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pythonpandas
Prince Bhatti
Prince Bhatti

Reputation: 5031

PANDAS: Extracting values from a column by applying a condition on other columnns

I have the following table

 Label   B   C
 1       5   91  
 1       5   65
 1       5   93
-1       5   54
-1       5   48
 1       10  66
 1       10  54
-1       10  15

I want only those values in C which are labeled '1' for each set of values in B.I want to extract those values from C in a list like this: [[91 65 93],[66 54]] Implementing similar thing in python is easy but i want to do same thing using pandas.

Upvotes: 2

Views: 4792

Answers (3)

joaquin
joaquin

Reputation: 85615

Not so nice as other answers:

first select label and get useful columns:

df2 = df[df['Label'] == 1][['B','C']].set_index('B')

Then just a list comprehension to get values

print [list(df2.ix[index]['C']) for index in set(df2.index)]

you get:

[[66, 54], [91, 65, 93]]

Upvotes: 1

K.Chen
K.Chen

Reputation: 1286

You can groupby the Label column, apply the list constructor. Here is an minimal example. 

Label = [1, 1, 1, -1, -1, -1]
c = [91, 65, 93, 54, 48, 15]
df = pd.DataFrame({'Label': Label, 'c': c})
df['c'].groupby(df['Label']).apply(list)[1]  # Change 1 to -1 if you want the -1 group

If you only want unique entries, then you can do 

df['c'].groupby(df['Label']).unique()[1]

Upvotes: 1

EdChum
EdChum

Reputation: 393903

You can filter the df to just those values where Label is 1, then on the remaining columns groupby B and get the unique values of C:

In [26]:

gp = df[df['Label']==1][['B','C']].groupby('B')
gp['C'].unique()
Out[26]:
B
5     [91, 65, 93]
10        [66, 54]
Name: C, dtype: object

You can convert it to a list of arrays also:

In [36]:

list(gp['C'].unique().values)
Out[36]:
[array([91, 65, 93], dtype=int64), array([66, 54], dtype=int64)]

Upvotes: 4

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