Pandas Split Apply Combine

1. Split Apply Combine Strategy
2. Use Split Apply Combine Strategy
3. Conclusion

In this article, we’ll discuss Pandas split apply combine strategy. This strategy is beneficial when working with large data sets, as it can be difficult to analyze all the data at once.

Split Apply Combine Strategy

The pandas split apply combine strategy is a powerful data analysis technique that involves partitioning a dataset into groups, using a function for each group, and then combining the results. This strategy can perform various data analysis tasks, such as aggregating data, calculating statistics, and finding patterns.

Remember the following points when using the split-apply-combine strategy.

1. First, choosing an appropriate function to apply to the data is essential.
2. Second, the analysis results will be influenced by how the data is grouped. For example, if the information is grouped by year, the results will be different than if the data is grouped by country.

It can help us to understand relationships between variables and to see patterns that would be difficult to spot when looking at the data as a whole.

Use Split Apply Combine Strategy

The split apply combine strategy can be used to answer various types of questions, including:

1. What is the average age of people in each state?
2. What is the total number of people in each state?
3. What is the average income of people in each state?

First, we will take a set of data like the following code.

import numpy as np
import pandas as pd

df = pd.DataFrame(
    {
        "A": ["one", "two", "three", "four", "five", "six", "seven", "eight"],
        "B": ["AB", "BC", "CD", "DE", "EF", "FG", "GH", "HI"],
        "C": np.random.randn(8),
        "D": np.random.randn(8),
    }
)
print(df)

Output:

    A       B       C           D
0   one     AB  -1.178015   -0.718776
1   two     BC  -0.149049   0.557202
2   three   CD  -0.486704   1.491223
3   four    DE  0.143172    1.669733
4   five    EF  -0.627370   0.825338
5   six     FG  2.105268    -0.239559
6   seven   GH  1.203344    0.592531
7   eight   HI  1.756920    1.164611

To use the split apply combine strategy, you will need to:

1. Split the data into groups.
2. Apply a function to each group.
3. Combine the results.

Split The Data Into Groups

You must first split your data into groups to use the combined method. You can do this using the pandas groupby function.

To split the data into groups, you will need to decide on a variable to group by. This variable will determine how the data will be divided into groups.

grouped = df.groupby(["A", "B"])

Apply a Function to Each Group

Once your data is grouped, you can then apply a function to each group. This can be any function that you like, but it must be able to operate on a group of data.

df2 = df.set_index(["A", "B"])

Combine The Results

Finally, you can combine the results of the apply step into a single dataframe using the pandas concat function. This will give you a single dataframe that contains the results of the application step for each group.

This can be done in different ways, depending on the question you’re trying to answer.

grouped.sum()

Let’s combine each of the above statements and see how it works.

Code Example:

import numpy as np
import pandas as pd

df = pd.DataFrame(
    {
        "A": ["one", "two", "three", "four", "five", "six", "seven", "eight"],
        "B": ["AB", "BC", "CD", "DE", "EF", "FG", "GH", "HI"],
        "C": np.random.randn(8),
        "D": np.random.randn(8),
    }
)
# split the data
grouped = df.groupby(["A", "B"])

# apply a function to each group
df2 = df.set_index(["A", "B"])

# combine the group
grouded_data = grouped.sum()

print(grouded_data)

Output:

                 C         D
A     B
eight HI -0.398241 -1.145102
five  EF  0.439858 -0.923552
four  DE -1.150551 -1.466125
one   AB  0.882921  0.078129
seven GH -1.750068 -0.568044
six   FG -1.335543  0.562349
three CD -0.876180  1.007510
two   BC  1.275738  0.136052

Conclusion

The split apply combine strategy is one of the most used strategies in data science. It is a flexible and concise way to split data into groups, apply functions to those groups, and then combine the results.

The SAC process is a key part of the Pandas’ library and is used extensively by data scientists. There are many use cases of pandas split apply combine strategy. If you want to know more, read the blog and try it.