# 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 (opens new window). This is an extension types (opens new window) implemented within pandas. It is not the default dtype for integers, and will not be inferred; you must explicitly pass the dtype into array() (opens new window) or Series (opens new window):

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 (opens new window) or Series (opens new window) 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 (opens new window) 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