Change column type in pandas

In pandas, you can change the data type of a column using the astype() function. You can pass a dictionary to the astype() function where the keys are the column names and the values are the new data types.

For example, if you have a DataFrame called df and you want to change the data type of the column "A" to float, you would do the following:

Change a column's type in a Pandas DataFrame using the astype method in Python

df["A"] = df["A"].astype(float)

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You can also change the data type of multiple columns at once by passing a dictionary to the astype() function. For example:

Change multiple columns' types in a Pandas DataFrame using the astype method in Python

df = df.astype({"A": float, "B": int, "C": str})

If you want to change the data type of a particular column and also want to change the values of that column in case of error during conversion, you can use the pd.to_numeric and errors='coerce'

Change the data type of a Pandas DataFrame and also change the values of that column in case of error in Python

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

You can also use the pd.to_datetime to change datetime type of column.

Using pd.to_datetime method to change datetime type of a column in Python

df['date'] = pd.to_datetime(df['date'])

Please make sure the operation suits your needs and won't cause any unexpected values or data loss.