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.

Uppgift

Swipe to start coding

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

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

Lösning

import pandas as pd

# Sample DataFrame

df = pd.DataFrame({

'Name': ['Alice', 'Bob', 'Charlie'],

'Age': [25, 30, 35],

'City': ['New York', 'San Francisco', 'Los Angeles']

})

# 1. Add a new column to a DataFrame.

df['Occupation'] = ['Engineer', 'Doctor', 'Artist']

display(df)

print('-' * 40)

# 2. Rename columns in a DataFrame.

renamed_df = df.rename(columns={'Name': 'Full Name', 'Age': 'Age (years)'})

display(renamed_df)

print('-' * 40)

# 3. Drop a column from a DataFrame.

reduced_df = renamed_df.drop('City', axis=1)

display(reduced_df)

print('-' * 40)

# 4. Sort a DataFrame based on a specific column.

sorted_df = reduced_df.sort_values(by='Age (years)', ascending=False)

display(sorted_df)

Var allt tydligt?

Tack för dina kommentarer!

Avsnitt 3. Kapitel 4

import pandas as pd

# Sample DataFrame

df = pd.DataFrame({

'Name': ['Alice', 'Bob', 'Charlie'],

'Age': [25, 30, 35],

'City': ['New York', 'San Francisco', 'Los Angeles']

})

# 1. Add a new column to a DataFrame.

df['Occupation'] = ___

display(df)

print('-' * 40)

# 2. Rename columns in a DataFrame.

renamed_df = ___

display(renamed_df)

print('-' * 40)

# 3. Drop a column from a DataFrame.

reduced_df = ___

display(reduced_df)

print('-' * 40)

# 4. Sort a DataFrame based on a specific column.

sorted_df = ___

display(sorted_df)

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