Plotting with Plotly
While there are other plotting libraries, we will focus on plotly for the following reasons:
- has the ability to zoom
- images can be downloaded as
pngfiles - select features can highlight features of the plot
Basic Plot
Let's make a scatterplot:
import plotly.express as px
fig = px.scatter(df, # the data we are using
x="Day", # x axis data
y="OtuCount", # y axis data
color='Day', # how to color our data
template="simple_white") # what theme we would like
fig.show()
Scatterplot
Adding A TrendLine
We can add a trend line as well:
import plotly.express as px
fig = px.scatter(df,
x="Day",
y="OtuCount",
color='Day',
template="simple_white",
trendline="ols") # add in a trend line
fig.show()
Adding A Trend Line
Scaling
Now if one of your axes spans multiple magnitudes you can scale your data using the log_x or log_y arguements:
fig = px.scatter(df,
x="Day",
y="OtuCount",
color='Day',
template="simple_white",
trendline="ols",
log_y = True) # scale y axis
fig.show()
Scaling
Panels
Sometimes it is useful to separate data by some variable and create panels. We can easily do this by specifying the facet_row or facet_col arguements - where plots are stacked one on top of the other or side-by-side, respectively:
fig = px.scatter(df,
x="Day",
y="OtuCount",
color='Day',
template="simple_white",
facet_col = "DaySinceExperimentStart") # split plots by variable
fig.show()
Adding Panels
Modifying Text
To modify text we can use the labels and title option:
fig = px.scatter(df,
x="Day",
y="OtuCount",
color='Day',
template="simple_white",
labels={
"OtuCount": "OTU count" # add in a space and capitalize
},
title = "Figure 1") # add in figure title
fig.show()
Modifying Text
Tip
For more plots and plot customization options, checkout the Plotly Graphing Library Page for more information