matplotlib1

import pandas as pd
unrate=pd.read_csv("unrate.csv")
unrate['DATE']=pd.to_datetime(unrate['DATE'])
print(unrate.head(12))

>>>
         DATE  VALUE
0  1948-01-01    3.4
1  1948-02-01    3.8
2  1948-03-01    4.0
3  1948-04-01    3.9
4  1948-05-01    3.5
5  1948-06-01    3.6
6  1948-07-01    3.6
7  1948-08-01    3.9
8  1948-09-01    3.8
9  1948-10-01    3.7
10 1948-11-01    3.8
11 1948-12-01    4.0

import matplotlib.pyplot as plt
plt.plot()#画图
plt.show()#将画的图显示出来

>>>

first_year=unrate[0:12]
plt.plot(first_year['DATE'],first_year['VALUE'])#横轴为'DATE',纵轴为'VALUE'
plt.show()

import matplotlib.pyplot as plt
first_year=unrate[0:12:1]
plt.plot(first_year['DATE'],first_year['VALUE'])
plt.xticks(rotation=30) #横坐标倾斜30度
plt.show()

plt.plot(first_year['DATE'], first_year['VALUE'])
plt.xticks(rotation=90)
plt.xlabel('Month')#横轴标题
plt.ylabel('Unemployment Rate')#纵轴标题
plt.title('Monthly Unemployment Trends, 1948')#图形标题
plt.show()

import matplotlib.pyplot as plt
fig=plt.figure()
ax1=fig.add_subplot(2,2,1)#2行2列第一幅图
ax2=fig.add_subplot(2,2,2)#2行2列第二幅图
ax3=fig.add_subplot(2,2,4)#2行2列第四幅图
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
fig=plt.figure(figsize=(6,6))#图尺寸
ax1=fig.add_subplot(2,2,1)#2行2列第一幅图
ax2=fig.add_subplot(2,2,3)#2行2列第三幅图
ax1.plot(np.random.randint(1,5,5), np.arange(5))#随机生成数字
ax2.plot(np.arange(5))
plt.show()

fig = plt.figure(figsize=(6,3))

plt.plot(unrate[0:12]['DATE'], unrate[0:12]['VALUE'], c='red')#前12个数据为红色
plt.plot(unrate[12:24]['DATE'], unrate[12:24]['VALUE'], c='blue')#后12个数据为蓝色

plt.show()

unrate['MONTH'] = unrate['DATE'].dt.month# 也可以将'DATE'转换为数字月份

fig = plt.figure(figsize=(6,3))

plt.plot(unrate[0:12]['MONTH'], unrate[0:12]['VALUE'], c='red')
plt.plot(unrate[12:24]['MONTH'], unrate[12:24]['VALUE'], c='blue')

plt.show()

import pandas as pd
import matplotlib.pyplot as plt

unrate = pd.read_csv('unrate.csv')
unrate['DATE'] = pd.to_datetime(unrate['DATE'])
unrate['MONTH'] = unrate['DATE'].dt.month
fig = plt.figure(figsize=(10,6))
colors = ['red', 'blue', 'green', 'orange', 'black']

for i in range(5):
    start_index = i*12
    end_index = (i+1)*12
    subset = unrate[start_index:end_index]
    plt.plot(subset['MONTH'], subset['VALUE'], c=colors[i])
    
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

unrate = pd.read_csv('unrate.csv')
unrate['DATE'] = pd.to_datetime(unrate['DATE'])
unrate['MONTH'] = unrate['DATE'].dt.month
fig = plt.figure(figsize=(10,6))
colors = ['red', 'blue', 'green', 'orange', 'black']
for i in range(5):
    start_index = i*12
    end_index = (i+1)*12
    subset = unrate[start_index:end_index]
    plt.plot(subset['DATE'], subset['VALUE'], c=colors[i])  #DATE
    
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

unrate = pd.read_csv('unrate.csv')
unrate['DATE'] = pd.to_datetime(unrate['DATE'])
unrate['MONTH'] = unrate['DATE'].dt.month
fig = plt.figure(figsize=(10,6))
colors = ['red', 'blue', 'green', 'orange', 'black']

for i in range(5):
    start_index = i*12
    end_index = (i+1)*12
    subset = unrate[start_index:end_index]
    label=str(1948+i)   #方法二label=1948+i
    plt.plot(subset['MONTH'], subset['VALUE'], c=colors[i],label=label)  #MONTH
plt.legend(loc="best")   #plt.legend(loc=1)


plt.show()

import pandas as pd
import matplotlib.pyplot as plt

unrate = pd.read_csv('unrate.csv')
unrate['DATE'] = pd.to_datetime(unrate['DATE'])
unrate['MONTH'] = unrate['DATE'].dt.month
fig = plt.figure(figsize=(10,6))
colors = ['red', 'blue', 'green', 'orange', 'black']

for i in range(5):
    start_index = i*12
    end_index = (i+1)*12
    subset = unrate[start_index:end_index]
    label=str(1948+i)
    plt.plot(subset['MONTH'], subset['VALUE'], c=colors[i],label=label)  #MONTH
plt.legend(loc="best")
plt.xlabel("Month, Integer")
plt.ylabel('Unemployment Rate, Percent')
plt.title('Monthly Unemployment Trends, 1948-1952')

plt.show()