# 4.13. DataFrame NA¶

• pd.NA and np.nan Represents missing values

• pd.NA will be used in future, but for now, there are function which does not support it yet

• .isna()

• .dropna(how='any|all', axis='rows|columns')

• .any()

• .all()

• .fillna(value|dict)

• .ffill()

• .bfill()

• .interpolate() - works only with np.nan (not pd.NA)

A floating-point 'not a number' (NaN) value. Equivalent to the output of float('nan'). Due to the requirements of the IEEE-754 standard, math.nan and float('nan') are not considered to equal to any other numeric value, including themselves. To check whether a number is a NaN, use the isnan() function to test for NaNs instead of is or ==. Example 1:

Python Standard Library:

>>> import math
>>>
>>> math.nan == math.nan
False
>>> float('nan') == float('nan')
False
>>> math.isnan(math.nan)
True
>>> math.isnan(float('nan'))
True


## 4.13.1. SetUp¶

>>> import pandas as pd
>>> import numpy as np
>>>
>>>
>>> df = pd.DataFrame({
...     'A': [1, 2, np.nan, np.nan, 3, np.nan, 4],
...     'B': [1.1, 2.2, np.nan, np.nan, 3.3, np.nan, 4.4],
...     'C': ['a', 'b', np.nan, np.nan, 'c', np.nan, 'd'],
...     'D': [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
... })
>>>
>>> df
A    B    C   D
0  1.0  1.1    a NaN
1  2.0  2.2    b NaN
2  NaN  NaN  NaN NaN
3  NaN  NaN  NaN NaN
4  3.0  3.3    c NaN
5  NaN  NaN  NaN NaN
6  4.0  4.4    d NaN


## 4.13.2. Check if Any¶

>>> df.any()
A     True
B     True
C     True
D    False
dtype: bool


## 4.13.3. Check if All¶

>>> df.all()
A    True
B    True
C    True
D    True
dtype: bool


## 4.13.4. Check if Null¶

>>> df.isnull()
A      B      C     D
0  False  False  False  True
1  False  False  False  True
2   True   True   True  True
3   True   True   True  True
4  False  False  False  True
5   True   True   True  True
6  False  False  False  True


## 4.13.5. Check if NA¶

>>> df.isna()
A      B      C     D
0  False  False  False  True
1  False  False  False  True
2   True   True   True  True
3   True   True   True  True
4  False  False  False  True
5   True   True   True  True
6  False  False  False  True


## 4.13.6. Fill With Scalar Value¶

>>> df.fillna(0.0)
A    B    C    D
0  1.0  1.1    a  0.0
1  2.0  2.2    b  0.0
2  0.0  0.0  0.0  0.0
3  0.0  0.0  0.0  0.0
4  3.0  3.3    c  0.0
5  0.0  0.0  0.0  0.0
6  4.0  4.4    d  0.0


## 4.13.7. Fill With Dict Values¶

>>> df.fillna({
...     'A': 99,
...     'B': 88,
...     'C': 77
... })
A     B   C   D
0   1.0   1.1   a NaN
1   2.0   2.2   b NaN
2  99.0  88.0  77 NaN
3  99.0  88.0  77 NaN
4   3.0   3.3   c NaN
5  99.0  88.0  77 NaN
6   4.0   4.4   d NaN


## 4.13.8. Fill Forwards¶

ffill: propagate last valid observation forward:

>>> df.fillna(method='ffill')
A    B  C   D
0  1.0  1.1  a NaN
1  2.0  2.2  b NaN
2  2.0  2.2  b NaN
3  2.0  2.2  b NaN
4  3.0  3.3  c NaN
5  3.0  3.3  c NaN
6  4.0  4.4  d NaN


## 4.13.9. Fill Backwards¶

bfill: use NEXT valid observation to fill gap:

>>> df.fillna(method='bfill')
A    B  C   D
0  1.0  1.1  a NaN
1  2.0  2.2  b NaN
2  3.0  3.3  c NaN
3  3.0  3.3  c NaN
4  3.0  3.3  c NaN
5  4.0  4.4  d NaN
6  4.0  4.4  d NaN


## 4.13.10. Interpolate¶

>>> df.interpolate()
A         B    C   D
0  1.000000  1.100000    a NaN
1  2.000000  2.200000    b NaN
2  2.333333  2.566667  NaN NaN
3  2.666667  2.933333  NaN NaN
4  3.000000  3.300000    c NaN
5  3.500000  3.850000  NaN NaN
6  4.000000  4.400000    d NaN

Table 4.5. Interpolation techniques

Method

Description

linear

Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes

time

Works on daily and higher resolution data to interpolate given length of interval

index, values

use the actual numerical values of the index.

pad

Fill in NA using existing values

nearest, zero, slinear, quadratic, cubic, spline, barycentric, polynomial

Passed to scipy.interpolate.interp1d. These methods use the numerical values of the index. Both polynomial and spline require that you also specify an order (int), e.g. df.interpolate(method='polynomial', order=5)

krogh, piecewise_polynomial, spline, pchip, akima

Wrappers around the SciPy interpolation methods of similar names

from_derivatives

Refers to scipy.interpolate.BPoly.from_derivatives which replaces piecewise_polynomial interpolation method in scipy 0.18.

## 4.13.11. Drop Rows with NA¶

>>> df.dropna(how='all')
A    B  C   D
0  1.0  1.1  a NaN
1  2.0  2.2  b NaN
4  3.0  3.3  c NaN
6  4.0  4.4  d NaN

>>> df.dropna(how='all', axis='rows')
A    B  C   D
0  1.0  1.1  a NaN
1  2.0  2.2  b NaN
4  3.0  3.3  c NaN
6  4.0  4.4  d NaN

>>> df.dropna(how='all', axis=0)
A    B  C   D
0  1.0  1.1  a NaN
1  2.0  2.2  b NaN
4  3.0  3.3  c NaN
6  4.0  4.4  d NaN

>>> df.dropna(how='any')
Empty DataFrame
Columns: [A, B, C, D]
Index: []

>>> df.dropna(how='any', axis=0)
Empty DataFrame
Columns: [A, B, C, D]
Index: []

>>> df.dropna(how='any', axis='rows')
Empty DataFrame
Columns: [A, B, C, D]
Index: []


## 4.13.12. Drop Columns with NA¶

>>> df.dropna(how='all', axis='columns')
A    B    C
0  1.0  1.1    a
1  2.0  2.2    b
2  NaN  NaN  NaN
3  NaN  NaN  NaN
4  3.0  3.3    c
5  NaN  NaN  NaN
6  4.0  4.4    d

>>> df.dropna(how='all', axis=1)
A    B    C
0  1.0  1.1    a
1  2.0  2.2    b
2  NaN  NaN  NaN
3  NaN  NaN  NaN
4  3.0  3.3    c
5  NaN  NaN  NaN
6  4.0  4.4    d

>>> df.dropna(how='all', axis=-1)
Traceback (most recent call last):
ValueError: No axis named -1 for object type DataFrame

>>> df.dropna(how='any', axis='columns')
Empty DataFrame
Columns: []
Index: [0, 1, 2, 3, 4, 5, 6]

>>> df.dropna(how='any', axis=1)
Empty DataFrame
Columns: []
Index: [0, 1, 2, 3, 4, 5, 6]

>>> df.dropna(how='any', axis=-1)
Traceback (most recent call last):
ValueError: No axis named -1 for object type DataFrame


## 4.13.13. Recap¶

>>> data = pd.DataFrame({
...     'A': [1,2,3,4,5,6,7,8,9],
...     'B': [1,2,np.nan,np.nan,5,6,7,8,9]
... })
>>>
>>> a = data['A'].isnull()
>>> b = data['B'].isnull()
>>> c = data['B'].isnull().any()
>>> d = data['B'].isnull().all()
>>>
>>> e = data.fillna(0)
>>>
>>> f = data.dropna()
>>> g = data.dropna(how='any')
>>> h = data.dropna(how='any', axis='rows')
>>> i = data.dropna(how='all', axis='columns')
>>>
>>> j = data.ffill()
>>> k = data.bfill()
>>> l = data.interpolate('linear')
>>> n = data.interpolate('polynomial', order=3)


## 4.13.14. References¶

1

https://docs.python.org/3/library/math.html#math.nan

## 4.13.15. Assignments¶

"""
* Assignment: DataFrame NaN
* Complexity: easy
* Lines of code: 10 lines
* Time: 8 min

English:
TODO: English Translation
X. Run doctests - all must succeed

Polish:
1. Wczytaj dane z DATA jako df: pd.DataFrame
2. Pomiń pierwszą linię z metadanymi
3. Zmień nazwy kolumn na:
a. Sepal length
b. Sepal width
c. Petal length
d. Petal width
e. Species
4. Podmień wartości w kolumnie species
a. 0 -> 'setosa',
b. 1 -> 'versicolor',
c. 2 -> 'virginica'
5. Wybierz wartości w kolumnie 'Petal length' mniejsze od 4
6. Wybrane wartości ustaw na NaN
7. Interpoluj liniowo wszystkie wartości NaN
8. Usuń wiersze z pozostałymi wartościami NaN
9. Zdefiniuj result jako dwa pierwsze wiersze
10. Uruchom doctesty - wszystkie muszą się powieść

Tests:
>>> import sys; sys.tracebacklimit = 0

>>> pd.set_option('display.width', 500)
>>> pd.set_option('display.max_columns', 10)
>>> pd.set_option('display.max_rows', 10)

>>> assert result is not Ellipsis, \
'Assign result to variable: result'
>>> assert type(result) is pd.DataFrame, \
'Variable result must be a pd.DataFrame type'

>>> result  # doctest: +NORMALIZE_WHITESPACE
Sepal length  Sepal width  Petal length  Petal width     Species
1           5.9          3.0           5.1          1.8   virginica
2           6.0          3.4           4.5          1.6  versicolor
"""

import pandas as pd
import numpy as np

DATA = 'https://python.astrotech.io/_static/iris-dirty.csv'
COLUMNS = [
'Sepal length',
'Sepal width',
'Petal length',
'Petal width',
'Species']

# type: pd.DataFrame
result = ...