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sheriffff/main.ipynb

Created May 22, 2023 09:18
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IRPF: el mapeo bruto a neto podría ser continuo, y no por tramos...
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main.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"# cada tramo es (euros_hasta, perc_irpf)\n",
"tramos = [\n",
" (12450, 19),\n",
" (20200, 24),\n",
" (35200, 30),\n",
" (60000, 37),\n",
" (float(\"inf\"), 45) \n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {},
"outputs": [],
"source": [
"tramos = pd.DataFrame(tramos, columns=[\"stop_eur\", \"perc\"])\n",
"tramos[\"stop_eur_prev\"] = tramos.stop_eur.shift(1).fillna(0)\n",
"tramos[\"stop_eur_diff\"] = tramos.stop_eur - tramos.stop_eur_prev"
]
},
{
"cell_type": "code",
"execution_count": 186,
"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>stop_eur</th>\n",
" <th>perc</th>\n",
" <th>stop_eur_prev</th>\n",
" <th>stop_eur_diff</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>12450.0</td>\n",
" <td>19</td>\n",
" <td>0.0</td>\n",
" <td>12450.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20200.0</td>\n",
" <td>24</td>\n",
" <td>12450.0</td>\n",
" <td>7750.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>35200.0</td>\n",
" <td>30</td>\n",
" <td>20200.0</td>\n",
" <td>15000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>60000.0</td>\n",
" <td>37</td>\n",
" <td>35200.0</td>\n",
" <td>24800.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>inf</td>\n",
" <td>45</td>\n",
" <td>60000.0</td>\n",
" <td>inf</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" stop_eur perc stop_eur_prev stop_eur_diff\n",
"0 12450.0 19 0.0 12450.0\n",
"1 20200.0 24 12450.0 7750.0\n",
"2 35200.0 30 20200.0 15000.0\n",
"3 60000.0 37 35200.0 24800.0\n",
"4 inf 45 60000.0 inf"
]
},
"execution_count": 186,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tramos"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [],
"source": [
"def get_deducciones(bruto):\n",
" deducciones = 0\n",
"\n",
" for _, t in tramos.iterrows():\n",
" if bruto > t.stop_eur:\n",
" deducciones += t.stop_eur_diff * t.perc / 100\n",
" else:\n",
" deducciones += (bruto - t.stop_eur_prev) * t.perc / 100\n",
" break\n",
" \n",
" return deducciones"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {},
"outputs": [],
"source": [
"bs = np.arange(10000, 100000, 1000)"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {},
"outputs": [],
"source": [
"ds = np.array(list(map(get_deducciones, bs)))"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f6fa72f8c10>]"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(bs, ds)"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame({\"brutos\": bs, \"deducciones\": ds})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Quiero la relación $$deducción = k * bruto ^ N$$ luego hago $$log(deduccion) = log(k) + N log(bruto)$$"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {},
"outputs": [],
"source": [
"df[\"logd\"] = np.log10(df.deducciones)"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {},
"outputs": [],
"source": [
"df[\"logb\"] = np.log10(df.brutos)"
]
},
{
"cell_type": "code",
"execution_count": 142,
"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>brutos</th>\n",
" <th>deducciones</th>\n",
" <th>logd</th>\n",
" <th>logb</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10000</td>\n",
" <td>1900.0</td>\n",
" <td>3.278754</td>\n",
" <td>4.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11000</td>\n",
" <td>2090.0</td>\n",
" <td>3.320146</td>\n",
" <td>4.041393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>12000</td>\n",
" <td>2280.0</td>\n",
" <td>3.357935</td>\n",
" <td>4.079181</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>13000</td>\n",
" <td>2497.5</td>\n",
" <td>3.397505</td>\n",
" <td>4.113943</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>14000</td>\n",
" <td>2737.5</td>\n",
" <td>3.437354</td>\n",
" <td>4.146128</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" brutos deducciones logd logb\n",
"0 10000 1900.0 3.278754 4.000000\n",
"1 11000 2090.0 3.320146 4.041393\n",
"2 12000 2280.0 3.357935 4.079181\n",
"3 13000 2497.5 3.397505 4.113943\n",
"4 14000 2737.5 3.437354 4.146128"
]
},
"execution_count": 142,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {},
"outputs": [],
"source": [
"N, logk = np.polyfit(df.logb, df.logd, 1)\n",
"k = 10 ** logk"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {},
"outputs": [],
"source": [
"N = N.round(2)\n",
"k = k.round(3)"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1.32, 0.009)"
]
},
"execution_count": 158,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"N, k"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Es decir, $$deducción = 0.00937 * bruto ^ {1.316} \\sim bruto * (bruto ^ {0.31} / 100) $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Es decir, tu porcentaje de IRPF es $$\\%_{IRFP} = bruto ^ {0.31}$$"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {},
"outputs": [],
"source": [
"def get_perc_irpf(bruto):\n",
" return bruto ** 0.31"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [],
"source": [
"def get_deducciones_approx(bruto):\n",
" perc = get_perc_irpf(bruto)\n",
" \n",
" deduccion = bruto * perc / 100\n",
" \n",
" return deduccion"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [],
"source": [
"df[\"dapprox\"] = df.brutos.map(get_deducciones_approx).round()"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\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>brutos</th>\n",
" <th>deducciones</th>\n",
" <th>logd</th>\n",
" <th>logb</th>\n",
" <th>dapprox</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10000</td>\n",
" <td>1900.0</td>\n",
" <td>3.278754</td>\n",
" <td>4.000000</td>\n",
" <td>1738.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11000</td>\n",
" <td>2090.0</td>\n",
" <td>3.320146</td>\n",
" <td>4.041393</td>\n",
" <td>1969.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>12000</td>\n",
" <td>2280.0</td>\n",
" <td>3.357935</td>\n",
" <td>4.079181</td>\n",
" <td>2207.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>13000</td>\n",
" <td>2497.5</td>\n",
" <td>3.397505</td>\n",
" <td>4.113943</td>\n",
" <td>2451.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>14000</td>\n",
" <td>2737.5</td>\n",
" <td>3.437354</td>\n",
" <td>4.146128</td>\n",
" <td>2700.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>95000</td>\n",
" <td>33651.5</td>\n",
" <td>4.527004</td>\n",
" <td>4.977724</td>\n",
" <td>33176.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>96000</td>\n",
" <td>34101.5</td>\n",
" <td>4.532773</td>\n",
" <td>4.982271</td>\n",
" <td>33634.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>97000</td>\n",
" <td>34551.5</td>\n",
" <td>4.538467</td>\n",
" <td>4.986772</td>\n",
" <td>34093.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>98000</td>\n",
" <td>35001.5</td>\n",
" <td>4.544087</td>\n",
" <td>4.991226</td>\n",
" <td>34555.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>99000</td>\n",
" <td>35451.5</td>\n",
" <td>4.549635</td>\n",
" <td>4.995635</td>\n",
" <td>35017.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>90 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" brutos deducciones logd logb dapprox\n",
"0 10000 1900.0 3.278754 4.000000 1738.0\n",
"1 11000 2090.0 3.320146 4.041393 1969.0\n",
"2 12000 2280.0 3.357935 4.079181 2207.0\n",
"3 13000 2497.5 3.397505 4.113943 2451.0\n",
"4 14000 2737.5 3.437354 4.146128 2700.0\n",
".. ... ... ... ... ...\n",
"85 95000 33651.5 4.527004 4.977724 33176.0\n",
"86 96000 34101.5 4.532773 4.982271 33634.0\n",
"87 97000 34551.5 4.538467 4.986772 34093.0\n",
"88 98000 35001.5 4.544087 4.991226 34555.0\n",
"89 99000 35451.5 4.549635 4.995635 35017.0\n",
"\n",
"[90 rows x 5 columns]"
]
},
"execution_count": 162,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {},
"outputs": [],
"source": [
"df[\"err_abs\"] = (df.deducciones - df.dapprox).abs()"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {},
"outputs": [],
"source": [
"df[\"err_rel\"] = (df.deducciones / df.dapprox)"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>brutos</th>\n",
" <th>deducciones</th>\n",
" <th>logd</th>\n",
" <th>logb</th>\n",
" <th>dapprox</th>\n",
" <th>err_abs</th>\n",
" <th>err_rel</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>20000</td>\n",
" <td>4177.5</td>\n",
" <td>3.620916</td>\n",
" <td>4.301030</td>\n",
" <td>4309.0</td>\n",
" <td>131.5</td>\n",
" <td>0.969482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>30000</td>\n",
" <td>7165.5</td>\n",
" <td>3.855247</td>\n",
" <td>4.477121</td>\n",
" <td>7329.0</td>\n",
" <td>163.5</td>\n",
" <td>0.977691</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>40000</td>\n",
" <td>10501.5</td>\n",
" <td>4.021251</td>\n",
" <td>4.602060</td>\n",
" <td>10683.0</td>\n",
" <td>181.5</td>\n",
" <td>0.983010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>50000</td>\n",
" <td>14201.5</td>\n",
" <td>4.152334</td>\n",
" <td>4.698970</td>\n",
" <td>14310.0</td>\n",
" <td>108.5</td>\n",
" <td>0.992418</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>60000</td>\n",
" <td>17901.5</td>\n",
" <td>4.252889</td>\n",
" <td>4.778151</td>\n",
" <td>18171.0</td>\n",
" <td>269.5</td>\n",
" <td>0.985169</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>70000</td>\n",
" <td>22401.5</td>\n",
" <td>4.350277</td>\n",
" <td>4.845098</td>\n",
" <td>22237.0</td>\n",
" <td>164.5</td>\n",
" <td>1.007398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>80000</td>\n",
" <td>26901.5</td>\n",
" <td>4.429776</td>\n",
" <td>4.903090</td>\n",
" <td>26488.0</td>\n",
" <td>413.5</td>\n",
" <td>1.015611</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" brutos deducciones logd logb dapprox err_abs err_rel\n",
"10 20000 4177.5 3.620916 4.301030 4309.0 131.5 0.969482\n",
"20 30000 7165.5 3.855247 4.477121 7329.0 163.5 0.977691\n",
"30 40000 10501.5 4.021251 4.602060 10683.0 181.5 0.983010\n",
"40 50000 14201.5 4.152334 4.698970 14310.0 108.5 0.992418\n",
"50 60000 17901.5 4.252889 4.778151 18171.0 269.5 0.985169\n",
"60 70000 22401.5 4.350277 4.845098 22237.0 164.5 1.007398\n",
"70 80000 26901.5 4.429776 4.903090 26488.0 413.5 1.015611"
]
},
"execution_count": 184,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df.brutos.isin(np.arange(20000, 90000, 10000))]"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>brutos</th>\n",
" <th>deducciones</th>\n",
" <th>logd</th>\n",
" <th>logb</th>\n",
" <th>dapprox</th>\n",
" <th>err_abs</th>\n",
" <th>err_rel</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10000</td>\n",
" <td>1900.0</td>\n",
" <td>3.278754</td>\n",
" <td>4.000000</td>\n",
" <td>1738.0</td>\n",
" <td>162.0</td>\n",
" <td>109.321059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11000</td>\n",
" <td>2090.0</td>\n",
" <td>3.320146</td>\n",
" <td>4.041393</td>\n",
" <td>1969.0</td>\n",
" <td>121.0</td>\n",
" <td>106.145251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>12000</td>\n",
" <td>2280.0</td>\n",
" <td>3.357935</td>\n",
" <td>4.079181</td>\n",
" <td>2207.0</td>\n",
" <td>73.0</td>\n",
" <td>103.307657</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>13000</td>\n",
" <td>2497.5</td>\n",
" <td>3.397505</td>\n",
" <td>4.113943</td>\n",
" <td>2451.0</td>\n",
" <td>46.5</td>\n",
" <td>101.897185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>14000</td>\n",
" <td>2737.5</td>\n",
" <td>3.437354</td>\n",
" <td>4.146128</td>\n",
" <td>2700.0</td>\n",
" <td>37.5</td>\n",
" <td>101.388889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>95000</td>\n",
" <td>33651.5</td>\n",
" <td>4.527004</td>\n",
" <td>4.977724</td>\n",
" <td>33176.0</td>\n",
" <td>475.5</td>\n",
" <td>101.433265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>96000</td>\n",
" <td>34101.5</td>\n",
" <td>4.532773</td>\n",
" <td>4.982271</td>\n",
" <td>33634.0</td>\n",
" <td>467.5</td>\n",
" <td>101.389963</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>97000</td>\n",
" <td>34551.5</td>\n",
" <td>4.538467</td>\n",
" <td>4.986772</td>\n",
" <td>34093.0</td>\n",
" <td>458.5</td>\n",
" <td>101.344851</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>98000</td>\n",
" <td>35001.5</td>\n",
" <td>4.544087</td>\n",
" <td>4.991226</td>\n",
" <td>34555.0</td>\n",
" <td>446.5</td>\n",
" <td>101.292143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>99000</td>\n",
" <td>35451.5</td>\n",
" <td>4.549635</td>\n",
" <td>4.995635</td>\n",
" <td>35017.0</td>\n",
" <td>434.5</td>\n",
" <td>101.240826</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>90 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" brutos deducciones logd logb dapprox err_abs err_rel\n",
"0 10000 1900.0 3.278754 4.000000 1738.0 162.0 109.321059\n",
"1 11000 2090.0 3.320146 4.041393 1969.0 121.0 106.145251\n",
"2 12000 2280.0 3.357935 4.079181 2207.0 73.0 103.307657\n",
"3 13000 2497.5 3.397505 4.113943 2451.0 46.5 101.897185\n",
"4 14000 2737.5 3.437354 4.146128 2700.0 37.5 101.388889\n",
".. ... ... ... ... ... ... ...\n",
"85 95000 33651.5 4.527004 4.977724 33176.0 475.5 101.433265\n",
"86 96000 34101.5 4.532773 4.982271 33634.0 467.5 101.389963\n",
"87 97000 34551.5 4.538467 4.986772 34093.0 458.5 101.344851\n",
"88 98000 35001.5 4.544087 4.991226 34555.0 446.5 101.292143\n",
"89 99000 35451.5 4.549635 4.995635 35017.0 434.5 101.240826\n",
"\n",
"[90 rows x 7 columns]"
]
},
"execution_count": 177,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"irpf_perc_real\"]"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>stop_eur</th>\n",
" <th>perc</th>\n",
" <th>stop_eur_prev</th>\n",
" <th>stop_eur_diff</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>12450.0</td>\n",
" <td>19</td>\n",
" <td>0.0</td>\n",
" <td>12450.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20200.0</td>\n",
" <td>24</td>\n",
" <td>12450.0</td>\n",
" <td>7750.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>35200.0</td>\n",
" <td>30</td>\n",
" <td>20200.0</td>\n",
" <td>15000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>60000.0</td>\n",
" <td>37</td>\n",
" <td>35200.0</td>\n",
" <td>24800.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>inf</td>\n",
" <td>45</td>\n",
" <td>60000.0</td>\n",
" <td>inf</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" stop_eur perc stop_eur_prev stop_eur_diff\n",
"0 12450.0 19 0.0 12450.0\n",
"1 20200.0 24 12450.0 7750.0\n",
"2 35200.0 30 20200.0 15000.0\n",
"3 60000.0 37 35200.0 24800.0\n",
"4 inf 45 60000.0 inf"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tramos"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(5000, 14.0),\n",
" (15000, 19.7),\n",
" (25000, 23.1),\n",
" (35000, 25.6),\n",
" (45000, 27.7),\n",
" (55000, 29.5),\n",
" (65000, 31.0),\n",
" (75000, 32.5),\n",
" (85000, 33.7),\n",
" (95000, 34.9),\n",
" (105000, 36.0),\n",
" (115000, 37.1),\n",
" (125000, 38.0),\n",
" (135000, 38.9),\n",
" (145000, 39.8),\n",
" (155000, 40.6),\n",
" (165000, 41.4),\n",
" (175000, 42.2),\n",
" (185000, 42.9),\n",
" (195000, 43.6)]"
]
},
"execution_count": 169,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[(bruto, round(get_perc_irpf(bruto), 1)) for bruto in np.arange(5000, 205000, 10000)]"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.4045075"
]
},
"execution_count": 171,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_deducciones(200000) / 200000"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
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"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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