Data Visualization

A library that specializes in data visualization. The oldest and by far the most popular python plotting library

import matplotlib.pyplot as plt

plt.plot()

plt.bar()

plt.scatter()

It is a common practice to create histograms to explore your data as it can give you a general idea of what your data looks like

By entering in this function with value df[‘Object’].hist()

df[‘object’].hist() plt.tickable_format(useOffset=False, style=’plain’)

df[‘object’].hist() plt.xticks(rotation = 45) plt.ticklabel_format(useOffset=False, style=’plain’)

Each bin is a plotted bar whose height corresponds to how many data points are in that bin

A boxplot is a standardized way of displaying the distribution of data based on a five number summary(min,median,max). It can tell you about your outliers and what their values are.

sns.boxplot(x=’categorical’, y=’numerical’, pallet=’blugrn’)

As a data scientist, we are often interested in if (and how) different features of a dataset may be related. For numerical data, one key way to describe a relationship is the correlation

By using df.corr() function

To explain and tell a story to your audience with data

plt.style.available this will pull up all the styles you can use

You can use it as plt.style.use(‘’) And then plt.plot([],[]) to display