PYTHON

convert dataframe to float

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

convert dataframe column to float

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

pandas dataframe convert string to float

df_raw['PricePerSeat_Outdoor'] = pd.to_numeric(df_raw['PricePerSeat_Outdoor'], errors='coerce')

convert price to float pandas

df[df.columns[1:]] = df[df.columns[1:]].replace('[$,]', '', regex=True).astype(float)

convert all columns to float pandas

You have four main options for converting types in pandas:

to_numeric() - provides functionality to safely convert non-numeric types (e.g. strings) to a suitable numeric type. (See also to_datetime() and to_timedelta().)

astype() - convert (almost) any type to (almost) any other type (even if it's not necessarily sensible to do so). Also allows you to convert to categorial types (very useful).

infer_objects() - a utility method to convert object columns holding Python objects to a pandas type if possible.

convert_dtypes() - convert DataFrame columns to the "best possible" dtype that supports pd.NA (pandas' object to indicate a missing value).

Read on for more detailed explanations and usage of each of these methods.

pandas convert string to float

Series.astype()