pandas filter rows by value

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': 'foo bar foo bar foo bar foo foo'.split(),
                   'B': 'one one two three two two one three'.split(),
                   'C': np.arange(8), 'D': np.arange(8) * 2})
print(df)
sub_df = df.loc[df['A'] == 'foo'] # Subset based on specific A value

Method 1: Selecting rows of Pandas Dataframe based on particular column value using ‘>’, ‘=’, ‘=’, ‘<=’, ‘!=’ operator.

Example 1: Selecting all the rows from the given Dataframe in which ‘Percentage’ is greater than 75 using [ ].

Python3
# selecting rows based on condition 
rslt_df = dataframe[dataframe['Percentage'] > 70] 
    
print('\nResult dataframe :\n', rslt_df)

df.loc[(df['column_name'] >= A) & (df['column_name'] <= B)]

# does year equals to 2002?
# is_2002 is a boolean variable with True or False in it
>is_2002 =  gapminder['year']==2002
>print(is_2002.head())
0    False
1    False
2    False
3    False
4    False
# filter rows for year 2002 using  the boolean variable
>gapminder_2002 = gapminder[is_2002]
>print(gapminder_2002.shape)
(142, 6)