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Read_csv

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

A pandas function that reads delimited tabular data into a DataFrame.

Use read_csv to turn comma-separated or otherwise delimited tabular data into a pandas DataFrame for analysis. CSV fields vary in position but retain their column order, and quoted text can safely contain separators. section 2.4.1 section 7.1.2

Choose the reader according to the source format:

  • Use read_csv for delimited data from a local file, URL, or file-like object; commas are the default delimiter. section 2.4.1
  • Use read_fwf when values occupy fixed character positions rather than being separated by a delimiter. section 2.4.1
  • Use read_json, read_xml, read_html, or read_excel when the data is stored in those formats. section 2.4.1

Load listing data

import pandas as pd summary_listings = pd.read_csv("listings.csv") After loading, inspect the columns and their types to understand identifiers, text fields, numeric values, and missing data before feature engineering. section 7.1.2

Load a second dataset

calendar = pd.read_csv("calendar.csv") The loaded calendar data can then support preparation steps such as cleaning currency-formatted prices and grouping values by listing_id.

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

How deep it goes
Defines delimiter-based loading and demonstrates two commands, but does not detail broader parameter configuration.
Examples
Uses Auto MPG CSV structure and Tokyo Airbnb listings.csv and calendar.csv datasets.
What it assumes
Assumes basic Python and pandas familiarity while demonstrating CSV structure and DataFrame loading directly.
What you can do after
Apply it via pandas to ingest local CSV files and begin inspecting or transforming tabular datasets.
How it connects
Connects CSV loading with DataFrame inspection, fixed-width reading, feature engineering, and alternative pandas format readers.
Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
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This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.