python教程-4.数据处理numpy-pandas

4. 数据库处理

0. 简介

0.1 安装

pip install numpy pandas

或者

pip3 install numpy pandas

1. numpy

1.1 基本属性

import numpy as np

array=[[1,2,3],[4,5,6]]
arr=np.array(array)
# 打印矩阵
print(arr)
# 维度 --二维矩阵
print(arr.ndim)
# 矩阵的维度
print(arr.shape)
# 矩阵元素的个数
print(arr.size)

import numpy as np

# 一维
arr1=np.array([1,3,4])
print(arr1)

# 定义一个二维
array=[[1,2,3],[4,5,6]]
# 这边类型有int16,int32，int64,float32,float64
arr=np.array(array,dtype=np.float64)
print(arr.dtype)

# 定义一个全部为0 的数组
a=np.zeros(5)
print("定义一个全部为0 的数组",a)
# 定义一个二维矩阵
a=np.zeros((2,4),dtype=np.int64)
print(a)
# 定义一个三维举证
a=np.zeros((2,4,1),dtype=np.int32)
print(a)


# 定义全为1的
a=np.ones(3)
print("定义全为1的",a)
a=np.empty(3)
print("生成空的矩阵",a)
a=np.arange(3)
print(a)
# 生成3*4 的二维矩阵
a=np.arange(12).reshape(3,4)
print("生成3*4 的二维矩阵",a)

1.2 基础运算

一维

import numpy as np
a=np.array([10,20,30,40])
b=np.arange(4)
print(a,b)
# 加法运算
c=a+b
print(c)

# 平方
c=b**2
print(c)

# 平方，一样的效果
c=b*b
print(c)

# 对每个元素进行tan
print(np.tan(a))

# 对每个元素进行比较
print(a<30)
print(a==30)

二维

import numpy as np
a=np.array([[1,2],[3,4]])
b=np.arange(4).reshape((2,2))
c=np.arange(4).reshape((2,2))
print(a)
print(b)

# 每个元素自己相乘
print(a*b)

# 矩阵相乘
c_dot=np.dot(a,b)
print(c_dot)
# 矩阵相乘的另一种写法
print(a.dot(b))

# 随机生成一个矩阵
a=np.random.random((2,4))
print(a)

print("所有元素求和",np.sum(c_dot))
print("所有元素的最小值",np.min(c_dot))
# 1 表示行，0 表示列
print("每一行的最小值",np.min(c_dot,axis=1))
print("每一列的最小值",np.min(c_dot,axis=0))
print("所有元素的最大值",np.max(c_dot))
print("所有元素求和",np.sum(c_dot,axis=1))

import numpy as np
A=np.arange(2,14).reshape((3,4))
print(A)

# 最小值的索引值
print(np.argmin(A))

# 最大值的索引值
print(np.argmax(A))

# 所有值的平均值
print(np.mean(A))
# 所有值的平均值
print(np.average(A))

# 逐渐累加的过程
print(np.cumsum(A))

# 中位数
print(np.median(A))

# 累差，每两个数之间的差
print(np.diff(A))

#逐行进行排序
A_1=np.arange(14,2,-1).reshape((3,4))
print(A_1)
print(np.sort(A_1))

# 矩阵的转置
print(np.transpose(A))

# 截取矩阵
print(np.clip(A,5,9))
# 平均值
print(np.mean(A,axis=0))
# 对行进行平均值
print(np.mean(A,axis=1))

1.3 索引

import numpy as npA=np.arange(3,15).reshape(3,4)print(A)print("打印的是一行",A[2])print("打印一个数字",A[1][1])print("打印一个数字",A[2,1])print("打印范围",A[2,1:3])# 打印列for column in A.T:    print(column)    # 返回一个arrayprint(A.flatten())# 打印所有数字for item in A.flat:    print(item)

1.4 array 合并

• np.append()
• np.concatenate()
• np.stack()
• np.hstack()
• np.vstack()
• np.dstack()

其中最泛用的是第一个和第二个。第一个可读性好，比较灵活，但是占内存大。第二个则没有内存占用大的问题。

parameters	introduction
arr	待合并的数组的复制（特别主页是复制，所以要多耗费很多内存）
values	用来合并到上述数组复制的值。如果指定了下面的参数axis的话，则这些值必须和arr的shape一致(shape[axis]之外都相等)，否则的话，则没有要求。
axis	要合并的轴

import numpy as npA=np.array([1,1,1])B=np.array([2,2,2])C=np.vstack((A,B))# 1 为列进行合并D=np.append([[1,2,3]],[[3,4,54]],axis=1)# 左右合并F=np.hstack((A,B))# 垂直合并print(C)print(A.shape)print(D)print(F)# 实现变成数列的print(A[:,np.newaxis])print(np.vstack(A))print(np.concatenate((A,B,A,B),axis=0))

concatenate

parameters	introduction
*arrays	这些数组除了在待合并的axis（默认为axis=0）上之外，必须具有相同的shape
axis	待合并的轴，默认为0

1.5 array 分割

import numpy as npA=np.arange(12).reshape((3,4))print(A)# 分成两列print(np.split(A,2,axis=1))# 分成三列print(np.split(A,[1,1,2],axis=1))# 水平分割print(np.hsplit(A,2))# 垂直分割print(np.vsplit(A,3))a = np.arange(24).reshape(2,3,4)print(a)#深度分割print(np.dsplit(a,2))

1.6 拷贝

import numpy as npa=np.arange(4,dtype=float)print(a)# 浅拷贝，当原来的array 修改时，b 也跟着修改b=aprint(b is a)a[0]=0.3print(a)print(b)# 深度拷贝b=a.copy()a[0]=0.55print(a)print(b)b[0]=12print(a)print(b)

2.pandas

2.1 基本介绍

import pandas as pdimport numpy as nps=pd.Series([1,3,6,np.nan,44,1])print(s)dates=pd.date_range("20210701",periods=6)print(dates)df=pd.DataFrame(np.random.randn(6,4),index=dates,columns=["a","b","c","d"])print(df)# 可以使用字典的方式进行df2=pd.DataFrame({"A":1,"B":"kk","C":np.array([1,2,3])})print(df2)# 打印每一列的属性print(df2.dtypes)# 打印列的值print(df2.columns)print(df2.T)# 进行排序print(df2.sort_index(axis=1,ascending=False))print(df2.sort_index(axis=0,ascending=False))# 对于矩阵中的值进行排序print(df2.sort_values(by="C",ascending=False))

2.2 选择数据

import pandas as pdimport numpy as npdates=pd.date_range("20210703",periods=6)df=pd.DataFrame(np.arange(24).reshape((6,4)),index=dates,columns={"A","B","C","D"})print(df["A"])print(df.A)print("0-3 行",df[0:3])print(df["20210703":"20210705"])# loc 是纯标签的筛选print(df.loc["20210704"])print(df.loc["20210704",["A","B"]])# iloc 是纯数字的筛选print(df.iloc[3,1])print(df.iloc[3:5,1:3])# ix 是混合的筛选print(df.ix[:3,["A","C"]])

2.3 设置值

import pandas as pdimport numpy as npdates=pd.date_range("20210703",periods=6)df=pd.DataFrame(np.arange(24).reshape((6,4)),index=dates,columns={"A","B","C","D"})# 选择标签，然后设置值df.iloc[2,2]=1111print(df)df.loc["20210703","B"]=2222print(df)df.B[df.A>8]=0print(df)df["E"]=np.nanprint(df)df["F"]=[1,2,3,4,5,6]print(df)

2.4 处理丢失数据

import pandas as pdimport numpy as npdates=pd.date_range("20210703",periods=6)df=pd.DataFrame(np.arange(24).reshape((6,4)),index=dates,columns={"A","B","C","D"})df["E"]=np.nandf.iloc[0,1]=np.nandf.iloc[1,2]=np.nanprint(df)# 如果一行中，有nan ，就全部丢掉，这边是全部丢掉print(df.dropna(axis=0,how="any"))# 丢掉列，某一列全部是nan，才全部丢掉print(df.dropna(axis=1,how="all"))# 填充其中的nanprint(df.fillna(value=0))print(df.isnull())# 返回是否有一个nanprint(np.any(df.isnull())==True)

2.5 导入导出数据

api 网址

https://pandas.pydata.org/docs/reference/io.html

样例

import pandas as pdimport numpy as np# 首先准备一个csvdata=pd.read_csv(r"C:\Users\bn\Desktop\1.csv")print(data)data.to_pickle("1.pickle")

2.6 合并

concat

import pandas as pdimport numpy as npdf1=pd.DataFrame(np.ones((3,4))*0,columns=["a","b","c","d"])df2=pd.DataFrame(np.ones((3,4))*1,columns=["a","b","c","d"])df3=pd.DataFrame(np.ones((3,4))*2,columns=["a","b","c","d"])print(df1)print(df2)res=pd.concat([df1,df2,df3],axis=0)print(res)df1=pd.DataFrame(np.ones((3,4))*0,columns=["a","b","c","d"],index=[1,2,3])df2=pd.DataFrame(np.ones((3,4))*1,columns=["b","c","d","e"],index=[2,3,4])res=pd.concat([df1,df2])# 多余的部分会用NaN 连接print(res)# 寻找相同的列res=pd.concat([df1,df2],join="inner",ignore_index=True)print(res)# 还有append数据df1=pd.DataFrame(np.ones((3,4))*0,columns=["a","b","c","d"])df2=pd.DataFrame(np.ones((3,4))*1,columns=["a","b","c","d"])df3=pd.DataFrame(np.ones((3,4))*2,columns=["a","b","c","d"])res=df1.append([df2,df3])print(res)

merge

import pandas as pdimport numpy as npleft=pd.DataFrame({"key":["K0","K1","K2","K3"],                   "A":["A0","A1","A2","A3"],                   "B":["B0","B1","B2","B3"],                  })right=pd.DataFrame({"key":["K0","K1","K2","K3"],                   "C":["C0","C1","C2","C3"],                   "D":["D0","D1","D2","D3"],                  })print(left)print(right)res=pd.merge(left,right,on="key")print(res)

2.7 打印

import matplotlib.pyplot as pltimport pandas as pdimport numpy as npdata=pd.Series(np.random.randn(1000),index=np.arange(1000))data=data.cumsum()data.plot()plt.show()# 矩阵的数据data=pd.DataFrame(np.random.randn(1000,4),                  index=np.arange(1000),columns=list("ABCD"))print(data.head())data=data.cumsum()data.plot()plt.show()

3.matplotllib

3.1 基本用法

import matplotlib.pyplot as pltimport pandas as pdimport numpy as npx=np.linspace(-1,1,50)y=2*x+1plt.plot(x,y)plt.show()

3.2 figure 用法

每一个figure 中，就会有不同的图片

import matplotlib.pyplot as pltimport pandas as pdimport numpy as npx=np.linspace(-1,1,50)y1=2*x+1y2=x**2plt.figure()plt.plot(x,y1)plt.show()# 展示第二张图# 一个figure 就是一张图plt.figure()plt.plot(x,y2,color="red",linewidth=2.0,linestyle="--")plt.show()

3.3 设置坐标轴

import matplotlib.pyplot as pltimport pandas as pdimport numpy as npx=np.linspace(-3,3,50)y1=2*x+1y2=x**2plt.figure()plt.xlim((-1,2))plt.ylim((-2,3))plt.xlabel("t am x")plt.ylabel("t am y")new_ticks=np.linspace(-1,2,5)plt.xticks(new_ticks)plt.yticks([-2,-1.8,-1,1,3],["really bad","bad","normal","good","really god"])plt.plot(x,y1)plt.show()plt.figure()# 移动x 轴或者y 轴ax=plt.gca()ax.spines["right"].set_color("none")ax.spines["top"].set_color("none")ax.xaxis.set_ticks_position("bottom")ax.yaxis.set_ticks_position("left")ax.spines["bottom"].set_position(("data",0))ax.spines["left"].set_position(("data",0))plt.plot(x,y2,color="red",linewidth=2.0,linestyle="--")plt.show()

3.4 图例

import matplotlib.pyplot as pltimport pandas as pdimport numpy as npx=np.linspace(-3,3,50)y1=2*x+1y2=x**2# 这边命名一定要加, ，为l1, 返回值不加逗号会报错l1,=plt.plot(x,y1,label="up")l2,=plt.plot(x,y2,label="down",linestyle="--",linewidth=2,color="red")plt.legend(handles=[l1,l2,],labels=["aaa","bbbb"],loc="best")plt.show()

3.5 标注

s 为注释文本内容

xy 为被注释的坐标点

xytext 为注释文字的坐标位置

xycoords 参数如下:

• figure points：图左下角的点
• figure pixels：图左下角的像素
• figure fraction：图的左下部分
• axes points：坐标轴左下角的点
• axes pixels：坐标轴左下角的像素
• axes fraction：左下轴的分数
• data：使用被注释对象的坐标系统(默认)
• polar(theta,r)：if not native ‘data’ coordinates t

weight 设置字体线型

{‘ultralight’, ‘light’, ‘normal’, ‘regular’, ‘book’, ‘medium’, ‘roman’, ‘semibold’, ‘demibold’, ‘demi’, ‘bold’, ‘heavy’, ‘extra bold’, ‘black’}

color 设置字体颜色

• {‘b’, ‘g’, ‘r’, ‘c’, ‘m’, ‘y’, ‘k’, ‘w’}
• ‘black’,'red’等
• [0,1]之间的浮点型数据
• RGB或者RGBA, 如: (0.1, 0.2, 0.5)、(0.1, 0.2, 0.5, 0.3)等

arrowprops #箭头参数,参数类型为字典dict

• width：箭头的宽度(以点为单位)
• headwidth：箭头底部以点为单位的宽度
• headlength：箭头的长度(以点为单位)
• shrink：总长度的一部分，从两端“收缩”
• facecolor：箭头颜色

bbox给标题增加外框 ，常用参数如下：

• boxstyle：方框外形
• facecolor：(简写fc)背景颜色
• edgecolor：(简写ec)边框线条颜色
• edgewidth：边框线条大小

import matplotlib.pyplot as pltimport numpy as npx = np.arange(0, 6)y = x * xplt.plot(x, y, marker='o')for xy in zip(x, y):    plt.annotate("(%s,%s)" % xy, xy=xy, xytext=(-20, 10), textcoords='offset points')plt.show()

例二

import matplotlib.pyplot as pltimport numpy as npx = np.arange(0, 6)y = x * xplt.plot(x, y, marker='o')for xy in zip(x, y):    plt.annotate("(%s,%s)" % xy, xy=xy, xytext=(-20, 10), textcoords='offset points',     bbox=dict(boxstyle='round,pad=0.5', fc='yellow', ec='k', lw=1, alpha=0.5)plt.show()

例三

import matplotlib.pyplot as pltimport pandas as pdimport numpy as npx=np.linspace(-3,3,50)y1=2*x+1y2=x**2plt.scatter(x,y1)plt.figure(num=1,figsize=(8,5))ax=plt.gca()ax.spines["right"].set_color("none")ax.spines["top"].set_color("none")ax.xaxis.set_ticks_position("bottom")ax.yaxis.set_ticks_position("left")ax.spines["bottom"].set_position(("data",0))ax.spines["left"].set_position(("data",0))x0=1y0=2*x0+1plt.scatter(x0,y0,s=50,color='b')plt.plot([x0,x0],[y0,0],"k--",lw=2.5)# xytext 就是文字偏离点的位置plt.annotate(r"$2x+1=%s$" % y0,             xy=(x0,y0),             xycoords="data",             xytext=(+30,-30),             textcoords="offset points",             fontsize=6,             arrowprops=dict(arrowstyle="->",connectionstyle="arc3,rad=.2")             )plt.text(-3.7,3,r"$hi ,\mu \alpha$",fontdict={"size":16,"color":"r"})plt.show()

3.6 能见度

import matplotlib.pyplot as pltimport pandas as pdimport numpy as npx=np.linspace(-3,3,50)y1=2*x+1y2=x**2plt.plot(x,y1)plt.figure(num=1,figsize=(8,5))plt.ylim(-2,2)ax=plt.gca()ax.spines["right"].set_color("none")ax.spines["top"].set_color("none")ax.xaxis.set_ticks_position("bottom")ax.yaxis.set_ticks_position("left")ax.spines["bottom"].set_position(("data",0))ax.yaxis.set_ticks_position("left")ax.spines["left"].set_position(("data",0))for label in ax.get_xticklabels()+ax.get_yticklabels():    label.set_fontsize(12)    label.set_bbox(dict(facecolor="red",edgecolor="None",alpha=0.7))    plt.show()

3.7 各种图

3.7.1 散点图

import matplotlib.pyplot as pltimport pandas as pdimport numpy as npn=1024X=np.random.normal(0,1,n)Y=np.random.normal(0,1,n)T=np.arctan2(Y,X)# 生成plt.scatter(X,Y,s=75,c=T,alpha=0.5)plt.xlim((-1.5,1.5))plt.ylim((-1.5,1.5))plt.xticks(())plt.yticks(())plt.show()

3.7.2 柱状图

import matplotlib.pyplot as pltimport pandas as pdimport numpy as npn=12X=np.arange(n)Y1=(1-X/float(n))*np.random.uniform(0.5,1.0,n)Y2=(1-X/float(n))*np.random.uniform(0.5,1.0,n)plt.bar(X,+Y1,facecolor="#9999ff",edgecolor="white")plt.bar(X,-Y2,facecolor="#ff9999",edgecolor="white")for x,y in zip(X,Y1):    plt.text(x,y+0.05,"%.2f" %y,ha='center',va="bottom")for x,y in zip(X,Y2):    plt.text(x,-y-0.05,"%.2f" %y,ha='center',va="top")    plt.xlim(-.5,n)plt.xticks(())plt.ylim(-1.25,1.25)plt.yticks(())plt.show()

3.7.3 等高线

import numpy as npimport matplotlib.pyplot as plt#建立步长为0.01，即每隔0.01取一个点step = 0.01x = np.arange(-10,10,step)y = np.arange(-10,10,step)#也可以用x = np.linspace(-10,10,100)表示从-10到10，分100份#将原始数据变成网格数据形式X,Y = np.meshgrid(x,y)#写入函数，z是大写Z = X**2+Y**2#设置打开画布大小,长10，宽6#plt.figure(figsize=(10,6))#填充颜色，f即filledplt.contourf(X,Y,Z)#画等高线plt.contour(X,Y,Z)plt.show()

例二

import numpy as npimport pandas as pdimport matplotlib.pyplot as plt # 计算x,y坐标对应的高度值def f(x, y):    return (1-x/2+x**5+y**3) * np.exp(-x**2-y**2)# 生成x,y的数据n = 256x = np.linspace(-3, 3, n)y = np.linspace(-3, 3, n) # 把x,y数据生成mesh网格状的数据，因为等高线的显示是在网格的基础上添加上高度值X, Y = np.meshgrid(x, y) # 填充等高线plt.contourf(X, Y, f(X, Y),8, alpha=0.75,cmap=plt.cm.hot)# 添加等高线C = plt.contour(X, Y, f(X, Y), 8,colors="black")plt.clabel(C, inline=True, fontsize=12)# 显示图表plt.show()

3.8 图

3.8.1 图片

import numpy as npimport pandas as pdimport matplotlib.pyplot as plta=np.array([0.31,0.36,0.42,0.365,0.459,0.525,0.4237,0.5250,0.6515]).reshape(3,3)plt.imshow(a,interpolation="nearest",cmap="bone",origin="upper")# 添加图像plt.colorbar()plt.xticks(())plt.yticks(())plt.show()

3.8.2 3D 图像

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import Axes3D fig=plt.figure()ax=Axes3D(fig)X=np.arange(-4,4,0.25)Y=np.arange(-4,4,0.25)X,Y=np.meshgrid(X,Y)R=np.sqrt(X**2+Y**2)Z=np.sin(R)ax.plot_surface(X,Y,Z,rstride=1,cstride=1,cmap=plt.get_cmap("rainbow"))ax.contourf(X,Y,Z,zdir='z',offset=-2,cmap="rainbow")ax.set_zlim(-2,2)plt.show()

3.8.3 多图合一

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltplt.figure()plt.subplot(2,1,1)plt.plot([0,1],[0,1])plt.subplot(2,3,4)plt.plot([0,1],[0,1])plt.subplot(235)plt.plot([0,1],[0,1])plt.subplot(236)plt.plot([0,1],[0,4])plt.show()

例二

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport matplotlib.gridspec as gridspecplt.figure()# 第一种方法ax1=plt.subplot2grid((3,3),(0,0),colspan=3,rowspan=1)ax1.plot([1,2],[1,2])ax1.set_title("title")ax2=plt.subplot2grid((3,3),(1,0),colspan=2,rowspan=1)ax2.plot([1,2],[1,2])ax3=plt.subplot2grid((3,3),(1,2),colspan=1,rowspan=2)ax3.plot([1,2],[1,2])ax4=plt.subplot2grid((3,3),(2,0),colspan=1,rowspan=1)ax4.plot([1,2],[1,2])ax5=plt.subplot2grid((3,3),(2,1),colspan=1,rowspan=1)ax5.plot([1,2],[1,2])plt.show()## 第二种方法plt.figure()gs=gridspec.GridSpec(3,3)ax1=plt.subplot(gs[0,:])ax2=plt.subplot(gs[1,:2])ax3=plt.subplot(gs[1:,2])ax4=plt.subplot(gs[-1,0])ax5=plt.subplot(gs[-1,-2])plt.show()# 第三种方法plt.figure()f,((ax11,ax22),(ax21,ax22))=plt.subplots(2,2,sharex=True,sharey=True)ax11.scatter([1,2],[1,3])plt.show()

3.8.4 图中图

[x:y]为对列表取坐标为x到y的值，左边为闭区间取得到，右边为开区间取不到。

[x:y:z]为对列表取坐标为x到y的值，每间隔z个取1个值，同样为左闭右开，可以认为[x:y]是[x:y:z]的特例，其中z取1。

也可以输入负数：

[:-1]为剔除列表最后一个数字。

[::-1]为从列表最后一个开始取（即逆序），可以用a[::-1]取a的逆序。

[::-2]为从列表最后一个开始取（即逆序），每间隔2个取一次。

>>> a = [i for i in range(10)]>>> a[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]>>> a[0:3][0, 1, 2]>>> a[0:3:1][0, 1, 2]>>> a[0:3:2][0, 2]>>> a[0:-1][0, 1, 2, 3, 4, 5, 6, 7, 8]>>> a[0:0:-1][]>>> a[::-1][9, 8, 7, 6, 5, 4, 3, 2, 1, 0]>>> a[::-2][9, 7, 5, 3, 1]

例子

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport matplotlib.gridspec as gridspecfig=plt.figure()x=[1,2,3,4,5,6,7,8]y=[1,3,4,3,6,4,5,1]left,bottom,width,height=0.1,0.1,0.8,0.8ax1=fig.add_axes([left,bottom,width,height])ax1.plot(x,y,'r')ax1.set_xlabel("x")ax1.set_ylabel("y")ax1.set_title("title")left,bottom,width,height=0.2,0.6,0.25,0.25ax2=fig.add_axes([left,bottom,width,height])ax2.plot(y,x,'b')ax2.set_xlabel("x")ax2.set_ylabel("y")ax2.set_title("titleinside 1")plt.axes([0.6,0.2,0.25,0.25])plt.plot(y[::-1],x,'g')plt.xlabel("x")plt.ylabel("y")plt.title("title inside 2")plt.show()print(y[::-1])

3.9 主次坐标轴

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltx=np.arange(0,10,0.1)y1=0.05*x**2y2=-1*y1fig,ax1=plt.subplots()ax2=ax1.twinx()ax1.plot(x,y1,'g-')ax2.plot(x,y2,'b--')ax1.set_xlabel("X data")ax1.set_ylabel("Y1",color='g')ax2.set_ylabel("Y2",color='b')plt.show()

3.10 动画

import numpy as npimport matplotlibimport matplotlib.pyplot as pltimport matplotlib.animation as animation # 指定渲染环境%matplotlib notebook# %matplotlib inline def update_points(num):    '''    更新数据点    '''    point_ani.set_data(x[num], y[num])    return point_ani,  x = np.linspace(0, 2*np.pi, 100)y = np.sin(x) fig = plt.figure(tight_layout=True)plt.plot(x,y)point_ani, = plt.plot(x[0], y[0], "ro")plt.grid(ls="--")# 开始制作动画ani = animation.FuncAnimation(fig, update_points, np.arange(0, 100), interval=100, blit=True) # ani.save('sin_test2.gif', writer='imagemagick', fps=10)plt.show()

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltfrom matplotlib import animation %matplotlib notebookfig,ax=plt.subplots()def animate(i):    line.set_ydata(np.sin(x+i/100))    return line,def init():    line.set_ydata(np.sin(x))    return line,x=np.arange(0,2*np.pi,0.01)line,=ax.plot(x,np.sin(x))ani=animation.FuncAnimation(fig=fig,                            func=animate,                            frames=100,                            init_func=init,                            interval=20,                            blit=False)plt.show()