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[] Udemy - The Complete Pandas Bootcamp 2020 Data Science with Python

  • 收录时间:2020-06-07 19:11:27
  • 文件大小:11GB
  • 下载次数:63
  • 最近下载:2021-01-05 02:09:12
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文件列表

  1. 23. Python Basics/7. Data Types Lists (Part 2).mp4 134MB
  2. 11. Cleaning Data/17. Coding Exercise 11 (Solution).mp4 130MB
  3. 17. ---PART 3 COMPREHENSIVE PROJECT CHALLENGE---/4. Olympic Medal Tables (Solution Part 2).mp4 129MB
  4. 23. Python Basics/18. Visualization with Matplotlib.mp4 124MB
  5. 19. Time Series Basics/5. Creating a customized DatetimeIndex with pd.date_range().mp4 115MB
  6. 8. Visualization with Matplotlib/3. Customization of Plots.mp4 103MB
  7. 14. Reshaping and Pivoting DataFrames/6. pd.crosstab().mp4 99MB
  8. 12. Merging, Joining, and Concatenating Data/13. Joining on different Column Names Indexes.mp4 95MB
  9. 25. Statistical Concepts/1. Statistics - Overview, Terms and Vocabulary.mp4 93MB
  10. 10. Importing Data/1. Importing csv-files with pd.read_csv.mp4 91MB
  11. 11. Cleaning Data/5. Detection of missing Values.mp4 89MB
  12. 11. Cleaning Data/10. Handling Removing Duplicates.srt 89MB
  13. 11. Cleaning Data/10. Handling Removing Duplicates.mp4 89MB
  14. 12. Merging, Joining, and Concatenating Data/2. Adding Rows with append() and pd.concat() (Part 1).mp4 88MB
  15. 1. Getting Started/5. Installation of Anaconda.mp4 86MB
  16. 23. Python Basics/11. Conditional Statements (if, elif, else, while).mp4 86MB
  17. 19. Time Series Basics/7. Downsampling Time Series with resample() (Part 1).mp4 85MB
  18. 11. Cleaning Data/6. Removing missing values.mp4 85MB
  19. 15. Data Preparation and Feature Creation/8. Discretization and Binning with pd.qcut().mp4 85MB
  20. 16. Advanced Visualization with Seaborn/3. Categorical Plots.mp4 85MB
  21. 24. The Numpy Package/11. Visualization and (Linear) Regression.mp4 85MB
  22. 13. GroupBy Operations/16. Coding Exercise 13 (Solution).mp4 82MB
  23. 11. Cleaning Data/2. String Operations.mp4 81MB
  24. 12. Merging, Joining, and Concatenating Data/6. Outer Joins with merge().mp4 80MB
  25. 16. Advanced Visualization with Seaborn/4. Joint Plots Regression Plots.mp4 80MB
  26. 11. Cleaning Data/9. Detection of Duplicates.mp4 79MB
  27. 13. GroupBy Operations/13. stack() and unstack().mp4 79MB
  28. 20. Time Series Advanced Financial Time Series/12. Filling NA Values with bfill, ffill and interpolation.mp4 78MB
  29. 23. Python Basics/5. Data Types Strings.mp4 78MB
  30. 3. Pandas Basics (DataFrame Basics I)/16. Slicing Rows and Columns with loc (label-based indexing).mp4 78MB
  31. 4. Pandas Series and Index Objects/17. Changing Row Index with set_index() and reset_index().mp4 75MB
  32. 7. DataFrame Basics III/9. User-defined Functions with apply(), map() and applymap().mp4 74MB
  33. 10. Importing Data/3. Importing Data from Excel with pd.read_excel().mp4 74MB
  34. 24. The Numpy Package/5. Numpy Arrays Indexing and Slicing of multi-dimensional Arrays.mp4 74MB
  35. 15. Data Preparation and Feature Creation/6. Discretization and Binning with pd.cut() (Part 1).mp4 73MB
  36. 7. DataFrame Basics III/11. Hierarchical Indexing (Part 2).mp4 73MB
  37. 7. DataFrame Basics III/10. Hierarchical Indexing (Part 1).mp4 73MB
  38. 10. Importing Data/4. Importing messy Data from Excel with pd.read_excel().mp4 72MB
  39. 20. Time Series Advanced Financial Time Series/3. Importing Stock Price Data from Yahoo Finance (it still works!).mp4 72MB
  40. 13. GroupBy Operations/5. split-apply-combine applied.mp4 71MB
  41. 8. Visualization with Matplotlib/2. The plot() method.mp4 70MB
  42. 7. DataFrame Basics III/2. Sorting DataFrames with sort_index() and sort_values() (Version 1.0 Update).mp4 69MB
  43. 14. Reshaping and Pivoting DataFrames/2. Transposing Rows and Columns.mp4 68MB
  44. 11. Cleaning Data/1. First Inspection & Handling of inconsistent Data.mp4 68MB
  45. 24. The Numpy Package/7. Generating Random Numbers.mp4 68MB
  46. 4. Pandas Series and Index Objects/3. Analyzing Numerical Series with unique(), nunique() and value_counts().mp4 67MB
  47. 1. Getting Started/7. How to use Jupyter Notebooks.mp4 66MB
  48. 4. Pandas Series and Index Objects/7. Indexing and Slicing Pandas Series.mp4 66MB
  49. 5. DataFrame Basics II/5. Advanced Filtering with between(), isin() and ~.mp4 66MB
  50. 1. Getting Started/6. Opening a Jupyter Notebook.mp4 65MB
  51. 3. Pandas Basics (DataFrame Basics I)/12. Selecting Rows with iloc (position-based indexing).mp4 65MB
  52. 24. The Numpy Package/2. Numpy Arrays Vectorization.mp4 65MB
  53. 23. Python Basics/15. User Defined Functions (Part 1).mp4 64MB
  54. 15. Data Preparation and Feature Creation/2. Arithmetic Operations (Part 1).mp4 64MB
  55. 10. Importing Data/2. Importing messy csv-files with pd.read_csv.mp4 63MB
  56. 23. Python Basics/6. Data Types Lists (Part 1).mp4 63MB
  57. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/3. Chained Indexing How you should NOT do it (Part 1).mp4 60MB
  58. 23. Python Basics/10. Operators & Booleans.mp4 60MB
  59. 3. Pandas Basics (DataFrame Basics I)/1. Create your very first Pandas DataFrame (from csv).mp4 59MB
  60. 25. Statistical Concepts/26. Probabilities and Z-Values with scipy.stats.mp4 59MB
  61. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/4. Chained Indexing How you should NOT do it (Part 2).mp4 59MB
  62. 15. Data Preparation and Feature Creation/3. Arithmetic Operations (Part 2).mp4 58MB
  63. 23. Python Basics/12. For Loops.mp4 58MB
  64. 14. Reshaping and Pivoting DataFrames/4. Limits of pivot().mp4 58MB
  65. 7. DataFrame Basics III/15. Coding Exercise 8 (Solution).mp4 58MB
  66. 14. Reshaping and Pivoting DataFrames/5. pivot_table().mp4 58MB
  67. 10. Importing Data/5. Importing Data from the Web with pd.read_html().mp4 58MB
  68. 19. Time Series Basics/2. Converting strings to datetime objects with pd.to_datetime().mp4 58MB
  69. 17. ---PART 3 COMPREHENSIVE PROJECT CHALLENGE---/2. Olympic Medal Tables (Instruction & Hints).mp4 58MB
  70. 5. DataFrame Basics II/16. Coding Exercise 5 (Solution).mp4 58MB
  71. 7. DataFrame Basics III/5. Summary Statistics and Accumulations.mp4 57MB
  72. 23. Python Basics/16. User Defined Functions (Part 2).mp4 57MB
  73. 12. Merging, Joining, and Concatenating Data/3. Adding Rows with pd.concat() (Part 2).mp4 57MB
  74. 15. Data Preparation and Feature Creation/10. Scaling Standardization.mp4 56MB
  75. 3. Pandas Basics (DataFrame Basics I)/3. First Data Inspection.mp4 56MB
  76. 14. Reshaping and Pivoting DataFrames/3. Pivoting DataFrames with pivot().mp4 56MB
  77. 15. Data Preparation and Feature Creation/11. Creating Dummy Variables.mp4 55MB
  78. 7. DataFrame Basics III/13. String Operations (Part 2).mp4 55MB
  79. 3. Pandas Basics (DataFrame Basics I)/5. Make it easy TAB Completion and Tooltip.mp4 54MB
  80. 20. Time Series Advanced Financial Time Series/9. Financial Time Series - Return and Risk.mp4 54MB
  81. 24. The Numpy Package/3. Numpy Arrays Indexing and Slicing.mp4 53MB
  82. 5. DataFrame Basics II/2. Filtering DataFrames by one Condition.mp4 53MB
  83. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/2. Best Practice (How you should do it).mp4 53MB
  84. 23. Python Basics/17. User Defined Functions (Part 3).mp4 52MB
  85. 19. Time Series Basics/10. Advanced Indexing with reindex().mp4 50MB
  86. 13. GroupBy Operations/3. Splitting with many Keys.mp4 50MB
  87. 24. The Numpy Package/8. Performance Issues.mp4 50MB
  88. 5. DataFrame Basics II/8. Removing Rows.mp4 50MB
  89. 23. Python Basics/4. Data Types Integers and Floats.mp4 49MB
  90. 14. Reshaping and Pivoting DataFrames/7. melting DataFrames with melt().mp4 49MB
  91. 19. Time Series Basics/8. Downsampling Time Series with resample (Part 2).mp4 49MB
  92. 1. Getting Started/1. Overview Student FAQ.mp4 48MB
  93. 19. Time Series Basics/4. Indexing and Slicing Time Series.mp4 48MB
  94. 25. Statistical Concepts/27. Confidence Intervals with scipy.stats.mp4 48MB
  95. 13. GroupBy Operations/4. split-apply-combine explained.mp4 47MB
  96. 25. Statistical Concepts/29. Cleaning and preparing the Data - Movies Database (Part 1).mp4 47MB
  97. 3. Pandas Basics (DataFrame Basics I)/4. Built-in Functions, Attributes and Methods with Pandas.mp4 47MB
  98. 13. GroupBy Operations/2. Understanding the GroupBy Object.mp4 46MB
  99. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/6. Simple Rules what to do when....mp4 46MB
  100. 11. Cleaning Data/4. Intro NA values missing values.mp4 46MB
  101. 24. The Numpy Package/9. Case Study Numpy vs. Python Standard Library.mp4 46MB
  102. 11. Cleaning Data/14. Categorical Data.mp4 45MB
  103. 24. The Numpy Package/13. Numpy Quiz Solution.mp4 45MB
  104. 24. The Numpy Package/10. Summary Statistics.mp4 45MB
  105. 13. GroupBy Operations/10. Replacing NA Values by group-specific Values.mp4 45MB
  106. 20. Time Series Advanced Financial Time Series/11. Helpful DatetimeIndex Attributes and Methods.mp4 44MB
  107. 20. Time Series Advanced Financial Time Series/5. Normalizing Time Series to a Base Value (100).mp4 44MB
  108. 24. The Numpy Package/6. Numpy Arrays Boolean Indexing.mp4 44MB
  109. 11. Cleaning Data/12. Detection of Outliers.mp4 44MB
  110. 20. Time Series Advanced Financial Time Series/8. Measuring Stock Performance with MEAN Returns and STD of Returns.mp4 44MB
  111. 1. Getting Started/2. Tips How to get the most out of this course.mp4 44MB
  112. 7. DataFrame Basics III/3. Ranking DataFrames with rank().mp4 43MB
  113. 5. DataFrame Basics II/12. Creating DataFrames from Scratch with pd.DataFrame().mp4 43MB
  114. 4. Pandas Series and Index Objects/15. First Steps with Pandas Index Objects.mp4 43MB
  115. 4. Pandas Series and Index Objects/4. Analyzing non-numerical Series with unique(), nunique(), value_counts().mp4 43MB
  116. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/13. Removal of prior Version Deprecations.mp4 43MB
  117. 16. Advanced Visualization with Seaborn/5. Matrixplots Heatmaps.mp4 43MB
  118. 13. GroupBy Operations/11. Generalizing split-apply-combine with apply().mp4 43MB
  119. 15. Data Preparation and Feature Creation/4. TransformationMapping with map().mp4 43MB
  120. 20. Time Series Advanced Financial Time Series/4. Initial Inspection and Visualization.mp4 42MB
  121. 3. Pandas Basics (DataFrame Basics I)/19. Summary, Best Practices and Outlook.mp4 42MB
  122. 23. Python Basics/8. Data Types Tuples.mp4 42MB
  123. 19. Time Series Basics/1. Importing Time Series Data from csv-files.mp4 42MB
  124. 4. Pandas Series and Index Objects/8. Sorting of Series and Introduction to the inplace - parameter.mp4 41MB
  125. 7. DataFrame Basics III/12. String Operations (Part 1).mp4 41MB
  126. 24. The Numpy Package/1. Introduction to Numpy Arrays.mp4 41MB
  127. 3. Pandas Basics (DataFrame Basics I)/2. Pandas Display Options and the methods head() & tail().mp4 40MB
  128. 20. Time Series Advanced Financial Time Series/7. The methods diff() and pct_change().mp4 40MB
  129. 7. DataFrame Basics III/8. Coding Exercise 7 (Solution).mp4 40MB
  130. 25. Statistical Concepts/34. A simple Linear Regression Model with numpy & Scipy.mp4 40MB
  131. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/9. Coding Exercise 6 (Solution).mp4 39MB
  132. 15. Data Preparation and Feature Creation/9. Floors and Caps.mp4 39MB
  133. 3. Pandas Basics (DataFrame Basics I)/18. Indexing and Slicing with reindex().mp4 39MB
  134. 12. Merging, Joining, and Concatenating Data/4. Arithmetic with Pandas Objects Data Alignment.mp4 39MB
  135. 11. Cleaning Data/3. Changing Datatype of Columns with astype().mp4 39MB
  136. 19. Time Series Basics/9. The PeriodIndex object.mp4 39MB
  137. 12. Merging, Joining, and Concatenating Data/14. Joining on more than one Column.mp4 39MB
  138. 25. Statistical Concepts/24. The Standard Normal Distribution and Z-Values.mp4 39MB
  139. 4. Pandas Series and Index Objects/14. Coding Exercise 3 (Solution).mp4 39MB
  140. 17. ---PART 3 COMPREHENSIVE PROJECT CHALLENGE---/3. Olympic Medal Tables (Solution Part 1).mp4 38MB
  141. 23. Python Basics/20. Python Basics Quiz Solution.mp4 38MB
  142. 23. Python Basics/14. Generating Random Numbers.mp4 38MB
  143. 4. Pandas Series and Index Objects/5. Creating Pandas Series (Part 1).mp4 38MB
  144. 4. Pandas Series and Index Objects/11. Manipulating Pandas Series.mp4 38MB
  145. 8. Visualization with Matplotlib/9. Coding Exercise 9 (Solution).mp4 37MB
  146. 23. Python Basics/13. Key words break, pass, continue.mp4 37MB
  147. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/4. Important Recap Pandas Display Options (Changed in Version 0.25).mp4 37MB
  148. 25. Statistical Concepts/5. Relative and Cumulative Frequencies with plt.hist().mp4 36MB
  149. 8. Visualization with Matplotlib/7. Scatterplots.mp4 36MB
  150. 5. DataFrame Basics II/7. Removing Columns.mp4 36MB
  151. 20. Time Series Advanced Financial Time Series/6. The shift() method.mp4 36MB
  152. 25. Statistical Concepts/17. Probability Distributions - Overview.mp4 36MB
  153. 25. Statistical Concepts/3. Population vs. Sample.mp4 36MB
  154. 24. The Numpy Package/4. Numpy Arrays Shape and Dimensions.mp4 36MB
  155. 12. Merging, Joining, and Concatenating Data/15. pd.merge() and join().mp4 35MB
  156. 13. GroupBy Operations/9. Transformation with transform().mp4 35MB
  157. 19. Time Series Basics/3. Initial Analysis Visualization of Time Series.mp4 35MB
  158. 5. DataFrame Basics II/10. Creating Columns based on other Columns.mp4 35MB
  159. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/5. View vs. Copy.mp4 35MB
  160. 23. Python Basics/2. First Steps.mp4 34MB
  161. 8. Visualization with Matplotlib/5. Histograms (Part 2).mp4 34MB
  162. 3. Pandas Basics (DataFrame Basics I)/23. Advanced Indexing and Slicing (optional).mp4 34MB
  163. 15. Data Preparation and Feature Creation/5. Conditional Transformation.mp4 33MB
  164. 13. GroupBy Operations/12. Hierarchical Indexing with Groupby.mp4 33MB
  165. 15. Data Preparation and Feature Creation/7. Discretization and Binning with pd.cut() (Part 2).mp4 33MB
  166. 7. DataFrame Basics III/4. nunique() and nlargest() nsmallest() with DataFrames.mp4 33MB
  167. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/10. The NEW StringDtype.mp4 32MB
  168. 12. Merging, Joining, and Concatenating Data/8. Outer Joins (without Intersection) with merge().mp4 31MB
  169. 23. Python Basics/3. Variables.mp4 31MB
  170. 1. Getting Started/3. Did you know that....mp4 31MB
  171. 3. Pandas Basics (DataFrame Basics I)/8. Explore your own Dataset Coding Exercise 1 (Solution).mp4 31MB
  172. 25. Statistical Concepts/30. Cleaning and preparing the Data - Movies Database (Part 2).mp4 31MB
  173. 5. DataFrame Basics II/4. Filtering DataFrames by many Conditions (OR).mp4 31MB
  174. 13. GroupBy Operations/7. Advanced aggregation with agg().mp4 30MB
  175. 11. Cleaning Data/13. Handling Removing Outliers.mp4 30MB
  176. 15. Data Preparation and Feature Creation/12. String Operations.mp4 30MB
  177. 4. Pandas Series and Index Objects/10. idxmin() and idxmax().mp4 29MB
  178. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/6. NEW Extension dtypes (nullable dtypes) Why do we need them.mp4 28MB
  179. 25. Statistical Concepts/18. Discrete Uniform Distributions.mp4 28MB
  180. 3. Pandas Basics (DataFrame Basics I)/22. Coding Exercise 2 (Solution).mp4 28MB
  181. 4. Pandas Series and Index Objects/19. Renaming Index & Column Labels with rename().mp4 28MB
  182. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/8. NEW pd.NA value for missing values.mp4 28MB
  183. 25. Statistical Concepts/9. Variability around the Central Tendency Dispersion (Theory).mp4 28MB
  184. 25. Statistical Concepts/28. Covariance and Correlation Coefficient (Theory).mp4 28MB
  185. 25. Statistical Concepts/14. How to calculate Skew and Kurtosis with scipy.stats.mp4 27MB
  186. 12. Merging, Joining, and Concatenating Data/12. Right Joins with merge().mp4 27MB
  187. 17. ---PART 3 COMPREHENSIVE PROJECT CHALLENGE---/5. Olympic Medal Tables (Solution Part 3).mp4 27MB
  188. 25. Statistical Concepts/22. Normal Distribution - Probability Density Function (pdf) with scipy.stats.mp4 27MB
  189. 4. Pandas Series and Index Objects/6. Creating Pandas Series (Part 2).mp4 27MB
  190. 3. Pandas Basics (DataFrame Basics I)/9. Selecting Columns.mp4 27MB
  191. 3. Pandas Basics (DataFrame Basics I)/7. Explore your own Dataset Coding Exercise 1 (Intro).mp4 27MB
  192. 4. Pandas Series and Index Objects/22. Coding Exercise 4 (Solution).mp4 26MB
  193. 25. Statistical Concepts/36. Case Study (Part 1) The Market Model (Single Factor Model).mp4 26MB
  194. 5. DataFrame Basics II/3. Filtering DataFrames by many Conditions (AND).mp4 26MB
  195. 20. Time Series Advanced Financial Time Series/10. Financial Time Series - Covariance and Correlation.mp4 26MB
  196. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/7. Creating the NEW extension dtypes with convert_dtypes().mp4 26MB
  197. 25. Statistical Concepts/15. How to generate Random Numbers with Numpy.mp4 25MB
  198. 11. Cleaning Data/7. Replacing missing values.mp4 25MB
  199. 8. Visualization with Matplotlib/4. Histograms (Part 1).mp4 25MB
  200. 3. Pandas Basics (DataFrame Basics I)/13. Slicing Rows and Columns with iloc (position-based indexing).mp4 24MB
  201. 25. Statistical Concepts/21. Creating a normally distributed Random Variable.mp4 24MB
  202. 12. Merging, Joining, and Concatenating Data/11. Left Joins with merge().mp4 24MB
  203. 25. Statistical Concepts/31. How to calculate Covariance and Correlation in Python.mp4 24MB
  204. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/11. The NEW nullable BooleanDtype.mp4 23MB
  205. 7. DataFrame Basics III/6. The agg() method.mp4 23MB
  206. 25. Statistical Concepts/4. Visualizing Frequency Distributions with plt.hist().mp4 23MB
  207. 25. Statistical Concepts/7. Coding Measures of Central Tendency - Mean and Median.mp4 22MB
  208. 16. Advanced Visualization with Seaborn/2. First Steps in Seaborn.mp4 22MB
  209. 12. Merging, Joining, and Concatenating Data/9. Left Joins (without Intersection) with merge().mp4 22MB
  210. 20. Time Series Advanced Financial Time Series/2. Getting Ready (Installing required package).mp4 22MB
  211. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/12. Addition of the ignore_index parameter.mp4 22MB
  212. 23. Python Basics/9. Data Types Sets.mp4 21MB
  213. 3. Pandas Basics (DataFrame Basics I)/15. Selecting Rows with loc (label-based indexing).mp4 21MB
  214. 4. Pandas Series and Index Objects/18. Changing Column Labels.mp4 21MB
  215. 25. Statistical Concepts/6. Measures of Central Tendency (Theory).mp4 21MB
  216. 13. GroupBy Operations/8. GroupBy Aggregation with Relabeling (NEW - Pandas Version 0.25).mp4 21MB
  217. 11. Cleaning Data/8. Intro Duplicates.mp4 20MB
  218. 25. Statistical Concepts/19. Continuous Uniform Distributions.mp4 20MB
  219. 25. Statistical Concepts/32. Correlation and Scatterplots – visual Interpretation.mp4 20MB
  220. 8. Visualization with Matplotlib/6. Barcharts and Piecharts.mp4 20MB
  221. 1. Getting Started/8. How to tackle Pandas Version 1.0.mp4 19MB
  222. 4. Pandas Series and Index Objects/2. First Steps with Pandas Series.mp4 19MB
  223. 2. ---PART 1 PANDAS FROM ZERO TO HERO (BUILDING BLOCKS)---/2. Download Part 1 Course Materials.mp4 19MB
  224. 11. Cleaning Data/15. Pandas Version 1.0 New dtypes and pd.NA.mp4 18MB
  225. 25. Statistical Concepts/20. The Normal Distribution (Theory).mp4 18MB
  226. 11. Cleaning Data/15. Pandas Version 1.0 New dtypes and pd.NA.srt 18MB
  227. 2. ---PART 1 PANDAS FROM ZERO TO HERO (BUILDING BLOCKS)---/1. Intro to Tabular Data Pandas.mp4 18MB
  228. 25. Statistical Concepts/13. Skew and Kurtosis (Theory).mp4 18MB
  229. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/9. The NEW nullable Int64Dtype.mp4 18MB
  230. 5. DataFrame Basics II/9. Adding new Columns to a DataFrame.mp4 18MB
  231. 5. DataFrame Basics II/6. any() and all().mp4 18MB
  232. 25. Statistical Concepts/11. Percentiles with PythonNumpy.mp4 18MB
  233. 25. Statistical Concepts/16. Reproducibility with np.random.seed().mp4 17MB
  234. 5. DataFrame Basics II/13. Adding new Rows (hands-on approach).mp4 17MB
  235. 4. Pandas Series and Index Objects/9. nlargest() and nsmallest().mp4 17MB
  236. 25. Statistical Concepts/8. Coding Measures of Central Tendency - Geometric Mean.mp4 17MB
  237. 25. Statistical Concepts/12. Variance and Standard Deviation with PythonNumpy.mp4 16MB
  238. 12. Merging, Joining, and Concatenating Data/7. Inner Joins with merge().mp4 16MB
  239. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/1. Intro and Overview.mp4 15MB
  240. 25. Statistical Concepts/23. Normal Distribution - Cumulative Distribution Function (cdf) with scipy.stats.mp4 15MB
  241. 12. Merging, Joining, and Concatenating Data/10. Right Joins (without Intersection) with merge().mp4 15MB
  242. 4. Pandas Series and Index Objects/16. Creating Index Objects from Scratch.mp4 15MB
  243. 25. Statistical Concepts/25. Properties of the Standard Normal Distribution (Theory).mp4 15MB
  244. 5. DataFrame Basics II/11. Adding Columns with insert().mp4 13MB
  245. 25. Statistical Concepts/2.1 Course_Materials_Statistics.zip 12MB
  246. 19. Time Series Basics/6. More on pd.date_range().mp4 12MB
  247. 25. Statistical Concepts/35. How to interpret Intercept and Slope Coefficient.mp4 12MB
  248. 25. Statistical Concepts/10. Minimum, Maximum and Range with PythonNumpy.mp4 12MB
  249. 25. Statistical Concepts/33. What is Linear Regression (Theory).mp4 12MB
  250. 25. Statistical Concepts/37. Case Study (Part 2) The Market Model (Single Factor Model).mp4 10MB
  251. 3. Pandas Basics (DataFrame Basics I)/11. Zero-based Indexing and Negative Indexing.mp4 10MB
  252. 13. GroupBy Operations/1. Intro.mp4 10MB
  253. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/5. Info() method - new and extended output.mp4 10MB
  254. 3. Pandas Basics (DataFrame Basics I)/10. Selecting one Column with the dot notation.mp4 9MB
  255. 17. ---PART 3 COMPREHENSIVE PROJECT CHALLENGE---/1.1 Course_Materials_Part3.zip 8MB
  256. 3. Pandas Basics (DataFrame Basics I)/21. Coding Exercise 2 (Intro).mp4 8MB
  257. 26. Download .py files/1.2 Course_Materials_Part2.zip 6MB
  258. 23. Python Basics/1. Intro.mp4 6MB
  259. 11. Cleaning Data/11. The ignore_index parameter (NEW in Pandas 1.0).mp4 6MB
  260. 9. ----PART 2 FULL DATA WORKFLOW A-Z----/2.1 Course_Materials_Part2.zip 5MB
  261. 26. Download .py files/1.1 Course_Materials_Part1.zip 1MB
  262. 2. ---PART 1 PANDAS FROM ZERO TO HERO (BUILDING BLOCKS)---/2.1 Course_Materials_Part1.zip 1MB
  263. 25. Statistical Concepts/1.1 Overview.pdf 1023KB
  264. 18. ----PART 4 MANAGING TIME SERIES DATA WITH PANDAS----/2.1 Course_Materials_Part4.zip 831KB
  265. 25. Statistical Concepts/17.1 Prob_distr.pdf 478KB
  266. 2. ---PART 1 PANDAS FROM ZERO TO HERO (BUILDING BLOCKS)---/1.1 tabdata.pdf 472KB
  267. 25. Statistical Concepts/13.1 skew_kurtosis.pdf 425KB
  268. 25. Statistical Concepts/20.1 Normal.pdf 412KB
  269. 25. Statistical Concepts/25.1 standard_normal.pdf 394KB
  270. 25. Statistical Concepts/6.1 Central_tendency.pdf 299KB
  271. 25. Statistical Concepts/9.1 Dispersion.pdf 299KB
  272. 25. Statistical Concepts/28.1 Cov_Corr.pdf 228KB
  273. 3. Pandas Basics (DataFrame Basics I)/11.1 positions.pdf 194KB
  274. 25. Statistical Concepts/35.1 Coeff.pdf 178KB
  275. 25. Statistical Concepts/33.1 Regression.pdf 150KB
  276. 24. The Numpy Package/1.1 Numpy_basics.zip 106KB
  277. 3. Pandas Basics (DataFrame Basics I)/14.1 pandas-iloc.pdf 72KB
  278. 3. Pandas Basics (DataFrame Basics I)/17.1 Pandas-loc.pdf 68KB
  279. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/3.1 Course_Materials_Version_1_0.zip 27KB
  280. 17. ---PART 3 COMPREHENSIVE PROJECT CHALLENGE---/4. Olympic Medal Tables (Solution Part 2).srt 23KB
  281. 14. Reshaping and Pivoting DataFrames/6. pd.crosstab().srt 21KB
  282. 23. Python Basics/7. Data Types Lists (Part 2).srt 21KB
  283. 11. Cleaning Data/17. Coding Exercise 11 (Solution).srt 20KB
  284. 11. Cleaning Data/6. Removing missing values.srt 18KB
  285. 19. Time Series Basics/5. Creating a customized DatetimeIndex with pd.date_range().srt 18KB
  286. 11. Cleaning Data/5. Detection of missing Values.srt 17KB
  287. 16. Advanced Visualization with Seaborn/3. Categorical Plots.srt 17KB
  288. 1. Getting Started/7. How to use Jupyter Notebooks.srt 17KB
  289. 4. Pandas Series and Index Objects/3. Analyzing Numerical Series with unique(), nunique() and value_counts().srt 17KB
  290. 23. Python Basics/18. Visualization with Matplotlib.srt 17KB
  291. 13. GroupBy Operations/13. stack() and unstack().srt 17KB
  292. 7. DataFrame Basics III/9. User-defined Functions with apply(), map() and applymap().srt 16KB
  293. 24. The Numpy Package/13. Numpy Quiz Solution.srt 16KB
  294. 19. Time Series Basics/7. Downsampling Time Series with resample() (Part 1).srt 16KB
  295. 12. Merging, Joining, and Concatenating Data/13. Joining on different Column Names Indexes.srt 16KB
  296. 15. Data Preparation and Feature Creation/8. Discretization and Binning with pd.qcut().srt 16KB
  297. 14. Reshaping and Pivoting DataFrames/5. pivot_table().srt 16KB
  298. 12. Merging, Joining, and Concatenating Data/6. Outer Joins with merge().srt 16KB
  299. 12. Merging, Joining, and Concatenating Data/2. Adding Rows with append() and pd.concat() (Part 1).srt 16KB
  300. 23. Python Basics/11. Conditional Statements (if, elif, else, while).srt 16KB
  301. 13. GroupBy Operations/16. Coding Exercise 13 (Solution).srt 15KB
  302. 11. Cleaning Data/9. Detection of Duplicates.srt 15KB
  303. 25. Statistical Concepts/1. Statistics - Overview, Terms and Vocabulary.srt 15KB
  304. 11. Cleaning Data/2. String Operations.srt 15KB
  305. 15. Data Preparation and Feature Creation/2. Arithmetic Operations (Part 1).srt 15KB
  306. 7. DataFrame Basics III/11. Hierarchical Indexing (Part 2).srt 15KB
  307. 15. Data Preparation and Feature Creation/3. Arithmetic Operations (Part 2).srt 15KB
  308. 10. Importing Data/3. Importing Data from Excel with pd.read_excel().srt 14KB
  309. 24. The Numpy Package/11. Visualization and (Linear) Regression.srt 14KB
  310. 23. Python Basics/20. Python Basics Quiz Solution.srt 14KB
  311. 3. Pandas Basics (DataFrame Basics I)/3. First Data Inspection.srt 14KB
  312. 13. GroupBy Operations/5. split-apply-combine applied.srt 14KB
  313. 8. Visualization with Matplotlib/3. Customization of Plots.srt 14KB
  314. 16. Advanced Visualization with Seaborn/4. Joint Plots Regression Plots.srt 14KB
  315. 15. Data Preparation and Feature Creation/6. Discretization and Binning with pd.cut() (Part 1).srt 14KB
  316. 11. Cleaning Data/1. First Inspection & Handling of inconsistent Data.srt 13KB
  317. 14. Reshaping and Pivoting DataFrames/2. Transposing Rows and Columns.srt 13KB
  318. 25. Statistical Concepts/26. Probabilities and Z-Values with scipy.stats.srt 13KB
  319. 7. DataFrame Basics III/10. Hierarchical Indexing (Part 1).srt 13KB
  320. 5. DataFrame Basics II/2. Filtering DataFrames by one Condition.srt 13KB
  321. 14. Reshaping and Pivoting DataFrames/4. Limits of pivot().srt 12KB
  322. 1. Getting Started/1. Overview Student FAQ.srt 12KB
  323. 20. Time Series Advanced Financial Time Series/12. Filling NA Values with bfill, ffill and interpolation.srt 12KB
  324. 7. DataFrame Basics III/13. String Operations (Part 2).srt 12KB
  325. 4. Pandas Series and Index Objects/17. Changing Row Index with set_index() and reset_index().srt 12KB
  326. 7. DataFrame Basics III/5. Summary Statistics and Accumulations.srt 12KB
  327. 13. GroupBy Operations/4. split-apply-combine explained.srt 12KB
  328. 24. The Numpy Package/5. Numpy Arrays Indexing and Slicing of multi-dimensional Arrays.srt 11KB
  329. 12. Merging, Joining, and Concatenating Data/3. Adding Rows with pd.concat() (Part 2).srt 11KB
  330. 14. Reshaping and Pivoting DataFrames/3. Pivoting DataFrames with pivot().srt 11KB
  331. 3. Pandas Basics (DataFrame Basics I)/16. Slicing Rows and Columns with loc (label-based indexing).srt 11KB
  332. 4. Pandas Series and Index Objects/7. Indexing and Slicing Pandas Series.srt 11KB
  333. 23. Python Basics/5. Data Types Strings.srt 11KB
  334. 3. Pandas Basics (DataFrame Basics I)/12. Selecting Rows with iloc (position-based indexing).srt 11KB
  335. 23. Python Basics/12. For Loops.srt 11KB
  336. 23. Python Basics/10. Operators & Booleans.srt 11KB
  337. 19. Time Series Basics/2. Converting strings to datetime objects with pd.to_datetime().srt 11KB
  338. 15. Data Preparation and Feature Creation/11. Creating Dummy Variables.srt 11KB
  339. 1. Getting Started/6. Opening a Jupyter Notebook.srt 11KB
  340. 8. Visualization with Matplotlib/2. The plot() method.srt 11KB
  341. 10. Importing Data/2. Importing messy csv-files with pd.read_csv.srt 11KB
  342. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/2. Best Practice (How you should do it).srt 11KB
  343. 14. Reshaping and Pivoting DataFrames/7. melting DataFrames with melt().srt 11KB
  344. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/3. Chained Indexing How you should NOT do it (Part 1).srt 11KB
  345. 7. DataFrame Basics III/15. Coding Exercise 8 (Solution).srt 11KB
  346. 11. Cleaning Data/4. Intro NA values missing values.srt 11KB
  347. 4. Pandas Series and Index Objects/8. Sorting of Series and Introduction to the inplace - parameter.srt 11KB
  348. 3. Pandas Basics (DataFrame Basics I)/1. Create your very first Pandas DataFrame (from csv).srt 11KB
  349. 20. Time Series Advanced Financial Time Series/8. Measuring Stock Performance with MEAN Returns and STD of Returns.srt 11KB
  350. 5. DataFrame Basics II/16. Coding Exercise 5 (Solution).srt 11KB
  351. 23. Python Basics/15. User Defined Functions (Part 1).srt 10KB
  352. 11. Cleaning Data/12. Detection of Outliers.srt 10KB
  353. 3. Pandas Basics (DataFrame Basics I)/4. Built-in Functions, Attributes and Methods with Pandas.srt 10KB
  354. 7. DataFrame Basics III/2. Sorting DataFrames with sort_index() and sort_values() (Version 1.0 Update).srt 10KB
  355. 16. Advanced Visualization with Seaborn/5. Matrixplots Heatmaps.srt 10KB
  356. 19. Time Series Basics/10. Advanced Indexing with reindex().srt 10KB
  357. 24. The Numpy Package/7. Generating Random Numbers.srt 10KB
  358. 23. Python Basics/2. First Steps.srt 10KB
  359. 19. Time Series Basics/8. Downsampling Time Series with resample (Part 2).srt 10KB
  360. 20. Time Series Advanced Financial Time Series/3. Importing Stock Price Data from Yahoo Finance (it still works!).srt 10KB
  361. 20. Time Series Advanced Financial Time Series/9. Financial Time Series - Return and Risk.srt 10KB
  362. 23. Python Basics/6. Data Types Lists (Part 1).srt 10KB
  363. 13. GroupBy Operations/11. Generalizing split-apply-combine with apply().srt 10KB
  364. 13. GroupBy Operations/2. Understanding the GroupBy Object.srt 10KB
  365. 24. The Numpy Package/2. Numpy Arrays Vectorization.srt 10KB
  366. 5. DataFrame Basics II/12. Creating DataFrames from Scratch with pd.DataFrame().srt 10KB
  367. 15. Data Preparation and Feature Creation/10. Scaling Standardization.srt 10KB
  368. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/6. Simple Rules what to do when....srt 10KB
  369. 19. Time Series Basics/1. Importing Time Series Data from csv-files.srt 10KB
  370. 12. Merging, Joining, and Concatenating Data/14. Joining on more than one Column.srt 9KB
  371. 17. ---PART 3 COMPREHENSIVE PROJECT CHALLENGE---/2. Olympic Medal Tables (Instruction & Hints).srt 9KB
  372. 4. Pandas Series and Index Objects/11. Manipulating Pandas Series.srt 9KB
  373. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/4. Chained Indexing How you should NOT do it (Part 2).srt 9KB
  374. 11. Cleaning Data/14. Categorical Data.srt 9KB
  375. 7. DataFrame Basics III/12. String Operations (Part 1).srt 9KB
  376. 10. Importing Data/5. Importing Data from the Web with pd.read_html().srt 9KB
  377. 12. Merging, Joining, and Concatenating Data/4. Arithmetic with Pandas Objects Data Alignment.srt 9KB
  378. 5. DataFrame Basics II/5. Advanced Filtering with between(), isin() and ~.srt 9KB
  379. 7. DataFrame Basics III/3. Ranking DataFrames with rank().srt 9KB
  380. 24. The Numpy Package/1. Introduction to Numpy Arrays.srt 9KB
  381. 1. Getting Started/5. Installation of Anaconda.srt 9KB
  382. 15. Data Preparation and Feature Creation/9. Floors and Caps.srt 9KB
  383. 10. Importing Data/4. Importing messy Data from Excel with pd.read_excel().srt 9KB
  384. 25. Statistical Concepts/28. Covariance and Correlation Coefficient (Theory).srt 9KB
  385. 23. Python Basics/17. User Defined Functions (Part 3).srt 9KB
  386. 23. Python Basics/4. Data Types Integers and Floats.srt 9KB
  387. 13. GroupBy Operations/10. Replacing NA Values by group-specific Values.srt 9KB
  388. 24. The Numpy Package/9. Case Study Numpy vs. Python Standard Library.srt 9KB
  389. 4. Pandas Series and Index Objects/4. Analyzing non-numerical Series with unique(), nunique(), value_counts().srt 9KB
  390. 15. Data Preparation and Feature Creation/5. Conditional Transformation.srt 9KB
  391. 19. Time Series Basics/4. Indexing and Slicing Time Series.srt 9KB
  392. 24. The Numpy Package/10. Summary Statistics.srt 9KB
  393. 20. Time Series Advanced Financial Time Series/6. The shift() method.srt 9KB
  394. 25. Statistical Concepts/27. Confidence Intervals with scipy.stats.srt 8KB
  395. 20. Time Series Advanced Financial Time Series/7. The methods diff() and pct_change().srt 8KB
  396. 23. Python Basics/3. Variables.srt 8KB
  397. 24. The Numpy Package/3. Numpy Arrays Indexing and Slicing.srt 8KB
  398. 8. Visualization with Matplotlib/7. Scatterplots.srt 8KB
  399. 13. GroupBy Operations/3. Splitting with many Keys.srt 8KB
  400. 5. DataFrame Basics II/8. Removing Rows.srt 8KB
  401. 11. Cleaning Data/3. Changing Datatype of Columns with astype().srt 8KB
  402. 15. Data Preparation and Feature Creation/4. TransformationMapping with map().srt 8KB
  403. 8. Visualization with Matplotlib/5. Histograms (Part 2).srt 8KB
  404. 5. DataFrame Basics II/10. Creating Columns based on other Columns.srt 8KB
  405. 25. Statistical Concepts/17. Probability Distributions - Overview.srt 8KB
  406. 25. Statistical Concepts/34. A simple Linear Regression Model with numpy & Scipy.srt 8KB
  407. 25. Statistical Concepts/9. Variability around the Central Tendency Dispersion (Theory).srt 8KB
  408. 23. Python Basics/14. Generating Random Numbers.srt 8KB
  409. 23. Python Basics/16. User Defined Functions (Part 2).srt 8KB
  410. 23. Python Basics/8. Data Types Tuples.srt 8KB
  411. 20. Time Series Advanced Financial Time Series/5. Normalizing Time Series to a Base Value (100).srt 8KB
  412. 3. Pandas Basics (DataFrame Basics I)/19. Summary, Best Practices and Outlook.srt 8KB
  413. 3. Pandas Basics (DataFrame Basics I)/2. Pandas Display Options and the methods head() & tail().srt 8KB
  414. 25. Statistical Concepts/29. Cleaning and preparing the Data - Movies Database (Part 1).srt 8KB
  415. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/13. Removal of prior Version Deprecations.srt 8KB
  416. 13. GroupBy Operations/12. Hierarchical Indexing with Groupby.srt 8KB
  417. 4. Pandas Series and Index Objects/14. Coding Exercise 3 (Solution).srt 7KB
  418. 13. GroupBy Operations/9. Transformation with transform().srt 7KB
  419. 25. Statistical Concepts/24. The Standard Normal Distribution and Z-Values.srt 7KB
  420. 23. Python Basics/13. Key words break, pass, continue.srt 7KB
  421. 4. Pandas Series and Index Objects/5. Creating Pandas Series (Part 1).srt 7KB
  422. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/9. Coding Exercise 6 (Solution).srt 7KB
  423. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/8. NEW pd.NA value for missing values.srt 7KB
  424. 24. The Numpy Package/6. Numpy Arrays Boolean Indexing.srt 7KB
  425. 20. Time Series Advanced Financial Time Series/11. Helpful DatetimeIndex Attributes and Methods.srt 7KB
  426. 19. Time Series Basics/9. The PeriodIndex object.srt 7KB
  427. 16. Advanced Visualization with Seaborn/2. First Steps in Seaborn.srt 7KB
  428. 25. Statistical Concepts/30. Cleaning and preparing the Data - Movies Database (Part 2).srt 7KB
  429. 25. Statistical Concepts/18. Discrete Uniform Distributions.srt 7KB
  430. 17. ---PART 3 COMPREHENSIVE PROJECT CHALLENGE---/5. Olympic Medal Tables (Solution Part 3).srt 7KB
  431. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/5. View vs. Copy.srt 7KB
  432. 24. The Numpy Package/8. Performance Issues.srt 7KB
  433. 24. The Numpy Package/4. Numpy Arrays Shape and Dimensions.srt 7KB
  434. 13. GroupBy Operations/7. Advanced aggregation with agg().srt 7KB
  435. 25. Statistical Concepts/14. How to calculate Skew and Kurtosis with scipy.stats.srt 7KB
  436. 12. Merging, Joining, and Concatenating Data/15. pd.merge() and join().srt 7KB
  437. 25. Statistical Concepts/20. The Normal Distribution (Theory).srt 7KB
  438. 19. Time Series Basics/3. Initial Analysis Visualization of Time Series.srt 7KB
  439. 11. Cleaning Data/13. Handling Removing Outliers.srt 7KB
  440. 25. Statistical Concepts/3. Population vs. Sample.srt 7KB
  441. 4. Pandas Series and Index Objects/15. First Steps with Pandas Index Objects.srt 7KB
  442. 1. Getting Started/2. Tips How to get the most out of this course.srt 7KB
  443. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/4. Important Recap Pandas Display Options (Changed in Version 0.25).srt 7KB
  444. 4. Pandas Series and Index Objects/6. Creating Pandas Series (Part 2).srt 7KB
  445. 17. ---PART 3 COMPREHENSIVE PROJECT CHALLENGE---/3. Olympic Medal Tables (Solution Part 1).srt 7KB
  446. 3. Pandas Basics (DataFrame Basics I)/9. Selecting Columns.srt 6KB
  447. 25. Statistical Concepts/21. Creating a normally distributed Random Variable.srt 6KB
  448. 25. Statistical Concepts/6. Measures of Central Tendency (Theory).srt 6KB
  449. 11. Cleaning Data/8. Intro Duplicates.srt 6KB
  450. 7. DataFrame Basics III/4. nunique() and nlargest() nsmallest() with DataFrames.srt 6KB
  451. 25. Statistical Concepts/31. How to calculate Covariance and Correlation in Python.srt 6KB
  452. 3. Pandas Basics (DataFrame Basics I)/18. Indexing and Slicing with reindex().srt 6KB
  453. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/10. The NEW StringDtype.srt 6KB
  454. 3. Pandas Basics (DataFrame Basics I)/23. Advanced Indexing and Slicing (optional).srt 6KB
  455. 25. Statistical Concepts/32. Correlation and Scatterplots – visual Interpretation.srt 6KB
  456. 15. Data Preparation and Feature Creation/7. Discretization and Binning with pd.cut() (Part 2).srt 6KB
  457. 5. DataFrame Basics II/7. Removing Columns.srt 6KB
  458. 4. Pandas Series and Index Objects/10. idxmin() and idxmax().srt 6KB
  459. 20. Time Series Advanced Financial Time Series/4. Initial Inspection and Visualization.srt 6KB
  460. 7. DataFrame Basics III/8. Coding Exercise 7 (Solution).srt 6KB
  461. 25. Statistical Concepts/5. Relative and Cumulative Frequencies with plt.hist().srt 6KB
  462. 5. DataFrame Basics II/4. Filtering DataFrames by many Conditions (OR).srt 6KB
  463. 25. Statistical Concepts/36. Case Study (Part 1) The Market Model (Single Factor Model).srt 6KB
  464. 8. Visualization with Matplotlib/9. Coding Exercise 9 (Solution).srt 6KB
  465. 25. Statistical Concepts/15. How to generate Random Numbers with Numpy.srt 6KB
  466. 20. Time Series Advanced Financial Time Series/10. Financial Time Series - Covariance and Correlation.srt 6KB
  467. 12. Merging, Joining, and Concatenating Data/8. Outer Joins (without Intersection) with merge().srt 5KB
  468. 2. ---PART 1 PANDAS FROM ZERO TO HERO (BUILDING BLOCKS)---/1. Intro to Tabular Data Pandas.srt 5KB
  469. 1. Getting Started/4. More FAQ Important Information.html 5KB
  470. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/6. NEW Extension dtypes (nullable dtypes) Why do we need them.srt 5KB
  471. 13. GroupBy Operations/8. GroupBy Aggregation with Relabeling (NEW - Pandas Version 0.25).srt 5KB
  472. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/11. The NEW nullable BooleanDtype.srt 5KB
  473. 15. Data Preparation and Feature Creation/12. String Operations.srt 5KB
  474. 8. Visualization with Matplotlib/4. Histograms (Part 1).srt 5KB
  475. 25. Statistical Concepts/13. Skew and Kurtosis (Theory).srt 5KB
  476. 3. Pandas Basics (DataFrame Basics I)/13. Slicing Rows and Columns with iloc (position-based indexing).srt 5KB
  477. 1. Getting Started/3. Did you know that....srt 5KB
  478. 12. Merging, Joining, and Concatenating Data/11. Left Joins with merge().srt 5KB
  479. 11. Cleaning Data/7. Replacing missing values.srt 5KB
  480. 5. DataFrame Basics II/3. Filtering DataFrames by many Conditions (AND).srt 5KB
  481. 3. Pandas Basics (DataFrame Basics I)/8. Explore your own Dataset Coding Exercise 1 (Solution).srt 5KB
  482. 25. Statistical Concepts/8. Coding Measures of Central Tendency - Geometric Mean.srt 5KB
  483. 4. Pandas Series and Index Objects/2. First Steps with Pandas Series.srt 5KB
  484. 4. Pandas Series and Index Objects/19. Renaming Index & Column Labels with rename().srt 5KB
  485. 25. Statistical Concepts/19. Continuous Uniform Distributions.srt 5KB
  486. 5. DataFrame Basics II/6. any() and all().srt 5KB
  487. 3. Pandas Basics (DataFrame Basics I)/22. Coding Exercise 2 (Solution).srt 5KB
  488. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/7. Creating the NEW extension dtypes with convert_dtypes().srt 5KB
  489. 4. Pandas Series and Index Objects/22. Coding Exercise 4 (Solution).srt 5KB
  490. 12. Merging, Joining, and Concatenating Data/12. Right Joins with merge().srt 5KB
  491. 8. Visualization with Matplotlib/6. Barcharts and Piecharts.srt 5KB
  492. 25. Statistical Concepts/4. Visualizing Frequency Distributions with plt.hist().srt 5KB
  493. 25. Statistical Concepts/22. Normal Distribution - Probability Density Function (pdf) with scipy.stats.srt 4KB
  494. 25. Statistical Concepts/7. Coding Measures of Central Tendency - Mean and Median.srt 4KB
  495. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/12. Addition of the ignore_index parameter.srt 4KB
  496. 25. Statistical Concepts/16. Reproducibility with np.random.seed().srt 4KB
  497. 3. Pandas Basics (DataFrame Basics I)/7. Explore your own Dataset Coding Exercise 1 (Intro).srt 4KB
  498. 4. Pandas Series and Index Objects/9. nlargest() and nsmallest().srt 4KB
  499. 7. DataFrame Basics III/6. The agg() method.srt 4KB
  500. 23. Python Basics/9. Data Types Sets.srt 4KB
  501. 25. Statistical Concepts/11. Percentiles with PythonNumpy.srt 4KB
  502. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/9. The NEW nullable Int64Dtype.srt 4KB
  503. 25. Statistical Concepts/12. Variance and Standard Deviation with PythonNumpy.srt 4KB
  504. 4. Pandas Series and Index Objects/18. Changing Column Labels.srt 4KB
  505. 12. Merging, Joining, and Concatenating Data/9. Left Joins (without Intersection) with merge().srt 4KB
  506. 5. DataFrame Basics II/9. Adding new Columns to a DataFrame.srt 4KB
  507. 5. DataFrame Basics II/13. Adding new Rows (hands-on approach).srt 4KB
  508. 25. Statistical Concepts/25. Properties of the Standard Normal Distribution (Theory).srt 4KB
  509. 3. Pandas Basics (DataFrame Basics I)/11. Zero-based Indexing and Negative Indexing.srt 4KB
  510. 3. Pandas Basics (DataFrame Basics I)/15. Selecting Rows with loc (label-based indexing).srt 4KB
  511. 19. Time Series Basics/6. More on pd.date_range().srt 4KB
  512. 27. What´s next/1. Get your special BONUS here!.html 4KB
  513. 5. DataFrame Basics II/11. Adding Columns with insert().srt 4KB
  514. 4. Pandas Series and Index Objects/16. Creating Index Objects from Scratch.srt 3KB
  515. 12. Merging, Joining, and Concatenating Data/7. Inner Joins with merge().srt 3KB
  516. 1. Getting Started/8. How to tackle Pandas Version 1.0.srt 3KB
  517. 25. Statistical Concepts/35. How to interpret Intercept and Slope Coefficient.srt 3KB
  518. 25. Statistical Concepts/23. Normal Distribution - Cumulative Distribution Function (cdf) with scipy.stats.srt 3KB
  519. 25. Statistical Concepts/33. What is Linear Regression (Theory).srt 3KB
  520. 3. Pandas Basics (DataFrame Basics I)/10. Selecting one Column with the dot notation.srt 3KB
  521. 2. ---PART 1 PANDAS FROM ZERO TO HERO (BUILDING BLOCKS)---/2. Download Part 1 Course Materials.srt 3KB
  522. 23. Python Basics/1. Intro.srt 3KB
  523. 25. Statistical Concepts/37. Case Study (Part 2) The Market Model (Single Factor Model).srt 3KB
  524. 20. Time Series Advanced Financial Time Series/2. Getting Ready (Installing required package).srt 3KB
  525. 12. Merging, Joining, and Concatenating Data/5. EXCURSUS Comparing two DataFrames Identify Differences.html 3KB
  526. 13. GroupBy Operations/1. Intro.srt 3KB
  527. 12. Merging, Joining, and Concatenating Data/10. Right Joins (without Intersection) with merge().srt 3KB
  528. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/1. Intro and Overview.srt 3KB
  529. 25. Statistical Concepts/10. Minimum, Maximum and Range with PythonNumpy.srt 2KB
  530. 11. Cleaning Data/11. The ignore_index parameter (NEW in Pandas 1.0).srt 2KB
  531. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/5. Info() method - new and extended output.srt 2KB
  532. 3. Pandas Basics (DataFrame Basics I)/21. Coding Exercise 2 (Intro).srt 2KB
  533. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/1. Intro.html 1019B
  534. 20. Time Series Advanced Financial Time Series/1. Intro.html 976B
  535. 14. Reshaping and Pivoting DataFrames/1. Intro.html 894B
  536. 4. Pandas Series and Index Objects/1. Intro.html 827B
  537. 9. ----PART 2 FULL DATA WORKFLOW A-Z----/1. Welcome to PART 2 Full Data Workflow A-Z.html 814B
  538. 3. Pandas Basics (DataFrame Basics I)/17. Label-based Indexing Cheat Sheets.html 786B
  539. 16. Advanced Visualization with Seaborn/1. Intro.html 775B
  540. 15. Data Preparation and Feature Creation/1. Intro.html 710B
  541. 8. Visualization with Matplotlib/1. Intro.html 680B
  542. 7. DataFrame Basics III/1. Intro.html 643B
  543. 18. ----PART 4 MANAGING TIME SERIES DATA WITH PANDAS----/1. Welcome to PART 4 Time Series Data with Pandas.html 637B
  544. 12. Merging, Joining, and Concatenating Data/1. Intro.html 585B
  545. 10. Importing Data/6. Coding Exercise 10.html 557B
  546. 12. Merging, Joining, and Concatenating Data/16. Coding Exercise 12.html 557B
  547. 14. Reshaping and Pivoting DataFrames/8. Coding Exercise 14.html 557B
  548. 15. Data Preparation and Feature Creation/13. Coding Exercise 15.html 557B
  549. 16. Advanced Visualization with Seaborn/6. Coding Exercise 16.html 557B
  550. 20. Time Series Advanced Financial Time Series/13. Coding Exercise 17.html 557B
  551. 3. Pandas Basics (DataFrame Basics I)/14. Position-based Indexing Cheat Sheets.html 495B
  552. 22. ---APPENDIX PYTHON BASICS, NUMPY & STATISTICS---/1. Welcome to the Appendix.html 422B
  553. 5. DataFrame Basics II/1. Intro.html 406B
  554. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/2. How to update Pandas to Version 1.0.html 313B
  555. 11. Cleaning Data/16. Coding Exercise 11 (Intro).html 159B
  556. 13. GroupBy Operations/15. Coding Exercise 13 (Intro).html 159B
  557. 4. Pandas Series and Index Objects/13. Coding Exercise 3 (Intro).html 158B
  558. 4. Pandas Series and Index Objects/21. Coding Exercise 4 (Intro).html 158B
  559. 5. DataFrame Basics II/15. Coding Exercise 5 (Intro).html 158B
  560. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/8. Coding Exercise 6 (Intro).html 158B
  561. 7. DataFrame Basics III/14. Coding Exercise 8 (Intro).html 158B
  562. 7. DataFrame Basics III/7. Coding Exercise 7 (Intro).html 158B
  563. 8. Visualization with Matplotlib/8. Coding Exercise 9 (Intro).html 158B
  564. 3. Pandas Basics (DataFrame Basics I)/4.1 DataFrame Methods and Attributes.html 141B
  565. 3. Pandas Basics (DataFrame Basics I)/4.2 Pandas Series Methods and Attributes.html 138B
  566. 17. ---PART 3 COMPREHENSIVE PROJECT CHALLENGE---/1. Download Part 3 Course Materials.html 131B
  567. 18. ----PART 4 MANAGING TIME SERIES DATA WITH PANDAS----/2. Download Part 4 Course Materials.html 131B
  568. 9. ----PART 2 FULL DATA WORKFLOW A-Z----/2. Download Part 2 Course Materials.html 131B
  569. 13. GroupBy Operations/14. GroupBy 2.html 130B
  570. 13. GroupBy Operations/6. GroupBy 1.html 130B
  571. 23. Python Basics/19. Python Basics.html 130B
  572. 24. The Numpy Package/12. Numpy.html 130B
  573. 3. Pandas Basics (DataFrame Basics I)/20. Indexing and Slicing.html 130B
  574. 3. Pandas Basics (DataFrame Basics I)/6. First Steps.html 130B
  575. 4. Pandas Series and Index Objects/12. Pandas Series.html 130B
  576. 4. Pandas Series and Index Objects/20. Pandas Index objects.html 130B
  577. 5. DataFrame Basics II/14. DataFrame Basics II.html 130B
  578. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/7. Manipulating DataFrames Slices.html 130B
  579. 1. Getting Started/5.1 Installing on Windows.html 112B
  580. 1. Getting Started/5.2 Installing on macOS.html 111B
  581. 1. Getting Started/5.3 Installing on Linux.html 110B
  582. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/3. Downloads for this Section.html 84B
  583. 25. Statistical Concepts/2. Downloads for this Section.html 84B
  584. 26. Download .py files/1. Parts 1 & 2 .py files.html 64B
  585. 0. Websites you may like/[FreeCourseWorld.Com].url 54B
  586. 14. Reshaping and Pivoting DataFrames/[FreeCourseWorld.Com].url 54B
  587. 25. Statistical Concepts/[FreeCourseWorld.Com].url 54B
  588. 5. DataFrame Basics II/[FreeCourseWorld.Com].url 54B
  589. [FreeCourseWorld.Com].url 54B
  590. 0. Websites you may like/[DesireCourse.Net].url 51B
  591. 14. Reshaping and Pivoting DataFrames/[DesireCourse.Net].url 51B
  592. 25. Statistical Concepts/[DesireCourse.Net].url 51B
  593. 5. DataFrame Basics II/[DesireCourse.Net].url 51B
  594. [DesireCourse.Net].url 51B
  595. 0. Websites you may like/[CourseClub.Me].url 48B
  596. 14. Reshaping and Pivoting DataFrames/[CourseClub.Me].url 48B
  597. 25. Statistical Concepts/[CourseClub.Me].url 48B
  598. 5. DataFrame Basics II/[CourseClub.Me].url 48B
  599. [CourseClub.Me].url 48B