Look Up And Replace Values In A List (pandas)

I have a list z = ['111111','222222','333333','4444444'] and I want to look up each item in z and replace it with the equivalent value in stored in my dataframe 'data', which has

Solution 1:

You can create a mapping using a dictionary comprehension:

d = {k: v for k, v inzip(df.old_ids, df.new_ids)}
z_new = [d.get(k, k) for k in z]

>>> z_new
['121212', '202020', '303030', '4444444']

Note that the d.get(k, k) defaults to the original value if no lookup value is found in df.old_ids.

Solution 2:

I would use Pandas power for that:

In [109]: z = ['111111','222222','333333','4444444']

In [110]: df = pd.DataFrame({'z': z})

In [111]: data
Out[111]:
  new_ids  old_ids
01212121111111202020222222230303033333334040404444444

In [112]: df
Out[112]:
         z
01111111222222233333334444444

In [118]: dict(data[['old_ids','new_ids']].values)
Out[118]:
{'111111': '121212',
 '222222': '202020',
 '333333': '303030',
 '4444444': '404040'}

In [119]: df.replace(dict(data[['old_ids','new_ids']].values), inplace=True)

In [120]: df
Out[120]:
        z
0121212120202023030303404040

Solution 3:

Something like this should work:

for index, i in enumerate(z):
    if i in old_numbers:
        z[index] = new_numbers[old_numbers.index(i)]

This won't work if you have duplicates in old_numbers, as index only returns the first match.