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from sklearn.impute import SimpleImputer
import pandas as pd
import numpy as np
import json
def data_quality(df: pd.DataFrame):
print("Missing values before handling:")
print(df.isnull().sum())
print("Duplicate rows before handling:")
print(int(df.duplicated().sum()))
df.drop_duplicates(inplace=True)
print("Duplicate rows after handling:")
print(int(df.duplicated().sum()))
return df
def standardize_data_types(df: pd.DataFrame) -> pd.DataFrame:
# Convert string-based dates to datetime, but ignore boolean values
for col in df.columns:
if df[col].dtype == 'object' and not df[col].isin([True, False]).all():
try:
df[col] = pd.to_datetime(df[col], errors='coerce') # Invalid values become NaT
except Exception as e:
print(f"Skipping column {col}: {e}")
# Convert numeric strings to actual numbers
for col in df.select_dtypes(include=['object']).columns:
if df[col].str.replace('.', '', 1).str.isnumeric().all():
df[col] = pd.to_numeric(df[col])
return df
def handle_missing_data(df: pd.DataFrame) -> pd.DataFrame:
print("Before Imputation (NA Counts):")
print(df.isnull().sum())
numeric_col = df.select_dtypes(include=['number']).columns
if not numeric_col.empty:
num_imputer = SimpleImputer(strategy='median')
df[numeric_col] = num_imputer.fit_transform(df[numeric_col])
categorical_col = df.select_dtypes(include=['object', 'category']).columns
if not categorical_col.empty:
cat_imputer = SimpleImputer(strategy='most_frequent')
df[categorical_col] = cat_imputer.fit_transform(df[categorical_col])
print("After Imputation (NA Counts):")
print(df.isnull().sum())
return df
def handle_outliers(df: pd.DataFrame) -> pd.DataFrame:
numeric_col = df.select_dtypes(include=['number','int64', 'float64']).columns
if not numeric_col.empty:
for col in numeric_col:
Q1 = df[col].quantile(0.25)
Q3 = df[col].quantile(0.75)
IQR = Q3 - Q1
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
df[col] = df[col].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)
return df
def generate_final_report(df: pd.DataFrame, file_path: str):
with open(file_path, "w") as file:
file.write("FINAL DATA PREPROCESSING REPORT\n")
file.write("=" * 50 + "\n\n")
file.write("Missing Values (After Preprocessing):\n")
missing_values = df.isnull().sum()
for col, count in missing_values.items():
file.write(f"{col}: {count} missing values\n")
file.write("\nDuplicate Rows (After Preprocessing):\n")
file.write(f"Total Duplicate Rows: {df.duplicated().sum()}\n\n")
file.write("Preprocessing Completed Successfully!\n")
def save_cleaned_data(df: pd.DataFrame, file_path: str):
df.to_csv(file_path, index=False)
return file_path
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