{ "cells": [ { "cell_type": "code", "execution_count": 306, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns \n", "import numpy as np " ] }, { "cell_type": "code", "execution_count": 307, "metadata": {}, "outputs": [], "source": [ "#Change the path if you are working from a different machine \n", "\n", "df = pd.read_csv(\"../datasets/SampledLitter_SchoneRivieren.csv\", index_col= 0)" ] }, { "cell_type": "code", "execution_count": 308, "metadata": {}, "outputs": [], "source": [ "df = df.iloc[:,:11]" ] }, { "cell_type": "code", "execution_count": 309, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Width [cm]Length [cm]Fragmentation level (0-5)Degradation level (1-5)Biofueling surface [%]
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" ], "text/plain": [ " Width [cm] Length [cm] Fragmentation level (0-5) \\\n", "count 164.000000 164.00000 164.000000 \n", "mean 7.735976 8.50061 1.792683 \n", "std 7.038613 9.48355 1.688923 \n", "min 0.200000 0.10000 0.000000 \n", "25% 3.000000 2.00000 0.000000 \n", "50% 6.000000 5.00000 1.000000 \n", "75% 9.000000 11.00000 3.000000 \n", "max 38.000000 47.00000 5.000000 \n", "\n", " Degradation level (1-5) Biofueling surface [%] \n", "count 164.000000 164.000000 \n", "mean 1.957317 24.573171 \n", "std 0.867622 17.456365 \n", "min 1.000000 0.000000 \n", "25% 1.000000 10.000000 \n", "50% 2.000000 20.000000 \n", "75% 3.000000 40.000000 \n", "max 5.000000 100.000000 " ] }, "execution_count": 309, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 310, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 165 entries, 1 to 164\n", "Data columns (total 11 columns):\n", "Mass [gr] 164 non-null object\n", "Width [cm] 164 non-null float64\n", "Length [cm] 164 non-null float64\n", "Item category (OSPAR ID) 162 non-null object\n", "Polymer type 110 non-null object\n", "Brand 37 non-null object\n", "Language 31 non-null object\n", "Year 3 non-null object\n", "Fragmentation level (0-5) 164 non-null float64\n", "Degradation level (1-5) 164 non-null float64\n", "Biofueling surface [%] 164 non-null float64\n", "dtypes: float64(5), object(6)\n", "memory usage: 15.5+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 311, "metadata": {}, "outputs": [], "source": [ "df.columns = [\"mass\", \"width\", \"length\", \"OSPAR_ID\", \"polymer_type\", \"brand\", \"language\", \"year\", \"fragmentation\", \"degradation\", \"biofouling\"]" ] }, { "cell_type": "code", "execution_count": 312, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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masswidthlengthOSPAR_IDpolymer_typebrandlanguageyearfragmentationdegradationbiofouling
Item_code
1136.014.078NaNFernandesNLNaN0.01.010.0
2116.014.078NaNBullitNLNaN0.01.00.0
3116.014.078NaNBullitNLNaN0.02.010.0
4116.014.078NaNBullitNLNaN0.02.010.0
5416.014.078NaNBullitNLNaN0.03.050.0
6136.014.078NaNColaNLNaN0.02.020.0
7167.011.078NaNHeinekenNLNaN0.02.030.0
8137.011.078NaNHighway Cola LightNLNaN0.01.00.0
92527.011.078NaNHertog JanNLNaN0.01.010.0
10126.014.078NaNBullitNLNaN0.02.040.0
11467.011.078NaNAmstelNLNaN0.02.020.0
12188.016.078NaNNaNNLNaN1.03.050.0
13909.09.089NaNFroyakNLNaN0.01.010.0
14397.020.04.2PETLiptonNLNaN0.01.010.0
15246.016.04.2PETAA DrinkNLNaN0.01.00.0
16127.018.04.2PETAlbert HeinzNLNaN0.02.020.0
17139.018.04.2PETNaNNLNaN2.01.010.0
18278.021.04.2PETCola JumboNLNaN0.02.030.0
192347.020.091NaNHeinekenNLNaN0.02.030.0
202447.020.091NaNAmstelNLNaN0.03.050.0
214339.029.091NaNNaNNLNaN0.01.010.0
221913.07.06PETWelde MelkNLApr-200.01.00.0
232113.07.06PETHealthy HandNLJun-200.01.010.0
2433.01.015PO HardNaNNLNaN0.01.00.0
251216.016.091NaNCoronaNaNNaN1.01.010.0
26196.06.091NaNNaNNaNNaN1.01.00.0
27146.04.091NaNNaNNaNNaN1.01.00.0
2863.07.091NaNNaNNaNNaN1.01.00.0
29279.09.091NaNCoronaNaNNaN1.01.00.0
3065.03.091NaNNaNNaNNaN2.01.010.0
....................................
1350.14.01.046.2PO softNaNNaNNaN4.03.040.0
1360.13.01.046.2PO sfotNaNNaNNaN4.01.010.0
1370.11.02.519MultilayerNaNNaNNaN4.03.030.0
1380.12.01.048PO hardNaNNaNNaN4.02.020.0
1390.12.01.048PO hardNaNNaNNaN4.02.020.0
1400.12.00.591NaNNaNNaNNaN4.02.020.0
1410.11.00.591NaNNaNNaNNaN4.02.020.0
1420.12.02.091NaNNaNNaNNaN4.02.020.0
1430.13.00.248PO hardNaNNaNNaN1.03.030.0
1440.15.00.148PO hardNaNNaNNaN1.02.020.0
1450.16.00.148PO hardNaNNaNNaN1.02.020.0
1460.12.02.091NaNNaNNaNNaN4.02.020.0
1470.11.02.091NaNNaNNaNNaN4.02.020.0
1480.11.02.091NaNNaNNaNNaN5.02.020.0
1490.11.01.048EPSNaNNaNNaN5.03.050.0
1500.11.01.048EPSNaNNaNNaN5.03.050.0
1510.10.20.248EPSNaNNaNNaN5.03.050.0
1520.13.01.064NaNNaNNaNNaN0.04.070.0
1530.10.51.019MultilayerNaNNaNNaN5.02.020.0
1540.17.01.059NaNNaNNaNNaN0.01.010.0
1550.10.20.519MultilayerNaNNaNNaN5.03.030.0
1560.12.02.091NaNNaNNaNNaN5.03.030.0
1570.13.01.064NaNNaNNaNNaN0.05.0100.0
1580.10.20.248EPSNaNNaNNaN5.03.050.0
1590.10.20.248EPSNaNNaNNaN5.03.050.0
1600.10.20.248EPSNaNNaNNaN5.03.050.0
1610.10.20.291NaNNaNNaNNaN5.01.010.0
1620.11.01.091NaNNaNNaNNaN5.03.040.0
1630.11.01.548MultilayerNaNNaNNaN5.01.010.0
1640.13.01.064NaNNaNNaNNaN1.04.070.0
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165 rows × 11 columns

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" ], "text/plain": [ " mass width length OSPAR_ID polymer_type brand \\\n", "Item_code \n", "1 13 6.0 14.0 78 NaN Fernandes \n", "2 11 6.0 14.0 78 NaN Bullit \n", "3 11 6.0 14.0 78 NaN Bullit \n", "4 11 6.0 14.0 78 NaN Bullit \n", "5 41 6.0 14.0 78 NaN Bullit \n", "6 13 6.0 14.0 78 NaN Cola \n", "7 16 7.0 11.0 78 NaN Heineken \n", "8 13 7.0 11.0 78 NaN Highway Cola Light \n", "9 252 7.0 11.0 78 NaN Hertog Jan \n", "10 12 6.0 14.0 78 NaN Bullit \n", "11 46 7.0 11.0 78 NaN Amstel \n", "12 18 8.0 16.0 78 NaN NaN \n", "13 90 9.0 9.0 89 NaN Froyak \n", "14 39 7.0 20.0 4.2 PET Lipton \n", "15 24 6.0 16.0 4.2 PET AA Drink \n", "16 12 7.0 18.0 4.2 PET Albert Heinz \n", "17 13 9.0 18.0 4.2 PET NaN \n", "18 27 8.0 21.0 4.2 PET Cola Jumbo \n", "19 234 7.0 20.0 91 NaN Heineken \n", "20 244 7.0 20.0 91 NaN Amstel \n", "21 433 9.0 29.0 91 NaN NaN \n", "22 19 13.0 7.0 6 PET Welde Melk \n", "23 21 13.0 7.0 6 PET Healthy Hand \n", "24 3 3.0 1.0 15 PO Hard NaN \n", "25 121 6.0 16.0 91 NaN Corona \n", "26 19 6.0 6.0 91 NaN NaN \n", "27 14 6.0 4.0 91 NaN NaN \n", "28 6 3.0 7.0 91 NaN NaN \n", "29 27 9.0 9.0 91 NaN Corona \n", "30 6 5.0 3.0 91 NaN NaN \n", "... ... ... ... ... ... ... \n", "135 0.1 4.0 1.0 46.2 PO soft NaN \n", "136 0.1 3.0 1.0 46.2 PO sfot NaN \n", "137 0.1 1.0 2.5 19 Multilayer NaN \n", "138 0.1 2.0 1.0 48 PO hard NaN \n", "139 0.1 2.0 1.0 48 PO hard NaN \n", "140 0.1 2.0 0.5 91 NaN NaN \n", "141 0.1 1.0 0.5 91 NaN NaN \n", "142 0.1 2.0 2.0 91 NaN NaN \n", "143 0.1 3.0 0.2 48 PO hard NaN \n", "144 0.1 5.0 0.1 48 PO hard NaN \n", "145 0.1 6.0 0.1 48 PO hard NaN \n", "146 0.1 2.0 2.0 91 NaN NaN \n", "147 0.1 1.0 2.0 91 NaN NaN \n", "148 0.1 1.0 2.0 91 NaN NaN \n", "149 0.1 1.0 1.0 48 EPS NaN \n", "150 0.1 1.0 1.0 48 EPS NaN \n", "151 0.1 0.2 0.2 48 EPS NaN \n", "152 0.1 3.0 1.0 64 NaN NaN \n", "153 0.1 0.5 1.0 19 Multilayer NaN \n", "154 0.1 7.0 1.0 59 NaN NaN \n", "155 0.1 0.2 0.5 19 Multilayer NaN \n", "156 0.1 2.0 2.0 91 NaN NaN \n", "157 0.1 3.0 1.0 64 NaN NaN \n", "158 0.1 0.2 0.2 48 EPS NaN \n", "159 0.1 0.2 0.2 48 EPS NaN \n", "160 0.1 0.2 0.2 48 EPS NaN \n", "161 0.1 0.2 0.2 91 NaN NaN \n", "162 0.1 1.0 1.0 91 NaN NaN \n", "163 0.1 1.0 1.5 48 Multilayer NaN \n", "164 0.1 3.0 1.0 64 NaN NaN \n", "\n", " language year fragmentation degradation biofouling \n", "Item_code \n", "1 NL NaN 0.0 1.0 10.0 \n", "2 NL NaN 0.0 1.0 0.0 \n", "3 NL NaN 0.0 2.0 10.0 \n", "4 NL NaN 0.0 2.0 10.0 \n", "5 NL NaN 0.0 3.0 50.0 \n", "6 NL NaN 0.0 2.0 20.0 \n", "7 NL NaN 0.0 2.0 30.0 \n", "8 NL NaN 0.0 1.0 0.0 \n", "9 NL NaN 0.0 1.0 10.0 \n", "10 NL NaN 0.0 2.0 40.0 \n", "11 NL NaN 0.0 2.0 20.0 \n", "12 NL NaN 1.0 3.0 50.0 \n", "13 NL NaN 0.0 1.0 10.0 \n", "14 NL NaN 0.0 1.0 10.0 \n", "15 NL NaN 0.0 1.0 0.0 \n", "16 NL NaN 0.0 2.0 20.0 \n", "17 NL NaN 2.0 1.0 10.0 \n", "18 NL NaN 0.0 2.0 30.0 \n", "19 NL NaN 0.0 2.0 30.0 \n", "20 NL NaN 0.0 3.0 50.0 \n", "21 NL NaN 0.0 1.0 10.0 \n", "22 NL Apr-20 0.0 1.0 0.0 \n", "23 NL Jun-20 0.0 1.0 10.0 \n", "24 NL NaN 0.0 1.0 0.0 \n", "25 NaN NaN 1.0 1.0 10.0 \n", "26 NaN NaN 1.0 1.0 0.0 \n", "27 NaN NaN 1.0 1.0 0.0 \n", "28 NaN NaN 1.0 1.0 0.0 \n", "29 NaN NaN 1.0 1.0 0.0 \n", "30 NaN NaN 2.0 1.0 10.0 \n", "... ... ... ... ... ... \n", "135 NaN NaN 4.0 3.0 40.0 \n", "136 NaN NaN 4.0 1.0 10.0 \n", "137 NaN NaN 4.0 3.0 30.0 \n", "138 NaN NaN 4.0 2.0 20.0 \n", "139 NaN NaN 4.0 2.0 20.0 \n", "140 NaN NaN 4.0 2.0 20.0 \n", "141 NaN NaN 4.0 2.0 20.0 \n", "142 NaN NaN 4.0 2.0 20.0 \n", "143 NaN NaN 1.0 3.0 30.0 \n", "144 NaN NaN 1.0 2.0 20.0 \n", "145 NaN NaN 1.0 2.0 20.0 \n", "146 NaN NaN 4.0 2.0 20.0 \n", "147 NaN NaN 4.0 2.0 20.0 \n", "148 NaN NaN 5.0 2.0 20.0 \n", "149 NaN NaN 5.0 3.0 50.0 \n", "150 NaN NaN 5.0 3.0 50.0 \n", "151 NaN NaN 5.0 3.0 50.0 \n", "152 NaN NaN 0.0 4.0 70.0 \n", "153 NaN NaN 5.0 2.0 20.0 \n", "154 NaN NaN 0.0 1.0 10.0 \n", "155 NaN NaN 5.0 3.0 30.0 \n", "156 NaN NaN 5.0 3.0 30.0 \n", "157 NaN NaN 0.0 5.0 100.0 \n", "158 NaN NaN 5.0 3.0 50.0 \n", "159 NaN NaN 5.0 3.0 50.0 \n", "160 NaN NaN 5.0 3.0 50.0 \n", "161 NaN NaN 5.0 1.0 10.0 \n", "162 NaN NaN 5.0 3.0 40.0 \n", "163 NaN NaN 5.0 1.0 10.0 \n", "164 NaN NaN 1.0 4.0 70.0 \n", "\n", "[165 rows x 11 columns]" ] }, "execution_count": 312, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 313, "metadata": {}, "outputs": [], "source": [ "df.mass = df.mass.replace(to_replace=',', value='.')" ] }, { "cell_type": "code", "execution_count": 314, "metadata": {}, "outputs": [], "source": [ "df.mass = df.mass.replace(to_replace='4,2', value=4.2)" ] }, { "cell_type": "code", "execution_count": 315, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['13', '11', '41', '16', '252', '12', '46', '18', '90', '39', '24',\n", " '27', '234', '244', '433', '19', '21', '3', '121', '14', '6', '9',\n", " '38', '4', '158', '5', '7', '1.4', '1', '4.5', '5.7', '4.3', 4.2,\n", " '0.8', '0.3', '2.3', '0.03', '0.02', '0.01', '0.39', '0.26',\n", " '0.27', '0.13', '0.1', '0.15', '0.28', '0.21', '0.41', '1.06',\n", " '2.9', '7.8', '3.8', '0.7', '2.5', '16.13', '3.89', '3.74', '1.57',\n", " '0.17', '2.81', '0.5', '0.76', '0.11', '1.61', '0.33', '17.58',\n", " '2.22', '0.53', '0.09', '0.05', '0.42', '0.55', '9.1', '2.4',\n", " '1.6', '11.2', '26.5', '10.1', '0.23', '49.7', '3.3', '4.4', '6.1',\n", " '0.43', '0.81', '0.24', '0.07', '0.4', '0.04', '0.06', '0.2',\n", " '1.07', '9.4', '115.15', '48.3', '5.1', '1.8', nan], dtype=object)" ] }, "execution_count": 315, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.mass.unique()" ] }, { "cell_type": "code", "execution_count": 316, "metadata": {}, "outputs": [], "source": [ "df.mass = df.mass.astype(float)" ] }, { "cell_type": "code", "execution_count": 317, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 165 entries, 1 to 164\n", "Data columns (total 11 columns):\n", "mass 164 non-null float64\n", "width 164 non-null float64\n", "length 164 non-null float64\n", "OSPAR_ID 162 non-null object\n", "polymer_type 110 non-null object\n", "brand 37 non-null object\n", "language 31 non-null object\n", "year 3 non-null object\n", "fragmentation 164 non-null float64\n", "degradation 164 non-null float64\n", "biofouling 164 non-null float64\n", "dtypes: float64(6), object(5)\n", "memory usage: 15.5+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 318, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "67.07317073170732" ] }, "execution_count": 318, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Calculate the proportion of plastic items over the total anthropogenic litter found (by count, not mass)\n", "\n", "110/164 * 100\n", "\n" ] }, { "cell_type": "code", "execution_count": 319, "metadata": {}, "outputs": [], "source": [ "df.polymer_type = df.polymer_type.replace(np.nan, 'Not plastic', regex=True)\n" ] }, { "cell_type": "code", "execution_count": 320, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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masswidthlengthOSPAR_IDpolymer_typebrandlanguageyearfragmentationdegradationbiofouling
Item_code
113.06.014.078Not plasticFernandesNLNaN0.01.010.0
211.06.014.078Not plasticBullitNLNaN0.01.00.0
311.06.014.078Not plasticBullitNLNaN0.02.010.0
411.06.014.078Not plasticBullitNLNaN0.02.010.0
541.06.014.078Not plasticBullitNLNaN0.03.050.0
613.06.014.078Not plasticColaNLNaN0.02.020.0
716.07.011.078Not plasticHeinekenNLNaN0.02.030.0
813.07.011.078Not plasticHighway Cola LightNLNaN0.01.00.0
9252.07.011.078Not plasticHertog JanNLNaN0.01.010.0
1012.06.014.078Not plasticBullitNLNaN0.02.040.0
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" ], "text/plain": [ " mass width length OSPAR_ID polymer_type brand \\\n", "Item_code \n", "1 13.0 6.0 14.0 78 Not plastic Fernandes \n", "2 11.0 6.0 14.0 78 Not plastic Bullit \n", "3 11.0 6.0 14.0 78 Not plastic Bullit \n", "4 11.0 6.0 14.0 78 Not plastic Bullit \n", "5 41.0 6.0 14.0 78 Not plastic Bullit \n", "6 13.0 6.0 14.0 78 Not plastic Cola \n", "7 16.0 7.0 11.0 78 Not plastic Heineken \n", "8 13.0 7.0 11.0 78 Not plastic Highway Cola Light \n", "9 252.0 7.0 11.0 78 Not plastic Hertog Jan \n", "10 12.0 6.0 14.0 78 Not plastic Bullit \n", "\n", " language year fragmentation degradation biofouling \n", "Item_code \n", "1 NL NaN 0.0 1.0 10.0 \n", "2 NL NaN 0.0 1.0 0.0 \n", "3 NL NaN 0.0 2.0 10.0 \n", "4 NL NaN 0.0 2.0 10.0 \n", "5 NL NaN 0.0 3.0 50.0 \n", "6 NL NaN 0.0 2.0 20.0 \n", "7 NL NaN 0.0 2.0 30.0 \n", "8 NL NaN 0.0 1.0 0.0 \n", "9 NL NaN 0.0 1.0 10.0 \n", "10 NL NaN 0.0 2.0 40.0 " ] }, "execution_count": 320, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(10)" ] }, { "cell_type": "code", "execution_count": 321, "metadata": {}, "outputs": [], "source": [ "df_not_plastic = df.loc[df['polymer_type'] == 'Not plastic']" ] }, { "cell_type": "code", "execution_count": 322, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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masswidthlengthfragmentationdegradationbiofouling
count54.00000054.00000054.00000054.00000054.00000054.000000
mean39.2603706.9481487.8370371.4444441.83333322.037037
std81.8440065.8698066.2885131.7009060.94669221.046187
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50%9.6000006.0000007.0000001.0000002.00000020.000000
75%24.6250007.00000011.0000002.0000002.00000030.000000
max433.00000028.00000029.0000005.0000005.000000100.000000
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" ], "text/plain": [ " mass width length fragmentation degradation \\\n", "count 54.000000 54.000000 54.000000 54.000000 54.000000 \n", "mean 39.260370 6.948148 7.837037 1.444444 1.833333 \n", "std 81.844006 5.869806 6.288513 1.700906 0.946692 \n", "min 0.050000 0.200000 0.200000 0.000000 1.000000 \n", "25% 0.127500 3.250000 2.250000 0.000000 1.000000 \n", "50% 9.600000 6.000000 7.000000 1.000000 2.000000 \n", "75% 24.625000 7.000000 11.000000 2.000000 2.000000 \n", "max 433.000000 28.000000 29.000000 5.000000 5.000000 \n", "\n", " biofouling \n", "count 54.000000 \n", "mean 22.037037 \n", "std 21.046187 \n", "min 0.000000 \n", "25% 10.000000 \n", "50% 20.000000 \n", "75% 30.000000 \n", "max 100.000000 " ] }, "execution_count": 322, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_not_plastic.describe()" ] }, { "cell_type": "code", "execution_count": 323, "metadata": {}, "outputs": [], "source": [ "# 54 items are non-plastic, 110 plastic. \n", "#Mean mass for non-plastic items is 39 gr. Biofouling estimated at 22% on avergae, 1.44 of fragmentation \n", "# Average width and lenght: 6 cm. " ] }, { "cell_type": "code", "execution_count": 324, "metadata": {}, "outputs": [], "source": [ "df_plastic = df.loc[df['polymer_type'] != 'Not plastic']" ] }, { "cell_type": "code", "execution_count": 325, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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masswidthlengthfragmentationdegradationbiofouling
count110.000000110.000000110.000000110.000000110.000000110.000000
mean3.1642738.1227278.8263641.9636362.01818225.818182
std7.5306767.54150210.7210221.6641240.82375115.348061
min0.0100000.2000000.1000000.0000001.0000000.000000
25%0.1000003.0000002.0000000.0000001.00000010.000000
50%0.3150006.0000004.5000002.0000002.00000020.000000
75%2.37500010.75000010.0000003.0000003.00000040.000000
max49.70000038.00000047.0000005.0000003.00000060.000000
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" ], "text/plain": [ " mass width length fragmentation degradation \\\n", "count 110.000000 110.000000 110.000000 110.000000 110.000000 \n", "mean 3.164273 8.122727 8.826364 1.963636 2.018182 \n", "std 7.530676 7.541502 10.721022 1.664124 0.823751 \n", "min 0.010000 0.200000 0.100000 0.000000 1.000000 \n", "25% 0.100000 3.000000 2.000000 0.000000 1.000000 \n", "50% 0.315000 6.000000 4.500000 2.000000 2.000000 \n", "75% 2.375000 10.750000 10.000000 3.000000 3.000000 \n", "max 49.700000 38.000000 47.000000 5.000000 3.000000 \n", "\n", " biofouling \n", "count 110.000000 \n", "mean 25.818182 \n", "std 15.348061 \n", "min 0.000000 \n", "25% 10.000000 \n", "50% 20.000000 \n", "75% 40.000000 \n", "max 60.000000 " ] }, "execution_count": 325, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_plastic.describe()" ] }, { "cell_type": "code", "execution_count": 326, "metadata": {}, "outputs": [], "source": [ "#Mean mass for non-plastic items is 3.16 gr. Biofouling estimated at 26% on avergae, 1.96 of fragmentation. \n", "#Average length and width: 8 cm" ] }, { "cell_type": "code", "execution_count": 327, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5096: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " self[name] = value\n" ] } ], "source": [ "df_plastic.polymer_type = df_plastic.polymer_type.replace('Mulitlayer', 'Multilayer', regex=True)\n", "df_plastic.polymer_type = df_plastic.polymer_type.replace('Multilyaer', 'Multilayer', regex=True)\n", "df_plastic.polymer_type = df_plastic.polymer_type.replace('Multilayer', 'Multilayer', regex=True)\n", "df_plastic.polymer_type = df_plastic.polymer_type.replace('PO Hard', 'PO hard', regex=True)\n", "df_plastic.polymer_type = df_plastic.polymer_type.replace('PO sfot', 'PO soft', regex=True)\n", "df_plastic.polymer_type = df_plastic.polymer_type.replace(' ', '', regex=True)\n" ] }, { "cell_type": "code", "execution_count": 328, "metadata": {}, "outputs": [], "source": [ "polymer_type_df = df_plastic.groupby(\"polymer_type\").polymer_type.count() " ] }, { "cell_type": "code", "execution_count": 329, "metadata": {}, "outputs": [], "source": [ "polymer_type_df = polymer_type_df.sort_values(ascending = True)" ] }, { "cell_type": "code", "execution_count": 330, "metadata": {}, "outputs": [], "source": [ "polymer_type_df = polymer_type_df.to_frame()" ] }, { "cell_type": "code", "execution_count": 331, "metadata": {}, "outputs": [], "source": [ "polymer_type_df['Percentage'] = (polymer_type_df.polymer_type / 110) * 100 " ] }, { "cell_type": "code", "execution_count": 332, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['PS', 'Other', 'PET', 'POhard', 'EPS', 'Multilayer', 'POsoft'], dtype='object', name='polymer_type')" ] }, "execution_count": 332, "metadata": {}, "output_type": "execute_result" } ], "source": [ "polymer_type_prop.index" ] }, { "cell_type": "code", "execution_count": 333, "metadata": {}, "outputs": [], "source": [ "polymer_type = polymer_type_df.iloc[:,1:]" ] }, { "cell_type": "code", "execution_count": 334, "metadata": {}, "outputs": [], "source": [ "polymer_type.reset_index(inplace = True)" ] }, { "cell_type": "code", "execution_count": 335, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Polymer composition')" ] }, "execution_count": 335, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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masswidthlengthfragmentationdegradationbiofouling
mass1.0000000.1963680.363639-0.374618-0.107737-0.114121
width0.1963681.0000000.699370-0.3604000.0779080.206671
length0.3636390.6993701.000000-0.4062850.0806620.201283
fragmentation-0.374618-0.360400-0.4062851.0000000.3685770.346006
degradation-0.1077370.0779080.0806620.3685771.0000000.927638
biofouling-0.1141210.2066710.2012830.3460060.9276381.000000
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" ], "text/plain": [ " mass width length fragmentation degradation \\\n", "mass 1.000000 0.196368 0.363639 -0.374618 -0.107737 \n", "width 0.196368 1.000000 0.699370 -0.360400 0.077908 \n", "length 0.363639 0.699370 1.000000 -0.406285 0.080662 \n", "fragmentation -0.374618 -0.360400 -0.406285 1.000000 0.368577 \n", "degradation -0.107737 0.077908 0.080662 0.368577 1.000000 \n", "biofouling -0.114121 0.206671 0.201283 0.346006 0.927638 \n", "\n", " biofouling \n", "mass -0.114121 \n", "width 0.206671 \n", "length 0.201283 \n", "fragmentation 0.346006 \n", "degradation 0.927638 \n", "biofouling 1.000000 " ] }, "execution_count": 336, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_plastic.corr()" ] }, { "cell_type": "code", "execution_count": 340, "metadata": {}, "outputs": [], "source": [ "OSPAR_type = df_plastic.groupby(\"OSPAR_ID\").OSPAR_ID.count().to_frame()" ] }, { "cell_type": "code", "execution_count": 341, "metadata": {}, "outputs": [], "source": [ "OSPAR_type.columns = ['Count']" ] }, { "cell_type": "code", "execution_count": 342, "metadata": {}, "outputs": [], "source": [ "OSPAR_type.reset_index(inplace = True)" ] }, { "cell_type": "code", "execution_count": 343, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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OSPAR_IDCount
01001
11051
2117.11
3117.21
4154
5161
61919
719.11
823
9201
10211
11253
1239
13321
1438.11
154.11
164.25
174.31
1843.11
1946.13
2046.210
2146212
224823
2364
24991
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" ], "text/plain": [ " OSPAR_ID Count\n", "0 100 1\n", "1 105 1\n", "2 117.1 1\n", "3 117.2 1\n", "4 15 4\n", "5 16 1\n", "6 19 19\n", "7 19.1 1\n", "8 2 3\n", "9 20 1\n", "10 21 1\n", "11 25 3\n", "12 3 9\n", "13 32 1\n", "14 38.1 1\n", "15 4.1 1\n", "16 4.2 5\n", "17 4.3 1\n", "18 43.1 1\n", "19 46.1 3\n", "20 46.2 10\n", "21 462 12\n", "22 48 23\n", "23 6 4\n", "24 99 1" ] }, "execution_count": 343, "metadata": {}, "output_type": "execute_result" } ], "source": [ "OSPAR_type" ] }, { "cell_type": "code", "execution_count": 344, "metadata": {}, "outputs": [], "source": [ "OSPAR_type = OSPAR_type.sort_values(by = 'Count', ascending = False)" ] }, { "cell_type": "code", "execution_count": 345, "metadata": {}, "outputs": [], "source": [ "OSPAR_type =OSPAR_type.replace('48', 'Other items')\n", "OSPAR_type =OSPAR_type.replace('19', 'Crisp/sweet packets and lolly sticks')\n", "OSPAR_type =OSPAR_type.replace('462', 'Indefinable pieces of styrofoam [2.5 - 50 cm]')\n", "OSPAR_type =OSPAR_type.replace('46.2', 'Plastic foils or pieces of soft plastic [2.5 - 50 cm]')\n", "OSPAR_type =OSPAR_type.replace('3', 'Small plastic bags')\n", "OSPAR_type =OSPAR_type.replace('4.2', 'Plastic bottles < 0.5 L')\n", "OSPAR_type =OSPAR_type.replace('6', 'Food containers incl. fast food containers')\n", "OSPAR_type =OSPAR_type.replace('15', 'Caps/lids')\n", "OSPAR_type =OSPAR_type.replace('46.1', 'Undefined plastic pieces 2.5 - 50 cm (hard plastic)')\n", "OSPAR_type =OSPAR_type.replace('25', 'Household gloves (soft plastic)')\n", "OSPAR_type =OSPAR_type.replace('2', 'Bags')\n" ] }, { "cell_type": "code", "execution_count": 346, "metadata": {}, "outputs": [], "source": [ "OSPAR_top = OSPAR_type.iloc[:10,]" ] }, { "cell_type": "code", "execution_count": 347, "metadata": {}, "outputs": [], "source": [ "OSPAR_top.columns = ['OSPAR','Count']" ] }, { "cell_type": "code", "execution_count": 348, "metadata": {}, "outputs": [], "source": [ "OSPAR_top.set_index('OSPAR', inplace = True)" ] }, { "cell_type": "code", "execution_count": 349, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Count
OSPAR
Other items23
Crisp/sweet packets and lolly sticks19
Indefinable pieces of styrofoam [2.5 - 50 cm]12
Plastic foils or pieces of soft plastic [2.5 - 50 cm]10
Small plastic bags9
Plastic bottles < 0.5 L5
Food containers incl. fast food containers4
Caps/lids4
Bags3
Undefined plastic pieces 2.5 - 50 cm (hard plastic)3
\n", "
" ], "text/plain": [ " Count\n", "OSPAR \n", "Other items 23\n", "Crisp/sweet packets and lolly sticks 19\n", "Indefinable pieces of styrofoam [2.5 - 50 cm] 12\n", "Plastic foils or pieces of soft plastic [2.5 - ... 10\n", "Small plastic bags 9\n", "Plastic bottles < 0.5 L 5\n", "Food containers incl. fast food containers 4\n", "Caps/lids 4\n", "Bags 3\n", "Undefined plastic pieces 2.5 - 50 cm (hard plas... 3" ] }, "execution_count": 349, "metadata": {}, "output_type": "execute_result" } ], "source": [ "OSPAR_top" ] }, { "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }