31-dataframe-timeseries-code.ipynb

{
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   "cell_type": "code",
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    {
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     "text": [
      "/home/matt/envs/menv/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3418: DtypeWarning: Columns (7) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "url = 'https://github.com/mattharrison/datasets/raw/master'\\\n",
    "    '/data/dirtydevil.txt'\n",
    "df = pd.read_csv(url, skiprows=lambda num: num <34 or num == 35,\n",
    "                 sep='\\t')\n",
    "def tweak_river(df_):\n",
    "    return (df_\n",
    " .assign(datetime=pd.to_datetime(df_.datetime))\n",
    " .rename(columns={'144166_00060': 'cfs',\n",
    "                  '144167_00065': 'gage_height'})\n",
    " .set_index('datetime')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "lines_to_next_cell": 2
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   "outputs": [
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       "  <tbody>\n",
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       "      <th>2001-05-07 01:00:00</th>\n",
       "      <td>USGS</td>\n",
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       "      <td>9.53</td>\n",
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       "      <td>6.16</td>\n",
       "      <td>P</td>\n",
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       "      <th>2020-09-28 08:45:00</th>\n",
       "      <td>USGS</td>\n",
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       "      <td>9.20</td>\n",
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       "      <td>P</td>\n",
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       "    <tr>\n",
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       "      <td>USGS</td>\n",
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       "      <th>2020-09-28 09:15:00</th>\n",
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       "                    agency_cd  site_no tz_cd    cfs 144166_00060_cd  \\\n",
       "datetime                                                              \n",
       "2001-05-07 01:00:00      USGS  9333500   MDT  71.00          A:[91]   \n",
       "2001-05-07 01:15:00      USGS  9333500   MDT  71.00          A:[91]   \n",
       "2001-05-07 01:30:00      USGS  9333500   MDT  71.00          A:[91]   \n",
       "2001-05-07 01:45:00      USGS  9333500   MDT  70.00          A:[91]   \n",
       "2001-05-07 02:00:00      USGS  9333500   MDT  70.00          A:[91]   \n",
       "...                       ...      ...   ...    ...             ...   \n",
       "2020-09-28 08:30:00      USGS  9333500   MDT   9.53               P   \n",
       "2020-09-28 08:45:00      USGS  9333500   MDT   9.20               P   \n",
       "2020-09-28 09:00:00      USGS  9333500   MDT   9.20               P   \n",
       "2020-09-28 09:15:00      USGS  9333500   MDT   9.20               P   \n",
       "2020-09-28 09:30:00      USGS  9333500   MDT   9.20               P   \n",
       "\n",
       "                     gage_height 144167_00065_cd  \n",
       "datetime                                          \n",
       "2001-05-07 01:00:00          NaN             NaN  \n",
       "2001-05-07 01:15:00          NaN             NaN  \n",
       "2001-05-07 01:30:00          NaN             NaN  \n",
       "2001-05-07 01:45:00          NaN             NaN  \n",
       "2001-05-07 02:00:00          NaN             NaN  \n",
       "...                          ...             ...  \n",
       "2020-09-28 08:30:00         6.16               P  \n",
       "2020-09-28 08:45:00         6.15               P  \n",
       "2020-09-28 09:00:00         6.15               P  \n",
       "2020-09-28 09:15:00         6.15               P  \n",
       "2020-09-28 09:30:00         6.15               P  \n",
       "\n",
       "[539305 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "dd = tweak_river(df)\n",
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime\n",
       "2001-05-07 01:00:00    MDT\n",
       "2001-05-07 01:15:00    MDT\n",
       "2001-05-07 01:30:00    MDT\n",
       "2001-05-07 01:45:00    MDT\n",
       "2001-05-07 02:00:00    MDT\n",
       "                      ... \n",
       "2020-09-28 08:30:00    MDT\n",
       "2020-09-28 08:45:00    MDT\n",
       "2020-09-28 09:00:00    MDT\n",
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       "Name: tz_cd, Length: 539305, dtype: object"
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    "dd.tz_cd"
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tweak_river(df_):\n",
    "    return (df_\n",
    "      .assign(datetime=lambda df_:\n",
    "          pd.to_datetime(df_.datetime + \" \" +\n",
    "              df_.tz_cd.str.replace('MST', '-0700')\n",
    "                 .str.replace('MDT', '-0600'),\n",
    "              format='%Y-%m-%d %H:%M %z', utc=True))\n",
    "      .rename(columns={'144166_00060': 'cfs',\n",
    "                       '144167_00065': 'gage_height'})\n",
    "      .set_index('datetime')\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_america_denver_time(df_, time_col, tz_col):\n",
    "    return (df_\n",
    "            .assign(**{tz_col: df_[tz_col].replace('MDT', 'MST7MDT')})\n",
    "            .groupby(tz_col)\n",
    "            [time_col]\n",
    "            .transform(lambda s: pd.to_datetime(s)\n",
    "                .dt.tz_localize(s.name, ambiguous=True)\n",
    "                .dt.tz_convert('America/Denver'))\n",
    "           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tweak_river(df_):\n",
    "    return (df_\n",
    "      .assign(datetime=to_america_denver_time(df_, 'datetime',\n",
    "              'tz_cd'))\n",
    "      .rename(columns={'144166_00060': 'cfs',\n",
    "                       '144167_00065': 'gage_height'})\n",
    "      .set_index('datetime')\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "lines_to_next_cell": 2
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   "outputs": [],
   "source": [
    "dd = tweak_river(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "lines_to_next_cell": 2
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      "text/plain": [
       "                          agency_cd  site_no tz_cd    cfs 144166_00060_cd  \\\n",
       "datetime                                                                    \n",
       "2001-05-07 01:00:00-06:00      USGS  9333500   MDT  71.00          A:[91]   \n",
       "2001-05-07 01:15:00-06:00      USGS  9333500   MDT  71.00          A:[91]   \n",
       "2001-05-07 01:30:00-06:00      USGS  9333500   MDT  71.00          A:[91]   \n",
       "2001-05-07 01:45:00-06:00      USGS  9333500   MDT  70.00          A:[91]   \n",
       "2001-05-07 02:00:00-06:00      USGS  9333500   MDT  70.00          A:[91]   \n",
       "...                             ...      ...   ...    ...             ...   \n",
       "2020-09-28 08:30:00-06:00      USGS  9333500   MDT   9.53               P   \n",
       "2020-09-28 08:45:00-06:00      USGS  9333500   MDT   9.20               P   \n",
       "2020-09-28 09:00:00-06:00      USGS  9333500   MDT   9.20               P   \n",
       "2020-09-28 09:15:00-06:00      USGS  9333500   MDT   9.20               P   \n",
       "2020-09-28 09:30:00-06:00      USGS  9333500   MDT   9.20               P   \n",
       "\n",
       "                           gage_height 144167_00065_cd  \n",
       "datetime                                                \n",
       "2001-05-07 01:00:00-06:00          NaN             NaN  \n",
       "2001-05-07 01:15:00-06:00          NaN             NaN  \n",
       "2001-05-07 01:30:00-06:00          NaN             NaN  \n",
       "2001-05-07 01:45:00-06:00          NaN             NaN  \n",
       "2001-05-07 02:00:00-06:00          NaN             NaN  \n",
       "...                                ...             ...  \n",
       "2020-09-28 08:30:00-06:00         6.16               P  \n",
       "2020-09-28 08:45:00-06:00         6.15               P  \n",
       "2020-09-28 09:00:00-06:00         6.15               P  \n",
       "2020-09-28 09:15:00-06:00         6.15               P  \n",
       "2020-09-28 09:30:00-06:00         6.15               P  \n",
       "\n",
       "[539305 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='datetime'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 3600x2400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(dpi=600)  # doctest: +SKIP\n",
    "dd.cfs.plot()  # doctest: +SKIP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    493124.000000\n",
       "mean        104.460537\n",
       "std         477.341329\n",
       "min           0.000000\n",
       "25%          34.700000\n",
       "50%          81.000000\n",
       "75%         115.000000\n",
       "max       35800.000000\n",
       "Name: cfs, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd.cfs.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime\n",
       "2018-01-01 00:00:00-07:00    92.80\n",
       "2018-01-01 00:15:00-07:00    88.30\n",
       "2018-01-01 00:30:00-07:00    90.50\n",
       "2018-01-01 00:45:00-07:00    90.50\n",
       "2018-01-01 01:00:00-07:00    94.00\n",
       "                             ...  \n",
       "2020-09-28 08:30:00-06:00     9.53\n",
       "2020-09-28 08:45:00-06:00     9.20\n",
       "2020-09-28 09:00:00-06:00     9.20\n",
       "2020-09-28 09:15:00-06:00     9.20\n",
       "2020-09-28 09:30:00-06:00     9.20\n",
       "Name: cfs, Length: 95886, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dd\n",
    " .cfs\n",
    " .loc['2018':]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime\n",
       "2018-03-01 00:00:00-07:00    104.0\n",
       "2018-03-01 00:15:00-07:00    107.0\n",
       "2018-03-01 00:30:00-07:00    107.0\n",
       "2018-03-01 00:45:00-07:00    105.0\n",
       "2018-03-01 01:00:00-07:00    103.0\n",
       "                             ...  \n",
       "2019-05-31 22:45:00-06:00    121.0\n",
       "2019-05-31 23:00:00-06:00    123.0\n",
       "2019-05-31 23:15:00-06:00    123.0\n",
       "2019-05-31 23:30:00-06:00    125.0\n",
       "2019-05-31 23:45:00-06:00    123.0\n",
       "Name: cfs, Length: 43862, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dd\n",
    " .cfs\n",
    " .loc['2018/3':'2019/5']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='datetime'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(dd   # doctest: +SKIP\n",
    " .cfs\n",
    " .loc['2018/3':'2019/5']\n",
    " .plot()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='datetime'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(dd   # doctest: +SKIP\n",
    " .cfs\n",
    " .loc['2018/3':'2019/5']\n",
    " .clip(upper=400)\n",
    " .plot()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "dd2018 = (dd\n",
    " .cfs\n",
    " .loc['2018/3':'2019/5']\n",
    " .clip(upper=400))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (dd2018\n",
    " .resample('D')\n",
    " .mean()\n",
    " .plot(figsize=(10,4), alpha=.5, linewidth=1, label='Daily')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Dirty Devil Flow 2018 (cfs)')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax = (dd2018\n",
    " .resample('D')\n",
    " .mean()\n",
    " .rolling(7)\n",
    " .mean()\n",
    " .plot(figsize=(10,4), ax=ax, label='7-day Rolling')\n",
    ")\n",
    "ax.legend()\n",
    "ax.set_title('Dirty Devil Flow 2018 (cfs)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "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>cfs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07-07 13:15:00-06:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-07 13:30:00-06:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-07 13:45:00-06:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-07 14:00:00-06:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-07 14:15:00-06:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-18 08:15:00-06:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-18 08:30:00-06:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-18 08:45:00-06:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-18 09:15:00-06:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-18 10:30:00-06:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>337 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           cfs\n",
       "datetime                      \n",
       "2018-07-07 13:15:00-06:00  NaN\n",
       "2018-07-07 13:30:00-06:00  NaN\n",
       "2018-07-07 13:45:00-06:00  NaN\n",
       "2018-07-07 14:00:00-06:00  NaN\n",
       "2018-07-07 14:15:00-06:00  NaN\n",
       "...                        ...\n",
       "2018-08-18 08:15:00-06:00  NaN\n",
       "2018-08-18 08:30:00-06:00  NaN\n",
       "2018-08-18 08:45:00-06:00  NaN\n",
       "2018-08-18 09:15:00-06:00  NaN\n",
       "2018-08-18 10:30:00-06:00  NaN\n",
       "\n",
       "[337 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dd\n",
    "  [['cfs']]\n",
    "  .loc['2018/3':'2019/5']\n",
    "  .query('cfs.isna()')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='datetime'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(dd     # doctest: +SKIP\n",
    "  [['cfs']]\n",
    "  .loc['2018/7/7':'2018/7/8']\n",
    "  .plot(figsize=(10,3))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 6000x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(dpi=600, figsize=(10,3))   # doctest: +SKIP\n",
    "dd_july = (dd\n",
    " ['cfs']\n",
    " .loc['2018/7/7 11:00':'2018/7/7 20:00']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='datetime'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd_july.plot(ax=ax, label='original', linewidth=2)   # doctest: +SKIP\n",
    "(dd_july   # doctest: +SKIP\n",
    " .bfill()\n",
    " .add(.05)\n",
    " .plot(label='bfill', ax=ax, linewidth=.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='datetime'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dd_july   # doctest: +SKIP\n",
    " .ffill()\n",
    " .add(.1)\n",
    " .plot(label='ffill', ax=ax, linewidth=.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='datetime'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dd_july   # doctest: +SKIP\n",
    " .interpolate(method='polynomial', order=3)\n",
    " .add(.15)\n",
    " .plot(label='interpolate poly (order 3)', ax=ax, linewidth=.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='datetime'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dd_july   # doctest: +SKIP\n",
    " .interpolate()\n",
    " .add(.2)\n",
    " .plot(label='interpolate default', ax=ax, linewidth=.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='datetime'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dd_july   # doctest: +SKIP\n",
    " .interpolate(method='nearest')\n",
    " .add(.25)\n",
    " .plot(label='interpolate nearest', ax=ax, linewidth=.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='datetime'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dd_july   # doctest: +SKIP\n",
    " .fillna(1)\n",
    " .add(.3)\n",
    " .plot(label='fillna 1', ax=ax, linewidth=.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Missing Values Demo')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.legend()   # doctest: +SKIP\n",
    "ax.set_title('Missing Values Demo')   # doctest: +SKIP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26011.0</td>\n",
       "      <td>117.268802</td>\n",
       "      <td>29.000354</td>\n",
       "      <td>24.30</td>\n",
       "      <td>101.00</td>\n",
       "      <td>114.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41309.0</td>\n",
       "      <td>125.890293</td>\n",
       "      <td>24.280297</td>\n",
       "      <td>56.00</td>\n",
       "      <td>109.00</td>\n",
       "      <td>125.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>303.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51807.0</td>\n",
       "      <td>127.037609</td>\n",
       "      <td>48.885942</td>\n",
       "      <td>60.30</td>\n",
       "      <td>100.00</td>\n",
       "      <td>116.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50669.0</td>\n",
       "      <td>82.786214</td>\n",
       "      <td>74.133528</td>\n",
       "      <td>20.50</td>\n",
       "      <td>52.50</td>\n",
       "      <td>70.0</td>\n",
       "      <td>97.8</td>\n",
       "      <td>2140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>49507.0</td>\n",
       "      <td>63.007851</td>\n",
       "      <td>68.791835</td>\n",
       "      <td>0.31</td>\n",
       "      <td>27.20</td>\n",
       "      <td>43.9</td>\n",
       "      <td>78.5</td>\n",
       "      <td>1960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>41379.0</td>\n",
       "      <td>74.327241</td>\n",
       "      <td>139.857378</td>\n",
       "      <td>0.02</td>\n",
       "      <td>8.93</td>\n",
       "      <td>32.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>2460.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>37089.0</td>\n",
       "      <td>62.775011</td>\n",
       "      <td>115.285805</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>17.4</td>\n",
       "      <td>68.2</td>\n",
       "      <td>1660.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37584.0</td>\n",
       "      <td>74.676246</td>\n",
       "      <td>247.800553</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.30</td>\n",
       "      <td>25.6</td>\n",
       "      <td>59.1</td>\n",
       "      <td>7320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42272.0</td>\n",
       "      <td>128.309332</td>\n",
       "      <td>546.921269</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.55</td>\n",
       "      <td>20.0</td>\n",
       "      <td>55.9</td>\n",
       "      <td>9540.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>44647.0</td>\n",
       "      <td>196.285529</td>\n",
       "      <td>1455.942059</td>\n",
       "      <td>6.44</td>\n",
       "      <td>40.90</td>\n",
       "      <td>57.4</td>\n",
       "      <td>80.9</td>\n",
       "      <td>35800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>42165.0</td>\n",
       "      <td>97.194344</td>\n",
       "      <td>39.743333</td>\n",
       "      <td>53.40</td>\n",
       "      <td>81.50</td>\n",
       "      <td>89.3</td>\n",
       "      <td>105.0</td>\n",
       "      <td>766.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28685.0</td>\n",
       "      <td>100.042608</td>\n",
       "      <td>26.700535</td>\n",
       "      <td>17.00</td>\n",
       "      <td>86.50</td>\n",
       "      <td>97.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>407.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count        mean          std    min     25%    50%    75%  \\\n",
       "datetime                                                                  \n",
       "1         26011.0  117.268802    29.000354  24.30  101.00  114.0  132.0   \n",
       "2         41309.0  125.890293    24.280297  56.00  109.00  125.0  141.0   \n",
       "3         51807.0  127.037609    48.885942  60.30  100.00  116.0  136.0   \n",
       "4         50669.0   82.786214    74.133528  20.50   52.50   70.0   97.8   \n",
       "5         49507.0   63.007851    68.791835   0.31   27.20   43.9   78.5   \n",
       "6         41379.0   74.327241   139.857378   0.02    8.93   32.0   82.5   \n",
       "7         37089.0   62.775011   115.285805   0.00    4.25   17.4   68.2   \n",
       "8         37584.0   74.676246   247.800553   0.00    6.30   25.6   59.1   \n",
       "9         42272.0  128.309332   546.921269   0.00    7.55   20.0   55.9   \n",
       "10        44647.0  196.285529  1455.942059   6.44   40.90   57.4   80.9   \n",
       "11        42165.0   97.194344    39.743333  53.40   81.50   89.3  105.0   \n",
       "12        28685.0  100.042608    26.700535  17.00   86.50   97.8  113.0   \n",
       "\n",
       "              max  \n",
       "datetime           \n",
       "1           265.0  \n",
       "2           303.0  \n",
       "3           750.0  \n",
       "4          2140.0  \n",
       "5          1960.0  \n",
       "6          2460.0  \n",
       "7          1660.0  \n",
       "8          7320.0  \n",
       "9          9540.0  \n",
       "10        35800.0  \n",
       "11          766.0  \n",
       "12          407.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dd\n",
    " .groupby(dd.index.month)\n",
    " .cfs\n",
    " .describe()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='datetime'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 6000x2400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(dpi=600, figsize=(10,4)) # doctest: +SKIP\n",
    "(dd  # doctest: +SKIP\n",
    " .groupby(dd.index.month)\n",
    " ['cfs']\n",
    " .describe()\n",
    " ['mean']\n",
    " .plot.bar(ax=ax)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='datetime'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 6000x2400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(dpi=600, figsize=(10,4))  # doctest: +SKIP\n",
    "(dd  # doctest: +SKIP\n",
    " .groupby(dd.index.month)\n",
    " ['cfs']\n",
    " .describe()\n",
    " .loc[:, 'min':'75%']\n",
    " .plot.bar(ax=ax)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month', ylabel='cfs'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 6000x2400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "fig, ax = plt.subplots(dpi=600, figsize=(10,4))   # doctest: +SKIP\n",
    "sns.boxplot(data=dd.assign(cfs=dd.cfs.clip(upper=400)),   # doctest: +SKIP\n",
    "   x=dd.index.month.rename('Month'), y='cfs', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime\n",
       "2001-05-07 01:00:00-06:00    71.00\n",
       "2001-05-07 01:15:00-06:00    71.00\n",
       "2001-05-07 01:30:00-06:00    71.00\n",
       "2001-05-07 01:45:00-06:00    70.00\n",
       "2001-05-07 02:00:00-06:00    70.00\n",
       "                             ...  \n",
       "2020-09-28 08:30:00-06:00     9.53\n",
       "2020-09-28 08:45:00-06:00     9.20\n",
       "2020-09-28 09:00:00-06:00     9.20\n",
       "2020-09-28 09:15:00-06:00     9.20\n",
       "2020-09-28 09:30:00-06:00     9.20\n",
       "Name: cfs, Length: 539305, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd.cfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "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>site_no</th>\n",
       "      <th>cfs</th>\n",
       "      <th>gage_height</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-05-07 00:00:00-06:00</th>\n",
       "      <td>9333500.0</td>\n",
       "      <td>71.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-08 00:00:00-06:00</th>\n",
       "      <td>9333500.0</td>\n",
       "      <td>69.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-09 00:00:00-06:00</th>\n",
       "      <td>9333500.0</td>\n",
       "      <td>63.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-10 00:00:00-06:00</th>\n",
       "      <td>9333500.0</td>\n",
       "      <td>55.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-11 00:00:00-06:00</th>\n",
       "      <td>9333500.0</td>\n",
       "      <td>55.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-24 00:00:00-06:00</th>\n",
       "      <td>9333500.0</td>\n",
       "      <td>9.53</td>\n",
       "      <td>6.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-25 00:00:00-06:00</th>\n",
       "      <td>9333500.0</td>\n",
       "      <td>10.20</td>\n",
       "      <td>6.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-26 00:00:00-06:00</th>\n",
       "      <td>9333500.0</td>\n",
       "      <td>10.90</td>\n",
       "      <td>6.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-27 00:00:00-06:00</th>\n",
       "      <td>9333500.0</td>\n",
       "      <td>10.20</td>\n",
       "      <td>6.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-28 00:00:00-06:00</th>\n",
       "      <td>9333500.0</td>\n",
       "      <td>9.53</td>\n",
       "      <td>6.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7085 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             site_no    cfs  gage_height\n",
       "datetime                                                \n",
       "2001-05-07 00:00:00-06:00  9333500.0  71.50          NaN\n",
       "2001-05-08 00:00:00-06:00  9333500.0  69.00          NaN\n",
       "2001-05-09 00:00:00-06:00  9333500.0  63.50          NaN\n",
       "2001-05-10 00:00:00-06:00  9333500.0  55.00          NaN\n",
       "2001-05-11 00:00:00-06:00  9333500.0  55.00          NaN\n",
       "...                              ...    ...          ...\n",
       "2020-09-24 00:00:00-06:00  9333500.0   9.53         6.16\n",
       "2020-09-25 00:00:00-06:00  9333500.0  10.20         6.18\n",
       "2020-09-26 00:00:00-06:00  9333500.0  10.90         6.20\n",
       "2020-09-27 00:00:00-06:00  9333500.0  10.20         6.18\n",
       "2020-09-28 00:00:00-06:00  9333500.0   9.53         6.16\n",
       "\n",
       "[7085 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dd\n",
    "  .resample('D')\n",
    "  .median()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
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       "                             site_no    cfs  gage_height\n",
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    "(dd\n",
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    "(dd\n",
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       "                             site_no     cfs  gage_height\n",
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    "(dd\n",
    "  .resample('Q')\n",
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    ")"
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   "cell_type": "code",
   "execution_count": 37,
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      "text/plain": [
       "                             site_no    cfs  gage_height\n",
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       "...                              ...    ...          ...\n",
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       "\n",
       "[78 rows x 3 columns]"
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     "execution_count": 37,
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    "(dd\n",
    "  .resample('Q-JAN')\n",
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    ")"
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  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
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       "                             site_no   cfs  gage_height\n",
       "datetime                                               \n",
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       "...                              ...   ...          ...\n",
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       "2020-09-28 09:30:00-06:00  9333500.0   9.2         6.15\n",
       "\n",
       "[5100736 rows x 3 columns]"
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    "(dd\n",
    "  .resample('2min')\n",
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    ")"
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       "                             site_no   cfs  gage_height\n",
       "datetime                                               \n",
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       "2001-05-07 01:02:00-06:00  9333500.0  71.0          NaN\n",
       "2001-05-07 01:04:00-06:00  9333500.0  71.0          NaN\n",
       "2001-05-07 01:06:00-06:00  9333500.0  71.0          NaN\n",
       "2001-05-07 01:08:00-06:00  9333500.0  71.0          NaN\n",
       "...                              ...   ...          ...\n",
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       "2020-09-28 09:26:00-06:00  9333500.0   9.2         6.15\n",
       "2020-09-28 09:28:00-06:00  9333500.0   9.2         6.15\n",
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       "\n",
       "[5100736 rows x 3 columns]"
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    "(dd\n",
    "  .resample('2min')\n",
    "  .median()\n",
    "  .interpolate()\n",
    ")"
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       "                             site_no    cfs  gage_height\n",
       "datetime                                                \n",
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       "2001-10-31 00:00:00-07:00  9333500.0   39.0          NaN\n",
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       "2002-07-31 00:00:00-06:00  9333500.0   13.0          NaN\n",
       "...                              ...    ...          ...\n",
       "2019-10-31 00:00:00-06:00  9333500.0   12.8         6.25\n",
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       "2020-04-30 00:00:00-06:00  9333500.0  116.0         6.98\n",
       "2020-07-31 00:00:00-06:00  9333500.0   13.9         6.37\n",
       "2020-10-31 00:00:00-06:00  9333500.0    0.5         5.49\n",
       "\n",
       "[78 rows x 3 columns]"
      ]
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     "execution_count": 40,
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   "source": [
    "(dd\n",
    "  .resample('Q-JAN')\n",
    "  .median()\n",
    ")"
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  {
   "cell_type": "code",
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      "text/plain": [
       "                             site_no    cfs  gage_height\n",
       "datetime                                                \n",
       "2001-07-31 00:00:00-06:00  9333500.0   42.0          NaN\n",
       "2001-10-31 00:00:00-07:00  9333500.0   39.0          NaN\n",
       "2002-01-31 00:00:00-07:00  9333500.0  116.0          NaN\n",
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       "2002-07-31 00:00:00-06:00  9333500.0   13.0          NaN\n",
       "...                              ...    ...          ...\n",
       "2019-10-31 00:00:00-06:00  9333500.0   12.8         6.25\n",
       "2020-01-31 00:00:00-07:00  9333500.0  116.0         6.84\n",
       "2020-04-30 00:00:00-06:00  9333500.0  116.0         6.98\n",
       "2020-07-31 00:00:00-06:00  9333500.0   13.9         6.37\n",
       "2020-10-31 00:00:00-06:00  9333500.0    0.5         5.49\n",
       "\n",
       "[78 rows x 3 columns]"
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     },
     "execution_count": 41,
     "metadata": {},
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   "source": [
    "(dd\n",
    "  .reset_index()\n",
    "  .groupby(pd.Grouper(key='datetime', freq='Q-JAN'))\n",
    "  .median()\n",
    ")"
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  }
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