prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# -*- coding: utf-8 -*-
"""
Function to save laminate design set-up
- save_objective_function_BELLA:
saves the objective function parameters on Sheet [Objective function]
- save_multipanel:
saves the data of the multipanel structure:
- panel geometry
- panel thickness targets
... | pd.DataFrame() | pandas.DataFrame |
# Futu Algo: Algorithmic High-Frequency Trading Framework
#
# 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 appli... | pd.read_csv(input_file, index_col=None) | pandas.read_csv |
import os, glob
import pandas as pd
def selectX(df_dict=df_dict, ids=ids, x=100):
selectX = | pd.DataFrame(columns=df_dict[ids[0]].columns) | pandas.DataFrame |
# parser.py - Special parser for reading and writing *.tsv files with pandas.
# Import pandas library for parsing dataframes.
import pandas as pd
# For parsing MFI tables specifically.
def read_mfi(path, title="Metric", countries=['AFG', 'JPN']):
"""Special parser for reading an MFI table.
:param path: Path ... | pd.DataFrame(data) | pandas.DataFrame |
import os
import requests
import json
from mapLight.dirs import *
from mapLight.key import apiKey
def downloadBills(jurisdiction,session,includePositions=True,allBills=False):
params = {'jurisdiction':jurisdiction,
'session':session,
'include_organizations':int(includePositions)... | pd.concat(billDFs) | pandas.concat |
from datetime import datetime
from io import StringIO
import numpy
import pandas
import pytest
from hts.hierarchy import HierarchyTree
from hts.utilities.load_data import load_hierarchical_sine_data, load_mobility_data
@pytest.fixture
def events():
s = """ts,start_latitude,start_longitude,city
2019-12-06 12... | pandas.date_range(start="1998-01-01", periods=8, freq="QS") | pandas.date_range |
import cv2
import numpy as np
import pandas as pd
import shutil
from tqdm import tqdm
from pathlib import Path
from utils import get_all_files_in_folder
from sklearn.model_selection import train_test_split
def create_splits_files(root_dir, val_split, test_split):
train_dir = Path('denred0_data/train_test_split/t... | pd.DataFrame(labels, columns=["x"]) | pandas.DataFrame |
# Импортируем стандартный модуль для рендеринга страниц
from django.shortcuts import render
# Импортируем стандартные модули для пагинации страниц
from django.core.paginator import Paginator, EmptyPage, PageNotAnInteger
# Подключаем модуль для фиксирования времени
import time
# Подключаем модуль для анализа pandas
impo... | pd.concat(f) | pandas.concat |
import pandas as pd
import numpy as np
import datetime
import os
from scipy import array
from scipy.interpolate import interp1d
def subst(x, str_re, loc):
"""
Parameters:
-----------
x : str, the string to be updated
str_re : str, the new string to replace
loc : int or numpy.array, the index ... | pd.to_datetime(discharge['Time'], format="%H:%M:%S %d/%m/%Y") | pandas.to_datetime |
import numpy as np
import pandas as pd
import os
import sys
import matplotlib.pyplot as plt
import matplotlib
import datetime
import sklearn.datasets, sklearn.decomposition
from sklearn.cluster import KMeans
from sklearn_extra.cluster import KMedoids
from sklearn.preprocessing import StandardScaler
import sk... | pd.read_csv(demand_data_path) | pandas.read_csv |
from io import StringIO
import pandas as pd
import numpy as np
import pytest
import bioframe
import bioframe.core.checks as checks
# import pyranges as pr
# def bioframe_to_pyranges(df):
# pydf = df.copy()
# pydf.rename(
# {"chrom": "Chromosome", "start": "Start", "end": "End"},
# axis="col... | pd.Int64Dtype() | pandas.Int64Dtype |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 2 17:59:33 2019
@author: anna
This script computes the Director order parameter, the Deuterium Order parameters,
lipids tilt and splay angles.
"""
import MDAnalysis
import matplotlib.pyplot as plt
import MDAnalysis.lib.NeighborSearch as NS
impo... | pd.DataFrame({'Time':times[0] , 'box_x': [box[0]], 'box_y': [box[1]], 'box_z': [box[2]], 'alpha' : [box[3]], 'beta' : [box[4]], 'gamma' : [box[5]]}) | pandas.DataFrame |
import json
import re
from glob import glob
from os import makedirs, path
import pandas as pd
from mne import Evoked, write_evokeds
from mne.channels.layout import _find_topomap_coords
from mne.time_frequency import AverageTFR, write_tfrs
def files_from_dir(dir_path, extensions, natsort_files=True):
"""Retrieves... | pd.concat([metadata_df, epochs_df], axis=1) | pandas.concat |
import numpy as np
import pandas as pd
import pandas_datareader.data as web
import datetime as dt
import requests
import io
import zipfile
from kungfu.series import FinancialSeries
from kungfu.frame import FinancialDataFrame
def download_factor_data(freq='D'):
'''
Downloads factor data from Kenneth French's... | pd.read_excel(url, sheet_name='Sheet1') | pandas.read_excel |
"""
Show completed state for a given set of experiments.
"""
import os
import sys
from datetime import datetime
import numpy as np
import pandas as pd
here = os.path.abspath(os.path.dirname(__file__))
sys.path.insert(0, here + '/../')
import util
from config import status_args
def get_experiment_hash(args):
"""... | pd.DataFrame(results) | pandas.DataFrame |
#!/usr/bin/env python3
'''
Splits dataset into train/test/val
Author: <NAME>
Date: 10/16/2019
'''
import os
import argparse
import pandas as pd
import numpy as np
import csv
import shutil
from sklearn.model_selection import GroupShuffleSplit
from sklearn.model_selection import train_test_split
try:
im... | pd.concat([train_pos, train_neg], ignore_index=True) | pandas.concat |
import os
import tempfile
import unittest
import numpy as np
import pandas as pd
from sqlalchemy import create_engine
from tests.settings import POSTGRESQL_ENGINE, SQLITE_ENGINE
from tests.utils import get_repository_path, DBTest
from ukbrest.common.pheno2sql import Pheno2SQL
class Pheno2SQLTest(DBTest):
@unitt... | pd.isnull(query_result.loc[3, 'c150_0_0']) | pandas.isnull |
import time
from multiprocess import Process
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import pandas as pd
import os
import time
from tqdm import tqdm
import numpy as np
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from multiproc... | pd.DataFrame(tmp_l) | pandas.DataFrame |
# -----------------------------------------------------------------------------
# Account statement helpers
import logging
import datetime
import camelot
import pandas as pd
import os
import json
from pdfquery.cache import FileCache
import pdfquery
import pdfminer
from .utils import *
logger = logging.getLogger("h... | pd.DataFrame(self.statement['entries']) | pandas.DataFrame |
# Must run example4.py first
# Read an Excel sheet and save running config of devices using pandas
import pandas as pd
from netmiko import ConnectHandler
# Read Excel file of .xlsx format
data = | pd.read_excel(io="Example4-Device-Details.xlsx", sheet_name=0) | pandas.read_excel |
__author__ = 'rhythmicstar'
import gffutils
import pandas as pd
def possible_nmd(nmd_file):
splicing_data = pd.read_csv(nmd_file, header=None, sep='\s+')
index = pd.Index(splicing_data[3])
event_ids = | pd.Series(index, name='event_id') | pandas.Series |
# coding: utf-8
# ### **Loading Libraries**
# In[ ]:
import numpy as np
from sklearn.ensemble import RandomForestClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn import preprocessing
import os
print(os.listdir("../input"))
from sklea... | pd.DataFrame(predict_test,columns=['predict_0', 'predict_1', 'predict_2']) | pandas.DataFrame |
"""DataFrameToMatrix: Convert a DataFrame to a Numpy Matrix (ndarray) Class"""
from __future__ import print_function
# Third Party
import pandas as pd
import numpy as np
# Local imports
from bat.utils import dummy_encoder
class DataFrameToMatrix(object):
"""DataFrameToMatrix: Convert a DataFrame to a Numpy Matr... | pd.Categorical(df[column]) | pandas.Categorical |
from collections import OrderedDict
import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
)
import pandas._testing as tm
from pandas.core.construction import create_series_with_explicit_dtype
class TestFromDict:
# Note: these tests are specif... | Series([1.5, 3, 4], idx, dtype="O", name="x") | pandas.Series |
from pprint import pprint
import joblib
import pandas as pd
from lightgbm import LGBMClassifier
from sklearn.model_selection import train_test_split
from cian_similarity.utils import calc_metrics, get_connection, get_features, get_offers, get_pairs
class Model:
RANDOM_STATE_SKLEARN = 42
TARGET = "resolution... | pd.Series(self.clf.feature_importances_, index=self.X_train.columns) | pandas.Series |
"""convert XML results to CSV data
"""
import os
import xml.etree.ElementTree as ET
from typing import NamedTuple, List
import re
import pandas as pd
THIS_DIR = os.path.dirname(os.path.realpath(__file__))
DATA_DIR = THIS_DIR
class Catch2BenchResult(NamedTuple):
"""Benchmark result
Attributes:
name:... | pd.concat([parsed_name, xml_results], axis=1) | pandas.concat |
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[0], expected[0]) | pandas._testing.assert_series_equal |
from collections import OrderedDict
import numpy as np
import pandas as pd
from sklearn.ensemble import BaggingClassifier, RandomForestRegressor
from sklearn.tree import DecisionTreeClassifier
from unittest.mock import patch
from zipline.data import bundles
from tests import assert_output, project_test, generate_rand... | pd.Series(targets[18:24], index[18:24]) | pandas.Series |
"""Copyright (c) Facebook, Inc. and its affiliates."""
# pylint: disable=unused-argument,too-many-statements,unused-variable
import functools
import glob
import os
from collections import defaultdict
from pathlib import Path
from typing import List, Optional, Union
import altair as alt
import altair_saver
import numpy... | pd.concat([dev_df, test_df]) | pandas.concat |
""" Test cases for misc plot functions """
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas import (
DataFrame,
Index,
Series,
Timestamp,
)
import pandas._testing as tm
from pandas.tests.plotting.common import (
TestPlotBase,
_check_plot_works,
)
import ... | tm.makeTimeSeries(name="ts") | pandas._testing.makeTimeSeries |
import sys
assert sys.version_info >= (3, 5) # make sure we have Python 3.5+
import pandas as pd
# ------------------------Function to combine df of all gear type-----------------------------
def main(p1, p2, p3, p4, p5, p6, version):
df1 = pd.read_csv('../data/' + p1)
df2 = pd.read_csv('../data/' + p2)
... | pd.read_csv('../data/' + p4) | pandas.read_csv |
import urllib
import urllib.parse
import urllib.request
import json
import pandas as pd
from datetime import datetime
import os
class RetrieveByAttribute(object):
'''
This class extracts Historical user defined weather attributes from the
WorldWeatherOnline API. The data is extracted by city.
---------------... | pd.date_range(self.start_date_datetime, self.end_date_datetime, freq='M', closed='left') | pandas.date_range |
import pandapower as pp
from pandapower.grid_equivalents.auxiliary import drop_internal_branch_elements
import pandas as pd
import numpy as np
try:
import pandaplan.core.pplog as logging
except ImportError:
import logging
logger = logging.getLogger(__name__)
def _calculate_ward_and_impedance_parameters(Ybus... | pd.concat([net_external.res_ext_grid.p_mw, net_external.res_gen.p_mw[slack_gen]]) | pandas.concat |
import os
import pandas as pd
import numpy as np
import copy
from pprint import pprint
def work(pres):
count = [0, 0]
for i in pres:
count[i] += 1
out = count.index(max(count))
return out
def simple_vote(model_name, date, dataset, pseudo=False):
if pseudo:
DATA_DIR = '../predict_... | pd.read_csv(DATA_DIR + fname) | pandas.read_csv |
import decimal
import numpy as np
from numpy import iinfo
import pytest
import pandas as pd
from pandas import to_numeric
from pandas.util import testing as tm
class TestToNumeric(object):
def test_empty(self):
# see gh-16302
s = pd.Series([], dtype=object)
res = to_numeric(s)
... | to_numeric(s, errors='coerce') | pandas.to_numeric |
"""
Provide a generic structure to support window functions,
similar to how we have a Groupby object.
"""
from collections import defaultdict
from datetime import timedelta
from textwrap import dedent
from typing import List, Optional, Set
import warnings
import numpy as np
import pandas._libs.window as libwindow
fro... | Appender(_agg_doc) | pandas.util._decorators.Appender |
# -*- coding: utf-8 -*-
from unittest import TestCase
import pandas as pd
from alphaware.base import (Factor,
FactorContainer)
from alphaware.enums import (FactorType,
OutputDataFormat,
FreqType,
FactorNo... | assert_frame_equal(calculate, expected) | pandas.util.testing.assert_frame_equal |
"""
Class Features
Name: lib_data_io_nc
Author(s): <NAME> (<EMAIL>)
Date: '20200401'
Version: '3.0.0'
"""
#######################################################################################
# Libraries
import logging
import os
import netCDF4
import time
import re
import warnings
impor... | pd.DatetimeIndex([var_time_end]) | pandas.DatetimeIndex |
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 22 12:05:22 2017
@author: rgryan
"""
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import glob
import datetime
sh = False # Plotting the scale height info?
zc = False # Plotting the zero height concentration info?
re = True ... | pd.DataFrame() | pandas.DataFrame |
import os
import pandas as pd
import csv
from sklearn.model_selection import train_test_split
import numpy as np
import random
import tensorflow as tf
import torch
#directory of tasks dataset
os.chdir("original_data")
#destination path to create tsv files, dipends on data cutting
path_0 = "mttransformer/... | pd.concat([labeled3, unlabeled3]) | pandas.concat |
#
# Copyright (c) 2015 - 2022, Intel Corporation
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions a... | pandas.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import pandas as pd
from usal_echo.d00_utils.log_utils import setup_logging
from usal_echo.d00_utils.db_utils import (
dbReadWriteClean,
dbReadWriteViews,
dbReadWriteMeasurement,
)
logger = setup_logging(__name__, __name__)
def get_recommendation(row):
... | pd.Series(start + ground_truth_df.index) | pandas.Series |
# -*- coding: utf-8 -*-
import pytest
import os
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal, assert_series_equal
import numpy.testing as npt
from numpy.linalg import norm, lstsq
from numpy.random import randn
from flaky import flaky
from lifelines import CoxPHFitter, WeibullA... | pd.DataFrame.from_records([{"id": 1, "t": 1, "var1": 1.0}, {"id": 1, "t": 2, "var1": 2.0}]) | pandas.DataFrame.from_records |
from __future__ import print_function
import collections
import os
import re
import sys
import numpy as np
import pandas as pd
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import StandardScaler, MinMaxScaler, MaxAbsScaler
file_path = os.path.dirname(os.path.realpath(__file__))
lib_path = os.... | pd.DataFrame(mat, columns=df.columns) | pandas.DataFrame |
import pandas as pd
#import geopandas as gpd
import numpy as np
import os
#from sqlalchemy import create_engine
from scipy import stats
from sklearn.preprocessing import MinMaxScaler
import math
#from shapely import wkt
from datetime import datetime, timedelta, date
import time
from sklearn.ensemble import RandomForest... | pd.DataFrame({'datetime':prediction_date_range_hour}) | pandas.DataFrame |
""" Junk code from developing the method which might come in handy later.
"""
################################################################################
# Old version of run from analysis.py #
################################################################################
import os
import numpy ... | pd.DataFrame(index=lrs, columns=['n_spots']) | pandas.DataFrame |
import sqlite3
import json
import pandas as pd
class MamphiDataFetcher:
mamphi_db = ""
def __init__(self, mamphi_db=mamphi_db):
self.mamphi_db = mamphi_db
def fetch_center(self):
conn = sqlite3.connect(self.mamphi_db)
conn.row_factory = sqlite3.Row
cursor = conn.cursor()... | pd.read_json(week2) | pandas.read_json |
# -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101
import itertools
import warnings
from warnings import catch_warnings
from datetime import datetime
from pandas.types.common import (is_integer_dtype,
is_float_dtype,
is_scalar)
from pandas.compat... | tm.assert_frame_equal(df, expected) | pandas.util.testing.assert_frame_equal |
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import DBSCAN, KMeans
from configs import Level, LEVEL_MAP
from db.QueryBuilder import get_level_refactorings
from refactoring_statistics.query_utils import retrieve_columns
import pandas as pd
from pathlib import Path
from os impor... | pd.DataFrame(data=data) | pandas.DataFrame |
import numpy as np
import pandas as pd
import hydrostats.data as hd
import hydrostats.visual as hv
import HydroErr as he
import matplotlib.pyplot as plt
import os
from netCDF4 import Dataset
# *****************************************************************************************************
# *************ERA Inter... | pd.DataFrame(data=Q[:, counter], index=dates, columns=['flowrate (cms)']) | pandas.DataFrame |
"""
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 pandas as pd
import sqlalchemy as sa
##########... | pd.StringDtype() | pandas.StringDtype |
import string
from flask import Blueprint
from root.modules.consolidations.dao.consolidation_dao_impl import getFileDetails, getFileList, getProjectDetails, saveConsolidation
import os
from flask import Flask, render_template, url_for, json
import re
import pandas as pd
from flask import request, jsonify
impo... | pd.DataFrame.merge(A, B, how='inner', on=common_col) | pandas.DataFrame.merge |
import os
import cv2
import numpy as np
import pandas as pd
import dataset_settings
from util import insert_into_df, write_info, process_bimcv_image, resize_image
def prepare_bimcv_plus_data(data_path, v1_csv_path, v2_csv_path, source_url):
v1_csv = pd.read_excel(v1_csv_path, engine='openpyxl')
v2_csv = pd.... | pd.concat([v1_csv, v2_csv]) | pandas.concat |
import datetime
import numpy as np
import pandas as pd
from six import iteritems
from six.moves import zip
from zipline.utils.numpy_utils import NaTns, NaTD
def next_date_frame(dates, events_by_sid, event_date_field_name):
"""
Make a DataFrame representing the simulated next known date for an event.
Pa... | pd.DatetimeIndex(df.loc[:, ts_field], tz='utc') | pandas.DatetimeIndex |
import sys
import os
import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
def preprocess(df):
df.Age = df.Age.fillna(value=df.Age.mean())
# create new class U for unkown embar... | pd.concat([X,encoded],axis=1) | pandas.concat |
# pip.main(['install', 'nibabel'])
# pip.main(['install', 'pynrrd'])
# pip.main(['install', 'h5py'])
# pip.main(['install', 'scikit-image'])
# pip.main(['install', 'future'])
import os
import sys
import shutil
import difflib
import itertools
import numpy as np
import pandas as pd
import matplotlib.pyplot as pp
import ... | pd.DataFrame(timesers) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# Copyright (c) 2016 by University of Kassel and Fraunhofer Institute for Wind Energy and Energy
# System Technology (IWES), Kassel. All rights reserved. Use of this source code is governed by a
# BSD-style license that can be found in the LICENSE file.
import numpy as np
import pandas as pd
... | pd.Series() | pandas.Series |
#
# Copyright 2016 Quantopian, 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 applicable law or agreed to in wr... | pd.Timestamp("2015-01-07 8:45", tz='US/Eastern') | pandas.Timestamp |
# coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# 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 applicab... | pd.concat((train, valid, test), axis=0) | pandas.concat |
# x-by-y.py - dataviz module for quick X by Y charts.
__version__ = '0.1'
__all__ = ['layout', 'callback']
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import plotly.graph_objs as go
import pandas as pd
from lib.components import x_axis_dropdown, y... | pd.read_csv(DATA_DIR + '/' + xdata) | pandas.read_csv |
import pandas as pd
from glob import glob
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import numpy as np
### Function to Create Colormap
def custom_div_cmap(numcolors=256, name='custom_div_cmap',colors=['black','dimgrey','lightgrey','white','palegreen','forestgreen', 'darkgre... | pd.DataFrame(data.values[9,:][11,]) | pandas.DataFrame |
import numpy as np
import pandas as pd
import math
# step 1/2 数据生成器
Batch_size = 20
Lens = 528 # 取640为训练和验证截点。
TEST_MANIFEST_DIR = "../data/test_data.csv"
def ts_gen(path = TEST_MANIFEST_DIR, batch_size = Batch_size):
data_list = | pd.read_csv(path) | pandas.read_csv |
# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/02-spec-gen.ipynb (unless otherwise specified).
__all__ = ['init_spec', 'load_endpoints_df', 'get_endpoint_single_attr', 'init_stream_dict',
'add_params_to_stream_dict', 'add_streams_to_spec', 'construct_spec', 'save_spec', 'load_API_yaml']
# Cell
import nump... | pd.read_csv(endpoints_fp) | pandas.read_csv |
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# This file contains dummy data for the model unit tests
import numpy as np
import pandas as pd
AIR_FCST_LINEAR_95 = pd.DataFrame(
{
... | pd.Timestamp("2013-04-12 00:00:00") | pandas.Timestamp |
#!/usr/bin/env python
# coding: utf-8
# In[2]:
import pandas as pd
# In[3]:
sub_1_p = pd.read_csv('./output/submission_1020.csv')
sub_2_p = pd.read_csv('./output/submission_1021.csv')
sub_3_p = pd.read_csv('./output/submission_12345.csv')
sub_4_p = pd.read_csv('./output/submission_1234.csv')
sub_5_p = pd.read_cs... | pd.read_csv('type.csv') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 12 12:30:30 2021
@author: sahand
"""
import pandas as pd
import numpy as np
from tqdm import tqdm
dir_root = '/home/sahand/GoogleDrive/Data/Corpus/Dimensions AI unlimited citations/clean/' # ryzen
# dir_root = '/mnt/6016589416586D52/Users/z5204044/G... | pd.read_csv(dir_root+'publication idx',names=['id']) | pandas.read_csv |
from django.db import models
from django.utils import timezone
from django.db.models import Q
import asyncio
from ib_insync import IB, Stock, MarketOrder, util
from core.common import empty_append
from core.indicators import rel_dif
import vectorbtpro as vbt
import sys
import math
import pandas as pd
import numpy a... | pd.DataFrame(data=volume,index=df["date"],columns=symbols) | pandas.DataFrame |
import dash
import dash_core_components as dcc
import dash_bootstrap_components as dbc
import dash_html_components as html
import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
from datetime import date
import dash_loading_spinners as dls
from dash.dependencies import Input, Output, ClientsideF... | pd.to_datetime(data2['Time']) | pandas.to_datetime |
import pandas as pd
from sqlalchemy import create_engine
from nyc_ccci_etl.commons.configuration import get_database_connection_parameters
class DataPreparator:
def __init__(self):
host, database, user, password = get_database_connection_parameters()
engine_string = "postgresql+psycopg2://{user}:{p... | pd.read_sql_table('inspections', self.engine, schema="transformed") | pandas.read_sql_table |
"""Text Prediction Model based on Pretrained Language Model. Version 1"""
from typing import Optional
import collections
import logging
import pandas as pd
import os
import random
import numpy as np
from ..abstract.abstract_model import AbstractModel
from ...features.feature_metadata import R_OBJECT, R_INT, R_FLOAT, R... | pd.concat([y_train, y_val]) | pandas.concat |
#Copyright 2019 <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 to in writing, software
#d... | pd.get_dummies(X,drop_first=False) | pandas.get_dummies |
import csv
import re
import string
import math
import warnings
import pandas as pd
import numpy as np
import ipywidgets as wg
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.ticker as mtick
from itertools import product
from scipy.optimize import curve_fit
from IPython.display i... | pd.ExcelWriter(f"{path}Anisotropy Data.xlsx", engine='openpyxl', mode='a') | pandas.ExcelWriter |
# type: ignore
### Standard imports ###
import os
import glob
import logging
import argparse
import itertools
import operator
import timeit
from multiprocessing import Pool
### Non-standard imports ###
import yaml
import numpy as np
import pandas
### Local imports ###
from riptide import TimeSeries, ffa_search, find... | pandas.DataFrame(data, columns=columns) | pandas.DataFrame |
import unittest
import qteasy as qt
import pandas as pd
from pandas import Timestamp
import numpy as np
import math
from numpy import int64
import itertools
import datetime
from qteasy.utilfuncs import list_to_str_format, regulate_date_format, time_str_format, str_to_list
from qteasy.utilfuncs import maybe_trade_day, ... | pd.Timestamp('1970-01-01 00:00:00.000000008') | pandas.Timestamp |
import math
import string
from typing import Optional, Sequence, Tuple
import hypothesis.strategies as st
import numpy as np
import pandas as pd
import pandas.testing as tm
import pyarrow as pa
import pytest
from hypothesis import example, given, settings
import fletcher as fr
from fletcher.testing import examples
t... | pd.Series(fr_array) | pandas.Series |
import os
import glob
import argparse
import pandas as pd
import xml.etree.ElementTree as ET
def process(path, prefix):
xml_list = []
for xml_file in glob.glob(path + '/*.xml'):
tree = ET.parse(xml_file)
root = tree.getroot()
for member in root.findall('object'):
value = (pr... | pd.DataFrame(xml_list, columns=column_name) | pandas.DataFrame |
import logging
import time
from functools import reduce
from typing import List, Iterator, Callable, Any
import pandas
log = logging.getLogger(__name__)
def flatten(l: Iterator[Any]) -> Iterator[Any]:
"""
Thanks to this StackOverflow answer: https://stackoverflow.com/a/10824420
"""
for i in l:
... | pandas.concat([update[indices], using], ignore_index=True) | pandas.concat |
from myutils.utils import getConnection, cronlog
import pandas as pd
import numpy as np
import datetime
import requests
class TestRequest:
def __init__(self, url, method='GET', META=None, postdata=None):
self.method = method
u = url.split('?')
self.path_info = u[0]
self.META = META... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
import datetime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pytz
import random
# Date and Time
# =============
print(datetime.datetime(2000, 1, 1))
print(datetime.datetime.strptime("2000/1/1", "%Y/%m/%d"))
print(datetime.datetime(2000, 1, 1, 0, ... | pd.tseries.offsets.BusinessHour(start='07:00', end='22:00') | pandas.tseries.offsets.BusinessHour |
#!/usr/bin/env python3
import re
import tqdm
import sqlite3
import matplotlib.pyplot as plt
import itertools
import pandas as pd
from ShortestPathDepParse import Dependencies
from EntitiesExtraction import EntityExtractor, Entity
from nltk import CoreNLPParser
import numpy as np
np.random.seed(17)
punctuation = r'[\.... | pd.DataFrame() | pandas.DataFrame |
# pylint: disable-msg=W0612,E1101,W0141
import nose
from numpy.random import randn
import numpy as np
from pandas.core.index import Index, MultiIndex
from pandas import Panel, DataFrame, Series, notnull, isnull
from pandas.util.testing import (assert_almost_equal,
assert_series_equal... | assert_almost_equal(df.values, values) | pandas.util.testing.assert_almost_equal |
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
#from sklearn import metrics
import scipy
#Regressao linear com Adaline e Pseudo-Inversa
entrada = np.array([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0])
target = np.array([2.26, 3.8, 4.43, 5.91, 6.18, 7.26, 8.15, ... | pd.Series(entrada, name="X") | pandas.Series |
"""Unit tests for orbitpy.coveragecalculator.gridcoverage class.
``TestGridCoverage`` class:
* ``test_execute_0``: Test format of output access files.
* ``test_execute_1``: Roll Circular sensor tests
* ``test_execute_2``: Yaw Circular sensor tests
* ``test_execute_3``: Pitch Circular sensor tests
* ``test_execute_4``... | pd.read_csv(out_file_access, skiprows = [0,1,2,3]) | pandas.read_csv |
# Copyright 2018 Corti
#
# 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 to in writing, software
#... | pd.concat(segments, ignore_index=True) | pandas.concat |
#!/usr/bin/env python
"""Tests for `featureeng` package."""
import pytest
import pandas as pd
from featureeng import featureeng
from numpy.testing import assert_almost_equal, assert_equal
# @pytest.mark.xfail(reason ="Being Lazy, test_aggs_by_columns() has not yet been implemented")
def test_aggs_by_columns():
... | pd.DataFrame(data=[[4,7], [4,7]], columns=[expected_column_name, 'column_two']) | pandas.DataFrame |
import calendar
from datetime import datetime
import ccxt
import numpy as np
import pandas as pd
from stockstats import StockDataFrame as Sdf
class CCXTEngineer:
def __init__(self):
self.binance = ccxt.binance()
def data_fetch(self, start, end, pair_list=["BTC/USDT"], period="1m"):
def min_o... | pd.MultiIndex.from_product([pair_list, ["close"]]) | pandas.MultiIndex.from_product |
from typing import List, Text, Dict
from dataclasses import dataclass
import ssl
import urllib.request
from io import BytesIO
from zipfile import ZipFile
from urllib.parse import urljoin
from logging import exception
import os
from re import findall
from datetime import datetime, timedelta
import lxml.html... | pd.read_html(url, header=0) | pandas.read_html |
import flask
from flask import request
import pandas as pd
import spacy
import nltk
import numpy as np
from sklearn.cluster import KMeans
import os
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
import pickle
import gensim
from gensim import corpora
from sklearn import svm... | pd.DataFrame.from_dict(documents, orient='index') | pandas.DataFrame.from_dict |
import sys
import pandas as pd
from sqlalchemy import create_engine
def load_data(messages_filepath, categories_filepath):
''' Load and merge two CSV files - one containing messages and the other containing categories
Args:
messages_filepath (str): Path to the CSV file containing messages
... | pd.read_csv(categories_filepath) | pandas.read_csv |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import xgboost
import sklearn
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.grid_search import GridSearchCV, RandomizedSearchCV
from sklearn.datasets import make_classification
from sklearn.cross... | pd.merge(meta,shared,on=['sample']) | pandas.merge |
import numpy as np
import pytest
from pandas.core.dtypes.common import is_integer_dtype
import pandas as pd
from pandas import Categorical, CategoricalIndex, DataFrame, Series, get_dummies
import pandas._testing as tm
from pandas.core.arrays.sparse import SparseArray, SparseDtype
class TestGetDummies:
@pytest.f... | get_dummies(data) | pandas.get_dummies |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
#For User 1
User_1 = pd.read_csv('acceleration_labelled_data.csv')
User_1 = pd.DataFrame(User_1.iloc[:, 1:6].values)
User_1.columns = ["Activity", "Timeframe", "X axis", "Y axis", "Z axis"]
User_1["Timeframe"] = User_1["Timeframe"] - 0.017856
"""... | pd.DataFrame(User_6_annotations) | pandas.DataFrame |
"""
Full Pipeline: A-B-C
Development Pipeline: B-C
A) Making negative examples
B) Offering categories for redirect(Filter)
C) Predict probability for (query, category) (redirects)
"""
import pandas as pd
import re
from json import load
from utils.merge_tables import merge_product_external_id_to_categories
from utils.co... | pd.DataFrame({'query': query, 'external_id': product_external_id}) | pandas.DataFrame |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for Period dtype
import operator
import numpy as np
import pytest
from pandas._libs.tslibs.period import IncompatibleFrequency
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas impo... | tm.assert_frame_equal(p - df, exp) | pandas.util.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
import os
import ast
import pandas as pd
import numpy as np
import torch
from torch.utils.data import Dataset
class AffectiveMonitorDataset(Dataset):
"""
Affective Monitor Dataset in Pytorch version class
raw dataframe read from csv file is in .face_df
fil... | pd.read_csv(filepath,header=6) | pandas.read_csv |
#############################################################
# ActivitySim verification against TM1
# <NAME>, <EMAIL>, 02/22/19
# C:\projects\activitysim\verification>python compare_results.py
#############################################################
import pandas as pd
import openmatrix as omx
################... | pd.Categorical(tm1_trips["orig_purpose"]) | pandas.Categorical |
from datetime import (
datetime,
timedelta,
timezone,
)
import numpy as np
import pytest
import pytz
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
NaT,
Period,
Series,
Timedelta,
Timestamp,
date_range,
isna,
)
import pandas._testing as tm
class TestS... | Timestamp("20130103 9:01:01") | pandas.Timestamp |
import joblib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.cluster import KMeans
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_sel... | pd.DataFrame(data['风险值']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 16 19:59:22 2020
@author: Dell
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
sicData= | pd.read_excel(r'C:\Users\Dell\Desktop\data\SIC(2011-2018).xlsx',parse_dates=[0]) | pandas.read_excel |
# -*- coding: utf-8 -*-
"""
Created on Sat Feb 1 18:29:16 2020
@author: POI-PC
"""
from PyQt5.QtWidgets import*
from PyQt5.QtCore import pyqtSlot
from PyQt5 import QtGui
from PyQt5 import QtCore, QtWidgets
import sys
from selenium import webdriver
import time
import pandas as pd
import numpy as np
from xlrd import o... | pd.read_html('Sirketler/Sirketler.xls') | pandas.read_html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.