Modul 2: NumPy & Pandas

NumPy = array multidimensi super cepat (di bawah kapnya C). Operasi vektor menghindari loop manual: arr * 2 memproses jutaan elemen sekaligus. Statistik dasar built-in: mean, std, max.

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr * 2)          # [ 2  4  6  8 10]
print(arr.mean())       # 3.0
m = np.array([[1, 2], [3, 4]])
print(m.shape)          # (2, 2)

Output:
[ 2  4  6  8 10]
3.0
(2, 2)

Pandas DataFrame = spreadsheet dalam kode. read_csv() membaca file data, df.head() mengintip, df[df['gaji'] > 5000000] memfilter, df.groupby('dept')['gaji'].mean() merangkum. Ini 80% pekerjaan sehari-hari analis data.

import pandas as pd
df = pd.DataFrame({
    "nama": ["Budi", "Sari", "Agus"],
    "dept": ["IT", "HR", "IT"],
    "gaji": [8000000, 6000000, 9500000],
})
print(df[df["gaji"] > 7000000][["nama"]])
print(df.groupby("dept")["gaji"].mean())

Output:
    nama
0   Budi
2   Agus
dept
HR    6000000.0
IT    8750000.0
Name: gaji, dtype: float64

Cleaning data: df.isna().sum() menghitung nilai kosong; df.fillna(0) / df.dropna() menanganinya; df['tanggal'] = pd.to_datetime(...) membetulkan tipe; df.drop_duplicates() membuang duplikat. Aturan emas: selalu cek dulu dengan df.info() dan df.describe().