Python Pandas indexing provides KeyError: (slice(None, None, None), ) - python-3.x

Question

I am indexing and slicing my data using Pandas in Python3 to calculate spatial statistics.
When I am running a for loop over the range of latitude and longitude using .loc, gives an error KeyError: (slice(None, None, None), ) for the particular set of latitude and longitude for what no values are available in the input file. Instead of skipping those values, it gives an error and stops running the code. Following is my code.
import numpy as np
import pandas as pd
from scipy import stats
filename='input.txt'
df = pd.read_csv(filename,delim_whitespace=True, header=None, names = ['year','month','lat','lon','aod'], index_col = ['year','month','lat','lon'])
idx=pd.IndexSlice
for i in range (1, 13):
for lat0 in N.arange(0.,40.25,0.25,dtype=float):
for lon0 in N.arange(20.0,75.25,0.25,dtype=float):
tmp = df.loc[idx[:,i,lat0,lon0],:]
if (len(tmp) <= 0):
continue
tmp2 = tmp.index.tolist()
In the code above, if I run for tmp = df.loc[idx[:,1,0.0,34.0],:], it works well and provides the following output, which I used for the further calculation.
aod
year month lat lon
2003 1 0.0 34.0 0.032000
2006 1 0.0 34.0 0.114000
2007 1 0.0 34.0 0.035000
2008 1 0.0 34.0 0.026000
2011 1 0.0 34.0 0.097000
2012 1 0.0 34.0 0.106333
2013 1 0.0 34.0 0.081000
2014 1 0.0 34.0 0.038000
2015 1 0.0 34.0 0.278500
2016 1 0.0 34.0 0.033000
2017 1 0.0 34.0 0.036333
2019 1 0.0 34.0 0.064333
2020 1 0.0 34.0 0.109500
But, a same code I run for tmp = df.loc[idx[:,1,0.0,32.75],:], for the respective latitude and longitude no values available in the input file. Instead of skipping those, it gives me the following error:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3/dist-packages/pandas/core/indexing.py", line 925, in __getitem__
return self._getitem_tuple(key)
File "/usr/lib/python3/dist-packages/pandas/core/indexing.py", line 1100, in _getitem_tuple
return self._getitem_lowerdim(tup)
File "/usr/lib/python3/dist-packages/pandas/core/indexing.py", line 822, in _getitem_lowerdim
return self._getitem_nested_tuple(tup)
File "/usr/lib/python3/dist-packages/pandas/core/indexing.py", line 906, in _getitem_nested_tuple
obj = getattr(obj, self.name)._getitem_axis(key, axis=axis)
File "/usr/lib/python3/dist-packages/pandas/core/indexing.py", line 1157, in _getitem_axis
locs = labels.get_locs(key)
File "/usr/lib/python3/dist-packages/pandas/core/indexes/multi.py", line 3347, in get_locs
indexer = _update_indexer(
File "/usr/lib/python3/dist-packages/pandas/core/indexes/multi.py", line 3296, in _update_indexer
raise KeyError(key)
KeyError: (slice(None, None, None), 1, 0.0, 32.75)
I tried to replace .loc with .iloc, but it came out with a too many indexers error. However, I tried solutions from internet using .to_numpy(), .values and .as_matrix(), but nothing work.

Answer 1

But, a same code I run for tmp = df.loc[idx[:,1,0.0,32.75],:], for the respective latitude and longitude no values available in the input file. Instead of skipping those, it gives me the following error:
The idiomatic Pandas solution would be to write this as a groupby. Example:
# split df into groups by the keys month, lat, and lon
for index, tmp in df.groupby(['month','lat','lon']):
# tmp is a dataframe where all rows have identical month, lat, and lon values
# ... do something with the tmp dataframe ...
This has three benefits.
Speed. A groupby will be faster because it only needs to loop over the dataframe once, rather than searching the whole dataframe for everything matching the first group, then searching for the second group, etc.
Simplicity.
Robustness. From a robustness perspective, if a dataframe doesn't have, for example, any rows matching "month=1,lat=0.0,lon=32.75", then it will not create that group.
More information: User guide on grouping
Remark about groupby aggregation functions
You'll also sometimes see groupby used with aggregation functions. For example, suppose you wanted to get the sum of each column within each group.
>>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])
>>> df.groupby(by=["b"]).sum()
a c
b
1.0 2 3
2.0 2 5
These aggregation functions are faster and easier to use, but sometimes I need something that is custom and unusual, so I'll write a loop. But if you're doing something common, like getting the average of a group, consider looking for an aggregation function.