Use a list of values to select rows from a Pandas dataframe – Dev

The best answers to the question “Use a list of values to select rows from a Pandas dataframe” in the category Dev.

QUESTION:

Let’s say I have the following Pandas dataframe:

df = DataFrame({'A' : [5,6,3,4], 'B' : [1,2,3, 5]})
df

     A   B
0    5   1
1    6   2
2    3   3
3    4   5

I can subset based on a specific value:

x = df[df['A'] == 3]
x

     A   B
2    3   3

But how can I subset based on a list of values? – something like this:

list_of_values = [3,6]

y = df[df['A'] in list_of_values]

To get:

     A    B
1    6    2
2    3    3

ANSWER:

You can use the method query:

df.query('A in [6, 3]')
# df.query('A == [6, 3]')

or

lst = [6, 3]
df.query('A in @lst')
# df.query('A == @lst')

ANSWER:

You can use the isin method:

In [1]: df = pd.DataFrame({'A': [5,6,3,4], 'B': [1,2,3,5]})

In [2]: df
Out[2]:
   A  B
0  5  1
1  6  2
2  3  3
3  4  5

In [3]: df[df['A'].isin([3, 6])]
Out[3]:
   A  B
1  6  2
2  3  3

And to get the opposite use ~:

In [4]: df[~df['A'].isin([3, 6])]
Out[4]:
   A  B
0  5  1
3  4  5

ANSWER:

Another method;

df.loc[df.apply(lambda x: x.A in [3,6], axis=1)]

Unlike the isin method, this is particularly useful in determining if the list contains a function of the column A. For example, f(A) = 2*A - 5 as the function;

df.loc[df.apply(lambda x: 2*x.A-5 in [3,6], axis=1)]

It should be noted that this approach is slower than the isin method.