5 Best Ways to Write a Program in Python to Find the Minimum Age, Employee ID, and Salary in a DataFrame

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πŸ’‘ Problem Formulation: When working with employee data in a DataFrame, it’s often necessary to pinpoint the youngest employee, or to find the entry with the lowest salary or employee ID. This task is about fetching these specific entries given a dataset structured in a DataFrame format. For example, with a DataFrame containing columns ‘Age’, ‘Employee_ID’, and ‘Salary’, our desired output would be the minimum value from each of these columns.

Method 1: Using DataFrame min() Method

In this method, we employ the min() method provided by the pandas DataFrame that directly computes the minimum value for each column. It is straightforward and designed to be used on DataFrame objects, making it the go-to solution for such tasks.

Here’s an example:

import pandas as pd

# Sample DataFrame
data = {'Employee_ID': [101, 102, 103],
        'Age': [25, 22, 30],
        'Salary': [50000, 45000, 60000]}
df = pd.DataFrame(data)

# Find the minimum values
min_age = df['Age'].min()
min_id = df['Employee_ID'].min()
min_salary = df['Salary'].min()

print('Minimum Age:', min_age)
print('Minimum Employee ID:', min_id)
print('Minimum Salary:', min_salary)

Output:

Minimum Age: 22
Minimum Employee ID: 101
Minimum Salary: 45000

This code snippet first constructs a pandas DataFrame by providing a dictionary of lists, each list corresponding to a column. Then, it retrieves the minimum value for each column by applying the DataFrame’s min() method.

Method 2: Using sort_values() Method

This method involves sorting the DataFrame by the desired column and then selecting the top row, thereby getting the minimum value. It serves as an indirect approach to finding the minimum through sorting and is particularly useful when you need the entire row of data, not just the minimum value.

Here’s an example:

import pandas as pd

# Sample DataFrame
data = {'Employee_ID': [101, 102, 103],
        'Age': [25, 22, 30],
        'Salary': [50000, 45000, 60000]}
df = pd.DataFrame(data)

# Sort by 'Age' and select the first row
sorted_df = df.sort_values('Age')
min_age_row = sorted_df.iloc[0]

print(min_age_row)

Output:

Employee_ID    102
Age             22
Salary       45000
Name: 1, dtype: int64

The code sorts the DataFrame based on the ‘Age’ column and then uses iloc[0] to select the first row, which, after sorting, will contain the minimum age.

Method 3: Using idxmin() Method

With this approach, we make use of the idxmin() function to retrieve the index of the minimum value per column. Armed with the index, we can then ascertain the corresponding values, including the employee ID and salary of the youngest employee in the dataset.

Here’s an example:

import pandas as pd

# Sample DataFrame
data = {'Employee_ID': [101, 102, 103],
        'Age': [25, 22, 30],
        'Salary': [50000, 45000, 60000]}
df = pd.DataFrame(data)

# Get the index of minimum values
index_min_age = df['Age'].idxmin()
min_age_row = df.loc[index_min_age]

print(min_age_row)

Output:

Employee_ID    102
Age             22
Salary       45000
Name: 1, dtype: int64

By calling idxmin() on the ‘Age’ column, this snippet retrieves the index of the minimum value and then uses this index to select the entire row from the DataFrame.

Method 4: Using agg() Method

The agg() method allows us to apply different aggregation functions to each column of the DataFrame. This is a flexible method for simultaneously applying multiple custom operations, making it suitable for more complex data aggregation tasks.

Here’s an example:

import pandas as pd

# Sample DataFrame
data = {'Employee_ID': [101, 102, 103],
        'Age': [25, 22, 30],
        'Salary': [50000, 45000, 60000]}
df = pd.DataFrame(data)

# Aggregate minimum values for each column
minimums = df.agg({'Age': 'min', 'Employee_ID': 'min', 'Salary': 'min'})

print(minimums)

Output:

Age              22
Employee_ID     101
Salary        45000
dtype: int64

This snippet uses the agg() function to compute the minimum value for each column specified in the dictionary given as a parameter. It thus provides a concise and clear way to obtain our needed information.

Bonus One-Liner Method 5: Using a Lambda Function

If you’re looking for a quick, one-liner solution, you can use a lambda function within the agg() method to calculate the minimum for each column. This is best suited when you want a terse yet flexible code.

Here’s an example:

import pandas as pd

# Sample DataFrame
data = {'Employee_ID': [101, 102, 103],
        'Age': [25, 22, 30],
        'Salary': [50000, 45000, 60000]}
df = pd.DataFrame(data)

# Use lambda to find minimum values
minimums = df.agg(lambda x: x.min())

print(minimums)

Output:

Employee_ID     101
Age              22
Salary        45000
dtype: int64

Here, we’ve passed a lambda function that calls min() across each column in the DataFrame, encapsulating the process in a single line of elegant code.

Summary/Discussion

  • Method 1: DataFrame min() Method. Direct and efficient. Best for simplifying code and performance optimization. Does not provide the entire row if needed.
  • Method 2: Using sort_values() Method. Provides the entire entry, not just the minimum value. More resource-intensive for large data sets due to the sort operation.
  • Method 3: Using idxmin() Method. Accurate and relatively efficient. Like Method 2, it allows retrieval of the whole row of the youngest employee.
  • Method 4: Using agg() Method. Great for applying complex aggregation logic and obtaining multiple metrics simultaneously. Can be slightly less intuitive for beginners.
  • Method 5: Using a Lambda Function. Quick and succinct. Provides a one-line solution at the expense of being a bit more abstract and less explicit in what is calculated.