Use Numpy polyadd() to add two polynomials - python

I'm trying to add two polynomials using Numpy's polyadd(), but i'm getting the wrong result
a = [60, 51, 64, 38, 9, 79, 96, 81, 11]
e = [1,0,1,0,1,0,1]
b = np.polyadd(a, e)
b
array([60, 51, 65, 38, 10, 79, 97, 81, 12])
Is there an easy way to get the correct result (61, 51, 65, 38, 10, 79, 97, 81, 11) ?
Numpy treats coefficients from lowest to the highest order right? So here it should be (60+51x+64x^2+38x^3+9x^4+79x^5+96x^6+81x^7+11x^8)+(1+x^2+x^4+x^6) = 61+51x+65x^2+38x^3+10x^4+79x^5+97x^6+81x^7+11x^8


You are seeing the docs for different function. np.polynomial.polynomial.polyadd uses from lowest order to highest from left to right, whereas, np.polyadd ranks from highest to lowest.
>>> a = [60, 51, 64, 38, 9, 79, 96, 81, 11]
>>> e = [1,0,1,0,1,0,1]
>>> np.polyadd(a, e)
array([60, 51, 65, 38, 10, 79, 97, 81, 12])
>>> np.polynomial.polynomial.polyadd(a,e)
array([61., 51., 65., 38., 10., 79., 97., 81., 11.])
The result you are asking for can be obtained using np.polyadd by reversing the lists and then reversing the obtained result as suggested by #Mad Physicist:
>>> np.polyadd(a[::-1], e[::-1])[::-1]
array([61, 51, 65, 38, 10, 79, 97, 81, 11])

Related

given an random 100 number with duplicate. I want to count how many number is inside an interval of number in python

For Example
first_interval = [40, 50, 60, 70, 80, 90]
second_interval = [49, 59, 69, 79, 89, 99]
Data = [40, 42, 47, 49, 50, 52, 55, 56, 57, 59, 60, 61, 63, 65, 65, 65, 66, 68, 68, 69, 72, 74, 78, 79, 81, 85, 87, 88, 90, 98]
x = first_interval[0] <= data <= second_interval[0]
y = first_interval[1] <= data <= second_intercal[1] # and so on
I want to know how many numbers from data is between 40-49, 50-59, 60-69 and so on
frequency = [4, 6] # 4 is x and 6 is y

Iterate on the bounds using zip, then with a list comprehension you can filter the correct values
first_interval = [40, 50, 60, 70, 80, 90]
second_interval = [49, 59, 69, 79, 89, 99]
data = [40, 42, 47, 49, 50, 52, 55, 56, 57, 59, 60, 61, 63, 65, 65,
65, 66, 68, 68, 69, 72, 74, 78, 79, 81, 85, 87, 88, 90, 98]
result = {}
for start, end in zip(first_interval, second_interval):
result[(start, end)] = len([v for v in data if start <= v <= end])
print(result)
# {(40, 49): 4, (50, 59): 6, (60, 69): 10, (70, 79): 4, (80, 89): 4, (90, 99): 2}
print(result[(40, 49)])
# 4
The version with a list and len is easier to understand
result[(start, end)] = len([v for v in data if start <= v <= end])
But the following version would be more performant for bigger size, as it's a generator, it won't have to build the whole list to just forget it after
result[(start, end)] = sum((1 for v in data if start <= v <= end))
Another version, that doesn't use the predefined bounds, and so is much performant as it's complexity is O(n) and not O(n*m) as the first one : you iterate once on values, not on values for each bounds
result = defaultdict(int) # from collections import defaultdict
for value in data:
start = 10 * (value // 10)
result[(start, start + 9)] += 1

This may help you :
first_interval = [40, 50, 60, 70, 80, 90]
second_interval = [49, 59, 69, 79, 89, 99]
Data = [40, 42, 47, 49, 50, 52, 55, 56, 57, 59, 60, 61, 63, 65, 65, 65, 66, 68, 68, 69, 72, 74, 78, 79, 81, 85, 87, 88, 90, 98]
def find_occurence(start,end,data):
counter = 0
for i in data :
if start<=i<=end :
counter += 1
return counter
print(find_occurence(first_interval[0],second_interval[0],Data)) #this gives you the anser for x and the same thing for y
Note : start :means from where you want to start.
end : mean where you want to stop.

We can use numpy.histogram with bins defined by:
first_interval bins, but open on the right
max(second_interval) to determine the close of rightmost bin
Code
# Generate counts and bins (right most edge given by max(second_interval))
frequency, bins = np.histogram(data, bins = first_interval + [max(second_interval)])
# Show Results
for i in range(len(frequency)):
if i < len(frequency) - 1:
print(f'{bins[i]}-{bins[i+1]-1} : {frequency[i]}') # frequency doesn't include right edge
else:
print(f'{bins[i]}-{bins[i+1]} : {frequency[i]}') # frequency includes right edge in last bin
Output
40-49 : 4
50-59 : 6
60-69 : 10
70-79 : 4
80-89 : 4
90-99 : 2

Using a python for loop to print numbers from a list which are not in order of their index

I have a little query I'm currently trying to use a for loop to print the numbers in my list, I would like to print several numbers which are next to each other, but then I would also like to print several numbers which are further down the list. My code is below what I would like to do is print the numbers 51 and 52 as well as printing the numbers 56 and 57. Is it possible to do this within one loop or will I need to create another on?
Test = [50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62]
for i in range (1,3), (6, 8):
print (Test[i])

Test = [50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62]
indices = (1, 2, 6, 7)
for i in indices:
print(Test[i])
>>>51 52 56, 57 (on separate lines, of course)
If you always want to print in pairs, you can only specify the first element's index and print twice in the loop:
Test = [50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62]
indices = (1, 6)
for i in indices:
print(Test[i])
print(Test[i+1])
>>>51 52 56, 57 (on separate lines, of course)

You can do this output
Test = [50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62]
for i in range (1,3):
print (Test[i])
for i in range (6, 8):
print (Test[i])

How to generate a list of numbers in python

guys. I am now working on a python algorithm and I am new to python. I'd like to generate a list of numbers like 4, 7, 8, 11, 12, 13, 16, 17, 18, 19, 22, 23, 24, 25... with 2 for loops.
I've done some work to find some numbers and I am close to the result I want, which is generate a list contains this numbers
My code is here:
for x in range(0, 6, 1):
start_ind = int(((x+3) * (x+2)) / 2 + 1)
print("start index is ", [start_ind], x)
start_node = node[start_ind]
for y in range(0, x):
ind = start_ind + y + 1
ind_list = node[ind]
index = [ind_list]
print(index)
Node is a list:
node = ['n%d' % i for i in range(0, 36, 1)]
What I received from this code is:
start index is [7] 1
['n8']
start index is [11] 2
['n12']
['n13']
start index is [16] 3
['n17']
['n18']
['n19']
start index is [22] 4
['n23']
['n24']
['n25']
['n26']
start index is [29] 5
['n30']
['n31']
['n32']
['n33']
['n34']

This seems to give the same list: and I think it's much clearer what's happening!
val=4
result=[]
for i in range(1,7):
for j in range(val,val+i):
val = val+1
result.append(j)
val = j+3
print(result)

Do not think you need a loop for this, let alone two:
import numpy as np
dif = np.ones(100, dtype = np.int32)
dif[np.cumsum(np.arange(14))] = 3
(1+np.cumsum(dif)).tolist()
output
[4, 7, 8, 11, 12, 13, 16, 17, 18, 19, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33, 34, 37, 38, 39, 40, 41, 42, 43, 46, 47, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 121, 122, 123, 124, 125, 126, 127, 128, 129]

ind_list = []
start_ind = 4
for x in range(0, 6):
ind_list.append(start_ind)
for y in range(1, x+1):
ind_list.append(start_ind + y)
start_ind = ind_list[len(ind_list)-1]+3
print(ind_list)
You could probably use this. the print function works fine, the list I assume works fairly well for the numbers provided. It appends the new number at the beginning of the loop, with a cotinually longer loop each time for x. I'm assuming the number sequence is 4, 4+3, 4+3+1, 4+3+1+3, 4+3+1+3+1, 4+3+1+3+1+1, 4+3+1+3+1+1+3, ....

Python Nested list function to return score for students exam's.

I am trying to write a function named studentGrades(gradeList) that takes a nested list and returns a list with the average score for each student.
An example would be:
grades= [['Student','Quiz 1','Quiz 2','Quiz 3','Final'],
['John', 100, 90, 80, 90],
['McVay', 88, 99, 11, 15],
['Rita', 45, 56, 67, 89],
['Ketan', 59, 61, 67, 32],
['Saranya', 73, 79, 83, 45],
['Min', 89, 97, 101, 100]]
studentGrades(grades)
# Sample output below
>>> [90, 53, 64, 54, 70, 96]
I don't know how to do this using a nested loop. Any help or guidance is appreciated.

Incase you need a one liner
[int(sum(i[1:])/len(i[1:])) for i in grades[1:]]
Output:
[90, 53, 64, 54, 70, 96]

As I suggested in the comments, you can also use a dictionary for that. Dictionaries are basically made for situations like these and if you want to use your data any further, they might prove beneficial.
You would first have to convert your current structure, which I assumed is fixed in the sense that you have a "header" in grades, and then lists of the form [name,points..]. You can do:
grades= [['Student','Quiz 1','Quiz 2','Quiz 3','Final'],
['John', 100, 90, 80, 90],
['McVay', 88, 99, 11, 15],
['Rita', 45, 56, 67, 89],
['Ketan', 59, 61, 67, 32],
['Saranya', 73, 79, 83, 45],
['Min', 89, 97, 101, 100]]
gradedict = {}
for rows in grades[1:]:
gradedict[rows[0]] = rows[1:]
To initialize the dictionary. Then the following function:
def studentGrades(gradedict):
avgdict = {}
for key,lst in gradedict.items():
avgdict[key] = sum(lst)/len(lst)
return avgdict
Returns another dictionary with the corresponding averages. You could loop through the names, i.e. the keys, of either of those to print that. For example:
gradedict = studentGrades(gradedict)
for x in gradedict:
print("student ",x, " achieved an average of: ",gradedict[x])
which is just to show you how to access the elements. you can of course also loop through keys,items as I did in the function.
Hope this helps.

You can do something like this:
def studentGrades(a):
# return [sum(k[1:])/(len(k)-1) for k in a[1:]]
# Or
l = len(a[0]) - 1 # how many quizzes, thanks to #Mad Physicist
return [sum(k[1:])/l for k in a[1:]]
grades= [['Student','Quiz 1','Quiz 2','Quiz 3','Final'],
['John', 100, 90, 80, 90],
['McVay', 88, 99, 11, 15],
['Rita', 45, 56, 67, 89],
['Ketan', 59, 61, 67, 32],
['Saranya', 73, 79, 83, 45],
['Min', 89, 97, 101, 100]]
final = studentGrades(grades)
print(final)
Output:
[90, 53, 64, 54, 70, 96]

Why not just use pandas?
df = pd.DataFrame(grades).set_index(0).iloc[1:].mean(axis=1).astype(int)
Output:
John 90
McVay 53
Rita 64
Ketan 54
Saranya 70
Min 96
or
list(df.values)
Output:
[90, 53, 64, 54, 70, 96]

grades= [['Student','Quiz 1','Quiz 2','Quiz 3','Final'],
['John', 100, 90, 80, 90],
['McVay', 88, 99, 11, 15],
['Rita', 45, 56, 67, 89],
['Ketan', 59, 61, 67, 32],
['Saranya', 73, 79, 83, 45],
['Min', 89, 97, 101, 100]]
def average(grades):
for m in grades[1:]:
t = m[1:]
l = len(t)
s = sum(t)
yield s//l
list(average(grades))

Simple boolean indexing gives AssertionError: Cannot create BlockManager

I have a simple DataFrame, AR, with 83 columns and 1428 rows:
In [128]:
AR.index
Out[128]:
Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...], dtype='int64')
In [129]:
AR.columns
Out[129]:
Index([u'ARKOD', u'ANSNR', u'PNR', u'NAMN', u'MEDB', u'LAEN', u'GATA', u'ORT1', u'ORT2', u'LAND', u'TFNA', u'TFNB', u'BEH_BEHAR', u'BEH_BEHS1', u'BEH_BEHS2', u'BEH_BEHKV', u'BEH_BEHAR2', u'BEH_BEHS1_2', u'BEH_BEHS2_2', u'BEH_BEHKV2', u'BEH_BEHAR3', u'BEH_BEHS1_3', u'BEH_BEHS2_3', u'BEH_BEHKV_3', u'BEH_BEHAR_4', u'BEH_BEHS1_4', u'BEH_BEHS2_4', u'BEH_BEHKV_4', u'BEH25', u'FILLER1', u'BEHFT', u'SLP_SPLAR', u'SLP_SLPP', u'MOTSV', u'FILLER2', u'ATG_ATG25', u'ATG_ATG9', u'ATG_ATGFT', u'ATG_ATGOB', u'ATG_ATGUT', u'ATG_ATGSI', u'ATG_ATGDI', u'ATG_ATGFO', u'ATG_ATGUG', u'ATG_ATGAL ', u'ATG_ATGUL1', u'ATG_ATGUL2', u'ATG_ATGUL3', u'ATG_ATGUL4', u'ATG_ATGUL5', u'ATG_ATGUL6', u'ATG_ATGUL7', u'ATG_ATGUL8', u'ATG_ATGUL9', u'ATG_ATGUL10', u'ATG_ATGUL11', u'ATG_ATGUL12', u'ATG_ATGFU1', u'ATG_ATGFU2', u'ATG_ATGFU3', u'ATG_ATGFU4', u'ATG_ATGB1', u'ATG_ATGB2', u'SLUMP', u'STAT_STATF', u'STAT_STATO', u'STAT_STATA', u'STAT_STATK', u'STAT_STATU', u'STAT_STATH', u'STAT_STATR', u'ANTAL', u'ANTBT', u'ANTSM', u'ANTAE', u'ANTFU', u'ANTZL', u'ANTYL', u'STATL', u'ATB', u'ANTB ', u'FILLER2'], dtype='object')
When I do for example:
In [121]:
AR[AR.ANSNR==10042]
I get
AssertionError: Cannot create BlockManager._ref_locs because block [IntBlock: [ANSNR, PNR, MEDB, SLUMP, ANTAL, ANTBT, ANTSM, ANTAE, ANTFU, ANTZL, ANTYL, ATB], 12 x 1, dtype: int64] with duplicate items [Index([u'ARKOD', u'ANSNR', u'PNR', u'NAMN', u'MEDB', u'LAEN', u'GATA', u'ORT1', u'ORT2', u'LAND', u'TFNA', u'TFNB', u'BEH_BEHAR', u'BEH_BEHS1', u'BEH_BEHS2', u'BEH_BEHKV', u'BEH_BEHAR2', u'BEH_BEHS1_2', u'BEH_BEHS2_2', u'BEH_BEHKV2', u'BEH_BEHAR3', u'BEH_BEHS1_3', u'BEH_BEHS2_3', u'BEH_BEHKV_3', u'BEH_BEHAR_4', u'BEH_BEHS1_4', u'BEH_BEHS2_4', u'BEH_BEHKV_4', u'BEH25', u'FILLER1', u'BEHFT', u'SLP_SPLAR', u'SLP_SLPP', u'MOTSV', u'FILLER2', u'ATG_ATG25', u'ATG_ATG9', u'ATG_ATGFT', u'ATG_ATGOB', u'ATG_ATGUT', u'ATG_ATGSI', u'ATG_ATGDI', u'ATG_ATGFO', u'ATG_ATGUG', u'ATG_ATGAL ', u'ATG_ATGUL1', u'ATG_ATGUL2', u'ATG_ATGUL3', u'ATG_ATGUL4', u'ATG_ATGUL5', u'ATG_ATGUL6', u'ATG_ATGUL7', u'ATG_ATGUL8', u'ATG_ATGUL9', u'ATG_ATGUL10', u'ATG_ATGUL11', u'ATG_ATGUL12', u'ATG_ATGFU1', u'ATG_ATGFU2', u'ATG_ATGFU3', u'ATG_ATGFU4', u'ATG_ATGB1', u'ATG_ATGB2', u'SLUMP', u'STAT_STATF', u'STAT_STATO', u'STAT_STATA', u'STAT_STATK', u'STAT_STATU', u'STAT_STATH', u'STAT_STATR', u'ANTAL', u'ANTBT', u'ANTSM', u'ANTAE', u'ANTFU', u'ANTZL', u'ANTYL', u'STATL', u'ATB', u'ANTB ', u'FILLER2'], dtype='object')] does not have _ref_locs set
Thank you for any suggestions
Edit: sorry, here is the Pandas version:
in [136]:
pd.__version__
Out[136]:
'0.13.1'
Jeff's question:
In [139]:
AR.index.is_unique
Out[139]:
True
In [140]:
AR.columns.is_unique
Out[140]:
False
Is is the last one making problems?

Categories

Resources