Selecting Data from Last X Months

Question

I want to select data from the last 4 months. I would want to start from the beginning of the month, so if it is currently July 28, I would want data from March1-July28.

Currently I use DateOffset, and I realized that it is calling March28-July28 and leaving out a lot of my data.

df = pd.read_csv('MyData.csv')

df['recvd_dttm'] = pd.to_datetime(df['recvd_dttm'])

#Only retrieve data before now (ignore typos that are future dates)

mask = df['recvd_dttm'] <= datetime.datetime.now()
df = df.loc[mask]
# get first and last datetime for final week of data

range_max = df['recvd_dttm'].max()
range_min = range_max - pd.DateOffset(months=4)

# take slice with final week of data
df = df[(df['recvd_dttm'] >= range_min) & 
               (df['recvd_dttm'] <= range_max)]

I looked up other answers and found this one: How do I calculate the date six months from the current date using the datetime Python module? So I tried using relativedelta(months=-4) and got a ValueError: Length mismatch: Expected axis has 1 elements, new values have 3 elements

Any help would be appreciated.

Answer 1

You can use pd.tseries.offsets.MonthBegin.

import pandas as pd

# simulate some data
# =================================
np.random.seed(0)
date_rng = pd.date_range('2015-01-01', '2015-07-28', freq='D')
df = pd.DataFrame(np.random.randn(len(date_rng)), index=date_rng, columns=['col'])
df

               col
2015-01-01  1.7641
2015-01-02  0.4002
2015-01-03  0.9787
2015-01-04  2.2409
2015-01-05  1.8676
2015-01-06 -0.9773
2015-01-07  0.9501
2015-01-08 -0.1514
...            ...
2015-07-21 -0.2394
2015-07-22  1.0997
2015-07-23  0.6553
2015-07-24  0.6401
2015-07-25 -1.6170
2015-07-26 -0.0243
2015-07-27 -0.7380
2015-07-28  0.2799

[209 rows x 1 columns]

# processing
# ===============================
start_date = df.index[-1] - pd.tseries.offsets.MonthBegin(5)
# output: Timestamp('2015-03-01 00:00:00')

df[start_date:]

               col
2015-03-01 -0.3627
2015-03-02 -0.6725
2015-03-03 -0.3596
2015-03-04 -0.8131
2015-03-05 -1.7263
2015-03-06  0.1774
2015-03-07 -0.4018
2015-03-08 -1.6302
...            ...
2015-07-21 -0.2394
2015-07-22  1.0997
2015-07-23  0.6553
2015-07-24  0.6401
2015-07-25 -1.6170
2015-07-26 -0.0243
2015-07-27 -0.7380
2015-07-28  0.2799

[150 rows x 1 columns]