CODEWITHSUNDEEP
pythonarrayspandas
Tanvi Mirza
Tanvi Mirza

Reputation: 863

Generate numpy array using multiple columns of pandas dataframe

Sorry for the long post. I'm using python 3.6 on windows 10.I have a pandas data frame that contain around 100,000 rows. From this data frame I need to generate Four numpy arrays. First 5 relevant rows of my data frame looks like below

A          B      x      UB1     LB1     UB2    LB2
0.2134  0.7866  0.2237  0.1567  0.0133  1.0499  0.127
0.24735 0.75265 0.0881  0.5905  0.422   1.4715  0.5185
0.0125  0.9875  0.1501  1.3721  0.5007  2.0866  2.0617
0.8365  0.1635  0.0948  1.9463  1.0854  2.4655  1.9644
0.1234  0.8766  0.0415  2.7903  2.2602  3.5192  3.2828

Column B is (1-Column A), Actually column B is not there in my data frame. I have added it to explain my problem From this data frame, I need to generate three arrays. My arrays looks like

My array c looks like array([-0.2134, -0.7866,-0.24735, -0.75265,-0.0125, -0.9875,-0.8365, -0.1635,-0.1234, -0.8766],dtype=float32)

Where first element is first row of column A with added negative sign, similarly 2nd element is taken from 1st row of column B, third element is from second row of column A,fourth element is 2nd row of column B & so on My second array UB looks like 

array([ 0.2237, 0.0881, 0.1501, 0.0948, 0.0415, 0.2237],dtype=float32)

where elements are rows of column X.

My third array,bounds, looks like

   array([[0.0133 , 0.1567],
       [0.127 , 1.0499],
       [0.422 , 0.5905],
       [0.5185 , 1.4715],
       [0.5007 , 1.3721],
       [2.0617 , 2.0866],
       [1.0854 , 1.9463],
       [1.9644 , 2.4655],
       [2.2602 , 2.7903],
       [3.2828 , 3.5192]])

Where bounds[0][0] is first row of LB1,bounds[0][1] is first row of UB1. bounds[1][0] is first row of LB2, bounds [1][1] is first row of UB2. Again bounds[2][0] is 2nd row of LB1 & so on. My fourth array looks like

array([[-1,  1,  0,  0,  0,  0,  0,  0,  0,  0],
       [ 0,  0, -1,  1,  0,  0,  0,  0,  0,  0],
       [ 0,  0,  0,  0, -1,  1,  0,  0,  0,  0],
       [ 0,  0,  0,  0,  0,  0, -1,  1,  0,  0],
       [ 0,  0,  0,  0,  0,  0,  0,  0, -1,  1]])

It contains same number of rows as data frame rows & column=2*data frame rows.

Can you please tell me for 100,000 rows of record what is the efficient way to generate these arrays

Upvotes: 0

Views: 2513

Answers (1)

javidcf
javidcf

Reputation: 59701

This should be rather straightforward:

from io import StringIO
import pandas as pd
import numpy as np

data = """A          B      x      UB1     LB1     UB2    LB2
0.2134  0.7866  0.2237  0.1567  0.0133  1.0499  0.127
0.24735 0.75265 0.0881  0.5905  0.422   1.4715  0.5185
0.0125  0.9875  0.1501  1.3721  0.5007  2.0866  2.0617
0.8365  0.1635  0.0948  1.9463  1.0854  2.4655  1.9644
0.1234  0.8766  0.0415  2.7903  2.2602  3.5192  3.2828"""

df = pd.read_csv(StringIO(data), sep='\\s+', header=0)

c = -np.stack([df['A'], 1 - df['A']], axis=1).ravel()
print(c)
# [-0.2134  -0.7866  -0.24735 -0.75265 -0.0125  -0.9875  -0.8365  -0.1635
#  -0.1234  -0.8766 ]

ub = df['x'].values
print(ub)
# [0.2237 0.0881 0.1501 0.0948 0.0415]

bounds = np.stack([df['LB1'], df['UB1'], df['LB2'], df['UB2']], axis=1).reshape((-1, 2))
print(bounds)
# [[0.0133 0.1567]
#  [0.127  1.0499]
#  [0.422  0.5905]
#  [0.5185 1.4715]
#  [0.5007 1.3721]
#  [2.0617 2.0866]
#  [1.0854 1.9463]
#  [1.9644 2.4655]
#  [2.2602 2.7903]
#  [3.2828 3.5192]]

n = len(df)
fourth = np.zeros((n, 2 * n))
idx = np.arange(n)
fourth[idx, 2 * idx] = -1
fourth[idx, 2 * idx + 1] = 1
print(fourth)
# [[-1.  1.  0.  0.  0.  0.  0.  0.  0.  0.]
#  [ 0.  0. -1.  1.  0.  0.  0.  0.  0.  0.]
#  [ 0.  0.  0.  0. -1.  1.  0.  0.  0.  0.]
#  [ 0.  0.  0.  0.  0.  0. -1.  1.  0.  0.]
#  [ 0.  0.  0.  0.  0.  0.  0.  0. -1.  1.]]

Upvotes: 1

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