Python - Revision history

Lrogerson: /* Resources For Learning Python */

2015-10-13T15:44:52Z

Resources For Learning Python

← Older revision		Revision as of 17:44, 13 October 2015
Line 5:		Line 5:
	For the absolute beginner Codeacademy's free, interactive [https://www.codecademy.com/en/tracks/python learning module for Python] is a good starting point. Though it recommends approximately 13 hours for the course, those with a background in programming could probably finish it in under a day. For scientific computing it helps to have some familiarity with the Scipy stack, for which there is a nice introduction at [http://www.scipy-lectures.org/ scipy-lectures.org], particularly the Getting Started section.		For the absolute beginner Codeacademy's free, interactive [https://www.codecademy.com/en/tracks/python learning module for Python] is a good starting point. Though it recommends approximately 13 hours for the course, those with a background in programming could probably finish it in under a day. For scientific computing it helps to have some familiarity with the Scipy stack, for which there is a nice introduction at [http://www.scipy-lectures.org/ scipy-lectures.org], particularly the Getting Started section.

	Beyond that, it will be worth taking a look at the [https://web.stanford.edu/~mwaskom/software/seaborn/ Seaborn] module for attractive plotting functions. Those interested in Datajoint should also take a look at the ~~Datajoint~~ project page and that of [https://pythonhosted.org/neo/ Neo], the latter of which may be used for Python IO. Finally, if you are thinking of using Python for important code which will likely be reused, I would recommend reading the [https://www.python.org/dev/peps/pep-0008/ PEP0008 styleguide], to ensure you are writing correctly formatted code.		Beyond that, it will be worth taking a look at the [https://web.stanford.edu/~mwaskom/software/seaborn/ Seaborn] module for attractive plotting functions. Those interested in Datajoint should also take a look at the [https://datajoint.github.io/ project page] and that of [https://pythonhosted.org/neo/ Neo], the latter of which may be used for Python IO. Finally, if you are thinking of using Python for important code which will likely be reused, I would recommend reading the [https://www.python.org/dev/peps/pep-0008/ PEP0008 styleguide], to ensure you are writing correctly formatted code.

Lrogerson: /* Resources For Learning Python */

2015-10-13T15:02:12Z

Resources For Learning Python

← Older revision		Revision as of 17:02, 13 October 2015
Line 1:		Line 1:
	===Resources For Learning Python===		===Resources For Learning Python===

	Though not comparable in size to the truly enormous userbase of C++, Java and C#, Python still boasts a highly active community and a correspondingly large number of resources. In the Euler group all users are encouraged to use Python 3. While some modules only exist for the older Python 2 codebase, in the overwhelming majority of cases they have been re-established in the Py3 framework (cf. [https://python3wos.appspot.com/ Python 3 Wall Of Superpowers] for current status of module transfer).		Though not comparable in size to the truly enormous userbase of C++, Java and C#, Python still boasts a highly active community and a correspondingly large number of resources. In the Euler group all users are strongly encouraged to use Python 3. While some modules only exist for the older Python 2 codebase, in the overwhelming majority of cases they have been re-established in the Py3 framework (cf. [https://python3wos.appspot.com/ Python 3 Wall Of Superpowers] for current status of module transfer).

	For the absolute beginner Codeacademy's free, interactive [https://www.codecademy.com/en/tracks/python learning module for Python] is a good starting point. Though it recommends approximately 13 hours for the course, those with a background in programming could probably finish it in under a day. For scientific computing it helps to have some familiarity with the Scipy stack, for which there is a nice introduction at [http://www.scipy-lectures.org/ scipy-lectures.org], particularly the Getting Started section.		For the absolute beginner Codeacademy's free, interactive [https://www.codecademy.com/en/tracks/python learning module for Python] is a good starting point. Though it recommends approximately 13 hours for the course, those with a background in programming could probably finish it in under a day. For scientific computing it helps to have some familiarity with the Scipy stack, for which there is a nice introduction at [http://www.scipy-lectures.org/ scipy-lectures.org], particularly the Getting Started section.

	Beyond that, it will be worth taking a look at the [https://web.stanford.edu/~mwaskom/software/seaborn/ Seaborn] module for attractive plotting functions. Those interested in Datajoint should also take a look at the Datajoint project page and that of [https://pythonhosted.org/neo/ Neo], the latter of which may be used for Python IO. Finally, if you are thinking of using Python for important code which will likely be reused, I would recommend reading the [https://www.python.org/dev/peps/pep-0008/ PEP0008 styleguide], to ensure you are writing correctly formatted code.		Beyond that, it will be worth taking a look at the [https://web.stanford.edu/~mwaskom/software/seaborn/ Seaborn] module for attractive plotting functions. Those interested in Datajoint should also take a look at the Datajoint project page and that of [https://pythonhosted.org/neo/ Neo], the latter of which may be used for Python IO. Finally, if you are thinking of using Python for important code which will likely be reused, I would recommend reading the [https://www.python.org/dev/peps/pep-0008/ PEP0008 styleguide], to ensure you are writing correctly formatted code.

Lrogerson: /* Resources For Learning Python */

2015-10-13T15:02:01Z

Resources For Learning Python

← Older revision		Revision as of 17:02, 13 October 2015
Line 1:		Line 1:
	===Resources For Learning Python===		===Resources For Learning Python===

	Though not comparable in size to the truly enormous userbase of C++, Java and C#, Python still boasts a highly active community and a correspondingly large number of resources. In the AG Euler group all users are encouraged to use Python 3. While some modules only exist for the older Python 2 codebase, in the overwhelming majority of cases they have been re-established in the Py3 framework (cf. [https://python3wos.appspot.com/ Python 3 Wall Of Superpowers] for current status of module transfer).		Though not comparable in size to the truly enormous userbase of C++, Java and C#, Python still boasts a highly active community and a correspondingly large number of resources. In the Euler group all users are encouraged to use Python 3. While some modules only exist for the older Python 2 codebase, in the overwhelming majority of cases they have been re-established in the Py3 framework (cf. [https://python3wos.appspot.com/ Python 3 Wall Of Superpowers] for current status of module transfer).

	For the absolute beginner Codeacademy's free, interactive [https://www.codecademy.com/en/tracks/python learning module for Python] is a good starting point. Though it recommends approximately 13 hours for the course, those with a background in programming could probably finish it in under a day. For scientific computing it helps to have some familiarity with the Scipy stack, for which there is a nice introduction at [http://www.scipy-lectures.org/ scipy-lectures.org], particularly the Getting Started section.		For the absolute beginner Codeacademy's free, interactive [https://www.codecademy.com/en/tracks/python learning module for Python] is a good starting point. Though it recommends approximately 13 hours for the course, those with a background in programming could probably finish it in under a day. For scientific computing it helps to have some familiarity with the Scipy stack, for which there is a nice introduction at [http://www.scipy-lectures.org/ scipy-lectures.org], particularly the Getting Started section.

	Beyond that, it will be worth taking a look at the [https://web.stanford.edu/~mwaskom/software/seaborn/ Seaborn] module for attractive plotting functions. Those interested in Datajoint should also take a look at the Datajoint project page and that of [https://pythonhosted.org/neo/ Neo], the latter of which may be used for Python IO. Finally, if you are thinking of using Python for important code which will likely be reused, I would recommend reading the [https://www.python.org/dev/peps/pep-0008/ PEP0008 styleguide], to ensure you are writing correctly formatted code.		Beyond that, it will be worth taking a look at the [https://web.stanford.edu/~mwaskom/software/seaborn/ Seaborn] module for attractive plotting functions. Those interested in Datajoint should also take a look at the Datajoint project page and that of [https://pythonhosted.org/neo/ Neo], the latter of which may be used for Python IO. Finally, if you are thinking of using Python for important code which will likely be reused, I would recommend reading the [https://www.python.org/dev/peps/pep-0008/ PEP0008 styleguide], to ensure you are writing correctly formatted code.

Lrogerson: /* Resources For Learning Python */

2015-10-13T15:01:41Z

Resources For Learning Python

← Older revision		Revision as of 17:01, 13 October 2015
Line 5:		Line 5:
	For the absolute beginner Codeacademy's free, interactive [https://www.codecademy.com/en/tracks/python learning module for Python] is a good starting point. Though it recommends approximately 13 hours for the course, those with a background in programming could probably finish it in under a day. For scientific computing it helps to have some familiarity with the Scipy stack, for which there is a nice introduction at [http://www.scipy-lectures.org/ scipy-lectures.org], particularly the Getting Started section.		For the absolute beginner Codeacademy's free, interactive [https://www.codecademy.com/en/tracks/python learning module for Python] is a good starting point. Though it recommends approximately 13 hours for the course, those with a background in programming could probably finish it in under a day. For scientific computing it helps to have some familiarity with the Scipy stack, for which there is a nice introduction at [http://www.scipy-lectures.org/ scipy-lectures.org], particularly the Getting Started section.

	Beyond that, it will be worth taking a look at the [https://web.stanford.edu/~mwaskom/software/seaborn/ Seaborn] module for attractive plotting functions. Those interested in Datajoint should also take a look at the Datajoint project page and that of Neo, the latter of which may be used for Python IO. Finally, if you are thinking of using Python for important code which will likely be reused, I would recommend reading the [https://www.python.org/dev/peps/pep-0008/ PEP0008 styleguide], to ensure you are writing correctly formatted code.		Beyond that, it will be worth taking a look at the [https://web.stanford.edu/~mwaskom/software/seaborn/ Seaborn] module for attractive plotting functions. Those interested in Datajoint should also take a look at the Datajoint project page and that of [https://pythonhosted.org/neo/ Neo], the latter of which may be used for Python IO. Finally, if you are thinking of using Python for important code which will likely be reused, I would recommend reading the [https://www.python.org/dev/peps/pep-0008/ PEP0008 styleguide], to ensure you are writing correctly formatted code.

Lrogerson: Created page with "===Resources For Learning Python=== Though not comparable in size to the truly enormous userbase of C++, Java and C#, Python still boasts a highly active community and a corr..."

2015-10-13T15:01:11Z

Created page with "===Resources For Learning Python=== Though not comparable in size to the truly enormous userbase of C++, Java and C#, Python still boasts a highly active community and a corr..."

New page

===Resources For Learning Python===

Though not comparable in size to the truly enormous userbase of C++, Java and C#, Python still boasts a highly active community and a correspondingly large number of resources. In the AG Euler group all users are encouraged to use Python 3. While some modules only exist for the older Python 2 codebase, in the overwhelming majority of cases they have been re-established in the Py3 framework (cf. [https://python3wos.appspot.com/ Python 3 Wall Of Superpowers] for current status of module transfer).

For the absolute beginner Codeacademy's free, interactive [https://www.codecademy.com/en/tracks/python learning module for Python] is a good starting point. Though it recommends approximately 13 hours for the course, those with a background in programming could probably finish it in under a day. For scientific computing it helps to have some familiarity with the Scipy stack, for which there is a nice introduction at [http://www.scipy-lectures.org/ scipy-lectures.org], particularly the Getting Started section.

Beyond that, it will be worth taking a look at the [https://web.stanford.edu/~mwaskom/software/seaborn/ Seaborn] module for attractive plotting functions. Those interested in Datajoint should also take a look at the Datajoint project page and that of Neo, the latter of which may be used for Python IO. Finally, if you are thinking of using Python for important code which will likely be reused, I would recommend reading the [https://www.python.org/dev/peps/pep-0008/ PEP0008 styleguide], to ensure you are writing correctly formatted code.