Create an function from data-frame column values and insert into blank elements of another column

  • I have a data-frame (df) which looks like:

    0           1
    1           1
    2           1
    4           1
    5  0.77027027

    I am trying to create a new column called FHE_TO_USE which copies the FHE column and creates the mean of the FHE column and fills in any blanks with the mean. So the solution would look like:

            FHE     FHE_TO_USE
    0           1            1
    1           1            1
    2           1            1
    3               0.95405405
    4           1            1
    5  0.77027027   0.77027027

    I've tried the folling without success:

    df["FHE"] = np.where(df["PATTERN"]=="FT",1,df["FHE"])
    df["FHE"] = df["FHE"].convert_objects(convert_numeric=True)
    df["FHE_TO_USE"] = np.where(df["FHE"]=="",statistics.mean(df["FHE"]),df["FHE"])

    This also gives me a future warning:

    FutureWarning: convert_objects is deprecated.  To re-infer data dtypes for object columns, use Series.infer_objects()
    For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.

    What is the correct way to create the mean?

  • You can use the function fillna():

    df['FHE'] = pd.to_numeric(df.FHE, errors='coerce')
    df['FHE_TO_USE'] = df.FHE.fillna(df.FHE.mean())


           FHE  FHE_TO_USE
    0  1.00000    1.000000
    1  1.00000    1.000000
    2  1.00000    1.000000
    3      NaN    0.954054
    4  1.00000    1.000000
    5  0.77027    0.770270



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