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jrjames83/dist between points.ipynb

Created January 2, 2019 22:17
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dist between points.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import geopy.distance\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def combine_lat_long(df_obj):\n",
" return (df_obj.lat, df_obj.long)\n",
"\n",
"def get_distance(df_obj):\n",
" try:\n",
" return geopy.distance.distance(df_obj.latlong, df_obj.next_lat_long).m\n",
" except ValueError:\n",
" return 0"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" <th>latlong</th>\n",
" <th>next_lat_long</th>\n",
" <th>distance_between</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10.354395</td>\n",
" <td>53.594442</td>\n",
" <td>(10.354395, 53.5944416667)</td>\n",
" <td>(10.354395, 53.5944416667)</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.354395</td>\n",
" <td>53.594442</td>\n",
" <td>(10.354395, 53.5944416667)</td>\n",
" <td>(10.3543983333, 53.5944416667)</td>\n",
" <td>0.368701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.354398</td>\n",
" <td>53.594442</td>\n",
" <td>(10.3543983333, 53.5944416667)</td>\n",
" <td>(10.3544016667, 53.59444)</td>\n",
" <td>0.411409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>10.354402</td>\n",
" <td>53.594440</td>\n",
" <td>(10.3544016667, 53.59444)</td>\n",
" <td>(10.3543983333, 53.5944383333)</td>\n",
" <td>0.411409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>10.354398</td>\n",
" <td>53.594438</td>\n",
" <td>(10.3543983333, 53.5944383333)</td>\n",
" <td>(10.3543983333, 53.5944383333)</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>10.354398</td>\n",
" <td>53.594438</td>\n",
" <td>(10.3543983333, 53.5944383333)</td>\n",
" <td>(10.3543983333, 53.5944383333)</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>10.354398</td>\n",
" <td>53.594438</td>\n",
" <td>(10.3543983333, 53.5944383333)</td>\n",
" <td>(10.3543966667, 53.59444)</td>\n",
" <td>0.259427</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>10.354397</td>\n",
" <td>53.594440</td>\n",
" <td>(10.3543966667, 53.59444)</td>\n",
" <td>(10.3543933333, 53.59444)</td>\n",
" <td>0.368701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>10.354393</td>\n",
" <td>53.594440</td>\n",
" <td>(10.3543933333, 53.59444)</td>\n",
" <td>(10.3543933333, 53.59444)</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10.354393</td>\n",
" <td>53.594440</td>\n",
" <td>(10.3543933333, 53.59444)</td>\n",
" <td>(10.354395, 53.59444)</td>\n",
" <td>0.184350</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" lat long latlong \\\n",
"0 10.354395 53.594442 (10.354395, 53.5944416667) \n",
"1 10.354395 53.594442 (10.354395, 53.5944416667) \n",
"2 10.354398 53.594442 (10.3543983333, 53.5944416667) \n",
"3 10.354402 53.594440 (10.3544016667, 53.59444) \n",
"4 10.354398 53.594438 (10.3543983333, 53.5944383333) \n",
"5 10.354398 53.594438 (10.3543983333, 53.5944383333) \n",
"6 10.354398 53.594438 (10.3543983333, 53.5944383333) \n",
"7 10.354397 53.594440 (10.3543966667, 53.59444) \n",
"8 10.354393 53.594440 (10.3543933333, 53.59444) \n",
"9 10.354393 53.594440 (10.3543933333, 53.59444) \n",
"\n",
" next_lat_long distance_between \n",
"0 (10.354395, 53.5944416667) 0.000000 \n",
"1 (10.3543983333, 53.5944416667) 0.368701 \n",
"2 (10.3544016667, 53.59444) 0.411409 \n",
"3 (10.3543983333, 53.5944383333) 0.411409 \n",
"4 (10.3543983333, 53.5944383333) 0.000000 \n",
"5 (10.3543983333, 53.5944383333) 0.000000 \n",
"6 (10.3543966667, 53.59444) 0.259427 \n",
"7 (10.3543933333, 53.59444) 0.368701 \n",
"8 (10.3543933333, 53.59444) 0.000000 \n",
"9 (10.354395, 53.59444) 0.184350 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('/Users/jeff/Downloads/Coordinates.csv')\n",
"df.columns = ['lat', 'long']\n",
"df['latlong'] = df.apply(combine_lat_long, axis=1)\n",
"df['next_lat_long'] = df.latlong.shift(-1)\n",
"df['distance_between'] = df.apply(get_distance, axis=1)\n",
"\n",
"df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" <th>latlong</th>\n",
" <th>next_lat_long</th>\n",
" <th>distance_between</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.354398</td>\n",
" <td>53.594442</td>\n",
" <td>(10.3543983333, 53.5944416667)</td>\n",
" <td>(10.3544016667, 53.59444)</td>\n",
" <td>0.411409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>10.354402</td>\n",
" <td>53.594440</td>\n",
" <td>(10.3544016667, 53.59444)</td>\n",
" <td>(10.3543983333, 53.5944383333)</td>\n",
" <td>0.411409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>10.354395</td>\n",
" <td>53.594440</td>\n",
" <td>(10.354395, 53.59444)</td>\n",
" <td>(10.3543933333, 53.5944366667)</td>\n",
" <td>0.408968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>10.354393</td>\n",
" <td>53.594437</td>\n",
" <td>(10.3543933333, 53.5944366667)</td>\n",
" <td>(10.3543833333, 53.5944283333)</td>\n",
" <td>1.434015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>10.354383</td>\n",
" <td>53.594428</td>\n",
" <td>(10.3543833333, 53.5944283333)</td>\n",
" <td>(10.3543766667, 53.5944166667)</td>\n",
" <td>1.475235</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>10.354377</td>\n",
" <td>53.594417</td>\n",
" <td>(10.3543766667, 53.5944166667)</td>\n",
" <td>(10.3543666667, 53.594405)</td>\n",
" <td>1.689976</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>10.354367</td>\n",
" <td>53.594405</td>\n",
" <td>(10.3543666667, 53.594405)</td>\n",
" <td>(10.3543566667, 53.594395)</td>\n",
" <td>1.556564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>10.354357</td>\n",
" <td>53.594395</td>\n",
" <td>(10.3543566667, 53.594395)</td>\n",
" <td>(10.354345, 53.5943866667)</td>\n",
" <td>1.580571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>10.354345</td>\n",
" <td>53.594387</td>\n",
" <td>(10.354345, 53.5943866667)</td>\n",
" <td>(10.3543366667, 53.594375)</td>\n",
" <td>1.575495</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" lat long latlong \\\n",
"2 10.354398 53.594442 (10.3543983333, 53.5944416667) \n",
"3 10.354402 53.594440 (10.3544016667, 53.59444) \n",
"12 10.354395 53.594440 (10.354395, 53.59444) \n",
"13 10.354393 53.594437 (10.3543933333, 53.5944366667) \n",
"14 10.354383 53.594428 (10.3543833333, 53.5944283333) \n",
"15 10.354377 53.594417 (10.3543766667, 53.5944166667) \n",
"16 10.354367 53.594405 (10.3543666667, 53.594405) \n",
"17 10.354357 53.594395 (10.3543566667, 53.594395) \n",
"18 10.354345 53.594387 (10.354345, 53.5943866667) \n",
"\n",
" next_lat_long distance_between \n",
"2 (10.3544016667, 53.59444) 0.411409 \n",
"3 (10.3543983333, 53.5944383333) 0.411409 \n",
"12 (10.3543933333, 53.5944366667) 0.408968 \n",
"13 (10.3543833333, 53.5944283333) 1.434015 \n",
"14 (10.3543766667, 53.5944166667) 1.475235 \n",
"15 (10.3543666667, 53.594405) 1.689976 \n",
"16 (10.3543566667, 53.594395) 1.556564 \n",
"17 (10.354345, 53.5943866667) 1.580571 \n",
"18 (10.3543366667, 53.594375) 1.575495 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Then you can filter\n",
"df.query('distance_between > .4')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Raw
gistfile1.txt
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import geopy.distance\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def combine_lat_long(df_obj):\n",
" return (df_obj.lat, df_obj.long)\n",
"\n",
"def get_distance(df_obj):\n",
" try:\n",
" return geopy.distance.distance(df_obj.latlong, df_obj.next_lat_long).m\n",
" except ValueError:\n",
" return 0"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" <th>latlong</th>\n",
" <th>next_lat_long</th>\n",
" <th>distance_between</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10.354395</td>\n",
" <td>53.594442</td>\n",
" <td>(10.354395, 53.5944416667)</td>\n",
" <td>(10.354395, 53.5944416667)</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.354395</td>\n",
" <td>53.594442</td>\n",
" <td>(10.354395, 53.5944416667)</td>\n",
" <td>(10.3543983333, 53.5944416667)</td>\n",
" <td>0.368701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.354398</td>\n",
" <td>53.594442</td>\n",
" <td>(10.3543983333, 53.5944416667)</td>\n",
" <td>(10.3544016667, 53.59444)</td>\n",
" <td>0.411409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>10.354402</td>\n",
" <td>53.594440</td>\n",
" <td>(10.3544016667, 53.59444)</td>\n",
" <td>(10.3543983333, 53.5944383333)</td>\n",
" <td>0.411409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>10.354398</td>\n",
" <td>53.594438</td>\n",
" <td>(10.3543983333, 53.5944383333)</td>\n",
" <td>(10.3543983333, 53.5944383333)</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>10.354398</td>\n",
" <td>53.594438</td>\n",
" <td>(10.3543983333, 53.5944383333)</td>\n",
" <td>(10.3543983333, 53.5944383333)</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>10.354398</td>\n",
" <td>53.594438</td>\n",
" <td>(10.3543983333, 53.5944383333)</td>\n",
" <td>(10.3543966667, 53.59444)</td>\n",
" <td>0.259427</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>10.354397</td>\n",
" <td>53.594440</td>\n",
" <td>(10.3543966667, 53.59444)</td>\n",
" <td>(10.3543933333, 53.59444)</td>\n",
" <td>0.368701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>10.354393</td>\n",
" <td>53.594440</td>\n",
" <td>(10.3543933333, 53.59444)</td>\n",
" <td>(10.3543933333, 53.59444)</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10.354393</td>\n",
" <td>53.594440</td>\n",
" <td>(10.3543933333, 53.59444)</td>\n",
" <td>(10.354395, 53.59444)</td>\n",
" <td>0.184350</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" lat long latlong \\\n",
"0 10.354395 53.594442 (10.354395, 53.5944416667) \n",
"1 10.354395 53.594442 (10.354395, 53.5944416667) \n",
"2 10.354398 53.594442 (10.3543983333, 53.5944416667) \n",
"3 10.354402 53.594440 (10.3544016667, 53.59444) \n",
"4 10.354398 53.594438 (10.3543983333, 53.5944383333) \n",
"5 10.354398 53.594438 (10.3543983333, 53.5944383333) \n",
"6 10.354398 53.594438 (10.3543983333, 53.5944383333) \n",
"7 10.354397 53.594440 (10.3543966667, 53.59444) \n",
"8 10.354393 53.594440 (10.3543933333, 53.59444) \n",
"9 10.354393 53.594440 (10.3543933333, 53.59444) \n",
"\n",
" next_lat_long distance_between \n",
"0 (10.354395, 53.5944416667) 0.000000 \n",
"1 (10.3543983333, 53.5944416667) 0.368701 \n",
"2 (10.3544016667, 53.59444) 0.411409 \n",
"3 (10.3543983333, 53.5944383333) 0.411409 \n",
"4 (10.3543983333, 53.5944383333) 0.000000 \n",
"5 (10.3543983333, 53.5944383333) 0.000000 \n",
"6 (10.3543966667, 53.59444) 0.259427 \n",
"7 (10.3543933333, 53.59444) 0.368701 \n",
"8 (10.3543933333, 53.59444) 0.000000 \n",
"9 (10.354395, 53.59444) 0.184350 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('/Users/jeff/Downloads/Coordinates.csv')\n",
"df.columns = ['lat', 'long']\n",
"df['latlong'] = df.apply(combine_lat_long, axis=1)\n",
"df['next_lat_long'] = df.latlong.shift(-1)\n",
"df['distance_between'] = df.apply(get_distance, axis=1)\n",
"\n",
"df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" <th>latlong</th>\n",
" <th>next_lat_long</th>\n",
" <th>distance_between</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.354398</td>\n",
" <td>53.594442</td>\n",
" <td>(10.3543983333, 53.5944416667)</td>\n",
" <td>(10.3544016667, 53.59444)</td>\n",
" <td>0.411409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>10.354402</td>\n",
" <td>53.594440</td>\n",
" <td>(10.3544016667, 53.59444)</td>\n",
" <td>(10.3543983333, 53.5944383333)</td>\n",
" <td>0.411409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>10.354395</td>\n",
" <td>53.594440</td>\n",
" <td>(10.354395, 53.59444)</td>\n",
" <td>(10.3543933333, 53.5944366667)</td>\n",
" <td>0.408968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>10.354393</td>\n",
" <td>53.594437</td>\n",
" <td>(10.3543933333, 53.5944366667)</td>\n",
" <td>(10.3543833333, 53.5944283333)</td>\n",
" <td>1.434015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>10.354383</td>\n",
" <td>53.594428</td>\n",
" <td>(10.3543833333, 53.5944283333)</td>\n",
" <td>(10.3543766667, 53.5944166667)</td>\n",
" <td>1.475235</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>10.354377</td>\n",
" <td>53.594417</td>\n",
" <td>(10.3543766667, 53.5944166667)</td>\n",
" <td>(10.3543666667, 53.594405)</td>\n",
" <td>1.689976</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>10.354367</td>\n",
" <td>53.594405</td>\n",
" <td>(10.3543666667, 53.594405)</td>\n",
" <td>(10.3543566667, 53.594395)</td>\n",
" <td>1.556564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>10.354357</td>\n",
" <td>53.594395</td>\n",
" <td>(10.3543566667, 53.594395)</td>\n",
" <td>(10.354345, 53.5943866667)</td>\n",
" <td>1.580571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>10.354345</td>\n",
" <td>53.594387</td>\n",
" <td>(10.354345, 53.5943866667)</td>\n",
" <td>(10.3543366667, 53.594375)</td>\n",
" <td>1.575495</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" lat long latlong \\\n",
"2 10.354398 53.594442 (10.3543983333, 53.5944416667) \n",
"3 10.354402 53.594440 (10.3544016667, 53.59444) \n",
"12 10.354395 53.594440 (10.354395, 53.59444) \n",
"13 10.354393 53.594437 (10.3543933333, 53.5944366667) \n",
"14 10.354383 53.594428 (10.3543833333, 53.5944283333) \n",
"15 10.354377 53.594417 (10.3543766667, 53.5944166667) \n",
"16 10.354367 53.594405 (10.3543666667, 53.594405) \n",
"17 10.354357 53.594395 (10.3543566667, 53.594395) \n",
"18 10.354345 53.594387 (10.354345, 53.5943866667) \n",
"\n",
" next_lat_long distance_between \n",
"2 (10.3544016667, 53.59444) 0.411409 \n",
"3 (10.3543983333, 53.5944383333) 0.411409 \n",
"12 (10.3543933333, 53.5944366667) 0.408968 \n",
"13 (10.3543833333, 53.5944283333) 1.434015 \n",
"14 (10.3543766667, 53.5944166667) 1.475235 \n",
"15 (10.3543666667, 53.594405) 1.689976 \n",
"16 (10.3543566667, 53.594395) 1.556564 \n",
"17 (10.354345, 53.5943866667) 1.580571 \n",
"18 (10.3543366667, 53.594375) 1.575495 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Then you can filter\n",
"df.query('distance_between > .4')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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