From b3fecef77ed50e650d76884f07a6bee62c75a086 Mon Sep 17 00:00:00 2001 From: Duvall Pinkney <14938639+Dpinkney001@users.noreply.github.com> Date: Fri, 12 Mar 2021 02:05:01 -0500 Subject: [PATCH] Add files via upload --- Duvall Pinkney Data Science Project.ipynb | 720 +++++++++++++++++++++- 1 file changed, 686 insertions(+), 34 deletions(-) diff --git a/Duvall Pinkney Data Science Project.ipynb b/Duvall Pinkney Data Science Project.ipynb index a7fbace..2580a4d 100644 --- a/Duvall Pinkney Data Science Project.ipynb +++ b/Duvall Pinkney Data Science Project.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Duvall Pinkney Project\n", + "Duvall Pinkney Project Spring 2021\n", "\n", "Step 1: Choose your dataset\n", "Choose any interesting dataset that meets these criteria:\n", @@ -33,7 +33,7 @@ "b) Create a new repository for your project. See main Project page or Project sub-channel in the Video section for an introductory video to GitHub that shows how to create a new repository, add a file, and update a file.\n", "\n", "c) Upload your data file to this repository. See main Project page or Project sub-channel in the Video section for an introductory video to GitHub that shows how to create a new repository, add a file, and update a file.\n", - "\n", + "https://github.com/Dpinkney001/Data-Science-MAT328-Project-Spring-2021\n", "\n", "\n", "Item\n", @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -114,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -123,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -709,7 +709,7 @@ "[153954 rows x 29 columns]" ] }, - "execution_count": 3, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -720,7 +720,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -935,7 +935,7 @@ "max 1.000000 4.223769e+06 " ] }, - "execution_count": 4, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -946,7 +946,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -970,22 +970,22 @@ { "data": { "text/plain": [ - "array([[,\n", - " ,\n", - " ],\n", - " [,\n", - " ,\n", - " ],\n", - " [,\n", - " ,\n", - " ],\n", - " [,\n", - " ,\n", - " ]],\n", + "array([[,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ]],\n", " dtype=object)" ] }, - "execution_count": 5, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, @@ -1008,25 +1008,679 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "url = \"NYPD_Motor_Vehicle_Collisions_-_Crashes (bronx only).csv\"\n", + "df = pd.read_csv(url)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "'Series' object has no attribute 'valuse_counts'", + "data": { + "text/html": [ + "
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DATETIMEBOROUGHZIP CODELATITUDELONGITUDELOCATIONON STREET NAMECROSS STREET NAMEOFF STREET NAMENUMBER OF PERSONS INJUREDNUMBER OF PERSONS KILLEDNUMBER OF PEDESTRIANS INJUREDNUMBER OF PEDESTRIANS KILLEDNUMBER OF CYCLIST INJUREDNUMBER OF CYCLIST KILLEDNUMBER OF MOTORIST INJUREDNUMBER OF MOTORIST KILLEDCONTRIBUTING FACTOR VEHICLE 1CONTRIBUTING FACTOR VEHICLE 2CONTRIBUTING FACTOR VEHICLE 3CONTRIBUTING FACTOR VEHICLE 4CONTRIBUTING FACTOR VEHICLE 5COLLISION_IDVEHICLE TYPE CODE 1VEHICLE TYPE CODE 2VEHICLE TYPE CODE 3VEHICLE TYPE CODE 4VEHICLE TYPE CODE 5
106/08/201710:00BRONX10458.0NaNNaNNaNEAST FORDHAM ROADBELMONT AVENUENaN0.00.0000000Following Too CloselyUnspecifiedNaNNaNNaN3686573SPORT UTILITY / STATION WAGONPASSENGER VEHICLENaNNaNNaN
1705/25/20179:00BRONX10458.040.860060-73.891136(40.86006, -73.891136)3 AVENUEEAST 189 STREETNaN1.00.0100000Driver Inattention/DistractionNaNNaNNaNNaN3679454PASSENGER VEHICLENaNNaNNaNNaN
3205/18/201711:00BRONX10458.0NaNNaNNaNFORDHAM ROADSOUTHERN BLVDNaN0.00.0000000Following Too CloselyUnspecifiedNaNNaNNaN3674817PASSENGER VEHICLEPASSENGER VEHICLENaNNaNNaN
3405/30/20170:00BRONX10458.040.863960-73.894600(40.86396, -73.8946)VALENTINE AVENUEEAST 192 STREETNaN0.00.0000000Failure to Yield Right-of-WayUnspecifiedNaNNaNNaN3682928BusNaNNaNNaNNaN
3706/09/20177:30BRONX10458.040.868366-73.889810(40.868366, -73.88981)EAST 197 STREETBRIGGS AVENUENaN0.00.0000000Turning ImproperlyUnspecifiedNaNNaNNaN3687220SPORT UTILITY / STATION WAGONNaNNaNNaNNaN
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15393906/12/201320:43BRONX10458.040.864256-73.888123(40.8642556, -73.8881227)WEBSTER AVENUEEAST 195 STREETNaN1.00.0000010UnspecifiedNaNNaNNaNNaN111680MOTORCYCLENaNNaNNaNNaN
15394206/12/201322:03BRONX10458.040.869522-73.889893(40.8695216, -73.8898927)EAST 198 STREETVALENTINE AVENUENaN0.00.0000000Driver Inattention/DistractionUnspecifiedNaNNaNNaN111681PASSENGER VEHICLESPORT UTILITY / STATION WAGONNaNNaNNaN
15394606/10/201311:45BRONX10458.040.856435-73.886816(40.8564346, -73.8868164)EAST 188 STREETARTHUR AVENUENaN0.00.0000000UnspecifiedUnspecifiedNaNNaNNaN101467PASSENGER VEHICLEPASSENGER VEHICLENaNNaNNaN
15394806/09/201311:00BRONX10458.040.861831-73.892852(40.861831, -73.8928519)MARION AVENUEEAST FORDHAM ROADNaN0.00.0000000Driver Inattention/DistractionUnspecifiedNaNNaNNaN111655SPORT UTILITY / STATION WAGONSPORT UTILITY / STATION WAGONNaNNaNNaN
15395306/09/201318:10BRONX10458.040.862259-73.895884(40.8622592, -73.8958844)VALENTINE AVENUEEAST FORDHAM ROADNaN0.00.0000000UnspecifiedUnspecifiedNaNNaNNaN93557PASSENGER VEHICLEPASSENGER VEHICLENaNNaNNaN
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8729 rows × 29 columns

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" + ], + "text/plain": [ + " DATE TIME BOROUGH ZIP CODE LATITUDE LONGITUDE \\\n", + "1 06/08/2017 10:00 BRONX 10458.0 NaN NaN \n", + "17 05/25/2017 9:00 BRONX 10458.0 40.860060 -73.891136 \n", + "32 05/18/2017 11:00 BRONX 10458.0 NaN NaN \n", + "34 05/30/2017 0:00 BRONX 10458.0 40.863960 -73.894600 \n", + "37 06/09/2017 7:30 BRONX 10458.0 40.868366 -73.889810 \n", + "... ... ... ... ... ... ... \n", + "153939 06/12/2013 20:43 BRONX 10458.0 40.864256 -73.888123 \n", + "153942 06/12/2013 22:03 BRONX 10458.0 40.869522 -73.889893 \n", + "153946 06/10/2013 11:45 BRONX 10458.0 40.856435 -73.886816 \n", + "153948 06/09/2013 11:00 BRONX 10458.0 40.861831 -73.892852 \n", + "153953 06/09/2013 18:10 BRONX 10458.0 40.862259 -73.895884 \n", + "\n", + " LOCATION ON STREET NAME \\\n", + "1 NaN EAST FORDHAM ROAD \n", + "17 (40.86006, -73.891136) 3 AVENUE \n", + "32 NaN FORDHAM ROAD \n", + "34 (40.86396, -73.8946) VALENTINE AVENUE \n", + "37 (40.868366, -73.88981) EAST 197 STREET \n", + "... ... ... \n", + "153939 (40.8642556, -73.8881227) WEBSTER AVENUE \n", + "153942 (40.8695216, -73.8898927) EAST 198 STREET \n", + "153946 (40.8564346, -73.8868164) EAST 188 STREET \n", + "153948 (40.861831, -73.8928519) MARION AVENUE \n", + "153953 (40.8622592, -73.8958844) VALENTINE AVENUE \n", + "\n", + " CROSS STREET NAME OFF STREET NAME \\\n", + "1 BELMONT AVENUE NaN \n", + "17 EAST 189 STREET NaN \n", + "32 SOUTHERN BLVD NaN \n", + "34 EAST 192 STREET NaN \n", + "37 BRIGGS AVENUE NaN \n", + "... ... ... \n", + "153939 EAST 195 STREET NaN \n", + "153942 VALENTINE AVENUE NaN \n", + "153946 ARTHUR AVENUE NaN \n", + "153948 EAST FORDHAM ROAD NaN \n", + "153953 EAST FORDHAM ROAD NaN \n", + "\n", + " NUMBER OF PERSONS INJURED NUMBER OF PERSONS KILLED \\\n", + "1 0.0 0.0 \n", + "17 1.0 0.0 \n", + "32 0.0 0.0 \n", + "34 0.0 0.0 \n", + "37 0.0 0.0 \n", + "... ... ... \n", + "153939 1.0 0.0 \n", + "153942 0.0 0.0 \n", + "153946 0.0 0.0 \n", + "153948 0.0 0.0 \n", + "153953 0.0 0.0 \n", + "\n", + " NUMBER OF PEDESTRIANS INJURED NUMBER OF PEDESTRIANS KILLED \\\n", + "1 0 0 \n", + "17 1 0 \n", + "32 0 0 \n", + "34 0 0 \n", + "37 0 0 \n", + "... ... ... \n", + "153939 0 0 \n", + "153942 0 0 \n", + "153946 0 0 \n", + "153948 0 0 \n", + "153953 0 0 \n", + "\n", + " NUMBER OF CYCLIST INJURED NUMBER OF CYCLIST KILLED \\\n", + "1 0 0 \n", + "17 0 0 \n", + "32 0 0 \n", + "34 0 0 \n", + "37 0 0 \n", + "... ... ... \n", + "153939 0 0 \n", + "153942 0 0 \n", + "153946 0 0 \n", + "153948 0 0 \n", + "153953 0 0 \n", + "\n", + " NUMBER OF MOTORIST INJURED NUMBER OF MOTORIST KILLED \\\n", + "1 0 0 \n", + "17 0 0 \n", + "32 0 0 \n", + "34 0 0 \n", + "37 0 0 \n", + "... ... ... \n", + "153939 1 0 \n", + "153942 0 0 \n", + "153946 0 0 \n", + "153948 0 0 \n", + "153953 0 0 \n", + "\n", + " CONTRIBUTING FACTOR VEHICLE 1 CONTRIBUTING FACTOR VEHICLE 2 \\\n", + "1 Following Too Closely Unspecified \n", + "17 Driver Inattention/Distraction NaN \n", + "32 Following Too Closely Unspecified \n", + "34 Failure to Yield Right-of-Way Unspecified \n", + "37 Turning Improperly Unspecified \n", + "... ... ... \n", + "153939 Unspecified NaN \n", + "153942 Driver Inattention/Distraction Unspecified \n", + "153946 Unspecified Unspecified \n", + "153948 Driver Inattention/Distraction Unspecified \n", + "153953 Unspecified Unspecified \n", + "\n", + " CONTRIBUTING FACTOR VEHICLE 3 CONTRIBUTING FACTOR VEHICLE 4 \\\n", + "1 NaN NaN \n", + "17 NaN NaN \n", + "32 NaN NaN \n", + "34 NaN NaN \n", + "37 NaN NaN \n", + "... ... ... \n", + "153939 NaN NaN \n", + "153942 NaN NaN \n", + "153946 NaN NaN \n", + "153948 NaN NaN \n", + "153953 NaN NaN \n", + "\n", + " CONTRIBUTING FACTOR VEHICLE 5 COLLISION_ID \\\n", + "1 NaN 3686573 \n", + "17 NaN 3679454 \n", + "32 NaN 3674817 \n", + "34 NaN 3682928 \n", + "37 NaN 3687220 \n", + "... ... ... \n", + "153939 NaN 111680 \n", + "153942 NaN 111681 \n", + "153946 NaN 101467 \n", + "153948 NaN 111655 \n", + "153953 NaN 93557 \n", + "\n", + " VEHICLE TYPE CODE 1 VEHICLE TYPE CODE 2 \\\n", + "1 SPORT UTILITY / STATION WAGON PASSENGER VEHICLE \n", + "17 PASSENGER VEHICLE NaN \n", + "32 PASSENGER VEHICLE PASSENGER VEHICLE \n", + "34 Bus NaN \n", + "37 SPORT UTILITY / STATION WAGON NaN \n", + "... ... ... \n", + "153939 MOTORCYCLE NaN \n", + "153942 PASSENGER VEHICLE SPORT UTILITY / STATION WAGON \n", + "153946 PASSENGER VEHICLE PASSENGER VEHICLE \n", + "153948 SPORT UTILITY / STATION WAGON SPORT UTILITY / STATION WAGON \n", + "153953 PASSENGER VEHICLE PASSENGER VEHICLE \n", + "\n", + " VEHICLE TYPE CODE 3 VEHICLE TYPE CODE 4 VEHICLE TYPE CODE 5 \n", + "1 NaN NaN NaN \n", + "17 NaN NaN NaN \n", + "32 NaN NaN NaN \n", + "34 NaN NaN NaN \n", + "37 NaN NaN NaN \n", + "... ... ... ... \n", + "153939 NaN NaN NaN \n", + "153942 NaN NaN NaN \n", + "153946 NaN NaN NaN \n", + "153948 NaN NaN NaN \n", + "153953 NaN NaN NaN \n", + "\n", + "[8729 rows x 29 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "zipcode_filter = df['ZIP CODE'] == 10458\n", + "df[zipcode_filter]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ]],\n", + " dtype=object)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "my_neigborhood_data = df[zipcode_filter]\n", + "my_neigborhood_data.hist()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "x and y must be the same size", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mbronxData_zipcode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbronxData\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ZIP CODE'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvaluse_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mbronxData_zipcode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 5177\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5178\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5179\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5180\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5181\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mAttributeError\u001b[0m: 'Series' object has no attribute 'valuse_counts'" + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Time\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"COLLISION_ID\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mzipcode_filter\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[1;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[0;32m 2814\u001b[0m \u001b[0mverts\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0medgecolors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0medgecolors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2815\u001b[0m plotnonfinite=plotnonfinite, **({\"data\": data} if data is not\n\u001b[1;32m-> 2816\u001b[1;33m None else {}), **kwargs)\n\u001b[0m\u001b[0;32m 2817\u001b[0m \u001b[0msci\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m__ret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2818\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m__ret\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1589\u001b[0m args_and_kwargs.get(label_namer), auto_label)\n\u001b[0;32m 1590\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1591\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnew_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mnew_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1592\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1593\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_add_data_doc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreplace_names\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 356\u001b[0m \u001b[1;34mf\"%(removal)s. If any parameter follows {name!r}, they \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 357\u001b[0m f\"should be pass as keyword, not positionally.\")\n\u001b[1;32m--> 358\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 359\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 360\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[1;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[0;32m 4389\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4390\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4391\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"x and y must be the same size\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4392\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4393\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: x and y must be the same size" ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "bronxData_zipcode = bronxData['ZIP CODE'].valuse_counts()\n", - "bronxData_zipcode.hist()" + "plt.scatter(x = \"TIME\", y = \"COLLISION_ID\", data = df[zipcode_filter])" ] }, { @@ -1034,9 +1688,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "bronx_filtered_data = " - ] + "source": [] } ], "metadata": {