Selecting multiple columns based on conditional values
Create a DataFrame with data
import pandas as pd
import numpy as np
df = pd.DataFrame()
df['Name'] = ['John', 'Doe', 'Bill','Jim','Harry','Ben']
df['TotalMarks'] = [82, 38, 63,22,55,40]
df['Grade'] = ['A', 'E', 'B','E','C','D']
df['Promoted'] = [True, False,True,False,True,True]
Name TotalMarks Grade Promoted
0 John 82 A True
1 Doe 38 E False
2 Bill 63 B True
3 Jim 22 E False
4 Harry 55 C True
5 Ben 40 D True
Select all column with conditional values
example-1
df[df['Grade'] == 'E']
Name TotalMarks Grade Promoted
1 Doe 38 E False
3 Jim 22 E False
example-2
df[df['TotalMarks'] > 50]
Name TotalMarks Grade Promoted
0 John 82 A True
2 Bill 63 B True
4 Harry 55 C True
Select two columns with conditional values
df[['Name','TotalMarks']] [df['Promoted'] == True]
Name TotalMarks
0 John 82
2 Bill 63
4 Harry 55
5 Ben 40
Select all column with multiple conditional values
df[(df['TotalMarks'] > 50) & (df['TotalMarks'] < 80) ]
Name TotalMarks Grade Promoted
2 Bill 63 B True
4 Harry 55 C True
Select two column with multiple conditional values
example-1
df[['Name','TotalMarks']][(df["Promoted"] == True) & (df["Grade"] == 'A')]
Name TotalMarks
0 John 82
example-2
df[['Name','TotalMarks']] [(df['TotalMarks'] > 50) & (df['TotalMarks'] < 80) ]
Name TotalMarks
2 Bill 63
4 Harry 55

Using isin()
Pandas isin() method is used to check each element in the DataFrame is contained in values or not.
df[df.Grade.isin(['E'])]
Name TotalMarks Grade Promoted
1 Doe 38 E False
3 Jim 22 E False
isin() with multiple values
filter = df['Grade'].isin(['A', 'B'])
df[filter]
Name TotalMarks Grade Promoted
0 John 82 A True
2 Bill 63 B True
Also, you can retrieve data of selected columns using isin and loc
df.loc[df['Grade'].isin(['A', 'B']),['Name','TotalMarks', 'Grade'] ]
Name TotalMarks Grade
0 John 82 A
2 Bill 63 B
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