Python：df.mean似乎给出了错误的输出，为什么？

Question

背景： 我忙于分析各种实验工作的数据。目标是导入一个excel文件与各种表。然后从数据中“过滤”噪音并找出所有样品的平均值。然后绘制一张图并保存图。

进展&问题： 我已经能够做到所有上述步骤，然而，随着各种样品VS平均最终图形上看来我错了。我不确定“df.mean”是否是找到平均值的正确方法。我附上了我得到的图表，不知何故，我不同意平均数可以如此之低？ It can be seen that the saved image from my code cuts off the legend, how can I change this?

需要改善： 这是我在计算器的第一个问题，我还是新的Python。代码看起来非常“蓬松”，我希望对缩短代码有任何建议。

我的代码：

#IMPORT LIBRARIES 
import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 

#IMPORT DATA 
excel_df= pd.ExcelFile('data.xlsx',delimiter = ';') #import entire excel file 
sheet1=pd.read_excel('data.xlsx',sheetname=0,names=['time','void1','pressure1']) 
sheet2=pd.read_excel('data.xlsx',sheetname=1,names=['time','void2','pressure2']) 
sheet3=pd.read_excel('data.xlsx',sheetname=2,names=['time','void3','pressure3']) 
sheet4=pd.read_excel('data.xlsx',sheetname=3,names=['time','void4','pressure4']) 
sheet5=pd.read_excel('data.xlsx',sheetname=4,names=['time','void5','pressure5']) 
sheet6=pd.read_excel('data.xlsx',sheetname=5,names=['time','void6','pressure6']) 
sheet7=pd.read_excel('data.xlsx',sheetname=6,names=['time','void7','pressure7']) 
sheet8=pd.read_excel('data.xlsx',sheetname=7,names=['time','void8','pressure8']) 
sheet10=pd.read_excel('data.xlsx',sheetname=9,names=['time','void10','pressure10']) 

#SORT VALUES TO FIND THE UNWANTED DATA 
sheet1.sort_values('pressure1',ascending=False).head() #the pressure has noise so sort accordingly 

#GET ONLY WANTED DATA WITHOUT NOISE 
sheet1_new = sheet1[sheet1.pressure1 <=8] #exclude the noise above 8 bar 
sheet2_new = sheet2[sheet2.pressure2 <=8] #exclude the noise above 8 bar 
sheet3_new= sheet3[sheet3.pressure3 <=8] #exclude the noise above 8 bar 
sheet4_new = sheet4[sheet4.pressure4 <=8] #exclude the noise above 8 bar 
sheet5_new = sheet5[sheet5.pressure5 <=8] #exclude the noise above 8 bar 
sheet6_new = sheet6[sheet6.pressure6 <=8] #exclude the noise above 8 bar 
sheet7_new = sheet7[sheet7.pressure7 <=8] #exclude the noise above 8 bar 
sheet8_new = sheet8[sheet8.pressure8 <=8] #exclude the noise above 8 bar 
sheet10_new = sheet10[sheet10.pressure10 <=8] #exclude the noise above 8 bar 

#MERGE THE DATASETS TO FIND AVERAGE OF ALL SAMPLES 

#'MERGE' ONLY MERGES 2 DATAFRAMES AT A TIME 
merge12_df = pd.merge(sheet1_new,sheet2_new, on='time') 
merge34_df = pd.merge(sheet3_new,sheet4_new, on='time') 
merge56_df = pd.merge(sheet5_new,sheet6_new, on='time') 
merge78_df = pd.merge(sheet7_new,sheet8_new, on='time') 

#MERGE ON FIRST OUTPUT 
all_merged = merge12_df.merge(merge34_df, on='time').merge(merge56_df, on = 'time').merge(merge78_df, on = 'time').merge(sheet10_new, on = 'time') 
#print(all_merged.head()) #check that all data is merged into one dataframe 

#AVERAGE ALL PRESSURES 
mean_all_pressures = all_merged[["pressure1", "pressure2","pressure3", "pressure4","pressure5", "pressure6","pressure7", "pressure8", "pressure10"]].mean(axis=1) 

#PRINT AVERAGE VS ALL THE SAMPLES GRAPH 
plt.figure(1) 
plt.plot(all_merged.time,mean_all_pressures,'r.') #plot the average of all samples. 
plt.plot(sheet1_new.time,sheet1_new.pressure1) 
plt.plot(sheet2_new.time,sheet2_new.pressure2) 
plt.plot(sheet3_new.time,sheet3_new.pressure3) 
plt.plot(sheet4_new.time,sheet4_new.pressure4) 
plt.plot(sheet5_new.time,sheet5_new.pressure5) 
plt.plot(sheet6_new.time,sheet6_new.pressure6) 
plt.plot(sheet7_new.time,sheet7_new.pressure7) 
plt.plot(sheet8_new.time,sheet8_new.pressure8) 
plt.plot(sheet10_new.time,sheet10_new.pressure10) 
plt.legend(['Average','Sample 1','Sample 2','Sample 3','Sample 4','Sample 5','Sample 6','Sample 7','Sample 8','Sample 10'],bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) 
plt.xlabel('Time (s)'),plt.ylabel('Pressure (bar)') #Specify the plot details 
plt.savefig('AllPressures_vs_Average.png') #Save the plot for later use 
plt.show() #Display the plot 

Answer 1

大多数在你的代码重复的来自于你定义每个表中单独变量，然后做他们每个人相同的操作的事实。

您可以通过将每个工作表的内容存储到单个字典中而不是单独的变量来改进当前的代码。

从documentation中可以看到，通过指定s heetname = None，可以将所有工作表导入为字典。或者，您可以提供您想要阅读的工作表清单，在您的案例[0,1,2,...,11]中，因为它们是0索引的。

sheets_dict = pd.read_excel('data.xlsx',sheetname=None,names=['time','void1','pressure1']) 

您可以快速地看到你使用的是什么：

for name, sheet in sheets_dict.iteritems(): 
    print name, sheet.head() 

时，您可以根据需要单独访问每个表：

sheets_dict['sheet_1_name'] 

，这将避免大量的重复。 例如，滤波将仅仅是：

new_sheets_dict = {key: el[el.pressure1 <=8] for key, el in sheets_dict.iteritems)}