Challenge 5: Iterating Over Data

Iterating over datasets in Pandas is a critical operation, especially when custom data processing steps need to be applied to each row or column. Pandas offers:

Flexibility: Whether you need to process data row-wise, column-wise, or cell-wise, Pandas has you covered with multiple methods.
Efficiency: While it's typically more efficient to use vectorized Pandas operations, sometimes iteration is the most straightforward approach.

Understanding how to iterate effectively over datasets in Pandas can greatly aid in the data cleaning and pre-processing phase.

Compito

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Discover different ways to iterate over datasets in Pandas:

Iterate over rows of a DataFrame.
Iterate over column names of a DataFrame.
Apply a custom function to each cell in a DataFrame column.
Use the map function to format the entire DataFrame.

Soluzione

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. Iterate over rows of a DataFrame.

print('--- Task 1 ---')

for index, row in df.iterrows():

print(row)

print('-' * 20)

# 2. Iterate over columns of a DataFrame.

print('\n--- Task 2 ---')

for column in df:

print(column)

# 3. Apply a custom function to each cell in a DataFrame column.

print('\n--- Task 3 ---')

def add_suffix(cell):

return str(cell) + "_suffix"

df['Name'] = df['Name'].apply(add_suffix)

print(df)

# 4. Replace values in the City column according replacement_dict.

print('\n--- Task 4 ---')

replacement_dict = {

'New York': 'NY',

'San Francisco': 'SF',

'Los Angeles': 'LA'

}

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Sezione 3. Capitolo 5