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How to Improve Your Analytical Report With Conditional Formatting In Pandas

How to Improve Your Analytical Report With Conditional Formatting In Pandas

Last Updated on July 3, 2022 by Editorial Team

Author(s): Hrishikesh Patel

Originally published on Towards AI the World’s Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses.

How To Improve Your Analytical Report With Conditional Formatting In Pandas

Excel-like Conditional Formatting in Pandas Using Styler API

How to Improve Your Analytical Report With Conditional Formatting In Pandas
Image by author

Clearly communicating analytical insights with stakeholders is crucial for data scientists/analysts.

The use of conditional formatting in analytical reports can help in quickly identifying insights into a data frame (table).

Let’s start with an example first. The following pivot table shows the total sales of different products from 2016 to 2022.

Pivot table showing total sales of products from 2016 to 2022 — can you identify max sales in 2016? (image by author)
  • Can you identify the largest selling 💰 product in 2016? — Yes it is Product_B with a total sale of 169 but it’s difficult to identify just by looking👀 at the table.

Now let’s color the largest selling💰 product for each year. After highlighting, it becomes so much easier to answer the above question, isn’t it?

Highlighting maximum selling products in each year — now it’s easy to identify Product_B was the largest-selling product of 2016 with sales of 169 (image by author)

Let me show you how to do this in Pandas.


1. Highlight missing values
2. Highlight the maximum (or minimum) value in each row/column
3. Highlight values within a range
4. Plot in-column bar chart
5. Highlight values using a color gradient
6. Bonus🎁

Note: I strongly recommend using the latest version of Pandas. You can run pip install –upgrade pandas to get Pandas’ latest stable release.

1. Highlight missing values

Using dataframe.style.highlight_null() you can color null values as shown below. I stored the pivot table in the variable df_pivoted .

Highlighting nan values in red using `.highlight_null` (image by author)

It’s okay😀 if you don’t prefer red. Let’s customize the text and background color of missing values using the argument props=’color:white;background-color:black’ .

`props` argument allows customizing text and background color of highlights (image by author)

After highlighting, we can quickly get the insight that Product_H was not sold in 2018.

2. Highlight maximum (or minimum) values

To highlight maximum values in each column, you can use dataframe.style.highlight_max() . The method by default colors maximum values in each column as illustrated in the below image.

`.highlight_max` by default colors max values in each column (image by author)

To color max values in each row, you can specify the argument axis=1 .

Setting axis=1 in `.highlight_max` colors max values in each row (image by author)

Note: Similarly you can use the method dataframe.style.highlight_min() with proper arguments to color minimum values in rows/columns.

3. Highlight values within a range

Let’s consider that we want to highlight values between 100 and 200 — it’s quite easy to using dataframe.style.highlight_between(left, right) .

Values between 100 and 200 are highlighted in yellow using the `.highlight_between` method (image by author)

4. Plot in-column bar chart

A bar chart plotted within a column can be visually appealing and useful. Such bar charts can be created using dataframe.style.bar() the method as shown below.

Bar chart plotted within each column using the `dataframe.style.bar` method (image by author)

Let’s customize the bar chart to change its color and size.

The customized bar charts in columns (image by author)

5. Highlight values using a color gradient

What if you want to highlight the entire column with a color gradient. It can be done using dataframe.style.background_gradient() as depicted below. In the image, the color changes from red to green as the value increases. You can set subset=None to apply the gradient to the entire data frame.

Column ‘Product_C’ is colored using a gradient of red, yellow, and green colors (image by author)

6. Bonus 🎁

How can we highlight min, max, and missing values together in the data? Well, you can define a function as illustrated below. The function highlights min, max, and nan values in the column ‘Product_C’. By setting subset=None , it highlights the values in the entire data frame. Isn’t this function really cool? Let me know your thoughts in the comments!

Defining and using a function to highlight minimum, maximum, and missing values in the data frame (image by author)

Please feel free to explore highlighting methods in Pandas documentation.

Before you go!

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