Course Content

Gaining Insights with Data Visualization

## Gaining Insights with Data Visualization

# Line Plots

A **line plot** is a graphical representation of data that displays information along a number line. It consists of a series of points plotted on a graph, where the x-axis typically represents the **independent variable**, and the y-axis represents the **dependent variable**.

Line plots are particularly useful for displaying **trends in data** over time or across different categories. They are often used to visualize stock prices or weather patterns, and to compare the values of different variables, like sales volumes across various products or the performance of different sports teams. These trends or patterns can aid in **making predictions** about future values.

In Python, line plots can be created using the `plot()`

function from the `matplotlib.pyplot`

module.

Task

- Import the
`pyplot`

module of the`matplotlib`

library with the`plt`

alias. - Generate a random array of 1000 elements (1000 x 1 array) called
`values`

using`numpy`

. - Create a single line plot based on the
`values`

array. - Generate a second random array of 1000 elements called
`values1`

using`numpy`

. - Create a second line plot based on the
`values1`

array.

Task

- Import the
`pyplot`

module of the`matplotlib`

library with the`plt`

alias. - Generate a random array of 1000 elements (1000 x 1 array) called
`values`

using`numpy`

. - Create a single line plot based on the
`values`

array. - Generate a second random array of 1000 elements called
`values1`

using`numpy`

. - Create a second line plot based on the
`values1`

array.

Everything was clear?

A **line plot** is a graphical representation of data that displays information along a number line. It consists of a series of points plotted on a graph, where the x-axis typically represents the **independent variable**, and the y-axis represents the **dependent variable**.

Line plots are particularly useful for displaying **trends in data** over time or across different categories. They are often used to visualize stock prices or weather patterns, and to compare the values of different variables, like sales volumes across various products or the performance of different sports teams. These trends or patterns can aid in **making predictions** about future values.

In Python, line plots can be created using the `plot()`

function from the `matplotlib.pyplot`

module.

Task

- Import the
`pyplot`

module of the`matplotlib`

library with the`plt`

alias. - Generate a random array of 1000 elements (1000 x 1 array) called
`values`

using`numpy`

. - Create a single line plot based on the
`values`

array. - Generate a second random array of 1000 elements called
`values1`

using`numpy`

. - Create a second line plot based on the
`values1`

array.