Scatterplot with plotly vs pyplot / different approach in data table needed?

I’m trying to create a scatterplot in plotly, but have some difficulties. I think I need to rearrange my data table to be able to work with it, but am note sure.

This is how my data table looks:

table structure

The “Average Price” is the “real” data and the prices in the “Predictions” column are what my model predicted.

I want to display it in a scatterplot, showing both the predicted and real prices as dots, like this:

scatterplot created through matplotlib

This, I created with pyplot

plt.scatter(x_axis, result['Average Price'], label='Real')
plt.scatter(x_axis, result['Predictions'], label='Predictions')
plt.xlabel('YYY-MM-DD')
plt.ylabel('Average Price')
plt.legend(loc='lower right')
plt.show()

However, I wanted to do the same with plotly, which I can’t seem to figure out. I have no problems with one column, but don’t know how to access both. Do I need to rearrange the table so that I have all prices (predicted and real) in one column and an additional column labeling the data as “real” or “predicted”?

chart_model = px.scatter(result, x='YYYY-MM-DD', y='Predictions', title='Predictions')
chart_model.update_layout(title_x=0.5, plot_bgcolor='#ecf0f1', yaxis_title='Average Price Predicted',
                         font_color='#2c3e50')
chart_model.update_traces(marker=dict(color='blue'))

Thanks in advance for any tips on how to proceed!

Answer

• have simulated dataframe of same structure as your question
• have used pandas melt() to reshape in line to long dataframe that is then simple to use with plotly

import pandas as pd
import numpy as np
import plotly.express as px

# simulate data frame
df = pd.DataFrame(
    {
        "YYYY-MM-DD": pd.date_range("4-jan-2015", freq="7D", periods=300),
        "Average Price": np.random.uniform(1.2, 1.4, 300),
    }
).pipe(
    lambda d: d.assign(
        Predictions=d["Average Price"] * np.random.uniform(0.9, 1.1, 300)
    )
)

# simple inline restructure of data frame
px.scatter(df.set_index("YYYY-MM-DD").melt(ignore_index=False), y="value", color="variable")

alternate

• just move data into index and define columns to be plotted

px.scatter(df.set_index("YYYY-MM-DD"), y=["Average Price", "Predictions"])