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Data Analytics and Data Sets- Python with Pandas:

 

Please Note: All below course content will be covered in practical scenarios and regular assignments will be shared. All sessions will be recorded and shared with student for future reference (free of cost). Along with below course.

  1. Introduction to Data Analysis with Pandas and Python
  2. MacOS - Download the Anaconda Distribution, our Python development environment
  3. MacOS - Install Anaconda Distribution
  4. MacOS - Access the Terminal Application
  5. MacOS - Create conda Environment and Install pandas and Jupyter Notebook
  6. MacOS - Unpack Course Materials + The Start and Shutdown Process
  7. Windows - Download the Anaconda Distribution
  8. Windows - Install Anaconda Distribution
  9. Windows - Create conda Environment and Install pandas and Jupyter Notebook
  10. Windows - Unpack Course Materials + The Startdown and Shutdown Process
  11. Intro to the Jupyter Notebook Interface
  12. Cell Types and Cell Modes in Jupyter Notebook
  13. Code Cell Execution in Jupyter Notebook
  14. Popular Keyboard Shortcuts in Jupyter Notebook
  15. Import Libraries into Jupyter Notebook
  16. Python Crash Course, Part 1 - Data Types and Variables
  17. Python Crash Course, Part 2 - Lists
  18. Python Crash Course, Part 3 - Dictionaries
  19. Python Crash Course, Part 4 - Operators
  20. Python Crash Course, Part 5 - Functions
  21. Series
  22. Create Jupyter Notebook for the Series Module
  23. Create A Series Object from a Python List
  24. Create A Series Object from a Python Dictionary
  25. Assessment Create a Series Object
  26. Intro to Attributes on a Series Object
  27. Intro to Methods on a Series Object
  28. Parameters and Arguments
  29. Create Series from Dataset with the pd.read_csv Method
  30. Assessment Import Series with the read_csv Method
  31. Use the head and tail Methods to Return Rows from Beginning and End of Dataset
  32. Passing pandas Objects to Python Built-In Functions
  33. Accessing More Series Attributes
  34. Use the sort_values method to sort a Series in ascending or descending order
  35. Use the inplace Parameter to permanently mutate a pandas data structure
  36. Use the sort_index Method to Sort the Index of a pandas Series object
  37. Assessment: The sort_values and sort_index Methods
  38. Use Python's in Keyword to Check for Inclusion in Series values or index
  39. Extract Series Values by Index Position
  40. Extract Series Values by Index Label
  41. Assessment: Extract Series Values by Index Position or Index Label
  42. Use the get Method to Retrieve a Value for an index label in a Series
  43. Math Methods on Series Objects
  44. Use the idxmax and idxmin Methods to Find Index of Greatest or Smallest Value
  45. Use the value_counts Method to See Counts of Unique Values within a Series
  46. Use the apply Method to Invoke a Function on Every Series Values
  47. The .map() Method
  48. Assessment A Review of the Series Module
  49. DataFrames I: Introduction
  50. Intro to DataFrames I Module
  51. Shared Methods and Attributes between Series and DataFrames
  52. Differences between Shared Methods
  53. Select One Column from a DataFrame
  54. Assessment: Select One Column from a DataFrame
  55. Select Two or More Columns from a DataFrame
  56. Assessment Select Two or More Columns from a DataFrame
  57. Add New Column to DataFrame
  58. Broadcasting Operations on DataFrames
  59. A Review of the value_counts Method
  60. Drop DataFrame Rows with Null Values with the dropna Method
  61. Fill in Null DataFrame Values with the fillna Method
  62. Convert DataFrame Column Types with the astype Method
  63. Sort a DataFrame with the sort_values Method, Part I
  64. Sort a DataFrame with the sort_values Method, Part II
  65. Assessment: The sort_values Method on a DataFrame
  66. Sort DataFrame Indexwith the sort_index Method
  67. Rank Series Values with the rank Method
  68. DataFrames II: Filtering Data
  69. This Module's Dataset + Memory Optimization
  70. Filter a DataFrame Based on A Condition
  71. Filter DataFrame with More than One Condition (AND - &)
  72. Filter DataFrame with More than One Condition (OR - |)
  73. Check for Inclusion with the isin Method
  74. Check for Null and Present DataFrame Values with the isnull and notnull Methods
  75. Check For Inclusion Within a Range of Values with the between Method
  76. Check for Duplicate DataFrame Rows with the duplicated Method
  77. Delete Duplicate DataFrame Rows with the drop_duplicates Method
  78. Identify and Count Unique Values with the unique and nunique Methods
  79. DataFrames III: Data Extraction
  80. Intro to the DataFrames III Module + Import Dataset
  81. Use the set_index and reset_index methods to define a new DataFrame index
  82. Retrieve Rows by Index Label with loc Accessor
  83. Retrieve Rows by Index Position with iloc Accessor
  84. Passing second arguments to the loc and iloc Accessors
  85. Set New Value for a Specific Cell or Cells In a Row
  86. Set Multiple Values in a DataFrame
  87. Rename Index Labels or Columns in a DataFrame
  1. Delete Rows or Columns from a DataFrame
  2. Create Random Sample with the .sample() Method
  3. Use the nsmallest / nlargest methods to get rows with smallest / largest
  4. Filter A DataFrame with the where method
  5. Filter A DataFrame with the query method
  6. A Review of the apply Method on a pandas Series Object
  7. Apply a Function to every DataFrame Row with the apply Method
  8. Create a Copy of a DataFrame with the copy Method
  9. Working with Text Data
  10. Intro to the Working with Text Data Section
  11. Common String Methods - lower, upper, title, and len
  12. Use the str.replace method to replace all occurrences of character with another
  13. Filter a DataFrame's Rows with String Methods
  14. More DataFrame String Methods - strip, lstrip, and rstrip
  15. Invoke String Methods on DataFrame Index and Columns
  16. Split Strings by Characters with the str.split Method
  17. More Practice with the str.split method on a Series
  18. Exploring the expand and n Parameters of the str.split Method
  19. MultiIndex
  20. Intro to the MultiIndex Module
  21. Create a MultiIndex on a DataFrame with the set_index Method
  22. Extract Index Level Values with the get_level_values Method
  23. Change Index Level Name with the set_names Method
  24. The sort_index Method on a MultiIndex DataFrame
  25. Extract Rows from a MultiIndex DataFrame
  26. The transpose Method on a MultiIndex DataFrame
  27. The .swaplevel() Method
  28. The .stack() Method
  29. The .unstack() Method, Part 1
  30. The .unstack() Method, Part 2
  31. The .unstack() Method, Part 3
  32. The pivot Method
  33. Use the pivot_table method to create an aggregate summary of a DataFrame
  34. Use the pd.melt method to create a narrow dataset from a wide one
  35. The GroupBy Object
  36. Intro to the Groupby Module
  37. First Operations with groupby Object
  38. Retrieve a group from a GroupBy object with the get_group Method
  39. Methods on the Groupby Object and DataFrame Columns
  40. Grouping by Multiple Columns
  41. The .agg() Method
  42. Iterating through Groups
  43. Merging, Joining, and Concatenating DataFrames
  44. Intro to the Merging, Joining, and Concatenating Section
  45. The pd.concat Method, Part 1
  46. The pd.concat Method, Part 2
  47. Inner Joins, Part 1
  48. Inner Joins, Part 2
  49. Outer Joins
  50. Left Joins
  51. The left_on and right_on Parameters
  52. Merging by Indexes with the left_index and right_index Parameters
  53. The .join() Method
  54. The pd.merge() Method
  55. Working with Dates and Times in Datasets
  56. Intro to the Working with Dates and Times Module
  57. Review of Python's datetime Module
  58. The pandas Timestamp Object
  59. The pandas DateTimeIndex Object
  60. The pd.to_datetime() Method
  61. Create Range of Dates with the pd.date_range() Method, Part 1
  62. Create Range of Dates with the pd.date_range() Method, Part 2
  63. Create Range of Dates with the pd.date_range() Method, Part 3
  64. The .dt Accessor
  65. Install pandas-datareader Library
  66. Import Financial Data Set with pandas_datareader Library
  67. Selecting Rows from a DataFrame with a DateTimeIndex
  68. Timestamp Object Attributes and Methods
  69. The pd.DateOffset Object
  70. Timeseries Offsets
  71. The Timedelta Object
  72. Timedeltas in a Dataset
  73. Input and Output in pandas
  74. Intro to the Input and Output Section
  75. Pass a URL to the pd.read_csv Method
  76. Quick Object Conversions
  77. Export CSV File with the to_csv Method
  78. Install xlrd and openpyxl Libraries to Read and Write Excel Files
  79. Import Excel File into pandas with the read_excel Method
  80. Export Excel File with the to_excel Method
  81. Assessment Input and Output
  82. Visualization
  83. Intro to Visualization Section
  84. Use the plot Method to Render a Line Chart
  85. Modifying Plot Aesthetics with matplotlib Templates
  86. Creating Bar Graphs to Show Counts
  87. Creating Pie Charts to Represent Proportions
  88. Assessment Visualization
  89. Options and Settings in pandas
  90. Introduction to the Options and Settings Module
  91. Changing pandas Options with Attributes and Dot Syntax
  92. Changing pandas Options with Methods
  93. The precision Option
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