E-Commerce-EDA

E-Commerce Exploratory Data Analysis (EDA) Case Study in Python

Input Data:

df.info() <class 'pandas.core.frame.DataFrame'> RangeIndex: 550068 entries, 0 to 550067 Data columns (total 12 columns):

Column Non-Null Count Dtype

---

0 User_ID 550068 non-null int64
1 Product_ID 550068 non-null object 2 Gender 550068 non-null object 3 Age 550068 non-null object 4 Occupation 550068 non-null int64
5 City_Category 550068 non-null object 6 Stay_In_Current_City_Years 550068 non-null object 7 Marital_Status 550068 non-null int64
8 Product_Category_1 550068 non-null int64
9 Product_Category_2 376430 non-null float64 10 Product_Category_3 166821 non-null float64 11 Purchase 550068 non-null int64
dtypes: float64(2), int64(5), object(5) memory usage: 50.4+ MB

Count null features in the dataset

df.isnull().sum() User_ID 0 Product_ID 0 Gender 0 Age 0 Occupation 0 City_Category 0 Stay_In_Current_City_Years 0 Marital_Status 0 Product_Category_1 0 Product_Category_2 173638 Product_Category_3 383247 Purchase 0 dtype: int64 print(purchase_by_age) Age Purchase 0 0-17 8933.464640 1 18-25 9169.663606 2 26-35 9252.690633 3 36-45 9331.350695 4 46-50 9208.625697 5 51-55 9534.808031 6 55+ 9336.280459

References

https://github.com/Jotaherrer/DataAnalysis/tree/master/e_commerce https://towardsdatascience.com/data-science-for-e-commerce-with-python-a0a97dd7721d