How to Calculate Cumulative Sum

How to calculate cumulative sum?

# replace the second column for the cumsum of the initial second column
data[, 2] <- cumsum(data[, 2])

How to calculate cumulative sum (reversed) of a Python DataFrame within given groups?

You can try with series groupby

df['new'] = df.loc[::-1, 'Chi'].groupby(df['Basin']).cumsum()
df
Out[858]: 
   Basin (n=17 columns)  Chi  new
0   13.0            ...    4   14
1   13.0            ...    8   10
2   13.0            ...    2    2
3   21.0            ...    4   10
4   21.0            ...    6    6
5   38.0            ...    1   14
6   38.0            ...    7   13
7   38.0            ...    2    6
8   38.0            ...    4    4

How to get cumulative sum

select t1.id, t1.SomeNumt, SUM(t2.SomeNumt) as sum
from @t t1
inner join @t t2 on t1.id >= t2.id
group by t1.id, t1.SomeNumt
order by t1.id

SQL Fiddle example

Output

| ID | SOMENUMT | SUM |
-----------------------
|  1 |       10 |  10 |
|  2 |       12 |  22 |
|  3 |        3 |  25 |
|  4 |       15 |  40 |
|  5 |       23 |  63 |

Edit: this is a generalized solution that will work across most db platforms. When there is a better solution available for your specific platform (e.g., gareth's), use it!

How to calculate cumulative sums in MySQL

use this

select day,product_count,
sum(product_count) over (order by t.day ROWS UNBOUNDED PRECEDING) as cumulative_sum from (
SELECT
  date(purchase_date) as day,
  count(product_id) as product_count
  FROM products
  where day > DATE_SUB(now(), INTERVAL 6 MONTH)
  AND customer_city = 'Seattle'
  GROUP BY day 
  ORDER BY product_count desc
)t

Cumulative values in Power BI

Try this.

cumulative = 

VAR d = test[Date] 

RETURN CALCULATE(SUM(test[Number]),ALL('test'),test[Date] <=d)

Cumulative sum by column in pandas dataframe

You can perform the cumsum per group using groupby + cumsum:

df['z'] = df.groupby('x')['y'].cumsum()

output:

   x    y    z
0  0   67   67
1  0   -5   62
2  1   78   78
3  1   47  125
4  1   88  213
5  1   12  225
6  1   -4  221
7  2   14   14
8  2  232  246
9  2   28  274

Calculate cumulative sum and average based on column values in spark dataframe

You need to chain when() clauses as you want to populate one single column:

windowval=(Window.partitionBy('Location','Brand').orderBy('month_in_timestamp')
               .rangeBetween(Window.unboundedPreceding, 0))

df = df.withColumn('TotalSumValue',
         F.when(F.col('Brand').isin('brand1', 'brand2'), F.sum('TrueValue').over(windowval)) \
         .when(F.col('Brand').isin('brand3'), F.avg('TrueValue').over(windowval)))

How to calculate cumulative sum of a column based on Month column values

Convert your String date to timestamp using to_timestamp function of pyspark SQL function. Then sorting based on this timestamp column will give correct order.

from pyspark.sql.functions import to_timestamp

df.withColumn("month_in_timestamp", to_timestamp(df.Month, 'dd-MM-yyyy'))

windowval = (Window.partitionBy('Brand','Sector').orderBy('month_in_timestamp')
         .rangeBetween(Window.unboundedPreceding, 0))
df1 = df1.withColumn('TotalSumValue', F.sum('TrueValue').over(windowval))

Anylogic: how to calculate the cumulative sum?

Create a parameter with an initial value of 0. Call it sum. In the event action field use:

name_parameter = round(max(normal(10,200),0));
sum += name_parameter;