Pandas Conditional Creation of a Series/Dataframe Column
In this tutorial, we will learn how to add a new column to a Pandas dataframe based on certain conditions.
np.where()
Solution 1: Using If we have only two choices to select from, we can use np.where()
to create a new column conditionally. The syntax is as follows:
df['new_column'] = np.where(condition, value_if_condition_true, value_if_condition_false)
Suppose we have a DataFrame that contains information about the sales of different products in different regions. We want to add a new column called "Sale Status" that is set to "High" if the sales are greater than 1000, otherwise "Low". We can use np.where()
to create the new column "Sale Status" based on the sales values.
import pandas as pd
import numpy as np
df = pd.DataFrame({'Product': ['A', 'B', 'C', 'D'],
'Region': ['North', 'South', 'East', 'West'],
'Sales': [1200, 400, 900, 1100]})
df['Sale Status'] = np.where(df['Sales'] > 1000, 'High', 'Low')
print(df)
Output:
Product Region Sales Sale Status
0 A North 1200 High
1 B South 400 Low
2 C East 900 Low
3 D West 1100 High
If we have more than two conditions to select from, we can use np.select()
.
Here's an example where we have a DataFrame of employee information and we want to create a new column indicating their job level based on their salary:
import pandas as pd
import numpy as np
df = pd.DataFrame({
'employee': ['Alice', 'Bob', 'Charlie', 'Dave', 'Eve'],
'salary': [50000, 70000, 90000, 110000, 130000]
})
# Create new column indicating job level
conditions = [
df['salary'] < 60000,
(df['salary'] >= 60000) & (df['salary'] < 80000),
(df['salary'] >= 80000) & (df['salary'] < 100000),
df['salary'] >= 100000
]
choices = ['Junior', 'Mid-level', 'Senior', 'Executive']
df['job_level'] = np.select(conditions, choices, default='Unknown')
print(df)
In this example, we create four conditions based on salary ranges and assign a corresponding job level using np.select()
. The default parameter is set to 'Unknown' so that if none of the conditions match, the job level will be 'Unknown'.
This code will output the following DataFrame:
employee salary job_level
0 Alice 50000 Junior
1 Bob 70000 Mid-level
2 Charlie 90000 Senior
3 Dave 110000 Executive
4 Eve 130000 Executive
Solution 2: Using List Comprehension
List comprehension is another way to create another column conditionally. If we are working with object dtypes in columns, like in our example, list comprehensions typically outperform most other methods.
Here's how we can use list comprehension to create the Sale Status column in a previous example:
import pandas as pd
import numpy as np
df = pd.DataFrame({'Product': ['A', 'B', 'C', 'D'],
'Region': ['North', 'South', 'East', 'West'],
'Sales': [1200, 400, 900, 1100]})
df['Sale Status'] = ['High' if x > 1000 else 'Low' for x in df['Sales']]
print(df)
Output:
Product Region Sales Sale Status
0 A North 1200 High
1 B South 400 Low
2 C East 900 Low
3 D West 1100 High
When to use each solution
When it comes to adding a conditional column to a pandas DataFrame, there are multiple ways to achieve the desired result, such as using np.where()
or list comprehension. However, which one you choose will depend on the specific requirements of your task and the size of your data.
np.where()
is a NumPy function that allows you to create a new column based on a condition. It is very fast and efficient, especially for large datasets, as it uses vectorization. This means that the operation is performed on an entire array rather than on individual elements.
On the other hand, list comprehension is another way to create a conditional column in pandas. It is a more flexible method than np.where()
since it can handle more complex conditions and operations. List comprehension is especially useful when working with columns that have object dtype, like in the example provided in the question. It is also generally faster than other methods for small datasets. However, it can become slower as the size of the data increases.
Therefore, if you have a large dataset, and your condition is simple, such as in the example provided in the question, np.where()
is likely to be the most efficient method to use. However, if you have a smaller dataset or a more complex condition, then list comprehension might be a better option to use. Ultimately, the best approach will depend on the specific requirements of your task and the characteristics of your data.
Conclusion
In conclusion, adding a conditional column to a Pandas dataframe can be achieved using various methods such as np.where()
and list comprehension. np.where()
is useful when there are only a few conditions to be met, while list comprehension is a good option when dealing with object data types in the columns. Understanding the pros and cons of these methods can help you choose the most efficient and effective solution for your specific use case. With this knowledge, you can easily add conditional columns to your Pandas dataframes and make data manipulation easier and more efficient.