Pandas Query for SQL-like Querying
Pandas provide a query() method that enables users to analyze and filter the data just like where clause in SQL. DataFrame.query() method offers a simple way of making the selection and also capable of simplifying the task of index-based selection .Lets crate a DataFrame..
import pandas as pd
import numpy as np
df = pd.DataFrame()
df['Name'] = ['John', 'Doe', 'Bill','Jim','Harry','Ben']
df['Age'] = [14, 12, 14,11,12,14]
df['Category'] = ['A', 'E', 'B','E','C','D']
df['Height'] = [145, 152,167,136,149,161]
df['Weight'] = [34, 54,38,39,44,51]
df
Name Age Category Height Weight
0 John 14 A 145 34
1 Doe 12 E 152 54
2 Bill 14 B 167 38
3 Jim 11 E 136 39
4 Harry 12 C 149 44
5 Ben 14 D 161 51
Filtering with DataFrame.query()
df.query('Age == 12')
Name Age Category Height Weight
1 Doe 12 E 152 54
4 Harry 12 C 149 44
Multiple condition with DataFrame.query()
df.query('Age >= 11 & Age<=14')
Name Age Category Height Weight
0 John 14 A 145 34
1 Doe 12 E 152 54
2 Bill 14 B 167 38
3 Jim 11 E 136 39
4 Harry 12 C 149 44
5 Ben 14 D 161 51
Select specific columns with DataFrame.query()
df.query('Age == 14') [['Name','Height','Weight']]
Name Height Weight
0 John 145 34
2 Bill 167 38
5 Ben 161 51
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