flying-sheep/igraph-defaults-violin.ipynb

## igraph-defaults-violin.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "942c2268-cc52-4519-9f89-26dab452628b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import ipywidgets as w\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "81fa17b4-db97-4b5b-899e-031f7abb3994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<details>\n",
       "<summary>Click to view session information</summary>\n",
       "<pre>\n",
       "-----\n",
       "ipywidgets          8.1.2\n",
       "matplotlib          3.8.2\n",
       "pandas              2.2.0\n",
       "seaborn             0.13.2\n",
       "session_info        1.0.0\n",
       "-----\n",
       "</pre>\n",
       "<details>\n",
       "<summary>Click to view modules imported as dependencies</summary>\n",
       "<pre>\n",
       "PIL                 10.2.0\n",
       "asttokens           NA\n",
       "cloudpickle         3.0.0\n",
       "comm                0.2.1\n",
       "cycler              0.12.1\n",
       "cython_runtime      NA\n",
       "dateutil            2.8.2\n",
       "debugpy             1.8.1\n",
       "decorator           5.1.1\n",
       "executing           2.0.1\n",
       "ipykernel           6.29.2\n",
       "jedi                0.19.1\n",
       "kiwisolver          1.4.5\n",
       "mpl_toolkits        NA\n",
       "numpy               1.26.4\n",
       "packaging           23.2\n",
       "parso               0.8.3\n",
       "patsy               0.5.6\n",
       "platformdirs        4.2.0\n",
       "prompt_toolkit      3.0.43\n",
       "psutil              5.9.8\n",
       "pure_eval           0.2.2\n",
       "pyarrow             15.0.0\n",
       "pydev_ipython       NA\n",
       "pydevconsole        NA\n",
       "pydevd              2.9.5\n",
       "pydevd_file_utils   NA\n",
       "pydevd_plugins      NA\n",
       "pydevd_tracing      NA\n",
       "pygments            2.17.2\n",
       "pyparsing           3.1.1\n",
       "pytz                2024.1\n",
       "scipy               1.12.0\n",
       "six                 1.16.0\n",
       "stack_data          0.6.3\n",
       "statsmodels         0.14.1\n",
       "tornado             6.4\n",
       "traitlets           5.14.1\n",
       "wcwidth             0.2.13\n",
       "zmq                 25.1.2\n",
       "</pre>\n",
       "</details> <!-- seems like this ends pre, so might as well be explicit -->\n",
       "<pre>\n",
       "-----\n",
       "IPython             8.21.0\n",
       "jupyter_client      8.6.0\n",
       "jupyter_core        5.7.1\n",
       "-----\n",
       "Python 3.11.7 (main, Jan 29 2024, 16:03:57) [GCC 13.2.1 20230801]\n",
       "Linux-6.7.4-zen1-1-zen-x86_64-with-glibc2.39\n",
       "-----\n",
       "Session information updated at 2024-02-16 13:56\n",
       "</pre>\n",
       "</details>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import session_info\n",
    "session_info.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4e99844b-df6e-4e49-8d75-3a737c2aee71",
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_side_by_side(*dfs):\n",
    "    from IPython.display import display_html\n",
    "\n",
    "    html_str = ''.join(df.to_html(max_rows=20) for df in dfs)\n",
    "    display_html(f'<div style=\"display: flex; gap: 1em\">{html_str}</div>', raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "efc056a4-88ea-450b-9c74-e7381df90bbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"display: flex; gap: 1em\"><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>leiden</th>\n",
       "      <th>CST3</th>\n",
       "      <th>NKG7</th>\n",
       "      <th>PPBP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAACATACAACCAC-1</th>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACATTGAGCTAC-1</th>\n",
       "      <td>B cells</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACATTGATCAGC-1</th>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCGTGCTTCCG-1</th>\n",
       "      <td>NK cells</td>\n",
       "      <td>3.135494</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCGTGTATGCG-1</th>\n",
       "      <td>NK cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.484907</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACGCACTGGTAC-1</th>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACGCTGACCAGT-1</th>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACGCTGGTTCTT-1</th>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACGCTGTAGCCA-1</th>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACGCTGTTTCTG-1</th>\n",
       "      <td>NK cells</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCAGTGTCACGA-1</th>\n",
       "      <td>B cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCAGTGTCTATC-1</th>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCAGTGTGCAGT-1</th>\n",
       "      <td>B cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCCAGAGGTGAG-1</th>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCGAACACCTGA-1</th>\n",
       "      <td>Dendritic cells</td>\n",
       "      <td>2.484907</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCGAACTCTCAT-1</th>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>3.367296</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCTACTGAGGCA-1</th>\n",
       "      <td>B cells</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCTACTTCCTCG-1</th>\n",
       "      <td>B cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGCATGAGAGGC-1</th>\n",
       "      <td>B cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGCATGCCTCAC-1</th>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>leiden</th>\n",
       "      <th>CST3</th>\n",
       "      <th>NKG7</th>\n",
       "      <th>PPBP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAACATACAACCAC-1</th>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACATTGAGCTAC-1</th>\n",
       "      <td>B cells</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACATTGATCAGC-1</th>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCGTGCTTCCG-1</th>\n",
       "      <td>NK cells</td>\n",
       "      <td>3.135494</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCGTGTATGCG-1</th>\n",
       "      <td>FCGR3A+ Monocytes</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.484907</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACGCACTGGTAC-1</th>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACGCTGACCAGT-1</th>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACGCTGGTTCTT-1</th>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACGCTGTAGCCA-1</th>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACGCTGTTTCTG-1</th>\n",
       "      <td>NK cells</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCAGTGTCACGA-1</th>\n",
       "      <td>B cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCAGTGTCTATC-1</th>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCAGTGTGCAGT-1</th>\n",
       "      <td>B cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCCAGAGGTGAG-1</th>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCGAACACCTGA-1</th>\n",
       "      <td>Dendritic cells</td>\n",
       "      <td>2.484907</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCGAACTCTCAT-1</th>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>3.367296</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCTACTGAGGCA-1</th>\n",
       "      <td>B cells</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTCTACTTCCTCG-1</th>\n",
       "      <td>B cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGCATGAGAGGC-1</th>\n",
       "      <td>B cells</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGCATGCCTCAC-1</th>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_igraph = pd.read_parquet(\"violin-mess-igraph.parquet\")\n",
    "data_master = pd.read_parquet(\"violin-mess-master.parquet\")\n",
    "display_side_by_side(data_igraph, data_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8507e932-1395-4347-b1d5-a693a66e7338",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"display: flex; gap: 1em\"><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leiden</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CD4 T cells</th>\n",
       "      <td>0.148481</td>\n",
       "      <td>0.102739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD8 T cells</th>\n",
       "      <td>0.122549</td>\n",
       "      <td>0.082438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B cells</th>\n",
       "      <td>0.138724</td>\n",
       "      <td>0.116192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FCGR3A+ Monocytes</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NK cells</th>\n",
       "      <td>1.503327</td>\n",
       "      <td>1.960576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD14+ Monocytes</th>\n",
       "      <td>2.512173</td>\n",
       "      <td>0.449653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dendritic cells</th>\n",
       "      <td>3.576688</td>\n",
       "      <td>0.551062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Megakaryocytes</th>\n",
       "      <td>0.835071</td>\n",
       "      <td>0.771584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leiden</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CD4 T cells</th>\n",
       "      <td>0.147538</td>\n",
       "      <td>0.102402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD14+ Monocytes</th>\n",
       "      <td>2.495065</td>\n",
       "      <td>0.457043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B cells</th>\n",
       "      <td>0.138724</td>\n",
       "      <td>0.116192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD8 T cells</th>\n",
       "      <td>0.132876</td>\n",
       "      <td>0.090062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NK cells</th>\n",
       "      <td>2.790381</td>\n",
       "      <td>0.359713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FCGR3A+ Monocytes</th>\n",
       "      <td>0.187679</td>\n",
       "      <td>0.118766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dendritic cells</th>\n",
       "      <td>3.576688</td>\n",
       "      <td>0.551062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Megakaryocytes</th>\n",
       "      <td>0.835071</td>\n",
       "      <td>0.771584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_side_by_side(*[data.groupby(\"leiden\", observed=False)[\"CST3\"].agg([\"mean\", \"var\"]) for data in [data_igraph, data_master]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "45486378-eb17-47be-af07-e4f393c57c22",
   "metadata": {},
   "outputs": [],
   "source": [
    "palette_igraph = {\n",
    "    \"CD4 T cells\": \"#1f77b4\", \"CD8 T cells\": \"#ff7f0e\", \"B cells\": \"#2ca02c\",\n",
    "    \"FCGR3A+ Monocytes\": \"#d62728\", \"NK cells\": \"#9467bd\", \"CD14+ Monocytes\": \"#8c564b\",\n",
    "    \"Dendritic cells\": \"#e377c2\", \"Megakaryocytes\": \"#7f7f7f\",\n",
    "}\n",
    "palette_master = {\n",
    "    \"CD4 T cells\": \"#1f77b4\", \"CD14+ Monocytes\": \"#ff7f0e\", \"B cells\": \"#2ca02c\",\n",
    "    \"CD8 T cells\": \"#d62728\", \"NK cells\": \"#9467bd\", \"FCGR3A+ Monocytes\": \"#8c564b\",\n",
    "    \"Dendritic cells\": \"#e377c2\", \"Megakaryocytes\": \"#7f7f7f\",\n",
    "}\n",
    "kwargs = {\n",
    "    \"x\": \"leiden\", \"y\": \"CST3\", \"order\": None, \"orient\": \"vertical\", \"density_norm\": \"width\",\n",
    "    \"hue\": \"leiden\",\n",
    "    \"cut\": 0,\n",
    "    \"inner\": None,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e2b8b0d3-93d2-4591-b398-173f419a8bba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Axes: xlabel='leiden', ylabel='CST3'>,\n",
       " <Axes: xlabel='leiden', ylabel='CST3'>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, axs = plt.subplots(1, 2, figsize=(12, 5))\n",
    "[sns.violinplot(data=data, palette=pal, ax=ax, **kwargs) for ax, data, pal in zip(axs, [data_igraph, data_master], [palette_igraph, palette_master])]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.11 (Scanpy Tests)",
   "language": "python",
   "name": "scanpy-test-py3.11"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}