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Seems to be an attempt of mine to learn a bit more on how matplotlib assistant libraries can work in datasci, from march of 2024. Wow, its been two years since then.
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import mataplotlib.pyplot as plt\n", | |
| "import mplleaflet\n", | |
| "import os" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "ename": "ValueError", | |
| "evalue": "String or int arguments are only possible when a DataFrame or an array is provided in the `data_frame` argument. No DataFrame was provided, but argument 'size' is of type str or int.", | |
| "output_type": "error", | |
| "traceback": [ | |
| "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", | |
| "Cell \u001b[1;32mIn[7], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mpx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m73.5921337\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m45.4803403\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m91.44\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mgreen\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopacity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m px\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMercator Projection\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 3\u001b[0m os\u001b[38;5;241m.\u001b[39mchdir(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexpanduser(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m~\u001b[39m\u001b[38;5;124m\"\u001b[39m), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDocuments\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRepositories\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLearnModeling\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n", | |
| "File \u001b[1;32mc:\\Users\\adnan\\Documents\\Repositories\\LearnModeling\\.venv\\Lib\\site-packages\\plotly\\express\\_chart_types.py:66\u001b[0m, in \u001b[0;36mscatter\u001b[1;34m(data_frame, x, y, color, symbol, size, hover_name, hover_data, custom_data, text, facet_row, facet_col, facet_col_wrap, facet_row_spacing, facet_col_spacing, error_x, error_x_minus, error_y, error_y_minus, animation_frame, animation_group, category_orders, labels, orientation, color_discrete_sequence, color_discrete_map, color_continuous_scale, range_color, color_continuous_midpoint, symbol_sequence, symbol_map, opacity, size_max, marginal_x, marginal_y, trendline, trendline_options, trendline_color_override, trendline_scope, log_x, log_y, range_x, range_y, render_mode, title, template, width, height)\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mscatter\u001b[39m(\n\u001b[0;32m 13\u001b[0m data_frame\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m 14\u001b[0m x\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 60\u001b[0m height\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m 61\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m go\u001b[38;5;241m.\u001b[39mFigure:\n\u001b[0;32m 62\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 63\u001b[0m \u001b[38;5;124;03m In a scatter plot, each row of `data_frame` is represented by a symbol\u001b[39;00m\n\u001b[0;32m 64\u001b[0m \u001b[38;5;124;03m mark in 2D space.\u001b[39;00m\n\u001b[0;32m 65\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m---> 66\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmake_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlocals\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconstructor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mScatter\u001b[49m\u001b[43m)\u001b[49m\n", | |
| "File \u001b[1;32mc:\\Users\\adnan\\Documents\\Repositories\\LearnModeling\\.venv\\Lib\\site-packages\\plotly\\express\\_core.py:2090\u001b[0m, in \u001b[0;36mmake_figure\u001b[1;34m(args, constructor, trace_patch, layout_patch)\u001b[0m\n\u001b[0;32m 2087\u001b[0m layout_patch \u001b[38;5;241m=\u001b[39m layout_patch \u001b[38;5;129;01mor\u001b[39;00m {}\n\u001b[0;32m 2088\u001b[0m apply_default_cascade(args)\n\u001b[1;32m-> 2090\u001b[0m args \u001b[38;5;241m=\u001b[39m \u001b[43mbuild_dataframe\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconstructor\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2091\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m constructor \u001b[38;5;129;01min\u001b[39;00m [go\u001b[38;5;241m.\u001b[39mTreemap, go\u001b[38;5;241m.\u001b[39mSunburst, go\u001b[38;5;241m.\u001b[39mIcicle] \u001b[38;5;129;01mand\u001b[39;00m args[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpath\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 2092\u001b[0m args \u001b[38;5;241m=\u001b[39m process_dataframe_hierarchy(args)\n", | |
| "File \u001b[1;32mc:\\Users\\adnan\\Documents\\Repositories\\LearnModeling\\.venv\\Lib\\site-packages\\plotly\\express\\_core.py:1492\u001b[0m, in \u001b[0;36mbuild_dataframe\u001b[1;34m(args, constructor)\u001b[0m\n\u001b[0;32m 1489\u001b[0m args[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolor\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1490\u001b[0m \u001b[38;5;66;03m# now that things have been prepped, we do the systematic rewriting of `args`\u001b[39;00m\n\u001b[1;32m-> 1492\u001b[0m df_output, wide_id_vars \u001b[38;5;241m=\u001b[39m \u001b[43mprocess_args_into_dataframe\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1493\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwide_mode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvar_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue_name\u001b[49m\n\u001b[0;32m 1494\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1496\u001b[0m \u001b[38;5;66;03m# now that `df_output` exists and `args` contains only references, we complete\u001b[39;00m\n\u001b[0;32m 1497\u001b[0m \u001b[38;5;66;03m# the special-case and wide-mode handling by further rewriting args and/or mutating\u001b[39;00m\n\u001b[0;32m 1498\u001b[0m \u001b[38;5;66;03m# df_output\u001b[39;00m\n\u001b[0;32m 1500\u001b[0m count_name \u001b[38;5;241m=\u001b[39m _escape_col_name(df_output, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m\"\u001b[39m, [var_name, value_name])\n", | |
| "File \u001b[1;32mc:\\Users\\adnan\\Documents\\Repositories\\LearnModeling\\.venv\\Lib\\site-packages\\plotly\\express\\_core.py:1195\u001b[0m, in \u001b[0;36mprocess_args_into_dataframe\u001b[1;34m(args, wide_mode, var_name, value_name)\u001b[0m\n\u001b[0;32m 1193\u001b[0m df_output[col_name] \u001b[38;5;241m=\u001b[39m to_unindexed_series(real_argument, col_name)\n\u001b[0;32m 1194\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m df_provided:\n\u001b[1;32m-> 1195\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 1196\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mString or int arguments are only possible when a \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1197\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataFrame or an array is provided in the `data_frame` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1198\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124margument. No DataFrame was provided, but argument \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1199\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is of type str or int.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m field\n\u001b[0;32m 1200\u001b[0m )\n\u001b[0;32m 1201\u001b[0m \u001b[38;5;66;03m# Check validity of column name\u001b[39;00m\n\u001b[0;32m 1202\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m argument \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m df_input\u001b[38;5;241m.\u001b[39mcolumns:\n", | |
| "\u001b[1;31mValueError\u001b[0m: String or int arguments are only possible when a DataFrame or an array is provided in the `data_frame` argument. No DataFrame was provided, but argument 'size' is of type str or int." | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "plt.scatter([-73.5921337,45.4803403], s=91.44, c='green', alpha=0.5)\n", | |
| "px.title(\"Mercator Projection\")\n", | |
| "os.chdir(os.path.join(os.path.expanduser(\"~\"), \"Documents\", \"Repositories\", \"LearnModeling\"))\n", | |
| "mplleaflet.show()" | |
| ] | |
| } | |
| ], | |
| "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": "3.11.4" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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