Convert dataframe to float

Pandas dataframe convert string to float

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

Source: stackoverflow.com

Convert column to numeric pandas

# 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)

Source: stackoverflow.com

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.

Convert dataframe to float

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

Source: app.dataquest.io

Convert dataframe column to float

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

Source: app.dataquest.io

Convert price to float pandas

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

Source: stackoverflow.com

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