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12. Pandas Series: String Operations

Status

Done

TABLE OF CONTENT

1. Case Conversion

df['text_series'].str.lower() → Convert to lowercase
df['text_series'].str.upper() → Convert to uppercase
df['text_series'].str.title() → Convert to title case (first letter capitalized)

2. Cleaning & Replacement

df['text_series'].str.strip() → Remove whitespace from both ends
df['text_series'].str.replace(old, new) → Replace substring patterns
df['text_series'].str.repeat(n) → Repeat each string n times

3. Pattern Matching

df['text_series'].str.contains(pattern) → Check for substring/regex matches
df['text_series'].str.startswith(pattern) → Check starting characters
df['text_series'].str.endswith(pattern) → Check ending characters
df['text_series'].str.extract(regex) → Extract regex groups into columns

4. Splitting & Joining

df['text_series'].str.split(sep) → Split strings by delimiter
df['text_series'].str.get(i) → Get element at position i after split
df['text_series'].str.join(sep) → Join list elements with separator

5. String Properties & Encoding

df['text_series'].str.len() → Get length of each string
df['text_series'].str.get_dummies(sep) → Convert delimited strings to dummy variables

Example Usage:

names = pd.Series([' John Doe ', 'jane SMITH', 'alice cooper'])

# Case conversion
names.str.title()        # → [' John Doe ', 'Jane Smith', 'Alice Cooper']

# Cleaning
names.str.strip()        # → ['John Doe', 'jane SMITH', 'alice cooper']

# Pattern matching
names.str.contains('oh') # → [True, False, False]

# Splitting
names.str.split().str.get(0)  # → ['John', 'jane', 'alice']

Key Notes:

All methods return new Series (original remains unchanged)
Most methods accept regex patterns (contains, replace, etc.)
Use na=False in pattern matching to handle missing values
For complex string operations, chain multiple .str methods

Common Use Cases:

Standardizing text data (case, whitespace)
Extracting parts of strings (e.g., first names)
Creating features from text patterns
Preparing text for machine learning (via get_dummies)

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1. Case Conversion Methods

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2. Text Cleaning Methods

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3. Pattern Matching & Extraction

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4. Splitting & Combining Strings

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5. String Properties & Encoding

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Practical Applications

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Performance Considerations

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