Become a Python data analyst perform exploratory data analysis and gain insight into scientific computing using Python
Enhance your data analysis and predictive modeling skills using popular Python tools Key Features Cover all fundamental libraries for operation and manipulation of Python for data analysis Implement real-world datasets to perform predictive analytics with Python Access modern data analysis technique...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham ; Mumbai :
Packt
2018.
|
| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630689306719 |
Tabla de Contenidos:
- 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.
