[英]How to merge dataframe rows to a single row with all row values concenated for each column?
I have a df like:我有一个像这样的df:
| col1 | col2 | col3
0 | Text1 | a,b ,c | klra-tk³,t54 ?
1 | Text2 | NaN | gimbal3, gimbal4
2 | Text3 | a,k,m | NaN
I want to get a single row with all unique values of a column in a single line and NaNs ignored like:我想在一行中获得一列的所有唯一值的单行,并且忽略 NaN,例如:
| col1 | col2 | col3
0 | Text1, Text2, Text3 | a,b,c,k,m | klra-tk³,t54,gimbal3, gimbal4
How can I do this with pandas?我怎么能用 pandas 做到这一点?
Use custom function with Series.str.split
, DataFrame.stack
, reove duplicates by Series.drop_duplicates
and remove missing values by Series.dropna
, last join by ,
and convert Series
to one row DataFrame by Series.to_frame
and transpose: Use custom function with Series.str.split
, DataFrame.stack
, reove duplicates by Series.drop_duplicates
and remove missing values by Series.dropna
, last join by ,
and convert Series
to one row DataFrame by Series.to_frame
and transpose:
f = lambda x: ','.join(x.str.split(',', expand=True).stack().drop_duplicates().dropna())
df = df.apply(f).to_frame().T
print (df)
col1 col2 col3
0 Text1,Text2,Text3 a,b,c,k,m klra-tk,t54,gimbal3,gimbal4
Or use list comprehension like:或使用列表理解,如:
f = lambda x: ','.join(x.str.split(',', expand=True).stack().drop_duplicates().dropna())
df = pd.DataFrame([[f(df[x]) for x in df.columns]], columns=df.columns)
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