Understanding Repeating Sequences in Pandas DataFrames

As a data analyst, working with data from different sources can be challenging, especially when the data is scattered or disorganized. In this article, we’ll explore how to count repeating sequences in a Pandas DataFrame, specifically focusing on sorting and grouping by a column containing period IDs.

Introduction to Periods and Sales Volumes

The problem statement describes a scenario where sales volumes are recorded over time, with each record representing the duration of a specific period. The period ID in this context represents the length of the process, which is relevant for each store ID. When the series is interrupted, it means that a new period has started.

For example, let’s consider the following DataFrame:

In this example, the period ID of 208 represents a continuous duration for store 41685, while the period ID of 209 marks the end of that process and the start of another.

Grouping by Store ID

The problem states that the sales volumes are grouped by store ID using the df.groupby('store_id').agg(lambda x: x.tolist()) function. This produces a DataFrame with each store ID as a row, containing a list of period IDs for each store:

Counting Repeating Sequences

To count the repeating sequences of contiguous period IDs for each store ID, we need to sort the period IDs within each group and then analyze the resulting sequence.

Sorting by Period ID

One approach is to use the df.sort_values function to sort the DataFrame by both store_id and period_id, reset the index, and create a new column period_group that indicates whether each row represents the start or end of a period:

df['period_group'] = df['period_id'].diff().fillna(1).ne(1).astype(int).cumsum()

This produces a new DataFrame with the following columns:

Grouping by Period Group

Now that we have the period_group column, we can group the DataFrame by this new column to analyze the repeating sequences:

df.groupby('period_group').size()

This produces a Series with the count of rows for each period group. The resulting sequence is the number of times each period ID appears in a row-by-row manner.

Conclusion

In conclusion, counting repeating sequences in Pandas DataFrames involves sorting and grouping by a column containing period IDs. By using the df.sort_values function to sort the DataFrame and creating a new column period_group that indicates whether each row represents the start or end of a period, we can then group the DataFrame by this new column to analyze the resulting sequence.

This approach is useful for identifying patterns in the data, such as repeated sequences of contiguous period IDs, which can be valuable insights for data analysis and interpretation.

Last modified on 2023-06-04