When your everyday tasks scope includes lots of document editing, you realize that every document format needs its own approach and in some cases specific applications. Handling a seemingly simple Troff file can often grind the entire process to a halt, especially if you are trying to edit with inadequate software. To avoid this kind of troubles, find an editor that can cover all your requirements regardless of the file extension and delete index in Troff with zero roadblocks.
With DocHub, you will work with an editing multitool for any occasion or document type. Reduce the time you used to devote to navigating your old software’s functionality and learn from our intuitive interface while you do the work. DocHub is a streamlined online editing platform that handles all of your document processing requirements for virtually any file, including Troff. Open it and go straight to efficiency; no prior training or reading instructions is required to enjoy the benefits DocHub brings to document management processing. Start with taking a few moments to register your account now.
See improvements within your document processing just after you open your DocHub account. Save your time on editing with our one solution that can help you be more efficient with any document format with which you have to work.
this video explains how to drop columns from a pandas data frame by index position using the Python programming language so without much talk lets dive into the python code as a first step we need to import the pandas Library as you can see in the first line of code and then in the next lines of code Im creating an example data frame using the data frame Constructor so after running these lines of code a new data frame is appearing which is called data and this data frame contains six rows and five columns which are called X1 X2 X3 X4 and X5 now lets assume that we want to drop the column at the second index position of our data frame then we can apply the code that you can see in the next code box and in this code box Im using the drop function and within this function Im using the columns attribute and within this attribute Im specifying the index position 2 and Im also specifying the axis argument to be equal to 1. and then Im storing the output of this in a new data frame