prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
"""
Data loader for telemetry log files
"""
from functools import reduce
import math
from matplotlib import pyplot
import pandas as pd
from scipy.optimize import curve_fit
import statistics
from typing import Iterable, List, Optional, Tuple, Union
from telemetrydisc.database import get_logs_table, get_raw_data
from t... | pd.Series(index=data.index) | pandas.Series |
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)
... | tm.assert_series_equal(res, expected) | pandas.util.testing.assert_series_equal |
# Project: fuelmeter-tools
# Created by: # Created on: 5/7/2020
from pandas.tseries.offsets import MonthEnd
from puma.Report import Report
import pandas as pd
import numpy as np
import puma.plot as pplot
import puma.tex as ptex
import datetime
import os
class MultiMonthReport(Report):
def __init__(self,start,end... | pd.Grouper(freq='M') | pandas.Grouper |
#########################################################################
#########################################################################
# Classes for handling genome-wide association input and output files, ##
# analysis and qc programs, and post-hoc analyses ##
########################... | pd.DataFrame(dup_dict) | pandas.DataFrame |
"""
<NAME>017
PanCancer Classifier
scripts/pancancer_classifier.py
Usage: Run in command line with required command argument:
python pancancer_classifier.py --genes $GENES
Where GENES is a comma separated string. There are also optional arguments:
--diseases comma separated string of disease ty... | pd.DataFrame(y_alt_df) | pandas.DataFrame |
from calendar import month_name, monthrange
from pathlib import Path, PureWindowsPath
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import math as mt
from dataclima import helioclim3
class energycalc:
def __init__(self, df,
horizon,
... | pd.concat([self.df, dfprod], axis=1, sort=False) | pandas.concat |
# Code written by <NAME>
# Purpose: Converts all subdirectories of CSV and TSV files into one Excel file.
# Required modules installed from PIP: pandas, xlsxwriter
import os
import pandas as pd
class DataToExcel:
""" Converts all subdirectory data into an unified Excel file."""
def __init__(self):
... | pd.read_csv(each_file, sep='\t', skiprows=skipping_rows) | pandas.read_csv |
import pandas as pd
import numpy as np
import unicodedata
import re
import json
import nltk
nltk.download('stopwords')
nltk.download('wordnet')
from nltk.tokenize.toktok import ToktokTokenizer
from nltk.corpus import stopwords
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import S... | pd.to_datetime(date_df[column_name]) | pandas.to_datetime |
import numpy as np
import tempfile
import logging
import pandas as pd
from sklearn.metrics import jaccard_score
import rpGraph
######################################################################################################################
############################################## UTILITIES ###############... | pd.DataFrame(meas_sim) | pandas.DataFrame |
import sys
import os
import glob as gb
import sqlite3
import pandas as pd
def open_eso(file):
with open(file, 'r') as f:
flist = f.readlines()
return flist
def get_data_dict(flist):
data_dict = []
for f in flist:
if "End of Data Dictionary" in f:
... | pd.MultiIndex.from_tuples(multi_rows) | pandas.MultiIndex.from_tuples |
# The analyser
import pandas as pd
import matplotlib.pyplot as plt
import dill
import os
import numpy as np
from funcs import store_namespace
from funcs import load_namespace
import datetime
from matplotlib.font_manager import FontProperties
from matplotlib import rc
community = 'ResidentialCommunity'
sim_ids = ['M... | pd.to_datetime(opt_stats_df1.index) | pandas.to_datetime |
import json
from datetime import datetime
import pandas as pd
import scrapy
class MatchesSpider(scrapy.Spider):
# set the attributes for the spider
name = "matches"
def __init__(self, **kwargs):
"""initialize the data"""
super().__init__(**kwargs)
# create data frames and safe t... | pd.to_datetime(self.matches_df["Start_Time"]) | pandas.to_datetime |
# Preprocessing time series data
import pandas as pd
import numpy as np
from tsfresh import extract_features
df = pd.read_csv('complete_df_7.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
df['stock_open'] = df['stock_open'].astype(float)
# Create aggregate of sales down to product level
aggregate = df.groupby(['sku... | pd.to_datetime(df['tran_date']) | pandas.to_datetime |
import itertools
import pandas as pd
import requests
from task_geo.dataset_builders.nasa.references import PARAMETERS
def nasa_data_loc(lat, lon, str_start_date, str_end_date, parms_str):
"""
Extract data for a single location.
Parameters
----------
lat : string
lon : string
str_start_d... | pd.DataFrame(data_json['features'][0]['properties']['parameter']) | pandas.DataFrame |
import pandas as pd
import os
os.chdir("/Users/laurahughes/GitHub/cvisb_data/sample-viewer-api/src/static/data/")
# Helper functions for cleanup...
import helpers
viral_ebola = "/Users/laurahughes/GitHub/cvisb_data/sample-viewer-api/src/static/data/input_data/expt_summary_data/viral_seq/survival_dataset_ebov_02262019... | pd.concat([lsv, ebv]) | pandas.concat |
# Copyright (c) 2021 Sony Group Corporation and Hanjuku-kaso Co., Ltd. All Rights Reserved.
#
# This software is released under the MIT License.
# http://opensource.org/licenses/mit-license.php
import argparse
from distutils.util import strtobool
from pathlib import Path
import pickle
import warnings
from sklearn.exc... | DataFrame() | pandas.DataFrame |
from utils.utils import load_yaml
import pandas as pd
import logging
logger = logging.getLogger(__name__)
| pd.set_option('display.max_columns', 10) | pandas.set_option |
"""
Unit test of Inverse Transform
"""
import unittest
import pandas as pd
import numpy as np
import category_encoders as ce
import catboost as cb
import sklearn
import lightgbm
import xgboost
from shapash.utils.transform import inverse_transform, apply_preprocessing, get_col_mapping_ce
class TestInverseTransformCate... | pd.DataFrame({'city': ['chicago', np.nan]}) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 2 14:43:48 2020
@author: afo
"""
import pandas as pd
import os
from os import listdir
from os.path import abspath, isfile, join
from inspect import getsourcefile
import operator
from math import nan
import numpy as np
# custom function
from get_av... | pd.concat([end_results, all_tickers], axis=1) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 17 13:06:36 2018
@author: shlomi
OK
1)fix and get time series of SAOD volcanic index - see how kuchar did it
he did it SAD = surface area density of aerosols at 54 hPa
2)use enso3.4 anom for enso
3)use singapore qbo(level=50) for qbo
4)use solar f10... | pd.date_range('1979-01-01', '2019-01-01', freq='MS') | pandas.date_range |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for datetime64 and datetime64tz dtypes
from datetime import (
datetime,
time,
timedelta,
)
from itertools import (
product,
starmap,
)
import operator
import warnings
import numpy as np
impo... | pd.offsets.DateOffset(years=1) | pandas.offsets.DateOffset |
# Copyright (c) 2020 Spanish National Research Council
#
# 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 ... | pd.to_datetime(df.fecha, format='%Y%m%d') | pandas.to_datetime |
#!/usr/bin/env python3
import numpy as np
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
if __name__ == '__main__':
# read data file
df = | pd.read_csv('durante_etal_2013_study1.txt', delimiter='\t') | pandas.read_csv |
# -*- coding: utf-8 -*-
from Functions import utils as ut
from plotly.subplots import make_subplots
from statistics import mean, stdev
from datetime import timedelta
from functools import reduce
import plotly.graph_objs as go
import plotly as py
import pandas as pd
import numpy as np
import collections
import itertools... | pd.read_csv(filename) | pandas.read_csv |
#!/usr/bin/python
# -*- coding: utf-8 -*-
from abc import ABC
import logging
import os
import sys
import pandas as pd
# setup logger
logging.basicConfig()
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
from .operator import IOperator
class CSV(IOperator, ABC):
""" Instance Object for COCO ... | pd.isnull(csv_ann_df['class']) | pandas.isnull |
#
# process_species_by_dataset
#
# We generated a list of all the annotations in our universe; this script is
# used to (interactively) map them onto the GBIF and iNat taxonomies. Don't
# try to run this script from top to bottom; it's used like a notebook, not like
# a script, since manual review steps are required.
... | pd.read_csv(master_table_file) | pandas.read_csv |
import numpy as np
import pandas as pd
from scipy.stats import mode
from tqdm import tqdm
from geopy.geocoders import Nominatim
from datetime import datetime
def handle_bornIn(x):
skip_vals = ['16-Mar', '23-May', 'None']
if x not in skip_vals:
return datetime(2012, 1, 1).year - datetime(int(x), 1, 1)... | pd.isna(data_content.bikers_df['latitude']) | pandas.isna |
# -*- coding: utf-8 -*-
# Copyright (c) 2016-2017 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 pandas as pd
from numpy impo... | pd.Series() | pandas.Series |
# -*- coding: utf-8 -*-
"""
This module is for running predictions.
Examples:
Example command line executable::
$ python predict.py
"""
import logging
from pathlib import Path
import click
import pandas as pd
from cloudpickle import load
from orbyter_demo.util.config import parse_config
from orbyter_dem... | pd.DataFrame(yhat, columns=["MedianHouseValue"]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable=E1101,E1103,W0232
import os
import sys
from datetime import datetime
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
import pandas.compat as compat
import pandas.core.common as com
import pandas.util.testing as tm
from pandas import (Categor... | pd.Categorical(1) | pandas.Categorical |
from django.views.generic import TemplateView, CreateView
import pandas as pd
import numpy as np
###importing surprise library to implement the recommending systems needed
from surprise import NMF, SVD, SVDpp, KNNBasic, KNNWithMeans, KNNWithZScore, CoClustering
from surprise.model_selection import cross_validate
from s... | pd.read_csv('main/ml-100k/u.data', sep='\t', names=columns) | pandas.read_csv |
import unittest
import numpy as np
import pandas as pd
from sklearn.cluster import DBSCAN, KMeans
from sklearn.covariance import EmpiricalCovariance, MinCovDet
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.mixture import GaussianMixture
from dsbox.ml.outliers import CovarianceOutliers, Ga... | pd.DataFrame([1, 0, 0, 1, 10, 2, 115, 110, 32, 16, 2, 0, 15, 1]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
'''
Created on Mon Sep 28 16:26:09 2015
@author: r4dat
'''
# ICD9 procs from NHSN definition.
# Diabetes diagnoses from AHRQ version 5 SAS program, CMBFQI32.TXT
# sample string generator print((','.join(map(str, [str(x) for x in range(25040,25094)]))).replace(',','","'))
#
# "25000"-"250... | pd.set_option('expand_frame_repr', False) | pandas.set_option |
import numpy as np
import pandas as pd
import scipy.integrate
import tqdm
def single_nutrient(params, time, gamma_max, nu_max, precursor_mass_ref, Km,
omega, phi_R, phi_P, num_muts=1, volume=1E-3):
"""
Defines the system of ordinary differenetial equations (ODEs) which describe
accu... | pd.DataFrame(out, columns=colnames) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""Fatal Police Shooting
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1Zg-tic0ZjTQSkN0YXI2CtB3ix9H---Fh
"""
import pandas as pd
df = | pd.read_csv('database.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Authors: <NAME>, <NAME>, <NAME>, and
<NAME>
IHE Delft 2017
Contact: <EMAIL>
Repository: https://github.com/gespinoza/hants
Module: hants
"""
from __future__ import division
import netCDF4
import pandas as pd
import numpy as np
import datetime
import math
import os
import o... | pd.np.arange(ni) | pandas.np.arange |
# Contributions to SBDF reader functionality provided by PDF Solutions, Inc. (C) 2021
"""
TODOS:
* Return table/column metadata as well as the table data
* Support Decimal type
* Support _ValueArrayEncodingId.RUN_LENGTH array type
* Contemplate making an SBDF writer
"""
from contextlib import ExitStack
from pathlib im... | pd.DataFrame(pandas_data) | pandas.DataFrame |
"""
#
# scikit_optim.py
#
# Copyright (c) 2018 <NAME>. MIT License.
#
"""
import numpy as np
import os
import pandas as pd
import sys
import time
import warnings
import sklearn.metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.mixture import G... | pd.DataFrame.from_dict(summary_dict_cv, orient='index') | pandas.DataFrame.from_dict |
"""
.. _twitter:
Twitter Data API
================
"""
import logging
from functools import wraps
from twython import Twython
import pandas as pd
from pandas.io.json import json_normalize
TWITTER_LOG_FMT = ('%(asctime)s | %(levelname)s | %(filename)s:%(lineno)d '
'| %(funcName)s | %(message)s')
... | pd.DataFrame(place_trends[0]['trends']) | pandas.DataFrame |
import argparse
import pandas as pd
from forexconnect import ForexConnect, fxcorepy
import common_samples
def parse_args():
parser = argparse.ArgumentParser(description='Process command parameters.')
common_samples.add_main_arguments(parser)
common_samples.add_instrument_timeframe_arguments(parser)
... | pd.Series(doi) | pandas.Series |
#!/usr/bin/env python
"""Extract subcatchment runoff summary results from SWMM report file.
Reads subcatchment geometries from a GisToSWMM5 generated subcatchment geometry
file (*_subcatchments.wkt) file and subcatchment runoff results from a SWMM
report (by default .rpt) file. The script merges the information and sa... | pd.merge(df1, df2, on='name') | pandas.merge |
import pandas as pd
from utils.constants import *
def parse_devices(filename: str) -> pd.DataFrame:
data = | pd.read_json(filename, orient="records") | pandas.read_json |
from datetime import datetime
from io import StringIO
import itertools
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
Period,
Series,
Timedelta,
date_range,
)
import pandas._testing as tm
... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
import pandas as pd
import numpy as np
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
import pickle
from sklearn.metrics import r2_score
import warnings
from scipy.interpolate import ... | pd.read_csv("data/JHU/jhu_data.csv", dtype={"FIPS":str}) | pandas.read_csv |
import logging
from concurrent.futures import ThreadPoolExecutor
from io import BytesIO
from typing import List
import requests
import numpy as np
import pandas as pd
from catboost import CatBoost, Pool
from metaspace import SMInstance
from sm.engine.annotation.diagnostics import (
get_dataset_diagnostics,
Di... | pd.DataFrame(results) | pandas.DataFrame |
"""
This module provides a helper object to manage an updateable timechart search
through the export API which doesn't support aggregated live searches.
NOTE: IF you stumbled upon this, know that this is pretty much just a POC/playground.
"""
import json
from threading import Lock
from snaptime import snap_tz
import ... | pd.pivot_table(df, index=timefield, values=datafields, columns=groupby, fill_value=fill) | pandas.pivot_table |
# fmt: off
import os
import h5py
import torch
import copy
import ipywidgets as ipyw
import scipy
import pandas as pd
import datetime
import time
import itertools
import qgrid
import shutil
import subprocess
from random import shuffle
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
... | pd.concat(df_list) | pandas.concat |
import pandas as pd
import numpy as np
import os
# acquire
from pydataset import data
from datetime import date
from scipy import stats
# turn off pink warning boxes
import warnings
warnings.filterwarnings("ignore")
import sklearn
from sklearn.model_selection import train_test_split
# Train/Split the data~~~~~~... | pd.concat([zero_val, null_count, mis_val_percent], axis=1) | pandas.concat |
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from datetime import datetime, timedelta
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
import dash_actions... | pd.to_datetime(df["date"], format="%Y-%m-%d %H:%M:%S") | pandas.to_datetime |
#!/usr/bin/python
import unittest
import cv2
import numpy as np
import os
import pandas as pd
from pandas.testing import assert_frame_equal
from numpy.testing import assert_allclose
from PIE import track_colonies
# load in a test timecourse colony property dataframe
# NB: this case is quite pathological, preliminary ... | assert_frame_equal(expected_property_df, test_property_df,
check_index_type = False) | pandas.testing.assert_frame_equal |
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
CategoricalIndex,
DataFrame,
Index,
NaT,
Series,
date_range,
offsets,
)
import pandas._testing as tm
class TestDataFrameShift:
@pytest.mark.parametrize(
"input_... | tm.assert_equal(dtobj, unshifted) | pandas._testing.assert_equal |
# coding: utf-8
# Author: <NAME> <<EMAIL>>
# License: BSD 3 clause
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
from copy import copy as make_copy
from .classifier import *
import time
class StackingClassifier():
... | pd.concat([X, preds], axis=1) | pandas.concat |
#!/usr/bin/env python
import torch
from torch.utils.data import DataLoader
import pickle
from rdkit import Chem
from rdkit import rdBase
from tqdm import tqdm
from rdkit.Chem import AllChem
from data_structs import MolData, Vocabulary
from model import RNN
from utils import Variable, decrease_learning_rate, unique
imp... | pd.Series(data=epoch_lst) | pandas.Series |
import pandas as pd
from upgrade_model import k8s_releases_loader
k8s_releases = k8s_releases_loader.load()
def compute(id, start_date, end_date, first_version, upgrade_every):
days = pd.date_range(start=start_date, end=end_date, freq='D')
environment_ids = [id]
environment_state = pd.DataFrame(
... | pd.MultiIndex.from_product([environment_ids,days],names=['environment_id','at_date']) | pandas.MultiIndex.from_product |
import pandas as pd
from sklearn import metrics
from sklearn.linear_model import LogisticRegression
import time
import multiprocessing as mp
start_time=time.time()
def svm(location1,location2):
data=pd.read_csv(location1)
data_columns=data.columns
xtrain = data[data_columns[data_columns != 'typeoffraud']... | pd.read_csv(location5) | pandas.read_csv |
import os
import json
import pandas as pd
statements = []
evidences = []
adjective_frequencies = {'sub': {}, 'obj': {}}
_adjective_frequencies = {'sub': {}, 'obj': {}}
adjective_names = {'sub': {}, 'obj': {}}
_adjective_names = {'sub': {}, 'obj': {}}
adjective_pairs = {}
_adjective_pairs = {}
with open('../../data/c... | pd.DataFrame({'Adjective': adjective, 'frequency': frequency}) | pandas.DataFrame |
"""
The ``risk_models`` module provides functions for estimating the covariance matrix given
historical returns.
The format of the data input is the same as that in :ref:`expected-returns`.
**Currently implemented:**
- fix non-positive semidefinite matrices
- general risk matrix function, allowing you to run any ris... | pd.DataFrame(raw_cov_array, index=assets, columns=assets) | pandas.DataFrame |
#!/usr/bin/env python3
# various functions and mixins for downstream genomic and epigenomic anlyses
import os
import glob
import re
import random
from datetime import datetime
import time
from pybedtools import BedTool
import pandas as pd
import numpy as np
from tqdm import tqdm_notebook, tqdm
# Get Current Git Co... | pd.DataFrame(index=order) | pandas.DataFrame |
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from joblib import dump, load
from rulevetting.templates.model import ModelTemplate
class Model(ModelTemplate):
def __init__(self):
self.model = load('./notebooks/models/lr_model_all.joblib')
def predict(... | pd.concat((df_train, df_tune, df_test)) | pandas.concat |
import logging
import pandas as pd
from easysparql import easysparqlclass
import seaborn as sns
import matplotlib.pyplot as plt
from pandas.api.types import CategoricalDtype
from tadaqq.util import compute_scores
PRINT_DIFF = True
def get_logger(name, level=logging.INFO):
logger = logging.getLogger(name)
for... | pd.concat(dfs, ignore_index=True) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 10 17:05:23 2018
@author: <NAME>
"""
# -*- coding: utf-8 -*-
"""
Description: Risky Comments Extractor Based on Risky category bag of words.
"""
import numpy as np
import os
import pandas as pd
import re
from textblob import TextBlob;
from textblob import Wor... | pd.ExcelFile(Sowfile) | pandas.ExcelFile |
# importar bibliotecas
import pandas as pd
import numpy as np
import win32com.client as win32
import xlsxwriter
# importar a base de dados
tabela_vendas = pd.read_excel('Vendas.xlsx')
# visualizar a base de dados
pd.set_option('display.max_columns', None)
# print(tabela_vendas)
print('\nTabela de Vendas: ')
pri... | pd.pivot_table(tabela_vendas, index= ['ID Loja'], values='Valor Final', aggfunc='sum') | pandas.pivot_table |
import pandas
import bmeg.ioutils
from bmeg.emitter import JSONEmitter
from bmeg import (Aliquot, DrugResponse, Project, Compound,
Compound_Projects_Project,
DrugResponse_Aliquot_Aliquot,
DrugResponse_Compounds_Compound)
def transform(cellline_lookup_path='source... | pandas.read_csv(drug_annots_path, sep="\t") | pandas.read_csv |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from collections import OrderedDict
import pandas as pd
import pathlib
class Report:
# daily report of the account
# contain those followings: returns, costs turnovers, accounts, cash, bench, value
# update report
def __init__(... | pd.Series(self.values) | pandas.Series |
import numpy as np
import os.path
import pandas as pd
import sys
import math
# find parent directory and import base (travis)
parentddir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))
sys.path.append(parentddir)
from base.uber_model import UberModel, ModelSharedInputs
# print(sys.path)
# ... | pd.Series([], dtype="float") | pandas.Series |
import pandas as pd
from .datastore import merge_postcodes
from .types import ErrorDefinition
from .utils import add_col_to_tables_CONTINUOUSLY_LOOKED_AFTER as add_CLA_column # Check 'Episodes' present before use!
def validate_165():
error = ErrorDefinition(
code = '165',
description = 'Data entry for moth... | pd.to_datetime(L2_eps['DEC'], format='%d/%m/%Y', errors='coerce') | pandas.to_datetime |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2021/7/8 22:08
Desc: 金十数据中心-经济指标-美国
https://datacenter.jin10.com/economic
"""
import json
import time
import pandas as pd
import demjson
import requests
from akshare.economic.cons import (
JS_USA_NON_FARM_URL,
JS_USA_UNEMPLOYMENT_RATE_URL,
JS_USA_EIA_... | pd.to_datetime(date_list) | pandas.to_datetime |
import os, sys
from pathlib import Path
import math, random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as patches
try:
from data_handle.sad_object import *
except:
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from data_handle.sad_obj... | pd.DataFrame(sample_list, columns=df_all.columns) | pandas.DataFrame |
import os
import cv2
import numpy as np
from keras.models import load_model
import keras.layers as layers
import keras.models as models
files = []
path_to_dataset = os.getcwd()
files.extend(os.listdir(path_to_dataset + "/dataset5"))
path = 'dataset5/'
images = []
for i in files:
img = cv2.imread(path+i)
img = ... | pd.DataFrame(finale) | pandas.DataFrame |
#!/usr/bin/env python3
"""
DrugCentral db utility functions.
"""
import os,sys,re,json,logging,yaml
import pandas as pd
from pandas.io.sql import read_sql_query
import psycopg2,psycopg2.extras
#############################################################################
def Connect(dbhost, dbport, dbname, dbusr, dbpw)... | pd.concat([df, df_this]) | pandas.concat |
#read a csv file, loading it into a DataFrame
import numpy as np #python's array proccesing / linear algebra library
import pandas as pd #data processing / stats library
import matplotlib.pyplot as plt #data visualization
import csv
#read in some data
fn = 'polling_data.csv'
df=pd.read_csv(fn... | pd.to_datetime('1899-12-30') | pandas.to_datetime |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
from argcheck import (expect_types,
optional,
preprocess)
from xutils import py_assert
from alphaware.const import INDEX_FACTOR
from alphaware.enums import (FreqType,
OutputDataFormat... | pd.concat([ret, data_concat], axis=0) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 10 15:36:18 2021
Who would be so cruel to someone like you?
No one but you
Who would make the rules for the things that you do?
No one but you
I woke up this morning, looked at the sky
I thought of all of the time passing by
It didn't matter how hard we tried
'... | pd.DataFrame([GAS_TG_Base]) | pandas.DataFrame |
from io import StringIO
from copy import deepcopy
import numpy as np
import pandas as pd
import re
from glypnirO_GUI.get_uniprot import UniprotParser
from sequal.sequence import Sequence
from sequal.resources import glycan_block_dict
# Defining important colume names within the dataset
sequence_column_name = "Peptide... | pd.read_csv(area_filename, sep="\t") | pandas.read_csv |
## Convert .Bed to .HDF5 file (saving mean and std genotype seperately)
import pandas as pd
import os
from pysnptools.snpreader import SnpData
from pysnptools.snpreader import Pheno, Bed
import h5py
import numpy as np
from tqdm import tqdm
import argparse
def main(args):
genome_path = args.genome_path
... | pd.merge(iid, phenotype, on=['IID','FID']) | pandas.merge |
# NO TIENE LOS DATOS QUE HACEN FALTA
# SIN PROCESAR, MUCHO TRABAJO POR MENOS DE 100k REGISTROS UTILES
# %%
import os
import pandas as pd
import numpy as np
import datetime
from scripts import motor, quitardecimal, valores, modelogeneral, especifico, origensegunvin, version, modelogenerico, especifico2, corregirmodelo,... | pd.set_option('display.max_colwidth', -1) | pandas.set_option |
"""
Copyright 2021 <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
distribute... | pd.DataFrame.from_dict(training_data, orient='index') | pandas.DataFrame.from_dict |
# script with function to prepare and support evaluation
import glob
import yaml
import pandas as pd
import os
# load simulation results and calculate RTT
# network and algorithm name are used to filter the results
def sim_delays(network, algorithm):
sim_results = glob.glob('../eval/{}/{}/{}*.yaml'.format(network... | pd.DataFrame(columns=input_cols + ['src', 'dest', 'sim_rtt', 'emu_rtt']) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Monday 3 December 2018
@author: <NAME>
"""
import os
import pandas as pd
import numpy as np
import feather
import time
from datetime import date
import sys
from sklearn.cluster import MiniBatchKMeans, KMeans
from sklearn.metrics import silhouette_score
fr... | pd.DataFrame() | pandas.DataFrame |
import os
import sys
from copy import copy
from functools import wraps
from time import time
import skimage.filters
import funcs
import numpy as np
import pandas as pd
import seaborn as sns
import uncertainties as un
from funcs.post_processing.images.soot_foil import deltas as pp_deltas
from matplotlib import patches... | pd.HDFStore("/d/Data/Processed/Data/data_soot_foil.h5", "r") | pandas.HDFStore |
import os
import zipfile as zp
import pandas as pd
import numpy as np
import core
import requests
class Labels:
init_cols = [
'station_id', 'station_name', 'riv_or_lake', 'hydroy', 'hydrom', 'day',
'lvl', 'flow', 'temp', 'month']
trans_cols = [
'date', 'year', 'month', 'day', 'hydroy', 'hydrom', 'station_id'... | pd.read_csv('metadata/hydro_stations.csv', encoding='utf-8') | pandas.read_csv |
from common.util import ReviewUtil
import pandas as pd
from collections import Counter
import numpy as np
from scipy.stats import norm
import os
class ZYJTemporalAnalysis:
def __init__(self, input_dir: str, threshold: float = 0.9999, num_day_thres: float = 100.):
self.input_path = input_dir
self.t... | pd.to_datetime(df["referenceTime"]) | pandas.to_datetime |
import os
from io import BytesIO
import zipfile
import time
import warnings
import json
from pathlib import Path
import argparse
import requests
import pandas as pd
import geopandas as gpd
import fiona
DATA_DIR = Path(os.path.dirname(__file__), "../data")
RAW_DIR = Path(DATA_DIR, "raw")
PROCESSED_DIR = Path(DATA_DIR,... | pd.DataFrame(sensors) | pandas.DataFrame |
"""
json 불러와서 캡션 붙이는 것
"""
import json
import pandas as pd
path = './datasets/vqa/v2_OpenEnded_mscoco_train2014_questions.json'
with open(path) as question:
question = json.load(question)
# question['questions'][0]
# question['questions'][1]
# question['questions'][2]
df = pd.DataFrame(question['questions'])
d... | pd.DataFrame(val_cap['data']) | pandas.DataFrame |
from datetime import datetime, time, timedelta
from pandas.compat import range
import sys
import os
import nose
import numpy as np
from pandas import Index, DatetimeIndex, Timestamp, Series, date_range, period_range
import pandas.tseries.frequencies as frequencies
from pandas.tseries.tools import to_datetime
impor... | frequencies.get_freq_code('H') | pandas.tseries.frequencies.get_freq_code |
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... | tm.assert_frame_equal(df, store["b"]) | pandas.util.testing.assert_frame_equal |
import numpy as np
import pandas as pd
import matplotlib
from importlib import reload
import matplotlib.pyplot as plt
import elements
elements = reload(elements)
from elements.event import Event
import os
from scipy.fft import fft, fftfreq, ifft
#%%
#meta data
meta_event = pd.read_csv('data/meta_data.csv')
#List of ev... | pd.read_pickle('data/causes.pkl') | pandas.read_pickle |
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 21 10:00:33 2018
@author: jdkern
"""
from __future__ import division
from sklearn import linear_model
from statsmodels.tsa.api import VAR
import scipy.stats as st
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
##############... | pd.read_csv('PNW_hydro/FCRPS/Path_dams.csv',header=None) | pandas.read_csv |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.9.1+dev
# kernelspec:
# display_name: Python [conda env:biovectors]
# language: python
# name: conda-env-biovectors-... | pd.DataFrame(remaining_ids, columns=["pmid"]) | pandas.DataFrame |
import numpy as np
import pytest
from pandas import Categorical, Series
import pandas._testing as tm
@pytest.mark.parametrize(
"keep, expected",
[
("first", Series([False, False, False, False, True, True, False])),
("last", Series([False, True, True, False, False, False, False])),
(Fa... | Series([False, False, True, True]) | pandas.Series |
import numpy as np
import pandas as pd
from sklearn.metrics import roc_auc_score, auc, roc_curve, confusion_matrix, fbeta_score
from imblearn.over_sampling import BorderlineSMOTE
from collections import Counter
import gc as gc
from sklearn.feature_selection import RFE
#------------------------------------------------... | pd.merge(dataframe, dataframe_work, on=fix_column) | pandas.merge |
'''Some helper functions for data ETL including:
- Load features from dataframe
- Normalization and denormalize
- Load dataset, pytorch dataset
- Load adjacent matrix, load graph network
- Preprocess dataset
'''
import numpy as np
import pandas as pd
import torch
from datetime import datet... | pd.read_csv(feat_path) | pandas.read_csv |
from datetime import datetime, timedelta
import inspect
import numpy as np
import pytest
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
Index,
IntervalIndex,
MultiIndex... | RangeIndex(stop=2) | pandas.RangeIndex |
import inspect
import os
from unittest.mock import MagicMock, patch
import numpy as np
import pandas as pd
import pytest
import woodwork as ww
from evalml.model_understanding.graphs import visualize_decision_tree
from evalml.pipelines.components import ComponentBase
from evalml.utils.gen_utils import (
SEED_BOUND... | pd.Int64Index([1]) | pandas.Int64Index |
import pandas as pd
import numpy as np
import os
# Function to import multiple files into a dictionary - use for global country and region data.
def get_data(path, name):
'''Function to read in data files from csv and import it into a dictionary of dataframes - used for global country and region data.'''
file... | pd.DatetimeIndex(df['date']) | pandas.DatetimeIndex |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def plot_testing_acc(x, y):
df = pd.DataFrame({'x': x, 'y': y, 'z': y})
f1 = plt.figure(1)
plt.plot('x','y',data=df, marker='o', color='blue')
plt.title("Testing accuracy vs Number of Batches (100s)")
plt.xlabel("Number of Batches")
plt.y... | pd.DataFrame({'x1': batches, 'y1': label_1_height}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import functools
import os
from collections import Counter
from multiprocessing import Pool as ThreadPool
from random import sample
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
import pandas as pd
from apps.kemures.kernel.config.global_var import MAX_TH... | pd.merge(self.__song_msd_df, gender_df, how='inner', left_on='track_id', right_on='track_id') | pandas.merge |
# get the latest version of pandas_profiling
from pathlib import Path
import numpy as np
import pandas as pd
from pandas_profiling import ProfileReport
if __name__ == "__main__":
# data set location http://eforexcel.com/wp/downloads-18-sample-csv-files-data-sets-for-testing-sales/
df=pd.read_csv("/home/prasad/Down... | pd.to_datetime(df['Ship Date'],infer_datetime_format=True ) | pandas.to_datetime |
# coding: utf8
import torch
import numpy as np
import os
import warnings
import pandas as pd
from time import time
import logging
from torch.nn.modules.loss import _Loss
import torch.nn.functional as F
from sklearn.utils import column_or_1d
import scipy.sparse as sp
from clinicadl.tools.deep_learning.iotools import c... | pd.DataFrame(metrics, index=[0]) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Purpose: Perform Incremental PCA on QPESUMS data.
Description:
--input: directory that contains QPESUMS data as *.npy (6*275*162)
--output: the prefix of output files.
--filter: the file contains a list of timestamp that filters the input data for processing.
--... | pd.read_csv(args.filter) | pandas.read_csv |
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