How to get the last N rows of a pandas DataFrame?

Checking pandas is up-to-date

Firstly, it's important to keep your pandas routinely updated to harness the latest features and improvements:

pip install --upgrade pandas
# Excuse me, panda coming through!
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Want to know your current version? Check with:

import pandas as pd
print(pd.__version__)
# Panda's birthdate!
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Find the tail() method in versions 0.10.1 and onwards. Always staying up-to-date opens up latest functions and ensures compatibility.

Say no to deprecated methods

Heads up! ix has retired. It used to allow for both label and integer-based indexing, but could lead to unforeseen outcomes. Instead, use loc[] for label-based or iloc[] for position-based indexing:

# For position-based, get last 3 rows
last_three_rows = df.iloc[-3:]
# And they lived happily ever after!
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Choosing iloc[] ensures an outcome as predictable as sunrise, especially in older pandas versions where old is not gold and you need to stick with index-based selection.

Grouping: every group gets the tail treatment

For grouped data, you need to use GroupBy.tail(). This method proves instrumental when your requirement is to extract the last N rows of each group.

last_n_per_group = df.groupby('column_name').tail(N)
# Even pandas like to group and gab!
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Clear as day with loc and iloc

Use loc or iloc over the old player ix. Your code's readability and clarity increase leaps and bounds when you choose label or position-based row selection.

Performance considerations & constraints

When working with mammoth DataFrames, don't skip the memory usage and execution time when plucking the last N rows:

• Avoid copy-pasting: Steer clear from unnecessary data copying particularly when you're just observing it.
• Racing with time: Know that tail() is the sea-biscuit here, usually faster than iloc slicing owing to its optimization for this purpose.

Heads-up for common pitfalls

Watch out for these while working with pandas:

• Your DataFrame returns empty if N equals 0 or the DataFrame itself is vacant.
• In a DataFrame housing duplicates, tail() picks based on the given DataFrame's sequence.
• Be sure tail() is the final operation to mirror the most updated data.

Making time series fetch last N rows

When working with time series data, the concept of last N observations could be nuanced. Where your data is indexed by date, make use of last():

df_last_period = df.last('3D')  # For last 3 days
# Last three days: Yesterday, Today and Tomorrow!
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To max out your row fetching efficiency you need to decipher the dynamic indexing and resampling capabilities of time-indexed pandas data.