How to Create New Column Based on Condition in Pandas
In data analysis, it is often necessary to create new columns based on certain conditions. Pandas, being a powerful data manipulation library in Python, provides various methods to achieve this. In this article, we will discuss how to create new columns based on conditions in Pandas, focusing on the most commonly used techniques.
Firstly, let’s consider a simple example. Suppose we have a DataFrame containing information about students, including their names, ages, and grades. We want to create a new column that indicates whether the student is above average in terms of grades. To achieve this, we can use the `apply()` function along with a lambda function.
“`python
import pandas as pd
Create a sample DataFrame
data = {‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘David’],
‘Age’: [20, 22, 19, 21],
‘Grade’: [85, 90, 75, 95]}
df = pd.DataFrame(data)
Calculate the average grade
average_grade = df[‘Grade’].mean()
Create a new column based on the condition
df[‘Above Average’] = df[‘Grade’].apply(lambda x: ‘Yes’ if x > average_grade else ‘No’)
“`
In the above code, we first calculate the average grade using the `mean()` function. Then, we use the `apply()` function to apply a lambda function to each element in the ‘Grade’ column. The lambda function checks if the grade is greater than the average grade and assigns ‘Yes’ or ‘No’ accordingly.
Another method to create new columns based on conditions is by using boolean indexing. This approach is often more concise and readable, especially when dealing with complex conditions.
“`python
Create a new column based on the condition using boolean indexing
df[‘Above Average’] = df[‘Grade’] > average_grade
“`
In this example, we directly use boolean indexing to create the ‘Above Average’ column. The resulting column will contain `True` for students with grades above the average and `False` otherwise.
Additionally, we can also use the `loc` and `iloc` functions to create new columns based on conditions. These functions allow us to access and modify specific rows and columns in a DataFrame.
“`python
Create a new column based on the condition using loc function
df.loc[df[‘Grade’] > average_grade, ‘Above Average’] = ‘Yes’
df.loc[df[‘Grade’] <= average_grade, 'Above Average'] = 'No'
Alternatively, use the iloc function to create a new column
df.iloc[df['Grade'].idxmax():, 'Highest Grade'] = 'Yes'
```
In the first example, we use the `loc` function to assign values to the 'Above Average' column based on the condition. In the second example, we use the `iloc` function to create a new column named 'Highest Grade' and assign the value 'Yes' to the corresponding row.
In conclusion, creating new columns based on conditions in Pandas can be achieved using various methods, such as `apply()`, boolean indexing, and `loc`/`iloc` functions. These techniques provide flexibility and efficiency in data manipulation, making them invaluable tools for data analysts and scientists.
