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

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DesireCourse Net Udemy - The Complete Pandas Bootcamp 2020 Data Science with 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....mp431.24MB
  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.mp466.29MB
  7. 1. Getting Started/8. How to tackle Pandas Version 1.0.mp419.03MB
  8. 10. Importing Data/1. Importing csv-files with pd.read_csv.mp490.94MB
  9. 10. Importing Data/2. Importing messy csv-files with pd.read_csv.mp463.28MB
  10. 10. Importing Data/3. Importing Data from Excel with pd.read_excel().mp473.9MB
  11. 10. Importing Data/4. Importing messy Data from Excel with pd.read_excel().mp472.44MB
  12. 10. Importing Data/5. Importing Data from the Web with pd.read_html().mp458MB
  13. 11. Cleaning Data/1. First Inspection & Handling of inconsistent Data.mp468.24MB
  14. 11. Cleaning Data/10. Handling Removing Duplicates.mp488.67MB
  15. 11. Cleaning Data/11. The ignore_index parameter (NEW in Pandas 1.0).mp45.67MB
  16. 11. Cleaning Data/12. Detection of Outliers.mp444.07MB
  17. 11. Cleaning Data/13. Handling Removing Outliers.mp429.69MB
  18. 11. Cleaning Data/14. Categorical Data.mp445.48MB
  19. 11. Cleaning Data/15. Pandas Version 1.0 New dtypes and pd.NA.mp418.47MB
  20. 11. Cleaning Data/17. Coding Exercise 11 (Solution).mp4129.71MB
  21. 11. Cleaning Data/2. String Operations.mp480.88MB
  22. 11. Cleaning Data/3. Changing Datatype of Columns with astype().mp438.8MB
  23. 11. Cleaning Data/4. Intro NA values missing values.mp445.64MB
  24. 11. Cleaning Data/5. Detection of missing Values.mp489.4MB
  25. 11. Cleaning Data/6. Removing missing values.mp485.5MB
  26. 11. Cleaning Data/7. Replacing missing values.mp424.59MB
  27. 11. Cleaning Data/8. Intro Duplicates.mp420.26MB
  28. 11. Cleaning Data/9. Detection of Duplicates.mp479.21MB
  29. 12. Merging, Joining, and Concatenating Data/10. Right Joins (without Intersection) with merge().mp415.03MB
  30. 12. Merging, Joining, and Concatenating Data/11. Left Joins with merge().mp424.09MB
  31. 12. Merging, Joining, and Concatenating Data/12. Right Joins with merge().mp427.41MB
  32. 12. Merging, Joining, and Concatenating Data/13. Joining on different Column Names Indexes.mp495.32MB
  33. 12. Merging, Joining, and Concatenating Data/14. Joining on more than one Column.mp438.69MB
  34. 12. Merging, Joining, and Concatenating Data/15. pd.merge() and join().mp435.48MB
  35. 12. Merging, Joining, and Concatenating Data/2. Adding Rows with append() and pd.concat() (Part 1).mp488.07MB
  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. Replacing NA Values by group-specific Values.mp444.75MB
  44. 13. GroupBy Operations/11. Generalizing split-apply-combine with apply().mp442.78MB
  45. 13. GroupBy Operations/12. Hierarchical Indexing with Groupby.mp432.86MB
  46. 13. GroupBy Operations/13. stack() and unstack().mp478.81MB
  47. 13. GroupBy Operations/16. Coding Exercise 13 (Solution).mp481.56MB
  48. 13. GroupBy Operations/2. Understanding the GroupBy Object.mp446.27MB
  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. GroupBy Aggregation with Relabeling (NEW - Pandas Version 0.25).mp420.62MB
  54. 13. GroupBy Operations/9. Transformation with transform().mp435.41MB
  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().mp458.25MB
  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. 15. Data Preparation and Feature Creation/10. Scaling Standardization.mp456.33MB
  62. 15. Data Preparation and Feature Creation/11. Creating Dummy Variables.mp455.25MB
  63. 15. Data Preparation and Feature Creation/12. String Operations.mp429.66MB
  64. 15. Data Preparation and Feature Creation/2. Arithmetic Operations (Part 1).mp463.52MB
  65. 15. Data Preparation and Feature Creation/3. Arithmetic Operations (Part 2).mp458.45MB
  66. 15. Data Preparation and Feature Creation/4. TransformationMapping with map().mp442.68MB
  67. 15. Data Preparation and Feature Creation/5. Conditional Transformation.mp433.41MB
  68. 15. Data Preparation and Feature Creation/6. Discretization and Binning with pd.cut() (Part 1).mp473.03MB
  69. 15. Data Preparation and Feature Creation/7. Discretization and Binning with pd.cut() (Part 2).mp432.7MB
  70. 15. Data Preparation and Feature Creation/8. Discretization and Binning with pd.qcut().mp485.39MB
  71. 15. Data Preparation and Feature Creation/9. Floors and Caps.mp439.25MB
  72. 16. Advanced Visualization with Seaborn/2. First Steps in Seaborn.mp422.1MB
  73. 16. Advanced Visualization with Seaborn/3. Categorical Plots.mp485.2MB
  74. 16. Advanced Visualization with Seaborn/4. Joint Plots Regression Plots.mp479.61MB
  75. 16. Advanced Visualization with Seaborn/5. Matrixplots Heatmaps.mp442.78MB
  76. 17. ---PART 3 COMPREHENSIVE PROJECT CHALLENGE---/2. Olympic Medal Tables (Instruction & Hints).mp457.71MB
  77. 17. ---PART 3 COMPREHENSIVE PROJECT CHALLENGE---/3. Olympic Medal Tables (Solution Part 1).mp438.42MB
  78. 17. ---PART 3 COMPREHENSIVE PROJECT CHALLENGE---/4. Olympic Medal Tables (Solution Part 2).mp4128.78MB
  79. 17. ---PART 3 COMPREHENSIVE PROJECT CHALLENGE---/5. Olympic Medal Tables (Solution Part 3).mp426.99MB
  80. 19. Time Series Basics/1. Importing Time Series Data from csv-files.mp441.75MB
  81. 19. Time Series Basics/10. Advanced Indexing with reindex().mp450.5MB
  82. 19. Time Series Basics/2. Converting strings to datetime objects with pd.to_datetime().mp458MB
  83. 19. Time Series Basics/3. Initial Analysis Visualization of Time Series.mp435MB
  84. 19. Time Series Basics/4. Indexing and Slicing Time Series.mp448.16MB
  85. 19. Time Series Basics/5. Creating a customized DatetimeIndex with pd.date_range().mp4114.64MB
  86. 19. Time Series Basics/6. More on pd.date_range().mp412.36MB
  87. 19. Time Series Basics/7. Downsampling Time Series with resample() (Part 1).mp485.5MB
  88. 19. Time Series Basics/8. Downsampling Time Series with resample (Part 2).mp449.11MB
  89. 19. Time Series Basics/9. The PeriodIndex object.mp438.78MB
  90. 2. ---PART 1 PANDAS FROM ZERO TO HERO (BUILDING BLOCKS)---/1. Intro to Tabular Data Pandas.mp418.07MB
  91. 2. ---PART 1 PANDAS FROM ZERO TO HERO (BUILDING BLOCKS)---/2. Download Part 1 Course Materials.mp418.73MB
  92. 20. Time Series Advanced Financial Time Series/10. Financial Time Series - Covariance and Correlation.mp425.73MB
  93. 20. Time Series Advanced Financial Time Series/11. Helpful DatetimeIndex Attributes and Methods.mp444.3MB
  94. 20. Time Series Advanced Financial Time Series/12. Filling NA Values with bfill, ffill and interpolation.mp478.44MB
  95. 20. Time Series Advanced Financial Time Series/2. Getting Ready (Installing required package).mp421.77MB
  96. 20. Time Series Advanced Financial Time Series/3. Importing Stock Price Data from Yahoo Finance (it still works!).mp471.92MB
  97. 20. Time Series Advanced Financial Time Series/4. Initial Inspection and Visualization.mp442.33MB
  98. 20. Time Series Advanced Financial Time Series/5. Normalizing Time Series to a Base Value (100).mp444.26MB
  99. 20. Time Series Advanced Financial Time Series/6. The shift() method.mp435.78MB
  100. 20. Time Series Advanced Financial Time Series/7. The methods diff() and pct_change().mp440.23MB
  101. 20. Time Series Advanced Financial Time Series/8. Measuring Stock Performance with MEAN Returns and STD of Returns.mp443.93MB
  102. 20. Time Series Advanced Financial Time Series/9. Financial Time Series - Return and Risk.mp453.62MB
  103. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/1. Intro and Overview.mp415.5MB
  104. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/10. The NEW StringDtype.mp432.1MB
  105. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/11. The NEW nullable BooleanDtype.mp423.2MB
  106. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/12. Addition of the ignore_index parameter.mp421.75MB
  107. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/13. Removal of prior Version Deprecations.mp442.87MB
  108. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/4. Important Recap Pandas Display Options (Changed in Version 0.25).mp436.51MB
  109. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/5. Info() method - new and extended output.mp49.86MB
  110. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/6. NEW Extension dtypes (nullable dtypes) Why do we need them.mp428.29MB
  111. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/7. Creating the NEW extension dtypes with convert_dtypes().mp425.7MB
  112. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/8. NEW pd.NA value for missing values.mp427.91MB
  113. 21. +++ WHAT´S NEW IN PANDAS VERSION 1.0 - A HANDS-ON GUIDE +++/9. The NEW nullable Int64Dtype.mp418MB
  114. 23. Python Basics/1. Intro.mp45.89MB
  115. 23. Python Basics/10. Operators & Booleans.mp459.52MB
  116. 23. Python Basics/11. Conditional Statements (if, elif, else, while).mp486.04MB
  117. 23. Python Basics/12. For Loops.mp458.42MB
  118. 23. Python Basics/13. Key words break, pass, continue.mp436.71MB
  119. 23. Python Basics/14. Generating Random Numbers.mp438.13MB
  120. 23. Python Basics/15. User Defined Functions (Part 1).mp464.35MB
  121. 23. Python Basics/16. User Defined Functions (Part 2).mp457.39MB
  122. 23. Python Basics/17. User Defined Functions (Part 3).mp452.12MB
  123. 23. Python Basics/18. Visualization with Matplotlib.mp4124.22MB
  124. 23. Python Basics/2. First Steps.mp434.22MB
  125. 23. Python Basics/20. Python Basics Quiz Solution.mp438.25MB
  126. 23. Python Basics/3. Variables.mp431.46MB
  127. 23. Python Basics/4. Data Types Integers and Floats.mp449.47MB
  128. 23. Python Basics/5. Data Types Strings.mp477.78MB
  129. 23. Python Basics/6. Data Types Lists (Part 1).mp462.7MB
  130. 23. Python Basics/7. Data Types Lists (Part 2).mp4134.41MB
  131. 23. Python Basics/8. Data Types Tuples.mp441.8MB
  132. 23. Python Basics/9. Data Types Sets.mp421.44MB
  133. 24. The Numpy Package/1. Introduction to Numpy Arrays.mp441.13MB
  134. 24. The Numpy Package/10. Summary Statistics.mp444.82MB
  135. 24. The Numpy Package/11. Visualization and (Linear) Regression.mp484.55MB
  136. 24. The Numpy Package/13. Numpy Quiz Solution.mp445.45MB
  137. 24. The Numpy Package/2. Numpy Arrays Vectorization.mp464.74MB
  138. 24. The Numpy Package/3. Numpy Arrays Indexing and Slicing.mp453.44MB
  139. 24. The Numpy Package/4. Numpy Arrays Shape and Dimensions.mp435.52MB
  140. 24. The Numpy Package/5. Numpy Arrays Indexing and Slicing of multi-dimensional Arrays.mp473.64MB
  141. 24. The Numpy Package/6. Numpy Arrays Boolean Indexing.mp444.22MB
  142. 24. The Numpy Package/7. Generating Random Numbers.mp467.53MB
  143. 24. The Numpy Package/8. Performance Issues.mp449.88MB
  144. 24. The Numpy Package/9. Case Study Numpy vs. Python Standard Library.mp445.61MB
  145. 25. Statistical Concepts/1. Statistics - Overview, Terms and Vocabulary.mp493.36MB
  146. 25. Statistical Concepts/10. Minimum, Maximum and Range with PythonNumpy.mp412.3MB
  147. 25. Statistical Concepts/11. Percentiles with PythonNumpy.mp417.57MB
  148. 25. Statistical Concepts/12. Variance and Standard Deviation with PythonNumpy.mp416.35MB
  149. 25. Statistical Concepts/13. Skew and Kurtosis (Theory).mp418.02MB
  150. 25. Statistical Concepts/14. How to calculate Skew and Kurtosis with scipy.stats.mp427.44MB
  151. 25. Statistical Concepts/15. How to generate Random Numbers with Numpy.mp425.21MB
  152. 25. Statistical Concepts/16. Reproducibility with np.random.seed().mp417.25MB
  153. 25. Statistical Concepts/17. Probability Distributions - Overview.mp435.69MB
  154. 25. Statistical Concepts/18. Discrete Uniform Distributions.mp428.19MB
  155. 25. Statistical Concepts/19. Continuous Uniform Distributions.mp420.11MB
  156. 25. Statistical Concepts/20. The Normal Distribution (Theory).mp418.42MB
  157. 25. Statistical Concepts/21. Creating a normally distributed Random Variable.mp424.11MB
  158. 25. Statistical Concepts/22. Normal Distribution - Probability Density Function (pdf) with scipy.stats.mp426.95MB
  159. 25. Statistical Concepts/23. Normal Distribution - Cumulative Distribution Function (cdf) with scipy.stats.mp415.39MB
  160. 25. Statistical Concepts/24. The Standard Normal Distribution and Z-Values.mp438.66MB
  161. 25. Statistical Concepts/25. Properties of the Standard Normal Distribution (Theory).mp414.85MB
  162. 25. Statistical Concepts/26. Probabilities and Z-Values with scipy.stats.mp459.28MB
  163. 25. Statistical Concepts/27. Confidence Intervals with scipy.stats.mp448.1MB
  164. 25. Statistical Concepts/28. Covariance and Correlation Coefficient (Theory).mp427.58MB
  165. 25. Statistical Concepts/29. Cleaning and preparing the Data - Movies Database (Part 1).mp447MB
  166. 25. Statistical Concepts/3. Population vs. Sample.mp435.57MB
  167. 25. Statistical Concepts/30. Cleaning and preparing the Data - Movies Database (Part 2).mp431.1MB
  168. 25. Statistical Concepts/31. How to calculate Covariance and Correlation in Python.mp423.99MB
  169. 25. Statistical Concepts/32. Correlation and Scatterplots – visual Interpretation.mp420MB
  170. 25. Statistical Concepts/33. What is Linear Regression (Theory).mp411.64MB
  171. 25. Statistical Concepts/34. A simple Linear Regression Model with numpy & Scipy.mp439.67MB
  172. 25. Statistical Concepts/35. How to interpret Intercept and Slope Coefficient.mp412.35MB
  173. 25. Statistical Concepts/36. Case Study (Part 1) The Market Model (Single Factor Model).mp426.34MB
  174. 25. Statistical Concepts/37. Case Study (Part 2) The Market Model (Single Factor Model).mp410.31MB
  175. 25. Statistical Concepts/4. Visualizing Frequency Distributions with plt.hist().mp422.64MB
  176. 25. Statistical Concepts/5. Relative and Cumulative Frequencies with plt.hist().mp436.41MB
  177. 25. Statistical Concepts/6. Measures of Central Tendency (Theory).mp420.74MB
  178. 25. Statistical Concepts/7. Coding Measures of Central Tendency - Mean and Median.mp422.34MB
  179. 25. Statistical Concepts/8. Coding Measures of Central Tendency - Geometric Mean.mp416.56MB
  180. 25. Statistical Concepts/9. Variability around the Central Tendency Dispersion (Theory).mp427.69MB
  181. 3. Pandas Basics (DataFrame Basics I)/1. Create your very first Pandas DataFrame (from csv).mp459.42MB
  182. 3. Pandas Basics (DataFrame Basics I)/10. Selecting one Column with the dot notation.mp48.53MB
  183. 3. Pandas Basics (DataFrame Basics I)/11. Zero-based Indexing and Negative Indexing.mp410.18MB
  184. 3. Pandas Basics (DataFrame Basics I)/12. Selecting Rows with iloc (position-based indexing).mp465MB
  185. 3. Pandas Basics (DataFrame Basics I)/13. Slicing Rows and Columns with iloc (position-based indexing).mp424.29MB
  186. 3. Pandas Basics (DataFrame Basics I)/15. Selecting Rows with loc (label-based indexing).mp421.34MB
  187. 3. Pandas Basics (DataFrame Basics I)/16. Slicing Rows and Columns with loc (label-based indexing).mp477.55MB
  188. 3. Pandas Basics (DataFrame Basics I)/18. Indexing and Slicing with reindex().mp438.92MB
  189. 3. Pandas Basics (DataFrame Basics I)/19. Summary, Best Practices and Outlook.mp441.99MB
  190. 3. Pandas Basics (DataFrame Basics I)/2. Pandas Display Options and the methods head() & tail().mp440.46MB
  191. 3. Pandas Basics (DataFrame Basics I)/21. Coding Exercise 2 (Intro).mp48.06MB
  192. 3. Pandas Basics (DataFrame Basics I)/22. Coding Exercise 2 (Solution).mp428.07MB
  193. 3. Pandas Basics (DataFrame Basics I)/23. Advanced Indexing and Slicing (optional).mp434.11MB
  194. 3. Pandas Basics (DataFrame Basics I)/3. First Data Inspection.mp456.01MB
  195. 3. Pandas Basics (DataFrame Basics I)/4. Built-in Functions, Attributes and Methods with Pandas.mp446.87MB
  196. 3. Pandas Basics (DataFrame Basics I)/5. Make it easy TAB Completion and Tooltip.mp454.43MB
  197. 3. Pandas Basics (DataFrame Basics I)/7. Explore your own Dataset Coding Exercise 1 (Intro).mp426.55MB
  198. 3. Pandas Basics (DataFrame Basics I)/8. Explore your own Dataset Coding Exercise 1 (Solution).mp431.2MB
  199. 3. Pandas Basics (DataFrame Basics I)/9. Selecting Columns.mp426.63MB
  200. 4. Pandas Series and Index Objects/10. idxmin() and idxmax().mp428.69MB
  201. 4. Pandas Series and Index Objects/11. Manipulating Pandas Series.mp437.87MB
  202. 4. Pandas Series and Index Objects/14. Coding Exercise 3 (Solution).mp438.65MB
  203. 4. Pandas Series and Index Objects/15. First Steps with Pandas Index Objects.mp443.09MB
  204. 4. Pandas Series and Index Objects/16. Creating Index Objects from Scratch.mp415.02MB
  205. 4. Pandas Series and Index Objects/17. Changing Row Index with set_index() and reset_index().mp475.03MB
  206. 4. Pandas Series and Index Objects/18. Changing Column Labels.mp421.16MB
  207. 4. Pandas Series and Index Objects/19. Renaming Index & Column Labels with rename().mp428MB
  208. 4. Pandas Series and Index Objects/2. First Steps with Pandas Series.mp419MB
  209. 4. Pandas Series and Index Objects/22. Coding Exercise 4 (Solution).mp426.35MB
  210. 4. Pandas Series and Index Objects/3. Analyzing Numerical Series with unique(), nunique() and value_counts().mp467.12MB
  211. 4. Pandas Series and Index Objects/4. Analyzing non-numerical Series with unique(), nunique(), value_counts().mp442.89MB
  212. 4. Pandas Series and Index Objects/5. Creating Pandas Series (Part 1).mp438.09MB
  213. 4. Pandas Series and Index Objects/6. Creating Pandas Series (Part 2).mp426.74MB
  214. 4. Pandas Series and Index Objects/7. Indexing and Slicing Pandas Series.mp466.22MB
  215. 4. Pandas Series and Index Objects/8. Sorting of Series and Introduction to the inplace - parameter.mp441.4MB
  216. 4. Pandas Series and Index Objects/9. nlargest() and nsmallest().mp416.77MB
  217. 5. DataFrame Basics II/10. Creating Columns based on other Columns.mp434.56MB
  218. 5. DataFrame Basics II/11. Adding Columns with insert().mp413.07MB
  219. 5. DataFrame Basics II/12. Creating DataFrames from Scratch with pd.DataFrame().mp443.3MB
  220. 5. DataFrame Basics II/13. Adding new Rows (hands-on approach).mp416.95MB
  221. 5. DataFrame Basics II/16. Coding Exercise 5 (Solution).mp457.67MB
  222. 5. DataFrame Basics II/2. Filtering DataFrames by one Condition.mp452.92MB
  223. 5. DataFrame Basics II/3. Filtering DataFrames by many Conditions (AND).mp425.93MB
  224. 5. DataFrame Basics II/4. Filtering DataFrames by many Conditions (OR).mp430.82MB
  225. 5. DataFrame Basics II/5. Advanced Filtering with between(), isin() and ~.mp465.68MB
  226. 5. DataFrame Basics II/6. any() and all().mp417.58MB
  227. 5. DataFrame Basics II/7. Removing Columns.mp436.02MB
  228. 5. DataFrame Basics II/8. Removing Rows.mp449.62MB
  229. 5. DataFrame Basics II/9. Adding new Columns to a DataFrame.mp417.88MB
  230. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/2. Best Practice (How you should do it).mp452.6MB
  231. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/3. Chained Indexing How you should NOT do it (Part 1).mp460.07MB
  232. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/4. Chained Indexing How you should NOT do it (Part 2).mp458.85MB
  233. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/5. View vs. Copy.mp434.54MB
  234. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/6. Simple Rules what to do when....mp445.85MB
  235. 6. Manipulating Elements in a DataFrame Slice +++Important, know the Pitfalls!+++/9. Coding Exercise 6 (Solution).mp439.4MB
  236. 7. DataFrame Basics III/10. Hierarchical Indexing (Part 1).mp472.59MB
  237. 7. DataFrame Basics III/11. Hierarchical Indexing (Part 2).mp472.59MB
  238. 7. DataFrame Basics III/12. String Operations (Part 1).mp441.19MB
  239. 7. DataFrame Basics III/13. String Operations (Part 2).mp455.19MB
  240. 7. DataFrame Basics III/15. Coding Exercise 8 (Solution).mp458.22MB
  241. 7. DataFrame Basics III/2. Sorting DataFrames with sort_index() and sort_values() (Version 1.0 Update).mp468.86MB
  242. 7. DataFrame Basics III/3. Ranking DataFrames with rank().mp443.48MB
  243. 7. DataFrame Basics III/4. nunique() and nlargest() nsmallest() with DataFrames.mp432.6MB
  244. 7. DataFrame Basics III/5. Summary Statistics and Accumulations.mp457.47MB
  245. 7. DataFrame Basics III/6. The agg() method.mp422.83MB
  246. 7. DataFrame Basics III/8. Coding Exercise 7 (Solution).mp439.82MB
  247. 7. DataFrame Basics III/9. User-defined Functions with apply(), map() and applymap().mp474.33MB
  248. 8. Visualization with Matplotlib/2. The plot() method.mp470.25MB
  249. 8. Visualization with Matplotlib/3. Customization of Plots.mp4102.99MB
  250. 8. Visualization with Matplotlib/4. Histograms (Part 1).mp424.58MB
  251. 8. Visualization with Matplotlib/5. Histograms (Part 2).mp434.13MB
  252. 8. Visualization with Matplotlib/6. Barcharts and Piecharts.mp419.99MB
  253. 8. Visualization with Matplotlib/7. Scatterplots.mp436.15MB
  254. 8. Visualization with Matplotlib/9. Coding Exercise 9 (Solution).mp436.78MB
友情提示
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