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

Kursinhalt

Data Science Interview Challenge

Data Science Interview Challenge

1. Python
2. NumPy
3. Pandas
4. Matplotlib
5. Seaborn
6. Statistics
7. Scikit-learn

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.

Aufgabe

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).

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 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.

Aufgabe

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).

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 4
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
We're sorry to hear that something went wrong. What happened?
some-alt