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How to change data type of values in pandas

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1

I have a pandas dataframe that has both floats and numpy arrays as follows.

 name x1 x2 X3
0 a 4.7 [0.] 3
1 b 3.2 [1.7] 5
2 e 1.2 [2.] 6

I want to change all the numpy values in the dataframe into floats.
i.e.

 name x1 x2 X3
0 a 4.7 0. 3
1 b 3.2 1.7 5
2 e 1.2 2. 6

Can we do that using pandas?

I am happy to provide more details if needed.

pandas

share|improve this question

asked Mar 26 at 14:27

EmJEmJ

77455 silver badges2525 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  There are only one element lists?
  
  – jezrael
  Mar 26 at 14:28
  

  
  
  
  

add a comment | 

1

I have a pandas dataframe that has both floats and numpy arrays as follows.

 name x1 x2 X3
0 a 4.7 [0.] 3
1 b 3.2 [1.7] 5
2 e 1.2 [2.] 6

I want to change all the numpy values in the dataframe into floats.
i.e.

 name x1 x2 X3
0 a 4.7 0. 3
1 b 3.2 1.7 5
2 e 1.2 2. 6

Can we do that using pandas?

I am happy to provide more details if needed.

pandas

share|improve this question

asked Mar 26 at 14:27

EmJEmJ

77455 silver badges2525 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  There are only one element lists?
  
  – jezrael
  Mar 26 at 14:28
  

  
  
  
  

add a comment | 

I have a pandas dataframe that has both floats and numpy arrays as follows.

 name x1 x2 X3
0 a 4.7 [0.] 3
1 b 3.2 [1.7] 5
2 e 1.2 [2.] 6

I want to change all the numpy values in the dataframe into floats.
i.e.

 name x1 x2 X3
0 a 4.7 0. 3
1 b 3.2 1.7 5
2 e 1.2 2. 6

Can we do that using pandas?

I am happy to provide more details if needed.

pandas

share|improve this question

asked Mar 26 at 14:27

EmJEmJ

77455 silver badges2525 bronze badges

 name x1 x2 X3
0 a 4.7 [0.] 3
1 b 3.2 [1.7] 5
2 e 1.2 [2.] 6

 name x1 x2 X3
0 a 4.7 0. 3
1 b 3.2 1.7 5
2 e 1.2 2. 6

share|improve this question

asked Mar 26 at 14:27

EmJEmJ

77455 silver badges2525 bronze badges

share|improve this question

asked Mar 26 at 14:27

EmJEmJ

77455 silver badges2525 bronze badges

asked Mar 26 at 14:27

EmJEmJ

77455 silver badges2525 bronze badges

asked Mar 26 at 14:27

EmJEmJ

77455 silver badges2525 bronze badges

1 Answer
1

active

oldest

votes

1

If only one element lists use:

df['x2'] = df['x2'].str[0]

Or:

df['x2'] = df['x2'].apply(lambda x: x[0])

EDIT:

After comments there are strings in columns, so need:

mask = df.iloc[0].astype(str).str.startswith('[')

df.loc[:, mask] = df.loc[:, mask].apply(lambda x: x.str.strip('[]')).astype(float)

share|improve this answer

edited Mar 26 at 15:19

answered Mar 26 at 14:28

jezraeljezrael

392k2828 gold badges402402 silver badges475475 bronze badges

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  Hi, thanks a lot for the great answer. However, in my real dataset, I do not know what column names have numpy. Is there any way to do this without specifically mentioning the column name? :)
  
  – EmJ
  Mar 26 at 14:33
  
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @Emi - S there are only one element lists?
  
  – jezrael
  Mar 26 at 14:34
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @Emi - Or one element arrays in column, or floats ?
  
  – jezrael
  Mar 26 at 14:35
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Not sure what you meant. There are multiple columns (say 25 columns) and about 15 of the columns have numpy values :) Please let me know if you want an example. I can update the question.
  
  – EmJ
  Mar 26 at 14:37
  
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @Emi - ya, I think if is possible some value is [0,2.1] - length 2 or all arrays have only one value - [0.]
  
  – jezrael
  Mar 26 at 14:38
  

  
  
  
  

 | 
show 2 more comments

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1

If only one element lists use:

df['x2'] = df['x2'].str[0]

Or:

df['x2'] = df['x2'].apply(lambda x: x[0])

EDIT:

After comments there are strings in columns, so need:

mask = df.iloc[0].astype(str).str.startswith('[')

df.loc[:, mask] = df.loc[:, mask].apply(lambda x: x.str.strip('[]')).astype(float)

share|improve this answer

edited Mar 26 at 15:19

answered Mar 26 at 14:28

jezraeljezrael

392k2828 gold badges402402 silver badges475475 bronze badges

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  Hi, thanks a lot for the great answer. However, in my real dataset, I do not know what column names have numpy. Is there any way to do this without specifically mentioning the column name? :)
  
  – EmJ
  Mar 26 at 14:33
  
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @Emi - S there are only one element lists?
  
  – jezrael
  Mar 26 at 14:34
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @Emi - Or one element arrays in column, or floats ?
  
  – jezrael
  Mar 26 at 14:35
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Not sure what you meant. There are multiple columns (say 25 columns) and about 15 of the columns have numpy values :) Please let me know if you want an example. I can update the question.
  
  – EmJ
  Mar 26 at 14:37
  
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @Emi - ya, I think if is possible some value is [0,2.1] - length 2 or all arrays have only one value - [0.]
  
  – jezrael
  Mar 26 at 14:38
  

  
  
  
  

 | 
show 2 more comments

1

If only one element lists use:

df['x2'] = df['x2'].str[0]

Or:

df['x2'] = df['x2'].apply(lambda x: x[0])

EDIT:

After comments there are strings in columns, so need:

mask = df.iloc[0].astype(str).str.startswith('[')

df.loc[:, mask] = df.loc[:, mask].apply(lambda x: x.str.strip('[]')).astype(float)

share|improve this answer

edited Mar 26 at 15:19

answered Mar 26 at 14:28

jezraeljezrael

392k2828 gold badges402402 silver badges475475 bronze badges

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  Hi, thanks a lot for the great answer. However, in my real dataset, I do not know what column names have numpy. Is there any way to do this without specifically mentioning the column name? :)
  
  – EmJ
  Mar 26 at 14:33
  
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @Emi - S there are only one element lists?
  
  – jezrael
  Mar 26 at 14:34
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @Emi - Or one element arrays in column, or floats ?
  
  – jezrael
  Mar 26 at 14:35
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Not sure what you meant. There are multiple columns (say 25 columns) and about 15 of the columns have numpy values :) Please let me know if you want an example. I can update the question.
  
  – EmJ
  Mar 26 at 14:37
  
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @Emi - ya, I think if is possible some value is [0,2.1] - length 2 or all arrays have only one value - [0.]
  
  – jezrael
  Mar 26 at 14:38
  

  
  
  
  

 | 
show 2 more comments

If only one element lists use:

df['x2'] = df['x2'].str[0]

Or:

df['x2'] = df['x2'].apply(lambda x: x[0])

EDIT:

After comments there are strings in columns, so need:

mask = df.iloc[0].astype(str).str.startswith('[')

df.loc[:, mask] = df.loc[:, mask].apply(lambda x: x.str.strip('[]')).astype(float)

share|improve this answer

edited Mar 26 at 15:19

answered Mar 26 at 14:28

jezraeljezrael

392k2828 gold badges402402 silver badges475475 bronze badges

df['x2'] = df['x2'].str[0]

df['x2'] = df['x2'].apply(lambda x: x[0])

mask = df.iloc[0].astype(str).str.startswith('[')

df.loc[:, mask] = df.loc[:, mask].apply(lambda x: x.str.strip('[]')).astype(float)

share|improve this answer

edited Mar 26 at 15:19

answered Mar 26 at 14:28

jezraeljezrael

392k2828 gold badges402402 silver badges475475 bronze badges

answered Mar 26 at 14:28

jezraeljezrael

392k2828 gold badges402402 silver badges475475 bronze badges

answered Mar 26 at 14:28

jezraeljezrael

392k2828 gold badges402402 silver badges475475 bronze badges