Selecting rows for panda dataframe based on a condition

up vote
1
down vote

favorite

import numpy as np

import pandas as pd





lst2 = [[0.23,"f1"],[5.36,'f2']]

lst2_df = pd.DataFrame(lst2,index=list('pd'),columns=list('ab'))

lst2_df = lst2_df.rename({'a':'A'},axis='columns')



print(lst2_df)



m = ['1','f2']

print(lst2_df.loc[lst2_df['b'].isin(m)])

If I wish to iterate this condition for every column and not hard code it what do I write?
I tried print(lst2_df.loc[lst2_df['A':'b'].isin(m)]) it didnt worked.
I know there are similar question on the site but I could not find one that addresses my issue.

edited Nov 10 at 20:55

asked Nov 10 at 20:47

user10089194

386

Sorry, what is expected output? Do you need print([lst2_df.loc[lst2_df[c].isin(m)] for c in lst2_df.columns]) ?
– jezrael
Nov 10 at 20:50

I need that it performs the operation for every column. I have hardcoded it for a column 'b'. If I need to do it for every column,what is the solution? I hope i cleared your doubt
– user10089194
Nov 10 at 20:54

Can you explain more? What is expected output?
– jezrael
Nov 10 at 20:56

print(lst2_df.loc[lst2_df['b'].isin(m)]) is hardcoded for specific column 'b'. Let's say I had a huge dataframe and I wish to print rows whose element matches the condition. print(lst2_df.loc[lst2_df['all column'].isin(m)])
– user10089194
Nov 10 at 21:02

1

Something like print(lst2_df.loc[lst2_df.isin(m).any(axis=1)]) ?
– jezrael
Nov 10 at 21:07

|
show 1 more comment

up vote
1
down vote

favorite

import numpy as np

import pandas as pd





lst2 = [[0.23,"f1"],[5.36,'f2']]

lst2_df = pd.DataFrame(lst2,index=list('pd'),columns=list('ab'))

lst2_df = lst2_df.rename({'a':'A'},axis='columns')



print(lst2_df)



m = ['1','f2']

print(lst2_df.loc[lst2_df['b'].isin(m)])

edited Nov 10 at 20:55

asked Nov 10 at 20:47

user10089194

386

Sorry, what is expected output? Do you need print([lst2_df.loc[lst2_df[c].isin(m)] for c in lst2_df.columns]) ?
– jezrael
Nov 10 at 20:50

I need that it performs the operation for every column. I have hardcoded it for a column 'b'. If I need to do it for every column,what is the solution? I hope i cleared your doubt
– user10089194
Nov 10 at 20:54

Can you explain more? What is expected output?
– jezrael
Nov 10 at 20:56

print(lst2_df.loc[lst2_df['b'].isin(m)]) is hardcoded for specific column 'b'. Let's say I had a huge dataframe and I wish to print rows whose element matches the condition. print(lst2_df.loc[lst2_df['all column'].isin(m)])
– user10089194
Nov 10 at 21:02

1

Something like print(lst2_df.loc[lst2_df.isin(m).any(axis=1)]) ?
– jezrael
Nov 10 at 21:07

|
show 1 more comment

up vote
1
down vote

favorite

import numpy as np

import pandas as pd





lst2 = [[0.23,"f1"],[5.36,'f2']]

lst2_df = pd.DataFrame(lst2,index=list('pd'),columns=list('ab'))

lst2_df = lst2_df.rename({'a':'A'},axis='columns')



print(lst2_df)



m = ['1','f2']

print(lst2_df.loc[lst2_df['b'].isin(m)])

edited Nov 10 at 20:55

asked Nov 10 at 20:47

user10089194

386

import numpy as np

import pandas as pd





lst2 = [[0.23,"f1"],[5.36,'f2']]

lst2_df = pd.DataFrame(lst2,index=list('pd'),columns=list('ab'))

lst2_df = lst2_df.rename({'a':'A'},axis='columns')



print(lst2_df)



m = ['1','f2']

print(lst2_df.loc[lst2_df['b'].isin(m)])

pandas dataframe pycharm

edited Nov 10 at 20:55

asked Nov 10 at 20:47

user10089194

386

edited Nov 10 at 20:55

asked Nov 10 at 20:47

user10089194

386

edited Nov 10 at 20:55

asked Nov 10 at 20:47

user10089194

386

asked Nov 10 at 20:47

user10089194

386

asked Nov 10 at 20:47

user10089194

386

Sorry, what is expected output? Do you need print([lst2_df.loc[lst2_df[c].isin(m)] for c in lst2_df.columns]) ?
– jezrael
Nov 10 at 20:50

I need that it performs the operation for every column. I have hardcoded it for a column 'b'. If I need to do it for every column,what is the solution? I hope i cleared your doubt
– user10089194
Nov 10 at 20:54

Can you explain more? What is expected output?
– jezrael
Nov 10 at 20:56

print(lst2_df.loc[lst2_df['b'].isin(m)]) is hardcoded for specific column 'b'. Let's say I had a huge dataframe and I wish to print rows whose element matches the condition. print(lst2_df.loc[lst2_df['all column'].isin(m)])
– user10089194
Nov 10 at 21:02

1

Something like print(lst2_df.loc[lst2_df.isin(m).any(axis=1)]) ?
– jezrael
Nov 10 at 21:07

|
show 1 more comment

Sorry, what is expected output? Do you need print([lst2_df.loc[lst2_df[c].isin(m)] for c in lst2_df.columns]) ?
– jezrael
Nov 10 at 20:50

I need that it performs the operation for every column. I have hardcoded it for a column 'b'. If I need to do it for every column,what is the solution? I hope i cleared your doubt
– user10089194
Nov 10 at 20:54

Can you explain more? What is expected output?
– jezrael
Nov 10 at 20:56

print(lst2_df.loc[lst2_df['b'].isin(m)]) is hardcoded for specific column 'b'. Let's say I had a huge dataframe and I wish to print rows whose element matches the condition. print(lst2_df.loc[lst2_df['all column'].isin(m)])
– user10089194
Nov 10 at 21:02

1

Something like print(lst2_df.loc[lst2_df.isin(m).any(axis=1)]) ?
– jezrael
Nov 10 at 21:07

Sorry, what is expected output? Do you need print([lst2_df.loc[lst2_df[c].isin(m)] for c in lst2_df.columns]) ?
– jezrael
Nov 10 at 20:50

I need that it performs the operation for every column. I have hardcoded it for a column 'b'. If I need to do it for every column,what is the solution? I hope i cleared your doubt
– user10089194
Nov 10 at 20:54

Can you explain more? What is expected output?
– jezrael
Nov 10 at 20:56

print(lst2_df.loc[lst2_df['b'].isin(m)]) is hardcoded for specific column 'b'. Let's say I had a huge dataframe and I wish to print rows whose element matches the condition. print(lst2_df.loc[lst2_df['all column'].isin(m)])
– user10089194
Nov 10 at 21:02

Something like print(lst2_df.loc[lst2_df.isin(m).any(axis=1)]) ?
– jezrael
Nov 10 at 21:07

|
show 1 more comment

1 Answer
1

active

oldest

votes

up vote
1
down vote

accepted

Use:

m = ['1','f2']

print(lst2_df.loc[lst2_df.isin(m).any(axis=1)])

      A   b

d  5.36  f2

Explanation:

Compare DataFrame (all columns) by DataFrame.isin for boolean:

print (lst2_df.isin(m))

       A      b

p  False  False

d  False   True

And then add DataFrame.any for check at least one True per row:

print (lst2_df.isin(m).any(axis=1))

p    False

d     True

dtype: bool

And last filter by boolean indexing.

answered Nov 10 at 21:12

jezrael

308k20244319

just one doubt what difference will it make if I use print(lst2_df.loc[lst2_df.isin(m).all(axis=1)]) instead?
– user10089194
Nov 11 at 8:49

1

@user10089194 - It check if all Trues per rows, so filtering only rows with matched all values of m per rows.
– jezrael
Nov 11 at 8:51

1

@user10089194 - if use (lst2_df.isin(m) get boolean mask. So there is 2 possibility - check if any of value per row is True .any(axis=1) or check if all values per row is True by .all(axis=1). So if not all values matched then .all(axis=1) return False and output is empty df.
– jezrael
Nov 11 at 8:55

@user10089194 - try test it by m = ['1','f2', 5.36] with sample data
– jezrael
Nov 11 at 8:57

1

I did I understood. .any is like or. If any of the element matches it will print the entire rows however .all is and. All the element should match for it to print the row. That is why in my case it gave Empty dataframe as output. Thanks :)
– user10089194
Nov 11 at 9:03

|
show 2 more comments

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1 Answer
1

active

oldest

votes

1 Answer
1

active

oldest

votes

up vote
1
down vote

accepted

Use:

m = ['1','f2']

print(lst2_df.loc[lst2_df.isin(m).any(axis=1)])

      A   b

d  5.36  f2

Explanation:

Compare DataFrame (all columns) by DataFrame.isin for boolean:

print (lst2_df.isin(m))

       A      b

p  False  False

d  False   True

And then add DataFrame.any for check at least one True per row:

print (lst2_df.isin(m).any(axis=1))

p    False

d     True

dtype: bool

And last filter by boolean indexing.

answered Nov 10 at 21:12

jezrael

308k20244319

just one doubt what difference will it make if I use print(lst2_df.loc[lst2_df.isin(m).all(axis=1)]) instead?
– user10089194
Nov 11 at 8:49

1

@user10089194 - It check if all Trues per rows, so filtering only rows with matched all values of m per rows.
– jezrael
Nov 11 at 8:51

1

@user10089194 - if use (lst2_df.isin(m) get boolean mask. So there is 2 possibility - check if any of value per row is True .any(axis=1) or check if all values per row is True by .all(axis=1). So if not all values matched then .all(axis=1) return False and output is empty df.
– jezrael
Nov 11 at 8:55

@user10089194 - try test it by m = ['1','f2', 5.36] with sample data
– jezrael
Nov 11 at 8:57

1

I did I understood. .any is like or. If any of the element matches it will print the entire rows however .all is and. All the element should match for it to print the row. That is why in my case it gave Empty dataframe as output. Thanks :)
– user10089194
Nov 11 at 9:03

|
show 2 more comments

up vote
1
down vote

accepted

Use:

m = ['1','f2']

print(lst2_df.loc[lst2_df.isin(m).any(axis=1)])

      A   b

d  5.36  f2

Explanation:

Compare DataFrame (all columns) by DataFrame.isin for boolean:

print (lst2_df.isin(m))

       A      b

p  False  False

d  False   True

And then add DataFrame.any for check at least one True per row:

print (lst2_df.isin(m).any(axis=1))

p    False

d     True

dtype: bool

And last filter by boolean indexing.

answered Nov 10 at 21:12

jezrael

308k20244319

just one doubt what difference will it make if I use print(lst2_df.loc[lst2_df.isin(m).all(axis=1)]) instead?
– user10089194
Nov 11 at 8:49

1

@user10089194 - It check if all Trues per rows, so filtering only rows with matched all values of m per rows.
– jezrael
Nov 11 at 8:51

1

@user10089194 - if use (lst2_df.isin(m) get boolean mask. So there is 2 possibility - check if any of value per row is True .any(axis=1) or check if all values per row is True by .all(axis=1). So if not all values matched then .all(axis=1) return False and output is empty df.
– jezrael
Nov 11 at 8:55

@user10089194 - try test it by m = ['1','f2', 5.36] with sample data
– jezrael
Nov 11 at 8:57

1

I did I understood. .any is like or. If any of the element matches it will print the entire rows however .all is and. All the element should match for it to print the row. That is why in my case it gave Empty dataframe as output. Thanks :)
– user10089194
Nov 11 at 9:03

|
show 2 more comments

up vote
1
down vote

accepted

Use:

m = ['1','f2']

print(lst2_df.loc[lst2_df.isin(m).any(axis=1)])

      A   b

d  5.36  f2

Explanation:

Compare DataFrame (all columns) by DataFrame.isin for boolean:

print (lst2_df.isin(m))

       A      b

p  False  False

d  False   True

And then add DataFrame.any for check at least one True per row:

print (lst2_df.isin(m).any(axis=1))

p    False

d     True

dtype: bool

And last filter by boolean indexing.

answered Nov 10 at 21:12

jezrael

308k20244319

Use:

m = ['1','f2']

print(lst2_df.loc[lst2_df.isin(m).any(axis=1)])

      A   b

d  5.36  f2

Explanation:

Compare DataFrame (all columns) by DataFrame.isin for boolean:

print (lst2_df.isin(m))

       A      b

p  False  False

d  False   True

And then add DataFrame.any for check at least one True per row:

print (lst2_df.isin(m).any(axis=1))

p    False

d     True

dtype: bool

And last filter by boolean indexing.

answered Nov 10 at 21:12

jezrael

308k20244319

answered Nov 10 at 21:12

jezrael

308k20244319

answered Nov 10 at 21:12

jezrael

308k20244319

answered Nov 10 at 21:12

jezrael

308k20244319

just one doubt what difference will it make if I use print(lst2_df.loc[lst2_df.isin(m).all(axis=1)]) instead?
– user10089194
Nov 11 at 8:49

1

@user10089194 - It check if all Trues per rows, so filtering only rows with matched all values of m per rows.
– jezrael
Nov 11 at 8:51

1

@user10089194 - if use (lst2_df.isin(m) get boolean mask. So there is 2 possibility - check if any of value per row is True .any(axis=1) or check if all values per row is True by .all(axis=1). So if not all values matched then .all(axis=1) return False and output is empty df.
– jezrael
Nov 11 at 8:55

@user10089194 - try test it by m = ['1','f2', 5.36] with sample data
– jezrael
Nov 11 at 8:57

1

I did I understood. .any is like or. If any of the element matches it will print the entire rows however .all is and. All the element should match for it to print the row. That is why in my case it gave Empty dataframe as output. Thanks :)
– user10089194
Nov 11 at 9:03

|
show 2 more comments

just one doubt what difference will it make if I use print(lst2_df.loc[lst2_df.isin(m).all(axis=1)]) instead?
– user10089194
Nov 11 at 8:49

1

@user10089194 - It check if all Trues per rows, so filtering only rows with matched all values of m per rows.
– jezrael
Nov 11 at 8:51

1

@user10089194 - if use (lst2_df.isin(m) get boolean mask. So there is 2 possibility - check if any of value per row is True .any(axis=1) or check if all values per row is True by .all(axis=1). So if not all values matched then .all(axis=1) return False and output is empty df.
– jezrael
Nov 11 at 8:55

@user10089194 - try test it by m = ['1','f2', 5.36] with sample data
– jezrael
Nov 11 at 8:57

1

I did I understood. .any is like or. If any of the element matches it will print the entire rows however .all is and. All the element should match for it to print the row. That is why in my case it gave Empty dataframe as output. Thanks :)
– user10089194
Nov 11 at 9:03

just one doubt what difference will it make if I use print(lst2_df.loc[lst2_df.isin(m).all(axis=1)]) instead?
– user10089194
Nov 11 at 8:49

@user10089194 - It check if all Trues per rows, so filtering only rows with matched all values of m per rows.
– jezrael
Nov 11 at 8:51

@user10089194 - if use (lst2_df.isin(m) get boolean mask. So there is 2 possibility - check if any of value per row is True .any(axis=1) or check if all values per row is True by .all(axis=1). So if not all values matched then .all(axis=1) return False and output is empty df.
– jezrael
Nov 11 at 8:55

@user10089194 - try test it by m = ['1','f2', 5.36] with sample data
– jezrael
Nov 11 at 8:57

I did I understood. .any is like or. If any of the element matches it will print the entire rows however .all is and. All the element should match for it to print the row. That is why in my case it gave Empty dataframe as output. Thanks :)
– user10089194
Nov 11 at 9:03

|
show 2 more comments

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