Become a Python Data Analyst: Perform exploratory data analysis and gain insight into scientific computing using Python

Description

In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques. Cover Title Page Copyright and Credits Packt Upsell Contributor Table of Contents Preface Chapter 1: The Anaconda Distribution and Jupyter Notebook The Anaconda distribution Installing Anaconda Jupyter Notebook Creating your own Jupyter Notebook Notebook user interfaces Using the Jupyter Notebook Running code in a code cell Running markdown syntax in a text cell Styles and formats Lists Useful keyboard shortcuts Summary Chapter 2: Vectorizing Operations with NumPy Introduction to NumPy Problems and solutions  NumPy arrays Creating arrays in NumPy Creating arrays from lists Creating arrays from built-in NumPy functions Attributes of arrays Basic math with arrays Common manipulations with arrays Indexing arrays Slicing arrays Reshaping arrays Using NumPy for simulations Coin flips Simulating stock returns Summary Chapter 3: Pandas - Everyone's Favorite Data Analysis Library Introduction to the pandas library Important objects in pandas Series Creating a pandas series DataFrames Creating a pandas DataFrame Anatomy of a DataFrame Operations and manipulations of pandas Inspection of data Selection, addition, and deletion of data Slicing DataFrames Selection by labels Answering simple questions about a dataset Total employees by department in the dataset Overall attrition rate Average hourly rate Average number of years Employees with the most number of years Overall employee satisfaction Answering further questions Employees with Low JobSatisfaction Employees with both Low JobSatisfaction and JobInvolvement Employee comparison Summary Chapter 4: Visualization and Exploratory Data Analysis Introducing Matplotlib Terminologies in Matplotlib Introduction to pyplot Object-oriented interface Common customizations Colors Colornames Setting axis limits Setting ticks and tick labels Legend Annotations Producing grids, horizontal, and vertical lines EDA with seaborn and pandas Understanding the seaborn library Performing exploratory data analysis Key objectives when performing data analysis Types of variable Analyzing variables individually Understanding the main variable Numerical variables Categorical variables Relationships between variables Scatter plot Box plot Complex conditional plots Summary Chapter 5: Statistical Computing with Python Introduction to SciPy Statistics subpackage  Confidence intervals Probability calculations Hypothesis testing Performing statistical tests  Summary Chapter 6: Introduction to Predictive Analytics Models Predictive analytics and machine learning Understanding the scikit-learn library scikit-learn Building a regression model using scikit-learn Regression model to predict house prices Summary Other Books You May Enjoy Index