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@mdengler
Created October 26, 2015 20:37
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"import pandas as pd, numpy as np"
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"df = pd.DataFrame(index=pd.date_range(\"2015-03-12\", periods=6 * 24 *5, freq=\"10min\"))"
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"df[\"S1\"] = np.random.randn(len(df.index))\n",
"df[\"S2\"] = np.random.randn(len(df.index))"
],
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"input": [
"df.head(), df.tail()"
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"text": [
"( S1 S2\n",
" 2015-03-12 00:00:00 -0.955864 -1.033496\n",
" 2015-03-12 00:10:00 0.696620 1.232513\n",
" 2015-03-12 00:20:00 -0.806641 2.178203\n",
" 2015-03-12 00:30:00 -0.008553 1.334902\n",
" 2015-03-12 00:40:00 -0.364000 0.066461,\n",
" S1 S2\n",
" 2015-03-16 23:10:00 0.199640 0.073161\n",
" 2015-03-16 23:20:00 -0.444428 -1.657780\n",
" 2015-03-16 23:30:00 -0.725248 -1.685963\n",
" 2015-03-16 23:40:00 0.260387 -0.256123\n",
" 2015-03-16 23:50:00 -0.310167 0.175540)"
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"input": [
"s1 = pd.Series([(pd.Timestamp(\"2015-03-12 10:15:00\"), pd.Timestamp(\"2015-03-12 15:23:35\")),\n",
" (pd.Timestamp(\"2015-03-13 10:05:00\"), pd.Timestamp(\"2015-03-13 17:23:35\")),\n",
" (pd.Timestamp(\"2015-03-14 09:45:00\"), pd.Timestamp(\"2015-03-14 16:23:35\")),\n",
" ],\n",
" name=\"S1\")\n",
"s2 = pd.Series([(pd.Timestamp(\"2015-03-12 10:35:00\"), pd.Timestamp(\"2015-03-12 15:45:35\")),\n",
" (pd.Timestamp(\"2015-03-13 09:55:00\"), pd.Timestamp(\"2015-03-13 17:23:35\")),\n",
" (pd.Timestamp(\"2015-03-14 10:00:01\"), pd.Timestamp(\"2015-03-14 16:23:35\")),\n",
" ],\n",
" name=\"S2\")\n",
"mask_extents = pd.DataFrame([s1, s2]).T"
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"<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>S1</th>\n",
" <th>S2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> (2015-03-12 10:15:00, 2015-03-12 15:23:35)</td>\n",
" <td> (2015-03-12 10:35:00, 2015-03-12 15:45:35)</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td> (2015-03-13 10:05:00, 2015-03-13 17:23:35)</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td> (2015-03-14 09:45:00, 2015-03-14 16:23:35)</td>\n",
" <td> (2015-03-14 10:00:01, 2015-03-14 16:23:35)</td>\n",
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"</div>"
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"text": [
" S1 \\\n",
"0 (2015-03-12 10:15:00, 2015-03-12 15:23:35) \n",
"1 (2015-03-13 10:05:00, 2015-03-13 17:23:35) \n",
"2 (2015-03-14 09:45:00, 2015-03-14 16:23:35) \n",
"\n",
" S2 \n",
"0 (2015-03-12 10:35:00, 2015-03-12 15:45:35) \n",
"1 (2015-03-13 09:55:00, 2015-03-13 17:23:35) \n",
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"cell_type": "code",
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"input": [
"mask_extents[\"S1\"][0]"
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{
"metadata": {},
"output_type": "pyout",
"prompt_number": 198,
"text": [
"(Timestamp('2015-03-12 10:15:00'), Timestamp('2015-03-12 15:23:35'))"
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"cell_type": "code",
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"input": [
"mask_extents.loc[0,\"S1\"][0]"
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{
"metadata": {},
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"prompt_number": 199,
"text": [
"Timestamp('2015-03-12 10:15:00')"
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"prompt_number": 199
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"cell_type": "code",
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"input": [
"df2 = df.copy()\n",
"for col in df2.columns:\n",
" df2[col][~np.logical_or.reduce([(df2.index >= start) & (df2.index <= end) for start, end in mask_extents[col]])] = np.nan"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 200
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"cell_type": "code",
"collapsed": false,
"input": [
"df2[\"2015-03-12 10:00\":\"2015-03-12 10:50\"]"
],
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"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>S1</th>\n",
" <th>S2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2015-03-12 10:00:00</th>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-12 10:10:00</th>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-12 10:20:00</th>\n",
" <td>-0.889332</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-12 10:30:00</th>\n",
" <td>-1.176910</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-12 10:40:00</th>\n",
" <td> 0.515438</td>\n",
" <td>-2.142870</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-12 10:50:00</th>\n",
" <td>-1.541615</td>\n",
" <td>-0.617614</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text": [
" S1 S2\n",
"2015-03-12 10:00:00 NaN NaN\n",
"2015-03-12 10:10:00 NaN NaN\n",
"2015-03-12 10:20:00 -0.889332 NaN\n",
"2015-03-12 10:30:00 -1.176910 NaN\n",
"2015-03-12 10:40:00 0.515438 -2.142870\n",
"2015-03-12 10:50:00 -1.541615 -0.617614"
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],
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"cell_type": "code",
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"input": [
"print(df2.to_string())"
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{
"output_type": "stream",
"stream": "stdout",
"text": [
" S1 S2\n",
"2015-03-12 00:00:00 NaN NaN\n",
"2015-03-12 00:10:00 NaN NaN\n",
"2015-03-12 00:20:00 NaN NaN\n",
"2015-03-12 00:30:00 NaN NaN\n",
"2015-03-12 00:40:00 NaN NaN\n",
"2015-03-12 00:50:00 NaN NaN\n",
"2015-03-12 01:00:00 NaN NaN\n",
"2015-03-12 01:10:00 NaN NaN\n",
"2015-03-12 01:20:00 NaN NaN\n",
"2015-03-12 01:30:00 NaN NaN\n",
"2015-03-12 01:40:00 NaN NaN\n",
"2015-03-12 01:50:00 NaN NaN\n",
"2015-03-12 02:00:00 NaN NaN\n",
"2015-03-12 02:10:00 NaN NaN\n",
"2015-03-12 02:20:00 NaN NaN\n",
"2015-03-12 02:30:00 NaN NaN\n",
"2015-03-12 02:40:00 NaN NaN\n",
"2015-03-12 02:50:00 NaN NaN\n",
"2015-03-12 03:00:00 NaN NaN\n",
"2015-03-12 03:10:00 NaN NaN\n",
"2015-03-12 03:20:00 NaN NaN\n",
"2015-03-12 03:30:00 NaN NaN\n",
"2015-03-12 03:40:00 NaN NaN\n",
"2015-03-12 03:50:00 NaN NaN\n",
"2015-03-12 04:00:00 NaN NaN\n",
"2015-03-12 04:10:00 NaN NaN\n",
"2015-03-12 04:20:00 NaN NaN\n",
"2015-03-12 04:30:00 NaN NaN\n",
"2015-03-12 04:40:00 NaN NaN\n",
"2015-03-12 04:50:00 NaN NaN\n",
"2015-03-12 05:00:00 NaN NaN\n",
"2015-03-12 05:10:00 NaN NaN\n",
"2015-03-12 05:20:00 NaN NaN\n",
"2015-03-12 05:30:00 NaN NaN\n",
"2015-03-12 05:40:00 NaN NaN\n",
"2015-03-12 05:50:00 NaN NaN\n",
"2015-03-12 06:00:00 NaN NaN\n",
"2015-03-12 06:10:00 NaN NaN\n",
"2015-03-12 06:20:00 NaN NaN\n",
"2015-03-12 06:30:00 NaN NaN\n",
"2015-03-12 06:40:00 NaN NaN\n",
"2015-03-12 06:50:00 NaN NaN\n",
"2015-03-12 07:00:00 NaN NaN\n",
"2015-03-12 07:10:00 NaN NaN\n",
"2015-03-12 07:20:00 NaN NaN\n",
"2015-03-12 07:30:00 NaN NaN\n",
"2015-03-12 07:40:00 NaN NaN\n",
"2015-03-12 07:50:00 NaN NaN\n",
"2015-03-12 08:00:00 NaN NaN\n",
"2015-03-12 08:10:00 NaN NaN\n",
"2015-03-12 08:20:00 NaN NaN\n",
"2015-03-12 08:30:00 NaN NaN\n",
"2015-03-12 08:40:00 NaN NaN\n",
"2015-03-12 08:50:00 NaN NaN\n",
"2015-03-12 09:00:00 NaN NaN\n",
"2015-03-12 09:10:00 NaN NaN\n",
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"2015-03-12 09:30:00 NaN NaN\n",
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"2015-03-12 10:00:00 NaN NaN\n",
"2015-03-12 10:10:00 NaN NaN\n",
"2015-03-12 10:20:00 -0.889332 NaN\n",
"2015-03-12 10:30:00 -1.176910 NaN\n",
"2015-03-12 10:40:00 0.515438 -2.142870\n",
"2015-03-12 10:50:00 -1.541615 -0.617614\n",
"2015-03-12 11:00:00 1.588800 -1.282048\n",
"2015-03-12 11:10:00 0.465224 -0.278852\n",
"2015-03-12 11:20:00 1.199132 0.619324\n",
"2015-03-12 11:30:00 0.275081 -1.518271\n",
"2015-03-12 11:40:00 0.310088 0.343522\n",
"2015-03-12 11:50:00 1.042649 -0.116924\n",
"2015-03-12 12:00:00 -0.271621 -1.708400\n",
"2015-03-12 12:10:00 0.241241 1.236083\n",
"2015-03-12 12:20:00 0.527285 0.877077\n",
"2015-03-12 12:30:00 -0.257959 0.271352\n",
"2015-03-12 12:40:00 0.063406 0.338541\n",
"2015-03-12 12:50:00 -0.386308 -0.614765\n",
"2015-03-12 13:00:00 -0.199649 1.605412\n",
"2015-03-12 13:10:00 -0.696712 -0.176439\n",
"2015-03-12 13:20:00 -1.152970 0.226250\n",
"2015-03-12 13:30:00 0.342870 0.996011\n",
"2015-03-12 13:40:00 1.145065 1.593637\n",
"2015-03-12 13:50:00 -0.243509 0.069178\n",
"2015-03-12 14:00:00 0.166362 1.045921\n",
"2015-03-12 14:10:00 1.063363 -2.481133\n",
"2015-03-12 14:20:00 0.420532 -0.546011\n",
"2015-03-12 14:30:00 -0.097262 0.249988\n",
"2015-03-12 14:40:00 0.556301 -1.466058\n",
"2015-03-12 14:50:00 -0.125264 -0.427387\n",
"2015-03-12 15:00:00 -0.835824 -0.506506\n",
"2015-03-12 15:10:00 0.569680 -0.856827\n",
"2015-03-12 15:20:00 0.778034 -1.307289\n",
"2015-03-12 15:30:00 NaN -0.081794\n",
"2015-03-12 15:40:00 NaN -0.243035\n",
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"2015-03-13 10:30:00 -0.557355 2.358190\n",
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"2015-03-13 11:00:00 0.613380 -0.871587\n",
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{
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],
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]
}
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