016.mito_counts.ipynb

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   "source": [
    "#!/usr/bin/env python\n",
    "# -*- encoding: utf-8 -*-\n",
    "'''\n",
    "@文件        :016.mito_counts.ipynb\n",
    "@说明        :\n",
    "@时间        :2023/07/23 10:20:49\n",
    "@作者        :Wu Baosheng\n",
    "@版本        :1.0\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc\n",
    "import os,re,sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scanpy.external as sce\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import tspex\n",
    "import plotly.express as px\n",
    "import plotly.io as pio\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "mpl.rcParams['ps.fonttype'] = 42\n",
    "# sns.set(style='white', font_scale=1)\n",
    "# %config InlineBackend.figure_format = 'svg'\n",
    "cmap = sns.blend_palette(['#5F6F94','#FFFAFA','#DD5353'],as_cmap=True)   ##4C6793\n",
    "os.chdir('H:/002.singcell/01.liver/10.mutileSpecies/05.SAMap/07.Heps')"
   ]
  },
  {
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   "execution_count": 4,
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   "source": [
    "df = pd.read_excel('./mito-counts.xlsx',engine='openpyxl')"
   ]
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"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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"ticks\")\n",
    "sns.boxplot( data=df)\n",
    "sns.stripplot(data=df, color=\"black\",alpha=0.5)"
   ]
  }
 ],
 "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}