cmap_mutate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "643e8564-5d5f-4f09-bd0a-334e6b122c3f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-21T00:56:16.915105Z",
     "iopub.status.busy": "2024-10-21T00:56:16.914886Z",
     "iopub.status.idle": "2024-10-21T00:56:17.275566Z",
     "shell.execute_reply": "2024-10-21T00:56:17.275275Z",
     "shell.execute_reply.started": "2024-10-21T00:56:16.915086Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import palettable\n",
    "from lonboard.colormap import apply_continuous_cmap\n",
    "\n",
    "cmap = palettable.colorbrewer.sequential.YlOrRd_9\n",
    "# cmap = matplotlib.colormaps['magma']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77fd41ec-9854-4c2e-9b3a-da5e908af4a1",
   "metadata": {},
   "source": [
    "# `apply_continuous_cmap` mutates the column it operates on\n",
    "\n",
    "- this happens for both `palettable` and `matplotlib` colormaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ff6dad62-0170-4086-95a9-0477efe025e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-21T00:56:17.275957Z",
     "iopub.status.busy": "2024-10-21T00:56:17.275862Z",
     "iopub.status.idle": "2024-10-21T00:56:17.280191Z",
     "shell.execute_reply": "2024-10-21T00:56:17.280005Z",
     "shell.execute_reply.started": "2024-10-21T00:56:17.275951Z"
    }
   },
   "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>name</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  value\n",
       "0    a    0.1\n",
       "1    b    0.2\n",
       "2    c    0.3\n",
       "3    d    0.4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'name': ['a', 'b', 'c', 'd'], 'value': [.1, .2, .3, .4]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c7fa9f84-a508-49c5-8dea-dc1115297291",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-21T00:56:17.280598Z",
     "iopub.status.busy": "2024-10-21T00:56:17.280503Z",
     "iopub.status.idle": "2024-10-21T00:56:17.282779Z",
     "shell.execute_reply": "2024-10-21T00:56:17.282586Z",
     "shell.execute_reply.started": "2024-10-21T00:56:17.280591Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[255, 240, 169],\n",
       "       [254, 225, 134],\n",
       "       [254, 202, 101],\n",
       "       [253, 170,  72]], dtype=uint8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apply_continuous_cmap(df['value'], cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "80334cad-f621-471d-ab3d-a181d38438a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-21T00:56:17.283121Z",
     "iopub.status.busy": "2024-10-21T00:56:17.283064Z",
     "iopub.status.idle": "2024-10-21T00:56:17.285371Z",
     "shell.execute_reply": "2024-10-21T00:56:17.285203Z",
     "shell.execute_reply.started": "2024-10-21T00:56:17.283115Z"
    }
   },
   "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>name</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>25.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>51.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>76.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d</td>\n",
       "      <td>102.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  value\n",
       "0    a   25.6\n",
       "1    b   51.2\n",
       "2    c   76.8\n",
       "3    d  102.4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value column is changed!\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56552964-e78f-47d3-9c62-cf13abedd3e8",
   "metadata": {},
   "source": [
    "# Curiously, it doesn't do the same to a numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ceaabc5b-c484-4181-a1e2-ed626bb61f32",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-21T00:56:17.285685Z",
     "iopub.status.busy": "2024-10-21T00:56:17.285630Z",
     "iopub.status.idle": "2024-10-21T00:56:17.287116Z",
     "shell.execute_reply": "2024-10-21T00:56:17.286913Z",
     "shell.execute_reply.started": "2024-10-21T00:56:17.285678Z"
    }
   },
   "outputs": [],
   "source": [
    "value = np.array([.1, .2, .3, .4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "95113463-ec06-488a-bb3b-af7474073d2a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-21T00:56:17.288215Z",
     "iopub.status.busy": "2024-10-21T00:56:17.288091Z",
     "iopub.status.idle": "2024-10-21T00:56:17.290105Z",
     "shell.execute_reply": "2024-10-21T00:56:17.289947Z",
     "shell.execute_reply.started": "2024-10-21T00:56:17.288208Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[255, 240, 169],\n",
       "       [254, 225, 134],\n",
       "       [254, 202, 101],\n",
       "       [253, 170,  72]], dtype=uint8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apply_continuous_cmap(value, cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b0a73f3f-552f-4624-9dff-d0f5ea242f03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-21T00:56:17.290339Z",
     "iopub.status.busy": "2024-10-21T00:56:17.290289Z",
     "iopub.status.idle": "2024-10-21T00:56:17.291895Z",
     "shell.execute_reply": "2024-10-21T00:56:17.291744Z",
     "shell.execute_reply.started": "2024-10-21T00:56:17.290333Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.1, 0.2, 0.3, 0.4])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value array stays the same :)\n",
    "value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b8a83a8-60fa-464c-a668-32474640f367",
   "metadata": {},
   "source": [
    "# It also mutates a regular pandas Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f14111fc-1c0f-40ec-b84c-bee12330f542",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-21T00:56:17.292194Z",
     "iopub.status.busy": "2024-10-21T00:56:17.292084Z",
     "iopub.status.idle": "2024-10-21T00:56:17.293632Z",
     "shell.execute_reply": "2024-10-21T00:56:17.293482Z",
     "shell.execute_reply.started": "2024-10-21T00:56:17.292185Z"
    }
   },
   "outputs": [],
   "source": [
    "value = pd.Series([.1, .2, .3, .4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "84535138-36fb-41bc-95a7-bbf0ccc15830",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-21T00:56:17.293997Z",
     "iopub.status.busy": "2024-10-21T00:56:17.293912Z",
     "iopub.status.idle": "2024-10-21T00:56:17.295988Z",
     "shell.execute_reply": "2024-10-21T00:56:17.295777Z",
     "shell.execute_reply.started": "2024-10-21T00:56:17.293991Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[255, 240, 169],\n",
       "       [254, 225, 134],\n",
       "       [254, 202, 101],\n",
       "       [253, 170,  72]], dtype=uint8)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apply_continuous_cmap(value, cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ace08e33-588a-4f97-b42d-6795b12089fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-21T00:56:17.296279Z",
     "iopub.status.busy": "2024-10-21T00:56:17.296224Z",
     "iopub.status.idle": "2024-10-21T00:56:17.299273Z",
     "shell.execute_reply": "2024-10-21T00:56:17.298771Z",
     "shell.execute_reply.started": "2024-10-21T00:56:17.296273Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     25.6\n",
       "1     51.2\n",
       "2     76.8\n",
       "3    102.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value Series is changed!\n",
    "value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb72b3c0-7b1b-49b5-bd44-2893121ce36c",
   "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}