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app.py
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import flask
import pandas
import numpy
import plotly
import json
import plotly.express as px
app1 = flask.Flask("__name__")
data_url = "https://sip.einvoice.nat.gov.tw/ods-main/ODS308E/download/3886F055-EB77-4DF9-98E2-F3F49A7D3434/1/E265F67E-6CDA-4FB2-B4E9-ACF40ECA3476/0/?fileType=csv"
data = pandas.read_csv(data_url, encoding='utf8', usecols=["發票年月","縣市代碼","縣市名稱","鄉鎮市區代碼","鄉鎮市區名稱","行業名稱","平均開立張數","平均開立金額","平均客單價"])
@app1.route("/")
def hello():
return flask.render_template("home.html")
@app1.route("/selectDistrict")
def selectDistrict():
return flask.render_template("selectDistrict.html")
@app1.route("/selectDistrictVS")
def selectDistrictVS():
return flask.render_template("selectDistrictVS.html")
# selectDistrict.html 中的city下拉式選單顯示出來
@app1.route("/getCity", methods=["GET"])
def getCity():
dataCity = data["縣市名稱"].drop_duplicates()
result = dataCity.to_json(orient="values")
parsed = json.loads(result)
json_string = json.dumps(parsed, ensure_ascii=False).encode('utf8')
print("json_string.decode(): ", json_string.decode(), "type: ",
type(json_string.decode())) #json_string.decode() is a str
#convert string to object
json_object = json.loads(json_string.decode())
#check new data type
print("json_object: ",json_object,"type: ",type(json_object)) # json_object is a list
return json_object
# selectDistrict.html 中選擇 縣市名稱 後，鄉鎮市區名稱 下拉式選單顯示出來
@app1.route("/getDistrict", methods=["POST"])
def getDistrict():
city = flask.request.form.get('city')
print(city, type(city))
filter_City = (data["縣市名稱"] == city)
# 去除該縣市中，重複的區
dataDistricts = data[filter_City]["鄉鎮市區名稱"].drop_duplicates()
# dataframe to json
result = dataDistricts.to_json(orient="values")
parsed = json.loads(result)
json_string = json.dumps(parsed, ensure_ascii=False).encode('utf8')
print("json_string.decode(): ", json_string.decode(), "type: ",
type(json_string.decode())) # json_string.decode() is a str
# convert string to object
json_object = json.loads(json_string.decode())
# check new data type
print("json_object: ", json_object, "type: ",
type(json_object)) # json_object is a list
return json_object
@app1.route("/getIndustry",methods=["POST"])
def getIndustry():
city = flask.request.form.get('city')
district = flask.request.form.get('district')
print(city, type(city))
print(district, type(district))
filter_City = (data["縣市名稱"] == city)
filter_District = (data["鄉鎮市區名稱"] == district)
# 去除該縣市中，重複的區
dataIndustry = data[filter_City&filter_District]["行業名稱"].drop_duplicates()
result = dataIndustry.to_json(orient="values")
parsed = json.loads(result)
json_string = json.dumps(parsed, ensure_ascii=False).encode('utf8')
print("json_string.decode(): ", json_string.decode(), "type: ",
type(json_string.decode())) #json_string.decode() is a str
#convert string to object
json_object = json.loads(json_string.decode())
#check new data type
print("json_object: ",json_object,"type: ",type(json_object)) # json_object is a list
return json_object
@app1.route("/monthlyGrowthRate")
def monthlyGrowthRate():
city = flask.request.args.get("city")
district = flask.request.args.get("district")
industry = flask.request.args.get("industry")
filter_City = (data['縣市名稱'] == city)
filter_District = (data['鄉鎮市區名稱'] == district)
filter_Industry = (data['行業名稱'] == industry)
# data_new篩選出來特定地區特定產業
data_new=data[filter_City&filter_District&filter_Industry].sort_values(by=['發票年月'],ascending=False)
data_new['平均開立張數月成長率'] = numpy.NaN
data_new['平均開立金額月成長率'] = numpy.NaN
data_new['平均客單價月成長率'] = numpy.NaN
data_new.reset_index(inplace = True,drop = True) #把原本的index_col drop
for index in range(len(data_new.index)-1):
# at[資料索引值,欄位名稱]：利用資料索引值及欄位名稱來取得「單一值」
data_new.at[index,'平均開立張數月成長率'] = (data_new.at[index,'平均開立張數'] - data_new.at[index+1,'平均開立張數'])/data_new.at[index+1,'平均開立張數']
data_new.at[index,'平均開立金額月成長率'] = (data_new.at[index,'平均開立金額'] - data_new.at[index+1,'平均開立金額'])/data_new.at[index+1,'平均開立金額']
data_new.at[index,'平均客單價月成長率'] = (data_new.at[index,'平均客單價'] - data_new.at[index+1,'平均客單價'])/data_new.at[index+1,'平均客單價']
# 發票年月 轉成 yyyy-MM 型態字串 方便 plotly繪圖辨識時間
data_new['發票年月'] = (data_new['發票年月']/100).astype('int').astype('str')+'-'+(data_new['發票年月']%100).astype('int').astype('str')
print(data_new)
# here I'm adding a column "Num_Color" with colors
# "平均開立張數月成長率"正為紅色，負為綠色
data_new["Num_Color"] = numpy.where(data_new["平均開立張數月成長率"]<0, 'green', 'red')
fig1 = px.bar(data_new, x='發票年月', y='平均開立張數月成長率')
fig1.update_traces(marker_color=data_new["Num_Color"])
# fig1.show(renderer='colab')
graphJSON1 = json.dumps(fig1, cls=plotly.utils.PlotlyJSONEncoder)
# here I'm adding a column "Sum_Color" with colors
# "平均開立金額月成長率"正為紅色，負為綠色
data_new["Sum_Color"] = numpy.where(data_new["平均開立金額月成長率"]<0, 'green', 'red')
fig2 = px.bar(data_new, x='發票年月', y='平均開立金額月成長率')
fig2.update_traces(marker_color=data_new["Sum_Color"])
# fig2.show(renderer='colab')
graphJSON2 = json.dumps(fig2, cls=plotly.utils.PlotlyJSONEncoder)
# here I'm adding a column "Per_Price_Color" with colors
# "平均客單價月成長率"正為紅色，負為綠色
data_new["Per_Price_Color"] = numpy.where(data_new["平均客單價月成長率"]<0, 'green', 'red')
fig3 = px.bar(data_new, x='發票年月', y='平均客單價月成長率')
fig3.update_traces(marker_color=data_new["Per_Price_Color"])
# fig3.show(renderer='colab')
graphJSON3 = json.dumps(fig3, cls=plotly.utils.PlotlyJSONEncoder)
return flask.render_template("monthlyGrowthRate.html",graphJSON1=graphJSON1,graphJSON2=graphJSON2,graphJSON3=graphJSON3,city=city,district=district,industry=industry)
# 比較兩個地區同產業 的 平均開立張數月成長率、平均開立金額月成長率、平均客單價月成長率
@app1.route("/monthlyGrowthRateVS",methods=["GET"])
def monthlyGrowthRateVS():
city1 = flask.request.args.get("city1")
district1 = flask.request.args.get("district1")
industry1 = flask.request.args.get("industry1")
city2 = flask.request.args.get("city2")
district2 = flask.request.args.get("district2")
industry2 = flask.request.args.get("industry2")
filter_City = (data['縣市名稱'] == city1)
filter_District = (data['鄉鎮市區名稱'] == district1)
filter_Industry = (data['行業名稱'] == industry1)
filter_City2 = (data['縣市名稱'] == city2)
filter_District2 = (data['鄉鎮市區名稱'] == district2)
filter_Industry2 = (data['行業名稱'] == industry2)
data_new=data[filter_City&filter_District&filter_Industry].sort_values(by=['發票年月'],ascending=False)
data_new['平均開立張數月成長率'] = numpy.NaN
data_new['平均開立金額月成長率'] = numpy.NaN
data_new['平均客單價月成長率'] = numpy.NaN
data_new.reset_index(inplace = True,drop = True) #把原本的index_col drop
data_new2=data[filter_City2&filter_District2&filter_Industry2].sort_values(by=['發票年月'],ascending=False)
data_new2['平均開立張數月成長率'] = numpy.NaN
data_new2['平均開立金額月成長率'] = numpy.NaN
data_new2['平均客單價月成長率'] = numpy.NaN
data_new2.reset_index(inplace = True,drop = True) #把原本的index_col drop
for index in range(len(data_new.index)-1):
# at[資料索引值,欄位名稱]：利用資料索引值及欄位名稱來取得「單一值」
data_new.at[index,'平均開立張數月成長率'] = (data_new.at[index,'平均開立張數'] - data_new.at[index+1,'平均開立張數'])/data_new.at[index+1,'平均開立張數']
data_new.at[index,'平均開立金額月成長率'] = (data_new.at[index,'平均開立金額'] - data_new.at[index+1,'平均開立金額'])/data_new.at[index+1,'平均開立金額']
data_new.at[index,'平均客單價月成長率'] = (data_new.at[index,'平均客單價'] - data_new.at[index+1,'平均客單價'])/data_new.at[index+1,'平均客單價']
for index in range(len(data_new2.index)-1):
# at[資料索引值,欄位名稱]：利用資料索引值及欄位名稱來取得「單一值」
data_new2.at[index,'平均開立張數月成長率'] = (data_new2.at[index,'平均開立張數'] - data_new2.at[index+1,'平均開立張數'])/data_new2.at[index+1,'平均開立張數']
data_new2.at[index,'平均開立金額月成長率'] = (data_new2.at[index,'平均開立金額'] - data_new2.at[index+1,'平均開立金額'])/data_new2.at[index+1,'平均開立金額']
data_new2.at[index,'平均客單價月成長率'] = (data_new2.at[index,'平均客單價'] - data_new2.at[index+1,'平均客單價'])/data_new2.at[index+1,'平均客單價']
# 發票年月 轉成 yyyy-MM 型態字串 方便 plotly繪圖辨識時間
data_new['發票年月'] = (data_new['發票年月']/100).astype('int').astype('str')+'-'+(data_new['發票年月']%100).astype('int').astype('str')
# 發票年月 轉成 yyyy-MM 型態字串 方便 plotly繪圖辨識時間
data_new2['發票年月'] = (data_new2['發票年月']/100).astype('int').astype('str')+'-'+(data_new2['發票年月']%100).astype('int').astype('str')
# 開始繪圖
fig = []
graphJSON = []
purpose_list = ['平均開立張數月成長率','平均開立金額月成長率','平均客單價月成長率']
for purpose in purpose_list:
filter_greater_than_0 = (data_new[purpose] >= 0)
filter_lower_than_0 = (data_new[purpose] < 0)
data_new_greater_than_0 = data_new[filter_greater_than_0]
data_new_lower_than_0 = data_new[filter_lower_than_0]
print(data_new_greater_than_0)
# print(data_new_lower_than_0)
filter_greater_than_0 = (data_new2[purpose] >= 0)
filter_lower_than_0 = (data_new2[purpose] < 0)
data_new2_greater_than_0 = data_new2[filter_greater_than_0]
data_new2_lower_than_0 = data_new2[filter_lower_than_0]
print(data_new2_greater_than_0)
# print(data_new2_lower_than_0)
fig1 = plotly.graph_objects.Figure()
fig1.add_trace(plotly.graph_objects.Bar(x=data_new_greater_than_0['發票年月'],
y=data_new_greater_than_0[purpose],
name=city1+' '+district1+' '+industry1+" 大於0",
marker_color='red'
))
fig1.add_trace(plotly.graph_objects.Bar(x=data_new_lower_than_0['發票年月'],
y=data_new_lower_than_0[purpose],
name=city1+' '+district1+' '+industry1+" 小於0",
marker_color='green'
))
fig1.add_trace(plotly.graph_objects.Bar(x=data_new2_greater_than_0['發票年月'],
y=data_new2_greater_than_0[purpose],
name=city2+' '+district2+' '+industry2+" 大於0",
marker_color='orange'
))
fig1.add_trace(plotly.graph_objects.Bar(x=data_new2_lower_than_0['發票年月'],
y=data_new2_lower_than_0[purpose],
name=city2+' '+district2+' '+industry2+" 小於0",
marker_color='lime'
))
fig1.update_layout(
title=city1+' '+district1+' '+industry1+" vs "+city2+' '+district2+' '+industry2,
xaxis_tickfont_size=14,
yaxis=dict(
title=purpose,
titlefont_size=16,
tickfont_size=14,
),
legend=dict(
x=0,
y=1.0,
bgcolor='rgba(255, 255, 255, 0)',
bordercolor='rgba(255, 255, 255, 0)'
),
barmode='group',
bargap=0.15, # gap between bars of adjacent location coordinates.
bargroupgap=0.1 # gap between bars of the same location coordinate.
)
# fig1.show(renderer='colab')
graphJSON.append(json.dumps(fig1, cls=plotly.utils.PlotlyJSONEncoder))
return flask.render_template("monthlyGrowthRateVS.html",graphJSON1=graphJSON[0],graphJSON2=graphJSON[1],graphJSON3=graphJSON[2],city1=city1,district1=district1,industry1=industry1,city2=city2,district2=district2,industry2=industry2)
# 分析該地區該產業的折線圖(3個折線圖)(包括平均發票開立張數、金額、客單價)
@app1.route("/page3",methods=['POST','GET'])
def page3():
#存在空值，即删除该行 #重置索引值.reset_index(drop=True)--drop=True刪除原來索引值
raw_dataset=data.dropna(axis=0, how='any',inplace=False).reset_index(drop=True)
temp=[]
if flask.request.method =='POST':
if flask.request.values['send']=='送出': #收取表單資訊
select1 = flask.request.form.get(key='city',type=str)
select2 = flask.request.form.get(key='district',type=str)
select3 = flask.request.form.get(key='industry',type=str)
filter1=(raw_dataset["縣市名稱"]==select1)
filter2=(raw_dataset["鄉鎮市區名稱"]==select2)
filter3=(raw_dataset["行業名稱"]==select3)
temp=raw_dataset[filter1&filter2&filter3].reset_index(drop=True).loc[:,['發票年月','平均開立張數','平均開立金額','平均客單價']]
time=[]
for i in temp["發票年月"]:
i=str(i)
time.append(i[0:4]+"-"+i[4:6]) #["2020","08"]
#時間格式不對
temp={"time":time,"num":list(temp["平均開立張數"]),"amount":list(temp["平均開立金額"]),"price":list(temp["平均客單價"])}
temp= pandas.DataFrame(temp)
temp = temp.sort_values(by="time")
if len(temp)==0:#查無結果--NotFound
return "<form method='post' action='/page3'><title>page3:</title><p>縣市:"+select1+"</p><p>鄉鎮市區:"+select2+"</p><p>產業:"+select3+"</p><input type ='button' onclick='history.back()' value='回到上一頁'></input></form>"+"<img src='https://user-images.githubusercontent.com/24848110/33519396-7e56363c-d79d-11e7-969b-09782f5ccbab.png'>"
else:
#1
fig = px.line(temp, x="time", y="num", title="產業平均開立張數折線圖")
graphJSON1 = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
#2
fig = px.line(temp, x="time", y="amount", title="產業平均開立金額折線圖")
graphJSON2 = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
#3
fig = px.line(temp, x="time", y="price", title="產業平均客單價折線圖")
graphJSON3 = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
return "<form method='post' action='/page3'><title>page3:</title><p>縣市:"+select1+"</p><p>鄉鎮市區:"+select2+"</p><p>產業:"+select3+"</p><input type ='button' onclick='history.back()' value='回到上一頁'></input></form>"+"<div id='line-chart1' class='chart' ”></div><div id='line-chart2' class='chart' ”></div><div id='line-chart3' class='chart' ”></div><script src='https://cdn.plot.ly/plotly-latest.min.js'></script><script type='text/javascript'>var graphs = "+graphJSON1+";Plotly.plot('line-chart1', graphs, {});</script><script type='text/javascript'>var graphs = "+graphJSON2+";Plotly.plot('line-chart2', graphs, {});</script><script type='text/javascript'>var graphs = "+graphJSON3+";Plotly.plot('line-chart3', graphs, {});</script>"
return "send error!"
return flask.render_template('page3.html')
@app1.route('/pieChartBSelectDistrict',methods=['GET'])
def pieChartBSelectDistrict():
return flask.render_template("pieChartBSelectDistrict.html")
@app1.route('/pieChartB',methods=['GET'])
def pieChartB():
city = flask.request.args.get("city")
industry = flask.request.args.get("industry")
print("你抓的東西是",city,"和",industry)
af = data[data["縣市名稱"].isin([city]) & data["行業名稱"].isin([industry]) ]
fig01 = px.pie(af,values=af['平均開立張數'], names=af['鄉鎮市區名稱'], title='plotly pie Chart', color='鄉鎮市區名稱')
graphJSON1 = json.dumps(fig01, cls=plotly.utils.PlotlyJSONEncoder)
fig02 = px.pie(af,values=af['平均開立金額'], names=af['鄉鎮市區名稱'], title='plotly pie Chart', color='鄉鎮市區名稱')
graphJSON2 = json.dumps(fig02, cls=plotly.utils.PlotlyJSONEncoder)
fig03 = px.pie(af,values=af['平均客單價'], names=af['鄉鎮市區名稱'], title='plotly pie Chart', color='鄉鎮市區名稱')
graphJSON3 = json.dumps(fig03, cls=plotly.utils.PlotlyJSONEncoder)
return flask.render_template("pieChartB.html",graphJSON1=graphJSON1,graphJSON2=graphJSON2,graphJSON3=graphJSON3,city=city,industry=industry)
@app1.route('/pieChartASelectDistrict',methods=['GET'])
def pieChartASelectDistrict():
return flask.render_template("pieChartASelectDistrict.html")
@app1.route('/pieChartA',methods=['GET'])
def pieChartA():
#將index中選單的資料傳進來
city = flask.request.args.get("city")
district = flask.request.args.get("district")
#print("你抓的東西是",area01,"和",area02)-->這是單純測試值是否有抓近來
#利用Pandas DataFrame中的方法抓到想要的值
af = data[data["縣市名稱"].isin([city]) & data["鄉鎮市區名稱"].isin([district])]
#將各個圓餅圖的資料傳進graphJSON中
fig = px.pie(af,values=af['平均開立張數'], names=af['行業名稱'], title='平均開立張數', color='行業名稱')
graphJSON1 = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
fig2 = px.pie(af,values=af['平均開立金額'], names=af['行業名稱'], title='平均開立金額', color='行業名稱')
graphJSON2 = json.dumps(fig2, cls=plotly.utils.PlotlyJSONEncoder)
fig3 = px.pie(af,values=af['平均客單價'], names=af['行業名稱'], title='平均客單價', color='行業名稱')
graphJSON3 = json.dumps(fig3, cls=plotly.utils.PlotlyJSONEncoder)
#最後一次傳進plotly_charts.html網頁中
return flask.render_template("pieChartA.html",graphJSON1=graphJSON1,graphJSON2=graphJSON2,graphJSON3=graphJSON3,city=city,district=district)
if __name__ == "__main__":
app1.run()