I had a quick application question on using pandas in python to analyze large excel sheets.
For data that have millions of rows (beyond Excel's limit), how can we deal with analyzing them through pandas?
I know excel lets you load data from a text file and have your excel spreadsheet "create a connection" to the source file without having to load all the millions of rows directly. If we call this excel spreadsheet using pandas in python, will we be able to use our filter operations (and all the other table data analysis operations we've learned ) on all the millions of rows from the source file? Or will it just execute on only what shows up on the excel sheet (assuming we have selected the "create a connection" option to the source text file )?
Is there a more efficient way of using pandas with sas files directly?
I think getting files via small chunks can make the process efficient. Please look at this link
Related
I'm currently working with a pandas data frame and need to save data via CSV for different categories.so I thought to maintain one CSV and add separate sheets to each category. As per my research via CSV, we can't save data for multiple sheets. is there any workaround for this? I need to keep the format as CSV(cant use excel)
No.
A CSV file is just a text file, it doesn't have a standard facility for "multiple sheets" like spreadsheet files do.
You could save each "sheet" as a separate file, but that's about it.
I'm working with slightly big data and i need to write this data to an xlsx file. Sometimes the size of this files can be 15GB. I have a python code that gets data as dataframes and writes data to excel continuously so i need to write data to an existing excel and the existing sheet. I was using 'openpyxl'.
There are two problems that I faced while working with that library.
Firstly to append an existing excel it needs to load workbook which is an impossible thing for me because of the data size. I must use
the lowest RAM I can use. -
Secondly this lib is useful only writing
to the different sheets. When I'm trying to write data to same sheet
even if I give the 'startrow' for the saving process it deletes the
old data and writes new one starting from that row.
I already tried the solution available here to address my problem but it doesn't fit my requirements.
Do you have any idea how I can do this?.
Assuming I have an excel sheet already open, make some changes in the file and use pd.read_excel to create a dataframe based on that sheet, I understand that the dataframe will only reflect the data in the last saved version of the excel file. I would have to save the sheet first in order for pandas dataframe to take into account the change.
Is there anyway for pandas or other python packages to read an opened excel file and be able to refresh its data real time (without saving or closing the file)?
Have you tried using mitosheet package? It doesn't answer your question directly, but it allows you working on pandas dataframes as you would do in excel sheets. In this way, you may edit the data on the fly as in excel and still get a pandas dataframe as a result (meanwhile generating the code to perform the same operations with python). Does this help?
There is no way to do this. The table is not saved to disk, so pandas can not read it from disk.
Be careful not to over-engineer, that being said:
Depending on your use case, if this is really needed, I could theoretically imagine a Robotic Process Automation like e.g. BluePrism, UiPath or PowerAutomate loading live data from Excel into a Python environment with a pandas DataFrame continuously and then changing it.
This use case would have to be a really important process though, otherwise licensing RPA is not worth it here.
df = pd.read_excel("path")
In variable explorer you can see the data if you run the program in SPYDER ide
I am looking to write certain columns of data from an excel sheet to a HTML table. Not looking to write specific/fixed cells into the table always, need to do this based on conditions. For example, if I have a table with columns Name/Age/Occupation, I would like to make an HTML table using just columns Name and Occupation. Also, within Name, I would only like to write the names starting with 'N' onto the table and corresponding Occupation. The Excel sheet dynamically changes with new data everytime. Essentially, I would not want to write specific cells or range of cells into the table but only the data based on conditions I set. Any suggestions using python/html/jquery or other methods are welcome.
First you should edit the Excel file, export it as a .csv file and then work on the file using a program language of your preference. It would be much much more complicated if you try to work on the .xls or .xlsx files. I recommend using python with its library panda that works on csv files.
For parsing excel files, I've had good success using openpyxl
A Python library to read/write Excel 2010 xlsx/xlsm files
I have a complex excel spreadsheet that I'm trying to ingest and cleanse via xlrd. The existing spreadsheet is really designed to be more of a "readable" document, but I'm tasked with ingesting it as a data source. The trouble is that there is frequently lots of spacing between the field names and the actual data. Ultimately I'd like to read in the contents of the excel file, process it, and write a simplified file with just the data. Any ideas?
For example:
Have:
Want:
Here's the example spreadsheet: download