Setting freq of pandas DatetimeIndex after DataFrame creation

Question

Im using pandas datareader to get stock data.

import pandas as pd
import pandas_datareader.data as web
ABB = web.DataReader(name='ABB.ST', 
                     data_source='yahoo',
                     start='2000-1-1')

However by default freq is not set on the resulting dataframe. I need freq to be able to navigate using the index like this:

for index, row in ABB.iterrows():
    ABB.loc[[index + 1]]

If freq is not set on DatetimeIndex im not able to use +1 etc to navigate.

What I have found are two functions astype and resample. Since I already know to freq resample looks like overkill, I just want to set freq to daily.

Now my question is how can i use astype on ABB to set freq to daily?

Solution 1

Try:

ABB = ABB.asfreq('d')

This should change the frequency to daily with NaN for days without data.

Also, you should rewrite your for-loop as follows:

for index, row in ABB.iterrows():
    print(ABB.loc[[index + pd.Timedelta(days = 1)]])

Thanks!

Solution 2

ABB is pandas DataFrame, whose index type is DatetimeIndex.

DatetimeIndex has freq attribute which can be set as below

ABB.index.freq = 'd'

Check out the change

ABB.index

Comments

[Abdou]

ABB = ABB.asfreq('d') should change the frequency to daily with NaN for days without data. Also, change ABB.loc[[index + 1]] to ABB.loc[[index + pd.Timedelta(days = 1)]] inside that for-loop.

[Rich Andrews]

The resample is essential if there is no way to pre-validate the regularity of the index - big data sets from external systems always have error and blindly setting the index frequency is likely not safe. While OP knows the frequency ahead of time, this answer is more literal.