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Check for Duplicate values and Pull Info to New Dataframe

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0

I have a dataframe (df_data) with 14 columns for info over 1 month. I pulled out one week's data (df1) and made a list of all the account numbers there (accounts1)

What I am trying to do is take that accounts1 list and have it go through each value in the list, checking if it is counted more than once in df_data and if so, to save that account number to a new list for repeats only.

Then I want to take that repeats list and pull the 14 columns out of the original df_data so I can have all the rows of all 14 columns for each occurrence of that account number.

I'm getting stuck with the list of repeated account numbers, I used the following code, which seems to have worked to create a list with results

cnt = collections.Counter(accounts1)
repeats.append([k for k, v in cnt.items() if v > 1])
print((repeats).count) 

but the amount of elements in that list is right under 3,000. When I used the .unique and checked the difference it should be a little over 5,000. What am I doing wrong? And how can I then use those elements to pull the columns from the original dataframe?

Basically say I had

accounts1 = df1['accntnum'] = [0,1,2,5,8,2,5,0,0,7]

I would want it to cycle through and pull out each repeat from df_data and return a list of them like

repeats = [0, 2, 5, 7] 
(There are numbers in the monthly df_data that are in weekly df1 but may not be repeated there yet)

Then I'd like to use that list to pull from df_data['accntnum'], thinking something like

df_repeats = df_data[df_data['accntnum'] isin repeats]]

Oh also, I'm really only interested in the first occurrence of a repeat. There is a date and time column that can help sort those out in the end though. Thank you in advance!

python pandas

share|improve this question

asked Mar 8 at 23:40

RiverValRiverVal

11

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  df_data[df_data['acctnum'].isin(repeats)].sort_values(['date', 'time']).drop_duplicates(keep='first') ?
  
  – panktijk
  Mar 8 at 23:53
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Didn't quite work since it only kept the initial occurrence and not the first repetition as well like I needed, but it helped put me on the right track, thank you! I ended up sorting my df_data, creating a new df_first using the drop_duplicates(keep = 'first') like you suggested for the initial occurrence of each account number. Then, I used drop() to get rid of those first occurrences from df_data and repeated process to get second occurrences in their own dataframe, then combined the 2. Thank you!
  
  – RiverVal
  Mar 11 at 22:41
  
  

  
  
  
  

add a comment | 

0

I have a dataframe (df_data) with 14 columns for info over 1 month. I pulled out one week's data (df1) and made a list of all the account numbers there (accounts1)

What I am trying to do is take that accounts1 list and have it go through each value in the list, checking if it is counted more than once in df_data and if so, to save that account number to a new list for repeats only.

Then I want to take that repeats list and pull the 14 columns out of the original df_data so I can have all the rows of all 14 columns for each occurrence of that account number.

I'm getting stuck with the list of repeated account numbers, I used the following code, which seems to have worked to create a list with results

cnt = collections.Counter(accounts1)
repeats.append([k for k, v in cnt.items() if v > 1])
print((repeats).count) 

but the amount of elements in that list is right under 3,000. When I used the .unique and checked the difference it should be a little over 5,000. What am I doing wrong? And how can I then use those elements to pull the columns from the original dataframe?

Basically say I had

accounts1 = df1['accntnum'] = [0,1,2,5,8,2,5,0,0,7]

I would want it to cycle through and pull out each repeat from df_data and return a list of them like

repeats = [0, 2, 5, 7] 
(There are numbers in the monthly df_data that are in weekly df1 but may not be repeated there yet)

Then I'd like to use that list to pull from df_data['accntnum'], thinking something like

df_repeats = df_data[df_data['accntnum'] isin repeats]]

Oh also, I'm really only interested in the first occurrence of a repeat. There is a date and time column that can help sort those out in the end though. Thank you in advance!

python pandas

share|improve this question

asked Mar 8 at 23:40

RiverValRiverVal

11

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  df_data[df_data['acctnum'].isin(repeats)].sort_values(['date', 'time']).drop_duplicates(keep='first') ?
  
  – panktijk
  Mar 8 at 23:53
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Didn't quite work since it only kept the initial occurrence and not the first repetition as well like I needed, but it helped put me on the right track, thank you! I ended up sorting my df_data, creating a new df_first using the drop_duplicates(keep = 'first') like you suggested for the initial occurrence of each account number. Then, I used drop() to get rid of those first occurrences from df_data and repeated process to get second occurrences in their own dataframe, then combined the 2. Thank you!
  
  – RiverVal
  Mar 11 at 22:41
  
  

  
  
  
  

add a comment | 

I have a dataframe (df_data) with 14 columns for info over 1 month. I pulled out one week's data (df1) and made a list of all the account numbers there (accounts1)

What I am trying to do is take that accounts1 list and have it go through each value in the list, checking if it is counted more than once in df_data and if so, to save that account number to a new list for repeats only.

Then I want to take that repeats list and pull the 14 columns out of the original df_data so I can have all the rows of all 14 columns for each occurrence of that account number.

I'm getting stuck with the list of repeated account numbers, I used the following code, which seems to have worked to create a list with results

cnt = collections.Counter(accounts1)
repeats.append([k for k, v in cnt.items() if v > 1])
print((repeats).count) 

but the amount of elements in that list is right under 3,000. When I used the .unique and checked the difference it should be a little over 5,000. What am I doing wrong? And how can I then use those elements to pull the columns from the original dataframe?

Basically say I had

accounts1 = df1['accntnum'] = [0,1,2,5,8,2,5,0,0,7]

I would want it to cycle through and pull out each repeat from df_data and return a list of them like

repeats = [0, 2, 5, 7] 
(There are numbers in the monthly df_data that are in weekly df1 but may not be repeated there yet)

Then I'd like to use that list to pull from df_data['accntnum'], thinking something like

df_repeats = df_data[df_data['accntnum'] isin repeats]]

Oh also, I'm really only interested in the first occurrence of a repeat. There is a date and time column that can help sort those out in the end though. Thank you in advance!

python pandas

share|improve this question

asked Mar 8 at 23:40

RiverValRiverVal

11

cnt = collections.Counter(accounts1)
repeats.append([k for k, v in cnt.items() if v > 1])
print((repeats).count) 

accounts1 = df1['accntnum'] = [0,1,2,5,8,2,5,0,0,7]

repeats = [0, 2, 5, 7] 
(There are numbers in the monthly df_data that are in weekly df1 but may not be repeated there yet)

df_repeats = df_data[df_data['accntnum'] isin repeats]]

share|improve this question

asked Mar 8 at 23:40

RiverValRiverVal

11

share|improve this question

asked Mar 8 at 23:40

RiverValRiverVal

11

asked Mar 8 at 23:40

RiverValRiverVal

11

asked Mar 8 at 23:40

RiverValRiverVal

11