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I have the following df,
type id date code
exact 9720 2017-10-01 515
exact 9720 2017-10-01 515
fuzzy 8242 2017-11-01 122
fuzzy 8242 2017-11-01 122
I was trying
exact_rows = df['type'] != 'fuzzy'
grouped = df.loc[~exact_rows].groupby('id').apply(
lambda g: g.sort_values('date', ascending=True))
a = np.where(grouped['code'].transform('nunique') == 1, 20, 0)
but I got an error,
ValueError: transforms cannot produce aggregated results
I am wondering how to resolve the issue.
627
Transform 🐣 Compared with Aggregation, transform takes an additional step called “Broadcasting”. It broadcasts the results from sub dataframes to the original full dataframe. You could view it as left merge the results to original full dataframe.
transform() can take a function, a string function, a list of functions, and a dict. However, apply() is only allowed a function. apply() works with multiple Series at a time. But, transform() is only allowed to work with a single Series at a time.
Pandas Series: transform() functionThe transform() function is used to call function on self producing a Series with transformed values and that has the same axis length as self. Function to use for transforming the data. If a function, must either work when passed a Series or when passed to Series. apply.
What is the GroupBy function? Pandas' GroupBy is a powerful and versatile function in Python. It allows you to split your data into separate groups to perform computations for better analysis.
IIUC, you have to use transform in a groupby object, so just regroup with the existing whatever index
grouped.groupby(grouped.index)['code'].transform('nunique')
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