首页 磁力链接怎么用

[FreeCourseSite.com] Udemy - 2022 Python Data Analysis & Visualization Masterclass

文件类型 收录时间 最后活跃 资源热度 文件大小 文件数量
视频 2022-6-9 20:06 2024-11-19 08:47 154 8.38 GB 199
磁力链接
magnet:?xt=urn:btih:4b65bdf67178fcb9f00f4d8baa22fb72b2ae7344
迅雷链接
thunder://QUFtYWduZXQ6P3h0PXVybjpidGloOjRiNjViZGY2NzE3OGZjYjlmMDBmNGQ4YmFhMjJmYjcyYjJhZTczNDRaWg==
二维码链接
[FreeCourseSite.com] Udemy - 2022 Python Data Analysis & Visualization Masterclass的二维码
种子下载(838888不存储任何种子文件)
种子下载线路1(迅雷)--推荐
种子下载线路2(比特彗星)
种子下载线路3(torcache)
3条线路均为国内外知名下载网站种子链接，内容跟本站无关！
相关链接

FreeCourseSite com Udemy - 2022 Python Data Analysis & Visualization Masterclass

文件列表
  1. 01 - Introduction/001 Course Welcome & Curriculum Walkthrough.mp4116.97MB
  2. 01 - Introduction/002 What Do You Need To Know To Take This Course.mp416.78MB
  3. 01 - Introduction/003 Downloading The Course Materials IMPORTANT!!.mp428.41MB
  4. 01 - Introduction/004 How The Exercises Work.mp418.66MB
  5. 02 - Setup & Installation/001 Introducing Jupyter Notebook!.mp444.62MB
  6. 02 - Setup & Installation/002 Mac Installation Walkthrough.mp457.42MB
  7. 02 - Setup & Installation/003 Windows Installation Walkthrough.mp454.66MB
  8. 02 - Setup & Installation/004 Installing Pandas & Matplotlib (Mac & Windows).mp420.8MB
  9. 03 - Working With Jupyter Notebook/001 Creating Notebooks & Running Cells.mp439.05MB
  10. 03 - Working With Jupyter Notebook/002 Shutting Down The Notebook Server.mp433.64MB
  11. 03 - Working With Jupyter Notebook/003 How Cell Output Works.mp46.12MB
  12. 03 - Working With Jupyter Notebook/004 Command Mode Shortcuts.mp435.46MB
  13. 03 - Working With Jupyter Notebook/005 Cell Types Markdown Time!.mp434.21MB
  14. 03 - Working With Jupyter Notebook/006 Restarting The Kernel.mp426.2MB
  15. 03 - Working With Jupyter Notebook/007 Viewing The Docs Inside A Notebook.mp421.9MB
  16. 03 - Working With Jupyter Notebook/008 EXERCISE Jupyter Notebook.mp415.98MB
  17. 03 - Working With Jupyter Notebook/009 SOLUTION Jupyter Notebook.mp439.11MB
  18. 04 - Dataframes & Datasets/001 Datasets & CSV.mp442.9MB
  19. 04 - Dataframes & Datasets/002 pd.read_csv & DataFrames.mp447.9MB
  20. 04 - Dataframes & Datasets/003 Inspecting DataFrames head(), tail(), etc.mp468.09MB
  21. 04 - Dataframes & Datasets/004 DataTypes and info().mp439.75MB
  22. 04 - Dataframes & Datasets/005 The House Sales Dataset Walkthrough.mp453.06MB
  23. 04 - Dataframes & Datasets/006 The Titanic Passenger Dataset Walkthrough.mp479.67MB
  24. 04 - Dataframes & Datasets/007 Non-comma Separators Netflix Dataset.mp485.13MB
  25. 04 - Dataframes & Datasets/008 Overriding Headers Country Population Dataset.mp440.92MB
  26. 04 - Dataframes & Datasets/009 EXERCISE DataFrames & Datasets.mp417.49MB
  27. 04 - Dataframes & Datasets/010 SOLUTION DataFrames & Datasets.mp482.32MB
  28. 05 - Basic DataFrame Methods & Computations/001 Min & Max.mp437.56MB
  29. 05 - Basic DataFrame Methods & Computations/002 Sum & Count.mp471.06MB
  30. 05 - Basic DataFrame Methods & Computations/003 Mean, Median, & Mode.mp430.25MB
  31. 05 - Basic DataFrame Methods & Computations/004 Describe With Numeric Values.mp429.28MB
  32. 05 - Basic DataFrame Methods & Computations/005 Describe With Objects (Text) Values.mp454.48MB
  33. 05 - Basic DataFrame Methods & Computations/006 EXERCISE Basic DataFrame Methods.mp413.47MB
  34. 05 - Basic DataFrame Methods & Computations/007 SOLUTION Basic DataFrame Methods.mp431.66MB
  35. 06 - Series & Columns/001 Selecting A Single Column.mp443.19MB
  36. 06 - Series & Columns/002 A Closer Look At Series.mp451.03MB
  37. 06 - Series & Columns/003 Important Series Methods.mp417.36MB
  38. 06 - Series & Columns/004 unique & nunique.mp429.62MB
  39. 06 - Series & Columns/005 nlargest & nsmallest.mp453.88MB
  40. 06 - Series & Columns/006 Selecting Multiple Columns.mp421.79MB
  41. 06 - Series & Columns/007 The powerful value_counts() method.mp447.57MB
  42. 06 - Series & Columns/008 Using plot() to visualize!.mp459.72MB
  43. 06 - Series & Columns/009 EXERCISE Series & Plotting.mp418.2MB
  44. 06 - Series & Columns/010 SOLUTION Series & Plotting.mp463.63MB
  45. 07 - Indexing & Sorting/001 Set_Index Basics.mp457.21MB
  46. 07 - Indexing & Sorting/002 set_index The World Happiness Index Dataset.mp436.41MB
  47. 07 - Indexing & Sorting/003 setting index with read_csv.mp420.54MB
  48. 07 - Indexing & Sorting/004 sort_values intro.mp434.9MB
  49. 07 - Indexing & Sorting/005 sorting by multiple columns.mp426.35MB
  50. 07 - Indexing & Sorting/006 sorting text columns.mp426.74MB
  51. 07 - Indexing & Sorting/007 sort_index.mp419.39MB
  52. 07 - Indexing & Sorting/008 Sorting and Plotting!.mp422.45MB
  53. 07 - Indexing & Sorting/009 loc.mp453.8MB
  54. 07 - Indexing & Sorting/010 iloc.mp431.01MB
  55. 07 - Indexing & Sorting/011 loc & iloc with Series.mp431.51MB
  56. 07 - Indexing & Sorting/012 EXERCISE Indexes & Sorting.mp429.02MB
  57. 07 - Indexing & Sorting/013 SOLUTION Indexes & Sorting.mp483.17MB
  58. 08 - Filtering DataFrames/001 Filtering DataFrames With A Boolean Series.mp451.22MB
  59. 08 - Filtering DataFrames/002 Filtering With Comparison Operators.mp456.47MB
  60. 08 - Filtering DataFrames/003 The Between Method.mp420.9MB
  61. 08 - Filtering DataFrames/004 The isin() Method.mp426.75MB
  62. 08 - Filtering DataFrames/005 Combining Conditions Using AND (&).mp483.67MB
  63. 08 - Filtering DataFrames/006 Combining Conditions Using OR ().mp491.18MB
  64. 08 - Filtering DataFrames/007 Bitwise Negation.mp440.97MB
  65. 08 - Filtering DataFrames/008 isna() and notna() Methods.mp427.4MB
  66. 08 - Filtering DataFrames/009 Filtering + Plotting Examples.mp434.79MB
  67. 08 - Filtering DataFrames/010 EXERCISE Filtering.mp412.83MB
  68. 08 - Filtering DataFrames/011 SOLUTION Filtering Exercise.mp485.89MB
  69. 09 - Adding & Removing Columns/001 Dropping Columns.mp447.57MB
  70. 09 - Adding & Removing Columns/002 Dropping Rows.mp445.86MB
  71. 09 - Adding & Removing Columns/003 Adding Static Columns.mp441.25MB
  72. 09 - Adding & Removing Columns/004 Creating New Dynamic Columns.mp441.07MB
  73. 09 - Adding & Removing Columns/005 Finding The Highest pricesqft homes.mp428.23MB
  74. 09 - Adding & Removing Columns/006 Finding Largest Bitcoin Price Changes.mp437.6MB
  75. 09 - Adding & Removing Columns/007 EXERCISE AddingRemoving Columns & Rows.mp424.59MB
  76. 09 - Adding & Removing Columns/008 SOLUTION AddingRemoving Columns & Rows.mp446.7MB
  77. 10 - Updating Values/001 Renaming Columns and Index Labels.mp435.53MB
  78. 10 - Updating Values/002 The replace() method.mp447.05MB
  79. 10 - Updating Values/003 Updating Values Using loc[].mp427.09MB
  80. 10 - Updating Values/004 Updating Multiple Values Using loc[].mp431.7MB
  81. 10 - Updating Values/005 Making Updates With loc[] and Boolean Masks.mp454.61MB
  82. 10 - Updating Values/006 EXERCISE Updating Values.mp416.12MB
  83. 10 - Updating Values/007 SOLUTION Updating Values Exercise.mp470.35MB
  84. 11 - Working With Types and NA Values/001 Casting Types With astype().mp445.21MB
  85. 11 - Working With Types and NA Values/002 Introducing the Category Type.mp432.05MB
  86. 11 - Working With Types and NA Values/003 Casting With pd.to_numeric().mp432.66MB
  87. 11 - Working With Types and NA Values/004 dropna() and isna().mp455.98MB
  88. 11 - Working With Types and NA Values/005 fillna().mp435.73MB
  89. 11 - Working With Types and NA Values/006 EXERCISE Dealing With NA Values.mp412.55MB
  90. 11 - Working With Types and NA Values/007 SOLUTION Dealing With NA Values.mp442.3MB
  91. 12 - Working With Dates & Times/001 Why Dates Matter.mp439.68MB
  92. 12 - Working With Dates & Times/002 Converting With pd.to_datetime().mp444.79MB
  93. 12 - Working With Dates & Times/003 Specifying Fancy Formats With pd.to_datetime().mp468MB
  94. 12 - Working With Dates & Times/004 Dates and DataFrames.mp467.39MB
  95. 12 - Working With Dates & Times/005 The Useful dt Properties.mp456.24MB
  96. 12 - Working With Dates & Times/006 Comparing Dates.mp440.45MB
  97. 12 - Working With Dates & Times/007 Finding StarLink Flybys In UFO Dataset.mp472.24MB
  98. 12 - Working With Dates & Times/008 Date Math & TimeDeltas.mp468.03MB
  99. 12 - Working With Dates & Times/009 Billboard Charts Dataset Exploration.mp488.93MB
  100. 12 - Working With Dates & Times/010 EXERCISE Dates & Times.mp433.52MB
  101. 12 - Working With Dates & Times/011 SOLUTION Dates & Times.mp4115.74MB
  102. 13 - Matplotlib/001 Intro to Matplotlib.mp432.8MB
  103. 13 - Matplotlib/002 Our First Matplotlib Plots!.mp438.12MB
  104. 13 - Matplotlib/003 Do We Need plt.show().mp414.98MB
  105. 13 - Matplotlib/004 Anatomy of Plots.mp445.36MB
  106. 13 - Matplotlib/005 Figsize & Plot Dimensions.mp428.7MB
  107. 13 - Matplotlib/006 Changing Matplotlib Stylesheets.mp428.77MB
  108. 13 - Matplotlib/007 Line Styles, Colors, Widths, and More!.mp471.39MB
  109. 13 - Matplotlib/008 Plot Labels & Titles.mp449.62MB
  110. 13 - Matplotlib/009 Changing X & Y Ticks.mp453.16MB
  111. 13 - Matplotlib/010 Adding Legends To Plots.mp439.61MB
  112. 13 - Matplotlib/011 EXERCISE Matplotlib Challenge #1.mp435.76MB
  113. 13 - Matplotlib/012 Creating Bar Plots.mp469.01MB
  114. 13 - Matplotlib/013 Creating Histograms.mp477.71MB
  115. 13 - Matplotlib/014 EXERCISE Matplotlib Challenge #2.mp428.29MB
  116. 13 - Matplotlib/015 Creating Scatter Plots.mp435.21MB
  117. 13 - Matplotlib/016 Creating Pie Charts.mp450.96MB
  118. 13 - Matplotlib/017 EXERCISE Matplotlib Challenge #3.mp434.9MB
  119. 13 - Matplotlib/018 Working With Subplots.mp474.84MB
  120. 13 - Matplotlib/019 Putting It All Together.mp446.88MB
  121. 13 - Matplotlib/020 EXERCISE Matplotlib Challenge #4.mp474.81MB
  122. 14 - Revisiting Pandas Plotting/001 A Pandas Plotting Recap.mp436.15MB
  123. 14 - Revisiting Pandas Plotting/002 Changing Pandas Plot Styles.mp418.74MB
  124. 14 - Revisiting Pandas Plotting/003 Adding Labels and Titles to Pandas Plots.mp455.16MB
  125. 14 - Revisiting Pandas Plotting/004 Using rename() When Plotting.mp421.28MB
  126. 14 - Revisiting Pandas Plotting/005 Closer Look at Pandas Bar Plots.mp456.31MB
  127. 14 - Revisiting Pandas Plotting/006 EXERCISE Pandas Plotting Challenge #1.mp461.17MB
  128. 14 - Revisiting Pandas Plotting/007 Pandas Histograms.mp419.7MB
  129. 14 - Revisiting Pandas Plotting/008 Box Plots.mp431.98MB
  130. 14 - Revisiting Pandas Plotting/009 Pandas Line Plots.mp434.65MB
  131. 14 - Revisiting Pandas Plotting/010 EXERCISE Pandas Plotting Challenge #2.mp429.89MB
  132. 14 - Revisiting Pandas Plotting/011 Pandas Scatter Plots.mp423.65MB
  133. 14 - Revisiting Pandas Plotting/012 Multiple Plots On The Same Axes.mp435.74MB
  134. 14 - Revisiting Pandas Plotting/013 UFOS Plotting Challenge!.mp446.51MB
  135. 14 - Revisiting Pandas Plotting/014 EXERCISE Pandas Plotting Challenge #3.mp430.59MB
  136. 14 - Revisiting Pandas Plotting/015 Pandas Automatic Subplots.mp454.58MB
  137. 14 - Revisiting Pandas Plotting/016 Manual Subplots With Pandas.mp447.22MB
  138. 14 - Revisiting Pandas Plotting/017 EXERCISE Pandas Plotting Challenge #4.mp4107.87MB
  139. 14 - Revisiting Pandas Plotting/018 EXERCISE Pandas Plotting Challenge #5.mp4109.16MB
  140. 14 - Revisiting Pandas Plotting/019 Exporting Figures With savefig().mp427.17MB
  141. 15 - Grouping & Aggregating/001 Introducing Groupby.mp451.4MB
  142. 15 - Grouping & Aggregating/002 Exploring Groups.mp472.18MB
  143. 15 - Grouping & Aggregating/003 Split-Apply-Combine.mp459.01MB
  144. 15 - Grouping & Aggregating/004 Using The Agg Method.mp454.23MB
  145. 15 - Grouping & Aggregating/005 Agg with Custom Functions.mp415.12MB
  146. 15 - Grouping & Aggregating/006 Named Aggregation.mp429.09MB
  147. 16 - Hierarchical Indexing/001 Groupby With Multiple Columns.mp445.78MB
  148. 16 - Hierarchical Indexing/002 Creating a MultiIndex With set_index.mp438.37MB
  149. 16 - Hierarchical Indexing/003 Sorting A MultiIndex.mp441.24MB
  150. 16 - Hierarchical Indexing/004 Using .loc[] With A MultiIndex.mp449.59MB
  151. 16 - Hierarchical Indexing/005 Cross Sections With The XS Method.mp412.89MB
  152. 16 - Hierarchical Indexing/006 get_level_values().mp449.27MB
  153. 16 - Hierarchical Indexing/007 Hierarchical Columns.mp429.92MB
  154. 16 - Hierarchical Indexing/008 Stack() and Unstack().mp430.91MB
  155. 16 - Hierarchical Indexing/009 Plotting With Unstack().mp443.56MB
  156. 16 - Hierarchical Indexing/010 Grouping By Index.mp426.51MB
  157. 17 - Working With Text/001 The String Datatype Vs. Object Datatype.mp450.2MB
  158. 17 - Working With Text/002 Upper(), Lower(), and Capitalize().mp428.48MB
  159. 17 - Working With Text/003 Indexing String Series With [].mp440.1MB
  160. 17 - Working With Text/004 Stripping Whitespace With Strip().mp418.66MB
  161. 17 - Working With Text/005 Splitting Text Values With Split().mp449.17MB
  162. 17 - Working With Text/006 Replacing Portions of Strings With Replace().mp452.2MB
  163. 17 - Working With Text/007 Testing Strings With Contains().mp428.39MB
  164. 18 - Apply, Map, & Applymap/001 Applying Functions To Series.mp450.51MB
  165. 18 - Apply, Map, & Applymap/002 Apply() With Lambdas & Arguments.mp432.69MB
  166. 18 - Apply, Map, & Applymap/003 Apply() w DataFrames Columns.mp424.77MB
  167. 18 - Apply, Map, & Applymap/004 Apply() w DataFrames Rows.mp442.06MB
  168. 18 - Apply, Map, & Applymap/005 The Series Map() Method.mp416.72MB
  169. 18 - Apply, Map, & Applymap/006 The ApplyMap() Method.mp424.71MB
  170. 19 - Combining Series & DataFrames/001 Concatenating Series.mp429.7MB
  171. 19 - Combining Series & DataFrames/002 Concatenating Series By Index.mp422.12MB
  172. 19 - Combining Series & DataFrames/003 Inner vs. Outer Joins.mp418.38MB
  173. 19 - Combining Series & DataFrames/004 Concatenating DataFrames By Columns.mp426.67MB
  174. 19 - Combining Series & DataFrames/005 Concatenating DataFrames By Index.mp416.4MB
  175. 19 - Combining Series & DataFrames/006 The DataFrame Merge() Method.mp434.14MB
  176. 19 - Combining Series & DataFrames/007 Merge() w Left, Right, Inner, & Outer Joins.mp441.98MB
  177. 19 - Combining Series & DataFrames/008 Merge() On and Suffixes Arguments.mp459.14MB
  178. 20 - Seaborn/001 Intro to Seaborn.mp463.95MB
  179. 20 - Seaborn/002 The Helpful load_dataset() method.mp426.58MB
  180. 20 - Seaborn/003 Seaborn Scatterplots.mp475.23MB
  181. 20 - Seaborn/004 Seaborn Lineplots.mp486.54MB
  182. 20 - Seaborn/005 The relplot() Method.mp484.97MB
  183. 20 - Seaborn/006 Resizing Seaborn Plots Aspect & Height.mp463.21MB
  184. 20 - Seaborn/007 Seaborn Histograms.mp438.67MB
  185. 20 - Seaborn/008 KDE Plots.mp421.03MB
  186. 20 - Seaborn/009 Bivariate Distribution Plots.mp440.73MB
  187. 20 - Seaborn/010 Rugplots.mp439.19MB
  188. 20 - Seaborn/011 The Amazing displot() Method.mp452.18MB
  189. 21 - Seaborn Categorical Plots/001 Countplot.mp419.19MB
  190. 21 - Seaborn Categorical Plots/002 Strip & Swarm Plots.mp467.72MB
  191. 21 - Seaborn Categorical Plots/003 Boxplots.mp449.14MB
  192. 21 - Seaborn Categorical Plots/004 Boxenplots.mp415.38MB
  193. 21 - Seaborn Categorical Plots/005 Violinplots.mp428.98MB
  194. 21 - Seaborn Categorical Plots/006 Barplots.mp455.43MB
  195. 21 - Seaborn Categorical Plots/007 The Big Boy Catplot Method.mp461.61MB
  196. 22 - Controlling Seaborn Aesthetics/001 Changing Seaborn Themes.mp427.29MB
  197. 22 - Controlling Seaborn Aesthetics/002 Customizing Styles with set_style().mp448.9MB
  198. 22 - Controlling Seaborn Aesthetics/003 Altering Spines With despine().mp417.67MB
  199. 22 - Controlling Seaborn Aesthetics/004 Changing Color Palettes.mp468.68MB
友情提示
不会用的朋友看这里 把磁力链接复制到离线下载，或者bt下载软件里即可下载文件，或者直接复制迅雷链接到迅雷里下载！ 亲，你造吗？将网页分享给您的基友，下载的人越多速度越快哦！

违规内容投诉邮箱：[email protected]

概述 838888磁力搜索是一个磁力链接搜索引擎，是学术研究的副产品，用于解决资源过度分散的问题 它通过BitTorrent协议加入DHT网络，实时的自动采集数据，仅存储文件的标题、大小、文件列表、文件标识符（磁力链接）等基础信息 838888磁力搜索不下载任何真实资源，无法判断资源的合法性及真实性，使用838888磁力搜索服务的用户需自行鉴别内容的真伪 838888磁力搜索不上传任何资源，不提供Tracker服务，不提供种子文件的下载，这意味着838888磁力搜索 838888磁力搜索是一个完全合法的系统
838888磁力链接 磁力链接bt种子搜索引擎磁力搜索 DMCA