Prompt Engineering for Data: Basic Data Manipulation Using Generative AI

Prompt engineering, data science    |    Beginner
  • 12 videos | 1h 36m 5s
  • Includes Assessment
  • Earns a Badge
Rating 5.0 of 1 users Rating 5.0 of 1 users (1)
With DataFrames in pandas you can filter, aggregate, join, pivot, and manipulate data efficiently. These operations enable data analysts and scientists to work with datasets for various data-driven tasks. Prompt engineering tools are adept at generating code to make these tasks simple. You will start this course by exploring the configurations you can apply to read in your data. You'll present your problem statement to ChatGPT and explore the use of arguments to configure various aspects of the file reading, such as defining column names, and specifying which columns to include in the DataFrame. Additionally, you will learn how to read data from different sources, including JSON, Excel, and the Clipboard and write files out to these different formats. Next, you'll delve into common DataFrame operations, examine statistics on your data, rename columns, iterate over, and sort your data. As you encounter issues, you will turn to prompt engineering to help debug them. Finally, you'll explore how you can enhance your data using computed columns. You'll harness the power of two essential functions, apply and map, to transform your records. You will also focus on utilizing generative AI for code generation and you will employ the chain-of-thought prompting method to guide the chatbot in generating code effectively.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Read csv files into pandas dataframes
    Read data from a variety of sources
    Write data out to files
    Drop and select columns from dataframes
    Perform operations on dataframes
  • Iterate over and sort dataframes
    Debug issues with sorting dataframes
    Compute new columns in a dataframes
    Use the apply and filter functions
    Generate code with chain-of-thought prompting
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 10s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 13m 20s
    In this video, find out how to read CSV files into pandas DataFrames. FREE ACCESS
  • Locked
    3.  Reading Data from Multiple Sources
    10m 39s
    Learn how to read data from a variety of sources. FREE ACCESS
  • Locked
    4.  Writing Data Using Multiple Formats
    6m 36s
    In this video, you will learn how to write data out to files. FREE ACCESS
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    5.  Column Dropping and Selection Using Generative AI Tools
    8m 54s
    Find out how to drop and select columns from DataFrames. FREE ACCESS
  • Locked
    6.  Performing Basic Data Frame Operations
    7m 56s
    In this video, learn how to perform operations on DataFrames. FREE ACCESS
  • Locked
    7.  Using ChatGPT to Iterate over and Sort DataFrames
    11m 35s
    During this video, discover how to iterate over and sort DataFrames. FREE ACCESS
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    8.  Debugging Sorting Issues with Generative AI Help
    8m 3s
    In this video, discover how to debug issues with sorting DataFrames. FREE ACCESS
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    9.  Computing New Columns Using Existing Data
    7m 41s
    Discover how to compute new columns in a DataFrames. FREE ACCESS
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    10.  Learning to Use the Apply Function
    11m 15s
    Find out how to use the apply and filter functions. FREE ACCESS
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    11.  Using Chain-of-Thought Prompting for a Sequence of Operations
    5m 27s
    In this video, you will learn how to generate code with Chain-of-Thought prompting. FREE ACCESS
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    12.  Course Summary
    2m 31s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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