Set up environment and load data

Before you start, make sure that:

• You have installed DataSpell. This tutorial was created in DataSpell 2022.2.1.

  note

  You can download DataSpell and use all its features for free during a 30-day trial period. Also, consider taking part in the DataSpell Early Access Program.

• You have Python 3.6 or newer on your computer. If you're using macOS or Linux, your computer already has Python installed. You can get Python from python.org.

Let's start with running DataSpell.

For more information about DataSpell initial setup, refer to Run DataSpell for the first time. When you are ready, click Launch DataSpell.

In DataSpell, you can see all your notebooks and other work-related files in the workspace. You can attach existing directories or create them. Let's create one for our project.

Now it's time to get some data for research. In this tutorial, we will use the "Airline Delays from 2003-2016" dataset by Priank Ravichandar licensed under CC0 1.0. This dataset contains the information on flight delays and cancellations in the US airports for the period of 2003-2016.

We will load the data, analyze it, and find out which airport had the highest ratios of delayed and cancelled flights.

Let's create a Jupyter notebook:

The newly created notebook contains one empty cell:

Now let's import the pandas library and load airlines.csv. Click inside the notebook cell and type the following code:

import pandas as pd

# Read file
data = pd.read_csv('airlines.csv')

If you see the following warning, you can either click the first link to use the system interpreter, or choose to configure a Python interpreter for your project (see "Creating a new virtual environment " for details):

Let's run the notebook. There are several ways to do that:

• To execute all code cells in your notebook, click on the notebook toolbar.

• To run just the current cell, press CtrlEnter.

  When executing one cell at a time, mind code dependencies. If a cell relies on some code in another cell, that cell should be executed first.

Depending on the environment that you've chosen during the initial setup, you may need to install Jupyter:

If there is a red squiggly line under pandas, it means that this package isn't available in your environment:

Place the caret at the highlighted expression and press AltEnter to reveal the list of available quick fixes. Choose to install the package:

The required package will be installed and the red curvy line will no longer be displayed.

DataSpell allows viewing variables during notebooks execution. To do that, click Jupyter Variables in the lower-right part of the DataSpell window. Now there is a data variable that points to a pandas DataFrame structure:

To view the data as a table in a separate tab, click the View as DataFrame link.

You can also print out the data by typing the DataFrame name:

import pandas as pd

# Read file
data = pd.read_csv('airlines.csv')

# View DataFrame
data

Press CtrlEnter to run the cell. The output is displayed under the code cell:

You can scroll the output cell. DataSpell will load and display the data dynamically.

Congratulations on completing this basic data science tutorial! Here's what you have done:

• Created a directory in the workspace

• Downloaded a dataset and prepared it for research

• Created a notebook and ran it for the first time

As a next step, learn to visualize data with matplotlib.