## Tamilnadu Samacheer Kalvi 12th Computer Science Notes Chapter 16 Data Visualization Using Pyplot: Line Chart, Pie Chart and Bar Chart Notes

**Data Visualization:**

- Data Visualization is the graphical representation of information and data.
- The objective of Data Visualization is to communicate information visually to users.
- Data visualization uses statistical graphics.
- Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message.

**General types of Data Visualization:**

- Charts
- Tables
- Graphs
- Maps
- Infographics
- Dashboards

**Uses of Data Visualization:**

- Data Visualization help users to analyze and interpret the data easily.
- Data Visualization makes complex data understandable and usable.
- Various Charts in Data Visualization helps to show relationship in the data for one or more variables.

**Types of Visualizations in Matplotlib:**

There are many types of Visualizations under Matplotlib.

**Some of them are:**

- Line plot
- Scatter plot
- Histogram
- Box plot
- Bar chart and
- Pie chart

**Infographics:**

An infographic (information graphic) is the representation of information in a graphic format.

**Dashboard:**

- A dashboard is a collection of resources assembled to create a single unified visual display.
- Data visualizations and dashboards translate complex ideas and concepts into a simple visual format.
- Patterns and relationships that are undetectable in text are detectable at a glance using dashboard.

**Matplotlib:**

Matplotlib is the most popular data visualization library in Python. It allows to create charts in few lines of code.

**Box plot:**

The box plot is a standardized way of displaying the distribution of data based on the five number summary: minimum, first quartile, median, third quartile, and maximum.

**Types of pyplots using Matplotlib.**

Matplotlib allows us to create different kinds of plots ranging from histograms and scatter plots to bar graphs and bar charts.

**Line Chart:**

- A Line Chart or Line Graph is a type of chart which displays information as a series of , data points called ‘markers’ connected by straight line segments.
- A Line Chart is often used to visualize a trend in data over intervals of time – a time series – thus the line is often drawn chronologically.

**Bar Chart:**

- A BarPlot (or Bar Chart) is one of. the most common type of plot.
- It shows the relationship between a numerical variable and a categorical variable.
- Bar chart represents categorical data with rectangular bars.
- Each bar has a height corresponds to the value it represents.
- The bars can be plotted vertically or horizontally.
- It’s useful when we want to compare a given numeric value on different categories.
- To make a bar chart with Matplotlib, we can use the plt.bar() function.

**Pie Chart:**

- Pie Chart is probably one of the most common type of chart.
- It is a circular graphic which is divided into slices to illustrate numerical proportion.
- The point of a pie chart is to show the relationship of parts out of a whole.
- To make a Pie Chart with Matplotlib, we can use the pit.pie () function.
- The autopct parameter allows us to display the percentage value using the Python string formatting.

**Various buttons in a matplotlib window:**

**Home Button:**

The Home Button will help one to begun navigating the chart. If we ever want to return back to the original view, we can click on this.

**Forward/Back buttons :**

Forward/Back buttons can be used like the Forward and Back buttons in browser. Click these to move back to the previous point vou were at, or forward again.

**Pan Axis:**

This cross-looking button allows us to click it, and then click and drag graph around

**Zoom:**

- The Zoom button lets you click on it, then click and drag a square would like to zoom into specifically.
- Zooming in will require a left click and drag. Zoom out with a right click and drag.

**Configure Subplots:**

Configure Subplots button allows us to configure various spacing options with figure and plot.

**Save Figure :**

Save Figure button will allow you to save figure in various forms.