Author: panda

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Pandas fillna: Complete Guide to Handling Missing Values

What is fillna?

The fillna() method is one of the most critical pandas functions for data cleaning. It replaces NaN (Not a Number) and missing values with specified values, methods, or strategies.

Why is this important?

  • Many pandas operations fail with missing values
  • Machine learning algorithms can’t handle NaN values
  • Data analysis becomes unreliable with incomplete data
  • fillna() is the primary solution for data imputation

Common use cases:

  • Fill missing ages with mean age
  • Fill missing values with previous observation (forward fill)
  • Fill missing values with next observation (backward fill)
  • Fill missing values with interpolated values (for time series)
  • Fill different columns with different values

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How to Write DataFrames to SQL Databases in Pandas

Writing DataFrames to SQL databases is one of the most practical skills for data engineers and analysts. Pandas makes this straightforward with the to_sql() method, which allows you to export data to various databases like SQLite, PostgreSQL, MySQL, and more. This guide covers everything you need to know about storing your data persistently.

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Pandas merge() vs concat(): Which Should You Use?

When combining DataFrames in Pandas, you have two primary options: merge() and concat(). While they both combine data, they work differently and serve different purposes. This guide explains when to use each method and provides practical examples to help you make the right choice for your data analysis tasks.

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Merge DataFrames on Multiple Columns in Pandas

Merging DataFrames on multiple columns is essential when working with real-world datasets. While merging on a single key is common, many scenarios require matching on multiple columns to ensure accurate combinations. This guide covers everything you need to know about merging on multiple columns in Pandas, from basic syntax to advanced techniques.

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Pandas groupby(): Complete Guide with Examples

The groupby() function is one of the most powerful and frequently used methods in Pandas. It allows you to split a DataFrame into groups based on one or more columns, apply operations to each group independently, and combine the results back together. This split-apply-combine workflow is essential for data analysis, aggregation, and summarization tasks.

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