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    Speed Up Your Process Using the Workspace AI Assistant

    Discover the power of our AI Assistant. Get started with exciting prompts that will supercharge your data workflow!

    The sample dataset we'll use here consists of orders made with a UK-based online retailer from December 2010 to December 2011. Source of dataset.

    Get started with AI, follow these steps:

    1. Hover on the space in between cells and add a new cell by clicking the "plus" icon or the line.
    2. Type in your first prompt.
    3. Click on "Ask AI" or press the return key.

    1. Automatically Handle All Your Package Imports

    Try this prompt:

    Import the packages I will need to perform a machine learning classification task.

    2. Build Beautiful Visualizations

    Try this Prompt:

    Create a Plotly plot of monthly sales in 2011 based on online_retail.csv.
    # Import the required packages
    import folium
    # Create a map centered at the Eiffel Tower
    eiffel_tower_location = [48.8584, 2.2945]
    map_eiffel_tower = folium.Map(location=eiffel_tower_location, zoom_start=15)
    # Add a marker for the Eiffel Tower
    folium.Marker(location=eiffel_tower_location, popup='Eiffel Tower').add_to(map_eiffel_tower)
    # Display the map

    3. Speed Up Your SQL

    First create a SQL cell and select the "employees" sample database source. To the question "which countries do our customers purchase from?" using a SQL query, try this prompt:

    Write a SQL query that lists the top 3 countries from which customers make purchases and run it against "online_retail.csv".
    # Import the required packages
    import pandas as pd
    from sklearn.model_selection import train_test_split
    from sklearn.linear_model import LogisticRegression
    from sklearn.metrics import accuracy_score
    # Load the dataset
    url = ''
    df = pd.read_csv(url)
    # Preprocess the data
    X = df[['Pclass', 'Sex', 'Age', 'Fare']]
    y = df['Survived']
    X['Sex'] = X['Sex'].map({'male': 0, 'female': 1})
    X['Age'].fillna(X['Age'].mean(), inplace=True)
    X['Fare'].fillna(X['Fare'].mean(), inplace=True)
    # Split the data into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    # Build the classification model
    model = LogisticRegression(), y_train)
    # Make predictions
    y_pred = model.predict(X_test)
    # Calculate accuracy
    accuracy = accuracy_score(y_test, y_pred)

    4. Write a Summary

    Try this prompt:

    Write a summary of the analysis in this workspace.
    Unknown integration
    DataFrameavailable as
    SELECT, funding.valuation
    FROM companies
    JOIN funding ON companies.company_id = funding.company_id;
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

    5. Format Your Code

    Directly below the code cell that follows, try this prompt:

    Update the cell above to follow PEP 8 standards.

    Looking for more prompts to try? The following tutorial has more: 10 Ways to Speed Up Your Analysis With the Workspace AI Assistant