Appending integers from a list to each row to a new dataframe column
I have a list of integers x and I am trying to create a new column to an existing dataframe that has each of the x[0] ... x[1345] in the appropriate place.
This is what I did:
i = 0
while i < len(x):
df['Paystrings'] = x[i]
i = i + 1
df.head()
But I only get this:
Which is just the last element of x
python pandas dataframe
asked Nov 26 '18 at 21:17
SnorrlaxxxSnorrlaxxx
13611
|
show 1 more comment
I have a list of integers x and I am trying to create a new column to an existing dataframe that has each of the x[0] ... x[1345] in the appropriate place.
This is what I did:
i = 0
while i < len(x):
df['Paystrings'] = x[i]
i = i + 1
df.head()
But I only get this:
Which is just the last element of x
python pandas dataframe
asked Nov 26 '18 at 21:17
SnorrlaxxxSnorrlaxxx
13611
You overwritedf['Paystrings']in each iteration. What's your desired output? Do you want to create 1346 new columns?
– pault
Nov 26 '18 at 21:20
That's because you are redefining the column with the valuex[i]. So at the end of the loop, that will bex[-1]for the whole column
– C.Nivs
Nov 26 '18 at 21:21
Yes because df['Paystrings'] = x[i] sets the whole column to the value x[i]
– MisterMonk
Nov 26 '18 at 21:21
@pault I want to create a new column called Paystring, then for each row in that coilumn put x[0] in the first row x[1] in the second etc... does that make sense?'
– Snorrlaxxx
Nov 26 '18 at 21:22
I'd probably wrapxin aSeriesto stick into the Dataframe
– C.Nivs
Nov 26 '18 at 21:23
|
show 1 more comment
I have a list of integers x and I am trying to create a new column to an existing dataframe that has each of the x[0] ... x[1345] in the appropriate place.
This is what I did:
i = 0
while i < len(x):
df['Paystrings'] = x[i]
i = i + 1
df.head()
But I only get this:
Which is just the last element of x
python pandas dataframe
asked Nov 26 '18 at 21:17
SnorrlaxxxSnorrlaxxx
13611
I have a list of integers x and I am trying to create a new column to an existing dataframe that has each of the x[0] ... x[1345] in the appropriate place.
This is what I did:
i = 0
while i < len(x):
df['Paystrings'] = x[i]
i = i + 1
df.head()
But I only get this:
Which is just the last element of x
python pandas dataframe
python pandas dataframe
asked Nov 26 '18 at 21:17
SnorrlaxxxSnorrlaxxx
13611
asked Nov 26 '18 at 21:17
SnorrlaxxxSnorrlaxxx
13611
asked Nov 26 '18 at 21:17
SnorrlaxxxSnorrlaxxx
13611
asked Nov 26 '18 at 21:17
SnorrlaxxxSnorrlaxxx
13611
asked Nov 26 '18 at 21:17
SnorrlaxxxSnorrlaxxx
13611
13611
You overwritedf['Paystrings']in each iteration. What's your desired output? Do you want to create 1346 new columns?
– pault
Nov 26 '18 at 21:20
That's because you are redefining the column with the valuex[i]. So at the end of the loop, that will bex[-1]for the whole column
– C.Nivs
Nov 26 '18 at 21:21
Yes because df['Paystrings'] = x[i] sets the whole column to the value x[i]
– MisterMonk
Nov 26 '18 at 21:21
@pault I want to create a new column called Paystring, then for each row in that coilumn put x[0] in the first row x[1] in the second etc... does that make sense?'
– Snorrlaxxx
Nov 26 '18 at 21:22
I'd probably wrapxin aSeriesto stick into the Dataframe
– C.Nivs
Nov 26 '18 at 21:23
|
show 1 more comment
You overwritedf['Paystrings']in each iteration. What's your desired output? Do you want to create 1346 new columns?
– pault
Nov 26 '18 at 21:20
That's because you are redefining the column with the valuex[i]. So at the end of the loop, that will bex[-1]for the whole column
– C.Nivs
Nov 26 '18 at 21:21
Yes because df['Paystrings'] = x[i] sets the whole column to the value x[i]
– MisterMonk
Nov 26 '18 at 21:21
@pault I want to create a new column called Paystring, then for each row in that coilumn put x[0] in the first row x[1] in the second etc... does that make sense?'
– Snorrlaxxx
Nov 26 '18 at 21:22
I'd probably wrapxin aSeriesto stick into the Dataframe
– C.Nivs
Nov 26 '18 at 21:23
You overwrite
df['Paystrings'] in each iteration. What's your desired output? Do you want to create 1346 new columns?– pault
Nov 26 '18 at 21:20
You overwrite
df['Paystrings'] in each iteration. What's your desired output? Do you want to create 1346 new columns?– pault
Nov 26 '18 at 21:20
That's because you are redefining the column with the value
x[i]. So at the end of the loop, that will be x[-1] for the whole column– C.Nivs
Nov 26 '18 at 21:21
That's because you are redefining the column with the value
x[i]. So at the end of the loop, that will be x[-1] for the whole column– C.Nivs
Nov 26 '18 at 21:21
Yes because df['Paystrings'] = x[i] sets the whole column to the value x[i]
– MisterMonk
Nov 26 '18 at 21:21
Yes because df['Paystrings'] = x[i] sets the whole column to the value x[i]
– MisterMonk
Nov 26 '18 at 21:21
@pault I want to create a new column called Paystring, then for each row in that coilumn put x[0] in the first row x[1] in the second etc... does that make sense?'
– Snorrlaxxx
Nov 26 '18 at 21:22
@pault I want to create a new column called Paystring, then for each row in that coilumn put x[0] in the first row x[1] in the second etc... does that make sense?'
– Snorrlaxxx
Nov 26 '18 at 21:22
I'd probably wrap
x in a Series to stick into the Dataframe– C.Nivs
Nov 26 '18 at 21:23
I'd probably wrap
x in a Series to stick into the Dataframe– C.Nivs
Nov 26 '18 at 21:23
|
show 1 more comment
1 Answer
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If you don't have a index explizit set in the creation you can do this:
df['Paystrings'] = pd.Series(x)
Elsewise you can reset the index, then all elements have the number like they are sorted in this moment.
df.reset_index()
answered Nov 26 '18 at 21:26
MisterMonkMisterMonk
1549
add a comment |
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1 Answer
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1 Answer
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active
oldest
votes
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oldest
votes
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oldest
votes
If you don't have a index explizit set in the creation you can do this:
df['Paystrings'] = pd.Series(x)
Elsewise you can reset the index, then all elements have the number like they are sorted in this moment.
df.reset_index()
answered Nov 26 '18 at 21:26
MisterMonkMisterMonk
1549
add a comment |
If you don't have a index explizit set in the creation you can do this:
df['Paystrings'] = pd.Series(x)
Elsewise you can reset the index, then all elements have the number like they are sorted in this moment.
df.reset_index()
answered Nov 26 '18 at 21:26
MisterMonkMisterMonk
1549
add a comment |
If you don't have a index explizit set in the creation you can do this:
df['Paystrings'] = pd.Series(x)
Elsewise you can reset the index, then all elements have the number like they are sorted in this moment.
df.reset_index()
answered Nov 26 '18 at 21:26
MisterMonkMisterMonk
1549
If you don't have a index explizit set in the creation you can do this:
df['Paystrings'] = pd.Series(x)
Elsewise you can reset the index, then all elements have the number like they are sorted in this moment.
df.reset_index()
answered Nov 26 '18 at 21:26
MisterMonkMisterMonk
1549
answered Nov 26 '18 at 21:26
MisterMonkMisterMonk
1549
answered Nov 26 '18 at 21:26
MisterMonkMisterMonk
1549
answered Nov 26 '18 at 21:26
MisterMonkMisterMonk
1549
1549
add a comment |
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You overwrite
df['Paystrings']in each iteration. What's your desired output? Do you want to create 1346 new columns?– pault
Nov 26 '18 at 21:20
That's because you are redefining the column with the value
x[i]. So at the end of the loop, that will bex[-1]for the whole column– C.Nivs
Nov 26 '18 at 21:21
Yes because df['Paystrings'] = x[i] sets the whole column to the value x[i]
– MisterMonk
Nov 26 '18 at 21:21
@pault I want to create a new column called Paystring, then for each row in that coilumn put x[0] in the first row x[1] in the second etc... does that make sense?'
– Snorrlaxxx
Nov 26 '18 at 21:22
I'd probably wrap
xin aSeriesto stick into the Dataframe– C.Nivs
Nov 26 '18 at 21:23