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import numpy as np
import re
# extract numbers
def extract_number(x):
try:
x = str(x).strip()
# invalid values
if x in ["", "|", "nan", "None"]:
return np.nan
# remove common symbols
x = (
x.replace(",", "")
.replace("₹", "")
.replace("$", "")
.replace("%", "")
)
# range values
if "-" in x:
nums = x.split("-")
if len(nums) == 2:
try:
return (
float(nums[0]) +
float(nums[1])
) / 2
except:
pass
# first numeric pattern
num = re.findall(
r"\d+\.?\d*",
x
)
if num:
return float(num[0])
return np.nan
except:
return np.nan
# numeric detection
def is_numeric_like(series, threshold=0.70):
try:
converted = (
series.dropna()
.astype(str)
.apply(extract_number)
)
success_rate = (
converted.notnull().mean()
)
return success_rate >= threshold
except:
return False
# data detection
def looks_like_date(series):
try:
sample = (
series.dropna()
.astype(str)
.head(20)
)
keywords = [
"-", "/", ":",
"jan", "feb", "mar",
"apr", "may", "jun",
"jul", "aug", "sep",
"oct", "nov", "dec",
"202", "201", "200"
]
return any(
any(
k in val.lower()
for k in keywords
)
for val in sample
)
except:
return False
# date check
def is_datetime_like(series, threshold=0.80):
try:
parsed = pd.to_datetime(
series,
errors="coerce"
)
success_rate = (
parsed.notnull().mean()
)
return success_rate >= threshold
except:
return False
### main function
def preprocess(df, target):
print("\n--- SMART PREPROCESSING ---")
df = df.copy()
report = {
"converted_numeric": [],
"converted_datetime": [],
"dropped_columns": [],
"warnings": [],
"high_cardinality": [],
"outliers": {},
"recommendations": []
}
# remove duplicate
dup_count = df.duplicated().sum()
if dup_count > 0:
print(
f"Removing duplicate rows: "
f"{dup_count}"
)
df = df.drop_duplicates()
# comvert date time
for col in list(df.columns):
if df[col].dtype == "object":
if (
df[col].nunique() < 50
and looks_like_date(df[col])
and is_datetime_like(df[col])
):
print(
f"Converting '{col}' "
f"to datetime"
)
df[col] = pd.to_datetime(
df[col],
errors="coerce"
)
report[
"converted_datetime"
].append(col)
# numeric conversion
for col in list(df.columns):
if df[col].dtype == "object":
if is_numeric_like(df[col]):
print(
f"Converting '{col}' "
f"to numeric"
)
df[col] = (
df[col]
.astype(str)
.apply(extract_number)
)
report[
"converted_numeric"
].append(col)
# remove invalid target rows
if target in df.columns:
before = len(df)
df = df.dropna(subset=[target])
removed = before - len(df)
if removed > 0:
print(
f"Removed {removed} rows "
f"with invalid target values"
)
# drop id columns
keywords = [
"id",
"index",
"code",
"serial",
"number"
]
id_cols = [
col for col in df.columns
if (
df[col].nunique(dropna=False)
== len(df)
)
and any(
k in col.lower()
for k in keywords
)
and col != target
]
if id_cols:
print(
"Dropping ID-like columns:",
id_cols
)
df = df.drop(columns=id_cols)
report[
"dropped_columns"
].extend(id_cols)
# drop constant column
const_cols = [
col for col in df.columns
if (
df[col]
.nunique(dropna=False)
<= 1
)
]
if const_cols:
print(
"Dropping constant columns:",
const_cols
)
df = df.drop(columns=const_cols)
report[
"dropped_columns"
].extend(const_cols)
# handle nulls
for col in list(df.columns):
if col == target:
continue
null_pct = (
df[col]
.isnull()
.mean() * 100
)
# too many nulls
if null_pct > 40:
print(
f"Dropping '{col}' "
f"(too many nulls: "
f"{null_pct:.2f}%)"
)
df = df.drop(columns=[col])
report[
"dropped_columns"
].append(col)
continue
# numerical
if pd.api.types.is_numeric_dtype(df[col]):
if null_pct > 0:
if abs(df[col].skew()) > 1:
df[col] = (
df[col]
.fillna(
df[col].median()
)
)
else:
df[col] = (
df[col]
.fillna(
df[col].mean()
)
)
# categorical
else:
if null_pct > 0:
if not df[col].mode().empty:
df[col] = (
df[col]
.fillna(
df[col].mode()[0]
)
)
# high card
for col in df.select_dtypes(
include="object"
):
unique_vals = df[col].nunique()
if unique_vals > 50:
print(
f"High cardinality "
f"column: '{col}' "
f"({unique_vals} unique)"
)
report[
"high_cardinality"
].append(col)
# outlier detection
print("\nOutlier Detections:")
for col in df.select_dtypes(
include=["int64", "float64"]
):
if df[col].nunique() <= 10:
continue
Q1 = df[col].quantile(0.25)
Q3 = df[col].quantile(0.75)
IQR = Q3 - Q1
if IQR == 0:
continue
lower = Q1 - 1.5 * IQR
upper = Q3 + 1.5 * IQR
count = (
(
(df[col] < lower)
|
(df[col] > upper)
)
).sum()
ratio = count / len(df)
if ratio > 0.05:
print(
f"- {col}: "
f"{count} outliers "
f"({ratio:.1%}) "
f"--> high"
)
elif ratio > 0:
print(
f"- {col}: "
f"{count} outliers "
f"({ratio:.1%})"
)
report["outliers"][col] = int(count)
#### final summary
num_cols = df.select_dtypes(
include=["int64", "float64"]
).columns
cat_cols = df.select_dtypes(
include="object"
).columns
date_cols = df.select_dtypes(
include="datetime64[ns]"
).columns
print("\nFinal Data Summary:")
print(f"- Rows: {df.shape[0]}")
print(f"- Columns: {df.shape[1]}")
print(f"- Numerical columns: {len(num_cols)}")
print(f"- Categorical columns: {len(cat_cols)}")
print(f"- Datetime columns: {len(date_cols)}")
return df
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