Selecting values from lists to be put in graphs

[v for v in aList if v in ['d', 'e', 'f']]

[v for v in aList if v in 'def']

BEGIN

CREATE TABLE `mytable` AS (

SELECT Email FROM `anothertable` A
WHERE Email NOT IN (SELECT Email FROM `optout`)
AND Email NOT IN (SELECT emailbounced FROM `allbounced`)

GROUP BY Email
HAVING 
(SUM(CASE WHEN target = 'M' THEN 1 ELSE 0 END) > 0 OR   -- MAN  is present
SUM(CASE WHEN target = 'W' THEN 1 ELSE 0 END) > 0 AND   -- WOMAN  is present
SUM(CASE WHEN target NOT IN ('M', 'W') THEN 1 ELSE 0 END) = 0)  -- only MAN or WOMAN

);

END

In [6]: pd.Series(df.lookup(df.index, df.columns), index=df.columns)
Out[6]:
A    0
B    4
C    8
dtype: int32

In [14]: pd.Series(df.lookup(ind, cols), index=df.columns)
Out[14]:
A    0
B    4
C    8
dtype: int32

In [12]: df.lookup(df.index, df.columns)
Out[12]: array([0, 4, 8])

d = [
  {
    'id': 0,
    'foo': [1],
    'bar': ['a', 'b']
  },
  {
    'id': 3,
    'foo': [2],
    'bar': ['a', 'b']
  },
  {
    'id': 7,
    'foo': [1, 2],
    'bar': ['a']
  },
]

all_idx = [
    (el['id'], i, j)
    for el in d
    for i in el['foo']
    for j in el['bar']
]

# [(0, 1, 'a'), (0, 1, 'b'), (3, 2, 'a'), (3, 2, 'b'), (7, 1, 'a'), (7, 2, 'a')]

df.loc[all_idx].groupby(level=0).sum()

        col1
id
0  -0.225873
3  -3.048268
7  -1.315756