I have a data frame wherein a column is of "object" data type. I use pd.to_numeric() with errors = 'coerce' to convert this to "float" data type. However, the converted column appears as NaN for all entries. If I let errors = 'ignore', none of the entries are converted to float. Is there something I am missing? The following is the code snippet:

pd.to_numeric(df['gender'],errors = 'coerce')

The column df['gender'] comprises 'Male' and 'Female' entries. I would like to convert these to 'float' data type.

Thank you!

CodePudding user response：

to_numeric can only convert numeric-ish things. For example it can convert the string '10' into the number 10, but it can't convert something like 'Male' into a number.

Instead use pd.factorize:

df['gender'] = pd.factorize(df['gender'])[0].astype(float)

Or Series.factorize:

df['gender'] = df['gender'].factorize()[0].astype(float)

The first element of factorize contains the integer codes, so then we convert them astype(float).

Or as you commented, Series.map also works:

df['gender'] = df['gender'].map({'Male': 0, 'Female': 1}).astype(float)