Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Oppiskele Challenge 4: Altering DataFrame | Pandas
Data Science Interview Challenge
course content

Kurssisisältö

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 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.

Tehtävä

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).

Ratkaisu

Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Oliko kaikki selvää?

Miten voimme parantaa sitä?

Kiitos palautteestasi!

Osio 3. Luku 4
toggle bottom row

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.

Tehtävä

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).

Ratkaisu

Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Oliko kaikki selvää?

Miten voimme parantaa sitä?

Kiitos palautteestasi!

Osio 3. Luku 4
Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Pahoittelemme, että jotain meni pieleen. Mitä tapahtui?
some-alt