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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: Adding Text to Plots

Annotating and adding textual elements to plots can provide context and clarity to the data being presented. These textual elements can:

  • Highlight specific data points or trends in the data.
  • Explain the significance of certain features of the plot.
  • Provide additional details or supplementary information that doesn't fit neatly into the x or y axis labels.

Matplotlib offers versatile text and annotation functions that allow you to place text in arbitrary positions, adjust its style, and even point to specific parts of the chart.

Enhancing your plots with appropriate textual elements ensures that the viewer can grasp the full story behind the data.

Compito

Swipe to start coding

Using Matplotlib, add text and annotations to a simple line plot:

  1. Label the x-axis as X-Axis and the y-axis as Y-Axis.
  2. Determine the maximum and minimum y-values from the given data and annotate these points on the plot. Also, include arrows pointing to the max and min points.
  3. Insert a textual note at the coordinate (2.45, 2) with the content 'The greatest decline', rotated at an angle of -72 degrees.

Soluzione

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Sezione 4. Capitolo 5
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book
Challenge 5: Adding Text to Plots

Annotating and adding textual elements to plots can provide context and clarity to the data being presented. These textual elements can:

  • Highlight specific data points or trends in the data.
  • Explain the significance of certain features of the plot.
  • Provide additional details or supplementary information that doesn't fit neatly into the x or y axis labels.

Matplotlib offers versatile text and annotation functions that allow you to place text in arbitrary positions, adjust its style, and even point to specific parts of the chart.

Enhancing your plots with appropriate textual elements ensures that the viewer can grasp the full story behind the data.

Compito

Swipe to start coding

Using Matplotlib, add text and annotations to a simple line plot:

  1. Label the x-axis as X-Axis and the y-axis as Y-Axis.
  2. Determine the maximum and minimum y-values from the given data and annotate these points on the plot. Also, include arrows pointing to the max and min points.
  3. Insert a textual note at the coordinate (2.45, 2) with the content 'The greatest decline', rotated at an angle of -72 degrees.

Soluzione

Switch to desktopCambia al desktop per esercitarti nel mondo realeContinua da dove ti trovi utilizzando una delle opzioni seguenti
Tutto è chiaro?

Come possiamo migliorarlo?

Grazie per i tuoi commenti!

Sezione 4. Capitolo 5
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