Removing Timestamps that are Close to Each Other or Within a Threshold in a DataFrame

In this article, we will explore how to remove timestamps that are close to each other or within a specified threshold in a Pandas DataFrame.

Problem Statement

The problem statement is as follows: given a DataFrame with timestamps and values, remove all rows where the timestamp of one row is within 5 seconds of another row. This means that for each element in the DataFrame, drop all elements that are within a 5-second range from it.

Sample Code

Here is an example of how this can be done:

import pandas as pd

def add_ms(timestamp, ms):
    return int(timestamp) + 1000000 * ms

test_df = pd.DataFrame({
    'timestamp': [1675434673166538783, 1675434684419325010, 
                  1675434684420505291, 1675434673164456809, 
                  1675434700772654637],
    'value': [14.62, 14.62, 14.62, 14.63, 14.50]
})

test_df.sort_values(by='timestamp', inplace=True)
for i in range(0, len(test_df)):
    currentTimestamp = test_df['timestamp'].loc[i]
    temp_row = test_df.loc[i]
    temp_5_secs = add_ms(currentTimestamp, 5000)
    test_df = test_df[(test_df['timestamp']) >= temp_5_secs].reset_index(drop=True)
    test_df = test_df.append(temp_row, ignore_index=True)
    test_df.sort_values(by='timestamp', inplace=True)

print(test_df)

This code will remove all rows where the timestamp is within 5 seconds of another row.

Alternative Solution

However, this approach can be inefficient and may lead to an error if there are multiple consecutive timestamps that are close together.

One way to accomplish what you are trying to do is as follows:

Given a sample df:

def mask_stamp(k: list, time_incr: int) -> list[bool]:
    # return a list of bool values where True implies drop row
    last_time = 0
    delta_t = time_incr * 1000000000
    rslt = []
    for tstmp in k:
        if tstmp > last_time:
            rslt.append(False)
            last_time = tstmp + delta_t
        else:
            rslt.append(True)
    return rslt

def filter_timestamps(dfx: pd.DataFrame, time_inc:int) -> None:
    # return df with timestamps filtered by time increment
    drp_list = mask_stamp(dfx['timestamp'].to_list(), time_inc)
    for i in range(len(drp_list)):
        if drp_list[i]:
            dfx.drop(index=i, inplace=True)

df.sort_values(by=['timestamp'], ignore_index=True, inplace=True) 
filter_timestamps(df, 5)  

This code will also remove all rows where the timestamp is within 5 seconds of another row.

Advantages and Disadvantages

The new approach has several advantages:

• It is more efficient than the original approach.
• It avoids potential errors that can occur when trying to drop consecutive timestamps.

However, there are some disadvantages to this approach as well:

• It requires sorting the DataFrame before filtering. This can be time-consuming if the DataFrame is large.
• If the timestamp values are not unique within the specified range, it may lead to incorrect results.

Conclusion

In conclusion, removing timestamps that are close to each other or within a threshold in a DataFrame can be done using different approaches. The new approach presented above is more efficient and avoids potential errors but requires sorting the DataFrame before filtering.

Last modified on 2024-02-14