Pandas – Using DataFrame.assign() method (5 examples)

Introduction.

The assign() method in Pandas is a powerful tool for adding new columns to a DataFrame in a fluent and flexible way. This method is particularly useful in data preprocessing, feature engineering, and exploratory data analysis, enabling data scientists and analysts to prepare and transform data efficiently. In this tutorial, we will explore the assign() method through five comprehensive examples, ranging from basic to more advanced use cases.

Syntax & Parameters

Pandas is a paramount library in the Python data science ecosystem, known for its versatile and high-performance data manipulation capabilities. The assign() method exemplifies these qualities by offering a dynamic approach to modify DataFrames. Before diving into examples, it’s crucial to understand the syntax of assign() :

Where **kwargs are keyword arguments in the form of column=value . Here, ‘column’ is the name of the new or existing column, and ‘value’ can be a scalar, array-like, or a callable.

Example 1: Basic Usage

Let’s begin with a basic example by creating a DataFrame and adding a new column:

This example demonstrates how to add a new column ‘C’ that is twice the value of column ‘A’.

Example 2: Using Callables

The assign() method allows for the use of callables, enhancing its flexibility. Here’s how:

This illustrates adding a new column ‘D’ by applying a lambda function that sums columns ‘A’ and ‘C’.

Example 3: Chaining Assignments

The real power of assign() shines when used in a chaining method to perform multiple operations in a single line:

This compact syntax illustrates how to sequentially add columns ‘C’ and ‘D’, showcasing the method’s efficiency in data manipulation.

Example 4: Conditional Column Creation

Now, let’s see how to add a new column based on conditions:

This demonstrates dynamically creating a new column ‘E’ that categorizes values from column ‘A’ into ‘High’ and ‘Low’ based on a condition.

Example 5: Using External Functions

Finally, let’s utilize an external function within assign() for more complex operations:

This example shows how to integrate an external function to create a new column ‘F’, further demonstrating the method’s adaptability.

This tutorial provided a thorough exploration of the assign() method in Pandas, showcasing its versatility through five practical examples. By leveraging assign() , data manipulation becomes more concise and expressive, enabling efficient and dynamic DataFrame transformations.

Next Article: Pandas: Convert a list of dicts into a DataFrame

Previous Article: Pandas – Using DataFrame.melt() method (5 examples)

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How to Use the assign() Method in Pandas (With Examples)

The assign() method can be used to add new columns to a pandas DataFrame.

This method uses the following basic syntax:

It’s important to note that this method will only output the new DataFrame to the console, but it won’t actually modify the original DataFrame.

To modify the original DataFrame, you would need to store the results of the assign() method in a new variable.

The following examples show how to use the assign() method in different ways with the following pandas DataFrame:

Example 1: Assign New Variable to DataFrame

The following code shows how to use the assign() method to add a new variable to the DataFrame called points2 whose values are equal to the values in the points column multiplied by two:

Note that this assign() method doesn’t change the original DataFrame.

If we print the original DataFrame, we’ll see that it remains unchanged:

To save the results of the assign() method, we can store the results in a new DataFrame:

The new DataFrame called df_new now contains the points2 column that we created.

Example 2: Assign Multiple New Variables to DataFrame

The following code shows how to use the assign() method to add three new variables to the DataFrame:

Notice that three new columns have been added to the DataFrame.

Note : You can find the complete documentation for the pandas assign() method here .

Additional Resources

The following tutorials explain how to use other common functions in pandas:

How to Use describe() Function in Pandas How to Use idxmax() Function in Pandas How to Apply a Function to Selected Columns in Pandas

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Cleaning data, correlations, quiz/exercises, pandas dataframe assign() method.

❮ DataFrame Reference

Add a new column to the DataFrame:

Definition and Usage

The assign() method adds a new column to an existing DataFrame.

Return Value

A DataFrame with the new column(s) added.

This method does not change the original DataFrame.

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pandas: Add rows/columns to DataFrame with assign(), insert()

This article explains how to add new rows/columns to a pandas.DataFrame .

Add a column using bracket notation []

The pandas.dataframe.assign() method, the pandas.dataframe.insert() method, the pandas.concat() function, add a row using .loc[], the pandas.dataframe.append() method (deprecated in version 1.4.0 ), add multiple rows at once, add multiple columns at once, processing speed comparison.

Note that the append() method was deprecated in version 1.4.0 and removed in 2.0.0 .

  • What’s new in 1.4.0 (January 22, 2022) — pandas 2.0.3 documentation

The sample code in this article uses pandas version 2.0.3 .

Add a column to a pandas.DataFrame

You can select a column using [column_name] and assign values to it.

  • pandas: Select rows/columns by index (numbers and names)

If you specify a non-existent column name, a new column will be added with the assigned value.

Assign a scalar value

When a scalar value is assigned, all elements in the column are set to that value.

Assign an array-like object

If an array-like object such as a list or a NumPy array ndarray is assigned, each element is assigned directly. Note that a mismatch between the number of elements in the list and the number of rows will result in an error.

Assign a pandas.Series

You can also assign a Series .

Since each column of a DataFrame is treated as a Series , you can add new columns based on the results of operations or the processed results of these methods.

  • pandas: Handle strings (replace, strip, case conversion, etc.)

If the index label of the Series does not correspond to the column name of the DataFrame , a missing value NaN is assigned.

  • Missing values in pandas (nan, None, pd.NA)

The values attribute of a Series returns a NumPy array ndarray , treated as an array-like object. Elements are assigned in order, regardless of the index . Note that an error will occur if the number of elements does not match the number of rows.

  • Convert pandas.DataFrame, Series and numpy.ndarray to each other

The assign() method either appends a new column or assigns new values to an existing column.

  • pandas.DataFrame.assign — pandas 2.0.3 documentation

You can specify the column name and its value using the keyword argument structure, column_name=value .

If the column name exists, the method assigns the value to it. If the column name is new, it adds a new column. This method returns a new object, while the original object remains unchanged.

Just like when adding a column with [column_name] , you can specify lists or Series with the assign() method. You can also add/assign multiple columns simultaneously by specifying multiple keyword arguments.

Note that in the assign() method, you specify the column name as a keyword argument. Therefore, names that are not valid as argument names, such as those with symbols other than underscores _ , and reserved words, will result in an error. For information on acceptable argument names in Python, refer to the following article.

  • Valid variable names and naming rules in Python

The insert() method allows you to add a column at any position in a DataFrame .

  • pandas.DataFrame.insert — pandas 2.0.3 documentation

Specify the position as the first argument, the column name as the second, and the value to be assigned as the third.

The third argument can accept a scalar value, an array-like object such as a list, or a Series . The concept is similar to the previous examples.

The original DataFrame is directly updated.

Note that specifying a value exceeding the number of rows as the first argument will cause an error. Using a negative value to specify the position from the end is not allowed. To specify the end as the position for the new column, use len(df.columns) or df.shape[1] to get the number of existing columns.

  • pandas: Get the number of rows, columns, elements (size) of DataFrame

Also, assigning an existing column name as the second argument will lead to an error. Although it's possible to allow duplicates by setting the allow_duplicates argument to True , it's not recommended due to potential confusion caused by duplicated column names.

You can concatenate multiple DataFrame and Series objects using the concat() function.

  • pandas: Concat multiple DataFrame/Series with concat()

By concatenating a Series to a DataFrame , you can add a new column.

In the previous examples, when adding a Series , its name attribute was ignored. However, when concatenating horizontally with the concat() function with axis=1 , the name of the Series is used as the column name.

Specify a list or tuple of objects you want to concatenate as the first argument to concat() .

To keep only the rows sharing common indices, specify join='inner' .

The function allows you to concatenate multiple Series and DataFrame objects.

Add a row to a pandas.DataFrame

You can select a row using loc[row_name] and assign values to it.

  • pandas: Get/Set values with loc, iloc, at, iat

As with columns, by specifying a non-existent row name, you can add the row and assign values to it.

The approach is the same as for columns. You can assign a scalar value or an array-like object.

For array-like objects, ensure that the number of elements matches the number of columns; otherwise, it will cause an error.

Like columns, Series can also be assigned to rows. If the labels do not match, missing values NaN are assigned. If you want to ignore the labels, you can use values to convert to NumPy array ndarray .

The append() method was formerly used to add new rows to DataFrame . However, this method was deprecated in version 1.4.0 and removed in version 2.0.0 .

  • pandas.DataFrame.append — pandas 1.4.4 documentation

In the release notes, it is recommended to use the pandas.concat() function instead.

Specify a list or tuple of objects you want to concatenate as the first argument to concat() . By default, they are concatenated vertically.

To retain only the columns that share common names, specify join='inner' .

You need to exercise caution when concatenating DataFrame and Series vertically.

By default, it looks like this.

By converting the Series to DataFrame with the to_frame() method and transposing it with T , you get a one-row DataFrame . You can concatenate this.

  • pandas: Transpose DataFrame (swap rows and columns)

Note: Add a large number of rows or columns

It's not recommended to add a large number of rows or columns to a DataFrame individually due to inefficiency.

For example, when you add one column at a time in a for loop, a PerformanceWarning is issued. It seems to be issued when you add more than 100 columns.

Unless you need to use the features of DataFrame every time you add a row or column, it is better to concatenate all at once using concat() , as the warning message suggests.

A comparison of processing speed between adding one by one and adding all at once will be introduced at the end.

Take the following DataFrame as an example.

Add the data and row names for each row to separate lists. Although the content is simply created here, in the actual code it is created by some data processing.

Create a DataFrame from these lists and the column names columns of the original DataFrame , and concatenate it with the original DataFrame .

The concept is the same when adding columns as it was for adding rows as described above.

Add the data and column names for each column to separate lists.

Create a DataFrame from these lists and the row names index of the original DataFrame , and concatenate it with the original DataFrame . Note that you need to transpose the two-dimensional list holding the data.

  • Transpose 2D list in Python (swap rows and columns)

Compare the processing speed between adding rows or columns one by one and adding them all at once.

The following examples use the Jupyter Notebook magic command %%timeit . Note that these will not work if run as Python scripts.

  • Measure execution time with timeit in Python

In the case of adding 1000 rows:

In the case of adding 1000 columns:

In both cases, adding all rows or columns at once proves to be significantly faster.

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Learn By Example

Pandas Assign New Columns to a DataFrame

Author: Aditya Raj Last Updated: March 8, 2023

Pandas dataframes are the data structures that we use to handle tabular data in python. This article discusses different ways to assign new columns to pandas dataframe using the assign() method.

The Pandas assign() Method

Pandas assign a column to a dataframe, assign a column based on another column, assign multiple columns to a dataframe.

The assign() method is used to assign new columns to a pandas dataframe. It has the following syntax.

In the above function, the column names and the values for the columns are passed as keyword arguments. Here, column names are the keywords, and list or series objects containing the data for the column are the corresponding values.

assign to dataframe

When we invoke the assign() method on a dataframe df, it takes column names and series objects as its input argument. After execution, the assign() method adds the specified column to the dataframe df and returns the modified dataframe. 

To assign a new column to the pandas dataframe using the assign() method, we will pass the column name as its input argument and the values in the column as the value assigned to the column name parameter.

After execution, the assign() method returns the modified dataframe. You can observe this in the following example.

In the above example, we created a column "Name" in the input dataframe using a list and the a ssign() method.

Instead of a list, we can also assign a pandas series to the column name parameter to create a new column in the dataframe as shown below.

In this example, we passed a series to the assign() method as an input argument instead of a list. However, you can observe that the output dataframe in this example is the same as the previous example.

We can also create a column based on another column in a pandas dataframe. For this, we will first create a series based on another column. Then, we can use the assign() method and pass the column name with the series as its input to assign the column to the pandas dataframe.

You can observe this in the following example.

In this example, we have created the GPI column using the "Maths" column. For this, we created a series by dividing the Maths column by 10. Then, we assigned the new series to the GPI keyword as an input argument in the assign() method. You can also use the pandas apply method to create a new series in this case.

To assign multiple columns to the pandas dataframe, you can use the assign() method as shown below.

In this example, we assigned two columns to the pandas dataframe using the assign() method. For this, we passed both the column names and their values as keyword arguments to the assign() method.

In this article, we have discussed different ways to assign columns to a pandas dataframe. To learn more about python programming, you can read this article on how to use the insert() method to insert a column into a dataframe . You might also like this article on how to convert epoch to datetime in python .

I hope you enjoyed reading this article. Stay tuned for more informative articles.

Happy Learning!

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5 Practical Ways to Set Column Names in pandas Series

Method 1: using the name attribute.

One intuitive approach to assign a column name to a pandas Series is by setting the name attribute directly. This not only is simple and clear but also allows for fluent code readability when chaining methods. The name attribute can be accessed just like any regular property of the Series object.

Here’s an example:

This code snippet creates a pandas Series and then sets its column name to ‘Revenue’ by assigning a value to the name attribute. The modified Series now has a name that can be used to refer to the column once it’s integrated into a DataFrame.

Method 2: Using the rename Method

The rename method in pandas allows for more flexibility as it can be used to rename the Series itself or the labels of the Series’ index. By providing a scalar value to the axis parameter, the rename method sets the Series name without altering the contained data.

The example demonstrates the use of rename to set the name of the Series to ‘Profit’. This is particularly useful when you wish to return a new Series with the name set, leaving the original Series unmodified.

Method 3: At the Time of Series Creation

When creating the Series object initially, the name can be specified as a parameter. This is the most straightforward approach when the name is known at creation time, making the process efficient and clean.

The code snippet above demonstrates setting the Series name directly upon initialization by using the name keyword argument. This efficiently creates a Series with the specified name ‘Quantity’ right from the start.

Method 4: Utilizing the DataFrame Structure

Since pandas Series can be thought of as a single-column DataFrame, we can convert a Series into a DataFrame and then use DataFrame methods to set column names. This could be overkill for just renaming but may be convenient within a context where the Series is being expanded into a DataFrame.

By converting the Series into a DataFrame and providing the name argument, this method deploys DataFrame techniques to rename a Series. This can be powerful in contexts where further DataFrame manipulation is required.

Bonus One-Liner Method 5: The pd.Series() Constructor Shortcut

Lastly, as a concise one-liner, you can redefine the Series altogether using the pd.Series() constructor with the previously mentioned name parameter. It’s quick but not memory-efficient as it creates a new object.

This demonstrates re-creating the pandas Series while setting a new name in the constructor. The new Series is named ‘Fibonacci’, quickly renaming the Series in just one line of code.

Summary/Discussion

  • Method 1: Using the name Attribute. Straightforward and makes in-place renaming possible. However, it doesn’t lend itself to method chaining well.
  • Method 2: Using the rename Method. Flexible and allows for method chaining or returning a copy. But slightly more verbose for simple renaming tasks.
  • Method 3: At the Time of Series Creation. Most efficient if the name is known ahead. Not useful for existing Series.
  • Method 4: Utilizing the DataFrame Structure. Can be part of broader data manipulations in DataFrame form. Overkill if only renaming is needed.
  • Method 5: The pd.Series() Constructor Shortcut. Quick and clean but not memory-efficient as it creates a new Series object.

Emily Rosemary Collins is a tech enthusiast with a strong background in computer science, always staying up-to-date with the latest trends and innovations. Apart from her love for technology, Emily enjoys exploring the great outdoors, participating in local community events, and dedicating her free time to painting and photography. Her interests and passion for personal growth make her an engaging conversationalist and a reliable source of knowledge in the ever-evolving world of technology.

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How to Set Cell Value in Pandas DataFrame?

In this article, we will discuss how to set cell values in Pandas DataFrame in Python .

Method 1: Set value for a particular cell in pandas using dataframe.at

This method is used to set the value of an existing value or set a new record.

assign to dataframe

Method 2: Set value for a particular cell in pandas using loc() method

Here we are using the Pandas loc() method to set the column value based on row index and column name

assign to dataframe

Method 3: Update the value for a particular cell in pandas using replace

Here, we are updating the “suraj” value to “geeks” using Pandas replace .

assign to dataframe

Method 4: Update the value for a particular cell in pandas using iloc

Here, we are updating the value of multiple indexes of the 0 th column to 45 using Python iloc .

assign to dataframe

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  1. pandas.DataFrame.assign

    Assign new columns to a DataFrame. Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten. Parameters: **kwargsdict of {str: callable or Series} The column names are keywords. If the values are callable, they are computed on the DataFrame and assigned to the new columns.

  2. Pandas

    The assign() method in Pandas is a powerful tool for adding new columns to a DataFrame in a fluent and flexible way. This method is particularly useful in data preprocessing, feature engineering, and exploratory data analysis, enabling data scientists and analysts to prepare and transform data efficiently.

  3. How to Use the assign() Method in Pandas (With Examples)

    The assign () method can be used to add new columns to a pandas DataFrame. This method uses the following basic syntax: df.assign(new_column = values) It's important to note that this method will only output the new DataFrame to the console, but it won't actually modify the original DataFrame.

  4. Pandas DataFrame assign () Method

    The Dataframe.assign () method assigns new columns to a DataFrame, returning a new object (a copy) with the new columns added to the original ones. Existing columns that are re-assigned will be overwritten. The length of the newly assigned column must match the number of rows in the DataFrame. Example: Python3 import numpy as np

  5. How to Use the Pandas Assign Method to Add New Variables

    October 29, 2020 by Joshua Ebner In this tutorial, I'll explain how to use the Pandas assign method to add new variables to a Pandas dataframe. In this tutorial, I'll explain what the assign method does and how it works. I'll explain the syntax, and I'll show you step-by-step examples of how to use it.

  6. Pandas DataFrame assign() Method

    Definition and Usage The assign () method adds a new column to an existing DataFrame. Syntax dataframe .assign (kwargs) Parameters Return Value A DataFrame with the new column (s) added. This method does not change the original DataFrame. DataFrame Reference W3schools Pathfinder Track your progress - it's free! Log in Sign Up

  7. Indexing, Selecting, and Assigning Data in Pandas • datagy

    January 5, 2022 In this tutorial, you'll learn how to index, select and assign data in a Pandas DataFrame. Understanding how to index and select data is an important first step in almost any exploratory work you'll take on in data science.

  8. pandas: Add rows/columns to DataFrame with assign(), insert()

    The pandas.DataFrame.assign() method. The assign() method either appends a new column or assigns new values to an existing column.. pandas.DataFrame.assign — pandas 2.0.3 documentation; You can specify the column name and its value using the keyword argument structure, column_name=value. If the column name exists, the method assigns the value to it.

  9. Pandas Assign New Columns to a DataFrame

    The assign () method is used to assign new columns to a pandas dataframe. It has the following syntax. df.assign (col_1=series_1, col_2=series2,...) In the above function, the column names and the values for the columns are passed as keyword arguments.

  10. Set value for particular cell in pandas DataFrame using index

    Asked 11 years, 2 months ago Modified 4 months ago Viewed 1.7m times 785 I have created a Pandas DataFrame df = DataFrame(index=['A','B','C'], columns=['x','y']) Now, I would like to assign a value to particular cell, for example to row C and column x. In other words, I would like to perform the following transformation: x y x y

  11. Adding New Variable to Pandas DataFrame

    Method 2: Using [] to add a new column. In this example, instead of using the assign () method, we use square brackets ( []) to create a new variable or column for an existing Dataframe. The syntax goes like this: dataframe_name ['column_name'] = data column_name is the name of the new column to be added in our dataframe.

  12. pandas

    how to assign values to a new data frame from another data frame in python. columns = ['Tenor','5x16', '7x8', '2x16H'] index = range (0,12) SimMean = pd.DataFrame (index=index, columns=columns) SimMean Tenor 5x16 7x8 2x16H 0 NaN NaN NaN NaN 1 NaN NaN NaN NaN 2 NaN NaN NaN NaN 3 NaN NaN NaN NaN 4 NaN NaN NaN NaN 5 NaN NaN NaN NaN 6 NaN NaN NaN ...

  13. pandas.DataFrame.assign

    pandas.DataFrame.assign #. pandas.DataFrame.assign. #. Assign new columns to a DataFrame. Returns a new object with all original columns in addition to 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.

  14. Adding new column to existing DataFrame in Pandas

    Adding new columns to an existing DataFrame is a fundamental task in data analysis using Pandas. It allows you to enrich your data with additional information and facilitate further analysis and manipulation. This article will explore various methods for adding new columns, including simple assignment, the insert () method, the assign () method.

  15. Assign pandas dataframe column dtypes

    Another way to set the column types is to first construct a numpy record array with your desired types, fill it out and then pass it to a DataFrame constructor. import pandas as pd import numpy as np x = np.empty((10,), dtype=[('x', np.uint8), ('y', np.float64)]) df = pd.DataFrame(x) df.dtypes -> x uint8 y float64

  16. 5 Practical Ways to Set Column Names in pandas Series

    One intuitive approach to assign a column name to a pandas Series is by setting the name attribute directly. This not only is simple and clear but also allows for fluent code readability when chaining methods. ... By converting the Series into a DataFrame and providing the name argument, this method deploys DataFrame techniques to rename a ...

  17. pandas.DataFrame.assign

    pandas.DataFrame.assign ¶ DataFrame.assign(**kwargs) [source] ¶ Assign new columns to a DataFrame. Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten. Parameters **kwargsdict of {str: callable or Series} The column names are keywords.

  18. Assigning dataframe to dataframe in Pandas Python

    1 Answer Sorted by: 1 Use .copy to create a separate dataframe in memory: interest_margin_data = initial_margin_data.copy() It creates a different object in memory, rather than just pointing to the same place. This is done so if you create a "view" of the dataframe it does not require substantially extra memory.

  19. Assign values with for loops to pandas DataFrame columns

    2 Answers Sorted by: 3 The simplier is to pass a list to the DataFrame constructor, then no loop is necessary: df = pd.DataFrame ( [ [100,200,300]], columns= ['a', 'b', 'c'], index=range (100)) print (df.head ()) a b c 0 100 200 300 1 100 200 300 2 100 200 300 3 100 200 300 4 100 200 300 But if you want a loop solution:

  20. Add column names to dataframe in Pandas

    Creating the DataFrame. Let's first create an example DataFrame for demonstration reasons before moving on to adding column names. There are several ways in Pandas to add column names to your DataFrame: python3 # importing the pandas library. import pandas as pd # creating lists.

  21. How to Set Cell Value in Pandas DataFrame?

    In this article, we will discuss how to set cell values in Pandas DataFrame in Python. Method 1: Set value for a particular cell in pandas using dataframe.at. This method is used to set the value of an existing value or set a new record. Python3 # import pandas module. import pandas as pd

  22. how to multisplit dataframe column and assign identical column

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