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Duplicate of Certification - Data Analyst Associate - Pet Supplies

Data Analyst Associate Practical Exam Submission

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Company Background

PetMind is a retailer of products for pets. They are based in the United States. PetMind sells products that are a mix of luxury items and everyday items. Luxury items include toys. Everyday items include food.

Company questions

  • The company wants to increase sales by selling more everyday products repeatedly.
  • They have been testing this approach for the last year. They now want a report on how repeat purchases impact sales.

Importing Modules

# import libraries
import pandas as pd

1: Loading and Assessing repeat purchases Table

# Loading data from url
url='https://s3.amazonaws.com/talent-assets.datacamp.com/pet_supplies_2212.csv'
#read csv file at url and save as raw_data
repeat=pd.read_csv(url,sep=',')

# Table preview and shape
print('\nthe head of data as below:')
print(repeat.shape)
repeat.head()

# check the row_data info
repeat.info()
# Checking for duplicates
repeat.duplicated().sum()
# Checking for empty values
repeat.isnull().sum()
# Checking unique values of variables
print(len(repeat['product_id'].unique()))
print(repeat['category'].unique())
print(repeat['animal'].unique())
print(repeat['size'].unique())
print(repeat['rating'].unique())
print(repeat['repeat_purchase'].unique())

# data statistical check for categorical data type (object) columns
repeat.describe(include='object')

# data statistical check for numerical data type (int, float64) columns
repeat.describe(include='number')

2. Data issues and Cleaning Data

Data Issues

Tidiness Issue

  1. Valuable should have same format : size column should have same capatilized string.

Quality issue

  1. Missing values in the variable: category, rating
  2. Price variable data type convert from string to float64, price

#check the raw_data description
#display the category columns
print('\n the category data statastic summary as below:\n')
print(repeat.describe(include='object'))
print('\n the numerical data statastic summary as below:\n')
print(repeat.describe(include='number'))

#backup the raw data to make sure to keep raw_data intergrity.
df=repeat.copy()
# making product_id as an Index
df.set_index('product_id')
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