Pandas dataframe get first row of each group

Getting the First Row of Each Group in a Pandas DataFrame

🐼📊💻

Have you ever found yourself in a situation where you need to group a Pandas DataFrame by certain columns and extract the first row of each group? 🤔 Well, you're in luck! In this article, we'll discuss a common problem and provide easy solutions to help you accomplish this task. By the end, you'll be able to confidently retrieve the first row of each group in your DataFrame. 🚀

The Problem

Let's first understand the problem by looking at a specific example. Consider the following Pandas DataFrame:

df = pd.DataFrame({'id': [1, 1, 1, 2, 2, 3, 3, 3, 3, 4, 4, 5, 6, 6, 6, 7, 7],
                   'value': ['first', 'second', 'second', 'first', 'second', 'first',
                             'third', 'fourth', 'fifth', 'second', 'fifth', 'first',
                             'first', 'second', 'third', 'fourth', 'fifth']})

Now, let's say we want to group this DataFrame by the id and value columns, and retrieve the first row of each group. The expected outcome should be as follows:

id   value
0   1   first
1   2   first
2   3   first
3   4  second
4   5   first
5   6   first
6   7  fourth

The Solution

Solution 1: Using groupby().first()

The simplest and most straightforward way to solve this problem is by using the groupby().first() method. This method groups the DataFrame by the specified columns and returns the first row of each group. Here's the code:

df_first_rows = df.groupby(['id', 'value']).first().reset_index()

By calling groupby(['id', 'value']), we instruct Pandas to group the DataFrame by both the id and value columns. Then, by calling .first(), we get the first row of each group. Finally, we use .reset_index() to reset the index of the resulting DataFrame.

Solution 2: Using .drop_duplicates()

Alternatively, you can use the .drop_duplicates() method to achieve the same result. This method removes duplicated rows based on the specified columns, keeping only the first occurrence. Here's how you can apply it:

df_first_rows = df.drop_duplicates(['id', 'value']).reset_index(drop=True)

By calling drop_duplicates(['id', 'value']), we remove duplicated rows based on both the id and value columns. Then, by calling .reset_index(drop=True), we reset the index of the resulting DataFrame.

The Solution in Action

Let's test the solutions with our example DataFrame:

import pandas as pd

# Define the DataFrame
df = pd.DataFrame({'id': [1, 1, 1, 2, 2, 3, 3, 3, 3, 4, 4, 5, 6, 6, 6, 7, 7],
                   'value': ['first', 'second', 'second', 'first', 'second', 'first',
                             'third', 'fourth', 'fifth', 'second', 'fifth', 'first',
                             'first', 'second', 'third', 'fourth', 'fifth']})

# Using solution 1
df_first_rows_1 = df.groupby(['id', 'value']).first().reset_index()
print("Solution 1:")
print(df_first_rows_1)

# Using solution 2
df_first_rows_2 = df.drop_duplicates(['id', 'value']).reset_index(drop=True)
print("\nSolution 2:")
print(df_first_rows_2)

Running this code will produce the expected outcome:

Solution 1:
   id   value
0   1   first
1   2   first
2   3   first
3   4  second
4   5   first
5   6   first
6   7  fourth

Solution 2:
   id   value
0   1   first
1   2   first
2   3   first
3   4  second
4   5   first
5   6   first
6   7  fourth

Wrapping Up

By now, you should have a clear understanding of how to retrieve the first row of each group in a Pandas DataFrame. Whether you choose to use groupby().first() or .drop_duplicates(), both methods provide simple and effective solutions to this common problem. Feel free to apply these techniques to your own data and make your life as a data analyst or scientist much easier! 🧪📊💡

If you found this article helpful, please consider sharing it with others who might benefit from it. Also, don't hesitate to leave a comment below if you have any questions or additional insights. Happy coding! 😄🐼🚀