Python Pandas

This Python Pandas training course will teach you all about using Pandas for data analysis, from the beginning to creating one- and two-dimensional indexed data structures, indexing and slicing-and-dicing that data to derive results, loading data from local and Internet-based resources, and finally creating effective visualizations to form quick insights.

Prerequisites / Further Training

Have a look at our Python Bootcamp

Intended Audience

  • Analysts who wants to more on data analysis and reporting
  • Programmers who wants to performing data exploration and analysis on Python using pandas

DAY 1

Pandas Foundations 

  • Introduction
  • Dissecting the anatomy of a DataFrame
  • Accessing the main DataFrame components
  • Understanding data types
  • Selecting a single column of data as a Series
  • Calling Series methods
  • Working with operators on a Series
  • Chaining Series methods together
  • Making the index meaningful
  • Renaming row and column names
  • Creating and deleting columns

Essential DataFrame Operations

  • Selecting multiple DataFrame columns
  • Selecting columns with methods
  • Ordering column names sensibly
  • Operating on the entire DataFrame
  • Chaining DataFrame methods together
  • Working with operators on a DataFrame
  • Comparing missing values
  • Transposing the direction of a DataFrame operation
  • Determining college campus diversity

DAY 2

Beginning Data Analysis 

  • Developing a data analysis routine
  • Reducing memory by changing data types
  • Selecting the smallest of the largest
  • Selecting the largest of each group by sorting
  • Replicating nlargest with sort_values
  • Calculating a trailing stop order price

Selecting Subsets of Data

  • Selecting Series data
  • Selecting DataFrame rows
  • Selecting DataFrame rows and columns simultaneously
  • Selecting data with both integers and labels
  • Speeding up scalar selection
  • Slicing rows lazily
  • Slicing lexicographically

DAY 3

Boolean Indexing 

  • Calculating boolean statistics
  • Constructing multiple boolean conditions
  • Filtering with boolean indexing
  • Replicating boolean indexing with index selection
  • Selecting with unique and sorted indexes
  • Gaining perspective on stock prices
  • Translating SQL WHERE clauses
  • Determining the normality of stock market returns
  • Improving readability of boolean indexing with the query method
  • Preserving Series with the where method
  • Masking DataFrame rows
  • Selecting with booleans, integer location, and labels

Index Alignment 

  • Examining the Index object
  • Producing Cartesian products
  • Exploding indexes
  • Filling values with unequal indexes
  • Appending columns from different DataFrames
  • Highlighting the maximum value from each column
  • Replicating idxmax with method chaining
  • Finding the most common maximum

DAY 4

Grouping for Aggregation, Filtration, and Transformation 

  • Defining an aggregation
  • Grouping and aggregating with multiple columns and functions
  • Removing the MultiIndex after grouping
  • Customizing an aggregation function
  • Customizing aggregating functions with *args and **kwargs
  • Examining the groupby object
  • Filtering for states with a minority majority
  • Transforming through a weight loss bet
  • Calculating weighted mean SAT scores per state with apply
  • Grouping by continuous variables
  • Counting the total number of flights between cities
  • Finding the longest streak of on-time flights

Restructuring Data into a Tidy Form 

  • Tidying variable values as column names with stack
  • Tidying variable values as column names with melt
  • Stacking multiple groups of variables simultaneously
  • Inverting stacked data
  • Unstacking after a groupby aggregation
  • Replicating pivot_table with a groupby aggregation
  • Renaming axis levels for easy reshaping
  • Tidying when multiple variables are stored as column values
  • Tidying when two or more values are stored in the same cell
  • Tidying when variables are stored in column names and values
  • Tidying when multiple observational units are stored in the same table

DAY 5

Combining Pandas Objects 

  • Appending new rows to DataFrames
  • Concatenating multiple DataFrames together
  • Comparing President Trump’s and Obama’s approval ratings
  • Understanding the differences between concat, join, and merge
  • Connecting to SQL databases

Time Series Analysis 

  • Understanding the difference between Python and pandas date tools
  • Slicing time series intelligently
  • Using methods that only work with a DatetimeIndex
  • Counting the number of weekly crimes
  • Aggregating weekly crime and traffic accidents separately
  • Measuring crime by weekday and year
  • Grouping with anonymous functions with a DatetimeIndex
  • Grouping by a Timestamp and another column
  • Finding the last time crime was 20% lower with merge_asof

Visualization with Matplotlib, Pandas, and Seaborn 

  • Getting started with matplotlib
  • Object-oriented guide to matplotlib
  • Visualizing data with matplotlib
  • Plotting basics with pandas
  • Visualizing the flights dataset
  • Stacking area charts to discover emerging trends
  • Understanding the differences between seaborn and pandas
  • Doing multivariate analysis with seaborn Grids
  • Uncovering Simpson’s paradox in the diamonds dataset with Seaborn

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