{ "cells": [ { "cell_type": "markdown", "id": "f02f6e74-b42d-4f33-86e1-e233b711fe2c", "metadata": {}, "source": [ "## Exploring Maritime Piracy Incidents\n", "\n", "National Geospatial-Intelligence Agency’s Maritime Safety Information portal provides a shapefile of all incidencts of maritine piracy in the form on Anti-shipping Activity Messages. \n", "\n", "This notebook demonstrates how to read the shapefile via geopandas and explore the trends of pirate incidents over the years.\n", "\n", "Download [ASAM_shp.zip](https://msi.nga.mil/api/publications/download?key=16920958/SFH00000/ASAM_shp.zip&type=download)" ] }, { "cell_type": "code", "execution_count": 205, "id": "9246510e-3a68-41df-b910-9ac9da5677ef", "metadata": {}, "outputs": [], "source": [ "import geopandas as gpd\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import os" ] }, { "cell_type": "code", "execution_count": 206, "id": "8f825922-80eb-49ed-b594-ca21389d3112", "metadata": {}, "outputs": [], "source": [ "data_pkg_path = 'data'\n", "filename = 'ASAM_events.shp'\n", "file_path = os.path.join(data_pkg_path, filename)\n", "gdf = gpd.read_file(file_path)" ] }, { "cell_type": "code", "execution_count": 207, "id": "16096d7d-b658-4f70-b57c-dc8694cab399", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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referencedateofoccsubreghostility_victim_ddescriptiohostilitythostilit_Dvictim_lvictim_l_Dnavareageometry
01990-91990-06-0326CUBAN GUNBOATBELESBAT QUEENA CUBAN GUNBOAT COMMANDEERED LUXURY YACHT BELE...2Naval Engagement11VesselIVPOINT (-75.13333 21.93333)
11990-101990-03-2071PIRATESRO/RO SEA DRAGON20 MARCH 1990. BORNEO. ...1Pirate Assault3Cargo ShipXIPOINT (108.00000 3.00000)
21990-111990-03-2061PIRATESRO/RO SUNRISE20 MARCH 1990. SINGAPORE. ...1Pirate Assault3Cargo ShipVIIIPOINT (90.00000 -1.00000)
31989-161989-01-0162PEOPLES DEMOCRATIC REPUBLIC OF YEMENU.S. MARINERSRED SEA, YEMEN ...2Naval Engagement13OtherIXPOINT (42.00000 14.00000)
41989-171989-09-2363PIRATESLASH STONEWALL JACKSONIndian ocean ...1Pirate Assault11VesselVIIIPOINT (80.30000 13.10000)
.......................................
84982020-1382020-03-2762NoneNoneOn 27 March at 1226 UTC, two skiffs with 5-6 p...3Suspicious Approach9TankerIXPOINT (54.51667 26.01667)
84992020-132020-02-0271NoneNoneOn 20 January, a product tanker was boarded by...9Attempted Boarding9TankerXIPOINT (103.88333 1.05000)
85002020-2082020-05-1457NoneNoneOn 14 May, individuals in two skiffs approache...3Suspicious Approach5Merchant VesselIIPOINT (2.48333 4.56667)
85012021-1322021-06-2922NoneTankerPERU: On 28 June, at 16:00 local time, robbers...11Robbery1Anchored ShipXVIPOINT (-77.51667 -12.01667)
85022020-2732020-09-2024robberyNORD NEPTUNEOn 20 September at 0410 UTC, a robbery occurre...6Other3Cargo ShipVPOINT (-51.00000 0.01667)
\n", "

8503 rows × 12 columns

\n", "
" ], "text/plain": [ " reference dateofocc subreg hostility_ \\\n", "0 1990-9 1990-06-03 26 CUBAN GUNBOAT \n", "1 1990-10 1990-03-20 71 PIRATES \n", "2 1990-11 1990-03-20 61 PIRATES \n", "3 1989-16 1989-01-01 62 PEOPLES DEMOCRATIC REPUBLIC OF YEMEN \n", "4 1989-17 1989-09-23 63 PIRATES \n", "... ... ... ... ... \n", "8498 2020-138 2020-03-27 62 None \n", "8499 2020-13 2020-02-02 71 None \n", "8500 2020-208 2020-05-14 57 None \n", "8501 2021-132 2021-06-29 22 None \n", "8502 2020-273 2020-09-20 24 robbery \n", "\n", " victim_d \\\n", "0 BELESBAT QUEEN \n", "1 RO/RO SEA DRAGON \n", "2 RO/RO SUNRISE \n", "3 U.S. MARINERS \n", "4 LASH STONEWALL JACKSON \n", "... ... \n", "8498 None \n", "8499 None \n", "8500 None \n", "8501 Tanker \n", "8502 NORD NEPTUNE \n", "\n", " descriptio hostilityt \\\n", "0 A CUBAN GUNBOAT COMMANDEERED LUXURY YACHT BELE... 2 \n", "1 20 MARCH 1990. BORNEO. ... 1 \n", "2 20 MARCH 1990. SINGAPORE. ... 1 \n", "3 RED SEA, YEMEN ... 2 \n", "4 Indian ocean ... 1 \n", "... ... ... \n", "8498 On 27 March at 1226 UTC, two skiffs with 5-6 p... 3 \n", "8499 On 20 January, a product tanker was boarded by... 9 \n", "8500 On 14 May, individuals in two skiffs approache... 3 \n", "8501 PERU: On 28 June, at 16:00 local time, robbers... 11 \n", "8502 On 20 September at 0410 UTC, a robbery occurre... 6 \n", "\n", " hostilit_D victim_l victim_l_D navarea \\\n", "0 Naval Engagement 11 Vessel IV \n", "1 Pirate Assault 3 Cargo Ship XI \n", "2 Pirate Assault 3 Cargo Ship VIII \n", "3 Naval Engagement 13 Other IX \n", "4 Pirate Assault 11 Vessel VIII \n", "... ... ... ... ... \n", "8498 Suspicious Approach 9 Tanker IX \n", "8499 Attempted Boarding 9 Tanker XI \n", "8500 Suspicious Approach 5 Merchant Vessel II \n", "8501 Robbery 1 Anchored Ship XVI \n", "8502 Other 3 Cargo Ship V \n", "\n", " geometry \n", "0 POINT (-75.13333 21.93333) \n", "1 POINT (108.00000 3.00000) \n", "2 POINT (90.00000 -1.00000) \n", "3 POINT (42.00000 14.00000) \n", "4 POINT (80.30000 13.10000) \n", "... ... \n", "8498 POINT (54.51667 26.01667) \n", "8499 POINT (103.88333 1.05000) \n", "8500 POINT (2.48333 4.56667) \n", "8501 POINT (-77.51667 -12.01667) \n", "8502 POINT (-51.00000 0.01667) \n", "\n", "[8503 rows x 12 columns]" ] }, "execution_count": 207, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdf" ] }, { "cell_type": "markdown", "id": "abe4b3b3-104b-48aa-8eaa-c5934e63017d", "metadata": {}, "source": [ "`dateofocc` field contains event dates. We use Pandas `to_datetime()` function to convert them to datetime objects" ] }, { "cell_type": "code", "execution_count": 208, "id": "2ebfedfc-eae6-4f11-9a6b-44f0ef7b8f0a", "metadata": {}, "outputs": [], "source": [ "gdf['dateofocc'] = pd.to_datetime(gdf['dateofocc'])" ] }, { "cell_type": "code", "execution_count": 209, "id": "df83c80b-80c9-49d8-89ad-6e45520b0202", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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referencedateofoccsubreghostility_victim_ddescriptiohostilitythostilit_Dvictim_lvictim_l_Dnavareageometryyear
01990-91990-06-0326CUBAN GUNBOATBELESBAT QUEENA CUBAN GUNBOAT COMMANDEERED LUXURY YACHT BELE...2Naval Engagement11VesselIVPOINT (-75.13333 21.93333)1990
11990-101990-03-2071PIRATESRO/RO SEA DRAGON20 MARCH 1990. BORNEO. ...1Pirate Assault3Cargo ShipXIPOINT (108.00000 3.00000)1990
21990-111990-03-2061PIRATESRO/RO SUNRISE20 MARCH 1990. SINGAPORE. ...1Pirate Assault3Cargo ShipVIIIPOINT (90.00000 -1.00000)1990
31989-161989-01-0162PEOPLES DEMOCRATIC REPUBLIC OF YEMENU.S. MARINERSRED SEA, YEMEN ...2Naval Engagement13OtherIXPOINT (42.00000 14.00000)1989
41989-171989-09-2363PIRATESLASH STONEWALL JACKSONIndian ocean ...1Pirate Assault11VesselVIIIPOINT (80.30000 13.10000)1989
..........................................
84982020-1382020-03-2762NoneNoneOn 27 March at 1226 UTC, two skiffs with 5-6 p...3Suspicious Approach9TankerIXPOINT (54.51667 26.01667)2020
84992020-132020-02-0271NoneNoneOn 20 January, a product tanker was boarded by...9Attempted Boarding9TankerXIPOINT (103.88333 1.05000)2020
85002020-2082020-05-1457NoneNoneOn 14 May, individuals in two skiffs approache...3Suspicious Approach5Merchant VesselIIPOINT (2.48333 4.56667)2020
85012021-1322021-06-2922NoneTankerPERU: On 28 June, at 16:00 local time, robbers...11Robbery1Anchored ShipXVIPOINT (-77.51667 -12.01667)2021
85022020-2732020-09-2024robberyNORD NEPTUNEOn 20 September at 0410 UTC, a robbery occurre...6Other3Cargo ShipVPOINT (-51.00000 0.01667)2020
\n", "

8503 rows × 13 columns

\n", "
" ], "text/plain": [ " reference dateofocc subreg hostility_ \\\n", "0 1990-9 1990-06-03 26 CUBAN GUNBOAT \n", "1 1990-10 1990-03-20 71 PIRATES \n", "2 1990-11 1990-03-20 61 PIRATES \n", "3 1989-16 1989-01-01 62 PEOPLES DEMOCRATIC REPUBLIC OF YEMEN \n", "4 1989-17 1989-09-23 63 PIRATES \n", "... ... ... ... ... \n", "8498 2020-138 2020-03-27 62 None \n", "8499 2020-13 2020-02-02 71 None \n", "8500 2020-208 2020-05-14 57 None \n", "8501 2021-132 2021-06-29 22 None \n", "8502 2020-273 2020-09-20 24 robbery \n", "\n", " victim_d \\\n", "0 BELESBAT QUEEN \n", "1 RO/RO SEA DRAGON \n", "2 RO/RO SUNRISE \n", "3 U.S. MARINERS \n", "4 LASH STONEWALL JACKSON \n", "... ... \n", "8498 None \n", "8499 None \n", "8500 None \n", "8501 Tanker \n", "8502 NORD NEPTUNE \n", "\n", " descriptio hostilityt \\\n", "0 A CUBAN GUNBOAT COMMANDEERED LUXURY YACHT BELE... 2 \n", "1 20 MARCH 1990. BORNEO. ... 1 \n", "2 20 MARCH 1990. SINGAPORE. ... 1 \n", "3 RED SEA, YEMEN ... 2 \n", "4 Indian ocean ... 1 \n", "... ... ... \n", "8498 On 27 March at 1226 UTC, two skiffs with 5-6 p... 3 \n", "8499 On 20 January, a product tanker was boarded by... 9 \n", "8500 On 14 May, individuals in two skiffs approache... 3 \n", "8501 PERU: On 28 June, at 16:00 local time, robbers... 11 \n", "8502 On 20 September at 0410 UTC, a robbery occurre... 6 \n", "\n", " hostilit_D victim_l victim_l_D navarea \\\n", "0 Naval Engagement 11 Vessel IV \n", "1 Pirate Assault 3 Cargo Ship XI \n", "2 Pirate Assault 3 Cargo Ship VIII \n", "3 Naval Engagement 13 Other IX \n", "4 Pirate Assault 11 Vessel VIII \n", "... ... ... ... ... \n", "8498 Suspicious Approach 9 Tanker IX \n", "8499 Attempted Boarding 9 Tanker XI \n", "8500 Suspicious Approach 5 Merchant Vessel II \n", "8501 Robbery 1 Anchored Ship XVI \n", "8502 Other 3 Cargo Ship V \n", "\n", " geometry year \n", "0 POINT (-75.13333 21.93333) 1990 \n", "1 POINT (108.00000 3.00000) 1990 \n", "2 POINT (90.00000 -1.00000) 1990 \n", "3 POINT (42.00000 14.00000) 1989 \n", "4 POINT (80.30000 13.10000) 1989 \n", "... ... ... \n", "8498 POINT (54.51667 26.01667) 2020 \n", "8499 POINT (103.88333 1.05000) 2020 \n", "8500 POINT (2.48333 4.56667) 2020 \n", "8501 POINT (-77.51667 -12.01667) 2021 \n", "8502 POINT (-51.00000 0.01667) 2020 \n", "\n", "[8503 rows x 13 columns]" ] }, "execution_count": 209, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdf['year'] = gdf.dateofocc.dt.to_period('Y')\n", "gdf" ] }, { "cell_type": "markdown", "id": "1e58be7b-6156-4e36-aaa3-84de60fee9cd", "metadata": {}, "source": [ "Group values by year and calculate total piracy incidents for each year" ] }, { "cell_type": "code", "execution_count": null, "id": "b2961e58-677a-4a64-a4c1-976e0ffcadf6", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 210, "id": "b84af647-d8b4-403d-a8dc-399445185a97", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "year\n", "1978 2\n", "1979 2\n", "1980 2\n", "1981 1\n", "1983 2\n", "1984 8\n", "1985 17\n", "1986 29\n", "1987 24\n", "1988 28\n", "1989 17\n", "1990 21\n", "1991 28\n", "1992 37\n", "1993 102\n", "1994 94\n", "1995 90\n", "1996 48\n", "1997 73\n", "1998 67\n", "1999 133\n", "2000 424\n", "2001 311\n", "2002 374\n", "2003 390\n", "2004 346\n", "2005 342\n", "2006 298\n", "2007 324\n", "2008 500\n", "2009 491\n", "2010 545\n", "2011 483\n", "2012 345\n", "2013 367\n", "2014 291\n", "2015 275\n", "2016 321\n", "2017 354\n", "2018 162\n", "2019 153\n", "2020 317\n", "2021 262\n", "2022 3\n", "Freq: A-DEC, dtype: int64" ] }, "execution_count": 210, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counts = gdf.groupby('year').size()\n", "counts" ] }, { "cell_type": "markdown", "id": "56fb55e1-e159-4959-a3e3-870589b78587", "metadata": {}, "source": [ "Plot the trend of piracy incidents over time." ] }, { "cell_type": "code", "execution_count": 211, "id": "ecd06661-1843-48ad-8ef8-f982bab9187a", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1)\n", "fig.set_size_inches(15,7)\n", "counts.plot(kind='bar', ax=ax)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 212, "id": "ee9590c0-2eb0-4905-87ce-ec7922ac2ba4", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1)\n", "fig.set_size_inches(15,7)\n", "counts.plot(kind='bar', color='red', ax=ax)\n", "plt.xlabel('Year', size = 15)\n", "plt.ylabel('Number of Incidents', size = 15)\n", "plt.title('Global Maritime Piracy Incidents', size = 18)\n", "plt.legend(['total incients'])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 213, "id": "11a98c4e-f775-423a-9a53-97d982a046c9", "metadata": {}, "outputs": [], "source": [ "gdf = gdf.dropna(subset=['hostilit_D'])" ] }, { "cell_type": "code", "execution_count": 214, "id": "4e502cf2-2745-4d66-8ca7-b7cbb94cc0ad", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{2: 'Naval Engagement',\n", " 1: 'Pirate Assault',\n", " 6: 'Other',\n", " 5: 'Unknown',\n", " 4: 'Kidnapping',\n", " 3: 'Suspicious Approach',\n", " 11: 'Robbery',\n", " 9: 'Attempted Boarding',\n", " 10: 'Mothership Activity',\n", " 7: 'Hijacking'}" ] }, "execution_count": 214, "metadata": {}, "output_type": "execute_result" } ], "source": [ "types = gdf['hostilityt'].unique()\n", "description = gdf['hostilit_D'].unique()\n", "type_dict = dict(zip(types, description))\n", "type_dict" ] }, { "cell_type": "code", "execution_count": 215, "id": "838379bb-3508-4576-b99d-62938cc70e1e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "year hostilityt\n", "1978 1 1\n", " 5 1\n", "1979 5 2\n", "1980 1 1\n", " 5 1\n", " ... \n", "2021 10 2\n", " 11 144\n", "2022 3 1\n", " 7 1\n", " 11 1\n", "Length: 153, dtype: int64" ] }, "execution_count": 215, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counts = gdf.groupby(['year', 'hostilityt']).size()\n", "counts" ] }, { "cell_type": "code", "execution_count": 216, "id": "cb09196c-1b90-4410-87e6-10a6065b9f63", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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hostilitytPirate AssaultNaval EngagementSuspicious ApproachKidnappingUnknownOtherHijackingAttempted BoardingMothership ActivityRobbery
year
19781.0NaNNaNNaN1.0NaNNaNNaNNaNNaN
1979NaNNaNNaNNaN2.0NaNNaNNaNNaNNaN
19801.0NaNNaNNaN1.0NaNNaNNaNNaNNaN
19811.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
19831.0NaNNaNNaN1.0NaNNaNNaNNaNNaN
19843.0NaNNaNNaN5.0NaNNaNNaNNaNNaN
19856.08.0NaNNaN1.02.0NaNNaNNaNNaN
198612.05.0NaN2.03.07.0NaNNaNNaNNaN
198717.0NaNNaNNaN1.05.0NaNNaNNaNNaN
198822.03.01.0NaNNaN2.0NaNNaNNaNNaN
198910.03.0NaNNaN1.03.0NaNNaNNaNNaN
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\n", "
" ], "text/plain": [ "hostilityt Pirate Assault Naval Engagement Suspicious Approach Kidnapping \\\n", "year \n", "1978 1.0 NaN NaN NaN \n", "1979 NaN NaN NaN NaN \n", "1980 1.0 NaN NaN NaN \n", "1981 1.0 NaN NaN NaN \n", "1983 1.0 NaN NaN NaN \n", "1984 3.0 NaN NaN NaN \n", "1985 6.0 8.0 NaN NaN \n", "1986 12.0 5.0 NaN 2.0 \n", "1987 17.0 NaN NaN NaN \n", "1988 22.0 3.0 1.0 NaN \n", "1989 10.0 3.0 NaN NaN \n", "1990 15.0 2.0 NaN NaN \n", "1991 18.0 4.0 NaN 1.0 \n", "1992 35.0 NaN NaN 1.0 \n", "1993 98.0 3.0 NaN NaN \n", "1994 86.0 NaN NaN NaN \n", "1995 89.0 NaN NaN NaN \n", "1996 48.0 NaN NaN NaN \n", "1997 70.0 1.0 NaN NaN \n", "1998 60.0 5.0 NaN NaN \n", "1999 117.0 4.0 NaN NaN \n", "2000 342.0 3.0 1.0 55.0 \n", "2001 271.0 1.0 2.0 31.0 \n", "2002 323.0 NaN 48.0 NaN \n", "2003 343.0 1.0 46.0 NaN \n", "2004 296.0 1.0 49.0 NaN \n", "2005 299.0 NaN 43.0 NaN \n", "2006 264.0 NaN 32.0 2.0 \n", "2007 246.0 NaN 73.0 2.0 \n", "2008 379.0 1.0 69.0 35.0 \n", "2009 467.0 2.0 1.0 21.0 \n", "2010 543.0 NaN 2.0 NaN \n", "2011 483.0 NaN NaN NaN \n", "2012 334.0 NaN 10.0 1.0 \n", "2013 321.0 NaN 38.0 4.0 \n", "2014 277.0 NaN 13.0 1.0 \n", "2015 264.0 NaN 10.0 1.0 \n", "2016 301.0 NaN 19.0 NaN \n", "2017 291.0 NaN 58.0 2.0 \n", "2018 132.0 NaN 19.0 1.0 \n", "2019 89.0 NaN 2.0 14.0 \n", "2020 63.0 NaN 44.0 16.0 \n", "2021 20.0 NaN 33.0 16.0 \n", "2022 NaN NaN 1.0 NaN \n", "\n", "hostilityt Unknown Other Hijacking Attempted Boarding \\\n", "year \n", "1978 1.0 NaN NaN NaN \n", "1979 2.0 NaN NaN NaN \n", "1980 1.0 NaN NaN NaN \n", "1981 NaN NaN NaN NaN \n", "1983 1.0 NaN NaN NaN \n", "1984 5.0 NaN NaN NaN \n", "1985 1.0 2.0 NaN NaN \n", "1986 3.0 7.0 NaN NaN \n", "1987 1.0 5.0 NaN NaN \n", "1988 NaN 2.0 NaN NaN \n", "1989 1.0 3.0 NaN NaN \n", "1990 NaN 4.0 NaN NaN \n", "1991 2.0 3.0 NaN NaN \n", "1992 1.0 NaN NaN NaN \n", "1993 NaN 1.0 NaN NaN \n", "1994 8.0 NaN NaN NaN \n", "1995 NaN 1.0 NaN NaN \n", "1996 NaN NaN NaN NaN \n", "1997 2.0 NaN NaN NaN \n", "1998 1.0 1.0 NaN NaN \n", "1999 2.0 10.0 NaN NaN \n", "2000 NaN 23.0 NaN NaN \n", "2001 NaN 6.0 NaN NaN \n", "2002 NaN 3.0 NaN NaN \n", "2003 NaN NaN NaN NaN \n", "2004 NaN NaN NaN NaN \n", "2005 NaN NaN NaN NaN \n", "2006 NaN NaN NaN NaN \n", "2007 NaN 2.0 NaN NaN \n", "2008 13.0 3.0 NaN NaN \n", "2009 NaN NaN NaN NaN \n", "2010 NaN NaN NaN NaN \n", "2011 NaN NaN NaN NaN \n", "2012 NaN NaN NaN NaN \n", "2013 3.0 1.0 NaN NaN \n", "2014 NaN NaN NaN NaN \n", "2015 NaN NaN NaN NaN \n", "2016 NaN 1.0 NaN NaN \n", "2017 NaN 2.0 NaN NaN \n", "2018 NaN 1.0 NaN NaN \n", "2019 1.0 33.0 3.0 5.0 \n", "2020 1.0 114.0 14.0 34.0 \n", "2021 NaN 37.0 5.0 5.0 \n", "2022 NaN NaN 1.0 NaN \n", "\n", "hostilityt Mothership Activity Robbery \n", "year \n", "1978 NaN NaN \n", "1979 NaN NaN \n", "1980 NaN NaN \n", "1981 NaN NaN \n", "1983 NaN NaN \n", "1984 NaN NaN \n", "1985 NaN NaN \n", "1986 NaN NaN \n", "1987 NaN NaN \n", "1988 NaN NaN \n", "1989 NaN NaN \n", "1990 NaN NaN \n", "1991 NaN NaN \n", "1992 NaN NaN \n", "1993 NaN NaN \n", "1994 NaN NaN \n", "1995 NaN NaN \n", "1996 NaN NaN \n", "1997 NaN NaN \n", "1998 NaN NaN \n", "1999 NaN NaN \n", "2000 NaN NaN \n", "2001 NaN NaN \n", "2002 NaN NaN \n", "2003 NaN NaN \n", "2004 NaN NaN \n", "2005 NaN NaN \n", "2006 NaN NaN \n", "2007 NaN NaN \n", "2008 NaN NaN \n", "2009 NaN NaN \n", "2010 NaN NaN \n", "2011 NaN NaN \n", "2012 NaN NaN \n", "2013 NaN NaN \n", "2014 NaN NaN \n", "2015 NaN NaN \n", "2016 NaN NaN \n", "2017 NaN NaN \n", "2018 NaN NaN \n", "2019 1.0 NaN \n", "2020 NaN 23.0 \n", "2021 2.0 144.0 \n", "2022 NaN 1.0 " ] }, "execution_count": 216, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counts_df = counts.unstack()\n", "counts_df.rename(columns=type_dict, inplace=True)\n", "counts_df" ] }, { "cell_type": "code", "execution_count": 217, "id": "715e4c3f-1894-46a3-b736-115444b59d67", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1)\n", "fig.set_size_inches(15,7)\n", "counts_df.plot(kind='bar', stacked=True, ax=ax, colormap='tab10')\n", "plt.legend()\n", "plt.xlabel('Year', size = 15)\n", "plt.ylabel('Number of Incidents', size = 15)\n", "plt.title('Global Maritime Piracy Incidents', size = 18)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 218, "id": "022f75d1-8446-499e-9a07-e3991fbbea62", "metadata": {}, "outputs": [], "source": [ "output_dir = 'output'\n", "if not os.path.exists(output_dir):\n", " os.mkdir(output_dir)\n", "\n", "filename = 'stacked_barchart.jpg'\n", "output_path = os.path.join(output_dir, filename)" ] }, { "cell_type": "code", "execution_count": 219, "id": "1e6cbaf4-ca7c-4d95-a6c5-c8c35f391e50", "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots(1, 1)\n", "fig.set_size_inches(15,7)\n", "counts_df.plot(kind='bar', stacked=True, ax=ax, colormap='tab10')\n", "plt.legend()\n", "plt.xlabel('Year', size = 15)\n", "plt.ylabel('Number of Incidents', size = 15)\n", "plt.title('Global Maritime Piracy Incidents', size = 18)\n", "plt.savefig(output_path, dpi=300, transparent=False, bbox_inches='tight')\n", "plt.close()" ] }, { "cell_type": "markdown", "id": "b0cc3724-a1c9-48b6-b169-7ae2a764df81", "metadata": {}, "source": [ "![](stacked_barchart.jpg)" ] }, { "cell_type": "code", "execution_count": null, "id": "6fb5d697-f342-449c-8b0f-6b319d734345", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.2" } }, "nbformat": 4, "nbformat_minor": 5 }