Pandas

TL;DR

Library for processing and analyzing tabular data from sources such as CSV, Excel, and SQL databases.
Provides built-in handling for missing data (e.g., fillna, dropping rows).
Supports aggregation and summarization (e.g., groupby and sum) for extracting insights from datasets.

Definition

Pandas is a powerful and popular data manipulation library in Python used to process and analyze data in a variety of formats, including CSV, Excel, and SQL databases.

Explanation

Pandas offers an easy-to-use interface for loading, transforming, and summarizing data. It includes features for handling common real-world issues such as missing values, with methods to fill missing values or drop incomplete records. It also provides aggregation and summarization capabilities (for example, grouping data with groupby() and applying aggregations like sum()) that are useful when working with large datasets to extract totals, trends, or other summary statistics.

Examples

Handling missing data

import pandas as pd

# Load the student grades data

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

# Fill in missing values with 0

df = df.fillna(0)

# View the modified dataframe

df.head()

Output:

Student	Exam 1	Exam 2	Exam 3
Alice	89	92	95
Bob	75	0	80
Charlie	87	92	0
Dave	0	85	90

In this example, the fillna() function replaced missing grade values with 0.

Aggregation and summarization

import pandas as pd

# Load the sales data

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

# Group the data by product and calculate the total sales for each product

product_sales = df.groupby("product").sum()

# View the resulting dataframe

product_sales.head()