In [9]:

    

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline


    

In [2]:

    

train = pd.read_csv('data/train.csv')
print(train[:5])


    

    




   Store  DayOfWeek        Date  Sales  Customers  Open  Promo StateHoliday  \
0      1          5  2015-07-31   5263        555     1      1            0   
1      2          5  2015-07-31   6064        625     1      1            0   
2      3          5  2015-07-31   8314        821     1      1            0   
3      4          5  2015-07-31  13995       1498     1      1            0   
4      5          5  2015-07-31   4822        559     1      1            0   

   SchoolHoliday  
0              1  
1              1  
2              1  
3              1  
4              1  






    




F:\py34\Anaconda3\lib\site-packages\pandas\io\parsers.py:1170: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.
  data = self._reader.read(nrows)

In [3]:

    

train['Date'] = pd.to_datetime(train['Date'])
train[:5]['Date'].dt.dayofweek


    

    
Out[3]:





0    4
1    4
2    4
3    4
4    4
dtype: int64

In [13]:

    

train['DayOfWeek'] = train['Date'].dt.dayofweek
train['Month'] = train['Date'].dt.month
train['Year'] = train['Date'].dt.year
avg_month = train[['Sales', 'Month']].groupby('Month').mean()
avg_month.plot(kind='bar')
avg_day = train[['Sales', 'DayOfWeek']].groupby('DayOfWeek').mean()
avg_day.plot(kind='bar')


    

    
Out[13]:





<matplotlib.axes._subplots.AxesSubplot at 0x243f3dd8>






    













    

In [17]:

    

#group_dayofweek = train.groupby('DayOfWeek')
#sales by day of week
sale_dayofweek = pd.pivot_table(train, values='Sales', index=['Year','Store'], columns=['DayOfWeek'])
#sales by month
sale_month = pd.pivot_table(train, values='Sales', index=['Year','Store'], columns=['Month'])
sale_month[:5]


    

    
Out[17]:






  

    

      

      
Month
      
1
      
2
      
3
      
4
      
5
      
6
      
7
      
8
      
9
      
10
      
11
      
12
    
    

      
Year
      
Store
      

      

      

      

      

      

      

      

      

      

      

      

    
  
  

    

      
2013
      
1
      
4142.935484
      
4473.964286
      
4682.870968
      
3933.633333
      
3813.709677
      
3780.366667
      
4130.064516
      
3816.774194
      
3628.900000
      
3698.096774
      
4253.200000
      
4682.838710
    
    

      
2
      
3715.193548
      
3968.357143
      
4210.483871
      
3895.933333
      
3754.161290
      
3862.366667
      
4476.709677
      
4037.516129
      
3969.366667
      
3882.645161
      
4287.600000
      
4845.612903
    
    

      
3
      
5343.645161
      
5720.250000
      
6102.548387
      
5739.600000
      
5495.935484
      
5682.066667
      
6658.903226
      
5751.677419
      
5443.000000
      
5428.290323
      
5652.666667
      
6915.064516
    
    

      
4
      
7571.387097
      
8231.178571
      
8242.387097
      
7566.033333
      
7589.806452
      
7840.900000
      
7357.935484
      
7685.903226
      
7318.333333
      
7254.935484
      
7964.600000
      
9184.677419
    
    

      
5
      
3530.387097
      
3611.642857
      
3769.677419
      
3538.033333
      
3557.806452
      
3509.333333
      
4899.741935
      
4063.516129
      
3677.933333
      
3735.645161
      
3985.566667
      
4758.580645
    
  

In [18]:

    

sale_dayofweek.plot(kind='box')
sale_month.plot(kind='box')


    

    
Out[18]:





<matplotlib.axes._subplots.AxesSubplot at 0x22204be0>






    













    

In [ ]: