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- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Section 1: Overview of Data Analysis and pandas
- Introduction to pandas and Data Analysis
- Motivation for data analysis
- Data analytics pipeline
- How Python and pandas fit into the data analytics pipeline
- What is pandas?
- Where does pandas fit in the pipeline?
- Benefits of using pandas
- History of pandas
- Usage pattern and adoption of pandas
- pandas on the technology adoption curve
- Popular applications of pandas
- Summary
- References
- Installation of pandas and Supporting Software
- Selecting a version of Python to use
- Standalone Python installation
- Installation of Python and pandas using Anaconda
- Dependency packages for pandas
- Review of items installed with Anaconda
- Cross tooling – combining pandas awesomeness with R, Julia, H20.ai, and Azure ML Studio
- Command line tricks for pandas
- Options and settings for pandas
- Summary
- Further reading
- Section 2: Data Structures and I/O in pandas
- Using NumPy and Data Structures with pandas
- NumPy ndarrays
- Implementing neural networks with NumPy
- Practical applications of multidimensional arrays
- Data structures in pandas
- Summary
- References
- I/Os of Different Data Formats with pandas
- Data sources and pandas methods
- CSV and TXT
- Excel
- URL and S3
- HTML
- JSON
- Reading HDF formats
- Reading feather files
- Reading parquet files
- Reading a SQL file
- Reading a SAS/Stata file
- Reading from Google BigQuery
- Reading from a clipboard
- Managing sparse data
- Writing JSON objects to a file
- Serialization/deserialization
- Writing to exotic file types
- GeoPandas
- Open source APIs – Quandl
- Pandas plotting
- pandas-datareader
- Summary
- Section 3: Mastering Different Data Operations in pandas
- Indexing and Selecting in pandas
- Grouping, Merging, and Reshaping Data in pandas
- Special Data Operations in pandas
- Writing and applying one-liner custom functions
- Handling missing values
- A survey of methods on series
- pandas string methods
- Binary operations on DataFrames and series
- Binning values
- Using mathematical methods on DataFrames
- The abs() function
- corr() and cov()
- cummax(), cumin(), cumsum(), and cumprod()
- The describe() function
- The diff() function
- The rank() function
- The quantile() function
- The round() function
- The pct_change() function
- min(), max(), median(), mean(), and mode()
- all() and any()
- The clip() function
- The count() function
- Summary
- Time Series and Plotting Using Matplotlib
- Handling time series data
- Reading in time series data
- Assigning date indexes and subsetting in time series data
- Plotting the time series data
- Resampling and rolling of the time series data
- Separating timestamp components
- DateOffset and TimeDelta objects
- Time series-related instance methods
- Aliases for time series frequencies
- Time series concepts and datatypes
- Conversion between time series datatypes
- A summary of time series-related objects
- Plotting using matplotlib
- Summary
- Handling time series data
- Section 4: Going a Step Beyond with pandas
- Making Powerful Reports In Jupyter Using pandas
- pandas styling
- Navigating Jupyter Notebook
- Summary
- A Tour of Statistics with pandas and NumPy
- Descriptive statistics versus inferential statistics
- Measures of central tendency and variability
- Hypothesis testing – the null and alternative hypotheses
- Summary
- A Brief Tour of Bayesian Statistics and Maximum Likelihood Estimates
- Introduction to Bayesian statistics
- The mathematical framework for Bayesian statistics
- Probability distributions
- Bayesian statistics versus frequentist statistics
- Conducting Bayesian statistical analysis
- Monte Carlo estimation of the likelihood function and PyMC
- References
- Summary
- Data Case Studies Using pandas
- The pandas Library Architecture
- pandas Compared with Other Tools
- Comparison with R
- Slicing and selection
- Comparison with SQL
- Comparison with SAS
- Summary
- A Brief Tour of Machine Learning
- The role of pandas in machine learning
- Installation of scikit-learn
- Introduction to machine learning
- Application of machine learning – Kaggle Titanic competition
- Data analysis and preprocessing using pandas
- A naive approach to the Titanic problem
- The scikit-learn ML/classifier interface
- Supervised learning algorithms
- Unsupervised learning algorithms
- Summary
- Other Books You May Enjoy
Strategies for handling missing values
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