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Complete Python for Data Analysis

Video Introducing this tutorial
1. Section 1: Introduction
2. Application Download & installation
3. Install Python on Windows
4. Installing Python on Macbook
5. Python Files
6. Section 2 - Data Types & Variables
7. What is Python
8. Data Types
9. Variables
10. Section 3 : Operators & Numbers
11. Operators
12. Numbers in Python
13. String Data Type
14. Section 4: Using Strings in Python
15. String Methods
16. String Operators
17. Section 5: Slicing, Format Function & Casting
18. Slice
19. Format Function
20. Casting
21. Bill Payment System - Project
22. Section 6: List Data Structure
23. List - Data Structure
24. List Methods
25. Section 7: Control Flow
26. IF Statement
27. Guessing Game Project - Part 1
28. While Loops
29. Guessing Game - Part 2
30. For Loops
31. Break and Continue Statement
32. Section 8: Tuple Data Structure
33. Tuples - Data Structure
34. Section 9: Dictionary Data Structure
35. Dictionary
36. Dictionary Methods
37. Create List inside a Dictionary
38. Concert Ticket Project
39. Section 10: Functions
40. Python Built-In Functions
41. User Defined Functions
42. Variable Scope
43. Unpack Data - ARGS
44. Unpack Dictionaries - KWARGS
45. Section 11: Series
46. Introduction to Series
47. Create Series from List
48. Create Series from Tuple
49. Create Series from Dictionary
50. Create Series from CSV Dataset
51. Head & Tail Method
52. Count & describe Method
53. sort_values ( )
54. Inplace Parameter
55. sort_index ( )
56. Retrieve Records by Index Position
57. Retrieve Records by Index Label
58. Retrieve Records - get ( )
59. Use Attributes on Series
60. Section 12: Dataframe I
61. Introduction to Dataframe
62. Create Dataframe from List
63. Create Dataframe from Dictionary of List
64. Create Dataframe from Imported File
65. Retrieve Single Column
66. Retrieve Multiple Columns
67. Add New Column
68. Delete Column(s)
69. Find Sum of Null Values
70. Drop Rows with Missing Values
71. Replace Missing Values - fillna ( )
72. Broadcasting Operation
73. Count Unique Occurrences - value_count ( )
74. Sort Values - sort_values ( )
75. Sort Dataframe by Index - sort_index ( )
76. Remove & Replace Missing Values
77. Change Data Type - astype ( )
78. Section 13: Dataframe II
79. Optimizing Dataset
80. Refine Dataset By a Condition
81. Refine Dataset By Multiple Conditions - AND Condition
82. Select Specific Columns after Condition
83. Refine Dataframe Using Multiple Conditions - OR Condition
84. Retrieve Rows Having Specific Values - isin ( )
85. Return Null & Not Null Values - isnull ( ) & notnull ( )
86. Return Values within Range - between ( )
87. Retrieve Duplicate Records - duplicated ( )
88. Delete Duplicate Records - drop_duplicates ( )
89. unique ( ) & nunique ( )
90. Section 14: Dataframe III
91. Optimize Dataset
92. set_index ( ) & reset_index ( )
93. Retrieve Rows by Index Label .loc [ ] Accessor
94. Retrieve Rows by Index Position .iloc [ ] Accessor
95. Return Specific Index Label Values
96. Change Values in a Cell
97. Change Values of Unique Groups
98. Change Label or Column Name - rename ( )
99. Delete Rows or Columns - drop ( )
100. Retrieve Random Sample from Dataframe
101. Retrieve Smallest or Largest Values
102. Rank Values - rank ( )
103. Create a Copy of Dataset
104. Section 15: Manipulating Text Data
105. Optimizing Text Data
106. Change Case - upper ( ), lower ( ), title ( ), capitalize ( )
107. Remove White Spaces - lstrip ( ), rstrip ( ) , strip ( )
108. Replace Characters in a Column
109. Filter Dataframe for Specific Values - contains ( )
110. Split String Columns I
111. Split String Columns II
112. Section 16: Multi_Index in Dataframe
113. Create Multi Index
114. Sort Multi-Index
115. Retrieve Records from Multi-Index Dataframe
116. stack ( ) & unstack ( )
117. Aggregate Values using pivot_table ( )
118. Section 17: Groupby Object
119. Groupby Object I
120. Groupby Object II
121. get_group ( )
122. Group by Multiple Columns
123. Pass different Operation - agg ( )
124. For Loop & Groupby Object
125. Section 18: Data Relationship
126. What is Data Relationship
127. Data Normalization
128. Introduction to Join
129. Inner Join I
130. Inner Join II
131. Left Join
132. Right Join
133. Outer Join
134. Merge More than 2 Dataframes
135. Many to Many Data Relationship
136. left_on ( ) & right_on ( )
137. Combine Dataframes - pd.concat ( )
138. Section 19: Date & Time
139. Working with Date & Time
140. Pandas Timestamp Object
141. to_datetime ( )
142. pd.date_range ( ) I
143. pd.date_range ( ) II
144. dt.accessor
145. Format Datetime Objects - dt.strftime ( ) I
146. dt.strftime ( ) II
147. Section 20: Import & Export Dataset
148. Import Dataset from URL
149. Export Dataset as Files
150. Section 21: Conclusion
151. Next Steps