high_quality_barplots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b173b621",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "569d4896",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [67, 56, 62, 70, 89]\n",
    "b = [42, 55, 61, 51, 62]\n",
    "\n",
    "vals = a + b\n",
    "groups = ['Gene_a']*5 + ['Control']*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "280b161c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=22.5, pvalue=0.046532985074510584)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "stats.mannwhitneyu(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d37f53ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (2,4))\n",
    "\n",
    "ax = sns.barplot(x = groups, y = vals, capsize = 0.5, edgecolor = '0.2', lw = 2.5, errwidth = 2.5, \n",
    "                palette = ['brown', 'mistyrose'], errcolor = '0.2')\n",
    "\n",
    "kwargs = {'edgecolor':'0.2', 'linewidth':2.5, 'fc': 'none'}\n",
    "\n",
    "ax = sns.swarmplot(x = groups, y = vals, marker = 's', s = 10, **kwargs)\n",
    "\n",
    "prev_lim = ax.get_ylim()[1]\n",
    "\n",
    "max_val = max(vals)\n",
    "plt.plot([0,0,1,1], [max_val+5, max_val+8, max_val+8, max_val+5], lw = 2.5, color = '0.2', \n",
    "        clip_on = False)\n",
    "\n",
    "plt.text(x = 0.5, y = max_val+8, s = '*', ha = 'center', size = 20, weight = 'bold', color = '0.2')\n",
    "\n",
    "\n",
    "for axis in ['bottom', 'left']:\n",
    "    ax.spines[axis].set_linewidth(2.5)\n",
    "    ax.spines[axis].set_color('0.2')\n",
    "    \n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "plt.xticks(size = 14, rotation = 35, rotation_mode = 'anchor', ha = 'right', weight = 'bold', color = '0.2')\n",
    "plt.yticks(size = 14, weight = 'bold', color = '0.2')\n",
    "\n",
    "ax.tick_params(width = 2.5, color = '0.2')\n",
    "\n",
    "plt.ylim(top = prev_lim)\n",
    "\n",
    "\n",
    "plt.savefig('bar_test.svg', bbox_inches = 'tight')\n",
    "plt.savefig('bar_test.png', bbox_inches = 'tight', dpi = 250, facecolor = ax.get_facecolor())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dd2b76bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dfc3cee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [67, 56, 62, 70, 89, 45, 37, 50, 60, 34]\n",
    "b = [42, 55, 61, 51, 62, 25, 41, 35, 38, 22]\n",
    "\n",
    "vals = a + b\n",
    "gene = ['Gene_a']*10 + ['Control']*10\n",
    "\n",
    "groups = ['Treated']*5 + ['Untreated']*5 + ['Treated']*5 + ['Untreated']*5\n",
    "\n",
    "df = pd.DataFrame(zip(vals, gene, groups), columns = ['Value', 'Gene', 'Treatment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f8f6280a",
   "metadata": {},
   "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>Value</th>\n",
       "      <th>Gene</th>\n",
       "      <th>Treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67</td>\n",
       "      <td>Gene_a</td>\n",
       "      <td>Treated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>Gene_a</td>\n",
       "      <td>Treated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>Gene_a</td>\n",
       "      <td>Treated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>Gene_a</td>\n",
       "      <td>Treated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>89</td>\n",
       "      <td>Gene_a</td>\n",
       "      <td>Treated</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Value    Gene Treatment\n",
       "0     67  Gene_a   Treated\n",
       "1     56  Gene_a   Treated\n",
       "2     62  Gene_a   Treated\n",
       "3     70  Gene_a   Treated\n",
       "4     89  Gene_a   Treated"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "db7749d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (4,4))\n",
    "\n",
    "ax = sns.barplot(data = df, x = 'Gene', y = 'Value', capsize = 0.25, edgecolor = '0.2', lw = 2.5, errwidth = 2.5, \n",
    "                palette = ['brown', 'mistyrose'], errcolor = '0.2', hue = 'Treatment')\n",
    "\n",
    "kwargs = {'edgecolor':'0.2', 'linewidth':2.5, 'fc': 'none'}\n",
    "\n",
    "ax = sns.swarmplot(data = df, x = 'Gene', y = 'Value', hue = 'Treatment',\n",
    "                   dodge = True, marker = 's', s = 10, **kwargs)\n",
    "\n",
    "prev_lim = ax.get_ylim()[1]\n",
    "\n",
    "max_val = max(vals)\n",
    "\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "\n",
    "plt.legend(handles[2:], labels[2:], loc = 10, bbox_to_anchor = (0.5, -0.15), \n",
    "          ncol = 2, frameon = False, fontsize = 14, labelcolor = '0.2', prop = {'weight': 'bold'})\n",
    "\n",
    "\n",
    "\n",
    "for axis in ['bottom', 'left']:\n",
    "    ax.spines[axis].set_linewidth(2.5)\n",
    "    ax.spines[axis].set_color('0.2')\n",
    "    \n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "plt.xticks(size = 14, ha = 'center', weight = 'bold', color = '0.2')\n",
    "plt.yticks(size = 14, weight = 'bold', color = '0.2')\n",
    "\n",
    "ax.tick_params(width = 2.5, color = '0.2')\n",
    "\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Values', size = 14, weight = 'bold', color = '0.2')\n",
    "\n",
    "#plt.ylim(top = prev_lim)\n",
    "\n",
    "\n",
    "plt.savefig('bar_test_big.svg', bbox_inches = 'tight')\n",
    "plt.savefig('bar_test_big.png', bbox_inches = 'tight', dpi = 250, facecolor = ax.get_facecolor())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8341a2ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.2 60.4]\n",
      " [-0.2 79.2]]\n",
      "[[ 0.8 47.4]\n",
      " [ 0.8 60.2]]\n",
      "[[ 0.2   37.8  ]\n",
      " [ 0.2   53.415]]\n",
      "[[ 1.2 25.8]\n",
      " [ 1.2 38.6]]\n"
     ]
    }
   ],
   "source": [
    "for e in ax.lines:\n",
    "    xy = e.get_xydata()\n",
    "    if xy[0,0] == xy[1,0]:\n",
    "        print(xy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a64e0933",
   "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": "3.10.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}