[英]How to only keep rows which have more than one value in a pandas DataFrame?
[英]how to calculate value counts when we have more than one value in a colum in pandas dataframe
df
Name
Sri
Sri,Ram
Sri,Ram,kumar
Ram
我正在嘗試計算每個值的值計數。 使用時我沒有得到輸出
df["Name"].values_count()
我想要的輸出是
Sri 3
Ram 3
Kumar 1
split
列, stack
為長格式,然后count
:
df.Name.str.split(',', expand=True).stack().value_counts()
#Sri 3
#Ram 3
#kumar 1
#dtype: int64
或者可能:
df.Name.str.get_dummies(',').sum()
#Ram 3
#Sri 3
#kumar 1
#dtype: int64
或在value_counts之前連接:
pd.value_counts(pd.np.concatenate(df.Name.str.split(',')))
#Sri 3
#Ram 3
#kumar 1
#dtype: int64
時間 :
%timeit df.Name.str.split(',', expand=True).stack().value_counts()
#1000 loops, best of 3: 1.02 ms per loop
%timeit df.Name.str.get_dummies(',').sum()
#1000 loops, best of 3: 1.18 ms per loop
%timeit pd.value_counts(pd.np.concatenate(df.Name.str.split(',')))
#1000 loops, best of 3: 573 µs per loop
# option from @Bharathshetty
from collections import Counter
%timeit pd.Series(Counter((df['Name'].str.strip() + ',').sum().rstrip(',').split(',')))
# 1000 loops, best of 3: 498 µs per loop
# option inspired by @Bharathshetty
%timeit pd.value_counts(df.Name.str.cat(sep=',').split(','))
# 1000 loops, best of 3: 483 µs per loop
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