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This tutorial explored the concept of grouping data in a DataFrame and sorting it in Pandas.

Grouping and Sorting DataFrame in Pandas

As we know, Pandas is an advanced data analysis tool or package extension in Python. Most of the companies and organizations that use Python and require high-quality data analysis use this tool on a large scale.

This tutorial lets us understand how and why to group and sort certain data in a Pandas DataFrame. Most businesses and organizations that use Python and Pandas for data analysis need to gather insights from their data to better plan their business.

Pandas helps analysts gather such insights through groupbyits functions. For example, consider a product-based company.

The company may need to group certain products and categorize them in its sales orders. Therefore, grouping and sorting have many advantages in data analysis and interpretation.

Before we start, we create a dummy DataFrame to work with. Here we create a DataFrame, viz df.

dfWe add some columns and some data to this DataFrame. We can do this using the following code.

import pandas as pd

df = pd.DataFrame({"dat1": [9, 5]})
df = pd.DataFrame(
    {
        "name": ["Foo", "Foo", "Baar", "Foo", "Baar", "Foo", "Baar", "Baar"],
        "count_1": [5, 10, 12, 15, 20, 25, 30, 35],
        "count_2": [100, 150, 100, 25, 250, 300, 400, 500],
    }
)

The above code creates a DataFrame with some entries. To see the entries in the data, we use the following code.

print(df)

The above code gives the following output.

	name	count_1	count_2
0	Foo		5		100
1	Foo		10		150
2	Baar	12		100
3	Foo		15		25
4	Baar	20		250
5	Foo		25		300
6	Baar	30		400
7	Baar	35		500

As we can see, we have 4 columns and 8 rows indexed from value 0 to value 7. If we look at our DataFrame, we see some repeated names, named df.

Since we already have a DataFrame set up, let’s group the data in this DataFrame and then sort the values ​​within those groups.

groupbyGrouping and Sorting DataFrames Using Functions in Pandas

Let's group this data since we already have it in place. We can group this data so that we 名称have the names of similar products grouped with each other under the columns for better data analysis.

We can groupbydo this in Pandas using the function. This function ensures that the products or values ​​under the specified columns are kept together or grouped.

We can perform any additional operations on these grouped data. This grouping operation can be done in Pandas as shown below.

df.groupby(["name"])

As we can see, we dfuse groupbythe function on the DataFrame named and namepass the columns as arguments.

Now let's use this groupbysorting function to sort our data so that we not only have groups but also have the data sorted in a specific format.

After performing groupbythe operation, we want to sort the data to have the three largest values ​​in our grouping.

This means that we want to dfget the three largest values ​​after sorting the grouped DataFrame in our . We can do this using the following code.

print(df.groupby(["name"])["count_1"].nlargest(3))

The code obtains the following results.

name
Baar  7    35
      6    30
      4    20
Foo   5    25
      3    15
      1    10
Name: count_1, dtype: int64

As we can see, we sorted the grouping in such a way that we have only count_1the first three names with the highest count displayed in the column.

So for Name Baar, we can see that we have three entries with Counts listed as 35, , 30and 20, and two entries Foowith Counts listed as 25, , 15and 10.

In Pandas, we can also visualize the data type and column name associated with the grouped data type. In our case, we have count_1the grouping column named with a data type of in the output at the bottom int64.

Thus, using groupbythe and nlargest()functions, we have grouped, sorted, and fetched certain records on the columns in the DataFrame.