Let's check out the data within the dataframe. Print("movies_df is of the type: ", type(movies_df))Īs you saw, the movies_df is actually a pandas DataFrame and has 3201 movies (row) with 16 feature information (columns) each. # Importing the Vega Datasetįrom vega_datasets import data as vega_data Let's move ahead and get the dataset and perform some simple analysis to understand it better. We will not deal with all the columns but instead only use a few to learn more about the functioning of Altair as a visualization tool. The dataset contains information related to movies such as the title of the movie, how much money did the movie gross in America and worldwide, along with the production budget, genre, ratings from IMDB and Rotten Tomatoes. However, we will only perform data analysis on it using visualizations with Altair. This or similar dataset related to movies are famous in the data science community, especially in the context of Recommendation Engines. In this tutorial, we will be working with the Movies Dataset from the Vega dataset. as an object that supports the geo_interface (e.g., Geopandas GeoDataFrame, Shapely Geometries, GeoJSON Objects).as a URL string pointing to a JSON or CSV formatted text file.Start with the following imports: import pandas as pdīecause data in Altair is built around the Pandas Dataframe, it means that you can manipulate data in Altair the same way you would deal with Pandas DataFrame.Īnd although internally Altair stores data in the format of a Pandas DataFrame, there are multiple ways of inputting data: Now that we have installed Altair along with the Vega dataset, it's time to load some data. To learn more about Jupyter notebook, be sure to check out the Jupyter Notebook Definitive guide from DataCamp. If you are using the conda package manager, the equivalent is: $ conda install -c conda-forge altair vega_datasetsĪfter this, you can move on to your Jupyter Notebook and import Altair to get started. To install Altier, along with the Vega datasets, type the following in your console window: $ pip install altair vega_datasets In this tutorial, we will make use of an example datasets from Vega datasets. This means you can define the data and the output you expect to see (what the visualization should look like in the end), and Altair will do the necessary manipulations automatically for you. You will have to think more about the code required to create the visualization, and manually specify the plotting steps - axis limits, size, rotation, legends, etc.Īs a data scientist, Altair will allow you to focus your time, and effort more on your data - understanding, analyzing and visualizing it rather than on the code needed to do so. With imperative APIs, you need to focus your attention on 'how to do something' rather than 'what you want to do'. The answer lies in its declarative nature as compared to the other imperative APIs. Declarative APIsĪs mentioned earlier, Python already has a list of great tools and libraries at your disposal for the task of data visualization, and many even support interactive visualization. It is based on Vega and Vega-Lite, which are both visualization grammar that allows you to describe the visual appearance and interactive behavior of a visualization in a JSON format. It is declarative in nature (we shall come to this definition later on). So let's get started! What is Altair?Īltair is a Python library designed for statistical visualization. With Altair, you will be able to create meaningful, elegant, and effective visualizations with just a few lines of code and in a very short time. In this tutorial, we will introduce you to Altair. Python and R both provide a vast range of tools and tricks to assist you with the task. Unfortunately, as a novice, it can seem like a daunting task. But you know what is even better than visualizations for data analysis? Interactive visualizations! It highlights the relationships within the data and reveals information visible to the human eye that cannot be conveyed with just numbers and digits. Creating visualizations is a great way to tell the underlying story in your data. Being able to create easily understandable yet sophisticated plots is vital to becoming a successful data scientist.
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