Materi 08 · Tools & Methods

Python & Pandas untuk BA

Saat Excel mulai lambat di juta rows, saatnya naik kelas ke Python. Pandas = Excel on steroids. Plus jupyter notebook, matplotlib, seaborn untuk EDA professional.

⏱ 35 Menit🎯 Intermediate🐍 Python

1. Kenapa Python?

Excel Limit, Python Unlimited

Excel struggle > 100K rows. Python tangani 10M rows tanpa keringat. Plus reproducible, version-controllable, integrasi langsung dengan API/database/cloud. BA serius wajib up-skill ke Python di 2026.

2. Setup Environment

Anaconda / Miniconda: all-in-one Python + pandas + jupyter.
Google Colab: gratis, cloud, no install. Cocok untuk pemula.
Jupyter Notebook: interactive coding + markdown + plot di 1 doc.
VS Code + Python ext: kalau prefer IDE penuh.
pip install: pandas, numpy, matplotlib, seaborn, openpyxl.

3. PANDAS — DataFrame Fundamentals

Import & Load Data
import pandas as pd

import numpy as np

# Baca data dari berbagai source

df = pd.read_csv('sales.csv')

df = pd.read_excel('data.xlsx', sheet_name='Sheet1')

df = pd.read_sql(query, connection)

# Eksplorasi cepat

df.head(10)         # 10 row pertama

df.info()         # tipe data, null count

df.describe()     # statistik numerical

df.shape          # (rows, cols)

4. SELECT & FILTER

Indexing & Filtering
# Pilih kolom

df['name']

df[['name', 'email', 'country']]

# Filter row

df[df['amount'] > 1000]

df[(df['country'] == 'Indonesia') & (df['amount'] > 500)]

# Loc & iloc

df.loc[0:5, ['name', 'amount']]

df.iloc[0:5, 2:5]

5. GROUPBY — Aggregation

Group & Aggregate
# Total revenue per country

df.groupby('country')['amount'].sum()

# Multiple metric

df.groupby('country').agg({

  'amount': ['sum', 'mean', 'count'],

  'order_id': 'nunique'

})

# Multiple group columns

df.groupby(['country', 'month'])['amount'].sum().unstack()

6. MERGE — Join Tables

Pandas Merge / Join
# INNER JOIN

result = pd.merge(customers, orders, on='customer_id')

# LEFT JOIN

result = pd.merge(customers, orders, on='customer_id', how='left')

# Different column names

result = pd.merge(c, o, left_on='id', right_on='cust_id')

7. DATA CLEANING

Handle Missing & Duplicates
# Cek missing

df.isnull().sum()

# Drop / fill

df.dropna(subset=['email'])

df['age'].fillna(df['age'].median(), inplace=True)

# Drop duplicates

df.drop_duplicates(subset=['email'], keep='first')

# String operations

df['name'] = df['name'].str.strip().str.title()

df['email_lower'] = df['email'].str.lower()

8. APPLY & LAMBDA — Custom Logic

Custom Transformation
# Tagging segment

df['segment'] = df['amount'].apply(

  lambda x: 'High' if x > 1000 else 'Low'

)

# Function untuk multiple kolom

def classify(row):

  if row['amount'] > 5000 and row['frequency'] > 10:

    return 'VIP'

  return 'Regular'

df['tier'] = df.apply(classify, axis=1)

9. VISUALIZATION dengan Matplotlib & Seaborn

Quick Visualization
import matplotlib.pyplot as plt

import seaborn as sns

# Bar chart

df.groupby('country')['amount'].sum().plot(kind='bar')

# Line chart trend

df.groupby('month')['amount'].sum().plot()

# Heatmap correlation

sns.heatmap(df.corr(), annot=True, cmap='coolwarm')

# Distribution

sns.histplot(df['amount'], bins=30)

10. PROFESSIONAL EDA WORKFLOW

Step	Aksi	Function Pandas
1. Load	Import data	read_csv, read_excel, read_sql
2. Quick look	Lihat struktur	head(), info(), describe()
3. Clean	Handle missing/duplicates	isnull, dropna, fillna, drop_duplicates
4. Engineer	Buat feature baru	apply, lambda, str methods
5. Aggregate	Group & summarize	groupby, agg, pivot_table
6. Visualize	Plot insight	matplotlib, seaborn
7. Export	Share hasil	to_csv, to_excel, to_sql

📝 Tugas Praktik

Buka Google Colab. Install pandas (sudah preinstalled).
Download dataset Online Retail dari Kaggle. Load ke pandas.
Lakukan EDA lengkap: head, info, missing, duplicates, basic stats.
Hitung top 10 customer by revenue, AOV per country, monthly trend.
Buat 5 chart dengan matplotlib/seaborn untuk insight utama.
Export hasil ke Excel dengan multiple sheet (raw, summary, charts).

Rangkuman

Python + pandas = Excel pada level enterprise. Tangani jutaan row.
DataFrame = struktur data inti. Tabel di pandas = DataFrame.
groupby + agg = senjata utama untuk aggregation pivot-style.
merge() = JOIN versi pandas, mirip SQL.
Apply + lambda untuk custom transformation flexible.
EDA workflow 7-step: Load → Look → Clean → Engineer → Aggregate → Visualize → Export.