Pandas for Data Analysis

Pandas is the most important Python library for data manipulation and analysis. Built on top of NumPy, it provides two primary data structures — Series (1D) and DataFrame (2D) — that make working with structured data intuitive and efficient. The name comes from "Panel Data", a term from econometrics.

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Series

A Series is a one-dimensional labelled array:

import pandas as pd
import numpy as np

# Create from a list
s = pd.Series([10, 20, 30, 40, 50])
print(s)
# 0    10
# 1    20
# 2    30
# 3    40
# 4    50
# dtype: int64

# Create with custom index
s = pd.Series([10, 20, 30], index=['a', 'b', 'c'])
print(s['b'])  # 20

# Create from a dictionary
s = pd.Series({'London': 9000000, 'Paris': 2200000, 'Berlin': 3600000})
print(s)

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DataFrame

A DataFrame is a two-dimensional labelled table — the workhorse of Pandas: