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Having issues filtering some 'name' values. I want to groupby month and name, after that filter a specific value from column name 'ar street3' and make a trend of that.

this is the code i wrote below:

df = pd.DataFrame( 
'name' : ['ar street3', 'ar street 3', 'ba foo', 'br', ' oo', 'ke'],
'month' : [1, 2, 3, 4, 5, 6],
'score' : [2.0, 5., 8., 1., 2., 9.])

fig, ax = plt.subplots(figsize=(11,7))
df.groupby(['month', 'name']).filter(lambda x: 'ar street3' in x,df).mean(['score']).unstack().plot(ax.ax)

I get this type of error:

ValueError: The truth value of a GeoDataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Can somebody point me to the right direction ?

Thanks in advance.

If you want to combine filter and groupby in one line of code:

df = pd.DataFrame('name' : ['ar street3', 'ar street3', 'ba foo', 'br', ' oo', 'ke'],
 'month' : [1, 2, 3, 4, 5, 6],
 'score' : [2.0, 5., 8., 1., 2., 9.])

tag = 'ar street3'
df = df.groupby(['month', 'name']).filter(lambda x: tag in x.name)

print(df)

output:

 name month score
0 ar street3 1 2.0
1 ar street3 2 5.0

if you want to add month condition:

tag = 'ar street3'; month = 1
df = df.groupby(['month', 'name']).filter(lambda x: tag in x.name and month == x.month)

 name month score
0 ar street3 1 2.0

if you want only month:

#i have adapted the sample
df = pd.DataFrame('name' : ['ar street3', 'ar street3', 'ba foo', 'br', ' oo', 'ke'],
 'month' : [1, 2, 3, 4, 5, 1],
 'score' : [2.0, 5., 8., 1., 2., 9.])

month = 1
df = df.groupby(['month', 'name']).filter(lambda x: month == x.month)

output:

 name month score
0 ar street3 1 2.0
5 ke 1 9.0