获取熊猫数据框中特定行和列的值

Question

itemsets = [["26"], ["51", "28", "27"], ["50"], ["8"], ["81", "15"], ["10"], ["81"]]
support = [0.06421, 0.00123, 0.04112, 0.0112, 0.12097, 0.08123, 0.0021334]
df = pd.DataFrame()
df["itemsets"]= itemsets
df["support"] = support
print(df)

    itemsets    support
0   [26]    0.064210
1   [51, 28, 27]    0.001230
2   [50]    0.041120
3   [8] 0.011200
4   [81, 15]    0.120970
5   [10]    0.081230
6   [81]    0.002133

For example I tried :

df.support.where(itemsets == ["26"])

I need to have 0.064210 as result, I would have a support of a given itemset.

Answer 1

Use tuple to perform your comparison, not list s.

Then, use loc and specify the column you want as below

df['itemsets'] = df.itemsets.transform(tuple)
df.loc[df.itemsets == ('26',), 'support']

0    0.06421
Name: support, dtype: float64

---

Notice you dont have to actually change your columns, eg

var = df.itemsets.transform(tuple)
val_to_compare = ['26']
df.loc[var == tuple(val_to_compare), 'support']

---

For any ordering

var = df.itemsets.transform(lambda x: tuple(sorted(x)))
val = ['26']
df.loc[var == tuple(sorted(val)), 'support']