Adventures in Machine Learning

Efficient Ways to Add Columns to Pandas DataFrames

Are you having trouble merging data from one pandas DataFrame to another? Do you need to add a column to an existing DataFrame but don’t know where to start?

In this article, we will explore two easy and efficient ways to add columns to pandas DataFrames. Method 1: Add Column from One DataFrame to Last Column Position in Another

Adding a column from one DataFrame to another is a simple process with pandas.

The first method we will explore is adding a column from one DataFrame to the last column position in another. To do this, we need to create a new column in the destination DataFrame and assign the values of the source DataFrame to it.

Here’s an example of adding a column to the last position of a DataFrame:

“`

import pandas as pd

# Define the source DataFrame

source = pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6]})

# Define the destination DataFrame

destination = pd.DataFrame({‘X’: [7, 8, 9], ‘Y’: [10, 11, 12]})

# Add a new column from the source DataFrame to the last column position of the destination DataFrame

destination[‘Z’] = source[‘A’]

# Print the new DataFrame

print(destination)

“`

Output:

“`

X Y Z

0 7 10 1

1 8 11 2

2 9 12 3

“`

As you can see, the new column ‘Z’ has been added to the last column position of the destination DataFrame, and the values of the ‘A’ column from the source DataFrame have been assigned to it. We used the indexing operator [] to assign the values of the ‘A’ column to the new column ‘Z’.

Method 2: Add Column from One DataFrame to Specific Position in Another

The second method we will explore is adding a column from one DataFrame to a specific position in another. To do this, we need to insert a new column into the destination DataFrame at the specified position and then assign the values of the source DataFrame to it.

Here’s an example of adding a column to a specific position in a DataFrame:

“`

import pandas as pd

# Define the source DataFrame

source = pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6]})

# Define the destination DataFrame

destination = pd.DataFrame({‘X’: [7, 8, 9], ‘Y’: [10, 11, 12]})

# Define the position where we want to insert the new column

position = 1

# Insert a new column from the source DataFrame into the specified position of the destination DataFrame

destination.insert(position, ‘Z’, source[‘A’])

# Print the new DataFrame

print(destination)

“`

Output:

“`

X Z Y

0 7 1 10

1 8 2 11

2 9 3 12

“`

In this example, we used the insert() method to insert a new column ‘Z’ from the source DataFrame into the specified position 1 of the destination DataFrame. We assigned the values of the ‘A’ column from the source DataFrame to the new column ‘Z’.

Example 1: Adding Column to Last Column Position

Let’s look at a practical example of adding a column to the last column position of a DataFrame. Suppose we have a DataFrame that contains information about dogs, including their names, breeds, ages, and weights.

We want to add a new column that indicates whether each dog is a puppy or an adult based on its age. “`

import pandas as pd

# Define the dog DataFrame

dog_data = {‘Name’: [‘Buddy’, ‘Charlie’, ‘Lola’, ‘Luna’, ‘Rocky’], ‘Breed’: [‘Husky’, ‘Golden Retriever’, ‘Poodle’, ‘Labrador Retriever’, ‘Chihuahua’], ‘Age’: [2, 5, 1, 3, 4], ‘Weight’: [60, 75, 10, 80, 5]}

dog_df = pd.DataFrame(data=dog_data)

# Define the cutoff age for puppies

puppy_age = 1.5

# Create a new column indicating whether each dog is a puppy or an adult based on its age

dog_df[‘Puppy/Adult’] = [‘Puppy’ if x <= puppy_age else 'Adult' for x in dog_df['Age']]

# Print the new DataFrame

print(dog_df)

“`

Output:

“`

Name Breed Age Weight Puppy/Adult

0 Buddy Husky 2 60 Adult

1 Charlie Golden Retriever 5 75 Adult

2 Lola Poodle 1 10 Puppy

3 Luna Labrador Retriever 3 80 Adult

4 Rocky Chihuahua 4 5 Adult

“`

In this example, we first defined the dog DataFrame that contains information about the dogs. We then created a new column ‘Puppy/Adult’ that indicates whether each dog is a puppy or an adult based on its age.

We used a list comprehension to assign the values ‘Puppy’ or ‘Adult’ to the new column based on the value of the ‘Age’ column.

Conclusion

In this article, we explored two methods for adding columns from one DataFrame to another. The first method involves adding a column to the last column position of the destination DataFrame, while the second method involves inserting a column into a specific position in the destination DataFrame.

We also provided a practical example of adding a new column to a DataFrame. Now that you know how to add columns to pandas DataFrames, you can use this knowledge to manipulate and analyze your data more efficiently.

In the previous section, we discussed two methods for adding columns from one pandas DataFrame to another. Method 1 involves adding a column to the last column position of the destination DataFrame, while Method 2 involves inserting a column into a specific position in the destination DataFrame.

In this section, we’ll explore a practical example of adding a new column to a DataFrame at a specific column position. Example 2: Adding Column to Specific Column Position

Suppose we have the following DataFrame named `student_data` that contains information about students, including their names, ages, grades in different subjects, and their average grade:

“`

import pandas as pd

# Define the student DataFrame

student_data = {‘Name’: [‘John’, ‘Jane’, ‘Bill’, ‘Lisa’],

‘Age’: [18, 17, 19, 18],

‘Mathematics’: [80, 90, 70, 85],

‘Physics’: [85, 92, 75, 90],

‘Chemistry’: [90, 85, 75, 80],

‘Average’: [85.0, 89.0, 73.3, 85.0]}

student_df = pd.DataFrame(data=student_data)

# Print the DataFrame

print(student_df)

“`

Output:

“`

Name Age Mathematics Physics Chemistry Average

0 John 18 80 85 90 85.0

1 Jane 17 90 92 85 89.0

2 Bill 19 70 75 75 73.3

3 Lisa 18 85 90 80 85.0

“`

Suppose we want to add a new column that indicates whether each student passed or failed their final exams based on a passing grade cutoff of 70. Let’s assume that we want to insert this new column after the `Average` column.

To do so, we will use the `insert()` method to insert the new column at the specified position. The method requires three arguments: the position at which to insert the column, the name of the new column, and the values that will populate the column.

Here’s how to add the new column ‘Pass/Fail’ at a specific column position:

“`

# Define the passing grade cutoff

passing_grade = 70

# Create a new column that indicates whether each student passed or failed their final exams

pass_fail = [‘Pass’ if x >= passing_grade else ‘Fail’ for x in student_df[‘Average’]]

# Insert the new column after the ‘Average’ column

student_df.insert(6, ‘Pass/Fail’, pass_fail)

# Print the new DataFrame

print(student_df)

“`

Output:

“`

Name Age Mathematics Physics Chemistry Average Pass/Fail

0 John 18 80 85 90 85.0 Pass

1 Jane 17 90 92 85 89.0 Pass

2 Bill 19 70 75 75 73.3 Pass

3 Lisa 18 85 90 80 85.0 Pass

“`

As you can see, the new column ‘Pass/Fail’ has been added to the DataFrame at the specified position and indicates whether each student passed or failed their final exams based on the passing grade cutoff of 70.

Additional Resources

While briefly touching on the topic of adding columns to DataFrames, it is worth mentioning that pandas library provides extensive documentation and various tutorials that cover DataFrame manipulation in depth. The documentation provides multiple examples for creating, modifying, and manipulating DataFrames with pandas.

Pandas documentation: https://pandas.pydata.org/docs/

Pandas Tutorials: https://pandas.pydata.org/pandas-docs/stable/getting_started/tutorials.html

These resources can help you deepen your knowledge and improve your skills in working with pandas DataFrames.

Conclusion

In this article, we explored two straightforward methods for adding columns from one pandas DataFrame to another. In the first method, we added a column to the last column position of the destination DataFrame, while in the second method, we inserted a column into a specific position in the destination DataFrame.

We also covered an example of adding a new column to a DataFrame at a specific column position. Finally, we introduced various pandas documentation and tutorials resources to help further improve your pandas skills.

In summary, adding columns to pandas DataFrames is a simple process that involves creating a new column in the destination DataFrame and assigning the values of the source DataFrame to it using the appropriate method. We explored two methods for adding columns, one which involves adding a column to the last column position of the DataFrame, and the other which involves inserting a column into a specific position in the DataFrame.

We also provided practical examples to illustrate the implementation of these methods. By understanding how to add columns, you can manipulate and analyze your data more efficiently.

Remember to refer to pandas documentation and tutorials for more information on DataFrame manipulation with pandas.

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