About line chart
A line chart, also known as a line graph, is a type of data visualization that displays information as a series of data points connected by straight line segments. It is commonly used to show the trend or pattern of data over a continuous interval, such as time.
Libraries
Plotly is a library designed for interactive visualization with python. If you want to use plotly, you can either use the plotly.express module (px) or the plotly.graph_objects module (go). The main difference between these modules is the "level": the px module is easier to use (high level), but if you want to do something very specific and need flexibility, I recommend you use the go module (lower level).
Don't forget to install plotly with the pip install plotly command.
In our case, we'll use the go module (or graph_objects). We will also use numpy and pandas to generate data and put it into a dataframe.
- plotlty is used for creating the chart witht the
Scatter()function numpyis used to generate some datapandasis used to put the data into a dataframe
# Libraries
import plotly.graph_objects as go
import numpy as np
import pandas as pdDataset
Line chart are generally used to represent variations over time, but they don't have to be. For example, you can look at connected scatter plots.
In our case, however, we need to generate dates. To do this, we will simply use a for loop and generate annual data, from 1990 to 2022.
Then we generate a random walk (which is just a variable that has a 0.5 chance of going up (+1) and a 0.5 chance of going down (-1), and calculate the cumulative amount). (We're doing this three times because we'll be displaying several variables in a single graph at the end).
# Init an empty list that will stores the dates
dates = []
# Iterates over our range and add the value to the list
start = 1990
end = 2022
for date in range(start, end):
dates.append(str(date))
# Generate random steps (+1 or -1) with equal probabilities
random_steps = np.random.choice([-1, 1], size=end-start, p=[0.5, 0.5])
# Calculate the cumulative sum
random_walk1 = np.cumsum(random_steps)
random_steps = np.random.choice([-1, 1], size=end-start, p=[0.5, 0.5])
random_walk2 = np.cumsum(random_steps)
random_steps = np.random.choice([-1, 1], size=end-start, p=[0.5, 0.5])
random_walk3 = np.cumsum(random_steps)
df = pd.DataFrame({'date': dates,
'value1': random_walk1,
'value2': random_walk2,
'value3': random_walk3, })Basic line chart
The following code displays a simple line chart built wiht Plotly, with a title and an axis name, thanks to the Scatter() function.
# Create a line chart using Plotly graph objects
fig = go.Figure()
fig.add_trace(go.Scatter(
x=df['date'], # x-axis
y=df['value1'], # y-axis
mode='lines', # Connect data points with lines
name='Line' # Name in the legend
))
# Layout parameters
fig.update_layout(
title='Line Chart Example', # Title
xaxis_title='X Axis Label', # y-axis name
yaxis_title='Y Axis Label', # x-axis name
xaxis_tickangle=45, # Set the x-axis label angle
showlegend=True, # Display the legend
)# save this file as a standalong html file:
fig.write_html("../../static/interactiveCharts/linechart-plotly-basic.html")%%html
<iframe
src="../../interactiveCharts/linechart-plotly-basic.html"
width="800"
height="600"
title="scatterplot with plotly"
style="border:none">
</iframe>Add customization features
In this section, we will see how to add some different customization features that will make the line chart more aesthetic.
- add markers: we change the
modeargument from'lines'to'lines+markers' - specify markers style: the
symbol(must be in the following list:'circle','square','diamond','cross','x','star'), thesizeand thecolor. - specify line style: the
colorand thewidth
# Create a line chart using Plotly graph objects
fig = go.Figure()
fig.add_trace(go.Scatter(
x=df['date'], # x-axis
y=df['value1'], # y-axis
mode='lines+markers', # Connect data points with lines and add markers
name='Line', # Name in the legend
marker=dict(
symbol='circle', # Change the marker symbol to a circle
size=10, # Set the marker size
color='#cb1dd1'), # Set the marker color
line=dict(
color='#dea4e0', # Change the line color to red
width=2, # Set the line width
)
))
# Layout parameters
fig.update_layout(
title='Customized Line Chart', # Title
xaxis_title='X Axis Label', # y-axis name
yaxis_title='Y Axis Label', # x-axis name
xaxis_tickangle=45, # Set the x-axis label angle
showlegend=True, # Display the legend
plot_bgcolor='white', # Set the plot background color
# Set the paper (outside plot area) background color
paper_bgcolor='lightgray',
)# save this file as a standalong html file:
fig.write_html("../../static/interactiveCharts/linechart-plotly-custom.html")%%html
<iframe
src="../../interactiveCharts/linechart-plotly-custom.html"
width="800"
height="600"
title="line chart with plotly"
style="border:none">
</iframe>Line chart with mutliple variables
It's quite common to want to have several lines in a single graph. In practice, all you need to do is write the add_trace() with go.Scatter() function code several times (one for each variable).
This allows us to easily specify the parameters of each variable and ensure that the graph has a very precise style.
# Create a line chart using Plotly graph objects
fig = go.Figure()
# Variable 1
fig.add_trace(go.Scatter(
x=df['date'], # x-axis
y=df['value1'], # y-axis
mode='lines+markers', # Connect data points with lines and add markers
name='Line1', # Name in the legend
marker=dict(
symbol='square', # Change the marker symbol to a circle
size=10, # Set the marker size
color='red'), # Set the marker color
line=dict(
color='blue', # Change the line color to red
width=5) # Set the line width
))
# Variable 2
fig.add_trace(go.Scatter(
x=df['date'], # x-axis
y=df['value2'], # y-axis
mode='lines+markers', # Connect data points with lines and add markers
name='Line2', # Name in the legend
marker=dict(
symbol='circle', # Change the marker symbol to a circle
size=7, # Set the marker size
color='purple'), # Set the marker color
line=dict(
color='orange', # Change the line color to red
width=8) # Set the line width
))
# Variable 3
fig.add_trace(go.Scatter(
x=df['date'], # x-axis
y=df['value3'], # y-axis
mode='lines+markers', # Connect data points with lines and add markers
name='Line3', # Name in the legend
marker=dict(
symbol='diamond', # Change the marker symbol to a circle
size=15, # Set the marker size
color='yellow'), # Set the marker color
line=dict(
color='green', # Change the line color to red
width=4) # Set the line width
))
# Layout parameters
fig.update_layout(
title='Customized Line Chart', # Title
xaxis_title='X Axis Label', # y-axis name
yaxis_title='Y Axis Label', # x-axis name
xaxis_tickangle=45, # Set the x-axis label angle
showlegend=True, # Display the legend
plot_bgcolor='white', # Set the plot background color
# Set the paper (outside plot area) background color
paper_bgcolor='lightblue',
)# save this file as a standalong html file:
fig.write_html(
"../../static/interactiveCharts/linechart-plotly-multigroup.html")%%html
<iframe
src="../../interactiveCharts/linechart-plotly-multigroup.html"
width="800"
height="600"
title="line chart with plotly"
style="border:none">
</iframe>Going further
This article explains how to create a basic line chart with Plotly with various customization features, such as changing color, overall style or display multiple lines.
For more examples of how to create or customize your line charts with Python, see the line chart section. You may also be interested in creating an area chart.
