对于data = np.array(dt1)，np.array形式的csv文件dt1，倒数第1列数据是不是data[:,-1]

是的，对于一个二维的 NumPy 数组，可以使用 `array_name[:, -1]` 的方式来获取最后一列的数据。在你的例子中，`data = np.array(dt1)` 生成了一个二维的 NumPy 数组，因此 `data[:, -1]` 就是最后一列的数据。注意，这里的 `-1` 表示倒数第一列，如果要获取倒数第二列，可以使用 `-2`。

相关问题

解决import pandas as pd import numpy as np # 读取数据 train = pd.read_csv("D:\数据挖掘\实验\实验六\clean_train.csv") test = pd.read_csv("D:\数据挖掘\实验\实验六\clean_test.csv") # 抽取正负样本 train = train[train['label'] == 1].sample(sum(train['label'] == 1)).append(train[train['label'] == 0].sample(sum(train['label'] == 0))) test = test[test['label'] == 1].sample(sum(test['label'] == 1)).append(test[test['label'] == 0].sample(sum(test['label'] == 0))) # 删除列 x_train = train.drop(['user_id', 'merchant_id', 'coupon_id','date_received', 'date'], axis=1) x_test = test.drop(['user_id', 'merchant_id', 'coupon_id','date_received', 'date', 'label'], axis=1) # 处理无穷数据（无穷数据大或者无穷数据小） x_train[np.isinf(x_train)] = 0 x_test[np.isinf(x_test)] = 0from sklearn.tree import DecisionTreeClassifier model_dt1 = DecisionTreeClassifier(max_leaf_nodes=16, random_state=123).fit(x_train.drop(['label'], axis=1), x_train['label'])报错ValueError: Input contains NaN, infinity or a value too large for dtype('float32').问题

你在处理无穷数据时使用了 np.isinf() 函数，但是你并没有处理 NaN 值。你可以使用 Pandas 库中的 fillna() 函数来处理 NaN 值。下面是一个处理 NaN 和无穷数据的代码示例：

import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeClassifier

# 读取数据
train = pd.read_csv("D:\数据挖掘\实验\实验六\clean_train.csv")
test = pd.read_csv("D:\数据挖掘\实验\实验六\clean_test.csv")

# 抽取正负样本
train = train[train['label'] == 1].sample(sum(train['label'] == 1)).append(train[train['label'] == 0].sample(sum(train['label'] == 0)))
test = test[test['label'] == 1].sample(sum(test['label'] == 1)).append(test[test['label'] == 0].sample(sum(test['label'] == 0)))

# 删除列
x_train = train.drop(['user_id', 'merchant_id', 'coupon_id','date_received', 'date'], axis=1)
x_test = test.drop(['user_id', 'merchant_id', 'coupon_id','date_received', 'date', 'label'], axis=1)

# 处理无穷数据和 NaN
x_train = x_train.replace([np.inf, -np.inf], np.nan)
x_train.fillna(0, inplace=True)
x_test = x_test.replace([np.inf, -np.inf], np.nan)
x_test.fillna(0, inplace=True)

# 训练决策树模型
model_dt1 = DecisionTreeClassifier(max_leaf_nodes=16, random_state=123).fit(x_train.drop(['label'], axis=1), x_train['label'])

这样处理之后，你的数据集中就不应该包含 NaN 和无穷值了。

SELECT t1.id, t1.[kh] AS '客户', t1.[lrsj] AS '录入时间', t1.[htbh] AS '合同编号', t1.[rq] AS '签订日期', t1.[htqsrq] AS '起始日期', t1.[htjzrq] AS '截止日期', t1.[zjey] AS '总金额', t1.[hjzld] AS '总吨数', t1.[lrsj] AS '创建时间', t1.[chpt] AS '撮合平台', CAST ( t1.[htwj] AS VARCHAR ( MAX ) ) AS '合同文件', STUFF( ( SELECT ',' + CONVERT ( VARCHAR, CASE WHEN ISNUMERIC( REPLACE( t2.ppcs, ',', '' ) ) = 1 THEN CAST ( REPLACE( t2.ppcs, ',', '' ) AS FLOAT ) END ) FROM uf_xsht_dt1 t2 WHERE t1.id = t2.mainid FOR XML PATH ( '' ) ), 1, 1, '' ) AS '品牌', STUFF( ( SELECT ' ' + CONVERT ( VARCHAR, CASE WHEN ISNUMERIC( REPLACE( t2.hsdjyd, ' ', '' ) ) = 1 THEN CAST ( REPLACE( t2.hsdjyd, ' ', '' ) AS FLOAT ) END ) FROM uf_xsht_dt1 t2 WHERE t1.id = t2.mainid FOR XML PATH ( '' ) ), 1, 1, '' ) AS '销售单价', (SELECT SUM(sjfk) FROM uf_xshksj2 WHERE cght = t1.id) AS '实际已收', (SELECT SUM(hkje) FROM uf_xshksj2 WHERE cght = t1.id) AS '已收货款', (SELECT SUM(sjfk) FROM uf_xshksj2 WHERE kxlx = 0 AND cght = t1.id) AS '订金', CASE WHEN ((SELECT SUM(ISNULL(hkje, 0)) FROM uf_xshksj2 WHERE cght = t1.id) + (SELECT SUM(ISNULL(sjfk, 0)) FROM uf_xshksj2 WHERE kxlx = 0 AND cght = t1.id)) < t1.[zjey] THEN (SELECT SUM(ISNULL(hkje, 0)) FROM uf_xshksj2 WHERE cght = t1.id) / t1.[zjey] ELSE ((SELECT SUM(ISNULL(hkje, 0)) FROM uf_xshksj2 WHERE cght = t1.id) + (SELECT SUM(ISNULL(sjfk, 0)) FROM uf_xshksj2 WHERE kxlx = 0 AND cght = t1.id)) / t1.[zjey] END AS '已收比例' FROM uf_xsht t1 INNER JOIN uf_xsht_dt1 t2 ON t1.id = t2.mainid LEFT JOIN uf_xshksj2 t3 ON t1.id = t3.cght GROUP BY t1.id, t1.[kh], t1.[lrsj], t1.[htbh], t1.[rq], t1.[htqsrq], t1.[htjzrq], t1.[zjey], t1.[hjzld], t1.[chpt], CAST ( t1.[htwj] AS VARCHAR ( MAX ) );为什么只有第一个有数据

根据你提供的查询语句，我注意到在计算已收比例的部分使用了一个 CASE 表达式。然而，在这个查询中，你使用了两个子查询来计算已收货款和订金，并在 CASE 表达式中引用了这两个子查询。这可能导致只有第一个子查询的结果被使用，而第二个子查询的结果被忽略。

为了解决这个问题，你可以通过将已收货款和订金的子查询合并成一个子查询，并在 CASE 表达式中引用该合并后的子查询来计算已收比例。以下是修改后的查询语句：

SELECT
    t1.id,
    t1.[kh] AS '客户',
    t1.[lrsj] AS '录入时间',
    t1.[htbh] AS '合同编号',
    t1.[rq] AS '签订日期',
    t1.[htqsrq] AS '起始日期',
    t1.[htjzrq] AS '截止日期',
    t1.[zjey] AS '总金额',
    t1.[hjzld] AS '总吨数',
    t1.[lrsj] AS '创建时间',
    t1.[chpt] AS '撮合平台',
    CAST ( t1.[htwj] AS VARCHAR ( MAX ) ) AS '合同文件',
    STUFF(
        (
        SELECT
            ',' + CONVERT (
                VARCHAR,
            CASE
                WHEN ISNUMERIC( REPLACE( t2.ppcs, ',', '' ) ) = 1 THEN
                    CAST ( REPLACE( t2.ppcs, ',', '' ) AS FLOAT ) 
            END 
            ) 
        FROM uf_xsht_dt1 t2 
        WHERE t1.id = t2.mainid FOR XML PATH ( '' ) 
        ),
        1,
        1,
        '' 
    ) AS '品牌',
    STUFF(
        (
        SELECT
            ' ' + CONVERT (
                VARCHAR,
            CASE
                WHEN ISNUMERIC( REPLACE( t2.hsdjyd, ' ', '' ) ) = 1 THEN
                    CAST ( REPLACE( t2.hsdjyd, ' ', '' ) AS FLOAT ) 
            END 
            ) 
        FROM uf_xsht_dt1 t2 
        WHERE t1.id = t2.mainid FOR XML PATH ( '' ) 
        ),
        1,
        1,
        '' 
    ) AS '销售单价',
    t3.已收货款 AS '实际已收',
    t3.订金 AS '已收货款',
    (t3.已收货款 + t3.订金) / t1.[zjey] AS '已收比例'
FROM
    uf_xsht t1
    INNER JOIN uf_xsht_dt1 t2 ON t1.id = t2.mainid
    LEFT JOIN (
        SELECT
            cght,
            SUM(ISNULL(hkje, 0)) AS 已收货款,
            SUM(CASE WHEN kxlx = 0 THEN ISNULL(sjfk, 0) ELSE 0 END) AS 订金
        FROM
            uf_xshksj2
        GROUP BY
            cght
    ) t3 ON t1.id = t3.cght
GROUP BY
    t1.id,
    t1.[kh],
    t1.[lrsj],
    t1.[htbh],
    t1.[rq],
    t1.[htqsrq],
    t1.[htjzrq],
    t1.[zjey],
    t1.[hjzld],
    t1.[chpt],
    CAST ( t1.[htwj] AS VARCHAR ( MAX ) );

在上述查询中，我将已收货款和订金的子查询合并为一个子查询，并将其命名为 t3。然后，在 SELECT 子句中引用了 t3 中的已收货款和订金字段，并使用 `(t3.已收货款 + t3.订金) / t1.[zjey]` 计算已收比例。

请根据你的实际需求和数据结构进行适当的调整。希望这能解决你的问题！