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989.54 MB | 00:08:36 | mp4 | 1920X1080  | 16:9
Genre:eLearning |Language:English

Files Included :
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)