## What will you learn?

In this comprehensive guide, you will master the art of converting columns with ‘yes’/’no’ values into a numerical format (1s and 0s) using Python. By understanding the nuances of different methods, you’ll be equipped to handle such data transformations efficiently for various analytical purposes.

## Introduction to Problem and Solution

Dealing with categorical data like ‘yes’ or ‘no’ responses often requires converting them into a binary numeric format, typically 1s and 0s. However, this conversion process can sometimes yield unexpected results, such as all values becoming zeros. To address this challenge effectively, we will explore two main methods: utilizing conditional expressions within pandas DataFrame operations and employing the map method with a dictionary. Understanding these techniques will empower you to seamlessly convert textual categorical data into binary numeric representations without encountering unintended uniform outputs.

## Code

```
import pandas as pd
# Assuming df is your DataFrame and 'column_name' is the target column
# Method 1: Using .apply() with lambda function
df['column_name'] = df['column_name'].apply(lambda x: 1 if x == 'yes' else 0)
# Method 2: Using .map() with a dictionary
mapping_dict = {'yes': 1, 'no': 0}
df['column_name'] = df['column_name'].map(mapping_dict)
# Copyright PHD
```

## Explanation

**Understanding the Code**

**Lambda Function**: Utilizing .apply() enables the application of a lambda function across a DataFrame column. The lambda function contains a conditional expression that checks each value; returning 1 if it matches ‘yes’ and 0 otherwise.**Mapping Dictionary**: Employing .map() maps each element in the specified series based on a predefined dictionary. This dictionary explicitly defines how each category (‘yes’, ‘no’) should be translated into binary digits (1s and Os).

By choosing between these approaches based on your requirements for data manipulation within pandas DataFrames, you can accurately convert categorical text data (‘yes’, ‘no’) into binary numeric form (1s and Os).

#### How do I handle missing values during conversion?

To manage missing values, consider using .fillna() before applying conversions or incorporate conditions within your mapping logic to account for NaN values specifically.

#### Can these methods be applied on multiple columns simultaneously?

Yes! You can iterate over multiple columns using a loop structure where each iteration applies one of these methods onto each targeted column separately.

#### What if there are more categories than just yes/no?

For scenarios involving more than binary categories, consider utilizing pd.get_dummies(df) for one-hot encoding to convert categorical variables into indicator variables.

#### Is there an advantage between choosing .apply() vs .map()?

.apply() offers flexibility for complex functions while .map() is slightly faster for simple mappings based on existing dictionaries/lists.

#### How do I revert back from numeric representation to textual form (‘yes’, �no�)?

You can revert by using inverse mapping through reversing keys/values in the original dictionary passed through map again or by applying reversed conditionals via apply.

Converting categorical text data such as ‘yes/no’ responses into numerical form (binary digits) is crucial for preparing datasets for analytical models or machine learning applications. By mastering the techniques outlined here – including when to use .apply() versus .map() – practitioners can ensure accurate transformations leading to effective analytical outcomes from their efforts.