Highlight the maximum value in last two columns in Pandas – Python

When working with data, it's often crucial to identify and highlight the maximum value within specific columns in a pandas dataframe. In Python, the Pandas library is widely used for data manipulation and offers efficient built-in functions.

This article focuses on highlighting the maximum value in the last two columns of a Pandas dataframe. By utilizing different methods, we can quickly locate and emphasize the highest values in our dataframe, which will facilitate easier analysis and comprehension of the dataset.

Method 1: Using the style.highlight_max() Function

The simplest approach is to use the built-in highlight_max() function with the subset parameter to target only the last two columns ?

import pandas as pd

# Create a sample dataframe
data = {'Column1': [10, 15, 8],
        'Column2': [20, 5, 12],
        'Column3': [7, 18, 9]}

df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

# Highlight the maximum values in the last two columns
df_styled = df.style.highlight_max(subset=df.columns[-2:])
print("\nDataFrame with highlighted maximum values in last two columns:")
df_styled

Original DataFrame:
   Column1  Column2  Column3
0       10       20        7
1       15        5       18
2        8       12        9

This method utilizes the style property of a Pandas dataframe, specifically the highlight_max() function. By passing subset=df.columns[-2:], we specify the last two columns of the dataframe. This function highlights the maximum value in each column with a yellow background.

Method 2: Using Custom Conditional Formatting

For more control over the styling, you can create a custom function that applies specific formatting to maximum values ?

import pandas as pd

# Create a sample dataframe
data = {'Column1': [10, 15, 8],
        'Column2': [20, 5, 12],
        'Column3': [7, 18, 9]}

df = pd.DataFrame(data)

# Define a function to highlight the maximum value
def highlight_max(series):
    is_max = series == series.max()
    return ['background-color: lightcoral' if v else '' for v in is_max]

# Apply the function to the last two columns
df_styled = df.style.apply(highlight_max, subset=df.columns[-2:])
print("DataFrame with custom highlighting:")
df_styled

In this approach, we define a custom function called highlight_max, which compares each element in a series with the maximum value of that series. The function returns a list of style directives, where the maximum value is highlighted with a light coral background. By using the apply() function and specifying subset=df.columns[-2:], we apply the conditional formatting only to the last two columns.

Method 3: Highlighting with Different Colors

You can also highlight maximum values with different colors for each column ?

import pandas as pd

# Create a sample dataframe
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Score1': [85, 92, 78],
        'Score2': [90, 88, 95]}

df = pd.DataFrame(data)

# Highlight max in last two columns with different colors
df_styled = df.style.highlight_max(subset=['Score1'], color='lightblue') \
                  .highlight_max(subset=['Score2'], color='lightgreen')
                  
print("DataFrame with different colors for each column:")
df_styled

Key Points

• df.columns[-2:] selects the last two columns of the dataframe
• highlight_max() automatically finds and highlights maximum values
• Custom functions provide more control over styling options
• You can chain multiple styling operations for different effects

Conclusion

Highlighting maximum values in the last two columns helps quickly identify peak performance or important data points. Use highlight_max() for simple highlighting or create custom functions for more advanced styling control.