New DataFrame column using the key of a dictionary as row value when one of it's values is found in a given row

Question

I have a Pandas DataFrame with a large number of unique values. I would like to group these values with a more general column. By doing so I expect to add hierarchies to my data and thus make analysis easier.

One thing that worked was to copy the column and replaced the values as follows:

data.loc[data['new_col'].str.contains('string0|string1'), 'new_col']\
         = 'substitution'

However, I am trying to find a way to reproduce this easily without adding a condition for each entry.

Also tried using without success using the following methods:

• dict.items()
• pd.df.replace() Those attempts were futile for me.

I would like to hear your advice to know how to approach this.

import pandas as pd
# My DataFrame looks similar to this:
>>> df = pd.DataFrame({'A': ['a', 'w', 'c', 'd', 'z']})

# The dictionary were I store the generalization:
>>> subs = {'g1': ['a', 'b', 'c', 'd'],
...         'g2': ['w', 'x', 'y', 'z']}

>>> df
   A  H
0  a  g1
1  w  g2
2  c  g1
3  d  g1
4  z  g2

Answer 1

create a new dict by swapping key with values of list. Next, map df.A with the swapped dict.

swap_dict = {x: k for k, v in d.items() for x in v}

Out[1054]:
{'a': 's1',
 'b': 's1',
 'c': 's1',
 'd': 's1',
 'w': 's2',
 'x': 's2',
 'y': 's2',
 'z': 's2'}

df['H'] = df.A.map(swap_dict)

Out[1058]:
   A   H
0  a  s1
1  w  s2
2  c  s1
3  d  s1
4  z  s2

---

Note : I directly use keys of your dict as values of H instead of g1 , g2 ,.... because I think it is enough to identify each group of values. If you still want g1 , g2 ,..., it is easy to accomplish. Just let me know.
I also named your dict as d in my code