Definition of downsampling:
In data engineering, "downsampling" refers to the process of reducing the amount of data for analysis, storage, or processing. This is done by systematically selecting a subset of the data at a lower rate than the original.
The main goal of downsampling is to reduce the computational requirements, storage needs, and potentially noise in your data. For instance, in time series data, it is common to downsample to different granularities (daily, hourly, etc.) to make the data easier to work with and to align it more closely with the problem you're trying to solve.
In image or audio file processing, downsampling can be used to reduce the resolution of an image or sound file.
Downsampling can be performed in various ways, such as:
Average Pooling: This method averages a group of data points to create a single representative data point. For example, you might reduce the granularity of time-series data from seconds to minutes by averaging all the data points within each minute.
Decimation: This method discards some data points without any replacement. For instance, you might keep every nth data point and discard the rest.
Reservoir Sampling: This is a randomized algorithm that allows you to select a sample of n items from a dataset of an unknown size, m, where m > n. The algorithm guarantees that every possible subset of m items has an equal chance of being the sampled subset.
Downsampling has to be done carefully. If not handled properly, important information can be lost, which could lead to inaccurate model predictions or analysis. It's also worth mentioning that downsampling is different from data compression, which aims to reduce the storage space required for data without losing any information through techniques like encoding and quantization.
Example of downsampling in Python
Here's an example of downsampling a time series data set using the Pandas library in Python.
Please note that you need to have the necessary Python libraries installed in your Python environment to run this code.
import pandas as pd import numpy as np # Create a date range date_rng = pd.date_range(start='1/1/2023', end='12/31/2023', freq='H') # Create a DataFrame with the date_rng as the index and random data df = pd.DataFrame(date_rng, columns=['date']) df['data'] = np.random.randint(0,500,size=(len(date_rng))) df.set_index('date', inplace=True) # Print original DataFrame print("Original DataFrame:") print(df) # Downsampling: reduce datetime rows to daily df_daily = df.resample('D').mean() # Print downsampled DataFrame print("\nDownsampled DataFrame:") print(df_daily)
In this script, we first generate a date range from January 1, 2020 to January 10, 2020 with an hourly frequency. This serves as the index of our DataFrame. The data column of the DataFrame is populated with random integers between 0 and 100. The DataFrame is then resampled (downsampled) to a daily frequency using the resample method, and the mean of the hourly data for each day is computed. Both the original and downsampled DataFrames are then printed.
resample function is a flexible and high-performance method in pandas for frequency conversion and resampling of time-series data. It allows you to convert the data into different frequencies. The 'D' argument makes it daily, 'H' makes it hourly, 'M' is for monthly, and so on.
Your output will look something like this:
Original DataFrame: data date 2023-01-01 00:00:00 402 2023-01-01 01:00:00 191 2023-01-01 02:00:00 432 2023-01-01 03:00:00 288 2023-01-01 04:00:00 274 ... ... 2023-12-30 20:00:00 329 2023-12-30 21:00:00 277 2023-12-30 22:00:00 469 2023-12-30 23:00:00 289 2023-12-31 00:00:00 193 [8737 rows x 1 columns] Downsampled DataFrame: data date 2023-01-01 253.083333 2023-01-02 226.041667 2023-01-03 259.208333 2023-01-04 225.208333 2023-01-05 296.166667 ... ... 2023-12-27 244.666667 2023-12-28 261.625000 2023-12-29 248.708333 2023-12-30 250.375000 2023-12-31 193.000000 [365 rows x 1 columns]
So we downsampled a dataset of 8,737 rows to a smaller set of just 365 rows.
You can replace
.mean() with any other function (
median(), etc.) depending on what kind of downsampling you need.