項(xiàng)目一大數(shù)據(jù)技術(shù)在財(cái)務(wù)中的應(yīng)用單選題題號12345答案BABAB多選題題號1234答案ABCDABCDABCDABC判斷題題號12345答案××√××項(xiàng)目二搭建Python財(cái)務(wù)編程基礎(chǔ)單選題題號12345答案BCDCA多選題題號12345答案ABCACABCACDABC判斷題題號12345答案√√×√√實(shí)訓(xùn)題1、【輸入】#定義字典存儲產(chǎn)品名稱與銷售額（鍵為產(chǎn)品名，值為銷售額，單位：元）sales_data={"服裝類":125800.50,"電子產(chǎn)品":368250.75,"食品類":89500.20,"日用品類":156200.80}#計(jì)算月度總銷售額total_sales=sum(sales_data.values())#輸出總銷售額（千分位分隔，保留兩位小數(shù)）print(f"月度總銷售額：￥{total_sales:,.2f}元")print("=====各產(chǎn)品銷售額占比分析=====")#遍歷字典，計(jì)算并輸出各產(chǎn)品銷售額占比forproduct,amountinsales_data.items():proportion=(amount/total_sales)*100print(f"{product}銷售額：￥{amount:,.2f}元，占比：{proportion:.2f}%")【輸出】月度總銷售額：￥739,752.25元=====各產(chǎn)品銷售額占比分析=====服裝類銷售額：￥125,800.50元，占比：17.01%電子產(chǎn)品銷售額：￥368,250.75元，占比：49.78%食品類銷售額：￥89,500.20元，占比：12.10%日用品類銷售額：￥156,200.80元，占比：21.12%2、【輸入】#定義變量存儲收入與成本費(fèi)用數(shù)據(jù)（實(shí)際場景中可通過input()輸入）main_income=256800.50#主營業(yè)務(wù)收入other_income=32500.80#其他業(yè)務(wù)收入main_cost=185600.20#主營業(yè)務(wù)成本sales_expense=28900.50#銷售費(fèi)用admin_expense=15600.30#管理費(fèi)用#計(jì)算總收入與總費(fèi)用（使用算術(shù)運(yùn)算符）total_income=main_income+other_incometotal_expense=main_cost+sales_expense+admin_expense#計(jì)算利潤總額（收入-費(fèi)用）profit=total_income-total_expense#格式化輸出財(cái)務(wù)結(jié)果（千分位分隔，保留兩位小數(shù)）print("=====月度利潤核算報(bào)告=====")print(f"主營業(yè)務(wù)收入：￥{main_income:,.2f}元")print(f"其他業(yè)務(wù)收入：￥{other_income:,.2f}元")print(f"總收入：￥{total_income:,.2f}元")print("-"*30)print(f"主營業(yè)務(wù)成本：￥{main_cost:,.2f}元")print(f"銷售費(fèi)用：￥{sales_expense:,.2f}元")print(f"管理費(fèi)用：￥{admin_expense:,.2f}元")print(f"總費(fèi)用：￥{total_expense:,.2f}元")print("-"*30)print(f"利潤總額：￥{profit:,.2f}元")【輸出】=====月度利潤核算報(bào)告=====主營業(yè)務(wù)收入：￥256,800.50元其他業(yè)務(wù)收入：￥32,500.80元總收入：￥289,301.30元------------------------------主營業(yè)務(wù)成本：￥185,600.20元銷售費(fèi)用：￥28,900.50元管理費(fèi)用：￥15,600.30元總費(fèi)用：￥230,101.00元------------------------------利潤總額：￥59,200.30元項(xiàng)目三搭建Python財(cái)務(wù)應(yīng)用模型單選題題號12345答案BBBCA多選題題號12345答案ABCABABCDABCDABC判斷題題號12345答案√√×××實(shí)訓(xùn)題1、【輸入】#定義已知變量sales=73500#銷售額base_salary=3000#基礎(chǔ)工資#判斷提成比例ifsales<=10000:ratio=0.01elif10000<sales<=30000:ratio=0.03elif30000<sales<=50000:ratio=0.05elif50000<sales<=100000:ratio=0.08else:ratio=0.10#計(jì)算獎金bonus=sales/1.13*0.9*ratio#計(jì)算應(yīng)發(fā)工資（基礎(chǔ)工資+獎金）salary=base_salary+bonus#保留兩位小數(shù)并輸出結(jié)果print(f"銷售額(sales)：{sales:.2f}元")print(f"獎金(bonus)：{bonus:.2f}元")print(f"應(yīng)發(fā)工資(salary)：{salary:.2f}元")【輸出】銷售額(sales)：73500.00元獎金(bonus)：4683.19元應(yīng)發(fā)工資(salary)：7683.19元2、【輸入】principal=float(input("請輸入初始存款金額（元）："))rate=float(input("請輸入年利率："))years=int(input("請輸入存款年數(shù)："))year=1whileyear<=years:principal=principal*(1+rate)print(f"第{year}年末本息總額：{principal:.2f}元")year+=1【輸出】項(xiàng)目四AI+Python數(shù)據(jù)采集與預(yù)處理單選題題號12345答案ABCCA多選題題號12345答案ABDABCDABCABCDABC判斷題題號12345答案××√×√實(shí)訓(xùn)題1、【輸入】importpandasaspdimportnumpyasnp#1.構(gòu)造包含財(cái)務(wù)數(shù)據(jù)的DataFrame（含異常值）data={'訂單號':['ORD2025001','ORD2025001','ORD2025002','ORD2025003','ORD2025004'],'訂單日期':['2025-01-15','2025-01-15','2025-02-20','2025-02-25','2025-03-10'],'客戶ID':[101,101,102,103,104],'銷售金額':[5000,5000,np.nan,8000,-2000],'成本金額':[3000,3000,4500,5000,1200]}df=pd.DataFrame(data)#2.數(shù)據(jù)清洗#刪除重復(fù)訂單df.drop_duplicates(inplace=True)#填充銷售金額缺失值（用均值）df['銷售金額'].fillna(df['銷售金額'].mean(),inplace=True)#剔除負(fù)金額異常值df=df[(df['銷售金額']>0)&(df['成本金額']>0)]#3.計(jì)算各月總利潤并排序df['訂單月份']=pd.to_datetime(df['訂單日期']).dt.strftime('%Y-%m')profit_by_month=df.groupby('訂單月份')['銷售金額'].sum()-df.groupby('訂單月份')['成本金額'].sum()profit_by_month=profit_by_month.sort_index(ascending=True).reset_index(name='總利潤')print("清洗后數(shù)據(jù)：")print(df)print("\n各月總利潤統(tǒng)計(jì)：")print(profit_by_month)【輸出】2、【輸入】importpandasaspd#(1)使用字典構(gòu)建三張表#客戶表customer_data={'客戶ID':[101,102,103,104],'客戶名稱':['企業(yè)A','企業(yè)B','企業(yè)C','企業(yè)D']}customers=pd.DataFrame(customer_data)#訂單表order_data={'訂單ID':['ORD001','ORD002','ORD003','ORD004'],'客戶ID':[101,102,101,103],'產(chǎn)品ID':[1,2,1,3],'銷售金額':[10000,15000,8000,12000],'訂單日期':['2025-01-05','2025-02-10','2025-03-15','2025-04-20']}orders=pd.DataFrame(order_data)#產(chǎn)品表product_data={'產(chǎn)品ID':[1,2,3,4],'產(chǎn)品名稱':['產(chǎn)品X','產(chǎn)品Y','產(chǎn)品Z','產(chǎn)品W'],'成本金額':[6000,9000,7000,5000]}products=pd.DataFrame(product_data)#(2)使用左連接合并三張表#先合并訂單表和客戶表merged=pd.merge(orders,customers,on='客戶ID',how='left')#再合并產(chǎn)品表merged=pd.merge(merged,products,on='產(chǎn)品ID',how='left')print("合并后的完整表：")print(merged)#(3)構(gòu)建數(shù)據(jù)透視表#計(jì)算毛利和毛利率merged['毛利']=merged['銷售金額']-merged['成本金額']merged['毛利率']=(merged['毛利']/merged['銷售金額']).round(4)#保留4位小數(shù)#創(chuàng)建數(shù)據(jù)透視表pivot_table=pd.pivot_table(merged,values=['銷售金額','成本金額','毛利率'],index='產(chǎn)品名稱',aggfunc={'銷售金額':'sum','成本金額':'sum','毛利率':'mean'})#按毛利率降序排列pivot_table=pivot_table.sort_values('毛利率',ascending=False)print("\n數(shù)據(jù)透視表（按毛利率降序排列）：")print(pivot_table)【輸出】項(xiàng)目五搭建數(shù)據(jù)可視化分析模型單選題題號12345答案BCBAC多選題題號12345答案ABCACABCBCABD判斷題題號12345答案√×××√實(shí)訓(xùn)題1、【輸入】importmatplotlib.pyplotaspltimportnumpyasnp#設(shè)置中文字體和負(fù)號顯示plt.rcParams['font.family']='Simhei'plt.rcParams['axes.unicode_minus']=False#準(zhǔn)備數(shù)據(jù)months=['1月','2月','3月','4月','5月','6月']revenue=[120,135,142,150,165,180]profit_rate=[15.2,16.8,16.5,17.3,18.1,17.8]#創(chuàng)建畫布和雙Y軸fig,ax1=plt.subplots(figsize=(10,6))ax2=ax1.twinx()#繪制營業(yè)收入柱狀圖bars=ax1.bar(months,revenue,color='#3f72af',label='營業(yè)收入（萬元）')ax1.bar_label(bars,padding=3)#添加數(shù)據(jù)標(biāo)簽#繪制利潤率折線圖line,=ax2.plot(months,profit_rate,color='#8ac926',marker='o',label='利潤率（%）')forx,yinzip(months,profit_rate):ax2.text(x,y,f'{y}%',ha='center',va='bottom',color='#8ac926')#添加數(shù)據(jù)標(biāo)簽#設(shè)置標(biāo)題和坐標(biāo)軸標(biāo)簽ax1.set_title('2025年上半年?duì)I業(yè)收入與利潤率分析',fontsize=14)ax1.set_xlabel('月份',fontsize=12)ax1.set_ylabel('營業(yè)收入（萬元）',color='#3f72af',fontsize=12)ax2.set_ylabel('利潤率（%）',color='#8ac926',fontsize=12)#合并圖例lines1,labels1=ax1.get_legend_handles_labels()lines2,labels2=ax2.get_legend_handles_labels()ax1.legend(lines1+lines2,labels1+labels2,loc='upperleft')#調(diào)整布局并顯示plt.tight_layout()plt.show()【輸出】2、【輸入】frompyecharts.chartsimportLinefrompyechartsimportoptionsasopts#1.準(zhǔn)備數(shù)據(jù)departments=["市場部","技術(shù)部","銷售部","人事部","財(cái)務(wù)部"]#部門名稱（橫軸）budget=[200,300,250,100,150]#預(yù)算金額（萬元）actual=[180,320,240,90,150]#實(shí)際支出（萬元）#2.創(chuàng)建折線圖line=(Line()#設(shè)置橫軸：部門名稱.add_xaxis(departments)#添加“預(yù)算金額”折線.add_yaxis(series_name="預(yù)算金額（萬元）",#系列名稱（圖例顯示）y_axis=budget,#數(shù)據(jù)label_opts=opts.LabelOpts(is_show=True),#顯示每個點(diǎn)的具體數(shù)值itemstyle_opts=opts.ItemStyleOpts(color="#675bba")#自定義線條顏色（可選）)#添加“實(shí)際支出”折線.add_yaxis(series_name="實(shí)際支出（萬元）",y_axis=actual,label_opts=opts.LabelOpts(is_show=True),#顯示具體數(shù)值itemstyle_opts=opts.ItemStyleOpts(color="#ff6347")#自定義線條顏色（可選）)#設(shè)置全局配置（標(biāo)題、工具箱、懸浮提示等）.set_global_opts(title_opts=opts.TitleOpts(title="2025年甲公司各部門預(yù)算與實(shí)際支出情況"),#圖表標(biāo)題tooltip_opts=opts.TooltipOpts(trigger="axis"),#懸浮提示：鼠標(biāo)hover時顯示坐標(biāo)軸關(guān)聯(lián)數(shù)據(jù)toolbox_opts=opts.ToolboxOpts(),#顯示工具箱（保存圖片、重置縮放等功能）legend_opts=opts.LegendOpts(pos_top="10%"),#圖例位置（頂部10%處，避免遮擋標(biāo)題）xaxis_opts=opts.AxisOpts(name="部門名稱"),#橫軸名稱yaxis_opts=opts.AxisOpts(name="金額（萬元）")#縱軸名稱))line.render_notebook()【輸出】項(xiàng)目六搭建財(cái)務(wù)初階應(yīng)用模型單選題題號12345答案CDABB多選題題號12345答案ABCABACABABCD判斷題題號12345答案√√×√×實(shí)訓(xùn)題1、【輸入】importpandasaspd#1.創(chuàng)建預(yù)算數(shù)據(jù)和實(shí)際支出數(shù)據(jù)budget_data={'部門名稱':['研發(fā)部','研發(fā)部','市場部','市場部','銷售部','銷售部'],'預(yù)算項(xiàng)目':['設(shè)備采購','人員薪酬','廣告宣傳','市場調(diào)研','差旅費(fèi)','業(yè)務(wù)招待費(fèi)'],'預(yù)算金額（元）':[500000,800000,300000,100000,200000,150000]}actual_expense_data={'部門名稱':['研發(fā)部','研發(fā)部','市場部','市場部','銷售部','銷售部'],'支出項(xiàng)目':['設(shè)備采購','人員薪酬','廣告宣傳','市場調(diào)研','差旅費(fèi)','業(yè)務(wù)招待費(fèi)'],'實(shí)際支出金額（元）':[480000,820000,320000,90000,210000,160000]}df_budget=pd.DataFrame(budget_data)df_actual=pd.DataFrame(actual_expense_data)#2.統(tǒng)一列名df_budget.rename(columns={'預(yù)算項(xiàng)目':'項(xiàng)目'},inplace=True)df_actual.rename(columns={'支出項(xiàng)目':'項(xiàng)目'},inplace=True)#3.合并數(shù)據(jù)merged_df=pd.merge(df_budget,df_actual,on=['部門名稱','項(xiàng)目'],how='outer')#4.計(jì)算差額和差異率merged_df['差額（元）']=merged_df['預(yù)算金額（元）']-merged_df['實(shí)際支出金額（元）']merged_df['差異率（%）']=(merged_df['差額（元）']/merged_df['預(yù)算金額（元）']*100).round(2)#5.篩選差異率超過5%的項(xiàng)目significant_diff=merged_df[abs(merged_df['差異率（%）'])>5]#6.輸出結(jié)果print("各部門預(yù)算與實(shí)際支出差異分析：")print(merged_df[['部門名稱','項(xiàng)目','預(yù)算金額（元）','實(shí)際支出金額（元）','差額（元）','差異率（%）']])print("\n差異率超過5%的項(xiàng)目：")print(significant_diff[['部門名稱','項(xiàng)目','預(yù)算金額（元）','實(shí)際支出金額（元）','差額（元）','差異率（%）']])【輸出】2、【輸入】importpandasaspdfromsklearn.linear_modelimportLinearRegression#1.創(chuàng)建數(shù)據(jù)data={'研發(fā)投入（萬元）':[500,600,750,800,900],'新產(chǎn)品銷售額（萬元）':[2000,2300,2800,3000,3400]}df=pd.DataFrame(data)#2.劃分自變量（X）和因變量（y）X=df[['研發(fā)投入（萬元）']]#二維數(shù)組格式（適配模型要求）y=df['新產(chǎn)品銷售額（萬元）']#3.構(gòu)建并訓(xùn)練線性回歸模型model=LinearRegression()model.fit(X,y)#4.提取模型參數(shù)intercept=ercept_#截距（固定基礎(chǔ)值）coef=model.coef_[0]#系數(shù)（研發(fā)投入每增1萬，銷售額的變化）r_squared=model.score(X,y)#決定系數(shù)（模型擬合效果，越接近1越好）#5.輸出回歸方程與模型效果print(f"===線性回歸模型結(jié)果===")print(f"回歸方程：新產(chǎn)品銷售額={intercept:.2f}+{coef:.2f}×研發(fā)投入")print(f"決定系數(shù)R2：{r_squared:.2f}（越接近1，模型擬合效果越好）")#6.預(yù)測2025年（研發(fā)投入1000萬元時的銷售額）predicted_sales=model.predict([[1000]])print(f"2025年研發(fā)投入1000萬元時，預(yù)測銷售額：{predicted_sales[0]:.2f}萬元")【輸出】項(xiàng)目七搭建財(cái)務(wù)高階應(yīng)用模型單選題題號12345答案CBCBD多選題題號12345答案ABCABCDABCACABC判斷題題號12345答案×√√××實(shí)訓(xùn)題1、【輸入】importjiebaimportrefromcollectionsimportCounterfromwordcloudimportWordCloudimportmatplotlib.pyplotaspltimportdocx#步驟1：讀取Word文件defread_word_file(file_path):doc=docx.Document(file_path)text=""forparagraphindoc.paragraphs:text+=paragraph.text+"\n"returntextfile_path='2025年ESG報(bào)告文本.docx'try:text=read_word_file(file_path)exceptExceptionase:print(f"讀取文件失敗:{e}")exit()#步驟2：文本預(yù)處理（分詞和去停用詞）defpreprocess_text(text):#去除標(biāo)點(diǎn)符號和數(shù)字text=re.sub(r'[^\w\s]','',text)text=re.sub(r'\d+','',text)#分詞words=jieba.lcut(text)#去停用詞stopwords=set(['的','了','是','和','在','有','年','月','日','等','為','與'])filtered_words=[wordforwordinwordsifwordnotinstopwordsandlen(word)>1]returnfiltered_wordsprocessed_words=preprocess_text(text)#步驟3：統(tǒng)計(jì)高頻詞word_counts=Counter(processed_words)top_words=word_counts.most_common(10)print("高頻詞統(tǒng)計(jì)（前10）：")forword,countintop_words:print(f"{word}:{count}")#步驟4：生成詞云圖plt.rcParams['font.family']='SimHei'wordcloud=WordCloud(font_path='simhei.ttf',width=800,height=400,background_color='white').generate(''.join(processed_words))plt.figure(figsize=(10,5))plt.imshow(wordcloud,interpolation='bilinear')plt.axis('off')plt.title('2025年ESG報(bào)告高頻詞詞云圖')plt.show()【輸出】2、【輸入】importreimportpandasaspdimportmatplotlib.pyplotaspl