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Funnel Charts in Python

How to make funnel charts in Python with Plotly.

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Version Check¶

Plotly's Python API is updated frequently. Run pip install plotly --upgrade to update your Plotly version.

In [1]:
import plotly
plotly.__version__

Out[1]:
'2.0.6'

Funnel Chart Outline¶

Funnel charts are often used to represent data in different stages of a business process. It’s an important mechanism in Business Intelligence to identify potential problem areas of a process. For example, it’s used to observe the revenue or loss in a sales process for each stage. A funnel chart has multiple phases and values associated with them. Here is a table that represents a user flow funnel for a social media campaign. The column named 'Values' represents the total number of users at that Phase.

In [1]:
import plotly.plotly as py
import plotly.figure_factory as ff

data_table = [['Phases', 'Values'],
['Visit', 13873],
['Sign-up', 10553],
['Selection', 5443],
['Purchase', 3703],
['Review', 1708]]

table = ff.create_table(data_table)
py.iplot(table)

Out[1]:

Basic Funnel Chart¶

In [2]:
import plotly.plotly as py
from plotly import graph_objs as go

from __future__ import division

# chart stages data
values = [13873, 10553, 5443, 3703, 1708]
phases = ['Visit', 'Sign-up', 'Selection', 'Purchase', 'Review']

# color of each funnel section
colors = ['rgb(32,155,160)', 'rgb(253,93,124)', 'rgb(28,119,139)', 'rgb(182,231,235)', 'rgb(35,154,160)']


A funnel section will be drawn using Plotly shapes, in the shape of a Rectangle or Isosceles Trapezoid depending on the value of the next phase. The phase having maximum value will have the width equal to the plot.

In [3]:
n_phase = len(phases)
plot_width = 400

# height of a section and difference between sections
section_h = 100
section_d = 10

# multiplication factor to calculate the width of other sections
unit_width = plot_width / max(values)

# width of each funnel section relative to the plot width
phase_w = [int(value * unit_width) for value in values]

# plot height based on the number of sections and the gap in between them
height = section_h * n_phase + section_d * (n_phase - 1)


Each section will have a height of 100px and there will be a difference of 10px in successive sections. To draw a section, we are going to use SVG paths.

In [5]:
# list containing all the plot shapes
shapes = []

# list containing the Y-axis location for each section's name and value text
label_y = []

for i in range(n_phase):
if (i == n_phase-1):
points = [phase_w[i] / 2, height, phase_w[i] / 2, height - section_h]
else:
points = [phase_w[i] / 2, height, phase_w[i+1] / 2, height - section_h]

path = 'M {0} {1} L {2} {3} L -{2} {3} L -{0} {1} Z'.format(*points)

shape = {
'type': 'path',
'path': path,
'fillcolor': colors[i],
'line': {
'width': 1,
'color': colors[i]
}
}
shapes.append(shape)

# Y-axis location for this section's details (text)
label_y.append(height - (section_h / 2))

height = height - (section_h + section_d)


To draw the phase names and values, we are using the text mode in scatter plots. To style the plot, we are changing the background color of the plot and the plot paper, hiding the legend and tick labels, and removing the zeroline.

In [6]:
# For phase names
label_trace = go.Scatter(
x=[-350]*n_phase,
y=label_y,
mode='text',
text=phases,
textfont=dict(
color='rgb(200,200,200)',
size=15
)
)

# For phase values
value_trace = go.Scatter(
x=[350]*n_phase,
y=label_y,
mode='text',
text=values,
textfont=dict(
color='rgb(200,200,200)',
size=15
)
)

data = [label_trace, value_trace]

layout = go.Layout(
title="<b>Funnel Chart</b>",
titlefont=dict(
size=20,
color='rgb(203,203,203)'
),
shapes=shapes,
height=560,
width=800,
showlegend=False,
paper_bgcolor='rgba(44,58,71,1)',
plot_bgcolor='rgba(44,58,71,1)',
xaxis=dict(
showticklabels=False,
zeroline=False,
),
yaxis=dict(
showticklabels=False,
zeroline=False
)
)

fig = go.Figure(data=data, layout=layout)
py.iplot(fig)

Out[6]:

Segmented Funnel Chart¶

Instead of having a single source of data like the funnel charts, the segmented funnel charts have multiple data sources.

In [7]:
import plotly.plotly as py
import plotly.graph_objs as go

from __future__ import division
import pandas as pd

# campaign data

# color for each segment
colors = ['rgb(63,92,128)', 'rgb(90,131,182)', 'rgb(255,255,255)', 'rgb(127,127,127)', 'rgb(84,73,75)']


You can calculate the total number of users in each phase using DataFrame.iterrows() method.

In [8]:
total = [sum(row[1]) for row in df.iterrows()]


Number of phases and segments can be calculated using the shape (returns a tuple) attribute of DataFrame.

In [9]:
n_phase, n_seg = df.shape


We are using a fixed width for the plot and the width of each phase will be calculated according to the total users compared to the initial phase.

In [10]:
plot_width = 600
unit_width = plot_width / total[0]

phase_w = [int(value * unit_width) for value in total]

# height of a section and difference between sections
section_h = 100
section_d = 10

# shapes of the plot
shapes = []

# plot traces data
data = []

# height of the phase labels
label_y = []


A phase in the chart will be a rectangle made of smaller rectangles representing different segments.

In [11]:
height = section_h * n_phase + section_d * (n_phase-1)

# rows of the DataFrame
df_rows = list(df.iterrows())

# iteration over all the phases
for i in range(n_phase):
# phase name
row_name = df.index[i]

# width of each segment (smaller rectangles) will be calculated
# according to their contribution in the total users of phase
seg_unit_width = phase_w[i] / total[i]
seg_w = [int(df_rows[i][1][j] * seg_unit_width) for j in range(n_seg)]

# starting point of segment (the rectangle shape) on the X-axis
xl = -1 * (phase_w[i] / 2)

# iteration over all the segments
for j in range(n_seg):
# name of the segment
seg_name = df.columns[j]

# corner points of a segment used in the SVG path
points = [xl, height, xl + seg_w[j], height, xl + seg_w[j], height - section_h, xl, height - section_h]
path = 'M {0} {1} L {2} {3} L {4} {5} L {6} {7} Z'.format(*points)

shape = {
'type': 'path',
'path': path,
'fillcolor': colors[j],
'line': {
'width': 1,
'color': colors[j]
}
}
shapes.append(shape)

# to support hover on shapes
hover_trace = go.Scatter(
x=[xl + (seg_w[j] / 2)],
y=[height - (section_h / 2)],
mode='markers',
marker=dict(
size=min(seg_w[j]/2, (section_h / 2)),
color='rgba(255,255,255,1)'
),
text="Segment : %s" % (seg_name),
name="Value : %d" % (df[seg_name][row_name])
)
data.append(hover_trace)

xl = xl + seg_w[j]

label_y.append(height - (section_h / 2))

height = height - (section_h + section_d)


We will use text mode to draw the name of phase and its value.

In [12]:
# For phase names
label_trace = go.Scatter(
x=[-350]*n_phase,
y=label_y,
mode='text',
text=df.index.tolist(),
textfont=dict(
color='rgb(200,200,200)',
size=15
)
)

data.append(label_trace)

# For phase values (total)
value_trace = go.Scatter(
x=[350]*n_phase,
y=label_y,
mode='text',
text=total,
textfont=dict(
color='rgb(200,200,200)',
size=15
)
)

data.append(value_trace)


We will style the plot by changing the background color of the plot and the plot paper, hiding the legend and tick labels, and removing the zeroline.

In [13]:
layout = go.Layout(
title="<b>Segmented Funnel Chart</b>",
titlefont=dict(
size=20,
color='rgb(230,230,230)'
),
hovermode='closest',
shapes=shapes,
showlegend=False,
paper_bgcolor='rgba(44,58,71,1)',
plot_bgcolor='rgba(44,58,71,1)',
xaxis=dict(
showticklabels=False,
zeroline=False,
),
yaxis=dict(
showticklabels=False,
zeroline=False
)
)

fig = go.Figure(data=data, layout=layout)
py.iplot(fig)

Out[13]: