Series and DataFrame

In Pandas, Series and DataFrame are the two main data structures used for handling data.

They help you organize, analyze, and manipulate structured datasets efficiently.

First, import pandas:

import pandas as pd

1. Series

A Series is a one-dimensional labeled array.
It is similar to a single column in a table.

Creating a Series

data = pd.Series([10, 20, 30, 40])
print(data)

By default, pandas assigns index values starting from 0.

Creating a Series with Custom Index

data = pd.Series([10, 20, 30], index=["A", "B", "C"])
print(data)

Accessing Values

data["A"]
data[0]

Series from Dictionary

data = pd.Series({
    "Ali": 85,
    "Sara": 90,
    "Ahmed": 78
})
print(data)

2. DataFrame

A DataFrame is a two-dimensional data structure.
It is like a table with rows and columns.

Creating a DataFrame

data = {
    "Name": ["Ali", "Sara", "Ahmed"],
    "Age": [25, 28, 30],
    "Salary": [50000, 60000, 70000]
}df = pd.DataFrame(data)
print(df)

Viewing DataFrame Information

First 5 rows:

df.head()

Basic information:

df.info()

Statistical summary:

df.describe()

Selecting Data in DataFrame

Select a single column:

df["Name"]

Select multiple columns:

df[["Name", "Salary"]]

Select rows by index:

df.loc[0]

Filter data using condition:

df[df["Age"] > 26]

Key Differences Between Series and DataFrame

Series:

• One-dimensional
• Represents a single column
• Has index and values

DataFrame:

• Two-dimensional
• Contains multiple columns
• Each column is a Series

Why Series and DataFrame Matter

These structures allow you to:

Store structured data
Analyze real-world datasets
Perform filtering and grouping
Handle missing values
Prepare data for visualization and machine learning

Key Takeaway

Series and DataFrame are the foundation of data analysis in Pandas.
Understanding these two structures is essential for working with real-world datasets efficiently.