Load Necessary Libraries

Code

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

Basic Graph

Code

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()

Bar Chart

Code

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()

Real World Examples

Download data from his Github (gas_prices.csv & fifa_data.csv)

Line Graph

Code

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()

Load Fifa Data

Code

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

Code

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()

Pie Chart

Code

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()

Pie Chart #2

Code

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()

Box and Whiskers Chart

Code

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()