001 Welcome (8.22 MB)
002 Installing Jupyter in a Virtual Environment (5.84 MB)
003 Running in Github Codespaces (3.29 MB)
004 How to use Jupyter (3.9 MB)
005 How to use VS Code (2.21 MB)
006 Remember the Exercises (5.95 MB)
007 Intro csv v2 (3.42 MB)
008 Loading CSV data from a ZIP file with Pandas and Pyarrow (20.5 MB)
009 Summary stats in Pandas using describe, dtypes, and quantile (44.96 MB)
010 Pearson and Spearman Correlations in Pandas and Heatmaps (42.63 MB)
011 Understanding Pandas Categoricals with value counts and Cross Tabulations (22.76 MB)
012 Visualizations in Pandas, with Histograms, Scatterplots, and Barplots (48.83 MB)
013 Summary (4.32 MB)
014 Intro excel (5.16 MB)
015 Create an Excel in Pandas with to excel (10.81 MB)
016 Read Excel file in Pandas with read excel and Pyarrow (8.55 MB)
017 Understanding Counts and Frequencies of Missing Data in Pandas with isna, any, sum, and mean (14.69 MB)
018 Quantifying Strings with filter and value counts (8.76 MB)
019 Understanding Numbers with Correlations, Scatterplots, and Histograms (14.2 MB)
020 Writing and Formatting Excel Sheets in Pandas with to excel and XlsxWriter add format (4.54 MB)
021 Summary 2 (1.52 MB)
022 Intro (2.64 MB)
023 Loading Data for Merging with Pyarrow (3.38 MB)
024 Merging Dataframes with the merge method and left on, right on parameters (6.33 MB)
025 Validating one to one and one to many merges (13.47 MB)
026 Debugging Merging by piping dataframe size (10.85 MB)
027 Cleanup columns after merging with loc (14.73 MB)
028 Export Merged data to Excel (11.51 MB)
029 Merging summary (4.93 MB)
030 Intro grouping (3.49 MB)
031 Loading Retail Data from Excel into Pandas Dataframe (1.19 MB)
032 Using Feather and Pyarrow to Speed up loading Retail Data in Pandas (1.65 MB)
033 Exploratory Data Analysis (EDA) in Pandas with describe, histograms, and value counts (19.03 MB)
034 Aggregating in Pandas to Calculate Sales by Year (13.26 MB)
035 Using Groupby in Pandas to visualize Sales by country (21.49 MB)
036 Using Grouper in Pandas to Groupby by Month Frequency (12.48 MB)
037 Grouping by Month and Country and Visualizing with a Line Plot (27.72 MB)
038 Summary 3 (4.38 MB)
039 Intro cleaning (3.27 MB)
040 Loading Multiple Files into a Single Pandas Datafarme with Glob (2.02 MB)
041 Understanding the Heart Data to Cleanup (12.72 MB)
042 Fixing the Age Column Type to Int8 (2.18 MB)
043 Converting the Numeric Sex Column into a String (5.91 MB)
044 Converting the Chest Pain Column into an Int8 (5.25 MB)
045 Dealing with Characters in the Trestbps Numeric Column (11.59 MB)
046 Creating a Function to Repeat Common Cleanup in the Chol Column (20.4 MB)
047 Using the Cleanup Function for the Fbs Column (6.78 MB)
048 Fixing the Restecg Column (13.47 MB)
049 Fixing the Thalach Column (1.52 MB)
050 Fixing the Exang Column (1.63 MB)
051 Updating the Cleanup Function to Clean the Oldpeak Column (1.96 MB)
052 Cleaning the Slope Column (1.76 MB)
053 Cleaning the Ca Column (2.09 MB)
054 Converting Numeric Values to Catgoricals with the Thal Column (3.11 MB)
055 Fixing the Num Column (7.39 MB)
056 Comparing Memory usage in Pandas with memory usage (7.18 MB)
057 Refactoring to a Function in Pandas for Cleanup (28.25 MB)
058 Cleaning summary (999.89 KB)
059 Intro time series air quality dataset (3.1 MB)
060 Load CSV file from a Zip file with Pandas (4.43 MB)
061 Checking for Missing Values and Shape in Pandas (2.24 MB)
062 Parsing Dates Using Format Strings and to datetime (7.49 MB)
063 Rename columns in Pandas to Remove Invalid Characters (16.57 MB)
064 Make a Function to Clean up Pandas Data (4.28 MB)
065 Converting Dates to UTC in Pandas (3.95 MB)
066 Converting Dates to Italian time in Pandas and pytz (10.85 MB)
067 Making Line Plots for Time Series Data in Pandas (19.66 MB)
068 Interpolating and Filling in Missing values in Pandas (22.34 MB)
069 Resampling Time Series Data in Pandas with resample (11.02 MB)
070 Creating 7 Day Rolling Averages in Pandas with rolling (12.67 MB)
071 Updating the Function with Cleanup Functionality (1.66 MB)
072 Summary 4 (5.8 MB)
073 Intro text v2 (1.99 MB)
074 Load movie review text data from a directory (8.05 MB)
075 Exploring the str attribute in Pandas for String manipulation (6 MB)
076 Using Spacy to Remove Stop words in Pandas (9.8 MB)
077 Using scikit-learn to calculate Tfidf for Pandas text (11.3 MB)
078 Using XGBoost to Create a Classification Model (19.91 MB)
079 Predicting Values with XGBoost and Pandas (4.04 MB)
080 Intro v2 (1.94 MB)
081 Combining Multiple Datasets with Pandas and concat (12.65 MB)
082 Exploring heart disease with aggregations and scatterplots (18.07 MB)
083 Preparing a Pandas Dataset to Create an XGBoost Model (29.15 MB)
084 Tuning an XGBoost Model with Hyperopt (47.1 MB)
085 Using a Confusion matrix to Understand the Model (5.71 MB)
086 Ml summary (1.44 MB)
087 Intro SQL (958.2 KB)
088 Load CSV data into a Pandas dataframe and cleaning it (3.63 MB)
089 Using SqlAlchemy to Connect to a SQLite Database (5.03 MB)
090 Create a database table with Pandas using to sql (2.39 MB)
091 Query a SQLite table from Pandas using read sql (4.93 MB)
092 Query a SQLite table with Pandas (12.4 MB)
093 Visualize SQLite Data using Pandas (6.52 MB)
094 Summary SQL (4.67 MB)
095 Intro plotly (1.53 MB)
096 Load CSV data into Pandas dataframe (3.18 MB)
097 Clean Pandas data with a function for plotly (5.51 MB)
098 Creating a Line Plot in Plotly for Pandas (9.25 MB)
099 Creating a Bar plot in Plotly (7.21 MB)
100 Creating a Scatter plot in Plotly (9.4 MB)
101 Creating a Dashboard with Dash and Plotly Graphs (7.33 MB)
102 Creating a Plotly Dashboard using Dash with Widgets (3.3 MB)
103 Summary plotly (1.13 MB)
104 Conclusion (11.96 MB)