SciPy Descriptive Stats

In SciPy, descriptive statistics refers to summarizing and analyzing a dataset’s key characteristics. It helps summarize essential properties such as central tendency, variability, and distribution shape.

The .describe() function in the scipy.stats module is used to calculate common descriptive statistics of a given array, such as:

• Number of observations (nobs)
• Minimum and maximum values (minmax)
• Mean (mean)
• Variance (variance)
• Skewness (skewness)
• Kurtosis (kurtosis)

Syntax

stats.describe(a, axis=0, ddof=1, bias=True, nan_policy='propagate')

• a: The input data to describe.
• axis (Optional): The axis along which to compute the descriptive statistics (default is 0). If set to None, the statistics are calculated for the whole array.
• ddof (Optional): Delta Degrees of Freedom for calculating variance (default is 1).
• bias (Optional): If set to False, it corrects the skewness and kurtosis calculations for statistical bias.
• nan_policy (Optional): Defines the handling method to use when the input contains NaN. The options include:
  • propagate (Default): Returns NaN.
  • raise: Raises an error.
  • omit: Ignores NaN values and performs the calculations.

Example

The following example demonstrates the usage of the .describe() function to calculate the descriptive statistics of a given array:

import numpy as np
from scipy import stats

# Define an array
arr = np.array([12, 23, 34, 45, 56])

# Calculate the descriptive statistics of the array
res = stats.describe(arr)

# Print the result
print(res)

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The above code produces the following output:

DescribeResult(nobs=5, minmax=(12, 56), mean=34.0, variance=302.5, skewness=0.0, kurtosis=-1.3)

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Codebyte Example

The following codebyte example demonstrates the usage of the .describe() function to calculate the descriptive statistics of a given array:

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