Data Analysis with Pandas and Python

19h 5m 40s
English
Paid
April 30, 2024

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!

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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 a 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 do anything and everything with 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 take you step-by-step through Pandas, from installation to visualization! We'll cover hundreds of different methods, attributes, features, and functionalities packed away inside this awesome library. We'll dive into tons of different datasets, 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!

Whether you're a new data analyst or have spent years (*cough* too long *cough*) in Excel, Data Analysis with pandas and Python offers you an incredible introduction to one of the most powerful data toolkits available today!

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

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
  • Possess a strong understanding of manipulating 1D, 2D, and 3D data sets
  • Resolve common issues in broken or incomplete data sets

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# Title Duration
1 Introduction to the Course 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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