Challenge 3: Indexing and MultiIndexing

Pandas, an indispensable library in the data scientist's toolkit, offers robust indexing capabilities which are integral for data manipulation and retrieval.

Efficiency: Fast data access and manipulation is often dependent on smart indexing strategies, especially for larger datasets.
Flexibility: Whether it's basic row/column labels, hierarchical labels, or even date-time based indexing, Pandas has got you covered.
Readability: Descriptive indexing can render the code more intuitive and easier to follow, thereby streamlining the data exploration phase.

A solid grasp of indexing techniques, inclusive of multi indexing, can expedite tasks such as data retrieval, aggregation, and restructuring.

Compito

Swipe to start coding

Dive into indexing with Pandas through these tasks:

Set a column Date as the index of a DataFrame.
Reset the index of a DataFrame.
Create a DataFrame with a MultiIndex.
Access data from a MultiIndexed DataFrame with indices A and 1.

Soluzione

import pandas as pd

# Sample DataFrame

df = pd.DataFrame({

'Date': ['2023-01-01', '2023-01-02', '2023-01-03'],

'Temperature': [22, 24, 23],

'Humidity': [56, 58, 57]

})

# 1. Set a column as the index of a DataFrame.

indexed_df = df.set_index('Date')

display(indexed_df)

print('-' * 40)

# 2. Reset the index of a DataFrame.

reset_df = indexed_df.reset_index()

display(reset_df)

print('-' * 40)

# 3. Create a DataFrame with a MultiIndex.

arrays = [['A', 'A', 'B', 'B'], [1, 2, 1, 2]]

multi_indexed_df = pd.DataFrame({

'Value': [10, 20, 30, 40]

}, index=pd.MultiIndex.from_arrays(arrays, names=('Letter', 'Number')))

display(multi_indexed_df)

print('-' * 20)

# 4. Access data from a MultiIndexed DataFrame.

retrieved_data = multi_indexed_df.loc['A', 1]

display(retrieved_data)

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Sezione 3. Capitolo 3

import pandas as pd

# Sample DataFrame

df = pd.DataFrame({

'Date': ['2023-01-01', '2023-01-02', '2023-01-03'],

'Temperature': [22, 24, 23],

'Humidity': [56, 58, 57]

})

# 1. Set a column as the index of a DataFrame.

indexed_df = ___

display(indexed_df)

print('-' * 40)

# 2. Reset the index of a DataFrame.

reset_df = ___

display(reset_df)

print('-' * 40)

# 3. Create a DataFrame with a MultiIndex.

arrays = [['A', 'A', 'B', 'B'], [1, 2, 1, 2]]

multi_indexed_df = pd.DataFrame({

'Value': [10, 20, 30, 40]

}, index=pd.___.___(arrays, ___=('Letter', 'Number')))

display(multi_indexed_df)

print('-' * 20)

# 4. Access data from a MultiIndexed DataFrame.

retrieved_data = multi_indexed_df.___

display(retrieved_data)

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