prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
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(result, df) | pandas.util.testing.assert_frame_equal |
import sys
import os
import logging
import datetime
import pandas as pd
from job import Job, Trace
from policies import ShortestJobFirst, FirstInFirstOut, ShortestRemainingTimeFirst, QuasiShortestServiceFirst
sys.path.append('..')
def simulate_vc(trace, vc, placement, log_dir, policy, logger, start_ts, *args):
if... | pd.Timestamp(start) | pandas.Timestamp |
# settings.configure()
# import os
# import django
# os.environ.setdefault("DJANGO_SETTINGS_MODULE", "mysite.settings")
# django.setup()
# from . import models
import email
import pandas
from datetime import time
import random
from django.core.management.base import BaseCommand
from django.conf import settings
from... | tetime(medics['Срок действия']) | pandas.to_datetime |
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('2000-01-01 00:00:00') | pandas.Timestamp |
import argparse
import datetime
import numpy as np
import pandas as pd
import pysam
def convert_table_to_vcf(genotypes_filename, calls_filename, reference_filename, vcf_filename):
# Load genotypes in long format.
genotypes = | pd.read_table(genotypes_filename) | pandas.read_table |
from utils.model import Perceptron
from utils.all_utils import prepare_data, save_plot, save_model
import pandas as pd
import logging
import os
logging_str = "[%(asctime)s: %(levelname)s: %(module)s] %(message)s"
log_dir = "logs"
os.makedirs(log_dir, exist_ok=True)
logging.basicConfig(filename= os.path.join(log_dir,"r... | pd.DataFrame(data) | pandas.DataFrame |
import os
import configparser
import pandas as pd
import numpy as np
import psycopg2
import psycopg2.extras
# Set up GCP API
from google.cloud import bigquery
# Construct a BigQuery client object.
client = bigquery.Client()
import sql_queries as sql_q
def convert_int_zipcode_to_str(df, col):
"""
Converts i... | pd.read_csv(filename) | pandas.read_csv |
from brightics.common.report import ReportBuilder, strip_margin, plt2MD, \
pandasDF2MD, keyValues2MD
from brightics.function.utils import _model_dict
from brightics.common.utils import check_required_parameters
import numpy as np
import pandas as pd
import math
from math import sqrt
import seaborn as sns
... | pd.DataFrame(list, columns=cols) | pandas.DataFrame |
# A collection of helper functions that are used throughout. This file is aimed to avoid replication of code.
import pandas as pd
def read_in_NNDSS(date_string, apply_delay_at_read=False, apply_inc_at_read=False, running_epyreff=False):
"""
A general function to read in the NNDSS data. Alternatively this can ... | pd.to_datetime(date_string) | pandas.to_datetime |
from typing import List
import numpy as np
import pandas as pd
import stockstats
import talib
import copy
class BasicProcessor:
def __init__(self, data_source: str, start_date, end_date, time_interval, **kwargs):
assert data_source in {
"alpaca",
"baostock",
"ccxt",
... | pd.DataFrame() | pandas.DataFrame |
from itertools import chain
import operator
import numpy as np
import pytest
from pandas.core.dtypes.common import is_number
from pandas import (
DataFrame,
Index,
Series,
)
import pandas._testing as tm
from pandas.core.groupby.base import maybe_normalize_deprecated_kernels
from pandas.tests.apply.common... | Series(dtype="float64") | pandas.Series |
import pandas as pd
import pytest
import woodwork as ww
from woodwork.logical_types import Boolean, Double, Integer
from rayml.exceptions import MethodPropertyNotFoundError
from rayml.pipelines.components import (
ComponentBase,
FeatureSelector,
RFClassifierSelectFromModel,
RFRegressorSelectFromModel,
... | pd.Series([1.0, 2.0, 3.0], dtype="float") | pandas.Series |
from bs4 import BeautifulSoup
import requests
import pandas as pd
import datetime
from selenium import webdriver
page_link = 'http://lefthandditchcompany.com/SystemStatus.aspx'
page_response = requests.get(page_link, timeout=60, verify=False)
body = BeautifulSoup(page_response.content, 'lxml')
Creekflow = bod... | pd.to_numeric(df.Issues) | pandas.to_numeric |
import logging, os, sys, pickle, json, time, yaml, glob
from datetime import datetime as dt
import warnings
warnings.filterwarnings('ignore')
import subprocess
from itertools import chain
from tqdm import tqdm
import networkx as nx
import pandas as pd
from math import pi
import numpy as np
from kedro.io import DataCat... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import joblib, os, pickle
from Fuzzy_clustering.version3.project_manager.PredictModelManager.Clusterer import clusterer
from Fuzzy_clustering.version3.project_manager.PredictModelManager.ClusterPredictManager import ClusterPredict
class FullClusterPredictManager(object):
def... | pd.DataFrame() | pandas.DataFrame |
import anemoi as an
import pandas as pd
import numpy as np
import scipy as sp
import statsmodels.api as sm
import scipy.odr.odrpack as odrpack
import warnings
def compare_sorted_df_columns(cols_1, cols_2):
return sorted(cols_1) == sorted(cols_2)
def valid_ws_correlation_data(data, ref_ws_col='ref', s... | pd.concat([ref_ws_data, site_ws_data, ref_dir_data], axis=1, join='inner') | pandas.concat |
from __future__ import print_function
'''
This module should be organized as follows:
Main function:
chi_estimate() = returns chi_n, chi_b
- calls:
wealth.get_wealth_data() - returns data moments on wealth distribution
labor.labor_data_moments() - returns data moments on labor supply
minst... | pd.cut(ages, age_bins, right=False, include_lowest=True, labels=labels) | pandas.cut |
from pathlib import Path
from typing import Union, Dict, List
import medvision as mv
import numpy as np
import pandas as pd
def load_det_dsmd(
dsmd_path: Union[str, Path],
class2label: Union[str, Dict[str, int]]
):
""" load detection dataset metadata.
Args:
dsmd_path (str or Path): dataset me... | pd.read_csv(dsmd_path, header=None) | pandas.read_csv |
# -*- coding: utf-8 -*-
# Arithmetc tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import timedelta
import operator
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.compat import long
from pandas.core import ops
from pan... | TimedeltaIndex(['1 Day', '12 Hours']) | pandas.TimedeltaIndex |
# -*- coding: utf-8 -*-
"""
Analyzes code age in a git repository
Writes reports in the following locations
e.g. For repository "cpython"
[root] Defaults to ~/git.stats
├── cpython Directory for https://github.com/python/cpython.git... | DataFrame(dir_loc_frac, columns=['dir', 'LoC', 'frac']) | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as pt
from sklearn import linear_model
from sklearn import metrics
from keras import models
from keras import layers
import pickle
df=pd.read_csv("C://Users//Dell//Desktop//SNU//Seventh Semester//Data Mining//Project//Data//finalData.csv")
dfW=pd.read_cs... | pd.DataFrame(columns=["Date","Output"]) | pandas.DataFrame |
import os
os.chdir("D:/George/Projects/PaperTrends/src")
import sys
from twitter import TwitterParser
from arxiv import ArxivAPI
import pandas as pd
from designer import generateIntro
from tqdm import tqdm
class Trend:
def __init__(self, user='arxivtrends', ignoreposted=False):
print("Trend initialized"... | pd.read_csv("../db/csv/posted.csv") | pandas.read_csv |
from numpy import *
from numpy.random import *
import pandas as pd
import sqlite3
from os import remove
from os.path import exists
from itertools import combinations
db_path = 'db.sqlite3'
force = 1
nb_client = 1e1
nb_guarantee = 1e1
nb_fund_price = 1e1
nb_address_N = 5
nb_address_p = 0.1
nb_purchase_mu = 5
nb_purc... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
from datetime import timedelta
import pandas as pd
import pandas.tslib as tslib
import pandas.util.testing as tm
import pandas.tseries.period as period
from pandas import (DatetimeIndex, PeriodIndex, period_range, Series, Period,
_np_version_under1p10, Index, Timedelta, offsets)
... | offsets.Hour(2) | pandas.offsets.Hour |
import sys
sys.path.insert(0,'/usr/local/lib/python3.7/site-packages')
import pandas as pd
import numpy as np
import scipy
import bottleneck as bn
from sklearn.model_selection import KFold
from sklearn.preprocessing import normalize
from sklearn.feature_selection import f_regression
from scipy.stats import pea... | pd.read_csv(path_data+dm_file) | pandas.read_csv |
import pandas as pd
passageiros = pd.read_csv('Passageiros.csv')
passageiros.head()
import seaborn as sns
import matplotlib as mpl
mpl.rcParams['figure.figsize'] = (10, 6)
mpl.rcParams['font.size'] = 22
sns.lineplot(x='tempo',y='passageiros', data=passageiros,label='dado_completo')
## Escalando os dados
from skl... | pd.DataFrame(ytreino) | pandas.DataFrame |
# _*_ coding:utf-8 _*_
'''=================================
@Author :tix_hjq
@Date :19-10-30 下午9:36
================================='''
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import f1_score, r2_score
from numpy.random im... | pd.set_option('display.max_rows', None) | pandas.set_option |
from copy import deepcopy
import logging
import time
import traceback
from typing import List, Set, Tuple, Union
import uuid
import numpy as np
import pandas as pd
from ..features.types import R_FLOAT
from ..models.abstract.abstract_model import AbstractModel
from ..models.ensemble.bagged_ensemble_model import Bagged... | pd.concat([evaluated_both_rows, evaluated_new_only_rows, evaluated_old_only_rows, evaluated_neither_rows]) | pandas.concat |
import pandas as pd
import requests
from tqdm import tqdm
import os
from os import listdir
from os.path import isfile, join
from datetime import datetime
from functools import cache
"""
Test welke van de leden+descendants in een refset er in de VT (totaal en lijst gyn) zitten.
146481000146103 |simpele referentieset me... | pd.DataFrame(output2) | pandas.DataFrame |
""""
Created by <NAME>, based on the Master Thesis:
"A proposed method for unsupervised anomaly detection for arg_from multivariate building dataset "
University of Bern/Neutchatel/Fribourg - 2017
Any copy of this code should be notified at <EMAIL>
to avoid intellectual property's problems.
Not... | pd.to_datetime(df.index) | pandas.to_datetime |
# Copyright 2020 (c) Netguru S.A.
#
# 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 writ... | pd.concat([numerical_df, bool_df, categorical_df], axis=1) | pandas.concat |
import argparse
import logging
import os
import pickle
import re
from tqdm import tqdm
tqdm.pandas()
import boto3
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
logging.basicConfig(format='%(asctime)s,%(msecs)d %(levelname)-8s [%(filename)s:%(lineno)d] %(message... | pd.DataFrame.from_dict(card_id_card_feature_data, orient='index', columns=col_names) | pandas.DataFrame.from_dict |
"""
This network uses the last 26 observations of gwl, tide, and rain to predict the next 18
values of gwl for well MMPS-125. The data for MMPS-125 is missing <NAME>.
"""
import pandas as pd
from pandas import DataFrame
from pandas import concat
from pandas import read_csv
from sklearn.metrics import mean_squ... | pd.concat([df_t18, dates_18], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 11 16:31:58 2021
@author: snoone
"""
import os
import glob
import pandas as pd
import numpy as np
pd.options.mode.chained_assignment = None # default='warn'
OUTDIR2= "D:/Python_CDM_conversion/daily/cdm_out/head"
OUTDIR = "D:/Python_CDM_conversion/daily/cd... | pd.to_numeric(df["observation_value"],errors='coerce') | pandas.to_numeric |
"""Requires installation of requirements-extras.txt"""
import pandas as pd
import os
import seaborn as sns
from absl import logging
from ._nlp_constants import PROMPTS_PATHS, PERSPECTIVE_API_MODELS
from credoai.data.utils import get_data_path
from credoai.modules.credo_module import CredoModule
from credoai.utils.com... | pd.concat(dfrunst_assess_lst) | pandas.concat |
'''
@Author = Ollie
'''
import yfinance as yf
from pandas_datareader import data as pdr
yf.pdr_override()
import pandas as pd
from datetime import datetime, timedelta, date
class stock_dataframe():
def __init__(self, ticker, start_date, df):
'''This class represents a dataframe that can be used to scrape ... | pd.concat([old_df, self.df]) | pandas.concat |
"""
Processing data from the output database.
"""
import logging
from typing import List
from datetime import date
import numpy as np
import pandas as pd
from autumn.tools.db.database import get_database
from autumn.tools.db.load import load_mcmc_tables
from autumn.tools.utils.runs import read_run_id
logger = loggin... | pd.Series(index=index, data=target["values"]) | pandas.Series |
"""
A set of classes for aggregation of TERA data sources into common formats.
"""
from rdflib import Graph, Namespace, Literal, URIRef, BNode
from rdflib.namespace import RDF, OWL, RDFS
UNIT = Namespace('http://qudt.org/vocab/unit#')
import pandas as pd
import validators
import glob
import math
from tqdm import tqdm
... | pd.read_csv(path,sep=',',usecols=['child','parent'],na_values = nan_values, dtype=str) | pandas.read_csv |
import concurrent.futures as cf
from functools import partial
import os
import pandas as pd
import utility_functions as utilfunc
import config
# load logger
logger = utilfunc.get_logger()
class Agents(object):
"""
Agents class instance
"""
def __init__(self, agents_df):
"""
Initialize... | pd.merge(self.df, attr_df, how='left', on=on) | pandas.merge |
import pandas as pd
import glob
import matplotlib.pyplot as plt
import seaborn as sns
language = 'en'
embed='glove'
plot=True
df = | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import pandas.tseries.period as period
from pandas import period_range, PeriodIndex, Index, date_range
def _permute(obj):
return obj.take(np.random.permutation(len(obj)))
class TestPeriodIndex(tm.TestCase):
def setUp(self):
pa... | period_range('1/1/2000', '1/20/2000', freq='D') | pandas.period_range |
from influxdb import InfluxDBClient
import time
import pandas as pd
import numpy as np
from pprint import pprint
import plotly.graph_objs as go
import plotly.io as pio
from datetime import datetime, timedelta
import pandas as pd
import os
host = 'hs-04.ipa.psnc.pl'
port = 8086
user = 'root'
password = '<PASSWORD>'
db... | pd.DataFrame(timestamps, dtype='float64') | pandas.DataFrame |
# =========================================================================================================================================
# =================================== Extract Data from XML files and create Lua Tables. =================================
# ================================... | pandas.DataFrame([{"@index": 0, "@value": 1}]) | pandas.DataFrame |
import pandas as pd
import numpy as np
import sys
import os
clear = lambda: os.system('cls')
clear()
print("\n3. FILTRO BASADO EN CONTENIDO: PALABRAS CLAVES\n")
path="ml-latest-small"
movies = pd.read_csv(path+'/moviesES.csv', sep=',', encoding='latin-1', usecols=['movieId', 'title', 'genres'])
ratings = pd.read_cs... | pd.read_csv(path+'/tags.csv', sep=',', encoding='latin-1', usecols=['movieId', 'tag']) | pandas.read_csv |
from flask import Flask, render_template, request, redirect, url_for, session
import pandas as pd
import pymysql
import os
import io
#from werkzeug.utils import secure_filename
from pulp import *
import numpy as np
import pymysql
import pymysql.cursors
from pandas.io import sql
#from sqlalchemy import create... | pd.DataFrame(data=0,index=["ME","MAE","MAPE"],columns=["Moving Average","ARIMA","Exponential Smoothing","Regression"]) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This script saves bid and ask data for specified ETFs to files for each day
during market open hours.
It assumes the computer is at US East Coast Time.
@author: mark
"""
import os
import pandas as pd
import numpy as np
from itertools import product
import streaml... | pd.Timestamp('2021-01-01 9:30') | pandas.Timestamp |
# General purpose packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import randint
# Image processing packages
from skimage import io, color
from skimage.transform import resize
from skimage.segmentation import slic
from skimage.color import label2rgb
from skim... | pd.read_csv('signatures_data.csv', index_col=0) | pandas.read_csv |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from read_input import read_study
########################American#############################################
def main_analysis():
output_folder = './output_ref/'
studies = ['bermudean', 'maxcall2', 'maxcall10', 'strangle']
losses_ref... | pd.DataFrame() | pandas.DataFrame |
import sys
import os
from tqdm import tqdm
import pmdarima as pm
from pmdarima.model_selection import train_test_split
import numpy as np
from datetime import timedelta
import pandas as pd
from bokeh.io import output_file, show
from bokeh.models import Select, Slider
from bokeh.models import ColumnDataSource
from boke... | pd.to_datetime(data['dateRep'], infer_datetime_format=True) | pandas.to_datetime |
from datetime import datetime, timedelta
import operator
from typing import Any, Sequence, Type, Union, cast
import warnings
import numpy as np
from pandas._libs import NaT, NaTType, Timestamp, algos, iNaT, lib
from pandas._libs.tslibs.c_timestamp import integer_op_not_supported
from pandas._libs.tslibs.period import... | make_invalid_op("__rdivmod__") | pandas.core.ops.invalid.make_invalid_op |
# -*- coding: utf-8 -*-
"""
Copyright (c) 2019, <NAME> <akoenzen | uvic.ca>
All rights reserved.
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... | pd.Series(testing_labels) | pandas.Series |
import numpy as np
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Index,
Series,
Timestamp,
)
import pandas._testing as tm
from pandas.core.arrays import IntervalArray
class TestGetNumericData:
def test_get_numeric_data_preserve_dtype(self):
# get the numeric data
... | Index(["a", "b", "e"]) | pandas.Index |
import pandas as pd
import numpy as np
file4 = '../data/VITALS_BP1.xlsx'
x4 = pd.ExcelFile(file4)
bp = x4.parse('Sheet1')
print(bp.shape)
print(bp.iloc[0:1])
print(bp.dtypes)
bp = bp.dropna(subset=['START_DATE'])
bp['RECORDED_TIME'] = bp['RECORDED_TIME'].str[0:7] + '20' + bp['RECORDED_TIME'].str[7:]
bp['START_DATE'] = ... | pd.to_datetime(bp['RECORDED_TIME']) | pandas.to_datetime |
import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
Timestamp,
date_range,
)
import pandas._testing as tm
@pytest.mark.parametrize("bad_raw", [None, 1, 0])
def test_rolling_apply_invalid_raw(bad_raw):
with pytest.raises(ValueError, m... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
import re
import pandas as pd
from config import Config
class Dataset(Config):
"""
Attributes
----------
ukbb_vars: list
Variable names based on user selections as coded in the Biobank.
recoded_vars: list
Variable names based on user selections as will be recoded.
... | pd.DataFrame(new_vars) | 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.offsets.Hour(2) | pandas.offsets.Hour |
"""
Python module to do preliminary preprocessing
Creates train and test .csv files
"""
import pandas as pd
from sklearn.model_selection import train_test_split
import os
seed = 42
raw_data_dir = r'C:\Users\adrian.bergem\Google Drive\Data science\Projects\AI Credit Default\data\raw'
# Load in data
borrower = pd.read... | pd.concat([y_train, X_train], axis=1) | pandas.concat |
from __future__ import print_function
import os.path
import random
from functools import partial
import datetime as dt
from flask import Flask, json, Response
import h5py
import numpy as np
import pandas as pd
import dask.array as da
from subsample import coarsen
from bokeh.server.crossdomain import crossdomain
from S... | pd.read_csv('data/aapl.csv') | pandas.read_csv |
import streamlit as st
import pandas as pd
import numpy as np
import nltk
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from sentence_transformers import SentenceTransformer, util
import copy
import random
import requests
from bs4 import BeautifulSoup
import random
import time
from newspaper... | pd.DataFrame(columns=stories_columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import pytest
import re
from numpy import nan as NA
import numpy as np
from numpy.random import randint
from pandas.compat import range, u
import pandas.compat as compat
from pandas import Index, Series, DataFrame, isn... | tm.assert_index_equal(res, exp) | pandas.util.testing.assert_index_equal |
""" test scalar indexing, including at and iat """
from datetime import (
datetime,
timedelta,
)
import numpy as np
import pytest
from pandas import (
DataFrame,
Series,
Timedelta,
Timestamp,
date_range,
)
import pandas._testing as tm
from pandas.tests.indexing.common import Base
class T... | DataFrame({"A": ser, "B": ser2}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 17 2015
This script will grab the feature data from extracted feature files
for all images in an automated class file.
Can bin data by category or leave each image separate.
This particular script was edited to use neural nets to estimate the
number of cells in a diatom ... | pd.read_csv(feature_path + in_feature, index_col=0) | pandas.read_csv |
import ast
import numpy as np
import pandas as pd
from clevercsv import csv2df
from collections import Counter
from src import constants
def get_sequence(dataset, column, annotations):
for item in annotations[dataset]:
if item["header"] == column:
return item["sequence"], item["tokens"], list... | pd.DataFrame({"tokens": tokens, "tags": tags, "labels": labels}) | pandas.DataFrame |
#!/usr/bin/env python
### Up to date as of 10/2019 ###
'''Section 0: Import python libraries
This code has a number of dependencies, listed below.
They can be installed using the virtual environment "slab23"
that is setup using script 'library/setup3env.sh'.
Additional functions are housed in file ... | pd.DataFrame({'lon':output[:,0], 'lat': output[:,1], 'depth':output[:,3]*-1.0}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# @Time : 2021/4/20 12:54
# @File : danjuan_fund_data_analysis.py
# @Author : Rocky <EMAIL>
# 蛋卷数据分析
import datetime
import sys
from collections import defaultdict
sys.path.append('..')
from configure.settings import DBSelector
from common.BaseService import BaseService
import pandas as pd
WEE... | pd.DataFrame(data,columns=['fund','clear_num']) | pandas.DataFrame |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import re
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import TruncatedSVD
from sklearn import preprocessing, model_select... | pd.read_csv('../input/periods_test.csv', parse_dates=['date_from', 'date_to']) | pandas.read_csv |
import os
import time
import torch
import torch.nn.modules.distance
import torch.utils.data as td
import pandas as pd
import numpy as np
import datetime
from csl_common.utils import log
from csl_common.utils.nn import Batch
import csl_common.utils.ds_utils as ds_utils
from datasets import multi, affectnet, vggface2, w... | pd.DataFrame(stats) | pandas.DataFrame |
import numpy as np
import partitioning
import pickle
import h5py
import pandas as pd
import sys
VGGM = -5.24
VGGS = 8.17
def map_labels(labels):
return labels - 1
def soften_ordinal_labels(labels, m=0.05):
# this function softens the ordinal labels for better training.
labels_ = labels.copy()
labels_[... | pd.DataFrame(data=data_matrix,columns=['img_id', 'pcd', 'oa11', 'lsoa11',self.label_name,'predicted']) | pandas.DataFrame |
import os
from deepblast.dataset.utils import state_f, revstate_f
import pandas as pd
import numpy as np
from collections import Counter
def read_mali(root, tool='manual', report_ids=False):
""" Reads in all alignments.
Parameters
----------
root : path
Path to root directory
tool : str
... | pd.DataFrame(res) | pandas.DataFrame |
import pytest
from pandas import Interval, DataFrame
from pandas.testing import assert_frame_equal
from datar.base.funs import *
from datar.base import table, pi, paste0
from datar.stats import rnorm
from .conftest import assert_iterable_equal
def test_cut():
z = rnorm(10000)
tab = table(cut(z, breaks=range(-... | Interval(2, 3, closed='right') | pandas.Interval |
from datetime import timedelta
import pandas as pd
from estimate_start_times.concurrency_oracle import HeuristicsConcurrencyOracle
from estimate_start_times.config import Configuration as StartTimeConfiguration
from estimate_start_times.config import EventLogIDs as StartTimeEventLogIDs
from .config import Configurati... | pd.isna(self.batch_event_log[self.log_ids.batch_id]) | pandas.isna |
import boto3
import xmltodict
import requests
import pandas as pd
from custom_tokenizer import find_start_end
from tqdm import tqdm_notebook
class AMT:
def __init__(self, production=False):
environments = {
"production": {
"endpoint": "https://mturk-requester.us-east-1.amazonaws.com",... | pd.isnull(df.r_relevant) | pandas.isnull |
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.concat(dfs) | pandas.concat |
import csv
import GetOldTweets3 as got
import numpy as np
import pandas as pd
import re
import time
from datetime import datetime, timezone, date, timedelta
from urllib.error import HTTPError, URLError
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
counter = 0
since = | pd.to_datetime('2019-07-22') | pandas.to_datetime |
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import pandas as pd
import numpy as np
import seaborn as sns
from lightfm.evaluation import precision_at_k, recall_at_k
def model_perf_plots(df):
"""Function to plot model performance metrics.
Args:
df (pa... | pd.DataFrame(data={"userID": users, "itemID": items}) | pandas.DataFrame |
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
begge_kjonn_5 = pd.read_csv("begge_kjonn_5.csv")
gutter_5 = pd.read_csv("gutter_5.csv")
jenter_5 = pd.read_csv("jenter_5.csv")
jenter_gutter_5 = pd.concat([gutter_5, jenter_5]).reset_index(drop=True)
begge_kjonn_8 = pd.r... | pd.concat([gutter_9, jenter_9]) | pandas.concat |
from faker import Faker
import pandas as pd
import datetime
import numpy as np
import matplotlib.pylab as plt
'''
class which generates fake and sample data
'''
class fake_data:
@staticmethod
def one_sentence():
"""
Returns text(string)
Parameters
-----------
"""
tem... | pd.DataFrame(data, columns=["text"]) | pandas.DataFrame |
"""Parsers to convert uncontrolled cell grids into representations of StarTable blocks.
parse_blocks() emits a stream of blocks objects.
This in principle allows early abort of reads as well as generic postprocessing (
as discussed in store-module docstring).
parse_blocks() switches between different parsers dependin... | pd.DataFrame(json_precursor["columns"]) | pandas.DataFrame |
from pathlib import Path
import numba as nb
import numpy as np
import pandas as pd
from astropy.time import Time
from scipy.optimize import curve_fit
import ysvisutilpy2005ud as yvu
PI = np.pi
D2R = PI / 180
DATAPATH = Path('data')
SAVEPATH = Path('figs')
SAVEPATH.mkdir(exist_ok=True)
# ***************************... | pd.read_csv(DATAPATH/"2020PSJ.....1...15D.csv") | pandas.read_csv |
import math
__author__ = 'r_milk01'
import os
import pandas as pd
from configparser import ConfigParser
import matplotlib.pyplot as plt
import matplotlib
import itertools
import logging
import difflib
import colors as color_util
TIMINGS = ['usr', 'sys', 'wall']
MEASURES = ['max', 'avg']
SPECIALS = ['run', 'threads'... | pd.Series(values) | pandas.Series |
import os
from nose.tools import *
import unittest
import pandas as pd
from py_entitymatching.utils.generic_helper import get_install_path
import py_entitymatching.catalog.catalog_manager as cm
import py_entitymatching.utils.catalog_helper as ch
from py_entitymatching.io.parsers import read_csv_metadata
datasets_path... | pd.read_csv(path_a) | pandas.read_csv |
# *****************************************************************************
# Copyright (c) 2020, Intel Corporation All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# Redistributions of sou... | pd.DataFrame({'key2': ['baz', 'bar', 'baz'], 'B': ['b', 'zzz', 'ss']}) | pandas.DataFrame |
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score, confusion_matrix
from lifelines import CoxPHFitter
from datautils.dataset import Dataset
from datautils.data import Data
from datautils.helper import save_output
from tqdm import tqdm
i... | pd.DataFrame(processed, columns=["x","t","s"]) | pandas.DataFrame |
import os
import logging
from datetime import datetime, timedelta
import configparser
from data import \
download_yahoo_data,\
map_tickers,\
generate_rsi_features,\
add_targets_and_split, \
get_rsi_feature_names
import joblib
import numerapi
import pandas as pd
from sklearn.ensemble import Gradien... | pd.read_csv('full_data.csv') | pandas.read_csv |
from __future__ import annotations
import numpy as np
import pandas as pd
from lamarck.utils import objective_ascending_map
def rank_formatter(name):
def deco(rank_func):
def wrapper(obj, *a, **kw):
return rank_func(obj, *a, **kw).astype(int).rename(name)
return wrapper
return dec... | pd.Series([np.inf]) | pandas.Series |
"""
Optimizer Class Constructs Mean-Variance Related Optimization Problems with Constraints
2 Major Functionality:
- Optimize Weight based on Constraints & Objectives
- Simulate Random Weight Scenarios
For the first functionality, all the addition of objective/constraints are performed with the following methods.
- a... | pd.DataFrame(columns=self.assets, data=weight_vals) | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import models, layers
import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
dftrain_raw = pd.read_csv('data/titanic/train.csv')
dftest_raw = pd.read_csv('data/titanic/test.csv')
dftrain_raw.head(10)
... | pd.isna(dfdata['Cabin']) | pandas.isna |
import streamlit as st
import datetime
import pandas as pd
from plotly.subplots import make_subplots
from fbprophet import Prophet
from fbprophet.plot import plot_plotly
from plotly import graph_objs as go
import json
# App title
st.markdown('''
# Eindhoven STAR (Sound, Temperature, Air Quality, Rain) Environment Das... | pd.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
"""
Classes and methods to load datasets.
"""
import numpy as np
import struct
from scipy.misc import imresize
from scipy import ndimage
import os
import os.path
import pandas as pd
import json
from collections import defaultdict
from pathlib import Path as pathlib_path
import pickle
'''
Contains helper methods and c... | pd.Series(is_train) | pandas.Series |
from importlib import reload
import scipy
import numpy as np
#import matplotlib.pyplot as plt
import pandas as pd
import demosaurus
app = demosaurus.create_app()
def score_candidates(row):
print(row.publication_ppn)
author_name=str(row['name'])
author_role = row.role
publication_title = row.titelverme... | pd.DataFrame() | pandas.DataFrame |
import re
import numpy as np
import pandas.compat as compat
import pandas as pd
from pandas.compat import u
from pandas.core.base import FrozenList, FrozenNDArray
from pandas.util.testing import assertRaisesRegexp, assert_isinstance
from pandas import Series, Index, DatetimeIndex, PeriodIndex
from pandas import _np_ver... | FrozenList(self.lst) | pandas.core.base.FrozenList |
"""
Function and classes used to identify barcodes
"""
from typing import *
import pandas as pd
import numpy as np
import pickle
import logging
from sklearn.neighbors import NearestNeighbors
# from pynndescent import NNDescent
from pathlib import Path
from itertools import groupby
from pysmFISH.logger_utils import sel... | pd.concat([reference_round_df,ref_selected_df_no_duplicates]) | pandas.concat |
import string
import warnings
import numpy as np
from pandas import (
DataFrame,
MultiIndex,
NaT,
Series,
date_range,
isnull,
period_range,
timedelta_range,
)
from .pandas_vb_common import tm
class GetNumericData:
def setup(self):
self.df = DataFrame(np.random.randn(1000... | DataFrame(data) | pandas.DataFrame |
#!/usr/bin/python3
"""
AbxRxPro: Antibiotic Resistance Profiler
Version: 2.1.1-alpha
Last modified: 25/03/2021
Github: https://github.com/CaileanCarter/AbxRxPro
Author: <NAME>
Email: <EMAIL>
Institute affiliation: Quadra... | pd.DataFrame.from_dict(self.GeneFrequencies, orient="index") | pandas.DataFrame.from_dict |
import logging
import yaml
import os
import docker
import re
import sys
from tempfile import NamedTemporaryFile
import numpy as np
import pandas as pd
from pandas.errors import EmptyDataError
from docker.errors import NotFound, APIError
from io import StringIO
# from pynomer.client import NomerClient
# from ..core imp... | pd.notnull(id) | pandas.notnull |
# -----------------------------------------------------------------------------
# Copyright (c) 2014--, The Qiita Development Team.
#
# Distributed under the terms of the BSD 3-clause License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | pd.update_insdc_status('failed') | pandas.update_insdc_status |
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 12 12:37:58 2022
@author: gojja och willi
"""
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.api import VAR
from scipy.stats import pearsonr
import numpy... | pd.read_excel(r"...\Data\Raw Data\Other Variables\Anxious Index\anxious_index_chart.xlsx") | pandas.read_excel |
# -*- coding: utf-8 -*-
"""Device curtailment plots.
This module creates plots are related to the curtailment of generators.
@author: <NAME>
"""
import os
import logging
import pandas as pd
from collections import OrderedDict
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.dates as mdates
... | pd.notna(start_date_range) | pandas.notna |
import pandas as pd
from pm4py.objects.log.importer.xes import factory as xes_import_factory
from pm4py.objects.conversion.log.versions.to_dataframe import get_dataframe_from_event_stream
from pm4py.objects.conversion.log import converter as log_converter
from pm4py.algo.discovery.dfg import factory as dfg_factory
from... | pd.to_datetime(dataset['time:timestamp'],utc=True) | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 31 13:31:13 2019
@author: mehrdad
"""
import pandas as pd
import numpy as np
import tslib.trip_detection
# Compute the difference between observed trips and computed trips ----------------------
# Any mode to any mode
def compute_observed_vs_compu... | pd.merge(alts, computed_[['mainmode']], left_index=True, right_index=True, how='inner') | pandas.merge |
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