commit e5e5d9f93ae171094186726adc9b6badf4bd82dc Author: thpc-ensae Date: Thu May 4 11:42:53 2023 +0000 deuxieme essai diff --git a/.ipynb_checkpoints/test-checkpoint.ipynb b/.ipynb_checkpoints/test-checkpoint.ipynb new file mode 100644 index 0000000..da0ea2e --- /dev/null +++ b/.ipynb_checkpoints/test-checkpoint.ipynb @@ -0,0 +1,665 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 18, + "id": "c6b75e30-ce16-46c4-ab91-9ec69b3a5a9a", + "metadata": {}, + "outputs": [], + "source": [ + "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data\"" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "617f1d2a-49ab-4a1b-840c-73ca28e70ae1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#Importation des données \n", + "import pandas as pd\n", + "data = pd.io.parsers.read_csv(url)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "ec96f94d-f855-42c0-b056-0520af40a8f5", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
63.01.01.0.1145.0233.01.0.22.0150.00.02.33.00.0.16.00
067.01.04.0160.0286.00.02.0108.01.01.52.03.03.02
167.01.04.0120.0229.00.02.0129.01.02.62.02.07.01
237.01.03.0130.0250.00.00.0187.00.03.53.00.03.00
341.00.02.0130.0204.00.02.0172.00.01.41.00.03.00
456.01.02.0120.0236.00.00.0178.00.00.81.00.03.00
\n", + "
" + ], + "text/plain": [ + " 63.0 1.0 1.0.1 145.0 233.0 1.0.2 2.0 150.0 0.0 2.3 3.0 0.0.1 \\\n", + "0 67.0 1.0 4.0 160.0 286.0 0.0 2.0 108.0 1.0 1.5 2.0 3.0 \n", + "1 67.0 1.0 4.0 120.0 229.0 0.0 2.0 129.0 1.0 2.6 2.0 2.0 \n", + "2 37.0 1.0 3.0 130.0 250.0 0.0 0.0 187.0 0.0 3.5 3.0 0.0 \n", + "3 41.0 0.0 2.0 130.0 204.0 0.0 2.0 172.0 0.0 1.4 1.0 0.0 \n", + "4 56.0 1.0 2.0 120.0 236.0 0.0 0.0 178.0 0.0 0.8 1.0 0.0 \n", + "\n", + " 6.0 0 \n", + "0 3.0 2 \n", + "1 7.0 1 \n", + "2 3.0 0 \n", + "3 3.0 0 \n", + "4 3.0 0 " + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "0a9b659c-28c2-4463-8be8-204b6b45e5da", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], dtype='int64')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a540a17a-deae-4d8d-affd-f987f9379cbe", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012345678910111213
29845.01.01.0110.0264.00.00.0132.00.01.22.00.07.01
29968.01.04.0144.0193.01.00.0141.00.03.42.02.07.02
30057.01.04.0130.0131.00.00.0115.01.01.22.01.07.03
30157.00.02.0130.0236.00.02.0174.00.00.02.01.03.01
30238.01.03.0138.0175.00.00.0173.00.00.01.0?3.00
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 9 10 11 12 \\\n", + "298 45.0 1.0 1.0 110.0 264.0 0.0 0.0 132.0 0.0 1.2 2.0 0.0 7.0 \n", + "299 68.0 1.0 4.0 144.0 193.0 1.0 0.0 141.0 0.0 3.4 2.0 2.0 7.0 \n", + "300 57.0 1.0 4.0 130.0 131.0 0.0 0.0 115.0 1.0 1.2 2.0 1.0 7.0 \n", + "301 57.0 0.0 2.0 130.0 236.0 0.0 2.0 174.0 0.0 0.0 2.0 1.0 3.0 \n", + "302 38.0 1.0 3.0 138.0 175.0 0.0 0.0 173.0 0.0 0.0 1.0 ? 3.0 \n", + "\n", + " 13 \n", + "298 1 \n", + "299 2 \n", + "300 3 \n", + "301 1 \n", + "302 0 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "e8d814ee-52a0-40d4-bb25-9d3eff3629be", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
01234567891013
count303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000
mean54.4389440.6798683.158416131.689769246.6930690.1485150.990099149.6072610.3267331.0396041.6006600.937294
std9.0386620.4672990.96012617.59974851.7769180.3561980.99497122.8750030.4697941.1610750.6162261.228536
min29.0000000.0000001.00000094.000000126.0000000.0000000.00000071.0000000.0000000.0000001.0000000.000000
25%48.0000000.0000003.000000120.000000211.0000000.0000000.000000133.5000000.0000000.0000001.0000000.000000
50%56.0000001.0000003.000000130.000000241.0000000.0000001.000000153.0000000.0000000.8000002.0000000.000000
75%61.0000001.0000004.000000140.000000275.0000000.0000002.000000166.0000001.0000001.6000002.0000002.000000
max77.0000001.0000004.000000200.000000564.0000001.0000002.000000202.0000001.0000006.2000003.0000004.000000
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4 5 \\\n", + "count 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \n", + "mean 54.438944 0.679868 3.158416 131.689769 246.693069 0.148515 \n", + "std 9.038662 0.467299 0.960126 17.599748 51.776918 0.356198 \n", + "min 29.000000 0.000000 1.000000 94.000000 126.000000 0.000000 \n", + "25% 48.000000 0.000000 3.000000 120.000000 211.000000 0.000000 \n", + "50% 56.000000 1.000000 3.000000 130.000000 241.000000 0.000000 \n", + "75% 61.000000 1.000000 4.000000 140.000000 275.000000 0.000000 \n", + "max 77.000000 1.000000 4.000000 200.000000 564.000000 1.000000 \n", + "\n", + " 6 7 8 9 10 13 \n", + "count 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \n", + "mean 0.990099 149.607261 0.326733 1.039604 1.600660 0.937294 \n", + "std 0.994971 22.875003 0.469794 1.161075 0.616226 1.228536 \n", + "min 0.000000 71.000000 0.000000 0.000000 1.000000 0.000000 \n", + "25% 0.000000 133.500000 0.000000 0.000000 1.000000 0.000000 \n", + "50% 1.000000 153.000000 0.000000 0.800000 2.000000 0.000000 \n", + "75% 2.000000 166.000000 1.000000 1.600000 2.000000 2.000000 \n", + "max 2.000000 202.000000 1.000000 6.200000 3.000000 4.000000 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "de75f7d4-dad0-451d-bdf8-5b2f32b7d34a", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0 206\n", + "0.0 97\n", + "Name: 1, dtype: int64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[1].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "42f3cfc7-0daa-4c34-9c8d-9a9e37901735", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data[1].value_counts().plot.bar()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "973dcd7c-4c13-4992-bd5a-21419c245e11", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b13946d0-7731-459d-b1bd-e2a89752df0e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/test.ipynb b/test.ipynb new file mode 100644 index 0000000..da0ea2e --- /dev/null +++ b/test.ipynb @@ -0,0 +1,665 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 18, + "id": "c6b75e30-ce16-46c4-ab91-9ec69b3a5a9a", + "metadata": {}, + "outputs": [], + "source": [ + "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data\"" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "617f1d2a-49ab-4a1b-840c-73ca28e70ae1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#Importation des données \n", + "import pandas as pd\n", + "data = pd.io.parsers.read_csv(url)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "ec96f94d-f855-42c0-b056-0520af40a8f5", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
63.01.01.0.1145.0233.01.0.22.0150.00.02.33.00.0.16.00
067.01.04.0160.0286.00.02.0108.01.01.52.03.03.02
167.01.04.0120.0229.00.02.0129.01.02.62.02.07.01
237.01.03.0130.0250.00.00.0187.00.03.53.00.03.00
341.00.02.0130.0204.00.02.0172.00.01.41.00.03.00
456.01.02.0120.0236.00.00.0178.00.00.81.00.03.00
\n", + "
" + ], + "text/plain": [ + " 63.0 1.0 1.0.1 145.0 233.0 1.0.2 2.0 150.0 0.0 2.3 3.0 0.0.1 \\\n", + "0 67.0 1.0 4.0 160.0 286.0 0.0 2.0 108.0 1.0 1.5 2.0 3.0 \n", + "1 67.0 1.0 4.0 120.0 229.0 0.0 2.0 129.0 1.0 2.6 2.0 2.0 \n", + "2 37.0 1.0 3.0 130.0 250.0 0.0 0.0 187.0 0.0 3.5 3.0 0.0 \n", + "3 41.0 0.0 2.0 130.0 204.0 0.0 2.0 172.0 0.0 1.4 1.0 0.0 \n", + "4 56.0 1.0 2.0 120.0 236.0 0.0 0.0 178.0 0.0 0.8 1.0 0.0 \n", + "\n", + " 6.0 0 \n", + "0 3.0 2 \n", + "1 7.0 1 \n", + "2 3.0 0 \n", + "3 3.0 0 \n", + "4 3.0 0 " + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "0a9b659c-28c2-4463-8be8-204b6b45e5da", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], dtype='int64')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a540a17a-deae-4d8d-affd-f987f9379cbe", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012345678910111213
29845.01.01.0110.0264.00.00.0132.00.01.22.00.07.01
29968.01.04.0144.0193.01.00.0141.00.03.42.02.07.02
30057.01.04.0130.0131.00.00.0115.01.01.22.01.07.03
30157.00.02.0130.0236.00.02.0174.00.00.02.01.03.01
30238.01.03.0138.0175.00.00.0173.00.00.01.0?3.00
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 9 10 11 12 \\\n", + "298 45.0 1.0 1.0 110.0 264.0 0.0 0.0 132.0 0.0 1.2 2.0 0.0 7.0 \n", + "299 68.0 1.0 4.0 144.0 193.0 1.0 0.0 141.0 0.0 3.4 2.0 2.0 7.0 \n", + "300 57.0 1.0 4.0 130.0 131.0 0.0 0.0 115.0 1.0 1.2 2.0 1.0 7.0 \n", + "301 57.0 0.0 2.0 130.0 236.0 0.0 2.0 174.0 0.0 0.0 2.0 1.0 3.0 \n", + "302 38.0 1.0 3.0 138.0 175.0 0.0 0.0 173.0 0.0 0.0 1.0 ? 3.0 \n", + "\n", + " 13 \n", + "298 1 \n", + "299 2 \n", + "300 3 \n", + "301 1 \n", + "302 0 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "e8d814ee-52a0-40d4-bb25-9d3eff3629be", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
01234567891013
count303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000
mean54.4389440.6798683.158416131.689769246.6930690.1485150.990099149.6072610.3267331.0396041.6006600.937294
std9.0386620.4672990.96012617.59974851.7769180.3561980.99497122.8750030.4697941.1610750.6162261.228536
min29.0000000.0000001.00000094.000000126.0000000.0000000.00000071.0000000.0000000.0000001.0000000.000000
25%48.0000000.0000003.000000120.000000211.0000000.0000000.000000133.5000000.0000000.0000001.0000000.000000
50%56.0000001.0000003.000000130.000000241.0000000.0000001.000000153.0000000.0000000.8000002.0000000.000000
75%61.0000001.0000004.000000140.000000275.0000000.0000002.000000166.0000001.0000001.6000002.0000002.000000
max77.0000001.0000004.000000200.000000564.0000001.0000002.000000202.0000001.0000006.2000003.0000004.000000
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4 5 \\\n", + "count 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \n", + "mean 54.438944 0.679868 3.158416 131.689769 246.693069 0.148515 \n", + "std 9.038662 0.467299 0.960126 17.599748 51.776918 0.356198 \n", + "min 29.000000 0.000000 1.000000 94.000000 126.000000 0.000000 \n", + "25% 48.000000 0.000000 3.000000 120.000000 211.000000 0.000000 \n", + "50% 56.000000 1.000000 3.000000 130.000000 241.000000 0.000000 \n", + "75% 61.000000 1.000000 4.000000 140.000000 275.000000 0.000000 \n", + "max 77.000000 1.000000 4.000000 200.000000 564.000000 1.000000 \n", + "\n", + " 6 7 8 9 10 13 \n", + "count 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \n", + "mean 0.990099 149.607261 0.326733 1.039604 1.600660 0.937294 \n", + "std 0.994971 22.875003 0.469794 1.161075 0.616226 1.228536 \n", + "min 0.000000 71.000000 0.000000 0.000000 1.000000 0.000000 \n", + "25% 0.000000 133.500000 0.000000 0.000000 1.000000 0.000000 \n", + "50% 1.000000 153.000000 0.000000 0.800000 2.000000 0.000000 \n", + "75% 2.000000 166.000000 1.000000 1.600000 2.000000 2.000000 \n", + "max 2.000000 202.000000 1.000000 6.200000 3.000000 4.000000 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "de75f7d4-dad0-451d-bdf8-5b2f32b7d34a", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0 206\n", + "0.0 97\n", + "Name: 1, dtype: int64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[1].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "42f3cfc7-0daa-4c34-9c8d-9a9e37901735", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGlCAYAAADQyw0eAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAgYklEQVR4nO3df2xV9f3H8ddZS69A2qul9P4Il9JtdVkoMikOLCgtQqEiBHEDxGwQWaMDWbrSKJ1ZqMtGnQs/jEyCTn4JDLJEkA0mFpECQZICQwE3V7RIGb12MLi3rd1tLff7x8L97tqCu3Av99P2+UhO4jnnc2/fN7HlmXNPe61gMBgUAACAQb4W7wEAAAC+jEABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYJzEeA9wI65cuaLz588rOTlZlmXFexwAAPA/CAaDamxslNvt1te+dv1rJF0yUM6fPy+PxxPvMQAAwA2oq6vTgAEDrrumSwZKcnKypP+8wJSUlDhPAwAA/hd+v18ejyf07/j1dMlAufq2TkpKCoECAEAX87/cnsFNsgAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAME5ivAdAZAYt2hnvEXALnXl+UrxHAIC44AoKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADBORIFSUVGhe+65R8nJyUpPT9fUqVP10Ucfha0JBoMqLy+X2+1W7969lZeXp1OnToWtCQQCWrBggdLS0tS3b19NmTJF586du/lXAwAAuoWIAqWqqkrz58/X4cOHVVlZqS+++EIFBQVqbm4OrXnhhRe0bNkyrVy5UtXV1XI6nRo/frwaGxtDa4qLi7Vt2zZt2bJFBw8eVFNTkx566CG1t7dH75UBAIAuywoGg8EbffA///lPpaenq6qqSvfff7+CwaDcbreKi4v1zDPPSPrP1RKHw6Ff//rXeuKJJ+Tz+dS/f3+9/vrrmjFjhiTp/Pnz8ng82rVrlyZMmPCVX9fv98tut8vn8yklJeVGx++S+DsoPQt/BwVAdxLJv983dQ+Kz+eTJKWmpkqSamtr5fV6VVBQEFpjs9k0ZswYHTp0SJJ09OhRtbW1ha1xu93Kzs4OrfmyQCAgv98ftgEAgO7rhgMlGAyqpKREo0ePVnZ2tiTJ6/VKkhwOR9hah8MROuf1epWUlKQ77rjjmmu+rKKiQna7PbR5PJ4bHRsAAHQBNxwoTz31lD744AP9/ve/73DOsqyw/WAw2OHYl11vTVlZmXw+X2irq6u70bEBAEAXcEOBsmDBAu3YsUPvvvuuBgwYEDrudDolqcOVkIaGhtBVFafTqdbWVl26dOmaa77MZrMpJSUlbAMAAN1XRIESDAb11FNP6Y033tDevXuVmZkZdj4zM1NOp1OVlZWhY62traqqqlJubq4kKScnR7169QpbU19fr5MnT4bWAACAni2iTzOeP3++Nm/erDfffFPJycmhKyV2u129e/eWZVkqLi7WkiVLlJWVpaysLC1ZskR9+vTRrFmzQmvnzp2rhQsXql+/fkpNTVVpaamGDBmicePGRf8VAgCALieiQFm1apUkKS8vL+z42rVrNWfOHEnS008/rZaWFs2bN0+XLl3SiBEj9Pbbbys5OTm0fvny5UpMTNT06dPV0tKiBx54QOvWrVNCQsLNvRoAANAt3NTfQYkX/g4Kegr+DgqA7uSW/R0UAACAWCBQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABgn4kDZv3+/Jk+eLLfbLcuytH379rDzlmV1uv3mN78JrcnLy+twfubMmTf9YgAAQPcQcaA0Nzdr6NChWrlyZafn6+vrw7Y1a9bIsiw98sgjYeuKiorC1q1evfrGXgEAAOh2EiN9QGFhoQoLC6953ul0hu2/+eabys/P19e//vWw43369OmwFgAAQIrxPSifffaZdu7cqblz53Y4t2nTJqWlpWnw4MEqLS1VY2PjNZ8nEAjI7/eHbQAAoPuK+ApKJNavX6/k5GRNmzYt7Phjjz2mzMxMOZ1OnTx5UmVlZXr//fdVWVnZ6fNUVFToueeei+WoAADAIDENlDVr1uixxx7TbbfdFna8qKgo9N/Z2dnKysrS8OHDdezYMQ0bNqzD85SVlamkpCS07/f75fF4Yjc4AACIq5gFyoEDB/TRRx9p69atX7l22LBh6tWrl2pqajoNFJvNJpvNFosxAQCAgWJ2D8prr72mnJwcDR069CvXnjp1Sm1tbXK5XLEaBwAAdCERX0FpamrS6dOnQ/u1tbU6fvy4UlNTNXDgQEn/eQvmD3/4g5YuXdrh8R9//LE2bdqkBx98UGlpafrwww+1cOFC3X333Ro1atRNvBQAANBdRBwoR44cUX5+fmj/6r0hs2fP1rp16yRJW7ZsUTAY1KOPPtrh8UlJSXrnnXf04osvqqmpSR6PR5MmTdLixYuVkJBwgy8DAAB0J1YwGAzGe4hI+f1+2e12+Xw+paSkxHucW2rQop3xHgG30JnnJ8V7BACImkj+/eazeAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGiThQ9u/fr8mTJ8vtdsuyLG3fvj3s/Jw5c2RZVtg2cuTIsDWBQEALFixQWlqa+vbtqylTpujcuXM39UIAAED3EXGgNDc3a+jQoVq5cuU110ycOFH19fWhbdeuXWHni4uLtW3bNm3ZskUHDx5UU1OTHnroIbW3t0f+CgAAQLeTGOkDCgsLVVhYeN01NptNTqez03M+n0+vvfaaXn/9dY0bN06StHHjRnk8Hu3Zs0cTJkyIdCQAANDNxOQelH379ik9PV133nmnioqK1NDQEDp39OhRtbW1qaCgIHTM7XYrOztbhw4d6vT5AoGA/H5/2AYAALqvqAdKYWGhNm3apL1792rp0qWqrq7W2LFjFQgEJEler1dJSUm64447wh7ncDjk9Xo7fc6KigrZ7fbQ5vF4oj02AAAwSMRv8XyVGTNmhP47Oztbw4cPV0ZGhnbu3Klp06Zd83HBYFCWZXV6rqysTCUlJaF9v99PpAAA0I3F/NeMXS6XMjIyVFNTI0lyOp1qbW3VpUuXwtY1NDTI4XB0+hw2m00pKSlhGwAA6L5iHigXL15UXV2dXC6XJCknJ0e9evVSZWVlaE19fb1Onjyp3NzcWI8DAAC6gIjf4mlqatLp06dD+7W1tTp+/LhSU1OVmpqq8vJyPfLII3K5XDpz5ox+9rOfKS0tTQ8//LAkyW63a+7cuVq4cKH69eun1NRUlZaWasiQIaHf6gEAAD1bxIFy5MgR5efnh/av3hsye/ZsrVq1SidOnNCGDRt0+fJluVwu5efna+vWrUpOTg49Zvny5UpMTNT06dPV0tKiBx54QOvWrVNCQkIUXhIAAOjqrGAwGIz3EJHy+/2y2+3y+Xw97n6UQYt2xnsE3EJnnp8U7xEAIGoi+febz+IBAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGCfiQNm/f78mT54st9sty7K0ffv20Lm2tjY988wzGjJkiPr27Su3260f/vCHOn/+fNhz5OXlybKssG3mzJk3/WIAAED3EHGgNDc3a+jQoVq5cmWHc59//rmOHTumn//85zp27JjeeOMN/f3vf9eUKVM6rC0qKlJ9fX1oW7169Y29AgAA0O0kRvqAwsJCFRYWdnrObrersrIy7NhLL72k7373uzp79qwGDhwYOt6nTx85nc5IvzwAAOgBYn4Pis/nk2VZuv3228OOb9q0SWlpaRo8eLBKS0vV2Nh4zecIBALy+/1hGwAA6L4ivoISiX//+99atGiRZs2apZSUlNDxxx57TJmZmXI6nTp58qTKysr0/vvvd7j6clVFRYWee+65WI4KAAAMErNAaWtr08yZM3XlyhW9/PLLYeeKiopC/52dna2srCwNHz5cx44d07Bhwzo8V1lZmUpKSkL7fr9fHo8nVqMDAIA4i0mgtLW1afr06aqtrdXevXvDrp50ZtiwYerVq5dqamo6DRSbzSabzRaLUQEAgIGiHihX46Smpkbvvvuu+vXr95WPOXXqlNra2uRyuaI9DgAA6IIiDpSmpiadPn06tF9bW6vjx48rNTVVbrdb3/ve93Ts2DH96U9/Unt7u7xeryQpNTVVSUlJ+vjjj7Vp0yY9+OCDSktL04cffqiFCxfq7rvv1qhRo6L3ygAAQJcVcaAcOXJE+fn5of2r94bMnj1b5eXl2rFjhyTpO9/5Ttjj3n33XeXl5SkpKUnvvPOOXnzxRTU1Ncnj8WjSpElavHixEhISbuKlAACA7iLiQMnLy1MwGLzm+eudkySPx6OqqqpIvywAAOhB+CweAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMaJOFD279+vyZMny+12y7Isbd++Pex8MBhUeXm53G63evfurby8PJ06dSpsTSAQ0IIFC5SWlqa+fftqypQpOnfu3E29EAAA0H1EHCjNzc0aOnSoVq5c2en5F154QcuWLdPKlStVXV0tp9Op8ePHq7GxMbSmuLhY27Zt05YtW3Tw4EE1NTXpoYceUnt7+42/EgAA0G0kRvqAwsJCFRYWdnouGAxqxYoVevbZZzVt2jRJ0vr16+VwOLR582Y98cQT8vl8eu211/T6669r3LhxkqSNGzfK4/Foz549mjBhwk28HAAA0B1E9R6U2tpaeb1eFRQUhI7ZbDaNGTNGhw4dkiQdPXpUbW1tYWvcbreys7NDa74sEAjI7/eHbQAAoPuKaqB4vV5JksPhCDvucDhC57xer5KSknTHHXdcc82XVVRUyG63hzaPxxPNsQEAgGFi8ls8lmWF7QeDwQ7Hvux6a8rKyuTz+UJbXV1d1GYFAADmiWqgOJ1OSepwJaShoSF0VcXpdKq1tVWXLl265povs9lsSklJCdsAAED3FdVAyczMlNPpVGVlZehYa2urqqqqlJubK0nKyclRr169wtbU19fr5MmToTUAAKBni/i3eJqamnT69OnQfm1trY4fP67U1FQNHDhQxcXFWrJkibKyspSVlaUlS5aoT58+mjVrliTJbrdr7ty5Wrhwofr166fU1FSVlpZqyJAhod/qAQAAPVvEgXLkyBHl5+eH9ktKSiRJs2fP1rp16/T000+rpaVF8+bN06VLlzRixAi9/fbbSk5ODj1m+fLlSkxM1PTp09XS0qIHHnhA69atU0JCQhReEgB0TYMW7Yz3CLiFzjw/Kd4jGM0KBoPBeA8RKb/fL7vdLp/P1+PuR+EHWM/CD7Cehe/vnqUnfn9H8u83n8UDAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAME7UA2XQoEGyLKvDNn/+fEnSnDlzOpwbOXJktMcAAABdWGK0n7C6ulrt7e2h/ZMnT2r8+PH6/ve/Hzo2ceJErV27NrSflJQU7TEAAEAXFvVA6d+/f9j+888/r2984xsaM2ZM6JjNZpPT6Yz2lwYAAN1ETO9BaW1t1caNG/X444/LsqzQ8X379ik9PV133nmnioqK1NDQcN3nCQQC8vv9YRsAAOi+Yhoo27dv1+XLlzVnzpzQscLCQm3atEl79+7V0qVLVV1drbFjxyoQCFzzeSoqKmS320Obx+OJ5dgAACDOrGAwGIzVk0+YMEFJSUn64x//eM019fX1ysjI0JYtWzRt2rRO1wQCgbCA8fv98ng88vl8SklJifrcJhu0aGe8R8AtdOb5SfEeAbcQ3989S0/8/vb7/bLb7f/Tv99Rvwflqk8//VR79uzRG2+8cd11LpdLGRkZqqmpueYam80mm80W7REBAIChYvYWz9q1a5Wenq5Jk65fiBcvXlRdXZ1cLlesRgEAAF1MTALlypUrWrt2rWbPnq3ExP+/SNPU1KTS0lK99957OnPmjPbt26fJkycrLS1NDz/8cCxGAQAAXVBM3uLZs2ePzp49q8cffzzseEJCgk6cOKENGzbo8uXLcrlcys/P19atW5WcnByLUQAAQBcUk0ApKChQZ/fe9u7dW7t3747FlwQAAN0In8UDAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAME7UA6W8vFyWZYVtTqczdD4YDKq8vFxut1u9e/dWXl6eTp06Fe0xAABAFxaTKyiDBw9WfX19aDtx4kTo3AsvvKBly5Zp5cqVqq6ultPp1Pjx49XY2BiLUQAAQBcUk0BJTEyU0+kMbf3795f0n6snK1as0LPPPqtp06YpOztb69ev1+eff67NmzfHYhQAANAFxSRQampq5Ha7lZmZqZkzZ+qTTz6RJNXW1srr9aqgoCC01mazacyYMTp06NA1ny8QCMjv94dtAACg+4p6oIwYMUIbNmzQ7t279eqrr8rr9So3N1cXL16U1+uVJDkcjrDHOByO0LnOVFRUyG63hzaPxxPtsQEAgEGiHiiFhYV65JFHNGTIEI0bN047d+6UJK1fvz60xrKssMcEg8EOx/5bWVmZfD5faKurq4v22AAAwCAx/zXjvn37asiQIaqpqQn9Ns+Xr5Y0NDR0uKry32w2m1JSUsI2AADQfcU8UAKBgP7617/K5XIpMzNTTqdTlZWVofOtra2qqqpSbm5urEcBAABdRGK0n7C0tFSTJ0/WwIED1dDQoF/+8pfy+/2aPXu2LMtScXGxlixZoqysLGVlZWnJkiXq06ePZs2aFe1RAABAFxX1QDl37pweffRRXbhwQf3799fIkSN1+PBhZWRkSJKefvpptbS0aN68ebp06ZJGjBiht99+W8nJydEeBQAAdFFRD5QtW7Zc97xlWSovL1d5eXm0vzQAAOgm+CweAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMaJeqBUVFTonnvuUXJystLT0zV16lR99NFHYWvmzJkjy7LCtpEjR0Z7FAAA0EVFPVCqqqo0f/58HT58WJWVlfriiy9UUFCg5ubmsHUTJ05UfX19aNu1a1e0RwEAAF1UYrSf8K233grbX7t2rdLT03X06FHdf//9oeM2m01OpzPaXx4AAHQDMb8HxefzSZJSU1PDju/bt0/p6em68847VVRUpIaGhms+RyAQkN/vD9sAAED3FdNACQaDKikp0ejRo5WdnR06XlhYqE2bNmnv3r1aunSpqqurNXbsWAUCgU6fp6KiQna7PbR5PJ5Yjg0AAOIs6m/x/LennnpKH3zwgQ4ePBh2fMaMGaH/zs7O1vDhw5WRkaGdO3dq2rRpHZ6nrKxMJSUloX2/30+kAADQjcUsUBYsWKAdO3Zo//79GjBgwHXXulwuZWRkqKamptPzNptNNpstFmMCAAADRT1QgsGgFixYoG3btmnfvn3KzMz8ysdcvHhRdXV1crlc0R4HAAB0QVG/B2X+/PnauHGjNm/erOTkZHm9Xnm9XrW0tEiSmpqaVFpaqvfee09nzpzRvn37NHnyZKWlpenhhx+O9jgAAKALivoVlFWrVkmS8vLywo6vXbtWc+bMUUJCgk6cOKENGzbo8uXLcrlcys/P19atW5WcnBztcQAAQBcUk7d4rqd3797avXt3tL8sAADoRvgsHgAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBx4hooL7/8sjIzM3XbbbcpJydHBw4ciOc4AADAEHELlK1bt6q4uFjPPvus/vKXv+i+++5TYWGhzp49G6+RAACAIeIWKMuWLdPcuXP1ox/9SN/+9re1YsUKeTwerVq1Kl4jAQAAQyTG44u2trbq6NGjWrRoUdjxgoICHTp0qMP6QCCgQCAQ2vf5fJIkv98f20ENdCXwebxHwC3UE/8f78n4/u5ZeuL399XXHAwGv3JtXALlwoULam9vl8PhCDvucDjk9Xo7rK+oqNBzzz3X4bjH44nZjIAJ7CviPQGAWOnJ39+NjY2y2+3XXROXQLnKsqyw/WAw2OGYJJWVlamkpCS0f+XKFf3rX/9Sv379Ol2P7sXv98vj8aiurk4pKSnxHgdAFPH93bMEg0E1NjbK7XZ/5dq4BEpaWpoSEhI6XC1paGjocFVFkmw2m2w2W9ix22+/PZYjwkApKSn8AAO6Kb6/e46vunJyVVxukk1KSlJOTo4qKyvDjldWVio3NzceIwEAAIPE7S2ekpIS/eAHP9Dw4cN177336pVXXtHZs2f15JNPxmskAABgiLgFyowZM3Tx4kX94he/UH19vbKzs7Vr1y5lZGTEayQYymazafHixR3e5gPQ9fH9jWuxgv/L7/oAAADcQnwWDwAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwTlz/1D0AoGdqb2/XhQsXZFmW+vXrp4SEhHiPBMNwBQVGa29v12effaaGhga1t7fHexwAN2nbtm0aNWqU+vTpI7fbLZfLpT59+mjUqFHavn17vMeDQQgUGIkfYkD3s3r1as2cOVN33XWXtm7dqoMHD+rAgQPaunWr7rrrLs2cOVOvvvpqvMeEIfhDbTDO6tWr9ZOf/ESPP/64JkyYIIfDoWAwqIaGBu3evVtr167VSy+9pKKioniPCiAC3/zmN1VWVqa5c+d2en7NmjX61a9+pY8//vgWTwYTESgwDj/EgO6pd+/eOn78uL71rW91ev5vf/ub7r77brW0tNziyWAi3uKBcf7xj39o9OjR1zyfm5ur8+fP38KJAETD4MGD9corr1zz/KuvvqrBgwffwolgMn6LB8a5+kNs6dKlnZ7nhxjQNS1dulSTJk3SW2+9pYKCAjkcDlmWJa/Xq8rKSn366afatWtXvMeEIXiLB8apqqrSpEmTlJGRcd0fYvfdd1+8RwUQoTNnzmjVqlU6fPiwvF6vJMnpdOree+/Vk08+qUGDBsV3QBiDQIGR+CEGAD0bgQIAAIzDTbIAACPMnj1bY8eOjfcYMASBgi6HH2JA9+R2u5WRkRHvMWAIfosHXY7b7dbXvkZbA91NRUVFvEeAQbgHBQBwy5w7d06rVq3SoUOH5PV6ZVmWHA6HcnNz9eMf/1gDBgyI94gwBIGCLqeurk6LFy/WmjVr4j0KgAgcPHhQhYWF8ng8oT8hcPVjLCorK1VXV6c///nPGjVqVLxHhQEIFHQ577//voYNG8anGwNdzD333KPRo0dr+fLlnZ7/6U9/qoMHD6q6uvoWTwYTESgwzo4dO657/pNPPtHChQsJFKCL4bN4EAlukoVxpk6dKsuydL12tizrFk4EIBpcLpcOHTp0zUB577335HK5bvFUMBWBAuO4XC799re/1dSpUzs9f/z4ceXk5NzaoQDctNLSUj355JM6evSoxo8f3+FjLH73u99pxYoV8R4ThiBQYJycnBwdO3bsmoHyVVdXAJhp3rx56tevn5YvX67Vq1eH3qZNSEhQTk6ONmzYoOnTp8d5SpiCe1BgnAMHDqi5uVkTJ07s9Hxzc7OOHDmiMWPG3OLJAERLW1ubLly4IElKS0tTr1694jwRTEOgAAAA4/DnOAEAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADG+T/VVs+FuDoj5wAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data[1].value_counts().plot.bar()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "973dcd7c-4c13-4992-bd5a-21419c245e11", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b13946d0-7731-459d-b1bd-e2a89752df0e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}