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

Tags:

#AI-ML-DS With Python #Python-numpy #Python-scipy #Numpy #Python #python

Binning Data using Numpy

Conclusion

Binning Data using Scipy

Binned Mean with Scipy

Python3

Binned Sum with Scipy

Python3

Binned Quantiles with Scipy

Python3

Binning Data In Python With Scipy & Numpy

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