matplotlib、seaborn
1、matplotlib
anaconda安裝 matplotlib: conda install matplotlib
python 下安裝matplotlib: pip install matplotlib
import matplotlib.pyplot asplt
%matplotlib inline
import numpy as np
x=np.linspace(0,5,11)
y=x**2
#functional
plt.plot(x,y,'r-')
plt.xlabel('xlabel‘）
plt.ylabel('ylabel')
plt.title('title')
plt.show() # if not in the jypyter notebook
#plot many plots functions
plt.subplot(1,2,1)
plt.plt(x,y,'r')
plt.subplot(1,2,2)
plt.plot(y,x,'b')
plt.show()
# figure object
fig=plt.figure()
axes=fig.add_axes([0.1,0.1,0.8,0.8]) # add the axes
axes.plot(x,y) #plot the axes
axes.set_xlabel('x label')
axes.set_ylabel('y label')
axes.set_title('set title‘）
# plot many plots ,the plots in diffent axes
fig=plt.figure()
axes1=fig.add_axes([0.1,0.1,0.8,0.8])
axes2=fig.add_axes([0.2,0.5,0.4,0.3])
axes1.plot(x,y)
axes2.plot(y,x)
axes1.set_title('larger plot')
axes2.set_title('smaller plot')
# subplots
fig,axes=plt.subplots(nrows=1,ncols=2) # 3*3plots
for current_ax in axes:
current_ax.plot(x,y)
fig.axes=plt.subplots(nrows=1,ncols=2)
axes[0].plot(x,y)
axes[0].set_title('')
axes[1].plot(y,x）
axes[1].set_title('second plot’)
# figure size and dpi
fig=plt.figure(figsize=（8,2）)
ax=fig.add_axes([0,0,1,1])
ax.plot(x,y)
#figure size and dpi
fig.axes=plt.subplots(nrows=2,figsize=(8,2))
axes[0].plot(x,y)
axes[1].plot(y,x)
plt.tight_layout()
#save the figure
fig.savefig('my_picture.png',dpi=200)
#figure size and dpi
fig=plt.figure()
ax=fig.add_axes([0,0,1,1])
ax.plot(x,y)
ax.set_tilte()
ax.set_xlabel()
ax.set_ylabel()
# figure size and dpi
fig=plt.figure()
ax=fig.add_axes([0,0,1,1])
ax.plot(x,x*9,label='x squared')
ax.plot(x,x*3,label='x cubed')
ax.legend(loc=(0.1,0.1))
#plot appearance
fig=plt.figure()
ax=fig.add_axes([0,0,1,1,])
ax.plot(x,y,color='blue')== ax.plot(x,y,color='#fffff')
#plot appearance line marke style
fig=plt.figure()
ax=fig.add_axes([0,0,1,1])
ax.plot(x,y,color='purple',linewidth=3,aplha=0.5)
#ax.plot(x,y,color='purple',lw=3,alpha=0.5)
ax.plt(x,y,color='purple',lw=3,linestyle='--')
ax.plot(x,y,color='purple',lw=3,ls='--')
ax.plot(x,y,color='purple',lw=3,ls='-',marker='2',markersie=10,markerfacefolor='yellow',markeredgewith=3,markeredgecolor='gree')# marker and markersize
# apperanrance style xlimt,ylimt
fig=plot.figure()
ax=fig.add_axes()
ax.plot(x,y,color='purple',lw=2)
ax.set_xlim([0,1])
ax.set_ylim([0,1])
# special plot types
plt.scatter(x,y)
from random import sample
data=sample(range(1,1000),100)
plt.hist(data)
data=[np.random.normal(0,std,100) for std in range(1,4）
#retangular box plot
plt.boxplot(data,vert=Ture,patch_artist=True);
further reading
http://www.loria.fr/~rougier/teaching/matplotlib
#matplotlib exercises
#data
import numpy as np
x=np.arange(0,100)
y=x*2
z=x**2
import matplotlinb.pyplot as plt
%matplpot inline
fig=plt.figure()
ax=fig.add_axes([0,0,1,1])
ax.plot(x,y)
ax.set_alabel('x')
ax.set_ylabel('title)
##########################
#exerise 2
fig=plt.figure()
ax1=fig.add_axes([0,0,1,1])
ax2=fig.add_axes(0.2,0.5,0.2,0.2])
ax1.plot(x,y,color='red')
ax2.plot(x,y,color='red')
#########################333
#exercise 3
fig=plt.figure()
ax=fig.add_axes([0,0,1,1])
ax2=fig.add_axes([0.2,0.5,0.4,0.4])
ax.plot(x,z)
ax.set_xlabel（‘x’）
ax.set_ylabel('z')
ax2.plot(x,y)
ax2.set_title('zoom')
ax2.set_xlabel('x')
ax2.set_ylabe('y')
ax2.set_xlim([20,22])
ax2.set_ylim([30,50])
##########################33
# exerciser 4.01
fig.axes=plt.subplots(1,2)
axes[0].plot(x,y,ls='-',color='blue',lw=3)
axes[1].plot(x,z,color='red,lw=3)
# exerciser 4.01
fig.axes=plt.subplots(1,2,figsize=(12,2))
axes[0].plot(x,y,ls='-',color='blue',lw=3)
axes[1].plot(x,z,color='red,lw=3)
2、seaborn
anconda 安裝 seaborn: conda install seaborn
python 環境下安裝seaborn: pip install seaborn
# Distribtion Plots
import seaborn as sns
%matplorlib inline
tips=sns.load_dataset('tips')
tips.head()
sns.displot(tips['total_bill'])
sns.displot(tips['total_bill'],kde=False,bins=30)
################
sns.jointplot(x='total_bill',y='tip',data=tips)
sns.jointplot(x='total-bill',y='tip',data=tips,kind='hex')
sns.jointplot(x='total-bill',y='tip',data=tips,kind='reg')
#######################
sns.pairplot(tips)
sns.pairplot(tips,hue='sex',palette='coolwards')
############################
sns.rugplot(tips['total_bill'])
##############
sns.kdeplot(tips['total_bill'])
###########################################
#Categorical plots
import seaborn as sns
import numpy as np
% matplotlib inline
tips=sns.load_dataset('tops')
tips.head()
sns.barplot(x='sex',y='total_bill',data=tips)
sns.barplot(x='sex',y='total_bill',data=tips,estimator=np.std)
sns.countplot(X='sex',data=tips)
sns.boxplot(x='day',y='total_bill',data=tips)
sns.boxplot(x='day',y='total_bill',data=tips,hue='smoker')
sns.violinplot(x='day',y='total_bill',data=tips)
sns.violinplot(x='day',y='total_bill',data=tips,hue='sex',spit=True)
sns.stripplot(x='day',y='total_bill',data=tips,jitter=True,hue='sex',split=True)
#######
sns.violinplot(x='day',y='total_bill',data=tips)
sns.swarmplot(x='day',y='total_bill',data=tips,color='black')
##########
sns.factorplot(x='day',y='total_bill',data=tips,kind='bar')
#####################################################
#Matrix Plots
import seaborn as sns
%matplotlib inline
tips=sns.load_dataset('tips')
flights=sns.load_dataset('flights')
tips.head()
flights.head()
tc=.corr()
sns.heatmap(tc)
sns.heatmap(tc,annot=True,cmap='colorwarm')
flights.pivot_table(index='month',columns='years',values='passengers')
fp=flights.pivot_table(index='month',columns='years',values='passengers')
sns.headmap(fp)
sns.heatmap(fp,cmap='magma',linecolor='white',linwidths=1)
sns.clustermap(fp)
sns.clustermap(fp,cmap='coolwarm',standard_scale=1)
############################################################
####GRID
import seaborn as sns
%matplotlib inline
iris=sns.load_dataset('iris')
iris.head()
sns.pairplot(iris)
sns.PairGrid(iris)
g=snsn.PairGrid(iris)
g.map(plt.scatter)
g.map_diag(sns.displot)
g.map_upper(plt.scatter)
g.map_lower(sns.kedplot)
tips=sns.load_dataset('tips')
tips.head()
g=sns.FaceGrid(data=tips,col='time',row='smoker')
g.map(sns.distplot,'total_bill')
g.map(plt.scatter,'total_bill','tip')
##############################################
#regression plots
import seaborn sa sns
%matplotlib inline
tips=sns.load_dataset('tips')
tips.head()
sns.lmplot(x='total_bill',y='tip',data=tips,hue='sex',markers=['o','v'],scatter_kws={'s:100})
sns.lmplot(x='total_bill',y='tip',data=tips,col='sex',row='time')
sns.lmplot(x='total_bill',y='tip',data=tips,col='day',hue='sex',aspect=0.6,size=8)
#################################
#style and color
import seaborn as sns
%matplotlib inline
tips=sns.load_datasets(tips)
sns.setstyle('white') # whitegrid
sns.countplot(x='sex',data=tips)
sns.set_style('tickes')
sns.despine(left=Ture,riht=False)
plt.figure=(figsize=(12,3))
sns.countplot(x='sex',data=tips)
sns.set_context('poster',font_scal=13) #notbook
sns.countplot(x='sex',data=tips)
sns.lmplot(x='total_bill',y=tip,data=tips,hue='sex',palette='coolwarm')#semisc
######################################
#seaborn exercises
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
sns.set_style('whitegrid')
titanic=sns.load_ dataset('titanic')
sns.jointplot(x='fare',y='age',data=titanic)
sns.distplot(titanic['fare'],ked=False,color='red',bins=30)
sns.boxplot(x='class',y='age',data=titanic,palette='rainbow')
sns.swrmplot(x='class',y='age',data=titanic,palette='Set2')
sns.countplot(x='sex',data=titanic)
sns.heatmap(titanic.corr(),cmap='collwarm')
plt.title('titanic’)
sns.FaceGrid(data=titanic,col='sex')
g.map(plt.hist,'age')
g.map(sns.displot,'age')
pandas data visualization exercise solutions
import pandas as pd
import matplotlib.pyplot as plt
df3=pd.read_csv('df3')
%matplotlib inline
df3.plot.scatter(x='a',y='b',s=50,c='red',figsize=(10,10))
df3['a'].hist()
df3['a'].plot.hist()
plt.style.use('ggplot')
df3['a'].plot.hist(bins=20,alpha=0.5)