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BDD Shell example 03 - Spark and Pandas
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BDD Shell example 03 - Spark and Pandas.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Instantiate the BDD Shell"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"execfile('ipython/00-bdd-shell-init.py')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use the BDD Context to show a count of datasets\n",
"\n",
"See [documentation](https://docs.oracle.com/cd/E71457_01/bigData.Doc/bdd_shell_onPrem/src/rbs_im_access_ds.html) for more info"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"17"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dss = bc.datasets()\n",
"dss.count"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## List out all datasets"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Name: media_demo_customer\tKey:edp_cli_edp_2c7f41ee-65bf-43ac-8bb4-5b6b59a55d75\n",
"Name: movie_genre\tKey:default_edp_7d4c18a5-6f02-4067-9f63-91f950078b1e\n",
"Name: media_demo_customer\tKey:default_edp_89c616b6-aa10-4827-aa82-1e9c3fcc419e\n",
"Name: Road-Accident-Safety-Data-Guide_Casualty Class\tKey:default_edp_828ea9d8-e2d3-4b61-8dbe-2d7a735906db\n",
"Name: Road-Accident-Safety-Data-Guide_Day of Week\tKey:default_edp_15fd6694-d0cf-4586-bf71-e44ab72a4f96\n",
"Name: DfTRoadSafety_Accidents_2014\tKey:default_edp_503409d1-a571-4040-ab51-c4c4bb15d3dc\n",
"Name: media_demo_movielog\tKey:edp_cli_edp_1c5465a1-75bd-4f49-9e10-c552e2c0ff65\n",
"Name: media_3rdparty_activity\tKey:default_edp_ae82f0cb-5a27-42c5-a423-c726f0f256c9\n",
"Name: DfTRoadSafety_Casualties_2014\tKey:default_edp_cefae5ba-ee9f-4b98-8f8d-1cc545101bff\n",
"Name: DfTRoadSafety_Casualties_2014\tKey:default_edp_48565ebb-c218-41a9-8ea5-df70de6e7114\n",
"Name: 14tstcar\tKey:default_edp_a672a1a6-13a9-4bf2-bd15-fcaf4cc6516c\n",
"Name: Road-Accident-Safety-Data-Guide_Police Force\tKey:default_edp_1ebbc840-8d7c-4602-9632-97301bc86c62\n",
"Name: DfTRoadSafety_Vehicles_2014\tKey:default_edp_2fe129f3-79f5-44d4-991c-8f67830b5c03\n",
"Name: Road-Accident-Safety-Data-Guide_Junction Detail\tKey:default_edp_fc83be52-cb46-4295-b253-faa9c90da1c6\n",
"Name: Road-Accident-Safety-Data-Guide_Carriageway Hazards\tKey:default_edp_8efe8820-e32c-41fa-aad4-f624749f72f6\n",
"Name: Road-Accident-Safety-Data-Guide_Local Authority (District)\tKey:default_edp_044548c8-6631-4375-b148-16904f9ddd43\n",
"Name: Road-Accident-Safety-Data-Guide_Ped Location\tKey:default_edp_dad8a0db-7b09-49a8-b100-377163495f55\n"
]
}
],
"source": [
"for ds in dss:\n",
" print('Name: %s\\tKey:%s'%(ds.properties()['displayName'] ,ds.properties()['databaseKey'] ))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Assign a single dataset to the `ds` variable, inspect all properties"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"accessType\": \"private\", \n",
" \"attributeCount\": \"33\", \n",
" \"attributeDisplayNames\": \"Weather_Conditions\", \n",
" \"attributeKeys\": \"weather_conditions\", \n",
" \"attributeNotes\": null, \n",
" \"attributeSemanticTypes\": null, \n",
" \"attributeTags\": null, \n",
" \"authorizedGroup\": null, \n",
" \"authorizedReadGroup\": null, \n",
" \"authorizedReadUser\": \"10098\", \n",
" \"authorizedUser\": \"10098\", \n",
" \"collectionIdToBeReplaced\": null, \n",
" \"collectionKey\": \"default_edp_503409d1-a571-4040-ab51-c4c4bb15d3dc\", \n",
" \"creationTime\": \"2016-06-09T13:38:47.489Z\", \n",
" \"curated\": \"false\", \n",
" \"databaseKey\": \"default_edp_503409d1-a571-4040-ab51-c4c4bb15d3dc\", \n",
" \"dateTimePresent\": \"true\", \n",
" \"deleted\": \"false\", \n",
" \"description\": null, \n",
" \"displayName\": \"DfTRoadSafety_Accidents_2014\", \n",
" \"dpLockTimestamp\": null, \n",
" \"fullDataSet\": \"true\", \n",
" \"geocodePresent\": \"false\", \n",
" \"hasCustomDescription\": \"false\", \n",
" \"hasCustomLanguages\": \"false\", \n",
" \"id\": \"default_edp_503409d1-a571-4040-ab51-c4c4bb15d3dc.default_edp_503409d1-a571-4040-ab51-c4c4bb15d3dc\", \n",
" \"ingestStatus\": \"FINISHED\", \n",
" \"language\": null, \n",
" \"lastModifiedDate\": \"2016-06-09T13:38:49.501Z\", \n",
" \"logicalName\": \"10421:DfTRoadSafety_Accidents_2014\", \n",
" \"originalSource\": \"DfTRoadSafety_Accidents_2014.csv\", \n",
" \"originalSourceType\": \"Delimited file\", \n",
" \"parentCollectionName\": null, \n",
" \"parentDatabaseName\": null, \n",
" \"previewData\": null, \n",
" \"primaryKey\": null, \n",
" \"projectCount\": \"0\", \n",
" \"projectId\": \"10421\", \n",
" \"published\": \"true\", \n",
" \"ready\": \"true\", \n",
" \"recordCount\": \"146322\", \n",
" \"relatedDataSet\": null, \n",
" \"sourceName\": \"default.dftroadsafety_accidents_2014\", \n",
" \"sourceType\": \"Hive\", \n",
" \"tag\": null, \n",
" \"timesFavorited\": \"0\", \n",
" \"timesViewed\": \"0\", \n",
" \"transformed\": \"false\", \n",
" \"uploadUserId\": \"10098\", \n",
" \"uploadUserName\": \"Admin Admin\", \n",
" \"version\": \"3\"\n",
"}\n"
]
}
],
"source": [
"ds = dss.dataset('default_edp_503409d1-a571-4040-ab51-c4c4bb15d3dc')\n",
"\n",
"import json\n",
"print json.dumps(ds.properties(),indent=2,sort_keys=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Hive\tdefault.dftroadsafety_accidents_2014"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.source()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Convert the dataset to a Spark dataframe"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"spark_df = ds.to_spark()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Show the data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+--------------+---------------------+----------------------+---------+---------+------------+-----------------+------------------+--------------------+-------------+-----------+--------+--------------------------+-------------------------+---------------+----------------+---------+-----------+---------------+----------------+---------------+----------------+---------------------------------+---------------------------------------+----------------+------------------+-----------------------+--------------------------+-------------------+-------------------+-------------------------------------------+-------------------------+-----------+\n",
"|accident_index|location_easting_osgr|location_northing_osgr|longitude| latitude|police_force|accident_severity|number_of_vehicles|number_of_casualties| date|day_of_week| time|local_authority__district_|local_authority__highway_|_1st_road_class|_1st_road_number|road_type|speed_limit|junction_detail|junction_control|_2nd_road_class|_2nd_road_number|pedestrian_crossing_human_control|pedestrian_crossing_physical_facilities|light_conditions|weather_conditions|road_surface_conditions|special_conditions_at_site|carriageway_hazards|urban_or_rural_area|did_police_officer_attend_scene_of_accident|lsoa_of_accident_location|PRIMARY_KEY|\n",
"+--------------+---------------------+----------------------+---------+---------+------------+-----------------+------------------+--------------------+-------------+-----------+--------+--------------------------+-------------------------+---------------+----------------+---------+-----------+---------------+----------------+---------------+----------------+---------------------------------+---------------------------------------+----------------+------------------+-----------------------+--------------------------+-------------------+-------------------+-------------------------------------------+-------------------------+-----------+\n",
"| 201401BS70001| 524600| 179020|-0.206443|51.496345| 1| 3| 2| 1|1389225600000| 5|48060000| 12| E09000020| 3| 315| 6| 30| 0| -1| -1| 0| 0| 0| 1| 2| 2| 0| 0| 1| 2| E01002814| 0-0-0|\n",
"| 201401BS70002| 525780| 178290|-0.189713|51.489523| 1| 3| 2| 1|1390176000000| 2|82800000| 12| E09000020| 3| 3218| 6| 30| 5| 4| 3| 3220| 0| 5| 7| 1| 1| 0| 0| 1| 2| E01002894| 0-0-1|\n",
"| 201401BS70003| 526880| 178430|-0.173827|51.490536| 1| 3| 2| 1|1390262400000| 3|38400000| 12| E09000020| 3| 308| 6| 30| 3| 4| 6| 0| 0| 0| 1| 1| 1| 0| 0| 1| 1| E01002822| 0-0-2|\n",
"| 201401BS70004| 525580| 179080|-0.192311|51.496668| 1| 3| 1| 1|1389744000000| 4|63900000| 12| E09000020| 5| 0| 6| 30| 3| 4| 6| 0| 0| 1| 4| 1| 1| 0| 0| 1| 2| E01002812| 0-0-3|\n",
"| 201401BS70006| 527040| 179030|-0.171308|51.495892| 1| 3| 2| 1|1389225600000| 5|31800000| 12| E09000020| 3| 4| 6| 30| 7| 4| 3| 4| 0| 8| 1| 1| 1| 0| 0| 1| 1| E01002821| 0-0-4|\n",
"+--------------+---------------------+----------------------+---------+---------+------------+-----------------+------------------+--------------------+-------------+-----------+--------+--------------------------+-------------------------+---------------+----------------+---------+-----------+---------------+----------------+---------------+----------------+---------------------------------+---------------------------------------+----------------+------------------+-----------------------+--------------------------+-------------------+-------------------+-------------------------------------------+-------------------------+-----------+\n",
"only showing top 5 rows\n",
"\n"
]
}
],
"source": [
"spark_df.show(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import pandas library and convert spark dataframe to Pandas DataFrame\n",
"([Documentation](https://docs.oracle.com/cd/E71457_01/bigData.Doc/bdd_shell_onPrem/toc.htm#Pandas%20integration))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"pandas_df = spark_df.toPandas()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Do some Panda stuff"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show first five records"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>accident_index</th>\n",
" <th>location_easting_osgr</th>\n",
" <th>location_northing_osgr</th>\n",
" <th>longitude</th>\n",
" <th>latitude</th>\n",
" <th>police_force</th>\n",
" <th>accident_severity</th>\n",
" <th>number_of_vehicles</th>\n",
" <th>number_of_casualties</th>\n",
" <th>date</th>\n",
" <th>...</th>\n",
" <th>pedestrian_crossing_physical_facilities</th>\n",
" <th>light_conditions</th>\n",
" <th>weather_conditions</th>\n",
" <th>road_surface_conditions</th>\n",
" <th>special_conditions_at_site</th>\n",
" <th>carriageway_hazards</th>\n",
" <th>urban_or_rural_area</th>\n",
" <th>did_police_officer_attend_scene_of_accident</th>\n",
" <th>lsoa_of_accident_location</th>\n",
" <th>PRIMARY_KEY</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>201401BS70001</td>\n",
" <td>524600</td>\n",
" <td>179020</td>\n",
" <td>-0.206443</td>\n",
" <td>51.496345</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1389225600000</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>E01002814</td>\n",
" <td>0-0-0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>201401BS70002</td>\n",
" <td>525780</td>\n",
" <td>178290</td>\n",
" <td>-0.189713</td>\n",
" <td>51.489523</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1390176000000</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>E01002894</td>\n",
" <td>0-0-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>201401BS70003</td>\n",
" <td>526880</td>\n",
" <td>178430</td>\n",
" <td>-0.173827</td>\n",
" <td>51.490536</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1390262400000</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>E01002822</td>\n",
" <td>0-0-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>201401BS70004</td>\n",
" <td>525580</td>\n",
" <td>179080</td>\n",
" <td>-0.192311</td>\n",
" <td>51.496668</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1389744000000</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>E01002812</td>\n",
" <td>0-0-3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>201401BS70006</td>\n",
" <td>527040</td>\n",
" <td>179030</td>\n",
" <td>-0.171308</td>\n",
" <td>51.495892</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1389225600000</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>E01002821</td>\n",
" <td>0-0-4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 33 columns</p>\n",
"</div>"
],
"text/plain": [
" accident_index location_easting_osgr location_northing_osgr longitude \\\n",
"0 201401BS70001 524600 179020 -0.206443 \n",
"1 201401BS70002 525780 178290 -0.189713 \n",
"2 201401BS70003 526880 178430 -0.173827 \n",
"3 201401BS70004 525580 179080 -0.192311 \n",
"4 201401BS70006 527040 179030 -0.171308 \n",
"\n",
" latitude police_force accident_severity number_of_vehicles \\\n",
"0 51.496345 1 3 2 \n",
"1 51.489523 1 3 2 \n",
"2 51.490536 1 3 2 \n",
"3 51.496668 1 3 1 \n",
"4 51.495892 1 3 2 \n",
"\n",
" number_of_casualties date ... \\\n",
"0 1 1389225600000 ... \n",
"1 1 1390176000000 ... \n",
"2 1 1390262400000 ... \n",
"3 1 1389744000000 ... \n",
"4 1 1389225600000 ... \n",
"\n",
" pedestrian_crossing_physical_facilities light_conditions \\\n",
"0 0 1 \n",
"1 5 7 \n",
"2 0 1 \n",
"3 1 4 \n",
"4 8 1 \n",
"\n",
" weather_conditions road_surface_conditions special_conditions_at_site \\\n",
"0 2 2 0 \n",
"1 1 1 0 \n",
"2 1 1 0 \n",
"3 1 1 0 \n",
"4 1 1 0 \n",
"\n",
" carriageway_hazards urban_or_rural_area \\\n",
"0 0 1 \n",
"1 0 1 \n",
"2 0 1 \n",
"3 0 1 \n",
"4 0 1 \n",
"\n",
" did_police_officer_attend_scene_of_accident lsoa_of_accident_location \\\n",
"0 2 E01002814 \n",
"1 2 E01002894 \n",
"2 1 E01002822 \n",
"3 2 E01002812 \n",
"4 1 E01002821 \n",
"\n",
" PRIMARY_KEY \n",
"0 0-0-0 \n",
"1 0-0-1 \n",
"2 0-0-2 \n",
"3 0-0-3 \n",
"4 0-0-4 \n",
"\n",
"[5 rows x 33 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pandas_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show information about the dataframe"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 146322 entries, 0 to 146321\n",
"Data columns (total 33 columns):\n",
"accident_index 146322 non-null object\n",
"location_easting_osgr 146322 non-null int64\n",
"location_northing_osgr 146322 non-null int64\n",
"longitude 146322 non-null float64\n",
"latitude 146322 non-null float64\n",
"police_force 146322 non-null int64\n",
"accident_severity 146322 non-null int64\n",
"number_of_vehicles 146322 non-null int64\n",
"number_of_casualties 146322 non-null int64\n",
"date 146322 non-null int64\n",
"day_of_week 146322 non-null int64\n",
"time 146322 non-null int64\n",
"local_authority__district_ 146322 non-null int64\n",
"local_authority__highway_ 146322 non-null object\n",
"_1st_road_class 146322 non-null int64\n",
"_1st_road_number 146322 non-null int64\n",
"road_type 146322 non-null int64\n",
"speed_limit 146322 non-null int64\n",
"junction_detail 146322 non-null int64\n",
"junction_control 146322 non-null int64\n",
"_2nd_road_class 146322 non-null int64\n",
"_2nd_road_number 146322 non-null int64\n",
"pedestrian_crossing_human_control 146322 non-null int64\n",
"pedestrian_crossing_physical_facilities 146322 non-null int64\n",
"light_conditions 146322 non-null int64\n",
"weather_conditions 146322 non-null int64\n",
"road_surface_conditions 146322 non-null int64\n",
"special_conditions_at_site 146322 non-null int64\n",
"carriageway_hazards 146322 non-null int64\n",
"urban_or_rural_area 146322 non-null int64\n",
"did_police_officer_attend_scene_of_accident 146322 non-null int64\n",
"lsoa_of_accident_location 137045 non-null object\n",
"PRIMARY_KEY 146322 non-null object\n",
"dtypes: float64(2), int64(27), object(4)\n",
"memory usage: 36.8+ MB\n"
]
}
],
"source": [
"pandas_df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show stats summary of the data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>location_easting_osgr</th>\n",
" <th>location_northing_osgr</th>\n",
" <th>longitude</th>\n",
" <th>latitude</th>\n",
" <th>police_force</th>\n",
" <th>accident_severity</th>\n",
" <th>number_of_vehicles</th>\n",
" <th>number_of_casualties</th>\n",
" <th>date</th>\n",
" <th>day_of_week</th>\n",
" <th>...</th>\n",
" <th>_2nd_road_number</th>\n",
" <th>pedestrian_crossing_human_control</th>\n",
" <th>pedestrian_crossing_physical_facilities</th>\n",
" <th>light_conditions</th>\n",
" <th>weather_conditions</th>\n",
" <th>road_surface_conditions</th>\n",
" <th>special_conditions_at_site</th>\n",
" <th>carriageway_hazards</th>\n",
" <th>urban_or_rural_area</th>\n",
" <th>did_police_officer_attend_scene_of_accident</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>146322.000000</td>\n",
" <td>1.463220e+05</td>\n",
" <td>146322.000000</td>\n",
" <td>146322.000000</td>\n",
" <td>146322.000000</td>\n",
" <td>146322.000000</td>\n",
" <td>146322.000000</td>\n",
" <td>146322.000000</td>\n",
" <td>1.463220e+05</td>\n",
" <td>146322.00000</td>\n",
" <td>...</td>\n",
" <td>146322.000000</td>\n",
" <td>146322.000000</td>\n",
" <td>146322.000000</td>\n",
" <td>146322.000000</td>\n",
" <td>146322.000000</td>\n",
" <td>146322.000000</td>\n",
" <td>146322.000000</td>\n",
" <td>146322.000000</td>\n",
" <td>146322.000000</td>\n",
" <td>146322.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>447029.362475</td>\n",
" <td>2.903741e+05</td>\n",
" <td>-1.328712</td>\n",
" <td>52.500882</td>\n",
" <td>29.895839</td>\n",
" <td>2.836033</td>\n",
" <td>1.835179</td>\n",
" <td>1.329103</td>\n",
" <td>1.404500e+12</td>\n",
" <td>4.09868</td>\n",
" <td>...</td>\n",
" <td>377.446023</td>\n",
" <td>0.008386</td>\n",
" <td>0.857725</td>\n",
" <td>1.924099</td>\n",
" <td>1.500595</td>\n",
" <td>1.328132</td>\n",
" <td>0.096985</td>\n",
" <td>0.069135</td>\n",
" <td>1.341951</td>\n",
" <td>1.182577</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>95158.326674</td>\n",
" <td>1.578227e+05</td>\n",
" <td>1.397358</td>\n",
" <td>1.421337</td>\n",
" <td>25.190838</td>\n",
" <td>0.399682</td>\n",
" <td>0.700208</td>\n",
" <td>0.857469</td>\n",
" <td>9.045011e+09</td>\n",
" <td>1.91668</td>\n",
" <td>...</td>\n",
" <td>1288.061037</td>\n",
" <td>0.119877</td>\n",
" <td>1.947539</td>\n",
" <td>1.627001</td>\n",
" <td>1.514131</td>\n",
" <td>0.568551</td>\n",
" <td>0.685515</td>\n",
" <td>0.612831</td>\n",
" <td>0.474365</td>\n",
" <td>0.386320</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>70951.000000</td>\n",
" <td>1.030400e+04</td>\n",
" <td>-7.450342</td>\n",
" <td>49.913077</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.388534e+12</td>\n",
" <td>1.00000</td>\n",
" <td>...</td>\n",
" <td>-1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>384364.250000</td>\n",
" <td>1.765900e+05</td>\n",
" <td>-2.235527</td>\n",
" <td>51.474928</td>\n",
" <td>6.000000</td>\n",
" <td>3.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.396656e+12</td>\n",
" <td>2.00000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>451306.000000</td>\n",
" <td>2.416200e+05</td>\n",
" <td>-1.238819</td>\n",
" <td>52.055124</td>\n",
" <td>30.000000</td>\n",
" <td>3.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.404691e+12</td>\n",
" <td>4.00000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>526830.000000</td>\n",
" <td>3.911760e+05</td>\n",
" <td>-0.170586</td>\n",
" <td>53.414623</td>\n",
" <td>45.000000</td>\n",
" <td>3.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.412467e+12</td>\n",
" <td>6.00000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>655309.000000</td>\n",
" <td>1.190858e+06</td>\n",
" <td>1.758797</td>\n",
" <td>60.597984</td>\n",
" <td>98.000000</td>\n",
" <td>3.000000</td>\n",
" <td>21.000000</td>\n",
" <td>93.000000</td>\n",
" <td>1.419984e+12</td>\n",
" <td>7.00000</td>\n",
" <td>...</td>\n",
" <td>9999.000000</td>\n",
" <td>2.000000</td>\n",
" <td>8.000000</td>\n",
" <td>7.000000</td>\n",
" <td>9.000000</td>\n",
" <td>5.000000</td>\n",
" <td>7.000000</td>\n",
" <td>7.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 29 columns</p>\n",
"</div>"
],
"text/plain": [
" location_easting_osgr location_northing_osgr longitude \\\n",
"count 146322.000000 1.463220e+05 146322.000000 \n",
"mean 447029.362475 2.903741e+05 -1.328712 \n",
"std 95158.326674 1.578227e+05 1.397358 \n",
"min 70951.000000 1.030400e+04 -7.450342 \n",
"25% 384364.250000 1.765900e+05 -2.235527 \n",
"50% 451306.000000 2.416200e+05 -1.238819 \n",
"75% 526830.000000 3.911760e+05 -0.170586 \n",
"max 655309.000000 1.190858e+06 1.758797 \n",
"\n",
" latitude police_force accident_severity number_of_vehicles \\\n",
"count 146322.000000 146322.000000 146322.000000 146322.000000 \n",
"mean 52.500882 29.895839 2.836033 1.835179 \n",
"std 1.421337 25.190838 0.399682 0.700208 \n",
"min 49.913077 1.000000 1.000000 1.000000 \n",
"25% 51.474928 6.000000 3.000000 1.000000 \n",
"50% 52.055124 30.000000 3.000000 2.000000 \n",
"75% 53.414623 45.000000 3.000000 2.000000 \n",
"max 60.597984 98.000000 3.000000 21.000000 \n",
"\n",
" number_of_casualties date day_of_week \\\n",
"count 146322.000000 1.463220e+05 146322.00000 \n",
"mean 1.329103 1.404500e+12 4.09868 \n",
"std 0.857469 9.045011e+09 1.91668 \n",
"min 1.000000 1.388534e+12 1.00000 \n",
"25% 1.000000 1.396656e+12 2.00000 \n",
"50% 1.000000 1.404691e+12 4.00000 \n",
"75% 1.000000 1.412467e+12 6.00000 \n",
"max 93.000000 1.419984e+12 7.00000 \n",
"\n",
" ... _2nd_road_number \\\n",
"count ... 146322.000000 \n",
"mean ... 377.446023 \n",
"std ... 1288.061037 \n",
"min ... -1.000000 \n",
"25% ... 0.000000 \n",
"50% ... 0.000000 \n",
"75% ... 0.000000 \n",
"max ... 9999.000000 \n",
"\n",
" pedestrian_crossing_human_control \\\n",
"count 146322.000000 \n",
"mean 0.008386 \n",
"std 0.119877 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 0.000000 \n",
"max 2.000000 \n",
"\n",
" pedestrian_crossing_physical_facilities light_conditions \\\n",
"count 146322.000000 146322.000000 \n",
"mean 0.857725 1.924099 \n",
"std 1.947539 1.627001 \n",
"min 0.000000 1.000000 \n",
"25% 0.000000 1.000000 \n",
"50% 0.000000 1.000000 \n",
"75% 0.000000 4.000000 \n",
"max 8.000000 7.000000 \n",
"\n",
" weather_conditions road_surface_conditions \\\n",
"count 146322.000000 146322.000000 \n",
"mean 1.500595 1.328132 \n",
"std 1.514131 0.568551 \n",
"min 1.000000 -1.000000 \n",
"25% 1.000000 1.000000 \n",
"50% 1.000000 1.000000 \n",
"75% 1.000000 2.000000 \n",
"max 9.000000 5.000000 \n",
"\n",
" special_conditions_at_site carriageway_hazards urban_or_rural_area \\\n",
"count 146322.000000 146322.000000 146322.000000 \n",
"mean 0.096985 0.069135 1.341951 \n",
"std 0.685515 0.612831 0.474365 \n",
"min -1.000000 -1.000000 1.000000 \n",
"25% 0.000000 0.000000 1.000000 \n",
"50% 0.000000 0.000000 1.000000 \n",
"75% 0.000000 0.000000 2.000000 \n",
"max 7.000000 7.000000 2.000000 \n",
"\n",
" did_police_officer_attend_scene_of_accident \n",
"count 146322.000000 \n",
"mean 1.182577 \n",
"std 0.386320 \n",
"min 1.000000 \n",
"25% 1.000000 \n",
"50% 1.000000 \n",
"75% 1.000000 \n",
"max 2.000000 \n",
"\n",
"[8 rows x 29 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pandas_df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show a subset of the data based on a filter"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>accident_index</th>\n",
" <th>location_easting_osgr</th>\n",
" <th>location_northing_osgr</th>\n",
" <th>longitude</th>\n",
" <th>latitude</th>\n",
" <th>police_force</th>\n",
" <th>accident_severity</th>\n",
" <th>number_of_vehicles</th>\n",
" <th>number_of_casualties</th>\n",
" <th>date</th>\n",
" <th>...</th>\n",
" <th>pedestrian_crossing_physical_facilities</th>\n",
" <th>light_conditions</th>\n",
" <th>weather_conditions</th>\n",
" <th>road_surface_conditions</th>\n",
" <th>special_conditions_at_site</th>\n",
" <th>carriageway_hazards</th>\n",
" <th>urban_or_rural_area</th>\n",
" <th>did_police_officer_attend_scene_of_accident</th>\n",
" <th>lsoa_of_accident_location</th>\n",
" <th>PRIMARY_KEY</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>201401BS70001</td>\n",
" <td>524600</td>\n",
" <td>179020</td>\n",
" <td>-0.206443</td>\n",
" <td>51.496345</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1389225600000</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>E01002814</td>\n",
" <td>0-0-0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>201401BS70002</td>\n",
" <td>525780</td>\n",
" <td>178290</td>\n",
" <td>-0.189713</td>\n",
" <td>51.489523</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1390176000000</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>E01002894</td>\n",
" <td>0-0-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>201401BS70003</td>\n",
" <td>526880</td>\n",
" <td>178430</td>\n",
" <td>-0.173827</td>\n",
" <td>51.490536</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1390262400000</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>E01002822</td>\n",
" <td>0-0-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>201401BS70004</td>\n",
" <td>525580</td>\n",
" <td>179080</td>\n",
" <td>-0.192311</td>\n",
" <td>51.496668</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1389744000000</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>E01002812</td>\n",
" <td>0-0-3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>201401BS70006</td>\n",
" <td>527040</td>\n",
" <td>179030</td>\n",
" <td>-0.171308</td>\n",
" <td>51.495892</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1389225600000</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>E01002821</td>\n",
" <td>0-0-4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 33 columns</p>\n",
"</div>"
],
"text/plain": [
" accident_index location_easting_osgr location_northing_osgr longitude \\\n",
"0 201401BS70001 524600 179020 -0.206443 \n",
"1 201401BS70002 525780 178290 -0.189713 \n",
"2 201401BS70003 526880 178430 -0.173827 \n",
"3 201401BS70004 525580 179080 -0.192311 \n",
"4 201401BS70006 527040 179030 -0.171308 \n",
"\n",
" latitude police_force accident_severity number_of_vehicles \\\n",
"0 51.496345 1 3 2 \n",
"1 51.489523 1 3 2 \n",
"2 51.490536 1 3 2 \n",
"3 51.496668 1 3 1 \n",
"4 51.495892 1 3 2 \n",
"\n",
" number_of_casualties date ... \\\n",
"0 1 1389225600000 ... \n",
"1 1 1390176000000 ... \n",
"2 1 1390262400000 ... \n",
"3 1 1389744000000 ... \n",
"4 1 1389225600000 ... \n",
"\n",
" pedestrian_crossing_physical_facilities light_conditions \\\n",
"0 0 1 \n",
"1 5 7 \n",
"2 0 1 \n",
"3 1 4 \n",
"4 8 1 \n",
"\n",
" weather_conditions road_surface_conditions special_conditions_at_site \\\n",
"0 2 2 0 \n",
"1 1 1 0 \n",
"2 1 1 0 \n",
"3 1 1 0 \n",
"4 1 1 0 \n",
"\n",
" carriageway_hazards urban_or_rural_area \\\n",
"0 0 1 \n",
"1 0 1 \n",
"2 0 1 \n",
"3 0 1 \n",
"4 0 1 \n",
"\n",
" did_police_officer_attend_scene_of_accident lsoa_of_accident_location \\\n",
"0 2 E01002814 \n",
"1 2 E01002894 \n",
"2 1 E01002822 \n",
"3 2 E01002812 \n",
"4 1 E01002821 \n",
"\n",
" PRIMARY_KEY \n",
"0 0-0-0 \n",
"1 0-0-1 \n",
"2 0-0-2 \n",
"3 0-0-3 \n",
"4 0-0-4 \n",
"\n",
"[5 rows x 33 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pandas_df[pandas_df.accident_severity>1].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot a correlation matrix"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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H3ihmNReAXQbOKyw2wNzqYlflmFfdp+VK7bB6wd9EVNUtKi54kbGBia8XuyLN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v8HNgS0oliNOa56q+V5qfpVcyUnniYuBkV7qg7s5P0HVuVqxwlPQtSr6P19verBnIuKRWjogx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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd4bdf0c610>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Display the image in the Notebook output\n",
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"# Source: http://www.akshayshah.org/post/language-use-on-github/\n",
"def plot_correlation(dataframe, title='', corr_type=''):\n",
" lang_names = dataframe.columns.tolist()\n",
" tick_indices = np.arange(0.5, len(lang_names) + 0.5)\n",
" plt.figure(figsize=(16,10))\n",
" plt.pcolor(dataframe.values, cmap='RdBu', vmin=-1, vmax=1)\n",
" colorbar = plt.colorbar()\n",
" colorbar.set_label(corr_type)\n",
" plt.title(title)\n",
" plt.xticks(tick_indices, lang_names, rotation='vertical')\n",
" plt.yticks(tick_indices, lang_names)\n",
" \n",
"corr = pandas_df.corr()\n",
"plot_correlation(corr,ds.properties()['displayName'],'Correlation')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a new column with a windowing function\n",
"([Documentation](http://pandas.pydata.org/pandas-docs/stable/computation.html#rolling-windows))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pandas_df['severity_rolling_median'] = pandas_df.accident_severity.rolling(window=10,center=False).median()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a new Spark dataframe from the updated Pandas data"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"spark_df2 = sqlContext.createDataFrame(pandas_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Write the new Spark dataframe back to a new Hive table"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"spark_df2.write.saveAsTable('default.updated_accident_data')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show the schema - note the new column"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"root\n",
" |-- accident_index: string (nullable = true)\n",
" |-- location_easting_osgr: long (nullable = true)\n",
" |-- location_northing_osgr: long (nullable = true)\n",
" |-- longitude: double (nullable = true)\n",
" |-- latitude: double (nullable = true)\n",
" |-- police_force: long (nullable = true)\n",
" |-- accident_severity: long (nullable = true)\n",
" |-- number_of_vehicles: long (nullable = true)\n",
" |-- number_of_casualties: long (nullable = true)\n",
" |-- date: long (nullable = true)\n",
" |-- day_of_week: long (nullable = true)\n",
" |-- time: long (nullable = true)\n",
" |-- local_authority__district_: long (nullable = true)\n",
" |-- local_authority__highway_: string (nullable = true)\n",
" |-- _1st_road_class: long (nullable = true)\n",
" |-- _1st_road_number: long (nullable = true)\n",
" |-- road_type: long (nullable = true)\n",
" |-- speed_limit: long (nullable = true)\n",
" |-- junction_detail: long (nullable = true)\n",
" |-- junction_control: long (nullable = true)\n",
" |-- _2nd_road_class: long (nullable = true)\n",
" |-- _2nd_road_number: long (nullable = true)\n",
" |-- pedestrian_crossing_human_control: long (nullable = true)\n",
" |-- pedestrian_crossing_physical_facilities: long (nullable = true)\n",
" |-- light_conditions: long (nullable = true)\n",
" |-- weather_conditions: long (nullable = true)\n",
" |-- road_surface_conditions: long (nullable = true)\n",
" |-- special_conditions_at_site: long (nullable = true)\n",
" |-- carriageway_hazards: long (nullable = true)\n",
" |-- urban_or_rural_area: long (nullable = true)\n",
" |-- did_police_officer_attend_scene_of_accident: long (nullable = true)\n",
" |-- lsoa_of_accident_location: string (nullable = true)\n",
" |-- PRIMARY_KEY: string (nullable = true)\n",
" |-- severity_rolling_median: double (nullable = true)\n",
"\n"
]
}
],
"source": [
"spark_df2.printSchema()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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