Pandas DataFrame Grouping by Local Minima

In this article, we will explore how to group a Pandas DataFrame by local minima. This is particularly useful when dealing with time series data that have repeating patterns of maxima and minima.

Problem Statement

We are given a large Pandas DataFrame that consists of two columns: A (for x-axis values) and B (for y-axis values). The data is plotted to form a simple x-y coordinate graph, with the goal of creating smaller chunks of data. However, unlike the original sample data, the actual data has arbitrary peaks and minima.

Current Approach

The current approach involves splitting the large data file into smaller files based on the local minima and maxima of each cycle. This is done using the following code:

nrows = int(df['B'].max() * 2) - 1

alphabet: list = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
groups = df.groupby(df.index // nrows)
for (frameno, frame) in groups:
    frame.to_csv("/Users/_______/Desktop/Cycle Test/" + alphabet[frameno] + "%s.csv" % frameno, index=False)

However, this approach is not suitable for data with arbitrary peaks and minima. We need to find a way to group the data by local minima.

Solution

To solve this problem, we will use the following steps:

1. Find the local minima in the data.
2. Create groups based on the cumulative sum of the local minima.
3. Group the data by these groups using the groupby function.

Here is an example implementation in Python:

import pandas as pd

# Load the data
df = pd.read_csv(r'/Users/_______/Desktop/Data Packets/Cycle Data.csv')

# Find local minima
col = df['B']
minima = (col <= col.shift()) & (col < col.shift(-1))

# Create groups based on cumulative sum of local minima
groups = minima.cumsum()

# Group the data by these groups
df_grouped = df.groupby(groups).mean()

Note that we use the mean function as an example, but you can replace it with any other aggregation function that makes sense for your specific problem.

How it Works

1. First, we find the local minima in the data by checking if each value is less than or equal to its previous value and also less than its next value.
2. We then create groups based on the cumulative sum of these local minima. This gives us a sequence of indices that correspond to the start of each group.
3. Finally, we group the original DataFrame by these groups using the groupby function. The resulting DataFrame has one row for each group.

Example

Let’s consider an example where we have the following data:

The local minima in this data are the values that have a single peak. In this case, the local minima are:

• B = 3
• B = 5

We can create groups based on these local minima as follows:

minima = (df['B'] == 3) | (df['B'] == 5)
groups = minima.cumsum()

This gives us two groups: one starting at index 0 and another starting at index 4.

We can then group the original DataFrame by these groups using the groupby function as follows:

df_grouped = df.groupby(groups).mean()

The resulting DataFrame has one row for each group:

Note that we use the mean function as an example, but you can replace it with any other aggregation function that makes sense for your specific problem.

Conclusion

In this article, we explored how to group a Pandas DataFrame by local minima. We used the following steps:

1. Find the local minima in the data.
2. Create groups based on the cumulative sum of these local minima.
3. Group the original DataFrame by these groups using the groupby function.

We provided an example implementation and explained how each step works. This should give you a better understanding of how to group your own data using Pandas.

Last modified on 2023-07-17