Python pandas -> select by condition in columns name

Question

I have df with column names: 'a', 'b', 'c' ... 'z'.

print(my_df.columns)
Index(['a', 'b', 'c', ... 'y', 'z'],
  dtype='object', name=0)

I have function which determine which columns should be displayed. For example:

start = con_start()
stop = con_stop()
print(my_df.columns >= start) & (my_df <= stop)

My result is:

[False False ... False False False False  True  True
True  True False False]

My goal is display dataframe only with columns that satisfy my condition. If start = 'a' and stop = 'b', I want to have:

0                                      a              b         
index1       index2                                                  
New York     New York           0.000000       0.000000          
California   Los Angeles   207066.666667  214466.666667     
Illinois     Chicago       138400.000000  143633.333333     
Pennsylvania Philadelphia   53000.000000   53633.333333      
Arizona      Phoenix       111833.333333  114366.666667 

Answer 1

You can use slicing to achieve this with .loc:

 df.loc[:,'a':'b']

Answer 2

I want to make this robust and with as few assumptions as possible.

option 1
use iloc with array slicing
Assumptions:

• my_df.columns.is_unique evaluates to True
• columns are already in order

---

start = df.columns.get_loc(con_start())
stop = df.columns.get_loc(con_stop())

df.iloc[:, start:stop + 1]

option 2
use loc with boolean slicing
Assumptions:

• column values are comparable

---

start = con_start()
stop = con_stop()

c = df.columns.values
m = (start <= c) & (stop >= c)

df.loc[:, m]