{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example notebook" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)
05.13.51.40.2
14.93.01.40.2
24.73.21.30.2
34.63.11.50.2
45.03.61.40.2
...............
1456.73.05.22.3
1466.32.55.01.9
1476.53.05.22.0
1486.23.45.42.3
1495.93.05.11.8
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150 rows × 4 columns

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" ], "text/plain": [ " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", "0 5.1 3.5 1.4 0.2\n", "1 4.9 3.0 1.4 0.2\n", "2 4.7 3.2 1.3 0.2\n", "3 4.6 3.1 1.5 0.2\n", "4 5.0 3.6 1.4 0.2\n", ".. ... ... ... ...\n", "145 6.7 3.0 5.2 2.3\n", "146 6.3 2.5 5.0 1.9\n", "147 6.5 3.0 5.2 2.0\n", "148 6.2 3.4 5.4 2.3\n", "149 5.9 3.0 5.1 1.8\n", "\n", "[150 rows x 4 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "from sklearn.datasets import load_iris\n", "\n", "iris = load_iris()\n", "df = pd.DataFrame(iris.data, columns=iris.feature_names)\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3.9.12 ('py39')", "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": "3.9.12" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "a499a41969c0d18fcc13633407a37f033c7498c9502f1cce41536c22334cc4bb" } } }, "nbformat": 4, "nbformat_minor": 2 }