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Grokking Data Science
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Python Fundamentals for Data Science
Creating the Workspace - Jupyter Notebooks
Python Libraries
Learning NumPy - An Introduction
NumPy Basics - Creating NumPy Arrays and Array Attributes
NumPy Basics - Array Indexing and Slicing
NumPy Basics - Reshaping and Concatenation
NumPy Arithmetic and Statistics - Computations and Aggregations
NumPy Arithmetic and Statistics - Comparison and Boolean Masks
Exercises: NumPy
Learning Pandas - An Introduction
Pandas Core Components - The Series Object
Pandas Core Components - The DataFrame Object
Pandas DataFrame Operations - Read, View and Extract Information
Pandas DataFrame Operations - Selection, Slicing, and Filtering
Pandas DataFrame Operations - Grouping and Sorting
Pandas DataFrame Operations - Dealing With Missing and Duplicates
Pandas DataFrame Operations - Pivot Tables and Functions
Pandas: Further Readings and Cheat Sheet
Exercises: Pandas
Data Visualization - An Introduction
Data Visualization - Matplotlib Tips
Data Visualization Techniques - Scatter, Line, and Histogram
Data Visualization Techniques - Bar and Box Plot
Data Visualization Cheat Sheet
Quiz: Data Visualization
The Fundamentals of Statistics
IntroductionStatistical Features - BasicsStatistical Features - Working With Box PlotsBasics of ProbabilityBayesian StatisticsProbability Distributions - An IntroductionTypes of Distributions - Uniform, Bernoulli, and BinomialTypes of Distributions - NormalTypes of Distributions - Poisson and ExponentialProbability Distributions RecapStatistical SignificanceQuiz: Statistics
Machine Learning 101
IntroductionUnderstanding Machine LearningTypes of Machine Learning AlgorithmsMachine Learning Algorithms IMachine Learning Algorithms IIQuiz: Machine Learning AlgorithmsEvaluating a ModelQuiz: Evaluating a ModelKey Points to RememberMachine Learning Project Checklist
End-to-End Machine Learning Project
IntroductionKaggle Challenge - Exploratory Data AnalysisKaggle Challenge - Data PreprocessingKaggle Challenge - Data TransformationKaggle Challenge - Machine Learning ModelsKaggle Challenge - Fine Tune ParametersKaggle Challenge - Present, Launch and Maintain the SystemAssignmentFurther Study Material
The Real Talk
How to Get That High-Paying JobImposter SyndromeFinal Thoughts

Exercises: Pandas

We'll cover the following
    • Time to Test Your Skills!
        • Q1. Create a DataFrame from the given dictionary data and index labels and store it in the variable called “df”.
        • Q2. a) Select the column labelled “Listeners” and store it in the variable called “col”. b) Select the first row and store it in the variable called “row”.
        • Q3. Select all the rows where the Genre is ‘Pop’ and store the result in the variable “pop_artists”.
        • Q4. Select the artists who have more than 2,000,000 listeners and whose Genre is ‘Pop’ and save the output in the variable called “top_pop”.
        • Q5. Perform a grouping by Genre using sum() as the aggregation function and store the results in the variable called “grouped”.

Time to Test Your Skills!

Note: We are going to create a DataFrame called “df” in the first exercise, and we will keep referring to the same DataFrame as our input in the rest of the exercises.

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