For data crunchers and scientists, this book is excellent. They will learn how to use IPython and Jupyter, which offer computational environments for data scientists using Python, to manipulate, transform, and clean data, visualise various types of data, and use data to develop statistical or machine learning models. You’ll learn about Python’s DataFrame capabilities for effective labeled/columnar data storage and manipulation. You’ll also learn about Scikit-Learn, which includes ndarray for effective storage and manipulation of dense data arrays in Python, as well as capabilities for a flexible range of data visualisations in Python. Scikit-Learn provides efficient and clean Python implementations of the most significant and well-established machine learning algorithms. I hope you’ll find this book to be both valuable and helpful.

Python for Data science:

Python is a multipurpose programming language appropriate for data scientists, software developers, and programmers. Python has risen to the top of the extensive list of programming languages, alongside C and Java, because to its adaptability and user-friendliness. For large data, machine learning, and artificial intelligence, data scientists utilise Python. As the Internet of Things develops, a greater volume of data is being collected and shared at faster speeds from more sources, creating a significant demand for big data processing skills.
Data scientists must be able to swiftly and effectively gather, clean, prepare, analyse, and understand this enormous amount of data in order to properly advise businesses using data-driven insights.A general-purpose language like Python is more appropriate to the task at hand for some projects, whereas statistical computing languages like R are the most effective tool for others. Python provides additional ease and flexibility. The main quality of Python is its readability. Python is easy to learn and can be used for a variety of tasks because it is so similar to the user’s natural language and only needs a small number of lines of code. Python enables you to accomplish more in less time regardless of your level of programming experience.

Topics covered by this book:

  • Chapter 1 is about IPython,  Beyond Normal Python. Help and Documentation in IPython. Keyboard Shortcuts in the IPython Shell. IPython Magic Commands. Input and Output History. IPython and Shell Commands. Shell-Related Magic Commands. Profiling and Timing Code.

  • Chapter 2 is about Introduction to NumPy. Understanding Data Types in Python. The Basics of NumPy Arrays. Computation on NumPy Arrays: Universal Functions.   Aggregations: Min, Max, and Everything in Between. Computation on Arrays: Broadcasting. Comparisons, Masks, and Boolean Logic. Sorting Arrays. Structured Data: NumPy’s Structured Arrays.

  • Chapter 3 is about Installing and Using Pandas. Introducing Pandas Objects.  Data Indexing and Selection. Operating on Data in Pandas. Handling Missing Data. Hierarchical Indexing. Combining Datasets, Concat and Append.

  • Chapter 4 is about Visualization with Matplotlib. General Matplotlib Tips. Simple Scatter Plots. Visualizing Errors. Density and Contour Plots. Histograms, Binnings, and Density. Customizing Plot Legends. Customizing Colorbars. Text and Annotation. Customizing Ticks. Customizing Matplotlib: Configurations and Stylesheets. Geographic Data with Basemap.

  • Chapter 5 is about Machine Learning. What Is Machine Learning? Introducing Scikit-Learn. Hyperparameters and Model Validation. Hyperparameters and Model Validation. Feature Engineering.  Naive Bayes Classification. Introducing Principal Component Analysis. Some Thoughts on Manifold Methods.