Question :
I have a dataframe df, with two columns, I want to groupby one column and join the lists belongs to same group, example:
column_a, column_b
1, [1,2,3]
1, [2,5]
2, [5,6]
after the process:
column_a, column_b
1, [1,2,3,2,5]
2, [5,6]
I want to keep all the duplicates. I have the following questions:
- The dtypes of the dataframe are object(s). convert_objects() doesn’t convert column_b to list automatically. How can I do this?
- what does the function in df.groupby(…).apply(lambda x: …) apply to ? what is the form of x ? list?
- the solution to my main problem?
Thanks in advance.
Answer #1:
object dtype is a catch-all dtype that basically means not int, float, bool, datetime, or timedelta. So it is storing them as a list. convert_objects tries to convert a column to one of those dtypes.
You want
In [63]: df
Out[63]:
a b c
0 1 [1, 2, 3] foo
1 1 [2, 5] bar
2 2 [5, 6] baz
In [64]: df.groupby('a').agg({'b': 'sum', 'c': lambda x: ' '.join(x)})
Out[64]:
c b
a
1 foo bar [1, 2, 3, 2, 5]
2 baz [5, 6]
This groups the data frame by the values in column a. Read more about [groupby].(http://pandas.pydata.org/pandas-docs/stable/groupby.html).
This is doing a regular list sum (concatenation) just like [1, 2, 3] + [2, 5]
Answer #2:
df.groupby('column_a').agg(sum)
This works because of operator overloading sum concatenates the lists together. The index of the resulting df will be the values from column_a:
Answer #3:
Use numpy and simple “for” or “map”:
import numpy as np
u_clm = np.unique(df.column_a.values)
all_lists = []
for clm in u_clm:
df_process = df.query('column_a == @clm')
list_ = np.concatenate(df.column_b.values)
all_lists.append((clm, list_.tolist()))
df_sum_lists = pd.DataFrame(all_lists)
It’s faster in 350 times than a simple “groupby-agg-sum” approach for huge datasets.
