Search A pandas Column For A Value

Last Updated : 17 Dec, 2020

Prerequisites: pandas

In this article let’s discuss how to search data frame for a given specific value using pandas.

Function used

where() -is used to check a data frame for one or more condition and return the result accordingly. By default, The rows not satisfying the condition are filled with NaN value.
dropna() -This method allows the user to analyze and drop Rows/Columns with Null values. In this article it is used to deal with the cases where the rows that will have value as NaN because they will not satisfy a certain condition.

Approach

Import modules
Create data
Traverse through the column looking for a specific value
If matched, select

There is a basic difference between selecting a specific values and selecting rows that have a specific value. For the later case the indices to be retrieved has to be stored in a list. Implementation of both cases is included in this article:

Data frame in use:

Example 1: Select tuple containing salary as 200

Python3

import pandas as pd 
x = pd.DataFrame([["A", 100, "D"], ["B", 200, "E"], ["C", 100, "F"]], 
                 columns=["Name", "Salary", "Department"]) 
  
# Searching in whole column 
for i in range(len(x.Name)): 
    if 200 == x.Salary[i]: 
          
        # indx will store the tuple having that  
        # particular value in column. 
        indx = i 
  
# below line will print that tuple 
x.iloc[indx] 

Output:

Example 2: Search for people having salary of 100 and store the output in a dataframe again.

Python3

import pandas as pd 
x = pd.DataFrame([["A", 100, "D"], ["B", 200, "E"], ["C", 100, "F"]],  
                 columns=[ "Name", "Salary", "Department"]) 
  
# initialize the indx as a list 
indx = [] 
  
# Searching in whole column 
for i in range(len(x.Name)): 
    if 100 == x.Salary[i]: 
        
        # indx will store all the tuples having  
        # that particular value in column. 
        indx.append(i) 
  
# Final Dataframe having tuples 
df = pd.DataFrame() 
  
# this will append all tuples to the final 
# dataframe. 
for indexes in indx: 
    df = df.append(x.iloc[indexes]) 
  
df = x.where(x.Salary == 100) 
  
# It will remove NaN rows. 
df.dropna() 