Start from pandas 0.13, this is the most efficient way.

df.query('Gender=="Male" & Year=="2014" ')

You are missing a pair of parentheses to get comparable items on both sides of the | operator - which has higher precedence than ==:

df = df.loc[(df['Col 2'] == 'High') | (df['Col2'] == 'Medium')]

1. For filtering single column you can use: df.loc[df['column_name'] == some_value]

df = pd.DataFrame({'A': 'foo bar foo bar foo bar foo foo'.split(),
                   'B': 'one one two three two two one three'.split(),
                   'C': np.random.randint(3, size=8), 'D':np.random.randint(6, size=8)})
df
#returns
#     A      B  C  D
#0  foo    one  1  0
#1  bar    one  1  4
#2  foo    two  0  0
#3  bar  three  2  5
#4  foo    two  0  2
#5  bar    two  2  4
#6  foo    one  1  5
#7  foo  three  1  0

df_filtered = df.loc[df['C'] == 1] 

df_filtered #gives:
#     A      B  C  D
#0  foo    one  1  0
#1  bar    one  1  4
#6  foo    one  1  5
#7  foo  three  1  0

2. For filtering with more values of single column you can use the '|' operator (for multiple conditions): df.loc[(df['column_name'] >= A) | (df['column_name'] <= B)].

since you mention filter by single column values (the name of the single column you call 'values' which has different values 2, 4, 5, 9 for example), you can use this approach:

df_filtered = df.loc[(df['C'] == 1)| (df['C'] == 2)] 

df_filtered #returns:

#     A      B  C  D
#0  foo    one  1  0
#1  bar    one  2  1
#2  foo    two  1  1
#3  bar  three  2  2
#4  foo    two  1  1
#5  bar    two  2  2
#6  foo    one  2  2

3. You can even filter by multiple column values using the same approch:

df_filtered = df.loc[(df['C'] == 1)| (df['D'] == 2)]

df_filtered  #gives:

#     A      B  C  D
#0  foo    one  1  0
#2  foo    two  1  1
#3  bar  three  2  2
#4  foo    two  1  1
#5  bar    two  2  2
#6  foo    one  2  2
# note that this time we filter all the df values corresponding to columns C == 1 & D == 2