使用下列代码创建数据框,完成以下的填空题。 import numpy as np import pandas as pd index = pd.Index(data=["Tom", "Bob", "Mary", "James", "Andy", "Alice"], name="name") data = { "age": [18, 30, np.nan, 40, np.nan, 30], "city": ["Bei Jing ", "Shang Hai ", "Guang Zhou", "Shen Zhen", np.nan, " "], "income": [None, 11, 22,33, np.nan, 55], "sex": [None, "male", "female", "male", np.nan, "male"], "birth": ["2000-02-10", "1988-10-17", None, "1978-08-08", np.nan, "1988-10-17"] } (1)使用data为数据,ind


以下是针对问题的代码填空和答案: (1)创建数据框 `df` 的代码如下: ```python df = pd.DataFrame(data, index=index) ``` (2)查看存在缺失值的字段,完善以下代码: ```python print(df.isnull().any()) ``` 填空1:`isnull` (3)查看 `city` 存在缺失值的记录,完善以下代码: ```python y2 = df[df['city'].isnull()] print(y2) ``` 填空2:`df` (4)使用赋值操作和数据框对象的 `fillna()` 方法填充缺失值,完善以下代码: ```python adict = {'age': 22, 'city': 'tian jin', 'income': 36, 'sex': 'female', 'birth': '1986-6-5'} df.fillna(value=adict, inplace=True) ``` 填空3:`adict` 完整代码如下: ```python import numpy as np import pandas as pd index = pd.Index(data=["Tom", "Bob", "Mary", "James", "Andy", "Alice"], name="name") data = { "age": [18, 30, np.nan, 40, np.nan, 30], "city": ["Bei Jing ", "Shang Hai ", "Guang Zhou", "Shen Zhen", np.nan, " "], "income": [None, 11, 22, 33, np.nan, 55], "sex": [None, "male", "female", "male", np.nan, "male"], "birth": ["2000-02-10", "1988-10-17", None, "1978-08-08", np.nan, "1988-10-17"] } # (1) 创建数据框 df = pd.DataFrame(data, index=index) # (2) 查看存在缺失值的字段 print(df.isnull().any()) # (3) 查看 city 存在缺失值的记录 y2 = df[df['city'].isnull()] print(y2) # (4) 使用 fillna() 填充缺失值 adict = {'age': 22, 'city': 'tian jin', 'income': 36, 'sex': 'female', 'birth': '1986-6-5'} df.fillna(value=adict, inplace=True) ``` [2025-04-14 18:30:52 | AI问答 | 563点数解答]
- 使用下列代码创建数据框,完成以下的填空题。 import numpy as np import pandas as pd index = pd.Index(data=["Tom", "Bob", "Mary", "James", "Andy", "Alice"], name="name") data = { "age": [18, 30, np.nan, 40, np.nan, 30], "city": ["Bei Jing ", "Shang Hai ", "Guang Zhou", "Shen Zhen", np.nan, " "], "income": [None, 11, 22,33, np.nan, 55], "sex": [None, "male", "female", "male", np.nan, "male"], "birth": ["2000-02-10", "1988-10-17", None, "1978-08-08", np.nan, "1988-10-17"] } (1)使用data为数据,ind(563点数解答 | 2025-04-14 18:30:52)83
- 使用下列代码创建数据框,完成以下的填空。 import numpy as np import pandas as pd data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'], 'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3], 'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], 'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']} labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'] 1、使用data为数据,labels为索引,创建数据框df 2、取出 df 的 animal 和 age 列 填空1:df[________](317点数解答 | 2025-04-14 18:32:16)119
- 要求:创建一个对象 const person = {name: 'alice', age: 25, city: 'new york'},使用解构赋值从对象中提取 name 和 age,然后使用模板字符串创建一句话,例如“姓名是 alice,年龄是 25。” const person = {name: 'alice', age: 25, city: 'new york'}; // 使用解构赋值和模板字符串(138点数解答 | 2024-10-29 09:40:13)130
- 解构赋值和模板字符串 要求:创建一个对象 const person = {name: 'alice', age: 25, city: 'new york'},使用解构赋值从对象中提取 name 和 age,然后使用模板字符串创建一句话,例如“姓名是 alice,年龄是 25。” const person = {name: 'alice', age: 25, city: 'new york'}; // 使用解构赋值和模板字符串(64点数解答 | 2024-10-29 21:04:59)168
- import os import datetime from flask import Flask, request, jsonify import requests from flask_cors import CORS import re import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import numpy as np import base64 from io import BytesIO import pandas as pd import traceback # 添加traceback以获取详细错误信息 plt.rcParams["font.sans-serif"] = ["SimHei"] app = Flask(__name__) CORS(app) FASTGPT_API_URL = 'http://localhost:3000/api/v1/chat/completions' FASTGPT_API_KEY = 'fastgpt-gWzitHpBa8XRr0q(713点数解答 | 2025-06-18 16:00:34)92
- import numpy as np import math import xlrd #读excel数据用 from operator import itemgetter #排序用 import pandas as pd import random import matplotlib.pyplot as plt #*******************************读取数据************************************* # 读取excel数据,储存用户数据,出发点编号为0,用户1的编号为1,用户120的编号为120 data = pd.read_excel('data.xlsx', engine='openpyxl') print(data) # 获取节点坐标和需求 # 假设excel文件的第一列是x坐标,第二列是y坐标,第三列是需求 x = data.iloc[:, 0].tolist() # 获取所有行的第一列数据 y = data.iloc[:, 1].tolist() # 获取所有行的第二列数据 demands(2729点数解答 | 2024-11-25 15:32:50)161
- import openpyxl import smtplib import imaplib import email import json import os import re from bs4 import BeautifulSoup from fastapi import FastAPI, Form from openpyxl.styles import Alignment from email.mime.multipart import MIMEMultipart from email.mime.base import MIMEBase from email.header import decode_header from email import encoders import pandas as pd app = FastAPI() RECEIVER_EMAILS = { 0: "yundongshijie001@protonmail.com", 1: "xiaobudian001@protonmail.com" } email_address = "(182点数解答 | 2025-04-12 00:49:09)144
- ```python import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation from mpl_toolkits.mplot3d import Axes3D # 设置图形 fig = plt.figure(figsize=(10, 8)) ax = fig.add_subplot(111, projection='3d') ax.set_facecolor('black') fig.patch.set_facecolor('black') # 爱心参数方程 def heart(t): x = 16 * np.sin(t) 3 y = 13 * np.cos(t) - 5 * np.cos(2*t) - 2 * np.cos(3*t) - np.cos(4*t) return x, y # 生成爱心形状的点 t = np.linspace(0, 2*np.pi, 1000) x, y = heart(t) z = np.(1487点数解答 | 2025-08-07 11:24:56)42
- ```python import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation from mpl_toolkits.mplot3d import Axes3D # 设置图形 fig = plt.figure(figsize=(10, 8)) ax = fig.add_subplot(111, projection='3d') ax.set_facecolor('black') fig.patch.set_facecolor('black') # 爱心参数方程 def heart(t): x = 16 * np.sin(t) 3 y = 13 * np.cos(t) - 5 * np.cos(2*t) - 2 * np.cos(3*t) - np.cos(4*t) return x, y # 生成爱心形状的点 t = np.linspace(0, 2*np.pi, 1000) x, y = heart(t) z = np.(130点数解答 | 2025-08-29 21:24:33)31
- 以下代码生成包含广告成本和销售额的模拟数据,用来分析广告投入与销售额之间的关系。请补全以下代码,完成从数据生成到可视化分析的全过程。代码包含8个空缺(空1至空8),请根据上下文和注释提示填入正确的代码。 import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns plt.rcParams['font.sans-serif'] = ['SimHei'] # 生成模拟数据 np.random.seed(123) # 设置随机种子 ad_cost = np.random.uniform(10, 100, 50) # 生成均匀分布数据 sales = 50 + 2.5 * ad_cost + np.random.normal(0, 20, 50) # 生成正态分布噪声 data = pd.______({'Ad_Cost': ad_cost, 'Sales': sales}) # 空1:创建DataFrame print(data) (939点数解答 | 2025-05-26 23:04:07)95
- import numpy as np import pandas as pd import matplotlib.pyplot as plt # 读取教学账目数据 teaching_df = pd.read_excel('教学.xlsx') # 读取后勤账目数据 logistics_df = pd.read_excel('后勤.xlsx') # 读取行政账目数据 administrative_df = pd.read_excel('行政.xlsx') # 将各表格的取得日期列转换为日期时间类型,并提取年份作为新列 teaching_df['年份'] = pd.to_datetime(teaching_df['取得日期'], format='%y-%m-%d').dt.year logistics_df['年份'] = pd.to_datetime(logistics_df['取得日期'], format='%y-%m-%d').dt.year administrative_df['年份'] = pd.to_datetime(administrative_df['取得日期'], fo(69点数解答 | 2024-10-31 17:39:14)197
- 写出enter your name. alice hello alice, do you want to repeat this program? yes enter your name. bob hello bob, do you want to repeat this program? yes enter your name. charlie hello charlie, do you want to repeat this program? no goodbye, charlie! 的c++代码(267点数解答 | 2024-10-22 08:36:28)149