{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "bd557e01", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python: 3.9.12 (main, Apr 4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]\n", "numpy: 1.21.5\n", "pandas: 1.4.2\n", "matplotlib: 3.5.1\n", "seaborn: 0.11.2\n", "sklearn: 1.0.2\n" ] } ], "source": [ "import sys\n", "print(f'Python: {sys.version}')\n", " \n", "import numpy as np\n", "print(f'numpy: {np.__version__}')\n", " \n", "import pandas as pd\n", "print(f'pandas: {pd.__version__}')\n", " \n", "import matplotlib\n", "print(f'matplotlib: {matplotlib.__version__}')\n", " \n", "import seaborn as sns\n", "print(f'seaborn: {sns.__version__}')\n", " \n", "import sklearn as sk\n", "print(f'sklearn: {sk.__version__}')" ] }, { "cell_type": "code", "execution_count": 2, "id": "df976e18", "metadata": {}, "outputs": [], "source": [ "iris = sns.load_dataset('iris')" ] }, { "cell_type": "code", "execution_count": 3, "id": "15fc8379", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(iris)" ] }, { "cell_type": "code", "execution_count": 4, "id": "31d83945", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | sepal_length | \n", "sepal_width | \n", "petal_length | \n", "petal_width | \n", "species | \n", "
---|---|---|---|---|---|
0 | \n", "5.1 | \n", "3.5 | \n", "1.4 | \n", "0.2 | \n", "setosa | \n", "
1 | \n", "4.9 | \n", "3.0 | \n", "1.4 | \n", "0.2 | \n", "setosa | \n", "
2 | \n", "4.7 | \n", "3.2 | \n", "1.3 | \n", "0.2 | \n", "setosa | \n", "
3 | \n", "4.6 | \n", "3.1 | \n", "1.5 | \n", "0.2 | \n", "setosa | \n", "
4 | \n", "5.0 | \n", "3.6 | \n", "1.4 | \n", "0.2 | \n", "setosa | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
145 | \n", "6.7 | \n", "3.0 | \n", "5.2 | \n", "2.3 | \n", "virginica | \n", "
146 | \n", "6.3 | \n", "2.5 | \n", "5.0 | \n", "1.9 | \n", "virginica | \n", "
147 | \n", "6.5 | \n", "3.0 | \n", "5.2 | \n", "2.0 | \n", "virginica | \n", "
148 | \n", "6.2 | \n", "3.4 | \n", "5.4 | \n", "2.3 | \n", "virginica | \n", "
149 | \n", "5.9 | \n", "3.0 | \n", "5.1 | \n", "1.8 | \n", "virginica | \n", "
150 rows × 5 columns
\n", "\n", " | sepal_length | \n", "sepal_width | \n", "petal_length | \n", "petal_width | \n", "
---|---|---|---|---|
count | \n", "150.000000 | \n", "150.000000 | \n", "150.000000 | \n", "150.000000 | \n", "
mean | \n", "5.843333 | \n", "3.057333 | \n", "3.758000 | \n", "1.199333 | \n", "
std | \n", "0.828066 | \n", "0.435866 | \n", "1.765298 | \n", "0.762238 | \n", "
min | \n", "4.300000 | \n", "2.000000 | \n", "1.000000 | \n", "0.100000 | \n", "
25% | \n", "5.100000 | \n", "2.800000 | \n", "1.600000 | \n", "0.300000 | \n", "
50% | \n", "5.800000 | \n", "3.000000 | \n", "4.350000 | \n", "1.300000 | \n", "
75% | \n", "6.400000 | \n", "3.300000 | \n", "5.100000 | \n", "1.800000 | \n", "
max | \n", "7.900000 | \n", "4.400000 | \n", "6.900000 | \n", "2.500000 | \n", "
\n", " | PCA1 | \n", "PCA2 | \n", "species | \n", "
---|---|---|---|
0 | \n", "-2.684126 | \n", "0.319397 | \n", "setosa | \n", "
1 | \n", "-2.714142 | \n", "-0.177001 | \n", "setosa | \n", "
2 | \n", "-2.888991 | \n", "-0.144949 | \n", "setosa | \n", "
3 | \n", "-2.745343 | \n", "-0.318299 | \n", "setosa | \n", "
4 | \n", "-2.728717 | \n", "0.326755 | \n", "setosa | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "
145 | \n", "1.944110 | \n", "0.187532 | \n", "virginica | \n", "
146 | \n", "1.527167 | \n", "-0.375317 | \n", "virginica | \n", "
147 | \n", "1.764346 | \n", "0.078859 | \n", "virginica | \n", "
148 | \n", "1.900942 | \n", "0.116628 | \n", "virginica | \n", "
149 | \n", "1.390189 | \n", "-0.282661 | \n", "virginica | \n", "
150 rows × 3 columns
\n", "