FreeCourseSite com Udemy - Data Analysis with Pandas and Python

1. 1. Installation and Setup/1. Introduction to the Course.mp434MB
2. 1. Installation and Setup/10. Windows - Access the Command Prompt and Update Anaconda Libraries.mp419.06MB
3. 1. Installation and Setup/11. Windows - Unpack Course Materials + The Startdown and Shutdown Process.mp415.47MB
4. 1. Installation and Setup/12. Intro to the Jupyter Notebook Interface.mp49.31MB
5. 1. Installation and Setup/13. Cell Types and Cell Modes.mp411.68MB
6. 1. Installation and Setup/14. Code Cell Execution.mp48.22MB
7. 1. Installation and Setup/15. Popular Keyboard Shortcuts.mp46.26MB
8. 1. Installation and Setup/16. Import Libraries into Jupyter Notebook.mp411.54MB
9. 1. Installation and Setup/17. Python Crash Course, Part 1 - Data Types and Variables.mp411.98MB
10. 1. Installation and Setup/18. Python Crash Course, Part 2 - Lists.mp49MB
11. 1. Installation and Setup/19. Python Crash Course, Part 3 - Dictionaries.mp47.2MB
12. 1. Installation and Setup/20. Python Crash Course, Part 4 - Operators.mp47.88MB
13. 1. Installation and Setup/21. Python Crash Course, Part 5 - Functions.mp410.12MB
14. 1. Installation and Setup/3. Mac OS - Download the Anaconda Distribution.mp47.93MB
15. 1. Installation and Setup/4. Mac OS - Install Anaconda Distribution.mp418.07MB
16. 1. Installation and Setup/5. Mac OS - Access the Terminal.mp46.35MB
17. 1. Installation and Setup/6. Mac OS - Update Anaconda Libraries.mp435.27MB
18. 1. Installation and Setup/7. Mac OS - Unpack Course Materials + The Startdown and Shutdown Process.mp422.15MB
19. 1. Installation and Setup/8. Windows - Download the Anaconda Distribution.mp47.65MB
20. 1. Installation and Setup/9. Windows - Install Anaconda Distribution.mp415.2MB
21. 10. Working with Dates and Times/1. Intro to the Working with Dates and Times Module.mp46.33MB
22. 10. Working with Dates and Times/10. Install pandas-datareader Library.mp45.9MB
23. 10. Working with Dates and Times/11. Import Financial Data Set with pandas_datareader Library.mp425.48MB
24. 10. Working with Dates and Times/12. Selecting Rows from a DataFrame with a DateTimeIndex.mp418.34MB
25. 10. Working with Dates and Times/13. Timestamp Object Attributes.mp419.57MB
26. 10. Working with Dates and Times/14. The .truncate() Method.mp49.04MB
27. 10. Working with Dates and Times/15. pd.DateOffset Objects.mp425.58MB
28. 10. Working with Dates and Times/16. More Fun with pd.DateOffset Objects.mp431.91MB
29. 10. Working with Dates and Times/17. The pandas Timedelta Object.mp415.41MB
30. 10. Working with Dates and Times/18. Timedeltas in a Dataset.mp419.55MB
31. 10. Working with Dates and Times/2. Review of Python's datetime Module.mp416.74MB
32. 10. Working with Dates and Times/3. The pandas Timestamp Object.mp412.81MB
33. 10. Working with Dates and Times/4. The pandas DateTimeIndex Object.mp49.65MB
34. 10. Working with Dates and Times/5. The pd.to_datetime() Method.mp422.88MB
35. 10. Working with Dates and Times/6. Create Range of Dates with the pd.date_range() Method, Part 1.mp419.68MB
36. 10. Working with Dates and Times/7. Create Range of Dates with the pd.date_range() Method, Part 2.mp418.54MB
37. 10. Working with Dates and Times/8. Create Range of Dates with the pd.date_range() Method, Part 3.mp416.33MB
38. 10. Working with Dates and Times/9. The .dt Accessor.mp413.69MB
39. 11. Panels/1. Intro to the Module + Fetch Panel Dataset from Google Finance.mp413.68MB
40. 11. Panels/10. The .swapaxes() Method.mp49.71MB
41. 11. Panels/2. The Axes of a Panel Object.mp416.31MB
42. 11. Panels/3. Panel Attributes.mp410.5MB
43. 11. Panels/4. Use Bracket Notation to Extract a DataFrame from a Panel.mp48.25MB
44. 11. Panels/5. Extracting with the .loc, .iloc, and .ix Methods.mp413.54MB
45. 11. Panels/6. Convert Panel to a MultiIndex DataFrame (and Vice Versa).mp48.69MB
46. 11. Panels/7. The .major_xs() Method.mp412.12MB
47. 11. Panels/8. The .minor_xs() Method.mp413.62MB
48. 11. Panels/9. Transpose a Panel with the .transpose() Method.mp415.73MB
49. 12. Input and Output/1. Intro to the Input and Output Module.mp42.81MB
50. 12. Input and Output/2. Feed pd.read_csv() Method a URL Argument.mp47.61MB
51. 12. Input and Output/3. Quick Object Conversions.mp411.36MB
52. 12. Input and Output/4. Export DataFrame to CSV File with the .to_csv() Method.mp410.67MB
53. 12. Input and Output/5. Install xlrd and openpyxl Libraries to Read and Write Excel Files.mp45.98MB
54. 12. Input and Output/6. Import Excel File into pandas.mp419.13MB
55. 12. Input and Output/7. Export Excel File.mp417.8MB
56. 13. Visualization/1. Intro to Visualization Module.mp47.31MB
57. 13. Visualization/2. The .plot() Method.mp418.97MB
58. 13. Visualization/3. Modifying Aesthetics with Templates.mp412.09MB
59. 13. Visualization/4. Bar Graphs.mp412.27MB
60. 13. Visualization/5. Pie Charts.mp49.87MB
61. 13. Visualization/6. Histograms.mp412.17MB
62. 14. Options and Settings/1. Introduction to the Options and Settings Module.mp43.33MB
63. 14. Options and Settings/2. Changing pandas Options with Attributes and Dot Syntax.mp419.83MB
64. 14. Options and Settings/3. Changing pandas Options with Methods.mp413.92MB
65. 14. Options and Settings/4. The precision Option.mp46.1MB
66. 15. Conclusion/1. Conclusion.mp42.95MB
67. 2. Series/1. Create Jupyter Notebook for the Series Module.mp43.81MB
68. 2. Series/10. More Series Attributes.mp411.66MB
69. 2. Series/11. The .sort_values() Method.mp410.83MB
70. 2. Series/12. The inplace Parameter.mp49.39MB
71. 2. Series/13. The .sort_index() Method.mp48.58MB
72. 2. Series/14. Python's in Keyword.mp47.3MB
73. 2. Series/15. Extract Series Values by Index Position.mp48.9MB
74. 2. Series/16. Extract Series Values by Index Label.mp413.74MB
75. 2. Series/17. The .get() Method on a Series.mp49.56MB
76. 2. Series/18. Math Methods on Series Objects.mp410.16MB
77. 2. Series/19. The .idxmax() and .idxmin() Methods.mp45.75MB
78. 2. Series/2. Create A Series Object from a Python List.mp418.12MB
79. 2. Series/20. The .value_counts() Method.mp46.73MB
80. 2. Series/21. The .apply() Method.mp412.32MB
81. 2. Series/22. The .map() Method.mp413.09MB
82. 2. Series/3. Create A Series Object from a Python Dictionary.mp45.19MB
83. 2. Series/4. Intro to Attributes.mp412.86MB
84. 2. Series/5. Intro to Methods.mp47.91MB
85. 2. Series/6. Parameters and Arguments.mp418.28MB
86. 2. Series/7. Import Series with the .read_csv() Method.mp421.14MB
87. 2. Series/8. The .head() and .tail() Methods.mp46.47MB
88. 2. Series/9. Python Built-In Functions.mp49.87MB
89. 3. DataFrames I/1. Intro to DataFrames I Module.mp417.63MB
90. 3. DataFrames I/10. Fill in Null Values with the .fillna() Method.mp410.75MB
91. 3. DataFrames I/11. The .astype() Method.mp423.86MB
92. 3. DataFrames I/12. Sort a DataFrame with the .sort_values() Method, Part I.mp413.28MB
93. 3. DataFrames I/13. Sort a DataFrame with the .sort_values() Method, Part II.mp48.83MB
94. 3. DataFrames I/14. Sort DataFrame with the .sort_index() Method.mp46.56MB
95. 3. DataFrames I/15. Rank Values with the .rank() Method.mp413.16MB
96. 3. DataFrames I/2. Shared Methods and Attributes between Series and DataFrames.mp415.62MB
97. 3. DataFrames I/3. Differences between Shared Methods.mp413.1MB
98. 3. DataFrames I/4. Select One Column from a DataFrame.mp414.87MB
99. 3. DataFrames I/5. Select Two or More Columns from a DataFrame.mp49.94MB
100. 3. DataFrames I/6. Add New Column to DataFrame.mp417.23MB
101. 3. DataFrames I/7. Broadcasting Operations.mp418.23MB
102. 3. DataFrames I/8. A Review of the .value_counts() Method.mp48.42MB
103. 3. DataFrames I/9. Drop Rows with Null Values.mp419.2MB
104. 4. DataFrames II/1. This Module's Dataset + Memory Optimization.mp424.44MB
105. 4. DataFrames II/10. The .unique() and .nunique() Methods.mp48.19MB
106. 4. DataFrames II/2. Filter a DataFrame Based on A Condition.mp427.4MB
107. 4. DataFrames II/3. Filter with More than One Condition (AND - &).mp49.29MB
108. 4. DataFrames II/4. Filter with More than One Condition (OR - ).mp416.75MB
109. 4. DataFrames II/5. The .isin() Method.mp412.54MB
110. 4. DataFrames II/6. The .isnull() and .notnull() Methods.mp412.26MB
111. 4. DataFrames II/7. The .between() Method.mp416.76MB
112. 4. DataFrames II/8. The .duplicated() Method.mp419.56MB
113. 4. DataFrames II/9. The .drop_duplicates() Method.mp417.55MB
114. 5. DataFrames III/1. Intro to the DataFrames III Module + Import Dataset.mp47.67MB
115. 5. DataFrames III/10. Delete Rows or Columns from a DataFrame.mp416.21MB
116. 5. DataFrames III/11. Create Random Sample with the .sample() Method.mp49.33MB
117. 5. DataFrames III/12. The .nsmallest() and .nlargest() Methods.mp412.07MB
118. 5. DataFrames III/13. Filtering with the .where() Method.mp413.56MB
119. 5. DataFrames III/14. The .query() Method.mp419.93MB
120. 5. DataFrames III/15. A Review of the .apply() Method on Single Columns.mp411.75MB
121. 5. DataFrames III/16. The .apply() Method with Row Values.mp413.41MB
122. 5. DataFrames III/17. The .copy() Method.mp415.45MB
123. 5. DataFrames III/2. The .set_index() and .reset_index() Methods.mp413.19MB
124. 5. DataFrames III/3. Retrieve Rows by Index Label with .loc[].mp425.87MB
125. 5. DataFrames III/4. Retrieve Rows by Index Position with .iloc[].mp413.31MB
126. 5. DataFrames III/5. The Catch-All .ix[] Method.mp418.56MB
127. 5. DataFrames III/6. Second Arguments to .loc[], .iloc[], and .ix[] Methods.mp412.36MB
128. 5. DataFrames III/7. Set New Values for a Specific Cell or Row.mp48.89MB
129. 5. DataFrames III/8. Set Multiple Values in DataFrame.mp420.54MB
130. 5. DataFrames III/9. Rename Index Labels or Columns in a DataFrame.mp413.4MB
131. 6. Working with Text Data/1. Intro to the Working with Text Data Module.mp413.87MB
132. 6. Working with Text Data/2. Common String Methods - lower, upper, title, and len.mp414.88MB
133. 6. Working with Text Data/3. The .str.replace() Method.mp416MB
134. 6. Working with Text Data/4. Filtering with String Methods.mp415.54MB
135. 6. Working with Text Data/5. More String Methods - strip, lstrip, and rstrip.mp49.54MB
136. 6. Working with Text Data/6. String Methods on Index and Columns.mp411.13MB
137. 6. Working with Text Data/7. Split Strings by Characters with .str.split() Method.mp417.52MB
138. 6. Working with Text Data/8. More Practice with Splits.mp411.93MB
139. 6. Working with Text Data/9. The expand and n Parameters of the .str.split() Method.mp415.3MB
140. 7. MultiIndex/1. Intro to the MultiIndex Module.mp48.33MB
141. 7. MultiIndex/10. The .unstack() Method, Part 1.mp48.48MB
142. 7. MultiIndex/11. The .unstack() Method, Part 2.mp414.53MB
143. 7. MultiIndex/12. The .unstack() Method, Part 3.mp411.97MB
144. 7. MultiIndex/13. The .pivot() Method.mp412.11MB
145. 7. MultiIndex/14. The .pivot_table() Method.mp422.17MB
146. 7. MultiIndex/15. The pd.melt() Method.mp417.26MB
147. 7. MultiIndex/2. Create a MultiIndex with the set_index() Method.mp421.05MB
148. 7. MultiIndex/3. The .get_level_values() Method.mp416.55MB
149. 7. MultiIndex/4. The .set_names() Method.mp46.09MB
150. 7. MultiIndex/5. The sort_index() Method.mp410.25MB
151. 7. MultiIndex/6. Extract Rows from a MultiIndex DataFrame.mp417.34MB
152. 7. MultiIndex/7. The .transpose() Method and MultiIndex on Column Level.mp411.92MB
153. 7. MultiIndex/8. The .swaplevel() Method.mp45.18MB
154. 7. MultiIndex/9. The .stack() Method.mp413.19MB
155. 8. GroupBy/1. Intro to the Groupby Module.mp414.29MB
156. 8. GroupBy/2. First Operations with groupby Object.mp423.08MB
157. 8. GroupBy/3. Retrieve A Group with the .get_group() Method.mp410.14MB
158. 8. GroupBy/4. Methods on the Groupby Object and DataFrame Columns.mp420.49MB
159. 8. GroupBy/5. Grouping by Multiple Columns.mp410.35MB
160. 8. GroupBy/6. The .agg() Method.mp413.18MB
161. 8. GroupBy/7. Iterating through Groups.mp421.37MB
162. 9. Merging, Joining, and Concatenating/1. Intro to the Merging, Joining, and Concatenating Module.mp411.46MB
163. 9. Merging, Joining, and Concatenating/10. Merging by Indexes with the left_index and right_index Parameters.mp422.7MB
164. 9. Merging, Joining, and Concatenating/11. The .join() Method.mp46.27MB
165. 9. Merging, Joining, and Concatenating/12. The pd.merge() Method.mp46.85MB
166. 9. Merging, Joining, and Concatenating/2. The pd.concat() Method, Part 1.mp412.56MB
167. 9. Merging, Joining, and Concatenating/3. The pd.concat() Method, Part 2.mp413.21MB
168. 9. Merging, Joining, and Concatenating/4. The .append() Method on a DataFrame.mp45.13MB
169. 9. Merging, Joining, and Concatenating/5. Inner Joins, Part 1.mp417.92MB
170. 9. Merging, Joining, and Concatenating/6. Inner Joins, Part 2.mp416.61MB
171. 9. Merging, Joining, and Concatenating/7. Outer Joins.mp425.94MB
172. 9. Merging, Joining, and Concatenating/8. Left Joins.mp421MB
173. 9. Merging, Joining, and Concatenating/9. The left_on and right_on Parameters.mp420.24MB