p4_Visualizing+Correlation+Table_.ipynb

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   "execution_count": 6,
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ESX-FUTURE</th>\n",
       "      <th>SP-FUTURE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ESX-FUTURE</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.665239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SP-FUTURE</th>\n",
       "      <td>0.665239</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ESX-FUTURE  SP-FUTURE\n",
       "ESX-FUTURE    1.000000   0.665239\n",
       "SP-FUTURE     0.665239   1.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#https://www.youtube.com/watch?v=afjIo7gO86o ; Visualizing Correlation Table - Data Analysis with Python and Pandas p.4\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"datasets/market_data.csv\")\n",
    "\n",
    "act_market_data = pd.DataFrame()\n",
    "\n",
    "for name, group in df.groupby(\"Instrument\"):\n",
    "    if act_market_data.empty:\n",
    "        act_market_data = group.set_index(\"Timestamp\")[[\"Bid Price\"]].rename(columns={\"Bid Price\":name})\n",
    "    else:\n",
    "        act_market_data = act_market_data.join(group.set_index(\"Timestamp\")[[\"Bid Price\"]].rename(columns={\"Bid Price\":name}))\n",
    "\n",
    "#df.pivot_table(df, index=\"Timestamp\", values=\"Bid Price\", columns=\"Instrument\")\n",
    "\n",
    "act_market_data.head()\n",
    "\n",
    "market_data_corr = act_market_data.corr()\n",
    "\n",
    "market_data_corr.head()"
   ]
  },
  {
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   "execution_count": 7,
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      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
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   ],
   "source": [
    "plt.matshow(market_data_corr)\n",
    "# cont watching from 2:25"
   ]
  },
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