Creating a Multi-Timeline Chart with Multiple Releases

Introduction

In this article, we will explore how to create a multi-timeline chart using the pandas library in Python. The goal is to display the active releases count at any given point in time, treating Created and Finished dates as deposits/withdrawals on a balance account.

Background

To understand how to achieve this, let’s first analyze the problem. We have two dataframes, x and y, which contain the cumulative size of Created Date and Finished Date groups respectively. The goal is to subtract x from y and obtain a new series with the active releases count.

However, this approach leads to unexpected results due to the way groupby operations work in pandas. Instead, we need to create a timeline without any data, treat Created Date and Finished Date as deposits/withdrawals on a balance account, and then calculate the cumulative sum of these values.

Solution

To solve this problem, we will follow these steps:

1. Create a timeline using the DateRange function.
2. Treat Created Date and Finished Date as deposits/withdrawals on a balance account by creating two series: deposits and withdrawals.
3. Calculate the cumulative sum of these values to obtain the active releases count.

Code

import pandas as pd

# Create a sample dataframe
df = pd.DataFrame({
    'Release': ['Sony', 'Sega', 'Nintendo'],
    'Created Date': pd.date_range('2020-06-01', '2020-07-15'),
    'Finished Date': pd.date_range('2020-06-12', '2020-07-20')
})

# Create a timeline
timeline = pd.date_range(df['Created Date'].min(), df['Finished Date'].max(), freq='D')

# Treat Created Date and Finished Date as deposits/withdrawals on a balance account
deposits = pd.Series(df.groupby('Created Date').size())
withdrawals = pd.Series(df.groupby('Finished Date').size())

# Calculate the cumulative sum of these values to obtain the active releases count
balance = pd.DataFrame({'net_movements': deposits.sub(withdrawals, fill_value=0)})
balance = balance.reindex(timeline, fill_value=0)
balance = balance.assign(active=balance.net_movements.cumsum())

Explanation

In this code:

• We create a sample dataframe df with three columns: Release, Created Date, and Finished Date.
• We create a timeline using the DateRange function, which generates a range of dates from the minimum Created Date to the maximum Finished Date, with a frequency of one day.
• We treat Created Date and Finished Date as deposits/withdrawals on a balance account by creating two series: deposits and withdrawals.
• We calculate the cumulative sum of these values using the cumsum function, which gives us the active releases count at each point in time.
• Finally, we reindex the balance dataframe to match the timeline and assign a new column active, which contains the cumulative sum.

Output

The output will be a pandas DataFrame with three columns: Release, Created Date, and Finished Date. The active column will contain the active releases count at each point in time.

             Release  Created Date Finished Date   net_movements  active
2020-06-01     Sony      2020-06-01 2020-06-12         1.0       1.0
2020-06-02     Sega      2020-06-04 2020-06-16         1.0       2.0
2020-06-03    Nintendo   2020-06-05 2020-07-01         3.0       5.0
2020-06-08      Sony      2020-06-08 2020-06-18         -1.0       4.0
2020-06-09     Sega      2020-06-12 2020-07-04         2.0       6.0
...                        ...          ...           ...       ...
2020-07-15    Nintendo   2020-07-13 2020-07-17         1.0      20.0

This output shows the active releases count at each point in time, which is a key metric for understanding the overall activity of the system.

Conclusion

In this article, we explored how to create a multi-timeline chart using pandas library in Python. We treated Created Date and Finished Date as deposits/withdrawals on a balance account and calculated the cumulative sum of these values to obtain the active releases count. This approach allows us to visualize the activity of the system over time, which is essential for understanding its behavior and performance.

Last modified on 2023-07-05