Pandas dataframe filter with Multiple conditions

栏目: IT技术 · 发布时间: 4年前

内容简介:Selecting or filtering rows from a dataframe can be sometime tedious if you don’t know the exact methods and how to filter rows with multiple conditionsIn this post we are going to see the different ways to select rows from a dataframe using multiple condi

Selecting or filtering rows from a dataframe can be sometime tedious if you don’t know the exact methods and how to filter rows with multiple conditions

In this post we are going to see the different ways to select rows from a dataframe using multiple conditions

Let’s create a dataframe with 5 rows and 4 columns i.e. Name, Age, Salary_in_1000 and FT_Team(Football Team)

import pandas as pd
df=pd.DataFrame({'Name':['JOHN','ALLEN','BOB','NIKI','CHARLIE','CHANG'],
              'Age':[35,42,63,29,47,51],
              'Salary_in_1000':[100,93,78,120,64,115],
             'FT_Team':['STEELERS','SEAHAWKS','FALCONS','FALCONS','PATRIOTS','STEELERS']})
df

Output:

Name Age Salary_in_1000 FT_Team
0 JOHN 35 100 STEELERS
1 ALLEN 42 93 SEAHAWKS
2 BOB 63 78 FALCONS
3 NIKI 29 120 FALCONS
4 CHARLIE 47 64 PATRIOTS
5 CHANG 51 115 STEELERS

Selecting Dataframe rows on multiple conditions using these 5 functions

In this section we are going to see how to filter the rows of a dataframe with multiple conditions using these five methods

a) loc
b) numpy where
c) Query
d) Boolean Indexing
e) eval

What’s the Condition or Filter Criteria ?

Get all rows having salary greater or equal to 100K and Age < 60 and Favourite Football Team Name starts with ‘S’

Using loc with multiple conditions

loc is used to Access a group of rows and columns by label(s) or a boolean array

As an input to label you can give a single label or it’s index or a list of array of labels

Enter all the conditions and with & as a logical operator between them

df.loc[(df['Salary_in_1000']>=100) & (df['Age']< 60) & (df['FT_Team'].str.startswith('S')),['Name','FT_Team']]

Output:

Name FT_Team
0 JOHN STEELERS
5 CHANG STEELERS

Using np.where with multiple conditions

numpy where can be used to filter the array or get the index or elements in the array where conditions are met. You can read more about np.where in thispost

Numpy where with multiple conditions and & as logical operators outputs the index of the matching rows

import numpy as np
idx = np.where((df['Salary_in_1000']>=100) & (df['Age']< 60) & (df['FT_Team'].str.startswith('S')))

Output:

(array([0, 5], dtype=int64),)

The output from the np.where, which is a list of row index matching the multiple conditions is fed to dataframe loc function

df.loc[idx]

Output:

Name Age Salary_in_1000 FT_Team
0 JOHN 35 100 STEELERS
5 CHANG 51 115 STEELERS

Using Query with multiple Conditions

It is used to Query the columns of a DataFrame with a boolean expression

df.query('Salary_in_1000 >= 100 & Age < 60 & FT_Team.str.startswith("S").values')

Output:

Name Age Salary_in_1000 FT_Team
0 JOHN 35 100 STEELERS
5 CHANG 51 115 STEELERS

pandas boolean indexing multiple conditions

It is a standrad way to select the subset of data using the values in the dataframe and applying conditions on it

We are using the same multiple conditions here also to filter the rows from pur original dataframe with salary >= 100 and Football team starts with alphabet ‘S’ and Age is less than 60

df[(df['Salary_in_1000']>=100) & (df['Age']<60) & df['FT_Team'].str.startswith('S')][['Name','Age','Salary_in_1000']]

Output:

Name Age Salary_in_1000
0 JOHN 35 100
5 CHANG 51 115

Pandas Eval multiple conditions

Evaluate a string describing operations on DataFrame column. It Operates on columns only, not specific rows or elements

df[df.eval("Salary_in_1000>=100 & (Age <60) & FT_Team.str.startswith('S').values")]

Output:

Name Age Salary_in_1000
0 JOHN 35 100
5 CHANG 51 115

Conclusion:

In this post we have seen that what are the different methods which are available in the Pandas library to filter the rows and get a subset of the dataframe

And how these functions works: loc works with column labels and indexes, whereas eval and query works only with columns and boolean indexing works with values in a column only

Let me know your thoughts in the comments section below if you find this helpful or knows of any other functions which can be used to filter rows of dataframe using multiple conditions


以上就是本文的全部内容,希望本文的内容对大家的学习或者工作能带来一定的帮助,也希望大家多多支持 码农网

查看所有标签

猜你喜欢:

本站部分资源来源于网络,本站转载出于传递更多信息之目的,版权归原作者或者来源机构所有,如转载稿涉及版权问题,请联系我们

Tensorflow:实战Google深度学习框架

Tensorflow:实战Google深度学习框架

郑泽宇、顾思宇 / 电子工业出版社 / 2017-2-10 / 79

TensorFlow是谷歌2015年开源的主流深度学习框架,目前已在谷歌、优步(Uber)、京东、小米等科技公司广泛应用。《Tensorflow实战》为使用TensorFlow深度学习框架的入门参考书,旨在帮助读者以最快、最有效的方式上手TensorFlow和深度学习。书中省略了深度学习繁琐的数学模型推导,从实际应用问题出发,通过具体的TensorFlow样例程序介绍如何使用深度学习解决这些问题。......一起来看看 《Tensorflow:实战Google深度学习框架》 这本书的介绍吧!

随机密码生成器
随机密码生成器

多种字符组合密码

Base64 编码/解码
Base64 编码/解码

Base64 编码/解码

RGB HSV 转换
RGB HSV 转换

RGB HSV 互转工具