Bar plot with error

Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>cell_age</th>\n",
       "      <th>cell_mean</th>\n",
       "      <th>ci_lo</th>\n",
       "      <th>ci_hi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>0.427711</td>\n",
       "      <td>0.395802</td>\n",
       "      <td>0.458543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>0.448410</td>\n",
       "      <td>0.412520</td>\n",
       "      <td>0.484413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cell_age  cell_mean     ci_lo     ci_hi\n",
       "0        18   0.427711  0.395802  0.458543\n",
       "1        32   0.448410  0.412520  0.484413"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat = dict(\n",
    "    cell_age= (18,32),\n",
    "    cell_mean = (0.4277114502555463,0.44841020643768326),\n",
    "    ci_lo = (0.3958021553533243, 0.412519854795916), \n",
    "    ci_hi = (0.4585425031102684, 0.4844134134481588)\n",
    ")\n",
    "df = pd.DataFrame(dat)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "err = np.array(((df.cell_mean - df.ci_lo).values,\n",
    "                (df.cell_mean - df.ci_hi).values)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(2, 6))\n",
    "ax.bar(df.cell_age, \n",
    "       df.cell_mean, \n",
    "       width = 12, \n",
    "       yerr=err,\n",
    "       color=(\"skyblue\", \"pink\"))\n",
    "ax.set(xticks=df.cell_age,\n",
    "      xlabel=\"Cell age\", \n",
    "      ylabel=\"Spearman Correlation\", \n",
    "      )\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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