python matplotlib库_Python的Matplotlib库简述

Matplotlib 库是 python 的数据可视化库

import matplotlib.pyplot as plt

1、字符串转化为日期

unrate = pd.read_csv("unrate.csv")

unrate["DATE"] = pd.to_datetime(unrate["DATE"])

2、拆线图

data1 = unrate[0: 12]

plt.plot(data1["DATE"], data1["VALUE"]) #x轴数据和y轴数据

plt.xticks(rotation = 45) #将x轴的属性旋转一个角度

plt.xlabel("Date Month") #x轴描述

plt.ylabel("Rate Value") #y轴描述

plt.title("my first plt") #标题

plt.show()

3、多图拼切

fig =plt.figure()

ax1= fig.add_subplot(2, 1, 1)

ax2= fig.add_subplot(2, 1, 2)

ax1.plot(np.random.randint(1, 5, 5), np.arange(5))

ax2.plot(np.arange(10)*3, np.arange(10))

plt.show()

4、一图多线

fig = plt.figure(figsize=(6, 3)) #设定图尺寸

data1= unrate[0: 12]

data1["MONTH"] = data1["DATE"].dt.month

plt.plot(data1["MONTH"], data1["VALUE"], c="red")

data2= unrate[12: 24]

data2["MONTH"] = data2["DATE"].dt.month

plt.plot(data2["MONTH"], data2["VALUE"], c="blue")

plt.xticks(rotation= 45) #将x轴的属性旋转一个角度

plt.xlabel("Date Month")

plt.ylabel("Rate Value")

plt.title("my first plt")

plt.show()

5、一图多线 - 自动跑代码(带图例)

fig = plt.figure(figsize=(10, 6))

colors= ['red', 'blue', 'green', 'orange', 'black']for i in range(5):

start_index= i*12end_index= (i+1)*12subset=unrate[start_index: end_index]

label= str(1948 +i)

plt.plot(subset['MONTH'], subset['VALUE'], c=colors[i], label=label)#plt.legend(loc='best')

plt.legend(loc = 'upper left') #位置

plt.show()

6、条形图

fand_col = ["Fandango_Stars", "Fandango_Ratingvalue", "Metacritic_norm", "RT_user_norm_round", "IMDB_norm_round"]

bar_heights= fand_new.ix[0, fand_col].values #条形图高度

bar_positions = np.arange(5) + 0.75 #条形图起始位置

tick_positions = range(1, 6)

fig, ax=plt.subplots()

ax.bar(bar_positions, bar_heights,0.5) #0.5表示条形图的宽度

ax.set_xticks(tick_positions)

ax.set_xticklabels(fand_col, rotation= 90)

ax.set_xlabel('Rating Source')

ax.set_ylabel('Average Rating')

ax.set_title('Average User Rating For Avengers: Age of Ultron (2015)')

plt.show()

7、条形图 - 横向

fand_col = ["Fandango_Stars", "Fandango_Ratingvalue", "Metacritic_norm", "RT_user_norm_round", "IMDB_norm_round"]

bar_heights=fand_new.ix[0, fand_col].values

bar_positions= np.arange(5) + 0.75tick_positions= range(1, 6)

fig, ax=plt.subplots()

ax.barh(bar_positions, bar_heights,0.5) #横向

ax.set_yticks(tick_positions)

ax.set_yticklabels(fand_col, rotation=0)

ax.set_xlabel('Rating Source')

ax.set_ylabel('Average Rating')

ax.set_title('Average User Rating For Avengers: Age of Ultron (2015)')

plt.show()

8、散点图

fig, ax =plt.subplots()

ax.scatter(fand_new['Fandango_Stars'], fand_new['Metacritic_norm']) #散点图

ax.set_xlabel('Fandango')

ax.set_ylabel('Rotten Tomatoes')

plt.show()

9、直方图

fandango_distribution = fand_new['Fandango_Stars'].value_counts()

fandango_distribution=fandango_distribution.sort_index()

imdb_distribution= fand_new['IMDB_norm_round'].value_counts()

imdb_distribution=imdb_distribution.sort_index()#bins 是什么？通俗一点就是分组，将N多数据分成X组。默认：bins=10

fig, ax =plt.subplots()

ax.hist(fand_new['Fandango_Stars'], range=(4, 5), bins=5) #range 需要查看x轴的范围

plt.show()

10、多图

fig = plt.figure(figsize=(12, 12))

ax1= fig.add_subplot(2,2,1)

ax2= fig.add_subplot(2,2,2)

ax3= fig.add_subplot(2,2,3)

ax4= fig.add_subplot(2,2,4)

ax1.hist(fand_new['Fandango_Stars'], bins=20, range=(0, 5))

ax1.set_title('Distribution of Fandango Ratings')

ax1.set_ylim(0,50)

ax2.hist(fand_new['IMDB_norm_round'], 20, range=(0, 5))

ax2.set_title('Distribution of Rotten Tomatoes Ratings')

ax2.set_ylim(0,50)

ax3.hist(fand_new['Metacritic_norm'], 20, range=(0, 5))

ax3.set_title('Distribution of Metacritic Ratings')

ax3.set_ylim(0,50)

ax4.hist(fand_new['RT_user_norm_round'], 20, range=(0, 5))

ax4.set_title('Distribution of IMDB Ratings')

ax4.set_ylim(0,50)

plt.show()

11、四分图

fig, ax =plt.subplots()

ax.boxplot(fand_new['Metacritic_norm'])

ax.set_xticklabels(['Rotten Tomatoes'])

ax.set_ylim(0,5)

plt.show()

12、多图 - 通过数组

num_cols = ['Fandango_Stars', 'IMDB_norm_round', 'Metacritic_norm', 'RT_user_norm_round']

fig, ax=plt.subplots()

ax.boxplot(fand_new[num_cols].values)

ax.set_xticklabels(num_cols, rotation=90)

ax.set_ylim(0,5)

plt.show()

13、数据可视化 - 简洁一些

fig, ax =plt.subplots()

ax.plot(women_degrees['Year'], women_degrees['Biology'], c='blue', label='Women')

ax.plot(women_degrees['Year'], 100-women_degrees['Biology'], c='green', label='Men')

ax.tick_params(bottom="off", top="off", left="off", right="off") #可配置参数

for key,spine inax.spines.items():

spine.set_visible(False)

ax.legend(loc='upper right')

plt.show()

14、数据可视化 - 多图 - 通过程序

major_cats = ['Biology', 'Computer Science', 'Engineering', 'Math and Statistics']

fig= plt.figure(figsize=(12, 12))for sp in range(0, 4):

ax= fig.add_subplot(2, 2, sp+1)

ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c='blue', label='Women')

ax.plot(women_degrees['Year'], 100-women_degrees[major_cats[sp]], c='green', label='Men')

plt.legend(loc='upper right')

plt.show()

15、数据可视化 - 多图 - 通过程序跑 - 多图 简洁

major_cats = ['Biology', 'Computer Science', 'Engineering', 'Math and Statistics']

fig= plt.figure(figsize=(12, 12))for sp in range(0, 4):

ax= fig.add_subplot(2, 2, sp+1)

ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c='blue', label='Women')

ax.plot(women_degrees['Year'], 100-women_degrees[major_cats[sp]], c='green', label='Men')for key,spine inax.spines.items():

spine.set_visible(False)

ax.set_xlim(1968, 2011)

ax.set_ylim(0,100)

ax.set_title(major_cats[sp])

ax.tick_params(bottom="off", top="off", left="off", right="off")

plt.legend(loc='upper right')

plt.show()

16、如何使图表更好看？

cb_dark_blue = (0/255, 107/255, 164/255) #自定义颜色

cb_orange = (255/255, 128/255, 14/255)

fig= plt.figure(figsize=(12, 12))for sp in range(0, 4):

ax= fig.add_subplot(2, 2, sp+1)

ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c=cb_dark_blue, label='Women')

ax.plot(women_degrees['Year'], 100-women_degrees[major_cats[sp]], c=cb_orange, label='Men')for key,spine inax.spines.items():

spine.set_visible(False)

ax.set_xlim(1968, 2011)

ax.set_ylim(0,100)

ax.set_title(major_cats[sp])

ax.tick_params(bottom="off", top="off", left="off", right="off")

plt.legend(loc='upper right')

plt.show()

17、加粗线

cb_dark_blue = (0/255, 107/255, 164/255)

cb_orange= (255/255, 128/255, 14/255)

fig= plt.figure(figsize=(18, 3))for sp in range(0, 4):

ax= fig.add_subplot(1, 4, sp+1)

ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c=cb_dark_blue, label='Women', linewidth=3) #线条粗细

ax.plot(women_degrees['Year'], 100-women_degrees[major_cats[sp]], c=cb_orange, label='Men', linewidth=3)for key,spine inax.spines.items():

spine.set_visible(False)

ax.set_xlim(1968, 2011)

ax.set_ylim(0,100)

ax.set_title(major_cats[sp])

ax.tick_params(bottom="off", top="off", left="off", right="off")

plt.legend(loc='upper right')

plt.show()

18、加注释

fig = plt.figure(figsize=(18, 3))for sp in range(0, 4):

ax= fig.add_subplot(1, 4, sp+1)

ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c=cb_dark_blue, label='Women', linewidth=3)

ax.plot(women_degrees['Year'], 100-women_degrees[major_cats[sp]], c=cb_orange, label='Men', linewidth=3)for key,spine inax.spines.items():

spine.set_visible(False)

ax.set_xlim(1968, 2011)

ax.set_ylim(0,100)

ax.set_title(major_cats[sp])

ax.tick_params(bottom="off", top="off", left="off", right="off")if sp ==0:

ax.text(2005, 87, 'Men') #注释

ax.text(2002, 8, 'Women')elif sp == 3:

ax.text(2005, 62, 'Men')

ax.text(2001, 35, 'Women')

plt.show()