pandas groupby; if condition: sum else: max for given column based on another column
1
Say for df I do a groupby on group:
df = pd.DataFrame(np.random.rand(4,4), columns=list('abcd'))
df['group'] = [0, 0, 1, 1]
then I want to collapse the df so that A is summed if the sum of B is greater than 1 and I want the max of A if the sum of B is less than or equal to 1.
Then I want the min() of B and other various operations on the remaining columns.
python pandas
asked Nov 9 '18 at 23:39
jchaykowjchaykow
507318
add a comment |
1
Say for df I do a groupby on group:
df = pd.DataFrame(np.random.rand(4,4), columns=list('abcd'))
df['group'] = [0, 0, 1, 1]
then I want to collapse the df so that A is summed if the sum of B is greater than 1 and I want the max of A if the sum of B is less than or equal to 1.
Then I want the min() of B and other various operations on the remaining columns.
python pandas
asked Nov 9 '18 at 23:39
jchaykowjchaykow
507318
add a comment |
1
1
1
Say for df I do a groupby on group:
df = pd.DataFrame(np.random.rand(4,4), columns=list('abcd'))
df['group'] = [0, 0, 1, 1]
then I want to collapse the df so that A is summed if the sum of B is greater than 1 and I want the max of A if the sum of B is less than or equal to 1.
Then I want the min() of B and other various operations on the remaining columns.
python pandas
asked Nov 9 '18 at 23:39
jchaykowjchaykow
507318
Say for df I do a groupby on group:
df = pd.DataFrame(np.random.rand(4,4), columns=list('abcd'))
df['group'] = [0, 0, 1, 1]
then I want to collapse the df so that A is summed if the sum of B is greater than 1 and I want the max of A if the sum of B is less than or equal to 1.
Then I want the min() of B and other various operations on the remaining columns.
python pandas
python pandas
asked Nov 9 '18 at 23:39
jchaykowjchaykow
507318
asked Nov 9 '18 at 23:39
jchaykowjchaykow
507318
asked Nov 9 '18 at 23:39
jchaykowjchaykow
507318
asked Nov 9 '18 at 23:39
jchaykowjchaykow
507318
asked Nov 9 '18 at 23:39
jchaykowjchaykow
507318
507318
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
1
For better performance use where:
np.random.seed(15)
N = 1000
df = pd.DataFrame(np.random.rand(N,10), columns=list('abcdefghij'))
df['group'] = np.random.randint(100, size=N)
df_grouped = df.groupby('group')
s1 = df_grouped.apply(lambda grp: grp['a'].sum() if grp['b'].sum() > 1 else grp['a'].max())
print (s1)
df_grouped = df.groupby('group')
s2 = df_grouped['a'].sum().where(df_grouped['b'].sum() > 1, df_grouped['a'].max())
print (s2)
In [69]: %%timeit
...: df_grouped = df.groupby('group')
...: s1 = df_grouped.apply(lambda grp: grp['a'].sum() if grp['b'].sum() > 1 else grp['a'].max())
...:
24.8 ms ± 228 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [70]: %%timeit
...: df_grouped = df.groupby('group')
...: s2 = df_grouped['a'].sum().where(df_grouped['b'].sum() > 1, df_grouped['a'].max())
...:
1.63 ms ± 58 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
answered Nov 13 '18 at 7:32
jezraeljezrael
322k23265342
add a comment |
0
I figured it out with apply:
df_grouped = df.groupby('group')
df_grouped.apply(lambda grp: grp['a'].sum() if grp['b'].sum() > 1 else grp['a'].max())
group
0 0.834666
1 1.096652
dtype: float64
answered Nov 9 '18 at 23:56
jchaykowjchaykow
507318
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
1
For better performance use where:
np.random.seed(15)
N = 1000
df = pd.DataFrame(np.random.rand(N,10), columns=list('abcdefghij'))
df['group'] = np.random.randint(100, size=N)
df_grouped = df.groupby('group')
s1 = df_grouped.apply(lambda grp: grp['a'].sum() if grp['b'].sum() > 1 else grp['a'].max())
print (s1)
df_grouped = df.groupby('group')
s2 = df_grouped['a'].sum().where(df_grouped['b'].sum() > 1, df_grouped['a'].max())
print (s2)
In [69]: %%timeit
...: df_grouped = df.groupby('group')
...: s1 = df_grouped.apply(lambda grp: grp['a'].sum() if grp['b'].sum() > 1 else grp['a'].max())
...:
24.8 ms ± 228 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [70]: %%timeit
...: df_grouped = df.groupby('group')
...: s2 = df_grouped['a'].sum().where(df_grouped['b'].sum() > 1, df_grouped['a'].max())
...:
1.63 ms ± 58 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
answered Nov 13 '18 at 7:32
jezraeljezrael
322k23265342
add a comment |
1
For better performance use where:
np.random.seed(15)
N = 1000
df = pd.DataFrame(np.random.rand(N,10), columns=list('abcdefghij'))
df['group'] = np.random.randint(100, size=N)
df_grouped = df.groupby('group')
s1 = df_grouped.apply(lambda grp: grp['a'].sum() if grp['b'].sum() > 1 else grp['a'].max())
print (s1)
df_grouped = df.groupby('group')
s2 = df_grouped['a'].sum().where(df_grouped['b'].sum() > 1, df_grouped['a'].max())
print (s2)
In [69]: %%timeit
...: df_grouped = df.groupby('group')
...: s1 = df_grouped.apply(lambda grp: grp['a'].sum() if grp['b'].sum() > 1 else grp['a'].max())
...:
24.8 ms ± 228 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [70]: %%timeit
...: df_grouped = df.groupby('group')
...: s2 = df_grouped['a'].sum().where(df_grouped['b'].sum() > 1, df_grouped['a'].max())
...:
1.63 ms ± 58 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
answered Nov 13 '18 at 7:32
jezraeljezrael
322k23265342
add a comment |
1
1
1
For better performance use where:
np.random.seed(15)
N = 1000
df = pd.DataFrame(np.random.rand(N,10), columns=list('abcdefghij'))
df['group'] = np.random.randint(100, size=N)
df_grouped = df.groupby('group')
s1 = df_grouped.apply(lambda grp: grp['a'].sum() if grp['b'].sum() > 1 else grp['a'].max())
print (s1)
df_grouped = df.groupby('group')
s2 = df_grouped['a'].sum().where(df_grouped['b'].sum() > 1, df_grouped['a'].max())
print (s2)
In [69]: %%timeit
...: df_grouped = df.groupby('group')
...: s1 = df_grouped.apply(lambda grp: grp['a'].sum() if grp['b'].sum() > 1 else grp['a'].max())
...:
24.8 ms ± 228 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [70]: %%timeit
...: df_grouped = df.groupby('group')
...: s2 = df_grouped['a'].sum().where(df_grouped['b'].sum() > 1, df_grouped['a'].max())
...:
1.63 ms ± 58 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
answered Nov 13 '18 at 7:32
jezraeljezrael
322k23265342
For better performance use where:
np.random.seed(15)
N = 1000
df = pd.DataFrame(np.random.rand(N,10), columns=list('abcdefghij'))
df['group'] = np.random.randint(100, size=N)
df_grouped = df.groupby('group')
s1 = df_grouped.apply(lambda grp: grp['a'].sum() if grp['b'].sum() > 1 else grp['a'].max())
print (s1)
df_grouped = df.groupby('group')
s2 = df_grouped['a'].sum().where(df_grouped['b'].sum() > 1, df_grouped['a'].max())
print (s2)
In [69]: %%timeit
...: df_grouped = df.groupby('group')
...: s1 = df_grouped.apply(lambda grp: grp['a'].sum() if grp['b'].sum() > 1 else grp['a'].max())
...:
24.8 ms ± 228 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [70]: %%timeit
...: df_grouped = df.groupby('group')
...: s2 = df_grouped['a'].sum().where(df_grouped['b'].sum() > 1, df_grouped['a'].max())
...:
1.63 ms ± 58 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
answered Nov 13 '18 at 7:32
jezraeljezrael
322k23265342
answered Nov 13 '18 at 7:32
jezraeljezrael
322k23265342
answered Nov 13 '18 at 7:32
jezraeljezrael
322k23265342
answered Nov 13 '18 at 7:32
jezraeljezrael
322k23265342
322k23265342
add a comment |
add a comment |
0
I figured it out with apply:
df_grouped = df.groupby('group')
df_grouped.apply(lambda grp: grp['a'].sum() if grp['b'].sum() > 1 else grp['a'].max())
group
0 0.834666
1 1.096652
dtype: float64
answered Nov 9 '18 at 23:56
jchaykowjchaykow
507318
add a comment |
0
I figured it out with apply:
df_grouped = df.groupby('group')
df_grouped.apply(lambda grp: grp['a'].sum() if grp['b'].sum() > 1 else grp['a'].max())
group
0 0.834666
1 1.096652
dtype: float64
answered Nov 9 '18 at 23:56
jchaykowjchaykow
507318
add a comment |
0
0
0
I figured it out with apply:
df_grouped = df.groupby('group')
df_grouped.apply(lambda grp: grp['a'].sum() if grp['b'].sum() > 1 else grp['a'].max())
group
0 0.834666
1 1.096652
dtype: float64
answered Nov 9 '18 at 23:56
jchaykowjchaykow
507318
I figured it out with apply:
df_grouped = df.groupby('group')
df_grouped.apply(lambda grp: grp['a'].sum() if grp['b'].sum() > 1 else grp['a'].max())
group
0 0.834666
1 1.096652
dtype: float64
answered Nov 9 '18 at 23:56
jchaykowjchaykow
507318
answered Nov 9 '18 at 23:56
jchaykowjchaykow
507318
answered Nov 9 '18 at 23:56
jchaykowjchaykow
507318
answered Nov 9 '18 at 23:56
jchaykowjchaykow
507318
507318
add a comment |
add a comment |
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