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2. Pandas Series: Access & Selection

Status

Done

TABLE OF CONTENT

1. Label-Based & Positional Access

df['series'].at → Fast label-based scalar access (single value, like a dictionary).
df['series'].iat → Fast integer-position-based scalar access (like at but for positions).
df['series'].loc → Label-based indexing (slicing, single/multiple values).
df['series'].iloc → Integer-position-based indexing (slicing, single/multiple values).

Key Difference:

Use at/iat for single values (optimized for speed).
Use loc/iloc for slices or multiple values.

2. Value Retrieval & Conversion

df['series'].get(key) → Safe value access (returns None/default if key missing, like dict.get).
df['series'].item() → Extract the single value from a 1-element Series (raises error if size ≠ 1).
df['series'].to_numpy() → Convert Series to a NumPy array (preferred over .values in modern Pandas).
df['series'].array → Returns the underlying Pandas extension array (e.g., IntegerArray, StringArray).

3. Data Removal & Iteration

df['series'].pop(key) → Remove and return a value by label (modifies the Series in-place).
df['series'].items() → Iterate over (index, value) pairs (like dict.items).
df['series'].iter() → Alias for .items() (older Pandas versions).
df['series'].keys() → Alias for .index (returns index labels).

4. Advanced Selection

df['series'].xs(key) → Cross-section (select value by label, similar to .loc but less common).

When to Use What?

Need speed for single values? → at (label) / iat (position).
Slicing or filtering? → loc (label) / iloc (position).
Safe access with fallback? → get().
Convert to NumPy? → to_numpy() (modern) or .values (legacy).
Iterate? → items() or iter().

🔍 Access & Selection in Pandas Series (Titanic Dataset)

We will use the age column from the Titanic dataset to demonstrate all examples.


import pandas as pd
import seaborn as sns

# Load Titanic dataset
df = sns.load_dataset('titanic')

survived  pclass     sex     age  sibsp  parch     fare embarked  class     who  adult_male deck  embark_town    alive  alone
0         0       3     male   22.0      1      0   7.2500        S  Third     man        True  NaN  Southampton      no  False
1         1       1   female   38.0      1      0  71.2833        C  First   woman       False    C    Cherbourg     yes  False
2         1       3   female   26.0      0      0   7.9250        S  Third   woman       False  NaN  Southampton     yes   True
3         1       1   female   35.0      1      0  53.1000        S  First   woman       False    C  Southampton     yes  False
4         0       3     male   35.0      0      0   8.0500        S  Third     man        True  NaN  Southampton      no   True

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1. Label-Based & Positional Access

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2. Value Retrieval & Conversion

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3. Data Removal & Iteration

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4. Advanced Selection

When to Use What? – Summary Table

Task	Use
Access single label	`at`
Access single position	`iat`
Access multiple rows (labels)	`loc`
Access multiple rows (positions)	`iloc`
Safe access with fallback	`get()`
Extract from 1-element Series	`item()`
Convert to NumPy	`to_numpy()`
Remove value	`pop()`
Iterate over index-value pairs	`items()` / `iter()`
Access index labels	`keys()`
Cross-section selection (MultiIndex)	`xs()`