diff --git a/EDA/EDA_Notebook_Spring_2023.ipynb b/EDA/EDA_Notebook_Spring_2023.ipynb
index d3a80c84..fecabd74 100644
--- a/EDA/EDA_Notebook_Spring_2023.ipynb
+++ b/EDA/EDA_Notebook_Spring_2023.ipynb
@@ -11,20 +11,39 @@
},
{
"cell_type": "markdown",
- "source": [
- "# EDA for the primary and secondary task"
- ],
"metadata": {
"id": "nQEchFbcFZ65"
- }
+ },
+ "source": [
+ "# EDA for the primary and secondary task"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {
"id": "5ik4r_RR8GiN"
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting python-dwca-reader\n",
+ " Downloading python_dwca_reader-0.15.1-py3-none-any.whl (38 kB)\n",
+ "Installing collected packages: python-dwca-reader\n",
+ "Successfully installed python-dwca-reader-0.15.1\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING: You are using pip version 20.2.3; however, version 23.3.1 is available.\n",
+ "You should consider upgrading via the 'c:\\users\\smrit\\appdata\\local\\programs\\python\\python39\\python.exe -m pip install --upgrade pip' command.\n"
+ ]
+ }
+ ],
"source": [
"import requests\n",
"import shutil\n",
@@ -56,7 +75,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {
"id": "Z9QAr2if8GiN"
},
@@ -121,26 +140,28 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {
"id": "zw_XyCIk8GiN",
"outputId": "8da0260c-a827-4ede-8896-5f3494751704"
},
"outputs": [
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/share/pkg.7/python3/3.8.10/install/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3169: DtypeWarning: Columns (2,14,16,17,19,21,24,25,26,32,33,34,36,37,38,39,40,41,43,45,46) have mixed types.Specify dtype option on import or set low_memory=False.\n",
- " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "colnames: ['gbifID', 'datasetKey', 'occurrenceID', 'kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species', 'infraspecificEpithet', 'taxonRank', 'scientificName', 'verbatimScientificName', 'verbatimScientificNameAuthorship', 'countryCode', 'locality', 'stateProvince', 'occurrenceStatus', 'individualCount', 'publishingOrgKey', 'decimalLatitude', 'decimalLongitude', 'coordinateUncertaintyInMeters', 'coordinatePrecision', 'elevation', 'elevationAccuracy', 'depth', 'depthAccuracy', 'eventDate', 'day', 'month', 'year', 'taxonKey', 'speciesKey', 'basisOfRecord', 'institutionCode', 'collectionCode', 'catalogNumber', 'recordNumber', 'identifiedBy', 'dateIdentified', 'license', 'rightsHolder', 'recordedBy', 'typeStatus', 'establishmentMeans', 'lastInterpreted', 'mediaType', 'issue']\n",
- "df.shape: (7982741, 50)\n"
+ "ename": "FileNotFoundError",
+ "evalue": "[Errno 2] No such file or directory: '//projectnb/sparkgrp/ml-herbarium-grp/ml-herbarium-data/data.csv'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32mc:\\Users\\smrit\\Documents\\Boston\\Semester-3\\Spark\\Herbaria\\ml-herbarium\\EDA\\EDA_Notebook_Spring_2023.ipynb Cell 7\u001b[0m line \u001b[0;36m4\n\u001b[0;32m 2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpandas\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpd\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_csv(DATASET_CSV, sep\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m\\t\u001b[39;49;00m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m 5\u001b[0m DATASET_TYPE \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mcsv\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 6\u001b[0m colnames \u001b[39m=\u001b[39m []\n",
+ "File \u001b[1;32mc:\\Users\\smrit\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\util\\_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.._deprecate_kwarg..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 209\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 210\u001b[0m kwargs[new_arg_name] \u001b[39m=\u001b[39m new_arg_value\n\u001b[1;32m--> 211\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
+ "File \u001b[1;32mc:\\Users\\smrit\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\util\\_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 325\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m>\u001b[39m num_allow_args:\n\u001b[0;32m 326\u001b[0m warnings\u001b[39m.\u001b[39mwarn(\n\u001b[0;32m 327\u001b[0m msg\u001b[39m.\u001b[39mformat(arguments\u001b[39m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[0;32m 328\u001b[0m \u001b[39mFutureWarning\u001b[39;00m,\n\u001b[0;32m 329\u001b[0m stacklevel\u001b[39m=\u001b[39mfind_stack_level(),\n\u001b[0;32m 330\u001b[0m )\n\u001b[1;32m--> 331\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
+ "File \u001b[1;32mc:\\Users\\smrit\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m 935\u001b[0m kwds_defaults \u001b[39m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 936\u001b[0m dialect,\n\u001b[0;32m 937\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 946\u001b[0m defaults\u001b[39m=\u001b[39m{\u001b[39m\"\u001b[39m\u001b[39mdelimiter\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39m,\u001b[39m\u001b[39m\"\u001b[39m},\n\u001b[0;32m 947\u001b[0m )\n\u001b[0;32m 948\u001b[0m kwds\u001b[39m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 950\u001b[0m \u001b[39mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
+ "File \u001b[1;32mc:\\Users\\smrit\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 602\u001b[0m _validate_names(kwds\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mnames\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m))\n\u001b[0;32m 604\u001b[0m \u001b[39m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 605\u001b[0m parser \u001b[39m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)\n\u001b[0;32m 607\u001b[0m \u001b[39mif\u001b[39;00m chunksize \u001b[39mor\u001b[39;00m iterator:\n\u001b[0;32m 608\u001b[0m \u001b[39mreturn\u001b[39;00m parser\n",
+ "File \u001b[1;32mc:\\Users\\smrit\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 1439\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moptions[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m kwds[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m 1441\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles: IOHandles \u001b[39m|\u001b[39m \u001b[39mNone\u001b[39;00m \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m-> 1442\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_engine(f, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mengine)\n",
+ "File \u001b[1;32mc:\\Users\\smrit\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m 1733\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m mode:\n\u001b[0;32m 1734\u001b[0m mode \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m-> 1735\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39m=\u001b[39m get_handle(\n\u001b[0;32m 1736\u001b[0m f,\n\u001b[0;32m 1737\u001b[0m mode,\n\u001b[0;32m 1738\u001b[0m encoding\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[0;32m 1739\u001b[0m compression\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mcompression\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[0;32m 1740\u001b[0m memory_map\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mmemory_map\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mFalse\u001b[39;49;00m),\n\u001b[0;32m 1741\u001b[0m is_text\u001b[39m=\u001b[39;49mis_text,\n\u001b[0;32m 1742\u001b[0m errors\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding_errors\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mstrict\u001b[39;49m\u001b[39m\"\u001b[39;49m),\n\u001b[0;32m 1743\u001b[0m storage_options\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mstorage_options\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[0;32m 1744\u001b[0m )\n\u001b[0;32m 1745\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m 1746\u001b[0m f \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles\u001b[39m.\u001b[39mhandle\n",
+ "File \u001b[1;32mc:\\Users\\smrit\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\io\\common.py:856\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 851\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(handle, \u001b[39mstr\u001b[39m):\n\u001b[0;32m 852\u001b[0m \u001b[39m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m 853\u001b[0m \u001b[39m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m 854\u001b[0m \u001b[39mif\u001b[39;00m ioargs\u001b[39m.\u001b[39mencoding \u001b[39mand\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m ioargs\u001b[39m.\u001b[39mmode:\n\u001b[0;32m 855\u001b[0m \u001b[39m# Encoding\u001b[39;00m\n\u001b[1;32m--> 856\u001b[0m handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(\n\u001b[0;32m 857\u001b[0m handle,\n\u001b[0;32m 858\u001b[0m ioargs\u001b[39m.\u001b[39;49mmode,\n\u001b[0;32m 859\u001b[0m encoding\u001b[39m=\u001b[39;49mioargs\u001b[39m.\u001b[39;49mencoding,\n\u001b[0;32m 860\u001b[0m errors\u001b[39m=\u001b[39;49merrors,\n\u001b[0;32m 861\u001b[0m newline\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m 862\u001b[0m )\n\u001b[0;32m 863\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 864\u001b[0m \u001b[39m# Binary mode\u001b[39;00m\n\u001b[0;32m 865\u001b[0m handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(handle, ioargs\u001b[39m.\u001b[39mmode)\n",
+ "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '//projectnb/sparkgrp/ml-herbarium-grp/ml-herbarium-data/data.csv'"
]
}
],
@@ -466,7 +487,7 @@
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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",
"text/plain": [
"