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Python调查分析器：李克特量表、交叉分析与情感分析工具，自动生成可视化报告

survey-analyzer by dkyazzentwatwa/chatgpt-skills

72 周安装量

45 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill survey-analyzer

Python Web框架数据可视化数据分析

🇨🇳中文介绍

调查分析器

使用李克特量表、交叉分析和情感分析进行全面的调查数据分析。

功能

李克特量表分析：同意程度量表评分和可视化
交叉分析：分类变量之间的关系分析
频率分析：回答分布和百分比
情感评分：文本回答的情感分析
开放式问题分析：从文本回答中提取主题
统计检验：卡方检验、相关性、显著性检验
可视化：条形图、热力图、词云、分布图
报告生成：全面的 PDF/HTML 报告

快速开始

from survey_analyzer import SurveyAnalyzer

analyzer = SurveyAnalyzer()

# 加载调查数据
analyzer.load_csv('survey_responses.csv')

# 分析李克特量表问题
results = analyzer.likert_analysis('satisfaction', scale_type='agreement')
print(f"Mean score: {results['mean_score']:.2f}")

# 交叉分析
crosstab = analyzer.crosstab('age_group', 'product_preference')
print(crosstab)

# 生成报告
analyzer.generate_report('survey_report.pdf')

CLI 用法

# 分析李克特量表
python survey_analyzer.py --data survey.csv --likert satisfaction --output results.pdf

# 交叉分析
python survey_analyzer.py --data survey.csv --crosstab age_group product --output crosstab.png

# 情感分析
python survey_analyzer.py --data survey.csv --sentiment comments --output sentiment.html

# 完整报告
python survey_analyzer.py --data survey.csv --report --output full_report.pdf

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李克特量表分析

# 5点同意程度量表
analyzer.likert_analysis('satisfaction', scale_type='agreement')
# 1=非常不同意, 2=不同意, 3=中立, 4=同意, 5=非常同意

# 5点频率量表
analyzer.likert_analysis('usage', scale_type='frequency')
# 1=从不, 2=很少, 3=有时, 4=经常, 5=总是

# 自定义量表
analyzer.likert_analysis('rating', scale_type='custom',
                        labels=['Poor', 'Fair', 'Good', 'Excellent'])

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
# }

# 堆叠条形图
analyzer.plot_likert('satisfaction', 'likert_chart.png')

# 比较多个问题
analyzer.likert_comparison(['quality', 'value', 'service'])
analyzer.plot_likert_comparison(['quality', 'value', 'service'],
                               'comparison.png')

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%

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

对于允许多项选择的问题：

# 数据格式："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)

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

# 带百分比
crosstab_pct = analyzer.crosstab('age_group', 'satisfaction',
                                 normalize='index')  # 行百分比

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)'
# }

# 热力图
analyzer.plot_crosstab('age_group', 'satisfaction', 'crosstab_heatmap.png')

分析开放式文本回答：

# 分析所有评论
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

# 摘要
summary = analyzer.sentiment_summary('comments')
# {
#     'positive': 65%,
#     'neutral': 20%,
#     'negative': 15%,
#     'avg_polarity': 0.35
# }

# 可视化
analyzer.plot_sentiment('comments', 'sentiment_distribution.png')

开放式问题分析

words = analyzer.word_frequency('comments', top_n=20)
#        Word   Frequency
# 0     great       45
# 1   quality       38
# 2     price       32
# ...

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

themes = analyzer.extract_themes('feedback', n_themes=5)
# ['product quality', 'customer service', 'pricing',
#  'delivery speed', 'user experience']

nps = analyzer.nps_score('recommendation')  # 0-10 量表
# {
#     'promoters': 65%,   # 9-10
#     'passives': 25%,    # 7-8
#     'detractors': 10%,  # 0-6
#     'nps': 55
# }

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
# }

人口统计信息分析

demographics = analyzer.demographics_summary([
    'age_group',
    'gender',
    'location',
    'income_range'
])

# 返回每个人口统计变量的频率表

response_rate = analyzer.response_rate()
# {
#     'total_respondents': 200,
#     'completion_rate': 85%,
#     'average_time': '5m 30s',
#     'dropout_points': {
#         'question_5': 8%,
#         'question_12': 5%
#     }
# }

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

执行摘要
回答率和人口统计信息
逐题分析
李克特量表可视化
交叉分析
情感分析
关键发现和建议

自定义报告部分

analyzer.set_report_sections([
    'executive_summary',
    'demographics',
    'likert_questions',
    'cross_tabs',
    'sentiment',
    'recommendations'
])

按细分市场筛选

# 分析回答的子集
analyzer.filter('age_group', '25-34')
results = analyzer.likert_analysis('satisfaction')
analyzer.clear_filter()

comparison = analyzer.compare_segments(
    segment_col='age_group',
    metric_col='satisfaction'
)
# 显示不同细分市场对该指标的评分情况

针对纵向调查：

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

🇺🇸English

Survey Analyzer

Comprehensive survey data analysis with Likert scales, cross-tabs, and sentiment analysis.

Features

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

Quick Start

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')

CLI Usage

# 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

API Reference

SurveyAnalyzer Class

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

Likert Scale Analysis

Standard Scales

# 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

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

# 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')

Frequency Analysis

Single Choice

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:

# 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)

Cross-Tabulation

Basic Cross-Tab

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

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

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

Sentiment Analysis

Analyze open-ended text responses:

# 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')

Open-Ended Analysis

Word Frequency

words = analyzer.word_frequency('comments', top_n=20)
#        Word   Frequency
# 0     great       45
# 1   quality       38
# 2     price       32
# ...

Word Cloud

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

Theme Extraction

themes = analyzer.extract_themes('feedback', n_themes=5)
# ['product quality', 'customer service', 'pricing',
#  'delivery speed', 'user experience']

Satisfaction Metrics

Net Promoter Score (NPS)

nps = analyzer.nps_score('recommendation')  # 0-10 scale
# {
#     'promoters': 65%,   # 9-10
#     'passives': 25%,    # 7-8
#     'detractors': 10%,  # 0-6
#     'nps': 55
# }

Overall Satisfaction

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
# }

Demographics Analysis

demographics = analyzer.demographics_summary([
    'age_group',
    'gender',
    'location',
    'income_range'
])

# Returns frequency tables for each demographic variable

Response Rate Analysis

response_rate = analyzer.response_rate()
# {
#     'total_respondents': 200,
#     'completion_rate': 85%,
#     'average_time': '5m 30s',
#     'dropout_points': {
#         'question_5': 8%,
#         'question_12': 5%
#     }
# }

Report Generation

Comprehensive Report

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

analyzer.set_report_sections([
    'executive_summary',
    'demographics',
    'likert_questions',
    'cross_tabs',
    'sentiment',
    'recommendations'
])

Advanced Features

Filter by Segment

# Analyze subset of responses
analyzer.filter('age_group', '25-34')
results = analyzer.likert_analysis('satisfaction')
analyzer.clear_filter()

Compare Segments

comparison = analyzer.compare_segments(
    segment_col='age_group',
    metric_col='satisfaction'
)
# Shows how different segments scored the metric

Trend Analysis

For longitudinal surveys:

trends = analyzer.trend_analysis(
    metric='satisfaction',
    time_col='survey_date',
    period='month'
)
analyzer.plot_trends(trends, 'satisfaction_trend.png')

Dependencies

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

Weekly Installs

Repository

dkyazzentwatwa/…t-skills

GitHub Stars

First Seen

Jan 24, 2026

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57,700 周安装

Python调查分析器：李克特量表、交叉分析与情感分析工具，自动生成可视化报告

🇨🇳中文介绍

调查分析器

功能

快速开始

CLI 用法

相关 Skills

API 参考

SurveyAnalyzer 类

李克特量表分析

标准量表

结果

可视化

频率分析

单选题

多选题

交叉分析

基础交叉表

统计检验

可视化

情感分析

开放式问题分析

词频

词云

主题提取

满意度指标

净推荐值 (NPS)

总体满意度

人口统计信息分析

回答率分析

报告生成

综合报告

自定义报告部分

高级功能

按细分市场筛选

比较细分市场

趋势分析

依赖项

🇺🇸English

Survey Analyzer

Features

Quick Start

CLI Usage

API Reference

SurveyAnalyzer Class

Likert Scale Analysis

Standard Scales

Results

Visualization

Frequency Analysis

Single Choice

Multiple Choice

Cross-Tabulation

Basic Cross-Tab

Statistical Testing

Visualization

Sentiment Analysis

Open-Ended Analysis

Word Frequency

Word Cloud

Theme Extraction

Satisfaction Metrics

Net Promoter Score (NPS)

Overall Satisfaction

Demographics Analysis

Response Rate Analysis

Report Generation

Comprehensive Report

Custom Report Sections

Advanced Features

Filter by Segment

Compare Segments

Trend Analysis

Dependencies

最新 Skills