How to add column to dataframe pandas

How to create new columns derived from existing columns?В¶

I want to express the \(NO_2\) concentration of the station in London in mg/m \(^3\)

(If we assume temperature of 25 degrees Celsius and pressure of 1013 hPa, the conversion factor is 1.882)

To create a new column, use the [] brackets with the new column name at the left side of the assignment.

The calculation of the values is done element_wise. This means all values in the given column are multiplied by the value 1.882 at once. You do not need to use a loop to iterate each of the rows!

I want to check the ratio of the values in Paris versus Antwerp and save the result in a new column

The calculation is again element-wise, so the / is applied for the values in each row.

I want to rename the data columns to the corresponding station identifiers used by openAQ

The rename() function can be used for both row labels and column labels. Provide a dictionary with the keys the current names and the values the new names to update the corresponding names.

The mapping should not be restricted to fixed names only, but can be a mapping function as well. For example, converting the column names to lowercase letters can be done using a function as well:

REMEMBER

Operations are element-wise, no need to loop over rows.

Use rename with a dictionary or function to rename row labels or column names.

How to create plots in pandas?

How to calculate summary statistics?

© Copyright 2008-2022, the pandas development team.

Pandas: Add Column to Dataframe

In this article, we will discuss different ways to how to add a new column to dataframe in pandas i.e. using operator [] or assign() function or insert() function or using a dictionary. We will also discuss adding a new column by populating values from a list, using the same value in all indices, or calculating value on a new column based on another column.

Table of Contents

Let’s create a Dataframe object i.e.

Contents of the dataframe dfobj are,

Now lets discuss different ways to add new columns to this data frame in pandas.

Add column to Pandas Dataframe using [] operator

Pandas: Add Column from List

Suppose we want to add a new column ‘Marks’ with default values from a list. Let’s see how to do this,

But we need to keep these things in mind i.e.

Pandas: Add column to DataFrame with same value

Now add a new column ‘Total’ with same value 50 in each index i.e each item in this column will have same default value 50,

It added a new column ‘Total‘ and set value 50 at each items in that column.

Pandas: Add column based on another column

Let’s add a new column ‘Percentage‘ where entry at each index will be calculated by the values in other columns at that index i.e.

Append column to dataFrame using assign() function

In Python, Pandas Library provides a function to add columns i.e.

It accepts a keyword & value pairs, where a keyword is column name and value is either list / series or a callable entry. It returns a new dataframe and doesn’t modify the current dataframe.

Let’s add columns in DataFrame using assign().

First of all reset dataframe i.e.

Contents dataframe df_obj are,

Add column to DataFrame in Pandas using assign()

Let’s add a column ‘Marks’ i.e.

It will return a new dataframe with a new column ‘Marks’ in that Dataframe. Values provided in list will used as column values.

Contents of new dataframe mod_fd are,

Add multiple columns in DataFrame using assign()

We can also add multiple columns using assign() i.e.

It added both column Marks & Total. Contents of the returned dataframe is,

Add a columns in DataFrame based on other column using lambda function

Add column ‘Percentage’ in dataframe, it’s each value will be calculated based on other columns in each row i.e.

Contents of the returned dataframe are,

Pandas: Insert column to Dataframe using insert()

First of all reset dataframe i.e.

Contents dataframe df_obj are,

In all the previous solution, we added new column at the end of the dataframe, but suppose we want to add or insert a new column in between the other columns of the dataframe, then we can use the insert() function i.e.

It inserted the column ‘Marks’ in between other columns.

Pandas: Add a column to Dataframe using dictionary

Create a dictionary with keys as the values of new columns and values in dictionary will be the values of any existing column i.e.

Here we created a dictionary by zipping the a list of values and existing column ‘Name’. Then set this dictionary as the new column ‘ID’ in the dataframe.

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How to add new columns to Pandas dataframe?

In this article, I will use examples to show you how to add columns to a dataframe in Pandas. There is more than one way of adding columns to a Pandas dataframe, let’s review the main approaches.

Create a Dataframe

As usual let’s start by creating a dataframe.

Create a simple dataframe with a dictionary of lists, and column names: name, age, city, country.

I. Add a column to Pandas Dataframe with a default value

When trying to set the entire column of a dataframe to a specific value, use one of the four methods shown below.

Method I.1: By declaring a new list as a column

df[‘New_Column’]=’value’ will add the new column and set all rows to that value.

In this example, we will create a dataframe df and add a new column with the name Course to it.

Your Dataframe before we add a new column:

Your Dataframe after adding a new column:

This error is usually a result of creating a slice of the original dataframe before declaring your new column. To avoid the error add your new column to the original dataframe and then create the slice:

Python can do unexpected things when new objects are defined from existing ones. A slice of dataframe is just a stand-in for the rows stored in the original dataframe object: a new object is not created in memory.

To avoid these issues altogether use the copy or deepcopy module, which explicitly forces objects to be copied in memory so that methods called on the new objects are not applied to the source object.

The pandas.DataFrame.loc allows to access a group of rows and columns by label(s) or a boolean array.

.loc[] is primarily label based, but may also be used with a boolean array.

Allowed inputs are:

Your Dataframe before we add a new column:

Your Dataframe after adding a new column:

The .assign() function returns a new object with all original columns as well as the new ones. Existing columns that are re-assigned will be overwritten. The column names are keywords. If the values are callable, they are computed on the dataframe and assigned to the new columns.

df = df.assign( New_Column =’value’)

Your Dataframe before we add a new column:

Your Dataframe after adding a new column:

Your Dataframe before we add a new column:

Your Dataframe after adding a new column:

II. Add a new column with different values

All the methods that are cowered above can also be used to assign a new column with different values to a dataframe.

Method II.1: By declaring a new list as a column

You can append a new column with different values to a dataframe using method I.1 but with a list that contains multiple values. So instead of df[‘New_Column’]=’value’ use

When using this method you will need to keep the following in mind:

Your Dataframe before we add a new column:

Your Dataframe after adding a new column:

In this case you will need to change method I.2

df.loc[:,’ New_Column ‘] = ‘value’

df.loc[:, ‘ New_Column ‘] = [‘value1′,’value2′,’value3’]

Your Dataframe before we add a new column:

Your Dataframe after adding a new column:

df = df.assign( New_Column =’value’)

df = df.assign(New_column=[‘value1’, ‘value2’, ‘value3’])

Your Dataframe before we add a new column:

Your Dataframe after adding a new column:

df.insert(loc=1, column=»New Column», value=[‘value1’, ‘value2′,’value3’])

Your Dataframe before we add a new column:

Your Dataframe after adding a new column:

Please note that there are many more ways of adding a column to a Pandas dataframe. However, knowing these four should be more than sufficient.

Conclusion:

Now you should understand the basics of adding columns to a dataset in Pandas. I hope you’ve found this post helpful. If you want to go deeper into the subject, there are some great answers on StackOverflow.

How to Add Column to Pandas Dataframe – Definitive Guide

Pandas Data frame is a two-dimensional data structure that stores data in rows and columns structure.

You can add column to pandas dataframe using the df.insert(col_index_position, “Col_Name”, Col_Values_As_List, True) statement.

In this tutorial, you’ll see different methods available to add columns to pandas dataframe.

If You’re in Hurry…

You can use the below code snippet to add a new column to the pandas dataframe.

To add a column with empty values

To add a column with values

This is how you can add a new column to the pandas dataframe.

If You Want to Understand Details, Read on…

In this tutorial, you’ll learn the different methods available to add columns to the pandas dataframe. You can add columns using

Let’s look at the details of the scenario of adding a new column to the existing dataframe.

Table of Contents

Sample Dataframe

This is the sample dataframe used throughout the tutorial.

Dataframe Looks Like

Let’s see the different types of adding a column to pandas dataframe.

Using Subscript Notation or Assignment operator**

You can add a column by using the = operator with a list of values. The length of the list of values must be equal to the length of the rows in the dataframe. Otherwise, an error will be raised.

Dataframe Looks Like

Using Insert() method

You can add a column to pandas dataframe using the insert() method available in the pandas dataframe.

Usage

Below is the code snippet to add column using the insert() method.

Dataframe Looks Like

Using Assign() method

You can add a column to pandas dataframe using the assign() method available in the pandas dataframe.

Usage

Below is the code snippet to add column using the assign() method.

Dataframe Looks Like

This is how you can add columns with value in three different methods available in the pandas dataframe.

Next, you’ll add a column at a specific index.

Add column At Specific Index

In this section, you’ll add a column at a specific position.

You can add a column at a specific index by using the df.insert() method.

Use the below snippet to add a column at a specific index.

An index is zero-based. Hence you’ll see the new column State Tax added in the fourth position of the dataframe.

Dataframe Looks Like

The State Tax and the Remarks column are added for demonstration.

Let’s delete these columns. Refer to how to drop column in pandas dataframe to know about deleting columns in pandas dataframe.

Now, use the below snippet to delete the columns at positions 3 and 6.

The columns in indexes 3 and 6 are deleted.

Dataframe Looks Like

You’ve learned how to add columns at a specific indexes.

Next, you’ll learn how to add columns with a constant value.

Add Column to Dataframe With Constant Value

In this section, you’ll learn how to add a column to a dataframe with a constant value. This means, all the cells in the newly added column will have the same constant value.

You can do this by assigning a single value using the assignment operator as shown below.

Now, a new column called Price_Increase_Col will be added to the dataframe with the value 200 in all the cells.

Dataframe Looks Like

You’ve learned how to add columns to the dataframe in various cases.

Next, you’ll learn how to add multiple columns to the dataframe at once.

Add Multiple Column to Dataframe

In this section, you’ll learn how to add multiple columns to the dataframe in pandas.

You can add multiple columns to the dataframe by using the assignment operator.

Syntax

You can use this to add multiple columns at once and the cells will have the same constant values when you use the above syntax.

Example

Now, two new columns are added to the dataframe.

Dataframe Looks Like

You’ve learned how to append multiple columns to the dataframe at once.

Next, you’ll need to drop the added columns to clean up the dataframe. So we can use the same for the upcoming use cases.

Four columns added are Total_Price, Price_Increase_Col, Product_Category, Available_Units in the index 4,5,6,7 respectively.

Use the below snippet to drop these columns.

Dataframe Looks Like

This is how you can multiple columns at once to the existing dataframe.

Add Empty Column to Dataframe

Snippet

pd.NaT is used to denote the missing values in the Pandas dataframe. When you assign this value to a new column, a new column will be added to the dataframe with values as NaT which ideally means a null value.

When you execute the below line, a new column called Total_Price will be added to the dataframe with NaT values.

Dataframe Looks Like

You’ve learned how to add a column to pandas dataframe with empty values.

Next, you’ll learn how to add columns with values.

Conclusion

To summarize, you’ve learned how to add columns to pandas dataframe. You’ve learned different methods available in the pandas Dataframe to add a new column in the existing dataframe along with the different use-cases to add new columns.

If you’ve any questions or feedback feel free to comment below.

How To Add A New Column To An Existing Pandas DataFrame

Discussing 4 ways you can insert a new column to a pandas DataFrame

Introduction

In today’s short guide we will discuss four distinct ways you can add a new column into a DataFrame. Specifically, we’ll explore how to

First, let’s create an example DataFrame that we’ll reference throughout this guide to demonstrate a few concepts related to adding columns to pandas frames.

Using simple assignment

The easiest way to insert a new column is to simply assign the values of your Series into the existing frame:

Note that the above will work for most cases assuming that the indices of the new column match those of the DataFrame otherwise NaN values will be assigned to missing indices. For example,

Using assign()

pandas.DataFrame.assign() method can be used when you need to insert multiple new columns in a DataFrame, when you need to ignore the index of the column to be added or when you need to overwrite the values of an existing columns.

The method will return a new DataFrame object (a copy) containing all the original columns in addition to new ones:

Always remember that with assign:

Using insert()

For example, to add colC to the end of the DataFrame:

To insert colC in between colA and colB :

Additionally, insert() can even be used to add a duplicate column name. By default, a ValueError is raised when a column already exists in the DataFrame:

However, if you pass allow_duplicates=True to insert() method, the DataFrame will have two columns with the same name:

Using concat()

The above operation will concatenate the Series with the original DataFrame using the index. In most of the cases, you should use concat() if the indices of the objects to be concatenated match with each other. If indices don’t match then the all indices for every object will be present in the result:

Changing the index of the column to be added

On of the trickiest part when it comes to adding new columns to DataFrames is the index. You should be careful as each of the methods we discussed in this guide may handle indices in a different way.

If for any reason the index of the new column to be added has not any special meaning and you don’t want it to be taken into account when inserted, you can even specify the index of the Series to be the same as the index of the DataFrame.

Final Thoughts

Additionally, we discussed when you should be using each of the methods based on the end goal you want to achieve (for example if you want to ignore or take into account the index of the new column to be added).

When inserting new columns you must pick the method that is most suitable as each may behave in a different way when indices of the new column and the existing frame don’t match.

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