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Comparing using lower() on column’s data in pandas<\/p>\n
I’m trying to find the difference in data I have using the date column ‘date’. I want to display the difference between the number of sales and marketing. The sales column indicates the date sold the product and the marketing column indicates the date when a specific push was initiated. If a sales value was greater than a marketing value I would want to display 1.
\nI’ve come up with this code:
\nperf_data = perf_df.groupby(‘ID’)[‘revenue’].diff().apply(lambda r: r>=0).astype(float).astype(int)
\ndiff_sales = (perf_data.sales.diff().fillna(0).astype(int).groupby([pd.Grouper(key=’ID’, freq=’D’), ‘date’]).sum().astype(float))
\ndiff_marketing = (perf_data.marketing.diff().fillna(0).astype(int).groupby([pd.Grouper(key=’ID’, freq=’D’), ‘date’]).sum().astype(float))
\nif diff_sales>diff_marketing:
\n print’sales came out ahead<\/p>\n","protected":false},"excerpt":{"rendered":"
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