IFT_Step08_ffc_results_by_year.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Paths, input, and output file names\n",
    "\n",
    "outer_dir = os.path.abspath(os.path.join(os.getcwd() ,\"../..\"))\n",
    "path = os.path.join(outer_dir, 'IFT_files', 'IFT Results')\n",
    "\n",
    "version = 'V1_0_0'\n",
    "dry_pct = 1\n",
    "in_csv = os.path.join(path, 'Water_year_types_{}.csv'.format(version))\n",
    "in_ffc = os.path.join(path, 'ffc_results_long.csv')\n",
    "out_csv = os.path.join(path, 'ffc_dry_year_compare_{}pct_{}.csv'.format(int(dry_pct * 100), version))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "130\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stream</th>\n",
       "      <th>water_year</th>\n",
       "      <th>wy_mean_flow_cfs</th>\n",
       "      <th>day_count</th>\n",
       "      <th>pct_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>indian</td>\n",
       "      <td>1951</td>\n",
       "      <td>80.799484</td>\n",
       "      <td>365</td>\n",
       "      <td>0.732394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>indian</td>\n",
       "      <td>1952</td>\n",
       "      <td>97.464302</td>\n",
       "      <td>366</td>\n",
       "      <td>0.830986</td>\n",
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       "      <th>2</th>\n",
       "      <td>indian</td>\n",
       "      <td>1953</td>\n",
       "      <td>78.676068</td>\n",
       "      <td>365</td>\n",
       "      <td>0.676056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>indian</td>\n",
       "      <td>1954</td>\n",
       "      <td>67.919191</td>\n",
       "      <td>365</td>\n",
       "      <td>0.577465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>indian</td>\n",
       "      <td>1955</td>\n",
       "      <td>32.950972</td>\n",
       "      <td>365</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   stream  water_year  wy_mean_flow_cfs  day_count  pct_rank\n",
       "0  indian        1951         80.799484        365  0.732394\n",
       "1  indian        1952         97.464302        366  0.830986\n",
       "2  indian        1953         78.676068        365  0.676056\n",
       "3  indian        1954         67.919191        365  0.577465\n",
       "4  indian        1955         32.950972        365  0.253521"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import data\n",
    "df = pd.read_csv(in_csv)\n",
    "print(len(df))\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>water_year</th>\n",
       "      <th>stream</th>\n",
       "      <th>ift</th>\n",
       "      <th>ffm</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1951</td>\n",
       "      <td>indian</td>\n",
       "      <td>unimp</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>158.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1952</td>\n",
       "      <td>indian</td>\n",
       "      <td>unimp</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1953</td>\n",
       "      <td>indian</td>\n",
       "      <td>unimp</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1954</td>\n",
       "      <td>indian</td>\n",
       "      <td>unimp</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1955</td>\n",
       "      <td>indian</td>\n",
       "      <td>unimp</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>170.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   water_year  stream    ift        ffm  value\n",
       "0        1951  indian  unimp  DS_Dur_WS  158.0\n",
       "1        1952  indian  unimp  DS_Dur_WS  219.0\n",
       "2        1953  indian  unimp  DS_Dur_WS  195.0\n",
       "3        1954  indian  unimp  DS_Dur_WS  169.0\n",
       "4        1955  indian  unimp  DS_Dur_WS  170.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import data\n",
    "df1 = pd.read_csv(in_ffc)\n",
    "df1.rename(columns={'Year': 'water_year'}, inplace=True)\n",
    "print(len(df1))\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>water_year</th>\n",
       "      <th>stream</th>\n",
       "      <th>ift</th>\n",
       "      <th>ffm</th>\n",
       "      <th>value</th>\n",
       "      <th>wy_mean_flow_cfs</th>\n",
       "      <th>day_count</th>\n",
       "      <th>pct_rank</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1951</td>\n",
       "      <td>indian</td>\n",
       "      <td>unimp</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>158.0</td>\n",
       "      <td>80.799484</td>\n",
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       "      <td>0.732394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1952</td>\n",
       "      <td>indian</td>\n",
       "      <td>unimp</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>219.0</td>\n",
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       "      <td>1953</td>\n",
       "      <td>indian</td>\n",
       "      <td>unimp</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>195.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>1954</td>\n",
       "      <td>indian</td>\n",
       "      <td>unimp</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>169.0</td>\n",
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       "      <td>365</td>\n",
       "      <td>0.577465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1955</td>\n",
       "      <td>indian</td>\n",
       "      <td>unimp</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>170.0</td>\n",
       "      <td>32.950972</td>\n",
       "      <td>365</td>\n",
       "      <td>0.253521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   water_year  stream    ift        ffm  value  wy_mean_flow_cfs  day_count  \\\n",
       "0        1951  indian  unimp  DS_Dur_WS  158.0         80.799484        365   \n",
       "1        1952  indian  unimp  DS_Dur_WS  219.0         97.464302        366   \n",
       "2        1953  indian  unimp  DS_Dur_WS  195.0         78.676068        365   \n",
       "3        1954  indian  unimp  DS_Dur_WS  169.0         67.919191        365   \n",
       "4        1955  indian  unimp  DS_Dur_WS  170.0         32.950972        365   \n",
       "\n",
       "   pct_rank  \n",
       "0  0.732394  \n",
       "1  0.830986  \n",
       "2  0.676056  \n",
       "3  0.577465  \n",
       "4  0.253521  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.merge(df1, df, on=['stream', 'water_year'], how='left')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df3 = df2.loc[df2.pct_rank <= dry_pct]\n",
    "df4 = (df3.groupby(['stream', 'ift', 'ffm'])\n",
    "           .agg({'value':'mean'})\n",
    "           .reset_index()\n",
    "           .rename(columns={'value':'dry_val_mean'})\n",
    "          )\n",
    "df4 = df4.sort_values(by=['stream','ift'], ascending=[True,False])"
   ]
  },
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   "cell_type": "code",
   "execution_count": 18,
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    "ffm = 'DS_Mag_90'\n",
    "df5 = df4.loc[(df4.ffm == ffm) & (df4.stream == stream)]\n",
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       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>159.0</td>\n",
       "      <td>80.799484</td>\n",
       "      <td>365</td>\n",
       "      <td>0.732394</td>\n",
       "      <td>158.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1952</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>220.0</td>\n",
       "      <td>97.464302</td>\n",
       "      <td>366</td>\n",
       "      <td>0.830986</td>\n",
       "      <td>219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1953</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>195.0</td>\n",
       "      <td>78.676068</td>\n",
       "      <td>365</td>\n",
       "      <td>0.676056</td>\n",
       "      <td>195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1954</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>170.0</td>\n",
       "      <td>67.919191</td>\n",
       "      <td>365</td>\n",
       "      <td>0.577465</td>\n",
       "      <td>169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1955</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>171.0</td>\n",
       "      <td>32.950972</td>\n",
       "      <td>365</td>\n",
       "      <td>0.253521</td>\n",
       "      <td>170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1956</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>283.0</td>\n",
       "      <td>116.140401</td>\n",
       "      <td>366</td>\n",
       "      <td>0.887324</td>\n",
       "      <td>283.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1957</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>175.0</td>\n",
       "      <td>41.925103</td>\n",
       "      <td>365</td>\n",
       "      <td>0.394366</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1958</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>229.0</td>\n",
       "      <td>124.676122</td>\n",
       "      <td>365</td>\n",
       "      <td>0.901408</td>\n",
       "      <td>229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1959</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>256.0</td>\n",
       "      <td>41.579588</td>\n",
       "      <td>365</td>\n",
       "      <td>0.380282</td>\n",
       "      <td>254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1960</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>127.0</td>\n",
       "      <td>43.260980</td>\n",
       "      <td>366</td>\n",
       "      <td>0.422535</td>\n",
       "      <td>127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1961</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>145.0</td>\n",
       "      <td>46.742975</td>\n",
       "      <td>365</td>\n",
       "      <td>0.450704</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1962</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>220.0</td>\n",
       "      <td>49.370364</td>\n",
       "      <td>365</td>\n",
       "      <td>0.464789</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1963</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>148.0</td>\n",
       "      <td>69.705711</td>\n",
       "      <td>365</td>\n",
       "      <td>0.591549</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1964</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>175.0</td>\n",
       "      <td>27.424636</td>\n",
       "      <td>366</td>\n",
       "      <td>0.211268</td>\n",
       "      <td>175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1965</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>183.0</td>\n",
       "      <td>103.935032</td>\n",
       "      <td>365</td>\n",
       "      <td>0.845070</td>\n",
       "      <td>183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1966</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>195.0</td>\n",
       "      <td>49.821766</td>\n",
       "      <td>365</td>\n",
       "      <td>0.478873</td>\n",
       "      <td>194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1967</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>186.0</td>\n",
       "      <td>78.204506</td>\n",
       "      <td>365</td>\n",
       "      <td>0.661972</td>\n",
       "      <td>186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1968</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>217.0</td>\n",
       "      <td>40.423712</td>\n",
       "      <td>366</td>\n",
       "      <td>0.366197</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1969</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>254.0</td>\n",
       "      <td>109.408495</td>\n",
       "      <td>365</td>\n",
       "      <td>0.873239</td>\n",
       "      <td>254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1970</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>213.0</td>\n",
       "      <td>92.852301</td>\n",
       "      <td>365</td>\n",
       "      <td>0.816901</td>\n",
       "      <td>211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1971</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>177.0</td>\n",
       "      <td>66.465648</td>\n",
       "      <td>365</td>\n",
       "      <td>0.563380</td>\n",
       "      <td>177.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1972</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>180.0</td>\n",
       "      <td>24.492674</td>\n",
       "      <td>366</td>\n",
       "      <td>0.154930</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1973</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>188.0</td>\n",
       "      <td>80.438832</td>\n",
       "      <td>365</td>\n",
       "      <td>0.718310</td>\n",
       "      <td>187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1974</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>236.0</td>\n",
       "      <td>140.727383</td>\n",
       "      <td>365</td>\n",
       "      <td>0.971831</td>\n",
       "      <td>236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1975</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>226.0</td>\n",
       "      <td>78.693342</td>\n",
       "      <td>365</td>\n",
       "      <td>0.690141</td>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1976</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>206.0</td>\n",
       "      <td>15.963928</td>\n",
       "      <td>366</td>\n",
       "      <td>0.070423</td>\n",
       "      <td>205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1977</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>223.0</td>\n",
       "      <td>3.507721</td>\n",
       "      <td>365</td>\n",
       "      <td>0.014085</td>\n",
       "      <td>223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1978</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>234.0</td>\n",
       "      <td>104.860561</td>\n",
       "      <td>365</td>\n",
       "      <td>0.859155</td>\n",
       "      <td>233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1979</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>188.0</td>\n",
       "      <td>37.443202</td>\n",
       "      <td>365</td>\n",
       "      <td>0.323944</td>\n",
       "      <td>187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1980</td>\n",
       "      <td>indian</td>\n",
       "      <td>mpof</td>\n",
       "      <td>DS_Dur_WS</td>\n",
       "      <td>222.0</td>\n",
       "      <td>76.832308</td>\n",
       "      <td>366</td>\n",
       "      <td>0.647887</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13794</th>\n",
       "      <td>1989</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.252853</td>\n",
       "      <td>365</td>\n",
       "      <td>0.389831</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13795</th>\n",
       "      <td>1990</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.052878</td>\n",
       "      <td>365</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13796</th>\n",
       "      <td>1991</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.001208</td>\n",
       "      <td>365</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13797</th>\n",
       "      <td>1992</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.454817</td>\n",
       "      <td>366</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13798</th>\n",
       "      <td>1993</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.562573</td>\n",
       "      <td>365</td>\n",
       "      <td>0.559322</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13799</th>\n",
       "      <td>1994</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.383805</td>\n",
       "      <td>365</td>\n",
       "      <td>0.135593</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13800</th>\n",
       "      <td>1995</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.580668</td>\n",
       "      <td>365</td>\n",
       "      <td>0.881356</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13801</th>\n",
       "      <td>1996</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.729783</td>\n",
       "      <td>366</td>\n",
       "      <td>0.677966</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13802</th>\n",
       "      <td>1997</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.877200</td>\n",
       "      <td>365</td>\n",
       "      <td>0.796610</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13803</th>\n",
       "      <td>1998</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.866201</td>\n",
       "      <td>365</td>\n",
       "      <td>0.847458</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13804</th>\n",
       "      <td>1999</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.591429</td>\n",
       "      <td>365</td>\n",
       "      <td>0.661017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13805</th>\n",
       "      <td>2000</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.810757</td>\n",
       "      <td>366</td>\n",
       "      <td>0.508475</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13806</th>\n",
       "      <td>2001</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.871370</td>\n",
       "      <td>365</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13807</th>\n",
       "      <td>2002</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.246748</td>\n",
       "      <td>365</td>\n",
       "      <td>0.491525</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13808</th>\n",
       "      <td>2003</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.857028</td>\n",
       "      <td>365</td>\n",
       "      <td>0.779661</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13809</th>\n",
       "      <td>2004</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.865942</td>\n",
       "      <td>366</td>\n",
       "      <td>0.525424</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13810</th>\n",
       "      <td>2005</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.582321</td>\n",
       "      <td>365</td>\n",
       "      <td>0.440678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13811</th>\n",
       "      <td>2006</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.524876</td>\n",
       "      <td>365</td>\n",
       "      <td>0.932203</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13812</th>\n",
       "      <td>2007</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.006688</td>\n",
       "      <td>365</td>\n",
       "      <td>0.305085</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13813</th>\n",
       "      <td>2008</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.637617</td>\n",
       "      <td>366</td>\n",
       "      <td>0.372881</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13814</th>\n",
       "      <td>2009</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.402356</td>\n",
       "      <td>365</td>\n",
       "      <td>0.169492</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13815</th>\n",
       "      <td>2010</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.956125</td>\n",
       "      <td>365</td>\n",
       "      <td>0.593220</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13816</th>\n",
       "      <td>2011</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.126325</td>\n",
       "      <td>365</td>\n",
       "      <td>0.644068</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13817</th>\n",
       "      <td>2012</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.624554</td>\n",
       "      <td>366</td>\n",
       "      <td>0.254237</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13818</th>\n",
       "      <td>2013</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.437512</td>\n",
       "      <td>365</td>\n",
       "      <td>0.406780</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13819</th>\n",
       "      <td>2014</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.422825</td>\n",
       "      <td>365</td>\n",
       "      <td>0.033898</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13820</th>\n",
       "      <td>2015</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.519797</td>\n",
       "      <td>365</td>\n",
       "      <td>0.355932</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13821</th>\n",
       "      <td>2016</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.968470</td>\n",
       "      <td>366</td>\n",
       "      <td>0.542373</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13822</th>\n",
       "      <td>2017</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.207655</td>\n",
       "      <td>365</td>\n",
       "      <td>0.915254</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13823</th>\n",
       "      <td>2018</td>\n",
       "      <td>sproul</td>\n",
       "      <td>tessman</td>\n",
       "      <td>Peak_Fre_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.552816</td>\n",
       "      <td>365</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13824 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       water_year  stream      ift         ffm  value  wy_mean_flow_cfs  \\\n",
       "0            1951  indian     mpof   DS_Dur_WS  159.0         80.799484   \n",
       "1            1952  indian     mpof   DS_Dur_WS  220.0         97.464302   \n",
       "2            1953  indian     mpof   DS_Dur_WS  195.0         78.676068   \n",
       "3            1954  indian     mpof   DS_Dur_WS  170.0         67.919191   \n",
       "4            1955  indian     mpof   DS_Dur_WS  171.0         32.950972   \n",
       "5            1956  indian     mpof   DS_Dur_WS  283.0        116.140401   \n",
       "6            1957  indian     mpof   DS_Dur_WS  175.0         41.925103   \n",
       "7            1958  indian     mpof   DS_Dur_WS  229.0        124.676122   \n",
       "8            1959  indian     mpof   DS_Dur_WS  256.0         41.579588   \n",
       "9            1960  indian     mpof   DS_Dur_WS  127.0         43.260980   \n",
       "10           1961  indian     mpof   DS_Dur_WS  145.0         46.742975   \n",
       "11           1962  indian     mpof   DS_Dur_WS  220.0         49.370364   \n",
       "12           1963  indian     mpof   DS_Dur_WS  148.0         69.705711   \n",
       "13           1964  indian     mpof   DS_Dur_WS  175.0         27.424636   \n",
       "14           1965  indian     mpof   DS_Dur_WS  183.0        103.935032   \n",
       "15           1966  indian     mpof   DS_Dur_WS  195.0         49.821766   \n",
       "16           1967  indian     mpof   DS_Dur_WS  186.0         78.204506   \n",
       "17           1968  indian     mpof   DS_Dur_WS  217.0         40.423712   \n",
       "18           1969  indian     mpof   DS_Dur_WS  254.0        109.408495   \n",
       "19           1970  indian     mpof   DS_Dur_WS  213.0         92.852301   \n",
       "20           1971  indian     mpof   DS_Dur_WS  177.0         66.465648   \n",
       "21           1972  indian     mpof   DS_Dur_WS  180.0         24.492674   \n",
       "22           1973  indian     mpof   DS_Dur_WS  188.0         80.438832   \n",
       "23           1974  indian     mpof   DS_Dur_WS  236.0        140.727383   \n",
       "24           1975  indian     mpof   DS_Dur_WS  226.0         78.693342   \n",
       "25           1976  indian     mpof   DS_Dur_WS  206.0         15.963928   \n",
       "26           1977  indian     mpof   DS_Dur_WS  223.0          3.507721   \n",
       "27           1978  indian     mpof   DS_Dur_WS  234.0        104.860561   \n",
       "28           1979  indian     mpof   DS_Dur_WS  188.0         37.443202   \n",
       "29           1980  indian     mpof   DS_Dur_WS  222.0         76.832308   \n",
       "...           ...     ...      ...         ...    ...               ...   \n",
       "13794        1989  sproul  tessman  Peak_Fre_5    1.0         13.252853   \n",
       "13795        1990  sproul  tessman  Peak_Fre_5    2.0          8.052878   \n",
       "13796        1991  sproul  tessman  Peak_Fre_5    NaN          6.001208   \n",
       "13797        1992  sproul  tessman  Peak_Fre_5    1.0          7.454817   \n",
       "13798        1993  sproul  tessman  Peak_Fre_5    1.0         17.562573   \n",
       "13799        1994  sproul  tessman  Peak_Fre_5    1.0          8.383805   \n",
       "13800        1995  sproul  tessman  Peak_Fre_5    1.0         27.580668   \n",
       "13801        1996  sproul  tessman  Peak_Fre_5    3.0         19.729783   \n",
       "13802        1997  sproul  tessman  Peak_Fre_5    3.0         22.877200   \n",
       "13803        1998  sproul  tessman  Peak_Fre_5    1.0         26.866201   \n",
       "13804        1999  sproul  tessman  Peak_Fre_5    1.0         19.591429   \n",
       "13805        2000  sproul  tessman  Peak_Fre_5    1.0         16.810757   \n",
       "13806        2001  sproul  tessman  Peak_Fre_5    2.0          6.871370   \n",
       "13807        2002  sproul  tessman  Peak_Fre_5    3.0         16.246748   \n",
       "13808        2003  sproul  tessman  Peak_Fre_5    1.0         22.857028   \n",
       "13809        2004  sproul  tessman  Peak_Fre_5    3.0         16.865942   \n",
       "13810        2005  sproul  tessman  Peak_Fre_5    3.0         15.582321   \n",
       "13811        2006  sproul  tessman  Peak_Fre_5    1.0         29.524876   \n",
       "13812        2007  sproul  tessman  Peak_Fre_5    1.0         12.006688   \n",
       "13813        2008  sproul  tessman  Peak_Fre_5    2.0         12.637617   \n",
       "13814        2009  sproul  tessman  Peak_Fre_5    1.0          9.402356   \n",
       "13815        2010  sproul  tessman  Peak_Fre_5    2.0         17.956125   \n",
       "13816        2011  sproul  tessman  Peak_Fre_5    2.0         19.126325   \n",
       "13817        2012  sproul  tessman  Peak_Fre_5    1.0         11.624554   \n",
       "13818        2013  sproul  tessman  Peak_Fre_5    1.0         13.437512   \n",
       "13819        2014  sproul  tessman  Peak_Fre_5    NaN          3.422825   \n",
       "13820        2015  sproul  tessman  Peak_Fre_5    NaN         12.519797   \n",
       "13821        2016  sproul  tessman  Peak_Fre_5    1.0         16.968470   \n",
       "13822        2017  sproul  tessman  Peak_Fre_5    1.0         29.207655   \n",
       "13823        2018  sproul  tessman  Peak_Fre_5    1.0          9.552816   \n",
       "\n",
       "       day_count  pct_rank  value_unimp  \n",
       "0            365  0.732394        158.0  \n",
       "1            366  0.830986        219.0  \n",
       "2            365  0.676056        195.0  \n",
       "3            365  0.577465        169.0  \n",
       "4            365  0.253521        170.0  \n",
       "5            366  0.887324        283.0  \n",
       "6            365  0.394366        174.0  \n",
       "7            365  0.901408        229.0  \n",
       "8            365  0.380282        254.0  \n",
       "9            366  0.422535        127.0  \n",
       "10           365  0.450704        145.0  \n",
       "11           365  0.464789        220.0  \n",
       "12           365  0.591549        148.0  \n",
       "13           366  0.211268        175.0  \n",
       "14           365  0.845070        183.0  \n",
       "15           365  0.478873        194.0  \n",
       "16           365  0.661972        186.0  \n",
       "17           366  0.366197        216.0  \n",
       "18           365  0.873239        254.0  \n",
       "19           365  0.816901        211.0  \n",
       "20           365  0.563380        177.0  \n",
       "21           366  0.154930        179.0  \n",
       "22           365  0.718310        187.0  \n",
       "23           365  0.971831        236.0  \n",
       "24           365  0.690141        226.0  \n",
       "25           366  0.070423        205.0  \n",
       "26           365  0.014085        223.0  \n",
       "27           365  0.859155        233.0  \n",
       "28           365  0.323944        187.0  \n",
       "29           366  0.647887        221.0  \n",
       "...          ...       ...          ...  \n",
       "13794        365  0.389831          NaN  \n",
       "13795        365  0.118644          NaN  \n",
       "13796        365  0.050847          NaN  \n",
       "13797        366  0.084746          NaN  \n",
       "13798        365  0.559322          NaN  \n",
       "13799        365  0.135593          NaN  \n",
       "13800        365  0.881356          2.0  \n",
       "13801        366  0.677966          NaN  \n",
       "13802        365  0.796610          1.0  \n",
       "13803        365  0.847458          NaN  \n",
       "13804        365  0.661017          NaN  \n",
       "13805        366  0.508475          NaN  \n",
       "13806        365  0.067797          NaN  \n",
       "13807        365  0.491525          NaN  \n",
       "13808        365  0.779661          NaN  \n",
       "13809        366  0.525424          NaN  \n",
       "13810        365  0.440678          NaN  \n",
       "13811        365  0.932203          NaN  \n",
       "13812        365  0.305085          NaN  \n",
       "13813        366  0.372881          NaN  \n",
       "13814        365  0.169492          NaN  \n",
       "13815        365  0.593220          NaN  \n",
       "13816        365  0.644068          NaN  \n",
       "13817        366  0.254237          NaN  \n",
       "13818        365  0.406780          1.0  \n",
       "13819        365  0.033898          NaN  \n",
       "13820        365  0.355932          1.0  \n",
       "13821        366  0.542373          NaN  \n",
       "13822        365  0.915254          1.0  \n",
       "13823        365  0.186441          NaN  \n",
       "\n",
       "[13824 rows x 9 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compare management hydrographs to unimpaired hydrograph\n",
    "df6 = df3.loc[df3.ift == 'unimp'].copy()\n",
    "df6.rename(columns={'value': 'value_unimp'}, inplace=True)\n",
    "df7 = df3.loc[df3.ift != 'unimp'].copy()\n",
    "df8 = pd.merge(df7, df6[['water_year', 'stream', 'ffm', 'value_unimp']], on=['water_year', 'stream', 'ffm'], how='left')\n",
    "df8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export data to CSV\n",
    "\n",
    "df8.to_csv(out_csv, encoding='utf-8', index=False)"
   ]
  }
 ],
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