mirror of
https://github.com/donnemartin/data-science-ipython-notebooks.git
synced 2024-03-22 13:30:56 +08:00
1329 lines
26 KiB
Plaintext
1329 lines
26 KiB
Plaintext
{
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"metadata": {
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"name": "",
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"signature": "sha256:a19ef4e563c399be8f5e56aff348924149bc045dc0b070bd846325354013e9ed"
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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"worksheets": [
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Data Structures"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"* tuple\n",
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"* list\n",
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"* dict"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## tuple"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"One dimensional, fixed-length, immutable sequence"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"tup = (1, 2, 3)\n",
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"tup"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 1,
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"text": [
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"(1, 2, 3)"
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]
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}
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],
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"prompt_number": 1
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"list_1 = [1, 2, 3]"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 2
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Convert to a tuple"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"type(tuple(list_1))"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 3,
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"text": [
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"tuple"
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]
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}
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],
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"prompt_number": 3
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Nested tuples"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"nested_tup = ([1, 2, 3], (4, 5))\n",
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"nested_tup"
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],
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"language": "python",
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"metadata": {},
|
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"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 4,
|
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"text": [
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"([1, 2, 3], (4, 5))"
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]
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}
|
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],
|
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"prompt_number": 4
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|
},
|
|
{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Access by index O(1)"
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|
]
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|
},
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{
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"cell_type": "code",
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"collapsed": false,
|
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"input": [
|
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"nested_tup[0]"
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],
|
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"language": "python",
|
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"metadata": {},
|
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"outputs": [
|
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{
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"metadata": {},
|
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"output_type": "pyout",
|
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"prompt_number": 5,
|
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"text": [
|
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"[1, 2, 3]"
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]
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}
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],
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"prompt_number": 5
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Although tuples are immutable, their contents can contain mutable objects"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"nested_tup[0].append(4)\n",
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"nested_tup[0]"
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],
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"language": "python",
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"metadata": {},
|
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"outputs": [
|
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{
|
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"metadata": {},
|
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"output_type": "pyout",
|
|
"prompt_number": 6,
|
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"text": [
|
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"[1, 2, 3, 4]"
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]
|
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}
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],
|
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"prompt_number": 6
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},
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|
{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"Concatenate tuples by creating a new tuple and copying objects"
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|
]
|
|
},
|
|
{
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"cell_type": "code",
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"collapsed": false,
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|
"input": [
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"(1, 3, 2) + (4, 5, 6)"
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],
|
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"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
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"output_type": "pyout",
|
|
"prompt_number": 7,
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"text": [
|
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"(1, 3, 2, 4, 5, 6)"
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]
|
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}
|
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],
|
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"prompt_number": 7
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Multiply copies references to objects (objects themselves are not copied)"
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]
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},
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|
{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"('foo', 'bar') * 2"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
|
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{
|
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"metadata": {},
|
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"output_type": "pyout",
|
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"prompt_number": 8,
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"text": [
|
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"('foo', 'bar', 'foo', 'bar')"
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]
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}
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],
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"prompt_number": 8
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Unpack tuples"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"a, b = nested_tup\n",
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"a, b"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
|
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"output_type": "pyout",
|
|
"prompt_number": 9,
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"text": [
|
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"([1, 2, 3, 4], (4, 5))"
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]
|
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}
|
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],
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"prompt_number": 9
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},
|
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{
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"cell_type": "markdown",
|
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"metadata": {},
|
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"source": [
|
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"Unpack nested tuples"
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]
|
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},
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{
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"cell_type": "code",
|
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"collapsed": false,
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"input": [
|
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"(a, b, c, d), (e, f) = nested_tup\n",
|
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"a, b, c, d, e, f"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 10,
|
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"text": [
|
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"(1, 2, 3, 4, 4, 5)"
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]
|
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}
|
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],
|
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"prompt_number": 10
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},
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{
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"cell_type": "markdown",
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"metadata": {},
|
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"source": [
|
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"A common use of variable unpacking is when iterating over sequences of tuples or lists"
|
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]
|
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},
|
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{
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"cell_type": "code",
|
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"collapsed": false,
|
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"input": [
|
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"seq = [( 1, 2, 3), (4, 5, 6), (7, 8, 9)] \n",
|
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"for a, b, c in seq: \n",
|
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" print(a, b, c)"
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],
|
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"language": "python",
|
|
"metadata": {},
|
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"outputs": [
|
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{
|
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"output_type": "stream",
|
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"stream": "stdout",
|
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"text": [
|
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"(1, 2, 3)\n",
|
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"(4, 5, 6)\n",
|
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"(7, 8, 9)\n"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 11
|
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},
|
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"## list"
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]
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},
|
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{
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"cell_type": "markdown",
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|
"metadata": {},
|
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"source": [
|
|
"One dimensional, variable-length, mutable sequence"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"list_1 = [1, 2, 3]\n",
|
|
"list_1"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 12,
|
|
"text": [
|
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"[1, 2, 3]"
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|
]
|
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}
|
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],
|
|
"prompt_number": 12
|
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},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Convert to a list"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"type(list(tup))"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 13,
|
|
"text": [
|
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"list"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 13
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Nested list"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"nested_list = [(1, 2, 3), [4, 5]]\n",
|
|
"nested_list"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 14,
|
|
"text": [
|
|
"[(1, 2, 3), [4, 5]]"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 14
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Access by index"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
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"nested_list[1]"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 15,
|
|
"text": [
|
|
"[4, 5]"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 15
|
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},
|
|
{
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"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
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"Append an element O(1)"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"nested_list.append(6)\n",
|
|
"nested_list"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 16,
|
|
"text": [
|
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"[(1, 2, 3), [4, 5], 6]"
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|
]
|
|
}
|
|
],
|
|
"prompt_number": 16
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Insert an element at a specific index. Insert is expensive as it has to shift subsequent elements O(n)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"nested_list.insert(0, 'start')\n",
|
|
"nested_list"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 17,
|
|
"text": [
|
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"['start', (1, 2, 3), [4, 5], 6]"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 17
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Pop removes and returns an element from a specified index. Pop is expensive as it has to shift subsequent elements O(n). O(1) if pop is used for the last element"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"nested_list.pop(0)\n",
|
|
"nested_list"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 18,
|
|
"text": [
|
|
"[(1, 2, 3), [4, 5], 6]"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 18
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Remove locates the first such value and removes it O(n)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"nested_list.remove((1, 2, 3))\n",
|
|
"nested_list"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 19,
|
|
"text": [
|
|
"[[4, 5], 6]"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 19
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Check if a list contains a value O(n)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"6 in nested_list"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 20,
|
|
"text": [
|
|
"True"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 20
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Concatenate lists by creating a new list and copying objects"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"[1, 3, 2] + [4, 5, 6]"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 21,
|
|
"text": [
|
|
"[1, 3, 2, 4, 5, 6]"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 21
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Extend a list by appending elements. Faster than concatenating lists."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"nested_list.extend([7, 8, 9])\n",
|
|
"nested_list"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 22,
|
|
"text": [
|
|
"[[4, 5], 6, 7, 8, 9]"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 22
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## dict"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Also known as a hash map or associative array. A dict is a mutable collection of key-value pairs.\n",
|
|
"\n",
|
|
"Big O complexities are listed as average case, with most worst case complexities being O(n)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"dict_1 = { 'a' : 'foo', 'b' : [0, 1, 2, 3] }\n",
|
|
"dict_1"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 23,
|
|
"text": [
|
|
"{'a': 'foo', 'b': [0, 1, 2, 3]}"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 23
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Access by index O(1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"dict_1['b']"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 24,
|
|
"text": [
|
|
"[0, 1, 2, 3]"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 24
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Insert or set by index O(1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"dict_1[5] = 'bar'\n",
|
|
"dict_1"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 25,
|
|
"text": [
|
|
"{5: 'bar', 'a': 'foo', 'b': [0, 1, 2, 3]}"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 25
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Check if a dict contains a key O(1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"5 in dict_1"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 26,
|
|
"text": [
|
|
"True"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 26
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Delete a value O(1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"dict_2 = dict(dict_1)\n",
|
|
"del dict_2[5]\n",
|
|
"dict_2"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 27,
|
|
"text": [
|
|
"{'a': 'foo', 'b': [0, 1, 2, 3]}"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 27
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Pop a value O(1) deletes the key and returns the value"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"value = dict_2.pop('b')\n",
|
|
"print(value)\n",
|
|
"print(dict_2)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"[0, 1, 2, 3]\n",
|
|
"{'a': 'foo'}\n"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 28
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Get or pop with a default value if the key is not found. By default, get() will return None and pop() will throw an exception if the key is not found."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"value = dict_1.get('z', 0)\n",
|
|
"value"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 29,
|
|
"text": [
|
|
"0"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 29
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"setdefault() is similar to get(), but returns a default value if the key is not found"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"print(dict_1.setdefault('b', None))\n",
|
|
"print(dict_1.setdefault('z', None))"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"[0, 1, 2, 3]\n",
|
|
"None\n"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 30
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"By contrast to setdefault(), defaultdict lets you specify the default when the container is initialized, which works well if the default is appropriate for all keys."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"from collections import defaultdict\n",
|
|
"\n",
|
|
"seq = ['foo', 'bar', 'baz']\n",
|
|
"first_letter = defaultdict(list)\n",
|
|
"for elem in seq:\n",
|
|
" first_letter[elem[0]].append(elem)\n",
|
|
"first_letter"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 31,
|
|
"text": [
|
|
"defaultdict(<type 'list'>, {'b': ['bar', 'baz'], 'f': ['foo']})"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 31
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"dict keys must be \"hashable\": immutable objects like scalars (int, float, string) or tuples whose objects are all immutable."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"print(hash('string'))\n",
|
|
"print(hash((1, 2, (3, 4))))"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"-9167918882415130555\n",
|
|
"-2725224101759650258\n"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 32
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Lists are mutable and therefore are not hashable, although you can convert the list portion to a tuple as a quick fix"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"hash((1, 2, [3, 4]))"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"ename": "TypeError",
|
|
"evalue": "unhashable type: 'list'",
|
|
"output_type": "pyerr",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
|
|
"\u001b[0;32m<ipython-input-33-94f25bbf31b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhash\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
|
"\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 33
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Get the list of keys in no particular order (although keys() outputs the keys in the same order). In Python 3, keys() returns an iterator instead of a list."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"dict_1.keys()"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 34,
|
|
"text": [
|
|
"['a', 'b', 5, 'z']"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 34
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Get the list of values in no particular order (although values() outputs the keys in the same order). In Python 3, keys() returns an iterator instead of a list."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"dict_1.values()"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 35,
|
|
"text": [
|
|
"['foo', [0, 1, 2, 3], 'bar', None]"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 35
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Merge one dict into another"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"dict_1.update({'e' : 'elephant', 'f' : 'fish'})\n",
|
|
"dict_1"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 36,
|
|
"text": [
|
|
"{5: 'bar',\n",
|
|
" 'a': 'foo',\n",
|
|
" 'b': [0, 1, 2, 3],\n",
|
|
" 'e': 'elephant',\n",
|
|
" 'f': 'fish',\n",
|
|
" 'z': None}"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 36
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"A common operation is to pair up two sequences element-wise in a dict"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"mapping = dict(zip(range(7), reversed(range(7))))\n",
|
|
"mapping"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 37,
|
|
"text": [
|
|
"{0: 6, 1: 5, 2: 4, 3: 3, 4: 2, 5: 1, 6: 0}"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 37
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## set"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"A set is an unordered sequence of unique elements. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"set_1 = set([0, 1, 2, 3, 4, 5])\n",
|
|
"set_1"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 38,
|
|
"text": [
|
|
"{0, 1, 2, 3, 4, 5}"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 38
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"set_2 = {1, 2, 3, 5, 8, 13}\n",
|
|
"set_2"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 39,
|
|
"text": [
|
|
"{1, 2, 3, 5, 8, 13}"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 39
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Sets support set operations like union, intersection, difference, and symmetric difference"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Union O(len(set_1) + len(set_2))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"set_1 | set_2"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 40,
|
|
"text": [
|
|
"{0, 1, 2, 3, 4, 5, 8, 13}"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 40
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Intersection O(min(len(set_1), len(set_2))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"set_1 & set_2"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 41,
|
|
"text": [
|
|
"{1, 2, 3, 5}"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 41
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Difference O(len(set_1))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"set_1 - set_2"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 42,
|
|
"text": [
|
|
"{0, 4}"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 42
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Symmetric Difference O(len(set_1))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"set_1 ^ set_2"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 43,
|
|
"text": [
|
|
"{0, 4, 8, 13}"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 43
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Subset"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"set_3 = {1, 2, 3}\n",
|
|
"set_3.issubset(set_2)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 44,
|
|
"text": [
|
|
"True"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 44
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Superset"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"set_2.issuperset(set_3)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 45,
|
|
"text": [
|
|
"True"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 45
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Equal"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"{1, 2, 3} == {3, 2, 1}"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 46,
|
|
"text": [
|
|
"True"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 46
|
|
}
|
|
],
|
|
"metadata": {}
|
|
}
|
|
]
|
|
} |