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Pandas DataFrame | astype method

Programming
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Python
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Pandas
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Documentation
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DataFrame
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Type Conversion
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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Pandas DataFrame.astype(~) method converts the data type of the columns of a DataFrame to the specified type.

Parameters

1. dtype | string or type or dict of (string, string or type)

The desired data type to convert the DataFrame to.

2. copy | boolean | optional

Whether or not to return a new DataFrame:

  • If True, then a copy is returned - modifying the return value will not mutate the original DataFrame and vice versa.

  • If False, then a view is returned - modifying the return value will mutate the original DataFrame and vice versa.

By default, copy=True.

3. errorslink | string | optional

How to deal with cases where the type of the data in the source DataFrame cannot be converted to the specified type:

Value

Description

"raise"

Throw an error.

"ignore"

Return the source DataFrame in case of errors.

By default, errors="raise".

Return Value

A DataFrame with the new specified type.

Examples

Consider the following DataFrame:

df = pd.DataFrame({"A":[4,5],"B":["6","7"]})
df
   A  B
0  4  6
1  5  7

Converting to float

To convert df to type float:

df.astype("float")
   A    B
0  4.0  6.0
1  5.0  7.0

Note that the original df is still of type int.

Handling errors

Raise

The default behaviour when type conversion fails is to throw an error:

df = pd.DataFrame({"A":["a","b"]})
df.astype("int")
ValueError: invalid literal for int() with base 10: 'a'

Ignore

We could suppress the error, and simply get the original DataFrame back:

df = pd.DataFrame({"A":["a","b"]})
df.astype("int", errors="ignore")
   A
0  a
1  b
robocat
Published by Isshin Inada
Edited by 0 others
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