diff --git a/python_project_2.ipynb b/python_project_2.ipynb index c44a07d..d4925d0 100644 --- a/python_project_2.ipynb +++ b/python_project_2.ipynb @@ -21,7 +21,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_6745/2268714868.py:1: DtypeWarning: Columns (18,20) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_13417/2268714868.py:1: DtypeWarning: Columns (18,20) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df = pd.read_csv('~/Downloads/NYPD_Complaint_Data_Historic.csv')\n" ] } @@ -402,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "id": "3cbcbd7c", "metadata": {}, "outputs": [ @@ -412,7 +412,7 @@ "" ] }, - "execution_count": 12, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, @@ -437,7 +437,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "id": "b07d2228", "metadata": {}, "outputs": [ @@ -447,7 +447,7 @@ "6761" ] }, - "execution_count": 15, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -460,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "id": "1ef4d375", "metadata": {}, "outputs": [], @@ -483,7 +483,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "id": "b838819a", "metadata": {}, "outputs": [], @@ -495,7 +495,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 13, "id": "e9c16848", "metadata": {}, "outputs": [ @@ -520,7 +520,7 @@ "Name: PUBLIC_SPACE, dtype: int64" ] }, - "execution_count": 35, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -534,13 +534,178 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "id": "91ae572a", "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LARCENY,PETIT FROM OPEN AREAS, 563\n", + "LARCENY,GRAND FROM OPEN AREAS, UNATTENDED 501\n", + "ASSAULT 3 473\n", + "CONTROLLED SUBSTANCE, POSSESSI 272\n", + "HARASSMENT,SUBD 3,4,5 252\n", + "CRIMINAL MISCHIEF 4TH, GRAFFIT 209\n", + "ASSAULT 2,1,UNCLASSIFIED 187\n", + "HARASSMENT,SUBD 1,CIVILIAN 187\n", + "MARIJUANA, POSSESSION 4 & 5 174\n", + "ROBBERY,OPEN AREA UNCLASSIFIED 143\n", + "LARCENY,PETIT OF VEHICLE ACCES 141\n", + "LARCENY,PETIT FROM BUILDING,UN 133\n", + "LARCENY,PETIT OF BICYCLE 131\n", + "ROBBERY,PERSONAL ELECTRONIC DEVICE 115\n", + "LEWDNESS,PUBLIC 91\n", + "SEXUAL ABUSE 3,2 77\n", + "LARCENY,GRAND FROM BUILDING (NON-RESIDENCE) UNATTENDED 71\n", + "CRIMINAL MISCHIEF,UNCLASSIFIED 4 66\n", + "MENACING,UNCLASSIFIED 66\n", + "MARIJUANA, SALE 4 & 5 65\n", + "LARCENY,GRAND FROM PERSON,PICK 64\n", + "RESISTING ARREST 63\n", + "LARCENY,GRAND FROM VEHICLE/MOTORCYCLE 59\n", + "LARCENY,PETIT BY DISHONEST EMP 54\n", + "WEAPONS, POSSESSION, ETC 53\n", + "MISCHIEF, CRIMINAL 4, OF MOTOR 53\n", + "PUBLIC ADMINISTATION,UNCLASS M 52\n", + "ASSAULT POLICE/PEACE OFFICER 47\n", + "FORGERY,ETC.-MISD. 46\n", + "AGGRAVATED HARASSMENT 2 45\n", + "LARCENY,GRAND FROM PERSON,PERSONAL ELECTRONIC DEVICE(SNATCH) 45\n", + "LARCENY,PETIT FROM AUTO 44\n", + "BURGLARY,COMMERCIAL,NIGHT 36\n", + "LEAVING SCENE-ACCIDENT-PERSONA 33\n", + "LARCENY,GRAND FROM PERSON, BAG OPEN/DIP 33\n", + "RECKLESS ENDANGERMENT 2 31\n", + "CONTROLLED SUBSTANCE,INTENT TO 31\n", + "BRIBERY,PUBLIC ADMINISTRATION 30\n", + "LARCENY,GRAND BY THEFT OF CREDIT CARD 27\n", + "RAPE 1 26\n", + "MISCHIEF, CRIMINAL 3 & 2, OF M 26\n", + "WEAPONS POSSESSION 3 26\n", + "LARCENY,GRAND OF BICYCLE 25\n", + "LARCENY,PETIT FROM STORE-SHOPL 24\n", + "LARCENY,GRAND BY ACQUIRING LOST CREDIT CARD 23\n", + "LARCENY,GRAND FROM PERSON,UNCL 22\n", + "LARCENY,PETIT BY ACQUIRING LOS 21\n", + "ROBBERY,BICYCLE 21\n", + "LARCENY,GRAND OF AUTO 20\n", + "RECKLESS ENDANGERMENT 1 19\n", + "Name: DESCRIPTION, dtype: int64" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Each of the top four have more than 1000 incidents.\n", + "# What kind of incidents occurred there?\n", + "\n", + "subset = df[(df['PUBLIC_SPACE'] == 'CENTRAL PARK')\n", + " | (df['PUBLIC_SPACE'] == 'FLUSHING MEADOWS CORONA PARK')\n", + " | (df['PUBLIC_SPACE'] == 'CONEY ISLAND BEACH & BOARDWALK')\n", + " | (df['PUBLIC_SPACE'] == 'WASHINGTON SQUARE PARK')\n", + " ]\n", + "subset['DESCRIPTION'].value_counts().head(50)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "30c5dab8", + "metadata": {}, "outputs": [], "source": [ - "# Each of the top four have more than 1000 incidents.\n", - "# We'll pick Central Park and Coney Island." + "# Many incidents. This is grim.\n", + "# Choose just a few (to avoid psychological fatigue).\n", + "\n", + "bikes = subset[subset['DESCRIPTION'].str.contains('BICYCLE')]\n", + "cars = subset[subset['DESCRIPTION'].str.contains('VEHICLE')]" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "be093f45", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Visualizations comparing these two incidents in each location\n", + "# I wonder if there's a way to combine them into one plot using different colors.\n", + "\n", + "bikes.groupby(['PUBLIC_SPACE'])['DESCRIPTION'].count().plot(kind = 'bar')" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "c52dda8e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "cars.groupby(['PUBLIC_SPACE'])['DESCRIPTION'].count().plot(kind = 'bar')" + ] + }, + { + "cell_type": "markdown", + "id": "483a4ff9", + "metadata": {}, + "source": [ + "## Conclusions\n", + "\n", + "### The dataset comprises NYPD criminal complaints from 2006 to 2019. The downloaded csv file contains more than 7 million rows and 35 columns. The complaint number for each row was found to be non-unique. Exploratory analysis shows the dataset to be well formatted, with mostly string type data providing time, date, location and descriptions of each incident. No columns seemed to contain numerical data that would merit any summary statistics.\n", + "\n", + "### The first research question looks at the category of each incident and where those incidents occurred. I made a visualization to show the number of felony, misdemeanor and violations for each borough.\n", + "\n", + "### The second research quesion intended to show the top criminal complaints that occurred in NYC parks, playgrounds and greenspaces. But the task was found to be a nauseating experience, so I abandoned it. Instead, I compared the number of bike and car incidents for each of these public spaces. All incidents involving bikes or cars were considered: e.g. theft of, assault with, damage to, etc. Plotting showed Central Park to be where the greatest number of bike incidents occurred, but very few car incidents. Surprisingly, Flushing Meadows Park was very high for cars, whereas Washington Square Park had no car incidents at all." ] } ],