{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('../data/auto-mpg.csv')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mpgcylindersdisplacementhorsepowerweightaccelerationyearoriginname
018.08307.0130.03504.012.0701chevrolet chevelle malibu
115.08350.0165.03693.011.5701buick skylark 320
218.08318.0150.03436.011.0701plymouth satellite
316.08304.0150.03433.012.0701amc rebel sst
417.08302.0140.03449.010.5701ford torino
..............................
38727.04140.086.02790.015.6821ford mustang gl
38844.0497.052.02130.024.6822vw pickup
38932.04135.084.02295.011.6821dodge rampage
39028.04120.079.02625.018.6821ford ranger
39131.04119.082.02720.019.4821chevy s-10
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392 rows × 9 columns

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" ], "text/plain": [ " mpg cylinders displacement horsepower weight acceleration year \\\n", "0 18.0 8 307.0 130.0 3504.0 12.0 70 \n", "1 15.0 8 350.0 165.0 3693.0 11.5 70 \n", "2 18.0 8 318.0 150.0 3436.0 11.0 70 \n", "3 16.0 8 304.0 150.0 3433.0 12.0 70 \n", "4 17.0 8 302.0 140.0 3449.0 10.5 70 \n", ".. ... ... ... ... ... ... ... \n", "387 27.0 4 140.0 86.0 2790.0 15.6 82 \n", "388 44.0 4 97.0 52.0 2130.0 24.6 82 \n", "389 32.0 4 135.0 84.0 2295.0 11.6 82 \n", "390 28.0 4 120.0 79.0 2625.0 18.6 82 \n", "391 31.0 4 119.0 82.0 2720.0 19.4 82 \n", "\n", " origin name \n", "0 1 chevrolet chevelle malibu \n", "1 1 buick skylark 320 \n", "2 1 plymouth satellite \n", "3 1 amc rebel sst \n", "4 1 ford torino \n", ".. ... ... \n", "387 1 ford mustang gl \n", "388 2 vw pickup \n", "389 1 dodge rampage \n", "390 1 ford ranger \n", "391 1 chevy s-10 \n", "\n", "[392 rows x 9 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(392, 9)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mpgcylindersdisplacementhorsepowerweightaccelerationyearoriginname
018.08307.0130.03504.012.0701chevrolet chevelle malibu
115.08350.0165.03693.011.5701buick skylark 320
218.08318.0150.03436.011.0701plymouth satellite
316.08304.0150.03433.012.0701amc rebel sst
417.08302.0140.03449.010.5701ford torino
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" ], "text/plain": [ " mpg cylinders displacement horsepower weight acceleration year \\\n", "0 18.0 8 307.0 130.0 3504.0 12.0 70 \n", "1 15.0 8 350.0 165.0 3693.0 11.5 70 \n", "2 18.0 8 318.0 150.0 3436.0 11.0 70 \n", "3 16.0 8 304.0 150.0 3433.0 12.0 70 \n", "4 17.0 8 302.0 140.0 3449.0 10.5 70 \n", "\n", " origin name \n", "0 1 chevrolet chevelle malibu \n", "1 1 buick skylark 320 \n", "2 1 plymouth satellite \n", "3 1 amc rebel sst \n", "4 1 ford torino " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mpgcylindersdisplacementhorsepowerweightaccelerationyearorigin
count392.000000392.000000392.000000392.000000392.000000392.000000392.000000392.000000
mean23.4459185.471939194.411990104.4693882977.58418415.54132775.9795921.576531
std7.8050071.705783104.64400438.491160849.4025602.7588643.6837370.805518
min9.0000003.00000068.00000046.0000001613.0000008.00000070.0000001.000000
25%17.0000004.000000105.00000075.0000002225.25000013.77500073.0000001.000000
50%22.7500004.000000151.00000093.5000002803.50000015.50000076.0000001.000000
75%29.0000008.000000275.750000126.0000003614.75000017.02500079.0000002.000000
max46.6000008.000000455.000000230.0000005140.00000024.80000082.0000003.000000
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" ], "text/plain": [ " mpg cylinders displacement horsepower weight \\\n", "count 392.000000 392.000000 392.000000 392.000000 392.000000 \n", "mean 23.445918 5.471939 194.411990 104.469388 2977.584184 \n", "std 7.805007 1.705783 104.644004 38.491160 849.402560 \n", "min 9.000000 3.000000 68.000000 46.000000 1613.000000 \n", "25% 17.000000 4.000000 105.000000 75.000000 2225.250000 \n", "50% 22.750000 4.000000 151.000000 93.500000 2803.500000 \n", "75% 29.000000 8.000000 275.750000 126.000000 3614.750000 \n", "max 46.600000 8.000000 455.000000 230.000000 5140.000000 \n", "\n", " acceleration year origin \n", "count 392.000000 392.000000 392.000000 \n", "mean 15.541327 75.979592 1.576531 \n", "std 2.758864 3.683737 0.805518 \n", "min 8.000000 70.000000 1.000000 \n", "25% 13.775000 73.000000 1.000000 \n", "50% 15.500000 76.000000 1.000000 \n", "75% 17.025000 79.000000 2.000000 \n", "max 24.800000 82.000000 3.000000 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mpgcylindersdisplacementhorsepowerweightaccelerationyearorigin
mpg1.000000-0.777618-0.805127-0.778427-0.8322440.4233290.5805410.565209
cylinders-0.7776181.0000000.9508230.8429830.897527-0.504683-0.345647-0.568932
displacement-0.8051270.9508231.0000000.8972570.932994-0.543800-0.369855-0.614535
horsepower-0.7784270.8429830.8972571.0000000.864538-0.689196-0.416361-0.455171
weight-0.8322440.8975270.9329940.8645381.000000-0.416839-0.309120-0.585005
acceleration0.423329-0.504683-0.543800-0.689196-0.4168391.0000000.2903160.212746
year0.580541-0.345647-0.369855-0.416361-0.3091200.2903161.0000000.181528
origin0.565209-0.568932-0.614535-0.455171-0.5850050.2127460.1815281.000000
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" ], "text/plain": [ " mpg cylinders displacement horsepower weight \\\n", "mpg 1.000000 -0.777618 -0.805127 -0.778427 -0.832244 \n", "cylinders -0.777618 1.000000 0.950823 0.842983 0.897527 \n", "displacement -0.805127 0.950823 1.000000 0.897257 0.932994 \n", "horsepower -0.778427 0.842983 0.897257 1.000000 0.864538 \n", "weight -0.832244 0.897527 0.932994 0.864538 1.000000 \n", "acceleration 0.423329 -0.504683 -0.543800 -0.689196 -0.416839 \n", "year 0.580541 -0.345647 -0.369855 -0.416361 -0.309120 \n", "origin 0.565209 -0.568932 -0.614535 -0.455171 -0.585005 \n", "\n", " acceleration year origin \n", "mpg 0.423329 0.580541 0.565209 \n", "cylinders -0.504683 -0.345647 -0.568932 \n", "displacement -0.543800 -0.369855 -0.614535 \n", "horsepower -0.689196 -0.416361 -0.455171 \n", "weight -0.416839 -0.309120 -0.585005 \n", "acceleration 1.000000 0.290316 0.212746 \n", "year 0.290316 1.000000 0.181528 \n", "origin 0.212746 0.181528 1.000000 " ] }, "execution_count": 8, "metadata": {}, 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "sns.distplot(df.acceleration)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }