Openpyxl tutorial Openpyxl. The openpyxl is a Python library to read and write Excel 2010 xlsx/xlsm/xltx/xltm files. In this tutorial, we will work with xlsx files. Creating a new file. In the first example, we will create a new xlsx file with openpyxl. Writing to a cell. What’s more, this software is widely used in many different application fields all over the world. And, whether you like it or not, this applies to data science. You’ll need to deal with these spreadsheets at some point, but you won’t always want to continue working in it either. That’s why Python developers have implemented ways to read, write and manipulate not only these files, but also many other types of files. Today’s tutorial will give you some insights into how you can work with Excel and Python. It will provide you with an overview of packages that you can use to load and write these spreadsheets to files with the help of Python. You’ll learn how to work with packages such as pandas, openpyxl, xlrd, xlutils and pyexcel. (Try this interactive course:, to work with CSV and Excel files in Python.). The Data As Your Starting Point When you’re starting a data science project, you will often work from data that you have gathered maybe from web scraping, but probably mostly from datasets that you download from other places, such as,, etc. But more often than not, you’ll also find data on Google or on repositories that are shared by other users. This data might be in an Excel file or saved to a file with.csv extension, The possibilities can seem endless sometimes. But whenever you have data, your first step should be to make sure that you’re working with a qualitative data. In the case of a spreadsheet, you should corroborate that it's qualitative because you might not only want to check if this data can answer the research question that you have in mind but also if you can trust the data that the spreadsheet holds. Check The Quality of Your Spreadsheet To check the overall quality of your spreadsheet, you can go over the following checklist: • Does the spreadsheet represent static data? • Does your spreadsheet mix data, calculation, and reporting? Vray for sketchup 2015 crack. • Is the data in your spreadsheet complete and consistent? • Does your spreadsheet have a systematic worksheet structure? • Did you check if the live formulas in the spreadsheet are valid? This list of questions is to make sure that your spreadsheet doesn’t ‘sin’ against the best practices that are generally accepted in the industry. Of course, the above list is not exhaustive: there are many more general rules that you can follow to make sure your spreadsheet is not an ugly duckling. However, the questions that have been formulated above are most relevant for when you want to make sure if the spreadsheet is qualitative. Preparing Your Workspace Preparing your workspace is one of the first things that you can do to make sure that you start off well. The first step is to check your working directory. When you’re working in the terminal, you might first navigate to the directory that your file is located in and then start up Python. That also means that you have to make sure that your file is located in the directory that you want to work from! But perhaps more importantly, if you have already started your Python session and you’ve got no clue of the directory that you’re working in, you should consider executing the following commands: # Import `os` import os # Retrieve current working directory (`cwd`) cwd = os.getcwd() cwd # Change directory os.chdir('/path/to/your/folder') # List all files and directories in current directory os.listdir('.' ) Great, huh? You’ll see that these commands are pretty vital not only for loading your data but also for further analysis. For now, let’s just continue: you have gone through all the checkups, you have saved your data and prepped your workspace. Can you already start with reading the data in Python? Additional Workspace Preparations: pip That’s why you need to have pip and setuptools installed. If you have Python 2 >=2.7.9 or Python 3 >=3.4 installed, you won’t need to worry because then you’ll normally already have it ready. In such cases, just make sure you have upgraded to the latest version. To do this, run the following command in your terminal: # For Linux/OS X pip install -U pip setuptools # For Windows python -m pip install -U pip setuptools In case you haven’t installed pip installed yet, run the python get-pip.py script that you can find. Additionally, you can follow the installation instructions on the page if you need more help to get everything installed properly. Installing Anaconda Another option that you could consider if you’re using Python for data science is installing the Anaconda Python distribution. By doing this, you’ll have an easy and quick way to get started with doing data science because you don’t need to worry about separately installing the packages that you need to do data science.
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