3.7. Series NA¶

3.7.1. Rationale¶

https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html
https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html#missing-data-na
Experimental: the behaviour of pd.NA can still change without warning.
None
float('nan')
np.nan
pd.NA

3.7.2. Boolean Value¶

import pandas as pd
import numpy as np


bool(None)
# False

bool(float('nan'))
# True

bool(np.nan)
# True

bool(pd.NA)
# Traceback (most recent call last):
# TypeError: boolean value of NA is ambiguous

3.7.3. Type¶

import pandas as pd
import numpy as np


pd.Series([1, None, 3]).dtype                    # dtype('float64')
pd.Series([1.0, None, 3.0]).dtype                # dtype('float64')
pd.Series([True, None, False]).dtype             # dtype('O')
pd.Series(['a', None, 'c']).dtype                # dtype('O')

pd.Series([1, float('nan'), 3]).dtype            # dtype('float64')
pd.Series([1.0, float('nan'), 3.0]).dtype        # dtype('float64')
pd.Series([True, float('nan'), False]).dtype     # dtype('O')
pd.Series(['a', float('nan'), 'c']).dtype        # dtype('O')

pd.Series([1, np.nan, 3]).dtype                  # dtype('float64')
pd.Series([1.0, np.nan, 3.0]).dtype              # dtype('float64')
pd.Series([True, np.nan, False]).dtype           # dtype('O')
pd.Series(['a', np.nan, 'c']).dtype              # dtype('O')

pd.Series([1, pd.NA, 3]).dtype                   # dtype('O')
pd.Series([1.0, pd.NA, 3.0]).dtype               # dtype('O')
pd.Series([True, pd.NA, False]).dtype            # dtype('O')
pd.Series(['a', pd.NA, 'c']).dtype               # dtype('O')

3.7.4. Comparison¶

import pandas as pd
import numpy as np


None == None                     # True
None == float('nan')             # False
None == np.nan                   # False
None == pd.NA                    # False

float('nan') == None             # False
float('nan') == float('nan')     # False
float('nan') == np.nan           # False
float('nan') == pd.NA            # <NA>

np.nan == None                   # False
np.nan == float('nan')           # False
np.nan == np.nan                 # False
np.nan == pd.NA                  # <NA>

pd.NA == None                    # False
pd.NA == float('nan')            # <NA>
pd.NA == np.nan                  # <NA>
pd.NA == pd.NA                   # <NA>

3.7.5. Identity¶

import pandas as pd
import numpy as np


None is None                     # True
None is float('nan')             # False
None is np.nan                   # False
None is pd.NA                    # False

float('nan') is None             # False
float('nan') is float('nan')     # False
float('nan') is np.nan           # False
float('nan') is pd.NA            # False

np.nan is None                   # False
np.nan is float('nan')           # False
np.nan is np.nan                 # True
np.nan is pd.NA                  # False

pd.NA is None                    # False
pd.NA is float('nan')            # False
pd.NA is np.nan                  # False
pd.NA is pd.NA                   # True

3.7.6. Check¶

Negated ~ versions of all above methods

import pandas as pd
import numpy as np


s = pd.Series([1.0, np.nan, 3.0])

s.any()      # True
~s.any()     # False

s.all()      # True
~s.all()     # False

3.7.7. Select¶

s.isnull() and s.notnull()
s.isna() and s.notna()
Negated ~ versions of all above methods

import pandas as pd
import numpy as np


s = pd.Series([1.0, np.nan, 3.0])

s.isnull()
# 0    False
# 1     True
# 2    False
# dtype: bool

~s.isnull()
# 0     True
# 1    False
# 2     True
# dtype: bool

s.notnull()
# 0     True
# 1    False
# 2     True
# dtype: bool

~s.notnull()
# 0    False
# 1     True
# 2    False
# dtype: bool

import pandas as pd
import numpy as np


s = pd.Series([1.0, np.nan, 3.0])

s.isna()
# 0    False
# 1     True
# 2    False
# dtype: bool

s.notna()
# 0     True
# 1    False
# 2     True
# dtype: bool

~s.isna()
# 0     True
# 1    False
# 2     True
# dtype: bool

~s.notna()
# 0    False
# 1     True
# 2    False
# dtype: bool

3.7.8. Update¶

Works with inplace=True parameter.

Fill NA - Scalar value:

import pandas as pd


s = pd.Series([1.0, None, None, 4.0, None, 6.0])

s.fillna(0.0)
# 0    1.0
# 1    0.0
# 2    0.0
# 3    4.0
# 4    0.0
# 5    6.0
# dtype: float64

Forward Fill. ffill: propagate last valid observation forward:

import pandas as pd


s = pd.Series([1.0, None, None, 4.0, None, 6.0])

s.ffill()
# 0    1.0
# 1    1.0
# 2    1.0
# 3    4.0
# 4    4.0
# 5    6.0
# dtype: float64

Backward Fill. bfill: use NEXT valid observation to fill gap:

import pandas as pd


s = pd.Series([1.0, None, None, 4.0, None, 6.0])
s.bfill()
# 0    1.0
# 1    4.0
# 2    4.0
# 3    4.0
# 4    6.0
# 5    6.0
# dtype: float64

Interpolate. method: str, default linear. Does not have inplace=True:

import pandas as pd


s = pd.Series([1.0, None, None, 4.0, None, 6.0])

s.interpolate()
# 0    1.0
# 1    2.0
# 2    3.0
# 3    4.0
# 4    5.0
# 5    6.0
# dtype: float64

s.interpolate('nearest')   # requires installation of ``scipy`` library
# 0    1.0
# 1    1.0
# 2    4.0
# 3    4.0
# 4    4.0
# 5    6.0
# dtype: float64

s.interpolate('polynomial', order=2)  # requires installation of ``scipy`` library
# 0    1.0
# 1    2.0
# 2    3.0
# 3    4.0
# 4    5.0
# 5    6.0
# dtype: float64

Table 3.1. 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.

3.7.9. Drop¶

Drop Rows. Has inplace=True parameter:

import pandas as pd


s = pd.Series([1.0, None, None, 4.0, None, 6.0])

s.dropna()
# 0    1.0
# 1    2.0
# 2    2.0
# 4    5.0
# dtype: float64

3.7.10. Conversion¶

If you have a DataFrame or Series using traditional types that have missing data represented using np.nan
There are convenience methods convert_dtypes() in Series and DataFrame that can convert data to use the newer dtypes for integers, strings and booleans
This is especially helpful after reading in data sets when letting the readers such as read_csv() and read_excel() infer default dtypes.

data = pd.read_csv('data/baseball.csv', index_col='id')
data[data.columns[:10]].dtypes
# player    object
# year       int64
# stint      int64
# team      object
# lg        object
# g          int64
# ab         int64
# r          int64
# h          int64
# X2b        int64
# dtype: object

data = pd.read_csv('data/baseball.csv', index_col='id')
data = data.convert_dtypes()
data[data.columns[:10]].dtypes
# player    string
# year       Int64
# stint      Int64
# team      string
# lg        string
# g          Int64
# ab         Int64
# r          Int64
# h          Int64
# X2b        Int64
# dtype: object

3.7.11. Assignments¶

Code 3.10. Solution¶

"""
* Assignment: Series NA
* Complexity: easy
* Lines of code: 10 lines
* Time: 5 min

English:
    1. Use data from "Given" section (see below)
    2. From input data create `pd.Series`
    3. Fill first missing value with zero
    4. Drop missing values
    5. Reindex series (without old copy)
    6. Run doctests - all must succeed

Polish:
    1. Użyj danych z sekcji "Given" (patrz poniżej)
    2. Z danych wejściowych stwórz `pd.Series`
    3. Wypełnij pierwszą brakującą wartość zerem
    4. Usuń brakujące wartości
    5. Zresetuj indeks (bez kopii starego)
    6. Uruchom doctesty - wszystkie muszą się powieść
    X. Uruchom doctesty - wszystkie muszą się powieść

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

    >>> type(result) is pd.Series
    True
    >>> result
    0    1.0
    1    0.0
    2    5.0
    3    1.0
    4    2.0
    5    1.0
    dtype: float64
"""


# Given
import pandas as pd

DATA = [1, None, 5, None, 1, 2, 1]