Python tutorial for reading row data and exporting to Excel (Arabic)
This video is a python program tutorial for reading row data (ASCII) with pandas library to a data frame, making some calculations on the selected data then exporting the results to excel along with generating graphs.
هذا الفيديو التعليمي باستخدام لغه البايثوتن لقراءه ملفات الرو داتا و اجراء عمليات حسابيه عليها ثم اصدار البيانات الى ملف اكسل شيت و رسوم بيانيه حسب طلب المستخدم
Docs for python libraries: https://docs.python.org/3/library/ind...
The code in the tutorial is below:
---------
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
#================================ PARAMETERS =============================
n = 215 # Start point
m = 1000 # End point
N = 5 #space between two readings
#Read the folder path
package_dir = os.path.dirname(os.path.abspath(__file__))
add the name of the file to the folder path to get the absolute path
file= package_dir+'/RowData.log'
#Read the Row data file to the DATAFRAME and choosing the splitter option (tab '\t' or comma ',')
df_data = pd.read_csv(file,sep='\t')
df_data = pd.read_csv(file,sep=',')
print(df_data)
#========================= CONVERTING TIME AND DATE ======================
#Combine the [date] yyyy-mm-dd with the [clock] hh:mm:ss in a string and use the to_datetime method to convert it
df_data['comb_datetime'] = pd.to_datetime(df_data['Date']+' '+df_data['Clock'])
#============================ SUMMARY OF RESULTS ==========================
'''
Calculate the average value of the data in each required column
'''
avg_P = np.average(df_data.loc[n:m,'Pressure'])
avg_T = np.average(df_data.loc[n:m,'Temperature'])
avg_dP = np.average(df_data.loc[n:m,'dP'])
avg_oilRate = np.average(df_data.loc[n:m,'Std.OilFlowrate'])
avg_waterRate = np.average(df_data.loc[n:m,'WaterFlowrate'])
avg_std_gasRate= np.average(df_data.loc[n:m,'Std.GasFlowrate'])
avg_act_gasRate= np.average(df_data.loc[n:m,'Act.GasFlowrate'])
avg_GOR = np.average(df_data.loc[n:m,'GOR(std)'])
avg_WC = np.average(df_data.loc[n:m,'Std.Watercut'])
avg_oilSG = np.average(df_data.loc[n:m,'OilDensity'])
avg_waterSG = np.average(df_data.loc[n:m,'WaterDensity'])
avg_gasSG = np.average(df_data.loc[n:m,'GasDensity'])
avg_liquid = avg_oilRate + avg_waterRate
API = (141.5/(avg_oilSG/1000) - 131.5)
'''
Add all the averaged values into a dictionary called dict_summary
Then convert it to a DataFrame called summary
'''
dict_summary = { 'Delta time':'??????',
'Choke Size':'???????',
'WHP':avg_P,
'WHT':avg_T,
'Diff dP':avg_dP,
'Oil Rate':avg_oilRate,
'Water Rate':avg_waterRate,
'Liquid Rate':avg_liquid,
'Gas Rate':avg_std_gasRate,
'Actual Gas Rate':avg_act_gasRate,
'Total GOR':avg_GOR,
'Gas SG':avg_gasSG,
'Oil SG':avg_oilSG,
'Oil API':API,
'BSW':avg_WC,
}
summary = pd.DataFrame([dict_summary])
'''
Open an ExcelWriter (Panda function) and name it writer
define all the data that will be written in the excel sheet using the dataframe df_data with the .loc method
make sure to add the starting point and end point 'n' , 'm' & spaces between points 'N'
call function .to_excel and add the writer and the name of the sheet 'sheet1'
'''
writer = pd.ExcelWriter('output.xlsx')
df_data.loc[n:m:N,['Date', 'Clock', 'Pressure',
'Temperature', 'dP',
'Std.OilFlowrate', 'WaterFlowrate',
'Std.GasFlowrate', 'Act.GasFlowrate', 'GOR(std)',
'Act.OilFlowrate', 'Std.Watercut','OilDensity',
'WaterDensity', 'GasDensity'
]
].to_excel(writer,'sheet1')
'''
The same for the average sheet and call it 'sheet2'
Save to excel sheet with .save() method
'''
summary.to_excel(writer,'sheet2')
writer.save()
Make x as the x-axis value
x = df_data.loc[n:m,'comb_datetime']
Create the figure and add all the lines needed to be viewed in the graph
plt.figure()
plt.plot(x,df_data.loc[n:m,'Pressure'],'b-')
plt.plot(x,df_data.loc[n:m,'dP'],'g-')
plt.plot(x,df_data.loc[n:m,'Temperature'],'r-')
plt.margins(0.05)
plt.subplots_adjust(bottom=0.15)
plt.title( 'Pressure and Temperature')
plt.legend(['Pressure', 'dp','Temp'])
Create another figure
plt.figure()
plt.plot(x,df_data.loc[n:m,'Std.OilFlowrate'],'k-')
plt.plot(x,df_data.loc[n:m,'GOR(std)'],'g-')
plt.plot(x,df_data.loc[n:m,'WaterFlowrate'],'b-')
plt.legend(['Oil rate', 'GOR','Water rate'])
plt.title( 'Flow rate and GOR')
Create a third figure
plt.figure()
plt.plot(x,df_data.loc[n:m,'Std.GasFlowrate'],'y-')
plt.plot(x,df_data.loc[n:m,'Std.Watercut'],'b-')
plt.legend(['Gas rate', 'water rate'])
plt.title( 'Gas and water rate')
Show the graph with .show() method
plt.show()
En esta página del sitio puede ver el video en línea Python tutorial for reading row data and exporting to Excel (Arabic) de Duración hora minuto segunda en buena calidad , que subió el usuario mohammed albatati 22 febrero 2021, comparta el enlace con amigos y conocidos, en youtube este video ya ha sido visto 456 veces y le gustó 60 a los espectadores. Disfruta viendo!
