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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

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Challenge 4: Altering DataFrame

Pandas provides a plethora of tools that allow for easy modification of both data and structure of DataFrames. These capabilities are essential because:

  • Data Cleaning: Real-world datasets are often messy. The ability to transform and clean data ensures its readiness for analysis.
  • Versatility: Frequently, the structure of a dataset may not align with the requirements of a given task. Being able to reshape data can be a lifesaver.
  • Efficiency: Direct modifications to DataFrames, as opposed to creating new ones, can save memory and improve performance.

Getting familiar with the techniques to alter data and the structure of DataFrames is a key step in becoming proficient with Pandas.

Oppgave

Swipe to start coding

Harness the power of Pandas to alter data and the structure of DataFrames:

  1. Add a new column to a DataFrame with values Engineer, Doctor and Artist.
  2. Rename columns in a DataFrame. Change the Name column into Full Name and the Age column into Age (years).
  3. Drop a column City from a DataFrame.
  4. Sort a DataFrame based on the Age column (descending).

Løsning

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Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

Seksjon 3. Kapittel 4
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book
Challenge 4: Altering DataFrame

Pandas provides a plethora of tools that allow for easy modification of both data and structure of DataFrames. These capabilities are essential because:

  • Data Cleaning: Real-world datasets are often messy. The ability to transform and clean data ensures its readiness for analysis.
  • Versatility: Frequently, the structure of a dataset may not align with the requirements of a given task. Being able to reshape data can be a lifesaver.
  • Efficiency: Direct modifications to DataFrames, as opposed to creating new ones, can save memory and improve performance.

Getting familiar with the techniques to alter data and the structure of DataFrames is a key step in becoming proficient with Pandas.

Oppgave

Swipe to start coding

Harness the power of Pandas to alter data and the structure of DataFrames:

  1. Add a new column to a DataFrame with values Engineer, Doctor and Artist.
  2. Rename columns in a DataFrame. Change the Name column into Full Name and the Age column into Age (years).
  3. Drop a column City from a DataFrame.
  4. Sort a DataFrame based on the Age column (descending).

Løsning

Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

Seksjon 3. Kapittel 4
Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
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