Understanding Pandas and SciPy Filtering with Row Numbers Retention

Introduction

In this article, we will explore how to apply a scipy filter function to a pandas DataFrame while retaining the original row numbers. We’ll dive into the details of using scipy’s signal processing functions in conjunction with pandas DataFrames.

The Problem

We are given a pandas DataFrame df containing a single column ‘PT011’ with some NaN values:

   PT011
0 -0.160
1 -0.162
2    NaN
3 -0.164
4    NaN
5    NaN
6 -0.166
7 -0.167

After dropping the NaN rows using df.dropna(), we apply scipy’s signal processing functions to filter the data. However, when applying the filter function, we lose access to the original row numbers.

Solution

The solution lies in modifying our filtering approach to retain the original row numbers.

We will create a new pandas Series from the filtered output of the filter function and assign it an index equal to the input indices of the filter function. This way, we can maintain the correspondence between the filtered data and its original row numbers.

Applying SciPy Filtering with Row Numbers Retention

Step 1: Define the Filter Function

We’ll define a custom filter function butter_lowpass_filter that incorporates the retention of original row numbers.

def butter_lowpass_filter(data, cutoff, fs, order=2):
    nyq = 0.5 * fs
    normal_cutoff = cutoff / nyq
    b, a = butter(order, normal_cutoff, btype='low', analog=False)
    y = lfilter(b, a, data)
    input_to_filter = data.index  # Get the index of the original data
    output_of_filter = y        # Apply the filter function
    
    new_output = pd.Series(output_of_filter, index=input_to_filter)  # Create a new Series with the filtered output and retained row numbers
    
    return new_output

Step 2: Filter the Data

Now that we have our custom filter function, let’s apply it to the ‘PT011’ column of our DataFrame df.

signal_PT011_filtered = butter_lowpass_filter(df['PT011'], cutoff, fs)
print("Filtered signal PT011:\n", signal_PT011_filtered)

Step 3: Combine the Filtered Data with Original Row Numbers

To maintain correspondence between the filtered data and its original row numbers, we’ll add a new column to our DataFrame df that contains the filtered output.

df['signal_PT011_filtered'] = butter_lowpass_filter(df['PT011'], cutoff, fs)
print("Dataframe with filtered signal:\n", df)

Expected Output

Our expected output should look like this:

Conclusion

By using scipy’s signal processing functions in conjunction with pandas DataFrames, we can apply filters while retaining the original row numbers of our data. This approach allows us to maintain correspondence between the filtered data and its original context.

We have also seen how to create a custom filter function that incorporates this retention mechanism. By following these steps, you should be able to apply scipy filtering functions to your pandas DataFrames with row number retention in mind.

Last modified on 2023-07-08