--- jupyter: jupytext: notebook_metadata_filter: all text_representation: extension: .md format_name: markdown format_version: '1.3' jupytext_version: 1.14.1 kernelspec: display_name: Python 3 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: 3.8.8 plotly: description: How to create charts from csv files with Plotly and Python display_as: advanced_opt has_thumbnail: false language: python layout: base name: Plot CSV Data order: 1 page_type: example_index permalink: python/plot-data-from-csv/ thumbnail: thumbnail/csv.jpg --- CSV or comma-delimited-values is a very popular format for storing structured data. In this tutorial, we will see how to plot beautiful graphs using csv data, and Pandas. We will learn how to import csv data from an external source (a url), and plot it using Plotly and pandas. First we import the data and look at it. ```python import pandas as pd df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv') df.head() ``` ### Plot from CSV with Plotly Express ```python import pandas as pd import plotly.express as px df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv') fig = px.line(df, x = 'AAPL_x', y = 'AAPL_y', title='Apple Share Prices over time (2014)') fig.show() ``` ### Plot from CSV in Dash [Dash](https://plotly.com/dash/) is the best way to build analytical apps in Python using Plotly figures. To run the app below, run `pip install dash`, click "Download" to get the code and run `python app.py`. Get started with [the official Dash docs](https://dash.plotly.com/installation) and **learn how to effortlessly [style](https://plotly.com/dash/design-kit/) & [deploy](https://plotly.com/dash/app-manager/) apps like this with <a class="plotly-red" href="https://plotly.com/dash/">Dash Enterprise</a>.** ```python hide_code=true from IPython.display import IFrame snippet_url = 'https://python-docs-dash-snippets.herokuapp.com/python-docs-dash-snippets/' IFrame(snippet_url + 'plot-data-from-csv', width='100%', height=1200) ``` <div style="font-size: 0.9em;"><div style="width: calc(100% - 30px); box-shadow: none; border: thin solid rgb(229, 229, 229);"><div style="padding: 5px;"><div><p><strong>Sign up for Dash Club</strong> → Free cheat sheets plus updates from Chris Parmer and Adam Schroeder delivered to your inbox every two months. Includes tips and tricks, community apps, and deep dives into the Dash architecture. <u><a href="https://go.plotly.com/dash-club?utm_source=Dash+Club+2022&utm_medium=graphing_libraries&utm_content=inline">Join now</a></u>.</p></div></div></div></div> ### Plot from CSV with `graph_objects` ```python import pandas as pd import plotly.graph_objects as go df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv') fig = go.Figure(go.Scatter(x = df['AAPL_x'], y = df['AAPL_y'], name='Share Prices (in USD)')) fig.update_layout(title=dict(text='Apple Share Prices over time (2014)'), plot_bgcolor='rgb(230, 230,230)', showlegend=True) fig.show() ``` #### Reference See https://plotly.com/python/getting-started for more information about Plotly's Python API!