import numpy as np
import matplotlib.pyplot as plt
plt.plot([1,2,3,4,5],[1,2,3,4,5])
plt.show()
plt.plot([1,2,3,4,5],[1,4,9,16,25])
plt.show()
plt.plot([1,2,3,4,5],[1,2,3,4,5])
plt.xlabel('xlabel')
plt.ylabel('ylabel')
plt.show()plt.plot([1,2,3,4,5],[1,2,3,4,5])
plt.xlabel('xlabel',fontsize=16)
plt.ylabel('ylabel')
plt.show()plt.plot([1,2,3,4,5],[1,2,3,4,5],'--')
plt.xlabel('xlabel',fontsize=16)
plt.ylabel('ylabel',fontsize=16)
plt.show()plt.plot([1,2,3,4,5],[1,2,3,4,5],':')
plt.xlabel('xlabel',fontsize=16)
plt.ylabel('ylabel',fontsize=16)
plt.show()plt.plot([1,2,3,4,5],[1,2,3,4,5],'.')
plt.xlabel('xlabel',fontsize=16)
plt.ylabel('ylabel',fontsize=16)
plt.show()plt.plot([1,2,3,4,5],[1,2,3,4,5],'_')
plt.xlabel('xlabel',fontsize=16)
plt.ylabel('ylabel',fontsize=16)
plt.show()plt.plot([1,2,3,4,5],[1,2,3,4,5],'x')
plt.xlabel('xlabel',fontsize=16)
plt.ylabel('ylabel',fontsize=16)
plt.show()plt.plot([1,2,3,4,5],[1,2,3,4,5],'-.')
plt.xlabel('xlabel',fontsize=16)
plt.ylabel('ylabel',fontsize=16)
plt.show()plt.plot([1,2,3,4,5],[1,2,3,4,5],'-.',color='r')
plt.xlabel('xlabel',fontsize=16)
plt.ylabel('ylabel',fontsize=16)
plt.show()plt.plot([1,2,3,4,5],[1,2,3,4,5],'r-.')
plt.xlabel('xlabel',fontsize=16)
plt.ylabel('ylabel',fontsize=16)
plt.show()plt.plot([1,2,3,4,5],[1,2,3,4,5],'ro')
plt.xlabel('xlabel',fontsize=16)
plt.ylabel('ylabel',fontsize=16)
plt.show()test_numpy = np.arange(0,10,0.5)
plt.plot(test_numpy, test_numpy, 'r--')
plt.show()
test_numpy = np.arange(0,10,0.5)
plt.plot(test_numpy, test_numpy, 'r--')
plt.plot(test_numpy, test_numpy**2, 'bs')
plt.show()
test_numpy = np.arange(0,10,0.5)
plt.plot(test_numpy, test_numpy, 'r--')
plt.plot(test_numpy, test_numpy**2, 'bs')
plt.plot(test_numpy, test_numpy**3, 'go')
plt.show()
test_numpy = np.arange(0,10,0.5)
plt.plot(test_numpy, test_numpy, 'r--',
test_numpy, test_numpy**2, 'bs',
test_numpy, test_numpy**3, 'go')
plt.show()x = np.linspace(-10,10)
y = np.sin(x)
plt.plot(x,y)
plt.show()
x = np.linspace(-10,10)
y = np.sin(x)
plt.plot(x,y, linewidth=3.0)
plt.show()
plt.plot(x,y,color='b',linestyle=':',marker='o')
plt.show()
plt.plot(x,y,color='b',linestyle=':',marker='o',markerfacecolor='r')
plt.show()
plt.plot(x,y,color='b',linestyle=':',marker='o',markerfacecolor='r',markersize=10)
plt.show()
line = plt.plot(x,y)
plt.setp(line, color='r', linewidth=2.0, alpha=0.1)
plt.show()
plt.subplot(211)
plt.plot(x,y,color='r')
plt.subplot(212)
plt.plot(x,y,color='b')
plt.show()
plt.subplot(121)
plt.plot(x,y,color='r')
plt.subplot(122)
plt.plot(x,y,color='b')
plt.show()
plt.subplot(321)
plt.plot(x,y,color='r')
plt.subplot(324)
plt.plot(x,y,color='b')
plt.show()
plt.plot(x,y,color='b',linestyle=':',marker='o',markerfacecolor='r',markersize=10)
plt.xlabel('x:---')
plt.ylabel('y:---')
plt.title('title')
plt.show()plt.plot(x,y,color='b',linestyle=':',marker='o',markerfacecolor='r',markersize=10)
plt.xlabel('x:---')
plt.ylabel('y:---')
plt.title('title')
plt.text(0,0, 'text')
plt.show()plt.plot(x,y,color='b',linestyle=':',marker='o',markerfacecolor='r',markersize=10)
plt.xlabel('x:---')
plt.ylabel('y:---')
plt.title('title')
plt.text(0,0, 'text')
plt.grid(True)
plt.show()plt.plot(x,y,color='b',linestyle=':',marker='o',markerfacecolor='r',markersize=10)
plt.xlabel('x:---')
plt.ylabel('y:---')
plt.title('title')
plt.text(0,0, 'text')
plt.grid(True)
plt.annotate('anotate', xy=(-5,0), xytext=(-2,0.3), arrowprops = dict(facecolor='black', shrink=0.05))
plt.show()plt.plot(x,y,color='b',linestyle=':',marker='o',markerfacecolor='r',markersize=10)
plt.xlabel('x:---')
plt.ylabel('y:---')
plt.title('title')
plt.text(0,0, 'text')
plt.grid(True)
plt.annotate('anotate', xy=(-5,0), xytext=(-2,0.3), arrowprops = dict(facecolor='black', shrink=0.05, headwidth=20))
plt.show()plt.plot(x,y,color='b',linestyle=':',marker='o',markerfacecolor='r',markersize=10)
plt.xlabel('x:---')
plt.ylabel('y:---')
plt.title('title')
plt.text(0,0, 'text')
plt.grid(True)
plt.annotate('anotate', xy=(-5,0), xytext=(-2,0.3), arrowprops = dict(facecolor='black', shrink=0.05, headlength=20, headwidth=20))
plt.show()x = range(10)
y = range(10)
fig = plt.gca()
plt.plot(x,y)
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
plt.show()
import math
x = np.random.normal(loc=0.0, scale=1.0, size=300)
width = 0.5
bins = np.arange(math.floor(x.min())-width,math.ceil(x.max())+width, width)
bins
ax = plt.subplot(111)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.tick_params(bottom=True,top=False,left=True,right=False)
plt.grid()
plt.hist(x, alpha=0.5, bins=bins)
plt.show()
x = range(10)
y = range(10)
labels = ['tttssstttt' for i in range(10)]
fig,ax = plt.subplots()
plt.plot(x, y)
plt.title('title')
ax.set_xticklabels(labels, rotation = 45, horizontalalignment='right')
plt.show()import matplotlib as mpl
mpl.rcParams['axes.titlesize'] = '30'
x = range(10)
y = range(10)
labels = ['tttssstttt' for i in range(10)]
fig,ax = plt.subplots()
plt.plot(x, y)
plt.title('title')
ax.set_xticklabels(labels, rotation = 45, horizontalalignment='right')
plt.show()x = np.arange(10)
for i in range(1,4):
plt.plot(x, i*x**2, label='Group %d'%i)
plt.legend(loc='best')
plt.show()fig = plt.figure()
ax = plt.subplot(111)
x = np.arange(10)
for i in range(1,4):
plt.plot(x, i*x**2, label='Group %d'%i)
ax.legend(loc='upper center', bbox_to_anchor=(0.5,1.15), ncol=3)
plt.show()fig = plt.figure()
ax = plt.subplot(111)
x = np.arange(10)
for i in range(1,4):
plt.plot(x, i*x**2, label='Group %d'%i)
ax.legend(loc='upper center', bbox_to_anchor=(1.15,1), ncol=1)
plt.show()x = np.arange(10)
for i in range(1,4):
plt.plot(x, i*x**2, label='Group %d'%i)
plt.legend(loc='upper right', framealpha = 0.1)
plt.show()x = np.arange(10)
for i in range(1,4):
plt.plot(x, i*x**2, label='Group %d'%i, marker='o')
plt.legend(loc='upper right', framealpha = 0.1)
plt.show()x = np.linspace(-10,10)
y = np.sin(x)
plt.plot(x,y)
plt.show()
plt.style.use('dark_background')
plt.plot(x,y)
plt.show()plt.style.use('bmh')
plt.plot(x,y)
plt.show()plt.style.use('ggplot')
plt.plot(x,y)
plt.show()plt.style.use(['ggplot','bmh'])
plt.plot(x,y)
plt.show()
plt.xkcd()
plt.plot(x,y)
plt.show()
np.random.seed(0)
x = np.arange(5)
y = np.random.randn(5)
fig, axes = plt.subplots(ncols=2)
v_bars = axes[0].bar(x,y,color='red')
h_bars = axes[1].barh(x,y,color='red')
plt.show()
np.random.seed(0)
x = np.arange(5)
y = np.random.randint(-5,5,5)
fig, axes = plt.subplots(ncols=2)
v_bars = axes[0].bar(x,y,color='red')
h_bars = axes[1].barh(x,y,color='red')
plt.show()
np.random.seed(0)
x = np.arange(5)
y = np.random.randint(-5,5,5)
fig, axes = plt.subplots(ncols=2)
v_bars = axes[0].bar(x,y,color='red')
h_bars = axes[1].barh(x,y,color='red')
axes[0].axhline(0,color='grey',linewidth=2)
axes[1].axvline(0,color='grey',linewidth=2)
plt.show()
fig, ax = plt.subplots()
v_bars = ax.bar(x,y,color='lightblue')
for bar,height in zip(v_bars, y):
if height < 0:
bar.set(edgecolor='darkred', color='green', linewidth=3)
plt.show()x = np.random.randn(100).cumsum()
y = np.linspace(0,10,100)
fig,ax = plt.subplots()
ax.fill_between(x,y,'lightblue')
plt.show()
x = np.linspace(0,10,200)
y1 = 2*x+1
y2 = 3*x+1.2
y_mean = 0.5*x*np.cos(2*x) + 2.5*x + 1.1
fig, ax = plt.subplots()
ax.fill_between(x,y1,y2,color='red')
ax.plot(x,y_mean,color='black')
plt.show()
mean_values = [1,2,3]
variance = [0.2,0.4,0.5]
bar_label = ['bar1','bar2','bar3']
x_pos = list(range(len(bar_label)))
plt.bar(x_pos, mean_values, yerr=variance, alpha=0.3)
max_y = max(zip(mean_values, variance))
plt.ylim([0, (max_y[0] + max_y[1]) * 1.2])
plt.ylabel('variable y')
plt.xticks(x_pos, bar_label)
plt.show()x1 = np.array([1,2,3])
x2 = np.array([2,2,3])
bar_labels = ['bar1', 'bar2', 'bar3']
fig = plt.figure(figsize=(8,6))
y_pos = [x for x in np.arange(len(x1))]
plt.barh(y_pos, x1, color='g', alpha=0.5)
plt.barh(y_pos, -x2, color='b', alpha=0.5)
plt.xlim(-max(x2)-1, max(x1)+1)
plt.ylim(-1, len(x1))
plt.show()
green_data = [1,2,3]
blue_data = [3,2,1]
red_data = [2,3,3]
labels = ['group1','group2','group3']
pos = list(range(len(green_data)))
width = 0.2
fig,ax = plt.subplots(figsize=(8,6))
plt.bar(pos, green_data, width, alpha=0.5, color='g', label=labels[0])
plt.bar([p+width for p in pos], blue_data, width, alpha=0.5, color='b', label=labels[1])
plt.bar([p+width*2 for p in pos], red_data, width, alpha=0.5, color='r', label=labels[2])
plt.show()
data = range(200, 225, 5)
bar_labels = ['a','b','c','d','e']
fig = plt.figure(figsize=(10,8))
y_pos = np.arange(len(data))
plt.yticks(y_pos, bar_labels, fontsize=16)
bars = plt.barh(y_pos, data, alpha=0.5, color='g')
plt.vlines(min(data), -1, len(data)+0.5, linestyle='dashed')
for b,d in zip(bars, data):
plt.text(b.get_width()+b.get_width()*0.05, b.get_y() + b.get_height()/2, '{0:.2%}'.format(d/min(data)))
plt.show()mean_values = range(10,18)
x_pos = range(len(mean_values))
print(list(mean_values))
print(list(x_pos))
import matplotlib.colors as col
import matplotlib.cm as cm
cmap1 = cm.ScalarMappable(col.Normalize(min(mean_values), max(mean_values)), cm.hot)
cmap2 = cm.ScalarMappable(col.Normalize(0, 20), cm.hot)
plt.subplot(121)
plt.bar(x_pos, mean_values, color=cmap1.to_rgba(mean_values))
plt.subplot(122)
plt.bar(x_pos, mean_values, color=cmap2.to_rgba(mean_values))
plt.show()
patterns = ('-', '+', 'x', '\\', '*', 'o', 'O', '.')
fig = plt.gca()
mean_value = range(1, len(patterns) + 1)
x_pos = list(range(len(mean_value)))
bars = plt.bar(x_pos, mean_value, color='white')
for bar,pattern in zip(bars, patterns):
bar.set_hatch(pattern)
plt.show()test_data = [np.random.normal(0, std, 100) for std in range(1,4)]
test_data
fig = plt.figure(figsize=(8,6))
plt.boxplot(test_data,notch=False,sym='s',vert=True)
plt.xticks([y+1 for y in range(len(test_data))], ['x1','x2','x3'])
plt.xlabel('x')
plt.title('box plot')
plt.show()fig = plt.figure(figsize=(8,6))
bplot= plt.boxplot(test_data,notch=False,sym='s',vert=True)
plt.xticks([y+1 for y in range(len(test_data))], ['x1','x2','x3'])
plt.xlabel('x')
plt.title('box plot')
for components in bplot.keys():
for line in bplot[components]:
line.set_color('black')
plt.show()fig = plt.figure(figsize=(8,6))
plt.boxplot(test_data,notch=False,sym='s',vert=False)
plt.yticks([y+1 for y in range(len(test_data))], ['y1','y2','y3'])
plt.ylabel('y')
plt.title('box plot')
plt.show()fig = plt.figure(figsize=(8,6))
plt.boxplot(test_data,notch=True,sym='s',vert=True)
plt.xticks([y+1 for y in range(len(test_data))], ['x1','x2','x3'])
plt.xlabel('x')
plt.title('box plot')
plt.show()fig = plt.figure(figsize=(8,6))
bplot = plt.boxplot(test_data,notch=False,sym='s',vert=True,patch_artist=True)
plt.xticks([y+1 for y in range(len(test_data))], ['x1','x2','x3'])
plt.xlabel('x')
plt.title('box plot')
colors = ['pink','lightblue','lightgreen']
for pathch,color in zip(bplot['boxes'], colors):
pathch.set_facecolor(color)
plt.show()fig, axes = plt.subplots(nrows=1,ncols=2,figsize=(12,5))
test_data = [np.random.normal(0, std, 100) for std in range(6,10)]
axes[0].violinplot(test_data,showmeans=False,showmedians=True)
axes[0].set_title('violin plot')
axes[1].boxplot(test_data)
axes[1].set_title('box plot')
plt.show()fig, axes = plt.subplots(nrows=1,ncols=2,figsize=(12,5))
test_data = [np.random.normal(0, std, 100) for std in range(6,10)]
axes[0].violinplot(test_data,showmeans=False,showmedians=True)
axes[0].set_title('violin plot')
axes[1].boxplot(test_data)
axes[1].set_title('box plot')
for ax in axes:
ax.yaxis.grid(True)
ax.set_xticks([y+1 for y in range(len(test_data))])
plt.setp(axes, xticks=[y+1 for y in range(len(test_data))], xticklabels=['x1','x2','x3','x4'])
plt.show()data = np.random.normal(0,20,1000)
bins = np.arange(-100,100,5)
plt.hist(data, bins=bins)
plt.show()
bins = np.arange(-100,100,5)
plt.hist(data, bins=bins)
plt.xlim([min(data)-5,max(data)+5])
plt.show()
data1 = [random.gauss(15,10) for i in range(500)]
data2 = [random.gauss(5,5) for i in range(500)]
bins = np.arange(-50,50,2.5)
plt.hist(data1, bins=bins, label='class1', alpha=0.3)
plt.hist(data2, bins=bins, label='class2', alpha=0.3)
plt.legend()
plt.show()
mu_vec1 = np.array([0,0])
cov_mat1 = np.array([[2,0],[0,2]])
x1_samples = np.random.multivariate_normal(mu_vec1, cov_mat1, 100)
x2_samples = np.random.multivariate_normal(mu_vec1+0.2, cov_mat1+0.2, 100)
x3_samples = np.random.multivariate_normal(mu_vec1+0.4, cov_mat1+0.4, 100)
plt.figure(figsize=(8,6))
plt.scatter(x1_samples[:,0],x1_samples[:,1],marker='x',color='blue',alpha=0.6,label='x1')
plt.scatter(x2_samples[:,0],x2_samples[:,1],marker='o',color='red',alpha=0.6,label='x2')
plt.scatter(x3_samples[:,0],x3_samples[:,1],marker='^',color='green',alpha=0.6,label='x3')
plt.legend()
plt.show()
x_coords = [0.13, 0.22, 0.39, 0.59, 0.68, 0.74, 0.93]
y_coords = [0.75, 0.34, 0.44, 0.52, 0.80, 0.25, 0.55]
plt.figure(figsize=(8,6))
plt.scatter(x_coords, y_coords, marker='s', s=50)
plt.show()
x_coords = [0.13, 0.22, 0.39, 0.59, 0.68, 0.74, 0.93]
y_coords = [0.75, 0.34, 0.44, 0.52, 0.80, 0.25, 0.55]
plt.figure(figsize=(8,6))
plt.scatter(x_coords, y_coords, marker='s', s=50)
for x,y in zip(x_coords, y_coords):
plt.annotate('(%s,%s)' % (x,y), xy=(x,y), xytext=(0,-15), textcoords='offset points', ha='center')
plt.show()mu_vec1 = np.array([0,0])
cov_mat1 = np.array([[1,0],[0,1]])
X = np.random.multivariate_normal(mu_vec1, cov_mat1, 500)
R = X**2
R_sum = R.sum(axis=1)
fig = plt.figure(figsize=(8,6))
plt.scatter(X[:,0],X[:,1],color='grey',marker='o',s=20*R_sum, alpha=0.5)
plt.show()
from mpl_toolkits.mplot3d import Axes3D
x = np.arange(-4,4,0.25)
y = np.arange(-4,4,0.25)
X,Y = np.meshgrid(x,y)
Z = np.sin(np.sqrt(X**2+Y**2))
Z
fig = plt.figure()
ax = Axes3D(fig)
ax.plot_surface(X,Y,Z,rstride=1,cstride=1,cmap='rainbow')
plt.show()
fig = plt.figure()
ax = Axes3D(fig)
ax.plot_surface(X,Y,Z,rstride=1,cstride=1,cmap='rainbow')
ax.contour(X,Y,Z,zdim='z',offset=-2,cmap='rainbow')
ax.set_zlim(-2,2)
plt.show()
fig = plt.figure()
ax = fig.add_subplot(111,projection='3d')
plt.show()
fig = plt.figure()
ax = fig.gca(projection='3d')
theta = np.linspace(-4*np.pi,4*np.pi,100)
z = np.linspace(-2,2,100)
r = z**2 + 1
x = r * np.sin(theta)
y = r * np.cos(theta)
ax.plot(x,y,z)
plt.show()
np.random.seed(1)
def randrange(n, vmin, vmax):
return (vmax-vmin) * np.random.rand(n) + vmin
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
n = 100
for c,m,zlow,zhigh in [('r','o',-50,-25),('b','x',-30,-5)]:
xs = randrange(n,23,32)
ys = randrange(n,0,100)
zs = randrange(n,zlow,zhigh)
ax.scatter(xs,ys,zs,marker=m,color=c)
plt.show()np.random.seed(1)
def randrange(n, vmin, vmax):
return (vmax-vmin) * np.random.rand(n) + vmin
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
n = 100
for c,m,zlow,zhigh in [('r','o',-50,-25),('b','x',-30,-5)]:
xs = randrange(n,23,32)
ys = randrange(n,0,100)
zs = randrange(n,zlow,zhigh)
ax.scatter(xs,ys,zs,marker=m,color=c)
ax.view_init(20,0)
plt.show()fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for c,z in zip(['r','g','b','y'],[30,20,10,0]):
xs = np.arange(20)
ys = np.random.rand(20)
ax.bar(xs,ys,zs=z,zdir='y',color=c, alpha=0.5)
plt.show()m = 51212.
f = 40742.
m_perc = m/(m+f)
f_perc = f/(m+f)
colors = ['navy', 'lightcoral']
labels = ['Male', 'Female']
plt.figure(figsize=(8,8))
plt.pie([m_perc,f_perc],labels=labels,autopct='%1.1f%%',explode=[0,0.05],colors=colors)
plt.show()
colors = ['navy', 'lightcoral']
labels = ['Male', 'Female']
plt.figure(figsize=(8,8))
paches,texts,autotexts = plt.pie([m_perc,f_perc],labels=labels,autopct='%1.1f%%',explode=[0,0.05],colors=colors)
for text in texts+autotexts:
text.set_fontsize(20)
for text in autotexts:
text.set_color('white')
plt.show()ax1 = plt.subplot2grid((3,3),(0,0))
ax2 = plt.subplot2grid((3,3),(1,0))
ax3 = plt.subplot2grid((3,3),(0,2), rowspan=3)
ax4 = plt.subplot2grid((3,3),(2,0), colspan=2)
ax5 = plt.subplot2grid((3,3),(0,1), rowspan=2)
plt.show()
x = np.linspace(0,10,1000)
y2 = np.sin(x**2)
y1 = x**2
fig, ax1 = plt.subplots()
ax1.plot(x,y1)
left, bottom, width, height = [0.22, 0.45, 0.3, 0.35]
ax2 = fig.add_axes([left, bottom, width, height])
ax2.plot(x,y2)
plt.show()
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
top10_arricals_countries = ['CANADA','MEXICO','UNITED\nKINGDOM','JAPAN',
'CHINA','GERMANY','SOUHTH\nKOREA','FRANCE','BRAZIL','AUSTRALIA']
top10_arricals_values = [16.625687,15.378026,3.934508,2.999718,2.618737,1.769498,1.628563,1.419409,1.393710,1.36974]
arrivals_countries = ['WESTERN\nEUROPE','ASIA','SOUTH\nAMERICA','OCEANIA','CARIBBEAN',
'MIDDLE\nEAST','CENTRAL\nAMERICA','EASTERN\nEUROPE','AFRICA']
arrivals_percent = [36.9,30.4,13.8,4.4,4.0,3.6,2.9,2.6,1.5]fig, ax1 = plt.subplots(figsize=(20,12))
ax1.bar(range(10),top10_arricals_values,color='blue')
for spine in ax1.spines.values():
spine.set_visible(False)
plt.xticks(range(10), top10_arricals_countries,fontsize=18)
ax2 = inset_axes(ax1, width=6, height=6, loc=5)
explode = (0.08,0.08,0.05,0.05,0.05,0.05,0.05,0.05,0.05)
patches, texts, autotexts = ax2.pie(arrivals_percent, labels=arrivals_countries, autopct='%1.1f%%', explode=explode)
for text in texts+autotexts:
text.set_fontsize(16)
plt.show()def autolabel(rects):
for rect in rects:
height = rect.get_height()
ax1.text(rect.get_x() + rect.get_width()/2, 1.02*height, "{:,}".format(float(height)), ha='center', va='bottom', fontsize=18)fig, ax1 = plt.subplots(figsize=(20,12))
rects = ax1.bar(range(10),top10_arricals_values,color='blue')
for spine in ax1.spines.values():
spine.set_visible(False)
plt.xticks(range(10), top10_arricals_countries,fontsize=18)
autolabel(rects)
ax2 = inset_axes(ax1, width=6, height=6, loc=5)
explode = (0.08,0.08,0.05,0.05,0.05,0.05,0.05,0.05,0.05)
patches, texts, autotexts = ax2.pie(arrivals_percent, labels=arrivals_countries, autopct='%1.1f%%', explode=explode)
for text in texts+autotexts:
text.set_fontsize(16)
plt.show()np.random.seed(0)
df = pd.DataFrame({'Condition1': np.random.rand(20),'Condition2':np.random.rand(20)*0.9,'Condition3':np.random.rand(20)*1.1})
df.head()fig,ax = plt.subplots()
df.plot.bar(ax=ax)
plt.show()
fig,ax = plt.subplots()
df.plot.bar(ax=ax,stacked=True)
plt.show()
from matplotlib.ticker import FuncFormatter
df_ratio = df.div(df.sum(axis=1), axis=0)
df_ratio
fig,ax = plt.subplots()
df_ratio.plot.bar(ax=ax, stacked=True)
ax.yaxis.set_major_formatter(FuncFormatter(lambda y,_:'{:.0%}'.format(y)))
plt.show()url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00383/risk_factors_cervical_cancer.csv'
df = pd.read_csv('./data/risk_factors_cervical_cancer.csv', na_values='?')
df.head()from sklearn.impute import SimpleImputer as Imputer
impute = pd.DataFrame(Imputer().fit_transform(df))
impute.columns = df.columns
impute.index = df.index
impute.head()
import seaborn as sns
from sklearn.decomposition import PCA
from mpl_toolkits.mplot3d import Axes3D
features = impute.drop('Dx:Cancer', axis=1)
y = impute['Dx:Cancer']
pca = PCA(n_components=3)
X_r = pca.fit_transform(features)
print("Explained variance:\nPC1 {:.2%}\nPC2 {:.2%}\nPC3 {:.2%}".format(pca.explained_variance_ratio_[0],pca.explained_variance_ratio_[1],pca.explained_variance_ratio_[2]))fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(X_r[:,0],X_r[:,1],X_r[:,2], c=y, cmap=plt.cm.coolwarm)
ax.set_xlabel('PC1')
ax.set_ylabel('PC2')
ax.set_zlabel('PC3')
plt.show()