Pandas Select DataFrame columns using boolean

Question

I want to use a boolean to select the columns with more than 4000 entries from a dataframe comb which has over 1,000 columns. This expression gives me a Boolean (True/False) result:

criteria = comb.ix[:,'c_0327':].count()>4000 

I want to use it to select only the True columns to a new Dataframe.
The following just gives me "Unalignable boolean Series key provided":

comb.loc[criteria,] 

I also tried:

comb.ix[:, comb.ix[:,'c_0327':].count()>4000]  

Similar to this question answer dataframe boolean selection along columns instead of row but that gives me the same error: "Unalignable boolean Series key provided"

comb.ix[:,'c_0327':].count()>4000 

yields:

c_0327    False c_0328    False c_0329    False c_0330    False c_0331    False c_0332    False c_0333    False c_0334    False c_0335    False c_0336    False c_0337     True c_0338    False ..... 

Answer 1

What is returned is a Series with the column names as the index and the boolean values as the row values.

I think actually you want:

this should now work:

comb[criteria.index[criteria]] 

Basically this uses the index values from criteria and the boolean values to mask them, this will return an array of column names, we can use this to select the columns of interest from the orig df.

Answer 2

In pandas 0.25:

comb.loc[:, criteria] 

Returns a DataFrame with columns selected by the Boolean list or Series.

For multiple criteria:

comb.loc[:, criteria1 & criteria2] 

And for selecting rows with an index criteria:

comb[criteria] 

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Note: The bit-wise operator & is required (not and). See Logical operators for boolean indexing in Pandas.

Other Note: If the criteria is an expression (e.g., comb.columnX > 3), and multiple criteria are used, remember to enclose each expression in parentheses! This is because &, | have higher precedence than >, ==, ect. (whereas and, or are lower precedence).