pandas.excelwriter

Pandas.ExcelWriter() file corruption unless use .save() function

Pandas.ExcelWriter() file corruption unless use .save() function Question: I am trying to use Pandas to write a dataframe to Excel. The data is just some price data from a public Crypto API: My code is as follows: writer = pd.ExcelWriter(‘crypto.xlsx’) btc.to_excel(writer, sheet_name=’Bitcoin’) # btc = dataframe This code produces a corrupt .xlsx file. I have …

Total answers: 1

Write Dataframe outputs from a for loop to Excel without overwriting Pandas

Write Dataframe outputs from a for loop to Excel without overwriting Pandas Question: I have an extensive set of code that results in 6 DataFrames per for-loop run. The column names are tailored to what vehicle I have running through the loops so the column names are different but the sizes of the dataframes are …

Total answers: 1

How can we write new data to existing Excel spreadsheet?

How can we write new data to existing Excel spreadsheet? Question: I have a process that creates a dataframe of almost 1,000 rows that runs each week. I would like to be able to append to an existing sheet without having to re-read the spreadsheet because that will take a long time as the file …

Total answers: 3

how to download excel file in python and streamlit?

how to download excel file in python and streamlit? Question: I have a Python script that read files and convert it to dataframe using Python and streamlit. Then I want to create a function to allows the user to download this dataframe as an Excel file with extension .xls. So I tried to read the …

Total answers: 4

Exporting many excel files based on column value in pandas dataframe while maintaining formatting?

Exporting many excel files based on column value in pandas dataframe while maintaining formatting? Question: I have a dataframe that I would like to parse to individual files based on unique strings in a column "names." I can do this with a simple function like: f= lambda x: x.to_excel(os.getcwd()+’\{}.xlsx’.format(x.name), index= False) df.groupby(‘names’).apply(f) The caveat being …

Total answers: 2