种子简介

种子名称： [FreeCourseSite.com] Udemy - Data Analysis with Pandas and Python
文件类型： 视频
文件数目： 166个文件
文件大小： 4.16 GB
收录时间： 2021-1-26 17:45
已经下载： 3次
资源热度： 208
最近下载： 2024-11-4 11:26

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:030c5b8a9ac159291ce9f3255d727f2d0b5a2ada&dn=[FreeCourseSite.com] 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

[FreeCourseSite.com] Udemy - Data Analysis with Pandas and Python.torrent

1. Installation and Setup/1. Introduction to Data Analysis with Pandas and Python.mp434MB

1. Installation and Setup/10. Windows - Install Anaconda Distribution.mp438.65MB

1. Installation and Setup/11. Windows - Create conda Environment and Install pandas and Jupyter Notebook.mp4111.78MB

1. Installation and Setup/12. Windows - Unpack Course Materials + The Startdown and Shutdown Process.mp447.43MB

1. Installation and Setup/13. Intro to the Jupyter Notebook Interface.mp433.27MB

1. Installation and Setup/14. Cell Types and Cell Modes in Jupyter Notebook.mp419.96MB

1. Installation and Setup/15. Code Cell Execution in Jupyter Notebook.mp48.81MB

1. Installation and Setup/16. Popular Keyboard Shortcuts in Jupyter Notebook.mp417MB

1. Installation and Setup/17. Import Libraries into Jupyter Notebook.mp425.83MB

1. Installation and Setup/2. About Me.mp421.3MB

1. Installation and Setup/4. MacOS - Download the Anaconda Distribution, our Python development environment.mp416.22MB

1. Installation and Setup/5. MacOS - Install Anaconda Distribution.mp490.99MB

1. Installation and Setup/6. MacOS - Access the Terminal Application.mp473.07MB

1. Installation and Setup/7. MacOS - Create conda Environment and Install pandas and Jupyter Notebook.mp4112.47MB

1. Installation and Setup/8. MacOS - Unpack Course Materials + The Start and Shutdown Process.mp4110.84MB

1. Installation and Setup/9. Windows - Download the Anaconda Distribution.mp417.87MB

10. Merging, Joining, and Concatenating DataFrames/1. Intro to the Merging, Joining, and Concatenating Section.mp420.99MB

10. Merging, Joining, and Concatenating DataFrames/10. The .join() Method.mp46.27MB

10. Merging, Joining, and Concatenating DataFrames/11. The pd.merge() Method.mp46.85MB

10. Merging, Joining, and Concatenating DataFrames/2. The pd.concat Method, Part 1.mp421.79MB

10. Merging, Joining, and Concatenating DataFrames/3. The pd.concat Method, Part 2.mp430.59MB

10. Merging, Joining, and Concatenating DataFrames/4. Inner Joins, Part 1.mp417.92MB

10. Merging, Joining, and Concatenating DataFrames/5. Inner Joins, Part 2.mp417.75MB

10. Merging, Joining, and Concatenating DataFrames/6. Outer Joins.mp425.94MB

10. Merging, Joining, and Concatenating DataFrames/7. Left Joins.mp420.99MB

10. Merging, Joining, and Concatenating DataFrames/8. The left_on and right_on Parameters.mp420.25MB

10. Merging, Joining, and Concatenating DataFrames/9. Merging by Indexes with the left_index and right_index Parameters.mp422.7MB

11. Working with Dates and Times in Datasets/1. Intro to the Working with Dates and Times Module.mp414.11MB

11. Working with Dates and Times in Datasets/10. Install pandas-datareader Library.mp432.99MB

11. Working with Dates and Times in Datasets/11. Import Financial Data Set with pandas_datareader Library.mp441.66MB

11. Working with Dates and Times in Datasets/12. Selecting Rows from a DataFrame with a DateTimeIndex.mp474.26MB

11. Working with Dates and Times in Datasets/13. Timestamp Object Attributes and Methods.mp454.13MB

11. Working with Dates and Times in Datasets/14. The pd.DateOffset Object.mp443.83MB

11. Working with Dates and Times in Datasets/15. Timeseries Offsets.mp474.55MB

11. Working with Dates and Times in Datasets/16. The Timedelta Object.mp432.25MB

11. Working with Dates and Times in Datasets/17. Timedeltas in a Dataset.mp419.55MB

11. Working with Dates and Times in Datasets/2. Review of Python's datetime Module.mp416.74MB

11. Working with Dates and Times in Datasets/3. The pandas Timestamp Object.mp412.81MB

11. Working with Dates and Times in Datasets/4. The pandas DateTimeIndex Object.mp49.65MB

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

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

11. Working with Dates and Times in Datasets/7. Create Range of Dates with the pd.date_range() Method, Part 2.mp418.55MB

11. Working with Dates and Times in Datasets/8. Create Range of Dates with the pd.date_range() Method, Part 3.mp416.34MB

11. Working with Dates and Times in Datasets/9. The .dt Accessor.mp413.69MB

12. Input and Output in pandas/1. Intro to the Input and Output Section.mp45.52MB

12. Input and Output in pandas/2. Pass a URL to the pd.read_csv Method.mp419.83MB

12. Input and Output in pandas/3. Quick Object Conversions.mp443.65MB

12. Input and Output in pandas/4. Export CSV File with the to_csv Method.mp427.23MB

12. Input and Output in pandas/5. Install xlrd and openpyxl Libraries to Read and Write Excel Files.mp440.14MB

12. Input and Output in pandas/6. Import Excel File into pandas with the read_excel Method.mp449.2MB

12. Input and Output in pandas/7. Export Excel File with the to_excel Method.mp446.97MB

13. Visualization/1. Intro to Visualization Section.mp415.79MB

13. Visualization/2. Use the plot Method to Render a Line Chart.mp433.33MB

13. Visualization/3. Modifying Plot Aesthetics with matplotlib Templates.mp423.91MB

13. Visualization/4. Creating Bar Graphs to Show Counts.mp427.23MB

13. Visualization/5. Creating Pie Charts to Represent Proportions.mp421.44MB

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

14. Options and Settings in pandas/2. Changing pandas Options with Attributes and Dot Syntax.mp419.84MB

14. Options and Settings in pandas/3. Changing pandas Options with Methods.mp413.93MB

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

15. Conclusion/1. Conclusion.mp42.95MB

2. BONUS Python Crash Course/1. Intro to the Python Crash Course.mp414.4MB

2. BONUS Python Crash Course/10. Index Positions and Slicing.mp455.64MB

2. BONUS Python Crash Course/11. Dictionaries.mp453.76MB

2. BONUS Python Crash Course/2. Comments.mp49.18MB

2. BONUS Python Crash Course/3. Basic Data Types.mp432.46MB

2. BONUS Python Crash Course/4. Operators.mp450.67MB

2. BONUS Python Crash Course/5. Variables.mp424.01MB

2. BONUS Python Crash Course/6. Built-in Functions.mp432.96MB

2. BONUS Python Crash Course/7. Custom Functions.mp456.46MB

2. BONUS Python Crash Course/8. String Methods.mp474.71MB

2. BONUS Python Crash Course/9. Lists.mp444.53MB

3. Series/1. Create Jupyter Notebook for the Series Module.mp47.19MB

3. Series/10. Use the head and tail Methods to Return Rows from Beginning and End of Dataset.mp46.47MB

3. Series/11. Passing pandas Objects to Python Built-In Functions.mp49.87MB

3. Series/12. Accessing More Series Attributes.mp411.66MB

3. Series/13. Use the sort_values method to sort a Series in ascending or descending order.mp410.84MB

3. Series/14. Use the inplace Parameter to permanently mutate a pandas data structure.mp49.39MB

3. Series/15. Use the sort_index Method to Sort the Index of a pandas Series object.mp48.58MB

3. Series/17. Use Python's in Keyword to Check for Inclusion in Series values or index.mp47.31MB

3. Series/18. Extract Series Values by Index Positiox.mp48.91MB

3. Series/19. Extract Series Values by Index Label.mp446.24MB

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

3. Series/21. Use the get Method to Retrieve a Value for an index label in a Series.mp441.11MB

3. Series/22. Math Methods on Series Objects.mp410.16MB

3. Series/23. Use the idxmax and idxmin Methods to Find Index of Greatest or Smallest Value.mp45.75MB

3. Series/24. Use the value_counts Method to See Counts of Unique Values within a Series.mp46.73MB

3. Series/25. Use the apply Method to Invoke a Function on Every Series Values.mp412.32MB

3. Series/26. The Series#map Method.mp413.1MB

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

3. Series/5. Intro to Attributes on a Series Object.mp412.86MB

3. Series/6. Intro to Methods on a Series Object.mp47.92MB

3. Series/7. Parameters and Arguments.mp418.28MB

3. Series/8. Create Series from Dataset with the pd.read_csv Method.mp460.35MB

4. DataFrames I Introduction/1. Intro to DataFrames I Module.mp449.79MB

4. DataFrames I Introduction/10. A Review of the value_counts Method.mp48.43MB

4. DataFrames I Introduction/11. Drop DataFrame Rows with Null Values with the dropna Method.mp419.21MB

4. DataFrames I Introduction/13. Fill in Null DataFrame Values with the fillna Method.mp410.75MB

4. DataFrames I Introduction/14. Convert DataFrame Column Types with the astype Method.mp423.86MB

4. DataFrames I Introduction/15. Sort a DataFrame with the sort_values Method, Part I.mp413.28MB

4. DataFrames I Introduction/16. Sort a DataFrame with the sort_values Method, Part II.mp48.83MB

4. DataFrames I Introduction/18. Sort DataFrame Indexwith the sort_index Method.mp46.57MB

4. DataFrames I Introduction/19. Rank Series Values with the rank Method.mp413.17MB

4. DataFrames I Introduction/2. Shared Methods and Attributes between Series and DataFrames.mp467.52MB

4. DataFrames I Introduction/3. Differences between Shared Methods.mp413.11MB

4. DataFrames I Introduction/4. Select One Column from a DataFrame.mp414.88MB

4. DataFrames I Introduction/6. Select Two or More Columns from a DataFrame.mp49.93MB

4. DataFrames I Introduction/8. Add New Column to DataFrame.mp417.23MB

4. DataFrames I Introduction/9. Broadcasting Operations on DataFrames.mp418.23MB

5. DataFrames II Filtering Data/1. This Module's Dataset + Memory Optimization.mp497.89MB

5. DataFrames II Filtering Data/10. Identify and Count Unique Values with the unique and nunique Methods.mp48.2MB

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

5. DataFrames II Filtering Data/3. Filter DataFrame with More than One Condition (AND - &).mp49.3MB

5. DataFrames II Filtering Data/4. Filter DataFrame with More than One Condition (OR - ).mp416.76MB

5. DataFrames II Filtering Data/5. Check for Inclusion with the isin Method.mp412.53MB

5. DataFrames II Filtering Data/6. Check for Null and Present DataFrame Values with the isnull and notnull Methods.mp412.26MB

5. DataFrames II Filtering Data/7. Check For Inclusion Within a Range of Values with the between Method.mp416.77MB

5. DataFrames II Filtering Data/8. Check for Duplicate DataFrame Rows with the duplicated Method.mp419.56MB

5. DataFrames II Filtering Data/9. Delete Duplicate DataFrame Rows with the drop_duplicates Method.mp417.55MB

6. DataFrames III Data Extraction/1. Intro to the DataFrames III Module + Import Dataset.mp423.37MB

6. DataFrames III Data Extraction/10. Create Random Sample with the sample Method.mp49.33MB

6. DataFrames III Data Extraction/11. Use the nsmallest nlargest methods to get rows with smallest largest values..mp412.07MB

6. DataFrames III Data Extraction/12. Filter A DataFrame with the where method.mp413.56MB

6. DataFrames III Data Extraction/13. Filter A DataFrame with the query method.mp419.92MB

6. DataFrames III Data Extraction/14. A Review of the apply Method on a pandas Series Object.mp411.75MB

6. DataFrames III Data Extraction/15. Apply a Function to every DataFrame Row with the apply Method.mp413.41MB

6. DataFrames III Data Extraction/16. Create a Copy of a DataFrame with the copy Method.mp415.44MB

6. DataFrames III Data Extraction/2. Use the set_index and reset_index methods to define a new DataFrame index.mp439.21MB

6. DataFrames III Data Extraction/3. Retrieve Rows by Index Label with loc Accessor.mp461.12MB

6. DataFrames III Data Extraction/4. Retrieve Rows by Index Position with iloc Accessor.mp439.56MB

6. DataFrames III Data Extraction/5. Passing second arguments to the loc and iloc Accessors.mp445.85MB

6. DataFrames III Data Extraction/6. Set New Value for a Specific Cell or Cells In a Row.mp419.94MB

6. DataFrames III Data Extraction/7. Set Multiple Values in a DataFrame.mp438.56MB

6. DataFrames III Data Extraction/8. Rename Index Labels or Columns in a DataFrame.mp458.06MB

6. DataFrames III Data Extraction/9. Delete Rows or Columns from a DataFrame.mp416.21MB

7. Working with Text Data/1. Intro to the Working with Text Data Section.mp432.29MB

7. Working with Text Data/2. Common String Methods - lower, upper, title, and len.mp414.89MB

7. Working with Text Data/3. Use the str.replace method to replace all occurrences of character with another.mp416MB

7. Working with Text Data/4. Filter a DataFrame's Rows with String Methods.mp415.55MB

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

7. Working with Text Data/6. Invoke String Methods on DataFrame Index and Columns.mp411.13MB

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

7. Working with Text Data/8. More Practice with the str.split method on a Series.mp411.92MB

7. Working with Text Data/9. Exploring the expand and n Parameters of the str.split Method.mp415.31MB

8. MultiIndex/1. Intro to the MultiIndex Module.mp419.97MB

8. MultiIndex/10. The .unstack() Method, Part 1.mp48.48MB

8. MultiIndex/11. The .unstack() Method, Part 2.mp414.53MB

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

8. MultiIndex/13. The pivot Method.mp412.11MB

8. MultiIndex/14. Use the pivot_table method to create an aggregate summary of a DataFrame.mp422.16MB

8. MultiIndex/15. Use the pd.melt method to create a narrow dataset from a wide one.mp417.26MB

8. MultiIndex/2. Create a MultiIndex on a DataFrame with the set_index Method.mp445.69MB

8. MultiIndex/3. Extract Index Level Values with the get_level_values Method.mp420.66MB

8. MultiIndex/4. Change Index Level Name with the set_names Method.mp419.01MB

8. MultiIndex/5. The sort_index Method on a MultiIndex DataFrame.mp435.24MB

8. MultiIndex/6. Extract Rows from a MultiIndex DataFrame.mp447.01MB

8. MultiIndex/7. The transpose Method on a MultiIndex DataFrame.mp435.64MB

8. MultiIndex/8. The .swaplevel() Method.mp414.35MB

8. MultiIndex/9. The .stack() Method.mp413.19MB

9. The GroupBy Object/1. Intro to the Groupby Module.mp414.29MB

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

9. The GroupBy Object/3. Retrieve a group from a GroupBy object with the get_group Method.mp410.14MB

9. The GroupBy Object/4. Methods on the Groupby Object and DataFrame Columns.mp420.49MB

9. The GroupBy Object/5. Grouping by Multiple Columns.mp410.34MB

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

9. The GroupBy Object/7. Iterating through Groups.mp421.37MB