pshmulevich / python-data-visualization

Data Visualization is an important aspect of working with datasets.

Basic example of data visualization:

Some libraries most commonly used: Pandas, Matplotlib, Seaborn

Begin by importing the libraries

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

Usually you're working with a dataset, bring it to the Pandas DataFrame

iris = pd.read_csv(<filepath>/input/<filename>.csv)
iris.head()

Important to note, the head() command is used to inspect a portion of your data

How to inspect the number of species in your data you have so far

iris["Species"].value_counts()

Next is some actual categorization of the data that we have

Below code produces a scatter plot with legends of the sepal length versus the sepal width on the x and y axes respectively

iris.plot(kind="scatter", x="SepalLengthCm", y="SepalWidthCm")

Additinal information

Customizing your marker types and details:

Creating the plot object:

ax = plt.subplots()
ax.scatter(x_data, y_data, s = 10, color = color, alpha = 0.75)

What did this do? Create a plot object, designate it to be a scatter plot, set the size and transparancy of the marker, the alpha.

Other marker-related distinguishing types:

for marker in['o', '.', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd']:
plt.plot(rng.rand(5), rng.rand(5), marker, label = "marker = '{0}'".format(marker))
'o' = circle marker
'.' = small dot marker
',' = exceptionally small dot marker
'x' = x marker
'+' = plus marker
'v' = downward facing triangle marker
'^' = upward facing triangle marker
'<' = leftward facing triangle marker
'>' = rightward facing triangle marker
's' = square marker
'd' = diamond marker

This was applicable for a scatter plot example. There are other types of graphs like bar graphs which will be in the next update.