3.10. Series Arithmetic

3.10.1. Vectorized Operations

  • s + 2, s.add(2), s.__add__(2)

  • s - 2, s.sub(2), s.subtract(2), s.__sub__(2)

  • s * 2, s.mul(2), s.multiply(2), s.__mul__(2)

  • s ** 2, s.pow(2), s.__pow__(2)

  • s ** (1/2), s.pow(1/2), s.__sub__(1/2)

  • s / 2, s.div(2), s.divide(), s.__div__(2)

  • s // 2, s.truediv(2), s.__truediv__(2)

  • s % 2, s.mod(2), s.__mod__(2)

  • divmod(s, 2), s.divmod(2), s.__divmod__(2), (s//2, s%2)

import pandas as pd
import numpy as np

s = pd.Series(
    data = [1.1, 2.2, np.nan, 4.4],
    index = ['a', 'b', 'c', 'd'])

s
# a    1.1
# b    2.2
# c    NaN
# d    4.4
# dtype: float64
s + 2
# a    3.1
# b    4.2
# c    NaN
# d    6.4
# dtype: float64
s ** 2
# a     1.21
# b     4.84
# c      NaN
# d    19.36
# dtype: float64
s ** (1/2)
# a    1.048809
# b    1.483240
# c         NaN
# d    2.097618
# dtype: float64

3.10.2. Broadcasting

  • Uses inner join

  • fill_value: If data in both corresponding Series locations is missing the result will be missing

import pandas as pd


a = pd.Series([1, 2, 3])
b = pd.Series([4, 5, 6])

a + b
# 0    5
# 1    7
# 2    9
# dtype: int64
import pandas as pd


a = pd.Series([1, 2, 3, 4])
b = pd.Series([4, 5, 6])

a + b
# 0    5.0
# 1    7.0
# 2    9.0
# 3    NaN
# dtype: float64
import pandas as pd

a = pd.Series([1, 2, 3])
b = pd.Series([4, 5, 6, 7])

a + b
# 0    5.0
# 1    7.0
# 2    9.0
# 3    NaN
# dtype: float64
import pandas as pd

a = pd.Series([1, 2, None])
b = pd.Series([4, 5, 6])

a + b
# 0    5.0
# 1    7.0
# 2    NaN
# dtype: float64
import pandas as pd

a = pd.Series([1, 2, None])
b = pd.Series([4, 5, None])

a + b
# 0    5.0
# 1    7.0
# 2    NaN
# dtype: float64
import pandas as pd

a = pd.Series(data=[1, 2, 3], index=['a', 'b', 'c'])
b = pd.Series(data=[4, 5, 6], index=['a', 'b', 'x'])

a + b
# a    5.0
# b    7.0
# c    NaN
# x    NaN
# dtype: float64

fill_value: If data in both corresponding Series locations is missing the result will be missing:

a = pd.Series(data=[1, 2, 3], index=['a', 'b', 'c'])
b = pd.Series(data=[4, 5, 6], index=['a', 'b', 'x'])

a.add(b, fill_value=0)
# a    5.0
# b    7.0
# c    3.0
# x    6.0
# dtype: float64

3.10.3. Assignments

Code 3.66. Solution
"""
* Assignment: Series Arithmetic
* Complexity: easy
* Lines of code: 5 lines
* Time: 5 min

English:
    1. Set random seed to zero
    2. Generate `data: ndarray` with 5 random digits [0, 9]
    3. Create `index: list` with index names as sequential letters in english alphabet
    4. Create `s: pd.Series` from `data` and `index`
    5. Multiply `s` by 10
    6. Multiply `s` by `s`
    7. Run doctests - all must succeed

Polish:
    1. Ustaw random ziarno losowości na zero
    2. Wygeneruj `data: np.ndarray` z 5 losowymi cyframi <0, 9>
    3. Stwórz `index: list` z indeksami jak kolejne listery alfabetu angielskiego
    4. Stwórz `s: pd.Series` z `data` oraz `index`
    5. Pomnóż `s` przez 10
    6. Pomnóż `s` przez  wartości `s`
    7. Uruchom doctesty - wszystkie muszą się powieść

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

    >>> type(result) is pd.Series
    True
    >>> result
    a    2500
    b       0
    c     900
    d     900
    e    4900
    dtype: int64
"""

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


result = ...