notebook.community

Edit and run



In [89]:

    
%matplotlib inline

import pandas as pd
import matplotlib.pyplot as plt

plt.style.use('seaborn-notebook')



In [115]:

    
!ls data/*.csv









    



data/amzn-2016-01-01_2017-06-16.csv  data/vlgea-2016-01-01_2017-06-16.csv
data/kr-2016-01-01_2017-06-16.csv    data/wfm-2016-01-01_2017-06-16.csv
data/ngvc-2016-01-01_2017-06-16.csv  data/wmk-2016-01-01_2017-06-16.csv
data/sfm-2016-01-01_2017-06-16.csv   data/wmt-2016-01-01_2017-06-16.csv



In [119]:

    
dfs = []

for symbol in [
    'kr',
    'ngvc',
    'sfm',
    'vlgea',
    'wfm',
    'wmk',
    'wmt',
]:
    df = pd.read_csv('./data/{}-2016-01-01_2017-06-16.csv'.format(symbol),
                     parse_dates=['Date'], usecols=['Date', 'Volume'], index_col=['Date'])
    df = df.rename(columns={'Volume': symbol.upper()})
    
    dfs.append(df)

df = pd.concat(dfs, axis=1)
df = df.sort_index(ascending=True)

df.head()



In [118]:

    
df.loc['2017-05-01':].plot()









    Out[118]:





<matplotlib.axes._subplots.AxesSubplot at 0x1158a8a90>



In [ ]:

	KR	NGVC	SFM	VLGEA	WFM	WMK	WMT
Date
2016-01-04	9917582	65443	2313382	40464	4159849	64808	11926062
2016-01-05	7329300	43542	3887346	37950	3490645	42554	13325958
2016-01-06	9802706	111936	11009063	29714	5741480	41952	16564620
2016-01-07	9434006	73349	3584302	29358	5309019	73626	26430005
2016-01-08	8231126	102829	2336274	29510	4021226	48532	17767864