Pandas Data Manipulation
Learn powerful data manipulation and analysis with Pandas
60 min•By Priygop Team•Last updated: Feb 2026
What is Pandas?
Pandas is a powerful data manipulation and analysis library for Python. It provides data structures for efficiently storing and manipulating large datasets, with tools for reading and writing data in various formats.
Key Data Structures
- Series: 1-dimensional labeled array
- DataFrame: 2-dimensional labeled data structure
- Panel: 3-dimensional labeled data structure
Creating DataFrames
Example
import pandas as pd
import numpy as np
# Create DataFrame from dictionary
data = {
'Name': ['Alice', 'Bob', 'Charlie', 'Diana'],
'Age': [25, 30, 35, 28],
'City': ['NYC', 'LA', 'Chicago', 'Boston'],
'Salary': [50000, 60000, 70000, 55000]
}
df = pd.DataFrame(data)
# Create DataFrame from list of lists
data_list = [
['Alice', 25, 'NYC', 50000],
['Bob', 30, 'LA', 60000],
['Charlie', 35, 'Chicago', 70000]
]
df2 = pd.DataFrame(data_list, columns=['Name', 'Age', 'City', 'Salary'])
print(df.head())Data Selection and Filtering
Example
# Select columns
print(df['Name'])
print(df[['Name', 'Age']])
# Filter data
young_people = df[df['Age'] < 30]
high_salary = df[df['Salary'] > 60000]
# Multiple conditions
filtered = df[(df['Age'] > 25) & (df['Salary'] > 55000)]
# Sort data
sorted_df = df.sort_values('Age', ascending=False)