Data Science - Average of Rows [Code Coach]

Question

Data Science - Average of Rows
In a matrix, or 2-d array X, the averages (or means) of the elements of rows is called row means.
Task
Given a 2D array, return the rowmeans.
Input Format
First line: two integers separated by spaces, the first indicates the rows of matrix X (n) and the second indicates the columns of X (p)
Next n lines: values of the row in X
Output Format
An numpy 1d array of values rounded to the second decimal.
2 2
1.5 1
2 2.9
Sample Output
[1.25 2.45]
_______________
My code:
import numpy as np
import pandas as pd
n, p = [int(x) for x in input().split()]
lst = []
for i in range(n):
lst.append(input().split())
x = pd.DataFrame(lst).astype(float)
y = x[:].sum(axis=1) / 2
y = np.array(y)
print(y)
the code successfully passes the first 3 test cases, but does not pass 4 and 5. The input data for 4 and 5 test cases is hidden. please help me understand what could be the matter

Answer 1

My Solution with no Pandas:
import numpy as np
n, p = [int(x) for x in input().split()]
lista = []
for i in range(n):
lista.append(input().split())
print(np.array(lista).astype(np.float16).mean(axis=1).round(2))
Please vote :D

Answer 2

import numpy as np
n, p = [int(x) for x in input().split()] #it taking first two input of n and p
x= [] # array for the list
for i in range(n): # taking input for each row
x.append(input().split()) # taking input and spliting the data into columnwise input
arr = np.array(x) # making this numpy array
arr = arr.astype(np.float16)#making data type into float
mn = arr.mean(axis = 1) # having mean value in rowwise with axis = 1
mean= mn.round(2) #as output need to be two value after point value
print(mean)
I tried to explain the code as well. Please vote me to encourage me more.

Answer 3

Here my solution:
import numpy as np
n, p = [int(x) for x in input().split()]
m = []
for i in range(n):
m.append(input().split())
m_array = np.array([m], dtype= 'float')
rm_array = m_array.reshape(n, p)
resul = []
y = 0
while y < n:
resul.append(rm_array [y, :].mean().round(2))
y += 1
resul_array = np.array(resul)
print(resul_array)

Answer 4

You are calculating the mean for 2-column arrays but the number of columns varies.
[...and the second indicates the columns of X (p)...]
You forgot to round:
[Output format:
A numpy 1d array of values ROUNDED TO THE SECOND DECIMAL]
Hope this helps. Don't doubt to keep inquiring.

Answer 5

I'm upset that this example is 100% unrelated to everything that has been taught in this course so far. Yes it uses Python but not the concepts that were taught in this course. I thought it was just me but none of these solutions use concepts that were taught here.

Answer 6

import numpy as np
row, col = [int(x) for x in input().split()]
list = [float(j) for i in range(row) for j in input().split()]
array = np.array(list).reshape((row, col))
print(array.mean(axis=1).round(2))
# -------------------------------------------------------------
## for loop solution (more readable)
import numpy as np
row, col = [int(x) for x in input().split()]
list = []
for i in range(row):
list += [float(j) for j in input().split()]
array = np.array(list).reshape((row, col))
print(array.mean(axis=1).round(2))

Answer 7

Does anybody else get the same problem? In test 2, the code result shows two spaces between the first 2 elements, in the expected result it's just 1.
It looks like they edited it and no code works now.
How to report that

Answer 8

Hi, here's my solution. Thanks to the community since i used some of your answers to build mine. I found very confusing to have to call "input" again. As the problem is written, i thought that the whole bunch of numbers were given in one shot in the first input. Got stuck for while!
n, p = [int(x) for x in input().split()]
import numpy as np
data = []
for i in range(n):
list = [float(x) for x in input().split()]
data.append(list)
X = np.array(data)
means = X.mean(axis=1).round(2)
print(means)
Thanks!

Answer 9

"""i used numpy empty method to create an empty array with 0 rows and p columns. looping through n rows, i used numpy append method to insert new inputs and astype method to convert to float along the 0 axis. then used numpy mean method along axis 1 and rounded to 2 digits.
"""
n, p = [int(x) for x in input().split()]
import numpy as np
a = np.empty((0,p), float)
for i in range(n):
a = np.append(a, np.array([input().split()]).astype(float), axis=0)
print(a.mean(axis=1).round(2))

Answer 10

Here was my solution for the problem. This failed the last two test cases. Can anyone explain me why? Thank you.
import numpy as np
contents = []
while True:
try:
contents.append(input().strip().split())
except EOFError:
break
print(np.array(contents[1:]).astype(np.float).mean(axis=1))

Answer 11

BTW, they said nothing about astype() before.

Answer 12

import numpy as np
n, p = [int(x) for x in input().split()]
x = []
for i in range(n): x += [float(k) for k in input().split()]
x = np.array(x).reshape((n, p))
print(np.around(np.mean(x, axis=1),decimals=2))

Answer 13

I had the same problem, this was my initial idea
import numpy as np
n, p = [int(x) for x in input().split()]
arr = []
for i in range(0,n):
arr.append((input().split()))
np_arr = np.array(arr)
np_arr = np_arr.astype(float)
np_arr = np_arr.reshape(n,p)
print(np_arr.mean(axis=1))
I'm new to python, and i'm starting in data Science It was difficult to find a way to get it done, só I think about round the number, I went to Mother Google, and I found the function and I did this.
print(np_arr.mean(axis=1).round(2))
and worked.
Happy to share, I deserve a up vote no?

Answer 14

n, p = [int(x) for x in input().split()]
X = []
for i in range(n):
X.append([float(x) for x in input().split()])
y = [float(x) for x in input().split()]
try this code.... Credit to Amir Mehrabi Jorshari
import numpy as np
X=np.array(X).reshape(n,p)
y=np.array(y)
b=np.linalg.pinv(X) @ y.transpose()
print(np.around(b,decimals=2))

Answer 15

My code. Work perfectly
import numpy as np
row = []
n, p = [int(x) for x in input().split()]
while True:
try:
row.append(input().split())
except EOFError:
break
row_arr = np.array(row).astype(float)
rowMean = row_arr.mean(axis=1)
print(rowMean.round(2))

Answer 16

import numpy as np
n, p = [int(x) for x in input().split()]
arr=[]
for i in range (n):
arr+= input().split()
arr = np.array(arr)
arr = arr.astype(np.float16)
arr =np.reshape(arr,(n, p))
print (arr.mean(axis=1).round(2))
## Try not to follow me

Answer 17

import numpy as np
n, p = [int(x) for x in input().split()]
rowmean = np.zeros(n)
for i in range(n):
array = [float(j) for j in input().split()]
rowmean[i] = np.round(np.mean(array),2)
print(rowmean)

Answer 18

import numpy as np
n, p = [int(x) for x in input().split()]
data = []
for i in range(0,n):
liste = [float(x) for x in input().split()]
data.append(liste[0:p])
arr = np.array(data)
print(arr.mean(axis=1).round(2))

Answer 19

n, p = [int(x) for x in input().split()]
import numpy as np
new_arr = []
for i in range(n):
arr = input().split()
lis = [float(j) for j in arr]
new_arr.append(lis)
arr = np.array(new_arr)
print(arr.mean(axis=1).round(2))

Answer 20

I tried with numpy only
hope it helps :D
import numpy as np
n, p = [int(x) for x in input().split()]
data=[]
for i in range(n):
nar=np.asarray([float(x) for x in input().split()])
data=np.append(data,nar)
data=np.reshape(data,(n,p))
print(data.mean(axis=1).round(2))