I have been trying to rotate a list left and right in python
def rotate(l, r):
return l[r:] + l[:r]
l = eval(input())
r = int(input())
print(rotate(l, r))
but if i give input list as ['A','B','C','D',1,2,3,4,5] and r = -34 I'm getting output as
['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
but actual output is this :
['C', 'D', 1, 2, 3, 4, 5, 'A', 'B']
Can anyone tell how can I do it?
First you could use print() and test it for different values
def rotate(l, r):
return l[r:] + l[:r]
l = ['A','B','C','D',1,2,3,4,5]
print('len(l):', len(l))
for r in range(0, -34, -1):
print(f"{r:3}", rotate(l, r))
And you see
len(l): 9
0 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-1 [5, 'A', 'B', 'C', 'D', 1, 2, 3, 4]
-2 [4, 5, 'A', 'B', 'C', 'D', 1, 2, 3]
-3 [3, 4, 5, 'A', 'B', 'C', 'D', 1, 2]
-4 [2, 3, 4, 5, 'A', 'B', 'C', 'D', 1]
-5 [1, 2, 3, 4, 5, 'A', 'B', 'C', 'D']
-6 ['D', 1, 2, 3, 4, 5, 'A', 'B', 'C']
-7 ['C', 'D', 1, 2, 3, 4, 5, 'A', 'B']
-8 ['B', 'C', 'D', 1, 2, 3, 4, 5, 'A']
-9 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-10 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-11 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-12 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-13 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-14 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-15 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-16 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-17 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-18 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-19 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-20 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-21 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-22 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-23 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-24 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-25 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-26 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-27 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-28 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-29 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-30 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-31 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-32 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-33 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
When -r is bigger then len(r) then it doesn't work as you would expect.
It gets empty list + full list or full list + empty list
The same problem is with +34 and -34.
Because you get the same list for r=len(l), r=len(l)*2, ...r=len(l)*n so you would use modulo (r % len(l)) to have value smaller then len(l) and get what you need.
def rotate(l, r):
r = r % len(l)
return l[r:] + l[:r]
l = ['A','B','C','D',1,2,3,4,5]
print('len(l):', len(l))
for r in range(0, -34, -1):
print(f"{r:3}", rotate(l, r))
Result:
len(l): 9
0 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-1 [5, 'A', 'B', 'C', 'D', 1, 2, 3, 4]
-2 [4, 5, 'A', 'B', 'C', 'D', 1, 2, 3]
-3 [3, 4, 5, 'A', 'B', 'C', 'D', 1, 2]
-4 [2, 3, 4, 5, 'A', 'B', 'C', 'D', 1]
-5 [1, 2, 3, 4, 5, 'A', 'B', 'C', 'D']
-6 ['D', 1, 2, 3, 4, 5, 'A', 'B', 'C']
-7 ['C', 'D', 1, 2, 3, 4, 5, 'A', 'B']
-8 ['B', 'C', 'D', 1, 2, 3, 4, 5, 'A']
-9 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-10 [5, 'A', 'B', 'C', 'D', 1, 2, 3, 4]
-11 [4, 5, 'A', 'B', 'C', 'D', 1, 2, 3]
-12 [3, 4, 5, 'A', 'B', 'C', 'D', 1, 2]
-13 [2, 3, 4, 5, 'A', 'B', 'C', 'D', 1]
-14 [1, 2, 3, 4, 5, 'A', 'B', 'C', 'D']
-15 ['D', 1, 2, 3, 4, 5, 'A', 'B', 'C']
-16 ['C', 'D', 1, 2, 3, 4, 5, 'A', 'B']
-17 ['B', 'C', 'D', 1, 2, 3, 4, 5, 'A']
-18 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-19 [5, 'A', 'B', 'C', 'D', 1, 2, 3, 4]
-20 [4, 5, 'A', 'B', 'C', 'D', 1, 2, 3]
-21 [3, 4, 5, 'A', 'B', 'C', 'D', 1, 2]
-22 [2, 3, 4, 5, 'A', 'B', 'C', 'D', 1]
-23 [1, 2, 3, 4, 5, 'A', 'B', 'C', 'D']
-24 ['D', 1, 2, 3, 4, 5, 'A', 'B', 'C']
-25 ['C', 'D', 1, 2, 3, 4, 5, 'A', 'B']
-26 ['B', 'C', 'D', 1, 2, 3, 4, 5, 'A']
-27 ['A', 'B', 'C', 'D', 1, 2, 3, 4, 5]
-28 [5, 'A', 'B', 'C', 'D', 1, 2, 3, 4]
-29 [4, 5, 'A', 'B', 'C', 'D', 1, 2, 3]
-30 [3, 4, 5, 'A', 'B', 'C', 'D', 1, 2]
-31 [2, 3, 4, 5, 'A', 'B', 'C', 'D', 1]
-32 [1, 2, 3, 4, 5, 'A', 'B', 'C', 'D']
-33 ['D', 1, 2, 3, 4, 5, 'A', 'B', 'C']
BTW:
Without modulo you would have to use for-loops with [1:], [:1] or [-1:],[:-1] - but it need many moves - so it may need more time and memory (but for small list it is not visible).
def rotate(l, r):
if r >= 0:
for _ in range(0, r, 1):
l = l[1:] + l[:1]
else:
for _ in range(0, r, -1):
l = l[-1:] + l[:-1]
return l
l = ['A','B','C','D',1,2,3,4,5]
print('len(l):', len(l))
#for r in range(0, -34, -1):
# print(f"{r:3}", rotate(l, r))
for r in range(0, 34, 1):
print(f"{r:3}", rotate(l, r))
The same with one for-loop
def rotate(l, r):
if r >= 0:
s = 1
else:
s = -1
for _ in range(0, r, s):
l = l[s:] + l[:s]
return l
If r can be bigger than the list you need to add the modulo operater as #tim-roberts mentioned:
def rotate(l, r):
r = r % len(l)
return l[r:] + l[:r]
Outputs
l = [1,2,3]
print(rotate(l,0))
[1, 2, 3]
print(rotate(l,1))
[2, 3, 1]
print(rotate(l,-1))
[3, 1, 2]
print(rotate(l,4))
[2, 3, 1]
print(rotate(l,-4))
[3, 1, 2]
(personally I'd also turn around the rotation direction, using e.g. -r)
Related
I have a dataframe in Python below:
import pandas as pd
df = pd.DataFrame({
'CRDACCT_DLQ_CYC_1_MNTH_AGO' : [3, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'],
'CRDACCT_DLQ_CYC_2_MNTH_AGO': [4, 3, 3, 3, 3, 3, 2, 0, 5, 4, 3, 2, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2],
'CRDACCT_DLQ_CYC_3_MNTH_AGO': [8, 7, 6, 5, 4, 3, 2, 'F', 'F', 0, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'F', 'C', 'C', 'F', 'F'],
'CRDACCT_DLQ_CYC_4_MNTH_AGO' : [0, 2, 'F', 'F', 'C', 'C', 'C', 'C', 0, 2, 0, 2, 0, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'F', 'C', 'F'],
'CRDACCT_DLQ_CYC_5_MNTH_AGO' : [2, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'],
'CRDACCT_DLQ_CYC_6_MNTH_AGO' : [2, 2, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 0, 2, 0, 2, 0],
'CRDACCT_DLQ_CYC_7_MNTH_AGO' : [3, 3, 2, 'C', 'C', 'C', 'F', 0, 6, 5, 4, 3, 2, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'],
'CRDACCT_DLQ_CYC_8_MNTH_AGO' : [5, 4, 4, 3, 3, 2, 3, 2, 2, 2, 1, 2, 0, 2, 'C', 'C', 0, 2, 2, 2, 'C', 'C', 0, 'Z'],
'CRDACCT_DLQ_CYC_9_MNTH_AGO' : [2, 2, 'C', 0, 2, 0, 2, 'C', 'C', 'C', 'C', 'C', 0, 3, 2, 'C', 'F', 'C', 'F', 'F', 'F', 'F', 'F', 'F'],
'CRDACCT_DLQ_CYC_10_MNTH_AGO' : [5, 4, 3, 2, 3, 2, 0, 2, 0, 2, 'C', 'C', 'F', 2, 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'C'],
'CRDACCT_DLQ_CYC_11_MNTH_AGO' : [4, 3, 2, 'F', 2, 0, 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z'],
'CRDACCT_DLQ_CYC_12_MNTH_AGO' : ['F', 8, 7, 6, 5, 4, 3, 2, 'C', 'C', 'C', 0, 2, 'C', 'C', 0, 2, 0, 3, 2, 'C', 'C', 'F', 2]
})
df.head()
I want to convert those values (string value: C, F, and Z) into some categories with this condition: if values in column CRDACCT_DLQ_CYC_1_MNTH_AGO, CRDACCT_DLQ_CYC_2_MNTH_AGO, ......., CRDACCT_DLQ_CYC_12_MNTH_AGO consist:
C = 0
F = 0
Z = 0
else value = value
#Convert value
df = df.replace({'C': 0, 'F': 0, 'Z': 0,' ':0}).astype(int)
Then, I want to create a new column with the name of MSD. MSD stands for Month Since Delinquent. MSD is calculated by identifying each of 12 columns CRDACCT_DLQ_CYC_1_MNTH_AGO, CRDACCT_DLQ_CYC_2_MNTH_AGO, .......up until CRDACCT_DLQ_CYC_12_MNTH_AGO with this kind of condition:
If value in CRDACCT_DLQ_CYC_1_MNTH_AGO > 1 then MSD = 1, otherwise MSD=0 or
If value in CRDACCT_DLQ_CYC_2_MNTH_AGO > 1 then MSD = 2, otherwise MSD=0 or
If value in CRDACCT_DLQ_CYC_3_MNTH_AGO > 1 then MSD = 3, otherwise MSD=0 or
If value in CRDACCT_DLQ_CYC_4_MNTH_AGO > 1 then MSD = 4, otherwise MSD=0 or
If value in CRDACCT_DLQ_CYC_5_MNTH_AGO > 1 then MSD = 5, otherwise MSD=0 or
If value in CRDACCT_DLQ_CYC_6_MNTH_AGO > 1 then MSD = 6, otherwise MSD=0 or
If value in CRDACCT_DLQ_CYC_7_MNTH_AGO > 1 then MSD = 7, otherwise MSD=0 or
If value in CRDACCT_DLQ_CYC_8_MNTH_AGO > 1 then MSD = 8, otherwise MSD=0 or
If value in CRDACCT_DLQ_CYC_9_MNTH_AGO > 1 then MSD = 9, otherwise MSD=0 or
If value in CRDACCT_DLQ_CYC_10_MNTH_AGO > 1 then MSD = 10, otherwise MSD=0 or
If value in CRDACCT_DLQ_CYC_11_MNTH_AGO > 1 then MSD = 11, otherwise MSD=0 or
If value in CRDACCT_DLQ_CYC_12_MNTH_AGO > 1 then MSD = 12, otherwise MSD=0
Note: otherwise if value 1 and 0, then MSD = 0.
For example:
index 0, MSD =1,because value 3 > 1 is in CRDACCT_DLQ_CYC_1_MNTH_AGO
(we no need to check CRDACCT_DLQ_CYC_2_MNTH_AGO > 1 because we have
found month since delinquent in CRDACCT_DLQ_CYC_1_MNTH_AGO) , hence
MSD is in 1 MNTH AGO
index 1, MSD=1,
index 2, MSD=2,
index 3, MSD=2, because value 3 > 1 is in
CRDACCT_DLQ_CYC_2_MNTH_AGO, hence MSD is in 2 MNTH AGO
index 4, MSD=2
Note: by checking each 12 columns with those conditions, If all values = 0 in each column CRDACCT_DLQ_CYC_1_MNTH_AGO, .....and CRDACCT_DLQ_CYC_12_MNTH_AGO, then MSD should be = 0.
Generally it is to check value > 1 in each 12 columns then determine the MSD value based on column name CRDACCT_DLQ_CYC_x_MNTH_AGO, x will be the value of MSD if > 1.
It ain't pretty but this one-liner should do the trick ;)
df['MSD'] = (df > 1).astype(int).apply(lambda row: int(row.idxmax().split('_')[3]) if row.sum() >=1 else 0, axis=1)
basically - check which values are over 1, get the first column for each row which is above one (the MSD as you defined it), and don't forget to check the edge case when it is 0.
I want to create a list in Python from the dataset below:
import pandas as pd
df = pd.DataFrame({
`'CRDACCT_DLQ_CYC_1_MNTH_AGO' : [3, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C',` `'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'],`
'CRDACCT_DLQ_CYC_2_MNTH_AGO': [4, 3, 3, 3, 3, 3, 2, 0, 5, 4, 3, 2, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2],
'CRDACCT_DLQ_CYC_3_MNTH_AGO': [8, 7, 6, 5, 4, 3, 2, 'F', 'F', 0, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'F', 'C', 'C', 'F', 'F'],
'CRDACCT_DLQ_CYC_4_MNTH_AGO' : [0, 2, 'F', 'F', 'C', 'C', 'C', 'C', 0, 2, 0, 2, 0, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'F', 'C', 'F'],
'CRDACCT_DLQ_CYC_5_MNTH_AGO' : [2, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'],
'CRDACCT_DLQ_CYC_6_MNTH_AGO' : [2, 2, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 0, 2, 0, 2, 0],
'CRDACCT_DLQ_CYC_7_MNTH_AGO' : [3, 3, 2, 'C', 'C', 'C', 'F', 0, 6, 5, 4, 3, 2, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'],
'CRDACCT_DLQ_CYC_8_MNTH_AGO' : [5, 4, 4, 3, 3, 2, 3, 2, 2, 2, 1, 2, 0, 2, 'C', 'C', 0, 2, 2, 2, 'C', 'C', 0, 'Z'],
'CRDACCT_DLQ_CYC_9_MNTH_AGO' : [2, 2, 'C', 0, 2, 0, 2, 'C', 'C', 'C', 'C', 'C', 0, 3, 2, 'C', 'F', 'C', 'F', 'F', 'F', 'F', 'F', 'F'],
'CRDACCT_DLQ_CYC_10_MNTH_AGO' : [5, 4, 3, 2, 3, 2, 0, 2, 0, 2, 'C', 'C', 'F', 2, 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'C'],
'CRDACCT_DLQ_CYC_11_MNTH_AGO' : [4, 3, 2, 'F', 2, 0, 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z'],
'CRDACCT_DLQ_CYC_12_MNTH_AGO' : ['F', 8, 7, 6, 5, 4, 3, 2, 'C', 'C', 'C', 0, 2, 'C', 'C', 0, 2, 0, 3, 2, 'C', 'C', 'F', 2]
})
With some data wrangling by transposing the data to convert the value with this code:
#Transpose data
dfT = pd.DataFrame(df.T).reset_index(inplace=False)
dfT
#Data converting
df = df.replace({'C': -1, 'F': -2, 'Z': -3}).astype(int).T
df
The data frame look like this:
For example,
#in column 0, max value is 8,
#in column 1, max value is 8,
#in column 2, max value is 7,
.....
and so on until column 23.
Final result that I expected should be a list that consists a maximum value from each column:
max_val = [8,8,7,6,5,4,3,2,6,5,...,2,2,2,2,2,2,2,2,2,2]
You can try this:
import pandas as pd
df = pd.DataFrame({
'CRDACCT_DLQ_CYC_1_MNTH_AGO' : [3, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'],
'CRDACCT_DLQ_CYC_2_MNTH_AGO': [4, 3, 3, 3, 3, 3, 2, 0, 5, 4, 3, 2, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2],
'CRDACCT_DLQ_CYC_3_MNTH_AGO': [8, 7, 6, 5, 4, 3, 2, 'F', 'F', 0, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'F', 'C', 'C', 'F', 'F'],
'CRDACCT_DLQ_CYC_4_MNTH_AGO' : [0, 2, 'F', 'F', 'C', 'C', 'C', 'C', 0, 2, 0, 2, 0, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'F', 'C', 'F'],
'CRDACCT_DLQ_CYC_5_MNTH_AGO' : [2, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'],
'CRDACCT_DLQ_CYC_6_MNTH_AGO' : [2, 2, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 0, 2, 0, 2, 0],
'CRDACCT_DLQ_CYC_7_MNTH_AGO' : [3, 3, 2, 'C', 'C', 'C', 'F', 0, 6, 5, 4, 3, 2, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'],
'CRDACCT_DLQ_CYC_8_MNTH_AGO' : [5, 4, 4, 3, 3, 2, 3, 2, 2, 2, 1, 2, 0, 2, 'C', 'C', 0, 2, 2, 2, 'C', 'C', 0, 'Z'],
'CRDACCT_DLQ_CYC_9_MNTH_AGO' : [2, 2, 'C', 0, 2, 0, 2, 'C', 'C', 'C', 'C', 'C', 0, 3, 2, 'C', 'F', 'C', 'F', 'F', 'F', 'F', 'F', 'F'],
'CRDACCT_DLQ_CYC_10_MNTH_AGO' : [5, 4, 3, 2, 3, 2, 0, 2, 0, 2, 'C', 'C', 'F', 2, 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'C'],
'CRDACCT_DLQ_CYC_11_MNTH_AGO' : [4, 3, 2, 'F', 2, 0, 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z'],
'CRDACCT_DLQ_CYC_12_MNTH_AGO' : ['F', 8, 7, 6, 5, 4, 3, 2, 'C', 'C', 'C', 0, 2, 'C', 'C', 0, 2, 0, 3, 2, 'C', 'C', 'F', 2]
})
#Transpose data
dfT = pd.DataFrame(df.T).reset_index(inplace=False)
#Data converting
df = df.replace({'C': -1, 'F': -2, 'Z': -3}).astype(int).T
max_val = list(df.max())
print(max_val)
Output:
[8, 8, 7, 6, 5, 4, 3, 2, 6, 5, 4, 3, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2]
I have a dataframe in Python below:
import pandas as pd
df = pd.DataFrame({
'CRDACCT_DLQ_CYC_1_MNTH_AGO' : [3, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'],
'CRDACCT_DLQ_CYC_2_MNTH_AGO': [4, 3, 3, 3, 3, 3, 2, 0, 5, 4, 3, 2, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2],
'CRDACCT_DLQ_CYC_3_MNTH_AGO': [8, 7, 6, 5, 4, 3, 2, 'F', 'F', 0, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'F', 'C', 'C', 'F', 'F'],
'CRDACCT_DLQ_CYC_4_MNTH_AGO' : [0, 2, 'F', 'F', 'C', 'C', 'C', 'C', 0, 2, 0, 2, 0, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'F', 'C', 'F'],
'CRDACCT_DLQ_CYC_5_MNTH_AGO' : [2, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'],
'CRDACCT_DLQ_CYC_6_MNTH_AGO' : [2, 2, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 0, 2, 0, 2, 0],
'CRDACCT_DLQ_CYC_7_MNTH_AGO' : [3, 3, 2, 'C', 'C', 'C', 'F', 0, 6, 5, 4, 3, 2, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'],
'CRDACCT_DLQ_CYC_8_MNTH_AGO' : [5, 4, 4, 3, 3, 2, 3, 2, 2, 2, 1, 2, 0, 2, 'C', 'C', 0, 2, 2, 2, 'C', 'C', 0, 'Z'],
'CRDACCT_DLQ_CYC_9_MNTH_AGO' : [2, 2, 'C', 0, 2, 0, 2, 'C', 'C', 'C', 'C', 'C', 0, 3, 2, 'C', 'F', 'C', 'F', 'F', 'F', 'F', 'F', 'F'],
'CRDACCT_DLQ_CYC_10_MNTH_AGO' : [5, 4, 3, 2, 3, 2, 0, 2, 0, 2, 'C', 'C', 'F', 2, 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'C'],
'CRDACCT_DLQ_CYC_11_MNTH_AGO' : [4, 3, 2, 'F', 2, 0, 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z'],
'CRDACCT_DLQ_CYC_12_MNTH_AGO' : ['F', 8, 7, 6, 5, 4, 3, 2, 'C', 'C', 'C', 0, 2, 'C', 'C', 0, 2, 0, 3, 2, 'C', 'C', 'F', 2]
})
df.head()
I want to transform those values (string value: C, F, and Z) into some categories with this condition:
if values in column CRDACCT_DLQ_CYC_1_MNTH_AGO, CRDACCT_DLQ_CYC_2_MNTH_AGO, ......., CRDACCT_DLQ_CYC_12_MNTH_AGO consist:
C = -1
F = -2
Z = -3
else value = value
Then I transpose the table to identify Month Since Dlq (MSD).
dfT =pd.DataFrame(df.T).reset_index(inplace=False)
dfT
I want to create a list with the name of MSD. MSD is identified for value if it is greater than 1 ( value > 1). For example, in index 2 CRDACCT_DLQ_CYC_1_MNTH_AGO = C or after it has changed = -1 which is not greater than 1. Then, check CRDACCT_DLQ_CYC_2_MNTH_AGO is greater than 1? CRDACCT_DLQ_CYC_2_MNTH_AGO = 3 is greater than 1. Hence, the MSD is 2 because it's in CRDACCT_DLQ_CYC_2_MNTH_AGO. Detail flow chart & overview table for identification .
The MSD value is between 1 and 12 depends on i in CRDACCT_DLQ_CYC_i_MNTH_AGO, for i = 1,2,3,...,12.
So the final result is a MSD list with 24 value, identified for each index 0 -23.
Does it what you are looking for:
# From your dataframe
MSD = df.T.apply(pd.to_numeric, errors='coerce').ge(1).idxmax(axis=0) \
.str.extract(r'CYC_(\d+)_MNTH', expand=False).astype(int).tolist()
print(MSD)
# Output:
[1, 1, 2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 7, 2, 2, 2, 2, 2, 2, 8, 2, 2, 6, 2]
I am trying to create all combinations of two sets of lists using as follows:
x = [[1,2,3],[4,5,6]]
y = [['a','b','c'],['d','e','f']]
combos = [[1,2,3,'a','b','c'],[4,5,6,'d','e','f'],[4,5,6,'a','b','c'],[4,5,6,'d','e','f']]
I think itertools may be of some help but not sure how. Thanks
You can use product and chain:
from itertools import product, chain
[list(chain(*i)) for i in product(x, y)]
#[[1, 2, 3, 'a', 'b', 'c'],
# [1, 2, 3, 'd', 'e', 'f'],
# [4, 5, 6, 'a', 'b', 'c'],
# [4, 5, 6, 'd', 'e', 'f']]
Or you can use a list comprehension:
[i + j for i in x for j in y]
#[[1, 2, 3, 'a', 'b', 'c'],
# [1, 2, 3, 'd', 'e', 'f'],
# [4, 5, 6, 'a', 'b', 'c'],
# [4, 5, 6, 'd', 'e', 'f']]
I have an example multidimensional list:
example_list=[
["a","b","c", 2,4,5,7],
["e","f","g",0,0,1,5],
["e","f","g", 1,4,5,7],
["a","b","c", 3,2,5,7]
]
How is it possible to put them in groups like this:
out_list=[
[["a","b","c", 2,4,5,7],
["a","b","c",3,2,5,7]
],
[["e","f","g", 0,0,1,5],
["e","f","g", 1,4,5,7]
]
]
I have tried this:
example_list=[["a","b","c", 2,4,5,7],["e","f","g", 0,0,1,5],["e","f","g",1,4,5,7],["a","b","c", 3,2,5,7]]
unique=[]
index=0
for i in range(len(example_list)):
newlist=[]
if example_list[i][:3]==example_list[index][:3]:
newlist.append(example_list[i])
index=index+1
unique.append(newlist)
print unique
My results is this:
[[['a', 'b', 'c', 2, 4, 5, 7]], [['e', 'f', 'g',0, 0, 1, 5]], [['e', 'f', 'g', 1, 4, 5, 7]], [['a', 'b', 'c', 3, 2, 5,7]]]
I could not figure it out.
If the grouping is decided by the first three elements in each list following code will do what you're asking for:
from collections import defaultdict
example_list=[["a","b","c", 2,4,5,7],["e","f","g",0,0,1,5],["e","f","g", 1,4,5,7],["a","b","c", 3,2,5,7]]
d = defaultdict(list)
for l in example_list:
d[tuple(l[:3])].append(l)
print d.values() # [[['a', 'b', 'c', 2, 4, 5, 7], ['a', 'b', 'c', 3, 2, 5, 7]], ...]
This will use defaultdict to generate a dictionary where keys are the first three elements and values are list of lists which start with those elements.
First sort the list simply using sorted(), providing a lambda function as key.
>>> a = sorted(example_list, key=lambda x:x[:3])
[['a', 'b', 'c', 2, 4, 5, 7], ['a', 'b', 'c', 3, 2, 5, 7], ['e', 'f', 'g', 0, 0, 1, 5], ['e', 'f', 'g', 1, 4, 5, 7]]
And then use itertools.groupby() on the sorted list:
>>> [list(v) for k, v in groupby(a, lambda x:x[:3])]
[
[['a', 'b', 'c', 2, 4, 5, 7], ['a', 'b', 'c', 3, 2, 5, 7]],
[['e', 'f', 'g', 0, 0, 1, 5], ['e', 'f', 'g', 1, 4, 5, 7]]
]