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

Data scientists rely heavily on a variety of tools, such as SQL and the Python package Pandas, to effectively analyse, manipulate, and interpret data. SQL is an essential tool for creating data views or new tables, while Pandas is best utilised for data preparation and visualisation processes or machine learning. This post will discuss the SQL-like window methods in Pandas and how one can run SQL in Python.

A window function is defined.

In order to understand the concept of a window function, it is important to first define what it is. A window function is an operation that is performed on a set of rows in a table which are related to the current row in some manner. It is also referred to as an SQL analytic function, which essentially yields a result for each row based on the information taken from one or more other rows.

The use of the OVER clause is an essential aspect of window functions, and any function that does not include this qualifier should not be classified as a window function; rather, it should be identified as either a single-row function or an aggregate function.

It is possible to expedite and increase the precision of calculations and data aggregation across multiple divisions of target data with the application of window functions. Unlike single-row functions which generate a single value for each row present in the query, the window functions allow for a value to be generated for each division.

The benefits of using window features are not immediately apparent.

Rather than relying on lengthy and complex SQL queries for the purpose of cross-sectional data analysis, window functions offer a more efficient and straightforward approach. These functions allow for the rapid and effective aggregation of data from multiple sources, streamlining the entire analysis process.

Some frequent use of window functions are as follows:

  • Utilising information stored on a separate partition, as in inventory reporting that compares two time periods.
  • Placement of results within a time frame.
  • Making alterations to existing machine learning models to include new features.
  • Adding up numbers throughout a certain time frame, as in a running tally.
  • Interpolating or assuming missing values based on the distribution of the remaining data.

When utilising panel data, windowing functions can be employed to provide an overview of the aggregate characteristics of a data window through the addition of extra features.

Creating a window function in SQL requires meeting certain criteria.

The following three components are needed to build a window function in SQL:

  1. The name of the aggregate function or computation to use on that column. Functions like RANK() and SUM ().
  2. The phrase that specifies the data partition type for use in the aggregate function or computation is called the “PARTITION BY” clause.
  3. The window function initiation clause, OVER().

In addition to the aforementioned three options, the ORDER BY function may be used to provide the sorting criteria for each data window.

Functionality of the window in Pandas

The use of the `groupby` keyword in Pandas is often preferred to the use of the `GROUP BY` clause in SQL when translating window functions from the database. This is due to the fact that `groupby` is the foundation of Pandas’ window functions and is comparable to the `PARTITION BY` command in SQL. This keyword is used to identify which subsets of the dataset should be included in the aggregation operation.

If you require assistance with a more complex transformation, the .transform clause can provide the necessary support. You can specify the calculation or aggregation that should be used in order to apply the divisions within the dataset. This clause accepts a function as an argument and returns a data frame that is of the same length as the original. It has been said that the transformed values produced by this clause are in proportion to the length of the original axis.

The function provided to the `convert()` method may be a callable object, such as a lambda function, for more advanced operations, or it may be specified as a string for simpler aggregation functions such as `mean`, `count`, and `sum`.

It is imperative to recall that the transform() function can only be applied to aggregate methods that return a single row. This eliminates the necessity of having to utilise transform on any data set that is not an aggregate function.

Pandas Python window routines that mimic SQL

This lesson will use free, publicly available stock price data for illustrative purposes.

Useful libraries and components

ffn (Financial functions for Python)

The ffn library offers a wealth of useful functions for quantitative finance professionals, ranging from basic graphing operations to sophisticated statistical analysis. It is built upon a range of popular Python programs, such as Pandas, NumPy, and SciPy, providing an extensive suite of tools to support financial analysis.


The Python standard library’s datetime module provides powerful tools for working with dates and times. It is essential in a wide range of use cases, including financial and banking systems, stock market analysis, and time series analysis. With this module, developers can easily manipulate and interpret date and time data in a wide variety of ways.

The `datetime` module can be imported in order to classify date and time objects as either “aware” or “naive”, depending on whether or not they have timezone data. The `date` class within the `datetime` module can be utilised to transform numerical date properties into the standard YYYY-MM-DD format. The `date` class accepts three attributes in the order of year, month, and day (Year, Month, Day).


SQLite is a stand-alone SQL database that operates from a file, making it an ideal choice for use with Python applications. As it is already a part of the Python programming language, no additional software downloads are necessary. PySQLite is a library that provides an interface for SQLite databases that is compliant with the Python Database API Specification 2.0 (DBI 2.0). If you require compatibility with databases other than SQLite, such as MySQL, PostgreSQL, or Oracle, PySQLite is a suitable option.


The Python programming language offers a built-in module for generating sequences of numbers that are seemingly random. However, it should be noted that these numbers are not truly random, and the module should not be used for applications that require true randomness. This module can be used for a variety of purposes, such as generating random numbers, selecting an element from a string or list at random, and more.

The SystemRandom class, also available in the random module, generates random numbers using the system function os.urandom ().


When it comes to data libraries, Pandas is certainly the most popular. It is an open-source Python library for data analysis, built on top of NumPy, that has become the go-to set of utilities for data manipulation and aggregation in Python. Pandas, Series, and DataFrame are tabular data structures that give users access to features that resemble those found in relational databases and spreadsheets.


Some basic settings for acquiring this data collection are shown in the code below.

First, we’ll make sure all the required libraries are installed.

Second, we must import library files.

All that’s left to do is load the demo’s necessary libraries.

In the third stage, information is gathered.

The [ffn]( library will be employed to gather the necessary data for this stage. It only requires a single line of code to initiate the process of collecting data using this popular Python utility.

Phase 4: Putting the information in a SQLite database

The SQLite database is being used so that we may contrast Pandas with SQL to better understand how to implement SQL in Python.

Starting now, Python users may access the SQL language. Now, we can execute window functions in Python that are analogous to those in SQL.

Pandas as stock market calculators

Stepping onto the next section of this guide, we will do the following calculations in Pandas:

  • The highest point reached by any one company’s stock throughout that time frame.
  • The average of a stock’s closing price over the previous 28 days.
  • Individual ticker share of the previous day’s closing price.
  • Calculating ROI each day.
  • Interpolation of missing data.

For each company, the highest possible stock price

In order to achieve our desired result, we will use the groupby function to separate our dataset into subsets based on ticker. We will then use the max parameter in the convert() function to generate a new column indicating the maximum share price for each ticker.

Given that the max function is a simple calculation or aggregation, it is more efficient to send it as a string to convert rather than calling a new function. This is an example of the strength of the Python SQL language.

The average of the last 28 days’ closing prices as a moving average

By employing the groupby() and transform functions, a new partition in the dataframe can be created and named “ma 28-day” in this section. The lambda syntax can be used to specify the type of function that will be applied to each group in a SQL query when working with the Pandas DataFrame.

Individual ticker share of the previous day’s closing price

We don’t need transform since the shift clause delivers a value for each split in the data on its own ().

A Percentage Yield Every Day

Due to the intricate nature of the calculations necessary for each division, we will be making use of the lambda function syntax for this procedure.

Interpolation of missing data

Pandas’ window-type routines for missing data interpolation are likely more effective than SQL’s in this case.

At the outset of our analysis, we will simulate missing data in our real dataset by deliberately omitting certain pieces of data. We will then utilise imputation or interpolation techniques to fill in the missing information with values that are consistent with the remainder of the dataset.

In this scenario, we will use the “forward fill” method to fill in the missing data with the last reported value for the stock price.

Python’s integrated support for Structured Query Language (SQL) allows data scientists to seamlessly transition between Pandas and SQL when manipulating data. This capability enables analysts to seamlessly switch between tools and data sources without having to remember the syntax of each.

The integration of this module posed a challenge in that it required the establishment of a unified framework for converting between SQL and Python data types. Luckily, with this framework in place, the conversion of a SQL query to a Pandas DataFrame is made much easier. Even though the complexity of carrying out window operations may vary depending on the dataset, it is still essential to become well-versed in both languages.

Once a Structured Query Language (SQL) query has been written, it can be converted into a Pandas DataFrame through various methods. These include inserting columns into the database individually using the sqlmap function built into Pandas, or using an external module such as sqlite3. As demonstrated during the lesson, many of the same operations that can be done in SQL can also be performed when constructing a table in Pandas.

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