In this post, we will learn How Logical Operators For Boolean Indexing In Pandas Works as these operate like and(&), or(|), and are useful where we have to deal with multiple conditions. And Examples of the same are given below.
Logical Operator
In pandas, the operator ‘&’ is called (and), and another ‘|’, also known as (or), combines an array that represents conditions. and understand it better we have given an example below to understand it better and implement it to learn more.
import pandas as pd
# Create a sample DataFrame
df = pd.Data Frame({ 'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10], 'C': [11, 12, 13, 14, 15]})
# Select rows where A is greater than 2 and B is less than 10
subset = df[( df[ 'A'] > 2) & (df['B'] < 10)]
print( subset)
In this example given above, we have first created a data frame named and stored as ‘df’ which has three columns as ‘A’, ‘B’, and ‘C’ where we can see how we have used boolean indexes for selecting the rows where column ‘A’ is greater than two and in the column ‘B’ has less than 10. Here the’&’ and operator is used to combine the two conditions. which means nothing but that both conditions must be true for a row to be selected.
As we can use the this indexes with conditions that have multiple columns with some extra conditions which we have given as an example where we have used some complex conditions here to learn the concept you want to use in the project we are working on.
import pandas as pd
# Create a sample DataFrame
df = pd.DataFrame({ 'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10], 'C': [11, 12, 13, 14, 15]})
# Select rows where A is greater than B and C is even
subset = df[( df[ 'A'] > df[ 'B']) & (df[ 'C'] % 2 == 0)]
print( subset)
In the example given above, where we used this indexing which simply means greater or less to select the rows where column ‘A’ we have given the name as per our choice is greater than column ‘B’ and column ‘C’ is even. We use the ‘>’ operator to compare column A and column B, and the % operator to check if column C is even. The ‘and’ operator (&) is used to combine the two conditions, which means that both conditions must be true for a row to be selected.
Here we need to keep in mind that we can also use the parenthesis to group conditions and control the order of the operator and we can enhance the complexities as per our choices.
To learn more about How Logical Operators For Boolean Indexing In Pandas Work visit: by stack overflow.
To learn more about python solutions to different python problems and tutorials for the concepts we need to know to work on python programming along with different ways to solve any generally asked problems: How To Pass-Variables From A Java & Python Client To A Linux/Ubuntu Server Which Is Running C?.
