ch06.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Loading, Storage, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "np.random.seed(12345)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "np.set_printoptions(precision=4, suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading and Writing Data in Text Format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a,b,c,d,message\r\n",
      "1,2,3,4,hello\r\n",
      "5,6,7,8,world\r\n",
      "9,10,11,12,foo"
     ]
    }
   ],
   "source": [
    "!cat examples/ex1.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>hello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b   c   d message\n",
       "0  1   2   3   4   hello\n",
       "1  5   6   7   8   world\n",
       "2  9  10  11  12     foo"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('examples/ex1.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>hello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b   c   d message\n",
       "0  1   2   3   4   hello\n",
       "1  5   6   7   8   world\n",
       "2  9  10  11  12     foo"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_table('examples/ex1.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1,2,3,4,hello\r\n",
      "5,6,7,8,world\r\n",
      "9,10,11,12,foo"
     ]
    }
   ],
   "source": [
    "!cat examples/ex2.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>hello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0   1   2   3      4\n",
       "0  1   2   3   4  hello\n",
       "1  5   6   7   8  world\n",
       "2  9  10  11  12    foo"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('examples/ex2.csv', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>hello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   a   b   c   d message\n",
       "0  1   2   3   4   hello\n",
       "1  5   6   7   8   world\n",
       "2  9  10  11  12     foo"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('examples/ex2.csv', names=['a', 'b', 'c', 'd', 'message'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>message</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hello</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>world</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         a   b   c   d\n",
       "message               \n",
       "hello    1   2   3   4\n",
       "world    5   6   7   8\n",
       "foo      9  10  11  12"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = ['a', 'b', 'c', 'd', 'message']\n",
    "pd.read_csv('examples/ex2.csv', names=names, index_col='message')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key1,key2,value1,value2\r\n",
      "one,a,1,2\r\n",
      "one,b,3,4\r\n",
      "one,c,5,6\r\n",
      "one,d,7,8\r\n",
      "two,a,9,10\r\n",
      "two,b,11,12\r\n",
      "two,c,13,14\r\n",
      "two,d,15,16\r\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>value1</th>\n",
       "      <th>value2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">one</th>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">two</th>\n",
       "      <th>a</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           value1  value2\n",
       "key1 key2                \n",
       "one  a          1       2\n",
       "     b          3       4\n",
       "     c          5       6\n",
       "     d          7       8\n",
       "two  a          9      10\n",
       "     b         11      12\n",
       "     c         13      14\n",
       "     d         15      16"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!cat examples/csv_mindex.csv\n",
    "parsed = pd.read_csv('examples/csv_mindex.csv',\n",
    "                     index_col=['key1', 'key2'])\n",
    "parsed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['            A         B         C\\n',\n",
       " 'aaa -0.264438 -1.026059 -0.619500\\n',\n",
       " 'bbb  0.927272  0.302904 -0.032399\\n',\n",
       " 'ccc -0.264273 -0.386314 -0.217601\\n',\n",
       " 'ddd -0.871858 -0.348382  1.100491\\n']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(open('examples/ex3.txt'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aaa</th>\n",
       "      <td>-0.264438</td>\n",
       "      <td>-1.026059</td>\n",
       "      <td>-0.619500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbb</th>\n",
       "      <td>0.927272</td>\n",
       "      <td>0.302904</td>\n",
       "      <td>-0.032399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ccc</th>\n",
       "      <td>-0.264273</td>\n",
       "      <td>-0.386314</td>\n",
       "      <td>-0.217601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ddd</th>\n",
       "      <td>-0.871858</td>\n",
       "      <td>-0.348382</td>\n",
       "      <td>1.100491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A         B         C\n",
       "aaa -0.264438 -1.026059 -0.619500\n",
       "bbb  0.927272  0.302904 -0.032399\n",
       "ccc -0.264273 -0.386314 -0.217601\n",
       "ddd -0.871858 -0.348382  1.100491"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.read_table('examples/ex3.txt', sep='\\s+')\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# hey!\r\n",
      "a,b,c,d,message\r\n",
      "# just wanted to make things more difficult for you\r\n",
      "# who reads CSV files with computers, anyway?\r\n",
      "1,2,3,4,hello\r\n",
      "5,6,7,8,world\r\n",
      "9,10,11,12,foo"
     ]
    },
    {
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       "      <th></th>\n",
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       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>hello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b   c   d message\n",
       "0  1   2   3   4   hello\n",
       "1  5   6   7   8   world\n",
       "2  9  10  11  12     foo"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!cat examples/ex4.csv\n",
    "pd.read_csv('examples/ex4.csv', skiprows=[0, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "something,a,b,c,d,message\r\n",
      "one,1,2,3,4,NA\r\n",
      "two,5,6,,8,world\r\n",
      "three,9,10,11,12,foo"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>something</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>two</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>three</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  something  a   b     c   d message\n",
       "0       one  1   2   3.0   4     NaN\n",
       "1       two  5   6   NaN   8   world\n",
       "2     three  9  10  11.0  12     foo"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!cat examples/ex5.csv\n",
    "result = pd.read_csv('examples/ex5.csv')\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>something</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   something      a      b      c      d  message\n",
       "0      False  False  False  False  False     True\n",
       "1      False  False  False   True  False    False\n",
       "2      False  False  False  False  False    False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>something</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>two</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>three</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  something  a   b     c   d message\n",
       "0       one  1   2   3.0   4     NaN\n",
       "1       two  5   6   NaN   8   world\n",
       "2     three  9  10  11.0  12     foo"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.read_csv('examples/ex5.csv', na_values=['NULL'])\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
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       "\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>something</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>three</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  something  a   b     c   d message\n",
       "0       one  1   2   3.0   4     NaN\n",
       "1       NaN  5   6   NaN   8   world\n",
       "2     three  9  10  11.0  12     NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinels = {'message': ['foo', 'NA'], 'something': ['two']}\n",
    "pd.read_csv('examples/ex5.csv', na_values=sentinels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading Text Files in Pieces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.467976</td>\n",
       "      <td>-0.038649</td>\n",
       "      <td>-0.295344</td>\n",
       "      <td>-1.824726</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.358893</td>\n",
       "      <td>1.404453</td>\n",
       "      <td>0.704965</td>\n",
       "      <td>-0.200638</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.501840</td>\n",
       "      <td>0.659254</td>\n",
       "      <td>-0.421691</td>\n",
       "      <td>-0.057688</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.204886</td>\n",
       "      <td>1.074134</td>\n",
       "      <td>1.388361</td>\n",
       "      <td>-0.982404</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.354628</td>\n",
       "      <td>-0.133116</td>\n",
       "      <td>0.283763</td>\n",
       "      <td>-0.837063</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>2.311896</td>\n",
       "      <td>-0.417070</td>\n",
       "      <td>-1.409599</td>\n",
       "      <td>-0.515821</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>-0.479893</td>\n",
       "      <td>-0.650419</td>\n",
       "      <td>0.745152</td>\n",
       "      <td>-0.646038</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>0.523331</td>\n",
       "      <td>0.787112</td>\n",
       "      <td>0.486066</td>\n",
       "      <td>1.093156</td>\n",
       "      <td>K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>-0.362559</td>\n",
       "      <td>0.598894</td>\n",
       "      <td>-1.843201</td>\n",
       "      <td>0.887292</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>-0.096376</td>\n",
       "      <td>-1.012999</td>\n",
       "      <td>-0.657431</td>\n",
       "      <td>-0.573315</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           one       two     three      four key\n",
       "0     0.467976 -0.038649 -0.295344 -1.824726   L\n",
       "1    -0.358893  1.404453  0.704965 -0.200638   B\n",
       "2    -0.501840  0.659254 -0.421691 -0.057688   G\n",
       "3     0.204886  1.074134  1.388361 -0.982404   R\n",
       "4     0.354628 -0.133116  0.283763 -0.837063   Q\n",
       "...        ...       ...       ...       ...  ..\n",
       "9995  2.311896 -0.417070 -1.409599 -0.515821   L\n",
       "9996 -0.479893 -0.650419  0.745152 -0.646038   E\n",
       "9997  0.523331  0.787112  0.486066  1.093156   K\n",
       "9998 -0.362559  0.598894 -1.843201  0.887292   G\n",
       "9999 -0.096376 -1.012999 -0.657431 -0.573315   0\n",
       "\n",
       "[10000 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.read_csv('examples/ex6.csv')\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.467976</td>\n",
       "      <td>-0.038649</td>\n",
       "      <td>-0.295344</td>\n",
       "      <td>-1.824726</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.358893</td>\n",
       "      <td>1.404453</td>\n",
       "      <td>0.704965</td>\n",
       "      <td>-0.200638</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.501840</td>\n",
       "      <td>0.659254</td>\n",
       "      <td>-0.421691</td>\n",
       "      <td>-0.057688</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.204886</td>\n",
       "      <td>1.074134</td>\n",
       "      <td>1.388361</td>\n",
       "      <td>-0.982404</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.354628</td>\n",
       "      <td>-0.133116</td>\n",
       "      <td>0.283763</td>\n",
       "      <td>-0.837063</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one       two     three      four key\n",
       "0  0.467976 -0.038649 -0.295344 -1.824726   L\n",
       "1 -0.358893  1.404453  0.704965 -0.200638   B\n",
       "2 -0.501840  0.659254 -0.421691 -0.057688   G\n",
       "3  0.204886  1.074134  1.388361 -0.982404   R\n",
       "4  0.354628 -0.133116  0.283763 -0.837063   Q"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('examples/ex6.csv', nrows=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chunker = pd.read_csv('examples/ex6.csv', chunksize=1000)\n",
    "chunker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "chunker = pd.read_csv('examples/ex6.csv', chunksize=1000)\n",
    "\n",
    "tot = pd.Series([])\n",
    "for piece in chunker:\n",
    "    tot = tot.add(piece['key'].value_counts(), fill_value=0)\n",
    "\n",
    "tot = tot.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "E    368.0\n",
       "X    364.0\n",
       "L    346.0\n",
       "O    343.0\n",
       "Q    340.0\n",
       "M    338.0\n",
       "J    337.0\n",
       "F    335.0\n",
       "K    334.0\n",
       "H    330.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Writing Data to Text Format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>something</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>two</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>three</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  something  a   b     c   d message\n",
       "0       one  1   2   3.0   4     NaN\n",
       "1       two  5   6   NaN   8   world\n",
       "2     three  9  10  11.0  12     foo"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('examples/ex5.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",something,a,b,c,d,message\r\n",
      "0,one,1,2,3.0,4,\r\n",
      "1,two,5,6,,8,world\r\n",
      "2,three,9,10,11.0,12,foo\r\n"
     ]
    }
   ],
   "source": [
    "data.to_csv('examples/out.csv')\n",
    "!cat examples/out.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|something|a|b|c|d|message\n",
      "0|one|1|2|3.0|4|\n",
      "1|two|5|6||8|world\n",
      "2|three|9|10|11.0|12|foo\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "data.to_csv(sys.stdout, sep='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",something,a,b,c,d,message\n",
      "0,one,1,2,3.0,4,NULL\n",
      "1,two,5,6,NULL,8,world\n",
      "2,three,9,10,11.0,12,foo\n"
     ]
    }
   ],
   "source": [
    "data.to_csv(sys.stdout, na_rep='NULL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one,1,2,3.0,4,\n",
      "two,5,6,,8,world\n",
      "three,9,10,11.0,12,foo\n"
     ]
    }
   ],
   "source": [
    "data.to_csv(sys.stdout, index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a,b,c\n",
      "1,2,3.0\n",
      "5,6,\n",
      "9,10,11.0\n"
     ]
    }
   ],
   "source": [
    "data.to_csv(sys.stdout, index=False, columns=['a', 'b', 'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000-01-01,0\r\n",
      "2000-01-02,1\r\n",
      "2000-01-03,2\r\n",
      "2000-01-04,3\r\n",
      "2000-01-05,4\r\n",
      "2000-01-06,5\r\n",
      "2000-01-07,6\r\n"
     ]
    }
   ],
   "source": [
    "dates = pd.date_range('1/1/2000', periods=7)\n",
    "ts = pd.Series(np.arange(7), index=dates)\n",
    "ts.to_csv('examples/tseries.csv')\n",
    "!cat examples/tseries.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with Delimited Formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"a\",\"b\",\"c\"\r\n",
      "\"1\",\"2\",\"3\"\r\n",
      "\"1\",\"2\",\"3\"\r\n"
     ]
    }
   ],
   "source": [
    "!cat examples/ex7.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "f = open('examples/ex7.csv')\n",
    "\n",
    "reader = csv.reader(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'b', 'c']\n",
      "['1', '2', '3']\n",
      "['1', '2', '3']\n"
     ]
    }
   ],
   "source": [
    "for line in reader:\n",
    "    print(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('examples/ex7.csv') as f:\n",
    "    lines = list(csv.reader(f))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "header, values = lines[0], lines[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': ('1', '1'), 'b': ('2', '2'), 'c': ('3', '3')}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dict = {h: v for h, v in zip(header, zip(*values))}\n",
    "data_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "class my_dialect(csv.Dialect):\n",
    "    lineterminator = '\\n'\n",
    "    delimiter = ';'\n",
    "    quotechar = '\"'\n",
    "    quoting = csv.QUOTE_MINIMAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a,\"b\",\"c\"']\n",
      "['1,\"2\",\"3\"']\n",
      "['1,\"2\",\"3\"']\n"
     ]
    }
   ],
   "source": [
    "f = open('examples/ex7.csv')\n",
    "reader = csv.reader(f, dialect=my_dialect)\n",
    "for line in reader:\n",
    "    print(line)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a,\"b\",\"c\"']\n",
      "['1,\"2\",\"3\"']\n",
      "['1,\"2\",\"3\"']\n"
     ]
    }
   ],
   "source": [
    "f = open('examples/ex7.csv')\n",
    "reader = csv.reader(f, delimiter='|')\n",
    "for line in reader:\n",
    "    print(line)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one;two;three\r\n",
      "1;2;3\r\n",
      "4;5;6\r\n",
      "7;8;9\r\n"
     ]
    }
   ],
   "source": [
    "with open('mydata.csv', 'w') as f:\n",
    "    writer = csv.writer(f, dialect=my_dialect)\n",
    "    writer.writerow(('one', 'two', 'three'))\n",
    "    writer.writerow(('1', '2', '3'))\n",
    "    writer.writerow(('4', '5', '6'))\n",
    "    writer.writerow(('7', '8', '9'))\n",
    "!cat mydata.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JSON Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = \"\"\"\n",
    "{\"name\": \"Wes\",\n",
    " \"places_lived\": [\"United States\", \"Spain\", \"Germany\"],\n",
    " \"pet\": null,\n",
    " \"siblings\": [{\"name\": \"Scott\", \"age\": 30, \"pets\": [\"Zeus\", \"Zuko\"]},\n",
    "              {\"name\": \"Katie\", \"age\": 38,\n",
    "               \"pets\": [\"Sixes\", \"Stache\", \"Cisco\"]}]\n",
    "}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Wes',\n",
       " 'places_lived': ['United States', 'Spain', 'Germany'],\n",
       " 'pet': None,\n",
       " 'siblings': [{'name': 'Scott', 'age': 30, 'pets': ['Zeus', 'Zuko']},\n",
       "  {'name': 'Katie', 'age': 38, 'pets': ['Sixes', 'Stache', 'Cisco']}]}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "result = json.loads(obj)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"name\": \"Wes\", \"places_lived\": [\"United States\", \"Spain\", \"Germany\"], \"pet\": null, \"siblings\": [{\"name\": \"Scott\", \"age\": 30, \"pets\": [\"Zeus\", \"Zuko\"]}, {\"name\": \"Katie\", \"age\": 38, \"pets\": [\"Sixes\", \"Stache\", \"Cisco\"]}]}'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asjson = json.dumps(result)\n",
    "asjson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Scott</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Katie</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  age\n",
       "0  Scott   30\n",
       "1  Katie   38"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "siblings = pd.DataFrame(result['siblings'], columns=['name', 'age'])\n",
    "siblings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{\"a\": 1, \"b\": 2, \"c\": 3},\r\n",
      " {\"a\": 4, \"b\": 5, \"c\": 6},\r\n",
      " {\"a\": 7, \"b\": 8, \"c\": 9}]\r\n"
     ]
    }
   ],
   "source": [
    "!cat examples/example.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c\n",
       "0  1  2  3\n",
       "1  4  5  6\n",
       "2  7  8  9"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_json('examples/example.json')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"a\":{\"0\":1,\"1\":4,\"2\":7},\"b\":{\"0\":2,\"1\":5,\"2\":8},\"c\":{\"0\":3,\"1\":6,\"2\":9}}\n",
      "[{\"a\":1,\"b\":2,\"c\":3},{\"a\":4,\"b\":5,\"c\":6},{\"a\":7,\"b\":8,\"c\":9}]\n"
     ]
    }
   ],
   "source": [
    "print(data.to_json())\n",
    "print(data.to_json(orient='records'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XML and HTML: Web Scraping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "conda install lxml\n",
    "pip install beautifulsoup4 html5lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tables = pd.read_html('examples/fdic_failed_bank_list.html')\n",
    "len(tables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Bank Name</th>\n",
       "      <th>City</th>\n",
       "      <th>ST</th>\n",
       "      <th>CERT</th>\n",
       "      <th>Acquiring Institution</th>\n",
       "      <th>Closing Date</th>\n",
       "      <th>Updated Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Allied Bank</td>\n",
       "      <td>Mulberry</td>\n",
       "      <td>AR</td>\n",
       "      <td>91</td>\n",
       "      <td>Today's Bank</td>\n",
       "      <td>September 23, 2016</td>\n",
       "      <td>November 17, 2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Woodbury Banking Company</td>\n",
       "      <td>Woodbury</td>\n",
       "      <td>GA</td>\n",
       "      <td>11297</td>\n",
       "      <td>United Bank</td>\n",
       "      <td>August 19, 2016</td>\n",
       "      <td>November 17, 2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>First CornerStone Bank</td>\n",
       "      <td>King of Prussia</td>\n",
       "      <td>PA</td>\n",
       "      <td>35312</td>\n",
       "      <td>First-Citizens Bank &amp; Trust Company</td>\n",
       "      <td>May 6, 2016</td>\n",
       "      <td>September 6, 2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Trust Company Bank</td>\n",
       "      <td>Memphis</td>\n",
       "      <td>TN</td>\n",
       "      <td>9956</td>\n",
       "      <td>The Bank of Fayette County</td>\n",
       "      <td>April 29, 2016</td>\n",
       "      <td>September 6, 2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>North Milwaukee State Bank</td>\n",
       "      <td>Milwaukee</td>\n",
       "      <td>WI</td>\n",
       "      <td>20364</td>\n",
       "      <td>First-Citizens Bank &amp; Trust Company</td>\n",
       "      <td>March 11, 2016</td>\n",
       "      <td>June 16, 2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Bank Name             City  ST   CERT  \\\n",
       "0                   Allied Bank         Mulberry  AR     91   \n",
       "1  The Woodbury Banking Company         Woodbury  GA  11297   \n",
       "2        First CornerStone Bank  King of Prussia  PA  35312   \n",
       "3            Trust Company Bank          Memphis  TN   9956   \n",
       "4    North Milwaukee State Bank        Milwaukee  WI  20364   \n",
       "\n",
       "                 Acquiring Institution        Closing Date       Updated Date  \n",
       "0                         Today's Bank  September 23, 2016  November 17, 2016  \n",
       "1                          United Bank     August 19, 2016  November 17, 2016  \n",
       "2  First-Citizens Bank & Trust Company         May 6, 2016  September 6, 2016  \n",
       "3           The Bank of Fayette County      April 29, 2016  September 6, 2016  \n",
       "4  First-Citizens Bank & Trust Company      March 11, 2016      June 16, 2016  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "failures = tables[0]\n",
    "failures.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2010    157\n",
       "2009    140\n",
       "2011     92\n",
       "2012     51\n",
       "2008     25\n",
       "       ... \n",
       "2004      4\n",
       "2001      4\n",
       "2007      3\n",
       "2003      3\n",
       "2000      2\n",
       "Name: Closing Date, Length: 15, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_timestamps = pd.to_datetime(failures['Closing Date'])\n",
    "close_timestamps.dt.year.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parsing XML with lxml.objectify"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<INDICATOR>\n",
    "  <INDICATOR_SEQ>373889</INDICATOR_SEQ>\n",
    "  <PARENT_SEQ></PARENT_SEQ>\n",
    "  <AGENCY_NAME>Metro-North Railroad</AGENCY_NAME>\n",
    "  <INDICATOR_NAME>Escalator Availability</INDICATOR_NAME>\n",
    "  <DESCRIPTION>Percent of the time that escalators are operational\n",
    "  systemwide. The availability rate is based on physical observations performed\n",
    "  the morning of regular business days only. This is a new indicator the agency\n",
    "  began reporting in 2009.</DESCRIPTION>\n",
    "  <PERIOD_YEAR>2011</PERIOD_YEAR>\n",
    "  <PERIOD_MONTH>12</PERIOD_MONTH>\n",
    "  <CATEGORY>Service Indicators</CATEGORY>\n",
    "  <FREQUENCY>M</FREQUENCY>\n",
    "  <DESIRED_CHANGE>U</DESIRED_CHANGE>\n",
    "  <INDICATOR_UNIT>%</INDICATOR_UNIT>\n",
    "  <DECIMAL_PLACES>1</DECIMAL_PLACES>\n",
    "  <YTD_TARGET>97.00</YTD_TARGET>\n",
    "  <YTD_ACTUAL></YTD_ACTUAL>\n",
    "  <MONTHLY_TARGET>97.00</MONTHLY_TARGET>\n",
    "  <MONTHLY_ACTUAL></MONTHLY_ACTUAL>\n",
    "</INDICATOR>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lxml import objectify\n",
    "\n",
    "path = 'datasets/mta_perf/Performance_MNR.xml'\n",
    "parsed = objectify.parse(open(path))\n",
    "root = parsed.getroot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "\n",
    "skip_fields = ['PARENT_SEQ', 'INDICATOR_SEQ',\n",
    "               'DESIRED_CHANGE', 'DECIMAL_PLACES']\n",
    "\n",
    "for elt in root.INDICATOR:\n",
    "    el_data = {}\n",
    "    for child in elt.getchildren():\n",
    "        if child.tag in skip_fields:\n",
    "            continue\n",
    "        el_data[child.tag] = child.pyval\n",
    "    data.append(el_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>AGENCY_NAME</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>FREQUENCY</th>\n",
       "      <th>INDICATOR_NAME</th>\n",
       "      <th>INDICATOR_UNIT</th>\n",
       "      <th>MONTHLY_ACTUAL</th>\n",
       "      <th>MONTHLY_TARGET</th>\n",
       "      <th>PERIOD_MONTH</th>\n",
       "      <th>PERIOD_YEAR</th>\n",
       "      <th>YTD_ACTUAL</th>\n",
       "      <th>YTD_TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Metro-North Railroad</td>\n",
       "      <td>Service Indicators</td>\n",
       "      <td>Percent of commuter trains that arrive at thei...</td>\n",
       "      <td>M</td>\n",
       "      <td>On-Time Performance (West of Hudson)</td>\n",
       "      <td>%</td>\n",
       "      <td>96.9</td>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>96.9</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Metro-North Railroad</td>\n",
       "      <td>Service Indicators</td>\n",
       "      <td>Percent of commuter trains that arrive at thei...</td>\n",
       "      <td>M</td>\n",
       "      <td>On-Time Performance (West of Hudson)</td>\n",
       "      <td>%</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>96</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Metro-North Railroad</td>\n",
       "      <td>Service Indicators</td>\n",
       "      <td>Percent of commuter trains that arrive at thei...</td>\n",
       "      <td>M</td>\n",
       "      <td>On-Time Performance (West of Hudson)</td>\n",
       "      <td>%</td>\n",
       "      <td>96.9</td>\n",
       "      <td>95</td>\n",
       "      <td>3</td>\n",
       "      <td>2008</td>\n",
       "      <td>96.3</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Metro-North Railroad</td>\n",
       "      <td>Service Indicators</td>\n",
       "      <td>Percent of commuter trains that arrive at thei...</td>\n",
       "      <td>M</td>\n",
       "      <td>On-Time Performance (West of Hudson)</td>\n",
       "      <td>%</td>\n",
       "      <td>98.3</td>\n",
       "      <td>95</td>\n",
       "      <td>4</td>\n",
       "      <td>2008</td>\n",
       "      <td>96.8</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Metro-North Railroad</td>\n",
       "      <td>Service Indicators</td>\n",
       "      <td>Percent of commuter trains that arrive at thei...</td>\n",
       "      <td>M</td>\n",
       "      <td>On-Time Performance (West of Hudson)</td>\n",
       "      <td>%</td>\n",
       "      <td>95.8</td>\n",
       "      <td>95</td>\n",
       "      <td>5</td>\n",
       "      <td>2008</td>\n",
       "      <td>96.6</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AGENCY_NAME            CATEGORY  \\\n",
       "0  Metro-North Railroad  Service Indicators   \n",
       "1  Metro-North Railroad  Service Indicators   \n",
       "2  Metro-North Railroad  Service Indicators   \n",
       "3  Metro-North Railroad  Service Indicators   \n",
       "4  Metro-North Railroad  Service Indicators   \n",
       "\n",
       "                                         DESCRIPTION FREQUENCY  \\\n",
       "0  Percent of commuter trains that arrive at thei...         M   \n",
       "1  Percent of commuter trains that arrive at thei...         M   \n",
       "2  Percent of commuter trains that arrive at thei...         M   \n",
       "3  Percent of commuter trains that arrive at thei...         M   \n",
       "4  Percent of commuter trains that arrive at thei...         M   \n",
       "\n",
       "                         INDICATOR_NAME INDICATOR_UNIT MONTHLY_ACTUAL  \\\n",
       "0  On-Time Performance (West of Hudson)              %           96.9   \n",
       "1  On-Time Performance (West of Hudson)              %             95   \n",
       "2  On-Time Performance (West of Hudson)              %           96.9   \n",
       "3  On-Time Performance (West of Hudson)              %           98.3   \n",
       "4  On-Time Performance (West of Hudson)              %           95.8   \n",
       "\n",
       "  MONTHLY_TARGET  PERIOD_MONTH  PERIOD_YEAR YTD_ACTUAL YTD_TARGET  \n",
       "0             95             1         2008       96.9         95  \n",
       "1             95             2         2008         96         95  \n",
       "2             95             3         2008       96.3         95  \n",
       "3             95             4         2008       96.8         95  \n",
       "4             95             5         2008       96.6         95  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf = pd.DataFrame(data)\n",
    "perf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from io import StringIO\n",
    "tag = '<a href=\"http://www.google.com\">Google</a>'\n",
    "root = objectify.parse(StringIO(tag)).getroot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Google'"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "root\n",
    "root.get('href')\n",
    "root.text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binary Data Formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = pd.read_csv('examples/ex1.csv')\n",
    "frame\n",
    "frame.to_pickle('examples/frame_pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>hello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b   c   d message\n",
       "0  1   2   3   4   hello\n",
       "1  5   6   7   8   world\n",
       "2  9  10  11  12     foo"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_pickle('examples/frame_pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm examples/frame_pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using HDF5 Format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.io.pytables.HDFStore'>\n",
       "File path: mydata.h5"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame({'a': np.random.randn(100)})\n",
    "store = pd.HDFStore('mydata.h5')\n",
    "store['obj1'] = frame\n",
    "store['obj1_col'] = frame['a']\n",
    "store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "�HDF\r",
      "\r\n",
      "\u001a\r\n",
      "\u0000\u0000\u0000\u0000\u0000\b\b\u0000\u0004\u0000\u0010\u0000\u0001\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000��������\u0000\b\u0000\u0000\u0000\u0000\u0000\u0000��������\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000`\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0001\u0000\u0000\u0000\u0000\u0000\u0000\u0000�\u0000\u0000\u0000\u0000\u0000\u0000\u0000�\u0002\u0000\u0000\u0000\u0000\u0000\u0000"
     ]
    }
   ],
   "source": [
    "!cat mydata.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.127481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.568363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.309362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.577385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.168634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>-0.225524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1.349726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1.350300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>-0.386653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.865990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           a\n",
       "0   1.127481\n",
       "1  -0.568363\n",
       "2   0.309362\n",
       "3  -0.577385\n",
       "4  -1.168634\n",
       "..       ...\n",
       "95 -0.225524\n",
       "96  1.349726\n",
       "97  1.350300\n",
       "98 -0.386653\n",
       "99  0.865990\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store['obj1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1.127481\n",
       "1    -0.568363\n",
       "2     0.309362\n",
       "3    -0.577385\n",
       "4    -1.168634\n",
       "        ...   \n",
       "95   -0.225524\n",
       "96    1.349726\n",
       "97    1.350300\n",
       "98   -0.386653\n",
       "99    0.865990\n",
       "Name: a, Length: 100, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store['obj1_col']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "store.put('obj2', frame, format='table')\n",
    "store.select('obj2', where=['index >= 10 and index <= 15'])\n",
    "store.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.127481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.568363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.309362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.577385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.168634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a\n",
       "0  1.127481\n",
       "1 -0.568363\n",
       "2  0.309362\n",
       "3 -0.577385\n",
       "4 -1.168634"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.to_hdf('mydata.h5', 'obj3', format='table')\n",
    "pd.read_hdf('mydata.h5', 'obj3', where=['index < 5'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.remove('mydata.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading Microsoft Excel Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "xlsx = pd.ExcelFile('examples/ex1.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sheet1']"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xlsx.sheet_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>hello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b   c   d message\n",
       "0  1   2   3   4   hello\n",
       "1  5   6   7   8   world\n",
       "2  9  10  11  12     foo"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel(xlsx, 'Sheet1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>hello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b   c   d message\n",
       "0  1   2   3   4   hello\n",
       "1  5   6   7   8   world\n",
       "2  9  10  11  12     foo"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.read_excel('examples/ex1.xlsx', 'Sheet1')\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "writer = pd.ExcelWriter('examples/ex2.xlsx')\n",
    "frame.to_excel(writer, 'Sheet1')\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame.to_excel('examples/ex2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm examples/ex2.xlsx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interacting with Web APIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "url = 'https://api.github.com/repos/pandas-dev/pandas/issues'\n",
    "resp = requests.get(url)\n",
    "resp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'move late imports to top'"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = resp.json()\n",
    "data[0]['title']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "  'body': \"In #26414 we splitted the pandas plotting module into a general plotting framework able to call different backends and the current matplotlib backends. The idea is that other backends can be implemented in a simpler way, and be used with a common API by pandas users.\\r\\n\\r\\nThe API defined by the current matplotlib backend includes the objects listed next, but this API can probably be simplified. Here is the list with questions/proposals:\\r\\n\\r\\nNon-controversial methods to keep in the API (They provide the `Series.plot(kind='line')`... functionality):\\r\\n- LinePlot\\r\\n- BarPlot\\r\\n- BarhPlot\\r\\n- HistPlot\\r\\n- BoxPlot\\r\\n- KdePlot\\r\\n- AreaPlot\\r\\n- PiePlot\\r\\n- ScatterPlot\\r\\n- HexBinPlot\\r\\n\\r\\nPlotting functions provided in pandas (e.g. `pandas.plotting.andrews_curves(df)`)\\r\\n- andrews_curves\\r\\n- autocorrelation_plot\\r\\n- bootstrap_plot\\r\\n- lag_plot\\r\\n- parallel_coordinates\\r\\n- radviz\\r\\n- scatter_matrix\\r\\n- table\\r\\n\\r\\nShould those be part of the API and other backends should also implement them? Would it make sense to convert to the format `.plot` (e.g. `DataFrame.plot(kind='autocorrelation')`...)? Does it make sense to keep out of the API, or move to a third-party module?\\r\\n\\r\\nRedundant methods that can possibly be removed:\\r\\n- hist_series\\r\\n- hist_frame\\r\\n- boxplot\\r\\n- boxplot_frame\\r\\n- boxplot_frame_groupby\\r\\n\\r\\nIn the case of `boxplot`, we currently have several ways of generating a plot (calling mainly the same code):\\r\\n1. `DataFrame.plot.boxplot()`\\r\\n2. `DataFrame.plot(kind='box')`\\r\\n3. `DataFrame.boxplot()`\\r\\n4. `pandas.plotting.boxplot(df)`\\r\\n\\r\\nPersonally, I'd deprecate number 4, and for number 3, deprecate or at least not require a separate `boxplot_frame` method in the backend, but try to reuse `BoxPlot` (for number 3 comments, same applies to `hist`).\\r\\n\\r\\nFor `boxplot_frame_groupby`, didn't check in detail, but not sure if `BoxPlot` could be reused for this?\\r\\n\\r\\nFunctions to register converters:\\r\\n- register\\r\\n- deregister\\r\\n\\r\\nDo those make sense for other backends? \\r\\n\\r\\nDeprecated in pandas 0.23, to be removed:\\r\\n- tsplot\\r\\n\\r\\nTo see what each of these functions do in practise, it may be useful this notebook by @liirusuk: https://github.com/python-sprints/pandas_plotting_library/blob/master/AllPlottingExamples.ipynb\\r\\n\\r\\nCC: @pandas-dev/pandas-core @tacaswell, @jakevdp, @philippjfr, @PatrikHlobil\"},\n",
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       "  'body': \"#26414 splitted the pandas plotting code into two different modules:\\r\\n- `pandas.plotting`: Generic plotting framework that defined an API (with docs) and is able to plot with a selectable backend (will be selectable with a pandas option)\\r\\n- `pandas.plotting._matplotlib`: Matplotlib backend\\r\\n\\r\\nOur tests in `pandas/tests/plotting` are currently testing both together (calling the pandas plotting framework, which will call the matplotlib backend). But ideally we'd like to have two different groups of tests:\\r\\n- One with the current code but calling the matplotlib backend directly\\r\\n- A test set of the generic pandas plotting framework, using a mock backend (needs to be implemented)\\r\\n\\r\\n@jreback let me know if this makes sense to you\"},\n",
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       "  'body': \"I'd like to be able to _prepend_ indices to existing ones using `.set_index()`, in the same vein as the `append` argument.\\r\\n\\r\\nThis avoid having to do the awkward alternatives:\\r\\n- `.reset_index().set_index(...)`\\r\\n- `.set_index(append=True).swaplevel(...)` or `.set_index(append=True).reorder_levels(...)`\\r\\n\\r\\nMy first suggestion is to add a `prepend` keyword, mutually exclusive with `append`, but discussion is welcomed.\\r\\n\\r\\nAssociated PR: https://github.com/pandas-dev/pandas/pull/26724\"},\n",
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       "  'body': 'xref #25632\\r\\n\\r\\nremoved small amount of code related to self.ndim > 2\\r\\n\\r\\nalso a few changes to make the NotImplemented errors for self.ndim>2 more consistent and added test for these. (might be worth having a decorator for these)'},\n",
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       "  'body': '- [x] closes #26566\\r\\n- [x] tests added / passed\\r\\n- [x] passes `git diff upstream/master -u -- \"*.py\" | flake8 --diff`\\r\\n- [ ] whatsnew entry \\r\\n\\r\\ncc. @WillAyd \\r\\n\\r\\nNote I only decided to raise an error when the engine specified to `read_excel` was different to that of the ExcelFile - otherwise around 15 test cases would need to be changed - let me know what you think\\r\\n'},\n",
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       "  'body': 'Begin from 0.24.0 next code raise exception\\r\\n```python\\r\\nimport decimal\\r\\nimport pandas as pd\\r\\nser = pd.Series([1, 2])\\r\\nser.astype(decimal.Decimal)\\r\\n```\\r\\nTraceback\\r\\n```\\r\\nTraceback (most recent call last):\\r\\n  File \"<stdin>\", line 1, in <module>\\r\\n  File \"/home/shadchin/t_d/venv/local/lib/python2.7/site-packages/pandas/core/generic.py\", line 5691, in astype\\r\\n    **kwargs)\\r\\n  File \"/home/shadchin/t_d/venv/local/lib/python2.7/site-packages/pandas/core/internals/managers.py\", line 531, in astype\\r\\n    return self.apply(\\'astype\\', dtype=dtype, **kwargs)\\r\\n  File \"/home/shadchin/t_d/venv/local/lib/python2.7/site-packages/pandas/core/internals/managers.py\", line 395, in apply\\r\\n    applied = getattr(b, f)(**kwargs)\\r\\n  File \"/home/shadchin/t_d/venv/local/lib/python2.7/site-packages/pandas/core/internals/blocks.py\", line 534, in astype\\r\\n    **kwargs)\\r\\n  File \"/home/shadchin/t_d/venv/local/lib/python2.7/site-packages/pandas/core/internals/blocks.py\", line 595, in _astype\\r\\n    dtype = pandas_dtype(dtype)\\r\\n  File \"/home/shadchin/t_d/venv/local/lib/python2.7/site-packages/pandas/core/dtypes/common.py\", line 2029, in pandas_dtype\\r\\n    raise TypeError(\"dtype \\'{}\\' not understood\".format(dtype))\\r\\nTypeError: dtype \\'<class \\'decimal.Decimal\\'>\\' not understood\\r\\n```\\r\\n#### Problem description\\r\\n\\r\\nThis begin from #21160\\r\\n\\r\\nAfter was https://github.com/pandas-dev/pandas/blob/0409521665bd436a10aea7e06336066bf07ff057/pandas/core/dtypes/common.py#L2013\\r\\nNow https://github.com/pandas-dev/pandas/blob/cb00deb94500205fcb27a33cc1d0df79a9727f8b/pandas/core/internals/blocks.py#L595\\r\\n\\r\\n`pandas_dtype()` does not understand `decimal.Decimal`\\r\\n\\r\\nIs it expected behavior or regress?\\r\\n\\r\\n#### Output of ``pd.show_versions()``\\r\\n\\r\\n<details>\\r\\n\\r\\n\\r\\nINSTALLED VERSIONS\\r\\n------------------\\r\\ncommit: None\\r\\npython: 2.7.12.final.0\\r\\npython-bits: 64\\r\\nOS: Linux\\r\\nOS-release: 4.9.101-26\\r\\nmachine: x86_64\\r\\nprocessor: x86_64\\r\\nbyteorder: little\\r\\nLC_ALL: None\\r\\nLANG: en_US.UTF-8\\r\\nLOCALE: None.None\\r\\n\\r\\npandas: 0.24.2\\r\\npytest: None\\r\\npip: 19.1.1\\r\\nsetuptools: 41.0.1\\r\\nCython: None\\r\\nnumpy: 1.16.4\\r\\nscipy: None\\r\\npyarrow: None\\r\\nxarray: None\\r\\nIPython: None\\r\\nsphinx: None\\r\\npatsy: None\\r\\ndateutil: 2.8.0\\r\\npytz: 2019.1\\r\\nblosc: None\\r\\nbottleneck: None\\r\\ntables: None\\r\\nnumexpr: None\\r\\nfeather: None\\r\\nmatplotlib: None\\r\\nopenpyxl: None\\r\\nxlrd: None\\r\\nxlwt: None\\r\\nxlsxwriter: None\\r\\nlxml.etree: None\\r\\nbs4: None\\r\\nhtml5lib: None\\r\\nsqlalchemy: None\\r\\npymysql: None\\r\\npsycopg2: None\\r\\njinja2: None\\r\\ns3fs: None\\r\\nfastparquet: None\\r\\npandas_gbq: None\\r\\npandas_datareader: None\\r\\ngcsfs: None\\r\\n\\r\\n</details>\\r\\n'},\n",
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       "  'body': 'Associated issue: https://github.com/pandas-dev/pandas/issues/26742\\r\\n\\r\\nThis PR adds the ability to _prepend_ indices to existing ones using `.set_index()`, in the same vein as the `append` argument.\\r\\n\\r\\nThis avoid having to do the awkward alternatives:\\r\\n- `.reset_index().set_index(...)`\\r\\n- `.set_index(append=True).swaplevel(...)` or `.set_index(append=True).reorder_levels(...)`\\r\\n\\r\\n`prepend` is mutually exclusive with `append`. The code will test for this and raise ValueError if not.\\r\\n\\r\\n- [x] tests added / passed\\r\\n- [x] passes `git diff upstream/master -u -- \"*.py\" | flake8 --diff`\\r\\n- [ ] whatsnew entry\\r\\n'},\n",
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       "  'number': 26723,\n",
       "  'title': 'DOC/CI: Doc build is failing',\n",
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       "  'created_at': '2019-06-07T23:15:03Z',\n",
       "  'updated_at': '2019-06-08T03:37:08Z',\n",
       "  'closed_at': None,\n",
       "  'author_association': 'MEMBER',\n",
       "  'body': 'The doc build is failing with:\\r\\n```\\r\\nRecursion error:\\r\\nmaximum recursion depth exceeded while calling a Python object\\r\\n\\r\\nThis can happen with very large or deeply nested source files.  You can carefully increase the default Python recursion limit of 1000 in conf.py with e.g.:\\r\\n    import sys; sys.setrecursionlimit(1500)\\r\\n```\\r\\n\\r\\nI tried increasing the limit from 1,000 to 20,000 but still the same error, so I guess the problem is something else.\\r\\n\\r\\nI think this is the first job that failed: https://travis-ci.org/pandas-dev/pandas/jobs/542714631 (PR #26697)'},\n",
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       "  'title': 'REF: Refactor signature of RangeIndex._simple_new, allow range in construction',\n",
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       "   'created_at': '2018-10-23T02:34:15Z',\n",
       "   'updated_at': '2019-06-09T00:57:29Z',\n",
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       "  'created_at': '2019-06-07T22:25:36Z',\n",
       "  'updated_at': '2019-06-08T19:52:27Z',\n",
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       "  'body': \"This PR refactors ``RangeIndex._simple_new``, so its signature is the same as ``Index._simple_new``. This will help typing later on, as currently mypy complains about the different signatures. In short a ``_simple_new`` now expects a ``range`` as its input. As a ``range`` is immutable, the code is easy to reason about.\\r\\n\\r\\nAdditionally, ``RangeIndex`` now accepts a ``range`` as its input. Because ``pd.Index(range(10))`` works, I think ``pd.RangeIndex(range(10))`` should also work, for symmetry.\\r\\n\\r\\nEven more additionally, the signature of ``RangeIndex.from_range`` has dropped the ``**kwargs`` part of its signature. This makes the class method a bit easier to reason about. Other classes with a ``from_*`` class method don't have a ``**kwargs`` part.\\r\\n\\r\\nAfter this PR, ``RangeIndex`` will be ready for adding type hints.\\r\\n\\r\\nEDIT; Performance improvements:\\r\\n\\r\\n```python\\r\\n>>> rng = pd.RangeIndex(1_000_000)\\r\\n>>> sl = slice(0, 900_000)\\r\\n>>> %timeit rng[sl]  # slicing\\r\\n7.65 µs ± 8.11 ns per loop  # master\\r\\n1.85 µs ± 8.11 ns per loop  # this PR\\r\\n```\\r\\n\"},\n",
       " {'url': 'https://api.github.com/repos/pandas-dev/pandas/issues/26720',\n",
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       "  'number': 26720,\n",
       "  'title': 'SettingWithCopyWarning Triggers Inconsistently Based On Number of Columns',\n",
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       "  'created_at': '2019-06-07T22:02:23Z',\n",
       "  'updated_at': '2019-06-07T22:28:41Z',\n",
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       "  'body': \"#### Code Sample, a copy-pastable example if possible\\r\\n\\r\\n```python\\r\\nimport pandas as pd\\r\\n\\r\\ndivider_one = '=' * 75\\r\\ndivider_two = '+' * 75\\r\\ndivider_three = '#' * 75\\r\\n\\r\\n####################################################################################################\\r\\n#   There are three declarations for the `base_df_*` DataFrame:\\r\\n#       one initializes it with two elements then adds one, two, and three more columns; and\\r\\n#       one initializes it with three elements then adds one, two, and three more columns; and\\r\\n#       one initializes it with four elements then adds one, two, and three more columns.\\r\\n#\\r\\n#   It appears that the `SettingWithcopyWarning` is triggered when a DataFrame with four or more columns is\\r\\n#   used to derive another DataFrame by slicing and then an attempt is made to add a new column to\\r\\n#   the derived DataFrame. There is no such warning when the original DataFrame has fewer\\r\\n#   columns.\\r\\n#\\r\\n#   To test this, uncomment the appropriate function to test.\\r\\n#\\r\\n####################################################################################################\\r\\n\\r\\n\\r\\ndef test_base_df_with_two_initial_elements():\\r\\n    base_df_one = pd.DataFrame({\\r\\n        'col1': ['col1 value one', 'col1 value two', 'col1 value three'],\\r\\n        'col2': ['col2 value one', 'col2 value two', 'col2 value three'],\\r\\n    })\\r\\n\\r\\n    print(divider_one)#                                           This is with a base df w/ 2\\r\\n    print('Setting the df sub from slice.')\\r\\n    base_df_one_sub = base_df_one[['col1', 'col2']]\\r\\n    print('Adding newcol to sub.')\\r\\n    print('Should be no error.')\\r\\n    base_df_one_sub['newcol'] = 'values'\\r\\n    print(divider_one)\\r\\n\\r\\n    print(divider_two)#                                           This is with a base df w/ 3\\r\\n    print('Adding col3 to base.')\\r\\n    base_df_one['col3'] = 'col3 values'\\r\\n    print('Resetting the df sub from slice.')\\r\\n    base_df_one_sub = base_df_one[['col1', 'col2']]\\r\\n    print('Adding newcol to sub.')\\r\\n    print('Should be no error.')\\r\\n    base_df_one_sub['newcol'] = 'values'\\r\\n    print(divider_two)\\r\\n\\r\\n    print(divider_three)#                                         This is with a base df w/ 4\\r\\n    print('Adding col4 to base.')\\r\\n    base_df_one['col4'] = 'col4 values'\\r\\n    print('Resetting the df sub from slice.')\\r\\n    base_df_one_sub = base_df_one[['col1', 'col2']]\\r\\n    print('Adding newcol to sub.')\\r\\n    print('Should be an error.')\\r\\n    base_df_one_sub['newcol'] = 'values'\\r\\n    print(divider_three)\\r\\n\\r\\n\\r\\ndef test_base_df_with_three_initial_elements():\\r\\n    base_df_one = pd.DataFrame({\\r\\n        'col1': ['col1 value one', 'col1 value two', 'col1 value three'],\\r\\n        'col2': ['col2 value one', 'col2 value two', 'col2 value three'],\\r\\n        'col3': ['col3 value one', 'col3 value two', 'col3 value three'],\\r\\n    })\\r\\n\\r\\n    print(divider_one)#                                           This is with a base df w/ 3\\r\\n    print('Setting the df sub from slice.')\\r\\n    base_df_one_sub = base_df_one[['col1', 'col2']]\\r\\n    print('Adding newcol to sub.')\\r\\n    print('Should be no error.')\\r\\n    base_df_one_sub['newcol'] = 'values'\\r\\n    print(divider_one)\\r\\n\\r\\n    print(divider_two)#                                           This is with a base df w/ 4\\r\\n    print('Adding col4 to base.')\\r\\n    base_df_one['col4'] = 'col3 values'\\r\\n    print('Resetting the df sub from slice.')\\r\\n    base_df_one_sub = base_df_one[['col1', 'col2']]\\r\\n    print('Adding newcol to sub.')\\r\\n    print('Should be an error.')\\r\\n    base_df_one_sub['newcol'] = 'values'\\r\\n    print(divider_two)\\r\\n\\r\\n    print(divider_three)#                                         This is with a base df w/ 5\\r\\n    print('Adding col5 to base.')\\r\\n    base_df_one['col5'] = 'col4 values'\\r\\n    print('Resetting the df sub from slice.')\\r\\n    base_df_one_sub = base_df_one[['col1', 'col2']]\\r\\n    print('Adding newcol to sub.')\\r\\n    print('Should be an error.')\\r\\n    base_df_one_sub['newcol'] = 'values'\\r\\n    print(divider_three)\\r\\n\\r\\n\\r\\ndef test_base_df_with_four_initial_elements():\\r\\n    base_df_one = pd.DataFrame({\\r\\n        'col1': ['col1 value one', 'col1 value two', 'col1 value three'],\\r\\n        'col2': ['col2 value one', 'col2 value two', 'col2 value three'],\\r\\n        'col3': ['col3 value one', 'col3 value two', 'col3 value three'],\\r\\n        'col4': ['col4 value one', 'col4 value two', 'col4 value three'],\\r\\n    })\\r\\n\\r\\n    print(divider_one)#                                           This is with a base df w/ 4\\r\\n    print('Setting the df sub from slice.')\\r\\n    base_df_one_sub = base_df_one[['col1', 'col2']]\\r\\n    print('Adding newcol to sub.')\\r\\n    print('Should be an error.')\\r\\n    base_df_one_sub['newcol'] = 'values'\\r\\n    print(divider_one)\\r\\n\\r\\n    print(divider_two)#                                           This is with a base df w/ 5\\r\\n    print('Adding col5 to base.')\\r\\n    base_df_one['col5'] = 'col3 values'\\r\\n    print('Resetting the df sub from slice.')\\r\\n    base_df_one_sub = base_df_one[['col1', 'col2']]\\r\\n    print('Adding newcol to sub.')\\r\\n    print('Should be an error.')\\r\\n    base_df_one_sub['newcol'] = 'values'\\r\\n    print(divider_two)\\r\\n\\r\\n    print(divider_three)#                                         This is with a base df w/ 6\\r\\n    print('Adding col6 to base.')\\r\\n    base_df_one['col6'] = 'col4 values'\\r\\n    print('Resetting the df sub from slice.')\\r\\n    base_df_one_sub = base_df_one[['col1', 'col2']]\\r\\n    print('Adding newcol to sub.')\\r\\n    print('Should be an error.')\\r\\n    base_df_one_sub['newcol'] = 'values'\\r\\n    print(divider_three)\\r\\n\\r\\n# Choose which function to test to confirm and uncomment.\\r\\n\\r\\n# test_base_df_with_two_initial_elements()\\r\\n\\r\\n# test_base_df_with_three_initial_elements()\\r\\n\\r\\n# test_base_df_with_four_initial_elements()\\r\\n\\r\\n```\\r\\n#### Problem description\\r\\n\\r\\nIt appears that the `SettingWithcopyWarning` is triggered when a DataFrame with four or more columns is used to derive another DataFrame by slicing and then an attempt is made to add a new column to the derived DataFrame. There is no such warning when the original DataFrame has fewer columns.\\r\\n\\r\\n#### Expected Output\\r\\n\\r\\nI'm not precisely sure what to expect, but I would imagine that the number of columns in a source DataFrame shouldn't drive a warning against misuse of views of DataFrames. The documentation directs users to use `.loc[]` but only when the warning is issued. If there is potential wonkiness due to setting values from views or copies generally, I'd expect it'd be more helpful when the warning is triggered consistently.\\r\\n\\r\\n#### Output of ``pd.show_versions()``\\r\\n\\r\\n<details>\\r\\n\\r\\n[paste the output of ``pd.show_versions()`` here below this line]\\r\\nINSTALLED VERSIONS\\r\\n------------------\\r\\ncommit: None\\r\\npython: 3.7.3.final.0\\r\\npython-bits: 64\\r\\nOS: Windows\\r\\nOS-release: 10\\r\\nmachine: AMD64\\r\\nprocessor: Intel64 Family 6 Model 142 Stepping 10, GenuineIntel\\r\\nbyteorder: little\\r\\nLC_ALL: None\\r\\nLANG: en_US.UTF-8\\r\\nLOCALE: None.None\\r\\n\\r\\npandas: 0.24.2\\r\\npytest: None\\r\\npip: 19.1.1\\r\\nsetuptools: 41.0.1\\r\\nCython: None\\r\\nnumpy: 1.16.4\\r\\nscipy: None\\r\\npyarrow: None\\r\\nxarray: None\\r\\nIPython: 7.5.0\\r\\nsphinx: 2.1.0\\r\\npatsy: None\\r\\ndateutil: 2.8.0\\r\\npytz: 2019.1\\r\\nblosc: None\\r\\nbottleneck: None\\r\\ntables: None\\r\\nnumexpr: None\\r\\nfeather: None\\r\\nmatplotlib: None\\r\\nopenpyxl: None\\r\\nxlrd: None\\r\\nxlwt: None\\r\\nxlsxwriter: None\\r\\nlxml.etree: None\\r\\nbs4: None\\r\\nhtml5lib: None\\r\\nsqlalchemy: None\\r\\npymysql: None\\r\\npsycopg2: None\\r\\njinja2: 2.10.1\\r\\ns3fs: None\\r\\nfastparquet: None\\r\\npandas_gbq: None\\r\\npandas_datareader: None\\r\\ngcsfs: None\\r\\n\\r\\n</details>\\r\\n\"},\n",
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       "  'number': 26719,\n",
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       "  'body': '#### Code Sample, a copy-pastable example if possible\\r\\n\\r\\n```python\\r\\ndf[\\'Quarter\\'] = pd.PeriodIndex(df[\\'date\\'], freq=\\'Q\\')\\r\\nfig, ax = plt.subplots(figsize=(10,5))\\r\\ndf.plot.bar(x=\\'Quarter\\', y =\\'ape\\', ax=ax, xlim=(\\'2015Q1\\', \\'2019Q1\\'), color=\"tab:green\").grid(alpha=.4)\\r\\n```\\r\\n#### Problem description\\r\\n\\r\\nfor bar plot, adjusting xlim values gives the same range; but line plot (code below) works: \\r\\n\\r\\n```python\\r\\nfig, ax = plt.subplots(figsize=(10,5))\\r\\ndf.plot.line(x=\\'Quarter\\', y =\\'ape\\', ax=ax, xlim=(\\'2015Q1\\', \\'2019Q1\\'), color=\"tab:green\").grid(alpha=.4)\\r\\n```\\r\\n\\r\\n\\r\\n\\r\\n'},\n",
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       "  'body': '\\r\\n- [ ] closes #22529\\r\\n- [ ] tests added / passed\\r\\n- [ ] passes `git diff upstream/master -u -- \"*.py\" | flake8 --diff`\\r\\n- [ ] whatsnew entry\\r\\n'},\n",
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       "  'body': 'Present in the last two builds on master\\r\\n\\r\\n- https://travis-ci.org/pandas-dev/pandas/jobs/542915088\\r\\n- https://travis-ci.org/pandas-dev/pandas/jobs/542931830\\r\\n\\r\\n```\\r\\ngw0 I\\r\\n/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/hypothesis/_settings.py:193: HypothesisDeprecationWarning: The timeout setting can safely be removed with no effect.\\r\\n  verbosity=self.verbosity,\\r\\n[gw0] node down: Not properly terminated\\r\\nReplacing crashed worker gw0\\r\\n\\r\\n```\\r\\n\\r\\nSomething is crashing our pytest-xdist worker. We used pytest-xdist 1.28.0 in the last green build https://travis-ci.org/pandas-dev/pandas/jobs/542868383, so it\\'s likely not pytest-xdist itself.\\r\\n\\r\\nEdit: Seems to be an issue with feather / pyarrow. We segfault when collecting tests: https://travis-ci.org/pandas-dev/pandas/jobs/542977519#L3052. https://github.com/pandas-dev/pandas/pull/26715 is skipping, use this issue to restore.\\r\\n\\r\\n```\\r\\ncollecting 34877 items / 1 skipped / 34876 selected                            Fatal Python error: Segmentation fault\\r\\nCurrent thread 0x00007fb9015dd740 (most recent call first):\\r\\n  File \"<frozen importlib._bootstrap>\", line 219 in _call_with_frames_removed\\r\\n  File \"<frozen importlib._bootstrap_external>\", line 922 in create_module\\r\\n  File \"<frozen importlib._bootstrap>\", line 571 in module_from_spec\\r\\n  File \"<frozen importlib._bootstrap>\", line 658 in _load_unlocked\\r\\n  File \"<frozen importlib._bootstrap>\", line 955 in _find_and_load_unlocked\\r\\n  File \"<frozen importlib._bootstrap>\", line 971 in _find_and_load\\r\\n  File \"/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/pyarrow/__init__.py\", line 47 in <module>\\r\\n  File \"<frozen importlib._bootstrap>\", line 219 in _call_with_frames_removed\\r\\n  File \"<frozen importlib._bootstrap_external>\", line 678 in exec_module\\r\\n  File \"<frozen importlib._bootstrap>\", line 665 in _load_unlocked\\r\\n  File \"<frozen importlib._bootstrap>\", line 955 in _find_and_load_unlocked\\r\\n  File \"<frozen importlib._bootstrap>\", line 971 in _find_and_load\\r\\n  File \"/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/_pytest/outcomes.py\", line 168 in importorskip\\r\\n  File \"/home/travis/build/pandas-dev/pandas/pandas/tests/io/test_feather.py\", line 13 in <module>\\r\\n  File \"/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/_pytest/assertion/rewrite.py\", line 304 in load_module\\r\\n  File \"<frozen importlib._bootstrap>\", line 626 in _load_backward_compatible\\r\\n  File \"<frozen importlib._bootstrap>\", line 656 in _load_unlocked\\r\\n  File \"<frozen importlib._bootstrap>\", line 955 in _find_and_load_unlocked\\r\\n  File \"<frozen importlib._bootstrap>\", line 971 in _find_and_load\\r\\n  File \"/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/py/_path/local.py\", line 701 in pyimport\\r\\n\\r\\n```'},\n",
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       "  'body': 'Resolves https://github.com/pandas-dev/pandas/issues/26709\\r\\n```\\r\\n       before           after         ratio\\r\\n     [3ff4f38f]       [30bd5369]\\r\\n     <datetime_iter~1>\\r\\n-       749±100μs         365±70μs     0.49  index_object.IntervalIndexMethod.time_intersection_duplicate(1000)\\r\\n-        59.6±3ms       8.04±0.2ms     0.13  index_object.IntervalIndexMethod.time_intersection_duplicate(100000)\\r\\n           failed       2.50±0.01s      n/a  index_object.IntervalIndexMethod.time_intersection_duplicate(10000000)\\r\\n```\\r\\n\\r\\n- [ ] closes #xxxx\\r\\n- [ ] tests added / passed\\r\\n- [ ] passes `git diff upstream/master -u -- \"*.py\" | flake8 --diff`\\r\\n- [ ] whatsnew entry\\r\\n'},\n",
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       "  'created_at': '2019-06-07T17:58:19Z',\n",
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       "  'body': 'Currently, to extend pandas `Series`, `DataFrame` and `Index` with user-defined methods, we use accessors in the next way:\\r\\n\\r\\n```python\\r\\n@pandas.api.extensions.register_series_accessor(\\'emoji\\')\\r\\nclass Emoji:\\r\\n    def __init__(self, data):\\r\\n        self.data = data\\r\\n\\r\\n    def is_monkey(self):\\r\\n        \"\"\"\\r\\n        This would create `Series().emoji.is_monkey`\\r\\n        \"\"\"\\r\\n        return self.data.isin([\\'🙈\\', \\'🙉\\', \\'🙊\\'])\\r\\n```\\r\\n\\r\\nWhile this works well, I think there are two problems with this approach:\\r\\n- The API looks somehow intimidating, and it\\'s not well known. I think because `pandas.api.extensions.register_series_accessor` is too long and lives in `pandas.api`, separate of functionality most users know.\\r\\n- It\\'s not possible to register methods directly (`Series().is_monkey` instead of `Series().emoji.is_monkey`)\\r\\n\\r\\nI think all the projects extending pandas I\\'ve seen, simply \"inject\" the methods (except the ones implemented by pandas maintainers). For example:\\r\\n- https://github.com/PatrikHlobil/Pandas-Bokeh/blob/master/pandas_bokeh/__init__.py#L20\\r\\n- https://github.com/nalepae/pandarallel/blob/master/pandarallel/pandarallel.py#L52\\r\\n\\r\\nWhat I propose is to have a easier/simpler API for the user. To be specific, this is the syntax I\\'d like when extending `Series`...\\r\\n\\r\\n```python\\r\\nimport pandas\\r\\n\\r\\n@pandas.Series.extend(\\'emoji\\')\\r\\nclass Emoji:\\r\\n    def __init__(self, data):\\r\\n        self.data = data\\r\\n\\r\\n    def is_monkey(self):\\r\\n        \"\"\"\\r\\n        This would create `Series().emoji.is_monkey`\\r\\n        \"\"\"\\r\\n        return self.data.isin([\\'🙈\\', \\'🙉\\', \\'🙊\\'])\\r\\n\\r\\n@pandas.Series.extend(namespace=\\'emoji\\')\\r\\ndef is_monkey(data):\\r\\n    \"\"\"\\r\\n    This would also create `Series().emoji.is_monkey`\\r\\n    \"\"\"\\r\\n    return data.isin([\\'🙈\\', \\'🙉\\', \\'🙊\\'])\\r\\n\\r\\n@pandas.Series.extend\\r\\nclass Emoji:\\r\\n    def __init__(self, data):\\r\\n        self.data = data\\r\\n\\r\\n    def is_monkey(self):\\r\\n        \"\"\"\\r\\n        This would directly create `Series().is_monkey`\\r\\n        \"\"\"\\r\\n        return self.data.isin([\\'🙈\\', \\'🙉\\', \\'🙊\\'])\\r\\n\\r\\n@pandas.Series.extend\\r\\ndef is_monkey(data):\\r\\n    \"\"\"\\r\\n    This would create `Series().emoji.is_monkey`\\r\\n    \"\"\"\\r\\n    return data.isin([\\'🙈\\', \\'🙉\\', \\'🙊\\'])\\r\\n```\\r\\n\\r\\nThis would make things much easier for the user, because:\\r\\n- The name `pandas.Series.extend` is much easier to remember\\r\\n- A single function can be used (without creating a class)\\r\\n- A direct method of `Series`... can be created\\r\\n\\r\\nCC: @pandas-dev/pandas-core '},\n",
       " {'url': 'https://api.github.com/repos/pandas-dev/pandas/issues/26706',\n",
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       "  'title': 'Recommended way to check for sparse data (DataFrame or Series)',\n",
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       "  'body': \"Previously, you check for the SparseDataFrame class, but now we want people to store sparse data in normal DataFrames/Series. \\r\\n\\r\\nSo for a library developer, what is the recommended way to check for sparse data?\\r\\n\\r\\nFor a Series (or SparseArray), the `pd.api.types.is_sparse` still works on a plain Series with sparse data:\\r\\n\\r\\n```\\r\\nIn [42]: df = pd.DataFrame({'a': pd.SparseArray([1, np.nan, 1])}) \\r\\n\\r\\nIn [43]: s = df['a']\\r\\n\\r\\nIn [44]: type(s)\\r\\nOut[44]: pandas.core.series.Series\\r\\n\\r\\nIn [45]: pd.api.types.is_sparse(s)\\r\\nOut[45]: True\\r\\n```\\r\\n\\r\\nbut for a DataFrame that function does not work. So you could apply that method on each column/dtype, and check with `any` or `all` depending on your requirements:\\r\\n\\r\\n```\\r\\nIn [56]: df = pd.DataFrame({'a': pd.SparseArray([1, np.nan, 1]), 'b': [1, 2, 3]})\\r\\n\\r\\nIn [57]: df.dtypes.apply(pd.api.types.is_sparse)\\r\\nOut[57]: \\r\\na     True\\r\\nb    False\\r\\ndtype: bool\\r\\n\\r\\nIn [58]: df.dtypes.apply(pd.api.types.is_sparse).any()\\r\\nOut[58]: True\\r\\n\\r\\nIn [59]: df.dtypes.apply(pd.api.types.is_sparse).all()\\r\\nOut[59]: False\\r\\n```\\r\\n\\r\\nSo that actually works quite OK, now I am writing it. \\r\\nDo we want to make this even easier somehow? Or document this?\"},\n",
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       "   'created_at': '2018-10-23T02:34:15Z',\n",
       "   'updated_at': '2019-06-09T00:57:29Z',\n",
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       "  'body': \"With the `ftype(s)` property, you could check if data was sparse:\\r\\n\\r\\n```\\r\\nIn [26]: df = pd.DataFrame({'a': pd.SparseArray([1, np.nan, 1])})\\r\\n\\r\\nIn [27]: df\\r\\nOut[27]: \\r\\n     a\\r\\n0  1.0\\r\\n1  NaN\\r\\n2  1.0\\r\\n\\r\\nIn [28]: df.dtypes\\r\\nOut[28]: \\r\\na    Sparse[float64, nan]\\r\\ndtype: object\\r\\n\\r\\nIn [29]: df.ftypes\\r\\nOut[29]: \\r\\na    float64:sparse\\r\\ndtype: object\\r\\n```\\r\\n\\r\\nBut now that the dtype itself is a EA SparseDtype, I don't think those `ftype` and `ftypes` attributes serve any good purpose. So let's deprecate them.\"},\n",
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       "  'body': \"The CI job that is checking that the benchmarks don't break is reporting that an `asv` setting has been renamed.\\r\\n\\r\\nI think we've got a server where we run the benchmarks, not sure if we replicate the environment there at every run from `environment.yml`. Otherwise I guess we should do it manually before merging this.\\r\\n\\r\\n@TomAugspurger I think you manage that server? does this make sense?\\r\\n\"},\n",
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       "  'body': 'Is there is any standard method to replace flat columns with hierarchical while merge the dataframes?\\r\\n\\r\\nI mean something like:\\r\\n\\r\\n\\r\\n```python\\r\\ndef columns_as_multiindex(df, inplace=False):\\r\\n    \"\"\"Replace flat (:obj:`pandas.Index`) columns with hierarchical (:obj:`pandas.MultiIndex`).\\r\\n\\r\\n    Parameters\\r\\n    ----------\\r\\n    df : pandas.DataFrame\\r\\n        Dataframe wich contains the columns to be converted to MultiIndex.\\r\\n    inplace : bool (default False)\\r\\n        If ``True``, do operation inplace and return ``None``.\\r\\n\\r\\n    Returns\\r\\n    -------\\r\\n    pandas.DataFrame or None\\r\\n        Returns dataframe with modifed columns or ``None`` (depends on `inplace` parameter value).\\r\\n\\r\\n    Examples\\r\\n    --------\\r\\n    >>> df0 = pd.DataFrame([[1,2], [10, 20], [100, 200]], columns=[\"A\", \"B\"])\\r\\n    >>> df0\\r\\n    ...      A    B\\r\\n    ... 0    1    2\\r\\n    ... 1   10   20\\r\\n    ... 2  100  200\\r\\n\\r\\n    >>> columns = pd.MultiIndex.from_tuples([(\"C\", \"C0\"), (\"D\", \"D0\")])\\r\\n    >>> df1 = pd.DataFrame([[1,3], [10, 30], [100, 300]], columns=columns)\\r\\n    >>> df1\\r\\n    ...      C    D\\r\\n    ...     C0   D0\\r\\n    ... 0    1    3\\r\\n    ... 1   10   30\\r\\n    ... 2  100  300\\r\\n\\r\\n    Merging ``df0`` with ``df1`` returns the dataframe contains tuples in the column names\\r\\n\\r\\n    >>> df = pd.merge(df0, df1, left_on=\"A\", right_on=[(\"C\", \"C0\")])\\r\\n    >>> df\\r\\n    ...      A    B  (C, C0)  (D, D0)\\r\\n    ... 0    1    2        1        3\\r\\n    ... 1   10   20       10       30\\r\\n    ... 2  100  200      100      300\\r\\n\\r\\n    It\\'s a bit ugly. Let\\'s beautify the header\\r\\n\\r\\n    >>> columns_as_multiindex(df)\\r\\n    >>> df\\r\\n    ...      A    B    C    D\\r\\n    ...               C0   D0\\r\\n    ... 0    1    2    1    3\\r\\n    ... 1   10   20   10   30\\r\\n    ... 2  100  200  100  300\\r\\n    \"\"\"\\r\\n    columns = df.columns.tolist()\\r\\n    max_len = max([len(c) for c in columns])\\r\\n    for i, c in enumerate(columns):\\r\\n        if isinstance(c, str):\\r\\n            c = [c, ]\\r\\n        else:\\r\\n            c = list(c)\\r\\n        columns[i] = tuple(c + [\"\", ] * (max_len - len(c)))\\r\\n    columns = pd.MultiIndex.from_tuples(columns)\\r\\n    if inplace:\\r\\n        df.columns = columns\\r\\n    else:\\r\\n        df_new = df.copy()\\r\\n        df_new.columns = columns\\r\\n        return df_new\\r\\n```\\r\\n'},\n",
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       "   'description': 'Changes that would be nice to have in the next release. These issues are not blocking. They will be pushed to the next release if no one has time to fix them.',\n",
       "   'creator': {'login': 'jreback',\n",
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       "   'open_issues': 1257,\n",
       "   'closed_issues': 382,\n",
       "   'state': 'open',\n",
       "   'created_at': '2015-01-13T10:53:19Z',\n",
       "   'updated_at': '2019-06-08T22:14:16Z',\n",
       "   'due_on': '2020-12-31T08:00:00Z',\n",
       "   'closed_at': None},\n",
       "  'comments': 0,\n",
       "  'created_at': '2019-06-06T18:58:04Z',\n",
       "  'updated_at': '2019-06-06T18:58:04Z',\n",
       "  'closed_at': None,\n",
       "  'author_association': 'MEMBER',\n",
       "  'body': 'We have a `ci/print_skipped.py` script to print the skipped tests, that is currently used in Travis (see end of `.travis.yml` file). It would be to also do this in Azure as an extra step.'},\n",
       " {'url': 'https://api.github.com/repos/pandas-dev/pandas/issues/26692',\n",
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       "  'node_id': 'MDU6SXNzdWU0NTMwODEwNTY=',\n",
       "  'number': 26692,\n",
       "  'title': \"If tuples used as index pd.read_json( orient='split') does not read file saved by df.to_json(orient='split)\",\n",
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       "  'labels': [{'id': 49379259,\n",
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       "    'url': 'https://api.github.com/repos/pandas-dev/pandas/labels/IO%20JSON',\n",
       "    'name': 'IO JSON',\n",
       "    'color': '207de5',\n",
       "    'default': False}],\n",
       "  'state': 'open',\n",
       "  'locked': False,\n",
       "  'assignee': None,\n",
       "  'assignees': [],\n",
       "  'milestone': None,\n",
       "  'comments': 1,\n",
       "  'created_at': '2019-06-06T15:00:11Z',\n",
       "  'updated_at': '2019-06-07T01:51:02Z',\n",
       "  'closed_at': None,\n",
       "  'author_association': 'NONE',\n",
       "  'body': '#### Code Sample, a copy-pastable example if possible\\r\\n```python\\r\\na = pd.DataFrame()\\r\\na[\\'example\\'] = [0, 1 ,2, 3 ,4 ,5 ,6]\\r\\na.index = [(i, i) for i in range(7)]\\r\\na.to_json(\\'test2.json\\', orient=\\'split\\')\\r\\nb = pd.read_json(\\'test2.json\\', orient=\\'split\\')\\r\\n```\\r\\n#### Problem description\\r\\n\\r\\nAfter using tuples as index and saving DataFrame with orient=\\'split\\', error is thrown during loading via pd.read_json( orient=\\'split\\').\\r\\n\\r\\nFull traceback:\\r\\n--------------------------------------------------------\\r\\nValueError             Traceback (most recent call last)\\r\\n~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in create_block_manager_from_arrays(arrays, names, axes)\\r\\n   1666         blocks = form_blocks(arrays, names, axes)\\r\\n-> 1667         mgr = BlockManager(blocks, axes)\\r\\n   1668         mgr._consolidate_inplace()\\r\\n\\r\\n~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in __init__(self, blocks, axes, do_integrity_check)\\r\\n    113         if do_integrity_check:\\r\\n--> 114             self._verify_integrity()\\r\\n    115 \\r\\n\\r\\n~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in _verify_integrity(self)\\r\\n    310             if block._verify_integrity and block.shape[1:] != mgr_shape[1:]:\\r\\n--> 311                 construction_error(tot_items, block.shape[1:], self.axes)\\r\\n    312         if len(self.items) != tot_items:\\r\\n\\r\\n~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in construction_error(tot_items, block_shape, axes, e)\\r\\n   1690     raise ValueError(\"Shape of passed values is {0}, indices imply {1}\".format(\\r\\n-> 1691         passed, implied))\\r\\n   1692 \\r\\n\\r\\nValueError: Shape of passed values is (7, 1), indices imply (3, 1)\\r\\n\\r\\nDuring handling of the above exception, another exception occurred:\\r\\n\\r\\nValueError             Traceback (most recent call last)\\r\\n<ipython-input-36-f5b064e2d9e4> in <module>\\r\\n      3 a.index = [(i, i, i) for i in range(7)]\\r\\n      4 a.to_json(\\'test2.json\\', orient=\\'split\\')\\r\\n----> 5 b = pd.read_json(\\'test2.json\\', orient=\\'split\\')\\r\\n\\r\\n~/anaconda3/lib/python3.7/site-packages/pandas/io/json/json.py in read_json(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines, chunksize, compression)\\r\\n    425         return json_reader\\r\\n    426 \\r\\n--> 427     result = json_reader.read()\\r\\n    428     if should_close:\\r\\n    429         try:\\r\\n\\r\\n~/anaconda3/lib/python3.7/site-packages/pandas/io/json/json.py in read(self)\\r\\n    535             )\\r\\n    536         else:\\r\\n--> 537             obj = self._get_object_parser(self.data)\\r\\n    538         self.close()\\r\\n    539         return obj\\r\\n\\r\\n~/anaconda3/lib/python3.7/site-packages/pandas/io/json/json.py in _get_object_parser(self, json)\\r\\n    554         obj = None\\r\\n    555         if typ == \\'frame\\':\\r\\n--> 556             obj = FrameParser(json, **kwargs).parse()\\r\\n    557 \\r\\n    558         if typ == \\'series\\' or obj is None:\\r\\n\\r\\n~/anaconda3/lib/python3.7/site-packages/pandas/io/json/json.py in parse(self)\\r\\n    650 \\r\\n    651         else:\\r\\n--> 652             self._parse_no_numpy()\\r\\n    653 \\r\\n    654         if self.obj is None:\\r\\n\\r\\n~/anaconda3/lib/python3.7/site-packages/pandas/io/json/json.py in _parse_no_numpy(self)\\r\\n    874                 loads(json, precise_float=self.precise_float))}\\r\\n    875             self.check_keys_split(decoded)\\r\\n--> 876             self.obj = DataFrame(dtype=None, **decoded)\\r\\n    877         elif orient == \"index\":\\r\\n    878             self.obj = DataFrame(\\r\\n\\r\\n~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)\\r\\n    446 \\r\\n    447                     mgr = arrays_to_mgr(arrays, columns, index, columns,\\r\\n--> 448                                         dtype=dtype)\\r\\n    449                 else:\\r\\n    450                     mgr = init_ndarray(data, index, columns, dtype=dtype,\\r\\n\\r\\n~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/construction.py in arrays_to_mgr(arrays, arr_names, index, columns, dtype)\\r\\n     59     axes = [ensure_index(columns), index]\\r\\n     60 \\r\\n---> 61     return create_block_manager_from_arrays(arrays, arr_names, axes)\\r\\n     62 \\r\\n     63 \\r\\n\\r\\n~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in create_block_manager_from_arrays(arrays, names, axes)\\r\\n   1669         return mgr\\r\\n   1670     except ValueError as e:\\r\\n-> 1671         construction_error(len(arrays), arrays[0].shape, axes, e)\\r\\n   1672 \\r\\n   1673 \\r\\n\\r\\n~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in construction_error(tot_items, block_shape, axes, e)\\r\\n   1689         raise ValueError(\"Empty data passed with indices specified.\")\\r\\n   1690     raise ValueError(\"Shape of passed values is {0}, indices imply {1}\".format(\\r\\n-> 1691         passed, implied))\\r\\n   1692 \\r\\n   1693 \\r\\n\\r\\nValueError: Shape of passed values is (7, 1), indices imply (2, 1)\\r\\n\\r\\n2 in last tuple is number of elements of tuples I use as index.\\r\\n\\r\\n#### Expected Output\\r\\n\\r\\nb = a -- dataframe with same content as a\\r\\n\\r\\n        example\\r\\n(0, 0)    0\\r\\n(1, 1)    1\\r\\n(2, 2)    2\\r\\n(3, 3)    3\\r\\n(4, 4)    4\\r\\n(5, 5)    5\\r\\n(6, 6)    6\\r\\n\\r\\n#### Output of ``pd.show_versions()``\\r\\n\\r\\n<details>\\r\\nINSTALLED VERSIONS\\r\\n------------------\\r\\ncommit: None\\r\\npython: 3.7.3.final.0\\r\\npython-bits: 64\\r\\nOS: Linux\\r\\nOS-release: 4.18.0-20-generic\\r\\nmachine: x86_64\\r\\nprocessor: x86_64\\r\\nbyteorder: little\\r\\nLC_ALL: None\\r\\nLANG: en_GB.UTF-8\\r\\nLOCALE: en_GB.UTF-8\\r\\n\\r\\npandas: 0.24.2\\r\\npytest: 4.5.0\\r\\npip: 19.1.1\\r\\nsetuptools: 41.0.1\\r\\nCython: 0.29.7\\r\\nnumpy: 1.16.3\\r\\nscipy: 1.2.1\\r\\npyarrow: None\\r\\nxarray: None\\r\\nIPython: 7.5.0\\r\\nsphinx: 2.0.1\\r\\npatsy: 0.5.1\\r\\ndateutil: 2.8.0\\r\\npytz: 2019.1\\r\\nblosc: None\\r\\nbottleneck: 1.2.1\\r\\ntables: 3.4.4\\r\\nnumexpr: 2.6.9\\r\\nfeather: None\\r\\nmatplotlib: 3.1.0\\r\\nopenpyxl: 2.6.2\\r\\nxlrd: 1.2.0\\r\\nxlwt: 1.3.0\\r\\nxlsxwriter: 1.1.8\\r\\nlxml.etree: 4.3.3\\r\\nbs4: 4.7.1\\r\\nhtml5lib: 1.0.1\\r\\nsqlalchemy: 1.3.3\\r\\npymysql: None\\r\\npsycopg2: None\\r\\njinja2: 2.10.1\\r\\ns3fs: None\\r\\nfastparquet: None\\r\\npandas_gbq: None\\r\\npandas_datareader: None\\r\\ngcsfs: None\\r\\n</details>\\r\\n'},\n",
       " {'url': 'https://api.github.com/repos/pandas-dev/pandas/issues/26690',\n",
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       "  'number': 26690,\n",
       "  'title': \"ImportError: cannot import name 'Timedelta'\",\n",
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       "  'created_at': '2019-06-06T12:35:18Z',\n",
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       "  'body': '#### Code Sample, a copy-pastable example if possible\\r\\n\\r\\n```python\\r\\n# X@ubuntu:~/Desktop$ pylivetrader run -f algo.py --backend-config config.yaml\\r\\n\\r\\n\\r\\n```\\r\\n#### Problem description\\r\\n\\r\\nI\\'m trying to run pylivetrader locally in a conda environment with all requisites met, so far as I can see.\\r\\nPandas continually fails.\\r\\n\\r\\nI have tried multiple Pandas versions and explored both the help sections here as well as thoroughly searched online for possible solutions.\\r\\n\\r\\nI believe there is a conflict of Pandas versions going on, but based on the output of ``pd.show_versions()``, as I detail below, I\\'m not quite sure what is going on.\\r\\n\\r\\n#### Expected Output\\r\\n\\r\\nI expect pylivetrader to be able to run the algo.py, instead I am faced with ``` ImportError: cannot import name \\'Timedelta\\' ```.\\r\\n\\r\\n\\r\\n#### Output of ``pd.show_versions()``\\r\\n\\r\\nFurthermore, the output of the above is:\\r\\n\\r\\ndetails>\\r\\n\\r\\n ```AttributeError: module \\'pandas\\' has no attribute \\'show_versions\\'```\\r\\n\\r\\nHowever, if I run ```conda list``` in the specific env then I see that:\\r\\n\\r\\n```  pandas                    0.24.2           py37he6710b0_0  ```\\r\\n\\r\\n/details>\\r\\n ```\\r\\nTraceback (most recent call last):\\r\\n  File \"/home/X.local/bin/pylivetrader\", line 7, in <module>\\r\\n    from pylivetrader.__main__ import main\\r\\n  File \"/home/X/.local/lib/python3.5/site-packages/pylivetrader/__init__.py\", line 16, in <module>\\r\\n    from pylivetrader import api # noqa\\r\\n  File \"/home/X/.local/lib/python3.5/site-packages/pylivetrader/api.py\", line 25, in <module>\\r\\n    from pylivetrader.finance.asset_restrictions import (\\r\\n  File \"/home/X/.local/lib/python3.5/site-packages/pylivetrader/finance/asset_restrictions.py\", line 20, in <module>\\r\\n    import pandas as pd\\r\\n  File \"/home/X/.local/lib/python3.5/site-packages/pandas/__init__.py\", line 42, in <module>\\r\\n    from pandas.core.api import *\\r\\n  File \"/home/X/.local/lib/python3.5/site-packages/pandas/core/api.py\", line 10, in <module>\\r\\n    from pandas.core.groupby.groupby import Grouper\\r\\n  File \"/home/X/.local/lib/python3.5/site-packages/pandas/core/groupby/__init__.py\", line 2, in <module>\\r\\n    from pandas.core.groupby.groupby import (\\r\\n  File \"/home/X/.local/lib/python3.5/site-packages/pandas/core/groupby/groupby.py\", line 49, in <module>\\r\\n    from pandas.core.frame import DataFrame\\r\\n  File \"/home/X/.local/lib/python3.5/site-packages/pandas/core/frame.py\", line 66, in <module>\\r\\n    from pandas.core.generic import NDFrame, _shared_docs\\r\\n  File \"/home/X/.local/lib/python3.5/site-packages/pandas/core/generic.py\", line 43, in <module>\\r\\n    from pandas.core.internals import BlockManager\\r\\n  File \"/home/X/.local/lib/python3.5/site-packages/pandas/core/internals/__init__.py\", line 2, in <module>\\r\\n    from .blocks import (  # noqa:F401\\r\\n  File \"/home/X/.local/lib/python3.5/site-packages/pandas/core/internals/blocks.py\", line 11, in <module>\\r\\n    from pandas._libs.tslibs import Timedelta, conversion, is_null_datetimelike\\r\\nImportError: cannot import name \\'Timedelta\\'\\r\\n```\\r\\n\\r\\n'}]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
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       "      <th>26</th>\n",
       "      <td>26698</td>\n",
       "      <td>Add print skiped into azure step</td>\n",
       "      <td>[{'id': 48070600, 'node_id': 'MDU6TGFiZWw0ODA3...</td>\n",
       "      <td>open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>26695</td>\n",
       "      <td>CI - Azure: print skipped tests</td>\n",
       "      <td>[{'id': 48070600, 'node_id': 'MDU6TGFiZWw0ODA3...</td>\n",
       "      <td>open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>26692</td>\n",
       "      <td>If tuples used as index pd.read_json( orient='...</td>\n",
       "      <td>[{'id': 49379259, 'node_id': 'MDU6TGFiZWw0OTM3...</td>\n",
       "      <td>open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>26690</td>\n",
       "      <td>ImportError: cannot import name 'Timedelta'</td>\n",
       "      <td>[{'id': 129350, 'node_id': 'MDU6TGFiZWwxMjkzNT...</td>\n",
       "      <td>open</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    number                                              title  \\\n",
       "0    26750                           move late imports to top   \n",
       "1    26749                               Isort __init__ files   \n",
       "2    26748                      PLOT: Fix imports of plotting   \n",
       "3    26747       API: Define API for pandas plotting backends   \n",
       "4    26746   Read from HDF with empty `where` throws an error   \n",
       "..     ...                                                ...   \n",
       "25   26699  Replace flat columns with hierarchical while m...   \n",
       "26   26698                   Add print skiped into azure step   \n",
       "27   26695                    CI - Azure: print skipped tests   \n",
       "28   26692  If tuples used as index pd.read_json( orient='...   \n",
       "29   26690        ImportError: cannot import name 'Timedelta'   \n",
       "\n",
       "                                               labels state  \n",
       "0   [{'id': 211029535, 'node_id': 'MDU6TGFiZWwyMTE...  open  \n",
       "1   [{'id': 106935113, 'node_id': 'MDU6TGFiZWwxMDY...  open  \n",
       "2   [{'id': 211029535, 'node_id': 'MDU6TGFiZWwyMTE...  open  \n",
       "3   [{'id': 35818298, 'node_id': 'MDU6TGFiZWwzNTgx...  open  \n",
       "4   [{'id': 35818298, 'node_id': 'MDU6TGFiZWwzNTgx...  open  \n",
       "..                                                ...   ...  \n",
       "25  [{'id': 35818298, 'node_id': 'MDU6TGFiZWwzNTgx...  open  \n",
       "26  [{'id': 48070600, 'node_id': 'MDU6TGFiZWw0ODA3...  open  \n",
       "27  [{'id': 48070600, 'node_id': 'MDU6TGFiZWw0ODA3...  open  \n",
       "28  [{'id': 49379259, 'node_id': 'MDU6TGFiZWw0OTM3...  open  \n",
       "29  [{'id': 129350, 'node_id': 'MDU6TGFiZWwxMjkzNT...  open  \n",
       "\n",
       "[30 rows x 4 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "issues = pd.DataFrame(data, columns=['number', 'title',\n",
    "                                     'labels', 'state'])\n",
    "issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interacting with Databases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "query = \"\"\"\n",
    "CREATE TABLE test\n",
    "(a VARCHAR(20), b VARCHAR(20),\n",
    " c REAL,        d INTEGER\n",
    ");\"\"\"\n",
    "con = sqlite3.connect('mydata.sqlite')\n",
    "con.execute(query)\n",
    "con.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [('Atlanta', 'Georgia', 1.25, 6),\n",
    "        ('Tallahassee', 'Florida', 2.6, 3),\n",
    "        ('Sacramento', 'California', 1.7, 5)]\n",
    "stmt = \"INSERT INTO test VALUES(?, ?, ?, ?)\"\n",
    "con.executemany(stmt, data)\n",
    "con.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Atlanta', 'Georgia', 1.25, 6),\n",
       " ('Tallahassee', 'Florida', 2.6, 3),\n",
       " ('Sacramento', 'California', 1.7, 5)]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cursor = con.execute('select * from test')\n",
    "rows = cursor.fetchall()\n",
    "rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('a', None, None, None, None, None, None),\n",
       " ('b', None, None, None, None, None, None),\n",
       " ('c', None, None, None, None, None, None),\n",
       " ('d', None, None, None, None, None, None))"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cursor.description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>1.25</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tallahassee</td>\n",
       "      <td>Florida</td>\n",
       "      <td>2.60</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>California</td>\n",
       "      <td>1.70</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             a           b     c  d\n",
       "0      Atlanta     Georgia  1.25  6\n",
       "1  Tallahassee     Florida  2.60  3\n",
       "2   Sacramento  California  1.70  5"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(rows, columns=[x[0] for x in cursor.description])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>1.25</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tallahassee</td>\n",
       "      <td>Florida</td>\n",
       "      <td>2.60</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>California</td>\n",
       "      <td>1.70</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             a           b     c  d\n",
       "0      Atlanta     Georgia  1.25  6\n",
       "1  Tallahassee     Florida  2.60  3\n",
       "2   Sacramento  California  1.70  5"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sqlalchemy as sqla\n",
    "db = sqla.create_engine('sqlite:///mydata.sqlite')\n",
    "pd.read_sql('select * from test', db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm mydata.sqlite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  }
 ],
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