CODEWITHSUNDEEP
pythonarrayssubtraction
Quazi Nizam
Quazi Nizam

Reputation: 31

How to get array after adding a number to specific data of that array?

import numpy as np 
import xlrd
import xlwt

wb = xlrd.open_workbook('Scatter plot.xlsx')
workbook = xlwt.Workbook() 

sheet = workbook.add_sheet("Sheet1")

sh1 = wb.sheet_by_name('T180')
sh2=wb.sheet_by_name("T181")

x= np.array([sh1.col_values(1, start_rowx=51, end_rowx=315)])
y= np.array([sh1.col_values(2, start_rowx=51, end_rowx=315)])

x1= np.array([sh2.col_values(1, start_rowx=50, end_rowx=298)])
y1= np.array([sh2.col_values(2, start_rowx=50, end_rowx=298)])

condition = [(x1<=1000) & (x1>=0) ]
condition1 = [(y1<=1000) & (y1>=0) ]

x_prime=x1[condition]-150
y_prime= y[condition1]+20

plt.plot(x,y, "ro", label="T180")
plt.plot(x_prime,y_prime,"gs")
plt.show()

I want to subtract 150 from the values less than 1000 of x1 array and finally I need all values (subtracted+remaining). But with this code I got only the values that are less than 1000. But I need both (less than 1000+ greater than 1000). But greater than 1000 values will be unchanged. How can I will do this. As you can see there 248 elements in x1 array so after subtraction I will need 248 element as x_prime. Same as for y. Thanks in advance for your kind co-operation.

Upvotes: 1

Views: 257

Answers (3)

MaxU - stand with Ukraine
MaxU - stand with Ukraine

Reputation: 210832

Here is a Pandas solution:

import matplotlib
import matplotlib.pyplot as plt
import pandas as pd

matplotlib.style.use('ggplot')

fn = r'/path/to/ExcelFile.xlsx'
sheetname = 'T181'
df = pd.read_excel(fn, sheetname=sheetname, skiprows=47, parse_cols='B:C').dropna(how='any')

# customize X-values
df.ix[df.eval('0 <= GrvX <= 1000'), 'GrvX'] -= 150
df.ix[df.eval('2500 < GrvX <= 3000'), 'GrvX'] += 50
df.ix[df.eval('3000 < GrvX'), 'GrvX'] += 30

# customize Y-values
df.ix[df.eval('0 <= GrvY <= 1000'), 'GrvX'] += 20

df.plot.scatter(x='GrvX', y='GrvY', marker='s', s=30, label=sheetname, figsize=(14,12))

plt.show()

enter image description here

Upvotes: 1

Sayali Sonawane
Sayali Sonawane

Reputation: 12599

import numpy as np 
#random initialization
x1=np.random.randint(1,high=3000, size=10)

x_prime=x1.tolist()

for i in range(len(x_prime)):
    if(x_prime[i]<=1000 and x_prime[i]>=0): 
        x_prime[i]=x_prime[i]-150

x_prime=np.asarray(x_prime)

Answer:

x1
Out[151]: array([2285, 2243, 1716,  632, 2489, 2837, 2324, 2154,  562, 2508])

x_prime
Out[152]: array([2285, 2243, 1716,  482, 2489, 2837, 2324, 2154,  412, 2508])

Upvotes: 0

eistaa
eistaa

Reputation: 94

You can use numpy.place to modify arrays where a logic expression holds. For complex logic expressions on the array there are the logic functions that combines boolean arrays.

E.g.:

A = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
np.place(A, np.logical_and(A > 1, A <= 8), A-10)

will subtract 10 from every element of A that is > 1 and <= 8. After this A will be

array([ 1, -9, -8, -7, -6, -5, -4, -3,  9, 10])

Upvotes: 1

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