I'm trying to delete cells from an Excel spreadsheet using openpyxl. It seems like a pretty basic command, but I've looked around and can't find out how to do it. I can set their values to None, but they still exist as empty cells. worksheet.garbage_collect() throws an error saying that it's deprecated. I'm using the most recent version of openpyxl. Is there any way of just deleting an empty cell (as one would do in Excel), or do I have to manually shift all the cells up? Thanks.
In openpyxl cells are stored individually in a dictionary. This makes aggregate actions like deleting or adding columns or rows difficult as code has to process lots of individual cells. However, even moving to a tabular or matrix implementation is tricky as the coordinates of each cell are stored on each cell meaning that you have process all cells to the right and below an inserted or deleted cell. This is why we have not yet added any convenience methods for this as they could be really, really slow and we don't want the responsibility for that.
Hoping to move towards a matrix implementation in a future version but there's still the problem of cell coordinates to deal with.
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I'm having trouble finding a solution to fill out an excel template using python. I currently have a pandas dataframe where I use openpyxl to write the necessary data to specific Rows and Cells in a for loop. The issue I have is that in my next project several of the cells I have to write are not continuous so for example instead of going A1,A2,A3 it can go A1,A5,A9. However this time if I were to list the cells like I did in the past it would be impractical.
So I was looking for something that would work similar to a Vlookup in excel. Where in the template we have Python would match the necessary Row and Column to drop the information. I know I might need to use different commands.
I added a picture below as an example. So I would need to drop values in the empty cells and ideally Python would read "USA and Revenue" and know to drop that information on cell B2. I know I might need something to map it also I am just not sure on how to start or if it is even possible.
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In my excel file, I have a list of some 7000-8000 binary chemical compounds. (Consists of 2 elements only).
And I have segregated them into their component elements, i.e., I have 2 columns of elements, namely: First Element and Second Element.
I have attached a screenshot below:
Now I want to fill in the respective Atomic Number and Atomic Weight beside every element as per a predefined list using Python.
How do I do that?
I have attached a screenshot of my predefined list below, as well:
People have told me things like, use the "CSV" package or the "pandas" package, but I would request some more procedural help wrt to the above packages or any other method you might suggest.
Also, if it cannot be done via Python, I am open to other languages as well.
I noticed that your task does not require python programming. The reason is :
You already have a predefined list of items stored in a excel sheet.
Excel already has built in function (VLOOKUP) for this task.
We just have to use VLOOKUP function in column Atomic number, Atomic weight ( you have to create columns in data2 sheet ) which will take care of searching for particular element atomic weight, number and return it in active cell.
Next, use fill handle to apply the function to all the cells or ( if data is in table , great!! no need to use fill handle because table automatically applies the function to whole column range )
I expect that you already know how to work with excel formulas and functions, if not comment down below for further assistance. Kindly upvote the answer if you liked it.
NOTE: If you need automation, then be sure to check out Excel VBA, google sheets, Apps script.
I'm pretty new to Pandas and Python, but have solid coding background. I've decided to pick this up because it will help me automate certain financial reports at work..
To give you a basic background of my issue, I'm taking a PDF and using Tabula to reformat it into a CSV file, which is working fine but giving me certain formatting issues. The reports come in about 60 page PDF files, which I am exporting to a CSV and then trying to manipulate the data in Python using Pandas.
The issue: when I reformat the data, I get a CSV file that looks something like this -
The issue here is that certain tables are shifting and I think it is due to the amount of pages and multiple headings within those.
Would it be possible for me to reformat this data using Pandas, and basically create a set of rules for how it gets reformatted?
Basically, I would like to shift the rows that are misplaced back into their respective places based on something like blank spaces.
Is it possible for me to delete rows with certain strings - deleting extra/unnecessary headers.
Can I somehow save the 'Total' data at the bottom by searching for the row with 'Total' and placing it somewhere else?
In essence, is there a way to partition this data by a set of commands (without specifying row numbers - because this changes daily) and then reposition it accordingly so that I can manipulate the data however necessary?
I am writing a program that will process a bunch of data and fill a column in excel. I am using openpyxl, and strictly using write_only mode as well. Each column will have a fixed 75 cell size, and each cell in the row will have the same formula applied to it. However, I can only process the data one column at a time, I cannot process an entire row, then iterate through all of the rows.
How can I write to a column, then move onto the next column once I have filled the previous one?
This is a rather open ended question, but may I suggest using Pandas. Without some kind of example of what you are trying to achieve it's difficult to make a great recommendation, but I have used pandas in the past a ton for automating processing of excel files. Basically you would just load whatever data into a Pandas DataFrame, then do your transformations/calculations and whenever you are done write it back to either the same or a new excel file (or a number of other formats).
Because the OOXML file format is row-oriented, you must write in rows in write-only mode, it is simply not possible otherwise.
What you might be able to do is to create some kind transitional object that will allow to fill it with columns and then use this to write to openpyxl. A Pandas DataFrame would probably be suitable for this and openpyxl supports converting these into rows.
I used the Python Pandas library as a wrap-around instead of using SQL. Everything worked perfectly, except when I open the output excel file, the cells appear blank, but when I click on the cell, I can see the value in the cell above. Additionally, Python and Stata recognize the value in the cell, even though the eye cannot see it. Furthermore, if I do "text to columns", then the values in the cell become visible to the eye.
Clearly it's a pain to go through every column and click "text to columns", and I'm wondering the following:
(1) Why is the value not visible to the eye when it exists in the cell?
(2) What's the easiest way to make all the values visible to the eye aside from the cumbersome "text to columns" for all columns approach?
(3) I did a large number of tests to make sure the non-visible values in the cells in fact worked in analysis. Is my assumption that the non-visible values in the cells will always be accurate, true?
Thanks in advance for any help you can provide!
It sounds to me like your python code is inserting a carriage return either before or after the value.
I've replicated this behavior in Excel 2016 and can confirm that the cell appears blank, but does contain a value.
Furthermore, I've verified that using the text to columns will parse the carriage return out.