按条件分组并创建三个新列 [Low, Hit, High]

Question

我有一个大型数据集（约 5 Mio 行），其中包含机器学习训练的结果。现在我想检查结果是否达到“目标范围”。假设这个范围包含-0.25 和+0.25 之间的所有值。如果它在这个范围内，它是一个Hit，如果它低于Low，另一边是High。

我现在将创建这三列 Hit、Low、High 并计算适用条件的每一行并将1 放入此列，另外两列将变为0。之后，我会对这些值进行分组并总结它们。但是我怀疑肯定有更好更快的方法，比如分组的时候直接计算。

---

数据

import pandas as pd

df = pd.DataFrame({"Type":["RF", "RF", "RF", "MLP", "MLP", "MLP"], "Value":[-1.5,-0.1,1.7,0.2,-0.7,-0.6]})

+----+--------+---------+
|    | Type   |   Value |
|----+--------+---------|
|  0 | RF     |    -1.5 | <- Low
|  1 | RF     |    -0.1 | <- Hit
|  2 | RF     |     1.7 | <- High
|  3 | MLP    |     0.2 | <- Hit
|  4 | MLP    |    -0.7 | <- Low
|  5 | MLP    |    -0.6 | <- Low
+----+--------+---------+

---

预期产出

pd.DataFrame({"Type":["RF", "MLP"], "Low":[1,2], "Hit":[1,1], "High":[1,0]})

+----+--------+-------+-------+--------+
|    | Type   |   Low |   Hit |   High |
|----+--------+-------+-------+--------|
|  0 | RF     |     1 |     1 |      1 |
|  1 | MLP    |     2 |     1 |      0 |
+----+--------+-------+-------+--------+

Answer 1

您可以使用 cut 定义组并使用 pivot_table 重塑：

(df.assign(group=pd.cut(df['Value'],
                        [float('-inf'), -0.25, 0.25, float('inf')],
                        labels=['Low', 'Hit', 'High']))
   .pivot_table(index='Type', columns='group', values='Value', aggfunc='count')
   .reset_index()
   .rename_axis(None, axis=1)
)

或crosstab:

(pd.crosstab(df['Type'],
             pd.cut(df['Value'],
                    [float('-inf'), -0.25, 0.25, float('inf')],
                    labels=['Low', 'Hit', 'High'])
             )
   .reset_index().rename_axis(None, axis=1)
 )

输出：

  Type  Low  Hit  High
0  MLP    2    1     0
1   RF    1    1     1

Answer 2

你可以用np.select assign 它然后crosstab

c1 = df.Value<=-0.25
c2 = df.Value>=0.25
out = pd.crosstab(df['Type'], np.select([c1,c2], ['Low','High'], default='Hit'))
out
Out[32]: 
col_0  High  Hit  Low
Type                 
MLP       0    1    2
RF        1    1    1

Answer 3

你可以试试这个：

# Your code
import pandas as pd

df = pd.DataFrame({"Type":["RF", "RF", "RF", "MLP", "MLP", "MLP"], "Value":[-1.5,-0.1,1.7,0.2,-0.7,-0.6]})

# Set your range 
RANGE_MIN = -0.25
RANGE_MAX = 0.25

# --- define functions to be applied to df ---
# evaluate if value is a low
def eval_low(value):
    if value < RANGE_MIN:
        return 1
    else:
        return 0

# evaluate if value is a high
def eval_high(value):
    if value > RANGE_MAX:
        return 1
    else:
        return 0

# evaluate if value is a hit
def eval_hit(value):
    if value >= RANGE_MIN and value <= RANGE_MAX:
        return 1
    else:
        return 0

# Evaluate the functions in new columns
df['Low'] = df.Value.apply(eval_low)
df['Hit'] = df.Value.apply(eval_hit)
df['High'] = df.Value.apply(eval_high)

# get the summary
df.groupby('Type').sum()

Answer 4

df.assign(Value=pd.cut(df.Value,[-np.inf,-0.25,0.25,np.inf],labels=['Low','Hit','High']))
    .groupby('Type').value_counts().reset_index()
    .pivot_table(index='Type',columns='Value',values=0)

Value  Low  Hit  High
Type                 
MLP      2    1     0
RF       1    1     1