Nullable Integer Data Type¶
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 using
arrays.IntegerArray
. This is an extension types
implemented 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]:
<IntegerArray>
[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]:
<IntegerArray>
[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