目录
- 前言
- 一、numpy
* - 1、数组创建
- 2、数组运算
- 3、矩阵运算
- 二、pandas
* - 1、数据结构
- 2、数据处理
– - 三、matplotlib
* - 1、matplotlib图形绘制
– - 2、seaborn图形绘制
- 3、实际案例
- 结语
前言
之前写了那么多的关于金融数据分析的实战案例，我想是时候将这些例子中的使用到的python基础知识进行总结了……
想要学好数据分析，必须掌握的numpy、pandas、matplotlib三大数据分析相关库的知识， 收藏这一篇万字长文就足够了！！！
一、numpy
numpy为python提供了大量高效实现复杂数组和矩阵运算的函数
1、数组创建
import numpy as np
a = np.array([1,2,3])
print('a = ', a)
print('数组元素类型：', a.dtype)
Out: a = [1 2 3]
数组元素类型：int32
b = np.array([1.2, 2.3, 3.4])
print('b = ', b)
print('数组元素类型：', b.dtype)
Out: b = [1.2 2.3 3.4]
数组元素类型：float64
c = np.array([[1,2],[3,4]], dtype=np.complex64)
print('c = ', c)
print('数组元素类型：', c.dtype)
Out: c = [[1.+0.j 2.+0.j]
[3.+0.j 4.+0.j]]
数组元素类型：complex64
d = np.array([(1.2, 2.3),(3, 4)])
print('d = ', d)
print('数组元素类型：', d.dtype)
Out: d = [[1.2 2.3]
[3. 4. ]]
数组元素类型： float64
e = np.zeros((3,4))
print('e = ', e)
print('数组元素类型：', e.dtype)
e = [[0. 0. 0. 0.]
[0. 0. 0. 0.]
[0. 0. 0. 0.]]
数组元素类型： float64
f = np.ones((2,3,4), dtype=np.int16)
print('f = ', f)
print('数组元素类型：', f.dtype)
f = [[[1 1 1 1]
[1 1 1 1]
[1 1 1 1]]
[[1 1 1 1]
[1 1 1 1]
[1 1 1 1]]]
数组元素类型： int16
h = np.arrange(0, 2, 0.3)
print('h = ', h)
print('数组元素类型：', h.dtype)
h = [0. 0.3 0.6 0.9 1.2 1.5 1.8]
数组元素类型： float64
numpy使用ndarray类对象来处理多维数组，ndarray一些可以直接访问的属性：
属性名含义ndarray.ndim数组轴的个数ndarray.shape数组的形状ndarray.size数组中元素的总数ndarray.dtype数组中元素的数据类型ndarray.itemsize数组中每个元素的字节大小ndarray.data实际数组元素的缓冲区
x = np.arange(15).reshape(3,5)
print('x = ', x)
print('数组轴的个数:',x.ndim)
print('数组的形状:',x.shape)
print('数组中元素的总数:',x.size)
print('数组中元素的数据类型:',x.dtype)
print('数组中每个元素的字节大小:',x.itemsize)
print('实际数组元素的缓冲区:',x.data)
x = [[ 0 1 2 3 4]
[ 5 6 7 8 9]
[10 11 12 13 14]]
数组轴的个数: 2
数组的形状: (3, 5)
数组中元素的总数: 15
数组中元素的数据类型: int32
数组中每个元素的字节大小: 4
实际数组元素的缓冲区: <memory at 0x0000024C2033D708>
numpy数组输出基本规则：一维数组输出成行，二维数组输出为矩阵，三维数组输出为矩阵列表。
2、数组运算
a = np.array([20,30,40,50])
b = np.arange(4)
print('a = ',a)
print('b = ',b)
a = [20 30 40 50]
b = [0 1 2 3]
c = a - b
print('c = (a - b) = ',c)
c = (a - b) = [20 29 38 47]
b_2 = b**2
print('b_2 = b * b = ',b_2)
b_2 = b * b = [0 1 4 9]
a_sin = np.sin(a)
print('a_sin = sin(a) = ',a_sin)
a_sin = sin(a) = [ 0.91294525 -0.98803162 0.74511316 -0.26237485]
a_cond = (a<35)
print('a_cond = (a,a_cond)
print('a数组中小于35的元素是：',a[a<35])
a_cond = (a<35) = [ True True False False]
a数组中小于35的元素是： [20 30]
f = np.ones((2,3),dtype=np.int)
g = np.random.random((2,3))
print('f = ',f)
print('g = ',g)
f*=3
g+=f
print('f = ',f)
print('g = ',g)
f = [[1 1 1]
[1 1 1]]
g = [[0.48818655 0.14805382 0.24190023]
[0.23753093 0.87251939 0.53028234]]
f = [[3 3 3]
[3 3 3]]
g = [[3.48818655 3.14805382 3.24190023]
[3.23753093 3.87251939 3.53028234]]
print('g.sum()=',g.sum())
print('g.min()=',g.min())
print('g.max()=',g.max())
g.sum()= 20.518473272469617
g.min()= 3.1480538249037986
g.max()= 3.872519388164515
print('axis=0,表示按列进行运算，g.max(axis=0)=',g.max(axis=0))
print('axis=1,表示按行进行运算，g.max(axis=1)=',g.max(axis=1))
axis=0,表示按列进行运算，g.max(axis=0)= [3.48818655 3.87251939 3.53028234]
axis=1,表示按行进行运算，g.max(axis=1)= [3.48818655 3.87251939]
h = np.random.random(15)
print('h = ',h)
print('排序后的h = ',np.sort(h))
h = [0.05010685 0.16184821 0.59109447 0.98844731 0.68039122 0.13331593
0.61283281 0.49090379 0.30930569 0.0817642 0.30148594 0.18140083
0.03374513 0.75961934 0.990532 ]
排序后的h = [0.03374513 0.05010685 0.0817642 0.13331593 0.16184821 0.18140083
0.30148594 0.30930569 0.49090379 0.59109447 0.61283281 0.68039122
0.75961934 0.98844731 0.990532 ]
numpy常见函数：
函数名功能np.array(X)或np.array(X, dtype)将数据转化为一个ndarray；数组中元素类型为dtypenp.asarray(array)cope一个新的数组np.ones(X)或np.ones(X, dtype)或np.ones_like(array)形成一个元素全为1的数组；数组中元素类型为dtype；形状与参数array相同np.zeros(X)或np.zeros(X, dtype)或np.zeros_like(array)形成一个元素全为0的数组；数组中元素类型为dtype；形状与参数array相同np.empty(X)或np.empty(X, dtype)或np.empty_like(array)形成一个未初始化的数组；数组中元素类型为dtype；形状与参数array相同np.eye(N)或np.identity(N)形成一个NN维的数组，对角线为1，其余为0np.arange(num)或np.arange(begin,end)或np.arange(begin,end,step)形成一个[0，num-1)的数组；形成一个[begin，end-1)的数组；步长为stepnp.mershgrid(ndarray,…)生成一个ndarrayndarray*…的多维ndarraynp.where(cond,ndarray1,ndarray2)根据条件cond，选取ndarray1或ndarray2np.in1d(ndarray,[a,b,…])检查ndarray中是否有元素等于[a,b,…]，返回bool数组np.diag(ndarray)或np.diag([x,y…])以一维数组形式返回方阵的对角线；将一维数组转化为方阵np.dot(ndarray,ndarray)矩阵乘法np.trace(ndarray)对角线元素和np.sort(ndarray)或np.unique(ndarray)排序；排除重复元素再排序np.save(string.ndarray)将ndarray保存为string.npy的文件中np.savez(string,ndarray1,ndarray2…)将所有ndarray压缩保存为string.npy的文件中np.savetxt(string,ndarray,fmt,newline=’\n’)将ndarray保存到fmt格式的文件中np.load(string)读取文件转化为ndarray对象np.loadtxt(string,delimiter)读取文件，以delimiter为分隔符转化为ndarray对象
3、矩阵运算
矩阵式金融数据分析的重要数据结构之一。矩阵运算主要包括：生成矩阵、矩阵加减乘除，点积和内积运算、矩阵转置、矩阵特征值和特征向量，矩阵行列式值等等
a = np.array([[1,2],[3,4]])
b = np.array([[5,6],[7,8]])
print('a = ',a)
print('b = ',b)
print('元素乘法a*b:',a*b)
print('矩阵相乘:',np.dot(a,b))
a = [[1 2]
[3 4]]
b = [[5 6]
[7 8]]
元素乘法a*b: [[ 5 12]
[21 32]]
矩阵相乘: [[19 22]
[43 50]]
a = np.array([[1,2],[3,4]])
b = np.linalg.det(a)
print('矩阵a对应的行列式的值：',b)
c = np.linalg.inv(a)
print('矩阵a的逆矩阵：',c)
d = np.linalg.eig(a)
print('矩阵a的特征值',d[0])
print('矩阵a的特征向量',d[1])
矩阵a对应的行列式的值： -2.0000000000000004
矩阵a的逆矩阵： [[-2. 1. ]
[ 1.5 -0.5]]
矩阵a的特征值 [-0.37228132 5.37228132]
矩阵a的特征向量 [[-0.82456484 -0.41597356]
[ 0.56576746 -0.90937671]]
1. 3x + y - 2z = 5
2. x - y + 4z = -2
3. 2x + 3z = 2.5
from scipy.linalg import solve
a = np.array([[3,1,-2],[1,-1,4],[2,0,3]])
b = np.array([5,-2,2.5])
print('方程形如Ax=b,其中A={},\nb={}'.format(a,b))
x = solve(a,b)
print('方程的解为：',x)
方程形如Ax=b,其中A=[[ 3 1 -2]
[ 1 -1 4]
[ 2 0 3]],
b=[ 5. -2. 2.5]
方程的解为： [0.5 4.5 0.5]
二、pandas
pandas是以numpy为基础的python程序包，具备丰富的表格型数据操作功能
1、数据结构
Series：一维数组，类似list数据类型，用下标索引方式访问数据元素
Time-Series：时间序列，采用时间索引方式访问数据元素
DataFrame：二维表格型数据结构
Panel：三维数据，可装载多个DataFrame
import pandas as pd
s1 = pd.Series([100,'gd','gz'])
print(s1)
print('s1.shape=',s1.shape)
print('s1.index=',s1.index)
print('s1.values=',s1.values)
0 100
1 gd
2 gz
dtype: object
s1.shape= (3,)
s1.index= RangeIndex(start=0, stop=3, step=1)
s1.values= [100 'gd' 'gz']
s2 = pd.Series([100,'gd','gz'], index=['mark','university','city'])
print(s1)
print('s2.shape=',s2.shape)
print('s2.index=',s2.index)
print('s2.values=',s2.values)
print('s2["mark"]=',s2['mark'])
0 100
1 gd
2 gz
dtype: object
s2.shape= (3,)
s2.index= Index(['mark', 'university', 'city'], dtype='object')
s2.values= [100 'gd' 'gz']
s2["mark"]= 100
data = {'name':['python','java','c++'],'score':[95,90,99],'year':[2020,2021,2022]}
print('字典data：',data)
s3 = pd.DataFrame(data)
print('DataFrame s3:\n',s3)
print('s3.shape=',s3.shape)
print('s3.index=',s3.index)
print('s3.values=',s3.values)
字典data： {'name': ['python', 'java', 'c++'], 'score': [95, 90, 99], 'year': [2020, 2021, 2022]}
DataFrame s3:
name score year
0 python 95 2020
1 java 90 2021
2 c++ 99 2022
s3.shape= (3, 3)
s3.index= RangeIndex(start=0, stop=3, step=1)
s3.values= [['python' 95 2020]
['java' 90 2021]
['c++' 99 2022]]
2、数据处理
2.1、数据结构与描述性统计
dates = pd.date_range('20220401',periods=6)
df = pd.DataFrame(np.random.randn(6,3),index=dates,columns=['a','b','c'])
print('df:\n',df)
df:
a b c
2022-04-01 1.157682 1.248710 0.252461
2022-04-02 1.890162 -0.460040 -0.952669
2022-04-03 -0.089896 1.124548 0.842600
2022-04-04 1.088276 0.359554 1.005010
2022-04-05 -0.087399 0.242903 0.389513
2022-04-06 1.431301 1.716398 0.374692
df_1 = pd.DataFrame({'a':1.,
'b':pd.Timestamp('20220401'),
'c':pd.Series(1,index=list(range(4)),dtype='float32'),
'd':np.array([3]*4,dtype='int32'),
'e':pd.Categorical(["test","train","test","train"]),
'f':'foo'})
print('df_1:\n',df_1)
print('各列数据类型：\n',df_1.dtypes)
df_1:
a b c d e f
0 1.0 2022-04-01 1.0 3 test foo
1 1.0 2022-04-01 1.0 3 train foo
2 1.0 2022-04-01 1.0 3 test foo
3 1.0 2022-04-01 1.0 3 train foo
各列数据类型：
a float64
b datetime64[ns]
c float32
d int32
e category
f object
dtype: object
print('df_1数据描述：\n',df_1.describe())
df_1数据描述：
a c d
count 4.0 4.0 4.0
mean 1.0 1.0 3.0
std 0.0 0.0 0.0
min 1.0 1.0 3.0
25% 1.0 1.0 3.0
50% 1.0 1.0 3.0
75% 1.0 1.0 3.0
max 1.0 1.0 3.0
print('按e列的值进行排序：\n',df_1.sort_values(by='e'))
按e列的值进行排序：
a b c d e f
0 1.0 2022-04-01 1.0 3 test foo
2 1.0 2022-04-01 1.0 3 test foo
1 1.0 2022-04-01 1.0 3 train foo
3 1.0 2022-04-01 1.0 3 train foo
2.2、切片访问与缺失处理
print('访问b、c列：\n',df[['b','c']])
访问b、c列：
b c
2022-04-01 0.057102 -0.183773
2022-04-02 -0.487013 0.513329
2022-04-03 0.184821 0.509904
2022-04-04 -0.392358 1.952551
2022-04-05 -0.740828 0.595205
2022-04-06 1.425204 0.604636
print('访问前2行：\n',df[0:2])
访问前2行：
a b c
2022-04-01 0.400813 0.057102 -0.183773
2022-04-02 1.652287 -0.487013 0.513329
print('利用loc:\n',df.loc['20220402',['a','b']])
print('利用iloc:\n',df.iloc[3:5,0:2])
利用loc:
a 1.652287
b -0.487013
Name: 2022-04-02 00:00:00, dtype: float64
利用iloc:
a b
2022-04-04 0.915201 -0.392358
2022-04-05 1.037104 -0.740828
print('df中b列大于0的数据：\n',df[df.b>0])
df中b列大于0的数据：
a b c
2022-04-01 0.400813 0.057102 -0.183773
2022-04-03 1.124814 0.184821 0.509904
2022-04-06 0.733508 1.425204 0.604636
df['d'] = pd.Series([1,2,3,4,5,6],index=pd.date_range('20220402',periods=6))
print(df)
a b c d
2022-04-01 0.400813 0.057102 -0.183773 NaN
2022-04-02 1.652287 -0.487013 0.513329 1.0
2022-04-03 1.124814 0.184821 0.509904 2.0
2022-04-04 0.915201 -0.392358 1.952551 3.0
2022-04-05 1.037104 -0.740828 0.595205 4.0
2022-04-06 0.733508 1.425204 0.604636 5.0
df1 = df.dropna(axis=0,how='any')
print(df1)
a b c d
2022-04-02 1.652287 -0.487013 0.513329 1.0
2022-04-03 1.124814 0.184821 0.509904 2.0
2022-04-04 0.915201 -0.392358 1.952551 3.0
2022-04-05 1.037104 -0.740828 0.595205 4.0
2022-04-06 0.733508 1.425204 0.604636 5.0
df2 = df.fillna(value=0)
print(df2)
a b c d
2022-04-01 0.400813 0.057102 -0.183773 0.0
2022-04-02 1.652287 -0.487013 0.513329 1.0
2022-04-03 1.124814 0.184821 0.509904 2.0
2022-04-04 0.915201 -0.392358 1.952551 3.0
2022-04-05 1.037104 -0.740828 0.595205 4.0
2022-04-06 0.733508 1.425204 0.604636 5.0
2.3、多表合并
df3 = pd.DataFrame({'id':[1001,1002,1003,1004,1005,1006],
'date':pd.date_range('20220401',periods=6),
'city':['beijing','shanghai','guanzhou','shenzhen','tianjin','xian'],
'age':[23,44,54,32,34,32],
'category':['100a','100b','110a','110c','210a','130f'],
'price':[1200,np.nan,2133,5433,np.nan,4432]})
print('df3:\n',df3)
df3:
id date city age category price
0 1001 2022-04-01 beijing 23 100a 1200.0
1 1002 2022-04-02 shanghai 44 100b NaN
2 1003 2022-04-03 guanzhou 54 110a 2133.0
3 1004 2022-04-04 shenzhen 32 110c 5433.0
4 1005 2022-04-05 tianjin 34 210a NaN
5 1006 2022-04-06 xian 32 130f 4432.0
df4 = pd.DataFrame({'id':[1001,1002,1003,1004,1005,1006,1007,1008],
'gender':['male','female','male','female','male','male','female','female'],
'pay':['y','n','n','y','n','n','y','y'],
'point':[10,12,20,40,40,40,30,20]})
print('df4:\n',df4)
df4:
id gender pay point
0 1001 male y 10
1 1002 female n 12
2 1003 male n 20
3 1004 female y 40
4 1005 male n 40
5 1006 male n 40
6 1007 female y 30
7 1008 female y 20
print('df3、df4的交集：\n',pd.merge(df3,df4,how='inner'))
df3、df4的交集：
id date city age category price gender pay point
0 1001 2022-04-01 beijing 23 100a 1200.0 male y 10
1 1002 2022-04-02 shanghai 44 100b NaN female n 12
2 1003 2022-04-03 guanzhou 54 110a 2133.0 male n 20
3 1004 2022-04-04 shenzhen 32 110c 5433.0 female y 40
4 1005 2022-04-05 tianjin 34 210a NaN male n 40
5 1006 2022-04-06 xian 32 130f 4432.0 male n 40
print('df3_df4_right:\n',pd.merge(df3,df4,how='outer'))
df3_df4_right:
id date city age category price gender pay point
0 1001 2022-04-01 beijing 23.0 100a 1200.0 male y 10
1 1002 2022-04-02 shanghai 44.0 100b NaN female n 12
2 1003 2022-04-03 guanzhou 54.0 110a 2133.0 male n 20
3 1004 2022-04-04 shenzhen 32.0 110c 5433.0 female y 40
4 1005 2022-04-05 tianjin 34.0 210a NaN male n 40
5 1006 2022-04-06 xian 32.0 130f 4432.0 male n 40
6 1007 NaT NaN NaN NaN NaN female y 30
7 1008 NaT NaN NaN NaN NaN female y 20
pandas文件读取
函数功能pd.read_csv(‘file.csv’)读取csv文件pd.read_json(‘file.json’)读取json文件pd.read_excel(‘file.xls’,sheetname=[0,1…])读取excel文件多个sheet页，返回多个df的字典
pandas数据预处理函数
函数功能df.duplicated()或df.drop_duplicated()返回各行是否是上一行的重复行；删除重复行df.fillna(0)用0填充nandf.dropna(axis,how)axis=0:按行，axis=1:按列；how=’any’:有nan就删，how=’all’:全是nan才删del df([‘col’,…],axis)删除行列df.column=col_name指定列名
pandas数据筛选函数
函数名功能df.columns列名df.index索引名df.shape行x列df.head(n=N)前几行df.tail(n=N)后几行df.valuesnp对象的二维数组df.reindex(index=[‘row1’…],columns=[‘col1’…]重新排序df[n:m]切片，n~m-1
pandas数学运算和描述性统计函数
函数名功能df.T转置df1+df2合并，空值nandf1.add(df2,fill_value=0)合并，空值0df.sort_index(axis=0)按行索引升序df.sort_index(by=[‘col1’…])按特定值排序df.rank()计算排名rank值df.describe()描述性统计df.count()计算非nan值df.max/min/sum/mean/median/var/std()最大值最小值…df.cumsum()累计和df.cov()协方差df.groupby(‘col1’)按列分组
三、matplotlib
1、matplotlib图形绘制
绘图区域有figure表示，一个figure表示一个图形窗口
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False
x = np.arange(100,201)
y = 2*x + 1
plt.figure(figsize=(10,6))
plt.xlim(100,201)
plt.plot(x,y)
plt.xlabel('X值',fontsize=16)
plt.ylabel('Y值',fontsize=16)
plt.xticks(fontproperties='Time New Roman',size=14)
plt.yticks(fontproperties='Time New Roman',size=14)
plt.savefig('pic1.png',dpi=300,bbox_inches='tight')
plt.show()
1.1、绘制散点图
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False
plt.figure(figsize=(10,6))
n = 20
plt.scatter(np.random.rand(n)*100,np.random.rand(n)*100,c='r',s=100,alpha=0.8)
plt.scatter(np.random.rand(n)*100,np.random.rand(n)*100,c='g',s=200,alpha=0.5)
plt.scatter(np.random.rand(n)*100,np.random.rand(n)*100,c='k',s=300,alpha=0.2)
plt.xlabel('X值',fontsize=16)
plt.ylabel('Y值',fontsize=16)
plt.xticks(fontproperties='Time New Roman',size=14)
plt.yticks(fontproperties='Time New Roman',size=14)
plt.show()
1.2、绘制柱状图
y2021 = [15600,12700,11300,4270,3620]
y2022 = [17400,14800,12000,5200,4020]
labels = ['bj','sh','hk','sz','gz']
bar_width=0.4
plt.figure(figsize=(10,6))
plt.bar(np.arange(5),y2021,label='2021',color='g',alpha=0.8,width=bar_width)
plt.bar(np.arange(5)+bar_width,y2022,label='2022',color='r',alpha=0.8,width=bar_width)
plt.xlabel('top5 city',fontproperties='Time New Roman',size=16)
plt.ylabel('family amount',fontproperties='Time New Roman',size=16)
plt.xticks(np.arange(5)+bar_width/2,labels)
plt.xticks(fontproperties='Time New Roman',size=14)
plt.yticks(fontproperties='Time New Roman',size=14)
plt.title('millions family amount top5 city distribution',fontproperties='Time New Roman',size=16)
for x1,y1 in enumerate(y2021):
plt.text(x1-0.1,y1+150,y2021[x1])
for x2,y2 in enumerate(y2022):
plt.text(x2+0.25,y2+150,y2022[x2])
plt.legend()
plt.show()
1.3、绘制饼状图
plt.figure(figsize=(10,6))
edu = [0.2515,0.0057,0.3724,0.3336,0.0368]
labels = ['highschool','others','junior college','bachelor','master']
exp = [0.1,0,0,0,0]
colors = ['r','g','y','purple','blue']
plt.axes(aspect='equal')
plt.xlim(0,4)
plt.ylim(0,4)
plt.pie(x = edu,
explode = exp,
labels = labels,
colors = colors,
autopct = '%.1f%%',
pctdistance = 0.5,
labeldistance = 0.8,
startangle = 180,
radius = 1.5,
counterclock = False,
wedgeprops = {'linewidth':1.5,'edgecolor':'white'},
textprops = {'fontsize':15,'color':'k'},
center = (2,2),
frame = 1)
plt.xticks(())
plt.yticks(())
plt.title('xx客户分析')
plt.show()
1.4、绘制3D图
from mpl_toolkits.mplot3d import Axes3D
plt.figure(figsize=(10,6))
data = np.random.randint(0,255,size=[6,6,6])
x,y,z = data[0],data[1],data[2]
ax = plt.subplot(111,projection='3d')
ax.scatter(x[0:2],y[0:2],z[0:2],c='y',s=100)
ax.scatter(x[2:4],y[2:4],z[2:4],c='r',marker='*',s=100)
ax.scatter(x[4:6],y[4:6],z[4:6],c='g',marker='v',s=100)
ax.set_zlabel('Z')
ax.set_ylabel('Y')
ax.set_xlabel('X')
plt.show()
2、seaborn图形绘制
seaborn是基于matplotlib的python数据可视化程序包，它简化了繁琐复杂的参数设置，有助于用户创建具有统计意义的图形
import matplotlib.pyplot as plt
import seaborn as sns
plt.figure(figsize=(10,6))
x = [1,3,5,7,9,11,13,15,17,19]
y_bar = [3,4,6,8,9,10,9,11,7,8]
y_line = [2,3,5,7,8,9,8,10,6,7]
sns.set()
plt.bar(x,y_bar)
plt.plot(x,y_line,'-o',color='y')
plt.show
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
plt.figure(figsize=(10,6))
sns.set(palette="muted",color_codes=True)
rs = np.random.RandomState(10)
d = rs.normal(size=100)
sns.distplot(d,kde=False,color='b')
plt.show()
f,axes = plt.subplots(2,2,figsize=(7,7),sharex=True)
sns.distplot(d,kde=False,color='b',ax=axes[0,0])
sns.distplot(d,hist=False,rug=True,color='r',ax=axes[0,1])
sns.distplot(d,hist=False,color='g',kde_kws={"shade":True},ax=axes[1,0])
sns.distplot(d,color='m',ax=axes[1,1])
plt.show()
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
from scipy import stats
mean,cov = [0,1],[(1,.5),(.5,1)]
data = np.random.multivariate_normal(mean,cov,1000)
df = pd.DataFrame(data,columns=['x','y'])
plt.figure(figsize=(10,6))
g = sns.jointplot(x='x',y='y',data=df,kind='hex',color='k')
g.set_axis_labels('X','Y',size=16)
g.annotate(stats.pearsonr,fontsize=12)
g.show()
x = np.arange(0,100)
y = 2*x+1
var = np.random.normal(0,50,100)
y = y + var
df1 = pd.DataFrame()
df1['x'] = x
df1['y'] = y
plt.figure(figsize=(10,6))
g1 = sns.jointplot(x='x',y='y',data=df1,kind='reg',color='k')
g1.set_axis_labels('X值','Y值',size=16)
g1.savefig('sea05.png',dpi=300,bbox_inches='tight')
3、实际案例
import akshare as ak
stock1 = ak.stock_zh_a_daily('sh000001','20201231','20211231')
stock2 = ak.stock_zh_a_daily('sz300750','20201231','20211231')
stock3 = ak.stock_zh_a_daily('sh603738','20201231','20211231')
df = pd.DataFrame()
df['sh000001'] = stock1['close'].pct_change()
df['sz300750'] = stock2['close'].pct_change()
df['sh603738'] = stock3['close'].pct_change()
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False
sns.set_style("whitegrid",{'font.sans-serif':['simhei','Arial']})
sns.PairGrid(df).map(plt.scatter)
plt.figure(figsize=(10,8))
plt.title("三只股票相关系数矩阵",y=1.05,size=16)
sns.heatmap(df.corr(),linewidths=0.1,vmax=1.0,square=True,linecolor='white',
annot=True,annot_kws={'size':20,'weight':'bold','color':'white'})
plt.savefig('相关矩阵.png',dpi=300,bbox_inches='tight')
结语
看完这些基础知识后再看以前写的实战案例文章栏目【金融数据分析】，或许会有新的收获~
大家也可以在评论区留言，一起交流学习吧！
Original: https://blog.csdn.net/weixin_47974364/article/details/123893669
Author: 貮叁
Title: 【基础汇总】——python数据分析必备三大工具
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