Data Analysis with Pandas and Python

1. 01 Installation and Setup/006 Mac OS - Update Anaconda Libraries.mp435.27MB
2. 01 Installation and Setup/001 Introduction to the Course.mp434MB
3. 01 Installation and Setup/003 Mac OS - Download the Anaconda Distribution.mp47.92MB
4. 01 Installation and Setup/004 Mac OS - Install Anaconda Distribution.mp422.76MB
5. 01 Installation and Setup/005 Mac OS - Access the Terminal.mp48.84MB
6. 01 Installation and Setup/007 Mac OS - Unpack Course Materials The Startdown and Shutdown Process.mp422.15MB
7. 01 Installation and Setup/008 Windows - Download the Anaconda Distribution.mp49.27MB
8. 01 Installation and Setup/009 Windows - Install Anaconda Distribution.mp418.58MB
9. 01 Installation and Setup/010 Windows - Access the Command Prompt and Update Anaconda Libraries.mp422.5MB
10. 01 Installation and Setup/011 Windows - Unpack Course Materials The Startdown and Shutdown Process.mp417.72MB
11. 01 Installation and Setup/012 Intro to the Jupyter Notebook Interface.mp49.31MB
12. 01 Installation and Setup/013 Cell Types and Cell Modes.mp411.67MB
13. 01 Installation and Setup/014 Code Cell Execution.mp48.22MB
14. 01 Installation and Setup/015 Popular Keyboard Shortcuts.mp46.26MB
15. 01 Installation and Setup/016 Import Libraries into Jupyter Notebook.mp411.53MB
16. 01 Installation and Setup/017 Python Crash Course Part 1 - Data Types and Variables.mp411.98MB
17. 01 Installation and Setup/018 Python Crash Course Part 2 - Lists.mp49MB
18. 01 Installation and Setup/019 Python Crash Course Part 3 - Dictionaries.mp47.2MB
19. 01 Installation and Setup/020 Python Crash Course Part 4 - Operators.mp47.87MB
20. 01 Installation and Setup/021 Python Crash Course Part 5 - Functions.mp410.12MB
21. 02 Series/022 Create Jupyter Notebook for the Series Module.mp43.8MB
22. 02 Series/023 Create A Series Object from a Python List.mp418.12MB
23. 02 Series/024 Create A Series Object from a Python Dictionary.mp45.19MB
24. 02 Series/025 Intro to Attributes.mp412.85MB
25. 02 Series/026 Intro to Methods.mp47.91MB
26. 02 Series/027 Parameters and Arguments.mp418.28MB
27. 02 Series/028 Import Series with the .read_csv() Method.mp421.14MB
28. 02 Series/029 The .head() and .tail() Methods.mp46.47MB
29. 02 Series/030 Python Built-In Functions.mp49.87MB
30. 02 Series/031 More Series Attributes.mp411.66MB
31. 02 Series/032 The .sort_values() Method.mp410.83MB
32. 02 Series/033 The inplace Parameter.mp49.39MB
33. 02 Series/034 The .sort_index() Method.mp48.57MB
34. 02 Series/035 Pythons in Keyword.mp47.3MB
35. 02 Series/036 Extract Series Values by Index Position.mp48.9MB
36. 02 Series/037 Extract Series Values by Index Label.mp413.73MB
37. 02 Series/038 The .get() Method on a Series.mp49.56MB
38. 02 Series/039 Math Methods on Series Objects.mp410.16MB
39. 02 Series/040 The .idxmax() and .idxmin() Methods.mp45.75MB
40. 02 Series/041 The .value_counts() Method.mp46.73MB
41. 02 Series/042 The .apply() Method.mp412.31MB
42. 02 Series/043 The .map() Method.mp413.09MB
43. 03 DataFrames I/044 Intro to DataFrames I Module.mp417.63MB
44. 03 DataFrames I/045 Shared Methods and Attributes between Series and DataFrames.mp415.62MB
45. 03 DataFrames I/046 Differences between Shared Methods.mp413.1MB
46. 03 DataFrames I/047 Select One Column from a DataFrame.mp414.87MB
47. 03 DataFrames I/048 Select Two or More Columns from a DataFrame.mp49.93MB
48. 03 DataFrames I/049 Add New Column to DataFrame.mp417.23MB
49. 03 DataFrames I/050 Broadcasting Operations.mp418.23MB
50. 03 DataFrames I/051 A Review of the .value_counts() Method.mp48.42MB
51. 03 DataFrames I/052 Drop Rows with Null Values.mp419.2MB
52. 03 DataFrames I/053 Fill in Null Values with the .fillna() Method.mp410.75MB
53. 03 DataFrames I/054 The .astype() Method.mp423.86MB
54. 03 DataFrames I/055 Sort a DataFrame with the .sort_values() Method Part I.mp413.27MB
55. 03 DataFrames I/056 Sort a DataFrame with the .sort_values() Method Part II.mp48.83MB
56. 03 DataFrames I/057 Sort DataFrame with the .sort_index() Method.mp46.56MB
57. 03 DataFrames I/058 Rank Values with the .rank() Method.mp413.16MB
58. 04 DataFrames II/059 This Modules Dataset Memory Optimization.mp424.44MB
59. 04 DataFrames II/060 Filter a DataFrame Based on A Condition.mp427.4MB
60. 04 DataFrames II/061 Filter with More than One Condition (AND - ).mp49.29MB
61. 04 DataFrames II/062 Filter with More than One Condition (OR - ).mp416.75MB
62. 04 DataFrames II/063 The .isin() Method.mp412.53MB
63. 04 DataFrames II/064 The .isnull() and .notnull() Methods.mp412.26MB
64. 04 DataFrames II/065 The .between() Method.mp416.76MB
65. 04 DataFrames II/066 The .duplicated() Method.mp419.56MB
66. 04 DataFrames II/067 The .drop_duplicates() Method.mp417.55MB
67. 04 DataFrames II/068 The .unique() and .nunique() Methods.mp48.19MB
68. 05 DataFrames III/069 Intro to the DataFrames III Module Import Dataset.mp47.67MB
69. 05 DataFrames III/070 The .set_index() and .reset_index() Methods.mp413.19MB
70. 05 DataFrames III/071 Retrieve Rows by Index Label with .loc.mp425.87MB
71. 05 DataFrames III/072 Retrieve Rows by Index Position with .iloc.mp413.3MB
72. 05 DataFrames III/073 The Catch-All .ix Method.mp418.56MB
73. 05 DataFrames III/074 Second Arguments to .loc .iloc and .ix Methods.mp412.36MB
74. 05 DataFrames III/075 Set New Values for a Specific Cell or Row.mp48.89MB
75. 05 DataFrames III/076 Set Multiple Values in DataFrame.mp420.54MB
76. 05 DataFrames III/077 Rename Index Labels or Columns in a DataFrame.mp413.39MB
77. 05 DataFrames III/078 Delete Rows or Columns from a DataFrame.mp416.21MB
78. 05 DataFrames III/079 Create Random Sample with the .sample() Method.mp49.33MB
79. 05 DataFrames III/080 The .nsmallest() and .nlargest() Methods.mp412.07MB
80. 05 DataFrames III/081 Filtering with the .where() Method.mp413.56MB
81. 05 DataFrames III/082 The .query() Method.mp419.92MB
82. 05 DataFrames III/083 A Review of the .apply() Method on Single Columns.mp411.75MB
83. 05 DataFrames III/084 The .apply() Method with Row Values.mp413.41MB
84. 05 DataFrames III/085 The .copy() Method.mp415.44MB
85. 06 Working with Text Data/086 Intro to the Working with Text Data Module.mp413.86MB
86. 06 Working with Text Data/087 Common String Methods - lower upper title and len.mp414.88MB
87. 06 Working with Text Data/088 The .str.replace() Method.mp416MB
88. 06 Working with Text Data/089 Filtering with String Methods.mp415.54MB
89. 06 Working with Text Data/090 More String Methods - strip lstrip and rstrip.mp49.54MB
90. 06 Working with Text Data/091 String Methods on Index and Columns.mp411.12MB
91. 06 Working with Text Data/092 Split Strings by Characters with .str.split() Method.mp417.52MB
92. 06 Working with Text Data/093 More Practice with Splits.mp411.92MB
93. 06 Working with Text Data/094 The expand and n Parameters of the .str.split() Method.mp415.3MB
94. 07 MultiIndex/095 Intro to the MultiIndex Module.mp48.32MB
95. 07 MultiIndex/096 Create a MultiIndex with the set_index() Method.mp421.05MB
96. 07 MultiIndex/097 The .get_level_values() Method.mp416.54MB
97. 07 MultiIndex/098 The .set_names() Method.mp46.09MB
98. 07 MultiIndex/099 The sort_index() Method.mp410.25MB
99. 07 MultiIndex/100 Extract Rows from a MultiIndex DataFrame.mp417.34MB
100. 07 MultiIndex/101 The .transpose() Method and MultiIndex on Column Level.mp411.92MB
101. 07 MultiIndex/102 The .swaplevel() Method.mp45.18MB
102. 07 MultiIndex/103 The .stack() Method.mp413.19MB
103. 07 MultiIndex/104 The .unstack() Method Part 1.mp48.48MB
104. 07 MultiIndex/105 The .unstack() Method Part 2.mp414.53MB
105. 07 MultiIndex/106 The .unstack() Method Part 3.mp411.96MB
106. 07 MultiIndex/107 The .pivot() Method.mp412.11MB
107. 07 MultiIndex/108 The .pivot_table() Method.mp422.16MB
108. 07 MultiIndex/109 The pd.melt() Method.mp417.26MB
109. 08 GroupBy/110 Intro to the Groupby Module.mp414.29MB
110. 08 GroupBy/111 First Operations with groupby Object.mp423.08MB
111. 08 GroupBy/112 Retrieve A Group with the .get_group() Method.mp410.14MB
112. 08 GroupBy/113 Methods on the Groupby Object and DataFrame Columns.mp420.49MB
113. 08 GroupBy/114 Grouping by Multiple Columns.mp410.34MB
114. 08 GroupBy/115 The .agg() Method.mp413.18MB
115. 08 GroupBy/116 Iterating through Groups.mp421.37MB
116. 09 Merging Joining and Concatenating/117 Intro to the Merging Joining and Concatenating Module.mp411.46MB
117. 09 Merging Joining and Concatenating/118 The pd.concat() Method Part 1.mp412.56MB
118. 09 Merging Joining and Concatenating/119 The pd.concat() Method Part 2.mp413.2MB
119. 09 Merging Joining and Concatenating/120 The .append() Method on a DataFrame.mp45.13MB
120. 09 Merging Joining and Concatenating/121 Inner Joins Part 1.mp417.92MB
121. 09 Merging Joining and Concatenating/122 Inner Joins Part 2.mp417.75MB
122. 09 Merging Joining and Concatenating/123 Outer Joins.mp425.94MB
123. 09 Merging Joining and Concatenating/124 Left Joins.mp420.99MB
124. 09 Merging Joining and Concatenating/125 The left_on and right_on Parameters.mp420.24MB
125. 09 Merging Joining and Concatenating/126 Merging by Indexes with the left_index and right_index Parameters.mp422.7MB
126. 09 Merging Joining and Concatenating/127 The .join() Method.mp46.27MB
127. 09 Merging Joining and Concatenating/128 The pd.merge() Method.mp46.84MB
128. 10 Working with Dates and Times/129 Intro to the Working with Dates and Times Module.mp46.32MB
129. 10 Working with Dates and Times/130 Review of Pythons datetime Module.mp416.73MB
130. 10 Working with Dates and Times/131 The pandas Timestamp Object.mp412.8MB
131. 10 Working with Dates and Times/132 The pandas DateTimeIndex Object.mp49.65MB
132. 10 Working with Dates and Times/133 The pd.to_datetime() Method.mp422.88MB
133. 10 Working with Dates and Times/134 Create Range of Dates with the pd.date_range() Method Part 1.mp419.68MB
134. 10 Working with Dates and Times/135 Create Range of Dates with the pd.date_range() Method Part 2.mp418.54MB
135. 10 Working with Dates and Times/136 Create Range of Dates with the pd.date_range() Method Part 3.mp416.33MB
136. 10 Working with Dates and Times/137 The .dt Accessor.mp413.68MB
137. 10 Working with Dates and Times/138 Install pandas-datareader Library.mp45.9MB
138. 10 Working with Dates and Times/139 Import Financial Data Set with pandas_datareader Library.mp425.48MB
139. 10 Working with Dates and Times/140 Selecting Rows from a DataFrame with a DateTimeIndex.mp418.34MB
140. 10 Working with Dates and Times/141 Timestamp Object Attributes.mp419.57MB
141. 10 Working with Dates and Times/142 The .truncate() Method.mp49.04MB
142. 10 Working with Dates and Times/143 pd.DateOffset Objects.mp425.58MB
143. 10 Working with Dates and Times/144 More Fun with pd.DateOffset Objects.mp431.91MB
144. 10 Working with Dates and Times/145 The pandas Timedelta Object.mp415.41MB
145. 10 Working with Dates and Times/146 Timedeltas in a Dataset.mp419.55MB
146. 11 Panels/147 Intro to the Module Fetch Panel Dataset from Google Finance.mp413.67MB
147. 11 Panels/148 The Axes of a Panel Object.mp416.31MB
148. 11 Panels/149 Panel Attributes.mp410.49MB
149. 11 Panels/150 Use Bracket Notation to Extract a DataFrame from a Panel.mp48.25MB
150. 11 Panels/151 Extracting with the .loc .iloc and .ix Methods.mp413.53MB
151. 11 Panels/152 Convert Panel to a MultiIndex DataFrame (and Vice Versa).mp48.68MB
152. 11 Panels/153 The .major_xs() Method.mp412.11MB
153. 11 Panels/154 The .minor_xs() Method.mp413.62MB
154. 11 Panels/155 Transpose a Panel with the .transpose() Method.mp415.73MB
155. 11 Panels/156 The .swapaxes() Method.mp49.71MB
156. 12 Input and Output/157 Intro to the Input and Output Module.mp42.8MB
157. 12 Input and Output/158 Feed pd.read_csv() Method a URL Argument.mp47.6MB
158. 12 Input and Output/159 Quick Object Conversions.mp411.35MB
159. 12 Input and Output/160 Export DataFrame to CSV File with the .to_csv() Method.mp411.36MB
160. 12 Input and Output/161 Install xlrd and openpyxl Libraries to Read and Write Excel Files.mp45.98MB
161. 12 Input and Output/162 Import Excel File into pandas.mp419.13MB
162. 12 Input and Output/163 Export Excel File.mp417.8MB
163. 13 Visualization/164 Intro to Visualization Module.mp47.31MB
164. 13 Visualization/165 The .plot() Method.mp418.97MB
165. 13 Visualization/166 Modifying Aesthetics with Templates.mp412.08MB
166. 13 Visualization/167 Bar Graphs.mp412.27MB
167. 13 Visualization/168 Pie Charts.mp49.87MB
168. 13 Visualization/169 Histograms.mp412.16MB
169. 14 Options and Settings/170 Introduction to the Options and Settings Module.mp43.32MB
170. 14 Options and Settings/171 Changing pandas Options with Attributes and Dot Syntax.mp419.83MB
171. 14 Options and Settings/172 Changing pandas Options with Methods.mp413.92MB
172. 14 Options and Settings/173 The precision Option.mp46.1MB
173. 15 Conclusion/174 Conclusion.mp42.95MB