Missing values and NANs are commonplace occurrences in a dataset and need to be taken care of before data can be put to any use. We will look into various sources of missing values and the different types, as well as how to handle them in the upcoming sections.
- 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
Handling missing values
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