3 Preparing the data, part 1: Exploring and cleansing the data
This chapter covers
- Using config files in Python
- Ingesting XLS files into Pandas dataframes and saving dataframes with pickle
- Exploring the input dataset
- Categorizing data into continuous, categorical, and text categories
- Correcting gaps and errors in the dataset
- Calculating data volume for a successful deep learning project
In this chapter, you’ll learn how to bring tabular structured data from an XLS file into your Python program and how to use the pickle facility in Python to save your data structure between Python sessions. You’ll learn how to categorize the structured data in the three categories needed by the deep learning model: continuous, categorical, and text. You will ...
Get Deep Learning with Structured Data now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
