ID
001
002
REQ
ID ITEM QUANT
001 chips 20
002 chips 100
SCHEDULE
1 001 cleaning
1 002 normal
2 001 normal
2 002 remodel
3 001 normal
3 002 remodel
4 001 remodel
4 002 cleaning
item = corn chips
id_store
1
2
3
4
001
phase
cleaning
normal
normal
remodel
quant
0
20
20
5
002
phase
normal
remodel
remodel
cleaning
quant
100
5
5
0
I want to calculate a quant given a store phase. if the store is cleaning then its 0 quant, if remodeling then 5 quant else its the quant from requirements.
normally I would do this with a switch statement in dax but the phase data is not in my table. Please assist.
it turns out a simple switch statement looking at different tables works just fine.
Num Items :=
VAR T = sum(REQ[Quant])
RETURN
SWITCH(
TRUE(),
VALUES(SCHEDULE[PHASE]) = "cleaning", 0,
VALUES(SCHEDULE[PHASE]) = "remodeling", 5,
T
)
Related
I have the following
Input:
samples = [('001', 'RENAL', 'CHROMOPHOBE', 'KICH'),
('002', 'OVARIAN', 'HIGH_GRADE_SEROUS_CARCINOMA', 'LGSOC'),
('003', 'OVARIAN', 'OTHER', 'NaN'),
('001', 'COLORECTAL', 'ADENOCARCINOMA', 'KICH')]
labels = ['id', 'disease_type', 'disease_sub_type', 'study_abbreviation']
df = pd.DataFrame.from_records(samples, columns=labels)
df
id disease_type disease_sub_type study_abbreviation
0 001 RENAL CHROMOPHOBE KICH
1 002 OVARIAN HIGH_GRADE_SEROUS_CARCINOMA LGSOC
2 003 OVARIAN OTHER NaN
3 001 COLORECTAL ADENOCARCINOMA KICH
I want to be able to compress the repeated id, say 001 in this case so that I can have the disease_type and disease_sub_type, study_abbreviation merged into 1 cell each (nested).
Output
id disease_type disease_sub_type study_abbreviation
0 001 RENAL,COLORECTAL CHROMOPHOBE,ADENOCARCINOMA KICH, KICH
1 002 OVARIAN HIGH_GRADE_SEROUS_CARCINOMA LGSOC
2 003 OVARIAN OTHER NaN
This is not for anything but admin work hence the stupid ask but would help greatly when I need to merge on other datasets, thanks again.
You could group by your 'id' column and use list as an aggregation:
df.groupby('id',as_index=False).agg(','.join)
id disease_type disease_sub_type study_abbreviation
0 001 RENAL,COLORECTAL CHROMOPHOBE,ADENOCARCINOMA KICH,KICH
1 002 OVARIAN HIGH_GRADE_SEROUS_CARCINOMA LGSOC
2 003 OVARIAN OTHER NaN
So I have columns like this,
REF_NO
LCY_AMOUNT
TAG
001
200
NEGO
001
300
EXCH
001
350
POST
001
400
CONF
002
300
NEGO
002
400
EXCH
002
450
POST
002
500
CONF
What I need is Tag rows into columns like this
REF_NO
NEGO
EXCH
POST
CONF
001
200
300
350
400
002
300
400
450
500
So went with sumifs():
SUMIFS($B$3:$B$10,$A$3:$A$10,$A15,$C$3:$C$10,B$14)
And to produce the values in A15 and A16, you can check out unique().
For the following dataframe, I need calculate the change in 'count', for each set of date, location_id, uid and include the set in the results.
# Sample DataFrame
df = pd.DataFrame({'date': ['2021-01-01', '2021-01-01','2021-01-01','2021-01-02', '2021-01-02','2021-01-02'],
'location_id':[1001,2001,3001, 1001,2001,3001],
'uid': ['001', '003', '002','001', '004','002'],
'uid_count':[1, 2,3 ,2, 2, 4]})
date location_id uid count
0 2021-01-01 1001 001 1
1 2021-01-01 2001 003 2
2 2021-01-01 3001 002 3
3 2021-01-02 1001 001 2
4 2021-01-02 2001 004 2
5 2021-01-02 3001 002 4
My desired results would look like:
# Desired Results
date location_id uid
2021-01-01 1001 001 0
2001 003 0
3001 002 0
2021-01-02 1001 001 1
2001 004 0
3001 002 1
I thought I could do this via groupby by using the following, but the desired calculation isn't made:
# Current code:
df.groupby(['date','location_id','uid'],sort=False).apply(lambda x: (x['count'].values[-1] - x['count'].values[0]))
# Current results:
date location_id uid
2021-01-01 1001 001 0
2001 003 0
3001 002 0
2021-01-02 1001 001 0
2001 004 0
3001 002 0
How can I get the desired results?
The following code works with the test dataframe, I'm not certain about a larger dataframe
.transform() is used to calculate the differences for consecutive occurrences of 'uid_count', for each uid, with the same index as df.
The issue with .groupby(['date','location_id','uid'], is that each group only contains a single value.
Remove 'uid_count' at the end, with .drop(columns='uid_count'), if desired.
import pandas as pd
# sort the dataframe
df = df.sort_values(['date', 'location_id', 'uid'])
# groupby and transform based on the difference in uid_count
uid_count_diff = df.groupby(['location_id', 'uid']).uid_count.transform(lambda x: x.diff()).fillna(0).astype(int)
# create a column in df
df['uid_count_diff'] = uid_count_diff
# set the index
df = df.set_index(['date', 'location_id', 'uid'])
# result
uid_count uid_count_diff
date location_id uid
2021-01-01 1001 001 1 0
2001 003 2 0
3001 002 3 0
2021-01-02 1001 001 2 1
2001 004 2 0
3001 002 4 1
Here is a sample data table:
ID Number Test Type Score
001 A 81
001 A 75
001 A 93
001 B 62
001 B 87
001 B 82
002 A 91
002 A 83
002 B 94
002 B 97
What I want, in excel, is a return of the maximum score of each test type for each id number so it would look like this...
ID Number Test Type Score
001 A 93
001 B 87
002 A 91
002 B 97
Is that possible?
You can use MAXIFS(). On your second table, if you have the ID number and Test Types, in the C column you can do:
=MAXIFS(Sheet1!$C$2:$C$1000,Sheet1!$A$2:$A$1000,$A2,Sheet1!$B$2:$B$1000,$B2)
Where Sheet1 is your main table.
I want to convert number type to string with format as:
number -> string
1 -> 001
2 -> 002
12 -> 012
340 -> 340
You can use either TO_CHAR() (preferable in this situation) function or LPAD() function to achieve the desired result:
SQL> with t1(col) as(
2 select 1 from dual union all
3 select 2 from dual union all
4 select 12 from dual union all
5 select 340 from dual
6 )
7 select to_char(col, '000') as num_1
8 , lpad(to_char(col), 3, '0') as num_2
9 from t1
10 ;
NUM_1 NUM_2
----- ------------
001 001
002 002
012 012
340 340