3.Reading and Working with time-based data.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pylab\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1950    1  -0.60310E-01\n",
      "\n",
      " 1950    2   0.62681E+00\n",
      "\n",
      " 1950    3  -0.81275E-02\n",
      "\n",
      " 1950    4   0.55510E+00\n",
      "\n",
      " 1950    5   0.71577E-01\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('data/monthly-ao.txt') as f:\n",
    "    for x in range(5):\n",
    "        print(next(f))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.060310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>0.626810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1950</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.008128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1950</td>\n",
       "      <td>4</td>\n",
       "      <td>0.555100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1950</td>\n",
       "      <td>5</td>\n",
       "      <td>0.071577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0  1         2\n",
       "0  1950  1 -0.060310\n",
       "1  1950  2  0.626810\n",
       "2  1950  3 -0.008128\n",
       "3  1950  4  0.555100\n",
       "4  1950  5  0.071577"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pandas 'read_fwf'- read fixed width file\n",
    "data = pd.read_fwf('data/monthly-ao.txt', header=None)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>0.626810</td>\n",
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       "      <th>2</th>\n",
       "      <td>1950</td>\n",
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       "      <td>-0.008128</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1950</td>\n",
       "      <td>4</td>\n",
       "      <td>0.555100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1950</td>\n",
       "      <td>5</td>\n",
       "      <td>0.071577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1950</td>\n",
       "      <td>6</td>\n",
       "      <td>0.538570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1950</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.802480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1950</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.851010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1950</td>\n",
       "      <td>9</td>\n",
       "      <td>0.357970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1950</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.378900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1950</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.515110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1950</td>\n",
       "      <td>12</td>\n",
       "      <td>-1.928100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1951</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.084969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0   1         2\n",
       "1   1950   2  0.626810\n",
       "2   1950   3 -0.008128\n",
       "3   1950   4  0.555100\n",
       "4   1950   5  0.071577\n",
       "5   1950   6  0.538570\n",
       "6   1950   7 -0.802480\n",
       "7   1950   8 -0.851010\n",
       "8   1950   9  0.357970\n",
       "9   1950  10 -0.378900\n",
       "10  1950  11 -0.515110\n",
       "11  1950  12 -1.928100\n",
       "12  1951   1 -0.084969"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1:13] # So it's a monthy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0_1</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950-01-01</th>\n",
       "      <td>-0.060310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-02-01</th>\n",
       "      <td>0.626810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-03-01</th>\n",
       "      <td>-0.008128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-04-01</th>\n",
       "      <td>0.555100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-05-01</th>\n",
       "      <td>0.071577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-06-01</th>\n",
       "      <td>0.538570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-07-01</th>\n",
       "      <td>-0.802480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-08-01</th>\n",
       "      <td>-0.851010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-09-01</th>\n",
       "      <td>0.357970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-10-01</th>\n",
       "      <td>-0.378900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-11-01</th>\n",
       "      <td>-0.515110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-12-01</th>\n",
       "      <td>-1.928100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                   2\n",
       "0_1                 \n",
       "1950-01-01 -0.060310\n",
       "1950-02-01  0.626810\n",
       "1950-03-01 -0.008128\n",
       "1950-04-01  0.555100\n",
       "1950-05-01  0.071577\n",
       "1950-06-01  0.538570\n",
       "1950-07-01 -0.802480\n",
       "1950-08-01 -0.851010\n",
       "1950-09-01  0.357970\n",
       "1950-10-01 -0.378900\n",
       "1950-11-01 -0.515110\n",
       "1950-12-01 -1.928100"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_fwf('data/monthly-ao.txt', header=None, index_col=0, parse_dates=[[0,1]], infer_datetime_format=True)\n",
    "data.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1950-01-01', '1950-02-01', '1950-03-01', '1950-04-01',\n",
       "               '1950-05-01', '1950-06-01', '1950-07-01', '1950-08-01',\n",
       "               '1950-09-01', '1950-10-01',\n",
       "               ...\n",
       "               '2017-06-01', '2017-07-01', '2017-08-01', '2017-09-01',\n",
       "               '2017-10-01', '2017-11-01', '2017-12-01', '2018-01-01',\n",
       "               '2018-02-01', '2018-03-01'],\n",
       "              dtype='datetime64[ns]', name='0_1', length=819, freq=None)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950-01-01</th>\n",
       "      <td>-0.060310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-02-01</th>\n",
       "      <td>0.626810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-03-01</th>\n",
       "      <td>-0.008128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-04-01</th>\n",
       "      <td>0.555100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-05-01</th>\n",
       "      <td>0.071577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "               Value\n",
       "Month               \n",
       "1950-01-01 -0.060310\n",
       "1950-02-01  0.626810\n",
       "1950-03-01 -0.008128\n",
       "1950-04-01  0.555100\n",
       "1950-05-01  0.071577"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index.names = ['Month']\n",
    "data.columns = ['Value']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Range of dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1950-01-01 00:00:00')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-03-01 00:00:00')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.datetimes.DatetimeIndex"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950-01</th>\n",
       "      <td>-0.060310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-02</th>\n",
       "      <td>0.626810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-03</th>\n",
       "      <td>-0.008128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-04</th>\n",
       "      <td>0.555100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-05</th>\n",
       "      <td>0.071577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            Value\n",
       "Month            \n",
       "1950-01 -0.060310\n",
       "1950-02  0.626810\n",
       "1950-03 -0.008128\n",
       "1950-04  0.555100\n",
       "1950-05  0.071577"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.to_period()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More about reading the data\n",
    "When reading in dates with a pd.read_fwf function, you have several time-related parameters you can adjust:<br />\n",
    "**parse_dates, infer_datetime_format, date_parse**\n",
    "<br /><br />\n",
    "Experiment with these using %timeit to see if there are parameter difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "infer_datetime_format = True, no date parser\n",
      "9.42 ms ± 908 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "\n",
      "infer_datetime_format = False, no date parser\n",
      "8.17 ms ± 637 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "\n",
      "infer_datetime_format = True, date parser provided\n",
      "30.9 ms ± 2.56 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "\n",
      "infer_datetime_format = False, date parser provided\n",
      "31.3 ms ± 2.72 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "import timeit\n",
    "dateparse = lambda x, y: pd.datetime.strptime('%s-%s' % (x, y), '%Y-%m')\n",
    "\n",
    "print(\"infer_datetime_format = True, no date parser\")\n",
    "%timeit pd.read_fwf('data/monthly-ao.txt',\\\n",
    "                    header=None,\\\n",
    "                    index_col=0,\\\n",
    "                    parse_dates=[[0,1]],\\\n",
    "                    infer_datetime_format=True)\n",
    "\n",
    "print(\"\\ninfer_datetime_format = False, no date parser\")\n",
    "%timeit pd.read_fwf('data/monthly-ao.txt',\\\n",
    "                    header=None,\\\n",
    "                    index_col=0,\\\n",
    "                    parse_dates=[[0,1]],\\\n",
    "                    infer_datetime_format=False)\n",
    "\n",
    "print(\"\\ninfer_datetime_format = True, date parser provided\")\n",
    "%timeit pd.read_fwf('data/monthly-ao.txt',\\\n",
    "                    header=None,\\\n",
    "                    index_col=0,\\\n",
    "                    parse_dates=[[0,1]],\\\n",
    "                    infer_datetime_format=True,\\\n",
    "                    date_parser=dateparse)\n",
    "\n",
    "print(\"\\ninfer_datetime_format = False, date parser provided\")\n",
    "%timeit pd.read_fwf('data/monthly-ao.txt',\\\n",
    "                    header=None,\\\n",
    "                    index_col=0,\\\n",
    "                    parse_dates=[[0,1]],\\\n",
    "                    infer_datetime_format=False,\\\n",
    "                    date_parser=dateparse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract datetime from existing dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   day  hour  month  year\n",
       "0    4    10      1  2017\n",
       "1    5    12      1  2018"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'year':[2017, 2018], 'month':[1,1], 'day':[4, 5], 'hour':[10, 12]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2017-01-04 10:00:00\n",
       "1   2018-01-05 12:00:00\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Truncate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-04-30    0\n",
       "2018-05-31    1\n",
       "2018-06-30    2\n",
       "2018-07-31    3\n",
       "2018-08-31    4\n",
       "2018-09-30    5\n",
       "2018-10-31    6\n",
       "2018-11-30    7\n",
       "2018-12-31    8\n",
       "2019-01-31    9\n",
       "Freq: M, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(range(10), index=pd.date_range('04/10/2018', freq='M', periods=10)) # 10 Apr, 2018\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-05-31    1\n",
       "2018-06-30    2\n",
       "2018-07-31    3\n",
       "2018-08-31    4\n",
       "2018-09-30    5\n",
       "2018-10-31    6\n",
       "2018-11-30    7\n",
       "2018-12-31    8\n",
       "Freq: M, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.truncate(before='5/31/2018', after='12/31/2018') # May to December"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-05-31', '2018-10-31', '2018-11-30'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[[1, 6, 7]].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-04-30    0\n",
       "2018-06-30    2\n",
       "2018-08-31    4\n",
       "2018-10-31    6\n",
       "2018-12-31    8\n",
       "Freq: 2M, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[0:10:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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