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Assuming your column names (df.columns) are ['index','a','b','c'], then the data you want is in the third and fourth columns. If you don't know their names when your script runs, you can do this

newdf = df[df.columns[2:4]] # Remember, Python is 0-offset! The "third" entry is at slot two.

As EMS points out in his answer, df.ix slices columns a bit more concisely, but the .columns slicing interface might be more natural, because it uses the vanilla one-dimensional Python list indexing/slicing syntax.

Warning: 'index' is a bad name for a DataFrame column. That same label is also used for the real df.index attribute, an Index array. So your column is returned by df['index'] and the real DataFrame index is returned by df.index. An Index is a special kind of Series optimized for lookup of its elements' values. For df.index it's for looking up rows by their label. That df.columns attribute is also a pd.Index array, for looking up columns by their labels.

Assuming your column names (df.columns) are ['index','a','b','c'], then the data you want is in the third and fourth columns. If you don't know their names when your script runs, you can do this

newdf = df[df.columns[2:4]] # Remember, Python is zero-offset! The "third" entry is at slot two.

As EMS points out in his answer, df.ix slices columns a bit more concisely, but the .columns slicing interface might be more natural, because it uses the vanilla one-dimensional Python list indexing/slicing syntax.

Warning: 'index' is a bad name for a DataFrame column. That same label is also used for the real df.index attribute, an Index array. So your column is returned by df['index'] and the real DataFrame index is returned by df.index. An Index is a special kind of Series optimized for lookup of its elements' values. For df.index it's for looking up rows by their label. That df.columns attribute is also a pd.Index array, for looking up columns by their labels.

Assuming your column names (df.columns) are ['index','a','b','c'], then the data you want is in the 3rd & 4th columns. If you don't know their names when your script runs, you can do this

newdf = df[df.columns[2:4]] # Remember, Python is 0-offset! The "3rd" entry is at slot 2.

As EMS points out in his answer, df.ix slices columns a bit more concisely, but the .columns slicing interface might be more natural because it uses the vanilla 1-D python list indexing/slicing syntax.

WARN: 'index' is a bad name for a DataFrame column. That same label is also used for the real df.index attribute, a Index array. So your column is returned by df['index'] and the real DataFrame index is returned by df.index. An Index is a special kind of Series optimized for lookup of it's elements' values. For df.index it's for looking up rows by their label. That df.columns attribute is also a pd.Index array, for looking up columns by their labels.

Assuming your column names (df.columns) are ['index','a','b','c'], then the data you want is in the third and fourth columns. If you don't know their names when your script runs, you can do this

newdf = df[df.columns[2:4]] # Remember, Python is 0-offset! The "third" entry is at slot two.

As EMS points out in his answer, df.ix slices columns a bit more concisely, but the .columns slicing interface might be more natural, because it uses the vanilla one-dimensional Python list indexing/slicing syntax.

Warning: 'index' is a bad name for a DataFrame column. That same label is also used for the real df.index attribute, an Index array. So your column is returned by df['index'] and the real DataFrame index is returned by df.index. An Index is a special kind of Series optimized for lookup of its elements' values. For df.index it's for looking up rows by their label. That df.columns attribute is also a pd.Index array, for looking up columns by their labels.

Assuming your column names (df.columns) are ['index','a','b','c'], then the data you want is in the 3rd & 4th columns. If you don't know their names when your script runs, you can do this

newdf = df[df.columns[2:4]] # Remember, Python is 0-offset! The "3rd" entry is at slot 2.

As EMS points out in his answer, df.ix slices columns a bit more concisely, but the .columns slicing interface might be more natural because it uses the vanilla 1-D python list indexing/slicing syntax.

WARN: 'index' is a bad name for a DataFrame column. That same label is also used for the real df.index attribute, a Index array. So your column is returned by df['index'] and the real DataFrame index is returned by df.index. An Index is a special kind of Series optimized for lookup of it's elements' values. For df.index it's for looking up rows by their label. That df.columns attribute is also a pd.Index array, for looking up columns by their labels.

Assuming your column names (df.columns) are ['index','a','b','c'], then the data you want is in the 3rd & 4th columns. If you don't know their names when your script runs, you can do this

newdf = df[df.columns[2:4]] # Remember, Python is 0-offset! The "3rd" entry is at slot 2.

As EMS points out in his answer, df.ix slices columns a bit more concisely, but the .columns slicing interface might be more natural because it uses the vanilla 1-D python list indexing/slicing syntax.

WARN: 'index' is a bad name for a DataFrame column. That same label is also used for the real df.index attribute, a Index array. So your column is returned by df['index'] and the real DataFrame index is returned by df.index. An Index is a special kind of Series optimized for lookup of it's elements' values. For df.index it's for looking up rows by their label. That df.columns attribute is also a pd.Index array, for looking up columns by their labels.