gtordjma

## python-df_null2.py
#Nombre de valeurs nulles pour les variables numériques:
null_counts = numerical_columns_df.isnull().sum()
print(null_counts)

## python-value_obj.py
for x in object_columns_df:
    print( x + ': ' + str(object_columns_df[x].unique()))

## python-df_null.py
#Nombre de valeurs nulles pour les variables catégorielles:
null_counts = object_columns_df.isnull().sum()
print(null_counts)

## python-obj_none_obj.py
object_columns_df = df.select_dtypes(include=['object'])
numerical_columns_df =df.select_dtypes(exclude=['object'])

## python-df_drop.py
df=df.drop('Alley',axis=1)

## python-df_isna.py
df.isna().sum()

## python-df_describe.py
df.describe()

## python-df_info.py
df.info()

## python-import_dataset.py
df = pd.read_csv("house-dataset.csv")
df.head()

## python-import_lib.py
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn as sk
	#Nombre de valeurs nulles pour les variables numériques:
	null_counts = numerical_columns_df.isnull().sum()
	print(null_counts)
	for x in object_columns_df:
	print( x + ': ' + str(object_columns_df[x].unique()))
	#Nombre de valeurs nulles pour les variables catégorielles:
	null_counts = object_columns_df.isnull().sum()
	print(null_counts)
	object_columns_df = df.select_dtypes(include=['object'])
	numerical_columns_df =df.select_dtypes(exclude=['object'])
	import numpy as np
	import pandas as pd
	import matplotlib.pyplot as plt
	import sklearn as sk