Mixing
is it possible to split column from specific element in list?
I have tried to transform the data frame into the list like this.
here is the data frame before transformation.
df_gr = df_com.groupby(['Publication_Year_x', 'UPC', 'CPC']).size().unstack(fill_value=0)
df_gr_peryear = df_gr.replace(0, '', regex=True)
df_gr_peryear
and here is the list that i have tried with this code
list_allyear = [(list(i), v) for i, v in df_gr_peryear.stack().iteritems()]
list_allyear = pd.DataFrame(list_allyear, columns=["MOC", "count"])
list_allyear
here is the result
but I would like to split it just only the first element in all rows. the expected result will be
year MOC count
1971 [136,-]
1971 [136, A01D]
.
.
1972 [231, H01L] 5
I have tried the pd.DataFrame(list_allyear.MOC.values.tolist()) but it splits all elements that contain in the rows
python pandas list
asked Nov 22 '18 at 6:38
Hook ImHook Im
1318
add a comment |
I have tried to transform the data frame into the list like this.
here is the data frame before transformation.
df_gr = df_com.groupby(['Publication_Year_x', 'UPC', 'CPC']).size().unstack(fill_value=0)
df_gr_peryear = df_gr.replace(0, '', regex=True)
df_gr_peryear
and here is the list that i have tried with this code
list_allyear = [(list(i), v) for i, v in df_gr_peryear.stack().iteritems()]
list_allyear = pd.DataFrame(list_allyear, columns=["MOC", "count"])
list_allyear
here is the result
but I would like to split it just only the first element in all rows. the expected result will be
year MOC count
1971 [136,-]
1971 [136, A01D]
.
.
1972 [231, H01L] 5
I have tried the pd.DataFrame(list_allyear.MOC.values.tolist()) but it splits all elements that contain in the rows
python pandas list
asked Nov 22 '18 at 6:38
Hook ImHook Im
1318
One question - Is not problem mixed empty values with numeric? Not better omitdf_gr_peryear = df_gr.replace(0, '', regex=True)?
– jezrael
Nov 22 '18 at 6:55
yes, there is not the problem
– Hook Im
Nov 22 '18 at 6:56
add a comment |
I have tried to transform the data frame into the list like this.
here is the data frame before transformation.
df_gr = df_com.groupby(['Publication_Year_x', 'UPC', 'CPC']).size().unstack(fill_value=0)
df_gr_peryear = df_gr.replace(0, '', regex=True)
df_gr_peryear
and here is the list that i have tried with this code
list_allyear = [(list(i), v) for i, v in df_gr_peryear.stack().iteritems()]
list_allyear = pd.DataFrame(list_allyear, columns=["MOC", "count"])
list_allyear
here is the result
but I would like to split it just only the first element in all rows. the expected result will be
year MOC count
1971 [136,-]
1971 [136, A01D]
.
.
1972 [231, H01L] 5
I have tried the pd.DataFrame(list_allyear.MOC.values.tolist()) but it splits all elements that contain in the rows
python pandas list
asked Nov 22 '18 at 6:38
Hook ImHook Im
1318
I have tried to transform the data frame into the list like this.
here is the data frame before transformation.
df_gr = df_com.groupby(['Publication_Year_x', 'UPC', 'CPC']).size().unstack(fill_value=0)
df_gr_peryear = df_gr.replace(0, '', regex=True)
df_gr_peryear
and here is the list that i have tried with this code
list_allyear = [(list(i), v) for i, v in df_gr_peryear.stack().iteritems()]
list_allyear = pd.DataFrame(list_allyear, columns=["MOC", "count"])
list_allyear
here is the result
but I would like to split it just only the first element in all rows. the expected result will be
year MOC count
1971 [136,-]
1971 [136, A01D]
.
.
1972 [231, H01L] 5
I have tried the pd.DataFrame(list_allyear.MOC.values.tolist()) but it splits all elements that contain in the rows
python pandas list
python pandas list
asked Nov 22 '18 at 6:38
Hook ImHook Im
1318
asked Nov 22 '18 at 6:38
Hook ImHook Im
1318
asked Nov 22 '18 at 6:38
Hook ImHook Im
1318
asked Nov 22 '18 at 6:38
Hook ImHook Im
1318
asked Nov 22 '18 at 6:38
Hook ImHook Im
1318
1318
One question - Is not problem mixed empty values with numeric? Not better omitdf_gr_peryear = df_gr.replace(0, '', regex=True)?
– jezrael
Nov 22 '18 at 6:55
yes, there is not the problem
– Hook Im
Nov 22 '18 at 6:56
add a comment |
One question - Is not problem mixed empty values with numeric? Not better omitdf_gr_peryear = df_gr.replace(0, '', regex=True)?
– jezrael
Nov 22 '18 at 6:55
yes, there is not the problem
– Hook Im
Nov 22 '18 at 6:56
One question - Is not problem mixed empty values with numeric? Not better omit
df_gr_peryear = df_gr.replace(0, '', regex=True) ?– jezrael
Nov 22 '18 at 6:55
One question - Is not problem mixed empty values with numeric? Not better omit
df_gr_peryear = df_gr.replace(0, '', regex=True) ?– jezrael
Nov 22 '18 at 6:55
yes, there is not the problem
– Hook Im
Nov 22 '18 at 6:56
yes, there is not the problem
– Hook Im
Nov 22 '18 at 6:56
add a comment |
1 Answer
1
active
oldest
votes
Just simplified your code where you can use fill_value='' instead replace and then creating a dictionaries for list comprehension to have a final DataFrame:
cols = ['Publication_Year_x', 'UPC', 'CPC']
s = df_com.groupby(cols).size().unstack(fill_value='').stack()
L = [{'year': idx[0], 'MOC': list(idx[1:]), 'count': vals} for idx, vals in s.items()]
list_allyear = pd.DataFrame(L)
edited Nov 22 '18 at 7:03
pygo
3,1961619
answered Nov 22 '18 at 6:42
jezraeljezrael
340k25294365
@HookIm - You are welcome! Be free upvote solution too :)
– jezrael
Nov 22 '18 at 7:00
1
@pygo - Thank you.
– jezrael
Nov 22 '18 at 7:03
1
Nice solution +1 :-) , something new to learn
– pygo
Nov 22 '18 at 7:04
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
Just simplified your code where you can use fill_value='' instead replace and then creating a dictionaries for list comprehension to have a final DataFrame:
cols = ['Publication_Year_x', 'UPC', 'CPC']
s = df_com.groupby(cols).size().unstack(fill_value='').stack()
L = [{'year': idx[0], 'MOC': list(idx[1:]), 'count': vals} for idx, vals in s.items()]
list_allyear = pd.DataFrame(L)
edited Nov 22 '18 at 7:03
pygo
3,1961619
answered Nov 22 '18 at 6:42
jezraeljezrael
340k25294365
@HookIm - You are welcome! Be free upvote solution too :)
– jezrael
Nov 22 '18 at 7:00
1
@pygo - Thank you.
– jezrael
Nov 22 '18 at 7:03
1
Nice solution +1 :-) , something new to learn
– pygo
Nov 22 '18 at 7:04
add a comment |
Just simplified your code where you can use fill_value='' instead replace and then creating a dictionaries for list comprehension to have a final DataFrame:
cols = ['Publication_Year_x', 'UPC', 'CPC']
s = df_com.groupby(cols).size().unstack(fill_value='').stack()
L = [{'year': idx[0], 'MOC': list(idx[1:]), 'count': vals} for idx, vals in s.items()]
list_allyear = pd.DataFrame(L)
edited Nov 22 '18 at 7:03
pygo
3,1961619
answered Nov 22 '18 at 6:42
jezraeljezrael
340k25294365
@HookIm - You are welcome! Be free upvote solution too :)
– jezrael
Nov 22 '18 at 7:00
1
@pygo - Thank you.
– jezrael
Nov 22 '18 at 7:03
1
Nice solution +1 :-) , something new to learn
– pygo
Nov 22 '18 at 7:04
add a comment |
Just simplified your code where you can use fill_value='' instead replace and then creating a dictionaries for list comprehension to have a final DataFrame:
cols = ['Publication_Year_x', 'UPC', 'CPC']
s = df_com.groupby(cols).size().unstack(fill_value='').stack()
L = [{'year': idx[0], 'MOC': list(idx[1:]), 'count': vals} for idx, vals in s.items()]
list_allyear = pd.DataFrame(L)
edited Nov 22 '18 at 7:03
pygo
3,1961619
answered Nov 22 '18 at 6:42
jezraeljezrael
340k25294365
Just simplified your code where you can use fill_value='' instead replace and then creating a dictionaries for list comprehension to have a final DataFrame:
cols = ['Publication_Year_x', 'UPC', 'CPC']
s = df_com.groupby(cols).size().unstack(fill_value='').stack()
L = [{'year': idx[0], 'MOC': list(idx[1:]), 'count': vals} for idx, vals in s.items()]
list_allyear = pd.DataFrame(L)
edited Nov 22 '18 at 7:03
pygo
3,1961619
answered Nov 22 '18 at 6:42
jezraeljezrael
340k25294365
edited Nov 22 '18 at 7:03
pygo
3,1961619
edited Nov 22 '18 at 7:03
pygo
3,1961619
edited Nov 22 '18 at 7:03
pygo
3,1961619
3,1961619
answered Nov 22 '18 at 6:42
jezraeljezrael
340k25294365
answered Nov 22 '18 at 6:42
jezraeljezrael
340k25294365
answered Nov 22 '18 at 6:42
jezraeljezrael
340k25294365
340k25294365
@HookIm - You are welcome! Be free upvote solution too :)
– jezrael
Nov 22 '18 at 7:00
1
@pygo - Thank you.
– jezrael
Nov 22 '18 at 7:03
1
Nice solution +1 :-) , something new to learn
– pygo
Nov 22 '18 at 7:04
add a comment |
@HookIm - You are welcome! Be free upvote solution too :)
– jezrael
Nov 22 '18 at 7:00
1
@pygo - Thank you.
– jezrael
Nov 22 '18 at 7:03
1
Nice solution +1 :-) , something new to learn
– pygo
Nov 22 '18 at 7:04
@HookIm - You are welcome! Be free upvote solution too :)
– jezrael
Nov 22 '18 at 7:00
@HookIm - You are welcome! Be free upvote solution too :)
– jezrael
Nov 22 '18 at 7:00
1
1
@pygo - Thank you.
– jezrael
Nov 22 '18 at 7:03
@pygo - Thank you.
– jezrael
Nov 22 '18 at 7:03
1
1
Nice solution +1 :-) , something new to learn
– pygo
Nov 22 '18 at 7:04
Nice solution +1 :-) , something new to learn
– pygo
Nov 22 '18 at 7:04
add a comment |
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One question - Is not problem mixed empty values with numeric? Not better omit
df_gr_peryear = df_gr.replace(0, '', regex=True)?– jezrael
Nov 22 '18 at 6:55
yes, there is not the problem
– Hook Im
Nov 22 '18 at 6:56