I'm trying to replace my column with NaN
group_choices = ['Group1', 'Group2', 'Group3']
Groups limit
1 NaN NaN
2 Group1 2
3 Group2 2
4 Group3 2
5 NaN NaN
6 NaN NaN
7 NaN NaN
How can I replace NaN, randomly based on the group_choises?
I'm also trying to limit how often a group_choise can be randomised selected because of the limit value in the limit column.
I'm trying to get this result:
Groups limit
1 Group3 NaN
2 Group1 2
3 Group2 2
4 Group3 2
5 Group1 NaN
6 Group2 NaN
7 Out of groups
fillna with dictionariesdct = dict(zip(df.Groups.loc[pd.isna].index, group_choices))
df.fillna({'Groups': dct}).fillna({'Groups': 'Out of groups'})
Groups limit
1 Group1 NaN
2 Group1 2.0
3 Group2 2.0
4 Group3 2.0
5 Group2 NaN
6 Group3 NaN
7 Out of groups NaN
Useful but I like the new one better. It illustrates the evolution of my thought process.
def get_some(i, n):
for x in [*i] * n:
yield x
def fill(s, i, n):
gs = get_some(i, n)
for x in s:
if pd.isnull(x):
try:
yield next(gs)
except StopIteration:
yield "Out of groups"
else:
yield x
df.assign(Groups=[*fill(df.Groups, group_choices, 1)])
Groups limit
1 Group1 NaN
2 Group1 2.0
3 Group2 2.0
4 Group3 2.0
5 Group2 NaN
6 Group3 NaN
7 Out of groups NaN
Alternative
def get_some(i, n):
for x in [*i] * n:
yield x
df.assign(Groups=df.Groups.fillna(
df.Groups.loc[pd.isna].pipe(
lambda s: pd.Series(dict(zip(s.index, get_some(group_choices, 1))))
)
).fillna('Out of groups'))
