City_walls

City walls

import pandas as pd
import re
from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
import squarify # pip install squarify
import seaborn as sns
from wordcloud import WordCloud
import plotly.express as px # Requires: pip install plotly

file_path = '/Users/meredithsmith/Desktop/TØPAnalysis/BreachSongs2.xlsx'
df = pd.read_excel(file_path)

stop_words = [
    "i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you", "your",
    "yours", "he", "him", "his", "she", "her", "it", "its", "they", "them",
    "their", "what", "which", "who", "whom", "this", "that", "these", "those",
    "am", "is", "are", "was", "were", "be", "been", "being", "have", "has",
    "had", "having", "do", "does", "did", "doing", "a", "an", "the", "and",
    "but", "if", "or", "because", "as", "until", "while", "of", "at", "by",
    "for", "with", "about", "against", "between", "into", "through", "during",
    "before", "after", "above", "below", "to", "from", "up", "down", "in",
    "out", "on", "off", "over", "under", "again", "further", "then", "once"
]

# Function to remove stop words from a string
def remove_stopwords(text):
    words = text.split()
    filtered_words = [word for word in words if word.lower() not in stop_words]
    return " ".join(filtered_words)

# Apply to your column
# df1=filtered_df.copy()
# df1['unique'] = df1['unique'].apply(remove_stopwords)

lyrics = """Square up with me, I can take your right, throw a left
 Pair up with me, I'ma take you right to the chest
 Warpath etched in the surface
 Lines of the yellow tape, they're moving quiet like a
 Serpentine in a formation
 Buddy, that's my team, pretty impatient
 Now they're waiting on me
 It might be the furthest we've reached
 Now move it up, move it up, it's a breach

 I wonder where you are
 I wanted you to show me
 The way around the city walls
 The way on through
 I wonder where you are
 I wanted you to show me
 But now the night has fallen
 Abandoned by the sun

 Square up with me, I can come to you, tell me when
 Pair up with me, I can run on you, in the end
 I write a promise in pencil, but my loyalty's in pen
 Use a mistake as a crooked stencil, then we trace it back again
 From the mainland to the island of violence
 It was the same plan for a while, decided
 To send me up and rip you outta your seat
 You see, in a city with no entrance, there is not a retreat
 I'm wondering what you thought would happen, who you thought I would be
 Was this a sideswipe or did you picture this in a dream?
 Buckle down, this is possibly the furthest we've reached
 Now move it up, move it up, it's a breach

 I wonder where you are
 I wanted you to show me
 The way around the city walls
 The way on through
 I wonder where you are
 I wanted you to show me
 But now the night has fallen
 Abandoned by the sun
 Abandoned by the sun

 My smile wraps around my head, splitting it in
 Two, two, two, two
 I don't have a clue how I can keep the top half
 Glued, glued, glued, glued
  My smile wraps around my head, splitting it in
 Two, two, two, two
  I don't have a clue how I can keep the top half
 Glued, glued, glued, glued

  I wonder where you are
I wanted you to show me

 Entertain my, entertain my
 Entertain my, entertain my
 Entertain my, entertain my
 Entertain my, entertain my faith
  This is the last time, this is the last time
 Entertain my faith
 This is the last time that I try
 Address my soul, address my soul, address my soul
 Entertain my faith
 This is the last time that I try"""

# 1. Normalize: lowercase and remove punctuation
# We do this first so "Sun." becomes "sun"
clean_lyrics = re.sub(r'[^\w\s]', '', lyrics.lower())

# 2. Filter: Use your function directly on the string
# No .apply() needed for standard strings
filtered_lyrics = remove_stopwords(clean_lyrics)

# 3. Split: Break the string into a list of words
words = filtered_lyrics.split()

# 4. Tally: Count occurrences
counts = Counter(words)

# 5. Output: View the most frequent meaningful words
print(counts.most_common(10))

[('entertain', 10), ('two', 8), ('glued', 8), ('can', 5), ('now', 5), ('wonder', 5), ('where', 5), ('wanted', 5), ('show', 5), ('move', 4)]

City Walls Bar Chart

# Convert your Counter object to a sorted DataFrame
df_counts = pd.DataFrame(counts.most_common(15), columns=['Word', 'Count'])
df_counts = df_counts.sort_values(by='Count', ascending=True)

# Create the plot
plt.barh(df_counts['Word'], df_counts['Count'], color='skyblue')
plt.xlabel('Frequency')
plt.title('Top 15 Most Frequent Words')
plt.tight_layout()

# Save or display
plt.savefig('lyric_counts_bar.png')

City Walls Lollipop Chart

# 1. Prepare your data (from your existing code)
df_counts = pd.DataFrame(counts.most_common(15), columns=['Word', 'Count'])
# Sorting ensures the "longest" lollipops are at the top
df_counts = df_counts.sort_values(by='Count', ascending=True)

# 2. Create the plot
plt.figure(figsize=(10, 8))

# Draw the "stems" (horizontal lines)
plt.hlines(y=df_counts['Word'], xmin=0, xmax=df_counts['Count'], color='skyblue', alpha=0.5, linewidth=2)

# Draw the "candies" (the dots)
plt.plot(df_counts['Count'], df_counts['Word'], "o", markersize=10, color='slateblue')

# 3. Styling
plt.xlabel('Frequency', fontsize=12)
plt.title('Top 15 Most Frequent Words (Lollipop Chart)', fontsize=14, fontweight='bold')
plt.grid(axis='x', linestyle='--', alpha=0.3) # Horizontal grids help track the values

# Add labels to the end of the dots (optional)
for i, count in enumerate(df_counts['Count']):
    plt.text(count + 0.2, i, str(count), va='center', fontsize=9)

plt.tight_layout()

# 4. Save or display
plt.savefig('lyric_counts_lollipop.png')
plt.show()

City Walls Bubble Chart

# 1. Prepare your data (from your existing code)
df_counts = pd.DataFrame(counts.most_common(15), columns=['Word', 'Count'])

# 2. Setup positions for the bubbles (random or ordered)
# We use random X and Y coordinates to spread the bubbles out
np.random.seed(42) # Keeps the layout the same every time you run it
df_counts['x'] = np.random.rand(15)
df_counts['y'] = np.random.rand(15)

# 3. Create the plot
plt.figure(figsize=(12, 8))

# Squaring the count allows for more dramatic size differences
df_counts['size'] = df_counts['Count'] ** 2

# s= size of bubbles (multiplied by a factor to make them visible)
# c= color (can be mapped to the count)
scatter = plt.scatter(
    df_counts['x'],
    df_counts['y'],
    s=df_counts['Count'] * 1500,  # Adjust the multiplier (100) based on your data scale
    c=df_counts['Count'],
    cmap='viridis',
    alpha=0.6,
)

# 4. Add the word labels inside or near the bubbles
for i, row in df_counts.iterrows():
    plt.annotate(
        f"{row['Word']}\n({row['Count']})",
        (row['x'], row['y']),
        ha='center',
        va='center',
        fontsize=10,
        fontweight='bold'
    )

# 5. Styling
plt.title('City Walls Bubble Frequency Chart', fontsize=16)
plt.axis('off') # Hide axes for a cleaner "cloud" look
plt.colorbar(scatter, label='Frequency') # Optional: adds a color scale legend

plt.tight_layout()
plt.savefig('city_walls_bubble.png')
plt.show()

City Walls Tree Map

# 1. Prepare your data (from your existing Counter object)
df_counts = pd.DataFrame(counts.most_common(15), columns=['Word', 'Count'])

# 2. Create the plot
plt.figure(figsize=(12, 8))

# Define colors (using a nice Seaborn palette)
colors = sns.color_palette('viridis', len(df_counts))

# Create labels that include the word and the count
labels = [f"{row['Word']}\n({row['Count']})" for i, row in df_counts.iterrows()]

# 3. Draw the Treemap
squarify.plot(
    sizes=df_counts['Count'],
    label=labels,
    color=colors,
    alpha=0.8,
    text_kwargs={'fontsize': 10, 'fontweight': 'bold'}
)

# 4. Styling
plt.title('Top 15 Words: Treemap Distribution', fontsize=16)
plt.axis('off') # Treemaps don't need axes

plt.tight_layout()
plt.savefig('lyric_counts_treemap.png')
plt.show()

City Walls Word Cloud

# Join all words into one string weighted by their count
word_freq = dict(counts.most_common(15))

wordcloud = WordCloud(
    width=800,
    height=400,
    background_color='white',
    colormap='viridis'
).generate_from_frequencies(word_freq)

plt.figure(figsize=(15, 7))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()

The Radial Bar Chart

# Prepare data
df_counts = pd.DataFrame(counts.most_common(15), columns=['Word', 'Count'])
n = len(df_counts)
angles = np.linspace(0, 2 * np.pi, n, endpoint=False)
widths = [2 * np.pi / n] * n

plt.figure(figsize=(10, 10))
ax = plt.subplot(111, polar=True)

# Create the bars
bars = ax.bar(angles, df_counts['Count'], width=widths, bottom=10,
              color=plt.cm.viridis(df_counts['Count'] / df_counts['Count'].max()),
              edgecolor='white')

# Add labels
ax.set_xticks(angles)
ax.set_xticklabels(df_counts['Word'], fontsize=10)
ax.set_yticklabels([]) # Hide radial grid numbers
plt.title("Radial Word Frequency", va='bottom')
plt.show()

Rawfear Funnel Chart

df_counts = pd.DataFrame(counts.most_common(15), columns=['Word', 'Count'])

fig = px.funnel(df_counts, x='Count', y='Word',
                title='Top 15 Word Frequency Funnel',
                color_discrete_sequence=['#636EFA'])
fig.show()