4.4. Series Sample

4.4.1. SetUp

>>> import pandas as pd
>>> import numpy as np
>>> np.random.seed(0)
>>>
>>>
>>> s = pd.Series(
...     data = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
...     index = pd.date_range(start='1999-12-28', periods=10))
>>> s
1999-12-28    0
1999-12-29    1
1999-12-30    2
1999-12-31    3
2000-01-01    4
2000-01-02    5
2000-01-03    6
2000-01-04    7
2000-01-05    8
2000-01-06    9
Freq: D, dtype: int64

4.4.3. Tail

>>> s.tail(2)
2000-01-05    8
2000-01-06    9
Freq: D, dtype: int64

>>> s.tail(n=1)
2000-01-06    9
Freq: D, dtype: int64

4.4.4. First

>>> s.first('Y')
1999-12-28    0
1999-12-29    1
1999-12-30    2
1999-12-31    3
Freq: D, dtype: int64

>>> s.first('M')
1999-12-28    0
1999-12-29    1
1999-12-30    2
1999-12-31    3
Freq: D, dtype: int64

>>> s.first('D')
1999-12-28    0
Freq: D, dtype: int64

>>> s.first('3D')
1999-12-28    0
1999-12-29    1
1999-12-30    2
Freq: D, dtype: int64

>>> s.first('W')
1999-12-28    0
1999-12-29    1
1999-12-30    2
1999-12-31    3
2000-01-01    4
2000-01-02    5
Freq: D, dtype: int64

4.4.5. Last

>>> s.last('Y')
2000-01-01    4
2000-01-02    5
2000-01-03    6
2000-01-04    7
2000-01-05    8
2000-01-06    9
Freq: D, dtype: int64

>>> s.last('M')
2000-01-01    4
2000-01-02    5
2000-01-03    6
2000-01-04    7
2000-01-05    8
2000-01-06    9
Freq: D, dtype: int64

>>> s.last('D')
2000-01-06    9
Freq: D, dtype: int64

>>> s.last('2D')
2000-01-05    8
2000-01-06    9
Freq: D, dtype: int64

>>> s.last('W')
2000-01-03    6
2000-01-04    7
2000-01-05    8
2000-01-06    9
Freq: D, dtype: int64

4.4.6. Sample

  • 1/4 is 25%

  • .05 is 5%

  • 0.5 is 50%

  • 1.0 is 100%

n number or fraction random rows with and without repetition:

>>> s.sample()
1999-12-30    2
Freq: D, dtype: int64

>>> s.sample(2)
1999-12-31    3
2000-01-02    5
Freq: 2D, dtype: int64

>>> s.sample(n=2, replace=True)
2000-01-04    7
2000-01-04    7
dtype: int64

>>> s.sample(frac=1/4)
1999-12-30    2
1999-12-31    3
Freq: D, dtype: int64

>>> s.sample(frac=0.5)
2000-01-01    4
1999-12-29    1
2000-01-04    7
2000-01-05    8
1999-12-30    2
dtype: int64

4.4.7. Reset Index

>>> s.sample(frac=1.0).reset_index()
       index  0
0 2000-01-02  5
1 1999-12-30  2
2 2000-01-04  7
3 2000-01-01  4
4 1999-12-29  1
5 1999-12-28  0
6 2000-01-03  6
7 2000-01-05  8
8 2000-01-06  9
9 1999-12-31  3

4.4.8. Assignments

Code 4.79. Solution
"""
* Assignment: Series Sample
* Complexity: easy
* Lines of code: 5 lines
* Time: 5 min

English:
    1. Set random seed to zero
    2. Create `pd.Series` with 100 random numbers from standard normal distribution
    3. Series Index are following dates since 2000
    4. Print values:
        a. first in the series,
        b. last 5 elements in the series,
        c. first two weeks in the series,
        d. last month in the series,
        e. three random elements,
        f. 125% of random elements with replacement.
    5. Run doctests - all must succeed

Polish:
    1. Ustaw ziarno losowości na zero
    2. Stwórz `pd.Series` z 100 losowymi liczbami z rozkładu normalnego
    3. Indeksem w serii mają być kolejne dni od 2000 roku
    4. Wypisz wartości:
        a. pierwszy w serii,
        b. ostatnie 5 elementów w serii,
        c. dwa pierwsze tygodnie w serii,
        d. ostatni miesiąc w serii,
        e. trzy losowe element,
        f. 125% losowych elementów z powtórzeniami.
    5. Uruchom doctesty - wszystkie muszą się powieść

Hints:
    * `np.random.seed(0)`
    * `np.random.randn(n)`

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

    >>> assert result is not Ellipsis, \
    'Assign result to variable: `result`'
    >>> assert all(type(x) is not Ellipsis for x in result.values()), \
    'Assign result to dict values in `result`'
    >>> assert type(result) is dict, \
    'Variable `result` has invalid type, should be `dict`'

    >>> result  # doctest: +NORMALIZE_WHITESPACE
    {'head': 2000-01-01    1.764052
    Freq: D, dtype: float64, 'tail': 2000-04-05    0.706573
    2000-04-06    0.010500
    2000-04-07    1.785870
    2000-04-08    0.126912
    2000-04-09    0.401989
    Freq: D, dtype: float64, 'first': 2000-01-01    1.764052
    2000-01-02    0.400157
    2000-01-03    0.978738
    2000-01-04    2.240893
    2000-01-05    1.867558
    2000-01-06   -0.977278
    2000-01-07    0.950088
    2000-01-08   -0.151357
    2000-01-09   -0.103219
    Freq: D, dtype: float64, 'last': 2000-04-01    1.222445
    2000-04-02    0.208275
    2000-04-03    0.976639
    2000-04-04    0.356366
    2000-04-05    0.706573
    2000-04-06    0.010500
    2000-04-07    1.785870
    2000-04-08    0.126912
    2000-04-09    0.401989
    Freq: D, dtype: float64, 'sample_n': 2000-01-20   -0.854096
    2000-01-07    0.950088
    2000-02-15   -0.438074
    dtype: float64, 'sample_frac': 2000-03-07   -1.630198
    2000-04-01    1.222445
    2000-03-26    1.895889
    2000-02-09   -0.302303
    2000-02-09   -0.302303
                    ...
    2000-01-08   -0.151357
    2000-03-21   -1.165150
    2000-01-23    0.864436
    2000-03-20    0.056165
    2000-03-30    1.054452
    Length: 125, dtype: float64}
"""

import pandas as pd
import numpy as np
np.random.seed(0)

# type: dict[str, pd.Series]
result = {
    'head': ...,
    'tail': ...,
    'first': ...,
    'last': ...,
    'sample_n': ...,
    'sample_frac': ...,
}