prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pandas as pd
import numpy as np
import holidays
import statsmodels.formula.api as sm
import time
from Helper import helper
import datetime
class DR(object):
def __init__(self, dataframe):
df = dataframe.copy()
self.lm_data = helper.DR_Temp_data_cleaning(df)
self.name = 'DR'
de... | pd.read_csv(path) | pandas.read_csv |
import json, os, sys
import pandas as pd
from urllib.request import urlopen
from xml.dom import minidom
from json import load
from pandas.io.json import json_normalize
def filterIQM(apidf, filter_list):
""" Loads the API info and filters based on user-provided
parameters. Filter parameters should be a l... | pd.concat([userdf,filtered_apidf], sort=True) | pandas.concat |
"""
"""
"""
>>> # ---
>>> # SETUP
>>> # ---
>>> import os
>>> import logging
>>> logger = logging.getLogger('PT3S.Rm')
>>> # ---
>>> # path
>>> # ---
>>> if __name__ == "__main__":
... try:
... dummy=__file__
... logger.debug("{0:s}{1:s}{2:s}".format('DOCTEST: __main__ Context: ','path = os.p... | pd.merge(vWBLZ_vKNOT,pFWVB,left_on='NAME_y',right_on='NAME_i') | pandas.merge |
import os
import lightgbm as lgb
import neptune
from neptunecontrib.monitoring.lightgbm import neptune_monitor
from neptunecontrib.versioning.data import log_data_version
from neptunecontrib.api.utils import get_filepaths
from neptunecontrib.monitoring.reporting import send_binary_classification_report
from neptunecon... | pd.merge(submission, test, on='TransactionID') | pandas.merge |
import pandas as pd
from multiprocessing import Pool
import logging
from src.helper import create_logger
# Create the logger object
logger = create_logger('Parser', 'logs/Hedging.log',
logging.DEBUG, logging.WARNING)
class Parser():
"""
Parser class that calls parent and divide the dat... | pd.DataFrame() | pandas.DataFrame |
__author__ = "unknow"
__copyright__ = "Sprace.org.br"
__version__ = "1.0.0"
import pandas as pd
import sys
from math import sqrt
import sys
import os
import ntpath
import scipy.stats
import seaborn as sns
from matplotlib import pyplot as plt
#sys.path.append('/home/silvio/git/track-ml-1/utils')
#sys.path.append('.... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
import argparse
labels = [
"0",
"B-answer",
"I-answer",
]
def find_answer_start(answer, sent):
answer = [x.lower() for x in answer]
sent = [x.lower() for x in sent]
for idx, word in enumerate(sent):
if answer[0] in word:
is_match = Tru... | pd.read_pickle(args.data_path) | pandas.read_pickle |
import sys
import numpy as np
import pandas as pd
import wgdi.base as base
class karyotype_mapping():
def __init__(self, options):
self.position = 'order'
self.limit_length = 5
for k, v in options:
setattr(self, str(k), v)
print(str(k), ' = ', v)
def karyoty... | pd.read_csv(self.blockinfo, index_col='id') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys, os, platform, copy
import logging, re
import pandas as pd
import numpy as np
import itertools
import time, random
from tqdm import tqdm
tqdm.pandas()
from conf import getworkdir, conf
from models import run_model, CoxNMF_initialization
from utils import feat... | pd.MultiIndex.from_arrays(columns) | pandas.MultiIndex.from_arrays |
import datetime
import integrationutils as ius
import numpy as np
import os
import pathlib
import pandas as pd
import sqlite3
import sys
import warnings
''' Direct questions and concerns regarding this script to <NAME>
<EMAIL>
'''
def find_abund_col(df):
clist = []
for c in df.columns:
... | pd.read_csv('WorkflowInputFile.txt',delimiter='\t') | pandas.read_csv |
import pandas as pd
import numpy as np
import sys
from tabulate import tabulate
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
import matplotlib.ticker as ticker
# from pyutils import *
# import dtale
# dtale.show(df)
# from pandas_profiling import ProfileRepo... | pd.value_counts(s) | pandas.value_counts |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2021/8/27 15:59
Desc: REITs 行情及信息
http://quote.eastmoney.com/center/gridlist.html#fund_reits_all
https://www.jisilu.cn/data/cnreits/#CnReits
"""
import pandas as pd
import requests
def reits_realtime_em() -> pd.DataFrame:
"""
东方财富网-行情中心-REITs-沪深 REITs
... | o_numeric(temp_df['涨幅']) | pandas.to_numeric |
# LIBRARIES
# set up backend for ssh -x11 figures
import matplotlib
matplotlib.use('Agg')
# read and write
import os
import sys
import glob
import re
import fnmatch
import csv
import shutil
from datetime import datetime
# maths
import numpy as np
import pandas as pd
import math
import random
# miscellaneous
import ... | pd.DataFrame({'version': versions, 'R2': r2s}) | pandas.DataFrame |
import time
from collections import defaultdict
from datetime import timedelta
import cvxpy as cp
import empiricalutilities as eu
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm
from transfer_entropy import TransferEntropy
plt.style.use('fivethirtyei... | pd.DataFrame({'returns': returns, 'vol': vols, 'ete': ete_out}) | pandas.DataFrame |
#Copyright 2021 <NAME>, <NAME>, <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 writ... | is_numeric_dtype(dat[col]) | pandas.api.types.is_numeric_dtype |
########################################################
# <NAME> - drigols #
# Last update: 21/09/2021 #
########################################################
def OLS(dic):
import pandas as pd
df = | pd.DataFrame(dic) | pandas.DataFrame |
import numpy as np
import pandas as pd
from pyopenms import FeatureMap, FeatureXMLFile
def extractNamesAndIntensities(feature_dir, sample_names, database):
"""
This function takes .featureXML files, the output of SmartPeak
pre-processing, and extracts the metabolite's reference and its measured
intens... | pd.DataFrame.from_dict(extracted_data_dict, "index") | pandas.DataFrame.from_dict |
import warnings
import os
import numpy as np
import pandas as pd
from preprocessing.utils import remove_french_accents_and_cedillas_from_dataframe
columns_to_drop = ['nr', 'patient_id', 'eds_end_4digit', 'eds_manual', 'DOB', 'begin_date',
'end_date', 'death_date', 'death_hosp', 'eds_final_id',
... | pd.to_numeric(equalized_reorganised_lab_df['value'], errors='coerce') | pandas.to_numeric |
import pytest
import os
from mapping import util
from pandas.util.testing import assert_frame_equal, assert_series_equal
import pandas as pd
from pandas import Timestamp as TS
import numpy as np
@pytest.fixture
def price_files():
cdir = os.path.dirname(__file__)
path = os.path.join(cdir, 'data/')
files = ... | TS('2015-01-04') | pandas.Timestamp |
"""Electric grid models module."""
import cvxpy as cp
import itertools
from multimethod import multimethod
import natsort
import numpy as np
import opendssdirect
import pandas as pd
import scipy.sparse as sp
import scipy.sparse.linalg
import typing
import mesmo.config
import mesmo.data_interface
import mesmo.utils
l... | pd.DataFrame(columns=nodes, index=self.electric_grid_model.timesteps, dtype=float) | pandas.DataFrame |
import os
import argparse
import numpy as np
import pandas as pd
import nibabel as nib
from ukbb_cardiac.common.cardiac_utils import get_frames
from ukbb_cardiac.common.image_utils import np_categorical_dice
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--output_c... | pd.DataFrame(init) | pandas.DataFrame |
# --------------
# import the libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
import warnings
warnings.filterwarnings('ignore')
# Code starts here
df = pd.read_json(path,lines=True)
df.columns=df.columns.str.st... | pd.get_dummies(data=X_test,columns=["category", "cup_size","length"],prefix=["category", "cup_size","length"]) | pandas.get_dummies |
"""
PRESSGRAPHS DASH CLIENT
WEB GUI interface for PressGraphs WebAPI
"""
###################################
# IMPORTS
###################################
#builtins
from datetime import datetime
from datetime import timedelta
#3rd party
import dash
import dash_core_components as dcc
import dash_html_components as html... | pd.DataFrame(s_2_content) | pandas.DataFrame |
from datetime import timedelta
from functools import partial
from operator import attrgetter
import dateutil
import numpy as np
import pytest
import pytz
from pandas._libs.tslibs import OutOfBoundsDatetime, conversion
import pandas as pd
from pandas import (
DatetimeIndex, Index, Timestamp, date_range, datetime,... | date_range(freq='D', start=start, end=end, tz=tz) | pandas.date_range |
import requests
import pandas as pd
import ftplib
import io
import re
import json
import datetime
try:
from requests_html import HTMLSession
except Exception:
print("""Warning - Certain functionality
requires requests_html, which is not installed.
Install ... | pd.Timestamp(start_date) | pandas.Timestamp |
import fire
from rest_api_asyncio import UniprotClient, get_db
import pandas as pd
from pandas import DataFrame
import gtfparse
import time
from tqdm import tqdm
from pathlib import Path
from functools import reduce
import glob
import sys
import urllib3
import asyncio
OUT_HEADER_BASE = [
'gene_id',
'gene_name... | DataFrame([None], columns=['protein_existence']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
@author: <NAME> <<EMAIL>>
Date: Oct 2019
"""
import numpy as np
import pandas as pd
from .pipe import Pipe
from .. import precomp_funs as _pf
class CHPPlant(Pipe):
"""
Construct a CHP plant.
Can be added to the simulation environment by using the following method:
.a... | pd.DataFrame(data=self.res_dQ[:array_length, ...], index=hdf_idx) | pandas.DataFrame |
import pandas as pd
from recalibrate.unarycalibration.singelsystematiccalibration import single_systematic_calibration
from pprint import pprint
from sklearn.metrics import brier_score_loss
from xgboost import XGBClassifier
# Illustrates calibration of a single set of model probabilities (user selecting a product)
if... | pd.read_csv('https://raw.githubusercontent.com/microprediction/recalibrate/main/examples/default_data/default.csv') | pandas.read_csv |
"""Step 1: Solving the problem in a deterministic manner."""
import cvxpy as cp
import fledge
import numpy as np
import os
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import shutil
def main():
# Settings.
scenario_name = 'course_project_step_1'
results_path = os.pat... | pd.DataFrame(0.0, index=der_model_set.timesteps, columns=der_model_set.outputs) | pandas.DataFrame |
import numpy as np
import pandas as pd
import math
from elopackage.elo import Elo
from elopackage.player import Player
class ResultsTable:
def __init__(self, df):
"""
df - pd DataFrame of tournamenent results
"""
# self.df = df.sort_values(by='match_date_dt', ascending=True)
... | pd.concat([df_unique, df_tmp]) | pandas.concat |
import os, sys, platform, json, operator, multiprocessing, io, random, itertools, warnings, h5py, \
statistics, inspect, requests, validators, math, time, pprint, datetime, importlib, fsspec, scipy
# Python utils.
from textwrap import dedent
# External utils.
from tqdm import tqdm #progress bar.
from natsort import na... | pd.DataFrame(data=ndarray) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# # Avatar : The Last Airbender
# ### Machine Learning and Analysis of the show
# In[1]:
from IPython.display import Image
Image (filename = "images (1).jpg")
# ## Introduction :
#
# **Avatar: The Last Airbender (Avatar: The Legend of Aang in some regions)** is an American ... | pd.read_csv('series_names.csv') | pandas.read_csv |
# Copyright (c) 2019 Uber Technologies, 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... | pd.DataFrame(sig_errs) | pandas.DataFrame |
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
import sklearn
import json
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix
from sklearn.linear_model import LogisticRegr... | pd.read_csv('./public/Python_Scripts/Dataset.csv', header=None) | pandas.read_csv |
import csv
from io import StringIO
import os
import numpy as np
import pytest
from pandas.errors import ParserError
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
NaT,
Series,
Timestamp,
date_range,
read_csv,
to_datetime,
)
import pandas._testing as tm
impo... | read_csv(path, index_col=0, encoding="UTF-8") | pandas.read_csv |
import numpy as np
import xarray as xr
import pandas as pd
import os
from collections import OrderedDict
# from astropy.time import Time
import logging
import copy
from typing import List, Dict, Union, Tuple
import pysagereader
class SAGEIILoaderV700(object):
"""
Class designed to load the v7.00 SAGE II spec ... | pd.Timestamp('1858-11-17') | pandas.Timestamp |
import sys
import pandas as pd
import numpy as np
import json
import os
from datetime import date
from scipy.stats import linregress
import yaml
from momentum_data import cfg
DIR = os.path.dirname(os.path.realpath(__file__))
pd.set_option('display.max_rows', None)
pd.set_option('display.width', None)
pd.set_option('d... | pd.Series(closes[-slope_days:]) | pandas.Series |
# kaggleのSMS Spam Collection Datasetでナイーブベイズを体験する
# コード：https://qiita.com/fujin/items/50fe0e0227ef8457a473
import matplotlib.pyplot as pyplot
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naiv... | pd.read_csv("./datasets/spam.csv", encoding="latin-1") | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from src.tasks.preprocessing_funcs import load_dataloaders
from src.tasks.trainer import train_and_fit
from src.tasks.infer import infer_from_trained
import logging
from argparse import ArgumentParser
from src.tasks.visualization import Graph
from src.tasks.pdf_to_txt imp... | pd.DataFrame(input_sents) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
import covsirphy as cs
def md(scenario, filename, name=None):
with open(filename, "w") as fh:
fh.write(scenario.summary(name=name).to_markdown())
def main():
print(cs.__version__)
# Data loading
data_loader = cs.DataLoader("i... | pd.DataFrame.from_dict(opt_dict, orient="index") | pandas.DataFrame.from_dict |
import re
import pandas as pd
import numpy as np
class Resampler(object):
"""Resamples time-series data from one frequency to another frequency.
"""
min_in_freqs = {
'MIN': 1,
'MINUTE': 1,
'DAILY': 1440,
'D': 1440,
'HOURLY': 60,
'HOUR': 60,
'H': 60,... | pd.infer_freq(idx) | pandas.infer_freq |
import re
import time
import argparse
import numpy as np
import pandas as pd
import util as ut
from collections import Counter
from collections import defaultdict
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.neighbors import NearestNeigh... | pd.read_csv(fname) | pandas.read_csv |
import time
import sorting
import bst
import timeit
import platform
import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import heapq
import copy
random.seed(521)
DEFAULT_NUMBER = 100000 # 100k
DEFAULT_POPULATION = range(100000) # 1m
DEFAULT_SIZES = [10, 100, 1000, 10000, 100000]
de... | pd.DataFrame.from_dict(self.test_result) | pandas.DataFrame.from_dict |
r"""
Baseline Calculation
"""
# Standard Library imports
import argparse
import cartopy.crs as ccrs
import datetime
import h5py
import json
import matplotlib.colors
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import netCDF4
import numpy as np
import os
import pandas as pd
impor... | pd.to_datetime("1990-01") | pandas.to_datetime |
# this class is aimed at generating data for the redundant and noisy contexts
import os
import numpy as np
import pandas as pd
from os.path import join
import random
from runs.experiments import Experiment
def generate_tr_vl_ts_splits(id,
source_path,
split_pa... | pd.concat([train_sentences.loc[p_], train_sentences.loc[n_]]) | pandas.concat |
import argparse
import json
import os
import re
from glob import glob
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sns.set_style("dark")
def parse_args():
description = """Measure how you spend time by tracking key presses and
changes to window focus or title."""
p = argpars... | pd.Grouper(key="time", freq=freq) | pandas.Grouper |
from __future__ import print_function
import numpy as np
import time, os, sys
import matplotlib.pyplot as plt
from scipy import ndimage as ndi
from skimage import color, feature, filters, io, measure, morphology, segmentation, img_as_ubyte, transform
import warnings
import math
import pandas as pd
import argparse
impor... | pd.DataFrame() | pandas.DataFrame |
#Usage
# import sys
# sys.path.insert(0,'path to this file')
# import functions as f
import pickle
import pandas as pd
import os
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_s... | pd.read_pickle("C:/Users/nik00/py/proj/hyppi-independent.pkl") | pandas.read_pickle |
"""
The aim of this project was to build a classifier on the titanic kaggle dataset.
"""
### import libraries
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use('Pdf')
import matplotlib.pyplot as plt
# import data preprocessing modules
from sklearn.preprocessing import Imputer
from sklearn.prepro... | pd.read_csv("test.csv") | pandas.read_csv |
#! /usr/bin/env python3
"""
Model Checker Collection for the Model Checking Contest.
"""
import argparse
import hashlib
import math
import logging
import os
import random
import statistics
# import getpass
import json
import pathlib
import pickle
import platform
import re
import sys
import tempfile
import tarfile
imp... | pandas.DataFrame([test]) | pandas.DataFrame |
import logging
import pandas as pd
from scipy.cluster.vq import kmeans2
from django.http import JsonResponse
from django.views import View
from . import forms
from . import models
logger = logging.getLogger(__name__)
class LocationListAPI(View):
def get(self, request, *args, **kwargs):
"""Summarize... | pd.concat(result, axis=1) | pandas.concat |
from IPython.display import display
import pandas as pd
import pyomo.environ as pe
import numpy as np
import csv
import os
import shutil
class inosys:
def __init__(self, inp_folder, ref_bus, dshed_cost = 1000000, rshed_cost = 500, phase = 3, vmin=0.85, vmax=1.15, sbase = 1, sc_fa = 1):
'''
... | pd.read_csv(inp_folder + os.sep + 'qrep_dist.csv') | pandas.read_csv |
from unittest.mock import ANY, MagicMock, patch
import pytest
import pandas as pd
from pandas._testing import assert_frame_equal
from muttlib.dbconn.base import BaseClient, EngineBaseClient
@pytest.fixture
def engine_baseClient():
client = EngineBaseClient(
database="database",
host="host",
... | pd.DataFrame({'col1': ['1'], 'col2': ['3.0']}) | pandas.DataFrame |
import pathlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from prophet import Prophet
from sklearn import metrics
def get_prophet_data(stock_path):
with open(stock_path, 'r', encoding='utf-8') as f:
df = pd.read_json(f.read(), orient='records')
print(df)
# rename
... | pd.set_option('display.max_columns', None) | pandas.set_option |
from pandas import (
DataFrame,
Series,
)
import pandas._testing as tm
class TestToFrame:
def test_to_frame(self, datetime_series):
datetime_series.name = None
rs = datetime_series.to_frame()
xp = DataFrame(datetime_series.values, index=datetime_series.index)
tm... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = '<NAME>'
from pathlib import Path
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from gensim.models import LdaModel
from gensim.matutils import Sparse2Corp... | pd.DataFrame() | pandas.DataFrame |
from typing import Union, Optional
import pytest
import scanpy as sc
import cellrank.external as cre
from anndata import AnnData
from cellrank.tl.kernels import ConnectivityKernel
from cellrank.external.kernels._utils import MarkerGenes
from cellrank.external.kernels._wot_kernel import LastTimePoint
import numpy as ... | pd.Series(terminal_states) | pandas.Series |
#!/usr/bin/env python
"""Tests for `pubchem_api` package."""
import os
import numpy as np
import pandas as pd
import scipy
from scipy.spatial import distance
import unittest
# from click.testing import CliRunner
# from structure_prediction import cli
class TestDataPreprocessing(unittest.TestCase):
"""Tests for ... | pd.DataFrame(make_square) | pandas.DataFrame |
import numpy as np
import pandas as pd
import os
import pickle
from sklearn.model_selection import KFold
from sklearn.metrics import precision_recall_curve
import sklearn.metrics as metrics
from model import lightgbm_train
from glob import glob
from utils import *
import shap
from collections import defaultdict
def l... | pd.DataFrame.from_dict(t_shap) | pandas.DataFrame.from_dict |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2017 Alibaba Group Holding Ltd.
#
# 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/LICENS... | pd.DataFrame([['Col1', 1], ['Col2', 2]], columns=['Field1', 'Field2']) | pandas.DataFrame |
"""
Also test support for datetime64[ns] in Series / DataFrame
"""
from datetime import datetime, timedelta
import re
import numpy as np
import pytest
from pandas._libs import iNaT
import pandas._libs.index as _index
import pandas as pd
from pandas import DataFrame, DatetimeIndex, NaT, Series, Timestamp, date_range
... | tm.assert_series_equal(cp, expected) | pandas._testing.assert_series_equal |
# Copyright 2021 Fedlearn 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 applicable law or agreed to in writi... | pandas.merge(uid, g2.loc[:, ["uid"]], on="uid", how="inner") | pandas.merge |
import numpy as np
import pandas as pd
from numba import njit
import pytest
from vectorbt import defaults
from vectorbt.utils import checks, config, decorators, math, array
from tests.utils import hash
# ############# config.py ############# #
class TestConfig:
def test_config(self):
conf = config.Conf... | pd.Series([1, 2, 3], index=index) | pandas.Series |
import pandas
import os
import re
import numpy as np
import math
import warnings
from modin.error_message import ErrorMessage
from modin.engines.base.io import BaseIO
from modin.data_management.utils import compute_chunksize
from modin import __execution_engine__
if __execution_engine__ == "Ray":
import ray
PQ_... | pandas.DataFrame(index=index) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
from sklearn import metrics
from epiquark import ScoreCalculator
def test_non_case_imputation(shared_datadir, paper_example_score: ScoreCalculator) -> None:
cases = pd.read_csv(shared_datadir / "paper_example/cases_long.csv")
imputed = paper_example_score.... | pd.read_csv(shared_datadir / "paper_example/p_hat_di.csv") | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 14 10:59:05 2021
@author: franc
"""
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
import json
from collections import Counter, OrderedDict
import math
import torchtext
from torchtext.data import get_tokenizer
... | pd.DataFrame({'spanish': ["orca"], 'english': ["killer_whale"]}) | pandas.DataFrame |
import uuid
import traceback
import os
import numpy as np
import pandas
import nrrd
import glob
import argparse
import random
from PIL import Image
import csv
from shutil import rmtree
from collections import defaultdict
from keras.preprocessing.image import ImageDataGenerator, Iterator
from keras.utils import to_categ... | pandas.concat(all_train) | pandas.concat |
"""
Created on Thu Jan 26 17:04:11 2017
@author: <NAME>, <EMAIL>
"""
#%matplotlib inline
import numpy as np
import pandas as pd
import dicom
import os
import scipy.ndimage as ndimage
import matplotlib.pyplot as plt
import scipy.ndimage # added for scaling
import cv2
import time
import glob
from skimage import me... | pd.read_csv(LUNA_ANNOTATIONS) | pandas.read_csv |
"""This module is meant to contain the Solscan class"""
from messari.dataloader import DataLoader
from messari.utils import validate_input
from string import Template
from typing import Union, List, Dict
from .helpers import unpack_dataframe_of_dicts
import pandas as pd
#### Block
BLOCK_LAST_URL = 'https://public-api... | pd.concat(df_list, keys=accounts, axis=1) | pandas.concat |
# TODO(*): Move to ib/medata and rename contract_metadata.py
import logging
import os
from typing import List
import ib_insync
import pandas as pd
import helpers.io_ as hio
import im.ib.data.extract.gateway.utils as videgu
_LOG = logging.getLogger(__name__)
class IbMetadata:
def __init__(self, file_name: str) ... | pd.concat(dfs, axis=0) | pandas.concat |
import pandas as pd
import numpy as np
import unittest
from dstools.preprocessing.Bucketizer import Bucketizer
class TestBucketizer(unittest.TestCase):
def compare_DataFrame(self, df_transformed, df_transformed_correct):
"""
helper function to compare the values of the transformed DataFrame with ... | pd.DataFrame({'x':[1,2,3]}) | pandas.DataFrame |
import numpy as np
import pandas as pd
from scipy import interpolate
import pickle # to serialise objects
from scipy import stats
import seaborn as sns
from sklearn import metrics
from sklearn.model_selection import train_test_split
sns.set(style='whitegrid', palette='muted', font_scale=1.5)
RANDOM_SEED = 42
dataset... | pd.DataFrame(training_set) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 25 16:14:45 2021
@author: bdobson
"""
import os
import pandas as pd
import geopandas as gpd
from matplotlib import pyplot as plt
root = os.path.join("C:\\", "Users", "bdobson", "Documents", "GitHub", "cwsd_sewer","data")
catchment = "cranbrook"
cluster = 'cluster_Louv_... | pd.read_parquet(node_fid) | pandas.read_parquet |
import pandas as pd
import argparse
from matplotlib_venn import venn3
import matplotlib.pyplot as plt
import math
def get_args():
desc = 'Given sj files, see which splice junctions are shared/unique between datasets'
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('-sj_1', dest='sj_1',
h... | pd.merge(dfa, dfc, how='inner', on=['chrom', 'start', 'stop', 'strand']) | pandas.merge |
#!/usr/bin/env python3
#libraries
import pandas as pd
import numpy as np
import re
import os
pd.set_option('display.max_rows',200)
pd.set_option('display.max_columns',200)
import matplotlib.pyplot as plt
import seaborn as sns
import pymysql
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
f... | pd.merge(billing_avg,uspa_prod_dataframe, left_on = 'mobile',right_on='mobile_x',how = 'left') | pandas.merge |
# Author: <NAME> <<EMAIL>>
#
# License: BSD (3-clause)
import os.path as op
import pandas as pd
import numpy as np
import mne
from mne.transforms import apply_trans, _get_trans
from mne.utils import _validate_type, _check_fname
from mne.io import BaseRaw
def _read_fold_xls(fname, atlas="Juelich"):
"""Read fOLD... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""dlw9383-bandofthehawk-output.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/154b5GvPxORu_mhpHDIsNlWvyBxMIwEw2
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc=... | pd.DataFrame(y_kmeans, columns=['Clusters']) | pandas.DataFrame |
import pandas as pd
import plotly.express as px
# Passo 1 -Importar a basa de dados para o python
tabela = | pd.read_csv(r"C:\Users\jose_\OneDrive\Documentos\Estudos\arquivos_pyton\telecom_users.csv") | pandas.read_csv |
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... | isna(ser) | pandas.isna |
from typing import List
import pandas as pd
# Not covered: Essentially a script over other unit tested functions
def clean_data(df: pd.DataFrame) -> pd.DataFrame: # pragma: no cover
"""Parse
* location information to append city, state, zip code, and neighborhood columns
* salary information to append m... | pd.merge(df, df_expanded_salary, how="outer", on="link") | pandas.merge |
"""
Also test support for datetime64[ns] in Series / DataFrame
"""
from datetime import datetime, timedelta
import re
import numpy as np
import pytest
from pandas._libs import iNaT
import pandas._libs.index as _index
import pandas as pd
from pandas import DataFrame, DatetimeIndex, NaT, Series, Timestamp, date_range
... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
import numpy as np
np.random.seed(1337) # for reproducibility
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics._regression import r2_score, mean_squared_error
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import accuracy_score
from dbn import Superv... | pd.to_datetime(df['Date']) | pandas.to_datetime |
import pandas
from copy import deepcopy
from palm.base.target_data import TargetData
from palm.discrete_state_trajectory import DiscreteStateTrajectory,\
DiscreteDwellSegment
class BlinkTargetData(TargetData):
"""
A dwell trajectory loaded from a file. The trajectory
... | pandas.read_csv(data_file, header=0) | pandas.read_csv |
import unittest
import pandas as pd
import numpy as np
from ..timeseries import TimeSeries
class TimeSeriesTestCase(unittest.TestCase):
times = pd.date_range('20130101', '20130110')
pd_series1 = pd.Series(range(10), index=times)
pd_series2 = pd.Series(range(5, 15), index=times)
pd_series3 = pd.Serie... | pd.Timestamp('20130107') | pandas.Timestamp |
#python imports
import os
import gc
import string
import random
import time
import pickle
import shutil
from datetime import datetime
#internal imports
from modules.Signal import Signal
from modules.Database import Database
from modules.Predictor import Classifier, ComplexBuilder
from modules.utils import calcula... | pd.DataFrame(out,index=combinedPeakModelsFiltered.index, columns = quantColumnNames) | pandas.DataFrame |
from __future__ import division
from datetime import datetime
import sys
if sys.version_info < (3, 3):
import mock
else:
from unittest import mock
import pandas as pd
import numpy as np
import random
from nose.tools import assert_almost_equal as aae
import bt
import bt.algos as algos
def test_algo_name():... | pd.DateOffset(months=3) | pandas.DateOffset |
#!/usr/bin/python -u
# +
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import random
import os
SEED = 123
random.seed(SEED)
np.random.seed(SEED)
# +
#Load the train and test files with pchembl values (used for end-to-end deep learning)
train_with_label_df = pd.read_csv("../data/Train_Compound_... | pd.read_csv("../data/Train_Protein_LS.csv",header=None) | pandas.read_csv |
import os
from src.corpus.brat_writer import write_file
from typing import Dict, List
import pandas as pd
class DocumentMerger:
def __init__(
self,
ent_id2label,
rel_id2label,
true_doc_tokens: Dict[str, List[List[str]]],
save_dir="val"
) -> None:
super().__init... | pd.DataFrame() | pandas.DataFrame |
# author: <NAME>, <NAME>
# date: 2021-11-25
"""This script takes two file paths. It takes in the input path which includes the clean train and test data
and the output directory to store the results in. It performs machine learning analysis.
This script will have 4 outputs: 3 tables and 1 figure.
Usage: src/machine_... | pd.Series(data=out_col, index=mean_scores.index) | pandas.Series |
import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
import yfinance as yf
from pandas_datareader import data as web
import datetime as dt
from empyrical import*
import quantstats as qs
from darts.models import*
from darts import TimeSeries
from darts.utils.missing_values import... | pd.DataFrame(ret_data) | pandas.DataFrame |
import sys
import csv
import numpy as np
import gpflow
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import pandas as pd
import h5py
from sklearn.model_selection import train_test_split
import tensorflow as tf
from scipy.cluster.vq import kmeans
tf.set_random_seed(1234)
i... | pd.DataFrame(targets) | pandas.DataFrame |
import pytest
import numpy as np
import pandas as pd
from six import StringIO
from dae.tools.generate_histogram import (
ScoreHistogramInfo,
GenerateScoresHistograms,
)
# pytestmark = pytest.mark.xfail
class MyStringIO(StringIO):
def __add__(self, other):
return ""
@pytest.fixture
def score_fi... | pd.DataFrame({"RANKSCORE_0": [10000], "start": [100], "end": [100]}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""data_augmentation.py
# Notebook: Generate Training Dataset
In this Notebook, we want to simulate training dataset from the real world dataset. There are two steps in making such data:
* 1) Create pair of trajectories from the original set
* 2) Create label per pair of trajectories
# Requir... | pd.concat(data_list) | pandas.concat |
import numpy as np
import pandas as pd
import os
# http://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29
# Load .csv file
path = 'german/german_final.csv'
data = | pd.read_csv(path, header=None) | pandas.read_csv |
# coding: utf-8
"""
Aurelio_Amerio_Higgs_v4.py
In this analysis, I have used several MLP models, applied to the Kaggle Higgs dataset,
in order to distinguish signal from noise.
----------------------------------------------------------------------
author: <NAME> (<EMAIL>)
Student ID: QT08313
... | pd.read_csv(train_sig_path_sml, header=0) | pandas.read_csv |
# -*- coding: utf-8 -*-
import numpy as np
import pytest
from numpy.random import RandomState
from numpy import nan
from datetime import datetime
from itertools import permutations
from pandas import (Series, Categorical, CategoricalIndex,
Timestamp, DatetimeIndex, Index, IntervalIndex)
import pan... | algos.value_counts(s, bins=1) | pandas.core.algorithms.value_counts |
import numpy as np
import pandas as pd
import pytest
from sid.shared import boolean_choices
from src.create_initial_states.create_initial_immunity import (
_calculate_endog_immunity_prob,
)
from src.create_initial_states.create_initial_immunity import (
_calculate_exog_immunity_prob,
)
from src.create_initial_... | pd.DataFrame() | pandas.DataFrame |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pytest
import numpy as np
import pandas
from modin.pandas.utils import to_pandas
import modin.pandas as pd
from pathlib import Path
import pyarrow as pa
import os
import sys
from .utils import df_equals... | pandas.read_clipboard() | pandas.read_clipboard |
import os
import sys
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, compat
from pandas.util import testing as tm
class TestToCSV:
@pytest.mark.xfail((3, 6, 5) > sys.version_info >= (3, 5),
reason=("Python csv library bug "
... | pd.Series([1], ind, name="data") | pandas.Series |
import requests
from bs4 import BeautifulSoup
import pandas as pd
def get_tables(urls, link=False):
"""Returns a dataframes list with the tables of the different groups.
Keyword arguments:
urls -- list with urls of the different groups
link -- indicates whether you want to include the url of ... | pd.concat(tables_list, axis=0) | pandas.concat |
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