Pandas DataFrame.lt

Pandas DataFrame.lt

The DataFrame.lt method in pandas is used to perform element-wise comparison, checking whether each element in a DataFrame is less than the corresponding element in another DataFrame or scalar value. This method is a shorthand for the less-than (<) operator and returns a DataFrame of boolean values indicating the comparison result.

Syntax

The syntax for DataFrame.lt is:

DataFrame.lt(other, axis='columns', level=None)

Here, DataFrame refers to the pandas DataFrame being compared.

Parameters

Returns

A DataFrame of boolean values indicating where the condition (<) is satisfied.

Examples

Element-Wise Comparison with a Scalar

Compare all elements of a DataFrame with a scalar value.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
df = pd.DataFrame(data)

# Compare elements with a scalar
print("Is each element in 'Age' and 'Salary' less than 30,000?")
result = df[['Age', 'Salary']].lt(30000)
print(result)

Output

Is each element in 'Age' and 'Salary' less than 30,000?
     Age  Salary
0   True   False
1  False   False
2  False   False

Element-Wise Comparison with Another DataFrame

Compare two DataFrames element-wise to check for less-than conditions.

Python Program

import pandas as pd

# Create two DataFrames
data1 = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
data2 = {
    'Age': [30, 25, 40],
    'Salary': [75000, 75000, 85000]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# Compare the two DataFrames
print("Element-wise comparison of 'Age' and 'Salary':")
result = df1[['Age', 'Salary']].lt(df2)
print(result)

Output

Element-wise comparison of 'Age' and 'Salary':
     Age  Salary
0   True    True
1  False   False
2  False   False

Comparison Along Rows

Use axis='index' to perform element-wise comparison along rows.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
df = pd.DataFrame(data)

# Compare along rows
print("Comparison of 'Age' with 'Salary' along rows:")
result = df[['Age', 'Salary']].lt(df['Salary'], axis='index')
print(result)

Output

Comparison of 'Age' with 'Salary' along rows:
     Age  Salary
0   True   False
1   True   False
2   True   False

Handling MultiIndex Levels

Use the level parameter to compare elements at a specific level in a MultiIndex DataFrame.

Python Program

import pandas as pd

# Create a MultiIndex DataFrame
data = {
    'Age': [25, 30, 35, 40],
    'Salary': [70000, 80000, 90000, 100000]
}
index = pd.MultiIndex.from_tuples([
    ('Group1', 'Arjun'),
    ('Group1', 'Ram'),
    ('Group2', 'Priya'),
    ('Group2', 'Karan')
], names=['Group', 'Name'])
df = pd.DataFrame(data, index=index)

# Compare elements at the 'Group' level
print("Comparison of 'Age' < 35 at 'Group' level:")
result = df['Age'].lt(35, level='Group')
print(result)

Output

Comparison of 'Age' < 35 at 'Group' level:
Group   Name 
Group1  Arjun     True
        Ram       True
Group2  Priya    False
        Karan    False
Name: Age, dtype: bool

Summary

In this tutorial, we explored the DataFrame.lt method in pandas. Key takeaways include:

• Using lt to perform element-wise less-than comparisons.
• Comparing with scalars, DataFrames, or along specific axes.
• Utilizing MultiIndex levels for advanced comparisons.

The DataFrame.lt method is a versatile tool for applying conditional logic to pandas DataFrames.