English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 188 lectures (19h 21m) | 5.93 GB
Master Pandas and Python with real-world datasets and 200+ hands-on exercises! Go from beginner to expert Data Analyst!
Master data analysis with Python and Pandas: the most comprehensive AND effective course anywhere!
Welcome to the ultimate course on data analysis using Python and the powerful Pandas library. Whether you’re a complete beginner or an experienced programmer looking to level up your analytical skills, this course is designed to take you from zero to data manipulation and analysis guru.
What makes this course special?
- Proven success: Over 100,000 students have used my courses to master other data analysis tools like SQL, Excel, and Power BI
- No prerequisites: Start from scratch or jump ahead if you’re an experienced programmer
- Step-by-step approach: I break down every concept step-by-step, never assuming knowledge of any concepts that haven’t already been covered
- Real-world problem-solving: This course is jam-packed with examples using real-world datasets, from house sales data to UFO sightings!
- LOTS of practice: Literally hundreds(!) of exercises are integrated throughout the course, providing immediate reinforcement after each concept
- Comprehensive coverage: We progress from basic Python programming to advanced data transformations with Pandas, covering every step in between
Here’s what you’ll learn:
- Master the fundamentals of Python programming, specifically tailored for data analysis
- Harness the full power of the Pandas library to manipulate and analyze complex datasets
- Learn how to fetch and import external datasets into Pandas from various sources
- Perform exploratory data analysis (EDA), including a range of statistical measures
- Dive deep into indexing, sorting, filtering, and updating Pandas DataFrames
- Learn how to handle missing data
- Filter large datasets using SQL-like operations and logical criteria
- Merge and combine multiple datasets efficiently
- Create insightful summary views by grouping and aggregating data
- Manipulate string data and harness the power of regular expressions
- Perform time series analysis and calculations on datetime data
- Create insightful visualizations to communicate your findings effectively
- Apply functional programming concepts to streamline your data analysis
- Transform data using custom Python functions and Pandas methods
By the end of this course, you’ll have the skills to:
- Confidently work with large datasets using Python and Pandas
- Perform complex data transformations and analysis
- Create insightful visualizations to communicate your findings
- Apply functional programming concepts to data analysis
- Tackle real-world data problems with ease
Table of Contents
Introduction
1 Welcome to the Course!
2 IMPORTANT Example Files (and Exercise Solutions!)
Python Crash Course
3 Note to Students PLEASE READ
4 What Is Programming
5 The Programming Environment
6 The Programming Environment – Exercises
7 Values and Types
8 Functions
9 Expressions
10 Expressions in Colab
11 Expressions in Colab – Exercises
12 Variables
13 Variables – Exercises
14 Naming Variables
15 Errors
16 Comments
17 Text Cells
18 Text Cells – Exercises
19 Colab Tips and Pitfalls
20 Objects, Attributes, and Methods
21 Modules and Libraries
22 Lists
23 Tuples
24 Dictionaries
25 Data Structures – Exercises
Working with DataFrames
26 IMPORTANT DOWNLOAD EXAMPLE DATASETS
27 Introducing DataFrames
28 Introducing DataFrames – Exercises
29 Introducing the Example Datasets
30 DataFrames and the `read_csv` Method – Part I
31 DataFrames and the `read_csv` Method – Part II
32 DataFrames and the `read_csv` Method – Exercises
33 Providing DataFrame Column Names
34 Providing DataFrame Column Names – Exercises
35 Inspecting DataFrames
36 Inspecting DataFrames – Exercises
37 Data Types and the `info` Method
38 Data Types and the `info` Method – Exercises
39 Renaming Columns
40 Renaming Columns – Exercises
41 Dropping Columns
42 Dropping Columns – Exercises
43 Selecting Columns
44 Selecting Columns – Exercises
Working with Series
45 Series 101
46 Series 101 – Exercises
47 Converting Series with `to_numeric`
48 Converting Series with `to_numeric` – Exercises
49 Converting Series with `to_datetime`
50 Converting Series with `to_datetime` – Exercises
51 Adding Columns (Series) to DataFrames
52 Adding Columns (Series) to DataFrames – Exercises
53 Creating Derived Columns
54 Creating Derived Columns – Exercises
55 The `assign` Method
56 The `assign` Method – Exercises
Basic Data Analysis with Pandas
57 The `sum` Method
58 The `sum` Method – Exercises
59 The `count` Method
60 The `count` Method – Exercises
61 Mean and Median
62 Mean and Median – Exercises
63 Standard Deviation and the `describe` Method
64 Standard Deviation and the `describe` Method – Exercises
65 Using `describe` on Non-Numeric Fields
66 Using `describe` on Non-Numeric Fields – Exercises
67 The `unique` and `nunique` Methods
68 The `unique` and `nunique` Methods – Exercises
69 The `value_counts` Method
70 The `value_counts` Method – Exercises
Indexing and Sorting
71 The `iloc` Method
72 The `iloc` Method – Exercises
73 Indexing Basics
74 Indexing Basics – Exercises
75 The `loc` Method
76 The `loc` Method – Exercises
77 Sorting by Index
78 Sorting by Index – Exercises
79 Sorting By Columns
80 Sorting By Columns – Exercises
81 Dropping Rows By Index
82 Dropping Rows By Index – Exercises
Selecting Data with Criteria
83 Filtering DataFrames with a Boolean Series
84 Filtering DataFrames with a Boolean Series – Exercises
85 Applying Other Logical Conditions
86 Applying Other Logical Conditions – Exercises
87 The `between` and `isin` Methods
88 The `between` and `isin` Methods – Exercises
89 Combining Conditions Using the `&` Operator
90 Combining Conditions Using the `&` Operator – Exercises
91 Combining Conditions Using the “ Operator
92 Combining Conditions Using the “ Operator – Exercises
93 Combining ‘And’ & ‘Or’ Logic
94 Combining ‘And’ & ‘Or’ Logic – Exercises
95 Negation
96 Negation – Exercises
97 The `isna` Method
98 The `isna` Method – Exercises
Updating DataFrames
99 Updating DataFrame Values with `loc`
100 Updating DataFrame Values with `loc` – Exercises
101 Replacing DataFrame Values
102 Replacing DataFrame Values – Exercises
103 Updating Values with Boolean Masks
104 Updating Values with Boolean Masks – Exercises
105 Removing Null Values
106 Removing Null Values – Exercises
107 Replacing Null Values
108 Replacing Null Values – Exercises
109 Identifying Duplicate Data
110 Removing Duplicate Data
111 Identifying and Removing Duplicate Data – Exercises
Combining Datasets
112 Stacking Datasets Vertically I
113 Stacking Datasets Vertically II
114 Stacking Datasets Vertically – Exercises
115 Fetching Excel Data Into Pandas
116 Fetching Excel Data Into Pandas – Exercises
117 Joining DataFrames Horizontally I
118 Joining DataFrames Horizontally II
119 Joining DataFrames Horizontally – Exercises
120 Left and Right Joins
121 Full Outer Joins
122 Outer Joins – Exercises
123 Combining More Than Two Tables
124 Combining More Than Two Tables – Exercises
Grouping and Aggregation
125 Grouping and Aggregation 101
126 Grouping and Aggregation 101 – Exercises
127 Applying Multiple Aggregations
128 Applying Multiple Aggregations – Exercises
129 Grouping By Multiple Columns
130 Grouping By Multiple Columns – Exercises
131 The `transform` Method
132 The `transform` Method – Exercises
133 Pythonic Pivot Tables
134 Pythonic Pivot Tables – Exercises
Working with String Data
135 `upper`, `lower`, and `capitalize`
136 `upper`, `lower`, and `capitalize` – Exercises
137 The `len` Method
138 The `len` Method – Exercises
139 Regular Expressions 101
140 Regular Expressions 101 – Exercise
141 Matching Digits with Regular Expressions
142 Matching Digits with Regular Expressions – Exercises
143 The `contains` Method
144 The `contains` Method – Exercises
145 The `replace` Method I
146 The `replace` Method II
147 The `replace` Method – Exercises
Working with Datetime Data
148 Using Datetime Values as Criteria
149 Using Datetime Values as Criteria – Exercises
150 The `datetime` Module I
151 The `datetime` Module II
152 The `datetime` Module – Exercises
153 Date Math in Pandas
154 Date Math in Pandas – Exercises
155 The `shift` Method I
156 The `shift` Method II
157 The `shift` Method – Exercises
158 Calculating `rolling` Averages
159 Calculating `rolling` Averages – Exercises
Data Visualization with Pandas
160 Data Visualization 101.1
161 Data Visualization 101.2
162 Data Visualization – Exercises
163 Bar Plots
164 Bar Plots – Exercises
165 Scatter Plots
166 Scatter Plots – Exercises
167 Customizing Plot Appearance
168 Customizing Plot Axes
169 Customizing Plots – Exercises
Functional Programming in Python
170 Apply-ing Functions to Data Analysis
171 If Statements in Python
172 Incorporating Multiple Logical Conditions
173 Incorporating And and Or Logic
174 If Statements – Exercises
175 Functions in Python
176 Returning Values From Functions I
177 Returning Values From Functions II
178 Functions – Exercises
Leveraging the `map` and `apply` Methods
179 The `map` Method
180 The `map` Method – Exercises
181 Using `map` with Custom Functions I
182 Using `map` with Custom Functions II
183 Using `map` with Custom Functions – Exercises
184 The `apply` Method
185 The `apply` Method – Exercises
186 Applying `apply` to Multiple Columns
187 Applying `apply` to Multiple Columns – Exercises
BONUS LESSON
188 BONUS LESSON
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