Highlight the maximum value in last two columns in Pandas – Python
In this article, we will discuss how to highlight the maximum values in Pandas Dataframe. Let’s first make a dataframe:
Example:
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Python3
# Import Required Librariesimport pandas as pdimport numpy as np # Create a dictionary for the dataframedict = {'Name': ['Sumit Tyagi', 'Sukritin', 'Akriti Goel', 'Sanskriti', 'Abhishek Jain'], 'Age': [22, 20, np.nan, np.nan, 22], 'Marks': [90, 84, 33, 87, 82]} # Converting Dictionary to Pandas Dataframedf = pd.DataFrame(dict) # Print Dataframedf |
Output:
Now, come to the highlighting part. Our objective is to highlight cells with maximum values in last 2 columns.
Method 1: Highlighting Cell with maximum value in last 2 columns
We will do this by using the highlight_max() method of DataFrame property. highlight_max() method takes 3 arguments,
- subset: name of the columns of which you want to find the maximum
- color: name of the color with which you want to highlight the cell
- axis: (0/1) based on which axis you want to find the maximum.
Example:
Python3
# Highlighting the maximum values # of last 2 columnsdf.style.highlight_max(subset = ['Age', 'Marks'], color = 'lightgreen', axis = 0) |
Output:
Method 2: Instead of using column names we generalise it to last two columns
Example:
Python3
# Highlighting the maximum values of# last 2 columnsdf.style.highlight_max(subset = df.columns[-2:], color = 'lightgreen', axis = 0) |
Output:
Method 3: Highlighting the text instead of cell
Example:
Python3
# Defining custom function which returns# the list for df.style.apply() methoddef highlight_max(s): is_max = s == s.max() return ['color: green' if cell else '' for cell in is_max] df.style.apply(highlight_max, subset = df.columns[-2:]) |
Output:
Method 4: Highlighting cell with maximum values
Example:
Python3
# Defining custom function which returns# the list for df.style.apply() methoddef highlight_max(s): is_max = s == s.max() return ['background: lightgreen' if cell else '' for cell in is_max] df.style.apply(highlight_max, subset = df.columns[-2:]) |
Output:
