Introduction to Data Wrangling - Data Wrangling And Preprocessing

This topic will cover the importance of data wrangling in the data preprocessing pipeline, including understanding messy data, identifying data quality issues, and the role of data wrangling in preparing data for analysis.

Introduction to Data Wrangling
The essential first step in turning raw data into actionable insights.

1. What Is Data Wrangling?

Term	Meaning
Data Wrangling (also called data munging, data cleaning, or data preprocessing)	The process of transforming raw, messy, or unstructured data into a tidy, analysis‑ready format.
Goal	Reduce errors, handle inconsistencies, and create a reproducible dataset that can be explored, modeled, and visualized with confidence.

2. Why It Matters

Improves model performance – Clean data leads to more accurate predictions.
Saves time – Early detection of problems prevents costly re‑work later.
Ensures reproducibility – Documented steps make analyses transparent and repeatable.
Facilitates collaboration – Well‑structured data is easier for teammates to understand and use.

3. Typical Workflow

Acquire Data – Load files, query databases, or pull from APIs.
Inspect & Profile – Get a quick sense of size, types, missingness, and distribution.
Clean – Handle missing values, remove duplicates, correct typos, standardize formats.
Transform – Reshape, aggregate, create derived variables, encode categories.
Validate – Verify that transformations preserve meaning and meet business rules.
Export – Save the tidy dataset in a format suited for downstream analysis (CSV, Parquet, database table, etc.).

4. Core Tasks & Common Techniques

Task	Typical Operations	Tools / Functions (Python)
Missing Data	- Identify (`isnull`) - Impute (mean, median, mode, K‑NN) - Flag or drop rows/columns	`pandas.isna()`, `SimpleImputer`, `fillna()`
Duplicates	- Detect (`duplicated`) - Remove or consolidate	`drop_duplicates()`
Data Types	- Cast (`astype`) - Parse dates (`to_datetime`) - Convert categorical strings to `category`	`astype()`, `pd.to_datetime()`
String Cleaning	- Trim whitespace, lower‑case - Regex replace, extract patterns	`str.strip()`, `str.replace()`, `str.extract()`
Outliers	- Visual inspection (boxplot) - Statistical rules (IQR, Z‑score) - Cap or remove	`np.percentile()`, `scipy.stats.zscore`
Reshaping	- Pivot / unpivot - Melt, stack, wide ↔ long	`pivot()`, `melt()`, `stack()`
Feature Engineering	- Create ratios, flags, rolling windows - Encode categorical variables (one‑hot, label)	`pd.get_dummies()`, `OrdinalEncoder`
Joining Data	- Merge tables on keys, handle many‑to‑many - Concatenate datasets	`merge()`, `concat()`

5. Practical Example (Python)

import pandas as pd
import numpy as np
from sklearn.impute import SimpleImputer

# 1️⃣ Load raw data
df = pd.read_csv('sales_raw.csv')

# 2️⃣ Profile
print(df.info())
print(df.describe(include='all'))

# 3️⃣ Clean

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Introduction to Data Wrangling

Data Cleaning Techniques

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Data Transformation Methods

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Data Integration and Aggregation

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Handling Time Series Data

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Exploratory Data Analysis (EDA)

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Dimensionality Reduction Techniques

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Handling Imbalanced Data

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