anaconda安装 matplotlib: conda install matplotlib python 下安装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 readingnode
http://www.loria.fr/~rougier/teaching/matplotlibpython
#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)
anconda 安装 seaborn: conda install seaborn python 环境下安装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 solutionsapp
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)
df3['d'].plot.kde(lw=5,ls='--')
df3.ix[0:30].plot.area()
#####
f=plt.figure()
df3.ix[0:30].plot.area(alpha=0.4)
plt.legend(loc='center left',bbox_to_anchor=(1.0,0.5))
plt.show()
#######
######################################
#pandas built-in data visualization
import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline
df1=pd.read_csv('df1',index_col=0)
df2=pd.read_csv('df2')
df1['A'].hist(bins=30)
df1['A'].plot(kind='hist',bins=30)
df1['A'].hist()
df2.plot.bar()
df.plt.bar(stacked=True)
df1['A'].plot.hist(bins=50)
df1.head()
df1.plot.line(x=df1,index,y='B')
df2.plot.area()
df1.plot.line(x=df1,inde,y='B',figsize=(12,10))
df1.plot.scatter(x='A',y='B',c='C',cmap='coolwarm')
df1.plot.scatter(x='A',y='B',s=df1['C']*10)
df2.plot.box()
df=pd.DataFrame(np.random.randn(1000,2),columns=['a','b'])
df.plot.hexbin(x='a',y='b',gridsize=25,cmap='coolwarm')
df2['a'].plot.kde()
df2['a'].plot.density()
df2.plot.density()
############################################
df2.plot.density()
python 下安装 plotly 和 cufflinks:(使用pip安装完成后，在jupyter note book 中能正常使用) pip install plotly pip install cufflinks anaconda 安装 plotly，，命令 conda install -c https://conda.anaconda.org/plotly 或 conda install -c plotly plotly=3.6.0
import pandas as pd
import numpy as np
from ploty import __version__
print(__version__)
#######
import cufflinks as cf
from plotly.offline import download_plotlyjs,init_notebook_node,plot,iplot
init_notebook_mode(connected=True)
cf.go_offline()
###DATA
df=pd.DataFrame(np.random.randn(100,4),columns='A B C D'.split())
df.head()
df2=pd.DataFrame({'Category':['A','B','C'],'Values':[32,43,50]})
df2.plot()
df.plot()
%matplotlib inline
df.iplot()
df.iplot(kind='scatter',x='A',y='B',mode='markers',size=20)
df2.iplot(kind='bar',x='Category',y='values')
df.iplot.(kind='bar')
df.count().iplot(kind='bar')
df.sum().iplot(kind='bar')
df.iplot(kind='box')
df3=pd.DataFrame({'x':[1,2,3,4,5],'y':[10,20,30,20,10],'z':[500,400,300,200,100]})
df3.iplot(kind='surface',colorscale='rdylbu')
df['A'].iplot(kind='hist',bins=50)
df.iplot(kind='hist')
df[['A','B']].iplot(kind='spread')
df.ipot(kind='bubble',x='A',y='B',size='C')
df.scatter_matrix()
method 1: plotly
method 2: matplotlib basemap
#################
choropleth maps
#################################
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot
init_notebook_mode(connected=True)
import pandas as pd
data=dict(type='choropleth',locations=['A2','CA','WV'],
locationmode='USA-states',
colorscale='Porland',
text=['text 1','text 2','text 3'],
z=[1.0,2.0,3.0],
colorbar={'title:'Colorbar Title Goes Here'})
)
layout=dict(geo=['scope':'usa'})
choromap=go.Figure(data=[data],layout=layout)
iplot(choromap)