Visualizing_PallindromNumbers

Pallindrom.ipynb

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   "execution_count": 1,
   "metadata": {},
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   "source": [
    "import numpy as np\n",
    "x_data = []\n",
    "y_data = []\n",
    "p_data = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "source": [
    "# for n in range(, 4):\n",
    "def f(n):\n",
    "    low  = 10**(n-1)\n",
    "    high = 10**(n)\n",
    "    mx = 0\n",
    "    for i in range(low, high):\n",
    "             for j in range(low, high):\n",
    "                        p = str(i*j)\n",
    "                        if p == p[::-1]:\n",
    "                            if (i*j) not in p_data:\n",
    "                                x_data.append(i)\n",
    "                                y_data.append(j)\n",
    "                                p_data.append(i*j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "for n in range(2,4):\n",
    "    x = f(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High five! You successfully sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~danbo_95/0 or inside your plot.ly account where it is named 'basic-scatter'\n"
     ]
    },
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       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "import plotly\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "plotly.tools.set_credentials_file(username='', api_key='')\n",
    "\n",
    "# Create a trace\n",
    "trace = go.Scatter(\n",
    "    x = x_data,\n",
    "    y = y_data,\n",
    "    mode = 'markers'\n",
    ")\n",
    "\n",
    "data = [trace]\n",
    "\n",
    "# Plot and embed in ipython notebook!\n",
    "py.iplot(data, filename='basic-scatter')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "906609"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "max(p_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~danbo_95/2.embed\" height=\"525px\" width=\"100%\"></iframe>"
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       "<plotly.tools.PlotlyDisplay object>"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracex = go.Histogram(\n",
    "    x=x_data\n",
    ")\n",
    "tracey = go.Histogram(\n",
    "    x=y_data\n",
    ")\n",
    "data = [tracex, tracey]\n",
    "layout = go.Layout(barmode='stack')\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "py.iplot(fig, filename='stacked histogram')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Things to notice for max numbers:\n",
    "    1) Odd numbers\n",
    "    2) Corresponding product length even"
   ]
  }
 ],
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