Statistics

Learn how to apply statistical metrics to NumPy data.

We'll cover the following

Chapter Goals:

  • Learn about basic statistical analysis in NumPy
  • Write code to obtain statistics for NumPy arrays

A. Analysis

It is often useful to analyze data for its main characteristics and interesting trends. There are few techniques in NumPy that allow us to quickly inspect data arrays.

For example, we can obtain minimum and maximum values of a NumPy array using its inherent min and max functions. This gives us an initial sense of the data's range, and can alert us to extreme outliers in the data.

The code below shows example usages of the min and max functions.

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arr = np.array([[0, 72, 3],
                [1, 3, -60],
                [-3, -2, 4]])
print(arr.min())
print(arr.max())
print(repr(arr.min(axis=0)))
print(repr(arr.max(axis=-1)))

The axis keyword argument is identical to how it was used in np.argmin and np.argmax from the chapter on Indexing. In our example, we use axis=0 to find an array of the minimum values in each column of arr and axis=1 to find an array of the maximum values in each row of arr.

B. Statistical metrics

NumPy also provides basic statistical functions such as np.mean, np.var, and np.median, to calculate the mean, variance, and median of the data, respectively.

The code below shows how to obtain basic statistics with NumPy. Note that np.median applied without axis takes the median of the flattened array.

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arr = np.array([[0, 72, 3],
                [1, 3, -60],
                [-3, -2, 4]])
print(np.mean(arr))
print(np.var(arr))
print(np.median(arr))
print(repr(np.median(arr, axis=-1)))

Each of these functions takes in the data array as a required argument and axis as a keyword argument. For a more comprehensive list of statistical functions (e.g. calculating percentiles, creating histograms, etc.), check out the NumPy statistics page.

Time to Code!

Each coding exercise in this chapter will be to complete a small function that takes in a 2-D NumPy matrix (data) as input. The first function to complete is get_min_max, which returns the overall minimum and maximum element in data.

Set overall_min equal to data.min applied with no arguments.

Set overall_max equal to data.max applied with no arguments.

Return a tuple of overall_min and overall_max, in that order.

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def get_min_max(data):
  # CODE HERE
  pass

Next, we'll complete col_min, which returns the minimums across each column of data.

Set min0 equal to data.min with the axis keyword argument set to 0.

Then return min0.

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def col_min(data):
  # CODE HERE
  pass

The final function to complete is basic_stats, which returns the mean, median, and variance of data.

Set mean equal to np.mean applied to data.

Set median equal to np.median applied to data.

Set var equal to np.var applied to data.

Return a tuple containing mean, median, and var, in that order.

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def basic_stats(data):
  # CODE HERE
  pass

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