Pandas Introduction: Series & DataFrame Architecture

1D labeled Series vs 2D tabular DataFrames with explicit row and column indexes

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Pandas is the premier Python library for data manipulation and analysis, built directly on top of NumPy to provide fast, labeled tabular data structures.

Series vs DataFrame

Series: A 1D labeled array capable of holding any data type. It consists of an array of data paired with an explicit index array.

DataFrame: A 2D labeled tabular data structure with columns of potentially different types, resembling an Excel spreadsheet or SQL table.

Every column in a DataFrame is internally stored as a Pandas Series sharing a common index.

Creating DataFrames from Dictionaries

Try It Yourself »

import pandas as pd

# Create DataFrame from dictionary of lists
data = {
    "product": ["MacBook Pro", "Dell XPS", "ThinkPad X1"],
    "price": [1999.00, 1499.50, 1650.00],
    "in_stock": [True, True, False]
}

df = pd.DataFrame(data, index=["sku_101", "sku_102", "sku_103"])
print("Tabular DataFrame:
", df)
print("
DataFrame Info:")
print(f"Shape: {df.shape}, Columns: {list(df.columns)}")

DataFrames feature named columns and custom index labels.

Pro Tip

Use df.info() and df.describe() as the very first commands whenever loading any new dataset into Pandas.

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Test Yourself With Exercise

+15 XP

What is the relationship between a Pandas DataFrame and a Pandas Series?

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