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CHNEP Time Series step3
Raw
gistfile1.txt
{
"metadata": {
"name": "step3"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": "Read data into a Pandas Data Frame. Create Month and Year columns"
},
{
"cell_type": "code",
"collapsed": false,
"input": "import pandas as pd\n#df = pd.read_csv(\"DataDownload_745449_row.txt\", sep=\"\\t\",infer_datetime_format=True)\ndf = pd.read_csv(\"DataDownload_745449_row.txt\", \n usecols=[\"SampleDate\",\"Rainfall_IN\"],\n sep=\"\\t\",\n infer_datetime_format=True)\ndf[\"SampleDate_dt\"] = pd.to_datetime(df[\"SampleDate\"])\ndf[\"SampleDate_mo\"] = df[\"SampleDate_dt\"].apply(lambda x: x.month)\ndf[\"SampleDate_yr\"] = df[\"SampleDate_dt\"].apply(lambda x: x.year)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": "From the Janicki report:\n\"\"\"In the third step of the analysis, a correlation analysis is performed for each monthly value, the previous month\u2019s value, two months prior, etc., until correlation statistics have been calculated for all previous months up to 15 months prior. A table of these values is provided in the output (Display 3 not shown).\"\"\""
},
{
"cell_type": "code",
"collapsed": true,
"input": "gp = df.groupby([\"SampleDate_yr\",\"SampleDate_mo\"])\n#total_precipitation_grid = gp[\"Rainfall_IN\"].agg(\"sum\").unstack() #FixMe: different # days in each mo.\ngp[\"Rainfall_IN\"].agg(\"mean\")",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": "SampleDate_yr SampleDate_mo\n2000 12 0.041818\n2001 1 0.012903\n 2 0.018571\n 3 0.167097\n 4 0.000333\n 5 0.000000\n 6 0.283000\n 7 0.270000\n 8 0.129355\n 9 0.273000\n 10 0.043226\n 11 0.003333\n 12 0.033226\n2002 1 0.056129\n 2 0.074286\n...\n2007 9 0.143667\n 10 0.124516\n 11 0.000333\n 12 0.058710\n2008 1 0.096774\n 2 0.075714\n 3 0.099355\n 4 0.117931\n 5 0.011613\n 6 0.257333\n 7 0.438065\n 8 0.220645\n 9 0.038462\n 10 0.054286\n 11 0.000000\nName: Rainfall_IN, Length: 96, dtype: float64"
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": "total_precipitation_grid = gp[\"Rainfall_IN\"].agg(\"mean\").unstack()\ntotal_precipitation_grid",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>SampleDate_mo</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n <th>8</th>\n <th>9</th>\n <th>10</th>\n <th>11</th>\n <th>12</th>\n </tr>\n <tr>\n <th>SampleDate_yr</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 <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2000</th>\n <td> NaN</td>\n <td> NaN</td>\n <td> NaN</td>\n <td> NaN</td>\n <td> NaN</td>\n <td> NaN</td>\n <td> NaN</td>\n <td> NaN</td>\n <td> NaN</td>\n <td> NaN</td>\n <td> NaN</td>\n <td> 0.041818</td>\n </tr>\n <tr>\n <th>2001</th>\n <td> 0.012903</td>\n <td> 0.018571</td>\n <td> 0.167097</td>\n <td> 0.000333</td>\n <td> 0.000000</td>\n <td> 0.283000</td>\n <td> 0.270000</td>\n <td> 0.129355</td>\n <td> 0.273000</td>\n <td> 0.043226</td>\n <td> 0.003333</td>\n <td> 0.033226</td>\n </tr>\n <tr>\n <th>2002</th>\n <td> 0.056129</td>\n <td> 0.074286</td>\n <td> 0.013548</td>\n <td> 0.061667</td>\n <td> 0.064194</td>\n <td> 0.240333</td>\n <td> 0.204828</td>\n <td> 0.107333</td>\n <td> 0.106000</td>\n <td> 0.036129</td>\n <td> 0.026333</td>\n <td> 0.169032</td>\n </tr>\n <tr>\n <th>2003</th>\n <td> 0.000645</td>\n <td> 0.038929</td>\n <td> 0.062258</td>\n <td> 0.084667</td>\n <td> 0.030323</td>\n <td> 0.317000</td>\n <td> 0.175161</td>\n <td> 0.399677</td>\n <td> 0.218000</td>\n <td> 0.026129</td>\n <td> 0.029333</td>\n <td> 0.044516</td>\n </tr>\n <tr>\n <th>2004</th>\n <td> 0.141613</td>\n <td> 0.142593</td>\n <td> 0.044516</td>\n <td> 0.074000</td>\n <td> 0.060968</td>\n <td> 0.329333</td>\n <td> 0.332581</td>\n <td> 0.411613</td>\n <td> 0.343667</td>\n <td> 0.041613</td>\n <td> 0.027000</td>\n <td> 0.040323</td>\n </tr>\n <tr>\n <th>2005</th>\n <td> 0.019355</td>\n <td> 0.070000</td>\n <td> 0.112258</td>\n <td> 0.087000</td>\n <td> 0.106129</td>\n <td> 0.394000</td>\n <td> 0.338065</td>\n <td> 0.185161</td>\n <td> 0.079667</td>\n <td> 0.114516</td>\n <td> 0.066333</td>\n <td> 0.039677</td>\n </tr>\n <tr>\n <th>2006</th>\n <td> 0.033871</td>\n <td> 0.230000</td>\n <td> 0.000000</td>\n <td> 0.026333</td>\n <td> 0.031290</td>\n <td> 0.360000</td>\n <td> 0.324839</td>\n <td> 0.125161</td>\n <td> 0.290667</td>\n <td> 0.032903</td>\n <td> 0.072333</td>\n <td> 0.010455</td>\n </tr>\n <tr>\n <th>2007</th>\n <td> 0.041667</td>\n <td> 0.044286</td>\n <td> 0.021613</td>\n <td> 0.067000</td>\n <td> 0.000000</td>\n <td> 0.238462</td>\n <td> 0.444516</td>\n <td> 0.316452</td>\n <td> 0.143667</td>\n <td> 0.124516</td>\n <td> 0.000333</td>\n <td> 0.058710</td>\n </tr>\n <tr>\n <th>2008</th>\n <td> 0.096774</td>\n <td> 0.075714</td>\n <td> 0.099355</td>\n <td> 0.117931</td>\n <td> 0.011613</td>\n <td> 0.257333</td>\n <td> 0.438065</td>\n <td> 0.220645</td>\n <td> 0.038462</td>\n <td> 0.054286</td>\n <td> 0.000000</td>\n <td> NaN</td>\n </tr>\n </tbody>\n</table>\n<p>9 rows \u00d7 12 columns</p>\n</div>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": "SampleDate_mo 1 2 3 4 5 6 \\\nSampleDate_yr \n2000 NaN NaN NaN NaN NaN NaN \n2001 0.012903 0.018571 0.167097 0.000333 0.000000 0.283000 \n2002 0.056129 0.074286 0.013548 0.061667 0.064194 0.240333 \n2003 0.000645 0.038929 0.062258 0.084667 0.030323 0.317000 \n2004 0.141613 0.142593 0.044516 0.074000 0.060968 0.329333 \n2005 0.019355 0.070000 0.112258 0.087000 0.106129 0.394000 \n2006 0.033871 0.230000 0.000000 0.026333 0.031290 0.360000 \n2007 0.041667 0.044286 0.021613 0.067000 0.000000 0.238462 \n2008 0.096774 0.075714 0.099355 0.117931 0.011613 0.257333 \n\nSampleDate_mo 7 8 9 10 11 12 \nSampleDate_yr \n2000 NaN NaN NaN NaN NaN 0.041818 \n2001 0.270000 0.129355 0.273000 0.043226 0.003333 0.033226 \n2002 0.204828 0.107333 0.106000 0.036129 0.026333 0.169032 \n2003 0.175161 0.399677 0.218000 0.026129 0.029333 0.044516 \n2004 0.332581 0.411613 0.343667 0.041613 0.027000 0.040323 \n2005 0.338065 0.185161 0.079667 0.114516 0.066333 0.039677 \n2006 0.324839 0.125161 0.290667 0.032903 0.072333 0.010455 \n2007 0.444516 0.316452 0.143667 0.124516 0.000333 0.058710 \n2008 0.438065 0.220645 0.038462 0.054286 0.000000 NaN \n\n[9 rows x 12 columns]"
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": true,
"input": "# create a Data Frame with the correlation matrix\ncorr_df = total_precipitation_grid.corr()\n# Build the Correlation Matrix\ncorr_df",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>SampleDate_mo</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n <th>8</th>\n <th>9</th>\n <th>10</th>\n <th>11</th>\n <th>12</th>\n </tr>\n <tr>\n <th>SampleDate_mo</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 <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> 1.000000</td>\n <td> 0.350484</td>\n <td>-0.224925</td>\n <td> 0.381166</td>\n <td> 0.094439</td>\n <td>-0.169756</td>\n <td> 0.411895</td>\n <td> 0.320225</td>\n <td> 0.113643</td>\n <td>-0.124106</td>\n <td>-0.237011</td>\n <td> 0.130827</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> 0.350484</td>\n <td> 1.000000</td>\n <td>-0.580071</td>\n <td>-0.145371</td>\n <td> 0.209631</td>\n <td> 0.466265</td>\n <td> 0.134017</td>\n <td>-0.124904</td>\n <td> 0.424337</td>\n <td>-0.296166</td>\n <td> 0.646267</td>\n <td>-0.285992</td>\n </tr>\n <tr>\n <th>3 </th>\n <td>-0.224925</td>\n <td>-0.580071</td>\n <td> 1.000000</td>\n <td>-0.093879</td>\n <td>-0.086285</td>\n <td> 0.105694</td>\n <td> 0.003981</td>\n <td>-0.151649</td>\n <td>-0.121141</td>\n <td> 0.086517</td>\n <td>-0.286080</td>\n <td>-0.305772</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> 0.381166</td>\n <td>-0.145371</td>\n <td>-0.093879</td>\n <td> 1.000000</td>\n <td> 0.285583</td>\n <td>-0.049826</td>\n <td> 0.302548</td>\n <td> 0.460272</td>\n <td>-0.618390</td>\n <td> 0.244684</td>\n <td>-0.108678</td>\n <td> 0.216951</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 0.094439</td>\n <td> 0.209631</td>\n <td>-0.086285</td>\n <td> 0.285583</td>\n <td> 1.000000</td>\n <td> 0.613682</td>\n <td>-0.252434</td>\n <td>-0.059837</td>\n <td>-0.156369</td>\n <td> 0.146272</td>\n <td> 0.679235</td>\n <td> 0.222199</td>\n </tr>\n <tr>\n <th>6 </th>\n <td>-0.169756</td>\n <td> 0.466265</td>\n <td> 0.105694</td>\n <td>-0.049826</td>\n <td> 0.613682</td>\n <td> 1.000000</td>\n <td>-0.127930</td>\n <td> 0.033869</td>\n <td> 0.307216</td>\n <td> 0.024959</td>\n <td> 0.852314</td>\n <td>-0.630949</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 0.411895</td>\n <td> 0.134017</td>\n <td> 0.003981</td>\n <td> 0.302548</td>\n <td>-0.252434</td>\n <td>-0.127930</td>\n <td> 1.000000</td>\n <td> 0.064338</td>\n <td>-0.289626</td>\n <td> 0.630237</td>\n <td>-0.239335</td>\n <td>-0.372976</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 0.320225</td>\n <td>-0.124904</td>\n <td>-0.151649</td>\n <td> 0.460272</td>\n <td>-0.059837</td>\n <td> 0.033869</td>\n <td> 0.064338</td>\n <td> 1.000000</td>\n <td> 0.277668</td>\n <td> 0.048433</td>\n <td>-0.235686</td>\n <td>-0.257799</td>\n </tr>\n <tr>\n <th>9 </th>\n <td> 0.113643</td>\n <td> 0.424337</td>\n <td>-0.121141</td>\n <td>-0.618390</td>\n <td>-0.156369</td>\n <td> 0.307216</td>\n <td>-0.289626</td>\n <td> 0.277668</td>\n <td> 1.000000</td>\n <td>-0.474526</td>\n <td> 0.170692</td>\n <td>-0.548800</td>\n </tr>\n <tr>\n <th>10</th>\n <td>-0.124106</td>\n <td>-0.296166</td>\n <td> 0.086517</td>\n <td> 0.244684</td>\n <td> 0.146272</td>\n <td> 0.024959</td>\n <td> 0.630237</td>\n <td> 0.048433</td>\n <td>-0.474526</td>\n <td> 1.000000</td>\n <td>-0.040806</td>\n <td>-0.087543</td>\n </tr>\n <tr>\n <th>11</th>\n <td>-0.237011</td>\n <td> 0.646267</td>\n <td>-0.286080</td>\n <td>-0.108678</td>\n <td> 0.679235</td>\n <td> 0.852314</td>\n <td>-0.239335</td>\n <td>-0.235686</td>\n <td> 0.170692</td>\n <td>-0.040806</td>\n <td> 1.000000</td>\n <td>-0.272437</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 0.130827</td>\n <td>-0.285992</td>\n <td>-0.305772</td>\n <td> 0.216951</td>\n <td> 0.222199</td>\n <td>-0.630949</td>\n <td>-0.372976</td>\n <td>-0.257799</td>\n <td>-0.548800</td>\n <td>-0.087543</td>\n <td>-0.272437</td>\n <td> 1.000000</td>\n </tr>\n </tbody>\n</table>\n<p>12 rows \u00d7 12 columns</p>\n</div>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 31,
"text": "SampleDate_mo 1 2 3 4 5 6 \\\nSampleDate_mo \n1 1.000000 0.350484 -0.224925 0.381166 0.094439 -0.169756 \n2 0.350484 1.000000 -0.580071 -0.145371 0.209631 0.466265 \n3 -0.224925 -0.580071 1.000000 -0.093879 -0.086285 0.105694 \n4 0.381166 -0.145371 -0.093879 1.000000 0.285583 -0.049826 \n5 0.094439 0.209631 -0.086285 0.285583 1.000000 0.613682 \n6 -0.169756 0.466265 0.105694 -0.049826 0.613682 1.000000 \n7 0.411895 0.134017 0.003981 0.302548 -0.252434 -0.127930 \n8 0.320225 -0.124904 -0.151649 0.460272 -0.059837 0.033869 \n9 0.113643 0.424337 -0.121141 -0.618390 -0.156369 0.307216 \n10 -0.124106 -0.296166 0.086517 0.244684 0.146272 0.024959 \n11 -0.237011 0.646267 -0.286080 -0.108678 0.679235 0.852314 \n12 0.130827 -0.285992 -0.305772 0.216951 0.222199 -0.630949 \n\nSampleDate_mo 7 8 9 10 11 12 \nSampleDate_mo \n1 0.411895 0.320225 0.113643 -0.124106 -0.237011 0.130827 \n2 0.134017 -0.124904 0.424337 -0.296166 0.646267 -0.285992 \n3 0.003981 -0.151649 -0.121141 0.086517 -0.286080 -0.305772 \n4 0.302548 0.460272 -0.618390 0.244684 -0.108678 0.216951 \n5 -0.252434 -0.059837 -0.156369 0.146272 0.679235 0.222199 \n6 -0.127930 0.033869 0.307216 0.024959 0.852314 -0.630949 \n7 1.000000 0.064338 -0.289626 0.630237 -0.239335 -0.372976 \n8 0.064338 1.000000 0.277668 0.048433 -0.235686 -0.257799 \n9 -0.289626 0.277668 1.000000 -0.474526 0.170692 -0.548800 \n10 0.630237 0.048433 -0.474526 1.000000 -0.040806 -0.087543 \n11 -0.239335 -0.235686 0.170692 -0.040806 1.000000 -0.272437 \n12 -0.372976 -0.257799 -0.548800 -0.087543 -0.272437 1.000000 \n\n[12 rows x 12 columns]"
}
],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": "import matplotlib.pylab as plt\nfig = plt.figure(figsize=(16, 6))\n#corr_df[1].plot()\nax = fig.add_subplot(111)\nax.scatter(range(12),corr_df[1])\nax.plot(range(12),corr_df[1])\nax.set_title(\"Correlogram\\nUnadjusted for Seasonal Median\")#\n#fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold')\n#fig.subplots_adjust(top=0.85)\nax.set_ylabel('Correlation')\n",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 39,
"text": "<matplotlib.text.Text at 0xed84198>"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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fLOdiMSpYS8uW0pNPGisxAwAAALANua35TN1Hd8WKFerbt6/S09PVtWtXDR48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\nRJry/1buMipsoxKBAAAAAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0xeb2acc0>"
}
],
"prompt_number": 39
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Cells below are scratchpad\n=========================="
},
{
"cell_type": "code",
"collapsed": true,
"input": "",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": "\n\n",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>SampleDate_mo</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n <th>8</th>\n <th>9</th>\n <th>10</th>\n <th>11</th>\n <th>12</th>\n </tr>\n <tr>\n <th>SampleDate_mo</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 <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> 1.000000</td>\n <td> 0.376793</td>\n <td>-0.141185</td>\n <td> 0.349763</td>\n <td> 0.189664</td>\n <td> 0.035628</td>\n <td> 0.255588</td>\n <td> 0.241348</td>\n <td> 0.137903</td>\n <td>-0.302395</td>\n <td>-0.130989</td>\n <td> 0.120852</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> 0.376793</td>\n <td> 1.000000</td>\n <td>-0.581101</td>\n <td>-0.150145</td>\n <td> 0.204755</td>\n <td> 0.255135</td>\n <td> 0.133635</td>\n <td>-0.139765</td>\n <td> 0.410056</td>\n <td>-0.289660</td>\n <td> 0.652112</td>\n <td>-0.301006</td>\n </tr>\n <tr>\n <th>3 </th>\n <td>-0.141185</td>\n <td>-0.581101</td>\n <td> 1.000000</td>\n <td>-0.105291</td>\n <td>-0.086285</td>\n <td> 0.294823</td>\n <td> 0.020898</td>\n <td>-0.147432</td>\n <td>-0.123988</td>\n <td> 0.074511</td>\n <td>-0.286080</td>\n <td>-0.293345</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> 0.349763</td>\n <td>-0.150145</td>\n <td>-0.105291</td>\n <td> 1.000000</td>\n <td> 0.303083</td>\n <td> 0.080386</td>\n <td> 0.284596</td>\n <td> 0.470801</td>\n <td>-0.614747</td>\n <td> 0.224152</td>\n <td>-0.095538</td>\n <td> 0.224351</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 0.189664</td>\n <td> 0.204755</td>\n <td>-0.086285</td>\n <td> 0.303083</td>\n <td> 1.000000</td>\n <td> 0.701341</td>\n <td>-0.260162</td>\n <td>-0.062403</td>\n <td>-0.150278</td>\n <td> 0.159747</td>\n <td> 0.679235</td>\n <td> 0.222912</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 0.035628</td>\n <td> 0.255135</td>\n <td> 0.294823</td>\n <td> 0.080386</td>\n <td> 0.701341</td>\n <td> 1.000000</td>\n <td>-0.215617</td>\n <td> 0.092148</td>\n <td> 0.222448</td>\n <td>-0.015871</td>\n <td> 0.726601</td>\n <td>-0.488138</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 0.255588</td>\n <td> 0.133635</td>\n <td> 0.020898</td>\n <td> 0.284596</td>\n <td>-0.260162</td>\n <td>-0.215617</td>\n <td> 1.000000</td>\n <td> 0.087268</td>\n <td>-0.275079</td>\n <td> 0.600943</td>\n <td>-0.232765</td>\n <td>-0.414130</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 0.241348</td>\n <td>-0.139765</td>\n <td>-0.147432</td>\n <td> 0.470801</td>\n <td>-0.062403</td>\n <td> 0.092148</td>\n <td> 0.087268</td>\n <td> 1.000000</td>\n <td> 0.277756</td>\n <td> 0.052937</td>\n <td>-0.234442</td>\n <td>-0.255481</td>\n </tr>\n <tr>\n <th>9 </th>\n <td> 0.137903</td>\n <td> 0.410056</td>\n <td>-0.123988</td>\n <td>-0.614747</td>\n <td>-0.150278</td>\n <td> 0.222448</td>\n <td>-0.275079</td>\n <td> 0.277756</td>\n <td> 1.000000</td>\n <td>-0.441002</td>\n <td> 0.175703</td>\n <td>-0.551937</td>\n </tr>\n <tr>\n <th>10</th>\n <td>-0.302395</td>\n <td>-0.289660</td>\n <td> 0.074511</td>\n <td> 0.224152</td>\n <td> 0.159747</td>\n <td>-0.015871</td>\n <td> 0.600943</td>\n <td> 0.052937</td>\n <td>-0.441002</td>\n <td> 1.000000</td>\n <td>-0.021206</td>\n <td>-0.080424</td>\n </tr>\n <tr>\n <th>11</th>\n <td>-0.130989</td>\n <td> 0.652112</td>\n <td>-0.286080</td>\n <td>-0.095538</td>\n <td> 0.679235</td>\n <td> 0.726601</td>\n <td>-0.232765</td>\n <td>-0.234442</td>\n <td> 0.175703</td>\n <td>-0.021206</td>\n <td> 1.000000</td>\n <td>-0.283965</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 0.120852</td>\n <td>-0.301006</td>\n <td>-0.293345</td>\n <td> 0.224351</td>\n <td> 0.222912</td>\n <td>-0.488138</td>\n <td>-0.414130</td>\n <td>-0.255481</td>\n <td>-0.551937</td>\n <td>-0.080424</td>\n <td>-0.283965</td>\n <td> 1.000000</td>\n </tr>\n </tbody>\n</table>\n<p>12 rows \u00d7 12 columns</p>\n</div>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 98,
"text": "SampleDate_mo 1 2 3 4 5 6 \\\nSampleDate_mo \n1 1.000000 0.376793 -0.141185 0.349763 0.189664 0.035628 \n2 0.376793 1.000000 -0.581101 -0.150145 0.204755 0.255135 \n3 -0.141185 -0.581101 1.000000 -0.105291 -0.086285 0.294823 \n4 0.349763 -0.150145 -0.105291 1.000000 0.303083 0.080386 \n5 0.189664 0.204755 -0.086285 0.303083 1.000000 0.701341 \n6 0.035628 0.255135 0.294823 0.080386 0.701341 1.000000 \n7 0.255588 0.133635 0.020898 0.284596 -0.260162 -0.215617 \n8 0.241348 -0.139765 -0.147432 0.470801 -0.062403 0.092148 \n9 0.137903 0.410056 -0.123988 -0.614747 -0.150278 0.222448 \n10 -0.302395 -0.289660 0.074511 0.224152 0.159747 -0.015871 \n11 -0.130989 0.652112 -0.286080 -0.095538 0.679235 0.726601 \n12 0.120852 -0.301006 -0.293345 0.224351 0.222912 -0.488138 \n\nSampleDate_mo 7 8 9 10 11 12 \nSampleDate_mo \n1 0.255588 0.241348 0.137903 -0.302395 -0.130989 0.120852 \n2 0.133635 -0.139765 0.410056 -0.289660 0.652112 -0.301006 \n3 0.020898 -0.147432 -0.123988 0.074511 -0.286080 -0.293345 \n4 0.284596 0.470801 -0.614747 0.224152 -0.095538 0.224351 \n5 -0.260162 -0.062403 -0.150278 0.159747 0.679235 0.222912 \n6 -0.215617 0.092148 0.222448 -0.015871 0.726601 -0.488138 \n7 1.000000 0.087268 -0.275079 0.600943 -0.232765 -0.414130 \n8 0.087268 1.000000 0.277756 0.052937 -0.234442 -0.255481 \n9 -0.275079 0.277756 1.000000 -0.441002 0.175703 -0.551937 \n10 0.600943 0.052937 -0.441002 1.000000 -0.021206 -0.080424 \n11 -0.232765 -0.234442 0.175703 -0.021206 1.000000 -0.283965 \n12 -0.414130 -0.255481 -0.551937 -0.080424 -0.283965 1.000000 \n\n[12 rows x 12 columns]"
}
],
"prompt_number": 98
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 71
},
{
"cell_type": "code",
"collapsed": false,
"input": "\n",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 94
},
{
"cell_type": "code",
"collapsed": false,
"input": "import scipy.stats\nscipy.stats.pearsonr(rand(100), rand(100)), scipy.stats.pearsonr(range(100), range(100))",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": "((-0.057268524401161133, 0.57142779593403481), (1.0, 0.0))"
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": "import pandas as pd\n\ndf = pd.read_csv(\"DataDownload_745449_row.txt\", \n usecols=[\"SampleDate\",\"Rainfall_IN\"],\n sep=\"\\t\",\n infer_datetime_format=True)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": "df[\"SampleDate_dt\"] = pd.to_datetime(df[\"SampleDate\"])\ngetmo = lambda x: x.month\ndf[\"SampleDate_mo\"] = df[\"SampleDate_dt\"].apply(getmo)\ncols = df.columns.tolist()\ncols = [cols[-1],cols[1]]\ndf = df[cols]\n#df = df.drop('SampleDate',1)\n#df = df.drop('SampleDate_dt',1)\n#df = df[[\"SampleDate_mo\"],[\"Rainfall_IN\"]]",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": "\n\n \n \n",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 79
},
{
"cell_type": "code",
"collapsed": true,
"input": "",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>SampleDate</th>\n <th>WBodyID</th>\n <th>WaterBodyName</th>\n <th>DataSource</th>\n <th>Column1</th>\n <th>Column2</th>\n <th>ReachCode</th>\n <th>POR_Min</th>\n <th>POR_Max</th>\n <th>Rainfall_IN</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0 </th>\n <td> 12/10/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>1 </th>\n <td> 12/11/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.03</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> 12/12/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.17</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> 12/13/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> 12/14/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 12/15/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 12/16/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 12/17/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.30</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 12/18/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>9 </th>\n <td> 12/19/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.01</td>\n </tr>\n <tr>\n <th>10</th>\n <td> 12/20/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>11</th>\n <td> 12/21/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 12/22/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>13</th>\n <td> 12/23/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>14</th>\n <td> 12/24/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>15</th>\n <td> 12/25/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>16</th>\n <td> 12/26/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>17</th>\n <td> 12/27/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>18</th>\n <td> 12/28/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.41</td>\n </tr>\n <tr>\n <th>19</th>\n <td> 12/29/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>20</th>\n <td> 12/30/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>21</th>\n <td> 12/31/2000 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>22</th>\n <td> 1/1/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>23</th>\n <td> 1/2/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>24</th>\n <td> 1/3/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>25</th>\n <td> 1/4/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>26</th>\n <td> 1/5/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>27</th>\n <td> 1/6/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>28</th>\n <td> 1/7/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>29</th>\n <td> 1/8/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.26</td>\n </tr>\n <tr>\n <th>30</th>\n <td> 1/9/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>31</th>\n <td> 1/10/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>32</th>\n <td> 1/11/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>33</th>\n <td> 1/12/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.07</td>\n </tr>\n <tr>\n <th>34</th>\n <td> 1/13/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>35</th>\n <td> 1/14/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>36</th>\n <td> 1/15/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>37</th>\n <td> 1/16/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>38</th>\n <td> 1/17/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>39</th>\n <td> 1/18/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>40</th>\n <td> 1/19/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>41</th>\n <td> 1/20/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.06</td>\n </tr>\n <tr>\n <th>42</th>\n <td> 1/21/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>43</th>\n <td> 1/22/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>44</th>\n <td> 1/23/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>45</th>\n <td> 1/24/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>46</th>\n <td> 1/25/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>47</th>\n <td> 1/26/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>48</th>\n <td> 1/27/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>49</th>\n <td> 1/28/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>50</th>\n <td> 1/29/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>51</th>\n <td> 1/30/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>52</th>\n <td> 1/31/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.01</td>\n </tr>\n <tr>\n <th>53</th>\n <td> 2/1/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.25</td>\n </tr>\n <tr>\n <th>54</th>\n <td> 2/2/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>55</th>\n <td> 2/3/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.26</td>\n </tr>\n <tr>\n <th>56</th>\n <td> 2/4/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>57</th>\n <td> 2/5/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.01</td>\n </tr>\n <tr>\n <th>58</th>\n <td> 2/6/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</td>\n </tr>\n <tr>\n <th>59</th>\n <td> 2/7/2001 0:00</td>\n <td> 41</td>\n <td> Hillsborough River</td>\n <td> USGS_NWIS</td>\n <td> 2304500</td>\n <td> HILLSBOROUGH RIVER NEAR TAMPA FL</td>\n <td> 3.100210e+12</td>\n <td> 12/10/2000 0:00</td>\n <td> 11/3/2008 0:00</td>\n <td> 0.00</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 <td>...</td>\n </tr>\n </tbody>\n</table>\n<p>2803 rows \u00d7 10 columns</p>\n</div>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": " SampleDate WBodyID WaterBodyName DataSource Column1 \\\n0 12/10/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n1 12/11/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n2 12/12/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n3 12/13/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n4 12/14/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n5 12/15/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n6 12/16/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n7 12/17/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n8 12/18/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n9 12/19/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n10 12/20/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n11 12/21/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n12 12/22/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n13 12/23/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n14 12/24/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n15 12/25/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n16 12/26/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n17 12/27/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n18 12/28/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n19 12/29/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n20 12/30/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n21 12/31/2000 0:00 41 Hillsborough River USGS_NWIS 2304500 \n22 1/1/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n23 1/2/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n24 1/3/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n25 1/4/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n26 1/5/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n27 1/6/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n28 1/7/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n29 1/8/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n30 1/9/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n31 1/10/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n32 1/11/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n33 1/12/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n34 1/13/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n35 1/14/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n36 1/15/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n37 1/16/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n38 1/17/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n39 1/18/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n40 1/19/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n41 1/20/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n42 1/21/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n43 1/22/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n44 1/23/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n45 1/24/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n46 1/25/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n47 1/26/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n48 1/27/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n49 1/28/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n50 1/29/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n51 1/30/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n52 1/31/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n53 2/1/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n54 2/2/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n55 2/3/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n56 2/4/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n57 2/5/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n58 2/6/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n59 2/7/2001 0:00 41 Hillsborough River USGS_NWIS 2304500 \n ... ... ... ... ... \n\n Column2 ReachCode POR_Min \\\n0 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n1 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n2 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n3 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n4 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n5 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n6 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n7 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n8 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n9 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n10 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n11 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n12 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n13 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n14 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n15 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n16 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n17 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n18 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n19 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n20 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n21 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n22 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n23 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n24 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n25 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n26 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n27 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n28 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n29 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n30 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n31 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n32 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n33 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n34 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n35 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n36 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n37 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n38 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n39 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n40 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n41 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n42 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n43 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n44 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n45 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n46 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n47 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n48 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n49 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n50 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n51 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n52 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n53 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n54 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n55 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n56 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n57 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n58 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n59 HILLSBOROUGH RIVER NEAR TAMPA FL 3.100210e+12 12/10/2000 0:00 \n ... ... ... \n\n POR_Max Rainfall_IN \n0 11/3/2008 0:00 0.00 \n1 11/3/2008 0:00 0.03 \n2 11/3/2008 0:00 0.17 \n3 11/3/2008 0:00 0.00 \n4 11/3/2008 0:00 0.00 \n5 11/3/2008 0:00 0.00 \n6 11/3/2008 0:00 0.00 \n7 11/3/2008 0:00 0.30 \n8 11/3/2008 0:00 0.00 \n9 11/3/2008 0:00 0.01 \n10 11/3/2008 0:00 0.00 \n11 11/3/2008 0:00 0.00 \n12 11/3/2008 0:00 0.00 \n13 11/3/2008 0:00 0.00 \n14 11/3/2008 0:00 0.00 \n15 11/3/2008 0:00 0.00 \n16 11/3/2008 0:00 0.00 \n17 11/3/2008 0:00 0.00 \n18 11/3/2008 0:00 0.41 \n19 11/3/2008 0:00 0.00 \n20 11/3/2008 0:00 0.00 \n21 11/3/2008 0:00 0.00 \n22 11/3/2008 0:00 0.00 \n23 11/3/2008 0:00 0.00 \n24 11/3/2008 0:00 0.00 \n25 11/3/2008 0:00 0.00 \n26 11/3/2008 0:00 0.00 \n27 11/3/2008 0:00 0.00 \n28 11/3/2008 0:00 0.00 \n29 11/3/2008 0:00 0.26 \n30 11/3/2008 0:00 0.00 \n31 11/3/2008 0:00 0.00 \n32 11/3/2008 0:00 0.00 \n33 11/3/2008 0:00 0.07 \n34 11/3/2008 0:00 0.00 \n35 11/3/2008 0:00 0.00 \n36 11/3/2008 0:00 0.00 \n37 11/3/2008 0:00 0.00 \n38 11/3/2008 0:00 0.00 \n39 11/3/2008 0:00 0.00 \n40 11/3/2008 0:00 0.00 \n41 11/3/2008 0:00 0.06 \n42 11/3/2008 0:00 0.00 \n43 11/3/2008 0:00 0.00 \n44 11/3/2008 0:00 0.00 \n45 11/3/2008 0:00 0.00 \n46 11/3/2008 0:00 0.00 \n47 11/3/2008 0:00 0.00 \n48 11/3/2008 0:00 0.00 \n49 11/3/2008 0:00 0.00 \n50 11/3/2008 0:00 0.00 \n51 11/3/2008 0:00 0.00 \n52 11/3/2008 0:00 0.01 \n53 11/3/2008 0:00 0.25 \n54 11/3/2008 0:00 0.00 \n55 11/3/2008 0:00 0.26 \n56 11/3/2008 0:00 0.00 \n57 11/3/2008 0:00 0.01 \n58 11/3/2008 0:00 0.00 \n59 11/3/2008 0:00 0.00 \n ... ... \n\n[2803 rows x 10 columns]"
}
],
"prompt_number": 29
},
{
"cell_type": "markdown",
"metadata": {},
"source": ""
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": "import numpy\nimport matplotlib.pyplot as plt\n\n# define lambda function to get Month from DateTime\ngetmo = lambda x: x.month\n\n# Create a new column in the data frame with the numeric month for each row \n# (this is irrespective of year)\ndf[\"SampleDate_dt\"] = pd.to_datetime(df[\"SampleDate\"])\ndf[\"SampleDate_mo\"] = df[\"SampleDate_dt\"].apply(getmo)\n\n#grouped = df[\"SampleDate_dt\"].groupby(lambda x: x.month) #Someday/Maybe\ngrouped = df.groupby(\"SampleDate_mo\")\nbp = boxplot_frame_groupby(grouped)\n\n",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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rwoQJ2LFjB6qqqnD9+nV88803+P3vfy98QWCdm9Krl3bRmnZoUq1Py2Tav5mS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\nAAAAAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x21883390>"
}
],
"prompt_number": 29
},
{
"cell_type": "markdown",
"metadata": {},
"source": ""
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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