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What is python-docx?

The python-docx library is specifically crafted for the purpose of handling Microsoft Word (.docx) files through Python programming. Within Word documents, paragraphs are essential as they serve multiple functions, including body text, headings, and list items such as bullet points. Users have the ability to set both width and height for certain elements, although it is usually best practice to refrain from doing so. When one dimension is established, python-docx will automatically determine the other dimension to preserve the aspect ratio, ensuring that images appear as intended without any distortion. For those who may not be well-acquainted with Word paragraph styles, it is beneficial to delve into this feature, as it allows users to apply a wide array of formatting options to a paragraph simultaneously. This library not only permits the creation of new documents but also enables the modification of pre-existing ones, and while it mainly emphasizes making adjustments to current files, beginning with a blank document can create the illusion of constructing a project from scratch. Furthermore, by leveraging this library, users can significantly enhance their efficiency when producing and modifying Word documents, making it a valuable tool for anyone working with this format.

What is pandas?

Pandas is a versatile open-source library for data analysis and manipulation that excels in speed and power while maintaining a user-friendly interface within the Python ecosystem. It supports a wide range of data formats for both importing and exporting, such as CSV, text documents, Microsoft Excel, SQL databases, and the efficient HDF5 format. The library stands out with its intelligent data alignment features and its adept handling of missing values, allowing for seamless label-based alignment during calculations, which greatly aids in the organization of chaotic datasets. Moreover, pandas includes a sophisticated group-by engine that facilitates complex aggregation and transformation tasks, making it simple for users to execute split-apply-combine operations on their data. In addition to these capabilities, pandas is equipped with extensive time series functions that allow for the creation of date ranges, frequency conversions, and moving window statistics, as well as managing date shifting and lagging. Users also have the flexibility to define custom time offsets for specific applications and merge time series data without losing any critical information. Ultimately, the comprehensive array of features offered by pandas solidifies its status as an indispensable resource for data professionals utilizing Python, ensuring they can efficiently handle a diverse range of data-related tasks.

Media

Media

Integrations Supported

3LC
Activeeon ProActive
Amazon SageMaker Data Wrangler
ApertureDB
Avanzai
Cleanlab
Coiled
Dash
Flyte
Giskard
Kedro
MLJAR Studio
Microsoft Word
Netdata
Python
Spyder
TeamStation
ThinkData Works
Union Pandera
Yandex Data Proc

Integrations Supported

3LC
Activeeon ProActive
Amazon SageMaker Data Wrangler
ApertureDB
Avanzai
Cleanlab
Coiled
Dash
Flyte
Giskard
Kedro
MLJAR Studio
Microsoft Word
Netdata
Python
Spyder
TeamStation
ThinkData Works
Union Pandera
Yandex Data Proc

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

python-docx

Company Website

python-docx.readthedocs.io/en/latest/

Company Facts

Organization Name

pandas

Date Founded

2008

Company Website

pandas.pydata.org

Categories and Features

Categories and Features

Data Analysis

Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics

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