[Lab_Customer Analysis Round 1] Sara

[sarasilva123]

No description provided.

[pt-data-ta-lis]

Good Job!

Comments:

• Rearrange the columns in the dataframe as needed
  tab2_data = tab2_data[tab1_data.columns]
tab3_data = tab3_data[tab1_data.columns]

• Which columns are numerical?
  datatotal_data.select_dtypes('float')

• Which columns are categorial?
  datatotal_data.select_dtypes('object')

• Delete the column education and number_of_open_complaints from the dataframe
  datatotal_data = datatotal_data.drop(['education', 'number_of_open_complaints'], axis=1)

• Correct the values in the column customer_lifetime_value. They are given as percent, so multiply them by 100 and change dtype to numerical type
  datatotal_data['customer_lifetime_value'] = datatotal_data['customer_lifetime_value'].str.replace('%',''") --> Excluding the '%' character from the colum so that you can convert all the column to float.
  datatotal_data['customer_lifetime_value'] = pd.to_numeric(datatotal_data['customer_lifetime_value'], errors='coerce')*100

• Filter out the data for customers who have an income of 0 or less
  The logic is right, but it was intendend to take out who has an income of 0 or less so that the final data frame shows only who has a positive income. The condition should be:
  datatotal_data[datatotal_data['income'] > 0