Numpy is a popular Python library for data science focusing on arrays, vectors, and matrices. Did you already learn something new today? Let’s master the popular variance function in NumPy!
Problem: How to calculate the variance of a NumPy array?
Solution: To calculate the variance of a Python NumPy array
x, use the function
Here is an example:
import numpy as np # Goals in five matches goals_croatia = np.array([0,2,2,0,2]) goals_france = np.array([1,0,1,1,0]) c = np.var(goals_croatia) f = np.var(goals_france) print(c<f) # False
What is the output of this puzzle?
*Intermediate Level* (solution here)
This puzzle introduces a new feature of the NumPy library: the variance function. The variance is the average squared deviation from the mean of the values in the array.
When applied to a 1D numpy array, this function returns the variance of the array values.
In the puzzle, the variance of the goals of the last five games of Croatia is 0.96 and of France is 0.24. But you do not need to know the exact values to see that the variance of goals shot by Croatia is larger.
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Syntax NumPy Variance
numpy.var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False)
|array_like||Array of values for which to compute variance. NumPy converts it to an array if it isn’t already one (broadcasting).|
|int, optional||Axis along which you calculate the variance. Default: variance of flattened array.|
|data-type, optional||Data type of variance values. Default: float32 for integers.|
|ndarray, optional||Store the result in this array (overwrite) –> array shape must be the same.|
|bool, optional||If |
|variance||ndarray, see dtype parameter above||If out=None, returns a new array containing the variance; otherwise, a reference to the output array is returned.|
How to Calculate the Row Variance of a Numpy 2D Array?
You can play with the following interactive Python code to calculate the variance of a 2D array (total, row, and column variance).
Where to Go From Here?
Enough theory. Let’s get some practice!
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