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This article mainly introduces Python how to slice a field and achieve cumulative summation of the relevant knowledge, the content is detailed and easy to understand, simple and fast operation, with a certain reference value, I believe that after reading this Python how to slice a field and achieve the cumulative sum of the article will have a harvest, let's take a look.

Question: group according to the first column, add the above accumulation to the following grouping, how to achieve?

Train of thought:

1. First pivot perspective, scale is extended to multiple columns

2.cumsum () accumulation

3.melt reverse perspective, restore the original shape

Load the data source:

Import pandas as pddf = pd.read_table (r "D:\ Jupyter\ data\ df_test.txt") df

Data cleaning process:

Years = [before 1980', '1980-1989', '1990-1999', '2000-2009', '2010-2020'] result = (df.pivot (index= "period", columns= "scale", values= "count") .loc [years] .Cumsum () .reset _ index () .melt (id_vars='period') Value_name='count') result.dropna (inplace=True) result ["count"] = result ["count"] .astype ("int16") result.period = result.period.astype ("category") result.period = result.period.cat.reorder_categories (years) result.sort_values (by= ["period", "scale"], inplace=True) result

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