Matplotlib 实例

Matplotlib Learning --- Examples

 

 

Real world examples¶

 

 

Load Necessary Libraries¶

 

In [3]:

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

 

 

Line graph¶

 

In [4]:

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

plt.figure(figsize=(7,5),dpi=100)

plt.title('Gas Price over Time (in USD)')

plt.plot(gas.Year,gas.USA,label='United States')
plt.plot(gas.Year,gas.Canada,label='Canada')
plt.plot(gas["Year"],gas['South Korea'],label='South Korea')

plt.xticks(gas.Year[::3])

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

plt.show()

 

 

 

In [13]:

plt.figure(figsize=(7,5),dpi=100)

plt.title('Gas Price over Time (in USD)',fontdict={'fontweight':'bold','fontsize':22})

for country in gas:
    if country != 'Year':
        plt.plot(gas.Year,gas[country])
        
plt.xticks(gas.Year[::3].tolist()+[2011])

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

plt.savefig('gas_price_figure',dpi=300)
plt.show()

 

 

 

 

Load Fifa Data¶

 

In [16]:

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

 

Out[16]:

 

	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¶

 

In [31]:

bins = [i*10 for i in range(4,11)]

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

plt.xticks(bins)

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

 

 

 

 

Pie chart¶

 

In [56]:

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

labels = ['Left','Right']
colors = ['#ababab','#aabbcc']
plt.pie([left,right],labels = labels,colors = colors,autopct='%.2f%%')
plt.title('Foot Preference of FIFA Players')

plt.show()

 

 

 

In [90]:

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

plt.figure(dpi=100)
plt.style.use('ggplot')

light = fifa.loc[fifa.Weight<125].count()[0]
light_medium = fifa.loc[(fifa.Weight>=125) & (fifa.Weight<150)].count()[0]
medium = fifa.loc[(fifa.Weight>=150) & (fifa.Weight<175)].count()[0]
medium_heavy = fifa.loc[(fifa.Weight>=175) & (fifa.Weight<200)].count()[0]
heavy = fifa.loc[fifa.Weight>=200].count()[0]
explode = [0.3,0.2,0,0,0.3]

labels = ['light','light_medium','medium','medium_heavy','heavy']
plt.pie([light,light_medium,medium,medium_heavy,heavy],labels=labels,autopct='%.2f%%',pctdistance=0.9,explode=explode)
plt.show()

 

 

 

In [119]:

plt.style.use('default')

plt.figure(figsize=(6,8),dpi=100)

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']

labels = ['FC Barcelona','Real Madrid','NE Reavolution']

boxes = plt.boxplot([barcelona,madrid,revs],labels = labels,patch_artist=True,medianprops={'linewidth':2})
for box in boxes['boxes']:
    box.set(color='#4286f4',linewidth=2)
    box.set(facecolor = "#e0e0e0")
    
plt.show()