Pandas Data Series: Compare the elements of the two Pandas Series
Pandas: Data Series Exercise-4 with Solution
Write a Pandas program to compare the elements of the two Pandas Series.
Sample Series: [2, 4, 6, 8, 10], [1, 3, 5, 7, 10]
Sample Solution:
Python Code :
import pandas as pd
ds1 = pd.Series([2, 4, 6, 8, 10])
ds2 = pd.Series([1, 3, 5, 7, 10])
print("Series1:")
print(ds1)
print("Series2:")
print(ds2)
print("Compare the elements of the said Series:")
print("Equals:")
print(ds1 == ds2)
print("Greater than:")
print(ds1 > ds2)
print("Less than:")
print(ds1 < ds2)
Sample Output:
Series1: 0 2 1 4 2 6 3 8 4 10 dtype: int64 Series2: 0 1 1 3 2 5 3 7 4 10 dtype: int64 Compare the elements of the said Series: Equals: 0 False 1 False 2 False 3 False 4 True dtype: bool Greater than: 0 True 1 True 2 True 3 True 4 False dtype: bool Less than: 0 False 1 False 2 False 3 False 4 False dtype: bool
Explanation:
The above exercise demonstrates how to perform element-wise comparison operations between two Pandas Series objects using Python's comparison operators.
Following code creates two Pandas Series objects named 'ds1' and 'ds2' containing a sequence of five integers each:
ds1 = [2, 4, 6, 8, 10] ds2 = [1, 3, 5, 7, 10]
print(ds1 == ds2): This line compares the two Series objects using the '==' operator, resulting in a new Series object with boolean values indicating whether the corresponding elements in ‘ds1’ and ‘ds2’ are equal or not. The output will be: [False, False, False, False, True]
print(ds1 > ds2): This line compares the two Series objects using the '>' operator, resulting in a new Series object with boolean values indicating whether the corresponding elements in ‘ds1’ are greater than those in ‘ds2’ or not. The output will be: [True, True, True, True, False]
print(ds1 < ds2): This line compares the two Series objects using the '<' operator, resulting in a new Series object with boolean values indicating whether the corresponding elements in ‘ds1’ are less than those in ‘ds2’ or not. The output will be: [False, False, False, False, False]
Python-Pandas Code Editor:
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