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"""
Statistical analysis and insights generation
"""
import pandas as pd
import numpy as np
from scipy import stats
from typing import Dict, Tuple, Union
def calculate_descriptive_stats(df: pd.DataFrame, column: str) -> Dict:
"""
Calculate descriptive statistics for a column
Args:
df: Input DataFrame
column: Column name
Returns:
Dictionary with statistics
"""
stats_dict = {
"count": df[column].count(),
"mean": df[column].mean(),
"median": df[column].median(),
"std": df[column].std(),
"min": df[column].min(),
"25%": df[column].quantile(0.25),
"75%": df[column].quantile(0.75),
"max": df[column].max(),
"skewness": df[column].skew(),
"kurtosis": df[column].kurtosis(),
}
return stats_dict
def correlation_analysis(df: pd.DataFrame, method: str = "pearson") -> pd.DataFrame:
"""
Perform correlation analysis
Args:
df: Input DataFrame with numeric columns
method: 'pearson', 'spearman', or 'kendall'
Returns:
Correlation matrix
"""
numeric_df = df.select_dtypes(include=[np.number])
corr_matrix = numeric_df.corr(method=method)
return corr_matrix
def hypothesis_testing(group1: pd.Series, group2: pd.Series,
test_type: str = "ttest") -> Dict:
"""
Perform hypothesis testing between two groups
Args:
group1: First group data
group2: Second group data
test_type: 't-test', 'mannwhitneyu', or 'chi2'
Returns:
Dictionary with test results
"""
results = {}
if test_type == "ttest":
statistic, p_value = stats.ttest_ind(group1.dropna(), group2.dropna())
results = {
"test": "Independent t-test",
"statistic": statistic,
"p_value": p_value,
"significant": p_value < 0.05
}
elif test_type == "mannwhitneyu":
statistic, p_value = stats.mannwhitneyu(group1.dropna(), group2.dropna())
results = {
"test": "Mann-Whitney U Test",
"statistic": statistic,
"p_value": p_value,
"significant": p_value < 0.05
}
return results
def anova_test(groups: list) -> Dict:
"""
Perform ANOVA test
Args:
groups: List of group data Series
Returns:
Dictionary with ANOVA results
"""
clean_groups = [g.dropna() for g in groups]
f_stat, p_value = stats.f_oneway(*clean_groups)
return {
"test": "ANOVA",
"f_statistic": f_stat,
"p_value": p_value,
"significant": p_value < 0.05
}
def chi_square_test(contingency_table: pd.DataFrame) -> Dict:
"""
Perform Chi-square test for independence
Args:
contingency_table: Contingency table (DataFrame)
Returns:
Dictionary with test results
"""
chi2, p_value, dof, expected = stats.chi2_contingency(contingency_table)
return {
"test": "Chi-square",
"statistic": chi2,
"p_value": p_value,
"degrees_of_freedom": dof,
"significant": p_value < 0.05
}
def trend_analysis(df: pd.DataFrame, time_col: str, value_col: str) -> Dict:
"""
Perform simple trend analysis
Args:
df: Input DataFrame
time_col: Column name for time/date
value_col: Column name for values
Returns:
Dictionary with trend metrics
"""
df_sorted = df.sort_values(time_col).copy()
x = np.arange(len(df_sorted))
y = df_sorted[value_col].values
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
return {
"slope": slope,
"intercept": intercept,
"r_squared": r_value**2,
"p_value": p_value,
"trend": "upward" if slope > 0 else "downward",
"significant": p_value < 0.05
}
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