diff --git a/Cryptocurrency.jpeg b/Cryptocurrency.jpeg
new file mode 100644
index 0000000..5a8f387
Binary files /dev/null and b/Cryptocurrency.jpeg differ
diff --git a/Untitled.ipynb b/Untitled.ipynb
index 55f2dd1..b27e83c 100644
--- a/Untitled.ipynb
+++ b/Untitled.ipynb
@@ -7,154 +7,208 @@
     "# Kriptovalute"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Općenito"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Kriptovalute su digitalne imovine prvotno osmišljene kao sredstvo za razmjenu vrijednosti koje koriste kriptografiju za validaciju i sigurnost transakcija, te kontrolu stvaranja dodatnih jedinica.\n",
+    "Kriptovalute koriste decentraliziranu kontrolu za razliku od centraliziranih bankarskih sustava."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7ff652e9e080>"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff652f8dc50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "figa, axa = plt.subplots()\n",
+    "crypto = plt.imread('Cryptocurrency.jpeg')\n",
+    "axa.imshow(crypto)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bitcoin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bitcoin je prva decentralizirana kriptovaluta napravljena 2009. Od tada je napravljeno mnoštvo kriptovaluta sa raznim idejama za primjenu."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Blochchain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Velika većina kriptovaluta je napravljena na blockchain tehnologiji. Blockchain možemo poistovjetiti sa bazom podataka koja je podjeljena između svih čvorova koji sudjeluju u sustavu.\n",
+    "Osnovna građevna jedinica svakog blockshaina je Blok:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import csv\n",
-    "lista = []\n",
-    "with open('data/bitcoin_price.csv') as file:\n",
-    "    read = csv.DictReader(file)\n",
-    "    for row in read:\n",
-    "        for feature in row:\n",
-    "            \n",
-    "        lista.append(row)\n",
-    "        print (row)"
+    "import hashlib\n",
+    "\n",
+    "class Block:\n",
+    "\n",
+    "    def __init__(self, index, timpestamp, previousHash, data):\n",
+    "        \n",
+    "        self.index = index\n",
+    "        self.timestamp = timpestamp\n",
+    "        self.data = data\n",
+    "        self.previousHash = previousHash\n",
+    "        self.encoded = (str(self.index) + str(self.timestamp) + \n",
+    "                str(self.data) + str(self.previousHash)).encode('utf-8')\n",
+    "        self.hash = self.hashMyBlock().hexdigest()\n",
+    "\n",
+    "    def hashMyBlock(self):\n",
+    "        sha = hashlib.sha256()\n",
+    "        sha.update(self.encoded)\n",
+    "        return sha"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 159,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'12f78dfc1079a49cbde40cd3f749b160f40cba132f65a812794163459308967c'"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import datetime as date\n",
+    "genesisBlock = Block(0, date.datetime.now(),\"\",\"First block\")\n",
+    "genesisBlock.hash"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vidimo da za isti ulaz funkcija sha256 daje isti izlaz:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
    "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>Date</th>\n",
-       "      <th>Open</th>\n",
-       "      <th>High</th>\n",
-       "      <th>Low</th>\n",
-       "      <th>Close</th>\n",
-       "      <th>Volume</th>\n",
-       "      <th>Market Cap</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Feb 20, 2018</td>\n",
-       "      <td>11231.8</td>\n",
-       "      <td>11958.5</td>\n",
-       "      <td>11231.80</td>\n",
-       "      <td>11403.7</td>\n",
-       "      <td>9,926,540,000</td>\n",
-       "      <td>189,536,000,000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Feb 19, 2018</td>\n",
-       "      <td>10552.6</td>\n",
-       "      <td>11273.8</td>\n",
-       "      <td>10513.20</td>\n",
-       "      <td>11225.3</td>\n",
-       "      <td>7,652,090,000</td>\n",
-       "      <td>178,055,000,000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Feb 18, 2018</td>\n",
-       "      <td>11123.4</td>\n",
-       "      <td>11349.8</td>\n",
-       "      <td>10326.00</td>\n",
-       "      <td>10551.8</td>\n",
-       "      <td>8,744,010,000</td>\n",
-       "      <td>187,663,000,000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Feb 17, 2018</td>\n",
-       "      <td>10207.5</td>\n",
-       "      <td>11139.5</td>\n",
-       "      <td>10149.40</td>\n",
-       "      <td>11112.7</td>\n",
-       "      <td>8,660,880,000</td>\n",
-       "      <td>172,191,000,000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Feb 16, 2018</td>\n",
-       "      <td>10135.7</td>\n",
-       "      <td>10324.1</td>\n",
-       "      <td>9824.82</td>\n",
-       "      <td>10233.9</td>\n",
-       "      <td>7,296,160,000</td>\n",
-       "      <td>170,960,000,000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "           Date     Open     High       Low    Close         Volume  \\\n",
-       "0  Feb 20, 2018  11231.8  11958.5  11231.80  11403.7  9,926,540,000   \n",
-       "1  Feb 19, 2018  10552.6  11273.8  10513.20  11225.3  7,652,090,000   \n",
-       "2  Feb 18, 2018  11123.4  11349.8  10326.00  10551.8  8,744,010,000   \n",
-       "3  Feb 17, 2018  10207.5  11139.5  10149.40  11112.7  8,660,880,000   \n",
-       "4  Feb 16, 2018  10135.7  10324.1   9824.82  10233.9  7,296,160,000   \n",
-       "\n",
-       "        Market Cap  \n",
-       "0  189,536,000,000  \n",
-       "1  178,055,000,000  \n",
-       "2  187,663,000,000  \n",
-       "3  172,191,000,000  \n",
-       "4  170,960,000,000  "
+       "'12f78dfc1079a49cbde40cd3f749b160f40cba132f65a812794163459308967c'"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 160,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "copyOfGenesisBlock = Block(genesisBlock.index,genesisBlock.timestamp,\n",
+    "                           genesisBlock.previousHash,genesisBlock.data)\n",
+    "copyOfGenesisBlock.hash"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv\n",
+    "lista = []\n",
+    "with open('data/bitcoin_price.csv') as file:\n",
+    "    read = csv.DictReader(file)\n",
+    "    for row in read:\n",
+    "        for feature in row:\n",
+    "            lista.append(row)\n",
+    "            print (row)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "test = pd.read_csv('data/bitcoin_price.csv')\n",
-    "test.head()"
+    "test = pd.read_csv('data/bitcoin_price.csv')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline \n",
-    "\n",
-    "import matplotlib.pyplot as plt\n"
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -170,7 +224,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -181,88 +235,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0       Feb 20, 2018\n",
-       "1       Feb 19, 2018\n",
-       "2       Feb 18, 2018\n",
-       "3       Feb 17, 2018\n",
-       "4       Feb 16, 2018\n",
-       "5       Feb 15, 2018\n",
-       "6       Feb 14, 2018\n",
-       "7       Feb 13, 2018\n",
-       "8       Feb 12, 2018\n",
-       "9       Feb 11, 2018\n",
-       "10      Feb 10, 2018\n",
-       "11      Feb 09, 2018\n",
-       "12      Feb 08, 2018\n",
-       "13      Feb 07, 2018\n",
-       "14      Feb 06, 2018\n",
-       "15      Feb 05, 2018\n",
-       "16      Feb 04, 2018\n",
-       "17      Feb 03, 2018\n",
-       "18      Feb 02, 2018\n",
-       "19      Feb 01, 2018\n",
-       "20      Jan 31, 2018\n",
-       "21      Jan 30, 2018\n",
-       "22      Jan 29, 2018\n",
-       "23      Jan 28, 2018\n",
-       "24      Jan 27, 2018\n",
-       "25      Jan 26, 2018\n",
-       "26      Jan 25, 2018\n",
-       "27      Jan 24, 2018\n",
-       "28      Jan 23, 2018\n",
-       "29      Jan 22, 2018\n",
-       "            ...     \n",
-       "1730    May 27, 2013\n",
-       "1731    May 26, 2013\n",
-       "1732    May 25, 2013\n",
-       "1733    May 24, 2013\n",
-       "1734    May 23, 2013\n",
-       "1735    May 22, 2013\n",
-       "1736    May 21, 2013\n",
-       "1737    May 20, 2013\n",
-       "1738    May 19, 2013\n",
-       "1739    May 18, 2013\n",
-       "1740    May 17, 2013\n",
-       "1741    May 16, 2013\n",
-       "1742    May 15, 2013\n",
-       "1743    May 14, 2013\n",
-       "1744    May 13, 2013\n",
-       "1745    May 12, 2013\n",
-       "1746    May 11, 2013\n",
-       "1747    May 10, 2013\n",
-       "1748    May 09, 2013\n",
-       "1749    May 08, 2013\n",
-       "1750    May 07, 2013\n",
-       "1751    May 06, 2013\n",
-       "1752    May 05, 2013\n",
-       "1753    May 04, 2013\n",
-       "1754    May 03, 2013\n",
-       "1755    May 02, 2013\n",
-       "1756    May 01, 2013\n",
-       "1757    Apr 30, 2013\n",
-       "1758    Apr 29, 2013\n",
-       "1759    Apr 28, 2013\n",
-       "Name: Date, Length: 1760, dtype: object"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "test.Date"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -278,23 +260,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
-     "ename": "AttributeError",
-     "evalue": "'numpy.str_' object has no attribute 'toordinal'",
+     "ename": "NameError",
+     "evalue": "name 'dates' is not defined",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-27-9c82eacb52b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdatees\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdates\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate2num\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_date\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatees\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/anaconda3/lib/python3.6/site-packages/matplotlib/dates.py\u001b[0m in \u001b[0;36mdate2num\u001b[0;34m(d)\u001b[0m\n\u001b[1;32m    394\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 396\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_to_ordinalf_np_vectorized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/anaconda3/lib/python3.6/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2753\u001b[0m             \u001b[0mvargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_n\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_n\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2754\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2755\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_vectorize_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2756\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2757\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_ufunc_and_otypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/anaconda3/lib/python3.6/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_vectorize_call\u001b[0;34m(self, func, args)\u001b[0m\n\u001b[1;32m   2823\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2824\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2825\u001b[0;31m             \u001b[0mufunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0motypes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_ufunc_and_otypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2826\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2827\u001b[0m             \u001b[0;31m# Convert args to object arrays first\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/anaconda3/lib/python3.6/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_get_ufunc_and_otypes\u001b[0;34m(self, func, args)\u001b[0m\n\u001b[1;32m   2783\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2784\u001b[0m             \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2785\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2786\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2787\u001b[0m             \u001b[0;31m# Performance note: profiling indicates that -- for simple\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/anaconda3/lib/python3.6/site-packages/matplotlib/dates.py\u001b[0m in \u001b[0;36m_to_ordinalf\u001b[0;34m(dt)\u001b[0m\n\u001b[1;32m    243\u001b[0m         \u001b[0mtzi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUTC\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m     \u001b[0mbase\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoordinal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m     \u001b[0;31m# If it's sufficiently datetime-like, it will have a `date()` method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.str_' object has no attribute 'toordinal'"
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-6-9c82eacb52b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdatees\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdates\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate2num\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_date\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatees\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'dates' is not defined"
      ]
     }
    ],
@@ -306,33 +283,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f874dfc7898>]"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f874e02d550>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.plot(index[-200:], opens[-200:])"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {