Why is first argument sometimes rows and sometimes columns?

Why is first argument sometimes rows and sometimes columns? - Printable Version

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Why is first argument sometimes rows and sometimes columns? - Mark17 - Sep-02-2020

I can do this:

# creating a value with all null values in new data frame 
new["Null Column"]= None

In this example, the first argument in brackets calls for a column.

I can also do this:

>>>df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
...  index=['cobra', 'viper', 'sidewinder'],
...  columns=['max_speed', 'shield'])

>>>df.loc['viper']
max_speed    4
shield       5
Name: viper, dtype: int64

In this example, the first argument in brackets references a row.

Is it accurate to say in more general terms that .loc and .iloc methods (and maybe others?) call for (row, column) whereas subsetting/slicing methods/syntax call for (column, row)?

If so, then is there a reason why it's not more consistent?

RE: Why is first argument sometimes rows and sometimes columns? - perfringo - Sep-03-2020

You probably read the documentation (as this example is from there) but somehow missed concept of 'label' i.e. row and column names (two first rows in documentation of pd.DataFrame.loc):.

Quote:Access a group of rows and columns by label(s) or a boolean array.

.loc[] is primarily label based, but may also be used with a boolean array.

These labels are positional, which means that you can skip columns:

In [18]: df.loc['viper']       # no column labels specified, all are selected implicitly
Out[18]:
max_speed    3
shield       4
Name: viper, dtype: int64

In [19]: df.loc['viper', :]    # all column labels selected explicitly with :
Out[19]:
max_speed    3
shield       4
Name: viper, dtype: int64

But not rows:

In [20]: df.loc[:, 'shield']   # all rows are explicitly selected
Out[20]:
cobra         2
viper         4
sidewinder    6
Name: shield, dtype: int64

In [21]: df.loc['shield']      # no row label 'shield' (first positional argument)
/...../
KeyError: 'shield'