DesireCourse Net Udemy - Complete Data Analysis Course with Pandas & NumPy Python

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