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How to edit cells in a Pandas DataFrame without header?

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0

I have a Pandas DataFrame without header and I need to edit all cells.

Cells are composed by a product name and a certain number, for example:

'Pasta=3'

Example:

 0 1 2 3 
0 'Pasta=3' 'Soup=2' 'Potatoes=4' None
1 'Cheese=1' 'Milk=2' None None
2 'Eggs=6' 'Cleaners=1' 'Beef=2' 'Fish=1'
3 'Apples=3' 'Banana=2' 'Pear=4' None

Now I have to remove the equality symbol and the number. So, if I have

'Pasta=3' 

I have to remove '=3' to obtain

'Pasta'

How can I do it, if the dataframe hasn't header?

Thank you.

python python-3.x pandas dataframe

share|improve this question

edited Mar 26 at 1:08

hrakkar

asked Mar 26 at 0:52

hrakkarhrakkar

322 bronze badges

add a comment | 

0

I have a Pandas DataFrame without header and I need to edit all cells.

Cells are composed by a product name and a certain number, for example:

'Pasta=3'

Example:

 0 1 2 3 
0 'Pasta=3' 'Soup=2' 'Potatoes=4' None
1 'Cheese=1' 'Milk=2' None None
2 'Eggs=6' 'Cleaners=1' 'Beef=2' 'Fish=1'
3 'Apples=3' 'Banana=2' 'Pear=4' None

Now I have to remove the equality symbol and the number. So, if I have

'Pasta=3' 

I have to remove '=3' to obtain

'Pasta'

How can I do it, if the dataframe hasn't header?

Thank you.

python python-3.x pandas dataframe

share|improve this question

edited Mar 26 at 1:08

hrakkar

asked Mar 26 at 0:52

hrakkarhrakkar

322 bronze badges

add a comment | 

I have a Pandas DataFrame without header and I need to edit all cells.

Cells are composed by a product name and a certain number, for example:

'Pasta=3'

Example:

 0 1 2 3 
0 'Pasta=3' 'Soup=2' 'Potatoes=4' None
1 'Cheese=1' 'Milk=2' None None
2 'Eggs=6' 'Cleaners=1' 'Beef=2' 'Fish=1'
3 'Apples=3' 'Banana=2' 'Pear=4' None

Now I have to remove the equality symbol and the number. So, if I have

'Pasta=3' 

I have to remove '=3' to obtain

'Pasta'

How can I do it, if the dataframe hasn't header?

Thank you.

python python-3.x pandas dataframe

share|improve this question

edited Mar 26 at 1:08

hrakkar

asked Mar 26 at 0:52

hrakkarhrakkar

322 bronze badges

'Pasta=3'

 0 1 2 3 
0 'Pasta=3' 'Soup=2' 'Potatoes=4' None
1 'Cheese=1' 'Milk=2' None None
2 'Eggs=6' 'Cleaners=1' 'Beef=2' 'Fish=1'
3 'Apples=3' 'Banana=2' 'Pear=4' None

'Pasta=3' 

'Pasta'

share|improve this question

edited Mar 26 at 1:08

hrakkar

asked Mar 26 at 0:52

hrakkarhrakkar

322 bronze badges

share|improve this question

edited Mar 26 at 1:08

hrakkar

asked Mar 26 at 0:52

hrakkarhrakkar

322 bronze badges

asked Mar 26 at 0:52

hrakkarhrakkar

322 bronze badges

asked Mar 26 at 0:52

hrakkarhrakkar

322 bronze badges

1 Answer
1

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oldest

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2

First of all, they have a "header" (i.e. column name). In your case, it is either 0 or "0" (integer 0, or string 0). So you can still access the columns by df[0] or df["0"] depending on whether that's a integer or string.

Second, to remove everything after the =, just use df.replace on every column by stating regex=True

df.replace(r'=.*','', regex=True)

You can also use apply (slower, prefer the first method)

df.apply(lambda s: s.str.replace(r'=.*',''))

share|improve this answer

answered Mar 26 at 1:17

rafaelcrafaelc

31.2k88 gold badges3232 silver badges5555 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  It worked very well! Thank you so much :)
  
  – hrakkar
  Mar 26 at 10:33
  

  
  
  
  

add a comment | 

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2

First of all, they have a "header" (i.e. column name). In your case, it is either 0 or "0" (integer 0, or string 0). So you can still access the columns by df[0] or df["0"] depending on whether that's a integer or string.

Second, to remove everything after the =, just use df.replace on every column by stating regex=True

df.replace(r'=.*','', regex=True)

You can also use apply (slower, prefer the first method)

df.apply(lambda s: s.str.replace(r'=.*',''))

share|improve this answer

answered Mar 26 at 1:17

rafaelcrafaelc

31.2k88 gold badges3232 silver badges5555 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  It worked very well! Thank you so much :)
  
  – hrakkar
  Mar 26 at 10:33
  

  
  
  
  

add a comment | 

2

First of all, they have a "header" (i.e. column name). In your case, it is either 0 or "0" (integer 0, or string 0). So you can still access the columns by df[0] or df["0"] depending on whether that's a integer or string.

Second, to remove everything after the =, just use df.replace on every column by stating regex=True

df.replace(r'=.*','', regex=True)

You can also use apply (slower, prefer the first method)

df.apply(lambda s: s.str.replace(r'=.*',''))

share|improve this answer

answered Mar 26 at 1:17

rafaelcrafaelc

31.2k88 gold badges3232 silver badges5555 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  It worked very well! Thank you so much :)
  
  – hrakkar
  Mar 26 at 10:33
  

  
  
  
  

add a comment | 

First of all, they have a "header" (i.e. column name). In your case, it is either 0 or "0" (integer 0, or string 0). So you can still access the columns by df[0] or df["0"] depending on whether that's a integer or string.

Second, to remove everything after the =, just use df.replace on every column by stating regex=True

df.replace(r'=.*','', regex=True)

You can also use apply (slower, prefer the first method)

df.apply(lambda s: s.str.replace(r'=.*',''))

share|improve this answer

answered Mar 26 at 1:17

rafaelcrafaelc

31.2k88 gold badges3232 silver badges5555 bronze badges

df.replace(r'=.*','', regex=True)

df.apply(lambda s: s.str.replace(r'=.*',''))

share|improve this answer

answered Mar 26 at 1:17

rafaelcrafaelc

31.2k88 gold badges3232 silver badges5555 bronze badges

answered Mar 26 at 1:17

rafaelcrafaelc

31.2k88 gold badges3232 silver badges5555 bronze badges

answered Mar 26 at 1:17

rafaelcrafaelc

31.2k88 gold badges3232 silver badges5555 bronze badges