Getting Started

Overview / Student FAQ
Tips : How to get the most out of this course
Did you know that...?
More FAQ / Important Information
Installation of Anaconda
Opening a Jupyter Notebook
How to use Jupyter Notebooks
How to tackle Pandas Version 1.0

Part 1: Pandas from Zero to Hero (Building Blocks)

Intro to Tabular Data / Pandas
Download: Part 1 Course Materials

Pandas Basics (DataFrame Basics I)

Create your very first Pandas DataFrame (from csv)
Pandas Display Options and the methods head() & tail()
First Data Inspection
Built-in Functions, Attributes and Methods with Pandas
Make it easy: TAB Completion and Tooltip
First Steps
Explore your own Dataset: Coding Exercise 1 (Intro)
Explore your own Dataset: Coding Exercise 1 (Solution)
Selecting Columns
Selecting one Column with the "dot notation"
Zero-based Indexing and Negative Indexing
Selecting Rows with iloc (position-based indexing)
Slicing Rows and Columns with iloc (position-based indexing
Position-based indexing Cheat Sheets
Selecting Rows with loc (label-based indexing)
Slicing Rows and Columns with loc (label-based indexing)
Label-based Indexing Cheat Sheets
Indexing and Slicing with reindex()
Summary, Best Practices and Outlook
Indexing and Slicing
Coding Exercise 2 (Intro)
Coding Exercise 2 (Solution)
Advanced Indexing and Slicing (optional)

Pandas Series and Index Objects

Intro
First Steps with Pandas Series
Analyzing Numerical Series with unique(), nunique() and value_counts()
Analyzing non-numerical Series with unique(), nunique(), value_counts()
Creating Pandas Series (Part 1)
Creating Pandas Series (Part 2)
Indexing and Slicing Pandas Series
Sorting of Series and Introduction to the inplace - parameter
nlargest() and nsmallest()
idxmin() and idxmax()
Manipulating Pandas Series
Pandas Series
Coding Exercise 3 (Intro)
Coding Exercise 3 (Solution)
First Steps with Pandas Index Objects
Creating Index Objects from Scratch
Changing Row Index with set_index() and reset_index()
Changing Column Labels
Renaming Index & Column Labels with rename()
Pandas Index objects
Coding Exercise 4 (Intro)
Coding Exercise 4 (Solution)

DataFrame Basics II

Intro
Filtering DataFrames by one Condition
Filtering DataFrames by many Conditions (AND)
Filtering DataFrames by many Conditions (OR)
Advanced Filtering with between(), isin() and ~
any() and all()
Removing Columns
Removing Rows
Adding new Columns to a DataFrame
Creating Columns based on other Columns
Adding Columns with insert()
Creating DataFrames from Scratch with pd.DataFrame()
Adding new Rows (hands-on approach)
DataFrame Basics II
Coding Exercise 5 (Intro)
Coding Exercise 5 (Solution)

Manipulating Elements in a DataFrame / Slice +++Important, know the Pitfalls!+++

Intro
Best Practice (How you should do it)
Chained Indexing: How you should NOT do it (Part 1)
Chained Indexing: How you should NOT do it (Part 2)
View vs. Copy
Simple Rules what to do when...
Manipulating DataFrames / Slices
Coding Exercise 6 (Intro)
Coding Exercise 6 (Solution)

DataFrame Basics III

Intro
Sorting DataFrames with sort_index() and sort_values() (Version 1.0 Update)
Ranking DataFrames with rank()
nunique() and nlargest() / nsmallest() with DataFrames
Summary Statistics and Accumulations
The agg() method
Coding Exercise 7 (Intro)
Coding Exercise 7 (Solution)
User-defined Functions with apply(), map() and applymap()
Hierarchical Indexing (Part 1)
Hierarchical Indexing (Part 2)
String Operations (Part 1)
String Operations (Part 2)
Coding Exercise 8 (Intro)
Coding Exercise 8 (Solution)

Visualization with Matplotlib

Intro
The plot() method
Customization of Plots
Histograms (Part 1)
Histograms (Part 2)
Barcharts and Piecharts
Scatterplots
Coding Exercise 9 (Intro)
Coding Exercise 9 (Solution)

Part 2: Full Data Workflow A-Z

Welcome to PART 2: Full Data Workflow A-Z
Download: Part 2 Course Materials

Importing Data

Importing csv-files with pd.read_csv
Importing messy csv-files with pd.read_csv
Importing Data from Excel with pd.read_excel()
Importing messy Data from Excel with pd.read_excel()
Importing Data from the Web with pd.read_html()
Coding Exercise 10

Cleaning Data

First Inspection and Handling of Inconsistent Data
String Operations
Changing Datatype of Columns with astype()
Intro NA values / missing values
Detection of missing Values
Removing missing values
Replacing missing values
Intro Duplicates
Detection of Duplicates
Handling / Removing Duplicates
The ignore_index parameter (NEW in Pandas 1.0)
Detection of Outliers
Handling / Removing Outliers
Categorical Data
Pandas Version 1.0: New dtypes and pd.NA
Coding Exercise 11 (Intro)
Coding Exercise 11 (Solution)

Merging, Joining, and Concatenating Data

Intro
Adding rows with append() and pd.concat()_(Part 1)
Adding Rows with pd.concat() (Part 2)
Arithmetic with Pandas Objects / Data Alignment
EXCURSUS: Comparing two DataFrames / Identify Differences
Outer Joins with merge()
Inner Joins with merge()
Outer Joins (without Intersection) with merge()
Left Joins (without Intersection) with merge()
Right Joins (without Intersection) with merge()
Left Joins with merge()
Right Joins with merge()
Joining on different Column Names / Indexes
Joining on more than one Column
pd.merge() and join()
Coding Exercise 12

GroupBy Operations

Intro
Understanding the GroupBy Object
Splitting with many Keys
split-apply-combine explained
split-apply-combine applied
GroupBy 1
Advanced aggregation with agg()
GroupBy Aggregation with Relabeling (NEW - Pandas Version 0.25)
Transformation with transform()
Replacing NA Values by group-specific Values
Generalizing split-apply-combine with apply()
Hierarchical Indexing with Groupby
stack() and unstack()
GroupBy 2
Coding Exercise 13 (Intro)
Coding Exercise 13 (Solution)

Reshaping and Pivoting DataFrames

Intro
Transposing Rows and Columns
Pivoting DataFrames with pivot()
Limits of pivot()
pivot_table()
pd.crosstab()
melting DataFrames with melt()
Coding Exercise 14

Data Preparation and Feature Creation

Intro
Arithmetic Operations (Part 1)
Arithmetic Operations (Part 2)
Transformation/Mapping with map()
Conditional Transformation
Discretization and Binning with pd.cut() (Part 1)
Discretization and Binning with pd.cut() (Part 2)
Discretization and Binning with pd.qcut()
Floors and Caps
Scaling / Standardization
Creating Dummy Variables
String Operations
Coding Exercise 15

Advance Visualization with Seaborn

Intro
First Steps in Seaborn
Categorical Plots
Joint Plots / Regression Plots
Matrixplots / Heatmaps
Coding Exercise 16

Part 3: Comprehensive Project Challenges

Intro and Downloads

Data Manipulation and Aggregation Challenge (Olympic Medal Tables)

Olympic Medal Tables (Instruction & Hints)
Olympic Medal Tables (Solution Part 1)
Olympic Medal Tables (Solution Part 2)
Olympic Medal Tables (Solution Part 3)

Explanatory Data Analysis Challenge

Challenge Introduction & Overview
Merging and Concatenating (Solution Part 1)
Data Cleaning 1 (Solution Part 2)
Data Cleaning 2 (Solution Part 3)
The most successful Countries (Solution Part 4)
Impact of GDP, Population and Politics (Solution Part 5)
Statistical Analysis and Hypothesis Testing (Solution Part 6)
Aggregating and Ranking (Solution Part 7)
Summer Games vs. Winter Games - does Location matter? (Solution Part 8)
Men vs. Women - do Culture & Religion matter? (Solution Part 9)
National Sports and Traditions (Solution Part 10)

Part 4: Pandas for Finance, Invensting & Time Series

Welcome to PART 4: Finance and Investments with Pandas
Download: Part 4 Course Materials

Time Series Basics

Importing Time Series Data from csv-files
Converting strings to datetime objects with pd.to_datetime()
Initial Analysis / Visualization of Time Series
Indexing and Slicing Time Series
Creating a customized DatetimeIndex with pd.date_range()
More on pd.date_range()
Downsampling Time Series with resample() (Part 1)
Downsampling Time Series with resample (Part 2)
The PeriodIndex object
Advanced Indexing with reindex()

Pandas for Finance and Investing

Intro
Getting Ready (Installing required package)
Importing Stock Price Data from Yahoo Finance (it still works!)
Initial Inspection and Visualization
Normalizing Time Series to a Base Value (100)
The shift() method
The methods diff() and pct_change()
Measuring Stock Performance with MEAN Returns and STD of Returns
Financial Time Series - Return and Risk
Financial Time Series - Covariance and Correlation
Helpful DatetimeIndex Attributes and Methods
Filling NA Values with bfill, ffill and interpolation
Coding Exercise 17

Part 5: Machine Learning With Pandas and SCIKIT-Learn

Overview & Downloads

Introduction to Regression & Classification

Machine Learning - an Overview
Linear Regression with scikit-learn - a simple Introduction
Making Predictions with Linear Regression
Overfitting
Underfitting
Logistic Regression with scikit-learn - a simple Introduction (Part 1)
Logistic Regression with scikit-learn - a simple Introduction (Part 2)

Bonus: Machine Learning Project A-Z (Regression)

Project Intro
Importing the Dataset and first Inspection
Cleaning the Data and Creating more Features
Explanatory Data Analysis (Part 1)
Explanatory Data Analysis (Part 2)
Feature Engineering (Part 1)
Feature Engineering (Part 2)
Splitting the Data into Training Set and Test Set
Training the Machine Learning Model
Testing/Evaluating the Model with the Test Set
Feature Importance

WHAT´S NEW IN PANDAS VERSION 1.0? - A HANDS-ON GUIDE

Intro and Overview
How to update Pandas to Version 1.0
Downloads for this Section
Important Recap: Pandas Display Options (Changed in Version 0.25)
Info() method - new and extended output
NEW Extension dtypes ("nullable" dtypes): Why do we need them?
Creating the NEW extension dtypes with convert_dtypes()
NEW pd.NA value for missing values
The NEW "nullable" Int64Dtype
The NEW StringDtype
The NEW "nullable" BooleanDtype
Addition of the ignore_index parameter
Removal of prior Version Deprecations

Appendix: Python Basics, Numpy & Statistics

Welcome to the Appendix

Python Basics

Downloads
Intro
First Steps
Variables
Data Types: Integers and Floats
Data Types: Strings
Data Types: Lists (Part 1)
Data Types: Lists (Part 2)
Data Types: Tuples
Data Types: Sets
Operators & Booleans
Conditional Statements (if, elif, else, while)
For Loops
Key words break, pass, continue
Generating Random Numbers
User Defined Functions (Part 1)
User Defined Functions (Part 2)
User Defined Functions (Part 3)
Visualization with Matplotlib
Python Basics
Python Basics Quiz: Solution

The Numpy Package

Downloads
Introduction to Numpy Arrays
Numpy Arrays: Vectorization
Numpy Arrays: Indexing and Slicing
Numpy Arrays: Shape and Dimensions
Numpy Arrays: Indexing and Slicing of multi-dimensional Arrays
Numpy Arrays: Boolean Indexing
Generating Random Numbers
Performance Issues
Case Study: Numpy vs. Python Standard Library
Summary Statistics
Visualization and (Linear) Regression
Numpy
Numpy Quiz: Solution

Statistical Concepts

Statistics - Overview, Terms and Vocabulary
Downloads for this Section
Population vs. Sample
Visualizing Frequency Distributions with plt.hist()
Relative and Cumulative Frequencies with plt.hist()
Measures of Central Tendency (Theory)
Coding Measures of Central Tendency - Mean and Median
Coding Measures of Central Tendency - Geometric Mean
Variability around the Central Tendency / Dispersion (Theory)
Minimum, Maximum and Range with Python/Numpy
Percentiles with Python/Numpy
Variance and Standard Deviation with Python/Numpy
Skew and Kurtosis (Theory)
How to calculate Skew and Kurtosis with scipy.stats
How to generate Random Numbers with Numpy
Reproducibility with np.random.seed()
Probability Distributions - Overview
Discrete Uniform Distributions
Continuous Uniform Distributions
The Normal Distribution (Theory)
Creating a normally distributed Random Variable
Normal Distribution - Probability Density Function (pdf) with scipy.stats
Normal Distribution - Cumulative Distribution Function (cdf) with scipy.stats
The Standard Normal Distribution and Z-Values
Properties of the Standard Normal Distribution (Theory)
Probabilities and Z-Values with scipy.stats
Confidence Intervals with scipy.stats
Covariance and Correlation Coefficient (Theory)
Cleaning and preparing the Data - Movies Database (Part 1)
Cleaning and preparing the Data - Movies Database (Part 2)
How to calculate Covariance and Correlation in Python
Correlation and Scatterplots – visual Interpretation
What is Linear Regression? (Theory)
A simple Linear Regression Model with numpy & Scipy
How to interpret Intercept and Slope Coefficient
Case Study (Part 1): The Market Model (Single Factor Model)
Case Study (Part 2): The Market Model (Single Factor Model)

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Getting Started

Part 1: Pandas from Zero to Hero (Building Blocks)

Pandas Basics (DataFrame Basics I)

Pandas Series and Index Objects

DataFrame Basics II

Manipulating Elements in a DataFrame / Slice +++Important, know the Pitfalls!+++

DataFrame Basics III

Visualization with Matplotlib

Part 2: Full Data Workflow A-Z

Importing Data

Cleaning Data

Merging, Joining, and Concatenating Data

GroupBy Operations

Reshaping and Pivoting DataFrames

Data Preparation and Feature Creation

Advance Visualization with Seaborn

Part 3: Comprehensive Project Challenges

Data Manipulation and Aggregation Challenge (Olympic Medal Tables)

Explanatory Data Analysis Challenge

Part 4: Pandas for Finance, Invensting & Time Series

Time Series Basics

Pandas for Finance and Investing

Part 5: Machine Learning With Pandas and SCIKIT-Learn

Introduction to Regression & Classification

Bonus: Machine Learning Project A-Z (Regression)

WHAT´S NEW IN PANDAS VERSION 1.0? - A HANDS-ON GUIDE

Appendix: Python Basics, Numpy & Statistics

Python Basics

The Numpy Package

Statistical Concepts

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