Introduction to Python

To start

• There are two popular versions of Python (2.7 and 3.5);

• Ide \(\rightarrow\) Pycharm, Netbeans, Eclipse etc, I suggest Spyder (similar to Rstudio);

• Jupyter Notebook \(\rightarrow\) web application to create and share documents (code, formulas, text, etc);

• Anaconda \(\rightarrow\) contains R, Python, various IDE, Anaconda navigator (GUI), Anaconda Prompt and Jupyter Notebook.

Some tutorials

• https://docs.python.org/3/tutorial/ \(\rightarrow\) official python tutorial;
• https://courses.edx.org/courses/course-v1:UCSanDiegoX+DSE200x+1T2018/course/ \(\rightarrow\) Videos, exercises, etc;
• http://www.pymvpa.org/tutorial.html \(\rightarrow\) Multivariate Pattern Analysis.

Some basic information

• Python uses white space to identify code blocks (Avoid using tabs and prefer whitespaces) \(\rightarrow\) \(4\) spaces per indentation level;
• No \(;\) at the end of each line;
• No variable declaration as R;
• assignment operator \(=\), i.e not <-.

# One line comment

"""
Two lines comment
"""

Some useful packages

• Numpy \(\rightarrow\) array

• Scipy \(\rightarrow\) math (https://docs.scipy.org/doc/scipy-0.18.1/reference/index.html)

• Pandas \(\rightarrow\) dataframes (https://pandas.pydata.org/pandas-docs/stable/)

• Matplotlib \(\rightarrow\) plot (https://matplotlib.org/)

• Seaborn \(\rightarrow\) plot (https://seaborn.pydata.org/)

• scikit learn \(\rightarrow\) Classification, Regression, Clustering etc (https://scikit-learn.org/stable/tutorial/index.html)

• statmodels \(\rightarrow\) Models (http://www.statsmodels.org/devel/index.html)

Install packages

• Anaconda Prompt \(\rightarrow\) https://anaconda.org/anaconda/repo Example:

conda install -c conda-forge numpy

• Anaconda Navigator \(\rightarrow\) enviroments \(\rightarrow\) search package

In both cases, it is useful to create an enviroments:

conda create -n yourenvname python=x.x anaconda
activate yourenvname

and then install packages into yourenvname.