How to Dynamically Convert Column Types in Pandas DataFrames?

Convert Column Types in Pandas

In your example, you can convert columns 2 and 3 to floats during DataFrame creation. Pandas provides several methods to convert column types dynamically, here are the approaches:

Using to_numeric():

df[['Col2', 'Col3']] = df[['Col2', 'Col3']].apply(pd.to_numeric, errors='coerce')

Using astype():

df[['Col2', 'Col3']] = df[['Col2', 'Col3']].astype(float)

Both methods allow for specifying the data type as an argument, and ignore invalid values (coerce option).

Using infer_objects():

df[['Col2', 'Col3']] = df[['Col2', 'Col3']].infer_objects()

This method tries to infer the correct data type (e.g., integers to int64) based on column values.

Using convert_dtypes():

convert_dtypes = {'Col2': float, 'Col3': float}
df = df.convert_dtypes(convert_dtypes)

This method allows for specifying the desired data types for each column explicitly.

By choosing the appropriate method and dynamically specifiying the column names, you can convert column types in your DataFrame as needed.

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