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Python - Grouping Rows into List

By : user2175708
Date : October 14 2020, 08:10 PM
I wish this helpful for you Something like this might help.
You simply group them by the TransactionID and then take the ProductID from it and convert to list
code :
 grouped_list = list(df.groupby('TransactionID')['ProductID'].apply(list))

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Converting table to list grouping 3 rows

By : WicTy
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further Try this more explicit version (fiddle):
code :
var list = $("<ul/>");      
var listitem = null;

// iterate over each cell (not each row)
$('#tab1').find("tr > td").each(function(i) {

    // starting with the first item, start a new listitem every three items
    // and append it to the ul
    if(i%3 == 0){
        listitem = $("<li/>");

    // append the current item as a paragraph to the listitem
    var p = "<p>" + $(this).html() + "</p>";         


// replace the table with the ul

grouping by rows in python

By : Mahfuz soikot
Date : March 29 2020, 07:55 AM
should help you out How huge is the data? If memory isn't a problem and you have the data in a list this works for you example input:
code :
input = [['SE01_inf', 'name', [], 'NA', []],\
         ['SE01_loc', 'NA', 'loc', 'NA', []],\
         ['SE01_id', 'NA', [], '123', []],\
         ['SE01_1_inf', 'name1', [], 'NA', []],\
         ['SE01_1_loc', 'NA', 'loc', 'NA', []]]

output = {}
for row in input:
    id = row[0][:row[0].rfind('_')]
    if id not in output:
        output[id] = [id] + row[1:]
        output[id] = [new if old in ('NA', []) else old for new, old in zip(row, output[id])]

print output.values()
[['SE01', 'name', 'loc', '123', []], ['SE01_1', 'name1', 'loc', 'NA', []]]

grouping rows in list in pandas groupby

By : CSMgameclub
Date : March 29 2020, 07:55 AM
To fix this issue You can do this using groupby to group on the column of interest and then apply list to every group:
code :
In [1]: df = pd.DataFrame( {'a':['A','A','B','B','B','C'], 'b':[1,2,5,5,4,6]})

   a  b
0  A  1
1  A  2
2  B  5
3  B  5
4  B  4
5  C  6

In [2]: df.groupby('a')['b'].apply(list)
A       [1, 2]
B    [5, 5, 4]
C          [6]
Name: b, dtype: object

In [3]: df1 = df.groupby('a')['b'].apply(list).reset_index(name='new')
   a        new
0  A     [1, 2]
1  B  [5, 5, 4]
2  C        [6]

grouping rows in a list of lists in pandas

By : karishma Jain
Date : March 29 2020, 07:55 AM
this one helps. This is slightly different to @jezrael in that the listifying of strings is done via map. In addition call reset_index() adds "Description" explicitly to output.
code :
import pandas as pd

df = pd.DataFrame([[1, 'A'], [1, 'B'], [1, 'C'], [2, 'A'], [2, 'C'], [3, 'A']], columns=['ID', 'Description'])

df.groupby('ID')['Description'].apply(list).apply(lambda x: list(map(list, x))).reset_index()

# ID Description
# 1 [[A], [B], [C]] 
# 2 [[A], [C]] 
# 3 [[A]] 

Pandas: Grouping rows by list in CSV file?

By : user3721965
Date : March 29 2020, 07:55 AM
it should still fix some issue In an effort to make our budgeting life a bit easier and help myself learn; I am creating a small program in python that takes data from our exported bank csv. , You can assign a new column using str.extract and then groupby:
code :
df = pd.DataFrame({"description":['Macdonald something', 'Whataburger something', 'pizza hut something',
                                  'Whataburger something','Macdonald something','Macdonald otherthing',],

fast_food = ['Macdonald', 'Whataburger', 'pizza hut']

df["found"] = df["description"].str.extract(f'({"|".join(fast_food)})',flags=re.I)

print (df.groupby("found").sum())

Macdonald     5.25
Whataburger   6.50
pizza hut     3.50
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