pandas.DataFrame.to_numpy — pandas 0.24.0rc1 documentation (original) (raw)
DataFrame. to_numpy(dtype=None, copy=False)[source]¶
Convert the DataFrame to a NumPy array.
New in version 0.24.0.
By default, the dtype of the returned array will be the common NumPy dtype of all types in the DataFrame. For example, if the dtypes arefloat16 and float32, the results dtype will be float32. This may require copying data and coercing values, which may be expensive.
| Parameters: | dtype : str or numpy.dtype, optional The dtype to pass to numpy.asarray() copy : bool, default False Whether to ensure that the returned value is a not a view on another array. Note that copy=False does not ensure thatto_numpy() is no-copy. Rather, copy=True ensure that a copy is made, even if not strictly necessary. |
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| Returns: | array : numpy.ndarray |
Examples
pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy() array([[1, 3], [2, 4]])
With heterogenous data, the lowest common type will have to be used.
df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]}) df.to_numpy() array([[1. , 3. ], [2. , 4.5]])
For a mix of numeric and non-numeric types, the output array will have object dtype.
df['C'] = pd.date_range('2000', periods=2) df.to_numpy() array([[1, 3.0, Timestamp('2000-01-01 00:00:00')], [2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)