Hands-on data analysis with Pandas : efficiently perform data collection, wrangling, analysis, and visualization using Python /

This book will be a handy guide to quickly learn pandas and understand how it can empower you in the exciting world of data manipulation, analysis, and data science. You will learn how to use pandas to perform numeric and statistical analysis using real-world examples. You will also visualize statis...

وصف كامل

التفاصيل البيبلوغرافية
المؤلف الرئيسي: Molin, Stefanie (مؤلف)
التنسيق: Licensed eBooks
اللغة:الإنجليزية
منشور في: Birmingham, UK : Packt Publishing, [2019]
الوصول للمادة أونلاين:https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2215604
جدول المحتويات:
  • Cover; Title Page; Copyright and Credits; Dedication; About Packt; Foreword; Contributors; Table of Contents; Preface; Section 1: Getting Started with Pandas; Chapter 1: Introduction to Data Analysis; Chapter materials; Fundamentals of data analysis; Data collection; Data wrangling; Exploratory data analysis; Drawing conclusions; Statistical foundations; Sampling; Descriptive statistics; Measures of central tendency; Mean; Median; Mode; Measures of spread; Range; Variance; Standard deviation; Coefficient of variation; Interquartile range; Quartile coefficient of dispersion; Summarizing data
  • Common distributionsScaling data; Quantifying relationships between variables; Pitfalls of summary statistics; Prediction and forecasting; Inferential statistics; Setting up a virtual environment; Virtual environments; venv; Windows; Linux/macOS; Anaconda; Installing the required Python packages; Why pandas?; Jupyter Notebooks; Launching JupyterLab; Validating the virtual environment; Closing JupyterLab; Summary; Exercises; Further reading; Chapter 2: Working with Pandas DataFrames; Chapter materials; Pandas data structures; Series; Index; DataFrame; Bringing data into a pandas DataFrame
  • From a Python objectFrom a file; From a database; From an API; Inspecting a DataFrame object; Examining the data; Describing and summarizing the data; Grabbing subsets of the data; Selection; Slicing; Indexing; Filtering; Adding and removing data; Creating new data; Deleting unwanted data; Summary; Exercises; Further reading; Section 2: Using Pandas for Data Analysis; Chapter 3: Data Wrangling with Pandas; Chapter materials; What is data wrangling?; Data cleaning; Data transformation; The wide data format; The long data format; Data enrichment; Collecting temperature data
  • Cleaning up the dataRenaming columns; Type conversion; Reordering, reindexing, and sorting data; Restructuring the data; Pivoting DataFrames; Melting DataFrames; Handling duplicate, missing, or invalid data; Finding the problematic data; Mitigating the issues; Summary; Exercises; Further reading; Chapter 4: Aggregating Pandas DataFrames; Chapter materials; Database-style operations on DataFrames; Querying DataFrames; Merging DataFrames; DataFrame operations; Arithmetic and statistics; Binning and thresholds; Applying functions; Window calculations; Pipes; Aggregations with pandas and numpy
  • Summarizing DataFramesUsing groupby; Pivot tables and crosstabs; Time series; Time-based selection and filtering; Shifting for lagged data; Differenced data; Resampling; Merging; Summary; Exercises; Further reading; Chapter 5: Visualizing Data with Pandas and Matplotlib; Chapter materials; An introduction to matplotlib; The basics; Plot components; Additional options; Plotting with pandas; Evolution over time; Relationships between variables; Distributions; Counts and frequencies; The pandas.plotting subpackage; Scatter matrices; Lag plots; Autocorrelation plots; Bootstrap plots; Summary

مواد مشابهة

اكتشف القنوات ذات الصلة