Pandas Series: astype() function
Change data type of a series in Pandas
The astype() function is used to cast a pandas object to a specified data type.
Syntax:
Series.astype(self, dtype, copy=True, errors='raise', **kwargs)
Parameters:
| Name | Description | Type/Default Value | Required / Optional |
|---|---|---|---|
| dtype | Use a numpy.dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, …}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame’s columns to column-specific types. | data type, or dict of column name -> data type | Required |
| copy | Return a copy when copy=True (be very careful setting copy=False as changes to values then may propagate to other pandas objects). | bool Default Value: True |
Required |
| errors | Control raising of exceptions on invalid data for provided dtype.
|
{‘raise’, ‘ignore’} Default Value: ‘raise’ |
Required |
| kwargs | keyword arguments to pass on to the constructor |
Returns: casted - same type as caller
Example - Create a DataFrame:
Python-Pandas Code:
import numpy as np
import pandas as pd
d = {'c1': [2, 3], 'c2': [4, 5]}
df = pd.DataFrame(data=d)
df.dtypes
Output:
c1 int64 c2 int64 dtype: object
Example - Cast all columns to int32:
Python-Pandas Code:
import numpy as np
import pandas as pd
d = {'c1': [2, 3], 'c2': [4, 5]}
df = pd.DataFrame(data=d)
df.astype('int32').dtypes
Output:
c1 int32 c2 int32 dtype: object
Example - Cast c1 to int32 using a dictionary:
Python-Pandas Code:
import numpy as np
import pandas as pd
d = {'c1': [2, 3], 'c2': [4, 5]}
df = pd.DataFrame(data=d)
df.astype({'c1': 'int32'}).dtypes
Output:
c1 int32 c2 int64 dtype: object
Example - Create a series:
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series([2, 3], dtype='int32')
s
Output:
0 2 1 3 dtype: int32
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series([2, 3], dtype='int32')
s.astype('int64')
Output:
0 2 1 3 dtype: int64
Example - Convert to categorical type:
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series([2, 3], dtype='int32')
s.astype('category')
Output:
0 2 1 3 dtype: category Categories (2, int64): [2, 3]
Example - Convert to ordered categorical type with custom ordering:
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series([2, 3], dtype='int32')
cat_dtype = pd.api.types.CategoricalDtype(
categories=[3, 2], ordered=True)
s.astype(cat_dtype)
Output:
0 2 1 3 dtype: category Categories (2, int64): [3 < 2]
Example - Note that using copy=False and changing data on a new pandas object may propagate changes:
Python-Pandas Code:
import numpy as np
import pandas as pd
s1 = pd.Series([3,2])
s2 = s1.astype('int64', copy=False)
s2[0] = 10
s1 # note that s1[0] has changed too
Output:
0 10 1 2 dtype: int64
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