Matplotlib繪圖雙縱座標軸設置及控制設置時間格式

雙y軸座標軸圖

今天利用matplotlib繪圖,想要完成一個雙座標格式的圖。html

fig=plt.figure(figsize=(20,15))
ax1=fig.add_subplot(111)
ax1.plot(demo0719['TPS'],'b-',label='TPS',linewidth=2)
ax2=ax1.twinx()#這是雙座標關鍵一步
ax2.plot(demo0719['successRate']*100,'r-',label='successRate',linewidth=2)

橫座標設置時間間隔

import matplotlib.dates as mdate
ax1.xaxis.set_major_formatter(mdate.DateFormatter('%Y-%m-%d %H:%M:%S'))#設置時間標籤顯示格式
plt.xticks(pd.date_range(demo0719.index[0],demo0719.index[-1],freq='1min'))

縱座標設置顯示百分比

import matplotlib.ticker as mtick
fmt='%.2f%%'
yticks = mtick.FormatStrFormatter(fmt)
ax2.yaxis.set_major_formatter(yticks)

知識點

在matplotlib中,整個圖像爲一個Figure對象。在Figure對象中能夠包含一個,或者多個Axes對象。每一個Axes對象都是一個擁有本身座標系統的繪圖區域。其邏輯關係以下:

一個Figure對應一張圖片。api

Title爲標題。Axis爲座標軸,Label爲座標軸標註。Tick爲刻度線,Tick Label爲刻度註釋。1
spa

Title爲標題。Axis爲座標軸,Label爲座標軸標註。Tick爲刻度線,Tick Label爲刻度註釋。
code

add_subplot()

The Axes instance will be returned.對象

twinx()

ax = twinx()

create a twin of Axes for generating a plot with a sharex x-axis but independent y axis. The y-axis of self will have ticks on left and the returned axes will have ticks on the right.
意思就是,建立了一個獨立的Y軸,共享了X軸。雙座標軸!圖片

相似的還有twiny()utf-8

ax1.xaxis.set_major_formatter

Set the formatter of the major ticker
ACCEPTS: A Formatter instance

DateFormatter()

strftime方法(傳入格式化字符串)。

strftime(dt, fmt=None)
Refer to documentation for datetime.strftime.
fmt is a strftime() format string.

FormatStrFormatter()

Use a new-style format string (as used by str.format()) to format the tick. The field formatting must be labeled x
定義字符串格式。

plt.xticks

# return locs, labels where locs is an array of tick locations and
# labels is an array of tick labels.
locs, labels = xticks()

# set the locations of the xticks
xticks( arange(6) )

# set the locations and labels of the xticks
xticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') )

代碼彙總

#coding:utf-8
import matplotlib.pyplot as plt 
import matplotlib as mpl
import matplotlib.dates as mdate
import matplotlib.ticker as mtick
import numpy as np
import pandas as pd
import os


mpl.rcParams['font.sans-serif']=['SimHei'] #用來正常顯示中文標籤
mpl.rcParams['axes.unicode_minus']=False #用來正常顯示負號
mpl.rc('xtick', labelsize=20) #設置座標軸刻度顯示大小
mpl.rc('ytick', labelsize=20) 
font_size=30
#matplotlib.rcParams.update({'font.size': 60})

%matplotlib inline
plt.style.use('ggplot')

data=pd.read_csv('simsendLogConvert_20160803094801.csv',index_col=0,encoding='gb2312',parse_dates=True)

columns_len=len(data.columns)
data_columns=data.columns

for x in range(0,columns_len,2):
    print('第{}列'.format(x))
    total=data.ix[:,x]
    print('第{}列'.format(x+1))
    successRate=(data.ix[:,x+1]/data.ix[:,x]).fillna(0)
    
    
    yLeftLabel=data_columns[x]
    yRightLable=data_columns[x+1]
    
    
    print('------------------開始繪製類型{}曲線圖------------------'.format(data_columns[x]))
    
    fig=plt.figure(figsize=(25,20))
    ax1=fig.add_subplot(111)
    #繪製Total曲線圖
    ax1.plot(total,color='#4A7EBB',label=yLeftLabel,linewidth=4)

    # 設置X軸的座標刻度線顯示間隔
    ax1.xaxis.set_major_formatter(mdate.DateFormatter('%Y-%m-%d %H:%M:%S'))#設置時間標籤顯示格式
    plt.xticks(pd.date_range(data.index[0],data.index[-1],freq='1min'))#時間間隔
    plt.xticks(rotation=90)
    
    #設置雙座標軸,右側Y軸
    ax2=ax1.twinx()
    
    #設置右側Y軸顯示百分數
    fmt='%.2f%%'
    yticks = mtick.FormatStrFormatter(fmt)
    
    # 繪製成功率圖像
    ax2.set_ylim(0,110)
    ax2.plot(successRate*100,color='#BE4B48',label=yRightLable,linewidth=4)
    ax2.yaxis.set_major_formatter(yticks)

    ax1.set_xlabel('Time',fontsize=font_size) 
    ax1.set_ylabel(yLeftLabel,fontsize=font_size)
    ax2.set_ylabel(yRightLable,fontsize=font_size)
    
    legend1=ax1.legend(loc=(.02,.94),fontsize=16,shadow=True)
    legend2=ax2.legend(loc=(.02,.9),fontsize=16,shadow=True)
    
    legend1.get_frame().set_facecolor('#FFFFFF')
    legend2.get_frame().set_facecolor('#FFFFFF')
    
    plt.title(yLeftLabel+'&'+yRightLable,fontsize=font_size)

    plt.savefig('D:\\JGT\\Work-YL\\01佈置的任務\\04繪製曲線圖和報告文件\\0803\\出圖\\{}-{}'.format(yLeftLabel.replace(r'/',' '),yRightLable.replace(r'/',' ')),dpi=300)

參考


  1. Vami-繪圖: matplotlib核心剖析
  2. Secondary axis with twinx(): how to add to legend?
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