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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