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[DesireCourse.Net] Udemy - The Complete Pandas Bootcamp Master your Data in Python

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DesireCourse Net Udemy - The Complete Pandas Bootcamp Master your Data in Python

文件列表
  1. 1. Getting Started/1. Overview Student FAQ.mp448.47MB
  2. 1. Getting Started/2. Tips How to get the most out of this course.mp443.63MB
  3. 1. Getting Started/3. Did you know that....mp431.23MB
  4. 1. Getting Started/5. Installation of Anaconda.mp486.27MB
  5. 1. Getting Started/6. Opening a Jupyter Notebook.mp465.09MB
  6. 1. Getting Started/7. How to use Jupyter Notebooks.mp466.28MB
  7. 10. Importing Data/1. Importing csv-files with pd.read_csv.mp490.93MB
  8. 10. Importing Data/2. Importing messy csv-files with pd.read_csv.mp463.27MB
  9. 10. Importing Data/3. OLD Importing Data from Excel with pd.read_excel().mp451.9MB
  10. 10. Importing Data/4. NEW Importing Data from Excel with pd.read_excel().mp473.9MB
  11. 10. Importing Data/5. Importing messy Data from Excel with pd.read_excel().mp472.44MB
  12. 10. Importing Data/6. Importing Data from the Web with pd.read_html().mp458MB
  13. 10. Importing Data/7. Coding Exercise 10 (Intro).mp412.37MB
  14. 11. Cleaning Data/1. First Inspection & Handling of inconsistent Data.mp468.24MB
  15. 11. Cleaning Data/10. Handling Removing Duplicates.mp488.67MB
  16. 11. Cleaning Data/11. Detection of Outliers.mp444.07MB
  17. 11. Cleaning Data/12. Handling Removing Outliers.mp429.69MB
  18. 11. Cleaning Data/13. Categorical Data.mp445.47MB
  19. 11. Cleaning Data/14. Coding Exercise 11 (Intro).mp410.83MB
  20. 11. Cleaning Data/2. String Operations.mp480.88MB
  21. 11. Cleaning Data/3. Changing Datatype of Columns with astype().mp438.79MB
  22. 11. Cleaning Data/4. Intro NA values missing values.mp445.64MB
  23. 11. Cleaning Data/5. Detection of missing Values.mp489.4MB
  24. 11. Cleaning Data/6. Removing missing values.mp485.5MB
  25. 11. Cleaning Data/7. Replacing missing values.mp424.59MB
  26. 11. Cleaning Data/8. Intro Duplicates.mp420.26MB
  27. 11. Cleaning Data/9. Detection of Duplicates.mp479.21MB
  28. 12. Merging, Joining, and Concatenating Data/10. Right Joins (without Intersection) with merge().mp415.03MB
  29. 12. Merging, Joining, and Concatenating Data/11. Left Joins with merge().mp424.08MB
  30. 12. Merging, Joining, and Concatenating Data/12. Right Joins with merge().mp427.41MB
  31. 12. Merging, Joining, and Concatenating Data/13. Joining on different Column Names Indexes.mp495.32MB
  32. 12. Merging, Joining, and Concatenating Data/14. Joining on more than one Column.mp438.69MB
  33. 12. Merging, Joining, and Concatenating Data/15. pd.merge() and join().mp435.47MB
  34. 12. Merging, Joining, and Concatenating Data/16. Coding Exercise 12 (Intro).mp48.6MB
  35. 12. Merging, Joining, and Concatenating Data/2. Adding Rows with append() and pd.concat() (Part 1).mp488.06MB
  36. 12. Merging, Joining, and Concatenating Data/3. Adding Rows with pd.concat() (Part 2).mp456.9MB
  37. 12. Merging, Joining, and Concatenating Data/4. Arithmetic with Pandas Objects Data Alignment.mp438.91MB
  38. 12. Merging, Joining, and Concatenating Data/6. Outer Joins with merge().mp480.1MB
  39. 12. Merging, Joining, and Concatenating Data/7. Inner Joins with merge().mp415.56MB
  40. 12. Merging, Joining, and Concatenating Data/8. Outer Joins (without Intersection) with merge().mp431.49MB
  41. 12. Merging, Joining, and Concatenating Data/9. Left Joins (without Intersection) with merge().mp421.85MB
  42. 13. GroupBy Operations/1. Intro.mp410.09MB
  43. 13. GroupBy Operations/10. Generalizing split-apply-combine with apply().mp442.78MB
  44. 13. GroupBy Operations/11. Hierarchical Indexing with Groupby.mp432.86MB
  45. 13. GroupBy Operations/12. stack() and unstack().mp478.81MB
  46. 13. GroupBy Operations/14. Coding Exercise 13 (Intro).mp411.78MB
  47. 13. GroupBy Operations/15. Coding Exercise 13 (Solution).mp481.56MB
  48. 13. GroupBy Operations/2. Understanding the GroupBy Object.mp446.26MB
  49. 13. GroupBy Operations/3. Splitting with many Keys.mp449.91MB
  50. 13. GroupBy Operations/4. split-apply-combine explained.mp447.07MB
  51. 13. GroupBy Operations/5. split-apply-combine applied.mp470.7MB
  52. 13. GroupBy Operations/7. Advanced aggregation with agg().mp430.26MB
  53. 13. GroupBy Operations/8. Transformation with transform().mp435.41MB
  54. 13. GroupBy Operations/9. Replacing NA Values by group-specific Values.mp444.75MB
  55. 14. Reshaping and Pivoting DataFrames/2. Transposing Rows and Columns.mp468.43MB
  56. 14. Reshaping and Pivoting DataFrames/3. Pivoting DataFrames with pivot().mp455.9MB
  57. 14. Reshaping and Pivoting DataFrames/4. Limits of pivot().mp458.24MB
  58. 14. Reshaping and Pivoting DataFrames/5. pivot_table().mp458.07MB
  59. 14. Reshaping and Pivoting DataFrames/6. pd.crosstab().mp499.48MB
  60. 14. Reshaping and Pivoting DataFrames/7. melting DataFrames with melt().mp449.45MB
  61. 14. Reshaping and Pivoting DataFrames/8. Coding Exercsie 14 (Intro).mp46.76MB
  62. 15. Data Preparation and Feature Creation/10. Scaling Standardization.mp456.33MB
  63. 15. Data Preparation and Feature Creation/11. Creating Dummy Variables.mp455.25MB
  64. 15. Data Preparation and Feature Creation/12. String Operations.mp429.66MB
  65. 15. Data Preparation and Feature Creation/13. Coding Exercise 15 (Intro).mp415.03MB
  66. 15. Data Preparation and Feature Creation/2. Arithmetic Operations (Part 1).mp463.52MB
  67. 15. Data Preparation and Feature Creation/3. Arithmetic Operations (Part 2).mp458.45MB
  68. 15. Data Preparation and Feature Creation/4. TransformationMapping with map().mp442.68MB
  69. 15. Data Preparation and Feature Creation/5. Conditional Transformation.mp433.41MB
  70. 15. Data Preparation and Feature Creation/6. Discretization and Binning with pd.cut() (Part 1).mp473.03MB
  71. 15. Data Preparation and Feature Creation/7. Discretization and Binning with pd.cut() (Part 2).mp432.7MB
  72. 15. Data Preparation and Feature Creation/8. Discretization and Binning with pd.qcut().mp485.39MB
  73. 15. Data Preparation and Feature Creation/9. Floors and Caps.mp439.25MB
  74. 16. Advanced Visualization with Seaborn/2. First Steps in Seaborn.mp422.09MB
  75. 16. Advanced Visualization with Seaborn/3. Categorical Plots.mp485.2MB
  76. 16. Advanced Visualization with Seaborn/4. Joint Plots Regression Plots.mp479.61MB
  77. 16. Advanced Visualization with Seaborn/5. Matrixplots Heatmaps.mp442.78MB
  78. 16. Advanced Visualization with Seaborn/6. Coding Exercise 16 (Intro).mp48.97MB
  79. 17. -----PART III COMPREHENSIVE PROJECT CHALLENGE -------/1. Olympic Medal Tables (Intro).mp44.46MB
  80. 17. -----PART III COMPREHENSIVE PROJECT CHALLENGE -------/2. Olympic Medal Tables (Instruction & Hints).mp457.71MB
  81. 17. -----PART III COMPREHENSIVE PROJECT CHALLENGE -------/3. Olympic Medal Tables (Solution Part 1).mp438.42MB
  82. 17. -----PART III COMPREHENSIVE PROJECT CHALLENGE -------/4. Olympic Medal Tables (Solution Part 2).mp4128.78MB
  83. 17. -----PART III COMPREHENSIVE PROJECT CHALLENGE -------/5. Olympic Medal Tables (Solution Part 3).mp426.99MB
  84. 19. Time Series Basics/1. Importing Time Series Data from csv-files.mp441.75MB
  85. 19. Time Series Basics/10. Advanced Indexing with reindex().mp450.5MB
  86. 19. Time Series Basics/2. Converting strings to datetime objects with pd.to_datetime().mp458MB
  87. 19. Time Series Basics/3. Initial Analysis Visualization of Time Series.mp434.99MB
  88. 19. Time Series Basics/4. Indexing and Slicing Time Series.mp448.16MB
  89. 19. Time Series Basics/5. Creating a customized DatetimeIndex with pd.date_range().mp4114.64MB
  90. 19. Time Series Basics/6. More on pd.date_range().mp412.36MB
  91. 19. Time Series Basics/7. Downsampling Time Series with resample() (Part 1).mp485.5MB
  92. 19. Time Series Basics/8. Downsampling Time Series with resample (Part 2).mp449.11MB
  93. 19. Time Series Basics/9. The PeriodIndex object.mp438.77MB
  94. 20. Time Series Advanced Financial Time Series/10. Financial Time Series - Covariance and Correlation.mp425.73MB
  95. 20. Time Series Advanced Financial Time Series/11. Helpful DatetimeIndex Attributes and Methods.mp444.3MB
  96. 20. Time Series Advanced Financial Time Series/12. Filling NA Values with bfill, ffill and interpolation.mp478.44MB
  97. 20. Time Series Advanced Financial Time Series/13. Coding Exercise 17 (Intro).mp410.74MB
  98. 20. Time Series Advanced Financial Time Series/2. NEW Getting Ready (Installing required package).mp421.77MB
  99. 20. Time Series Advanced Financial Time Series/3. NEW Importing Stock Price Data from Yahoo Finance (it still works!).mp471.92MB
  100. 20. Time Series Advanced Financial Time Series/4. Initial Inspection and Visualization.mp442.33MB
  101. 20. Time Series Advanced Financial Time Series/5. Normalizing Time Series to a Base Value (100).mp444.26MB
  102. 20. Time Series Advanced Financial Time Series/6. The shift() method.mp435.78MB
  103. 20. Time Series Advanced Financial Time Series/7. The methods diff() and pct_change().mp440.23MB
  104. 20. Time Series Advanced Financial Time Series/8. Measuring Stock Performance with MEAN Returns and STD of Returns.mp443.92MB
  105. 20. Time Series Advanced Financial Time Series/9. Financial Time Series - Return and Risk.mp453.62MB
  106. 22. Python Basics/1. Intro.mp45.89MB
  107. 22. Python Basics/10. Operators & Booleans.mp459.52MB
  108. 22. Python Basics/11. Conditional Statements (if, elif, else, while).mp486.04MB
  109. 22. Python Basics/12. For Loops.mp458.42MB
  110. 22. Python Basics/13. Key words break, pass, continue.mp436.71MB
  111. 22. Python Basics/14. Generating Random Numbers.mp438.12MB
  112. 22. Python Basics/15. User Defined Functions (Part 1).mp464.35MB
  113. 22. Python Basics/16. User Defined Functions (Part 2).mp457.39MB
  114. 22. Python Basics/17. User Defined Functions (Part 3).mp452.12MB
  115. 22. Python Basics/18. Visualization with Matplotlib.mp4124.22MB
  116. 22. Python Basics/2. First Steps.mp434.22MB
  117. 22. Python Basics/20. Python Basics Quiz Solution.mp438.25MB
  118. 22. Python Basics/3. Variables.mp431.46MB
  119. 22. Python Basics/4. Data Types Integers and Floats.mp449.47MB
  120. 22. Python Basics/5. Data Types Strings.mp477.78MB
  121. 22. Python Basics/6. Data Types Lists (Part 1).mp462.7MB
  122. 22. Python Basics/7. Data Types Lists (Part 2).mp4134.4MB
  123. 22. Python Basics/8. Data Types Tuples.mp441.8MB
  124. 22. Python Basics/9. Data Types Sets.mp421.44MB
  125. 23. The Numpy Package/1. Introduction to Numpy Arrays.mp441.13MB
  126. 23. The Numpy Package/10. Summary Statistics.mp444.82MB
  127. 23. The Numpy Package/11. Visualization and (Linear) Regression.mp484.55MB
  128. 23. The Numpy Package/13. Numpy Quiz Solution.mp445.45MB
  129. 23. The Numpy Package/2. Numpy Arrays Vectorization.mp464.74MB
  130. 23. The Numpy Package/3. Numpy Arrays Indexing and Slicing.mp453.44MB
  131. 23. The Numpy Package/4. Numpy Arrays Shape and Dimensions.mp435.52MB
  132. 23. The Numpy Package/5. Numpy Arrays Indexing and Slicing of multi-dimensional Arrays.mp473.64MB
  133. 23. The Numpy Package/6. Numpy Arrays Boolean Indexing.mp444.22MB
  134. 23. The Numpy Package/7. Generating Random Numbers.mp467.53MB
  135. 23. The Numpy Package/8. Performance Issues.mp449.88MB
  136. 23. The Numpy Package/9. Case Study Numpy vs. Python Standard Library.mp445.61MB
  137. 3. Pandas Basics (DataFrame Basics I)/1. Intro to Tabular Data Pandas.mp425.8MB
  138. 3. Pandas Basics (DataFrame Basics I)/10. Explore your own Dataset Coding Exercise 1 (Solution).mp429.88MB
  139. 3. Pandas Basics (DataFrame Basics I)/11. Selecting Columns.mp438.53MB
  140. 3. Pandas Basics (DataFrame Basics I)/12. Selecting Rows with Square Brackets (not advisable).mp422.03MB
  141. 3. Pandas Basics (DataFrame Basics I)/13. Selecting Rows with iloc (position-based indexing).mp453.95MB
  142. 3. Pandas Basics (DataFrame Basics I)/14. Slicing Rows and Columns with iloc (position-based indexing).mp425.95MB
  143. 3. Pandas Basics (DataFrame Basics I)/16. Selecting Rows with loc (label-based indexing).mp430.37MB
  144. 3. Pandas Basics (DataFrame Basics I)/17. Slicing Rows and Columns with loc (label-based indexing).mp491.38MB
  145. 3. Pandas Basics (DataFrame Basics I)/19. Summary and Outlook.mp462.15MB
  146. 3. Pandas Basics (DataFrame Basics I)/21. Coding Exercise 2 (Intro).mp48.6MB
  147. 3. Pandas Basics (DataFrame Basics I)/22. Coding Exercise 2 (Solution).mp439.11MB
  148. 3. Pandas Basics (DataFrame Basics I)/3. First Steps (Inspection of Data, Part 1).mp451.52MB
  149. 3. Pandas Basics (DataFrame Basics I)/4. First Steps (Inspection of Data, Part 2).mp456.78MB
  150. 3. Pandas Basics (DataFrame Basics I)/5. Coding Exercise 0 Coding the Video Lectures.mp4109.32MB
  151. 3. Pandas Basics (DataFrame Basics I)/6. Built-in Functions, Attributes and Methods with Pandas.mp450.64MB
  152. 3. Pandas Basics (DataFrame Basics I)/7. Make it easy TAB Completion and Tooltip.mp454.43MB
  153. 3. Pandas Basics (DataFrame Basics I)/9. Explore your own Dataset Coding Exercise 1 (Intro).mp463.16MB
  154. 4. Pandas Series and Index Objects/10. Sorting of Series and Introduction to the inplace - parameter.mp441.4MB
  155. 4. Pandas Series and Index Objects/11. nlargest() and nsmallest().mp416.76MB
  156. 4. Pandas Series and Index Objects/12. idxmin() and idxmax().mp428.68MB
  157. 4. Pandas Series and Index Objects/13. Manipulating Pandas Series.mp437.87MB
  158. 4. Pandas Series and Index Objects/15. Coding Exercise 3 (Intro).mp414.12MB
  159. 4. Pandas Series and Index Objects/16. Coding Exercise 3 (Solution).mp438.65MB
  160. 4. Pandas Series and Index Objects/17. First Steps with Pandas Index Objects.mp443.09MB
  161. 4. Pandas Series and Index Objects/18. Creating Index Objects from Scratch.mp415.02MB
  162. 4. Pandas Series and Index Objects/19. Changing Row Index with set_index() and reset_index().mp475.03MB
  163. 4. Pandas Series and Index Objects/2. First Steps with Pandas Series.mp435.98MB
  164. 4. Pandas Series and Index Objects/20. Changing Column Labels.mp421.15MB
  165. 4. Pandas Series and Index Objects/21. Renaming Index & Column Labels with rename().mp433.57MB
  166. 4. Pandas Series and Index Objects/23. Coding Exercise 4 (Intro).mp49MB
  167. 4. Pandas Series and Index Objects/24. Coding Exercise 4 (Solution).mp426.35MB
  168. 4. Pandas Series and Index Objects/3. Analyzing Numerical Series with unique(), nunique() and value_counts().mp472.72MB
  169. 4. Pandas Series and Index Objects/5. EXCURSUS Updating Pandas Anaconda.mp477.11MB
  170. 4. Pandas Series and Index Objects/6. Analyzing non-numerical Series with unique(), nunique(), value_counts().mp442.89MB
  171. 4. Pandas Series and Index Objects/7. Creating Pandas Series (Part 1).mp438.08MB
  172. 4. Pandas Series and Index Objects/8. Creating Pandas Series (Part 2).mp426.74MB
  173. 4. Pandas Series and Index Objects/9. Indexing and Slicing Pandas Series.mp466.22MB
  174. 5. DataFrame Basics II/10. Creating Columns based on other Columns.mp434.56MB
  175. 5. DataFrame Basics II/11. Adding Columns with insert().mp413.07MB
  176. 5. DataFrame Basics II/14. Coding Exercise 5 (Intro).mp410.73MB
  177. 5. DataFrame Basics II/15. Coding Exercise 5 (Solution).mp457.67MB
  178. 5. DataFrame Basics II/2. Filtering DataFrames by one Condition.mp452.92MB
  179. 5. DataFrame Basics II/3. Filtering DataFrames by many Conditions (AND).mp425.93MB
  180. 5. DataFrame Basics II/4. Filtering DataFrames by many Conditions (OR).mp430.82MB
  181. 5. DataFrame Basics II/5. Advanced Filtering with between(), isin() and ~.mp465.67MB
  182. 5. DataFrame Basics II/6. any() and all().mp417.57MB
  183. 5. DataFrame Basics II/7. Removing Columns.mp436.01MB
  184. 5. DataFrame Basics II/8. Removing Rows.mp449.62MB
  185. 5. DataFrame Basics II/9. Adding new Columns to a DataFrame.mp417.88MB
  186. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/2. Best Practice (How you should do it).mp452.6MB
  187. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/3. Chained Indexing How you should NOT do it (Part 1).mp460.07MB
  188. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/4. Chained Indexing How you should NOT do it (Part 2).mp458.84MB
  189. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/5. View vs. Copy.mp434.54MB
  190. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/6. Simple Rules what to do when....mp445.85MB
  191. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/8. Coding Exercise 6 (Intro).mp48.73MB
  192. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/9. Coding Exercise 6 (Solution).mp439.4MB
  193. 7. DataFrame Basics III/10. Hierarchical Indexing (Part 1).mp472.59MB
  194. 7. DataFrame Basics III/11. Hierarchical Indexing (Part 2).mp472.59MB
  195. 7. DataFrame Basics III/12. String Operations (Part 1).mp441.19MB
  196. 7. DataFrame Basics III/13. String Operations (Part 2).mp455.19MB
  197. 7. DataFrame Basics III/14. Coding Exercise 8 (Intro).mp48.19MB
  198. 7. DataFrame Basics III/15. Coding Exercise 8 (Solution).mp458.22MB
  199. 7. DataFrame Basics III/2. Sorting DataFrames with sort_index() and sort_values().mp454.32MB
  200. 7. DataFrame Basics III/3. Ranking DataFrames with rank().mp443.47MB
  201. 7. DataFrame Basics III/4. nunique() and nlargest() nsmallest() with DataFrames.mp432.6MB
  202. 7. DataFrame Basics III/5. Summary Statistics and Accumulations.mp457.47MB
  203. 7. DataFrame Basics III/6. The agg() method.mp422.83MB
  204. 7. DataFrame Basics III/7. Coding Exercise 7 (Intro).mp49.98MB
  205. 7. DataFrame Basics III/8. Coding Exercise 7 (Solution).mp439.82MB
  206. 7. DataFrame Basics III/9. User-defined Functions with apply(), map() and applymap().mp474.33MB
  207. 8. Visualization with Matplotlib/2. The plot() method.mp470.25MB
  208. 8. Visualization with Matplotlib/3. Customization of Plots.mp4102.99MB
  209. 8. Visualization with Matplotlib/4. Histograms (Part 1).mp424.58MB
  210. 8. Visualization with Matplotlib/5. Histograms (Part 2).mp434.12MB
  211. 8. Visualization with Matplotlib/6. Barcharts and Piecharts.mp419.99MB
  212. 8. Visualization with Matplotlib/7. Scatterplots.mp436.14MB
  213. 8. Visualization with Matplotlib/8. Coding Exercise 9 (Intro).mp49.67MB
  214. 8. Visualization with Matplotlib/9. Coding Exercise 9 (Solution).mp436.78MB
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