Time Series and Date Axes in Python
Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.
Time Series using Axes of type date ¶
Time series can be represented using either plotly.express functions ( px.line , px.scatter , px.bar etc) or plotly.graph_objects charts objects ( go.Scatter , go.Bar etc). For more examples of such charts, see the documentation of line and scatter plots or bar charts.
For financial applications, Plotly can also be used to create Candlestick charts and OHLC charts, which default to date axes.
Plotly auto-sets the axis type to a date format when the corresponding data are either ISO-formatted date strings or if they’re a date pandas column or datetime NumPy array.
# Using plotly.express import plotly.express as px df = px.data.stocks() fig = px.line(df, x='date', y="GOOG") fig.show()
# Using graph_objects import plotly.graph_objects as go import pandas as pd df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv') fig = go.Figure([go.Scatter(x=df['Date'], y=df['AAPL.High'])]) fig.show()
Time Series in Dash¶
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 .
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Different Chart Types on Date Axes¶
Any kind of cartesian chart can be placed on date axes, for example this bar chart of relative stock ticker values.
import plotly.express as px df = px.data.stocks(indexed=True)-1 fig = px.bar(df, x=df.index, y="GOOG") fig.show()
import plotly.express as px df = px.data.stocks(indexed=True)-1 fig = px.area(df, facet_col="company", facet_col_wrap=2) fig.show()
Configuring Tick Labels¶
By default, the tick labels (and optional ticks) are associated with a specific grid-line, and represent an instant in time, for example, «00:00 on February 1, 2018». Tick labels can be formatted using the tickformat attribute (which accepts the d3 time-format formatting strings) to display only the month and year, but they still represent an instant by default, so in the figure below, the text of the label «Feb 2018» spans part of the month of January and part of the month of February. The dtick attribute controls the spacing between gridlines, and the «M1» setting means «1 month». This attribute also accepts a number of milliseconds, which can be scaled up to days by multiplying by 24*60*60*1000 .
Date axis tick labels have the special property that any portion after the first instance of ‘\n’ in tickformat will appear on a second line only once per unique value, as with the year numbers in the example below. To have the year number appear on every tick label, ‘
‘ should be used instead of ‘\n’ .
Note that by default, the formatting of values of X and Y values in the hover label matches that of the tick labels of the corresponding axes, so when customizing the tick labels to something broad like «month», it’s usually necessary to customize the hover label to something narrower like the actual date, as below.
import plotly.express as px df = px.data.stocks() fig = px.line(df, x="date", y=df.columns, hover_data="date": "|%B %d, %Y">, title='custom tick labels') fig.update_xaxes( dtick="M1", tickformat="%b\n%Y") fig.show()