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种子名称： [DesireCourse.Net] Udemy - Data Analysis with Pandas and Python
文件类型： 视频
文件数目： 160个文件
文件大小： 3.77 GB
收录时间： 2020-10-21 07:45
已经下载： 3次
资源热度： 188
最近下载： 2024-12-24 15:07

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[DesireCourse.Net] Udemy - Data Analysis with Pandas and Python.torrent

1. Installation and Setup/1. Introduction to Data Analysis with Pandas and Python.mp434.01MB

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.mp4111.79MB

1. Installation and Setup/12. Windows - Unpack Course Materials + The Startdown and Shutdown Process.mp447.44MB

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.mp48.82MB

1. Installation and Setup/16. Popular Keyboard Shortcuts in Jupyter Notebook.mp417.01MB

1. Installation and Setup/17. Import Libraries into Jupyter Notebook.mp425.84MB

1. Installation and Setup/18. Python Crash Course, Part 1 - Data Types and Variables.mp411.99MB

1. Installation and Setup/19. Python Crash Course, Part 2 - Lists.mp49.01MB

1. Installation and Setup/2. About Me.mp421.31MB

1. Installation and Setup/20. Python Crash Course, Part 3 - Dictionaries.mp47.21MB

1. Installation and Setup/21. Python Crash Course, Part 4 - Operators.mp47.88MB

1. Installation and Setup/22. Python Crash Course, Part 5 - Functions.mp410.13MB

1. Installation and Setup/4. MacOS - Download the Anaconda Distribution, our Python development environment.mp416.23MB

1. Installation and Setup/5. MacOS - Install Anaconda Distribution.mp491MB

1. Installation and Setup/6. MacOS - Access the Terminal Application.mp473.08MB

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.mp417.88MB

10. Working with Dates and Times in Datasets/1. Intro to the Working with Dates and Times Module.mp414.12MB

10. Working with Dates and Times in Datasets/10. Install pandas-datareader Library.mp433MB

10. Working with Dates and Times in Datasets/11. Import Financial Data Set with pandas_datareader Library.mp441.67MB

10. Working with Dates and Times in Datasets/12. Selecting Rows from a DataFrame with a DateTimeIndex.mp474.27MB

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

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

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

10. Working with Dates and Times in Datasets/16. The Timedelta Object.mp432.26MB

10. Working with Dates and Times in Datasets/17. Timedeltas in a Dataset.mp419.56MB

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

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

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

10. Working with Dates and Times in Datasets/5. The pd.to_datetime() Method.mp422.89MB

10. Working with Dates and Times in Datasets/6. Create Range of Dates with the pd.date_range() Method, Part 1.mp419.69MB

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

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

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

11. Input and Output in pandas/1. Intro to the Input and Output Section.mp45.53MB

11. Input and Output in pandas/2. Pass a URL to the pd.read_csv Method.mp419.84MB

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

11. Input and Output in pandas/4. Export CSV File with the to_csv Method.mp427.24MB

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

11. Input and Output in pandas/6. Import Excel File into pandas with the read_excel Method.mp449.19MB

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

12. Visualization/1. Intro to Visualization Section.mp415.8MB

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

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

12. Visualization/4. Creating Bar Graphs to Show Counts.mp427.24MB

12. Visualization/5. Creating Pie Charts to Represent Proportions.mp421.45MB

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

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

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

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

14. Conclusion/1. Conclusion.mp42.96MB

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

2. Series/10. Use the head and tail Methods to Return Rows from Beginning and End of Dataset.mp46.48MB

2. Series/11. Passing pandas Objects to Python Built-In Functions.mp49.88MB

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

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

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

2. Series/15. Use the sort_index Method to Sort the Index of a pandas Series object.mp48.57MB

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

2. Series/18. Extract Series Values by Index Position.mp48.91MB

2. Series/19. Extract Series Values by Index Label.mp446.25MB

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

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

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

2. Series/23. Use the idxmax and idxmin Methods to Find Index of Greatest or Smallest Value.mp45.76MB

2. Series/24. Use the value_counts Method to See Counts of Unique Values within a Series.mp46.74MB

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

2. Series/26. The .map() Method.mp413.1MB

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

2. Series/5. Intro to Attributes on a Series Object.mp412.85MB

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

2. Series/7. Parameters and Arguments.mp418.29MB

2. Series/8. Create Series from Dataset with the pd.read_csv Method.mp460.36MB

3. DataFrames I Introduction/1. Intro to DataFrames I Module.mp449.8MB

3. DataFrames I Introduction/10. A Review of the .value_counts() Method.mp48.42MB

3. DataFrames I Introduction/11. Drop Rows with Null Values.mp419.21MB

3. DataFrames I Introduction/12. Fill in Null Values with the .fillna() Method.mp410.76MB

3. DataFrames I Introduction/13. The .astype() Method.mp423.87MB

3. DataFrames I Introduction/14. Sort a DataFrame with the .sort_values() Method, Part I.mp413.28MB

3. DataFrames I Introduction/15. Sort a DataFrame with the .sort_values() Method, Part II.mp48.84MB

3. DataFrames I Introduction/17. Sort DataFrame with the .sort_index() Method.mp46.57MB

3. DataFrames I Introduction/18. Rank Values with the .rank() Method.mp413.17MB

3. DataFrames I Introduction/2. Shared Methods and Attributes between Series and DataFrames.mp467.53MB

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

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

3. DataFrames I Introduction/6. Select Two or More Columns from a DataFrame.mp49.94MB

3. DataFrames I Introduction/8. Add New Column to DataFrame.mp417.24MB

3. DataFrames I Introduction/9. Broadcasting Operations.mp418.23MB

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

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

4. DataFrames II Filtering Data/2. Filter a DataFrame Based on A Condition.mp427.41MB

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

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

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

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

4. DataFrames II Filtering Data/7. The .between() Method.mp416.77MB

4. DataFrames II Filtering Data/8. The .duplicated() Method.mp419.57MB

4. DataFrames II Filtering Data/9. The .drop_duplicates() Method.mp417.56MB

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

5. DataFrames III Data Extraction/10. Create Random Sample with the .sample() Method.mp49.34MB

5. DataFrames III Data Extraction/11. Use the nsmallest nlargest methods to get rows with smallest largest values..mp412.08MB

5. DataFrames III Data Extraction/12. Filtering the DataFrame with the where method.mp413.56MB

5. DataFrames III Data Extraction/13. Filtering the DataFrame with the query method.mp419.93MB

5. DataFrames III Data Extraction/14. A Review of the .apply() Method on Single Columns.mp411.75MB

5. DataFrames III Data Extraction/15. The .apply() Method with Row Values.mp413.42MB

5. DataFrames III Data Extraction/16. The .copy() Method.mp415.44MB

5. DataFrames III Data Extraction/2. Use the set_index and reset_index methods to define a new DataFrame index.mp439.22MB

5. DataFrames III Data Extraction/3. Retrieve Rows by Index Label with loc Accessor.mp461.13MB

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

5. DataFrames III Data Extraction/5. Passing second arguments to the loc and iloc Accessors.mp445.86MB

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

5. DataFrames III Data Extraction/7. Set Multiple Values in a DataFrame.mp438.57MB

5. DataFrames III Data Extraction/8. Rename Index Labels or Columns in a DataFrame.mp458.07MB

5. DataFrames III Data Extraction/9. Delete Rows or Columns from a DataFrame.mp416.22MB

6. Working with Text Data/1. Intro to the Working with Text Data Section.mp432.3MB

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

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

6. Working with Text Data/4. Filtering a DataFrame's rows with string methods.mp415.55MB

6. Working with Text Data/5. More String Methods - strip, lstrip, and rstrip for removing whitespace.mp49.55MB

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

6. Working with Text Data/7. Split Strings by Characters with the str.split Method.mp417.53MB

6. Working with Text Data/8. More Practice with the str.split method on a Series.mp411.93MB

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

7. MultiIndex/1. Intro to the MultiIndex Module.mp419.98MB

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

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

7. MultiIndex/12. The .unstack() Method, Part 3.mp411.97MB

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

7. MultiIndex/14. Use the pivot_table method to create an aggregate summary of a DataFrame.mp422.17MB

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

7. MultiIndex/2. Create a MultiIndex on a DataFrame with the set_index Method.mp445.7MB

7. MultiIndex/3. Extract Index Level Values with the get_level_values Method.mp420.67MB

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

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

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

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

7. MultiIndex/8. The .swaplevel() Method.mp414.34MB

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

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

8. The GroupBy Object/2. First Operations with groupby Object.mp423.09MB

8. The GroupBy Object/3. Retrieve a group from a GroupBy object with the get_group Method.mp410.15MB

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

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

8. The GroupBy Object/6. The .agg() Method.mp413.19MB

8. The GroupBy Object/7. Iterating through Groups.mp421.38MB

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

9. Merging, Joining, and Concatenating DataFrames/10. The .join() Method.mp46.28MB

9. Merging, Joining, and Concatenating DataFrames/11. The pd.merge() Method.mp46.84MB

9. Merging, Joining, and Concatenating DataFrames/2. The pd.concat Method, Part 1.mp421.8MB

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

9. Merging, Joining, and Concatenating DataFrames/4. Inner Joins, Part 1.mp417.93MB

9. Merging, Joining, and Concatenating DataFrames/5. Inner Joins, Part 2.mp417.76MB

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

9. Merging, Joining, and Concatenating DataFrames/7. Left Joins.mp421MB

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

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