计算字符串/类别 pandas groupby 聚合的出现次数

Question

I have data in a tabular format with id per row. In columns I have set a flag that has one or more categorical values ie condition_one, condition_two

I'm generating summary statistics using the below:

function_count_certain_condition = lambda x: x.str.count("condition_two").sum()

function_count_certain_condition.__name__ = 'number_of_two_conditions'

# ---
aggregations = {
'column_one': ['count','first','last','nunique'],
'conditions_column': [function_count_certain_condition]
} 

df_aggregate_stats = df.groupby(['id_column']).agg(aggregations)

This works but doesn't seem particularly pythonic or performant. I tried using value_counts() but got a key error

Answer 1

particularly pythonic

Yes, you are using lambda which is stored in variable (whole point of lambda is missing if it is not nameless) and then shove name for it. Just use def to define function, that is replace

function_count_certain_condition = lambda x: x.str.count("condition_two").sum()

function_count_certain_condition.__name__ = 'number_of_two_conditions'

using

def number_of_two_conditions(x):
    return x.str.count("condition_two").sum()

performant

Firstly be warned against premature optimization. If that code works fast enough for your code do not try force it to be faster. Regarding that particular function I do not see anything to cause excessive execution time as both substring counting and addition are generally fast operations.