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1

I have a pandas dataframe of 182 rows that comes from read_csv. The first column, sys_code, contains various alphanumeric codes. I want to drop ones that start with 'FB' (there are 14 of these). I loop through the dataframe, adding what I assume would be the index to a list, then try to drop by index using the list. But this doesn't work unless I add 18 to each index number.

Without adding 18, I get a list containing numbers from 84 - 97. When I try to drop the rows using this list for indexes, I get KeyError: '[84] not found in axis'. But when I add 18 to each number, it works fine, at least for this particular dataset. But why is this? Shouldn't i be the same as the index number?

fb = []
i = 0
df.reset_index(drop=True)
for x in df['sys_code']:
 if x[:2] == 'FB':
 fb.append(i+18) #works
 fb.append(i) # doesn't work
 i += 1
df.drop(fb, axis=0, inplace=True)

python python-3.x pandas

share|improve this question

edited Mar 7 at 13:18

Shanteshwar Inde

7421918

asked Mar 7 at 12:49

recurvatarecurvata

225

add a comment | 

1

I have a pandas dataframe of 182 rows that comes from read_csv. The first column, sys_code, contains various alphanumeric codes. I want to drop ones that start with 'FB' (there are 14 of these). I loop through the dataframe, adding what I assume would be the index to a list, then try to drop by index using the list. But this doesn't work unless I add 18 to each index number.

Without adding 18, I get a list containing numbers from 84 - 97. When I try to drop the rows using this list for indexes, I get KeyError: '[84] not found in axis'. But when I add 18 to each number, it works fine, at least for this particular dataset. But why is this? Shouldn't i be the same as the index number?

fb = []
i = 0
df.reset_index(drop=True)
for x in df['sys_code']:
 if x[:2] == 'FB':
 fb.append(i+18) #works
 fb.append(i) # doesn't work
 i += 1
df.drop(fb, axis=0, inplace=True)

python python-3.x pandas

share|improve this question

edited Mar 7 at 13:18

Shanteshwar Inde

7421918

asked Mar 7 at 12:49

recurvatarecurvata

225

add a comment | 

I have a pandas dataframe of 182 rows that comes from read_csv. The first column, sys_code, contains various alphanumeric codes. I want to drop ones that start with 'FB' (there are 14 of these). I loop through the dataframe, adding what I assume would be the index to a list, then try to drop by index using the list. But this doesn't work unless I add 18 to each index number.

Without adding 18, I get a list containing numbers from 84 - 97. When I try to drop the rows using this list for indexes, I get KeyError: '[84] not found in axis'. But when I add 18 to each number, it works fine, at least for this particular dataset. But why is this? Shouldn't i be the same as the index number?

fb = []
i = 0
df.reset_index(drop=True)
for x in df['sys_code']:
 if x[:2] == 'FB':
 fb.append(i+18) #works
 fb.append(i) # doesn't work
 i += 1
df.drop(fb, axis=0, inplace=True)

python python-3.x pandas

share|improve this question

edited Mar 7 at 13:18

Shanteshwar Inde

7421918

asked Mar 7 at 12:49

recurvatarecurvata

225

fb = []
i = 0
df.reset_index(drop=True)
for x in df['sys_code']:
 if x[:2] == 'FB':
 fb.append(i+18) #works
 fb.append(i) # doesn't work
 i += 1
df.drop(fb, axis=0, inplace=True)

share|improve this question

edited Mar 7 at 13:18

Shanteshwar Inde

7421918

asked Mar 7 at 12:49

recurvatarecurvata

225

share|improve this question

edited Mar 7 at 13:18

Shanteshwar Inde

7421918

asked Mar 7 at 12:49

recurvatarecurvata

225

edited Mar 7 at 13:18

Shanteshwar Inde

7421918

edited Mar 7 at 13:18

Shanteshwar Inde

7421918

asked Mar 7 at 12:49

recurvatarecurvata

225

asked Mar 7 at 12:49

recurvatarecurvata

225

1 Answer
1

active

oldest

votes

2

You could use Series.str.startswith. Here's an example:

df = pd.DataFrame('col1':['some string', 'FBsomething', 'FB', 'etc'])
print(df)

 col1
0 some string
1 FBsomething
2 FB
3 etc

You could remove those strings that do not start with FB using:

df[~df.col1.str.startswith('FB')]

 col1
0 some string
3 etc

share|improve this answer

answered Mar 7 at 12:53

yatuyatu

15k41542

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  Thanks, was not aware of that syntax with the ~ symbol. I had to modify the code a bit - df = df[~df.col1.str.startswith('FB')], but works great. Still don't know why original code gave error though.
  
  – recurvata
  Mar 7 at 13:28
  

  
  
  
  

add a comment | 

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2

You could use Series.str.startswith. Here's an example:

df = pd.DataFrame('col1':['some string', 'FBsomething', 'FB', 'etc'])
print(df)

 col1
0 some string
1 FBsomething
2 FB
3 etc

You could remove those strings that do not start with FB using:

df[~df.col1.str.startswith('FB')]

 col1
0 some string
3 etc

share|improve this answer

answered Mar 7 at 12:53

yatuyatu

15k41542

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  Thanks, was not aware of that syntax with the ~ symbol. I had to modify the code a bit - df = df[~df.col1.str.startswith('FB')], but works great. Still don't know why original code gave error though.
  
  – recurvata
  Mar 7 at 13:28
  

  
  
  
  

add a comment | 

2

You could use Series.str.startswith. Here's an example:

df = pd.DataFrame('col1':['some string', 'FBsomething', 'FB', 'etc'])
print(df)

 col1
0 some string
1 FBsomething
2 FB
3 etc

You could remove those strings that do not start with FB using:

df[~df.col1.str.startswith('FB')]

 col1
0 some string
3 etc

share|improve this answer

answered Mar 7 at 12:53

yatuyatu

15k41542

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  Thanks, was not aware of that syntax with the ~ symbol. I had to modify the code a bit - df = df[~df.col1.str.startswith('FB')], but works great. Still don't know why original code gave error though.
  
  – recurvata
  Mar 7 at 13:28
  

  
  
  
  

add a comment | 

You could use Series.str.startswith. Here's an example:

df = pd.DataFrame('col1':['some string', 'FBsomething', 'FB', 'etc'])
print(df)

 col1
0 some string
1 FBsomething
2 FB
3 etc

You could remove those strings that do not start with FB using:

df[~df.col1.str.startswith('FB')]

 col1
0 some string
3 etc

share|improve this answer

answered Mar 7 at 12:53

yatuyatu

15k41542

df = pd.DataFrame('col1':['some string', 'FBsomething', 'FB', 'etc'])
print(df)

 col1
0 some string
1 FBsomething
2 FB
3 etc

df[~df.col1.str.startswith('FB')]

 col1
0 some string
3 etc

share|improve this answer

answered Mar 7 at 12:53

yatuyatu

15k41542

answered Mar 7 at 12:53

yatuyatu

15k41542

answered Mar 7 at 12:53

yatuyatu

15k41542