Binning Data using Scipy
The SciPy library’s binned_statistic function efficiently bins data into specified bins, providing statistics such as mean, sum, or median for each bin. It takes input data, bin edges, and a chosen statistic, returning binned results for further analysis.
Binned Mean with Scipy
Calculate the mean within each bin using scipy’s binned_statistic function. This approach demonstrates how to use binned_statistic to calculate the mean of data points within specified bins.
Python3
import random import statistics from scipy.stats import binned_statistic # Generate some example data data = [random.random() for _ in range ( 100 )] # Define the number of bins num_bins = 10 # Use binned_statistic to calculate mean within each bin result = binned_statistic(data, data, bins = num_bins, statistic = 'mean' ) # Extract bin edges and binned mean from the result bin_edges = result.bin_edges bin_means = result.statistic # Print the result print ( "Bin Edges:" , bin_edges) print ( "Binned Mean:" , bin_means) |
Output:
Bin Edges: [0.0337853 0.12594314 0.21810098 0.31025882 0.40241666 0.4945745
0.58673234 0.67889019 0.77104803 0.86320587 0.95536371]
Binned Mean: [0.07024781 0.15714129 0.26879363 0.36394539 0.44062907 0.54527985
0.63046277 0.72201578 0.84474723 0.91074019]
Binned Sum with Scipy
Calculate the sum within each bin using scipy’s binned_statistic function. Similar to the mean Approach, this calculates the sum within each bin, providing a different perspective on aggregating data.
Python3
from scipy.stats import binned_statistic # Generate some example data data = np.random.rand( 100 ) # Define the number of bins num_bins = 10 # Use binned_statistic to calculate sum within each bin result = binned_statistic(data, data, bins = num_bins, statistic = 'sum' ) # Print the result print ( "Bin Edges:" , result.bin_edges) print ( "Binned Sum:" , result.statistic) |
Output:
Bin Edges: [0.00222855 0.1014526 0.20067665 0.29990071 0.39912476 0.49834881
0.59757286 0.69679692 0.79602097 0.89524502 0.99446907]
Binned Sum: [ 0.60435816 1.60018494 2.47764912 3.49905238 2.73274596 6.07700391
3.15241481 8.89573616 7.75076402 11.36858964]
Binned Quantiles with Scipy
Calculate quantiles (75th percentile) within each bin using scipy’s binned_statistic function. This demonstrates how to calculate a specific quantile (75th percentile) within each bin, useful for analyzing the spread of data.
Python3
from scipy.stats import binned_statistic # Generate some example data data = np.random.randn( 1000 ) # Define the number of bins num_bins = 20 # Use binned_statistic to calculate quantiles within each bin result = binned_statistic(data, data, bins = num_bins, statistic = lambda x: np.percentile(x, q = 75 )) # Print the result print ( "Bin Edges:" , result.bin_edges) print ( "75th Percentile within Each Bin:" , result.statistic) |
Output:
Bin Edges: [-3.8162536 -3.46986707 -3.12348054 -2.777094 -2.43070747 -2.08432094
-1.73793441 -1.39154788 -1.04516135 -0.69877482 -0.35238828 -0.00600175
0.34038478 0.68677131 1.03315784 1.37954437 1.72593091 2.07231744
2.41870397 2.7650905 3.11147703]
75th Percentile within Each Bin: [-3.8162536 nan nan -2.53157311 -2.14902013 -1.82057818
-1.43829609 -1.10931775 -0.76699539 -0.43874444 -0.09672504 0.25824355
0.61470027 0.95566003 1.27059392 1.58331292 1.98752497 2.34089378
2.55623431 3.07407641]
The array contains the calculated 75th percentile within each bin. The values in the array correspond to the 75th percentile of the data within the respective bins.
Some bins may not have enough data points to calculate the 75th percentile, resulting in nan
(not a number) values. For example, the second bin has a nan
value because there might not be enough data in that bin to compute the 75th percentile.
Binning Data In Python With Scipy & Numpy
Binning data is an essential technique in data analysis that enables the transformation of continuous data into discrete intervals, providing a clearer picture of the underlying trends and distributions. In the Python ecosystem, the combination of numpy and scipy libraries offers robust tools for effective data binning.
In this article, we’ll explore the fundamental concepts of binning and guide you through how to perform binning using these libraries.
Table of Content
- Why Binning Data is Important?
- Binning Data using Numpy
- Binning Data using Scipy
- Binning Data In Python – FAQs