The `pylab` module is a part of the `matplotlib`  library in Python. It combines  `matplotlib.pyplot` (to create visualizations)  and `numpy` packages (to create, modify, and manipulate datasets) to provide users with a convenient way of plotting charts.

# Installation

The `pylab` module is a part of the `matplotlib` plotting library, so it is installed alongside `matplotlib`. The following command installs the `matplotlib` library (this includes the `pylab` module):

```sh
pip install matplotlib  
```

Moreover, we must install the `numpy` library because the `pylab` module depends on it. The following command installs the `numpy` library:

```sh
pip install numpy  
```



# Plotting curves

Now that we have an overview of installing the required libraries, let's look at how to use `pylab` to create data visualizations.

>**Note:** The `pylab` package has already been installed for you in the working example set up below.

from pylab import *
## Setting the x-axis
x = linspace(-3, 3)
## Plotting curves
plot(x,sin(x),'r.',label="sin graph")
plot(x,cos(x), 'b--',label="cos graph")
plot(x, -sin(x), 'g+',label="- sin graph")
## Adding labels
title("Sine and cosine graphs", color='b')
xlabel("Angle/ radians", color='r')
ylabel("Function value", color= 'r')
legend(loc='best')
## Displaying the final chart
show()


check

# Explanation

**Line 1:** We import all the dependencies from the `pylab` module.

**Line 2:** We create the x-axis for our chart using a linearly spaced vector.  

**Lines 5–7:** These lines create our sine, cosine, and negative cosine curves and set their respective titles.

**Lines 9–11:** These lines add the title, x-axis, and y-axis labels.

**Line 12:** This line creates a legend that adds the labels we gave to our plots to the chart. Moreover, by setting `location= best`, the function chooses the most suitable location to add the label to the chart.

**Line 14:** The `show()` function displays the final chart.

final.tar.gz

What is Pylab?

Pylab, part of matplotlib in Python, combines plotting and data manipulation using matplotlib.pyplot and numpy, facilitating convenient data visualizations.