survey-analyzer

• Likert Scale Analysis: Agreement scale scoring and visualization
• Cross-Tabulation: Relationship analysis between categorical variables
• Frequency Analysis: Response distributions and percentages
• Sentiment Scoring: Text response sentiment analysis
• Open-Ended Analysis: Theme extraction from text responses
• Statistical Tests: Chi-square, correlations, significance testing
• Visualizations: Bar charts, heatmaps, word clouds, distribution plots
• Report Generation: Comprehensive PDF/HTML reports

```python

from survey_analyzer import SurveyAnalyzer

analyzer = SurveyAnalyzer()

# Load survey data

analyzer.load_csv('survey_responses.csv')

# Analyze Likert scale question

results = analyzer.likert_analysis('satisfaction', scale_type='agreement')

print(f"Mean score: {results['mean_score']:.2f}")

# Cross-tabulation

crosstab = analyzer.crosstab('age_group', 'product_preference')

print(crosstab)

# Generate report

analyzer.generate_report('survey_report.pdf')

```

```bash

# Analyze Likert scale

python survey_analyzer.py --data survey.csv --likert satisfaction --output results.pdf

# Cross-tabulation

python survey_analyzer.py --data survey.csv --crosstab age_group product --output crosstab.png

# Sentiment analysis

python survey_analyzer.py --data survey.csv --sentiment comments --output sentiment.html

# Full report

python survey_analyzer.py --data survey.csv --report --output full_report.pdf

```

SurveyAnalyzer Class

```python

class SurveyAnalyzer:

def __init__(self)

# Data Loading

def load_csv(self, filepath, **kwargs) -> 'SurveyAnalyzer'

def load_data(self, data: pd.DataFrame) -> 'SurveyAnalyzer'

# Likert Scale Analysis

def likert_analysis(self, column, scale_type='agreement') -> Dict

def likert_comparison(self, columns: List[str]) -> pd.DataFrame

def plot_likert(self, column, output, scale_type='agreement') -> str

# Frequency Analysis

def frequency_table(self, column) -> pd.DataFrame

def multiple_choice(self, column, delimiter=',') -> pd.DataFrame

def plot_frequencies(self, column, output, top_n=None) -> str

# Cross-Tabulation

def crosstab(self, row_var, col_var, normalize=None) -> pd.DataFrame

def chi_square_test(self, row_var, col_var) -> Dict

def plot_crosstab(self, row_var, col_var, output) -> str

# Sentiment Analysis

def sentiment_analysis(self, column) -> pd.DataFrame

def sentiment_summary(self, column) -> Dict

def plot_sentiment(self, column, output) -> str

# Open-Ended Analysis

def word_frequency(self, column, top_n=20) -> pd.DataFrame

def word_cloud(self, column, output) -> str

def extract_themes(self, column, n_themes=5) -> List[str]

# Statistics

def satisfaction_score(self, columns: List[str]) -> Dict

def response_rate(self) -> Dict

def demographics_summary(self, columns: List[str]) -> pd.DataFrame

# Reporting

def generate_report(self, output, format='pdf') -> str

def summary(self) -> str

```

Standard Scales

```python

# 5-point agreement scale

analyzer.likert_analysis('satisfaction', scale_type='agreement')

# 1=Strongly Disagree, 2=Disagree, 3=Neutral, 4=Agree, 5=Strongly Agree

# 5-point frequency scale

analyzer.likert_analysis('usage', scale_type='frequency')

# 1=Never, 2=Rarely, 3=Sometimes, 4=Often, 5=Always

# Custom scale

analyzer.likert_analysis('rating', scale_type='custom',

labels=['Poor', 'Fair', 'Good', 'Excellent'])

```

Results

```python

results = analyzer.likert_analysis('satisfaction')

# {

# 'mean_score': 4.2,

# 'median': 4,

# 'mode': 5,

# 'distribution': {1: 2, 2: 5, 3: 15, 4: 40, 5: 38},

# 'percentages': {1: 2%, 2: 5%, 3: 15%, 4: 40%, 5: 38%},

# 'top_2_box': 78%, # % Agree + Strongly Agree

# 'bottom_2_box': 7% # % Disagree + Strongly Disagree

# }

```

Visualization

```python

# Stacked bar chart

analyzer.plot_likert('satisfaction', 'likert_chart.png')

# Compare multiple questions

analyzer.likert_comparison(['quality', 'value', 'service'])

analyzer.plot_likert_comparison(['quality', 'value', 'service'],

'comparison.png')

```

Single Choice

```python

freq = analyzer.frequency_table('age_group')

# Count Percentage

# 18-24 45 22.5%

# 25-34 78 39.0%

# 35-44 52 26.0%

# 45+ 25 12.5%

# Plot

analyzer.plot_frequencies('age_group', 'age_distribution.png')

```

Multiple Choice

For questions allowing multiple selections:

```python

# Data format: "Option A, Option B, Option C"

results = analyzer.multiple_choice('features_liked', delimiter=',')

# Count Percentage

# Price 120 60%

# Quality 95 47.5%

# Design 80 40%

# Durability 70 35%

analyzer.plot_frequencies('features_liked', 'features.png', top_n=10)

```

Basic Cross-Tab

```python

crosstab = analyzer.crosstab('age_group', 'satisfaction')

# Satisfied Neutral Dissatisfied

# 18-24 30 10 5

# 25-34 60 15 3

# 35-44 40 8 4

# 45+ 18 5 2

# With percentages

crosstab_pct = analyzer.crosstab('age_group', 'satisfaction',

normalize='index') # Row percentages

```

Statistical Testing

```python

result = analyzer.chi_square_test('age_group', 'satisfaction')

# {

# 'statistic': 12.45,

# 'p_value': 0.014,

# 'significant': True,

# 'interpretation': 'There is a significant relationship between

# age_group and satisfaction (p=0.014)'

# }

```

Visualization

```python

# Heatmap

analyzer.plot_crosstab('age_group', 'satisfaction', 'crosstab_heatmap.png')

```

Analyze open-ended text responses:

```python

# Analyze all comments

sentiment_df = analyzer.sentiment_analysis('comments')

# comment polarity sentiment

# 0 "Great product!" 0.8 Positive

# 1 "Could be better" 0.1 Neutral

# 2 "Very disappointed" -0.6 Negative

# Summary

summary = analyzer.sentiment_summary('comments')

# {

# 'positive': 65%,

# 'neutral': 20%,

# 'negative': 15%,

# 'avg_polarity': 0.35

# }

# Visualize

analyzer.plot_sentiment('comments', 'sentiment_distribution.png')

```

Word Frequency

```python

words = analyzer.word_frequency('comments', top_n=20)

# Word Frequency

# 0 great 45

# 1 quality 38

# 2 price 32

# ...

```

Word Cloud

```python

analyzer.word_cloud('comments', 'wordcloud.png')

```

Theme Extraction

```python

themes = analyzer.extract_themes('feedback', n_themes=5)

# ['product quality', 'customer service', 'pricing',

# 'delivery speed', 'user experience']

```

Net Promoter Score (NPS)

```python

nps = analyzer.nps_score('recommendation') # 0-10 scale

# {

# 'promoters': 65%, # 9-10

# 'passives': 25%, # 7-8

# 'detractors': 10%, # 0-6

# 'nps': 55

# }

```

Overall Satisfaction

```python

satisfaction = analyzer.satisfaction_score([

'product_quality',

'customer_service',

'value_for_money',

'ease_of_use'

])

# {

# 'overall_score': 4.3,

# 'category_scores': {...},

# 'satisfaction_rate': 86% # % scoring 4-5

# }

```

```python

demographics = analyzer.demographics_summary([

'age_group',

'gender',

'location',

'income_range'

])

# Returns frequency tables for each demographic variable

```

```python

response_rate = analyzer.response_rate()

# {

# 'total_respondents': 200,

# 'completion_rate': 85%,

# 'average_time': '5m 30s',

# 'dropout_points': {

# 'question_5': 8%,

# 'question_12': 5%

# }

# }

```

Comprehensive Report

```python

analyzer.generate_report('survey_report.pdf', format='pdf')

```

Report includes:

• Executive summary
• Response rate and demographics
• Question-by-question analysis
• Likert scale visualizations
• Cross-tabulations
• Sentiment analysis
• Key findings and recommendations

Custom Report Sections

```python

analyzer.set_report_sections([

'executive_summary',

'demographics',

'likert_questions',

'cross_tabs',

'sentiment',

'recommendations'

])

```

Filter by Segment

```python

# Analyze subset of responses

analyzer.filter('age_group', '25-34')

results = analyzer.likert_analysis('satisfaction')

analyzer.clear_filter()

```

Compare Segments

```python

comparison = analyzer.compare_segments(

segment_col='age_group',

metric_col='satisfaction'

)

# Shows how different segments scored the metric

```

Trend Analysis

For longitudinal surveys:

```python

trends = analyzer.trend_analysis(

metric='satisfaction',

time_col='survey_date',

period='month'

)

analyzer.plot_trends(trends, 'satisfaction_trend.png')

```

• pandas>=2.0.0
• numpy>=1.24.0
• scipy>=1.10.0
• textblob>=0.17.0
• matplotlib>=3.7.0
• seaborn>=0.12.0
• wordcloud>=1.9.0
• reportlab>=4.0.0