# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))
        

import matplotlib.pyplot as plt
import seaborn as sns

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
"""
# Lets explore the Sales Data
"""
df = pd.read_csv('../input/supermarket-sales/supermarket_sales - Sheet1.csv')
df
df.describe(include='all')
"""
## Let's start with easy questions:
1. sales volume by gender
2. sales volume by product line
3. sales volume by payment type
4. which city has which type of customers
"""
#1. sales volume by gender

dfgender = df.groupby(['Gender'], sort=False)['Gender','Total'].sum()
dfgender.head(10)
#2. sales volume by product line

dfpline = df.groupby(['Product line'], sort=False)['Total','Quantity'].sum()
dfpline.head(10)
#3. sales volume by payment type

dfpmnttype = df.groupby(['Payment'], sort=False)['Total','Quantity'].sum()
dfpmnttype.head(10)
#4. which city has which type of customers

dfcust = df.groupby(['City', 'Customer type'], sort=False)['Total'].count()
dfcust.head(10)
#how many Customer type "Member" are there in Yangon?
df['City'].value_counts()
#how many times does value "Yangon" occur?
df.City.value_counts().Yangon
dfplot = df[["City","Gender","Product line","Total"]]
dfplot
#Which city sells how much of each Product line?
pd.pivot_table(dfplot , values = 'Total' , index = 'City' , columns = 'Product line' , aggfunc = 'count')
#Which type of payment is more popular when paying for a certain product line?
pd.pivot_table(df , values = 'Total' , index = 'Product line' , columns = 'Payment' , aggfunc = 'count')
sns.countplot(data = df , x = 'Payment' , hue = 'Product line'  , palette = 'magma')
pd.pivot_table(df , values = 'Total' , index = 'City' , columns = 'Payment' , aggfunc = 'count')
sns.countplot(data = df , x = 'City' , hue = 'Payment'  , palette = 'magma')
df.groupby('Product line')['Gender'].value_counts()