{"cells": [{"cell_type": "markdown", "metadata": {"papermill": {"exception": false, "start_time": "2020-10-29T02:58:14.912145", "end_time": "2020-10-29T02:58:14.924131", "duration": 0.011986, "status": "completed"}, "tags": []}, "source": "# Scatter Plots"}, {"cell_type": "code", "execution_count": 1, "metadata": {"execution": {"iopub.execute_input": "2020-10-29T02:58:14.950137Z", "iopub.status.busy": "2020-10-29T02:58:14.949177Z", "iopub.status.idle": "2020-10-29T02:58:17.386167Z", "shell.execute_reply": "2020-10-29T02:58:17.386167Z"}, "papermill": {"exception": false, "start_time": "2020-10-29T02:58:14.933133", "end_time": "2020-10-29T02:58:17.387160", "duration": 2.454027, "status": "completed"}, "tags": []}, "outputs": [], "source": "import pandas as pd\nimport data_describe as dd"}, {"cell_type": "code", "execution_count": 2, "metadata": {"execution": {"iopub.execute_input": "2020-10-29T02:58:17.411166Z", "iopub.status.busy": "2020-10-29T02:58:17.410167Z", "iopub.status.idle": "2020-10-29T02:58:17.500160Z", "shell.execute_reply": "2020-10-29T02:58:17.499169Z"}, "papermill": {"exception": false, "start_time": "2020-10-29T02:58:17.396134", "end_time": "2020-10-29T02:58:17.500160", "duration": 0.104026, "status": "completed"}, "tags": []}, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "(442, 11)"}, "execution_count": 2}], "source": "from sklearn.datasets import load_diabetes\ndata = load_diabetes()\ndf = pd.DataFrame(data.data, columns=list(data.feature_names))\ndf['target'] = data.target\ndf.shape"}, {"cell_type": "code", "execution_count": 3, "metadata": {"execution": {"iopub.execute_input": "2020-10-29T02:58:17.534162Z", "iopub.status.busy": "2020-10-29T02:58:17.533160Z", "iopub.status.idle": "2020-10-29T02:58:17.545131Z", "shell.execute_reply": "2020-10-29T02:58:17.546160Z"}, "papermill": {"exception": false, "start_time": "2020-10-29T02:58:17.511136", "end_time": "2020-10-29T02:58:17.546160", "duration": 0.035024, "status": "completed"}, "tags": []}, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": " age sex bmi bp s1 s2 s3 \\\n0 0.038076 0.050680 0.061696 0.021872 -0.044223 -0.034821 -0.043401 \n1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163 0.074412 \n\n s4 s5 s6 target \n0 -0.002592 0.019908 -0.017646 151.0 \n1 -0.039493 -0.068330 -0.092204 75.0 ", "text/html": "
\n | age | \nsex | \nbmi | \nbp | \ns1 | \ns2 | \ns3 | \ns4 | \ns5 | \ns6 | \ntarget | \n
---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n0.038076 | \n0.050680 | \n0.061696 | \n0.021872 | \n-0.044223 | \n-0.034821 | \n-0.043401 | \n-0.002592 | \n0.019908 | \n-0.017646 | \n151.0 | \n
1 | \n-0.001882 | \n-0.044642 | \n-0.051474 | \n-0.026328 | \n-0.008449 | \n-0.019163 | \n0.074412 | \n-0.039493 | \n-0.068330 | \n-0.092204 | \n75.0 | \n