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I have a simplified Dataframe which can be set up as follows:
indexes =['01/10/2017', '28/10/2018', '27/10/2019', '30/10/2019']
cols = ['Period', 'A', 'B', 'C']
df= pd.DataFrame(index = indexes, columns= cols)
df.Period = 1
df = pd.concat([df, 2*df.copy(), 3*df.copy()])
df.sort_index()
The Dataframe looks like:
Period A B C
01/10/2017 1 NaN NaN NaN
01/10/2017 2 NaN NaN NaN
01/10/2017 3 NaN NaN NaN
27/10/2019 1 NaN NaN NaN
27/10/2019 2 NaN NaN NaN
27/10/2019 3 NaN NaN NaN
28/10/2018 1 NaN NaN NaN
28/10/2018 2 NaN NaN NaN
28/10/2018 3 NaN NaN NaN
30/10/2019 1 NaN NaN NaN
30/10/2019 2 NaN NaN NaN
30/10/2019 3 NaN NaN NaN
I want to find the rows that are in this list:
FwdTimeChangeDates = ['28/10/2018', '27/10/2019']
with Period that is > 2.
I want to +=2 to the Period with those conditions (So Period 3-->5, and 4-->6, etc.).
How do I filter based on the two conditions?
df.loc[FwdTimeChangeDates] Gives:
Period A B C
28/10/2018 1 NaN NaN NaN
28/10/2018 2 NaN NaN NaN
28/10/2018 3 NaN NaN NaN
27/10/2019 1 NaN NaN NaN
27/10/2019 2 NaN NaN NaN
27/10/2019 3 NaN NaN NaN
and
df.loc[df.Period>2]
Gives
Period A B C
01/10/2017 3 NaN NaN NaN
28/10/2018 3 NaN NaN NaN
27/10/2019 3 NaN NaN NaN
30/10/2019 3 NaN NaN NaN
and I want:
Period A B C
28/10/2018 3 NaN NaN NaN
27/10/2019 3 NaN NaN NaN
But I can't join the two conditions with:
df.loc[FwdTimeChangeDates & df.Period>1] or
df.loc[(FwdTimeChangeDates) & (df.Period>1)]
450
The Boolean values like True & false and 1&0 can be used as indexes in panda dataframe. They can help us filter out the required records. In the below exampels we will see different methods that can be used to carry out the Boolean indexing operations.
Boolean indexing is defined as a very important feature of numpy, which is frequently used in pandas. Its main task is to use the actual values of the data in the DataFrame. We can filter the data in the boolean indexing in different ways, which are as follows: Access the DataFrame with a boolean index.
The main distinction between the two methods is: loc gets rows (and/or columns) with particular labels. iloc gets rows (and/or columns) at integer locations.
Combine the two conditions, use isin for the first.
df[df.index.isin(['28/10/2018', '27/10/2019']) & (df.Period > 2)]
Period A B C
28/10/2018 3 NaN NaN NaN
27/10/2019 3 NaN NaN NaN
If the dataframe has a multiindex:
df.loc[(df.index.isin(FwdTimeChangeDates, level=0)) & (df.Period > 2), 'Period']
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