FreeCourseSite com Udemy - Data Analysis with Pandas and Python

1. 1. Installation and Setup/1. Introduction to Data Analysis with Pandas and Python.mp434MB
2. 1. Installation and Setup/10. Windows - Install Anaconda Distribution.mp438.64MB
3. 1. Installation and Setup/11. Windows - Create conda Environment and Install pandas and Jupyter Notebook.mp4111.79MB
4. 1. Installation and Setup/12. Windows - Unpack Course Materials + The Startdown and Shutdown Process.mp447.44MB
5. 1. Installation and Setup/13. Intro to the Jupyter Notebook Interface.mp433.26MB
6. 1. Installation and Setup/14. Cell Types and Cell Modes in Jupyter Notebook.mp419.97MB
7. 1. Installation and Setup/15. Code Cell Execution in Jupyter Notebook.mp48.81MB
8. 1. Installation and Setup/16. Popular Keyboard Shortcuts in Jupyter Notebook.mp417MB
9. 1. Installation and Setup/17. Import Libraries into Jupyter Notebook.mp425.84MB
10. 1. Installation and Setup/2. About Me.mp421.31MB
11. 1. Installation and Setup/4. MacOS - Download the Anaconda Distribution, our Python development environment.mp416.22MB
12. 1. Installation and Setup/5. MacOS - Install Anaconda Distribution.mp490.99MB
13. 1. Installation and Setup/6. MacOS - Access the Terminal Application.mp473.07MB
14. 1. Installation and Setup/7. MacOS - Create conda Environment and Install pandas and Jupyter Notebook.mp4112.47MB
15. 1. Installation and Setup/8. MacOS - Unpack Course Materials + The Start and Shutdown Process.mp4110.84MB
16. 1. Installation and Setup/9. Windows - Download the Anaconda Distribution.mp417.87MB
17. 10. Merging, Joining, and Concatenating DataFrames/1. Intro to the Merging, Joining, and Concatenating Section.mp420.99MB
18. 10. Merging, Joining, and Concatenating DataFrames/10. The .join() Method.mp46.27MB
19. 10. Merging, Joining, and Concatenating DataFrames/11. The pd.merge() Method.mp46.84MB
20. 10. Merging, Joining, and Concatenating DataFrames/2. The pd.concat Method, Part 1.mp421.79MB
21. 10. Merging, Joining, and Concatenating DataFrames/3. The pd.concat Method, Part 2.mp430.58MB
22. 10. Merging, Joining, and Concatenating DataFrames/4. Inner Joins, Part 1.mp417.93MB
23. 10. Merging, Joining, and Concatenating DataFrames/5. Inner Joins, Part 2.mp417.75MB
24. 10. Merging, Joining, and Concatenating DataFrames/6. Outer Joins.mp425.94MB
25. 10. Merging, Joining, and Concatenating DataFrames/7. Left Joins.mp420.99MB
26. 10. Merging, Joining, and Concatenating DataFrames/8. The left_on and right_on Parameters.mp420.25MB
27. 10. Merging, Joining, and Concatenating DataFrames/9. Merging by Indexes with the left_index and right_index Parameters.mp422.7MB
28. 11. Working with Dates and Times in Datasets/1. Intro to the Working with Dates and Times Module.mp414.11MB
29. 11. Working with Dates and Times in Datasets/10. Install pandas-datareader Library.mp432.99MB
30. 11. Working with Dates and Times in Datasets/11. Import Financial Data Set with pandas_datareader Library.mp441.66MB
31. 11. Working with Dates and Times in Datasets/12. Selecting Rows from a DataFrame with a DateTimeIndex.mp474.26MB
32. 11. Working with Dates and Times in Datasets/13. Timestamp Object Attributes and Methods.mp454.13MB
33. 11. Working with Dates and Times in Datasets/14. The pd.DateOffset Object.mp443.83MB
34. 11. Working with Dates and Times in Datasets/15. Timeseries Offsets.mp474.54MB
35. 11. Working with Dates and Times in Datasets/16. The Timedelta Object.mp432.25MB
36. 11. Working with Dates and Times in Datasets/17. Timedeltas in a Dataset.mp419.56MB
37. 11. Working with Dates and Times in Datasets/2. Review of Python's datetime Module.mp416.73MB
38. 11. Working with Dates and Times in Datasets/3. The pandas Timestamp Object.mp412.8MB
39. 11. Working with Dates and Times in Datasets/4. The pandas DateTimeIndex Object.mp49.65MB
40. 11. Working with Dates and Times in Datasets/5. The pd.to_datetime() Method.mp422.88MB
41. 11. Working with Dates and Times in Datasets/6. Create Range of Dates with the pd.date_range() Method, Part 1.mp419.69MB
42. 11. Working with Dates and Times in Datasets/7. Create Range of Dates with the pd.date_range() Method, Part 2.mp418.54MB
43. 11. Working with Dates and Times in Datasets/8. Create Range of Dates with the pd.date_range() Method, Part 3.mp416.34MB
44. 11. Working with Dates and Times in Datasets/9. The .dt Accessor.mp413.68MB
45. 12. Input and Output in pandas/1. Intro to the Input and Output Section.mp45.53MB
46. 12. Input and Output in pandas/2. Pass a URL to the pd.read_csv Method.mp419.83MB
47. 12. Input and Output in pandas/3. Quick Object Conversions.mp443.64MB
48. 12. Input and Output in pandas/4. Export CSV File with the to_csv Method.mp427.23MB
49. 12. Input and Output in pandas/5. Install xlrd and openpyxl Libraries to Read and Write Excel Files.mp440.13MB
50. 12. Input and Output in pandas/6. Import Excel File into pandas with the read_excel Method.mp449.19MB
51. 12. Input and Output in pandas/7. Export Excel File with the to_excel Method.mp446.97MB
52. 13. Visualization/1. Intro to Visualization Section.mp415.8MB
53. 13. Visualization/2. Use the plot Method to Render a Line Chart.mp433.33MB
54. 13. Visualization/3. Modifying Plot Aesthetics with matplotlib Templates.mp423.9MB
55. 13. Visualization/4. Creating Bar Graphs to Show Counts.mp427.23MB
56. 13. Visualization/5. Creating Pie Charts to Represent Proportions.mp421.44MB
57. 14. Options and Settings in pandas/1. Introduction to the Options and Settings Module.mp43.32MB
58. 14. Options and Settings in pandas/2. Changing pandas Options with Attributes and Dot Syntax.mp419.83MB
59. 14. Options and Settings in pandas/3. Changing pandas Options with Methods.mp413.92MB
60. 14. Options and Settings in pandas/4. The precision Option.mp46.11MB
61. 15. Conclusion/1. Conclusion.mp42.95MB
62. 2. BONUS Python Crash Course/1. Intro to the Python Crash Course.mp414.41MB
63. 2. BONUS Python Crash Course/10. Index Positions and Slicing.mp455.64MB
64. 2. BONUS Python Crash Course/11. Dictionaries.mp453.76MB
65. 2. BONUS Python Crash Course/2. Comments.mp49.19MB
66. 2. BONUS Python Crash Course/3. Basic Data Types.mp432.45MB
67. 2. BONUS Python Crash Course/4. Operators.mp450.66MB
68. 2. BONUS Python Crash Course/5. Variables.mp424.02MB
69. 2. BONUS Python Crash Course/6. Built-in Functions.mp432.96MB
70. 2. BONUS Python Crash Course/7. Custom Functions.mp456.46MB
71. 2. BONUS Python Crash Course/8. String Methods.mp474.71MB
72. 2. BONUS Python Crash Course/9. Lists.mp444.53MB
73. 3. Series/1. Create Jupyter Notebook for the Series Module.mp47.2MB
74. 3. Series/10. Use the head and tail Methods to Return Rows from Beginning and End of Dataset.mp46.48MB
75. 3. Series/11. Passing pandas Objects to Python Built-In Functions.mp49.88MB
76. 3. Series/12. Accessing More Series Attributes.mp411.67MB
77. 3. Series/13. Use the sort_values method to sort a Series in ascending or descending order.mp410.83MB
78. 3. Series/14. Use the inplace Parameter to permanently mutate a pandas data structure.mp49.39MB
79. 3. Series/15. Use the sort_index Method to Sort the Index of a pandas Series object.mp48.58MB
80. 3. Series/17. Use Python's in Keyword to Check for Inclusion in Series values or index.mp47.3MB
81. 3. Series/18. Extract Series Values by Index Positiox.mp48.9MB
82. 3. Series/19. Extract Series Values by Index Label.mp446.24MB
83. 3. Series/2. Create A Series Object from a Python List.mp418.12MB
84. 3. Series/21. Use the get Method to Retrieve a Value for an index label in a Series.mp441.1MB
85. 3. Series/22. Math Methods on Series Objects.mp410.16MB
86. 3. Series/23. Use the idxmax and idxmin Methods to Find Index of Greatest or Smallest Value.mp45.75MB
87. 3. Series/24. Use the value_counts Method to See Counts of Unique Values within a Series.mp46.73MB
88. 3. Series/25. Use the apply Method to Invoke a Function on Every Series Values.mp412.32MB
89. 3. Series/26. The Series#map Method.mp413.09MB
90. 3. Series/3. Create A Series Object from a Python Dictionary.mp45.2MB
91. 3. Series/5. Intro to Attributes on a Series Object.mp412.85MB
92. 3. Series/6. Intro to Methods on a Series Object.mp47.91MB
93. 3. Series/7. Parameters and Arguments.mp418.28MB
94. 3. Series/8. Create Series from Dataset with the pd.read_csv Method.mp460.35MB
95. 4. DataFrames I Introduction/1. Intro to DataFrames I Module.mp449.79MB
96. 4. DataFrames I Introduction/10. A Review of the value_counts Method.mp48.42MB
97. 4. DataFrames I Introduction/11. Drop DataFrame Rows with Null Values with the dropna Method.mp419.2MB
98. 4. DataFrames I Introduction/13. Fill in Null DataFrame Values with the fillna Method.mp410.76MB
99. 4. DataFrames I Introduction/14. Convert DataFrame Column Types with the astype Method.mp423.86MB
100. 4. DataFrames I Introduction/15. Sort a DataFrame with the sort_values Method, Part I.mp413.28MB
101. 4. DataFrames I Introduction/16. Sort a DataFrame with the sort_values Method, Part II.mp48.83MB
102. 4. DataFrames I Introduction/18. Sort DataFrame Indexwith the sort_index Method.mp46.56MB
103. 4. DataFrames I Introduction/19. Rank Series Values with the rank Method.mp413.16MB
104. 4. DataFrames I Introduction/2. Shared Methods and Attributes between Series and DataFrames.mp467.52MB
105. 4. DataFrames I Introduction/3. Differences between Shared Methods.mp413.11MB
106. 4. DataFrames I Introduction/4. Select One Column from a DataFrame.mp414.87MB
107. 4. DataFrames I Introduction/6. Select Two or More Columns from a DataFrame.mp49.93MB
108. 4. DataFrames I Introduction/8. Add New Column to DataFrame.mp417.24MB
109. 4. DataFrames I Introduction/9. Broadcasting Operations on DataFrames.mp418.23MB
110. 5. DataFrames II Filtering Data/1. This Module's Dataset + Memory Optimization.mp497.88MB
111. 5. DataFrames II Filtering Data/10. Identify and Count Unique Values with the unique and nunique Methods.mp48.19MB
112. 5. DataFrames II Filtering Data/2. Filter a DataFrame Based on A Condition.mp427.4MB
113. 5. DataFrames II Filtering Data/3. Filter DataFrame with More than One Condition (AND - &).mp49.3MB
114. 5. DataFrames II Filtering Data/4. Filter DataFrame with More than One Condition (OR - ).mp416.75MB
115. 5. DataFrames II Filtering Data/5. Check for Inclusion with the isin Method.mp412.53MB
116. 5. DataFrames II Filtering Data/6. Check for Null and Present DataFrame Values with the isnull and notnull Methods.mp412.26MB
117. 5. DataFrames II Filtering Data/7. Check For Inclusion Within a Range of Values with the between Method.mp416.76MB
118. 5. DataFrames II Filtering Data/8. Check for Duplicate DataFrame Rows with the duplicated Method.mp419.56MB
119. 5. DataFrames II Filtering Data/9. Delete Duplicate DataFrame Rows with the drop_duplicates Method.mp417.56MB
120. 6. DataFrames III Data Extraction/1. Intro to the DataFrames III Module + Import Dataset.mp423.36MB
121. 6. DataFrames III Data Extraction/10. Create Random Sample with the sample Method.mp49.33MB
122. 6. DataFrames III Data Extraction/11. Use the nsmallest nlargest methods to get rows with smallest largest values..mp412.07MB
123. 6. DataFrames III Data Extraction/12. Filter A DataFrame with the where method.mp413.57MB
124. 6. DataFrames III Data Extraction/13. Filter A DataFrame with the query method.mp419.92MB
125. 6. DataFrames III Data Extraction/14. A Review of the apply Method on a pandas Series Object.mp411.75MB
126. 6. DataFrames III Data Extraction/15. Apply a Function to every DataFrame Row with the apply Method.mp413.41MB
127. 6. DataFrames III Data Extraction/16. Create a Copy of a DataFrame with the copy Method.mp415.44MB
128. 6. DataFrames III Data Extraction/2. Use the set_index and reset_index methods to define a new DataFrame index.mp439.21MB
129. 6. DataFrames III Data Extraction/3. Retrieve Rows by Index Label with loc Accessor.mp461.12MB
130. 6. DataFrames III Data Extraction/4. Retrieve Rows by Index Position with iloc Accessor.mp439.56MB
131. 6. DataFrames III Data Extraction/5. Passing second arguments to the loc and iloc Accessors.mp445.85MB
132. 6. DataFrames III Data Extraction/6. Set New Value for a Specific Cell or Cells In a Row.mp419.94MB
133. 6. DataFrames III Data Extraction/7. Set Multiple Values in a DataFrame.mp438.56MB
134. 6. DataFrames III Data Extraction/8. Rename Index Labels or Columns in a DataFrame.mp458.06MB
135. 6. DataFrames III Data Extraction/9. Delete Rows or Columns from a DataFrame.mp416.22MB
136. 7. Working with Text Data/1. Intro to the Working with Text Data Section.mp432.29MB
137. 7. Working with Text Data/2. Common String Methods - lower, upper, title, and len.mp414.88MB
138. 7. Working with Text Data/3. Use the str.replace method to replace all occurrences of character with another.mp416MB
139. 7. Working with Text Data/4. Filter a DataFrame's Rows with String Methods.mp415.54MB
140. 7. Working with Text Data/5. More DataFrame String Methods - strip, lstrip, and rstrip.mp49.55MB
141. 7. Working with Text Data/6. Invoke String Methods on DataFrame Index and Columns.mp411.13MB
142. 7. Working with Text Data/7. Split Strings by Characters with the str.split Method.mp417.52MB
143. 7. Working with Text Data/8. More Practice with the str.split method on a Series.mp411.93MB
144. 7. Working with Text Data/9. Exploring the expand and n Parameters of the str.split Method.mp415.3MB
145. 8. MultiIndex/1. Intro to the MultiIndex Module.mp419.97MB
146. 8. MultiIndex/10. The .unstack() Method, Part 1.mp48.48MB
147. 8. MultiIndex/11. The .unstack() Method, Part 2.mp414.53MB
148. 8. MultiIndex/12. The .unstack() Method, Part 3.mp411.97MB
149. 8. MultiIndex/13. The pivot Method.mp412.11MB
150. 8. MultiIndex/14. Use the pivot_table method to create an aggregate summary of a DataFrame.mp422.16MB
151. 8. MultiIndex/15. Use the pd.melt method to create a narrow dataset from a wide one.mp417.27MB
152. 8. MultiIndex/2. Create a MultiIndex on a DataFrame with the set_index Method.mp445.7MB
153. 8. MultiIndex/3. Extract Index Level Values with the get_level_values Method.mp420.66MB
154. 8. MultiIndex/4. Change Index Level Name with the set_names Method.mp419MB
155. 8. MultiIndex/5. The sort_index Method on a MultiIndex DataFrame.mp435.23MB
156. 8. MultiIndex/6. Extract Rows from a MultiIndex DataFrame.mp447MB
157. 8. MultiIndex/7. The transpose Method on a MultiIndex DataFrame.mp435.63MB
158. 8. MultiIndex/8. The .swaplevel() Method.mp414.34MB
159. 8. MultiIndex/9. The .stack() Method.mp413.19MB
160. 9. The GroupBy Object/1. Intro to the Groupby Module.mp414.29MB
161. 9. The GroupBy Object/2. First Operations with groupby Object.mp423.08MB
162. 9. The GroupBy Object/3. Retrieve a group from a GroupBy object with the get_group Method.mp410.14MB
163. 9. The GroupBy Object/4. Methods on the Groupby Object and DataFrame Columns.mp420.5MB
164. 9. The GroupBy Object/5. Grouping by Multiple Columns.mp410.35MB
165. 9. The GroupBy Object/6. The .agg() Method.mp413.18MB
166. 9. The GroupBy Object/7. Iterating through Groups.mp421.37MB