You can use the assign function:

df = df.assign(industry='yyy')

Rename Specific Columns

Use the df.rename() function and refer the columns to be renamed. Not all the columns have to be renamed:

df = df.rename(columns={'oldName1': 'newName1', 'oldName2': 'newName2'})

# Or rename the existing DataFrame (rather than creating a copy) 
df.rename(columns={'oldName1': 'newName1', 'oldName2': 'newName2'}, inplace=True)

Minimal Code Example

df = pd.DataFrame('x', index=range(3), columns=list('abcde'))
df

   a  b  c  d  e
0  x  x  x  x  x
1  x  x  x  x  x
2  x  x  x  x  x

The following methods all work and produce the same output:

df2 = df.rename({'a': 'X', 'b': 'Y'}, axis=1)
df2 = df.rename({'a': 'X', 'b': 'Y'}, axis='columns')
df2 = df.rename(columns={'a': 'X', 'b': 'Y'}) 

df2

   X  Y  c  d  e
0  x  x  x  x  x
1  x  x  x  x  x
2  x  x  x  x  x

Remember to assign the result back, as the modification is not-inplace. Alternatively, specify inplace=True:

df.rename({'a': 'X', 'b': 'Y'}, axis=1, inplace=True)
df

   X  Y  c  d  e
0  x  x  x  x  x
1  x  x  x  x  x
2  x  x  x  x  x
 

You can specify errors='raise' to raise errors if an invalid column-to-rename is specified.

Reassign Column Headers

Use df.set_axis() with axis=1.

df2 = df.set_axis(['V', 'W', 'X', 'Y', 'Z'], axis=1)
df2

   V  W  X  Y  Z
0  x  x  x  x  x
1  x  x  x  x  x
2  x  x  x  x  x

Headers can be assigned directly:

df.columns = ['V', 'W', 'X', 'Y', 'Z']
df

   V  W  X  Y  Z
0  x  x  x  x  x
1  x  x  x  x  x
2  x  x  x  x  x

If you only need to get list of unique values, you can just use unique method. If you want to have Python's set, then do set(some_series)

In [1]: s = pd.Series([1, 2, 3, 1, 1, 4])

In [2]: s.unique()
Out[2]: array([1, 2, 3, 4])

In [3]: set(s)
Out[3]: {1, 2, 3, 4}

However, if you have DataFrame, just select series out of it ( some_data_frame['<col_name>'] ).

df.reset_index().melt(id_vars='index').drop('variable',1)

Output:

       index   value
0    Saturday  2540.0
1      Sunday  1313.0
2      Monday  1360.0
3     Tuesday  1089.0
4   Wednesday  1329.0
5    Thursday   798.0
6    Saturday  2441.0
7      Sunday  1891.0
8      Monday  1558.0
9     Tuesday  2105.0
10  Wednesday  1658.0
11   Thursday  1195.0
12   Saturday  3832.0
13     Sunday  2968.0
14     Monday  2967.0
15    Tuesday  2476.0
16  Wednesday  2073.0
17   Thursday  2183.0
18   Saturday  4093.0
19     Sunday  2260.0
20     Monday  2156.0
21    Tuesday  1577.0
22  Wednesday  2403.0
23   Thursday  1287.0
24   Saturday  1455.0
25     Sunday  1454.0
26     Monday  1564.0
27    Tuesday  1744.0
28  Wednesday  1231.0
29   Thursday  1460.0
30   Saturday  2552.0
31     Sunday  1798.0
32     Monday  1752.0
33    Tuesday  1457.0
34  Wednesday   874.0
35   Thursday  1269.0

Note: just noted a commented suggesting to do the same thing, I will delete my post if requested :)

I got this to work, but I had to back up a few steps in order to get a code working. Things are still pretty clunky, for example I am not using the variable 'set' within the forloop. Recommendations to get something more elegant working would be appreciated, thanks!

from multiprocessing.resource_sharer import stop
import pandas as pd
import numpy as np

'''Create df'''
boxes = {'Color': ['Red','Orange','Yellow','Green','Red','Orange','Yellow','Green','Red','Orange','Yellow','Green'],
         'Shape': ['Square','Square','Square','Rectangle','Rectangle','Rectangle','Square','Rectangle','Circle','Circle','Circle','Circle'],
        'Length': [15,25,25,15,15,15,20,25,26,26,23,29],
         'Width': [8,5,5,4,8,8,5,4,2,2,3,5,],
        'Height': [30,35,35,40,30,35,40,40,36,35,39,46]}

df = pd.DataFrame(boxes, columns = ['Color','Shape','Length','Width','Height'])
df

'''Create new dataframe, populate it with data w/ columns relabeled'''
renamed_data = pd.DataFrame(columns = ['Color'])
a = df.iloc[0,0]
repeats = df['Color'].value_counts()[a]
i=1
for set in df:
    if i <= repeats:
        d = df.duplicated(subset=['Color'], keep = 'first')
        time = (df.iloc[0,2])
        df1 = df[d]
        df = df[~d]
        df = df.set_index('Color')
        df = df.add_prefix('_')
        df = df.add_prefix(time)
        df.reset_index(inplace=True)
        renamed_data = renamed_data.merge(df, on = 'Color', how='outer')
        df = df1
        i=i+1
    else:
        stop
renamed_data