We Keep Coding

Find top N values within each group

I have a dataset similar to the sample below:
| id | size | old_a | old_b | new_a | new_b |
|----|--------|-------|-------|-------|-------|
| 6 | small | 3 | 0 | 21 | 0 |
| 6 | small | 9 | 0 | 23 | 0 |
| 13 | medium | 3 | 0 | 12 | 0 |
| 13 | medium | 37 | 0 | 20 | 1 |
| 20 | medium | 30 | 0 | 5 | 6 |
| 20 | medium | 12 | 2 | 3 | 0 |
| 12 | small | 7 | 0 | 2 | 0 |
| 10 | small | 8 | 0 | 12 | 0 |
| 15 | small | 19 | 0 | 3 | 0 |
| 15 | small | 54 | 0 | 8 | 0 |
| 87 | medium | 6 | 0 | 9 | 0 |
| 90 | medium | 11 | 1 | 16 | 0 |
| 90 | medium | 25 | 0 | 4 | 0 |
| 90 | medium | 10 | 0 | 5 | 0 |
| 9 | large | 8 | 1 | 23 | 0 |
| 9 | large | 19 | 0 | 2 | 0 |
| 1 | large | 1 | 0 | 0 | 0 |
| 50 | large | 34 | 0 | 7 | 0 |
This is the input for above table:
data=[[6,'small',3,0,21,0],[6,'small',9,0,23,0],[13,'medium',3,0,12,0],[13,'medium',37,0,20,1],[20,'medium',30,0,5,6],[20,'medium',12,2,3,0],[12,'small',7,0,2,0],[10,'small',8,0,12,0],[15,'small',19,0,3,0],[15,'small',54,0,8,0],[87,'medium',6,0,9,0],[90,'medium',11,1,16,0],[90,'medium',25,0,4,0],[90,'medium',10,0,5,0],[9,'large',8,1,23,0],[9,'large',19,0,2,0],[1,'large',1,0,0,0],[50,'large',34,0,7,0]]
data= pd.DataFrame(data,columns=['id','size','old_a','old_b','new_a','new_b'])
I want to have an output which will group the dataset on size and would list out top 2 id based on the values of 'new_a' column within each group of size. Since, some of the ids are repeating multiple times, I would want to sum the values of new_a for such ids and then find top 2 values. My final table should look like the one below:
| size | id | new_a |
|--------|----|-------|
| large | 9 | 25 |
| large | 50 | 7 |
| medium | 13 | 32 |
| medium | 90 | 25 |
| small | 6 | 44 |
| small | 10 | 12 |
I have tried the below code but it isn't showing top 2 values of new_a for each group within 'size' column.
nlargest = data.groupby(['size','id'])['new_a'].sum().nlargest(2).reset_index()

print(
df.groupby('size').apply(
lambda x: x.groupby('id').sum().nlargest(2, columns='new_a')
).reset_index()[['size', 'id', 'new_a']]
)
Prints:
size id new_a
0 large 9 25
1 large 50 7
2 medium 13 32
3 medium 90 25
4 small 6 44
5 small 10 12

You can set size, id as the index to avoid double groupby here, and use Series.sum leveraging level parameter.
df.set_index(["size", "id"]).groupby(level=0).apply(
lambda x: x.sum(level=1).nlargest(2)
).reset_index()
size id new_a
0 large 9 25
1 large 50 7
2 medium 13 32
3 medium 90 25
4 small 6 44
5 small 10 12

You can chain two groupby methods:
data.groupby(['id', 'size'])['new_a'].sum().groupby('size').nlargest(2)\
.droplevel(0).to_frame('new_a').reset_index()
Output:
id size new_a
0 9 large 25
1 50 large 7
2 13 medium 32
3 90 medium 25
4 6 small 44
5 10 small 12

Get a combination of values in dataframe and implement a function

I want to take the combination of values ​​in a column and apply a function to each combination.What is the easiest way to do this?
Example Data
| name | value |
|------|-------|
| 6A | 1 |
| 6A | 1 |
| 6A | 1 |
| 6B | 3 |
| 6B | 3 |
| 6B | 3 |
| 6C | 7 |
| 6C | 5 |
| 6C | 4 |
The Result I Want
i used sum as a function in the example:
| pair | result |
|-------|--------|
| 6A_6B | 4 |
| 6A_6B | 4 |
| 6A_6B | 4 |
| 6A_6C | 8 |
| 6A_6C | 6 |
| 6A_6C | 5 |
| 6B_6C | 10 |
| 6B_6C | 8 |
| 6B_6C | 7 |
Note
My function takes "pandas.Series" as parameters.
For example:
x=a series of "6A"
and
y=a series of "6B"
6A_6B = sum(x,y)

I find it more straightforward to reshape the data, then it's a simple addition of all pairwise combinations.
import pandas as pd
from itertools import combinations
u = (df.assign(idx = df.groupby('name').cumcount()+1)
.pivot(index='idx', columns='name', values='value'))
#name 6A 6B 6C
#idx
#1 1 3 7
#2 1 3 5
#3 1 3 4
l = []
for items in combinations(u.columns, 2):
l.append(u.loc[:, items].sum(1).to_frame('result').assign(pair='_'.join(items)))
df = pd.concat(l)
result pair
idx
1 4 6A_6B
2 4 6A_6B
3 4 6A_6B
1 8 6A_6C
2 6 6A_6C
3 5 6A_6C
1 10 6B_6C
2 8 6B_6C
3 7 6B_6C

itertools.combinations
Off the top of my head
from itertools import combinations
g = dict(tuple(df.groupby('name')))
pd.DataFrame([
(f'{x}_{y}', a + b)
for x, y in combinations(g, 2)
for a, b in zip(g[x]['value'], g[y]['value'])
], columns=df.columns)
name value
0 6A_6B 4
1 6A_6B 4
2 6A_6B 4
3 6A_6C 8
4 6A_6C 6
5 6A_6C 5
6 6B_6C 10
7 6B_6C 8
8 6B_6C 7

Removing duplicates with three conditions, Pandas

I have the following dataframe:
reference | topcredit | currentbalance | creditlimit
1 1 | 50 | 20 | 70
2 1 | 30 | 28 | 50
3 1 | 50 | 20 | 70
4 1 | 81 | 32 | 100
5 2 | 70 | 0 | 56
6 2 | 50 | 20 | 70
7 2 | 100 | 0 | 150
8 3 | 85 | 85 | 95
9 3 | 85 | 85 | 95
And so on...
I want to drop duplicates based on the 'reference' only those that have the same topcredit, currentbalance and creditlimit.
In the reference 1 I have two that have the same numbers in the three columns in line 1 and 3, but also in reference 2, line 6 I would like to keep 1 of reference 1 and also line 6 of reference 2. In reference 3 both lines have the same information too.
The expected output is:
reference | topcredit | currentbalance | creditlimit
1 | 50 | 20 | 70
1 | 30 | 28 | 50
1 | 81 | 32 | 100
2 | 70 | 24 | 56
2 | 50 | 20 | 70
2 | 100 | 80 | 150
3 | 85 | 85 | 95
I would apreciate the help, I've been searching how to do it for a while.

Pandas: sum multiple columns based on similar consecutive numbers in another column

Given the following table
+----+--------+--------+--------------+
| Nr | Price | Volume | Transactions |
+----+--------+--------+--------------+
| 1 | 194.6 | 100 | 1 |
| 2 | 195 | 10 | 1 |
| 3 | 194.92 | 100 | 1 |
| 4 | 194.92 | 52 | 1 |
| 5 | 194.9 | 99 | 1 |
| 6 | 194.86 | 74 | 1 |
| 7 | 194.85 | 900 | 1 |
| 8 | 194.85 | 25 | 1 |
| 9 | 194.85 | 224 | 1 |
| 10 | 194.6 | 101 | 1 |
| 11 | 194.85 | 19 | 1 |
| 12 | 194.6 | 10 | 1 |
| 13 | 194.6 | 25 | 1 |
| 14 | 194.53 | 12 | 1 |
| 15 | 194.85 | 14 | 1 |
| 16 | 194.6 | 11 | 1 |
| 17 | 194.85 | 93 | 1 |
| 18 | 195 | 90 | 1 |
| 19 | 195 | 100 | 1 |
| 20 | 195 | 50 | 1 |
| 21 | 195 | 50 | 1 |
| 22 | 195 | 25 | 1 |
| 23 | 195 | 5 | 1 |
| 24 | 195 | 500 | 1 |
| 25 | 195 | 100 | 1 |
| 26 | 195.09 | 100 | 1 |
| 27 | 195 | 120 | 1 |
| 28 | 195 | 60 | 1 |
| 29 | 195 | 40 | 1 |
| 30 | 195 | 10 | 1 |
| 31 | 194.6 | 1 | 1 |
| 32 | 194.99 | 1 | 1 |
| 33 | 194.81 | 20 | 1 |
| 34 | 194.81 | 50 | 1 |
| 35 | 194.97 | 17 | 1 |
| 36 | 194.99 | 25 | 1 |
| 37 | 195 | 75 | 1 |
+----+--------+--------+--------------+
For faster testing you can also find here the same table in a pandas dataframe
pd_data_before = pd.DataFrame([[1,194.6,100,1],[2,195,10,1],[3,194.92,100,1],[4,194.92,52,1],[5,194.9,99,1],[6,194.86,74,1],[7,194.85,900,1],[8,194.85,25,1],[9,194.85,224,1],[10,194.6,101,1],[11,194.85,19,1],[12,194.6,10,1],[13,194.6,25,1],[14,194.53,12,1],[15,194.85,14,1],[16,194.6,11,1],[17,194.85,93,1],[18,195,90,1],[19,195,100,1],[20,195,50,1],[21,195,50,1],[22,195,25,1],[23,195,5,1],[24,195,500,1],[25,195,100,1],[26,195.09,100,1],[27,195,120,1],[28,195,60,1],[29,195,40,1],[30,195,10,1],[31,194.6,1,1],[32,194.99,1,1],[33,194.81,20,1],[34,194.81,50,1],[35,194.97,17,1],[36,194.99,25,1],[37,195,75,1]],columns=['Nr','Price','Volume','Transactions'])
The question is how do we sum up the volume and transactions based on similar consecutive prices? The end result would be something like this:
+----+--------+--------+--------------+
| Nr | Price | Volume | Transactions |
+----+--------+--------+--------------+
| 1 | 194.6 | 100 | 1 |
| 2 | 195 | 10 | 1 |
| 4 | 194.92 | 152 | 2 |
| 5 | 194.9 | 99 | 1 |
| 6 | 194.86 | 74 | 1 |
| 9 | 194.85 | 1149 | 3 |
| 10 | 194.6 | 101 | 1 |
| 11 | 194.85 | 19 | 1 |
| 13 | 194.6 | 35 | 2 |
| 14 | 194.53 | 12 | 1 |
| 15 | 194.85 | 14 | 1 |
| 16 | 194.6 | 11 | 1 |
| 17 | 194.85 | 93 | 1 |
| 25 | 195 | 920 | 8 |
| 26 | 195.09 | 100 | 1 |
| 30 | 195 | 230 | 4 |
| 31 | 194.6 | 1 | 1 |
| 32 | 194.99 | 1 | 1 |
| 34 | 194.81 | 70 | 2 |
| 35 | 194.97 | 17 | 1 |
| 36 | 194.99 | 25 | 1 |
| 37 | 195 | 75 | 1 |
+----+--------+--------+--------------+
You can also find the result ready made in a pandas dataframe below:
pd_data_after = pd.DataFrame([[1,194.6,100,1],[2,195,10,1],[4,194.92,152,2],[5,194.9,99,1],[6,194.86,74,1],[9,194.85,1149,3],[10,194.6,101,1],[11,194.85,19,1],[13,194.6,35,2],[14,194.53,12,1],[15,194.85,14,1],[16,194.6,11,1],[17,194.85,93,1],[25,195,920,8],[26,195.09,100,1],[30,195,230,4],[31,194.6,1,1],[32,194.99,1,1],[34,194.81,70,2],[35,194.97,17,1],[36,194.99,25,1],[37,195,75,1]],columns=['Nr','Price','Volume','Transactions'])
I managed to achieve this in a for loop. But the problem is that it is very slow when iterating each row. My data set is huge, around 50 million rows.
Is there any way to achieve this without looping?

A common trick to groupby consecutive values is the following:
df.col.ne(df.col.shift()).cumsum()
We can use that here, then use agg to keep the first values of the columns we aren't summing, and to sum the values we do want to sum.
(df.groupby(df.Price.ne(df.Price.shift()).cumsum())
.agg({'Nr': 'last', 'Price': 'first', 'Volume':'sum', 'Transactions': 'sum'})
).reset_index(drop=True)
Nr Price Volume Transactions
0 1 194.60 100 1
1 2 195.00 10 1
2 4 194.92 152 2
3 5 194.90 99 1
4 6 194.86 74 1
5 9 194.85 1149 3
6 10 194.60 101 1
7 11 194.85 19 1
8 13 194.60 35 2
9 14 194.53 12 1
10 15 194.85 14 1
11 16 194.60 11 1
12 17 194.85 93 1
13 25 195.00 920 8
14 26 195.09 100 1
15 30 195.00 230 4
16 31 194.60 1 1
17 32 194.99 1 1
18 34 194.81 70 2
19 35 194.97 17 1
20 36 194.99 25 1
21 37 195.00 75 1

Pandas: How to create a multi-indexed pivot

I have a set of experiments defined by two variables: scenario and height. For each experiment, I take 3 measurements: result 1, 2 and 3.
The dataframe that collects all the results looks like this:
import numpy as np
import pandas as pd
df = pd.DataFrame()
df['Scenario']= np.repeat(['Scenario a','Scenario b','Scenario c'],3)
df['height'] = np.tile([0,1,2],3)
df['Result 1'] = np.arange(1,10)
df['Result 2'] = np.arange(20,29)
df['Result 3'] = np.arange(30,39)
If I run the following:
mypiv = df.pivot('Scenario','height').transpose()
writer = pd.ExcelWriter('test_df_pivot.xlsx')
mypiv.to_excel(writer,'test df pivot')
writer.save()
I obtain a dataframe where columns are the scenarios, and the rows have a multi-index defined by result and height:
+----------+--------+------------+------------+------------+
| | height | Scenario a | Scenario b | Scenario c |
+----------+--------+------------+------------+------------+
| Result 1 | 0 | 1 | 4 | 7 |
| | 1 | 2 | 5 | 8 |
| | 2 | 3 | 6 | 9 |
| Result 2 | 0 | 20 | 23 | 26 |
| | 1 | 21 | 24 | 27 |
| | 2 | 22 | 25 | 28 |
| Result 3 | 0 | 30 | 33 | 36 |
| | 1 | 31 | 34 | 37 |
| | 2 | 32 | 35 | 38 |
+----------+--------+------------+------------+------------+
How can I create a pivot where the indices are swapped, i.e. height first, then result?
I couldn't find a way to create it directly. I managed to get what I want swapping the levels and the re-sorting the results:
mypiv2 = mypiv.swaplevel(0,1 , axis=0).sortlevel(level=0,axis=0,sort_remaining=True)
but I was wondering if there is a more direct way.

You can first set_index and then stack with unstack:
print (df.set_index(['height','Scenario']).stack().unstack(level=1))
Scenario Scenario a Scenario b Scenario c
height
0 Result 1 1 4 7
Result 2 20 23 26
Result 3 30 33 36
1 Result 1 2 5 8
Result 2 21 24 27
Result 3 31 34 37
2 Result 1 3 6 9
Result 2 22 25 28
Result 3 32 35 38