Question

If read a file with default column names, how do call them after?
df[1] seems to work almost all of the time. However, it complains about types when writing conditions like:

In [60]: cond = ((df[1] != node) & (df[2] != deco))
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/home/ferreirafm/work/colab/SNP/rawdata/<ipython-input-60-513a433bfeb5> in <module>()
----> 1 cond = ((df[1] != node) & (df[2] != deco))

/usr/lib64/python2.7/site-packages/pandas/core/series.pyc in wrapper(self, other)
140             if np.isscalar(res):
141                 raise TypeError('Could not compare %s type with Series'
--> 142                                 % type(other))
143             return Series(na_op(values, other),
144                           index=self.index, name=self.name)

TypeError: Could not compare <type 'str'> type with Series

Treat dataframe columns by default names are more appropriate for my applications.

Answer 1

It seems that you compare a series of scalar values to a string:

In [73]: node = 'a'

In [74]: deco = 'b'

In [75]: data = [(10, 'a', 1), (11, 'b', 2), (12, 'c', 3)]

In [76]: df = pd.DataFrame(data)

In [77]: df
Out[77]: 
    0  1  2
0  10  a  1
1  11  b  2
2  12  c  3

In [78]: cond = ((df[1] != node) & (df[2] != deco))
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-78-0afad3702859> in <module>()
----> 1 cond = ((df[1] != node) & (df[2] != deco))

/home/.../python2.7/site-packages/pandas/core/series.pyc in wrapper(self, other)
    140             if np.isscalar(res):
    141                 raise TypeError('Could not compare %s type with Series'
--> 142                                 % type(other))
    143             return Series(na_op(values, other),
    144                           index=self.index, name=self.name)

TypeError: Could not compare <type 'str'> type with Series

Note that pandas can handle strings and numbers in a series, but it not really makes sense to compare strings and numbers, so the error message is useful.
However pandas should perhaps give a more detailed error message.

If your condition for the column 2 would be a number it would work:

In [79]: deco = 3

In [80]: cond = ((df[1] != node) & (df[2] != deco))

In [81]: df[cond]
Out[81]: 
    0  1  2
1  11  b  2

Some comments:

Maybe some of your confusion is due to a design decision in pandas:

If you read data from a file with read_csv the default column names of the resulting data frame are set to X.1 to X.N (and to X1 to XN for versions >= 0.9), which are strings.

If you create a data frame from exiting arrays or lists or something the column names default to 0 to N and are integers.

In [23]: df = pd.read_csv(StringIO(data), header=None)

In [24]: df.columns
Out[24]: Index([X.1, X.2, X.3], dtype=object)

In [25]: df.columns[0]
Out[25]: 'X.1'

In [26]: type(df.columns[0])
Out[26]: str

In [27]: df = pd.DataFrame(randn(2,3))

In [30]: df.columns
Out[30]: Int64Index([0, 1, 2])

In [31]: df.columns[0]
Out[31]: 0

In [32]: type(df.columns[0])
Out[32]: numpy.int64

I opened a ticket to discuss this.

So your

In [60]: cond = ((df[1] != node) & (df[2] != deco))

should work for a dataframe created from an array or something, if the type of df[1] and df[2] is the same as the type of node and deco.

If you have read a file with read_csv than

In [60]: cond = ((df['X.2'] != node) & (df['X.3'] != deco))

should work with versions < 0.9, while it should be

In [60]: cond = ((df['X2'] != node) & (df['X3'] != deco))

with versions >= 0.9.