diff --git a/README.md b/README.md index f99623a..2e0ae99 100644 --- a/README.md +++ b/README.md @@ -41,9 +41,11 @@ Are there geographic hotspots? If so, what are the probabilities that volunteers When are people most likely to spot pythons, and why? (Analysis + viz) Viz: frequency by observation date (OBSDATE) Analysis: research python activity - are they diurnal? do we have a timeseries for the observations? not in the official dataset, but maybe inaturalist + Are sightings cyclical? (Analysis + viz) Viz: TimeSeries Analysis Analysis: explanation + How many python observations do you predict will be recorded for the full 2019 year? (time-series analysis) time-series - use it to predit a value(sum of 2019 sightings) diff --git a/main.ipynb b/main.ipynb index 6d503cc..2d51adb 100644 --- a/main.ipynb +++ b/main.ipynb @@ -2,73 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "array(['Credible', 'Verified', 'Possible', 'Negative', 'Corrected', ''],\n", - " dtype=object)" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Project\n", - "import mysql.connector\n", - "import pandas as pd\n", - "import numpy as np\n", - "import chart_studio.plotly as py\n", - "import cufflinks as cf\n", - "from ipywidgets import interact\n", - "\n", - "cf.go_offline()\n", - "conn = mysql.connector.connect(\n", - " host=\"localhost\",\n", - " user=\"root\",\n", - " passwd=\"\",\n", - " database='burmese_python_project')\n", - "\n", - "\n", - "df = pd.read_sql_query(\n", - "'''select *\n", - "from pythons''', conn)\n", - "\n", - "df.head(5)\n", - "df['IDCred'].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 47, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -103,142 +37,142 @@ " DateUp\n", " Location\n", " ...\n", + " IDCred\n", + " Verified\n", + " Reviewer\n", + " ReviewDate\n", " OrgSrcID\n", " PID\n", " Voucher\n", " Museum\n", " MuseumRec\n", " Reference\n", - " County\n", - " ObsDate_Year\n", - " ObsDate_Month\n", - " ObsDate_Day\n", " \n", " \n", " \n", " \n", - " 0\n", + " 0\n", " 8303498\n", " Travis Mangione FWC\n", " Burmese python\n", " Python molurus ssp. bivittatus\n", " Positive\n", - " 2019-11-24\n", + " 24 Nov 2019\n", " \n", " 26 Nov 2019\n", " \n", " Miami-Dade, Florida, United States\n", " ...\n", + " Credible\n", + " Verified\n", + " FWCC Exotic Species Database\n", + " 05 Dec 2019\n", " \n", " \n", " 0\n", " \n", " \n", " \n", - " Miami-Dade\n", - " 2019\n", - " 11\n", - " 24\n", " \n", " \n", - " 1\n", + " 1\n", " 8303147\n", " Edward F. Metzger III\n", " Burmese python\n", " Python molurus ssp. bivittatus\n", " Positive\n", - " 2019-11-20\n", + " 20 Nov 2019\n", " \n", " 22 Nov 2019\n", " 22 Nov 2019\n", " Broward, Florida, United States\n", " ...\n", + " Verified\n", + " Verified\n", + " FWCC Exotic Species Database\n", + " 05 Dec 2019\n", " \n", " \n", " \n", " \n", " \n", " \n", - " Broward\n", - " 2019\n", - " 11\n", - " 20\n", " \n", " \n", - " 2\n", + " 2\n", " 8298008\n", " Michael Reupert NPS Big Cypress National Pres...\n", " Burmese python\n", " Python molurus ssp. bivittatus\n", " Positive\n", - " 2019-11-07\n", + " 07 Nov 2019\n", " \n", " 12 Nov 2019\n", " \n", " Collier, Florida, United States\n", " ...\n", + " Credible\n", + " Verified\n", + " FWCC Exotic Species Database\n", + " 14 Nov 2019\n", " \n", " \n", " 0\n", " HQ freezer ...\n", " \n", " \n", - " Collier\n", - " 2019\n", - " 11\n", - " 7\n", " \n", " \n", - " 3\n", + " 3\n", " 8295649\n", " matthew mccollister National Park Service, Big...\n", " Burmese python\n", " Python molurus ssp. bivittatus\n", " Positive\n", - " 2019-11-06\n", + " 06 Nov 2019\n", " \n", " 07 Nov 2019\n", " \n", " Collier, Florida, United States\n", " ...\n", + " Credible\n", + " Verified\n", + " FWCC Exotic Species Database\n", + " 08 Nov 2019\n", " \n", " \n", " 0\n", " \n", " \n", " \n", - " Collier\n", - " 2019\n", - " 11\n", - " 6\n", " \n", " \n", - " 4\n", + " 4\n", " 8295647\n", " matthew mccollister National Park Service, Big...\n", " Burmese python\n", " Python molurus ssp. bivittatus\n", " Positive\n", - " 2019-11-05\n", + " 05 Nov 2019\n", " \n", " 07 Nov 2019\n", " \n", " Collier, Florida, United States\n", " ...\n", + " Credible\n", + " Verified\n", + " FWCC Exotic Species Database\n", + " 08 Nov 2019\n", " \n", " \n", " 0\n", " \n", " \n", " \n", - " Collier\n", - " 2019\n", - " 11\n", - " 5\n", " \n", " \n", "\n", - "

5 rows × 58 columns

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5 rows × 54 columns

\n", "" ], "text/plain": [ @@ -249,12 +183,12 @@ "3 8295649 matthew mccollister National Park Service, Big... \n", "4 8295647 matthew mccollister National Park Service, Big... \n", "\n", - " ComName SciName OccStatus ObsDate \\\n", - "0 Burmese python Python molurus ssp. bivittatus Positive 2019-11-24 \n", - "1 Burmese python Python molurus ssp. bivittatus Positive 2019-11-20 \n", - "2 Burmese python Python molurus ssp. bivittatus Positive 2019-11-07 \n", - "3 Burmese python Python molurus ssp. bivittatus Positive 2019-11-06 \n", - "4 Burmese python Python molurus ssp. bivittatus Positive 2019-11-05 \n", + " ComName SciName OccStatus ObsDate \\\n", + "0 Burmese python Python molurus ssp. bivittatus Positive 24 Nov 2019 \n", + "1 Burmese python Python molurus ssp. bivittatus Positive 20 Nov 2019 \n", + "2 Burmese python Python molurus ssp. bivittatus Positive 07 Nov 2019 \n", + "3 Burmese python Python molurus ssp. bivittatus Positive 06 Nov 2019 \n", + "4 Burmese python Python molurus ssp. bivittatus Positive 05 Nov 2019 \n", "\n", " DateAcc DateEnt DateUp Location ... \\\n", "0 26 Nov 2019 Miami-Dade, Florida, United States ... \n", @@ -263,1032 +197,6201 @@ "3 07 Nov 2019 Collier, Florida, United States ... \n", "4 07 Nov 2019 Collier, Florida, United States ... \n", "\n", - " OrgSrcID PID Voucher Museum \\\n", - "0 0 \n", - "1 \n", - "2 0 HQ freezer ... \n", - "3 0 \n", - "4 0 \n", - "\n", - " MuseumRec Reference County ObsDate_Year ObsDate_Month ObsDate_Day \n", - "0 Miami-Dade 2019 11 24 \n", - "1 Broward 2019 11 20 \n", - "2 Collier 2019 11 7 \n", - "3 Collier 2019 11 6 \n", - "4 Collier 2019 11 5 \n", - "\n", - "[5 rows x 58 columns]" + " IDCred Verified Reviewer ReviewDate OrgSrcID PID \\\n", + "0 Credible Verified FWCC Exotic Species Database 05 Dec 2019 \n", + "1 Verified Verified FWCC Exotic Species Database 05 Dec 2019 \n", + "2 Credible Verified FWCC Exotic Species Database 14 Nov 2019 \n", + "3 Credible Verified FWCC Exotic Species Database 08 Nov 2019 \n", + "4 Credible Verified FWCC Exotic Species Database 08 Nov 2019 \n", + "\n", + " Voucher Museum MuseumRec \\\n", + "0 0 \n", + "1 \n", + "2 0 HQ freezer ... \n", + "3 0 \n", + "4 0 \n", + "\n", + " Reference \n", + "0 \n", + "1 \n", + "2 \n", + "3 \n", + "4 \n", + "\n", + "[5 rows x 54 columns]" ] }, - 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" + "text/plain": [ + "Index(['objectid', 'Reporter', 'ComName', 'SciName', 'OccStatus', 'ObsDate',\n", + " 'DateAcc', 'DateEnt', 'DateUp', 'Location', 'Latitude', 'Longitude',\n", + " 'Datum', 'CoordAcc', 'Method', 'DataType', 'LocalOwner', 'Habitat',\n", + " 'Locality', 'Site', 'InfestAcre', 'GrossAcre', 'Abundance', 'Density',\n", + " 'NumCollect', 'Percentcov', 'TreatArea', 'TreatComm', 'Quantity',\n", + " 'QuantityU', 'TrapType', 'NumTraps', 'Comments', 'VisitType',\n", + " 'CollectTme', 'Surveyor', 'RecSource', 'RecOwner', 'RecSrcTyp',\n", + " 'OrigName', 'Nativity', 'Host', 'Host_Name', 'VerifyMthd', 'IDCred',\n", + " 'Verified', 'Reviewer', 'ReviewDate', 'OrgSrcID', 'PID', 'Voucher',\n", + " 'Museum', 'MuseumRec', 'Reference'],\n", + " dtype='object')" ] }, + "execution_count": 2, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "by_year=df.groupby(['ObsDate_Year'])['objectid'].agg('count')\n", - "by_year.iplot(kind='line',\n", - " xTitle = 'Year',\n", - " yTitle = 'Number of Observations',\n", - " title= 'Total Number of Observations per Year')" + "df.columns" ] }, { - "cell_type": "code", - "execution_count": 68, + "cell_type": "markdown", "metadata": {}, + "source": [ + "##### Client Question: How many python observations do you predict will be recorded for the full 2019 year? " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { "text/plain": [ - "array([22, 11, 26, 20, 18, 16, 25, 9, 31, 17, 21, 28, 19, 29, 4, 12, 7,\n", - " 15, 23, 8, 30, 27, 1, 6, 13, 5, 24, 14, 3, 2, 10])" + "0 24 Nov 2019\n", + "1 20 Nov 2019\n", + "2 07 Nov 2019\n", + "3 06 Nov 2019\n", + "4 05 Nov 2019\n", + "Name: ObsDate, dtype: object" ] }, - "execution_count": 68, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "counties=['Miami-Dade','Collier','Monroe']\n", - "dec_set=df.loc[(df['County'].isin(counties)) & (df['ObsDate_Month']== 12) & (df['Verified']=='Verified')]\n" + "df['ObsDate'].head() # need to convert to a date-time index" ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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objectidReporterComNameSciNameOccStatusObsDateDateAccDateEntDateUpLocation...IDCredVerifiedReviewerReviewDateOrgSrcIDPIDVoucherMuseumMuseumRecReference
ObsDate
20198303498Travis Mangione FWCBurmese pythonPython molurus ssp. bivittatusPositive24 Nov 201926 Nov 2019Miami-Dade, Florida, United States...CredibleVerifiedFWCC Exotic Species Database05 Dec 20190
20198303147Edward F. Metzger IIIBurmese pythonPython molurus ssp. bivittatusPositive20 Nov 201922 Nov 201922 Nov 2019Broward, Florida, United States...VerifiedVerifiedFWCC Exotic Species Database05 Dec 2019
20198298008Michael Reupert NPS Big Cypress National Pres...Burmese pythonPython molurus ssp. bivittatusPositive07 Nov 201912 Nov 2019Collier, Florida, United States...CredibleVerifiedFWCC Exotic Species Database14 Nov 20190HQ freezer ...
20198295649matthew mccollister National Park Service, Big...Burmese pythonPython molurus ssp. bivittatusPositive06 Nov 201907 Nov 2019Collier, Florida, United States...CredibleVerifiedFWCC Exotic Species Database08 Nov 20190
20198295647matthew mccollister National Park Service, Big...Burmese pythonPython molurus ssp. bivittatusPositive05 Nov 201907 Nov 2019Collier, Florida, United States...CredibleVerifiedFWCC Exotic Species Database08 Nov 20190
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5 rows × 54 columns

\n", + "
" + ], + "text/plain": [ + " objectid Reporter \\\n", + "ObsDate \n", + "2019 8303498 Travis Mangione FWC \n", + "2019 8303147 Edward F. Metzger III \n", + "2019 8298008 Michael Reupert NPS Big Cypress National Pres... \n", + "2019 8295649 matthew mccollister National Park Service, Big... \n", + "2019 8295647 matthew mccollister National Park Service, Big... \n", + "\n", + " ComName SciName OccStatus \\\n", + "ObsDate \n", + "2019 Burmese python Python molurus ssp. bivittatus Positive \n", + "2019 Burmese python Python molurus ssp. bivittatus Positive \n", + "2019 Burmese python Python molurus ssp. bivittatus Positive \n", + "2019 Burmese python Python molurus ssp. bivittatus Positive \n", + "2019 Burmese python Python molurus ssp. bivittatus Positive \n", + "\n", + " ObsDate DateAcc DateEnt DateUp \\\n", + "ObsDate \n", + "2019 24 Nov 2019 26 Nov 2019 \n", + "2019 20 Nov 2019 22 Nov 2019 22 Nov 2019 \n", + "2019 07 Nov 2019 12 Nov 2019 \n", + "2019 06 Nov 2019 07 Nov 2019 \n", + "2019 05 Nov 2019 07 Nov 2019 \n", + "\n", + " Location ... IDCred Verified \\\n", + "ObsDate ... \n", + "2019 Miami-Dade, Florida, United States ... Credible Verified \n", + "2019 Broward, Florida, United States ... Verified Verified \n", + "2019 Collier, Florida, United States ... Credible Verified \n", + "2019 Collier, Florida, United States ... Credible Verified \n", + "2019 Collier, Florida, United States ... Credible Verified \n", + "\n", + " Reviewer ReviewDate OrgSrcID PID Voucher \\\n", + "ObsDate \n", + "2019 FWCC Exotic Species Database 05 Dec 2019 0 \n", + "2019 FWCC Exotic Species Database 05 Dec 2019 \n", + "2019 FWCC Exotic Species Database 14 Nov 2019 0 \n", + "2019 FWCC Exotic Species Database 08 Nov 2019 0 \n", + "2019 FWCC Exotic Species Database 08 Nov 2019 0 \n", + "\n", + " Museum MuseumRec Reference \n", + "ObsDate \n", + "2019 \n", + "2019 \n", + "2019 HQ freezer ... \n", + "2019 \n", + "2019 \n", + "\n", + "[5 rows x 54 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.index = pd.to_datetime(df['ObsDate']).dt.year\n", + "\n", + "# df.drop(columns = ['ObsDate'], inplace = True)\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plan of attack\n", + "set the obsdate as the index, as a datetime, drop everything but \"objectid\", (group on date here?), and then fill in the missing dates with 0. from there, pass it to the model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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objectidReporterComNameSciNameOccStatusObsDateDateAccDateEntDateUpLocation...IDCredVerifiedReviewerReviewDateOrgSrcIDPIDVoucherMuseumMuseumRecReference
ObsDate
1979-10-243067952FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive24 Oct 197917 Oct 2013Miami-Dade, Florida, United States...VerifiedVerified...U. S. Geological Survey. 2012. USGS Everglades...
1995-12-12615739FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive12 Dec 199503 May 200903 May 2009Miami-Dade, Florida, United States...VerifiedVerifiedFlorida Museum of Natural History ...UF 146714, 147499 ...Florida Museum of Natural History. Herpetolog...
1996-02-123067680FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive12 Feb 199617 Oct 2013Miami-Dade, Florida, United States...VerifiedVerified...U. S. Geological Survey. 2012. USGS Everglades...
1996-11-183067679FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive18 Nov 199617 Oct 2013Miami-Dade, Florida, United States...VerifiedVerified...U. S. Geological Survey. 2012. USGS Everglades...
1997-11-03615765FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive03 Nov 199703 May 200903 May 2009Miami-Dade, Florida, United States...VerifiedVerifiedFlorida Museum of Natural History Herpetology ...UF 147495 ...Florida Museum of Natural History. Herpetology...
..................................................................
2019-11-068295649matthew mccollister National Park Service, Big...Burmese pythonPython molurus ssp. bivittatusPositive06 Nov 201907 Nov 2019Collier, Florida, United States...CredibleVerifiedFWCC Exotic Species Database08 Nov 20190
2019-11-078295635Jeffrey Fobb Miami-Dade Fire RescueBurmese pythonPython molurus ssp. bivittatusPositive07 Nov 201907 Nov 201907 Nov 2019Miami-Dade, Florida, United States...VerifiedVerifiedFWCC Exotic Species Database08 Nov 2019
2019-11-078298008Michael Reupert NPS Big Cypress National Pres...Burmese pythonPython molurus ssp. bivittatusPositive07 Nov 201912 Nov 2019Collier, Florida, United States...CredibleVerifiedFWCC Exotic Species Database14 Nov 20190HQ freezer ...
2019-11-208303147Edward F. Metzger IIIBurmese pythonPython molurus ssp. bivittatusPositive20 Nov 201922 Nov 201922 Nov 2019Broward, Florida, United States...VerifiedVerifiedFWCC Exotic Species Database05 Dec 2019
2019-11-248303498Travis Mangione FWCBurmese pythonPython molurus ssp. bivittatusPositive24 Nov 201926 Nov 2019Miami-Dade, Florida, United States...CredibleVerifiedFWCC Exotic Species Database05 Dec 20190
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4798 rows × 54 columns

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" + ], + "text/plain": [ + " objectid Reporter \\\n", + "ObsDate \n", + "1979-10-24 3067952 FWCC Exotic Species Database Florida Fish and ... \n", + "1995-12-12 615739 FWCC Exotic Species Database Florida Fish and ... \n", + "1996-02-12 3067680 FWCC Exotic Species Database Florida Fish and ... \n", + "1996-11-18 3067679 FWCC Exotic Species Database Florida Fish and ... \n", + "1997-11-03 615765 FWCC Exotic Species Database Florida Fish and ... \n", + "... ... ... \n", + "2019-11-06 8295649 matthew mccollister National Park Service, Big... \n", + "2019-11-07 8295635 Jeffrey Fobb Miami-Dade Fire Rescue \n", + "2019-11-07 8298008 Michael Reupert NPS Big Cypress National Pres... \n", + "2019-11-20 8303147 Edward F. Metzger III \n", + "2019-11-24 8303498 Travis Mangione FWC \n", + "\n", + " ComName SciName OccStatus \\\n", + "ObsDate \n", + "1979-10-24 Burmese python Python molurus ssp. bivittatus Positive \n", + "1995-12-12 Burmese python Python molurus ssp. bivittatus Positive \n", + "1996-02-12 Burmese python Python molurus ssp. bivittatus Positive \n", + "1996-11-18 Burmese python Python molurus ssp. bivittatus Positive \n", + "1997-11-03 Burmese python Python molurus ssp. bivittatus Positive \n", + "... ... ... ... \n", + "2019-11-06 Burmese python Python molurus ssp. bivittatus Positive \n", + "2019-11-07 Burmese python Python molurus ssp. bivittatus Positive \n", + "2019-11-07 Burmese python Python molurus ssp. bivittatus Positive \n", + "2019-11-20 Burmese python Python molurus ssp. bivittatus Positive \n", + "2019-11-24 Burmese python Python molurus ssp. bivittatus Positive \n", + "\n", + " ObsDate DateAcc DateEnt DateUp \\\n", + "ObsDate \n", + "1979-10-24 24 Oct 1979 17 Oct 2013 \n", + "1995-12-12 12 Dec 1995 03 May 2009 03 May 2009 \n", + "1996-02-12 12 Feb 1996 17 Oct 2013 \n", + "1996-11-18 18 Nov 1996 17 Oct 2013 \n", + "1997-11-03 03 Nov 1997 03 May 2009 03 May 2009 \n", + "... ... ... ... ... \n", + "2019-11-06 06 Nov 2019 07 Nov 2019 \n", + "2019-11-07 07 Nov 2019 07 Nov 2019 07 Nov 2019 \n", + "2019-11-07 07 Nov 2019 12 Nov 2019 \n", + "2019-11-20 20 Nov 2019 22 Nov 2019 22 Nov 2019 \n", + "2019-11-24 24 Nov 2019 26 Nov 2019 \n", + "\n", + " Location ... IDCred Verified \\\n", + "ObsDate ... \n", + "1979-10-24 Miami-Dade, Florida, United States ... Verified Verified \n", + "1995-12-12 Miami-Dade, Florida, United States ... Verified Verified \n", + "1996-02-12 Miami-Dade, Florida, United States ... Verified Verified \n", + "1996-11-18 Miami-Dade, Florida, United States ... Verified Verified \n", + "1997-11-03 Miami-Dade, Florida, United States ... Verified Verified \n", + "... ... ... ... ... \n", + "2019-11-06 Collier, Florida, United States ... Credible Verified \n", + "2019-11-07 Miami-Dade, Florida, United States ... Verified Verified \n", + "2019-11-07 Collier, Florida, United States ... Credible Verified \n", + "2019-11-20 Broward, Florida, United States ... Verified Verified \n", + "2019-11-24 Miami-Dade, Florida, United States ... Credible Verified \n", + "\n", + " Reviewer ReviewDate OrgSrcID PID Voucher \\\n", + "ObsDate \n", + "1979-10-24 \n", + "1995-12-12 \n", + "1996-02-12 \n", + "1996-11-18 \n", + "1997-11-03 \n", + "... ... ... ... .. ... \n", + "2019-11-06 FWCC Exotic Species Database 08 Nov 2019 0 \n", + "2019-11-07 FWCC Exotic Species Database 08 Nov 2019 \n", + "2019-11-07 FWCC Exotic Species Database 14 Nov 2019 0 \n", + "2019-11-20 FWCC Exotic Species Database 05 Dec 2019 \n", + "2019-11-24 FWCC Exotic Species Database 05 Dec 2019 0 \n", + "\n", + " Museum \\\n", + "ObsDate \n", + "1979-10-24 ... \n", + "1995-12-12 Florida Museum of Natural History ... \n", + "1996-02-12 ... \n", + "1996-11-18 ... \n", + "1997-11-03 Florida Museum of Natural History Herpetology ... \n", + "... ... \n", + "2019-11-06 \n", + "2019-11-07 \n", + "2019-11-07 HQ freezer ... \n", + "2019-11-20 \n", + "2019-11-24 \n", + "\n", + " MuseumRec \\\n", + "ObsDate \n", + "1979-10-24 \n", + "1995-12-12 UF 146714, 147499 ... \n", + "1996-02-12 \n", + "1996-11-18 \n", + "1997-11-03 UF 147495 ... \n", + "... ... \n", + "2019-11-06 \n", + "2019-11-07 \n", + "2019-11-07 \n", + "2019-11-20 \n", + "2019-11-24 \n", + "\n", + " Reference \n", + "ObsDate \n", + "1979-10-24 U. S. Geological Survey. 2012. USGS Everglades... \n", + "1995-12-12 Florida Museum of Natural History. Herpetolog... \n", + "1996-02-12 U. S. Geological Survey. 2012. USGS Everglades... \n", + "1996-11-18 U. S. Geological Survey. 2012. USGS Everglades... \n", + "1997-11-03 Florida Museum of Natural History. Herpetology... \n", + "... ... \n", + "2019-11-06 \n", + "2019-11-07 \n", + "2019-11-07 \n", + "2019-11-20 \n", + "2019-11-24 \n", + "\n", + "[4798 rows x 54 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.index = pd.to_datetime(df['ObsDate'])\n", + "\n", + "time_df = df\n", + "\n", + "# time_df['sightings'] = df['objectid'].value_counts() # gives warning\n", + "\n", + "time_df.sort_index(inplace = True)\n", + "\n", + "time_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "so now I need to extract 2019 from this time_df, so that it can be my target" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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objectidReporterComNameSciNameOccStatusObsDateDateAccDateEntDateUpLocation...IDCredVerifiedReviewerReviewDateOrgSrcIDPIDVoucherMuseumMuseumRecReference
ObsDate
2019-01-028249408FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated02 Jan 201903 Aug 2019Miami-Dade, Florida, United States...VerifiedVerified
2019-01-028250994FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated02 Jan 201903 Aug 2019Miami-Dade, Florida, United States...VerifiedVerified
2019-01-048250999FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated04 Jan 201903 Aug 2019Miami-Dade, Florida, United States...VerifiedVerified
2019-01-048250985FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated04 Jan 201903 Aug 2019Miami-Dade, Florida, United States...VerifiedVerified
2019-01-058251407FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated05 Jan 201903 Aug 2019Miami-Dade, Florida, United States...VerifiedVerified
..................................................................
2019-11-068295649matthew mccollister National Park Service, Big...Burmese pythonPython molurus ssp. bivittatusPositive06 Nov 201907 Nov 2019Collier, Florida, United States...CredibleVerifiedFWCC Exotic Species Database08 Nov 20190
2019-11-078295635Jeffrey Fobb Miami-Dade Fire RescueBurmese pythonPython molurus ssp. bivittatusPositive07 Nov 201907 Nov 201907 Nov 2019Miami-Dade, Florida, United States...VerifiedVerifiedFWCC Exotic Species Database08 Nov 2019
2019-11-078298008Michael Reupert NPS Big Cypress National Pres...Burmese pythonPython molurus ssp. bivittatusPositive07 Nov 201912 Nov 2019Collier, Florida, United States...CredibleVerifiedFWCC Exotic Species Database14 Nov 20190HQ freezer ...
2019-11-208303147Edward F. Metzger IIIBurmese pythonPython molurus ssp. bivittatusPositive20 Nov 201922 Nov 201922 Nov 2019Broward, Florida, United States...VerifiedVerifiedFWCC Exotic Species Database05 Dec 2019
2019-11-248303498Travis Mangione FWCBurmese pythonPython molurus ssp. bivittatusPositive24 Nov 201926 Nov 2019Miami-Dade, Florida, United States...CredibleVerifiedFWCC Exotic Species Database05 Dec 20190
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167 rows × 54 columns

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objectidReporterComNameSciNameOccStatusObsDateDateAccDateEntDateUpLocation...IDCredVerifiedReviewerReviewDateOrgSrcIDPIDVoucherMuseumMuseumRecReference
ObsDate
1979-10-243067952FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive24 Oct 197917 Oct 2013Miami-Dade, Florida, United States...VerifiedVerified...U. S. Geological Survey. 2012. USGS Everglades...
1995-12-12615739FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive12 Dec 199503 May 200903 May 2009Miami-Dade, Florida, United States...VerifiedVerifiedFlorida Museum of Natural History ...UF 146714, 147499 ...Florida Museum of Natural History. Herpetolog...
1996-02-123067680FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive12 Feb 199617 Oct 2013Miami-Dade, Florida, United States...VerifiedVerified...U. S. Geological Survey. 2012. USGS Everglades...
1996-11-183067679FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive18 Nov 199617 Oct 2013Miami-Dade, Florida, United States...VerifiedVerified...U. S. Geological Survey. 2012. USGS Everglades...
1997-11-03615765FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive03 Nov 199703 May 200903 May 2009Miami-Dade, Florida, United States...VerifiedVerifiedFlorida Museum of Natural History Herpetology ...UF 147495 ...Florida Museum of Natural History. Herpetology...
..................................................................
2018-12-308250925FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated30 Dec 201803 Aug 2019Miami-Dade, Florida, United States...VerifiedVerified
2018-12-308241927FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive30 Dec 201803 Aug 2019Broward, Florida, United States...VerifiedVerified
2018-12-318251395FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated31 Dec 201803 Aug 2019Miami-Dade, Florida, United States...VerifiedVerified
2018-12-318251351FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated31 Dec 201803 Aug 2019Miami-Dade, Florida, United States...VerifiedVerified
2018-12-317854740Bob goreBurmese pythonPython molurus ssp. bivittatusPositive31 Dec 201831 Dec 201831 Dec 2018Miami-Dade, Florida, United States...PossibleVerifiedFWCC Exotic Species Database10 Jan 2019
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IDCred Verified \\\n", + "ObsDate ... \n", + "1979-10-24 Miami-Dade, Florida, United States ... Verified Verified \n", + "1995-12-12 Miami-Dade, Florida, United States ... Verified Verified \n", + "1996-02-12 Miami-Dade, Florida, United States ... Verified Verified \n", + "1996-11-18 Miami-Dade, Florida, United States ... Verified Verified \n", + "1997-11-03 Miami-Dade, Florida, United States ... Verified Verified \n", + "... ... ... ... ... \n", + "2018-12-30 Miami-Dade, Florida, United States ... Verified Verified \n", + "2018-12-30 Broward, Florida, United States ... Verified Verified \n", + "2018-12-31 Miami-Dade, Florida, United States ... Verified Verified \n", + "2018-12-31 Miami-Dade, Florida, United States ... Verified Verified \n", + "2018-12-31 Miami-Dade, Florida, United States ... Possible Verified \n", + "\n", + " Reviewer ReviewDate OrgSrcID PID Voucher \\\n", + "ObsDate \n", + "1979-10-24 \n", + "1995-12-12 \n", + "1996-02-12 \n", + "1996-11-18 \n", + "1997-11-03 \n", + "... ... ... ... .. ... \n", + "2018-12-30 \n", + "2018-12-30 \n", + "2018-12-31 \n", + "2018-12-31 \n", + "2018-12-31 FWCC Exotic Species Database 10 Jan 2019 \n", + "\n", + " Museum \\\n", + "ObsDate \n", + "1979-10-24 ... \n", + "1995-12-12 Florida Museum of Natural History ... \n", + "1996-02-12 ... \n", + "1996-11-18 ... \n", + "1997-11-03 Florida Museum of Natural History Herpetology ... \n", + "... ... \n", + "2018-12-30 \n", + "2018-12-30 \n", + "2018-12-31 \n", + "2018-12-31 \n", + "2018-12-31 \n", + "\n", + " MuseumRec \\\n", + "ObsDate \n", + "1979-10-24 \n", + "1995-12-12 UF 146714, 147499 ... \n", + "1996-02-12 \n", + "1996-11-18 \n", + "1997-11-03 UF 147495 ... \n", + "... ... \n", + "2018-12-30 \n", + "2018-12-30 \n", + "2018-12-31 \n", + "2018-12-31 \n", + "2018-12-31 \n", + "\n", + " Reference \n", + "ObsDate \n", + "1979-10-24 U. S. Geological Survey. 2012. USGS Everglades... \n", + "1995-12-12 Florida Museum of Natural History. Herpetolog... \n", + "1996-02-12 U. S. Geological Survey. 2012. USGS Everglades... \n", + "1996-11-18 U. S. Geological Survey. 2012. USGS Everglades... \n", + "1997-11-03 Florida Museum of Natural History. Herpetology... \n", + "... ... \n", + "2018-12-30 \n", + "2018-12-30 \n", + "2018-12-31 \n", + "2018-12-31 \n", + "2018-12-31 \n", + "\n", + "[4631 rows x 54 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now collect everything before 2019 as my training data\n", + "begin = pd.Timestamp('1979-10-24')\n", + "end = pd.Timestamp('2018-12-31') # should adjust to july 31 2019 to account for the data gathered this year?\n", + "\n", + "training = time_df.truncate(before=begin, after=end)\n", + "\n", + "training" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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objectidReporterComNameSciNameOccStatusDateAccDateEntDateUpLocationLatitude...IDCredVerifiedReviewerReviewDateOrgSrcIDPIDVoucherMuseumMuseumRecReference
ObsDate
1979-10-243067952FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive17 Oct 2013Miami-Dade, Florida, United States25.75931...VerifiedVerified...U. S. Geological Survey. 2012. USGS Everglades...
1995-12-12615739FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive03 May 200903 May 2009Miami-Dade, Florida, United States25.22278...VerifiedVerifiedFlorida Museum of Natural History ...UF 146714, 147499 ...Florida Museum of Natural History. Herpetolog...
1996-02-123067680FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive17 Oct 2013Miami-Dade, Florida, United States25.26356...VerifiedVerified...U. S. Geological Survey. 2012. USGS Everglades...
1996-11-183067679FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive17 Oct 2013Miami-Dade, Florida, United States25.64142...VerifiedVerified...U. S. Geological Survey. 2012. USGS Everglades...
1997-11-03615765FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive03 May 200903 May 2009Miami-Dade, Florida, United States25.38235...VerifiedVerifiedFlorida Museum of Natural History Herpetology ...UF 147495 ...Florida Museum of Natural History. Herpetology...
..................................................................
2018-12-308250925FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.69986...VerifiedVerified
2018-12-308241927FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive03 Aug 2019Broward, Florida, United States26.33000...VerifiedVerified
2018-12-318251395FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.84732...VerifiedVerified
2018-12-318251351FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.81880...VerifiedVerified
2018-12-317854740Bob goreBurmese pythonPython molurus ssp. bivittatusPositive31 Dec 201831 Dec 2018Miami-Dade, Florida, United States25.75688...PossibleVerifiedFWCC Exotic Species Database10 Jan 2019
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4631 rows × 53 columns

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ObsDateobjectidReporterComNameSciNameOccStatusDateAccDateEntDateUpLocation...IDCredVerifiedReviewerReviewDateOrgSrcIDPIDVoucherMuseumMuseumRecReference
01979-10-243067952FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive17 Oct 2013Miami-Dade, Florida, United States...VerifiedVerified...U. S. Geological Survey. 2012. USGS Everglades...
11995-12-12615739FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive03 May 200903 May 2009Miami-Dade, Florida, United States...VerifiedVerifiedFlorida Museum of Natural History ...UF 146714, 147499 ...Florida Museum of Natural History. Herpetolog...
21996-02-123067680FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive17 Oct 2013Miami-Dade, Florida, United States...VerifiedVerified...U. S. Geological Survey. 2012. USGS Everglades...
31996-11-183067679FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive17 Oct 2013Miami-Dade, Florida, United States...VerifiedVerified...U. S. Geological Survey. 2012. USGS Everglades...
41997-11-03615765FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive03 May 200903 May 2009Miami-Dade, Florida, United States...VerifiedVerifiedFlorida Museum of Natural History Herpetology ...UF 147495 ...Florida Museum of Natural History. Herpetology...
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5 rows × 54 columns

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objectid
ObsDate
1979-10-241
1995-12-121
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......
2018-12-274
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2018-12-302
2018-12-313
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2249 rows × 1 columns

\n", + "" + ], + "text/plain": [ + " objectid\n", + "ObsDate \n", + "1979-10-24 1\n", + "1995-12-12 1\n", + "1996-02-12 1\n", + "1996-11-18 1\n", + "1997-11-03 2\n", + "... ...\n", + "2018-12-27 4\n", + "2018-12-28 3\n", + "2018-12-29 3\n", + "2018-12-30 2\n", + "2018-12-31 3\n", + "\n", + "[2249 rows x 1 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now, group by the dates so that the sightings are counted by Date\n", + "\n", + "# training.pivot_table(index = 'ObsDate', values = 'objectid', aggfunc = 'count') # why isn't this working?\n", + "training_pivot = pd.pivot_table(training, index = 'ObsDate', values = 'objectid', aggfunc = 'count' )\n", + "training_pivot" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "training_pivot.index = pd.to_datetime(training_pivot.index) # doesn't work because the dtype of the index is '\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
objectid
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......
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14314 rows × 1 columns

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objectidReporterComNameSciNameOccStatusDateAccDateEntDateUpLocationLatitude...IDCredVerifiedReviewerReviewDateOrgSrcIDPIDVoucherMuseumMuseumRecReference
ObsDate
2019-01-028249408FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.33997...VerifiedVerified
2019-01-028250994FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.73368...VerifiedVerified
2019-01-048250999FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.73535...VerifiedVerified
2019-01-048250985FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.73313...VerifiedVerified
2019-01-058251407FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.86322...VerifiedVerified
..................................................................
2019-11-068295649matthew mccollister National Park Service, Big...Burmese pythonPython molurus ssp. bivittatusPositive07 Nov 2019Collier, Florida, United States25.83131...CredibleVerifiedFWCC Exotic Species Database08 Nov 20190
2019-11-078295635Jeffrey Fobb Miami-Dade Fire RescueBurmese pythonPython molurus ssp. bivittatusPositive07 Nov 201907 Nov 2019Miami-Dade, Florida, United States25.82564...VerifiedVerifiedFWCC Exotic Species Database08 Nov 2019
2019-11-078298008Michael Reupert NPS Big Cypress National Pres...Burmese pythonPython molurus ssp. bivittatusPositive12 Nov 2019Collier, Florida, United States25.86434...CredibleVerifiedFWCC Exotic Species Database14 Nov 20190HQ freezer ...
2019-11-208303147Edward F. Metzger IIIBurmese pythonPython molurus ssp. bivittatusPositive22 Nov 201922 Nov 2019Broward, Florida, United States26.29298...VerifiedVerifiedFWCC Exotic Species Database05 Dec 2019
2019-11-248303498Travis Mangione FWCBurmese pythonPython molurus ssp. bivittatusPositive26 Nov 2019Miami-Dade, Florida, United States25.76191...CredibleVerifiedFWCC Exotic Species Database05 Dec 20190
\n", + "

167 rows × 53 columns

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objectid
ObsDate
2019-01-021
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......
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2019-11-230
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327 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " objectid\n", + "ObsDate \n", + "2019-01-02 1\n", + "2019-01-03 0\n", + "2019-01-04 1\n", + "2019-01-05 1\n", + "2019-01-06 0\n", + "... ...\n", + "2019-11-20 1\n", + "2019-11-21 0\n", + "2019-11-22 0\n", + "2019-11-23 0\n", + "2019-11-24 1\n", + "\n", + "[327 rows x 1 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "target.reset_index(inplace = True)\n", + "target_pivot = pd.pivot_table(target, index = 'ObsDate', values = 'objectid', aggfunc = 'count' )\n", + "# target.index = pd.to_datetime(target_pivot.index)\n", + "target_pivot2 = target_pivot.resample('D').count().fillna(0)\n", + "target_pivot2" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# training / test data is ready" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "from statsmodels.tsa.arima_model import ARMA\n", + "\n", + "model = ARMA(\n", + " training_pivot2,\n", + " freq = 'D', # daily frequency\n", + " order = (2, 1),\n", + ").fit()\n", + "\n", + "predictions = model.predict(\n", + " start = len(training_pivot2), # in terms of index, where do we want to start and end? start at the END of your training data\n", + " end = len(training_pivot2) + len(target_pivot2) - 1, # and end at the END of our testing data\n", + ")\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = model.predict(\n", + " start = len(training_pivot2), # in terms of index, where do we want to start and end? start at the END of your training data\n", + " end = len(training_pivot2) + len(target_pivot2) - 1, # and end at the END of our testing data\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Expected Number of Python Sightings in 2019" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "204.0" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# how to get the sum of the 2019 year? \n", + "round(predictions.sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###### Are there geographic hotspots? If so, what are the probabilities that volunteers will find pythons in those areas between today's date and Dec 31st?" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# from this Time-Series data above, work on Probability of finding snakes in the geographic hotspots between now and Dec 31\n", + "# group the data by Month-day, ignoring the year,\n", + "# \n", + "# a python was sighted on this day for x out of the total years, \n", + "# sum the probabilities for today's date through 12/31\n", + "\n", + "# first need to review git. i need the counties data from elda, how can I make sure I effectively merge that with my own code? \n", + "\n", + "# based on 2018's December data,\n", + "# every day is a success/failure for those days\n", + "# probability for 2018 - days of success / total days\n", + "\n", + "# apply that prob to 2019, with the remaining days" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/sampoad/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py:4102: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " errors=errors,\n" + ] + }, + { + "data": { + "text/html": [ + "
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objectidReporterComNameSciNameOccStatusDateAccDateEntDateUpLocationLatitude...IDCredVerifiedReviewerReviewDateOrgSrcIDPIDVoucherMuseumMuseumRecReference
ObsDate
2018-12-018240169FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Collier, Florida, United States25.94577...VerifiedVerified
2018-12-018240170FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Collier, Florida, United States25.94577...VerifiedVerified
2018-12-017827675Mark ProcterBurmese pythonPython molurus ssp. bivittatusPositive01 Dec 201801 Dec 2018Collier, Florida, United States26.10031...PossibleVerifiedFWCC Exotic Species Database03 Dec 2018
2018-12-038243480FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Manatee, Florida, United States27.50771...VerifiedVerified
2018-12-037829338Robert Hinson Big Cypress National PreserveBurmese pythonPython molurus ssp. bivittatusPositive10 Dec 2018Collier, Florida, United States25.80743...VerifiedVerifiedFWCC Exotic Species Database13 Dec 20180USGS ...
2018-12-047827802Michael Reupert NPS Big Cypress National Pres...Burmese pythonPython molurus ssp. bivittatusPositive04 Dec 201810 Dec 2018Collier, Florida, United States25.90142...CredibleVerifiedFWCC Exotic Species Database04 Dec 20180BICY freezer for USGS pickup ...
2018-12-048250869FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.67811...VerifiedVerified
2018-12-048242509FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive03 Aug 2019Lee, Florida, United States26.43832...VerifiedVerified
2018-12-058250330FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive03 Aug 2019Miami-Dade, Florida, United States25.46568...VerifiedVerified
2018-12-087829502Ozzie RomeroBurmese pythonPython molurus ssp. bivittatusPositive13 Dec 2018Broward, Florida, United States26.05082...PossibleVerifiedFWCC Exotic Species Database14 Dec 20180
2018-12-088250954FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.72530...VerifiedVerified
2018-12-097829752Brian Waters NPSBurmese pythonPython molurus ssp. bivittatusPositive15 Dec 201815 Dec 2018Miami-Dade, Florida, United States25.36813...VerifiedVerifiedFWCC Exotic Species Database17 Dec 2018
2018-12-097829306Michael Reupert NPS Big Cypress National Pres...Burmese pythonPython molurus ssp. bivittatusPositive09 Dec 2018Collier, Florida, United States25.87741...VerifiedVerifiedFWCC Exotic Species Database10 Dec 20180Left at sight, dead for a while ...
2018-12-117829459Elizabeth Scarlett USGSBurmese pythonPython molurus ssp. bivittatusPositive12 Dec 201812 Dec 2018Collier, Florida, United States25.85093...VerifiedVerifiedFWCC Exotic Species Database13 Dec 2018
2018-12-117829473Jeremy Dixon USFWSBurmese pythonPython molurus ssp. bivittatusPositive13 Dec 2018Miami-Dade, Florida, United States25.24072...VerifiedVerifiedFWCC Exotic Species Database13 Dec 20181Sent to USGS (Jill J.) for necropsy ...
2018-12-118251069FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.76087...VerifiedVerified
2018-12-118243500FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive03 Aug 2019Monroe, Florida, United States25.08652...CredibleVerified
2018-12-118251068FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.76087...VerifiedVerified
2018-12-128251027FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.74407...VerifiedVerified
2018-12-128251028FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.74420...VerifiedVerified
2018-12-128251112FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.76173...VerifiedVerified
2018-12-138250346FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive03 Aug 2019Miami-Dade, Florida, United States25.47014...VerifiedVerified
2018-12-138239911FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive03 Aug 2019Monroe, New York, United States43.12627...CredibleVerified
2018-12-147829751Kevin ReichBurmese pythonPython molurus ssp. bivittatusPositive14 Dec 201814 Dec 2018Miami-Dade, Florida, United States25.76155...VerifiedVerifiedFWCC Exotic Species Database17 Dec 2018
2018-12-147847067Deborah Jansen Big Cypress National PreserveBurmese pythonPython molurus ssp. bivittatusPositive18 Dec 2018Collier, Florida, United States25.90140...VerifiedVerifiedFWCC Exotic Species Database09 Jan 20190BICY for USGS pickup ...
2018-12-148241748FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusNegative03 Aug 2019Broward, Florida, United States26.14542...VerifiedVerified
2018-12-157847023Jeremy Dixon USFWSBurmese pythonPython molurus ssp. bivittatusPositive17 Dec 2018Monroe, Florida, United States25.19408...VerifiedVerifiedFWCC Exotic Species Database17 Dec 20180Transferred to Eric S. (FWC) and Brian S. (USG...
2018-12-158251056FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.76066...VerifiedVerified
2018-12-168251026FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.74389...VerifiedVerified
2018-12-168251073FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.76092...VerifiedVerified
2018-12-168251430FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.88362...VerifiedVerified
2018-12-168250982FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.73243...VerifiedVerified
2018-12-178251061FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusNegative03 Aug 2019Miami-Dade, Florida, United States25.76068...VerifiedVerified
2018-12-198249308FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.22257...VerifiedVerified
2018-12-198250924FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.69950...VerifiedVerified
2018-12-198251254FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.78384...VerifiedVerified
2018-12-197852533Jeremy Dixon USFWSBurmese pythonPython molurus ssp. bivittatusPositive20 Dec 2018Monroe, Florida, United States25.18203...VerifiedVerifiedFWCC Exotic Species Database09 Jan 20190Sent to USGS (Jill J.) ...
2018-12-207854293Deborah Jansen Big Cypress National PreserveBurmese pythonPython molurus ssp. bivittatusPositive21 Dec 2018Collier, Florida, United States25.92421...VerifiedVerifiedFWCC Exotic Species Database09 Jan 20190BICY for USGS pickup ...
2018-12-218246406FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive03 Aug 2019Monroe, Florida, United States25.08652...VerifiedVerified
2018-12-228250997FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.73497...VerifiedVerified
2018-12-238251003FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.73765...VerifiedVerified
2018-12-247854741Anne Wood University of OregonBurmese pythonPython molurus ssp. bivittatusPositive31 Dec 2018Monroe, Florida, United States25.19796...PossibleVerifiedFWCC Exotic Species Database10 Jan 20190
2018-12-268247598FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Monroe, Florida, United States25.20877...VerifiedVerified
2018-12-278241431FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Broward, Florida, United States25.98608...VerifiedVerified
2018-12-278250853FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive03 Aug 2019Miami-Dade, Florida, United States25.67309...VerifiedVerified
2018-12-278241728FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Broward, Florida, United States26.13188...VerifiedVerified
2018-12-278019334Deborah Jansen Big Cypress National PreserveBurmese pythonPython molurus ssp. bivittatusPositive30 Jan 2019Collier, Florida, United States25.88258...VerifiedVerifiedFWCC Exotic Species Database31 Jan 20190Big Cypress ...
2018-12-288251344FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.81509...VerifiedVerified
2018-12-288247587FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Monroe, Florida, United States25.18852...VerifiedVerified
2018-12-287854455jarek anzelmoBurmese pythonPython molurus ssp. bivittatusPositive28 Dec 2018Miami-Dade, Florida, United States25.55459...PossibleVerifiedFWCC Exotic Species Database10 Jan 20190
2018-12-297854459Brian Waters NPSBurmese pythonPython molurus ssp. bivittatusPositive29 Dec 201829 Dec 2018Miami-Dade, Florida, United States25.43341...CredibleVerifiedFWCC Exotic Species Database10 Jan 2019
2018-12-298251032FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.74828...VerifiedVerified
2018-12-298240148FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Collier, Florida, United States25.87766...VerifiedVerified
2018-12-308250925FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.69986...VerifiedVerified
2018-12-308241927FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusPositive03 Aug 2019Broward, Florida, United States26.33000...VerifiedVerified
2018-12-318251395FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.84732...VerifiedVerified
2018-12-318251351FWCC Exotic Species Database Florida Fish and ...Burmese pythonPython molurus ssp. bivittatusTreated03 Aug 2019Miami-Dade, Florida, United States25.81880...VerifiedVerified
2018-12-317854740Bob goreBurmese pythonPython molurus ssp. bivittatusPositive31 Dec 201831 Dec 2018Miami-Dade, Florida, United States25.75688...PossibleVerifiedFWCC Exotic Species Database10 Jan 2019
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Verified Verified \n", + "2018-12-17 25.76068 ... Verified Verified \n", + "2018-12-19 25.22257 ... Verified Verified \n", + "2018-12-19 25.69950 ... Verified Verified \n", + "2018-12-19 25.78384 ... Verified Verified \n", + "2018-12-19 25.18203 ... Verified Verified FWCC Exotic Species Database \n", + "2018-12-20 25.92421 ... Verified Verified FWCC Exotic Species Database \n", + "2018-12-21 25.08652 ... Verified Verified \n", + "2018-12-22 25.73497 ... Verified Verified \n", + "2018-12-23 25.73765 ... Verified Verified \n", + "2018-12-24 25.19796 ... Possible Verified FWCC Exotic Species Database \n", + "2018-12-26 25.20877 ... Verified Verified \n", + "2018-12-27 25.98608 ... Verified Verified \n", + "2018-12-27 25.67309 ... Verified Verified \n", + "2018-12-27 26.13188 ... Verified Verified \n", + "2018-12-27 25.88258 ... Verified Verified FWCC Exotic Species Database \n", + "2018-12-28 25.81509 ... Verified Verified \n", + "2018-12-28 25.18852 ... Verified Verified \n", + "2018-12-28 25.55459 ... Possible Verified FWCC Exotic Species Database \n", + "2018-12-29 25.43341 ... Credible Verified FWCC Exotic Species Database \n", + "2018-12-29 25.74828 ... Verified Verified \n", + "2018-12-29 25.87766 ... Verified Verified \n", + "2018-12-30 25.69986 ... Verified Verified \n", + "2018-12-30 26.33000 ... Verified Verified \n", + "2018-12-31 25.84732 ... Verified Verified \n", + "2018-12-31 25.81880 ... Verified Verified \n", + "2018-12-31 25.75688 ... Possible Verified FWCC Exotic Species Database \n", + "\n", + " ReviewDate OrgSrcID PID Voucher \\\n", + "ObsDate \n", + "2018-12-01 \n", + "2018-12-01 \n", + "2018-12-01 03 Dec 2018 \n", + "2018-12-03 \n", + "2018-12-03 13 Dec 2018 0 \n", + "2018-12-04 04 Dec 2018 0 \n", + "2018-12-04 \n", + "2018-12-04 \n", + "2018-12-05 \n", + "2018-12-08 14 Dec 2018 0 \n", + "2018-12-08 \n", + "2018-12-09 17 Dec 2018 \n", + "2018-12-09 10 Dec 2018 0 \n", + "2018-12-11 13 Dec 2018 \n", + "2018-12-11 13 Dec 2018 1 \n", + "2018-12-11 \n", + "2018-12-11 \n", + "2018-12-11 \n", + "2018-12-12 \n", + "2018-12-12 \n", + "2018-12-12 \n", + "2018-12-13 \n", + "2018-12-13 \n", + "2018-12-14 17 Dec 2018 \n", + "2018-12-14 09 Jan 2019 0 \n", + "2018-12-14 \n", + "2018-12-15 17 Dec 2018 0 \n", + "2018-12-15 \n", + "2018-12-16 \n", + "2018-12-16 \n", + "2018-12-16 \n", + "2018-12-16 \n", + "2018-12-17 \n", + "2018-12-19 \n", + "2018-12-19 \n", + "2018-12-19 \n", + "2018-12-19 09 Jan 2019 0 \n", + "2018-12-20 09 Jan 2019 0 \n", + "2018-12-21 \n", + "2018-12-22 \n", + "2018-12-23 \n", + "2018-12-24 10 Jan 2019 0 \n", + "2018-12-26 \n", + "2018-12-27 \n", + "2018-12-27 \n", + "2018-12-27 \n", + "2018-12-27 31 Jan 2019 0 \n", + "2018-12-28 \n", + "2018-12-28 \n", + "2018-12-28 10 Jan 2019 0 \n", + "2018-12-29 10 Jan 2019 \n", + "2018-12-29 \n", + "2018-12-29 \n", + "2018-12-30 \n", + "2018-12-30 \n", + "2018-12-31 \n", + "2018-12-31 \n", + "2018-12-31 10 Jan 2019 \n", + "\n", + " Museum MuseumRec \\\n", + "ObsDate \n", + "2018-12-01 \n", + "2018-12-01 \n", + "2018-12-01 \n", + "2018-12-03 \n", + "2018-12-03 USGS ... \n", + "2018-12-04 BICY freezer for USGS pickup ... \n", + "2018-12-04 \n", + "2018-12-04 \n", + "2018-12-05 \n", + "2018-12-08 \n", + "2018-12-08 \n", + "2018-12-09 \n", + "2018-12-09 Left at sight, dead for a while ... \n", + "2018-12-11 \n", + "2018-12-11 Sent to USGS (Jill J.) for necropsy ... \n", + "2018-12-11 \n", + "2018-12-11 \n", + "2018-12-11 \n", + "2018-12-12 \n", + "2018-12-12 \n", + "2018-12-12 \n", + "2018-12-13 \n", + "2018-12-13 \n", + "2018-12-14 \n", + "2018-12-14 BICY for USGS pickup ... \n", + "2018-12-14 \n", + "2018-12-15 Transferred to Eric S. (FWC) and Brian S. (USG... \n", + "2018-12-15 \n", + "2018-12-16 \n", + "2018-12-16 \n", + "2018-12-16 \n", + "2018-12-16 \n", + "2018-12-17 \n", + "2018-12-19 \n", + "2018-12-19 \n", + "2018-12-19 \n", + "2018-12-19 Sent to USGS (Jill J.) ... \n", + "2018-12-20 BICY for USGS pickup ... \n", + "2018-12-21 \n", + "2018-12-22 \n", + "2018-12-23 \n", + "2018-12-24 \n", + "2018-12-26 \n", + "2018-12-27 \n", + "2018-12-27 \n", + "2018-12-27 \n", + "2018-12-27 Big Cypress ... \n", + "2018-12-28 \n", + "2018-12-28 \n", + "2018-12-28 \n", + "2018-12-29 \n", + "2018-12-29 \n", + "2018-12-29 \n", + "2018-12-30 \n", + "2018-12-30 \n", + "2018-12-31 \n", + "2018-12-31 \n", + "2018-12-31 \n", + "\n", + " Reference \n", + "ObsDate \n", + "2018-12-01 \n", + "2018-12-01 \n", + "2018-12-01 \n", + "2018-12-03 \n", + "2018-12-03 \n", + "2018-12-04 \n", + "2018-12-04 \n", + "2018-12-04 \n", + "2018-12-05 \n", + "2018-12-08 \n", + "2018-12-08 \n", + "2018-12-09 \n", + "2018-12-09 \n", + "2018-12-11 \n", + "2018-12-11 \n", + "2018-12-11 \n", + "2018-12-11 \n", + "2018-12-11 \n", + "2018-12-12 \n", + "2018-12-12 \n", + "2018-12-12 \n", + "2018-12-13 \n", + "2018-12-13 \n", + "2018-12-14 \n", + "2018-12-14 \n", + "2018-12-14 \n", + "2018-12-15 \n", + "2018-12-15 \n", + "2018-12-16 \n", + "2018-12-16 \n", + "2018-12-16 \n", + "2018-12-16 \n", + "2018-12-17 \n", + "2018-12-19 \n", + "2018-12-19 \n", + "2018-12-19 \n", + "2018-12-19 \n", + "2018-12-20 \n", + "2018-12-21 \n", + "2018-12-22 \n", + "2018-12-23 \n", + "2018-12-24 \n", + "2018-12-26 \n", + "2018-12-27 \n", + "2018-12-27 \n", + "2018-12-27 \n", + "2018-12-27 \n", + "2018-12-28 \n", + "2018-12-28 \n", + "2018-12-28 \n", + "2018-12-29 \n", + "2018-12-29 \n", + "2018-12-29 \n", + "2018-12-30 \n", + "2018-12-30 \n", + "2018-12-31 \n", + "2018-12-31 \n", + "2018-12-31 \n", + "\n", + "[58 rows x 53 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "last_year = time_df.loc['2018-12']\n", + "\n", + "last_year.drop(columns = 'ObsDate', axis = 1, inplace = True)\n", + "\n", + "last_year" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "last_year_ridx = last_year.reset_index()\n", + "last_year_pivot = pd.pivot_table(last_year_ridx, index = 'ObsDate', values = 'objectid', aggfunc = 'count' )" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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objectid
ObsDate
2018-12-013
2018-12-032
2018-12-043
2018-12-051
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2018-12-092
2018-12-115
2018-12-123
2018-12-132
2018-12-143
2018-12-152
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2018-12-194
2018-12-201
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2018-12-231
2018-12-241
2018-12-261
2018-12-274
2018-12-283
2018-12-293
2018-12-302
2018-12-313
\n", + "
" + ], + "text/plain": [ + " objectid\n", + "ObsDate \n", + "2018-12-01 3\n", + "2018-12-03 2\n", + "2018-12-04 3\n", + "2018-12-05 1\n", + "2018-12-08 2\n", + "2018-12-09 2\n", + "2018-12-11 5\n", + "2018-12-12 3\n", + "2018-12-13 2\n", + "2018-12-14 3\n", + "2018-12-15 2\n", + "2018-12-16 4\n", + "2018-12-17 1\n", + "2018-12-19 4\n", + "2018-12-20 1\n", + "2018-12-21 1\n", + "2018-12-22 1\n", + "2018-12-23 1\n", + "2018-12-24 1\n", + "2018-12-26 1\n", + "2018-12-27 4\n", + "2018-12-28 3\n", + "2018-12-29 3\n", + "2018-12-30 2\n", + "2018-12-31 3" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "last_year_pivot" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# every day had multiple python sightings in Dec2018,\n", + "# so my PRIORS would be the number of python sightings that day, divided by the total number of sightings that month?\n", + "# subset down to \"so far this month\" first" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "month_remaining2018 = last_year_pivot.loc['2018-12-14':]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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objectid
ObsDate
2018-12-143
2018-12-152
2018-12-164
2018-12-171
2018-12-194
2018-12-201
2018-12-211
2018-12-221
2018-12-231
2018-12-241
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2018-12-302
2018-12-313
\n", + "
" + ], + "text/plain": [ + " objectid\n", + "ObsDate \n", + "2018-12-14 3\n", + "2018-12-15 2\n", + "2018-12-16 4\n", + "2018-12-17 1\n", + "2018-12-19 4\n", + "2018-12-20 1\n", + "2018-12-21 1\n", + "2018-12-22 1\n", + "2018-12-23 1\n", + "2018-12-24 1\n", + "2018-12-26 1\n", + "2018-12-27 4\n", + "2018-12-28 3\n", + "2018-12-29 3\n", + "2018-12-30 2\n", + "2018-12-31 3" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "month_remaining2018" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "month_remaining2018_ridx = month_remaining2018.reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "16" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "days_remaining = len(month_remaining2018)\n", + "\n", + "days_remaining" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ObsDate\n", + "2018-12-14 0.1875\n", + "2018-12-15 0.1250\n", + "2018-12-16 0.2500\n", + "2018-12-17 0.0625\n", + "2018-12-19 0.2500\n", + "2018-12-20 0.0625\n", + "2018-12-21 0.0625\n", + "2018-12-22 0.0625\n", + "2018-12-23 0.0625\n", + "2018-12-24 0.0625\n", + "2018-12-26 0.0625\n", + "2018-12-27 0.2500\n", + "2018-12-28 0.1875\n", + "2018-12-29 0.1875\n", + "2018-12-30 0.1250\n", + "2018-12-31 0.1875\n", + "Name: objectid, dtype: float64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "month_remaining2018['objectid'] / 16" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "month_remaining2018_ridx['percSnake'] = month_remaining2018_ridx['objectid'] / int(days_remaining)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ObsDateobjectidpercSnake
02018-12-1430.1875
12018-12-1520.1250
22018-12-1640.2500
32018-12-1710.0625
42018-12-1940.2500
52018-12-2010.0625
62018-12-2110.0625
72018-12-2210.0625
82018-12-2310.0625
92018-12-2410.0625
102018-12-2610.0625
112018-12-2740.2500
122018-12-2830.1875
132018-12-2930.1875
142018-12-3020.1250
152018-12-3130.1875
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" + ], + "text/plain": [ + " ObsDate objectid percSnake\n", + "0 2018-12-14 3 0.1875\n", + "1 2018-12-15 2 0.1250\n", + "2 2018-12-16 4 0.2500\n", + "3 2018-12-17 1 0.0625\n", + "4 2018-12-19 4 0.2500\n", + "5 2018-12-20 1 0.0625\n", + "6 2018-12-21 1 0.0625\n", + "7 2018-12-22 1 0.0625\n", + "8 2018-12-23 1 0.0625\n", + "9 2018-12-24 1 0.0625\n", + "10 2018-12-26 1 0.0625\n", + "11 2018-12-27 4 0.2500\n", + "12 2018-12-28 3 0.1875\n", + "13 2018-12-29 3 0.1875\n", + "14 2018-12-30 2 0.1250\n", + "15 2018-12-31 3 0.1875" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "month_remaining2018_ridx" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# BAYES\n", + "# bowls = [0.5, 0.5] # this will be my Priors, the probability of selecting each bowl\n", + "# vanilla = [30/40, 20/40] # Likelihoods, the probability of selecting vanilla from each bowl\n", + "\n", + "# def bayes_theorem(priors: list, likelihoods: list) -> np.array: # each should be a list, returns an array\n", + "# marginal_prob = sum(np.multiply(priors, likelihoods))\n", + "# posterior_prob = np.divide(np.multiply(priors, likelihoods), marginal_prob)\n", + "# return posterior_prob\n", + "\n", + "# bayes_theorem(bowls, vanilla) # returns an array([0.6, 0.4]), so the first value is bowlOne, and second is bowlTwo\n", + "\n", + "# print(\"The Probability that the Vanilla cookie I selected was from bowl one is \" + str(bayes_theorem(bowls, vanilla)[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.1875,\n", + " 0.125,\n", + " 0.25,\n", + " 0.0625,\n", + " 0.25,\n", + " 0.0625,\n", + " 0.0625,\n", + " 0.0625,\n", + " 0.0625,\n", + " 0.0625,\n", + " 0.0625,\n", + " 0.25,\n", + " 0.1875,\n", + " 0.1875,\n", + " 0.125,\n", + " 0.1875]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(month_remaining2018_ridx['percSnake'])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.08571429, 0.05714286, 0.11428571, 0.02857143, 0.11428571,\n", + " 0.02857143, 0.02857143, 0.02857143, 0.02857143, 0.02857143,\n", + " 0.02857143, 0.11428571, 0.08571429, 0.08571429, 0.05714286,\n", + " 0.08571429])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def bayes_theorem(priors: list, likelihoods: list) -> np.array: # each should be a list, returns an array\n", + " marginal_prob = sum(np.multiply(priors, likelihoods))\n", + " posterior_prob = np.divide(np.multiply(priors, likelihoods), marginal_prob)\n", + " return posterior_prob\n", + "\n", + "priors = [1/16, 1/16, 1/16, 1/16, 1/16, 1/16, 1/16, 1/16, 1/16, 1/16, 1/16, 1/16, 1/16, 1/16, 1/16, 1/16, ] # same probability for every remaining day, of which there are 16\n", + "likelihoods = list(month_remaining2018_ridx['percSnake']) # likelihood of finding snakes that day\n", + "\n", + "bayes_theorem(priors, likelihoods)\n", + "# sum(bayes_theorem(priors, likelihoods)) # sum the probabilities is equal to one. this is \"given that a python was sighted between now and EOY, what is the probability it was on a specific day of the month, based on 2018 December data\"\n", + "# this also fails to account for the geographic hotspots, I need to have subsetted the data down to those three counties before calculating likelihoods\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "month_remaining2018_ridx['Prob_by_Day2019'] = pd.Series(bayes_theorem(priors, likelihoods))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"['Prob_by_Day'] not found in axis\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmonth_remaining2018_ridx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Prob_by_Day'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m 4100\u001b[0m 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\u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5342\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: \"['Prob_by_Day'] not found in axis\"" + ] + } + ], + "source": [ + "month_remaining2018_ridx.drop(columns = 'Prob_by_Day', axis = 1, inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "month_remaining2018_ridx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import sem, t\n", + "\n", + "def conf_interval(sample, confidence):\n", + " return t.interval(\n", + " confidence, # confidence level, passed in as an argument\n", + " len(sample) - 1, # degrees of freedom\n", + " loc = sample.mean(),\n", + " scale = sem(sample),\n", + " )\n", + "\n", + "conf_interval(bayes_theorem(priors, likelihoods), 0.9) # 90% confidence" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are 90% confident that the average probability of python sightings is between 0.47 and 0.78" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Are the sightings cyclical?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Analysis: It is important to understand the difference between \"seasonal\" and \"cyclical\". \n", + "\n", + "Seasonal data is impacted by the seasons\n", + "
Cyclical data fluctuates without a fixed time period\n", + "\n", + "In this case, as we see in the below graphs, we believe that the data is seasonal, the fluctuations occur at a fixed time period." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from statsmodels.tsa.seasonal import seasonal_decompose\n", + "\n", + "my_graph = seasonal_decompose(target_pivot2)\n", + "\n", + "my_graph.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {