return default if pandas dataframe.loc location doesn't exist

Question

I find myself often having to check whether a column or row exists in a dataframe before trying to reference it. For example I end up adding a lot of code like:

if 'mycol' in df.columns and 'myindex' in df.index: x = df.loc[myindex, mycol]
else: x = mydefault

Is there any way to do this more nicely? For example on an arbitrary object I can do x = getattr(anobject, 'id', default) – is there anything similar to this in pandas? Really any way to achieve what I’m doing more gracefully?

Asked By: fantabolous

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Source

Answer 1

Python has this mentality to ask for forgiveness instead of permission. You’ll find a lot of posts on this matter, such as this one.

In Python catching exceptions is relatively inexpensive, so you’re encouraged to use it. This is called the EAFP approach.

For example:

try:
    x = df.loc['myindex', 'mycol']
except KeyError:
    x = mydefault

Answered By: FooBar

Answer 2

There is a method for Series:

So you could do:

df.mycol.get(myIndex, NaN)

Example:

In [117]:

df = pd.DataFrame({'mycol':arange(5), 'dummy':arange(5)})
df
Out[117]:
   dummy  mycol
0      0      0
1      1      1
2      2      2
3      3      3
4      4      4

[5 rows x 2 columns]
In [118]:

print(df.mycol.get(2, NaN))
print(df.mycol.get(5, NaN))
2
nan

Answered By: EdChum

Answer 3

Use reindex:

df.reindex(index=['myindex'], columns=['mycol'], fill_value=mydefault)

What’s great here is using lists for the index and columns, where some of them exist and some of them don’t, and you get the fallback value whenever either the index or column is missing.

Example:

In[1]:
df = pd.DataFrame({ 
 'A':[1, 2, 3],
 'B':[5, 3, 7],
})
df

Out[1]:
    A   B
0   1   5
1   2   3
2   3   7

In[2]:
df.reindex(index=[0, 1, 100], columns=['A', 'C'], fill_value='FV')

Out[2]:
    A   C
0   1   FV
1   2   FV
100 FV  FV

Answered By: Joe

return default if pandas dataframe.loc location doesn't exist

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