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Data science basics. Working with Numpy, Pandas and Jupyter Notebooks

Created for

Iva E. Popova, 2016-2025, Creative Commons License
Iva E. Popova on LinkedIn

Introduction

Introduction

What is Data Science

Data Science is an interdisciplinary field that combines statistics, computer science, and domain knowledge to extract meaningful insights from data.
Datascience_venn_diagram.jpg

The Data Science Workflow (Simplified)

Data Acquisition: Gathering data from various sources.
Data Cleaning and Preprocessing: Handling missing values, errors, and transforming data into a usable format.
Exploratory Data Analysis (EDA): Understanding the data through visualization and statistical summaries.
Modeling and Algorithm Development: Building predictive or descriptive models using machine learning or statistical techniques.
Evaluation and Interpretation: Assessing the performance of the model and drawing meaningful conclusions.
Deployment and Communication: Applying the insights or deploying the model for practical use and communicating the findings to stakeholders.

Why Python for Data Science?

Python has become the dominant programming language in the Data Science field due to several key advantages:
Simplicity and Readability: Python's syntax is clear and easy to learn, making it accessible for beginners.
Extensive Libraries: A vast ecosystem of powerful libraries specifically designed for data manipulation, analysis, visualization, and machine learning.
Large and Active Community: This means ample resources, tutorials, and support are available.
Integration Capabilities: Python can easily integrate with other tools and systems.
Versatility: Beyond Data Science, Python is used in web development, automation, and more.

Core Python Libraries for Data Science

Core Python Libraries for Data Science

Overview

NumPy for numerical computing
Pandas for data manipulation
Matplotlib & Seaborn for data visualization
Scikit-learn for machine learning
TensorFlow & PyTorch for deep learning

Setting Up the Environment

Create and activate the virtual environment for your DataScience projects

                # Create
                python -m venv .venv

                # Activate (CMD)
                .venv\Scripts\activate.bat
Install the necessary libraries using

                pip install numpy pandas matplotlib seaborn scikit-learn
You can also use Jupyter Notebook for an interactive coding experience:

                pip install jupyterlab
Note, that VSCode supports working with Jupyter Notebooks: Jupyter Notebooks in VS Code
Make sure you have installed the Jupyter extension from Microsoft.s

NumPy (Numerical Python)

NumPy (Numerical Python)

Overview

Provides support for large, multi-dimensional arrays and matrices, along with a collection of high-level mathematical functions to operate on these arrays.
Foundation for many other scientific computing libraries.
numpy_ndim_array.png

Example

Performing mathematical operations on arrays efficiently

                import numpy as np

                # Create a NumPy array
                array = np.array([1, 2, 3, 4, 5])
                print(array)

                # Basic operations
                print(array + 5)  # Element-wise addition
                print(array * 2)  # Element-wise multiplication

                # Multi-dimensional arrays
                matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
                print(matrix)

Resources

NumPy Full Python Course - Data Science Fundamentals

Pandas

Pandas

Overview

Offers powerful data structures for data analysis, primarily the DataFrame.
Provides tools for data manipulation, cleaning, filtering, merging, and more.
Makes working with tabular data (like spreadsheets or SQL tables) incredibly easy.
/dataframe.webp"

Example


                import pandas as pd

                # Create a DataFrame
                data = {
                    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
                    'Age': [25, 30, 35, 40],
                    'Department': ['HR', 'IT', 'Finance', 'Marketing']
                }

                df = pd.DataFrame(data)
                print(df)

                # Basic operations
                print(df.describe())  # Statistical summary
                print(df['Age'].mean())  # Mean of Age column
                print(df[df['Age'] > 30])  # Filter rows where Age > 30

Data Handling with Pandas

Examples are given in next Jupyter Notebook: Pandas_data_handling.ipynb

Resources

Python Pandas Tutorial (Part 1): Getting Started with Data Analysis - Installation and Loading Data

Data Visualization

Data Visualization

Matplotlib

A comprehensive library for creating static, interactive, and animated visualizations in Python.
Provides a wide range of plot types (line plots, scatter plots, bar charts, histograms, etc.).
Often used in conjunction with Pandas for visualizing data.

Example: Creating a simple line plot with Matplotlib


                import matplotlib.pyplot as plt

                # Simple line plot
                x = [1, 2, 3, 4, 5]
                y = [1, 4, 9, 16, 25]

                plt.plot(x, y)
                plt.title('Square Numbers')
                plt.xlabel('Number')
                plt.ylabel('Square')
                plt.show()
simpleLinePlot.png

Example: Plotting DataFrame with Matplotlib

Examples are given in next Jupyter Notebook: Matplotlib_examples.ipynb

Resources

Matplotlib Tutorial (Part 1): Creating and Customizing Our First Plots

These slides are based on

customised version of

Hakimel's reveal.js

framework