How to add new column with values based on another column in pandas

Question

Lets say I have the following df:

rs = np.random.RandomState(1979)
x = rs.randn(500)
g = np.tile(list("ABCDEFGHIJ"), 50)
df = pd.DataFrame(dict(x=x, g=g))
m = df.g.map(ord)
df["x"] += m

df

           x    g
0   64.038123   A
1   66.147050   B
2   66.370011   C
3   68.791019   D
4   70.583534   E
... ... ...
495 69.358022   F
496 72.212877   G
497 70.474247   H
498 73.251022   I
499 74.461828   J

I would Like to add a new column that consists of a number 1-10 that matches the letter of the alphabet, like so:

g new
A  1
B  2
C  3
D  4

The actual data I'm working with uses names instead of alphabet letters, but I obviously don't want to put those on here. But again, my goal is make a new column with a values I want for certain names.

Thanks!

Answer 1

It sounds like a dictionary that maps names to their respective ordered position you want in your graph is the way to go. Here's a simple example:

>>> import pandas as pd
>>> df = pd.DataFrame({"Name": ["Alice", "Bob", "Bob", "Alice", "Charlie"]})
>>> ordering = {"Alice": 1, "Bob": 3, "Charlie": 2}
>>> df["Ordering"] = df["Name"].map(ordering)
>>> df
      Name  Ordering
0    Alice         1
1      Bob         3
2      Bob         3
3    Alice         1
4  Charlie         2