Data Analysis with Python Competency (Intermediate Level)

Topics covered

• apply a join operation on two related but dissimilar DataFrames using the merge function
• apply the loc and iloc functions to access specific rows and columns
• calculate deltas and percentage returns in stock prices
• compare deep copies of arrays with views and know when to use each of them
• configure an univariate distribution's appearance, including color, size, and the components of the plot
• define the aesthetic parameters for a plot and make use of Seaborn's built-in templates for creating shareable graphs
• describe and apply different techniques involved in sorting the contents of a pandas DataFrame
• describe and apply the different techniques involved in handling datasets where some information is missing
• describe the concept of a hierarchical index or multi-index and how it can be useful
• distinguish between scatter plots, hexbin plots, and KDE plots
• filter data using the loc, iloc, at, and iat functions
• identify and deal with duplicate records
• identify and work with time-series data
• implement a hierarchical index and access the DataFrame's contents based on that index
• look up data using different techniques
• perform a regression analysis on a pair of variables in your dataset by using the Seaborn lmplot
• perform grouping and aggregations on data
• perform reshape operations on arrays to visualize its contents in different ways
• recognize what a normal distribution is and what is defined as an outlier
• retrieve specific parts of an array using row and column indices
• specify grouping and aggregations with multiple indexes
• summarize records into bins or categories
• work with Seaborn to glean patterns in a dataset by visualizing the relationships between several pairs of variables