statsmodels VS python xlrd

Compare statsmodels VS python xlrd and see what are their differences

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statsmodels

Statsmodels: statistical modeling and econometrics in Python - statsmodels/statsmodels

python xlrd

Please use openpyxl where you can... Contribute to python-excel/xlrd development by creating an account on GitHub.

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2023-08-18

Landing page //
2023-08-18

statsmodels features and specs

No features have been listed yet.

python xlrd features and specs

Simplicity
xlrd provides a straightforward and easy-to-use API for reading Excel files, making it accessible for beginners and quick implementations.
Widely Used
xlrd has been a popular choice for handling Excel files in Python, which means there is a lot of available documentation and community support.
Efficient Reading
It is optimized for reading data from Excel files without loading entire data into memory, which is beneficial for handling large files.

Possible disadvantages of python xlrd

No Write Support
xlrd is designed solely for reading, and it does not support writing or modifying Excel files.
Limited to Older Excel Formats
With version 2.0 and above, xlrd only supports the older .xls Excel file format and does not support .xlsx files.
Deprecated Features
Due to changes in dependencies and updates to Excel formats, some features in xlrd have become deprecated or removed, which can limit its functionality.

Analysis of statsmodels

Overall verdict

statsmodels is a robust, well-established open-source Python library for statistical modeling, offering rigorous implementations of a wide range of statistical methods with strong documentation and academic credibility.

Why this product is good

Comprehensive coverage of statistical models including linear regression, generalized linear models, time series analysis (ARIMA, VAR), and mixed effects models
Provides detailed statistical output such as p-values, confidence intervals, and diagnostic tests, which is often lacking in machine-learning-focused libraries
Well-integrated with the broader scientific Python ecosystem including NumPy, SciPy, and pandas
Open-source with an active community, thorough documentation, and extensive examples
Emphasizes statistical rigor and inference rather than just prediction, making results interpretable and defensible

Recommended for

Statisticians and data scientists who need detailed statistical inference and hypothesis testing
Researchers and academics performing econometric or time series analysis
Analysts who require interpretable model outputs like coefficients, p-values, and confidence intervals
Python users who want R-like statistical modeling capabilities
Educational settings teaching applied statistics and econometrics

statsmodels videos

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Linear Regressions with StatsModels

python xlrd videos

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Category Popularity

0-100% (relative to statsmodels and python xlrd)

python xlrd

Application Builder

33 33%

Application Builder

67% 67

Development Tools

33 33%

Development Tools

67% 67

Data Science And Machine Learning

33 33%

Data Science And Machine Learning

67% 67

Developer Tools

100 100%

Developer Tools

0% 0

User comments

Share your experience with using statsmodels and python xlrd. For example, how are they different and which one is better?

Social recommendations and mentions

Based on our record, statsmodels should be more popular than python xlrd. It has been mentiond 4 times since March 2021. 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.

statsmodels mentions (4)

[P] statsmodels.tsa.holtwinters.ExponentialSmoothing results in NaN forecasts and parameters when fitting on entire dataset using known parameters from training model.
I reckon you're more likely to get a good response on their Github page than here. Unless a dev happens to see this post. Source: over 3 years ago
How do you usually build your models?
Since you are using python, pandas, scikit-learn, scipy, and statsmodels are what you are looking for. Source: almost 4 years ago
Can we solve serverless cold starts?
In case you're really worried about cold start latency and your application load shows high variance in the number of concurrent requests, you might want to get a bit fancier. You could use time-series forecasting to anticipate how many containers should be warmed at each point in time. StatsModels is an open-source project that offers the most common algorithms for working with time-series. Here's a good... - Source: dev.to / about 5 years ago
Advice required to choose appropriate software for an assignment
Can't you get a student discount for Stata? R would definitely be able to handle everything. For Python, have a look through the statsmodel package https://github.com/statsmodels/statsmodels. Source: over 5 years ago

python xlrd mentions (2)

I need to read multiple excel files, extract a column from each and compose a new file
So to get this out of the way first, xlrd has less features than openpyxl and in addition only works with the old '.xls' format, not the newer '.xlsx' format. Even on the xlrd's Github repo it says: 'Please use openpyxl where you can... '. Source: over 4 years ago
Sending Bulk SMS using Africas Talking, Python and Excel
There are few alternative libraries for reading and writing excel files: Pandas, Xlrd , openpyxl among others. In the end I settled for openpyxl as I had the most experience Using it and it had support for .xlsx files. - Source: dev.to / over 5 years ago