Learn Pandas
Pandas is a fast, powerful, and flexible open-source Python library built for working with structured data.
What Is Pandas?
Pandas is an open-source Python library that provides high-performance, easy-to-use data structures and data analysis tools. Built on top of NumPy, it introduces two core objects - the Series and the DataFrame - that let you load, clean, transform, and analyze tabular data in just a few lines of code. It was created by Wes McKinney in 2008 and has since become the de facto standard for data manipulation in Python.
What Can You Do With Pandas?
With pandas you can read data from CSV, JSON, Excel, and SQL sources; filter and sort rows; handle missing values; group and aggregate data; merge multiple datasets together; and reshape data for analysis or visualization. Because it integrates tightly with libraries like NumPy, Matplotlib, and scikit-learn, pandas is usually the first stop in any Python data workflow.
- Clean and prepare messy, real-world data
- Explore datasets with summary statistics
- Read and write CSV, JSON, Excel, and SQL data
- Filter, sort, and group large tables of data
- Merge and reshape multiple datasets
- Handle missing or duplicate data automatically
A Pandas Series
import pandas as pd
data = [10, 20, 30]
series = pd.Series(data)
print(series)Series and DataFrame at a Glance
A Pandas DataFrame
import pandas as pd
data = {
"name": ["Ana", "Ben", "Cy"],
"age": [28, 34, 22]
}
df = pd.DataFrame(data)
print(df)A DataFrame Column Is a Series
import pandas as pd
df = pd.DataFrame({"name": ["Ana", "Ben"], "age": [28, 34]})
ages = df["age"]
print(type(ages))
print(ages)Exercise: Pandas Introduction
What is pandas primarily used for?
Frequently Asked Questions
- What is pandas used for?
- Loading, cleaning and analysing tabular data in Python. It reads CSV, Excel, JSON and SQL into a DataFrame, then filters, groups and aggregates it, covering most of what a spreadsheet does but reproducibly.
- What is the difference between a Series and a DataFrame?
- A Series is a single labelled column. A DataFrame is a table of Series sharing one index, so each column can hold a different type. Selecting one column from a DataFrame gives you a Series.
- Is pandas hard to learn?
- The basics take a few days if you know Python. The difficulty is that pandas usually offers several ways to do the same thing, and indexing with loc and iloc trips up most beginners early.
