3.6. Series Slicing

3.6.1. Numeric Index

import pandas as pd

s = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])

s
# 0    1.0
# 1    2.0
# 2    3.0
# 3    4.0
# 4    5.0
# dtype: float64

s[:2]
# 0    1.0
# 1    2.0
# dtype: float64

s[2:]
# 2    3.0
# 3    4.0
# 4    5.0
# dtype: float64

s[1:-2]
# 1    2.0
# 2    3.0
# dtype: float64

s[::2]
# 0    1.0
# 2    3.0
# 4    5.0
# dtype: float64

s[1::2]
# 1    2.0
# 3    4.0
# dtype: float64

3.6.2. String Index

  • Using string index upper and lower bound are inclusive!

  • String indexes has also numeric index underneath

import pandas as pd

s = pd.Series(
    data = [1.0, 2.0, 3.0, 4.0, 5.0],
    index = ['a', 'b', 'c', 'd', 'e'])

s
# a    1.0
# b    2.0
# c    3.0
# d    4.0
# e    5.0
# dtype: float64

s['a':'d']
# a    1.0
# b    2.0
# c    3.0
# d    4.0
# dtype: float64

s['a':'d':2]
# a    1.0
# c    3.0
# dtype: float64

s['a':'d':'b']
# Traceback (most recent call last):
# TypeError: '>=' not supported between instances of 'str' and 'int'

s['d':'a']
# Series([], dtype: float64)
import pandas as pd

s = pd.Series(
    data = [1.0, 2.0, 3.0, 4.0, 5.0],
    index = ['a', 'b', 'c', 'd', 'e'])

s
# a    1.0
# b    2.0
# c    3.0
# d    4.0
# e    5.0
# dtype: float64

s[:2]
# a    1.0
# b    2.0
# dtype: float64

s[2:]
# c    3.0
# d    4.0
# e    5.0
# dtype: float64

s[1:-2]
# b    2.0
# c    3.0
# dtype: float64

s[::2]
# a    1.0
# c    3.0
# e    5.0
# dtype: float64

s[1::2]
# b    2.0
# d    4.0
# dtype: float64
import pandas as pd

s = pd.Series(
    data = [1.0, 2.0, 3.0, 4.0, 5.0],
    index = ['aaa', 'bbb', 'ccc', 'ddd', 'eee'])

s
# aaa    1.0
# bbb    2.0
# ccc    3.0
# ddd    4.0
# eee    5.0
# dtype: float64

s['a':'b']
# aaa    1.0
# dtype: float64

s['a':'c']
# aaa    1.0
# bbb    2.0
# dtype: float64

3.6.3. Date Index

import pandas as pd

s = pd.Series(
    data = [1.0, 2.0, 3.0, 4.0, 5.0],
    index = pd.date_range('1999-12-30', periods=5))

s
# 1999-12-30    1.0
# 1999-12-31    2.0
# 2000-01-01    3.0
# 2000-01-02    4.0
# 2000-01-03    5.0
# Freq: D, dtype: float64

s['2000-01-02':'2000-01-04']
# 2000-01-02    4.0
# 2000-01-03    5.0
# Freq: D, dtype: float64

s['1999-12-30':'2000-01-04':2]
# 1999-12-30    1.0
# 2000-01-01    3.0
# 2000-01-03    5.0
# Freq: 2D, dtype: float64

s['1999-12-30':'2000-01-04':-1]
# Series([], Freq: -1D, dtype: float64)

s['2000-01-04':'1999-12-30':-1]
# 2000-01-03    5.0
# 2000-01-02    4.0
# 2000-01-01    3.0
# 1999-12-31    2.0
# 1999-12-30    1.0
# Freq: -1D, dtype: float64

s[:'1999']
# 1999-12-30    1.0
# 1999-12-31    2.0
# Freq: D, dtype: float64

s['2000':]
# 2000-01-01    3.0
# 2000-01-02    4.0
# 2000-01-03    5.0
# Freq: D, dtype: float64

s[:'1999-12']
# 1999-12-30    1.0
# 1999-12-31    2.0
# Freq: D, dtype: float64

s['2000-01':]
# 2000-01-01    3.0
# 2000-01-02    4.0
# 2000-01-03    5.0
# Freq: D, dtype: float64

s[:'2000-01-02']
# 1999-12-30    1.0
# 1999-12-31    2.0
# 2000-01-01    3.0
# 2000-01-02    4.0
# Freq: D, dtype: float64

s['2000-01-02':]
# 2000-01-02    4.0
# 2000-01-03    5.0
# Freq: D, dtype: float64

s['1999-12':'1999-12']
# 1999-12-30    1.0
# 1999-12-31    2.0
# Freq: D, dtype: float64

s['2000-01':'2000-01-05']
# 2000-01-01    3.0
# 2000-01-02    4.0
# 2000-01-03    5.0
# Freq: D, dtype: float64

s[:'2000-01-05':2]
# 1999-12-30    1.0
# 2000-01-01    3.0
# 2000-01-03    5.0
# Freq: 2D, dtype: float64

s[:'2000-01-03':-1]
# 2000-01-03    5.0
# Freq: -1D, dtype: float64
import pandas as pd

s = pd.Series(
    data = [1.0, 2.0, 3.0, 4.0, 5.0],
    index = pd.date_range('1999-12-30', periods=5))

s
# 1999-12-30    1.0
# 1999-12-31    2.0
# 2000-01-01    3.0
# 2000-01-02    4.0
# 2000-01-03    5.0

s[1:3]
# 1999-12-31    2.0
# 2000-01-01    3.0
# Freq: D, dtype: float64

s[:3]
# 1999-12-30    1.0
# 1999-12-31    2.0
# 2000-01-01    3.0
# Freq: D, dtype: float64

s[:3:2]
# 1999-12-30    1.0
# 2000-01-01    3.0
# Freq: 2D, dtype: float64

s[::-1]
# 2000-01-03    5.0
# 2000-01-02    4.0
# 2000-01-01    3.0
# 1999-12-31    2.0
# 1999-12-30    1.0
# Freq: -1D, dtype: float64

3.6.4. Assignments

Code 3.56. Solution
"""
* Assignment: Series Slice Datetime
* Complexity: easy
* Lines of code: 5 lines
* Time: 3 min

English:
    1. Set random seed to zero
    2. Create `s: pd.Series` with 100 random numbers from standard distribution
    3. Series Index are following dates since 2000
    4. Define `result: pd.Series` with slice dates from 2000-02-14 to end of February 2000
    5. Compare result with "Tests" section (see below)

Polish:
    1. Ustaw ziarno losowości na zero
    2. Stwórz `s: pd.Series` z 100 losowymi liczbami z rozkładu normalnego
    3. Indeksem w serii mają być kolejne dni od 2000 roku
    4. Zdefiniuj `result: pd.Series` z wytciętymi datami od 2000-02-14 do końca lutego 2000
    5. Porównaj wyniki z sekcją "Tests" (patrz poniżej)

Hints:
    * `np.random.randn()`

Tests:
    >>> type(result) is pd.Series
    True
    >>> pd.set_option('display.width', 500)
    >>> pd.set_option('display.max_columns', 10)
    >>> pd.set_option('display.max_rows', 10)
    >>> result  # doctest: +NORMALIZE_WHITESPACE
    2000-02-14   -0.509652
    2000-02-15   -0.438074
    2000-02-16   -1.252795
    2000-02-17    0.777490
    2000-02-18   -1.613898
                    ...
    2000-02-25    0.428332
    2000-02-26    0.066517
    2000-02-27    0.302472
    2000-02-28   -0.634322
    2000-02-29   -0.362741
    Freq: D, Length: 16, dtype: float64
"""


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

NUMBER = 100


Code 3.57. Solution
"""
* Assignment: Slicing Slice Str
* Complexity: easy
* Lines of code: 10 lines
* Time: 13 min

English:
    1. Use data from "Given" section (see below)
    2. Create `pd.Series` with 26 random integers in range `[10, 100)`
    3. Name indexes like letters from ASCII alphabet (`ascii_lowercase: str`)
    4. Find middle letter of alphabet
    5. Slice from series 3 elements up and down from middle
    6. Compare result with "Tests" section (see below)

Polish:
    1. Użyj danych z sekcji "Given" (patrz poniżej)
    2. Stwórz `pd.Series` z 26 losowymi liczbami całkowitymi z przedziału `<10; 100)`
    3. Nazwij indeksy jak kolejne litery alfabetu ASCII (`ascii_lowercase: str`)
    4. Znajdź środkową literę alfabetu
    5. Wytnij z serii po 3 elementy w górę i w dół od wyszukanego środka
    6. Porównaj wyniki z sekcją "Tests" (patrz poniżej)

Hints:
    * `np.random.randint(..., ..., size=...)`

Tests:
    >>> type(result) is pd.Series
    True
    >>> result
    j    97
    k    80
    l    98
    m    98
    n    22
    o    68
    p    75
    dtype: int64
"""


# Given
from statistics import median_low
import pandas as pd
import numpy as np
np.random.seed(0)


ascii_lowercase = 'abcdefghijklmnopqrstuvwxyz'