Arithmetic Operations on Pandas Series

Just like with NumPy ndarrays, we can perform element-wise arithmetic operations on Pandas Series. In this lesson we will look at arithmetic operations between Pandas Series and single numbers. Let's create a new Pandas Series that will hold a grocery list of just fruits.

# We create a Pandas Series that stores a grocery list of just fruits
fruits= pd.Series(data = [10, 6, 3,], index = ['apples', 'oranges', 'bananas'])

# We display the fruits Pandas Series
fruits

apples         10
oranges        6
bananas       3
dtype: int64

We can now modify the data in fruits by performing basic arithmetic operations. Let's see some examples

Example 1. Element-wise basic arithmetic operations

# We print fruits for reference
print('Original grocery list of fruits:\n ', fruits)

# We perform basic element-wise operations using arithmetic symbols
print()
print('fruits + 2:\n', fruits + 2) # We add 2 to each item in fruits
print()
print('fruits - 2:\n', fruits - 2) # We subtract 2 to each item in fruits
print()
print('fruits * 2:\n', fruits * 2) # We multiply each item in fruits by 2 
print()
print('fruits / 2:\n', fruits / 2) # We divide each item in fruits by 2
print()

Original grocery list of fruits:
apples         10
oranges        6
bananas       3
dtype: int64

fruits + 2:
apples         12
oranges        8
bananas       5
dtype: int64

fruits - 2:
apples           8
oranges        4
bananas       1
dtype: int64

fruits * 2:
apples         20
oranges      12
bananas       6
dtype: int64

fruits / 2:
apples           5.0
oranges        3.0
bananas       1.5
dtype: float64

You can also apply mathematical functions from NumPy, such assqrt(x), to all elements of a Pandas Series.

Example 2. Use mathematical functions from NumPy to operate on Series

# We import NumPy as np to be able to use the mathematical functions
import numpy as np

# We print fruits for reference
print('Original grocery list of fruits:\n', fruits)

# We apply different mathematical functions to all elements of fruits
print()
print('EXP(X) = \n', np.exp(fruits))
print() 
print('SQRT(X) =\n', np.sqrt(fruits))
print()
print('POW(X,2) =\n',np.power(fruits,2)) # We raise all elements of fruits to the power of 2

Original grocery list of fruits:
apples         10
oranges        6
bananas       3
dtype: int64

EXP(X) =
apples        22026.465795
oranges         403.428793
bananas          20.085537
dtype: float64

SQRT(X) =
apples            3.162278
oranges         2.449490
bananas        1.732051
dtype: float64

POW(X,2) =
apples         100
oranges        36
bananas         9
dtype: int64

Pandas also allows us to only apply arithmetic operations on selected items in our fruits grocery list. Let's see some examples

Example 3. Perform arithmetic operations on selected elements

# We print fruits for reference
print('Original grocery list of fruits:\n ', fruits)
print()

# We add 2 only to the bananas
print('Amount of bananas + 2 = ', fruits['bananas'] + 2)
print()

# We subtract 2 from apples
print('Amount of apples - 2 = ', fruits.iloc[0] - 2)
print()

# We multiply apples and oranges by 2
print('We double the amount of apples and oranges:\n', fruits[['apples', 'oranges']] * 2)
print()

# We divide apples and oranges by 2
print('We half the amount of apples and oranges:\n', fruits.loc[['apples', 'oranges']] / 2)

Original grocery list of fruits:
apples         10
oranges        6
bananas       3
dtype: int64

Amount of bananas + 2 = 5

Amount of apples - 2 = 8

We double the amount of apples and oranges:
apples         20
oranges      12
dtype: int64

We half the amount of apples and oranges:
apples         5.0
oranges      3.0
dtype: float64

You can also apply arithmetic operations on Pandas Series of mixed data type provided that the arithmetic operation is defined for all data types in the Series, otherwise, you will get an error. Let's see what happens when we multiply our grocery list by 2

Example 4. Perform multiplication on a Series having integer and string elements

# We multiply our grocery list by 2
groceries * 2

eggs                 60
apples             12
milk         YesYes
bread        NoNo
dtype: object

As we can see, in this case, since we multiplied by 2, Pandas doubles the data of each item including the strings. Pandas can do this because the multiplication operation * is defined both for numbers and strings. If you were to apply an operation that was valid for numbers but not strings, say for instance, / you will get an error. So when you have mixed data types in your Pandas Series make sure the arithmetic operations are valid on all the data types of your elements.