Python：测向平均值似乎给出了错误的输出，为什么？

Question

背景： 我正忙于分析各种实验工作的数据。目的是导入包含各种工作表的excel文件。然后“过滤”数据中的噪声，找到所有样本的平均值。然后绘制一个图形并保存该图形。你知道吗

进度和问题： 我已经能够做所有上述步骤，但是，最终的图表与各种样本的平均数对我来说似乎是错误的。我不确定测向平均值是求平均数的正确方法。我已经附上了我得到的图表，不知何故我不能同意平均值可以这么低？ It can be seen that the saved image from my code cuts off the legend, how can I change this?

需要改进： 这是我关于stackoverflow的第一个问题，我对Python还是个新手。代码似乎非常“蓬松”，我将感谢任何建议缩短代码。你知道吗

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

代码中的大部分重复都是因为您为每个工作表定义了一个单独的变量，然后对每个工作表执行相同的操作。你知道吗

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代码。你知道吗

从documentation可以看到，通过指定sheetname = None，可以将所有工作表作为字典导入。或者，您也可以提供您想要阅读的工作表列表，在您的例子中[0,1,2,...,11]，因为它们是0索引的。你知道吗

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

您可以使用以下工具快速查看您拥有的功能：

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

您可以在需要时分别访问每张图纸：

sheets_dict['sheet_1_name']

这样可以避免很多重复。 例如，过滤将只是：

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