Data Science — Python

Key libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

NumPy basics

arr = np.array([1, 2, 3, 4, 5])
print(arr.mean())
print(arr.std())
print(arr.reshape(5, 1))

Pandas basics

# Create DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Read CSV
df = pd.read_csv('data.csv')

# Select columns
df['A']
df[['A', 'B']]

# Filter
df[df['A'] > 1]

# Group by
df.groupby('category').mean()

Functions

def analyze_data(df):
    return {
        'shape': df.shape,
        'dtypes': df.dtypes,
        'missing': df.isnull().sum(),
        'describe': df.describe()
    }

List comprehensions

squares = [x**2 for x in range(10)]
even = [x for x in range(10) if x % 2 == 0]

Mini Practice

1. Load data with Pandas
2. Manipulate DataFrames
3. Create visualizations
4. Build analysis functions

Up Next

Continue with R - Statistical computing.