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   "source": [
    "# Introduction to NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Update/Upgrade the NumPy package to the latest version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting numpy\n",
      "  Using cached numpy-1.19.2-cp37-cp37m-win_amd64.whl (12.9 MB)\n",
      "Installing collected packages: numpy\n",
      "Successfully installed numpy-1.19.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  WARNING: The script f2py.exe is installed in 'C:\\Users\\vikto\\AppData\\Roaming\\Python\\Python37\\Scripts' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\n"
     ]
    }
   ],
   "source": [
    "# Parts 1 and 2 can be executed in Jupyter or the Anacodna Prompt\n",
    "\n",
    "pip install numpy --upgrade\n",
    "# pip install numpy --upgrade --user # You may need to specify this if you're not running it as admin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Install/Upgrade another package (e.g. Pandas) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already up-to-date: pandas in c:\\programdata\\anaconda3\\lib\\site-packages (1.1.2)\n",
      "Requirement already satisfied, skipping upgrade: pytz>=2017.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2019.3)\n",
      "Requirement already satisfied, skipping upgrade: python-dateutil>=2.7.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2.8.1)\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.15.4 in c:\\users\\vikto\\appdata\\roaming\\python\\python37\\site-packages (from pandas) (1.19.2)\n",
      "Requirement already satisfied, skipping upgrade: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas --upgrade\n",
    "#pip install pandas --upgrade --user  # You may need to specify this based if you're not running it as admin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Import NumPy as <i> np </i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Use the<i> np.array() </i>function to create an array variable - <i>array_hw_1</i> with the numbers 4,5,7 and 10 in it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "array_hw_1 = np.array([4,5,7,10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Display the new array on screen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  5,  7, 10])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_hw_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Create a 2x2 array with the same values as <i> array_hw_1 </i> and display it on the screen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "array_hw_2 = np.array([[4,5],[7,10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  5],\n",
       "       [ 7, 10]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_hw_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Call the <i> np.mean() </i> function on each array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(array_hw_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(array_hw_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Use the <i> axis </i> argument to find the mean for all the rows of <i> array_hw_2</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.5, 8.5])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(array_hw_2, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Use the <i>axis</i> argument to find the mean for all the columns of <i>array_hw_2</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.5, 7.5])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(array_hw_2, axis = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Use the <i> Shift + Tab </i> shorcut on <i> np.mean() </i> to check what possible arguments we can specify. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. Set the <i> dtype </i> argument equal to <i> np.int </i> and compare the results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 7])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(array_hw_2, axis = 0, dtype = np.int)"
   ]
  }
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