Accelerate Pandas Operations with Cython, Numba, and eval()

Introduction

This lab guides you through various techniques to speed up operations on pandas DataFrame using Cython, Numba, and pandas.eval(). These techniques can provide significant speed improvements when working with large datasets.

VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.

Skills Graph

%%%%{init: {'theme':'neutral'}}%%%% flowchart RL python(("`Python`")) -.-> python/BasicConceptsGroup(["`Basic Concepts`"]) python(("`Python`")) -.-> python/ControlFlowGroup(["`Control Flow`"]) python(("`Python`")) -.-> python/DataStructuresGroup(["`Data Structures`"]) python(("`Python`")) -.-> python/FunctionsGroup(["`Functions`"]) python(("`Python`")) -.-> python/ModulesandPackagesGroup(["`Modules and Packages`"]) python(("`Python`")) -.-> python/PythonStandardLibraryGroup(["`Python Standard Library`"]) python(("`Python`")) -.-> python/DataScienceandMachineLearningGroup(["`Data Science and Machine Learning`"]) python/BasicConceptsGroup -.-> python/comments("`Comments`") python/ControlFlowGroup -.-> python/for_loops("`For Loops`") python/DataStructuresGroup -.-> python/tuples("`Tuples`") python/DataStructuresGroup -.-> python/dictionaries("`Dictionaries`") python/FunctionsGroup -.-> python/function_definition("`Function Definition`") python/ModulesandPackagesGroup -.-> python/importing_modules("`Importing Modules`") python/ModulesandPackagesGroup -.-> python/standard_libraries("`Common Standard Libraries`") python/PythonStandardLibraryGroup -.-> python/math_random("`Math and Random`") python/DataScienceandMachineLearningGroup -.-> python/numerical_computing("`Numerical Computing`") python/DataScienceandMachineLearningGroup -.-> python/data_analysis("`Data Analysis`") python/FunctionsGroup -.-> python/build_in_functions("`Build-in Functions`") subgraph Lab Skills python/comments -.-> lab-65445{{"`Speed Up Pandas Operations`"}} python/for_loops -.-> lab-65445{{"`Speed Up Pandas Operations`"}} python/tuples -.-> lab-65445{{"`Speed Up Pandas Operations`"}} python/dictionaries -.-> lab-65445{{"`Speed Up Pandas Operations`"}} python/function_definition -.-> lab-65445{{"`Speed Up Pandas Operations`"}} python/importing_modules -.-> lab-65445{{"`Speed Up Pandas Operations`"}} python/standard_libraries -.-> lab-65445{{"`Speed Up Pandas Operations`"}} python/math_random -.-> lab-65445{{"`Speed Up Pandas Operations`"}} python/numerical_computing -.-> lab-65445{{"`Speed Up Pandas Operations`"}} python/data_analysis -.-> lab-65445{{"`Speed Up Pandas Operations`"}} python/build_in_functions -.-> lab-65445{{"`Speed Up Pandas Operations`"}} end

Setup and Create Sample Data

Before we start, let's import necessary modules and create a sample DataFrame.

## Import necessary modules import pandas as pd import numpy as np ## Create a sample DataFrame df = pd.DataFrame( { "a": np.random.randn(1000), "b": np.random.randn(1000), "N": np.random.randint(100, 1000, (1000)), "x": "x", } ) df

Implementing Pure Python Function

We will begin by creating a function in pure Python that operates row-wise on the DataFrame.

## Define a function def f(x): return x * (x - 1) ## Define another function that uses the first function def integrate_f(a, b, N): s = 0 dx = (b - a) / N for i in range(N): s += f(a + i * dx) return s * dx

Speed Up Pandas Operations

Introduction

VM Tips

Skills Graph

Setup and Create Sample Data

Implementing Pure Python Function

Summary

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