Python爬虫

Numpy

创建数组

import numpy as np

创建数组

arr1=np.array([1,2,3])

arr2=np.array([[3,4,5],[2,4,1]])

print(arr1)

print(arr2)

print(type(arr1))

[1 2 3]

[[3 4 5]

[2 4 1]]

<class 'numpy.ndarray'>

查看数组的基础属性

print(arr1.shape)

print(arr1.ndim)

print(arr1.dtype)

print(arr2.shape)

print(arr2.ndim)

print(arr2.dtype)

(3,)

1

int32

(2, 3)

2

int32

初识数组特点

list1=([0.3,0.5,4.2])

arr1=np.array([0.3,0.5,4.2])

print(list1)

print(arr1)

print(list1 ** 2)

print([i**2 for i in list1])

print(arr1 ** 2)

[0.3, 0.5, 4.2]

[0.3 0.5 4.2]

[0.09, 0.25, 17.64]

[ 0.09 0.25 17.64]

创建常见数组

arr3=np.arange(0,10)

arr4=np.arange(10)

arr5=np.arange(0,1,0.1)

print(arr3)

print(arr4)

print(arr5)

[0 1 2 3 4 5 6 7 8 9]

[0 1 2 3 4 5 6 7 8 9]

[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9]

arr6=np.linspace(0,1,10)

print(arr6)

[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]

arr7=np.zeros([3,4,5])

print(arr7)

[[[ 0. 0. 0. 0. 0.]

[ 0. 0. 0. 0. 0.]

[ 0. 0. 0. 0. 0.]

[ 0. 0. 0. 0. 0.]]

[[ 0. 0. 0. 0. 0.]

[ 0. 0. 0. 0. 0.]

[ 0. 0. 0. 0. 0.]

[ 0. 0. 0. 0. 0.]]

[[ 0. 0. 0. 0. 0.]

[ 0. 0. 0. 0. 0.]

[ 0. 0. 0. 0. 0.]

[ 0. 0. 0. 0. 0.]]]

arr8=np.ones([3,4,5])

print(arr8)

[[[ 1. 1. 1. 1. 1.]

[ 1. 1. 1. 1. 1.]

[ 1. 1. 1. 1. 1.]

[ 1. 1. 1. 1. 1.]]

[[ 1. 1. 1. 1. 1.]

[ 1. 1. 1. 1. 1.]

[ 1. 1. 1. 1. 1.]

[ 1. 1. 1. 1. 1.]]

[[ 1. 1. 1. 1. 1.]

[ 1. 1. 1. 1. 1.]

[ 1. 1. 1. 1. 1.]

[ 1. 1. 1. 1. 1.]]]

数组的数据类型

arr8=np.array([3,4,5],dtype=np.float) # 声明数组的数据类型

print(arr8)

print(arr8.dtype)

arr8[0]=1.2

print(arr8)

print(np.int32(arr8)) # 转换数组的数据类型

[ 3. 4. 5.]

float64

[ 1.2 4. 5. ]

[1 4 5]

生成随机数

print(np.random.random(10))

[ 0.73744669 0.80080002 0.69015703 0.51743988 0.05570415 0.34901843

0.0734643 0.86541768 0.18830607 0.07036505]

print(np.random.rand(10))

[ 0.27580848 0.69278954 0.12912278 0.53180506 0.21617686 0.4357371

0.92604774 0.19278177 0.81396217 0.15550441]

print(np.random.rand(3,4))

print(np.random.randn(3,4))

[[ 0.03654586 0.23675581 0.35543946 0.31476077]

[ 0.12875907 0.78566879 0.87653987 0.78687239]

[ 0.79805071 0.46032083 0.08375826 0.60476404]]

[[ 0.22623064 -0.34199973 -0.50766523 0.01726679]

[ 0.71462127 -1.19509683 -0.3916739 0.67375221]

[-1.02150652 0.98995901 1.64691806 0.81784057]]

数组的索引

arr1=np.array([0.3,0.78,0.24,5,3.2])

print(arr1)

print(arr1[0])

print(arr1[-5])

print(arr1[1:2])

print(arr1[-4:-2])

[0.3 0.78 0.24 5. 3.2 ]

0.3

0.3

[0.78]

[0.78 0.24]

逻辑型索引

arr2=np.array([2.3,1.8,4.5])

print(arr2)

print(arr2[[False,False,True]])

index=arr2>2

print(arr2[index])

[2.3 1.8 4.5]

[4.5]

[2.3 4.5]

多维数组的索引

arr3=np.arange(1,13).reshape([3,4])

print(arr3)

print(arr3[2,3])

print(arr3[2,0:])

print(arr3[:,3])

print(arr3[1:,1:3])

[[ 1 2 3 4]

[ 5 6 7 8]

[ 9 10 11 12]]

12

[ 9 10 11 12]

[ 4 8 12]

[[ 6 7]

[10 11]]

[[ 5 6 7 8]

[ 9 10 11 12]]

[False True True]

修改数组中的元素

arr3=np.arange(1,13).reshape([3,4])

print(arr3)

arr3[0,0]=15

print(arr3)

[[ 1 2 3 4]

[ 5 6 7 8]

[ 9 10 11 12]]

[[15 2 3 4]

[ 5 6 7 8]

[ 9 10 11 12]]

求解距离矩阵

n=10 # 样本个数

x=np.linspace(1,100,n) # 样本的横坐标

y=np.linspace(1,100,n) # 样本的纵坐标

dist=np.sqrt((x[0]-x[1])**2+(y[0]-y[1])**2)

dist = np.zeros([n, n]) # 初始距离矩阵

for i in range(n):

for j in range(n):

dist[i, j] = np.sqrt((x[i] - x[j])**2 + (y[i] - y[j])**2) # 计算欧式距离

print(x)

print(y)

print(dist)

[ 1. 12. 23. 34. 45. 56. 67. 78. 89. 100.]

[ 1. 12. 23. 34. 45. 56. 67. 78. 89. 100.]

[[ 0. 15.55634919 31.11269837 46.66904756 62.22539674

77.78174593 93.33809512 108.8944443 124.45079349 140.00714267]

[ 15.55634919 0. 15.55634919 31.11269837 46.66904756

62.22539674 77.78174593 93.33809512 108.8944443 124.45079349]

[ 31.11269837 15.55634919 0. 15.55634919 31.11269837

46.66904756 62.22539674 77.78174593 93.33809512 108.8944443 ]

[ 46.66904756 31.11269837 15.55634919 0. 15.55634919

31.11269837 46.66904756 62.22539674 77.78174593 93.33809512]

[ 62.22539674 46.66904756 31.11269837 15.55634919 0.

15.55634919 31.11269837 46.66904756 62.22539674 77.78174593]

[ 77.78174593 62.22539674 46.66904756 31.11269837 15.55634919

0. 15.55634919 31.11269837 46.66904756 62.22539674]

[ 93.33809512 77.78174593 62.22539674 46.66904756 31.11269837

15.55634919 0. 15.55634919 31.11269837 46.66904756]

[108.8944443 93.33809512 77.78174593 62.22539674 46.66904756

31.11269837 15.55634919 0. 15.55634919 31.11269837]

[124.45079349 108.8944443 93.33809512 77.78174593 62.22539674

46.66904756 31.11269837 15.55634919 0. 15.55634919]

[140.00714267 124.45079349 108.8944443 93.33809512 77.78174593

62.22539674 46.66904756 31.11269837 15.55634919 0. ]]

数组形态变化

arr4=np.arange(1,13)

print(arr4)

print(arr4.reshape([3,4]))

arr5=arr4.reshape([3,4])

print(arr5.ravel()) # 数组的展平

print(arr5.flatten('F')) #数组的纵向展平

arr6=arr4.reshape([3,4])

print(np.hstack((arr5,arr6))) # 数组的横向拼接

print(np.vstack((arr5,arr6))) # 数组的纵向拼接

[ 1 2 3 4 5 6 7 8 9 10 11 12]

[[ 1 2 3 4]

[ 5 6 7 8]

[ 9 10 11 12]]

[ 1 2 3 4 5 6 7 8 9 10 11 12]

[ 1 5 9 2 6 10 3 7 11 4 8 12]

[[ 1 2 3 4 1 2 3 4]

[ 5 6 7 8 5 6 7 8]

[ 9 10 11 12 9 10 11 12]]

[[ 1 2 3 4]

[ 5 6 7 8]

[ 9 10 11 12]

[ 1 2 3 4]

[ 5 6 7 8]

[ 9 10 11 12]]

掌握 NumPy 矩阵与通用函数

import numpy as np

matr1 = np.mat("1 2 3;4 5 6;7 8 9")

print(type(matr1))

matr2 = np.matrix([[1, 2, 3],[4, 5, 6],[7, 8, 9]])

np.bmat('matr1 matr2; matr1, matr2')

<class 'numpy.matrix'>

matrix([[1, 2, 3, 1, 2, 3],

[4, 5, 6, 4, 5, 6],

[7, 8, 9, 7, 8, 9],

[1, 2, 3, 1, 2, 3],

[4, 5, 6, 4, 5, 6],

[7, 8, 9, 7, 8, 9]])

通用函数

arr1 = np.array([0.2, 0.4, 0.6])

arr2=np.array([0.2,0.6,0.78])

list1 = [0.2, 0.4, 0.6]

print(arr1+1)

print(arr1-1)

print(arr1*2)

print(arr1/2)

print(list1*2)

print(arr1+arr2)

print(arr1>0)

print(arr1<arr2)

print(np.any(arr1==0.2))

print(np.all(arr2==0.2))

[1.2 1.4 1.6]

[-0.8 -0.6 -0.4]

[0.4 0.8 1.2]

[0.1 0.2 0.3]

[0.2, 0.4, 0.6, 0.2, 0.4, 0.6]

[0.4 1. 1.38]

[ True True True]

[False True True]

True

False

广播机制

arr3 = np.arange(1, 13).reshape([4, 3])

arr4 = np.array([1, 2, 3])

arr5 = np.array([[1], [2], [3], [4]])

print(arr3)

print(arr4)

print(arr5)

print(arr3+arr4)

print(arr3+arr5)

[[ 1 2 3]

[ 4 5 6]

[ 7 8 9]

[10 11 12]]

[1 2 3]

[[1]

[2]

[3]

[4]]

[[ 2 4 6]

[ 5 7 9]

[ 8 10 12]

[11 13 15]]

[[ 2 3 4]

[ 6 7 8]

[10 11 12]

[14 15 16]]

利用 NumPy 进行统计分析

import numpy as np

读写二进制文件

arr1 = np.arange(1, 13).reshape([4, 3])

arr2 = np.arange(1, 13).reshape([3, 4])

print(arr1)

print(arr2)

[[ 1 2 3]

[ 4 5 6]

[ 7 8 9]

[10 11 12]]

[[ 1 2 3 4]

[ 5 6 7 8]

[ 9 10 11 12]]

读写txt文件

np.savetxt('tmp/arr1.txt', arr1, delimiter=',') # 保存数据

np.loadtxt('tmp/arr1.txt', delimiter=',') # 读取数据

array([[ 1., 2., 3.],

[ 4., 5., 6.],

[ 7., 8., 9.],

[10., 11., 12.]])

使用数组进行简单统计分析

arr3 = np.random.randint(1, 10, (3, 4))

print(arr3)

arr3.sort(axis=0)

print(arr3)

print(arr3.argsort(axis=0))

print(np.tile(arr3, 2))

print(np.repeat(arr3, 2, axis=1))

print(arr3.mean())

print(arr3.mean(axis=0))

print(arr3.max(axis=0))

print(arr3.argmax(axis=0))

[[8 6 7 3]

[2 9 9 4]

[2 5 2 3]]

[[2 5 2 3]

[2 6 7 3]

[8 9 9 4]]

[[0 0 0 0]

[1 1 1 1]

[2 2 2 2]]

[[2 5 2 3 2 5 2 3]

[2 6 7 3 2 6 7 3]

[8 9 9 4 8 9 9 4]]

[[2 2 5 5 2 2 3 3]

[2 2 6 6 7 7 3 3]

[8 8 9 9 9 9 4 4]]

5.0

[ 4. 6.66666667 6. 3.33333333]

[8 9 9 4]

[2 2 2 2]

Pandas

import pandas as pd

读取文本数据

pd.read_csv?

data_txt = pd.read_csv('data/meal_order_info.txt',sep=' ')

data_csv = pd.read_csv('data/meal_order_info.csv', encoding='gbk', header=0)

data_csv

将数据框存储为文本文件数据

data_csv.to_csv('tmp/data_csv.csv',index=None, encoding='gbk')

data_csv

读取Excel文件

data_excel = pd.read_excel('data/meal_order_detail.xlsx',sheet_name='meal_order_detail2')

data_excel

data_excel.to_excel('tmp/data_excel.xlsx', index=None, sheet_name='test1')

掌握DataFrame的常用操作

import pandas as pd

Series系列

ser1 = pd.Series([1,2,'a'],index=['a','b','c'])

print(ser1)

ser2 = pd.Series({'a':[1,2,3],'b':['1','2','3']})

print(ser2)

a 1

b 2

c a

dtype: object

a [1, 2, 3]

b [1, 2, 3]

dtype: object

构造数据框（DataFrame）

d=[[1.3,2.0,3,4],[2,4,1,4],[2,5,1.9,7],[3,1,0,11]]

print(d)

df = pd.DataFrame(d, index=['a', 'b', 'c', 'd'], columns=['A', 'B', 'C', 'D'])

print(df)

d={'color':['blue','green','yellow','red','white'],

'object':['ball','pen','pencil','paper','mug'],

'price':[1.2,1.0,0.6,0.9,1.7]}

frame = pd.DataFrame(d,index=['a','b','c','d','e'])

print(frame)

print(pd.DataFrame(index=[1, 2], columns=[1, 2]))

print(pd.DataFrame(1, index=[1, 2], columns=[1, 2]))

[[1.3, 2.0, 3, 4], [2, 4, 1, 4], [2, 5, 1.9, 7], [3, 1, 0, 11]]

A B C D

a 1.3 2.0 3.0 4

b 2.0 4.0 1.0 4

c 2.0 5.0 1.9 7

d 3.0 1.0 0.0 11

color object price

a blue ball 1.2

b green pen 1.0

c yellow pencil 0.6

d red paper 0.9

e white mug 1.7

1 2

1 NaN NaN

2 NaN NaN

1 2

1 1 1

2 1 1

数据框的常用属性

d=[[1.3,2.0,3,4],[2,4,1,4],[2,5,1.9,7],[3,1,0,11]]

df = pd.DataFrame(d, index=['a', 'b', 'c', 'd'], columns=['A', 'B', 'C', 'D'])

print(df)

print(df.values)

print(df.index)

print(df.shape)

print(df.dtypes)

A B C D

a 1.3 2.0 3.0 4

b 2.0 4.0 1.0 4

c 2.0 5.0 1.9 7

d 3.0 1.0 0.0 11

[[ 1.3 2. 3. 4. ]

[ 2. 4. 1. 4. ]

[ 2. 5. 1.9 7. ]

[ 3. 1. 0. 11. ]]

Index(['a', 'b', 'c', 'd'], dtype='object')

(4, 4)

A float64

B float64

C float64

D int64

dtype: object

数据框的查改增删操作

import pandas as pd

访问数据框中的元素

d=[[1.3,2.0,3,4],[2,4,1,4],[2,5,1.9,7],[3,1,0,11]]

df = pd.DataFrame(d, index=['a', 'b', 'c', 'd'], columns=['A', 'B', 'C', 'D'])

print(df)

print(df['A']) # 单列数据访问

print(df[['A', 'C']]) # 多列数据访问

A B C D

a 1.3 2.0 3.0 4

b 2.0 4.0 1.0 4

c 2.0 5.0 1.9 7

d 3.0 1.0 0.0 11

a 1.3

b 2.0

c 2.0

d 3.0

Name: A, dtype: float64

A C

a 1.3 3.0

b 2.0 1.0

c 2.0 1.9

d 3.0 0.0

print(df.head(3)) # 访问某几行数据

print(df.tail(3))

A B C D

a 1.3 2.0 3.0 4

b 2.0 4.0 1.0 4

c 2.0 5.0 1.9 7

A B C D

b 2.0 4.0 1.0 4

c 2.0 5.0 1.9 7

d 3.0 1.0 0.0 11

print(df)

print(df.iloc[0, 0]) # 按照行列顺序进行数据访问

print(df.iloc[0:3, 0])

print(df.iloc[:, 0])

print(df.iloc[0, :])

print(df.iloc[1:3, 1:3])

A B C D

a 1.3 2.0 3.0 4

b 2.0 4.0 1.0 4

c 2.0 5.0 1.9 7

d 3.0 1.0 0.0 11

1.3

a 1.3

b 2.0

c 2.0

Name: A, dtype: float64

a 1.3

b 2.0

c 2.0

d 3.0

Name: A, dtype: float64

A 1.3

B 2.0

C 3.0

D 4.0

Name: a, dtype: float64

B C

b 4.0 1.0

c 5.0 1.9

<class 'pandas.core.series.Series'>

print(df.loc['a', 'A']) # 按照行列名称进行数据访问

print(df.loc['a':'c', 'A'])

print(df.loc[:, 'A'])

print(df.loc['a', :])

print(df.loc[['b','c'], ['B', 'C']])

1.3

a 1.3

b 2.0

c 2.0

Name: A, dtype: float64

a 1.3

b 2.0

c 2.0

d 3.0

Name: A, dtype: float64

A 1.3

B 2.0

C 3.0

D 4.0

Name: a, dtype: float64

B C

b 4.0 1.0

c 5.0 1.9

注意如下方式返回值的区别

print(df.iloc[:, 0])

print(df.iloc[:, 0:1])

print(type(df.iloc[:, 0]))

print(type(df.iloc[:, 0:1]))

a 1.3

b 2.0

c 2.0

d 3.0

Name: A, dtype: float64

A

a 1.3

b 2.0

c 2.0

d 3.0

<class 'pandas.core.series.Series'>

<class 'pandas.core.frame.DataFrame'>

修改数据框中的元素

d=[[1.3,2.0,3,4],[2,4,1,4],[2,5,1.9,7],[3,1,0,11]]

df = pd.DataFrame(d, index=['a', 'b', 'c', 'd'], columns=['A', 'B', 'C', 'D'])

print(df)

df.loc['a', 'A'] = 101

df.loc[:, 'B'] = 0.25

df.loc[:, 'C'] = [1, 2, 3, 4]

print(df)

A B C D

a 1.3 2.0 3.0 4

b 2.0 4.0 1.0 4

c 2.0 5.0 1.9 7

d 3.0 1.0 0.0 11

A B C D

a 101.0 0.25 1 4

b 2.0 0.25 2 4

c 2.0 0.25 3 7

d 3.0 0.25 4 11

C:\Users\Administrator\AppData\Local\Temp\ipykernel_70148\2679651701.py:7: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`

df.loc[:, 'C'] = [1, 2, 3, 4]

为数据框增添数据

d=[[1.3,2.0,3,4],[2,4,1,4],[2,5,1.9,7],[3,1,0,11]]

df = pd.DataFrame(d, index=['a', 'b', 'c', 'd'], columns=['A', 'B', 'C', 'D'])

print(df)

df['E'] = 5

df['F'] = [1, 2, 3, 4]

print(df)

A B C D

a 1.3 2.0 3.0 4

b 2.0 4.0 1.0 4

c 2.0 5.0 1.9 7

d 3.0 1.0 0.0 11

A B C D E F

a 1.3 2.0 3.0 4 5 1

b 2.0 4.0 1.0 4 5 2

c 2.0 5.0 1.9 7 5 3

d 3.0 1.0 0.0 11 5 4

import pandas as pd

删除数据框中的元素

d=[[1.3,2.0,3,4],[2,4,1,4],[2,5,1.9,7],[3,1,0,11]]

df = pd.DataFrame(d, index=['a', 'b', 'c', 'd'], columns=['A', 'B', 'C', 'D'])

print(df)

print(df.drop('D'))

print(df.drop('D', axis=1, inplace=False)) # 删除数据框的列元素

print(df)

print(df.drop(['a', 'c'], axis=0)) # 输出数据框的行元素

A B C D

a 1.3 2.0 3.0 4

b 2.0 4.0 1.0 4

c 2.0 5.0 1.9 7

d 3.0 1.0 0.0 11

A B C

a 1.3 2.0 3.0

b 2.0 4.0 1.0

c 2.0 5.0 1.9

d 3.0 1.0 0.0

A B C D

a 1.3 2.0 3.0 4

b 2.0 4.0 1.0 4

c 2.0 5.0 1.9 7

d 3.0 1.0 0.0 11

A B C D

b 2.0 4.0 1.0 4

d 3.0 1.0 0.0 11

描述分析DataFrame数据

import numpy as np

import pandas as pd

d=[[1.3,2.0,3,4],[2,4,1,4],[2,5,1.9,7],[3,1,0,11]]

df = pd.DataFrame(d, index=['a', 'b', 'c', 'd'], columns=['A', 'B', 'C', 'D'])

print(df)

print(np.mean(df, axis=1))

print(df.mean(axis=1))

print(df.std())

print(df.describe())

print(df.T.describe())

df['A'].value_counts()

转换与处理时间序列数据

import pandas as pd

order = pd.read_csv('data/meal_order_info.csv', encoding='gbk')

print(order)

print(order['lock_time'].dtypes)

order['lock_time'] = pd.to_datetime(order['lock_time'])

print(order['lock_time'].dtypes)

print(pd.DatetimeIndex(order['lock_time']))

print(pd.PeriodIndex(order['lock_time'], freq='H'))

order['lock_time']

print(order['lock_time'][0].year) # 获取数据年份信息

print(order['lock_time'].dt.year) # 获取数据年份信息

print(order['lock_time'].dt.month) # 获取数据月份信息

print(order['lock_time'].dt.week) # 获取数据周次信息

print(order['lock_time'] + pd.Timedelta(days=1)) # 时间平移

print(order['lock_time'][1] - order['lock_time'][0]) # 求时间差别

使用分组聚合进行组内计算

import pandas as pd

detail = pd.read_excel('data/meal_order_detail.xlsx')

detail.head()

detail_group = detail[['order_id', 'counts', 'amounts']].groupby(by='order_id') # 分组操作

detail_group.agg('mean').head(3) # 对分组数据的所有列都执行mean操作

detail_group.agg(['mean', 'sum']).head(3) # 对分组数据的所有列都执行mean和sum操作

detail_group.agg({'counts': ['mean', np.max], 'amounts': 'std'}).head(3) # 对分组数据的不同列执行不同操作

detail_group.agg({'counts': lambda x: sum(x)**2}).head(3) # 将自定义函数放入聚合操作中

创建透视表与交叉表

import pandas as pd

detail = pd.read_excel('data/meal_order_detail.xlsx')

detail.head()

pd.pivot_table(detail[['order_id', 'counts', 'amounts']], index='order_id', aggfunc='sum').head(3)

pd.pivot_table(detail[['order_id', 'dishes_name', 'counts']], index='order_id', columns='dishes_name',aggfunc='sum').head(3)

pd.pivot_table(detail[['order_id', 'dishes_name', 'counts']], index='order_id', columns='dishes_name',values='counts', fill_value=0).head()

pd.crosstab(index=detail['order_id'], columns=detail['dishes_name']).head(3)

pd.crosstab(index=detail['order_id'], columns=detail['dishes_name'], values=detail['counts'], aggfunc='sum').fillna(0).head(3)

Matplotlib

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

x = np.arange(0, 1.1, 0.1)
print(x)
plt.figure()        # 第一环节，创建画布
plt.plot(x, x**2)   # 第二环节，绘制图形
plt.plot(x, x**4)
plt.show()          # 第三环节，显示图形

plt.figure

x = np.arange(0, 1.1, 0.1)
print(x)
plt.figure()        # 第一环节，创建画布
plt.plot(x, x**2)   # 第二环节，绘制图形
plt.plot(x, x**4)
plt.xlim(0, 1)
plt.ylim(0, 1)
plt.title('lines')
plt.xlabel('x')
plt.ylabel('y')
plt.legend(['y=x^2', 'y=x^4'])

plt.savefig('tmp/examplt.png')
plt.show()          # 第三环节，显示图形

import numpy as np
import matplotlib.pyplot as plt

data = np.load('国民经济核算季度数据.npz', allow_pickle=True)
columns = data['columns']
values = data['values']
print(columns)
print(values)
data['values'].shape


# 绘制散点图
plt.figure(figsize=(8, 6))
plt.rcParams['font.sans-serif'] = 'SimHei'  # 设置中文显示
plt.rcParams['axes.unicode_minus'] = False

plt.scatter(values[:, 1], values[:, 3], marker='o')
plt.scatter(values[:, 1], values[:, 4], marker='*')
plt.scatter(values[:, 1], values[:, 5], marker='D')

plt.xticks(range(0, 70, 4), values[range(0, 70, 4), 1], rotation=45)
plt.legend(['第一产业生产总值', '第二产业生产总值', '第三产业生产总值'])
plt.title('2000-2017年各产业生产总值散点图')
plt.ylabel('生产总值（亿元）')
plt.savefig('tmp/2000-2017年各产业生产总值散点图.png')
plt.show()

# 绘制折线图
plt.figure(figsize=(8, 6))
plt.rcParams['font.sans-serif'] = 'SimHei'  # 设置中文显示
plt.rcParams['axes.unicode_minus'] = False

plt.plot(values[:, 1], values[:, 3], linestyle='solid')
plt.plot(values[:, 1], values[:, 4], marker='*')
plt.plot(values[:, 1], values[:, 5], marker='D')

plt.xticks(range(0, 70, 4), values[range(0, 70, 4), 1], rotation=45)
plt.legend(['第一产业生产总值', '第二产业生产总值', '第三产业生产总值'])
plt.title('2000-2017年各产业生产总值折线图')
plt.ylabel('生产总值（亿元）')
plt.savefig('tmp/2000-2017年各产业生产总值折线图.png')
plt.show()

# 绘制直方图
plt.figure(figsize=(8, 6))
plt.rcParams['font.sans-serif'] = 'SimHei'  # 设置中文显示
plt.rcParams['axes.unicode_minus'] = False

plt.title('2017年第一季度各产业生产总值直方图')
plt.ylabel('生产总值（亿元）')
plt.bar(columns[3:6], values[-1, 3:6])
my_height = values[-1, 3:6]
for i in range(len(my_height)):
    plt.text(i, my_height[i]+1000, my_height[i], va='bottom', ha='center')

plt.show()

# 绘制饼图
plt.figure(figsize=(6, 6))
plt.rcParams['font.sans-serif'] = 'SimHei'  # 设置中文显示
plt.rcParams['axes.unicode_minus'] = False

labels = ['第一产业', '第二产业', '第三产业']

plt.pie(values[-1, 3:6], explode=[0.01, 0.01, 0.01], labels=labels, autopct='%1.1f%%')
plt.title('2017年第一季度各产业生产总值饼图')
plt.show()

# 绘制箱线图
plt.figure(figsize=(6, 6))
plt.rcParams['font.sans-serif'] = 'SimHei'  # 设置中文显示
plt.rcParams['axes.unicode_minus'] = False

labels = ['第一产业', '第二产业', '第三产业']
plt.boxplot(values[:, 3:6], notch=True, labels=labels)
plt.show()

plt.figure(figsize=(6, 6))
plt.rcParams['font.sans-serif'] = 'SimHei'  # 设置中文显示
plt.rcParams['axes.unicode_minus'] = False

labels = ['第一产业', '第二产业', '第三产业']
plt.boxplot(values[:, 3], notch=True)
plt.show()

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