Note
IntegerArray is currently experimental. Its API or implementation may change without warning. Uses pandas.NA as the missing value.
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 using arrays.IntegerArray. This is an extension type implemented within pandas.
In [1]: arr = pd.array([1, 2, None], dtype=pd.Int64Dtype()) In [2]: arr Out[2]: <IntegerArray> [1, 2, <NA>] Length: 3, dtype: Int64
Or the string alias "Int64" (note the capital "I") to differentiate from NumPyâs 'int64' dtype:
In [3]: pd.array([1, 2, np.nan], dtype="Int64") Out[3]: <IntegerArray> [1, 2, <NA>] Length: 3, dtype: Int64
All NA-like values are replaced with pandas.NA.
In [4]: pd.array([1, 2, np.nan, None, pd.NA], dtype="Int64") Out[4]: <IntegerArray> [1, 2, <NA>, <NA>, <NA>] Length: 5, dtype: Int64
This array can be stored in a DataFrame or Series like any NumPy array.
In [5]: pd.Series(arr) Out[5]: 0 1 1 2 2 <NA> dtype: Int64
You can also pass the list-like object to the Series constructor with the dtype.
Warning
Currently pandas.array() and pandas.Series() use different rules for dtype inference. pandas.array() will infer a nullable-integer dtype
In [6]: pd.array([1, None]) Out[6]: <IntegerArray> [1, <NA>] Length: 2, dtype: Int64 In [7]: pd.array([1, 2]) Out[7]: <IntegerArray> [1, 2] Length: 2, dtype: Int64
For backwards-compatibility, Series infers these as either integer or float dtype.
In [8]: pd.Series([1, None]) Out[8]: 0 1.0 1 NaN dtype: float64 In [9]: pd.Series([1, 2]) Out[9]: 0 1 1 2 dtype: int64
We recommend explicitly providing the dtype to avoid confusion.
In [10]: pd.array([1, None], dtype="Int64") Out[10]: <IntegerArray> [1, <NA>] Length: 2, dtype: Int64 In [11]: pd.Series([1, None], dtype="Int64") Out[11]: 0 1 1 <NA> dtype: Int64
In the future, we may provide an option for Series to infer a nullable-integer dtype.
Operations involving an integer array will behave similar to NumPy arrays. Missing values will be propagated, and the data will be coerced to another dtype if needed.
In [12]: s = pd.Series([1, 2, None], dtype="Int64")
# arithmetic
In [13]: s + 1
Out[13]:
0 2
1 3
2 <NA>
dtype: Int64
# comparison
In [14]: s == 1
Out[14]:
0 True
1 False
2 <NA>
dtype: boolean
# slicing operation
In [15]: s.iloc[1:3]
Out[15]:
1 2
2 <NA>
dtype: Int64
# operate with other dtypes
In [16]: s + s.iloc[1:3].astype("Int8")
Out[16]:
0 <NA>
1 4
2 <NA>
dtype: Int64
# coerce when needed
In [17]: s + 0.01
Out[17]:
0 1.01
1 2.01
2 <NA>
dtype: Float64
These dtypes can operate as part of a DataFrame.
In [18]: df = pd.DataFrame({"A": s, "B": [1, 1, 3], "C": list("aab")})
In [19]: df
Out[19]:
A B C
0 1 1 a
1 2 1 a
2 <NA> 3 b
In [20]: df.dtypes
Out[20]:
A Int64
B int64
C object
dtype: object
These dtypes can be merged, reshaped & casted.
In [21]: pd.concat([df[["A"]], df[["B", "C"]]], axis=1).dtypes Out[21]: A Int64 B int64 C object dtype: object In [22]: df["A"].astype(float) Out[22]: 0 1.0 1 2.0 2 NaN Name: A, dtype: float64
Reduction and groupby operations such as sum() work as well.
In [23]: df.sum(numeric_only=True)
Out[23]:
A 3
B 5
dtype: Int64
In [24]: df.sum()
Out[24]:
A 3
B 5
C aab
dtype: object
In [25]: df.groupby("B").A.sum()
Out[25]:
B
1 3
3 0
Name: A, dtype: Int64
Scalar NA Value#
arrays.IntegerArray uses pandas.NA as its scalar missing value. Slicing a single element thatâs missing will return pandas.NA
In [26]: a = pd.array([1, None], dtype="Int64") In [27]: a[1] Out[27]: <NA>
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