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 pdx = pd.DataFrame([["A", 100, "D"], ["B", 200, "E"], ["C", 100, "F"]], columns=["Name", "Salary", "Department"]) # Searching in whole columnfor 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 tuplex.iloc[indx] |
Output:
Example 2: Search for people having salary of 100 and store the output in a dataframe again.
Python3
import pandas as pdx = pd.DataFrame([["A", 100, "D"], ["B", 200, "E"], ["C", 100, "F"]], columns=[ "Name", "Salary", "Department"]) # initialize the indx as a listindx = [] # Searching in whole columnfor 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 tuplesdf = 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:
