Working with datasets is one of the most important skills in Data Analytics. A dataset is a collection of structured information, usually stored in files like CSV, Excel, or databases.<\/p>\n\n\n\n
In Python, we commonly use the pandas library to handle datasets efficiently.<\/p>\n\n\n\n
A dataset is a structured collection of data organized in rows and columns.<\/p>\n\n\n\n
Rows represent records
Columns represent fields or features<\/p>\n\n\n\n
Example:<\/p>\n\n\n\n
Name | Age | Salary
Ali | 25 | 50000
Sara | 28 | 60000<\/p>\n\n\n\n
CSV (Comma Separated Values)
Excel (.xlsx)
JSON
SQL Databases<\/p>\n\n\n\n
CSV is the most commonly used format in analytics.<\/p>\n\n\n\n
First, install pandas if not installed:<\/p>\n\n\n\n
pip install pandas<\/pre>\n\n\n\nImport pandas and load a CSV file:<\/p>\n\n\n\n
import pandas as pddf = pd.read_csv(\"data.csv\")<\/pre>\n\n\n\nFor Excel files:<\/p>\n\n\n\n
df = pd.read_excel(\"data.xlsx\")<\/pre>\n\n\n\nViewing Data<\/h2>\n\n\n\n
Display first 5 rows:<\/p>\n\n\n\n
df.head()<\/pre>\n\n\n\nDisplay last 5 rows:<\/p>\n\n\n\n
df.tail()<\/pre>\n\n\n\nCheck shape (rows and columns):<\/p>\n\n\n\n
df.shape<\/pre>\n\n\n\nCheck column names:<\/p>\n\n\n\n
df.columns<\/pre>\n\n\n\nGet summary information:<\/p>\n\n\n\n
df.info()<\/pre>\n\n\n\nGet statistical summary:<\/p>\n\n\n\n
df.describe()<\/pre>\n\n\n\nSelecting Data<\/h2>\n\n\n\n
Select a single column:<\/p>\n\n\n\n
df[\"Age\"]<\/pre>\n\n\n\nSelect multiple columns:<\/p>\n\n\n\n
df[[\"Name\", \"Salary\"]]<\/pre>\n\n\n\nSelect rows using condition:<\/p>\n\n\n\n
df[df[\"Age\"] > 25]<\/pre>\n\n\n\nHandling Missing Data<\/h2>\n\n\n\n
Check missing values:<\/p>\n\n\n\n
df.isnull().sum()<\/pre>\n\n\n\nRemove missing values:<\/p>\n\n\n\n
df.dropna()<\/pre>\n\n\n\nFill missing values:<\/p>\n\n\n\n
df.fillna(0)<\/pre>\n\n\n\nSorting Data<\/h2>\n\n\n\n
Sort by a column:<\/p>\n\n\n\n
df.sort_values(\"Salary\")<\/pre>\n\n\n\nSort in descending order:<\/p>\n\n\n\n
df.sort_values(\"Salary\", ascending=False)<\/pre>\n\n\n\nSaving the Dataset<\/h2>\n\n\n\n
Save as CSV:<\/p>\n\n\n\n
df.to_csv(\"new_data.csv\", index=False)<\/pre>\n\n\n\nSave as Excel:<\/p>\n\n\n\n
df.to_excel(\"new_data.xlsx\", index=False)<\/pre>\n\n\n\nWhy Working with Datasets is Important<\/h2>\n\n\n\n
Data analysis starts with understanding and cleaning data.
Proper dataset handling helps you:<\/p>\n\n\n\nUnderstand data structure
Identify errors
Clean missing values
Filter useful information
Prepare data for visualization and modeling<\/p>\n\n\n\nKey Takeaway<\/h2>\n\n\n\n
Working with datasets means loading, exploring, cleaning, filtering, and saving data. Mastering these steps is essential for building a strong foundation in Data Analytics.<\/p>\n\n\n