Numerical Computing with Python and Numpy
Part 6 of "Data Analysis with Python: Zero to Pandas"
This tutorial series is a beginner-friendly introduction to programming and data analysis using the Python programming language. These tutorials take a practical and coding-focused approach. The best way to learn the material is to execute the code and experiment with it yourself. Check out the full series here:
- First Steps with Python and Jupyter
- A Quick Tour of Variables and Data Types
- Branching using Conditional Statements and Loops
- Writing Reusable Code Using Functions
- Reading from and Writing to Files
- Numerical Computing with Python and Numpy
- Analyzing Tabular Data using Pandas
- Data Visualization using Matplotlib & Seaborn
- Exploratory Data Analysis - A Case Study
This tutorial covers the following topics:
- Working with numerical data in Python
- Going from Python lists to Numpy arrays
- Multi-dimensional Numpy arrays and their benefits
- Array operations, broadcasting, indexing, and slicing
- Working with CSV data files using Numpy
How to run the code
This tutorial is an executable Jupyter notebook hosted on Jovian. You can run this tutorial and experiment with the code examples in a couple of ways: using free online resources (recommended) or on your computer.
Option 1: Running using free online resources (1-click, recommended)
The easiest way to start executing the code is to click the Run button at the top of this page and select Run on Binder. You can also select "Run on Colab" or "Run on Kaggle", but you'll need to create an account on Google Colab or Kaggle to use these platforms.
Option 2: Running on your computer locally
To run the code on your computer locally, you'll need to set up Python, download the notebook and install the required libraries. We recommend using the Conda distribution of Python. Click the Run button at the top of this page, select the Run Locally option, and follow the instructions.
Jupyter Notebooks: This tutorial is a Jupyter notebook - a document made of cells. Each cell can contain code written in Python or explanations in plain English. You can execute code cells and view the results, e.g., numbers, messages, graphs, tables, files, etc., instantly within the notebook. Jupyter is a powerful platform for experimentation and analysis. Don't be afraid to mess around with the code & break things - you'll learn a lot by encountering and fixing errors. You can use the "Kernel > Restart & Clear Output" menu option to clear all outputs and start again from the top.
Working with numerical data
The "data" in Data Analysis typically refers to numerical data, e.g., stock prices, sales figures, sensor measurements, sports scores, database tables, etc. The Numpy library provides specialized data structures, functions, and other tools for numerical computing in Python. Let's work through an example to see why & how to use Numpy for working with numerical data.
Suppose we want to use climate data like the temperature, rainfall, and humidity to determine if a region is well suited for growing apples. A simple approach for doing this would be to formulate the relationship between the annual yield of apples (tons per hectare) and the climatic conditions like the average temperature (in degrees Fahrenheit), rainfall (in millimeters) & average relative humidity (in percentage) as a linear equation.
yield_of_apples = w1 * temperature + w2 * rainfall + w3 * humidity
We're expressing the yield of apples as a weighted sum of the temperature, rainfall, and humidity. This equation is an approximation since the actual relationship may not necessarily be linear, and there may be other factors involved. But a simple linear model like this often works well in practice.
Based on some statical analysis of historical data, we might come up with reasonable values for the weights
w3. Here's an example set of values:
w1, w2, w3 = 0.3, 0.2, 0.5