Certainly! I assume you want to create this chart using Python. We can use the Plotly library to generate a waterfall chart. First, you need to install Plotly using pip:

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pip install plotly

Now, here's a simple script that creates a waterfall chart with the given values:

pythonCopy code
import plotly.graph_objects as go

# Define the columns and values
columns = ['Beginning ARR', 'Net New', 'Upsell', 'Downsell', 'Churn', 'Ending ARR']
values = [10000000, 2000000, 1000000, -250000, -500000]

# Calculate the Ending ARR
ending_arr = sum(values)
values.append(ending_arr)

# Create a waterfall chart
fig = go.Figure(go.Waterfall(
    name="ARR",
    orientation="v",
    x=columns,
    y=values,
    textposition="outside",
    texttemplate='$%{y:.2s}',
    connector={"line": {"color": "rgb(63, 63, 63)"}},
))

# Update the layout
fig.update_layout(
    title="Waterfall Chart",
    showlegend=False,
    xaxis_title="Categories",
    yaxis_title="Amount ($ in millions)",
    yaxis_tickprefix="$",
    yaxis_ticksuffix="M",
)

# Show the plot
fig.show()

This script creates a waterfall chart with the specified columns and values, and it calculates the Ending ARR as the sum of the other columns. When you run the script, it will display the waterfall chart using the Plotly library.

I apologize for the confusion. It seems like the Plotly library is trying to open the chart in a web browser, but it's unable to do so in your environment. Instead, let's modify the script to save the chart as an HTML file, which you can open with your web browser.

Please replace the last line fig.show() with the following code:

pythonCopy code
import plotly.io as pio

# Save the plot as an HTML file
pio.write_html(fig, file='waterfall_chart.html', auto_open=True)

Now, when you run the modified script, it will save the waterfall chart as an HTML file named waterfall_chart.html in the same directory as the script. The auto_open=True parameter will attempt to open the chart in your default web browser automatically. If it still doesn't open, you can manually open the generated HTML file with your preferred web browser to view the chart.

Here's the fully revised script that saves the waterfall chart as an HTML file:

pythonCopy code
import plotly.graph_objects as go
import plotly.io as pio

# Define the columns and values
columns = ['Beginning ARR', 'Net New', 'Upsell', 'Downsell', 'Churn', 'Ending ARR']
values = [10000000, 2000000, 1000000, -250000, -500000]

# Calculate the Ending ARR
ending_arr = sum(values)
values.append(ending_arr)

# Create a waterfall chart
fig = go.Figure(go.Waterfall(
    name="ARR",
    orientation="v",
    x=columns,
    y=values,
    textposition="outside",
    texttemplate='$%{y:.2s}',
    connector={"line": {"color": "rgb(63, 63, 63)"}},
))

# Update the layout
fig.update_layout(
    title="Waterfall Chart",
    showlegend=False,
    xaxis_title="Categories",
    yaxis_title="Amount ($ in millions)",
    yaxis_tickprefix="$",
    yaxis_ticksuffix="M",
)

# Save the plot as an HTML file and attempt to open it in the default web browser
pio.write_html(fig, file='waterfall_chart.html', auto_open=True)

This script creates a waterfall chart with the specified columns and values, and it calculates the Ending ARR as the sum of the other columns. It saves the chart as an HTML file called waterfall_chart.html and attempts to open it in your default web browser. If it doesn't open automatically, you can manually open the generated HTML file with your preferred web browser to view the chart.

I apologize for the confusion. Replit does not support opening files in a web browser directly from the script. Instead, you can use the Replit built-in web server to serve the generated HTML file.

First, install Flask with the following command:

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pip install Flask

Now, replace the entire script with the following code:

pythonCopy code
from flask import Flask, send_file
import plotly.graph_objects as go
import plotly.io as pio

app = Flask(__name__)

@app.route('/')
def waterfall_chart():
    # Define the columns and values
    columns = ['Beginning ARR', 'Net New', 'Upsell', 'Downsell', 'Churn', 'Ending ARR']
    values = [10000000, 2000000, 1000000, -250000, -500000]

    # Calculate the Ending ARR
    ending_arr = sum(values)
    values.append(ending_arr)

    # Create a waterfall chart
    fig = go.Figure(go.Waterfall(
        name="ARR",
        orientation="v",
        x=columns,
        y=values,
        textposition="outside",
        texttemplate='$%{y:.2s}',
        connector={"line": {"color": "rgb(63, 63, 63)"}},
    ))

    # Update the layout
    fig.update_layout(
        title="Waterfall Chart",
        showlegend=False,
        xaxis_title="Categories",
        yaxis_title="Amount ($ in millions)",
        yaxis_tickprefix="$",
        yaxis_ticksuffix="M",
    )

    # Save the plot as an HTML file
    pio.write_html(fig, file='waterfall_chart.html')

    return send_file('waterfall_chart.html')

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)

This script creates a simple Flask application that serves the waterfall chart as an HTML file. Once you run the script, you should see a message like:

csharpCopy code
* Running on http://0.0.0.0:8080/ (Press CTRL+C to quit)

Click on the URL, or copy and paste it into your web browser to view the waterfall chart.

Certainly! You can make the Ending ARR column a total column by setting the measure attribute in the Waterfall trace. Here's the updated script:

pythonCopy code
from flask import Flask, send_file
import plotly.graph_objects as go
import plotly.io as pio

app = Flask(__name__)

@app.route('/')
def waterfall_chart():
    # Define the columns and values
    columns = ['Beginning ARR', 'Net New', 'Upsell', 'Downsell', 'Churn', 'Ending ARR']
    values = [10000000, 2000000, 1000000, -250000, -500000]

    # Calculate the Ending ARR
    ending_arr = sum(values)

    # Define measure attribute for Waterfall trace
    measure = ['absolute'] + ['relative'] * (len(columns) - 2) + ['total']

    # Create a waterfall chart
    fig = go.Figure(go.Waterfall(
        name="ARR",
        orientation="v",
        x=columns,
        y=values + [ending_arr],
        measure=measure,
        textposition="outside",
        texttemplate='$%{y:.2s}',
        connector={"line": {"color": "rgb(63, 63, 63)"}},
    ))

    # Update the layout
    fig.update_layout(
        title="Waterfall Chart",
        showlegend=False,
        xaxis_title="Categories",
        yaxis_title="Amount ($ in millions)",
        yaxis_tickprefix="$",
        yaxis_ticksuffix="M",
    )

    # Save the plot as an HTML file
    pio.write_html(fig, file='waterfall_chart.html')

    return send_file('waterfall_chart.html')

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)

This script now sets the Ending ARR column as a total column, starting at 0 and ending at the summation figure. Run the script again, and you should see the updated waterfall chart with the correct Ending ARR column representation.

Certainly! To create a cyberpunk-themed waterfall chart, we can customize the chart colors and apply a dark theme. Here's the updated script with a cyberpunk color scheme:

pythonCopy code
from flask import Flask, send_file
import plotly.graph_objects as go
import plotly.io as pio

app = Flask(__name__)

@app.route('/')
def waterfall_chart():
    # Define the columns and values
    columns = ['Beginning ARR', 'Net New', 'Upsell', 'Downsell', 'Churn', 'Ending ARR']
    values = [10000000, 2000000, 1000000, -250000, -500000]

    # Calculate the Ending ARR
    ending_arr = sum(values)

    # Define measure attribute for Waterfall trace
    measure = ['absolute'] + ['relative'] * (len(columns) - 2) + ['total']

    # Create a waterfall chart
    fig = go.Figure(go.Waterfall(
        name="ARR",
        orientation="v",
        x=columns,
        y=values + [ending_arr],
        measure=measure,
        textposition="outside",
        texttemplate='$%{y:.2s}',
        connector={"line": {"color": "rgb(100, 100, 100)"}},
        increasing={"marker": {"color": "rgb(80, 200, 120)"}},
        decreasing={"marker": {"color": "rgb(200, 80, 120)"}},
        totals={"marker": {"color": "rgb(255, 215, 0)"}},
    ))

    # Update the layout
    fig.update_layout(
        title="Waterfall Chart",
        showlegend=False,
        xaxis_title="Categories",
        yaxis_title="Amount ($ in millions)",
        yaxis_tickprefix="$",
        yaxis_ticksuffix="M",
        plot_bgcolor="rgb(20, 20, 20)",
        paper_bgcolor="rgb(20, 20, 20)",
        font=dict(color="rgb(210, 210, 210)"),
        xaxis=dict(
            gridcolor="rgb(80, 80, 80)",
            linecolor="rgb(80, 80, 80)",
            tickfont=dict(color="rgb(210, 210, 210)")
        ),
        yaxis=dict(
            gridcolor="rgb(80, 80, 80)",
            linecolor="rgb(80, 80, 80)",
            tickfont=dict(color="rgb(210, 210, 210)")
        )
    )

    # Save the plot as an HTML file
    pio.write_html(fig, file='waterfall_chart.html')

    return send_file('waterfall_chart.html')

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)

This updated script applies a dark theme with a cyberpunk-inspired color scheme. The background is set to a dark gray, and the text is light gray. The chart uses different colors for increasing, decreasing, and total bars. The gridlines and axis lines are also styled to match the dark theme.

Run the script again, and you should see a cyberpunk-themed waterfall chart with a dark mode and interesting color gradients.