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Course Skill Level:


Course Duration:

4 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Developers, analysts, and others with Python experience

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others with Python skills who want to get to grips with pandas, a versatile and high-performance Python library for data manipulation, analysis, and discovery.
  • Skill-level: Foundation-level Pandas skills for Intermediate skilled team members. This is not a basic class.
  • Developing: Basic (1-2 years’ experience)
  • Python: Basic (1-2 years’ experience)
  • Spreadsheet software: Intermediate (2-5 years’ experience)

About this course

You will learn how to use pandas to perform data analysis in Python. You will start with an overview of data analysis and iteratively progress from modeling data, to accessing data from remote sources, performing numeric and statistical analysis, through indexing and performing aggregate analysis, and finally to visualizing statistical data and applying pandas to finance. With the knowledge you gain from this course, you will quickly learn pandas and how it can empower you in the exciting world of data manipulation, analysis and science.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Pandas expert instructor, students will learn about and explore:
  • Getting comfortable using pandas and Python as an effective data exploration and analysis tool
  • Pandas through a framework of data analysis, with an explanation of how pandas is well suited for the various stages in a data analysis process
  • A comprehensive guide to pandas with many of clear and practical examples to help you get up and using pandas
  • How data analysts and scientists think about of the processes of gathering and understanding data
  • How pandas can be used to support the end-to-end process of data analysis
  • Using pandas Series and DataFrame objects to represent single and multivariate data
  • Slicing and dicing data with pandas, as well as combining, grouping, and aggregating data from multiple sources
  • Accessing data from external sources such as files, databases, and web services
  • Representing and manipulating time-series data and the many of the intricacies involved with this type of data
  • Visualizing statistical information
  • Using pandas to solve several common data representation and analysis problems within finance

Course breakdown / modules

  • Introducing pandas
  • Data manipulation, analysis, science, and pandas
  • The process of data analysis
  • Relating the book to the process
  • Concepts of data and analysis in our tour of pandas
  • Other Python libraries of value with pandas

  • Installation of Anaconda
  • IPython and Jupyter Notebook
  • Introducing the pandas Series and DataFrame
  • Visualization

  • Configuring pandas
  • Creating a Series
  • The .index and .values properties
  • The size and shape of a Series
  • Specifying an index at creation
  • Heads, tails, and takes
  • Retrieving values in a Series by label or position
  • Slicing a Series into subsets
  • Alignment via index labels
  • Performing Boolean selection
  • Re-indexing a Series
  • Modifying a Series in-place

  • Configuring pandas
  • Creating DataFrame objects
  • Accessing data within a DataFrame
  • Selecting rows using Boolean selection
  • Selecting across both rows and columns

  • Configuring pandas
  • Renaming columns
  • Adding new columns with [] and .insert()
  • Adding columns through enlargement
  • Adding columns using concatenation
  • Reordering columns
  • Replacing the contents of a column
  • Deleting columns
  • Appending new rows
  • Concatenating rows
  • Adding and replacing rows via enlargement
  • Removing rows using .drop()
  • Removing rows using Boolean selection
  • Removing rows using a slice

  • Configuring pandas
  • The importance of indexes
  • The pandas index types
  • Working with Indexes
  • Hierarchical indexing

  • Configuring pandas
  • Creating Categoricals
  • Renaming categories
  • Appending new categories
  • Removing categories
  • Removing unused categories
  • Setting categories
  • Descriptive information of a Categorical
  • Munging school grades

  • Configuring pandas
  • Performing numerical methods on pandas objects
  • Performing statistical processes on pandas objects

  • Configuring pandas
  • Working with CSV and text/tabular format data
  • Reading and writing data in Excel format
  • Reading and writing JSON files
  • Reading HTML data from the web
  • Reading and writing HDF5 format files
  • Accessing CSV data on the web
  • Reading and writing from/to SQL databases
  • Reading data from remote data services

  • Configuring pandas
  • What is tidying your data?
  • How to work with missing data
  • Handling duplicate data
  • Transforming data

  • Configuring pandas
  • Concatenating data in multiple objects
  • Merging and joining data
  • Pivoting data to and from value and indexes
  • Stacking and unstacking
  • Performance benefits of stacked data

  • Configuring pandas
  • The split, apply, and combine (SAC) pattern
  • Data for the examples
  • Splitting data
  • Applying aggregate functions, transforms, and filters
  • Transforming groups of data
  • Filtering groups from aggregation

  • Setting up the IPython notebook
  • Representation of dates, time, and intervals
  • Introducing time-series data
  • Calculating new dates using offsets
  • Representing durations of time using Period
  • Handling holidays using calendars
  • Normalizing timestamps using time zones
  • Manipulating time-series data
  • Time-series moving-window operations

  • Configuring pandas
  • Plotting basics with pandas
  • Creating time-series charts
  • Common plots used in statistical analyses
  • Manually rendering multiple plots in a single chart

  • Setting up the IPython notebook
  • Obtaining and organizing stock data from Google
  • Plotting time-series prices
  • Plotting volume-series data
  • Calculating the simple daily percentage change in closing price
  • Calculating simple daily cumulative returns of a stock
  • Resampling data from daily to monthly returns
  • Analyzing distribution of returns
  • Performing a moving-average calculation
  • Comparison of average daily returns across stocks
  • Correlation of stocks based on the daily percentage change of the closing price
  • Calculating the volatility of stocks
  • Determining risk relative to expected returns