Search A pandas Column For A Value

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()

Output: