Adventures in Machine Learning

Mastering Data Manipulation with Pandas: Using the Concat() Function

Pandas is an excellent data manipulation library in Python that is widely used in data analysis, financial modeling, and much more. It is built on top of the NumPy library and offers great features for data manipulation such as the powerful DataFrame and Series classes.

In this article, we will explore how to use the concat() function in pandas to stack DataFrames while discussing the importance of ignore_index and provide additional resources for those that want to learn more.

Using pandas concat() function to stack DataFrames

The pandas concat() function is an effective way to combine two or more data sources such as DataFrames into a single object. The concat() function concatenates the rows or columns of two or more data frames into a single data frame.

Example 1: Stack two pandas DataFrames

In this example, we create two pandas DataFrames, each containing some fictional employee data. The first DataFrame consists of three columns, i.e., Name, Age, and Year of Employment.

The second DataFrame has four columns, i.e., Name, Age, Salary, and Department. To stack these two data frames `df1` and `df2` into a single data frame using the concat() function, we can use the following code:

“`

import pandas as pd

# creating the first DataFrame

df1 = pd.DataFrame({‘Name’: [‘Alice’, ‘Bob’, ‘Chris’, ‘Eve’],

‘Age’: [23, 54, 32, 32],

‘Year of Employment’: [2018, 2010, 2012, 2017]})

# creating the second DataFrame

df2 = pd.DataFrame({‘Name’: [‘Alice’, ‘Bob’, ‘Chris’, ‘Eve’],

‘Age’: [23, 54, 32, 32],

‘Salary’: [50000, 70000, 45000, 60000],

‘Department’: [‘IT’, ‘Sales’, ‘Marketing’, ‘HR’]})

# combining the data frames

df_combined = pd.concat([df1, df2], axis=1)

print(df_combined)

“`

Output:

“`

Name Age Year of Employment Name Age Salary Department

0 Alice 23 2018 Alice 23 50000 IT

1 Bob 54 2010 Bob 54 70000 Sales

2 Chris 32 2012 Chris 32 45000 Marketing

3 Eve 32 2017 Eve 32 60000 HR

“`

When we concatenate the two DataFrames, we must ensure we choose a value for the axis parameter, which can be either 0 or 1. An axis parameter of 0 will stack the DataFrames vertically, while an axis of 1 will stack them horizontally.

The output shows that we have concatenated the two data frames horizontally. Example 2: Stack three pandas DataFrames

We can also concatenate more than two data frames using the concat() function.

In this example, we will use three data frames `df1`, `df2`, and `df3`. `df1` and `df2` are the same DataFrames as in the previous example, and `df3` has two more columns `Name` and `Maximum Education`.

“`

# creating the third DataFrame

df3 = pd.DataFrame({‘Name’: [‘Alice’, ‘Bob’, ‘Chris’, ‘Eve’],

‘Maximum Education’: [‘PhD’, ‘MBA’, ‘BSc’, ‘MSc’]})

# combining the data frames

df_combined = pd.concat([df1, df2, df3], axis=1)

print(df_combined)

“`

Output:

“`

Name Age Year of Employment Name Age Salary Department Name Maximum Education

0 Alice 23 2018 Alice 23 50000 IT Alice PhD

1 Bob 54 2010 Bob 54 70000 Sales Bob MBA

2 Chris 32 2012 Chris 32 45000 Marketing Chris BSc

3 Eve 32 2017 Eve 32 60000 HR Eve MSc

“`

The output of this code shows that we have concatenated the three data frames horizontally, producing a data frame with nine columns.

The Importance of ignore_index

The ignore_index parameter in the concat() function ensures that a new index will be assigned to the resulting data frame instead of keeping the original index numbers of the original data frames. For example, consider the result after concatenating `df1` and `df2` without the ignore_index parameter as shown below:

“`

df_combined = pd.concat([df1, df2], axis=1)

print(df_combined)

“`

Output:

“`

Name Age Year of Employment Name Age Salary Department

0 Alice 23 2018 Alice 23 50000 IT

1 Bob 54 2010 Bob 54 70000 Sales

2 Chris 32 2012 Chris 32 45000 Marketing

3 Eve 32 2017 Eve 32 60000 HR

“`

Notice that the output includes the original index numbers from the input data frames. This behavior can cause problems when trying to merge the concatenated data with other data frames later.

Hence, we usually set ignore_index to `True`. “`

df_combined = pd.concat([df1, df2], axis=1, ignore_index=True)

print(df_combined)

“`

Output:

“`

0 1 2 3 4 5 6

0 Alice 23 2018 Alice 23 50000 IT

1 Bob 54 2010 Bob 54 70000 Sales

2 Chris 32 2012 Chris 32 45000 Marketing

3 Eve 32 2017 Eve 32 60000 HR

“`

The ignore_index parameter removes the original index numbers from the input data frames, creating a new index that starts from 0. The resulting output is now more manageable and consists of six columns and four rows.

Additional resources

As you continue to learn and explore pandas, you might want additional resources to help you expand your knowledge. Here are some of the best resources available on pandas:

– The official pandas documentation: This is a great place to start to learn about the different features and functions in pandas.

It is very detailed and includes examples and use cases. You can find it on the pandas website.

– The pandas cookbook: This is an excellent resource for pandas users of all levels. It provides real-world use cases and examples along with explanations of how to manipulate the data.

The cookbook is available on the pandas website. – The pandas code examples repository on GitHub: The pandas team has created over 500 examples of using pandas across a wide range of use cases like finance, movie databases, housing, social media, and much more.

You can find the examples on the pandas repository on GitHub.

Conclusion

In conclusion, the concat() function in pandas is a powerful tool for combining DataFrames. By using the parameters of the function effectively, we can combine any number of DataFrames into a single object.

The ignore_index parameter is essential for ensuring that a new index is used and for avoiding conflicts when merging data frames later. With the additional resources provided, you can continue to expand your knowledge of pandas and become more proficient with data manipulation in Python.

In summary, using the concat() function in pandas is an efficient method for combining DataFrames. By stacking multiple DataFrames, we can create a single object that contains all the information we need without sacrificing its structural integrity.

The ignore_index parameter is also crucial for avoiding conflicts when merging data frames later. It is crucial to understand the multiple possibilities of using the concat() functions effectively.

Moreover, these skills in pandas are invaluable to data analysts, financial modelers, and anyone else who frequently works with large datasets. With the additional resources provided, individuals can further expand their knowledge of pandas and mastering data manipulation in Python.