种子简介

种子名称： [FreeCourseSite.com] Udemy - The Ultimate Pandas Bootcamp Advanced Python Data Analysis
文件类型： 视频
文件数目： 319个文件
文件大小： 9.62 GB
收录时间： 2022-4-25 12:36
已经下载： 3次
资源热度： 140
最近下载： 2024-5-24 10:01

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:a0c4d102befdc70c9df1368c0ee9f671ad3da227&dn=[FreeCourseSite.com] Udemy - The Ultimate Pandas Bootcamp Advanced Python Data Analysis 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

[FreeCourseSite.com] Udemy - The Ultimate Pandas Bootcamp Advanced Python Data Analysis.torrent

1. Introduction/1. Course Structure.mp414.06MB

1. Introduction/2. Pandas Is Not Single.mp417.81MB

1. Introduction/3. Anaconda.mp420.71MB

1. Introduction/4. Jupyter Notebooks.mp448.02MB

1. Introduction/5. Cloud vs Local.mp426.53MB

1. Introduction/6. Hello, Python.mp432.79MB

1. Introduction/7. NumPy.mp462.19MB

10. Handling Date And Time/1. Section Intro.mp422.33MB

10. Handling Date And Time/10. A Cool Shorcut read_csv() With parse_dates.mp417.61MB

10. Handling Date And Time/11. Indexing Dates.mp426.63MB

10. Handling Date And Time/12. Skill Challenge.mp43.79MB

10. Handling Date And Time/13. Solution.mp417.1MB

10. Handling Date And Time/14. DateTimeIndex Attribute Accessors.mp438.15MB

10. Handling Date And Time/15. Creating Date Ranges.mp436.53MB

10. Handling Date And Time/16. Shifting Dates With pd.DateOffset.mp436.22MB

10. Handling Date And Time/17. BONUS Timedeltas And Absolute Time.mp428.36MB

10. Handling Date And Time/18. Resampling Timeseries.mp438.53MB

10. Handling Date And Time/19. Upsampling And Interpolation.mp449.4MB

10. Handling Date And Time/2. The Python datetime Module.mp440.29MB

10. Handling Date And Time/20. What About asfreq().mp436.61MB

10. Handling Date And Time/21. BONUS Rolling Windows.mp443.49MB

10. Handling Date And Time/22. Skill Challenge.mp44.65MB

10. Handling Date And Time/23. Solution.mp422.9MB

10. Handling Date And Time/3. Parsing Dates From Text.mp452.82MB

10. Handling Date And Time/4. Even Better dateutil.mp423.85MB

10. Handling Date And Time/5. From Datetime To String.mp422.37MB

10. Handling Date And Time/6. Performant Datetimes With Numpy.mp435.33MB

10. Handling Date And Time/7. The Pandas Timestamp.mp424.04MB

10. Handling Date And Time/8. Our Dataset Brent Prices.mp429.43MB

10. Handling Date And Time/9. Date Parsing And DatetimeIndex.mp424.53MB

11. Regex And Text Manipulation/1. Section Intro.mp416.68MB

11. Regex And Text Manipulation/10. Skill Challenge.mp43.23MB

11. Regex And Text Manipulation/11. Solution.mp421.97MB

11. Regex And Text Manipulation/12. Slicing Substrings.mp424.19MB

11. Regex And Text Manipulation/13. Masking With String Methods.mp436.91MB

11. Regex And Text Manipulation/14. BONUS Parsing Indicators With get_dummies().mp466.3MB

11. Regex And Text Manipulation/15. Text Replacement.mp441.78MB

11. Regex And Text Manipulation/16. Introduction To Regular Expressions.mp475.02MB

11. Regex And Text Manipulation/17. More Regex Concepts.mp465.17MB

11. Regex And Text Manipulation/18. How To Approach Regex.mp463.52MB

11. Regex And Text Manipulation/19. Is This A Valid Email.mp480.08MB

11. Regex And Text Manipulation/2. Our Data Boston Marathon Runners.mp423.57MB

11. Regex And Text Manipulation/20. BONUS What's The Point Of re.compile().mp418.31MB

11. Regex And Text Manipulation/21. Pandas str contains(), split() And replace() With Regex.mp476.29MB

11. Regex And Text Manipulation/22. Skill Challenge.mp45.42MB

11. Regex And Text Manipulation/23. Solution.mp472.37MB

11. Regex And Text Manipulation/3. String Methods In Python.mp428.77MB

11. Regex And Text Manipulation/4. Vectorized String Operations In Pandas.mp418.43MB

11. Regex And Text Manipulation/5. Case Operations.mp414.03MB

11. Regex And Text Manipulation/6. Finding Characters And Words.mp425.73MB

11. Regex And Text Manipulation/7. Strips And Whitespace.mp431.73MB

11. Regex And Text Manipulation/8. String Splitting And Concatenation.mp446.35MB

11. Regex And Text Manipulation/9. More Split Parameters.mp440.08MB

12. Visualizing Data/1. Section Intro.mp410.33MB

12. Visualizing Data/10. BONUS Data Ink And Chartjunk.mp432.34MB

12. Visualizing Data/11. Skill Challenge.mp47.52MB

12. Visualizing Data/12. Solution.mp454.25MB

12. Visualizing Data/2. The Art Of Data Visualization.mp413.01MB

12. Visualizing Data/3. The Preliminaries Of matplotlib.mp462.88MB

12. Visualizing Data/4. Line Graphs.mp454.18MB

12. Visualizing Data/5. Bar Charts.mp450.14MB

12. Visualizing Data/6. Pie Plots.mp454.89MB

12. Visualizing Data/7. Histograms.mp445.78MB

12. Visualizing Data/8. Scatter Plots.mp463.39MB

12. Visualizing Data/9. Other Visualization Options.mp463.65MB

13. Data Formats And IO/1. Section Intro.mp45.21MB

13. Data Formats And IO/10. Solution.mp445.82MB

13. Data Formats And IO/2. Reading JSON.mp419.74MB

13. Data Formats And IO/3. Reading HTML.mp4103.72MB

13. Data Formats And IO/4. Reading Excel.mp455.72MB

13. Data Formats And IO/5. Creating Output The to_ Family Of Methods.mp474.01MB

13. Data Formats And IO/6. BONUS Introduction To Pickling.mp431.71MB

13. Data Formats And IO/7. Pickles In Pandas.mp422.93MB

13. Data Formats And IO/8. The Many Other Formats.mp427.91MB

13. Data Formats And IO/9. Skill Challenge.mp411.71MB

14. Appendix A - Rapid-Fire Python Fundamentals/1. Section Intro.mp48.88MB

14. Appendix A - Rapid-Fire Python Fundamentals/10. Lists vs. Strings.mp427.56MB

14. Appendix A - Rapid-Fire Python Fundamentals/11. List Methods And Functions.mp432.99MB

14. Appendix A - Rapid-Fire Python Fundamentals/12. Containers II Tuples.mp420.03MB

14. Appendix A - Rapid-Fire Python Fundamentals/13. Containers III Sets.mp452.97MB

14. Appendix A - Rapid-Fire Python Fundamentals/14. Containers IV Dictionaries.mp422.74MB

14. Appendix A - Rapid-Fire Python Fundamentals/15. Dictionary Keys And Values.mp436.32MB

14. Appendix A - Rapid-Fire Python Fundamentals/16. Membership Operators.mp419.28MB

14. Appendix A - Rapid-Fire Python Fundamentals/17. Controlling Flow if, else, And elif.mp441.66MB

14. Appendix A - Rapid-Fire Python Fundamentals/18. Truth Value Of Non-booleans.mp415.92MB

14. Appendix A - Rapid-Fire Python Fundamentals/19. For Loops.mp420.57MB

14. Appendix A - Rapid-Fire Python Fundamentals/2. Data Types.mp410.16MB

14. Appendix A - Rapid-Fire Python Fundamentals/20. The range() Immutable Sequence.mp423.72MB

14. Appendix A - Rapid-Fire Python Fundamentals/21. While Loops.mp429.23MB

14. Appendix A - Rapid-Fire Python Fundamentals/22. Break And Continue.mp419.14MB

14. Appendix A - Rapid-Fire Python Fundamentals/23. Zipping Iterables.mp417.19MB

14. Appendix A - Rapid-Fire Python Fundamentals/24. List Comprehensions.mp431.78MB

14. Appendix A - Rapid-Fire Python Fundamentals/25. Defining Functions.mp457.77MB

14. Appendix A - Rapid-Fire Python Fundamentals/26. Function Arguments Positional vs Keyword.mp430.44MB

14. Appendix A - Rapid-Fire Python Fundamentals/27. Lambdas.mp423.21MB

14. Appendix A - Rapid-Fire Python Fundamentals/28. Importing Modules.mp434.15MB

14. Appendix A - Rapid-Fire Python Fundamentals/3. Variables.mp439.13MB

14. Appendix A - Rapid-Fire Python Fundamentals/4. Arithmetic And Augmented Assignment Operators.mp427.46MB

14. Appendix A - Rapid-Fire Python Fundamentals/5. Ints And Floats.mp442.8MB

14. Appendix A - Rapid-Fire Python Fundamentals/6. Booleans And Comparison Operators.mp421.88MB

14. Appendix A - Rapid-Fire Python Fundamentals/7. Strings.mp432.13MB

14. Appendix A - Rapid-Fire Python Fundamentals/8. Methods.mp425.33MB

14. Appendix A - Rapid-Fire Python Fundamentals/9. Containers I Lists.mp429.49MB

15. Appendix B - Going Local Installation And Setup/1. Installing Anaconda And Python - Windows.mp471.34MB

15. Appendix B - Going Local Installation And Setup/2. Installing Anaconda And Python - Mac.mp417.14MB

15. Appendix B - Going Local Installation And Setup/3. Installing Anaconda And Python - Linux.mp430.95MB

2. Series At A Glance/1. Section Intro.mp46.93MB

2. Series At A Glance/10. Solution.mp422.9MB

2. Series At A Glance/11. Another Solution.mp411.24MB

2. Series At A Glance/12. The head() And tail() Methods.mp422.98MB

2. Series At A Glance/13. Extracting By Index Position.mp429.06MB

2. Series At A Glance/14. Accessing Elements By Label.mp427.06MB

2. Series At A Glance/15. BONUS The add_prefix() And add_suffix() Methods.mp416.49MB

2. Series At A Glance/16. Using Dot Notation.mp413.25MB

2. Series At A Glance/17. Boolean Masks And The .loc Indexer.mp429.47MB

2. Series At A Glance/18. Extracting By Position With .iloc.mp411.61MB

2. Series At A Glance/19. BONUS Using Callables With .loc And .iloc.mp437.14MB

2. Series At A Glance/2. What Is A Series.mp412.54MB

2. Series At A Glance/20. Selecting With .get().mp430.55MB

2. Series At A Glance/21. Selection Recap.mp428.19MB

2. Series At A Glance/22. Skill Challenge.mp46.38MB

2. Series At A Glance/23. Solution.mp423.39MB

2. Series At A Glance/3. Parameters vs Arguments.mp48.07MB

2. Series At A Glance/4. What’s In The Data.mp420.41MB

2. Series At A Glance/5. The .dtype Attribute.mp46.37MB

2. Series At A Glance/6. BONUS What Is dtype('o'), Really.mp410.1MB

2. Series At A Glance/7. Index And RangeIndex.mp433.16MB

2. Series At A Glance/8. Series And Index Names.mp419.12MB

2. Series At A Glance/9. Skill Challenge.mp47.71MB

3. Series Methods And Handling/1. Section Intro.mp412.93MB

3. Series Methods And Handling/10. Skill Challenge.mp44.05MB

3. Series Methods And Handling/11. Solution.mp413.45MB

3. Series Methods And Handling/12. Dropping And Filling NAs.mp421.52MB

3. Series Methods And Handling/13. Descriptive Statistics.mp433.67MB

3. Series Methods And Handling/14. The describe() Method.mp49.7MB

3. Series Methods And Handling/15. mode() And value_counts().mp431.73MB

3. Series Methods And Handling/16. idxmax() And idxmin().mp422MB

3. Series Methods And Handling/17. Sorting With sort_values().mp419.63MB

3. Series Methods And Handling/18. nlargest() And nsmallest().mp412.17MB

3. Series Methods And Handling/19. Sorting With sort_index().mp415.3MB

3. Series Methods And Handling/2. Reading In Data With read_csv().mp452.81MB

3. Series Methods And Handling/20. Skill Challenge.mp43.18MB

3. Series Methods And Handling/21. Solution.mp49.91MB

3. Series Methods And Handling/22. Series Arithmetics And fill_value().mp440.2MB

3. Series Methods And Handling/23. BONUS Calculating Variance And Standard Deviation.mp417.36MB

3. Series Methods And Handling/24. Cumulative Operations.mp417.94MB

3. Series Methods And Handling/25. Pairwise Differences With diff().mp412.79MB

3. Series Methods And Handling/26. Series Iteration.mp416.07MB

3. Series Methods And Handling/27. Filtering filter(), where(), And mask().mp455.05MB

3. Series Methods And Handling/28. Transforming With update(), apply() And map().mp469.92MB

3. Series Methods And Handling/29. Skill Challenge.mp410.2MB

3. Series Methods And Handling/3. Series Sizing With .size, .shape, And len().mp423.26MB

3. Series Methods And Handling/30. Solution I - Reading Data.mp414.55MB

3. Series Methods And Handling/31. Solution II - Mean, Median, And Standard Deviation.mp420.47MB

3. Series Methods And Handling/32. Solution III - Z-scores.mp448.2MB

3. Series Methods And Handling/4. Unique Values And Series Monotonicity.mp417.8MB

3. Series Methods And Handling/5. The count() Method.mp46.03MB

3. Series Methods And Handling/6. Accessing And Counting NAs.mp436.79MB

3. Series Methods And Handling/7. BONUS Another Approach.mp421.33MB

3. Series Methods And Handling/8. The Other Side notnull() And notna().mp411.04MB

3. Series Methods And Handling/9. BONUS Booleans Are Literally Numbers In Python.mp411.62MB

4. Working With DataFrames/1. Section Intro.mp410.81MB

4. Working With DataFrames/10. BONUS - How Are Random Numbers Generated.mp442.94MB

4. Working With DataFrames/11. DataFrame Axes.mp423.31MB

4. Working With DataFrames/12. Changing The Index.mp450.38MB

4. Working With DataFrames/13. Extracting From DataFrames By Label.mp436.01MB

4. Working With DataFrames/14. DataFrame Extraction by Position.mp446.71MB

4. Working With DataFrames/15. Single Value Access With .at And .iat.mp426.34MB

4. Working With DataFrames/16. BONUS - The get_loc() Method.mp425.07MB

4. Working With DataFrames/17. Skill Challenge.mp44.1MB

4. Working With DataFrames/18. Solution.mp445.19MB

4. Working With DataFrames/19. More Cleanup Going Numeric.mp418.63MB

4. Working With DataFrames/2. What Is A DataFrame.mp445.86MB

4. Working With DataFrames/20. The astype() Method.mp425.17MB

4. Working With DataFrames/21. DataFrame replace() + A Glimpse At Regex.mp444.28MB

4. Working With DataFrames/22. Part I Collecting The Units.mp466.82MB

4. Working With DataFrames/23. The rename() Method.mp427.59MB

4. Working With DataFrames/24. DataFrame dropna().mp440.08MB

4. Working With DataFrames/25. BONUS - dropna() With Subset.mp429.26MB

4. Working With DataFrames/26. Part II Merging Units With Column Names.mp457.28MB

4. Working With DataFrames/27. Part III Removing Units From Values.mp435.62MB

4. Working With DataFrames/28. Filtering in 2D.mp442.35MB

4. Working With DataFrames/29. DataFrame Sorting.mp449.42MB

4. Working With DataFrames/3. Creating A DataFrame.mp422.42MB

4. Working With DataFrames/30. Using Series between() With DataFrames.mp434.97MB

4. Working With DataFrames/31. BONUS - Min, Max and Idx[MinMax], And Good Foods.mp462.98MB

4. Working With DataFrames/32. DataFrame nlargest() And nsmallest().mp435.36MB

4. Working With DataFrames/33. Skill Challenge.mp44.31MB

4. Working With DataFrames/34. Solution.mp442.25MB

4. Working With DataFrames/35. Another Skill Challenge.mp46.79MB

4. Working With DataFrames/36. Solution.mp436.86MB

4. Working With DataFrames/4. BONUS - Four More Ways To Build DataFrames.mp473.23MB

4. Working With DataFrames/5. The info() Method.mp419.04MB

4. Working With DataFrames/6. Reading In Nutrition Data.mp427.29MB

4. Working With DataFrames/7. Some Cleanup Removing The Duplicated Index.mp435.62MB

4. Working With DataFrames/8. The sample() Method.mp422.61MB

4. Working With DataFrames/9. BONUS - Sampling With Replacement Or Weights.mp440.48MB

5. DataFrames In Depth/1. Section Intro.mp421.13MB

5. DataFrames In Depth/10. Solution.mp440.04MB

5. DataFrames In Depth/11. 2d Indexing.mp440.02MB

5. DataFrames In Depth/12. Fancy Indexing With lookup().mp446.21MB

5. DataFrames In Depth/13. Sorting By Index Or Column.mp445.02MB

5. DataFrames In Depth/14. Sorting vs. Reordering.mp465.24MB

5. DataFrames In Depth/15. BONUS - Another Way.mp412.95MB

5. DataFrames In Depth/16. 15. BONUS - Please Avoid Sorting Like This.mp417.07MB

5. DataFrames In Depth/17. Skill Challenge.mp44.48MB

5. DataFrames In Depth/18. Solution.mp425.76MB

5. DataFrames In Depth/19. Identifying Dupes.mp460.88MB

5. DataFrames In Depth/2. Introducing A New Dataset.mp418.3MB

5. DataFrames In Depth/20. Removing Duplicates.mp429.82MB

5. DataFrames In Depth/21. Removing DataFrame Rows.mp419.78MB

5. DataFrames In Depth/22. BONUS - Removing Columns.mp416.19MB

5. DataFrames In Depth/23. BONUS - Another Way pop().mp419.07MB

5. DataFrames In Depth/24. BONUS - A Sophisticated Alternative.mp433.17MB

5. DataFrames In Depth/25. Null Values In DataFrames.mp442.16MB

5. DataFrames In Depth/26. Dropping And Filling DataFrame NAs.mp449MB

5. DataFrames In Depth/27. BONUS - Methods And Axes With fillna().mp457.38MB

5. DataFrames In Depth/28. Skill Challenge.mp45.3MB

5. DataFrames In Depth/29. Solution.mp442.49MB

5. DataFrames In Depth/3. Quick Review Indexing With Boolean Masks.mp423.33MB

5. DataFrames In Depth/30. Calculating Aggregates With agg().mp437.08MB

5. DataFrames In Depth/31. Same-shape Transforms.mp466.98MB

5. DataFrames In Depth/32. More Flexibility With apply().mp459.38MB

5. DataFrames In Depth/33. Element-wise Operations With applymap().mp468.51MB

5. DataFrames In Depth/34. Skill Challenge.mp48.76MB

5. DataFrames In Depth/35. Solution.mp426.47MB

5. DataFrames In Depth/36. Setting DataFrame Values.mp443.55MB

5. DataFrames In Depth/37. The SettingWithCopy Warning.mp439.81MB

5. DataFrames In Depth/38. View vs Copy.mp449.3MB

5. DataFrames In Depth/39. Adding DataFrame Columns.mp436.47MB

5. DataFrames In Depth/4. More Approaches To Boolean Masking.mp468.42MB

5. DataFrames In Depth/40. Adding Rows To DataFrames.mp449.9MB

5. DataFrames In Depth/41. BONUS - How Are DataFrames Stored In Memory.mp421.73MB

5. DataFrames In Depth/42. Skill Challenge.mp45.04MB

5. DataFrames In Depth/43. Solution.mp431.94MB

5. DataFrames In Depth/5. Binary Operators With Booleans.mp437.94MB

5. DataFrames In Depth/6. BONUS - XOR and Complement Binary Ops.mp450.47MB

5. DataFrames In Depth/7. Combining Conditions.mp445.57MB

5. DataFrames In Depth/8. Conditions As Variables.mp419.9MB

5. DataFrames In Depth/9. Skill Challenge.mp43.96MB

6. Working With Multiple DataFrames/1. Section Intro.mp47.95MB

6. Working With Multiple DataFrames/10. Skill Challenge.mp45.99MB

6. Working With Multiple DataFrames/11. Solution.mp459.47MB

6. Working With Multiple DataFrames/12. The merge() Method.mp435.38MB

6. Working With Multiple DataFrames/13. The left_on And right_on Params.mp432.2MB

6. Working With Multiple DataFrames/14. Inner vs Outer Joins.mp427.11MB

6. Working With Multiple DataFrames/15. Left vs Right Joins.mp420.27MB

6. Working With Multiple DataFrames/16. One-to-One and One-to-Many Joins.mp457.01MB

6. Working With Multiple DataFrames/17. Many-to-Many Joins.mp455.62MB

6. Working With Multiple DataFrames/18. Merging By Index.mp438.15MB

6. Working With Multiple DataFrames/19. The join() Method.mp422.87MB

6. Working With Multiple DataFrames/2. Introducing (Five) New Datasets.mp440.6MB

6. Working With Multiple DataFrames/20. Skill Challenge.mp43.81MB

6. Working With Multiple DataFrames/21. Solution.mp446.08MB

6. Working With Multiple DataFrames/3. Concatenating DataFrames.mp442.12MB

6. Working With Multiple DataFrames/4. The Duplicated Index Issue.mp451.32MB

6. Working With Multiple DataFrames/5. Enforcing Unique Indices.mp458.39MB

6. Working With Multiple DataFrames/6. BONUS - Creating Multiple Indices With concat().mp428.45MB

6. Working With Multiple DataFrames/7. Column Axis Concatenation.mp427.09MB

6. Working With Multiple DataFrames/8. The append() Method A Special Case Of concat().mp414.48MB

6. Working With Multiple DataFrames/9. Concat On Different Columns.mp438.21MB

7. Going MultiDimensional/1. Section Intro.mp426.42MB

7. Going MultiDimensional/10. Skill Challenge.mp43.78MB

7. Going MultiDimensional/11. Solution.mp444.8MB

7. Going MultiDimensional/12. The Anatomy Of A MultiIndex Object.mp434.85MB

7. Going MultiDimensional/13. Adding Another Level.mp433.59MB

7. Going MultiDimensional/14. Shuffling Levels.mp424.32MB

7. Going MultiDimensional/15. Removing MultiIndex Levels.mp437.7MB

7. Going MultiDimensional/16. MultiIndex sort_index().mp435.62MB

7. Going MultiDimensional/17. More MultiIndex Methods.mp437.92MB

7. Going MultiDimensional/18. Reshaping With stack().mp430.57MB

7. Going MultiDimensional/19. The Flipside unstack().mp445.95MB

7. Going MultiDimensional/2. Introducing New Data.mp422.11MB

7. Going MultiDimensional/20. BONUS Creating MultiLevel Columns Manually.mp458.73MB

7. Going MultiDimensional/21. An Easier Way transpose().mp418.6MB

7. Going MultiDimensional/22. BONUS - What About Panels.mp427.89MB

7. Going MultiDimensional/23. Skill Challenge.mp48.01MB

7. Going MultiDimensional/24. Solution.mp449.18MB

7. Going MultiDimensional/3. Index And RangeIndex.mp426.87MB

7. Going MultiDimensional/4. Creating A MultiIndex.mp420.15MB

7. Going MultiDimensional/5. MultiIndex From read_csv().mp427.7MB

7. Going MultiDimensional/6. Indexing Hierarchical DataFrames.mp439.39MB

7. Going MultiDimensional/7. Indexing Ranges And Slices.mp459.11MB

7. Going MultiDimensional/8. BONUS - Use With pd.IndexSlice!.mp416.97MB

7. Going MultiDimensional/9. Cross Sections With xs().mp433.15MB

8. GroupBy And Aggregates/1. Section Intro.mp417.09MB

8. GroupBy And Aggregates/10. Skill Challenge.mp43.22MB

8. GroupBy And Aggregates/11. Solution.mp427.59MB

8. GroupBy And Aggregates/12. Iterating Through Groups.mp421.03MB

8. GroupBy And Aggregates/13. Handpicking Subgroups.mp423.65MB

8. GroupBy And Aggregates/14. MultiIndex Grouping.mp426.54MB

8. GroupBy And Aggregates/15. Fine-tuned Aggregates.mp444.14MB

8. GroupBy And Aggregates/16. Named Aggregations.mp436.49MB

8. GroupBy And Aggregates/17. The filter() Method.mp426.12MB

8. GroupBy And Aggregates/18. GroupBy Transformations.mp438.79MB

8. GroupBy And Aggregates/19. BONUS - There's Also apply().mp441.18MB

8. GroupBy And Aggregates/2. New Data Game Sales.mp414.89MB

8. GroupBy And Aggregates/20. Skill Challenge.mp44.05MB

8. GroupBy And Aggregates/21. Solution.mp424.51MB

8. GroupBy And Aggregates/3. Simple Aggregations Review.mp429.02MB

8. GroupBy And Aggregates/4. Conditional Aggregates.mp424.51MB

8. GroupBy And Aggregates/5. The Split-Apply-Combine Pattern.mp422.51MB

8. GroupBy And Aggregates/6. The groupby() Method.mp421.56MB

8. GroupBy And Aggregates/7. The DataFrameGroupBy Object.mp419.81MB

8. GroupBy And Aggregates/8. Customizing Index To Group Mappings.mp420.48MB

8. GroupBy And Aggregates/9. BONUS - Series groupby().mp420.8MB

9. Reshaping With Pivots/1. Section Intro.mp423.83MB

9. Reshaping With Pivots/10. MultiIndex Pivot Tables.mp419.05MB

9. Reshaping With Pivots/11. Applying Multiple Functions.mp418.33MB

9. Reshaping With Pivots/12. Skill Challenge.mp45.48MB

9. Reshaping With Pivots/13. Solution.mp436.64MB

9. Reshaping With Pivots/2. New Data New York City SAT Scores.mp426.77MB

9. Reshaping With Pivots/3. Pivoting Data.mp441.9MB

9. Reshaping With Pivots/4. Undoing Pivots.mp427.89MB

9. Reshaping With Pivots/5. What About Aggregates.mp434.25MB

9. Reshaping With Pivots/6. The pivot_table().mp433.66MB

9. Reshaping With Pivots/7. BONUS The Problem With Average Percentage.mp436.16MB

9. Reshaping With Pivots/8. Replicating Pivot Tables With GroupBy.mp412.5MB

9. Reshaping With Pivots/9. Adding Margins.mp424.59MB