Aggregate dataframe rows into a dictionary
1
I have a pandas DataFrame object where each row represents one object in an image.
One example of a possible row would be:
{'img_filename': 'img1.txt', 'img_size':'20', 'obj_size':'5', 'obj_type':'car'}
I want to aggregate all the objects that belong to the same image, and get something whose rows would be like:
{'img_filename': 'img1.txt', 'img_size':'20', 'obj': [{'obj_size':'5', 'obj_type':'car'}, {{'obj_size':'6', 'obj_type':'bus'}}]}
That is, the third column is a list of columns containing the data of each group.
How can I do this?
EDIT:
Consider the following example.
import pandas as pd
df1 = pd.DataFrame([
{'img_filename': 'img1.txt', 'img_size':'20', 'obj_size':'5', 'obj_type':'car'},
{'img_filename': 'img1.txt', 'img_size':'20', 'obj_size':'6', 'obj_type':'bus'},
{'img_filename': 'img2.txt', 'img_size':'25', 'obj_size':'4', 'obj_type':'car'}
])
df2 = pd.DataFrame([
{'img_filename': 'img1.txt', 'img_size':'20', 'obj': [{'obj_size':'5', 'obj_type':'car'}, {'obj_size':'6', 'obj_type':'bus'}]},
{'img_filename': 'img2.txt', 'img_size':'25', 'obj': [{'obj_size':'4', 'obj_type':'car'}]}
])
I want to turn df1 into df2.
python pandas
asked Nov 22 at 17:46
Jsevillamol
628617
add a comment |
1
I have a pandas DataFrame object where each row represents one object in an image.
One example of a possible row would be:
{'img_filename': 'img1.txt', 'img_size':'20', 'obj_size':'5', 'obj_type':'car'}
I want to aggregate all the objects that belong to the same image, and get something whose rows would be like:
{'img_filename': 'img1.txt', 'img_size':'20', 'obj': [{'obj_size':'5', 'obj_type':'car'}, {{'obj_size':'6', 'obj_type':'bus'}}]}
That is, the third column is a list of columns containing the data of each group.
How can I do this?
EDIT:
Consider the following example.
import pandas as pd
df1 = pd.DataFrame([
{'img_filename': 'img1.txt', 'img_size':'20', 'obj_size':'5', 'obj_type':'car'},
{'img_filename': 'img1.txt', 'img_size':'20', 'obj_size':'6', 'obj_type':'bus'},
{'img_filename': 'img2.txt', 'img_size':'25', 'obj_size':'4', 'obj_type':'car'}
])
df2 = pd.DataFrame([
{'img_filename': 'img1.txt', 'img_size':'20', 'obj': [{'obj_size':'5', 'obj_type':'car'}, {'obj_size':'6', 'obj_type':'bus'}]},
{'img_filename': 'img2.txt', 'img_size':'25', 'obj': [{'obj_size':'4', 'obj_type':'car'}]}
])
I want to turn df1 into df2.
python pandas
asked Nov 22 at 17:46
Jsevillamol
628617
You have dictionaries inside pandas cells?
– roganjosh
Nov 22 at 17:51
@roganjosh I represented the rows as dictionaries, but they are not
– Jsevillamol
Nov 22 at 17:53
Could you add a small reproducible example to help understand the problem?
– Sven Harris
Nov 22 at 17:54
1
Then this is actually pretty confusing. Please make a small DF that actually illustrates your task. It need only be 3 or 4 rows of junk data, so long as it illustrates the point
– roganjosh
Nov 22 at 17:55
1
@roganjosh done
– Jsevillamol
Nov 22 at 17:59
add a comment |
1
1
1
I have a pandas DataFrame object where each row represents one object in an image.
One example of a possible row would be:
{'img_filename': 'img1.txt', 'img_size':'20', 'obj_size':'5', 'obj_type':'car'}
I want to aggregate all the objects that belong to the same image, and get something whose rows would be like:
{'img_filename': 'img1.txt', 'img_size':'20', 'obj': [{'obj_size':'5', 'obj_type':'car'}, {{'obj_size':'6', 'obj_type':'bus'}}]}
That is, the third column is a list of columns containing the data of each group.
How can I do this?
EDIT:
Consider the following example.
import pandas as pd
df1 = pd.DataFrame([
{'img_filename': 'img1.txt', 'img_size':'20', 'obj_size':'5', 'obj_type':'car'},
{'img_filename': 'img1.txt', 'img_size':'20', 'obj_size':'6', 'obj_type':'bus'},
{'img_filename': 'img2.txt', 'img_size':'25', 'obj_size':'4', 'obj_type':'car'}
])
df2 = pd.DataFrame([
{'img_filename': 'img1.txt', 'img_size':'20', 'obj': [{'obj_size':'5', 'obj_type':'car'}, {'obj_size':'6', 'obj_type':'bus'}]},
{'img_filename': 'img2.txt', 'img_size':'25', 'obj': [{'obj_size':'4', 'obj_type':'car'}]}
])
I want to turn df1 into df2.
python pandas
asked Nov 22 at 17:46
Jsevillamol
628617
I have a pandas DataFrame object where each row represents one object in an image.
One example of a possible row would be:
{'img_filename': 'img1.txt', 'img_size':'20', 'obj_size':'5', 'obj_type':'car'}
I want to aggregate all the objects that belong to the same image, and get something whose rows would be like:
{'img_filename': 'img1.txt', 'img_size':'20', 'obj': [{'obj_size':'5', 'obj_type':'car'}, {{'obj_size':'6', 'obj_type':'bus'}}]}
That is, the third column is a list of columns containing the data of each group.
How can I do this?
EDIT:
Consider the following example.
import pandas as pd
df1 = pd.DataFrame([
{'img_filename': 'img1.txt', 'img_size':'20', 'obj_size':'5', 'obj_type':'car'},
{'img_filename': 'img1.txt', 'img_size':'20', 'obj_size':'6', 'obj_type':'bus'},
{'img_filename': 'img2.txt', 'img_size':'25', 'obj_size':'4', 'obj_type':'car'}
])
df2 = pd.DataFrame([
{'img_filename': 'img1.txt', 'img_size':'20', 'obj': [{'obj_size':'5', 'obj_type':'car'}, {'obj_size':'6', 'obj_type':'bus'}]},
{'img_filename': 'img2.txt', 'img_size':'25', 'obj': [{'obj_size':'4', 'obj_type':'car'}]}
])
I want to turn df1 into df2.
python pandas
python pandas
asked Nov 22 at 17:46
Jsevillamol
628617
asked Nov 22 at 17:46
Jsevillamol
628617
edited Nov 22 at 17:59
asked Nov 22 at 17:46
Jsevillamol
628617
asked Nov 22 at 17:46
Jsevillamol
628617
asked Nov 22 at 17:46
Jsevillamol
628617
628617
You have dictionaries inside pandas cells?
– roganjosh
Nov 22 at 17:51
@roganjosh I represented the rows as dictionaries, but they are not
– Jsevillamol
Nov 22 at 17:53
Could you add a small reproducible example to help understand the problem?
– Sven Harris
Nov 22 at 17:54
1
Then this is actually pretty confusing. Please make a small DF that actually illustrates your task. It need only be 3 or 4 rows of junk data, so long as it illustrates the point
– roganjosh
Nov 22 at 17:55
1
@roganjosh done
– Jsevillamol
Nov 22 at 17:59
add a comment |
You have dictionaries inside pandas cells?
– roganjosh
Nov 22 at 17:51
@roganjosh I represented the rows as dictionaries, but they are not
– Jsevillamol
Nov 22 at 17:53
Could you add a small reproducible example to help understand the problem?
– Sven Harris
Nov 22 at 17:54
1
Then this is actually pretty confusing. Please make a small DF that actually illustrates your task. It need only be 3 or 4 rows of junk data, so long as it illustrates the point
– roganjosh
Nov 22 at 17:55
1
@roganjosh done
– Jsevillamol
Nov 22 at 17:59
You have dictionaries inside pandas cells?
– roganjosh
Nov 22 at 17:51
You have dictionaries inside pandas cells?
– roganjosh
Nov 22 at 17:51
@roganjosh I represented the rows as dictionaries, but they are not
– Jsevillamol
Nov 22 at 17:53
@roganjosh I represented the rows as dictionaries, but they are not
– Jsevillamol
Nov 22 at 17:53
Could you add a small reproducible example to help understand the problem?
– Sven Harris
Nov 22 at 17:54
Could you add a small reproducible example to help understand the problem?
– Sven Harris
Nov 22 at 17:54
1
1
Then this is actually pretty confusing. Please make a small DF that actually illustrates your task. It need only be 3 or 4 rows of junk data, so long as it illustrates the point
– roganjosh
Nov 22 at 17:55
Then this is actually pretty confusing. Please make a small DF that actually illustrates your task. It need only be 3 or 4 rows of junk data, so long as it illustrates the point
– roganjosh
Nov 22 at 17:55
1
1
@roganjosh done
– Jsevillamol
Nov 22 at 17:59
@roganjosh done
– Jsevillamol
Nov 22 at 17:59
add a comment |
2 Answers
2
active
oldest
votes
1
One way using to_dict
df2 = df1.groupby('img_filename')['obj_size','obj_type'].apply(lambda x: x.to_dict('records'))
df2 = df2.reset_index(name='obj')
# Assuming you have multiple same img files with different sizes then I'm choosing first.
# If this not the case then groupby directly and reset index.
#df1.groupby('img_filename, 'img_size')['obj_size','obj_type'].apply(lambda x: x.to_dict('records'))
df2['img_size'] = df1.groupby('img_filename')['img_size'].first().values
print (df2)
img_filename obj img_size
0 img1.txt [{'obj_size': '5', 'obj_type': 'car'}, {'obj_s... 20
1 img2.txt [{'obj_size': '4', 'obj_type': 'car'}] 25
answered Nov 22 at 18:24
Abhi
2,479320
add a comment |
1
One liner.
Suppose you have same img_filename and different img_size and you want to join the value.
For ex:
img_filename img_size obj_size obj_type
0 img1.txt 20 5 car
1 img1.txt 22 6 bus
2 img2.txt 25 4 car
# if you want to join the img_size of img1.txt like 20, 22
df2 = df1.groupby("img_filename")["img_size", "obj_size", "obj_type"].apply(lambda x: pd.Series({"obj": x[["obj_size", "obj_type"]].to_json(orient="records"), "img_size": ','.join(x["img_size"])})).reset_index()
Output:
img_filename obj img_size
0 img1.txt [{"obj_size":"5","obj_type":"car"},{"obj_size"... 20,22
1 img2.txt [{"obj_size":"4","obj_type":"car"}] 25
Considering first value
#if you want to consider only first value i.e. 20
df2 = df1.groupby("img_filename")["img_size", "obj_size", "obj_type"].apply(lambda x: pd.Series({"obj": x[["obj_size", "obj_type"]].to_json(orient="records"), "img_size": x["img_size"].iloc[0]})).reset_index()
Output:
img_filename obj img_size
0 img1.txt [{"obj_size":"5","obj_type":"car"},{"obj_size"... 20
1 img2.txt [{"obj_size":"4","obj_type":"car"}] 25
answered Nov 22 at 18:42
Srce Cde
1,136411
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
One way using to_dict
df2 = df1.groupby('img_filename')['obj_size','obj_type'].apply(lambda x: x.to_dict('records'))
df2 = df2.reset_index(name='obj')
# Assuming you have multiple same img files with different sizes then I'm choosing first.
# If this not the case then groupby directly and reset index.
#df1.groupby('img_filename, 'img_size')['obj_size','obj_type'].apply(lambda x: x.to_dict('records'))
df2['img_size'] = df1.groupby('img_filename')['img_size'].first().values
print (df2)
img_filename obj img_size
0 img1.txt [{'obj_size': '5', 'obj_type': 'car'}, {'obj_s... 20
1 img2.txt [{'obj_size': '4', 'obj_type': 'car'}] 25
answered Nov 22 at 18:24
Abhi
2,479320
add a comment |
1
One way using to_dict
df2 = df1.groupby('img_filename')['obj_size','obj_type'].apply(lambda x: x.to_dict('records'))
df2 = df2.reset_index(name='obj')
# Assuming you have multiple same img files with different sizes then I'm choosing first.
# If this not the case then groupby directly and reset index.
#df1.groupby('img_filename, 'img_size')['obj_size','obj_type'].apply(lambda x: x.to_dict('records'))
df2['img_size'] = df1.groupby('img_filename')['img_size'].first().values
print (df2)
img_filename obj img_size
0 img1.txt [{'obj_size': '5', 'obj_type': 'car'}, {'obj_s... 20
1 img2.txt [{'obj_size': '4', 'obj_type': 'car'}] 25
answered Nov 22 at 18:24
Abhi
2,479320
add a comment |
1
1
1
One way using to_dict
df2 = df1.groupby('img_filename')['obj_size','obj_type'].apply(lambda x: x.to_dict('records'))
df2 = df2.reset_index(name='obj')
# Assuming you have multiple same img files with different sizes then I'm choosing first.
# If this not the case then groupby directly and reset index.
#df1.groupby('img_filename, 'img_size')['obj_size','obj_type'].apply(lambda x: x.to_dict('records'))
df2['img_size'] = df1.groupby('img_filename')['img_size'].first().values
print (df2)
img_filename obj img_size
0 img1.txt [{'obj_size': '5', 'obj_type': 'car'}, {'obj_s... 20
1 img2.txt [{'obj_size': '4', 'obj_type': 'car'}] 25
answered Nov 22 at 18:24
Abhi
2,479320
One way using to_dict
df2 = df1.groupby('img_filename')['obj_size','obj_type'].apply(lambda x: x.to_dict('records'))
df2 = df2.reset_index(name='obj')
# Assuming you have multiple same img files with different sizes then I'm choosing first.
# If this not the case then groupby directly and reset index.
#df1.groupby('img_filename, 'img_size')['obj_size','obj_type'].apply(lambda x: x.to_dict('records'))
df2['img_size'] = df1.groupby('img_filename')['img_size'].first().values
print (df2)
img_filename obj img_size
0 img1.txt [{'obj_size': '5', 'obj_type': 'car'}, {'obj_s... 20
1 img2.txt [{'obj_size': '4', 'obj_type': 'car'}] 25
answered Nov 22 at 18:24
Abhi
2,479320
edited Nov 22 at 19:01
answered Nov 22 at 18:24
Abhi
2,479320
answered Nov 22 at 18:24
Abhi
2,479320
answered Nov 22 at 18:24
Abhi
2,479320
2,479320
add a comment |
add a comment |
1
One liner.
Suppose you have same img_filename and different img_size and you want to join the value.
For ex:
img_filename img_size obj_size obj_type
0 img1.txt 20 5 car
1 img1.txt 22 6 bus
2 img2.txt 25 4 car
# if you want to join the img_size of img1.txt like 20, 22
df2 = df1.groupby("img_filename")["img_size", "obj_size", "obj_type"].apply(lambda x: pd.Series({"obj": x[["obj_size", "obj_type"]].to_json(orient="records"), "img_size": ','.join(x["img_size"])})).reset_index()
Output:
img_filename obj img_size
0 img1.txt [{"obj_size":"5","obj_type":"car"},{"obj_size"... 20,22
1 img2.txt [{"obj_size":"4","obj_type":"car"}] 25
Considering first value
#if you want to consider only first value i.e. 20
df2 = df1.groupby("img_filename")["img_size", "obj_size", "obj_type"].apply(lambda x: pd.Series({"obj": x[["obj_size", "obj_type"]].to_json(orient="records"), "img_size": x["img_size"].iloc[0]})).reset_index()
Output:
img_filename obj img_size
0 img1.txt [{"obj_size":"5","obj_type":"car"},{"obj_size"... 20
1 img2.txt [{"obj_size":"4","obj_type":"car"}] 25
answered Nov 22 at 18:42
Srce Cde
1,136411
add a comment |
1
One liner.
Suppose you have same img_filename and different img_size and you want to join the value.
For ex:
img_filename img_size obj_size obj_type
0 img1.txt 20 5 car
1 img1.txt 22 6 bus
2 img2.txt 25 4 car
# if you want to join the img_size of img1.txt like 20, 22
df2 = df1.groupby("img_filename")["img_size", "obj_size", "obj_type"].apply(lambda x: pd.Series({"obj": x[["obj_size", "obj_type"]].to_json(orient="records"), "img_size": ','.join(x["img_size"])})).reset_index()
Output:
img_filename obj img_size
0 img1.txt [{"obj_size":"5","obj_type":"car"},{"obj_size"... 20,22
1 img2.txt [{"obj_size":"4","obj_type":"car"}] 25
Considering first value
#if you want to consider only first value i.e. 20
df2 = df1.groupby("img_filename")["img_size", "obj_size", "obj_type"].apply(lambda x: pd.Series({"obj": x[["obj_size", "obj_type"]].to_json(orient="records"), "img_size": x["img_size"].iloc[0]})).reset_index()
Output:
img_filename obj img_size
0 img1.txt [{"obj_size":"5","obj_type":"car"},{"obj_size"... 20
1 img2.txt [{"obj_size":"4","obj_type":"car"}] 25
answered Nov 22 at 18:42
Srce Cde
1,136411
add a comment |
1
1
1
One liner.
Suppose you have same img_filename and different img_size and you want to join the value.
For ex:
img_filename img_size obj_size obj_type
0 img1.txt 20 5 car
1 img1.txt 22 6 bus
2 img2.txt 25 4 car
# if you want to join the img_size of img1.txt like 20, 22
df2 = df1.groupby("img_filename")["img_size", "obj_size", "obj_type"].apply(lambda x: pd.Series({"obj": x[["obj_size", "obj_type"]].to_json(orient="records"), "img_size": ','.join(x["img_size"])})).reset_index()
Output:
img_filename obj img_size
0 img1.txt [{"obj_size":"5","obj_type":"car"},{"obj_size"... 20,22
1 img2.txt [{"obj_size":"4","obj_type":"car"}] 25
Considering first value
#if you want to consider only first value i.e. 20
df2 = df1.groupby("img_filename")["img_size", "obj_size", "obj_type"].apply(lambda x: pd.Series({"obj": x[["obj_size", "obj_type"]].to_json(orient="records"), "img_size": x["img_size"].iloc[0]})).reset_index()
Output:
img_filename obj img_size
0 img1.txt [{"obj_size":"5","obj_type":"car"},{"obj_size"... 20
1 img2.txt [{"obj_size":"4","obj_type":"car"}] 25
answered Nov 22 at 18:42
Srce Cde
1,136411
One liner.
Suppose you have same img_filename and different img_size and you want to join the value.
For ex:
img_filename img_size obj_size obj_type
0 img1.txt 20 5 car
1 img1.txt 22 6 bus
2 img2.txt 25 4 car
# if you want to join the img_size of img1.txt like 20, 22
df2 = df1.groupby("img_filename")["img_size", "obj_size", "obj_type"].apply(lambda x: pd.Series({"obj": x[["obj_size", "obj_type"]].to_json(orient="records"), "img_size": ','.join(x["img_size"])})).reset_index()
Output:
img_filename obj img_size
0 img1.txt [{"obj_size":"5","obj_type":"car"},{"obj_size"... 20,22
1 img2.txt [{"obj_size":"4","obj_type":"car"}] 25
Considering first value
#if you want to consider only first value i.e. 20
df2 = df1.groupby("img_filename")["img_size", "obj_size", "obj_type"].apply(lambda x: pd.Series({"obj": x[["obj_size", "obj_type"]].to_json(orient="records"), "img_size": x["img_size"].iloc[0]})).reset_index()
Output:
img_filename obj img_size
0 img1.txt [{"obj_size":"5","obj_type":"car"},{"obj_size"... 20
1 img2.txt [{"obj_size":"4","obj_type":"car"}] 25
answered Nov 22 at 18:42
Srce Cde
1,136411
edited Nov 22 at 19:28
answered Nov 22 at 18:42
Srce Cde
1,136411
answered Nov 22 at 18:42
Srce Cde
1,136411
answered Nov 22 at 18:42
Srce Cde
1,136411
1,136411
add a comment |
add a comment |
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You have dictionaries inside pandas cells?
– roganjosh
Nov 22 at 17:51
@roganjosh I represented the rows as dictionaries, but they are not
– Jsevillamol
Nov 22 at 17:53
Could you add a small reproducible example to help understand the problem?
– Sven Harris
Nov 22 at 17:54
1
Then this is actually pretty confusing. Please make a small DF that actually illustrates your task. It need only be 3 or 4 rows of junk data, so long as it illustrates the point
– roganjosh
Nov 22 at 17:55
1
@roganjosh done
– Jsevillamol
Nov 22 at 17:59