FreeCourseSite com Udemy - The Ultimate Pandas Bootcamp Advanced Python Data Analysis

1. 1. Introduction/1. Course Structure.mp414.06MB
2. 1. Introduction/2. Pandas Is Not Single.mp417.81MB
3. 1. Introduction/3. Anaconda.mp420.71MB
4. 1. Introduction/4. Jupyter Notebooks.mp448.02MB
5. 1. Introduction/5. Cloud vs Local.mp426.53MB
6. 1. Introduction/6. Hello, Python.mp432.79MB
7. 1. Introduction/7. NumPy.mp462.19MB
8. 10. Handling Date And Time/1. Section Intro.mp422.33MB
9. 10. Handling Date And Time/10. A Cool Shorcut read_csv() With parse_dates.mp417.61MB
10. 10. Handling Date And Time/11. Indexing Dates.mp426.63MB
11. 10. Handling Date And Time/12. Skill Challenge.mp43.79MB
12. 10. Handling Date And Time/13. Solution.mp417.1MB
13. 10. Handling Date And Time/14. DateTimeIndex Attribute Accessors.mp438.15MB
14. 10. Handling Date And Time/15. Creating Date Ranges.mp436.53MB
15. 10. Handling Date And Time/16. Shifting Dates With pd.DateOffset.mp436.22MB
16. 10. Handling Date And Time/17. BONUS Timedeltas And Absolute Time.mp428.36MB
17. 10. Handling Date And Time/18. Resampling Timeseries.mp438.53MB
18. 10. Handling Date And Time/19. Upsampling And Interpolation.mp449.4MB
19. 10. Handling Date And Time/2. The Python datetime Module.mp440.29MB
20. 10. Handling Date And Time/20. What About asfreq().mp436.61MB
21. 10. Handling Date And Time/21. BONUS Rolling Windows.mp443.49MB
22. 10. Handling Date And Time/22. Skill Challenge.mp44.65MB
23. 10. Handling Date And Time/23. Solution.mp422.9MB
24. 10. Handling Date And Time/3. Parsing Dates From Text.mp452.82MB
25. 10. Handling Date And Time/4. Even Better dateutil.mp423.85MB
26. 10. Handling Date And Time/5. From Datetime To String.mp422.37MB
27. 10. Handling Date And Time/6. Performant Datetimes With Numpy.mp435.33MB
28. 10. Handling Date And Time/7. The Pandas Timestamp.mp424.04MB
29. 10. Handling Date And Time/8. Our Dataset Brent Prices.mp429.43MB
30. 10. Handling Date And Time/9. Date Parsing And DatetimeIndex.mp424.53MB
31. 11. Regex And Text Manipulation/1. Section Intro.mp416.68MB
32. 11. Regex And Text Manipulation/10. Skill Challenge.mp43.23MB
33. 11. Regex And Text Manipulation/11. Solution.mp421.97MB
34. 11. Regex And Text Manipulation/12. Slicing Substrings.mp424.19MB
35. 11. Regex And Text Manipulation/13. Masking With String Methods.mp436.91MB
36. 11. Regex And Text Manipulation/14. BONUS Parsing Indicators With get_dummies().mp466.3MB
37. 11. Regex And Text Manipulation/15. Text Replacement.mp441.78MB
38. 11. Regex And Text Manipulation/16. Introduction To Regular Expressions.mp475.02MB
39. 11. Regex And Text Manipulation/17. More Regex Concepts.mp465.17MB
40. 11. Regex And Text Manipulation/18. How To Approach Regex.mp463.52MB
41. 11. Regex And Text Manipulation/19. Is This A Valid Email.mp480.08MB
42. 11. Regex And Text Manipulation/2. Our Data Boston Marathon Runners.mp423.57MB
43. 11. Regex And Text Manipulation/20. BONUS What's The Point Of re.compile().mp418.31MB
44. 11. Regex And Text Manipulation/21. Pandas str contains(), split() And replace() With Regex.mp476.29MB
45. 11. Regex And Text Manipulation/22. Skill Challenge.mp45.42MB
46. 11. Regex And Text Manipulation/23. Solution.mp472.37MB
47. 11. Regex And Text Manipulation/3. String Methods In Python.mp428.77MB
48. 11. Regex And Text Manipulation/4. Vectorized String Operations In Pandas.mp418.43MB
49. 11. Regex And Text Manipulation/5. Case Operations.mp414.03MB
50. 11. Regex And Text Manipulation/6. Finding Characters And Words.mp425.73MB
51. 11. Regex And Text Manipulation/7. Strips And Whitespace.mp431.73MB
52. 11. Regex And Text Manipulation/8. String Splitting And Concatenation.mp446.35MB
53. 11. Regex And Text Manipulation/9. More Split Parameters.mp440.08MB
54. 12. Visualizing Data/1. Section Intro.mp410.33MB
55. 12. Visualizing Data/10. BONUS Data Ink And Chartjunk.mp432.34MB
56. 12. Visualizing Data/11. Skill Challenge.mp47.52MB
57. 12. Visualizing Data/12. Solution.mp454.25MB
58. 12. Visualizing Data/2. The Art Of Data Visualization.mp413.01MB
59. 12. Visualizing Data/3. The Preliminaries Of matplotlib.mp462.88MB
60. 12. Visualizing Data/4. Line Graphs.mp454.18MB
61. 12. Visualizing Data/5. Bar Charts.mp450.14MB
62. 12. Visualizing Data/6. Pie Plots.mp454.89MB
63. 12. Visualizing Data/7. Histograms.mp445.78MB
64. 12. Visualizing Data/8. Scatter Plots.mp463.39MB
65. 12. Visualizing Data/9. Other Visualization Options.mp463.65MB
66. 13. Data Formats And IO/1. Section Intro.mp45.21MB
67. 13. Data Formats And IO/10. Solution.mp445.82MB
68. 13. Data Formats And IO/2. Reading JSON.mp419.74MB
69. 13. Data Formats And IO/3. Reading HTML.mp4103.72MB
70. 13. Data Formats And IO/4. Reading Excel.mp455.72MB
71. 13. Data Formats And IO/5. Creating Output The to_ Family Of Methods.mp474.01MB
72. 13. Data Formats And IO/6. BONUS Introduction To Pickling.mp431.71MB
73. 13. Data Formats And IO/7. Pickles In Pandas.mp422.93MB
74. 13. Data Formats And IO/8. The Many Other Formats.mp427.91MB
75. 13. Data Formats And IO/9. Skill Challenge.mp411.71MB
76. 14. Appendix A - Rapid-Fire Python Fundamentals/1. Section Intro.mp48.88MB
77. 14. Appendix A - Rapid-Fire Python Fundamentals/10. Lists vs. Strings.mp427.56MB
78. 14. Appendix A - Rapid-Fire Python Fundamentals/11. List Methods And Functions.mp432.99MB
79. 14. Appendix A - Rapid-Fire Python Fundamentals/12. Containers II Tuples.mp420.03MB
80. 14. Appendix A - Rapid-Fire Python Fundamentals/13. Containers III Sets.mp452.97MB
81. 14. Appendix A - Rapid-Fire Python Fundamentals/14. Containers IV Dictionaries.mp422.74MB
82. 14. Appendix A - Rapid-Fire Python Fundamentals/15. Dictionary Keys And Values.mp436.32MB
83. 14. Appendix A - Rapid-Fire Python Fundamentals/16. Membership Operators.mp419.28MB
84. 14. Appendix A - Rapid-Fire Python Fundamentals/17. Controlling Flow if, else, And elif.mp441.66MB
85. 14. Appendix A - Rapid-Fire Python Fundamentals/18. Truth Value Of Non-booleans.mp415.92MB
86. 14. Appendix A - Rapid-Fire Python Fundamentals/19. For Loops.mp420.57MB
87. 14. Appendix A - Rapid-Fire Python Fundamentals/2. Data Types.mp410.16MB
88. 14. Appendix A - Rapid-Fire Python Fundamentals/20. The range() Immutable Sequence.mp423.72MB
89. 14. Appendix A - Rapid-Fire Python Fundamentals/21. While Loops.mp429.23MB
90. 14. Appendix A - Rapid-Fire Python Fundamentals/22. Break And Continue.mp419.14MB
91. 14. Appendix A - Rapid-Fire Python Fundamentals/23. Zipping Iterables.mp417.19MB
92. 14. Appendix A - Rapid-Fire Python Fundamentals/24. List Comprehensions.mp431.78MB
93. 14. Appendix A - Rapid-Fire Python Fundamentals/25. Defining Functions.mp457.77MB
94. 14. Appendix A - Rapid-Fire Python Fundamentals/26. Function Arguments Positional vs Keyword.mp430.44MB
95. 14. Appendix A - Rapid-Fire Python Fundamentals/27. Lambdas.mp423.21MB
96. 14. Appendix A - Rapid-Fire Python Fundamentals/28. Importing Modules.mp434.15MB
97. 14. Appendix A - Rapid-Fire Python Fundamentals/3. Variables.mp439.13MB
98. 14. Appendix A - Rapid-Fire Python Fundamentals/4. Arithmetic And Augmented Assignment Operators.mp427.46MB
99. 14. Appendix A - Rapid-Fire Python Fundamentals/5. Ints And Floats.mp442.8MB
100. 14. Appendix A - Rapid-Fire Python Fundamentals/6. Booleans And Comparison Operators.mp421.88MB
101. 14. Appendix A - Rapid-Fire Python Fundamentals/7. Strings.mp432.13MB
102. 14. Appendix A - Rapid-Fire Python Fundamentals/8. Methods.mp425.33MB
103. 14. Appendix A - Rapid-Fire Python Fundamentals/9. Containers I Lists.mp429.49MB
104. 15. Appendix B - Going Local Installation And Setup/1. Installing Anaconda And Python - Windows.mp471.34MB
105. 15. Appendix B - Going Local Installation And Setup/2. Installing Anaconda And Python - Mac.mp417.14MB
106. 15. Appendix B - Going Local Installation And Setup/3. Installing Anaconda And Python - Linux.mp430.95MB
107. 2. Series At A Glance/1. Section Intro.mp46.93MB
108. 2. Series At A Glance/10. Solution.mp422.9MB
109. 2. Series At A Glance/11. Another Solution.mp411.24MB
110. 2. Series At A Glance/12. The head() And tail() Methods.mp422.98MB
111. 2. Series At A Glance/13. Extracting By Index Position.mp429.06MB
112. 2. Series At A Glance/14. Accessing Elements By Label.mp427.06MB
113. 2. Series At A Glance/15. BONUS The add_prefix() And add_suffix() Methods.mp416.49MB
114. 2. Series At A Glance/16. Using Dot Notation.mp413.25MB
115. 2. Series At A Glance/17. Boolean Masks And The .loc Indexer.mp429.47MB
116. 2. Series At A Glance/18. Extracting By Position With .iloc.mp411.61MB
117. 2. Series At A Glance/19. BONUS Using Callables With .loc And .iloc.mp437.14MB
118. 2. Series At A Glance/2. What Is A Series.mp412.54MB
119. 2. Series At A Glance/20. Selecting With .get().mp430.55MB
120. 2. Series At A Glance/21. Selection Recap.mp428.19MB
121. 2. Series At A Glance/22. Skill Challenge.mp46.38MB
122. 2. Series At A Glance/23. Solution.mp423.39MB
123. 2. Series At A Glance/3. Parameters vs Arguments.mp48.07MB
124. 2. Series At A Glance/4. What’s In The Data.mp420.41MB
125. 2. Series At A Glance/5. The .dtype Attribute.mp46.37MB
126. 2. Series At A Glance/6. BONUS What Is dtype('o'), Really.mp410.1MB
127. 2. Series At A Glance/7. Index And RangeIndex.mp433.16MB
128. 2. Series At A Glance/8. Series And Index Names.mp419.12MB
129. 2. Series At A Glance/9. Skill Challenge.mp47.71MB
130. 3. Series Methods And Handling/1. Section Intro.mp412.93MB
131. 3. Series Methods And Handling/10. Skill Challenge.mp44.05MB
132. 3. Series Methods And Handling/11. Solution.mp413.45MB
133. 3. Series Methods And Handling/12. Dropping And Filling NAs.mp421.52MB
134. 3. Series Methods And Handling/13. Descriptive Statistics.mp433.67MB
135. 3. Series Methods And Handling/14. The describe() Method.mp49.7MB
136. 3. Series Methods And Handling/15. mode() And value_counts().mp431.73MB
137. 3. Series Methods And Handling/16. idxmax() And idxmin().mp422MB
138. 3. Series Methods And Handling/17. Sorting With sort_values().mp419.63MB
139. 3. Series Methods And Handling/18. nlargest() And nsmallest().mp412.17MB
140. 3. Series Methods And Handling/19. Sorting With sort_index().mp415.3MB
141. 3. Series Methods And Handling/2. Reading In Data With read_csv().mp452.81MB
142. 3. Series Methods And Handling/20. Skill Challenge.mp43.18MB
143. 3. Series Methods And Handling/21. Solution.mp49.91MB
144. 3. Series Methods And Handling/22. Series Arithmetics And fill_value().mp440.2MB
145. 3. Series Methods And Handling/23. BONUS Calculating Variance And Standard Deviation.mp417.36MB
146. 3. Series Methods And Handling/24. Cumulative Operations.mp417.94MB
147. 3. Series Methods And Handling/25. Pairwise Differences With diff().mp412.79MB
148. 3. Series Methods And Handling/26. Series Iteration.mp416.07MB
149. 3. Series Methods And Handling/27. Filtering filter(), where(), And mask().mp455.05MB
150. 3. Series Methods And Handling/28. Transforming With update(), apply() And map().mp469.92MB
151. 3. Series Methods And Handling/29. Skill Challenge.mp410.2MB
152. 3. Series Methods And Handling/3. Series Sizing With .size, .shape, And len().mp423.26MB
153. 3. Series Methods And Handling/30. Solution I - Reading Data.mp414.55MB
154. 3. Series Methods And Handling/31. Solution II - Mean, Median, And Standard Deviation.mp420.47MB
155. 3. Series Methods And Handling/32. Solution III - Z-scores.mp448.2MB
156. 3. Series Methods And Handling/4. Unique Values And Series Monotonicity.mp417.8MB
157. 3. Series Methods And Handling/5. The count() Method.mp46.03MB
158. 3. Series Methods And Handling/6. Accessing And Counting NAs.mp436.79MB
159. 3. Series Methods And Handling/7. BONUS Another Approach.mp421.33MB
160. 3. Series Methods And Handling/8. The Other Side notnull() And notna().mp411.04MB
161. 3. Series Methods And Handling/9. BONUS Booleans Are Literally Numbers In Python.mp411.62MB
162. 4. Working With DataFrames/1. Section Intro.mp410.81MB
163. 4. Working With DataFrames/10. BONUS - How Are Random Numbers Generated.mp442.94MB
164. 4. Working With DataFrames/11. DataFrame Axes.mp423.31MB
165. 4. Working With DataFrames/12. Changing The Index.mp450.38MB
166. 4. Working With DataFrames/13. Extracting From DataFrames By Label.mp436.01MB
167. 4. Working With DataFrames/14. DataFrame Extraction by Position.mp446.71MB
168. 4. Working With DataFrames/15. Single Value Access With .at And .iat.mp426.34MB
169. 4. Working With DataFrames/16. BONUS - The get_loc() Method.mp425.07MB
170. 4. Working With DataFrames/17. Skill Challenge.mp44.1MB
171. 4. Working With DataFrames/18. Solution.mp445.19MB
172. 4. Working With DataFrames/19. More Cleanup Going Numeric.mp418.63MB
173. 4. Working With DataFrames/2. What Is A DataFrame.mp445.86MB
174. 4. Working With DataFrames/20. The astype() Method.mp425.17MB
175. 4. Working With DataFrames/21. DataFrame replace() + A Glimpse At Regex.mp444.28MB
176. 4. Working With DataFrames/22. Part I Collecting The Units.mp466.82MB
177. 4. Working With DataFrames/23. The rename() Method.mp427.59MB
178. 4. Working With DataFrames/24. DataFrame dropna().mp440.08MB
179. 4. Working With DataFrames/25. BONUS - dropna() With Subset.mp429.26MB
180. 4. Working With DataFrames/26. Part II Merging Units With Column Names.mp457.28MB
181. 4. Working With DataFrames/27. Part III Removing Units From Values.mp435.62MB
182. 4. Working With DataFrames/28. Filtering in 2D.mp442.35MB
183. 4. Working With DataFrames/29. DataFrame Sorting.mp449.42MB
184. 4. Working With DataFrames/3. Creating A DataFrame.mp422.42MB
185. 4. Working With DataFrames/30. Using Series between() With DataFrames.mp434.97MB
186. 4. Working With DataFrames/31. BONUS - Min, Max and Idx[MinMax], And Good Foods.mp462.98MB
187. 4. Working With DataFrames/32. DataFrame nlargest() And nsmallest().mp435.36MB
188. 4. Working With DataFrames/33. Skill Challenge.mp44.31MB
189. 4. Working With DataFrames/34. Solution.mp442.25MB
190. 4. Working With DataFrames/35. Another Skill Challenge.mp46.79MB
191. 4. Working With DataFrames/36. Solution.mp436.86MB
192. 4. Working With DataFrames/4. BONUS - Four More Ways To Build DataFrames.mp473.23MB
193. 4. Working With DataFrames/5. The info() Method.mp419.04MB
194. 4. Working With DataFrames/6. Reading In Nutrition Data.mp427.29MB
195. 4. Working With DataFrames/7. Some Cleanup Removing The Duplicated Index.mp435.62MB
196. 4. Working With DataFrames/8. The sample() Method.mp422.61MB
197. 4. Working With DataFrames/9. BONUS - Sampling With Replacement Or Weights.mp440.48MB
198. 5. DataFrames In Depth/1. Section Intro.mp421.13MB
199. 5. DataFrames In Depth/10. Solution.mp440.04MB
200. 5. DataFrames In Depth/11. 2d Indexing.mp440.02MB
201. 5. DataFrames In Depth/12. Fancy Indexing With lookup().mp446.21MB
202. 5. DataFrames In Depth/13. Sorting By Index Or Column.mp445.02MB
203. 5. DataFrames In Depth/14. Sorting vs. Reordering.mp465.24MB
204. 5. DataFrames In Depth/15. BONUS - Another Way.mp412.95MB
205. 5. DataFrames In Depth/16. 15. BONUS - Please Avoid Sorting Like This.mp417.07MB
206. 5. DataFrames In Depth/17. Skill Challenge.mp44.48MB
207. 5. DataFrames In Depth/18. Solution.mp425.76MB
208. 5. DataFrames In Depth/19. Identifying Dupes.mp460.88MB
209. 5. DataFrames In Depth/2. Introducing A New Dataset.mp418.3MB
210. 5. DataFrames In Depth/20. Removing Duplicates.mp429.82MB
211. 5. DataFrames In Depth/21. Removing DataFrame Rows.mp419.78MB
212. 5. DataFrames In Depth/22. BONUS - Removing Columns.mp416.19MB
213. 5. DataFrames In Depth/23. BONUS - Another Way pop().mp419.07MB
214. 5. DataFrames In Depth/24. BONUS - A Sophisticated Alternative.mp433.17MB
215. 5. DataFrames In Depth/25. Null Values In DataFrames.mp442.16MB
216. 5. DataFrames In Depth/26. Dropping And Filling DataFrame NAs.mp449MB
217. 5. DataFrames In Depth/27. BONUS - Methods And Axes With fillna().mp457.38MB
218. 5. DataFrames In Depth/28. Skill Challenge.mp45.3MB
219. 5. DataFrames In Depth/29. Solution.mp442.49MB
220. 5. DataFrames In Depth/3. Quick Review Indexing With Boolean Masks.mp423.33MB
221. 5. DataFrames In Depth/30. Calculating Aggregates With agg().mp437.08MB
222. 5. DataFrames In Depth/31. Same-shape Transforms.mp466.98MB
223. 5. DataFrames In Depth/32. More Flexibility With apply().mp459.38MB
224. 5. DataFrames In Depth/33. Element-wise Operations With applymap().mp468.51MB
225. 5. DataFrames In Depth/34. Skill Challenge.mp48.76MB
226. 5. DataFrames In Depth/35. Solution.mp426.47MB
227. 5. DataFrames In Depth/36. Setting DataFrame Values.mp443.55MB
228. 5. DataFrames In Depth/37. The SettingWithCopy Warning.mp439.81MB
229. 5. DataFrames In Depth/38. View vs Copy.mp449.3MB
230. 5. DataFrames In Depth/39. Adding DataFrame Columns.mp436.47MB
231. 5. DataFrames In Depth/4. More Approaches To Boolean Masking.mp468.42MB
232. 5. DataFrames In Depth/40. Adding Rows To DataFrames.mp449.9MB
233. 5. DataFrames In Depth/41. BONUS - How Are DataFrames Stored In Memory.mp421.73MB
234. 5. DataFrames In Depth/42. Skill Challenge.mp45.04MB
235. 5. DataFrames In Depth/43. Solution.mp431.94MB
236. 5. DataFrames In Depth/5. Binary Operators With Booleans.mp437.94MB
237. 5. DataFrames In Depth/6. BONUS - XOR and Complement Binary Ops.mp450.47MB
238. 5. DataFrames In Depth/7. Combining Conditions.mp445.57MB
239. 5. DataFrames In Depth/8. Conditions As Variables.mp419.9MB
240. 5. DataFrames In Depth/9. Skill Challenge.mp43.96MB
241. 6. Working With Multiple DataFrames/1. Section Intro.mp47.95MB
242. 6. Working With Multiple DataFrames/10. Skill Challenge.mp45.99MB
243. 6. Working With Multiple DataFrames/11. Solution.mp459.47MB
244. 6. Working With Multiple DataFrames/12. The merge() Method.mp435.38MB
245. 6. Working With Multiple DataFrames/13. The left_on And right_on Params.mp432.2MB
246. 6. Working With Multiple DataFrames/14. Inner vs Outer Joins.mp427.11MB
247. 6. Working With Multiple DataFrames/15. Left vs Right Joins.mp420.27MB
248. 6. Working With Multiple DataFrames/16. One-to-One and One-to-Many Joins.mp457.01MB
249. 6. Working With Multiple DataFrames/17. Many-to-Many Joins.mp455.62MB
250. 6. Working With Multiple DataFrames/18. Merging By Index.mp438.15MB
251. 6. Working With Multiple DataFrames/19. The join() Method.mp422.87MB
252. 6. Working With Multiple DataFrames/2. Introducing (Five) New Datasets.mp440.6MB
253. 6. Working With Multiple DataFrames/20. Skill Challenge.mp43.81MB
254. 6. Working With Multiple DataFrames/21. Solution.mp446.08MB
255. 6. Working With Multiple DataFrames/3. Concatenating DataFrames.mp442.12MB
256. 6. Working With Multiple DataFrames/4. The Duplicated Index Issue.mp451.32MB
257. 6. Working With Multiple DataFrames/5. Enforcing Unique Indices.mp458.39MB
258. 6. Working With Multiple DataFrames/6. BONUS - Creating Multiple Indices With concat().mp428.45MB
259. 6. Working With Multiple DataFrames/7. Column Axis Concatenation.mp427.09MB
260. 6. Working With Multiple DataFrames/8. The append() Method A Special Case Of concat().mp414.48MB
261. 6. Working With Multiple DataFrames/9. Concat On Different Columns.mp438.21MB
262. 7. Going MultiDimensional/1. Section Intro.mp426.42MB
263. 7. Going MultiDimensional/10. Skill Challenge.mp43.78MB
264. 7. Going MultiDimensional/11. Solution.mp444.8MB
265. 7. Going MultiDimensional/12. The Anatomy Of A MultiIndex Object.mp434.85MB
266. 7. Going MultiDimensional/13. Adding Another Level.mp433.59MB
267. 7. Going MultiDimensional/14. Shuffling Levels.mp424.32MB
268. 7. Going MultiDimensional/15. Removing MultiIndex Levels.mp437.7MB
269. 7. Going MultiDimensional/16. MultiIndex sort_index().mp435.62MB
270. 7. Going MultiDimensional/17. More MultiIndex Methods.mp437.92MB
271. 7. Going MultiDimensional/18. Reshaping With stack().mp430.57MB
272. 7. Going MultiDimensional/19. The Flipside unstack().mp445.95MB
273. 7. Going MultiDimensional/2. Introducing New Data.mp422.11MB
274. 7. Going MultiDimensional/20. BONUS Creating MultiLevel Columns Manually.mp458.73MB
275. 7. Going MultiDimensional/21. An Easier Way transpose().mp418.6MB
276. 7. Going MultiDimensional/22. BONUS - What About Panels.mp427.89MB
277. 7. Going MultiDimensional/23. Skill Challenge.mp48.01MB
278. 7. Going MultiDimensional/24. Solution.mp449.18MB
279. 7. Going MultiDimensional/3. Index And RangeIndex.mp426.87MB
280. 7. Going MultiDimensional/4. Creating A MultiIndex.mp420.15MB
281. 7. Going MultiDimensional/5. MultiIndex From read_csv().mp427.7MB
282. 7. Going MultiDimensional/6. Indexing Hierarchical DataFrames.mp439.39MB
283. 7. Going MultiDimensional/7. Indexing Ranges And Slices.mp459.11MB
284. 7. Going MultiDimensional/8. BONUS - Use With pd.IndexSlice!.mp416.97MB
285. 7. Going MultiDimensional/9. Cross Sections With xs().mp433.15MB
286. 8. GroupBy And Aggregates/1. Section Intro.mp417.09MB
287. 8. GroupBy And Aggregates/10. Skill Challenge.mp43.22MB
288. 8. GroupBy And Aggregates/11. Solution.mp427.59MB
289. 8. GroupBy And Aggregates/12. Iterating Through Groups.mp421.03MB
290. 8. GroupBy And Aggregates/13. Handpicking Subgroups.mp423.65MB
291. 8. GroupBy And Aggregates/14. MultiIndex Grouping.mp426.54MB
292. 8. GroupBy And Aggregates/15. Fine-tuned Aggregates.mp444.14MB
293. 8. GroupBy And Aggregates/16. Named Aggregations.mp436.49MB
294. 8. GroupBy And Aggregates/17. The filter() Method.mp426.12MB
295. 8. GroupBy And Aggregates/18. GroupBy Transformations.mp438.79MB
296. 8. GroupBy And Aggregates/19. BONUS - There's Also apply().mp441.18MB
297. 8. GroupBy And Aggregates/2. New Data Game Sales.mp414.89MB
298. 8. GroupBy And Aggregates/20. Skill Challenge.mp44.05MB
299. 8. GroupBy And Aggregates/21. Solution.mp424.51MB
300. 8. GroupBy And Aggregates/3. Simple Aggregations Review.mp429.02MB
301. 8. GroupBy And Aggregates/4. Conditional Aggregates.mp424.51MB
302. 8. GroupBy And Aggregates/5. The Split-Apply-Combine Pattern.mp422.51MB
303. 8. GroupBy And Aggregates/6. The groupby() Method.mp421.56MB
304. 8. GroupBy And Aggregates/7. The DataFrameGroupBy Object.mp419.81MB
305. 8. GroupBy And Aggregates/8. Customizing Index To Group Mappings.mp420.48MB
306. 8. GroupBy And Aggregates/9. BONUS - Series groupby().mp420.8MB
307. 9. Reshaping With Pivots/1. Section Intro.mp423.83MB
308. 9. Reshaping With Pivots/10. MultiIndex Pivot Tables.mp419.05MB
309. 9. Reshaping With Pivots/11. Applying Multiple Functions.mp418.33MB
310. 9. Reshaping With Pivots/12. Skill Challenge.mp45.48MB
311. 9. Reshaping With Pivots/13. Solution.mp436.64MB
312. 9. Reshaping With Pivots/2. New Data New York City SAT Scores.mp426.77MB
313. 9. Reshaping With Pivots/3. Pivoting Data.mp441.9MB
314. 9. Reshaping With Pivots/4. Undoing Pivots.mp427.89MB
315. 9. Reshaping With Pivots/5. What About Aggregates.mp434.25MB
316. 9. Reshaping With Pivots/6. The pivot_table().mp433.66MB
317. 9. Reshaping With Pivots/7. BONUS The Problem With Average Percentage.mp436.16MB
318. 9. Reshaping With Pivots/8. Replicating Pivot Tables With GroupBy.mp412.5MB
319. 9. Reshaping With Pivots/9. Adding Margins.mp424.59MB