Python: Changing values in a DataFrame
0
I'm new to python and pandas and I need some ideas. Say I have the following DataFrame:
0 1 2 3 4 5
1 5 5 5 5 5
2 5 5 5 5 5
3 5 5 5 5 5
4 5 5 5 5 5
I want to iterate through each row and change the values of specific columns. Say I wanted to change all of the values in columns (2,3,4) to a 3.
This is what I've tried, am I going down the right path?
for row in df.iterrows():
for col in range(2, 4):
df.set_value('row', 'col', 3)
EDIT:
Thanks for the responses. The simple solutions are obvious, but what if I wanted to change the values to this... for example:
0 1 2 3 4 5
1 1 2 3 4 5
2 6 7 8 9 10
3 11 12 13 14 15
4 16 17 18 19 20
python pandas dataframe
asked Nov 19 '18 at 11:35
embedded.95embedded.95
227
add a comment |
0
I'm new to python and pandas and I need some ideas. Say I have the following DataFrame:
0 1 2 3 4 5
1 5 5 5 5 5
2 5 5 5 5 5
3 5 5 5 5 5
4 5 5 5 5 5
I want to iterate through each row and change the values of specific columns. Say I wanted to change all of the values in columns (2,3,4) to a 3.
This is what I've tried, am I going down the right path?
for row in df.iterrows():
for col in range(2, 4):
df.set_value('row', 'col', 3)
EDIT:
Thanks for the responses. The simple solutions are obvious, but what if I wanted to change the values to this... for example:
0 1 2 3 4 5
1 1 2 3 4 5
2 6 7 8 9 10
3 11 12 13 14 15
4 16 17 18 19 20
python pandas dataframe
asked Nov 19 '18 at 11:35
embedded.95embedded.95
227
Just assign a value to columndf[2]=3,df[3]=3,df[4]=3
– AkshayNevrekar
Nov 19 '18 at 11:37
df[[2, 3, 4]] = 3?
– jpp
Nov 19 '18 at 11:38
There is really no reason to loop. you can call each column and asign the value. Is there anything else you will want, apart from asigning a value to the column?
– MEdwin
Nov 19 '18 at 11:38
add a comment |
0
0
0
I'm new to python and pandas and I need some ideas. Say I have the following DataFrame:
0 1 2 3 4 5
1 5 5 5 5 5
2 5 5 5 5 5
3 5 5 5 5 5
4 5 5 5 5 5
I want to iterate through each row and change the values of specific columns. Say I wanted to change all of the values in columns (2,3,4) to a 3.
This is what I've tried, am I going down the right path?
for row in df.iterrows():
for col in range(2, 4):
df.set_value('row', 'col', 3)
EDIT:
Thanks for the responses. The simple solutions are obvious, but what if I wanted to change the values to this... for example:
0 1 2 3 4 5
1 1 2 3 4 5
2 6 7 8 9 10
3 11 12 13 14 15
4 16 17 18 19 20
python pandas dataframe
asked Nov 19 '18 at 11:35
embedded.95embedded.95
227
I'm new to python and pandas and I need some ideas. Say I have the following DataFrame:
0 1 2 3 4 5
1 5 5 5 5 5
2 5 5 5 5 5
3 5 5 5 5 5
4 5 5 5 5 5
I want to iterate through each row and change the values of specific columns. Say I wanted to change all of the values in columns (2,3,4) to a 3.
This is what I've tried, am I going down the right path?
for row in df.iterrows():
for col in range(2, 4):
df.set_value('row', 'col', 3)
EDIT:
Thanks for the responses. The simple solutions are obvious, but what if I wanted to change the values to this... for example:
0 1 2 3 4 5
1 1 2 3 4 5
2 6 7 8 9 10
3 11 12 13 14 15
4 16 17 18 19 20
python pandas dataframe
python pandas dataframe
asked Nov 19 '18 at 11:35
embedded.95embedded.95
227
asked Nov 19 '18 at 11:35
embedded.95embedded.95
227
edited Nov 19 '18 at 11:50
embedded.95
asked Nov 19 '18 at 11:35
embedded.95embedded.95
227
asked Nov 19 '18 at 11:35
embedded.95embedded.95
227
asked Nov 19 '18 at 11:35
embedded.95embedded.95
227
227
Just assign a value to columndf[2]=3,df[3]=3,df[4]=3
– AkshayNevrekar
Nov 19 '18 at 11:37
df[[2, 3, 4]] = 3?
– jpp
Nov 19 '18 at 11:38
There is really no reason to loop. you can call each column and asign the value. Is there anything else you will want, apart from asigning a value to the column?
– MEdwin
Nov 19 '18 at 11:38
add a comment |
Just assign a value to columndf[2]=3,df[3]=3,df[4]=3
– AkshayNevrekar
Nov 19 '18 at 11:37
df[[2, 3, 4]] = 3?
– jpp
Nov 19 '18 at 11:38
There is really no reason to loop. you can call each column and asign the value. Is there anything else you will want, apart from asigning a value to the column?
– MEdwin
Nov 19 '18 at 11:38
Just assign a value to column
df[2]=3, df[3]=3, df[4]=3– AkshayNevrekar
Nov 19 '18 at 11:37
Just assign a value to column
df[2]=3, df[3]=3, df[4]=3– AkshayNevrekar
Nov 19 '18 at 11:37
df[[2, 3, 4]] = 3 ?– jpp
Nov 19 '18 at 11:38
df[[2, 3, 4]] = 3 ?– jpp
Nov 19 '18 at 11:38
There is really no reason to loop. you can call each column and asign the value. Is there anything else you will want, apart from asigning a value to the column?
– MEdwin
Nov 19 '18 at 11:38
There is really no reason to loop. you can call each column and asign the value. Is there anything else you will want, apart from asigning a value to the column?
– MEdwin
Nov 19 '18 at 11:38
add a comment |
2 Answers
2
active
oldest
votes
2
If you are using a loop when working with dataframes, you are almost always not on the right track.
For this you can use a vectorized assignment:
df[[2, 3, 4]] = 3
Example:
df = pd.DataFrame({1: [1, 2], 2: [1, 2]})
print(df)
# 1 2
# 0 1 1
# 1 2 2
df[[1, 2]] = 3
print(df)
# 1 2
# 0 3 3
# 1 3 3
answered Nov 19 '18 at 11:38
DeepSpaceDeepSpace
38.6k44471
Yes, this would be correct. But what if I wanted to apply this to 2000 rows of data, where the assigned values are different for each row. It now becomes more complicated, hence the loop. I created this dumbed down example, but I assume the principle would be the same.
– embedded.95
Nov 19 '18 at 11:45
@embedded.95 Then that is an entirely different question which has been asked and answered many times before. Use pandas indexing ornp.where.
– DeepSpace
Nov 19 '18 at 11:49
add a comment |
2
you can do this
df.iloc[:,1] = 3 #columns 2
df.iloc[:,2] = 3
df.iloc[:,3] = 3
edited Nov 19 '18 at 11:39
AkshayNevrekar
4,64491736
answered Nov 19 '18 at 11:38
runzhi xiaorunzhi xiao
813
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
2
If you are using a loop when working with dataframes, you are almost always not on the right track.
For this you can use a vectorized assignment:
df[[2, 3, 4]] = 3
Example:
df = pd.DataFrame({1: [1, 2], 2: [1, 2]})
print(df)
# 1 2
# 0 1 1
# 1 2 2
df[[1, 2]] = 3
print(df)
# 1 2
# 0 3 3
# 1 3 3
answered Nov 19 '18 at 11:38
DeepSpaceDeepSpace
38.6k44471
Yes, this would be correct. But what if I wanted to apply this to 2000 rows of data, where the assigned values are different for each row. It now becomes more complicated, hence the loop. I created this dumbed down example, but I assume the principle would be the same.
– embedded.95
Nov 19 '18 at 11:45
@embedded.95 Then that is an entirely different question which has been asked and answered many times before. Use pandas indexing ornp.where.
– DeepSpace
Nov 19 '18 at 11:49
add a comment |
2
If you are using a loop when working with dataframes, you are almost always not on the right track.
For this you can use a vectorized assignment:
df[[2, 3, 4]] = 3
Example:
df = pd.DataFrame({1: [1, 2], 2: [1, 2]})
print(df)
# 1 2
# 0 1 1
# 1 2 2
df[[1, 2]] = 3
print(df)
# 1 2
# 0 3 3
# 1 3 3
answered Nov 19 '18 at 11:38
DeepSpaceDeepSpace
38.6k44471
Yes, this would be correct. But what if I wanted to apply this to 2000 rows of data, where the assigned values are different for each row. It now becomes more complicated, hence the loop. I created this dumbed down example, but I assume the principle would be the same.
– embedded.95
Nov 19 '18 at 11:45
@embedded.95 Then that is an entirely different question which has been asked and answered many times before. Use pandas indexing ornp.where.
– DeepSpace
Nov 19 '18 at 11:49
add a comment |
2
2
2
If you are using a loop when working with dataframes, you are almost always not on the right track.
For this you can use a vectorized assignment:
df[[2, 3, 4]] = 3
Example:
df = pd.DataFrame({1: [1, 2], 2: [1, 2]})
print(df)
# 1 2
# 0 1 1
# 1 2 2
df[[1, 2]] = 3
print(df)
# 1 2
# 0 3 3
# 1 3 3
answered Nov 19 '18 at 11:38
DeepSpaceDeepSpace
38.6k44471
If you are using a loop when working with dataframes, you are almost always not on the right track.
For this you can use a vectorized assignment:
df[[2, 3, 4]] = 3
Example:
df = pd.DataFrame({1: [1, 2], 2: [1, 2]})
print(df)
# 1 2
# 0 1 1
# 1 2 2
df[[1, 2]] = 3
print(df)
# 1 2
# 0 3 3
# 1 3 3
answered Nov 19 '18 at 11:38
DeepSpaceDeepSpace
38.6k44471
edited Nov 19 '18 at 14:53
answered Nov 19 '18 at 11:38
DeepSpaceDeepSpace
38.6k44471
answered Nov 19 '18 at 11:38
DeepSpaceDeepSpace
38.6k44471
answered Nov 19 '18 at 11:38
DeepSpaceDeepSpace
38.6k44471
38.6k44471
Yes, this would be correct. But what if I wanted to apply this to 2000 rows of data, where the assigned values are different for each row. It now becomes more complicated, hence the loop. I created this dumbed down example, but I assume the principle would be the same.
– embedded.95
Nov 19 '18 at 11:45
@embedded.95 Then that is an entirely different question which has been asked and answered many times before. Use pandas indexing ornp.where.
– DeepSpace
Nov 19 '18 at 11:49
add a comment |
Yes, this would be correct. But what if I wanted to apply this to 2000 rows of data, where the assigned values are different for each row. It now becomes more complicated, hence the loop. I created this dumbed down example, but I assume the principle would be the same.
– embedded.95
Nov 19 '18 at 11:45
@embedded.95 Then that is an entirely different question which has been asked and answered many times before. Use pandas indexing ornp.where.
– DeepSpace
Nov 19 '18 at 11:49
Yes, this would be correct. But what if I wanted to apply this to 2000 rows of data, where the assigned values are different for each row. It now becomes more complicated, hence the loop. I created this dumbed down example, but I assume the principle would be the same.
– embedded.95
Nov 19 '18 at 11:45
Yes, this would be correct. But what if I wanted to apply this to 2000 rows of data, where the assigned values are different for each row. It now becomes more complicated, hence the loop. I created this dumbed down example, but I assume the principle would be the same.
– embedded.95
Nov 19 '18 at 11:45
@embedded.95 Then that is an entirely different question which has been asked and answered many times before. Use pandas indexing or
np.where.– DeepSpace
Nov 19 '18 at 11:49
@embedded.95 Then that is an entirely different question which has been asked and answered many times before. Use pandas indexing or
np.where.– DeepSpace
Nov 19 '18 at 11:49
add a comment |
2
you can do this
df.iloc[:,1] = 3 #columns 2
df.iloc[:,2] = 3
df.iloc[:,3] = 3
edited Nov 19 '18 at 11:39
AkshayNevrekar
4,64491736
answered Nov 19 '18 at 11:38
runzhi xiaorunzhi xiao
813
add a comment |
2
you can do this
df.iloc[:,1] = 3 #columns 2
df.iloc[:,2] = 3
df.iloc[:,3] = 3
edited Nov 19 '18 at 11:39
AkshayNevrekar
4,64491736
answered Nov 19 '18 at 11:38
runzhi xiaorunzhi xiao
813
add a comment |
2
2
2
you can do this
df.iloc[:,1] = 3 #columns 2
df.iloc[:,2] = 3
df.iloc[:,3] = 3
edited Nov 19 '18 at 11:39
AkshayNevrekar
4,64491736
answered Nov 19 '18 at 11:38
runzhi xiaorunzhi xiao
813
you can do this
df.iloc[:,1] = 3 #columns 2
df.iloc[:,2] = 3
df.iloc[:,3] = 3
edited Nov 19 '18 at 11:39
AkshayNevrekar
4,64491736
answered Nov 19 '18 at 11:38
runzhi xiaorunzhi xiao
813
edited Nov 19 '18 at 11:39
AkshayNevrekar
4,64491736
edited Nov 19 '18 at 11:39
AkshayNevrekar
4,64491736
edited Nov 19 '18 at 11:39
AkshayNevrekar
4,64491736
4,64491736
answered Nov 19 '18 at 11:38
runzhi xiaorunzhi xiao
813
answered Nov 19 '18 at 11:38
runzhi xiaorunzhi xiao
813
answered Nov 19 '18 at 11:38
runzhi xiaorunzhi xiao
813
813
add a comment |
add a comment |
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Just assign a value to column
df[2]=3,df[3]=3,df[4]=3– AkshayNevrekar
Nov 19 '18 at 11:37
df[[2, 3, 4]] = 3?– jpp
Nov 19 '18 at 11:38
There is really no reason to loop. you can call each column and asign the value. Is there anything else you will want, apart from asigning a value to the column?
– MEdwin
Nov 19 '18 at 11:38