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C T 09 Write Python statements to determine whether any of the record of an employee is missing in
the series: empNames and empSalary.
isnull(): For every element of the series, the method returns True if the value is missing from the series,
and False otherwise.
1.5.7 Mathematical Operations Involving More than One Series:
Pandas provides various mathematical operations that can be performed on Series data, making it an ideal for analyzing
data.
Example 1: Profit over four quarters
Suppose we have two Series that contain the sales and expenses data for a company over four quarters, respectively.
We can subtract the expenses from the sales to obtain the profit for each quarter, as given below:
01 import pandas as pd
02 salesData = [1000, 1200, 1400, 1600]
03 expensesData = [800, 900, 1000, 1100]
04 sales = pd.Series(salesData, index = ['Q1_2023', 'Q2_2023', 'Q3_2023', 'Q4_2023'])
05 expenses = pd.Series(expensesData, index = ['Q1_2023', 'Q2_2023', 'Q3_2023',
'Q4_2023'])
06 profit = sales - expenses
07 print("Profit over four quarters: ")
08 print(profit)
output:
Profit over four quarters:
Q1_2023 200
Q2_2023 300
Q3_2023 400
Q4_2023 500
dtype: int64
Example 2: Daily total sales across two different stores
Suppose we have two Series that contain the daily sales of two different stores of a company for the last week. We can
calculate the daily total sales for each day using the "+" operator as follows:
01 import pandas as pd
02 sales1 = [1000, 1200, 1400, 1600, 1800, 2000, 2000]
03 sales2 = [8000, 1000, 1200, 2400, 1600, 1800, 2200]
04 days = ['Day1', 'Day2', 'Day3', 'Day4', 'Day5', 'Day6', 'Day7']
05 store1Sales = pd.Series(sales1, index = days)
06 store2Sales = pd.Series(sales2, index = days)
07 totalSales = store1Sales + store2Sales
08 print(totalSales)
output:
Day1 9000
Day2 2200
Day3 2600
Day4 4000
Day5 3400
16 Touchpad Informatics Practices-XII

