## Introduction

Altair simplifies the process of turning data into beautiful, interactive charts. We'll be using a dataset of cars to demonstrate two types of visualizations: a Bar Chart and a Scatter Plot. First, we'll see how to compare the average horsepower of cars from different origins using a Bar Chart. This will give us insights into which region produces more powerful cars. Next, we'll dive into a Scatter Plot to examine how a car's weight influences its miles per gallon, offering a glimpse into the efficiency of different car models. It's built on top of Vega and Vega-Lite visualization grammars and offers a simple, intuitive, and consistent way to build a wide range of statistical visualizations.

## General Steps to use Altair package in Google Colab

**Step 1**. Install Altair using the below command

```
!pip install altair_viewer
```

**Step 2.** Install the dataset

```
!pip install altair vega_datasets
```

## Bar chart using Altair

```
import altair as alt
from vega_datasets import data
# Load sample data
source = data.cars()
# Create a bar chart
chart = alt.Chart(source).mark_bar().encode(
x='mean(Horsepower)',
y='Origin',
color='Origin'
)
# Display the chart
chart.show()
```

**Explanation**

- We import Altair and a sample dataset (
`cars`

) from`vega_datasets`

, a collection of datasets used for visualization examples. - We create a bar chart using
`alt.Chart(source)`

, where`source`

is the DataFrame containing our data. - We use
`mark_bar()`

to specify that we want a bar chart. - In the
`encode`

method, we define the x-axis as the mean horsepower (`mean(Horsepower)`

), the y-axis as the car origin (`Origin`

), and color the bars by the car origin. - Finally, we display the chart using
`chart.show()`

.

## Scatter Plot

```
import altair as alt
from vega_datasets import data
# Load sample data
source = data.cars()
# Create a scatter plot
scatter_plot = alt.Chart(source).mark_circle(size=60).encode(
x='Weight_in_lbs',
y='Miles_per_Gallon',
color='Origin',
tooltip=['Name', 'Origin', 'Weight_in_lbs', 'Miles_per_Gallon']
).interactive()
# Display the chart
scatter_plot.show()
```

**Explanation**

- We import Altair and the
`cars`

dataset from`vega_datasets`

. - We create a scatter plot using
`alt.Chart(source)`

. The`source`

is the DataFrame containing our data. `mark_circle(size=60)`

specifies that we want to use circles of size 60 to represent our data points.- In the
`encode`

method, we define:`x='Weight_in_lbs'`

: The x-axis represents the weight of the cars.`y='Miles_per_Gallon'`

: The y-axis represents the miles per gallon.`color='Origin'`

: The color of the points is determined by the car's origin.`tooltip=['Name', 'Origin', 'Weight_in_lbs', 'Miles_per_Gallon']`

: This adds a tooltip that displays the car's name, origin, weight, and MPG when you hover over a point.

`.interactive()`

makes the plot interactive, allowing you to zoom in and out and pan across the plot.- Finally,
`scatter_plot.show()`

displays the chart.

## Conclusion

With just a few steps, we've seen how Altair can help us easily turn car data into clear and engaging charts, making data analysis both fun and insightful.