What does the pandas.series.array attribute do?

The “.array” is one of the pandas series attributes. it will return a pandas ExtensionArray with the values stored in the series. The “.array” is used to get a zero-copy reference to the underlying data.

The resultant array is not like a NumPy array it is an ExtensionArray, and it has different array types based on the data present in the series (dtype).

Example 1

import pandas as pd

# create pandas series with numerical values
s1 = pd.Series([1,2,3,4])




The “s1” is the pandas series object which is created by using integer values with length 4. And we have applied the pandas array attribute to this series object s1.


0    1
1    2
2    3
3    4
dtype: int64

[1, 2, 3, 4]
Length: 4, dtype: int64

The resultant ExtensionArray is created for the series object “s1” that can be seen in the above output block which is PandasArray.

Example 2

import pandas as pd

# creating dates
date = pd.date_range("2021-06-01", periods=5, freq="M")

# creating pandas Series with date sequence
s2 = pd.Series(date)




Let’s take a series with date-time sequence values, and get an ExtensionArray using the series attribute “.array”.


0    2021-06-30
1    2021-07-31
2    2021-08-31
3    2021-09-30
4    2021-10-31
dtype: datetime64[ns]

['2021-06-30 00:00:00', '2021-07-31 00:00:00', '2021-08-31 00:00:00',
'2021-09-30 00:00:00', '2021-10-31 00:00:00']
Length: 5, dtype: datetime64[ns]

In the following example, we can see a DatatimeArray created by using the “array” attribute. This DatatimeArray is a type of ExtensionArray which is for DateTime dtype data in pandas.

For any other 3rd-party dtype, the array type will be the ExtensionArray only.

Updated on: 09-Mar-2022


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