notebook.community

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In [66]:

    
%matplotlib inline

import pandas as pd
import matplotlib.pyplot as plt



In [67]:

    
def process_year(year):
    df = pd.read_html('./data/{}.html'.format(year),
                      header=1,
                      index_col=['Category'])[0]

    df = df.drop(['Total', 'Average'], axis=1)

    df = df.rename(index=str, columns={'January': '{}-01'.format(year),
                                       'February': '{}-02'.format(year),
                                       'March': '{}-03'.format(year),
                                       'April': '{}-04'.format(year),
                                       'May': '{}-05'.format(year),
                                       'June': '{}-06'.format(year),
                                       'July': '{}-07'.format(year),
                                       'August': '{}-08'.format(year),
                                       'September': '{}-09'.format(year),
                                       'October': '{}-10'.format(year),
                                       'November': '{}-11'.format(year),
                                       'December': '{}-12'.format(year),
                                      })

    df = df[df.columns[1:]].replace('[\$,]', '', regex=True).astype(float)

    return df



In [68]:

    
df = process_year(2017)

df.head()









    Out[68]:







  
    
      
      2017-02
      2017-03
      2017-04
      2017-05
      2017-06
      2017-07
      2017-08
      2017-09
      2017-10
      2017-11
    
    
      Category
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      Bandwidth Charge
      0.00
      0.00
      0.00
      0.00
      0.00
      0.04
      0.00
      0.01
      0.01
      0.00
    
    
      DNS Charge
      0.09
      0.10
      0.10
      0.11
      0.10
      0.10
      0.10
      0.10
      0.11
      0.04
    
    
      Resource Charge
      0.01
      0.01
      0.01
      0.01
      0.01
      0.00
      0.00
      0.00
      0.01
      0.00
    
    
      Site Charge
      0.28
      0.31
      0.30
      0.31
      0.30
      0.31
      0.31
      0.30
      0.31
      0.78
    
    
      Storage Charge
      0.17
      0.15
      0.16
      0.08
      0.07
      0.11
      0.13
      0.12
      0.18
      0.08



In [69]:

    
dfs = []

for year in range(2009, 2018):
    dfs.append(process_year(year))

len(dfs)









    Out[69]:





9



In [70]:

    
df = pd.concat(dfs, axis=1)

df.head()









    Out[70]:







  
    
      
      2009-04
      2009-05
      2009-06
      2009-07
      2009-08
      2009-09
      2009-10
      2009-11
      2009-12
      2010-02
      ...
      2017-02
      2017-03
      2017-04
      2017-05
      2017-06
      2017-07
      2017-08
      2017-09
      2017-10
      2017-11
    
  
  
    
      Bandwidth Charge
      0.01
      0.0
      0.01
      0.00
      0.01
      0.01
      0.01
      0.01
      0.02
      0.02
      ...
      0.00
      0.0
      0.0
      0.00
      0.0
      0.04
      0.0
      0.01
      0.01
      0.00
    
    
      DNS Charge
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      0.00
      ...
      0.09
      0.1
      0.1
      0.11
      0.1
      0.10
      0.1
      0.10
      0.11
      0.04
    
    
      Deposit Fee
      0.00
      0.0
      0.00
      0.00
      0.00
      0.00
      0.00
      0.00
      0.00
      0.00
      ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
    
    
      Domain Registration
      0.00
      0.0
      0.00
      8.59
      0.00
      0.00
      0.00
      0.00
      0.00
      NaN
      ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
    
    
      Domain Renewal
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      0.00
      ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
    
  

5 rows × 96 columns



In [92]:

    
print(plt.style.available)
plt.style.use('seaborn-talk')









    



['_classic_test', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2']



In [96]:

    
df.transpose().plot(kind='area')









    Out[96]:





<matplotlib.axes._subplots.AxesSubplot at 0x119add2b0>



In [ ]:

	2017-02	2017-03	2017-04	2017-05	2017-06	2017-07	2017-08	2017-09	2017-10	2017-11
Category
Bandwidth Charge	0.00	0.00	0.00	0.00	0.00	0.04	0.00	0.01	0.01	0.00
DNS Charge	0.09	0.10	0.10	0.11	0.10	0.10	0.10	0.10	0.11	0.04
Resource Charge	0.01	0.01	0.01	0.01	0.01	0.00	0.00	0.00	0.01	0.00
Site Charge	0.28	0.31	0.30	0.31	0.30	0.31	0.31	0.30	0.31	0.78
Storage Charge	0.17	0.15	0.16	0.08	0.07	0.11	0.13	0.12	0.18	0.08