prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 23 23:11:09 2020
@author: esteban
"""
import os
print(os.getcwd())
import pandas as pd
import glob
from variables import pathInformesComunas,\
pathExport,\
nombreInformeConsolidadoComunas,\
nombreInformesComunas,pathReportesCOVID,\
... | pd.to_datetime(df["fecha"],format="%d-%m-%Y") | pandas.to_datetime |
import pandas as pd
import json
benchmarks = json.load(open("bench_out.json"))["benchmarks"]
classes = set()
datasets = set()
for benchmark in benchmarks:
name = benchmark["name"]
classes.add(name.split("_")[0])
datasets.add(name.split("<")[1].split(">")[0])
df = pd.DataFrame(benchmarks)
for class_name i... | pd.DataFrame() | pandas.DataFrame |
import random
from collections import defaultdict
from contextlib import redirect_stdout, redirect_stderr
from io import StringIO
from typing import Dict
from warnings import warn
from pandas import concat, DataFrame, Categorical
from tqdm import tqdm
from data_frames import to_nested_dicts
from data_sources.drug_con... | concat(data) | pandas.concat |
import os
import logging
import inspect
import time
import pandas as pd
import numpy as np
from talpa.visualization import *
from sklearn.model_selection import StratifiedShuffleSplit
from talpa.classifiers import *
from talpa.metrics import *
from sklearn.preprocessing import StandardScaler
from talpa.core.data_checks... | pd.DataFrame({'Accuracy':[acc_mean] , 'F1score': [f1_mean]}) | pandas.DataFrame |
#!/usr/bin/python3
import pandas as pd
import numpy as np
import mhyp_enrich as mh
import pdb
import time
import math
import statsmodels.stats.multitest as mt
import random
from scipy import stats as st
from scipy.stats import beta
def main():
num_MC_samp = 1000000 # Number of Monte-Carlo samples to use
alt... | pd.ExcelWriter('Analysis_Output/lit_confusion_matrices_tf_pbs_only.xlsx') | pandas.ExcelWriter |
import numpy as np
from numpy.testing import assert_allclose
import pandas as pd
import pytest
import quantopy as qp
@pytest.fixture(autouse=True)
def random():
np.random.seed(0)
class TestReturnSeries:
def test_from_price(self):
expected = [0.0625, 0.058824]
rs = qp.ReturnSeries.from_pric... | pd.Series([8.7, 8.91, 8.71, 8.43, 8.73]) | pandas.Series |
#!/usr/bin/env python
# encoding:utf-8
'''sklearn doc
'''
import re
import os
import sys
import numpy as np
import pandas as pd
from time import time
from sklearn.model_selection import GridSearchCV, cross_val_predict
# RandomizedSearchCV cross_val_score train_test_split
from skfeature.function.information_th... | pd.read_csv(y_file, index_col=0, header=0) | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
READ IN:
1) <NAME> Data "../../../AKJ_Replication/Replication/data/data_replication.csv"
2) Alternative data "../output/alternativedata.csv"
EXPORT:
"../output/alternativedata.csv"
@author: olivergiesecke
"""
import pandas as pd
import numpy ... | pd.to_datetime(ref_df["start_date"]) | pandas.to_datetime |
__author__ = "saeedamen" # <NAME>
#
# Copyright 2016 Cuemacro
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not
# use this file except in compliance with the License. You may obtain a copy of
# the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable la... | pd.to_datetime(df.index) | pandas.to_datetime |
# coding:utf-8
import sys
import numpy as np
import pandas as pd
import pendulum
import pyltr
from flasgger import Swagger
from flask import Flask, jsonify, render_template, request
from pymongo import MongoClient, DESCENDING
from sklearn.externals import joblib
import config
app = Flask(__name__)
swagger = Swagger(... | pd.DataFrame(score_list) | pandas.DataFrame |
from typing import NoReturn, Tuple, Any, Union, Optional, List
from copy import deepcopy, copy
from warnings import warn
from darts import TimeSeries as DartsTimeSeries
import numpy as np
from pandas import DataFrame, date_range, infer_freq, Series, DatetimeIndex, \
Timestamp, Timedelta, concat
from timeatlas.abs... | Timestamp(limit) | pandas.Timestamp |
#!/usr/bin/env python
"""get_map_grid_data.py: module is dedicated to fetch map2, mapex, grid2, grd, gridex data from files."""
__author__ = "<NAME>."
__copyright__ = "Copyright 2020, SuperDARN@VT"
__credits__ = []
__license__ = "MIT"
__version__ = "1.0."
__maintainer__ = "<NAME>."
__email__ = "<EMAIL>"
__status__ = ... | pd.concat([self.reco, o]) | pandas.concat |
# <NAME> - <EMAIL>
"""
Predict : Regression Methods(using scikit-learn package)
A prediction task for rental home company
Author: <NAME> - <EMAIL>
"""
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.metrics import fbeta_score, make_scorer
# Custom Loss funct... | DataFrame(dic) | pandas.DataFrame |
import pandas as pd
import numpy as np
import itertools
import warnings
import scipy.cluster.hierarchy as sch
from scipy.spatial import distance
from joblib import Parallel, delayed
__all__ = ['hcluster_tally',
'neighborhood_tally',
'running_neighborhood_tally',
'any_cluster_tally']
"""TO... | pd.DataFrame(res) | pandas.DataFrame |
import re
import pandas as pd
# Function that searches data.txt for email/phone numbers before returning a dictionary
def find_data(pattern, column_name):
with open('data.txt', 'r') as file:
contents = file.read()
matches = pattern.findall(contents)
matches_dict = {column_name: matches}... | pd.DataFrame(data=matches) | pandas.DataFrame |
import numpy as np
import pandas as pd
def getDailyVol(close, span0=100):
'''
Computes the daily volatility of price returns.
It takes a closing price series, applies a diff sample to sample
(assumes each sample is the closing price), computes an EWM with
`span0` samples and then the standard devi... | pd.Series(index=events.index) | pandas.Series |
import datetime
from datetime import timedelta
from distutils.version import LooseVersion
from io import BytesIO
import os
import re
from warnings import catch_warnings, simplefilter
import numpy as np
import pytest
from pandas.compat import is_platform_little_endian, is_platform_windows
import pandas.util._test_deco... | ensure_clean_store(setup_path, mode="w") | pandas.tests.io.pytables.common.ensure_clean_store |
import numpy as np
import pandas as pd
#import scipy.stats
import random
import math
from time import time
names = locals()
#from ast import literal_eval
#ๆฐๆฎๅฏผๅ
ฅ
df_area = pd.read_csv('/public/home/hpc204212088/connected_vehicle/xin3/shortest_path/area.csv')
list_county = list(df_area['c_id'])
df_density = pd.read_csv(... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 23 11:40:16 2017
@author: tobias
"""
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Read the input data
input_file = '/Users/tobias/GitHub/seqcap_processor/data/processed/target_contigs/match_table.txt'
workdir ... | pd.DataFrame({'index':num_x_labels,'locus_name': x_labels}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
##########################################################################
# NSAp - Copyright (C) CEA, 2019
# Distributed under the terms of the CeCILL-B license, as published by
# the CEA-CNRS-INRIA. Refer to the LICENSE file or to
# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
#... | pd.DataFrame(df_to_dump, columns=df.columns) | pandas.DataFrame |
# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/05-orchestrator.ipynb (unless otherwise specified).
__all__ = ['retry_request', 'if_possible_parse_local_datetime', 'SP_and_date_request', 'handle_capping',
'date_range_request', 'year_request', 'construct_year_month_pairs', 'year_and_month_request',
... | pd.concat([df, df_year]) | pandas.concat |
# Copyright (C) 2012 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to
# deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribut... | pd.isnull(row) | pandas.isnull |
"""
A warehouse for constant values required to initilize the PUDL Database.
This constants module stores and organizes a bunch of constant values which are
used throughout PUDL to populate static lists within the data packages or for
data cleaning purposes.
"""
import importlib.resources
import pandas as pd
import ... | pd.StringDtype() | pandas.StringDtype |
import pandas as pd
from uhxl import UhExcelFile
DATA = "tests/data/test_merged_cell.xlsx"
FILE = UhExcelFile(DATA)
def test_merged_cell():
df = pd.read_excel(FILE)
assert isinstance(df, pd.DataFrame)
assert df.equals(pd.DataFrame({"merged": ["col1", "a"], None: ["col2", "b"]}))
def test_merged_cell_mu... | pd.read_excel(FILE, header=(0, 1)) | pandas.read_excel |
from re import findall
from pandas import Series
from omics import get_ome_regexp, get_omics_regexp
ome_re = get_ome_regexp()
omics_re = get_omics_regexp()
def test_ome_re():
assert findall(ome_re, 'genome') == ['genome']
assert findall(ome_re, '(genome') == ['genome']
assert findall(ome_re, 'genome pro... | Series(['transcriptomic proteomic']) | pandas.Series |
import yfinance as yf
import matplotlib.pyplot as plt
import collections
import pandas as pd
import numpy as np
import cvxpy as cp
import efficient_frontier
import param_estimator
import backtest
import objective_functions
def port_opt(stock_picks, weight_constraints, control, trade_horizon, cardinality, target_retu... | pd.concat([train_stock_returns, train_etf_returns], axis=1) | pandas.concat |
import math
import os
import pathlib
from functools import reduce
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
from experiment_definitions import ExperimentDefinitions
from data_collectors import MemtierCollector, MiddlewareCollector
class ... | pd.merge(throughput_get, response_time_get) | pandas.merge |
import pandas as pd
import numpy as np
import re
import openpyxl as openpyxl
import os
from os import listdir
from pathlib import Path
import geopandas as gpd
from geopandas.tools import sjoin
import sys
import argparse
## define pathnames
dropbox_general = "/Users/euniceliu/Dropbox (Dartmouth College)/"
DROPBOX_DATA_... | pd.read_pickle(DF_ACS_PATH_2019) | pandas.read_pickle |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
date: 2021/9/28 16:02
desc: ไธๆน่ดขๅฏ็ฝ-ๆฐๆฎไธญๅฟ-็น่ฒๆฐๆฎ-ๆบๆ่ฐ็
http://data.eastmoney.com/jgdy/
ไธๆน่ดขๅฏ็ฝ-ๆฐๆฎไธญๅฟ-็น่ฒๆฐๆฎ-ๆบๆ่ฐ็ -ๆบๆ่ฐ็ ็ป่ฎก: http://data.eastmoney.com/jgdy/tj.html
ไธๆน่ดขๅฏ็ฝ-ๆฐๆฎไธญๅฟ-็น่ฒๆฐๆฎ-ๆบๆ่ฐ็ -ๆบๆ่ฐ็ ่ฏฆ็ป: http://data.eastmoney.com/jgdy/xx.html
"""
import pandas as pd
import requests
from tqdm impo... | numeric(big_df['ๆๆฐไปท'], errors="coerce") | pandas.to_numeric |
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Output, Input
import plotly.express as px
import pandas as pd
import geopandas as gpd
import numpy as np
import folium
from folium.plugins import FastMarkerCluster
from datetime import date
app = dash.Dash... | pd.read_csv('Police_Department_Incident_Reports__2018_to_Present.csv') | pandas.read_csv |
from django.db.models.fields import Field
from django.http import HttpResponse
from django.utils.translation import gettext_lazy as _
from django.template.response import TemplateResponse
from django.core.exceptions import PermissionDenied
from django.urls import reverse_lazy
import csv
import urllib.parse
from .forms ... | pd.read_csv(file_path, **read_csv_params) | pandas.pandas.read_csv |
# coding=utf-8
# Copyright 2016-2018 <NAME>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | pd.DataFrame(data, columns=cols) | pandas.DataFrame |
import pandas as pd
import numpy as np
from datetime import datetime
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
# from users import build_user_matrix
class Recommending:
def __init__(self):
'''Initializes the TFIDF Vectorizer Object'''... | pd.read_csv(fave_file) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
:Author: <NAME>
<NAME>
:Date: 2018. 7. 18
"""
import os
import platform
import sys
from copy import deepcopy as dc
from datetime import datetime
from warnings import warn
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas.core.com... | pd.merge(grouped_data, benchmark, on=[DATE]) | pandas.merge |
#!/usr/bin/env python3
import os
import sys
import pandas as pd
from json import load
infolder = sys.argv[1]
cwd = os.getcwd()
os.chdir(infolder)
with open('ica_decomposition.json', 'r') as f:
comp = load(f)
del(comp['Method'])
# Prepare list of components for projections
acc = ''
rej = ''
ign = ''
for n, ... | pd.DataFrame(comp_data, columns=['var', 'class']) | pandas.DataFrame |
import re
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import IntervalArray
class TestSeriesReplace:
def test_replace_explicit_none(self):
# GH#36984 if the user explicitly passes value=None, give it to them
ser = pd.Series([0, 0, ""],... | pd.IntervalDtype("float64") | pandas.IntervalDtype |
from opendatatools.common import RestAgent, md5
from progressbar import ProgressBar
import json
import pandas as pd
import io
import hashlib
import time
index_map = {
'Barclay_Hedge_Fund_Index' : 'ghsndx',
'Convertible_Arbitrage_Index' : 'ghsca',
'Distressed_Securities_Index' : 'ghsds',
'Emerg... | pd.DataFrame(jsonobj['data']) | pandas.DataFrame |
import pandas as pd
import pyspark
from flytekitplugins.spark.task import Spark
import flytekit
from flytekit import kwtypes, task, workflow
from flytekit.types.schema import FlyteSchema
try:
from typing import Annotated
except ImportError:
from typing_extensions import Annotated
def test_wf1_with_spark():
... | pd.DataFrame(data={"name": ["Alice"], "age": [5]}) | pandas.DataFrame |
import os
import pandas as pd
from tqdm import tqdm
import json
import numpy as np
from sklearn.model_selection import train_test_split
def bb_iou(boxA, boxB):
# determine the (x, y)-coordinates of the intersection rectangle
xA = max(boxA[0], boxB[0])
yA = max(boxA[1], boxB[1])
xB = min(boxA[2], boxB[... | pd.read_csv(DATA_DIR + "/train.csv") | pandas.read_csv |
from datetime import timedelta
import operator
import numpy as np
import pytest
import pytz
from pandas._libs.tslibs import IncompatibleFrequency
from pandas.core.dtypes.common import is_datetime64_dtype, is_datetime64tz_dtype
import pandas as pd
from pandas import (
Categorical,
Index,
IntervalIndex,
... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
# Modified version for Erie County, New York
# Contact: <EMAIL>
from functools import reduce
from typing import Generator, Tuple, Dict, Any, Optional
import os
import pandas as pd
import streamlit as st
import numpy as np
import matplotlib
from bs4 import BeautifulSoup
import requests
import ipyvuetify as v
from trait... | pd.api.types.is_integer_dtype(df.day) | pandas.api.types.is_integer_dtype |
import os
import pandas as pd
from google.cloud import storage
#็ฐๅขๅคๆฐ
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "../auth/credential.json"
def load_data_from_gcs(bucket_name="pj_horidasimono", prefix="dataset/train/ElectricalAppliance"):
client = storage.Client()
blobs = client.list_blobs(bucket_name, pref... | pd.read_json(content) | pandas.read_json |
import math
# from datetime import timedelta, datetime
from itertools import combinations
from datetime import datetime
import numpy as np
import pandas as pd
import scipy.stats as stats
from sklearn import linear_model
import matplotlib.pyplot as plt
# https://zhuanlan.zhihu.com/p/37605060
# https://realpython.com/n... | pd.read_csv(input_filepath, sep='\t') | pandas.read_csv |
"""Preprocessing WSDM Dataset.
Author: DHSong
Last Modified At: 2020.07.07
Preprocessing WSDM Dataset.
"""
import os
from collections import Counter
from tqdm import tqdm
import pandas as pd
import matplotlib.font_manager as fm
import matplotlib.pyplot as plt
import seaborn as sns
class PreprocessingWorker:
"""... | pd.cut(members.loc[~invalid_bd, 'bd'], 5) | pandas.cut |
import json
from typing import Optional
import pandas as pd
from .api_methods import API
from .namespaces import symbols_in_namespace
def search(search_string: str, namespace: Optional[str] = None, response_format: str = 'frame') -> json:
# search for string in a single namespace
if namespace:
df = ... | pd.DataFrame(json_response[1:], columns=json_response[0]) | pandas.DataFrame |
'''
Extracting Apple Watch Health Data
'''
import os
from datetime import datetime
from xml.dom import minidom
import numpy as np
import pandas as pd
class AppleWatchData(object):
'''
Object to contain all relevant data access calls for Apple Watch health data.
'''
# TODO: make parsing of xml file a he... | pd.to_numeric(apple_array[:, 2], errors='ignore') | pandas.to_numeric |
import tqdm
from offline.infra.netlink import NetLink
import pandas as pd
import numpy as np
from csv import QUOTE_ALL
from dataclasses import dataclass
from collections import namedtuple,defaultdict
from itertools import chain, combinations
from datetime import datetime
import functools
# Cross = namedtuple("Cross", ... | pd.read_html(entire_document) | pandas.read_html |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
from numpy import nan
import numpy as np
import pandas as pd
from pandas.types.common import is_integer, is_scalar
from pandas import Index, Series, DataFrame, isnull, date_range
from pandas.core.index import MultiIndex
from pa... | range(2, 6) | pandas.compat.range |
# -*- coding: utf-8 -*-
"""
Map ReEDS geographic regions and classes to Supply Curve points
"""
import logging
import numpy as np
import os
import pandas as pd
from warnings import warn
from reVX.utilities.exceptions import ReedsValueError, ReedsKeyError
from reVX.utilities.utilities import log_versions
from rex.utili... | pd.cut(x=cum_cap, bins=cap_bins, labels=labels) | pandas.cut |
# date: 2021-11-25
"""Train the model
Usage: train_model.py --train_file=<train_file> --test_file=<test_file> --out_file_train=<out_file_train> --out_file_result=<out_file_result>
Options:
--train_file=<train_file> the train dataframe to train
--test_file=<test_file> the test dataframe to evalua... | pd.crosstab(columns=rst_all_train["model"], index=rst_all_train["var_num"], values=rst_all_train["test_score"], aggfunc=sum) | pandas.crosstab |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = | pd.read_csv(data_path) | pandas.read_csv |
import numpy as np
from scipy.io import loadmat
import os
from pathlib import Path
# from mpl_toolkits.mplot3d import Axes3D
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
# plotting parameters
sns.set(font_scale=1.1)
sns.set_context("talk")
sns.set_palette(['#701f57', '#ad1759', '#e1... | pd.DataFrame() | pandas.DataFrame |
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | pd.Series([0, 0], index=[ts_min, ts_max]) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Process an EPW file
.. moduleauthor:: <NAME> (<EMAIL>, <EMAIL>)
"""
import csv
from collections import OrderedDict
import pandas as pd
from datetime import datetime
class EpwFile:
def __init__(self, filepath):
"""
Load an EPW file into memory
:param filepath:... | pd.DataFrame(self.data) | pandas.DataFrame |
#!/usr/bin/env python
import argparse
import pandas as pd
import re
#read arguments
parser = argparse.ArgumentParser(description="Subset the exon clusters by species pairs based on the pairwise reclustered gene orthogroups")
parser.add_argument("--exon_pairs", "-ep", required=True)
parser.add_argument("--reclustere... | pd.Series(exon_clusters_df.ExCluster_ID.values, index=exon_clusters_df.Coordinate) | pandas.Series |
from qutip import *
from ..mf import *
import pandas as pd
from scipy.interpolate import interp1d
from copy import deepcopy
import matplotlib.pyplot as plt
def ham_gen_jc(params, alpha=0):
sz = tensor(sigmaz(), qeye(params.c_levels))
sm = tensor(sigmam(), qeye(params.c_levels))
a = tensor(qeye(2), destroy... | pd.Series(alpha_0_lower, index=lower_midpoint_frequencies) | pandas.Series |
import py
from csvuploader import HeaderCsv
import pandas as pd
from pandas.util.testing import assert_frame_equal
from StringIO import StringIO
def test_load_file(request):
test_dir = py.path.local(request.module.__file__)
with test_dir.dirpath('data', 'simple.csv').open('r') as f:
text = f.read()
... | pd.DataFrame([[1, 2]], columns=['A', 'B']) | pandas.DataFrame |
# Copyright (C) 2020 University of Oxford
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed t... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import pandas as pd
sample1 = pd.read_table('MUT-1_2.annotate.csv', sep='\t', index_col=0)["score"]
sample2 = pd.read_table('MUT-2_2.annotate.csv', sep='\t', index_col=0)["score"]
sample3 = pd.read_table('MUT-4_2.annotate.csv', sep='\t', index_col=0)["score"]
sample4 = pd.read_table('MUT-5_2.annot... | pd.concat([concat, meta], axis=1) | pandas.concat |
import math
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_array_equal
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import median_absolute_error
from sklearn.metrics import r2_score
from greykite.common.constants import ACTUAL_COL
from greykite.common.con... | pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]) | pandas.Series |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
from config_fh import get_db_engine, get_db_session, get_cache_file_path, STR_FORMAT_DATE
from fh_tools.fh_utils import return_risk_analysis, str_2_date
from fh_tools import fh_utils
import matplotlib.pyplot as plt # pycharm ้่ฆ้่ฟ็ฐๅฎ่ฐ็จ plt.show ๆ่ฝๆพ็คบplo... | pd.read_sql_query(query_str, engine) | pandas.read_sql_query |
'''
ML-Based Trading Strategy
'''
import cbpro
import zmq
import sys
import json
import time
import os
import pickle
import pandas as pd
import numpy as np
import datetime as dt
# the following libraries are to update the persisted ML model
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
# overl... | pd.to_datetime(dataframe['time'], infer_datetime_format=True) | pandas.to_datetime |
"""
An exhaustive list of pandas methods exercising NDFrame.__finalize__.
"""
import operator
import re
import numpy as np
import pytest
import pandas as pd
# TODO:
# * Binary methods (mul, div, etc.)
# * Binary outputs (align, etc.)
# * top-level methods (concat, merge, get_dummies, etc.)
# * window
# * cumulative ... | pd.DataFrame(*frame_data) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/2/14 18:19
Desc: ๆฐๆตช่ดข็ป-่ก็ฅจๆๆ
https://stock.finance.sina.com.cn/option/quotes.html
ๆๆ-ไธญ้ๆ-ๆฒชๆทฑ 300 ๆๆฐ
https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php
ๆๆ-ไธไบคๆ-50ETF
ๆๆ-ไธไบคๆ-300ETF
https://stock.finance.sina.com.cn/option/quotes.html
"""
import json
i... | numeric(data_df['่กๆไปท']) | pandas.to_numeric |
import feedparser
import pprint
import requests
import pandas as pd
import numpy as np
def loadFiles( codes ):
"""Devuelve una lista de dataframes para solo codigo"""
#codes = ['Est_Mercat_Immobiliari_Lloguer_Mitja_Mensual']
parameters = {'rows': '1000'}
url = 'http://opendata-ajuntament.barcelona.ca... | pd.DataFrame(row['resources']) | pandas.DataFrame |
# Licensed to Elasticsearch B.V. under one or more contributor
# license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright
# ownership. Elasticsearch B.V. licenses this file to you under
# the Apache License, Version 2.0 (the "License"); you may
# not use ... | pd.to_datetime(value, format="%G-W%V-%uT%H:%M:%S%z") | pandas.to_datetime |
# Authors: <NAME> <<EMAIL>>
# License: BSD 3 clause
import os
import pickle as pkl
import numpy as np
from numba import njit
from numba.experimental import jitclass
from numba import types, _helperlib
from .types import float32, boolean, uint32, string, void, get_array_2d_type
from .checks import check_X_y, check_array... | pd.DataFrame(data, columns=columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import operator as op
import seaborn as sns
# http://data8.org/datascience/_modules/datascience/tables.html
#####################
# Frame Manipulation
def relabel(df, OriginalName, NewName):
return df.rename(index=str, columns={OriginalN... | pd.DataFrame(df) | pandas.DataFrame |
#! /usr/bin/env python3
import numpy as np
import matplotlib.pyplot as plt
import pandas
import os
from sklearn.cluster import KMeans
# Load in raw data files
county_data_filename = "county_facts.csv" # Census statistics
election_data_filename = "2016_US_County_Level_Presidential_Results.csv" # Election outcomes
coun... | pandas.read_csv(county_data_filename) | pandas.read_csv |
import numpy as np
import pandas as pd
import requests # Coleta de conteรบdo em Webpage
from requests.exceptions import HTTPError
from bs4 import BeautifulSoup as bs # Scraping webpages
from time import sleep
import json
import re #biblioteca para trabalhar com regular expressions - regex
import string
import unidecode... | pd.DataFrame(questions_overview['questions']) | pandas.DataFrame |
import os, glob, gc, time, yaml, shutil, random
import addict
import argparse
from collections import defaultdict
from tqdm import tqdm
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score, roc_auc_score
from sklearn.preprocessing import StandardScaler, LabelBinarizer, LabelEncoder, Quan... | pd.set_option("display.max_rows", 20) | pandas.set_option |
import pandas as pd
import numpy as np
df =pd.read_csv('user49.csv')
dfcp=pd.read_csv('mdbcp.csv')
dfData={'id': dfcp['id'],'avg':dfcp['avg']}
df2=pd.DataFrame(dfData)
#print(type(df['id'][0]))
df=df.set_index('id').join(df2.set_index('id'))
df=df.dropna()
df['ratio']=df['rating']-df['avg']
df=df.drop(columns=['ratin... | pd.read_csv('dir.csv') | pandas.read_csv |
# coding: utf-8
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import random
import seaborn as sns
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler
import glob, os
import errno
from sklearn.linear_model import LogisticRegression
from sklear... | pd.DataFrame(columns=['Classifier_name',"vald_precision","vald_recall"]) | pandas.DataFrame |
import pandas as pd
data = | pd.read_csv('data/T_UWWTPS.csv') | pandas.read_csv |
from os import sep
from numpy.core.fromnumeric import mean
import pandas as pd
import matplotlib.pyplot as plt
import math
from sklearn.cluster import KMeans
X = [7, 3, 1, 5, 1, 7, 8, 5]
Y = [1, 4, 5, 8, 3, 8, 2, 9]
labels = ["x1", "x2", "x3", "x4", "x5", "x6", "x7", "x8"]
kdata = pd.DataFrame({"X": X, "Y": Y}, index=... | pd.read_csv("./cdata.txt") | pandas.read_csv |
"""Provincial road network loss maps
"""
import os
import sys
from collections import OrderedDict
import geopandas as gpd
import pandas as pd
import cartopy.crs as ccrs
import cartopy.io.shapereader as shpreader
import matplotlib.pyplot as plt
from shapely.geometry import LineString
from vtra.utils import *
def main... | pd.merge(region_file,flow_file,how='left', on=['edge_id']) | pandas.merge |
import sys
import os
import os.path, time
import glob
import datetime
import pandas as pd
import numpy as np
import csv
import featuretools as ft
import pyasx
import pyasx.data.companies
def get_holdings(file):
'''
holdings can come from export or data feed (simple)
'''
simple_csv = False
with open... | pd.read_csv(file, skiprows=[0, 1, 3], header=0) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Tue May 21 12:10:32 2019
@author: gh2668
"""
import pandas as pd
import read_attributes_signatures
def read_data():
meta_df = read_attributes_signatures.read_meta()
att_df, sig_df = read_attributes_signatures.seperate_attributes_signatures(meta_df)
knoben = | pd.read_csv("catchment_clusters_with_continoues_climate.csv", index_col=1) | pandas.read_csv |
#!/usr/bin/env python
# ------------------------------------------------------------------------------------------------------%
# Created by "Thieu" at 13:56, 28/01/2021 %
# ... | DataFrame(list_fitness) | pandas.DataFrame |
import pandas as pd
import os
from utils.composition import _fractional_composition
def norm_form(formula):
comp = _fractional_composition(formula)
form = ''
for key, value in comp.items():
form += f'{key}{str(value)[0:9]}'
return form
def count_elems(string):
count = 0
switch = 1
... | pd.concat([df_preds, pred], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
import sys
from random import randint
import dash
import dash_core_components as dcc
import dash_html_components as html
from coronadash.dash_components import Col, Row
from coronadash.conf.config import myapp
from coronadash.conf.config import mydash
import pandas as pd
import datetime
from d... | pd.to_datetime(df["date"]) | pandas.to_datetime |
import pytest
import pandas as pd
from pathlib import Path
from eobox import sampledata
from eobox.raster import cube
@pytest.fixture
def eocube_input_1(tmpdir):
year = 2008
dataset = sampledata.get_dataset("lsts")
layers_paths = [Path(p) for p in dataset["raster_files"]]
layers_df = pd.Series([p.ste... | pd.to_datetime(layers_df.sceneid.str[9:16], format="%Y%j") | pandas.to_datetime |
import pandas as pd
def read_local_data(data_dir):
static_vars = | pd.read_csv(data_dir + 'static_vars.csv') | pandas.read_csv |
from context import dero
import pandas as pd
from pandas.util.testing import assert_frame_equal
from pandas import Timestamp
from numpy import nan
import numpy
class DataFrameTest:
df = pd.DataFrame([
(10516, 'a', '1/1/2000', 1.01),
(10516, 'a'... | Timestamp('2000-01-07 00:00:00') | pandas.Timestamp |
# -*- coding:utf-8 -*-
# !/usr/bin/env python
"""
Date: 2021/11/2 21:08
Desc: ๅ่ฑ้กบ-ๆฐๆฎไธญๅฟ-ๆๆฏ้่ก
http://data.10jqka.com.cn/rank/cxg/
"""
import pandas as pd
import requests
from bs4 import BeautifulSoup
from py_mini_racer import py_mini_racer
from tqdm import tqdm
from akshare.datasets import get_ths_js
def _get_file_co... | pd.read_html(r.text, converters={"่ก็ฅจไปฃ็ ": str}) | pandas.read_html |
from unittest import TestCase
from nose_parameterized import parameterized
import os
import gzip
import pandas as pd
from pandas import read_csv
from pyfolio.utils import to_utc
from pandas.util.testing import assert_frame_equal, assert_series_equal
from pyfolio.risk import (compute_style_factor_exposures,
... | pd.Panel() | pandas.Panel |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 17 17:32:41 2018
@author: brettwang
Daily Open + Volume plot for one cryptocurrency during specific date range
dependency:
beautifulsoup
pandas
"""
from bs4 import BeautifulSoup
import requests
import pandas as pd
#import seaborn as sns
imp... | pd.DataFrame(data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sun Oct 6 10:10:03 2019
@author: <NAME>
"""
import numpy as np
import pandas as pd
import glob as glob
from tg_set_globalplotting import tg_set_globalplotting
from tg_simulate_behaviour import tg_simulate_behaviour
from tg_suboptimal_goal_choice import tg_suboptimal_goal_cho... | pd.read_csv('../Results/preprocessed_results.csv') | pandas.read_csv |
import os
import datajoint as dj
import numpy as np
import pathlib
from datetime import datetime
import pandas as pd
import uuid
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
import io
from PIL import Ima... | pd.DataFrame([ipsi_cdend_1, ipsi_cdend_2]) | pandas.DataFrame |
# Copyright 2021 Research Institute of Systems Planning, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | pd.DataFrame(columns=column_names) | pandas.DataFrame |
import os
import glob
import psycopg2
import pandas as pd
import numpy as np
from sql_queries import *
from typing import Union
def _type_converter(data):
"""This is a simple utility method we use for type conversion
Args:
data (Union[np.float64, np.float32, np.int64), np.int32, object)]): Data we ar... | pd.read_json(filepath, lines=True) | pandas.read_json |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.5.0
# kernelspec:
# display_name: Python [conda env:PROJ_irox_oer] *
# language: python
# name: conda-env-PROJ... | pd.DataFrame(data_dict_list) | pandas.DataFrame |
'''
Library for Google Sheets functions.
'''
import configparser
import os
import pickle
import logging
import re
import math
from string import ascii_uppercase
from typing import List
import pandas as pd
import numpy as np
from constants import rgx_age, rgx_sex, rgx_date, rgx_lives_in_wuhan, date_columns, column... | pd.DataFrame(data=data, columns=columns) | pandas.DataFrame |
from __future__ import print_function
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from pandas import (Series, Index, Int64Index, Timestamp, Period,
DatetimeIndex, PeriodIndex, TimedeltaIndex,
Timedelta, timedelta_range, date_range, Float64Index... | pd.period_range('2014-05-01', '2014-05-15', freq='D') | pandas.period_range |
import pandas as pd
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from torch.optim import Adagrad
def run(dim, ds, epochs, attempts, lrs, reg_coef):
losses = | pd.DataFrame(columns=['lr', 'epoch', 'attempt', 'loss']) | pandas.DataFrame |
# Author: <NAME>
# github: sehovaclj
# code that uses a regular RNN to forecast energy consumption. Refer to Journal paper for more details
# importing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim... | pd.DataFrame(dataset) | pandas.DataFrame |
from functools import partial
from collections import defaultdict
import json
import warnings
from distutils.version import LooseVersion
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
from ....utils import getargspec
from ..utils import _get_pyarrow_dtypes, _meta_from_dtypes
from ...utils import... | pd.Series(dtypes) | pandas.Series |
import argparse
import glob
import os
import pandas as pd
from tabulate import tabulate
from texttable import Texttable
from dante_tokenizer.data.load import read_test_data
from dante_tokenizer.data.preprocessing import reconstruct_html_chars, remove_quotes
from dante_tokenizer.evaluate import evaluate_dataset
from d... | pd.DataFrame(table[1:], columns=table[0]) | pandas.DataFrame |
'''
#Step 3: Process all tweets and assign them โlabelโ
'''
import os
import pandas as pd
import re
import numpy as np
from poultryrate.data_model import data_model
class tweet_classifier():
tweet_summary = pd.DataFrame()
tweet_exploded = pd.DataFrame()
islamicmonths = | pd.DataFrame() | pandas.DataFrame |
import copy
import gc
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.patches import ConnectionPatch
from protocol_analysis import visualization_protocols as vis
def pie_plot_percentage(party_dict: dict, title, save_name, name_dict, fig_dpi):
plt.figure(figsize=(16, 10))
... | pd.DataFrame.from_dict(all_hosts, orient='index') | pandas.DataFrame.from_dict |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.