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 correspondingSeries
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¶
"""
* 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`
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`
Tests:
>>> type(result) is pd.Series
True
>>> result
a 2500
b 0
c 900
d 900
e 4900
dtype: int64
"""
# Given
import pandas as pd
import numpy as np
np.random.seed(0)
result = ...