How to get real and imaginary parts of a column in a Pandas dataframe?

Question

import pandas as pd

I have a dataframe:

df=pd.DataFrame({'cmplxnumbers':[1+1j,2-2j,3*(1+1j)]})

I need to get the imaginary parts of the numbers in the column.

I do it by:

df.cmplxnumbers.apply(lambda number: number.imag)

I get as a result:

0    1.0
1   -2.0
2    3.0
Name: cmplxnumbers, dtype: float64

Which is as expected.

Is there any quicker, more straightforward method, perhaps not involving the lambda function?

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Answer 1

Pandas DataFrame/Series builds on top of numpy array, so they can be passed to most numpy functions.

In this case, you can try the following, which should be faster than the non-vectorized .apply:

df['imag'] = np.imag(df.cmplxnumbers)
df['real'] = np.real(df.cmplxnumbers)

Output:

         cmplxnumbers  imag  real
0  1.000000+1.000000j   1.0   1.0
1  2.000000-2.000000j  -2.0   2.0
2  3.000000+3.000000j   3.0   3.0

Or you can do agg:

df[['real','imag']] = df.cmplxnumbers.agg([np.real, np.imag])

Answered By: Quang Hoang

Answer 2

The .values field of a Pandas object provides the underlying NumPy array, which has the real and imag fields. In summary:

real_part = pd_obj.values.real
imag_part = pd_obj.values.imag

Answered By: Dev-iL

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