{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9ed7a8b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open the database\n",
    "import pandas as pd\n",
    "\n",
    "vstar = pd.read_csv('vsx.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "761870f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "451272"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of rows\n",
    "len(vstar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "50036b28",
   "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>iod</th>\n",
       "      <th>name</th>\n",
       "      <th>code</th>\n",
       "      <th>type</th>\n",
       "      <th>mag_min</th>\n",
       "      <th>mag_max</th>\n",
       "      <th>period</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>270915</td>\n",
       "      <td>VSX J000000.6+620044</td>\n",
       "      <td>0</td>\n",
       "      <td>SR</td>\n",
       "      <td>12.300</td>\n",
       "      <td>12.550</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.00271</td>\n",
       "      <td>62.01222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>195</td>\n",
       "      <td>GM And</td>\n",
       "      <td>0</td>\n",
       "      <td>RRAB</td>\n",
       "      <td>12.430</td>\n",
       "      <td>13.300</td>\n",
       "      <td>0.706758</td>\n",
       "      <td>0.01521</td>\n",
       "      <td>35.36286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>287959</td>\n",
       "      <td>CSS_J000004.0+182425</td>\n",
       "      <td>0</td>\n",
       "      <td>RRAB</td>\n",
       "      <td>15.171</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.485160</td>\n",
       "      <td>0.01669</td>\n",
       "      <td>18.40698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>275753</td>\n",
       "      <td>Mis V1378</td>\n",
       "      <td>0</td>\n",
       "      <td>SR</td>\n",
       "      <td>11.400</td>\n",
       "      <td>12.700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01717</td>\n",
       "      <td>57.10467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>170899</td>\n",
       "      <td>UNSW-V 312</td>\n",
       "      <td>0</td>\n",
       "      <td>EA</td>\n",
       "      <td>13.630</td>\n",
       "      <td>0.066</td>\n",
       "      <td>1.057620</td>\n",
       "      <td>0.02500</td>\n",
       "      <td>-59.74675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64625</td>\n",
       "      <td>V0467 And</td>\n",
       "      <td>0</td>\n",
       "      <td>EW</td>\n",
       "      <td>15.210</td>\n",
       "      <td>15.610</td>\n",
       "      <td>0.353539</td>\n",
       "      <td>0.02721</td>\n",
       "      <td>35.36692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>25131</td>\n",
       "      <td>Z Peg</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>7.300</td>\n",
       "      <td>13.600</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>0.02733</td>\n",
       "      <td>25.88647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>251805</td>\n",
       "      <td>V1023 Cas</td>\n",
       "      <td>0</td>\n",
       "      <td>RRAB</td>\n",
       "      <td>18.130</td>\n",
       "      <td>18.750</td>\n",
       "      <td>0.643700</td>\n",
       "      <td>0.02771</td>\n",
       "      <td>56.65331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>25281</td>\n",
       "      <td>EP Peg</td>\n",
       "      <td>0</td>\n",
       "      <td>SR</td>\n",
       "      <td>10.700</td>\n",
       "      <td>11.400</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>0.02875</td>\n",
       "      <td>20.23736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>25257</td>\n",
       "      <td>DM Peg</td>\n",
       "      <td>0</td>\n",
       "      <td>EA/D:</td>\n",
       "      <td>10.800</td>\n",
       "      <td>11.600</td>\n",
       "      <td>2.588991</td>\n",
       "      <td>0.03037</td>\n",
       "      <td>18.73808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      iod                  name  code   type  mag_min  mag_max      period  \\\n",
       "0  270915  VSX J000000.6+620044     0     SR   12.300   12.550   68.000000   \n",
       "1     195                GM And     0   RRAB   12.430   13.300    0.706758   \n",
       "2  287959  CSS_J000004.0+182425     0   RRAB   15.171    0.970    0.485160   \n",
       "3  275753             Mis V1378     0     SR   11.400   12.700         NaN   \n",
       "4  170899            UNSW-V 312     0     EA   13.630    0.066    1.057620   \n",
       "5   64625             V0467 And     0     EW   15.210   15.610    0.353539   \n",
       "6   25131                 Z Peg     0      M    7.300   13.600  320.000000   \n",
       "7  251805             V1023 Cas     0   RRAB   18.130   18.750    0.643700   \n",
       "8   25281                EP Peg     0     SR   10.700   11.400  340.000000   \n",
       "9   25257                DM Peg     0  EA/D:   10.800   11.600    2.588991   \n",
       "\n",
       "        ra       dec  \n",
       "0  0.00271  62.01222  \n",
       "1  0.01521  35.36286  \n",
       "2  0.01669  18.40698  \n",
       "3  0.01717  57.10467  \n",
       "4  0.02500 -59.74675  \n",
       "5  0.02721  35.36692  \n",
       "6  0.02733  25.88647  \n",
       "7  0.02771  56.65331  \n",
       "8  0.02875  20.23736  \n",
       "9  0.03037  18.73808  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the first 10 rows\n",
    "vstar.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2bb3abef",
   "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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>mag_min</th>\n",
       "      <th>mag_max</th>\n",
       "      <th>period</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VSX J000000.6+620044</td>\n",
       "      <td>SR</td>\n",
       "      <td>12.300</td>\n",
       "      <td>12.550</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>62.01222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GM And</td>\n",
       "      <td>RRAB</td>\n",
       "      <td>12.430</td>\n",
       "      <td>13.300</td>\n",
       "      <td>0.706758</td>\n",
       "      <td>35.36286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CSS_J000004.0+182425</td>\n",
       "      <td>RRAB</td>\n",
       "      <td>15.171</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.485160</td>\n",
       "      <td>18.40698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mis V1378</td>\n",
       "      <td>SR</td>\n",
       "      <td>11.400</td>\n",
       "      <td>12.700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.10467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UNSW-V 312</td>\n",
       "      <td>EA</td>\n",
       "      <td>13.630</td>\n",
       "      <td>0.066</td>\n",
       "      <td>1.057620</td>\n",
       "      <td>-59.74675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>V0467 And</td>\n",
       "      <td>EW</td>\n",
       "      <td>15.210</td>\n",
       "      <td>15.610</td>\n",
       "      <td>0.353539</td>\n",
       "      <td>35.36692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Z Peg</td>\n",
       "      <td>M</td>\n",
       "      <td>7.300</td>\n",
       "      <td>13.600</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>25.88647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>V1023 Cas</td>\n",
       "      <td>RRAB</td>\n",
       "      <td>18.130</td>\n",
       "      <td>18.750</td>\n",
       "      <td>0.643700</td>\n",
       "      <td>56.65331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EP Peg</td>\n",
       "      <td>SR</td>\n",
       "      <td>10.700</td>\n",
       "      <td>11.400</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>20.23736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DM Peg</td>\n",
       "      <td>EA/D:</td>\n",
       "      <td>10.800</td>\n",
       "      <td>11.600</td>\n",
       "      <td>2.588991</td>\n",
       "      <td>18.73808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name   type  mag_min  mag_max      period       dec\n",
       "0  VSX J000000.6+620044     SR   12.300   12.550   68.000000  62.01222\n",
       "1                GM And   RRAB   12.430   13.300    0.706758  35.36286\n",
       "2  CSS_J000004.0+182425   RRAB   15.171    0.970    0.485160  18.40698\n",
       "3             Mis V1378     SR   11.400   12.700         NaN  57.10467\n",
       "4            UNSW-V 312     EA   13.630    0.066    1.057620 -59.74675\n",
       "5             V0467 And     EW   15.210   15.610    0.353539  35.36692\n",
       "6                 Z Peg      M    7.300   13.600  320.000000  25.88647\n",
       "7             V1023 Cas   RRAB   18.130   18.750    0.643700  56.65331\n",
       "8                EP Peg     SR   10.700   11.400  340.000000  20.23736\n",
       "9                DM Peg  EA/D:   10.800   11.600    2.588991  18.73808"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstar = vstar.drop(columns=['code', 'iod', 'ra'])\n",
    "vstar.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0ecd968f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VAR     65677\n",
       "EW      53229\n",
       "RRAB    50043\n",
       "ROT     41104\n",
       "SR      38206\n",
       "MISC    27823\n",
       "EA      22521\n",
       "M       17983\n",
       "RRC     15477\n",
       "E        9956\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many stars of each type?\n",
    "\n",
    "vstar.type\n",
    "vstar.type.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0b7981b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add an amplitude column: (brightest range)\n",
    "vstar['amplitude'] = vstar.mag_max - vstar.mag_min\n",
    "\n",
    "# Discard the negative amplitudes (The data is formatted differently)\n",
    "vstar = vstar[vstar.amplitude > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f5617e07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11049"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop any missing values\n",
    "vstar = vstar.dropna()\n",
    "\n",
    "# Filter for M-type variables\n",
    "miras = vstar[vstar.type == 'M']\n",
    "miras.head(10)\n",
    "len(miras)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "def8fee3",
   "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>mag_min</th>\n",
       "      <th>mag_max</th>\n",
       "      <th>period</th>\n",
       "      <th>dec</th>\n",
       "      <th>amplitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11049.000000</td>\n",
       "      <td>11049.000000</td>\n",
       "      <td>11049.000000</td>\n",
       "      <td>11049.000000</td>\n",
       "      <td>11049.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.227227</td>\n",
       "      <td>15.588874</td>\n",
       "      <td>305.924404</td>\n",
       "      <td>-19.270900</td>\n",
       "      <td>3.361647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.049992</td>\n",
       "      <td>2.051315</td>\n",
       "      <td>98.413172</td>\n",
       "      <td>30.089179</td>\n",
       "      <td>1.436329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.900000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>58.200000</td>\n",
       "      <td>-88.270190</td>\n",
       "      <td>0.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.200000</td>\n",
       "      <td>14.600000</td>\n",
       "      <td>235.500000</td>\n",
       "      <td>-33.708080</td>\n",
       "      <td>2.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.300000</td>\n",
       "      <td>15.700000</td>\n",
       "      <td>294.000000</td>\n",
       "      <td>-27.674060</td>\n",
       "      <td>3.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.500000</td>\n",
       "      <td>16.800000</td>\n",
       "      <td>364.500000</td>\n",
       "      <td>-7.928030</td>\n",
       "      <td>4.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19.172000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>948.000000</td>\n",
       "      <td>88.274390</td>\n",
       "      <td>10.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mag_min       mag_max        period           dec     amplitude\n",
       "count  11049.000000  11049.000000  11049.000000  11049.000000  11049.000000\n",
       "mean      12.227227     15.588874    305.924404    -19.270900      3.361647\n",
       "std        2.049992      2.051315     98.413172     30.089179      1.436329\n",
       "min        0.900000      3.100000     58.200000    -88.270190      0.070000\n",
       "25%       11.200000     14.600000    235.500000    -33.708080      2.400000\n",
       "50%       12.300000     15.700000    294.000000    -27.674060      3.200000\n",
       "75%       13.500000     16.800000    364.500000     -7.928030      4.200000\n",
       "max       19.172000     22.000000    948.000000     88.274390     10.900000"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miras.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2ef881af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 750x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Period vs. Amplitude in MIRA-type variable stars\n",
    "\n",
    "# Let's focus on MIRAs that we might sometimes observe with the\n",
    "# un-aided eye - that is, their minimum magnitude (brightest)\n",
    "# is less than magnitude 6.\n",
    "\n",
    "miras = miras[miras.mag_min < 6.0]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(5,4), dpi=150)\n",
    "plt.grid()\n",
    "plt.scatter(miras.period, miras.amplitude, marker='.', s=100, c=miras.mag_min)\n",
    "plt.xlabel('Period (days)')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.title('M-type Variable Stars')\n",
    "plt.text(420,11, '$\\chi$ cygni')\n",
    "plt.text(250,8.1, 'Mira')\n",
    "plt.colorbar(label='Minimum Magnitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "82134bc4",
   "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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>mag_min</th>\n",
       "      <th>mag_max</th>\n",
       "      <th>period</th>\n",
       "      <th>dec</th>\n",
       "      <th>amplitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>402961</th>\n",
       "      <td>khi Cyg</td>\n",
       "      <td>M</td>\n",
       "      <td>3.3</td>\n",
       "      <td>14.2</td>\n",
       "      <td>408.05</td>\n",
       "      <td>32.91406</td>\n",
       "      <td>10.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name type  mag_min  mag_max  period       dec  amplitude\n",
       "402961  khi Cyg    M      3.3     14.2  408.05  32.91406       10.9"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miras[miras.amplitude > 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9950378f",
   "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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>mag_min</th>\n",
       "      <th>mag_max</th>\n",
       "      <th>period</th>\n",
       "      <th>dec</th>\n",
       "      <th>amplitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>105266</th>\n",
       "      <td>S Car</td>\n",
       "      <td>M</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.9</td>\n",
       "      <td>149.49</td>\n",
       "      <td>-61.549</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name type  mag_min  mag_max  period     dec  amplitude\n",
       "105266  S Car    M      4.5      9.9  149.49 -61.549        5.4"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miras[miras.period>700]\n",
    "miras[miras.period<200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f662ae50",
   "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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>mag_min</th>\n",
       "      <th>mag_max</th>\n",
       "      <th>period</th>\n",
       "      <th>dec</th>\n",
       "      <th>amplitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6106</th>\n",
       "      <td>WX Psc</td>\n",
       "      <td>M</td>\n",
       "      <td>0.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>660.00</td>\n",
       "      <td>12.59800</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448104</th>\n",
       "      <td>IZ Peg</td>\n",
       "      <td>M</td>\n",
       "      <td>1.7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>486.00</td>\n",
       "      <td>10.91039</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12453</th>\n",
       "      <td>omi Cet</td>\n",
       "      <td>M</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>331.96</td>\n",
       "      <td>-2.97764</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402961</th>\n",
       "      <td>khi Cyg</td>\n",
       "      <td>M</td>\n",
       "      <td>3.3</td>\n",
       "      <td>14.2</td>\n",
       "      <td>408.05</td>\n",
       "      <td>32.91406</td>\n",
       "      <td>10.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name type  mag_min  mag_max  period       dec  amplitude\n",
       "6106     WX Psc    M      0.9      4.3  660.00  12.59800        3.4\n",
       "448104   IZ Peg    M      1.7      6.4  486.00  10.91039        4.7\n",
       "12453   omi Cet    M      2.0     10.1  331.96  -2.97764        8.1\n",
       "402961  khi Cyg    M      3.3     14.2  408.05  32.91406       10.9"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# variable stars YOU could monitor (sometimes) with your naked eye!\n",
    "\n",
    "vstar[(vstar.dec > -10) & (vstar.mag_min < 4) & (vstar.amplitude>3) & (vstar.type == 'M')].sort_values(['mag_min'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af86be28",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}