get string slices in a groupby statement python
up vote
1
down vote
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I have a dataframe where I want to group by the ID field and get last letters in GG field. For example, say I have the following:
df1 = pd.DataFrame({
'ID':['Q'] * 3,
'GG':['L3S_0097A','L3S_0097B','L3S_0097C']
})
print (df1)
ID GG
0 Q L3S_0097A
1 Q L3S_0097B
2 Q L3S_0097C
I am trying to groupby ID column and get only last letter in GG column and add it to the defaultdict like this:
{'Q': ['A','B','C']}
Here is the code I tried:
mm = df1.groupby('ID')['GG'].str[-1].apply(list).to_dict()
and also tried the following code:
for i, j in zip(df1.ID,df1.GG):
mm[i].append(j[-1])
but both din't work. May I know how to do it?
python-3.x pandas
add a comment |
up vote
1
down vote
favorite
I have a dataframe where I want to group by the ID field and get last letters in GG field. For example, say I have the following:
df1 = pd.DataFrame({
'ID':['Q'] * 3,
'GG':['L3S_0097A','L3S_0097B','L3S_0097C']
})
print (df1)
ID GG
0 Q L3S_0097A
1 Q L3S_0097B
2 Q L3S_0097C
I am trying to groupby ID column and get only last letter in GG column and add it to the defaultdict like this:
{'Q': ['A','B','C']}
Here is the code I tried:
mm = df1.groupby('ID')['GG'].str[-1].apply(list).to_dict()
and also tried the following code:
for i, j in zip(df1.ID,df1.GG):
mm[i].append(j[-1])
but both din't work. May I know how to do it?
python-3.x pandas
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
I have a dataframe where I want to group by the ID field and get last letters in GG field. For example, say I have the following:
df1 = pd.DataFrame({
'ID':['Q'] * 3,
'GG':['L3S_0097A','L3S_0097B','L3S_0097C']
})
print (df1)
ID GG
0 Q L3S_0097A
1 Q L3S_0097B
2 Q L3S_0097C
I am trying to groupby ID column and get only last letter in GG column and add it to the defaultdict like this:
{'Q': ['A','B','C']}
Here is the code I tried:
mm = df1.groupby('ID')['GG'].str[-1].apply(list).to_dict()
and also tried the following code:
for i, j in zip(df1.ID,df1.GG):
mm[i].append(j[-1])
but both din't work. May I know how to do it?
python-3.x pandas
I have a dataframe where I want to group by the ID field and get last letters in GG field. For example, say I have the following:
df1 = pd.DataFrame({
'ID':['Q'] * 3,
'GG':['L3S_0097A','L3S_0097B','L3S_0097C']
})
print (df1)
ID GG
0 Q L3S_0097A
1 Q L3S_0097B
2 Q L3S_0097C
I am trying to groupby ID column and get only last letter in GG column and add it to the defaultdict like this:
{'Q': ['A','B','C']}
Here is the code I tried:
mm = df1.groupby('ID')['GG'].str[-1].apply(list).to_dict()
and also tried the following code:
for i, j in zip(df1.ID,df1.GG):
mm[i].append(j[-1])
but both din't work. May I know how to do it?
python-3.x pandas
python-3.x pandas
edited Nov 20 at 12:01
jezrael
316k22256333
316k22256333
asked Nov 20 at 11:53
amrutha
827
827
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add a comment |
1 Answer
1
active
oldest
votes
up vote
1
down vote
Use syntactic sugar - groupby
by - 2 Series
- GG
Series with last value and df1['ID']
:
mm = df1['GG'].str[-1].groupby(df1['ID']).apply(list).to_dict()
Or assign
only last value back to GG
:
mm = df1.assign(GG = df1['GG'].str[-1]).groupby('ID')['GG'].apply(list).to_dict()
print (mm)
{'Q': ['A', 'B', 'C']}
Pure python solution:
from collections import defaultdict
mm = defaultdict(list)
#https://stackoverflow.com/a/10532492
for i, j in zip(df1.ID,df1.GG):
mm[i].append(j[-1])
print (mm)
defaultdict(<class 'list'>, {'Q': ['A', 'B', 'C']})
thanks.. it worked.. but may I know how can I do it with zip statement? because when I tried using the above posted zip code, I get the error like this: "AttributeError: 'list' object has no attribute 'str' "
– amrutha
Nov 20 at 12:07
@amrutha - I think needdefaultdict
for this.
– jezrael
Nov 20 at 12:14
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
Use syntactic sugar - groupby
by - 2 Series
- GG
Series with last value and df1['ID']
:
mm = df1['GG'].str[-1].groupby(df1['ID']).apply(list).to_dict()
Or assign
only last value back to GG
:
mm = df1.assign(GG = df1['GG'].str[-1]).groupby('ID')['GG'].apply(list).to_dict()
print (mm)
{'Q': ['A', 'B', 'C']}
Pure python solution:
from collections import defaultdict
mm = defaultdict(list)
#https://stackoverflow.com/a/10532492
for i, j in zip(df1.ID,df1.GG):
mm[i].append(j[-1])
print (mm)
defaultdict(<class 'list'>, {'Q': ['A', 'B', 'C']})
thanks.. it worked.. but may I know how can I do it with zip statement? because when I tried using the above posted zip code, I get the error like this: "AttributeError: 'list' object has no attribute 'str' "
– amrutha
Nov 20 at 12:07
@amrutha - I think needdefaultdict
for this.
– jezrael
Nov 20 at 12:14
add a comment |
up vote
1
down vote
Use syntactic sugar - groupby
by - 2 Series
- GG
Series with last value and df1['ID']
:
mm = df1['GG'].str[-1].groupby(df1['ID']).apply(list).to_dict()
Or assign
only last value back to GG
:
mm = df1.assign(GG = df1['GG'].str[-1]).groupby('ID')['GG'].apply(list).to_dict()
print (mm)
{'Q': ['A', 'B', 'C']}
Pure python solution:
from collections import defaultdict
mm = defaultdict(list)
#https://stackoverflow.com/a/10532492
for i, j in zip(df1.ID,df1.GG):
mm[i].append(j[-1])
print (mm)
defaultdict(<class 'list'>, {'Q': ['A', 'B', 'C']})
thanks.. it worked.. but may I know how can I do it with zip statement? because when I tried using the above posted zip code, I get the error like this: "AttributeError: 'list' object has no attribute 'str' "
– amrutha
Nov 20 at 12:07
@amrutha - I think needdefaultdict
for this.
– jezrael
Nov 20 at 12:14
add a comment |
up vote
1
down vote
up vote
1
down vote
Use syntactic sugar - groupby
by - 2 Series
- GG
Series with last value and df1['ID']
:
mm = df1['GG'].str[-1].groupby(df1['ID']).apply(list).to_dict()
Or assign
only last value back to GG
:
mm = df1.assign(GG = df1['GG'].str[-1]).groupby('ID')['GG'].apply(list).to_dict()
print (mm)
{'Q': ['A', 'B', 'C']}
Pure python solution:
from collections import defaultdict
mm = defaultdict(list)
#https://stackoverflow.com/a/10532492
for i, j in zip(df1.ID,df1.GG):
mm[i].append(j[-1])
print (mm)
defaultdict(<class 'list'>, {'Q': ['A', 'B', 'C']})
Use syntactic sugar - groupby
by - 2 Series
- GG
Series with last value and df1['ID']
:
mm = df1['GG'].str[-1].groupby(df1['ID']).apply(list).to_dict()
Or assign
only last value back to GG
:
mm = df1.assign(GG = df1['GG'].str[-1]).groupby('ID')['GG'].apply(list).to_dict()
print (mm)
{'Q': ['A', 'B', 'C']}
Pure python solution:
from collections import defaultdict
mm = defaultdict(list)
#https://stackoverflow.com/a/10532492
for i, j in zip(df1.ID,df1.GG):
mm[i].append(j[-1])
print (mm)
defaultdict(<class 'list'>, {'Q': ['A', 'B', 'C']})
edited Nov 20 at 12:14
answered Nov 20 at 11:56
jezrael
316k22256333
316k22256333
thanks.. it worked.. but may I know how can I do it with zip statement? because when I tried using the above posted zip code, I get the error like this: "AttributeError: 'list' object has no attribute 'str' "
– amrutha
Nov 20 at 12:07
@amrutha - I think needdefaultdict
for this.
– jezrael
Nov 20 at 12:14
add a comment |
thanks.. it worked.. but may I know how can I do it with zip statement? because when I tried using the above posted zip code, I get the error like this: "AttributeError: 'list' object has no attribute 'str' "
– amrutha
Nov 20 at 12:07
@amrutha - I think needdefaultdict
for this.
– jezrael
Nov 20 at 12:14
thanks.. it worked.. but may I know how can I do it with zip statement? because when I tried using the above posted zip code, I get the error like this: "AttributeError: 'list' object has no attribute 'str' "
– amrutha
Nov 20 at 12:07
thanks.. it worked.. but may I know how can I do it with zip statement? because when I tried using the above posted zip code, I get the error like this: "AttributeError: 'list' object has no attribute 'str' "
– amrutha
Nov 20 at 12:07
@amrutha - I think need
defaultdict
for this.– jezrael
Nov 20 at 12:14
@amrutha - I think need
defaultdict
for this.– jezrael
Nov 20 at 12:14
add a comment |
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