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Data Analysis Utilities
Common operations for data processing and analysis
"""
import pandas as pd
import numpy as np
from typing import Any, Dict, List, Optional, Union
import logging
logger = logging.getLogger(__name__)
def calculate_sum(df: pd.DataFrame, column_name: str, filter_condition: Optional[pd.Series] = None) -> float:
"""
Calculate sum of a numeric column
Args:
df: Input DataFrame
column_name: Name of column to sum
filter_condition: Optional boolean Series for filtering
Returns:
Sum as float
"""
try:
if filter_condition is not None:
filtered_df = df[filter_condition]
else:
filtered_df = df
result = filtered_df[column_name].sum()
logger.info(f"Sum of {column_name}: {result}")
return float(result)
except Exception as e:
logger.error(f"Error calculating sum: {e}")
raise
def count_rows(df: pd.DataFrame, filter_condition: Optional[pd.Series] = None) -> int:
"""
Count rows in DataFrame with optional filter
Args:
df: Input DataFrame
filter_condition: Optional boolean Series for filtering
Returns:
Count as integer
"""
try:
if filter_condition is not None:
result = df[filter_condition].shape[0]
else:
result = df.shape[0]
logger.info(f"Row count: {result}")
return int(result)
except Exception as e:
logger.error(f"Error counting rows: {e}")
raise
def aggregate_stats(df: pd.DataFrame, group_by: Union[str, List[str]],
agg_func: Union[str, Dict[str, str]]) -> Dict[str, Any]:
"""
Group by columns and apply aggregation functions
Args:
df: Input DataFrame
group_by: Column(s) to group by
agg_func: Aggregation function(s) - 'sum', 'mean', 'count', etc.
Can be a dict mapping column names to functions
Returns:
Dictionary with aggregated results
"""
try:
grouped = df.groupby(group_by)
if isinstance(agg_func, str):
result = grouped.agg(agg_func)
else:
result = grouped.agg(agg_func)
# Convert to JSON-serializable format
result_dict = result.to_dict()
logger.info(f"Aggregation completed: {len(result)} groups")
return result_dict
except Exception as e:
logger.error(f"Error in aggregation: {e}")
raise
def find_max_min(df: pd.DataFrame, column_name: str) -> Dict[str, Any]:
"""
Find maximum and minimum values in a column
Args:
df: Input DataFrame
column_name: Name of column
Returns:
Dictionary with 'max' and 'min' values
"""
try:
max_val = df[column_name].max()
min_val = df[column_name].min()
# Find rows with max and min values
max_row = df[df[column_name] == max_val].iloc[0].to_dict()
min_row = df[df[column_name] == min_val].iloc[0].to_dict()
result = {
"max": float(max_val) if pd.api.types.is_numeric_dtype(df[column_name]) else str(max_val),
"min": float(min_val) if pd.api.types.is_numeric_dtype(df[column_name]) else str(min_val),
"max_row": max_row,
"min_row": min_row
}
logger.info(f"Max: {max_val}, Min: {min_val}")
return result
except Exception as e:
logger.error(f"Error finding max/min: {e}")
raise
def calculate_mean(df: pd.DataFrame, column_name: str, filter_condition: Optional[pd.Series] = None) -> float:
"""
Calculate mean of a numeric column
Args:
df: Input DataFrame
column_name: Name of column
filter_condition: Optional boolean Series for filtering
Returns:
Mean as float
"""
try:
if filter_condition is not None:
filtered_df = df[filter_condition]
else:
filtered_df = df
result = filtered_df[column_name].mean()
logger.info(f"Mean of {column_name}: {result}")
return float(result)
except Exception as e:
logger.error(f"Error calculating mean: {e}")
raise
def calculate_median(df: pd.DataFrame, column_name: str) -> float:
"""
Calculate median of a numeric column
Args:
df: Input DataFrame
column_name: Name of column
Returns:
Median as float
"""
try:
result = df[column_name].median()
logger.info(f"Median of {column_name}: {result}")
return float(result)
except Exception as e:
logger.error(f"Error calculating median: {e}")
raise
def calculate_std(df: pd.DataFrame, column_name: str) -> float:
"""
Calculate standard deviation of a numeric column
Args:
df: Input DataFrame
column_name: Name of column
Returns:
Standard deviation as float
"""
try:
result = df[column_name].std()
logger.info(f"Std dev of {column_name}: {result}")
return float(result)
except Exception as e:
logger.error(f"Error calculating std: {e}")
raise
def value_counts(df: pd.DataFrame, column_name: str) -> Dict[str, int]:
"""
Count unique values in a column
Args:
df: Input DataFrame
column_name: Name of column
Returns:
Dictionary mapping values to counts
"""
try:
counts = df[column_name].value_counts().to_dict()
# Convert keys to strings for JSON serialization
result = {str(k): int(v) for k, v in counts.items()}
logger.info(f"Value counts for {column_name}: {len(result)} unique values")
return result
except Exception as e:
logger.error(f"Error calculating value counts: {e}")
raise
def filter_dataframe(df: pd.DataFrame, conditions: Dict[str, Any]) -> pd.DataFrame:
"""
Filter DataFrame based on multiple conditions
Args:
df: Input DataFrame
conditions: Dictionary of column: value pairs for filtering
Returns:
Filtered DataFrame
"""
try:
filtered = df.copy()
for column, value in conditions.items():
if isinstance(value, (list, tuple)):
# Filter for values in list
filtered = filtered[filtered[column].isin(value)]
else:
# Exact match
filtered = filtered[filtered[column] == value]
logger.info(f"Filtered DataFrame: {filtered.shape[0]} rows remaining")
return filtered
except Exception as e:
logger.error(f"Error filtering DataFrame: {e}")
raise
def get_column_unique_values(df: pd.DataFrame, column_name: str) -> List[Any]:
"""
Get unique values in a column
Args:
df: Input DataFrame
column_name: Name of column
Returns:
List of unique values
"""
try:
unique_vals = df[column_name].unique().tolist()
logger.info(f"Unique values in {column_name}: {len(unique_vals)}")
return unique_vals
except Exception as e:
logger.error(f"Error getting unique values: {e}")
raise
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