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Searching for an item within a list in a column and saving that item to a new column

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2

I am very new to Python and need help!

I want to search a column of a data frame for an item in a list and if found, store that item in a new column. My the location column is messy and am trying to extract a state abbreviation if there is one.

So far I have been able to find the columns where the search terms are found (I’m not sure if this is 100% correct), how would I take the search term that was found and store it in a new column?

state_search=('CO', 'CA', 'WI', 'VA', 'NY', 'PA', 'MA', 'TX',)

pattern = '|'.join(state_search) 

state_jobs_df=jobs_data_df.loc[jobs_data_df['location'].str.contains(pattern), :]

I want to take the state that was found and store that in a new 'state' column. Thanks for any help.

---

print (jobs_data_df)
 location
0 Madison, WI 53702
1 Senior Training Leader located in Raynham, MA ...
2 Dixon CA
3 Camphill, PA Weekends and nights
4 Charlottesville, VA Some travel required
5 Houston, TX
6 Denver, CO 80215
7 Respiratory Therapy Primary Location : TX- Som...

python string pandas list search

share|improve this question

edited Mar 27 at 15:31

jezrael

401k2929 gold badges412412 silver badges479479 bronze badges

asked Mar 27 at 15:05

KatipidersKatipiders

1322 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Since you're doing a regex search, I'd suppose to add b around state names, so that some SKY Hotel, NH won't match Kentucky.
  
  – 9000
  Mar 27 at 15:26
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @9000 - yes, word boundaries - in my answer.
  
  – jezrael
  Mar 27 at 15:26
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  If my answer was helpful, don't forget accept it - click on the check mark beside the answer to toggle it from greyed out to filled in. Thanks.
  
  – jezrael
  Mar 27 at 15:45
  

  
  
  
  

add a comment | 

2

I am very new to Python and need help!

I want to search a column of a data frame for an item in a list and if found, store that item in a new column. My the location column is messy and am trying to extract a state abbreviation if there is one.

So far I have been able to find the columns where the search terms are found (I’m not sure if this is 100% correct), how would I take the search term that was found and store it in a new column?

state_search=('CO', 'CA', 'WI', 'VA', 'NY', 'PA', 'MA', 'TX',)

pattern = '|'.join(state_search) 

state_jobs_df=jobs_data_df.loc[jobs_data_df['location'].str.contains(pattern), :]

I want to take the state that was found and store that in a new 'state' column. Thanks for any help.

---

print (jobs_data_df)
 location
0 Madison, WI 53702
1 Senior Training Leader located in Raynham, MA ...
2 Dixon CA
3 Camphill, PA Weekends and nights
4 Charlottesville, VA Some travel required
5 Houston, TX
6 Denver, CO 80215
7 Respiratory Therapy Primary Location : TX- Som...

python string pandas list search

share|improve this question

edited Mar 27 at 15:31

jezrael

401k2929 gold badges412412 silver badges479479 bronze badges

asked Mar 27 at 15:05

KatipidersKatipiders

1322 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Since you're doing a regex search, I'd suppose to add b around state names, so that some SKY Hotel, NH won't match Kentucky.
  
  – 9000
  Mar 27 at 15:26
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @9000 - yes, word boundaries - in my answer.
  
  – jezrael
  Mar 27 at 15:26
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  If my answer was helpful, don't forget accept it - click on the check mark beside the answer to toggle it from greyed out to filled in. Thanks.
  
  – jezrael
  Mar 27 at 15:45
  

  
  
  
  

add a comment | 

I am very new to Python and need help!

I want to search a column of a data frame for an item in a list and if found, store that item in a new column. My the location column is messy and am trying to extract a state abbreviation if there is one.

So far I have been able to find the columns where the search terms are found (I’m not sure if this is 100% correct), how would I take the search term that was found and store it in a new column?

state_search=('CO', 'CA', 'WI', 'VA', 'NY', 'PA', 'MA', 'TX',)

pattern = '|'.join(state_search) 

state_jobs_df=jobs_data_df.loc[jobs_data_df['location'].str.contains(pattern), :]

I want to take the state that was found and store that in a new 'state' column. Thanks for any help.

---

print (jobs_data_df)
 location
0 Madison, WI 53702
1 Senior Training Leader located in Raynham, MA ...
2 Dixon CA
3 Camphill, PA Weekends and nights
4 Charlottesville, VA Some travel required
5 Houston, TX
6 Denver, CO 80215
7 Respiratory Therapy Primary Location : TX- Som...

python string pandas list search

share|improve this question

edited Mar 27 at 15:31

jezrael

401k2929 gold badges412412 silver badges479479 bronze badges

asked Mar 27 at 15:05

KatipidersKatipiders

1322 bronze badges

state_search=('CO', 'CA', 'WI', 'VA', 'NY', 'PA', 'MA', 'TX',)

pattern = '|'.join(state_search) 

state_jobs_df=jobs_data_df.loc[jobs_data_df['location'].str.contains(pattern), :]

print (jobs_data_df)
 location
0 Madison, WI 53702
1 Senior Training Leader located in Raynham, MA ...
2 Dixon CA
3 Camphill, PA Weekends and nights
4 Charlottesville, VA Some travel required
5 Houston, TX
6 Denver, CO 80215
7 Respiratory Therapy Primary Location : TX- Som...

share|improve this question

edited Mar 27 at 15:31

jezrael

401k2929 gold badges412412 silver badges479479 bronze badges

asked Mar 27 at 15:05

KatipidersKatipiders

1322 bronze badges

share|improve this question

edited Mar 27 at 15:31

jezrael

401k2929 gold badges412412 silver badges479479 bronze badges

asked Mar 27 at 15:05

KatipidersKatipiders

1322 bronze badges

edited Mar 27 at 15:31

jezrael

401k2929 gold badges412412 silver badges479479 bronze badges

edited Mar 27 at 15:31

jezrael

401k2929 gold badges412412 silver badges479479 bronze badges

asked Mar 27 at 15:05

KatipidersKatipiders

1322 bronze badges

asked Mar 27 at 15:05

KatipidersKatipiders

1322 bronze badges

2 Answers
2

active

oldest

votes

1

Use Series.str.extract with word boundaries and filter non missing rows by Series.notna or DataFrame.dropna:

pat = '|'.join(r"bb".format(x) for x in state_search)
jobs_data_df['state'] = jobs_data_df['location'].str.extract('('+ pat + ')', expand=False)
jobs_data_df = jobs_data_df[jobs_data_df['state'].notna()]

Or:

jobs_data_df = jobs_data_df.dropna(subset=['state'])

share|improve this answer

answered Mar 27 at 15:23

jezraeljezrael

401k2929 gold badges412412 silver badges479479 bronze badges

add a comment | 

0

It's a bit hack-y, but a simpler solution might take a form similar to:

for row in dataRows:
 for state in state_search:
 if state in row:
 #put state in correct column here
 break #should break just the inner loop; if that doesn't happen, delete this line

It's probably helpful to think about how the underlying program would have to approach the problem (checking each row for a string that matches one of your states, then doing something with it), and go at that directly. Unless you're dealing with a huge load of data, it may not be worth going crazy fancy with regular expressions or the like.

share|improve this answer

edited Mar 27 at 15:16

answered Mar 27 at 15:10

Taylor NelmsTaylor Nelms

22522 silver badges1414 bronze badges

add a comment | 

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1

Use Series.str.extract with word boundaries and filter non missing rows by Series.notna or DataFrame.dropna:

pat = '|'.join(r"bb".format(x) for x in state_search)
jobs_data_df['state'] = jobs_data_df['location'].str.extract('('+ pat + ')', expand=False)
jobs_data_df = jobs_data_df[jobs_data_df['state'].notna()]

Or:

jobs_data_df = jobs_data_df.dropna(subset=['state'])

share|improve this answer

answered Mar 27 at 15:23

jezraeljezrael

401k2929 gold badges412412 silver badges479479 bronze badges

add a comment | 

1

Use Series.str.extract with word boundaries and filter non missing rows by Series.notna or DataFrame.dropna:

pat = '|'.join(r"bb".format(x) for x in state_search)
jobs_data_df['state'] = jobs_data_df['location'].str.extract('('+ pat + ')', expand=False)
jobs_data_df = jobs_data_df[jobs_data_df['state'].notna()]

Or:

jobs_data_df = jobs_data_df.dropna(subset=['state'])

share|improve this answer

answered Mar 27 at 15:23

jezraeljezrael

401k2929 gold badges412412 silver badges479479 bronze badges

add a comment | 

Use Series.str.extract with word boundaries and filter non missing rows by Series.notna or DataFrame.dropna:

pat = '|'.join(r"bb".format(x) for x in state_search)
jobs_data_df['state'] = jobs_data_df['location'].str.extract('('+ pat + ')', expand=False)
jobs_data_df = jobs_data_df[jobs_data_df['state'].notna()]

Or:

jobs_data_df = jobs_data_df.dropna(subset=['state'])

share|improve this answer

answered Mar 27 at 15:23

jezraeljezrael

401k2929 gold badges412412 silver badges479479 bronze badges

pat = '|'.join(r"bb".format(x) for x in state_search)
jobs_data_df['state'] = jobs_data_df['location'].str.extract('('+ pat + ')', expand=False)
jobs_data_df = jobs_data_df[jobs_data_df['state'].notna()]

jobs_data_df = jobs_data_df.dropna(subset=['state'])

share|improve this answer

answered Mar 27 at 15:23

jezraeljezrael

401k2929 gold badges412412 silver badges479479 bronze badges

answered Mar 27 at 15:23

jezraeljezrael

401k2929 gold badges412412 silver badges479479 bronze badges

answered Mar 27 at 15:23

jezraeljezrael

401k2929 gold badges412412 silver badges479479 bronze badges

0

It's a bit hack-y, but a simpler solution might take a form similar to:

for row in dataRows:
 for state in state_search:
 if state in row:
 #put state in correct column here
 break #should break just the inner loop; if that doesn't happen, delete this line

It's probably helpful to think about how the underlying program would have to approach the problem (checking each row for a string that matches one of your states, then doing something with it), and go at that directly. Unless you're dealing with a huge load of data, it may not be worth going crazy fancy with regular expressions or the like.

share|improve this answer

edited Mar 27 at 15:16

answered Mar 27 at 15:10

Taylor NelmsTaylor Nelms

22522 silver badges1414 bronze badges

add a comment | 

0

It's a bit hack-y, but a simpler solution might take a form similar to:

for row in dataRows:
 for state in state_search:
 if state in row:
 #put state in correct column here
 break #should break just the inner loop; if that doesn't happen, delete this line

It's probably helpful to think about how the underlying program would have to approach the problem (checking each row for a string that matches one of your states, then doing something with it), and go at that directly. Unless you're dealing with a huge load of data, it may not be worth going crazy fancy with regular expressions or the like.

share|improve this answer

edited Mar 27 at 15:16

answered Mar 27 at 15:10

Taylor NelmsTaylor Nelms

22522 silver badges1414 bronze badges

add a comment | 

It's a bit hack-y, but a simpler solution might take a form similar to:

for row in dataRows:
 for state in state_search:
 if state in row:
 #put state in correct column here
 break #should break just the inner loop; if that doesn't happen, delete this line

It's probably helpful to think about how the underlying program would have to approach the problem (checking each row for a string that matches one of your states, then doing something with it), and go at that directly. Unless you're dealing with a huge load of data, it may not be worth going crazy fancy with regular expressions or the like.

share|improve this answer

edited Mar 27 at 15:16

answered Mar 27 at 15:10

Taylor NelmsTaylor Nelms

22522 silver badges1414 bronze badges

for row in dataRows:
 for state in state_search:
 if state in row:
 #put state in correct column here
 break #should break just the inner loop; if that doesn't happen, delete this line

share|improve this answer

edited Mar 27 at 15:16

answered Mar 27 at 15:10

Taylor NelmsTaylor Nelms

22522 silver badges1414 bronze badges

answered Mar 27 at 15:10

Taylor NelmsTaylor Nelms

22522 silver badges1414 bronze badges

answered Mar 27 at 15:10

Taylor NelmsTaylor Nelms

22522 silver badges1414 bronze badges