hw_200.csv

Pandas Example.ipynb

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   "cell_type": "code",
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   "source": [
    "import pandas as pd"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can read in a csv file using Pandas' `read_csv` method:"
   ]
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   "source": [
    "df = pd.read_csv('hw_200.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have read the CSV into a `dataframe` object, we can look at the first five rows using the `head` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Index   Height(Inches)\"   \"Weight(Pounds)\"\n",
      "0      1             65.78             112.99\n",
      "1      2             71.52             136.49\n",
      "2      3             69.40             153.03\n",
      "3      4             68.22             142.34\n",
      "4      5             67.79             144.30\n"
     ]
    }
   ],
   "source": [
    "print df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `describe` method to output the names of the columns and summarizing statistics about the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Index   Height(Inches)\"   \"Weight(Pounds)\"\n",
      "count  200.000000        200.000000         200.000000\n",
      "mean   100.500000         67.949800         127.221950\n",
      "std     57.879185          1.940363          11.960959\n",
      "min      1.000000         63.430000          97.900000\n",
      "25%     50.750000         66.522500         119.895000\n",
      "50%    100.500000         67.935000         127.875000\n",
      "75%    150.250000         69.202500         136.097500\n",
      "max    200.000000         73.900000         158.960000\n"
     ]
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   ],
   "source": [
    "print df.describe()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The names of the columns are not very friendly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'Index', u' Height(Inches)\"', u' \"Weight(Pounds)\"'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's rename the columns in the dataframe using the `columns` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   index  height_inches  weight_pounds\n",
      "0      1          65.78         112.99\n"
     ]
    }
   ],
   "source": [
    "df.columns = ['index', 'height_inches', 'weight_pounds']\n",
    "print df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get the average (mean) weight of the `weight_pounds` series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "127.22195\n"
     ]
    }
   ],
   "source": [
    "print df['weight_pounds'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unsurprisingly, it's the same value as we got earlier with the `describe` method. You can also run `describe` on a single series to check the output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    200.000000\n",
      "mean     127.221950\n",
      "std       11.960959\n",
      "min       97.900000\n",
      "25%      119.895000\n",
      "50%      127.875000\n",
      "75%      136.097500\n",
      "max      158.960000\n",
      "Name: weight_pounds, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print df['weight_pounds'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations! You are now a Pandas expert."
   ]
  }
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