UdemyCourseDownloader Complete Data Analysis Course with Pandas & NumPy Python

1. 09. Pandas Exercise/2. Pandas Exercise Solution - I.mp4132.99MB
2. 01. Introduction/1. What is Data analysis.mp495.77MB
3. 01. Introduction/2. Introduction to Pandas.mp478.38MB
4. 01. Introduction/3. Course FAQ.mp473.66MB
5. 02. Installation and IDE/1. Different ways of installation.mp453.41MB
6. 02. Installation and IDE/2. Download and Install anaconda + Pandas.mp446.67MB
7. 02. Installation and IDE/4. Anaconda + Conda Command.mp457.09MB
8. 02. Installation and IDE/7. Getting started with Jupyter Lab.mp474.99MB
9. 02. Installation and IDE/9. Import Library.mp423.53MB
10. 04. Python Crash Course [Optional]/1. Introduction.mp434.27MB
11. 04. Python Crash Course [Optional]/2. Python Basics - I.mp449.84MB
12. 04. Python Crash Course [Optional]/4. Python Basics - II.mp428.25MB
13. 04. Python Crash Course [Optional]/6. Lists and tuples.mp462.73MB
14. 04. Python Crash Course [Optional]/8. Dictionary and set.mp435.31MB
15. 04. Python Crash Course [Optional]/10. Functions.mp426.43MB
16. 05. Python Exercises/1. Exercise Overview.mp429.16MB
17. 05. Python Exercises/2. Solutions.mp4109.43MB
18. 06. Numpy/1. Creating NumPy array.mp485.15MB
19. 06. Numpy/2. Numpy indexing and selection, Functions.mp493.62MB
20. 06. Numpy/3. Some more Numpy Functions.mp461.94MB
21. 06. Numpy/4. Linear algebra with NumPy.mp443.83MB
22. 06. Numpy/5. List vs NumPy Array.mp451.15MB
23. 06. Numpy/6. Views vs Copy - Numpy Array.mp434.75MB
24. 06. Numpy/7. Insert, Append and Delete NumPy array.mp445.16MB
25. 06. Numpy/8. Split, Concatenate, Tile and Repeat array.mp459.86MB
26. 07. Series Pandas/2. Introduction to Series.mp451.47MB
27. 07. Series Pandas/3. Create Series from Python Object.mp447.72MB
28. 07. Series Pandas/4. Create Series from CSV file.mp448.2MB
29. 07. Series Pandas/6. Series attributes & methods.mp458.37MB
30. 07. Series Pandas/8. Label indexing.mp419.43MB
31. 07. Series Pandas/10. inplace parameter, sort_values & sort_index.mp433.4MB
32. 07. Series Pandas/12. Apply Python built in function on Series.mp413.95MB
33. 07. Series Pandas/13. Extract Value from Series.mp425.76MB
34. 07. Series Pandas/15. .value_counts() Method.mp47.7MB
35. 07. Series Pandas/16. .apply() and .map() method.mp433.3MB
36. 08. Data Frame Pandas/1. Introduction to Data Frame.mp473.01MB
37. 08. Data Frame Pandas/2. Create Data Frame - random data + from File.mp485.44MB
38. 08. Data Frame Pandas/3. Data frame attributes and methods.mp483.4MB
39. 08. Data Frame Pandas/4. Adding new column.mp430.55MB
40. 08. Data Frame Pandas/5. Select one or more than one column.mp444.87MB
41. 08. Data Frame Pandas/6. Broadcasting operation.mp428.46MB
42. 08. Data Frame Pandas/7. Drop missing row or column.mp435.95MB
43. 08. Data Frame Pandas/8. Filtering Data with one condition.mp468.55MB
44. 08. Data Frame Pandas/9. Filtering Data with multiple condition.mp438.81MB
45. 08. Data Frame Pandas/10. Filtering Data with .isin() method.mp437.2MB
46. 08. Data Frame Pandas/11. Filtering Data with .between() method.mp428.55MB
47. 08. Data Frame Pandas/12. unique() & nunique() method.mp429.92MB
48. 08. Data Frame Pandas/13. sorting values.mp484.74MB
49. 08. Data Frame Pandas/14. sort index and inplace parameter.mp437.7MB
50. 08. Data Frame Pandas/15. .loc() and .iloc() method.mp475.04MB
51. 08. Data Frame Pandas/16. .ix() method.mp422.01MB
52. 08. Data Frame Pandas/17. .astype() method - optimize memory requirement.mp449.88MB
53. 08. Data Frame Pandas/18. set_index() change index column.mp434.22MB
54. 08. Data Frame Pandas/19. .apply() method on single column.mp434.76MB
55. 08. Data Frame Pandas/20. .apply() method on multiple column.mp455.58MB
56. 08. Data Frame Pandas/21. Fetch random sample.mp429.02MB
57. 09. Pandas Exercise/1. Exercise Overview Google App store dataset.mp428.68MB
58. 09. Pandas Exercise/3. Pandas Exercise Solution - II.mp4131.72MB
59. 10. Panel Pandas/1. Warning - Panel Data type.mp48.62MB
60. 11. Pandas Options/1. max_rows , max_columns.mp466.11MB
61. 11. Pandas Options/2. precision.mp413.02MB
62. 12. Visualize Data with Pandas/1. Display Stock data with Line Chart.mp463.8MB
63. 12. Visualize Data with Pandas/2. Pie, Histogram and Bar Chart.mp456.34MB
64. 13. Import and Export data from Pandas/1. read_csv() & to_csv() method.mp461.68MB
65. 14. Working with Text Data/1. Getting started with Data.mp444.42MB
66. 14. Working with Text Data/2. Some String methods.mp443.43MB
67. 14. Working with Text Data/3. More String methods.mp470.95MB
68. 14. Working with Text Data/4. Filtering Message with String.mp441.36MB
69. 14. Working with Text Data/5. Splitting Text.mp429.46MB
70. 14. Working with Text Data/6. Processing on Column names.mp410.1MB
71. 15. Data Grouping/1. Importing Data Grouping.mp448.54MB
72. 15. Data Grouping/2. Getting Group.mp437.42MB
73. 15. Data Grouping/3. Size, First and Last Method.mp431.05MB
74. 15. Data Grouping/4. Sum, Mean, Max, Min Method.mp448.31MB
75. 15. Data Grouping/5. .agg method.mp439.26MB
76. 16. Data Frame Multiindex/1. Import Data - Multiindex.mp49.58MB
77. 16. Data Frame Multiindex/2. Set multiple column as index.mp423.38MB
78. 16. Data Frame Multiindex/3. Sorting MultiIndex.mp432.01MB
79. 16. Data Frame Multiindex/4. Index - Meta Information.mp439.43MB
80. 16. Data Frame Multiindex/5. Change Index names.mp410.22MB
81. 16. Data Frame Multiindex/6. Fetch data from MultiIndex Dataframe.mp439.08MB
82. 16. Data Frame Multiindex/7. Transposing DataFrame.mp451MB
83. 16. Data Frame Multiindex/8. UnStack and Stack Data.mp448.66MB
84. 16. Data Frame Multiindex/9. Pivot and Pivot_table Method.mp438.91MB
85. 17. Working with Time series data/1. Python Date and Datetime module.mp436.19MB
86. 17. Working with Time series data/2. Pandas Timestamp and Datetimeindex object.mp486.31MB
87. 18. Data cleaning/1. Data cleaning - Youtube Dataset (warm up) Part - 1.mp418.5MB
88. 18. Data cleaning/2. Data cleaning - Youtube Channel Dataset Part - 2.mp448.73MB
89. 18. Data cleaning/3. Data cleaning - Youtube Channel Dataset Part - 3.mp490.72MB