{
"metadata": {
"name": "",
"signature": "sha256:b05a9dca93aaf2f8984b051998cc2dd098000e2319c64c9b2a774fca741c4910"
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"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<div align=\"center\">\n",
"<h1>\n",
"Crash Course on Python (via IPython) and Beginner Visualization and Numerical Computation\n",
"</h1>\n",
"<div width=1px height=100px>\n",
"</div>\n",
"<h2>\n",
"Jon Woodring\n",
"<br>\n",
"Los Alamos National Laboratory\n",
"</h2>\n",
"<h3>\n",
"The Ohio State University\n",
"<br>\n",
"October 10, 2014\n",
"</h3>\n",
"</div>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# To follow along in IPython #\n",
"\n",
"## Step One ##\n",
"\n",
"Download this IPython notebook at Github:\n",
"\n",
"https://github.com/DataScienceAtScale/python-at-osu-oct-10 *then click on the link in the README*\n",
"\n",
"or get it directly via\n",
"\n",
"https://raw.githubusercontent.com/DataScienceAtScale/python-at-osu-oct-10/master/Python_at_OSU_Oct_10.ipynb\n",
"\n",
"# Optionally follow along without IPython #\n",
"\n",
"Use nbviewer to see the static version of the notebook in the web:\n",
"\n",
"http://nbviewer.ipython.org/github/DataScienceAtScale/python-at-osu-oct-10/blob/master/Python_at_OSU_Oct_10.ipynb\n",
"\n",
"**You can stop here if you are viewing the static web version.**"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Step Two ##\n",
"\n",
"Download and install Anaconda Python from Continuum Analytics. Use Python 3.4, as there isn't a good reason to use 2.7 as a beginner:\n",
"\n",
"http://continuum.io/downloads#py34\n",
"\n",
"### Optional Install ###\n",
"\n",
"There are other Python options as well, such as *Python(X,Y)*, *WinPython*, *MiniConda* version of *Anaconda*, or using *pip* to install packages with an existing installed version of *Python*. \n",
"\n",
"If you are using any of these versions, also make sure that you install:\n",
"- IPython Notebook (will also get IPython command line as a dependency)\n",
"- SciPy (will also get NumPy as a dependency)\n",
"- Pandas\n",
"- Matplotlib"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Step Three ##\n",
"\n",
"Launch IPython Notebook in the directory where *Python_at_OSU_Oct_10.ipynb* is located.\n",
"\n",
"This will open a webpage for the IPython Notebook interface. From the terminal (for example if you downloaded the notebook in Downloads):\n",
"\n",
"```bash\n",
"$ cd ~/Downloads\n",
"$ ls Python_at_OSU_Oct_10.ipynb\n",
"Python_at_OSU_Oct_10.ipynb\n",
"$ ipython notebook\n",
"```\n",
"\n",
"Alternatively, launch IPython Notebook and copy the notebook file into the directory where ipython was launched:\n",
"\n",
"```bash\n",
"$ cp ~/Downloads/Python_at_OSU_Oct_10.ipynb .\n",
"$ ipython notebook\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### What it looks like if you successfully launched IPython Notebook ###\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Step Four ##\n",
"\n",
"Open the notebook (*Python_at_OSU_Oct_10.ipynb*) from the web interface that was opened in your browser:\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# 3 Reasons Why I like Python #\n",
"\n",
"## Rather than telling you why you should like it, I'm going to tell you why I like it. ##"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# #1 My 881 project written in Python (taken with with Professor Dey) still works #\n",
"## I haven't touched it in 10+ years and I just ran it last Friday to see if it worked ##\n",
"\n",
"\n",
"\n",
"# Yep! Still works! #"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# #2 import antigravity #\n",
"\n",
"\n",
"\n",
"## and a million other things: numerics, graphics, web, GPU, distributed, GUIs, databases, analytics, etc.\n",
"\n",
"### The things that Python isn't good at (yet): compilation and shared-memory threading ###"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# #3 IPython + Matplotlib + Numpy + SciPy + SQLite = Amazing #\n",
"\n",
"- ## Everything in this tutorial was created with these Python tools ##\n",
"- ## An honorable mention goes to Cython, which makes it really easy to call C code directly from Python ##\n",
"\n",
"# Plus, the language is fun. It is so easy to do anything. #\n",
"\n",
"- ## I have written volume renderers, high-dimensional visualizations, analysis tools, web servers, benchmarks, parallel processing, data manipulation, demonstrations, etc. all in Python ##\n",
"- ## I use Python because premature optimization is the downfall of coding productivity. There is no good reason to start with C/C++ when 95% of the time in Visualization and Analysis our bottleneck is I/O. If you need to make it go fast, find your bottlenecks, and *optimize LATER not at the start*. ##"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# So let's get into it\n",
"\n",
"print('Hello world!')"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Hello world!\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# An Interactive Demo of IPython Notebook - Shown by Jon #\n",
"\n",
"# To execute IPython Cells, press *Ctrl-Enter* or the play button on the toolbar #\n",
"\n",
"# The static view has the output cached already #"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Python #\n",
"\n",
"- A dynamically typed language (methods and functions are checked at run-time)\n",
"- Whitespace is meaningful for blocks (no semi-colons/terminators)\n",
"- Syntax is C-like (procedural, Algol-like)\n",
"- Built-in immutable data types: integers, floating point, strings, tuples, etc.\n",
"- Built-in mutable data structures: lists, associative maps (dicts/hashes), sets, etc.\n",
"- Automatic memory management (reference counting)\n",
"- Exception handling (try/except)\n",
"- Functions are first-class (they can be passed around as arguments)\n",
"- Object-oriented paradigms can be used (classes)\n",
"- A huge standard library\n",
"- CPython is the standard implementation, which is interpreted (there are efforts to make JIT compilers, such as *pypy* and *Numba*)\n",
"- Bind to C for speed (Python is an excellent \"glue\" language)\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# assignment & print function\n",
"\n",
"x = 0 # you don't have to declare x first\n",
"print(x) # calling built-in function\n",
"\n",
"y = 1.0 # just assign a literal to a name\n",
"print(y) # calling looks just like C and other Algol-languages\n",
"\n",
"z = 'foo' # a string with quotes\n",
"print(z) # no semi-colons or other terminators\n",
"\n",
"w = \"bar\" # another string with double quotes (either works)\n",
"print(w)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0\n",
"1.0\n",
"foo\n",
"bar\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# more assignment & expressions\n",
"\n",
"x = 1\n",
"y = 2\n",
"z = 3\n",
"r = 'one'\n",
"s = 'two'\n",
"t = 'three'\n",
"f = 1.0\n",
"\n",
"a = x * y + z # expressions look like most other infix notation\n",
"print(a)\n",
"\n",
"print(r + s + t) # concatenating strings & expression \n",
" # in a function argument\n",
"\n",
"b, c, d = x + f, 4 * f, y ** z # multiple assigments on one line\n",
"print(b, c, d) # adding numeric types casts integer to float\n",
"\n",
"print(x + r) # but this doesn't work \n",
" # (won't cast a numeric to a string, unlike Javascript)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"5\n",
"onetwothree\n",
"2.0 4.0 8\n"
]
},
{
"ename": "TypeError",
"evalue": "unsupported operand type(s) for +: 'int' and 'str'",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-3-1adb0bd1160d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# adding numeric types casts integer to float\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# but this doesn't work\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 21\u001b[0m \u001b[1;31m# (won't cast a numeric to a string, unlike Javascript)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# conversions and types\n",
"\n",
"x = '1.01'\n",
"\n",
"print(x, type(x)) # type is another built-in function\n",
"x = float(x) # x is now a float\n",
"print(x, type(x)) # print can take multiple arguments\n",
"x = int(x)\n",
"print(x, type(x)) # now it's an integer\n",
"x = str(x)\n",
"print(x, type(x))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1.01 <class 'str'>\n",
"1.01 <class 'float'>\n",
"1 <class 'int'>\n",
"1 <class 'str'>\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# lists (vectors, really)\n",
"\n",
"x = [1, 2, 3, 4, 5] # a list of integers\n",
"print(x)\n",
"\n",
"y = [1.0, 2.0, 3.0, 4.0, 5.0] # a list of floating point\n",
"print(y)\n",
"\n",
"z = ['a', 'b', 'c', 'd', 'e'] # a list of strings\n",
"print(z)\n",
"\n",
"w = [1, 'two', 3.0, \"four\", print, 'last'] # can we mix them?\n",
"print(w) # yes, we can"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1, 2, 3, 4, 5]\n",
"[1.0, 2.0, 3.0, 4.0, 5.0]\n",
"['a', 'b', 'c', 'd', 'e']\n",
"[1, 'two', 3.0, 'four', <built-in function print>, 'last']\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# accessing lists\n",
"\n",
"w = [1, 'two', 3.0, \"four\", print, 'last'] \n",
"print(w)\n",
"\n",
"# accessing is array notation\n",
"# a Python list is like a \"vector\" in C++\n",
"# random access, reverse is constant time, etc.\n",
"# i.e., deleting from the front is linear time\n",
"print(w[0]) # first item\n",
"print(w[1]) # second item\n",
"print(w[len(w)-1]) # last item, len is a built-in function\n",
"print(w[-1]) # this is the last item, too\n",
"print(w[-2]) # second to last\n",
"\n",
"w[0] = w[-1] # let's copy the last item to the first\n",
"print(w[0])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1, 'two', 3.0, 'four', <built-in function print>, 'last']\n",
"1\n",
"two\n",
"last\n",
"last\n",
"<built-in function print>\n",
"last\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# list slices\n",
"\n",
"w = [1, 2, 3, 4, 5]\n",
"\n",
"# slices - similar to Matlab and Fortran\n",
"print(w[2:]) # everything from 2 onwards\n",
"print(w[:2]) # right hand index is exclusive (Python uses 0-indexing)\n",
"print(w[:2] + w[2:]) # list concatenation\n",
"\n",
"# you can do subranges\n",
"print(w[1:3])\n",
"\n",
"# you can do skips\n",
"print(w[::2])\n",
"\n",
"# even in reverse\n",
"print(w[::-1])\n",
"\n",
"# you can combine them all together\n",
"print(w[3:0:-2]) # notice I had to do 3 to 0 by -2 to go in reverse"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[3, 4, 5]\n",
"[1, 2]\n",
"[1, 2, 3, 4, 5]\n",
"[2, 3]\n",
"[1, 3, 5]\n",
"[5, 4, 3, 2, 1]\n",
"[4, 2]\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"w = [1, 2, 3, 4, 5]\n",
"\n",
"# a slice is a copy\n",
"v = w[::-1]\n",
"v[0] = 'first'\n",
"print(v, w)\n",
"\n",
"# such that : means copy\n",
"u = v[:]\n",
"u[-1] = 'last'\n",
"print(u, v)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['first', 4, 3, 2, 1] [1, 2, 3, 4, 5]\n",
"['first', 4, 3, 2, 'last'] ['first', 4, 3, 2, 1]\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# lists of lists\n",
"\n",
"x = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] # what about lists of lists?\n",
"print(x)\n",
"print(x[0][0]) # double accessors to get the inner list items\n",
"print(x[-1][-1])\n",
"print(x[1:][1:]) # except this is probably not what you are expecting\n",
"\n",
"y = [['a', 1, 2], [3.0], [4 + 3, ['seven', ['eight']], 'nine']]\n",
"print(y) # nothing stopping arbitrary list nesting\n",
"print(y[2][1][1][0]) # anything can go in a list"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n",
"1\n",
"9\n",
"[[7, 8, 9]]\n",
"[['a', 1, 2], [3.0], [7, ['seven', ['eight']], 'nine']]\n",
"eight\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# list iteration\n",
"\n",
"z = [1, 2, 3]\n",
"n = []\n",
"for i in z: # iterating over a list\n",
" n.append(i + 1) # append is a method on a list \n",
" # that modifies it in place (it returns None)\n",
"print(n, z)\n",
"\n",
"w = [[1, 2], [3, 4], [5, 6]]\n",
"s = ''\n",
"for i in w: # iterate over a list\n",
" for j in i: # iterate over another list\n",
" s = s + str(j)\n",
"print(s)\n",
" \n",
"q = []\n",
"# iterate over two lists in tandem with zip\n",
"for i, j in zip(z, n):\n",
" print('i:', i, 'j:', j, 'i+j:', i + j)\n",
" q.append(i + j)\n",
"print(q)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[2, 3, 4] [1, 2, 3]\n",
"123456\n",
"i: 1 j: 2 i+j: 3\n",
"i: 2 j: 3 i+j: 5\n",
"i: 3 j: 4 i+j: 7\n",
"[3, 5, 7]\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# if you want to modify a list in place, use the accessors\n",
"# i.e., like how you would in C\n",
"z = [1, 2, 3, 4, 5]\n",
"for i in range(0, len(z)): # range is a special \"list\" \n",
" # (iterator, actually) \n",
" # that gives you integers\n",
" z[i] = z[i] + 1\n",
"print(z) # basically, that was a for loop from 0 to 5 (exclusive)\n",
"\n",
"z = [1, 2, 3, 4, 5]\n",
"for i in z: # this does nothing because i is a copy of the item in z\n",
" i = i + 1\n",
"print(z)\n",
"\n",
"# DON'T DO THIS: IT WILL NEVER RETURN\n",
"# for i in z: # iterating over a list\n",
"# z.append(i + 1) # but you just added to the end of z, \n",
"# # so, z keeps growing, such that \n",
"# # you will never hit the end of z\n",
"\n",
"# basically, don't try to modify the list while iterating over it\n",
"# lots of \"bad\" things can happen"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[2, 3, 4, 5, 6]\n",
"[1, 2, 3, 4, 5]\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# tuples - basically, immutable lists\n",
"# there are also sets and frozensets (mutable and immutable sets)\n",
"empty = ()\n",
"print(empty)\n",
"print(len(empty))\n",
"\n",
"a = (1, 2, 3)\n",
"print(a)\n",
"\n",
"print(a[0], a[-1], a[1:])\n",
"\n",
"for i in a:\n",
" print(i + 1)\n",
"\n",
"a[0] = 'a' # this is going to fail, because tuples are immutable\n",
"\n",
"# strings are immutable, too, as with all other basic data types,\n",
"# while containers are mutable (except tuples and frozensets)\n",
"s = 'a string'\n",
"s[0] = 'a' # this will fail too"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"()\n",
"0\n",
"(1, 2, 3)\n",
"1 3 (2, 3)\n",
"2\n",
"3\n",
"4\n"
]
},
{
"ename": "TypeError",
"evalue": "'tuple' object does not support item assignment",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-13-3f201cd96ca7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'a'\u001b[0m \u001b[1;31m# this is going to fail, because tuples are immutable\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[1;31m# strings are immutable, too, as with all other basic data types,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# dicts : maps, hashes, associative arrays\n",
"# lists, dicts, and tuples are all accessed in\n",
"# similar ways\n",
"\n",
"empty = {}\n",
"print(empty)\n",
"print(empty.keys()) # dicts have keys\n",
"print(empty.values()) # and values (it's a map)\n",
"\n",
"a = {'one': 1, 2: 'two', 'print': print} # we can store all \n",
" # sorts of things in a dict, \n",
" # just like a list and tuple\n",
"print(a)\n",
"print(a['one']) # and we can use all sorts of keys\n",
"print(a[2])\n",
"print(a['print']) # even functions can be fetched\n",
"a['print'](\"hi there, I'm a function in a dict!\") # call it\n",
"\n",
"for k in a:\n",
" print('key:', k, 'value:', a[k])\n",
" \n",
"print('a key' in a) # boolean is a built-in type: True or False\n",
"a['a key'] # this is going to fail"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{}\n",
"dict_keys([])\n",
"dict_values([])\n",
"{'print': <built-in function print>, 2: 'two', 'one': 1}\n",
"1\n",
"two\n",
"<built-in function print>\n",
"hi there, I'm a function in a dict!\n",
"key: print value: <built-in function print>\n",
"key: 2 value: two\n",
"key: one value: 1\n",
"False\n"
]
},
{
"ename": "KeyError",
"evalue": "'a key'",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-14-2361f8fa98e9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'a key'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# boolean is a built-in type: True or False\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'a key'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m# this is going to fail\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m: 'a key'"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# simple boolean expressions\n",
"\n",
"s_one = '1'\n",
"i_one = 1\n",
"f_one = 1.0\n",
"i_two = 2\n",
"i_other_one = 1\n",
"\n",
"print('\"1\" = 1?', s_one == i_one) # strings can't equal numerics\n",
"print('1 < 2?', i_one < i_two)\n",
"print('1.0 > 2?', f_one > i_two)\n",
"print('1 = 1.0?', i_one == f_one)\n",
"print('\"1\" = str(1)?', s_one == str(i_one)) \n",
"print('1 =/= 1?', i_one != i_other_one) "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\"1\" = 1? False\n",
"1 < 2? True\n",
"1.0 > 2? False\n",
"1 = 1.0? True\n",
"\"1\" = str(1)? True\n",
"1 =/= 1? False\n"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# is vs. equality\n",
"\n",
"a = [1, 2, 3, 4, 5]\n",
"b = [1, 2, 3, 4, 5]\n",
"c = [1, 2, 3, 4]\n",
"d = [2, 2, 3, 4, 5]\n",
"\n",
"print(a == a)\n",
"print(a == b) # equality works for lists and containers\n",
"print(a == c)\n",
"print(a == d)\n",
"\n",
"# but what if you want to know about references?\n",
"print(a is a)\n",
"print(a is b) # False, they are not the same reference\n",
"\n",
"# x is y -> id(x) == id(y)\n",
"print(id(a) == id(a))\n",
"print(id(a) == id(b)) # this is equivalent to 'a is b'"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"True\n",
"True\n",
"False\n",
"False\n",
"True\n",
"False\n",
"True\n",
"False\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# copies vs. references of lists\n",
"a = ['A']\n",
"b = ['B']\n",
"c = ['C']\n",
"d = [a, b, c] # list of lists\n",
"print('d:', d)\n",
"print('a:', a, 'b:', b, 'c:', c)\n",
"\n",
"d[0] = d[-1] # list c isn't copied\n",
"print('d:', d) # list c is in both places\n",
"print('a:', a, 'b:', b, 'c:', c) # a is still the same, \n",
" # and d[0] and d[-1] reference c\n",
"\n",
"temp = a[0]\n",
"a[0] = b[0] # this string will be copied\n",
"b[0] = temp\n",
"print('d:', d) # all the built-in types are copied \n",
" # (i.e., int, float, string, etc.)\n",
"print('a:', a, 'b:', b, 'c:', c) # but built-in data structures \n",
" # (i.e., lists, classes, maps, etc.)\n",
" # are referenced"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"d: [['A'], ['B'], ['C']]\n",
"a: ['A'] b: ['B'] c: ['C']\n",
"d: [['C'], ['B'], ['C']]\n",
"a: ['A'] b: ['B'] c: ['C']\n",
"d: [['C'], ['A'], ['C']]\n",
"a: ['B'] b: ['A'] c: ['C']\n"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# immutability and is\n",
"\n",
"# what if you try 'is' on two numbers?\n",
"a = 1\n",
"b = 1\n",
"print(a is b) # True? what's going on here?\n",
"print(id(a))\n",
"print(id(b)) # they point to the same thing\n",
"\n",
"# basic types, such as string, integers, floats, get reused \"interned\"\n",
"# and are actually immutable - the other types (lists, classes, maps) are mutable\n",
"\n",
"# so, when you call a function, you are always passing by reference\n",
"def is_it(x, y):\n",
" z = a # get a new reference to x\n",
" print(id(x), id(y), id(z))\n",
"\n",
"is_it(a, b)\n",
"is_it([1, 2, 3], [1, 2, 3]) # difference references\n",
"\n",
"# but only to a certain point\n",
"a = 100000000 # they will have different ids\n",
"b = 100000000\n",
"print(a is b)\n",
"print(a == b) # all of this is done because Python uses a lot of hashing\n",
" # for optimization - because you can hash immutable data\n",
" \n",
"# for example, you can only use immutable data for keys in dicts \n",
"a = {}\n",
"a[[1, 2, 3]] = b # a list is unhashable"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"True\n",
"140368470091648\n",
"140368470091648\n",
"140368470091648 140368470091648 140368470091648\n",
"140368256473544 140368256473800 140368470091648\n",
"False\n",
"True\n"
]
},
{
"ename": "TypeError",
"evalue": "unhashable type: 'list'",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-18-c7ade888dec8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[1;31m# for example, you can only use immutable data for keys in dicts\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 29\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 30\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mb\u001b[0m \u001b[1;31m# a list is unhashable\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m: unhashable type: 'list'"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# compound boolean expressions\n",
"\n",
"print(not False)\n",
"print(False or True)\n",
"print(True and False)\n",
"print((False and True) or not True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"True\n",
"True\n",
"False\n",
"False\n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# if-then-else & block indentation\n",
"\n",
"x = 1\n",
"y = 2\n",
"z = 3\n",
"\n",
"# see how blocks line up due to spacing?\n",
"# PEP 8 says the preferred tab stop is 4 spaces (don't use tabs)\n",
"# I prefer 2, personally\n",
"if x < 1 or False:\n",
" print('not here')\n",
"elif y < 2 and True:\n",
" print('not here either')\n",
"elif z > 0:\n",
" print('we got here')\n",
" if not x != y:\n",
" print('nope')\n",
" elif z == 3:\n",
" print('here too')\n",
" if z < y or y < x:\n",
" print('not here either')\n",
" else:\n",
" print('we got all the way here')\n",
" while z > x:\n",
" z = z - 1\n",
" if y > x:\n",
" y = y - x\n",
" else:\n",
" y = y - 2\n",
" while z >= y:\n",
" z = z - 1\n",
" if x > 0:\n",
" x = x + 1\n",
" else:\n",
" print('nada')\n",
"else:\n",
" print('not gonna get here')\n",
"print(x, y, z)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"we got here\n",
"here too\n",
"we got all the way here\n",
"4 -1 -2\n"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# 0-argument functions and returning\n",
"\n",
"def nop(): # no argument list\n",
" pass # do nothing\n",
"\n",
"print(nop) # nop is a function\n",
"print(nop()) # call it, functions return None \n",
" # if there isn't an explicit return\n",
"\n",
"def one(): # no argument list\n",
" return 1\n",
"\n",
"print(one())\n",
"\n",
"# functions are first-class\n",
"temp = nop\n",
"nop = one\n",
"one = temp\n",
"\n",
"print(nop(), one())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<function nop at 0x7faa0815cbf8>\n",
"None\n",
"1\n",
"1 None\n"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# arguments to functions and returning values\n",
"\n",
"def xyz(x): # one argument, no types needed\n",
" return x, x # this means it is returning a tuple\n",
"\n",
"print(xyz(1)) \n",
"\n",
"def uvw(u, v): # two arguments\n",
" return (u, v) # we can do it explicitly, too\n",
"\n",
"print(uvw(1, 2))\n",
"\n",
"def abc(a, b, c):\n",
" return [a, b, c] # we can return lists\n",
"\n",
"print(abc(1, 2, 3))\n",
"\n",
"# btw, these two are equivalent\n",
"i, j = (3, 4)\n",
"print(i, j)\n",
"(i, j) = 3, 4\n",
"print(i, j)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(1, 1)\n",
"(1, 2)\n",
"[1, 2, 3]\n",
"3 4\n",
"3 4\n"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# recursion works just fine\n",
"\n",
"def ye_old_fib(n):\n",
" if n < 2:\n",
" return 1\n",
" else:\n",
" return ye_old_fib(n - 1) + ye_old_fib(n - 2)\n",
" \n",
"fibs = []\n",
"for i in range(0, 10):\n",
" fibs = fibs + [ye_old_fib(i)] # append done another way\n",
"print(fibs)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1, 1, 2, 3, 5, 8, 13, 21, 34, 55]\n"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# named arguments and default values\n",
"\n",
"def plus_one(x):\n",
" return x + 1\n",
"\n",
"def plus_two(x):\n",
" return x + 2\n",
"\n",
"# argument v and f have default values\n",
"def func_caller(v = 1, f = plus_one): \n",
" return f(v) # we can call arguments that are functions\n",
"\n",
"print(func_caller()) # we don't have to pass an argument for v and f\n",
"print(func_caller(2)) # they are applied left to right\n",
"print(func_caller(2, plus_two))\n",
"print(func_caller(f=plus_two)) # we can bypass the order by naming them"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2\n",
"3\n",
"4\n",
"3\n"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# scope is function level NOT block level\n",
"# Local Function -> Outer Function -> Global -> Python Defined\n",
"\n",
"# assignment determines scope\n",
"# you can think of '=' as declaration in Python\n",
"value = 1 # global\n",
"\n",
"def modify_value(): \n",
" value = 2 # local scope for value\n",
" \n",
" def modify_modify_value():\n",
" value = 3 # inner scope\n",
" \n",
" if True:\n",
" value = 4 # we look at the function scope\n",
" \n",
" print(value) # this will be 4, not 3\n",
" \n",
" modify_modify_value()\n",
" print(value) # 2 not 3 or 4\n",
"\n",
"print(value)\n",
"modify_value()\n",
"print(value)\n",
"del value # remove a name from scope and reclaim memory\n",
"print(value) # this is going to be undefined"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1\n",
"4\n",
"2\n",
"1\n"
]
},
{
"ename": "NameError",
"evalue": "name 'value' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-26-88dc96fa7cc1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;31m# remove a name from scope and reclaim memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# this is going to be undefined\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'value' is not defined"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# assignment establishes scope\n",
"\n",
"if True:\n",
" some_thing = 1 # it gets \"declared\" here, but not \"lifted\"\n",
"print(some_thing)\n",
"\n",
"print(not_lifted + 1) # not_lifted is not in scope yet\n",
"if True:\n",
" not_lifted = 1"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1\n"
]
},
{
"ename": "NameError",
"evalue": "name 'not_lifted' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-27-2c757dd657b1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msome_thing\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnot_lifted\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# not_lifted is not in scope yet\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mnot_lifted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'not_lifted' is not defined"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# more scope examples\n",
"\n",
"foo = ['bar'] # global scope\n",
"\n",
"def pretend_modify():\n",
" foo = ['nope'] # new scope established\n",
"\n",
"def actually_modify():\n",
" foo.append('yep') # we use outer scope\n",
" # because it isn't assignment\n",
" \n",
"def gonna_fail():\n",
" foo.append('whoops') # this will break because foo isn't in scope\n",
" foo = ['broked'] # because this assignment created a new scope\n",
" \n",
"print(foo)\n",
"pretend_modify()\n",
"print(foo)\n",
"actually_modify()\n",
"print(foo)\n",
"gonna_fail()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['bar']\n",
"['bar']\n",
"['bar', 'yep']\n"
]
},
{
"ename": "UnboundLocalError",
"evalue": "local variable 'foo' referenced before assignment",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mUnboundLocalError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-28-4c3c00ae6438>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[0mactually_modify\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfoo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m \u001b[0mgonna_fail\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m<ipython-input-28-4c3c00ae6438>\u001b[0m in \u001b[0;36mgonna_fail\u001b[1;34m()\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mgonna_fail\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mfoo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'whoops'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# this will break because foo isn't in scope\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 14\u001b[0m \u001b[0mfoo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'broked'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m# because this assignment created a new scope\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mUnboundLocalError\u001b[0m: local variable 'foo' referenced before assignment"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# files\n",
"\n",
"f = open('foo.txt', 'w')\n",
"f.write('hi there!\\n')\n",
"f.close()\n",
"\n",
"g = open('foo.txt', 'r')\n",
"s = g.read(10)\n",
"g.close()\n",
"\n",
"print(s)\n",
"\n",
"# the struct module and ctypes are useful for mass binary\n",
"# conversion of data, as well as the numpy from_file/to_file\n",
"# operations\n",
"f = open('bar.bin', 'wb')\n",
"f.write((10).to_bytes(1, 'little')) # I'd normally use struct\n",
"f.close()\n",
"\n",
"g = open('bar.bin', 'rb')\n",
"i = int.from_bytes(g.read(1), 'little') # Or numpy.fromfile\n",
"g.close()\n",
"\n",
"print(i)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"hi there!\n",
"\n",
"10\n"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# modules, help, and dir\n",
"\n",
"l = [1, 2, 3, 4, 5]\n",
"print(dir(l)) # what's in l?\n",
"\n",
"import sys # sys module (a library, basically)\n",
"print(dir(sys)) # what's in the sys module?\n",
"\n",
"from os import * # import everything in os into this namespace\n",
"print(dir()) # what's in the global namespace, now?\n",
"\n",
"print(help(sys)) # get module help\n",
"\n",
"print(help(sys.exit)) # get help on sys.exit"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['__add__', '__class__', '__contains__', '__delattr__', '__delitem__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__', '__rmul__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 'append', 'clear', 'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']\n",
"['__displayhook__', '__doc__', '__excepthook__', '__interactivehook__', '__loader__', '__name__', '__package__', '__spec__', '__stderr__', '__stdin__', '__stdout__', '_clear_type_cache', '_current_frames', '_debugmallocstats', '_getframe', '_home', '_mercurial', '_xoptions', 'abiflags', 'api_version', 'argv', 'base_exec_prefix', 'base_prefix', 'builtin_module_names', 'byteorder', 'call_tracing', 'callstats', 'copyright', 'displayhook', 'dont_write_bytecode', 'exc_info', 'excepthook', 'exec_prefix', 'executable', 'exit', 'flags', 'float_info', 'float_repr_style', 'getallocatedblocks', 'getcheckinterval', 'getdefaultencoding', 'getdlopenflags', 'getfilesystemencoding', 'getprofile', 'getrecursionlimit', 'getrefcount', 'getsizeof', 'getswitchinterval', 'gettrace', 'hash_info', 'hexversion', 'implementation', 'int_info', 'intern', 'last_traceback', 'last_type', 'last_value', 'maxsize', 'maxunicode', 'meta_path', 'modules', 'path', 'path_hooks', 'path_importer_cache', 'platform', 'prefix', 'ps1', 'ps2', 'ps3', 'setcheckinterval', 'setdlopenflags', 'setprofile', 'setrecursionlimit', 'setswitchinterval', 'settrace', 'stderr', 'stdin', 'stdout', 'thread_info', 'version', 'version_info', 'warnoptions']\n",
"['In', 'Out', 'P_NOWAIT', 'P_NOWAITO', 'P_WAIT', 'SEEK_CUR', 'SEEK_END', 'SEEK_SET', '_', '__', '___', '__builtin__', '__builtins__', '__doc__', '__loader__', '__name__', '__package__', '__spec__', '_dh', '_exit', '_i', '_i1', '_i10', '_i11', '_i12', '_i13', '_i14', '_i15', '_i16', '_i17', '_i18', '_i19', '_i2', '_i20', '_i21', '_i22', '_i23', '_i24', '_i25', '_i26', '_i27', '_i28', '_i29', '_i3', '_i30', '_i4', '_i5', '_i6', '_i7', '_i8', '_i9', '_ih', '_ii', '_iii', '_oh', '_sh', 'a', 'abc', 'actually_modify', 'altsep', 'b', 'c', 'curdir', 'd', 'defpath', 'devnull', 'empty', 'environb', 'execl', 'execle', 'execlp', 'execlpe', 'execvp', 'execvpe', 'exit', 'extsep', 'f', 'f_one', 'fdopen', 'fibs', 'foo', 'fsdecode', 'fsencode', 'func_caller', 'fwalk', 'g', 'get_exec_path', 'get_ipython', 'getenv', 'getenvb', 'gonna_fail', 'i', 'i_one', 'i_other_one', 'i_two', 'is_it', 'j', 'k', 'l', 'linesep', 'makedirs', 'modify_value', 'n', 'name', 'nop', 'one', 'pardir', 'path', 'pathsep', 'plus_one', 'plus_two', 'popen', 'pretend_modify', 'putenv', 'q', 'quit', 'r', 'removedirs', 'renames', 's', 's_one', 'sep', 'some_thing', 'spawnl', 'spawnle', 'spawnlp', 'spawnlpe', 'spawnv', 'spawnve', 'spawnvp', 'spawnvpe', 'supports_bytes_environ', 'sys', 't', 'temp', 'u', 'unsetenv', 'uvw', 'v', 'w', 'walk', 'x', 'xyz', 'y', 'ye_old_fib', 'z']\n",
"Help on built-in module sys:\n",
"\n",
"NAME\n",
" sys\n",
"\n",
"MODULE REFERENCE\n",
" http://docs.python.org/3.4/library/sys\n",
" \n",
" The following documentation is automatically generated from the Python\n",
" source files. It may be incomplete, incorrect or include features that\n",
" are considered implementation detail and may vary between Python\n",
" implementations. When in doubt, consult the module reference at the\n",
" location listed above.\n",
"\n",
"DESCRIPTION\n",
" This module provides access to some objects used or maintained by the\n",
" interpreter and to functions that interact strongly with the interpreter.\n",
" \n",
" Dynamic objects:\n",
" \n",
" argv -- command line arguments; argv[0] is the script pathname if known\n",
" path -- module search path; path[0] is the script directory, else ''\n",
" modules -- dictionary of loaded modules\n",
" \n",
" displayhook -- called to show results in an interactive session\n",
" excepthook -- called to handle any uncaught exception other than SystemExit\n",
" To customize printing in an interactive session or to install a custom\n",
" top-level exception handler, assign other functions to replace these.\n",
" \n",
" stdin -- standard input file object; used by input()\n",
" stdout -- standard output file object; used by print()\n",
" stderr -- standard error object; used for error messages\n",
" By assigning other file objects (or objects that behave like files)\n",
" to these, it is possible to redirect all of the interpreter's I/O.\n",
" \n",
" last_type -- type of last uncaught exception\n",
" last_value -- value of last uncaught exception\n",
" last_traceback -- traceback of last uncaught exception\n",
" These three are only available in an interactive session after a\n",
" traceback has been printed.\n",
" \n",
" Static objects:\n",
" \n",
" builtin_module_names -- tuple of module names built into this interpreter\n",
" copyright -- copyright notice pertaining to this interpreter\n",
" exec_prefix -- prefix used to find the machine-specific Python library\n",
" executable -- absolute path of the executable binary of the Python interpreter\n",
" float_info -- a struct sequence with information about the float implementation.\n",
" float_repr_style -- string indicating the style of repr() output for floats\n",
" hash_info -- a struct sequence with information about the hash algorithm.\n",
" hexversion -- version information encoded as a single integer\n",
" implementation -- Python implementation information.\n",
" int_info -- a struct sequence with information about the int implementation.\n",
" maxsize -- the largest supported length of containers.\n",
" maxunicode -- the value of the largest Unicode codepoint\n",
" platform -- platform identifier\n",
" prefix -- prefix used to find the Python library\n",
" thread_info -- a struct sequence with information about the thread implementation.\n",
" version -- the version of this interpreter as a string\n",
" version_info -- version information as a named tuple\n",
" __stdin__ -- the original stdin; don't touch!\n",
" __stdout__ -- the original stdout; don't touch!\n",
" __stderr__ -- the original stderr; don't touch!\n",
" __displayhook__ -- the original displayhook; don't touch!\n",
" __excepthook__ -- the original excepthook; don't touch!\n",
" \n",
" Functions:\n",
" \n",
" displayhook() -- print an object to the screen, and save it in builtins._\n",
" excepthook() -- print an exception and its traceback to sys.stderr\n",
" exc_info() -- return thread-safe information about the current exception\n",
" exit() -- exit the interpreter by raising SystemExit\n",
" getdlopenflags() -- returns flags to be used for dlopen() calls\n",
" getprofile() -- get the global profiling function\n",
" getrefcount() -- return the reference count for an object (plus one :-)\n",
" getrecursionlimit() -- return the max recursion depth for the interpreter\n",
" getsizeof() -- return the size of an object in bytes\n",
" gettrace() -- get the global debug tracing function\n",
" setcheckinterval() -- control how often the interpreter checks for events\n",
" setdlopenflags() -- set the flags to be used for dlopen() calls\n",
" setprofile() -- set the global profiling function\n",
" setrecursionlimit() -- set the max recursion depth for the interpreter\n",
" settrace() -- set the global debug tracing function\n",
"\n",
"FUNCTIONS\n",
" __displayhook__ = displayhook(...)\n",
" displayhook(object) -> None\n",
" \n",
" Print an object to sys.stdout and also save it in builtins._\n",
" \n",
" __excepthook__ = excepthook(...)\n",
" excepthook(exctype, value, traceback) -> None\n",
" \n",
" Handle an exception by displaying it with a traceback on sys.stderr.\n",
" \n",
" call_tracing(...)\n",
" call_tracing(func, args) -> object\n",
" \n",
" Call func(*args), while tracing is enabled. The tracing state is\n",
" saved, and restored afterwards. This is intended to be called from\n",
" a debugger from a checkpoint, to recursively debug some other code.\n",
" \n",
" callstats(...)\n",
" callstats() -> tuple of integers\n",
" \n",
" Return a tuple of function call statistics, if CALL_PROFILE was defined\n",
" when Python was built. Otherwise, return None.\n",
" \n",
" When enabled, this function returns detailed, implementation-specific\n",
" details about the number of function calls executed. The return value is\n",
" a 11-tuple where the entries in the tuple are counts of:\n",
" 0. all function calls\n",
" 1. calls to PyFunction_Type objects\n",
" 2. PyFunction calls that do not create an argument tuple\n",
" 3. PyFunction calls that do not create an argument tuple\n",
" and bypass PyEval_EvalCodeEx()\n",
" 4. PyMethod calls\n",
" 5. PyMethod calls on bound methods\n",
" 6. PyType calls\n",
" 7. PyCFunction calls\n",
" 8. generator calls\n",
" 9. All other calls\n",
" 10. Number of stack pops performed by call_function()\n",
" \n",
" exc_info(...)\n",
" exc_info() -> (type, value, traceback)\n",
" \n",
" Return information about the most recent exception caught by an except\n",
" clause in the current stack frame or in an older stack frame.\n",
" \n",
" exit(...)\n",
" exit([status])\n",
" \n",
" Exit the interpreter by raising SystemExit(status).\n",
" If the status is omitted or None, it defaults to zero (i.e., success).\n",
" If the status is an integer, it will be used as the system exit status.\n",
" If it is another kind of object, it will be printed and the system\n",
" exit status will be one (i.e., failure).\n",
" \n",
" getallocatedblocks(...)\n",
" getallocatedblocks() -> integer\n",
" \n",
" Return the number of memory blocks currently allocated, regardless of their\n",
" size.\n",
" \n",
" getcheckinterval(...)\n",
" getcheckinterval() -> current check interval; see setcheckinterval().\n",
" \n",
" getdefaultencoding(...)\n",
" getdefaultencoding() -> string\n",
" \n",
" Return the current default string encoding used by the Unicode \n",
" implementation.\n",
" \n",
" getdlopenflags(...)\n",
" getdlopenflags() -> int\n",
" \n",
" Return the current value of the flags that are used for dlopen calls.\n",
" The flag constants are defined in the os module.\n",
" \n",
" getfilesystemencoding(...)\n",
" getfilesystemencoding() -> string\n",
" \n",
" Return the encoding used to convert Unicode filenames in\n",
" operating system filenames.\n",
" \n",
" getprofile(...)\n",
" getprofile()\n",
" \n",
" Return the profiling function set with sys.setprofile.\n",
" See the profiler chapter in the library manual.\n",
" \n",
" getrecursionlimit(...)\n",
" getrecursionlimit()\n",
" \n",
" Return the current value of the recursion limit, the maximum depth\n",
" of the Python interpreter stack. This limit prevents infinite\n",
" recursion from causing an overflow of the C stack and crashing Python.\n",
" \n",
" getrefcount(...)\n",
" getrefcount(object) -> integer\n",
" \n",
" Return the reference count of object. The count returned is generally\n",
" one higher than you might expect, because it includes the (temporary)\n",
" reference as an argument to getrefcount().\n",
" \n",
" getsizeof(...)\n",
" getsizeof(object, default) -> int\n",
" \n",
" Return the size of object in bytes.\n",
" \n",
" getswitchinterval(...)\n",
" getswitchinterval() -> current thread switch interval; see setswitchinterval().\n",
" \n",
" gettrace(...)\n",
" gettrace()\n",
" \n",
" Return the global debug tracing function set with sys.settrace.\n",
" See the debugger chapter in the library manual.\n",
" \n",
" intern(...)\n",
" intern(string) -> string\n",
" \n",
" ``Intern'' the given string. This enters the string in the (global)\n",
" table of interned strings whose purpose is to speed up dictionary lookups.\n",
" Return the string itself or the previously interned string object with the\n",
" same value.\n",
" \n",
" setcheckinterval(...)\n",
" setcheckinterval(n)\n",
" \n",
" Tell the Python interpreter to check for asynchronous events every\n",
" n instructions. This also affects how often thread switches occur.\n",
" \n",
" setdlopenflags(...)\n",
" setdlopenflags(n) -> None\n",
" \n",
" Set the flags used by the interpreter for dlopen calls, such as when the\n",
" interpreter loads extension modules. Among other things, this will enable\n",
" a lazy resolving of symbols when importing a module, if called as\n",
" sys.setdlopenflags(0). To share symbols across extension modules, call as\n",
" sys.setdlopenflags(os.RTLD_GLOBAL). Symbolic names for the flag modules\n",
" can be found in the os module (RTLD_xxx constants, e.g. os.RTLD_LAZY).\n",
" \n",
" setprofile(...)\n",
" setprofile(function)\n",
" \n",
" Set the profiling function. It will be called on each function call\n",
" and return. See the profiler chapter in the library manual.\n",
" \n",
" setrecursionlimit(...)\n",
" setrecursionlimit(n)\n",
" \n",
" Set the maximum depth of the Python interpreter stack to n. This\n",
" limit prevents infinite recursion from causing an overflow of the C\n",
" stack and crashing Python. The highest possible limit is platform-\n",
" dependent.\n",
" \n",
" setswitchinterval(...)\n",
" setswitchinterval(n)\n",
" \n",
" Set the ideal thread switching delay inside the Python interpreter\n",
" The actual frequency of switching threads can be lower if the\n",
" interpreter executes long sequences of uninterruptible code\n",
" (this is implementation-specific and workload-dependent).\n",
" \n",
" The parameter must represent the desired switching delay in seconds\n",
" A typical value is 0.005 (5 milliseconds).\n",
" \n",
" settrace(...)\n",
" settrace(function)\n",
" \n",
" Set the global debug tracing function. It will be called on each\n",
" function call. See the debugger chapter in the library manual.\n",
"\n",
"DATA\n",
" __stderr__ = <_io.TextIOWrapper name='<stderr>' mode='w' encoding='UTF...\n",
" __stdin__ = <_io.TextIOWrapper name='<stdin>' mode='r' encoding='UTF-8...\n",
" __stdout__ = <_io.TextIOWrapper name='<stdout>' mode='w' encoding='UTF...\n",
" abiflags = 'm'\n",
" api_version = 1013\n",
" argv = ['-c', '-f', '/home/woodring/.ipython/profile_default/security/...\n",
" base_exec_prefix = '/usr'\n",
" base_prefix = '/usr'\n",
" builtin_module_names = ('_ast', '_codecs', '_collections', '_functools...\n",
" byteorder = 'little'\n",
" copyright = 'Copyright (c) 2001-2014 Python Software Foundati...ematis...\n",
" displayhook = <IPython.kernel.zmq.displayhook.ZMQShellDisplayHook obje...\n",
" dont_write_bytecode = False\n",
" exec_prefix = '/usr'\n",
" executable = '/usr/bin/python3'\n",
" flags = sys.flags(debug=0, inspect=0, interactive=0, opt...ing=0, quie...\n",
" float_info = sys.float_info(max=1.7976931348623157e+308, max_...epsilo...\n",
" float_repr_style = 'short'\n",
" hash_info = sys.hash_info(width=64, modulus=2305843009213693...iphash2...\n",
" hexversion = 50594544\n",
" implementation = namespace(cache_tag='cpython-34', hexversion=505...in...\n",
" int_info = sys.int_info(bits_per_digit=30, sizeof_digit=4)\n",
" last_value = UnboundLocalError(\"local variable 'foo' referenced before...\n",
" maxsize = 9223372036854775807\n",
" maxunicode = 1114111\n",
" meta_path = [<class '_frozen_importlib.BuiltinImporter'>, <class '_fro...\n",
" modules = {'IPython': <module 'IPython' from '/usr/lib/python3.4/site-...\n",
" path = ['', '/usr/lib/python34.zip', '/usr/lib/python3.4', '/usr/lib/p...\n",
" path_hooks = [<class 'zipimport.zipimporter'>, <function FileFinder.pa...\n",
" path_importer_cache = {'/home/woodring/.ipython/extensions': FileFinde...\n",
" platform = 'linux'\n",
" prefix = '/usr'\n",
" ps1 = 'In : '\n",
" ps2 = '...: '\n",
" ps3 = 'Out: '\n",
" stderr = <IPython.kernel.zmq.iostream.OutStream object>\n",
" stdin = <_io.TextIOWrapper name='<stdin>' mode='r' encoding='UTF-8'>\n",
" stdout = <IPython.kernel.zmq.iostream.OutStream object>\n",
" thread_info = sys.thread_info(name='pthread', lock='semaphore', versio...\n",
" version = '3.4.2 (default, Oct 8 2014, 13:44:52) \\n[GCC 4.9.1 2014090...\n",
" version_info = sys.version_info(major=3, minor=4, micro=2, releaseleve...\n",
" warnoptions = []\n",
"\n",
"FILE\n",
" (built-in)\n",
"\n",
"\n",
"None\n",
"Help on built-in function exit in module sys:\n",
"\n",
"exit(...)\n",
" exit([status])\n",
" \n",
" Exit the interpreter by raising SystemExit(status).\n",
" If the status is omitted or None, it defaults to zero (i.e., success).\n",
" If the status is an integer, it will be used as the system exit status.\n",
" If it is another kind of object, it will be printed and the system\n",
" exit status will be one (i.e., failure).\n",
"\n",
"None\n"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# import syntax\n",
"\n",
"import sys # bring all of sys into global namespace as sys\n",
"print(sys.path) # use something in sys\n",
"\n",
"import sys as foobar # rename the sys module\n",
"print(foobar.path)\n",
"\n",
"from sys import * # import everything from sys, without having to do sys.something\n",
"from sys import path # only import path from sys\n",
"print(path)\n",
"\n",
"from sys import path as foobar # import argv and rename it\n",
"print(foobar)\n",
"\n",
"print(sys.path is foobar and path is foobar)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['', '/usr/lib/python34.zip', '/usr/lib/python3.4', '/usr/lib/python3.4/plat-linux', '/usr/lib/python3.4/lib-dynload', '/home/woodring/.local/lib/python3.4/site-packages', '/usr/lib/python3.4/site-packages', '/usr/lib/python3.4/site-packages/IPython/extensions']\n",
"['', '/usr/lib/python34.zip', '/usr/lib/python3.4', '/usr/lib/python3.4/plat-linux', '/usr/lib/python3.4/lib-dynload', '/home/woodring/.local/lib/python3.4/site-packages', '/usr/lib/python3.4/site-packages', '/usr/lib/python3.4/site-packages/IPython/extensions']\n",
"['', '/usr/lib/python34.zip', '/usr/lib/python3.4', '/usr/lib/python3.4/plat-linux', '/usr/lib/python3.4/lib-dynload', '/home/woodring/.local/lib/python3.4/site-packages', '/usr/lib/python3.4/site-packages', '/usr/lib/python3.4/site-packages/IPython/extensions']\n",
"['', '/usr/lib/python34.zip', '/usr/lib/python3.4', '/usr/lib/python3.4/plat-linux', '/usr/lib/python3.4/lib-dynload', '/home/woodring/.local/lib/python3.4/site-packages', '/usr/lib/python3.4/site-packages', '/usr/lib/python3.4/site-packages/IPython/extensions']\n",
"True\n"
]
}
],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# some final things to cover \n",
"# (though, this isn't exhaustive, but a few more useful features)\n",
"\n",
"s = set([1, 1, 2, 3, 4, 4]) # sets\n",
"print(s)\n",
"print(3 in s)\n",
"\n",
"def plus_n(n):\n",
" return lambda x: x + n # lambdas are \"anonymous functions\"\n",
" # though, limited to one line expressions in Python\n",
"\n",
"plus_two = plus_n(2) # make a plus two function\n",
"print(plus_two, plus_two(1))\n",
" \n",
"l = [2 ** i for i in range(0, 9) if i > 4] # list expressions\n",
" # basically, map and filter\n",
" # reduce is found in functools module\n",
"print(l)\n",
"\n",
"# also, you can create \"generator functions\" for lazy\n",
"# evaluation\n",
"def infinite_evens():\n",
" i = 0\n",
" while True:\n",
" i = i + 2\n",
" yield i\n",
"evens = infinite_evens() # infinite list of even numbers\n",
"for i, j in zip(range(0, 10), evens):\n",
" print(i, j)\n",
"\n",
"try: # exception handling\n",
" print(undefined)\n",
"except: # you can catch different types of exceptions\n",
" # which I am not showing here\n",
" print('I caught an error!')\n",
"finally:\n",
" print('and some cleanup')\n",
"\n",
"# classes\n",
"class test:\n",
" def __init__(self, k):\n",
" self.j = k\n",
" \n",
" def __call__(self):\n",
" return self.j\n",
" \n",
" def i(self, x):\n",
" self.j = x\n",
" return x + 1\n",
" \n",
"t = test(10)\n",
"print(t)\n",
"print(t())\n",
"print(t.i(2))\n",
"print(t())\n",
"t.foo = print\n",
"t.foo('bar')\n",
"print(dir(t))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{1, 2, 3, 4}\n",
"True\n",
"<function plus_n.<locals>.<lambda> at 0x7faa08116e18> 3\n",
"[32, 64, 128, 256]\n",
"0 2\n",
"1 4\n",
"2 6\n",
"3 8\n",
"4 10\n",
"5 12\n",
"6 14\n",
"7 16\n",
"8 18\n",
"9 20\n",
"I caught an error!\n",
"and some cleanup\n",
"<__main__.test object at 0x7faa08143be0>\n",
"10\n",
"3\n",
"2\n",
"bar\n",
"['__call__', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'foo', 'i', 'j']\n"
]
}
],
"prompt_number": 38
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# OK Break Time #\n",
"## We'll take a break here before we get into the really fun stuff ##\n",
"### Ask Questions, Try things out, Go to the Restroom ###\n",
"### We'll pick up again in a few minutes ###"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Running Python from the command line #\n",
"\n",
"```bash\n",
"$ python some_program.py\n",
"```\n",
"\n",
"It's as easy as that. If you are on bash, you can put `#!/usr/bin/python` on the first line of the program, and then launch it directly if the .py is set as executable.\n",
"\n",
"A better version is `#!/usr/bin/env python`, that way if you have different versions of Python, you can change your path to call the correct one.\n",
"\n",
"# Installing packages that you don't have #\n",
"\n",
"- With Anaconda Python\n",
"```bash\n",
"$ conda install <some package>\n",
"```\n",
"\n",
"- With pip installed\n",
"```bash\n",
"$ pip install --user <some package>\n",
"```\n",
"\n",
"## [PyPI](https://pypi.python.org/pypi) is the standard repository for Python packages ##"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How do you create modules? #\n",
"\n",
"Just create a file named *your_module.py* and you can `import your_module`.\n",
"\n",
"Doing nested submodules is a little more involved, but not that hard. Check the python documentation.\n",
"\n",
"# How about main function? #\n",
"\n",
"You don't really need one, because eveything in the .py will be executed.\n",
"\n",
"Though, if you want an explicit main, here's the code for that:\n",
"\n",
"```python\n",
"if __name__ == '__main__':\n",
" your code goes here\n",
"```\n",
"\n",
"This is useful for putting unit tests directly into \"library\" code.\n",
"\n",
"# How about command line arguments? #\n",
"\n",
"Those are found in the *sys* module.\n",
"\n",
"```python\n",
"import sys\n",
"\n",
"sys.argv # a list of command arguments\n",
"sys.argv[0] # the name of the program\n",
"sys.argv[1:] # everything else\n",
"len(sys.argv) # number of arguments\n",
"```\n",
"\n",
"There are standard modules for parsing command line arguments, to add switches, etc."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# what's next? numpy\n",
"\n",
"import numpy # also, it is standard convention to do\n",
" # import numpy as np\n",
" # I prefer typing numpy, as it is more explicit\n",
"\n",
"A = numpy.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
"print(A)\n",
"\n",
"# OK, so what's so special about that compared to the list?"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 1 2 3 4 5 6 7 8 9 10]\n"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# numpy arrays are fast, almost C speed\n",
"# as long as you do \"large amounts of work\"\n",
"\n",
"import time\n",
"\n",
"AL = range(0, 1000000)\n",
"BL = range(0, 1000000)\n",
"CL = [0] * len(AL)\n",
"\n",
"start = time.time()\n",
"for i in range(0, len(AL)):\n",
" CL[i] = AL[i] + BL[i]\n",
"print(time.time() - start)\n",
"\n",
"A = numpy.array(range(0, 1000000), numpy.int32)\n",
"B = numpy.array(range(0, 1000000), numpy.int32)\n",
"\n",
"start = time.time()\n",
"C = A + B\n",
"print(time.time() - start)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.8321845531463623\n",
"0.001194000244140625"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# numpy allows you to use lists (or any sequence) in place of an\n",
"# array and it will convert it for you\n",
"\n",
"A = numpy.array([1, 2, 3, 4]) # initialized with a list\n",
"B = [1, 2, 3, 4]\n",
"C = A + B\n",
"print(C)\n",
"\n",
"# but, it's better to start with arrays as your data structures\n",
"# if you are going to be using them a lot, rather than converting\n",
"\n",
"# creating arrays from scratch\n",
"A = numpy.empty((5,)) # length of 4\n",
"A = numpy.empty((5,2)) # 4x2 matrix\n",
"A = numpy.zeros((5,2)) # 4x2 matrix of zeroes\n",
"A = numpy.zeros((5,2), numpy.float64) # 4x2 matrix of zeroes, using doubles\n",
"\n",
"# things need to be the name size (or shape) -- or \"broadcastable\"\n",
"C = A + B # going to fail because A and B aren't the same shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[2 4 6 8]\n"
]
},
{
"ename": "ValueError",
"evalue": "operands could not be broadcast together with shapes (5,2) (4,) ",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-41-86277d246645>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[1;31m# things need to be the name size (or shape) -- or \"broadcastable\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m \u001b[0mC\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mA\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mB\u001b[0m \u001b[1;31m# going to fail because A and B aren't the same shape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (5,2) (4,) "
]
}
],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# also, numpy has the ability to get binary data to and from disk\n",
"\n",
"f = open('foo.bin', 'w')\n",
"a = numpy.array([1, 2, 3, 4, 5], numpy.int32)\n",
"\n",
"a.tofile(f)\n",
"f.close()\n",
"\n",
"f = open('foo.bin', 'r')\n",
"b = numpy.fromfile(f, numpy.int32, 5) \n",
"\n",
"f.close()\n",
"\n",
"print(a == b)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ True True True True True]\n"
]
}
],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# numpy notation is similar to array slicing\n",
"# and Matlab and Fortran matrix notation\n",
"\n",
"A = numpy.array(range(0, 10)) # numbers 0..9\n",
"\n",
"V = A[::2] # this is a view (shallow copy)\n",
"V[0] = -10 # slices are views in numpy\n",
"print(V, A) \n",
"B = A.copy() # this is a deep copy of A\n",
"B[0] = 0\n",
"print(B, A)\n",
"\n",
"C = A[::2] + B[::2]\n",
"print(C)\n",
"\n",
"C = A[1:9] * B[:8]\n",
"print(C)\n",
"\n",
"C = A[1:-3] - B[2:-2]\n",
"print(C)\n",
"\n",
"C = A / B[:5] # this is going to fail, because they aren't the same shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[-10 2 4 6 8] [-10 1 2 3 4 5 6 7 8 9]\n",
"[0 1 2 3 4 5 6 7 8 9] [-10 1 2 3 4 5 6 7 8 9]\n",
"[-10 4 8 12 16]\n",
"[ 0 2 6 12 20 30 42 56]\n",
"[-1 -1 -1 -1 -1 -1]\n"
]
},
{
"ename": "ValueError",
"evalue": "operands could not be broadcast together with shapes (10,) (5,) ",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-43-043c324e4d76>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m \u001b[0mC\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mA\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mB\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m# this is going to fail, because they aren't the same shape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (10,) (5,) "
]
}
],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# numpy also supports multi-dimensional arrays\n",
"# default memory layout is:\n",
"# C, row-major, right-most index varies fastest\n",
"\n",
"A = numpy.array(range(0, 8))\n",
"A = numpy.reshape(A, (2, 2, 2)) # change the shape of an array\n",
" # the total size (elements) must be the same\n",
"print(A)\n",
"\n",
"print(A[0,0,0]) # this is different from nested lists\n",
"print(A[1,1,1])\n",
"\n",
"A = numpy.transpose(A, axes=[0,2,1]) # swap around axes\n",
"print(A)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[[0 1]\n",
" [2 3]]\n",
"\n",
" [[4 5]\n",
" [6 7]]]\n",
"0\n",
"7\n",
"[[[0 2]\n",
" [1 3]]\n",
"\n",
" [[4 6]\n",
" [5 7]]]\n"
]
}
],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# numpy also supports \"broadcasting\"\n",
"\n",
"A = numpy.array(range(0, 4))\n",
"A = numpy.reshape(A, (2, 2))\n",
"\n",
"print(A) # a 2x2 matrix\n",
"\n",
"A = A + 1 # 1 is added to all elements\n",
"print(A)\n",
"\n",
"v = numpy.array([-1, 1]) # let's make a vector\n",
"v = numpy.reshape(v, (2, 1)) # a column vector\n",
"print(v)\n",
"\n",
"A = A * v # v gets broadcast over the columns\n",
"print(A)\n",
"\n",
"v = numpy.reshape(v, (1, 2)) # now it's a row vector\n",
"print(v)\n",
"\n",
"A = A - v # v gets broadcast over the rows\n",
"print(A)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[0 1]\n",
" [2 3]]\n",
"[[1 2]\n",
" [3 4]]\n",
"[[-1]\n",
" [ 1]]\n",
"[[-1 -2]\n",
" [ 3 4]]\n",
"[[-1 1]]\n",
"[[ 0 -3]\n",
" [ 4 3]]\n"
]
}
],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# you can broadcast all sorts of ways\n",
"\n",
"A = numpy.array(range(0, 3*3))\n",
"A = numpy.reshape(A, (3, 3))\n",
"\n",
"B = numpy.array(range(0, 3*1))\n",
"B = numpy.reshape(B, (3, 1))\n",
"\n",
"C = A + B\n",
"print(C)\n",
"\n",
"B = numpy.array(range(0, 3*1))\n",
"B = numpy.reshape(B, (1, 3))\n",
"\n",
"C = A + B\n",
"print(C)\n",
"\n",
"A = numpy.array(range(0, 2*2*2))\n",
"A = numpy.reshape(A, (2, 2, 2))\n",
"\n",
"B = numpy.array(range(0, 2*1))\n",
"B = numpy.reshape(B, (1, 1, 2))\n",
"\n",
"C = A + B\n",
"print(C)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 0 1 2]\n",
" [ 4 5 6]\n",
" [ 8 9 10]]\n",
"[[ 0 2 4]\n",
" [ 3 5 7]\n",
" [ 6 8 10]]\n",
"[[[0 2]\n",
" [2 4]]\n",
"\n",
" [[4 6]\n",
" [6 8]]]\n"
]
}
],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# you can use 1D arrays to index into arrays\n",
"\n",
"A = numpy.array(range(0, 4))\n",
"A = numpy.reshape(A, (2, 2))\n",
"\n",
"I = numpy.array([[0], [1], [1], [0]]) # the shape of the output\n",
"J = numpy.array([[0], [0], [1], [1]]) # is the same shape as the indices shape\n",
"print(A[I,J]) # (4, 1)\n",
"print((A[I,J])[3,0])\n",
"\n",
"I = numpy.array([0, 1, 1, 0]) # the shape of the output\n",
"J = numpy.array([0, 0, 1, 1]) # is the same shape as the indices\n",
"print(A[I,J]) # (4,)\n",
"print((A[I,J])[3])\n",
"B = A[I,J]\n",
"\n",
"B[0] = 1000 # indexing creates a copy\n",
"print(A, B)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[0]\n",
" [2]\n",
" [3]\n",
" [1]]\n",
"1\n",
"[0 2 3 1]\n",
"1\n",
"[[0 1]\n",
" [2 3]] [1000 2 3 1]\n"
]
}
],
"prompt_number": 47
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# you can use boolean arrays to filter out elements\n",
"A = numpy.array(range(1, 11))\n",
"b = numpy.array([i % 2 == 0 for i in range(1, 11)]) # all the even elements\n",
"\n",
"print(A[b]) # b is the same shape as A\n",
" # this is stream compaction\n",
" # the output size is equal to the number of Trues\n",
"\n",
"print(numpy.where(b, A, 0)) # where generates the same shape as A\n",
" # but replaces A with 0 where b is False\n",
"\n",
"print(A == 4) # this works\n",
"print(A[b==False]) # and this too\n",
"print(A[A > 5]) # and this\n",
"\n",
"print(numpy.argwhere(A > 5)) # get the indices where A > 5\n",
"\n",
"# there are other options as well, with any, all, logical_*\n",
"# for doing all sorts of indexing"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 2 4 6 8 10]\n",
"[ 0 2 0 4 0 6 0 8 0 10]\n",
"[False False False True False False False False False False]\n",
"[1 3 5 7 9]\n",
"[ 6 7 8 9 10]\n",
"[[5]\n",
" [6]\n",
" [7]\n",
" [8]\n",
" [9]]\n"
]
}
],
"prompt_number": 48
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# numpy has a lot of functionality\n",
"# beyond +, *, - and /\n",
"# http://docs.scipy.org/doc/numpy/reference/ufuncs.html#available-ufuncs\n",
"\n",
"A = numpy.array(range(0, 4))\n",
"\n",
"print(numpy.max(A))\n",
"print(numpy.min(A))\n",
"print(numpy.sign(A))\n",
"print(numpy.cos(A))\n",
"print(A > A)\n",
"print(A == A)\n",
"print(-A)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"3\n",
"0\n",
"[0 1 1 1]\n",
"[ 1. 0.54030231 -0.41614684 -0.9899925 ]\n",
"[False False False False]\n",
"[ True True True True]\n",
"[ 0 -1 -2 -3]\n"
]
}
],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# and a lot of what you want is probably\n",
"# in the linear algebra\n",
"# http://docs.scipy.org/doc/numpy/reference/routines.linalg.html\n",
" \n",
"from numpy.linalg import linalg # a submodule of a module\n",
"# numpy.linalg.linalg \n",
"\n",
"A = numpy.array([[0, 1], [2, 3]])\n",
"B = numpy.array([[0, -1], [1, 0]])\n",
"\n",
"print(linalg.dot(A, B)) # matrix multiply\n",
"print(numpy.outer(A, B)) # outer product\n",
"print(linalg.qr(A)) # qr factorization\n",
"print(linalg.svd(A)) # SVD\n",
"print(linalg.eig(A)) # eigenvectors and values\n",
"print(linalg.inv(A)) # inverse of A\n",
"# etc. "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 1 0]\n",
" [ 3 -2]]\n",
"[[ 0 0 0 0]\n",
" [ 0 -1 1 0]\n",
" [ 0 -2 2 0]\n",
" [ 0 -3 3 0]]\n",
"(array([[ 0., -1.],\n",
" [-1., 0.]]), array([[-2., -3.],\n",
" [ 0., -1.]]))\n",
"(array([[-0.22975292, -0.97324899],\n",
" [-0.97324899, 0.22975292]]), array([ 3.70245917, 0.54018151]), array([[-0.52573111, -0.85065081],\n",
" [ 0.85065081, -0.52573111]]))\n",
"(array([-0.56155281, 3.56155281]), array([[-0.87192821, -0.27032301],\n",
" [ 0.48963374, -0.96276969]]))"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[[-1.5 0.5]\n",
" [ 1. 0. ]]\n"
]
}
],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# next is scipy\n",
"#\n",
"# it has lots of specialized functionality\n",
"# for scientific computing:\n",
"# FFTs, signal processing, integration, statistics,\n",
"# interpolation, optimization, graphs, etc.\n",
"#\n",
"# http://docs.scipy.org/doc/scipy/reference/\n",
"\n",
"from scipy import fftpack\n",
"\n",
"A = numpy.array([0, 1, 2, 3, 4, 3, 2, 1])\n",
"\n",
"print(fftpack.fft(A)) # fft\n",
"print(fftpack.ifft(fftpack.fft(A))) # ifft and fft\n",
"\n",
"from scipy import optimize\n",
"\n",
"B = numpy.array([0, 1, 2, 3, 4, 5, 6, 7])\n",
"\n",
"def poly(x, a, b, c): # the model to fit to\n",
" return a + b*x + c*x*x\n",
"\n",
"print(optimize.curve_fit(poly, B, A)) # outputs a, b, c and covariance matrix"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 16.00000000 +0.00000000e+00j -6.82842712 -2.22044605e-16j\n",
" 0.00000000 -0.00000000e+00j -1.17157288 -2.22044605e-16j\n",
" 0.00000000 +0.00000000e+00j -1.17157288 +2.22044605e-16j\n",
" 0.00000000 +0.00000000e+00j -6.82842712 +2.22044605e-16j]\n",
"[ 0. +0.00000000e+00j 1. +5.55111512e-17j 2. +0.00000000e+00j\n",
" 3. -1.11022302e-16j 4. +0.00000000e+00j 3. -5.55111512e-17j\n",
" 2. +0.00000000e+00j 1. +1.11022302e-16j]\n",
"(array([-0.33333333, 1.85714286, -0.23809524]), array([[ 0.13492064, -0.07142857, 0.00793651],\n",
" [-0.07142857, 0.0600907 , -0.00793651],\n",
" [ 0.00793651, -0.00793651, 0.00113379]]))"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# the fun part, plotting the data\n",
"\n",
"%matplotlib inline \n",
"# this \"magic\" is necessary for ipython notebook\n",
"# it's not necessary (and will be an error)\n",
"# in normal python\n",
"\n",
"# there are other \"magic\" ipython commands, check the documentation\n",
"\n",
"import matplotlib.pyplot as plt # this is all you need in python\n",
" # pyplot is the Matlab like plotting interface\n",
" # plt is the standard rename for pyplot\n",
" \n",
"# examples of plots can be found at http://matplotlib.org/gallery.html"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import functools\n",
"\n",
"A = numpy.array([0, 1, 2, 3, 4, 3, 2, 1])\n",
"B = numpy.array([0, 1, 2, 3, 4, 5, 6, 7])\n",
"\n",
"# our model curve\n",
"def poly(x, a, b, c):\n",
" return a + b*x + c*x*x\n",
"\n",
"abc, cov = optimize.curve_fit(poly, B, A) # going to do the least squares fit like before\n",
"\n",
"fixed = functools.partial(poly, a=abc[0], b=abc[1], c=abc[2]) # freeze the polynomial\n",
"fixed = numpy.vectorize(fixed) # create a vectorized version of the function\n",
"\n",
"# the start of a plot\n",
"# pyplot is Matlab like, it is a state machine\n",
"plt.figure() # start a new plot\n",
"plt.xlabel('x') # labels\n",
"plt.ylabel('y')\n",
"plt.plot(B, fixed(B)) # the x and y values of the model\n",
"plt.legend('model')\n",
"plt.plot(B, A, 'o') # 'o' means plot it with circles\n",
"plt.legend('original')\n",
"plt.title('least squares fit to quadratic model') # a title\n",
"plt.show() # show it\n",
"\n",
"# plt.savefig('foo.png') # write it to an image instead"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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SSXDJJUoOIvnCLCSG116LP0HUhEoQRWjDhlDXud12YfpuzbEkkl9mzoQjjwxT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\n",
"text": [
"<matplotlib.figure.Figure at 0x7fa9e855cc18>"
]
}
],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from scipy import fftpack\n",
"from scipy import fft\n",
"\n",
"A = numpy.array([0, 1, 0, -1, 0, 1, 0, -1])\n",
"\n",
"f = numpy.abs(fftpack.fft(A)) ** 2 # power spectrum\n",
"q = fftpack.fftfreq(A.size, 1.0) # get the frequencies\n",
"i = numpy.argsort(q) # get the indices that would sort the frequencies\n",
"\n",
"plt.figure() # plot it\n",
"plt.title('power spectrum')\n",
"plt.xlabel('frequencies')\n",
"plt.ylabel('power')\n",
"plt.plot(q[i], f[i]) \n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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JM2t5uD33Om1mF5vZKWZ2KvAW4ObGIlAXPpNiaPPmhV8cXgiqwfN8aJ2dYTnq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\nQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7faa081347f0>"
]
}
],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# sqlite is awesome to store your data in\n",
"# stop using custom text files, and use sqlite\n",
"\n",
"import sqlite3\n",
"conn = sqlite3.connect('northwind.db') # connect to the database\n",
"cursor = conn.cursor()\n",
"\n",
"cursor.execute('select CustomerID from customers')\n",
"# this is a pretty simple query, but you can do all sorts\n",
"# of complex SQL queries, using SQLite\n",
"\n",
"# convert the first letter of the customer id to a number\n",
"data = cursor.fetchall()\n",
"customers = [ord(i[0][0]) - ord('A') + 1 for i in data]\n",
"bins = numpy.max(customers) - numpy.min(customers)\n",
"\n",
"# let's plot the distribution\n",
"plt.figure()\n",
"plt.title('distribution of customer ids')\n",
"plt.xlabel('first letter')\n",
"plt.ylabel('count')\n",
"plt.hist(customers, bins)\n",
"plt.show()\n",
"\n",
"# what's the max letter?\n",
"counts = numpy.histogram(customers, bins)[0]\n",
"print(chr(numpy.argmax(counts) + numpy.min(customers) + ord('A')))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7fa9e8194d68>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"W\n"
]
}
],
"prompt_number": 55
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Where to go from here? #\n",
"\n",
"- Lots of features I didn't cover in Matplotlib: [Matplotlib Gallery](http://matplotlib.org/gallery.html)\n",
"- Matplotlib can do 3D visualization\n",
"- More powerful 3D visualization is available through [VTK](http://vtk.org) and [ParaView](http://paraview.org) bindings\n",
"- Example of a really cool and sophisticated visualization and analysis toolchain, checkout [yt](http://yt-project.org/) for Cosmological and Astrophyics analysis\n",
"- To do web style graphics, try [Bokeh](http://bokeh.pydata.org/)\n",
"- You can even script [Blender](http://www.blender.org/documentation/blender_python_api_2_72_release/#blender-python-documentation) for modeling and rendering\n",
"- If you'd rather do it from scratch, there's [PyOpenGL](http://pyopengl.sourceforge.net/)\n",
"- Also, if you're interested, theres [PyGame](http://www.pygame.org/news.html) bindings to the SDL library\n",
"- For data sources, there's tons of bindings to SQL style databases, HDF5, NetCDF, etc. First, try using SQLite3. While it won't scale, it will certainly make smaller data sets managable\n",
"- For big data, cloud databases, etc. try [Blaze](http://blaze.pydata.org/docs/latest/index.html)\n",
"- For GUIs, try [PyQt](https://wiki.python.org/moin/PyQt) (there are other options as well)\n",
"\n",
"## And the list goes on, and on, and on... ##\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Now go out and write some Python #\n",
"\n",
"- From my experience at the lab, there are so many problems that can be solved just with a \"scripting\" language\n",
"- Though, it's unfair to call Python scripting, as many applications are written in Python as the main language\n",
"\n",
"## Too many problems are prematurely optimized ##\n",
"\n",
"- For example, in visualization and analysis, a lot the bottleneck is in the I/O\n",
"- Then, Python will typically go almost as fast as C \n",
"- Faster turn around time in your research if you rapid prototype, and then find the bottlenecks\n",
"- But, Python won't solve all your problems, especially if you are CPU bound or memory-bandwidth bound"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Thanks for your time #\n",
"## Questions? ##"
]
}
],
"metadata": {}
}
]
}