pythonpandasdataframe

Pandas compact rows when data is missing

I have a list of dicts where each dict can have different keys. I want to create a dataframe with one row where each key is a column and the row is its value:

import pandas as pd
data = [{"A":1}, {"B":2}, {"C":3}]
df = pd.DataFrame(data)
print(df.to_string(index=False))
#   A   B   C
# 1.0 NaN NaN
# NaN 2.0 NaN
# NaN NaN 3.0

What I want:

#   A   B   C
# 1.0 2.0 3.0

How can I drop/compact the rows with NaN values?

Solution

  • One option would be to stack:

    df.stack().droplevel(0).to_frame().T

    Or using a dummy groupby:

    import numpy as np

df.groupby(np.repeat(0, len(df))).first()

    Output:

         A    B    C
0  1.0  2.0  3.0