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种子名称： [GigaCourse.Com] Udemy - 2022 Python Data Analysis and Visualization Masterclass
文件类型： 视频
文件数目： 199个文件
文件大小： 8.38 GB
收录时间： 2022-11-22 04:57
已经下载： 3次
资源热度： 182
最近下载： 2024-11-25 08:23

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[GigaCourse.Com] Udemy - 2022 Python Data Analysis and Visualization Masterclass.torrent

01 - Introduction/001 Course Welcome & Curriculum Walkthrough.mp4116.97MB

01 - Introduction/002 What Do You Need To Know To Take This Course.mp416.78MB

01 - Introduction/003 Downloading The Course Materials IMPORTANT!!.mp428.41MB

01 - Introduction/004 How The Exercises Work.mp418.66MB

02 - Setup & Installation/001 Introducing Jupyter Notebook!.mp444.62MB

02 - Setup & Installation/002 Mac Installation Walkthrough.mp457.42MB

02 - Setup & Installation/003 Windows Installation Walkthrough.mp454.66MB

02 - Setup & Installation/004 Installing Pandas & Matplotlib (Mac & Windows).mp420.8MB

03 - Working With Jupyter Notebook/001 Creating Notebooks & Running Cells.mp439.05MB

03 - Working With Jupyter Notebook/002 Shutting Down The Notebook Server.mp433.64MB

03 - Working With Jupyter Notebook/003 How Cell Output Works.mp46.12MB

03 - Working With Jupyter Notebook/004 Command Mode Shortcuts.mp435.46MB

03 - Working With Jupyter Notebook/005 Cell Types Markdown Time!.mp434.21MB

03 - Working With Jupyter Notebook/006 Restarting The Kernel.mp426.2MB

03 - Working With Jupyter Notebook/007 Viewing The Docs Inside A Notebook.mp421.9MB

03 - Working With Jupyter Notebook/008 EXERCISE Jupyter Notebook.mp415.98MB

03 - Working With Jupyter Notebook/009 SOLUTION Jupyter Notebook.mp439.11MB

04 - Dataframes & Datasets/001 Datasets & CSV.mp442.9MB

04 - Dataframes & Datasets/002 pd.read_csv & DataFrames.mp447.9MB

04 - Dataframes & Datasets/003 Inspecting DataFrames head(), tail(), etc.mp468.09MB

04 - Dataframes & Datasets/004 DataTypes and info().mp439.75MB

04 - Dataframes & Datasets/005 The House Sales Dataset Walkthrough.mp453.06MB

04 - Dataframes & Datasets/006 The Titanic Passenger Dataset Walkthrough.mp479.67MB

04 - Dataframes & Datasets/007 Non-comma Separators Netflix Dataset.mp485.13MB

04 - Dataframes & Datasets/008 Overriding Headers Country Population Dataset.mp440.92MB

04 - Dataframes & Datasets/009 EXERCISE DataFrames & Datasets.mp417.49MB

04 - Dataframes & Datasets/010 SOLUTION DataFrames & Datasets.mp482.32MB

05 - Basic DataFrame Methods & Computations/001 Min & Max.mp437.56MB

05 - Basic DataFrame Methods & Computations/002 Sum & Count.mp471.06MB

05 - Basic DataFrame Methods & Computations/003 Mean, Median, & Mode.mp430.25MB

05 - Basic DataFrame Methods & Computations/004 Describe With Numeric Values.mp429.28MB

05 - Basic DataFrame Methods & Computations/005 Describe With Objects (Text) Values.mp454.48MB

05 - Basic DataFrame Methods & Computations/006 EXERCISE Basic DataFrame Methods.mp413.47MB

05 - Basic DataFrame Methods & Computations/007 SOLUTION Basic DataFrame Methods.mp431.66MB

06 - Series & Columns/001 Selecting A Single Column.mp443.19MB

06 - Series & Columns/002 A Closer Look At Series.mp451.03MB

06 - Series & Columns/003 Important Series Methods.mp417.36MB

06 - Series & Columns/004 unique & nunique.mp429.62MB

06 - Series & Columns/005 nlargest & nsmallest.mp453.88MB

06 - Series & Columns/006 Selecting Multiple Columns.mp421.79MB

06 - Series & Columns/007 The powerful value_counts() method.mp447.57MB

06 - Series & Columns/008 Using plot() to visualize!.mp459.72MB

06 - Series & Columns/009 EXERCISE Series & Plotting.mp418.2MB

06 - Series & Columns/010 SOLUTION Series & Plotting.mp463.63MB

07 - Indexing & Sorting/001 Set_Index Basics.mp457.21MB

07 - Indexing & Sorting/002 set_index The World Happiness Index Dataset.mp436.41MB

07 - Indexing & Sorting/003 setting index with read_csv.mp420.54MB

07 - Indexing & Sorting/004 sort_values intro.mp434.9MB

07 - Indexing & Sorting/005 sorting by multiple columns.mp426.35MB

07 - Indexing & Sorting/006 sorting text columns.mp426.74MB

07 - Indexing & Sorting/007 sort_index.mp419.39MB

07 - Indexing & Sorting/008 Sorting and Plotting!.mp422.45MB

07 - Indexing & Sorting/009 loc.mp453.8MB

07 - Indexing & Sorting/010 iloc.mp431.01MB

07 - Indexing & Sorting/011 loc & iloc with Series.mp431.51MB

07 - Indexing & Sorting/012 EXERCISE Indexes & Sorting.mp429.02MB

07 - Indexing & Sorting/013 SOLUTION Indexes & Sorting.mp483.17MB

08 - Filtering DataFrames/001 Filtering DataFrames With A Boolean Series.mp451.22MB

08 - Filtering DataFrames/002 Filtering With Comparison Operators.mp456.47MB

08 - Filtering DataFrames/003 The Between Method.mp420.9MB

08 - Filtering DataFrames/004 The isin() Method.mp426.75MB

08 - Filtering DataFrames/005 Combining Conditions Using AND (&).mp483.67MB

08 - Filtering DataFrames/006 Combining Conditions Using OR ().mp491.18MB

08 - Filtering DataFrames/007 Bitwise Negation.mp440.97MB

08 - Filtering DataFrames/008 isna() and notna() Methods.mp427.4MB

08 - Filtering DataFrames/009 Filtering + Plotting Examples.mp434.79MB

08 - Filtering DataFrames/010 EXERCISE Filtering.mp412.83MB

08 - Filtering DataFrames/011 SOLUTION Filtering Exercise.mp485.89MB

09 - Adding & Removing Columns/001 Dropping Columns.mp447.57MB

09 - Adding & Removing Columns/002 Dropping Rows.mp445.86MB

09 - Adding & Removing Columns/003 Adding Static Columns.mp441.25MB

09 - Adding & Removing Columns/004 Creating New Dynamic Columns.mp441.07MB

09 - Adding & Removing Columns/005 Finding The Highest pricesqft homes.mp428.23MB

09 - Adding & Removing Columns/006 Finding Largest Bitcoin Price Changes.mp437.6MB

09 - Adding & Removing Columns/007 EXERCISE AddingRemoving Columns & Rows.mp424.59MB

09 - Adding & Removing Columns/008 SOLUTION AddingRemoving Columns & Rows.mp446.7MB

10 - Updating Values/001 Renaming Columns and Index Labels.mp435.53MB

10 - Updating Values/002 The replace() method.mp447.05MB

10 - Updating Values/003 Updating Values Using loc[].mp427.09MB

10 - Updating Values/004 Updating Multiple Values Using loc[].mp431.7MB

10 - Updating Values/005 Making Updates With loc[] and Boolean Masks.mp454.61MB

10 - Updating Values/006 EXERCISE Updating Values.mp416.12MB

10 - Updating Values/007 SOLUTION Updating Values Exercise.mp470.35MB

11 - Working With Types and NA Values/001 Casting Types With astype().mp445.21MB

11 - Working With Types and NA Values/002 Introducing the Category Type.mp432.05MB

11 - Working With Types and NA Values/003 Casting With pd.to_numeric().mp432.66MB

11 - Working With Types and NA Values/004 dropna() and isna().mp455.98MB

11 - Working With Types and NA Values/005 fillna().mp435.73MB

11 - Working With Types and NA Values/006 EXERCISE Dealing With NA Values.mp412.55MB

11 - Working With Types and NA Values/007 SOLUTION Dealing With NA Values.mp442.3MB

12 - Working With Dates & Times/001 Why Dates Matter.mp439.68MB

12 - Working With Dates & Times/002 Converting With pd.to_datetime().mp444.79MB

12 - Working With Dates & Times/003 Specifying Fancy Formats With pd.to_datetime().mp468MB

12 - Working With Dates & Times/004 Dates and DataFrames.mp467.39MB

12 - Working With Dates & Times/005 The Useful dt Properties.mp456.24MB

12 - Working With Dates & Times/006 Comparing Dates.mp440.45MB

12 - Working With Dates & Times/007 Finding StarLink Flybys In UFO Dataset.mp472.24MB

12 - Working With Dates & Times/008 Date Math & TimeDeltas.mp468.03MB

12 - Working With Dates & Times/009 Billboard Charts Dataset Exploration.mp488.93MB

12 - Working With Dates & Times/010 EXERCISE Dates & Times.mp433.52MB

12 - Working With Dates & Times/011 SOLUTION Dates & Times.mp4115.74MB

13 - Matplotlib/001 Intro to Matplotlib.mp432.8MB

13 - Matplotlib/002 Our First Matplotlib Plots!.mp438.12MB

13 - Matplotlib/003 Do We Need plt.show().mp414.98MB

13 - Matplotlib/004 Anatomy of Plots.mp445.36MB

13 - Matplotlib/005 Figsize & Plot Dimensions.mp428.7MB

13 - Matplotlib/006 Changing Matplotlib Stylesheets.mp428.77MB

13 - Matplotlib/007 Line Styles, Colors, Widths, and More!.mp471.39MB

13 - Matplotlib/008 Plot Labels & Titles.mp449.62MB

13 - Matplotlib/009 Changing X & Y Ticks.mp453.16MB

13 - Matplotlib/010 Adding Legends To Plots.mp439.61MB

13 - Matplotlib/011 EXERCISE Matplotlib Challenge #1.mp435.76MB

13 - Matplotlib/012 Creating Bar Plots.mp469.01MB

13 - Matplotlib/013 Creating Histograms.mp477.71MB

13 - Matplotlib/014 EXERCISE Matplotlib Challenge #2.mp428.29MB

13 - Matplotlib/015 Creating Scatter Plots.mp435.21MB

13 - Matplotlib/016 Creating Pie Charts.mp450.96MB

13 - Matplotlib/017 EXERCISE Matplotlib Challenge #3.mp434.9MB

13 - Matplotlib/018 Working With Subplots.mp474.84MB

13 - Matplotlib/019 Putting It All Together.mp446.88MB

13 - Matplotlib/020 EXERCISE Matplotlib Challenge #4.mp474.81MB

14 - Revisiting Pandas Plotting/001 A Pandas Plotting Recap.mp436.15MB

14 - Revisiting Pandas Plotting/002 Changing Pandas Plot Styles.mp418.74MB

14 - Revisiting Pandas Plotting/003 Adding Labels and Titles to Pandas Plots.mp455.16MB

14 - Revisiting Pandas Plotting/004 Using rename() When Plotting.mp421.28MB

14 - Revisiting Pandas Plotting/005 Closer Look at Pandas Bar Plots.mp456.31MB

14 - Revisiting Pandas Plotting/006 EXERCISE Pandas Plotting Challenge #1.mp461.17MB

14 - Revisiting Pandas Plotting/007 Pandas Histograms.mp419.7MB

14 - Revisiting Pandas Plotting/008 Box Plots.mp431.98MB

14 - Revisiting Pandas Plotting/009 Pandas Line Plots.mp434.65MB

14 - Revisiting Pandas Plotting/010 EXERCISE Pandas Plotting Challenge #2.mp429.89MB

14 - Revisiting Pandas Plotting/011 Pandas Scatter Plots.mp423.65MB

14 - Revisiting Pandas Plotting/012 Multiple Plots On The Same Axes.mp435.74MB

14 - Revisiting Pandas Plotting/013 UFOS Plotting Challenge!.mp446.51MB

14 - Revisiting Pandas Plotting/014 EXERCISE Pandas Plotting Challenge #3.mp430.59MB

14 - Revisiting Pandas Plotting/015 Pandas Automatic Subplots.mp454.58MB

14 - Revisiting Pandas Plotting/016 Manual Subplots With Pandas.mp447.22MB

14 - Revisiting Pandas Plotting/017 EXERCISE Pandas Plotting Challenge #4.mp4107.87MB

14 - Revisiting Pandas Plotting/018 EXERCISE Pandas Plotting Challenge #5.mp4109.16MB

14 - Revisiting Pandas Plotting/019 Exporting Figures With savefig().mp427.17MB

15 - Grouping & Aggregating/001 Introducing Groupby.mp451.4MB

15 - Grouping & Aggregating/002 Exploring Groups.mp472.18MB

15 - Grouping & Aggregating/003 Split-Apply-Combine.mp459.01MB

15 - Grouping & Aggregating/004 Using The Agg Method.mp454.23MB

15 - Grouping & Aggregating/005 Agg with Custom Functions.mp415.12MB

15 - Grouping & Aggregating/006 Named Aggregation.mp429.09MB

16 - Hierarchical Indexing/001 Groupby With Multiple Columns.mp445.78MB

16 - Hierarchical Indexing/002 Creating a MultiIndex With set_index.mp438.37MB

16 - Hierarchical Indexing/003 Sorting A MultiIndex.mp441.24MB

16 - Hierarchical Indexing/004 Using .loc[] With A MultiIndex.mp449.59MB

16 - Hierarchical Indexing/005 Cross Sections With The XS Method.mp412.89MB

16 - Hierarchical Indexing/006 get_level_values().mp449.27MB

16 - Hierarchical Indexing/007 Hierarchical Columns.mp429.92MB

16 - Hierarchical Indexing/008 Stack() and Unstack().mp430.91MB

16 - Hierarchical Indexing/009 Plotting With Unstack().mp443.56MB

16 - Hierarchical Indexing/010 Grouping By Index.mp426.51MB

17 - Working With Text/001 The String Datatype Vs. Object Datatype.mp450.2MB

17 - Working With Text/002 Upper(), Lower(), and Capitalize().mp428.48MB

17 - Working With Text/003 Indexing String Series With [].mp440.1MB

17 - Working With Text/004 Stripping Whitespace With Strip().mp418.66MB

17 - Working With Text/005 Splitting Text Values With Split().mp449.17MB

17 - Working With Text/006 Replacing Portions of Strings With Replace().mp452.2MB

17 - Working With Text/007 Testing Strings With Contains().mp428.39MB

18 - Apply, Map, & Applymap/001 Applying Functions To Series.mp450.51MB

18 - Apply, Map, & Applymap/002 Apply() With Lambdas & Arguments.mp432.69MB

18 - Apply, Map, & Applymap/003 Apply() w DataFrames Columns.mp424.77MB

18 - Apply, Map, & Applymap/004 Apply() w DataFrames Rows.mp442.06MB

18 - Apply, Map, & Applymap/005 The Series Map() Method.mp416.72MB

18 - Apply, Map, & Applymap/006 The ApplyMap() Method.mp424.71MB

19 - Combining Series & DataFrames/001 Concatenating Series.mp429.7MB

19 - Combining Series & DataFrames/002 Concatenating Series By Index.mp422.12MB

19 - Combining Series & DataFrames/003 Inner vs. Outer Joins.mp418.38MB

19 - Combining Series & DataFrames/004 Concatenating DataFrames By Columns.mp426.67MB

19 - Combining Series & DataFrames/005 Concatenating DataFrames By Index.mp416.4MB

19 - Combining Series & DataFrames/006 The DataFrame Merge() Method.mp434.14MB

19 - Combining Series & DataFrames/007 Merge() w Left, Right, Inner, & Outer Joins.mp441.98MB

19 - Combining Series & DataFrames/008 Merge() On and Suffixes Arguments.mp459.14MB

20 - Seaborn/001 Intro to Seaborn.mp463.95MB

20 - Seaborn/002 The Helpful load_dataset() method.mp426.58MB

20 - Seaborn/003 Seaborn Scatterplots.mp475.23MB

20 - Seaborn/004 Seaborn Lineplots.mp486.54MB

20 - Seaborn/005 The relplot() Method.mp484.97MB

20 - Seaborn/006 Resizing Seaborn Plots Aspect & Height.mp463.21MB

20 - Seaborn/007 Seaborn Histograms.mp438.67MB

20 - Seaborn/008 KDE Plots.mp421.03MB

20 - Seaborn/009 Bivariate Distribution Plots.mp440.73MB

20 - Seaborn/010 Rugplots.mp439.19MB

20 - Seaborn/011 The Amazing displot() Method.mp452.18MB

21 - Seaborn Categorical Plots/001 Countplot.mp419.19MB

21 - Seaborn Categorical Plots/002 Strip & Swarm Plots.mp467.72MB

21 - Seaborn Categorical Plots/003 Boxplots.mp449.14MB

21 - Seaborn Categorical Plots/004 Boxenplots.mp415.38MB

21 - Seaborn Categorical Plots/005 Violinplots.mp428.98MB

21 - Seaborn Categorical Plots/006 Barplots.mp455.43MB

21 - Seaborn Categorical Plots/007 The Big Boy Catplot Method.mp461.61MB

22 - Controlling Seaborn Aesthetics/001 Changing Seaborn Themes.mp427.29MB

22 - Controlling Seaborn Aesthetics/002 Customizing Styles with set_style().mp448.9MB

22 - Controlling Seaborn Aesthetics/003 Altering Spines With despine().mp417.67MB

22 - Controlling Seaborn Aesthetics/004 Changing Color Palettes.mp468.68MB