{
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"name": "stdout",
"output_type": "stream",
"text": [
"Original array:\n",
" A B C D E\n",
"0 1.0 1.329212 NaN -0.316280 -0.990810\n",
"1 2.0 -1.070816 -1.438713 0.564417 0.295722\n",
"2 3.0 -1.626404 0.219565 0.678805 1.889273\n",
"3 4.0 0.961538 0.104011 NaN 0.850229\n",
"4 5.0 NaN 1.057737 0.165562 0.515018\n",
"5 6.0 -1.336936 0.562861 1.392855 -0.063328\n",
"6 7.0 0.121668 1.207603 -0.002040 1.627796\n",
"7 8.0 0.354493 1.037528 -0.385684 0.519818\n",
"8 9.0 1.686583 -1.325963 1.428984 -2.089354\n",
"9 10.0 -0.129820 0.631523 -0.586538 NaN\n",
"\n",
"Different background color:\n"
]
},
{
"data": {
"text/html": [
" | A | B | C | D | E |
\n",
" \n",
" | 0 | \n",
" 1 | \n",
" 1.32921 | \n",
" nan | \n",
" -0.31628 | \n",
" -0.99081 | \n",
"
\n",
" \n",
" | 1 | \n",
" 2 | \n",
" -1.07082 | \n",
" -1.43871 | \n",
" 0.564417 | \n",
" 0.295722 | \n",
"
\n",
" \n",
" | 2 | \n",
" 3 | \n",
" -1.6264 | \n",
" 0.219565 | \n",
" 0.678805 | \n",
" 1.88927 | \n",
"
\n",
" \n",
" | 3 | \n",
" 4 | \n",
" 0.961538 | \n",
" 0.104011 | \n",
" nan | \n",
" 0.850229 | \n",
"
\n",
" \n",
" | 4 | \n",
" 5 | \n",
" nan | \n",
" 1.05774 | \n",
" 0.165562 | \n",
" 0.515018 | \n",
"
\n",
" \n",
" | 5 | \n",
" 6 | \n",
" -1.33694 | \n",
" 0.562861 | \n",
" 1.39285 | \n",
" -0.063328 | \n",
"
\n",
" \n",
" | 6 | \n",
" 7 | \n",
" 0.121668 | \n",
" 1.2076 | \n",
" -0.00204021 | \n",
" 1.6278 | \n",
"
\n",
" \n",
" | 7 | \n",
" 8 | \n",
" 0.354493 | \n",
" 1.03753 | \n",
" -0.385684 | \n",
" 0.519818 | \n",
"
\n",
" \n",
" | 8 | \n",
" 9 | \n",
" 1.68658 | \n",
" -1.32596 | \n",
" 1.42898 | \n",
" -2.08935 | \n",
"
\n",
" \n",
" | 9 | \n",
" 10 | \n",
" -0.12982 | \n",
" 0.631523 | \n",
" -0.586538 | \n",
" nan | \n",
"
\n",
"
"
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"np.random.seed(24)\n",
"df = pd.DataFrame({'A': np.linspace(1, 10, 10)})\n",
"df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE'))],\n",
" axis=1)\n",
"df.iloc[0, 2] = np.nan\n",
"df.iloc[3, 3] = np.nan\n",
"df.iloc[4, 1] = np.nan\n",
"df.iloc[9, 4] = np.nan\n",
"print(\"Original array:\")\n",
"print(df)\n",
"print(\"\\nDifferent background color:\")\n",
"coldict = {'B':'red', 'D':'yellow'}\n",
"\n",
"def highlight_cols(x):\n",
" #copy df to new - original data are not changed\n",
" df = x.copy()\n",
" #select all values to default value - red color\n",
" df.loc[:,:] = 'background-color: red'\n",
" #overwrite values grey color\n",
" df[['B','C', 'E']] = 'background-color: grey'\n",
" #return color df\n",
" return df \n",
"\n",
"df.style.apply(highlight_cols, axis=None)"
]
}
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