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Hands-On Data Analysis with Pandas: Efficiently perform data collection, wrangling, analysis, and visualization using Python
Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discovery
Key FeaturesPerform efficient data analysis and manipulation tasks using pandasApply pandas to different real-world domains using step-by-step demonstrationsGet accustomed to using pandas as an effective data exploration toolBook DescriptionData analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value.
Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data.
By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.
What you will learnUnderstand how data analysts and scientists gather and analyze dataPerform data analysis and data wrangling in PythonCombine, group, and aggregate data from multiple sourcesCreate data visualizations with pandas, matplotlib, and seabornApply machine learning (ML) algorithms to identify patterns and make predictionsUse Python data science libraries to analyze real-world datasetsUse pandas to solve common data representation and analysis problemsBuild Python scripts, modules, and packages for reusable analysis codeWho this book is forThis book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial.
Table of ContentsIntroduction to Data AnalysisWorking with Pandas DataFramesData Wrangling with PandasAggregating Pandas DataFramesData Visualization with Pandas and MatplotlibPlotting with Seaborn and Customization TechniquesFinancial Analysis with Bitcoin and the Stock MarketRule-based Anomaly Catching HackersGetting started with Machine Learning in PythonMaking Better Optimizing ML ModelsML Anomaly Catching Hackers, Part 2The Road Ahead
Key FeaturesPerform efficient data analysis and manipulation tasks using pandasApply pandas to different real-world domains using step-by-step demonstrationsGet accustomed to using pandas as an effective data exploration toolBook DescriptionData analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value.
Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data.
By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.
What you will learnUnderstand how data analysts and scientists gather and analyze dataPerform data analysis and data wrangling in PythonCombine, group, and aggregate data from multiple sourcesCreate data visualizations with pandas, matplotlib, and seabornApply machine learning (ML) algorithms to identify patterns and make predictionsUse Python data science libraries to analyze real-world datasetsUse pandas to solve common data representation and analysis problemsBuild Python scripts, modules, and packages for reusable analysis codeWho this book is forThis book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial.
Table of ContentsIntroduction to Data AnalysisWorking with Pandas DataFramesData Wrangling with PandasAggregating Pandas DataFramesData Visualization with Pandas and MatplotlibPlotting with Seaborn and Customization TechniquesFinancial Analysis with Bitcoin and the Stock MarketRule-based Anomaly Catching HackersGetting started with Machine Learning in PythonMaking Better Optimizing ML ModelsML Anomaly Catching Hackers, Part 2The Road Ahead
- GenresProgramming
716 pages, Kindle Edition
Published July 26, 2019
31 people are currently reading
43 people want to read
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Displaying 1 - 3 of 3 reviews
November 24, 2021
Not for a beginner, unbalanced, not as good as video courses available. It's not a bad book, some approaches described are quite interesting and the use of real world data are appealing, but it's unable to break free from the competition because of incoherence and unbalanced topic coverage. Publicly available books like Python for Data analysis by Jake VanderPlas will cover more for the price of nothing.
A lot of tricky one-liners, lambda-approaches and chained operations. Ironically, chained indexing was mentioned only once while sending you to the docs that are famously ambiguous about this very topic.
Into on statistics and machine learning are waste of spaces because anyone reasonable enough to learn Pandas should go elsewhere for these topics.
If you're looking for a good Pandas book, either go for Cookbook or stick with video courses, there are highly appreciated and up to date ones. Only worth getting on the discount to check out some practical approaches, not to be treated as a manual.
Could be massively improved with editorial work, a lot of it.
A lot of tricky one-liners, lambda-approaches and chained operations. Ironically, chained indexing was mentioned only once while sending you to the docs that are famously ambiguous about this very topic.
Into on statistics and machine learning are waste of spaces because anyone reasonable enough to learn Pandas should go elsewhere for these topics.
If you're looking for a good Pandas book, either go for Cookbook or stick with video courses, there are highly appreciated and up to date ones. Only worth getting on the discount to check out some practical approaches, not to be treated as a manual.
Could be massively improved with editorial work, a lot of it.
October 13, 2021
For me it was pretty good at the beginning of the book but then I have to skip a few lasted chapters due to complex absorption of the topic. And last but not least the author made a great job. I certainly come back to this book later then my skills will grow up.
February 13, 2021
Excellent book. Well explained, week written, good examples... Interesting topic and there extra around machine learning was a very nice bonus!
Displaying 1 - 3 of 3 reviews