# Challenge 1: Fundamentals of Plotting

Matplotlib, a cornerstone visualization library in Python, provides a vast array of plotting capabilities that are both **expressive** and **efficient**. Some compelling advantages of using Matplotlib for your data visualization tasks are:

**Versatility:**Matplotlib supports a diverse range of plots, from basic line plots to more complex visualizations like contour plots.**Customization:**Every aspect of a plot, from its colors to its labels, can be tailored, providing full control to the user.**Integration:**It works seamlessly with other libraries, especially Pandas and NumPy, making it a fundamental tool in the data analysis workflow.

For budding data scientists, analysts, or anyone keen on visual representation, Matplotlib's plotting functions act as a bridge between raw data and insights.

Task

Plot three foundational graph types using Matplotlib:

- Plot a simple line graph.
- Create a scatter plot.
- Generate a histogram.

## Code Description

**plt.plot(x, y)**

The

`plt.plot()`

function plots y against x as lines. It is commonly used for displaying a series or trajectory in a two-dimensional space.**plt.scatter(x_scatter, y_scatter)**

The

`plt.scatter()`

function creates a scatter plot which displays values for two variables for a set of data. The dots in a scatter plot not only report what values occur together, but also show how often they do.**plt.hist(data, bins=30)**

The

`plt.hist()`

function computes and draws the histogram of the input data, offering insights into the distribution. The parameter `bins` denotes the number of bins (intervals) to be used for the histogram.
Everything was clear?

Course Content

Data Science Interview Challenge

## Data Science Interview Challenge

# Challenge 1: Fundamentals of Plotting

Matplotlib, a cornerstone visualization library in Python, provides a vast array of plotting capabilities that are both **expressive** and **efficient**. Some compelling advantages of using Matplotlib for your data visualization tasks are:

**Versatility:**Matplotlib supports a diverse range of plots, from basic line plots to more complex visualizations like contour plots.**Customization:**Every aspect of a plot, from its colors to its labels, can be tailored, providing full control to the user.**Integration:**It works seamlessly with other libraries, especially Pandas and NumPy, making it a fundamental tool in the data analysis workflow.

For budding data scientists, analysts, or anyone keen on visual representation, Matplotlib's plotting functions act as a bridge between raw data and insights.

Task

Plot three foundational graph types using Matplotlib:

- Plot a simple line graph.
- Create a scatter plot.
- Generate a histogram.

## Code Description

**plt.plot(x, y)**

The

`plt.plot()`

function plots y against x as lines. It is commonly used for displaying a series or trajectory in a two-dimensional space.**plt.scatter(x_scatter, y_scatter)**

The

`plt.scatter()`

function creates a scatter plot which displays values for two variables for a set of data. The dots in a scatter plot not only report what values occur together, but also show how often they do.**plt.hist(data, bins=30)**

The

`plt.hist()`

function computes and draws the histogram of the input data, offering insights into the distribution. The parameter `bins` denotes the number of bins (intervals) to be used for the histogram.
Everything was clear?