Data Science CookBook

These are the topics covered

1. Data Wrangling

• Creating a Data Frame
• Describing the Data
• Navigating DataFrames
• Selecting Rows Based on Conditionals
• Replacing Values
• Renaming Columns
• Finding the Minimum, Maximum, Sum, Average, and Count
• Finding Unique Values
• Handling Missing Values
• Deleting a Column
• Deleting a Row
• Dropping Duplicate Rows
• Grouping Rows by Values
• Grouping Rows by Time
• Looping Over a Column
• Applying a Function Over All Elements in a Column
• Applying a Function to Groups
• Concatenating DataFrames
• Merging DataFrames

2. Handling Numerical Data

• Rescaling a Feature
• Standardizing a Feature
• Normalizing Observations
• Generating Polynomial and Interaction Features
• Transforming Features
• Detecting Outliers
• Handling Outliers
• Discretizating Features
• Grouping Observations Using Clustering
• Deleting Observations with Missing Values
• Imputing Missing Values

3. Handling Categorial Data

• Encoding Nominal Categorical Features
• Encoding Ordinal Categorical Features
• Encoding Dictionaries of Features
• Imputing Missing Class Values
• Handling Imbalanced Classes

4. Handling Text

• Cleaning Text
• Parsing and Cleaning HTML
• Removing Punctuation
• Tokenizing Text
• Removing Stop Words
• Stemming Words
• Tagging Parts of Speech
• Encoding Text as a Bag of Words
• Weighting Word Importance