CF

Data Analysis with Pandas and Python

19h 5m 40s
English
Paid

Data Analysis with Pandas and Python is a 174-lesson 19 hours 5 minutes self-paced course by Udemy. Welcome to the most comprehensive Pandas course available on Udemy!

Course facts

Lessons
174
Duration
19 hours 5 minutes
Level
All levels
Language
English
Updated
Instructor
Udemy
Price
Premium

Welcome to the most comprehensive Pandas course available on Udemy! An excellent choice for both beginners and experts looking to expand their knowledge on one of the most popular Python libraries in the world!

Course Overview

Data Analysis with Pandas and Python offers 19+ hours of in-depth video tutorials on the most powerful data analysis toolkit available today. Lessons include:

  • Installing
  • Sorting
  • Filtering
  • Grouping
  • Aggregating
  • De-duplicating
  • Pivoting
  • Munging
  • Deleting
  • Merging
  • Visualizing
  • And more!

Why Learn Pandas?

If you've spent time in spreadsheet software like Microsoft Excel, Apple Numbers, or Google Sheets and are eager to take your data analysis skills to the next level, this course is for you!

Data Analysis with Pandas and Python introduces you to the popular Pandas library built on top of the Python programming language.

Pandas is a powerhouse tool that allows you to handle colossal data sets: analyzing, organizing, sorting, filtering, pivoting, aggregating, munging, cleaning, calculating, and more! I call it "Excel on steroids"! Over the course of more than 19 hours, I'll guide you step-by-step through Pandas, from installation to visualization.

Course Content

We will cover hundreds of different methods, attributes, features, and functionalities within this awesome library. We’ll dive into various datasets, both short and long, broken and pristine, to demonstrate the incredible versatility and efficiency of this package.

Data Analysis with Pandas and Python is bundled with dozens of datasets for you to use. Dive right in and follow along with my lessons to see how easy it is to get started with pandas!

Who Should Enroll

Who this course is for:
  • Data analysts and business analysts
  • Excel users looking to learn a more powerful software for data analysis

Requirements

  • Basic/intermediate experience with Microsoft Excel or another spreadsheet software (common functions, vlookups, Pivot Tables, etc.)
  • Basic experience with the Python programming language
  • Strong knowledge of data types (strings, integers, floating points, booleans, etc.)

Learning Outcomes

What you'll learn:

  • Perform a multitude of data operations in Python's popular "pandas" library, including grouping, pivoting, joining, and more!
  • Learn hundreds of methods and attributes across numerous pandas objects
  • Gain a strong understanding of manipulating 1D, 2D, and 3D data sets
  • Resolve common issues in broken or incomplete data sets

Who teaches Data Analysis with Pandas and Python? Udemy

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Udemy is the largest open marketplace for online courses on the internet. Founded in 2010 by Eren Bali, Oktay Caglar, and Gagan Biyani and headquartered in San Francisco, the company went public on the Nasdaq in 2021 under the ticker UDMY. The platform hosts well over two hundred thousand courses across software development, IT and cloud, data science, design, business, marketing, and creative skills, taught by tens of thousands of independent instructors. Roughly seventy million learners use it worldwide, and the corporate arm — Udemy Business — supplies a curated subset of that catalog to enterprise customers.

Because Udemy is a marketplace rather than a single editorial publisher, the catalog is uneven by design. The strongest material lives in the long-form, project-based courses authored by working engineers — full-stack JavaScript, React, Node.js, Python data science, AWS, Docker and Kubernetes, mobile development with Flutter and React Native, and cloud certification preparation. The CourseFlix listing under this source is the slice of that catalog that has been mirrored here for offline-friendly viewing, organized by topic and updated as new releases land. Pricing on Udemy itself swings dramatically with the site's near-permanent sales, which is why the platform is best treated as a deep reference catalog: pick instructors with strong reviews and a track record of updating their material rather than buying on the headline price alone.

What lessons are included in Data Analysis with Pandas and Python?

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#1: Introduction to the Course
All Course Lessons (174)
#Lesson TitleDurationAccess
1
Introduction to the Course Demo
12:15
2
About Me
00:57
3
MacOS - Download the Anaconda Distribution
03:56
4
MacOS - Install Anaconda Distribution
10:39
5
MacOS - Access the Terminal
08:35
6
MacOS - Create Conda Environment and Install Pandas
13:08
7
MacOS - Unpack Course Materials + The Start and Shutdown Process
12:33
8
Windows - Download the Anaconda Distribution
03:48
9
Windows - Install Anaconda Distribution
05:17
10
Windows - Access the Command Prompt and Update Anaconda Libraries
10:12
11
Windows - Unpack Course Materials + The Startdown and Shutdown Process
08:50
12
Intro to the Jupyter Notebook Interface
05:15
13
Cell Types and Cell Modes
07:04
14
Code Cell Execution
04:48
15
Popular Keyboard Shortcuts
03:07
16
Import Libraries into Jupyter Notebook
07:10
17
Python Crash Course, Part 1 - Data Types and Variables
07:06
18
Python Crash Course, Part 2 - Lists
05:07
19
Python Crash Course, Part 3 - Dictionaries
04:20
20
Python Crash Course, Part 4 - Operators
04:31
21
Python Crash Course, Part 5 - Functions
06:03
22
Create Jupyter Notebook for the Series Module
02:13
23
Create A Series Object from a Python List
10:33
24
Create A Series Object from a Python Dictionary
03:07
25
Intro to Attributes
07:18
26
Intro to Methods
04:43
27
Parameters and Arguments
10:11
28
Import Series with the .read_csv() Method
10:24
29
The .head() and .tail() Methods
03:43
30
Python Built-In Functions
05:21
31
More Series Attributes
06:14
32
The .sort_values() Method
06:05
33
The inplace Parameter
05:08
34
The .sort_index() Method
04:39
35
Python's in Keyword
04:01
36
Extract Series Values by Index Position
04:16
37
Extract Series Values by Index Label
07:23
38
The .get() Method on a Series
05:04
39
Math Methods on Series Objects
05:40
40
The .idxmax() and .idxmin() Methods
03:11
41
The .value_counts() Method
03:40
42
The .apply() Method
06:47
43
The .map() Method
06:53
44
Intro to DataFrames I Module
07:25
45
Shared Methods and Attributes between Series and DataFrames
07:38
46
Differences between Shared Methods
06:49
47
Select One Column from a DataFrame
07:58
48
Select Two or More Columns from a DataFrame
05:13
49
Add New Column to DataFrame
08:04
50
Broadcasting Operations
09:07
51
A Review of the .value_counts() Method
03:55
52
Drop Rows with Null Values
06:42
53
Fill in Null Values with the .fillna() Method
04:25
54
The .astype() Method
10:39
55
Sort a DataFrame with the .sort_values() Method, Part I
05:47
56
Sort a DataFrame with the .sort_values() Method, Part II
04:14
57
Sort DataFrame with the .sort_index() Method
03:00
58
Rank Values with the .rank() Method
05:54
59
This Module's Dataset + Memory Optimization
10:46
60
Filter a DataFrame Based on A Condition
12:58
61
Filter with More than One Condition (AND - &)
04:42
62
Filter with More than One Condition (OR - |)
08:36
63
The .isin() Method
06:18
64
The .isnull() and .notnull() Methods
05:08
65
The .between() Method
06:52
66
The .duplicated() Method
09:06
67
The .drop_duplicates() Method
08:17
68
The .unique() and .nunique() Methods
04:23
69
Intro to the DataFrames III Module + Import Dataset
03:24
70
The .set_index() and .reset_index() Methods
05:38
71
Retrieve Rows by Index Label with .loc[]
09:43
72
Retrieve Rows by Index Position with .iloc[]
06:08
73
The Catch-All .ix[] Method
08:45
74
Second Arguments to .loc[], .iloc[], and .ix[] Methods
06:22
75
Set New Values for a Specific Cell or Row
04:28
76
Set Multiple Values in DataFrame
09:17
77
Rename Index Labels or Columns in a DataFrame
06:50
78
Delete Rows or Columns from a DataFrame
07:30
79
Create Random Sample with the .sample() Method
04:44
80
The .nsmallest() and .nlargest() Methods
05:37
81
Filtering with the .where() Method
05:04
82
The .query() Method
09:07
83
A Review of the .apply() Method on Single Columns
05:54
84
The .apply() Method with Row Values
06:50
85
The .copy() Method
07:06
86
Intro to the Working with Text Data Module
05:56
87
Common String Methods - lower, upper, title, and len
07:15
88
The .str.replace() Method
08:08
89
Filtering with String Methods
06:44
90
More String Methods - strip, lstrip, and rstrip
04:31
91
String Methods on Index and Columns
05:31
92
Split Strings by Characters with .str.split() Method
08:41
93
More Practice with Splits
06:02
94
The expand and n Parameters of the .str.split() Method
07:01
95
Intro to the MultiIndex Module
04:27
96
Create a MultiIndex with the set_index() Method
09:51
97
The .get_level_values() Method
07:52
98
The .set_names() Method
03:09
99
The sort_index() Method
04:57
100
Extract Rows from a MultiIndex DataFrame
08:33
101
The .transpose() Method and MultiIndex on Column Level
05:49
102
The .swaplevel() Method
02:35
103
The .stack() Method
06:01
104
The .unstack() Method, Part 1
03:39
105
The .unstack() Method, Part 2
06:10
106
The .unstack() Method, Part 3
05:10
107
The .pivot() Method
06:35
108
The .pivot_table() Method
10:17
109
The pd.melt() Method
06:00
110
Intro to the Groupby Module
07:43
111
First Operations with groupby Object
09:34
112
Retrieve A Group with the .get_group() Method
03:48
113
Methods on the Groupby Object and DataFrame Columns
08:42
114
Grouping by Multiple Columns
04:36
115
The .agg() Method
06:12
116
Iterating through Groups
09:05
117
Intro to the Merging, Joining, and Concatenating Module
05:48
118
The pd.concat() Method, Part 1
05:40
119
The pd.concat() Method, Part 2
06:36
120
The .append() Method on a DataFrame
02:04
121
Inner Joins, Part 1
09:19
122
Inner Joins, Part 2
09:01
123
Outer Joins
12:24
124
Left Joins
09:20
125
The left_on and right_on Parameters
08:55
126
Merging by Indexes with the left_index and right_index Parameters
11:03
127
The .join() Method
03:16
128
The pd.merge() Method
03:07
129
Intro to the Working with Dates and Times Module
03:45
130
Review of Python's datetime Module
09:32
131
The pandas Timestamp Object
07:16
132
The pandas DateTimeIndex Object
05:24
133
The pd.to_datetime() Method
11:12
134
Create Range of Dates with the pd.date_range() Method, Part 1
10:23
135
Create Range of Dates with the pd.date_range() Method, Part 2
09:05
136
Create Range of Dates with the pd.date_range() Method, Part 3
07:51
137
The .dt Accessor
07:30
138
Install pandas-datareader Library
02:31
139
Import Financial Data Set with pandas_datareader Library
10:43
140
Selecting Rows from a DataFrame with a DateTimeIndex
08:02
141
Timestamp Object Attributes
07:28
142
The .truncate() Method
03:00
143
pd.DateOffset Objects
12:01
144
More Fun with pd.DateOffset Objects
14:07
145
The pandas Timedelta Object
08:40
146
Timedeltas in a Dataset
09:31
147
Intro to the Module + Fetch Panel Dataset from Google Finance
07:18
148
The Axes of a Panel Object
07:43
149
Panel Attributes
05:05
150
Use Bracket Notation to Extract a DataFrame from a Panel
04:00
151
Extracting with the .loc, .iloc, and .ix Methods
06:58
152
Convert Panel to a MultiIndex DataFrame (and Vice Versa)
04:05
153
The .major_xs() Method
05:47
154
The .minor_xs() Method
06:25
155
Transpose a Panel with the .transpose() Method
07:43
156
The .swapaxes() Method
04:23
157
Intro to the Input and Output Module
01:34
158
Feed pd.read_csv() Method a URL Argument
03:49
159
Quick Object Conversions
05:05
160
Export DataFrame to CSV File with the .to_csv() Method
05:49
161
Install xlrd and openpyxl Libraries to Read and Write Excel Files
02:37
162
Import Excel File into pandas
09:31
163
Export Excel File
08:43
164
Intro to Visualization Module
04:17
165
The .plot() Method
09:14
166
Modifying Aesthetics with Templates
05:21
167
Bar Graphs
06:25
168
Pie Charts
05:08
169
Histograms
06:10
170
Introduction to the Options and Settings Module
01:43
171
Changing pandas Options with Attributes and Dot Syntax
06:57
172
Changing pandas Options with Methods
06:14
173
The precision Option
03:11
174
Conclusion
01:39
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Frequently asked questions

What are the prerequisites for enrolling in the course?
The course includes a comprehensive introduction to Python, covering data types, variables, lists, dictionaries, operators, and functions. This makes it suitable for beginners who may not have prior experience with Python. However, some familiarity with spreadsheet software like Excel, Numbers, or Google Sheets is recommended to better understand the transition to data analysis using Pandas.
What type of projects or exercises will I work on during the course?
Throughout the course, you will work with various datasets to apply Pandas functionalities such as sorting, filtering, grouping, aggregating, and visualizing data. These exercises help in understanding how to handle and analyze large datasets effectively, similar to tasks performed in spreadsheet software but on a larger scale.
Who is the target audience for this course?
This course is designed for individuals who are familiar with spreadsheet software and wish to enhance their data analysis skills using Python and Pandas. It is suitable for both beginners looking to enter the field of data analysis and experienced users aiming to deepen their understanding of the Pandas library.
What tools and platforms does the course focus on?
The course focuses on using the Pandas library in Python for data analysis. It also introduces the Jupyter Notebook interface as a primary tool for executing code and analyzing data. Instructions are provided for setting up the Anaconda Distribution on both MacOS and Windows to ensure a seamless learning experience.
What topics are not covered in this course?
While the course covers extensive functionalities of the Pandas library, it does not delve into advanced topics such as machine learning algorithms or data visualization libraries beyond basic visualizations within Pandas. These topics may require additional courses or resources for a comprehensive understanding.
What is the expected time commitment for completing the course?
The course offers over 19 hours of video tutorials, divided into 174 lessons. The time commitment will depend on your pace of learning and familiarity with the content. It is advisable to allocate additional time for practicing exercises and reviewing material to fully grasp the concepts.
How does this course prepare me for further studies or a career in data analysis?
By mastering the Pandas library, you will gain a solid foundation in data manipulation and analysis, which is crucial for any data-related role. The skills acquired in this course will be beneficial for further studies in data science, machine learning, and other advanced topics, as Pandas is a fundamental tool in these fields.