Pandas-intro-demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.6.7'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from platform import python_version\n",
    "python_version()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.18.1'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.1'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Built-in Collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 3, 4, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers = (1, 2, 3, 4, 5)  # Immutable, ordered\n",
    "numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'apple', 'banana', 'cherry', 'orange', 'pomegranate'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruit_names = {'apple', 'banana', 'orange'}  # Unorded\n",
    "fruit_names = fruit_names.union({'pomegranate', 'cherry'})  # Mutable\n",
    "fruit_names = fruit_names.union({'apple'})  # No duplcates!\n",
    "fruit_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.564, 0.67, 0.465, 0.876, 0.1023]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [0.564, 0.670, 0.465]  # Mutable, ordered\n",
    "data.append(0.876)\n",
    "data.append(0.1023)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'apple': 5, 'banana': 10, 'orange': 0, 'pomegranate': 1}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock = {'apple': 5, 'banana': 10, 'orange': 0}  # unordered\n",
    "stock['pomegranate'] = 1  # Mutable\n",
    "stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['apple', 'banana', 'orange', 'pomegranate'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([5, 10, 0, 1])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple', 100, (1, 2, 3, 4, 5), 3.14159]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "things = ['apple', 100, numbers, 3.14159]  # Multifarious types allowed\n",
    "things"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 4, 5)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'potato' in fruit_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock['apple']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'apple': 15, 'banana': 10, 'orange': 0, 'pomegranate': 1}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock['apple'] = 15\n",
    "stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'apple': 15, 'banana': 10, 'orange': 0}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del stock['pomegranate']\n",
    "stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'apple': 15, 'banana': 10, 'orange': 0, 'grapes': 3}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock['grapes'] = 3\n",
    "stock"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.564 , 0.67  , 0.465 , 0.876 , 0.1023])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_array = np.array(data)\n",
    "data_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_array.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1, 255,   0,   0], dtype=uint8)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.array([1, 255, 0, 0], dtype='B')\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 1, 1], dtype=uint8)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = b + 1\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "data2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 1.41421356, 1.73205081],\n",
       "       [2.        , 2.23606798, 2.44948974]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_calc(x):\n",
    "    return x**2 - x + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  3,  7],\n",
       "       [13, 21, 31]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_calc(data2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'apple': 15, 'banana': 10, 'orange': 0, 'grapes': 3}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apple     15\n",
       "banana    10\n",
       "orange     0\n",
       "grapes     3\n",
       "Name: Stock level, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_series = pd.Series(stock, name='Stock level')\n",
    "stock_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_series['apple']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['apple', 'banana', 'orange', 'grapes'], dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_series.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15, 10,  0,  3])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_series.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Stock level'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_series.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_series['banana'] = stock_series['banana'] + 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apple     15\n",
       "banana    60\n",
       "orange     0\n",
       "grapes     3\n",
       "Name: Stock level, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_series['pomegranate'] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apple          15\n",
       "banana         60\n",
       "orange          0\n",
       "grapes          3\n",
       "pomegranate     3\n",
       "Name: Stock level, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_series['grapes']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apple          15\n",
       "banana         60\n",
       "orange          0\n",
       "grapes          3\n",
       "pomegranate     3\n",
       "Name: Stock level, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apple     1.24\n",
       "banana    1.67\n",
       "orange    1.95\n",
       "grapes    3.24\n",
       "Name: Price ($), dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_data = {\n",
    "    'apple': 1.24,\n",
    "    'banana': 1.67,\n",
    "    'orange': 1.95,\n",
    "    'grapes': 3.24\n",
    "}\n",
    "price_series = pd.Series(price_data, name='Price ($)')\n",
    "price_series"
   ]
  },
  {
   "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>Stock level</th>\n",
       "      <th>Price ($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>15</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>60</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>0</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grapes</th>\n",
       "      <td>3</td>\n",
       "      <td>3.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pomegranate</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Stock level  Price ($)\n",
       "apple                 15       1.24\n",
       "banana                60       1.67\n",
       "orange                 0       1.95\n",
       "grapes                 3       3.24\n",
       "pomegranate            3        NaN"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([stock_series, price_series], axis=1, sort=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apple          15\n",
       "banana         60\n",
       "orange          0\n",
       "grapes          3\n",
       "pomegranate     3\n",
       "Name: Stock level, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Stock level']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stock level    3.00\n",
       "Price ($)      3.24\n",
       "Name: grapes, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['grapes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['apple', 'banana', 'orange', 'grapes', 'pomegranate'], dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Stock level', 'Price ($)'], dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15.  ,  1.24],\n",
       "       [60.  ,  1.67],\n",
       "       [ 0.  ,  1.95],\n",
       "       [ 3.  ,  3.24],\n",
       "       [ 3.  ,   nan]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stock level      int64\n",
       "Price ($)      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Stock level</th>\n",
       "      <th>Price ($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>15</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>60</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>0</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grapes</th>\n",
       "      <td>3</td>\n",
       "      <td>3.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pomegranate</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Stock level  Price ($)\n",
       "apple                 15       1.24\n",
       "banana                60       1.67\n",
       "orange                 0       1.95\n",
       "grapes                 3       3.24\n",
       "pomegranate            3        NaN"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Stock level</th>\n",
       "      <th>Price ($)</th>\n",
       "      <th>Value in Stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>15</td>\n",
       "      <td>1.24</td>\n",
       "      <td>18.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>60</td>\n",
       "      <td>1.67</td>\n",
       "      <td>100.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grapes</th>\n",
       "      <td>3</td>\n",
       "      <td>3.24</td>\n",
       "      <td>9.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pomegranate</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Stock level  Price ($)  Value in Stock\n",
       "apple                 15       1.24           18.60\n",
       "banana                60       1.67          100.20\n",
       "orange                 0       1.95            0.00\n",
       "grapes                 3       3.24            9.72\n",
       "pomegranate            3        NaN             NaN"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Value in Stock'] = df['Stock level'] * df['Price ($)']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apple          False\n",
       "banana          True\n",
       "orange         False\n",
       "grapes          True\n",
       "pomegranate    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_on_sale = {\n",
    "    'banana': True,\n",
    "    'grapes': True,\n",
    "    'coconut': True\n",
    "}\n",
    "promotions = pd.Series(items_on_sale, index=df.index).fillna(False)\n",
    "promotions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Stock level</th>\n",
       "      <th>Price ($)</th>\n",
       "      <th>Value in Stock</th>\n",
       "      <th>Promotions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>15</td>\n",
       "      <td>1.24</td>\n",
       "      <td>18.60</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>60</td>\n",
       "      <td>1.67</td>\n",
       "      <td>100.20</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grapes</th>\n",
       "      <td>3</td>\n",
       "      <td>3.24</td>\n",
       "      <td>9.72</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pomegranate</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Stock level  Price ($)  Value in Stock  Promotions\n",
       "apple                 15       1.24           18.60       False\n",
       "banana                60       1.67          100.20        True\n",
       "orange                 0       1.95            0.00       False\n",
       "grapes                 3       3.24            9.72        True\n",
       "pomegranate            3        NaN             NaN       False"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Promotions'] = promotions\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Stock level</th>\n",
       "      <th>Price ($)</th>\n",
       "      <th>Value in Stock</th>\n",
       "      <th>Promotions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>15</td>\n",
       "      <td>1.24</td>\n",
       "      <td>18.60</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>60</td>\n",
       "      <td>1.67</td>\n",
       "      <td>100.20</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grapes</th>\n",
       "      <td>3</td>\n",
       "      <td>3.24</td>\n",
       "      <td>9.72</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Stock level  Price ($)  Value in Stock  Promotions\n",
       "apple            15       1.24           18.60       False\n",
       "banana           60       1.67          100.20        True\n",
       "orange            0       1.95            0.00       False\n",
       "grapes            3       3.24            9.72        True"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Stock level</th>\n",
       "      <th>Price ($)</th>\n",
       "      <th>Value in Stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.200000</td>\n",
       "      <td>2.025000</td>\n",
       "      <td>32.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25.153529</td>\n",
       "      <td>0.861027</td>\n",
       "      <td>46.011342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.240000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>7.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.810000</td>\n",
       "      <td>14.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>2.272500</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>3.240000</td>\n",
       "      <td>100.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Stock level  Price ($)  Value in Stock\n",
       "count     5.000000   4.000000        4.000000\n",
       "mean     16.200000   2.025000       32.130000\n",
       "std      25.153529   0.861027       46.011342\n",
       "min       0.000000   1.240000        0.000000\n",
       "25%       3.000000   1.562500        7.290000\n",
       "50%       3.000000   1.810000       14.160000\n",
       "75%      15.000000   2.272500       39.000000\n",
       "max      60.000000   3.240000      100.200000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fjoi91fao9nE8Ig5FxMGI2F/tG9Ve1kfElyLiu9XPzLuXq5eRCvSaH0Owkj0KbFuw7x7gyczcCjxZba9054GPZebvAtcCd1b/DqPYy8+A6zPzncAVwLbqSq1R7AXgLuDovO1R7QNgMjOvmHe99qj28jngHzPzd4B30vn3WZ5eMnNkHsC7ga/P2/4E8Ilh19VnD1uAw/O2XwA2Vs83Ai8Mu8YBetoNvHfUewF+DThA507okeuFzv0fTwLXA3urfSPXR1XrcWDDgn0j1wvwRuA/qC5AWe5eRuoMncU/YmCUjWd1zX719TeGXE9fImIL8C7gXxnRXqplioPAGeCJzBzVXv4K+HPgl/P2jWIf0LnT/J8iYrb6yBAYzV5+E/hv4G+qpbC/joi1LFMvoxbotT5iQK+OiBgDHgc+mpkvDrueQWXmLzLzCjpnuFdHxGXDrqlfEXEjcCYzZ4ddS0Ouy8wr6Syv3hkRfzDsgga0GrgSeDgz3wW8xDIuFY1aoJf4EQOnL3wyZfX1zJDrqSUi1tAJ812Z+eVq90j2ckFm/gSYofN3jlHr5TrgjyPiOJ1PRL0+Iv6O0esDgMw8WX09A3yFzqe+jmIvJ4AT1f/qA/gSnYBfll5GLdDrfAzBqNkDfKB6/gE669ErWkQE8AXgaGZ+dt6hUezlLRGxvnr+euA9wHcZsV4y8xOZeUlmbqHzc/HPmfknjFgfABGxNiLecOE58EfAYUawl8z8L+AHEfHb1a4/BJ5nuXoZ9h8NBvgjw3bg34B/B/5i2PX0WfsX6Xyk8P/Q+c39p8Cv0/lD1veqr28edp01+vh9OktdzwEHq8f2Ee3l94BvV70cBu6t9o9cL/N6avGrP4qOXB901p2/Uz2OXPg5H8VeqrqvAPZX/419FXjTcvXirf+SVIhRW3KRJC3CQJekQhjoklQIA12SCmGgS1IhDHRJKoSBLkmF+F8HNgTCF/+UvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Stock level'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": [
    "df['Value in Stock'].dropna().sort_values().plot.barh(title='Stock Value')\n",
    "plt.xlabel('$')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Stock level</th>\n",
       "      <th>Price ($)</th>\n",
       "      <th>Value in Stock</th>\n",
       "      <th>Promotions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>15</td>\n",
       "      <td>1.24</td>\n",
       "      <td>18.60</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>60</td>\n",
       "      <td>1.67</td>\n",
       "      <td>100.20</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grapes</th>\n",
       "      <td>3</td>\n",
       "      <td>3.24</td>\n",
       "      <td>9.72</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pomegranate</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Stock level  Price ($)  Value in Stock  Promotions\n",
       "apple                 15       1.24           18.60       False\n",
       "banana                60       1.67          100.20        True\n",
       "orange                 0       1.95            0.00       False\n",
       "grapes                 3       3.24            9.72        True\n",
       "pomegranate            3        NaN             NaN       False"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "banana    60\n",
       "grapes     3\n",
       "Name: Stock level, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Promotions'], 'Stock level']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.72"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Stock level'] < 10]['Value in Stock'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('stock_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example with Real Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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>Units</th>\n",
       "      <th>Tag</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Prim Crusher Power</th>\n",
       "      <td>kW</td>\n",
       "      <td>310-JI-20211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fresh Feed</th>\n",
       "      <td>MTPH</td>\n",
       "      <td>315-WQI-20327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total SAG Feed</th>\n",
       "      <td>MTPH</td>\n",
       "      <td>315-WQI-20325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAG Feed Density</th>\n",
       "      <td>%Sol</td>\n",
       "      <td>320-FX-20731A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fresh Water Flowrate</th>\n",
       "      <td>m3/hr</td>\n",
       "      <td>360-FI-23704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Units            Tag\n",
       "Name                                      \n",
       "Prim Crusher Power       kW   310-JI-20211\n",
       "Fresh Feed             MTPH  315-WQI-20327\n",
       "Total SAG Feed         MTPH  315-WQI-20325\n",
       "SAG Feed Density       %Sol  320-FX-20731A\n",
       "Fresh Water Flowrate  m3/hr   360-FI-23704"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'DCS grinding historical Rst data April 4th 2012.xlsx'\n",
    "filepath = os.path.join('data', filename)\n",
    "df = pd.read_excel(filepath, header=None)\n",
    "\n",
    "# Need to deal with first 3 header rows\n",
    "#df.loc[0:2]\n",
    "\n",
    "# Save info for later\n",
    "info = df.loc[0:2].transpose().drop(0)\n",
    "info.columns = ['Name', 'Units', 'Tag']\n",
    "info = info.set_index('Name')\n",
    "info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>Prim Crusher Power</th>\n",
       "      <th>Fresh Feed</th>\n",
       "      <th>Total SAG Feed</th>\n",
       "      <th>SAG Feed Density</th>\n",
       "      <th>Fresh Water Flowrate</th>\n",
       "      <th>Reclaim Water Flowrate</th>\n",
       "      <th>Discharge Right</th>\n",
       "      <th>SAG Sound 1</th>\n",
       "      <th>SAG Sound 2</th>\n",
       "      <th>SAG Power</th>\n",
       "      <th>...</th>\n",
       "      <th>BM1 CYC Feed Flowrate</th>\n",
       "      <th>BM2 CYC Feed Flowrate</th>\n",
       "      <th>BM1 Speed</th>\n",
       "      <th>BM2 Speed</th>\n",
       "      <th>BM1 CYC Feed Water</th>\n",
       "      <th>BM2 CYC Feed Water</th>\n",
       "      <th>BM1 CYC Feed Density</th>\n",
       "      <th>BM2 CYC Feed Density</th>\n",
       "      <th>BM1 CYC Press.</th>\n",
       "      <th>BM2 CYC Press.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-04-04 05:30:00</th>\n",
       "      <td>70.2831</td>\n",
       "      <td>1264.64</td>\n",
       "      <td>1586.36</td>\n",
       "      <td>63.0536</td>\n",
       "      <td>1068.44</td>\n",
       "      <td>1805.33</td>\n",
       "      <td>6831.73</td>\n",
       "      <td>111.918</td>\n",
       "      <td>111.413</td>\n",
       "      <td>13116</td>\n",
       "      <td>...</td>\n",
       "      <td>3982.06</td>\n",
       "      <td>4350.93</td>\n",
       "      <td>12.09</td>\n",
       "      <td>12.11</td>\n",
       "      <td>214.465</td>\n",
       "      <td>217.51</td>\n",
       "      <td>47.3381</td>\n",
       "      <td>43.5599</td>\n",
       "      <td>43.4479</td>\n",
       "      <td>48.8423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-04 05:31:00</th>\n",
       "      <td>60.5936</td>\n",
       "      <td>1234.74</td>\n",
       "      <td>1669.76</td>\n",
       "      <td>62.6013</td>\n",
       "      <td>1061.42</td>\n",
       "      <td>1802.52</td>\n",
       "      <td>6825</td>\n",
       "      <td>112.494</td>\n",
       "      <td>112.096</td>\n",
       "      <td>12903</td>\n",
       "      <td>...</td>\n",
       "      <td>4113.21</td>\n",
       "      <td>4311.93</td>\n",
       "      <td>12.1</td>\n",
       "      <td>12.11</td>\n",
       "      <td>216.571</td>\n",
       "      <td>209.407</td>\n",
       "      <td>47.4083</td>\n",
       "      <td>43.679</td>\n",
       "      <td>41.7285</td>\n",
       "      <td>48.4962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-04 05:32:00</th>\n",
       "      <td>0</td>\n",
       "      <td>1268.25</td>\n",
       "      <td>1596.89</td>\n",
       "      <td>62.1362</td>\n",
       "      <td>1068.44</td>\n",
       "      <td>1796.9</td>\n",
       "      <td>6865.4</td>\n",
       "      <td>113.106</td>\n",
       "      <td>110.515</td>\n",
       "      <td>12873</td>\n",
       "      <td>...</td>\n",
       "      <td>4050.19</td>\n",
       "      <td>4562.5</td>\n",
       "      <td>12.1</td>\n",
       "      <td>12.1</td>\n",
       "      <td>212.863</td>\n",
       "      <td>215.93</td>\n",
       "      <td>46.9444</td>\n",
       "      <td>45.069</td>\n",
       "      <td>42.851</td>\n",
       "      <td>43.827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-04 05:33:00</th>\n",
       "      <td>0</td>\n",
       "      <td>1282.12</td>\n",
       "      <td>1646.42</td>\n",
       "      <td>64.2787</td>\n",
       "      <td>1066.68</td>\n",
       "      <td>1805.33</td>\n",
       "      <td>6816.16</td>\n",
       "      <td>113.012</td>\n",
       "      <td>111.894</td>\n",
       "      <td>13073</td>\n",
       "      <td>...</td>\n",
       "      <td>4040.79</td>\n",
       "      <td>4499.12</td>\n",
       "      <td>12.11</td>\n",
       "      <td>12.09</td>\n",
       "      <td>214.465</td>\n",
       "      <td>215.404</td>\n",
       "      <td>46.2989</td>\n",
       "      <td>43.2792</td>\n",
       "      <td>45.9766</td>\n",
       "      <td>48.8844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-04 05:34:00</th>\n",
       "      <td>0</td>\n",
       "      <td>1311.01</td>\n",
       "      <td>1712.75</td>\n",
       "      <td>62.7837</td>\n",
       "      <td>1064.93</td>\n",
       "      <td>1795.19</td>\n",
       "      <td>6849.83</td>\n",
       "      <td>113.492</td>\n",
       "      <td>112.096</td>\n",
       "      <td>12858</td>\n",
       "      <td>...</td>\n",
       "      <td>4084.91</td>\n",
       "      <td>4366.29</td>\n",
       "      <td>12.12</td>\n",
       "      <td>12.11</td>\n",
       "      <td>213.939</td>\n",
       "      <td>210.987</td>\n",
       "      <td>47.6539</td>\n",
       "      <td>45.42</td>\n",
       "      <td>42.8089</td>\n",
       "      <td>49.9117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Prim Crusher Power Fresh Feed Total SAG Feed  \\\n",
       "Timestamp                                                          \n",
       "2012-04-04 05:30:00            70.2831    1264.64        1586.36   \n",
       "2012-04-04 05:31:00            60.5936    1234.74        1669.76   \n",
       "2012-04-04 05:32:00                  0    1268.25        1596.89   \n",
       "2012-04-04 05:33:00                  0    1282.12        1646.42   \n",
       "2012-04-04 05:34:00                  0    1311.01        1712.75   \n",
       "\n",
       "                    SAG Feed Density Fresh Water Flowrate  \\\n",
       "Timestamp                                                   \n",
       "2012-04-04 05:30:00          63.0536              1068.44   \n",
       "2012-04-04 05:31:00          62.6013              1061.42   \n",
       "2012-04-04 05:32:00          62.1362              1068.44   \n",
       "2012-04-04 05:33:00          64.2787              1066.68   \n",
       "2012-04-04 05:34:00          62.7837              1064.93   \n",
       "\n",
       "                    Reclaim Water Flowrate Discharge Right SAG Sound 1  \\\n",
       "Timestamp                                                                \n",
       "2012-04-04 05:30:00                1805.33         6831.73     111.918   \n",
       "2012-04-04 05:31:00                1802.52            6825     112.494   \n",
       "2012-04-04 05:32:00                 1796.9          6865.4     113.106   \n",
       "2012-04-04 05:33:00                1805.33         6816.16     113.012   \n",
       "2012-04-04 05:34:00                1795.19         6849.83     113.492   \n",
       "\n",
       "                    SAG Sound 2 SAG Power  ... BM1 CYC Feed Flowrate  \\\n",
       "Timestamp                                  ...                         \n",
       "2012-04-04 05:30:00     111.413     13116  ...               3982.06   \n",
       "2012-04-04 05:31:00     112.096     12903  ...               4113.21   \n",
       "2012-04-04 05:32:00     110.515     12873  ...               4050.19   \n",
       "2012-04-04 05:33:00     111.894     13073  ...               4040.79   \n",
       "2012-04-04 05:34:00     112.096     12858  ...               4084.91   \n",
       "\n",
       "                    BM2 CYC Feed Flowrate BM1 Speed BM2 Speed  \\\n",
       "Timestamp                                                       \n",
       "2012-04-04 05:30:00               4350.93     12.09     12.11   \n",
       "2012-04-04 05:31:00               4311.93      12.1     12.11   \n",
       "2012-04-04 05:32:00                4562.5      12.1      12.1   \n",
       "2012-04-04 05:33:00               4499.12     12.11     12.09   \n",
       "2012-04-04 05:34:00               4366.29     12.12     12.11   \n",
       "\n",
       "                    BM1 CYC Feed Water  BM2 CYC Feed Water   \\\n",
       "Timestamp                                                     \n",
       "2012-04-04 05:30:00             214.465              217.51   \n",
       "2012-04-04 05:31:00             216.571             209.407   \n",
       "2012-04-04 05:32:00             212.863              215.93   \n",
       "2012-04-04 05:33:00             214.465             215.404   \n",
       "2012-04-04 05:34:00             213.939             210.987   \n",
       "\n",
       "                    BM1 CYC Feed Density BM2 CYC Feed Density BM1 CYC Press.  \\\n",
       "Timestamp                                                                      \n",
       "2012-04-04 05:30:00              47.3381              43.5599        43.4479   \n",
       "2012-04-04 05:31:00              47.4083               43.679        41.7285   \n",
       "2012-04-04 05:32:00              46.9444               45.069         42.851   \n",
       "2012-04-04 05:33:00              46.2989              43.2792        45.9766   \n",
       "2012-04-04 05:34:00              47.6539                45.42        42.8089   \n",
       "\n",
       "                    BM2 CYC Press.  \n",
       "Timestamp                           \n",
       "2012-04-04 05:30:00        48.8423  \n",
       "2012-04-04 05:31:00        48.4962  \n",
       "2012-04-04 05:32:00         43.827  \n",
       "2012-04-04 05:33:00        48.8844  \n",
       "2012-04-04 05:34:00        49.9117  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trim rows and set columns and index\n",
    "df.loc[0, 0] = 'Timestamp'\n",
    "df.columns = df.loc[0]\n",
    "df.columns.name = None\n",
    "df = df.loc[3:]\n",
    "df = df.set_index('Timestamp')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2880, 28)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prim Crusher Power        float64\n",
       "Fresh Feed                float64\n",
       "Total SAG Feed            float64\n",
       "SAG Feed Density          float64\n",
       "Fresh Water Flowrate      float64\n",
       "Reclaim Water Flowrate    float64\n",
       "Discharge Right           float64\n",
       "SAG Sound 1               float64\n",
       "SAG Sound 2               float64\n",
       "SAG Power                   int64\n",
       "SAG Speed                 float64\n",
       "SAG Lube Temp             float64\n",
       "Pebble Return Load        float64\n",
       "Pebble Crusher Power      float64\n",
       "Pebble Crusher CCS        float64\n",
       "CV6                       float64\n",
       "BM1 Power                   int64\n",
       "BM2 Power                   int64\n",
       "BM1 CYC Feed Flowrate     float64\n",
       "BM2 CYC Feed Flowrate     float64\n",
       "BM1 Speed                 float64\n",
       "BM2 Speed                 float64\n",
       "BM1 CYC Feed Water        float64\n",
       "BM2 CYC Feed Water        float64\n",
       "BM1 CYC Feed Density      float64\n",
       "BM2 CYC Feed Density      float64\n",
       "BM1 CYC Press.            float64\n",
       "BM2 CYC Press.            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in df:\n",
    "    df[col] = pd.to_numeric(df[col])\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prim Crusher Power        0\n",
       "Fresh Feed                0\n",
       "Total SAG Feed            0\n",
       "SAG Feed Density          0\n",
       "Fresh Water Flowrate      0\n",
       "Reclaim Water Flowrate    0\n",
       "Discharge Right           0\n",
       "SAG Sound 1               0\n",
       "SAG Sound 2               0\n",
       "SAG Power                 0\n",
       "SAG Speed                 0\n",
       "SAG Lube Temp             0\n",
       "Pebble Return Load        0\n",
       "Pebble Crusher Power      0\n",
       "Pebble Crusher CCS        0\n",
       "CV6                       0\n",
       "BM1 Power                 0\n",
       "BM2 Power                 0\n",
       "BM1 CYC Feed Flowrate     0\n",
       "BM2 CYC Feed Flowrate     0\n",
       "BM1 Speed                 0\n",
       "BM2 Speed                 0\n",
       "BM1 CYC Feed Water        0\n",
       "BM2 CYC Feed Water        0\n",
       "BM1 CYC Feed Density      0\n",
       "BM2 CYC Feed Density      0\n",
       "BM1 CYC Press.            0\n",
       "BM2 CYC Press.            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Prim Crusher Power</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fresh Feed</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>1.464866</td>\n",
       "      <td>1514.71700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total SAG Feed</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>0.595102</td>\n",
       "      <td>2256.48900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAG Feed Density</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>0.274515</td>\n",
       "      <td>119.26610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fresh Water Flowrate</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>8.010986</td>\n",
       "      <td>1098.95500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reclaim Water Flowrate</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2752.78900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discharge Right</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>6302.750000</td>\n",
       "      <td>7690.64200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAG Sound 1</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>103.677300</td>\n",
       "      <td>117.12610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAG Sound 2</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>101.673600</td>\n",
       "      <td>116.27330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAG Power</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>-36.000000</td>\n",
       "      <td>13285.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAG Speed</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>-2.750000</td>\n",
       "      <td>10.38000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAG Lube Temp</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>36.101320</td>\n",
       "      <td>41.81735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pebble Return Load</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>424.12450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pebble Crusher Power</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>435.41310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pebble Crusher CCS</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>1.386002</td>\n",
       "      <td>18.24901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CV6</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>0.360113</td>\n",
       "      <td>400.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BM1 Power</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>-34.000000</td>\n",
       "      <td>12534.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BM2 Power</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>-33.000000</td>\n",
       "      <td>12594.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BM1 CYC Feed Flowrate</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>19.267570</td>\n",
       "      <td>5140.43800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BM2 CYC Feed Flowrate</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>0.837720</td>\n",
       "      <td>6094.78700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BM1 Speed</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>-0.090000</td>\n",
       "      <td>12.14000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BM2 Speed</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>-0.050000</td>\n",
       "      <td>12.15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BM1 CYC Feed Water</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>712.70310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BM2 CYC Feed Water</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>688.69310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BM1 CYC Feed Density</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>64.44495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BM2 CYC Feed Density</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>62.11948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BM1 CYC Press.</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>93.07576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BM2 CYC Press.</th>\n",
       "      <td>2880.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.02678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count          min          max\n",
       "Prim Crusher Power      2880.0     0.000000   1000.00000\n",
       "Fresh Feed              2880.0     1.464866   1514.71700\n",
       "Total SAG Feed          2880.0     0.595102   2256.48900\n",
       "SAG Feed Density        2880.0     0.274515    119.26610\n",
       "Fresh Water Flowrate    2880.0     8.010986   1098.95500\n",
       "Reclaim Water Flowrate  2880.0     0.000000   2752.78900\n",
       "Discharge Right         2880.0  6302.750000   7690.64200\n",
       "SAG Sound 1             2880.0   103.677300    117.12610\n",
       "SAG Sound 2             2880.0   101.673600    116.27330\n",
       "SAG Power               2880.0   -36.000000  13285.00000\n",
       "SAG Speed               2880.0    -2.750000     10.38000\n",
       "SAG Lube Temp           2880.0    36.101320     41.81735\n",
       "Pebble Return Load      2880.0     0.000000    424.12450\n",
       "Pebble Crusher Power    2880.0     0.000000    435.41310\n",
       "Pebble Crusher CCS      2880.0     1.386002     18.24901\n",
       "CV6                     2880.0     0.360113    400.00000\n",
       "BM1 Power               2880.0   -34.000000  12534.00000\n",
       "BM2 Power               2880.0   -33.000000  12594.00000\n",
       "BM1 CYC Feed Flowrate   2880.0    19.267570   5140.43800\n",
       "BM2 CYC Feed Flowrate   2880.0     0.837720   6094.78700\n",
       "BM1 Speed               2880.0    -0.090000     12.14000\n",
       "BM2 Speed               2880.0    -0.050000     12.15000\n",
       "BM1 CYC Feed Water      2880.0     0.000000    712.70310\n",
       "BM2 CYC Feed Water      2880.0     0.000000    688.69310\n",
       "BM1 CYC Feed Density    2880.0     0.000000     64.44495\n",
       "BM2 CYC Feed Density    2880.0     0.000000     62.11948\n",
       "BM1 CYC Press.          2880.0     0.000000     93.07576\n",
       "BM2 CYC Press.          2880.0     0.000000     91.02678"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()[['count', 'min', 'max']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2012-04-04 05:30:00'), Timestamp('2012-04-06 05:29:00'))"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index[0], df.index[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysing data visually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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": [
    "speeds_gt_10 = df['SAG Speed'] > 10\n",
    "df.loc[speeds_gt_10, 'SAG Speed'].hist(bins=31)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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": [
    "df.loc[speeds_gt_10, 'SAG Power'].hist(bins=31)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "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": [
    "df.loc[speeds_gt_10, 'SAG Speed'].plot(style='.')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "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": [
    "df.loc[speeds_gt_10, 'SAG Power'].plot(style='.')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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": [
    "x = df.loc[speeds_gt_10, 'SAG Speed']\n",
    "y = df.loc[speeds_gt_10, 'SAG Power']\n",
    "plt.scatter(x, y, alpha=0.2)\n",
    "plt.xlim(10.25, 10.4)\n",
    "plt.xlabel(f\"{x.name} ({info.loc[x.name, 'Units']})\")\n",
    "plt.ylabel(f\"{y.name} ({info.loc[y.name, 'Units']})\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "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": [
    "df.loc[speeds_gt_10, 'Total SAG Feed'].hist(bins=31)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaT</td>\n",
       "      <td>SAG rpm</td>\n",
       "      <td>Feed Bearing kPa</td>\n",
       "      <td>Dis Bearing kPa</td>\n",
       "      <td>SAG wtph</td>\n",
       "      <td>Pebble CSS</td>\n",
       "      <td>Recirc.</td>\n",
       "      <td>Pebble Reject Totalizer</td>\n",
       "      <td>Screen Deck amp</td>\n",
       "      <td>FD1 D80 inch</td>\n",
       "      <td>...</td>\n",
       "      <td>BM1 Cyc Feed Flow Rate</td>\n",
       "      <td>BM2 Pump Box Water flow</td>\n",
       "      <td>BM2 Pump Box Level</td>\n",
       "      <td>BM2 Power kW</td>\n",
       "      <td>BM2 SPEED</td>\n",
       "      <td>BM2 Feed Water Flow</td>\n",
       "      <td>BM2 Cyclone kPa</td>\n",
       "      <td>BM2 CYC. FEED DENSITY</td>\n",
       "      <td>BM2 Cyc Feed Flow Rate</td>\n",
       "      <td>OSA FF %Solid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaT</td>\n",
       "      <td>SM-PEC-Mill-nAct</td>\n",
       "      <td>320-PIT-20941</td>\n",
       "      <td>320-PIT-20947</td>\n",
       "      <td>315-WQI-20327</td>\n",
       "      <td>325-HY-Gap</td>\n",
       "      <td>320-WQI-20846</td>\n",
       "      <td>325-WQI-20319</td>\n",
       "      <td>320-II-20755</td>\n",
       "      <td>315-EXP-FDR1-P80</td>\n",
       "      <td>...</td>\n",
       "      <td>320-FI-21136</td>\n",
       "      <td>320-FI-21230</td>\n",
       "      <td>320-LI-21231</td>\n",
       "      <td>BM2-PEC-Mill-P</td>\n",
       "      <td>BM2-PEC-Mill-nAct</td>\n",
       "      <td>320-FI-21285</td>\n",
       "      <td>320-PI-21238</td>\n",
       "      <td>320-DI-21237</td>\n",
       "      <td>320-FI-21236</td>\n",
       "      <td>300-OA-0186-BM1Sol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0                 1                 2                3              4   \\\n",
       "0 NaT           SAG rpm  Feed Bearing kPa  Dis Bearing kPa       SAG wtph   \n",
       "1 NaT  SM-PEC-Mill-nAct     320-PIT-20941    320-PIT-20947  315-WQI-20327   \n",
       "\n",
       "           5              6                        7                8   \\\n",
       "0  Pebble CSS        Recirc.  Pebble Reject Totalizer  Screen Deck amp   \n",
       "1  325-HY-Gap  320-WQI-20846            325-WQI-20319     320-II-20755   \n",
       "\n",
       "                 9   ...                      28                       29  \\\n",
       "0      FD1 D80 inch  ...  BM1 Cyc Feed Flow Rate  BM2 Pump Box Water flow   \n",
       "1  315-EXP-FDR1-P80  ...            320-FI-21136             320-FI-21230   \n",
       "\n",
       "                   30              31                 32                   33  \\\n",
       "0  BM2 Pump Box Level    BM2 Power kW          BM2 SPEED  BM2 Feed Water Flow   \n",
       "1        320-LI-21231  BM2-PEC-Mill-P  BM2-PEC-Mill-nAct         320-FI-21285   \n",
       "\n",
       "                34                     35                      36  \\\n",
       "0  BM2 Cyclone kPa  BM2 CYC. FEED DENSITY  BM2 Cyc Feed Flow Rate   \n",
       "1     320-PI-21238           320-DI-21237            320-FI-21236   \n",
       "\n",
       "                   37  \n",
       "0       OSA FF %Solid  \n",
       "1  300-OA-0186-BM1Sol  \n",
       "\n",
       "[2 rows x 38 columns]"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'DCS Data Example.xlsx'\n",
    "filepath = os.path.join('data', filename)\n",
    "df = pd.read_excel(filepath, header=None)\n",
    "\n",
    "# Need to deal with first 2 header rows\n",
    "df.loc[0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "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>Tag</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SAG rpm</th>\n",
       "      <td>SM-PEC-Mill-nAct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feed Bearing kPa</th>\n",
       "      <td>320-PIT-20941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dis Bearing kPa</th>\n",
       "      <td>320-PIT-20947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAG wtph</th>\n",
       "      <td>315-WQI-20327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pebble CSS</th>\n",
       "      <td>325-HY-Gap</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Tag\n",
       "Name                              \n",
       "SAG rpm           SM-PEC-Mill-nAct\n",
       "Feed Bearing kPa     320-PIT-20941\n",
       "Dis Bearing kPa      320-PIT-20947\n",
       "SAG wtph             315-WQI-20327\n",
       "Pebble CSS              325-HY-Gap"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save info for later\n",
    "info = df.loc[0:1].transpose().drop(0)\n",
    "info.columns = ['Name', 'Tag']\n",
    "info = info.set_index('Name')\n",
    "info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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>SAG rpm</th>\n",
       "      <th>Feed Bearing kPa</th>\n",
       "      <th>Dis Bearing kPa</th>\n",
       "      <th>SAG wtph</th>\n",
       "      <th>Pebble CSS</th>\n",
       "      <th>Recirc.</th>\n",
       "      <th>Pebble Reject Totalizer</th>\n",
       "      <th>Screen Deck amp</th>\n",
       "      <th>FD1 D80 inch</th>\n",
       "      <th>FD2 D80 inch</th>\n",
       "      <th>...</th>\n",
       "      <th>BM1 Cyc Feed Flow Rate</th>\n",
       "      <th>BM2 Pump Box Water flow</th>\n",
       "      <th>BM2 Pump Box Level</th>\n",
       "      <th>BM2 Power kW</th>\n",
       "      <th>BM2 SPEED</th>\n",
       "      <th>BM2 Feed Water Flow</th>\n",
       "      <th>BM2 Cyclone kPa</th>\n",
       "      <th>BM2 CYC. FEED DENSITY</th>\n",
       "      <th>BM2 Cyc Feed Flow Rate</th>\n",
       "      <th>OSA FF %Solid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-06 05:30:00</th>\n",
       "      <td>10.4816</td>\n",
       "      <td>6952.17</td>\n",
       "      <td>6261.19</td>\n",
       "      <td>1780.89</td>\n",
       "      <td>16.136</td>\n",
       "      <td>292.654</td>\n",
       "      <td>0.863263</td>\n",
       "      <td>31.862</td>\n",
       "      <td>1.58594</td>\n",
       "      <td>1.40429</td>\n",
       "      <td>...</td>\n",
       "      <td>5055.65</td>\n",
       "      <td>60.7807</td>\n",
       "      <td>44.323</td>\n",
       "      <td>12605.7</td>\n",
       "      <td>12.3346</td>\n",
       "      <td>0.358288</td>\n",
       "      <td>39.9423</td>\n",
       "      <td>71.9821</td>\n",
       "      <td>5173.3</td>\n",
       "      <td>44.8878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06 05:31:00</th>\n",
       "      <td>10.4835</td>\n",
       "      <td>6953.01</td>\n",
       "      <td>6258.83</td>\n",
       "      <td>1791.12</td>\n",
       "      <td>16.136</td>\n",
       "      <td>294.881</td>\n",
       "      <td>0.863919</td>\n",
       "      <td>31.9225</td>\n",
       "      <td>1.58368</td>\n",
       "      <td>1.41579</td>\n",
       "      <td>...</td>\n",
       "      <td>5046.12</td>\n",
       "      <td>61.3404</td>\n",
       "      <td>45.0006</td>\n",
       "      <td>12618.8</td>\n",
       "      <td>12.3348</td>\n",
       "      <td>0.354895</td>\n",
       "      <td>40.1479</td>\n",
       "      <td>71.8553</td>\n",
       "      <td>5150.27</td>\n",
       "      <td>44.8878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06 05:32:00</th>\n",
       "      <td>10.4831</td>\n",
       "      <td>6955.81</td>\n",
       "      <td>6253.46</td>\n",
       "      <td>1800.24</td>\n",
       "      <td>16.136</td>\n",
       "      <td>293.065</td>\n",
       "      <td>0.899928</td>\n",
       "      <td>31.9358</td>\n",
       "      <td>1.55908</td>\n",
       "      <td>1.43375</td>\n",
       "      <td>...</td>\n",
       "      <td>5070.73</td>\n",
       "      <td>64.2192</td>\n",
       "      <td>44.8348</td>\n",
       "      <td>12603.4</td>\n",
       "      <td>12.3388</td>\n",
       "      <td>0.349461</td>\n",
       "      <td>39.9234</td>\n",
       "      <td>72.0669</td>\n",
       "      <td>5218.75</td>\n",
       "      <td>44.8878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06 05:33:00</th>\n",
       "      <td>10.4821</td>\n",
       "      <td>6956.2</td>\n",
       "      <td>6256.94</td>\n",
       "      <td>1788.26</td>\n",
       "      <td>16.136</td>\n",
       "      <td>291.898</td>\n",
       "      <td>0.886091</td>\n",
       "      <td>32.0162</td>\n",
       "      <td>1.55133</td>\n",
       "      <td>1.44379</td>\n",
       "      <td>...</td>\n",
       "      <td>5038.52</td>\n",
       "      <td>68.3223</td>\n",
       "      <td>44.4711</td>\n",
       "      <td>12603</td>\n",
       "      <td>12.3378</td>\n",
       "      <td>0.346761</td>\n",
       "      <td>40.1441</td>\n",
       "      <td>71.9733</td>\n",
       "      <td>5176.32</td>\n",
       "      <td>44.8878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06 05:34:00</th>\n",
       "      <td>10.4829</td>\n",
       "      <td>6943.73</td>\n",
       "      <td>6261.08</td>\n",
       "      <td>1792.45</td>\n",
       "      <td>16.136</td>\n",
       "      <td>294.364</td>\n",
       "      <td>0.926062</td>\n",
       "      <td>32.2032</td>\n",
       "      <td>1.53359</td>\n",
       "      <td>1.42751</td>\n",
       "      <td>...</td>\n",
       "      <td>5081.67</td>\n",
       "      <td>65.128</td>\n",
       "      <td>45.1929</td>\n",
       "      <td>12604.2</td>\n",
       "      <td>12.3391</td>\n",
       "      <td>0.346761</td>\n",
       "      <td>40.0629</td>\n",
       "      <td>72.1788</td>\n",
       "      <td>5193.33</td>\n",
       "      <td>44.8878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     SAG rpm Feed Bearing kPa Dis Bearing kPa SAG wtph  \\\n",
       "Timestamp                                                                \n",
       "2017-01-06 05:30:00  10.4816          6952.17         6261.19  1780.89   \n",
       "2017-01-06 05:31:00  10.4835          6953.01         6258.83  1791.12   \n",
       "2017-01-06 05:32:00  10.4831          6955.81         6253.46  1800.24   \n",
       "2017-01-06 05:33:00  10.4821           6956.2         6256.94  1788.26   \n",
       "2017-01-06 05:34:00  10.4829          6943.73         6261.08  1792.45   \n",
       "\n",
       "                    Pebble CSS  Recirc. Pebble Reject Totalizer  \\\n",
       "Timestamp                                                         \n",
       "2017-01-06 05:30:00     16.136  292.654                0.863263   \n",
       "2017-01-06 05:31:00     16.136  294.881                0.863919   \n",
       "2017-01-06 05:32:00     16.136  293.065                0.899928   \n",
       "2017-01-06 05:33:00     16.136  291.898                0.886091   \n",
       "2017-01-06 05:34:00     16.136  294.364                0.926062   \n",
       "\n",
       "                    Screen Deck amp FD1 D80 inch FD2 D80 inch  ...  \\\n",
       "Timestamp                                                      ...   \n",
       "2017-01-06 05:30:00          31.862      1.58594      1.40429  ...   \n",
       "2017-01-06 05:31:00         31.9225      1.58368      1.41579  ...   \n",
       "2017-01-06 05:32:00         31.9358      1.55908      1.43375  ...   \n",
       "2017-01-06 05:33:00         32.0162      1.55133      1.44379  ...   \n",
       "2017-01-06 05:34:00         32.2032      1.53359      1.42751  ...   \n",
       "\n",
       "                    BM1 Cyc Feed Flow Rate BM2 Pump Box Water flow  \\\n",
       "Timestamp                                                            \n",
       "2017-01-06 05:30:00                5055.65                 60.7807   \n",
       "2017-01-06 05:31:00                5046.12                 61.3404   \n",
       "2017-01-06 05:32:00                5070.73                 64.2192   \n",
       "2017-01-06 05:33:00                5038.52                 68.3223   \n",
       "2017-01-06 05:34:00                5081.67                  65.128   \n",
       "\n",
       "                    BM2 Pump Box Level BM2 Power kW BM2 SPEED  \\\n",
       "Timestamp                                                       \n",
       "2017-01-06 05:30:00             44.323      12605.7   12.3346   \n",
       "2017-01-06 05:31:00            45.0006      12618.8   12.3348   \n",
       "2017-01-06 05:32:00            44.8348      12603.4   12.3388   \n",
       "2017-01-06 05:33:00            44.4711        12603   12.3378   \n",
       "2017-01-06 05:34:00            45.1929      12604.2   12.3391   \n",
       "\n",
       "                    BM2 Feed Water Flow BM2 Cyclone kPa BM2 CYC. FEED DENSITY  \\\n",
       "Timestamp                                                                       \n",
       "2017-01-06 05:30:00            0.358288         39.9423               71.9821   \n",
       "2017-01-06 05:31:00            0.354895         40.1479               71.8553   \n",
       "2017-01-06 05:32:00            0.349461         39.9234               72.0669   \n",
       "2017-01-06 05:33:00            0.346761         40.1441               71.9733   \n",
       "2017-01-06 05:34:00            0.346761         40.0629               72.1788   \n",
       "\n",
       "                    BM2 Cyc Feed Flow Rate OSA FF %Solid  \n",
       "Timestamp                                                 \n",
       "2017-01-06 05:30:00                 5173.3       44.8878  \n",
       "2017-01-06 05:31:00                5150.27       44.8878  \n",
       "2017-01-06 05:32:00                5218.75       44.8878  \n",
       "2017-01-06 05:33:00                5176.32       44.8878  \n",
       "2017-01-06 05:34:00                5193.33       44.8878  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trim rows and set columns and index\n",
    "df.loc[0, 0] = 'Timestamp'\n",
    "df.columns = df.loc[0]\n",
    "df.columns.name = None\n",
    "df = df.loc[2:]\n",
    "df = df.set_index('Timestamp')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1440, 37)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SAG rpm                    float64\n",
       "Feed Bearing kPa           float64\n",
       "Dis Bearing kPa            float64\n",
       "SAG wtph                   float64\n",
       "Pebble CSS                 float64\n",
       "Recirc.                    float64\n",
       "Pebble Reject Totalizer    float64\n",
       "Screen Deck amp            float64\n",
       "FD1 D80 inch               float64\n",
       "FD2 D80 inch               float64\n",
       "FD3 D80 inch               float64\n",
       "FD1 Speed%                 float64\n",
       "FD2 Speed%                 float64\n",
       "FD3 Speed%                 float64\n",
       "SAG kW                     float64\n",
       "Pebble kW                  float64\n",
       "SAG Water                  float64\n",
       "Dis Water                  float64\n",
       "Push Water                 float64\n",
       "Transfer %Solid            float64\n",
       "BM1 Pump Box Water flow    float64\n",
       "BM1 Pump Box Level         float64\n",
       "BM1 Power kW               float64\n",
       "BM1 SPEED                  float64\n",
       "BM1 Feed Water Flow        float64\n",
       "BM1 Cyclone kPa            float64\n",
       "BM1 CYC. FEED DENSITY      float64\n",
       "BM1 Cyc Feed Flow Rate     float64\n",
       "BM2 Pump Box Water flow    float64\n",
       "BM2 Pump Box Level         float64\n",
       "BM2 Power kW               float64\n",
       "BM2 SPEED                  float64\n",
       "BM2 Feed Water Flow        float64\n",
       "BM2 Cyclone kPa            float64\n",
       "BM2 CYC. FEED DENSITY      float64\n",
       "BM2 Cyc Feed Flow Rate     float64\n",
       "OSA FF %Solid              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in df:\n",
    "    df[col] = pd.to_numeric(df[col])\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "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>SAG rpm</th>\n",
       "      <th>Feed Bearing kPa</th>\n",
       "      <th>Dis Bearing kPa</th>\n",
       "      <th>SAG wtph</th>\n",
       "      <th>Pebble CSS</th>\n",
       "      <th>Recirc.</th>\n",
       "      <th>Pebble Reject Totalizer</th>\n",
       "      <th>Screen Deck amp</th>\n",
       "      <th>FD1 D80 inch</th>\n",
       "      <th>FD2 D80 inch</th>\n",
       "      <th>...</th>\n",
       "      <th>BM1 Cyc Feed Flow Rate</th>\n",
       "      <th>BM2 Pump Box Water flow</th>\n",
       "      <th>BM2 Pump Box Level</th>\n",
       "      <th>BM2 Power kW</th>\n",
       "      <th>BM2 SPEED</th>\n",
       "      <th>BM2 Feed Water Flow</th>\n",
       "      <th>BM2 Cyclone kPa</th>\n",
       "      <th>BM2 CYC. FEED DENSITY</th>\n",
       "      <th>BM2 Cyc Feed Flow Rate</th>\n",
       "      <th>OSA FF %Solid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1440.00000</td>\n",
       "      <td>1440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.481519</td>\n",
       "      <td>6976.487751</td>\n",
       "      <td>6272.578553</td>\n",
       "      <td>1807.925945</td>\n",
       "      <td>15.958596</td>\n",
       "      <td>306.743716</td>\n",
       "      <td>0.995889</td>\n",
       "      <td>32.225269</td>\n",
       "      <td>1.560141</td>\n",
       "      <td>1.419372</td>\n",
       "      <td>...</td>\n",
       "      <td>5060.368817</td>\n",
       "      <td>54.763743</td>\n",
       "      <td>46.184280</td>\n",
       "      <td>12590.485243</td>\n",
       "      <td>12.335337</td>\n",
       "      <td>0.350923</td>\n",
       "      <td>39.999418</td>\n",
       "      <td>71.910470</td>\n",
       "      <td>5155.29411</td>\n",
       "      <td>45.221231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.003083</td>\n",
       "      <td>22.971586</td>\n",
       "      <td>23.366039</td>\n",
       "      <td>44.962537</td>\n",
       "      <td>2.217289</td>\n",
       "      <td>23.043017</td>\n",
       "      <td>0.215196</td>\n",
       "      <td>0.503777</td>\n",
       "      <td>0.103332</td>\n",
       "      <td>0.038855</td>\n",
       "      <td>...</td>\n",
       "      <td>38.989584</td>\n",
       "      <td>45.384543</td>\n",
       "      <td>3.478773</td>\n",
       "      <td>60.242943</td>\n",
       "      <td>0.002996</td>\n",
       "      <td>0.003967</td>\n",
       "      <td>0.168657</td>\n",
       "      <td>0.794754</td>\n",
       "      <td>45.63280</td>\n",
       "      <td>1.517136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.472630</td>\n",
       "      <td>6913.743000</td>\n",
       "      <td>6206.637000</td>\n",
       "      <td>1697.510000</td>\n",
       "      <td>12.944010</td>\n",
       "      <td>256.555500</td>\n",
       "      <td>0.759294</td>\n",
       "      <td>31.132540</td>\n",
       "      <td>1.373525</td>\n",
       "      <td>1.306794</td>\n",
       "      <td>...</td>\n",
       "      <td>4952.278000</td>\n",
       "      <td>0.088800</td>\n",
       "      <td>40.634440</td>\n",
       "      <td>12435.150000</td>\n",
       "      <td>12.323600</td>\n",
       "      <td>0.346761</td>\n",
       "      <td>39.396870</td>\n",
       "      <td>69.960480</td>\n",
       "      <td>5029.53000</td>\n",
       "      <td>41.071870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.479378</td>\n",
       "      <td>6962.583250</td>\n",
       "      <td>6257.459250</td>\n",
       "      <td>1780.932500</td>\n",
       "      <td>14.120010</td>\n",
       "      <td>290.711275</td>\n",
       "      <td>0.870844</td>\n",
       "      <td>31.880225</td>\n",
       "      <td>1.487003</td>\n",
       "      <td>1.394648</td>\n",
       "      <td>...</td>\n",
       "      <td>5032.513250</td>\n",
       "      <td>0.199455</td>\n",
       "      <td>44.590863</td>\n",
       "      <td>12554.907500</td>\n",
       "      <td>12.333410</td>\n",
       "      <td>0.346761</td>\n",
       "      <td>39.890313</td>\n",
       "      <td>71.381438</td>\n",
       "      <td>5123.79375</td>\n",
       "      <td>44.561560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.481475</td>\n",
       "      <td>6975.705500</td>\n",
       "      <td>6270.785000</td>\n",
       "      <td>1818.695500</td>\n",
       "      <td>15.128020</td>\n",
       "      <td>303.787350</td>\n",
       "      <td>0.908780</td>\n",
       "      <td>32.158715</td>\n",
       "      <td>1.538914</td>\n",
       "      <td>1.421941</td>\n",
       "      <td>...</td>\n",
       "      <td>5061.472000</td>\n",
       "      <td>57.229145</td>\n",
       "      <td>45.161420</td>\n",
       "      <td>12601.250000</td>\n",
       "      <td>12.335420</td>\n",
       "      <td>0.350667</td>\n",
       "      <td>39.995480</td>\n",
       "      <td>71.942695</td>\n",
       "      <td>5156.97950</td>\n",
       "      <td>45.385360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.483660</td>\n",
       "      <td>6992.094250</td>\n",
       "      <td>6288.244000</td>\n",
       "      <td>1843.820000</td>\n",
       "      <td>18.992000</td>\n",
       "      <td>320.190725</td>\n",
       "      <td>1.025239</td>\n",
       "      <td>32.495405</td>\n",
       "      <td>1.621079</td>\n",
       "      <td>1.446005</td>\n",
       "      <td>...</td>\n",
       "      <td>5087.724500</td>\n",
       "      <td>90.681482</td>\n",
       "      <td>45.903370</td>\n",
       "      <td>12636.650000</td>\n",
       "      <td>12.337272</td>\n",
       "      <td>0.353279</td>\n",
       "      <td>40.105150</td>\n",
       "      <td>72.503750</td>\n",
       "      <td>5188.02500</td>\n",
       "      <td>46.136358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.492360</td>\n",
       "      <td>7037.431000</td>\n",
       "      <td>6335.180000</td>\n",
       "      <td>1885.738000</td>\n",
       "      <td>19.374250</td>\n",
       "      <td>383.882700</td>\n",
       "      <td>2.005210</td>\n",
       "      <td>34.203490</td>\n",
       "      <td>2.001935</td>\n",
       "      <td>1.519087</td>\n",
       "      <td>...</td>\n",
       "      <td>5178.790000</td>\n",
       "      <td>168.368400</td>\n",
       "      <td>61.740430</td>\n",
       "      <td>12704.200000</td>\n",
       "      <td>12.346020</td>\n",
       "      <td>0.373074</td>\n",
       "      <td>40.567880</td>\n",
       "      <td>73.985370</td>\n",
       "      <td>5306.72300</td>\n",
       "      <td>48.339210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           SAG rpm  Feed Bearing kPa  Dis Bearing kPa     SAG wtph  \\\n",
       "count  1440.000000       1440.000000      1440.000000  1440.000000   \n",
       "mean     10.481519       6976.487751      6272.578553  1807.925945   \n",
       "std       0.003083         22.971586        23.366039    44.962537   \n",
       "min      10.472630       6913.743000      6206.637000  1697.510000   \n",
       "25%      10.479378       6962.583250      6257.459250  1780.932500   \n",
       "50%      10.481475       6975.705500      6270.785000  1818.695500   \n",
       "75%      10.483660       6992.094250      6288.244000  1843.820000   \n",
       "max      10.492360       7037.431000      6335.180000  1885.738000   \n",
       "\n",
       "        Pebble CSS      Recirc.  Pebble Reject Totalizer  Screen Deck amp  \\\n",
       "count  1440.000000  1440.000000              1440.000000      1440.000000   \n",
       "mean     15.958596   306.743716                 0.995889        32.225269   \n",
       "std       2.217289    23.043017                 0.215196         0.503777   \n",
       "min      12.944010   256.555500                 0.759294        31.132540   \n",
       "25%      14.120010   290.711275                 0.870844        31.880225   \n",
       "50%      15.128020   303.787350                 0.908780        32.158715   \n",
       "75%      18.992000   320.190725                 1.025239        32.495405   \n",
       "max      19.374250   383.882700                 2.005210        34.203490   \n",
       "\n",
       "       FD1 D80 inch  FD2 D80 inch  ...  BM1 Cyc Feed Flow Rate  \\\n",
       "count   1440.000000   1440.000000  ...             1440.000000   \n",
       "mean       1.560141      1.419372  ...             5060.368817   \n",
       "std        0.103332      0.038855  ...               38.989584   \n",
       "min        1.373525      1.306794  ...             4952.278000   \n",
       "25%        1.487003      1.394648  ...             5032.513250   \n",
       "50%        1.538914      1.421941  ...             5061.472000   \n",
       "75%        1.621079      1.446005  ...             5087.724500   \n",
       "max        2.001935      1.519087  ...             5178.790000   \n",
       "\n",
       "       BM2 Pump Box Water flow  BM2 Pump Box Level  BM2 Power kW    BM2 SPEED  \\\n",
       "count              1440.000000         1440.000000   1440.000000  1440.000000   \n",
       "mean                 54.763743           46.184280  12590.485243    12.335337   \n",
       "std                  45.384543            3.478773     60.242943     0.002996   \n",
       "min                   0.088800           40.634440  12435.150000    12.323600   \n",
       "25%                   0.199455           44.590863  12554.907500    12.333410   \n",
       "50%                  57.229145           45.161420  12601.250000    12.335420   \n",
       "75%                  90.681482           45.903370  12636.650000    12.337272   \n",
       "max                 168.368400           61.740430  12704.200000    12.346020   \n",
       "\n",
       "       BM2 Feed Water Flow  BM2 Cyclone kPa  BM2 CYC. FEED DENSITY  \\\n",
       "count          1440.000000      1440.000000            1440.000000   \n",
       "mean              0.350923        39.999418              71.910470   \n",
       "std               0.003967         0.168657               0.794754   \n",
       "min               0.346761        39.396870              69.960480   \n",
       "25%               0.346761        39.890313              71.381438   \n",
       "50%               0.350667        39.995480              71.942695   \n",
       "75%               0.353279        40.105150              72.503750   \n",
       "max               0.373074        40.567880              73.985370   \n",
       "\n",
       "       BM2 Cyc Feed Flow Rate  OSA FF %Solid  \n",
       "count              1440.00000    1440.000000  \n",
       "mean               5155.29411      45.221231  \n",
       "std                  45.63280       1.517136  \n",
       "min                5029.53000      41.071870  \n",
       "25%                5123.79375      44.561560  \n",
       "50%                5156.97950      45.385360  \n",
       "75%                5188.02500      46.136358  \n",
       "max                5306.72300      48.339210  \n",
       "\n",
       "[8 rows x 37 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SAG rpm                       0\n",
       "Feed Bearing kPa              0\n",
       "Dis Bearing kPa               0\n",
       "SAG wtph                      0\n",
       "Pebble CSS                    0\n",
       "Recirc.                       0\n",
       "Pebble Reject Totalizer       0\n",
       "Screen Deck amp               0\n",
       "FD1 D80 inch                  0\n",
       "FD2 D80 inch                  0\n",
       "FD3 D80 inch                  0\n",
       "FD1 Speed%                    0\n",
       "FD2 Speed%                    0\n",
       "FD3 Speed%                    0\n",
       "SAG kW                        0\n",
       "Pebble kW                     0\n",
       "SAG Water                     0\n",
       "Dis Water                     0\n",
       "Push Water                    0\n",
       "Transfer %Solid            1439\n",
       "BM1 Pump Box Water flow       0\n",
       "BM1 Pump Box Level            0\n",
       "BM1 Power kW                  0\n",
       "BM1 SPEED                     0\n",
       "BM1 Feed Water Flow           0\n",
       "BM1 Cyclone kPa               0\n",
       "BM1 CYC. FEED DENSITY         0\n",
       "BM1 Cyc Feed Flow Rate        0\n",
       "BM2 Pump Box Water flow       0\n",
       "BM2 Pump Box Level            0\n",
       "BM2 Power kW                  0\n",
       "BM2 SPEED                     0\n",
       "BM2 Feed Water Flow           0\n",
       "BM2 Cyclone kPa               0\n",
       "BM2 CYC. FEED DENSITY         0\n",
       "BM2 Cyc Feed Flow Rate        0\n",
       "OSA FF %Solid                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df['Feed Bearing kPa']\n",
    "y = df['Dis Bearing kPa']\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.scatter(x, y)\n",
    "plt.xlabel(x.name)\n",
    "plt.ylabel(y.name)\n",
    "plt.title('Correlation of bearing pressure measurements')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3))\n",
    "plt.plot(y/x)\n",
    "plt.title('Ratio of Bearing Pressures (Discharge/Feed)')\n",
    "plt.ylim(0.89, 0.91)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ur8LmXRmkZWQzf/1ObntzFg9d3CN/mUhz1+7ggie/JbtQF//PXvw+f3raim08c1WfIl8nkeIowVeQH3dncs8H8zi7R3PO7NbsUDfnkFmzbQ/nPBYcaX86fyMA5/dswaOX9uLWf6fw2pTVTFm+jfG3nkKVIn7sClu7fS8pa7ZzSsskXr62P2Zw+9uzeWtGKpt2ZfDKtf3KtLNQUTbtTGfEs5OLnb9+RzpDHg6S/+OXHcd5PVscrKbFtPnrdvL4l0sLlJ380Fc8dtlx9Glb8Eh+7fa9+dOPf7mE24d1iWob7s494+YxcckWLjyuJf9bxI5gxya1+PK2gZz28Nc8+vkSOtZL4JavPmFXRjYAQ49pyk/6tKRV/RrMSt3OtBXb6Ni4Fu0b1+ScHs2LTLRtGtbgw1sKHqnvSs9i5AvT+M/MVKav2kabBjX40zld6dS0FmbGpp3pnPv4pH3raFCDXHcu7duahz9bzGldmvDlwk18On8j5z/xLeN+dVKBbbs7O/ZmUa9GclSvjVS8tIxsalWNzVQam606DL303UreT1nH+ynr+OK2gUfskduL364E4JK+rfjv7PXszszhjrO6kJBgPDaiFzv2ZvHN4s089dUybh16dKnrm7VmOwCD2yTlJ/JfDDyKt2akMmnpFp6duJwbBx5VoTFsTcvg3R/W8pM+rahXI5nVW/fQukF1zIylm3Yx9JFvgOBUwZgRvcjIyuWcY5uzcWc6m3dlUL9mMtv3ZHHJM8EOwC1v/MCrk1fxp3OP4dhW9Urc9oxV27h73DxWbN7NuzefRKemtSs0tkNt9bY9ADxx+XEM6dKU61/5nm+XbuXip7/jtev6UyXRWLFlN9WqJDJh0ab85Walbo96G++lrOXlyatISjBGXdC92HodGtfi/ZtPYviT37J0ey4QnCv/+0+O5ad9W+fX696yLlf0b1vGSAO1q1XhPzedyFszUrn97dms2rqHMx/9Zr963VrU4T83nVjgFM8tQ4Lvx4vfruDeD+YzZ+0O2t/1Edec1I6/nNuVxRvTuOK5qWxJy6BOtSR+PeRo5q3bSdaOTH436XMa1UpmzIjj6Nwsvj5DsSI9K4efvTiNKcu3cd3J7fnzuV0PdZP2Y7E+YKlv374+ffr0g7a9CRMmMGjQoDIt8/HcDdz42gzq1ajC9j1ZALx/80n0bF3yj3llO5BYymPB+p2c+/gkhnVvxpOX9y6yzuZdGRw/+nOa1K7KxDsGUzWp5G7XB8Yv4MVJK3lySDVOP21wfvmOvVmc+MAXAHz8m1Np3aBGhcQwbtY6fv3GD0BwNLUzPYvte7I4o2tTerSsy8MR3b4f3nIy3VvWLXZd7s47M9dy21uz8suu6N+GofW2MHjw4AJ1d+zN4o/vzuHT+RvJzN43KOuEDg25/4JudGwSmz/SZf2M/eHdOYydtprZ95xJrapJuDvt7/qo2Po9WtalT9v6vDl9DSl/OYPkpOJ7fXJznRtenc7nC4Idg5euOZ5BnZuU2iZ3Z8xbX/DqIufhS3pGtcyB2JuZQ897PyWz0KC7h3/ak4t6tyyx+z0n1/nTe3N4Y9qaYuuU5M6zulT4jnB5rN66hy27M+jQqGaJvQ/RfL4ys3O58rmpXNi7JZf1a1Ni3fJIz8phV3o2jWtXZey01dz5zpz8eU9d0ZuzezQvcfmyfFfMbIa79y1Pe0FH8OW2eOMubnxtBgAv/Ox4tqZl8vNXpjP8yW95+dp+/Or1mYy75WTaN6p5iFtauXbszeKXr88MzkeWsCfbuHZVXrm2H1e/MI3j7vuMefeeud8P29a0DMbP3cCabXt45pvldG9ZhyoJOQXq1K1ehU9+eypDH/ma+z6cz7PlOD85J3UHNaom8sbU1Tw3KRgz0KhWVX7cnZnfZfvp/I35pxzuPq8rV/RvW2KyATAzLu7TiiHHNOGZb5bz4rcreH3qatr3r8ZggsSyfU8Wz09awRNfBd3W/do14Lend2LC4k1MXraVycu3MvSRb/jbxcdyyfGtS9xeLMnNdWav3UHHJrX4dN4Gtu3O5JHPFrMnM4fWDarnd2maGSseOJv3U9bxzg9r2bk3i7N7NOOvHy0EYPSF3dm4M4OXvlvJ+LnrGd6rZbHbfOjjhfnJ/a0bT+D4dg2iaquZ0atJEr+5ZFD5gi5F9eREFo8+i+ycXJZt3s2Gnek0r1stql6axATjgYuO5Y5hXeh132f55YM7N6ZpnWqYGWf3aMamnRmkzFtAj2O6ULVKAreOTQHgwfELGdy5CZ2a1mLFlt1k5uTy+7dmc3rXptxyWseDem6/8NiVibcPLtcO+j+/Xsa0lduYtnIbG3em85uhnfark5mdy7x1O2hfyg5FpIzsHGan7mBcyroCV01EGtS5MQ9c1IPmdasfcPsrkxJ8OWzamc4Z/xt0t737yxM5rk193J0bBx7FP79exsgXpgHw3g9r+e3p+3/oINir/2TeBrJycvn928EI3Kl/GELTOtUOThAV5JoXp7Fy627+76q+NCml7acc3QiAPZk5vDU9tUDimrRkC1c+P7VA/ZM7NgY27LeeVvVrcHm/trzw7Qq+WbKFgZ0al7ndz3y9jAfGLyxQNvmu02hetzqpP+5h4pItnN61KU9PWMbyzWncN7x7mX+M6tVI5o5hXbjmpHb0G/0Fo6amM2rqf/erd9WAttwfdimfcFRDAP728UKemrCMO9+Zzc70LAZ3aXJITv/syczmnZlreXtGKgvW72RAh4b53d9b0jLYlZ7N3ePm8c3izbSoW411O9KLXdczVxY8MDEzLjiuJRccty95j+jXhs27MjiqcS0ys3NpUDOZW8emcEbXZkUOtlu5ZTfPfLMcgPn3nUmN5Nj9aUtKTKBzs9oH1HVer0YyD13cgzv+M4eHLu6x3xUAAA13LWVQ+J0699gWfDBrHb/5d0qRpwbmrN2BO6WeLlu1dTertu5h5uofmbxsKzWSExnUuQkTl2whLSOLtg1q0q5RTZrUrsptb82if/sG3H1eN7q2qJO/jpxc58c9mbz7Q2qBdZ/7+CR+MbADN5561H7jaTbvyWX7nswSk/LEJZsxA3d49PMlfDx3AwM7N+aus47Jr3Pl81OZtmIbEOwU/e0nPflw9jqmLt/GrowscnPhvJ4taN+oJs9PWsHnCzaW+Hq0bViD609uz5UD2sb0wMeouujNrB7wHNAdcOBad59sZrcAvwKygf+6++1h/buA64Ac4Nfu/klY3gd4CagOfATc6qU0oDK76N2dnFwnLSObJ75cStUqCaxds5oLTunFqUc3JiHBePm7lYz5Ygm/HHQUrepXZ1j35vnLXv5/U5m8fCt3DOvCTYMKdn/97MVpTFi0GYCLjmu534jvrxdv5p2Zqbyfsq7IthU3srgsDkYX/Yotu7nu5e9Zvnk3Q49pwnMjj49qudmp2zn/iW8B+OK2gTSvW41/fLKYF77dN+p+6DFNGXVBd5rUrso333xdZCzpWTkMeOALduzN4svbBtG+UU1ycp1c9xIH8bk7N/9rJh/N2bfj0K5hDa4/pQNXDjiw863ReGrCUh79dBGZEb205/VswX3nd6N+zaJ/xLbtzuTK56Yyf/1OAE7v2pTHLzvugC7J+3rxZka+MI37L+jOVWGcm3amF7tT5u5s3pVBv79+UeZtQXBu+fh2Ddi6O5Ofn9K+1DEIxRn14Xyem7SC83u24NahR1OralL+TvDmXRkMf2ISW9IyefPGE+h1AKfGDvbprMpUVCwfzFrHrWN/IG+g/nk9W3Bmt6bc+8F8Nu/K4MnLe3POsft3MefkOq9MXsm9H8wvV5ueuaoP76eszf++dWlWm49/cypfLdzENS/tu1rg5I6NuO2MTjSvW52UNdvze0fH33oKxzSvs996M7Jz6HHPp1w9oC2ndWnC5c/tOzgY0KEBr1zbn0c+W8w/v152QO2uXiWRPm3rc1ybevxPeJCWnVvyb0txDkUXfbQJ/mVgors/Z2bJQA3gOOCPwDnunmFmTdx9k5l1Bd4A+gEtgM+BTu6eY2bTgFuBKQQJ/jF3H1/Stisywadn5VA17FZ9a0Yq94ybx57MnGLr16mWxM707AJlr1zbj+4t63Lbmyl8tWgzw3u1YMyI4/ZbdvnmNE4LR1HXTE5k6h+H5ndLTl+5jZ/8s+AI7B4t63JJ31b8+f15QPAFfPTSXiSWY4R4Zf5opWVk85f35/LOzLX5ZWXteXh6wjL+9slCCn8Ex4zotV9XbEmx5CWtXwzsQO2qSfnXKl/Wrw33nN+1yPP8vxn7A++lrCM5KYHpfxpKcmLCQbuG/auvvuLkUwcyO3UHa7btKXDkWpzM7Fye/WZZfmzDujXjn1f1KdN2V2zZzeB/TMh/npRg+Zdm5b3mf/1oAZ/N38jx7eqzY28W67anM2ftjvxlerauR4u61ViyKY2lm9L228ajl/aid5v6tGlYo0JHF7s7v397Nm/P2Hf0VyM5scD39w9nd+GGUw/sPHO8J/g8P+7OpG71KvlHyulZOQx5+Ov8Kxb6tW/AlQPact6xzVm3I52THvwyf9kTj2rISR0bMaBDA+rVSGbysq0kJhhVEhOYnbqd9o1q8uXCTWzamUHvtvWKHS/QvlFN7hjWOf9gacKiTQUuCSxKh8Y1eWHk8bRuUIMEg1wPBmzeM24eXy/ezHNX92Vo16bMXP0jn8zdkN+bEynlL6dTt3oVXpuyKv939hcDO3Bl/7ZMXbGNd2amYgandWnKkC5NaFGveqmn4coiJhO8mdUBZgEdIo+2zexN4Fl3/7xQ/bsA3P2B8PknwD3ASuArd+8Sll8GDHL3X5S0/YpM8L98fUaBI7ZI1askcvuwzsyYt4SJ64Nzynku7duaejWq7PehaVG3Gp/fNrDY7sClm3Yxd+1OfvPvFJ4f2ZchxzRlV3oWwx6dyM69WUz4/SAAGkbc+CU7J5frXp7O14s3c3STWnz621MxM3Jygzt6FXUdbXHK+qOVFp5v3rk3ixb1ij6n5O4s3pjGqP/OZ+KSLQzp0oTuLety8+COB/RlWLt9L69OXkXKmh/ZsTebV67tR+Pa+98Ip6RY3J0bXp3BZ/P3das1rJnM1t2ZQJDoR13QncQEY0taBu/9sJZR/10AwPd/HFrk9ipTeZLJt0u3cEV4lNKtRR0euvjY/Qb6Ld+cRsNaValbveCNgK596Xu+XLiJO8/qwoOFTktEo3CvkrvzwWcTOP+MweTmeqVfrpiVk8uv3/iB8XM3FNg5Abj+5PbcdkbnMl8rn+dISfBFmbt2B/d+MI/vV/5YbJ3P/2cgHZuU7dTQpl3pTFm+jekrt7F8826uPbkdgzs3KbZnck9mNh/P3cCf35tL1SqJbNudyUktkrjgxK75py+LcsrRjfi/q/sW2EHPzXWufmEak5ZuoWZyIm/cMKBA71FOrrM3K+egXt4Wq4PsOgCbgRfNrCcwg+AovBNwipmNBtKB37n790BLgiP0PKlhWVY4Xbj8oHB3erepn5/gf3ZiO/5w9jF8v3IbnZrWzv+Rb5+1isd/PpDNuzLYkpZJw1rJ+Uelp3ZqzO1vz2bt9r0M7NSYl645vsRu9I5NatOiXnXMgsu9hhzTlBtfm8Ha7Xu5f3i3Aok9T1JiAk9e0ZuTH/qSJZvSaH/XRwzr1oxJS7eQlpFNj5Z12bQrnV+ddnR+F2tFmLVmO8Of/Db/+bhfnZT/hdi0K50PZq3nhUkraF63GtNX/Zj/Gt5zfrdybbdlverceVZ01zcXxyy4HGrWmu1s2pWRf7e8j+du4KkJS3lj2mpmrNrG4M5NCuykPXRxj4Oe3MvrpI6NWDL6LF76diWjP1rAuY9P4uVr+xUYf5DXcxR5Lnrq8q18uXATvz+zMzcOPIpjmtdh0pLNDO7chCZ1qnLXO3Pyf+Bfv74/yzensW5HOred3okEM3Ld9/usmxl1koOyg3EvgiqJCTx1RW9yPbh18QMfLWRAhwaccnTjA07sElwG+MbPB7Dmx71MWb6V16euYu7anQzp0oSzezTn4j6tDmi9TWpX4/yeLTg/yntA1EhO4qLerbiodyvcnR/WbGfDohTO7tuaBjWTue7l/Q/0WtarzivX9tvvs5mQYDx5eW+Wbk7jqMb7D85IutoAABJDSURBVKxLTLCYvXa9IkVzBN+XIGGf5O5TzWwMsBO4EPiSINkfD/ybYGfgCWCyu78WLv88QXf8auABdx8alp8C3O7u5xWxzRuAGwCaNm3aZ+zYsRUQamBXprMr02lRq+ijzbS0NGrVqthBTPd+t5et6bmc3LIKH63I4sQWSfy8R3KJOwd7s527v9vLpj3Fvz8GJCfCHcdXo0O9/X/goo3lmVnpTF4fdHXmdX9VS4Tb+lYjORHu/q7ggKk6ycaZ7ZI4q30VEg7SAJNoYtmb7VRJCLqe8+S6c+0newrUa1LDGNqmCme0OzS3uq2oz9i4ZZm8syToaTq7fRWGtEmiSoLx66+CeH/eI5mTWgYx/mHSHnZmOH8fWIPqSUW/Z8t35NCyVgJVE6N/Tyvj+3IoxEscEL+xZOQ487bksDYtlzPbVSHXwYwyfV4PpbK8L4MHDz5oXfTNgCnu3i58fgpwJ5AIPOjuE8LyZcAA4HqIzS76aFRGV933K7fx04hz7nPvPTPqvcd563Ywb+1OTu3UmJpVE6lVNYnMnFwe/2Jp/qVVEJwf+9fPBxRYtqhY3J0tacFI1ot6t6LvqH1nWP55ZR+GdW/G+h17+cnTkwvcSQzggl4tuH1Yl2K77ytTed6XKcu3MuLZKfRr14BRF3Y/5DePqajPWGZ2LiNfmMbk5VuLrfOv6/vz0ncr+XT+Rv50zjEVfk/zeOnajpc4QLHEqpjsonf3DWa2xsw6u/siYAgwH1gGnAZMMLNOQDKwBRgH/MvMHiEYZHc0MC0cZLfLzAYAU4GrgcfLG8Dh4Ph2DTivZ3C5yns3n1SmrqFuLerSrUXBc6xVkxL53Zmd+e3pnRgZnmf6btlWJi7ZzClHl3yp2Kj/LuD58FrvvGuNWzeozuf/MzB/MFrzutUZe8MAfvn6zPwBVotGDSv1pjSxakCHhiz/69nAwelKPliSkxJ444YBfDx3A/+ZmUrLetV56buVdGhUk1EXdufy/5taYFTxgd6NTUQOT9FmmluA18MR9MuBa4DdwAtmNhfIBEaGg/DmhQPw5hNcPnezu+cNdb2JfZfJjQ8fR4QHL+rBz05sd0CX8BQnMcF49bp+LFi/i0ufmczfP1nECR0aFjkQb29mDvd9OG+/ka0X927Fw5fs/5+6WjeowQe3nMyWtAwa1Eg+7BPj4d7+kgzr3oxh3YP/fxA5JuIXp3bIH3Pw2GXH6Ty1yBEmqgTv7ilAUd0FVxZTfzQwuojy6QTX0h9xalZN2u+faVQEM6NrizqMurA7t45NoeMfx3NF/zYs3ZRGi8RMHkz5hpVbd5Oete/C669+F1wvHs0lkmX9164SO357eifqVK9Cn7b1GdCh4aFujogcZPE/jPAIMbxXSz6cvZ7P5m/k9amrI+ZkFai3eNRZ+ZezxfIdmKT8qlVJ5ObBHQ91M0TkEFGCjyNjRvTijv/MISs7l0GdG7Np1WL69zmO8XM30L1lXS48rmW5bpwjIiKHDyX4OFIjOYnHL9t3V70Je5bTv0ND+qt7VkTkiFNx9+ETERGRmKEELyIiEoeU4EVEROKQEryIiEgcUoIXERGJQ0rwIiIicUgJXkREJA4pwYuIiMQhJXgREZE4pAQvIiISh5TgRURE4pASvIiISBxSghcREYlDSvAiIiJxSAleREQkDinBi4iIxCEleBERkTikBC8iIhKHlOBFRETikBK8iIhIHFKCFxERiUNRJXgzq2dmb5vZQjNbYGYnRMz7nZm5mTWKKLvLzJaa2SIzOzOivI+ZzQnnPWZmVrHhiIiICER/BD8G+NjduwA9gQUAZtYaOB1YnVfRzLoCI4BuwDDgKTNLDGc/DdwAHB0+hlVADCIiIlJIqQnezOoApwLPA7h7prtvD2f/L3A74BGLDAfGunuGu68AlgL9zKw5UMfdJ7u7A68AF1RcKCIiIpInmiP4DsBm4EUz+8HMnjOzmmZ2PrDW3WcVqt8SWBPxPDUsaxlOFy4XERGRCmbBwXQJFcz6AlOAk9x9qpmNATIJjurPcPcdZrYS6OvuW8zsSWCyu78WLv888BFBN/4D7j40LD8FuN3dzytimzcQdOXTtGnTPmPHjq2YaKOQlpZGrVq1Dtr2KpNiiU2KJfbESxygWGJVWWIZPHjwDHfvW95tJkVRJxVIdfep4fO3gXuA9sCscJxcK2CmmfUL67eOWL4VsC4sb1VE+X7c/VngWYC+ffv6oEGDooumAkyYMIGDub3KpFhik2KJPfESByiWWHUoYim1i97dNwBrzKxzWDQEmOnuTdy9nbu3I0jevcO644ARZlbVzNoTDKab5u7rgV1mNiAcPX818H4lxCQiInLEi+YIHuAW4HUzSwaWA9cUV9Hd55nZm8B8IBu42d1zwtk3AS8B1YHx4UNEREQqWFQJ3t1TgGLPB4RH8ZHPRwOji6g3HehetiaKiIhIWelOdiIiInFICV5ERCQOKcGLiIjEISV4ERGROKQELyIiEoeU4EVEROKQEryIiEgcUoIXERGJQ0rwIiIicUgJXkREJA4pwYuIiMQhJXgREZE4pAQvIiISh5TgRURE4pASvIiISBxSghcREYlDSvAiIiJxSAleREQkDinBi4iIxCEleBERkTikBC8iIhKHlOBFRETikBK8iIhIHFKCFxERiUNRJXgzq2dmb5vZQjNbYGYnmNnfw+ezzexdM6sXUf8uM1tqZovM7MyI8j5mNiec95iZWWUEJSIicqSL9gh+DPCxu3cBegILgM+A7u5+LLAYuAvAzLoCI4BuwDDgKTNLDNfzNHADcHT4GFZBcYiIiEiEUhO8mdUBTgWeB3D3THff7u6funt2WG0K0CqcHg6MdfcMd18BLAX6mVlzoI67T3Z3B14BLqjgeERERASwINeWUMGsF/AsMJ/g6H0GcKu7746o8wHwb3d/zcyeAKa4+2vhvOeB8cBK4EF3HxqWnwLc4e7nFrHNGwiO9GnatGmfsWPHljfOqKWlpVGrVq2Dtr3KpFhik2KJPfESByiWWFWWWAYPHjzD3fuWd5tJUdbpDdzi7lPNbAxwJ/BnADP7I5ANvB7WL+q8updQvn+h+7MEOxX07dvXBw0aFEUzK8aECRM4mNurTIolNimW2BMvcYBiiVWHIpZozsGnAqnuPjV8/jZBwsfMRgLnAlf4vq6AVKB1xPKtgHVheasiykVERKSClZrg3X0DsMbMOodFQ4D5ZjYMuAM43933RCwyDhhhZlXNrD3BYLpp7r4e2GVmA8LR81cD71dkMCIiIhKIpose4BbgdTNLBpYD1wDfA1WBz8Kr3aa4+43uPs/M3iQ4Z58N3OzuOeF6bgJeAqoTnJcfX1GBiIiIyD5RJXh3TwEKn/DvWEL90cDoIsqnA93L0kAREREpO93JTkREJA4pwYuIiMQhJXgREZE4pAQvIiISh5TgRURE4pASvIiISBxSghcREYlDSvAiIiJxSAleREQkDinBi4iIxCEleBERkTikBC8iIhKHlOBFRETikBK8iIhIHFKCFxERiUNK8CIiInFICV5ERCQOKcGLiIjEISV4ERGROKQELyIiEoeU4EVEROKQEryIiEgcUoIXERGJQ1EleDOrZ2Zvm9lCM1tgZieYWQMz+8zMloR/60fUv8vMlprZIjM7M6K8j5nNCec9ZmZWGUGJiIgc6aI9gh8DfOzuXYCewALgTuALdz8a+CJ8jpl1BUYA3YBhwFNmlhiu52ngBuDo8DGsguIQERGRCKUmeDOrA5wKPA/g7pnuvh0YDrwcVnsZuCCcHg6MdfcMd18BLAX6mVlzoI67T3Z3B16JWEZEREQqUDRH8B2AzcCLZvaDmT1nZjWBpu6+HiD82ySs3xJYE7F8aljWMpwuXC4iIiIVLCnKOr2BW9x9qpmNIeyOL0ZR59W9hPL9V2B2A0FXPkCamS2Kop0VpRGw5SBurzIpltikWGJPvMQBiiVWlSWWthWxwWgSfCqQ6u5Tw+dvEyT4jWbW3N3Xh93vmyLqt45YvhWwLixvVUT5ftz9WeDZqKOoQGY23d37HoptVzTFEpsUS+yJlzhAscSqQxFLqV307r4BWGNmncOiIcB8YBwwMiwbCbwfTo8DRphZVTNrTzCYblrYjb/LzAaEo+evjlhGREREKlA0R/AAtwCvm1kysBy4hmDn4E0zuw5YDfwUwN3nmdmbBDsB2cDN7p4Trucm4CWgOjA+fIiIiEgFiyrBu3sKUFTXwpBi6o8GRhdRPh3oXpYGHgKH5NRAJVEssUmxxJ54iQMUS6w66LFYcMWaiIiIxBPdqlZERCQOHdYJ3syGhbfDXWpmd0aU/9TM5plZrpkVO2qxlNvtHmtmk8P1zDGzakUs397MpobL/zsco5A3b5CZpYTLf324xmJm9c3sXTObbWbTzKzEUywxEMevwm27mTWKKL8ijGG2mX1nZj1LiiPGYxlkZjvCz1eKmf3lMI6lrpl9YGazwuWvOQxieT3c/lwze8HMqoTlXcJlM8zsd6XFUZmxhJ/3lIhHrpn1KmL59lb0994suJ340vA70/swjWN42P4UM5tuZieXFEcsxxLOK1Newd0PyweQCCwjuBFPMjAL6BrOOwboDEwA+pawjr8Bd4bTdwIPhdNJwGygZ/i8IZBYxPJvAiPC6X8CN4XT9QgGGbYJnzc5jGP5O3B3ON2F4PbEsRzHcUA7YCXQKKL8RKB+OH0WMPUweE+Ki2UQ8OFh9l0pLpY/RKyrMbANSI7xWM4muK+HAW+w77vSBDieYPzR7w7l+1KoTg9geTHLF/e9P5tgELQBAyjh+xLjcdRi36noY4GFh/F7Uqa84u6HdYI/Afgk4vldwF2F6pT2RiwCmofTzYFFER/u10rZvhHctCCpcHuAXwKj4iSW/wInR9RdRnAXw5iLo9B6VhKRSArNqw+sjeX3pKRYKHuCj+VY7gKeCj+D7QlubZ1wOMQSLvNbYHShsnuILsFXWiyF6vy1cBvD8pK+988AlxW1ncMpjiLaueAwfk/KlFfc/bDuoi/ulrhlUdztdjsBbmafmNlMM7u9iGUbAtvdPbuI7XcC6pvZBDObYWZXH8axzAIuAjCzfgR3WGq13xpiI45oXUfpl2jGeiwnWNCtPd7MupVSN5ZjeYLgyGgdMAe41d1zS6gfM7GEXfNXAR+Xcft5KjOWSJcS9DQUVtL3vixti+U4MLMLzWwhwcHKtaW0I5ZjKWteifo6+FgU9a1vD0AScDJBd9se4Aszm+HuX0S5/SSgD8FlhNWByWY2xd0XF7O9WI7lQWCMmaUQ/AD/QHB/g6Ic6jhKZWaDCRJ8aefiYjmWmUBbd08zs7OB9whuKFWcWI7lTCAFOA04CvjMzCa6+85i6sdSLE8B37j7xAPcXmXGEmzArD+wx93nlnH7ZWlbLMeBu78LvGtmpwL3A0NL2lRJ66oI5YilrHnlsD6CL+6WuMUysxfDAQofhUUbLbjNLrb/7Xa/dvct7r4H+IjgfvyRtgD1zCxvJyly+6kE/153t7tvAb4h+De7h10s7r7T3a9x914Edx9sDKyI0ThKZGbHAs8Bw919aynVYzaW8D1JC6c/AqpYxMC1wykWgptmveOBpQSfrS6xHouZ3U3wXfifEqMrWWXGkmcERR8pQum/YdG2LZbjyOfu3wBHHcLvSnljKWteOazPwScR3FWvPfsGQ3QrVGcCJZ8r+TsFB0P8LZyuT3CUVCPczufAOUUs/xYFB0P8Mpw+BvgiXLYGMBfofpjGUo9w0BPwc+CVWI4jYj0rKXiutw3B+d0TD5fPVwmxNGPfwKF+BHeStMM0lqeBe8LppsBaihk7ESuxANcD3wHVi1n/PUR3Dr7SYgmfJxAkhQ4lLF/c9/4cCg6ym3aYxtGRfd+V3uHn65B8VyogljLlFXc/fBN8GPDZwGKCgV9/jCi/MHwRM4CNFDHgIqzXMHzBloR/G0TMuxKYF76Ifytm+Q7ANILE8RZQNWLe7wlGPM4FfnO4xkIwyGMJsBB4h3AkegzH8etwO9kEe77PheXPAT8SdAenANMPg/ekuFh+FS47C5hCFDstMRxLC+BTgtM/c4ErD4NYssNt532W/hKWNwu3vxPYHk7XOYSxDAKmlLL94r73BjwZtmsOJSS0GI/jjvD9TAEmEzFg+HCLJZxXpryiO9mJiIjEocP5HLyIiIgUQwleREQkDinBi4iIxCEleBERkTikBC8iIhKHlOBFRETikBK8iIhIHFKCFxERiUP/D32fpNPRg231AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3))\n",
    "plt.plot((x+y)/2)\n",
    "plt.title('Average Bearing Pressure')\n",
    "plt.ylim(6000,7000)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df['SAG wtph'].diff(10).rename('Change in feed (WTPH / 10 min)')\n",
    "y = (df['Feed Bearing kPa'] / df['Dis Bearing kPa'])\n",
    "y = y.rename('Pressure Ratio (Feed / Disch.)')\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.scatter(x, y)\n",
    "plt.xlabel(x.name)\n",
    "plt.ylabel(y.name)\n",
    "plt.title('Correlation of bearing pressures and feed rate')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.03451273536537047\n",
      "Coef., intercept: (2.6717235992935034e-05, 1.1122219157779847)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "delay = 3\n",
    "\n",
    "data_to_fit = pd.DataFrame({\n",
    "    f'Change in feed (WTPH) over {delay} min': df['SAG wtph'].diff(delay),\n",
    "    'Pressure Ratio (Feed / Disch.)': (df['Feed Bearing kPa'] / df['Dis Bearing kPa'])\n",
    "})\n",
    "\n",
    "data_to_fit = data_to_fit.dropna()\n",
    "x = data_to_fit[f'Change in feed (WTPH) over {delay} min'].values.reshape(-1,1)\n",
    "y = data_to_fit['Pressure Ratio (Feed / Disch.)'].values.reshape(-1,1)\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x, y)\n",
    "\n",
    "print(f\"R-squared: {model.score(x, y)}\")\n",
    "print(f\"Coef., intercept: ({model.coef_.item()}, {model.intercept_[0]})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data_to_fit[f'Change in feed (WTPH) over {delay} min']\n",
    "y = data_to_fit['Pressure Ratio (Feed / Disch.)']\n",
    "\n",
    "x_line = np.linspace(x.min(), x.max(), 100)\n",
    "best_fit_line = pd.Series(\n",
    "    data=model.predict(x_line.reshape(-1,1)).reshape(-1,), \n",
    "    index=x_line,\n",
    "    name='Best Fit'\n",
    ")\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.scatter(x, y, marker='.', alpha=0.25)\n",
    "best_fit_line.plot(style='r-') \n",
    "plt.xlabel(x.name)\n",
    "plt.ylabel(y.name)\n",
    "plt.title('Correlation of bearing pressures and feed rate')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (torch)",
   "language": "python",
   "name": "torch"
  },
  "language_info": {
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