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通过简单的matlplotlib第三方库，导入你的数据进行绘制柱状图。## 柱状图

import matplotlib.pyplot as pltimport stringplt.xlabel('hour')plt.ylabel('time')plt.title('Average number of rides per hour per week')xx = []#存放X值yy = []def savey(str1):    fx=open("f://"+str1,"r").read().split("\n")    ax=[]    x=[]    s=0    i=0    for i in range(len(fx)):        s=float(fx[i])        ax.append(s)    x=ax    return xdef savex(str1):    fx=open("f://"+str1,"r").read().split("\n")    ax=[]    x=[]    s=0    i=0    for i in range(len(fx)):        s=int(fx[i])        ax.append(s)    x=ax    return xxx=savex("datax.txt")yy=savex("datay.txt")plt.bar(xx,yy,label='bike sharing',color='r')plt.legend()plt.show()

绘制折线图

通过plot函数，可以绘制多条折线。

import matplotlib.pyplot as pltdef savex(str1):    fx=open("f://"+str1,"r").read().split("\n")    ax=[]    x=[]    s=0    i=0    for i in range(len(fx)):        s=float(fx[i])        ax.append(s)    x=ax    return x#x=[2013,2014,2015,2016,2017,2018]x=savex("datax.txt")y1=savex("data_50.txt")y2=savex("data_60.txt")y3=savex("data_70.txt")y4=savex("data_80.txt")y5=savex("data_90.txt")plt.plot(x,y1,label="1950",color='r')plt.plot(x,y2,label="1960",color='g')plt.plot(x,y3,label="1970",color='b')plt.plot(x,y4,label="1980",color='y')plt.plot(x,y5,label="1990",color='black')#plt.plot(x2,y2,label='Second one')plt.xlabel('Month')plt.ylabel("People")plt.title('People Monthly active membership in 2015.1~2017.12')plt.legend()plt.show()

山脉图

import matplotlib.pyplot as plt

y0=[145227,99538,211741,220121,215442,173487]

y1=[669732,651493,808558,1057055,1218525,1240965] y2=[219400,202856,269400,371680,444132,463433] x=[2013,2014,2015,2016,2017,2018] plt.stackplot(x,y0,y1,y2,colors = [‘g’,‘r’,‘b’])

plt.xlabel(‘year’)

plt.ylabel(‘times’) plt.title(‘Total number of rides by men and women each September in 2013.9-2018.9’) plt.legend() plt.show()

Seaborn库绘制多变量图

通过用pairgrid（）和map（）函数可以生成多种可视化图。

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns%matplotlib inlinedata = pd.read_excel('d:\\num.xls',sheet_name='Sheet1') data1=data["location"]data2=data["cost"]data3=data["impact"]a=pd.DataFrame({'location':data1,"impact":data3,"cost":data3})g = sns.PairGrid(a)g.map_diag(sns.distplot)g.map_upper(plt.scatter)g.map_lower(sns.kdeplot)

Seaborn多变量散点图

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns%matplotlib inlinetips = pd.read_excel('d:\\num.xls',sheet_name='Sheet1')   sns.stripplot(x = 'location', y = 'religious', data = tips, jitter= True,hue = 'sex', dodge = True)sns.set()# Load the brain networks example datasetdf = sns.load_dataset("brain_networks", header=[0, 1, 2], index_col=0)# Select a subset of the networksused_networks = [1, 5, 6, 7, 8, 12, 13, 17]used_columns = (df.columns.get_level_values("network")                          .astype(int)                          .isin(used_networks))df = df.loc[:, used_columns]# Create a categorical palette to identify the networksnetwork_pal = sns.husl_palette(8, s=.45)network_lut = dict(zip(map(str, used_networks), network_pal))# Convert the palette to vectors that will be drawn on the side of the matrixnetworks = df.columns.get_level_values("network")network_colors = pd.Series(networks, index=df.columns).map(network_lut)# Draw the full plotsns.clustermap(df.corr(), center=0, cmap="vlag",               row_colors=network_colors, col_colors=network_colors,               linewidths=.75, figsize=(13, 13))

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