Select Dataframe by Column and give np.NaN for the missing columns

Question

I have a dataframe:

df = pd.DataFrame([{"a": 1, "b": 2}])

I want to create another dataframe with column "a" and "c" and give np.NaN for column "c". I have something like this, but it throws error since "c" is not in df. What is a clean and working way to do it?

df1 = df["a","c"]

Asked By: topcan5

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Source

Answer 1

You can use pandas.DataFrame.join to add the "c" column with NaN values to your existing dataframe like this:

import pandas as pd
import numpy as np

df = pd.DataFrame([{"a": 1, "b": 2}])

df = df.join(pd.DataFrame(columns=["c"]))

display(df)

The output:

index	a	b	c
0	1	2	NaN

Answered By: Kim Tang

Select Dataframe by Column and give np.NaN for the missing columns

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