APIデータを自動投稿するpythonプログラム
■複数記事を自動でwordpressに投稿
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import pandas as pd import plotly.io as pio import plotly.offline as pyo import plotly.graph_objs as go import urllib.request from time import sleep from datetime import datetime, date, timedelta from dateutil.relativedelta import relativedelta import upftp import math import json from wordpress_xmlrpc import Client, WordPressPost from wordpress_xmlrpc.methods.posts import GetPosts, NewPost from wordpress_xmlrpc.methods.users import GetUserInfo import ssl ssl._create_default_https_context = ssl._create_unverified_context #hsコードから品名を取得する df_hsname = pd.read_csv("article.csv") #パラメーターの作成 classification = "&px=H4" fmt = "&fmt=csv" partner_area = "&p=0" amount_country = 50 #各国のコードを取得する準備で国の数を取得しjsonを読み込む url = urllib.request.urlopen("https://comtrade.un.org/data/cache/partnerAreas.json") jdata = json.loads(url.read().decode()) amount_country = int(len(jdata['results'])) #クエリ作成とトークン入力 def make_url(reporting_area,import_or_export): url = ("https://comtrade.un.org/api/get?type=C" + reporting_area+classification+partner_area + "&rg="+ str(import_or_export)+"&cc="+com + fmt + "&token=") # print(url) return url #APIが混雑している際に再取得する def fetch_url(url): for x in range(30): try: f = urllib.request.urlopen(url) except Exception as e: pass print("トライ30回中" + str(x + 1) + "回目") print("1分間の停止後リトライします。") sleep(60) else: return f break else: print("全て失敗しました") #URLからcsvをダウンロードし、dfに追加していく def get_data(import_or_export): df = pd.DataFrame(columns=[1,2]) for i in range(2,amount_country): reporting_area = "&r=" + (jdata['results'][i]["id"]) added_area = make_url(reporting_area,import_or_export) added_res = fetch_url(added_area) added_df = pd.read_csv(added_res) df = pd.concat([df,added_df],sort=False) # print(str(i)+"回目") df=df.dropna(subset=['Trade Value (US$)']) return df # hsコードから品名を取得する f = open('hscodeH4.json', 'r') jsonData = json.load(f) for i in range(len(df_hsname)): now = datetime.now() print('now:', now) com = str(df_hsname["HS"][i]) if len(com) == 5: com = "0" + com itemname = str(df_hsname["TITLE"][i]) for x in range(len(jsonData["results"])): if jsonData['results'][x]["id"] == com: itemname2 = jsonData['results'][x]["text"] itemname2 = itemname2[9:] fullname = str(itemname2) print("次の対象hsは" + com) print("記事タイトルは" + itemname) print("品名は" + fullname) #輸出グラフ作成用dfを作成 df = get_data(2) df.to_csv("Log/get_expt.csv") #valueが高い順に並べ替え df_s = df.sort_values('Trade Value (US$)', ascending=False) #国名リスト化 partner = [] for var in df_s["Reporter"]: partner.append(var) #金額のみのグラフ data = [] valuee = [] for i in partner: df2 = df[df["Reporter"] == i] trace = go.Bar(x=df2["Reporter"], y=df2['Trade Value (US$)'], text = "M=million$ B=billion$", name = i ) data.append(trace) valuee.append(df2.iat[0,33]) layoute1 = go.Layout( title= itemname + "(HScode:" + com + ") Export Trade Statistics in 2017") fig = go.Figure(data=data, layout=layoute1) # pio.write_image(fig,'IMG/' + com + '.jpeg') # print("画像書き出し完了") pyo.plot(fig,filename= com +"expvalue"+ ".html", auto_open=False) #個数、重量のグラフ df['value/kg'] = 0 df['value/unit'] = 0 df.dropna(subset=['Netweight (kg)'],inplace=True) df3 = df.reset_index(drop=True) df3["Netweight (kg)"] = df3["Netweight (kg)"].astype(int) df3['Year'] = df3['Year'].astype(int) for i in range(len(df3)): if df3["Netweight (kg)"][i] == 0: df3 = df3.drop(i, axis=0) df3 = df3.reset_index(drop=True) for i in range(len(df3)): if df3["Netweight (kg)"][i] < 10000: df3.drop(i, axis=0,inplace=True) df3 = df3.reset_index(drop=True) df3['value/kg'] = df3['value/kg'].astype(float) for i in range(len(df3)): df3['value/kg'][i] = df3['Trade Value (US$)'][i] / df3['Netweight (kg)'][i] #valueが高い順に並べ替え df3.sort_values('value/kg', ascending=True,inplace=True) df3 = df3.reset_index(drop=True) df3.to_csv("expt.csv") #少なすぎる場合のエラーを防ぐ為に20以下でも対応可能にする if len(df3["Reporter"]) > 20: append_times = 20 else: append_times = len(df3["Reporter"]) # #国名リスト化(top10) partner2 = [] for var in range(append_times): partner2.append(df3["Reporter"][var]) data2 = [] pkge = [] for i in partner2: df4 = df3[df3["Reporter"] == i] trace2 = go.Bar(x=df4["Reporter"], y=df4['value/kg'], text = "↑↑Value(US$) Per KG↑↑<br>↓↓Total Export Amount↓↓<br>" + str(df4.iat[0,31]) + "KG", name = i ) data2.append(trace2) pkge.append(round(df4.iat[0,37],2)) layoute2 = go.Layout( title= itemname + " Export Value Per KG for HScode:" + com, width=1000, height=250 ) fig = go.Figure(data=data2, layout=layoute2) pyo.plot(fig,filename= com +"expkg"+ ".html", auto_open=False) #輸入データ #輸入の引数は1 df = get_data(1) df.to_csv("Log/get_impt.csv") #valueが高い順に並べ替え dfi = df.sort_values('Trade Value (US$)', ascending=False) #国名リスト化 partneri = [] for var in dfi["Reporter"]: partneri.append(var) #金額のみのグラフ datai = [] valuei = [] for i in partneri: df2 = dfi[dfi["Reporter"] == i] trace = go.Bar(x=df2["Reporter"], y=df2['Trade Value (US$)'], text = "M=million$ B=billion$", name = i ) datai.append(trace) valuei.append(df2.iat[0,33]) layouti1 = go.Layout( title= itemname + " (HScode:" + com + ")Import Trade Statistics in 2017" ) fig = go.Figure(data=datai, layout=layouti1) pyo.plot(fig,filename= com +"ipvalue"+ ".html",auto_open=False) #個数、重量のグラフ df['value/kg'] = 0 df['value/unit'] = 0 dfi2 = df.dropna(subset=['Netweight (kg)']) dfi3 = dfi2.reset_index(drop=True) for i in range(len(dfi3)): if dfi3["Netweight (kg)"][i] == 0: dfi3 = dfi3.drop(i, axis=0) dfi3 = dfi3.reset_index(drop=True) for i in range(len(dfi3)): if dfi3["Netweight (kg)"][i] < 10000: dfi3.drop(i, axis=0,inplace=True) dfi3 = dfi3.reset_index(drop=True) dfi3['value/kg'] = dfi3['value/kg'].astype(float) dfi3['Year'] = dfi3['Year'].astype(int) for i in range(len(dfi3)): dfi3['value/kg'][i] = dfi3['Trade Value (US$)'][i] / dfi3['Netweight (kg)'][i] #valueが高い順に並べ替え dfi3.sort_values('value/kg', ascending=False,inplace=True) dfi3 = dfi3.reset_index(drop=True) dfi3.to_csv("impt.csv") # #国名リスト化(top10) partner2i = [] #少なすぎる場合のエラーを防ぐ為に20以下でも対応可能にする if len(dfi3["Reporter"]) > 20: append_timesi = 20 else: append_timesi = len(dfi3["Reporter"]) for var in range(append_timesi): partner2i.append(dfi3["Reporter"][var]) data3 = [] pkgi = [] for i in partner2i: dfi4 = dfi3[dfi3["Reporter"] == i] trace3 = go.Bar(x=dfi4["Reporter"], y=dfi4['value/kg'], text = "↑↑Value(US$) Per KG↑↑<br>↓↓Total Import Amount(KG)↓↓<br>" + str(dfi4.iat[0,31]) , name = i ) data3.append(trace3) pkgi.append(round(dfi4.iat[0,37],2)) layouti2 = go.Layout( title= itemname + " Import Value Per KG for HScode:" + com, width=1000, height=250 ) fig = go.Figure(data=data3, layout=layouti2) pyo.plot(fig,filename= com +"impkg"+ ".html", auto_open=False) print("輸入全体グラフ作成作業終了") #'Trade Value (US$)'が高い順に並べ替え df3.sort_values('Trade Value (US$)', ascending=False,inplace=True) df3 = df3.reset_index(drop=True) dfi3.sort_values('Trade Value (US$)', ascending=False,inplace=True) dfi3 = dfi3.reset_index(drop=True) #金額をカンマ区切りにする for i in range(len(df3['Trade Value (US$)'])): a = df3['Trade Value (US$)'][i] df3['Trade Value (US$)'][i] = "{:,}".format(float(a)) for i in range(len(dfi3['Trade Value (US$)'])): a = dfi3['Trade Value (US$)'][i] dfi3['Trade Value (US$)'][i] = "{:,}".format(float(a)) #ウェイトをカンマ区切りにする for i in range(len(df3['Netweight (kg)'])): a = df3['Netweight (kg)'][i] df3['Netweight (kg)'][i] = "{:,}".format(float(a)) for i in range(len(dfi3['Netweight (kg)'])): a = dfi3['Netweight (kg)'][i] dfi3['Netweight (kg)'][i] = "{:,}".format(float(a)) #value/kgを下２桁表示にする for i in range(len(dfi3['value/kg'])): a = dfi3['value/kg'][i] dfi3['value/kg'][i] = round(dfi3['value/kg'][i],2) for i in range(len(df3['value/kg'])): a = df3['value/kg'][i] df3['value/kg'][i] = round(df3['value/kg'][i],2) #テーブル化で数値文字化けを防ぐ dfi3['Trade Value (US$)'] = dfi3['Trade Value (US$)'].astype(str) df3['Trade Value (US$)'] = df3['Trade Value (US$)'].astype(str) upftp.up_ftptest() def bunsho(): #投稿用記事出力 up_url = "\"https://hstariffstat.com/html/" tag_st = "[advanced_iframe use_shortcode_attributes_only=\"true\" src=" end_value = " ""width=\"100%\" height=\"600\" id=\"advanced_iframe\" ]" end_kg = " ""width=\"100%\" height=\"300\" id=\"advanced_iframe\" ]" class_tip = "<div class=\"center-block\">" class_end = "</div>" #タイトル記事 titlekiji = class_tip + "This article has been written to share the Accurate Statistical Trade Ranking by country. <br>" + \ "The graphs that have been shared show the International Trade Price and Weight of <br><b>\"" +fullname + "\"</b>(HScode:" + com +")<br>" + \ "<br>These graphs may help you to find new Buyer and Supplier Country. " + \ "<br>All the Statistical data that is depicted here is taken from <a href=https://comtrade.un.org/>UN Comtrade Database.</a><p>" + class_end #輸出価格 exprice = "<h1 id=\"rittai\">Export Trade Statistics of \""+ itemname + "\"(HScode:" + com +")" + "</h1><br><br>" + class_tip + \ "Here is a graph which shows export statistics of<br><b>" + itemname + "</b>(HScode:" + com + ") worldwide in " + str(df3["Year"][0]) + \ " <br>With the help of these statistics, you can get the information about the ranking of the top export countries worldwide in" + str(df3["Year"][0]) + "<br>" + class_end + \ tag_st + up_url + com +"expvalue"+ ".html\"" + end_value+class_tip + \ "According to the stats, "+ \ partner[0] + " ranked at the top in exports of \"" + itemname +"\"" + " total export value " + "{:,}".format(valuee[0]) + "USD <br>" + \ partner[0] + " is followed by " + \ partner[1] + ": " + "{:,}".format(valuee[1]) + "USD <br>" +\ partner[2] + ": " + "{:,}".format(valuee[2]) + "USD <br>" +\ partner[3] + ": " + "{:,}".format(valuee[3]) + "USD <br>" +\ partner[4] + ": " + "{:,}".format(valuee[4]) + "USD and <br>" +\ partner[5] + ": " + "{:,}".format(valuee[5]) + "USD <br>" + "<p>"+class_end #輸出キロ exkgvalue = "<h1 id=\"rittai\">Supplier country’s \"export value per KG\" of \""+ itemname + "</h1><br><br>"+ class_tip + \ "Another graph below is displaying: <br><b>\"The export value per KG in USD\"</b>"+ \ "(The Total Weight divides the Total Export Value) <br>"+ \ "This graph shows which country exports items at a lower price in comparison with the other countries. <p>"+ \ class_end + \ tag_st +up_url + com +"expkg"+ ".html\"" + end_kg+ \ class_tip + \ partner2[0] + " ranked first in Exports \"" + itemname +"\" at the lowest price of " + str(pkge[0]) + "USD/KG " + \ partner2[0] + " is followed by " + partner2[1] + str(pkge[1]) + "USD/KG" + ", " + \ partner2[2] + str(pkge[2]) + "USD/KG"+ ", " + \ partner2[3] + str(pkge[3]) + "USD/KG"+ ", " + \ partner2[4] + str(pkge[4]) + "USD/KG"+ \ ", and " + \ partner2i[5] + str(pkge[4]) + "USD/KG" + "<p>" + class_end #輸入価格 imprice = "<h1 id=\"rittai\">Import Trade Statistics of \""+ itemname + "\"(HScode:" + com +")" + "</h1><br><br>" + class_tip + \ "The statistics show the ranking of<br><b>" + itemname + "</b>(HScode:" + com + ") worldwide in " + str(df3["Year"][0]) + class_end + \ tag_st + up_url + com + "ipvalue"+ ".html\"" + end_value + class_tip + \ "the top importing countries worldwide in " + str(df3["Year"][0]) + "<br>" +\ partneri[0] + " ranked first in imports of \"" + itemname +"\"" + " Total import value " + "{:,}".format(valuei[0]) + "USD <br>" + \ partneri[0] + " is followed by " + \ partneri[1] + ": " + "{:,}".format(valuei[1]) + "USD <br>" + \ partneri[2] + ": " + "{:,}".format(valuei[2]) + "USD <br>" + \ partneri[3] + ": " + "{:,}".format(valuei[3]) + "USD <br>" + \ partneri[4] + ": " + "{:,}".format(valuei[4]) + "USD and <br>" + \ partneri[5] + ": " + "{:,}".format(valuei[5]) + "USD <br>" + "<p>" + class_end #輸入キロ imkgvalue = "<h1 id=\"rittai\">Buyer country's \"import Value per KG\" of \""+ itemname + "</h1><br><br>" + class_tip + \ "The graph below is displaying: <br><b>\"Import Value per KG in USD\"</b>" + \ "(the total weight divides the total Import value)<br>" + \ "It shows which country imports items at a higher price and comparison with other countries." + class_end + \ tag_st +up_url + com +"impkg"+ ".html\"" + end_kg + class_tip + \ partner2i[0] + " ranked first in imports \"" + itemname +"\" at the highest price of " + str(pkgi[0]) + "/KG " + \ partner2i[0] + " is followed by " + partner2i[1] + ", " + partner2i[2] + ", " + partner2i[3]+ ", " + partner2i[4] + \ ", and " + partner2i[5] + "<p>" + class_end #輸出テーブル作成 cut_e = df3 to_remove = [1,2,'Classification','Period Desc."','Aggregate Level','Is Leaf Code','Trade Flow Code','Reporter Code', 'Partner Code','Partner','Partner ISO','2nd Partner Code','2nd Partner','2nd Partner ISO','Customs Proc. Code', 'Customs','Mode of Transport Code"','Mode of Transport','Commodity Code','Commodity','Qty Unit Code','Flag','value/unit', 'CIF Trade Value (US$)','FOB Trade Value (US$)','Gross weight (kg)','Mode of Transport Code','Period','Period Desc.', 'Qty','Qty Unit','Alt Qty Unit Code'] cut_e = cut_e[cut_e.columns.difference(to_remove)] cut_e = cut_e.rename(columns={'Reporter ISO':'ISO'}) cut_e.sort_values('Trade Value (US$)', ascending=False,inplace=True) cut_e = cut_e.reset_index(drop=True) cut_e2 = cut_e.loc[:,['Reporter','ISO','Year','Trade Flow','Trade Value (US$)','Netweight (kg)','Alt Qty','Alt Qty Unit','value/kg']] exphtml = cut_e2.to_html(classes="extable") cap_exphtml = "<h1 id=\"rittai\">Table data of \""+ itemname + "\"(HS:" + com +")" + "[Export]</h1><br><br>" + \ "<caption>" + itemname + " Export Trading Statistics (HScode:" + com + ")</caption>\n" + exphtml #輸入テーブル作成 cut_i = dfi3 to_remove = [1,2,'Classification','Period Desc."','Aggregate Level','Is Leaf Code','Trade Flow Code','Reporter Code', 'Partner Code','Partner','Partner ISO','2nd Partner Code','2nd Partner','2nd Partner ISO','Customs Proc. Code', 'Customs','Mode of Transport Code"','Mode of Transport','Commodity Code','Commodity','Qty Unit Code','Flag','value/unit', 'CIF Trade Value (US$)','FOB Trade Value (US$)','Gross weight (kg)','Mode of Transport Code','Period','Period Desc.', 'Qty','Qty Unit','Alt Qty Unit Code'] cut_i = cut_i[cut_i.columns.difference(to_remove)] cut_i = cut_i.rename(columns={'Reporter ISO':'ISO'}) cut_i.sort_values('Trade Value (US$)', ascending=False,inplace=True) cut_i = cut_i.reset_index(drop=True) cut_i2 = cut_i.loc[:,['Reporter','ISO','Year','Trade Flow','Trade Value (US$)','Netweight (kg)','Alt Qty','Alt Qty Unit','value/kg']] imphtml = cut_i2.to_html(classes="imtable") cap_imphtml = "<h1 id=\"rittai\">Table data of \""+ itemname + "\"(HS:" + com +")" + "[Import]</h1><br><br>" + \ "<caption>" + itemname + " Import Trading Statistics (HScode:" + com + ")</caption>\n" + imphtml title = itemname + "\"(HS:" + com +")" + "World Trade Statistics" #全て結合 kiji_all= titlekiji + exprice + exkgvalue + imprice + imkgvalue + "<p>" + cap_exphtml + "<p>" + cap_imphtml return kiji_all kiji_all = bunsho() title = itemname + "\"(HS:" + com +")" + "World Trade Statistics" wp = Client('http://hstariffstat.com/xmlrpc.php', 'id', 'pass') post = WordPressPost() post.title = title post.content = kiji_all post.terms_names = {'post_tag': [itemname, 'trade statistics'],'category': ['2017']} post.post_status = 'publish' wp.call(NewPost(post)) print("全て完了") |
■FTPアップロードを行うupftp.py
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#FTPにアップロード import glob import string import os from ftplib import FTP import urllib.request def up_ftp(): filelist=glob.glob('*.html') ftp=FTP('sv2142.xserver.jp') ftp.set_pasv("true") ftp.login('id','pass') ftp.cwd('/hstariffstat.com/public_html/html/') #ホスト側のディレクトリ for file in filelist: # f=open(file,'rb') with open(file, "rb") as f: filename=os.path.basename(file) cmd='STOR '+filename ftp.storbinary(cmd,f) f.close() ftp.quit() for file in filelist: os.remove(file) print("FTPアップロード完了") |
■個別記事を自動でwordpressに投稿
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import pandas as pd import plotly.io as pio import plotly.offline as pyo import plotly.graph_objs as go import urllib.request from time import sleep from datetime import datetime, date, timedelta from dateutil.relativedelta import relativedelta import upftp import json from wordpress_xmlrpc import Client, WordPressPost from wordpress_xmlrpc.methods.posts import GetPosts, NewPost from wordpress_xmlrpc.methods.users import GetUserInfo import ssl ssl._create_default_https_context = ssl._create_unverified_context com = input("HSコードを入力してください") com = com.replace('.', '') #hsコードから品名を取得する f = open('hscodeH4.json', 'r') jsonData = json.load(f) for i in range(len(jsonData["results"])): if jsonData['results'][i]["id"] == com: print("発見") itemname = jsonData['results'][i]["text"] itemname = itemname[9:] fullname = itemname print(itemname) prename = input("必要があれば上記品名を変更して下さい。") if prename != "": itemname = prename print("グラフは以下の名前で表示されます" + itemname) #パラメーターの作成 #trade data type C Commodities S Services datatype = "type=C" #china=156,UK=826,US=842,jp=392or all # reporting_area = "&r=842" # #Annual=A,Monthly=M # frequency = "&freq=A" # #YYYY or YYYYMM or now or recent # time_period = "&ps=recent" # Harmonized System (HS) classification = "&px=H4" #china=156,UK=826,US=842,or all=0 partner_area = "&p=0" # 1 (imports) and 2 (exports), # import_or_export = ("&rg=" + imp_or_exp) #HS CODE classification_code = ("&cc=" + com) #csv or json fmt = "&fmt=csv" #対象国の数 amount_country = 50 #TOKEN token = "" #各国のコードを取得する準備で国の数を取得しjsonを読み込む url = urllib.request.urlopen("https://comtrade.un.org/data/cache/partnerAreas.json") jdata = json.loads(url.read().decode()) amount_country = int(len(jdata['results'])) #クエリ作成とトークン入力 def make_url(reporting_area,import_or_export): url = ("https://comtrade.un.org/api/get?"+datatype + reporting_area+classification+partner_area+"&rg=" + str(import_or_export)+classification_code+fmt+"&token=") print(url) return url #APIが混雑している際に再取得する def fetch_url(url): for x in range(30): try: f = urllib.request.urlopen(url) except Exception as e: pass print("トライ30回中" + str(x + 1) + "回目") print("1分間の停止後リトライします。") sleep(60) else: return f break else: print("全て失敗しました") #URLからcsvをダウンロードし、dfに追加していく def get_data(import_or_export): df = pd.DataFrame(columns=[1,2]) for i in range(2,amount_country): reporting_area = "&r=" + (jdata['results'][i]["id"]) added_area = make_url(reporting_area,import_or_export) added_res = fetch_url(added_area) added_df = pd.read_csv(added_res) df = pd.concat([df,added_df],sort=False) print(str(i)+"回目") now = datetime.now() print('now:', now) df=df.dropna(subset=['Trade Value (US$)']) return df # #輸出データ #輸出の引数は2 df = get_data(2) #valueが高い順に並べ替え df_s = df.sort_values('Trade Value (US$)', ascending=False) #国名リスト化 partner = [] for var in df_s["Reporter"]: partner.append(var) #金額のみのグラフ data = [] valuee = [] for i in partner: df2 = df[df["Reporter"] == i] trace = go.Bar(x=df2["Reporter"], y=df2['Trade Value (US$)'], text = "M=million$ B=billion$", name = i ) data.append(trace) valuee.append(df2.iat[0,33]) layoute1 = go.Layout( title= itemname + "(HScode:" + com + ") Export Trade Statistics in 2017") fig = go.Figure(data=data, layout=layoute1) pio.write_image(fig, com + '.jpeg') pyo.plot(fig,filename= com +"expvalue"+ ".html", auto_open=False) #個数、重量のグラフ df['value/kg'] = 0 df['value/unit'] = 0 df.dropna(subset=['Netweight (kg)'],inplace=True) df3 = df.reset_index(drop=True) df3["Netweight (kg)"] = df3["Netweight (kg)"].astype(int) df3['Year'] = df3['Year'].astype(int) for i in range(len(df3)): if df3["Netweight (kg)"][i] == 0: df3 = df3.drop(i, axis=0) df3 = df3.reset_index(drop=True) for i in range(len(df3)): if df3["Netweight (kg)"][i] < 10000: df3.drop(i, axis=0,inplace=True) df3 = df3.reset_index(drop=True) df3['value/kg'] = df3['value/kg'].astype(float) for i in range(len(df3)): df3['value/kg'][i] = df3['Trade Value (US$)'][i] / df3['Netweight (kg)'][i] #valueが高い順に並べ替え df3.sort_values('value/kg', ascending=True,inplace=True) df3 = df3.reset_index(drop=True) df3.to_csv("expt.csv") #少なすぎる場合のエラーを防ぐ為に20以下でも対応可能にする if len(df3["Reporter"]) > 20: append_times = 20 else: append_times = len(df3["Reporter"]) # #国名リスト化(top10) partner2 = [] for var in range(append_times): partner2.append(df3["Reporter"][var]) data2 = [] pkge = [] for i in partner2: df4 = df3[df3["Reporter"] == i] trace2 = go.Bar(x=df4["Reporter"], y=df4['value/kg'], text = "↑↑Value(US$) Per KG↑↑<br>↓↓Total Export Amount↓↓<br>" + str(df4.iat[0,31]) + "KG", name = i ) data2.append(trace2) pkge.append(round(df4.iat[0,37],2)) layoute2 = go.Layout( title= itemname + " Export Value Per KG for HScode:" + com, width=1000, height=250 ) fig = go.Figure(data=data2, layout=layoute2) pyo.plot(fig,filename= com +"expkg"+ ".html", auto_open=False) #輸入データ #輸入の引数は1 df = get_data(1) #valueが高い順に並べ替え dfi = df.sort_values('Trade Value (US$)', ascending=False) #国名リスト化 partneri = [] for var in dfi["Reporter"]: partneri.append(var) #金額のみのグラフ datai = [] valuei = [] for i in partneri: df2 = dfi[dfi["Reporter"] == i] trace = go.Bar(x=df2["Reporter"], y=df2['Trade Value (US$)'], text = "M=million$ B=billion$", name = i ) datai.append(trace) valuei.append(df2.iat[0,33]) layouti1 = go.Layout( title= itemname + " (HScode:" + com + ")Import Trade Statistics in 2017" ) fig = go.Figure(data=datai, layout=layouti1) pyo.plot(fig,filename= com +"ipvalue"+ ".html",auto_open=False) #個数、重量のグラフ df['value/kg'] = 0 df['value/unit'] = 0 dfi2 = df.dropna(subset=['Netweight (kg)']) dfi3 = dfi2.reset_index(drop=True) for i in range(len(dfi3)): if dfi3["Netweight (kg)"][i] == 0: dfi3 = dfi3.drop(i, axis=0) dfi3 = dfi3.reset_index(drop=True) for i in range(len(dfi3)): if dfi3["Netweight (kg)"][i] < 10000: dfi3.drop(i, axis=0,inplace=True) dfi3 = dfi3.reset_index(drop=True) dfi3['value/kg'] = dfi3['value/kg'].astype(float) dfi3['Year'] = dfi3['Year'].astype(int) for i in range(len(dfi3)): dfi3['value/kg'][i] = dfi3['Trade Value (US$)'][i] / dfi3['Netweight (kg)'][i] #valueが高い順に並べ替え dfi3.sort_values('value/kg', ascending=False,inplace=True) dfi3 = dfi3.reset_index(drop=True) dfi3.to_csv("impt.csv") # #国名リスト化(top10) partner2i = [] #少なすぎる場合のエラーを防ぐ為に20以下でも対応可能にする if len(dfi3["Reporter"]) > 20: append_timesi = 20 else: append_timesi = len(dfi3["Reporter"]) for var in range(append_timesi): partner2i.append(dfi3["Reporter"][var]) data3 = [] pkgi = [] for i in partner2i: dfi4 = dfi3[dfi3["Reporter"] == i] trace3 = go.Bar(x=dfi4["Reporter"], y=dfi4['value/kg'], text = "↑↑Value(US$) Per KG↑↑<br>↓↓Total Import Amount(KG)↓↓<br>" + str(dfi4.iat[0,31]) , name = i ) data3.append(trace3) pkgi.append(round(dfi4.iat[0,37],2)) layouti2 = go.Layout( title= itemname + " Import Value Per KG for HScode:" + com, width=1000, height=250 ) fig = go.Figure(data=data3, layout=layouti2) pyo.plot(fig,filename= com +"impkg"+ ".html", auto_open=False) #'Trade Value (US$)'が高い順に並べ替え df3.sort_values('Trade Value (US$)', ascending=False,inplace=True) df3 = df3.reset_index(drop=True) dfi3.sort_values('Trade Value (US$)', ascending=False,inplace=True) dfi3 = dfi3.reset_index(drop=True) #金額をカンマ区切りにする for i in range(len(df3['Trade Value (US$)'])): a = df3['Trade Value (US$)'][i] df3['Trade Value (US$)'][i] = "{:,}".format(float(a)) for i in range(len(dfi3['Trade Value (US$)'])): a = dfi3['Trade Value (US$)'][i] dfi3['Trade Value (US$)'][i] = "{:,}".format(float(a)) #ウェイトをカンマ区切りにする for i in range(len(df3['Netweight (kg)'])): a = df3['Netweight (kg)'][i] df3['Netweight (kg)'][i] = "{:,}".format(float(a)) for i in range(len(dfi3['Netweight (kg)'])): a = dfi3['Netweight (kg)'][i] dfi3['Netweight (kg)'][i] = "{:,}".format(float(a)) #value/kgを下２桁表示にする for i in range(len(dfi3['value/kg'])): a = dfi3['value/kg'][i] dfi3['value/kg'][i] = round(dfi3['value/kg'][i],2) for i in range(len(df3['value/kg'])): a = df3['value/kg'][i] df3['value/kg'][i] = round(df3['value/kg'][i],2) #テーブル化で数値文字化けを防ぐ dfi3['Trade Value (US$)'] = dfi3['Trade Value (US$)'].astype(str) df3['Trade Value (US$)'] = df3['Trade Value (US$)'].astype(str) upftp.up_ftp() #全ての文字列を一つの変数に入れてpyperclip.copy()でコピーできるようにする def bunsho(): #投稿用記事出力 up_url = "\"https://hstariffstat.com/html/" tag_st = "[advanced_iframe use_shortcode_attributes_only=\"true\" src=" end_value = " ""width=\"100%\" height=\"600\" id=\"advanced_iframe\" ]" end_kg = " ""width=\"100%\" height=\"300\" id=\"advanced_iframe\" ]" class_tip = "<div class=\"center-block\">" class_end = "</div>" #タイトル記事 titlekiji = class_tip + "This article has been written to share the Accurate Statistical Trade Ranking by country. <br>" + \ "The graphs that have been shared show the International Trade Price and Weight of <br><b>\"" +fullname + "\"</b>(HScode:" + com +")" + \ "<br>These graphs may help you to find new Buyer and Supplier Country. " + \ "<br>All the Statistical data that is depicted here is taken from <a href=https://comtrade.un.org/>UN Comtrade Database.</a><p>" + class_end #輸出価格 exprice = "<h1 id=\"rittai\">Export Trade Statistics of \""+ itemname + "\"(HScode:" + com +")" + "</h1><br><br>" + class_tip + \ "Here is a graph which shows export statistics of<br><b>" + itemname + "</b>(HScode:" + com + ") worldwide in " + str(df3["Year"][0]) + \ " <br>With the help of these statistics, you can get the information about the ranking of the top export countries worldwide in" + str(df3["Year"][0]) + "<br>" + class_end + \ tag_st + up_url + com +"expvalue"+ ".html\"" + end_value+class_tip + \ "According to the stats, "+ \ partner[0] + " ranked at the top in exports of \"" + itemname +"\"" + " total export value " + "{:,}".format(valuee[0]) + "USD <br>" + \ partner[0] + " is followed by " + \ partner[1] + ": " + "{:,}".format(valuee[1]) + "USD <br>" +\ partner[2] + ": " + "{:,}".format(valuee[2]) + "USD <br>" +\ partner[3] + ": " + "{:,}".format(valuee[3]) + "USD <br>" +\ partner[4] + ": " + "{:,}".format(valuee[4]) + "USD and <br>" +\ partner[5] + ": " + "{:,}".format(valuee[5]) + "USD <br>" + "<p>"+class_end #輸出キロ exkgvalue = "<h1 id=\"rittai\">Supplier country’s \"export value per KG\" of \""+ itemname + "</h1><br><br>"+ class_tip + \ "Another graph below is displaying: <br><b>\"The export value per KG in USD\"</b>"+ \ "(The Total Weight divides the Total Export Value) <br>"+ \ "This graph shows which country exports items at a lower price in comparison with the other countries. <p>"+ \ class_end + \ tag_st +up_url + com +"expkg"+ ".html\"" + end_kg+ \ class_tip + \ partner2[0] + " ranked first in Exports \"" + itemname +"\" at the lowest price of " + str(pkge[0]) + "USD/KG " + \ partner2[0] + " is followed by " + partner2[1] + str(pkge[1]) + "USD/KG" + ", " + \ partner2[2] + str(pkge[2]) + "USD/KG"+ ", " + \ partner2[3] + str(pkge[3]) + "USD/KG"+ ", " + \ partner2[4] + str(pkge[4]) + "USD/KG"+ \ ", and " + \ partner2i[5] + str(pkge[4]) + "USD/KG" + "<p>" + class_end #輸入価格 imprice = "<h1 id=\"rittai\">Import Trade Statistics of \""+ itemname + "\"(HScode:" + com +")" + "</h1><br><br>" + class_tip + \ "The statistics show the ranking of<br><b>" + itemname + "</b>(HScode:" + com + ") worldwide in " + str(df3["Year"][0]) + class_end + \ tag_st + up_url + com + "ipvalue"+ ".html\"" + end_value + class_tip + \ "the top importing countries worldwide in " + str(df3["Year"][0]) + "<br>" +\ partneri[0] + " ranked first in imports of \"" + itemname +"\"" + " Total import value " + "{:,}".format(valuei[0]) + "USD <br>" + \ partneri[0] + " is followed by " + \ partneri[1] + ": " + "{:,}".format(valuei[1]) + "USD <br>" + \ partneri[2] + ": " + "{:,}".format(valuei[2]) + "USD <br>" + \ partneri[3] + ": " + "{:,}".format(valuei[3]) + "USD <br>" + \ partneri[4] + ": " + "{:,}".format(valuei[4]) + "USD and <br>" + \ partneri[5] + ": " + "{:,}".format(valuei[5]) + "USD <br>" + "<p>" + class_end #輸入キロ imkgvalue = "<h1 id=\"rittai\">Buyer country's \"import Value per KG\" of \""+ itemname + "</h1><br><br>" + class_tip + \ "The graph below is displaying: <br><b>\"Import Value per KG in USD\"</b>" + \ "(the total weight divides the total Import value)<br>" + \ "It shows which country imports items at a higher price and comparison with other countries." + class_end + \ tag_st +up_url + com +"impkg"+ ".html\"" + end_kg + class_tip + \ partner2i[0] + " ranked first in imports \"" + itemname +"\" at the highest price of " + str(pkgi[0]) + "/KG " + \ partner2i[0] + " is followed by " + partner2i[1] + ", " + partner2i[2] + ", " + partner2i[3]+ ", " + partner2i[4] + \ ", and " + partner2i[5] + "<p>" + class_end #輸出テーブル作成 cut_e = df3 to_remove = [1,2,'Classification','Period Desc."','Aggregate Level','Is Leaf Code','Trade Flow Code','Reporter Code', 'Partner Code','Partner','Partner ISO','2nd Partner Code','2nd Partner','2nd Partner ISO','Customs Proc. Code', 'Customs','Mode of Transport Code"','Mode of Transport','Commodity Code','Commodity','Qty Unit Code','Flag','value/unit', 'CIF Trade Value (US$)','FOB Trade Value (US$)','Gross weight (kg)','Mode of Transport Code','Period','Period Desc.', 'Qty','Qty Unit','Alt Qty Unit Code'] cut_e = cut_e[cut_e.columns.difference(to_remove)] cut_e = cut_e.rename(columns={'Reporter ISO':'ISO'}) cut_e.sort_values('Trade Value (US$)', ascending=False,inplace=True) cut_e = cut_e.reset_index(drop=True) cut_e2 = cut_e.loc[:,['Reporter','ISO','Year','Trade Flow','Trade Value (US$)','Netweight (kg)','Alt Qty','Alt Qty Unit','value/kg']] exphtml = cut_e2.to_html(classes="extable") cap_exphtml = "<h1 id=\"rittai\">Table data of \""+ itemname + "\"(HS:" + com +")" + "[Export]</h1><br><br>" + \ "<caption>" + itemname + " Export Trading Statistics (HScode:" + com + ")</caption>\n" + exphtml #輸入テーブル作成 cut_i = dfi3 to_remove = [1,2,'Classification','Period Desc."','Aggregate Level','Is Leaf Code','Trade Flow Code','Reporter Code', 'Partner Code','Partner','Partner ISO','2nd Partner Code','2nd Partner','2nd Partner ISO','Customs Proc. Code', 'Customs','Mode of Transport Code"','Mode of Transport','Commodity Code','Commodity','Qty Unit Code','Flag','value/unit', 'CIF Trade Value (US$)','FOB Trade Value (US$)','Gross weight (kg)','Mode of Transport Code','Period','Period Desc.', 'Qty','Qty Unit','Alt Qty Unit Code'] cut_i = cut_i[cut_i.columns.difference(to_remove)] cut_i = cut_i.rename(columns={'Reporter ISO':'ISO'}) cut_i.sort_values('Trade Value (US$)', ascending=False,inplace=True) cut_i = cut_i.reset_index(drop=True) cut_i2 = cut_i.loc[:,['Reporter','ISO','Year','Trade Flow','Trade Value (US$)','Netweight (kg)','Alt Qty','Alt Qty Unit','value/kg']] imphtml = cut_i2.to_html(classes="imtable") cap_imphtml = "<h1 id=\"rittai\">Table data of \""+ itemname + "\"(HS:" + com +")" + "[Import]</h1><br><br>" + \ "<caption>" + itemname + " Import Trading Statistics (HScode:" + com + ")</caption>\n" + imphtml title = itemname + "\"(HS:" + com +")" + "World Trade Statistics" #全て結合 kiji_all= titlekiji + exprice + exkgvalue + imprice + imkgvalue + "<p>" + cap_exphtml + "<p>" + cap_imphtml return kiji_all kiji_all = bunsho() title = itemname + "\"(HS:" + com +")" + "World Trade Statistics" wp = Client('http://hstariffstat.com/xmlrpc.php', 'id', 'pass') post = WordPressPost() post.title = title post.content = kiji_all post.terms_names = {'post_tag': [itemname, 'trade statistics'],'category': ['worldwide']} post.post_status = 'publish' wp.call(NewPost(post)) |