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Adding a New Column to an Existing Data Frame in Pandas

The world is moving quickly towards data-driven businesses to make decisions, perform actions, and use data for support. The process of collecting and organizing data to achieve helpful conclusions is the main objective of data analysis. This process uses analytical and logical reasoning to gain information from the data. 

One of the tools used for data analysis is Pandas. Pandas is an open-source, fundamental, high-level building block used to perform practical and real-world data analysis in Python. It is one of the most popular data-wrangling packages. It performs well with many other data science modules inside the Python ecosystem. 

In this article, we will learn and understand what Pandas is and how you can add a new column in an existing data frame.

  • What are data frames?
  • Adding a column to an existing data frame:

                Method 1: Declaring a new list as a column
                Method 2: Using DataFrame.insert()
                Method 3: Using the Dataframe.assign() method
                Method 4: Using the dictionary data structure

  • Advantages and  disadvantages of adding columns to a data frame in Pandas
  • FAANG interview questions on adding a column to a data frame using Pandas
  • FAQs on adding a column to a data frame using Pandas

What Are Data Frames?

Pandas provides powerful and flexible data structures that make data manipulation and analysis easy. A data frame is one of these structures. Data frames represent a method to store data in rectangular grids that can be easily overviewed for analysis. Each row of these grids corresponds to a value, while each column represents a vector containing data for a specific variable. 

In Pandas, a DataFrame represents a two-dimensional, heterogenous, tabular data structure with labeled rows and columns (axes). In simple words, it contains three components ― data, rows, columns.

Adding a Column to an Existing Data Frame

Consider the following data frame called df. It contains 14 columns.

You want to add another column called patient_name. There are multiple ways that you can perform this action. We’ll be covering four ways in the following sections.

Method 1: Declaring a New List as a Column

First, you create a list that contains the required information (names of patients). Then, you create a column name (patient_name) in the data frame (df) to which we assign the newly created list by using the ‘=’ operator. 

# Creating a list of names

names = ["Alice", "Mark", "John", "Bob", "David"]

# Creating the patient_name in the df data frame

df["patient_name"] = names

# Observe the result

df.head()

Result:

Method 2: Using DataFrame.insert()

If you use this method, you have the flexibility to add the required column at any position in the existing data frame.

The syntax is as follows:

Considering the df data frame, you can add the patient_name column in the first position after the age column.  

# Creating a list of names

names = ["Alice", "Mark", "John", "Bob", "David"]

# Using DataFrame.insert() to add the patient_name column

# Adding this column in position 1

df.insert(1, "patient_name", names)

# Observe the result

df.head()

Note: You can use column_position to add the column in any preferable position in the data frame. For example, if you want to add it in position 3, then the code will be: df.insert(3, "patient_name", names)

Result:

Method 3: Using the Dataframe.assign() method

This method allows you to assign a new column into an existing data frame. Here, the patient_name column is passed as a parameter, and its corresponding list of values is equated against it. 

# Creating a list of names

names = ["Alice", "Mark", "John", "Bob", "David"]

# Using assign() to create the patient_name column

# Column name must be equated with the corresponding list of values

df = df.assign(patient_name = names)

# Observe the result

df.head()

Result:

Method 4: Using the Dictionary Data Structure

You can use the Python dictionary (key-value pair) to add a new column in an existing data frame. In this method, you must use the new column as the key and an existing column as the value.

# Creating a dictionary

# {key: value}

# key contains values of the new column

# values contain inputs of an existing column

# Example

# key represents the new values for the patient_name column

# value represents the age column which is an existing column

names = {"Alice": 63, "Mark": 37, "John": 41, "Bob": 56, "David": 57}

# Creating the patient_name column in the df data frame

df["patient_name"] = names

# Observe the result

df.head()

Alternatively, you can use the map function to add a new column in the df dataframe. This can be performed using the following code:

# Creating a dictionary, where keys represent age as mentioned in the data frame

nameDict = {63: "Alice", 

            37: "Mark", 

            41: "John", 

            56: "Bob", 

            57: "David"

           }

# Using the map function to add new column in the pandas data frame

df["patient_name"] = df["Age"].map(nameDict)

# Observe the result

df.head()

Result:

Examples of Tech Interview Questions on Adding a Column to a Data Frame Using Pandas

Here are a few practice questions on Pandas and data frames:

  1. Define Pandas.
  2. Define DataFrame in Pandas.
  3. Define the different ways a DataFrame can be created in Pandas.
  4. How will you add a column to a Pandas data frame?
  5. How do you add a new column in the third position of an existing data frame?
Check out the interview questions and problems page to learn more

FAQs on Adding a Column to a Data Frame Using Pandas

Question 1: How do I create a data frame using Pandas in Python?
You can use the following code to create a DataFrame using Pandas in Python:

TextDescription automatically generated

Question 2: Can I add the values to a new column without creating a separate list?
It is important to provide a structure to the values that you want to add to a column. This structure is provided using the list data structure. Ideally, it is advisable to create a list then pass it into the functions to create a column.
Example: studentRecords = pd.DataFrame({"sName": ["Alice", "Bob"], "sAge": [20, 32]})

Question 3: Can I rewrite the values of a column once created?
Yes, we can overwrite the values of a column.

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Article contributed by Problem Setters Official

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