This article will discuss different ways to get a cell value from a Pandas Dataframe in Python.
Table of Contents:
 Get Cell value from Pandas Dataframe by row/column numbers
 Get Cell value from Pandas Dataframe by row/column names
 Pandas Dataframe: Get Cell value by condition
 Pandas Dataframe: Get the first cell value of a Column
First of all, we will create a Dataframe from a list of columns,
import pandas as pd # List of Tuples students = [('jack', 34, 'Sydeny', 'Australia'), ('Riti', 30, 'Delhi', 'France'), ('Vikas', 31, 'Mumbai', 'India'), ('Neelu', 32, 'Bangalore', 'Germany'), ('John', 16, 'New York', 'US'), ('Mike', 17, 'las vegas', 'US')] # Create a DataFrame from list of tuples df = pd.DataFrame( students, columns=['Name', 'Age', 'City', 'Country'], index=['a', 'b', 'c', 'd', 'e', 'f']) print(df)
Contents of this Dataframe are as follows,
Name Age City Country a jack 34 Sydeny Australia b Riti 30 Delhi France c Vikas 31 Mumbai India d Neelu 32 Bangalore Germany e John 16 New York US f Mike 17 las vegas US
Now we will explore different techniques to fetch a cell value from this dataframe using label names or index positions or conditions.
Get Cell Value of a Pandas Dataframe using row & column number
We can fetch a cell value from a Dataframe based on its index positions i.e. its row and column number. An important point to remember is that indexing starts from zero. It means the index position/number of Nth row or column will be N1. For example,
 3rd row of the Dataframe is row number 2
 4th Column of the Dataframe is column number 3.
To fetch the cell value by row/column number, we have different techniques i.e, either using Dataframe.iat[] or Dataframe.iloc[]. Let’s discuss them one by one,
Get a Cell value using Dataframe.iat[]
In Pandas, the Dataframe provides an attribute iat[] to access a single cell value based on its row & column numbers i.e.
DataFrame.iat[row_number, column_number]
It returns the cell value at the given row & column number. But if any of the given index positions/number is out of bound, it can give IndexError. Let’s understand by an example, fetch the cell value at the 3rd row and 4th column,
row_index_pos = 2 column_index_pos = 3 # Get Cell Value at 3rd row and 4th column # (Index positions starts from 0) value = df.iat[row_index_pos,column_index_pos] print (value)
Output:
India
It returned the cell value at the 3rd row and 4th column of the DataFrame as a string.
Important Point:
As Row and Column numbers start from 0 in DataFrame, row number 2 points to the third row of dataframe and column number 3 points to the fourth column of DataFrame.
Get a Cell value using Dataframe.iloc[]
In Pandas, the Dataframe provides a property iloc[], to select the subset of Dataframe based on position indexing. Contents of the subset will be decided based on the provided index positions/numbers of rows & columns. Although we can select single or multiple rows & columns using it. But today, we will choose a single cell using it with the following syntax,
DataFrame.iloc[row_number, column_number]
For example, let’s fetch the cell value at the 3rd row and 4th column of the Dataframe using iloc[]
row_index_pos = 2 column_index_pos = 3 # Get Cell Value at 3rd row and 4th column # (Index positions starts from 0) value = df.iloc[row_index_pos , column_index_pos] print (value)
Output:
India
It returned the cell value at the 3rd row and 4th column of the DataFrame.
Important Point:
As indexing starts from 0 in DataFrame, the index position of the 3rd row is 2, and for the 4th column, it is 3.
Get Cell Value of a Pandas Dataframe using row & column labels/names
We can fetch a cell value from a Dataframe based on row and column names using loc[] and at[] attributes. Let’s discuss them one by one.
Get call value using loc[] in Pandas Dataframe
In Pandas, the Dataframe provides a property loc[], to select the subset of Dataframe based on row and column names/labels. Although, we can choose single or multiple rows & columns using it. But today, we will select a single cell using it with the following syntax,
DataFrame.loc[row_label, column_name]
For example, let’s fetch the cell value at row ‘c’ and column ‘Age’ of the Dataframe using iloc[]
row_label = 'c' column_name = 'Age' # Get cell value at row 'c' and column 'Age' value = df.loc[row_label, column_name] print (value)
Output:
31
It returned the value at row ‘c’ and column ‘Age’ of the DataFrame as int.
Get call value using at[] in Pandas Dataframe
In Pandas, the DataFrame provides a property at[], to access the single values from a Dataframe by their row and column label name. The syntax is as follows,
pandas.DataFrame.at[row_label , column_name]
We will get the value of single cell using it. For example, let’s get cell value at row ‘c’ and column ‘Age’ of the DataFrame,
row_label = 'c' column_name = 'Age' # Get cell value at row 'c' and column 'Age' value = df.at[row_label, column_name] print (value)
Output:
31
It returned the value at row ‘c’ and column ‘Age’ of the DataFrame as int.
Pandas: Get cell value based on condition
We can select a cell value from a column based on conditions on other columns. For example, get cell value of column ‘Name’, where column ‘Age’ is 32,
# Get cell value of column 'Name', where column 'Age' is 32 values = df[df['Age'] == 32]['Name'].tolist() if len(values) > 0: print (values[0])
Output:
Neelu
Using df[df[‘Age’] == 32] it selected only those rows where column ‘Age’ has value 32. Then, it fetched the values of column ‘Name’ and then selected the first cell’s value from it.
Get the value of the first cell of a Column
To get the value of any column’s first cell, we first need to select the column as a Series object and then fetch the first entry from it. For example, let’s fetch the value of the first cell from column ‘Age’,
# Get value of first cell of Column 'Age' value = df['Age'].values[0] print (value)
Output:
34
It returned the value of the first cell of column ‘Age’.
Summary
Today we learned about different techniques to fetch a cell value from a Pandas Dataframe in Python.
Pandas Tutorials Learn Data Analysis with Python

Pandas Tutorial Part #1  Introduction to Data Analysis with Python

Pandas Tutorial Part #2  Basics of Pandas Series

Pandas Tutorial Part #3  Get & Set Series values

Pandas Tutorial Part #4  Attributes & methods of Pandas Series

Pandas Tutorial Part #5  Add or Remove Pandas Series elements

Pandas Tutorial Part #6  Introduction to DataFrame

Pandas Tutorial Part #7  DataFrame.loc[]  Select Rows / Columns by Indexing

Pandas Tutorial Part #8  DataFrame.iloc[]  Select Rows / Columns by Label Names

Pandas Tutorial Part #9  Filter DataFrame Rows

Pandas Tutorial Part #10  Add/Remove DataFrame Rows & Columns

Pandas Tutorial Part #11  DataFrame attributes & methods

Pandas Tutorial Part #12  Handling Missing Data or NaN values

Pandas Tutorial Part #13  Iterate over Rows & Columns of DataFrame

Pandas Tutorial Part #14  Sorting DataFrame by Rows or Columns

Pandas Tutorial Part #15  Merging or Concatenating DataFrames

Pandas Tutorial Part #16  DataFrame GroupBy explained with examples
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