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how to check all values in particular column has same data type or not?

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1

$begingroup$

I have column 'ABC' which has 5000 rows. Currently, dtype of column is object. Mostly it has string values but some values dtype is not string, I want to find all those rows and modify those rows. Column is as following:

1 abc
2 def
3 ghi
4 23
5 mno
6 null
7 qwe
8 12-11-2019
...
...
...
4900 ert
5000 tyu

In above case, I can use for loop to find out rows which do not have desired dtype. I just wanted to know, is their better way to solve this issue.

Note: I am using Pandas.

python pandas data-cleaning numpy

share|improve this question

asked 8 hours ago

KiranKiran

10844 bronze badges

New contributor

Kiran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  In pandas dtypes can be inferred by trying to cast them and making un-castable ones to string dtypes as in object, which means all elements in a single column will be in a same datatype. You cant have two diff. row elements in the same column to be of different datatypes.
  $endgroup$
  – Kiritee Gak
  7 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  @KiriteeGak: I think that is not quite true. You can test that yourself. Create a dataframe, with at least two rows indexed 1 and 2. Then do df.loc[1, 'new_column']= 'my_value'. Then do df['new_column'].map(type). You will see, that all but the first row contain floats. That is because the other rows contain NaN, which is a float and not a str. Likewise you could mix in other object types in your object column if you like (but it is probably not a very good idea).
  $endgroup$
  – jottbe
  7 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  I stand corrected. Thanks :)
  $endgroup$
  – Kiritee Gak
  6 hours ago
  

  
  
  
  

add a comment
 | 

1

$begingroup$

I have column 'ABC' which has 5000 rows. Currently, dtype of column is object. Mostly it has string values but some values dtype is not string, I want to find all those rows and modify those rows. Column is as following:

1 abc
2 def
3 ghi
4 23
5 mno
6 null
7 qwe
8 12-11-2019
...
...
...
4900 ert
5000 tyu

In above case, I can use for loop to find out rows which do not have desired dtype. I just wanted to know, is their better way to solve this issue.

Note: I am using Pandas.

python pandas data-cleaning numpy

share|improve this question

asked 8 hours ago

KiranKiran

10844 bronze badges

New contributor

Kiran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  In pandas dtypes can be inferred by trying to cast them and making un-castable ones to string dtypes as in object, which means all elements in a single column will be in a same datatype. You cant have two diff. row elements in the same column to be of different datatypes.
  $endgroup$
  – Kiritee Gak
  7 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  @KiriteeGak: I think that is not quite true. You can test that yourself. Create a dataframe, with at least two rows indexed 1 and 2. Then do df.loc[1, 'new_column']= 'my_value'. Then do df['new_column'].map(type). You will see, that all but the first row contain floats. That is because the other rows contain NaN, which is a float and not a str. Likewise you could mix in other object types in your object column if you like (but it is probably not a very good idea).
  $endgroup$
  – jottbe
  7 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  I stand corrected. Thanks :)
  $endgroup$
  – Kiritee Gak
  6 hours ago
  

  
  
  
  

add a comment
 | 

$begingroup$

I have column 'ABC' which has 5000 rows. Currently, dtype of column is object. Mostly it has string values but some values dtype is not string, I want to find all those rows and modify those rows. Column is as following:

1 abc
2 def
3 ghi
4 23
5 mno
6 null
7 qwe
8 12-11-2019
...
...
...
4900 ert
5000 tyu

In above case, I can use for loop to find out rows which do not have desired dtype. I just wanted to know, is their better way to solve this issue.

Note: I am using Pandas.

python pandas data-cleaning numpy

share|improve this question

asked 8 hours ago

KiranKiran

10844 bronze badges

New contributor

Kiran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

1 abc
2 def
3 ghi
4 23
5 mno
6 null
7 qwe
8 12-11-2019
...
...
...
4900 ert
5000 tyu

share|improve this question

asked 8 hours ago

KiranKiran

10844 bronze badges

New contributor

Kiran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

share|improve this question

asked 8 hours ago

KiranKiran

10844 bronze badges

New contributor

Kiran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked 8 hours ago

KiranKiran

10844 bronze badges

New contributor

Kiran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked 8 hours ago

KiranKiran

10844 bronze badges

1 Answer
1

active

oldest

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2

$begingroup$

You can get the type of the entries of your column with map:

df['ABC'].map(type)

So to filter on all values, which are not stored as str, you can use:

df['ABC'].map(type) != str

If however you just want to check if some of the rows contain a string, that has a special format (like a date), you can check this with a regex like:

df['ABC'].str.match('[0-9]4-[0-9]2-[0-9]2')

But of course, that is no exact date check. E.g. it would also return True for values like 0000-13-91, but this was only meant to give you an idea anyways.

share|improve this answer

edited 7 hours ago

answered 7 hours ago

jottbejottbe

1821010 bronze badges

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  thanks, it helped..
  $endgroup$
  – Kiran
  7 hours ago
  

  
  
  
  

add a comment
 | 

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2

$begingroup$

You can get the type of the entries of your column with map:

df['ABC'].map(type)

So to filter on all values, which are not stored as str, you can use:

df['ABC'].map(type) != str

If however you just want to check if some of the rows contain a string, that has a special format (like a date), you can check this with a regex like:

df['ABC'].str.match('[0-9]4-[0-9]2-[0-9]2')

But of course, that is no exact date check. E.g. it would also return True for values like 0000-13-91, but this was only meant to give you an idea anyways.

share|improve this answer

edited 7 hours ago

answered 7 hours ago

jottbejottbe

1821010 bronze badges

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  thanks, it helped..
  $endgroup$
  – Kiran
  7 hours ago
  

  
  
  
  

add a comment
 | 

2

$begingroup$

You can get the type of the entries of your column with map:

df['ABC'].map(type)

So to filter on all values, which are not stored as str, you can use:

df['ABC'].map(type) != str

If however you just want to check if some of the rows contain a string, that has a special format (like a date), you can check this with a regex like:

df['ABC'].str.match('[0-9]4-[0-9]2-[0-9]2')

But of course, that is no exact date check. E.g. it would also return True for values like 0000-13-91, but this was only meant to give you an idea anyways.

share|improve this answer

edited 7 hours ago

answered 7 hours ago

jottbejottbe

1821010 bronze badges

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  thanks, it helped..
  $endgroup$
  – Kiran
  7 hours ago
  

  
  
  
  

add a comment
 | 

$begingroup$

You can get the type of the entries of your column with map:

df['ABC'].map(type)

So to filter on all values, which are not stored as str, you can use:

df['ABC'].map(type) != str

If however you just want to check if some of the rows contain a string, that has a special format (like a date), you can check this with a regex like:

df['ABC'].str.match('[0-9]4-[0-9]2-[0-9]2')

But of course, that is no exact date check. E.g. it would also return True for values like 0000-13-91, but this was only meant to give you an idea anyways.

share|improve this answer

edited 7 hours ago

answered 7 hours ago

jottbejottbe

1821010 bronze badges

$endgroup$

df['ABC'].map(type)

df['ABC'].map(type) != str

df['ABC'].str.match('[0-9]4-[0-9]2-[0-9]2')

share|improve this answer

edited 7 hours ago

answered 7 hours ago

jottbejottbe

1821010 bronze badges

answered 7 hours ago

jottbejottbe

1821010 bronze badges

answered 7 hours ago

jottbejottbe

1821010 bronze badges