Scatter plot plotly python

Line Charts in Python

How to make line charts in Python with Plotly. Examples on creating and styling line charts in Python with Plotly.

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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.

Line Plots with plotly.express¶

Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures. With px.line , each data point is represented as a vertex (which location is given by the x and y columns) of a polyline mark in 2D space.

For more examples of line plots, see the line and scatter notebook.

import plotly.express as px df = px.data.gapminder().query("country=='Canada'") fig = px.line(df, x="year", y="lifeExp", title='Life expectancy in Canada') fig.show() 

Line Plots with column encoding color¶

import plotly.express as px df = px.data.gapminder().query("continent=='Oceania'") fig = px.line(df, x="year", y="lifeExp", color='country') fig.show() 

Line charts 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 .

Get started with the official Dash docs and learn how to effortlessly style & deploy apps like this with Dash Enterprise.

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Data Order in Line Charts¶

Plotly line charts are implemented as connected scatterplots (see below), meaning that the points are plotted and connected with lines in the order they are provided, with no automatic reordering.

This makes it possible to make charts like the one below, but also means that it may be required to explicitly sort data before passing it to Plotly to avoid lines moving «backwards» across the chart.

import plotly.express as px import pandas as pd df = pd.DataFrame(dict( x = [1, 3, 2, 4], y = [1, 2, 3, 4] )) fig = px.line(df, x="x", y="y", title="Unsorted Input") fig.show() df = df.sort_values(by="x") fig = px.line(df, x="x", y="y", title="Sorted Input") fig.show() 

Connected Scatterplots¶

In a connected scatterplot, two continuous variables are plotted against each other, with a line connecting them in some meaningful order, usually a time variable. In the plot below, we show the «trajectory» of a pair of countries through a space defined by GDP per Capita and Life Expectancy. Botswana’s life expectancy

import plotly.express as px df = px.data.gapminder().query("country in ['Canada', 'Botswana']") fig = px.line(df, x="lifeExp", y="gdpPercap", color="country", text="year") fig.update_traces(textposition="bottom right") fig.show() 

Line charts with markers¶

The markers argument can be set to True to show markers on lines.

import plotly.express as px df = px.data.gapminder().query("continent == 'Oceania'") fig = px.line(df, x='year', y='lifeExp', color='country', markers=True) fig.show() 

The symbol argument can be used to map a data field to the marker symbol. A wide variety of symbols are available.

import plotly.express as px df = px.data.gapminder().query("continent == 'Oceania'") fig = px.line(df, x='year', y='lifeExp', color='country', symbol="country") fig.show() 

Line plots on Date axes¶

Line plots can be made on using any type of cartesian axis, including linear, logarithmic, categorical or date axes. Line plots on date axes are often called time-series charts.

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.

import plotly.express as px df = px.data.stocks() fig = px.line(df, x='date', y="GOOG") fig.show() 

Sparklines with Plotly Express¶

Sparklines are scatter plots inside subplots, with gridlines, axis lines, and ticks removed.

import plotly.express as px df = px.data.stocks(indexed=True) fig = px.line(df, facet_row="company", facet_row_spacing=0.01, height=200, width=200) # hide and lock down axes fig.update_xaxes(visible=False, fixedrange=True) fig.update_yaxes(visible=False, fixedrange=True) # remove facet/subplot labels fig.update_layout(annotations=[], overwrite=True) # strip down the rest of the plot fig.update_layout( showlegend=False, plot_bgcolor="white", margin=dict(t=10,l=10,b=10,r=10) ) # disable the modebar for such a small plot fig.show(config=dict(displayModeBar=False)) 

Line Plot with go.Scatter¶

If Plotly Express does not provide a good starting point, it is possible to use the more generic go.Scatter class from plotly.graph_objects . Whereas plotly.express has two functions scatter and line , go.Scatter can be used both for plotting points (makers) or lines, depending on the value of mode . The different options of go.Scatter are documented in its reference page.

Simple Line Plot¶

import plotly.graph_objects as go import numpy as np x = np.arange(10) fig = go.Figure(data=go.Scatter(x=x, y=x**2)) fig.show() 

Line Plot Modes¶

import plotly.graph_objects as go # Create random data with numpy import numpy as np np.random.seed(1) N = 100 random_x = np.linspace(0, 1, N) random_y0 = np.random.randn(N) + 5 random_y1 = np.random.randn(N) random_y2 = np.random.randn(N) - 5 # Create traces fig = go.Figure() fig.add_trace(go.Scatter(x=random_x, y=random_y0, mode='lines', name='lines')) fig.add_trace(go.Scatter(x=random_x, y=random_y1, mode='lines+markers', name='lines+markers')) fig.add_trace(go.Scatter(x=random_x, y=random_y2, mode='markers', name='markers')) fig.show() 

Style Line Plots¶

This example styles the color and dash of the traces, adds trace names, modifies line width, and adds plot and axes titles.

import plotly.graph_objects as go # Add data month = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'] high_2000 = [32.5, 37.6, 49.9, 53.0, 69.1, 75.4, 76.5, 76.6, 70.7, 60.6, 45.1, 29.3] low_2000 = [13.8, 22.3, 32.5, 37.2, 49.9, 56.1, 57.7, 58.3, 51.2, 42.8, 31.6, 15.9] high_2007 = [36.5, 26.6, 43.6, 52.3, 71.5, 81.4, 80.5, 82.2, 76.0, 67.3, 46.1, 35.0] low_2007 = [23.6, 14.0, 27.0, 36.8, 47.6, 57.7, 58.9, 61.2, 53.3, 48.5, 31.0, 23.6] high_2014 = [28.8, 28.5, 37.0, 56.8, 69.7, 79.7, 78.5, 77.8, 74.1, 62.6, 45.3, 39.9] low_2014 = [12.7, 14.3, 18.6, 35.5, 49.9, 58.0, 60.0, 58.6, 51.7, 45.2, 32.2, 29.1] fig = go.Figure() # Create and style traces fig.add_trace(go.Scatter(x=month, y=high_2014, name='High 2014', line=dict(color='firebrick', width=4))) fig.add_trace(go.Scatter(x=month, y=low_2014, name = 'Low 2014', line=dict(color='royalblue', width=4))) fig.add_trace(go.Scatter(x=month, y=high_2007, name='High 2007', line=dict(color='firebrick', width=4, dash='dash') # dash options include 'dash', 'dot', and 'dashdot' )) fig.add_trace(go.Scatter(x=month, y=low_2007, name='Low 2007', line = dict(color='royalblue', width=4, dash='dash'))) fig.add_trace(go.Scatter(x=month, y=high_2000, name='High 2000', line = dict(color='firebrick', width=4, dash='dot'))) fig.add_trace(go.Scatter(x=month, y=low_2000, name='Low 2000', line=dict(color='royalblue', width=4, dash='dot'))) # Edit the layout fig.update_layout(title='Average High and Low Temperatures in New York', xaxis_title='Month', yaxis_title='Temperature (degrees F)') fig.show() 

Connect Data Gaps¶

connectgaps determines if missing values in the provided data are shown as a gap in the graph or not. In this tutorial, we showed how to take benefit of this feature and illustrate multiple areas in mapbox.

import plotly.graph_objects as go x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] fig = go.Figure() fig.add_trace(go.Scatter( x=x, y=[10, 20, None, 15, 10, 5, 15, None, 20, 10, 10, 15, 25, 20, 10], name = 'No Gaps', # Style name/legend entry with html tags connectgaps=True # override default to connect the gaps )) fig.add_trace(go.Scatter( x=x, y=[5, 15, None, 10, 5, 0, 10, None, 15, 5, 5, 10, 20, 15, 5], name='Gaps', )) fig.show() 

Interpolation with Line Plots¶

import plotly.graph_objects as go import numpy as np x = np.array([1, 2, 3, 4, 5]) y = np.array([1, 3, 2, 3, 1]) fig = go.Figure() fig.add_trace(go.Scatter(x=x, y=y, name="linear", line_shape='linear')) fig.add_trace(go.Scatter(x=x, y=y + 5, name="spline", text=["tweak line smoothness
with 'smoothing' in line object"], hoverinfo='text+name', line_shape='spline')) fig.add_trace(go.Scatter(x=x, y=y + 10, name="vhv", line_shape='vhv')) fig.add_trace(go.Scatter(x=x, y=y + 15, name="hvh", line_shape='hvh')) fig.add_trace(go.Scatter(x=x, y=y + 20, name="vh", line_shape='vh')) fig.add_trace(go.Scatter(x=x, y=y + 25, name="hv", line_shape='hv')) fig.update_traces(hoverinfo='text+name', mode='lines+markers') fig.update_layout(legend=dict(y=0.5, traceorder='reversed', font_size=16)) fig.show() 

Label Lines with Annotations¶

import plotly.graph_objects as go import numpy as np title = 'Main Source for News' labels = ['Television', 'Newspaper', 'Internet', 'Radio'] colors = ['rgb(67,67,67)', 'rgb(115,115,115)', 'rgb(49,130,189)', 'rgb(189,189,189)'] mode_size = [8, 8, 12, 8] line_size = [2, 2, 4, 2] x_data = np.vstack((np.arange(2001, 2014),)*4) y_data = np.array([ [74, 82, 80, 74, 73, 72, 74, 70, 70, 66, 66, 69], [45, 42, 50, 46, 36, 36, 34, 35, 32, 31, 31, 28], [13, 14, 20, 24, 20, 24, 24, 40, 35, 41, 43, 50], [18, 21, 18, 21, 16, 14, 13, 18, 17, 16, 19, 23], ]) fig = go.Figure() for i in range(0, 4): fig.add_trace(go.Scatter(x=x_data[i], y=y_data[i], mode='lines', name=labels[i], line=dict(color=colors[i], width=line_size[i]), connectgaps=True, )) # endpoints fig.add_trace(go.Scatter( x=[x_data[i][0], x_data[i][-1]], y=[y_data[i][0], y_data[i][-1]], mode='markers', marker=dict(color=colors[i], size=mode_size[i]) )) fig.update_layout( xaxis=dict( showline=True, showgrid=False, showticklabels=True, linecolor='rgb(204, 204, 204)', linewidth=2, ticks='outside', tickfont=dict( family='Arial', size=12, color='rgb(82, 82, 82)', ), ), yaxis=dict( showgrid=False, zeroline=False, showline=False, showticklabels=False, ), autosize=False, margin=dict( autoexpand=False, l=100, r=20, t=110, ), showlegend=False, plot_bgcolor='white' ) annotations = [] # Adding labels for y_trace, label, color in zip(y_data, labels, colors): # labeling the left_side of the plot annotations.append(dict(xref='paper', x=0.05, y=y_trace[0], xanchor='right', yanchor='middle', text=label + ' <>%'.format(y_trace[0]), font=dict(family='Arial', size=16), showarrow=False)) # labeling the right_side of the plot annotations.append(dict(xref='paper', x=0.95, y=y_trace[11], xanchor='left', yanchor='middle', text='<>%'.format(y_trace[11]), font=dict(family='Arial', size=16), showarrow=False)) # Title annotations.append(dict(xref='paper', yref='paper', x=0.0, y=1.05, xanchor='left', yanchor='bottom', text='Main Source for News', font=dict(family='Arial', size=30, color='rgb(37,37,37)'), showarrow=False)) # Source annotations.append(dict(xref='paper', yref='paper', x=0.5, y=-0.1, xanchor='center', yanchor='top', text='Source: PewResearch Center & ' + 'Storytelling with data', font=dict(family='Arial', size=12, color='rgb(150,150,150)'), showarrow=False)) fig.update_layout(annotations=annotations) fig.show() 

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