Add multiple text labels from DataFrame columns in Plotly

Question

The goal is to plot some data using plotly where the text param contains multiple columns.

Here is my DataFrame:

import pandas as pd
import numpy as np
import plotly as py
import plotly.graph_objs as go

np.random.seed(1)
df = pd.DataFrame({'Mean Age': np.random.randint(40,60,10),
                   'Percent': np.random.randint(20,80,10),
                   'Number Column': np.random.randint(100,500,10)},
                  index=list('ABCDEFGHIJ'))

df.index.name = 'Text Column'
df = df.sort_values('Mean Age')

Here is an example of how I plotted the data with text from one column to show on hover:

# trace for Percent
trace0 = go.Scatter(
    x = df.index,
    y = df['Percent'],
    name = 'Percent',
    text = df['Mean Age'], # text to show on hover from df column
    mode = 'lines+markers',
    line = dict(
        color = ('rgb(0,0,255)'), # blue
        width = 4)
)

layout = dict(title = 'Test Plot',
             xaxis = dict(title = 'Text Column'),
             yaxis = dict(title = 'Percent'),
              )

data = [trace0]
fig = dict(data=data, layout=layout)

py.offline.plot(fig, filename = 'Test_Plot.html')

I am looking to add another column's data to the text param. I can accomplish this by doing some list comprehensions but is there an easier/more efficient way to do this?

I am looking for an output similar to what is below but in a more efficient way than using list comprehension:

# column values to list
num = list(df['Number Column'])
age = list(df['Mean Age'])


# trace for Percent
trace0 = go.Scatter(
    x = df.index,
    y = df['Percent'],
    name = 'Percent',
    # list comprehension to get the data to show
    text = [f'Number Column: {x}; Mean Age: {y}' for x,y in list(zip(num, age))],
    mode = 'lines+markers',
    line = dict(
        color = ('rgb(0,0,255)'), # blue
        width = 4)
)

layout = dict(title = 'Test Plot',
             xaxis = dict(title = 'Text Column'),
             yaxis = dict(title = 'Percent'),
              )

data = [trace0]
fig = dict(data=data, layout=layout)

py.offline.plot(fig, filename = 'Test_Plot_Output.html')

text needs to be (string or array of strings) according to the documentation, so I guess you are stuck here. Optimizing the list comprehension seems the only option. — Maximilian Peters
– Maximilian Peters, Commented Jan 26, 2019 at 18:33
@MaximilianPeters thanks for that information. I just read the documentation and found something interesting...As you mentioned it can be an array of strings; however, the series or rather ndarray of df['Mean Age] is numpy.int32 and it works despite being array of int32. Irregardless, I suppose I can do something like list(map(str, zip(df['Mean Age'], df['Number Column']))), which should be faster than list comprehension. — It_is_Chris
– It_is_Chris, Commented Jan 26, 2019 at 20:46
In a lot of cases instead of arrays any iterable can be used with Plotly. In order to get the information from Python to Plotly's JS library everything will be serialized a JSON anyways. — Maximilian Peters
– Maximilian Peters, Commented Jan 27, 2019 at 0:06

Caleb Courtney · Accepted Answer · 2020-08-27 23:12:30Z

9

You could also do something along the lines of the following:

trace0 = go.Scatter(
    x = df.index,
    y = df['Percent'],
    name = 'Percent',
    # string concatenation in pandas
    # also the <br> puts the data on a new line in the hover text
    text = "Number Column: " + df["Number Column"].astype(str) + "<br>Mean Age: " + df["Mean Age"].astype(str),
    mode = 'lines+markers',
    line = dict(
        color = ('rgb(0,0,255)'),  # blue
        width = 4)
)

answered Aug 27, 2020 at 23:12

Caleb Courtney

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