Title: 10 Python Code Snippets That Solve Tasks Efficiently

Python Pandas Efficiency for Data Manipulation

Pandas, a go-to library for data manipulation in Python, offers an array of functionalities that empower data scientists and analysts. In this article, we’ll explore 10 Pandas code snippets designed to efficiently tackle specific tasks commonly encountered in data analysis. Each code snippet is accompanied by a practical example to showcase its effectiveness.

Section 1: Introduction to the Example Dataset

import pandas as pd

Creating a basic example dataset

data = { ‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘David’, ‘Emily’], ‘Age’: [25, 30, 22, 35, 28], ‘Salary’: [50000, 60000, 45000, 70000, 55000], ‘Department’: [‘HR’, ‘Finance’, ‘IT’, ‘Marketing’, ‘HR’] }

df = pd.DataFrame(data) print(df)

Section 2: 10 Efficient Pandas Code Snippets 2.1 Remove Rows with Missing Values

Remove rows with missing values

df_cleaned = df.dropna() print(df_cleaned)

2.2 Select Columns Based on Data Types

Select columns based on data types

numeric_columns = df.select_dtypes(include=[‘int’, ‘float’]).columns print(df[numeric_columns])

2.3 Filter Rows Based on Multiple Conditions

Filter rows based on multiple conditions

filtered_df = df[(df[‘Age’] > 25) & (df[‘Department’] == ‘HR’)] print(filtered_df)

2.4 Convert String Column to DateTime

Convert string column to DateTime

df[‘JoinDate’] = pd.to_datetime(df[‘JoinDate’], format=’%Y-%m-%d’)

2.5 Calculate Row-wise Sum

Calculate row-wise sum

df[‘Total’] = df.iloc[:, 1:].sum(axis=1)

2.6 Merge DataFrames on a Specific Column

Merge DataFrames on a specific column

other_data = { ‘Department’: [‘HR’, ‘Finance’, ‘IT’], ‘Location’: [‘City1’, ‘City2’, ‘City3’] }

other_df = pd.DataFrame(other_data) merged_df = pd.merge(