![]() We’ll use the same dataset that’s available in my pivot table tutorial and we’ll use some of the steps we outlined there. Let’s get started by loading our data first. Styler.apply - which applies styles column- / row- / dataframe-wise.Styler.applymap - which applies styles element-wise.The end styling is accomplished with CSS, through style-functions that are applied to scalars, series, or entire dataframes, via attribute:value pairs. ![]() The API returns a new Styler object, which has useful methods to apply formatting and styling to dataframes. Pandas developed the styling API in 2019 and it’s gone through active development since then. For example, 10% may be easier to understand than the value 0.10, but keeping the proportion of 0.10 is more usable for further analysis. Our end goal should be to make the data easier for our readers to understand while maintaining the usability of the underlying data available in the dataframe. In this post, we’ll explore how to take these features that are commonplace in Excel and demonstrate how to take these on using the Pandas Style API! Why would we want to style data? For that, many analysts still turn to Excel to add data styles (such as currencies) or conditional formatting before sharing the data with our broader audiences. Pandas is the quintessential tool for data analysis in Python, but it’s not always the easiest to make data look presentable. ![]() Let’s add some style to our Pandas Dataframes! Source: Nik Piepenbreier
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