Pandas pct_change(): Add Colors to Percent with style

Add colors to percent with style. Add monochrome colors to the percent with style.

Pct_change() is a pandas function that adds colors to the percent returned by pct_change() of a time series. It has been around for a while but I never used it. I just never really needed it. This is the second time I’ve seen color in a pct_change function. What is it about pandas functions that makes them so interesting?

Pandas pct_change() is a Python module that allows you to convert percent values to, and from, different units. This is a handy little tool that allows you to share pct_values between languages without having to convert the values.

The pct change() function in Pandas is a useful tool for quickly calculating percent change between two rows or columns. The pct change() function works with nearby rows and columns by default, but it can also compute percent change over a user-defined time period.

One of the great aspects of Pandas pct change is the ability to utilize the Pandas style function to add annotations with numerous colors. In this article, we’ll learn how to color the output of the pct change() function in Pandas, which computes the percent change between rows.

Add Annotations to Pandas pct_change resultsPandas pct change results can now have annotations added to them.

Let’s start by loading Pandas.

pandas as a pd import

We’ll make a basic dataframe out of the revenue of three tech companies over several years.

[2017, 2018, 2019, 2024]]]]]]]]]]]]]]]]]]] =[15934, 22112, 18485, 29146] facebook [12662, 30736, None, 40269] google [25489, 16571, 39240, 44281] Microsoft

Our input data is stored in various lists, and we can use Pandas’ DataFrame() function to turn the lists into a dataframe.

df = pd.DataFrame(“facebook”:facebook, “google”:google, “microsoft”:microsoft, index=year) df = pd.DataFrame(“facebook”:facebook, “google”:google, “microsoft”:microsoft, index=year)

Columns represent companies, and rows represent years in the example dataframe.

2017 15934 12662.0 25489 2018 22112 30736.0 16571 2019 18485 NaN 39240 2024 29146 40269.0 44281 df facebook google microsoft

We may use Pandas’ pct change() function on the dataframe to determine the change in revenue over time in terms of percent. Panda’s pct-change() method calculates percent change for each row by comparing it to the preceding row by default. As a result, the first row values in the results are NaNs.

For each year in our example, we get a percent change in revenue.

df.pct change() 2017 Facebook Google Microsoft -0.164029 0.000000 1.367992 2024 0.576738 0.310157 0.128466 2018 0.387724 1.427421 -0.349876 2019 -0.164029 0.000000 1.367992

In Pandas, add a percentage sign.

We may use the style method to add a percentage sign to the pct change() results and specify the format we want.

df.pct change(). “:.2 percent”), style.format(“:.2 percent”), style.format(“:.2 percent”), style.format( google microsoft facebook nan percent nan percent nan percent nan percent nan percent nan percent nan percent nan percent 2018 -34.99 percent 38.77 percent 142.74 percent -16.40 percent in 2019 0.00 percent 136.80% 57.67 percent in 2024 12.85 percent, 31.02 percent, 31.02 percent, 31.02 percent, 31.

It’s worth noting that the row with nan values also has a percentage indication, which is illogical. We can adjust the values of nan by passing “na rep” as an argument to the format() function. Instead of nan, we now get “-” dashes with the percentage symbol.

df.pct change().style.format(“:.2 percent”, na rep=”-“) df.pct change().style.format(“:.2 percent”, na rep=”-“) df.pct change().style. google microsoft facebook – – – – – – – – – – – 38.77 percentage point -34.99 percent 142.74 percent -16.40 percent in 2019 nil percent 3.68 percent 57.67 percent in 2024 12.85 percent, 31.02 percent, 31.02 percent, 31.02 percent, 31.

In Pandas, color-code maximum values in a column.

Maximum values are highlighted. We may use the highlight max() function to highlight the highest values in each column after converting to percent change with the chain operator.

(df. style. highlight max(). pct change(). pct change(). pct change(). pct change (). format(“:.2 percent”, na rep=”-“)) format(“:.2 percent”, na rep=”-“) format(“:.2 percent”, na rep

Take note of how we chained numerous functions differently. When combining many actions, writing each operation on its own line, as seen here, makes the code easier to read and understand. The maximum values in each column are highlighted in yellow by default when using the highlight max() function.

Highlight Max Value with highlight_maxWith highlight max, you can increase the maximum value of a highlight. The color argument to the highlight max() function allows us to define the color with which we want to highlight the maximum value.

(df. pct_change(). style. highlight_max(color=”lightgreen”). format(“{:.2%}”, na_rep=”-“)) Specify Color to highlight Max Value with highlight_maxSpecify Color to highlight Max Value with highlight_max

In Pandas, color-code the maximum and minimum values in a column.

Let’s use the highlight max/highlight min methods to highlight the maximum and least values in each column in two different colors.

(df. pct_change(). style. highlight_max(color=”lightgreen”). highlight_min(color=”yellow”). format(“{:.2%}”, na_rep=”-“)) Pandas add colors to maximum and minimum valuesPandas add colors to maximum and minimum values

Pandas pct_change() is a fantastic addition to the pysparkling library. It can return colors based on the value change of a label that you can display in your Jupyter notebook.. Read more about pandas percentage change between two columns and let us know what you think.

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