andbeonetraveler - 4 months ago 11x

Python Question

I have a square matrix as a dataframe in pandas. It should be symmetric, and nearly is, except for a few missing values that I filled with 0. I want to use the fact that it should be symmetric to fill the missing values, by taking the max of the absolute value over df.ix[x,y] and df.ix[y,x]. I.e.:

`df = pd.DataFrame({'b': {'b': 1, 'a': 0,'c':-1}, 'a': {'b': 1, 'a': 1,'c':0},'c':{'c':1,'a':0,'b':0}})`

>>> df

a b c

a 1 0 1

b 1 1 0

c 1 -1 1

should become:

`>>> df`

a b c

a 1 1 1

b 1 1 -1

c 1 -1 1

At first I thought of using a simple applymap with a function something like:

`def maxSymmetric(element):`

if abs(element) > df.T.ix[element.column,element.row]:

return element

else return df.T.ix[element.column,element.row]

But there doesn't seem to be a way to call the indices of an element within a function inside applymap (see related).

So then I tried making a multilevel dataframe of the original matrix and its transpose:

`pd.concat([df,df.T],axis=0,keys=['o','t'])`

a b c

o a 1 0 1

b 1 1 0

c 1 -1 1

t a 1 1 1

b 0 1 -1

c 1 0 1

Now I want to extract the correct (nonzero, if available) element from either 'o' or 't', for each element, using a similar function as above. But I'm not very experienced with multiindexing, and I can't figure out how to use applymap here, or if I should be using something else.

Suggestions?

Answer

I think you can first convert `df`

to `numpy array`

, use `numpy solution`

and last create `DataFrame`

with `constructor`

:

```
a = df.values
print (pd.DataFrame(data=a + a.T - np.diag(a.diagonal()),
columns=df.columns,
index=df.index))
a b c
a 1 1 2
b 1 1 -1
c 2 -1 1
```

EDIT by comment:

```
print (df + df.T - df[df==df.T].fillna(0))
a b c
a 1.0 1.0 1.0
b 1.0 1.0 -1.0
c 1.0 -1.0 1.0
```

Source (Stackoverflow)

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