Microsoft designed Excel to help users to analyze, organize and store data and can be used in different application fields. Specifically, companies directly apply the software in data science, which means they have to deal with spreadsheets every day. For that reason, they started to use Python as a simple way to manage these files. In this article, you will get some general insights on how to use Python and Excel for data science by exploring a couple of packages.
Collect the Dataset
A science project requires you to gather enough qualitative data from the web to analyze. The reason for not choosing quantitative data is that in addition to answering your research question you might need to check whether the data you have is reliable.
You can check the quality of the data by identifying several patterns such as a mix of data, calculation and reporting; consistency; completeness and whether it is static. These are just basic rules that correlate with the best practices accepted within any industry.
Another aspect to consider is to adjust the data file itself. For example, the first row is always reserved for the header. You should avoid fields and names with blank spaces meaning try to use underscores and dashes instead. Moreover, avoid symbols, delete the extra comments given before and mark any missing data with N/A. If you are confident about the dataset, you can start preparing the workspace.
Prepare the Workspace
The first thing to do is to check the working directory where the files are located and you will be working from. Besides, you should go through all the checkups and save the data you will work with.
Install Packages to Manage Excel Files
After all these steps, there is one more crucial action, which is the package installation. There are two options: either you install Setuptools/pip or Anaconda Python distribution. Data science analysis will become easier because you have the opportunity to test without separate installation.
Import Excel Files
Typically you import the Excel files as Pandas DataFrames that provide data analysis tools and easy structures for Python programming. Data scientists use Pandas library to have their data structured and self-explanatory.
However, Python offers a list of packages that allow you to work with Excel files. As an advice, try to use the virtual environments system to create a folder with required executables. After installing it in your directory, deactivate it once you are done. Other examples of packages include Openpyx1, x1rd (for reading and formatting Excel files) and x1wt (for writing the data in Excel).
Check your Data
Once you finished with the installation and preparation, make sure that the data is imported correctly. For reference, you can use the Pandas Cheat Sheet. For more detailed technical information on Python, you can ask Pyxll. If you are interested to use the Excel faster, try keyskillset tips and tricks. By combining the knowledge from both software, you can become a great asset for your company.