{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "7ydeifHy_KZ0" }, "source": [ "# **POLARIN's ERDDAP querying: tabledap**" ] }, { "cell_type": "markdown", "metadata": { "id": "9lCrRHAOPd9Y" }, "source": [ "*How can we query oceanographic data in tabular form using tabledap?*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook can be run with Binder by following the link:\n", "[polarin_erddap_querying_tabledap](https://mybinder.org/v2/gh/POLAR-RESEARCH-INFRASTRUCTURE-NETWORK/jupyter-notebooks/HEAD?urlpath=%2Fdoc%2Ftree%2Fnotebooks_binder%2Fpolarin_erddap_querying_tabledap.ipynb)" ] }, { "cell_type": "markdown", "metadata": { "id": "W2Lk2xzd_KZ2" }, "source": [ "This notebook will illustrate how to build queries and make requests to [https://erddap.s4polarin.eu/erddap/index.html](https://erddap.s4polarin.eu/erddap/index.html) using Python." ] }, { "cell_type": "markdown", "metadata": { "id": "OQSLQ574_KZ2", "vscode": { "languageId": "plaintext" } }, "source": [ "## **Setup**\n", "\n", "To begin we need to import the necessary libraries." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6FfRJZc2_KZ2" }, "outputs": [], "source": [ "!pip install requests pandas\n", "# these packages should be installed with the command above if running the code locally\n", "\n", "import requests\n", "import pandas as pd\n", "import io\n", "import warnings\n", "\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "metadata": { "id": "HI6J1aGWwOFq" }, "source": [ "## **Get a list of available tabledap datasets**" ] }, { "cell_type": "markdown", "metadata": { "id": "x0XY0JDFwTRv" }, "source": [ "To check what datasets are available in the ERDDAP and get their URLs the first step is to make a request to [https://erddap.s4polarin.eu/erddap/tabledap/allDatasets.html](https://erddap.s4polarin.eu/erddap/tabledap/allDatasets.html) using the URL that will allow us to get the datasets' ids and their URLs based on the data structure. After receiving the data it will be loaded into a pandas DataFrame." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "id": "Wdmc1ee3xNGp", "outputId": "5d658c5d-9b05-4465-d60a-fc0b937ad021" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "summary": "{\n \"name\": \"cleaned_df\",\n \"rows\": 319,\n \"fields\": [\n {\n \"column\": \"datasetID\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 319,\n \"samples\": [\n \"ARICE_de_pangaea_dataset326602\",\n \"ARICE_de_pangaea_dataset869036\",\n \"ARICE_ArcticNet1101_CTD_BaffinBay_NorthwestPassage_v20140922\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"url\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 319,\n \"samples\": [\n \"https://erddap.s4polarin.eu/erddap/tabledap/ARICE_de_pangaea_dataset326602\",\n \"https://erddap.s4polarin.eu/erddap/tabledap/ARICE_de_pangaea_dataset869036\",\n \"https://erddap.s4polarin.eu/erddap/tabledap/ARICE_ArcticNet1101_CTD_BaffinBay_NorthwestPassage_v20140922\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", "type": "dataframe", "variable_name": "cleaned_df" }, "text/html": [ "\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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
datasetIDurl
0allDatasetshttps://erddap.s4polarin.eu/erddap/tabledap/allDatasets
1cnr_iadc_119c_736f_bdbdhttps://erddap.s4polarin.eu/erddap/tabledap/cnr_iadc_119c_736f_bdbd
2cnr_iadc_a128_9f50_66cahttps://erddap.s4polarin.eu/erddap/tabledap/cnr_iadc_a128_9f50_66ca
3cnr_iadc_7668_30ee_5237https://erddap.s4polarin.eu/erddap/tabledap/cnr_iadc_7668_30ee_5237
4ARICE_CCIN80_Arcticnet_0501a_BaffinBayhttps://erddap.s4polarin.eu/erddap/tabledap/ARICE_CCIN80_Arcticnet_0501a_BaffinBay
.........
314cnr_iadc_aee4_8c82_1df6https://erddap.s4polarin.eu/erddap/tabledap/cnr_iadc_aee4_8c82_1df6
315cnr_antarcticdatacenter_a8e9_20db_433ehttps://erddap.s4polarin.eu/erddap/tabledap/cnr_antarcticdatacenter_a8e9_20db_433e
316cnr_iadc_8dd9_d4f8_1cf8https://erddap.s4polarin.eu/erddap/tabledap/cnr_iadc_8dd9_d4f8_1cf8
317ARICE_Amundsen_TSG_V1https://erddap.s4polarin.eu/erddap/tabledap/ARICE_Amundsen_TSG_V1
318cnr_iadc_25f1_464d_dfcchttps://erddap.s4polarin.eu/erddap/tabledap/cnr_iadc_25f1_464d_dfcc
\n", "

319 rows × 2 columns

\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" ], "text/plain": [ " datasetID \\\n", "0 allDatasets \n", "1 cnr_iadc_119c_736f_bdbd \n", "2 cnr_iadc_a128_9f50_66ca \n", "3 cnr_iadc_7668_30ee_5237 \n", "4 ARICE_CCIN80_Arcticnet_0501a_BaffinBay \n", ".. ... \n", "314 cnr_iadc_aee4_8c82_1df6 \n", "315 cnr_antarcticdatacenter_a8e9_20db_433e \n", "316 cnr_iadc_8dd9_d4f8_1cf8 \n", "317 ARICE_Amundsen_TSG_V1 \n", "318 cnr_iadc_25f1_464d_dfcc \n", "\n", " url \n", "0 https://erddap.s4polarin.eu/erddap/tabledap/allDatasets \n", "1 https://erddap.s4polarin.eu/erddap/tabledap/cnr_iadc_119c_736f_bdbd \n", "2 https://erddap.s4polarin.eu/erddap/tabledap/cnr_iadc_a128_9f50_66ca \n", "3 https://erddap.s4polarin.eu/erddap/tabledap/cnr_iadc_7668_30ee_5237 \n", "4 https://erddap.s4polarin.eu/erddap/tabledap/ARICE_CCIN80_Arcticnet_0501a_BaffinBay \n", ".. ... \n", "314 https://erddap.s4polarin.eu/erddap/tabledap/cnr_iadc_aee4_8c82_1df6 \n", "315 https://erddap.s4polarin.eu/erddap/tabledap/cnr_antarcticdatacenter_a8e9_20db_433e \n", "316 https://erddap.s4polarin.eu/erddap/tabledap/cnr_iadc_8dd9_d4f8_1cf8 \n", "317 https://erddap.s4polarin.eu/erddap/tabledap/ARICE_Amundsen_TSG_V1 \n", "318 https://erddap.s4polarin.eu/erddap/tabledap/cnr_iadc_25f1_464d_dfcc \n", "\n", "[319 rows x 2 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datasets_url = 'https://erddap.s4polarin.eu/erddap/tabledap/allDatasets.csv?datasetID%2Ctabledap'\n", "\n", "# request and load into DataFrame\n", "datasets_resp = requests.get(datasets_url)\n", "datasets_df = pd.read_csv(io.StringIO(datasets_resp.text), sep=',')\n", "\n", "# drop rows where tabledap is NaN\n", "datasets_df = datasets_df.dropna(subset=['tabledap'])\n", "\n", "# add url column\n", "datasets_df['url'] = datasets_df['tabledap']\n", "cleaned_df = datasets_df.drop(columns=['tabledap'])\n", "\n", "pd.set_option('display.max_colwidth', None)\n", "cleaned_df = cleaned_df.reset_index(drop=True)\n", "cleaned_df" ] }, { "cell_type": "markdown", "metadata": { "id": "NGuJYRDI5X-Q" }, "source": [ "Using these URLs we will than be able to get their data. \n", "In this example we will use the cnr_iadc_f5ff_4134_70dd dataset, with the URL:\n", "[https://erddap.s4polarin.eu/erddap/tabledap/cnr_iadc_f5ff_4134_70dd](https://erddap.s4polarin.eu/erddap/tabledap/cnr_iadc_f5ff_4134_70dd)" ] }, { "cell_type": "markdown", "metadata": { "id": "2GI1pHT8_KZ3" }, "source": [ "## **Get a list of variables for the dataset**" ] }, { "cell_type": "markdown", "metadata": { "id": "iHEskNpp_KZ3" }, "source": [ "Now we can make a request to the dataset's metadata, which will give us a list of all the available variables and their relative data type.\n", "These variables can be than used in the following requests." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 356 }, "id": "Y0gjus3u_KZ3", "outputId": "71cfe9f6-aaab-432e-8172-05fb59fbc955" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipython-input-1584371082.py:10: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " variables_df.drop(columns=['Row Type', 'Attribute Name', 'Value'], inplace=True)\n" ] }, { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "summary": "{\n \"name\": \"variables_df\",\n \"rows\": 7,\n \"fields\": [\n {\n \"column\": \"Variable Name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"time\",\n \"latitude\",\n \"TEMP_QC\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Data Type\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"float\",\n \"String\",\n \"double\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", "type": "dataframe", "variable_name": "variables_df" }, "text/html": [ "\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", "
Variable NameData Type
0timedouble
1latitudefloat
2longitudefloat
3depthfloat
4TEMPfloat
5TEMP_QCbyte
6station_idString
\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" ], "text/plain": [ " Variable Name Data Type\n", "0 time double\n", "1 latitude float\n", "2 longitude float\n", "3 depth float\n", "4 TEMP float\n", "5 TEMP_QC byte\n", "6 station_id String" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_url = 'https://erddap.s4polarin.eu/erddap/tabledap/cnr_iadc_f5ff_4134_70dd'\n", "\n", "# building the full url for the metadata and making the request\n", "metadata_url = dataset_url.replace('tabledap', 'info').replace('griddap', 'info') + '/index.csv'\n", "\n", "metadata_resp = requests.get(metadata_url)\n", "metadata_df = pd.read_csv(io.StringIO(metadata_resp.text), sep=',')\n", "variables_df = metadata_df.loc[metadata_df['Row Type'].isin(['variable', 'dimension'])]\n", "variables_df.reset_index(drop=True, inplace=True)\n", "variables_df.drop(columns=['Row Type', 'Attribute Name', 'Value'], inplace=True)\n", "variables_df" ] }, { "cell_type": "markdown", "metadata": { "id": "WsM4KzezErso" }, "source": [ "## **Get a list of platform codes**" ] }, { "cell_type": "markdown", "metadata": { "id": "G0gdlsevEgQB" }, "source": [ "We will then perform another request to check the station id for the selected dataset, which will be useful in the following queries to the ERDDAP." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 89 }, "id": "_xPd_j6bET-p", "outputId": "33030ccc-958f-4302-aa4e-7f21f98f18b3" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "summary": "{\n \"name\": \"platforms_df\",\n \"rows\": 1,\n \"fields\": [\n {\n \"column\": \"station_id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"vws21g\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", "type": "dataframe", "variable_name": "platforms_df" }, "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
station_id
0vws21g
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", " \n", " \n", "
\n", "\n", "
\n", "
\n" ], "text/plain": [ " station_id\n", "0 vws21g" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "platforms_query = '.csv?station_id&distinct()'\n", "\n", "# The data format specified is 'csv' (in which the first row contains the column names and the second the units of measurment, which will be removed from the dataframe in these examples).\n", "# Other possibilities are 'csv0' which will return only the data rows and 'csvp', which will return a csv with the column names (and their unit of measurment) as first row and data starting from the second.\n", "# the additional parameter &distinct() will ensure we will get only unique rows\n", "\n", "platform_resp = requests.get(dataset_url + platforms_query)\n", "platforms_df = pd.read_csv(io.StringIO(platform_resp.text), sep=',')\n", "platforms_df" ] }, { "cell_type": "markdown", "metadata": { "id": "g4Qqknls_KZ3" }, "source": [ "## **Data gathering**" ] }, { "cell_type": "markdown", "metadata": { "id": "IzKOfHrT_KZ3" }, "source": [ "Following are three examples of data queries:\n", "\n", "### With station_id and time range\n", "\n", " When building the URL to get the data a platform code can be inserted in the query to get the data relative to the platform.\n", " In the following example the platform code 'vws21g' has been chosen and the variables are:\n", " - station_id\n", " - time\n", " - latitude\n", " - longitude\n", " - TEMP\n", "\n", " The query will look like:\n", "\n", " ```?PLATFORMCODE%2Ctime%2Clatitude%2Clongitude%2CTEMP&station_id=%22vws21g%22&time%3E=2023-04-29T09%3A45%3A00Z&time%3C=2024-04-29T09%3A45%3A00Z```\n", "\n", " It can be divided into two main parts:\n", "\n", "1. ```?station_id%2Ctime%2Clatitude%2Clongitude%2CTEMP```\n", "\n", " Where ```?``` indicates the start of query parametes and the rest is a list of variables we want as columns in the csv, separated by ```%2C```, an encoded comma(,).\n", "\n", "2. ```&dtation_id=%22vws21g%22&time%3E=2022-04-29T09%3A45%3A00Z&time%3C=2024-04-29T09%3A45%3A00Z```\n", "\n", " After the list of variables we can add filters, separated by ```&```.\n", "\n", " The platform code chosen is vws21g and it has to be inserted between encoded double quotes(\"), represented by ```%22```.\n", "\n", " The syntax for the timerange is:\n", "\n", " ```time%3E=2022-04-29T09%3A45%3A00Z&time%3C=2024-04-29T09%3A45%3A00Z```\n", "\n", " Here the other encoded characters are ```%3E``` (>), ```%3C``` (<) and ```%3A``` (:).\n", " \n", " The time has to be passed as an ISO string, with the format YYYY-MM-DDThh:mm:ssZ." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "id": "Y_k4utuW_KZ4", "outputId": "f362e7fe-917d-45ed-f407-5edba06f7c57" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "summary": "{\n \"name\": \"data_df\",\n \"rows\": 50960,\n \"fields\": [\n {\n \"column\": \"station_id\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"vws21g\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"time\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 50960,\n \"samples\": [\n \"2023-08-21T18:50:00Z\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"latitude\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"79.12797\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"longitude\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"11.7237\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"TEMP\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 21304,\n \"samples\": [\n \"1.2145\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", "type": "dataframe", "variable_name": "data_df" }, "text/html": [ "\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", " \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", "
station_idtimelatitudelongitudeTEMP
0vws21g2023-04-29T00:00:00Z79.1271111.724883NaN
1vws21g2023-04-29T00:10:00Z79.1271111.724883NaN
2vws21g2023-04-29T00:20:00Z79.1271111.724883NaN
3vws21g2023-04-29T00:30:00Z79.1271111.724883NaN
4vws21g2023-04-29T00:40:00Z79.1271111.724883NaN
..................
50955vws21g2024-04-28T23:15:07Z79.1279711.72370.206
50956vws21g2024-04-28T23:25:07Z79.1279711.72370.2033
50957vws21g2024-04-28T23:35:07Z79.1279711.72370.2071
50958vws21g2024-04-28T23:45:07Z79.1279711.72370.2061
50959vws21g2024-04-28T23:55:07Z79.1279711.72370.202
\n", "

50960 rows × 5 columns

\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" ], "text/plain": [ " station_id time latitude longitude TEMP\n", "0 vws21g 2023-04-29T00:00:00Z 79.12711 11.724883 NaN\n", "1 vws21g 2023-04-29T00:10:00Z 79.12711 11.724883 NaN\n", "2 vws21g 2023-04-29T00:20:00Z 79.12711 11.724883 NaN\n", "3 vws21g 2023-04-29T00:30:00Z 79.12711 11.724883 NaN\n", "4 vws21g 2023-04-29T00:40:00Z 79.12711 11.724883 NaN\n", "... ... ... ... ... ...\n", "50955 vws21g 2024-04-28T23:15:07Z 79.12797 11.7237 0.206\n", "50956 vws21g 2024-04-28T23:25:07Z 79.12797 11.7237 0.2033\n", "50957 vws21g 2024-04-28T23:35:07Z 79.12797 11.7237 0.2071\n", "50958 vws21g 2024-04-28T23:45:07Z 79.12797 11.7237 0.2061\n", "50959 vws21g 2024-04-28T23:55:07Z 79.12797 11.7237 0.202\n", "\n", "[50960 rows x 5 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "platform_code = 'vws21g'\n", "\n", "variables = '.csv?station_id%2Ctime%2Clatitude%2Clongitude%2CTEMP'\n", "filters = f'&station_id=%22{platform_code}%22&time%3E=2023-04-29T00%3A00%3A00Z&time%3C=2024-04-29T00%3A00%3A00Z'\n", "\n", "data_resp = requests.get(dataset_url + variables + filters)\n", "data_df = pd.read_csv(io.StringIO(data_resp.text), sep=',')\n", "\n", "data_df=data_df.sort_values(by=[\"time\"])\n", "data_df.reset_index(drop=True, inplace=True)\n", "data_df = data_df.dropna(subset=['station_id'])\n", "data_df" ] }, { "cell_type": "markdown", "metadata": { "id": "qaWqgS56bnS3" }, "source": [ "### With coordinates range" ] }, { "cell_type": "markdown", "metadata": { "id": "R3Ua9HhUbtTE" }, "source": [ "Another possibility when querying the data is to specify a range of coordinates.\n", "This can be done by inserting in the query filters the following:\n", "\n", "```latitude%3E=75&latitude%3C=80&longitude%3E=-50&longitude%3C=50```\n", "\n", "Effectively selecting platforms inside a square delimited by:\n", "\n", "- latitude equal or greater than 75 and equal or less than 80\n", "\n", "and\n", "\n", "- longitude equal or greater than -50 and equal or less than 50." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 125 }, "id": "03XyfGE8fQ4e", "outputId": "e247d6b8-6c6d-4188-f2a3-40a9c61553a4" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "summary": "{\n \"name\": \"coords_data_df\",\n \"rows\": 2,\n \"fields\": [\n {\n \"column\": \"station_id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"vws21g\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"latitude\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"79.12797\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"longitude\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"11.7237\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", "type": "dataframe", "variable_name": "coords_data_df" }, "text/html": [ "\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", "
station_idlatitudelongitude
1vws21g79.1271111.724883
2vws21g79.1279711.7237
\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" ], "text/plain": [ " station_id latitude longitude\n", "1 vws21g 79.12711 11.724883\n", "2 vws21g 79.12797 11.7237" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coords_variables = '.csv?station_id%2Clatitude%2Clongitude'\n", "coords_filter = '&latitude%3E=75&latitude%3C=80&longitude%3E=-50&longitude%3C=50&distinct()'\n", "\n", "coords_data_resp = requests.get(dataset_url + coords_variables + coords_filter)\n", "coords_data_df = pd.read_csv(io.StringIO(coords_data_resp.text), sep=',')\n", "\n", "coords_data_df = coords_data_df.dropna(subset=['station_id'])\n", "\n", "coords_data_df" ] }, { "cell_type": "markdown", "metadata": { "id": "h8u9WXnHLpS5" }, "source": [ "### **Additional resources**" ] }, { "cell_type": "markdown", "metadata": { "id": "KiKTNro9LHFz" }, "source": [ "For additional information about ERDDAP please visit: \n", " \n", " [https://erddap.s4polarin.eu/erddap/information.html](https://erddap.s4polarin.eu//erddap/information.html)\n", "\n", "The webpages for the Python's libraries that have been used in this notebook are:\n", "- REQUESTS: https://requests.readthedocs.io/en/latest/\n", "- PANDAS: https://pandas.pydata.org/\n", "- IO: https://docs.python.org/3/library/io.html" ] }, { "cell_type": "markdown", "metadata": { "id": "X1gTBFd3Pd9q" }, "source": [ "This work has received funding from the European Union Horizon Europe project Polar Research Infrastructure Network (POLARIN) under grant agreement No. 101130949 (https://doi.org/10.3030/101130949).\n", "This notebook makes use of data available in the European Marine Observation and Data Network (EMODnet, https://emodnet.ec.europa.eu)." ] }, { "cell_type": "markdown", "metadata": { "id": "U1igB8nMPd9r" }, "source": [ "| | | |\n", "|:-:|:-:|:-:|\n", "| | | |\n" ] } ], "metadata": { "colab": { "provenance": [] }, "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": "3.9.0" } }, "nbformat": 4, "nbformat_minor": 0 }