What is the difference between read_excel and Excel file?

What does read_excel do in pandas?

I'm reading a book about data analysis in R.

In the chapter about reading in excel files with R, the author does something like this: data = readexcel("excelfile.xls") whereas I understand readexcel() is something like an alias for read.table(). But then, what does readexcel() actually do?

Readexcel reads a single excel file as a dataframe. You should probably use readcsv instead for reading multiple excel files Pandas (dataframe, a python library) can read multiple .xlsx and .xls files into a dataframe. I am not sure if the functions are aliases or direct calls to pandas functions
Also, here is a reference link for different pandas methods to read different file formats.

What is the difference between read_excel and Excel file?

Does readexcel have additional options like encoding and other settings to make the data available as read (and perhaps display, but that's not what my question is about). Library(readxl). # create a temporary file with some fake data. Testfile <- tempfile(). Write.csv(head(iris), testfile) readexcel(testfile). # convert it to data frame. Testdata <- readexcel(testfile). # print it. # class : data.frame # dim : 485 1. # dimnames : 2. Excel file. Testfile <- tempfile(). Write.xlsx(head(iris), testfile) # load the file to the system. Readexcel(testfile). As mentioned in the answer by @baptiste above, both functions use openxlsx package. This package is quite new (it was introduced with version 0.4 of readxl), and is also the one that allows you to write the data in Excel format without specifying any arguments in readexcel.

If you check the source code of readexcel, you will see that it returns an object from openxlsx package (it looks like openxlsx::newXlsxWorkbook). When you do the comparison, it is easier to see the difference: # install.packages("openxlsx") library(openxlsx). Library(readxl). Testfile <- tempfile(). Write.xlsx(head(iris), testfile) openxlsx::newXlsxWorkbook(). # Class : openxlsxworkbook. # Created on : Thu, 18 Nov 2026 19:16:17 GMT

Is CSV faster than XLSX in Python?

So I'm trying to load an arbitrary amount of data (approximately 500,000 lines). Loading all the data at once (into an Excel spreadsheet) seems like an unacceptable solution, and if I want it to be a nice readable format for other people to look at, I think I have to separate out the contents of each line into separate rows, but the only way I can do that is with CSV.

I've tried using openpyxl, which appears to be the best option for handling this, and while the file opens in Excel just fine, my script that iterates through every line, reads it as a row in a pandas dataframe, and creates an object, all fail. Import openpyxl. From collections import Counter. Df = pd.DataFrame() wb = openpyxl.loadworkbook(filename="filepath") numcolumns = 0. For s in l: numcolumns = s. The answer is that it's not faster to use CSV than it is to use XLSX, and openpyxl can't handle both without some additional programming.