{ "cells": [ { "cell_type": "markdown", "id": "4a64a25d", "metadata": {}, "source": [ "# pandas: Profile DataFrame" ] }, { "cell_type": "markdown", "id": "728144e4", "metadata": {}, "source": [ "## Package Import" ] }, { "cell_type": "code", "execution_count": 1, "id": "6d077a6a", "metadata": { "execution": { "iopub.execute_input": "2025-09-02T18:24:54.825809Z", "iopub.status.busy": "2025-09-02T18:24:54.825673Z", "iopub.status.idle": "2025-09-02T18:24:55.015759Z", "shell.execute_reply": "2025-09-02T18:24:55.015081Z" }, "tags": [ "hide-output", "scroll-output" ] }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "markdown", "id": "094df23f", "metadata": {}, "source": [ "## Dataset Import" ] }, { "cell_type": "markdown", "id": "f081f68c", "metadata": {}, "source": [ "The dataset used in this notebook is from [Kaggle - Pokemon](https://www.kaggle.com/datasets/abcsds/pokemon)." ] }, { "cell_type": "code", "execution_count": 2, "id": "eef143f1", "metadata": { "execution": { "iopub.execute_input": "2025-09-02T18:24:55.017703Z", "iopub.status.busy": "2025-09-02T18:24:55.017541Z", "iopub.status.idle": "2025-09-02T18:24:55.021904Z", "shell.execute_reply": "2025-09-02T18:24:55.021481Z" }, "tags": [ "hide-output", "scroll-output" ] }, "outputs": [], "source": [ "data = pd.read_csv('data/Pokemon.csv')" ] }, { "cell_type": "markdown", "id": "2d5f3a12", "metadata": {}, "source": [ "## Profile a DataFrame" ] }, { "cell_type": "code", "execution_count": 3, "id": "4d78cc8c", "metadata": { "execution": { "iopub.execute_input": "2025-09-02T18:24:55.023384Z", "iopub.status.busy": "2025-09-02T18:24:55.023261Z", "iopub.status.idle": "2025-09-02T18:25:06.131459Z", "shell.execute_reply": "2025-09-02T18:25:06.131053Z" }, "tags": [ "hide-output", "scroll-output" ] }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", " Upgrade to ydata-sdk\n", "

\n", " Improve your data and profiling with ydata-sdk, featuring data quality scoring, redundancy detection, outlier identification, text validation, and synthetic data generation.\n", "

\n", "
\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3d2e35a3b1c5489fa446cd53e3a9dcd3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Summarize dataset: 0%| | 0/5 [00:00" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ydata_profiling import ProfileReport\n", "\n", "report = ProfileReport(data)\n", "report.to_notebook_iframe()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (code-venv)", "language": "python", "name": "code-venv" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "0e38d630a75345a393c5f084546799d6": { "model_module": "@jupyter-widgets/base", 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