NumPy

NumPy Statistical Functions with Code Examples

NumPy is a powerful library for numerical computations in Python. One of its many features is a collection of statistical functions that help in analyzing a dataset. In this tutorial, we’ll explore some of the basic NumPy statistical functions.

Pre-requisites

  • Python 3.x
  • NumPy library

If you haven’t installed NumPy yet, you can install it using pip. To visualize the dataset, we can use Matplotlib, a popular data visualization library for Python.

Python
pip install numpy
pip install matplotlib

Dataset

For this tutorial, let’s use a simple dataset consisting of the scores of 10 students in Mathematics, Science, and English.

Python
# Sample Dataset
math_scores = [90, 85, 77, 95, 80, 84, 89, 92, 74, 88]
science_scores = [85, 90, 78, 92, 82, 88, 89, 90, 76, 91]
english_scores = [80, 79, 85, 87, 90, 86, 88, 91, 76, 84]

Step 1: Import NumPy

The first step is to import the NumPy library.

Python
import numpy as np

Step 2: Convert Dataset to NumPy Arrays

Convert the sample dataset into NumPy arrays for easier manipulation.

Python
# Convert lists to NumPy arrays
math_scores = np.array([90, 85, 77, 95, 80, 84, 89, 92, 74, 88])
science_scores = np.array([85, 90, 78, 92, 82, 88, 89, 90, 76, 91])
english_scores = np.array([80, 79, 85, 87, 90, 86, 88, 91, 76, 84])

Step 3: Mean

Calculate the mean (average) score for each subject.

Python
# Calculate mean
math_mean = np.mean(math_scores)
science_mean = np.mean(science_scores)
english_mean = np.mean(english_scores)

print(f"Math Mean: {math_mean}")
print(f"Science Mean: {science_mean}")
print(f"English Mean: {english_mean}")
Python
#Output
Math Mean: 85.4
Science Mean: 86.1
English Mean: 84.6

Bar Chart

Plot a bar chart to compare the mean scores for each subject.

mean scores
image 6

Step 4: Median

Calculate the median score for each subject.

Python
# Calculate median
math_median = np.median(math_scores)
science_median = np.median(science_scores)
english_median = np.median(english_scores)

print(f"Math Median: {math_median}")
print(f"Science Median: {science_median}")
print(f"English Median: {english_median}")
Python
#Output
Math Median: 86.5
Science Median: 88.5
English Median: 85.5

Step 5: Standard Deviation

Calculate the standard deviation for each subject to understand the variability.

Python
# Calculate standard deviation
math_std = np.std(math_scores)
science_std = np.std(science_scores)
english_std = np.std(english_scores)

print(f"Math Standard Deviation: {math_std}")
print(f"Science Standard Deviation: {science_std}")
print(f"English Standard Deviation: {english_std}")
Python
#Output
Math Standard Deviation: 6.390618123468184
Science Standard Deviation: 5.3563046963368315
English Standard Deviation: 4.651881339845203

Step 6: Minimum and Maximum

Find the minimum and maximum scores for each subject.

Python
# Calculate minimum and maximum
math_min, math_max = np.min(math_scores), np.max(math_scores)
science_min, science_max = np.min(science_scores), np.max(science_scores)
english_min, english_max = np.min(english_scores), np.max(english_scores)

print(f"Math Min-Max: {math_min}-{math_max}")
print(f"Science Min-Max: {science_min}-{science_max}")
print(f"English Min-Max: {english_min}-{english_max}")
Python
#Output
Math Min-Max: 74-95
Science Min-Max: 76-92
English Min-Max: 76-91

Step 7: Sum and Product

Find the sum and product of scores for each subject.

Python
# Calculate sum and product
math_sum, math_prod = np.sum(math_scores), np.prod(math_scores)
science_sum, science_prod = np.sum(science_scores), np.prod(science_scores)
english_sum, english_prod = np.sum(english_scores), np.prod(english_scores)

print(f"Math Sum-Product: {math_sum}-{math_prod}")
print(f"Science Sum-Product: {science_sum}-{science_prod}")
print(f"English Sum-Product: {english_sum}-{english_prod}")
Python
#Output
Math Sum-Product: 854-1604312601367568384
Science Sum-Product: 861-3497867111170832384
English Sum-Product: 846-46502495079440384

Boxplot

Create a boxplot to visualize the distribution, median, and outliers for each subject.

score distribution
image 7

With these simple functions, you can perform basic statistical analysis on a dataset. NumPy offers many more statistical functions, but these should provide a good starting point for basic data analysis.

Related Posts

Text Summarization

Text Summarization using Flan-T5 : A Simple Tutorial

The tutorial demonstrates how to use Google's Flan-T5 model for text summarization. The steps involve understanding Flan-T5, setting up the working environment with Python and TensorFlow, choosing and loading a dataset (here, CNN/DailyMail dataset), loading Flan-T5, preprocessing the data with tokenization, fine-tuning the model on the specific dataset, evaluating the model's performance with 'ROUGE' metric, using the trained model to summarize new texts, and experimenting with different model configurations for improved results.

Leave a Reply

Your email address will not be published. Required fields are marked *

We use cookies to improve your experience on our website