种子简介

种子名称： [Tutorialsplanet.NET] Udemy - Data Analysis with Pandas and Python
文件类型： 视频
文件数目： 173个文件
文件大小： 2.31 GB
收录时间： 2020-9-14 17:21
已经下载： 3次
资源热度： 184
最近下载： 2024-11-22 19:22

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:22c8df38077a2e49f6e015738aff41b851b4c697&dn=[Tutorialsplanet.NET] Udemy - Data Analysis with Pandas and Python 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

[Tutorialsplanet.NET] Udemy - Data Analysis with Pandas and Python.torrent

1. Installation and Setup/1. Introduction to the Course.mp434MB

1. Installation and Setup/10. Windows - Access the Command Prompt and Update Anaconda Libraries.mp419.06MB

1. Installation and Setup/11. Windows - Unpack Course Materials + The Startdown and Shutdown Process.mp415.47MB

1. Installation and Setup/12. Intro to the Jupyter Notebook Interface.mp49.32MB

1. Installation and Setup/13. Cell Types and Cell Modes.mp411.68MB

1. Installation and Setup/14. Code Cell Execution.mp48.23MB

1. Installation and Setup/15. Popular Keyboard Shortcuts.mp46.27MB

1. Installation and Setup/16. Import Libraries into Jupyter Notebook.mp411.53MB

1. Installation and Setup/17. Python Crash Course, Part 1 - Data Types and Variables.mp411.99MB

1. Installation and Setup/18. Python Crash Course, Part 2 - Lists.mp49.01MB

1. Installation and Setup/19. Python Crash Course, Part 3 - Dictionaries.mp47.21MB

1. Installation and Setup/20. Python Crash Course, Part 4 - Operators.mp47.88MB

1. Installation and Setup/21. Python Crash Course, Part 5 - Functions.mp410.12MB

1. Installation and Setup/3. Mac OS - Download the Anaconda Distribution.mp47.93MB

1. Installation and Setup/4. Mac OS - Install Anaconda Distribution.mp418.06MB

1. Installation and Setup/5. Mac OS - Access the Terminal.mp46.36MB

1. Installation and Setup/6. Mac OS - Update Anaconda Libraries.mp435.27MB

1. Installation and Setup/7. Mac OS - Unpack Course Materials + The Startdown and Shutdown Process.mp422.15MB

1. Installation and Setup/8. Windows - Download the Anaconda Distribution.mp47.65MB

1. Installation and Setup/9. Windows - Install Anaconda Distribution.mp415.2MB

10. Working with Dates and Times/1. Intro to the Working with Dates and Times Module.mp46.32MB

10. Working with Dates and Times/10. Install pandas-datareader Library.mp45.9MB

10. Working with Dates and Times/11. Import Financial Data Set with pandas_datareader Library.mp425.48MB

10. Working with Dates and Times/12. Selecting Rows from a DataFrame with a DateTimeIndex.mp418.34MB

10. Working with Dates and Times/13. Timestamp Object Attributes.mp419.58MB

10. Working with Dates and Times/14. The .truncate() Method.mp49.05MB

10. Working with Dates and Times/15. pd.DateOffset Objects.mp425.58MB

10. Working with Dates and Times/16. More Fun with pd.DateOffset Objects.mp431.91MB

10. Working with Dates and Times/17. The pandas Timedelta Object.mp415.41MB

10. Working with Dates and Times/18. Timedeltas in a Dataset.mp419.55MB

10. Working with Dates and Times/2. Review of Python's datetime Module.mp416.73MB

10. Working with Dates and Times/3. The pandas Timestamp Object.mp412.8MB

10. Working with Dates and Times/4. The pandas DateTimeIndex Object.mp49.66MB

10. Working with Dates and Times/5. The pd.to_datetime() Method.mp422.88MB

10. Working with Dates and Times/6. Create Range of Dates with the pd.date_range() Method, Part 1.mp419.68MB

10. Working with Dates and Times/7. Create Range of Dates with the pd.date_range() Method, Part 2.mp418.54MB

10. Working with Dates and Times/8. Create Range of Dates with the pd.date_range() Method, Part 3.mp416.33MB

10. Working with Dates and Times/9. The .dt Accessor.mp413.68MB

11. Panels/1. Intro to the Module + Fetch Panel Dataset from Google Finance.mp413.67MB

11. Panels/10. The .swapaxes() Method.mp49.72MB

11. Panels/2. The Axes of a Panel Object.mp416.31MB

11. Panels/3. Panel Attributes.mp410.5MB

11. Panels/4. Use Bracket Notation to Extract a DataFrame from a Panel.mp48.26MB

11. Panels/5. Extracting with the .loc, .iloc, and .ix Methods.mp413.53MB

11. Panels/6. Convert Panel to a MultiIndex DataFrame (and Vice Versa).mp48.68MB

11. Panels/7. The .major_xs() Method.mp412.12MB

11. Panels/8. The .minor_xs() Method.mp413.63MB

11. Panels/9. Transpose a Panel with the .transpose() Method.mp415.73MB

12. Input and Output/1. Intro to the Input and Output Module.mp42.8MB

12. Input and Output/2. Feed pd.read_csv() Method a URL Argument.mp47.61MB

12. Input and Output/3. Quick Object Conversions.mp411.35MB

12. Input and Output/4. Export DataFrame to CSV File with the .to_csv() Method.mp411.36MB

12. Input and Output/5. Install xlrd and openpyxl Libraries to Read and Write Excel Files.mp45.99MB

12. Input and Output/6. Import Excel File into pandas.mp419.14MB

12. Input and Output/7. Export Excel File.mp417.81MB

13. Visualization/1. Intro to Visualization Module.mp47.32MB

13. Visualization/2. The .plot() Method.mp418.98MB

13. Visualization/3. Modifying Aesthetics with Templates.mp412.08MB

13. Visualization/4. Bar Graphs.mp412.27MB

13. Visualization/5. Pie Charts.mp49.87MB

13. Visualization/6. Histograms.mp412.16MB

14. Options and Settings/1. Introduction to the Options and Settings Module.mp43.33MB

14. Options and Settings/2. Changing pandas Options with Attributes and Dot Syntax.mp419.83MB

14. Options and Settings/3. Changing pandas Options with Methods.mp413.92MB

14. Options and Settings/4. The precision Option.mp46.11MB

15. Conclusion/1. Conclusion.mp42.96MB

2. Series/1. Create Jupyter Notebook for the Series Module.mp43.8MB

2. Series/10. More Series Attributes.mp411.66MB

2. Series/11. The .sort_values() Method.mp410.83MB

2. Series/12. The inplace Parameter.mp49.4MB

2. Series/13. The .sort_index() Method.mp48.58MB

2. Series/14. Python's in Keyword.mp47.31MB

2. Series/15. Extract Series Values by Index Position.mp48.9MB

2. Series/16. Extract Series Values by Index Label.mp413.74MB

2. Series/17. The .get() Method on a Series.mp49.57MB

2. Series/18. Math Methods on Series Objects.mp410.16MB

2. Series/19. The .idxmax() and .idxmin() Methods.mp45.75MB

2. Series/2. Create A Series Object from a Python List.mp418.12MB

2. Series/20. The .value_counts() Method.mp46.73MB

2. Series/21. The .apply() Method.mp412.31MB

2. Series/22. The .map() Method.mp413.1MB

2. Series/3. Create A Series Object from a Python Dictionary.mp45.19MB

2. Series/4. Intro to Attributes.mp412.86MB

2. Series/5. Intro to Methods.mp47.92MB

2. Series/6. Parameters and Arguments.mp418.28MB

2. Series/7. Import Series with the .read_csv() Method.mp421.14MB

2. Series/8. The .head() and .tail() Methods.mp46.48MB

2. Series/9. Python Built-In Functions.mp49.87MB

3. DataFrames I/1. Intro to DataFrames I Module.mp417.63MB

3. DataFrames I/10. Fill in Null Values with the .fillna() Method.mp410.75MB

3. DataFrames I/11. The .astype() Method.mp423.87MB

3. DataFrames I/12. Sort a DataFrame with the .sort_values() Method, Part I.mp413.27MB

3. DataFrames I/13. Sort a DataFrame with the .sort_values() Method, Part II.mp48.84MB

3. DataFrames I/14. Sort DataFrame with the .sort_index() Method.mp46.57MB

3. DataFrames I/15. Rank Values with the .rank() Method.mp413.16MB

3. DataFrames I/2. Shared Methods and Attributes between Series and DataFrames.mp415.62MB

3. DataFrames I/3. Differences between Shared Methods.mp413.1MB

3. DataFrames I/4. Select One Column from a DataFrame.mp414.87MB

3. DataFrames I/5. Select Two or More Columns from a DataFrame.mp49.94MB

3. DataFrames I/6. Add New Column to DataFrame.mp417.24MB

3. DataFrames I/7. Broadcasting Operations.mp418.23MB

3. DataFrames I/8. A Review of the .value_counts() Method.mp48.43MB

3. DataFrames I/9. Drop Rows with Null Values.mp419.21MB

4. DataFrames II/1. This Module's Dataset + Memory Optimization.mp424.44MB

4. DataFrames II/10. The .unique() and .nunique() Methods.mp48.2MB

4. DataFrames II/2. Filter a DataFrame Based on A Condition.mp427.4MB

4. DataFrames II/3. Filter with More than One Condition (AND - &).mp49.3MB

4. DataFrames II/4. Filter with More than One Condition (OR - ).mp416.75MB

4. DataFrames II/5. The .isin() Method.mp412.54MB

4. DataFrames II/6. The .isnull() and .notnull() Methods.mp412.26MB

4. DataFrames II/7. The .between() Method.mp416.76MB

4. DataFrames II/8. The .duplicated() Method.mp419.56MB

4. DataFrames II/9. The .drop_duplicates() Method.mp417.55MB

5. DataFrames III/1. Intro to the DataFrames III Module + Import Dataset.mp47.67MB

5. DataFrames III/10. Delete Rows or Columns from a DataFrame.mp416.21MB

5. DataFrames III/11. Create Random Sample with the .sample() Method.mp49.34MB

5. DataFrames III/12. The .nsmallest() and .nlargest() Methods.mp412.08MB

5. DataFrames III/13. Filtering with the .where() Method.mp413.56MB

5. DataFrames III/14. The .query() Method.mp419.92MB

5. DataFrames III/15. A Review of the .apply() Method on Single Columns.mp411.76MB

5. DataFrames III/16. The .apply() Method with Row Values.mp413.41MB

5. DataFrames III/17. The .copy() Method.mp415.45MB

5. DataFrames III/2. The .set_index() and .reset_index() Methods.mp413.19MB

5. DataFrames III/3. Retrieve Rows by Index Label with .loc[].mp425.87MB

5. DataFrames III/4. Retrieve Rows by Index Position with .iloc[].mp413.3MB

5. DataFrames III/5. The Catch-All .ix[] Method.mp418.56MB

5. DataFrames III/6. Second Arguments to .loc[], .iloc[], and .ix[] Methods.mp412.37MB

5. DataFrames III/7. Set New Values for a Specific Cell or Row.mp48.9MB

5. DataFrames III/8. Set Multiple Values in DataFrame.mp420.55MB

5. DataFrames III/9. Rename Index Labels or Columns in a DataFrame.mp413.4MB

6. Working with Text Data/1. Intro to the Working with Text Data Module.mp413.87MB

6. Working with Text Data/2. Common String Methods - lower, upper, title, and len.mp414.88MB

6. Working with Text Data/3. The .str.replace() Method.mp416MB

6. Working with Text Data/4. Filtering with String Methods.mp415.54MB

6. Working with Text Data/5. More String Methods - strip, lstrip, and rstrip.mp49.54MB

6. Working with Text Data/6. String Methods on Index and Columns.mp411.12MB

6. Working with Text Data/7. Split Strings by Characters with .str.split() Method.mp417.52MB

6. Working with Text Data/8. More Practice with Splits.mp411.92MB

6. Working with Text Data/9. The expand and n Parameters of the .str.split() Method.mp415.31MB

7. MultiIndex/1. Intro to the MultiIndex Module.mp48.32MB

7. MultiIndex/10. The .unstack() Method, Part 1.mp48.49MB

7. MultiIndex/11. The .unstack() Method, Part 2.mp414.54MB

7. MultiIndex/12. The .unstack() Method, Part 3.mp411.96MB

7. MultiIndex/13. The .pivot() Method.mp412.12MB

7. MultiIndex/14. The .pivot_table() Method.mp422.16MB

7. MultiIndex/15. The pd.melt() Method.mp417.27MB

7. MultiIndex/2. Create a MultiIndex with the set_index() Method.mp421.06MB

7. MultiIndex/3. The .get_level_values() Method.mp416.54MB

7. MultiIndex/4. The .set_names() Method.mp46.1MB

7. MultiIndex/5. The sort_index() Method.mp410.25MB

7. MultiIndex/6. Extract Rows from a MultiIndex DataFrame.mp417.34MB

7. MultiIndex/7. The .transpose() Method and MultiIndex on Column Level.mp411.92MB

7. MultiIndex/8. The .swaplevel() Method.mp45.19MB

7. MultiIndex/9. The .stack() Method.mp413.2MB

8. GroupBy/1. Intro to the Groupby Module.mp414.3MB

8. GroupBy/2. First Operations with groupby Object.mp423.08MB

8. GroupBy/3. Retrieve A Group with the .get_group() Method.mp410.14MB

8. GroupBy/4. Methods on the Groupby Object and DataFrame Columns.mp420.5MB

8. GroupBy/5. Grouping by Multiple Columns.mp410.34MB

8. GroupBy/6. The .agg() Method.mp413.18MB

8. GroupBy/7. Iterating through Groups.mp421.37MB

9. Merging, Joining, and Concatenating/1. Intro to the Merging, Joining, and Concatenating Module.mp411.46MB

9. Merging, Joining, and Concatenating/10. Merging by Indexes with the left_index and right_index Parameters.mp422.71MB

9. Merging, Joining, and Concatenating/11. The .join() Method.mp46.28MB

9. Merging, Joining, and Concatenating/12. The pd.merge() Method.mp46.85MB

9. Merging, Joining, and Concatenating/2. The pd.concat() Method, Part 1.mp412.56MB

9. Merging, Joining, and Concatenating/3. The pd.concat() Method, Part 2.mp413.2MB

9. Merging, Joining, and Concatenating/4. The .append() Method on a DataFrame.mp45.13MB

9. Merging, Joining, and Concatenating/5. Inner Joins, Part 1.mp417.92MB

9. Merging, Joining, and Concatenating/6. Inner Joins, Part 2.mp417.76MB

9. Merging, Joining, and Concatenating/7. Outer Joins.mp425.94MB

9. Merging, Joining, and Concatenating/8. Left Joins.mp421MB

9. Merging, Joining, and Concatenating/9. The left_on and right_on Parameters.mp420.25MB