07_string_methods

String Methods

Description: String methods provide functionalities for manipulating and analyzing textual data within DataFrames and Series. They support operations such as searching, replacing, and splitting strings.

• DataFrame.str.contains(): Checks if a pattern or substring exists in each element of a Series.
• DataFrame.str.replace(): Replaces occurrences of a pattern or substring in each element of a Series.
• DataFrame.str.extract(): Extracts substrings based on a regular expression pattern.
• DataFrame.str.split(): Splits each element of a Series into a list based on a delimiter.

DataFrame.str.contains()

Explanation: The DataFrame.str.contains() function checks if each element in a Series contains a specified substring. This is useful for filtering or extracting rows based on substring matches.

Features:

• Substring Matching: Checks if a substring is present within each element.
• Regular Expressions: Supports regular expressions for more complex matching.
• Case Sensitivity: Can be controlled using the case parameter.

Problem Solved:

• Allows for filtering and extracting rows based on whether a substring or pattern is present in the text data.

Parameters:

• pat: The substring or regular expression pattern to search for.
• case: Boolean indicating whether to perform a case-sensitive search. Default is True.
• na: Value to return for missing values (optional).

Example Code:

import pandas as pd

# Sample DataFrame with text data
data = {
    'Description': ['Apple fruit', 'Banana fruit', 'Carrot vegetable']
}
df = pd.DataFrame(data)

# Checking if 'fruit' is in the Description column
df['Contains Fruit'] = df['Description'].str.contains('fruit')

# Displaying the result
print("DataFrame with 'Contains Fruit' column:")
print(df)

Explanation:

• df['Description'].str.contains('fruit') checks each entry in the 'Description' column to see if it contains the substring 'fruit' and creates a new column with the boolean result.

DataFrame.str.replace()

Explanation: The DataFrame.str.replace() function replaces occurrences of a specified substring or pattern within each element of a Series. It supports both simple replacements and regular expression-based replacements.

Features:

• Replacement: Replaces substrings or patterns with a specified value.
• Regular Expressions: Supports using regular expressions for advanced replacements.
• Case Sensitivity: Can be controlled using the case parameter.

Problem Solved:

• Facilitates modifying text data by replacing specified substrings or patterns.

Parameters:

• pat: The substring or regular expression pattern to replace.
• repl: The replacement value.
• case: Boolean indicating whether to perform a case-sensitive replacement. Default is True.
• regex: Boolean indicating whether pat is a regular expression. Default is True.

Example Code:

import pandas as pd

# Sample DataFrame with text data
data = {
    'Description': ['Apple fruit', 'Banana fruit', 'Carrot vegetable']
}
df = pd.DataFrame(data)

# Replacing 'fruit' with 'fresh fruit'
df['Description'] = df['Description'].str.replace('fruit', 'fresh fruit')

# Displaying the result
print("DataFrame with replaced text:")
print(df)

Explanation:

• df['Description'].str.replace('fruit', 'fresh fruit') replaces occurrences of 'fruit' with 'fresh fruit' in the 'Description' column.

DataFrame.str.extract()

Explanation: The DataFrame.str.extract() function extracts substrings that match a specified regular expression pattern. It is useful for parsing and extracting specific parts of text data.

Features:

• Regular Expressions: Extracts substrings based on regular expression patterns.
• Capture Groups: Supports extracting specific parts of a pattern using capture groups.

Problem Solved:

• Allows extraction of specific data from text based on patterns, which is useful for parsing and data cleaning.

Parameters:

• pat: The regular expression pattern to match.
• expand: Boolean or integer. If True, return a DataFrame with extracted groups; if False, return a Series.

Example Code:

import pandas as pd

# Sample DataFrame with text data
data = {
    'Description': ['Apple fruit - 1', 'Banana fruit - 2', 'Carrot vegetable - 3']
}
df = pd.DataFrame(data)

# Extracting the number after '-'
df['Number'] = df['Description'].str.extract(r'-(\d+)')

# Displaying the result
print("DataFrame with extracted numbers:")
print(df)

Explanation:

• df['Description'].str.extract(r'-(\d+)') extracts numbers following the '-' character using a regular expression.

DataFrame.str.split()

Explanation: The DataFrame.str.split() function splits each element in a Series into a list of substrings based on a specified delimiter. This is useful for tokenizing text or breaking text into components.

Features:

• Splitting: Splits each string based on a specified delimiter.
• Regular Expressions: Supports splitting based on regular expressions.

Problem Solved:

• Tokenizes or breaks text data into substrings for further analysis or processing.

Parameters:

• pat: The delimiter or regular expression to split by.
• n: Number of splits to perform (optional).
• expand: Boolean indicating whether to expand the split lists into separate columns.

Example Code:

import pandas as pd

# Sample DataFrame with text data
data = {
    'Description': ['Apple fruit', 'Banana fruit', 'Carrot vegetable']
}
df = pd.DataFrame(data)

# Splitting the Description column into two columns
df[['Fruit', 'Type']] = df['Description'].str.split(' ', n=1, expand=True)

# Displaying the result
print("DataFrame with split columns:")
print(df)

Explanation:

• df['Description'].str.split(' ', n=1, expand=True) splits each entry in the 'Description' column into two columns based on the first space.