Develop Reference

Selecting rows from another boolean dataframe in Python

I have two dataframes, df1 and df2.
df1 contains integers and df2 contains booleans.
df1 and df2 are exactly the same size (like both are 10x10).
I would like to create a df3 that would take the data from df1 only if the value in the same location in df2 is True. All False would be replaced by Nan in df3
Thanks in advance!

How to merge two different dataframe with different columns

As someone who is super new in merge/append on Python, I am trying to merge two different DF together.
DF1 has 2 columns with Text and ID columns and 100 rows
DF2 has 3 columns with Text, ID, and Match columns and has 20 rows
My goal is to combine the two DFs together so the "Match" column from DF2 can be merged into DF1.
The Match column is all "True" value, so when it gets merged over the other 80 rows on DF1 can be NaN and I can fix it later.
Thank you to everyone for the help and support!

Try a left merge using .merge(), like this:
DF_out = DF1.merge(DF2, on=['Text', 'ID'], how='left')

Python join dataframes by index

I'm working with multiple dataframes in Python and I'm looking to map one onto the other based on a common column (similar to index/match in Excel). I want to join the date column of one dataframe, to the index of the other dataframe (where the date is stored as the index). How would I call out the index? For reference, I want to subtract my ROI for dataframe 2 (awk_price) to the ROI from dataframe 1 (S&P 500). The dataframes are shown below.
I currently have a merged dataframe using
pd.merge(awk_price,sp_500, left_index=True, right_on='Date')
I would love to just add a column to df2 subtracting ROI from dataframe 2 by ROI from dataframe 1 but I can't figure out how to "map" the dates column from dataframe 1 to the index from dataframe 2.
Dataframe 2 (awk_price)
Dataframe 1 (sp_500)

You can use reset_index(), and then rename the column:
df=df1.reset_index().rename(columns={"index": "Date"})
df

Rows into columns in a multi-index Pandas DataFrame

I have a DataFrame with a multi-index, one of dates and the other numbered from 0 to 1267 as the image shows.
How do I have the index 0-1267 as columns instead of rows and have the dates as the only row index?

Select some column and then use Series.unstack by first level:
df1 = df['CUMULATIVE FRACTION'].unstack(0)
Or if need MultiIndex in columns use DataFrame.unstack:
df2 = df.unstack(0)

Check Series label does not exist in a separate DataFrame

I'm iterating over two separate dataframes, where one dataframe is a subset of the other. I need to ensure that only the columns in the set (df1) which are not contained in the subset (df2) pass the conditional statement.
In this case, it would be comparing the Series object during each iteration in df1 to the dataframe, df2. Ideally I would like to compare just the labels associated with each column, not the values contained in the columns. My code below. Any help would be greatly appreciated!
for i in df1:
for j in df2:
if df1[i] is not in df2:
...do some stuff between df1[i] and df2[j]

To find out if the values of df1 are in df2 you can use:
df1.isin(df2)
To find all values in df1 that are not in df2 you can use:
df1[~df1.isin(df2)]
The values that are in df1 and df2 will be a nan in this case