Course curriculum

  • 1

    Module 1: Introduction to Pandas

    • Resources
    • Module 1.1 - What is Pandas?
    • Segment 1 - What is Pandas
    • Segment 2 - Which Version of Pandas to Use
    • Segment 3 - Pandas Examples
    • Module 1.2 - The DataFrame and Series
    • Segment 4 - Introduction to the DataFrame and Series
    • Segment 5 - DataFrame Components
    • Segment 6 - Selecting a Series
    • Segment 7 - Components of a Series
    • Segment 8 - Getting Help in a Jupyter Notebook
    • Segment 9 - Exercises
    • Modules 1.3 - Data Types and Missing Values
    • Segment 10 - Introduction to Data Types and Missing Values
    • Segment 11 - Finding the Data Type of Each Column
    • Segment 12 - Getting More Metadata
    • Segment 13 - Exercises
    • Module 1.4 - Setting a Meaningful Index
    • Segment 14 - Setting an Index of a DataFrame
    • Segment 15 - Accessing the Index, Columns, and Data
    • Segment 16 - Accessing the Components of a Series
    • Segment 17 - The Default Index
    • Segment 18 - Setting an Index on Read
    • Segment 19 - Choosing a Good Index
    • Segment 20 - Exercises
    • Module 1.5 - Five-Step Process for Data Exploration
    • Segment 21 - Five-Step Process for Data Exploration
  • 2

    Module 2: Selecting Subsets of Data

    • Module 2.1 - Selecting Subsets of Data from DataFrames with Just Brackets
    • Segment 22 - Introduction to Subset Selection
    • Segment 23 -Selecting with Just the Brackets
    • Segment 24 -Exercises
    • Module 2.2 - Selecting Subsets of Data from DataFrames with loc
    • Segment 25 - Simultaneous Row and Column Subset Selection
    • Segment 26 - Slice Notation with loc
    • Segment 27 - Other Subset Selections with loc
    • Segment 28 -Exercises
    • Module 2.3 - Selecting Subsets of Data with Iloc
    • Segment 29 - Simultaneous Row and Column Subset Selection
    • Segment 30 -Exercises
    • Module 2.4 - Selecting Subsets of Data from a Series
    • Segment 31 - Selecting Subsets of Data from a Series
    • Segment 32 -Exercises
    • Module 2.5 - Boolean Selection Single Condition
    • Segment 33 - Boolean Selection Single Conditions
    • Segment 34 - Practical Boolean Selection
    • Segment 35 - Exercises
    • Module 2.6 - Boolean Selection Multiple Conditions
    • Segment 36 - Different Logical Operators for Boolean Series
    • Segment 37 - Inverting a Condition with the Not Operator
    • Segment 38 - Many Equality Conditions in a Single Column
    • Segment 39 - Exercises - Boolean Selection Multiple Conditions
    • Module 2.7 - Boolean Selection More
    • Segment 40 - Boolean Selection on a Series
    • Segment 41 - Simultaneous Boolean Selection of Rows and Column Labels with loc
    • Segment 42 - Column to Column Comparison
    • Segment 43 - Filter for Missing Values
    • Segment 44 -Exercises - Boolean Selection More
    • Module 2.8 - Filtering with the Query Method
    • Segment 45 - Introduction to the Query Method
    • Segment 46 - Column to Column Comparison with Query
    • Segment 48 - Arithmetic Operations within Query
    • Segment 49 - Reference Variable Names
    • Segment 50 - Selecting Columns with Query
    • Segment 51 - Summary of the Query Method
    • Segment 52 -Exercises
    • Module 2.9 - Miscellaneous Subset Selection
    • Segment 53 - Selecting a Column with Dot Notation
    • Segment 54 -Selecting Rows with just the Brackets using Slice Notation
    • Segment 55 - Selecting a Single Cell with at and iat
    • Module 2.10 - Taking Certification Exam
    • Segment 56 - Going to Exam Website
    • Segment 57 - Completing the Exam
    • Segment 58 - Submitting the Exam
  • 3

    Module 3: Essential Series Commands

    • Module 3.1 - Numeric Series Methods
    • Segment 59 - Numeric Series Methods
    • Segment 60 - Core Series Attributes
    • Segment 61 - Arithmetic Operators
    • Segment 62 - Comparison Operators
    • Segment 63 - Boolean and Bitwise Operators
    • Segment 64 - Aggregation Methods
    • Segment 65 - Non-Aggregation Methods
    • Segment 66 - Series Methods with a Non-Default Index
    • Segment 67 - Operations on a Boolean Series
    • Segment 68 - Exercises
    • Module 3.2 - Series Missing Value Methods
    • Segment 69 - The isna and notna Methods
    • Segment 70 - Dropping Missing Values with dropna
    • Segment 71 - Filling Missing Values with the fillna Method
    • Segment 72 - Filling Missing Values with interpolate
    • Segment 73 - Exercises
    • Segment 74 - Sorting the Value and the Index
    • Module 3.3 - Series Sorting, Ranking and Uniqueness
    • Segment 75 - Ranking
    • Segment 76 - Uniqueness
    • Segment 77 - Exercises
    • Module 3.4 - More Series Methods
    • Segment 78 - The agg, idxmin, idxmax, nsmallest, and nlargest Methods
    • Segment 79 - Differencing Methods diff and pct_change
    • Segment 80 - Randomly Sample a Series
    • Segment 81 - The replace Method
    • Segment 82 - Exercises
    • Module 3.5 - String Series Methods
    • Segment 83 - String Series Methods
    • Segment 84 - The value_counts Method
    • Segment 85 - The split String Method
    • Segment 86 - Special Methods Just for Object Columns
    • Segment 87 - More String-Only Methods
    • Segment 88 - The replace String Method
    • Segment 89 - Selecting Subsets with the Brackets
    • Segment 90 - Exericses
    • Module 3.6 - Datetime Series Methods
    • Segment 91 - Datetime Attributes
    • Segment 92 - Datetime Methods
    • Segment 93 - Format Time as a String with strftime
    • Segment 94 - Convert to Period
    • Segment 95 - Timedeltas
    • Segment 96 - Datetime Series Methods
    • Module 3.7 - Project - Testing Normality of Stock Market Returns
    • Segment 97 - Project - Testing Normality of Stock Market Returns
    • Segment 98 - Exercises
  • 4

    Module 4: Essential DataFrame Commands

    • Module 4.1 - Introduction to DataFrames
    • Segment 99- Introduction to DataFrames
    • Segment 100 - Arithmetic DataFrame Operations
    • Segment 102 - DataFrame Comparison Operators
    • Segment 103 - Overlap of DataFrame and Series Methods
    • Segment 104 - Data Dictionaries
    • Segment 105 - Exercises
    • Module 4.2 - Numeric DataFrame Methods
    • Segment 106 - Aggregation Methods
    • Segment 107 - Changing the Direction of the Operation
    • Segment 108 - Non-Aggregation Methods
    • Segment 109 - Summary Statistics for All Columns with the Describe Method
    • Segment 110 - Nuisance Columns
    • Segment 111 - Exercises
    • Module 4.3 - DataFrame Missing Value Methods
    • Segment 112 - The agg, idxmin, and idxmax Methods
    • Segment 113 - Dropping Rows and Columns with the dropna Method
    • Segment 114 - Filling missing values with the fillna Method
    • Segment 115 - The interpolate Method
    • Segment 116 - Exercises
    • Module 4.4 - DataFame Sorting, Ranking and Uniqueness
    • Segment 117 - Sorting
    • Segment 118 - Ranking
    • Segment 119 - Uniqueness
    • Segment 120 - Finding the Maximum or Minimum of a Group
    • Segment 121 - The value_counts Method
    • Segment 122 - Exercises
    • Module 4.5 - DataFrame Structure Methods
    • Segment 123 - Adding a New Column to the DataFrame
    • Segment 124 - Copying the DataFrame
    • Segment 125 - Column and Row Dropping and Renaming
    • Segment 126 - Inserting Columns in the Middle of a DataFrame
    • Segment 127 - Getting the Integer Location with the Index get_loc Method
    • Segment 128 - The pop Method
    • Segment 129 - Exercises
    • Module 4.6 - More DataFame Methods
    • Segment 130 - The isna and notna Methods
    • Segment 131 - Differencing methods diff and pct_change
    • Segment 132 - The Sample Method
    • Segment 133 - The nsmallest and nlargest methods
    • Segment 134 - The corr Method
    • Segment 135 - The replace Method
    • Segment 136 - Methods available only to Series and not DataFrames
    • Segment 137 - Exercises
    • Module 4.7 - Assigning Subsets of Data
    • Segment 138 - Setting New Data with loc
    • Segment 139 - Setting New Data with iloc
    • Segment 140 - Boolean Selection Assignment
    • Segment 141 - Improper Assignment
    • Segment 142 - Exercises
  • 5

    Module 5: Data Types

    • Module 5.1 - Integer, Float and Boolean Data Types
    • Segment 143 - Integer Data Type
    • Segment 144 - Changing Data Types with astype
    • Segment 145 - Unsigned Integers
    • Segment 146 - Nullable Integer Data Type
    • Segment 147 - Boolean Selection with Nullable Booleans
    • Segment 148 - Float Data Types
    • Segment 149 - Changing from Float to Int
    • Segment 150 - Pandas Nullable Float Data Type
    • Segment 151 - Boolean Data Type
    • Segment 152 - Nullable Boolean Data Type
    • Segment 153 - Different Syntax for Data Types
    • Segment 154 - Data Type Summary
    • Segment 155 - Exercises
    • Module 5.2 - Object, Categorical, and String Data Types
    • Segment 156 - 1 Object Data Types
    • Segment 157 - Categorical Data Type
    • Segment 158 - Internal Storage of Categorical Data
    • Segment 159 - The cat Acccessor
    • Segment 160 - Modifying Categories
    • Segment 161 - Massive Reduction in Memory Used
    • Segment 162 - Speeding Up Operations
    • Segment 163 - The str Accessor is Still Available
    • Segment 164 - Ordered Categories
    • Segment 165 - Integers can be Categories
    • Segment 166 - The New String Data Type
    • Segment 167 - Converting Strings to Numerica
    • Segment 168 - Exercises
    • Module 5.3 - Datetime, Timedelta, and Period Data Types
    • Segment 169 - The pandas datetime64 data type
    • Segment 170 - The pandas timedelta64 data type
    • Segment 171 - The pandas period data type
    • Segment 172 - Summary Table
    • Segment 173 - Exercises
    • Module 5.4 - DataFrame Data Type Conversion
    • Segment 174 - Discovering Strings in Numeric Columns
    • Segment 175 - Converting non-numeric values to missing
    • Segment 176 - The astype method for DataFrames
    • Segment 177 - Reading in data with known missing values
    • Segment 178 - More Data type Conversion with the Housing Dataset
    • Segment 179 - Exercises
  • 6

    Module 6: Grouping Data

    • Module 6.1 - Grouping Aggregation Basics
    • Segment 180- Grouping Aggregation Basics
    • Segment 181 - Grouping with the groupby Method
    • Segment 182 - Use String Names for Aggregation Functions
    • Segment 183 - Aligning the Dots when Method Chaining
    • Segment 184 - The Index When Grouping
    • Segment 185 - The GroupBy Object
    • Segment 186 - Exercises
    • Module 6.2 - Grouping and Aggregating Multiple Columns
    • Segment 187 - Grouping with Multiple Columns
    • Segment 188 - Aggregating Multiple Columns
    • Segment 189 - Getting the size of each group
    • Segment 190 - Exercises
    • Module 6.3 - Grouping with Pivot Tables
    • Segment 191 - Creating Pivot Tables with Pandas
    • Segment 192 - Where is the Pivoting
    • Segment 193 - Styling Pivot Tables
    • Segment 194 - Getting the Size of each Group
    • Segment 195 - Add Marging to get Row and Column Totals
    • Segment 196 - Non-Standard Pivot Tables
    • Segment 197 - Exercises
    • Module 6.4 - Counting with Crosstabs
    • Segment 198 - Counting the Frequency with the crosstab Function
    • Segment 199 - Normalizing Other Aggregations
    • Segment 200 - crosstab is almost unnecessary in pandas
    • Segment 201 - Exercises
    • Module 6.5 - Alternative Groupby Syntax
    • Segment 202 - Alternative Groupby Syntax
    • Segment 203 - Exercises
    • Module 6.6 - Custom Aggregation
    • Segment 204 - Using a Custom Aggregation Function
    • Segment 205 - Custom aggregation functions must return a single value
    • Segment 206 - Find the mean salary for the five highest paid employees per department
    • Segment 207 - What percent of total salary do these five employees represent
    • Segment 208 - Using a custom aggregation function in a pivot table
    • Segment 209 - Percentage of employees by department with salaries greater than 100,000
    • Segment 210 - Optimizing a custom aggregation function
    • Segment 211 - Complete operations that are independent of the group outside of the custom function
    • Segment 212 - Exercises
    • Module 6.7 - Filer and Transform with Groupby
    • Segment 213 - The filter Method
    • Segment 214 - Viewing each Sub-DataFrame
    • Segment 215 - Summary of the GroupBy filter Method
    • Segment 216 - Finding actors that appear in at least 25 movies
    • Segment 217 - The groupby transform Method
    • Segment 218 - transform second use case - return a new value for each row in the group
    • Segment 219 - Find Difference from the Mean
    • Segment 220 - Transforming multiple columns
    • Segment 221 - Summary of the groupby transform method
    • Segment 222 - Exercises
    • Module 6.8 - More Groupby Methods
    • Segment 223 - Kinds of groupby attributes and methods
    • Segment 224 - head, tail, and nth groupby methods
    • Segment 225 - Groupby Methods Unique to Series
    • Segment 226 - Non-aggregating Methods
    • Module 6.9 - Binning Numeric Columns
    • Segment 227 - Exercises
    • Segment 228 - Binning with pd.cut
    • Segment 229 - Cut into a specific number of bins
    • Segment 230 - Quantile binning with pd.qcut
    • Module 6.10 - Miscellaneous Grouping Functionality
    • Segment 231 - Grouping with Bins
    • Segment 232 - Exercises
    • Segment 233 - Grouping by Columns not in the DataFrame
    • Segment 234 - Grouping Series and aggregating other columns
    • Segment 235 - Change the Direction of Grouping
    • Segment 236 - Exercises