Pandas is a powerful and versatile Python library built on top of NumPy. It provides high-performance data structures and data analysis tools, making it an essential tool for data scientists, analysts, and engineers working with structured data.
Core Data Structures
- Series: A one-dimensional labeled array capable of holding any data type (integers, strings, floating-point numbers, Python objects, etc.). It can be thought of as a single column in a spreadsheet.
- DataFrame: A two-dimensional labeled data structure with columns of potentially different types. It represents tabular data, similar to a spreadsheet or SQL table.
Key Features
- Data ingestion: Reads data from various file formats (CSV, Excel, JSON, SQL databases).
- Data manipulation: Provides functions for cleaning, transforming, and reshaping data.
- Data analysis: Offers tools for statistical analysis, time series analysis, and exploratory data analysis.
- Data visualization: Integrates with plotting libraries like Matplotlib for creating visualizations.
Example
Python
import pandas as pd
# Create a DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35]}
df = pd.DataFrame(data)
# Access and manipulate data
print(df['Age'])
df['Age'] = df['Age'] + 5
Why Pandas?
- Ease of use: Provides a high-level interface for data manipulation.
- Performance: Built on NumPy, offers efficient operations on large datasets.
- Flexibility: Handles various data types and structures.
- Integration: Works seamlessly with other Python libraries.
Pandas has become a standard tool for data scientists and analysts due to its versatility and efficiency in handling structured data.
Introduction to Pandas
What are the core data structures in Pandas?
Series and DataFrame.
How does Pandas relate to NumPy?
Pandas is built on top of NumPy, utilizing its array structures for efficient computations.
What is a Pandas Series?
A one-dimensional labeled array capable of holding any data type.
How do I create a Pandas Series?
From a list, NumPy array, or dictionary.
Can a Pandas Series have multiple data types?
Yes, but it’s generally recommended to have a homogeneous data type for performance reasons.
What is a Pandas DataFrame?
A two-dimensional labeled data structure with columns of potentially different types.
How do I handle missing data in Pandas?
Use fillna() or dropna() methods.
How do I merge or join DataFrames?
Use merge() or join() methods
How do I group data in Pandas?
Use the groupby() method.