From f0ac0854abfd7e3da47ece43ce5049c7b4119a0e Mon Sep 17 00:00:00 2001 From: Jedrzej Nowacki Date: Tue, 22 Nov 2022 22:43:15 +0100 Subject: [PATCH 1/5] Solutions for homework --- zadanie_domowe/common_chars.py | 8 ++++++- zadanie_domowe/dog_data_task.py | 32 +++++++++++++++++++++---- zadanie_domowe/obwod.py | 9 +++++++- zadanie_domowe/terriers.csv | 41 +++++++++++++++++++++++++++++++++ 4 files changed, 83 insertions(+), 7 deletions(-) create mode 100644 zadanie_domowe/terriers.csv diff --git a/zadanie_domowe/common_chars.py b/zadanie_domowe/common_chars.py index 3c22163..88aa00d 100644 --- a/zadanie_domowe/common_chars.py +++ b/zadanie_domowe/common_chars.py @@ -9,10 +9,16 @@ """ +def get_unique_chars(string_to_process): + return set(''.join(string_to_process.split())) + + def common_chars(string1, string2): - pass + return get_unique_chars(string1).intersection(get_unique_chars(string2)) input1 = "this is a string" input2 = "ala ma kota" output = ['a', 't'] + +print(common_chars(input1, input2)) diff --git a/zadanie_domowe/dog_data_task.py b/zadanie_domowe/dog_data_task.py index 0ba7f29..bd8f99c 100644 --- a/zadanie_domowe/dog_data_task.py +++ b/zadanie_domowe/dog_data_task.py @@ -1,15 +1,13 @@ - import csv from pathlib import Path +import re +import pandas as pd +import statistics as st with open(Path(__file__).parent / './dogs-data.csv', encoding='utf-8') as data_file: dog_data = csv.DictReader(data_file) dog_data = list(dog_data) -print(dog_data[0]) - - - """ Zadanie 1 @@ -35,3 +33,27 @@ """ +# a +breeds = sorted(set(map(lambda entry: re.sub(r'[^A-Za-z ]+', '', entry['Breed']), dog_data))) + +# b +df = pd.DataFrame(dog_data) +the_most_popular_breed = df.groupby('OwnerAge')['Breed'].apply(list).to_dict() + +for k, v in the_most_popular_breed.items(): + the_most_popular_breed[k] = st.mode(v) + +# c +dogs_ages = list(map(lambda entry: int(entry['DogAge']), dog_data)) + +mode = st.mode(dogs_ages) +mean = st.mean(dogs_ages) +var = st.variance(dogs_ages) + +# d +terriers = df.groupby('Breed')['DogAge'].count().to_dict() +terriers = dict(filter(lambda entry: 'terrier' in entry[0].lower(), terriers.items())) + +with open(Path(__file__).parent / './terriers.csv', 'w', newline='') as f: + w = csv.writer(f) + w.writerows(terriers.items()) diff --git a/zadanie_domowe/obwod.py b/zadanie_domowe/obwod.py index e34e607..d95a875 100644 --- a/zadanie_domowe/obwod.py +++ b/zadanie_domowe/obwod.py @@ -6,7 +6,14 @@ Przykład: obwod([(0,0), (0,1), (1,1), (1,0)]) == 4 """ +import math def obwod(points): - pass \ No newline at end of file + perimeter = 0 + for i, point in enumerate(points): + perimeter += math.dist(point, points[(i + 1) % len(points)]) + return perimeter + + +print(obwod([(0, 0), (0, 1), (1, 1), (1, 0)])) diff --git a/zadanie_domowe/terriers.csv b/zadanie_domowe/terriers.csv new file mode 100644 index 0000000..73c5cec --- /dev/null +++ b/zadanie_domowe/terriers.csv @@ -0,0 +1,41 @@ +Airedale Terrier,131 +American Staffordshire Terrier,53 +Australian Silky Terrier,3 +Australian Terrier,38 +Bedlington Terrier,7 +Biewer Yorkshire Terrier,112 +Border Terrier,243 +Boston Terrier,268 +Brasilianischer Terrier,4 +Bull Terrier,3 +Cairn Terrier,324 +Deutscher Jagdterrier,14 +English Staffordshire Terrier,7 +English Toy Terrier,29 +Foxterrier,299 +Irish Soft Coated Wheaten Terrier,45 +Irish Terrier,136 +Jack Russel Terrier,2293 +Kerry Blue Terrier,6 +Lakeland Terrier,43 +Manchester Terrier,33 +Miniature Bull Terrier,111 +Norfolk Terrier,32 +Norwich Terrier,84 +Parson Jack Russell Terrier,291 +Parson Russell Terrier,480 +Parson Terrier,27 +Pit Bull Terrier,15 +Russischer schwarzer Terrier,49 +Scottish Terrier,66 +Silky Terrier,7 +Skye Terrier,16 +Soft Coated Wheaten Terrier,40 +Staffordshire Bullterrier,27 +Terrier,339 +Tibet Terrier,291 +Toyterrier,13 +Tschechischer Terrier,2 +Welsh Terrier,79 +West Highland White Terrier,674 +Yorkshire Terrier,2386 From 3acbfca0a124e93d6f0c801858199e03305a7bd4 Mon Sep 17 00:00:00 2001 From: jnowacki Date: Sun, 18 Dec 2022 09:19:35 +0100 Subject: [PATCH 2/5] ipynb updates --- pandas/data_analysis.ipynb | 6602 +++--------------------------------- 1 file changed, 398 insertions(+), 6204 deletions(-) diff --git a/pandas/data_analysis.ipynb b/pandas/data_analysis.ipynb index dc68942..6a9f818 100644 --- a/pandas/data_analysis.ipynb +++ b/pandas/data_analysis.ipynb @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "slideshow": { "slide_type": "slide" @@ -105,8 +105,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 18, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } @@ -126,7 +129,7 @@ "dtype: int64" ] }, - "execution_count": 13, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -164,28 +167,16 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "April 211819\n", - "May 682758\n", - "June 737011\n", - "July 779511\n", - "dtype: int64" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "members = {'April': 211819,'May': 682758, 'June': 737011, 'July': 779511}\n", "\n", @@ -207,28 +198,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "April 211819.0\n", - "May 682758.0\n", - "June 737011.0\n", - "July 779511.0\n", - "Name: Rides, dtype: float64" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "months = ['April', 'May', 'June', 'July']\n", "\n", @@ -252,36 +231,16 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "211819\n" - ] - }, - { - "data": { - "text/plain": [ - "April 211819\n", - "May 682758\n", - "June 737011\n", - "July 779511\n", - "August 673790\n", - "dtype: int64" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "members = {'April': 211819,'May': 682758, 'June': 737011, 'July': 779511}\n", "\n", @@ -306,29 +265,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "April 211819\n", - "May 682758\n", - "June 737011\n", - "July 779511\n", - "August 673790\n", - "dtype: int64" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "members = {'April': 211819,'May': 682758, 'June': 737011, 'July': 779511}\n", "\n", @@ -366,30 +312,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "August 880599.0\n", - "July 973827.0\n", - "June 908505.0\n", - "May 830656.0\n", - "October NaN\n", - "September 814282.0\n", - "dtype: float64" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "members = pd.Series({'May': 682758, 'June': 737011, 'August': 673790, 'July': 779511,\n", "'September': 673790, 'October': 444177})\n", @@ -416,30 +348,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "May 683758\n", - "June 738011\n", - "July 780511\n", - "August 674790\n", - "September 674790\n", - "October 445177\n", - "dtype: int64" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "members = pd.Series({'May': 682758, 'June': 737011, 'July': 779511, 'August': 673790,\n", "'September': 673790, 'October': 444177})\n", @@ -481,6 +399,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } @@ -523,88 +444,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
membersoccasionals
May682758147898
June737011171494
July779511194316
August673790206809
September673790140492
October44417753596
\n", - "
" - ], - "text/plain": [ - " members occasionals\n", - "May 682758 147898\n", - "June 737011 171494\n", - "July 779511 194316\n", - "August 673790 206809\n", - "September 673790 140492\n", - "October 444177 53596" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "members = pd.Series({'May': 682758, 'June': 737011, 'July': 779511})\n", "occasionals = pd.Series({'May': 147898, 'June': 171494, 'July': 194316})\n", @@ -626,70 +475,16 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
membersoccasionals
0682758147898
1737011171494
2779511194316
\n", - "
" - ], - "text/plain": [ - " members occasionals\n", - "0 682758 147898\n", - "1 737011 171494\n", - "2 779511 194316" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "data = [\n", " {'members': 682758, 'occasionals': 147898},\n", @@ -715,30 +510,16 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "index\n", - " members occasionals\n", - "May 682758 147898\n", - "June 737011 171494\n", - "July 779511 194316\n", - "\n", - "columns\n", - " May June July\n", - "members 682758 737011 779511\n", - "occasionals 147898 171494 194316\n" - ] - } - ], + "outputs": [], "source": [ "data = {\n", " 'May': {'members': 682758, 'occasionals': 147898},\n", @@ -799,206 +580,16 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Countryfemale_BMImale_BMIgdppopulationunder5mortalitylife_expectancyfertility
0Afghanistan21.0740220.620581311.026528741.0110.452.86.20
1Albania25.6572626.446578644.02968026.017.976.81.76
2Algeria26.3684124.5962012314.034811059.029.575.52.73
3Angola23.4843122.250837103.019842251.0192.056.76.43
4Antigua and Barbuda27.5054525.7660225736.085350.010.975.52.16
...........................
170Venezuela28.1340827.4450017911.028116716.017.174.22.53
171Vietnam21.0650020.916304085.086589342.026.274.11.86
172Palestine29.0264326.577503564.03854667.024.774.14.38
173Zambia23.0543620.683213039.013114579.094.951.15.88
174Zimbabwe24.6452222.026601286.013495462.098.347.33.85
\n", - "

175 rows × 8 columns

\n", - "
" - ], - "text/plain": [ - " Country female_BMI male_BMI gdp population \\\n", - "0 Afghanistan 21.07402 20.62058 1311.0 26528741.0 \n", - "1 Albania 25.65726 26.44657 8644.0 2968026.0 \n", - "2 Algeria 26.36841 24.59620 12314.0 34811059.0 \n", - "3 Angola 23.48431 22.25083 7103.0 19842251.0 \n", - "4 Antigua and Barbuda 27.50545 25.76602 25736.0 85350.0 \n", - ".. ... ... ... ... ... \n", - "170 Venezuela 28.13408 27.44500 17911.0 28116716.0 \n", - "171 Vietnam 21.06500 20.91630 4085.0 86589342.0 \n", - "172 Palestine 29.02643 26.57750 3564.0 3854667.0 \n", - "173 Zambia 23.05436 20.68321 3039.0 13114579.0 \n", - "174 Zimbabwe 24.64522 22.02660 1286.0 13495462.0 \n", - "\n", - " under5mortality life_expectancy fertility \n", - "0 110.4 52.8 6.20 \n", - "1 17.9 76.8 1.76 \n", - "2 29.5 75.5 2.73 \n", - "3 192.0 56.7 6.43 \n", - "4 10.9 75.5 2.16 \n", - ".. ... ... ... \n", - "170 17.1 74.2 2.53 \n", - "171 26.2 74.1 1.86 \n", - "172 24.7 74.1 4.38 \n", - "173 94.9 51.1 5.88 \n", - "174 98.3 47.3 3.85 \n", - "\n", - "[175 rows x 8 columns]" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = pd.read_csv('gapminder.csv')\n", "\n", @@ -1007,167 +598,16 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
103Braund\\t Mr. Owen Harrismale2210A/5 211717.2500NaNS
211Cumings\\t Mrs. John Bradley (Florence Briggs T...female3810PC 1759971.2833C85C
313Heikkinen\\t Miss. Lainafemale2600STON/O2. 31012827.9250NaNS
411Futrelle\\t Mrs. Jacques Heath (Lily May Peel)female351011380353.1000C123S
503Allen\\t Mr. William Henrymale35003734508.0500NaNS
\n", - "
" - ], - "text/plain": [ - " Survived Pclass \\\n", - "PassengerId \n", - "1 0 3 \n", - "2 1 1 \n", - "3 1 3 \n", - "4 1 1 \n", - "5 0 3 \n", - "\n", - " Name Sex Age \\\n", - "PassengerId \n", - "1 Braund\\t Mr. Owen Harris male 22 \n", - "2 Cumings\\t Mrs. John Bradley (Florence Briggs T... female 38 \n", - "3 Heikkinen\\t Miss. Laina female 26 \n", - "4 Futrelle\\t Mrs. Jacques Heath (Lily May Peel) female 35 \n", - "5 Allen\\t Mr. William Henry male 35 \n", - "\n", - " SibSp Parch Ticket Fare Cabin Embarked \n", - "PassengerId \n", - "1 1 0 A/5 21171 7.2500 NaN S \n", - "2 1 0 PC 17599 71.2833 C85 C \n", - "3 0 0 STON/O2. 3101282 7.9250 NaN S \n", - "4 1 0 113803 53.1000 C123 S \n", - "5 0 0 373450 8.0500 NaN S " - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = pd.read_csv('./titanic_train.tsv', delimiter='\\t', index_col=0, nrows=5)\n", "df" @@ -1186,113 +626,16 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
start_datestart_station_codeend_dateend_station_codeduration_secis_member
02019-04-14 07:55:2260012019-04-14 08:07:1661327131
12019-04-14 07:59:3164112019-04-14 08:09:1864115871
22019-04-14 07:59:5560972019-04-14 08:12:1160367361
32019-04-14 07:59:5763102019-04-14 08:27:58634516801
42019-04-14 08:00:3770292019-04-14 08:14:1262508140
\n", - "
" - ], - "text/plain": [ - " start_date start_station_code end_date \\\n", - "0 2019-04-14 07:55:22 6001 2019-04-14 08:07:16 \n", - "1 2019-04-14 07:59:31 6411 2019-04-14 08:09:18 \n", - "2 2019-04-14 07:59:55 6097 2019-04-14 08:12:11 \n", - "3 2019-04-14 07:59:57 6310 2019-04-14 08:27:58 \n", - "4 2019-04-14 08:00:37 7029 2019-04-14 08:14:12 \n", - "\n", - " end_station_code duration_sec is_member \n", - "0 6132 713 1 \n", - "1 6411 587 1 \n", - "2 6036 736 1 \n", - "3 6345 1680 1 \n", - "4 6250 814 0 " - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = pd.read_excel('./bikes.xlsx', engine='openpyxl', nrows=5)\n", "df" @@ -1312,127 +655,16 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
TitleArtistId
AlbumId
1For Those About To Rock We Salute You1
2Balls to the Wall2
3Restless and Wild2
4Let There Be Rock1
5Big Ones3
.........
343Respighi:Pines of Rome226
344Schubert: The Late String Quartets & String Qu...272
345Monteverdi: L'Orfeo273
346Mozart: Chamber Music274
347Koyaanisqatsi (Soundtrack from the Motion Pict...275
\n", - "

347 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - " Title ArtistId\n", - "AlbumId \n", - "1 For Those About To Rock We Salute You 1\n", - "2 Balls to the Wall 2\n", - "3 Restless and Wild 2\n", - "4 Let There Be Rock 1\n", - "5 Big Ones 3\n", - "... ... ...\n", - "343 Respighi:Pines of Rome 226\n", - "344 Schubert: The Late String Quartets & String Qu... 272\n", - "345 Monteverdi: L'Orfeo 273\n", - "346 Mozart: Chamber Music 274\n", - "347 Koyaanisqatsi (Soundtrack from the Motion Pict... 275\n", - "\n", - "[347 rows x 2 columns]" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = pd.read_sql('Album', con='sqlite:///Chinook.sqlite', index_col='AlbumId')\n", "\n", @@ -1441,127 +673,16 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
TitleArtistId
AlbumId
1For Those About To Rock We Salute You1
2Balls to the Wall2
3Restless and Wild2
4Let There Be Rock1
5Big Ones3
.........
343Respighi:Pines of Rome226
344Schubert: The Late String Quartets & String Qu...272
345Monteverdi: L'Orfeo273
346Mozart: Chamber Music274
347Koyaanisqatsi (Soundtrack from the Motion Pict...275
\n", - "

347 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - " Title ArtistId\n", - "AlbumId \n", - "1 For Those About To Rock We Salute You 1\n", - "2 Balls to the Wall 2\n", - "3 Restless and Wild 2\n", - "4 Let There Be Rock 1\n", - "5 Big Ones 3\n", - "... ... ...\n", - "343 Respighi:Pines of Rome 226\n", - "344 Schubert: The Late String Quartets & String Qu... 272\n", - "345 Monteverdi: L'Orfeo 273\n", - "346 Mozart: Chamber Music 274\n", - "347 Koyaanisqatsi (Soundtrack from the Motion Pict... 275\n", - "\n", - "[347 rows x 2 columns]" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import sqlalchemy\n", "\n", @@ -1585,210 +706,16 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
07.00.270.3620.70.045451701.00103.000.458.86
16.30.300.341.60.049141320.99403.300.499.56
28.10.280.406.90.05030970.99513.260.4410.16
37.20.230.328.50.058471860.99563.190.409.96
47.20.230.328.50.058471860.99563.190.409.96
58.10.280.406.90.05030970.99513.260.4410.16
66.20.320.167.00.045301360.99493.180.479.66
77.00.270.3620.70.045451701.00103.000.458.86
\n", - "
" - ], - "text/plain": [ - " fixed acidity volatile acidity citric acid residual sugar chlorides \\\n", - "0 7.0 0.27 0.36 20.7 0.045 \n", - "1 6.3 0.30 0.34 1.6 0.049 \n", - "2 8.1 0.28 0.40 6.9 0.050 \n", - "3 7.2 0.23 0.32 8.5 0.058 \n", - "4 7.2 0.23 0.32 8.5 0.058 \n", - "5 8.1 0.28 0.40 6.9 0.050 \n", - "6 6.2 0.32 0.16 7.0 0.045 \n", - "7 7.0 0.27 0.36 20.7 0.045 \n", - "\n", - " free sulfur dioxide total sulfur dioxide density pH sulphates \\\n", - "0 45 170 1.0010 3.00 0.45 \n", - "1 14 132 0.9940 3.30 0.49 \n", - "2 30 97 0.9951 3.26 0.44 \n", - "3 47 186 0.9956 3.19 0.40 \n", - "4 47 186 0.9956 3.19 0.40 \n", - "5 30 97 0.9951 3.26 0.44 \n", - "6 30 136 0.9949 3.18 0.47 \n", - "7 45 170 1.0010 3.00 0.45 \n", - "\n", - " alcohol quality \n", - "0 8.8 6 \n", - "1 9.5 6 \n", - "2 10.1 6 \n", - "3 9.9 6 \n", - "4 9.9 6 \n", - "5 10.1 6 \n", - "6 9.6 6 \n", - "7 8.8 6 " - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "url = \"https://docs.google.com/spreadsheets/d/1ycvVWmVJ2MTn3_1NRVmVrySoHEHdWlwi4-Kr1W0Nv28/export?format=csv&gid=848662053\"\n", "df = pd.read_csv(url)\n", @@ -1826,6 +753,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } @@ -1837,8 +767,11 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } @@ -1853,8 +786,11 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } @@ -1880,42 +816,32 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\"members\":{\"May\":682758,\"June\":737011,\"July\":779511},\"occasionals\":{\"May\":147898,\"June\":171494,\"July\":194316}}\n" - ] - } - ], + "outputs": [], "source": [ "print(df.to_json())" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'members': {'May': 682758, 'June': 737011, 'July': 779511}, 'occasionals': {'May': 147898, 'June': 171494, 'July': 194316}}\n" - ] - } - ], + "outputs": [], "source": [ "print(df.to_dict())\n" ] @@ -1933,8 +859,11 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } @@ -1976,6 +905,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } @@ -2010,116 +942,16 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
gdppopulationlife_expectancy
Country
Afghanistan1311.026528741.052.8
Albania8644.02968026.076.8
Algeria12314.034811059.075.5
Angola7103.019842251.056.7
Antigua and Barbuda25736.085350.075.5
Argentina14646.040381860.075.4
Armenia7383.02975029.072.3
Australia41312.021370348.081.6
\n", - "
" - ], - "text/plain": [ - " gdp population life_expectancy\n", - "Country \n", - "Afghanistan 1311.0 26528741.0 52.8\n", - "Albania 8644.0 2968026.0 76.8\n", - "Algeria 12314.0 34811059.0 75.5\n", - "Angola 7103.0 19842251.0 56.7\n", - "Antigua and Barbuda 25736.0 85350.0 75.5\n", - "Argentina 14646.0 40381860.0 75.4\n", - "Armenia 7383.0 2975029.0 72.3\n", - "Australia 41312.0 21370348.0 81.6" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = pd.read_csv('./gapminder.csv', index_col='Country', nrows=8, usecols=['Country', 'gdp', 'population','life_expectancy'])\n", "\n", @@ -2139,33 +971,16 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "Country\n", - "Afghanistan 26528741.0\n", - "Albania 2968026.0\n", - "Algeria 34811059.0\n", - "Angola 19842251.0\n", - "Antigua and Barbuda 85350.0\n", - "Argentina 40381860.0\n", - "Armenia 2975029.0\n", - "Australia 21370348.0\n", - "Name: population, dtype: float64" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# notacja z kropką\n", "df.population" @@ -2173,33 +988,16 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "Country\n", - "Afghanistan 26528741.0\n", - "Albania 2968026.0\n", - "Algeria 34811059.0\n", - "Angola 19842251.0\n", - "Antigua and Barbuda 85350.0\n", - "Argentina 40381860.0\n", - "Armenia 2975029.0\n", - "Australia 21370348.0\n", - "Name: population, dtype: float64" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Operator []\n", "df['population']" @@ -2218,106 +1016,16 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
gdppopulation
Country
Afghanistan1311.026528741.0
Albania8644.02968026.0
Algeria12314.034811059.0
Angola7103.019842251.0
Antigua and Barbuda25736.085350.0
Argentina14646.040381860.0
Armenia7383.02975029.0
Australia41312.021370348.0
\n", - "
" - ], - "text/plain": [ - " gdp population\n", - "Country \n", - "Afghanistan 1311.0 26528741.0\n", - "Albania 8644.0 2968026.0\n", - "Algeria 12314.0 34811059.0\n", - "Angola 7103.0 19842251.0\n", - "Antigua and Barbuda 25736.0 85350.0\n", - "Argentina 14646.0 40381860.0\n", - "Armenia 7383.0 2975029.0\n", - "Australia 41312.0 21370348.0" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df[['gdp','population']]" ] @@ -2335,142 +1043,32 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "fragment" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['female_BMI', 'male_BMI', 'gdp', 'population', 'under5mortality',\n", - " 'life_expectancy', 'fertility'],\n", - " dtype='object')" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.columns" ] }, { "cell_type": "code", - "execution_count": 93, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PKBPopulacjaODŻ
Country
Afghanistan1311.026528741.052.8
Albania8644.02968026.076.8
Algeria12314.034811059.075.5
Angola7103.019842251.056.7
Antigua and Barbuda25736.085350.075.5
Argentina14646.040381860.075.4
Armenia7383.02975029.072.3
Australia41312.021370348.081.6
\n", - "
" - ], - "text/plain": [ - " PKB Populacja ODŻ\n", - "Country \n", - "Afghanistan 1311.0 26528741.0 52.8\n", - "Albania 8644.0 2968026.0 76.8\n", - "Algeria 12314.0 34811059.0 75.5\n", - "Angola 7103.0 19842251.0 56.7\n", - "Antigua and Barbuda 25736.0 85350.0 75.5\n", - "Argentina 14646.0 40381860.0 75.4\n", - "Armenia 7383.0 2975029.0 72.3\n", - "Australia 41312.0 21370348.0 81.6" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.columns = ['PKB', 'Populacja', 'ODŻ']\n", "\n", @@ -2490,27 +1088,16 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "PKB 14646.0\n", - "Populacja 40381860.0\n", - "ODŻ 75.4\n", - "Name: Argentina, dtype: float64" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.loc['Argentina']" ] @@ -2528,74 +1115,16 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PKBPopulacjaODŻ
Country
Albania8644.02968026.076.8
Angola7103.019842251.056.7
\n", - "
" - ], - "text/plain": [ - " PKB Populacja ODŻ\n", - "Country \n", - "Albania 8644.0 2968026.0 76.8\n", - "Angola 7103.0 19842251.0 56.7" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.loc[['Albania', 'Angola']]" ] @@ -2613,70 +1142,16 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PKBPopulacja
Country
Albania8644.02968026.0
Angola7103.019842251.0
\n", - "
" - ], - "text/plain": [ - " PKB Populacja\n", - "Country \n", - "Albania 8644.0 2968026.0\n", - "Angola 7103.0 19842251.0" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df2 = df.loc[['Albania', 'Angola'], ['PKB', 'Populacja']]\n", "\n", @@ -2696,81 +1171,16 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PKBPopulacjaODŻ
Country
Albania8644.02968026.076.8
Algeria12314.034811059.075.5
Angola7103.019842251.056.7
\n", - "
" - ], - "text/plain": [ - " PKB Populacja ODŻ\n", - "Country \n", - "Albania 8644.0 2968026.0 76.8\n", - "Algeria 12314.0 34811059.0 75.5\n", - "Angola 7103.0 19842251.0 56.7" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.loc['Albania': 'Angola', 'PKB': 'ODŻ']" ] @@ -2788,24 +1198,16 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "7103.0" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.at['Angola', 'PKB']" ] @@ -2823,26 +1225,16 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Afghanistan', 'Albania', 'Algeria', 'Angola', 'Antigua and Barbuda',\n", - " 'Argentina', 'Armenia', 'Australia'],\n", - " dtype='object', name='Country')" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.index" ] @@ -2860,88 +1252,16 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
membersoccasionals
May682758147898
June737011171494
July779511194316
August673790206809
September673790140492
October44417753596
\n", - "
" - ], - "text/plain": [ - " members occasionals\n", - "May 682758 147898\n", - "June 737011 171494\n", - "July 779511 194316\n", - "August 673790 206809\n", - "September 673790 140492\n", - "October 444177 53596" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "members = pd.Series({'May': 682758, 'June': 737011, 'July': 779511, 'August': 673790,\n", "'September': 673790, 'October': 444177})\n", @@ -2963,58 +1283,16 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
membersoccasionals
May682758147898
\n", - "
" - ], - "text/plain": [ - " members occasionals\n", - "May 682758 147898" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.head()" ] @@ -3028,82 +1306,16 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
membersoccasionals
June737011171494
July779511194316
August673790206809
September673790140492
October44417753596
\n", - "
" - ], - "text/plain": [ - " members occasionals\n", - "June 737011 171494\n", - "July 779511 194316\n", - "August 673790 206809\n", - "September 673790 140492\n", - "October 444177 53596" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.tail()" ] @@ -3117,70 +1329,16 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
membersoccasionals
May682758147898
August673790206809
September673790140492
\n", - "
" - ], - "text/plain": [ - " members occasionals\n", - "May 682758 147898\n", - "August 673790 206809\n", - "September 673790 140492" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.sample(3)" ] @@ -3194,100 +1352,16 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
membersoccasionals
count6.0000006.000000
mean665172.833333152434.166667
std116216.04545654783.506738
min444177.00000053596.000000
25%673790.000000142343.500000
50%678274.000000159696.000000
75%723447.750000188610.500000
max779511.000000206809.000000
\n", - "
" - ], - "text/plain": [ - " members occasionals\n", - "count 6.000000 6.000000\n", - "mean 665172.833333 152434.166667\n", - "std 116216.045456 54783.506738\n", - "min 444177.000000 53596.000000\n", - "25% 673790.000000 142343.500000\n", - "50% 678274.000000 159696.000000\n", - "75% 723447.750000 188610.500000\n", - "max 779511.000000 206809.000000" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.describe()" ] @@ -3301,29 +1375,16 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Index: 6 entries, May to October\n", - "Data columns (total 2 columns):\n", - " # Column Non-Null Count Dtype\n", - "--- ------ -------------- -----\n", - " 0 members 6 non-null int64\n", - " 1 occasionals 6 non-null int64\n", - "dtypes: int64(2)\n", - "memory usage: 144.0+ bytes\n" - ] - } - ], + "outputs": [], "source": [ "df.info()" ] @@ -3337,24 +1398,16 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "6" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "len(df)" ] @@ -3368,24 +1421,16 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "(6, 2)" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.shape" ] @@ -3408,38 +1453,16 @@ }, { "cell_type": "code", - "execution_count": 167, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Tomasz\\AppData\\Local\\Temp\\ipykernel_49236\\3698961737.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", - " df.mean()\n" - ] - }, - { - "data": { - "text/plain": [ - "Survived 0.383838\n", - "Pclass 2.308642\n", - "Age 29.699118\n", - "SibSp 0.523008\n", - "Parch 0.381594\n", - "Fare 32.204208\n", - "dtype: float64" - ] - }, - "execution_count": 167, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.mean()" ] @@ -3457,25 +1480,16 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1 3 2]\n", - "3 4\n", - "1 3\n", - "2 3\n", - "dtype: int64\n" - ] - } - ], + "outputs": [], "source": [ "dane = pd.Series([1, 3, 2, 3, 1, 1, 2, 3, 2, 3])\n", "\n", @@ -3499,36 +1513,16 @@ }, { "cell_type": "code", - "execution_count": 164, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "PassengerId\n", - "1 False\n", - "2 False\n", - "3 False\n", - "4 False\n", - "5 False\n", - " ... \n", - "887 False\n", - "888 False\n", - "889 True\n", - "890 False\n", - "891 False\n", - "Name: Age, Length: 891, dtype: bool" - ] - }, - "execution_count": 164, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = pd.read_csv('./titanic_train.tsv', sep='\\t', index_col='PassengerId')\n", "df.Age.isnull()\n" @@ -3547,131 +1541,16 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
female_BMImale_BMIgdppopulationunder5mortalitylife_expectancyfertility
Country
Afghanistan21.0740220.620581311.026528741.0110.452.86.20
Albania25.6572626.446578644.02968026.017.976.81.76
Algeria26.3684124.5962012314.034811059.029.575.52.73
Angola23.4843122.250837103.019842251.0192.056.76.43
Antigua and Barbuda27.5054525.7660225736.085350.010.975.52.16
\n", - "
" - ], - "text/plain": [ - " female_BMI male_BMI gdp population \\\n", - "Country \n", - "Afghanistan 21.07402 20.62058 1311.0 26528741.0 \n", - "Albania 25.65726 26.44657 8644.0 2968026.0 \n", - "Algeria 26.36841 24.59620 12314.0 34811059.0 \n", - "Angola 23.48431 22.25083 7103.0 19842251.0 \n", - "Antigua and Barbuda 27.50545 25.76602 25736.0 85350.0 \n", - "\n", - " under5mortality life_expectancy fertility \n", - "Country \n", - "Afghanistan 110.4 52.8 6.20 \n", - "Albania 17.9 76.8 1.76 \n", - "Algeria 29.5 75.5 2.73 \n", - "Angola 192.0 56.7 6.43 \n", - "Antigua and Barbuda 10.9 75.5 2.16 " - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = pd.read_csv('./gapminder.csv', index_col='Country', nrows=5)\n", "\n", @@ -3680,153 +1559,16 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
female_BMImale_BMIgdppopulationunder5mortalitylife_expectancyfertilitycontinenttmp
Country
Afghanistan21.0740220.620581311.026528741.0110.452.86.20Asia1
Albania25.6572626.446578644.02968026.017.976.81.76Europe1
Algeria26.3684124.5962012314.034811059.029.575.52.73Africa1
Angola23.4843122.250837103.019842251.0192.056.76.43Africa1
Antigua and Barbuda27.5054525.7660225736.085350.010.975.52.16Americas1
\n", - "
" - ], - "text/plain": [ - " female_BMI male_BMI gdp population \\\n", - "Country \n", - "Afghanistan 21.07402 20.62058 1311.0 26528741.0 \n", - "Albania 25.65726 26.44657 8644.0 2968026.0 \n", - "Algeria 26.36841 24.59620 12314.0 34811059.0 \n", - "Angola 23.48431 22.25083 7103.0 19842251.0 \n", - "Antigua and Barbuda 27.50545 25.76602 25736.0 85350.0 \n", - "\n", - " under5mortality life_expectancy fertility continent \\\n", - "Country \n", - "Afghanistan 110.4 52.8 6.20 Asia \n", - "Albania 17.9 76.8 1.76 Europe \n", - "Algeria 29.5 75.5 2.73 Africa \n", - "Angola 192.0 56.7 6.43 Africa \n", - "Antigua and Barbuda 10.9 75.5 2.16 Americas \n", - "\n", - " tmp \n", - "Country \n", - "Afghanistan 1 \n", - "Albania 1 \n", - "Algeria 1 \n", - "Angola 1 \n", - "Antigua and Barbuda 1 " - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "conts = pd.Series({\n", " 'Afghanistan': 'Asia', 'Albania': 'Europe', 'Algeria':' Africa', 'Angola': 'Africa', 'Antigua and Barbuda': 'Americas'})\n", @@ -3840,168 +1582,16 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
female_BMImale_BMIgdppopulationunder5mortalitylife_expectancyfertilitycontinenttmp
Country
Afghanistan21.0740220.620581311.026528741.0110.452.86.20Asia1.0
Albania25.6572626.446578644.02968026.017.976.81.76Europe1.0
Algeria26.3684124.5962012314.034811059.029.575.52.73Africa1.0
Angola23.4843122.250837103.019842251.0192.056.76.43Africa1.0
Antigua and Barbuda27.5054525.7660225736.085350.010.975.52.16Americas1.0
Argentina27.4652327.5017014646.040381860.015.475.42.24NaNNaN
\n", - "
" - ], - "text/plain": [ - " female_BMI male_BMI gdp population \\\n", - "Country \n", - "Afghanistan 21.07402 20.62058 1311.0 26528741.0 \n", - "Albania 25.65726 26.44657 8644.0 2968026.0 \n", - "Algeria 26.36841 24.59620 12314.0 34811059.0 \n", - "Angola 23.48431 22.25083 7103.0 19842251.0 \n", - "Antigua and Barbuda 27.50545 25.76602 25736.0 85350.0 \n", - "Argentina 27.46523 27.50170 14646.0 40381860.0 \n", - "\n", - " under5mortality life_expectancy fertility continent \\\n", - "Country \n", - "Afghanistan 110.4 52.8 6.20 Asia \n", - "Albania 17.9 76.8 1.76 Europe \n", - "Algeria 29.5 75.5 2.73 Africa \n", - "Angola 192.0 56.7 6.43 Africa \n", - "Antigua and Barbuda 10.9 75.5 2.16 Americas \n", - "Argentina 15.4 75.4 2.24 NaN \n", - "\n", - " tmp \n", - "Country \n", - "Afghanistan 1.0 \n", - "Albania 1.0 \n", - "Algeria 1.0 \n", - "Angola 1.0 \n", - "Antigua and Barbuda 1.0 \n", - "Argentina NaN " - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.loc['Argentina'] = {\n", " 'female_BMI': 27.46523,\n", @@ -4017,125 +1607,16 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
female_BMImale_BMIpopulationunder5mortalitylife_expectancyfertilitycontinenttmp
Country
Afghanistan21.0740220.6205826528741.0110.452.86.20Asia1.0
Algeria26.3684124.5962034811059.029.575.52.73Africa1.0
Antigua and Barbuda27.5054525.7660285350.010.975.52.16Americas1.0
Argentina27.4652327.5017040381860.015.475.42.24NaNNaN
\n", - "
" - ], - "text/plain": [ - " female_BMI male_BMI population under5mortality \\\n", - "Country \n", - "Afghanistan 21.07402 20.62058 26528741.0 110.4 \n", - "Algeria 26.36841 24.59620 34811059.0 29.5 \n", - "Antigua and Barbuda 27.50545 25.76602 85350.0 10.9 \n", - "Argentina 27.46523 27.50170 40381860.0 15.4 \n", - "\n", - " life_expectancy fertility continent tmp \n", - "Country \n", - "Afghanistan 52.8 6.20 Asia 1.0 \n", - "Algeria 75.5 2.73 Africa 1.0 \n", - "Antigua and Barbuda 75.5 2.16 Americas 1.0 \n", - "Argentina 75.4 2.24 NaN NaN " - ] - }, - "execution_count": 122, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.drop('gdp', axis='columns')\n" ] @@ -4168,167 +1649,16 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
103Braund\\t Mr. Owen Harrismale22.010A/5 211717.2500NaNS
211Cumings\\t Mrs. John Bradley (Florence Briggs T...female38.010PC 1759971.2833C85C
313Heikkinen\\t Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
411Futrelle\\t Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
503Allen\\t Mr. William Henrymale35.0003734508.0500NaNS
\n", - "
" - ], - "text/plain": [ - " Survived Pclass \\\n", - "PassengerId \n", - "1 0 3 \n", - "2 1 1 \n", - "3 1 3 \n", - "4 1 1 \n", - "5 0 3 \n", - "\n", - " Name Sex Age \\\n", - "PassengerId \n", - "1 Braund\\t Mr. Owen Harris male 22.0 \n", - "2 Cumings\\t Mrs. John Bradley (Florence Briggs T... female 38.0 \n", - "3 Heikkinen\\t Miss. Laina female 26.0 \n", - "4 Futrelle\\t Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", - "5 Allen\\t Mr. William Henry male 35.0 \n", - "\n", - " SibSp Parch Ticket Fare Cabin Embarked \n", - "PassengerId \n", - "1 1 0 A/5 21171 7.2500 NaN S \n", - "2 1 0 PC 17599 71.2833 C85 C \n", - "3 0 0 STON/O2. 3101282 7.9250 NaN S \n", - "4 1 0 113803 53.1000 C123 S \n", - "5 0 0 373450 8.0500 NaN S " - ] - }, - "execution_count": 104, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = pd.read_csv('./titanic_train.tsv', sep='\\t', index_col='PassengerId')\n", "\n", @@ -4337,344 +1667,48 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "PassengerId\n", - "1 0\n", - "2 1\n", - "3 1\n", - "4 1\n", - "5 0\n", - " ..\n", - "887 0\n", - "888 1\n", - "889 0\n", - "890 1\n", - "891 0\n", - "Name: Survived, Length: 891, dtype: int64" - ] - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df['Survived']" ] }, { "cell_type": "code", - "execution_count": 108, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "PassengerId\n", - "1 False\n", - "2 True\n", - "3 True\n", - "4 True\n", - "5 False\n", - " ... \n", - "887 False\n", - "888 True\n", - "889 False\n", - "890 True\n", - "891 False\n", - "Name: Survived, Length: 891, dtype: bool" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df['Survived'] == 1" ] }, { "cell_type": "code", - "execution_count": 109, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
211Cumings\\t Mrs. John Bradley (Florence Briggs T...female38.010PC 1759971.2833C85C
411Futrelle\\t Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
701McCarthy\\t Mr. Timothy Jmale54.0001746351.8625E46S
1211Bonnell\\t Miss. Elizabethfemale58.00011378326.5500C103S
2411Sloper\\t Mr. William Thompsonmale28.00011378835.5000A6S
....................................
87211Beckwith\\t Mrs. Richard Leonard (Sallie Monypeny)female47.0111175152.5542D35S
87301Carlsson\\t Mr. Frans Olofmale33.0006955.0000B51 B53 B55S
88011Potter\\t Mrs. Thomas Jr (Lily Alexenia Wilson)female56.0011176783.1583C50C
88811Graham\\t Miss. Margaret Edithfemale19.00011205330.0000B42S
89011Behr\\t Mr. Karl Howellmale26.00011136930.0000C148C
\n", - "

216 rows × 11 columns

\n", - "
" - ], - "text/plain": [ - " Survived Pclass \\\n", - "PassengerId \n", - "2 1 1 \n", - "4 1 1 \n", - "7 0 1 \n", - "12 1 1 \n", - "24 1 1 \n", - "... ... ... \n", - "872 1 1 \n", - "873 0 1 \n", - "880 1 1 \n", - "888 1 1 \n", - "890 1 1 \n", - "\n", - " Name Sex Age \\\n", - "PassengerId \n", - "2 Cumings\\t Mrs. John Bradley (Florence Briggs T... female 38.0 \n", - "4 Futrelle\\t Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", - "7 McCarthy\\t Mr. Timothy J male 54.0 \n", - "12 Bonnell\\t Miss. Elizabeth female 58.0 \n", - "24 Sloper\\t Mr. William Thompson male 28.0 \n", - "... ... ... ... \n", - "872 Beckwith\\t Mrs. Richard Leonard (Sallie Monypeny) female 47.0 \n", - "873 Carlsson\\t Mr. Frans Olof male 33.0 \n", - "880 Potter\\t Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 \n", - "888 Graham\\t Miss. Margaret Edith female 19.0 \n", - "890 Behr\\t Mr. Karl Howell male 26.0 \n", - "\n", - " SibSp Parch Ticket Fare Cabin Embarked \n", - "PassengerId \n", - "2 1 0 PC 17599 71.2833 C85 C \n", - "4 1 0 113803 53.1000 C123 S \n", - "7 0 0 17463 51.8625 E46 S \n", - "12 0 0 113783 26.5500 C103 S \n", - "24 0 0 113788 35.5000 A6 S \n", - "... ... ... ... ... ... ... \n", - "872 1 1 11751 52.5542 D35 S \n", - "873 0 0 695 5.0000 B51 B53 B55 S \n", - "880 0 1 11767 83.1583 C50 C \n", - "888 0 0 112053 30.0000 B42 S \n", - "890 0 0 111369 30.0000 C148 C \n", - "\n", - "[216 rows x 11 columns]" - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df[df['Pclass'] == 1]" ] @@ -4697,272 +1731,16 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
211Cumings\\t Mrs. John Bradley (Florence Briggs T...female38.010PC 1759971.2833C85C
411Futrelle\\t Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
1211Bonnell\\t Miss. Elizabethfemale58.00011378326.5500C103S
3211Spencer\\t Mrs. William Augustus (Marie Eugenie)femaleNaN10PC 17569146.5208B78C
5311Harper\\t Mrs. Henry Sleeper (Myna Haxtun)female49.010PC 1757276.7292D33C
....................................
85711Wick\\t Mrs. George Dennick (Mary Hitchcock)female45.01136928164.8667NaNS
86311Swift\\t Mrs. Frederick Joel (Margaret Welles B...female48.0001746625.9292D17S
87211Beckwith\\t Mrs. Richard Leonard (Sallie Monypeny)female47.0111175152.5542D35S
88011Potter\\t Mrs. Thomas Jr (Lily Alexenia Wilson)female56.0011176783.1583C50C
88811Graham\\t Miss. Margaret Edithfemale19.00011205330.0000B42S
\n", - "

94 rows × 11 columns

\n", - "
" - ], - "text/plain": [ - " Survived Pclass \\\n", - "PassengerId \n", - "2 1 1 \n", - "4 1 1 \n", - "12 1 1 \n", - "32 1 1 \n", - "53 1 1 \n", - "... ... ... \n", - "857 1 1 \n", - "863 1 1 \n", - "872 1 1 \n", - "880 1 1 \n", - "888 1 1 \n", - "\n", - " Name Sex Age \\\n", - "PassengerId \n", - "2 Cumings\\t Mrs. John Bradley (Florence Briggs T... female 38.0 \n", - "4 Futrelle\\t Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", - "12 Bonnell\\t Miss. Elizabeth female 58.0 \n", - "32 Spencer\\t Mrs. William Augustus (Marie Eugenie) female NaN \n", - "53 Harper\\t Mrs. Henry Sleeper (Myna Haxtun) female 49.0 \n", - "... ... ... ... \n", - "857 Wick\\t Mrs. George Dennick (Mary Hitchcock) female 45.0 \n", - "863 Swift\\t Mrs. Frederick Joel (Margaret Welles B... female 48.0 \n", - "872 Beckwith\\t Mrs. Richard Leonard (Sallie Monypeny) female 47.0 \n", - "880 Potter\\t Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 \n", - "888 Graham\\t Miss. Margaret Edith female 19.0 \n", - "\n", - " SibSp Parch Ticket Fare Cabin Embarked \n", - "PassengerId \n", - "2 1 0 PC 17599 71.2833 C85 C \n", - "4 1 0 113803 53.1000 C123 S \n", - "12 0 0 113783 26.5500 C103 S \n", - "32 1 0 PC 17569 146.5208 B78 C \n", - "53 1 0 PC 17572 76.7292 D33 C \n", - "... ... ... ... ... ... ... \n", - "857 1 1 36928 164.8667 NaN S \n", - "863 0 0 17466 25.9292 D17 S \n", - "872 1 1 11751 52.5542 D35 S \n", - "880 0 1 11767 83.1583 C50 C \n", - "888 0 0 112053 30.0000 B42 S \n", - "\n", - "[94 rows x 11 columns]" - ] - }, - "execution_count": 110, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pierwsza_klasa = df['Pclass'] == 1\n", "kobiety = df['Sex'] == 'female'\n", @@ -4972,272 +1750,16 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
103Braund\\t Mr. Owen Harrismale22.010A/5 211717.2500NaNS
211Cumings\\t Mrs. John Bradley (Florence Briggs T...female38.010PC 1759971.2833C85C
411Futrelle\\t Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
803Palsson\\t Master. Gosta Leonardmale2.03134990921.0750NaNS
1012Nasser\\t Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNC
....................................
86103Hansen\\t Mr. Claus Petermale41.02035002614.1083NaNS
86202Giles\\t Mr. Frederick Edwardmale21.0102813411.5000NaNS
86403Sage\\t Miss. Dorothy Edith \"Dolly\"femaleNaN82CA. 234369.5500NaNS
86712Duran y More\\t Miss. Asuncionfemale27.010SC/PARIS 214913.8583NaNC
87512Abelson\\t Mrs. Samuel (Hannah Wizosky)female28.010P/PP 338124.0000NaNC
\n", - "

192 rows × 11 columns

\n", - "
" - ], - "text/plain": [ - " Survived Pclass \\\n", - "PassengerId \n", - "1 0 3 \n", - "2 1 1 \n", - "4 1 1 \n", - "8 0 3 \n", - "10 1 2 \n", - "... ... ... \n", - "861 0 3 \n", - "862 0 2 \n", - "864 0 3 \n", - "867 1 2 \n", - "875 1 2 \n", - "\n", - " Name Sex Age \\\n", - "PassengerId \n", - "1 Braund\\t Mr. Owen Harris male 22.0 \n", - "2 Cumings\\t Mrs. John Bradley (Florence Briggs T... female 38.0 \n", - "4 Futrelle\\t Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", - "8 Palsson\\t Master. Gosta Leonard male 2.0 \n", - "10 Nasser\\t Mrs. Nicholas (Adele Achem) female 14.0 \n", - "... ... ... ... \n", - "861 Hansen\\t Mr. Claus Peter male 41.0 \n", - "862 Giles\\t Mr. Frederick Edward male 21.0 \n", - "864 Sage\\t Miss. Dorothy Edith \"Dolly\" female NaN \n", - "867 Duran y More\\t Miss. Asuncion female 27.0 \n", - "875 Abelson\\t Mrs. Samuel (Hannah Wizosky) female 28.0 \n", - "\n", - " SibSp Parch Ticket Fare Cabin Embarked \n", - "PassengerId \n", - "1 1 0 A/5 21171 7.2500 NaN S \n", - "2 1 0 PC 17599 71.2833 C85 C \n", - "4 1 0 113803 53.1000 C123 S \n", - "8 3 1 349909 21.0750 NaN S \n", - "10 1 0 237736 30.0708 NaN C \n", - "... ... ... ... ... ... ... \n", - "861 2 0 350026 14.1083 NaN S \n", - "862 1 0 28134 11.5000 NaN S \n", - "864 8 2 CA. 2343 69.5500 NaN S \n", - "867 1 0 SC/PARIS 2149 13.8583 NaN C \n", - "875 1 0 P/PP 3381 24.0000 NaN C \n", - "\n", - "[192 rows x 11 columns]" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "\n", "df[df['SibSp'] > df['Parch']]" @@ -5258,630 +1780,64 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
211Cumings\\t Mrs. John Bradley (Florence Briggs T...female38.010PC 1759971.2833C85C
411Futrelle\\t Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
701McCarthy\\t Mr. Timothy Jmale54.0001746351.8625E46S
1211Bonnell\\t Miss. Elizabethfemale58.00011378326.5500C103S
2411Sloper\\t Mr. William Thompsonmale28.00011378835.5000A6S
\n", - "
" - ], - "text/plain": [ - " Survived Pclass \\\n", - "PassengerId \n", - "2 1 1 \n", - "4 1 1 \n", - "7 0 1 \n", - "12 1 1 \n", - "24 1 1 \n", - "\n", - " Name Sex Age \\\n", - "PassengerId \n", - "2 Cumings\\t Mrs. John Bradley (Florence Briggs T... female 38.0 \n", - "4 Futrelle\\t Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", - "7 McCarthy\\t Mr. Timothy J male 54.0 \n", - "12 Bonnell\\t Miss. Elizabeth female 58.0 \n", - "24 Sloper\\t Mr. William Thompson male 28.0 \n", - "\n", - " SibSp Parch Ticket Fare Cabin Embarked \n", - "PassengerId \n", - "2 1 0 PC 17599 71.2833 C85 C \n", - "4 1 0 113803 53.1000 C123 S \n", - "7 0 0 17463 51.8625 E46 S \n", - "12 0 0 113783 26.5500 C103 S \n", - "24 0 0 113788 35.5000 A6 S " - ] - }, - "execution_count": 139, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.query('Pclass == 1').head()" ] }, { "cell_type": "code", - "execution_count": 151, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
211Cumings\\t Mrs. John Bradley (Florence Briggs T...female38.010PC 1759971.2833C85C
411Futrelle\\t Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
1211Bonnell\\t Miss. Elizabethfemale58.00011378326.5500C103S
3211Spencer\\t Mrs. William Augustus (Marie Eugenie)femaleNaN10PC 17569146.5208B78C
5311Harper\\t Mrs. Henry Sleeper (Myna Haxtun)female49.010PC 1757276.7292D33C
\n", - "
" - ], - "text/plain": [ - " Survived Pclass \\\n", - "PassengerId \n", - "2 1 1 \n", - "4 1 1 \n", - "12 1 1 \n", - "32 1 1 \n", - "53 1 1 \n", - "\n", - " Name Sex Age \\\n", - "PassengerId \n", - "2 Cumings\\t Mrs. John Bradley (Florence Briggs T... female 38.0 \n", - "4 Futrelle\\t Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", - "12 Bonnell\\t Miss. Elizabeth female 58.0 \n", - "32 Spencer\\t Mrs. William Augustus (Marie Eugenie) female NaN \n", - "53 Harper\\t Mrs. Henry Sleeper (Myna Haxtun) female 49.0 \n", - "\n", - " SibSp Parch Ticket Fare Cabin Embarked \n", - "PassengerId \n", - "2 1 0 PC 17599 71.2833 C85 C \n", - "4 1 0 113803 53.1000 C123 S \n", - "12 0 0 113783 26.5500 C103 S \n", - "32 1 0 PC 17569 146.5208 B78 C \n", - "53 1 0 PC 17572 76.7292 D33 C " - ] - }, - "execution_count": 151, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.query('(Pclass == 1) and (Sex == \"female\")').head()" ] }, { "cell_type": "code", - "execution_count": 153, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
103Braund\\t Mr. Owen Harrismale22.010A/5 211717.2500NaNS
211Cumings\\t Mrs. John Bradley (Florence Briggs T...female38.010PC 1759971.2833C85C
411Futrelle\\t Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
803Palsson\\t Master. Gosta Leonardmale2.03134990921.0750NaNS
1012Nasser\\t Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNC
....................................
86103Hansen\\t Mr. Claus Petermale41.02035002614.1083NaNS
86202Giles\\t Mr. Frederick Edwardmale21.0102813411.5000NaNS
86403Sage\\t Miss. Dorothy Edith \"Dolly\"femaleNaN82CA. 234369.5500NaNS
86712Duran y More\\t Miss. Asuncionfemale27.010SC/PARIS 214913.8583NaNC
87512Abelson\\t Mrs. Samuel (Hannah Wizosky)female28.010P/PP 338124.0000NaNC
\n", - "

192 rows × 11 columns

\n", - "
" - ], - "text/plain": [ - " Survived Pclass \\\n", - "PassengerId \n", - "1 0 3 \n", - "2 1 1 \n", - "4 1 1 \n", - "8 0 3 \n", - "10 1 2 \n", - "... ... ... \n", - "861 0 3 \n", - "862 0 2 \n", - "864 0 3 \n", - "867 1 2 \n", - "875 1 2 \n", - "\n", - " Name Sex Age \\\n", - "PassengerId \n", - "1 Braund\\t Mr. Owen Harris male 22.0 \n", - "2 Cumings\\t Mrs. John Bradley (Florence Briggs T... female 38.0 \n", - "4 Futrelle\\t Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", - "8 Palsson\\t Master. Gosta Leonard male 2.0 \n", - "10 Nasser\\t Mrs. Nicholas (Adele Achem) female 14.0 \n", - "... ... ... ... \n", - "861 Hansen\\t Mr. Claus Peter male 41.0 \n", - "862 Giles\\t Mr. Frederick Edward male 21.0 \n", - "864 Sage\\t Miss. Dorothy Edith \"Dolly\" female NaN \n", - "867 Duran y More\\t Miss. Asuncion female 27.0 \n", - "875 Abelson\\t Mrs. Samuel (Hannah Wizosky) female 28.0 \n", - "\n", - " SibSp Parch Ticket Fare Cabin Embarked \n", - "PassengerId \n", - "1 1 0 A/5 21171 7.2500 NaN S \n", - "2 1 0 PC 17599 71.2833 C85 C \n", - "4 1 0 113803 53.1000 C123 S \n", - "8 3 1 349909 21.0750 NaN S \n", - "10 1 0 237736 30.0708 NaN C \n", - "... ... ... ... ... ... ... \n", - "861 2 0 350026 14.1083 NaN S \n", - "862 1 0 28134 11.5000 NaN S \n", - "864 8 2 CA. 2343 69.5500 NaN S \n", - "867 1 0 SC/PARIS 2149 13.8583 NaN C \n", - "875 1 0 P/PP 3381 24.0000 NaN C \n", - "\n", - "[192 rows x 11 columns]" - ] - }, - "execution_count": 153, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.query('SibSp > Parch')" ] }, { "cell_type": "code", - "execution_count": 113, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "(113, 11)" - ] - }, - "execution_count": 113, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "young = 18\n", "df.query('Age < @young').shape" @@ -5911,131 +1867,16 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
female_BMImale_BMIgdppopulationunder5mortalitylife_expectancyfertility
Country
Afghanistan21.0740220.620581311.026528741.0110.452.86.20
Albania25.6572626.446578644.02968026.017.976.81.76
Algeria26.3684124.5962012314.034811059.029.575.52.73
Angola23.4843122.250837103.019842251.0192.056.76.43
Antigua and Barbuda27.5054525.7660225736.085350.010.975.52.16
\n", - "
" - ], - "text/plain": [ - " female_BMI male_BMI gdp population \\\n", - "Country \n", - "Afghanistan 21.07402 20.62058 1311.0 26528741.0 \n", - "Albania 25.65726 26.44657 8644.0 2968026.0 \n", - "Algeria 26.36841 24.59620 12314.0 34811059.0 \n", - "Angola 23.48431 22.25083 7103.0 19842251.0 \n", - "Antigua and Barbuda 27.50545 25.76602 25736.0 85350.0 \n", - "\n", - " under5mortality life_expectancy fertility \n", - "Country \n", - "Afghanistan 110.4 52.8 6.20 \n", - "Albania 17.9 76.8 1.76 \n", - "Algeria 29.5 75.5 2.73 \n", - "Angola 192.0 56.7 6.43 \n", - "Antigua and Barbuda 10.9 75.5 2.16 " - ] - }, - "execution_count": 135, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = pd.read_csv('./gapminder.csv', index_col='Country', nrows=5)\n", "\n", @@ -6055,27 +1896,16 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "female_BMI\n", - "male_BMI\n", - "gdp\n", - "population\n", - "under5mortality\n", - "life_expectancy\n", - "fertility\n" - ] - } - ], + "outputs": [], "source": [ "for column_name in df:\n", " print(column_name)" @@ -6083,23 +1913,13 @@ }, { "cell_type": "code", - "execution_count": 147, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "female_BMI Country\n", - "Afghanistan 21.07402\n", - "Albania 25.65726\n", - "Algeria 26.36841\n", - "Angola 23.48431\n", - "Antigua and Barbuda 27.50545\n", - "Name: female_BMI, dtype: float64\n" - ] + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true } - ], + }, + "outputs": [], "source": [ "for col_name, series in df.items():\n", " print(col_name, series)\n", @@ -6108,29 +1928,16 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Afghanistan \n", - " female_BMI 2.107402e+01\n", - "male_BMI 2.062058e+01\n", - "gdp 1.311000e+03\n", - "population 2.652874e+07\n", - "under5mortality 1.104000e+02\n", - "life_expectancy 5.280000e+01\n", - "fertility 6.200000e+00\n", - "Name: Afghanistan, dtype: float64\n" - ] - } - ], + "outputs": [], "source": [ "for idx, row in df.iterrows():\n", " print(idx, '\\n', row)\n", @@ -6139,30 +1946,16 @@ }, { "cell_type": "code", - "execution_count": 162, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "Country\n", - "Afghanistan normal\n", - "Albania overweight\n", - "Algeria normal\n", - "Angola normal\n", - "Antigua and Barbuda overweight\n", - "Name: male_BMI, dtype: object" - ] - }, - "execution_count": 162, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def bmi_level(bmi):\n", " if bmi <= 18.5:\n", @@ -6182,30 +1975,16 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "Country\n", - "Afghanistan normal\n", - "Albania overweight\n", - "Algeria normal\n", - "Angola normal\n", - "Antigua and Barbuda overweight\n", - "dtype: object" - ] - }, - "execution_count": 163, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def bmi_level(row_data):\n", " bmi = row_data['male_BMI']\n", @@ -6222,127 +2001,16 @@ }, { "cell_type": "code", - "execution_count": 220, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CountryAfghanistanAlbaniaAlgeriaAngolaAntigua and Barbuda
female_BMI2.107402e+012.565726e+012.636841e+012.348431e+0127.50545
male_BMI2.062058e+012.644657e+012.459620e+012.225083e+0125.76602
gdp1.311000e+038.644000e+031.231400e+047.103000e+0325736.00000
population2.652874e+072.968026e+063.481106e+071.984225e+0785350.00000
under5mortality1.104000e+021.790000e+012.950000e+011.920000e+0210.90000
life_expectancy5.280000e+017.680000e+017.550000e+015.670000e+0175.50000
fertility6.200000e+001.760000e+002.730000e+006.430000e+002.16000
\n", - "
" - ], - "text/plain": [ - "Country Afghanistan Albania Algeria Angola \\\n", - "female_BMI 2.107402e+01 2.565726e+01 2.636841e+01 2.348431e+01 \n", - "male_BMI 2.062058e+01 2.644657e+01 2.459620e+01 2.225083e+01 \n", - "gdp 1.311000e+03 8.644000e+03 1.231400e+04 7.103000e+03 \n", - "population 2.652874e+07 2.968026e+06 3.481106e+07 1.984225e+07 \n", - "under5mortality 1.104000e+02 1.790000e+01 2.950000e+01 1.920000e+02 \n", - "life_expectancy 5.280000e+01 7.680000e+01 7.550000e+01 5.670000e+01 \n", - "fertility 6.200000e+00 1.760000e+00 2.730000e+00 6.430000e+00 \n", - "\n", - "Country Antigua and Barbuda \n", - "female_BMI 27.50545 \n", - "male_BMI 25.76602 \n", - "gdp 25736.00000 \n", - "population 85350.00000 \n", - "under5mortality 10.90000 \n", - "life_expectancy 75.50000 \n", - "fertility 2.16000 " - ] - }, - "execution_count": 220, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.transpose()" ] @@ -6358,167 +2026,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
103Braund\\t Mr. Owen Harrismale22.010A/5 211717.2500NaNS
211Cumings\\t Mrs. John Bradley (Florence Briggs T...female38.010PC 1759971.2833C85C
313Heikkinen\\t Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
411Futrelle\\t Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
503Allen\\t Mr. William Henrymale35.0003734508.0500NaNS
\n", - "
" - ], - "text/plain": [ - " Survived Pclass \\\n", - "PassengerId \n", - "1 0 3 \n", - "2 1 1 \n", - "3 1 3 \n", - "4 1 1 \n", - "5 0 3 \n", - "\n", - " Name Sex Age \\\n", - "PassengerId \n", - "1 Braund\\t Mr. Owen Harris male 22.0 \n", - "2 Cumings\\t Mrs. John Bradley (Florence Briggs T... female 38.0 \n", - "3 Heikkinen\\t Miss. Laina female 26.0 \n", - "4 Futrelle\\t Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", - "5 Allen\\t Mr. William Henry male 35.0 \n", - "\n", - " SibSp Parch Ticket Fare Cabin Embarked \n", - "PassengerId \n", - "1 1 0 A/5 21171 7.2500 NaN S \n", - "2 1 0 PC 17599 71.2833 C85 C \n", - "3 0 0 STON/O2. 3101282 7.9250 NaN S \n", - "4 1 0 113803 53.1000 C123 S \n", - "5 0 0 373450 8.0500 NaN S " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = pd.read_csv('./titanic_train.tsv', sep='\\t', index_col='PassengerId')\n", "\n", @@ -6534,86 +2051,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
SurvivedPclassAgeSibSpParchFare
Sex
female0.7420382.15923627.9157090.6942680.64968244.479818
male0.1889082.38994830.7266450.4298090.23570225.523893
\n", - "
" - ], - "text/plain": [ - " Survived Pclass Age SibSp Parch Fare\n", - "Sex \n", - "female 0.742038 2.159236 27.915709 0.694268 0.649682 44.479818\n", - "male 0.188908 2.389948 30.726645 0.429809 0.235702 25.523893" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.groupby('Sex').mean()" ] @@ -6627,122 +2074,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
SurvivedAgeSibSpParchFare
SexPclass
female10.96808534.6117650.5531910.457447106.125798
20.92105328.7229730.4868420.60526321.970121
30.50000021.7500000.8958330.79861116.118810
male10.36885241.2813860.3114750.27868967.226127
20.15740730.7407070.3425930.22222219.741782
30.13544726.5075890.4985590.22478412.661633
\n", - "
" - ], - "text/plain": [ - " Survived Age SibSp Parch Fare\n", - "Sex Pclass \n", - "female 1 0.968085 34.611765 0.553191 0.457447 106.125798\n", - " 2 0.921053 28.722973 0.486842 0.605263 21.970121\n", - " 3 0.500000 21.750000 0.895833 0.798611 16.118810\n", - "male 1 0.368852 41.281386 0.311475 0.278689 67.226127\n", - " 2 0.157407 30.740707 0.342593 0.222222 19.741782\n", - " 3 0.135447 26.507589 0.498559 0.224784 12.661633" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.groupby(['Sex', 'Pclass']).mean()" ] @@ -6766,616 +2107,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
pairsystemidis_constrainedmetricscore
0ha-enNiuTrans382Truebleu-all16.512243
1ha-enNiuTrans382Truechrf-all0.447248
2ha-enNiuTrans382Truebleu-A16.512243
3ha-enNiuTrans382Truechrf-A0.447248
4ha-enOnline-B1356Falsebleu-all18.655658
5ha-enOnline-B1356Falsechrf-all0.466582
6ha-enOnline-B1356Falsebleu-A18.655658
7ha-enOnline-B1356Falsechrf-A0.466582
8ha-enFacebook-AI181Falsebleu-all20.982704
9ha-enFacebook-AI181Falsechrf-all0.486538
10ha-enFacebook-AI181Falsebleu-A20.982704
11ha-enFacebook-AI181Falsechrf-A0.486538
12ha-enManifold437Truebleu-all16.943915
13ha-enManifold437Truechrf-all0.456384
14ha-enManifold437Truebleu-A16.943915
15ha-enManifold437Truechrf-A0.456384
16ha-enOnline-Y1374Falsebleu-all13.898531
17ha-enOnline-Y1374Falsechrf-all0.448429
18ha-enOnline-Y1374Falsebleu-A13.898531
19ha-enOnline-Y1374Falsechrf-A0.448429
20ha-enTWB1335Falsebleu-all12.326443
21ha-enTWB1335Falsechrf-all0.402826
22ha-enTWB1335Falsebleu-A12.326443
23ha-enTWB1335Falsechrf-A0.402826
24ha-enMS-EgDC896Truebleu-all17.133350
25ha-enMS-EgDC896Truechrf-all0.452663
26ha-enMS-EgDC896Truebleu-A17.133350
27ha-enMS-EgDC896Truechrf-A0.452663
28ha-enTRANSSION336Falsebleu-all18.834851
29ha-enTRANSSION336Falsechrf-all0.472383
30ha-enTRANSSION336Falsebleu-A18.834851
31ha-enTRANSSION336Falsechrf-A0.472383
32ha-enAMU628Truebleu-all14.132845
33ha-enAMU628Truechrf-all0.412566
34ha-enAMU628Truebleu-A14.132845
35ha-enAMU628Truechrf-A0.412566
36ha-enUEdin1149Truebleu-all14.887836
37ha-enUEdin1149Truechrf-all0.422474
38ha-enUEdin1149Truebleu-A14.887836
39ha-enUEdin1149Truechrf-A0.422474
40ha-enZMT553Falsebleu-all18.837023
41ha-enZMT553Falsechrf-all0.472315
42ha-enZMT553Falsebleu-A18.837023
43ha-enZMT553Falsechrf-A0.472315
44ha-enP3AI715Truebleu-all17.793617
45ha-enP3AI715Truechrf-all0.463074
46ha-enP3AI715Truebleu-A17.793617
47ha-enP3AI715Truechrf-A0.463074
48ha-enHuaweiTSC758Truebleu-all17.492440
49ha-enHuaweiTSC758Truechrf-all0.467957
50ha-enHuaweiTSC758Truebleu-A17.492440
51ha-enHuaweiTSC758Truechrf-A0.467957
52ha-enGTCOM1298Falsebleu-all17.794272
53ha-enGTCOM1298Falsechrf-all0.467148
54ha-enGTCOM1298Falsebleu-A17.794272
55ha-enGTCOM1298Falsechrf-A0.467148
\n", - "
" - ], - "text/plain": [ - " pair system id is_constrained metric score\n", - "0 ha-en NiuTrans 382 True bleu-all 16.512243\n", - "1 ha-en NiuTrans 382 True chrf-all 0.447248\n", - "2 ha-en NiuTrans 382 True bleu-A 16.512243\n", - "3 ha-en NiuTrans 382 True chrf-A 0.447248\n", - "4 ha-en Online-B 1356 False bleu-all 18.655658\n", - "5 ha-en Online-B 1356 False chrf-all 0.466582\n", - "6 ha-en Online-B 1356 False bleu-A 18.655658\n", - "7 ha-en Online-B 1356 False chrf-A 0.466582\n", - "8 ha-en Facebook-AI 181 False bleu-all 20.982704\n", - "9 ha-en Facebook-AI 181 False chrf-all 0.486538\n", - "10 ha-en Facebook-AI 181 False bleu-A 20.982704\n", - "11 ha-en Facebook-AI 181 False chrf-A 0.486538\n", - "12 ha-en Manifold 437 True bleu-all 16.943915\n", - "13 ha-en Manifold 437 True chrf-all 0.456384\n", - "14 ha-en Manifold 437 True bleu-A 16.943915\n", - "15 ha-en Manifold 437 True chrf-A 0.456384\n", - "16 ha-en Online-Y 1374 False bleu-all 13.898531\n", - "17 ha-en Online-Y 1374 False chrf-all 0.448429\n", - "18 ha-en Online-Y 1374 False bleu-A 13.898531\n", - "19 ha-en Online-Y 1374 False chrf-A 0.448429\n", - "20 ha-en TWB 1335 False bleu-all 12.326443\n", - "21 ha-en TWB 1335 False chrf-all 0.402826\n", - "22 ha-en TWB 1335 False bleu-A 12.326443\n", - "23 ha-en TWB 1335 False chrf-A 0.402826\n", - "24 ha-en MS-EgDC 896 True bleu-all 17.133350\n", - "25 ha-en MS-EgDC 896 True chrf-all 0.452663\n", - "26 ha-en MS-EgDC 896 True bleu-A 17.133350\n", - "27 ha-en MS-EgDC 896 True chrf-A 0.452663\n", - "28 ha-en TRANSSION 336 False bleu-all 18.834851\n", - "29 ha-en TRANSSION 336 False chrf-all 0.472383\n", - "30 ha-en TRANSSION 336 False bleu-A 18.834851\n", - "31 ha-en TRANSSION 336 False chrf-A 0.472383\n", - "32 ha-en AMU 628 True bleu-all 14.132845\n", - "33 ha-en AMU 628 True chrf-all 0.412566\n", - "34 ha-en AMU 628 True bleu-A 14.132845\n", - "35 ha-en AMU 628 True chrf-A 0.412566\n", - "36 ha-en UEdin 1149 True bleu-all 14.887836\n", - "37 ha-en UEdin 1149 True chrf-all 0.422474\n", - "38 ha-en UEdin 1149 True bleu-A 14.887836\n", - "39 ha-en UEdin 1149 True chrf-A 0.422474\n", - "40 ha-en ZMT 553 False bleu-all 18.837023\n", - "41 ha-en ZMT 553 False chrf-all 0.472315\n", - "42 ha-en ZMT 553 False bleu-A 18.837023\n", - "43 ha-en ZMT 553 False chrf-A 0.472315\n", - "44 ha-en P3AI 715 True bleu-all 17.793617\n", - "45 ha-en P3AI 715 True chrf-all 0.463074\n", - "46 ha-en P3AI 715 True bleu-A 17.793617\n", - "47 ha-en P3AI 715 True chrf-A 0.463074\n", - "48 ha-en HuaweiTSC 758 True bleu-all 17.492440\n", - "49 ha-en HuaweiTSC 758 True chrf-all 0.467957\n", - "50 ha-en HuaweiTSC 758 True bleu-A 17.492440\n", - "51 ha-en HuaweiTSC 758 True chrf-A 0.467957\n", - "52 ha-en GTCOM 1298 False bleu-all 17.794272\n", - "53 ha-en GTCOM 1298 False chrf-all 0.467148\n", - "54 ha-en GTCOM 1298 False bleu-A 17.794272\n", - "55 ha-en GTCOM 1298 False chrf-A 0.467148" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = pd.read_csv('https://raw.githubusercontent.com/wmt-conference/wmt21-news-systems/main/scores/automatic-scores.tsv', sep='\\t')\n", "df[df.pair == 'ha-en']" @@ -7383,174 +2124,16 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
metricbleu-Ableu-allchrf-Achrf-all
system
AMU14.13284514.1328450.4125660.412566
Facebook-AI20.98270420.9827040.4865380.486538
GTCOM17.79427217.7942720.4671480.467148
HuaweiTSC17.49244017.4924400.4679570.467957
MS-EgDC17.13335017.1333500.4526630.452663
Manifold16.94391516.9439150.4563840.456384
NiuTrans16.51224316.5122430.4472480.447248
Online-B18.65565818.6556580.4665820.466582
Online-Y13.89853113.8985310.4484290.448429
P3AI17.79361717.7936170.4630740.463074
TRANSSION18.83485118.8348510.4723830.472383
TWB12.32644312.3264430.4028260.402826
UEdin14.88783614.8878360.4224740.422474
ZMT18.83702318.8370230.4723150.472315
\n", - "
" - ], - "text/plain": [ - "metric bleu-A bleu-all chrf-A chrf-all\n", - "system \n", - "AMU 14.132845 14.132845 0.412566 0.412566\n", - "Facebook-AI 20.982704 20.982704 0.486538 0.486538\n", - "GTCOM 17.794272 17.794272 0.467148 0.467148\n", - "HuaweiTSC 17.492440 17.492440 0.467957 0.467957\n", - "MS-EgDC 17.133350 17.133350 0.452663 0.452663\n", - "Manifold 16.943915 16.943915 0.456384 0.456384\n", - "NiuTrans 16.512243 16.512243 0.447248 0.447248\n", - "Online-B 18.655658 18.655658 0.466582 0.466582\n", - "Online-Y 13.898531 13.898531 0.448429 0.448429\n", - "P3AI 17.793617 17.793617 0.463074 0.463074\n", - "TRANSSION 18.834851 18.834851 0.472383 0.472383\n", - "TWB 12.326443 12.326443 0.402826 0.402826\n", - "UEdin 14.887836 14.887836 0.422474 0.422474\n", - "ZMT 18.837023 18.837023 0.472315 0.472315" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df[df.pair == 'ha-en'].pivot(index='system', columns='metric', values='score')" ] @@ -7584,167 +2167,16 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
103Braund\\t Mr. Owen Harrismale22.010A/5 211717.2500NaNS
211Cumings\\t Mrs. John Bradley (Florence Briggs T...female38.010PC 1759971.2833C85C
313Heikkinen\\t Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
411Futrelle\\t Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
503Allen\\t Mr. William Henrymale35.0003734508.0500NaNS
\n", - "
" - ], - "text/plain": [ - " Survived Pclass \\\n", - "PassengerId \n", - "1 0 3 \n", - "2 1 1 \n", - "3 1 3 \n", - "4 1 1 \n", - "5 0 3 \n", - "\n", - " Name Sex Age \\\n", - "PassengerId \n", - "1 Braund\\t Mr. Owen Harris male 22.0 \n", - "2 Cumings\\t Mrs. John Bradley (Florence Briggs T... female 38.0 \n", - "3 Heikkinen\\t Miss. Laina female 26.0 \n", - "4 Futrelle\\t Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", - "5 Allen\\t Mr. William Henry male 35.0 \n", - "\n", - " SibSp Parch Ticket Fare Cabin Embarked \n", - "PassengerId \n", - "1 1 0 A/5 21171 7.2500 NaN S \n", - "2 1 0 PC 17599 71.2833 C85 C \n", - "3 0 0 STON/O2. 3101282 7.9250 NaN S \n", - "4 1 0 113803 53.1000 C123 S \n", - "5 0 0 373450 8.0500 NaN S " - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = pd.read_csv('./titanic_train.tsv', sep='\\t', index_col='PassengerId')\n", "\n", @@ -7753,79 +2185,32 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "PassengerId\n", - "1 BRAUND\\t MR. OWEN HARRIS\n", - "2 CUMINGS\\t MRS. JOHN BRADLEY (FLORENCE BRIGGS T...\n", - "3 HEIKKINEN\\t MISS. LAINA\n", - "4 FUTRELLE\\t MRS. JACQUES HEATH (LILY MAY PEEL)\n", - "5 ALLEN\\t MR. WILLIAM HENRY\n", - " ... \n", - "887 MONTVILA\\t REV. JUOZAS\n", - "888 GRAHAM\\t MISS. MARGARET EDITH\n", - "889 JOHNSTON\\t MISS. CATHERINE HELEN \"CARRIE\"\n", - "890 BEHR\\t MR. KARL HOWELL\n", - "891 DOOLEY\\t MR. PATRICK\n", - "Name: Name, Length: 891, dtype: object" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.Name.str.upper()" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PassengerId\n", - "1 Braund\\t Mr. Owen Harris\n", - "2 Cumings\\t Mrs. John Bradley (Florence Briggs T...\n", - "3 Heikkinen\\t Miss. Laina\n", - "4 Futrelle\\t Mrs. Jacques Heath (Lily May Peel)\n", - "5 Allen\\t Mr. William Henry\n", - "Name: Name, dtype: object\n" - ] - }, - { - "data": { - "text/plain": [ - "PassengerId\n", - "1 False\n", - "2 True\n", - "3 True\n", - "4 True\n", - "5 False\n", - "Name: Name, dtype: bool" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "print(df.Name.head())\n", "df.Name.str.contains('Miss|Mrs').head()" @@ -7833,163 +2218,32 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
01
PassengerId
1BraundMr. Owen Harris
2CumingsMrs. John Bradley (Florence Briggs Thayer)
3HeikkinenMiss. Laina
4FutrelleMrs. Jacques Heath (Lily May Peel)
5AllenMr. William Henry
.........
887MontvilaRev. Juozas
888GrahamMiss. Margaret Edith
889JohnstonMiss. Catherine Helen \"Carrie\"
890BehrMr. Karl Howell
891DooleyMr. Patrick
\n", - "

891 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - " 0 1\n", - "PassengerId \n", - "1 Braund Mr. Owen Harris\n", - "2 Cumings Mrs. John Bradley (Florence Briggs Thayer)\n", - "3 Heikkinen Miss. Laina\n", - "4 Futrelle Mrs. Jacques Heath (Lily May Peel)\n", - "5 Allen Mr. William Henry\n", - "... ... ...\n", - "887 Montvila Rev. Juozas\n", - "888 Graham Miss. Margaret Edith\n", - "889 Johnston Miss. Catherine Helen \"Carrie\"\n", - "890 Behr Mr. Karl Howell\n", - "891 Dooley Mr. Patrick\n", - "\n", - "[891 rows x 2 columns]" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.Name.str.split('\\t', expand=True)" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "PassengerId\n", - "1 [Braund, Mr. Owen Harris]\n", - "2 [Cumings, Mrs. John Bradley (Florence Briggs ...\n", - "3 [Heikkinen, Miss. Laina]\n", - "4 [Futrelle, Mrs. Jacques Heath (Lily May Peel)]\n", - "5 [Allen, Mr. William Henry]\n", - " ... \n", - "887 [Montvila, Rev. Juozas]\n", - "888 [Graham, Miss. Margaret Edith]\n", - "889 [Johnston, Miss. Catherine Helen \"Carrie\"]\n", - "890 [Behr, Mr. Karl Howell]\n", - "891 [Dooley, Mr. Patrick]\n", - "Name: Name, Length: 891, dtype: object" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "\n", "df.Name.str.split('\\t')" @@ -7997,108 +2251,48 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "PassengerId\n", - "1 Mr. Owen Harris\n", - "2 Mrs. John Bradley (Florence Briggs Thayer)\n", - "3 Miss. Laina\n", - "4 Mrs. Jacques Heath (Lily May Peel)\n", - "5 Mr. William Henry\n", - " ... \n", - "887 Rev. Juozas\n", - "888 Miss. Margaret Edith\n", - "889 Miss. Catherine Helen \"Carrie\"\n", - "890 Mr. Karl Howell\n", - "891 Mr. Patrick\n", - "Name: Name, Length: 891, dtype: object" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.Name.str.split('\\t').str[1]" ] }, { "cell_type": "code", - "execution_count": 81, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "PassengerId\n", - "1 Mr.\n", - "2 Mrs.\n", - "3 Miss.\n", - "4 Mrs.\n", - "5 Mr.\n", - " ... \n", - "887 Rev.\n", - "888 Miss.\n", - "889 Miss.\n", - "890 Mr.\n", - "891 Mr.\n", - "Name: Name, Length: 891, dtype: object" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.Name.str.split('\\t').str[1].str.strip().str.split(' ').str[0]" ] }, { "cell_type": "code", - "execution_count": 219, + "execution_count": null, "metadata": { + "pycharm": { + "is_executing": true + }, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 219, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dane.hist()" ] From 19974529fc4a9bc35a1506d007f8a02d5a732a56 Mon Sep 17 00:00:00 2001 From: jnowacki Date: Sun, 18 Dec 2022 09:19:41 +0100 Subject: [PATCH 3/5] ipynb updates --- .../data_analysis-checkpoint.ipynb | 2307 +++++++++++++++++ podstawy/podstawy.ipynb | 2 +- 2 files changed, 2308 insertions(+), 1 deletion(-) create mode 100644 pandas/.ipynb_checkpoints/data_analysis-checkpoint.ipynb diff --git a/pandas/.ipynb_checkpoints/data_analysis-checkpoint.ipynb b/pandas/.ipynb_checkpoints/data_analysis-checkpoint.ipynb new file mode 100644 index 0000000..6cbfd7d --- /dev/null +++ b/pandas/.ipynb_checkpoints/data_analysis-checkpoint.ipynb @@ -0,0 +1,2307 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Analiza Danych w Pythonie: `pandas`\n", + "\n", + "### 10 grudnia 2022" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### `pandas`\n", + "Biblioteka `pandas` jest podstawowym narzędziem w ekosystemie Pythona do analizy danych:\n", + " * dostarcza dwa podstawowe typy danych: \n", + " * `Series` (szereg, 1D)\n", + " * `DataFrame` (ramka danych, 2D)\n", + " * operacje na tych obiektach: obsługa brakujących wartości, łączenie danych;\n", + " * obsługuje dane różnego typu, np. szeregi czasowe;\n", + " * biblioteka bazuje na `numpy` -- bibliotece do obliczeń numerycznych;\n", + " * pozwala też na prostą wizualizację danych;\n", + " * ETL: extract, transform, load." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "Żeby zaimportowąc bibliotekę `pandas` wystarczy:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "#### __Zadanie 0__: sprawdź, czy masz zainstalowaną bibliotekę `pandas`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### [Szeregi](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html) (`pd.Series`)\n", + "\n", + " Szereg reprezentuje jednorodne dane jednowymiarowe - jest odpowiednikiem wektora w R.\n", + " * Szeregi możemy tworzyć na różne sposoby (więcej za chwilę), np. z obiektów tj. listy i słowniki.\n", + " * Dane muszą być jednorodne. W przeciwnym przypadku nastąpi automatyczna konwersja.\n", + " * Podczas tworzenia szeregu musimy podać jeden obowiązkowy argument `data` - dane.\n", + " * Ponadto możemy podać też indeks (`index`), typ danych (`dtype`) lub nazwę (`name`).\n", + " \n", + " \n", + " ```\n", + " class pandas.Series(data=None, index=None, dtype=None, name=None)\n", + " ```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "Podczas tworzenie szeregu mozemy podać dane w formacie listy lub słownika.\n", + "\n", + "Poniżej jest przykład przedstawiający tworzenie szeregu z danych, które są zawarte w liście:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "\n", + "data = [211819, 682758, 737011, 779511, 673790, 673790, 444177, 136791]\n", + "\n", + "s = pd.Series(data)\n", + "\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "W przypadku, gdy dane pochodzą z listy i nie podaliśmy indeksu, pandas doda automatyczny indeks liczbowy zaczynający się od 0." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "W przypadku przekazania słownika jako danych do szeregu, pandas wykorzysta klucze do stworzenia indeksu:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "members = {'April': 211819,'May': 682758, 'June': 737011, 'July': 779511}\n", + "\n", + "s = pd.Series(members)\n", + "\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "Podczas tworzenia szeregu możemy zdefiniować indeks, jak i nazwę szeregu:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "months = ['April', 'May', 'June', 'July']\n", + "\n", + "data = [211819, 682758, 737011, 779511]\n", + "\n", + "s = pd.Series(data=data, index=months, dtype=float, name='Rides')\n", + "\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "Odwołanie się do poszczególnego elementu odbywa się przy pomocy klucza z indeksu." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "members = {'April': 211819,'May': 682758, 'June': 737011, 'July': 779511}\n", + "\n", + "s = pd.Series(members)\n", + "\n", + "print(s['April'])\n", + "\n", + "s['August'] = 673790\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "Dodanie elementu do szeregu odbywa się poprzez definiowanie nowego klucza:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "members = {'April': 211819,'May': 682758, 'June': 737011, 'July': 779511}\n", + "\n", + "s = pd.Series(members)\n", + "\n", + "s['August'] = 673790\n", + "\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "Więcej nt. indeksowania w szeregach w dalszej części kursu." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "Podstawowa cechą szeregu jest wykonywanie operacji w sposób wektorowy. Działa to w następujący sposób:\n", + " * gdy w obu szeregach jest zawarty ten sam klucz, to są sumowane ich wartości;\n", + " * w przeciwnym przypadku wartość klucza w wynikowym szeregu to `pd.NaN`. \n", + " * Równoważnie możemy wykorzystać metodę `pandas.Series.add`. W tym przypadku możemy podać domyślną wartość w przypadku braku klucza." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "members = pd.Series({'May': 682758, 'June': 737011, 'August': 673790, 'July': 779511,\n", + "'September': 673790, 'October': 444177})\n", + "\n", + "occasionals = pd.Series({'May': 147898, 'June': 171494, 'July': 194316, 'August': 206809,\n", + "'September': 140492})\n", + "\n", + "all_data = members + occasionals\n", + "# Równoważnie\n", + "all_data = members.add(occasionals)\n", + "all_data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "Możemy wykonać operacje arytmetyczne na szeregu: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "members = pd.Series({'May': 682758, 'June': 737011, 'July': 779511, 'August': 673790,\n", + "'September': 673790, 'October': 444177})\n", + "\n", + "members += 1000\n", + "\n", + "members" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Podsumowanie\n", + " * Szeregi działają podobnie do słowników, z tą różnicą, że wartości muszą być jednorodne (tego samego typu).\n", + " * Odwołanie do poszczególnych elementów odbywa się poprzez nawiasy `[]` i podanie klucza.\n", + " * W przeciwieństwie do słowników, możemy w prosty sposób wykonywać operacje arytmetyczne." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Zadanie 1\n", + " * Stwórz szereg `n`, który będzie zawierać liczby od 0 do 10 (włącznie).\n", + " * Stwórz szereg `n2`, który będzie zawierać kwadraty liczb od 0 do 10 (włącznie).\n", + " * Następnie stwórz szereg `trojkatne`, który będzie sumą powyższych szeregów podzieloną przez 2." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### [Ramka danych](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) (`pd.DataFrame`)\n", + "\n", + "Ramka danych jest podstawową strukturą danych w bibliotece `pandas`, która pozwala na trzymanie i reprezentowanie danych tabelarycznych (dwuwymiarowych).\n", + " * Posiada kolumny (cechy) i wiersze (obserwacje, przykłady).\n", + " * Możemy też patrzeć na nią jak na słownik, którego wartościami są szeregi.\n", + "\n", + "```\n", + "class pandas.DataFrame(data=None, index=None, columns=None, dtype=None)\n", + "```\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "Ramkę danych możemy stworzyć na różne sposoby.\n", + "\n", + "Pierwszy z nich (\"kolumnowy\") polega na zdefiniowaniu ramki poprzez podanie szeregów jako kolumn:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "members = pd.Series({'May': 682758, 'June': 737011, 'July': 779511})\n", + "occasionals = pd.Series({'May': 147898, 'June': 171494, 'July': 194316})\n", + "\n", + "df = pd.DataFrame({'members': members, 'occasionals': occasionals})\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "Drugim popularnym sposobem jest przekazanie listy słowników. Wtedy `pandas` zinterpretuje to jako listę przykładów:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "data = [\n", + " {'members': 682758, 'occasionals': 147898},\n", + " {'occasionals': 171494,'members': 737011},\n", + " {'members': 779511, 'occasionals': 194316},\n", + "]\n", + "\n", + "df = pd.DataFrame(data)\n", + "\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "Możemy też wykorzystać metodę `from_dict` ([doc](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.from_dict.html)), która pozwala zdefiniować czy podane dane są w podane w postaci kolumnowej lub wierszowej:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "data = {\n", + " 'May': {'members': 682758, 'occasionals': 147898},\n", + " 'June': {'members': 737011, 'occasionals': 171494},\n", + " 'July': {'members': 779511, 'occasionals': 194316}\n", + "}\n", + "\n", + "df = pd.DataFrame.from_dict(data, orient='index')\n", + "print('index\\n', df)\n", + "print()\n", + "df = pd.DataFrame.from_dict(data, orient='columns')\n", + "print('columns\\n', df)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Wczytywanie danych" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Biblioteka `pandas` pozwala na wczytanie i zapis danych z różnych formatów:\n", + " * formaty tekstowe, np. `csv`, `json`\n", + " * pliki arkuszy kalkulacyjnych: Excel (xls, xlsx)\n", + " * bazy danych\n", + " * inne: `sas` `spss`\n", + "\n", + "\n", + "Efektem wczytania danych jest odpowiednio stworzona ramka danych (`DataFrame`)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Jednym z najprostszych formatów danych jest format `csv`, gdzie kolejne wartości są rozdzielone przecinkiem.\n", + "\n", + "Żeby wczytać dane w takim formacie należy użyć funkcji `pandas.read_csv`.\n", + "\n", + "Pandas pozwala na ustawienie wielu parametrów (np. separator, cudzysłowy). Więcej na ten temat w [dokumentacji](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df = pd.read_csv('gapminder.csv')\n", + "\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df = pd.read_csv('./titanic_train.tsv', delimiter='\\t', index_col=0, nrows=5)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Do wczytania danych z arkusza kalkulacyjnego służy funkcja `pandas.read_excel`. Do otworzenia pliku `xlsx` może być koniecnze ustawienie parametru: `engine='openpyxl`. Więcej opcji w [dokumentacji](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df = pd.read_excel('./bikes.xlsx', engine='openpyxl', nrows=5)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Innym ważnym źródłem informacji są bazy danych. Pandas potrafi komunikować się z bazą danych za pomocą biblioteki [SQLAlchemy](https://pypi.org/project/SQLAlchemy/) i dostarcza odpowiedną funkcję:\n", + " * `pandas.read_sql` - wczytanie całej tabeli lub zapytania do bazy danych" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df = pd.read_sql('Album', con='sqlite:///Chinook.sqlite', index_col='AlbumId')\n", + "\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "import sqlalchemy\n", + "\n", + "engine = sqlalchemy.create_engine('sqlite:///Chinook.sqlite', echo=True)\n", + "connection = engine.raw_connection()\n", + "\n", + "df = pd.read_sql('SELECT * FROM Album', con='sqlite:///Chinook.sqlite', index_col='AlbumId')\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Biblioteka `pandas` potrafi także automatycznie pobrać dane, które znajdują się w Internecie. Dzięki temu możemy zaciągnąć dane z Google spreadsheets:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "url = \"https://docs.google.com/spreadsheets/d/1ycvVWmVJ2MTn3_1NRVmVrySoHEHdWlwi4-Kr1W0Nv28/export?format=csv&gid=848662053\"\n", + "df = pd.read_csv(url)\n", + "\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "#### Podsumowanie\n", + "\n", + "\n", + " * Biblioteka `pandas` wspiera pobieranie danych z różnych formatów i źródeł.\n", + " * Każda funkcja ma listę argumentów, które pozwalają na ustawić poszczególne parametry (np. [read_csv](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html#pandas.read_csv))." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Zapis i eksport danych" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "Pandas pozwala w prosty sposób na zapisywanie ramki danych do pliku. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "members = pd.Series({'May': 682758, 'June': 737011, 'July': 779511})\n", + "occasionals = pd.Series({'May': 147898, 'June': 171494, 'July': 194316})\n", + "\n", + "df = pd.DataFrame({'members': members, 'occasionals': occasionals})\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "# zapis do formatu CSV\n", + "df.to_csv('tmp.csv')\n", + "# zapis do arkusza kalkulacyjnego \n", + "df.to_excel('tmp.xlsx')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "Ponadto możemy przekonwertować ramkę danych do JSONa lub Pythonowego słownika:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "print(df.to_json())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "print(df.to_dict())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "Lub przekopiować dane do schowka:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.to_clipboard()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Zadanie\n", + "\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + " * Przekonwertuj tabele `Customer` z bazy `Chinook.sqlite` do arkusza kalkulacyjnego. Plik wynikowy nazwij `customers.xlsx`.\n", + " * Tabela `Employee` zawiera informacje o pracownikach firmy Chinook. Wyswietl dane na ekranie i podaj miasta, w których mieszkają pracownicy.\n", + " * Tabela `Invoice` zawiera informacje o fakturach. Przekonwertuj kolumnę `BillingCountry` do pythonowego słownika, a następnie podaj najcześciej występującą wartość. Ile razy pojawiła się?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Ramka danych - podstawy" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "#### Kolumny\n", + "\n", + "Na ramkę danych możemy patrzeć jak na swego rodzaju słownik, którego wartościami są szeregi. Pozwoli to na uzyskanie lepszej intuicji.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df = pd.read_csv('./gapminder.csv', index_col='Country', nrows=8, usecols=['Country', 'gdp', 'population','life_expectancy'])\n", + "\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Dostęp do poszczególnej kolumny możemy uzystać na dwa sposoby:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "# notacja z kropką\n", + "df.population" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "# Operator []\n", + "df['population']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Do operatora `[]` możemy też podać listę nazw kolumn:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df[['gdp','population']]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Listę kolumn możemy pobrać za pomocą:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.columns = ['PKB', 'Populacja', 'ODŻ']\n", + "\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Żeby odwołać się do poszczególnych wierszy należy wykorzystać metodę `loc`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.loc['Argentina']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "Metoda `loc` również może przyjąć listę wierszy: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.loc[['Albania', 'Angola']]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "Możemy również podać drugi parametr: nazwy kolumn:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df2 = df.loc[['Albania', 'Angola'], ['PKB', 'Populacja']]\n", + "\n", + "df2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "Albo wykorzystać tzw. _slicing_, cyzli operator `:`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.loc['Albania': 'Angola', 'PKB': 'ODŻ']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Żeby odwołać się do pojedyńczej wartości możemy użyć metody `at`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.at['Angola', 'PKB']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "Dostęp do indeksu:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.index" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "#### Podstawowe metody `pd.Series` i `pd.DataFrame`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "members = pd.Series({'May': 682758, 'June': 737011, 'July': 779511, 'August': 673790,\n", + "'September': 673790, 'October': 444177})\n", + "\n", + "occasionals = pd.Series({'May': 147898, 'June': 171494, 'July': 194316, 'August': 206809,\n", + "'September': 140492, 'October': 53596})\n", + "\n", + "df = pd.DataFrame({'members': members, 'occasionals': occasionals})\n", + "\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Metoda `head` pozwala tworzy nową ramkę danych z pierwszymi 5 przykładami:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Metoda `tail` robi to samo, ale z 5 ostatnymi przykładami:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Metoda `sample` pozwala na stworzenie nowej ramki danych z wylosowanymi `n` przykładami:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.sample(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Metoda `describe` zwraca podstawowe statystyki m.in.: liczebność, średnią, wartości skrajne: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Metoda `info` zwraca informacje techniczne o kolumnach: np. typ danych:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Podstawową informacją o ramce danych to liczba przykładów w ramce danych. Możemy wykorzystać to tego funkcję `len`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "len(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Natomiast atrybut `shape` zwraca nam krotkę z liczbą przykładów i liczbą kolumn:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "#### Operacja arytmetyczne\n", + "\n", + " * `max`, `idxmax`\n", + " * `min`, `idxmin`\n", + " * `mean`\n", + " * `count`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Zbiór wartości i zliczanie wartości:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "dane = pd.Series([1, 3, 2, 3, 1, 1, 2, 3, 2, 3])\n", + "\n", + "print(dane.unique())\n", + "\n", + "dane = pd.Series([1, 3, 2, 3, 1, 1, 2, 3, 2, 3])\n", + "\n", + "print(dane.value_counts())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Sprawdzanie czy brakuje danych:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df = pd.read_csv('./titanic_train.tsv', sep='\\t', index_col='PassengerId')\n", + "df.Age.isnull()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Dodawanie i modyfikowanie danych" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df = pd.read_csv('./gapminder.csv', index_col='Country', nrows=5)\n", + "\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "conts = pd.Series({\n", + " 'Afghanistan': 'Asia', 'Albania': 'Europe', 'Algeria':' Africa', 'Angola': 'Africa', 'Antigua and Barbuda': 'Americas'})\n", + "\n", + "df['continent'] = conts\n", + "\n", + "df['tmp'] = 1\n", + "\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.loc['Argentina'] = {\n", + " 'female_BMI': 27.46523,\n", + " 'male_BMI': 27.5017,\n", + " 'gdp': 14646.0,\n", + " 'population': 40381860.0,\n", + " 'under5mortality': 15.4,\n", + " 'life_expectancy': 75.4,\n", + " 'fertility': 2.24\n", + "}\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.drop('gdp', axis='columns')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Filtrowanie danych" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Biblioteka pandas posiada 2 sposoby na filtrowanie danych zawartych w ramce danych:\n", + " * operator `[]` -- najbardziej rozpowszechniony;\n", + " * metoda `query()`.\n", + "Oba sposoby mają różną składnię.\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df = pd.read_csv('./titanic_train.tsv', sep='\\t', index_col='PassengerId')\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df['Survived']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df['Survived'] == 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df[df['Pclass'] == 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "#### Operatory\n", + "\n", + "* `&` - koniukcja (i)\n", + "* `|` - alternatywa (lub)\n", + "* `~` - negacja (nie)\n", + "* `()` - jeżeli mamy kilka warunków to warto je uporządkować w nawiasy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "pierwsza_klasa = df['Pclass'] == 1\n", + "kobiety = df['Sex'] == 'female'\n", + "\n", + "df[pierwsza_klasa & kobiety]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "\n", + "df[df['SibSp'] > df['Parch']]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "#### `pd.DataFrame.query`\n", + "\n", + "Innym sposobem na filtrowanie danych jest metoda `query`, która jako argument przyjmuje wyrażenie:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.query('Pclass == 1').head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.query('(Pclass == 1) and (Sex == \"female\")').head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.query('SibSp > Parch')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "young = 18\n", + "df.query('Age < @young').shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Zadanie" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "#### Operacje na wierszach i kolumnach" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df = pd.read_csv('./gapminder.csv', index_col='Country', nrows=5)\n", + "\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Iterowanie po ramce danych oznacza oznacza przejście po nazwach kolumn:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "for column_name in df:\n", + " print(column_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + } + }, + "outputs": [], + "source": [ + "for col_name, series in df.items():\n", + " print(col_name, series)\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "for idx, row in df.iterrows():\n", + " print(idx, '\\n', row)\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "def bmi_level(bmi):\n", + " if bmi <= 18.5:\n", + " level = 'underweight'\n", + " elif bmi < 25:\n", + " level = 'normal'\n", + " elif bmi < 30:\n", + " level = 'overweight'\n", + " else:\n", + " level = 'obese'\n", + " return level\n", + "\n", + "s = df['male_BMI'].map(bmi_level)\n", + " \n", + "s" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "def bmi_level(row_data):\n", + " bmi = row_data['male_BMI']\n", + " if bmi <= 18.5:\n", + " return 'underweight'\n", + " elif bmi < 25:\n", + " return 'normal'\n", + " elif bmi < 30:\n", + " return 'overweight'\n", + " return 'obese'\n", + "\n", + "df.apply(bmi_level, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.transpose()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Grupowanie (`groupby`)\n", + "\n", + "Często zdarza się, gdy potrzebujemy podzielić dane ze względu na wartości w zadanej kolumnie, a następnie obliczenie zebranie danych w każdej z grup. Do tego służy metody `groupby`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df = pd.read_csv('./titanic_train.tsv', sep='\\t', index_col='PassengerId')\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_Przykład_: chcemy obliczyć średnią dla każdej z kolumn z podziałem na płeć pasażera, która jest zawarta w kolumnie `Sex`. Stąd jako parametr do metody `groupby` podajemy nazwę kolumny `Sex`, a następnie wywołujemy metodę `mean`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.groupby('Sex').mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Możemy też podać listę nazw kolumn. Wtedy wartości zostaną obliczone dla każdej z wytworzonych grup:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.groupby(['Sex', 'Pclass']).mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Pivot\n", + "Metoda `pivot` pozwala na stworzenie nowej ramki danych, gdzie indeks i nazwy kolumn są wartościami początkowej ranki danych. \n", + "\n", + "_Przykład_: zobaczmy na poniższą ramkę danych, która zawiera informacje o jakości tłumaczenia dla pary językowej hausa-angielski. Kolumna `system` zawiera nazwę systemu, kolumna `metric` - nazwę metryki, zaś kolumna `score`- wartość metryki. Chcemy przedstawić te dane w następujący sposób: jako klucz chcemy mieć nazwę systemu, zaś jako kolumny - metryki. Możemy wykorzystać do tego metodę `pivot`, gdzie musimy podać 3 argumenty:\n", + " * `index`: nazwę kolumny, na podstawie której zostanie stworzony indeks;\n", + " * `columns`: nazwa kolumny, które zawiera nazwy kolumn dla nowej ramki danych;\n", + " * `values`: nazwa kolumny, która zawiera interesujące nas dane." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df = pd.read_csv('https://raw.githubusercontent.com/wmt-conference/wmt21-news-systems/main/scores/automatic-scores.tsv', sep='\\t')\n", + "df[df.pair == 'ha-en']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df[df.pair == 'ha-en'].pivot(index='system', columns='metric', values='score')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Dane tekstowe" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "`pandas` posiada udogodnienia do pracy z wartościami tekstowymi:\n", + " * dostęp następuje przez atrybut `str`;\n", + " * funkcje:\n", + " * formatujące: `lower()`, `upper()`;\n", + " * wyrażenia regularne: `contains()`, `match()`;\n", + " * inne: `split()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df = pd.read_csv('./titanic_train.tsv', sep='\\t', index_col='PassengerId')\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.Name.str.upper()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "print(df.Name.head())\n", + "df.Name.str.contains('Miss|Mrs').head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.Name.str.split('\\t', expand=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "\n", + "df.Name.str.split('\\t')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.Name.str.split('\\t').str[1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "df.Name.str.split('\\t').str[1].str.strip().str.split(' ').str[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "dane.hist()" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "interpreter": { + "hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/podstawy/podstawy.ipynb b/podstawy/podstawy.ipynb index 75d5a52..c1a9dc8 100644 --- a/podstawy/podstawy.ipynb +++ b/podstawy/podstawy.ipynb @@ -2494,7 +2494,7 @@ "evalue": "invalid syntax (250649810.py, line 1)", "output_type": "error", "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"/tmp/ipykernel_3661/250649810.py\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m Przykłady:\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + "\u001B[0;36m File \u001B[0;32m\"/tmp/ipykernel_3661/250649810.py\"\u001B[0;36m, line \u001B[0;32m1\u001B[0m\n\u001B[0;31m Przykłady:\u001B[0m\n\u001B[0m ^\u001B[0m\n\u001B[0;31mSyntaxError\u001B[0m\u001B[0;31m:\u001B[0m invalid syntax\n" ] } ], From cda8810f3f05605ca4d4ae80c6adeece716b3edf Mon Sep 17 00:00:00 2001 From: jnowacki Date: Sun, 15 Jan 2023 12:48:08 +0100 Subject: [PATCH 4/5] pandas homework --- zadanie_domowe_pandas/zadania.ipynb | 170 +++++++++++++++++++++++----- 1 file changed, 142 insertions(+), 28 deletions(-) diff --git a/zadanie_domowe_pandas/zadania.ipynb b/zadanie_domowe_pandas/zadania.ipynb index 756f552..d7358cc 100644 --- a/zadanie_domowe_pandas/zadania.ipynb +++ b/zadanie_domowe_pandas/zadania.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -25,10 +25,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": " Unnamed: 0 Id Expected Rooms SqrMeters Floor \\\n0 0 1 269000 3 55.00 1 \n1 1 2 320000 3 79.00 10 \n2 2 3 146000 1 31.21 1 \n3 3 4 189000 2 44.00 2 \n4 4 5 480240 2 65.25 1 \n\n Location \n0 Poznań Zawady \n1 Poznań Rataje ul. Orła Bialego \n2 Poznań Nowe Miasto ul. Kawalerka W Nowym Bloku... \n3 Poznań Grunwald Ogrody Jeżyce Centrum Łazarz u... \n4 Poznań ul. Droga Dębińska 19 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Unnamed: 0IdExpectedRoomsSqrMetersFloorLocation
001269000355.001Poznań Zawady
112320000379.0010Poznań Rataje ul. Orła Bialego
223146000131.211Poznań Nowe Miasto ul. Kawalerka W Nowym Bloku...
334189000244.002Poznań Grunwald Ogrody Jeżyce Centrum Łazarz u...
445480240265.251Poznań ul. Droga Dębińska 19
\n
" + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('dane_mieszkania.csv')\n", + "df.head()" + ] }, { "cell_type": "markdown", @@ -39,10 +52,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": "2 2208\nName: Rooms, dtype: int64" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Rooms'].value_counts().head(1)" + ] }, { "cell_type": "markdown", @@ -53,10 +77,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": " Unnamed: 0 Id Expected Rooms SqrMeters Floor \\\n971 971 972 1 5 99.76 1 \n4528 4528 4529 4000 3 85.50 2 \n1175 1175 1176 68000 1 33.10 1 \n4889 4889 4890 79000 2 38.10 10 \n470 470 471 85000 1 37.00 3 \n898 898 899 90000 1 24.26 4 \n2055 2055 2056 94000 2 54.00 2 \n4723 4723 4724 98000 2 50.00 1 \n3550 3550 3551 98000 2 53.00 2 \n911 911 912 99000 2 40.50 3 \n\n Location \n971 Poznań Kameralne Osiedle Domów Energooszczędny... \n4528 Poznań ul. Ułańska \n1175 Poznań Naramowice ul. Naramowicka \n4889 Poznań Nowe Miasto ul. Katowicka \n470 Poznań Nowe Miasto ul. Folwarczna \n898 Poznań Jeżyce ul. Szamarzewskiego \n2055 Poznań Okolica Rybno Rybno ul. Kłecko \n4723 Poznań Wilda ul. Robocza/Sikorskiego \n3550 Poznań Koziegłowy ul. Os. Leśne \n911 Poznań Nowe Miasto ul. Katowicka ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Unnamed: 0IdExpectedRoomsSqrMetersFloorLocation
9719719721599.761Poznań Kameralne Osiedle Domów Energooszczędny...
4528452845294000385.502Poznań ul. Ułańska
11751175117668000133.101Poznań Naramowice ul. Naramowicka
48894889489079000238.1010Poznań Nowe Miasto ul. Katowicka
47047047185000137.003Poznań Nowe Miasto ul. Folwarczna
89889889990000124.264Poznań Jeżyce ul. Szamarzewskiego
20552055205694000254.002Poznań Okolica Rybno Rybno ul. Kłecko
47234723472498000250.001Poznań Wilda ul. Robocza/Sikorskiego
35503550355198000253.002Poznań Koziegłowy ul. Os. Leśne
91191191299000240.503Poznań Nowe Miasto ul. Katowicka
\n
" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_values(by='Expected').head(10)" + ] }, { "cell_type": "markdown", @@ -67,9 +103,18 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": "'Piątkowo'" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "def find_borough(desc):\n", " dzielnice = ['Stare Miasto',\n", @@ -79,7 +124,17 @@ " 'Piątkowo',\n", " 'Winogrady',\n", " 'Miłostowo',\n", - " 'Dębiec']" + " 'Dębiec']\n", + " min_index = -1\n", + " selected_borough = 'Inne'\n", + "\n", + " for b in dzielnice:\n", + " current_index = desc.find(b)\n", + " if -1 < current_index and (current_index < min_index or min_index == -1):\n", + " selected_borough = b\n", + " min_index = current_index\n", + "\n", + " return selected_borough" ] }, { @@ -91,10 +146,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": " Unnamed: 0 Id Expected Rooms SqrMeters Floor \\\n0 0 1 269000 3 55.00 1 \n1 1 2 320000 3 79.00 10 \n2 2 3 146000 1 31.21 1 \n3 3 4 189000 2 44.00 2 \n4 4 5 480240 2 65.25 1 \n\n Location Borough \n0 Poznań Zawady Inne \n1 Poznań Rataje ul. Orła Bialego Rataje \n2 Poznań Nowe Miasto ul. Kawalerka W Nowym Bloku... Inne \n3 Poznań Grunwald Ogrody Jeżyce Centrum Łazarz u... Jeżyce \n4 Poznań ul. Droga Dębińska 19 Inne ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Unnamed: 0IdExpectedRoomsSqrMetersFloorLocationBorough
001269000355.001Poznań ZawadyInne
112320000379.0010Poznań Rataje ul. Orła BialegoRataje
223146000131.211Poznań Nowe Miasto ul. Kawalerka W Nowym Bloku...Inne
334189000244.002Poznań Grunwald Ogrody Jeżyce Centrum Łazarz u...Jeżyce
445480240265.251Poznań ul. Droga Dębińska 19Inne
\n
" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.assign(Borough = lambda r: r['Location'].apply(find_borough))\n", + "df.head()" + ] }, { "cell_type": "markdown", @@ -105,10 +173,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": "Borough\nDębiec 4855.836783\nInne 5681.655798\nJeżyce 5612.074472\nPiątkowo 5784.994686\nRataje 5475.153982\nStare Miasto 9008.936060\nWilda 5489.441432\nWinogrady 8889.044553\nName: SqPrice, dtype: float64" + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['SqPrice'] = df['Expected'] / df['SqrMeters']\n", + "df.groupby('Borough')['SqPrice'].mean()" + ] }, { "cell_type": "markdown", @@ -119,10 +199,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": "Rooms\n1 251,577.85\n2 303,861.86\n3 383,533.64\n4 456,560.24\n5 613,355.19\n6 1,069,136.69\n7 520,000.00\n10 320,000.00\nName: Expected, dtype: object" + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('Rooms')['Expected'].mean().map('{:,.2f}'.format)" + ] }, { "cell_type": "markdown", @@ -133,10 +224,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": "array(['Winogrady', 'Stare Miasto'], dtype=object)" + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df['Floor'] == 13]['Borough'].unique()" + ] }, { "cell_type": "markdown", @@ -147,10 +249,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": " Unnamed: 0 Id Expected Rooms SqrMeters Floor \\\n44 44 45 345000 3 84.00 1 \n105 105 106 275000 3 53.00 1 \n175 175 176 326073 3 53.02 1 \n521 521 522 359000 3 68.00 1 \n530 530 531 374906 3 67.10 1 \n618 618 619 300685 3 49.70 1 \n774 774 775 408456 3 65.88 1 \n796 796 797 279000 3 48.00 1 \n845 845 846 280000 3 48.00 1 \n863 863 864 342000 3 84.00 1 \n869 869 870 314782 3 52.03 1 \n940 940 941 312967 3 51.73 1 \n968 968 969 285000 3 53.00 1 \n1106 1106 1107 288446 3 48.35 1 \n1123 1123 1124 319000 3 57.00 1 \n1133 1133 1134 279000 3 47.60 1 \n1279 1279 1280 280000 3 48.00 1 \n1373 1373 1374 299000 3 57.17 1 \n1385 1385 1386 285000 3 53.00 1 \n1391 1391 1392 320771 3 53.02 1 \n1410 1410 1411 310401 3 52.03 1 \n1476 1476 1477 399000 3 80.00 1 \n1485 1485 1486 265000 3 52.80 1 \n1519 1519 1520 408456 3 65.88 1 \n1706 1706 1707 329271 3 53.54 1 \n1903 1903 1904 328636 3 54.32 1 \n2037 2037 2038 364935 3 65.40 1 \n2045 2045 2046 275397 3 44.78 1 \n2069 2069 2070 308066 3 50.92 1 \n2092 2092 2093 301452 3 54.70 1 \n2273 2273 2274 480000 3 62.77 1 \n2303 2303 2304 380932 3 64.13 1 \n2316 2316 2317 280000 3 48.00 1 \n2324 2324 2325 303779 3 50.92 1 \n2355 2355 2356 331816 3 56.24 1 \n2416 2416 2417 310673 3 50.93 1 \n2659 2659 2660 329000 3 75.00 1 \n2740 2740 2741 288446 3 48.35 1 \n2833 2833 2834 320000 3 53.00 1 \n2835 2835 2836 326761 3 54.01 1 \n3154 3154 3155 350000 3 76.80 1 \n3365 3365 3366 334928 3 55.36 1 \n3379 3379 3380 292518 3 48.35 1 \n3406 3406 3407 319000 3 64.90 1 \n3500 3500 3501 320000 3 53.00 1 \n3599 3599 3600 339000 3 58.00 1 \n3750 3750 3751 213000 3 59.30 1 \n4034 4034 4035 419000 3 78.00 1 \n4133 4133 4134 359000 3 59.00 1 \n4244 4244 4245 323917 3 53.54 1 \n4291 4291 4292 312442 3 51.22 1 \n4314 4314 4315 295068 3 49.46 1 \n4430 4430 4431 285000 3 53.00 1 \n4493 4493 4494 308611 3 51.73 1 \n4536 4536 4537 285000 3 48.00 1 \n4569 4569 4570 344101 3 56.41 1 \n4678 4678 4679 329000 3 75.20 1 \n4716 4716 4717 358765 3 59.30 1 \n4862 4862 4863 289000 3 58.00 1 \n4969 4969 4970 296560 3 49.71 1 \n\n Location Borough \\\n44 Poznań Winogrady Stare Miasto ul. Powstańców W... Winogrady \n105 Poznań Winogrady ul. Oś. Zwycięstwa 26 Winogrady \n175 Poznań Winogrady ul. Hawelańska Winogrady \n521 Poznań Winogrady Winogrady \n530 Poznań Winogrady Piątkowo Naramowice Stare Mia... Winogrady \n618 Poznań Winogrady ul. Hawelańska Winogrady \n774 Poznań Winogrady ul. Hawelańska Winogrady \n796 Poznań Winogrady ul. Winogrady Winogrady \n845 Poznań Winogrady ul. Os. Przyjaźni Winogrady \n863 Poznań Winogrady ul. Powstańców Warszawy Winogrady \n869 Poznań Winogrady ul. Hawelańska Winogrady \n940 Poznań Winogrady ul. Hawelańska Winogrady \n968 Poznań Winogrady ul. Osiedle Zwycięstwa Winogrady \n1106 Poznań Winogrady ul. Hawelańska Winogrady \n1123 Poznań Naramowice Winogrady Piątkowo ul. Naram... Winogrady \n1133 Poznań Winogrady ul. Os. Przyjaźni Winogrady \n1279 Poznań Winogrady Piątkowo Naramowice ul. Pod L... Winogrady \n1373 Poznań Winogrady ul. Hawelańska Winogrady \n1385 Poznań Winogrady Piątkowo Sołacz Naramowice ul... Winogrady \n1391 Poznań Winogrady ul. Hawelańska Winogrady \n1410 Poznań Winogrady ul. Hawelańska Winogrady \n1476 Poznań Piatkowo Winogrady Naramowice Sołacz Wi... Winogrady \n1485 Poznań Winogrady ul. Wichrowe Wzgóze Winogrady \n1519 Poznań Winogrady ul. Lechicka Okolice Winogrady \n1706 Poznań Winogrady ul. Hawelańska Winogrady \n1903 Poznań Winogrady ul. Hawelańska Winogrady \n2037 Poznań Winogrady Piątkowo Naramowice Stare Mia... Winogrady \n2045 Poznań Winogrady ul. Hawelańska Winogrady \n2069 Poznań Winogrady ul. Hawelańska Winogrady \n2092 Poznań Winogrady Piątkowo Naramowice Stare Mia... Winogrady \n2273 Poznań Winogrady ul. Naramowicka Winogrady \n2303 Poznań Winogrady ul. Hawelańska Winogrady \n2316 Poznań Winogrady Piątkowo Sołacz Naramowice Ce... Winogrady \n2324 Poznań Winogrady ul. Hawelańska Winogrady \n2355 Poznań Winogrady ul. Hawelańska Winogrady \n2416 Poznań Winogrady ul. Lechicka Okolice Winogrady \n2659 Poznań Winogrady ul. Idealne 3 Pok. Na Biuro Winogrady \n2740 Poznań Winogrady Winogrady \n2833 Poznań Winogrady ul. Os. Zwycięstwa Winogrady \n2835 Poznań Winogrady ul. Hawelańska Winogrady \n3154 Poznań Winogrady ul. Wilczak Winogrady \n3365 Poznań Winogrady ul. Hawelańska Winogrady \n3379 Poznań Winogrady ul. Hawelańska Winogrady \n3406 Poznań Winogrady ul. Os. Wichrowe Wzgórze Winogrady \n3500 Poznań Winogrady Winogrady \n3599 Poznań Winogrady ul. Strzeszynska Winogrady Po... Winogrady \n3750 Poznań Winogrady ul. Hawelańska Winogrady \n4034 Poznań Sołacz Bonin Winiary Winogrady Piątko u... Winogrady \n4133 Poznań Winogrady ul. Piątkowska Ataner Winogrady \n4244 Poznań Winogrady ul. Hawelańska Winogrady \n4291 Poznań Winogrady ul. Lechicka Okolice Winogrady \n4314 Poznań Winogrady ul. Hawelańska Winogrady \n4430 Poznań Winogrady Winogrady \n4493 Poznań Winogrady ul. Hawelańska Winogrady \n4536 Poznań Winogrady Winogrady \n4569 Poznań Winogrady ul. Hawelańska Winogrady \n4678 Poznań Winogrady ul. Wilczak Dopłata W Mdm Winogrady \n4716 Poznań Winogrady ul. Hawelańska Winogrady \n4862 Poznań Sołacz Winogrady Piątkowo Naramowice ul... Winogrady \n4969 Poznań Winogrady ul. Hawelańska Winogrady \n\n SqPrice \n44 4107.142857 \n105 5188.679245 \n175 6150.000000 \n521 5279.411765 \n530 5587.272727 \n618 6050.000000 \n774 6200.000000 \n796 5812.500000 \n845 5833.333333 \n863 4071.428571 \n869 6050.009610 \n940 6050.009666 \n968 5377.358491 \n1106 5965.791107 \n1123 5596.491228 \n1133 5861.344538 \n1279 5833.333333 \n1373 5230.015743 \n1385 5377.358491 \n1391 6050.000000 \n1410 5965.808188 \n1476 4987.500000 \n1485 5018.939394 \n1519 6200.000000 \n1706 6150.000000 \n1903 6050.000000 \n2037 5580.045872 \n2045 6150.000000 \n2069 6050.000000 \n2092 5511.005484 \n2273 7646.965111 \n2303 5939.996881 \n2316 5833.333333 \n2324 5965.809112 \n2355 5900.000000 \n2416 6100.000000 \n2659 4386.666667 \n2740 5965.791107 \n2833 6037.735849 \n2835 6050.009258 \n3154 4557.291667 \n3365 6050.000000 \n3379 6050.010341 \n3406 4915.254237 \n3500 6037.735849 \n3599 5844.827586 \n3750 3591.905565 \n4034 5371.794872 \n4133 6084.745763 \n4244 6050.000000 \n4291 6100.000000 \n4314 5965.790538 \n4430 5377.358491 \n4493 5965.803209 \n4536 5937.500000 \n4569 6100.000000 \n4678 4375.000000 \n4716 6050.000000 \n4862 4982.758621 \n4969 5965.801650 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Unnamed: 0IdExpectedRoomsSqrMetersFloorLocationBoroughSqPrice
444445345000384.001Poznań Winogrady Stare Miasto ul. Powstańców W...Winogrady4107.142857
105105106275000353.001Poznań Winogrady ul. Oś. Zwycięstwa 26Winogrady5188.679245
175175176326073353.021Poznań Winogrady ul. HawelańskaWinogrady6150.000000
521521522359000368.001Poznań WinogradyWinogrady5279.411765
530530531374906367.101Poznań Winogrady Piątkowo Naramowice Stare Mia...Winogrady5587.272727
618618619300685349.701Poznań Winogrady ul. HawelańskaWinogrady6050.000000
774774775408456365.881Poznań Winogrady ul. HawelańskaWinogrady6200.000000
796796797279000348.001Poznań Winogrady ul. WinogradyWinogrady5812.500000
845845846280000348.001Poznań Winogrady ul. Os. PrzyjaźniWinogrady5833.333333
863863864342000384.001Poznań Winogrady ul. Powstańców WarszawyWinogrady4071.428571
869869870314782352.031Poznań Winogrady ul. HawelańskaWinogrady6050.009610
940940941312967351.731Poznań Winogrady ul. HawelańskaWinogrady6050.009666
968968969285000353.001Poznań Winogrady ul. Osiedle ZwycięstwaWinogrady5377.358491
110611061107288446348.351Poznań Winogrady ul. HawelańskaWinogrady5965.791107
112311231124319000357.001Poznań Naramowice Winogrady Piątkowo ul. Naram...Winogrady5596.491228
113311331134279000347.601Poznań Winogrady ul. Os. PrzyjaźniWinogrady5861.344538
127912791280280000348.001Poznań Winogrady Piątkowo Naramowice ul. Pod L...Winogrady5833.333333
137313731374299000357.171Poznań Winogrady ul. HawelańskaWinogrady5230.015743
138513851386285000353.001Poznań Winogrady Piątkowo Sołacz Naramowice ul...Winogrady5377.358491
139113911392320771353.021Poznań Winogrady ul. HawelańskaWinogrady6050.000000
141014101411310401352.031Poznań Winogrady ul. HawelańskaWinogrady5965.808188
147614761477399000380.001Poznań Piatkowo Winogrady Naramowice Sołacz Wi...Winogrady4987.500000
148514851486265000352.801Poznań Winogrady ul. Wichrowe WzgózeWinogrady5018.939394
151915191520408456365.881Poznań Winogrady ul. Lechicka OkoliceWinogrady6200.000000
170617061707329271353.541Poznań Winogrady ul. HawelańskaWinogrady6150.000000
190319031904328636354.321Poznań Winogrady ul. HawelańskaWinogrady6050.000000
203720372038364935365.401Poznań Winogrady Piątkowo Naramowice Stare Mia...Winogrady5580.045872
204520452046275397344.781Poznań Winogrady ul. HawelańskaWinogrady6150.000000
206920692070308066350.921Poznań Winogrady ul. HawelańskaWinogrady6050.000000
209220922093301452354.701Poznań Winogrady Piątkowo Naramowice Stare Mia...Winogrady5511.005484
227322732274480000362.771Poznań Winogrady ul. NaramowickaWinogrady7646.965111
230323032304380932364.131Poznań Winogrady ul. HawelańskaWinogrady5939.996881
231623162317280000348.001Poznań Winogrady Piątkowo Sołacz Naramowice Ce...Winogrady5833.333333
232423242325303779350.921Poznań Winogrady ul. HawelańskaWinogrady5965.809112
235523552356331816356.241Poznań Winogrady ul. HawelańskaWinogrady5900.000000
241624162417310673350.931Poznań Winogrady ul. Lechicka OkoliceWinogrady6100.000000
265926592660329000375.001Poznań Winogrady ul. Idealne 3 Pok. Na BiuroWinogrady4386.666667
274027402741288446348.351Poznań WinogradyWinogrady5965.791107
283328332834320000353.001Poznań Winogrady ul. Os. ZwycięstwaWinogrady6037.735849
283528352836326761354.011Poznań Winogrady ul. HawelańskaWinogrady6050.009258
315431543155350000376.801Poznań Winogrady ul. WilczakWinogrady4557.291667
336533653366334928355.361Poznań Winogrady ul. HawelańskaWinogrady6050.000000
337933793380292518348.351Poznań Winogrady ul. HawelańskaWinogrady6050.010341
340634063407319000364.901Poznań Winogrady ul. Os. Wichrowe WzgórzeWinogrady4915.254237
350035003501320000353.001Poznań WinogradyWinogrady6037.735849
359935993600339000358.001Poznań Winogrady ul. Strzeszynska Winogrady Po...Winogrady5844.827586
375037503751213000359.301Poznań Winogrady ul. HawelańskaWinogrady3591.905565
403440344035419000378.001Poznań Sołacz Bonin Winiary Winogrady Piątko u...Winogrady5371.794872
413341334134359000359.001Poznań Winogrady ul. Piątkowska AtanerWinogrady6084.745763
424442444245323917353.541Poznań Winogrady ul. HawelańskaWinogrady6050.000000
429142914292312442351.221Poznań Winogrady ul. Lechicka OkoliceWinogrady6100.000000
431443144315295068349.461Poznań Winogrady ul. HawelańskaWinogrady5965.790538
443044304431285000353.001Poznań WinogradyWinogrady5377.358491
449344934494308611351.731Poznań Winogrady ul. HawelańskaWinogrady5965.803209
453645364537285000348.001Poznań WinogradyWinogrady5937.500000
456945694570344101356.411Poznań Winogrady ul. HawelańskaWinogrady6100.000000
467846784679329000375.201Poznań Winogrady ul. Wilczak Dopłata W MdmWinogrady4375.000000
471647164717358765359.301Poznań Winogrady ul. HawelańskaWinogrady6050.000000
486248624863289000358.001Poznań Sołacz Winogrady Piątkowo Naramowice ul...Winogrady4982.758621
496949694970296560349.711Poznań Winogrady ul. HawelańskaWinogrady5965.801650
\n
" + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[(df['Floor'] == 1) & (df['Rooms'] == 3) & (df['Borough'] == 'Winogrady')]" + ] } ], "metadata": { From db55a5535d44f19ee3ddc309b14685cba2186980 Mon Sep 17 00:00:00 2001 From: jnowacki Date: Sat, 28 Jan 2023 18:17:39 +0100 Subject: [PATCH 5/5] sklearn homework --- zadanie_domowe_sklearn/homework.ipynb | 487 +++++++++++++++++++++----- 1 file changed, 391 insertions(+), 96 deletions(-) diff --git a/zadanie_domowe_sklearn/homework.ipynb b/zadanie_domowe_sklearn/homework.ipynb index a025c72..f4b4f85 100644 --- a/zadanie_domowe_sklearn/homework.ipynb +++ b/zadanie_domowe_sklearn/homework.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -58,25 +58,21 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "" - ] + "text/plain": "" }, - "execution_count": 117, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] + "text/plain": "
", + "image/png": "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\n" }, "metadata": {}, "output_type": "display_data" @@ -98,10 +94,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": "-4" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "~3" + ] }, { "cell_type": "markdown", @@ -119,10 +126,12 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 6, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "from sklearn.cluster import KMeans" + ] }, { "cell_type": "markdown", @@ -133,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -149,23 +158,32 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 8, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n", + " warnings.warn(\n" + ] + }, { "data": { - "text/plain": [ - "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n", - " n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',\n", - " random_state=None, tol=0.0001, verbose=0)" - ] + "text/plain": "KMeans(n_clusters=3)", + "text/html": "
KMeans(n_clusters=3)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" }, - "execution_count": 121, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], - "source": [] + "source": [ + "kmeans.fit(points)" + ] }, { "cell_type": "markdown", @@ -176,10 +194,12 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 9, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "clusters = kmeans.predict(points)" + ] }, { "cell_type": "markdown", @@ -190,15 +210,13 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] + "text/plain": "
", + "image/png": "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\n" }, "metadata": {}, "output_type": "display_data" @@ -218,16 +236,16 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Claster ID: 0\tX: 1158.9296227871434\tY:-212.28055211754568\n", - "Claster ID: 1\tX: -844.3076877296985\tY:-450.0715318089522\n", - "Claster ID: 2\tX: 60.61234354820601\tY:444.84943020237415\n" + "Claster ID: 0\tX: -844.3076877296984\tY:-450.07153180895205\n", + "Claster ID: 1\tX: 1158.9296227871432\tY:-212.2805521175457\n", + "Claster ID: 2\tX: 60.61234354820599\tY:444.8494302023744\n" ] } ], @@ -245,10 +263,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": "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\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": "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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for number_of_clusters in [2, 4]:\n", + " kmeans = KMeans(n_clusters=number_of_clusters)\n", + " kmeans.fit(points)\n", + " clusters = kmeans.predict(points)\n", + " plt.scatter(x=points[0], y=points[1], c=clusters)\n", + " plt.show()" + ] }, { "cell_type": "markdown", @@ -259,15 +321,39 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 13, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n", + " warnings.warn(\n", + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n", + " warnings.warn(\n", + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n", + " warnings.warn(\n", + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n", + " warnings.warn(\n", + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n", + " warnings.warn(\n" + ] + }, { "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] + "text/plain": "
", + "image/png": "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\n" }, "metadata": {}, "output_type": "display_data" @@ -302,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -318,16 +404,14 @@ }, { "cell_type": "code", - "execution_count": 168, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "array([0, 0, 0, ..., 2, 2, 2])" - ] + "text/plain": "array([0, 0, 0, ..., 2, 2, 2], dtype=int64)" }, - "execution_count": 168, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -340,15 +424,13 @@ }, { "cell_type": "code", - "execution_count": 169, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] + "text/plain": "
", + "image/png": "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\n" }, "metadata": {}, "output_type": "display_data" @@ -368,10 +450,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": "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\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "scaler = StandardScaler()\n", + "scaler.fit(points)\n", + "normalized_data = scaler.transform(points)\n", + "\n", + "kmeans = KMeans(3)\n", + "kmeans.fit(normalized_data)\n", + "clusters = kmeans.predict(normalized_data)\n", + "plt.scatter(x=points[0], y=points[1], c=clusters)\n", + "plt.show()\n", + "\n", + "labels = db.fit_predict(normalized_data)\n", + "\n", + "plt.scatter(x=points[0], y=points[1], c=labels)\n", + "plt.show()" + ] }, { "cell_type": "markdown", @@ -384,7 +510,7 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -400,13 +526,33 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\datasets\\_openml.py:932: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n", + " warn(\n" + ] + }, + { + "data": { + "text/plain": "0 5\n1 0\n2 4\n3 1\n4 9\n ..\n69995 2\n69996 3\n69997 4\n69998 5\n69999 6\nName: class, Length: 70000, dtype: category\nCategories (10, object): ['0', '1', '2', '3', ..., '6', '7', '8', '9']" + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "mnist = fetch_mldata('MNIST original')\n", + "from sklearn.datasets import fetch_openml\n", + "\n", + "mnist = fetch_openml('mnist_784')\n", "X = mnist.data.astype('float64')\n", - "y = mnist.target" + "y = mnist.target\n", + "y" ] }, { @@ -418,17 +564,15 @@ }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "PCA(copy=True, iterated_power='auto', n_components=10, random_state=None,\n", - " svd_solver='auto', tol=0.0, whiten=False)" - ] + "text/plain": "PCA(n_components=10)", + "text/html": "
PCA(n_components=10)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" }, - "execution_count": 190, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -440,7 +584,7 @@ }, { "cell_type": "code", - "execution_count": 191, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -456,10 +600,32 @@ }, { "cell_type": "code", - "execution_count": 192, + "execution_count": 22, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": "array([8, 9, 4, ..., 1, 0, 6])" + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kmeans = KMeans(10)\n", + "kmeans.fit(mnist_pca)\n", + "mnist_clusters = kmeans.predict(mnist_pca)\n", + "mnist_clusters" + ] }, { "cell_type": "markdown", @@ -470,21 +636,60 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 76, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 22 10 20 3580 42 223 191 29 133 2653]\n", + " [3576 7 8 0 4 6 6 4259 11 0]\n", + " [ 317 73 4745 31 206 412 256 627 168 155]\n", + " [ 119 44 247 14 199 4528 37 647 1135 171]\n", + " [ 369 2049 36 16 3736 0 275 309 18 16]\n", + " [ 900 306 15 90 455 1898 83 504 1494 568]\n", + " [ 207 2 215 92 106 39 5321 627 69 198]\n", + " [ 294 4183 55 20 2088 4 4 620 10 15]\n", + " [ 619 265 93 45 215 1183 67 532 3740 66]\n", + " [ 148 2647 20 55 3374 84 14 496 95 25]]\n" + ] + }, { "data": { - "text/plain": [ - "array([1, 7, 8, 3, 0, 2, 4, 1, 6, 0])" - ] + "text/plain": "
", + "image/png": "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\n" }, - "execution_count": 198, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "array([3, 7, 2, 5, 4, 5, 6, 1, 8, 4], dtype=int64)" + }, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], - "source": [] + "source": [ + "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", + "import numpy as np\n", + "import seaborn as sns\n", + "\n", + "mnist_clusters_as_string = np.char.mod('%d', mnist_clusters)\n", + "cm = confusion_matrix(y, mnist_clusters_as_string)\n", + "print(cm)\n", + "\n", + "fig, ax = plt.subplots(figsize=(5,5))\n", + "plot = sns.heatmap(cm, ax=ax, cbar=False, annot=True, fmt='d', cmap='gist_yarg',\n", + " linewidths=0.1,linecolor='lightblue')\n", + "\n", + "plot.set(xlabel='predicted', ylabel='true')\n", + "plt.show()\n", + "\n", + "np.argmax(cm, axis=1)" + ] }, { "cell_type": "markdown", @@ -495,21 +700,21 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": 72, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "0.5762857142857143" - ] - }, - "execution_count": 200, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "4 ------ 10425\n" + ] } ], - "source": [] + "source": [ + "sums = np.sum(cm, axis=0)\n", + "print(np.argmax(sums),\"------\", np.max(sums))" + ] }, { "cell_type": "markdown", @@ -520,29 +725,119 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 79, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": "0.28754285714285716" + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "\n", + "accuracy_score(y, mnist_clusters_as_string)" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**zadanie 11** Spróbuj podwyższych wynik, stosując np. normalizację lub zmieniając parametry." + "**zadanie 11** Spróbuj podwyższych wynik, stosując np. normalizację lub zmieniając parametry.\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 89, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": "0.10692857142857143" + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#NORMALIZACJA\n", + "pca = PCA(n_components=10)\n", + "pca.fit(X)\n", + "\n", + "mnist_pca = pca.transform(X)\n", + "\n", + "scaler = StandardScaler()\n", + "scaler.fit(mnist_pca)\n", + "normalized_data = scaler.transform(mnist_pca)\n", + "\n", + "kmeans = KMeans(10)\n", + "kmeans.fit(normalized_data)\n", + "mnist_clusters = kmeans.predict(normalized_data)\n", + "\n", + "mnist_clusters_as_string = np.char.mod('%d', mnist_clusters)\n", + "\n", + "accuracy_score(y, mnist_clusters_as_string)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\conta\\anaconda3\\envs\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": "0.012071428571428571" + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#ZMIANA WSTEPNEJ LICZBY WYMIAROW\n", + "pca = PCA(n_components=20)\n", + "pca.fit(X)\n", + "\n", + "mnist_pca = pca.transform(X)\n", + "\n", + "kmeans = KMeans(10)\n", + "kmeans.fit(mnist_pca)\n", + "mnist_clusters = kmeans.predict(mnist_pca)\n", + "\n", + "mnist_clusters_as_string = np.char.mod('%d', mnist_clusters)\n", + "\n", + "accuracy_score(y, mnist_clusters_as_string)" + ], + "metadata": { + "collapsed": false + } }, { "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", "**Gratuluję!**" ] }