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Mastering Statistics and Calculus with Python: A Workshop Guide


The Statistics and Calculus with Python Workshop


In this workshop, we will dive into the exciting world of statistics and calculus using Python. We will provide you with detailed, step-by-step sample codes that are not only informative but also executable. By following along with these examples, you will gain a strong understanding of how to apply statistical and calculus concepts in real-world scenarios.

Getting Started

To begin, it is important to have Python installed on your computer. You can download and install Python from the official website ( Ensure that you choose the version compatible with your operating system.

Once Python is successfully installed, you can open the Python interpreter or any integrated development environment (IDE) of your choice to begin writing and executing Python code. Popular IDEs include PyCharm, Visual Studio Code, and Jupyter Notebook.

Calculus with Python


Differentiation is a fundamental concept in calculus, and Python provides powerful tools to perform differentiation calculations. Using the sympy library, we can easily differentiate functions symbolically. Let’s see an example:

from sympy import symbols, diff
x = symbols('x')
f = x ** 2 + 2 * x + 1
df = diff(f, x)

The output will be the derivative of the function f, which is 2*x + 2.


Integration is another crucial concept in calculus, and Python makes it simple to calculate integrals. The sympy library also provides functions to perform integration symbolically. Here’s an example:

from sympy import symbols, integrate
x = symbols('x')
f = x ** 2 + 2 * x + 1
integral = integrate(f, x)

The output will be the indefinite integral of the function f, which is (x**3/3) + x**2 + x.

Statistics with Python

Descriptive Statistics

Python offers various libraries to perform descriptive statistical analysis on datasets. One of the most commonly used libraries is numpy. Let’s say we have a list of numbers and want to compute basic statistics such as mean, median, and standard deviation. Here’s how you can do it:

import numpy as np
data = [1, 2, 3, 4, 5]
mean = np.mean(data)
median = np.median(data)
std_dev = np.std(data)
print("Mean:", mean)
print("Median:", median)
print("Standard Deviation:", std_dev)

The output will display the mean, median, and standard deviation of the given dataset.

Hypothesis Testing

Python’s scipy library provides functions to conduct hypothesis tests, which are vital in statistical analysis. Let’s take a hypothesis test for the mean of a single population as an example:

from scipy import stats
data = [1, 2, 3, 4, 5]
t_statistic, p_value = stats.ttest_1samp(data, 3)
print("T-Statistic:", t_statistic)
print("P-Value:", p_value)

The output will show the calculated t-statistic and the p-value. These values allow us to make conclusions about the hypothesis.


By following the step-by-step sample codes provided in this workshop, you have gained a solid understanding of how to apply statistics and calculus concepts using Python. Whether you are analyzing datasets or solving complex mathematical problems, Python offers a powerful and flexible environment to achieve your goals. Keep exploring and experimenting with Python to further enhance your skills in statistics and calculus.