{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Activity 1 - Python Primer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First let's import some key data attributes\n",
    "\n",
    "!pip install numpy pandas matplotlib seaborn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To begin - let's show a very quick example of generating some dataset and plotting this. Do not worry if you do not understand the commands being used - you will do soon. For now, this is just to show how quickly we can generate this using Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.random.normal(1.0, 0.3, [1000,1000])\n",
    "b = np.random.normal(1.8, 0.2, [100,100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa094a77a10>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(a[0], a[1])\n",
    "plt.scatter(b[0], b[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Operations in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "number = 1\n",
    "text = 'hello_everyone'\n",
    "l = [1,2,3,4,5]\n",
    "d = {'name':'bob', 'value':100}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic variables in Python include **numerical values** (integers, floats, doubles, etc.) and **string values** (text). Basic data structures include **lists** and **dictionaries**.\n",
    "\n",
    "- Lists are essentially like arrays. We can create a dynamic group of (mixed) variables. We can append and remove from this list, and we can access elements from the list.\n",
    "- Dictionaries are like objects. We can create name-value pairs to reference attributes that make up an object (e.g., properties of a car).\n",
    "- We can create lists of dictionaries, and we can have lists within dictionaries. We can also have a list of lists (nested lists), and we can have dictionaries as values in a dictionary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's walk through some Python examples\n",
    "\n",
    "First let's look at some basics of Python and get use to manipulating data using the built in variable types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First some simple variables\n",
    "\n",
    "an_integer = 12\n",
    "a_floating_point_number = 18.4732"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.4732"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can do some simple maths on these variables and see the output\n",
    "\n",
    "an_integer + a_floating_point_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "221.67839999999998"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also write functions to do simple maths\n",
    "\n",
    "def multiply(number1, number2):\n",
    "    return number1 * number2\n",
    "\n",
    "multiply(an_integer, a_floating_point_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello there!\n"
     ]
    }
   ],
   "source": [
    "# We can also create text variables just as easily\n",
    "\n",
    "a_string = 'Hello there!'\n",
    "print (a_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello there! My name is Phil\n",
      "Hello there! My name is Dave\n"
     ]
    }
   ],
   "source": [
    "# We can do some simple manipulation of text\n",
    "\n",
    "my_name = 'Phil'\n",
    "message = a_string + ' My name is ' + my_name\n",
    "print (message)\n",
    "\n",
    "# Including spliting sentences to corrupt a message\n",
    "imposter_name = 'Dave'\n",
    "s_m = message.split(\" \")\n",
    "new_message = ' '.join(s_m[:-1]) + ' ' + imposter_name\n",
    "print (new_message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'banana', 'orange', 'lemon']\n",
      "['apple', 'banana']\n",
      "['apple', 'banana', 'orange']\n",
      "['apple', 'banana', 'orange', 'lemon', 'mango']\n",
      "['apple', 'orange', 'lemon', 'mango']\n"
     ]
    }
   ],
   "source": [
    "# We also have lists of data that can sort variables\n",
    "fruits = ['apple','banana','orange','lemon']\n",
    "print (fruits)\n",
    "# We can access sets of variables using indexes\n",
    "print (fruits[0:2])\n",
    "print (fruits[:-1])\n",
    "# We can append items to the list, and remove items from the list\n",
    "fruits.append('mango')\n",
    "print (fruits)\n",
    "fruits.remove('banana')\n",
    "print (fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'bob', 'age': 23, 'height': 185, 'email': 'bob@bobmail.com'}\n"
     ]
    }
   ],
   "source": [
    "# We can also create dictionary objects \n",
    "# This is helpful for storing related variables about an object\n",
    "\n",
    "person = {}\n",
    "person['name'] = 'bob'\n",
    "person['age'] = 23\n",
    "person['height'] = 185\n",
    "person['email'] = 'bob@bobmail.com'\n",
    "print (person)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'name': 'bob', 'age': 23, 'height': 177, 'email': 'bob@bobmail.com'}, {'name': 'john', 'age': 41, 'height': 185, 'email': 'john@johnmail.com'}, {'name': 'sophie', 'age': 31, 'height': 157, 'email': 'sophie@sophiemail.com'}, {'name': 'wendy', 'age': 19, 'height': 174, 'email': 'wendy@wendymail.com'}]\n"
     ]
    }
   ],
   "source": [
    "# Like earlier, we could use a function to create 'person' objects\n",
    "people = []\n",
    "\n",
    "def create_person(name, age, height, email):\n",
    "    global people\n",
    "    new_person = {'name':name,\n",
    "                 'age':age,\n",
    "                 'height':height,\n",
    "                 'email':email}\n",
    "    people.append(new_person)\n",
    "\n",
    "create_person('bob', 23, 177, 'bob@bobmail.com')\n",
    "create_person('john', 41, 185, 'john@johnmail.com')\n",
    "create_person('sophie', 31, 157, 'sophie@sophiemail.com')\n",
    "create_person('wendy', 19, 174, 'wendy@wendymail.com')\n",
    "\n",
    "\n",
    "# Here we store our person objects in our people list\n",
    "# to make a group of 'persons' - a.k.a. people!\n",
    "print (people)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introducing data science libraries\n",
    "\n",
    "We have covered a lot very quickly here. You've now already used the main built in variables of Python, that allow you to store numerical and text data, and the data structures such as lists (which are essentially arrays), and dictionaries (which are essentially objects). Let's now explore this deeper by introducing some of the data science libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can import libraries using the following\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bob</td>\n",
       "      <td>23</td>\n",
       "      <td>177</td>\n",
       "      <td>bob@bobmail.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>john</td>\n",
       "      <td>41</td>\n",
       "      <td>185</td>\n",
       "      <td>john@johnmail.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sophie</td>\n",
       "      <td>31</td>\n",
       "      <td>157</td>\n",
       "      <td>sophie@sophiemail.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wendy</td>\n",
       "      <td>19</td>\n",
       "      <td>174</td>\n",
       "      <td>wendy@wendymail.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name  age  height                  email\n",
       "0     bob   23     177        bob@bobmail.com\n",
       "1    john   41     185      john@johnmail.com\n",
       "2  sophie   31     157  sophie@sophiemail.com\n",
       "3   wendy   19     174    wendy@wendymail.com"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our people dictionary is difficult for us to read clearly\n",
    "# Pandas DataFrames help manipulate tabular data like this very easily\n",
    "\n",
    "data = pd.DataFrame(people)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    23\n",
       "1    41\n",
       "2    31\n",
       "3    19\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can access individual columns of the data now\n",
    "data['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>john</td>\n",
       "      <td>41</td>\n",
       "      <td>185</td>\n",
       "      <td>john@johnmail.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name  age  height              email\n",
       "1  john   41     185  john@johnmail.com"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Who is the tallest of the users? Let's find out\n",
    "data[data['height'] == np.max(data['height'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sophie</td>\n",
       "      <td>31</td>\n",
       "      <td>157</td>\n",
       "      <td>sophie@sophiemail.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name  age  height                  email\n",
       "2  sophie   31     157  sophie@sophiemail.com"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Who is the shortest of the users? Let's find out\n",
    "data[data['height'] == np.min(data['height'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What if we want to plot this data quickly?\n",
    "data.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What next?\n",
    "\n",
    "Spend some time researching into Pandas, Matplotlib, and Numpy - these are core to manipulating numerical and tabular data, and then being able to visualize the results. There are many great examples online of getting started with these libraries.\n",
    "E.g., (Free e-book 'A Whirlwind Tour of Python': http://www.oreilly.com/programming/free/a-whirlwind-tour-of-python.csp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}