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import json
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from deepdoc.parser.resume.entities import degrees, regions, industries
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FIELDS = [
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"address STRING",
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"annual_salary int",
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"annual_salary_from int",
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"annual_salary_to int",
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"birth STRING",
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"card STRING",
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"certificate_obj string",
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"city STRING",
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"corporation_id int",
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"corporation_name STRING",
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"corporation_type STRING",
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"degree STRING",
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"discipline_name STRING",
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"education_obj string",
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"email STRING",
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"expect_annual_salary int",
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"expect_city_names string",
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"expect_industry_name STRING",
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"expect_position_name STRING",
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"expect_salary_from int",
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"expect_salary_to int",
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"expect_type STRING",
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"gender STRING",
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"industry_name STRING",
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"industry_names STRING",
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"is_deleted STRING",
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"is_fertility STRING",
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"is_house STRING",
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"is_management_experience STRING",
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"is_marital STRING",
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"is_oversea STRING",
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"language_obj string",
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"name STRING",
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"nation STRING",
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"phone STRING",
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"political_status STRING",
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"position_name STRING",
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"project_obj string",
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"responsibilities string",
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"salary_month int",
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"scale STRING",
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"school_name STRING",
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"self_remark string",
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"skill_obj string",
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"title_name STRING",
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"tob_resume_id STRING",
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"updated_at Timestamp",
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"wechat STRING",
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"work_obj string",
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"work_experience int",
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"work_start_time BIGINT"
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]
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def refactor(df):
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def deal_obj(obj, k, kk):
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if not isinstance(obj, type({})):
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return ""
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obj = obj.get(k, {})
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if not isinstance(obj, type({})):
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return ""
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return obj.get(kk, "")
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def loadjson(line):
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try:
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return json.loads(line)
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except Exception as e:
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pass
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return {}
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df["obj"] = df["resume_content"].map(lambda x: loadjson(x))
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df.fillna("", inplace=True)
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clms = ["tob_resume_id", "updated_at"]
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def extract(nms, cc=None):
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nonlocal clms
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clms.extend(nms)
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for c in nms:
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if cc:
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df[c] = df["obj"].map(lambda x: deal_obj(x, cc, c))
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else:
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df[c] = df["obj"].map(
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lambda x: json.dumps(
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x.get(
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c,
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{}),
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ensure_ascii=False) if isinstance(
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x,
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type(
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{})) and (
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isinstance(
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x.get(c),
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type(
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{})) or not x.get(c)) else str(x).replace(
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"None",
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""))
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extract(["education", "work", "certificate", "project", "language",
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"skill"])
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extract(["wechat", "phone", "is_deleted",
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"name", "tel", "email"], "contact")
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extract(["nation", "expect_industry_name", "salary_month",
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"industry_ids", "is_house", "birth", "annual_salary_from",
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"annual_salary_to", "card",
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"expect_salary_to", "expect_salary_from",
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"expect_position_name", "gender", "city",
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"is_fertility", "expect_city_names",
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"political_status", "title_name", "expect_annual_salary",
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"industry_name", "address", "position_name", "school_name",
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"corporation_id",
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"is_oversea", "responsibilities",
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"work_start_time", "degree", "management_experience",
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"expect_type", "corporation_type", "scale", "corporation_name",
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"self_remark", "annual_salary", "work_experience",
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"discipline_name", "marital", "updated_at"], "basic")
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df["degree"] = df["degree"].map(lambda x: degrees.get_name(x))
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df["address"] = df["address"].map(lambda x: " ".join(regions.get_names(x)))
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df["industry_names"] = df["industry_ids"].map(lambda x: " ".join([" ".join(industries.get_names(i)) for i in
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str(x).split(",")]))
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clms.append("industry_names")
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def arr2str(a):
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if not a:
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return ""
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if isinstance(a, list):
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a = " ".join([str(i) for i in a])
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return str(a).replace(",", " ")
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df["expect_industry_name"] = df["expect_industry_name"].map(
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lambda x: arr2str(x))
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df["gender"] = df["gender"].map(
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lambda x: "男" if x == 'M' else (
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"女" if x == 'F' else ""))
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for c in ["is_fertility", "is_oversea", "is_house",
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"management_experience", "marital"]:
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df[c] = df[c].map(
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lambda x: '是' if x == 'Y' else (
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'否' if x == 'N' else ""))
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df["is_management_experience"] = df["management_experience"]
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df["is_marital"] = df["marital"]
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clms.extend(["is_management_experience", "is_marital"])
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df.fillna("", inplace=True)
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for i in range(len(df)):
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if not df.loc[i, "phone"].strip() and df.loc[i, "tel"].strip():
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df.loc[i, "phone"] = df.loc[i, "tel"].strip()
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for n in ["industry_ids", "management_experience", "marital", "tel"]:
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for i in range(len(clms)):
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if clms[i] == n:
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del clms[i]
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break
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clms = list(set(clms))
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df = df.reindex(sorted(clms), axis=1)
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for c in clms:
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df[c] = df[c].map(
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lambda s: str(s).replace(
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"\t",
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" ").replace(
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"\n",
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"\\n").replace(
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"\r",
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"\\n"))
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return dict(zip([n.split(" ")[0] for n in FIELDS], df.values.tolist()[0]))
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