How to create new dataframe with pandas based on columns of other dataframes

Question

I have a dataframe with columns 'A', 'B', 'C' and I want to create a new dataframe that has columns named 'X', 'Y', 'Z' which will respectively be column 'A' divided by column 'B', 'B' by 'C' and 'A' by 'C'. Also, I want to keep the same index. I have tried the following

new_df = old_df['A'] / old_df['B']

However, I don't know how to add the other columns I want.

I understand how to add these new columns to the existing dataframe, but I want to create a new dataframe for these columns. — Kaashoed
– Kaashoed, Commented Jul 14, 2020 at 12:42
just declare that variable with new_df = pandas.DataFrame() and then you can new_df['X'] = old_df['A']/old_df['B'] — JenilDave
– JenilDave, Commented Jul 14, 2020 at 12:44
I really thought I tried this already but it's working, thanks. — Kaashoed
– Kaashoed, Commented Jul 14, 2020 at 12:49

NYC Coder · Accepted Answer · 2020-07-14 12:47:57Z

3

Just do this:

new_df = pd.DataFrame(columns=['X', 'Y', 'Z'])
new_df['X'] = df['A'] / df['B']
new_df['Y'] = df['B'] / df['C']
new_df['Z'] = df['A'] / df['C']
print(new_df)

          X         Y         Z
0  0.227273  0.647059  0.147059
1  1.600000  0.833333  1.333333
2  0.800000  1.666667  1.333333

answered Jul 14, 2020 at 12:47

NYC Coder

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Comments

Micah Johnson · Accepted Answer · 2020-07-14 13:16:33Z

0

You can also use pandas.DataFrame.div which allows you use fill_values. For instance:

new_df = pd.DataFrame(columns=['X','Y','Z'])
new_df['X'] = old_df['A'].div(old_df['B'], fill_value=-9999)

answered Jul 14, 2020 at 13:16

Micah Johnson

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