{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import dask.dataframe as dd\n", "import dask.distributed\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "client = dask.distributed.Client()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['dropoff_datetime', 'dropoff_latitude', 'dropoff_longitude',\n", " 'dropoff_taxizone_id', 'ehail_fee', 'extra', 'fare_amount',\n", " 'improvement_surcharge', 'mta_tax', 'passenger_count', 'payment_type',\n", " 'pickup_latitude', 'pickup_longitude', 'pickup_taxizone_id',\n", " 'rate_code_id', 'store_and_fwd_flag', 'tip_amount', 'tolls_amount',\n", " 'total_amount', 'trip_distance', 'trip_type', 'vendor_id', 'trip_id'],\n", " dtype='object')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Print column names\n", "\n", "df0 = dd.read_parquet('/data/all_trips.parquet', engine='fastparquet', index='pickup_datetime')\n", "df0.columns" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Load only the columns we need, large speedup.\n", "\n", "df = dd.read_parquet('/data/all_trips_spark.parquet', engine='arrow', \n", " columns=[\n", "# 'pickup_datetime', \n", " 'pickup_longitude', 'pickup_latitude', #'pickup_taxizone_id',\n", "# 'dropoff_datetime', \n", " 'dropoff_longitude', 'dropoff_latitude', #'dropoff_taxizone_id',\n", "# 'trip_type', \n", "# 'passenger_count'\n", " 'total_amount'\n", " ])\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " pickup_longitude pickup_latitude dropoff_longitude dropoff_latitude \\\n", "0 -73.965919 40.771244 -73.949608 40.777058 \n", "1 -73.997482 40.725952 -74.005936 40.735703 \n", "2 -73.964798 40.767391 -73.977753 40.773746 \n", "3 -74.011597 40.708832 -74.013466 40.709358 \n", "4 -74.000648 40.718578 -73.944580 40.712368 \n", "\n", " total_amount \n", "0 5.800000 \n", "1 5.400000 \n", "2 5.800000 \n", "3 4.600000 \n", "4 27.799999 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " pickup_longitude pickup_latitude dropoff_longitude \\\n", "1432504 NaN NaN NaN \n", "1432505 NaN NaN NaN \n", "1432506 NaN NaN NaN \n", "1432507 NaN NaN NaN \n", "1432508 NaN NaN NaN \n", "\n", " dropoff_latitude total_amount \n", "1432504 NaN 8.300000 \n", "1432505 NaN 17.299999 \n", "1432506 NaN 41.759998 \n", "1432507 NaN 6.300000 \n", "1432508 NaN 14.300000 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Select only those points within some reasonable bounds (half a degree)\n", "\n", "df = df[df.pickup_latitude.notnull() & df.pickup_longitude.notnull() \n", " & ((df.pickup_latitude - 40.75).abs() <= 0.5) \n", " & ((df.pickup_longitude + 73.9).abs() <= 0.5)\n", " ]\n", "df = df[df.dropoff_latitude.notnull() & df.dropoff_longitude.notnull() \n", " & ((df.dropoff_latitude - 40.75).abs() <= 0.5) \n", " & ((df.dropoff_longitude + 73.9).abs() <= 0.5)\n", " ]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "pickup_longitude 1268170371\n", "pickup_latitude 1268170371\n", "dropoff_longitude 1268170371\n", "dropoff_latitude 1268170371\n", "total_amount 1268170371\n", "dtype: int64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We get about 1.27 billion points\n", "df.count().compute()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " pickup_longitude pickup_latitude dropoff_longitude dropoff_latitude \\\n", "0 -73.965919 40.771244 -73.949608 40.777058 \n", "1 -73.997482 40.725952 -74.005936 40.735703 \n", "2 -73.964798 40.767391 -73.977753 40.773746 \n", "3 -74.011597 40.708832 -74.013466 40.709358 \n", "4 -74.000648 40.718578 -73.944580 40.712368 \n", "\n", " total_amount \n", "0 5.800000 \n", "1 5.400000 \n", "2 5.800000 \n", "3 4.600000 \n", "4 27.799999 " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def convert_lon(d, latvar):\n", " \"Convert longitude to web mercator\"\n", " k = d[latvar].copy()\n", " k = (20037508.34 / 180) * (np.log(np.tan((90. + d[latvar]) * np.pi/360))/(np.pi/180.))\n", " return k" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Convert lats and lons to web mercator projection\n", "df['pickup_longitude'] = df.pickup_longitude * (20037508.34 / 180)\n", "df['pickup_latitude'] = df.map_partitions(convert_lon, 'pickup_latitude')\n", "df['dropoff_longitude'] = df.dropoff_longitude * (20037508.34 / 180)\n", "df['dropoff_latitude'] = df.map_partitions(convert_lon, 'dropoff_latitude')" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Consolidate partitions for faster plotting\n", "df.repartition(npartitions=200).to_parquet('/tmp/filtered.parquet', compression='SNAPPY')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Read the consolidated data back in\n", "df = dd.read_parquet('/tmp/filtered.parquet')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Subsample the data \n", "# It's currently commented out, but it's useful \n", "# when iterating on plot details (axes, ranges, etc.), \n", "# as it greatly speeds up plot redrawing. \n", "\n", "# df = client.persist(df.sample(frac=0.02))" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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pickup_longitudepickup_latitudedropoff_longitudedropoff_latitudetotal_amount
0-8233848.54978660.0-8232033.04979513.05.800000
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" ], "text/plain": [ " pickup_longitude pickup_latitude dropoff_longitude dropoff_latitude \\\n", "0 -8233848.5 4978660.0 -8232033.0 4979513.0 \n", "1 -8237362.0 4972004.0 -8238303.0 4973436.5 \n", "2 -8233724.0 4978093.5 -8235166.0 4979025.5 \n", "3 -8238933.5 4969490.0 -8239141.5 4969566.0 \n", "4 -8237714.5 4970922.5 -8231473.0 4970009.0 \n", "\n", " total_amount \n", "0 5.800000 \n", "1 5.400000 \n", "2 5.800000 \n", "3 4.600000 \n", "4 27.799999 " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import datashader as ds\n", "import datashader.transfer_functions as tf\n", "\n", "import datashader as ds\n", "from datashader.bokeh_ext import InteractiveImage\n", "from functools import partial\n", "from datashader.utils import export_image\n", "from datashader.colors import colormap_select, Greys9, Hot, viridis, inferno\n", "from IPython.core.display import HTML, display" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", " window._bokeh_onload_callbacks = [];\n", " window._bokeh_is_loading = undefined;\n", " }\n", "\n", "\n", " \n", " if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n", " window._bokeh_timeout = Date.now() + 5000;\n", " window._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"
\\n\"+\n", " \"

\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"

\\n\"+\n", " \"\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
\"}};\n", "\n", " function display_loaded() {\n", " if (window.Bokeh !== undefined) {\n", " document.getElementById(\"8d091618-c77e-4686-a40c-8a673de9b2a6\").textContent = \"BokehJS successfully loaded.\";\n", " } else if (Date.now() < window._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", " function run_callbacks() {\n", " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " delete window._bokeh_onload_callbacks\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(js_urls, callback) {\n", " window._bokeh_onload_callbacks.push(callback);\n", " if (window._bokeh_is_loading > 0) {\n", " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " window._bokeh_is_loading = js_urls.length;\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = false;\n", " s.onreadystatechange = s.onload = function() {\n", " window._bokeh_is_loading--;\n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", " run_callbacks()\n", " }\n", " };\n", " s.onerror = function() {\n", " console.warn(\"failed to load library \" + url);\n", " };\n", " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", " }\n", " };var element = document.getElementById(\"8d091618-c77e-4686-a40c-8a673de9b2a6\");\n", " if (element == null) {\n", " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '8d091618-c77e-4686-a40c-8a673de9b2a6' but no matching script tag was found. \")\n", " return false;\n", " }\n", "\n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.js\"];\n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " \n", " function(Bokeh) {\n", " \n", " document.getElementById(\"8d091618-c77e-4686-a40c-8a673de9b2a6\").textContent = \"BokehJS is loading...\";\n", " },\n", " function(Bokeh) {\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if ((window.Bokeh !== undefined) || (force === true)) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\n", " }if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < window._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!window._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " window._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"8d091618-c77e-4686-a40c-8a673de9b2a6\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(this));" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bokeh.models import BoxZoomTool\n", "from bokeh.plotting import figure, output_notebook, show\n", "\n", "output_notebook()\n", "\n", "#set centers, bounds, and ranges in web mercator coords\n", "x_center = -8234000 \n", "y_center = 4973000\n", "\n", "x_half_range = 30000\n", "y_half_range = 25000\n", "\n", "NYC = x_range, y_range = ((x_center - x_half_range, x_center + x_half_range), \n", " (y_center-y_half_range, y_center+y_half_range))\n", "\n", "# plot_width scales (quadratically?) with memory consumption.\n", "# With 32GB RAM, I can set this to 2000, but 2500 crashes with MemoryError.\n", "# I used this setting for high quality, large plots. \n", "# plot_width = 2000 \n", "\n", "# plot_width of 400 seems to require less than 4GB, and makes the notebook more manageable. \n", "# Also changes aesthetic appearance by decreasing GPS \"noise\" due to coarse binning\n", "plot_width = 400 \n", "\n", "# auto calculate from width\n", "plot_height = int(plot_width/(x_half_range/y_half_range))\n", "\n", "def base_plot(tools='pan,wheel_zoom,reset,save',plot_width=plot_width, \n", " plot_height=plot_height, **plot_args):\n", " p = figure(tools=tools, plot_width=plot_width, plot_height=plot_height,\n", " x_range=x_range, y_range=y_range, outline_line_color=None,\n", " min_border=0, min_border_left=0, min_border_right=0,\n", " min_border_top=0, min_border_bottom=0, **plot_args)\n", " \n", " p.axis.visible = False\n", " p.xgrid.grid_line_color = None\n", " p.ygrid.grid_line_color = None\n", " \n", " p.add_tools(BoxZoomTool(match_aspect=True))\n", " \n", " return p\n", " \n", "options = dict(line_color=None, fill_color='blue', size=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pickups" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "background = \"black\"\n", "export = partial(export_image, export_path=\"export\", background=background)\n", "cm = partial(colormap_select, reverse=(background==\"black\"))\n", "\n", "\n", "def create_image_1(x_range, y_range, w=plot_width, h=plot_height):\n", " cvs = ds.Canvas(plot_width=w, plot_height=h, x_range=x_range, y_range=y_range)\n", " agg = cvs.points(df, 'pickup_longitude', 'pickup_latitude', ds.count('total_amount'))\n", " img = tf.shade(agg, cmap=viridis, how='eq_hist')\n", " return tf.dynspread(img, threshold=0.5, max_px=4)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "
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\n", "" ], "text/plain": [ "" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p = base_plot(background_fill_color=background)\n", "export(create_image_1(x_range, y_range, plot_width, plot_height),\"pickups_large_wide\")\n", "InteractiveImage(p, create_image_1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dropoffs" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "background = \"black\"\n", "export = partial(export_image, export_path=\"export\", background=background)\n", "cm = partial(colormap_select, reverse=(background==\"black\"))\n", "\n", "\n", "def create_image_2(x_range, y_range, w=plot_width, h=plot_height):\n", " cvs = ds.Canvas(plot_width=w, plot_height=h, x_range=x_range, y_range=y_range)\n", " agg = cvs.points(df, 'dropoff_longitude', 'dropoff_latitude', ds.count('total_amount'))\n", " img = tf.shade(agg, cmap=inferno, how='eq_hist')\n", " return tf.dynspread(img, threshold=0.5, max_px=4)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
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\n", "" ], "text/plain": [ "" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p = base_plot(background_fill_color=background)\n", "export(create_image_2(x_range, y_range, plot_width, plot_height),\"dropoffs_large_wide\")\n", "InteractiveImage(p, create_image_2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pickups (Green) vs Dropoffs (Orange)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "background = 'black'\n", "export = partial(export_image, export_path=\"export\", background=background)\n", "cm = partial(colormap_select, reverse=(background==\"black\"))\n", "\n", "\n", "def create_image_3(x_range, y_range, w=plot_width, h=plot_height):\n", " cvs = ds.Canvas(plot_width=w, plot_height=h, x_range=x_range, y_range=y_range)\n", " drops = cvs.points(df, 'dropoff_longitude', 'dropoff_latitude', ds.count('total_amount'))\n", " picks = cvs.points(df, 'pickup_longitude', 'pickup_latitude', ds.count('total_amount'))\n", " more_drops = tf.shade(drops.where(drops > picks), cmap=inferno, how='eq_hist')\n", " more_picks = tf.shade(picks.where(picks > drops), cmap=viridis, how='eq_hist')\n", " img = tf.stack(more_picks,more_drops)\n", " return tf.dynspread(img, threshold=0.5, max_px=4)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/shekhar/anaconda3/lib/python3.5/site-packages/toolz/functoolz.py:621: RuntimeWarning: invalid value encountered in over\n", " return func(b, a)\n" ] }, { "data": { "text/html": [ "\n", "\n", "
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pickup_latitudepickup_longitudepickup_taxizone_iddropoff_latitudedropoff_longitudedropoff_taxizone_id
pickup_datetime
2009-01-01 00:00:0040.771244-73.965919237.040.777058-73.949608263.0
2009-01-01 00:00:0040.725952-73.997482114.040.735703-74.005936249.0
2009-01-01 00:00:0240.767391-73.964798237.040.773746-73.97775343.0
2009-01-01 00:00:0440.708832-74.011597261.040.709358-74.013466261.0
2009-01-01 00:00:0740.718578-74.000648144.040.712368-73.94458080.0
\n", "
" ], "text/plain": [ " pickup_latitude pickup_longitude pickup_taxizone_id \\\n", "pickup_datetime \n", "2009-01-01 00:00:00 40.771244 -73.965919 237.0 \n", "2009-01-01 00:00:00 40.725952 -73.997482 114.0 \n", "2009-01-01 00:00:02 40.767391 -73.964798 237.0 \n", "2009-01-01 00:00:04 40.708832 -74.011597 261.0 \n", "2009-01-01 00:00:07 40.718578 -74.000648 144.0 \n", "\n", " dropoff_latitude dropoff_longitude dropoff_taxizone_id \n", "pickup_datetime \n", "2009-01-01 00:00:00 40.777058 -73.949608 263.0 \n", "2009-01-01 00:00:00 40.735703 -74.005936 249.0 \n", "2009-01-01 00:00:02 40.773746 -73.977753 43.0 \n", "2009-01-01 00:00:04 40.709358 -74.013466 261.0 \n", "2009-01-01 00:00:07 40.712368 -73.944580 80.0 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['pickup_datetime_str'] = df.index.astype(str)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = df[df.pickup_datetime_str < '2016-07-01']" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['pickup_datetime_str'] = df.pickup_datetime_str.str.slice(0, 7)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = df[df.pickup_latitude.notnull() & df.pickup_longitude.notnull() \n", " & ((df.pickup_latitude - 40.75).abs() <= 0.5) \n", " & ((df.pickup_longitude + 73.9).abs() <= 0.5)\n", " ]\n", "df = df[df.dropoff_latitude.notnull() & df.dropoff_longitude.notnull() \n", " & ((df.dropoff_latitude - 40.75).abs() <= 0.5) \n", " & ((df.dropoff_longitude + 73.9).abs() <= 0.5)\n", " ]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "z = df.groupby('pickup_datetime_str').count().compute()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "z['pickup_valid_fraction'] = z['pickup_taxizone_id'].astype(np.float64) / z['pickup_longitude'].astype(np.float64)\n", "z['dropoff_valid_fraction'] = z['dropoff_taxizone_id'].astype(np.float64) / z['dropoff_longitude'].astype(np.float64)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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bgnju89eImYjvW/cmj0uvjwua2Wbufi29yL5jV1rZIiLSEZQaS0REpHdFA0bp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\nvTmBzYmA/deJ12om4nl8g5iJfydRr6S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4hbZmzUjitvq+Ri4xU/ZCQms8tNRXxYfXTCjv/albfGfE0LIYQQQgghhBC6AnZFCIBS6nXDMN4G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g5alyRJkiRJkiRp3LMQIkmSJEmSJEmSOpZbY0mSJEmSJEmSpI5lIUSSJEmSJEmSJHUsCyGS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\n2xCWRsk8H1aZ7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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(z.index.astype('M8[M]').values, 100*(1.0 - z.pickup_valid_fraction.values), \n", " label='Pickups', lw=3)\n", "plt.plot(z.index.astype('M8[M]').values, 100*(1.0 - z.dropoff_valid_fraction.values), \n", " label='Dropoffs', lw=3)\n", "plt.legend(loc='upper right')\n", "plt.xlabel(\"Time\")\n", "plt.ylabel(\"GPS Error Rate [%]\")\n", "plt.title(\"GPS Coordinates Outside Taxi Zones\")\n", "plt.ylim(0, 1)\n", "plt.xlim('2009-01', '2016-07')\n", "plt.gcf().set_size_inches(6, 3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }