Python_Intro.ipynb

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   "source": [
    "# Introduction to Python\n",
    "\n",
    "By Daniel T. Quaye\n",
    "\n",
    "\n",
    "## Contents:\n",
    "\n",
    "- Basics of coding in Python\n",
    "- Objects\n",
    "- Data manipulation\n",
    "\n",
    "\n",
    "We want to have a general look at Python and some basic concepts underlying the more advanced analysis to be performed during the in-person and online sessions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coding Basics\n",
    "### Python as a Calculator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
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     "execution_count": 2,
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   "source": [
    "1+5"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
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     "execution_count": 3,
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   ],
   "source": [
    "25/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "data": {
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       "27.0"
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     "execution_count": 4,
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   "source": [
    "4.5 * 6"
   ]
  },
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   "metadata": {},
   "source": [
    "### Variables \n",
    "\n",
    "- Used to store information / objects. \n",
    "- '=' used for variable assignment\n",
    "- Important to use meaningful names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Object Types\n",
    "#### Type 1\n",
    "\n",
    "1. String: 'a', '23.5', \"good\", \"Hello world!\", \"TRUE\"\n",
    "2. Integer: 0, 1, 2, 3\n",
    "3. Float: 12.4, 30.5, \n",
    "4. Logical: True/False\n",
    "\n",
    "##### They can be used together with the operators below for data manipulation\n",
    "\n",
    "* '=='  'and'  'or'  '=!'  '>='  '<' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Type 2\n",
    "\n",
    "Dimension |Homogeneous |Heterogeneous\n",
    "----------|------------|------------- \n",
    "1-d       |vector      |list\n",
    "2-d       |matrix      |dataframe\n",
    "n-d       |array       |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sat', 'mon', 'sun', 'wed')"
      ]
     },
     "execution_count": 5,
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   ],
   "source": [
    "days = ['sat','mon','sun','wed']      #store the object / value\n",
    "days                                  #display the object contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 20, 30, 40, 50]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1 = [10,20,30,40,50] \n",
    "v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Days' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mDays\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'Days' is not defined"
     ]
    }
   ],
   "source": [
    "Days                               #Python is case sensitive"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 20, 30, 'mon', 'sun', 'wed']"
      ]
     },
     "execution_count": 8,
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   ],
   "source": [
    "list_1 = [10,20,30,'mon','sun','wed']\n",
    "list_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 4, 5], [-5, 8, 9]]"
      ]
     },
     "execution_count": 9,
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   "source": [
    "M1 = [[1, 4, 5], \n",
    "    [-5, 8, 9]]\n",
    "M1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.1, 2. , 3. ],\n",
       "       [3. , 4. , 5. ]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import numpy library\n",
    "import numpy as np\n",
    "\n",
    "A = np.array([[1.1, 2, 3], [3, 4, 5]])      # Array of floats\n",
    "A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tom</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nick</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chris</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age\n",
       "0    tom   18\n",
       "1   nick   25\n",
       "2  chris   10"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import pandas library\n",
    "import pandas as pd\n",
    " \n",
    "# create a list of lists\n",
    "data = [['tom', 18], ['nick', 25], ['chris', 10]]\n",
    " \n",
    "# Create the pandas DataFrame\n",
    "df = pd.DataFrame(data, columns = ['Name', 'Age'])\n",
    " \n",
    "# print dataframe.\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Variable Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 4,
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   "source": [
    "v1 = 10 \n",
    "\n",
    "type(v1)            "
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v2 = 456.78\n",
    "\n",
    "type(v2)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "numpy.ndarray"
      ]
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     "execution_count": 6,
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   "source": [
    "type(A)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    }
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   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing/ Subsetting\n",
    "\n",
    "- index of the first element is 0\n",
    "- can be done with positive or negative numbers \n",
    "- can be done with comparison operators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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     "data": {
      "text/plain": [
       "int"
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     "execution_count": 12,
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     "output_type": "execute_result"
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   "source": [
    "v1 = 10 \n",
    "\n",
    "type(v1)            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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     "data": {
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       "float"
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     "execution_count": 13,
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   "source": [
    "v2 = 456.78\n",
    "\n",
    "type(v2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "type(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "type(df)"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing/ Subsetting\n",
    "\n",
    "- index of the first element is 0\n",
    "- can be done with positive or negative numbers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_1[0]          #positive indexing   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'wed'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_1[-1]         #negative indexing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'nick'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.Name[1]         #index an element in a specific column of a dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditional Statements\n",
    "\n",
    "### if statement "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 53                             \n",
    "y = 500\n",
    "\n",
    "if y > x:\n",
    "  print(\"y is greater than x\")      #important to indent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### elif "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 53                             \n",
    "y = 500\n",
    "\n",
    "if x == y:\n",
    "  print(\"x is equal to y\")\n",
    "elif x < y:                        #if previous conditions are false, then do this instead\n",
    "  print(\"x is less than y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### else"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x is less than y\n"
     ]
    }
   ],
   "source": [
    "x = 53\n",
    "y = 500\n",
    "\n",
    "if x > y:\n",
    "  print(\"x is greater than y\")\n",
    "elif x == y:\n",
    "  print(\"x and y are equal\")\n",
    "else:                               #deals with anything not captured by preceding conditions.\n",
    "  print(\"x is less than y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### for loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n",
      "True\n",
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "v3 = [10,20,30,40,50] \n",
    "\n",
    "#iterating over a list\n",
    "for i in v3:                \n",
    "    print (i == v3[2])       # looks a bit complex here :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "baseball\n"
     ]
    }
   ],
   "source": [
    "sports = [\"football\", \"baseball\", \"volleyball\"]     \n",
    "\n",
    "for x in sports:\n",
    "  if x == \"baseball\": \n",
    "        print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='red'>Google is your friend; think about what you want to do, translate it into words and search in google ....+ python (Stackoverflow very good)</font>\n",
    "\n"
   ]
  }
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