#format rst

PyDLR34
=======
Basemap (Beispiel aus dem Buch von Jake VanderPlas 
https://jakevdp.github.io/PythonDataScienceHandbook/04.13-geographic-data-with-basemap.html)

::

   # https://github.com/matplotlib/basemap/issues/420
   # https://github.com/conda-forge/basemap-feedstock/issues/30
   # https://github.com/conda-forge/basemap-feedstock/issues/45

   import numpy as np
   import matplotlib.pyplot as plt
   from mpl_toolkits.basemap import Basemap

   plt.figure(figsize=(8, 8))
   m = Basemap(projection='ortho', resolution=None, lat_0=50, lon_0=-100)
   m.bluemarble(scale=0.5);



PyDLR33
=======

Oktober 2019

* Eric Matthes, Python Crash Course, 2nd edition, 2019, Cheat Sheets
  https://ehmatthes.github.io/pcc_2e/  

* f-string, multiline (https://realpython.com/python-f-strings)
  ::
  
     name = "Bob"
     profession = "baker"
     affiliation = "London"

     message = (
        f"Hi {name}. "
        f"You are a {profession}. "
        f"You were in {affiliation}."
     )

     print(message)

* Verschiedene Möglichkeiten, um Mehrzeilenstrings zu schreiben:
  https://www.techbeamers.com/python-multiline-string/

PyDLR32
=======

Juli 2019

* Python - die Alternative zu Matlab? 
  http://num.math.uni-goettingen.de/~schulz/data/python_talk.pdf

* Glumpy is a python library for scientific visualization that is both fast, scalable and beautiful. 
  Glumpy offers an intuitive interface between numpy and modern OpenGL.

  http://glumpy.github.io  

  Python & OpenGL for Scientific Visualization,
  Copyright (c) 2018 Nicolas P. Rougier,
  License: Creative Commons Attribution 4.0 International (CC BY-NC-SA 4.0),
  Website: http://www.labri.fr/perso/nrougier/python-opengl

  http://www.labri.fr/perso/nrougier/python-opengl/

* https://github.com/rougier/matplotlib-tutorial

* Python 4 Everybody by Charles Severance, https://www.py4e.com (free book)


PyDLR31
=======

April 2019 

::

  # UnboundLocalError: local variable 'D' referenced before assignment
  # Python Introductory Course 8.-10. April 2019
  # Python 3.7

  D = {}

  def foo():
      if False:
          D = {}
      D["blue"] = "blau"
      print("D local:", D)   
      return D
 

  D = foo()
  print(D)


  # shorter:
  # 1. Line '*' commented out: NameError: name 'D' is not defined                 
  # 2. With line '*': UnboundLocalError: local variable 'D' referenced before assignment
  # 3. With 'if True': works                                                      
  def g():                                                                        
      if False:                                                                   
          D = {}  # (*)                                                           
      print(D)                                                                    
                                                                                
  g()   

Explanation:

- https://docs.python.org/3/faq/programming.html#why-am-i-getting-an-unboundlocalerror-when-the-variable-has-a-value

- https://eli.thegreenplace.net/2011/05/15/understanding-unboundlocalerror-in-python


PyDLR23
=======

Switch/Case with Python

::

 def a():
     print("'a' was called")

 def b():
     print("'b' was called")
    
 def c():
     print("'c' was called")
    
    
 D = { 'a' : a, 'b' : b, 'c' : c}

 while True:
     s = input("your choice: ")
     if s in D:
         D[s]()
     else:
         break



PyDLR22
=======

Python 3
--------

* Jake VanderPlas, A Whirlwind Tour of Python (freier Text mit 100 Seiten plus
  Jupyter Notebooks)

  https://github.com/jakevdp/WhirlwindTourOfPython

* Kevin Sheppard, Python for Econometrics, 2017

  https://www.kevinsheppard.com/Python_for_Econometrics


SciPy
-----

* http://www.scipy-lectures.org (inkl. Datensaetzen und Aufgaben)

  - Numpy http://www.scipy-lectures.org/intro/numpy/index.html
  - Matplotlib http://www.scipy-lectures.org/intro/matplotlib/index.html
  - SciPy http://www.scipy-lectures.org/intro/matplotlib/index.html
  - Statistics (ein wenig Pandas) http://www.scipy-lectures.org/packages/statistics/index.html

* Matplotlib Gallery (viele Beispiele mit Sourcecode)

  http://matplotlib.org/gallery.html

* Hans Petter Langtangen, A very basic introduction to scientific Python programming,
  2016

  http://hplgit.github.io/bumpy/doc/pub/basics.html

* Svein Linge, Hans Petter Langtangen, Programming for Computations - Python
  A Gentle Introduction to Numerical Simulations with Python, 2016 (Open Access)

  http://link.springer.com/book/10.1007%2F978-3-319-32428-9



Pandas
------

* Pandas Homepage, siehe *10 Minutes to Pandas*, *Tutorials*, *Lessons for 
  new Pandas users*, ...

  http://pandas.pydata.org/pandas-docs/stable/

* Beispiele aus dem Netz, leicht modifiziert. U.a. Excel Writer, Pandas Reader, 
  Pandas Zeitserie, Numpy: Textdateien einlesen, ...

  http://www.hhoegl.de/start_python/bookonl/demos/py_data_io/

* Karlijn Willems, Pandas Tutorial: DataFrames in Python, October 2016


* https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python

* http://www.analyticsvidhya.com/blog/2015/04/comprehensive-guide-data-exploration-sas-using-python-numpy-scipy-matplotlib-pandas/

* http://blog.brakmic.com/data-science-for-losers

* Wes McKinney, Python for Data Analysis, O'Reilly 2013.

* Cheat sheets

  - https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf
  - https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.EBTvnMY