Pandas in 7 Days: Utilize Python to Manipulate Data, Conduct Scientific Computing, Time Series Analysis, and Exploratory Data Analysis

Cover Page
Title Page
Copyright Page
Dedication Page
About the Author
About the Reviewers
Acknowledgements
Preface
Errata
Table of Contents
1. Pandas, the Python Library
    Structure
    Objective
    A bit of history
    Why use Pandas (and Python) for data analysis?
        A trust gained over the years
        A flexible language that adapts to any context
        Automation, reproducibility, and interaction
    Data Analysis
        What is data analysis?
        The data scientist
        The analysis process
    Tabular form of data
        Tabular form of data
        Spreadsheets
        SQL tables from databases
        Pandas and Dataframes
    Conclusion
    References
2. Setting up a Data Analysis Environment
    Structure
    Objective
    The basic procedure
        Installing Python
            Python 3.x vs Python 2.7
        Installing Pandas and the other libraries
        Installing Jupyter Notebooks
        Considerations
    Anaconda
        The Anaconda distribution
        Installing Anaconda
        Installing Anaconda on Linux systems
        Conda and the Anaconda command line shell
        Creating and managing environments with Conda
        Conda – Create an environment with a different Python version
        Conda – Export the environments
        Creating and managing packages (and libraries) with Conda
        Jupyter Notebook
        Creating a Notebook on Jupyter
        First steps on a new Jupyter Notebook
    Working online on development environments: Replit
        Replit
        Creating a Python workspace for data analysis on Replit
    Conclusion
    Questions
    Multiple Choice Questions
    Answers
    Key Terms
    References/Further Readings
3. Day 1 - Data Structures in Pandas library
    Structure
    Objective
    Introduction to the Series
        A Series as a sequence of data
        The Index() constructor
        The name attribute of Series
    Various ways to build Series
        The Series() constructor
        Creating a Series from a NumPy array
        Creating a Series from a scalar value
        Creating a Series from a dictionary
        Creating a Series from a list of tuples
        Creating a Series from a 2-element array list
    Introduction to DataFrames
        The DataFrame as an indexed data table
        The DataFrame() constructor
            Viewing DataFrames in Jupyter Notebook
        Other constructor arguments
        Access the various elements of the DataFrame with iloc[ ] and loc[ ]
    Various ways to build DataFrames
        Creating a DataFrame from a Numpy ndarray
        Creating a DataFrame from a Series dictionary
        Creating a DataFrame from a ndarray dictionary
            Pass-by-copy with Dictionaries
        Creating a DataFrame from a structured array or a record array
        Creating a DataFrame from a list of dictionaries
        Creating a DataFrame from a list of Data Classes
        Creating a DataFrame from a list of Named Tuples
    MultiIndex DataFrame
        Creating a MultiIndex DataFrame from a dictionary of tuples
    Conclusion
    Questions
    Multiple Choice Questions
        Answers
    Key Terms
    References
4. Day 2 - Working within a DataFrame, Basic Functionalities
    Structure
    Objective
    Viewing Data
        Direct printing of the DataFrame values
        The head() and tail() methods
    Selection
        Selection by Subsetting
        Subsetting by index or by position
            Scalars and Series as return values
        Indexing Operators and Indexers
        loc[ ] – Selection by Labels
        iloc[ ] – Selection by location
        Indexer ix[ ]
        at[] and iat[] – Selection by value
        Selection on dtype
    Filtering
        The Boolean condition
        Filtering on the lines
        Filtering on columns
        Application of multiple conditions
        Boolean reductions
        Filtering with isin()
    Editing
        Adding, inserting, and deleting a column in a DataFrame
        Adding, inserting, and deleting a row in a DataFrame
        Adding new columns with assign()
    Descriptive Statistics
    The describe() method
        Calculation of individual statistics
            The Skipna option
        The standardization of data
    Transposition, Sorting, and Reindexing
        Transposition
        Sorting
        Sorting by label
        Sorting by values
        Reindexing
        Reindexing using another DataFrame as a reference
    Conclusion
    Questions
    MCQs
        Solution: 
    Key Terms
    References/Further Readings
5. Day 3 - Working within a DataFrame, Advanced Functionalities
    Structure
    Objective
    Shifting
    The shift() method
    Reshape
    The stack() and unstack() methods
    Pivoting
    Iteration
    Iteration with for loops
    Methods of iteration
    Row iterations in DataFrames
    Application of Functions on a Dataframe
    The levels of operability of the functions
    Applying NumPy functions directly on DataFrame
    Applying functions with the apply () method
    Applications of functions with the pipe() method
    Chaining pipe() and apply() methods
    Applying functions with arguments with the pipe() method
    The applymap() and map() methods
    Transforming
    The transform() method
    Reducing a MultiIndex DataFrame to a DataFrame
    Transform() does not aggregate elements
    Transform() works with a single column at a time
    Aggregation
        The agg() method
    Grouping
    The groupby() method
    The groupby() method for indexes
    Grouping with Transformation
    Grouping with Aggregation
    Apply() with groupby()
    Categorization
    Specifying a category column
    Conclusion
    Questions
    MCQs
        Solution:
    Key Terms
    References/Further Readings
6. Day 4 - Working with Two or More DataFrames
    Structure
    Objective
    Add data to a DataFrame
        The append() method
        Appending multiple Series
        The concat() function
        Concatenations between DataFrame and Series that are not homogeneous
        Multiple concatenations
    Joining between two DataFrames
        The merge() method
        The LEFT Join
        The RIGHT Join
        The OUTER Join
        The INNER Join
    Arithmetic with DataFrames
        Two homogeneous DataFrames as operands
        Two non-homogeneous DataFrames as operands
        Operations between a DataFrame and a Series
    Flexible binary arithmetic methods
        Flexible binary methods and arithmetic operators
        Differences between arithmetic operators and flexible binary methods
        Arithmetic operations between subsets of the same DataFrame
        Arithmetic operations between subsets of different DataFrames
        The relative binary methods
    Boolean operations
        Boolean operations between two DataFrames
        Flexible Boolean binary methods
    Aligning on DataFrames
        The align() method
        Aligning with the Join modalities
    Conclusion
    Questions
    Multiple choice questions
    Solution
    Key Terms
    References/Further Readings
7. Day 5 - Working with Data Sources and Real-World Datasets
    Structure
    Objective
    Files as external data sources
    Read and write data from files in CSV format
        Graphical display of DataFrames in Jupyter Notebook
        Preview of the content being written to file
        Some optional to_csv() parameters
    Read and Write Microsoft Excel File
    Read data from HTML pages on the web
    Read and write data from XML files
    Read and Write data in JSON
    Write and read data in binary format
        Final thoughts on saving data to file
    Interact with databases for data exchange
        SQLite installation
        Creation of test data on SQLite
    Other data sources
    Working with Time Data
        Date Times
        Time Deltas
        Create arrays of time values
        Import Date Time values from file
    Working with Text Data
        The methods of correcting the characters of the string
        Methods for splitting strings
        Methods for concatenating columns of text
        Separate alphanumeric from numeric characters in a column
        Adding characters to the rows of a column
        Recognition methods between alphanumeric and numeric characters
        Searching methods in strings
    Conclusion
    Questions
    Multiple choice questions
        Solution
    Key Terms
    References/Further Readings
8. Day 6 - Troubleshooting Challenges with Real Datasets
    Structure
    Objective
    Handling Missing Data
        How NaNs are formed
        Other operations that generate NaN
    Working with Missing values
        Find and count the missing values
        Drop missing values
        Filling in of Missing Values
        Fill Forward or Backward
    Interpolate
        Conversion of data types in the presence of NaN
    Other common operations for correcting newly imported DataFrame
        Data Replacing
        Data Duplicated
        Renaming Axis Indexes
        Sparse DataFrames
    Tidy Data
        Methods for inversion of Stacked Data
        Example with the stack() method
        Example with the melt() method
    Columns that contain values, not variables
        Several variables in the same column
        Columns that contain multiple variables
        Variables in both rows and columns
        Groups of Variables in the columns
        Names of columns with multiple variables
    Conclusion
    Questions
    Multiple choice questions
    Solution
    Key Terms
    References/Further Readings
9. Day 7 – Data Visualization and Reporting
    Structure
    Objective
    Matplotlib
        Making a Line Plot with Matplotlib
        Making a Scatter Plot
        Making a Bar Chart
        Making a Pie Chart
    Making a Box Plot
    Using the data in a DataFrame with Matplotlib
    Introduction to Seaborn
    Reporting
        Data Profiling and Reporting
    Reporting in HTML and PDF
    Conclusion
    Questions
    Multiple Choice Questions
    Answers
    Key Terms
    References/Further Readings
10. Beyond Pandas
    Structure
    Objective
    Where to learn more about Pandas?
    The official Pandas documentation
        Participate in the development of the Pandas library
    What can you learn after Pandas?
    The Data Scientist
    The knowledge of the Data Scientist
        The Task of a Data Scientist
        The goals of a Data Scientist
    Data Scientist and R
    Statistical analysis with StatsModels
    Machine Learning with Scikit-learn
    Image Processing and Deep Learning
    Big Data
    The Data Engineer, a new professional figure
    Conclusion
    Questions
    Multiple Choice Questions
    Answers
    Key Terms
    References/Further Readings
Index