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
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
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
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
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
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
