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2021-10-17 19:26:08
cumulative some by date for each user
df=df.sort_values(['date']).reset_index(drop=True) df["cum_sale"]=df.groupby(['item'])['sale'].cumsum(axis=0)
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SQL
2021-10-14 14:02:12
coursera spark sql max count
df.groupBy(color).max("count").show(); +-----------+----------+ |color |max(count)| +-----------+----------+ | red| 5| | blue| 3| | brown| 2| | green| 2| +-----------+----------+
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Python
2021-10-13 05:30:09
weigted average in pandas
g = df.groupby('Date') df.value / g.value.transform("sum") * df.wt
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Python
2021-10-06 05:30:02
groupby
>>> n_by_state = df.groupby("state")["state"].count() >>> n_by_state.head(10) state AK 16 AL 206 AR 117 AS 2 AZ 48 CA 361 CO 90 CT 240 DC 2 DE 97 Name: last_name, dtype: int64
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Python
2021-10-05 09:25:05
groupby count pandas
df.groupby(['col1', 'col2']).size().reset_index(name='counts')
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Python
2021-10-03 13:20:05
dataframe groupby to dictionary
In [81]: df.groupby('Column1')['Column3'].apply(list).to_dict() Out[81]: {0: [1], 1: [2, 3, 5], 2: [1, 2], 3: [4, 5], 4: [1], 5: [1, 2, 3]}
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Python
2021-09-30 07:03:03
pd count how many item occurs in another column
df['Counts'] = df.groupby(['Color'])['Value'].transform('count')
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Python
2021-09-28 04:33:05
create dictionary without removing duplicates from dataframe
{k: g.to_dict(orient='records') for k, g in df.groupby(level=0)} # {'bob': [{'age': 20, 'name': 'bob'}, {'age': 30, 'name': 'bob'}], # 'jim': [{'age': 25, 'name': 'jim'}]}
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Python
2021-09-21 05:02:06
pandas sort values group by
df.groupby(['job']).apply(lambda x: (x.groupby('source') .sum() .sort_values('count', ascending=False)) .head(3))
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Python
2021-09-17 04:16:02
python access each group
gb = df.groupby(['A']) for k, gp in gb: print 'key=' + str(k) print gp gb.get_group('foo')
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