CODEWITHSUNDEEP
pythondatetimepandas
Doubledown
Doubledown

Reputation: 468

Pandas datetime index all set to the same date in a month (1st)

I have a dataframe like:

Date          Value
2014-11-03  8767.83
2014-12-01  8948.96
2015-01-02  9098.98
2015-02-02  8774.11
2015-03-02  9102.82
2015-04-01  8672.13
2015-05-01  8737.00
2015-06-01  8394.23
2015-07-01  8103.86
2015-08-03  8416.86
2015-09-01  7677.32
2015-10-01  7832.70

I want all the dates to be the 1st of the month like:

Date          Value
2014-11-01  8767.83
2014-12-01  8948.96
2015-01-01  9098.98
2015-02-01  8774.11
2015-03-01  9102.82
2015-04-01  8672.13
2015-05-01  8737.00
2015-06-01  8394.23
2015-07-01  8103.86
2015-08-01  8416.86
2015-09-01  7677.32
2015-10-01  7832.70

I know this has to be really simple, but I'm new at pandas/python and looked for over an hour at this point. Thanks in advance.

Upvotes: 1

Views: 2145

Answers (2)

Abhay Nainan
Abhay Nainan

Reputation: 4234

I know this is an old question, but in case you want to create a monthly start index from scratch, you can make use of Panda's offset-alias functionality to do so.

import pandas as pd
monthly_start_index = pd.date_range(start='2018-01-01', 
                                    stop='2018-12-01',
                                    freq = 'MS')
df.index = monthly_start_index

Upvotes: 0

Anzel
Anzel

Reputation: 20553

You may just use simple list comprehension to "convert" and re-assign your index:

In [3]: df
Out[3]: 
              Value
Date               
2014-11-03  8767.83
2014-12-01  8948.96
2015-01-02  9098.98
2015-02-02  8774.11
2015-03-02  9102.82
2015-04-01  8672.13
2015-05-01  8737.00
2015-06-01  8394.23
2015-07-01  8103.86
2015-08-03  8416.86
2015-09-01  7677.32
2015-10-01  7832.70

In [4]: df.index = [pd.datetime(x.year, x.month, 1) for x in df.index.tolist()] 
In [5]: df
Out[5]: 
              Value
2014-11-01  8767.83
2014-12-01  8948.96
2015-01-01  9098.98
2015-02-01  8774.11
2015-03-01  9102.82
2015-04-01  8672.13
2015-05-01  8737.00
2015-06-01  8394.23
2015-07-01  8103.86
2015-08-01  8416.86
2015-09-01  7677.32
2015-10-01  7832.70

Upvotes: 2

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