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# analysis_modules.py
import pandas as pd
import plotly.express as px
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import adfuller
from sklearn.cluster import KMeans
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import io
import base64
# --- Time-Series Module ---
def analyze_time_series(df: pd.DataFrame, date_col: str, value_col: str):
"""Performs time-series decomposition and stationarity testing."""
df[date_col] = pd.to_datetime(df[date_col])
ts_df = df.set_index(date_col)[value_col].dropna()
# Decomposition
decomposition = seasonal_decompose(ts_df, model='additive', period=12) # Assuming monthly data
fig_decomp = px.line(pd.DataFrame({'trend': decomposition.trend, 'seasonal': decomposition.seasonal, 'residual': decomposition.resid}),
title=f"Time-Series Decomposition of {value_col}")
# Stationarity Test (ADF)
adf_result = adfuller(ts_df)
adf_md = f"""
### Stationarity Analysis (ADF Test)
- **Test Statistic:** `{adf_result[0]:.4f}`
- **p-value:** `{adf_result[1]:.4f}`
- **Conclusion:** The series is likely **{'stationary' if adf_result[1] < 0.05 else 'non-stationary'}**.
"""
return fig_decomp, adf_md
# --- Text Analysis Module ---
def generate_word_cloud(df: pd.DataFrame, text_col: str):
"""Generates a word cloud from a text column."""
text = ' '.join(df[text_col].dropna().astype(str))
wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)
# Convert matplotlib plot to a data URI for Gradio
buf = io.BytesIO()
wordcloud.to_image().save(buf, format='png')
img_str = "data:image/png;base64," + base64.b64encode(buf.getvalue()).decode('utf-8')
return img_str
# --- Clustering Module ---
def perform_clustering(df: pd.DataFrame, numeric_cols: list, n_clusters: int = 4):
"""Performs K-Means clustering and returns a scatter plot."""
cluster_data = df[numeric_cols].dropna()
kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init='auto').fit(cluster_data)
cluster_data['Cluster'] = kmeans.labels_.astype(str)
# For visualization, we'll use the first two numeric columns
fig_cluster = px.scatter(cluster_data, x=numeric_cols[0], y=numeric_cols[1], color='Cluster',
title=f"K-Means Clustering (k={n_clusters})")
return fig_cluster |