{ "metadata": { "celltoolbar": "Slideshow", "name": "", "signature": "sha256:093ade9f7a84a6d338065b17fb8569997604c42bcaa25d7d2f607f28e5ba3dec" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Data Summative Pt. 2" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "We've imported the schedules from NextBus (look at `schedules.py`), let's quickly do a sanity check and graph them. Their data has been aggregated into `schedules_times.pickle`, which is generated from `aggregate_schedules.py`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import pickle\n", "from datetime import datetime\n", "from matplotlib import pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "from pytz import timezone" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "with open('schedules_times.pickle', 'rb') as schedule_times_file:\n", " weekday_stop_times, sat_stop_times, sun_stop_times = pickle.load(schedule_times_file)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Let's define a function to make plotting these easier." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_schedule_dist(times, schedule_str=\"Weekday\"):\n", " vals, bin_edges = np.histogram(times, 100)\n", " fig, ax = plt.subplots()\n", " ax.plot([datetime.fromtimestamp(x // 1000, tz=timezone('UTC')) for x in bin_edges][:-1], vals)\n", " plt.title(\"Distribution of %s Stop Times\" % schedule_str)\n", " plt.xlabel(\"Time\")\n", " plt.ylabel(\"Number of Stops\")\n", " fig.autofmt_xdate()" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "plot_schedule_dist(weekday_stop_times, \"Weekday\")" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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9t9xJlGHpUuDzz4Fp08TEeI0by52ofKdOAWPHiilAmPEp732zws8l+f/Mg7Bz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\nf//3f/TSSy9RXl4eERENHTqU9u/fr/c8t2/fJhc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"text": [ "" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "plot_schedule_dist(sat_stop_times, 'Saturday')" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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MAGDUqFHYsmULCgoKEBcXh8uXL8PNzQ2tW7dGkyZNEBUVBSLChg0bNI9huu3o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\nFRYWEhHR8OHDad++fbWa5fjx4/9v796Doir7OIB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"text": [ "" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "plot_schedule_dist(sun_stop_times, \"Sunday\")" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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V6599xn/Wrwf+/BO4eBHYtKn28xHlkMn4pLm7d4VOQqpC6YPPsrOzMW7cOKxb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\ngezsbJibm9dYnqKLVTVr1gyOjo5o3rw5vvrqKwC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"text": [ "" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Clearly, weekdays involve two modes of stops (for the two rush hours) while the weekends only involve one. As well, while the shape of the distributions for Saturday and Sunday are very similar, Sundays have fewer stops than Saturdays, presumably because it is less busy." ] }, { "cell_type": "heading", "level": 1, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Punctuality" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "What are the factors that affect the punctuality of TTC vehicles?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Punctuality of vehicles can be determined by finding the times when vehicles stop at stops and comparing it to when they were scheduled to. This actually involved a fair bit of logic, which can be found in `punctuality.py`. The results are stored in the MongoDB database." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from pymongo import MongoClient, ASCENDING, DESCENDING\n", "client = MongoClient()\n", "db = client.datasummative\n", "punctuality_collection = db.punctuality" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "We'll begin by looking at the distribution of punctuality values." ] }, { "cell_type": "code", "collapsed": false, "input": [ "punctualities = []\n", "for punc_row in punctuality_collection.find(fields={'punctuality': 1}):\n", " punctualities.append(punc_row['punctuality'] / (1000 * 60))\n", "punctualities_series_orig = pd.Series(punctualities)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "n, bins, patches = plt.hist(punctualities_series_orig,bins=300)\n", "t = plt.title(\"Punctuality of TTC Vehicles\")\n", "t = plt.xlabel(\"Minutes Late\")\n", "t = plt.ylabel(\"Number of Instances\")" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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4OLi4uMDFxQXbt2831CESEdEDMFgALVy4ECkpKc3a/Pz8cPbsWZw6dQouLi6IjIwEAGRn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\n8UyIiMjAunUApaSkwM3NDRq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"text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "This appears clearly right-skewed. From last time, we can import some statistical functions and apply them here." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def pearsons_index(series):\n", " return 3 * (series.mean() - series.median()) / series.std()\n", "def data_range(series):\n", " return series.quantile(1) - series.quantile(0)\n", "def interquartile_range(series):\n", " return series.quantile(.75) - series.quantile(.25)\n", "def outlier_range(series):\n", " iqr = interquartile_range(series)\n", " return (series.quantile(.25) - 1.5 * iqr, series.quantile(.75) + 1.5 * iqr)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "print(punctualities_series_orig.describe())\n", "print(\"Pearson's index: \", pearsons_index(punctualities_series_orig))\n", "print(\"Interquartile range: \", interquartile_range(punctualities_series_orig))\n", "print(\"Data range: \", data_range(punctualities_series_orig))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "count 222510.000000\n", "mean 10.598743\n", "std 55.564036\n", "min 0.000000\n", "25% 1.350000\n", "50% 3.483333\n", "75% 7.916667\n", "max 1386.633333\n", "dtype: float64\n", "Pearson's index: 0.384173486614\n", "Interquartile range: 6.56666666667\n", "Data range: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 1386.63333333\n" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Once again, this data is clearly dirty and requires some cleaning up. The maximum value, 1387, corresponds to just over 23 hours, which is obviously wrong. We'll remove the outliers and try again.\n", "\n", "This time, we do not use 1.5 \\* IQR to remove the outliers. Instead, I'm making a judgement call and cutting off the data at a maximum of 40 ( = 40 minutes), since most of the data drops off after about 35. Using the 1.5 \\* IQR would have led to the highest value being 18 minutes, and would remove 10% of the data from the series. At 40 minutes, only approximately 1% of the dataset is removed." ] }, { "cell_type": "code", "collapsed": false, "input": [ "outlier_r = outlier_range(punctualities_series_orig)\n", "punctualities_series = pd.Series(x for x in punctualities\n", " if x >= outlier_r[0] and x <= 40)\n", "print(punctualities_series.describe())\n", "print(\"Pearson's index: \", pearsons_index(punctualities_series))\n", "print(\"Interquartile range: \", interquartile_range(punctualities_series))\n", "print(\"Data range: \", data_range(punctualities_series))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "count 219122.000000\n", "mean 5.580403\n", "std 6.187868\n", "min 0.000000\n", "25% 1.350000\n", "50% 3.416667\n", "75% 7.466667\n", "max 39.983333\n", "dtype: float64\n", "Pearson's index: 1.04902199392\n", "Interquartile range: 6.11666666667\n", "Data range: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 39.9833333333\n" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "n, bins, patches = plt.hist(punctualities_series,bins=20)\n", "t = plt.title(\"Punctuality of TTC Vehicles\")\n", "t = plt.xlabel(\"Minutes Late\")\n", "t = plt.ylabel(\"Number of Instances\")" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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VX1/PmDFj+OKLL7okQFdQD0VExHku76FYLBYqKyvN95WVlbrbsIiINNHq7et/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\n6xNKfX09Dz30ENnZ2RQUFLB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"text": [ "" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Here, we can see that the data is right-skewed, which makes sense since most vehicles arrive close to on top, whereas fewer and fewer vehicles arrive later. The Pearson's Index is greater than one, which confirms that the data is right-skewed." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "We can plot this on a box-and-whiskers plot, for the fun of it." ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.boxplot(punctualities_series, True, '+', vert=False, whis=np.inf)\n", "plt.xlim(0, 50)\n", "t = plt.title(\"Punctuality of TTC Vehicles\")\n", "t = plt.xlabel(\"Minutes Late\")\n", "t = plt.yticks((1,), ('TTC Vehicles',), rotation=90)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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FgAOApQhwALAUAQ4Alvo/u7+h0/DDnO0AAAAASUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 15 }, { "cell_type": "heading", "level": 2, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Routes and Punctuality" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "How is punctuality across routes?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# rt_and_punc = {}\n", "# for punc_row in punctuality_collection.find(fields={'punctuality': 1,\n", "# 'rt_tag': 1}):\n", "# rt_tag = punc_row['rt_tag']\n", "# if rt_tag in rt_and_punc:\n", "# rt_and_punc[rt_tag].append(punc_row['punctuality'] / (1000 * 60))\n", "# else:\n", "# rt_and_punc[rt_tag] = [punc_row['punctuality'] / (1000 * 60)]\n", "# ## convert to panda series, then find median\n", "# rt_and_punc_medians = {}\n", "# for rt in rt_and_punc:\n", "# s = pd.Series(rt_and_punc[rt])\n", "# rt_and_punc_medians[rt] = s.median()\n", "with open('median_punctuality.pkl', 'rb') as median_punc_file:\n", " rt_and_punc_medians = pickle.load(median_punc_file)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "N = len(rt_and_punc_medians)\n", "ind = np.arange(N)\n", "d = list(zip(*rt_and_punc_medians.items()))\n", "p = plt.bar(ind, d[1])\n", "t = plt.xlabel('Routes')\n", "t = plt.ylabel('Median Late Time (minutes)')\n", "t = plt.xticks(ind + 0.4, d[0], rotation=90)\n", "t = plt.title('Median Late Times of Routes')" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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vP599U8Yb++wbWnfL89n/Oe6dw2dfrwDtb7/9hkOHDuH69evib6NHj7aehdWN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\ng/g94N0AAAAASUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Well that's pretty much useless. Let's try again as a histogram." ] }, { "cell_type": "code", "collapsed": false, "input": [ "p = plt.hist(d[1], bins=30)\n", "t = plt.xlabel('Median Late Time (minutes)')\n", "t = plt.title('Median Late Times of Routes')" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Again, this is left-skewed, although taking the medians also forms two clusters. The mode is around 3 minutes." ] }, { "cell_type": "heading", "level": 2, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Velocity and Punctuality" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Are the median velocities of routes correlated with the median punctuality of routes?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "# velocity_collection = db.velocity\n", "# routes_velocity = {}\n", "# for velocity in velocity_collection.find(fields={'route_tag': 1,\n", "# 'vx': 1,\n", "# 'vy': 1}):\n", "# rt_tag = velocity['route_tag']\n", "# vel = math.hypot(velocity['vx'], velocity['vy'])\n", "# if rt_tag in routes_velocity:\n", "# routes_velocity[rt_tag].append(vel)\n", "# else:\n", "# routes_velocity[rt_tag] = [vel]\n", "# routes_velocity_median = {}\n", "# for rt in routes_velocity:\n", "# s = pd.Series(routes_velocity[rt])\n", "# routes_velocity_median[rt] = s.median()" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "import pickle\n", "# with open('median_velocities.pkl', 'wb') as median_vels_file:\n", "# pickle.dump(routes_velocity_median, median_vels_file)\n", "# with open('median_punctuality.pkl', 'wb') as median_punc_file:\n", "# pickle.dump(rt_and_punc_medians, median_punc_file)\n", "with open('median_velocities.pkl', 'rb') as median_vels_file:\n", " routes_velocity_median = pickle.load(median_vels_file)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "routes = []\n", "puncs = []\n", "velocities = []\n", "for rt in rt_and_punc_medians:\n", " if rt in routes_velocity_median:\n", " routes.append(rt)\n", " puncs.append(rt_and_punc_medians[rt])\n", " velocities.append(routes_velocity_median[rt])\n", "routes_puncs_df = pd.DataFrame({'route': routes, 'punc': puncs, 'vel': velocities}, index=routes)\n", "del routes\n", "del velocities\n", "del puncs" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "p = plt.scatter(routes_puncs_df['vel'], routes_puncs_df['punc'])\n", "t = plt.title('Median Punctuality vs. Median Velocity of Routes')\n", "t = plt.xlabel('Median Velocity (m/s)')\n", "t = plt.ylabel('Median Punctuality (minutes)')" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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u2LABu3fvRtu2bbFjxw4MGDAA06ZNq3KisbCwkD+YpKenhzZt2iAuLu5dEqpR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\njx8vtzqK4vPPv6TB4E6zuR4lqSq/+absE9vlxY0bNzh8+Ci2bNmZI0a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"text": [ "" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "This appears to indicate little correlation. We can verify this by running a least-squares regression." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from pandas.stats.api import ols\n", "res = ols(y=routes_puncs_df['punc'], x=routes_puncs_df['vel'])\n", "print(res)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "-------------------------Summary of Regression Analysis-------------------------\n", "\n", "Formula: Y ~ + \n", "\n", "Number of Observations: 172\n", "Number of Degrees of Freedom: 2\n", "\n", "R-squared: 0.0274\n", "Adj R-squared: 0.0217\n", "\n", "Rmse: 6.8197\n", "\n", "F-stat (1, 170): 4.7845, p-value: 0.0301\n", "\n", "Degrees of Freedom: model 1, resid 170\n", "\n", "-----------------------Summary of Estimated Coefficients------------------------\n", " Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%\n", "--------------------------------------------------------------------------------\n", " x 0.8392 0.3836 2.19 0.0301 0.0872 1.5911\n", " intercept 3.9364 1.9720 2.00 0.0475 0.0713 7.8014\n", "---------------------------------End of Summary---------------------------------\n", "\n" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "0.027 is a remarkably bad R-squared value. There is no correlation and the data does not fit." ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.hold(True)\n", "p = plt.scatter(routes_puncs_df['vel'], routes_puncs_df['punc'])\n", "t = plt.title('Median Punctuality vs. Median Velocity of Routes')\n", "t = plt.xlabel('Median Velocity (m/s)')\n", "t = plt.ylabel('Median Punctuality (minutes)')\n", "m, b = 0.8392, 3.9364\n", "p = plt.plot(np.arange(1, 11), np.poly1d((m, b))(np.arange(1,11)), '-r')\n", "plt.hold(False)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Nt99+O8vg37Pvhw4dSlJSEg4ODgQEBNCqVassZxE5bZudnN5r1qwZXbp0wcfHh9dee43g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\n8gt1unqFlpX+oUKhLTUe5ueff6ZW60qNZhh1umjWq1e/wim+SbJfv6H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"text": [ "" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Punctuality and Number of Trips\n", "Does a route having more trips make it more punctual on median?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "with open('trips_routes.pkl', 'rb') as trips_routes_file:\n", " routes_trips = pickle.load(trips_routes_file)\n", "rts = []\n", "num_trips_s = []\n", "for rt, num_trips in routes_trips:\n", " rts.append(rt)\n", " num_trips_s.append(num_trips)\n", "routes_trips_df = pd.DataFrame({'route': rts, 'num_trips': num_trips_s}, index=rts)\n", "del rts\n", "del num_trips_s\n", "del routes_trips\n", "puncs_trips_df = routes_puncs_df.merge(routes_trips_df, right_index=True, left_index=True)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "res = ols(y=puncs_trips_df['punc'], x=puncs_trips_df['num_trips'])\n", "print(res)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "-------------------------Summary of Regression Analysis-------------------------\n", "\n", "Formula: Y ~ + \n", "\n", "Number of Observations: 172\n", "Number of Degrees of Freedom: 2\n", "\n", "R-squared: 0.1862\n", "Adj R-squared: 0.1814\n", "\n", "Rmse: 6.2380\n", "\n", "F-stat (1, 170): 38.9004, p-value: 0.0000\n", "\n", "Degrees of Freedom: model 1, resid 170\n", "\n", "-----------------------Summary of Estimated Coefficients------------------------\n", " Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%\n", "--------------------------------------------------------------------------------\n", " x -0.0050 0.0008 -6.24 0.0000 -0.0065 -0.0034\n", " intercept 11.4722 0.7205 15.92 0.0000 10.0601 12.8844\n", "---------------------------------End of Summary---------------------------------\n", "\n" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_with_reg(x, y):\n", " plt.hold(True)\n", " plt.plot(x,y, 'b.')\n", " res = ols(y=y, x=x)\n", " m = res.beta['x']\n", " b = res.beta['intercept']\n", " x_fit = np.linspace(min(x), max(x))\n", " y_fit = np.poly1d((m,b))\n", " plt.plot(x_fit, y_fit(x_fit), '-k')\n", " plt.hold(False)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "plot_with_reg(puncs_trips_df['num_trips'], puncs_trips_df['punc'])\n", "t = plt.title('Number of Trips vs. Punctuality')\n", "t = plt.xlabel('Number of Trips (per six weeks)')\n", "t = plt.ylabel('Punctuality (minutes)')\n", "t = plt.xlim(0,5000)\n", "t = plt.ylim(0, 40)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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VBSAKDISQ1kLpZqXXr19j+PDhuCz+aK4mjakaKdLeT01BhJDWSGPbhNZWWlqq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\nxcfHIz4+nvt51apVLXeGdFl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"text": [ "" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "The fit, although weak in the strict sense (R-squared = 0.1862), does indicate that an increase in number of trips tends to decrease the median time waiting for a route. This makes sense, since more frequent vehicles means that even if one is missed, or is late, it is very difficult for many vehicles to be late at once." ] }, { "cell_type": "heading", "level": 2, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Ridership and Punctuality" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Are there correlations between ridership and punctuality?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ridership_collection = db.ridership\n", "rts = []\n", "costs = []\n", "riders = []\n", "for r in ridership_collection.find():\n", " rts.append(r['route'])\n", " costs.append(r['cost'])\n", " riders.append(r['ridership'])\n", "ridership_df = pd.DataFrame({'route': rts, 'cost': costs, 'ridership': riders},\n", " index=rts)\n", "ridership_punc_df = routes_puncs_df.merge(ridership_df, right_index=True, left_index=True)\n", "del rts\n", "del costs\n", "del riders\n", "print(ridership_punc_df.shape)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(136, 6)\n" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "print(ols(x=ridership_punc_df['ridership'], y=ridership_punc_df['punc']))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "-------------------------Summary of Regression Analysis-------------------------\n", "\n", "Formula: Y ~ + \n", "\n", "Number of Observations: 136\n", "Number of Degrees of Freedom: 2\n", "\n", "R-squared: 0.1538\n", "Adj R-squared: 0.1475\n", "\n", "Rmse: 5.0319\n", "\n", "F-stat (1, 134): 24.3621, p-value: 0.0000\n", "\n", "Degrees of Freedom: model 1, resid 134\n", "\n", "-----------------------Summary of Estimated Coefficients------------------------\n", " Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%\n", "--------------------------------------------------------------------------------\n", " x -0.0002 0.0000 -4.94 0.0000 -0.0002 -0.0001\n", " intercept 9.0460 0.5849 15.47 0.0000 7.8996 10.1924\n", "---------------------------------End of Summary---------------------------------\n", "\n" ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "plot_with_reg(ridership_punc_df['ridership'], ridership_punc_df['punc'])\n", "t = plt.ylim(0, 30)\n", "t = plt.title(\"Punctuality vs. Ridership\")\n", "t = plt.xlabel(\"Weekday Ridership\")\n", "t = plt.ylabel(\"Punctuality (minutes)\")" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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LFC3BREFE1HSt7oY7IiIyD0wURESkERMFERFpxERBREQaMVEQEZFGTBRERKQREwUREWnE\nREFERBoxURARkUZMFEREpBETBRERacREQUREGjFRaCE2FlAogMcfB7ggHxG1NUwUWjh7FjhwANi9\nW5U0iIjaEiYKLVhZqf4NCQHWrTNuLEREhsb1KLSgVKpqEuvWAV26GDsaIqL6ceEiIiLSiAsXERGR\nUVg09oLMzEwcPHgQOTk5kMlkcHd3x/Dhw+Hr62uI+BoVG6vqbLayArZuZdMQEZGuNdj0tGXLFqxe\nvRpdu3ZFaGgoXF1dIYRAfn4+kpOTcfPmTSxYsACzZs3SfVBNqD4pFKoRSQAwZQqwbZvOwyEiahX0\n1fTUYI2iqKgIv/76K2xtbet9vri4GJs2bdJ5QE3FEUlERPrV6juzOSKJiEjFaJ3Zb731Fm7fvo0H\nDx5gxIgRcHJywpYtW3QeSHN16aJqbmKSICLSj0YTxZ49e2Bvb49du3bB3d0dFy5cwIcffmiI2IiI\nyAQ0migqKioAALt27cLkyZNhb28PmUym98CIiMg0NDo8dvz48fD29kanTp3wz3/+E9evX0enTp0M\nERsREZkArTqzb926BXt7e7Rv3x6lpaW4c+cOXFxc9BcU78wmImoyo3Vml5aWYs2aNXjxxRcBAHl5\neTh+/LjOAyEiItPUaKKYPXs2OnTogMTERACAq6sr/vznP+s9MCIiMg2NJooLFy5g4cKF6NChAwDA\n2tpaqx3n5uYiMjISvr6+8PPzw6pVqwComrFGjRqFAQMGYPTo0VByJSAiIpPWaKLo2LEjysrKpMcX\nLlxAx44dG92xpaUlPvnkE2RmZuLIkSNYs2YNTp06heXLl2PUqFE4e/YsRowYgeXLl7esBEREpFeN\nJoq4uDiMHTsWV65cwYwZM/DYY49hxYoVje7YxcUFQUFBAAAbGxsMHDgQV69exc6dOxETEwMAiImJ\nwY4dO1pYBCIi0ietRj3dvHkTR44cAQCEhYWhW7duTTpITk4OHn30UZw8eRK9e/dGUVERAEAIAUdH\nR+mxFBRHPRERNZnBJwWsNmLECPz666948skn62zTRklJCZ5++mmsXLmyzgSDMpmswZv34uLipN8V\nCgUUCoVWxyMiaisSEhKQkJCg9+M0WKMoKyvD3bt3ERkZqRZIcXExxo4di9OnTze68wcPHuDJJ5/E\nuHHj8NprrwEAvL29kZCQABcXF+Tn5yMyMrLOvlijICJqOoPXKNauXYuVK1ciLy8PgwYNkrbb2tri\nD3/4Q6M7FkJg7ty58PHxkZIEAEyYMAGbN2/GwoULsXnzZkRFRbWwCEREpE+N9lGsXr0ar776apN3\n/Ntvv+GRRx5BQECA1Ly0bNkyhIaGYurUqbh8+TLc3d2xbds2dKk19StrFERETaevc2ejiWLz5s31\n9iM8++yzOg9GCoqJgoioyYzWmX3s2DEpUZSVlWHfvn0IDg7Wa6IgIiLT0eQV7pRKJaZNm4aff/5Z\nXzGxRkFE1AxGmxSwNisrK2RnZ+s8ECIiMk1arUdRraqqCllZWZg6dapegyIiItPRaNNTzXsoLCws\n0KdPH/Tq1Uu/QbHpiYioyYw26skYmCiIiJrOaH0U3333Hfr37w87OzvY2trC1tYWdnZ2Og+EiIhM\nU6M1Ck9PT+zatQsDBw40VEysURARNYPRahQuLi4GTRJERGRaGh31FBISgmnTpiEqKkpa5U4mk2HS\npEl6D46IiIyv0URx+/ZtdO7cGXv27FHbzkRBRNQ2cNQTEZGZMPhcTytWrMDChQvrnTlWJpNh1apV\nOg+GiIhMT4OJwsfHBwDU1qKo1tCqdEREZH7Y9EREZCaMOs340qVLkZOTg4qKCimY9PR0nQejSWws\ncPYsYGUFbN0K1FrrqNHniRrC7w6RZo0mipkzZ+Kjjz6Cn58f2rVr8mSzOnP2LHDggOr32Fhg27bG\nn+cJgLTR2HeLqK1rNFF069YNEyZMMEQsGllZqf4NCQHWrdPueZ4ASBuNfbeI2rpG+yj27NmDb775\nBiNHjjTYDXf1tbMplaqT/bp19dcM6nv+8ceB3btVJ4BffmGNgurX2HeLqLUw2uyxM2fOxJkzZ+Dr\n66vW9LRx40adByMFpaPCansCYBMVEZkDoyUKLy8vnD592qBDYltS2Oac9BWKh01UU6awiYqIWiej\nTQoYERGBrKwsnR9YX6r7JXbvViUNbbCNmoioYY12ZiclJSEoKAgeHh7o2LEjAMMNj21O7aA5J/2t\nW9lGTUTUkEabnnJycurd7u7urodwVKqrT81pEmLHJBG1VQbvo7hz5w5sbW01vlmb1zQrqP8Vtvao\npbffZqczEVFDDJ4oRo4cCS8vL0ycOBEhISFwdHQEABQWFuL48ePYsWMHzp07h7179+o+qP8Vtnbt\ngJ3OREQNM8qop3379mHr1q04fPgw8vLyAACurq4YNmwYZs6cCYVCofOAgIYLy/siiIgaZrThscbQ\nUGHZ/0BE1DCjDY9tiTlz5sDZ2Rn+/v7Stri4OLi5uUEul0MulyM+Pl7r/XXpompuYpIgIjIcvSaK\n2bNn10kEMpkMb7zxBk6cOIETJ05g7Nix+gyBiIhaSK+JYvjw4XBwcKiz3QRbu4iIqAGNJoo33ngD\nmZmZOj3o6tWrERgYiLlz50KpVOp030REpFuN3pk9cOBAxMbG4sGDB5gzZw6mT58Oe3v7Zh/wpZde\nwuLFiwEA77zzDt58802sX7++zuvi4uKk3xUKhd5GWBERtVYJCQlISEjQ+3G0HvV0+vRpbNq0CVu3\nbsWwYcMwb948REZGNvq+nJwcjB8/HhkZGVo/x6VQiYiazqijniorK3H69GmcOnUK3bp1Q2BgIP7v\n//4P06ZNa/IB8/Pzpd+///57tRFRTRUbq7oJ7/HHVUNniYhI9xqtUbz++uv48ccf8dhjj+H5559H\naGio9JyXlxfOnDnT4HunT5+OAwcO4ObNm3B2dsZ7772HhIQEpKWlQSaTwcPDA2vXroWzs7N6UFpm\nRd6pTUT0kNFuuNu4cSOmTp0Ka2vrOs8plUp00cNNDdoWlndqExE9ZLSmpy1bttRJEiNGjAAAvSSJ\npti6VVWTYJIgItKfBkc9lZWV4e7du7h58yZu3bolbS8uLsbVq1cNElxjqu/UBricKRGRvjSYKNau\nXYuVK1ciLy8PgwYNkrbb2triD3/4g0GCa4rqle0AVdJgfwURkW402kexevVqvPrqq4aKB0Dz2tnY\nX0FEbZ3BO7P37duHxx57DN999x1kMlmd5ydNmqTzYKSgmlFYzixLRG2dvhJFg01PBw4cwGOPPYYf\nf/zR4ImiOWr2VxARke60qvUoNGFnNhG1dQavUXz88ccNBlE9VbgpYWc2EZF+NJgo7ty5U2+TU3Wi\nMDVWVqp/Q0JU/RRERKQbZtP0xM5sImrrjDaFR1lZGdavX4+srCyUlZVJtYkNGzboPBgpKM4eS0TU\nZEabwiM6OhrXrl1DfHw8FAoFcnNzYWNjo/NAiIjINDVaowgKCkJaWhoCAgKQnp6OBw8eYNiwYTh6\n9Kj+gtJTVuTIKCIyZ0arUXTo0AEAYG9vj4yMDCiVSty4cUPngRhC9cio3btVSYNaL65FQmQ4jS6F\nOm/ePNy6dQvvv/8+JkyYgJKSEixZssQQsekcR0aZDw6HJjIcsxn1pA2OjDIfnNuLqC6jjXp67733\n1IKotnjxYp0HU/M4uigs+yTMF5M+UV0GvzO7mrW1tZQgysrKsGvXLvj4+Og8EH1oSvMEk0rrwrm9\niAynyU1P9+/fx+jRo3Gg+gysB7rKik1pnuD620TU2hlt1FNtpaWlJrPCXWOaslQqO7qJiOrXaNOT\nv7+/9HtVVRWuX7+u1/6JlqrdhKRtzWDrVrZ5ExHVp9Gmp0uXLklVGQsLCzg7O8PS0lK/QbWg+sQm\nJCJqq4zW9PSXv/wF7u7ucHd3h5ubGywtLREdHa3zQHSFTUhERLrVaKI4efKk2uOKigqkpKToLaCW\nakq/BBERNa7BRLF06VLY2toiIyMDtra20k/37t0xYcIEQ8bYJNXDJpkkiIh0o9E+ikWLFmHZsmWG\nigeA7tvZeI8EEbUFRuujGDx4MJQ1Zl1TKpXYsWOHzgPRJ0NOBsjJ6ojI3DSaKN577z10qXEJ3qVL\nF8TFxekzJp0zZAc3Z6glInPTaKKorxpTWVmp1c7nzJkDZ2dntXsxbt26hVGjRmHAgAEYPXq0Wm2l\nJTRdyVd3cPv4AFFR+r3a56grIjI3jSaKQYMG4Y033sCFCxdw/vx5vP766xg0aJBWO589ezbi4+PV\nti1fvhyjRo3C2bNnMWLECCxfvrx5kdei6Uq+uoP70iX9X+1z1BURmZtGE8Xq1athaWmJadOm4Zln\nnkGnTp2wZs0arXY+fPhwODg4qG3buXMnYmJiAAAxMTE66+/Q5kreEFf7HHVl2tiHRNR0el+PIicn\nB+PHj0dGRgYAwMHBAUVFRQBUzVqOjo7SYymoZvTcazPtdO3XcDRU28M798mcGW2a8TNnzuCjjz5C\nTk4OKioqpGD27dvX4oPLZDK1NS5qqtlhrlAooFAoNO6r9rTT9SWB2q/hKmltD/uQyJwkJCQgISFB\n78dptEYREBCAl156CcHBwWjfvr3qTTKZ1v0UtWsU3t7eSEhIgIuLC/Lz8xEZGYnTp0+rB6WDrKjN\nlSNXSWt7uOARmTOj1SgsLS3x0ksv6eyAEyZMwObNm7Fw4UJs3rwZUVFROtt3TdpcOXLG2LaHCx4R\nNV2jNYq4uDh069YNkyZNQseOHaXtjo6Oje58+vTpOHDgAG7evAlnZ2f89a9/xcSJEzF16lRcvnwZ\n7u7u2LZtm9p9GkDLs2JsLJCVBVy4ABw5AvTp0+xdERG1GkZbM9vd3b3efoTs7GydByMF1cLCssOS\niNoiozU95eTk6Pyg+lbd7OTkBOTlqfoiOKqJiKh5Gq1RbN68ud4axbPPPqu/oFqYFas7LPPygMOH\nVdtYsyAic2e0GsWxY8ekRFFWVoZ9+/YhODhYr4kCAMLCwhARESH99OzZU+v3VndYPv646nFzh0Ly\nPgsiombccKdUKjFt2jT8/PPP+ooJMpkMBw4cQGJiovRjbW2tljgCAwNhYaE5z7V0KCT7OoioNTFa\nZ3Zt5eXl8PPzw9mzZ3UeTLXahRVC4Ny5c1LSSEpKQk5ODkJCQqTEMWTIEHTt2lWncfA+CyJqTYyW\nKMaPHy/9XlVVhaysLEydOhUrVqzQeTBSUFoUVqlU4ujRo1LyOHr0KHr27KlW6/Dy8kK7do1OZ6Xh\nGLzPgohaD6MligP/a3sRQsDCwgJ9+vRBr169dB6IWlDNKGxlZSUyMjKQlJQkJY+ioiIMGTJEShyh\noaGwsbHRU9RERMZl8ERRVlaGzz77DOfPn0dAQADmzJkDS0tLnQdQb1A6KmxBQQGSkpJw+PBhJCUl\nIS0tDV5eXmq1jj59+jQ43xQRUWti8EQxdepUdOjQAcOHD8fu3bvRp08frFy5UucB1BtUCwqraaTS\n/fv3kZqaqtZJLpPJEB4eLiWO4OBgtTvQiYhaC4MnCn9/f2kiv4qKCgwePBgnTpzQeQD1BtWCwvbo\nARQUqH6fOBGoXu6ivgQihEBOTo5ac9WZM2cQFBQkJY7w8HC4uLjoqGRERPpj8Psoag49bWwYqim5\nf//h7zVblOqbUlwmk8HDwwMeHh6YMWMGYmMBa+sSlJcno0OHw/j8888xZ84cODo6qjVX+fn5STPp\nEhGZuwYzQHp6OmxtbaXHZWVl0mOZTIbi4mL9R9cMgwYBe/cCcjmwcePD7drMJnv2LPDbbzYAHkOf\nPo/hp59UI71Onz4t1TpWrVqFq1evIjQ0VG1obu2JDYmIzIXeV7hrjpZUnxoa0qrNUFdN903UbLr6\n9NNCnDp1RGquOn78OPr06aNW6+jfvz87yYnIoEzmhjtD0FdhG6MpmWi6S/vBgwdIT0+XRlclJiai\ntLRU6uOIiIjA4MGDYVVdrSEi0gMmCuh/7iVN+2/qXdpXr15V6yTPyMiAj48PIiIiMHToUOzYEYG8\nPDfOI0VEOsNEAfWrehcX4NQp3Z5g66s1VCcPS0vAxkbV79GcY5aVlSElJUVKHD/9lIgHDzoBiEBQ\nUATWrQs5fXLUAAAcLElEQVRHUFCQwe5VISLzY7TZY01JzZabgoKHo5eqNVbjaOz5+jq8a46WmjKl\n+Ympc+fOGDZsGIYNGwYAGDdOID7+AtzdEyGXJ+H559fjwoULGDRokNrQXCcnp+YdkIhIR1pVjUKp\nBAYOVCWJ+pqAGpvttbHn6+uj0LbJqanNYvUd6/bt20hOTpZqHUeOHIGLi4taJ/nAgQNbNH8VEZkv\nNj39j6YO5169gCtXADs7ID297lrZ2o5qqnmi13ZiQH1MSV5ZWYmsrCy1WXNv3LihtlZHWFiY2jBm\nImq7mCi0MGyY5hXtmjuqSRuGmpL8+vXrSEpKkjrKU1NT0a9fP7Vah4eHB4fmErVBTBT1qF0LmDGj\n+Sfrlp7ojTUleXl5OdLS0qRax+HDh1FZWamWOIKDg9GpUyfDBUVERtGmE0VDzUK1awHr1mk+WWvq\nR9DHid4YS6kKIXD58mW1obmnTp1CQECAWvLo0aOH/oPRIy5TS1RXm04UDTULVdcCnJwALy9V34Sm\nk0bt/XTpot+TjakspVpaWork5GQpeSQlJcHOzk4tcfj7++Plly1azcnXVD5bIlPSphNF7Waht99+\neG+DtTVw5Ahw7ZrqtTVnjK2turPb3h74/XcgJqbxk402V64NvcZUl1KtqqrC2bNn1aZbv3LlCtq3\nHwylMgJAOCZOHIIdOxyNHWqDTPWzJTImvc1qIUxQ7bCKioSYMkX1rxBCPPqoEIDqZ8oUIRwcHj6O\nimp4v0OHqr9v3DjV7yEhD/ddW+1jzZun2jZuXMPxNBS3KSssLBQhIT8J4C/C1vYxYWNjIwYOHCjm\nzp0r1q9fL06dOiUqKyuNHaakNX22RIair1N6q0gUtdU+wY8cqXosl9d/4qg+uTs5qb9Pm5ONm5vq\nPXZ2QuTk1J8UtEk49SUYU1Pz83jw4IFISUkRn376qZgxY4Zwd3cXjo6O4oknnhDvv/++2Ldvnygp\nKTF2yERUg74SRatoeqqtdsdzYx3RNRczcnUFMjO1b6qoPeS2pKRuk4c2HeH6nn7EEPLy8qShuYcP\nH0Z6ejq8vb3V+jp69+7NoblERtKm+yhaytERKCpS/R4VBXz/vep3bfofareFV7+vqaOjqvdTzRw6\nYO/du1dnadn27durJQ65XI4OHTro/Ngc9WT6+DcyPLNLFO7u7rCzs0P79u1haWmJ5OTkh0HpuLCj\nRj1czGjfvoaH19Z34tbVsNnGph8xB0IIXLx4UW101blz5yCXy9Xmr+revXuLj8VRT6aPfyPDM7tE\n4eHhgZSUFDg61h1Zo+vCNnSy18fIGUPfq2Hq7ty5I81fdfjwYRw9ehROTk5qtQ4fH58mLy3LUU+m\nj38jwzPLRHH8+HF07dq1znOGWrhIHyduXkVpVlVVhVOnTqk1V127dk1tadmwsDDY29vX+35dTftO\n+tcWL4yMzewSRd++fWFvb4/27dvjhRdewLx58x4GZaQV7hrSlLZWXV5FtZU23ps3b+LIkYdLy6ak\npMDDw0Ot1uHp6QmZTMZETKSB2SWK/Px89OjRAzdu3MCoUaOwevVqDB8+XBVUCwur6xNsU05OuryK\naqsnxQcPHuD333+XmquSkpJw7949RERE4PTpCJw5E4Hg4EH49dfOZps8iZrD7BYuqp5rqFu3bnjq\nqaeQnJwsJQoAiIuLk35XKBRQKBRa77vmYkO1FzdqjvoWNGpIly66O6E35bjmxNLSEiEhIQgJCcH8\n+fMBALm5uUhKSsL+/Ym4fv0NnD6diTFj/NRqHT179jRy5ESGlZCQgISEBL0fxyg1irt376KyshK2\ntrYoLS3F6NGj8e6772L06NGqoFqYFaubf2xsgCFDgO3bWz5iqaW1BE21nJauhdEW3b17F8ePH1fr\n67C2tlZLHAEBAVxaltoUs2p6ys7OxlNPPQUAqKiowMyZM7Fo0aKHQdVT2KY0JymVwIABwI0bqse6\nbrZpKBZvb9XwV0tL4Phx9YWTNDUjmWoTU3OSm7EIIXDu3Dm15qpLly4hJCQE4eHh0tDc+gZPEJmL\nNj3XkxDqU2d4eDQ+HYY202pUa+r0Gg3N7WRv/3C7m5v28TQlVkNqqJyNPddSuprupKioSMTHx4vF\nixeLkSNHCltbW+Hl5SVmz54tPv/8c5GZmWlS81cRtZS+TumtJlHUPJnWntyvPk2ZNK6pJ72GTuzV\nc0lZWanmhdI2HlOd4M5YyU1fSaiiokKkpaWJf/zjH2LWrFmib9++okuXLmLcuHFiyZIlYu/evaK4\nuFh3ByQysDafKGqeTHV9kmpq7WPoUCFcXOomg5wcVU2i9nZTUvtqXdPVu7GSmyFrWPn5+eI///mP\n+H//7/+JoUOHCisrKxEUFCRefvll8cUXX4iLFy+Kqqoq/QZBpCP6ShStcq4nbTt562tHr29bQ/ur\n77UN9SdoOtaFC6r+isYWVjKE2vFfv256/SPG7MS/f/8+Tpw4oba0LIA6S8t27NjRsIERaaHN91E0\nR31NGE1p1mjKlOKNHUtf7flNVTt+U+0fMRVVVVUiOztbfPnll+KVV14RcrlcWFlZifDwcPHmm2+K\n7777TuTn5xs7TCIhhP5qFEa7j8IQ6rsPoSn3JtT32q1b67/arX6tjY1qplql8uE2e3vg9m3j3g8R\nGwv8+CNQVgZ07w58+60q/obKQyoymQzu7u5wd3fHjBkzAAAlJSU4duwYDh8+jPXr1+P555+Hg4OD\nWq3Dz8+vyfNXEZmqVtn0pK36mjCa0qzR1NfWHpK7bp3q/R9+CLz1lnFPxjWbnKrjM4VmJsD0hto2\nVVVVFc6cOaPWXJWfny/NXxUeHo4hQ4agS2srGLU6ZnUfRWMaK6ypnFhq90GcOQPcvFl3nqfa8Vav\n+W3I+Guuh+HoqJry3Fh9JrU/j6go0+snqU9TvneFhYXS/FVJSUk4duwY+vTpo1br6N+/Pxd5Ip1i\noqhB2xvU9J1Qal+lA4CbG5CRoX4sQ3Yga7rLe/ZsVU/JzZvqq/YZ+sRc+/Oob9VAU1L9maanP1wA\nq7HPrfbfwcamAunp6dLNgImJiSgpKVFLHCEhIbCqbq80IENeeJnKRZ65Ymd2Ddp2wOpiPL6m4aPV\ncVTfaFc7nobW6jb0PQi1y2DsDuzaxzfV+0iq1R6UoM3nps1378qVK2L79u3i9ddfF2FhYcLKykqE\nhISI+fPni6+//lpcvnxZ10Vpdqyt8Vhtkb5O6SabKJo7vr8mXZwQXVwefrEnTqw/jpyc+uOp+Z/C\nze3h84a4B8HGRoiRI1XHqP2f09gnZmMfv6mqP9OgICGiorSLuznfvbt374pDhw6JFStWiIkTJ4pu\n3boJNzc3MXXqVPH3v/9dJCcni/Lycun1urqD3ZAXDsa+SDF3bS5RNOXKY9481QndweHhyVEI3ZyQ\nHBwexhEV1bT3GuM/RVGREN26qX92/M/ZMs35Huniu1dVVSXOnTsnNm3aJF544QXh7+8vrK2txSOP\nPCIWLlwofH1/EMD1Fl+dGzJxt7aLhNamzSWKppzcajcN6LJKO3Kkap9yedO/3Mb6T9HamnZIe0ql\nUvz8888iLi5OdO06SgB2omP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"text": [ "" ] } ], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "This indicates that there is a weak negative correlation between the number of riders and the average lateness of the routes. This is expected, since routes with more riders tend to come more frequently and thus are less late less often than routes with fewer riders." ] }, { "cell_type": "heading", "level": 2, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Ridership and Velocity" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Does having more riders slow down vehicles?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(ols(x=ridership_punc_df['ridership'], y=ridership_punc_df['vel']))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "-------------------------Summary of Regression Analysis-------------------------\n", "\n", "Formula: Y ~ + \n", "\n", "Number of Observations: 136\n", "Number of Degrees of Freedom: 2\n", "\n", "R-squared: 0.0104\n", "Adj R-squared: 0.0030\n", "\n", "Rmse: 0.9908\n", "\n", "F-stat (1, 134): 1.4055, p-value: 0.2379\n", "\n", "Degrees of Freedom: model 1, resid 134\n", "\n", "-----------------------Summary of Estimated Coefficients------------------------\n", " Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%\n", "--------------------------------------------------------------------------------\n", " x -0.0000 0.0000 -1.19 0.2379 -0.0000 0.0000\n", " intercept 4.8046 0.1152 41.72 0.0000 4.5789 5.0303\n", "---------------------------------End of Summary---------------------------------\n", "\n" ] } ], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "plot_with_reg(x=ridership_punc_df['ridership'], y=ridership_punc_df['vel'])\n", "t = plt.ylabel(\"Median Velocity (m/s)\")\n", "t = plt.xlabel(\"Weekday Ridership\")\n", "t = plt.title(\"Velocity vs. Ridership\")" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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PKSkpSXqsVCqhVCob3QdB2znpeXnAvXvqx8OGATk52uWVq2KJyJwlJyfLvq8c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\nRx99BBubP/+UraVsAKBQKDB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"text": [ "" ] } ], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "There is almost no correlation between ridership and velocity of routes. This means that routes that carry more people are not expected to be much faster or slower than those that do." ] }, { "cell_type": "heading", "level": 2, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Weather" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Is there any correlation between punctuality and temperature or pressure?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# weather_collection = db.weather\n", "# ts = []\n", "# ps = []\n", "# temps = []\n", "# pressures = []\n", "# for punctuality_row in punctuality_collection.find():\n", "# t = punctuality_row['datetime_real']\n", "# p = punctuality_row['punctuality'] / (1000* 60)\n", "# if p > 40:\n", "# continue\n", "# weather_row = weather_collection.find_one({'datetime': {'$lte': t}},\n", "# sort=[('datetime', DESCENDING)])\n", "# ts.append(t)\n", "# ps.append(p)\n", "# temps.append(weather_row['temp'])\n", "# pressures.append(weather_row['pressure'])\n", "# di = {'time': ts, 'punc': ps, 'temp': temps, 'pressure': pressures}\n", "# with open('weather_punc.pkl', 'wb') as weather_punc_file:\n", "# pickle.dump(di, weather_punc_file)\n", "# del ts\n", "# del ps\n", "# del temps\n", "# del pressures\n", "# del di" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 51 }, { "cell_type": "code", "collapsed": false, "input": [ "with open('weather_punc.pkl', 'rb') as weather_punc_file:\n", " di = pickle.load(weather_punc_file)\n", "weather_punc_df = pd.DataFrame(di, index=di['time'])\n", "del di" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 52 }, { "cell_type": "code", "collapsed": false, "input": [ "print(ols(x=weather_punc_df['temp'], y=weather_punc_df['punc']))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "-------------------------Summary of Regression Analysis-------------------------\n", "\n", "Formula: Y ~ + \n", "\n", "Number of Observations: 491464\n", "Number of Degrees of Freedom: 2\n", "\n", "R-squared: 0.0003\n", "Adj R-squared: 0.0003\n", "\n", "Rmse: 5.5336\n", "\n", "F-stat (1, 491462): 154.5662, p-value: 0.0000\n", "\n", "Degrees of Freedom: model 1, resid 491462\n", "\n", "-----------------------Summary of Estimated Coefficients------------------------\n", " Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%\n", "--------------------------------------------------------------------------------\n", " x 0.0241 0.0019 12.43 0.0000 0.0203 0.0279\n", " intercept 4.5434 0.0241 188.32 0.0000 4.4961 4.5907\n", "---------------------------------End of Summary---------------------------------\n", "\n" ] } ], "prompt_number": 55 }, { "cell_type": "code", "collapsed": false, "input": [ "plot_with_reg(weather_punc_df['temp'], weather_punc_df['punc'])\n", "t = plt.title('Punctuality and Temperature')\n", "t = plt.xlabel('Temperature (degrees Celcius)')\n", "t = plt.ylabel('Punctuality (minutes)')" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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TCGoycCtHJmMWJywnGbYozlDdPvJuI1SfDdvPkUyDSqzTKQZAVZW5Xi/BePjy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\n7aD0IpFga5DBHJd1sAMxM9pBqZShJhp0UBmqP0ztIB4VFSzaV7Z843z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"text": [ "" ] } ], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "print(ols(x=weather_punc_df['pressure'], y=weather_punc_df['punc']))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "-------------------------Summary of Regression Analysis-------------------------\n", "\n", "Formula: Y ~ + \n", "\n", "Number of Observations: 491464\n", "Number of Degrees of Freedom: 2\n", "\n", "R-squared: 0.0008\n", "Adj R-squared: 0.0008\n", "\n", "Rmse: 5.5324\n", "\n", "F-stat (1, 491462): 375.3727, p-value: 0.0000\n", "\n", "Degrees of Freedom: model 1, resid 491462\n", "\n", "-----------------------Summary of Estimated Coefficients------------------------\n", " Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%\n", "--------------------------------------------------------------------------------\n", " x -0.2921 0.0151 -19.37 0.0000 -0.3217 -0.2626\n", " intercept 34.0656 1.5092 22.57 0.0000 31.1077 37.0235\n", "---------------------------------End of Summary---------------------------------\n", "\n" ] } ], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [ "plot_with_reg(weather_punc_df['pressure'], weather_punc_df['punc'])\n", "t = plt.title('Punctuality and Pressure')\n", "t = plt.xlabel('Pressure (kPa)')\n", "t = plt.ylabel('Punctuality (minutes)')" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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1HnO7iYz3LOP15rwx2cmOdrd9bddHogeZqn7Z8WRmnm7g9h35CUklJBiOHj1K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\nGG1RkZzZMsYcj301/PegUr3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Ww4erfetlqhHeAUEsw4b1zsOooIA+a3l5JOOU1btli/nZ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\nXsLtSgKgu3F10qHkR3k5sGuX8Vv2LsXVOQ+7d58MLCnuK4m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"text": [ "" ] } ], "prompt_number": 54 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "From this, we can see that neither temperature nor pressure has any real effect on the punctuality of the vehicles." ] } ], "metadata": {} } ] }