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Data Science Interview Challenge
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Data Science Interview Challenge

Data Science Interview Challenge

1. Python
IntroductionChallenge 1: List ComprehensionChallenge 2: String ManipulationChallenge 3: Dictionary ManipulationChallenge 4: Nested LoopsChallenge 5: ClassesTheoretical Questions
2. NumPy
Challenge 1: Array CreationChallenge 2: Array ManipulationChallenge 3: Statistical InsightsChallenge 4: Handling Missing ValuesChallenge 5: Subarray SortingTheoretical Questions
3. Pandas
Challenge 1: DataFrame CreationChallenge 2: Data GroupingChallenge 3: Indexing and MultiIndexingChallenge 4: Altering DataFrameChallenge 5: Iterating Over DataTheoretical Questions
4. Matplotlib
Challenge 1: Fundamentals of PlottingChallenge 2: Other Graph TypesChallenge 3: SubplotsChallenge 4: Customizing PlotsChallenge 5: Adding Text to PlotsTheoretical Questions
5. Seaborn
Challenge 1: Visualizing DistributionsChallenge 2: Exploring Categorical DataChallenge 3: Relational PlotsChallenge 4: Regression PlotsChallenge 5: Matrix PlotsTheoretical Questions
6. Statistics
Challenge 1: Probabilities and DistributionsChallenge 2: Bayes' TheoremChallenge 3: Hypothesis TestingChallenge 4: Confidence IntervalsChallenge 5: CorrelationTheoretical Questions
7. Scikit-learn
Challenge 1: Data ScalingChallenge 2: Basic Model CreationChallenge 3: PipelinesChallenge 4: Cross-validationChallenge 5: Hyperparameter TuningTheoretical Questions

book
Challenge 5: Iterating Over Data

Iterating over datasets in Pandas is a critical operation, especially when custom data processing steps need to be applied to each row or column. Pandas offers:

  • Flexibility: Whether you need to process data row-wise, column-wise, or cell-wise, Pandas has you covered with multiple methods.
  • Efficiency: While it's typically more efficient to use vectorized Pandas operations, sometimes iteration is the most straightforward approach.

Understanding how to iterate effectively over datasets in Pandas can greatly aid in the data cleaning and pre-processing phase.

Aufgabe

Swipe to start coding

Discover different ways to iterate over datasets in Pandas:

  1. Iterate over rows of a DataFrame.
  2. Iterate over column names of a DataFrame.
  3. Apply a custom function to each cell in a DataFrame column.
  4. Use the map function to format the entire DataFrame.

Lösung

Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 3. Kapitel 5
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book
Challenge 5: Iterating Over Data

Iterating over datasets in Pandas is a critical operation, especially when custom data processing steps need to be applied to each row or column. Pandas offers:

  • Flexibility: Whether you need to process data row-wise, column-wise, or cell-wise, Pandas has you covered with multiple methods.
  • Efficiency: While it's typically more efficient to use vectorized Pandas operations, sometimes iteration is the most straightforward approach.

Understanding how to iterate effectively over datasets in Pandas can greatly aid in the data cleaning and pre-processing phase.

Aufgabe

Swipe to start coding

Discover different ways to iterate over datasets in Pandas:

  1. Iterate over rows of a DataFrame.
  2. Iterate over column names of a DataFrame.
  3. Apply a custom function to each cell in a DataFrame column.
  4. Use the map function to format the entire DataFrame.

Lösung

Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 3. Kapitel 5
Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
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