Nullable Integer Data Type — pandas 0.24.2 documentation (original) (raw)

New in version 0.24.0.

Note

IntegerArray is currently experimental. Its API or implementation may change without warning.

In Working with missing data, we saw that pandas primarily uses NaN to represent missing data. Because NaN is a float, this forces an array of integers with any missing values to become floating point. In some cases, this may not matter much. But if your integer column is, say, an identifier, casting to float can be problematic. Some integers cannot even be represented as floating point numbers.

Pandas can represent integer data with possibly missing values usingarrays.IntegerArray. This is an extension typesimplemented within pandas. It is not the default dtype for integers, and will not be inferred; you must explicitly pass the dtype into array() or Series:

In [1]: arr = pd.array([1, 2, np.nan], dtype=pd.Int64Dtype())

In [2]: arr Out[2]: [1, 2, NaN] Length: 3, dtype: Int64

Or the string alias "Int64" (note the capital "I", to differentiate from NumPy’s 'int64' dtype:

In [3]: pd.array([1, 2, np.nan], dtype="Int64") Out[3]: [1, 2, NaN] Length: 3, dtype: Int64

This array can be stored in a DataFrame or Series like any NumPy array.

In [4]: pd.Series(arr) Out[4]: 0 1 1 2 2 NaN dtype: Int64

You can also pass the list-like object to the Series constructor with the dtype.

In [5]: s = pd.Series([1, 2, np.nan], dtype="Int64")

In [6]: s Out[6]: 0 1 1 2 2 NaN dtype: Int64

By default (if you don’t specify dtype), NumPy is used, and you’ll end up with a float64 dtype Series:

In [7]: pd.Series([1, 2, np.nan]) Out[7]: 0 1.0 1 2.0 2 NaN dtype: float64

Operations involving an integer array will behave similar to NumPy arrays. Missing values will be propagated, and and the data will be coerced to another dtype if needed.

arithmetic

In [8]: s + 1 Out[8]: 0 2 1 3 2 NaN dtype: Int64

comparison

In [9]: s == 1 Out[9]: 0 True 1 False 2 False dtype: bool

indexing

In [10]: s.iloc[1:3] Out[10]: 1 2 2 NaN dtype: Int64

operate with other dtypes

In [11]: s + s.iloc[1:3].astype('Int8') Out[11]: 0 NaN 1 4 2 NaN dtype: Int64

coerce when needed

In [12]: s + 0.01 Out[12]: 0 1.01 1 2.01 2 NaN dtype: float64

These dtypes can operate as part of of DataFrame.

In [13]: df = pd.DataFrame({'A': s, 'B': [1, 1, 3], 'C': list('aab')})

In [14]: df Out[14]: A B C 0 1 1 a 1 2 1 a 2 NaN 3 b

In [15]: df.dtypes Out[15]: A Int64 B int64 C object dtype: object

These dtypes can be merged & reshaped & casted.

In [16]: pd.concat([df[['A']], df[['B', 'C']]], axis=1).dtypes Out[16]: A Int64 B int64 C object dtype: object

In [17]: df['A'].astype(float) Out[17]: 0 1.0 1 2.0 2 NaN Name: A, dtype: float64

Reduction and groupby operations such as ‘sum’ work as well.

In [18]: df.sum() Out[18]: A 3 B 5 C aab dtype: object

In [19]: df.groupby('B').A.sum() Out[19]: B 1 3 3 0 Name: A, dtype: Int64