I'm trying to read this .txt file in pandas and this is my result. I thought (naively) that I was getting a hang of this stuff last night, but I'm wrong apparently. If I simply run
rebull = pd.read_table('rebull.txt',sep=' ')
it works, but it gives my result with a disordered array of NaN's I assume from the separations in the initial .txt RESULT
Try skipinitialspace:
In [26]: pd.read_table('test.txt', sep=' ', skipinitialspace=True)
Out[26]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 386 entries, 0 to 385
Data columns (total 7 columns):
Mon 386 non-null values
id 386 non-null values
NA 386 non-null values
alpha_K24 386 non-null values
class 386 non-null values
alpha_K8 386 non-null values
class.1 0 non-null values
dtypes: float64(3), object(4)
Sorry for misunderstanding your problem. I think you can read the table as @DSM mentioned and also set the column names
In [55]: pd.read_table('test.txt', sep=r"\s\s+", header=None, skiprows=[0], names=['Mon id', 'Na', 'alpha_K24', 'class', 'alpha_8', 'class'])
Out[55]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 386 entries, 0 to 385
Data columns (total 6 columns):
Mon id 386 non-null values
Na 386 non-null values
alpha_K24 386 non-null values
class 386 non-null values
alpha_8 386 non-null values
class 386 non-null values
dtypes: float64(2), object(4)
Note that you might set your second class as another name. Or you'll get two columns by df['class']
r"\s+" here. The \s\s+ was a hypothetical which would have worked to keep "Mon id" together if everything (columns included) were separated by two or more spaces, which unf. isn't the case. Here it works okay on the data itself, but it doesn't offer any advantage because we don't have to let \s itself pass.Figured out my problem...always confirm your indices are joined by hyphens if necessary. In particular my 'Mon id' in the first column was my problem...should be 'Mon-id'.
pd.read_table("gistfile1.txt", sep=r"\s+", skiprows=1, header=None) and fix the columns after the fact.
sep=r"\s\s+" (i.e. only separate on one or more). You just got unlucky. :^)