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i tried to print the multiplication table but i was expecting to print the table according to given question image.enter image description here
One line version
print('\n'.join(' '.join(str(i*n) for n in range(1,i+1)) for i in range(1,9)))
1
2 4
3 6 9
4 8 12 16
5 10 15 20 25
6 12 18 24 30 36
7 14 21 28 35 42 49
8 16 24 32 40 48 56 64
Expanded Version
for i in range(1,9):
print(' '.join(str(i*n) for n in range(1,i+1)))
1
2 4
3 6 9
4 8 12 16
5 10 15 20 25
6 12 18 24 30 36
7 14 21 28 35 42 49
8 16 24 32 40 48 56 64
Beginner version
for i in range(1,9):
for j in range(1,i+1):
print(i*j," ",end="")
print()
1
2 4
3 6 9
4 8 12 16
5 10 15 20 25
6 12 18 24 30 36
7 14 21 28 35 42 49
8 16 24 32 40 48 56 64
Related
I have a dataframe with profit values, IDs, and week values. It looks a little like this
ID
Week
Profit
A
1
2
A
2
2
A
3
0
A
4
0
I want to create two new columns called "Bi-Weekly" and "Monthly", so week 1 would be label 2, week 2 would also be label 2, but week 3 would be labeled 4, and week 4 would be labeled 4, and they would all be labeled month 1, so I could groupby weekly, bi-weekly, or monthly profit as needed. Right now I've created two functions which work, but the weeks are going to go up to a year (52 weeks) so I was wondering if there's a more efficient way. My bi-weekly function below.
def biweek(prof_calc):
if (prof_calc['week']==2):
return 2
elif (prof_calc['week']==3):
return 2
elif (prof_calc['week']==4):
return 4
elif (prof_calc['week']==5):
return 4
elif (prof_calc['week']==6):
return 6
elif (prof_calc['week']==7):
return 6
elif (prof_calc['week']==8):
return 8
elif (prof_calc['week']==9):
return 8
elif (prof_calc['week']==10):
return 10
elif (prof_calc['week']==11):
return 10
prof_calc['BiWeek'] = prof_calc.apply(biweek, axis=1)
IIUC, you could try:
df["Biweekly"] = (df["Week"]-1)//2+1
df["Monthly"] = (df["Week"]-1)//4+1
>>> df
ID Week Profit Biweekly Monthly
0 A 1 42 1 1
1 A 2 69 1 1
2 A 3 53 2 1
3 A 4 63 2 1
4 A 5 56 3 2
5 A 6 57 3 2
6 A 7 86 4 2
7 A 8 23 4 2
8 A 9 35 5 3
9 A 10 10 5 3
10 A 11 25 6 3
11 A 12 21 6 3
12 A 13 39 7 4
13 A 14 82 7 4
14 A 15 76 8 4
15 A 16 20 8 4
16 A 17 97 9 5
17 A 18 67 9 5
18 A 19 21 10 5
19 A 20 22 10 5
20 A 21 88 11 6
21 A 22 67 11 6
22 A 23 33 12 6
23 A 24 38 12 6
24 A 25 8 13 7
25 A 26 67 13 7
26 A 27 16 14 7
27 A 28 49 14 7
28 A 29 3 15 8
29 A 30 17 15 8
30 A 31 79 16 8
31 A 32 19 16 8
32 A 33 21 17 9
33 A 34 9 17 9
34 A 35 56 18 9
35 A 36 83 18 9
36 A 37 1 19 10
37 A 38 53 19 10
38 A 39 66 20 10
39 A 40 55 20 10
40 A 41 85 21 11
41 A 42 90 21 11
42 A 43 34 22 11
43 A 44 3 22 11
44 A 45 9 23 12
45 A 46 28 23 12
46 A 47 58 24 12
47 A 48 14 24 12
48 A 49 42 25 13
49 A 50 69 25 13
50 A 51 76 26 13
51 A 52 49 26 13
I am working with python to create a new frame starting from two frame by using Pandas.
The first frame (called frame1) is composed by the following line:
A B C D E
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4
5 5 5 5 5
6 6 6 6 6
7 7 7 7 7
8 8 8 8 8
9 9 9 9 9
10 10 10 10 10
11 11 11 11 11
12 12 12 12 12
13 13 13 13 13
14 14 14 14 14
15 15 15 15 15
The second frame (called frame2) is:
A B C D E
19 19 19 19 19
24 24 24 24 24
29 29 29 29 29
34 34 34 34 34
39 39 39 39 39
44 44 44 44 44
49 49 49 49 49
54 54 54 54 54
59 59 59 59 59
64 64 64 64 64
69 69 69 69 69
74 74 74 74 74
79 79 79 79 79
84 84 84 84 84
89 89 89 89 89
94 94 94 94 94
99 99 99 99 99
Now i want to create a new dataset with this logic: starting from frame1 substitute every 5 row until the end of the frame1, the row of the frame1 with a random row of the frame2 (and remove the added row from frame2). A possible output should be:
A B C D E
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4
59 59 59 59 59
6 6 6 6 6
7 7 7 7 7
8 8 8 8 8
9 9 9 9 9
29 29 29 29 29
11 11 11 11 11
12 12 12 12 12
13 13 13 13 13
14 14 14 14 14
84 84 84 84 84
How can i do this operation?
It's quite simple:
frame1.loc[4::5] = frame2.sample(frac=1).reset_index(drop=True)
where
df.loc[4::5] selects every fifth element, starting with the fifth one in df, and
df.sample(frac=1).reset_index(drop=True) shuffles a df around randomly
One way is to first obtain the indices where to update (we could also slice assign, but we'd have the problem of the end not being included), and then assign back taking a sample from df2 of the corresponding size:
ix = np.flatnonzero(np.diff(np.arange(df.shape[0]+1)//5))
df1.iloc[ix] = df2.sample(df1.shape[0]//5).to_numpy()
print(df1)
A B C D E
0 1 1 1 1 1
1 2 2 2 2 2
2 3 3 3 3 3
3 4 4 4 4 4
4 84 84 84 84 84
5 6 6 6 6 6
6 7 7 7 7 7
7 8 8 8 8 8
8 9 9 9 9 9
9 89 89 89 89 89
10 11 11 11 11 11
11 12 12 12 12 12
12 13 13 13 13 13
13 14 14 14 14 14
14 99 99 99 99 99
Need help creating a 9 column descending half pyramid.
The first column must count 1-9.
Then with each row they should continue counting with that starting multiple. Would appreciate any help please.
for num in range(10):
for i in range(num):
print (num, end=" ")
print("\n")
>Current output
1
2 2
3 3 3
4 4 4 4
5 5 5 5 5
6 6 6 6 6 6
7 7 7 7 7 7 7
8 8 8 8 8 8 8 8
9 9 9 9 9 9 9 9 9
>I need it to output as:
1
2 4
3 6 9
4 8 12 16
5 10 15 20 25
6 12 18 24 30 36
7 14 21 28 27 35 42
8 16 24 32 40 48 56 64
9 18 27 36 45 54 63 72 81
You got your inner looping wrong. Considering the outer loop represents line numbers, the inner loop should start from line number, incrementing each time by line number till the square of line number:
for num in range(1, 10):
for i in range(num, num*num+1, num):
print(i, end=" ")
print("\n")
# 1
# 2 4
# 3 6 9
# 4 8 12 16
# 5 10 15 20 25
# 6 12 18 24 30 36
# 7 14 21 28 35 42 49
# 8 16 24 32 40 48 56 64
# 9 18 27 36 45 54 63 72 81
you were almost there! just a few minor adjustments:
for mul in range(1, 10):
for i in range(1, mul+1):
print (i * mul, end=" ")
print("\n")
what you need to print is i * mul; and the range needs to start at 1 and stop at (i.e. one before) mul+1.
a bit more compact and neatly aligned:
for mul in range(1, 10):
print(' '.join(f'{mul*i:2d}' for i in range(1, mul+1)))
this outputs:
1
2 4
3 6 9
4 8 12 16
...
9 18 27 36 45 54 63 72 81
I have some code here:
for i in range(self.size):
print('{:6d}'.format(self.data[i], end=' '))
if (i + 1) % NUMBER_OF_COLUMNS == 0:
print()
Right now this prints as:
1
1
1
1
1
2
3
3
3
3
(whitespace)
3
3
3
etc.
It creates a new line when it hits 10 digits, but it doens't print the initial 10 in a row...
This is what I want-
1 1 1 1 1 1 1 2 2 3
3 3 3 3 3 4 4 4 4 5
However when it hits two digit numbers it gets messed up -
8 8 8 8 8 9 9 9 9 10
10 10 10 10 10 10 etc.
I want it to be right-aligned like this-
8 8 8 8 8 9
10 10 10 10 11 12 etc.
When I remove the format piece it will print the rows out, but there wont be the extra spacing in there of course!
You can align strings by "padding" values using a string's .rjust method. Using some dummy data:
NUMBER_OF_COLUMNS = 10
for i in range(100):
print("{}".format(i//2).rjust(3), end=' ')
#print("{:3}".format(i//2), end=' ') edit: this also works. Thanks AChampion
if (i + 1) % NUMBER_OF_COLUMNS == 0:
print()
#Output:
0 0 1 1 2 2 3 3 4 4
5 5 6 6 7 7 8 8 9 9
10 10 11 11 12 12 13 13 14 14
15 15 16 16 17 17 18 18 19 19
20 20 21 21 22 22 23 23 24 24
25 25 26 26 27 27 28 28 29 29
30 30 31 31 32 32 33 33 34 34
35 35 36 36 37 37 38 38 39 39
40 40 41 41 42 42 43 43 44 44
45 45 46 46 47 47 48 48 49 49
Another approach is to just chunk up the data into rows and print each row, e.g.:
def chunk(iterable, n):
return zip(*[iter(iterable)]*n)
for row in chunk(self.data, NUMBER_OF_COLUMNS):
print(' '.join(str(data).rjust(6) for data in row))
e.g:
In []:
for row in chunk(range(100), 10):
print(' '.join(str(data//2).rjust(3) for data in row))
Out[]:
0 0 1 1 2 2 3 3 4 4
5 5 6 6 7 7 8 8 9 9
10 10 11 11 12 12 13 13 14 14
15 15 16 16 17 17 18 18 19 19
20 20 21 21 22 22 23 23 24 24
25 25 26 26 27 27 28 28 29 29
30 30 31 31 32 32 33 33 34 34
35 35 36 36 37 37 38 38 39 39
40 40 41 41 42 42 43 43 44 44
45 45 46 46 47 47 48 48 49 49
I am new to programming and have taken up learning python in an attempt to make some tasks I run in my research more efficient. I am running a PCA in the pandas module (I found a tutorial online) and have the script for this, but need to subselect part of a dataframe prior to the pca.
so far I have (just for example in reality I am reading a .csv file with a larger matrix)
x = np.random.randint(30, size=(8,8))
df = pd.DataFrame(x)
0 1 2 3 4 5 6 7
0 9 0 23 13 2 5 14 6
1 20 17 11 10 25 23 20 23
2 15 14 22 25 11 15 5 15
3 9 27 15 27 7 15 17 23
4 12 6 11 13 27 11 26 20
5 27 13 5 16 5 5 2 18
6 3 18 22 0 7 10 11 11
7 25 18 10 11 29 29 1 25
What I want to do is sub-select columns that satisfy a certain criteria in any of the rows, specifically I want every column that has at least one number =>27 (just for example) to produce a new dataframe
0 1 3 4 5
0 9 0 13 2 5
1 20 17 10 25 23
2 15 14 25 11 15
3 9 27 27 7 15
4 12 6 13 27 11
5 27 13 16 5 5
6 3 18 0 7 10
7 25 18 11 29 29
I have looked into the various slicing methods in pandas but none seem to do what I want (.loc and .iloc etc.).
The actual script I am using to read in thus far is
filename = 'Data.csv'
data = pd.read_csv(filename,sep = ',')
x = data.ix[:,1:] # variables - species
y = data.ix[:,0] # cases - age
so a sub dataframme of x is what I am after (as above).
Any advice is greatly appreciated.
Indexers like loc, iloc, and ix accept boolean arrays. For example if you have three columns, df.loc[:, [True, False, True]] will return all the rows and the columns 0 and 2 (when corresponding value is True). You can check whether any of the elements in a column is greater than or equal to 27 by (df>=27).any(). This will return True for the columns that has at least one value >=27. So you can slice the dataframe with:
df.loc[:, (df>=27).any()]
Out[34]:
0 1 3 4 5 7
0 8 2 28 9 14 21
1 24 26 23 17 0 0
2 3 24 7 15 4 28
3 29 17 12 7 7 6
4 5 3 10 24 29 14
5 23 21 0 16 23 13
6 22 10 27 1 7 24
7 9 27 2 27 17 12
And this is the initial dataframe:
df
Out[35]:
0 1 2 3 4 5 6 7
0 8 2 7 28 9 14 26 21
1 24 26 15 23 17 0 21 0
2 3 24 26 7 15 4 7 28
3 29 17 9 12 7 7 0 6
4 5 3 13 10 24 29 22 14
5 23 21 26 0 16 23 17 13
6 22 10 19 27 1 7 9 24
7 9 27 26 2 27 17 8 12