Working with Time Series Data in Pandas: Converting Rows of Categorical Values into Columns

In this article, we will explore how to convert rows of categorical values into columns using pandas. We’ll use the example provided by the Stack Overflow community as a starting point and delve deeper into the technical details behind this process.

Understanding the Problem

We have a dataset consisting of 34 movies with corresponding dates and values. Our goal is to find a correlation between the time trend and the values for each movie. However, the current dataset has rows representing individual timestamps, but we want to transform it into a format where each movie has a value for each timestamp.

The Original Dataset

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

Transforming the Dataset

To convert this dataset into a format where each movie has a value for each timestamp, we can use pandas’ pivot function.

# Filter only movies if necessary
df = df[df['movie'].str.startswith('Movie')]

This line of code ensures that we’re working with only the ‘Movie’ movies from our original dataset. This is not strictly necessary, but it helps to avoid any potential issues with missing data.

df = df.pivot(columns='movie', index='date', values='value')

Here, we use the pivot function to transform our dataset into a new format. The columns parameter specifies that we want to pivot on the ‘movie’ column, while the index parameter tells pandas to use the ‘date’ column as the row labels (or index). Finally, the values parameter indicates that we want to aggregate the values in each row based on the ‘value’ column.

The resulting dataset will have the following structure:

Note that the ‘NaN’ values indicate that there are no corresponding timestamp for those movies.

Adding Missing Timestamps

To add missing timestamps to our dataset, we can use pandas’ reindex function.

idx = pd.date_range(start=df.index.min(), end=df.index.max(), freq='5T')
df = df.reindex(idx)

Here, we create a new index of timestamps with 5-minute intervals between each timestamp. We then pass this new index to the reindex function, which fills in any missing values based on our original dataset.

The resulting dataset will now have all movies represented for every 5-minute interval:

Conclusion

In this article, we demonstrated how to convert rows of categorical values into columns using pandas. By using the pivot function and reindexing our dataset with missing timestamps, we were able to transform our original dataset into a format that allows for easy analysis of the correlation between time trends and movie values.

We hope that this article has provided you with a deeper understanding of how to work with time series data in pandas and inspire you to explore more advanced techniques.

Last modified on 2023-07-14