data$book_recommend<-apply(as.matrix(data$book_recommend),1,function(x){as.numeric}) data<-na.omit(data)

这段代码用于将图书数据中的“book_recommend”列转换为数值型，并删除其中的缺失值。以下是对代码的解释：

# 使用apply函数和as.numeric函数，将“book_recommend”列转换为数值型
data$book_recommend <- apply(as.matrix(data$book_recommend), 1, function(x) {as.numeric(x)})

# 使用na.omit函数，删除数据中的缺失值
data <- na.omit(data)

首先，使用apply函数和as.numeric函数将“book_recommend”列中的每个元素转换为数值型。其中，as.matrix函数用于将数据框中的一列转换为矩阵，1表示按行进行处理，而function(x) {as.numeric(x)}表示对每行中的元素进行转换。

接下来，使用na.omit函数删除数据中的缺失值。该函数会删除数据框中包含缺失值的整行数据。

这段代码的作用是将“book_recommend”列中的字符串型数据转换为数值型，并删除其中的缺失值。这通常是在进行数据分析和建模之前需要进行的预处理步骤之一。

希望这能够帮助您理解这段代码的作用。

相关问题

优化这个sql SELECT count( 1 ) FROM ( SELECT B.ID, B.PURCHASE_REQUEST_ID, B.MATERIAL_ID, B.MATERIAL_CODE, B.MATERIAL_NAME, B.STANDARD, B.MODEL_ID, B.BILL_ROW_ID, B.BILL_NO, BILL_NAME, B.MODEL_CODE, B.MODEL_NAME, B.PARENT_MODEL_ID, B.PARENT_MODEL_CODE, B.PARENT_MODEL_NAME, B.UNIT_CODE, B.UNIT_NAME, B.PURCHASE_TYPE_CODE, CAST( NVL( B.APPLY_NUM, 0 ) AS NUMBER ( 24, 10 ) ) AS APPLY_NUM, CAST( NVL( B.DEAL_NUM, 0 ) AS NUMBER ( 24, 10 ) ) AS DEAL_NUM, CAST( NVL( B.RETURN_NUM, 0 ) AS NUMBER ( 24, 10 ) ) AS RETURN_NUM, B.DEAL_USER_ID, B.DEAL_USER_NAME, CAST( NVL( B.PRICE, 0 ) AS NUMBER ( 24, 10 ) ) AS PRICE, CAST( NVL( B.AMOUNT, 0 ) AS NUMBER ( 24, 10 ) ) AMOUNT, B.IMPLEMENT_CODE, B.IMPLEMENT_NAME, B.IMPLEMENT_INVEST_AMOUNT, B.PURCHASE_MANAGER_ID, B.PURCHASE_MANAGER_NAME, B.PROVIDER_ID, B.PROVIDER_NAME, B.REMARK, B.DELIVER_AREA, B.DELIVER_ADDRESS, B.RECEIVE_PEOPLE, B.RECEIVE_PEOPLE_PHONE, B.ITEM_STATUS, B.COST_CENTER, B.COST_BUDGET_CODE, B.COST_IMPLEMENT_NAME, B.FRAME_CONT_ID, B.FRAME_CONT_CODE, B.FRAME_CONT_NAME, B.DETAIL_CONFIG, B.PURCHASE_CATEGORY_CODE, B.INVOICE_TITLE_CODE, B.INVOICE_SEND_ADDRRSS, B.MATERIAL_REQUEST_ITEM_ID, B.YEAR, B.DELETE_FLAG, B.PROVINCE_CODE, B.REASON, B.PARENT_ITEM_ID, B.FRAME_CONT_ITEM_ID, B.SUB_MATERIAL_REQUEST_ID, B.SUB_MATERIAL_REQUEST_CODE, B.MATERIAL_URL, B.RECOMMEND_PROVIDER_NAMES, C.PURCHASE_REQUEST_CODE, C.PURCHASE_REQUEST_NAME, C.APPLY_TYPE_CODE, C.CREATOR_NAME, C.APPLY_TELEPHONE, C.COMPANY_NAME, C.DEPT_NAME, B.CREATE_TIME, TO_CHAR( B.CREATE_TIME, 'YYYY-MM-DD' ) CREATE_TIME_STR, C.ARRIVE_TIME, C.IS_TO_END, C.MONEY_WAY_CODE, C.OWN, C.APPLY_CATEGORY_CODE, C.manu_Type, C.BILL_ID, MMD.MATERIAL_TYPE_CODE, B.BRANCH_COMPANY_DEAL_USER_ID, B.BRANCH_COMPANY_DEAL_USER_NAME, ( SELECT ORG_NAME FROM ORGANIZATIONS WHERE DELETE_FLAG = '0' AND ORG_CODE = ( SELECT PARENT_COMPANY_NO FROM ORGANIZATIONS WHERE ID = B.MATERIAL_DEPT_ID )) AS MATERIAL_COMPANY_NAME, B.ORIGINAL, B.PROVIDER_PRODUCT_MODEL, B.PROVIDER_PRODUCT_NAME, B.PRODUCT_DESC, B.Back_Flag, CASE WHEN MMD.material_type_code = 'WZ' THEN '1' WHEN MMD.material_type_code = 'FW' THEN '2' ELSE '3' END apply_category_code_item, NVL( C.IS_CARDSYSTEM_REQUEST, '0' ) IS_CARDSYSTEM_REQUEST, B.APPLY_GROUP_AUTHORITES, B.SCIENTIFIC_RESEARCH_ID, B.SCIENTIFIC_RESEARCH_CODE, B.SCIENTIFIC_RESEARCH_NAME, B.PREQUALFY_CODE, nvl( C.IS_QUICK, '0' ) AS IS_QUICK, C.PURCHASE_WAY_CODE, C.PURCHASE_TYPE_CODE PURCHASE_TYPE_CODE_P, C.ORIGINAL_TYPE, C.PURCHASE_REQUEST_BILLS_TYPE, B.IS_FRAME_CONT_MONAD FROM PURCHASE_REQUEST_ITEM B LEFT JOIN PURCHASE_REQUEST C ON B.PURCHASE_REQUEST_ID = C.ID LEFT JOIN MATERIAL_DATA MMD ON MMD.ID = B.MATERIAL_ID AND MMD.DELETE_FLAG = '0' WHERE B.delete_flag = '0' AND B.Item_Status IN ( 1 ) AND NOT EXISTS ( SELECT * FROM purchase_request_item_log pril WHERE B.id = pril.purchase_request_item_id AND pril.lock_status = '1' AND pril.delete_flag = '0' ) AND ( ( c.apply_type_code NOT IN ( '20', '41', '3' ) AND nvl( B.Apply_Num, 0 ) > nvl( B.Deal_Num, 0 )) OR c.apply_type_code IN ( '20', '41', '3' ) ) AND B.Deal_User_Id =: 1 AND C.MONEY_WAY_CODE =: 2 AND C.APPLY_TYPE_CODE =: 3 AND C.PAY_OUT_TYPE_CODE =: 4 AND C.APPLY_CATEGORY_CODE =: 5 AND NVL( C.IS_CARDSYSTEM_REQUEST, '0' ) = : 6 AND NOT EXISTS ( SELECT * FROM purchase_request_item p left join material_province mp ON p.material_id = mp.material_id WHERE p.delete_flag = 0 AND mp.delete_flag = 0 AND mp.material_status = 03 AND mp.org_code = p.province_code AND p.id = B.id ) ORDER BY C.ID, B.ID ASC)

首先，可以将子查询中的所有列名改为别名，这样可以避免重名和提高代码可读性。另外，可以尝试使用 EXISTS 子查询来替换 NOT EXISTS 子查询，因为 EXISTS 子查询的性能通常更好。最后，可以考虑对 WHERE 子句中的多个条件进行逻辑上的分组，以避免出现过于复杂的条件表达式。下面是优化后的 SQL：

SELECT COUNT(1)
FROM (
SELECT
b.ID,
b.PURCHASE_REQUEST_ID,
b.MATERIAL_ID,
b.MATERIAL_CODE,
b.MATERIAL_NAME,
b.STANDARD,
b.MODEL_ID,
b.BILL_ROW_ID,
b.BILL_NO,
b.BILL_NAME,
b.MODEL_CODE,
b.MODEL_NAME,
b.PARENT_MODEL_ID,
b.PARENT_MODEL_CODE,
b.PARENT_MODEL_NAME,
b.UNIT_CODE,
b.UNIT_NAME,
b.PURCHASE_TYPE_CODE,
CAST(NVL(b.APPLY_NUM, 0) AS NUMBER(24, 10)) AS APPLY_NUM,
CAST(NVL(b.DEAL_NUM, 0) AS NUMBER(24, 10)) AS DEAL_NUM,
CAST(NVL(b.RETURN_NUM, 0) AS NUMBER(24, 10)) AS RETURN_NUM,
b.DEAL_USER_ID,
b.DEAL_USER_NAME,
CAST(NVL(b.PRICE, 0) AS NUMBER(24, 10)) AS PRICE,
CAST(NVL(b.AMOUNT, 0) AS NUMBER(24, 10)) AMOUNT,
b.IMPLEMENT_CODE,
b.IMPLEMENT_NAME,
b.IMPLEMENT_INVEST_AMOUNT,
b.PURCHASE_MANAGER_ID,
b.PURCHASE_MANAGER_NAME,
b.PROVIDER_ID,
b.PROVIDER_NAME,
b.REMARK,
b.DELIVER_AREA,
b.DELIVER_ADDRESS,
b.RECEIVE_PEOPLE,
b.RECEIVE_PEOPLE_PHONE,
b.ITEM_STATUS,
b.COST_CENTER,
b.COST_BUDGET_CODE,
b.COST_IMPLEMENT_NAME,
b.FRAME_CONT_ID,
b.FRAME_CONT_CODE,
b.FRAME_CONT_NAME,
b.DETAIL_CONFIG,
b.PURCHASE_CATEGORY_CODE,
b.INVOICE_TITLE_CODE,
b.INVOICE_SEND_ADDRRSS,
b.MATERIAL_REQUEST_ITEM_ID,
b.YEAR,
b.DELETE_FLAG,
b.PROVINCE_CODE,
b.REASON,
b.PARENT_ITEM_ID,
b.FRAME_CONT_ITEM_ID,
b.SUB_MATERIAL_REQUEST_ID,
b.SUB_MATERIAL_REQUEST_CODE,
b.MATERIAL_URL,
b.RECOMMEND_PROVIDER_NAMES,
c.PURCHASE_REQUEST_CODE,
c.PURCHASE_REQUEST_NAME,
c.APPLY_TYPE_CODE,
c.CREATOR_NAME,
c.APPLY_TELEPHONE,
c.COMPANY_NAME,
c.DEPT_NAME,
b.CREATE_TIME,
TO_CHAR(b.CREATE_TIME, 'YYYY-MM-DD') CREATE_TIME_STR,
c.ARRIVE_TIME,
c.IS_TO_END,
c.MONEY_WAY_CODE,
c.OWN,
c.APPLY_CATEGORY_CODE,
c.manu_Type,
c.BILL_ID,
MMD.MATERIAL_TYPE_CODE,
b.BRANCH_COMPANY_DEAL_USER_ID,
b.BRANCH_COMPANY_DEAL_USER_NAME,
(SELECT ORG_NAME FROM ORGANIZATIONS WHERE DELETE_FLAG = '0' AND ORG_CODE = (SELECT PARENT_COMPANY_NO FROM ORGANIZATIONS WHERE ID = b.MATERIAL_DEPT_ID)) AS MATERIAL_COMPANY_NAME,
b.ORIGINAL,
b.PROVIDER_PRODUCT_MODEL,
b.PROVIDER_PRODUCT_NAME,
b.PRODUCT_DESC,
b.Back_Flag,
CASE
WHEN MMD.material_type_code = 'WZ' THEN '1'
WHEN MMD.material_type_code = 'FW' THEN '2'
ELSE '3'
END apply_category_code_item,
NVL(c.IS_CARDSYSTEM_REQUEST, '0') IS_CARDSYSTEM_REQUEST,
b.APPLY_GROUP_AUTHORITES,
b.SCIENTIFIC_RESEARCH_ID,
b.SCIENTIFIC_RESEARCH_CODE,
b.SCIENTIFIC_RESEARCH_NAME,
b.PREQUALFY_CODE,
NVL(c.IS_QUICK, '0') AS IS_QUICK,
c.PURCHASE_WAY_CODE,
c.PURCHASE_TYPE_CODE PURCHASE_TYPE_CODE_P,
c.ORIGINAL_TYPE,
c.PURCHASE_REQUEST_BILLS_TYPE,
b.IS_FRAME_CONT_MONAD
FROM PURCHASE_REQUEST_ITEM b
LEFT JOIN PURCHASE_REQUEST c ON b.PURCHASE_REQUEST_ID = c.ID
LEFT JOIN MATERIAL_DATA MMD ON MMD.ID = b.MATERIAL_ID AND MMD.DELETE_FLAG = '0'
WHERE b.delete_flag = '0'
AND b.Item_Status IN (1)
AND b.Deal_User_Id = :1
AND C.MONEY_WAY_CODE = :2
AND C.APPLY_TYPE_CODE = :3
AND C.PAY_OUT_TYPE_CODE = :4
AND C.APPLY_CATEGORY_CODE = :5
AND NVL(C.IS_CARDSYSTEM_REQUEST, '0') = :6
AND EXISTS (
SELECT *
FROM purchase_request_item_log pril
WHERE B.id = pril.purchase_request_item_id
AND pril.lock_status = '1'
AND pril.delete_flag = '0'
)
AND (
c.apply_type_code IN ('20', '41', '3')
OR (
c.apply_type_code NOT IN ('20', '41', '3')
AND NVL(b.Apply_Num, 0) > NVL(b.Deal_Num, 0)
)
)
AND NOT EXISTS (
SELECT *
FROM purchase_request_item p
LEFT JOIN material_province mp ON p.material_id = mp.material_id
WHERE p.delete_flag = 0
AND mp.delete_flag = 0
AND mp.material_status = 03
AND mp.org_code = p.province_code
AND p.id = b.id
)
ORDER BY c.ID, b.ID ASC
)

请用pithon帮我爬取网址：https://www.bilibili.com/video/BV1Us4y1q7MR/?spm_id_from=333.788.recommend_more_video.-1&vd_source=39eadfcf8faa9eab6a7679a8550bcae3并做做情感分析

好的，我可以为你提供一个简单的爬取和情感分析的示例代码。请注意，在运行代码之前，需要安装 requests、beautifulsoup4、textblob 等 Python 库。

import requests
from bs4 import BeautifulSoup
from textblob import TextBlob

# 请求页面并获取页面HTML数据
url = 'https://www.bilibili.com/video/BV1Us4y1q7MR/?spm_id_from=333.788.recommend_more_video.-1&vd_source=39eadfcf8faa9eab6a7679a8550bcae3'
response = requests.get(url)
html = response.text

# 使用BeautifulSoup解析HTML数据，并获取评论内容
soup = BeautifulSoup(html, 'html.parser')
comments = []
for comment in soup.select('.comment'):
    content = comment.select('.text')[0].text.strip()
    comments.append(content)

# 对评论内容进行情感分析
polarity = 0
subjectivity = 0
for comment in comments:
    blob = TextBlob(comment)
    polarity += blob.sentiment.polarity
    subjectivity += blob.sentiment.subjectivity

# 输出情感分析结果
avg_polarity = polarity / len(comments)
avg_subjectivity = subjectivity / len(comments)
print('情感极性：', avg_polarity)
print('情感主观性：', avg_subjectivity)

这段代码会请求指定的视频页面，使用 BeautifulSoup 解析页面 HTML 数据，并获取视频评论内容。接着，使用 TextBlob 对评论内容进行情感分析，最后输出情感极性和主观性的平均值。

请注意，该代码只是一个简单的示例，实际情况可能会更加复杂。在爬取和分析他人的评论数据时，请务必遵守法律法规和道德准则。