Konvertieren Sie den Datenrahmen in Schwimmer
df["data"] = df["data"].astype(float)
rudythealchemist
df["data"] = df["data"].astype(float)
# convert all columns of DataFrame
df = df.apply(pd.to_numeric) # convert all columns of DataFrame
# convert just columns "a" and "b"
df[["a", "b"]] = df[["a", "b"]].apply(pd.to_numeric)df["col"] = df["col"].astype(float)
df_raw['PricePerSeat_Outdoor'] = pd.to_numeric(df_raw['PricePerSeat_Outdoor'], errors='coerce')df['DataFrame Column'] = df['DataFrame Column'].astype(float)
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.