3/9/2018
c >>>编写>>> numpy >>>升级>>> pandas
https://www.bilibili.com/video/av16378934/
NO.3 - NO.5
3/10/2018
NO.6
3/11/2018
NO.10
3/12/2018
NO.17
3/13/2018
NO.18
补充了一些pandas的操作
#2.2 create np array import numpy as np # array = np.array([[1,2,3],[2,3,4]],dtype = np.float) # print(array) # print(array.dtype) # print('NO. of dim:',array.ndim) # print('shape:',array.shape) # print('size:',array.size) # brray = np.zeros((3,4)) # print(brray) # crray = np.ones((3,4),dtype=np.int16) # print(crray) # drray = np.empty((3,4)) # print(drray) # erray = np.arange(10,20,2) # print(erray) # frray = np.arange(12).reshape((3,4)) # print(frray) # grray = np.linspace(0,10,21) # print(grray) #2.3 np basic calculation I # a = np.linspace(10,20,6) # b = np.arange(6) # c = a - b #addition, substraction # print(a,' ',b,' ',c) # d = b**2 #power # f = a*b #multiplication # print(d, ' ',f) # g = np.sin(10*a*np.pi/180) #trigonometric function, exponential function # print(g) # print(b<3) #comparsion # print(b == 3) # h = np.array([[1,1], # [0,1]]) # i = np.arange(1,5,1).reshape((2,2)) # print(h*i) #multiplication one by one # print(np.dot(h,i)) #matrix dot product # print(h.dot(i)) # j = np.random.random((2,4)) #random number in [0,1)。 ↓summation, maximum, minimum # print(j) # print(np.sum(j,axis = 1)) #axis = 1 -> sum of every row, axis = 0 -> sum of every column # print(np.max(j)) # print(np.max(j,axis = 1)) #axis = 1 -> max in every row, axis = 0 -> max in every column # print(np.min(j)) #2.4 numpy basic calculation II # A = np.random.random((3,4)) # print(A) # print(np.argmin(A)) #seek for min & max number index # print(np.argmax(A)) # print(np.mean(A,axis = 1)) #axis = 1 -> mean of every row, axis = 0 -> mean of every column # print(A.mean()) # print(np.average(A)) # A = np.arange(15,0,-1).reshape(3,5) # print(np.cumsum(A)) #accumulation between the two adjacent number, output a 1*15 array # print(np.diff(A)) #difference between the two adjacent number, output a 3*4 = 3*(5-1) # print(np.transpose(np.nonzero(A))) #find the coordinate value of the nonzero numbers. # print(np.sort(A)) #sort every sub-array in A # print(np.transpose(A)) #transpose type I # print(A.T) #transpose type II # print((A.T).dot(A)) # print(np.clip(A,None,9)) #turn any number < None to it's original value, turn any number > 9 to 9 #2.5 index in numpy # A = np.arange(3,15).reshape((3,4)) # print(A) # print(A[2][1]) # print(A[2,1]) # print(A[2,:]) # print(A[:,1]) # print(A[1,1:2]) # for row in A: # print(row) # print(np.shape(row)) # vector is a row one by default(??really??) # print(A.T) # for column in A.T: # print(np.transpose(column).reshape(3,1)) # print(np.shape(column)[0]) #np.shape is a list, probably with more than one dimension # print(A.flatten()) # turn N-dimension list A into a vector # for i in A.flat: # Abbreviation of turning N-dimension list A into a vector when A is iterated. # print(i) #2.6 merging array in numpy # A = np.array([1,1,1]) # B = np.array([2,2,2]) # print(np.vstack((A,B))) #vertically stack # print(np.hstack((A,B))) #horizontally stack # print(A.shape) # print(A[np.newaxis,:].shape) #np.newaxis before comma means reshaping A to (1,old dimensions) # print(A[np.newaxis,:] == A) # print(A[np.newaxis,:] is A) # print(A[:,np.newaxis].shape) #np.newaxis after comma means reshaping A to (old dimensions,1) # print(A[:,np.newaxis]) # A = np.array([1,1,1])[:,np.newaxis] #turn a vector into a column vector # B = np.array([2,2,2])[:,np.newaxis] # print(np.vstack((A,B))) #vertically stack # print(np.hstack((A,B))) #horizontally stack # print(np.hstack((A,A,B,B))) #multiple stack # C = np.concatenate((A,A,B,B),axis = 0) #define the stacking mode, giving axis value at the last step #2.7 Partition array in numpy # A = np.arange(5,17).reshape(3,4) # print(A) # print(np.split(A,4,axis = 1)) #split array A uniformly into 4 sub-arrays # print(np.vsplit(A,4)) #split array A uniformly into 4 sub-arrays # print(np.split(A,[1],axis = 1)) #split array A into [0,1) and [1,A.shape[1]) two sub-arrays # print(np.array_split(A,3,axis = 1)) #split array A nonuniformly into 3 sub-arrays #2.8 Copy & Deep Copy in Numpy # a = np.arange(3,12,3) # b = a # c = a[:] #if a is a np array, this command will only shallowly copy a to c # d = a.copy() # a[0] = 0 # print(a,' ',b,' ',c,' ',d) #3.1 Pandas - A Brief Introduction import pandas as pd # s = pd.Series([1,3,6,np.nan,44,1]) #one-dimensional pd data # print(s) # dates = pd.date_range('20180311',periods = 6) #one-dimensional pd date data # print(dates) # df = pd.DataFrame(np.random.rand(6,4),index = dates,columns = ['a','b','c','d']) # print(df) #two-dimensional pd data, with index and columns names defined # df1 = pd.DataFrame(np.arange(12).reshape(3,4)) # print(df1) #two-dimensional pd data, without any index and column defination # df2 = pd.DataFrame({ # 'A':1., # 'B':pd.Timestamp('20180312'), # 'C':pd.Series(1,index = list(range(4)),dtype = 'float32'), # 'D':np.array([3]*4,dtype = 'int32'), # 'E':pd.Categorical(["test","train","test","train"]), #what is Categorical in pandas??? # 'F':'foo' # }) #two-dimensional pd data, defined by a dictionary # print(df2) # print(df2.dtypes) # print(df2.index) # print(df2.columns) # print(df2.values) # print(df2.describe()) # print(df2.T) # print(df2.sort_index(axis = 0, ascending = False)) #axis = 1 → sort column, axis = 0 → sort index # print(df2.sort_values(by = 'E')) #3.2 Selecting data from Pandas # dates = pd.date_range('20130101',periods = 6) # df = pd.DataFrame(np.arange(24).reshape(6,4),index = dates,columns = list('ABCD')) # print(df['A']) # print(df['A'] is df.A) # print(df[0:3]) # print(df['20130102':'20130104']) # print(df) # print(df.loc['20130102',['A','C']]) #loc treats only label names # print(df.iloc[1,[0,2]]) # iloc treats only label numbers # print(df.ix[1,['A','C']]) #ix is the mixture of loc and iloc # co = ['a','b','c'] # df = pd.DataFrame(np.random.rand(4,3),columns = co) # print(df) # print(df.query('a>0.6 and a<0.7')) # student = pd.read_csv('pyread.csv') # print(student.head(3)) # print(student.ix[:,['name','gender']].tail()) # print(student[(student['gender'] == 'Female') & (student['age'] < 24)]) #3.3 Setting values in Pandas # dates = pd.date_range('20130101',periods = 6) # df = pd.DataFrame(np.arange(24).reshape(6,4),index = dates,columns = list('ABCD')) # df.ix[2,2] = 1111 # df.ix['20130101',1] = 222 # df.A[df.A > 4] = 0 # print(df) # df = pd.DataFrame(np.arange(24).reshape(6,4),index = dates,columns = list('ABCD')) # df[df.A > 4] = 0 # print(df) # df = pd.DataFrame(np.arange(24).reshape(6,4),index = dates,columns = list('ABCD')) # df['CC'] = pd.Series(np.arange(1,7),index = dates) # df['E'] = np.nan # print(df) # print(df.sort_index(axis = 1)) # 3.4 Treating those missed values # dates = pd.date_range('20130101',periods = 6) # df = pd.DataFrame(np.arange(24).reshape(6,4),index = dates,columns = list('ABCD')) # df.iloc[0,1] = np.nan # df.iloc[3,3] = np.nan # print(df) # print(df.dropna(axis = 0, how = 'any')) # if how = 'any', then drop out any row that has a nan item, # # if how = 'all', only drop out rows that all items are nans. # print(df.dropna(axis = 1)) # print(df.fillna(value = -100)) # print(df.isnull()) # print(np.any(df.isnull()) == True) #3.5 Import and Export values # data = pd.read_csv('pyread.csv') # print(type(data)) # #data.index = [2,3,4,5] # data.to_csv('pyto.csv') #3.6 Concatenating DataFrame Object and Appending DataFrame # df1 = pd.DataFrame(np.ones((3,4))*0,columns = list('abcd')) # df2 = pd.DataFrame(np.ones((3,4))*1,columns = list('abcd')) # df3 = pd.DataFrame(np.ones((3,4))*2,columns = list('abcd')) # print(df1) # print(df2) # print(df3) # print(pd.concat([df1,df2,df3],axis = 0)) # #如果简单点来说，就是0轴匹配的是index， 涉及上下运算；1轴匹配的是columns, 涉及左右运算。 # print(pd.concat([df1,df2,df3],axis = 0,ignore_index = True)) # df1 = pd.DataFrame(np.ones((3,4))*0,columns = list('abcd'),index = [1,2,3]) # df2 = pd.DataFrame(np.ones((3,4))*1,columns = list('bcde'),index = [2,3,4]) # print(pd.concat([df1,df2])) # equals to → print(pd.concat([df1,df2],join = 'outer')) → df1 ∪ df2 # print(pd.concat([df1,df2],join = 'inner',ignore_index = True)) # df1 ∩ df2 # print(pd.concat([df1,df2],axis = 1,join_axes = [df1.index])) #ignore index not in df1 # df1 = pd.DataFrame(np.ones((3,4))*0,columns = list('abcd')) # df2 = pd.DataFrame(np.ones((3,4))*1,columns = list('abcd')) # df3 = pd.DataFrame(np.ones((3,4))*2,columns = list('abcd')) # res1 = df1.append(df2,ignore_index = True) # print(res1) # res2 = df1.append([df1,df2],ignore_index = False) # print(res2) # s1 = pd.Series([1,2,3,4],index = list('abcd')) # res3 = df1.append(s1,ignore_index = True) # print(res3) #3.7 Merging in Pandas # left = 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') #merge by column(axis = 1) # print(res) # left = pd.DataFrame({ # 'key1': ['K0', 'K0', 'K1', 'K2'], # 'key2': ['K0', 'K1', 'K0', 'K1'], # 'A': ['A0', 'A1', 'A2', 'A3'], # 'B': ['B0', 'B1', 'B2', 'B3'] # }) # right = pd.DataFrame({ # 'key1': ['K0', 'K1', 'K1', 'K2'], # 'key2': ['K0', 'K0', 'K0', 'K0'], # 'C': ['C0', 'C1', 'C2', 'C3'], # 'D': ['D0', 'D1', 'D2', 'D3'] # }) # print(left) # print(right) # res = pd.merge(left,right,on = ['key1','key2'],how = 'inner') #how = 'inner' by default # print(res) #how = ['inner','outer','left','right'] # res = pd.merge(left,right,on = ['key1','key2'],how = 'outer') # print(res) # res = pd.merge(left,right,on = ['key1','key2'],how = 'left') # print(res) # res = pd.merge(left,right,on = ['key1','key2'],how = 'right') # print(res) # df1 = pd.DataFrame({'col1':[0,1], 'col_left':['a','b']}) # df2 = pd.DataFrame({'col1':[1,2,2],'col_right':[2,2,2]}) # print(df1) # print(df2) # res = pd.merge(df1, df2, on='col1', how='outer', indicator=True) # print(res) #changing the indicator column's name by assigning indicator "??a_new_name??" # left = pd.DataFrame({ # 'A': ['A0', 'A1', 'A2'], # 'B': ['B0', 'B1', 'B2']}, # index=['K0', 'K1', 'K2']) # right = pd.DataFrame({ # 'C': ['C0', 'C2', 'C3'], # 'D': ['D0', 'D2', 'D3']}, # index=['K0', 'K2', 'K3']) # print(left) # print(right) # # left_index and right_index # res = pd.merge(left, right, left_index=True, right_index=True, how='outer') # print(res) #merge by index (axis = 0) # res = pd.merge(left, right, left_index=True, right_index=True, how='inner') # print(res) # boys = pd.DataFrame({ # 'k': ['K0', 'K1', 'K2'], # 'age': [1, 2, 3] # }) # girls = pd.DataFrame({ # 'k': ['K0', 'K0', 'K3'], # 'age': [4, 5, 6] # }) # print(boys) # print(girls) # res = pd.merge(boys, girls, on='k', suffixes=['_boy', '_girl'], how='inner') # print(res) # res = pd.merge(boys, girls, on='k', suffixes=['_boy', '_girl'], how='outer') # print(res) #3.8 Pandas Plot import matplotlib.pyplot as plt # data = pd.Series(np.random.randn(1000),index = np.arange(1000)) # data = data.cumsum() # data.plot() # plt.show() # data2 = pd.DataFrame(np.random.randn(1000,4),index = np.arange(1000),columns = list('ABCD')) # data2 = data2.cumsum() # ax1 = data2.plot.scatter(x = 'A', y = 'B', color = 'DarkBlue',label = 'Class 1') # data2.plot.scatter(x = 'A', y = 'C', color = 'DarkGreen',label = 'Class 2',ax = ax1) # plt.show() #plot methods: 'bar' 'hist' 'box' 'kde' 'area' 'scatter' 'hexbin' 'pie' #4.1 Arithmetic in Pandas # df1 = pd.DataFrame(np.random.rand(3,4),columns = list('abcd')) # df2 = pd.DataFrame(np.ones((3,4))*2,columns = list('bcde')) # print(df1+df2) # print(df1/df2) #4.2 Searching Data in Pandas Series # np.random.seed(1234) # d1 = pd.Series(2*np.random.randn(10)+3) # print(d1) # d11 = pd.Series(np.random.normal(loc = 3,scale = 2,size = 10)) # print(d11) # d2 = pd.Series(np.random.f(2,4,size = 10)) #Draw samples from an F distribution # print(d2) # d3 = pd.Series(np.random.randint(1,100,size = 10)) # print(d3) # d1.count() #非空元素计算 # d1.min() #最小值 # d1.max() #最大值 # d1.idxmin() #最小值的位置，类似于R中的which.min函数 # d1.idxmax() #最大值的位置，类似于R中的which.max函数 # d1.quantile(0.1) #10%分位数 # d1.sum() #求和 # d1.mean() #均值 # d1.median() #中位数 # d1.mode() #众数 # d1.var() #方差 # d1.std() #标准差 # d1.mad() #平均绝对偏差 # d1.skew() #偏度 # d1.kurt() #峰度 # d1.describe() #一次性输出多个描述性统计指标 #4.3 Delete Data in Pandas df = pd.DataFrame(np.arange(48).reshape(8,6),columns = list('abcdef')) print(df) print(df.drop([0,3])) #drop out row 0 and 3 print(list(df[df['a'] > 15].index)) print(df.drop(df[df['a'] > 15].index, axis = 0)) #delete certain items with some boolean rules print(df.drop(['b','d'],axis = 1).head()) print(df.shape[0]) #total row count print(df.shape[1]) #total column count
https://www.bilibili.com/video/av16378354/
NO.3开始
看到NO.7
3/14/2018
NO.19 matplotlib视频结束
import matplotlib.pyplot as plt import numpy as np from mpl_toolkits.mplot3d import Axes3D as ad #2.1 Basic Usage # x = np.linspace(-1,1,50) # y = 2*x + 1 # plt.plot(x,y) #plot methods: 'bar' 'hist' 'box' 'kde' 'area' 'scatter' 'hexbin' 'pie' # plt.show() # Domain of Definition x = np.linspace(-3,3,50) #Curve Equations y = 2*x + 1 z = x**2 #Try to plot #plt.figure() # ← ← ← ← ← ← Fig1 (the first fig) #plt.plot(x,y) # ← ← ← ← ← ← choose which curve to be plotted in Fig1 #2.2 Edit Figure Parameter plt.figure(num = 99,figsize = (8,8)) # ← ← ← ← ← ← Fig99 (the second fig) # ↑ ↑ ↑ ↑ ↑ ↑ (num=None, figsize=None, dpi=None, facecolor=None, edgecolor=None, # ↑ ↑ ↑ ↑ ↑ ↑ frameon=True, FigureClass=Figure, clear=False, **kwargs #2.3 Edit Axes Text Parameter plt.xlim((-1,2)) # ← ← ← ← ← ← horizontal axis's value range plt.ylim((-2,3)) # ← ← ← ← ← ← vertical axis's value range plt.xlabel('XXXX') plt.ylabel("I'm YYYYY") new_ticks = np.linspace(-1,2,5) plt.xticks(new_ticks) # ← ← ← ← ← ← change the ticks of the horizontal axis plt.yticks([-2,-1.8,-1,1.22,5],[r'$really bad$', r'$bad alpha$', 'normal','good','really good']) # ↑ ↑ ↑ ↑ ↑ ↑ r'$$' is to change the font of the ticks to a more mathematical one # ↑ ↑ ↑ ↑ ↑ ↑ Caution: between the two $, space should have a before it. # ↑ ↑ ↑ ↑ ↑ ↑ Use alpha to type Greek letters #2.4 Set Axes Position ax = plt.gca() # ← ← ← ← ← ← gca = 'get current axis' ax.spines['right'].set_color('none') # ← ← ← ← ← ← spine is 脊椎, meaning the boder lines here. ax.spines['top'].set_color('none') # ← ← ← ← ← ← color 'none' means to remove the boder line. # ↑ ↑ ↑ ↑ ↑ ↑ spines are 'top','bottom','right','left' ax.xaxis.set_ticks_position('bottom') # ← ← ← ← ← ← set x axis to bottom spine ax.yaxis.set_ticks_position('left') # ← ← ← ← ← ← set y axis to left spine ax.spines['bottom'].set_position(('data',0)) # ← ← ← ← ← ← set bottom spine to y = 0 ax.spines['left'].set_position(('data',0)) # ← ← ← ← ← ← set bottom spine to x = 0 # ↑ ↑ ↑ ↑ ↑ ↑ set_position(('axes',??)) # ↑ ↑ ↑ ↑ ↑ ↑ set_position(('outward',??)) #2.5 Legend # ↓ ↓ ↓ ↓ ↓ ↓ Using plt.plot(x,z) without 'line1, = '(the returned value) is also okay. line1, = plt.plot(x,z,color = 'red',linewidth = 1.0, linestyle = '--',label = 'down') # ↑ ↑ ↑ ↑ ↑ ↑ choose line parameter. The comma after line is to enable 'handles' in plt.legend. line2, = plt.plot(x,y,label = 'up') # ↑ ↑ ↑ ↑ ↑ ↑ line parameter: color, linewidth, linestyle, label plt.legend(handles = [line1,line2,], labels = ['aaa','bbb'], loc = 'best') # ↑ ↑ ↑ ↑ ↑ ↑ will plot out the legend # ↑ ↑ ↑ ↑ ↑ ↑ loc:['best','upper right','center? left','lower right',etc.], where to put the legend # ↑ ↑ ↑ ↑ ↑ ↑ labels = ['aaa','bbb'] here will overwrite label = 'up' and label = 'down' # ↑ ↑ ↑ ↑ ↑ ↑ handles = [line1,line2,] or handles = [line2,] are both okay. #2.6 Annotation x0 = 1 y0 = x0*2 + 1 plt.scatter(x0,y0,s = 50, c = 'b',) # ↑ ↑ ↑ ↑ ↑ ↑ plot a point. s means size. Both c or color are okay. b means blue. plt.plot([x0,x0],[y0,0],'k--', lw = 2.5) # ↑ ↑ ↑ ↑ ↑ ↑ plot a line. k means black, and -- means the linestyle (dashed). lw means linewidth. # ↑ ↑ ↑ ↑ ↑ ↑ [x0,x0],[y0,0] means the two ends of the line is [x0,y0] and [x0,0] # ↓ ↓ ↓ ↓ ↓ ↓ Annotation Method I plt.annotate(r'$2x+1=%s$' % y0, xy=(x0, y0), xycoords='data', xytext=(+30, -30), textcoords='offset points', fontsize=16, arrowprops=dict(arrowstyle='->', connectionstyle="arc3,rad=.2")) # ↓ ↓ ↓ ↓ ↓ ↓ Annotation Method II plt.text(-3.7, 3, r'$This is the some text. mu sigma_i alpha_t$', fontdict={'size': 16, 'color': 'r'}) #2.7 Tick Visibility for label in ax.get_xticklabels() + ax.get_yticklabels(): label.set_fontsize(12) label.set_bbox(dict(facecolor = 'white', edgecolor = 'None', alpha = 0.7)) # ↑ ↑ ↑ ↑ ↑ ↑ alpha = visibility ∈ [0,1] #3.1 Plot Scatter Chart plt.figure(num = 100,figsize = (8,8)) n = 1024 # number of the data X = np.random.normal(0,1,n) Y = np.random.normal(0,1,n) Co = np.arctan2(Y,X) #for color value plt.scatter(X,Y,s = 75,c = Co, alpha = 0.5) # alpha = visibility ∈ [0,1] plt.xlim((-1.5,1.5)) plt.ylim((-1.5,1.5)) plt.xticks() # remove x ticks plt.yticks() # remove y ticks #3.2 Plot Bar Chart plt.figure(num = 101,figsize = (8,8)) n = 12 x = np.arange(n) y1 = (1-x/float(n))*np.random.uniform(0.5,1,n) #uniform distribution y2 = (1-x/float(n))*np.random.uniform(0.5,1,n) plt.bar(x,+y1,facecolor = '#9999ff',edgecolor = 'white') plt.bar(x,-y2,facecolor = '#ff9999',edgecolor = 'white') for a,b in zip(x,y1): # zip(x,y1) enables value transforming simultaneously plt.text(a,b + 0.05, '%.2f' % b, ha = 'center',va = 'bottom') #'%.2f' % y means 保留两位小数的y。ha means horizontal alignment。va means vertical alignment（对齐方式）。 for a,b in zip(x,y2): # zip(x,y1) enables value transforming simultaneously plt.text(a,- b - 0.05, '-%.2f' % b, ha = 'center',va = 'top') #'%.2f' % y means 保留两位小数的y。ha means horizontal alignment。va means vertical alignment（对齐方式）。 plt.xlim(-.5, n) plt.xticks(()) plt.ylim(-1.25, 1.25) plt.yticks(()) #3.3 Plot Contour Chart plt.figure(num = 102,figsize = (8,8)) def f(x,y): # the height function return (1 - x / 2 + x**5 + y**3) * np.exp(-x**2 -y**2) n = 256 x = np.linspace(-3,3,n) y = np.linspace(-3,3,n) X,Y = np.meshgrid(x,y) #plot contour filling plt.contourf(X,Y,f(X,Y),8,alpha = 0.75,cmap = 'hot') # 8 means eight kinds of heights. #cmap is the mapping between data and color. cmp = plt.cm.hot, or plt.cm.cold is also okay. #plot contour lines C = plt.contour(X,Y,f(X,Y),8,colors = 'black',linewidth = 0.5) #add label plt.clabel(C, inline = True, fontsize = 10) #inline = True means lines will avoid pass through labels #3.4 Plot Image plt.figure(num = 103,figsize = (8,8)) a = np.array([0.313660827978, 0.365348418405, 0.423733120134, 0.365348418405, 0.439599930621, 0.525083754405, 0.423733120134, 0.525083754405, 0.651536351379]).reshape(3,3) plt.imshow(a,interpolation = 'nearest',cmap = 'bone',origin = 'lower') #cmap = plt.cm.bone is also okay. # origin = 'upper' will turn the image to the correct direction: # http://matplotlib.org/examples/pylab_examples/image_origin.html # interpolation's value: # http://matplotlib.org/examples/images_contours_and_fields/interpolation_methods.html plt.colorbar() # use this command to show collor bar # use plt.colorbar(shrink = 0.9) to shrink the colorbar #3.5 Plot 3D Data fig3d = plt.figure(num = 103,figsize = (8,8)) ax = ad(fig3d) 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')) # (rstride,cstride) means the (row,column) stride ax.contourf(X,Y,Z,zdir = 'z', offset = -10, cmap = 'rainbow') #zdir means the direction of projecting # offset means the offset of contour figure towards the axis ax.set_zlim(-2,2) #4.1 Subplot zero plt.figure(num = 104,figsize = (8,8)) #http://blog.csdn.net/yuan1125/article/details/69934785 plt.subplot(4,2,1) plt.plot([0,1],[0,1]) plt.subplot(4,2,2) plt.plot([0,1],[0,2]) plt.subplot(4,2,3) plt.plot([0,1],[0,3]) plt.subplot(4,2,4) plt.plot([0,1],[0,4]) plt.subplot(4,1,3) plt.plot([0,1],[0,5]) plt.subplot(4,2,7) plt.plot([0,1],[0,6]) plt.subplot(4,2,8) plt.plot([0,1],[0,7]) # another expression # ax1 = plt.subplot(4,2,1) # ax1.plot([0,1],[0,1]) # ax2 = plt.subplot(4,2,2) # ax2.plot([0,1],[0,2]) # ax3 = plt.subplot(4,2,3) # ax3.plot([0,1],[0,3]) # ax4 = plt.subplot(4,2,4) # ax4.plot([0,1],[0,4]) # ax5 = plt.subplot(4,1,3) # ax5.plot([0,1],[0,5]) # ax6 = plt.subplot(4,2,7) # ax6.plot([0,1],[0,6]) # ax7 = plt.subplot(4,2,8) # ax7.plot([0,1],[0,7]) #4.1 Subplot I, II, III # I subplot2grid plt.figure(num = 105,figsize = (8,8)) ax1 = plt.subplot2grid((3,3),(0,0),colspan = 3, rowspan = 1) ax1.plot([0,1],[0,1]) ax1.set_title('ax1_title') ax2 = plt.subplot2grid((3,3),(1,0),colspan = 2, rowspan = 1) ax2.plot([0,2],[0,2]) ax2.set_title('ax2_title') ax3 = plt.subplot2grid((3,3),(2,0),colspan = 1, rowspan = 1) ax3.plot([0,2],[0,2]) ax3.set_title('ax2_title') ax4 = plt.subplot2grid((3,3),(2,1),colspan = 1, rowspan = 1) ax4.plot([0,2],[0,2]) ax4.set_title('ax2_title') ax5 = plt.subplot2grid((3,3),(1,2),colspan = 1, rowspan = 2) ax5.plot([0,2],[0,2]) ax5.set_title('ax2_title') # II gridspec import matplotlib.gridspec as gridspec plt.figure(num = 106,figsize = (8,8)) gs = gridspec.GridSpec(3,3) ax1 = plt.subplot(gs[0,:]) #ax1.plot([0,2],[0,2]) ax2 = plt.subplot(gs[1,:2]) ax3 = plt.subplot(gs[2,0]) ax4 = plt.subplot(gs[2,1]) ax5 = plt.subplot(gs[1:,2]) # III easy to define structure plt.figure(num = 107,figsize = (8,8)) f,((ax11,ax12),(ax21,ax22)) = plt.subplots(2,2,sharex = True,sharey = True) ax11.plot([1,2],[1,2]) plt.tight_layout() #4.3 Picture-in-Picture fig = plt.figure(num = 107,figsize = (8,8)) x = np.arange(1,8) y = [1,3,4,2,5,8,6] left,bottom,width,height = 0.1,0.1,0.8,0.8 ax1 = 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 = 0.2,0.6,0.25,0.25 ax2 = fig.add_axes([left,bottom,width,height]) ax2.plot(y,x,'b') ax2.set_xlabel('x') ax2.set_ylabel('y') ax2.set_title('title inside I') plt.axes([0.6,.2,.25,.25]) plt.plot(y[::-1],x,'g') plt.xlabel('x') plt.ylabel('y') plt.title('title inside II') #4.4 Primary and Secondary Axis plt.figure(num = 108,figsize = (8,8)) x = np.arange(0,10,0.1) y1 = 0.05*x**2 y2 = -y1 fig, 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') ax2.set_ylabel('Y2') #5.1 Animation from matplotlib import animation plt.figure(num = 109,figsize = (8,8)) fg, ax = plt.subplots() x = np.arange(0,2*np.pi,0.01) line, = ax.plot(x,np.sin(x)) def animationfunc(i): line.set_ydata(np.sin(x + i/100)) return line, def animationinit(): line.set_ydata(np.sin(x)) return line, ani = animation.FuncAnimation(fig = fg, func = animationfunc, frames = 100, init_func = animationinit, interval = 20, blit = False) # frames = 100 means 100 frames(帧). interval = 20 means the time lag between two adjacent frames. # blit = False means the program will only refresh points that has changes. #999.999 plt.show() # ← ← ← ← ← ← OK. 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