Convert "informal" month-day index to a datetime index

Question

I have a series whose index is in month-day format. It is not officially a datetime index. In addition, the series contains one datum for each day of the year:

       Data_Value
01-01         156
01-02         139
01-03         133
01-04         106
01-05         128
01-06         189

My goal is to make a line plot with the "Data_Value" in the y-axis and the twelve months in the x-axis. But I want to plot the 365 data, not aggregate them into 12 months. Something like this:

Anyway, my first movement has been to plot the 365 data ignoring the fact of putting the months names in the x-axis:

s = np.array(s)
plt.figure()
plt.plot(s, '-o')

But in the last command I got the error "ValueError: could not convert string to float: '12-31'".

Does anybody how to convert "informal" month-day index to a datetime index omitting the year? Or any other solution to reach my final goal of plotting the series? Thx.

Answer 1

This is what I have done, as jezrael suggested:

Add one random year to index to convert it to datetime index:

s.index = '2014-' + s.index.astype(str)
s.index = pd.to_datetime(s.index)
days_s = s.index
days_s = np.array(days_s)

Then set and draw the plot:

plt.figure()
plt.plot(days_s, s, '-o')

And change x-axis from %y-%m-%d to %m format using mdates.DateFormatter:

import matplotlib.dates as mdates
monthsFmt = mdates.DateFormatter('%m')
plt.gca().xaxis.set_major_formatter(monthsFmt)

Finally, render plot:

plt.show()

I do not know if this solution is a bit sloppy but it works.

Answer 2

You can use:

np.random.seed(100)
rng = pd.date_range('2017-01-01', periods=365).strftime('%m-%d')
df = pd.DataFrame({ 'Data_value': np.random.randint(1000, size=365)}, index=rng)  
#print (df)

d = {'01':'Jan', '02':'Feb', '03':'Mar','04': 'Apr', '05':'May','06': 'Jun', 
     '07':'Jul', '08':'Aug','09': 'Sep','10': 'Oct', '11':'Nov','12': 'Dec'}

#add _for match only first value of string to dict
d = {'_' + k:v for k, v in d.items()}

#add _ to index
df.index = '_' + df.index
#split values by - to MultiIndex
df.index = df.index.str.split('-', expand=True) 

#reshape and replace NaN to 0
df = df['Data_value'].unstack(fill_value=0)
#rename index values by dict
df = df.rename(index=d)

print (df)
      01   02   03   04   05   06   07   08   09   10 ...    22   23   24  \
Jan  520  792  835  871  855   79  944  906  350  948 ...   316  570  912   
Feb  900  415  897  141  757  723  612    4  603  955 ...     2  889  617   
Mar  181  283  824  238  369  926  944  303  679  877 ...   618   30   17   
Apr  693  846    0   13  185  460  362  131  582  643 ...   811   36  773   
May  852   95  626  749  631   76  801  314  102  938 ...   419  407  765   
Jun  677  870  122  628  186  295  619  734  819  286 ...    16  524  854   
Jul  138  776  473  712  414  908  658  349  887  604 ...   389  435  346   
Aug  385   14  883  289  148  168  536  477  442  796 ...   730  250  477   
Sep   82  998  401  906  653  593  885  793  194  655 ...   944  754  506   
Oct  144  819  182  183   83  502  356  554  957  760 ...    70  309  994   
Nov  674  131  870  139  305  797  804  861  451  922 ...   723  119   71   
Dec  781  304  466  544  294  296  497  693   93  398 ...   915  716  322   

      25   26   27   28   29   30   31  
Jan  507  649   93   86  386  667  876  
Feb  478  403  994   63    0    0    0  
Mar   53   68  946  488  347  475  979  
Apr  839   38  214   94  738  170    0  
May  521  944  496  789  409  438  262  
Jun  466  621   67  220  223  788    0  
Jul   34  451  862  974  694   77  212  
Aug  736   74  437  798   67  668  933  
Sep  693  657  705  298  861  172    0  
Oct  736  943  944  905  689  821  879  
Nov  829   93  498  804  123  554    0  
Dec  141   87   65  324  182  640  343  

[12 rows x 31 columns]

Last DataFrame.plot:

df.plot()