Binary comparison operations in Pandas are used to compare elements in a Pandas Data structure such as, Series or DataFrame objects with a scalar value or another Data structure. These operations return Boolean results that indicate the outcome of each comparison, and these operations are useful for filtering, condition-based operations, and data analysis.
In this tutorial, you will learn how to perform binary comparison operations like less than, greater than, equal to, and others, on a Pandas Data structure with scalar values and between other DataFrames/Series objects.
Binary comparison operators are used to compare elements in a Pandas Series or DataFrame with a scalar value. The result of these operations is a boolean Data structure where True indicates the given condition is satisfied and False for not.
Here is a list of common binary comparison operators that can be used on a Pandas DataFrame or Series −
The following example demonstrates how to apply comparison operators to a Pandas DataFrame with a scalar value.
import pandas as pd
# Create a sample DataFrame
data = {'A': [1, 5, 3, 8], 'B': [4, 6, 2, 9]}
df = pd.DataFrame(data)
# Display the input DataFrame
print("Input DataFrame:\n", df)
# Perform binary comparison operations
print("\nLess than 5:\n", df < 5)
print("\nGreater than 5:\n", df > 5)
print("\nLess than or equal to 5:\n", df <= 5)
print("\nGreater than or equal to 5:\n", df >= 5)
print("\nEqual to 5:\n", df == 5)
print("\nNot equal to 5:\n", df != 5)
Input DataFrame:
A B
0 1 4
1 5 6
2 3 2
3 8 9
Less than 5:
A B
0 True True
1 False False
2 True True
3 False False
Greater than 5:
A B
0 False False
1 False True
2 False False
3 True True
Less than or equal to 5:
A B
0 True True
1 True False
2 True True
3 False False
Greater than or equal to 5:
A B
0 False False
1 True True
2 False False
3 True True
Equal to 5:
A B
0 False False
1 True False
2 False False
3 False False
Not equal to 5:
A B
0 True True
1 False True
2 True True
3 True True
In addition to the above operators, Pandas provides various functions to perform binary comparison operations on Pandas Data structure, by providing the additional options for customization, like selecting the axis and specifying levels for the MultiIndex objects.
Following is the list of binary comparison functions in Pandas −
| S.No | Function | Description |
|---|---|---|
| 1 | lt(other[, axis, level]) | Element-wise less than comparison. |
| 2 | gt(other[, axis, level]) | Element-wise greater than comparison. |
| 3 | le(other[, axis, level]) | Element-wise less than or equal comparison. |
| 4 | ge(other[, axis, level]) | Element-wise greater than or equal comparison. |
| 5 | ne(other[, axis, level]) | Element-wise not equal comparison. |
| 6 | eq(other[, axis, level]) | Element-wise equal comparison. |
This example demonstrates the applying the binary comparison functions between a Pandas Series and a scalar value.
import pandas as pd
# Create a Pandas Series
s = pd.Series([10, 20, 30, 40, 50])
# Display the Series
print("Pandas Series:\n", s)
# Perform comparison operations
print("\nLess than 25:\n", s.lt(25))
print("\nGreater than 25:\n", s.gt(25))
print("\nLess than or equal to 30:\n", s.le(30))
print("\nGreater than or equal to 40:\n", s.ge(40))
print("\nNot equal to 30:\n", s.ne(30))
print("\nEqual to 50:\n", s.eq(50))
Pandas Series: 0 10 1 20 2 30 3 40 4 50 dtype: int64 Less than 25: 0 True 1 True 2 False 3 False 4 False dtype: bool Greater than 25: 0 False 1 False 2 True 3 True 4 True dtype: bool Less than or equal to 30: 0 True 1 True 2 True 3 False 4 False dtype: bool Greater than or equal to 40: 0 False 1 False 2 False 3 True 4 True dtype: bool Not equal to 30: 0 True 1 True 2 False 3 True 4 True dtype: bool Equal to 50: 0 False 1 False 2 False 3 False 4 True dtype: bool
Similarly above example, this will perform binary comparison operations between a DataFrame and a scalar value using the binary comparison functions in Pandas.
import pandas as pd
# Create a DataFrame
data = {'A': [10, 20, 30], 'B': [40, 50, 60]}
df = pd.DataFrame(data)
# Display the DataFrame
print("DataFrame:\n", df)
# Perform comparison operations
print("\nLess than 25:\n", df.lt(25))
print("\nGreater than 50:\n", df.gt(50))
print("\nEqual to 30:\n", df.eq(30))
print("\nLess than or equal to 30:\n", df.le(30))
print("\nGreater than or equal to 40:\n", df.ge(40))
print("\nNot equal to 30:\n", df.ne(30))
DataFrame:
A B
0 10 40
1 20 50
2 30 60
Less than 25:
A B
0 True False
1 True False
2 False False
Greater than 50:
A B
0 False False
1 False False
2 False True
Equal to 30:
A B
0 False False
1 False False
2 True False
Less than or equal to 30:
A B
0 True False
1 True False
2 True False
Greater than or equal to 40:
A B
0 False True
1 False True
2 False True
Not equal to 30:
A B
0 True True
1 True True
2 False True
This example compares the two DataFrames element-wise using the eq(), ne(), lt(), gt(), le(), and ge() functions.
import pandas as pd
# Create two DataFrames
df1 = pd.DataFrame({'A': [1, 0, 3], 'B': [9, 5, 6]})
df2 = pd.DataFrame({'A': [1, 2, 1], 'B': [6, 5, 4]})
# Display the Input DataFrames
print("DataFrame 1:\n", df1)
print("\nDataFrame 2:\n", df2)
# Perform comparison operations between two DataFrames
print("\nEqual :\n", df1.eq(df2))
print("\nNot Equal:\n", df1.ne(df2))
print("\ndf1 Less than df2:\n", df1.lt(df2))
print("\ndf1 Greater than df2:\n", df1.gt(df2))
print("\ndf1 Less than or equal to df2:\n", df1.le(df2))
print("\ndf1 Greater than or equal to df2:\n", df1.ge(df2))
DataFrame 1:
A B
0 1 9
1 0 5
2 3 6
DataFrame 2:
A B
0 1 6
1 2 5
2 1 4
Equal :
A B
0 True False
1 False True
2 False False
Not Equal:
A B
0 False True
1 True False
2 True True
df1 Less than df2:
A B
0 False False
1 True False
2 False False
df1 Greater than df2:
A B
0 False True
1 False False
2 True True
df1 Less than or equal to df2:
A B
0 True False
1 True True
2 False False
df1 Greater than or equal to df2:
A B
0 True True
1 False True
2 True True