I have an Excel file that looks like this:

I have a sample data from which i am calculating the rank

I’m working with an Excel file and need to read its contents into a DataFrame. When I use pandas (with default engine), I can specify the data type of the columns to be strings, which works perfectly:

I have to read an online Excelsheet into a dataframe. This data frame consists formatted text in several cells. For example, as shown in Figure 1, a cell might contain a paragraph, followed by a space and a few bullet points etc. How can I read the Excelsheet as a Pandas data frame while preserving this text style format in each cell.

I am a new coder. I am having trouble with pandas.read_excel(). I had a lot of excel files. Pandas read most of it. However, Pandas couldn’t read some of the files and they return the exactly same corupted output on every file that pandas couldn’t read.

I have a lot excel files, all with the same format and I just want to make one big file with the concatenation of all the others.

My code is as under:

ugds = set() for sheet_name in excel_data.sheet_names: df = pd.read_excel(excel_data, sheet_name=sheet_name, header=1) ugds.update(df[“Pooln Gestão”].unique()) for UGD in ugds: new_file_name = f'{base_name}_{UGD}.xlsx’ with pd.ExcelWriter(new_file_name) as writer: for sheet_name in excel_data.sheet_names: df = pd.read_excel(excel_data, sheet_name=sheet_name, header=1) df_filtrado = df[df[“Pooln Gestão”] == UGD] df_filtrado.to_excel(writer, sheet_name=sheet_name, index=False) I need to skip the first row because what i am searching […]

I have an Excel file with merged columns and rows, and I want to read the excel file and parse it to convert it into a DataFrame.

My issue is simple,
I have an excel file containing multiple sheets, in every sheet there is a dataframe the dataframes on the different sheets have different columns but they all have one common column. The common column is called ‘Supervisor’ and has about 17 unique values.