Commit a5b9f6bc by dong

fix20230412

parent f93df474
...@@ -36,3 +36,32 @@ def get_jjzb(): ...@@ -36,3 +36,32 @@ def get_jjzb():
except Exception as e: except Exception as e:
current_app.logger.error(e) current_app.logger.error(e)
return jsonify(code=RET.DBERR, msg="查询出错!") return jsonify(code=RET.DBERR, msg="查询出错!")
@api_bigdata.route("/GetJjzb", methods=["GET"]) # 项目跟踪
def get_jjzb():
try:
obj_li = City.query.all()
data = [{
"id": obj.id,
"area": obj.area,
"size": obj.size,
"year": obj.year,
"people": obj.people,
"GDP": obj.GDP,
"addscale": obj.addscale,
"investment": obj.investment,
"retail": obj.retail,
"in_out": obj.in_out,
"public": obj.public,
"people_out": obj.people_out,
"people_per": obj.people_per,
"public_in": obj.public_in,
"info": obj.info,
"question": obj.question,
"flag": obj.flag
} for obj in obj_li]
return jsonify(code=RET.OK, data=data, msg="查询成功!")
except Exception as e:
current_app.logger.error(e)
return jsonify(code=RET.DBERR, msg="查询出错!")
\ No newline at end of file
...@@ -182,7 +182,8 @@ def attract_cnums(): ...@@ -182,7 +182,8 @@ def attract_cnums():
num = len([company for company in company1 if company.province == pro]) num = len([company for company in company1 if company.province == pro])
else: else:
num = enterprise.filter_by(province=pro).count() num = enterprise.filter_by(province=pro).count()
df.append({"name": pro, "value": num}) if num != 0:
df.append({"name": pro, "value": num})
df = sorted(df, key=lambda x: x["value"], reverse=True) df = sorted(df, key=lambda x: x["value"], reverse=True)
redis_store.setex(name_query, 30 * 24 * 3600, json.dumps(df[:5])) redis_store.setex(name_query, 30 * 24 * 3600, json.dumps(df[:5]))
return jsonify(code=RET.OK, msg="获取成功", data=df[:5]) return jsonify(code=RET.OK, msg="获取成功", data=df[:5])
...@@ -195,7 +196,8 @@ def attract_cnums(): ...@@ -195,7 +196,8 @@ def attract_cnums():
else: else:
num = enterprise.filter_by(province=province, city=cit).count() num = enterprise.filter_by(province=province, city=cit).count()
# num = enterprise.filter_by(province=province, city=cit).count() # num = enterprise.filter_by(province=province, city=cit).count()
df.append({"name": cit, "value": num}) if num != 0:
df.append({"name": cit, "value": num})
df = sorted(df, key=lambda x: x["value"], reverse=True) df = sorted(df, key=lambda x: x["value"], reverse=True)
redis_store.setex(name_query, 30 * 24 * 3600, json.dumps(df[:5])) redis_store.setex(name_query, 30 * 24 * 3600, json.dumps(df[:5]))
return jsonify(code=RET.OK, msg="获取成功", data=df[:5]) return jsonify(code=RET.OK, msg="获取成功", data=df[:5])
...@@ -210,7 +212,8 @@ def attract_cnums(): ...@@ -210,7 +212,8 @@ def attract_cnums():
num = enterprise.filter_by(province=province, city=city, district=dis).count() num = enterprise.filter_by(province=province, city=city, district=dis).count()
# num = enterprise.filter_by(province=province, city=city, district=dis).count() # num = enterprise.filter_by(province=province, city=city, district=dis).count()
df.append({"name": dis, "value": num}) if num != 0:
df.append({"name": dis, "value": num})
df = sorted(df, key=lambda x: x["value"], reverse=True) df = sorted(df, key=lambda x: x["value"], reverse=True)
redis_store.setex(name_query, 30 * 24 * 3600, json.dumps(df[:5])) redis_store.setex(name_query, 30 * 24 * 3600, json.dumps(df[:5]))
return jsonify(code=RET.OK, msg="获取成功", data=df[:5]) return jsonify(code=RET.OK, msg="获取成功", data=df[:5])
...@@ -222,7 +225,8 @@ def attract_cnums(): ...@@ -222,7 +225,8 @@ def attract_cnums():
num = enterprise.filter_by(province=province, city=city, district=district).count() num = enterprise.filter_by(province=province, city=city, district=district).count()
# num = enterprise.filter_by(province=province, city=city, district=district).count() # num = enterprise.filter_by(province=province, city=city, district=district).count()
df.append({"name": district, "value": num}) if num != 0:
df.append({"name": district, "value": num})
df = sorted(df, key=lambda x: x["value"], reverse=True) df = sorted(df, key=lambda x: x["value"], reverse=True)
data = df[:5] data = df[:5]
# redis缓存 # redis缓存
......
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