had to change python verion to 14.3 to match system python
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@@ -2,7 +2,7 @@
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"execution_count": 1,
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"id": "Q6ifg03dKPR4"
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"id": "Q6ifg03dKPR4"
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@@ -19,7 +19,7 @@
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 2,
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"metadata": {
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"metadata": {
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"id": "0ghtg7ecRQ50"
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"id": "0ghtg7ecRQ50"
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@@ -40,13 +40,7 @@
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"cell_type": "code",
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"cell_type": "code",
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"source": [
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"execution_count": 3,
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"####################################\n",
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"from sklearn.datasets import load_iris\n",
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"data = load_iris()\n",
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"df = pd.DataFrame(data.data, columns=data.feature_names)\n",
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"df"
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],
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"metadata": {
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"metadata": {
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"colab": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"base_uri": "https://localhost:8080/",
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@@ -55,31 +49,11 @@
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"id": "DjCaxH3BKJ_O",
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"id": "DjCaxH3BKJ_O",
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"outputId": "ab33e45d-d332-412e-db2a-88cc4c9528f4"
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"execution_count": null,
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{
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"output_type": "execute_result",
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"data": {
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" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
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"0 5.1 3.5 1.4 0.2\n",
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"1 4.9 3.0 1.4 0.2\n",
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"2 4.7 3.2 1.3 0.2\n",
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"3 4.6 3.1 1.5 0.2\n",
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"4 5.0 3.6 1.4 0.2\n",
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".. ... ... ... ...\n",
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"145 6.7 3.0 5.2 2.3\n",
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"146 6.3 2.5 5.0 1.9\n",
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"147 6.5 3.0 5.2 2.0\n",
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"148 6.2 3.4 5.4 2.3\n",
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"149 5.9 3.0 5.1 1.8\n",
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"\n",
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"[150 rows x 4 columns]"
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],
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"text/html": [
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"text/html": [
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"\n",
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"<div>\n",
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" <div id=\"df-a3976c98-d8d9-4643-9f56-c2532de2159a\" class=\"colab-df-container\">\n",
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" <div>\n",
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"<style scoped>\n",
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"<style scoped>\n",
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"</table>\n",
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"<p>150 rows × 4 columns</p>\n",
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"\n",
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" background-color: #E2EBFA;\n",
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"\n",
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" }\n",
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"\n",
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" background-color: #3B4455;\n",
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" }\n",
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"\n",
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" [theme=dark] .colab-df-convert:hover {\n",
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" background-color: #434B5C;\n",
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" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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" }\n",
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"\n",
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" <script>\n",
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" const buttonEl =\n",
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" document.querySelector('#df-a3976c98-d8d9-4643-9f56-c2532de2159a button.colab-df-convert');\n",
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" buttonEl.style.display =\n",
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" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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"\n",
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" async function convertToInteractive(key) {\n",
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" const element = document.querySelector('#df-a3976c98-d8d9-4643-9f56-c2532de2159a');\n",
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" const dataTable =\n",
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" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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" [key], {});\n",
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" if (!dataTable) return;\n",
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"\n",
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" const docLinkHtml = 'Like what you see? Visit the ' +\n",
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" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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" + ' to learn more about interactive tables.';\n",
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" element.innerHTML = '';\n",
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" dataTable['output_type'] = 'display_data';\n",
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||||||
" await google.colab.output.renderOutput(dataTable, element);\n",
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" const docLink = document.createElement('div');\n",
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" docLink.innerHTML = docLinkHtml;\n",
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" background-color: #E8F0FE;\n",
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" display: none;\n",
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" width: 32px;\n",
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" }\n",
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"\n",
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" background-color: #E2EBFA;\n",
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" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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" fill: #174EA6;\n",
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" }\n",
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"\n",
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" background-color: #3B4455;\n",
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" fill: #D2E3FC;\n",
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" }\n",
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"\n",
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" background-color: #434B5C;\n",
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" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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" fill: #FFFFFF;\n",
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" }\n",
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" </style>\n",
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||||||
" <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
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" title=\"Generate code using this dataframe.\"\n",
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" style=\"display:none;\">\n",
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"\n",
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" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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"\n",
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" buttonEl.onclick = () => {\n",
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" google.colab.notebook.generateWithVariable('df');\n",
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" }\n",
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" })();\n",
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" </script>\n",
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],
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"application/vnd.google.colaboratory.intrinsic+json": {
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"text/plain": [
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"type": "dataframe",
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" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
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"variable_name": "df",
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"0 5.1 3.5 1.4 0.2\n",
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"summary": "{\n \"name\": \"df\",\n \"rows\": 150,\n \"fields\": [\n {\n \"column\": \"sepal length (cm)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.8280661279778629,\n \"min\": 4.3,\n \"max\": 7.9,\n \"num_unique_values\": 35,\n \"samples\": [\n 6.2,\n 4.5,\n 5.6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sepal width (cm)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.435866284936698,\n \"min\": 2.0,\n \"max\": 4.4,\n \"num_unique_values\": 23,\n \"samples\": [\n 2.3,\n 4.0,\n 3.5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"petal length (cm)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.7652982332594667,\n \"min\": 1.0,\n \"max\": 6.9,\n \"num_unique_values\": 43,\n \"samples\": [\n 6.7,\n 3.8,\n 3.7\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"petal width (cm)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.7622376689603465,\n \"min\": 0.1,\n \"max\": 2.5,\n \"num_unique_values\": 22,\n \"samples\": [\n 0.2,\n 1.2,\n 1.3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
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"1 4.9 3.0 1.4 0.2\n",
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}
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"2 4.7 3.2 1.3 0.2\n",
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"3 4.6 3.1 1.5 0.2\n",
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"4 5.0 3.6 1.4 0.2\n",
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".. ... ... ... ...\n",
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"145 6.7 3.0 5.2 2.3\n",
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"146 6.3 2.5 5.0 1.9\n",
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"147 6.5 3.0 5.2 2.0\n",
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"148 6.2 3.4 5.4 2.3\n",
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"149 5.9 3.0 5.1 1.8\n",
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"\n",
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"[150 rows x 4 columns]"
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]
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},
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},
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"execution_count": 3,
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"metadata": {},
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"metadata": {},
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"execution_count": 9
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"output_type": "execute_result"
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}
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}
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],
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"source": [
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"####################################\n",
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"from sklearn.datasets import load_iris\n",
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"data = load_iris()\n",
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"df = pd.DataFrame(data.data, columns=data.feature_names)\n",
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"df"
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]
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 4,
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"metadata": {
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"metadata": {
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"id": "ANJhDRNPKPR5"
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"id": "ANJhDRNPKPR5"
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"execution_count": null,
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"execution_count": 5,
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"metadata": {
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"metadata": {
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"id": "8v3oVZYTKPR6",
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"colab": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"base_uri": "https://localhost:8080/",
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"height": 361
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"id": "8v3oVZYTKPR6",
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"outputId": "4110ac58-5c52-4cbf-a83f-16d9a7189ccb"
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"outputId": "4110ac58-5c52-4cbf-a83f-16d9a7189ccb"
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},
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"outputs": [
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"outputs": [
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{
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{
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"output_type": "execute_result",
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"data": {
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"data": {
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"text/plain": [
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" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
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"0 5.1 3.5 1.4 0.2\n",
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"1 4.9 3.0 1.4 0.2\n",
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"2 4.7 3.2 1.3 0.2\n",
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"3 4.6 3.1 1.5 0.2\n",
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".. ... ... ... ...\n",
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"146 6.3 2.5 5.0 1.9\n",
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"147 6.5 3.0 5.2 2.0\n",
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"148 6.2 3.4 5.4 2.3\n",
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"149 5.9 3.0 5.1 1.8\n",
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"\n",
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"text/html": [
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"\n",
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"<div>\n",
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" <div id=\"df-f9797485-14a5-43a3-a42e-5084fa2ba17a\" class=\"colab-df-container\">\n",
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||||||
" <div>\n",
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||||||
"<style scoped>\n",
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"<style scoped>\n",
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||||||
" .dataframe tbody tr th:only-of-type {\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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@@ -477,153 +316,26 @@
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" </tbody>\n",
|
" </tbody>\n",
|
||||||
"</table>\n",
|
"</table>\n",
|
||||||
"<p>150 rows × 4 columns</p>\n",
|
"<p>150 rows × 4 columns</p>\n",
|
||||||
"</div>\n",
|
"</div>"
|
||||||
" <div class=\"colab-df-buttons\">\n",
|
|
||||||
"\n",
|
|
||||||
" <div class=\"colab-df-container\">\n",
|
|
||||||
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f9797485-14a5-43a3-a42e-5084fa2ba17a')\"\n",
|
|
||||||
" title=\"Convert this dataframe to an interactive table.\"\n",
|
|
||||||
" style=\"display:none;\">\n",
|
|
||||||
"\n",
|
|
||||||
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
|
|
||||||
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
|
|
||||||
" </svg>\n",
|
|
||||||
" </button>\n",
|
|
||||||
"\n",
|
|
||||||
" <style>\n",
|
|
||||||
" .colab-df-container {\n",
|
|
||||||
" display:flex;\n",
|
|
||||||
" gap: 12px;\n",
|
|
||||||
" }\n",
|
|
||||||
"\n",
|
|
||||||
" .colab-df-convert {\n",
|
|
||||||
" background-color: #E8F0FE;\n",
|
|
||||||
" border: none;\n",
|
|
||||||
" border-radius: 50%;\n",
|
|
||||||
" cursor: pointer;\n",
|
|
||||||
" display: none;\n",
|
|
||||||
" fill: #1967D2;\n",
|
|
||||||
" height: 32px;\n",
|
|
||||||
" padding: 0 0 0 0;\n",
|
|
||||||
" width: 32px;\n",
|
|
||||||
" }\n",
|
|
||||||
"\n",
|
|
||||||
" .colab-df-convert:hover {\n",
|
|
||||||
" background-color: #E2EBFA;\n",
|
|
||||||
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
|
||||||
" fill: #174EA6;\n",
|
|
||||||
" }\n",
|
|
||||||
"\n",
|
|
||||||
" .colab-df-buttons div {\n",
|
|
||||||
" margin-bottom: 4px;\n",
|
|
||||||
" }\n",
|
|
||||||
"\n",
|
|
||||||
" [theme=dark] .colab-df-convert {\n",
|
|
||||||
" background-color: #3B4455;\n",
|
|
||||||
" fill: #D2E3FC;\n",
|
|
||||||
" }\n",
|
|
||||||
"\n",
|
|
||||||
" [theme=dark] .colab-df-convert:hover {\n",
|
|
||||||
" background-color: #434B5C;\n",
|
|
||||||
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
|
||||||
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
|
||||||
" fill: #FFFFFF;\n",
|
|
||||||
" }\n",
|
|
||||||
" </style>\n",
|
|
||||||
"\n",
|
|
||||||
" <script>\n",
|
|
||||||
" const buttonEl =\n",
|
|
||||||
" document.querySelector('#df-f9797485-14a5-43a3-a42e-5084fa2ba17a button.colab-df-convert');\n",
|
|
||||||
" buttonEl.style.display =\n",
|
|
||||||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
|
||||||
"\n",
|
|
||||||
" async function convertToInteractive(key) {\n",
|
|
||||||
" const element = document.querySelector('#df-f9797485-14a5-43a3-a42e-5084fa2ba17a');\n",
|
|
||||||
" const dataTable =\n",
|
|
||||||
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
|
||||||
" [key], {});\n",
|
|
||||||
" if (!dataTable) return;\n",
|
|
||||||
"\n",
|
|
||||||
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
|
||||||
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
|
||||||
" + ' to learn more about interactive tables.';\n",
|
|
||||||
" element.innerHTML = '';\n",
|
|
||||||
" dataTable['output_type'] = 'display_data';\n",
|
|
||||||
" await google.colab.output.renderOutput(dataTable, element);\n",
|
|
||||||
" const docLink = document.createElement('div');\n",
|
|
||||||
" docLink.innerHTML = docLinkHtml;\n",
|
|
||||||
" element.appendChild(docLink);\n",
|
|
||||||
" }\n",
|
|
||||||
" </script>\n",
|
|
||||||
" </div>\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
" <div id=\"id_49bdecce-3201-4032-8098-3c859f8fd670\">\n",
|
|
||||||
" <style>\n",
|
|
||||||
" .colab-df-generate {\n",
|
|
||||||
" background-color: #E8F0FE;\n",
|
|
||||||
" border: none;\n",
|
|
||||||
" border-radius: 50%;\n",
|
|
||||||
" cursor: pointer;\n",
|
|
||||||
" display: none;\n",
|
|
||||||
" fill: #1967D2;\n",
|
|
||||||
" height: 32px;\n",
|
|
||||||
" padding: 0 0 0 0;\n",
|
|
||||||
" width: 32px;\n",
|
|
||||||
" }\n",
|
|
||||||
"\n",
|
|
||||||
" .colab-df-generate:hover {\n",
|
|
||||||
" background-color: #E2EBFA;\n",
|
|
||||||
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
|
||||||
" fill: #174EA6;\n",
|
|
||||||
" }\n",
|
|
||||||
"\n",
|
|
||||||
" [theme=dark] .colab-df-generate {\n",
|
|
||||||
" background-color: #3B4455;\n",
|
|
||||||
" fill: #D2E3FC;\n",
|
|
||||||
" }\n",
|
|
||||||
"\n",
|
|
||||||
" [theme=dark] .colab-df-generate:hover {\n",
|
|
||||||
" background-color: #434B5C;\n",
|
|
||||||
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
|
||||||
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
|
||||||
" fill: #FFFFFF;\n",
|
|
||||||
" }\n",
|
|
||||||
" </style>\n",
|
|
||||||
" <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
|
|
||||||
" title=\"Generate code using this dataframe.\"\n",
|
|
||||||
" style=\"display:none;\">\n",
|
|
||||||
"\n",
|
|
||||||
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
|
|
||||||
" width=\"24px\">\n",
|
|
||||||
" <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
|
|
||||||
" </svg>\n",
|
|
||||||
" </button>\n",
|
|
||||||
" <script>\n",
|
|
||||||
" (() => {\n",
|
|
||||||
" const buttonEl =\n",
|
|
||||||
" document.querySelector('#id_49bdecce-3201-4032-8098-3c859f8fd670 button.colab-df-generate');\n",
|
|
||||||
" buttonEl.style.display =\n",
|
|
||||||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
|
||||||
"\n",
|
|
||||||
" buttonEl.onclick = () => {\n",
|
|
||||||
" google.colab.notebook.generateWithVariable('df');\n",
|
|
||||||
" }\n",
|
|
||||||
" })();\n",
|
|
||||||
" </script>\n",
|
|
||||||
" </div>\n",
|
|
||||||
"\n",
|
|
||||||
" </div>\n",
|
|
||||||
" </div>\n"
|
|
||||||
],
|
],
|
||||||
"application/vnd.google.colaboratory.intrinsic+json": {
|
"text/plain": [
|
||||||
"type": "dataframe",
|
" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
|
||||||
"variable_name": "df",
|
"0 5.1 3.5 1.4 0.2\n",
|
||||||
"summary": "{\n \"name\": \"df\",\n \"rows\": 150,\n \"fields\": [\n {\n \"column\": \"sepal length (cm)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.8280661279778629,\n \"min\": 4.3,\n \"max\": 7.9,\n \"num_unique_values\": 35,\n \"samples\": [\n 6.2,\n 4.5,\n 5.6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sepal width (cm)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.435866284936698,\n \"min\": 2.0,\n \"max\": 4.4,\n \"num_unique_values\": 23,\n \"samples\": [\n 2.3,\n 4.0,\n 3.5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"petal length (cm)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.7652982332594667,\n \"min\": 1.0,\n \"max\": 6.9,\n \"num_unique_values\": 43,\n \"samples\": [\n 6.7,\n 3.8,\n 3.7\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"petal width (cm)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.7622376689603465,\n \"min\": 0.1,\n \"max\": 2.5,\n \"num_unique_values\": 22,\n \"samples\": [\n 0.2,\n 1.2,\n 1.3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\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",
|
||||||
|
".. ... ... ... ...\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": 5,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"execution_count": 10
|
"output_type": "execute_result"
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"source": [
|
"source": [
|
||||||
@@ -643,20 +355,20 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": null,
|
"execution_count": 6,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"id": "aksGQcJ8KPR6",
|
|
||||||
"colab": {
|
"colab": {
|
||||||
"base_uri": "https://localhost:8080/"
|
"base_uri": "https://localhost:8080/"
|
||||||
},
|
},
|
||||||
|
"id": "aksGQcJ8KPR6",
|
||||||
"outputId": "4cbdedf5-cabe-4263-c06c-26bbb8f509b1"
|
"outputId": "4cbdedf5-cabe-4263-c06c-26bbb8f509b1"
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"output_type": "stream",
|
|
||||||
"name": "stdout",
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
"<class 'pandas.DataFrame'>\n",
|
||||||
"RangeIndex: 150 entries, 0 to 149\n",
|
"RangeIndex: 150 entries, 0 to 149\n",
|
||||||
"Data columns (total 4 columns):\n",
|
"Data columns (total 4 columns):\n",
|
||||||
" # Column Non-Null Count Dtype \n",
|
" # Column Non-Null Count Dtype \n",
|
||||||
@@ -683,20 +395,20 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": null,
|
"execution_count": 7,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"id": "ADWpUqeKKPR7",
|
|
||||||
"colab": {
|
"colab": {
|
||||||
"base_uri": "https://localhost:8080/"
|
"base_uri": "https://localhost:8080/"
|
||||||
},
|
},
|
||||||
|
"id": "ADWpUqeKKPR7",
|
||||||
"outputId": "76ac8d2f-0d98-4721-e0f2-b79b48736b00"
|
"outputId": "76ac8d2f-0d98-4721-e0f2-b79b48736b00"
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"output_type": "stream",
|
|
||||||
"name": "stdout",
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
"<class 'pandas.DataFrame'>\n",
|
||||||
"RangeIndex: 150 entries, 0 to 149\n",
|
"RangeIndex: 150 entries, 0 to 149\n",
|
||||||
"Data columns (total 4 columns):\n",
|
"Data columns (total 4 columns):\n",
|
||||||
" # Column Non-Null Count Dtype \n",
|
" # Column Non-Null Count Dtype \n",
|
||||||
@@ -727,22 +439,22 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": null,
|
"execution_count": 8,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"id": "9RAydRsiKPR7",
|
|
||||||
"colab": {
|
"colab": {
|
||||||
"base_uri": "https://localhost:8080/"
|
"base_uri": "https://localhost:8080/"
|
||||||
},
|
},
|
||||||
|
"id": "9RAydRsiKPR7",
|
||||||
"outputId": "18ec37ec-3a95-4ef9-c111-847ac8f6ecec"
|
"outputId": "18ec37ec-3a95-4ef9-c111-847ac8f6ecec"
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"output_type": "stream",
|
|
||||||
"name": "stdout",
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"FEATURES: Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
|
"FEATURES: Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
|
||||||
" 'petal width (cm)'],\n",
|
" 'petal width (cm)'],\n",
|
||||||
" dtype='object')\n",
|
" dtype='str')\n",
|
||||||
"\n",
|
"\n",
|
||||||
"First feature: sepal length (cm)\n",
|
"First feature: sepal length (cm)\n",
|
||||||
"\n",
|
"\n",
|
||||||
@@ -774,21 +486,21 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": null,
|
"execution_count": 9,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"id": "PWkPTOGnKPR8",
|
|
||||||
"colab": {
|
"colab": {
|
||||||
"base_uri": "https://localhost:8080/",
|
"base_uri": "https://localhost:8080/",
|
||||||
"height": 1000
|
"height": 1000
|
||||||
},
|
},
|
||||||
|
"id": "PWkPTOGnKPR8",
|
||||||
"outputId": "f6a46e8f-0169-4646-864f-1b3ed749f238"
|
"outputId": "f6a46e8f-0169-4646-864f-1b3ed749f238"
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"output_type": "stream",
|
|
||||||
"name": "stdout",
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
"<class 'pandas.DataFrame'>\n",
|
||||||
"RangeIndex: 150 entries, 0 to 149\n",
|
"RangeIndex: 150 entries, 0 to 149\n",
|
||||||
"Data columns (total 4 columns):\n",
|
"Data columns (total 4 columns):\n",
|
||||||
" # Column Non-Null Count Dtype \n",
|
" # Column Non-Null Count Dtype \n",
|
||||||
@@ -816,24 +528,24 @@
|
|||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"output_type": "error",
|
|
||||||
"ename": "KeyError",
|
"ename": "KeyError",
|
||||||
"evalue": "'irisname'",
|
"evalue": "'irisname'",
|
||||||
|
"output_type": "error",
|
||||||
"traceback": [
|
"traceback": [
|
||||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
||||||
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
|
"\u001b[31mKeyError\u001b[39m Traceback (most recent call last)",
|
||||||
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3804\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3805\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3806\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
"\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.14/site-packages/pandas/core/indexes/base.py:3641\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 3640\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m3641\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_engine\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3642\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
|
||||||
"\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
|
"\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:168\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m168\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n",
|
||||||
"\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
|
"\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:197\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m197\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n",
|
||||||
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
|
"\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7668\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m7668\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n",
|
||||||
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
|
"\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7676\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m7676\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n",
|
||||||
"\u001b[0;31mKeyError\u001b[0m: 'irisname'",
|
"\u001b[31mKeyError\u001b[39m: 'irisname'",
|
||||||
"\nThe above exception was the direct cause of the following exception:\n",
|
"\nThe above exception was the direct cause of the following exception:\n",
|
||||||
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
|
"\u001b[31mKeyError\u001b[39m Traceback (most recent call last)",
|
||||||
"\u001b[0;32m/tmp/ipykernel_6512/325098609.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;31m# or more to the point, grab that column and inspect:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Irisname entries: {df_clean['irisname'].unique()}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;31m# also note how the last two rows in the df have problems, among others...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 18\u001b[39m\n\u001b[32m 14\u001b[39m print(df_clean)\n\u001b[32m 15\u001b[39m print(\u001b[33m'='\u001b[39m * \u001b[32m70\u001b[39m)\n\u001b[32m 16\u001b[39m \n\u001b[32m 17\u001b[39m \u001b[38;5;66;03m# or more to the point, grab that column and inspect:\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m18\u001b[39m print(f\"Irisname entries: {df_clean[\u001b[33m'irisname'\u001b[39m].unique()}\")\n\u001b[32m 19\u001b[39m \n\u001b[32m 20\u001b[39m \u001b[38;5;66;03m# also note how the last two rows in the df have problems, among others...\u001b[39;00m\n",
|
||||||
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 4100\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4101\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4102\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4103\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4104\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
"\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.14/site-packages/pandas/core/frame.py:4378\u001b[39m, in \u001b[36mDataFrame.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 4374\u001b[39m \n\u001b[32m 4375\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_single_key:\n\u001b[32m 4376\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m self.columns.nlevels > \u001b[32m1\u001b[39m:\n\u001b[32m 4377\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m self._getitem_multilevel(key)\n\u001b[32m-> \u001b[39m\u001b[32m4378\u001b[39m indexer = self.columns.get_loc(key)\n\u001b[32m 4379\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[32m 4380\u001b[39m indexer = [indexer]\n\u001b[32m 4381\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
|
||||||
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3810\u001b[0m ):\n\u001b[1;32m 3811\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3812\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3813\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3814\u001b[0m \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
"\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.14/site-packages/pandas/core/indexes/base.py:3648\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 3643\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[32m 3644\u001b[39m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc.Iterable)\n\u001b[32m 3645\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[32m 3646\u001b[39m ):\n\u001b[32m 3647\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3648\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m 3649\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[32m 3650\u001b[39m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[32m 3651\u001b[39m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[32m 3652\u001b[39m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[32m 3653\u001b[39m \u001b[38;5;28mself\u001b[39m._check_indexing_error(key)\n",
|
||||||
"\u001b[0;31mKeyError\u001b[0m: 'irisname'"
|
"\u001b[31mKeyError\u001b[39m: 'irisname'"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -862,9 +574,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"source": [
|
"execution_count": null,
|
||||||
"df.columns"
|
|
||||||
],
|
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"colab": {
|
"colab": {
|
||||||
"base_uri": "https://localhost:8080/"
|
"base_uri": "https://localhost:8080/"
|
||||||
@@ -872,10 +582,8 @@
|
|||||||
"id": "4B2An3iML1ay",
|
"id": "4B2An3iML1ay",
|
||||||
"outputId": "5492db65-acd8-4c11-f669-742a79b6cb62"
|
"outputId": "5492db65-acd8-4c11-f669-742a79b6cb62"
|
||||||
},
|
},
|
||||||
"execution_count": null,
|
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"output_type": "execute_result",
|
|
||||||
"data": {
|
"data": {
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
|
"Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
|
||||||
@@ -883,9 +591,13 @@
|
|||||||
" dtype='object')"
|
" dtype='object')"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
|
"execution_count": 19,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"execution_count": 19
|
"output_type": "execute_result"
|
||||||
}
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"df.columns"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@@ -1763,7 +1475,7 @@
|
|||||||
"name": "python",
|
"name": "python",
|
||||||
"nbconvert_exporter": "python",
|
"nbconvert_exporter": "python",
|
||||||
"pygments_lexer": "ipython3",
|
"pygments_lexer": "ipython3",
|
||||||
"version": "3.12.2"
|
"version": "3.14.3"
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
"nbformat": 4,
|
"nbformat": 4,
|
||||||
|
|||||||
Reference in New Issue
Block a user