python docx VS Pandas

Compare python docx VS Pandas and see what are their differences

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python docx

Create and modify Word documents with Python. Contribute to python-openxml/python-docx development by creating an account on GitHub.

Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

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2023-08-18

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2023-05-12

python docx

Website: github.com
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Pandas

Website: pandas.pydata.org
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python docx features and specs

Ease of Use
python-docx provides a simple API for creating and manipulating .docx files, making it accessible for both beginners and experienced developers.
Free and Open Source
Being an open-source library with an active community, python-docx is freely available and continually improved by contributors.
Comprehensive Documentation
The library comes with comprehensive documentation, including examples and guidelines, which makes it easier to learn and use effectively.
Wide Range of Features
It supports a variety of features for creating and editing document elements like paragraphs, tables, and images, enabling robust document customization.
Cross-platform Compatibility
As a Python library, python-docx can run on multiple platforms that support Python, providing flexibility in deployment.

Possible disadvantages of python docx

Performance Limitations
Handling very large documents might be slow, as python-docx might not be optimized for performance-intensive tasks compared to some other solutions.
Limited Advanced Features
While useful for many applications, python-docx may not support all advanced features needed for highly complex document generation and manipulation.
Memory Consumption
The library can consume a significant amount of memory when dealing with large documents, which can be a constraint in memory-limited environments.
Lack of Built-in Validation
Python-docx does not inherently provide validation for document content, which means errors might not be detected until attempting to open the file.
Dependency on Microsoft Word
While not a direct dependency, testing the results of python-docx manipulation often requires Microsoft Word or a compatible reader to ensure fidelity.

Pandas features and specs

Data Wrangling
Pandas offers robust tools for manipulating, cleaning, and transforming data, making it easier to prepare data for analysis.
Flexible Data Structures
Pandas provides two primary data structures: Series and DataFrame, which are flexible and offer powerful capabilities for handling various types of datasets.
Integration with Other Libraries
Pandas integrates seamlessly with other Python libraries such as NumPy, Matplotlib, and SciPy, facilitating comprehensive data analysis workflows.
Performance with Data Size
For data sizes that fit into memory, Pandas performs excellently with operations and computations being highly optimized.
Rich Feature Set
Pandas provides a wide array of functionalities, including but not limited to group-by operations, merging and joining data sets, time-series functionality, and input/output tools.
Community and Documentation
Pandas has a strong community and extensive documentation, offering a wealth of tutorials, examples, and support for new and experienced users alike.

Possible disadvantages of Pandas

Memory Consumption
Pandas can become memory inefficient with very large datasets because it relies heavily on in-memory operations.
Single-threaded
Many Pandas operations are single-threaded, which can lead to performance bottlenecks when handling very large datasets.
Steep Learning Curve
For users who are new to data analysis or Pandas, there can be a steep learning curve due to its extensive capabilities and complex syntax at times.
Less Suitable for Real-time Analytics
Pandas is not designed for real-time analytics and is better suited for batch processing due to its in-memory operations and single-threaded nature.
Error Handling
Error messages in Pandas can sometimes be cryptic and hard to interpret, making debugging a challenge for users.

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Pandas videos

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Ozzy Man Reviews: Pandas

Category Popularity

0-100% (relative to python docx and Pandas)

Pandas

Data Science And Machine Learning

5 5%

Data Science And Machine Learning

95% 95

Development Tools

100 100%

Development Tools

0% 0

Data Science Tools

0 0%

Data Science Tools

100% 100

Game Development

100 100%

Game Development

0% 0

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare python docx and Pandas

python docx Reviews

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Pandas is a powerful and flexible open-source library used to perform data analysis in Python. It provides high-performance data structures (i.e., the famous DataFrame) and data analysis tools that make it easy to work with structured data.

Source: kinsta.com

Python & ETL 2020: A List and Comparison of the Top Python ETL Tools

When it comes to ETL, you can do almost anything with Pandas if you're willing to put in the time. Plus, pandas is extraordinarily easy to run. You can set up a simple script to load data from a Postgre table, transform and clean that data, and then write that data to another Postgre table.

Source: www.xplenty.com

Social recommendations and mentions

Based on our record, Pandas seems to be a lot more popular than python docx. While we know about 219 links to Pandas, we've tracked only 2 mentions of python docx. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

python docx mentions (2)

What Would Go in Your Dream Documentation Solution?
So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:. Source: over 1 year ago
See unknow person with a problem in Stackoverflow: writes a library for her
Here's the project: https://github.com/python-openxml/python-docx. Source: about 2 years ago

Pandas mentions (219)

Top Programming Languages for AI Development in 2025
Libraries for data science and deep learning that are always changing. - Source: dev.to / 17 days ago
How to import sample data into a Python notebook on watsonx.ai and other questions…
# Read the content of nda.txt Try: Import os, types Import pandas as pd From botocore.client import Config Import ibm_boto3 Def __iter__(self): return 0 # @hidden_cell # The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials. # You might want to remove those credentials before you share the notebook. Cos_client = ibm_boto3.client(service_name='s3', ... - Source: dev.to / about 1 month ago
How I Hacked Uber’s Hidden API to Download 4379 Rides
As with any web scraping or data processing project, I had to write a fair amount of code to clean this up and shape it into a format I needed for further analysis. I used a combination of Pandas and regular expressions to clean it up (full code here). - Source: dev.to / about 1 month ago
Must-Know 2025 Developer’s Roadmap and Key Programming Trends
Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 3 months ago
Sample Super Store Analysis Using Python & Pandas
This tutorial provides a concise and foundational guide to exploring a dataset, specifically the Sample SuperStore dataset. This dataset, which appears to originate from a fictional e-commerce or online marketplace company's annual sales data, serves as an excellent example for learning and how to work with real-world data. The dataset includes a variety of data types, which demonstrate the full range of... - Source: dev.to / 9 months ago