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种子名称： [UdemyCourseDownloader] Complete Data Analysis Course with Pandas & NumPy Python
文件类型： 视频
文件数目： 89个文件
文件大小： 4.18 GB
收录时间： 2020-7-28 05:31
已经下载： 3次
资源热度： 306
最近下载： 2024-12-30 09:15

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[UdemyCourseDownloader] Complete Data Analysis Course with Pandas & NumPy Python.torrent

09. Pandas Exercise/2. Pandas Exercise Solution - I.mp4132.99MB

01. Introduction/1. What is Data analysis.mp495.77MB

01. Introduction/2. Introduction to Pandas.mp478.38MB

01. Introduction/3. Course FAQ.mp473.66MB

02. Installation and IDE/1. Different ways of installation.mp453.41MB

02. Installation and IDE/2. Download and Install anaconda + Pandas.mp446.67MB

02. Installation and IDE/4. Anaconda + Conda Command.mp457.09MB

02. Installation and IDE/7. Getting started with Jupyter Lab.mp474.99MB

02. Installation and IDE/9. Import Library.mp423.53MB

04. Python Crash Course [Optional]/1. Introduction.mp434.27MB

04. Python Crash Course [Optional]/2. Python Basics - I.mp449.84MB

04. Python Crash Course [Optional]/4. Python Basics - II.mp428.25MB

04. Python Crash Course [Optional]/6. Lists and tuples.mp462.73MB

04. Python Crash Course [Optional]/8. Dictionary and set.mp435.31MB

04. Python Crash Course [Optional]/10. Functions.mp426.43MB

05. Python Exercises/1. Exercise Overview.mp429.16MB

05. Python Exercises/2. Solutions.mp4109.43MB

06. Numpy/1. Creating NumPy array.mp485.15MB

06. Numpy/2. Numpy indexing and selection, Functions.mp493.62MB

06. Numpy/3. Some more Numpy Functions.mp461.94MB

06. Numpy/4. Linear algebra with NumPy.mp443.83MB

06. Numpy/5. List vs NumPy Array.mp451.15MB

06. Numpy/6. Views vs Copy - Numpy Array.mp434.75MB

06. Numpy/7. Insert, Append and Delete NumPy array.mp445.16MB

06. Numpy/8. Split, Concatenate, Tile and Repeat array.mp459.86MB

07. Series Pandas/2. Introduction to Series.mp451.47MB

07. Series Pandas/3. Create Series from Python Object.mp447.72MB

07. Series Pandas/4. Create Series from CSV file.mp448.2MB

07. Series Pandas/6. Series attributes & methods.mp458.37MB

07. Series Pandas/8. Label indexing.mp419.43MB

07. Series Pandas/10. inplace parameter, sort_values & sort_index.mp433.4MB

07. Series Pandas/12. Apply Python built in function on Series.mp413.95MB

07. Series Pandas/13. Extract Value from Series.mp425.76MB

07. Series Pandas/15. .value_counts() Method.mp47.7MB

07. Series Pandas/16. .apply() and .map() method.mp433.3MB

08. Data Frame Pandas/1. Introduction to Data Frame.mp473.01MB

08. Data Frame Pandas/2. Create Data Frame - random data + from File.mp485.44MB

08. Data Frame Pandas/3. Data frame attributes and methods.mp483.4MB

08. Data Frame Pandas/4. Adding new column.mp430.55MB

08. Data Frame Pandas/5. Select one or more than one column.mp444.87MB

08. Data Frame Pandas/6. Broadcasting operation.mp428.46MB

08. Data Frame Pandas/7. Drop missing row or column.mp435.95MB

08. Data Frame Pandas/8. Filtering Data with one condition.mp468.55MB

08. Data Frame Pandas/9. Filtering Data with multiple condition.mp438.81MB

08. Data Frame Pandas/10. Filtering Data with .isin() method.mp437.2MB

08. Data Frame Pandas/11. Filtering Data with .between() method.mp428.55MB

08. Data Frame Pandas/12. unique() & nunique() method.mp429.92MB

08. Data Frame Pandas/13. sorting values.mp484.74MB

08. Data Frame Pandas/14. sort index and inplace parameter.mp437.7MB

08. Data Frame Pandas/15. .loc() and .iloc() method.mp475.04MB

08. Data Frame Pandas/16. .ix() method.mp422.01MB

08. Data Frame Pandas/17. .astype() method - optimize memory requirement.mp449.88MB

08. Data Frame Pandas/18. set_index() change index column.mp434.22MB

08. Data Frame Pandas/19. .apply() method on single column.mp434.76MB

08. Data Frame Pandas/20. .apply() method on multiple column.mp455.58MB

08. Data Frame Pandas/21. Fetch random sample.mp429.02MB

09. Pandas Exercise/1. Exercise Overview Google App store dataset.mp428.68MB

09. Pandas Exercise/3. Pandas Exercise Solution - II.mp4131.72MB

10. Panel Pandas/1. Warning - Panel Data type.mp48.62MB

11. Pandas Options/1. max_rows , max_columns.mp466.11MB

11. Pandas Options/2. precision.mp413.02MB

12. Visualize Data with Pandas/1. Display Stock data with Line Chart.mp463.8MB

12. Visualize Data with Pandas/2. Pie, Histogram and Bar Chart.mp456.34MB

13. Import and Export data from Pandas/1. read_csv() & to_csv() method.mp461.68MB

14. Working with Text Data/1. Getting started with Data.mp444.42MB

14. Working with Text Data/2. Some String methods.mp443.43MB

14. Working with Text Data/3. More String methods.mp470.95MB

14. Working with Text Data/4. Filtering Message with String.mp441.36MB

14. Working with Text Data/5. Splitting Text.mp429.46MB

14. Working with Text Data/6. Processing on Column names.mp410.1MB

15. Data Grouping/1. Importing Data Grouping.mp448.54MB

15. Data Grouping/2. Getting Group.mp437.42MB

15. Data Grouping/3. Size, First and Last Method.mp431.05MB

15. Data Grouping/4. Sum, Mean, Max, Min Method.mp448.31MB

15. Data Grouping/5. .agg method.mp439.26MB

16. Data Frame Multiindex/1. Import Data - Multiindex.mp49.58MB

16. Data Frame Multiindex/2. Set multiple column as index.mp423.38MB

16. Data Frame Multiindex/3. Sorting MultiIndex.mp432.01MB

16. Data Frame Multiindex/4. Index - Meta Information.mp439.43MB

16. Data Frame Multiindex/5. Change Index names.mp410.22MB

16. Data Frame Multiindex/6. Fetch data from MultiIndex Dataframe.mp439.08MB

16. Data Frame Multiindex/7. Transposing DataFrame.mp451MB

16. Data Frame Multiindex/8. UnStack and Stack Data.mp448.66MB

16. Data Frame Multiindex/9. Pivot and Pivot_table Method.mp438.91MB

17. Working with Time series data/1. Python Date and Datetime module.mp436.19MB

17. Working with Time series data/2. Pandas Timestamp and Datetimeindex object.mp486.31MB

18. Data cleaning/1. Data cleaning - Youtube Dataset (warm up) Part - 1.mp418.5MB

18. Data cleaning/2. Data cleaning - Youtube Channel Dataset Part - 2.mp448.73MB

18. Data cleaning/3. Data cleaning - Youtube Channel Dataset Part - 3.mp490.72MB