How to Group by column value in Pandas Data frame
4
I have pandas dataframe like this. I want group by App_Name in seperate variable
App_Name Date Response Gross Revenue
com.apple.tiles2 2018-10-13 3748.723574 24133394
com.orange.thescore 2018-10-13 2034.611964 8273607
com.number.studio 2018-10-13 1807.756545 33736740
com.orange.thescore 2018-10-14 4671.930435 38575556
com.number.studio 2018-10-14 3533.461547 38726087
com.banana.com 2018-10-14 2920.33747 86230313
com.apple.tiles2 2018-10-15 3986.434851 35928884
com.number.studio 2018-10-15 2044.759823 76526368
com.apple.tiles2 2018-10-16 2610.214035 30611434
com.alpha.studio 2018-10-16 1731.429858 11643154
com.banana.com 2018-10-16 1601.387403 13781285
com.alpha.studio 2018-10-17 2769.373388 13198984
com.banana.com 2018-10-17 2205.359489 21974901
com.orange.thescore 2018-10-17 1820.852862 7565015
com.alpha.studio 2018-10-18 2784.822039 24217875
com.banana.com 2018-10-18 2545.899329 28361412
com.orange.thescore 2018-10-18 2052.207745 7544861
I want to group data by App_Name and stored in sepearte list or dataframe for each App_Name, something like this given below:
App_Name Date Response Gross Revenue
com.alpha.studio 2018-10-16 1731.429858 11643154
com.alpha.studio 2018-10-17 2769.373388 13198984
com.alpha.studio 2018-10-18 2784.822039 24217875
App_Name Date Response Gross Revenue
com.apple.tiles2 2018-10-13 3748.723574 24133394
com.apple.tiles2 2018-10-15 3986.434851 35928884
com.apple.tiles2 2018-10-16 2610.214035 30611434
App_Name Date Response Gross Revenue
com.banana.com 2018-10-14 2920.33747 86230313
com.banana.com 2018-10-16 1601.387403 13781285
com.banana.com 2018-10-17 2205.359489 21974901
com.banana.com 2018-10-18 2545.899329 28361412
App_Name Date Response Gross Revenue
com.number.studio 2018-10-14 3533.461547 38726087
com.number.studio 2018-10-13 1807.756545 33736740
com.number.studio 2018-10-15 2044.759823 76526368
App_Name Date Response Gross Revenue
com.orange.thescore 2018-10-13 2034.611964 8273607
com.orange.thescore 2018-10-14 4671.930435 38575556
com.orange.thescore 2018-10-17 1820.852862 7565015
com.orange.thescore 2018-10-18 2052.207745 7544861
python pandas pandas-groupby data-science
asked Nov 12 at 10:18
hepiz
1478
add a comment |
4
I have pandas dataframe like this. I want group by App_Name in seperate variable
App_Name Date Response Gross Revenue
com.apple.tiles2 2018-10-13 3748.723574 24133394
com.orange.thescore 2018-10-13 2034.611964 8273607
com.number.studio 2018-10-13 1807.756545 33736740
com.orange.thescore 2018-10-14 4671.930435 38575556
com.number.studio 2018-10-14 3533.461547 38726087
com.banana.com 2018-10-14 2920.33747 86230313
com.apple.tiles2 2018-10-15 3986.434851 35928884
com.number.studio 2018-10-15 2044.759823 76526368
com.apple.tiles2 2018-10-16 2610.214035 30611434
com.alpha.studio 2018-10-16 1731.429858 11643154
com.banana.com 2018-10-16 1601.387403 13781285
com.alpha.studio 2018-10-17 2769.373388 13198984
com.banana.com 2018-10-17 2205.359489 21974901
com.orange.thescore 2018-10-17 1820.852862 7565015
com.alpha.studio 2018-10-18 2784.822039 24217875
com.banana.com 2018-10-18 2545.899329 28361412
com.orange.thescore 2018-10-18 2052.207745 7544861
I want to group data by App_Name and stored in sepearte list or dataframe for each App_Name, something like this given below:
App_Name Date Response Gross Revenue
com.alpha.studio 2018-10-16 1731.429858 11643154
com.alpha.studio 2018-10-17 2769.373388 13198984
com.alpha.studio 2018-10-18 2784.822039 24217875
App_Name Date Response Gross Revenue
com.apple.tiles2 2018-10-13 3748.723574 24133394
com.apple.tiles2 2018-10-15 3986.434851 35928884
com.apple.tiles2 2018-10-16 2610.214035 30611434
App_Name Date Response Gross Revenue
com.banana.com 2018-10-14 2920.33747 86230313
com.banana.com 2018-10-16 1601.387403 13781285
com.banana.com 2018-10-17 2205.359489 21974901
com.banana.com 2018-10-18 2545.899329 28361412
App_Name Date Response Gross Revenue
com.number.studio 2018-10-14 3533.461547 38726087
com.number.studio 2018-10-13 1807.756545 33736740
com.number.studio 2018-10-15 2044.759823 76526368
App_Name Date Response Gross Revenue
com.orange.thescore 2018-10-13 2034.611964 8273607
com.orange.thescore 2018-10-14 4671.930435 38575556
com.orange.thescore 2018-10-17 1820.852862 7565015
com.orange.thescore 2018-10-18 2052.207745 7544861
python pandas pandas-groupby data-science
asked Nov 12 at 10:18
hepiz
1478
1
df.groupby('App_Name')?
– juanpa.arrivillaga
Nov 12 at 10:21
5
I'd like to know how this question has 4 upvotes in 3 minutes.
– coldspeed
Nov 12 at 10:22
add a comment |
4
4
4
I have pandas dataframe like this. I want group by App_Name in seperate variable
App_Name Date Response Gross Revenue
com.apple.tiles2 2018-10-13 3748.723574 24133394
com.orange.thescore 2018-10-13 2034.611964 8273607
com.number.studio 2018-10-13 1807.756545 33736740
com.orange.thescore 2018-10-14 4671.930435 38575556
com.number.studio 2018-10-14 3533.461547 38726087
com.banana.com 2018-10-14 2920.33747 86230313
com.apple.tiles2 2018-10-15 3986.434851 35928884
com.number.studio 2018-10-15 2044.759823 76526368
com.apple.tiles2 2018-10-16 2610.214035 30611434
com.alpha.studio 2018-10-16 1731.429858 11643154
com.banana.com 2018-10-16 1601.387403 13781285
com.alpha.studio 2018-10-17 2769.373388 13198984
com.banana.com 2018-10-17 2205.359489 21974901
com.orange.thescore 2018-10-17 1820.852862 7565015
com.alpha.studio 2018-10-18 2784.822039 24217875
com.banana.com 2018-10-18 2545.899329 28361412
com.orange.thescore 2018-10-18 2052.207745 7544861
I want to group data by App_Name and stored in sepearte list or dataframe for each App_Name, something like this given below:
App_Name Date Response Gross Revenue
com.alpha.studio 2018-10-16 1731.429858 11643154
com.alpha.studio 2018-10-17 2769.373388 13198984
com.alpha.studio 2018-10-18 2784.822039 24217875
App_Name Date Response Gross Revenue
com.apple.tiles2 2018-10-13 3748.723574 24133394
com.apple.tiles2 2018-10-15 3986.434851 35928884
com.apple.tiles2 2018-10-16 2610.214035 30611434
App_Name Date Response Gross Revenue
com.banana.com 2018-10-14 2920.33747 86230313
com.banana.com 2018-10-16 1601.387403 13781285
com.banana.com 2018-10-17 2205.359489 21974901
com.banana.com 2018-10-18 2545.899329 28361412
App_Name Date Response Gross Revenue
com.number.studio 2018-10-14 3533.461547 38726087
com.number.studio 2018-10-13 1807.756545 33736740
com.number.studio 2018-10-15 2044.759823 76526368
App_Name Date Response Gross Revenue
com.orange.thescore 2018-10-13 2034.611964 8273607
com.orange.thescore 2018-10-14 4671.930435 38575556
com.orange.thescore 2018-10-17 1820.852862 7565015
com.orange.thescore 2018-10-18 2052.207745 7544861
python pandas pandas-groupby data-science
asked Nov 12 at 10:18
hepiz
1478
I have pandas dataframe like this. I want group by App_Name in seperate variable
App_Name Date Response Gross Revenue
com.apple.tiles2 2018-10-13 3748.723574 24133394
com.orange.thescore 2018-10-13 2034.611964 8273607
com.number.studio 2018-10-13 1807.756545 33736740
com.orange.thescore 2018-10-14 4671.930435 38575556
com.number.studio 2018-10-14 3533.461547 38726087
com.banana.com 2018-10-14 2920.33747 86230313
com.apple.tiles2 2018-10-15 3986.434851 35928884
com.number.studio 2018-10-15 2044.759823 76526368
com.apple.tiles2 2018-10-16 2610.214035 30611434
com.alpha.studio 2018-10-16 1731.429858 11643154
com.banana.com 2018-10-16 1601.387403 13781285
com.alpha.studio 2018-10-17 2769.373388 13198984
com.banana.com 2018-10-17 2205.359489 21974901
com.orange.thescore 2018-10-17 1820.852862 7565015
com.alpha.studio 2018-10-18 2784.822039 24217875
com.banana.com 2018-10-18 2545.899329 28361412
com.orange.thescore 2018-10-18 2052.207745 7544861
I want to group data by App_Name and stored in sepearte list or dataframe for each App_Name, something like this given below:
App_Name Date Response Gross Revenue
com.alpha.studio 2018-10-16 1731.429858 11643154
com.alpha.studio 2018-10-17 2769.373388 13198984
com.alpha.studio 2018-10-18 2784.822039 24217875
App_Name Date Response Gross Revenue
com.apple.tiles2 2018-10-13 3748.723574 24133394
com.apple.tiles2 2018-10-15 3986.434851 35928884
com.apple.tiles2 2018-10-16 2610.214035 30611434
App_Name Date Response Gross Revenue
com.banana.com 2018-10-14 2920.33747 86230313
com.banana.com 2018-10-16 1601.387403 13781285
com.banana.com 2018-10-17 2205.359489 21974901
com.banana.com 2018-10-18 2545.899329 28361412
App_Name Date Response Gross Revenue
com.number.studio 2018-10-14 3533.461547 38726087
com.number.studio 2018-10-13 1807.756545 33736740
com.number.studio 2018-10-15 2044.759823 76526368
App_Name Date Response Gross Revenue
com.orange.thescore 2018-10-13 2034.611964 8273607
com.orange.thescore 2018-10-14 4671.930435 38575556
com.orange.thescore 2018-10-17 1820.852862 7565015
com.orange.thescore 2018-10-18 2052.207745 7544861
python pandas pandas-groupby data-science
python pandas pandas-groupby data-science
asked Nov 12 at 10:18
hepiz
1478
asked Nov 12 at 10:18
hepiz
1478
asked Nov 12 at 10:18
hepiz
1478
asked Nov 12 at 10:18
hepiz
1478
asked Nov 12 at 10:18
hepiz
1478
1478
1
df.groupby('App_Name')?
– juanpa.arrivillaga
Nov 12 at 10:21
5
I'd like to know how this question has 4 upvotes in 3 minutes.
– coldspeed
Nov 12 at 10:22
add a comment |
1
df.groupby('App_Name')?
– juanpa.arrivillaga
Nov 12 at 10:21
5
I'd like to know how this question has 4 upvotes in 3 minutes.
– coldspeed
Nov 12 at 10:22
1
1
df.groupby('App_Name')?– juanpa.arrivillaga
Nov 12 at 10:21
df.groupby('App_Name')?– juanpa.arrivillaga
Nov 12 at 10:21
5
5
I'd like to know how this question has 4 upvotes in 3 minutes.
– coldspeed
Nov 12 at 10:22
I'd like to know how this question has 4 upvotes in 3 minutes.
– coldspeed
Nov 12 at 10:22
add a comment |
1 Answer
1
active
oldest
votes
3
Convert groupby object to dictionary of DataFrames:
d = dict(tuple(df.groupby('App_Name')))
print (d['com.alpha.studio'])
App_Name Date Response Gross Revenue
9 com.alpha.studio 2018-10-16 1731.429858 11643154 NaN
11 com.alpha.studio 2018-10-17 2769.373388 13198984 NaN
14 com.alpha.studio 2018-10-18 2784.822039 24217875 NaN
EDIT:
d1 =
for k, v in d.items():
d1[k] = v['Gross Revenue'].rolling(2).mean()
answered Nov 12 at 10:30
jezrael
319k22258337
2
do you have any idea whydict(df.groupby('App_Name')is giving anTypeError: attribute of type 'str' is not callableerror? It's very strange
– juanpa.arrivillaga
Nov 12 at 10:32
@juanpa.arrivillaga - Sorry, not idea.
– jezrael
Nov 12 at 11:03
@ jezrael I want to calculate the rolling mean for gross revenue and store separately date frame with calculated mean and corresponding date. can please help in it
– hepiz
Nov 13 at 12:22
1
What is formula for standard DataFrame?
– jezrael
Nov 13 at 12:22
1
@hamsa - check edited answer. Is possible loop by each DataFrame, caunt values and append back.
– jezrael
Nov 13 at 12:37
|
show 1 more comment
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3
Convert groupby object to dictionary of DataFrames:
d = dict(tuple(df.groupby('App_Name')))
print (d['com.alpha.studio'])
App_Name Date Response Gross Revenue
9 com.alpha.studio 2018-10-16 1731.429858 11643154 NaN
11 com.alpha.studio 2018-10-17 2769.373388 13198984 NaN
14 com.alpha.studio 2018-10-18 2784.822039 24217875 NaN
EDIT:
d1 =
for k, v in d.items():
d1[k] = v['Gross Revenue'].rolling(2).mean()
answered Nov 12 at 10:30
jezrael
319k22258337
2
do you have any idea whydict(df.groupby('App_Name')is giving anTypeError: attribute of type 'str' is not callableerror? It's very strange
– juanpa.arrivillaga
Nov 12 at 10:32
@juanpa.arrivillaga - Sorry, not idea.
– jezrael
Nov 12 at 11:03
@ jezrael I want to calculate the rolling mean for gross revenue and store separately date frame with calculated mean and corresponding date. can please help in it
– hepiz
Nov 13 at 12:22
1
What is formula for standard DataFrame?
– jezrael
Nov 13 at 12:22
1
@hamsa - check edited answer. Is possible loop by each DataFrame, caunt values and append back.
– jezrael
Nov 13 at 12:37
|
show 1 more comment
3
Convert groupby object to dictionary of DataFrames:
d = dict(tuple(df.groupby('App_Name')))
print (d['com.alpha.studio'])
App_Name Date Response Gross Revenue
9 com.alpha.studio 2018-10-16 1731.429858 11643154 NaN
11 com.alpha.studio 2018-10-17 2769.373388 13198984 NaN
14 com.alpha.studio 2018-10-18 2784.822039 24217875 NaN
EDIT:
d1 =
for k, v in d.items():
d1[k] = v['Gross Revenue'].rolling(2).mean()
answered Nov 12 at 10:30
jezrael
319k22258337
2
do you have any idea whydict(df.groupby('App_Name')is giving anTypeError: attribute of type 'str' is not callableerror? It's very strange
– juanpa.arrivillaga
Nov 12 at 10:32
@juanpa.arrivillaga - Sorry, not idea.
– jezrael
Nov 12 at 11:03
@ jezrael I want to calculate the rolling mean for gross revenue and store separately date frame with calculated mean and corresponding date. can please help in it
– hepiz
Nov 13 at 12:22
1
What is formula for standard DataFrame?
– jezrael
Nov 13 at 12:22
1
@hamsa - check edited answer. Is possible loop by each DataFrame, caunt values and append back.
– jezrael
Nov 13 at 12:37
|
show 1 more comment
3
3
3
Convert groupby object to dictionary of DataFrames:
d = dict(tuple(df.groupby('App_Name')))
print (d['com.alpha.studio'])
App_Name Date Response Gross Revenue
9 com.alpha.studio 2018-10-16 1731.429858 11643154 NaN
11 com.alpha.studio 2018-10-17 2769.373388 13198984 NaN
14 com.alpha.studio 2018-10-18 2784.822039 24217875 NaN
EDIT:
d1 =
for k, v in d.items():
d1[k] = v['Gross Revenue'].rolling(2).mean()
answered Nov 12 at 10:30
jezrael
319k22258337
Convert groupby object to dictionary of DataFrames:
d = dict(tuple(df.groupby('App_Name')))
print (d['com.alpha.studio'])
App_Name Date Response Gross Revenue
9 com.alpha.studio 2018-10-16 1731.429858 11643154 NaN
11 com.alpha.studio 2018-10-17 2769.373388 13198984 NaN
14 com.alpha.studio 2018-10-18 2784.822039 24217875 NaN
EDIT:
d1 =
for k, v in d.items():
d1[k] = v['Gross Revenue'].rolling(2).mean()
answered Nov 12 at 10:30
jezrael
319k22258337
edited Nov 13 at 12:36
answered Nov 12 at 10:30
jezrael
319k22258337
answered Nov 12 at 10:30
jezrael
319k22258337
answered Nov 12 at 10:30
jezrael
319k22258337
319k22258337
2
do you have any idea whydict(df.groupby('App_Name')is giving anTypeError: attribute of type 'str' is not callableerror? It's very strange
– juanpa.arrivillaga
Nov 12 at 10:32
@juanpa.arrivillaga - Sorry, not idea.
– jezrael
Nov 12 at 11:03
@ jezrael I want to calculate the rolling mean for gross revenue and store separately date frame with calculated mean and corresponding date. can please help in it
– hepiz
Nov 13 at 12:22
1
What is formula for standard DataFrame?
– jezrael
Nov 13 at 12:22
1
@hamsa - check edited answer. Is possible loop by each DataFrame, caunt values and append back.
– jezrael
Nov 13 at 12:37
|
show 1 more comment
2
do you have any idea whydict(df.groupby('App_Name')is giving anTypeError: attribute of type 'str' is not callableerror? It's very strange
– juanpa.arrivillaga
Nov 12 at 10:32
@juanpa.arrivillaga - Sorry, not idea.
– jezrael
Nov 12 at 11:03
@ jezrael I want to calculate the rolling mean for gross revenue and store separately date frame with calculated mean and corresponding date. can please help in it
– hepiz
Nov 13 at 12:22
1
What is formula for standard DataFrame?
– jezrael
Nov 13 at 12:22
1
@hamsa - check edited answer. Is possible loop by each DataFrame, caunt values and append back.
– jezrael
Nov 13 at 12:37
2
2
do you have any idea why
dict(df.groupby('App_Name') is giving an TypeError: attribute of type 'str' is not callable error? It's very strange– juanpa.arrivillaga
Nov 12 at 10:32
do you have any idea why
dict(df.groupby('App_Name') is giving an TypeError: attribute of type 'str' is not callable error? It's very strange– juanpa.arrivillaga
Nov 12 at 10:32
@juanpa.arrivillaga - Sorry, not idea.
– jezrael
Nov 12 at 11:03
@juanpa.arrivillaga - Sorry, not idea.
– jezrael
Nov 12 at 11:03
@ jezrael I want to calculate the rolling mean for gross revenue and store separately date frame with calculated mean and corresponding date. can please help in it
– hepiz
Nov 13 at 12:22
@ jezrael I want to calculate the rolling mean for gross revenue and store separately date frame with calculated mean and corresponding date. can please help in it
– hepiz
Nov 13 at 12:22
1
1
What is formula for standard DataFrame?
– jezrael
Nov 13 at 12:22
What is formula for standard DataFrame?
– jezrael
Nov 13 at 12:22
1
1
@hamsa - check edited answer. Is possible loop by each DataFrame, caunt values and append back.
– jezrael
Nov 13 at 12:37
@hamsa - check edited answer. Is possible loop by each DataFrame, caunt values and append back.
– jezrael
Nov 13 at 12:37
|
show 1 more comment
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1
df.groupby('App_Name')?– juanpa.arrivillaga
Nov 12 at 10:21
5
I'd like to know how this question has 4 upvotes in 3 minutes.
– coldspeed
Nov 12 at 10:22