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Plot CSV Data in Python

How to create charts from csv files with Plotly and Python

New to Plotly?

Plotly's Python library is free and open source! Get started by downloading the client and reading the primer.
You can set up Plotly to work in online or offline mode, or in jupyter notebooks.
We also have a quick-reference cheatsheet (new!) to help you get started!

Version Check

Plotly's python package is updated frequently. Run pip install plotly --upgrade to use the latest version.

In [1]:
import plotly
plotly.__version__
Out[1]:
'2.0.1'

Imports

In [2]:
import plotly.plotly as py
import plotly.graph_objs as go
import plotly.figure_factory as FF

import numpy as np
import pandas as pd

A Simple Example

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 import data from a local file sample-data.csv with the pandas function: read_csv().

In [3]:
df = pd.read_csv('sample-data.csv')

sample_data_table = FF.create_table(df.head())
py.iplot(sample_data_table, filename='sample-data-table')
Out[3]:
In [4]:
trace1 = go.Scatter(
                    x=df['x'], y=df['logx'], # Data
                    mode='lines', name='logx' # Additional options
                   )
trace2 = go.Scatter(x=df['x'], y=df['sinx'], mode='lines', name='sinx' )
trace3 = go.Scatter(x=df['x'], y=df['cosx'], mode='lines', name='cosx')

layout = go.Layout(title='Simple Plot from csv data',
                   plot_bgcolor='rgb(230, 230,230)')

fig = go.Figure(data=[trace1, trace2, trace3], layout=layout)

# Plot data in the notebook
py.iplot(fig, filename='simple-plot-from-csv')
Out[4]:

Plotting Data from External Source

In the next example, we will learn how to import csv data from an external source (a url), and plot it using Plotly and pandas. We are going to use this data for the example.

In [5]:
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv')

df_external_source = FF.create_table(df.head())
py.iplot(df_external_source, filename='df-external-source-table')
Out[5]:
In [6]:
trace = go.Scatter(x = df['AAPL_x'], y = df['AAPL_y'],
                  name='Share Prices (in USD)')
layout = go.Layout(title='Apple Share Prices over time (2014)',
                   plot_bgcolor='rgb(230, 230,230)', 
                   showlegend=True)
fig = go.Figure(data=[trace], layout=layout)

py.iplot(fig, filename='apple-stock-prices')
Out[6]:

Dash Example

Dash is an Open Source Python library which can help you convert plotly figures into a reactive, web-based application. Below is a simple example of a dashboard created using Dash. Its source code can easily be deployed to a PaaS.

In [1]:
from IPython.display import IFrame
IFrame(src= "https://dash-simple-apps.plotly.host/dash-plotfromcsvplot/", width="100%", height="650px", frameBorder="0")
Out[1]:
In [1]:
from IPython.display import IFrame
IFrame(src= "https://dash-simple-apps.plotly.host/dash-plotfromcsvplot/code", width="100%", height=500, frameBorder="0")
Out[1]: