Pandas Series: to_numpy() function
Values in Pandas Series or Index
A NumPy ndarray representing the values in this Series or Index.
Syntax:
Series.to_numpy(self, dtype=None, copy=False)
Parameters:
| Name | Description | Type/Default Value | Required / Optional |
|---|---|---|---|
| dtype | The dtype to pass to numpy.asarray() |
str or numpy.dtype | Optional |
| copy | Whether to ensure that the returned value is a not a view on another array. Note that copy=False does not ensure that to_numpy() is no-copy. Rather, copy=True ensure that a copy is made, even if not strictly necessary. | bool Default Value: False |
Required |
Returns: numpy.ndarray
Notes:
The returned array will be the same up to equality (values equal in self will be equal in the returned array; likewise for values that are not equal). When self contains an ExtensionArray, the dtype may be different. For example, for a category-dtype Series, to_numpy() will return a NumPy array and the categorical dtype will be lost.
For NumPy dtypes, this will be a reference to the actual data stored in this Series or Index (assuming copy=False). Modifying the result in place will modify the data stored in the Series or Index (not that we recommend doing that).
For extension types, to_numpy() may require copying data and coercing the result to a NumPy type (possibly object), which may be expensive. When you need a no-copy reference to the underlying data, Series.array should be used instead.
This table lays out the different dtypes and default return types of to_numpy() for various dtypes within pandas.
| dtype | array type |
|---|---|
| category[T] | ndarray[T] (same dtype as input) |
| period | ndarray[object] (Periods) |
| interval | ndarray[object] (Intervals) |
| IntegerNA | ndarray[object] |
| datetime64[ns] | datetime64[ns] |
| datetime64[ns, tz] | ndarray[object] (Timestamps) |
Example:
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series(pd.Categorical(['p', 'q', 'p']))
s.to_numpy()
Output:
array(['p', 'q', 'p'], dtype=object)
Specify the dtype to control how datetime-aware data is represented. Use dtype=object to return an ndarray of pandas Timestamp objects, each with the correct tz.
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series(pd.Categorical(['p', 'q', 'p']))
s = pd.Series(pd.date_range('2019', periods=4, tz="CET"))
s.to_numpy(dtype=object)
Output:
array([Timestamp('2019-01-01 00:00:00+0100', tz='CET', freq='D'),
Timestamp('2019-01-02 00:00:00+0100', tz='CET', freq='D'),
Timestamp('2019-01-03 00:00:00+0100', tz='CET', freq='D'),
Timestamp('2019-01-04 00:00:00+0100', tz='CET', freq='D')],
dtype=object)
Or dtype='datetime64[ns]' to return an ndarray of native datetime64 values. The values are converted to UTC and the timezone info is dropped.
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series(pd.Categorical(['p', 'q', 'p']))
s = pd.Series(pd.date_range('2019', periods=4, tz="CET"))
s.to_numpy(dtype="datetime64[ns]")
# doctest: +ELLIPSIS
Output:
array(['2018-12-31T23:00:00.000000000', '2019-01-01T23:00:00.000000000',
'2019-01-02T23:00:00.000000000', '2019-01-03T23:00:00.000000000'],
dtype='datetime64[ns]')
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