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Convert dataframe column to float

Pandas dataframe convert string to float

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

Source: stackoverflow.com

Pandas change to numeric

>>> s = pd.Series(["8", 6, "7.5", 3, "0.9"]) # mixed string and numeric values
>>> s
0      8
1      6
2    7.5
3      3
4    0.9
dtype: object

>>> pd.to_numeric(s) # convert everything to float values
0    8.0
1    6.0
2    7.5
3    3.0
4    0.9
dtype: float64

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

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