Data Analysis with Pandas and Python

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Boris Paskhaver

20:38:59

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00001 Introduction to Data Analysis with Pandas and Python.mp4

12:18

00002 MacOS - Download the Anaconda Distribution Our Python Development Environment.mp4

04:22

00003 MacOS - Install Anaconda Distribution.mp4

08:52

00004 MacOS - Access the Terminal Application.mp4

08:35

00005 MacOS - Create Conda Environment and Install Pandas and Jupyter Notebook.mp4

13:09

00006 MacOS - Unpack Course Materials + The Start and Shutdown Process.mp4

12:34

00007 Windows - Download the Anaconda Distribution.mp4

04:38

00008 Windows - Install Anaconda Distribution.mp4

06:42

00009 Windows - Create Conda Environment and Install Pandas and Jupyter Notebook.mp4

18:14

00010 Windows - Unpack Course Materials + The Start and Shutdown Process.mp4

13:16

00011 Introduction to the Jupyter Notebook Interface.mp4

09:54

00012 Cell Types and Cell Modes in Jupyter Notebook.mp4

07:39

00013 Code Cell Execution in Jupyter Notebook.mp4

03:15

00014 Popular Keyboard Shortcuts in Jupyter Notebook.mp4

03:44

00015 Import Libraries into Jupyter Notebook.mp4

08:34

00016 Introduction to the Python Crash Course.mp4

03:37

00017 Comments.mp4

03:21

00018 Basic Data Types.mp4

10:50

00019 Operators.mp4

15:38

00020 Variables.mp4

07:51

00021 Built-In Functions.mp4

10:44

00022 Custom Functions.mp4

16:36

00023 String Methods.mp4

20:54

00024 Lists.mp4

13:20

00025 Index Positions and Slicing.mp4

15:58

00026 Dictionaries.mp4

15:25

00027 Create Jupyter Notebook for the Series Module.mp4

02:18

00028 Create a Series Object from a Python List.mp4

10:35

00029 Create a Series Object from a Python Dictionary.mp4

03:08

00030 Introduction to Attributes on a Series Object.mp4

07:20

00031 Introduction to Methods on a Series Object.mp4

04:44

00032 Parameters and Arguments.mp4

10:12

00033 Create Series from a Dataset with the pd.read csv Method.mp4

15:05

00034 Use the Head and Tail Methods to Return Rows from the Beginning and End of a Dataset.mp4

03:45

00035 Passing Pandas Objects to Python Built-In Functions.mp4

05:23

00036 Accessing More Series Attributes.mp4

06:16

00037 Use the sort values Method to Sort a Series in Ascending or Descending Order.mp4

06:07

00038 Use the inplace Parameter to Permanently Mutate a Pandas Data Structure.mp4

05:10

00039 Use the sort index Method to Sort the Index of a Pandas Series Object.mp4

04:40

00040 Use Python s in Keyword to Check for Inclusion in Series Values or Index.mp4

04:03

00041 Extract Series Values by Index Position.mp4

04:17

00042 Extract Series Values by Index Label.mp4

10:38

00043 Use the get Method to Retrieve a Value for an Index label in a Series.mp4

09:41

00044 Math Methods on Series Objects.mp4

05:42

00045 Use the idxmax and idxmin Methods to Find Index of Greatest or Smallest Value.mp4

03:13

00046 Use the value counts Method to See Counts of Unique Values within a Series.mp4

03:41

00047 Use the apply Method to Invoke a Function on Every Series Values.mp4

06:48

00048 The Series map Method.mp4

06:56

00049 Introduction to DataFrames I Module.mp4

09:50

00050 Shared Methods and Attributes Between Series and DataFrames.mp4

13:58

00051 Differences Between Shared Methods.mp4

06:51

00052 Select One Column from a DataFrame.mp4

08:00

00053 Select Two or More Columns from a DataFrame.mp4

05:14

00054 Add a New Column to DataFrame.mp4

08:06

00055 Broadcasting Operations on DataFrames.mp4

09:09

00056 A Review of the value counts Method.mp4

03:57

00057 Drop DataFrame Rows with Null Values with the dropna Method.mp4

06:43

00058 Fill in Null DataFrame Values with the fillna Method.mp4

04:28

00059 Convert DataFrame Column Types with the astype Method.mp4

10:41

00060 Sort a DataFrame with the sort values Method Part I.mp4

05:49

00061 Sort a DataFrame with the sort values Method Part II.mp4

04:16

00062 Sort DataFrame Index with the sort index Method.mp4

03:01

00063 Rank Series Values with the rank Method.mp4

05:56

00064 This Module s Dataset + Memory Optimization.mp4

15:54

00065 Filter a DataFrame Based on a Condition.mp4

13:00

00066 Filter DataFrame with More than One Condition AND.mp4

04:42

00067 Filter DataFrame with More than One Condition OR.mp4

08:37

00068 Check for Inclusion with the isin Method.mp4

06:20

00069 Check for Null and Present DataFrame Values with the isnull and notnull Methods.mp4

05:09

00070 Check for Inclusion Within a Range of Values with the between Method.mp4

06:53

00071 Check for Duplicate DataFrame Rows with the duplicated Method.mp4

09:07

00072 Delete Duplicate DataFrame Rows with the drop duplicates Method.mp4

08:18

00073 Identify and Count Unique Values with the unique and nunique Methods.mp4

04:24

00074 Introduction to the DataFrames III Module + Import Dataset.mp4

04:57

00075 Use the set index and reset index Methods to Define a new DataFrame Index.mp4

07:28

00076 Retrieve Rows by Index Label with loc Accessor.mp4

12:44

00077 Retrieve Rows by Index Position with iloc Accessor.mp4

07:26

00078 Passing Second Arguments to the loc and iloc Accessors.mp4

09:13

00079 Set New Value for a Specific Cell or Cells in a Row.mp4

04:36

00080 Set Multiple Values in a DataFrame.mp4

06:10

00081 Rename Index Labels or Columns in a DataFrame.mp4

09:36

00082 Delete Rows or Columns from a DataFrame.mp4

07:31

00083 Create Random Sample with the sample Method.mp4

04:45

00084 Use the nsmallest nlargest Methods to Get Rows with Smallest Largest Values..mp4

05:37

00085 Filter a DataFrame with the where Method.mp4

05:05

00086 Filter a DataFrame with the query Method.mp4

09:10

00087 A Review of the apply Method on a Pandas Series Object.mp4

05:55

00088 Apply a Function to Every DataFrame Row with the apply Method.mp4

06:51

00089 Create a Copy of a DataFrame with the copy Method.mp4

07:07

00090 Introduction to the Working with Text Data Section.mp4

06:12

00091 Common String Methods - lower upper title and len.mp4

07:16

00092 Use the str.replace Method to Replace All Occurrences of a Character with Another.mp4

08:10

00093 Filter a DataFrame s Rows with String Methods.mp4

06:46

00094 More DataFrame String Methods - strip lstrip and rstrip.mp4

04:33

00095 Invoke String Methods on DataFrame Index and Columns.mp4

05:32

00096 Split Strings by Characters with the str.split Method.mp4

08:44

00097 More Practice with the str.split Method on a Series.mp4

06:03

00098 Exploring the expand and n Parameters of the str.split Method.mp4

07:02

00099 Introduction to the MultiIndex Module.mp4

04:52

00100 Create a MultiIndex on a DataFrame with the set index Method.mp4

10:39

00101 Extract Index Level Values with the get level values Method.mp4

04:21

00102 Change Index Level Name with the set names Method.mp4

04:17

00103 The sort index Method on a MultiIndex DataFrame.mp4

08:26

00104 Extract Rows from a MultiIndex DataFrame.mp4

11:01

00105 The transpose Method on a MultiIndex DataFrame.mp4

08:18

00106 The .swaplevel Method.mp4

03:31

00107 The .stack Method.mp4

06:03

00108 The .unstack Method Part 1.mp4

03:40

00109 The .unstack Method Part 2.mp4

06:11

00110 The .unstack Method Part 3.mp4

05:10

00111 The pivot Method.mp4

06:36

00112 Use the pivot table Method to Create an Aggregate Summary of a DataFrame.mp4

10:18

00113 Use the pd.melt Method to Create a Narrow Dataset from a Wide One.mp4

06:01

00114 Introduction to the GroupBy Module.mp4

07:43

00115 First Operations with the GroupBy Object.mp4

09:35

00116 Retrieve a Group from a GroupBy Object with the get group Method.mp4

03:48

00117 Methods on the GroupBy Object and DataFrame Columns.mp4

08:43

00118 Grouping by Multiple Columns.mp4

04:37

00119 The .agg Method.mp4

06:13

00120 Iterating Through Groups.mp4

09:06

00121 Introduction to the Merging Joining and Concatenating Section.mp4

04:52

00122 The pd.concat Method Part 1.mp4

05:23

00123 The pd.concat Method Part 2.mp4

07:08

00124 The append Method on a DataFrame.mp4

02:05

00125 Inner Joins Part 1.mp4

09:20

00126 Inner Joins Part 2.mp4

09:03

00127 Outer Joins.mp4

12:25

00128 Left Joins.mp4

09:21

00129 The left on and right on Parameters.mp4

08:56

00130 Merging by Indexes with the left index and right index Parameters.mp4

11:04

00131 The .join Method.mp4

03:17

00132 The pd.merge Method.mp4

03:08

00133 Introduction to the Working with Dates and Times Module.mp4

04:18

00134 Review of Python s Datetime Module.mp4

09:33

00135 The Pandas Timestamp Object.mp4

07:17

00136 The Pandas DateTimeIndex Object.mp4

05:25

00137 The pd.to datetime Method.mp4

11:13

00138 Create Range of Dates with the pd.date range Method Part 1.mp4

10:24

00139 Create Range of Dates with the pd.date range Method Part 2.mp4

09:06

00140 Create Range of Dates with the pd.date range Method Part 3.mp4

07:52

00141 The .dt Accessor.mp4

07:31

00142 Install Pandas-datareader Library.mp4

03:34

00143 Import Financial Dataset with pandas datareader Library.mp4

07:58

00144 Selecting Rows from a DataFrame with a DateTimeIndex.mp4

12:26

00145 Timestamp Object Attributes and Methods.mp4

09:43

00146 The pd.DateOffset Object.mp4

06:52

00147 Timeseries Offsets.mp4

12:33

00148 The Timedelta Object.mp4

08:24

00149 Timedeltas in a Dataset.mp4

09:32

00150 Introduction to the Input and Output Section.mp4

01:23

00151 Pass a URL to the pd.read csv Method.mp4

04:05

00152 Quick Object Conversions.mp4

07:04

00153 Export CSV File with the to csv Method.mp4

05:29

00154 Install xlrd and openpyxl Libraries to Read and Write Excel Files.mp4

04:05

00155 Import Excel File into Pandas with the read excel Method.mp4

09:24

00156 Export Excel File with the to excel Method.mp4

07:48

00157 Introduction to the Visualization Section.mp4

04:50

00158 Use the plot Method to Render a Line Chart.mp4

07:57

00159 Modifying Plot Aesthetics with matplotlib Templates.mp4

04:47

00160 Creating Bar Graphs to Show Counts.mp4

05:59

00161 Creating Pie Charts to Represent Proportions.mp4

04:51

00162 Introduction to the Options and Settings Module.mp4

01:44

00163 Changing Pandas Options with Attributes and Dot Syntax.mp4

06:59

00164 Changing Pandas Options with Methods.mp4

06:16

00165 The precision Option.mp4

03:12

00166 Conclusion.mp4

01:40

Data-Analysis-with-Pandas-and-Python-master.zip

Description

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 datasets—analyzing, organizing, sorting, filtering, pivoting, aggregating, munging, cleaning, calculating, and more! Hence, we call it “Excel on steroids”!

Over the course of more than 19 hours, we will go step-by-step through Pandas, from installation to visualization! We will cover hundreds of different methods, attributes, features, and functionalities packed away inside this awesome library. We will dive into tons of different datasets, short and long, broken, and pristine, to demonstrate the incredible versatility and efficiency of this package.

This course is bundled with dozens of datasets for you to use and improve your skills. Dive right in and follow along with the lessons to see how easy it is to get started with Pandas!

By the end of this course, you will be able to gain deeper insights into your data that would be impractical in Excel but is now possible with Pandas.

All resources for this course are available at https://github.com/PacktPublishing/Data-Analysis-with-Pandas-and-Python

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Python

Data Analysis

Pandas

Boris Paskhaver

Instructor's Courses

Boris Paskhaver is a NYC-based web developer and software engineer with experience in building apps in React/Redux and Ruby on Rails. Raised in New Jersey, he graduated from the Stern School of Business at New York University in 2013 with a double major in business economics and marketing. Since graduation, his work has taken him in a wide variety of directions—he spent years in marketing, then financial services, and now the tech industry. He has worked everywhere, from a 50-person digital agency to an international tech powerhouse with thousands of employees. He always had a love of learning but struggled with the traditional resources available for education. His goal is to create comprehensive courses that break down complex details into small, digestible pieces.

PacktPub

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