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Python sum aggregate numbers in a dataframe column into a new column

How to aggregate numbers in a dataframe into a new column gradual sum of column number into a new column:
Index
numbers
new column
0
1
1
1
2
3
2
3
6
3
4
10
4
5
15

The solusion for getting the result and new column as described in the table:
df.cumsum()

How to sum values of pandas dataframe by rows?

I have a data frame like this:
A
0
1
0
2
and I would like to sum the values "so far" of the dataframe in a cumulative format, so if A increases by 1 then I would like the sum to increase by 1 as well, as so:
A Sum
0 0
1 1
0 1
2 2
I have to keep a record of when this change occurs for the analysis, so I can't just sum the entire column at once.
I thought about doing:
df = df.assign(A_before=df.A.shift(1))
df['change'] = (df.A - df.A_before)
df['sum'] = df['A'] + df['A_before']
but it's not adding the sum values from the previous rows as well, only the values in the same rows.
Any solutions? Thank you.

You can do diff with cumsum
df.A.diff().ge(1).cumsum()
0 0
1 1
2 1
3 2
Name: A, dtype: int64
df['sum']=df.A.diff().ge(1).cumsum()

I want to add columns to a pandas data frame using data fromother columns [duplicate]

I have a pandas dataframe defined as:
A B SUM_C
1 1 10
1 2 20
I would like to do a cumulative sum of SUM_C and add it as a new column to the same dataframe. In other words, my end goal is to have a dataframe that looks like below:
A B SUM_C CUMSUM_C
1 1 10 10
1 2 20 30
Using cumsum in pandas on group() shows the possibility of generating a new dataframe where column name SUM_C is replaced with cumulative sum. However, my ask is to add the cumulative sum as a new column to the existing dataframe.
Thank you

Just apply cumsum on the pandas.Series df['SUM_C'] and assign it to a new column:
df['CUMSUM_C'] = df['SUM_C'].cumsum()
Result:
df
Out[34]:
A B SUM_C CUMSUM_C
0 1 1 10 10
1 1 2 20 30

Pandas groupby cumulative sum ignore current row

I know there's some questions about this topic (like Pandas: Cumulative sum of one column based on value of another) however, none of them fuull fill my requirements.
Let's say I have a dataframe like this one
.
I want to compute the cumulative sum of Cost grouping by month, avoiding taking into account the current value, in order to get the Desired column.By using groupby and cumsum I obtain colum CumSum
.
The DDL to generate the dataframe is
df = pd.DataFrame({'Month': [1,1,1,2,2,1,3],
'Cost': [5,8,10,1,3,4,1]})

IIUC you can use groupby.cumsum and then just subtract cost;
df['cumsum_'] = df.groupby('Month').Cost.cumsum().sub(df.Cost)
print(df)
Month Cost cumsum_
0 1 5 0
1 1 8 5
2 1 10 13
3 2 1 0
4 2 3 1
5 1 4 23
6 3 1 0

You can do the following:
df['agg']=df.groupby('Month')['Cost'].shift().fillna(0)
df['Cumsum']=df['Cost']+df['agg']

Sum of previous rows values

how I can sum previous rows values and current row value to a new column?
My current output:
index,value
0,1
1,2
2,3
3,4
4,5
My goal output is:
index,value,sum
0,1,1
1,2,3
2,3,6
3,4,10
4,5,15
I know that this is easy to do with Excel, but I'm looking solution to do with pandas.
My code:
import random, pandas
recordlist=[1,2,3,4,5]
df=pandas.DataFrame(recordlist, columns=["Values"])

use cumsum
df.assign(sum=df.value.cumsum())
value sum
index
0 1 1
1 2 3
2 3 6
3 4 10
4 5 15
Or
df['sum'] = df.value.cumsum()
df
value sum
index
0 1 1
1 2 3
2 3 6
3 4 10
4 5 15
If df is a series
pd.DataFrame(dict(value=df, sum=df.cumsum())

As already used in the previous posts, df.assign is a great function.
If you want to have a little bit more flexibility here, you can use a lambda function, like so
df.assign[ sum=lambda l: l['index'] + l['value'] ]
Just to do the summing, this could even be shortened with
df.assign[ sum=df['index'] + df['value'] ]
Note that sum (before the = sign) is not a function or variable, but the name for the new column. So this could be also df.assign[ mylongersumlabel=.. ]