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种子名称： [GigaCourse.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： 4.17 GB
收录时间： 2024-3-8 04:42
已经下载： 3次
资源热度： 94
最近下载： 2024-5-21 12:20

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[GigaCourse.Com] Udemy - Data Analysis with Pandas and Python.torrent

01 - Installation and Setup/001 Introduction to Data Analysis with Pandas and Python.mp4125.34MB

01 - Installation and Setup/002 About Me.mp45.99MB

01 - Installation and Setup/004 macOS - Download the Anaconda Distribution, our Python development environment.mp49.82MB

01 - Installation and Setup/005 macOS - Install Anaconda Distribution.mp441.22MB

01 - Installation and Setup/006 macOS - Access the Terminal Application.mp451.78MB

01 - Installation and Setup/007 macOS - Create conda Environment and Install pandas and Jupyter Notebook.mp459.24MB

01 - Installation and Setup/008 macOS - Unpack Course Materials + The Start and Shutdown Process.mp451.96MB

01 - Installation and Setup/009 Windows - Download the Anaconda Distribution.mp410.19MB

01 - Installation and Setup/010 Windows - Install Anaconda Distribution.mp416.31MB

01 - Installation and Setup/011 Windows - Create conda Environment and Install pandas and Jupyter Notebook.mp447.74MB

01 - Installation and Setup/012 Windows - Unpack Course Materials + The Startdown and Shutdown Process.mp427.14MB

01 - Installation and Setup/013 Intro to the Jupyter Notebook Interface.mp416.26MB

01 - Installation and Setup/014 Cell Types and Cell Modes in Jupyter Notebook.mp410.48MB

01 - Installation and Setup/015 Code Cell Execution in Jupyter Notebook.mp44.67MB

01 - Installation and Setup/016 Popular Keyboard Shortcuts in Jupyter Notebook.mp48.11MB

01 - Installation and Setup/017 Import Libraries into Jupyter Notebook.mp414.47MB

02 - BONUS_ Python Crash Course/001 Intro to the Python Crash Course.mp47.59MB

02 - BONUS_ Python Crash Course/002 Comments.mp44.1MB

02 - BONUS_ Python Crash Course/003 Basic Data Types.mp418.21MB

02 - BONUS_ Python Crash Course/004 Operators.mp428MB

02 - BONUS_ Python Crash Course/005 Variables.mp413.29MB

02 - BONUS_ Python Crash Course/006 Built-in Functions.mp418.32MB

02 - BONUS_ Python Crash Course/007 Custom Functions.mp430.91MB

02 - BONUS_ Python Crash Course/008 String Methods.mp440.14MB

02 - BONUS_ Python Crash Course/009 Lists.mp428.38MB

02 - BONUS_ Python Crash Course/010 Index Positions and Slicing.mp430.01MB

02 - BONUS_ Python Crash Course/011 Dictionaries.mp429.06MB

03 - Series/001 Create Jupyter Notebook for the Series Module.mp42.95MB

03 - Series/002 Create A Series Object from a Python List.mp422.59MB

03 - Series/003 Create A Series Object from a Python Dictionary.mp46.53MB

03 - Series/005 Intro to Attributes on a Series Object.mp418.87MB

03 - Series/006 Intro to Methods on a Series Object.mp49.53MB

03 - Series/007 Parameters and Arguments.mp429.7MB

03 - Series/008 Create Series from Dataset with the pd.read_csv Method.mp429.41MB

03 - Series/010 Use the head and tail Methods to Return Rows from Beginning and End of Dataset.mp48.55MB

03 - Series/011 Passing pandas Objects to Python Built-In Functions.mp412.15MB

03 - Series/012 Accessing More Series Attributes.mp425.61MB

03 - Series/013 Use the sort_values method to sort a Series in ascending or descending order.mp428.49MB

03 - Series/014 Use the inplace Parameter to permanently mutate a pandas data structure.mp414.34MB

03 - Series/015 Use the sort_index Method to Sort the Index of a pandas Series object.mp411.23MB

03 - Series/017 Use Python's in Keyword to Check for Inclusion in Series values or index.mp49.2MB

03 - Series/018 Extract Series Values by Index Positiox.mp423.5MB

03 - Series/019 Extract Series Values by Index Label.mp422.1MB

03 - Series/021 Use the get Method to Retrieve a Value for an index label in a Series.mp420.63MB

03 - Series/022 Math Methods on Series Objects.mp415.28MB

03 - Series/023 Use the idxmax and idxmin Methods to Find Index of Greatest or Smallest Value.mp48.78MB

03 - Series/024 Use the value_counts Method to See Counts of Unique Values within a Series.mp48.18MB

03 - Series/025 Use the apply Method to Invoke a Function on Every Series Values.mp418.33MB

03 - Series/026 The Series#map Method.mp422.63MB

04 - DataFrames I_ Introduction/001 Intro to DataFrames I Module.mp449.79MB

04 - DataFrames I_ Introduction/002 Shared Methods and Attributes between Series and DataFrames.mp442.53MB

04 - DataFrames I_ Introduction/003 Differences between Shared Methods.mp427.01MB

04 - DataFrames I_ Introduction/004 Select One Column from a DataFrame.mp442.2MB

04 - DataFrames I_ Introduction/006 Select Two or More Columns from a DataFrame.mp413.01MB

04 - DataFrames I_ Introduction/008 Add New Column to DataFrame.mp440.64MB

04 - DataFrames I_ Introduction/009 Broadcasting Operations on DataFrames.mp431.24MB

04 - DataFrames I_ Introduction/010 A Review of the value_counts Method.mp47.95MB

04 - DataFrames I_ Introduction/011 Drop DataFrame Rows with Null Values with the dropna Method.mp437.16MB

04 - DataFrames I_ Introduction/013 Fill in Null DataFrame Values with the fillna Method.mp414.77MB

04 - DataFrames I_ Introduction/014 Convert DataFrame Column Types with the astype Method.mp460.72MB

04 - DataFrames I_ Introduction/015 Sort a DataFrame with the sort_values Method, Part I.mp443.29MB

04 - DataFrames I_ Introduction/016 Sort a DataFrame with the sort_values Method, Part II.mp417.16MB

04 - DataFrames I_ Introduction/018 Sort DataFrame Indexwith the sort_index Method.mp418.7MB

04 - DataFrames I_ Introduction/019 Rank Series Values with the rank Method.mp421.78MB

05 - DataFrames II_ Filtering Data/001 This Module's Dataset + Memory Optimization.mp475.27MB

05 - DataFrames II_ Filtering Data/002 Filter a DataFrame Based on A Condition.mp482.28MB

05 - DataFrames II_ Filtering Data/003 Filter DataFrame with More than One Condition (AND - &).mp414.76MB

05 - DataFrames II_ Filtering Data/004 Filter DataFrame with More than One Condition (OR - _).mp427.47MB

05 - DataFrames II_ Filtering Data/005 Check for Inclusion with the isin Method.mp420.56MB

05 - DataFrames II_ Filtering Data/006 Check for Null and Present DataFrame Values with the isnull and notnull Methods.mp418.75MB

05 - DataFrames II_ Filtering Data/007 Check For Inclusion Within a Range of Values with the between Method.mp426.54MB

05 - DataFrames II_ Filtering Data/008 Check for Duplicate DataFrame Rows with the duplicated Method.mp453.7MB

05 - DataFrames II_ Filtering Data/009 Delete Duplicate DataFrame Rows with the drop_duplicates Method.mp432.23MB

05 - DataFrames II_ Filtering Data/010 Identify and Count Unique Values with the unique and nunique Methods.mp47.56MB

06 - DataFrames III_ Data Extraction/001 Intro to the DataFrames III Module + Import Dataset.mp412.48MB

06 - DataFrames III_ Data Extraction/002 Use the set_index and reset_index methods to define a new DataFrame index.mp417.59MB

06 - DataFrames III_ Data Extraction/003 Retrieve Rows by Index Label with loc Accessor.mp434.39MB

06 - DataFrames III_ Data Extraction/004 Retrieve Rows by Index Position with iloc Accessor.mp417.18MB

06 - DataFrames III_ Data Extraction/005 Passing second arguments to the loc and iloc Accessors.mp425.65MB

06 - DataFrames III_ Data Extraction/006 Set New Value for a Specific Cell or Cells In a Row.mp46.52MB

06 - DataFrames III_ Data Extraction/007 Set Multiple Values in a DataFrame.mp416.11MB

06 - DataFrames III_ Data Extraction/008 Rename Index Labels or Columns in a DataFrame.mp458.05MB

06 - DataFrames III_ Data Extraction/009 Delete Rows or Columns from a DataFrame.mp429.04MB

06 - DataFrames III_ Data Extraction/010 Create Random Sample with the sample Method.mp412.35MB

06 - DataFrames III_ Data Extraction/011 Use the nsmallest _ nlargest methods to get rows with smallest _ largest values.mp428.3MB

06 - DataFrames III_ Data Extraction/012 Filter A DataFrame with the where method.mp434.38MB

06 - DataFrames III_ Data Extraction/013 Filter A DataFrame with the query method.mp433.05MB

06 - DataFrames III_ Data Extraction/014 A Review of the apply Method on a pandas Series Object.mp418.63MB

06 - DataFrames III_ Data Extraction/015 Apply a Function to every DataFrame Row with the apply Method.mp417.18MB

06 - DataFrames III_ Data Extraction/016 Create a Copy of a DataFrame with the copy Method.mp436.08MB

07 - Working with Text Data/001 Intro to the Working with Text Data Section.mp414.41MB

07 - Working with Text Data/002 Common String Methods - lower, upper, title, and len.mp447.89MB

07 - Working with Text Data/003 Use the str.replace method to replace all occurrences of character with another.mp438.91MB

07 - Working with Text Data/004 Filter a DataFrame's Rows with String Methods.mp436.08MB

07 - Working with Text Data/005 More DataFrame String Methods - strip, lstrip, and rstrip.mp47.89MB

07 - Working with Text Data/006 Invoke String Methods on DataFrame Index and Columns.mp414.58MB

07 - Working with Text Data/007 Split Strings by Characters with the str.split Method.mp428.23MB

07 - Working with Text Data/008 More Practice with the str.split method on a Series.mp436.36MB

07 - Working with Text Data/009 Exploring the expand and n Parameters of the str.split Method.mp427.64MB

08 - MultiIndex/001 Intro to the MultiIndex Module.mp49.84MB

08 - MultiIndex/002 Create a MultiIndex on a DataFrame with the set_index Method.mp422.14MB

08 - MultiIndex/003 Extract Index Level Values with the get_level_values Method.mp49.89MB

08 - MultiIndex/004 Change Index Level Name with the set_names Method.mp49.09MB

08 - MultiIndex/005 The sort_index Method on a MultiIndex DataFrame.mp416.77MB

08 - MultiIndex/006 Extract Rows from a MultiIndex DataFrame.mp422.8MB

08 - MultiIndex/007 The transpose Method on a MultiIndex DataFrame.mp417.97MB

08 - MultiIndex/008 The .swaplevel() Method.mp44.88MB

08 - MultiIndex/009 The .stack() Method.mp436.38MB

08 - MultiIndex/010 The .unstack() Method, Part 1.mp411.42MB

08 - MultiIndex/011 The .unstack() Method, Part 2.mp431.22MB

08 - MultiIndex/012 The .unstack() Method, Part 3.mp427.27MB

08 - MultiIndex/013 The pivot Method.mp415.72MB

08 - MultiIndex/014 Use the pivot_table method to create an aggregate summary of a DataFrame.mp432.73MB

08 - MultiIndex/015 Use the pd.melt method to create a narrow dataset from a wide one.mp447.79MB

09 - The GroupBy Object/001 Intro to the Groupby Module.mp423.13MB

09 - The GroupBy Object/002 First Operations with groupby Object.mp461.64MB

09 - The GroupBy Object/003 Retrieve a group from a GroupBy object with the get_group Method.mp49.18MB

09 - The GroupBy Object/004 Methods on the Groupby Object and DataFrame Columns.mp433.03MB

09 - The GroupBy Object/005 Grouping by Multiple Columns.mp424.47MB

09 - The GroupBy Object/006 The .agg() Method.mp441.47MB

09 - The GroupBy Object/007 Iterating through Groups.mp433.74MB

10 - Merging, Joining, and Concatenating DataFrames/001 Intro to the Merging, Joining, and Concatenating Section.mp411.89MB

10 - Merging, Joining, and Concatenating DataFrames/002 The pd.concat Method, Part 1.mp410.48MB

10 - Merging, Joining, and Concatenating DataFrames/003 The pd.concat Method, Part 2.mp415.31MB

10 - Merging, Joining, and Concatenating DataFrames/004 Inner Joins, Part 1.mp443.41MB

10 - Merging, Joining, and Concatenating DataFrames/005 Inner Joins, Part 2.mp420.8MB

10 - Merging, Joining, and Concatenating DataFrames/006 Outer Joins.mp459.65MB

10 - Merging, Joining, and Concatenating DataFrames/007 Left Joins.mp463.15MB

10 - Merging, Joining, and Concatenating DataFrames/008 The left_on and right_on Parameters.mp448.52MB

10 - Merging, Joining, and Concatenating DataFrames/009 Merging by Indexes with the left_index and right_index Parameters.mp453.98MB

10 - Merging, Joining, and Concatenating DataFrames/010 The .join() Method.mp45.49MB

10 - Merging, Joining, and Concatenating DataFrames/011 The pd.merge() Method.mp410.28MB

11 - Working with Dates and Times in Datasets/001 Intro to the Working with Dates and Times Module.mp45.58MB

11 - Working with Dates and Times in Datasets/002 Review of Python's datetime Module.mp429.2MB

11 - Working with Dates and Times in Datasets/003 The pandas Timestamp Object.mp417.4MB

11 - Working with Dates and Times in Datasets/004 The pandas DateTimeIndex Object.mp413.06MB

11 - Working with Dates and Times in Datasets/005 The pd.to_datetime() Method.mp450MB

11 - Working with Dates and Times in Datasets/006 Create Range of Dates with the pd.date_range() Method, Part 1.mp462.28MB

11 - Working with Dates and Times in Datasets/007 Create Range of Dates with the pd.date_range() Method, Part 2.mp456.29MB

11 - Working with Dates and Times in Datasets/008 Create Range of Dates with the pd.date_range() Method, Part 3.mp425.69MB

11 - Working with Dates and Times in Datasets/009 The .dt Accessor.mp417.51MB

11 - Working with Dates and Times in Datasets/010 Install pandas-datareader Library.mp422.6MB

11 - Working with Dates and Times in Datasets/011 Import Financial Data Set with pandas_datareader Library.mp419.23MB

11 - Working with Dates and Times in Datasets/012 Selecting Rows from a DataFrame with a DateTimeIndex.mp432.14MB

11 - Working with Dates and Times in Datasets/013 Timestamp Object Attributes and Methods.mp428.3MB

11 - Working with Dates and Times in Datasets/014 The pd.DateOffset Object.mp418.74MB

11 - Working with Dates and Times in Datasets/015 Timeseries Offsets.mp432.43MB

11 - Working with Dates and Times in Datasets/016 The Timedelta Object.mp417.06MB

11 - Working with Dates and Times in Datasets/017 Timedeltas in a Dataset.mp432.94MB

12 - Input and Output in pandas/001 Intro to the Input and Output Section.mp41.92MB

12 - Input and Output in pandas/002 Pass a URL to the pd.read_csv Method.mp419.83MB

12 - Input and Output in pandas/003 Quick Object Conversions.mp421.65MB

12 - Input and Output in pandas/004 Export CSV File with the to_csv Method.mp424.83MB

12 - Input and Output in pandas/005 Install xlrd and openpyxl Libraries to Read and Write Excel Files.mp411.39MB

12 - Input and Output in pandas/006 Import Excel File into pandas with the read_excel Method.mp439.23MB

12 - Input and Output in pandas/007 Export Excel File with the to_excel Method.mp436.88MB

13 - Visualization/001 Intro to Visualization Section.mp46.27MB

13 - Visualization/002 Use the plot Method to Render a Line Chart.mp416.24MB

13 - Visualization/003 Modifying Plot Aesthetics with matplotlib Templates.mp413.8MB

13 - Visualization/004 Creating Bar Graphs to Show Counts.mp413.45MB

13 - Visualization/005 Creating Pie Charts to Represent Proportions.mp410.7MB

14 - Options and Settings in pandas/001 Introduction to the Options and Settings Module.mp42.54MB

14 - Options and Settings in pandas/002 Changing pandas Options with Attributes and Dot Syntax.mp440.81MB

14 - Options and Settings in pandas/003 Changing pandas Options with Methods.mp432.99MB

14 - Options and Settings in pandas/004 The precision Option.mp412.93MB

15 - Conclusion/001 Conclusion.mp42.39MB