NumPy Guide to Effortless Statistical Functions Analysis Using Sum, Mean, and Median

Master efficient calculation of sum, mean, and median for robust statistical functions analysis.

1. Introduction to NumPy & Statistical Functions

NumPy is a Python library specialized in array computations, offering fast and efficient statistical function processing. It supports central tendency measures like mean, median, and mode—but here we focus on mean and median.

2. Sum (sum)

2.1 1D Array Sum

import numpy as np  
arr = np.array([1,2,3,4,5])  
total_sum = np.sum(arr)  
print(total_sum)  # Output: 15

np.sum() returns the total of all elements.

2.2 2D Array Sum by Axis

arr2d = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(np.sum(arr2d, axis=0))  # Column sums: [12, 15, 18]
print(np.sum(arr2d, axis=1))  # Row sums: [6, 15, 24]

• axis=0 sums columns
• axis=1 sums rows

3. Mean (mean)

3.1 1D Array Mean

mean_value = np.mean(arr)
print(mean_value)  # Output: 3.0

np.mean() calculates the arithmetic mean.

3.2 2D Array Mean by Axis

print(np.mean(arr2d, axis=0))  # Column means: [4., 5., 6.]
print(np.mean(arr2d, axis=1))  # Row means: [2., 5., 8.]

3.3 Mean with Missing Values (nan)

arr_nan = np.array([1,2,np.nan,4])
print(np.mean(arr_nan))       # Output: nan
print(np.nanmean(arr_nan))    # Ignores nan, outputs 2.333...

• mean() returns nan if any nan present
• Use np.nanmean() to ignore nan

4. Median (median)

4.1 What Median Means

Median is the middle value after sorting data. For even-numbered sets, it’s the average of the two center values.

4.2 Median in 1D and 2D Arrays

print(np.median(arr))  # 3.0

print(np.median(arr2d, axis=0))  # [4., 5., 6.]
print(np.median(arr2d, axis=1))  # [2., 5., 8.]

np.median() also accepts an axis parameter.

5. Extra Tips: Range, Variance, Standard Deviation

Useful for understanding data spread:

• Range: np.ptp() gives the max-minus-min value
• Variance: np.var() measures data dispersion
• Standard Deviation: np.std() (and np.nanstd() if nan present)

6. Code Examples Summary

import numpy as np

arr = np.array([1,2,3,4,5])
arr2d = np.array([[1,2,3],[4,5,6],[7,8,9]])

# Sum
sum1 = np.sum(arr)
sum_col = np.sum(arr2d, axis=0)
sum_row = np.sum(arr2d, axis=1)

# Mean
mean1 = np.mean(arr)
mean_col = np.mean(arr2d, axis=0)
mean_row = np.mean(arr2d, axis=1)

# Median
median1 = np.median(arr)
median_col = np.median(arr2d, axis=0)
median_row = np.median(arr2d, axis=1)

print(sum1, sum_col, sum_row)
print(mean1, mean_col, mean_row)
print(median1, median_col, median_row)

Expected Output:

15
[12 15 18]
[6 15 24]

3.0
[4. 5. 6.]
[2. 5. 8.]

3.0
[4. 5. 6.]
[2. 5. 8.]

7. Handling Errors in Statistical Functions

np.sum() Common Errors

1. TypeError: cannot perform reduce with flexible type
   Caused by mixing strings/None with numbers (e.g., [1, 2, 'a']).
   Fix: Clean data and ensure numeric-only arrays.
2. ValueError: operands could not be broadcast together
   Happens when array shapes are incompatible.
   Fix: Check array shapes via .shape.

np.mean() Common Issues

1. RuntimeWarning: Mean of empty slice
   When calling np.mean([]).
   Fix: Check arr.size > 0 before computing.
2. nan result due to missing values
   Fix: Use np.nanmean() to ignore nan.

np.median() Common Errors

1. TypeError: ‘<‘ not supported between instances
   Caused by mixing numbers and strings (e.g., [1, 2, '3']).
   Fix: Filter out non-numeric elements.
2. ValueError: cannot convert float NaN to integer
   Occurs when integer arrays include nan.
   Fix: Use dtype=float or np.nanmedian().

Summary Table

8. Conclusion & Tags

NumPy’s sum, mean, and median make it easy to compute statistics on 1D and multi-dimensional arrays. With added tips like nanmean, nanstd and data cleaning, you’re set for real-world data analysis tasks.