From 1b4a49e83e2dee107fccb475b6f0b6aa2856e436 Mon Sep 17 00:00:00 2001 From: Guitlle Date: Mon, 8 Apr 2019 07:43:50 -0600 Subject: [PATCH] explore scoos data --- explorar-datos/SCOOS_explore_data.ipynb | 395 ++++++++++++++++++++++++ 1 file changed, 395 insertions(+) create mode 100644 explorar-datos/SCOOS_explore_data.ipynb diff --git a/explorar-datos/SCOOS_explore_data.ipynb b/explorar-datos/SCOOS_explore_data.ipynb new file mode 100644 index 0000000..ae09224 --- /dev/null +++ b/explorar-datos/SCOOS_explore_data.ipynb @@ -0,0 +1,395 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib as mlp\n", + "from matplotlib import pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "scoos = pd.read_csv(\"../data/SCOOS_Harmful_Algal_Blooms_1916-2019.csv\", skiprows= 7, encoding=\"latin-1\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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yearmonthdaytimelatitudelongitudedepth (m)locationAkashiwo sanguinea (cells/L)Alexandrium spp. (cells/L)...Phaeophytin 1 (mg/m3)Phaeophytin 2 (mg/m3)Phosphate (uM)Prorocentrum spp. (cells/L)Pseudo-nitzschia delicatissima group (cells/L)Pseudo-nitzschia seriata group (cells/L)Silicate (uM)Volume Settled for counting (mL)Water Temperature (°C)Volume for counting (mL)
01969123123:59:5933.606100-117.9311000.0Newport Pier0.00.0...NaNNaN0.3105200.05200.015599.0NaN25.018.0NaN
11969123123:59:5934.408000-119.6850000.0Stearns Wharf0.00.0...0.58NaN1.1022552.01392.00.06.56250.0NaNNaN
21969123123:59:5936.603686-121.8892710.0Monterey Wharf0.0858.0...NaNNaNNaN17503.01500.05574.0NaNNaNNaNNaN
31969123123:59:5934.408000-119.6850000.0Stearns WharfNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
41969123123:59:5934.008000-118.4990000.0Santa Monica Pier748.00.0...NaNNaNNaN2992.00.07480.0NaNNaN16.8NaN
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NaN \n", + "\n", + " Phaeophytin 2 (mg/m3) Phosphate (uM) Prorocentrum spp. (cells/L) \\\n", + "0 NaN 0.310 5200.0 \n", + "1 NaN 1.102 2552.0 \n", + "2 NaN NaN 17503.0 \n", + "3 NaN NaN NaN \n", + "4 NaN NaN 2992.0 \n", + "\n", + " Pseudo-nitzschia delicatissima group (cells/L) \\\n", + "0 5200.0 \n", + "1 1392.0 \n", + "2 1500.0 \n", + "3 NaN \n", + "4 0.0 \n", + "\n", + " Pseudo-nitzschia seriata group (cells/L) Silicate (uM) \\\n", + "0 15599.0 NaN \n", + "1 0.0 6.562 \n", + "2 5574.0 NaN \n", + "3 NaN NaN \n", + "4 7480.0 NaN \n", + "\n", + " Volume Settled for counting (mL) Water Temperature (°C) \\\n", + "0 25.0 18.0 \n", + "1 50.0 NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN 16.8 \n", + "\n", + " Volume for counting (mL) \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + "[5 rows x 33 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scoos.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist2d(scoos.longitude, scoos.latitude, bins=50, cmin=1)\n", + "plt.title(\"Geographical distribution of points\")\n", + "0" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "scoos[\"Date\"] = pd.to_datetime(scoos.apply(lambda x: str(x.year)+\"-\"+str(x.month)+\"-\"+str(x.day), axis=1))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Distribution of SCOOS data points over time')" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "scoos[scoos.Date > pd.to_datetime(\"2002-01-01\")].Date.hist(bins=20)\n", + "plt.title(\"Distribution of SCOOS data points over time\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1596,)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Unique dates:\n", + "scoos.groupby(\"Date\").apply(lambda.unique().shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}