Limited rows selection with given column in Pandas | Python

Selecting with complex criteria using query method in Pandas

Last Updated : 25 Oct, 2020

In this article, let's discuss how to select complex criteria using the Query() method in Pandas. In pandas for Selecting with complex criteria using the query method, first, we create data frames with the help of pandas.Dataframe() and store it one variable and then with the help of query() method we can select complex criteria. With the help of pandas.Dataframe.loc() we can find details of the data frame by passing the index of the data frame.
Example 1:

Python3

import pandas as pd

df = pd.DataFrame([[10, 20, 30, 40], [70, 14, 21, 80], 
                   [55, 15, 80, 12]],
                  
                  columns=['GFG_USER_1', 'GFG_USER_2',
                           'GFG_USER_3', 'GFG_USER_4'],
                  
                  index=['Practice1', 'Practice2', 'Practice3'])

print(df, "\n")

# Filter data using query method
df1 = df.loc[df.query(
    'GFG_USER_1 <= 80 & GFG_USER_2 > 10 & \
    GFG_USER_3 < 50 &  GFG_USER_4 == 80').index]

print(df1)

Output:

Example 2:

Python3

import pandas as pd

df = pd.DataFrame([[100, 200, 300], [70, 140, 210], 
                   [55, 150, 180]],
                  
                  columns=['PAK', 'AUS', 'IND'],
                  
                  index=['Match1', 'Match2', 'Match3'])

print(df, "\n")

# Filter data using query method
df1 = df.loc[df.query('PAK <= 80 & AUS > 100 & IND < 250').index]

print(df1)

Output:

Example 3:

Python3

import pandas as pd

df = pd.DataFrame([[1000, 2000, 3000, 4000], [7000, 1400, 2100, 2800], 
                   [5500, 1500, 800, 1200]],
                  
                  columns=['DSA_Self_Paced', 'OOPS', 'DBMS', 'System_design'],
                  
                  index=['Sale1', 'Sale2', 'Sale3'])

print(df, "\n")

# Filter data using query method
df1 = df.loc[df.query(
    'DSA_Self_Paced <= 6000 & OOPS > 1400 & DBMS < 1500 \
    &  System_design == 1200').index]

print(df1)

Output:

Limited rows selection with given column in Pandas | Python

vipinyadav15799

Improve

Article Tags :

Practice Tags :

python

Similar Reads

Limited rows selection with given column in Pandas | Python

Methods in Pandas like iloc[], iat[] are generally used to select the data from a given dataframe. In this article, we will learn how to select the limited rows with given columns with the help of these methods. Example 1: Select two columns Python3 # Import pandas package import pandas as pd # Defi

Select Rows With Multiple Filters in Pandas

In this article, we are going to select rows using multiple filters in pandas. We will select multiple rows in pandas using multiple conditions, logical operators and using loc() function. Selecting rows with logical operators i.e. AND and OR can be achieved easily with a combination of >, <,

How to join datasets with same columns and select one using Pandas?

It is usual that in Data manipulation operations, as the data comes from different sources, there might be a need to join two datasets to one. In this article, let us discuss how to join datasets with the same columns in python. Using Pandas concat() Python has a package called pandas that provides

How to Select Single Column of a Pandas Dataframe

In Pandas, a DataFrame is like a table with rows and columns. Sometimes, we need to extract a single column to analyze or modify specific data. This helps in tasks like filtering, calculations or visualizations. When we select a column, it becomes a Pandas Series, a one-dimensional data structure th

How to Filter Rows Based on Column Values with query function in Pandas?

In this article, let's see how to filter rows based on column values. Query function can be used to filter rows based on column values.Â Consider below Dataframe: Python3 import pandas as pd data = [['A', 10], ['B', 15], ['C', 14], ['D', 12]] df = pd.DataFrame(data, columns = ['Name', 'Age']) df Out

Using Apply in Pandas Lambda functions with multiple if statements

In this article, we are going to see how to apply multiple if statements with lambda function in a pandas dataframe. Sometimes in the real world, we will need to apply more than one conditional statement to a dataframe to prepare the data for better analysis.Â We normally use lambda functions to app

Reshape Wide DataFrame to Tidy with identifiers using Pandas Melt

Sometimes we need to reshape the Pandas data frame to perform analysis in a better way. Reshaping plays a crucial role in data analysis. Pandas provide functions like melt and unmelt for reshaping. In this article, we will see what is Pandas Melt and how to use it to reshape wide to Tidy with identi

Python | Data Comparison and Selection in Pandas

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages, and makes importing and analyzing data much easier. The most important thing in Data Analysis is comparing values and selecting data acco

Select first or last N rows in a Dataframe using head() and tail() method in Python-Pandas

Letâ€™s discuss how to select top or bottom N number of rows from a Dataframe using head() & tail() methods. 1) Select first N Rows from a Dataframe using head() method of Pandas DataFrame : Pandas head() method is used to return top n (5 by default) rows of a data frame or series Syntax: Dataframe.he

Select a single column of data as a Series in Pandas

In this article, we will discuss how to select a single column of data as a Series in Pandas. For example, Suppose we have a data frame : Name Age MotherTongue Akash 21 Hindi Ashish 23 Marathi Diksha 21 Bhojpuri Radhika 20 Nepali Ayush 21 Punjabi Now when we select column Mother Tongue as a Series w