CODEWITHSUNDEEP
pythonpandasnan
Simon Chemnitz-Thomsen
Simon Chemnitz-Thomsen

Reputation: 126

Python get coordinate with pair of nans

I have a dataframe like this 

>>df1 = pd.DataFrame({
          'A': ['1', '2', '3', '4', '5'],
          'B': ['1', '1', '1', '1', '1'],
          'C': ['c', 'A1', NaN, 'c3', Nan],
          'D': ['d0', 'B1', 'B2', Nan, 'B4'],
          'E': ['A', Nan, 'S', Nan, 'S'],
          'F': ['3', '4', '5', '6', '7'],
          'G': ['2', '2', NaN, '2', '2']
        })
>>df1

    A   B     C     D     E   F     G
0   1   1     c    d0     A   3     2
1   2   1    A1    B1   NaN   4     2
2   3   1   NaN    B2     S   5   NaN
3   4   1    c3   NaN   NaN   6     2
4   5   1   NaN    B4     S   7     2

and I would like to get the coordinates of all nans. that is the output should be:

[[1,"E"], [2,"C"] , [2,"G"] , [3,"D"] ,[3,"E"] , [4,"C"] ]

All other questions i looked at just want the column name and not the pairs.

Is there any efficient way to solve this problem? Thank you

Upvotes: 3

Views: 807

Answers (3)

Dani Mesejo
Dani Mesejo

Reputation: 61920

You could use np.argwhere with pd.isna, like this:

result = [[r, df1.columns[c]] for r, c in np.argwhere(pd.isna(df1).values)]
print(result)

Output

[[1, 'E'], [2, 'C'], [2, 'G'], [3, 'D'], [3, 'E'], [4, 'C']]

Upvotes: 1

Mohit Motwani
Mohit Motwani

Reputation: 4792

Try using np.where:

df = pd.DataFrame({'A': ['1', '2', '3', '4','5'],
          'B': ['1', '1', '1', '1','1'],
          'C': ['c', 'A1', np.nan, 'c3',np.nan],
          'D': ['d0', 'B1', 'B2', np.nan,'B4'],
          'E': ['A', np.nan, 'S', np.nan,'S'],
          'F': ['3', '4', '5', '6','7'],
          'G': ['2', '2', np.nan, '2','2']})

arr = np.where(df.isna())
arr
(array([1, 2, 2, 3, 3, 4], dtype=int64),
 array([4, 2, 6, 3, 4, 2], dtype=int64))

np.where returns the indices where the given condition is True, here where df is null.

[(x, df.columns[y]) for x, y in zip(arr[0], arr[1])]

[(1, 'E'), (2, 'C'), (2, 'G'), (3, 'D'), (3, 'E'), (4, 'C')]

Upvotes: 1

jezrael
jezrael

Reputation: 863291

Use stack with filter index values by missing values:

s = df1.stack(dropna=False)
L = [list(x) for x in s.index[s.isna()]]
print (L)
[[1, 'E'], [2, 'C'], [2, 'G'], [3, 'D'], [3, 'E'], [4, 'C']]

Upvotes: 5

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