Introduction to Pandas: Series and DataFrames

Pandas is the standard Python library for working with structured, tabular data — think spreadsheets, CSV files, and SQL query results. It is built directly on top of NumPy, and its two core data structures, Series and DataFrame, are what make real-world data analysis in Python practical.

Installing and Importing Pandas

# pip install pandas

import pandas as pd   # "pd" is the universal, near-mandatory alias

The Series — A One-Dimensional Labeled Array

A Series is similar to a NumPy array, but every value also has an associated label, called its index — making it behave somewhat like a hybrid between a list and a dictionary.

import pandas as pd

scores = pd.Series([85, 90, 78, 92])
print(scores)
# 0    85
# 1    90
# 2    78
# 3    92
# dtype: int64

Series With a Custom Index

scores = pd.Series([85, 90, 78], index=["Math", "Science", "English"])
print(scores)
# Math       85
# Science    90
# English    78
# dtype: int64

print(scores["Science"])   # 90 — access by label, just like a dictionary key

The DataFrame — A Two-Dimensional Labeled Table

A DataFrame is the core pandas structure — a table with labeled rows and columns, where each column is internally a Series. This maps almost directly onto a spreadsheet or a SQL table.

data = {
    "name": ["Vishwas", "Priya", "Arjun"],
    "age": [22, 21, 23],
    "city": ["Bengaluru", "Mumbai", "Chennai"]
}

df = pd.DataFrame(data)
print(df)
#       name  age       city
# 0  Vishwas   22  Bengaluru
# 1    Priya   21     Mumbai
# 2    Arjun   23    Chennai

Creating a DataFrame From a List of Lists

rows = [
    ["Vishwas", 22, "Bengaluru"],
    ["Priya", 21, "Mumbai"]
]
df = pd.DataFrame(rows, columns=["name", "age", "city"])
print(df)

Reading Data From a CSV File

df = pd.read_csv("students.csv")
print(df.head())    # shows the first 5 rows by default
print(df.tail(3))   # shows the LAST 3 rows

read_csv() is one of the single most-used functions in all of pandas — real-world data almost always starts life as a CSV, Excel file, or SQL query result before becoming a DataFrame.

Inspecting a DataFrame

print(df.shape)       # (3, 3) — 3 rows, 3 columns
print(df.columns)     # Index(['name', 'age', 'city'], dtype='object')
print(df.describe())  # statistics for the 'age' column, since it's numeric

Accessing a Column

print(df["age"])      # returns a Series — the entire "age" column
print(df.age)          # same result, dot notation (only works if the name has no spaces)
print(df[["name", "age"]])   # returns a DataFrame with just these two columns

Series vs DataFrame — At a Glance

Interview tip: "What is the relationship between a Series and a DataFrame?" is asked frequently — the precise answer is that a DataFrame is essentially a collection of Series objects sharing the same index, with each individual column of a DataFrame technically being a Series in its own right.