Question

背景我正在忙着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”数据中的噪声并找到所有样本的平均值。然后绘制图形并保存图形。

进展＆amp;问题： 我已经能够完成上述所有步骤，但是，各种样本的最终图表与其平均值相比似乎是错误的。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，不知怎的，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，我还是Python新手。代码似乎非常“蓬松”，我希望有任何关于缩短代码的建议。

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

代码中的大部分重复都来自于为每个工作表定义一个单独的变量，然后对每个工作表执行相同的操作。

您可以通过将每个工作表的内容存储到单个字典中来改进当前代码，而不是将变量分开。

从documentation，您可以看到通过指定s heetname = None，您可以将所有工作表导入为字典。或者，您可以在[0,1,2,...,11]的情况下提供您想要阅读的工作表列表，因为它们是0索引的。

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

您可以快速查看所使用的内容：

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

您可以在需要时单独访问每张图纸：

sheets_dict['sheet_1_name']

这样可以避免大量的重复。例如，过滤只是：

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

Python：df.mean似乎给错了输出，为什么？

1 个答案: