How to count unique values in one colulmn based on value in another column by group in Pandas - python-3.x

Question

I'm trying to count unique values in one column only when the value meets a certain condition based on another column. For example, the data looks like this:
GroupID ID Value
ABC TX123 0
ABC TX678 1
ABC TX678 2
DEF AG123 1
DEF AG123 1
DEF AG123 1
GHI TE203 0
GHI TE203 0
I want to count the number of unique ID by Group ID but only when the value column >0. When all values for a group ID =0, it will simply have 0. For example, the result dataset would look like this:
GroupID UniqueNum
ABC 1
DEF 1
GHI 0
I've tried the following but it simply returns the uique number of IDs regardless of its value. How do I add the condition of when value >0?
count_df = df.groupby(['GroupID'])['ID'].nunique()

Answer 1

positive counts only
You can use pre-filtering with loc and named aggregation with groupby.agg('nunique'):
(df.loc[df['Value'].gt(0), 'ID']
.groupby(df['GroupID'])
.agg(UniqueNum='nunique')
.reset_index()
)
Output:
GroupID UniqueNum
0 ABC 1
1 DEF 1
all counts (including zero)
If you want to count as zero, the groups with no match, you can reindex:
(df.loc[df['Value'].gt(0), 'ID']
.groupby(df['GroupID'])
.agg(UniqueNum='nunique')
.reindex(df['GroupID'].unique(), fill_value=0)
.reset_index()
)
Or mask:
(df['ID'].where(df['Value'].gt(0))
.groupby(df['GroupID'])
.agg(UniqueNum='nunique')
.reset_index()
)
Output:
GroupID UniqueNum
0 ABC 1
1 DEF 1
2 GHI 0
Used input:
GroupID ID Value
ABC TX123 0
ABC TX678 1
ABC TX678 2
DEF AG123 1
DEF AG123 1
DEF AG123 1
GHI AB123 0

Answer 2

If need 0 for non matched values use Series.where for NaNs for non matched condition, then aggregate by DataFrameGroupBy.nunique:
df = pd.DataFrame({ 'GroupID': ['ABC', 'ABC', 'ABC', 'DEF', 'DEF', 'NEW'],
'ID': ['TX123', 'TX678', 'TX678', 'AG123', 'AG123', 'AG123'],
'Value': [0, 1, 2, 1, 1, 0]})
df = (df['ID'].where(df["Value"].gt(0)).groupby(df['GroupID'])
.nunique()
.reset_index(name='nunique'))
print (df)
GroupID nunique
0 ABC 1
1 DEF 1
2 NEW 0
How it working:
print (df.assign(new=df['ID'].where(df["Value"].gt(0))))
GroupID ID Value new
0 ABC TX123 0 NaN
1 ABC TX678 1 TX678
2 ABC TX678 2 TX678
3 DEF AG123 1 AG123
4 DEF AG123 1 AG123
5 NEW AG123 0 NaN <- set NaN for non matched condition