Datalore 2024.2 Help

Quick start tutorial

Use this guide to learn the basics of Datalore.

Overview

Datalore is a collaborative data science platform that streamlines insight delivery and helps data science and business teams become more productive together. You can use Datalore for data collection and exploration, machine learning, interactive visualization, and reporting.

Datalore brings together:

  • Powerful coding assistance for Python, SQL, R, Scala, and Kotlin in Jupyter notebooks

  • Data integrations for SQL databases and cloud storage systems

  • Modern business intelligence and interactive data apps

  • Real-time collaboration on code and in team workspaces

For a quick overview of Datalore, check out the Getting Started with Datalore video.

First steps

Start learning Datalore by creating and editing your first notebook.

Open a new notebook in the editor

  1. On the Home page, click the New notebook. You are now redirected to the New notebook page.

  2. (Optional) Enter your text in the Title field.

  3. Press Enter to finish creating your notebook and open it in the editor.

At the moment, your notebook document contains only one cell. By default, it is an empty code cell.

Edit your notebook

  1. In the first cell, enter the following code.

    # Try it now import datetime now_moment = datetime.datetime.now() print('– What is the right time to start?') print('– Now!\n')
  2. To run the code of the currently active cell, press Alt+Shift+Enter.

  3. Add a Markdown cell by hovering over the middle of the bottom border of the cell and clicking Add markdown cell.

  4. Enter a text in your Markdown cell.

  5. To run the code of your entire notebook, select Run | Run all from the main menu.

Work with your data

Follow the procedures below to attach a .csv file and perform basic analytics on its data.

Prepare your dataset

  1. Download https://www.jetbrains.com/help/datalore/resources/libraries_by_python_version.csv. This dataset lists 20 popular Python libraries as imported by different Python versions.

  2. Attach the downloaded dataset to your notebook by dragging it into the editor.

  3. Make sure the downloaded file is attached to the notebook:

    1. Click the Attached data icon on the left-hand sidebar to open the list of notebook attachments.

    2. Click Notebook files. The file is expected to be there.

    Attached data tool

View your data as a table

Use pandas, a popular library for Python, to view your dataset.

  1. By convention, import pandas as pd:

    import pandas as pd
  2. Enter the code below to access the content of your dataset and display it as a table.

    libraries = pd.read_csv('libraries_by_python_version.csv') libraries

After you run your code cell, you should have a scrollable table like this:

Dataset table

Visualize your data set

Build a horizontal bar chart to display imports of different libraries to Python 3.4.

  1. Import the seaborn library.

    import seaborn as sns
  2. Enter the code below.

    sns.barplot(x="kernel_python 3.4", y="library", data=libraries) fig = sns.barplot

After you run your code cell, you should have a chart as shown below.

Barchart with seaborn library

Share your work

Now that you have done some work on your notebook, you can share it with your collaborators. In this procedure, you will grant view or edit access to every user who will have the link to the notebook.

  1. On the editor page, click the Share button in the upper right corner of the editor to open the Share [notebook_title] dialog shown in the picture below.

    Share notebook dialog
  2. Under Share notebook, expand the list and select the access type you want to grant using the link.

    Access type selection
  3. Click the icons next to the notebook link to open it in a new browser tab or copy the link.

    Open shared notebook icon
  4. (Optional) To disable the link, repeat steps 1 and 2, and select No access link.

Last modified: 17 May 2024