Python Pandas Basics

Series and DataFrame

pandas has two core data structures. A Series is a single labelled column of data, like a list with an index attached. A DataFrame is a full table made of multiple Series, with rows and named columns, similar to an Excel sheet or SQL table.

pip install pandas

import pandas as pd

ages = pd.Series([21, 24, 19], name="age")
print(ages)

data = {"name": ["Aarav", "Diya", "Kabir"], "age": [21, 24, 19]}
df = pd.DataFrame(data)
print(df)

Reading a CSV File

Real-world data usually comes from CSV files. pandas read_csv() loads a file straight into a DataFrame in one line, ready for exploration.

import pandas as pd

df = pd.read_csv("students.csv")
print(df.head())
print(df.info())

head() shows the first five rows by default, useful for a quick peek at your data, while info() prints column names, data types, and how many non-missing values each column has — a great first check on any new dataset.

Selecting Columns and Filtering Rows

You select a single column using square brackets with the column name, which returns a Series. To filter rows based on a condition, place a boolean expression inside the brackets — pandas keeps only the rows where it evaluates to True.

import pandas as pd

df = pd.read_csv("students.csv")

names = df["name"]
print(names)

adults = df[df["age"] >= 21]
print(adults)