Pandas monthly resample maintaining first date

Question

when resampling a time series into a monthly series, pandas changes the initial date of my time series with the start of the month. From:

2020-01-12    0.730439  
2020-01-13    0.559328    
                ...    
2021-06-29    0.188461  
2021-06-30    0.750668

To:

2020-01-01     8.613978
2020-02-01    14.614601
   ...              ...
2021-05-01    11.936765
2021-06-01    13.758198

Instead of the desired result, where in the fisrt month the date is the first date of my time series:

2020-01-12     8.613978
2020-02-01    14.614601
   ...              ...
2021-05-01    11.936765
2021-06-01    13.758198

Is there a way to perform a monthly resample without losing the initial date?

Currently I do correct it afterwards. I am asking if there is a way to do it on the fly. I have tried with all resample‘s parameters without achieving the desired result. I had a look at pd.Grouper but also without success.

Thank you,
Gonxo

PS: Small script to replicate the issue.

import pandas as pd
from numpy.random import random

index = pd.date_range('20200112', '20210630')
df = pd.Series(random(len(index)), index=index)

df.resample('MS').sum()

Asked By: gonzalo mr

||

Source

Answer 1

You can do a manual update:

s = df.resample('MS').sum()

s.index = [df.index.min()] + list(s.index[1:])

Output:

2020-01-12     7.345615
2020-02-01    15.873136
2020-03-01    14.083565
2020-04-01    17.547765
2020-05-01    15.321236
2020-06-01    11.787999
2020-07-01    16.619211
2020-08-01    17.292133
2020-09-01    16.866571
2020-10-01    17.772687
2020-11-01    13.371602
2020-12-01    17.037126
2021-01-01    15.907105
2021-02-01    13.887159
2021-03-01    13.660123
2021-04-01    16.534306
2021-05-01    15.055836
2021-06-01    15.818617
dtype: float64

Answered By: Quang Hoang

Answer 2

Try this:

df = df.close.resample('M').sum()

Alternatively, you can change ‘MS’ to ‘M’ in your code.

Answered By: Pragya Kapoor

Pandas monthly resample maintaining first date

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