DataFrame transform column values to new columns
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0
I have following series:
project id type
First 130403725 PRODUCT 68
EMPTY 2
Six 130405706 PRODUCT 24
132517244 PRODUCT 33
132607436 PRODUCT 87
How I can transform type values to new columns:
PRODUCT EMPTY
project id
First 130403725 68 2
Six 130405706 24 0
132517244 33 0
132607436 87 0
python dataframe
asked Nov 23 '18 at 11:01
Night WalkerNight Walker
8,36340131201
add a comment |
0
I have following series:
project id type
First 130403725 PRODUCT 68
EMPTY 2
Six 130405706 PRODUCT 24
132517244 PRODUCT 33
132607436 PRODUCT 87
How I can transform type values to new columns:
PRODUCT EMPTY
project id
First 130403725 68 2
Six 130405706 24 0
132517244 33 0
132607436 87 0
python dataframe
asked Nov 23 '18 at 11:01
Night WalkerNight Walker
8,36340131201
add a comment |
0
0
0
I have following series:
project id type
First 130403725 PRODUCT 68
EMPTY 2
Six 130405706 PRODUCT 24
132517244 PRODUCT 33
132607436 PRODUCT 87
How I can transform type values to new columns:
PRODUCT EMPTY
project id
First 130403725 68 2
Six 130405706 24 0
132517244 33 0
132607436 87 0
python dataframe
asked Nov 23 '18 at 11:01
Night WalkerNight Walker
8,36340131201
I have following series:
project id type
First 130403725 PRODUCT 68
EMPTY 2
Six 130405706 PRODUCT 24
132517244 PRODUCT 33
132607436 PRODUCT 87
How I can transform type values to new columns:
PRODUCT EMPTY
project id
First 130403725 68 2
Six 130405706 24 0
132517244 33 0
132607436 87 0
python dataframe
python dataframe
asked Nov 23 '18 at 11:01
Night WalkerNight Walker
8,36340131201
asked Nov 23 '18 at 11:01
Night WalkerNight Walker
8,36340131201
asked Nov 23 '18 at 11:01
Night WalkerNight Walker
8,36340131201
asked Nov 23 '18 at 11:01
Night WalkerNight Walker
8,36340131201
asked Nov 23 '18 at 11:01
Night WalkerNight Walker
8,36340131201
8,36340131201
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
1
Use unstack, because MultiIndex Series:
s1 = s.unstack(fill_value=0)
print (s1)
type EMPTY PRODUCT
project id
First 130403725 2 68
Six 130405706 0 24
132517244 0 33
132607436 0 87
For DataFrame:
df = s.unstack(fill_value=0).reset_index().rename_axis(None, axis=1)
print (df)
project id EMPTY PRODUCT
0 First 130403725 2 68
1 Six 130405706 0 24
2 Six 132517244 0 33
3 Six 132607436 0 87
answered Nov 23 '18 at 11:07
jezraeljezrael
355k26319395
add a comment |
3
This is a classic pivot table:
df_pivoted = df.pivot(index=["project", "id"], columns=["type"], values=[3])
I've used 3 as the index of the value column but it would be more clear if you would have named it.
answered Nov 23 '18 at 11:04
johnpatonjohnpaton
30517
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
Use unstack, because MultiIndex Series:
s1 = s.unstack(fill_value=0)
print (s1)
type EMPTY PRODUCT
project id
First 130403725 2 68
Six 130405706 0 24
132517244 0 33
132607436 0 87
For DataFrame:
df = s.unstack(fill_value=0).reset_index().rename_axis(None, axis=1)
print (df)
project id EMPTY PRODUCT
0 First 130403725 2 68
1 Six 130405706 0 24
2 Six 132517244 0 33
3 Six 132607436 0 87
answered Nov 23 '18 at 11:07
jezraeljezrael
355k26319395
add a comment |
1
Use unstack, because MultiIndex Series:
s1 = s.unstack(fill_value=0)
print (s1)
type EMPTY PRODUCT
project id
First 130403725 2 68
Six 130405706 0 24
132517244 0 33
132607436 0 87
For DataFrame:
df = s.unstack(fill_value=0).reset_index().rename_axis(None, axis=1)
print (df)
project id EMPTY PRODUCT
0 First 130403725 2 68
1 Six 130405706 0 24
2 Six 132517244 0 33
3 Six 132607436 0 87
answered Nov 23 '18 at 11:07
jezraeljezrael
355k26319395
add a comment |
1
1
1
Use unstack, because MultiIndex Series:
s1 = s.unstack(fill_value=0)
print (s1)
type EMPTY PRODUCT
project id
First 130403725 2 68
Six 130405706 0 24
132517244 0 33
132607436 0 87
For DataFrame:
df = s.unstack(fill_value=0).reset_index().rename_axis(None, axis=1)
print (df)
project id EMPTY PRODUCT
0 First 130403725 2 68
1 Six 130405706 0 24
2 Six 132517244 0 33
3 Six 132607436 0 87
answered Nov 23 '18 at 11:07
jezraeljezrael
355k26319395
Use unstack, because MultiIndex Series:
s1 = s.unstack(fill_value=0)
print (s1)
type EMPTY PRODUCT
project id
First 130403725 2 68
Six 130405706 0 24
132517244 0 33
132607436 0 87
For DataFrame:
df = s.unstack(fill_value=0).reset_index().rename_axis(None, axis=1)
print (df)
project id EMPTY PRODUCT
0 First 130403725 2 68
1 Six 130405706 0 24
2 Six 132517244 0 33
3 Six 132607436 0 87
answered Nov 23 '18 at 11:07
jezraeljezrael
355k26319395
edited Nov 23 '18 at 11:23
answered Nov 23 '18 at 11:07
jezraeljezrael
355k26319395
answered Nov 23 '18 at 11:07
jezraeljezrael
355k26319395
answered Nov 23 '18 at 11:07
jezraeljezrael
355k26319395
355k26319395
add a comment |
add a comment |
3
This is a classic pivot table:
df_pivoted = df.pivot(index=["project", "id"], columns=["type"], values=[3])
I've used 3 as the index of the value column but it would be more clear if you would have named it.
answered Nov 23 '18 at 11:04
johnpatonjohnpaton
30517
add a comment |
3
This is a classic pivot table:
df_pivoted = df.pivot(index=["project", "id"], columns=["type"], values=[3])
I've used 3 as the index of the value column but it would be more clear if you would have named it.
answered Nov 23 '18 at 11:04
johnpatonjohnpaton
30517
add a comment |
3
3
3
This is a classic pivot table:
df_pivoted = df.pivot(index=["project", "id"], columns=["type"], values=[3])
I've used 3 as the index of the value column but it would be more clear if you would have named it.
answered Nov 23 '18 at 11:04
johnpatonjohnpaton
30517
This is a classic pivot table:
df_pivoted = df.pivot(index=["project", "id"], columns=["type"], values=[3])
I've used 3 as the index of the value column but it would be more clear if you would have named it.
answered Nov 23 '18 at 11:04
johnpatonjohnpaton
30517
answered Nov 23 '18 at 11:04
johnpatonjohnpaton
30517
answered Nov 23 '18 at 11:04
johnpatonjohnpaton
30517
answered Nov 23 '18 at 11:04
johnpatonjohnpaton
30517
30517
add a comment |
add a comment |
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