From c7eaf01ec18e77404920e5766f0b1e786855131b Mon Sep 17 00:00:00 2001 From: Hannah Russell <hannah.russell@studium.uni-hamburg.de> Date: Tue, 14 Mar 2023 17:25:28 +0100 Subject: [PATCH] update elbe ipynb files and names --- ipynb/Elbe_Chlorophyll.ipynb | 299 ++++++++++++++++ ipynb/Elbe_Turbidity.ipynb | 659 +++++++++++++++++++++++++++++++++++ 2 files changed, 958 insertions(+) create mode 100644 ipynb/Elbe_Chlorophyll.ipynb create mode 100644 ipynb/Elbe_Turbidity.ipynb diff --git a/ipynb/Elbe_Chlorophyll.ipynb b/ipynb/Elbe_Chlorophyll.ipynb new file mode 100644 index 0000000..e2336a8 --- /dev/null +++ b/ipynb/Elbe_Chlorophyll.ipynb @@ -0,0 +1,299 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 15, + "id": "3d380a50", + "metadata": {}, + "outputs": [], + "source": [ + "import glob\n", + "import os\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "3c79ca53", + "metadata": {}, + "source": [ + "## pre-processing elbe Chlorophyll data\n", + "The general aim is to create concateable (non-2d i guess) data frames of all estuaries with unified column names " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bdd39076", + "metadata": {}, + "outputs": [], + "source": [ + "#os.getcwd()\n", + "os.chdir(\"C:\\\\Users\\\\Hannah Russell\\\\north_sea_estuaries_visualisations\")\n", + "\n", + "cwd = os.path.abspath(os.curdir)\n", + "elbe_clorophyll_df_1 = glob.glob(os.path.join(cwd, 'data', 'input', 'elbe', 'chlorophyll','df_1', '*.csv'))\n", + "elbe_clorophyll_df_1 = [pd.read_csv(file, sep = ';', encoding= 'unicode_escape') for file in elbe_clorophyll_df_1]\n", + "elbe_clorophyll_df_1 = pd.concat(elbe_clorophyll_df_1, ignore_index=True)\n", + "#elbe_clorophyll_df_1.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c68f4427", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\HANNAH~1\\AppData\\Local\\Temp/ipykernel_18444/3904582180.py:1: FutureWarning: The default value of regex will change from True to False in a future version.\n", + " elbe_clorophyll_df_1.columns = elbe_clorophyll_df_1.columns.str.replace(\"['']\", \"\")\n" + ] + } + ], + "source": [ + "elbe_clorophyll_df_1.columns = elbe_clorophyll_df_1.columns.str.replace(\"['']\", \"\")\n", + "elbe_clorophyll_df_1.drop(elbe_clorophyll_df_1[elbe_clorophyll_df_1.Messwert.str.contains('[<]', na=True)].index, inplace=True) # removed < from columns with <2.0 string\n", + "elbe_clorophyll_df_1['Stromkilometer'] = elbe_clorophyll_df_1['Stromkilometer'].str.replace(\",\", \".\")\n", + "elbe_clorophyll_df_1['Messwert'] = elbe_clorophyll_df_1['Messwert'].str.replace(\",\", \".\")\n", + "#elbe_clorophyll_df_1.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f98cec41", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Chlorophyll ug/L')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Stromkilometer = elbe_clorophyll_df_1['Stromkilometer'].astype(float)\n", + "Messwert = elbe_clorophyll_df_1['Messwert'].astype(float)\n", + "\n", + "# plot of all cholorphyll values from all years on one plot\n", + "plt.scatter(Stromkilometer, Messwert)\n", + "plt.gca().invert_xaxis()\n", + "plt.title('Elbe-- All Years')\n", + "plt.xlabel('Kilometer')\n", + "plt.ylabel('Chlorophyll ug/L')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "6b548829", + "metadata": {}, + "outputs": [], + "source": [ + "elbe_chlor_df_1_med = elbe_clorophyll_df_1[['Stromkilometer', 'Messwert']]\n", + "elbe_chlor_df_1_med.dropna()\n", + "\n", + "elbe_chlor_df_1_med = elbe_chlor_df_1_med.groupby('Stromkilometer', as_index=False).median() \n", + "\n", + "stromkilometer_med = elbe_chlor_df_1_med['Stromkilometer']\n", + "messwert_med = elbe_chlor_df_1_med['Messwert']\n", + "\n", + "#elbe_chlor_df_1_med.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "8ebebf61", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Chlorophyll ug/L')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Chlorophyll values from all years\n", + "plt.plot(stromkilometer_med, messwert_med)\n", + "plt.gca().invert_xaxis()\n", + "plt.title('Elbe-- Median at km')\n", + "plt.xlabel('Kilometer')\n", + "plt.ylabel('Chlorophyll ug/L')" + ] + }, + { + "cell_type": "markdown", + "id": "0e70af95", + "metadata": {}, + "source": [ + "## elbe depth" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b7f50efe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Depth')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "elbe_depth_df_1 = pd.read_csv(cwd + \"/data/input/elbe/depth/df_1/Schelde Depth.csv\")\n", + "\n", + "elbe_depth_df_1['Stromkilometer'] = elbe_depth_df_1['Stromkilometer'].astype(float).round(1)\n", + "Stromkilometer_d = elbe_depth_df_1['Stromkilometer']\n", + "Depth = elbe_depth_df_1['Depth']\n", + "elbe_depth_df_1\n", + "\n", + "# Depth plot\n", + "plt.plot(Stromkilometer_d, Depth)\n", + "plt.gca().invert_xaxis()\n", + "plt.gca().invert_yaxis()\n", + "plt.title('Elbe-- All Years')\n", + "plt.xlabel('Kilometer')\n", + "plt.ylabel('Depth')" + ] + }, + { + "cell_type": "markdown", + "id": "d37aec35", + "metadata": {}, + "source": [ + "## Elbe dephth and chlorophyll " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "02479b31", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# create figure and axis objects with subplots()\n", + "fig,ax1 = plt.subplots()\n", + "ax2 = ax1.twinx()\n", + "\n", + "ax1.plot(stromkilometer_med, messwert_med, color=\"red\") # this line won't show up when there is a limit on the x axis\n", + "ax2.plot(Stromkilometer_d, Depth, color=\"blue\")\n", + "\n", + "# x-axis\n", + "ax1.set_xlabel(\"Stromkilometer\", fontsize = 14)\n", + "ax2.set_xlim(586,830) # red line is apparently outside these limits even though the data is in that range\n", + "plt.xticks(np.arange(550, 850, step=50))\n", + "plt.gca().invert_xaxis()\n", + "\n", + "# y-axes\n", + "ax1.set_ylabel(\"Messwert\", color=\"red\", fontsize=14)\n", + "ax2.set_ylabel(\"Depth\",color=\"blue\",fontsize=14)\n", + "ax2.invert_yaxis()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "242cf914", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + }, + "vscode": { + "interpreter": { + "hash": "ae321efca05d5287feb4e18c73c84aa717d56d176335c74bbc73c515f0d20084" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ipynb/Elbe_Turbidity.ipynb b/ipynb/Elbe_Turbidity.ipynb new file mode 100644 index 0000000..15cce4d --- /dev/null +++ b/ipynb/Elbe_Turbidity.ipynb @@ -0,0 +1,659 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "id": "f6efd717", + "metadata": {}, + "outputs": [], + "source": [ + "import glob\n", + "import os\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "801e0dd2", + "metadata": {}, + "source": [ + "## pre-processing elbe Chlorophyll data" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9188cca4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Gew�sser</th>\n", + " <th>Wasserk�rper</th>\n", + " <th>Messstelle</th>\n", + " <th>Stromkilometer</th>\n", + " <th>Parameter</th>\n", + " <th>Messwert</th>\n", + " <th>Einheit</th>\n", + " <th>Messwerttyp</th>\n", + " <th>Medium</th>\n", + " <th>Messwertart</th>\n", + " <th>Messvorgang</th>\n", + " <th>Datum</th>\n", + " <th>Bezugsjahr</th>\n", + " <th>Zeit</th>\n", + " <th>Datum bis</th>\n", + " <th>Zeit bis</th>\n", + " <th>Status</th>\n", + " <th>Analysemethode</th>\n", + " <th>Bemerkung (Datenausgabe)</th>\n", + " <th>zus�tzliche Informationen</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Elbe</td>\n", + " <td>Elbe-West</td>\n", + " <td>Blankenese (Strom-km 634,3)</td>\n", + " <td>634.3</td>\n", + " <td>Trübung (FNU)</td>\n", + " <td>30,6</td>\n", + " <td>FNU</td>\n", + " <td>quantitativ nachgewiesen</td>\n", + " <td>Wasser - Gesamtprobe</td>\n", + " <td>Tagesmittelwert</td>\n", + " <td>kontinuierliche Messungen</td>\n", + " <td>02.01.1997</td>\n", + " <td>1997</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>freigegeben</td>\n", + " <td>-</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Elbe</td>\n", + " <td>Elbe-West</td>\n", + " <td>Blankenese (Strom-km 634,3)</td>\n", + " <td>634.3</td>\n", + " <td>Trübung (FNU)</td>\n", + " <td>39,7</td>\n", + " <td>FNU</td>\n", + " <td>quantitativ nachgewiesen</td>\n", + " <td>Wasser - Gesamtprobe</td>\n", + " <td>Tagesmittelwert</td>\n", + " <td>kontinuierliche Messungen</td>\n", + " <td>03.01.1997</td>\n", + " <td>1997</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>freigegeben</td>\n", + " <td>-</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Elbe</td>\n", + " <td>Elbe-West</td>\n", + " <td>Blankenese (Strom-km 634,3)</td>\n", + " <td>634.3</td>\n", + " <td>Trübung (FNU)</td>\n", + " <td>37,2</td>\n", + " <td>FNU</td>\n", + " <td>quantitativ nachgewiesen</td>\n", + " <td>Wasser - Gesamtprobe</td>\n", + " <td>Tagesmittelwert</td>\n", + " <td>kontinuierliche Messungen</td>\n", + " <td>06.01.1997</td>\n", + " <td>1997</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>freigegeben</td>\n", + " <td>-</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Elbe</td>\n", + " <td>Elbe-West</td>\n", + " <td>Blankenese (Strom-km 634,3)</td>\n", + " <td>634.3</td>\n", + " <td>Trübung (FNU)</td>\n", + " <td>34,0</td>\n", + " <td>FNU</td>\n", + " <td>quantitativ nachgewiesen</td>\n", + " <td>Wasser - Gesamtprobe</td>\n", + " <td>Tagesmittelwert</td>\n", + " <td>kontinuierliche Messungen</td>\n", + " <td>07.01.1997</td>\n", + " <td>1997</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>freigegeben</td>\n", + " <td>-</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Elbe</td>\n", + " <td>Elbe-West</td>\n", + " <td>Blankenese (Strom-km 634,3)</td>\n", + " <td>634.3</td>\n", + " <td>Trübung (FNU)</td>\n", + " <td>35,1</td>\n", + " <td>FNU</td>\n", + " <td>quantitativ nachgewiesen</td>\n", + " <td>Wasser - Gesamtprobe</td>\n", + " <td>Tagesmittelwert</td>\n", + " <td>kontinuierliche Messungen</td>\n", + " <td>08.01.1997</td>\n", + " <td>1997</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>freigegeben</td>\n", + " <td>-</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Gew�sser Wasserk�rper Messstelle Stromkilometer \\\n", + "0 Elbe Elbe-West Blankenese (Strom-km 634,3) 634.3 \n", + "1 Elbe Elbe-West Blankenese (Strom-km 634,3) 634.3 \n", + "2 Elbe Elbe-West Blankenese (Strom-km 634,3) 634.3 \n", + "3 Elbe Elbe-West Blankenese (Strom-km 634,3) 634.3 \n", + "4 Elbe Elbe-West Blankenese (Strom-km 634,3) 634.3 \n", + "\n", + " Parameter Messwert Einheit Messwerttyp \\\n", + "0 Trübung (FNU) 30,6 FNU quantitativ nachgewiesen \n", + "1 Trübung (FNU) 39,7 FNU quantitativ nachgewiesen \n", + "2 Trübung (FNU) 37,2 FNU quantitativ nachgewiesen \n", + "3 Trübung (FNU) 34,0 FNU quantitativ nachgewiesen \n", + "4 Trübung (FNU) 35,1 FNU quantitativ nachgewiesen \n", + "\n", + " Medium Messwertart Messvorgang \\\n", + "0 Wasser - Gesamtprobe Tagesmittelwert kontinuierliche Messungen \n", + "1 Wasser - Gesamtprobe Tagesmittelwert kontinuierliche Messungen \n", + "2 Wasser - Gesamtprobe Tagesmittelwert kontinuierliche Messungen \n", + "3 Wasser - Gesamtprobe Tagesmittelwert kontinuierliche Messungen \n", + "4 Wasser - Gesamtprobe Tagesmittelwert kontinuierliche Messungen \n", + "\n", + " Datum Bezugsjahr Zeit Datum bis Zeit bis Status \\\n", + "0 02.01.1997 1997 NaN NaN NaN freigegeben \n", + "1 03.01.1997 1997 NaN NaN NaN freigegeben \n", + "2 06.01.1997 1997 NaN NaN NaN freigegeben \n", + "3 07.01.1997 1997 NaN NaN NaN freigegeben \n", + "4 08.01.1997 1997 NaN NaN NaN freigegeben \n", + "\n", + " Analysemethode Bemerkung (Datenausgabe) zus�tzliche Informationen \n", + "0 - NaN NaN \n", + "1 - NaN NaN \n", + "2 - NaN NaN \n", + "3 - NaN NaN \n", + "4 - NaN NaN " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get xls files list from a folder\n", + "#os.getcwd()\n", + "os.chdir(\"C:\\\\Users\\\\Hannah Russell\\\\north_sea_estuaries_visualisations\")\n", + "\n", + "cwd = os.path.abspath(os.curdir)\n", + "elbe_turbidity_df_1 = glob.glob(os.path.join(cwd, 'data', 'input', 'elbe', 'turbidity','df_1', '*.xls'))\n", + "elbe_turbidity_df_1 = [pd.read_excel(file) for file in elbe_turbidity_df_1]\n", + "#df_list = (pd.read_excel(file) for file in xls_files)\n", + "elbe_turbidity_df_1 = pd.concat(elbe_turbidity_df_1, ignore_index=True)\n", + "\n", + "elbe_turbidity_df_1.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "094afe71", + "metadata": {}, + "outputs": [], + "source": [ + "elbe_turbidity_df_1['Messwert'] = elbe_turbidity_df_1['Messwert'].str.replace(\",\", \".\")\n", + "elbe_turbidity_df_1.drop(elbe_turbidity_df_1[elbe_turbidity_df_1.Stromkilometer.str.contains('[Nebenge]', na=False)].index, inplace=True)\n", + "Stromkilometer = elbe_turbidity_df_1['Stromkilometer'].astype(float)\n", + "Messwert = elbe_turbidity_df_1['Messwert'].astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bb4beeaa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Turbidity FNU')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#plot all values on one plot\n", + "plt.scatter(Stromkilometer, Messwert)\n", + "plt.gca().invert_xaxis()\n", + "plt.xlim(586,700) #Geestacht is at ~586 km\n", + "plt.title('Elbe-- All Years')\n", + "plt.xlabel('Kilometer')\n", + "plt.ylabel('Turbidity FNU')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b638e47d", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Stromkilometer</th>\n", + " <th>Messwert</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>3.9</td>\n", + " <td>10.5</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>26.5</td>\n", + " <td>3.8</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>89.6</td>\n", + " <td>11.7</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>172.6</td>\n", + " <td>14.6</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>216.6</td>\n", + " <td>13.4</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>318.1</td>\n", + " <td>20.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>470.0</td>\n", + " <td>10.9</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>474.5</td>\n", + " <td>19.5</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>609.8</td>\n", + " <td>18.2</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>628.9</td>\n", + " <td>30.6</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>634.3</td>\n", + " <td>25.6</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>660.6</td>\n", + " <td>73.8</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Stromkilometer Messwert\n", + "0 3.9 10.5\n", + "1 26.5 3.8\n", + "2 89.6 11.7\n", + "3 172.6 14.6\n", + "4 216.6 13.4\n", + "5 318.1 20.0\n", + "6 470.0 10.9\n", + "7 474.5 19.5\n", + "8 609.8 18.2\n", + "9 628.9 30.6\n", + "10 634.3 25.6\n", + "11 660.6 73.8" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "turb_avg = turbidity[['Stromkilometer', 'Messwert']]\n", + "turb_avg.dropna()\n", + "turb_avg = turb_avg.groupby('Stromkilometer', as_index = False).median() #for some reason this is invalid even though median() works... not sure yet what's wrong here\n", + "turb_avg" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c2739d4d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'turbidity')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "km_avg = turb_avg['Stromkilometer']\n", + "Messwert_avg = turb_avg['Messwert']\n", + "plt.plot(km_avg, Messwert_avg)\n", + "plt.gca().invert_xaxis()\n", + "plt.title('Elbe-- Median at km')\n", + "plt.xlabel('Kilometer')\n", + "plt.ylabel('turbidity')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f84bdb72", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Stromkilometer</th>\n", + " <th>Depth</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>537.9</td>\n", + " <td>-2.574074</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>546.2</td>\n", + " <td>-1.666667</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>561.8</td>\n", + " <td>0.131687</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>572.3</td>\n", + " <td>0.370370</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>579.6</td>\n", + " <td>1.267490</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>131</th>\n", + " <td>821.8</td>\n", + " <td>25.481481</td>\n", + " </tr>\n", + " <tr>\n", + " <th>132</th>\n", + " <td>823.2</td>\n", + " <td>26.518519</td>\n", + " </tr>\n", + " <tr>\n", + " <th>133</th>\n", + " <td>824.9</td>\n", + " <td>24.888889</td>\n", + " </tr>\n", + " <tr>\n", + " <th>134</th>\n", + " <td>826.0</td>\n", + " <td>24.444444</td>\n", + " </tr>\n", + " <tr>\n", + " <th>135</th>\n", + " <td>828.8</td>\n", + " <td>25.481481</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>136 rows × 2 columns</p>\n", + "</div>" + ], + "text/plain": [ + " Stromkilometer Depth\n", + "0 537.9 -2.574074\n", + "1 546.2 -1.666667\n", + "2 561.8 0.131687\n", + "3 572.3 0.370370\n", + "4 579.6 1.267490\n", + ".. ... ...\n", + "131 821.8 25.481481\n", + "132 823.2 26.518519\n", + "133 824.9 24.888889\n", + "134 826.0 24.444444\n", + "135 828.8 25.481481\n", + "\n", + "[136 rows x 2 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "elbe_depth = pd.read_csv('Elbe Depth.csv')\n", + "elbe_depth['Stromkilometer'] = elbe_depth['Stromkilometer'].astype(float).round(1)\n", + "Stromkilometer2 = elbe_depth['Stromkilometer']\n", + "Depth = elbe_depth['Depth']\n", + "elbe_depth" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2da93d63", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# create figure and axis objects with subplots()\n", + "fig,ax1 = plt.subplots()\n", + "ax2 = ax1.twinx()\n", + "\n", + "# make a plot\n", + "ax1.plot(km_avg, Messwert_avg, color=\"red\") # this line won't show up when there is a limit on the x axis\n", + "ax2.plot(Stromkilometer2, Depth, color=\"blue\")\n", + "\n", + "# x-axis\n", + "ax1.set_xlabel(\"Stromkilometer\", fontsize = 14)\n", + "ax2.set_xlim(586,830) \n", + "plt.xticks(np.arange(550, 850, step=50))\n", + "plt.gca().invert_xaxis()\n", + "\n", + "# y-axis labels\n", + "ax1.set_ylabel(\"Messwert\", color=\"red\", fontsize=14)\n", + "ax2.set_ylabel(\"Depth\",color=\"blue\",fontsize=14)\n", + "ax2.invert_yaxis()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81a50a6c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} -- GitLab