├── src
├── __init__.py
├── cache
│ └── .gitkeep
├── currency_exchange.py
├── predictor.py
├── parser.py
├── data_collector.py
└── analyzer.py
├── img
├── from_to.png
├── from_to2.png
├── predicted.png
├── prev_table.png
└── most_freq_keys.png
├── requirements.txt
├── .dockerignore
├── settings.json
├── Dockerfile
├── pyproject.toml
├── .pre-commit-config.yaml
├── .gitignore
├── researcher.py
├── README.md
└── LICENSE
/src/__init__.py:
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1 |
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/src/cache/.gitkeep:
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1 |
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/img/from_to.png:
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https://raw.githubusercontent.com/hukenovs/hh_research/HEAD/img/from_to.png
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/img/from_to2.png:
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https://raw.githubusercontent.com/hukenovs/hh_research/HEAD/img/from_to2.png
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/img/predicted.png:
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https://raw.githubusercontent.com/hukenovs/hh_research/HEAD/img/predicted.png
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/img/prev_table.png:
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https://raw.githubusercontent.com/hukenovs/hh_research/HEAD/img/prev_table.png
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/img/most_freq_keys.png:
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https://raw.githubusercontent.com/hukenovs/hh_research/HEAD/img/most_freq_keys.png
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/requirements.txt:
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1 | jupyter==1.0.0
2 | matplotlib==3.6.3
3 | nltk==3.8.1
4 | numpy==1.24.1
5 | pandas==1.5.3
6 | pre-commit==3.0.1
7 | requests==2.31.0
8 | scikit-learn==1.2.1
9 | scipy==1.10.0
10 | seaborn==0.12.2
11 | tqdm==4.64.1
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/.dockerignore:
--------------------------------------------------------------------------------
1 | .idea/
2 | .ipynb_checkpoints
3 | */.ipynb_checkpoints/*
4 |
5 | __pycache__/
6 | *.egg
7 | MANIFEST
8 |
9 | .cache
10 | .env
11 | .venv
12 | env/
13 | venv/
14 | ENV/
15 | env.bak/
16 | venv.bak/
17 |
18 | .mypy_cache/
19 |
20 | *.png
21 | *.csv
22 | img/**/*
23 | src/cache/**/*
24 |
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/settings.json:
--------------------------------------------------------------------------------
1 | {
2 | "options": {
3 | "text": "Data Scientist",
4 | "area": 1,
5 | "per_page": 50,
6 | "professional_roles": [96, 10]
7 | },
8 | "refresh": false,
9 | "num_workers": 10,
10 | "save_result": false,
11 | "rates": {
12 | "USD": 0.012641,
13 | "EUR": 0.010831,
14 | "UAH": 0.35902,
15 | "RUR": 1
16 | }
17 | }
18 |
--------------------------------------------------------------------------------
/Dockerfile:
--------------------------------------------------------------------------------
1 | # Dockerfile
2 | # #############################################################################
3 | #
4 | # Build image:
5 | # >> docker build -t hh-api .
6 | #
7 | # Run container:
8 | # >> docker run --rm --name my-app -it -p 3333:3333 hh-api
9 | #
10 | # #############################################################################
11 |
12 | FROM python:3.8
13 | LABEL maintainer="Alexander Kapitanov"
14 | LABEL source="https://github.com/capitanov/hh_research"
15 | WORKDIR /workdir
16 | COPY . .
17 |
18 | RUN pip install --upgrade pip && pip install --no-cache-dir -r /workdir/requirements.txt
19 |
20 | EXPOSE 3333
21 | CMD jupyter notebook --port 3333 --no-browser --ip 0.0.0.0 --allow-root
22 |
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/pyproject.toml:
--------------------------------------------------------------------------------
1 | # Example configuration for Black.
2 |
3 | # NOTE: you have to use single-quoted strings in TOML for regular expressions.
4 | # It's the equivalent of r-strings in Python. Multiline strings are treated as
5 | # verbose regular expressions by Black. Use [ ] to denote a significant space
6 | # character.
7 | [tool.pytest.ini_options]
8 | minversion = "6.1"
9 | addopts = "-ra -q"
10 | testpaths = "tests"
11 |
12 | [tool.flake8]
13 | ignore = ['E203', 'E266', 'E501', 'W503', 'F403', 'F401', 'E231']
14 | max-line-length = 120
15 | max-complexity = 18
16 | select = ['B','C','E','F','W','T4','B9']
17 |
18 | [tool.isort]
19 | known_third_party = ["matplotlib", "nltk", "numpy", "pandas", "requests", "scipy", "seaborn", "sklearn", "src", "tqdm"]
20 | multi_line_output = 3
21 | include_trailing_comma = true
22 | force_grid_wrap = 0
23 | use_parentheses = true
24 | line_length = 120
25 |
26 | [tool.black]
27 | line-length = 120
28 | target-version = ['py36', 'py37', 'py38', 'py39']
29 | exclude = '''
30 | /(
31 | \.eggs
32 | | \.git
33 | | \.hg
34 | | \.mypy_cache
35 | | \.tox
36 | | \.venv
37 | | _build
38 | | buck-out
39 | | build
40 | | dist
41 | # The following are specific to Black, you probably don't want those.
42 | | blib2to3
43 | | tests/data
44 | | profiling
45 | )/
46 | '''
47 |
--------------------------------------------------------------------------------
/.pre-commit-config.yaml:
--------------------------------------------------------------------------------
1 | exclude: '^$'
2 | fail_fast: false
3 | default_language_version:
4 | python: python3.9
5 | repos:
6 | - repo: https://github.com/pre-commit/pre-commit-hooks
7 | rev: v4.3.0
8 | hooks:
9 | - id: check-added-large-files
10 | # - id: check-docstring-first
11 | - id: check-case-conflict
12 | - id: check-merge-conflict
13 | - id: check-yaml
14 | - id: trailing-whitespace
15 | - id: end-of-file-fixer
16 | - id: requirements-txt-fixer
17 |
18 | - repo: https://github.com/PyCQA/autoflake
19 | rev: v1.4
20 | hooks:
21 | - id: autoflake
22 | args: ['-r', '--in-place',
23 | '--remove-all-unused-imports',
24 | '--ignore-init-module-imports',
25 | '--remove-unused-variables',
26 | '--remove-duplicate-keys'
27 | ]
28 |
29 | - repo: https://github.com/asottile/seed-isort-config
30 | rev: v2.2.0
31 | hooks:
32 | - id: seed-isort-config
33 | args: ['--application-directories=src']
34 |
35 | - repo: https://github.com/PyCQA/isort
36 | rev: 5.10.1
37 | hooks:
38 | - id: isort
39 | args:
40 | - "--profile=black"
41 |
42 | - repo: https://github.com/ambv/black
43 | rev: 22.6.0
44 | hooks:
45 | - id: black
46 | language_version: python3
47 | args:
48 | - "--line-length=120"
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/.gitignore:
--------------------------------------------------------------------------------
1 | # User added
2 | .idea/
3 |
4 | # Byte-compiled / optimized / DLL files
5 | __pycache__/
6 | *.py[cod]
7 | *$py.class
8 |
9 | # C extensions
10 | *.so
11 |
12 | # Distribution / packaging
13 | .Python
14 | build/
15 | develop-eggs/
16 | dist/
17 | downloads/
18 | eggs/
19 | .eggs/
20 | lib/
21 | lib64/
22 | parts/
23 | sdist/
24 | var/
25 | wheels/
26 | *.egg-info/
27 | .installed.cfg
28 | *.egg
29 | MANIFEST
30 |
31 | # PyInstaller
32 | # Usually these files are written by a python script from a template
33 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
34 | *.manifest
35 | *.spec
36 |
37 | # Installer logs
38 | pip-log.txt
39 | pip-delete-this-directory.txt
40 |
41 | # Unit test / coverage reports
42 | htmlcov/
43 | .tox/
44 | .coverage
45 | .coverage.*
46 | .cache
47 | nosetests.xml
48 | coverage.xml
49 | *.cover
50 | .hypothesis/
51 | .pytest_cache/
52 |
53 | # Translations
54 | *.mo
55 | *.pot
56 |
57 | # Django stuff:
58 | *.log
59 | local_settings.py
60 | db.sqlite3
61 |
62 | # Flask stuff:
63 | instance/
64 | .webassets-cache
65 |
66 | # Scrapy stuff:
67 | .scrapy
68 |
69 | # Sphinx documentation
70 | docs/_build/
71 |
72 | # PyBuilder
73 | target/
74 |
75 | # Jupyter Notebook
76 | .ipynb_checkpoints
77 | */.ipynb_checkpoints/*
78 |
79 | # pyenv
80 | .python-version
81 |
82 | # celery beat schedule file
83 | celerybeat-schedule
84 |
85 | # SageMath parsed files
86 | *.sage.py
87 |
88 | # Environments
89 | .env
90 | .venv
91 | env/
92 | venv/
93 | ENV/
94 | env.bak/
95 | venv.bak/
96 |
97 | # Spyder project settings
98 | .spyderproject
99 | .spyproject
100 |
101 | # Rope project settings
102 | .ropeproject
103 |
104 | # mkdocs documentation
105 | /site
106 |
107 | # mypy
108 | .mypy_cache/
109 |
110 | *.csv
111 | src/cache/**/*
112 | !src/cache/.gitkeep
113 |
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/src/currency_exchange.py:
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1 | """Get currency exchange for RUB, EUR, USD from remore server
2 |
3 | GNU GENERAL PUBLIC LICENSE
4 | Version 3, 29 June 2007
5 |
6 | Copyright (c) 2020 Kapitanov Alexander
7 |
8 | This program is free software: you can redistribute it and/or modify
9 | it under the terms of the GNU General Public License as published by
10 | the Free Software Foundation, either version 3 of the License, or
11 | (at your option) any later version.
12 |
13 | You should have received a copy of the GNU General Public License
14 | along with this program. If not, see .
15 |
16 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
17 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
18 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT
19 | WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT
20 | NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
21 | FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND
22 | PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE
23 | DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR
24 | OR CORRECTION.
25 |
26 | ------------------------------------------------------------------------
27 | """
28 | import json
29 | from typing import Dict
30 |
31 | import requests
32 |
33 |
34 | class Exchanger:
35 | __EXCHANGE_URL = "https://api.exchangerate-api.com/v4/latest/RUB"
36 |
37 | def __init__(self, config_path: str):
38 | self.config_path = config_path
39 |
40 | def update_exchange_rates(self, rates: Dict):
41 | """Parse exchange rates for RUB, USD, EUR and save them to `rates`
42 |
43 | Parameters
44 | ----------
45 | rates : dict
46 | Dict of currencies. For example: {"RUB": 1, "USD": 0.001}
47 | """
48 |
49 | try:
50 | response = requests.get(self.__EXCHANGE_URL)
51 | new_rates = response.json()["rates"]
52 | except requests.exceptions.SSLError:
53 | raise AssertionError("[FAIL] Cannot get exchange rate! Try later or change the host API")
54 |
55 | for curr in rates:
56 | rates[curr] = new_rates[curr]
57 |
58 | # Change 'RUB' to 'RUR'
59 | rates["RUR"] = rates.pop("RUB")
60 |
61 | def save_rates(self, rates: Dict):
62 | """Save rates to JSON config."""
63 |
64 | with open(self.config_path, "r") as cfg:
65 | data = json.load(cfg)
66 |
67 | data["rates"] = rates
68 |
69 | with open(self.config_path, "w") as cfg:
70 | json.dump(data, cfg, indent=2)
71 |
72 |
73 | if __name__ == "__main__":
74 | _exchanger = Exchanger("../settings.json")
75 | _default = {"RUB": None, "USD": None, "EUR": None, "UAH": None}
76 | _exchanger.update_exchange_rates(_default)
77 | _exchanger.save_rates(_default)
78 | for _k, _v in _default.items():
79 | print(f"{_k}: {_v :.05f}")
80 |
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/researcher.py:
--------------------------------------------------------------------------------
1 | """Head Hunter Researcher
2 |
3 | Description :
4 | HeadHunter (hh.ru) main research script.
5 |
6 | 1. Get data from hh.ru by user request (i.e. 'Machine learning')
7 | 2. Collect all vacancies.
8 | 3. Parse JSON and get useful values: salary, experience, name,
9 | skills, employer name etc.
10 | 4. Calculate some statistics: average salary, median, std, variance.
11 |
12 | ------------------------------------------------------------------------
13 |
14 | GNU GENERAL PUBLIC LICENSE
15 | Version 3, 29 June 2007
16 |
17 | Copyright (c) 2020 Kapitanov Alexander
18 |
19 | This program is free software: you can redistribute it and/or modify
20 | it under the terms of the GNU General Public License as published by
21 | the Free Software Foundation, either version 3 of the License, or
22 | (at your option) any later version.
23 |
24 | You should have received a copy of the GNU General Public License
25 | along with this program. If not, see .
26 |
27 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
28 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
29 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT
30 | WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT
31 | NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
32 | FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND
33 | PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE
34 | DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR
35 | OR CORRECTION.
36 |
37 | ------------------------------------------------------------------------
38 | """
39 |
40 | # Authors : Alexander Kapitanov
41 | # ...
42 | # Contacts :
43 | # License : GNU GENERAL PUBLIC LICENSE
44 |
45 | import os
46 | from typing import Optional
47 |
48 | from src.analyzer import Analyzer
49 | from src.currency_exchange import Exchanger
50 | from src.data_collector import DataCollector
51 | from src.parser import Settings
52 | from src.predictor import Predictor
53 |
54 | CACHE_DIR = os.path.join(os.path.abspath(os.path.dirname(__file__)), "cache")
55 | SETTINGS_PATH = "settings.json"
56 |
57 |
58 | class ResearcherHH:
59 | """Main class for searching vacancies and analyze them."""
60 |
61 | def __init__(self, config_path: str = SETTINGS_PATH, no_parse: bool = False):
62 | self.settings = Settings(config_path, no_parse=no_parse)
63 | self.exchanger = Exchanger(config_path)
64 | self.collector: Optional[DataCollector] = None
65 | self.analyzer: Optional[Analyzer] = None
66 | self.predictor = Predictor()
67 |
68 | def update(self, **kwargs):
69 | self.settings.update_params(**kwargs)
70 | if not any(self.settings.rates.values()) or self.settings.update:
71 | print("[INFO]: Trying to get exchange rates from remote server...")
72 | self.exchanger.update_exchange_rates(self.settings.rates)
73 | self.exchanger.save_rates(self.settings.rates)
74 |
75 | print(f"[INFO]: Get exchange rates: {self.settings.rates}")
76 | self.collector = DataCollector(self.settings.rates)
77 | self.analyzer = Analyzer(self.settings.save_result)
78 |
79 | def __call__(self):
80 | print("[INFO]: Collect data from JSON. Create list of vacancies...")
81 | vacancies = self.collector.collect_vacancies(
82 | query=self.settings.options, refresh=self.settings.refresh, num_workers=self.settings.num_workers
83 | )
84 | print("[INFO]: Prepare dataframe...")
85 | df = self.analyzer.prepare_df(vacancies)
86 | print("\n[INFO]: Analyze dataframe...")
87 | self.analyzer.analyze_df(df)
88 | print("\n[INFO]: Predict None salaries...")
89 | # total_df = self.predictor.predict(df)
90 | # self.predictor.plot_results(total_df)
91 | print("[INFO]: Done! Exit()")
92 |
93 |
94 | if __name__ == "__main__":
95 | hh_analyzer = ResearcherHH()
96 | hh_analyzer.update()
97 | hh_analyzer()
98 |
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/src/predictor.py:
--------------------------------------------------------------------------------
1 | r"""Predictor: getting words from vacancies (description, keywords) and
2 | make predictions for None salaries.
3 |
4 | ------------------------------------------------------------------------
5 |
6 | GNU GENERAL PUBLIC LICENSE
7 | Version 3, 29 June 2007
8 |
9 | Copyright (c) 2020 Kapitanov Alexander
10 |
11 | This program is free software: you can redistribute it and/or modify
12 | it under the terms of the GNU General Public License as published by
13 | the Free Software Foundation, either version 3 of the License, or
14 | (at your option) any later version.
15 |
16 | You should have received a copy of the GNU General Public License
17 | along with this program. If not, see .
18 |
19 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
20 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
21 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT
22 | WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT
23 | NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
24 | FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND
25 | PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE
26 | DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR
27 | OR CORRECTION.
28 |
29 | ------------------------------------------------------------------------
30 | """
31 |
32 | # Authors : Alexander Kapitanov
33 | # ...
34 | # Contacts :
35 | # License : GNU GENERAL PUBLIC LICENSE
36 |
37 | import matplotlib.pyplot as plt
38 | import numpy as np
39 | import pandas as pd
40 | import seaborn as sns
41 | from nltk.corpus import stopwords as nltk_stopwords
42 | from scipy.sparse import hstack
43 | from sklearn.feature_extraction import DictVectorizer
44 | from sklearn.feature_extraction.text import TfidfVectorizer
45 | from sklearn.linear_model import Ridge
46 |
47 |
48 | class Predictor:
49 | """Predictor: getting words from vacancies (description, keywords) and
50 | make predictions for None salaries.
51 |
52 | """
53 |
54 | @staticmethod
55 | def text_replace(text) -> pd.Series:
56 | """Clean text"""
57 | return text.apply(lambda x: [i.lower() for i in x]).replace("[^a-zA-Z]\bqout\b|\bamp\b", " ", regex=True)
58 |
59 | @staticmethod
60 | def prepare_dataframe(df: pd.DataFrame) -> pd.DataFrame:
61 | df_num = df[df["From"].notna() | df["From"].notna()]
62 | df_avg = df_num[["From", "To"]].mean(axis=1)
63 | df_num = df_num.drop(["Salary", "From", "To"], axis=1)
64 | df_num.insert(3, "Average", df_avg)
65 | return df_num
66 |
67 | @staticmethod
68 | def plot_results(df: pd.DataFrame):
69 | fp = plt.figure("Predicted salaries", figsize=(12, 8), dpi=80)
70 | fp.add_subplot(2, 2, 1)
71 | plt.title("Average Boxplot")
72 | sns.boxplot(data=df[["Average"]], width=0.4)
73 |
74 | fp.add_subplot(2, 2, 2)
75 | plt.title("Average Swarmplot")
76 | sns.swarmplot(data=df[["Average"]].dropna(), size=6)
77 |
78 | fp.add_subplot(2, 2, 3)
79 | plt.title("Average: Distribution ")
80 | sns.histplot(df[["Average"]].dropna(), bins=12, kde=True)
81 | plt.grid(False)
82 | plt.yticks([], [])
83 | plt.tight_layout()
84 | plt.show()
85 |
86 | def predict(self, df: pd.DataFrame, min_df_threshold: int = 5) -> pd.DataFrame:
87 | """Prepare data frame and save results
88 |
89 | Parameters
90 | ----------
91 | df: pd.DataFrame
92 | Dict of parsed vacancies.
93 | min_df_threshold: int
94 | Threshold for document freq.
95 |
96 | """
97 |
98 | # Create pandas dataframe
99 | # Set TF-IDF features
100 | stopwords_ru = set(nltk_stopwords.words("russian"))
101 | stopwords_en = set(nltk_stopwords.words("english"))
102 | stopwords = stopwords_ru | stopwords_en
103 |
104 | new_df = self.prepare_dataframe(df)
105 | tf_idf = TfidfVectorizer(min_df=min_df_threshold, stop_words=stopwords)
106 |
107 | # Training set
108 | txt = self.text_replace(new_df["Keys"])
109 | joined_text = []
110 | for i, x in enumerate(txt):
111 | print(f"{i :<4} {x}")
112 | joined_text.append(" ".join(x))
113 | x_train_text = tf_idf.fit_transform(joined_text)
114 |
115 | # Print top words used in keys
116 | idx = np.ravel(x_train_text.sum(axis=0).argsort(axis=1))[::-1][:7]
117 | top_words = np.array(tf_idf.get_feature_names())[idx].tolist()
118 | print("Top words used in keys: {}".format(top_words))
119 |
120 | # One-hot-encoding for data frame features
121 | dct_enc = DictVectorizer()
122 | x_train_cat = dct_enc.fit_transform(new_df[["Experience", "Name"]].to_dict("Records"))
123 |
124 | # Stack vectors
125 | x_train = hstack([x_train_text, x_train_cat])
126 |
127 | y_train = new_df["Average"]
128 | model = Ridge(alpha=1, random_state=255)
129 | model.fit(x_train, y_train)
130 |
131 | # Frame with NaNs
132 | x_test = df[df["From"].isna() & df["To"].isna()]
133 |
134 | # Test vectors
135 | print(x_test["Description"])
136 | x_desc = x_test["Description"].apply(str.lower)
137 | joined_desc = []
138 | for i, x in enumerate(x_desc):
139 | joined_text.append(" ".join(x))
140 | x_test_text = tf_idf.transform(joined_desc)
141 | x_test_cat = dct_enc.transform(x_test[["Experience", "Name"]].to_dict("Records"))
142 | x_test = hstack([x_test_text, x_test_cat])
143 |
144 | # Prediction model - result
145 | y_test = model.predict(x_test)
146 | print(
147 | f"[INFO]: Salary for vacancies with NaN:\n"
148 | f"Average is {y_test.mean(dtype=int)}"
149 | f"Maximum is {y_test.max(dtype=int)}"
150 | f"Maximum is {y_test.min(dtype=int)}"
151 | )
152 |
153 | df_tst = x_test.drop(["Salary", "From", "To"], axis=1)
154 | df_tst.insert(3, "Average", y_test.astype(int))
155 | return df_tst
156 |
--------------------------------------------------------------------------------
/src/parser.py:
--------------------------------------------------------------------------------
1 | r"""Parse command line arguments
2 |
3 | Command parameters:
4 | refresh : bool - Refresh data from remote server.
5 | num_workers : int - Number of workers for threading.
6 | options : dict - Options for GET request to hh api.
7 |
8 | Example:
9 | options:
10 | {
11 | "text": "Python Developer",
12 | "area": 1,
13 | "per_page": 50
14 | }
15 |
16 | Parser parameters:
17 | update : bool - Update JSON config if needed.
18 |
19 | ------------------------------------------------------------------------
20 |
21 | GNU GENERAL PUBLIC LICENSE
22 | Version 3, 29 June 2007
23 |
24 | Copyright (c) 2020 Kapitanov Alexander
25 |
26 | This program is free software: you can redistribute it and/or modify
27 | it under the terms of the GNU General Public License as published by
28 | the Free Software Foundation, either version 3 of the License, or
29 | (at your option) any later version.
30 |
31 | You should have received a copy of the GNU General Public License
32 | along with this program. If not, see .
33 |
34 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
35 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
36 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT
37 | WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT
38 | NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
39 | FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND
40 | PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE
41 | DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR
42 | OR CORRECTION.
43 |
44 | ------------------------------------------------------------------------
45 | """
46 |
47 | # Authors : Alexander Kapitanov
48 | # ...
49 | # Contacts :
50 | # License : GNU GENERAL PUBLIC LICENSE
51 |
52 | import argparse
53 | import json
54 | from typing import Dict, Optional, Sequence
55 |
56 |
57 | class Settings:
58 | r"""Researcher parameters
59 |
60 | Parameters
61 | ----------
62 | config_path : str
63 | Path to config file
64 | input_args : tuple
65 | Command line arguments for tests.
66 | no_parse : bool
67 | Disable parsing arguments from command line.
68 |
69 | Attributes
70 | ----------
71 | options : dict
72 | Options for GET request to API.
73 | refresh : bool
74 | Refresh data from remote server.
75 | save_result : bool
76 | Save DataFrame with parsed vacancies to CSV file
77 | num_workers : int
78 | Number of workers for threading.
79 | rates : dict
80 | Dict of currencies. For example: {"RUB": 1, "USD": 0.001}
81 | """
82 |
83 | def __init__(
84 | self, config_path: str, input_args: Optional[Sequence[str]] = None, no_parse: bool = False,
85 | ):
86 | self.options: Optional[Dict] = None
87 | self.rates: Optional[Dict] = None
88 | self.refresh: bool = False
89 | self.num_workers: int = 1
90 | self.save_result: bool = False
91 | self.update: bool = False
92 |
93 | # Get config from file
94 | with open(config_path, "r") as cfg:
95 | config: Dict = json.load(cfg)
96 |
97 | if not no_parse:
98 | params = self.__parse_args(input_args)
99 |
100 | for key, value in params.items():
101 | if value is not None:
102 | if key in config:
103 | config[key] = value
104 | if "options" in config and key in config["options"]:
105 | config["options"][key] = value
106 |
107 | self.update = params.get("update", False)
108 | if params["update"]:
109 | with open(config_path, "w") as cfg:
110 | json.dump(config, cfg, indent=2)
111 |
112 | # Update attributes:
113 | for key, value in config.items():
114 | if hasattr(self, key):
115 | setattr(self, key, value)
116 |
117 | def __repr__(self):
118 | txt = "\n".join([f"{k :<16}: {v}" for k, v in self.__dict__.items()])
119 | return f"Settings:\n{txt}"
120 |
121 | def update_params(self, **kwargs):
122 | """Update object params"""
123 | for key, value in kwargs.items():
124 | if hasattr(self, key) and value is not None:
125 | setattr(self, key, value)
126 |
127 | @staticmethod
128 | def __parse_args(inputs_args) -> Dict:
129 | """Read arguments from command line.
130 |
131 | Returns
132 | -------
133 | arguments : dict
134 | Parsed arguments from command line. Note: some arguments are positional.
135 |
136 | """
137 |
138 | parser = argparse.ArgumentParser(description="HeadHunter vacancies researcher")
139 | parser.add_argument(
140 | "-t", "--text", action="store", type=str, default=None, help='Search query text (e.g. "Machine learning")',
141 | )
142 | parser.add_argument(
143 | "-p", "--professional_roles", action="store", type=int, default=None,
144 | help='Professional role filter (Possible roles can be found here https://api.hh.ru/professional_roles)',
145 | nargs='*'
146 | )
147 | parser.add_argument(
148 | "-n", "--num_workers", action="store", type=int, default=None, help="Number of workers for multithreading.",
149 | )
150 | parser.add_argument(
151 | "-r", "--refresh", help="Refresh cached data from HH API", action="store_true", default=None,
152 | )
153 | parser.add_argument(
154 | "-s", "--save_result", help="Save parsed result as DataFrame to CSV file.", action="store_true", default=None,
155 | )
156 | parser.add_argument(
157 | "-u", "--update", action="store_true", default=None, help="Save command line args to file in JSON format.",
158 | )
159 |
160 | params, unknown = parser.parse_known_args(inputs_args)
161 | # Update config from command line
162 | return vars(params)
163 |
164 |
165 | if __name__ == "__main__":
166 | settings = Settings(
167 | config_path="../settings.json", input_args=("--num_workers", "5", "--refresh", "--text", "Data Scientist"),
168 | )
169 |
170 | print(settings)
171 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # Автоматизация поиска и анализа вакансий hh.ru
2 |
3 | **Идея**: проект упрощает поиск, анализ и исследование особенностей вакансий на hh.ru.
4 | Скрипт использует API hh.ru для доступа к данным вакансий.
5 |
6 | ### Общая информация
7 |
8 | | **Title** | HeadHunter Find & Research |
9 | | :-- | :-- |
10 | | **Author** | Alexander Kapitanov |
11 | | **Language** | Python (3+) |
12 | | **Release** | 14 Aug 2019 |
13 | | **License** | GNU GPL 3.0 |
14 |
15 | 
16 |
17 | ____
18 |
19 | ### Requirements
20 | Пример установки зависимостей для Python 3.9
21 |
22 | ```bash
23 | git clone
24 | cd
25 |
26 | conda create -n venv python=3.9
27 | conda activate venv
28 |
29 | python researcher.py
30 | ```
31 |
32 | ### Command line arguments
33 | ```bash
34 | usage: researcher.py [-h] [--text TEXT] [--professional_roles ROLE1 ROLE2 ...] [--num_workers MAX_WORKERS] [--refresh] [--save_result] [--update]
35 |
36 | HeadHunter (hh.ru) vacancies researcher
37 |
38 | optional arguments:
39 | -h, --help show this help message and exit
40 | -t TEXT, --text TEXT Search query text (e.g. "Machine learning")
41 | -p [PROFESSIONAL_ROLES ...], --professional_roles [PROFESSIONAL_ROLES ...]
42 | Professional role filter (Possible roles can be found here https://api.hh.ru/professional_roles)
43 | -n NUM_WORKERS, --num_workers NUM_WORKERS
44 | Number of workers for multithreading.
45 | -r, --refresh Refresh cached data from HH API
46 | -s, --save_result Save parsed result as DataFrame to CSV file.
47 | -u, --update Save command line args to file in JSON format.
48 | ```
49 |
50 | ### Config file
51 | Все параметры находятся в конфигурационном файле, пример:
52 | ```json
53 | {
54 | "options": {
55 | "text": "Data Scientist",
56 | "area": 1,
57 | "per_page": 50,
58 | "professional_roles": [96, 10]
59 | },
60 | "refresh": false,
61 | "num_workers": 10,
62 | "save_result": false,
63 | "exchanges": ["RUB", "USD", "EUR", "UAH"]
64 | }
65 | ```
66 |
67 | ### Input data
68 | Входные данные - словарь ключевых значений, формирующих запрос.
69 |
70 | **Основные параметры**:
71 | - `area` - локация поискового запроса (пример: `{area: 1}` - Москва),
72 | - `text` - поисковой запрос для вакансий (пример: `{text : Machine Learning}` или `{text: Java}`),
73 | - `per_page` - количество вакансий на страницу, по умолчанию **50**.
74 | - `professional_roles` - фильтр по роли в запросе ([возможные значения](https://api.hh.ru/openapi/redoc#tag/Obshie-spravochniki/paths/~1professional_roles/get))
75 |
76 | и другие параметры (в зависимости от требуемого запроса).
77 |
78 | Пример графика распределения зарплат:
79 |
80 | 
81 |
82 | ### Run
83 |
84 | Для запуска скрипта необходимо задать обязательный параметр ключевого запроса поиска. В системах Windows ключевой запрос в двойных кавычках! Например: `Machine Learning` или `JavaScript`.
85 | Скрипт запускается из командной строки:
86 |
87 | `python researcher.py --text "Python Developer"`
88 |
89 | Можно задать параметр `--refresh`, который обновляет кешируемые данные о вакансиях. Для повторных запросов, отличающихся от первичного, это обязательный параметр.
90 |
91 | `python researcher.py --text "Data Mining" --refresh`
92 |
93 | ### Processing
94 | - Ответ от удаленного ресурса в виде json-массива для текущего курса валют: `{RUR, USD, EUR, UAH}`.
95 | - На базе словаря **входных данных** формируется URL для запроса данных с hh.ru через API,
96 | - Создается список всех `id` вакансий,
97 | - Парсинг JSON в ответ на запрос по всем `id` вакансий,
98 | - Анализ параметра `salary` для формирования словаря зарплат:
99 | - Для зарплат, указанных в `USD` и `EUR` производится пересчёт по текущему курсу рубля,
100 | - Для зарплат, указанных до вычета НДФЛ производится пересчёт на реальную зарплату "на руки",
101 | - Для отсутствующих зарплат - пропуск.
102 | - Создаётся словарь ключевых элементов таблицы,
103 | - Массив содержит поля: `{колонки фрейма* - (см. ниже)}`,
104 | - Для полей `From` и `To` происходит перерасчет зарплаты,
105 | - Ключевые навыки формируются как перечисляемый список,
106 | - Описание вакансий очищается от HTML-тегов с помощью дополнительной функции.
107 | - Функция возвращает массив кортежей.
108 | - Преобразование сырых данных в `DataFrame` для дальнейшего анализа. Результат сохраняется на диск в виде `csv` файла.
109 | - Анализ `DataFrame` - поиск статстических параметров, поиск мат. ожидания, медианы и т.д от зарплат. Классификация по параметрам.
110 | - Предсказание зарплат для вакансий, у которых этот параметр не задан.
111 | - Построение информативных графиков.
112 |
113 | Пример графика для указанных зарплат:
114 |
115 | 
116 |
117 | **Колонки фрейма**:
118 |
119 | | Параметр | Тип | Описание |
120 | | :-- | :-- | :-- |
121 | | `Ids` | `str` | идентификатор вакансии (формирует ссылку на вакансию) |
122 | | `Employer` | `str` | работодатель |
123 | | `Name` | `str` | название вакансии |
124 | | `Salary` | `bool` | указание зарплаты: `True / False` |
125 | | `From` | `float` | нижний порог зарплаты |
126 | | `To` | `float` | верхний порог зарплаты |
127 | | `Experience` | `str` | опыт работы |
128 | | `Schedule` | `str` | график работы |
129 | | `Keys` | `list` | ключевые навыки |
130 | | `Description` | `str` | описание вакансиии |
131 |
132 | *Нижний и верхний порог зарплаты пересчитаны в рубли по текущему курсу валюты. Также для зарплат, указанных до вычета НДФЛ производится пересчёт на реальную зарплату "на руки".*
133 |
134 | Пример наиболее часто используемых ключевых значений:
135 |
136 | 
137 |
138 | ### Output data
139 | Выходные данные - таблица в формате `csv`, созданная с помощью фреймворка `pandas`. Дополнительно к этой таблице проводится анализ: поиск мат. ожидания, медианы и т.д от зарплат в зависимости от остальных критериев в таблице (например, от опыта работы). Проводится классификация по различным параметрам.
140 |
141 | Пример предсказанных зарплат:
142 |
143 | 
144 |
145 | [Документация API HeadHunter hh.ru](https://github.com/hhru/api "Head-Hunter API documentation")
146 | ____
147 |
148 | ### Link:
149 | * https://habr.com/users/hukenovs/
150 |
151 | ### Author:
152 | * Kapitanov Alexander
153 |
154 | ### Release:
155 | * 2019/08/14.
156 |
157 | ### License:
158 | * GNU GPL 3.0.
159 |
--------------------------------------------------------------------------------
/src/data_collector.py:
--------------------------------------------------------------------------------
1 | r"""Vacancy finder
2 |
3 | ------------------------------------------------------------------------
4 |
5 | GNU GENERAL PUBLIC LICENSE
6 | Version 3, 29 June 2007
7 |
8 | Copyright (c) 2020 Kapitanov Alexander
9 |
10 | This program is free software: you can redistribute it and/or modify
11 | it under the terms of the GNU General Public License as published by
12 | the Free Software Foundation, either version 3 of the License, or
13 | (at your option) any later version.
14 |
15 | You should have received a copy of the GNU General Public License
16 | along with this program. If not, see .
17 |
18 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
19 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
20 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT
21 | WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT
22 | NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
23 | FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND
24 | PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE
25 | DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR
26 | OR CORRECTION.
27 |
28 | ------------------------------------------------------------------------
29 | """
30 |
31 | # Authors : Alexander Kapitanov
32 | # ...
33 | # Contacts :
34 | # License : GNU GENERAL PUBLIC LICENSE
35 |
36 | import hashlib
37 | import os
38 | import pickle
39 | import re
40 | from concurrent.futures import ThreadPoolExecutor
41 | from typing import Dict, Optional
42 | from urllib.parse import urlencode
43 |
44 | import requests
45 | from tqdm import tqdm
46 |
47 | CACHE_DIR = os.path.join(os.path.abspath(os.path.dirname(__file__)), "cache")
48 |
49 |
50 | class DataCollector:
51 | r"""Researcher parameters
52 |
53 | Parameters
54 | ----------
55 | exchange_rates : dict
56 | Dict of exchange rates: RUR, USD, EUR.
57 |
58 | """
59 | __API_BASE_URL = "https://api.hh.ru/vacancies/"
60 | __DICT_KEYS = (
61 | "Ids",
62 | "Employer",
63 | "Name",
64 | "Salary",
65 | "From",
66 | "To",
67 | "Experience",
68 | "Schedule",
69 | "Keys",
70 | "Description",
71 | )
72 |
73 | def __init__(self, exchange_rates: Optional[Dict]):
74 | self._rates = exchange_rates
75 |
76 | @staticmethod
77 | def clean_tags(html_text: str) -> str:
78 | """Remove HTML tags from the string
79 |
80 | Parameters
81 | ----------
82 | html_text: str
83 | Input string with tags
84 |
85 | Returns
86 | -------
87 | result: string
88 | Clean text without HTML tags
89 |
90 | """
91 | pattern = re.compile("<.*?>")
92 | return re.sub(pattern, "", html_text)
93 |
94 | @staticmethod
95 | def __convert_gross(is_gross: bool) -> float:
96 | return 0.87 if is_gross else 1
97 |
98 | def get_vacancy(self, vacancy_id: str):
99 | # Get data from URL
100 | url = f"{self.__API_BASE_URL}{vacancy_id}"
101 | vacancy = requests.get(url).json()
102 |
103 | # Extract salary
104 | salary = vacancy.get("salary")
105 |
106 | # Calculate salary:
107 | # Get salary into {RUB, USD, EUR} with {Gross} parameter and
108 | # return a new salary in RUB.
109 | from_to = {"from": None, "to": None}
110 | if salary:
111 | is_gross = vacancy["salary"].get("gross")
112 | for k, v in from_to.items():
113 | if vacancy["salary"][k] is not None:
114 | _value = self.__convert_gross(is_gross)
115 | from_to[k] = int(_value * salary[k] / self._rates[salary["currency"]])
116 |
117 | # Create pages tuple
118 | return (
119 | vacancy_id,
120 | vacancy.get("name", ""),
121 | vacancy.get("employer", {}).get("name", ""),
122 | salary is not None,
123 | from_to["from"],
124 | from_to["to"],
125 | vacancy.get("experience", {}).get("name", ""),
126 | vacancy.get("schedule", {}).get("name", ""),
127 | [el["name"] for el in vacancy.get("key_skills", [])],
128 | self.clean_tags(vacancy.get("description", "")),
129 | )
130 |
131 | @staticmethod
132 | def __encode_query_for_url(query: Optional[Dict]) -> str:
133 | if 'professional_roles' in query:
134 | query_copy = query.copy()
135 |
136 | roles = '&'.join([f'professional_role={r}' for r in query_copy.pop('professional_roles')])
137 |
138 | return roles + (f'&{urlencode(query_copy)}' if len(query_copy) > 0 else '')
139 |
140 | return urlencode(query)
141 |
142 | def collect_vacancies(self, query: Optional[Dict], refresh: bool = False, num_workers: int = 1) -> Dict:
143 | """Parse vacancy JSON: get vacancy name, salary, experience etc.
144 |
145 | Parameters
146 | ----------
147 | query : dict
148 | Search query params for GET requests.
149 | refresh : bool
150 | Refresh cached data
151 | num_workers : int
152 | Number of workers for threading.
153 |
154 | Returns
155 | -------
156 | dict
157 | Dict of useful arguments from vacancies
158 |
159 | """
160 | if num_workers is None or num_workers < 1:
161 | num_workers = 1
162 |
163 | url_params = self.__encode_query_for_url(query)
164 |
165 | # Get cached data if exists...
166 | cache_name: str = url_params
167 | cache_hash = hashlib.md5(cache_name.encode()).hexdigest()
168 | cache_file = os.path.join(CACHE_DIR, cache_hash)
169 | try:
170 | if not refresh:
171 | print(f"[INFO]: Get results from cache! Enable refresh option to update results.")
172 | return pickle.load(open(cache_file, "rb"))
173 | except (FileNotFoundError, pickle.UnpicklingError):
174 | pass
175 |
176 | # Check number of pages...
177 | target_url = self.__API_BASE_URL + "?" + url_params
178 | num_pages = requests.get(target_url).json()["pages"]
179 |
180 | # Collect vacancy IDs...
181 | ids = []
182 | for idx in range(num_pages + 1):
183 | response = requests.get(target_url, {"page": idx})
184 | data = response.json()
185 | if "items" not in data:
186 | break
187 | ids.extend(x["id"] for x in data["items"])
188 |
189 | # Collect vacancies...
190 | jobs_list = []
191 | with ThreadPoolExecutor(max_workers=num_workers) as executor:
192 | for vacancy in tqdm(
193 | executor.map(self.get_vacancy, ids),
194 | desc="Get data via HH API",
195 | ncols=100,
196 | total=len(ids),
197 | ):
198 | jobs_list.append(vacancy)
199 |
200 | unzipped_list = list(zip(*jobs_list))
201 |
202 | result = {}
203 | for idx, key in enumerate(self.__DICT_KEYS):
204 | result[key] = unzipped_list[idx]
205 |
206 | pickle.dump(result, open(cache_file, "wb"))
207 | return result
208 |
209 |
210 | if __name__ == "__main__":
211 | dc = DataCollector(exchange_rates={"USD": 0.01264, "EUR": 0.01083, "RUR": 1.00000})
212 |
213 | vacancies = dc.collect_vacancies(
214 | query={"text": "FPGA", "area": 1, "per_page": 50},
215 | # refresh=True
216 | )
217 | print(vacancies["Employer"])
218 |
--------------------------------------------------------------------------------
/src/analyzer.py:
--------------------------------------------------------------------------------
1 | r"""Researcher: collect statistics, predict salaries etc.
2 |
3 | ------------------------------------------------------------------------
4 |
5 | GNU GENERAL PUBLIC LICENSE
6 | Version 3, 29 June 2007
7 |
8 | Copyright (c) 2020 Kapitanov Alexander
9 |
10 | This program is free software: you can redistribute it and/or modify
11 | it under the terms of the GNU General Public License as published by
12 | the Free Software Foundation, either version 3 of the License, or
13 | (at your option) any later version.
14 |
15 | You should have received a copy of the GNU General Public License
16 | along with this program. If not, see .
17 |
18 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
19 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
20 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT
21 | WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT
22 | NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
23 | FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND
24 | PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE
25 | DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR
26 | OR CORRECTION.
27 |
28 | ------------------------------------------------------------------------
29 | """
30 |
31 | # Authors : Alexander Kapitanov
32 | # ...
33 | # Contacts :
34 | # License : GNU GENERAL PUBLIC LICENSE
35 |
36 | import re
37 | from typing import Dict, List
38 |
39 | import matplotlib.pyplot as plt
40 | import nltk
41 | import numpy as np
42 | import pandas as pd
43 | import seaborn as sns
44 |
45 |
46 | class Analyzer:
47 | def __init__(self, save_csv: bool = False):
48 | self.save_csv = save_csv
49 | # try:
50 | # nltk.download("stopwords")
51 | # except:
52 | # print(r"[INFO] You have downloaded stopwords!")
53 |
54 | @staticmethod
55 | def find_top_words_from_keys(keys_list: List) -> pd.Series:
56 | """Find most used words into description of vacancies.
57 |
58 | Parameters
59 | ----------
60 | keys_list : list
61 | List of sentences from keywords of vacancies.
62 |
63 | Returns
64 | -------
65 | pd.Series
66 | List of sorted keywords.
67 |
68 | """
69 | # Create a list of keys for all vacancies
70 | lst_keys = []
71 | for keys_elem in keys_list:
72 | for el in keys_elem:
73 | if el != "":
74 | lst_keys.append(re.sub("'", "", el.lower()))
75 |
76 | # Unique keys and their counter
77 | set_keys = set(lst_keys)
78 | # Dict: {Key: Count}
79 | dct_keys = {el: lst_keys.count(el) for el in set_keys}
80 | # Sorted dict
81 | srt_keys = dict(sorted(dct_keys.items(), key=lambda x: x[1], reverse=True))
82 | # Return pandas series
83 | return pd.Series(srt_keys, name="Keys")
84 |
85 | @staticmethod
86 | def find_top_words_from_description(desc_list: List) -> pd.Series:
87 | """Find most used words into description of vacancies.
88 |
89 | Parameters
90 | ----------
91 | desc_list : list
92 | List of sentences from vacancy description.
93 |
94 | Returns
95 | -------
96 | pd.Series
97 | List of sorted words from descriptions.
98 |
99 | """
100 | words_ls = " ".join([re.sub(" +", " ", re.sub(r"\d+", "", el.strip().lower())) for el in desc_list])
101 | # Find all words
102 | words_re = re.findall("[a-zA-Z]+", words_ls)
103 | # Filter words with length < 3
104 | words_l2 = [el for el in words_re if len(el) > 2]
105 | # Unique words
106 | words_st = set(words_l2)
107 | # Remove 'stop words'
108 | try:
109 | _ = nltk.corpus.stopwords.words("english")
110 | except LookupError:
111 | nltk.download("stopwords")
112 | finally:
113 | stop_words = set(nltk.corpus.stopwords.words("english"))
114 |
115 | # XOR for dictionary
116 | words_st ^= stop_words
117 | words_st ^= {"amp", "quot"}
118 | # Dictionary - {Word: Counter}
119 | words_cnt = {el: words_l2.count(el) for el in words_st}
120 | # Pandas series
121 | return pd.Series(dict(sorted(words_cnt.items(), key=lambda x: x[1], reverse=True)))
122 |
123 | def prepare_df(self, vacancies: Dict) -> pd.DataFrame:
124 | """Prepare data frame and save results
125 |
126 | Parameters
127 | ----------
128 | vacancies: dict
129 | Dict of parsed vacancies.
130 |
131 | """
132 |
133 | # Create pandas dataframe
134 | df = pd.DataFrame.from_dict(vacancies)
135 | # Print some info from data frame
136 | with pd.option_context("display.max_rows", None, "display.max_columns", None):
137 | print(df[df["Salary"]][["Employer", "From", "To", "Experience"]][0:15])
138 | # Save to file
139 | if self.save_csv:
140 | print("\n\n[INFO]: Save dataframe to file...")
141 | df.to_csv(rf"hh_results.csv", index=False)
142 | return df
143 |
144 | def analyze_df(self, df: pd.DataFrame):
145 | """Load data frame and analyze results
146 |
147 | """
148 | sns.set()
149 | # with pd.option_context('display.max_rows', None, 'display.max_columns', None):
150 | print(df[df["Salary"]][0:7])
151 |
152 | print("\nNumber of vacancies: {}".format(df["Ids"].count()))
153 | print("\nVacancy with max salary: ")
154 | print(df.iloc[df[["From", "To"]].idxmax()])
155 | print("\nVacancy with min salary: ")
156 | print(df.iloc[df[["From", "To"]].idxmin()])
157 |
158 | print("\n[INFO]: Describe salary data frame")
159 | df_stat = df[["From", "To"]].describe().applymap(np.int32)
160 | print(df_stat.iloc[list(range(4)) + [-1]])
161 |
162 | print('\n[INFO]: Average statistics (filter for "From"-"To" parameters):')
163 | comb_ft = np.nanmean(df[df["Salary"]][["From", "To"]].to_numpy(), axis=1)
164 | print("Describe salary series:")
165 | print("Min : %d" % np.min(comb_ft))
166 | print("Max : %d" % np.max(comb_ft))
167 | print("Mean : %d" % np.mean(comb_ft))
168 | print("Median : %d" % np.median(comb_ft))
169 |
170 | print("\nMost frequently used words [Keywords]:")
171 | most_keys = self.find_top_words_from_keys(df["Keys"].to_list())
172 | print(most_keys[:12])
173 |
174 | print("\nMost frequently used words [Description]:")
175 | most_words = self.find_top_words_from_description(df["Description"].to_list())
176 | print(most_words[:12])
177 |
178 | print("\n[INFO]: Plot results. Close figure box to continue...")
179 | fz = plt.figure("Salary plots", figsize=(12, 8))
180 | fz.add_subplot(2, 2, 1)
181 | plt.title("From / To: Boxplot")
182 | sns.boxplot(data=df[["From", "To"]].dropna() / 1000, width=0.4)
183 | plt.ylabel("Salary x 1000 [RUB]")
184 | fz.add_subplot(2, 2, 2)
185 | plt.title("From / To: Swarmplot")
186 | sns.swarmplot(data=df[["From", "To"]].dropna() / 1000, size=6)
187 |
188 | fz.add_subplot(2, 2, 3)
189 | plt.title("From: Distribution ")
190 | sns.histplot(df["From"].dropna() / 1000, bins=14, color="C0", kde=True)
191 | plt.grid(True)
192 | plt.xlabel("Salary x 1000 [RUB]")
193 | plt.xlim([-50, df["From"].max() / 1000])
194 | plt.yticks([], [])
195 |
196 | fz.add_subplot(2, 2, 4)
197 | plt.title("To: Distribution")
198 | sns.histplot(df["To"].dropna() / 1000, bins=14, color="C1", kde=True)
199 | plt.grid(True)
200 | plt.xlim([-50, df["To"].max() / 1000])
201 | plt.xlabel("Salary x 1000 [RUB]")
202 | plt.yticks([], [])
203 | plt.tight_layout()
204 | plt.show()
205 |
206 |
207 | if __name__ == "__main__":
208 | analyzer = Analyzer()
209 |
--------------------------------------------------------------------------------
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493 | agreement or commitment, however denominated, not to enforce a patent
494 | (such as an express permission to practice a patent or covenant not to
495 | sue for patent infringement). To "grant" such a patent license to a
496 | party means to make such an agreement or commitment not to enforce a
497 | patent against the party.
498 |
499 | If you convey a covered work, knowingly relying on a patent license,
500 | and the Corresponding Source of the work is not available for anyone
501 | to copy, free of charge and under the terms of this License, through a
502 | publicly available network server or other readily accessible means,
503 | then you must either (1) cause the Corresponding Source to be so
504 | available, or (2) arrange to deprive yourself of the benefit of the
505 | patent license for this particular work, or (3) arrange, in a manner
506 | consistent with the requirements of this License, to extend the patent
507 | license to downstream recipients. "Knowingly relying" means you have
508 | actual knowledge that, but for the patent license, your conveying the
509 | covered work in a country, or your recipient's use of the covered work
510 | in a country, would infringe one or more identifiable patents in that
511 | country that you have reason to believe are valid.
512 |
513 | If, pursuant to or in connection with a single transaction or
514 | arrangement, you convey, or propagate by procuring conveyance of, a
515 | covered work, and grant a patent license to some of the parties
516 | receiving the covered work authorizing them to use, propagate, modify
517 | or convey a specific copy of the covered work, then the patent license
518 | you grant is automatically extended to all recipients of the covered
519 | work and works based on it.
520 |
521 | A patent license is "discriminatory" if it does not include within
522 | the scope of its coverage, prohibits the exercise of, or is
523 | conditioned on the non-exercise of one or more of the rights that are
524 | specifically granted under this License. You may not convey a covered
525 | work if you are a party to an arrangement with a third party that is
526 | in the business of distributing software, under which you make payment
527 | to the third party based on the extent of your activity of conveying
528 | the work, and under which the third party grants, to any of the
529 | parties who would receive the covered work from you, a discriminatory
530 | patent license (a) in connection with copies of the covered work
531 | conveyed by you (or copies made from those copies), or (b) primarily
532 | for and in connection with specific products or compilations that
533 | contain the covered work, unless you entered into that arrangement,
534 | or that patent license was granted, prior to 28 March 2007.
535 |
536 | Nothing in this License shall be construed as excluding or limiting
537 | any implied license or other defenses to infringement that may
538 | otherwise be available to you under applicable patent law.
539 |
540 | 12. No Surrender of Others' Freedom.
541 |
542 | If conditions are imposed on you (whether by court order, agreement or
543 | otherwise) that contradict the conditions of this License, they do not
544 | excuse you from the conditions of this License. If you cannot convey a
545 | covered work so as to satisfy simultaneously your obligations under this
546 | License and any other pertinent obligations, then as a consequence you may
547 | not convey it at all. For example, if you agree to terms that obligate you
548 | to collect a royalty for further conveying from those to whom you convey
549 | the Program, the only way you could satisfy both those terms and this
550 | License would be to refrain entirely from conveying the Program.
551 |
552 | 13. Use with the GNU Affero General Public License.
553 |
554 | Notwithstanding any other provision of this License, you have
555 | permission to link or combine any covered work with a work licensed
556 | under version 3 of the GNU Affero General Public License into a single
557 | combined work, and to convey the resulting work. The terms of this
558 | License will continue to apply to the part which is the covered work,
559 | but the special requirements of the GNU Affero General Public License,
560 | section 13, concerning interaction through a network will apply to the
561 | combination as such.
562 |
563 | 14. Revised Versions of this License.
564 |
565 | The Free Software Foundation may publish revised and/or new versions of
566 | the GNU General Public License from time to time. Such new versions will
567 | be similar in spirit to the present version, but may differ in detail to
568 | address new problems or concerns.
569 |
570 | Each version is given a distinguishing version number. If the
571 | Program specifies that a certain numbered version of the GNU General
572 | Public License "or any later version" applies to it, you have the
573 | option of following the terms and conditions either of that numbered
574 | version or of any later version published by the Free Software
575 | Foundation. If the Program does not specify a version number of the
576 | GNU General Public License, you may choose any version ever published
577 | by the Free Software Foundation.
578 |
579 | If the Program specifies that a proxy can decide which future
580 | versions of the GNU General Public License can be used, that proxy's
581 | public statement of acceptance of a version permanently authorizes you
582 | to choose that version for the Program.
583 |
584 | Later license versions may give you additional or different
585 | permissions. However, no additional obligations are imposed on any
586 | author or copyright holder as a result of your choosing to follow a
587 | later version.
588 |
589 | 15. Disclaimer of Warranty.
590 |
591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
599 |
600 | 16. Limitation of Liability.
601 |
602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
610 | SUCH DAMAGES.
611 |
612 | 17. Interpretation of Sections 15 and 16.
613 |
614 | If the disclaimer of warranty and limitation of liability provided
615 | above cannot be given local legal effect according to their terms,
616 | reviewing courts shall apply local law that most closely approximates
617 | an absolute waiver of all civil liability in connection with the
618 | Program, unless a warranty or assumption of liability accompanies a
619 | copy of the Program in return for a fee.
620 |
621 | END OF TERMS AND CONDITIONS
622 |
623 | How to Apply These Terms to Your New Programs
624 |
625 | If you develop a new program, and you want it to be of the greatest
626 | possible use to the public, the best way to achieve this is to make it
627 | free software which everyone can redistribute and change under these terms.
628 |
629 | To do so, attach the following notices to the program. It is safest
630 | to attach them to the start of each source file to most effectively
631 | state the exclusion of warranty; and each file should have at least
632 | the "copyright" line and a pointer to where the full notice is found.
633 |
634 |
635 | Copyright (C)
636 |
637 | This program is free software: you can redistribute it and/or modify
638 | it under the terms of the GNU General Public License as published by
639 | the Free Software Foundation, either version 3 of the License, or
640 | (at your option) any later version.
641 |
642 | This program is distributed in the hope that it will be useful,
643 | but WITHOUT ANY WARRANTY; without even the implied warranty of
644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
645 | GNU General Public License for more details.
646 |
647 | You should have received a copy of the GNU General Public License
648 | along with this program. If not, see .
649 |
650 | Also add information on how to contact you by electronic and paper mail.
651 |
652 | If the program does terminal interaction, make it output a short
653 | notice like this when it starts in an interactive mode:
654 |
655 | Copyright (C)
656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
657 | This is free software, and you are welcome to redistribute it
658 | under certain conditions; type `show c' for details.
659 |
660 | The hypothetical commands `show w' and `show c' should show the appropriate
661 | parts of the General Public License. Of course, your program's commands
662 | might be different; for a GUI interface, you would use an "about box".
663 |
664 | You should also get your employer (if you work as a programmer) or school,
665 | if any, to sign a "copyright disclaimer" for the program, if necessary.
666 | For more information on this, and how to apply and follow the GNU GPL, see
667 | .
668 |
669 | The GNU General Public License does not permit incorporating your program
670 | into proprietary programs. If your program is a subroutine library, you
671 | may consider it more useful to permit linking proprietary applications with
672 | the library. If this is what you want to do, use the GNU Lesser General
673 | Public License instead of this License. But first, please read
674 | .
675 |
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