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Day6 3800
Day7 4200
dtype: int64
Example 3: Computing total revenue
Let's consider two series, price and quantity, representing the price and quantity of five different products sold in past
one hour. We want to calculate the total revenue generated by selling these products. We can do this by multiplying
the price and quantity series as shown below:
01 import pandas as pd
02 products = ['Product1', 'Product2', 'Product3', 'Product4', 'Product5']
03 price = pd.Series([10, 20, 30, 40, 35], index = products)
04 quantity = pd.Series([100, 200, 300, 400, 300], index = products)
05 totalRevenue = price * quantity
06 print("Total Revenue: ")
07 print(totalRevenue)
08 print("Sum Total: ", totalRevenue.sum())
output:
Total Revenue:
Product1 1000
Product2 4000
Product3 9000
Product4 16000
Product5 10500
dtype: int64
Sum Total: 40500
Example 4: Comparing daily sales of two stores
Suppose we have two Series that contain the daily sales of two stores for the last week. We want to compare the daily
sales of the two stores to determine which store had higher sales on each day. We can perform this operation using
the ">" operator as follows:
>>> sales1 = [1000, 1200, 1400, 1600, 1800, 2000, 2000]
>>> sales2 = [8000, 1000, 1200, 2400, 1600, 1800, 2200]
>>> days = ['Day1', 'Day2', 'Day3', 'Day4', 'Day5', 'Day6', 'Day7']
>>> store1Sales = pd.Series(sales1, index = days)
>>> store2Sales = pd.Series(sales2, index = days)
>>> higherSales = store1Sales > store2Sales
>>> print(higherSales)
Day1 False
Day2 True
Day3 True
Day4 False
Day5 True
Day6 True
Day7 False
dtype: bool
>>> higherSales = higherSales.replace({False:'Store2', True:'Store1'})
>>> print(higherSales)
Day1 Store2
Day2 Store1
Day3 Store1
Day4 Store2
Day5 Store1
Day6 Store1
Day7 Store2
dtype: object
Data Handling using Pandas 17

