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It's hard to ignore the wealth of statistical packages available in R/CRAN. That said, I spend a lot of time in Python land and would never dissuade anyone from having as much fun as I do. :) Here are some libraries/links you might find useful for statistical work.

  • NumPy/Scipy You probably know about these already. But let me point out the Cookbook where you can read about many statistical facilities already available and the Example List which is a great reference for functions (including data manipulation and other operations). Another handy reference is John Cook's Distributions in Scipy.

  • pandaspandas This is a really nice library for working with statistical data -- tabular data, time series, panel data. Includes many builtin functions for data summaries, grouping/aggregation, pivoting. Also has a statistics/econometrics library.

  • larry Labeled array that plays nice with NumPy. Provides statistical functions not present in NumPy and good for data manipulation.

  • python-statlib A fairly recent effort which combined a number of scattered statistics libraries. Useful for basic and descriptive statistics if you're not using NumPy or pandas.

  • statsmodels Statistical modeling: Linear models, GLMs, among others.

  • scikits Statistical and scientific computing packages -- notably smoothing, optimization and machine learning.

  • PyMC For your Bayesian/MCMC/hierarchical modeling needs. Highly recommended.

  • PyMix Mixture models.

  • Biopython Useful for loading your biological data into python, and provides some rudimentary statistical/ machine learning tools for analysis.

If speed becomes a problem, consider Theano -- used with good success by the deep learning people.

There's plenty of other stuff out there, but this is what I find the most useful along the lines you mentioned.

It's hard to ignore the wealth of statistical packages available in R/CRAN. That said, I spend a lot of time in Python land and would never dissuade anyone from having as much fun as I do. :) Here are some libraries/links you might find useful for statistical work.

  • NumPy/Scipy You probably know about these already. But let me point out the Cookbook where you can read about many statistical facilities already available and the Example List which is a great reference for functions (including data manipulation and other operations). Another handy reference is John Cook's Distributions in Scipy.

  • pandas This is a really nice library for working with statistical data -- tabular data, time series, panel data. Includes many builtin functions for data summaries, grouping/aggregation, pivoting. Also has a statistics/econometrics library.

  • larry Labeled array that plays nice with NumPy. Provides statistical functions not present in NumPy and good for data manipulation.

  • python-statlib A fairly recent effort which combined a number of scattered statistics libraries. Useful for basic and descriptive statistics if you're not using NumPy or pandas.

  • statsmodels Statistical modeling: Linear models, GLMs, among others.

  • scikits Statistical and scientific computing packages -- notably smoothing, optimization and machine learning.

  • PyMC For your Bayesian/MCMC/hierarchical modeling needs. Highly recommended.

  • PyMix Mixture models.

  • Biopython Useful for loading your biological data into python, and provides some rudimentary statistical/ machine learning tools for analysis.

If speed becomes a problem, consider Theano -- used with good success by the deep learning people.

There's plenty of other stuff out there, but this is what I find the most useful along the lines you mentioned.

It's hard to ignore the wealth of statistical packages available in R/CRAN. That said, I spend a lot of time in Python land and would never dissuade anyone from having as much fun as I do. :) Here are some libraries/links you might find useful for statistical work.

  • NumPy/Scipy You probably know about these already. But let me point out the Cookbook where you can read about many statistical facilities already available and the Example List which is a great reference for functions (including data manipulation and other operations). Another handy reference is John Cook's Distributions in Scipy.

  • pandas This is a really nice library for working with statistical data -- tabular data, time series, panel data. Includes many builtin functions for data summaries, grouping/aggregation, pivoting. Also has a statistics/econometrics library.

  • larry Labeled array that plays nice with NumPy. Provides statistical functions not present in NumPy and good for data manipulation.

  • python-statlib A fairly recent effort which combined a number of scattered statistics libraries. Useful for basic and descriptive statistics if you're not using NumPy or pandas.

  • statsmodels Statistical modeling: Linear models, GLMs, among others.

  • scikits Statistical and scientific computing packages -- notably smoothing, optimization and machine learning.

  • PyMC For your Bayesian/MCMC/hierarchical modeling needs. Highly recommended.

  • PyMix Mixture models.

  • Biopython Useful for loading your biological data into python, and provides some rudimentary statistical/ machine learning tools for analysis.

If speed becomes a problem, consider Theano -- used with good success by the deep learning people.

There's plenty of other stuff out there, but this is what I find the most useful along the lines you mentioned.

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It's hard to ignore the wealth of statistical packages available in R/CRAN. That said, I spend a lot of time in Python land and would never dissuade anyone from having as much fun as I do. :) Here are some libraries/links you might find useful for statistical work.

  • NumPy/Scipy You probably know about these already. But let me point out the Cookbook where you can read about many statistical facilities already available and the Example List which is a great reference for functions (including data manipulation and other operations). Another handy reference is John Cook's Distributions in Scipy.

  • pandas This is a really nice library for working with statistical data -- tabular data, time series, panel data. Includes many builtin functions for data summaries, grouping/aggregation, pivoting. Also has a statistics/econometrics library.

  • larry Labeled array that plays nice with NumPy. Provides statistical functions not present in NumPy and good for data manipulation.

  • python-statlib A fairly recent effort which combined a number of scattered statistics libraries. Useful for basic and descriptive statistics if you're not using NumPy or pandas.

  • statsmodels Statistical modeling: Linear models, GLMs, among others.

  • scikits Statistical and scientific computing packages -- notably smoothing, optimization and machine learning.

  • PyMC For your Bayesian/MCMC/hierarchical modeling needs. Highly recommended.

  • PyMix Mixture models.

  • Biopython Useful for loading your biological data into python, and provides some rudimentary statistical/ machine learning tools for analysis.

If speed becomes a problem, consider Theano -- used with good success by the deep learning people.

There's plenty of other stuff out there, but this is what I find the most useful along the lines you mentioned.

It's hard to ignore the wealth of statistical packages available in R/CRAN. That said, I spend a lot of time in Python land and would never dissuade anyone from having as much fun as I do. :) Here are some libraries/links you might find useful for statistical work.

  • NumPy/Scipy You probably know about these already. But let me point out the Cookbook where you can read about many statistical facilities already available and the Example List which is a great reference for functions (including data manipulation and other operations). Another handy reference is John Cook's Distributions in Scipy.

  • pandas This is a really nice library for working with statistical data -- tabular data, time series, panel data. Includes many builtin functions for data summaries, grouping/aggregation, pivoting. Also has a statistics/econometrics library.

  • larry Labeled array that plays nice with NumPy. Provides statistical functions not present in NumPy and good for data manipulation.

  • python-statlib A fairly recent effort which combined a number of scattered statistics libraries. Useful for basic and descriptive statistics if you're not using NumPy or pandas.

  • statsmodels Statistical modeling: Linear models, GLMs, among others.

  • scikits Statistical and scientific computing packages -- notably smoothing, optimization and machine learning.

  • PyMC For your Bayesian/MCMC/hierarchical modeling needs. Highly recommended.

  • PyMix Mixture models.

If speed becomes a problem, consider Theano -- used with good success by the deep learning people.

There's plenty of other stuff out there, but this is what I find the most useful along the lines you mentioned.

It's hard to ignore the wealth of statistical packages available in R/CRAN. That said, I spend a lot of time in Python land and would never dissuade anyone from having as much fun as I do. :) Here are some libraries/links you might find useful for statistical work.

  • NumPy/Scipy You probably know about these already. But let me point out the Cookbook where you can read about many statistical facilities already available and the Example List which is a great reference for functions (including data manipulation and other operations). Another handy reference is John Cook's Distributions in Scipy.

  • pandas This is a really nice library for working with statistical data -- tabular data, time series, panel data. Includes many builtin functions for data summaries, grouping/aggregation, pivoting. Also has a statistics/econometrics library.

  • larry Labeled array that plays nice with NumPy. Provides statistical functions not present in NumPy and good for data manipulation.

  • python-statlib A fairly recent effort which combined a number of scattered statistics libraries. Useful for basic and descriptive statistics if you're not using NumPy or pandas.

  • statsmodels Statistical modeling: Linear models, GLMs, among others.

  • scikits Statistical and scientific computing packages -- notably smoothing, optimization and machine learning.

  • PyMC For your Bayesian/MCMC/hierarchical modeling needs. Highly recommended.

  • PyMix Mixture models.

  • Biopython Useful for loading your biological data into python, and provides some rudimentary statistical/ machine learning tools for analysis.

If speed becomes a problem, consider Theano -- used with good success by the deep learning people.

There's plenty of other stuff out there, but this is what I find the most useful along the lines you mentioned.

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source | link

It's hard to ignore the wealth of statistical packages available in R/CRAN. That said, I spend a lot of time in Python land and would never dissuade anyone from having as much fun as I do. :) Here are some libraries/links you might find useful for statistical work.

  • NumPy/Scipy You probably know about these already. But let me point out the Cookbook where you can read about many statistical facilities already available and the Example List which is a great reference for functions (including data manipulation and other operations). Another handy reference is John Cook's Distributions in Scipy.

  • pandas This is a really nice library for working with statistical data -- tabular data, time series, panel data. Includes many builtin functions for data summaries, grouping/aggregation, pivoting. Also has a statistics/econometrics library.

  • larry Labeled array that plays nice with NumPy. Provides statistical functions not present in NumPy and good for data manipulation.

  • python-statlib A fairly recent effort which combined a number of scattered statistics libraries. Useful for basic and descriptive statistics if you're not using NumPy or pandas.

  • statsmodels Statistical modeling: Linear models, GLMs, among others.

  • scikits Statistical and scientific computing packages -- notably smoothing, optimization and machine learning.

  • PyMCPyMC For your Bayesian/MCMC/hierarchical modeling needs. Highly recommended.

  • PyMix Mixture models.

If speed becomes a problem, consider Theano -- used with good success by the deep learning people.

There's plenty of other stuff out there, but this is what I find the most useful along the lines you mentioned.

It's hard to ignore the wealth of statistical packages available in R/CRAN. That said, I spend a lot of time in Python land and would never dissuade anyone from having as much fun as I do. :) Here are some libraries/links you might find useful for statistical work.

  • NumPy/Scipy You probably know about these already. But let me point out the Cookbook where you can read about many statistical facilities already available and the Example List which is a great reference for functions (including data manipulation and other operations). Another handy reference is John Cook's Distributions in Scipy.

  • pandas This is a really nice library for working with statistical data -- tabular data, time series, panel data. Includes many builtin functions for data summaries, grouping/aggregation, pivoting. Also has a statistics/econometrics library.

  • larry Labeled array that plays nice with NumPy. Provides statistical functions not present in NumPy and good for data manipulation.

  • python-statlib A fairly recent effort which combined a number of scattered statistics libraries. Useful for basic and descriptive statistics if you're not using NumPy or pandas.

  • statsmodels Statistical modeling: Linear models, GLMs, among others.

  • scikits Statistical and scientific computing packages -- notably smoothing, optimization and machine learning.

  • PyMC For your Bayesian/MCMC/hierarchical modeling needs. Highly recommended.

  • PyMix Mixture models.

If speed becomes a problem, consider Theano -- used with good success by the deep learning people.

There's plenty of other stuff out there, but this is what I find the most useful along the lines you mentioned.

It's hard to ignore the wealth of statistical packages available in R/CRAN. That said, I spend a lot of time in Python land and would never dissuade anyone from having as much fun as I do. :) Here are some libraries/links you might find useful for statistical work.

  • NumPy/Scipy You probably know about these already. But let me point out the Cookbook where you can read about many statistical facilities already available and the Example List which is a great reference for functions (including data manipulation and other operations). Another handy reference is John Cook's Distributions in Scipy.

  • pandas This is a really nice library for working with statistical data -- tabular data, time series, panel data. Includes many builtin functions for data summaries, grouping/aggregation, pivoting. Also has a statistics/econometrics library.

  • larry Labeled array that plays nice with NumPy. Provides statistical functions not present in NumPy and good for data manipulation.

  • python-statlib A fairly recent effort which combined a number of scattered statistics libraries. Useful for basic and descriptive statistics if you're not using NumPy or pandas.

  • statsmodels Statistical modeling: Linear models, GLMs, among others.

  • scikits Statistical and scientific computing packages -- notably smoothing, optimization and machine learning.

  • PyMC For your Bayesian/MCMC/hierarchical modeling needs. Highly recommended.

  • PyMix Mixture models.

If speed becomes a problem, consider Theano -- used with good success by the deep learning people.

There's plenty of other stuff out there, but this is what I find the most useful along the lines you mentioned.

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