Python - 将列值分组到类中

Question

我有一个包含以下数据的CSV：

Customer    Age
A           10
B           53
C           20
D            2
E           55
F           12

为此，我正在使用Pandas库来阅读csv。我的问题是如何对Ages值进行分组以获得具有以下间隔的新列：

Customer    Age   Age_Interval
A           10      [0-10]
B           53      [50-60]
C           20      [10-20]
D           2       [0-10]
E           55      [50-60]
F           12      [10-20]   

我怎样才能做到这一点？

谢谢！

Answer 1

我相信你需要cut：

df['Age_Interval'] = pd.cut(df['Age'], bins=np.arange(0,110,10))
print (df)
  Customer  Age Age_Interval
0        A   10      (0, 10]
1        B   53     (50, 60]
2        C   20     (10, 20]
3        D    2      (0, 10]
4        E   55     (50, 60]
5        F   12     (10, 20]

---

b = np.arange(0,110,10)
l = [ "{0}-{1}".format(i, i + 10) for i in range(0, 100, 10)]
df['Age_Interval'] = pd.cut(df['Age'], bins=b, labels=l)
print (df)
  Customer  Age Age_Interval
0        A   10         0-10
1        B   53        50-60
2        C   20        10-20
3        D    2         0-10
4        E   55        50-60
5        F   12        10-20

编辑：

print (df)
  Customer  Age
0        A   10
1        B   53
2        C   20
3        D    2
4        E   55
5        F   12
6        G    0

b = np.arange(0,110,10)
l = [ "{0}-{1}".format(i, i + 10) for i in range(0, 100, 10)]
df['Age_Interval'] = pd.cut(df['Age'], bins=b, labels=l, include_lowest=True)
print (df)
  Customer  Age Age_Interval
0        A   10         0-10
1        B   53        50-60
2        C   20        10-20
3        D    2         0-10
4        E   55        50-60
5        F   12        10-20
6        G    0         0-10

Answer 2

你可以试试这个

df['Age_Interval'] = pd.cut(df.Age, range(10,100,10), include_lowest=True)