Python row add column

4 Ways to Add a Column in Pandas

Pandas DataFrame presents data in tabular rows and columns. Adding new columns is an important task in data analysis. Here’s how to do it in pandas.

Soner Yıldırım is a data scientist for the travel company Wander, with an expertise in data analysis, data visualization and machine learning. Before joining Wander, he worked as a junior data scientist for Invent Analytics.

Pandas is a data analysis and manipulation library for Python. It provides numerous functions and methods to manage tabular data. The core data structure of pandas is DataFrame, which stores data in tabular form with labeled rows and columns.

4 Ways to Add a Column in Pandas

1. Add columns at the end of the table.
2. Add columns at a specific index.
3. Add columns with the loc method.
4. Add columns with the assign function.

From a data perspective, rows represent observations or data points. Columns represent features or attributes about the observations. Consider a DataFrame of house prices. Each row is a house and each column is a feature about the house such as age, number of rooms, price and so on.

Adding or dropping columns is a common operation in data analysis. We’ll go over four different ways of adding a new column to a DataFrame.

First, let’s create a simple DataFrame to use in the examples.

import numpy as np import pandas as pd df = pd.DataFrame() df 

4 Pandas Add Column Methods

Below are four methods for adding columns to a pandas DataFrame.

Method 1: Adding Columns on the End

This might be the most commonly used method for creating a new column.

We specify the column name like we are selecting a column in the DataFrame. Then, the values are assigned to this column. A new column is added as the last column, i.e. the column with the highest index.

We can also add multiple columns at once. Column names are passed in a list and values need to be two-dimensional compatible with the number of rows and columns. For instance, the following code adds three columns filled with random integers between zero and 10.

df[["1of3", "2of3", "3of3"]] = np.random.randint(10, size=(4,3)) df 

Let’s drop these three columns before going to the next method.

df.drop(["1of3", "2of3", "3of3"], axis=1, inplace=True) 

Method 2: Add Columns at a Specific Index

In the first method, the new column is added at the end. Pandas also allows for adding new columns at a specific index. The insert function can be used to customize the location of the new column. Let’s add a column next to column A.

The insert function takes three parameters that are the index, the name of the column and the values. The column indices start from zero, so we set the index parameter as one to add the new column next to column A. We can pass a constant value to be filled in all rows.

Method 3: Add Columns with Loc

The loc method allows you to select rows and columns using their labels. It’s also possible to create a new column with this method.

In order to select rows and columns, we pass the desired labels. The colon indicates that we want to select all the rows. In the column part, we specify the labels of the columns to be selected. Since the DataFrame does not have column E, pandas creates a new column.

Method 4: Add Columns With the Assign Function

The last method is the assign function.

df = df.assign(F = df.C * 10) df 

We specify both the column name and values inside the assign function. You may notice that we derive the values using another column in the DataFrame. The previous methods also allow for similar derivations.

There is an important difference between the insert and assign functions. The insert function works in place, which means adding a new column is saved in the DataFrame.

The situation is a little different with the assign function. It returns the modified DataFrame but does not change the original one. In order to use the modified version with the new column, we need to explicitly assign it.

We’ve now covered four different methods for adding new columns to a pandas DataFrame, a common operation in data analysis and manipulation. One of the things I like about pandas is that it usually provides multiple ways to perform a given task, making it a flexible and versatile tool for analyzing and manipulating data.

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How to Add Column to Pandas DataFrame?

To add a new column to the existing Pandas DataFrame, assign the new column values to the DataFrame, indexed using the new column name.

In this tutorial, we shall learn how to add a column to DataFrame, with the help of example programs, that are going to be very detailed and illustrative.

Syntax to add column

The syntax to add a column to DataFrame is:

mydataframe['new_column_name'] = column_values

where mydataframe is the dataframe to which you would like to add the new column with the label new_column_name. You can either provide all the column values as a list or a single value that is taken as default value for all of the rows.

Examples

1. Add column to DataFrame

In this example, we will create a dataframe df_marks and add a new column with name geometry.

Python Program

import pandas as pd mydictionary = #create dataframe df_marks = pd.DataFrame(mydictionary) print('Original DataFrame\n--------------') print(df_marks) #add column df_marks['geometry'] = [81, 92, 67, 76] print('\n\nDataFrame after adding "geometry" column\n--------------') print(df_marks)

Original DataFrame -------------- names physics chemistry algebra 0 Somu 68 84 78 1 Kiku 74 56 88 2 Amol 77 73 82 3 Lini 78 69 87 DataFrame after adding "geometry" column -------------- names physics chemistry algebra geometry 0 Somu 68 84 78 81 1 Kiku 74 56 88 92 2 Amol 77 73 82 67 3 Lini 78 69 87 76

The column is added to the dataframe with the specified list as column values.

The length of the list you provide for the new column should equal the number of rows in the dataframe. If this condition fails, you will get an error similar to the following.

ValueError: Length of values does not match length of index

2. Add column to DataFrame with a default value

In this example, we will create a dataframe df_marks and add a new column called geometry with a default value for each of the rows in the dataframe.

Python Program

import pandas as pd mydictionary = #create dataframe df_marks = pd.DataFrame(mydictionary) print('Original DataFrame\n--------------') print(df_marks) #add column df_marks['geometry'] = 65 print('\n\nDataFrame after adding "geometry" column\n--------------') print(df_marks)

Original DataFrame -------------- names physics chemistry algebra 0 Somu 68 84 78 1 Kiku 74 56 88 2 Amol 77 73 82 3 Lini 78 69 87 DataFrame after adding "geometry" column -------------- names physics chemistry algebra geometry 0 Somu 68 84 78 65 1 Kiku 74 56 88 65 2 Amol 77 73 82 65 3 Lini 78 69 87 65

The column is added to the dataframe with the specified value as default column value.

Summary

In this Pandas Tutorial, we learned how to add a new column to Pandas DataFrame with the help of detailed Python examples.

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Pandas Add Column to DataFrame

In pandas you can add/append a new column to the existing DataFrame using DataFrame.insert() method, this method updates the existing DataFrame with a new column. DataFrame.assign() is also used to insert a new column however, this method returns a new Dataframe after adding a new column.

In this article, I will cover examples of how to add/append multiple columns, add a constant value, deriving new columns from an existing column to the Pandas DataFrame.

1. Quick Examples of Add Column to DataFrame

 df['Tutors'] = df['Courses'].map(tutors) print(df) 

Let’s create a Pandas DataFrame with sample data and execute the above examples.

 df = pd.DataFrame(technologies) print(df) 

2. Pandas Add Column to DataFrame

DataFrame.assign() is used to add/append a column to the Pandas DataFrame, this method returns a new DataFrame after adding a column to the existing DataFrame.

Below is the syntax of the assign() method.

Now let’s add a column ‘ TutorsAssigned ” to the DataFrame. Using assign() we cannot modify the existing DataFrame in-place instead it returns a new DataFrame after adding a column. The below example adds a list of values as a new column to the DataFrame.

3. Add Multiple Columns to the DataFrame

You can also use assign() method to add multiple columns to the pandas DataFrame

4. Adding a Column From Existing

In real-time, we are mostly required to add a column by calculating from an existing column. The below example derives Discount_Percent column from Fee and Discount . Here, I will use lambda to derive a new column from the existing one.

Yields below output. Similarly, you can also derive multiple columns and add them to a DataFrame in a single statement, I will leave this to you to explore.

5. Add a Constant or Empty Column

The below example adds 3 new columns to the DataFrame, one column with all None values, a second column with 0 value, and the third column with an empty string value.

6. Append Column to Existing Pandas DataFrame

The above examples create a new DataFrame after adding new columns instead of appending a column to an existing DataFrame. The example explained in this section is used to append a new column to the existing DataFrame.

You can also use this approach to add a new column by deriving from an existing column,

7. Add Column to Specific Position of DataFrame

DataFrame.insert() method is used to add DataFrame at any position of the existing DataFrame. In most of the above examples you have seen inserts at the end of the DataFrame but this method gives the flexibility to add it at the beginning, in the middle, or at any column index of the DataFrame.

This example adds a Tutors column at the beginning of the DataFrame.

8. Add Column From Dictionary Mapping

If you wanted to add a column with specific values for each row based on an existing value, you can do this using a Dictionary. Here, The values from the dictionary will be added as Tutors column in df, by matching the key value with the column ‘Courses’ .

 df['Tutors'] = df['Courses'].map(tutors) print(df) 

Yields below output. Note that it is unable to map pandas as the key in the dictionary is not exactly matched with the value in the Courses column (case sensitive).

9. Using loc[] Add Column

Using pandas loc[] you can access rows and columns by labels or names however, you can also use this for adding a new columns to pandas DataFrame. This loc[] property uses the first argument as rows and second argument for columns hence, I will use the second argument to add a new column.

Conclusion

In this article, I have explained you can add/append a column to the existing DataFrame by using DataFrame.assing(), DataFrame.insert() e.t.c. Also learned insert() is used to add a column at any position of the DataFrame.

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This Post Has 2 Comments

Add Column From Dictionary Mapping:
your last example will not work as described in this article. The KEYS from the dictionary will be added as another COLUMN values in df, regardless of the dictionaly VALUES.

Thank you for pointing it out. I have fixed it now

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