import pandas as pddf = pd.read_excel('data.xlsx')df.groupby('region').sum()model.fit(X_train, y_train)import numpy as npplt.plot(forecast)df.dropna(inplace=True)from sklearn import metricsprint(df.describe())np.mean(returns) * 252

Data Engineering & Data Cleaning

Our data engineering and data cleaning services remove duplicates, standardize records and organize reporting pipelines. Explore data-quality rules, data dictionaries and lakehouse pipeline examples.

Examples

Data cleaning

Removing duplicates, fixing dates and filling missing values in customer lists.

Data dictionary

Explaining every column so anyone in the team understands the data.

Lakehouse pipeline

Raw → cleaned → business-ready layers (bronze, silver, gold).

How we do it in Python

df = df.drop_duplicates()
df["date"] = pd.to_datetime(df["date"])
df["city"] = df["city"].str.strip().str.title()
df.to_excel("clean.xlsx", index=False)

Excel sheets

Data Engineering Model

Pipeline design and data quality rules in one workbook.

Executive SummaryPipeline_ArchitectureData_Quality_Rules

Download Excel

Clean Data Master Workbook

Cleaned customer data, scenario analysis and data dictionary.

Executive SummaryCustomer Acquisition DataSales Data DictionaryData Science Challenge Q&A