{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook, we will \n", " * Create several plots to summarize individual features within our dataset\n", " * Apply several filtering and grouping operations on our pandas DataFrames\n", " * Compare numerical data across different categorical groups\n", " * Summarize our data with summary statistics\n", " \n", "We will examine [Austin Bikeshare](https://www.kaggle.com/jboysen/austin-bike) data." ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "\n", "%matplotlib inline\n", "sns.set()" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# read in data from a csv file\n", "df = pd.read_csv('../Data/austin_bikeshare_trips.csv')" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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bikeidcheckout_timeduration_minutesend_station_idend_station_namemonthstart_station_idstart_station_namestart_timesubscriber_typetrip_idyear
08.019:12:00412565.0Trinity & 6th Street3.02536.0Waller & 6th St.2015-03-19 19:12:00Walk Up99000828822015.0
1141.02:06:0462570.0South Congress & Academy10.02494.02nd & Congress2016-10-30 02:06:04Local365126176822016.0
2578.016:28:27132498.0Convention Center / 4th St. @ MetroRail3.02538.0Bullock Museum @ Congress & MLK2016-03-11 16:28:27Local36590753662016.0
3555.015:12:00802712.0Toomey Rd @ South Lamar11.02497.0Capitol Station / Congress & 11th2014-11-23 15:12:0024-Hour Kiosk (Austin B-cycle)99003192982014.0
486.015:39:13253377.0MoPac Pedestrian Bridge @ Veterans Drive4.02707.0Rainey St @ Cummings2017-04-16 15:39:13Walk Up144685972017.0
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" ], "text/plain": [ " bikeid checkout_time duration_minutes end_station_id \\\n", "0 8.0 19:12:00 41 2565.0 \n", "1 141.0 2:06:04 6 2570.0 \n", "2 578.0 16:28:27 13 2498.0 \n", "3 555.0 15:12:00 80 2712.0 \n", "4 86.0 15:39:13 25 3377.0 \n", "\n", " end_station_name month start_station_id \\\n", "0 Trinity & 6th Street 3.0 2536.0 \n", "1 South Congress & Academy 10.0 2494.0 \n", "2 Convention Center / 4th St. @ MetroRail 3.0 2538.0 \n", "3 Toomey Rd @ South Lamar 11.0 2497.0 \n", "4 MoPac Pedestrian Bridge @ Veterans Drive 4.0 2707.0 \n", "\n", " start_station_name start_time \\\n", "0 Waller & 6th St. 2015-03-19 19:12:00 \n", "1 2nd & Congress 2016-10-30 02:06:04 \n", "2 Bullock Museum @ Congress & MLK 2016-03-11 16:28:27 \n", "3 Capitol Station / Congress & 11th 2014-11-23 15:12:00 \n", "4 Rainey St @ Cummings 2017-04-16 15:39:13 \n", "\n", " subscriber_type trip_id year \n", "0 Walk Up 9900082882 2015.0 \n", "1 Local365 12617682 2016.0 \n", "2 Local365 9075366 2016.0 \n", "3 24-Hour Kiosk (Austin B-cycle) 9900319298 2014.0 \n", "4 Walk Up 14468597 2017.0 " ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can quickly compute summary statistics on our DataFrame with the `.describe()` method." ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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bikeidduration_minutesend_station_idmonthstart_station_idtrip_idyear
count648508.000000649231.000000629389.000000618479.000000630190.0000006.492310e+05618479.000000
mean471.61521429.1275062582.4706175.8871852584.2382885.384945e+092015.340026
std323.58837987.278642319.8985343.206358320.8409644.925349e+091.019771
min3.0000000.0000001001.0000001.0000001001.0000008.269930e+062013.000000
25%208.0000008.0000002499.0000003.0000002501.0000001.274709e+072014.000000
50%417.00000015.0000002548.0000005.0000002549.0000009.900028e+092015.000000
75%745.00000028.0000002571.0000009.0000002571.0000009.900190e+092016.000000
max5089.00000021296.0000003687.00000012.0000003687.0000009.900353e+092017.000000
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" ], "text/plain": [ " bikeid duration_minutes end_station_id month \\\n", "count 648508.000000 649231.000000 629389.000000 618479.000000 \n", "mean 471.615214 29.127506 2582.470617 5.887185 \n", "std 323.588379 87.278642 319.898534 3.206358 \n", "min 3.000000 0.000000 1001.000000 1.000000 \n", "25% 208.000000 8.000000 2499.000000 3.000000 \n", "50% 417.000000 15.000000 2548.000000 5.000000 \n", "75% 745.000000 28.000000 2571.000000 9.000000 \n", "max 5089.000000 21296.000000 3687.000000 12.000000 \n", "\n", " start_station_id trip_id year \n", "count 630190.000000 6.492310e+05 618479.000000 \n", "mean 2584.238288 5.384945e+09 2015.340026 \n", "std 320.840964 4.925349e+09 1.019771 \n", "min 1001.000000 8.269930e+06 2013.000000 \n", "25% 2501.000000 1.274709e+07 2014.000000 \n", "50% 2549.000000 9.900028e+09 2015.000000 \n", "75% 2571.000000 9.900190e+09 2016.000000 \n", "max 3687.000000 9.900353e+09 2017.000000 " ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice the minimum and maximum values achieved in the **duration_minutes** column.\n", "\n", "The minimum value is 0 minutes. These could be an incorrect value, or it could be a correct value for rides that were terminated immediately after starting. The maximum value is 21296, which is a trip lasting over 350 hours! This is almost certainly an error. This table also shows that 75% of the trips within the dataset last less than 28 minutes.\n", "\n", "\n", "We should examine what fraction of the dataset contains anomalous rides and decide how to treat these rows." ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.7% of trips in raw data are 0 minutes in duration\n" ] } ], "source": [ "duration_0_mins = df[df['duration_minutes'] == 0]\n", "frac_of_dataset = 100 * len(duration_0_mins) / len(df)\n", "\n", "print('{0:.1f}% of trips in raw data are 0 minutes in duration'.format(frac_of_dataset))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As such a small fraction of the dataset contains rides of 0 minute duration, lets filter these trips out, thereby treating them as invalid rides." ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Remove any trip with 0 minute duration\n", "df = df[df['duration_minutes'] > 0]\n", "\n", "# Reset the integer based index of the DataFrame\n", "df.reset_index(drop=True, inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets do the same for trips lasting longer than 2 hours." ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.3% of trips in remaining data are greater than 120 minutes in duration\n" ] } ], "source": [ "greater_than_2_hours = df[df['duration_minutes'] > 120]\n", "frac_of_dataset = 100 * len(greater_than_2_hours) / len(df)\n", "\n", "print('{0:.1f}% of trips in remaining data are greater than 120 minutes in duration'.format(frac_of_dataset))\n", "\n", "# Remove any trip lasting longer than 2 hours\n", "df = df[df['duration_minutes'] <= 120]\n", "\n", "# Reset the integer based index of the DataFrame\n", "df.reset_index(drop=True, inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's spend some time examining the **start_time** column, which tells us when individual bike trips started. \n", "\n", "First, let's convert the string based column to a datetime type, and then let's look at counts of trips by start time hour." ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# convert start_time column to datetime type\n", "df['start_time'] = pd.to_datetime(df['start_time'])" ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Pull out hour of day into a seperate column\n", "df['start_hour'] = df['start_time'].apply(lambda t: t.hour)" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indexstart_hour
007215
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224946
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" ], "text/plain": [ " index start_hour\n", "0 0 7215\n", "1 1 4968\n", "2 2 4946\n", "3 3 971\n", "4 4 654" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Count the number of trips that start by hour\n", "hourly_counts = df['start_hour'].value_counts()\n", "\n", "# reorder by hour of day\n", "hourly_counts.sort_index(inplace=True)\n", "\n", "# Return a DataFrame with the index as an additional column\n", "hourly_counts_df = hourly_counts.reset_index()\n", "\n", "# preview it for ease of understanding\n", "hourly_counts_df.head()" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "data": { "image/png": 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Y4zVML774YvbZZ58kH90AYuXjUqmUmpqaTJ8+vfwdAgAAVMkaA9M999xTqT4A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\n+uRxaO7cudl1112TJH379s2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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.barplot(x='index', y='start_hour', data=hourly_counts_df, color='teal')\n", "\n", "ax.figure.set_size_inches(14,8)\n", "ax.set_title('Number of trips by hour of the day')\n", "ax.set_xlabel('Hour of day')\n", "ax.set_ylabel('Frequency')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voila!\n", "\n", "Does this histogram make sense? What is it telling us? Does the behavior characterized in the plot demonstrate behavior your might expect?\n", "\n", "Here we've plotted counts of number of trips by hour. We can visualize the **duration_minutes** column in a similar way, by binning the column into 5 minute bins." ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [ { "data": { "image/png": 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l8vPz8yzLzMzU4MGD9eijj1qSqcDGjRv1/vvvy8fHR/3799fAgQM1\nYsQIS7KsXLlSS5cuVYUKFTzLrl27pv79+6t///5l9rqU4BI4duyYhg8frqysLH322Wdq3769/va3\nv6lKlSqWZRo/frxGjBih3Nxc+fn5KSsrSxUrVtTUqVMtyyTZ84ecHUtL1apV1bVrV126dEnvv/++\n+vbtq+eff97SH75t27bV4MGD9Yc//EH+/v7Kzs7Wli1bFBYWZlkm5skcO85Tw4YNNW7cOLVp08Yz\nT5s2bVLDhg0tyyTZ8/PcjnNlx21KsudcsU2ZY8dtyo7zJNlzm4qJiSn2984pU6ZYlkmy5++dubm5\nqly5cqFllSpVsvT3zvT0dO3fv19BQUHKyspS1apVdfXqVeXk5FiWKS8vTzk5OYVK8NWrV8t8nlyG\nYRhl+gq3mBMnTiglJUV33HGH7r//fsXHx2vYsGEKCAiwNFdWVpays7Pl6+tb6C9OVnnmmWd0/Phx\nXbp0SUOHDvX8kAsODta4ceMsyRQfH6/du3ffUFpatGhh2V+/Cpw/f165ubmqXr26kpKSLC1SkrR/\n/34lJyd7LvjUrFkzNW7c2NJM0r/nKSgoSFu3bmWefoad5skwDK1bt07JycnKzs6Wn5+fmjdvrkcf\nfdTSXwSkwp/nDzzwgObOnWvp5/n1c3X9NvXYY49ZPld22qakG+eqYLuyeq7s9jtCcduUXf792Wmb\nKu5zyi7/9uy2TRUo+L3Tz89Pvr6+lmaR7Pl7Z2JiohISEtSiRQv5+/srKytLycnJioyM1BNPPGFJ\npvfee0+pqalKTU1V165d9ac//UndunXTqFGj9Mc//tGSTBs2bNDMmTNVp04dzzwdP35cY8eO1SOP\nPFJmr0sJ/hVWrlypxx9/3OoYtmbnclfwS5OVpSUnJ0crVqxQpUqV1KNHD1WsWFGStGzZMvXr18+y\nXAcPHlSVKlV055136u2335aXl5eGDBmi2267zfJMv/vd7/T222/L5XJZnul6CxYs0J///GerYxRi\nh0y7d+9WSEiI8vPztWzZMu3fv1/333+/wsPD5e3tbVmuY8eO6e6775b008VB9u/fr8aNG1v6GXXt\n2jWlpaXp7rvv1vbt25WSkqL69eurbdu2lmW6ePGijh07piZNmmj16tVKSUnRPffco/DwcPn4WHcw\nWVxcnIYPH26bf/+StHz5coWHh1temorKzMyUy+WSn5+fPv30U2VmZqpXr16Wfv++/fZbeXl5qV69\nelq4cKEuXbqkp556Sv7+/pZl+uqrr7R7925dvXpV1apV0+9//3vVq1fPsjwF0tLStGfPHl25ckXV\nqlVT8+bNLb2S9smTJxUbG6vU1FR5e3vL7XarQYMGGjdunIKDgy3LVeD8+fPKy8tT9erVLf/jiiT9\n+OOP2rdvn+f3ziZNmqh69eqWZiqqIJuV8vLydOTIEU+WevXqlflnFCX4Vxg0aJDef/99SzOMHj36\nZx+bM2dOOSaxPzsWzueff1516tRRXl6edu7cqYULF+r222+3dNuaM2eO9u7dq6ysLAUFBem+++6T\nr6+vDh48aNk2ZcdMo0aNKvTL7vbt29WqVStPXqszGYahHTt2WJ6pYFueNWuWLl++rPbt22v79u26\nevWqJk+ebEmm63O9/fbbSk5OVtu2bbV9+3Y1aNDAsiNDnn/+ebVp00YXL15UUlKS2rRpo+TkZN11\n112KiYmxJFPBHpU9e/bo4sWLeuSRR7Rr1y79+OOPlv6MCQ0N1e9+9zu9+OKLevjhhy3Lcb2HHnpI\njRs31tSpU1WnTh2r40iSli5dqnfffVeS1K5dO50/f16BgYHKyspSbGysJZlef/117dixQzk5OapR\no4Zq166toKAg7dq1S2+88YYlmd566y199913atasmTZt2qS6devqxIkTat26tQYMGGBJJkn64IMP\n9Pe//10PPPCAtm3bpsaNG+vo0aOKjIxUx44dLck0aNAgjR49utA51Hv27NHMmTO1bNkySzIVWLdu\nnbZt21bo1k2dO3e23R+mUNixY8cUFxenihUrasSIEZ4/UE+ePLlMT+/knOD/kNUXBSnQuXNnvfba\na5afi1HUli1bfvax0NDQckzyby+99JKncEZERHgK58cff2xZCU5PT9frr78uSfr88881fPhwvffe\ne7Lyb1O7du3SsmXLlJ2dre7du2v+/PmSpMjISDJdp0GDBtq0aZOee+45eXl56ciRI+rbt69leYrL\n9N1331meqcC+ffu0ePFiST+dT23l9+56dro4yI8//qg+ffooMjJS7777rnx8fDR48GDLDpuTfto7\n3bFjRyUkJCghIUGS9Oijj1p6pIokBQcHa8aMGZoxY4beeOMNhYeHq02bNrr99tsty3Tvvfdq5MiR\nGjVqlBo0aKDw8HA1a9bMsjyStGrVKv3zn/9UTk6Ounfvrg0bNsjlclla7LZt26Zly5bp2rVr6tat\nm+bOnStJWr9+vWWZvvzyS8/nU3h4uJ5++mktWLBA/fr1s3SuPvzwQyUkJMjlcunKlSsaM2aMFi5c\nqEGDBllWgu16EbGpU6fK7XYrLCxMvr6+ys7O1ubNm7VlyxZNnz7dkkzLly//2ces+tkcFxf3s4+N\nGjWqHJP828SJE/X//t//U15enp599lnNnj1bjRo10nfffVemr0sJLoGCQ0BSUlLk4+Oj/Px8DRs2\nzNJDQB577DHt3LlT58+fV5cuXSzJUJzExESlpKQU+xd6q0qwHQtnbm6u0tPTFRgYqI4dOyotLU1j\nxoxRbm6uZZncbrfS0tJUo0YNzz0KL126ZOm97eyY6emnn9Z9992nxYsX6+WXX1ZAQIBatmxpWR67\nZjpz5ozWrl0rf39/zxVFz54967mSp1XseHEQ6aefM/Xr19fJkycVHByskydPWprHx8dH+/btU/Pm\nzbVr1y499NBDSk5OtvxKsC6XS7Vq1dK8efN06NAhffTRR3rnnXd0/vx5bdq0ybJMDz74oFauXKkN\nGzbob3/7m1588UX5+/tr9erVlmTKz8/X1atXdfHiRV2+fFmXL19WxYoVLf3szM3N1XfffaeMjAxl\nZGTo3Llzuu222yz9t3f58mWdPn1a//3f/60TJ04oJydHeXl5ln9OXbp0yXMu95UrV3ThwgVVrFjR\n0rmy60XEvv32Wy1atKjQsg4dOlj6B7vvvvtOX3zxhWXn2hYnMDBQS5cu1fDhwy39/beogm5Qu3Zt\nRUVF6f/+7//Kfg9+md6A6RZj1/vI2VFeXp7Rr18/48iRI1ZH8ejbt6/nnoSGYRjvvvuuERUVZen3\nLykpyejcubNx7tw5z7I333zTaNy4sWWZdu3aZfTu3dvIz8/3LIuIiLD0Xnt2zFTg+PHjxpAhQ4we\nPXpYHcXDTpnWrl1rvPbaa8ZTTz1lvPvuu8alS5eMdu3aGVu3brU017vvvmuMGTPG6NKlixEfH29k\nZmYabdu2NdasWWNZpr179xo9evQw/vSnPxlNmzY1unfvbnTo0MHYvn27ZZmOHz9uDBo0yOjWrZvR\nsGFDo0WLFsbjjz9uHDhwwLJMhmEYAwcOtPT1i/Nzma7/uVPe1qxZY/zhD38whg4dasycOdPo3Lmz\n0atXL2PZsmWWZdq6davRs2dPIyYmxnj33XeN1q1bG48++qil90798ssvjXbt2hk9evQwOnbsaOzZ\ns8eYO3eukZiYaFkmwzCM1atXG4888ojxzDPPGB07djQ2bNhgzJ0719J7urrdbuPzzz83YmNjjfHj\nxxuxsbHGZ599ZrjdbssyGcZP9y/etWtXoWU7d+60/LPiqaeeMvbu3WtphqJGjx5t+c/g6w0ePNhY\nv369kZeXZxiGYWzbts3o3r270aVLlzJ9Xc4JLoF+/foVe77Dzy23gp0u1nXy5EldvnzZ8r8OFti2\nbZtefvllJSQkeC5KMG/ePL3xxhtKSUmxON1PfvjhB91xxx06f/68/uu//svqOJL+nclO7Jbp8uXL\nWr9+vbp37251FA87ZpLs970rYBiG50qnVjt69KgyMjIUEBCgOnXqFLpthFVycnJ04cIFSdKdd95p\ncZob2WG7+vHHHwtd8MYwDNudi3jw4EEFBASoRo0aVkfxOHv2rKpVq+a5TodVDMNQRkaGAgMDbbE9\nFcjIyNDJkyd19913y9/fX26329KLCtrViRMnPBfskiQvLy/dd999io6O9pxjaoX09HRdvny50H2V\nrZaTk6OcnBzLrzBe4MyZM3r99dc1duxYz0Xftm/frtjYWK1Zs6bMXpcSXAKTJ0/WtWvXbjgExA73\n5S1gh4t1FcdOP1AK2LFw2vH7RyZzyGSOHTNJ9sxFJvPsmItM5pDJPDvkOnr06M8+ZoerQwNmcU5w\nCUyZMuWG+xI+8sgjeuyxx6yOJumnc3++//57W/71ecyYMZZ/cBdVkMkuBViSrc7PKEAmc8hkjh0z\nSfbMRSbz7JiLTOaQyTw75IqJidHJkydVt27dQnlcLpftfs8DboYSXAIul0uPPfaYbUqv9NOH0YwZ\nM7R3716NGTNGly5dUteuXTVjxgxbXK2vgB0+uIuyY6bOnTtbHeEGZDKHTObYMZMktWjRwuoIN7Bj\nJrt+/+yYy47fPztmsuP3zo6ZJHt8/9555x0NHDhQs2fPttWpEZGRkTdcVLRgp5BVpyzaMVP37t2V\nkZFR7GM3u7NLWbIqEyX4N+7UqVOSpNdee00LFizQ3XffrbNnz2r06NE3XCXPSnb8gWKXTEXva/fJ\nJ59Yel+7Tz75RF26dNHly5c1d+5cHThwQGfOnNHw4cPl6+trSSaJeTKLeTLv4MGDSkpKUmZmpm6/\n/Xbt27dPTZo0sSzP7t27FRISIrfbraV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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Select the bin intervals\n", "bins = np.arange(0,125,5)\n", "\n", "# Return indices of half-open bins to which each value of column belongs.\n", "duration_min_intervals = pd.cut(df['duration_minutes'], bins=bins, include_lowest=True)\n", "five_minute_counts = duration_min_intervals.value_counts(sort=False)\n", "\n", "# Create the plot\n", "ax = five_minute_counts.plot.bar(rot=90, figsize=(16, 6))\n", "\n", "# Formatting\n", "ax.set_title('Number of trips by duration')\n", "ax.set_xlabel('Duration (minutes)')\n", "ax.set_ylabel('Counts')\n", "\n", "# Format x tick labels\n", "labels = ['{}-{}'.format(bins[i], bins[i+1]) for i in range(len(bins)-1)]\n", "ax.set_xticklabels(labels)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An interesting question in this dataset is whether ridership patterns vary on different days of the week. We can begin to examine this by comparing the mean trip trip duration for each day of the week. This will also allow us to examine the mean trip duration for weekdays versus weekend trips." ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Pull out start day of the week into a seperate column where Monday=0, Sunday=6\n", "df['start_day_of_week'] = df['start_time'].apply(lambda t: t.dayofweek)" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmeanstdmin25%50%75%max
start_day_of_week
074269.019.77523620.0986801.07.013.024.0120.0
165626.017.60660418.9374461.06.011.021.0120.0
267188.017.40370318.3516311.06.011.021.0120.0
372337.018.13914019.0048341.07.011.022.0120.0
497297.020.70602420.0957581.08.014.025.0120.0
5132552.025.33631322.2685941.011.018.030.0120.0
6107812.025.30526322.5081041.010.018.031.0120.0
\n", "
" ], "text/plain": [ " count mean std min 25% 50% 75% \\\n", "start_day_of_week \n", "0 74269.0 19.775236 20.098680 1.0 7.0 13.0 24.0 \n", "1 65626.0 17.606604 18.937446 1.0 6.0 11.0 21.0 \n", "2 67188.0 17.403703 18.351631 1.0 6.0 11.0 21.0 \n", "3 72337.0 18.139140 19.004834 1.0 7.0 11.0 22.0 \n", "4 97297.0 20.706024 20.095758 1.0 8.0 14.0 25.0 \n", "5 132552.0 25.336313 22.268594 1.0 11.0 18.0 30.0 \n", "6 107812.0 25.305263 22.508104 1.0 10.0 18.0 31.0 \n", "\n", " max \n", "start_day_of_week \n", "0 120.0 \n", "1 120.0 \n", "2 120.0 \n", "3 120.0 \n", "4 120.0 \n", "5 120.0 \n", "6 120.0 " ] }, "execution_count": 113, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Group the rows by the start_day_of_week and compute summary statistics\n", "df.groupby('start_day_of_week')['duration_minutes'].describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the mean and median trip durations for Saturday and Sunday are larger than those for the remaining days of the week. \n", "\n", "We can visualize these differences by day by incorporating some of the plots from Chapter 1 of OpenStats. As we are comparing numerical data (trip duration) across categories (days of the week), let's use violin plots, which are similar to box plots." ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "data": { "image/png": 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b+14eDmDIVbgPLKuvpEinUwAA20rAsh0opXj8CxLMgTUZwBbDALaCREuIeRMr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\nA9iLLrooeGwYBgYOHIjrrru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NnTvK3KJ9U48B7FVXXYWmpia8+eabcF0XxxxzDPbbb79y\ntI2y9MWqJhTAVmX31OBcCBlc1w0CV4sZWFE8r3gAq1+n+MrtAxq6VDsWkgxgROjs7ABsB4Yuu+H+\n7yLogQvHKczC2U4NPM/jIn4xlkwm0c9uKPpelVmFFHchALAXc2D/8pe/YMqUKXj88cfxxBNP4Itf\n/CKeffbZcrSNsvRCDgm9ig+AKtuOvEfxlslkgsA1OwWWGVghPM8DECohNqKvU3zlAtjcuR+Oja5k\nMrKtHcVTR0dHkHUFAGQDFpYQx5vuXydRmIVzEjXZz3D6WBwppZBKJZGwEkXfT1gJKKW4CwX2IgP7\nk5/8BA8//DAOOuggAMDGjRtx1VVXYcKECb3eOPKlUjqAzXVXVTaY1e9RfHmeB8/zgsWbLM6BFMV1\niwew+nWKr9w2KqEA1rbguS4ymQwcxyn+gxQLHZ0dgJPLshmWBdicAx13Oji1i2RgdVa2o6MD/fr1\nL3ifKpvrZuB5HhJm8XO7DmxTqWRkXSKJeszAZjKZIHgFgIMOOogj/2WmAxXHynWXnS0pYhATf7qP\nC0uI2fcSBBlYXUKcDWCV4nk47oJjPHTu1495/MdfV2cnkHeTajgJZmBjTq8+XayE2MmuTKw/Q/Gi\nM6uOVTyA1SsTMwO7FwHssGHD8OCDD6KtrQ1tbW148MEHceCBB5ajbZSlF2uxjFx36cdcyCX+Mhn/\nRFUYwPIEJkF+CTFYQixGME3ADM11CrbRYgAbZ+l02h+kyM+yOAmWj8ZcMAe2SAmxnS0h5kJe8ZRK\nZQPYEhlYJ7sRPAPYvQhgFyxYgDfeeAOnnXYaTj31VLz++uu46aabytE2yioWwBqGAdMwGMAKkE5H\nM7C6mlCf6CjelMoe45wDK06wUJsZOvebHLyUIFiB2skLYBMMYONOTx0otg+szsDyOxBPOjGhA9V8\ndpCBTRV9X5Ie58AOGjQIt99+eznaQj3IX3DMMAwu5CGAPlHpwNVmBlaUgkWczOjrFGfZ83uRxSZ5\n7o+3VCp7g5o/z9l2kEoluQppjOnsarEA1s7OieYCnvGkp4bYRokMrGFnP8cBzJIB7OWXX4777rsP\nEydOLHqSXLp0aa82jHJ4oyJbcELTqxCbBgyDAawUXIWYSJ4gQMmfC2fnMjDcRiWedN/bdmH/2nZ1\n9jMsIY6j3P2eVfR9K/s6p5B0E8DOnz8fALBo0aKyNYaK0wGsUWwYHgxu407Pdchfx4VzIGQIViHO\nW8SJAWz8cexSLr3DgGFHb9MMy4YCkEwygI2rIANbNIDVGVgGsHEULNppFA9gbc6BDZScAztkyBAA\nwPe//30ceOCBkf9de+21ZWsgheSXEPdNK6jM9EhbfgDLEhIZPM/v5/wSYs6BjL9c9U3obG/kv0dx\nFKwybebdpll6DjQzMHGVTusMbOE2KZatt1HhHMg40se1XWIOrA5sef3vJgN75ZVXYtWqVdixYwdO\nPfXU4HXXdbH//vuXpXFEVLiNjn7MGxgZ9EBFkIFlACtIsTmw/hMGsPEWHN/5pYTcQi/2dHBqFc3A\nMoCNs9yirSVKiBnABkoGsLfeeiv27NmDBQsW4Lrrrsv9gG1j0KBBZWkc9Yz3MPGnT1ThgXjT4A2M\nFHqgwswrIeYARvwVPb+z9EaEXACbl4HlHLjYywWwhQv5MAMbb8H9nlG8QJZbaOaULCGur6/H8OHD\ncc8996C5uRlbt27Fli1bsHbtWvz2t78tZxvFy43IRO9cTMMMygspvvRcx/ytIJmBkUEf//p6phMy\nvIDFXzDPOXzwZ68DnAMdb+EANrN8GTLLl/nP2f+xlwtgS5cQcxuVeNLHvV0qA2syA6v1uI3OnDlz\n8Prrr6O5uRmjRo3CqlWrcNxxx+H8888vR/sIuTlw+SMyFveBFSG3Cm3uJtYweAMjhc60m3klxMzA\nx18wQGnkjV6BNzBxF5zfDQPu2tUAAHvs+OAEwPN/fOng1MpfgTr0GjOw8ZRb86SnEmJe/0tmYLVX\nX30Vf/jDHzB58mTMnz8fS5Ys4YFTZnoOnGUY+NXK1/Crla/5z02TNzECBAFs6DUGsHLoQDXIwGb/\n5SqE8ZfLwhXLwPLcH2fhADbCZAY27oIMrFUkA2vpEmLuAxtHufv97gNYDmDvRQA7ZMgQOI6DQw45\nBO+99x4OO+wwtLe3l6NtlKX3+3QsC69s3oBXNm/wn5smb2IFCLZRyl+IlCXEIujjXw/I6n95AYu/\novMgg0V8GMDGWTBAkTcH1gjmwDGAjatgH1inyCJOjt5Gh4mkONLX+1KrEDvZ13n934sS4qFDh+K+\n++7DuHHj8MMf/hAA0NHR0esNo5x02v+iOnklBY5p8UssQpEA1gA87gEsQn4GliXEchTNwLKEWIRg\n/+f8xVxMXULM/o+rrq4uGIYJs0gQY3Ef2FjL3e8XD89s0y8h5/V/LzKwCxYswPDhw3H00Ufj9NNP\nxzrm+icAACAASURBVP/+7//ie9/7XhmaRlqQgc0LYG3TYhmJAJ5XPFBVJV6neAnmwOZlYFl9EX/B\nPKeic2B5AxNnQf+aeSXE2e8CM/DxlUwmYTtVkXUvNMuyYZgWA9iY0vOfdaCaTwe2XMRrLzKws2fP\nxn//938DAGbOnImZM2f2eqMoSgepjhUNYBOWhVQnT2Jxp0uI85MwaZYQi5BOp2CauRgmV0LMADbu\ngvnv+XtogXMg4y7IsFh5t2mWLiHk8R9XXV2dcJyaku87Tg06ee8XSzowTZiF85/Dr3Mtor3IwHZ1\ndWHr1q3laAuVoL+oVUUC2HQ6xe1UYk6pUos4cQRegnQ6jXDxBTOwcgTzHCOLOHEVWgmC4zs/gLX1\nIi48/uOqs6sTtlNd8n3HqUZXV2cZW0Tlouc2J4os4BV+ndWXe5GBbWpqwsSJEzFo0CBUVVVBKQXD\nMLB06dJytI+Q+0I7eReyhGVDKYV0Oo1EoviXnSpfbhud3GtchViOVCoFo0gAyxHY+Cu6jU72IefA\nxltwfOcNXOuAlsd/fHV1dqLfgMEl37cTNejsbCpji6hcdGCaKFFCrF/nIl57EcA+8MAD5WgHdUPP\ndajKC2CrggtZkgFsjOXvAwoAlskbWClSqRQsK1dlYTGAFSO3lUroRZYQi6BXojXs6I2sYduR9yle\n0uk0MpkMnERtyc84Tg1a93QGCSWKD32/X20VrkANAFUWF/HSegxgX3311aKvH3jggR97Y6g4faGq\nsvNKiLPPk8kk6usbyt4uKg8dwFr5ASxXoRMhmUoivCChfswSovgruheowQBWguAG1c4vIdYZGB7/\ncdTZ6e/y0W0JcaIanuchmUyiurr056jydHXphFXxALba9vubAexeBLDLly8PHqfTabz22msYPXo0\nzjnnnF5tGOX0lIHlhSzeigewBlzPg+d5MM0ep7JTBUslk0iExqdyJcQ87uMuCFLNYgEsKzDiLLiu\nO3mlhEEAyxvYOOrs9Oe2Oonu5sD6Czx1dXUygI2ZIGHVQwZWB7qS9RjALly4MPJ8z549uOaaa3qt\nQVQo94UuFcDyixxnulTUCSXgbTP3Hi9g8aXnuNeE+t4KMrAsIY673FYqhasQBws8USzlMrD5JcTM\nwMaZ7tfuViHW2Vne+8WPXpxLZ1rzVTOADXzk1E1tbS02b97cG22hEnIlxNEAtppzYUQoFsA62a8C\n9wKLt0wmA6VUtISYc2DFCPb6jOyh5V+2uQ9svOnjO38OrM7I8rofTzow6a6EWL/HICZ+dJ9WW8X7\n3zZt2KbNvsdeZGBnzpwZTBJXSmHTpk045ZRTer1hlBOsSsYSYpH0KGt4CrR+nEwm0cDpz7Glb2LD\n2+gYJgCDgxcS6DJhb8V6GLXbYH7uiFAGliXEccY5sDIF13u7eAlp+D0GMfHT2dmRDVKtkp+ptqqC\nudKS9RjAzpo1K3hsGAYGDBiAQw89tFcbRVHJZBIJy4KZt9qczshyLly8BSVFdq7/nSCA5QUsznSQ\nGt5JwzD859wHNv7S6WyWdUsTlGkAnzsiyMBmuIhbrAUVFgUBrP+cA1jxpK/3ltNNAJt9j1U48dPV\n1YUaq3T5OADUWDUcvMBelBD3798fTU1N6OjowP7778/gtQ+kUsmC7CuQy8hyJDbedJD6tw88LH3L\nz7okbP0e+z7OdJBqWsD7r/r/08958xJ/RcuELQawEqTTaX+0Ki8TY1gMYONMX9Ntu/TWiFb2PQ5g\nx09nZyequ8m+A/782K7sYl+SlczA7tq1C7Nnz8bq1asxcuRIGIaBtWvX4phjjsGPf/xjNDY2lrOd\noiWTSVTlb2YOBK8xiIk3Hais3a5gmsCpR+fmwHIULt7CJcQ71/mvHfpZBrBSBEFqeBFiy4QCkMkw\nAx9n6XQKsOzCfT6Dyise/3GkK+qsvSgh5r1f/HR1dWI/a1C3n6mxqtHZzn2AS2Zg58+fj+OPPx4v\nvvgifvOb32DJkiV48cUXccQRR+CWW24pZxvFS6VSRTOwjsULmQTBhvah81TC8p+w7+NNZ1nMvMPf\ntBQzMAIULRPPzoFlBjbeUqk0jCID13o+AacQxFNuFeKeS4gZwMaLUsovIS6xArFWbVdDKSW+/0sG\nsO+99x6+8Y1vwAntQZZIJPCNb3wD77zzTlkaRz6/hLh0BpY3svFWbI6zzb1ARSi2iBPgB7QcvIi/\nXAlxaPSKJcQipNMpqCID1+DAdazpsmBmYOVJJruglCq5ArGW20pHdhlxyQC2qqr4wWMYBkzzI+++\nQ/8gpRRSqRScIiuScQ6sDMF2CqF7WCe0CjHFV6kA1mIJsQhBli1cJZYNYIMFniiWUqlUdOn5LMM0\nAcPg8R9TwTY6e7UKsewAJm562kJHq7b9RZ46hc+DLRmJdldX/c/WXO/atQunnHIK1qxZg/Xr1+PC\nCy/EjBkzMG/ePHgeN2cPc90MPM8r2AMWyGVgmYWLt1Qqpe9ZA/q+hmVk8ZabDxV93bT9DJzeZoXi\nqWiWNViFmMd+nCWTycI9YDXH4XU/pvT2KIlEbcnPONn3pAcwcaP7vtbuYRViW+8DLLv/Sy7itHr1\napx66qkFryulsHPnzn/4D6bTadxwww2orvY7YOHChbj66qsxduxY3HDDDVi6dCkmTZr0D//+uNEZ\ntiquQixWKpUqGIjPBbAchY+zYEuFvMPfClahTqGmpvuLHVWuogNUFgNYCZKpJNDQr+h7hu3wuh9T\nHR3tAHJBajFOoibyWYoHPSBR3cMc2Jpshlb6AEbJAPaPf/xjr/zBW2+9FdOnT8f9998PAFi5ciXG\njBkDABg/fjxefPFFBrAh+iJVdA6szQBWgnQ6BbtEBjaZZAAbZz0HsF0MYGMsk0mjoPwi+zyVYgAb\nV6lUCp7rls7A2g46hWdf4qq93c/COVWlA9hE9r32dgawcdLR4fd9TQ8lxDW2HsDo6PU27ctKBrAH\nHnjgx/7HHn/8cQwcOBAnn3xyEMCGl4Guq6tDa2trj79nwIBa2EXmhsRRV9ceAEBNkQtZdfY1pTIY\nPLihrO2i8kmnU3BsQKnca052FWLHAfs+xvQaegWrEGef19c77P8Yy2TSuQnvWvbaZ5oe+z6mdu3a\nBQAwqkrcyFZVo2P3h9hvv3rR22jEUWdnGyw70e0c2ERVAwADXV3tPAfEiGX5U4JqndKDF0CuxNiy\nXNH9XzKA7Q2PPfYYDMPAyy+/jHfffRdz5sxBU1NT8H57e/te7S+7e7ecUYctWz4EkAtWw6qzaZg9\ne1qwc2fPgT9Vps7OTtTbQDI0HU7vA7t7N/s+zpqaWgCUzsBu3doE264vc6uoXDo6OgsX8sk+b2lp\n47EfU5s3b/cflFhME1VVyGQy2Lx5V8kFN6ky7drVhOrqxm4HJkzTRFV1PXbtauI5IEa2bvXv9+vs\nHgJYpw4AsG3bhyL6v1SQXtblhB966CEsXrwYixYtwqc+9SnceuutGD9+PJYvXw4AWLZsGUaPHl3O\nJu3zcjXxhQGsZZpIWLb4idxxpvf6cvICmET2nlavWkfxpEuIS2VgOX0g3rqSXYUBbDYj29nJYz+u\nWlr8gSujqvj0AP16a2tL2dpEvc/zPLS0NKOqpudETnVNP+zZs6cMraJy0XOae8rA6gC3ra2t19u0\nL+vz/XDmzJmDu+66C9OmTUM6ncbkyZP7ukn7FP0FrU8kir5f5yTEf4njLJVKwXVdVDvR0djq7NeB\nizjEm94TMH8R8vAcWIonpRQ62tuBqrzBS9sCTAMdHTzvx9Xu3X5lmlFXvLrCqPUzMOEKNqp8ra0t\nyGQyqKsf1ONna+sHorOzI1i5lipfc7M/INEv0f0ARmP2/ZaW5l5v076srCXEYYsWLQoeL168uK+a\nsc9rb9cBbPEyofpEArsYwMaWDlCr88Yvqh39Pi9ecaYz7FZeDKOfM4CNr66uLn9buSoHaM/1s2EY\nQMLhAi4xpgNYlAhgUdcQ/RzFwocf+jt81O5FAFtXv1/2Zz7EQQeN6NV2UXnojHq/RPHVxzUd4ErP\nwPd5Bpa619bm17fXO6UC2Cp0dHYU3y+QKp7u/+qCJIwB28q9T/GkpxDkB7A2BzBiTx/bRn4GFgCq\nHZaPxtiuXf5cOKOu+Nwvo95/XQc8FA8fJYDVn9m5c0evtonKZ8+e3QByGdZSau0aOKaDPXtkD2Ax\ngN3H6S90v+ric2H6ZVcplF5KEFfNzX6/1lYVLuhQW5V7n+JJV2DYeRl4/ZxZuPjavds/96O2yOBl\nTRXa2tq4F2xMbdu2FQBgNPYv+r5+ffv2rWVrE/W+rVu3AAAa+x3Q42f1Z/R3hSrfjh3bMbBqAGyz\n+11WDMPAftWDxA9eMIDdx+mbmP4lAtgB1bXZz8keiYkrHaDWFdlNoa7KH7hQ4f11KFba2lph2YWr\nEOuCDGbg46u5uXQAa2Rf4wBWPG3bthVGTS2MEmtf6ACWwUu8BAFs/54D2Ib++2d/ZnOvtonKI5VK\nYffuJgyuGbxXnx9SMxjt7e2i7wEYwO7jdu9ugmUYaCgxB1YHtgxg40lP6q8v0v311QYymUyQpaP4\naWlpQbHtAJ1q/T4DmLhqavL3Ai2aga2rin6GYiOZTPqlpP0GlPyMYdtAfQM2b9lUxpZRb9u8eRNM\ny0Ftdn5rdxoah8IwDGzezO9AHOzYsR1KKQzZ2wC21v/ctm3berNZ+zQGsPu4HTu2Y3BtPcwSe4IN\nrq3Pfk52KUFcNTX5c6Eaagv7v6FGf4aDF3HkeR72NO9GVW1hhl3vriF9EYc40+VhRkORLRWyB7/0\nErI42rx5I5RSMAZ2fyNrDtgPLc3NHMSKiXQ6jU2bNmDAwINgmj3fmlt2Ao39D8SGDevgeW4ZWki9\naePG9QCAg+oP3KvPD89+Tv+cRAxg92GdnR1obW3BkBILOQAI3uNcmHjSGZbGIhXkjTVG5DMUL62t\nLfBcD1VF4he7CjAtVl7EWRCcFjn4dVDLADZ+Nm7cAAAwB3afhdMBrv48VbbNmzfCdV0M2O8Te/0z\nA/b7BJLJpOgsXFysX78OADCi4aC9+vzI+oMiPycRA9h9mJ7fMrSbAHZodpl9zoWJp127PoRlBhWD\nEY3ZwIYrUcbTrl3+wESxANYwgKpahV272PdxtX37NiBhF+4DCwRB7fbtvHGNm3XrPgAAGD0FsIP2\ny35+ba+3iXrf+++vBgAMHHzwXv/MoMGjsj/7915pE5WPPu4Pqh++V58fVjcMlmFh7do1vdmsfRoD\n2H3Yhg3ZkoISKxECQLXtYEhtPTZuWM/FfGJox47t6Feb3fsxT/9sWTGzMPG0Y4cfnNSUGL+qbvDn\nyHZ1dZaxVVQOmUzGH5TsX1f02EdjLWBy/lscrVnzPmCaMAb1UEI8eP/s51eXo1nUy1aufBsAMGTY\np/b6Z4YMOwIA8M47b/dKm6g8MpkMVq/+Ow6sG4Y6p8iIdREJy8HIhhFYv25tsF+8NAxg92FBSUE3\nizno91vbWllOGDOdnR1oa2tD/7ri85/71/n/MoCNJz2vvbpEAKsDW/Z//Gzfvg2u68IYUF/0fcM0\ngX512Lx5EwcuYySVSmHDhnUwBg2Gkb/0eL76RhjVNQxgY8DzXLz77grU1e+H+oYhe/1z/QYMR3VN\nI1auXMHzQAVbu3YNUqkkPtn/sI/0c0cMOAyu52L16vd6qWX7Ngaw+7C1a9fANAwMbyidgQWAkdkA\nd+3aD8rRLCoTXRY+oK74+/XVgGOxfDyu9PYItSX2NNcB7JYtW8rUIiqXDRvW+Q8GlJ4+YgysRzLZ\nxTLiGFmzZjVc14U5pOdtVAzDgDHkADQ17eI0kgr3/vvvo729HUMP/HTxiosSDMPA0GGfxp49u3Pn\nDKo4K1euAAAcMeDwj/Rzn+zvf15qBp4B7D4qmUxi7do1+ES/gaiyux+JPWygP2L33nvvlKNpVCa6\nPHBwv+IXNMMwsF+jH+i4LlchjJuNGzfAtEqXENcP9P/dtEnuKoRxpee0GUP6lf7QEH9gkxm4+Fix\n4i0AgDFsxF593jxwROTnqDItX/4iAOCggz/7kX9W/8xf//rSx9omKp/XXnsFlmHh0wP3vnwcAI4Y\n8EkkTAevvfZqL7Vs38YAdh/1/vt/h+u6OGK/nstJDh2wHyzTxKp3GcDGiV5dckhj6RHZwY3+XrDM\nwsSL67rYvHkj6vorGCXO0vXZmQV6rjzFx/vv/x0wDWC/Eul3AEY2gOUCLvGxYsVbgGnCPGDvViI1\nDhwJAHj77Td7s1nUizzPxfLlLyNRVY/9h3/6I//8ASM+A9upxl//+iLLiCvQhx/uxLp1H+CIAYej\nzilRbldClZXAvwz6NLZu3SJyPQQGsPsoPaJ6xKChPX62yrZxSP9BWLd+LVpbW3q7aVQmelW6waXv\nYTE0m51l+Xi8rF+/Fpn/z955R0dxnn37emZXq957RwVVRDW9umBsx72kOHGKU9/jN4mTuCSOk7i8\njgsfMbYDNja244a76UVUUYSEGkhCgCQQEqACEqhLK22Z749FxMYICbG7syvNdQ5HR6ud5/6J0ezM\n/dzNaMQ7sP/36NwtHYqPHq1QH1yGEV1dnZbOskE+CK2m/zcG+YBWw6HDZfYTp2Izzp07y/HjxxAh\nEQidblDHCF9/hJcPJSUH6O3tsbFCFVtQWlpCa2sL0XGTkaQB6p4vgVarIyp2Ek1NjRw5ogYxnI38\n/FwAJgVPGNLxE88fl5eXYzVNzoLqwDooRYX56DQa0oLCBvX+CWFRyLLM/v2FNlamYg+MRiNHj1YQ\n7ANuuv4jsFGBlp9VVBy2lzQVO9DXkdJvgFI4vzCZtrY2Tp06aQdVKvbg4MESzGYzIvryXWiFRoKI\nAOpqT6k1kMOA3Ny9yLKMJn7wdXBCCKT4JPT6boqL99tQnYqt2Lx5AwCJadcNeY2+Yzdv3mgVTSr2\nQZZlsrK2oZW0TA6Z1O/7zLK5359NDB6Pq8aVXTt3YDb3/77hiOrAOiB1dbXU1dcyNiRiwPrXPiad\nTzkqLMyzpTQVO3HiRDW9vb1EB16+oUOon6WRU3m56sAOJw4dsjR18B9g/8r/vIPb5/CqOD99joiI\nubwDC1xwcg8cKLKpJhXbk5Ozx5I+fAUOLICUkPLf41Wcivr6WkpKDhAclkRA0KghrxMUOpqAoDgK\nC/PUzSwnorKynLq6WiYFT8Bb9+2O86c6amnuaaa5p5k/5/ydUx2133qPu9aNqaGTaTrbOOJq4VUH\n1gHZt89SjD8pbHB1MADhXj5EevtSWlJMZ2enraSp2Im+D6KY4Ms7sBpJEBVomQepjlEaHnR2dnD4\ncBleATI698u/1z/C8rWoaGQ2cRhuGI1GCovywd31svWvffQ5uQUF+2wtTcWG1NRUU11dhRQZi3Ab\n4KK/CBEQhPAPpKiogNbWVhspVLEFGzasBSAp/cbLvk++TAQOLJH4pDE3IsvyhTVVHJ+tWzMBmBsx\n65I//3fpsgvR14au0/y7dNkl3zfv/PHbtmXaQKXjojqwDoYsy+zZsxOdRnshqjpYZkTFYTAaLjjA\nKs5LUVEBQkB86MAt9RPDLO9RozDDg8LCfEwmEyGjBn6vmyf4hMgcOXKIlpZmm2tTsS1lZaV0dnQg\n4kMHNU5DeLtDiC+HDh2ktbXFDgpVbMH27ZsBkFIyrvhYIQRScgYmk4ndu3dYW5qKjWhqamTXriy8\nfcOI6qf7cMu5k3R1NtPVeY51nz5Ky7n+S0ViEqbh6RVEVtZW9V7gBDQ1NbJv316iPCNI9U/+1s9b\nelpp6Dr9jdcauk7T0vPtTao4n1Ek+MRRVFRAXd23o7TDFdWBdTAqKo5w5sxpJodH4+7ickXHzoqO\nQwB79uy0jTgVu9Da2kpV1VGiA8H9MvWvfYwOt7xn//4CW0tTsQO5uZaRCoNxYAFCR1k2vvqaQag4\nL31poCJx4DmgfYiEcGRZHpFNPIYD3d3dZGfvRnh6I0XHDWkNzehU0LqwffuWEVcH56ysW7cKs9lE\n+oQ7kKRLP4rv2fIqsmwZkdfe2sCeLa/1u55GoyVt/G0YDAbWr19tE80q1mPTpnWYzWZujl1wyc1K\ng9lwyeMu9boQgltiFwCwYcMa6wp1YFQH1sHYtcuygzo7Jv6Kjw1w9yQ9OJzKynLq60fOLsxwY//+\nAmRZZnT44C5Pfy9BkLcl7Viv19tYnYotaWtrpaysFO9AGfeBM0gBCB5l+ZqTk20zXSq2p6enh4KC\nPPByvzDjdTCIBEuh9N69ag2kM7J37256evRIKRmIfhyZgRCubkjxyTQ2nuHgQXWkjqPT1NTIzp3b\n8fIJITZx+iXf093VQnvrN8fjtbfW093Vf6ZFXPJsPDwD2L59ixqFdWDa29vIytpGgKs/U0OvfPbv\npZgQPI4wj1Cys3dx7txZq6zp6KgOrAOh13ezb99egtw9SR1k9+GLmXPe8e1zhFWcj/x8Sz1bcsTA\n0dc+kiMFBoOBkhK1E6Uzk5eXi9lsJvQK9q9cPcAvXKayspzGxjO2E6diUw4cKKSnR49IDB9U+nAf\nwsMNIgM5erSCM2dOD3yAisMgy7IlfViS0CRf+QzQr6NJGwvAtm1brCFNxYasWfMVRqORMRPvQpIu\nPSrLZLp0BK6/1wE0GhfSJtxOb28v69atsopWFeuzadM6enp6uDn2RrT9nP8rRRISt8QuwGg0jpgI\nvOrAOhB5ebn09PQwOyYB6QoeYL7OpPBoPFx07NmzE5PJZGWFKramq6uTsrISQn0tkdXBkhJpuZT7\nnF8V52Tv3t0AhFxhJmHo+ferUVjnpe/cX0n6cB99x6idaJ2LY8cqOXGiBik2AeHx7S6kV4IUFIoI\nCmX//gLOnm2ykkIVa3PmzGl27tyBj284sYkzrL5+fPJcPL2D2LZty4iJxDkTnZ0dbN68CR+dT7/N\nm4bKjLBpBLoFsmPHyKiDVh1YB6KvdnUo6cN96DRapkeOoqWlhbKykdVSezhQXHwAk8lEcuSVXZqh\nvuDrAcXFRRiN/e/Qqjgura0tHD1agW+ojKvHlR0bHAtCqGO0nJWurk6KSw5AgDciwPuKjxdxoSBJ\n7Nun1sE6E32ZUprkK2/edCk0KRnIskx29m6rrKdifdas+cpS+zrprn5rX68GjUZL+oQ7MRoNrFs3\nMiJxzsTmzRvR67u5KeYGdBqdVdfWShq+E7sAg8HAxo3rrLq2I6I6sA5CS0szR44cIikgmOCr3Imd\nETUKsAxGV3Eu+hox9TVmGixCCJLCBd3d3ZSXH7GFNBUbU1y8H1mWCbqy5uMAuLiCb6hMVdVRtRut\nE7J/fxEmoxERHzqk44XOBaICOXmyhvr6OiurU7EFBoOBfftyEB6eiIghXPSXQIobDZKG7OydyLJs\nlTVVrEdj4xl2796Jj18EMfFTbWYnLmkmnt5BIyYS5yzo9d1kZm7A08WD6yLn2sTGrPAZ+Ln6sn3b\nZjo7O2xiw1FQHVgHYd++HGRZZlrkqKteKzEgmAB3DwoL8jAY1Gics2AymSgp2Y+PuyWieqWo3Yid\nmwMHCgGG5MACBEb1raOOU3I28vMtkVMRN7TeBwAiPuz8Wmo3ameguLiIrq5OpISUITdvuhjh6oYU\nE0ddXS01NdVWWVPFevR1Hk6bcLtNoq99SJKlI7HRqHYkdiR27NhGZ2cHN0Rdh5vWzSY2dBoXFkTf\ngL5Hz5Ytm2xiw1FQHVgHIT8/FwFMjoi56rUkIZgaEUtXd5eaRuxEHD1aQWdnJ4lh4oqauPQREyzQ\naVUHxhkxm02UlZXi5sWguw9fTJ8De/Cges07Ez09PZSUFoOfJ8J/6Nk3IjYEJEFhYb4V1anYir5x\nWVJCilXXlRJTv7G+imPQ3HyOXbt2WDoPJ0yzub24pDl4eAWyfftW2tvbbG5P5fIYjUY2bVyHTqNj\nfvS1NrU1L3IOni4eZGZuoLe3x6a2lER1YB2A9vY2KiqOkBgQjJ+bu1XWnBRuCePs319olfVUbE/f\n+IP4sKE18NJIglHBgtOnG9RupE5GdfVxurq68I+QGWL/Njx8QecOhw6VqumDTkRZWSmG3l6LA3oV\nCFcXCPOnquqomjbo4Oj1evbvL0L4+CECg626thQ1Clxc2Ldvr/o54EBs2rQeo9FI6rhb++08bE00\nGi0pGTfT29sz7CNxzkBeXg7nms8yN3wWXi5XVyY4EO5aN66NnEtHRzt79uyyqS0lUR1YB6Cv9m1i\nWJTV1kz0D8JL58r+ogL1JuYklJQUIwkYFTxEDwaIC7UcW1qqzgJ0Jg4eLAXA/8ob0F5ACPAPl2lr\na+PUqRNWUqZia4qKLBFTMerqHFjgghNcVKSWETgyxcX76e3tQYpPGlK2zeUQWi1STAJNTY1UVR21\n6toqQ6Ozs5Pt27fg7uFHXJJ1O89ejoSUuehcvS40DlJRBlmW2bhxLQLB/Jjr7GLz+qh5aISGTZvW\nYzab7WLT3qgOrAPQ97AxITTSamtqJIlxIRE0tzRTXV1ltXVVbEN7ezvHjx8jKhBcXYb+QJNwwYE9\nYC1pKnagL/p+NQ4sQECE5WtpqZpG7AyYzSbL57+7K4T4XfV6fU6wmkbs2GRnWyYOSPHJgz/oCjai\npYTk83bUbsSOwLZtm9Hru0kaswCNxsVudrUubiSNmU9nZwc7d263m12Vb1JRcYTq6uNMDB5PiLt1\nMy76w9/Vj2mhk6mvr6WkZHg+D6oOrMIYjUYOHiwmyMOTCO8hdO65DOPDLA5xcfF+q66rYn0OHixG\nlmXiQ6/ukvT3Evh7WtIS1XE6zkF3dxfl5YfxDpLRXWVfh4Dze2AlJeo17wxUVlbQ3t6GiA22SiRO\neHtAoDdlh0rp6uq0gkIVa9Pa2kpx8QFEYAhSQNCA7zefa4LODuhsp/fz/1i+HwApKhbh5k5Ozh71\nPqAwvb29ZGaux0XnTmKafaJvXycpfT4arY6NG9dhNBrtbl8FMjM3AHBj9PV2tTv/vL0tWzbaojtz\n0AAAIABJREFU1a69UB1YhamsLKe7u5vxoZGDfoAxD3InNiMkAkkItamPE9C3Q5YwxPrXr5MQJtDr\n9VRWVlz1Wiq2p6ysFLPZTKAVEjB07uAVKFNeflhNGXMC8vP3AefnuFoJMSoUk9Gofu47KDk5ezCb\nTUijUwf1fuO2dSBbUgDl1mbL9wMgJA1SYgodHe3q34HC7Nmzk7a2VhJTr0enu8IB31bA1c2bhOS5\nnD3bpDb2UoCzZ5soLMwj2iuKJL9Eu9oe5RPDaN8ESkoODMvxaqoDqzB9TZbGDyJ9+GRbC836Ls7p\nu3h062pOtl1+3qOni46kgGB1NqSDYzKZOHCgEC+3oY3PuZg+J1gdp+Mc9KV7BlipBD4w0pLZMVzT\nhoYLZrOJffv2gs4FIgKttm6fM7xvX47V1lSxDmazma1bM0HSoBlE92G5qxO59ZsNueTWZuRBRNel\npDEAFnsqimA2m1i/fjWSpCU5Y4FiOpLH3owQEuvWrR629ZCOyvbtmzGbzdwQfa3V690Hw/xoS9R/\nODbyUh1YBZFlmaKifNy0WtKCBp7/92reTkzno68Nne28lj9wd7GJYVHIsqx2I3ZgKiqO0NHRQVLE\n0MbnXMyo8+N0Cgry1AZeDo7RaKRofwGuHuAzcDbhoAiOtXwtKNhnnQVVbEJ5+RFaWpoR8aEIjfVu\nxSLAGwK8KS4uGvaD7J2N0tJiTp+uR0pIRrgPIhpn6ifls7/Xv4YUEIQIj6KsrJTa2pNXqFTFGuTl\n5XLmzGnikmbj7nH1Ne5Dxcs7mNiE6dTWnlQj8nakt7eXHdu34uXiyfTQKYpomBg8AX9XP3bvzqK7\ne3hlZakOrILU1Z3i9OkGMkIicNFcvq16i76bhs72b7xW39FGywBpghPPj9NRm3o4Ln2ORnKEdXbn\ntBpBYpigsfGMOszewTly5BBdnZ0ExQx9fM7FeAWAm5dlHrDBoNa/OSp791oa7IjEq+zcdQlEYjgm\nk4m8vFyrr60ydDIz1wOgSZ9gF3t9dvpq8FTshyzLrF27CiEEqeO+o7QcUsdbNKxdu1Ld2LYTubnZ\ntHe0MydiFjqNThENWknDtZFz0Ou72bMnSxENtkJ1YBWk7+Fi0iDG5xjMpit6vY9QT2+ivP0oO1hC\nZ6fa1MPRMJlM7Mvdi7sOYq9ifM7FpEZZ1srJ2WO1NVWsz759e4H/Rk2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bzbz55hJ6enr4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7+YOwb8vdi+fQuHD5cpLemS6PV6AOTeHoWVfBPt9bfC+QiM8PW3fO9AyAZLT5K+/7+RgKurK3/8\n459JTk7lRNU+9m5fitHoWH83V4LBoCd762vU1hSRnp7B73//iFr32g/f//6PmDx5KuUtFbxz+H3M\nsvIZhtZAlmU+qPiE0rNljB07np/85BcOHX0F1YGlpcVSl6M3Ghz6D9Gln9ao/b3uCHSdv7G1tDQr\npkGSBL09vbjpYN4YyeEvyJGAt7tgdprlo8dgsO0Opq+vH5Ik4e4t4x1kU1MqgyR4lOVrQID95mqH\nhZ2PuMo412dAfw2vnK3jq0YCkxmNRuNwjf36yMgYh4enJ6aiXMyn65SWcwEpIAg8vcDTG919P7V8\n7yCYjldiPlJKUHAICQmJSsuxK25ubvzpT38hKSmFk1V5bF39LB3tys9nv1LaW0+zZdVT1NYUkZY2\nhj/84XGHHZviCEiSxG9+81sSEkaT05DH0tK36DE57+YFQK/JwJuH3iWrdhcxMaP43//9o+LNFgfD\niHdgx44dz+TJU6k418iaCsfcGQbwc3MnzNP7G6+Fe/k4bL1uR28PbxRlI0kSDz74a8V0SJKGyVOm\noe+FE432q7lUuTzltZZzMXXqdJvacXNzJz4+gfYmgQOX340oWhosX1NS0u1mc+LEa4iPT0CuakBu\nUq6J1JUiPFzB96ImKr6ejtkx+TLI5bXQ1sW1195AcHCI0nIuSWBgEL/93z8iZDPGreuQOzuUlvRN\nHGzjxXy2EdPOTHSurvzxD4+NyGY/7u7u/PnPf2fevOtpPlvD5pV/p6HWcZ8jL6buRDGbV/2d1uZa\n5s+/mUcf/atTdAtXGp3OlUce+QspKWkUNBbxz8L/R7NeuUDN1dDW28ZL+18mp2EfCQmjeeyxv+Lu\n7ph+xcWMeAdWCMGDD/6aAP8AVpaXsOvEMYeNxP5uylw0529i4V4+/HbyHIUVXZqmrk6WFuzhXHcX\nd931XUaPTlZUz9SpMwDYWmKm6rTZrs2DrIG2n42w/l53ZHoMMjnlZo7UyQQFBREfb/td+7S0Mcgy\nFK0XnKkGJzv9/c6ldeDki0tiNEB1CVTkWD7D0tLG2M22EIL77rsfAHPmfswHa5CNzlG3Js2f8F/n\nxdcTab5j1yV9Hbm7F3NeBXLOEVx0Ou644x6lJV2WMWPGcv/9P0Hu7sSw+mNMFWXIDtCE0JGQTUaM\npYUYN3yBbDTwm1//lujoWKVlKYaLiws///lv+NnPfoXR0E3Whhcp278G2UGfIwHMZhOlhV+xc9Mi\nzCYDv/rVQ/z4xw+i1TpGczBnwMvLm8cff5K5c6+jpv0ETxc8T1VbtdKyroiT7ad4Ov8FjrYeY/r0\nmTzxxFP4+jrPWAZ1DiyW3ZS4uARy9u4hv66GvNoavF1difD2dah0M19XN3bWHMNN68K/5t+JrwM1\nmwA4193FZ4f389b+HBo620lPz+DBB3+FsGMHtUsRGBhIQ0M95cdOcvCEzNEGGQ8dBHo7RzqhTiso\nO2mm+2t/9oFeMCPFeTyY7l6ZnHKZVflmKutldK5uPPDAg0RHx9jcdnx8As3NzVRVnuBMNTSeEOjc\nwcPX4YIal0TrAqerwNjzX7EePjKxYxUUdQUYDXCyDA7tFJw9JXBz8+Tee3/A1KnT7Xr9WSJ/gmNH\nDmOqPm2JCgIEeCM0jruXK9xdkctPgYsGzQ/mItwdP0Iid+mRC48i7yiBunP4ePnws5/+itGjk5SW\nNiAJCaORJA2Vh0sxHq/EXFOF8PFF+Cj3YGc6aGmEqB0zUTENsixjPl6Bces6zFUVuLvq+MmPf87M\nmbMV0+RIxMUlkJ6eQXHxfmqqCmlsqCA0Mh0X3dVFs8pLMwGsNge2s72J3Ztfproym8DAIB5//EnG\njVPu78qZkSQNEyZcg5ubO4XF+eyp24uLpCXBN95q97bNJ7cBsCDmequsB5ZreeupHSw9+BYdhg7u\nued7/OhHP3PYDYz+5sAK2dnCUUBjY7tN1m1qamT16i/ZvSsLk9lEpLcvd6eM5ZrwGCQHedL9w+aV\nALx8410KK/kvLfou1laWsaP6KAaziZDgUO66+z6mT5/lUHn01dVVrFnzFQUFeciyTLAPzEyRSI0S\nDnN+++NMq8zb20yYZYvzevc0DSG+jq0ZoEMvk1dpprBKptcInp6eLFjwHW688WY8PW0/BP7r1NfX\nsXr1F+zduwdZlvEKsDiBgVEOM5WiXzqaoWANyLLAw0cm/Vrw8h/4OCXp7YaGY3DioMCgBw8PD26+\n+TYWLLhF0XTD1tZWNm1ax5Ytm+jp0YObDpERi0iPQegcs3GJaUUWAJr75ykpY0Dkjm7k4uPIR06B\nyYy/fwC33non8+Zd53R1dWfPNvHFF5+Qnb0LWZaRokahmTJbkRrUnk/eBsD1+z+3u20Ac0Mtxrzd\nyGfq0Wg03HDDTdxxxz14e3sPfPAIo729jeXLX6eoqABXNy+mzPkFUaMmDXm9NSv+AMDt97981dpO\nVO0jb9c7GHq7mDJlOg8++Cu734eHK6WlxSxb9m9aW1tI9U/mF2k/JdAt4KrX/VP2EwAsmvnPq14L\noKWnlXcOv0/J2YN4eXnzy1/+j8OPSwoOvvTnjOrAXoIzZ06zevWX7NmzE7PZTLSPH9eNGs2UiFh8\nFI56OooDK8sylc1N7D15nF0nj2EwmQgKCubOO+9l5sw5DruTA1Bbe5K1a1eSk/P/27vzsCrr/P/j\nz5t9VUHZVBDZRFFAEMUNcMkx17TQtGyZ0prpq01lWmnzs7IsW6fM0imzUTPNKS01tczEBRVEBBVR\ndkRkF2XnnHP//jhKOkm5sB14P65rruaSm/t8zvkc7vt+fdYD6HQ6bCzA01nBy1mhu6OCuWnLDIbL\nftSgqjB7TMv9bAFKyvS93Cm5KpmFKlodtG/fnrvvnsCIEXdh0czzts+fz2Hz5k0cOnQAVVUxNgE7\nF5WOrvowa95Cp3JFf6Mf/jxoSnOX5MZUFcovQmE2FGXDpQL935GlpSWjR+uDq7V1y9n+o6zsMjt3\nbmfnzu1UVlZc2bS2I0p3R5Ruji2qp7MlB1i1tBw1Ix81Iw/y9IsidurkwPjxkxg6NMLgVzPNyEhj\n/fo1nDp1AgClsyvGvn0w6uaF0kQNtM0RYNXaWnRpyWhPJ6AW6PfIDQkJZerU6Tg5ySbaf0RVVXbv\n3sVXX31JbW0t3r1G0jd0GsYmZrd8roYIsBpNNUcPrCEteS9mZubMmPEo4eHDDWIEmiG5dKmUzz9f\nQVxcDFYmVjzi+wD9nfrd0TkbMsAeKzjOqqT/cLm2jD59Apg16yk6dGjhLeFIgL0tFy7ksnnzJg4e\n3KdvgVUUeju4MKirO0HOrlg2w425uQNs9qWLRJ9LJzong8Ir++Z1tO/IhIn3EhYWgYkB7aObn5/H\ntm1biDlyiMtl+u+UkRF066QPs17OCva2LecCv+xH/Ybp/3d3ywqwWp1KduFvobXomrVP3Ny6MWzY\nXYSFDcPM7NZv3o0pJyebvXv3EB9/lNzc31Ydte2k0ulKmLWxbznDjKO/0f93YGTzluNaOi2UXNAH\n1qJzClVX6l5RFHx8fOnbtx8REcNbdCt/RUU5v/zyEwcP7ic7O1P/j4oCTh1Q3J30gda2eVs1WlKA\nVVUVii6jZuShpudBib7SFUWhR4+eDB0awaBBQ1t0I+atUlWV48ePsW3bFk6fPgWAYmmFkXcvjH37\nNPrw4qYMsLqiArSnE9GlJkFNDYqiEBAQxPjxk/Dxad71LAxNdnYWy5d/wLlz2XTo2I3BI56iXYdb\nC/93GmBLi89xYPcySkty6NatO0899TQuLl1u61ziz6mqyq+/7mbt2tXU1FQzwCmEGT3ux8b09u6B\nDRFgy2srWH92I/tzozE1MWXq/Q9y112jMTJquVNnriUB9g6UlBRz6NBBoqP3k56eCoCZsTFBzl0Z\n2LU7/o4umDTRiirNEWALK8qIPpdBdE4G2Zf0LewW5hb0CxnAwIFD8PPr06KGCt8qnU5LWloq8fFx\nxMfHkZmZXvczexvqwqybg4JxM+4h25ICbFmVSuoFfWhNy9MPDwYwMzOjd29/AgKCCAwMatKtUu7E\nhQu5HDsWy7FjR0lOTkJ3ZeEWcyvo6KrfP7aDs34+anNpKQG2uhyKz+t7WkvOK2iv1L2lpeWVeg8m\nICAQGxvDG16Yl3eBo0djiI09TErKmd8WfOtoi+LuhOLuBPY2Td5z0dwBVtXp4MJFfWjNyIeySgBM\nTEzo3TuAfv3607dvMO3atf691M+fz2HPnp/Yt28v5VdWKla6uGHs649RNw+URngWaOwAq2pq0aWd\nQXs6ETU/FwA7O3vCw4cTHj68xW59ZAhqaqpZu3Y1e/b8jImJOSFDH8Xde/BN//7tBlhVVUlLjuLo\nwf+g1dRw1113M23aDIMfEWEoLlzIZcWKZaSknKGdWTse9X2Qvg4Bt3yeOw2wiUUn+SJpDcXVJbi7\nd+eJJ2bTtavrbZ2ruUiAbSC5ueeJjj5A9MF9XMjTX+itTc0I6exG/85u9OzkjEkjtmo0VYAtrqwg\nNjeLwzmZnCnW721mbGxMQEAQgwYNJTAwqNUut15SUszx48eIjz/KiRMJVFfr9/gyNQG3jgrujvqh\nxo7tm3YRqOYMsDUalaxClYx8lfR8lfxrdiJxcHAkMDCYwMAgfH17tbie1ltVXl5OQkI88fFHOX48\njvJy/UgDxQjadVKx6wx2LtDOQd9j31SaK8BqavS9rCXnoSRXoeKaundyciYoqB+BgcH4+Pi2ql63\nixdLiIuL5ejRI5w8mYhWe2XVYmsLFNdOKG4O+iHHpo3/npsjwKqV1ajZhZBVgHquCGr0+1BZWFrS\nNzCI4OAB+PsHGsyWCw2tpqaGmJjD7NnzE8nJScCVXlmvnhj5+GFk13CNd40RYFVVRS3IQ3vmBLq0\n5LreVn//QIYPv4uAgCCDbphuaaKjD7Bq1Qqqqirx9I0gePBDGBv/eZi8nQCr0dQQs28VGWcPYGVl\nzcyZf6NfvwG3XXZxe3Q6LT/+uJVNm75Go9Ew2DmU6T5TsTa9+RE9txtgKzVVbDi7iV/P78PYyJiJ\n99zL+PGTDPIeLQG2gamqSnp6GtHR+zh06CAXL+r3gLIxMyfIuSv9O7vh5+Dc4D2zjRlgiyrLiTmf\nxZHzWZy9EloVRcHXtxeDBg0lJGRAix4K2Bhqa2s5ffoUx4/HkZAQf91QUytz6OagD7PuDgp2No0b\nZpsywGp1KueLIT1fJaNAR04R6K5cKUxMTPDx8cXfvy+BgUF07tyl1c6l0Wq1nD2bTGJiPCdOJJKe\nnlrXK2dsCh2cVOxcwK4zWHdo3OHGTRVgdVoozYeSXH1ovVSkwJW6Nzc3x9e3F35+fQgMDGozQ9Eq\nKyuIjz9GXFwMCYnxVFxp1MDICFzsUFwdUNw66fdobYQvQVMEWFWnQmEpalYBanYBFFyq+1nHjp0I\nCOhLcHB/evXyM6ipIk0hJyebPXt2s//AXsrLrvTKOrpg7OOHkYcPyh0uYNWQAVatrECbchrdmROo\nJUUA2Nl3JGxoBBERI6S3tRHl5eXy0Ufvk5mZTicnL4bc9TSWVn88/PxWA2xFWTH7dn1AcWE6np7e\n/N//PSN12sxycrJZseJj0tNTsbewY1avv+Jrd3Mrst9OgE0pTWPlyVXkVxbg6tqNJ554im7dut9W\n2VsCCbCNSKfTcuZMMkeOHCI25hAlV8KslalZXZjt7eCCaQO0ZjZ0gC2sKOPI+SxizmeRUlII/BZa\nQ0JCCQkZYBCTvJtKcXERp06d4OTJRE6eTKSkpLjuZ+2t0IfZK4HW2qJhH2QbM8Cqqkr+JcjIU0kv\n0M9pvTosWFEUunf3oFevPvj59cHHp4fBrSjaUMrLy0hKOsmJE4mcOpV4XYOGmaV+MSg7F/382Tvc\nPeF3GivAXl18qThHH1gv5inornQ2GhkZ4eXljZ+fP35+ffD09Grz4UWr1ZKamsLx43H6LTOumXKA\nreWVMOsAne1RGmiz5sYKsGp1rT6sZhWiniuEKv1eXUbGxvTw8b0yJLwvnTt3bSnQl7cAACAASURB\nVLWNVA2ptraWY8di2bv3FxITj+sbu0xMMerujXEPPxSn22vsu9MAq+p06HIy0SWfQJeVBjodxsbG\nBAf3Jzx8GL17+2NkaBtLG6iammo+++xToqP3Y2Vtx5C7/kFHR496j7+VAFtw4Qz7f/qQqspSwsKG\n8cgjM2XIcAuh1WrZsuVbtmzZhKpTubvbKCZ7TMDE6I+f524lwGp1Wr7P2M4PGdtRURk7dgL33jvV\n4O/ZEmCbiE6nIyXlDEeORHPkyKG6gGNpYkqwiyvhbp706Oh42w8DDRFgy2qqiT6Xwf7sNNIu6ltg\nFUWhV6/ehISE0q9ff4PazLi5qKrKhQvn68LMqVMnqKioqPt5Zzvw7myEj4uCQ7s7H27c0AFWo9UP\nCT6Tq3I2V6Ws6refubh0xs9PH1h79vRrcz3vN6uoqLCuMePEiQQuXfptfG27TvqVjTu5grXdnffO\nNmSA1WnhYp5+8aXC7N8WXwLo2tX1St374+vbs1m3vDEEFy+WXBlyHkfiieNUVernh2JqjOLmiOLh\nDK6d7ijMNmSAVatq9KsGp1/g2qEV7Tt0IDAgiICAIHr37iP1foeKigrZt28vUVG/UFCQD4DS0RHj\n3kH6XtlbaNC+3QCr1taiO3sK7Yk41CvrV3R1dSMifDiDBg3F1rbdLZ1PNAxVVdm+/Xs2bFiHkZEJ\noRFP4OZ54yG+Nxtg08/s50jUZ4DKgw8+wsiRo6XRqQVKSTnDJ8s/JL8gj262rjzh9zidrZ3rPf5m\nA2x+ZQErTnxO6qV07O078uSTs+nZ069By95cJMA2A51OR1paSl2YLSrS93A6WdsS5ubJEFcP7G/x\nIeF2A6xO1XGy4AJ7s1I5mpuNRqfDyMiIXr1607//QIKDQ9rEAhyNSafTkpGRzsmTiSQmHr9uMaAO\n1uDtouDjouDa6fYWg2qIAFterV8p+GyufvGl2is9bTY2NgQE9K3raTOUxZdaElVVyck5Vzd/9tr6\nt7CBjl31qxt3cIbb6ey40wBbWwVFOVcWX8pR0OinNGJpaYm/fyCBgcH07u0vIy7ugEaj4ezZZOLj\njxITc7guuGBijOLmoA+zbg63HGbvNMCqVTWo6XlXQmuxvtsdcHf3oF+//gQGBuPm1k0eeBuBTqfj\n9OlT/PzzTmJjD6OqKoq1DUa9AvUrGN/E1ny3GmDV8jK0p46jO52AWl2FsYkJgwcNZcSIUXTv7in1\n3EIkJBzjo4/ep6qqiuBBD+LTe9TvjvmzAKuqKknHt3H8yAasrK2ZM/s5/Pz6NGq5xZ2prKxk7dov\niIrag4WJBbN6PUqQQ+ANj72ZAJtYdJJPTnxGhaaCgQMH8/DDM1vUlnV3qkUHWJ1Ox6JFi0hOTsbM\nzIzFixfTrVu3eo83lAB7LZ1OR3JyEnv3/kJMzCFqriyY0MfBhfBunvR16npTQ4xvNcDml19mX1Ya\n+7JTKarU9w527tyFsLDhDBkSJj2tjai8vIzjx48RFxdLQsIxKq/0zJib6lc29nZR8HRSsDC7uYeJ\n2w2whZf0gfVMrn4u69U/eGdnF4KCQggK6oe3t48MIWtgZWWXSUiI/139G5uCfWeVTm7g0A2Mb7I6\nbyfAVldAXpo+tJbm/zaX1cHBkb59gwkKCqFHD1+DH2LUEqmqSkZGGkeOHOLIkWjy8/V7ad5OmL2d\nAKtW1aCmXdBvdXP+t9Dq4eFJSMhA+vcPxdHR6RbflbgT+fl57Nr1I7/+upvq6iowMcXYxw/j3n3/\ncCuemw2wuuJCtImx6FKTQafDxsaWkSP/wsiRf5F7fQuVmZnO22+/QWnpRXoGjiMgZMp1DQx/FGBV\nVUdc9FecObETe/uOzJu3gC5dDGuF2bYsOvoAn/17OTW1NUzsPo6J3cdipFy/MuQfBVhVVdmeuZNN\nqZsxNjHmkUdmEh4+vEnK3pRadIDdtWsXv/zyC2+++Sbx8fGsWLGCTz75pN7jDTHAXquiopxDhw4S\nFbWH1NSzgH7xp5HuPozx6omlaf2ruK4/eRSAaX7Bf/gaZ4oL+Pb0cU4WXADAwsKC0NDBhIcPx9PT\nW1pgm5hGU8vp00nExcUSFxdT1xtvbAS93RT6exnh2P6P62R3gr67dIT/nz/w6nQqp3NUjqToyLky\nTVdRFLy9exAU1I+goH5tZgGelkCj0ZCcnMSxY7HExcXW9cyZWoCLt0oXX7D4kwbTlBj9f71C/vg4\nVYVLBXAuCQoyFVSdvu49Pb3p21df9126yJzGpqSqKllZGRw5Es3hw4fIu7KCPZZmKL27ofRyQzGv\nvxFBd+g0AEahvn/+WpcrUBMyUJNzQKO/Znh6ehMSEkr//qE4ODje+RsSd6Siopw9e3azc9d2SoqL\nwMgY44B+GAf0R7nBKqGaw1EAmAwIu+H51OoqNLEH0SUdB8DFpQujR49lyJCwNrtegSHJz89j6dLX\nycvLxdM3gpChj6JcCTLHDq0HoG/otOt+R6fTcejXFWSmHKRLF1fmzVsgI6cMUGZmBh98sJTCwgL6\ndgpglt9fsTT5bVTG12c3AXC/933X/V61tobPT63mSP5R7OzsefrpuXh6ejdp2ZtKiw6wS5Yswd/f\nn7FjxwIwdOhQ9u3bV+/xhh5gr3XuXDZRUXvYv38vly9fwtbMnHt69GG4u/dtrWCce7mUjUnxxOZm\nA9CjR08iIkbQr98ALCz+fKiSaHyqqpKdnUVcXAz79v1a1zPT3VGhv7e+V/Z2w0VVjUp8hkpMqo5L\nV6bjBgT0ZcCAQQQE9JVh4i2AqqqcP3+O/fv3smfPz5SXl6Mo0MlNpWtPaO90e/NltRrIz4CcJLhc\npD9B5y5duWvkaEJCQmnfXuq+Jbj693/gQBR79vxMZWWFfr5sT1eUPu4o1rd3nVaLLqMeT0NNvQCq\nin3HToy6azQDBgySVUhbKI1Gw+HDB9mwYR0lJcUo7dpjMmgERl3rH4F2LVVV0aUloz20F7WyAheX\nzkyb9hABAX0xaso9vsQdu3SplLfffoOMjDR69P4LfQc+UO9zgKqqxO5fTUrSL3h5+TB37ouyToUB\nu3z5MsuWvc+pU4n4tPfiub5zMDeuv+GpRlvLvxI+5mRxEj4+vsye/WyrnvrTogPsggULGDVqFOHh\n4QBERETw888/17tfkUajxaSBVndsKaqqqvjuu+/YuHEjlZWVOFrbMqVnIP07u91UmLlYVcl3yQn8\nmpmCTlXp1asXjz32GH5+rWMSd2ul1Wo5cuQI3377LYmJiQB0tIX+Xkb0cVMwNbm5JFNSpu9tPZ6p\nUqvRb3dy1113cc8999C1a9fGfAviDlRXV/Prr7+yZcsW0tLSALC2U3HtBc5eNxdkNTWQdQLOn1Go\nrdL3tg4cOJAJEyYQEBAgPa0tWHl5Odu3b+fb776jpLgYjIxQvFxQArujdLi5B1I1txhdfBpk60d1\nuLu7ExkZSXh4uEHu+dcWVVRUsGbNGrZs2YJOp8PIwweT0AgUq/qHZailF6k9sBv1fBamZmY8MH06\n9957r6w6a8BKS0t5/vnnycrKok/wZHoH33iqWPzhDSQd34qHhwdLly7FxkbCq6HTarUsXbqUvXv3\n4mffk6f9n8LsBvsEa3RaliV+SnxhAgMGDGDhwoVt9m++RQTYJUuWEBAQwJgxYwAICwsjKiqq3uNb\nUw/s/7p0qZTNm//LL7t3odVp8bTrxJyQsD9c7OmXjLN8dfIo1RoNzs4uTJ36IMHBIfLgamAyMtLY\nsWMbhw4dQKvV0t5KIXKgEU4d6q9HVVU5clZl9wkdqgp2dnaMGjWGiIgR2NjcuNVKtDyqqnLmzGl2\n7fqR2NjD6HQ6Ormq+A4B0z8YAXi5CE7+qlB5GaysrRkWMZIRI0bJMFEDU1tby4EDUWzb/j0Xcs+D\nsRHKUD+MfOof5q/qVNSYM6jH9dv49OjRk3Hj7iEgoK9c+w1UZmY6X3zxb1JTz6LYtMN03BSUG1zH\ndUUFaLZvQq2uwt+/Lw8//JjMZ24liouLeO21lyksLKB/2ON4+oZf9/MzJ3/i6IH/4OzswsKFr8nI\nmlZEo9Hw0UfvEhcXS0CnPszu87frRmLqVB2fnviMI/lH6d3bn2eemY+ZWf1TDluLFt0Du3PnTvbs\n2VM3B3bZsmV89tln9R7fmgPsVXl5uWzYsI6YmMM4Wtnw4uCRdLL6fSvb9pRTrD8Zh42NDffdN43w\n8OHS6m7gSkqK2b79e3bs2IapMYzvZ0TPrr8fDlarVdkep+NElkqH9h2YNv0h+vcfKPVv4IqKClm5\n8mNOnTqBhQ30HqZi+z9Tm1QVcs/C2cP6PVvHjbuHe+65D3Nzme9myHQ6HTExh/h81QoqKypQ/N1R\n+vdA+Z9Vy9WaWnS7EyC7AGdnF2bNegpv7x7NVGrRkHQ6Hd999w2bN29CaW+nD7HXNGDrSkvQbN2I\nWlnBX//6BBERI6TBopXJy7vAP//5ArW1WsZEvoWVjT0AZZfy2b7pRSwtzHnttbdkakArVFNTw/vv\nL+XEieNM9pjIhO5j6n72U/YvrDuzgR49ejJ37kttZlpgfQHWeNGiRYuatii/5+Hhwb59+1ixYgX7\n9u1j0aJF2Nvb13t8RUVNE5auedjY2NK//0BUVcfRxOPE5mbT17krNtcsyLAlOZGNSfHY2dmzcOGr\n+PsHyryXVuDqtiZubu7ExR0lMVODVqfi7vDb3NhLFSrr92tJywNPTy9efPH/4ePjK/XfClhZWTF4\n8FBUVeVkYhIXUhXMragLsTotJB+EzOMKllbWzJ79HCNH/kUaLloBRVHo2tWVfv0GkHgigbLUbNSC\nUv2qxVemzail5ei2xUD+Rfr0CWDevIU4O7s0c8lFQ1EUhZ49/aipqeFMYjy6nEyMuvdAMTFBvXwJ\nzbZNqBXlPPzw44wYMUrCaytkY2ODra0tsbGHKLuUj5tnKAAHd3/M5dJcHnvsSXr06NnMpRSNwdjY\nmKCgYPZF7eFU/ilCnftjbWrFxepSliV+irmFOQtffrVNzXm2tr5xw3yLCLCKojBs2DDuu+8+IiMj\n/zC8QtsIsKD/XHr16o2xsTFHE44Rcz6bgV27YWFiyraUU2xMiqdTx04sWPCKPMC0Qp07dyEoKITE\nxHhOZ5Wh1UF3RyM0WpXPf9FRdBnCwoYxe/ZzMgemlVEU/R7NHh5exMcfIze1Fgc3MLPUz3fNPqnQ\nvbsHL77w/1rtyoNtma2tLUMGh5GVlUHe2TTUSxUYebqganXothyG0gruvnscs2Y9Jb3urZCiKPj5\n9eHSpUuknUpErarCuJsntbt/QC0uYOrUBxk9emxzF1M0om7dupOUdJL01ATsHdwpKcoi6fg2/P0D\nmTq1/gWehOEzNTWjfYcOHI6JpqiqmAFOIXyZ/BXplzJ54IFH6Nmzba1tU1+Ale4aAzBx4r1MnjyF\nkqoKdqSepqK2hs3JibRv154FC1+VuS+tWNeurrzyyhLs7e05kqJyuVIlLk2ltELlrrvu5vHH/9Ym\n5kC0VYGBQTz1938AkBoLNVWQlahgY2PD/Pn/lLmurZiVlTXPPfeCvoEiPQ+1oBQ1+RxcqmDkyL8w\nffrDsndzK6YoCg899FccnZzRpZ5GdyEH9Xw2PXv6MW7cxOYunmhkiqLw4IOPApCWvI/0M/qdOR58\n8FEJr23AoEFD6dGjJ3EF8RwvTCT6wmHc3bszbNiI5i5aiyEB1kCMHTuRdu3asSfzLDvTTlOlqeUv\no8fKHIg2wMbGlokT70OjhV9P6jiYrMPCwoJJkyLlRtYG9OkTQK9efSg+r5D4C2hqYeLE+7C2/pON\nY4XBMzIyJjJSv/+j7lAyalwqZmZm3HPPfX/ym6I1MDIyYuSIUaDVULt1IwB33TW6mUslmkq3bu64\nuHThXEYsudkJuLt3x8Wlc3MXSzQBRVGIiNCH1bXJXwMQHj5CGi2vIQHWQJiZmTFixGgqamv57nQC\nZmZmDBs2srmLJZpIWNgwHBwcSMhUKa+G0aPHYWsrqwy3BYqiMG3agyiKwqV8BQcHR0aMGNXcxRJN\nxM+vD7169YHcYqioZtSoMbRv36G5iyWaSFjYMEyvjLKxs7MnKCikmUskmlJISP+6/9+v34BmLIlo\nan36BKIoCgVV+i3SAgL6NnOJWhZZ9cOAjBw5ioMHoygpKWHcuImyTUobYmJiwpNPzmHv3l+wtLRk\nzJgJzV0k0YTc3T1YvPhtLl4sxs3Nvc3u+9ZWzZr1dw4dOoCJiQkREdJw2ZZYW9swf95C0tPT6NnT\nD2Nj6YFpS8LDR3Dy5AkURWHo0IjmLo5oQu3bt8fLy4ezZ5NxdXWTKUP/o0Vso3Or2sI2OkIIIYQQ\nQoi2qbi4iOTkJLy8fNpsgG3R+8DeKgmwQgghhBBCCNF61RdgZQ6sEEIIIYQQQgiDIAFWCCGEEEII\nIYRBkAArhBBCCCGEEMIgSIAVQgghhBBCCGEQJMAKIYQQQgghhDAIEmCFEEIIIYQQQhgECbBCCCGE\nEEIIIQyCBFghhBBCCCGEEAZBAqwQQgghhBBCCIMgAVYIIYQQQgghhEGQACuEEEIIIYQQwiBIgBVC\nCCGEEEIIYRAUVVXV5i6EEEIIIYQQQgjxZ6QHVgghhBBCCCGEQZAAK4QQQgghhBDCIEiAFUIIIYQQ\nQghhECTACiGEEEIIIYQwCBJghRBCCCGEEEIYBAmwQgghhBBCCCEMggTYJnb48GF69OjBtm3brvv3\n8ePH88ILL9z2edevX89HH310p8UTjeDNN99kxowZjB49moiICGbMmMGcOXMa9DWeeeYZDh8+3KDn\nFDfn4YcfJiEhAYCamhqCg4P57LPP6n4+Y8YMkpKS/vAc1dXVDB8+vMHLlpqayowZMxr8vOLP3ejv\nPjQ0lGeeeaZRXi8qKuqO7iGi6Zw7d46goCBmzJhR979ly5Zdd8wzzzxDTU3Ndf8mdWxYVq5cySOP\nPMKDDz7IjBkzOHHiRL3Hbtiwgdra2tt6ncGDB99uEUUTuJXvwZ+Re/pvTJq7AG2Rh4cH27ZtY+zY\nsQAkJydTWVnZzKUSjeXqA8e3335LWloac+fObeYSiYY0ePBgYmNj8ff35+jRowwZMoS9e/fy+OOP\nU11dTU5ODr6+vs1dTNHEbvR3f/jwYb7++utmLploCby8vFizZk29P3///febsDSioaWkpPDLL7+w\nfv16FEUhKSmJ+fPn8/3339/w+BUrVnDPPfc0cSlFY7vV74G4eRJgm4Gvry/p6elcvnwZW1tbvv/+\ne8aPH09ubi7ff/89X375JWZmZri7u/Pqq6/yww8/sHfvXqqqqsjKymLmzJlMnjyZ2NhY3njjDdq1\na4exsTGBgYEAvPvuu5w4cYKLFy/i6+vLkiVLuP/++3nttdfw9vZm79697Nmzh0WLFjXvB9GGXX2Q\nvfqQMnjwYA4cOEBubi4vv/wy1dXVmJub89prr2Fvb8/TTz9NWVkZlZWVPPPMMwwZMoR169bxzTff\n4ODgQFFREQBlZWUsWLCAy5cvk5+fz/Tp0xk/fjyTJk1i586dGBsb8/bbb+Pn58eYMWOa8yNoNQYN\nGsTy5cv561//yt69e4mMjOSdd97h8uXLnDx5kv79+xMTE8P777+PsbExrq6uvPrqq9TU1DB37lwu\nXbqEm5tb3flmzJiBr68vZ8+epaysjH/961906dKFNWvWsHXrVhRFYcyYMTz00EPs2rWLf//735iY\nmODo6Mj7779PYWEhc+fORVVVHBwc6s67Y8cO1q1bh0ajQVEUli1bxurVq3FycuKBBx6gtLSURx99\nlG+//bY5PsY2IzMzk8cff5zi4mKGDRvG7NmzmTFjBosWLcLT05P169dTWFjIpEmT+Nvf/kaHDh0I\nCwvDysqKzZs3Y2RkRJ8+fVi4cCGpqam89NJLWFpaYmlpSfv27QFYu3Ytu3btorKyEjs7O5YtW8aL\nL77I+PHjiYiIIDU1lbfeeouVK1c286chrjp8+DDvvPMOpqamTJkyhQ8//JAff/yRc+fOSR0bIFtb\nW86fP8+mTZsICwujZ8+ebNq0iSNHjrBs2TJUVaW8vJx3332X2NhYCgoKeOaZZ3j44Ydv+Gzwwgsv\ncPHiRS5evMgnn3zC22+/TUpKCq6urnU99WfOnOHNN99Eq9VSUlLCokWLqKioYOPGjXz44YcA3H//\n/fzrX//Cycmp2T6btqS+70F91/znnnsOZ2dnsrOz6dOnD6+88gr5+flyT78BGULcTEaNGsWuXbtQ\nVZWEhAT69u3LxYsX+eijj/jyyy9Zv349tra2bNiwAdAHkxUrVvDJJ5/U3ZBeeeUV3n33XVavXk3X\nrl3rjmvXrh1ffPEF//3vf4mPjycvL4/IyEi+++47AP773/8SGRnZPG9c/KG33nqLGTNmsGbNGh57\n7DHeeecdsrKyuHjxIp9++invvfceWq2WwsJC/vOf/7Bx40aWL19eN/QoMzOTsWPHsmrVKj7//HNW\nr16Nra0twcHB7N+/H61WS1RUFCNHjmzmd9p69OrVi7S0NFRVJSYmhv79+zNw4EAOHjzIkSNHGDJk\nCC+//DLLli1j7dq1ODk58d133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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n", "\n", "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(16, 6))\n", "\n", "sns.violinplot(x=\"start_day_of_week\", y=\"duration_minutes\", data=df, ax=ax)\n", "\n", "ax.set_title('Trip duration in minutes by day of week')\n", "ax.set_xlabel('Day of week')\n", "ax.set_ylabel('Duration (minutes)')\n", "ax.set_xticklabels(days)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This violin plot demonstrates the difference in trip durations by day of the week.\n", "\n", "First, we notice that the distribution of trip durations ocurring during weekday trips are similar in both the shape of the distribution (unimodal, similar centers, right skewed) and the median duration. This shape flattens out a bit on Fridays and is flattest on Saturday and Sunday, demonstrating higher variability. The median trip duration also increases on Friday and is highest on Saturday and Sunday, which continues to point towards different ridership patterns on weekdays versus weekends.\n", "\n", "In fact, we might begin to characterize weekend bike rides as being more \"joy ride-esque\", as these trips tend to be longer than typical weekday rides." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }