Did you know that Microsoft launched MS Excel in 1987? More than two decades later, this spreadsheet application has proved to be a valuable tool in data gathering, analysis, and presentation. The popular Visual Basic for Applications (VBA) programming language uses MS Excel for tables and databases. Most data scientists prefer it because it supports complex analysis of large data, using automated and customized commands.
Ever wondered why Python is preferred to Excel in some situations? Python contains advanced features that enable data scientists to perform swift and thorough data clean-ups. Data clean-up in Excel is a tedious process, because you have to manually navigate through tens or hundreds of spreadsheets to arrive at necessary adjustments. Python users can update multiple spreadsheets within seconds.
Guidelines on using Python and Excel for a data science project
If you want to experience the combined advantages of both MS Excel and Python when working on a data science project, you must pay attention to the following 8 steps:
Use qualitative data
Qualitative data is the information that cannot be quantified using numeric values. For example, color, tastes, dislike, among others. It’s impossible to determine the percentage of the blue color in a blue car, because a color cannot be measured in weight, distance, depth or height. Qualitative data helps data scientists capture important information that directly affects quantitative variables. For example, data scientists can determine how color influences the sales of a particular product.
Kaggle and Quandl datasets usually consist of both quantitative and qualitative data, since companies post actual research data. A client who doesn’t understand the difference between quantitative and qualitative data may instruct you to download MS Excel spreadsheet files from Google Drive or Dropbox. Your first task after downloading the spreadsheet files is sorting the data into their correct categories.
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Do a thorough check on the qualities of your spreadsheets
Data scientists use Excel spreadsheets because they support many data manipulation functions, allow distinct grouping of quantitative and qualitative data, and are compatible with data analysis software such as SPSS. Before you begin data analysis, you need to verify the following properties in your Excel spreadsheets.
- Do the spreadsheets contain static data? Static data refers to information that remains permanent after data entry. The printed information on a book or academic certificate is a good example of static data. For consistency purpose, ensure you always work with static data.
- Can you find raw information, data analysis, and presentations? A good spreadsheet should have clear distinctions between raw data and manipulated data. These two types of data should ideally appear in separate spreadsheets when displayed on the same Excel file.
- Is the data consistent? Before you roll up your sleeves and begin data analysis, you need to confirm whether the data is complete and adheres to the prescribed structure. You may realize the spreadsheet has missing data entries. Sometimes, data can end up in the wrong categories; for example, if the client entered January sales figures in the February column.
- Is there a systematic structure? There’s nothing more frustrating than trying to figure out the correct rows and columns in a disorganized Excel spreadsheet. Working with disorganized data leads to wrong analysis and data presentation.
- Are all the presented formulae valid? Smart data scientists test all formulae presented in Excel spreadsheets, because assuming everything is okay is risky. If you copy-paste and apply the wrong formula in multiple spreadsheets, you will have a hard time during the data clean-up stage.
Adhere to effective spreadsheet management techniques
Software developers use Python in data science because it is compatible with Microsoft Excel. You won’t have to worry about data loss when switching back to Excel. Here are nine ways to make your Excel spreadsheets suitable for Python data analysis:
- Set aside the first row in your spreadsheet for the header
- Reserve the first column for the sampling unit
- Use underscores, dashes, Camel casing, and concatenation to fill blank spaces in spreadsheets
- Always use short names where necessary
- Desist from using names containing exclamation marks, back and forward slashes, dollar signs, question marks, normal brackets, square brackets, hash symbols, and mathematical functions
- Delete all comments, because they will overlap into other columns
- Use N/A to represent missing values
- Always save your changes immediately
- Save your MS Excel documents in .CSV or .txt file formats for better Python compatibility
Ensure your workspace is okay
Proper preparation prevents poor performance. After downloading the required Excel files, confirm whether you’ve set up the correct working directory. This is where you’ll find the references to the file paths to your Excel spreadsheets, and relating to the work in progress. You can do this by locating the directory of your important spreadsheet files before launching Python.
Python contains a list of simplified commands to help you import Excel spreadsheet files, change locations of the working directory, and list all spreadsheet files available for use.
Get the right MS Excel packages
You can import Excel files to Python only after installing the correct package. The Anaconda package is suitable for novice computer programmers due to its ease of installation. You won’t struggle to perform complex data operations when using a simplified list of commands. Anaconda also comes with other optional packages that match projects of varying complexities.
Seasoned data scientists can install pip or setup for Python 2.0 and subsequent versions. Using pip allows computer programmers to perform a wide variety of data analysis and presentation. In addition, you can easily update it, using various Python-based Excel packages.
Here’s a step by step procedure for installing the pip package to your Python desktop application.
- Download the get-pip.py code
- Run the following command if you’re using the Linux operating system
pip install - U pip setup tools
Windows operating system users can follow the instruction below:
Python - m pip install - U pip setup tools
Import your MS Excel files as Panda Data Frames
A Panda Dataframe is a special type of format for organizing and storing data. Panda software library is open-source and only works with Python. One advantage of using Panda Data frames is they allow computer programmers to read data stored in different file formats. They also allow enhanced pivoting of datasets. Data pivoting is a case wherein a data analyst uses Excel to create a summarized table from a larger data table.
For you to use Panda Data frames, you need to install Anaconda on your Python software.
Use Panda Data Frames to confirm whether all the data is correct
Working with Panda Data Frames enables a data analyst to verify whether all the required data appears correctly in Python’s GUI. The data frames will help you to spot any blank fields, incorrect data entries, and inconsistent spreadsheets. Another advantage is that you’ll spend significantly less time making corrections, compared to manually cross-checking individual Excel spreadsheet documents.
When you need to verify the first entries of your spreadsheets using Panda Data Frames, type the following command.
# Check the first entries of the Data Frame
Dfl. Head ()
You can download the Panda Cheat Sheet to enable you to load Excel spreadsheets as Data Frames within seconds.
Use xlsxwriter to convert information in MS Excel spreadsheets
The xlsxwriter package enables data analysts to convert Panda Data Frames into MS Excel Spreadsheets. You can use this package to save more than one Panda Data Frame on a single Microsoft Excel file.
Summing it up
Python is convenient to use with Microsoft Excel spreadsheet files thanks to its simplified list of commands, and numerous data analysis features. However, you need to make sure your data andExcel spreadsheet documents adhere to the qualities explained in this article.
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