Pandas
pandas is an open-source Python library that provides labeled data structures and tools for manipulating and analyzing tabular and other structured data.
Pandas Workout: 200 exercises to make you a stronger data analyst
An open-source Python library for data analysis and manipulation, including labeled tabular data structures.
- How it is used
- Applies pandas to loading, cleaning, analyzing, and comparing real-world data sets.
- How deep it goes
- Explains Series indexing, alignment, broadcasting, and substantial data-frame analysis without cataloging all pandas capabilities.
- Examples
- Uses monthly test scores, New York City taxi passenger counts, and Python and Stack Overflow survey data.
- What you can do after
- Apply it via pandas to load, clean, join, analyze, and visualize data frames and Series.
Read how Pandas Workout: 200 exercises to make you a stronger data analyst explains pandas →
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
pandas is an open-source Python library providing data structures and operations for manipulating and analyzing structured and tabular data.
- How it is used
- Supports loading, assembling, cleaning, and exploring tabular data before data analysis or machine learning.
- How deep it goes
- Demonstrates DataFrame construction, indexing, readers, merges, and exploratory commands without detailing pandas internals.
- Examples
- Uses a labeled four-person table, merged product tables, and the Auto MPG dataset in fixed-width and CSV forms.
- What you can do after
- Apply it via pandas to load, merge, inspect, summarize, and visualize mixed-type tabular datasets.
Read how Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI explains pandas →
The Well-Grounded Data Analyst
pandas is a Python software library for working with tabular and labeled data, including data manipulation and analysis.
- How it is used
- Supports exploratory preparation of tabular data before metrics, visualizations, segmentation, forecasting, or interactive prototypes.
- How deep it goes
- Demonstrates executable concatenation, inspection, grouping, correction, merging, and filtering, while deferring specialized statistics, forecasting, and clustering to other libraries.
- Examples
- Uses combined November–December product events and a Streamlit house-price explorer with geographic filters, median prices, charts, and raw-data tables.
- What you can do after
- Apply it via pandas to prepare, validate, transform, summarize, and filter large tabular datasets for analysis or prototypes.
Starting Data Analytics with Generative AI and Python
pandas is a Python library providing labeled data structures and tools for manipulating and analyzing tabular data.
- How it is used
- Uses pandas DataFrames to inspect business datasets and filter customer reviews before drawing analytical conclusions.
- How deep it goes
- Demonstrates loading, inspection, grouping, filtering, and export through code; does not explain pandas internals.
- Examples
- Works with e-commerce CSV files, contrasting customer identifiers and selecting Portuguese reviews containing positive keywords.
- What you can do after
- Apply it via pandas to inspect imported data, test identifier assumptions, and produce a review subset for validation.
Read how Starting Data Analytics with Generative AI and Python explains pandas →
Cyber Threat Hunting
pandas is an open-source Python library for manipulating and analyzing tabular and labeled data.
- How it is used
- Supports beaconing hunts by preparing and analyzing network-event tables.
- How deep it goes
- Demonstrates practical DataFrame loading, conversion, grouping, filtering, deduplication, and plotting rather than pandas internals.
- Examples
- Uses 667,827 network events, connection tuples, destination IPs, web paths, line plots, and histograms.
- What you can do after
- Apply it via pandas to filter and summarize network events for beaconing investigation.
The Quick Python Book, Fourth Edition
pandas is an open-source Python library for data analysis that provides labeled data structures and operations for manipulating tabular and other structured data.
- How it is used
- Automates loading, cleaning, combining, exploring, and plotting tabular data in Python.
- How deep it goes
- Works through data frames and practical operations with small examples, while leaving the wider library and deeper visualization options for further study.
- Examples
- Uses a numeric grid, weather CSV and JSON data, and linked sales-call and revenue tables.
- What you can do after
- Apply it via pandas to load and clean files, select and join rows, calculate grouped totals, and make simple plots.
Read how The Quick Python Book, Fourth Edition explains pandas →
Fast Python: High performance techniques for large datasets
- How it is used
- Uses pandas for in-memory tabular analysis and as a baseline for comparing data-loading and larger-scale processing approaches.
- How deep it goes
- Works through loading, memory reduction, computation choices, and chunked processing; treats Arrow as a complement rather than detailing all its functionality.
- Examples
- Uses taxi-trip CSV benchmarks, a chunked CSV-to-Parquet conversion, and state-tax calculations.
- What you can do after
- Apply it via pandas to load and analyze tables, then measure and adjust memory use, computation, or chunk size.
Read how Fast Python: High performance techniques for large datasets explains pandas →
Statistics Every Programmer Needs
pandas is a Python software library for data manipulation and analysis, especially with tabular and labeled data.
- How it is used
- Supports data preprocessing, exploration, summarization, visualization, and preparation for statistical or predictive modeling.
- How deep it goes
- Demonstrates import, inspection, encoding, grouping, and visualization, but does not descend into pandas internals.
- Examples
- Uses a raisin CSV data set, DataFrame inspection, categorical encoding, grouped statistics, histograms, and correlation heat maps.
- What you can do after
- Apply pandas to load, inspect, clean, transform, summarize, and visualize tabular data for downstream analysis.
Read how Statistics Every Programmer Needs explains pandas →
Build Python Web Apps with Streamlit
pandas is a Python library for data manipulation and analysis, especially with labeled and tabular data.
- How it is used
- Supports loading, inspecting, selecting, wrangling, and visualizing tabular data in interactive Streamlit dashboards.
- How deep it goes
- Explains installation, CSV loading, dataframe inspection, column selection, and visualization preparation without detailing advanced dataframe operations.
- Examples
- Uses sales_data.csv, a large sales dataframe, column subsets, dataframe inspection, and dashboard charts as concrete examples.
- What you can do after
- Build data-loading and dashboard workflows that inspect, select, and prepare tabular records for interactive analysis and charts.
Read how Build Python Web Apps with Streamlit explains pandas →
Grokking Machine Learning
Pandas is an open-source Python library for manipulating and analyzing structured, especially tabular, data.
- How it is used
- Loads, manipulates, and saves datasets for data preparation before machine-learning modeling.
- How deep it goes
- Demonstrates CSV loading, inspecting dimensions and columns, selecting fields, and counting values; notes missing data but does not detail cleaning procedures.
- Examples
- Uses Titanic passenger data to inspect columns and count survival outcomes.
- What you can do after
- Apply Pandas to load a CSV, inspect its dimensions and columns, select fields, and count values.
Natural Language Processing in Action, Second Edition
pandas is a Python library for manipulating and analyzing labeled tabular and time-series data.
- How it is used
- Converts token-count dictionaries into labeled tabular vectors for text analysis.
- How deep it goes
- Explains DataFrame construction, missing-value filling, integer conversion, vocabulary columns, and vector arithmetic, but defers later high-dimensional improvements.
- Examples
- Uses 16 bias-related documents to produce a 246-column document-term matrix from token counts.
- What you can do after
- Apply pandas to construct, inspect, and normalize labeled text-count matrices.
Read how Natural Language Processing in Action, Second Edition explains pandas →
Related concepts
- read_csvused by · read_csv is a function that parses comma-separated or otherwise delimited text into a tabular data structure.
- data groupingused by · Data grouping is the organization of data into groups according to shared values, keys, or other criteria, supporting grouped processing, analysis, or retrieval.
- data frameuses · A data frame is a two-dimensional tabular data structure with labeled rows and columns that can contain values of different types.
- CSVuses · CSV is a plain-text format for tabular data, representing records as rows and fields as values separated by delimiters, conventionally commas, with quoting conventions for special content.
- Pythonuses · Python is a high-level, general-purpose, dynamically typed programming language known for readable syntax and automatic memory management.
- datasetuses · A dataset is an organized collection of related data items, records, or observations treated as a unit for storage, processing, analysis, or computation.
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