/matplotlib-data-visualization-basics

This is just a basic intro to Data visualization using Python Matplotlib

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
You can import matplotlib.pyplot the way you like but this the most used convention you'll see anywhere
%matplotlib inline

This is a magic function to enable inline plots

df = pd.read_csv('auto_mobile_price_data.csv')
df.head(2)
symboling normalized-losses make fuel-type aspiration num-of-doors body-style drive-wheels engine-location wheel-base ... engine-size fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg highway-mpg price
0 3 ? alfa-romero gas std two convertible rwd front 88.6 ... 130 mpfi 3.47 2.68 9.0 111 5000 21 27 13495
1 3 ? alfa-romero gas std two convertible rwd front 88.6 ... 130 mpfi 3.47 2.68 9.0 111 5000 21 27 16500

2 rows × 26 columns

Draw a line plot for Price against City MPG

#df.plot(x='price', y='city-mpg')

# OR
# df.plot.line(x='price', y='city-mpg')

# OR
df.plot(kind='line', x='price', y='city-mpg')
plt.show()

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Default plot type is line plot. other kinds can be scatter, bar, histo etc

If you want to have multiple plots within same figure, you need to create a figure with custom size first,

define axes

define title, x label, y label for each axes

A figure can have many axes (plots)

An axes have x axis and y axis

fig = plt.figure(figsize=(10, 5))

# A figure with 10 inches of width and 5 inches of height

axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8])
axes2 = fig.add_axes([0.2, 0.4, 0.4, 0.4])

# [0.1, 0.1, 0.8, 0.8]
# 0.1 Distance from Left
# 0.1 Distance from Bottom
# 0.8 Width of the axes or width of the current plot
# 0.8 Height of the axes or height of the current plot

axes1.set_title('Outer Plot')
axes2.set_title('Inner Plot')

axes1.set_xlabel('Outer X')
axes1.set_ylabel('Outer Y')

axes2.set_xlabel('Inner X')
axes2.set_ylabel('Inner Y')

x = np.linspace(0, 1, 50)
y = np.linspace(0, 10, 50)**4

axes1.plot(x, y)
axes2.plot(y, x)
plt.show()

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Add Legend to Plot


fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 0.8])
ax.plot(x, x**4, label='X Double Squared')
ax.plot(x, x**3, label='X Cubed')

ax.legend(loc=0)

# loc=0 : best fit
# these digits range from 0 to 10 for left, top left, bottom, center etc.
# (0.1, 0.5) = disance from X-axis and distance from Y-axis.

plt.show()


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If you want to show counts of categorical values like number of mobiles released by each company, earthquake hits per ear then you should consider Bar plot which shows counts against a category


get number of cars (counts) for each make (company) (category)


company_counts = df['make'].value_counts()


fig = plt.figure(figsize=(10,5))
axes = fig.gca()
# get current axes

axes.set_title("Bar for Number of Cars by a Company")
axes.set_xlabel('Company')
axes.set_ylabel('Number of Cars')
company_counts.plot(kind='bar', ax=axes)
plt.show()


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You can show markers on plot instead of Lines or Bars.


x = np.arange(0, 50)
y = np.arange(0, 1, 0.02)
plt.plot(x, y**2, '*', x, y**3, '8',  np.arange(50, 0, -1), np.arange(1, 0, -0.02), '3')
plt.show()
# 3rd argument will considered as marker. Marker value either be a string or numerical, depending on marker type


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This is the List of available markers. Details can be found here




xs, ys = np.mgrid[:4, 9:0:-1]

markers = [".", "+", ",", "x", "o", "D", "d", "", "8", "s", "p", "*", "|", "_", "h", "H", 0, 4, "<", "3",
           1, 5, ">", "4", 2, 6, "^", "2", 3, 7, "v", "1", "None", None, " ", ""]

descripts = ["point", "plus", "pixel", "cross", "circle", "diamond", "thin diamond", "",
             "octagon", "square", "pentagon", "star", "vertical bar", "horizontal bar", "hexagon 1", "hexagon 2",
             "tick left", "caret left", "triangle left", "tri left", "tick right", "caret right", "triangle right", "tri right",
             "tick up", "caret up", "triangle up", "tri up", "tick down", "caret down", "triangle down", "tri down",
             "Nothing", "Nothing", "Nothing", "Nothing"]

fig, ax = plt.subplots(1, 1, figsize=(10, 4))

for x, y, m, d in zip(xs.T.flat, ys.T.flat, markers, descripts):
    ax.scatter(x, y, marker=m, s=120)
    ax.text(x + 0.1, y - 0.1, d, size=12)
    ax.set_axis_off()
    
plt.show()


/usr/lib/pymodules/python2.7/matplotlib/collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if self._edgecolors == 'face':



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Show markers over the line instead of just markers


x = np.arange(0, 20)
y = np.arange(0, 1, 0.05)
plt.plot(x, y**2, '*--', x, y**3, '8-')
plt.show()

# First letter in 3rd/6th argument denotes marker type and seconds denotes line type i.e dotted, dashed, 
# dashed dotted


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Make a Plot that has dotted red line, Large yellow diamond markers having green edge


x = np.arange(0, 20)
y = np.arange(0, 1, 0.05)
plt.plot(x, y**2, 'r-', marker='D', markerfacecolor='y', markeredgecolor='g')

# OR

# plt.plot(x, y**2, 'r-', marker='D', mfc='y', mec='g')

# OR

# plt.plot(x, y**2, 'r-D', mfc='y', mec='g')

# mec = markeredgecolor
# mfc = markerfacecolor
# r-D = red, line with -, Diamond marker

plt.show()


png




Line and Marker with different available options


fig = plt.figure(figsize=(8, 3))
axes = fig.add_axes([0, 0, 1, 1])

x = np.linspace(0, 1, 15)

axes.plot(x, x**2, color='blue', linewidth=2, linestyle='-', marker='o', markerfacecolor='yellow', markersize=20,
         markeredgewidth=2, markeredgecolor='green')

axes.set_xlim(-0.05, 1.05)
axes.set_ylim(-0.5, 1.5)

plt.show()


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MathText


You can show Math symbols inside plot like sigma or pie or any other symbol


plt.plot(np.arange(0, 20), np.arange(0, 1, 0.05))
plt.title(r'$\sigma_{i=15}$', fontsize=20)
plt.show()

# To make subscripts and superscripts, use the '_' and '^' symbols


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