Pandas DataFrame.ge

Pandas DataFrame.ge

The DataFrame.ge method in pandas is used to compare elements of a DataFrame with another DataFrame, Series, or scalar value. The method performs element-wise greater than or equal to (>=) comparisons, returning a DataFrame of boolean values.

Syntax

The syntax for DataFrame.ge is:

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

Here, DataFrame refers to the pandas DataFrame being compared.

Parameters

Returns

A DataFrame of boolean values indicating the result of the greater than or equal to comparison for each element.

Examples

Comparing with a Scalar

Compare all elements in 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 value
print("Comparing with scalar value 30:")
result = df[['Age', 'Salary']].ge(30)
print(result)

Output

Comparing with scalar value 30:
     Age  Salary
0  False    True
1   True    True
2   True    True

Comparing with Another DataFrame

Perform element-wise comparison with another DataFrame of the same shape.

Python Program

import pandas as pd

# Create two DataFrames
data1 = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
data2 = {
    'Age': [20, 30, 40],
    'Salary': [75000, 75000, 95000]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# Perform element-wise comparison
print("Comparing with another DataFrame:")
result = df1[['Age', 'Salary']].ge(df2)
print(result)

Output

Comparing with another DataFrame:
     Age  Salary
0   True   False
1   True    True
2  False   False

Comparing Row-wise Using axis=0

Compare elements row-wise using axis=0.

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 row-wise with a Series
compare_series = pd.Series([25, 75000], index=['Age', 'Salary'])
print("Comparing row-wise with a Series:")
result = df[['Age', 'Salary']].ge(compare_series, axis=1)
print(result)

Output

Comparing row-wise with a Series:
     Age  Salary
0   True   False
1   True    True
2   True    True

Handling MultiIndex

Perform element-wise comparison on a DataFrame with a MultiIndex using the level parameter.

Python Program

import pandas as pd

# Create a MultiIndex DataFrame
data = {
    'Value': [5, 15, 25, 35]
}
index = pd.MultiIndex.from_tuples([
    ('A', 'one'), ('A', 'two'), ('B', 'one'), ('B', 'two')
], names=['Group', 'Number'])
df = pd.DataFrame(data, index=index)

# Compare with scalar, specifying level
print("Comparing with scalar 20 at level 'Group':")
result = df.ge(20, level='Group')
print(result)

Output

Comparing with scalar 20 at level 'Group':
              Value
Group Number       
A     one     False
      two     False
B     one      True
      two      True

Summary

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

• Performing element-wise greater than or equal to comparisons with scalars, Series, or DataFrames.
• Using the axis parameter for row-wise or column-wise comparison.
• Handling comparisons on MultiIndex DataFrames using the level parameter.

The DataFrame.ge method is a flexible tool for filtering and comparing DataFrame values in pandas.