[英]Split dataframe by certain condition but keep the original dataframe
I have a dataframe "bb" like this: 我有一个像这样的数据帧“bb”:
Response Unique Count
I love it so much! 246_0 1
This is not bad, but can be better. 246_1 2
Well done, let's do it. 247_0 1
If count is lager than 1, I would like to split the string and make the dataframe "bb" become this: (result I expected) 如果count大于1,我想分割字符串并使数据帧“bb”变为:(结果我预期)
Response Unique
I love it so much! 246_0
This is not bad 246_1_0
but can be better. 246_1_1
Well done, let's do it. 247_0
My code: 我的代码:
bb = DataFrame(bb[bb['Count'] > 1].Response.str.split(',').tolist(), index=bb[bb['Count'] > 1].Unique).stack()
bb = bb.reset_index()[[0, 'Unique']]
bb.columns = ['Response','Unique']
bb=bb.replace('', np.nan)
bb=bb.dropna()
print(bb)
But the result is like this: 但结果是这样的:
Response Unique
0 This is not bad 246_1
1 but can be better. 246_1
How can I keep the original dataframe in this case? 在这种情况下,如何保留原始数据帧?
First split only values per condition with to new helper Series
and then add counter values by GroupBy.cumcount
only per duplicated index values by Index.duplicated
: 首先将每个条件的值除以新的帮助程序Series
,然后仅通过GroupBy.cumcount
按重复的索引值按Index.duplicated
添加计数器值:
s = df.loc[df.pop('Count') > 1, 'Response'].str.split(',', expand=True).stack()
df1 = df.join(s.reset_index(drop=True, level=1).rename('Response1'))
df1['Response'] = df1.pop('Response1').fillna(df1['Response'])
mask = df1.index.duplicated(keep=False)
df1.loc[mask, 'Unique'] += df1[mask].groupby(level=0).cumcount().astype(str).radd('_')
df1 = df1.reset_index(drop=True)
print (df1)
Response Unique
0 I love it so much! 246_0
1 This is not bad 246_1_0
2 but can be better. 246_1_1
3 Well done! 247_0
EDIT: If need _0
for all another values remove mask: 编辑:如果需要_0
为所有其他值删除掩码:
s = df.loc[df.pop('Count') > 1, 'Response'].str.split(',', expand=True).stack()
df1 = df.join(s.reset_index(drop=True, level=1).rename('Response1'))
df1['Response'] = df1.pop('Response1').fillna(df1['Response'])
df1['Unique'] += df1.groupby(level=0).cumcount().astype(str).radd('_')
df1 = df1.reset_index(drop=True)
print (df1)
Response Unique
0 I love it so much! 246_0_0
1 This is not bad 246_1_0
2 but can be better. 246_1_1
3 Well done! 247_0_0
Step wise we can solve this problem the following: 我们可以逐步解决以下问题:
groupby
on index and use cumcount
to get the correct unique
column values. 我们groupby
对指数和使用cumcount
以获得正确的unique
列值。 concat
the dataframes together again. 最后,我们concat
又一起dataframes。 df1 = df[df['Count'].ge(2)] # all rows which have a count 2 or higher
df2 = df[df['Count'].eq(1)] # all rows which have count 1
df1 = explode_str(df1, 'Response', ',') # explode the string to rows on comma delimiter
# Create the correct unique column
df1['Unique'] = df1['Unique'] + '_' + df1.groupby(df1.index).cumcount().astype(str)
df = pd.concat([df1, df2]).sort_index().drop('Count', axis=1).reset_index(drop=True)
Response Unique
0 I love it so much! 246_0
1 This is not bad 246_1_0
2 but can be better. 246_1_1
3 Well done! 247_0
Function used from linked answer: 链接答案使用的功能:
def explode_str(df, col, sep):
s = df[col]
i = np.arange(len(s)).repeat(s.str.count(sep) + 1)
return df.iloc[i].assign(**{col: sep.join(s).split(sep)})
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