Use Cumcount on Pandas Dataframe With a Conditional Increment

Use cumcount on pandas dataframe with a conditional increment

Use groupby with combination of shift and cumsum.

df['new'] = df.groupby('key').cond.apply(
        lambda x: x.shift().fillna(1).cumsum()
).astype(int)

df
  key  cond  new
0   A     1    1
1   A     1    2
2   B     1    1
3   B     0    2
4   A     0    3
5   A     1    3
6   B     1    2

Conditional grouped CumCount pandas

You may need create a addtional key for groupby , by using cumsum after shfit

addkey=df.groupby(['user','#product']).action.apply(lambda x : x.eq('buy').shift().fillna(0).cumsum())
df['seebefore']=df.action.eq('see').groupby([df.user,df['#product'],addkey]).cumsum()
df
Out[131]: 
    index user  #product action  seebefore
0       0    A         1    see        1.0
1       1    A         1    see        2.0
2       2    B         2    see        1.0
3       3    A         2    see        1.0
4       4    A         1    buy        2.0
5       5    B         2    buy        1.0
6       6    A         1    see        1.0
7       7    A         2    see        2.0
8       8    A         1    see        2.0
9       9    A         1    see        3.0
10     10    A         2    buy        2.0
11     11    B         1    buy        0.0
12     12    A         1    buy        3.0

pandas - increment cumulative sum only when string is matched

You can replace Value where Condition is not equal to New with 0 and then do cumulative sum:

df['Cumulative Value'] = df.Value.where(df.Condition == 'New', 0).groupby(df.Name).cumsum()

df
  Name Condition  Value  Cumulative Value
0    A       New      5                 5
1    A   Not New      7                 5
2    A       New     15                20
3    B       Old     20                 0
4    B       New     14                14
5    B       Old      3                14

how to increment a value every time a certain condition is met in pandas dataframe

You can assign() the result of each cumsum() to its own column:

df = df.assign(**{f'sum_{v}': df.val.eq(v).cumsum() for v in df.val.unique()})

#    time  val  sum_1  sum_2  sum_3
# 0     0    1      1      0      0
# 1     1    1      2      0      0
# 2     2    2      2      1      0
# 3     3    3      2      1      1
# 4     4    1      3      1      1
# 5     5    2      3      2      1

pandas conditional cumcount of values in second column?

You want to get the cumcount and add one. Then use %2 to differentiate between odd or even rows. Then, take the cumulative sum and subtract 1 to start counting from zero.

You can use:

df0['flag'] = ((df0.groupby('KEY').cumcount() + 1) % 2).cumsum() - 1
df0
Out[1]: 
   KEY  flag
0    0      0
1    0      0
2    0      1
3    0      1
4    1      2
5    1      2
6    1      3
7    2      4
8    2      4
9    2      5
10   2      5
11   2      6
12   3      7
13   3      7
14   3      8
15   3      8
16   3      9
17   3      9
18   4     10
19   5     11
20   6     12

Increase Count for Conditional Values in Row

Compare value and use cumulative sum by Series.cumsum:

df['new'] = (df["B"]=="Yes").cumsum()
print (df)
         A    B  new
0  WalMart  Yes    1
1   Target  NaN    1
2  WalMart  Yes    2
3  BestBuy  NaN    2
4   Target  Yes    3

With some rows before first Yes:

df['new'] = (df["B"]=="Yes").cumsum()
print (df)
         A    B  new
0      gap  NaN    0
1  WalMart  Yes    1
2   Target  NaN    1
3  WalMart  Yes    2
4  BestBuy  NaN    2
5   Target  Yes    3

pandas: increment based on a condition in another column

You can try groupby with ngroup

m = dataframe['text'].str.contains('##').cumsum()

dataframe['id'] = dataframe.groupby(m).ngroup() + 100

print(dataframe)

                     text   id
0               ##weather  100
1           how is today?  100
2               we go out  100
3                  ##rain  101
4         my day is rainy  101
5   I am not feeling well  101
6                   rainy  101
7                   blues  101
8                ##flower  102
9         the blue flower  102
10          she likes red  102
11    this flower is nice  102

pandas increment under specific condition of other column

Let us try groupby and cumcount:

m = df['A'] != df['A'].shift()
df['B'] = df.groupby(m.cumsum()).cumcount()

---

   A  B
0  a  0
1  b  0
2  b  1
3  b  2
4  b  3
5  d  0

Pandas increment values on groupby with a condition

You can try cumcount with //

s = df.groupby('links').cumcount()//3
Out[125]: 
0    0
1    0
2    0
3    0
4    0
5    1
6    1
7    0
8    0
dtype: int64

df['links'] = df['links'] + s.astype(str)

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