Homework EDA

src/app.py

@@ -2,3 +2,65 @@
 engine = db_connect()
 
 # your code here
+
+# Step 0: Import Libraries and modules
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+#Herramienta de Machine Learning
+from sklearn.model_selection import train_test_split
+
+# Step 1: Load data
+url='https://breathecode.herokuapp.com/asset/internal-link?id=927&path=AB_NYC_2019.csv'
+df_raw = pd.read_csv(url)
+
+df_raw.sample(10, random_state=2025)
+
+# Step 2: Reprocessing
+df_baking = df_raw.copy()
+df_baking = df_baking.drop(columns=['id','name', 'host_name', 'last_review', 'reviews_per_month','latitude','longitude'])
+columnsCategory = ['host_id','neighbourhood_group','neighbourhood','room_type']
+df_baking[columnsCategory] = df_baking[columnsCategory].astype('category')
+df = df_baking.copy()
+df.info()
+df_raw.sample(5, random_state=2025)
+
+# Step 3: EDA
+df_train, df_test =  train_test_split(df, test_size=0.1, random_state= 2025)
+df_train = df_train.reset_index(drop=True)
+df_test = df_test.reset_index(drop=True)
+
+df_train.shape, df_test.shape
+
+display(df_train.describe(include = 'number').T)
+display(df_train.describe(include = 'category').T)
+
+#Análisis univariado
+df_train.hist()
+plt.tight_layout()
+plt.show()
+
+sns.countplot(data= df_train, x='host_id')
+plt.show()
+
+sns.countplot(data= df_train, x='neighbourhood_group')
+plt.show()
+
+sns.countplot(data= df_train, x='neighbourhood')
+plt.show()
+
+sns.countplot(data= df_train, x='room_type')
+plt.show()
+
+#Análisis bivariado para datos númericos
+sns.pairplot(data = df_train, corner= True)
+plt.show()
+
+#comparamos numericos contra categoricos
+sns.pairplot(df_train, hue='neighbourhood_group', corner=True)
+plt.show()
+
+#Comparamos categorico contra cateogtrico
+sns.countplot(df_train, x = "room_type", hue = "neighbourhood_group")
+plt.show()