In [1]:

    

# import libaries
import numpy as np
import pandas as pd
import matplotlib.pyplot as pyplt
from IPython.display import Image
import seaborn as sns


    

In [2]:

    

# Plot in ipython notebook
%matplotlib inline


    

In [3]:

    

# Set Display Options
pd.options.display.max_rows = 15


    

In [4]:

    

stores = pd.read_csv('kaggle_walmart_data/stores.csv')
sales = pd.read_csv('kaggle_walmart_data/train.csv')
features = pd.read_csv('kaggle_walmart_data/features.csv')


    

In [5]:

    

print('Rows of Weekly Sales: ', len(sales))


    

    




Rows of Weekly Sales:  421570

In [6]:

    

sales.dtypes


    

    
Out[6]:





Store             int64
Dept              int64
Date             object
Weekly_Sales    float64
IsHoliday          bool
dtype: object

In [7]:

    

sales.head()


    

    
Out[7]:






  

    

      

      
Store
      
Dept
      
Date
      
Weekly_Sales
      
IsHoliday
    
  
  

    

      
0
      
 1
      
 1
      
 2010-02-05
      
 24924.50
      
 False
    
    

      
1
      
 1
      
 1
      
 2010-02-12
      
 46039.49
      
  True
    
    

      
2
      
 1
      
 1
      
 2010-02-19
      
 41595.55
      
 False
    
    

      
3
      
 1
      
 1
      
 2010-02-26
      
 19403.54
      
 False
    
    

      
4
      
 1
      
 1
      
 2010-03-05
      
 21827.90
      
 False
    
  

In [8]:

    

sales.Date = pd.to_datetime(sales.Date)


    

In [9]:

    

sales.dtypes


    

    
Out[9]:





Store                    int64
Dept                     int64
Date            datetime64[ns]
Weekly_Sales           float64
IsHoliday                 bool
dtype: object

In [10]:

    

def topn(group, field, n=5):
    return group.sort_index(by=field, ascending=False)[:n]

sales.groupby('Store').apply(topn, 'Weekly_Sales', 3)


    

    
Out[10]:






  

    

      

      

      
Store
      
Dept
      
Date
      
Weekly_Sales
      
IsHoliday
    
    

      
Store
      

      

      

      

      

      

    
  
  

    

      
1 
      
7741  
      
  1
      
 72
      
2011-11-25
      
 203670.47
      
  True
    
    

      
7689  
      
  1
      
 72
      
2010-11-26
      
 196810.42
      
  True
    
    

      
9325  
      
  1
      
 92
      
2011-12-23
      
 194839.60
      
 False
    
    

      
2 
      
17896 
      
  2
      
 72
      
2010-11-26
      
 285353.53
      
  True
    
    

      
11148 
      
  2
      
  7
      
2010-12-24
      
 264837.42
      
 False
    
    

      
17948 
      
  2
      
 72
      
2011-11-25
      
 263558.03
      
  True
    
    

      
3 
      
25459 
      
  3
      
 38
      
2010-02-05
      
 155897.94
      
 False
    
    

      
...
      
...
      
...
      
...
      
...
      
...
      
...
    
    

      
43
      
403842
      
 43
      
 92
      
2011-09-09
      
 101792.33
      
  True
    
    

      
44
      
408236
      
 44
      
 38
      
2012-10-26
      
  66629.98
      
 False
    
    

      
408232
      
 44
      
 38
      
2012-09-28
      
  65599.95
      
 False
    
    

      
408204
      
 44
      
 38
      
2012-03-16
      
  64719.71
      
 False
    
    

      
45
      
419193
      
 45
      
 72
      
2010-11-26
      
 240758.86
      
  True
    
    

      
419245
      
 45
      
 72
      
2011-11-25
      
 198041.11
      
  True
    
    

      
412837
      
 45
      
  7
      
2010-12-24
      
 166757.11
      
 False
    
  

135 rows × 5 columns

In [14]:

    

sales_monthly_store1 = sales[sales.Store == 1].set_index('Date').sort_index().resample('M', how='sum')


    

In [16]:

    

sales_monthly.Weekly_Sales.plot()


    

    
Out[16]:





<matplotlib.axes.AxesSubplot at 0x10ef94710>






    

In [ ]: