5.17. Series Arithmetic

  • Vectorized Operations

  • s + 2, s.add(2)

  • s - 2, s.sub(2), s.subtract(2)

  • s * 2, s.mul(2), s.multiply(2)

  • s ** 2, s.pow(2)

  • s ** (1/2), s.pow(1/2)

  • s / 2, s.div(2), s.divide()

  • s // 2, s.truediv(2)

  • s % 2, s.mod(2)

  • divmod(s, 2), s.divmod(2)

5.17.1. SetUp

>>> import pandas as pd
>>>
>>> pd.set_option('display.max_columns', 50)
>>> pd.set_option('display.max_rows', 200)
>>> pd.set_option('display.width', 500)
>>> pd.set_option('display.memory_usage', 'deep')
>>> pd.set_option('display.precision', 4)
>>>
>>>
>>> data = [1, 2, 3, 4, 5]
>>> s = pd.Series(data)
>>>
>>> s
0    1
1    2
2    3
3    4
4    5
dtype: int64

5.17.2. Add

  • Series + 100

  • Series.add(100)

>>> s + 100
0    101
1    102
2    103
3    104
4    105
dtype: int64

>>> s.add(100)
0    101
1    102
2    103
3    104
4    105
dtype: int64

5.17.3. Subtract

  • Series - 100

  • Series.sub(100)

  • Series.subtract(100)

>>> s - 100
0   -99
1   -98
2   -97
3   -96
4   -95
dtype: int64

>>> s.sub(100)
0   -99
1   -98
2   -97
3   -96
4   -95
dtype: int64

>>> s.subtract(100)
0   -99
1   -98
2   -97
3   -96
4   -95
dtype: int64

5.17.4. Multiply

  • Series * 100

  • Series.mul(100)

  • Series.multiply(100)

>>> s * 100
0    100
1    200
2    300
3    400
4    500
dtype: int64

>>> s.mul(100)
0    100
1    200
2    300
3    400
4    500
dtype: int64

>>> s.multiply(100)
0    100
1    200
2    300
3    400
4    500
dtype: int64

5.17.5. Power

  • Series ** 2

  • Series.pow(2)

>>> s ** 2
0     1
1     4
2     9
3    16
4    25
dtype: int64

>>> s.pow(2)
0     1
1     4
2     9
3    16
4    25
dtype: int64

5.17.6. Root

  • Series ** (1/2)

>>> s ** (1/2)
0    1.0000
1    1.4142
2    1.7321
3    2.0000
4    2.2361
dtype: float64

>>> s ** 0.5
0    1.0000
1    1.4142
2    1.7321
3    2.0000
4    2.2361
dtype: float64

5.17.7. True Division

  • Series / 100

  • Series.div(100)

  • Series.divide(100)

  • Series.truediv(100)

>>> s / 100
0    0.01
1    0.02
2    0.03
3    0.04
4    0.05
dtype: float64

>>> s.div(100)
0    0.01
1    0.02
2    0.03
3    0.04
4    0.05
dtype: float64

>>> s.divide(100)
0    0.01
1    0.02
2    0.03
3    0.04
4    0.05
dtype: float64

>>> s.truediv(100)
0    0.01
1    0.02
2    0.03
3    0.04
4    0.05
dtype: float64

5.17.8. Floor Div

  • Series // 2

  • Series.floordiv(2)

>>> s
0    1
1    2
2    3
3    4
4    5
dtype: int64

>>> s // 2
0    0
1    1
2    1
3    2
4    2
dtype: int64

>>> s.floordiv(2)
0    0
1    1
2    1
3    2
4    2
dtype: int64

5.17.9. Modulo

  • Series % 2

  • Series.mod(2)

>>> s
0    1
1    2
2    3
3    4
4    5
dtype: int64

>>> s % 2
0    1
1    0
2    1
3    0
4    1
dtype: int64

>>> s.mod(2)
0    1
1    0
2    1
3    0
4    1
dtype: int64

Parity check:

>>> s % 2 == 0
0    False
1     True
2    False
3     True
4    False
dtype: bool

5.17.10. Arithmetic Expressions

  • ((s+100) * 10) ** 2

>>> ((s+100) * 10) ** 2
0    1020100
1    1040400
2    1060900
3    1081600
4    1102500
dtype: int64

5.17.11. Missing Values

>>> data = [1, 2, None, 4, 5]
>>> s = pd.Series(data)
>>>
>>> s
0    1.0
1    2.0
2    NaN
3    4.0
4    5.0
dtype: float64

>>> s + 100
0    101.0
1    102.0
2      NaN
3    104.0
4    105.0
dtype: float64

>>> s ** 2
0     1.0
1     4.0
2     NaN
3    16.0
4    25.0
dtype: float64

>>> ((s+100) * 10) ** 2
0    1020100.0
1    1040400.0
2          NaN
3    1081600.0
4    1102500.0
dtype: float64

5.17.12. Fill Values

>>> data = [1, 2, None, 4, 5]
>>> s = pd.Series(data)
>>>
>>> s
0    1.0
1    2.0
2    NaN
3    4.0
4    5.0
dtype: float64

>>> s.add(100, fill_value=0)
0    101.0
1    102.0
2    100.0
3    104.0
4    105.0
dtype: float64

5.17.13. Assignments

# %% About
# - Name: Series Arithmetic
# - Difficulty: easy
# - Lines: 5
# - Minutes: 3

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% English
# 1. Define variable `result` with result of
#    add 10 to each element of `DATA`
# 2. Do not use `.add()` method
# 3. Run doctests - all must succeed

# %% Polish
# 1. Zdefiniuj zmienną `result` z wynikiem
#    dodania 10 to każdego elementu z `DATA`
# 2. Nie używaj metody `.add()`
# 3. Uruchom doctesty - wszystkie muszą się powieść

# %% Expected
# >>> result
# 0    11.0
# 1    12.0
# 2    13.0
# 3    14.0
# 4    15.0
# dtype: float64

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0

>>> assert sys.version_info >= (3, 9), \
'Python has an is invalid version; expected: `3.9` or newer.'

>>> assert 'result' in globals(), \
'Variable `result` is not defined; assign result of your program to it.'

>>> assert result is not Ellipsis, \
'Variable `result` has an invalid value; assign result of your program to it.'

>>> assert type(result) is pd.Series, \
'Variable `result` has an invalid type; expected: `pd.Series`.'

>>> pd.set_option('display.max_columns', 50)
>>> pd.set_option('display.max_rows', 200)
>>> pd.set_option('display.width', 500)
>>> pd.set_option('display.memory_usage', 'deep')
>>> pd.set_option('display.precision', 4)

>>> result
0    11.0
1    12.0
2    13.0
3    14.0
4    15.0
dtype: float64
"""

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -f -v myfile.py`

# %% Imports
import pandas as pd

# %% Types
result: pd.Series

# %% Data
DATA = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])

# %% Result
result = ...


# %% About
# - Name: Series Arithmetic
# - Difficulty: easy
# - Lines: 5
# - Minutes: 3

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% English
# 1. Define variable `result` with result of
#    add 10 to each element of `DATA`
# 2. Use `.add()` method
# 3. Run doctests - all must succeed

# %% Polish
# 1. Zdefiniuj zmienną `result` z wynikiem
#    dodania 10 to każdego elementu z `DATA`
# 2. Użyj metodę `.add()`
# 3. Uruchom doctesty - wszystkie muszą się powieść

# %% Expected
# >>> result
# 0    11.0
# 1    12.0
# 2     NaN
# 3    14.0
# 4    15.0
# dtype: float64

# %% Hints
# - `Series.add(fill_value)`

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0

>>> assert sys.version_info >= (3, 9), \
'Python has an is invalid version; expected: `3.9` or newer.'

>>> assert 'result' in globals(), \
'Variable `result` is not defined; assign result of your program to it.'

>>> assert result is not Ellipsis, \
'Variable `result` has an invalid value; assign result of your program to it.'

>>> assert type(result) is pd.Series, \
'Variable `result` has an invalid type; expected: `pd.Series`.'

>>> pd.set_option('display.max_columns', 50)
>>> pd.set_option('display.max_rows', 200)
>>> pd.set_option('display.width', 500)
>>> pd.set_option('display.memory_usage', 'deep')
>>> pd.set_option('display.precision', 4)

>>> result
0    11.0
1    12.0
2     NaN
3    14.0
4    15.0
dtype: float64
"""

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -f -v myfile.py`

# %% Imports
import pandas as pd

# %% Types
result: pd.Series

# %% Data
DATA = pd.Series([1.0, 2.0, None, 4.0, 5.0])

# %% Result
result = ...


# %% About
# - Name: Series Arithmetic
# - Difficulty: easy
# - Lines: 5
# - Minutes: 3

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% English
# 1. Define variable `result` with result of
#    add 10 to each element of `DATA`
#    if value is not-a-number then treat it as zero
# 2. Use `.add()` method
# 3. Run doctests - all must succeed

# %% Polish
# 1. Zdefiniuj zmienną `result` z wynikiem
#    dodania 10 to każdego elementu z `DATA`
#    jeżeli wartość nie jest liczbą, to potraktuj ją jak zero
# 2. Użyj metodę `.add()`
# 3. Uruchom doctesty - wszystkie muszą się powieść

# %% Expected
# >>> result
# 0    11.0
# 1    12.0
# 2    10.0
# 3    14.0
# 4    15.0
# dtype: float64

# %% Hints
# - `Series.add(fill_value)`

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0

>>> assert sys.version_info >= (3, 9), \
'Python has an is invalid version; expected: `3.9` or newer.'

>>> assert 'result' in globals(), \
'Variable `result` is not defined; assign result of your program to it.'

>>> assert result is not Ellipsis, \
'Variable `result` has an invalid value; assign result of your program to it.'

>>> assert type(result) is pd.Series, \
'Variable `result` has an invalid type; expected: `pd.Series`.'

>>> pd.set_option('display.max_columns', 50)
>>> pd.set_option('display.max_rows', 200)
>>> pd.set_option('display.width', 500)
>>> pd.set_option('display.memory_usage', 'deep')
>>> pd.set_option('display.precision', 4)

>>> result
0    11.0
1    12.0
2    10.0
3    14.0
4    15.0
dtype: float64
"""

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -f -v myfile.py`

# %% Imports
import pandas as pd

# %% Types
result: pd.Series

# %% Data
DATA = pd.Series([1.0, 2.0, None, 4.0, 5.0])

# %% Result
result = ...