Installation and Setup
Python Crash Course
Intro to Pandas and Numpy
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Section Overview + All The Files for This Section
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Introduction to Pandas (6:24)
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How to Create a Dataframe (16:23)
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Different Ways to Display a Dataframe (6:48)
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Basic Attributes, Methods and Functions (11:50)
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Selecting One Column from a Dataframe (5:53)
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Selecting Two or More Columns from a Dataframe (5:35)
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Add New Column to a Dataframe (Simple Assignment) (9:59)
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Add New Column to a Dataframe with assign() and insert() (6:41)
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Operations on Dataframes (columns and rows) (8:10)
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The value_counts() method (4:10)
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Important Note
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Sort a Dataframe with sort_values() (9:37)
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The set_index() and sort_index() methods (6:56)
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Rename Columns and Index with rename() (5:58)
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Exercise for this Section
Project 1 - Web Scraping with Pandas
Filtering Data
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Download All The Files Used In this Section
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Filter a Dataframe Based on 1 Condition (14:14)
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Creating a Conditional Column from 2 Choices np.where() (10:29)
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Filter a Dataframe Based on 2 or More Conditions (12:07)
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Creating a Conditional Column from More Than 2 Choices np.select() (12:51)
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The .isin() Method (7:17)
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Find Duplicate Rows with the .duplicated() Method (17:55)
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Drop Duplicate Elements with the .drop_duplicates() Method (10:17)
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Get and Count Unique Values The unique() and nunique() methods (3:54)
Data Extraction
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Download All The Files Used In this Section
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Differences between the loc() and iloc() method (7:15)
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First Look at The Dataset Setting Index and Selecting Columns (5:31)
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Selecting elements by index label with .loc() (20:20)
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Selecting elements by index position with .iloc() (13:02)
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Set New Value for a Cell in a Dataframe (9:47)
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Drop Rows Or Columns from a Dataframe (9:36)
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Create Random Sample with the sample() Method (7:44)
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Filter a DataFrame with the query() method (12:29)
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The apply() Method (6:09)
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Lambda function + apply() method (17:52)
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Make a Copy of a Dataframe with copy() (Deep Copy vs Shallow Copy) (7:18)
Reshaping and Pivoting Dataframes
Project 2: Making Static and Interactive Data Visualization
GroupBy and Aggregate Function
Merging and Concatenating DataFrames
Regular Expressions
Project 3 - Data Cleaning with Pandas
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Dataset Overview (3:46)
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Identify Missing Data with the isnull() Method (10:28)
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Dealing with Missing Data: Remove a column or row with .drop, .dropna or .isnull (12:35)
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Dealing with Missing Data: Replace NaN by the mean, median, mode with .fillna() (14:06)
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Extracting data with split() and extract() methods (15:44)
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How to Identify and Deal with Outliers (17:26)
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Dealing with inconsistent capitalization lower(), upper(), title() (4:52)
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Remove blank spaces with strip(), lstrip(), and rstrip() (4:41)
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Replace strings with replace() or sub() (8:14)
Machine Learning with Python
Project 4: Text Classification with scikit-learn
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The Dataset, Selecting a Sample Datafame and The Problem to Solve (8:35)
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Dealing with Imbalanced Classes: Undersampling and Oversampling (17:19)
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Splitting data into train and test set (6:54)
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Text Representation with Bag of Words: CountVectorizer and TF-IDF (17:59)
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Turning our text data into numerical vectors (7:00)
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Model Selection: Types of Machine Learning Algorithms for out Project (2:05)
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Support Vector Machines (SVM) (6:23)
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Decision Tree (4:34)
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Naive Bayes (5:11)
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Logistic Regression (2:22)
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Model Evaluation - Confusion Matrix (6:32)
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Mean Accuracy (3:31)
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F1 Score (4:55)
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Classification Report (1:44)
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Tuning the Model - GridSearchCV (3:38)
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