Python program to find the frequency of the elements which are common in a list of strings

In this Python article, we explore how to find the frequency of elements that are common in a list of strings. We'll demonstrate three different approaches: finding character frequency using reduce(), word frequency using list operations, and word frequency using pandas functions.

Example 1: Character Frequency Using reduce() Function

This approach finds the frequency of characters that appear in all strings using the reduce() function with Counter ?

from functools import reduce
from collections import Counter

# Sample list of strings
strings = ['Types of Environment - Class Discussion', 
           'Management Structure and Nature  - Class Discussion', 
           'Macro- Demography, Natural, Legal & Political  - Class Discussion']

print("Input strings:")
for s in strings:
    print(f"  '{s}'")

# Find common characters and their frequency
char_freq = reduce(lambda m, n: m & n, 
                   (Counter(elem) for elem in strings[1:]), 
                   Counter(strings[0]))

print("\nCommon characters and their frequency:")
print(dict(char_freq))

Input strings:
  'Types of Environment - Class Discussion'
  'Management Structure and Nature  - Class Discussion'
  'Macro- Demography, Natural, Legal & Political  - Class Discussion'

Common characters and their frequency:
{'s': 5, ' ': 5, 'e': 2, 'i': 2, 'o': 1, 'n': 1, 'r': 1, 'm': 1, 't': 1, '-': 1, 'C': 1, 'l': 1, 'a': 1, 'D': 1, 'c': 1, 'u': 1}

Example 2: Word Frequency Using List Operations

This method splits each string into words, combines them into a single list, and uses Counter to find frequencies ?

from collections import Counter

# Sample list of strings
strings = ['Types of Environment - Class Discussion', 
           'Management Structure and Nature  - Class Discussion', 
           'Macro- Demography, Natural, Legal & Political  - Class Discussion']

print("Input strings:")
for s in strings:
    print(f"  '{s}'")

# Split each string into words and combine
words_list = []
for string in strings:
    words_list.extend(string.split())

print(f"\nCombined word list: {words_list}")

# Count frequency of words
word_freq = Counter(words_list)
print("\nWord frequency:")
for word, freq in word_freq.most_common():
    print(f"  '{word}': {freq}")

Input strings:
  'Types of Environment - Class Discussion'
  'Management Structure and Nature  - Class Discussion'
  'Macro- Demography, Natural, Legal & Political  - Class Discussion'

Combined word list: ['Types', 'of', 'Environment', '-', 'Class', 'Discussion', 'Management', 'Structure', 'and', 'Nature', '-', 'Class', 'Discussion', 'Macro-', 'Demography,', 'Natural,', 'Legal', '&', 'Political', '-', 'Class', 'Discussion']

Word frequency:
  '-': 3
  'Class': 3
  'Discussion': 3
  'Types': 1
  'of': 1
  'Environment': 1
  'Management': 1
  'Structure': 1
  'and': 1
  'Nature': 1
  'Macro-': 1
  'Demography,': 1
  'Natural,': 1
  'Legal': 1
  '&': 1
  'Political': 1

Example 3: Word Frequency Using Pandas

This approach uses pandas Series and the value_counts() method for a more elegant solution ?

import pandas as pd

# Sample list of strings
strings = ['Types of Environment - Class Discussion', 
           'Management Structure and Nature  - Class Discussion', 
           'Macro- Demography, Natural, Legal & Political  - Class Discussion']

print("Input strings:")
for s in strings:
    print(f"  '{s}'")

# Split each string into words and combine
words_list = []
for string in strings:
    words_list.extend(string.split())

print(f"\nCombined word list: {words_list}")

# Use pandas Series to count frequencies
word_freq = pd.Series(words_list).value_counts()
print("\nWord frequency using pandas:")
print(word_freq)

Input strings:
  'Types of Environment - Class Discussion'
  'Management Structure and Nature  - Class Discussion'
  'Macro- Demography, Natural, Legal & Political  - Class Discussion'

Combined word list: ['Types', 'of', 'Environment', '-', 'Class', 'Discussion', 'Management', 'Structure', 'and', 'Nature', '-', 'Class', 'Discussion', 'Macro-', 'Demography,', 'Natural,', 'Legal', '&', 'Political', '-', 'Class', 'Discussion']

Word frequency using pandas:
-              3
Class          3
Discussion     3
Types          1
of             1
Environment    1
Management     1
Structure      1
and            1
Nature         1
Macro-         1
Demography,    1
Natural,       1
Legal          1
&              1
Political      1
dtype: int64

Working with Excel Data

To read string data from Excel files, you can use openpyxl and pandas ?

import openpyxl
import pandas as pd

# Load Excel file
workbook = openpyxl.load_workbook("data.xlsx")
sheet = workbook.active

# Convert to DataFrame
df = pd.DataFrame(sheet.values)

# Filter rows containing specific text
filtered_df = df[df.iloc[:,3].str.contains('Discussion', na=False)]

# Extract the strings column
strings = filtered_df.iloc[:,3].values.tolist()

# Now apply any of the frequency methods above

Comparison of Methods

Conclusion

Use reduce() with Counter for character frequency analysis. For word frequency, Counter provides a simple solution while pandas value_counts() integrates well with data analysis workflows.