The popularity of the data science industry, and its many tools and programming languages, has led to a shift in the methods used by many data analytics professionals. For data analysts or business analysts, it has become increasingly common to transition from analytical to automated methods of working with data. Data science tools and software for data analytics reflect these changes within the industry. Standard data analytics software like Microsoft Excel includes a variety of features that can be used to streamline the process of managing and manipulating a dataset, with tools like Pivot Tables being a go-to method for Data Analysts.
The Pivot Table tool is Excel’s more advanced method for working with structured data and is used to aggregate and parse datasets by returning descriptive statistics about values and variables. Pivot tables are not only applicable within the spreadsheet environment, but can also be deployed within a Python environment. Data scientists familiar with Python libraries and packages can easily make the move from Pivot Tables to Python code.
What Are Pivot Tables?
Pivot Tables are a Microsoft Excel function used to easily summarize data and return descriptive statistics. These tables give dimension and clarity to the data stored in a spreadsheet, making it easier to see the different variables or categories in a dataset, as well as to query the data. To create a Pivot Table, a Data Analyst will create an object from a dataset by selecting different aspects, or fields, of the dataset to move into the position of rows, columns, and values. These fields can then be categorized in ways that pinpoint the exact relationships of interest. The Data Analyst can “pivot” the perspective of the data, by manipulating the fields contained within the table’s rows and columns.
Generally, pivot tables are used when working with datasets that are too large to display all of the variables onscreen and enable easier organization and exploration of these datasets. Once an object is created, the Pivot Table structure also allows data scientists to make calculations such as analyzing the sum, average, and mean of the Pivot Table. As a form of tabular data which aggregates rows and columns, pivot tables can provide both simple and complex comparisons, with some pivot tables examining just two variables, while others include multiple variables.
Pivot tables with multiple variables are known as multi-level tables and can be created by including multiple fields in an analysis. For example, a Pivot Table that includes a column displaying product names, and multiple rows that include product sales information (items sold and sales generated in a dollar amount over time), can be used by a Data Analyst to calculate the total sales of a product or which product is purchased most often and during what time period. In addition, data analysts can edit the format and style of the Pivot Table, including unique colors, fonts, sizes, and calculations to make visualizations clear and comprehensible.
Making the Move from Excel to Python
Due to the popularity of Python’s data science libraries, analysts can easily make the move from using pivot tables in Microsoft Excel to using pivot tables in Python. By importing data science libraries for Python such as Pandas and NumPy, data analysts can call upon pivot tables in a Python environment like IDLE or Jupyter Notebook. With Pandas, data scientists can use the functions “pandas.pivot” or “pandas.pivot_table” to produce a Pivot Table with set values and variables. This function can also be used to clean and analyze data by filling in missing values, aggregating the sum of values in a particular dataset, and applying statistical formulas and calculations to the dataset.
When using Excel data and pivot tables in Python, data is presented in a structure similar to a spreadsheet, but without the lines between rows and columns. So, if you’re looking to return data more visually similar to a spreadsheet, you can also import a data visualization library such as Seaborn or Matplotlib. These libraries are known for their capabilities when it comes to creating graphs and charts and can be used to replicate the look of a spreadsheet.
How Python Can Replace Pivot Tables
Python tools and software offer multiple functions and features that can replicate the uses of pivot tables. More advanced data analysts and scientists can use Python to replace pivot tables in their work. For example, Python’s “GroupBy” function can be used to compare and summarize aspects of a dataset. Similar to pivot tables, selecting different variables for comparison returns more information about the relationships between these variables. However, making comparisons using the “GroupBy” function is not the most efficient way to replace pivot tables when a dataset has several variables because this function was not created to cleanly handle multi-variate aggregation.
If you’re working with complex datasets with multiple variables that influence each other, the “DataFrames” function is the better option for replacing pivot tables. The DataFrames function found in the Pandas Python library replicates the form and structure of a Microsoft Excel spreadsheet. In Python, these DataFrames convert the spreadsheet cells of Excel data into a Pivot Table structure familiar to Python programs, while analyzing different dimensions of the data. Both pivot tables and DataFrames can be used to summarize and make inferences about a structured dataset.
Interested in Learning More About Python and Pivot Tables?
Data Analysts looking to learn more about Python and Pivot Tables can take part in any of Noble Desktop’s Python classes or Excel classes. The Excel Bootcamp focuses on teaching beginner students and professionals the fundamentals of working with spreadsheet data such as summarizing data with pivot tables and utilizing Excel formulas and functions. The Python for Data Science Bootcamp also pairs foundational knowledge in using pivot tables to analyze and visualize data with data science libraries, like Pandas and NumPy. In addition, Noble Desktop’s Data Analytics classes and certificates combine knowledge of Python and Microsoft Excel to create a more holistic approach to managing and analyzing data.