# pandas Published articles for pandas. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Quiz: Introduction to pandas DevFeed: [Quiz: Introduction to pandas]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-18T12:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [pandas](), [CSV](), [data](), [Data Science](), [Statistics](), [Web](), [save]() Tags: [csv](), [data](), [explore](), [file](), [how-to](), [interactive](), [introduction](), [pandas](), [save](), [statistics](), [table](), [tables](), [test](), [web]() ### AI overview An interactive seven-question quiz tests core pandas skills, including reading tables from web pages, saving data as CSV, understanding DataFrames, chaining methods, and calculating statistics. ### Source excerpt Test your pandas basics: read tables from the web, save data to CSV, explore a DataFrame, and calculate averages in this interactive quiz. ## Announcing Fedora Linux 45 Beta DevFeed: [Announcing Fedora Linux 45 Beta]() Original publisher: [Read original article]() Author: Aoife Moloney Published: 2026-09-15T14:05:00Z Content type: release Language: en Sources: [Fedora Magazine]() Topics: [Fedora](), [Linux](), [dnf](), [podman](), [pandas](), [Python](), [gcc](), [Go Language](), [MariaDB](), [MySQL](), [Filesystems]() Tags: [2026](), [dnf](), [fedora-project-community](), [filesystems](), [gcc](), [go](), [linux](), [mysql](), [pandas](), [partitioning](), [podman](), [pre-release](), [python](), [release](), [toolchains]() ### AI overview Fedora Linux 45 Beta is available for download or upgrade on September 15, 2026. The release includes changes to the kernel console, package signature verification, desktop secret management, software versions, Atomic Desktop image builds, Fedora CoreOS swap support, Anaconda installation flows, and Stratis filesystem partitioning. ### Source excerpt Fedora Linux 45 Beta is here! On Tuesday, 15 September 2026 you can download or upgrade your systems from your usual spots to start enjoying what F45 has to offer early. How to get the beta release You can download F45 Beta, or our pre-release edition versions, from any of the following places: You can [...] ## Automating EDA With fg-data-profiling DevFeed: [Automating EDA With fg-data-profiling]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-15T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Data analysis](), [pandas](), [Python](), [Data Quality](), [Statistics](), [HTML](), [JSON]() Tags: [data-analysis](), [data-quality](), [pandas](), [python](), [statistics](), [tutorial]() ### AI overview A video course on automating exploratory data analysis with fg-data-profiling. It covers generating interactive reports from DataFrames, exporting them to HTML or JSON, analyzing time series, and comparing datasets. ### Source excerpt Automate exploratory data analysis by transforming DataFrames into interactive reports with one command from fg-data-profiling. ## How to fix a weird pandas and pyarrow issue with BirdNetPi DevFeed: [How to fix a weird pandas and pyarrow issue with BirdNetPi]() Original publisher: [Read original article]() Author: Robin Wilson Published: 2026-08-27T17:57:36Z Content type: tutorial Language: en Sources: [Robin Wilson]() Topics: [Python](), [Raspberry Pi](), [pandas]() Tags: [how-to](), [pandas](), [programming](), [python](), [raspberry-pi]() ### AI overview A troubleshooting guide for BirdNetPi explains that immediate Python crashes and missing Streamlit visualizations can be caused by the pyarrow package. The author uses import profiling to identify pyarrow as the package present immediately before the crash and recommends trying its removal. ### Source excerpt Summary: If you get Python crashing immediately in BirdNetPi, try uninstalling the pyarrow Python package. I've got a BirdNetPi set up at home. This is a bit of software that runs on a Raspberry Pi and listens on a microphone (I've mounted mine on the outside of an upstairs window, using a 3D printed mount/cover [...] ## What Jobs Can You Get After Learning Python? 6 Careers and Their Salaries DevFeed: [What Jobs Can You Get After Learning Python? 6 Careers and Their Salaries]() Original publisher: [Read original article]() Author: Kari Brooks Published: 2026-08-10T20:00:19Z Content type: article Language: en Sources: [Treehouse Blog]() Topics: [Python](), [Back end](), [Django](), [Flask](), [pandas]() Tags: [backend](), [backend-development](), [career-advice](), [career-change](), [careers](), [data-analyst](), [developer](), [django](), [flask](), [learn](), [learn-to-code](), [learning](), [pandas](), [python](), [python-careers](), [tech-jobs]() ### AI overview This guide describes six career paths available after learning Python, including data analysis, data science, backend development, machine learning engineering, automation engineering, and DevOps. It discusses beginner accessibility, required skills, and salary ranges from roughly $60,000 for entry-level analysts to over $180,000 for senior machine learning engineers. ### Source excerpt Python opens doors to data, web, AI, and automation careers. Here are six real jobs you can land after learning Python, what they pay, and which ones welcome beginners. The post What Jobs Can You Get After Learning Python? 6 Careers and Their Salaries appeared first on Treehouse Blog. ## The Creator of Pandas on AI, Apache Arrow, and the Future of Software Engineering DevFeed: [The Creator of Pandas on AI, Apache Arrow, and the Future of Software Engineering]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-07-08T12:16:09Z Content type: article Language: en Sources: [Data Engineering Central]() Topics: [pandas](), [data-engineering](), [Artificial Intelligence](), [DuckDB](), [data-platforms](), [software-development](), [future of software](), [Apache Spark](), [Hadoop](), [Open Source](), [parquet]() Tags: [ai](), [apache-arrow](), [arrow](), [data](), [data-engineering](), [data-platforms](), [duckdb](), [hadoop](), [large-language-models](), [open-source](), [pandas](), [parquet](), [software-development]() ### AI overview An interview with Wes McKinney covers the origins of pandas and Apache Arrow, the evolution of modern data engineering from Hadoop to lakehouse architectures, and the roles of tools such as Parquet, DuckDB, DataFusion, and Spark. McKinney also discusses how AI affects software development, arguing that it can improve experienced engineers' productivity but does not replace software engineering, architecture, or judgment. ### Source excerpt interview with Wes McKinney ## Re-autoresearching MSMARCO BM25, on Vespa DevFeed: [Re-autoresearching MSMARCO BM25, on Vespa]() Original publisher: [Read original article]() Author: andreer thomas Published: 2026-05-29T00:00:00Z Content type: article Language: en Sources: [Vespa Blog]() Topics: [Benchmark](), [dataset](), [Python](), [Large Language Model](), [generalization in machine learning](), [pandas](), [Google Search]() Tags: [benchmark](), [bm25](), [dataset](), [evaluation](), [experiment](), [google-search](), [information-retrieval](), [openai](), [pandas](), [python](), [ranking](), [retrieval](), [zero-shot]() ### AI overview This article reproduces an MSMARCO BM25 autoresearch experiment in Vespa. It compares LLM-driven Python reranking with an approach restricted to existing Vespa rank features and reports a comparable improvement on a 650,000-passage subset, with better generalization to the full dataset. ### Source excerpt BM25 is having a moment. We reproduce Doug Turnbull's MSMARCO autoresearch experiment in Vespa and get a comparable MRR@10 lift from existing rank features -- with twice the generalization to full MSMARCO. ## Introducing Apache Arrow Support in mssql-python DevFeed: [Introducing Apache Arrow Support in mssql-python]() Original publisher: [Read original article]() Author: Saumya Garg Published: 2026-05-04T04:33:00Z Content type: release Language: en Sources: [Microsoft Python Engineering]() Topics: [sql-server](), [data-processing](), [interoperability](), [data](), [pandas]() Tags: [apache-arrow](), [arrow](), [azure](), [azure-sql](), [client-driver](), [data-processing](), [interoperability](), [pandas](), [python](), [python-driver-for-azure-sql](), [python-driver-for-sql-server](), [sql-server](), [sql-server-2025](), [zero-copy]() ### AI overview Microsoft introduces Apache Arrow support in mssql-python, enabling SQL Server data to be fetched directly into Arrow structures for Polars, Pandas, DuckDB, and other Arrow-native libraries. The approach is intended to reduce Python object creation and memory overhead during data processing. ### Source excerpt Reviewed by Sumit Sarabhai Fetching a million rows from SQL Server into a Polars DataFrame used to mean a million Python objects, a million GC allocations, and then throwing it all away to build a DataFrame. Not anymore. mssql-python now supports fetching SQL Server data directly as Apache Arrow structures - a faster and more [...] The post Introducing Apache Arrow Support in mssql-python appeared first on Microsoft for Python Developers Blog. ## The Schema-Driven LLM Query Pattern DevFeed: [The Schema-Driven LLM Query Pattern]() Original publisher: [Read original article]() Author: Rick Donato Published: 2026-02-27T15:09:32Z Content type: tutorial Language: en Sources: [Packet Coders - Learn Network Automation]() Topics: [Large Language Model](), [Structured-data](), [Security](), [data](), [pandas](), [Query (disambiguation)]() Tags: [ai](), [blog](), [data](), [databases](), [graphql](), [llm](), [pandas](), [rest](), [schema](), [security](), [structured]() ### AI overview This tutorial presents the Schema-Driven LLM Query Pattern. Instead of sending raw data to an external LLM, it sends the data schema, receives query syntax, and executes the query locally to reduce data exposure and token usage. ### Source excerpt Typically, when working with an LLM, you send your data along with a prompt and receive a response. But this pattern often introduces issues, especially with public LLMs, due to security concerns and token bloat. There is a different pattern we can use: the Schema-Driven LLM Query Pattern. With ## Pharmacy late-night opening hours analysis featured in the Financial Times DevFeed: [Pharmacy late-night opening hours analysis featured in the Financial Times]() Original publisher: [Read original article]() Author: Robin Wilson Published: 2026-02-04T14:10:37Z Content type: article Language: en Sources: [Robin Wilson]() Topics: [Data analysis](), [geospatial](), [Geographic Information System](), [Python](), [pandas]() Tags: [academic](), [analysis](), [article](), [data](), [data-analysis](), [geospatial](), [gis](), [pandas](), [python]() ### AI overview The article describes an analysis of NHS data on community pharmacy opening hours in England. It reports that the number of pharmacies open past 9pm on weekdays fell by approximately 95% between 2022 and 2025, leaving large areas without late-night pharmacy access. The analysis used Python, pandas, geospatial tools and mapping libraries, and was featured in the Financial Times. ### Source excerpt Some data analysis I've done has been featured in the Financial Times today - see this article (the link may not work any more unless you have a FT subscription - sorry). The brief story is that I had terrible back pain over Christmas, and spoke to an out-of-hours GP on the phone who prescribed [...] ## Streaming optimized data to S3 for analytics with Parquet DevFeed: [Streaming optimized data to S3 for analytics with Parquet]() Original publisher: [Read original article]() Author: Chandler Mayo Published: 2025-08-13T00:00:00Z Content type: tutorial Language: en Sources: [Redpanda]() Topics: [parquet](), [Redpanda-Connect](), [Amazon S3](), [data analytics](), [Streaming](), [Apache Spark](), [pandas](), [Serverless]() Tags: [analytics](), [analytics-systems-data-feed](), [apache-spark-and-athena-data-queries](), [athena](), [automating-data-pipelines-in-s3](), [building-dashboards-with-s3-data](), [compressing-data-with-parquet](), [data](), [data-pipeline](), [data-pipeline-for-analytics](), [event-driven-pipelines-with-s3](), [how-to](), [learn](), [pandas](), [parquet](), [parquet-files-in-amazon-s3](), [pipeline](), [real-time-data-streaming](), [redpanda-connect](), [redpanda-data-streaming](), [s3](), [serverless](), [setting-up-redpanda-observability](), [spark](), [streaming](), [streaming-data-to-s3-with-parquet](), [tutorial]() ### AI overview This tutorial explains how to use Redpanda Connect to continuously batch streaming data and write compressed Apache Parquet files to Amazon S3 for analytical workloads. It covers the benefits of Parquet and querying the resulting files with tools such as Pandas, Apache Spark, and Athena. ### Source excerpt Learn how to build a powerful data pipeline that feeds analytics systems from Redpanda using clean, compressed Parquet files in Amazon S3. ## Aequitas Flow step-by-step: a Fair ML optimization framework DevFeed: [Aequitas Flow step-by-step: a Fair ML optimization framework]() Original publisher: [Read original article]() Author: Sérgio Jesus Published: 2024-08-12T14:35:43Z Content type: tutorial Language: en Sources: [Feedzai]() Topics: [Machine learning](), [Optimization](), [audit](), [Benchmark](), [pandas](), [datasets](), [interfaces]() Tags: [audit](), [benchmark](), [datasets](), [fairness](), [interfaces](), [machine-learning](), [ml](), [open-source](), [optimization](), [pandas](), [reproducibility](), [research](), [responsible-ai]() ### AI overview This tutorial introduces Aequitas Flow, an open-source framework for standardized experiments with fair machine-learning algorithms. It explains how the framework extends Aequitas fairness auditing with reproducible experimentation, support for custom datasets and methods, and workflows for practitioners selecting models and researchers running benchmarks. ### Source excerpt By Sérgio Jesus, Inês Silva, Pedro Saleiro, Hugo Ferreira, Pedro Bizarro In this blog post we will visit Aequitas Flow, an Open-Source framework designed to run complete and standardized experiments of Fair ML algorithms. We encourage you to try Aequitas Flow with the Google Colab Notebooks, which are available in the project's GitHub repository. This blog post is based on the paper by Sérgio Jesus, Pedro Saleiro, Inês Silva, Beatriz M. Jorge, Rita P. Ribeiro, João Gama, Pedro Bizarro, and Rayid Ghani. Table of Contents: 1. What is Aequitas Flow? - 1.1. For Practitioners selecting a model - 1.2. For Researchers running a benchmark 2. Install Aequitas Flow 3. The components of Aequitas Flow - 3.1. Experiment - 3.2. Optimizer - 3.3. Datasets - 3.4. Methods - 3.5. Audit 4. Conclusion What is Aequitas Flow? Aequitas Flow is the codename for the latest version of Aequitas, a well-established package for fairness auditing in the ML community. This version extends the package to include experimentation with Fair ML algorithms. Aequitas started as a software to diagnose and alert for disparities in ML models' decisions depending on sensitive attributes, such as race, gender, or age. To achieve this, the package runs a Bias Audit, in which it calculates several metrics across all data groups, determined by the sensitive attributes, and compares them to identify any gap in performance. While some other packages already implement Fair ML methods, they also introduce an overhead of technical knowledge to configure, evaluate, and deploy these models. Because of this, we extended Aequitas to enable users to conduct experiments using a wide variety of fair ML methods from the literature in an intuitive user experience. Aequitas Flow was built on the principles of extensibility and reproducibility: it allows users to incorporate their own datasets and methods in the framework using familiar interfaces of sci-kit-learn and pandas. It ensures their work can be replicated by performin ## Optimizing AI Code for Performance DevFeed: [Optimizing AI Code for Performance]() Original publisher: [Read original article]() Author: admin Published: 2024-05-27T13:47:23Z Content type: tutorial Language: en Sources: [Posts on Chris Banes]() Topics: [AI Development](), [Optimization](), [Code](), [Feature Engineering](), [data]() Tags: [ai](), [batching](), [code](), [feature-engineering](), [format](), [machine-learning](), [optimization](), [pandas](), [parquet](), [performance](), [uncategorized]() ### AI overview This guide explains techniques for optimizing AI and machine learning code for faster execution and lower computing costs. It covers data loading and preprocessing, algorithm selection, hardware acceleration, distributed training, and batching. ### Source excerpt AI code optimization is about getting almost everything out of your algorithms and models. It's about making sure that your AI projects run smoothly, quickly, and cost-effectively. In this guide, we'll dive into the world of AI code optimization, exploring different techniques and best practices that can help you speed up your machine learning projects. [...] ## UK Bank Holidays with Pandas on Jupyter DevFeed: [UK Bank Holidays with Pandas on Jupyter]() Original publisher: [Read original article]() Author: Simone Spaccarotella Published: 2022-09-21T17:22:24Z Content type: tutorial Language: en Sources: [BBC]() Topics: [pandas](), [Jupyter Notebook](), [JSON](), [Python](), [Data Science](), [data]() Tags: [coding](), [data-science](), [json](), [jupyter-notebook](), [kaggle](), [pandas](), [python]() ### AI overview A hands-on tutorial about using Pandas in a Jupyter Notebook to retrieve UK bank holiday data in JSON format and prepare it for analysis in a DataFrame. The article also describes the author's self-study practice in Python, data science, and machine learning. ### Source excerpt How I learned to manipulate JSON data with Pandas on a Jupyter Notebook and deconstruct it to a DataFrame ready for queries.Image by author created from Jupiter photo by NASA and Pandas photo by Pascal Müller on UnsplashA bit of context first I started a self-study path to learn the theoretical fundamentals of Data Science and Machine Learning. I have also been playing with Python ever since, with coding exercises during the "Friday 10% time" here at the BBC and on my own time when I have some to spare. I created this playground repository to refresh my Pythonic knowledge that was buried deep inside the funny head of mine. This repo is a mix of material and exercises to keep my Python coding skills sharp. GitHub - simonespa/python-playground: A playground repo for Python I also created a specific playground focused on Data Science and Machine learning. In this playground I practice with libraries such as NumPy, Pandas, Scikit-Learn, TensorFlow (et. all) with a Jupyter Notebook. It also contains notes and summaries of what I learn in forms of README or notebooks. GitHub - simonespa/machine-learning-playground: A playground to practice machine learning, data modelling and visualisation Nothing fancy or ground-breaking, just a couple of playground repos for me to practice and learn at my own pace. The 10% time at the BBC This happens every other Friday and it is about learning. Engineers use this time to learn new technologies and/or languages, discover new approaches and techniques. We can organise the day as we want. It could be a self-paced learning session (reading, watching tutorials, etc.), a day of study for an apprenticeship or a university degree etc., an actual hands-on prototyping session to work on a long-running personal project, or a group collaboration with other like-minded people. There is no limitation to how we plan our day, as long as we keep in mind the end goal. In simple terms, satisfy our own curiosity and creativity by strengthening our technic ## Practical SQL for Data Analysis DevFeed: [Practical SQL for Data Analysis]() Original publisher: [Read original article]() Author: Haki Benita Published: 2021-04-25T21:00:00Z Content type: tutorial Language: en Sources: [Haki Benita]() Topics: [SQL](), [Data analysis](), [pandas](), [PostgreSQL](), [Python]() Tags: [analysis](), [articles](), [benchmark](), [data-analysis](), [database](), [how-to](), [pandas](), [postgresql](), [practical](), [python](), [sql]() ### AI overview This practical tutorial demonstrates how SQL can perform fast and efficient data analysis using a users table, comparing the approach with pandas and measuring memory usage. It reports that pandas alone uses about 37 MB and loading the data into memory adds about 300 MB, while the database table is 65 MB. ### Source excerpt Pandas is by far the most popular tool for data analysis. It's packed with useful features, it's battle tested and widely accepted. However, pandas comes at a cost which is often overlooked. SQL databases has been around since the 1970s. They contain many features that most developers never heard of, and I want to bring some of them to light. ## Working with SQLite using Go and Python DevFeed: [Working with SQLite using Go and Python]() Original publisher: [Read original article]() Published: 2020-11-30T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [SQLite](), [Go Language](), [Python](), [Databases](), [SQL](), [pandas](), [HTTP]() Tags: [ardan-labs](), [blog](), [code](), [database](), [go](), [go-programming](), [golang](), [http](), [pandas](), [programming](), [python](), [sql](), [sqlite]() ### AI overview A tutorial on using SQLite with Go and Python. It explains SQLite, transactions, schemas, and SQL, then builds a Go HTTP server that stores trade notifications in SQLite and a Python program that processes the data. ### Source excerpt Introduction I prefer to use relational (SQL) databases in general since they provide several features that are very useful when working with data. SQLite is a great choice since the database is a single file, which makes it easier to share data. Even though it's a single file, SQLite can handle up to 281 terabytes of data. SQLite also comes with a command line client called sqlite3 which is great for quick prototyping. ## Python and Pandas : First Contact with Data DevFeed: [Python and Pandas : First Contact with Data]() Original publisher: [Read original article]() Published: 2020-11-02T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [pandas](), [Python](), [Data Quality](), [Structured-data](), [CSV](), [Python 3.8]() Tags: [ardan-labs](), [blog](), [blog-post](), [data-quality](), [format](), [go](), [go-programming](), [golang](), [pandas](), [programming](), [python](), [python-3-8](), [schema]() ### AI overview This tutorial introduces working with data using Python 3.8 and pandas 1.1. It emphasizes that data quality is crucial and explains why understanding a dataset's schema, units, and representations is necessary before analyzing it. ### Source excerpt Introduction Every single company I've worked at and talked to has the same problem without a single exception so far - poor data quality, especially tracking data. Either there's incomplete data, missing tracking data, duplicative tracking data. - DJ Patil I spend a lot of my time digging into data at various companies. Most of the time I'm surprised by what I see and so are the engineers and analysts that work at these companies. I've seen missing data, bad data, data nobody knows anything about, and many other oddities. ## Find that bug! Using a search engine as a programmer DevFeed: [Find that bug! Using a search engine as a programmer]() Original publisher: [Read original article]() Author: Itamar Turner-Trauring Published: 2020-08-17T00:00:00Z Content type: tutorial Language: en Sources: [Itamar Turner-Trauring]() Topics: [Programming](), [issue tracker](), [Python](), [Stack Overflow](), [pandas](), [Logging](), [GitHub](), [JSON]() Tags: [bug](), [documentation](), [github](), [issue-tracker](), [json](), [pandas](), [python](), [search]() ### AI overview This article explains how programmers can find solutions to bugs and programming problems by searching project-specific issue trackers, documentation, Stack Overflow, and other local resources alongside general search engines. It uses an Eliot and Pandas error as an example of a GitHub issue that a general search engine did not index. ### Source excerpt Most bugs you encounter have been encountered by others before you; most programming problems you face have been faced by others as well. And many of those people have written down details about what they've learned--in issue trackers, documentation, and blog posts. All you have to do is find this information. Typing a phrase in to your search engine of choice will sometimes take you straight to the right answer. But quite often, the results aren't helpful. No need to give up, though: there are still plenty of ways you can productively keep searching. Use site-specific search too It's easy to believe that search engines have all the answers right at the top, but they actually hide quite a lot of content deep in their results. And some obscure content never gets indexed at all, which is unfortunate when it's the obscure content that you need to find. So instead of just using a search engine, use the local search engine of the project issue tracker, the documentation, StackOverflow, and so on. For example, let's saying you're using Eliot, a somewhat obscure Python logging library I maintain, and you want to use it with the Pandas library. Unfortunately, you get an error, so you search Google for the text of the error: eliot dataframe is not json serializable. Now, there is an actual issue in Eliot's GitHub issue tracker with this exact error message--but as of August 2020 Google doesn't return it, probably because it didn't bother to index that page. But if you were to use the search form on the Eliot GitHub project's issues page, you would find the issue that mentions this particular error. In this case, as in many others, the search engine isn't actually indexing everything: you have no choice but to use the local search engine. Local search engines often have the additional benefit of allowing more structured search, for example: An issue tracker might let you search by open/closed status, labels, or the affected version. StackOverflow questions are tagged with parti ## Python Tips - Playing with Pandas DevFeed: [Python Tips - Playing with Pandas]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2020-01-06T00:00:00Z Content type: tutorial Language: en Sources: [Scott Johnson]() Topics: [pandas](), [Python](), [data-processing](), [dataset](), [JSON](), [CSV]() Tags: [csv](), [data](), [data-processing](), [dataset](), [json](), [library](), [pandas](), [python](), [script]() ### AI overview A practical introduction to using pandas in Python for loading and inspecting a dataset. The article demonstrates reading JSON data, sampling and copying a DataFrame, checking its length and type, examining columns, accessing text values, iterating over them, and converting values to strings. ### Source excerpt As I write this it is 3:45 am and I am playing with Python. I have a small script that looks like this: import pandas as pd DATA_PATH = "data/" filename = "file.json" train_sample_size = 45000 dataset = pd.read_json(os.path.join(DATA_PATH, filename), lines=True) dataset_test = dataset.copy().sample(train_sample_size).reset_index() dataset_head = dataset_test.head(train_sample_size) pdb.set_trace() The pandas library give you a bunch of facilities for looking at data and organizing it into what are called data frames. If you think of pandas as a spreadsheet that you can use programmatically, well, that's not far off. Pandas is widely used for Python data processing and can read data from CSV and JSON formats (among others). If I want to investigate the length of a variable, I can do this: (Pdb) len(dataset) 45648 And if I want to know the type of a variable, I can do this: type(dataset) And if I want to know the columns that Pandas as loaded from the json, I can do this: dataset.columns Index(['tweet_id', 'timestamp_us_eastern', 'author_handle', 'author_screenname', 'authority', 'predicted_gender', 'predicted_language', 'predicted_country', 'predicted_state_or_province', 'predicted_city', 'given_location', 'followers', 'following', 'predicted_sentiment', 'context_notes', 'antisemitic_classification', 'author_bio', 'text', 'is_truncated', 'is_retweet', 'lang', 'Majority Model', 'label'], dtype='object') What this tells me is that I have a dataset variable with a text column that I can access by dataset.text. And then if I want the first 'row' of data in this column, I can do this: (Pdb) dataset.text[0] "Jerry Seinfeld spent a lot of time in Israel's apartheid State supporting the Zionists and giving the bastards credibility. Think its fair to shun his crappy unfunny TV Shows. Let him know what people think of him brown nosing the terrorists of Israel.\n" So [0] gives the first bit of data. And, correspondingly, [1] gives the next ## From gnuplot to Matplotlib & Pandas DevFeed: [From gnuplot to Matplotlib & Pandas]() Original publisher: [Read original article]() Published: 2019-04-01T08:59:00Z Content type: tutorial Language: en Sources: [Bert Hubert's writings]() Topics: [pandas](), [plotting](), [data](), [CSV](), [Shell]() Tags: [csv](), [data](), [pandas](), [plotting](), [shell]() ### AI overview This tutorial introduces Matplotlib and Pandas as an alternative to gnuplot for analyzing and plotting system data. It explains how Pandas and Matplotlib keep labeled data and graphing in one programming environment, using vmstat output and CSV files as examples. ### Source excerpt I've been using gnuplot since.. like forever. It is one of my best friends in plotting data and discovering what is going on. But for all its greatness, you do tend to run into a wall - once you step outside the things gnuplot is good at, suddenly large heaps of awk, sort, unique and odd shell scripts are required to get to the next level. This is no criticism of gnuplot - it is great for what it is for.