Matplotlib

Matplotlib#

Matplotlib is a Python 2D plotting library that produces high-quality charts and figures, which helps us visualize extensive data to understand better. Pandas is a handy and useful data-structure tool for analyzing large and complex data.

Load Necessary Libraries#

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

Basic Graph#

x = [0,1,2,3,4]
y = [0,2,4,6,8]

# Resize your Graph (dpi specifies pixels per inch. When saving probably should use 300 if possible)
plt.figure(figsize=(8,5), dpi=100)

# Line 1

# Keyword Argument Notation
#plt.plot(x,y, label='2x', color='red', linewidth=2, marker='.', linestyle='--', markersize=10, markeredgecolor='blue')

# Shorthand notation
# fmt = '[color][marker][line]'
plt.plot(x,y, 'b^--', label='2x')

## Line 2

# select interval we want to plot points at
x2 = np.arange(0,4.5,0.5)

# Plot part of the graph as line
plt.plot(x2[:6], x2[:6]**2, 'r', label='X^2')

# Plot remainder of graph as a dot
plt.plot(x2[5:], x2[5:]**2, 'r--')

# Add a title (specify font parameters with fontdict)
plt.title('Our First Graph!', fontdict={'fontname': 'Comic Sans MS', 'fontsize': 20})

# X and Y labels
plt.xlabel('X Axis')
plt.ylabel('Y Axis')

# X, Y axis Tickmarks (scale of your graph)
plt.xticks([0,1,2,3,4,])
#plt.yticks([0,2,4,6,8,10])

# Add a legend
plt.legend()

# Save figure (dpi 300 is good when saving so graph has high resolution)
plt.savefig('mygraph.png', dpi=300)

# Show plot
plt.show()

The line plot graph should look like this:

Bar Chart#

labels = ['A', 'B', 'C']
values = [1,4,2]

plt.figure(figsize=(5,3), dpi=100)

bars = plt.bar(labels, values)

patterns = ['/', 'O', '*']
for bar in bars:
    bar.set_hatch(patterns.pop(0))

plt.savefig('barchart.png', dpi=300)

plt.show()

The line bar chart should look like this:

Real World Examples#

Download datasets from my Github:

  1. gas_prices.csv

  2. fifa_data.csv

  3. iris_data.csv

Line Graph#

gas = pd.read_csv('gas_prices.csv')

plt.figure(figsize=(8,5))

plt.title('Gas Prices over Time (in USD)', fontdict={'fontweight':'bold', 'fontsize': 18})

plt.plot(gas.Year, gas.USA, 'b.-', label='United States')
plt.plot(gas.Year, gas.Canada, 'r.-')
plt.plot(gas.Year, gas['South Korea'], 'g.-')
plt.plot(gas.Year, gas.Australia, 'y.-')

# Another Way to plot many values!
# countries_to_look_at = ['Australia', 'USA', 'Canada', 'South Korea']
# for country in gas:
#     if country in countries_to_look_at:
#         plt.plot(gas.Year, gas[country], marker='.')

plt.xticks(gas.Year[::3].tolist()+[2011])

plt.xlabel('Year')
plt.ylabel('US Dollars')

plt.legend()

plt.savefig('Gas_price_figure.png', dpi=300)

plt.show()

The line graph should look like this:

Load Fifa Data#

fifa = pd.read_csv('fifa_data.csv')

fifa.head(5)
Unnamed: 0 ID Name Age Photo Nationality Flag Overall Potential Club ... Composure Marking StandingTackle SlidingTackle GKDiving GKHandling GKKicking GKPositioning GKReflexes Release Clause
0 0 158023 L. Messi 31 https://cdn.sofifa.org/players/4/19/158023.png Argentina https://cdn.sofifa.org/flags/52.png 94 94 FC Barcelona ... 96.0 33.0 28.0 26.0 6.0 11.0 15.0 14.0 8.0 €226.5M
1 1 20801 Cristiano Ronaldo 33 https://cdn.sofifa.org/players/4/19/20801.png Portugal https://cdn.sofifa.org/flags/38.png 94 94 Juventus ... 95.0 28.0 31.0 23.0 7.0 11.0 15.0 14.0 11.0 €127.1M
2 2 190871 Neymar Jr 26 https://cdn.sofifa.org/players/4/19/190871.png Brazil https://cdn.sofifa.org/flags/54.png 92 93 Paris Saint-Germain ... 94.0 27.0 24.0 33.0 9.0 9.0 15.0 15.0 11.0 €228.1M
3 3 193080 De Gea 27 https://cdn.sofifa.org/players/4/19/193080.png Spain https://cdn.sofifa.org/flags/45.png 91 93 Manchester United ... 68.0 15.0 21.0 13.0 90.0 85.0 87.0 88.0 94.0 €138.6M
4 4 192985 K. De Bruyne 27 https://cdn.sofifa.org/players/4/19/192985.png Belgium https://cdn.sofifa.org/flags/7.png 91 92 Manchester City ... 88.0 68.0 58.0 51.0 15.0 13.0 5.0 10.0 13.0 €196.4M

5 rows × 89 columns

Histogram#

bins = [40,50,60,70,80,90,100]

plt.figure(figsize=(8,5))

plt.hist(fifa.Overall, bins=bins, color='#abcdef')

plt.xticks(bins)

plt.ylabel('Number of Players')
plt.xlabel('Skill Level')
plt.title('Distribution of Player Skills in FIFA 2018')

plt.savefig('histogram.png', dpi=300)

plt.show()

The histogram should look like this:

Pie Chart#

Pie Chart #1#

left = fifa.loc[fifa['Preferred Foot'] == 'Left'].count()[0]
right = fifa.loc[fifa['Preferred Foot'] == 'Right'].count()[0]

plt.figure(figsize=(8,5))

labels = ['Left', 'Right']
colors = ['#abcdef', '#aabbcc']

plt.pie([left, right], labels = labels, colors=colors, autopct='%.2f %%')

plt.title('Foot Preference of FIFA Players')

plt.show()

The piechart should look like this:

Pie Chart #2#

plt.figure(figsize=(8,5), dpi=100)

plt.style.use('ggplot')

fifa.Weight = [int(x.strip('lbs')) if type(x)==str else x for x in fifa.Weight]

light = fifa.loc[fifa.Weight < 125].count()[0]
light_medium = fifa[(fifa.Weight >= 125) & (fifa.Weight < 150)].count()[0]
medium = fifa[(fifa.Weight >= 150) & (fifa.Weight < 175)].count()[0]
medium_heavy = fifa[(fifa.Weight >= 175) & (fifa.Weight < 200)].count()[0]
heavy = fifa[fifa.Weight >= 200].count()[0]

weights = [light,light_medium, medium, medium_heavy, heavy]
label = ['under 125', '125-150', '150-175', '175-200', 'over 200']
explode = (.4,.2,0,0,.4)

plt.title('Weight of Professional Soccer Players (lbs)')

plt.pie(weights, labels=label, explode=explode, pctdistance=0.8,autopct='%.2f %%')
plt.show()

The piechart should look like this:

Pie Chart #3#

import pandas as pd
data = pd.read_csv("iris_data.csv")
data.head()
Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species
0 1 5.1 3.5 1.4 0.2 Iris-setosa
1 2 4.9 3.0 1.4 0.2 Iris-setosa
2 3 4.7 3.2 1.3 0.2 Iris-setosa
3 4 4.6 3.1 1.5 0.2 Iris-setosa
4 5 5.0 3.6 1.4 0.2 Iris-setosa 
SepalLength = data['SepalLengthCm'].value_counts()

# Plot a pie chart
%matplotlib inline
from matplotlib import pyplot as plt
SepalLength.plot(kind='pie', title='Sepal Length', figsize=(9,9))
plt.legend()
plt.show()

The piechart should look like this:

Box and Whiskers Chart#

A box and whisker plot(box plot) displays the five-number summary of a set of data. The five-number summary is the minimum, first quartile, median, third quartile, and maximum.

Box plot #1#

plt.figure(figsize=(5,8), dpi=100)

plt.style.use('default')

barcelona = fifa.loc[fifa.Club == "FC Barcelona"]['Overall']
madrid = fifa.loc[fifa.Club == "Real Madrid"]['Overall']
revs = fifa.loc[fifa.Club == "New England Revolution"]['Overall']

#bp = plt.boxplot([barcelona, madrid, revs], labels=['a','b','c'], boxprops=dict(facecolor='red'))
bp = plt.boxplot([barcelona, madrid, revs], labels=['FC Barcelona','Real Madrid','NE Revolution'], patch_artist=True, medianprops={'linewidth': 2})

plt.title('Professional Soccer Team Comparison')
plt.ylabel('FIFA Overall Rating')

for box in bp['boxes']:
    # change outline color
    box.set(color='#4286f4', linewidth=2)
    # change fill color
    box.set(facecolor = '#e0e0e0' )
    # change hatch
    #box.set(hatch = '/')
    
plt.show()

The box plot should look like this:

Box plot #2#

#cateating data
import pandas as pd
df = pd.DataFrame({'Name': ['John', 'Rad', 'Var', 'Mathew', 'Alina', 'Lee', 'Rogers'],
                   'Salary':[60000,64000,60000,289000,66000,50000,60000],
                   'Hours':[41,40,36,30,35,39,40],
                   'Grade':[50,50,46,95,50,5,57]})
print(df)

     Name  Salary  Hours  Grade
0    John   60000     41     50
1     Rad   64000     40     50
2     Var   60000     36     46
3  Mathew  289000     30     95
4   Alina   66000     35     50
5     Lee   50000     39      5
6  Rogers   60000     40     57

# Quartiles of Hours
print(df['Hours'].quantile([0.25, 0.5, 0.75]))

0.25    35.5
0.50    39.0
0.75    40.0
Name: Hours, dtype: float64

# Plot a box-whisker chart
import matplotlib.pyplot as plt
df['Hours'].plot(kind='box', title='Weekly Hours Distribution', figsize=(10,8))
plt.show()

The box plot should look like this:

# Quartiles of Salary
print(df['Salary'].quantile([0.25, 0.5, 0.75]))

0.25    60000.0
0.50    60000.0
0.75    65000.0
Name: Salary, dtype: float64

# Plot a box-whisker chart
df['Salary'].plot(kind='box', title='Salary Distribution', figsize=(10,8))
plt.show()

The box plot should look like this:

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