diff --git a/Data Exploration and Conclusions.ipynb b/Data Exploration and Conclusions.ipynb new file mode 100644 index 0000000..921947d --- /dev/null +++ b/Data Exploration and Conclusions.ipynb @@ -0,0 +1,2170 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 236, + "id": "df82347c", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "id": "d8a59692", + "metadata": {}, + "outputs": [], + "source": [ + "df= pd.read_excel('measurements2.xlsx')" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "id": "035c15e6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " distance consume speed temp_inside temp_outside specials gas_type \\\n", + "0 28.0 5.0 26 21.5 12 NaN E10 \n", + "1 12.0 4.2 30 21.5 13 NaN E10 \n", + "2 11.2 5.5 38 21.5 15 NaN E10 \n", + "3 12.9 3.9 36 21.5 14 NaN E10 \n", + "4 18.5 4.5 46 21.5 15 NaN E10 \n", + ".. ... ... ... ... ... ... ... \n", + "383 16.0 3.7 39 24.5 18 NaN SP98 \n", + "384 16.1 4.3 38 25.0 31 AC SP98 \n", + "385 16.0 3.8 45 25.0 19 NaN SP98 \n", + "386 15.4 4.6 42 25.0 31 AC SP98 \n", + "387 14.7 5.0 25 25.0 30 AC SP98 \n", + "\n", + " AC rain sun refill liters refill gas \n", + "0 0 0 0 45.0 E10 \n", + "1 0 0 0 NaN NaN \n", + "2 0 0 0 NaN NaN \n", + "3 0 0 0 NaN NaN \n", + "4 0 0 0 NaN NaN \n", + ".. .. ... ... ... ... \n", + "383 0 0 0 NaN NaN \n", + "384 1 0 0 NaN NaN \n", + "385 0 0 0 NaN NaN \n", + "386 1 0 0 NaN NaN \n", + "387 1 0 0 NaN NaN \n", + "\n", + "[388 rows x 12 columns]" + ] + }, + "execution_count": 172, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "6fb31237", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.float64" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(df.distance[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "44f0e14c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "distance 0\n", + "consume 0\n", + "speed 0\n", + "temp_inside 12\n", + "temp_outside 0\n", + "specials 295\n", + "gas_type 0\n", + "AC 0\n", + "rain 0\n", + "sun 0\n", + "refill liters 375\n", + "refill gas 375\n", + "dtype: int64" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "7dc9a17a", + "metadata": {}, + "outputs": [], + "source": [ + "MediaConsumos= df.groupby(['gas_type']).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "0f7ff9b1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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distanceconsumespeedtemp_insidetemp_outsideACrainsunrefill liters
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" + ], + "text/plain": [ + " distance consume speed temp_inside temp_outside AC \\\n", + "gas_type \n", + "E10 21.096250 4.931250 43.506250 21.917197 10.11875 0.043750 \n", + "SP98 18.639912 4.899123 40.820175 21.938356 12.22807 0.100877 \n", + "\n", + " rain sun refill liters \n", + "gas_type \n", + "E10 0.100000 0.075000 39.6000 \n", + "SP98 0.140351 0.087719 35.5625 " + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "MediaConsumos" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "463a21ec", + "metadata": {}, + "outputs": [], + "source": [ + "#¿que nos interesa?\n", + "#consumo por kilometro a la misma velocidad\n", + "#precio\n", + "#specialidades" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "id": "df5d762b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", + " meane10=e10.mean()\n", + ":4: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", + " meansp=sp.mean()\n" + ] + } + ], + "source": [ + "e10= df[df.gas_type=='E10']\n", + "sp= df[df.gas_type=='SP98']\n", + "meane10=e10.mean()\n", + "meansp=sp.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "d3634051", + "metadata": {}, + "outputs": [], + "source": [ + "consumonormale10=(meane10.consume/meane10.speed)*40" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "id": "81dfeaff", + "metadata": {}, + "outputs": [], + "source": [ + "consumonormalsp=(meansp.consume/meansp.speed)*40" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "id": "0c04f810", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.533831346070967\n" + ] + } + ], + "source": [ + "print(consumonormale10)" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "id": "e079a5bb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.800687654453636\n" + ] + } + ], + "source": [ + "print(consumonormalsp)" + ] + }, + { + "cell_type": "markdown", + "id": "5301d2ab", + "metadata": {}, + "source": [ + "#### Primera conclusión:\n", + "A misma velocidad consume más el sp" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "7d20ea65", + "metadata": {}, + "outputs": [], + "source": [ + "consumonormaldiste10=(meane10.consume/meane10.distance)*1" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "id": "83fae437", + "metadata": {}, + "outputs": [], + "source": [ + "consumonormaldistsp=(meansp.consume/meansp.distance)*1" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "id": "7fc317f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.23375007406529597\n", + "0.26282971364032093\n" + ] + } + ], + "source": [ + "print(consumonormaldiste10)\n", + "print(consumonormaldistsp)" + ] + }, + { + "cell_type": "markdown", + "id": "4e4eddbb", + "metadata": {}, + "source": [ + "### Segunda conclusión:\n", + "A misma distancia se consume más con sp" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "id": "3e673bcf", + "metadata": {}, + "outputs": [], + "source": [ + "def limpieza(x):\n", + " if x== 'SP98':\n", + " return 1\n", + " if x== 'E10':\n", + " return 0\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "id": "806222ba", + "metadata": {}, + "outputs": [], + "source": [ + "df.gas_type=df.gas_type.apply(limpieza)" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "id": "ebfd14dc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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 distanceconsumespeedtemp_insidetemp_outsidegas_typeACrainsunrefill liters
distancenannannannannannannannannannan
consume-0.13nannannannannannannannannan
speed0.56-0.23nannannannannannannannan
temp_inside0.08-0.160.06nannannannannannannan
temp_outside0.09-0.320.020.36nannannannannannan
gas_type-0.05-0.02-0.100.010.15nannannannannan
AC-0.030.10-0.040.300.170.11nannannannan
rain-0.020.250.01-0.04-0.190.060.24nannannan
sun0.08-0.170.080.250.350.020.09-0.11nannan
refill liters0.130.100.050.03-0.08-0.24nan-0.49nannan
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mask = np.zeros_like(corr, dtype=bool)\n", + "mask[np.triu_indices_from(mask)] = True\n", + "corr[mask] = np.nan\n", + "(corr\n", + " .style\n", + " .background_gradient(cmap='coolwarm', axis=None, vmin=-1, vmax=1)\n", + " .highlight_null(null_color='#f1f1f1') \n", + " .set_precision(2))" + ] + }, + { + "cell_type": "markdown", + "id": "504d58e6", + "metadata": {}, + "source": [ + "No enseña correlación entre consume y gas type" + ] + }, + { + "cell_type": "markdown", + "id": "999b54ee", + "metadata": {}, + "source": [ + "#### Siguiente paso\n", + "Enriquecer datos con el precio de la gasolina. Aquí vamos a hacer la suposición, de que los dos tipos de gasolina que se muertan con sin plomo 98, uno siendo el estándar y el otro con ethanol 10.\n", + "Tras una extensiva búsqueda en internet he llegado a la conclusión de que no se puede hallar el precio de la e10 a día de hoy, pero cómo nos dicen que es más barata la e10, y es la que menos se consume, vamos a suponer que es más rentable económicamente la gasolina e10, dado que se consume menos con esta y es más barata. Para hacer estas conclusiones más fiables, vamos a intentar igualar las condiciones de prueba, tomando una temperatura normalizada, y eliminando los datos que contengan factores especiales como lluvia o aire acondicionado." + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "id": "15e7a7f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([nan, 'AC rain', 'AC', 'rain', 'snow', 'AC snow',\n", + " 'half rain half sun', 'sun', 'AC sun', 'sun ac', 'ac', 'AC Sun',\n", + " 'ac rain'], dtype=object)" + ] + }, + "execution_count": 179, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.specials.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "id": "fe498e37", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "30" + ] + }, + "execution_count": 180, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.AC.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "id": "a42647d6", + "metadata": {}, + "outputs": [], + "source": [ + "indexes = df[ df['AC'] == 1 ].index" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "id": "c45243b9", + "metadata": {}, + "outputs": [], + "source": [ + "df.drop(indexes, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "id": "7e9a54fc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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distanceconsumespeedtemp_insidetemp_outsidespecialsgas_typeACrainsunrefill litersrefill gas
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3808.35.05224.527NaN1000NaNNaN
3815.53.73324.528sun1001NaNNaN
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358 rows × 12 columns

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distanceconsumespeedtemp_insidetemp_outsidespecialsgas_typeACrainsunrefill litersrefill gas
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295 rows × 12 columns

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" + ], + "text/plain": [ + " TipoDeGasolina ConsumoMismaDistancia\n", + "0 E10 0.226760\n", + "1 SP98 0.276869" + ] + }, + "execution_count": 232, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dframe" + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "id": "40d2d10a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 238, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "id": "c2dcd09a", + "metadata": {}, + "source": [ + "Por distancia sigue consumiendo más sp" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "id": "bbd44684", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.11372583791452384\n", + "0.11946026986506746\n" + ] + } + ], + "source": [ + "speede10=(meane10.consume/meane10.speed)*1\n", + "speedsp=(meansp.consume/meansp.speed)*1\n", + "print(speede10)\n", + "print(speedsp)" + ] + }, + { + "cell_type": "markdown", + "id": "9c6390e3", + "metadata": {}, + "source": [ + "Lo mismo por velocidad, aunque la diferencia parece reducirse" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "id": "7116d92c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12.38030888030888\n", + "10.744336569579287\n" + ] + } + ], + "source": [ + "tempe10=(meane10.consume/meane10.temp_outside)*25\n", + "tempsp=(meansp.consume/meansp.temp_outside)*25\n", + "print(tempe10)\n", + "print(tempsp)" + ] + }, + { + "cell_type": "markdown", + "id": "5e25781c", + "metadata": {}, + "source": [ + "Aquí en cambio, vemos como el consumo de e10 se ve más afectado por una mayor temperatura que el sp." + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "id": "d46a505a", + "metadata": {}, + "outputs": [], + "source": [ + "totale10=(meane10.consume/(meane10.temp_outside*meane10.speed*meane10.distance))*1\n", + "totalsp=(meansp.consume/(meansp.temp_outside*meansp.speed*meansp.distance))*1" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "id": "d8d2a3aa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0005328895532414408\n", + "0.0006021792113958752\n" + ] + } + ], + "source": [ + "print(totale10)\n", + "print(totalsp)" + ] + }, + { + "cell_type": "markdown", + "id": "80cfd9e8", + "metadata": {}, + "source": [ + "#### Conclusion Final\n", + "En igualdad de condiciones en cuanto a temperatura fuera, velocidad, distancia y condiciones especiales, parece que se consume menos con e10. Es cierto, que la muestra es ciertamente pequeña, y el consumo se puede deber a muchas otras cosas como el coche y el tipo de motor, no solamente el tipo de gasolina, por lo que con estos datos concluir que el e10 resulta en un consumo menor resulta peligroso." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f559b524", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Ironhack", + "language": "python", + "name": "ironhack" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}