Python for Data Science: Getting Started with Anaconda and Jupyter Notebook
Python has become one of the most popular programming languages for data science. It's not hard to see why: it's easy to learn, has an expansive library ecosystem, and is open-source. In this article, we'll cover the essential tools you need to get started with Python for data science: Anaconda and Jupyter Notebook.
What is Anaconda?
Anaconda is a free and open-source distribution of Python and R languages for data science and machine learning. It includes over 250 packages for data science, math, engineering, and visualization. Anaconda also includes a package manager and environment manager so you can easily install, update, and manage packages and dependencies for your projects.
To get started with Anaconda, you'll need to download it from the official website and install it on your computer.
What is Jupyter Notebook?
Jupyter Notebook is an open-source web application that allows you to create and share interactive documents that contain live code, equations, visualizations, and narrative text. It supports over 40 programming languages, including Python.
Jupyter Notebook is an excellent tool for data science because it allows you to combine code, data, and text into a single document. This means you can easily document and share your analysis with others.
To use Jupyter Notebook with Anaconda, you'll need to launch it from the Anaconda Navigator or the command prompt. Once you've launched Jupyter Notebook, you can create a new notebook and start writing Python code.
Getting Started with Python for Data Science in Jupyter Notebook
Now that you have Anaconda and Jupyter Notebook installed, let's create a new notebook and start exploring Python for data science.
Step 1: Create a New Notebook
To create a new notebook, launch Jupyter Notebook and click on the "New" button in the top right corner. Then, select "Python 3" from the dropdown menu.
Step 2: Write your First Python Code
In the first cell of your new notebook, type the following code:
print("Hello, world!")Then, run the code by clicking on the "Run" button or pressing "Shift + Enter". You should see the output "Hello, world!" printed below the cell.
Step 3: Load a Data Set and Analyze it
To load a data set and analyze it, you'll need to import the necessary libraries. In this example, we'll use the pandas library to load a data set of wine reviews from Kaggle.
Add the following code to your notebook:
import pandas as pd
## Load the data set
wine_reviews = pd.read_csv("https://raw.githubusercontent.com/zynicide/wine-reviews/master/winemag-data-130k-v2.csv")
## Print the first 5 rows of the data set
wine_reviews.head()Then, run the code to load the data set and print the first 5 rows. You should see a table of wine reviews with columns such as "country", "description", "points", and "price".
Step 4: Visualize the Data
To visualize the data, you'll need to import another library called matplotlib. Add the following code to create a scatter plot of the wine reviews:
import matplotlib.pyplot as plt
## Create a scatter plot of points vs price
plt.scatter(wine_reviews["points"], wine_reviews["price"])
## Add labels and title
plt.xlabel("Points")
plt.ylabel("Price")
plt.title("Wine Reviews")
plt.show()Then, run the code to create the scatter plot. You should see a plot with points on the x-axis and price on the y-axis.
Conclusion
Now that you have a basic understanding of Anaconda and Jupyter Notebook, you can start exploring Python for data science. Python is a versatile language with many data science applications, and Anaconda and Jupyter Notebook are excellent tools to help you get started.
Happy coding!