{"id":136,"date":"2026-03-02T15:14:52","date_gmt":"2026-03-02T10:14:52","guid":{"rendered":"https:\/\/gigz.pk\/python\/?post_type=lesson&p=136"},"modified":"2026-03-17T07:55:09","modified_gmt":"2026-03-17T02:55:09","slug":"series-and-dataframe","status":"publish","type":"lesson","link":"https:\/\/gigz.pk\/python\/lesson\/series-and-dataframe\/","title":{"rendered":"Series and DataFrame"},"content":{"rendered":"\n

In Pandas, Series<\/strong> and DataFrame<\/strong> are the two main data structures used for handling data.<\/p>\n\n\n\n

They help you organize, analyze, and manipulate structured datasets efficiently.<\/p>\n\n\n\n

First, import pandas:<\/p>\n\n\n\n

import pandas as pd<\/pre>\n\n\n\n
1. Series<\/h2>\n\n\n\n
A Series is a one-dimensional labeled array.
It is similar to a single column in a table.<\/p>\n\n\n\n
Creating a Series<\/h3>\n\n\n\n
data = pd.Series([10, 20, 30, 40])
print(data)<\/pre>\n\n\n\n
By default, pandas assigns index values starting from 0.<\/p>\n\n\n\n
Creating a Series with Custom Index<\/h3>\n\n\n\n
data = pd.Series([10, 20, 30], index=[\"A\", \"B\", \"C\"])
print(data)<\/pre>\n\n\n\n
Accessing Values<\/h3>\n\n\n\n
data[\"A\"]
data[0]<\/pre>\n\n\n\n
Series from Dictionary<\/h3>\n\n\n\n
data = pd.Series({
    \"Ali\": 85,
    \"Sara\": 90,
    \"Ahmed\": 78
})
print(data)<\/pre>\n\n\n\n
2. DataFrame<\/h2>\n\n\n\n
A DataFrame is a two-dimensional data structure.
It is like a table with rows and columns.<\/p>\n\n\n\n
Creating a DataFrame<\/h3>\n\n\n\n
data = {
    \"Name\": [\"Ali\", \"Sara\", \"Ahmed\"],
    \"Age\": [25, 28, 30],
    \"Salary\": [50000, 60000, 70000]
}df = pd.DataFrame(data)
print(df)<\/pre>\n\n\n\n
Viewing DataFrame Information<\/h3>\n\n\n\n
First 5 rows:<\/p>\n\n\n\n
df.head()<\/pre>\n\n\n\n
Basic information:<\/p>\n\n\n\n
df.info()<\/pre>\n\n\n\n
Statistical summary:<\/p>\n\n\n\n
df.describe()<\/pre>\n\n\n\n
Selecting Data in DataFrame<\/h2>\n\n\n\n
Select a single column:<\/p>\n\n\n\n
df[\"Name\"]<\/pre>\n\n\n\n
Select multiple columns:<\/p>\n\n\n\n
df[[\"Name\", \"Salary\"]]<\/pre>\n\n\n\n
Select rows by index:<\/p>\n\n\n\n
df.loc[0]<\/pre>\n\n\n\n
Filter data using condition:<\/p>\n\n\n\n
df[df[\"Age\"] > 26]<\/pre>\n\n\n\n
Key Differences Between Series and DataFrame<\/h2>\n\n\n\n
Series:<\/p>\n\n\n\n