pandas.core.indexing.IndexingError: Too many indexers

Question

I want to extract electricity consumption for Site 2

>>> df4 = pd.read_excel(xls, 'Elec Monthly Cons')
>>> df4
     Site Unnamed: 1 2014-01-01 00:00:00 2014-02-01 00:00:00 2014-03-01 00:00:00         ...         2017-08-01 00:00:00 2017-09-01 00:00:00 2017-10-01 00:00:00 2017-11-01 00:00:00 2017-12-01 00:00:00
0    Site    Profile            JAN 2014            FEB 2014            MAR 2014         ...                    AUG 2017            SEP 2017            OCT 2017            NOV 2017            DEC 2017
1  Site 1        NHH               10344                 NaN                 NaN         ...                         NaN                 NaN                 NaN                 NaN                 NaN
2  Site 2         HH              258351              229513              239379         ...                         NaN                 NaN                 NaN                 NaN                 NaN

type

type(df4)
<class 'pandas.core.frame.DataFrame'>

My goal is to take out the numerical value but I do not know how to set the index properly. What I have tried so far does not work at all.

df1 = df.loc[idx[:,1:2],:]

But

    raise IndexingError('Too many indexers')
pandas.core.indexing.IndexingError: Too many indexers

It seems that I do not understand indexing. Does the series type play any role?

df.head
<bound method NDFrame.head of Site                   Site 2
Unnamed: 1                 HH

EDIT

print (df.index)
Index([             'Site',        'Unnamed: 1', 2014-01-01 00:00:00,
       2014-02-01 00:00:00, 2014-03-01 00:00:00, 2014-04-01 00:00:00,
       2014-05-01 00:00:00, 2014-06-01 00:00:00, 2014-07-01 00:00:00,

How to solve this?

Asked By: MikiBelavista

||

Source

Answer 1

In my opinion is necessary remove :, because it means select all columns, but Series have no column.

Also it seems no MultiIndex, so then need:

df1 = df.iloc[1:2]

There is problem first 2 rows are headers, so for MultiIndex DataFrame need:

df4 = pd.read_excel(xls, 'Elec Monthly Cons', header=[0,1], index_col=[0,1])

And then for select use:

idx = pd.IndexSlice
df1 = df.loc[:, idx[:,'FEB 2014':'MAR 2014']]

Answered By: jezrael

Answer 2

I got this error while using pd.apply function wrongly (using the axis option), which returns one Pandas series per row (Pandas series has no columns)

Example

#Before apply 
print(df.iloc[:,1]) # ok
df = df.apply(lambda row :(tokenizer(row[0]).input_ids,tokenizer(row[1]).input_ids), axis=1 )
print(df.iloc[:,1]) # NOT OK, throws pandas.core.indexing.IndexingError: Too many indexers

You can use iloc for selecting a particular row of the data frame and get the nth element of the series with [] notation.
But you cannot do something like df.iloc[:,1], that is select all rows but containing only values of the second column.

Answered By: Alex Punnen

pandas.core.indexing.IndexingError: Too many indexers

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