CODEWITHSUNDEEP
pythonpandasdataframemulti-index
June
June

Reputation: 345

Drop Non-equivalent Multiindex Rows in Pandas Dataframe

Goal

If sub-column min equals to sub-column max and if min and max sub-column do not equal to each other in any of the column (ao, his, cyp1a2s, cyp3a4s in this case), drop the row.

Example

arrays = [np.array(['ao', 'ao', 'hia', 'hia', 'cyp1a2s', 'cyp1a2s', 'cyp3a4s', 'cyp3a4s']),
          np.array(['min', 'max', 'min', 'max', 'min', 'max', 'min', 'max'])]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['',''])
df = pd.DataFrame(np.array([[1, 1, 0, 0, float('nan'), float('nan'), 0, 0], 
                            [1, 1, 0, 0, float('nan'), 1, 0, 0],
                            [0, 2, 0, 0, float('nan'), float('nan'), 1, 1],]), index=['1', '2', '3'], columns=index)
df

    ao      hia     cyp1a2s cyp3a4s
    min max min max min max min max
1   1.0 1.0 0.0 0.0 NaN NaN 0.0 0.0
2   1.0 1.0 0.0 0.0 NaN 1.0 0.0 0.0
3   0.0 2.0 0.0 0.0 NaN NaN 1.0 1.0

Want

df = pd.DataFrame(np.array([[1, 1, 0, 0, float('nan'), float('nan'), 0, 0]]), index=['1'], columns=index)
df

    ao      hia     cyp1a2s cyp3a4s
    min max min max min max min max
1   1.0 1.0 0.0 0.0 NaN NaN 0.0 0.0

Attempt

df.apply(lambda x: x['min'].map(str) == x['max'].map(str), axis=1)

KeyError: ('min', 'occurred at index 1')

Note

The actual dataframe has 50+ columns.

Upvotes: 3

Views: 167

Answers (2)

frankr6591
frankr6591

Reputation: 1247

The reason df.apply() didn't work is you needed to reference 2 levels of columns.

Also .map(str) was invalid for mapping from float64... used .astype(str)

The following work for >1 columns:

eqCols = ['cyp1a2s','hia']
neqCols = list(set(df.xs('min', level=1, axis=1).columns) - set(eqCols))
EQ = lambda r,c : r[c]['min'].astype(str) == r[c]['max'].astype(str)
df[df.apply(lambda r: ([EQ(r,c) for c in eqCols][0]) & ([(not EQ(r,c)) for c in neqCols][0]), axis=1)]

Upvotes: 1

jezrael
jezrael

Reputation: 862591

Use DataFrame.xs for DataFrame by second levels of MultiIndex, replace NaNs:

df1 = df.xs('min', axis=1, level=1).fillna('nan')
df2 = df.xs('max', axis=1, level=1).fillna('nan')

Or convert data to strings:

df1 = df.xs('min', axis=1, level=1).astype('str')
df2 = df.xs('max', axis=1, level=1).astype('str')

Compare Dataframes by DataFrame.eq and test if all Trues by DataFrame.all and last filter by boolean indexing:

df = df[df1.eq(df2).all(axis=1)]
print (df)
    ao       hia      cyp1a2s     cyp3a4s     
   min  max  min  max     min max     min  max
1  1.0  1.0  0.0  0.0     NaN NaN     0.0  0.0

Upvotes: 2

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