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 N-1. 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,

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### 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.