Notebook

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Time Series Plot with `datetime` Objects¶

In [1]:

import plotly.plotly as py
import plotly.graph_objs as go

import pandas as pd
from datetime import datetime

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')

data = [go.Scatter(x=df.Date, y=df['AAPL.High'])]

py.iplot(data, filename = 'time-series-simple')

Out[1]:

Date Strings¶

In [2]:

import plotly.plotly as py
import plotly.graph_objs as go

import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv")

data = [go.Scatter(
          x=df.Date,
          y=df['AAPL.Close'])]

py.iplot(data)

Out[2]:

Time Series Plot with Custom Date Range¶

In [3]:

import plotly.plotly as py
import plotly.graph_objs as go

import datetime

def to_unix_time(dt):
    epoch =  datetime.datetime.utcfromtimestamp(0)
    return (dt - epoch).total_seconds() * 1000

x = [datetime.datetime(year=2013, month=10, day=04),
    datetime.datetime(year=2013, month=11, day=05),
    datetime.datetime(year=2013, month=12, day=06)]
data = [go.Scatter(
            x=x,
            y=[1, 3, 6])]

layout = go.Layout(xaxis = dict(
                   range = [to_unix_time(datetime.datetime(2013, 10, 17)),
                            to_unix_time(datetime.datetime(2013, 11, 20))]
    ))

fig = go.Figure(data = data, layout = layout)
py.iplot(fig)

Out[3]:

Manually Set Range¶

In [4]:

import plotly.plotly as py
import plotly.graph_objs as go

import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv")

trace_high = go.Scatter(
                x=df.Date,
                y=df['AAPL.High'],
                name = "AAPL High",
                line = dict(color = '#17BECF'),
                opacity = 0.8)

trace_low = go.Scatter(
                x=df.Date,
                y=df['AAPL.Low'],
                name = "AAPL Low",
                line = dict(color = '#7F7F7F'),
                opacity = 0.8)

data = [trace_high,trace_low]

layout = dict(
    title = "Manually Set Date Range",
    xaxis = dict(
        range = ['2016-07-01','2016-12-31'])
)

fig = dict(data=data, layout=layout)
py.iplot(fig, filename = "Manually Set Range")

Out[4]:

Time Series With Rangeslider¶

In [5]:

import plotly.plotly as py
import plotly.graph_objs as go

import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv")

trace_high = go.Scatter(
    x=df.Date,
    y=df['AAPL.High'],
    name = "AAPL High",
    line = dict(color = '#17BECF'),
    opacity = 0.8)

trace_low = go.Scatter(
    x=df.Date,
    y=df['AAPL.Low'],
    name = "AAPL Low",
    line = dict(color = '#7F7F7F'),
    opacity = 0.8)

data = [trace_high,trace_low]

layout = dict(
    title='Time Series with Rangeslider',
    xaxis=dict(
        rangeselector=dict(
            buttons=list([
                dict(count=1,
                     label='1m',
                     step='month',
                     stepmode='backward'),
                dict(count=6,
                     label='6m',
                     step='month',
                     stepmode='backward'),
                dict(step='all')
            ])
        ),
        rangeslider=dict(
            visible = True
        ),
        type='date'
    )
)

fig = dict(data=data, layout=layout)
py.iplot(fig, filename = "Time Series with Rangeslider")

Out[5]:

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.