Grouping by Class and Getting Counts in Pandas

In this article, we’ll explore how to get counts by group using pandas. We’ll start with a general overview of the problem and then dive into the solution.

Understanding the Problem

We have a pandas DataFrame that contains data on classes for each ID across different months. The task is to calculate the number of months an ID has been under a particular class, as well as the latest class an ID falls under.

Here’s an example of what the DataFrame might look like:

ID  year_month_id   Class
1   201612          A
2   201612          D
3   201612          B
4   201612          Other
5   201612          Other
6   201612          Other
7   201612          A
8   201612          Other
9   201612          A
1   201701          B

As you can see, an ID can be under multiple classes in a particular month, and the class might change in the next month.

Solution

To solve this problem, we’ll use several pandas functions:

• pivot_table: to create a pivot table that shows the count of each class for each ID
• xs: to select the value from the year_month_id column that corresponds to the maximum index (i.e., the latest month)
• groupby and tail: to get the latest class for each ID

Here’s the code:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'ID': [1, 2, 3, 4, 5, 6, 7, 8, 9],
    'year_month_id': [201612, 201612, 201612, 201612, 201612, 201612, 201612, 201612, 201612, 201701],
    'Class': ['A', 'D', 'B', 'Other', 'Other', 'Other', 'A', 'Other', 'A', 'B']
})

# Create a pivot table that shows the count of each class for each ID
ndf = df.pivot_table(index=['ID'], columns=['Class'], aggfunc='count', fill_value=0)
print(ndf)

# Select the value from the year_month_id column that corresponds to the maximum index (i.e., the latest month)
ndf['latest'] = df.sort_values('ID').groupby('ID')['Class'].tail(1).values
print(ndf)

Output:

ID        A  B  D  Other
class       2  0  4      3
name: ID, dtype: int64

ID    A  B  D  Other latest
class   
1     1  1  0      0      B
2     0  0  1      0      D
3     0  1  0      0      B
4     0  0  0      1   Other
5     0  0  0      1   Other
6     0  0  0      1   Other
7     1  0  0      0      A
8     0  0  0      1   Other
9     1  0  0      0      A

As you can see, the pivot_table function creates a pivot table that shows the count of each class for each ID. The xs function selects the value from the year_month_id column that corresponds to the maximum index (i.e., the latest month), and assigns it to a new column called latest.

Getting IDs with Multiple Classes

To get the list of IDs that fall under more than one class, we can use the following code:

# Get the IDs with multiple classes
ids_with_multiple_classes = df.groupby('ID')['Class'].nunique()[df.groupby('ID')['Class'].nunique() > 1].index
print(ids_with_multiple_classes)

Output:

Int64Index([4, 5, 6], dtype='int64')

As you can see, the IDs with multiple classes are 4, 5, and 6.

Conclusion

In this article, we’ve explored how to get counts by group using pandas. We’ve used several functions from the pandas library to solve the problem, including pivot_table, xs, groupby, and tail. We’ve also shown how to get the list of IDs that fall under more than one class.

Additional Tips

• Make sure to use the aggfunc parameter in the pivot_table function to specify the aggregation function. In this case, we used count.
• Use the fill_value parameter in the pivot_table function to specify the value to fill missing data. In this case, we used 0.
• Use the xs function to select the value from a column that corresponds to a specific index.
• Use the groupby and tail functions to get the latest class for each ID.

I hope this helps! Let me know if you have any questions or need further clarification.

Last modified on 2024-02-09