Python: Referensi untuk Social Scientist - Revision history

Onnowpurbo: /* Using APIs */

2017-01-29T01:52:07Z

Using APIs

← Older revision		Revision as of 01:52, 29 January 2017
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	When a service wants you to use their data, they often provide it through an API. There are often specific Python libraries for accessing popularing, complex and/or APIs requiring authentication. Otherwises, requests is quite useful.		When a service wants you to use their data, they often provide it through an API. There are often specific Python libraries for accessing popularing, complex and/or APIs requiring authentication. Otherwises, requests is quite useful.

	* [http://nbviewer.ipython.org/github/ptwobrussell/Mining-the-Social-Web-2nd-Edition/blob/master/ipynb/Chapter%201%20-%20Mining%20Twitter.ipynb Mining Twitter: Exploring Trending Topics, Discovering What People Are Talking About, and More] by [http://twitter.com/ptwobrussell[http://newcoder.io/api/ Matthew Russell]		* [http://nbviewer.ipython.org/github/ptwobrussell/Mining-the-Social-Web-2nd-Edition/blob/master/ipynb/Chapter%201%20-%20Mining%20Twitter.ipynb Mining Twitter: Exploring Trending Topics, Discovering What People Are Talking About, and More] by [http://twitter.com/ptwobrussell[http://newcoder.io/api/ Matthew Russell]. Alternatif lain: https://github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition/blob/master/ipynb/Chapter%201%20-%20Mining%20Twitter.ipynb
	* [http://blog.crisis.net/sentiment-analysis-part-1/ Sentiment Analysis] by [https://twitter.com/chrisalbon Chris Albon]. Getting data from the CrisisNET API.		* [http://blog.crisis.net/sentiment-analysis-part-1/ Sentiment Analysis] by [https://twitter.com/chrisalbon Chris Albon]. Getting data from the CrisisNET API.
	* [http://nealcaren.github.io/sushi_bars.html Sushi Bars and Buffets: Measuring local culture with the Yelp API] by [https://twitter.com/haphazardsoc me]. Using the Yelp API to gather data about local food cultures and how they vary by SES.		* [http://nealcaren.github.io/sushi_bars.html Sushi Bars and Buffets: Measuring local culture with the Yelp API] by [https://twitter.com/haphazardsoc me]. Using the Yelp API to gather data about local food cultures and how they vary by SES.

Onnowpurbo: /* Using APIs */

2017-01-29T00:29:41Z

Using APIs

← Older revision		Revision as of 00:29, 29 January 2017
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	When a service wants you to use their data, they often provide it through an API. There are often specific Python libraries for accessing popularing, complex and/or APIs requiring authentication. Otherwises, requests is quite useful.		When a service wants you to use their data, they often provide it through an API. There are often specific Python libraries for accessing popularing, complex and/or APIs requiring authentication. Otherwises, requests is quite useful.

	* Mining Twitter: Exploring Trending Topics, Discovering What People Are Talking About, and More by Matthew Russell		* [http://nbviewer.ipython.org/github/ptwobrussell/Mining-the-Social-Web-2nd-Edition/blob/master/ipynb/Chapter%201%20-%20Mining%20Twitter.ipynb Mining Twitter: Exploring Trending Topics, Discovering What People Are Talking About, and More] by [http://twitter.com/ptwobrussell[http://newcoder.io/api/ Matthew Russell]
	* Sentiment Analysis by Chris Albon. Getting data from the CrisisNET API.		* [http://blog.crisis.net/sentiment-analysis-part-1/ Sentiment Analysis] by [https://twitter.com/chrisalbon Chris Albon]. Getting data from the CrisisNET API.
	* Sushi Bars and Buffets: Measuring local culture with the Yelp API by me. Using the Yelp API to gather data about local food cultures and how they vary by SES.		* [http://nealcaren.github.io/sushi_bars.html Sushi Bars and Buffets: Measuring local culture with the Yelp API] by [https://twitter.com/haphazardsoc me]. Using the Yelp API to gather data about local food cultures and how they vary by SES.
	* Diving into Flickr data with python by Olivier D'Hondt. Using the Flickr API to extract photo metadata.		* [http://nbviewer.ipython.org/github/odhondt/flickr_data/blob/master/flickr%20data.ipynb Diving into Flickr data with python] by [https://github.com/odhondt Olivier D'Hondt]. Using the Flickr API to extract photo metadata.

	==Web Scraping==		==Web Scraping==

Onnowpurbo: /* Overviews */

2017-01-29T00:25:56Z

Overviews

Show changes

Onnowpurbo: /* Walkthroughs */

2017-01-29T00:12:45Z

Walkthroughs

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	* Pinterest Analysis about the NYFW Fall 2014 by Rosario Gomez. Sraping, cleaning and descriptives. Nicely demonstrates how to store data in a Mongodb database. There is also a fancier version of the analysis.		* Pinterest Analysis about the NYFW Fall 2014 by Rosario Gomez. Sraping, cleaning and descriptives. Nicely demonstrates how to store data in a Mongodb database. There is also a fancier version of the analysis.

	Overviews		==Overviews==

	Introductions to using Python for data analysis that make sense to social scientists.		Introductions to using Python for data analysis that make sense to social scientists.

	Python for Data Science by Joe McCarthy. A comprehensive and accessible introduction to Python for scientific analysis, although I might start with the Data Mining Example section.		* [http://nbviewer.ipython.org/github/gumption/Python_for_Data_Science/blob/master/Python_for_Data_Science_all.ipynb Python for Data Science] by [http://interrelativity.com/joe/ Joe McCarthy]. A comprehensive and accessible introduction to Python for scientific analysis, although I might start with the Data Mining Example section.
	A Crash Course in Python for Scientists by Rick Muller. Basics of Python for folks with no experience with the languagew.		* [http://nbviewer.ipython.org/gist/rpmuller/5920182 A Crash Course in Python for Scientists] by [http://www.cs.sandia.gov/~rmuller/ Rick Muller]. Basics of Python for folks with no experience with the languagew.

	Using APIs		Using APIs

Onnowpurbo: /* Walkthroughs */

2017-01-29T00:11:07Z

Walkthroughs

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	* [http://nbviewer.ipython.org/urls/raw.githubusercontent.com/jvns/talks/master/pycon2014/bike_paths.ipynb?create=1 Diving into Open Data with IPython Notebook & Pandas] by [http://twitter.com/b0rk Julia Evans]. An analysis of whether people bike when it rains using Pandas.		* [http://nbviewer.ipython.org/urls/raw.githubusercontent.com/jvns/talks/master/pycon2014/bike_paths.ipynb?create=1 Diving into Open Data with IPython Notebook & Pandas] by [http://twitter.com/b0rk Julia Evans]. An analysis of whether people bike when it rains using Pandas.
	* [http://nbviewer.ipython.org/github/brianckeegan/Bechdel/blob/master/Bechdel_test.ipynb The Need for Openness in Data Journalism] by [https://twitter.com/bkeegan Brian Keegan] Reanalysis of a 538 posts on the Bechdel Test and films using Pandas and statsmodels. When I ran this one, I had an issue with the BeautifulSoup part.u		* [http://nbviewer.ipython.org/github/brianckeegan/Bechdel/blob/master/Bechdel_test.ipynb The Need for Openness in Data Journalism] by [https://twitter.com/bkeegan Brian Keegan] Reanalysis of a 538 posts on the Bechdel Test and films using Pandas and statsmodels. When I ran this one, I had an issue with the BeautifulSoup part.u
	* Predicting customer churn with scikit-learn by Eric Chiang. I don't care about customer churn, but it's a well-written walkthrough of machine learning classification.		* [http://blog.yhathq.com/posts/predicting-customer-churn-with-sklearn.html Predicting customer churn with scikit-learn] by [https://github.com/EricChiang Eric Chiang]. I don't care about customer churn, but it's a well-written walkthrough of machine learning classification.
	* 538 Model by Skipper Seabold. Recreation of the classic 538 prediction model using Pandas.		* [http://nbviewer.ipython.org/urls/raw.githubusercontent.com/jseabold/538model/master/silver_model.ipynb 538 Model] by [https://twitter.com/jseabold Skipper Seabold]. Recreation of the classic 538 prediction model using Pandas.
	* Heat and Violence in Chicago by Brian Keegan. Walkthrough of an impressive analysis of crime trends.		* Heat and Violence in Chicago by Brian Keegan. Walkthrough of an impressive analysis of crime trends.
	* Powerpoetry Analysis by SumAll Foundation. Analysis of how individual poetry styles change over time using pandas.		* Powerpoetry Analysis by SumAll Foundation. Analysis of how individual poetry styles change over time using pandas.

Onnowpurbo: /* Walkthroughs */

2017-01-29T00:07:35Z

Walkthroughs

← Older revision		Revision as of 00:07, 29 January 2017
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	One of the great things about IPython notebooks is that they can easily blend text and code. This has led to a sharp increase in the number of data analysis projects where people carefully explain an entire research project, including data collection/importation, management and analysis. The code is right there, and you can usually run it and/or modify yourself. Looking at a few of these is an excellent introduction to what people are currently doing, even if you don't understand everything.		One of the great things about IPython notebooks is that they can easily blend text and code. This has led to a sharp increase in the number of data analysis projects where people carefully explain an entire research project, including data collection/importation, management and analysis. The code is right there, and you can usually run it and/or modify yourself. Looking at a few of these is an excellent introduction to what people are currently doing, even if you don't understand everything.

	* Diving into Open Data with IPython Notebook & Pandas by Julia Evans. An analysis of whether people bike when it rains using Pandas.		* [http://nbviewer.ipython.org/urls/raw.githubusercontent.com/jvns/talks/master/pycon2014/bike_paths.ipynb?create=1 Diving into Open Data with IPython Notebook & Pandas] by [http://twitter.com/b0rk Julia Evans]. An analysis of whether people bike when it rains using Pandas.
	* The Need for Openness in Data Journalism by Brian Keegan Reanalysis of a 538 posts on the Bechdel Test and films using Pandas and statsmodels. When I ran this one, I had an issue with the BeautifulSoup part.u		* [http://nbviewer.ipython.org/github/brianckeegan/Bechdel/blob/master/Bechdel_test.ipynb The Need for Openness in Data Journalism] by [https://twitter.com/bkeegan Brian Keegan] Reanalysis of a 538 posts on the Bechdel Test and films using Pandas and statsmodels. When I ran this one, I had an issue with the BeautifulSoup part.u
	* Predicting customer churn with scikit-learn by Eric Chiang. I don't care about customer churn, but it's a well-written walkthrough of machine learning classification.		* Predicting customer churn with scikit-learn by Eric Chiang. I don't care about customer churn, but it's a well-written walkthrough of machine learning classification.
	* 538 Model by Skipper Seabold. Recreation of the classic 538 prediction model using Pandas.		* 538 Model by Skipper Seabold. Recreation of the classic 538 prediction model using Pandas.
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	Image Processing with scikit-image by Eric Chiang. Basics of using photos as data.		Image Processing with scikit-image by Eric Chiang. Basics of using photos as data.
	Building a fashion recommender by Rosario Gomez. Analyzing pictures of models to extract the colors of the clothes they are wearing. It's really impressive.		Building a fashion recommender by Rosario Gomez. Analyzing pictures of models to extract the colors of the clothes they are wearing. It's really impressive.





	==Reference==		==Reference==

	* https://nealcaren.github.io/python-tutorials/		* https://nealcaren.github.io/python-tutorials/

Onnowpurbo at 00:03, 29 January 2017

2017-01-29T00:03:13Z

← Older revision		Revision as of 00:03, 29 January 2017
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	* World Cup Learning by Juan Pedro Fisanotti. Predict winners of World Cup soccer matches using the PyBrain library for machine learning. Data is also on Github.		* World Cup Learning by Juan Pedro Fisanotti. Predict winners of World Cup soccer matches using the PyBrain library for machine learning. Data is also on Github.
	* Is Seattle Really Seeing an Uptick In Cycling? by Jake Vanderplas. Yes. An excellent time-series analysis.		* Is Seattle Really Seeing an Uptick In Cycling? by Jake Vanderplas. Yes. An excellent time-series analysis.
	* Predicting the World Cup with the Google Cloud Platform by Jordan Tigani. Soccer predictions. 99% of this doesn't have anything to do with the Google Cloud Platform.		* [http://nbviewer.ipython.org/github/GoogleCloudPlatform/ipython-soccer-predictions/blob/master/predict/wc-final.ipynb Predicting the World Cup with the Google Cloud Platform] by Jordan Tigani. Soccer predictions. 99% of this doesn't have anything to do with the Google Cloud Platform.
	* Finding the World's Economic Center of Gravity by Daniel Velkov. Using pandas to find and plot the coordinates of the GDP-weighted center of the world by year.		* Finding the World's Economic Center of Gravity by Daniel Velkov. Using pandas to find and plot the coordinates of the GDP-weighted center of the world by year.
	* Pinterest Analysis about the NYFW Fall 2014 by Rosario Gomez. Sraping, cleaning and descriptives. Nicely demonstrates how to store data in a Mongodb database. There is also a fancier version of the analysis.		* Pinterest Analysis about the NYFW Fall 2014 by Rosario Gomez. Sraping, cleaning and descriptives. Nicely demonstrates how to store data in a Mongodb database. There is also a fancier version of the analysis.

Onnowpurbo at 00:02, 29 January 2017

2017-01-29T00:02:05Z

← Older revision		Revision as of 00:02, 29 January 2017
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	I’ve compiled a list of Python tutorials and annotated analyses. I've tried to list pages that are accessible to social scientists with little background in Python and/or machine learning.		I’ve compiled a list of Python tutorials and annotated analyses. I've tried to list pages that are accessible to social scientists with little background in Python and/or machine learning.

	If you are totally new to Python, I would recommend installing Continuum's [[https://store.continuum.io/cshop/anaconda/ Anacoda]] Python distribution. It works on Macs and Windows, makes using IPython notebooks trivial, and solves most of the problems associated with installing various packages.		If you are totally new to Python, I would recommend installing Continuum's [https://store.continuum.io/cshop/anaconda/ Anacoda] Python distribution. It works on Macs and Windows, makes using IPython notebooks trivial, and solves most of the problems associated with installing various packages.

	If you know of anything I've left out or if links go dead, please let me know.		If you know of anything I've left out or if links go dead, please let me know.

Onnowpurbo at 00:01, 29 January 2017

2017-01-29T00:01:50Z

← Older revision		Revision as of 00:01, 29 January 2017
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	I’ve compiled a list of Python tutorials and annotated analyses. I've tried to list pages that are accessible to social scientists with little background in Python and/or machine learning.		I’ve compiled a list of Python tutorials and annotated analyses. I've tried to list pages that are accessible to social scientists with little background in Python and/or machine learning.

	If you are totally new to Python, I would recommend installing Continuum's [https://store.continuum.io/cshop/anaconda/ \| Anacoda] Python distribution. It works on Macs and Windows, makes using IPython notebooks trivial, and solves most of the problems associated with installing various packages.		If you are totally new to Python, I would recommend installing Continuum's [[https://store.continuum.io/cshop/anaconda/ Anacoda]] Python distribution. It works on Macs and Windows, makes using IPython notebooks trivial, and solves most of the problems associated with installing various packages.

	If you know of anything I've left out or if links go dead, please let me know.		If you know of anything I've left out or if links go dead, please let me know.

Onnowpurbo at 00:01, 29 January 2017

2017-01-29T00:01:29Z

← Older revision		Revision as of 00:01, 29 January 2017
Line 6:		Line 6:
	I’ve compiled a list of Python tutorials and annotated analyses. I've tried to list pages that are accessible to social scientists with little background in Python and/or machine learning.		I’ve compiled a list of Python tutorials and annotated analyses. I've tried to list pages that are accessible to social scientists with little background in Python and/or machine learning.

	If you are totally new to Python, I would recommend installing Continuum's [[https://store.continuum.io/cshop/anaconda/ \| Anacoda]] Python distribution. It works on Macs and Windows, makes using IPython notebooks trivial, and solves most of the problems associated with installing various packages.		If you are totally new to Python, I would recommend installing Continuum's [https://store.continuum.io/cshop/anaconda/ \| Anacoda] Python distribution. It works on Macs and Windows, makes using IPython notebooks trivial, and solves most of the problems associated with installing various packages.

	If you know of anything I've left out or if links go dead, please let me know.		If you know of anything I've left out or if links go dead, please let me know.