Python’s Pandas Library Has a SQL-Like Window Function that May Be Easily Performed

In the data science arena, professionals must master several tools for efficient data analysis, such as SQL and Pandas, a Python library. SQL is crucial in creating data views or tables, while Pandas is ideal for processing data, developing visualisations and even machine learning models. This article explores the SQL-inspired windows functions in Pandas and how SQL operations can be performed in Python.

The definition of a window function.

In comprehending the notion of a window function, it is crucial to establish its definition first. A window function is a calculation that is applied to a set of related rows in a table, relative to the current row. It is also known as an SQL analytic function, which outputs a value for each row based on the data obtained from one or more additional rows.

The OVER clause is a fundamental component of window functions, and without this syntax, any function cannot be categorised as a window function; instead, it should be recognised as either a single-row or aggregate function.

Window functions can expedite and enhance the accuracy of computations and data aggregation for numerous segments of target data. Unlike single-row functions, which output a single value for every row in the query, window functions produce a value for each segment.

The advantages of utilising window functions may not be immediately evident.

Window functions provide a faster and simpler alternative to using lengthy and intricate SQL queries for cross-sectional data analysis. This approach enables the swift and efficient aggregation of data from various sources, simplifying the analysis procedure.

Here are some common uses of window functions:

  • Comparing two time periods in inventory reporting by utilising data stored on a separate partition.
  • Arranging outcomes within a given time frame.
  • Integrating new features into existing machine learning models.
  • Summing up numbers over a specific period of time, such as a running total.
  • Estimating or assuming absent values based on the distribution of the remaining data.

In the case of panel data, window functions may be used to include supplementary features that provide an overview of the aggregate characteristics of a data window.

There are certain conditions that must be met to create a window function in SQL.

To construct a window function in SQL, the following three components are required:

  1. The name of the aggregate function or calculation to be applied to that column, such as RANK() and SUM().
  2. The “PARTITION BY” clause specifies the type of data partition to be used in the aggregate function or computation.
  3. The clause that begins a window function, OVER().

Apart from the three options mentioned earlier, the ORDER BY function can be used to specify the sorting criteria for each data window.

The Window Function in Pandas – How it Works

When translating window functions from the database, utilizing the `groupby` keyword in Pandas is often favoured over the `GROUP BY` clause in SQL. This is because `groupby` serves as the foundation of Pandas’ window functions and is similar to the `PARTITION BY` command in SQL. This keyword is employed to specify which subsets of the dataset should be included in the aggregation operation.

If you need assistance with a more intricate transformation, the .transform clause can offer the necessary aid. You may indicate the calculation or aggregation to use to implement the divisions within the dataset. This clause takes in a function as an input and returns a data frame with the same length as the original. According to sources, the transformed values generated by this clause are proportional to the length of the original axis.

In the `convert()` method, the function supplied may be a callable object, such as a lambda function, for more complex operations, or it can be specified as a string for simpler aggregation functions such as `mean`, `count`, and `sum`.

Remember that the transform() function can solely be applied to aggregate methods that return a single row. This averts the need to employ transform on any dataset that isn’t an aggregate function.

Pandas’ Python Window Functions that Emulate SQL

For demonstrative purposes, this tutorial employs publicly available stock price data that is free of charge.

Helpful Libraries and Components

ffn – Financial Functions for Python

For quantitative finance practitioners, the ffn library provides a plethora of helpful functions that range from basic graphing operations to advanced statistical analysis. It is constructed on top of widely-used Python programs such as Pandas, NumPy, and SciPy, giving financial analysis an extensive range of tools.


The datetime module present in the Python standard library furnishes powerful tools for dealing with dates and times. It’s indispensable in various use cases including finance and banking systems, analysis of stocks market and time series examination. This module enables developers to effortlessly manipulate and comprehend date and time information in a variety of ways.

Once the `datetime` module is imported, date and time objects can be classified as either “aware” or “naive” depending on whether or not they possess timezone data. Using the `date` class from the `datetime` module, numerical date properties can be converted into the standard YYYY-MM-DD format. The `date` class requires three attributes to be passed in the following order: year, month, and day (Year, Month, Day).


SQLite, a stand-alone SQL database that operates from a file, is an optimal choice for utilisation with Python applications. Since it’s already an inclusive component of the Python programming language, there’s no need for additional software downloads. PySQLite, a library that satisfies the Python Database API Specification 2.0 (DBI 2.0), grants an interface for SQLite databases. If there’s a requirement for compatibility with databases other than SQLite, PySQLite is an ideal option, such as MySQL, PostgreSQL, or Oracle.


The Python programming language possesses an integrated module that generates sequences of numbers that appear random. However, it’s important to acknowledge that these numbers aren’t genuinely random, and therefore, the module shouldn’t be utilised for applications that call for true randomness. This module can be utilised for various purposes like generating random numbers, choosing a random element from a string or list, and more.

The random module also features the SystemRandom class, which generates random numbers utilising the system function os.urandom().


When it comes to data libraries, Pandas indisputably steals the show. It is an open-source Python library for data analysis built on top of NumPy. It has evolved into data manipulation and aggregation Python’s ultimate arsenal of tools. Pandas, Series, and DataFrame are structures for tabular data that provide users with access to features equivalent to those found in relational databases and spreadsheets.


The following code presents some basic configurations for obtaining this collection of data.

Initially, let’s ensure that all the necessary libraries have been installed.

Secondly, we need to import the library files.

Now, all that’s left is to load the necessary libraries for the demo.

As part of the third stage, information is collected.

For this stage, the [ffn]( library will be utilised to acquire the essential data. The process of retrieving the data using this famous Python utility only necessitates one line of code.

Phase Four: Placing the Information into a SQLite Database

We are using the SQLite database to compare Pandas with SQL so that we can understand how to incorporate SQL in Python better.

Python users may now begin using the SQL language. We can now utilise window functions in Python that are equivalent to those found in SQL.

Using Pandas as Stock Market Calculators

Moving on to the following section of this tutorial, we will conduct the subsequent calculations utilising Pandas:

  • The peak stock price reached by any company during that time period.
  • The mean closing price of a stock over the last 28 days.
  • The individual shares of the previous day’s closing price for each ticker.
  • Computing the Return on Investment (ROI) on a daily basis. To learn more, kindly refer to Determining ROI in Recruitment.
  • Interpolating any absent data.

The Maximum Possible Stock Price for Each Company

To obtain our desired outcome, we will apply the groupby function to divide our dataset into subsets based on ticker. After that, we will utilise the max parameter in the convert() function to create a new column indicating the maximum stock price for each ticker.

As the max function is a basic calculation or aggregation, it is more efficient to send it as a string to convert, rather than invoking a new function. This is one of the advantages of utilising Python SQL language.

Using Moving Average to Determine the Mean Closing Prices Over the Last 28 days

In this section, we can use groupby() and transform functions to establish a new section in the dataframe, which will be called “ma 28-day”. The lambda syntax can be employed to specify the type of function that will be performed on each group in a SQL query while working with Pandas DataFrame.

Determining the Closing Price of Each Ticker of the Previous Day

Since the shift clause provides a value for each segment in the data individually, we do not need to use transform().

Calculating Daily Percentage Yield

Since the calculations required for each division are complex, we will use the lambda function syntax for this process.

Fill in for Absent Data Using Interpolation

In this scenario, the window-type routines for missing data interpolation in Pandas are more efficient than those in SQL.

To commence our analysis, we will create missing data in our original dataset by intentionally excluding certain data points. We will then employ imputation or interpolation methods to replace the absent information with values comparable to the rest of the dataset.

To replace the absent data with the most recent reported stock price, we will utilize the “forward fill” technique in this case.

Python’s built-in support for Structured Query Language (SQL) enables data scientists to easily switch between Pandas and SQL while working with data. This functionality allows analysts to switch between various tools and data sources without requiring them to learn the syntax of each one.

The integration of this module created a challenge as it involved establishing a universal framework for converting between SQL and Python data types. Fortunately, with this framework in place, converting a SQL query to a Pandas DataFrame is a lot simpler. Even though the difficulty of performing window operations may differ depending on the dataset, it is still critical to have a good understanding of both languages.

After writing a Structured Query Language (SQL) query, there are various techniques for converting it into a Pandas DataFrame. These include using the sqlmap function embedded in Pandas to insert columns into the database individually, or using an external module like sqlite3. As shown in the lesson, many of the same processes utilized in SQL can also be employed when constructing a table in Pandas.

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