{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from prettypandas import PrettyPandas"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\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)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
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" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
" D | \n",
" E | \n",
"
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" \n",
" \n",
" \n",
" 0 | \n",
" 1.0 | \n",
" 1.329212 | \n",
" -0.770033 | \n",
" -0.316280 | \n",
" -0.990810 | \n",
"
\n",
" \n",
" 1 | \n",
" 2.0 | \n",
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" -1.438713 | \n",
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" 0.295722 | \n",
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" \n",
" 2 | \n",
" 3.0 | \n",
" -1.626404 | \n",
" 0.219565 | \n",
" 0.678805 | \n",
" 1.889273 | \n",
"
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" \n",
" 3 | \n",
" 4.0 | \n",
" 0.961538 | \n",
" 0.104011 | \n",
" -0.481165 | \n",
" 0.850229 | \n",
"
\n",
" \n",
" 4 | \n",
" 5.0 | \n",
" 1.453425 | \n",
" 1.057737 | \n",
" 0.165562 | \n",
" 0.515018 | \n",
"
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" \n",
" 5 | \n",
" 6.0 | \n",
" -1.336936 | \n",
" 0.562861 | \n",
" 1.392855 | \n",
" -0.063328 | \n",
"
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" \n",
" 6 | \n",
" 7.0 | \n",
" 0.121668 | \n",
" 1.207603 | \n",
" -0.002040 | \n",
" 1.627796 | \n",
"
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" \n",
" 7 | \n",
" 8.0 | \n",
" 0.354493 | \n",
" 1.037528 | \n",
" -0.385684 | \n",
" 0.519818 | \n",
"
\n",
" \n",
" 8 | \n",
" 9.0 | \n",
" 1.686583 | \n",
" -1.325963 | \n",
" 1.428984 | \n",
" -2.089354 | \n",
"
\n",
" \n",
" 9 | \n",
" 10.0 | \n",
" -0.129820 | \n",
" 0.631523 | \n",
" -0.586538 | \n",
" 0.290720 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" A B C D E\n",
"0 1.0 1.329212 -0.770033 -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 -0.481165 0.850229\n",
"4 5.0 1.453425 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 0.290720"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summaries\n",
"\n",
"Supported summaries:\n",
"\n",
"* total\n",
"* average\n",
"* min\n",
"* max\n",
"* median"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
" \n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
" D | \n",
" E | \n",
" Total | \n",
"
\n",
" \n",
" 0 | \n",
" 1 | \n",
" 1.32921 | \n",
" -0.770033 | \n",
" -0.31628 | \n",
" -0.99081 | \n",
" 0.252088 | \n",
"
\n",
" 1 | \n",
" 2 | \n",
" -1.07082 | \n",
" -1.43871 | \n",
" 0.564417 | \n",
" 0.295722 | \n",
" 0.350609 | \n",
"
\n",
" 2 | \n",
" 3 | \n",
" -1.6264 | \n",
" 0.219565 | \n",
" 0.678805 | \n",
" 1.88927 | \n",
" 4.16124 | \n",
"
\n",
" 3 | \n",
" 4 | \n",
" 0.961538 | \n",
" 0.104011 | \n",
" -0.481165 | \n",
" 0.850229 | \n",
" 5.43461 | \n",
"
\n",
" 4 | \n",
" 5 | \n",
" 1.45342 | \n",
" 1.05774 | \n",
" 0.165562 | \n",
" 0.515018 | \n",
" 8.19174 | \n",
"
\n",
" 5 | \n",
" 6 | \n",
" -1.33694 | \n",
" 0.562861 | \n",
" 1.39285 | \n",
" -0.063328 | \n",
" 6.55545 | \n",
"
\n",
" 6 | \n",
" 7 | \n",
" 0.121668 | \n",
" 1.2076 | \n",
" -0.00204021 | \n",
" 1.6278 | \n",
" 9.95503 | \n",
"
\n",
" 7 | \n",
" 8 | \n",
" 0.354493 | \n",
" 1.03753 | \n",
" -0.385684 | \n",
" 0.519818 | \n",
" 9.52615 | \n",
"
\n",
" 8 | \n",
" 9 | \n",
" 1.68658 | \n",
" -1.32596 | \n",
" 1.42898 | \n",
" -2.08935 | \n",
" 8.70025 | \n",
"
\n",
" 9 | \n",
" 10 | \n",
" -0.12982 | \n",
" 0.631523 | \n",
" -0.586538 | \n",
" 0.29072 | \n",
" 10.2059 | \n",
"
\n",
" Total | \n",
" 55 | \n",
" 1.74294 | \n",
" 1.28612 | \n",
" 2.45891 | \n",
" 2.84508 | \n",
" | \n",
"
\n",
"
"
],
"text/plain": [
" A B C D E Total\n",
"0 1.0 1.329212 -0.770033 -0.316280 -0.990810 0.252088\n",
"1 2.0 -1.070816 -1.438713 0.564417 0.295722 0.350609\n",
"2 3.0 -1.626404 0.219565 0.678805 1.889273 4.161238\n",
"3 4.0 0.961538 0.104011 -0.481165 0.850229 5.434613\n",
"4 5.0 1.453425 1.057737 0.165562 0.515018 8.191742\n",
"5 6.0 -1.336936 0.562861 1.392855 -0.063328 6.555452\n",
"6 7.0 0.121668 1.207603 -0.002040 1.627796 9.955026\n",
"7 8.0 0.354493 1.037528 -0.385684 0.519818 9.526155\n",
"8 9.0 1.686583 -1.325963 1.428984 -2.089354 8.700249\n",
"9 10.0 -0.129820 0.631523 -0.586538 0.290720 10.205885\n",
"Total 55.0 1.742943 1.286118 2.458914 2.845083 NaN"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.pipe(PrettyPandas).total(axis=None)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
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" | \n",
" A | \n",
" B | \n",
" C | \n",
" D | \n",
" E | \n",
"
\n",
" \n",
" 0 | \n",
" 1 | \n",
" 1.32921 | \n",
" -0.770033 | \n",
" -0.31628 | \n",
" -0.99081 | \n",
"
\n",
" 1 | \n",
" 2 | \n",
" -1.07082 | \n",
" -1.43871 | \n",
" 0.564417 | \n",
" 0.295722 | \n",
"
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" 3 | \n",
" -1.6264 | \n",
" 0.219565 | \n",
" 0.678805 | \n",
" 1.88927 | \n",
"
\n",
" 3 | \n",
" 4 | \n",
" 0.961538 | \n",
" 0.104011 | \n",
" -0.481165 | \n",
" 0.850229 | \n",
"
\n",
" 4 | \n",
" 5 | \n",
" 1.45342 | \n",
" 1.05774 | \n",
" 0.165562 | \n",
" 0.515018 | \n",
"
\n",
" 5 | \n",
" 6 | \n",
" -1.33694 | \n",
" 0.562861 | \n",
" 1.39285 | \n",
" -0.063328 | \n",
"
\n",
" 6 | \n",
" 7 | \n",
" 0.121668 | \n",
" 1.2076 | \n",
" -0.00204021 | \n",
" 1.6278 | \n",
"
\n",
" 7 | \n",
" 8 | \n",
" 0.354493 | \n",
" 1.03753 | \n",
" -0.385684 | \n",
" 0.519818 | \n",
"
\n",
" 8 | \n",
" 9 | \n",
" 1.68658 | \n",
" -1.32596 | \n",
" 1.42898 | \n",
" -2.08935 | \n",
"
\n",
" 9 | \n",
" 10 | \n",
" -0.12982 | \n",
" 0.631523 | \n",
" -0.586538 | \n",
" 0.29072 | \n",
"
\n",
" Total | \n",
" 55 | \n",
" 1.74294 | \n",
" 1.28612 | \n",
" 2.45891 | \n",
" 2.84508 | \n",
"
\n",
"
"
],
"text/plain": [
" A B C D E\n",
"0 1.0 1.329212 -0.770033 -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 -0.481165 0.850229\n",
"4 5.0 1.453425 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 0.290720\n",
"Total 55.0 1.742943 1.286118 2.458914 2.845083"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PrettyPandas(df).total()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
" D | \n",
" E | \n",
"
\n",
" \n",
" 0 | \n",
" 1 | \n",
" 1.32921 | \n",
" -0.770033 | \n",
" -0.31628 | \n",
" -0.99081 | \n",
"
\n",
" 1 | \n",
" 2 | \n",
" -1.07082 | \n",
" -1.43871 | \n",
" 0.564417 | \n",
" 0.295722 | \n",
"
\n",
" 2 | \n",
" 3 | \n",
" -1.6264 | \n",
" 0.219565 | \n",
" 0.678805 | \n",
" 1.88927 | \n",
"
\n",
" 3 | \n",
" 4 | \n",
" 0.961538 | \n",
" 0.104011 | \n",
" -0.481165 | \n",
" 0.850229 | \n",
"
\n",
" 4 | \n",
" 5 | \n",
" 1.45342 | \n",
" 1.05774 | \n",
" 0.165562 | \n",
" 0.515018 | \n",
"
\n",
" 5 | \n",
" 6 | \n",
" -1.33694 | \n",
" 0.562861 | \n",
" 1.39285 | \n",
" -0.063328 | \n",
"
\n",
" 6 | \n",
" 7 | \n",
" 0.121668 | \n",
" 1.2076 | \n",
" -0.00204021 | \n",
" 1.6278 | \n",
"
\n",
" 7 | \n",
" 8 | \n",
" 0.354493 | \n",
" 1.03753 | \n",
" -0.385684 | \n",
" 0.519818 | \n",
"
\n",
" 8 | \n",
" 9 | \n",
" 1.68658 | \n",
" -1.32596 | \n",
" 1.42898 | \n",
" -2.08935 | \n",
"
\n",
" 9 | \n",
" 10 | \n",
" -0.12982 | \n",
" 0.631523 | \n",
" -0.586538 | \n",
" 0.29072 | \n",
"
\n",
" Average | \n",
" 5.5 | \n",
" 0.174294 | \n",
" 0.128612 | \n",
" 0.245891 | \n",
" 0.284508 | \n",
"
\n",
"
"
],
"text/plain": [
" A B C D E\n",
"0 1.0 1.329212 -0.770033 -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 -0.481165 0.850229\n",
"4 5.0 1.453425 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 0.290720\n",
"Average 5.5 0.174294 0.128612 0.245891 0.284508"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PrettyPandas(df).average()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" | \n",
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" Average | \n",
"
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" 0 | \n",
" 1 | \n",
" 1.32921 | \n",
" -0.770033 | \n",
" -0.31628 | \n",
" -0.99081 | \n",
" 0.0504176 | \n",
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" 2 | \n",
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" -0.481165 | \n",
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" 4 | \n",
" 5 | \n",
" 1.45342 | \n",
" 1.05774 | \n",
" 0.165562 | \n",
" 0.515018 | \n",
" 1.63835 | \n",
"
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" 5 | \n",
" 6 | \n",
" -1.33694 | \n",
" 0.562861 | \n",
" 1.39285 | \n",
" -0.063328 | \n",
" 1.31109 | \n",
"
\n",
" 6 | \n",
" 7 | \n",
" 0.121668 | \n",
" 1.2076 | \n",
" -0.00204021 | \n",
" 1.6278 | \n",
" 1.99101 | \n",
"
\n",
" 7 | \n",
" 8 | \n",
" 0.354493 | \n",
" 1.03753 | \n",
" -0.385684 | \n",
" 0.519818 | \n",
" 1.90523 | \n",
"
\n",
" 8 | \n",
" 9 | \n",
" 1.68658 | \n",
" -1.32596 | \n",
" 1.42898 | \n",
" -2.08935 | \n",
" 1.74005 | \n",
"
\n",
" 9 | \n",
" 10 | \n",
" -0.12982 | \n",
" 0.631523 | \n",
" -0.586538 | \n",
" 0.29072 | \n",
" 2.04118 | \n",
"
\n",
"
"
],
"text/plain": [
" A B C D E Average\n",
"0 1.0 1.329212 -0.770033 -0.316280 -0.990810 0.050418\n",
"1 2.0 -1.070816 -1.438713 0.564417 0.295722 0.070122\n",
"2 3.0 -1.626404 0.219565 0.678805 1.889273 0.832248\n",
"3 4.0 0.961538 0.104011 -0.481165 0.850229 1.086923\n",
"4 5.0 1.453425 1.057737 0.165562 0.515018 1.638348\n",
"5 6.0 -1.336936 0.562861 1.392855 -0.063328 1.311090\n",
"6 7.0 0.121668 1.207603 -0.002040 1.627796 1.991005\n",
"7 8.0 0.354493 1.037528 -0.385684 0.519818 1.905231\n",
"8 9.0 1.686583 -1.325963 1.428984 -2.089354 1.740050\n",
"9 10.0 -0.129820 0.631523 -0.586538 0.290720 2.041177"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PrettyPandas(df).average(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" \n",
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" | \n",
" A | \n",
" B | \n",
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" 0 | \n",
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" 6 | \n",
" 7 | \n",
" 0.121668 | \n",
" 1.2076 | \n",
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" 1.99101 | \n",
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" 7 | \n",
" 8 | \n",
" 0.354493 | \n",
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" -0.385684 | \n",
" 0.519818 | \n",
" 1.90523 | \n",
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" 8 | \n",
" 9 | \n",
" 1.68658 | \n",
" -1.32596 | \n",
" 1.42898 | \n",
" -2.08935 | \n",
" 1.74005 | \n",
"
\n",
" 9 | \n",
" 10 | \n",
" -0.12982 | \n",
" 0.631523 | \n",
" -0.586538 | \n",
" 0.29072 | \n",
" 2.04118 | \n",
"
\n",
" Average | \n",
" 5.5 | \n",
" 0.174294 | \n",
" 0.128612 | \n",
" 0.245891 | \n",
" 0.284508 | \n",
" | \n",
"
\n",
"
"
],
"text/plain": [
" A B C D E Average\n",
"0 1.0 1.329212 -0.770033 -0.316280 -0.990810 0.050418\n",
"1 2.0 -1.070816 -1.438713 0.564417 0.295722 0.070122\n",
"2 3.0 -1.626404 0.219565 0.678805 1.889273 0.832248\n",
"3 4.0 0.961538 0.104011 -0.481165 0.850229 1.086923\n",
"4 5.0 1.453425 1.057737 0.165562 0.515018 1.638348\n",
"5 6.0 -1.336936 0.562861 1.392855 -0.063328 1.311090\n",
"6 7.0 0.121668 1.207603 -0.002040 1.627796 1.991005\n",
"7 8.0 0.354493 1.037528 -0.385684 0.519818 1.905231\n",
"8 9.0 1.686583 -1.325963 1.428984 -2.089354 1.740050\n",
"9 10.0 -0.129820 0.631523 -0.586538 0.290720 2.041177\n",
"Average 5.5 0.174294 0.128612 0.245891 0.284508 NaN"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PrettyPandas(df).average(axis=None)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
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" | \n",
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" 3 | \n",
" 4 | \n",
" 0.961538 | \n",
" 0.104011 | \n",
" -0.481165 | \n",
" 0.850229 | \n",
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" 4 | \n",
" 5 | \n",
" 1.45342 | \n",
" 1.05774 | \n",
" 0.165562 | \n",
" 0.515018 | \n",
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" 5 | \n",
" 6 | \n",
" -1.33694 | \n",
" 0.562861 | \n",
" 1.39285 | \n",
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" 6 | \n",
" 7 | \n",
" 0.121668 | \n",
" 1.2076 | \n",
" -0.00204021 | \n",
" 1.6278 | \n",
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" 7 | \n",
" 8 | \n",
" 0.354493 | \n",
" 1.03753 | \n",
" -0.385684 | \n",
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" 8 | \n",
" 9 | \n",
" 1.68658 | \n",
" -1.32596 | \n",
" 1.42898 | \n",
" -2.08935 | \n",
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" 9 | \n",
" 10 | \n",
" -0.12982 | \n",
" 0.631523 | \n",
" -0.586538 | \n",
" 0.29072 | \n",
"
\n",
" Minimum | \n",
" 1 | \n",
" -1.6264 | \n",
" -1.43871 | \n",
" -0.586538 | \n",
" -2.08935 | \n",
"
\n",
" Maximum | \n",
" 10 | \n",
" 1.68658 | \n",
" 1.2076 | \n",
" 1.42898 | \n",
" 1.88927 | \n",
"
\n",
"
"
],
"text/plain": [
" A B C D E\n",
"0 1.0 1.329212 -0.770033 -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 -0.481165 0.850229\n",
"4 5.0 1.453425 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 0.290720\n",
"Minimum 1.0 -1.626404 -1.438713 -0.586538 -2.089354\n",
"Maximum 10.0 1.686583 1.207603 1.428984 1.889273"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PrettyPandas(df).min().max()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
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" 1.32921 | \n",
" -0.770033 | \n",
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" 1.05774 | \n",
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" 5 | \n",
" 6 | \n",
" -1.33694 | \n",
" 0.562861 | \n",
" 1.39285 | \n",
" -0.063328 | \n",
"
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" 6 | \n",
" 7 | \n",
" 0.121668 | \n",
" 1.2076 | \n",
" -0.00204021 | \n",
" 1.6278 | \n",
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" 7 | \n",
" 8 | \n",
" 0.354493 | \n",
" 1.03753 | \n",
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" 0.519818 | \n",
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" 8 | \n",
" 9 | \n",
" 1.68658 | \n",
" -1.32596 | \n",
" 1.42898 | \n",
" -2.08935 | \n",
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" 9 | \n",
" 10 | \n",
" -0.12982 | \n",
" 0.631523 | \n",
" -0.586538 | \n",
" 0.29072 | \n",
"
\n",
" Average | \n",
" 5.5 | \n",
" 0.174294 | \n",
" 0.128612 | \n",
" 0.245891 | \n",
" 0.284508 | \n",
"
\n",
"
"
],
"text/plain": [
" A B C D E\n",
"0 1.0 1.329212 -0.770033 -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 -0.481165 0.850229\n",
"4 5.0 1.453425 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 0.290720\n",
"Average 5.5 0.174294 0.128612 0.245891 0.284508"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PrettyPandas(df).summary(np.mean, \"Average\")"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
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" 6 | \n",
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" 1.39285 | \n",
" -0.063328 | \n",
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" 6 | \n",
" 7 | \n",
" 0.121668 | \n",
" 1.2076 | \n",
" -0.00204021 | \n",
" 1.6278 | \n",
"
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" 7 | \n",
" 8 | \n",
" 0.354493 | \n",
" 1.03753 | \n",
" -0.385684 | \n",
" 0.519818 | \n",
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" 8 | \n",
" 9 | \n",
" 1.68658 | \n",
" -1.32596 | \n",
" 1.42898 | \n",
" -2.08935 | \n",
"
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" 9 | \n",
" 10 | \n",
" -0.12982 | \n",
" 0.631523 | \n",
" -0.586538 | \n",
" 0.29072 | \n",
"
\n",
" > 0 | \n",
" 10 | \n",
" 6 | \n",
" 7 | \n",
" 5 | \n",
" 7 | \n",
"
\n",
"
"
],
"text/plain": [
" A B C D E\n",
"0 1.0 1.329212 -0.770033 -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 -0.481165 0.850229\n",
"4 5.0 1.453425 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 0.290720\n",
"> 0 10.0 6.000000 7.000000 5.000000 7.000000"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def count_greater_than_zero(column):\n",
" return (column > 0).sum()\n",
"\n",
"PrettyPandas(df).summary(count_greater_than_zero, title=\"> 0\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Multiple Summaries"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" 1 | \n",
" 1.32921 | \n",
" -0.770033 | \n",
" -0.31628 | \n",
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" 0.0504176 | \n",
" 0.252088 | \n",
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" 0.219565 | \n",
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" 0.832248 | \n",
" 4.16124 | \n",
"
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" 3 | \n",
" 4 | \n",
" 0.961538 | \n",
" 0.104011 | \n",
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" 0.850229 | \n",
" 1.08692 | \n",
" 5.43461 | \n",
"
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" 4 | \n",
" 5 | \n",
" 1.45342 | \n",
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" 0.515018 | \n",
" 1.63835 | \n",
" 8.19174 | \n",
"
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" 5 | \n",
" 6 | \n",
" -1.33694 | \n",
" 0.562861 | \n",
" 1.39285 | \n",
" -0.063328 | \n",
" 1.31109 | \n",
" 6.55545 | \n",
"
\n",
" 6 | \n",
" 7 | \n",
" 0.121668 | \n",
" 1.2076 | \n",
" -0.00204021 | \n",
" 1.6278 | \n",
" 1.99101 | \n",
" 9.95503 | \n",
"
\n",
" 7 | \n",
" 8 | \n",
" 0.354493 | \n",
" 1.03753 | \n",
" -0.385684 | \n",
" 0.519818 | \n",
" 1.90523 | \n",
" 9.52615 | \n",
"
\n",
" 8 | \n",
" 9 | \n",
" 1.68658 | \n",
" -1.32596 | \n",
" 1.42898 | \n",
" -2.08935 | \n",
" 1.74005 | \n",
" 8.70025 | \n",
"
\n",
" 9 | \n",
" 10 | \n",
" -0.12982 | \n",
" 0.631523 | \n",
" -0.586538 | \n",
" 0.29072 | \n",
" 2.04118 | \n",
" 10.2059 | \n",
"
\n",
" Average | \n",
" 5.5 | \n",
" 0.174294 | \n",
" 0.128612 | \n",
" 0.245891 | \n",
" 0.284508 | \n",
" | \n",
" | \n",
"
\n",
" Total | \n",
" 55 | \n",
" 1.74294 | \n",
" 1.28612 | \n",
" 2.45891 | \n",
" 2.84508 | \n",
" | \n",
" | \n",
"
\n",
"
"
],
"text/plain": [
" A B C D E Average Total\n",
"0 1.0 1.329212 -0.770033 -0.316280 -0.990810 0.050418 0.252088\n",
"1 2.0 -1.070816 -1.438713 0.564417 0.295722 0.070122 0.350609\n",
"2 3.0 -1.626404 0.219565 0.678805 1.889273 0.832248 4.161238\n",
"3 4.0 0.961538 0.104011 -0.481165 0.850229 1.086923 5.434613\n",
"4 5.0 1.453425 1.057737 0.165562 0.515018 1.638348 8.191742\n",
"5 6.0 -1.336936 0.562861 1.392855 -0.063328 1.311090 6.555452\n",
"6 7.0 0.121668 1.207603 -0.002040 1.627796 1.991005 9.955026\n",
"7 8.0 0.354493 1.037528 -0.385684 0.519818 1.905231 9.526155\n",
"8 9.0 1.686583 -1.325963 1.428984 -2.089354 1.740050 8.700249\n",
"9 10.0 -0.129820 0.631523 -0.586538 0.290720 2.041177 10.205885\n",
"Average 5.5 0.174294 0.128612 0.245891 0.284508 NaN NaN\n",
"Total 55.0 1.742943 1.286118 2.458914 2.845083 NaN NaN"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PrettyPandas(df).multi_summary([np.mean, np.sum],\n",
" ['Average', 'Total'],\n",
" axis=None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Number Formatting"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"from prettypandas import as_percent, as_currency, as_unit"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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" 0.562861 | \n",
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" 8 | \n",
" 9 | \n",
" 1.68658 | \n",
" -1.32596 | \n",
" 1.42898 | \n",
" -208.94% | \n",
"
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" 9 | \n",
" 10 | \n",
" -0.12982 | \n",
" 0.631523 | \n",
" -0.586538 | \n",
" 29.07% | \n",
"
\n",
"
"
],
"text/plain": [
""
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.style.format(as_percent(), subset='E')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" 2 | \n",
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" -1.33694 | \n",
" 0.562861 | \n",
" 1.39285 | \n",
" -0.063328 | \n",
"
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" 6 | \n",
" 700.00% | \n",
" 0.121668 | \n",
" 1.2076 | \n",
" -0.00204021 | \n",
" 1.6278 | \n",
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" 0.354493 | \n",
" 1.03753 | \n",
" -0.385684 | \n",
" 0.519818 | \n",
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" 8 | \n",
" 900.00% | \n",
" 1.68658 | \n",
" -1.32596 | \n",
" 1.42898 | \n",
" -2.08935 | \n",
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" -0.12982 | \n",
" 0.631523 | \n",
" -0.586538 | \n",
" 0.29072 | \n",
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\n",
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" A B C D E\n",
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"2 3.0 -1.626404 0.219565 0.678805 1.889273\n",
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"4 5.0 1.453425 1.057737 0.165562 0.515018\n",
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"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 0.290720"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PrettyPandas(df).as_percent(subset='A') # Format just column A"
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"metadata": {},
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{
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"execution_count": 38,
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"source": [
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"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 0.290720"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PrettyPandas(df).as_percent(subset=['B'])"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
" D | \n",
" E | \n",
"
\n",
" \n",
" 0 | \n",
" 1 | \n",
" 132.92% | \n",
" -0.770033 | \n",
" -0.31628 | \n",
" -0.99081 | \n",
"
\n",
" 1 | \n",
" 2 | \n",
" -107.08% | \n",
" -1.43871 | \n",
" 0.564417 | \n",
" 0.295722 | \n",
"
\n",
" 2 | \n",
" 3 | \n",
" -162.64% | \n",
" 0.219565 | \n",
" 0.678805 | \n",
" 1.88927 | \n",
"
\n",
" 3 | \n",
" 4 | \n",
" 96.15% | \n",
" 0.104011 | \n",
" -0.481165 | \n",
" 0.850229 | \n",
"
\n",
" 4 | \n",
" 5 | \n",
" 145.34% | \n",
" 1.05774 | \n",
" 0.165562 | \n",
" 0.515018 | \n",
"
\n",
" 5 | \n",
" 6 | \n",
" -133.69% | \n",
" 0.562861 | \n",
" 1.39285 | \n",
" -0.063328 | \n",
"
\n",
" 6 | \n",
" 7 | \n",
" 12.17% | \n",
" 1.2076 | \n",
" -0.00204021 | \n",
" 1.6278 | \n",
"
\n",
" 7 | \n",
" 8 | \n",
" 35.45% | \n",
" 1.03753 | \n",
" -0.385684 | \n",
" 0.519818 | \n",
"
\n",
" 8 | \n",
" 9 | \n",
" 168.66% | \n",
" -1.32596 | \n",
" 1.42898 | \n",
" -2.08935 | \n",
"
\n",
" 9 | \n",
" 10 | \n",
" -12.98% | \n",
" 0.631523 | \n",
" -0.586538 | \n",
" 0.29072 | \n",
"
\n",
" Total | \n",
" 55 | \n",
" 174.29% | \n",
" 1.28612 | \n",
" 2.45891 | \n",
" 2.84508 | \n",
"
\n",
"
"
],
"text/plain": [
" A B C D E\n",
"0 1.0 1.329212 -0.770033 -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 -0.481165 0.850229\n",
"4 5.0 1.453425 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 0.290720\n",
"Total 55.0 1.742943 1.286118 2.458914 2.845083"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PrettyPandas(df).as_percent(subset=['B']).total()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
" D | \n",
" E | \n",
"
\n",
" \n",
" 0 | \n",
" 100% | \n",
" 133% | \n",
" -77% | \n",
" -32% | \n",
" -99% | \n",
"
\n",
" 1 | \n",
" 200% | \n",
" -107% | \n",
" -144% | \n",
" 56% | \n",
" 30% | \n",
"
\n",
" 2 | \n",
" 300% | \n",
" -163% | \n",
" 22% | \n",
" 68% | \n",
" 189% | \n",
"
\n",
" 3 | \n",
" 400% | \n",
" 96% | \n",
" 10% | \n",
" -48% | \n",
" 85% | \n",
"
\n",
" 4 | \n",
" 500% | \n",
" 145% | \n",
" 106% | \n",
" 17% | \n",
" 52% | \n",
"
\n",
" 5 | \n",
" 600% | \n",
" -134% | \n",
" 56% | \n",
" 139% | \n",
" -6% | \n",
"
\n",
" 6 | \n",
" 700% | \n",
" 12% | \n",
" 121% | \n",
" -0% | \n",
" 163% | \n",
"
\n",
" 7 | \n",
" 800% | \n",
" 35% | \n",
" 104% | \n",
" -39% | \n",
" 52% | \n",
"
\n",
" 8 | \n",
" 900% | \n",
" 169% | \n",
" -133% | \n",
" 143% | \n",
" -209% | \n",
"
\n",
" 9 | \n",
" 1000% | \n",
" -13% | \n",
" 63% | \n",
" -59% | \n",
" 29% | \n",
"
\n",
" Median | \n",
" 550% | \n",
" 24% | \n",
" 39% | \n",
" 8% | \n",
" 41% | \n",
"
\n",
"
"
],
"text/plain": [
""
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(\n",
" df.pipe(PrettyPandas)\n",
" .as_percent(precision=0)\n",
" .median()\n",
" .style\n",
" .background_gradient()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
" D | \n",
" E | \n",
"
\n",
" \n",
" 0 | \n",
" 1 | \n",
" 1.32921 | \n",
" -0.770033 | \n",
" -0.31628 | \n",
" -0.99081 | \n",
"
\n",
" 1 | \n",
" 2 | \n",
" -1.07082 | \n",
" -1.43871 | \n",
" 0.564417 | \n",
" 0.295722 | \n",
"
\n",
" 2 | \n",
" 3 | \n",
" -1.6264 | \n",
" 0.219565 | \n",
" 0.678805 | \n",
" 1.88927 | \n",
"
\n",
" 3 | \n",
" 400.00% | \n",
" 96.15% | \n",
" 10.40% | \n",
" -48.12% | \n",
" 85.02% | \n",
"
\n",
" 4 | \n",
" 5 | \n",
" 1.45342 | \n",
" 1.05774 | \n",
" 0.165562 | \n",
" 0.515018 | \n",
"
\n",
" 5 | \n",
" 6 | \n",
" -1.33694 | \n",
" 0.562861 | \n",
" 1.39285 | \n",
" -0.063328 | \n",
"
\n",
" 6 | \n",
" 7 | \n",
" 0.121668 | \n",
" 1.2076 | \n",
" -0.00204021 | \n",
" 1.6278 | \n",
"
\n",
" 7 | \n",
" 8 | \n",
" 0.354493 | \n",
" 1.03753 | \n",
" -0.385684 | \n",
" 0.519818 | \n",
"
\n",
" 8 | \n",
" 9 | \n",
" 1.68658 | \n",
" -1.32596 | \n",
" 1.42898 | \n",
" -2.08935 | \n",
"
\n",
" 9 | \n",
" 10 | \n",
" -0.12982 | \n",
" 0.631523 | \n",
" -0.586538 | \n",
" 0.29072 | \n",
"
\n",
"
"
],
"text/plain": [
" A B C D E\n",
"0 1.0 1.329212 -0.770033 -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 -0.481165 0.850229\n",
"4 5.0 1.453425 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 0.290720"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PrettyPandas(df).as_percent(subset=pd.IndexSlice[3,:], precision=2)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
" \n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
" D | \n",
" E | \n",
" Total | \n",
"
\n",
" \n",
" 0 | \n",
" $1.00 | \n",
" $1.33 | \n",
" -$0.77 | \n",
" -$0.32 | \n",
" -$0.99 | \n",
" $0.25 | \n",
"
\n",
" 1 | \n",
" 200.00% | \n",
" -107.08% | \n",
" -143.87% | \n",
" 56.44% | \n",
" 29.57% | \n",
" 35.06% | \n",
"
\n",
" 2 | \n",
" 3 | \n",
" -1.6264 | \n",
" 0.219565 | \n",
" 0.678805 | \n",
" 1.88927 | \n",
" 4.16124 | \n",
"
\n",
" 3 | \n",
" 4 | \n",
" 0.961538 | \n",
" 0.104011 | \n",
" -0.481165 | \n",
" 0.850229 | \n",
" 5.43461 | \n",
"
\n",
" 4 | \n",
" 5 | \n",
" 1.45342 | \n",
" 1.05774 | \n",
" 0.165562 | \n",
" 0.515018 | \n",
" 8.19174 | \n",
"
\n",
" 5 | \n",
" 6 | \n",
" -1.33694 | \n",
" 0.562861 | \n",
" 1.39285 | \n",
" -0.063328 | \n",
" 6.55545 | \n",
"
\n",
" 6 | \n",
" 7 | \n",
" 0.121668 | \n",
" 1.2076 | \n",
" -0.00204021 | \n",
" 1.6278 | \n",
" 9.95503 | \n",
"
\n",
" 7 | \n",
" 8 | \n",
" 0.354493 | \n",
" 1.03753 | \n",
" -0.385684 | \n",
" 0.519818 | \n",
" 9.52615 | \n",
"
\n",
" 8 | \n",
" 9 | \n",
" 1.68658 | \n",
" -1.32596 | \n",
" 1.42898 | \n",
" -2.08935 | \n",
" 8.70025 | \n",
"
\n",
" 9 | \n",
" 10 | \n",
" -0.12982 | \n",
" 0.631523 | \n",
" -0.586538 | \n",
" 0.29072 | \n",
" 10.2059 | \n",
"
\n",
"
"
],
"text/plain": [
" A B C D E Total\n",
"0 1.0 1.329212 -0.770033 -0.316280 -0.990810 0.252088\n",
"1 2.0 -1.070816 -1.438713 0.564417 0.295722 0.350609\n",
"2 3.0 -1.626404 0.219565 0.678805 1.889273 4.161238\n",
"3 4.0 0.961538 0.104011 -0.481165 0.850229 5.434613\n",
"4 5.0 1.453425 1.057737 0.165562 0.515018 8.191742\n",
"5 6.0 -1.336936 0.562861 1.392855 -0.063328 6.555452\n",
"6 7.0 0.121668 1.207603 -0.002040 1.627796 9.955026\n",
"7 8.0 0.354493 1.037528 -0.385684 0.519818 9.526155\n",
"8 9.0 1.686583 -1.325963 1.428984 -2.089354 8.700249\n",
"9 10.0 -0.129820 0.631523 -0.586538 0.290720 10.205885"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"first_row_idx = pd.IndexSlice[0, :]\n",
"second_row_idx = pd.IndexSlice[1, :]\n",
"\n",
"(\n",
" df.pipe(PrettyPandas)\n",
" .as_currency(subset=first_row_idx)\n",
" .as_percent(subset=second_row_idx)\n",
" .total(axis=1)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
" D | \n",
" E | \n",
"
\n",
" \n",
" 0 | \n",
" £1.00 | \n",
" 132.92% | \n",
" -0.770033 | \n",
" -0.31628 | \n",
" -0.99081 | \n",
"
\n",
" 1 | \n",
" £2.00 | \n",
" -107.08% | \n",
" -1.43871 | \n",
" 0.564417 | \n",
" 0.295722 | \n",
"
\n",
" 2 | \n",
" £3.00 | \n",
" -162.64% | \n",
" 0.219565 | \n",
" 0.678805 | \n",
" 1.88927 | \n",
"
\n",
" 3 | \n",
" £4.00 | \n",
" 96.15% | \n",
" 0.104011 | \n",
" -0.481165 | \n",
" 0.850229 | \n",
"
\n",
" 4 | \n",
" £5.00 | \n",
" 145.34% | \n",
" 1.05774 | \n",
" 0.165562 | \n",
" 0.515018 | \n",
"
\n",
" 5 | \n",
" £6.00 | \n",
" -133.69% | \n",
" 0.562861 | \n",
" 1.39285 | \n",
" -0.063328 | \n",
"
\n",
" 6 | \n",
" £7.00 | \n",
" 12.17% | \n",
" 1.2076 | \n",
" -0.00204021 | \n",
" 1.6278 | \n",
"
\n",
" 7 | \n",
" £8.00 | \n",
" 35.45% | \n",
" 1.03753 | \n",
" -0.385684 | \n",
" 0.519818 | \n",
"
\n",
" 8 | \n",
" £9.00 | \n",
" 168.66% | \n",
" -1.32596 | \n",
" 1.42898 | \n",
" -2.08935 | \n",
"
\n",
" 9 | \n",
" £10.00 | \n",
" -12.98% | \n",
" 0.631523 | \n",
" -0.586538 | \n",
" 0.29072 | \n",
"
\n",
" Total | \n",
" £55.00 | \n",
" 174.29% | \n",
" 1.28612 | \n",
" 2.45891 | \n",
" 2.84508 | \n",
"
\n",
" Average | \n",
" £5.50 | \n",
" 17.43% | \n",
" 0.128612 | \n",
" 0.245891 | \n",
" 0.284508 | \n",
"
\n",
"
"
],
"text/plain": [
" A B C D E\n",
"0 1.0 1.329212 -0.770033 -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 -0.481165 0.850229\n",
"4 5.0 1.453425 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 0.290720\n",
"Total 55.0 1.742943 1.286118 2.458914 2.845083\n",
"Average 5.5 0.174294 0.128612 0.245891 0.284508"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(\n",
" df\n",
" .pipe(PrettyPandas)\n",
" .as_currency('GBP', subset='A')\n",
" .as_percent(subset='B')\n",
" .total()\n",
" .average()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
" D | \n",
" E | \n",
" Total | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1.0 | \n",
" 1.329212 | \n",
" -0.770033 | \n",
" -0.316280 | \n",
" -0.990810 | \n",
" 0.252088 | \n",
"
\n",
" \n",
" 1 | \n",
" 2.0 | \n",
" -1.070816 | \n",
" -1.438713 | \n",
" 0.564417 | \n",
" 0.295722 | \n",
" 0.350609 | \n",
"
\n",
" \n",
" 2 | \n",
" 3.0 | \n",
" -1.626404 | \n",
" 0.219565 | \n",
" 0.678805 | \n",
" 1.889273 | \n",
" 4.161238 | \n",
"
\n",
" \n",
" 3 | \n",
" 4.0 | \n",
" 0.961538 | \n",
" 0.104011 | \n",
" -0.481165 | \n",
" 0.850229 | \n",
" 5.434613 | \n",
"
\n",
" \n",
" 4 | \n",
" 5.0 | \n",
" 1.453425 | \n",
" 1.057737 | \n",
" 0.165562 | \n",
" 0.515018 | \n",
" 8.191742 | \n",
"
\n",
" \n",
" 5 | \n",
" 6.0 | \n",
" -1.336936 | \n",
" 0.562861 | \n",
" 1.392855 | \n",
" -0.063328 | \n",
" 6.555452 | \n",
"
\n",
" \n",
" 6 | \n",
" 7.0 | \n",
" 0.121668 | \n",
" 1.207603 | \n",
" -0.002040 | \n",
" 1.627796 | \n",
" 9.955026 | \n",
"
\n",
" \n",
" 7 | \n",
" 8.0 | \n",
" 0.354493 | \n",
" 1.037528 | \n",
" -0.385684 | \n",
" 0.519818 | \n",
" 9.526155 | \n",
"
\n",
" \n",
" 8 | \n",
" 9.0 | \n",
" 1.686583 | \n",
" -1.325963 | \n",
" 1.428984 | \n",
" -2.089354 | \n",
" 8.700249 | \n",
"
\n",
" \n",
" 9 | \n",
" 10.0 | \n",
" -0.129820 | \n",
" 0.631523 | \n",
" -0.586538 | \n",
" 0.290720 | \n",
" 10.205885 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" A B C D E Total\n",
"0 1.0 1.329212 -0.770033 -0.316280 -0.990810 0.252088\n",
"1 2.0 -1.070816 -1.438713 0.564417 0.295722 0.350609\n",
"2 3.0 -1.626404 0.219565 0.678805 1.889273 4.161238\n",
"3 4.0 0.961538 0.104011 -0.481165 0.850229 5.434613\n",
"4 5.0 1.453425 1.057737 0.165562 0.515018 8.191742\n",
"5 6.0 -1.336936 0.562861 1.392855 -0.063328 6.555452\n",
"6 7.0 0.121668 1.207603 -0.002040 1.627796 9.955026\n",
"7 8.0 0.354493 1.037528 -0.385684 0.519818 9.526155\n",
"8 9.0 1.686583 -1.325963 1.428984 -2.089354 8.700249\n",
"9 10.0 -0.129820 0.631523 -0.586538 0.290720 10.205885"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(\n",
" df\n",
" .pipe(PrettyPandas)\n",
" .total(axis=1)\n",
" .to_frame()\n",
")"
]
}
],
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"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
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"name": "ipython",
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"file_extension": ".py",
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"name": "python",
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