CODEWITHSUNDEEP
pythonarrayspandasdataframe
asmgx
asmgx

Reputation: 7984

Filter pandas dataframe based on column list values

My dataframe has many columns. one of these columns is array

df
Out[191]: 
       10012005  10029008  10197000  ...  filename_int  filename      result
0           0.0       0.0       0.0  ...             1       1.0  [280, NON]
1           0.0       0.0       0.0  ...            10      10.0  [286, NON]
2           0.0       0.0       0.0  ...           100     100.0  [NON, 285]
3           0.0       0.0       0.0  ...         10000   10000.0  [NON, 286]
4           0.0       0.0       0.0  ...         10001   10001.0       [NON]
        ...       ...       ...  ...           ...       ...         ...
52708       0.0       0.0       0.0  ...          9995    9995.0       [NON]
52709       0.0       0.0       0.0  ...          9996    9996.0       [NON]
52710       0.0       0.0       0.0  ...          9997    9997.0  [285, NON]
52711       0.0       0.0       0.0  ...          9998    9998.0       [NON]
52712       0.0       0.0       0.0  ...          9999    9999.0       [NON]

[52713 rows x 4289 columns]

the column result is an array of these values

[NON]
[123,NON]
[357,938,837]
[455,NON,288]
[388,929,NON,020]

I want my filter dataframe to only display records that has values other than NON

therefore values such as

[NON,NON]
[NON]
[]

these will be excluded

only in the filer values like

[123,NON]
[357,938,837]
[455,NON,288]
[388,929,NON,020]

I tried this code 

df[len(df["result"])!="NON"]

but I get this error !!

  File "pandas\_libs\hashtable_class_helper.pxi", line 1614, in pandas._libs.hashtable.PyObjectHashTable.get_item

KeyError: True

how to filter my dataframe?

Upvotes: 2

Views: 387

Answers (3)

BENY
BENY

Reputation: 323226

I will do 

s=pd.DataFrame(df.B.tolist())
df=df[(s.ne('NON')&s.notnull()).any(1).to_numpy()].copy()
   A           B
0  1  [280, NON]
3  4       [285]

Upvotes: 1

Eric Truett
Eric Truett

Reputation: 3010

You can use apply to identify rows that meet your criteria. Here, the filter works because apply returns a boolean.

import pandas as pd
import numpy as np

vals = [280, 285, 286, 'NON', 'NON', 'NON']
listcol = [np.random.choice(vals, 3) for _ in range(100)] 
df = pd.DataFrame({'vals': listcol})

def is_non(l):
    return len([i for i in l if i != 'NON']) > 0

df.loc[df.vals.apply(is_non), :]

Upvotes: 1

cs95
cs95

Reputation: 402323

Try map with lambda here:

df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [[280, 'NON'], ['NON'], [], [285]] })
df

   A           B
0  1  [280, NON]
1  2       [NON]
2  3          []
3  4       [285]

df[df['B'].map(lambda x: any(y != 'NON' for y in x))]

   A           B
0  1  [280, NON]
3  4       [285]

The generator expression inside map returns True if there are at least 1 items in the list which are "NON".

Upvotes: 2

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