DataFrame Architecture

Columnar Design

1. Structure

DataFrame is a dict-like container of Series:

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4.0, 5.0, 6.0],
    'C': ['x', 'y', 'z']
})

2. Columns as Series

col_a = df['A']
print(type(col_a))  # Series
print(col_a.dtype)  # int64

col_c = df['C']
print(col_c.dtype)  # object

3. Heterogeneous Types

Each column has its own dtype:

print(df.dtypes)
# A     int64
# B   float64
# C    object

Construction

1. From Dict

data = {
    'name': ['Alice', 'Bob'],
    'age': [25, 30],
    'salary': [50000, 60000]
}
df = pd.DataFrame(data)

2. From Lists

data = [
    [1, 4, 'x'],
    [2, 5, 'y'],
    [3, 6, 'z']
]
df = pd.DataFrame(data, columns=['A', 'B', 'C'])

3. From Records

df = pd.DataFrame([
    {'name': 'Alice', 'age': 25},
    {'name': 'Bob', 'age': 30}
])

Indexing

1. Column Selection

df['A']          # Single column (Series)
df[['A', 'B']]   # Multiple columns (DataFrame)

2. Row Selection

df.loc[0]        # By label
df.iloc[0]       # By position
df.loc[0:2]      # Slice by label

3. Boolean Indexing

df[df['age'] > 25]  # Filter rows

Operations

1. Column-wise

df['D'] = df['A'] + df['B']  # New column
df.drop('D', axis=1, inplace=True)  # Remove column

2. Row-wise

df.loc[3] = [4, 7, 'w']  # Add row
df = df.drop(3)  # Remove row

3. Aggregation

df.sum()      # Sum each column
df.mean()     # Mean each column
df.describe() # Summary statistics