# Visualization 1: Matplotlib Basics Exercises

``````

In [30]:

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from __future__ import print_function
from IPython.html.widgets import interact, interactive, fixed
from IPython.html import widgets

``````
``````

:0: FutureWarning: IPython widgets are experimental and may change in the future.

``````

## Scatter plots

Learn how to use Matplotlib's `plt.scatter` function to make a 2d scatter plot.

• Generate random data using `np.random.randn`.
• Style the markers (color, size, shape, alpha) appropriately.
• Include an x and y label and title.
``````

In [21]:

randx = np.random.randn(500)
randy = np.random.randn(500)
plt.scatter(randx, randy, color = "g", marker = "x")
plt.xlabel("Random X")
plt.ylabel("Random Y")
plt.title("Random Data!!!!!")
plt.box(False)
plt.grid(True)

``````
``````

``````

## Histogram

Learn how to use Matplotlib's `plt.hist` function to make a 1d histogram.

• Generate randpom data using `np.random.randn`.
• Figure out how to set the number of histogram bins and other style options.
• Include an x and y label and title.
``````

In [41]:

data = np.random.randn(500000)
def plothist(bins, numdata):
plt.hist(np.random.randn(numdata), bins=bins, color = "k", ec = "w")
interact(plothist, bins=widgets.IntSlider(min=1,max=100,step=1,value=10), numdata=\
widgets.IntSlider(min=10,max=10000,step=10,value=10));
plt.xlabel("Random Variable X")
plt.ylabel("Counts")
plt.title("Distribution of a random variable in abjustable bins.")

``````
``````

``````
``````

In [ ]:

``````