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Combining Different Scaled arrays in dataframe
2019 Community Moderator ElectionWhat is the difference between @staticmethod and @classmethod?Difference between append vs. extend list methods in PythonDoes Django scale?Difference between __str__ and __repr__?Selecting multiple columns in a pandas dataframeDelete column from pandas DataFrame by column name“Large data” work flows using pandasHow to iterate over rows in a DataFrame in Pandas?Select rows from a DataFrame based on values in a column in pandasGet list from pandas DataFrame column headers
Is there a built-in function (numpy or pandas I'm thinking) that would help combine multiple rows of one column in a dataframe, keeping the same dimensions, but different scale? Also, combined with that, summing the values from a different column between the intervals? Or is it something I just need to build from scratch? Example below, I'm not sure exactly how to ask. This would need to be scalable; the example is simple, in reality I'm working with a 250 dim array and theoretically unlimited rows.
Ex:
import pandas as pd
import numpy as np
#Creating DF
df = pd.DataFrame([[[-2,-1,0,1,2],[-10,-5,5,5,-10]],
[[-.5,.5,1.5,2.5,3.5],[-3,-2,0,-2,-3]]])
output: 0 1
0 [-2, -1, 0, 1, 2] [-10, -5, 5, 5, -10]
1 [-0.5, 0.5, 1.5, 2.5, 3.5] [-3, -2, 0, -2, -3]
where the answer is [-2,-0.625,0.75,2.125,3.5] (column0 combined with dim 5) , [-10,-5,0,-5,-5] (sum of column1 between steps of column0 where (interval-1) < x<=interval)
answer = pd.DataFrame([[[-2,-.625,.75,2.125,3.5],[-10,-5,0,-5,-5]]])
python pandas
asked Mar 6 at 22:11
J BeckfordJ Beckford
83
|
show 3 more comments
Is there a built-in function (numpy or pandas I'm thinking) that would help combine multiple rows of one column in a dataframe, keeping the same dimensions, but different scale? Also, combined with that, summing the values from a different column between the intervals? Or is it something I just need to build from scratch? Example below, I'm not sure exactly how to ask. This would need to be scalable; the example is simple, in reality I'm working with a 250 dim array and theoretically unlimited rows.
Ex:
import pandas as pd
import numpy as np
#Creating DF
df = pd.DataFrame([[[-2,-1,0,1,2],[-10,-5,5,5,-10]],
[[-.5,.5,1.5,2.5,3.5],[-3,-2,0,-2,-3]]])
output: 0 1
0 [-2, -1, 0, 1, 2] [-10, -5, 5, 5, -10]
1 [-0.5, 0.5, 1.5, 2.5, 3.5] [-3, -2, 0, -2, -3]
where the answer is [-2,-0.625,0.75,2.125,3.5] (column0 combined with dim 5) , [-10,-5,0,-5,-5] (sum of column1 between steps of column0 where (interval-1) < x<=interval)
answer = pd.DataFrame([[[-2,-.625,.75,2.125,3.5],[-10,-5,0,-5,-5]]])
python pandas
asked Mar 6 at 22:11
J BeckfordJ Beckford
83
It's very unclear what you're asking. How do you reach these outputs from these inputs?
– G. Anderson
Mar 6 at 22:41
column1 is based on stock options pricing from stock prices (column0) and in actuality column0 is the standard deviation from current price.
– J Beckford
Mar 6 at 23:02
I should clarify... I can figure out column 0 as x = np.linspace(min(min(df.iloc[:,0])),max(max(df.iloc[:,0])), 5) with 5 intervals. I need help summing the column 1 values that correspond to between the interval values.
– J Beckford
Mar 6 at 23:11
conceptually each row is a paired value so if in a dictionary it would be row0 = -2:-10 , -1:-5 , 0:5 , 1:5 , 2:-10 and row1 = -.5:-3, .5:-2, 1.5:0, 2.5:-2, 3.5:-3. Somehow I need to iterate through each row and sum each value between the intervals of new x linspace (min,max)
– J Beckford
Mar 6 at 23:42
I'm sorry, maybe I'm being dense, but how do you get from ` -2:-10 , -1:-5 , 0:5 , 1:5 , 2:-10` to-10,-5,0,-5,-5? If you could show some pseudocode for the algorithm to get to the answer that would really help my brain
– G. Anderson
Mar 7 at 17:10
|
show 3 more comments
Is there a built-in function (numpy or pandas I'm thinking) that would help combine multiple rows of one column in a dataframe, keeping the same dimensions, but different scale? Also, combined with that, summing the values from a different column between the intervals? Or is it something I just need to build from scratch? Example below, I'm not sure exactly how to ask. This would need to be scalable; the example is simple, in reality I'm working with a 250 dim array and theoretically unlimited rows.
Ex:
import pandas as pd
import numpy as np
#Creating DF
df = pd.DataFrame([[[-2,-1,0,1,2],[-10,-5,5,5,-10]],
[[-.5,.5,1.5,2.5,3.5],[-3,-2,0,-2,-3]]])
output: 0 1
0 [-2, -1, 0, 1, 2] [-10, -5, 5, 5, -10]
1 [-0.5, 0.5, 1.5, 2.5, 3.5] [-3, -2, 0, -2, -3]
where the answer is [-2,-0.625,0.75,2.125,3.5] (column0 combined with dim 5) , [-10,-5,0,-5,-5] (sum of column1 between steps of column0 where (interval-1) < x<=interval)
answer = pd.DataFrame([[[-2,-.625,.75,2.125,3.5],[-10,-5,0,-5,-5]]])
python pandas
asked Mar 6 at 22:11
J BeckfordJ Beckford
83
Is there a built-in function (numpy or pandas I'm thinking) that would help combine multiple rows of one column in a dataframe, keeping the same dimensions, but different scale? Also, combined with that, summing the values from a different column between the intervals? Or is it something I just need to build from scratch? Example below, I'm not sure exactly how to ask. This would need to be scalable; the example is simple, in reality I'm working with a 250 dim array and theoretically unlimited rows.
Ex:
import pandas as pd
import numpy as np
#Creating DF
df = pd.DataFrame([[[-2,-1,0,1,2],[-10,-5,5,5,-10]],
[[-.5,.5,1.5,2.5,3.5],[-3,-2,0,-2,-3]]])
output: 0 1
0 [-2, -1, 0, 1, 2] [-10, -5, 5, 5, -10]
1 [-0.5, 0.5, 1.5, 2.5, 3.5] [-3, -2, 0, -2, -3]
where the answer is [-2,-0.625,0.75,2.125,3.5] (column0 combined with dim 5) , [-10,-5,0,-5,-5] (sum of column1 between steps of column0 where (interval-1) < x<=interval)
answer = pd.DataFrame([[[-2,-.625,.75,2.125,3.5],[-10,-5,0,-5,-5]]])
python pandas
python pandas
asked Mar 6 at 22:11
J BeckfordJ Beckford
83
asked Mar 6 at 22:11
J BeckfordJ Beckford
83
edited Mar 6 at 22:29
J Beckford
asked Mar 6 at 22:11
J BeckfordJ Beckford
83
asked Mar 6 at 22:11
J BeckfordJ Beckford
83
asked Mar 6 at 22:11
J BeckfordJ Beckford
83
83
It's very unclear what you're asking. How do you reach these outputs from these inputs?
– G. Anderson
Mar 6 at 22:41
column1 is based on stock options pricing from stock prices (column0) and in actuality column0 is the standard deviation from current price.
– J Beckford
Mar 6 at 23:02
I should clarify... I can figure out column 0 as x = np.linspace(min(min(df.iloc[:,0])),max(max(df.iloc[:,0])), 5) with 5 intervals. I need help summing the column 1 values that correspond to between the interval values.
– J Beckford
Mar 6 at 23:11
conceptually each row is a paired value so if in a dictionary it would be row0 = -2:-10 , -1:-5 , 0:5 , 1:5 , 2:-10 and row1 = -.5:-3, .5:-2, 1.5:0, 2.5:-2, 3.5:-3. Somehow I need to iterate through each row and sum each value between the intervals of new x linspace (min,max)
– J Beckford
Mar 6 at 23:42
I'm sorry, maybe I'm being dense, but how do you get from ` -2:-10 , -1:-5 , 0:5 , 1:5 , 2:-10` to-10,-5,0,-5,-5? If you could show some pseudocode for the algorithm to get to the answer that would really help my brain
– G. Anderson
Mar 7 at 17:10
|
show 3 more comments
It's very unclear what you're asking. How do you reach these outputs from these inputs?
– G. Anderson
Mar 6 at 22:41
column1 is based on stock options pricing from stock prices (column0) and in actuality column0 is the standard deviation from current price.
– J Beckford
Mar 6 at 23:02
I should clarify... I can figure out column 0 as x = np.linspace(min(min(df.iloc[:,0])),max(max(df.iloc[:,0])), 5) with 5 intervals. I need help summing the column 1 values that correspond to between the interval values.
– J Beckford
Mar 6 at 23:11
conceptually each row is a paired value so if in a dictionary it would be row0 = -2:-10 , -1:-5 , 0:5 , 1:5 , 2:-10 and row1 = -.5:-3, .5:-2, 1.5:0, 2.5:-2, 3.5:-3. Somehow I need to iterate through each row and sum each value between the intervals of new x linspace (min,max)
– J Beckford
Mar 6 at 23:42
I'm sorry, maybe I'm being dense, but how do you get from ` -2:-10 , -1:-5 , 0:5 , 1:5 , 2:-10` to-10,-5,0,-5,-5? If you could show some pseudocode for the algorithm to get to the answer that would really help my brain
– G. Anderson
Mar 7 at 17:10
It's very unclear what you're asking. How do you reach these outputs from these inputs?
– G. Anderson
Mar 6 at 22:41
It's very unclear what you're asking. How do you reach these outputs from these inputs?
– G. Anderson
Mar 6 at 22:41
column1 is based on stock options pricing from stock prices (column0) and in actuality column0 is the standard deviation from current price.
– J Beckford
Mar 6 at 23:02
column1 is based on stock options pricing from stock prices (column0) and in actuality column0 is the standard deviation from current price.
– J Beckford
Mar 6 at 23:02
I should clarify... I can figure out column 0 as x = np.linspace(min(min(df.iloc[:,0])),max(max(df.iloc[:,0])), 5) with 5 intervals. I need help summing the column 1 values that correspond to between the interval values.
– J Beckford
Mar 6 at 23:11
I should clarify... I can figure out column 0 as x = np.linspace(min(min(df.iloc[:,0])),max(max(df.iloc[:,0])), 5) with 5 intervals. I need help summing the column 1 values that correspond to between the interval values.
– J Beckford
Mar 6 at 23:11
conceptually each row is a paired value so if in a dictionary it would be row0 = -2:-10 , -1:-5 , 0:5 , 1:5 , 2:-10 and row1 = -.5:-3, .5:-2, 1.5:0, 2.5:-2, 3.5:-3. Somehow I need to iterate through each row and sum each value between the intervals of new x linspace (min,max)
– J Beckford
Mar 6 at 23:42
conceptually each row is a paired value so if in a dictionary it would be row0 = -2:-10 , -1:-5 , 0:5 , 1:5 , 2:-10 and row1 = -.5:-3, .5:-2, 1.5:0, 2.5:-2, 3.5:-3. Somehow I need to iterate through each row and sum each value between the intervals of new x linspace (min,max)
– J Beckford
Mar 6 at 23:42
I'm sorry, maybe I'm being dense, but how do you get from ` -2:-10 , -1:-5 , 0:5 , 1:5 , 2:-10` to
-10,-5,0,-5,-5? If you could show some pseudocode for the algorithm to get to the answer that would really help my brain– G. Anderson
Mar 7 at 17:10
I'm sorry, maybe I'm being dense, but how do you get from ` -2:-10 , -1:-5 , 0:5 , 1:5 , 2:-10` to
-10,-5,0,-5,-5? If you could show some pseudocode for the algorithm to get to the answer that would really help my brain– G. Anderson
Mar 7 at 17:10
|
show 3 more comments
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It's very unclear what you're asking. How do you reach these outputs from these inputs?
– G. Anderson
Mar 6 at 22:41
column1 is based on stock options pricing from stock prices (column0) and in actuality column0 is the standard deviation from current price.
– J Beckford
Mar 6 at 23:02
I should clarify... I can figure out column 0 as x = np.linspace(min(min(df.iloc[:,0])),max(max(df.iloc[:,0])), 5) with 5 intervals. I need help summing the column 1 values that correspond to between the interval values.
– J Beckford
Mar 6 at 23:11
conceptually each row is a paired value so if in a dictionary it would be row0 = -2:-10 , -1:-5 , 0:5 , 1:5 , 2:-10 and row1 = -.5:-3, .5:-2, 1.5:0, 2.5:-2, 3.5:-3. Somehow I need to iterate through each row and sum each value between the intervals of new x linspace (min,max)
– J Beckford
Mar 6 at 23:42
I'm sorry, maybe I'm being dense, but how do you get from ` -2:-10 , -1:-5 , 0:5 , 1:5 , 2:-10` to
-10,-5,0,-5,-5? If you could show some pseudocode for the algorithm to get to the answer that would really help my brain– G. Anderson
Mar 7 at 17:10