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  1. Урок 1. 00:00:51
    Welcome
  2. Урок 2. 00:02:01
    Installing Jupyter in a Virtual Environment
  3. Урок 3. 00:01:37
    Running in Github Codespaces
  4. Урок 4. 00:02:09
    How to use Jupyter
  5. Урок 5. 00:01:11
    How to use VS Code
  6. Урок 6. 00:00:27
    Remember the Exercises
  7. Урок 7. 00:00:34
    Intro csv v2
  8. Урок 8. 00:05:26
    Loading CSV data from a ZIP file with Pandas and Pyarrow
  9. Урок 9. 00:06:35
    Summary stats in Pandas using describe, dtypes, and quantile
  10. Урок 10. 00:05:36
    Pearson and Spearman Correlations in Pandas and Heatmaps
  11. Урок 11. 00:04:50
    Understanding Pandas Categoricals with value_counts and Cross Tabulations
  12. Урок 12. 00:08:37
    Visualizations in Pandas, with Histograms, Scatterplots, and Barplots
  13. Урок 13. 00:00:25
    Summary
  14. Урок 14. 00:00:42
    Intro excel
  15. Урок 15. 00:01:46
    Create an Excel in Pandas with to_excel
  16. Урок 16. 00:01:31
    Read Excel file in Pandas with read_excel and Pyarrow
  17. Урок 17. 00:03:03
    Understanding Counts and Frequencies of Missing Data in Pandas with isna, any, sum, and mean
  18. Урок 18. 00:02:07
    Quantifying Strings with filter and value_counts
  19. Урок 19. 00:03:33
    Understanding Numbers with Correlations, Scatterplots, and Histograms
  20. Урок 20. 00:01:49
    Writing and Formatting Excel Sheets in Pandas with to_excel and XlsxWriter add_format
  21. Урок 21. 00:00:11
    Summary
  22. Урок 22. 00:00:15
    Intro
  23. Урок 23. 00:00:57
    Loading Data for Merging with Pyarrow
  24. Урок 24. 00:01:34
    Merging Dataframes with the merge method and left_on, right_on parameters
  25. Урок 25. 00:02:51
    Validating one to one and one to many merges
  26. Урок 26. 00:02:36
    Debugging Merging by piping dataframe size
  27. Урок 27. 00:02:19
    Cleanup columns after merging with loc
  28. Урок 28. 00:00:56
    Export Merged data to Excel
  29. Урок 29. 00:00:31
    Merging summary
  30. Урок 30. 00:00:38
    Intro grouping
  31. Урок 31. 00:00:33
    Loading Retail Data from Excel into Pandas Dataframe
  32. Урок 32. 00:00:49
    Using Feather and Pyarrow to Speed up loading Retail Data in Pandas
  33. Урок 33. 00:03:48
    Exploratory Data Analysis (EDA) in Pandas with describe, histograms, and value_counts
  34. Урок 34. 00:02:44
    Aggregating in Pandas to Calculate Sales by Year
  35. Урок 35. 00:06:06
    Using Groupby in Pandas to visualize Sales by country
  36. Урок 36. 00:03:36
    Using Grouper in Pandas to Groupby by Month Frequency
  37. Урок 37. 00:05:31
    Grouping by Month and Country and Visualizing with a Line Plot
  38. Урок 38. 00:00:26
    Summary
  39. Урок 39. 00:00:37
    Intro cleaning
  40. Урок 40. 00:00:47
    Loading Multiple Files into a Single Pandas Datafarme with Glob
  41. Урок 41. 00:02:47
    Understanding the Heart Data to Cleanup
  42. Урок 42. 00:00:44
    Fixing the Age Column Type to Int8
  43. Урок 43. 00:01:18
    Converting the Numeric Sex Column into a String
  44. Урок 44. 00:00:49
    Converting the Chest Pain Column into an Int8
  45. Урок 45. 00:02:25
    Dealing with ? Characters in the Trestbps Numeric Column
  46. Урок 46. 00:03:08
    Creating a Function to Repeat Common Cleanup in the Chol Column
  47. Урок 47. 00:01:05
    Using the Cleanup Function for the Fbs Column
  48. Урок 48. 00:01:28
    Fixing the Restecg Column
  49. Урок 49. 00:00:14
    Fixing the Thalach Column
  50. Урок 50. 00:00:15
    Fixing the Exang Column
  51. Урок 51. 00:00:23
    Updating the Cleanup Function to Clean the Oldpeak Column
  52. Урок 52. 00:00:19
    Cleaning the Slope Column
  53. Урок 53. 00:00:18
    Cleaning the Ca Column
  54. Урок 54. 00:00:39
    Converting Numeric Values to Catgoricals with the Thal Column
  55. Урок 55. 00:01:07
    Fixing the Num Column
  56. Урок 56. 00:00:50
    Comparing Memory usage in Pandas with memory_usage
  57. Урок 57. 00:04:19
    Refactoring to a Function in Pandas for Cleanup
  58. Урок 58. 00:00:06
    Cleaning summary
  59. Урок 59. 00:00:31
    Intro time series air quality dataset
  60. Урок 60. 00:00:51
    Load CSV file from a Zip file with Pandas
  61. Урок 61. 00:00:52
    Checking for Missing Values and Shape in Pandas
  62. Урок 62. 00:02:04
    Parsing Dates Using Format Strings and to_datetime
  63. Урок 63. 00:02:36
    Rename columns in Pandas to Remove Invalid Characters
  64. Урок 64. 00:00:52
    Make a Function to Clean up Pandas Data
  65. Урок 65. 00:00:57
    Converting Dates to UTC in Pandas
  66. Урок 66. 00:01:30
    Converting Dates to Italian time in Pandas and pytz
  67. Урок 67. 00:03:24
    Making Line Plots for Time Series Data in Pandas
  68. Урок 68. 00:03:27
    Interpolating and Filling in Missing values in Pandas
  69. Урок 69. 00:02:30
    Resampling Time Series Data in Pandas with resample
  70. Урок 70. 00:01:45
    Creating 7 Day Rolling Averages in Pandas with rolling
  71. Урок 71. 00:00:16
    Updating the Function with Cleanup Functionality
  72. Урок 72. 00:00:22
    Summary
  73. Урок 73. 00:00:25
    Intro text v2
  74. Урок 74. 00:01:32
    Load movie review text data from a directory
  75. Урок 75. 00:00:55
    Exploring the str attribute in Pandas for String manipulation
  76. Урок 76. 00:02:44
    Using Spacy to Remove Stop words in Pandas
  77. Урок 77. 00:01:44
    Using scikit-learn to calculate Tfidf for Pandas text
  78. Урок 78. 00:02:40
    Using XGBoost to Create a Classification Model
  79. Урок 79. 00:01:40
    Predicting Values with XGBoost and Pandas
  80. Урок 80. 00:00:21
    Intro v2
  81. Урок 81. 00:02:00
    Combining Multiple Datasets with Pandas and concat
  82. Урок 82. 00:05:01
    Exploring heart disease with aggregations and scatterplots
  83. Урок 83. 00:04:59
    Preparing a Pandas Dataset to Create an XGBoost Model
  84. Урок 84. 00:06:02
    Tuning an XGBoost Model with Hyperopt
  85. Урок 85. 00:01:48
    Using a Confusion matrix to Understand the Model
  86. Урок 86. 00:00:09
    Ml summary
  87. Урок 87. 00:00:13
    Intro SQL
  88. Урок 88. 00:01:32
    Load CSV data into a Pandas dataframe and cleaning it
  89. Урок 89. 00:00:55
    Using SqlAlchemy to Connect to a SQLite Database
  90. Урок 90. 00:00:31
    Create a database table with Pandas using to_sql
  91. Урок 91. 00:01:19
    Query a SQLite table from Pandas using read_sql
  92. Урок 92. 00:01:57
    Query a SQLite table with Pandas
  93. Урок 93. 00:01:54
    Visualize SQLite Data using Pandas
  94. Урок 94. 00:00:27
    Summary SQL
  95. Урок 95. 00:00:11
    Intro plotly
  96. Урок 96. 00:00:22
    Load CSV data into Pandas dataframe
  97. Урок 97. 00:01:45
    Clean Pandas data with a function for plotly
  98. Урок 98. 00:02:01
    Creating a Line Plot in Plotly for Pandas
  99. Урок 99. 00:02:29
    Creating a Bar plot in Plotly
  100. Урок 100. 00:03:41
    Creating a Scatter plot in Plotly
  101. Урок 101. 00:01:43
    Creating a Dashboard with Dash and Plotly Graphs
  102. Урок 102. 00:01:10
    Creating a Plotly Dashboard using Dash with Widgets
  103. Урок 103. 00:00:08
    Summary plotly
  104. Урок 104. 00:01:17
    Conclusion