经常重复使用的绘图代码

使用SciencePlots画论文配图可见：传送门

折线图

import matplotlib.pyplot as plt

import matplotlib as mpl

# 中文和负号的正常显示

mpl.rcParams['font.sans-serif'] = ['Times New Roman']

mpl.rcParams['font.sans-serif'] = [u'SimHei']

mpl.rcParams['axes.unicode_minus'] = False

#自定义刻度和标签

times=data1['start_time_noday'].tolist()

# 分时间区间,保证最后一位纳入标签

ticks=list(range(0,len(times),2))

if ticks[-1]!=len(times)-1:

ticks.append(len(times)-1)

labels=[times[i] for i in ticks]

##绘图

fig= plt.figure(figsize=(8, 4),dpi=100)

# 设置图形的显示风格

plt.style.use('ggplot')

ax1 = fig.add_subplot(111)

ax1.plot(data1['order_id'],'-*',linewidth=1.5,label='非雨天工作日')

ax1.plot(data2['order_id'],'-o',linewidth=1.5,label='非雨天周末')

ax1.plot(data3['order_id'],'-v',linewidth=1.5,label='雨天工作日')

ax1.plot(data4['order_id'],'-^',linewidth=1.5,label='雨天周末')

ax1.legend(loc='upper right', frameon=False,fontsize = 10)

ax1.set_xlabel('时间',fontsize =10)

ax1.set_ylabel('订单量',fontsize =10)

ax1.set(xlim=[0,len(times)-1])

ax1.set_xticks(ticks)

ax1.set_xticklabels(labels, rotation=45, horizontalalignment='right')

ax1.tick_params(labelsize=8)

ax1.set_title('骑行订单时间分布',fontsize =8)

plt.vlines(32, 0, 2358, colors = "black", linestyles = "dashed",linewidth=0.8)

plt.vlines(34, 0, 1366, colors = "black", linestyles = "dashed",linewidth=0.8)

plt.vlines(72, 0, 1702, colors = "black", linestyles = "dashed",linewidth=0.8)

bbox_props=dict(boxstyle="round",fc="w",ec="0.5",alpha=0)

ax1.text(30,100,'8:00',ha='center',va='center',size=8,bbox=bbox_props,horizontalalignment='left')

ax1.text(36,100,'8:30',ha='center',va='center',size=8,bbox=bbox_props,horizontalalignment='left')

ax1.text(74,100,'18:00',ha='center',va='center',size=8,bbox=bbox_props,horizontalalignment='left')

ax1.legend(loc='upper right', frameon=False,fontsize = 10)

#vlines(x, ymin, ymax)画竖直线,前三个参数分别是：横坐标,minof纵坐标，max纵坐标

#hlines(y, xmin, xmax)画水平线

# ax1.vlines(0, 0, 0.5, colors = "c", linestyles = "dashed")

plt.savefig('./time_distribute_15min_israin_isweekday.png',format='png', dpi=300)

plt.show()

times=data1['start_time_noday'].tolist()

# 分时间区间,保证最后一位纳入标签

ticks=list(range(0,len(times),2))

if ticks[-1]!=len(times)-1:

ticks.append(len(times)-1)

labels=[times[i] for i in ticks]

ax1.set_xticks(ticks)

ax1.set_xticklabels(labels, rotation=45, horizontalalignment='right')

是自定义横轴的刻度显示间隔及显示标签

柱状图

import matplotlib.pyplot as plt

from matplotlib.ticker import FuncFormatter

name_list = ["< 100k","100k-200k","200k-300k","300k-400k","400k-500k","> 500k"]

num_list = [0.2626,0.3717,0.2061,0.0788,0.0343,0.0465]

def auto_text(rects,ax):

for rect in rects:

ax.text(rect.get_x()+rect.get_width()/2, rect.get_height(), '%.2f%%'%(rect.get_height()*100), ha='center', va='bottom')

def to_percent(temp,position):

return '%1.0f'%(100*temp) + '%'

with plt.style.context(['science','no-latex']):

fig= plt.figure(figsize=(8, 4),dpi=200)

# 设置图形的显示风格

# plt.style.use('ggplot')

ax1 = fig.add_subplot(111)

rect=ax1.bar(range(len(num_list)), num_list,tick_label=name_list)

ax1.yaxis.set_major_formatter(FuncFormatter(to_percent))

ax1.set_ylabel('User Percentage',fontsize =10)

ax1.set_xlabel('Income',fontsize =10)

auto_text(rect,ax1)

# ax1.set_xticks(ticks)

# ax1.set_xticklabels(labels, rotation=45, horizontalalignment='right')

ax1.tick_params(labelsize=8)

plt.savefig('income_percentage.jpg',dpi=300,)

plt.show()

ax1.yaxis.set_major_formatter(FuncFormatter(to_percent))

作用是设置纵轴为百分比格式，可以换做，省去自定义函数 to_percent的麻烦

import matplotlib.ticker as mtick

ax1.yaxis.set_major_formatter(mtick.PercentFormatter())

多柱状图

import pandas as pd

import matplotlib.pyplot as plt

import matplotlib as mpl

# 中文和负号的正常显示

mpl.rcParams['font.sans-serif'] = ['Times New Roman']

mpl.rcParams['font.sans-serif'] = [u'SimHei']

mpl.rcParams['axes.unicode_minus'] = False

from matplotlib.ticker import FuncFormatter

def to_percent(temp,position):

return '%1.0f'%(100*temp) + '%'

data=pd.read_excel(r"C:\Users\fff507\Desktop\周内各天时变比例.xlsx")

value_list=data['order_id'].tolist()

x=list(range(len(value_list)))

total_width,n=1,1.5

width=total_width/n

with plt.style.context(['science','no-latex']):

fig= plt.figure(figsize=(12, 4),dpi=200)

# 设置图形的显示风格

# plt.style.use('ggplot')

ax1 = fig.add_subplot(111)

ax1.bar(x,value_list,width=width,fc='red',alpha=0.5)

# 设置刻度和标签

ticks=list(range(12,len(value_list),24))

labels=['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']

ax1.set_xticks(ticks)

ax1.set_xticklabels(labels, rotation=0, horizontalalignment='center')

ax1.tick_params(labelsize=10)

# ax1.set_title('骑行订单时间分布',fontsize =10)

ax1.yaxis.set_major_formatter(FuncFormatter(to_percent))

x1=0-width/2

plt.vlines(x1, 0, 0.2, colors = "grey", linestyles = "-",linewidth=1)

plt.vlines(23-width/2, 0, 0.2, colors = "grey", linestyles = "-",linewidth=1)

plt.vlines(47+width/2, 0, 0.2, colors = "grey", linestyles = "-",linewidth=1)

plt.vlines(71+width/2, 0, 0.2, colors = "grey", linestyles = "-",linewidth=1)

plt.vlines(95+width/2, 0, 0.2, colors = "grey", linestyles = "-",linewidth=1)

plt.vlines(119+width/2, 0, 0.2, colors = "grey", linestyles = "-",linewidth=1)

plt.vlines(143+width/2, 0, 0.2, colors = "grey", linestyles = "-",linewidth=1)

plt.vlines(167+width/2, 0, 0.2, colors = "grey", linestyles = "-",linewidth=1)

list2=list(range(12,157,24))

for i in list2:

plt.vlines(i, 0, 0.2, colors = "grey", linestyles = "--",linewidth=1)

ax1.set_ylabel('Trip Percentage/Day',fontsize =10)

# plt.savefig('周内各天时变比例.png',format='png',bbox_inches='tight', pad_inches = 0,dpi=300)

plt.savefig('周内各天时变比例.png',format='png',dpi=300)

plt.show()

上面用到了SciencePlots绘图，传送门，会自动去掉绘图时的白边，不用这个的话，下面这样也可以去掉白边

plt.savefig('周内各天时变比例.png',format='png',bbox_inches='tight', pad_inches = 0,dpi=300)

概率分布直方图&累计概率分布

def cum_prob_curve(data,bins,title,xlabel,pic_path):

'''

绘制概率分布直方图和累计概率分布曲线

'''

import matplotlib.pyplot as plt

import matplotlib as mpl

from matplotlib.ticker import FuncFormatter

#从pyplot导入MultipleLocator类，这个类用于设置刻度间隔

from matplotlib.pyplot import MultipleLocator

fig= plt.figure(figsize=(8, 4),dpi=100)

# 设置图形的显示风格

plt.style.use('ggplot')

# 中文和负号的正常显示

mpl.rcParams['font.sans-serif'] = ['Times New Roman']

mpl.rcParams['font.sans-serif'] = [u'SimHei']

mpl.rcParams['axes.unicode_minus'] = False

ax1 = fig.add_subplot(111)

##概率分布直方图

a1,a2,a3=ax1.hist(data,bins =bins, alpha = 0.65,density=0.7,edgecolor='k')

##累计概率曲线

#生成累计概率曲线的横坐标

indexs=[]

a2=a2.tolist()

for i,value in enumerate(a2):

if i<=len(a2)-2:

index=(a2[i]+a2[i+1])/2

indexs.append(index)

#生成累计概率曲线的纵坐标

def to_percent(temp,position):

return '%1.0f'%(100*temp) + '%'

dis=a2[1]-a2[0]

freq=[f*dis for f in a1]

acc_freq=[]

for i in range(0,len(freq)):

if i==0:

temp=freq[0]

else:

temp=sum(freq[:i+1])

acc_freq.append(temp)

#这是双坐标关键一步

ax2=ax1.twinx()

#绘制累计概率曲线

ax2.plot(indexs,acc_freq)

#设置累计概率曲线纵轴为百分比格式

ax2.yaxis.set_major_formatter(FuncFormatter(to_percent))

ax1.set_xlabel(xlabel,fontsize=8)

ax1.set_title(title,fontsize =8)

#把x轴的刻度间隔设置为1，并存在变量里

# x_major_locator=MultipleLocator(xlocator)

# ax1.xaxis.set_major_locator(x_major_locator)

ax1.set_ylabel('频率/组距',fontsize=8)

ax2.set_ylabel("累计频率",fontsize=8)

plt.savefig(pic_path,format='png', dpi=300)

plt.show()

本文地址：https://blog.csdn.net/qq_38412868/article/details/107470587

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