{ "cells": [ { "cell_type": "markdown", "id": "213a38a2", "metadata": {}, "source": [ "# Pandas DataFrame\n", "\n", "This notebook goes over how to load data from a [pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/index.html) DataFrame." ] }, { "cell_type": "code", "execution_count": null, "id": "f6a7a9e4-80d6-486a-b2e3-636c568aa97c", "metadata": {}, "outputs": [], "source": [ "#!pip install pandas" ] }, { "cell_type": "code", "execution_count": 1, "id": "79331964", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "id": "e487044c", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('example_data/mlb_teams_2012.csv')" ] }, { "cell_type": "code", "execution_count": 6, "id": "ac273ca1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Team\"Payroll (millions)\"\"Wins\"
0Nationals81.3498
1Reds82.2097
2Yankees197.9695
3Giants117.6294
4Braves83.3194
\n", "
" ], "text/plain": [ " Team \"Payroll (millions)\" \"Wins\"\n", "0 Nationals 81.34 98\n", "1 Reds 82.20 97\n", "2 Yankees 197.96 95\n", "3 Giants 117.62 94\n", "4 Braves 83.31 94" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "66e47a13", "metadata": {}, "outputs": [], "source": [ "from langchain.document_loaders import DataFrameLoader" ] }, { "cell_type": "code", "execution_count": 7, "id": "2334caca", "metadata": {}, "outputs": [], "source": [ "loader = DataFrameLoader(df, page_content_column=\"Team\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "d616c2b0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Document(page_content='Nationals', metadata={' \"Payroll (millions)\"': 81.34, ' \"Wins\"': 98}),\n", " Document(page_content='Reds', metadata={' \"Payroll (millions)\"': 82.2, ' \"Wins\"': 97}),\n", " Document(page_content='Yankees', metadata={' \"Payroll (millions)\"': 197.96, ' \"Wins\"': 95}),\n", " Document(page_content='Giants', metadata={' \"Payroll (millions)\"': 117.62, ' \"Wins\"': 94}),\n", " Document(page_content='Braves', metadata={' \"Payroll (millions)\"': 83.31, ' \"Wins\"': 94}),\n", " Document(page_content='Athletics', metadata={' \"Payroll (millions)\"': 55.37, ' \"Wins\"': 94}),\n", " Document(page_content='Rangers', metadata={' \"Payroll (millions)\"': 120.51, ' \"Wins\"': 93}),\n", " Document(page_content='Orioles', metadata={' \"Payroll (millions)\"': 81.43, ' \"Wins\"': 93}),\n", " Document(page_content='Rays', metadata={' \"Payroll (millions)\"': 64.17, ' \"Wins\"': 90}),\n", " Document(page_content='Angels', metadata={' \"Payroll (millions)\"': 154.49, ' \"Wins\"': 89}),\n", " Document(page_content='Tigers', metadata={' \"Payroll (millions)\"': 132.3, ' \"Wins\"': 88}),\n", " Document(page_content='Cardinals', metadata={' \"Payroll (millions)\"': 110.3, ' \"Wins\"': 88}),\n", " Document(page_content='Dodgers', metadata={' \"Payroll (millions)\"': 95.14, ' \"Wins\"': 86}),\n", " Document(page_content='White Sox', metadata={' \"Payroll (millions)\"': 96.92, ' \"Wins\"': 85}),\n", " Document(page_content='Brewers', metadata={' \"Payroll (millions)\"': 97.65, ' \"Wins\"': 83}),\n", " Document(page_content='Phillies', metadata={' \"Payroll (millions)\"': 174.54, ' \"Wins\"': 81}),\n", " Document(page_content='Diamondbacks', metadata={' \"Payroll (millions)\"': 74.28, ' \"Wins\"': 81}),\n", " Document(page_content='Pirates', metadata={' \"Payroll (millions)\"': 63.43, ' \"Wins\"': 79}),\n", " Document(page_content='Padres', metadata={' \"Payroll (millions)\"': 55.24, ' \"Wins\"': 76}),\n", " Document(page_content='Mariners', metadata={' \"Payroll (millions)\"': 81.97, ' \"Wins\"': 75}),\n", " Document(page_content='Mets', metadata={' \"Payroll (millions)\"': 93.35, ' \"Wins\"': 74}),\n", " Document(page_content='Blue Jays', metadata={' \"Payroll (millions)\"': 75.48, ' \"Wins\"': 73}),\n", " Document(page_content='Royals', metadata={' \"Payroll (millions)\"': 60.91, ' \"Wins\"': 72}),\n", " Document(page_content='Marlins', metadata={' \"Payroll (millions)\"': 118.07, ' \"Wins\"': 69}),\n", " Document(page_content='Red Sox', metadata={' \"Payroll (millions)\"': 173.18, ' \"Wins\"': 69}),\n", " Document(page_content='Indians', metadata={' \"Payroll (millions)\"': 78.43, ' \"Wins\"': 68}),\n", " Document(page_content='Twins', metadata={' \"Payroll (millions)\"': 94.08, ' \"Wins\"': 66}),\n", " Document(page_content='Rockies', metadata={' \"Payroll (millions)\"': 78.06, ' \"Wins\"': 64}),\n", " Document(page_content='Cubs', metadata={' \"Payroll (millions)\"': 88.19, ' \"Wins\"': 61}),\n", " Document(page_content='Astros', metadata={' \"Payroll (millions)\"': 60.65, ' \"Wins\"': 55})]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loader.load()" ] }, { "cell_type": "code", "execution_count": null, "id": "beb55c2f", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 5 }