{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"from IPython.display import Image\n",
"from IPython.display import clear_output\n",
"from IPython.display import FileLink, FileLinks"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Introduction to\n",
"\n",
"\n",
"\n",
"### with Application to Bioinformatics\n",
"\n",
"#### - Day 4"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Start by doing today's quiz\n",
"\n",
"Go to Canvas, `Modules -> Day 4 -> Review Day 3`\n",
" \n",
"~20 minutes\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### In what ways does the type of an object matter?\n",
"- Questions 1, 2 and 3"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The price is a string!\n"
]
}
],
"source": [
"row = 'sofa|2000|buy|Uppsala'\n",
"fields = row.split('|')\n",
"price = fields[1]\n",
"if price == 2000:\n",
" print('The price is a number!')\n",
"if price == '2000':\n",
" print('The price is a string!')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[30, 100, 2000]\n"
]
}
],
"source": [
"print(sorted([ 2000, 30, 100 ]))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['100', '2000', '30']\n"
]
}
],
"source": [
"print(sorted(['2000', '30', '100']))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"ord('3')"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### In what ways does the type of an object matter?\n",
"\n",
"- Each type store a specific type of information\n",
" - `int` for integers,\n",
" - `float` for floating point values (decimals),\n",
" - `str` for strings,\n",
" - `list` for lists,\n",
" - `dict` for dictionaries.\n",
"\n",
"- Each type supports different operations, functions and methods."
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"lines_to_next_cell": 0,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"- Each type supports different **operations**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"30 > 2000"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'30' > '2000'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"30 > int('2000')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"'12345'[2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"12345[2]"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"lines_to_next_cell": 0,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"- Each type supports different **functions**"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"data": {
"text/plain": [
"'2'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max('2000')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"ename": "TypeError",
"evalue": "'int' object is not iterable",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mmax\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2000\u001b[39;49m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
]
}
],
"source": [
"max(2000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"import math\n",
"math.cos(3.14)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"math.cos('3.14')"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"lines_to_next_cell": 0,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"- Each type supports different **methods**"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"data": {
"text/plain": [
"'actg'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'ACTG'.lower()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'list' object has no attribute 'lower'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m()\n",
"\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'lower'"
]
}
],
"source": [
"[1, 2, 3].lower()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"set([]).add('tiger')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"[].add('tiger')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- How to find what methods are available: Python documentation, or `dir()`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dir('ACTG') # list all attributes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"dir(str) # list all attributes"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### Convert string to number\n",
"- Questions 4, 5 and 6\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2000.0"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"float('2000')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"float('0.5')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1000000000.0"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"float('1e9')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 2,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"float('1e-2')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"int('2000')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"int('1.5')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 2,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"int('1e9')"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### Convert to boolean: `1`, `0`, `'1'`, `'0'`, `''`, `{}`\n",
"- Question 7\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"bool(1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"bool(0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"bool('0')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"bool('')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"bool([])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"bool({})"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"- Python and the truth: true and false values"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"lines_to_next_cell": 2
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 is true!\n",
"0 is false!\n",
"'' is false!\n",
"'0' is true!\n",
"'1' is true!\n",
"[] is false!\n",
"[0] is true!\n"
]
}
],
"source": [
"values = [1, 0, '', '0', '1', [], [0]]\n",
"for x in values:\n",
" if x:\n",
" print(repr(x), 'is true!')\n",
" else:\n",
" print(repr(x), 'is false!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- `if x` is equivalent to `if bool(x)`"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"- Is `1` equivalent to `True`?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 2,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"1 == True"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 2,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"x = 1\n",
"if x is True:\n",
" print(repr(x), 'is true!')\n",
"else:\n",
" print(repr(x), 'is false!')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 2,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"x = 1\n",
"if bool(x) is True:\n",
" print(repr(x), 'is true!')\n",
"else:\n",
" print(repr(x), 'is false!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"- Be careful: `if x is True` is **not** equivalent to `if bool(x) is True`"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### Container types, when should you use which? (Question 8)\n",
"\n",
"- **lists**: when order is important\n",
"- **dictionaries**: to keep track of the relation between keys and values\n",
"- **sets**: to check for membership. No order, no duplicates."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"data": {
"text/plain": [
"['comedy', 'drama', 'drama', 'sci-fi']"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"genre_list = [\"comedy\", \"drama\", \"drama\", \"sci-fi\"]\n",
"genre_list"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"data": {
"text/plain": [
"{'comedy', 'drama', 'sci-fi'}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"genres = set(genre_list)\n",
"genres"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'drama' in genre_list\n",
"'drama' in genres\n",
"# which operation is faster?"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"lines_to_next_cell": 0,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/plain": [
"{'comedy': 1, 'drama': 2, 'sci-fi': 1}"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"genre_counts = {\"comedy\": 1, \"drama\": 2, \"sci-fi\": 1}\n",
"genre_counts"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'rating': 10.0, 'title': 'Toy Story'}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"movie = {\"rating\": 10.0, \"title\": \"Toy Story\"}\n",
"movie"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### Python syntax (Question 9)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def echo(message): # starts a new function definition\n",
" # this function echos the message \n",
" print(message) # print state of the variable\n",
" return message # return the value to end the function\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### Converting between strings and lists\n",
"- Question 10"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['h', 'e', 'l', 'l', 'o']"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(\"hello\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"str(['h', 'e', 'l', 'l', 'o'])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'h_e_l_l_o'"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'_'.join('hello')"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### TODAY\n",
"\n",
"- More on functions:\n",
" - scope of variables\n",
" - positional arguments and keyword arguments\n",
" - `return` statement\n",
"- Reusing code:\n",
" - comments and documentation\n",
" - importing modules: using libraries\n",
"- Pandas - explore your data!\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### More on functions: scope - global vs local variables\n",
"- Global variables can be accessed inside the function"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"HOST inside the function = global\n",
"HOST outside the function = global\n"
]
}
],
"source": [
"HOST = 'global'\n",
"\n",
"def show_host():\n",
" print(f'HOST inside the function = {HOST}')\n",
"\n",
"show_host()\n",
"print(f'HOST outside the function = {HOST}')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"- Change in the function will not change the global variable"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"HOST outside the function before change = global\n",
"HOST inside the function = local\n",
"HOST outside the function after change = global\n",
"global\n"
]
}
],
"source": [
"HOST = 'global'\n",
"\n",
"def change_host():\n",
" HOST = 'local'\n",
" print(f'HOST inside the function = {HOST}')\n",
"def app2():\n",
" print(HOST)\n",
"print(f'HOST outside the function before change = {HOST}')\n",
"change_host()\n",
"print(f'HOST outside the function after change = {HOST}')\n",
"app2()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"- Pass global variable as argument"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"HOST = 'global'\n",
"\n",
"def change_host(HOST):\n",
" HOST = 'local'\n",
" print(f'HOST inside the function = {HOST}')\n",
"\n",
"print(f'HOST outside the function before change = {HOST}')\n",
"change_host(HOST)\n",
"print(f'HOST outside the function after change = {HOST}')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"### More on functions: scope - global vs local variables cont.\n",
"List as global variables\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"MOVIES = ['Toy story', 'Home alone']\n",
"\n",
"def change_movie():\n",
" MOVIES = ['Fargo', 'The Usual Suspects']\n",
" print(f'MOVIES inside the function = {MOVIES}')\n",
"\n",
"print(f'MOVIES outside the function before change = {MOVIES}')\n",
"change_movie()\n",
"print(f'MOVIES outside the function after change = {MOVIES}')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Will the global variable never to changed by function?"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MOVIES outside the function before change = ['Toy story', 'Home alone']\n",
"MOVIES inside the function = ['Toy story', 'Home alone', 'Fargo', 'The Usual Suspects']\n",
"MOVIES outside the function after change = ['Toy story', 'Home alone', 'Fargo', 'The Usual Suspects']\n"
]
}
],
"source": [
"MOVIES = ['Toy story', 'Home alone']\n",
"\n",
"def change_movie():\n",
" MOVIES.extend(['Fargo', 'The Usual Suspects'])\n",
" print(f'MOVIES inside the function = {MOVIES}')\n",
"\n",
"print(f'MOVIES outside the function before change = {MOVIES}')\n",
"change_movie()\n",
"print(f'MOVIES outside the function after change = {MOVIES}')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Take away: be careful when using global variables. Do not use it unless you know what you are doing."
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"lines_to_next_cell": 1,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### More on functions: `return` statement\n",
"A function that counts the number of occurences of `'C'` in the argument string."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"lines_to_next_cell": 1
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2 \n",
" 0\n"
]
}
],
"source": [
"def cytosine_count(nucleotides):\n",
" count = 0\n",
" for x in nucleotides:\n",
" if x == 'c' or x == 'C':\n",
" count += 1\n",
" return count\n",
"\n",
"count1 = cytosine_count('CATATTAC')\n",
"count2 = cytosine_count('tagtag')\n",
"print(count1, \"\\n\", count2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Functions that `return` are easier to repurpose than those that `print` their result"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"lines_to_next_cell": 1
},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cytosine_count('catattac') + cytosine_count('tactactac')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n",
"0\n"
]
}
],
"source": [
"def print_cytosine_count(nucleotides):\n",
" count = 0\n",
" for x in nucleotides:\n",
" if x == 'c' or x == 'C':\n",
" count += 1\n",
" print(count)\n",
"\n",
"print_cytosine_count('CATATTAC')\n",
"print_cytosine_count('tagtag')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n",
"3\n"
]
},
{
"ename": "TypeError",
"evalue": "unsupported operand type(s) for +: 'NoneType' and 'NoneType'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[27], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mprint_cytosine_count\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcatattac\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mprint_cytosine_count\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtactactac\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'NoneType' and 'NoneType'"
]
}
],
"source": [
"print_cytosine_count('catattac') + print_cytosine_count('tactactac')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"- Functions without any `return` statement returns `None`\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Return value of foo() = None\n"
]
}
],
"source": [
"def foo():\n",
" do_nothing = 1\n",
"\n",
"result = foo()\n",
"print(f'Return value of foo() = {result}')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- Use `return` for all values that you might want to use later in your program"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Small detour: Python's value for missing values: `None`\n",
"\n",
"- Default value for optional arguments\n",
"- Implicit return value of functions without a `return` statement\n",
"- `None` is `None`, not anything else"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"None == 0"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"None == False"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"None == ''"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bool(None)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"NoneType"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(None)"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Keyword arguments\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"fh = open('../files/fruits.txt', mode='w', encoding='utf-8'); fh.close()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[100, 6, 5, 4, 1]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted([1, 4, 100, 5, 6], reverse=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Why do we use keyword arguments?"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['gene_id', 'INSR', '\"insulin receptor\"']"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"record = 'gene_id INSR \"insulin receptor\"'\n",
"\n",
"record.split(' ', 2)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"['gene_id', 'INSR', '\"insulin receptor\"']"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"record.split(sep=' ', maxsplit=2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"* It increases the clarity and readability"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### The order of keyword arguments does not matter"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"fh = open('../files/fruits.txt', mode='w', encoding='utf-8'); fh.close()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"fh = open('../files/fruits.txt', encoding='utf-8', mode='w'); fh.close()"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Can be used in both ways, with or without keyword \n",
"- if there is no ambiguity"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"fh = open('../files/fruits.txt', 'w', encoding='utf-8'); fh.close()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"fh = open('../files/fruits.txt', mode='w', encoding='utf-8'); fh.close()"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### But there are some exceptions "
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "positional argument follows keyword argument (986095044.py, line 1)",
"output_type": "error",
"traceback": [
"\u001b[0;36m Cell \u001b[0;32mIn[42], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m fh = open('files/recipes.txt', encoding='utf-8', 'w'); fh.close()\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
]
}
],
"source": [
"fh = open('files/recipes.txt', encoding='utf-8', 'w'); fh.close()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"* Positional arguments must be in front of keyword arguments"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Restrictions by purpose"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[100, 6, 5, 4, 1]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted([1, 4, 100, 5, 6], reverse=True)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "sorted expected 1 argument, got 2",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[44], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43msorted\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mTypeError\u001b[0m: sorted expected 1 argument, got 2"
]
}
],
"source": [
"sorted([1, 4, 100, 5, 6], True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"sorted(iterable, /, *, key=None, reverse=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- arguments before `/` must be specified with position\n",
"- arguments after `*` must be specified with keyword"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"lines_to_next_cell": 1,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### How to define functions taking keyword arguments\n",
"\n",
"- Just define them as usual:"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The lecture is ongoing.\n"
]
},
{
"ename": "TypeError",
"evalue": "format_sentence() got multiple values for argument 'value'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[45], line 6\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m subject \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m value \u001b[38;5;241m+\u001b[39m end\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(format_sentence(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlecture\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mongoing\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mformat_sentence\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlecture\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m!\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mongoing\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(format_sentence(subject\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlecture\u001b[39m\u001b[38;5;124m'\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mongoing\u001b[39m\u001b[38;5;124m'\u001b[39m, end\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m...\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
"\u001b[0;31mTypeError\u001b[0m: format_sentence() got multiple values for argument 'value'"
]
}
],
"source": [
"def format_sentence(subject, value = 13, end = \"....\"):\n",
" return 'The ' + subject + ' is ' + value + end\n",
"\n",
"print(format_sentence('lecture', 'ongoing', '.'))\n",
"\n",
"print(format_sentence('lecture', '!', value='ongoing'))\n",
"\n",
"print(format_sentence(subject='lecture', value='ongoing', end='...'))"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"lines_to_next_cell": 1,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Defining functions with default arguments"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The lecture is ongoing...\n"
]
}
],
"source": [
"def format_sentence(subject, value, end='.'):\n",
" return 'The ' + subject + ' is ' + value + end\n",
"\n",
"#print(format_sentence('lecture', 'ongoing'))\n",
"\n",
"print(format_sentence('lecture', 'ongoing', '...'))"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"lines_to_next_cell": 1,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Defining functions with optional arguments\n",
"\n",
"- Convention: use the object `None`"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The lecture is ongoing.\n",
"The lecture is ongoing and self-referential!\n"
]
}
],
"source": [
"def format_sentence(subject, value, end='.', second_value=None):\n",
" if second_value is None:\n",
" return 'The ' + subject + ' is ' + value + end\n",
" else:\n",
" return 'The ' + subject + ' is ' + value + ' and ' + second_value + end\n",
"\n",
"print(format_sentence('lecture', 'ongoing'))\n",
"\n",
"print(format_sentence('lecture', 'ongoing', second_value='self-referential', end='!'))"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"#### Comparing `None`\n",
"\n",
"- To differentiate `None` to the other false values such as `0`, `False` and `''` use `is None`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"counts = {'drama': 2, 'romance': 0}\n",
"\n",
"counts.get('romance'), counts.get('thriller')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"counts.get('romance') is None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"counts.get('thriller') is None"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"- Python and the truth, take two"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 2,
"scrolled": true,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"values = [None, 1, 0, '', '0', '1', [], [0]]\n",
"for x in values:\n",
" if x is None:\n",
" print(repr(x), 'is None')\n",
" if not x:\n",
" print(repr(x), 'is false')\n",
" if x:\n",
" print(repr(x), 'is true')"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Exercise 1\n",
"\n",
"- Notebook Day_4_Exercise_1 (~30 minutes)\n",
"- Go to Canvas, `Modules -> Day 4 -> Exercise 1 - day 4`\n",
"\n",
"\n",
"- Extra reading:\n",
" - https://realpython.com/python-kwargs-and-args/\n",
" - https://able.bio/rhett/python-functions-and-best-practices--78aclaa\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### A short note on code structure\n",
"\n",
"- Functions\n",
" - e.g. sum(), print(), open()\n",
"- Modules\n",
" - files containing a collection of functions and methods, e.g. string.py \n",
"- Documentation\n",
" - docstring, comments"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Why functions?\n",
"- Cleaner code\n",
"- Better defined tasks in code\n",
"- Re-usability\n",
"- Better structure"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Why modules?\n",
"\n",
"- Cleaner code\n",
"- Better defined tasks in code\n",
"- Re-usability\n",
"- Better structure"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- Collect all related functions in one file\n",
"- Import a module to use its functions\n",
"- Only need to understand what the functions do, not how"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Example of modules"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'-f'"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import sys\n",
"\n",
"sys.argv[1]"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2024-11-14 14:43:47.521944\n"
]
}
],
"source": [
"from datetime import datetime\n",
"print(datetime.now())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.system(\"ls\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### How to find the right module and instructions?\n",
"\n",
"- Look at the [module index](https://docs.python.org/3/py-modindex.html) for Python standard modules\n",
"- Search [PyPI](http://pypi.org)\n",
"- Search https://www.w3schools.com/python/\n",
"- Ask your colleagues\n",
"- Search the web\n",
"- Use ChatGPT"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- Standard modules: no installation needed\n",
"- Other libraries: install with `pip install` or `conda install`"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### How to understand it?\n",
"- E.g. I want to know how to split a string by the separator `,`"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"text = 'Programming,is,cool'"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function split:\n",
"\n",
"split(sep=None, maxsplit=-1) method of builtins.str instance\n",
" Return a list of the words in the string, using sep as the delimiter string.\n",
" \n",
" sep\n",
" The delimiter according which to split the string.\n",
" None (the default value) means split according to any whitespace,\n",
" and discard empty strings from the result.\n",
" maxsplit\n",
" Maximum number of splits to do.\n",
" -1 (the default value) means no limit.\n",
"\n"
]
}
],
"source": [
"help(text.split)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"['Programming', 'is', 'cool']"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text.split(sep=',')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### For slightly more complicated problems\n",
"- e.g. how to download Python logo from internet with `urllib`, given the URL https://www.python.org/static/img/python-logo@2x.png"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on package urllib:\n",
"\n",
"NAME\n",
" urllib\n",
"\n",
"MODULE REFERENCE\n",
" https://docs.python.org/3.9/library/urllib\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",
"PACKAGE CONTENTS\n",
" error\n",
" parse\n",
" request\n",
" response\n",
" robotparser\n",
"\n",
"FILE\n",
" /Users/kostas/opt/miniconda3/envs/python-workshop-teacher/lib/python3.9/urllib/__init__.py\n",
"\n",
"\n"
]
}
],
"source": [
"import urllib\n",
"\n",
"help(urllib)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- Probably easier to find the answer by searching the web or using ChatGPT"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### One minute exercise \n",
"- get help from ChatGPT (https://chat.openai.com/)\n",
"\n",
"Using Python to download the Python logo from internet with urllib\n",
"providing the url as https://www.python.org/static/img/python-logo@2x.png"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"import urllib.request\n",
"\n",
"url = \"https://www.python.org/static/img/python-logo@2x.png\"\n",
"filename = \"python-logo.png\" # The name you want to give to the downloaded file\n",
"\n",
"urllib.request.urlretrieve(url, filename)\n",
"\n",
"print(\"Download completed.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"help(math.sqrt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"math.sqrt(3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"### Various ways of importing"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"import math\n",
"\n",
"math.sqrt(3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"import math as m\n",
"m.sqrt(3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"from math import sqrt\n",
"sqrt(3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"from pprint import pprint"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Documentation and commenting your code\n"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"def process_file(filename, chrom, pos):\n",
" \"\"\"\n",
" Read a very large vcf file, search for lines matching\n",
" chromosome chrom and position pos.\n",
"\n",
" Print the genotypes of the matching lines.\n",
" \"\"\"\n",
" for line in open(filename):\n",
" if not line.startswith('#'):\n",
" col = line.split('\\t')\n",
" if col[0] == chrom and int(col[1]) == pos:\n",
" print(col[9:])"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function process_file in module __main__:\n",
"\n",
"process_file(filename, chrom, pos)\n",
" Read a very large vcf file, search for lines matching\n",
" chromosome chrom and position pos.\n",
" \n",
" Print the genotypes of the matching lines.\n",
"\n"
]
}
],
"source": [
"help(process_file)"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- This works because somebody has documented the code!"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Your code may have two types of users:\n",
"\n",
"- library users\n",
"- maintainers (maybe yourself!)"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"### Write documentation for both of them!\n",
"\n",
"- library users (docstrings):\n",
" ```python\n",
" \"\"\"\n",
" What does this function do?\n",
" \"\"\"\n",
" ```\n",
"- maintainers (comments):\n",
" ```python\n",
" # implementation details\n",
" ```"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Places for documentation"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''"
},
"source": [
"- At the beginning of the file\n",
"\n",
" ```python\n",
" \"\"\"\n",
" This module provides functions for ...\n",
" \"\"\"\n",
" ```"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''"
},
"source": [
"- At every function definition"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"import random\n",
"def make_list(x):\n",
" \"\"\"Returns a random list of length x.\"\"\"\n",
" li = list(range(x))\n",
" random.shuffle(li)\n",
" return li"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Comments"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''"
},
"source": [
" - Wherever the code is hard to understand\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"my_list[5] += other_list[3] # explain why you do this!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"### Demo: write a Python script with documentation and use it"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"from files import mywork\n",
"mywork.pipeline([\"accctt\", \"gaccct\"])"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Read more:\n",
"\n",
"https://realpython.com/documenting-python-code/\n",
"\n",
"https://www.python.org/dev/peps/pep-0008/?#comments"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Quiz time\n",
"\n",
"Go to Canvas, `Modules -> Day 4 -> PyQuiz 4.1`\n",
"\n",
"~10 min"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Lunch"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"title = 'Toy Story'\n",
"rating = 10\n",
"print('The result is: ' + title + ' with rating: ' + str(rating))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"# f-strings (since python 3.6)\n",
"print(f'The result is: {title} with rating: {rating}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"# format method\n",
"print('The result is: {} with rating: {}'.format(title, rating))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"# the ancient way (python 2)\n",
"print('The result is: %s with rating: %s' % (title, rating))"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"Learn more from the Python docs: https://docs.python.org/3.9/library/string.html#format-string-syntax"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"#### Exercise 2 (??) wrong example?\n",
"\n",
"\n",
"```py\n",
"pick_movie(year=1996, rating_min=8.5)\n",
"The Bandit\n",
"pick_movie(rating_max=8.0, genre=\"Mystery\")\n",
"Twelve Monkeys\n",
"```\n",
"\n",
"- Notebook Day_4_Exercise_2"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"#### Exercise 2\n",
"\n",
"\n",
"Documentation\n",
"\n",
"- Notebook Day_4_Exercise_2"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Pandas!!!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Pandas\n",
"- Library for working with tabular data\n",
"- Data analysis: \n",
" - filter\n",
" - transform\n",
" - aggregate\n",
" - plot\n",
"- Main hero: the `DataFrame` type"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### DataFrame "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Creating a small DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"\n",
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" 1 \n",
" 2 \n",
" 3 \n",
" 35 \n",
" \n",
" \n",
" 2 \n",
" 3 \n",
" 5 \n",
" 40 \n",
" \n",
" \n",
" 3 \n",
" 4 \n",
" 10 \n",
" 50 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" age \n",
" circumference \n",
" height \n",
" \n",
" \n",
" \n",
" \n",
" count \n",
" 4.000000 \n",
" 4.000000 \n",
" 4.000000 \n",
" \n",
" \n",
" mean \n",
" 2.500000 \n",
" 5.000000 \n",
" 38.750000 \n",
" \n",
" \n",
" std \n",
" 1.290994 \n",
" 3.559026 \n",
" 8.539126 \n",
" \n",
" \n",
" min \n",
" 1.000000 \n",
" 2.000000 \n",
" 30.000000 \n",
" \n",
" \n",
" 25% \n",
" 1.750000 \n",
" 2.750000 \n",
" 33.750000 \n",
" \n",
" \n",
" 50% \n",
" 2.500000 \n",
" 4.000000 \n",
" 37.500000 \n",
" \n",
" \n",
" 75% \n",
" 3.250000 \n",
" 6.250000 \n",
" 42.500000 \n",
" \n",
" \n",
" max \n",
" 4.000000 \n",
" 10.000000 \n",
" 50.000000 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" age \n",
" circumference \n",
" height \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 1 \n",
" 2 \n",
" 30 \n",
" \n",
" \n",
" 1 \n",
" 2 \n",
" 3 \n",
" 35 \n",
" \n",
" \n",
" 2 \n",
" 3 \n",
" 5 \n",
" 40 \n",
" \n",
" \n",
" 3 \n",
" 4 \n",
" 10 \n",
" 50 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" age \n",
" circumference \n",
" height \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 1 \n",
" 2 \n",
" 30 \n",
" \n",
" \n",
" 1 \n",
" 2 \n",
" 3 \n",
" 35 \n",
" \n",
" \n",
" 2 \n",
" 3 \n",
" 5 \n",
" 40 \n",
" \n",
" \n",
" 3 \n",
" 4 \n",
" 10 \n",
" 50 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" age \n",
" height \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 1 \n",
" 30 \n",
" \n",
" \n",
" 1 \n",
" 2 \n",
" 35 \n",
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" \n",
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" 40 \n",
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" 50 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" height \n",
" age \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 30 \n",
" 1 \n",
" \n",
" \n",
" 1 \n",
" 35 \n",
" 2 \n",
" \n",
" \n",
" 2 \n",
" 40 \n",
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" \n",
" \n",
" 3 \n",
" 50 \n",
" 4 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" age \n",
" circumference \n",
" height \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 1 \n",
" 2 \n",
" 30 \n",
" \n",
" \n",
" 1 \n",
" 2 \n",
" 3 \n",
" 35 \n",
" \n",
" \n",
" 2 \n",
" 3 \n",
" 5 \n",
" 40 \n",
" \n",
" \n",
" 3 \n",
" 4 \n",
" 10 \n",
" 50 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" age \n",
" circumference \n",
" height \n",
" \n",
" \n",
" \n",
" \n",
" 1 \n",
" 2 \n",
" 3 \n",
" 35 \n",
" \n",
" \n",
" 2 \n",
" 3 \n",
" 5 \n",
" 40 \n",
" \n",
" \n",
" 3 \n",
" 4 \n",
" 10 \n",
" 50 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" age \n",
" circumference \n",
" height \n",
" \n",
" \n",
" \n",
" \n",
" 1 \n",
" 2 \n",
" 3 \n",
" 35 \n",
" \n",
" \n",
" 3 \n",
" 4 \n",
" 10 \n",
" 50 \n",
" \n",
" \n",
" 0 \n",
" 1 \n",
" 2 \n",
" 30 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" age \n",
" circumference \n",
" height \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 1 \n",
" 2 \n",
" 30 \n",
" \n",
" \n",
" 1 \n",
" 2 \n",
" 3 \n",
" 35 \n",
" \n",
" \n",
" 2 \n",
" 3 \n",
" 5 \n",
" 40 \n",
" \n",
" \n",
" 3 \n",
" 4 \n",
" 10 \n",
" 50 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" age \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 1 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" age \n",
" circumference \n",
" \n",
" \n",
" \n",
" \n",
" count \n",
" 4.000000 \n",
" 4.000000 \n",
" \n",
" \n",
" mean \n",
" 2.500000 \n",
" 5.000000 \n",
" \n",
" \n",
" std \n",
" 1.290994 \n",
" 3.559026 \n",
" \n",
" \n",
" min \n",
" 1.000000 \n",
" 2.000000 \n",
" \n",
" \n",
" 25% \n",
" 1.750000 \n",
" 2.750000 \n",
" \n",
" \n",
" 50% \n",
" 2.500000 \n",
" 4.000000 \n",
" \n",
" \n",
" 75% \n",
" 3.250000 \n",
" 6.250000 \n",
" \n",
" \n",
" max \n",
" 4.000000 \n",
" 10.000000 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" age \n",
" circumference \n",
" height \n",
" radius \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 1 \n",
" 2 \n",
" 30 \n",
" 0.318310 \n",
" \n",
" \n",
" 1 \n",
" 2 \n",
" 3 \n",
" 35 \n",
" 0.477465 \n",
" \n",
" \n",
" 2 \n",
" 3 \n",
" 5 \n",
" 40 \n",
" 0.795775 \n",
" \n",
" \n",
" 3 \n",
" 4 \n",
" 10 \n",
" 50 \n",
" 1.591549 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" age \n",
" circumference \n",
" height \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 1 \n",
" 2 \n",
" 30 \n",
" \n",
" \n",
" 1 \n",
" 2 \n",
" 3 \n",
" 35 \n",
" \n",
" \n",
" 2 \n",
" 3 \n",
" 5 \n",
" 40 \n",
" \n",
" \n",
" 3 \n",
" 4 \n",
" 10 \n",
" 50 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" name \n",
" price \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" palm \n",
" 1423 \n",
" \n",
" \n",
" 1 \n",
" ada \n",
" 2000 \n",
" \n",
" \n",
" 2 \n",
" ek \n",
" 102 \n",
" \n",
" \n",
" 3 \n",
" olive \n",
" 30 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" name \n",
" price \n",
" age \n",
" circumference \n",
" height \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" palm \n",
" 1423.0 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" \n",
" \n",
" 1 \n",
" ada \n",
" 2000.0 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" \n",
" \n",
" 2 \n",
" ek \n",
" 102.0 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" \n",
" \n",
" 3 \n",
" olive \n",
" 30.0 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" \n",
" \n",
" 4 \n",
" NaN \n",
" NaN \n",
" 1.0 \n",
" 2.0 \n",
" 30.0 \n",
" \n",
" \n",
" 5 \n",
" NaN \n",
" NaN \n",
" 2.0 \n",
" 3.0 \n",
" 35.0 \n",
" \n",
" \n",
" 6 \n",
" NaN \n",
" NaN \n",
" 3.0 \n",
" 5.0 \n",
" 40.0 \n",
" \n",
" \n",
" 7 \n",
" NaN \n",
" NaN \n",
" 4.0 \n",
" 10.0 \n",
" 50.0 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" Tree \n",
" age \n",
" circumference \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 1 \n",
" 118 \n",
" 30 \n",
" \n",
" \n",
" 1 \n",
" 1 \n",
" 484 \n",
" 58 \n",
" \n",
" \n",
" 2 \n",
" 1 \n",
" 664 \n",
" 87 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" Tree \n",
" age \n",
" circumference \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 1 \n",
" 118 \n",
" 30 \n",
" \n",
" \n",
" 1 \n",
" 1 \n",
" 484 \n",
" 58 \n",
" \n",
" \n",
" 2 \n",
" 1 \n",
" 664 \n",
" 87 \n",
" \n",
" \n",
" 3 \n",
" 1 \n",
" 1004 \n",
" 115 \n",
" \n",
" \n",
" 4 \n",
" 1 \n",
" 1231 \n",
" 120 \n",
" \n",
" \n",
" 5 \n",
" 1 \n",
" 1372 \n",
" 142 \n",
" \n",
" \n",
" 6 \n",
" 1 \n",
" 1582 \n",
" 145 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" Tree \n",
" age \n",
" circumference \n",
" \n",
" \n",
" \n",
" \n",
" 2 \n",
" 1 \n",
" 664 \n",
" 87 \n",
" \n",
" \n",
" 3 \n",
" 1 \n",
" 1004 \n",
" 115 \n",
" \n",
" \n",
" 4 \n",
" 1 \n",
" 1231 \n",
" 120 \n",
" \n",
" \n",
" 5 \n",
" 1 \n",
" 1372 \n",
" 142 \n",
" \n",
" \n",
" 6 \n",
" 1 \n",
" 1582 \n",
" 145 \n",
" \n",
" \n",
" 9 \n",
" 2 \n",
" 664 \n",
" 111 \n",
" \n",
" \n",
" 10 \n",
" 2 \n",
" 1004 \n",
" 156 \n",
" \n",
" \n",
" 11 \n",
" 2 \n",
" 1231 \n",
" 172 \n",
" \n",
" \n",
" 12 \n",
" 2 \n",
" 1372 \n",
" 203 \n",
" \n",
" \n",
" 13 \n",
" 2 \n",
" 1582 \n",
" 203 \n",
" \n",
" \n",
" 16 \n",
" 3 \n",
" 664 \n",
" 75 \n",
" \n",
" \n",
" 17 \n",
" 3 \n",
" 1004 \n",
" 108 \n",
" \n",
" \n",
" 18 \n",
" 3 \n",
" 1231 \n",
" 115 \n",
" \n",
" \n",
" 19 \n",
" 3 \n",
" 1372 \n",
" 139 \n",
" \n",
" \n",
" 20 \n",
" 3 \n",
" 1582 \n",
" 140 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" Tree \n",
" age \n",
" circumference \n",
" \n",
" \n",
" \n",
" \n",
" 2 \n",
" 1 \n",
" 664 \n",
" 87 \n",
" \n",
" \n",
" 16 \n",
" 3 \n",
" 664 \n",
" 75 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" Tree \n",
" age \n",
" circumference \n",
" \n",
" \n",
" \n",
" \n",
" 12 \n",
" 2 \n",
" 1372 \n",
" 203 \n",
" \n",
" \n",
" 13 \n",
" 2 \n",
" 1582 \n",
" 203 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" age \n",
" circumference \n",
" height \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 1 \n",
" 2 \n",
" 30 \n",
" \n",
" \n",
" 1 \n",
" 2 \n",
" 3 \n",
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" \n",
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" 2 \n",
" 3 \n",
" 5 \n",
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" \n",
" \n",
" 3 \n",
" 4 \n",
" 10 \n",
" 50 \n",
" \n",
" \n",
"
\n",
"
"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"small_df.plot(x='age', y='circumference', kind='line') # plot the relationship of age and height\n",
"# try with other types of plots, e.g. scatter"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Tips: what if no plots shows up?"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Plotting - bars"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"small_df[['age']].plot(kind='bar')"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Plotting multiple columns"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"small_df[['circumference', 'age']].plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"df[['circumference', 'age']].plot(kind='bar', figsize=(12, 8), fontsize=16)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Plotting histogram"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"small_df.plot(kind='hist', y = 'age', fontsize=18)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Plotting box"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"small_df.plot(kind='box', y = 'age')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Further reading: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"#### Scatterplot\n",
"\n",
"```py\n",
" df.plot(kind=\"scatter\", x=\"column_name\", y=\"other_column_name\")\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"df.plot(kind=\"scatter\", x='age', y='circumference',\n",
" figsize=(12, 8), fontsize=14)"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"#### Line plot\n",
"```py\n",
"dataframe.plot(kind=\"line\", x=..., y=...)\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"tree1 = df[df['Tree'] == 1]\n",
"tree1.plot(kind=\"line\", x='age', y='circumference',\n",
" fontsize=14, figsize=(12,8))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"#### Multiple graphs - grouping"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"df.groupby('Tree')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"df.groupby('Tree').plot(kind=\"line\", x='age', y='circumference')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"df.groupby('Tree').groups"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_marker": "'''",
"lines_to_next_cell": 2,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Exercise 2 (~30 minutes)\n",
"- Go to Canvas, `Modules -> Day 4 -> Exercise 2 - day 4` \n",
"- **Easy**:\n",
" - Explore the `Orange_1.tsv`\n",
"- **Medium/hard**:\n",
" - Use Pandas to read IMDB\n",
" - Explore it by making graphs\n",
"- **Extra exercises**:\n",
" - Read the pandas documentation :)\n",
" - Start exploring your own data\n",
"- After exercise, do Quiz 4.2 and then take a break\n",
"- After break, working on the project"
]
}
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