multiple-columns

I get how to use pd.MultiIndex.from_tuples() in order to change something like Value (A,a) 1 (B,a) 2 (B,b) 3 into Value Caps Lower A a 1 B a 2 B b 3 But how do I change column tuples in the form (A, a) (A, b) (B,a) (B,b) index 1 1 2 2 3 2 2 3 3 2 3 3 4 4 1 into the form Caps A B Lower a b a b index 1 1 2 2 3 2 2 3 3 2 3 3 4 4 1 Many thanks. Edit: The reason I have a tuple column header is that when I joined a DataFrame with a single level column onto a DataFrame with a Multi-Level column it turned the Multi-Column into a tuple of strings format and left the single level as single string. Edit

Suppose I've data similar to following: index id name value value2 value3 data1 val5 0 345 name1 1 99 23 3 66 1 12 name2 1 99 23 2 66 5 2 name6 1 99 23 7 66 How can we drop all those columns like ( value , value2 , value3 ) where all rows have same values, in one command or couple of commands using python ? Consider we have many columns similar to value , value2 , value3 ... value200 . Output: index id name data1 0 345 name1 3 1 12 name2 2 5 2 name6 7 EdChum What we can do is apply nunique to calc the number of unique values in the df and drop the columns which only have a single unique value:

Using the following data: $cat date1.csv Bob,2013-06-03T17:18:07 James,2013-06-03T17:18:07 Kevin,2013-06-03T17:18:07 $cat date2.csv 2012-12-02T18:30:31 2012-12-02T18:28:37 2013-06-01T12:16:05 How can date1.csv and date2.csv files be merged? Output desired: $cat merge-date1-date2.csv Bob,2013-06-03T17:18:07,2012-12-02T18:30:31 James,2013-06-03T17:18:07,2012-12-02T18:28:37 Kevin,2013-06-03T17:18:07,2013-06-01T12:16:05 Please note: the best solution will be able to quickly manage a massive number of lines. You were on track with paste(1) : $ paste -d , date1.csv date2.csv Bob,2013-06-03T17:18:07

I feel like this question must have been answered by someone before, but I can't find an answer on stack overflow! I have a dataframe result that looks like this and I want to remove all the values less than or equal to 10 >>> result Name Value Date 189 Sall 19.0 11/14/15 191 Sam 10.0 11/14/15 192 Richard 21.0 11/14/15 193 Ingrid 4.0 11/14/15 This command works and removes all the values that are 10: df2 = result[result['Value'] != 10] But when I try to add the <= qualifier I get the error message SyntaxError: invalid syntax df3 = result[result['Value'] ! <= 10] I feel like there is probably a