Hubble_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>distance</th>\n",
       "      <th>recession_velocity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.032</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.034</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.214</td>\n",
       "      <td>-130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.263</td>\n",
       "      <td>-70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.275</td>\n",
       "      <td>-185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance  recession_velocity\n",
       "0     0.032                 170\n",
       "1     0.034                 290\n",
       "2     0.214                -130\n",
       "3     0.263                 -70\n",
       "4     0.275                -185"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"hubble_data.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>dist</th>\n",
       "      <th>rec_vel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.032</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.034</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.214</td>\n",
       "      <td>-130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.263</td>\n",
       "      <td>-70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.275</td>\n",
       "      <td>-185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    dist  rec_vel\n",
       "0  0.032      170\n",
       "1  0.034      290\n",
       "2  0.214     -130\n",
       "3  0.263      -70\n",
       "4  0.275     -185"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headers = [\"dist\",\"rec_vel\"]\n",
    "\n",
    "data_no_headers = pd.read_csv(\"hubble_data_no_headers.csv\", names = headers)\n",
    "\n",
    "data_no_headers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.032\n",
       "1     0.034\n",
       "2     0.214\n",
       "3     0.263\n",
       "4     0.275\n",
       "5     0.275\n",
       "6     0.450\n",
       "7     0.500\n",
       "8     0.500\n",
       "9     0.630\n",
       "10    0.800\n",
       "11    0.900\n",
       "12    0.900\n",
       "13    0.900\n",
       "14    0.900\n",
       "15    1.000\n",
       "16    1.100\n",
       "17    1.100\n",
       "18    1.400\n",
       "19    1.700\n",
       "20    2.000\n",
       "21    2.000\n",
       "22    2.000\n",
       "23    2.000\n",
       "Name: dist, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_no_headers[\"dist\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.set_index(\"distance\", inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>recession_velocity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.032</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.034</th>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.214</th>\n",
       "      <td>-130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.263</th>\n",
       "      <td>-70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.275</th>\n",
       "      <td>-185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          recession_velocity\n",
       "distance                    \n",
       "0.032                    170\n",
       "0.034                    290\n",
       "0.214                   -130\n",
       "0.263                    -70\n",
       "0.275                   -185"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "data.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}