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Counting continues occurrence of a value in a column [duplicate]

1

This question already has an answer here:

• 
  Pandas DataFrame: How to groupby consecutive values
  
  2 answers
  
  

I have a data frame like this:

index value
---- -----
1 A
2 A
3 A
4 A
5 B
6 B
7 A
8 B
9 C
10 C

I want to add a column to count continues occurrence of my value, like this:

index value continues-count
---- ----- ----------
1 A 1
2 A 2
3 A 3
4 A 4
5 B 1
6 B 2
7 A 1
8 B 1
9 C 1
10 C 2

I am able to do it using a loop but as my dataset is huge it takes forever!

python pandas dataframe

share|improve this question

asked Mar 6 at 22:22

HamidHamid

12810

marked as duplicate by user3483203 pandas
Users with the  pandas badge can single-handedly close pandas questions as duplicates and reopen them as needed.

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Mar 6 at 23:06

This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  Can you please post your current solution?
  
  – blacksite
  Mar 6 at 22:26
  

  
  
  
  

add a comment | 

1

This question already has an answer here:

• 
  Pandas DataFrame: How to groupby consecutive values
  
  2 answers
  
  

I have a data frame like this:

index value
---- -----
1 A
2 A
3 A
4 A
5 B
6 B
7 A
8 B
9 C
10 C

I want to add a column to count continues occurrence of my value, like this:

index value continues-count
---- ----- ----------
1 A 1
2 A 2
3 A 3
4 A 4
5 B 1
6 B 2
7 A 1
8 B 1
9 C 1
10 C 2

I am able to do it using a loop but as my dataset is huge it takes forever!

python pandas dataframe

share|improve this question

asked Mar 6 at 22:22

HamidHamid

12810

marked as duplicate by user3483203 pandas
Users with the  pandas badge can single-handedly close pandas questions as duplicates and reopen them as needed.

StackExchange.ready(function()
if (StackExchange.options.isMobile) return;

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$hover.hover(
function()
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Mar 6 at 23:06

This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  Can you please post your current solution?
  
  – blacksite
  Mar 6 at 22:26
  

  
  
  
  

add a comment | 

This question already has an answer here:

• 
  Pandas DataFrame: How to groupby consecutive values
  
  2 answers
  
  

I have a data frame like this:

index value
---- -----
1 A
2 A
3 A
4 A
5 B
6 B
7 A
8 B
9 C
10 C

I want to add a column to count continues occurrence of my value, like this:

index value continues-count
---- ----- ----------
1 A 1
2 A 2
3 A 3
4 A 4
5 B 1
6 B 2
7 A 1
8 B 1
9 C 1
10 C 2

I am able to do it using a loop but as my dataset is huge it takes forever!

python pandas dataframe

share|improve this question

asked Mar 6 at 22:22

HamidHamid

12810

index value
---- -----
1 A
2 A
3 A
4 A
5 B
6 B
7 A
8 B
9 C
10 C

index value continues-count
---- ----- ----------
1 A 1
2 A 2
3 A 3
4 A 4
5 B 1
6 B 2
7 A 1
8 B 1
9 C 1
10 C 2

share|improve this question

asked Mar 6 at 22:22

HamidHamid

12810

share|improve this question

asked Mar 6 at 22:22

HamidHamid

12810

asked Mar 6 at 22:22

HamidHamid

12810

asked Mar 6 at 22:22

HamidHamid

12810

import itertools 
df['New']=list(itertools.chain(*[list(range(len(list(y))))for _,y in itertools.groupby(df.value)]))
df
Out[596]: 
 index value New
0 1 A 0
1 2 A 1
2 3 A 2
3 4 A 3
4 5 B 0
5 6 B 1
6 7 A 0
7 8 B 0
8 9 C 0
9 10 C 1

df['New']=df.groupby((df.value!=df.value.shift()).ne(0).cumsum()).cumcount()+1

import itertools 
df['New']=list(itertools.chain(*[list(range(len(list(y))))for _,y in itertools.groupby(df.value)]))
df
Out[596]: 
 index value New
0 1 A 0
1 2 A 1
2 3 A 2
3 4 A 3
4 5 B 0
5 6 B 1
6 7 A 0
7 8 B 0
8 9 C 0
9 10 C 1

df['New']=df.groupby((df.value!=df.value.shift()).ne(0).cumsum()).cumcount()+1

import itertools 
df['New']=list(itertools.chain(*[list(range(len(list(y))))for _,y in itertools.groupby(df.value)]))
df
Out[596]: 
 index value New
0 1 A 0
1 2 A 1
2 3 A 2
3 4 A 3
4 5 B 0
5 6 B 1
6 7 A 0
7 8 B 0
8 9 C 0
9 10 C 1

df['New']=df.groupby((df.value!=df.value.shift()).ne(0).cumsum()).cumcount()+1

import itertools 
df['New']=list(itertools.chain(*[list(range(len(list(y))))for _,y in itertools.groupby(df.value)]))
df
Out[596]: 
 index value New
0 1 A 0
1 2 A 1
2 3 A 2
3 4 A 3
4 5 B 0
5 6 B 1
6 7 A 0
7 8 B 0
8 9 C 0
9 10 C 1

df['New']=df.groupby((df.value!=df.value.shift()).ne(0).cumsum()).cumcount()+1

import itertools 
df['New']=list(itertools.chain(*[list(range(len(list(y))))for _,y in itertools.groupby(df.value)]))
df
Out[596]: 
 index value New
0 1 A 0
1 2 A 1
2 3 A 2
3 4 A 3
4 5 B 0
5 6 B 1
6 7 A 0
7 8 B 0
8 9 C 0
9 10 C 1

df['New']=df.groupby((df.value!=df.value.shift()).ne(0).cumsum()).cumcount()+1