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Changes from weeks 1 to 3 and removing unneeded data files
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‎Week_1_data_structures/Lecture_1_data_structures.ipynb‎

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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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"# Lecture 1: Jupyter Notebooks, lists, dictionaries, numpy arrays\n",
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"\n",
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"Also see the tutorials for lists and dictionaries. This lecture will focus on Fun Things with Numpy arrays.\n",
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"We're going to do stuff with lists, first, because it's easier to understand. After this lecture, you should always use numpy arrays, not lists, when storing/manipulating arrays of numbers."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Some typical list-based ways to store a list of numbers\n",
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"a_list = [0.0, 1.0, 2.0, 3.0]\n",
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"b_list = [-2.0, -1.0, 0.0, 0.2]\n",
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"\n",
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"print(a_list)\n",
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"print(b_list)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# print one element of the list (notice index, 1)\n",
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"print(a_list[1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Calculate the sum of a list\n",
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"my_sum = 0\n",
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"for e in a_list:\n",
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" # e is set to a_list[i] where i is 0, 1, 2 etc\n",
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" my_sum += e\n",
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"print(f\"Sum: {my_sum}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Perform a mathematical operation on each element in the list \n",
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"# In this case, 2 * x + 1\n",
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"#. Notice how we create a list to put the result in, and use append (rather than creating the list first)\n",
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"operate_on_a_list = []\n",
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"for e in a_list:\n",
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" operate_on_a_list.append( 2 * e + 1)\n",
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"print(operate_on_a_list)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Iterate through two lists together\n",
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"add_lists = []\n",
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"for a, b in zip(a_list, b_list):\n",
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" add_lists.append(a + b)\n",
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"print(f\"Adding lists: {add_lists}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# See if 2.0 is in the list\n",
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"b_found_two = False\n",
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"for a in a_list:\n",
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" if abs(a - 2.0) < 1e-12:\n",
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" b_found_two = True\n",
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"print(f\"Found 2: {b_found_two}\")"
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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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"## Just say NO to lists for mathematical operations on arrays of numbers\n",
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"Use numpy.\n",
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"Do numpy tutorial before lab."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import the library numpy and declare that you want to access numpy as np.[blah]\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"a_np_array = np.array(a_list) # Shortcut way to create a numpy array from a list\n",
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"b_np_array = np.array(b_list)\n",
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"print(a_np_array) # Notice different formatting from a list\n",
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"print(b_np_array)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Calculate the sum of a list\n",
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"print(f\"Sum: {np.sum(a_np_array)}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Perform a mathematical operation on each element in the list \n",
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"# In this case, 2 * x + 1\n",
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"#. Look - no for loop!!!\n",
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"operate_on_an_np_array = 2 * a_np_array + 1\n",
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"print(operate_on_an_np_array)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Add two lists together\n",
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"print(f\"Adding lists: {a_np_array + b_np_array}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# See if 2.0 is in the list\n",
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"#. isclose and any are a bit complicated to use, but very powerful\n",
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"all_twos = np.isclose(a_np_array, 2.0)\n",
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"if all_twos.any():\n",
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" print(\"Found a 2\")\n",
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"print(f\"Found 2: {all_twos}\")"
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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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"## Dictionaries\n",
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"See tutorial on dictionaries.\n",
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"\n",
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"Dictionaries are the heart of Python - pretty much everything in Python is a dictionary.\n",
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"\n",
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"Basic idea: Store data by a key (name) and value (what's in that key)\n",
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"\n",
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"Difference to lists: You can think of a list as a dictionary where all the keys are the numbers 0..n-1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Notice curly brackets\n",
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"my_dict = {\"key 1\": [-0.2, 0.2], \"key 2\": \"value\"}\n",
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"print(my_dict)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Getting one element out of the dictionary\n",
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"print(my_dict[\"key 1\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for k, v in my_dict.items():\n",
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" print(f\"Key {k}, value {v}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Adding one element to the dictionary\n",
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"my_dict[3] = \"Value for key 3\"\n",
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"print(my_dict)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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