prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pandas as pd
from pathlib import Path
from .utils import logging, get_cols, DATABASE
logger = logging.getLogger(__name__)
cwd = Path(__file__).parent
def main(_, name, level, row):
con = f'postgresql:///{DATABASE}'
file = (cwd / f'../../../inputs/cod/{name}.xlsx')
sheets = | pd.ExcelFile(file, engine='openpyxl') | pandas.ExcelFile |
import pandas as pd
import numpy as np
from pathlib import Path
from datetime import datetime as dt
from datetime import timedelta as td
import sys
import interpolate_matrix_test as interpolate_helpers
###############################
# 1. Set parameters
# 2. READ IN IN FILES
# 3. SET DATA OBJECTS
# 4. WRITE OUT... | pd.read_csv(dir2 + "CSV_data/d_flat_prices.csv") | pandas.read_csv |
import numpy as np
import arviz as az
import seaborn as sns
import pandas as pd
import pickle as pkl
import importlib
import anndata as ad
import ast
import matplotlib.pyplot as plt
from scdcdm.util import result_classes as res
from scdcdm.util import multi_parameter_sampling as mult
from scdcdm.util import multi_para... | pd.DataFrame() | pandas.DataFrame |
'''
Module that contains functions for intergenic mode.
'''
import subprocess
import os
from multiprocessing import Pool
from .misc import load_exp
import functools
import pandas as pd
import numpy as np
'''
Define a function that can get the gene expression given a tag directory, a GTF file, a normalization method, a... | pd.merge(gene_to_transcript,gene_exp,left_on='Transcript ID',right_index=True) | pandas.merge |
from lib.allgemein import liste_in_floats_umwandeln
import pandas as pd
import untangle
from decimal import *
#written by <NAME>
def get_xml_RecordTime_excitationwl(dateiname):
obj = untangle.parse(dateiname)
RecordTime = obj.XmlMain.Documents.Document['RecordTime']
excitationwl = float(obj.XmlMain.Docu... | pd.DataFrame(predf, index=colunames, columns=['x [µm]','y [µm]','z [µm]']) | pandas.DataFrame |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.2'
# jupytext_version: 1.2.1
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %% [markdown]
# # D... | pd.np.ones(BW) | pandas.np.ones |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: <NAME>
"""
import time
import seaborn as sns
import pandas as pd
import numpy as np
import os
from sklearn.cluster import KMeans, DBSCAN, Birch, SpectralClustering, AgglomerativeClustering
from sklearn import metrics
from sklearn import preprocessing
import... | pd.concat([mean_dataframe,aux_mean_dataframe]) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Module containing logic for table and graph with Ownership History
"""
__author__ = '<NAME>'
__email__ = '<EMAIL>'
from pandas import DataFrame
from PyQt5.QtCore import QObject, pyqtSlot
from source.util import Assertor
from .table_model import TableModel
from .model import Model
from ... | DataFrame(self.data[key + postfix]) | pandas.DataFrame |
from graph_build.graph_noise_join.conf import GraphNoiseJoinConf
import logging
import os
import fiona
import math
from pyproj import CRS
import numpy as np
import pandas as pd
import geopandas as gpd
import graph_build.graph_noise_join.utils as utils
import common.igraph as ig_utils
from common.igraph import Edge as E... | pd.concat([normal_samples, interpolated_samples], ignore_index=True) | pandas.concat |
# -*- coding: utf-8 -*-
# Copyright (c) 2019 SMHI, Swedish Meteorological and Hydrological Institute
# License: MIT License (see LICENSE.txt or http://opensource.org/licenses/mit).
import codecs
import datetime
import logging
import logging.config
import os
import re
import time
import numpy as np
import sharkpylib
... | pd.DataFrame() | pandas.DataFrame |
import typing
import unittest
import numpy as np
import pandas as pd
import sklearn.datasets
import sklearn.model_selection
from autoPyTorch.datasets.tabular_dataset import DataTypes, TabularDataset
from autoPyTorch.utils.backend import create
from autoPyTorch.utils.pipeline import get_dataset_requirements
class ... | pd.Series([1, 2]) | pandas.Series |
__all__ = ["spectrometer_sensitivity"]
# standard library
from typing import List, Union
# dependent packages
import numpy as np
import pandas as pd
from .atmosphere import eta_atm_func
from .instruments import eta_Al_ohmic_850, photon_NEP_kid, window_trans
from .physics import johnson_nyquist_psd, rad_trans, T_fro... | pd.Series(obs_hours * on_source_fraction, name="on_source_hours") | pandas.Series |
#%%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime
df = | pd.read_csv('data/cleanedData.csv') | pandas.read_csv |
# 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
# distributed ... | pd.Series(genes) | pandas.Series |
# %%
import os
import pandas as pd
import numpy as np
from fcutils.plotting.colors import colorMap
from analysis.misc.paths import cellfinder_cells_folder, cellfinder_out_dir, injections_folder
from analysis.anatomy.utils import *
# %%
import matplotlib.pyplot as plt
for i in range(100):
color = colorMap(i, ... | pd.concat([df, ipsi, contra], axis=1) | pandas.concat |
import pandas as pd
from pymongo import MongoClient
class KAnonymizer:
names = (
'age',
'region', #Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
'gender', # "weight" of that person in the dataset (i.e. how many people does that person... | pd.Series() | pandas.Series |
import itertools
import logging
import math
from datetime import datetime, timedelta, timezone
import boto3
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
import awswrangler as wr
from ._utils import ensure_data_types, get_df_list
logging.getLogger("awswrangler").setLevel(logging.DEBUG)
... | pd.DataFrame({"id": [1, 2, 3]}) | pandas.DataFrame |
#!/usr/bin/env python3
#
# Create model outputs with P.1203 software.
#
# Copyright 2018 <NAME>, <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including withou... | pd.concat(list_to_concat, ignore_index=True) | pandas.concat |
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from python.ftm.plot_events import get_refuel_events_from_events_csv, get_total_walking_distances_from_events_csv, \
get_parking_events_from_events_csv
from python.ftm.util import get_run_dir, get_latest_run, get_iterat... | pd.Series() | pandas.Series |
"""
Copyright 2022 HSBC Global Asset Management (Deutschland) GmbH
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 ... | pd.testing.assert_series_equal(actual, expectations) | pandas.testing.assert_series_equal |
import math
import os
import sys
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly.figure_factory as ff
from cell_defination import *
sys.path.insert(1, r'C:\Users\bunny\PycharmProjects\vccf_visualization')
import utils.kidney_nuclei_vessel... | pd.DataFrame(l_data) | pandas.DataFrame |
import pandas as pd
import numpy as np
import math
from deprecated import deprecated
from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error, mean_squared_log_error
import os
root_path = os.path.dirname(os.path.abspath('__file__'))
import sys
sys.path.append(root_path)
from tools.metrics_ import ... | pd.DataFrame([train_mape], columns=['train_mape']) | pandas.DataFrame |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import json
import logging
import random
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
import mct.Constants as Constants
import mct.Utilities as Utils
from mct.Hypothesi... | pd.DataFrame(index=input_frame.index) | pandas.DataFrame |
import shutil
import numpy as np
import pandas as pd
import logging
import time as _time
import requests
import random
from tqdm import tqdm
from concurrent import futures
from datetime import datetime, date, time
from dateutil.relativedelta import relativedelta, FR
from opensignals import utils
logger = logging.get... | pd.Series([], dtype='datetime64[ns]') | pandas.Series |
import pytest
import logging
import datetime
import json
import pandas as pd
from astropy.table import Table
from b_to_zooniverse import upload_decals
# logging.basicConfig(
# format='%(asctime)s %(message)s',
# level=logging.DEBUG)
@pytest.fixture
def calibration_dir(tmpdir):
return tmpdir.mkdir('cal... | pd.to_datetime('2018-01-01') | pandas.to_datetime |
import sklearn.linear_model
import sklearn.preprocessing
import sklearn.metrics
import pandas as pd
import numpy as np
import dataset_categories
import mushroom_classifier
"""
This class is used for reliable classification results, running the main method or
using the high level functions of mushroom_classifier.py for... | pd.read_csv(dataset_categories.FILE_PATH_SECONDARY_NO_MISS, header=0, sep=';') | pandas.read_csv |
import warnings
import cvxpy as cp
import numpy as np
import numpy.linalg as la
import pandas as pd
import scipy.stats as st
from _solver_fast import _cd_solver
from linearmodels.iv import IV2SLS, compare
from patsy import dmatrices
from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
from sklearn.u... | pd.DataFrame(var) | pandas.DataFrame |
# 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... | tm.box_expected(expected, xbox) | pandas._testing.box_expected |
from glob import glob
from PIL import Image
import pickle as pkl
import os
import configargparse
import configparser
import torch
import numpy as np
import argparse
import sys
import matplotlib.pyplot as plt
import yaml
from munch import munchify
import json
import PIL
from parse import parse
import collections
import ... | pd.Series(v[0]) | pandas.Series |
import gzip
import itertools as IT
import logging
import os
import random
from functools import partial, wraps
from pathlib import Path
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
from tqdm import tqdm
DEFAULT_CHUNK_SIZE = 8_192
def init():
print("Using DaskLike")
def from_sequence(i... | pd.DataFrame(chunk, columns=columns) | pandas.DataFrame |
import unittest
import pandas as pd
import pandas.util.testing as pdtest
import numpy as np
from tia.analysis.model import *
class TestAnalysis(unittest.TestCase):
def setUp(self):
self.closing_pxs = pd.Series(
np.arange(10, 19, dtype=float),
pd.date_range("12/5/2014", "12/17/2014"... | pdtest.assert_series_equal(expected.dly_upl, txnlvl.upl) | pandas.util.testing.assert_series_equal |
"""
Seed processing code
$Header: /nfs/slac/g/glast/ground/cvs/pointlike/python/uw/like2/seeds.py,v 1.7 2018/01/27 15:37:17 burnett Exp $
"""
import os, sys, time, pickle, glob, types
import numpy as np
import pandas as pd
from astropy.io import fits
from skymaps import SkyDir, Band
from uw.utilities import keyword_o... | pd.isnull(A.dup) | pandas.isnull |
import pytest
import pandas as pd
import numpy as np
from pandas import testing as pdt
from pandas import Timestamp
from datetime import datetime
from pyam import utils, META_IDX
TEST_VARS = ["foo", "foo|bar", "foo|bar|baz"]
TEST_CONCAT_SERIES = pd.Series(["foo", "bar", "baz"], index=["f", "b", "z"])
def test_patte... | pd.Series(["foo", "bar"]) | pandas.Series |
try:
import debug_settings
except:
print("Cannot import debug settings!")
import logging
logging.basicConfig()
logging.getLogger().setLevel(logging.INFO)
import glob
import numpy as np
import os
import pandas as pd
import sys
import yaml
from copy import deepcopy
from argparse import ArgumentParser
from coll... | pd.DataFrame(columns=["Step", "Action", "Agent", "Beliefs", "HyNum"]) | pandas.DataFrame |
import sys, io, json, base64, datetime as dt
sys.path.append("tmp")
import matplotlib
matplotlib.use('Agg') #not sure if this include / 'Agg' is necessary
import cntk
from helpers_cntk import *
####################################
# Parameters
####################################
classifier = 'svm' #must... | pandas.DataFrame(data=[[base64ImgString]], columns=['image base64 string']) | pandas.DataFrame |
from pytorch_lightning.core.step_result import TrainResult
import pandas as pd
import torch
import math
import numpy as np
from src.utils import simple_accuracy
from copy import deepcopy
from torch.optim.lr_scheduler import LambdaLR
class WeightEMA(object):
def __init__(self, model, ema_model, alpha=0.999):
... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MaxAbsScaler
from sklearn.cluster import KMeans
#%% load dataset
vehicles = pd.read_csv('../../data/raw/vehicles.csv')
print(vehicles.head())
print(vehicles.columns)
print(vehicles.shape)
#%%... | pd.qcut(vehicles['Engine Displacement'], 5, engine_categories) | pandas.qcut |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may... | pandas.DataFrame(columns=['day', 'ands', 'minus', 'amount', 'allocated']) | pandas.DataFrame |
import doctest
import os
from unittest import TestCase
import pandas as pd
import xarray as xr
from pysd.tools.benchmarking import assert_frames_close
_root = os.path.dirname(__file__)
class TestUtils(TestCase):
def test_xrsplit(self):
import pysd
array1d = xr.DataArray([0.5, 0., 1.],
... | pd.DataFrame(index=[1], columns=['elem1', 'elem2']) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
##################################
# Author: <NAME>
# Copyright © 2020 The Board of Trustees of the Royal Botanic Gardens, Kew
##################################
#
# # wcvp_taxo
# wcvp_taxo is a python3 script for matching and resolving scientific names against the WCVP databas... | pd.concat([return_df,return_syn]) | pandas.concat |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
from hashlib import md5
from typing import Dict, Iterable, Optional, Set, Type
import numpy as np
import p... | pd.DataFrame(records) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# ----------------------------------------------------------------------
# Name: csv_plot_heatmap.py
# Description:
#
# Author: m.akei
# Copyright: (c) 2020 by m.na.akei
# Time-stamp: <2020-08-30 09:56:44>
# Licence:
# Copyright (c) 2021 <NAME>
#
# Thi... | pd.to_datetime(csv_df[ani_col], format=t_format) | pandas.to_datetime |
import logging
import traceback
import pandas as pd
import numpy as np
import seaborn as sns
from collections import defaultdict
import matplotlib
matplotlib.use('Agg')
matplotlib.rcParams['pdf.fonttype'] = 42
import matplotlib.ticker as ticker
from matplotlib import pyplot as plt
import matplotlib.patches as mpatche... | pd.concat(dbs, sort=True) | pandas.concat |
import unittest
import pandas as pd
import numpy as np
from scipy.sparse.csr import csr_matrix
from string_grouper.string_grouper import DEFAULT_MIN_SIMILARITY, \
DEFAULT_REGEX, DEFAULT_NGRAM_SIZE, DEFAULT_N_PROCESSES, DEFAULT_IGNORE_CASE, \
StringGrouperConfig, StringGrouper, StringGrouperNotFitException, \
... | pd.DataFrame([(0, "hello")], columns=['group_rep_index', 'group_rep']) | pandas.DataFrame |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import operator
from itertools import product, starmap
from numpy import nan, inf
import numpy as np
import pandas as pd
from pandas import (Index, Series, DataFrame, isnull, bdate_range,
NaT, date_range, ti... | assert_series_equal(pd.NaT <= left, expected) | pandas.util.testing.assert_series_equal |
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.pipeline import Pipeline
from sklearn.externals import joblib
from nltk import WordNetLemmatizer
from nltk.corpus import stopwords as sw
from nltk.corpus import wordn... | pd.ExcelFile('../dictionary.xls') | pandas.ExcelFile |
#!/usr/bin/env python3
#
# This makes a dataframe containing a temporal average of navg last slices
# ========================================================================
#
# Imports
#
# ========================================================================
import os
import re
import glob
import argparse
import... | pd.concat(lst, ignore_index=True) | pandas.concat |
import sparse
import numpy as np
import pandas as pd
from sklearn.impute import SimpleImputer
from utils.clustering import *
from utils.plots import *
from lightgbm import LGBMClassifier
def retransform(arr: np.array, df: pd.DataFrame) -> pd.DataFrame:
"""Helper for scikit learn preprocessing."""
return pd.... | pd.DataFrame(X_te_sc) | pandas.DataFrame |
from enum import IntEnum
import os
import pandas as pd
from pathlib import Path
import time
class outcome(IntEnum):
UNCHANGED = 0
IMPROVED = 1
DETERIORATED = -1
class attribute:
geneName = str
variantNumber = int
drugName = str
outcome = str
relation = bool
sideEffect = bool
... | pd.concat(self.data, ignore_index=True) | pandas.concat |
import pandas as pd
import numpy as np
# from pandas.core.tools.datetimes import normalize_date
from pandas._libs import tslib
from backend.robinhood_api import RobinhoodAPI
class RobinhoodData:
"""
Wrapper to download orders and dividends from Robinhood accounts
Downloads two dataframes and saves to data... | pd.DataFrame(dividends) | pandas.DataFrame |
import asyncio
import sys
import random as rand
import os
from .integration_test_utils import setup_teardown_test, _generate_table_name, V3ioHeaders, V3ioError
from storey import build_flow, CSVSource, CSVTarget, SyncEmitSource, Reduce, Map, FlatMap, AsyncEmitSource, ParquetTarget, ParquetSource, \
DataframeSource... | pd.read_parquet(out_file, columns=columns) | pandas.read_parquet |
from typing import List
import pandas as pd
from matplotlib import pyplot as plt
import mundi
import sidekick as sk
from mundi import Region
from pydemic.utils import fmt, pc
from pydemic_ui import st
from pydemic_ui.app import SimpleApp
from pydemic_ui.apps.sitrep import abstract, cases_or_deaths, cases_plot
from py... | pd.concat([data, parents_col], axis=1) | pandas.concat |
# coding: utf-8
# In[1]:
import pandas as pd
tweets = | pd.read_csv("tweets.csv") | pandas.read_csv |
import sys
import pandas as pd
import numpy as np
from sqlalchemy import create_engine
def load_data(messages_filepath, categories_filepath):
'''
Load messages and categories files from a csv to a dataframe, merge them togheter and return the dataframe
Attributes:
messages_filepath = fullpath includin... | pd.concat([df, categories], axis=1) | pandas.concat |
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
from numba import njit
import vectorbt as vbt
from tests.utils import record_arrays_close
from vectorbt.generic.enums import range_dt, drawdown_dt
from vectorbt.portfolio.enums import order_dt, trade_dt, log_dt
day_dt = np.timedelta64... | pd.Timedelta('8 days 00:00:00') | pandas.Timedelta |
"""
Prepare training and testing datasets as CSV dictionaries 2.0 (Further modification required for GBM)
Created on 04/26/2019
@author: RH
"""
import os
import pandas as pd
import sklearn.utils as sku
import numpy as np
import re
# get all full paths of images
def image_ids_in(root_dir, ignore=['.DS_Store','dict.c... | pd.concat([train_tiles, tile_ids]) | pandas.concat |
from __future__ import print_function, division
#from nilmtk.stats import intersect_many_fast
import matplotlib.pyplot as plt
import pandas as pd
from datetime import timedelta
import matplotlib.dates as mdates
from copy import deepcopy
import numpy as np
# NILMTK imports
from nilmtk.consts import SECS_PER_DAY
from ni... | pd.Series() | pandas.Series |
import sys
from time import time, sleep
import pandas as pd
import psutil
import shutil
import glob
import os
try:
import _pickle as pickle
except:
import pickle
def print_progress_bar(count, total, start=0):
bar_len = 60
filled_len = int(round(bar_len * count / float(total)))
percents = round(100.0... | pd.DataFrame() | pandas.DataFrame |
import unittest
import qteasy as qt
import pandas as pd
from pandas import Timestamp
import numpy as np
import math
from numpy import int64
import itertools
import datetime
from qteasy.utilfuncs import list_to_str_format, regulate_date_format, time_str_format, str_to_list
from qteasy.utilfuncs import maybe_trade_day, ... | pd.to_datetime(prev_holiday) | pandas.to_datetime |
#!/usr/bin/env python3
import arbor
import pandas, seaborn
import matplotlib.pyplot as plt
# Construct chains of cells linked with gap junctions,
# Chains are connected by synapses.
# An event generator is attached to the first cell in the network.
#
# c --gj-- c --gj-- c --gj-- c --gj-- c
# ... | pandas.concat(df_list,ignore_index=True) | pandas.concat |
import math
import numpy as np
import pandas as pd
import seaborn as sns
import scipy.stats as ss
import matplotlib.pyplot as plt
from collections import Counter
def convert(data, to):
converted = None
if to == 'array':
if isinstance(data, np.ndarray):
converted = data
... | pd.get_dummies(dataset[col],prefix=col) | pandas.get_dummies |
# -*- coding: utf-8 -*-
import csv
import os
import platform
import codecs
import re
import sys
from datetime import datetime
import pytest
import numpy as np
from pandas._libs.lib import Timestamp
import pandas as pd
import pandas.util.testing as tm
from pandas import DataFrame, Series, Index, MultiIndex
from pand... | StringIO(data) | pandas.compat.StringIO |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 14 15:17:16 2018
@author: trevor
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as st
import time
from sklearn import linear_model
from pylab import mpl
from scipy.optimize import fsolve
from sklearn.l... | pd.read_csv('stock_price.csv',encoding='GBK') | pandas.read_csv |
import os
import gc
import time
import imghdr
from io import BytesIO
from typing import List, Optional
from datetime import datetime
import requests
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm # if you don't use IPython Kernel like jupyter, you should change "tqdm.notebook" to "tqdm"
from ca... | pd.to_datetime(df['last_sale.created_date']) | pandas.to_datetime |
import pandas
import string
import math
import csv
import os
import re
from unicodedata import normalize
import unicodedata
def corrigir_nomes(nome):
nome = nome.replace('Á', 'A').replace('É', 'E').replace('Í', 'I').replace('Ó', 'O').replace('Ú', 'U').replace('Ç', 'C')
return nome
def localize_... | pandas.concat([df1,df2],ignore_index=True) | pandas.concat |
import base64
import io
import textwrap
import dash
import dash_core_components as dcc
import dash_html_components as html
import gunicorn
import plotly.graph_objs as go
from dash.dependencies import Input, Output, State
import flask
import pandas as pd
import urllib.parse
from sklearn.preprocessing import StandardSca... | pd.concat([outlier_names, principalDf_outlier_scale_covar], axis=1) | pandas.concat |
import sys
#sys.path.append("..")
import numpy as np
import theano
import theano.tensor as T
import theano.tensor.signal.conv as C
from epimodel import EpidemiologicalParameters, preprocess_data
np.random.seed(123456)
import argparse
import copy
import datetime
import itertools
import pickle
import re
from datetime... | pd.concat([wearing, us_wearing]) | pandas.concat |
import pandas
import argparse
import ast
# Load arguments from the command line
parser = argparse.ArgumentParser()
parser.add_argument("--tp", type=str, help="The filename or path to the true positive csv", default='')
parser.add_argument("--tn", type=str, help="The filename or path to the true positive csv", default=... | pandas.read_csv('data/target_map.csv', converters={'cuis':ast.literal_eval}) | pandas.read_csv |
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from pandas.util._decorators import doc
from ..util import save_docx_table, get_top_substrs
from .dataset import _file_docs, _shared_docs
_plot_docs = _file_docs.copy()
_plot_docs['scope'] = _shared_docs['scope']
_plot_docs['s... | pd.DataFrame() | pandas.DataFrame |
import h5py
import numpy as np
import pandas as pd
import os
from multiprocessing import cpu_count, Pool
from alcokit.util import fs_dict, is_audio_file
from alcokit.hdf.api import Database
from alcokit.score import Score
import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# TODO : add handling... | pd.read_hdf(target, "layouts/" + feature) | pandas.read_hdf |
# Copyright 2021 The CGLB Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable la... | pd.concat([columns, column], axis=1) | pandas.concat |
import os
import json
import pandas as pd
import zipfile
from werkzeug.utils import secure_filename
import shutil
import time
from random import randint
from datetime import timedelta
import tempfile
import sys
from elasticsearch import Elasticsearch
##
##
# dataframes
from dataframes import dataframe
# functions
from ... | pd.concat([x[5] for x in lst_sub_df], ignore_index=True) | pandas.concat |
import requests, re, json, csv
import numpy as np
import pandas as pd
from bs4 import BeautifulSoup
confirmed_CSV_URL = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv'
deaths_CSV_URL = 'https://raw.githubusercontent.com/... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import time
import warnings
warnings.filterwarnings('ignore')
import pandas as pd, numpy as np
import math, json, gc, random, os, sys
import torch
import logging
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
from sklearn.model_selecti... | pd.concat([public_dataframe,private_dataframe1,private_dataframe2]) | pandas.concat |
# -*- coding: utf-8 -*-
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d, Axes3D
import random
import matplotlib
from collections import OrderedDict
import seaborn as sns
import matplotlib.gridspec as gridspec
from matplotlib.font_manager import F... | pd.DataFrame({}) | pandas.DataFrame |
# ---
# jupyter:
# jupytext:
# formats: jupyter_scripts//ipynb,ifis_tools//py
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.3'
# jupytext_version: 1.0.0
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# ... | pd.date_range(Data.index[0], Data.index[-1], freq='1h') | pandas.date_range |
"""Integration tests for the HyperTransformer."""
import re
from copy import deepcopy
from unittest.mock import patch
import numpy as np
import pandas as pd
import pytest
from rdt import HyperTransformer
from rdt.errors import Error, NotFittedError
from rdt.transformers import (
DEFAULT_TRANSFORMERS, BaseTransfo... | pd.testing.assert_frame_equal(reverse2, new_data) | pandas.testing.assert_frame_equal |
import pandas as pd
import logging
import numpy as np
import collections
import configparser
import shutil
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.dates as mdates
import requests
import io
from astropy.io import fits
from astropy.time import Time
from pathlib import Pat... | pd.to_datetime(atmos_info['TIME'], utc=False) | pandas.to_datetime |
from datetime import date
from google.oauth2 import service_account
from googleapiclient.discovery import build
import numpy as np
from repo_issues_dc import IssueReport as IR
import pandas as pd
import pathlib
credentials = service_account.Credentials.from_service_account_file(
str(pathlib.Path("auth/issue-repo... | pd.DataFrame(issue_list) | pandas.DataFrame |
import itertools
import pandas as pd
from sklearn import preprocessing
from learntools.core import *
class InteractionFeatures(CodingProblem):
_vars = ['clicks', 'interactions']
_hint = ("The easiest way to loop through the pairs is with itertools.combinations. "
"Once you have that working, fo... | pd.Series(series.index, index=series) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Thu May 22 10:14:40 2019
@author : Natacha
"""
from matplotlib.lines import Line2D
import datetime
import pandas as pd
import datetime
import numpy as np
from matplotlib import pyplot as plt
import glob
from astropy.io import ascii
import matplotlib.dates as mdates
from astropy... | pd.Series(blueb2_2018) | pandas.Series |
import os
import sqlite3
import pandas as pd
from pygbif import occurrences
from pygbif import species
from datetime import datetime
import geopandas as gpd
import shapely
import numpy as np
import fiona
from shapely.geometry import shape, Polygon, LinearRing, Point
from dwca.read import DwCAReader
import random
from s... | pd.read_sql(sql="SELECT * FROM taxon_concept;", con=conn) | pandas.read_sql |
import dask.array as da
import dask.dataframe as dd
import numpy as np
import numpy.linalg as LA
import pandas as pd
import pytest
import sklearn.linear_model
from dask.dataframe.utils import assert_eq
from dask_glm.regularizers import Regularizer
from sklearn.pipeline import make_pipeline
import dask_ml.linear_model
... | pd.DataFrame({"intercept": [1, 2, 3]}) | pandas.DataFrame |
from __future__ import division
import numpy as np
import pandas as pd
import pickle
import os
from math import ceil
import matplotlib
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
from sklearn.metrics import r2_score
warnings.simplefilter("ignore")
# col... | pd.DataFrame({'Remaining':[]}) | pandas.DataFrame |
import pandas
from collections import Counter
from tqdm import tqdm
user_df = pandas.read_csv('processed_data/prj_user.csv')
tweets_df = pandas.read_csv('original_data/prj_tweet.csv')
ids = user_df["id"]
ids = list(ids.values)
hobby_1_list = []
hobby_2_list = []
def get_users_most_popular_hashtags_list(tweets_df, u... | pandas.read_csv('processed_data/prj_user.csv') | pandas.read_csv |
import sys, os
import unittest
import pandas as pd
import numpy
import sys
from sklearn import datasets
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, Imputer, LabelEncoder, LabelBinarizer, MinMaxScaler, MaxAbsScaler, RobustScaler,\
Binarizer, PolynomialFeatures, OneHotEn... | pd.read_csv('nyoka/tests/auto-mpg.csv') | pandas.read_csv |
"""
Module: LMR_proxy_preprocess.py
Purpose: Takes proxy data in their native format (e.g. .pckl file for PAGES2k or collection of
NCDC-templated .txt files) and generates Pandas DataFrames stored in pickle files
containing metadata and actual data from proxy records. The "pickled" DataFrames
... | pd.DataFrame({'a':frame_data[:,0], 'b':frame_data[:,1]}) | pandas.DataFrame |
"""
Train VGG19
import os
os.system("pip install -U efficientnet")
"""
import argparse
import configparser
import datetime
import os
import keras.backend as K
import numpy as np
import pandas as pd
import tensorflow as tf
from efficientnet import EfficientNetB5, preprocess_input
from keras.applications.densenet import... | pd.DataFrame(TESTSET_ARRAY, columns=["Id", "Expected"]) | pandas.DataFrame |
""" This script reproduces content of Fig. 4 & Table 1 in the manuscript. """
import mne
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import helper
plt.ion()
results_dir = '../results/model_bursts/'
channels = ['C3-lap', 'C4-lap',... | pd.read_csv('../results/mean_laplacian_patterns.csv', index_col=0) | pandas.read_csv |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
import nose
import numpy as np
from numpy import nan
import pandas as pd
from distutils.version import LooseVersion
from pandas import (Index, Series, DataFrame, Panel, isnull,
date_range, period_range)
from pandas.core.index import MultiIn... | Series([True, True]) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 27 14:35:44 2018
@author: RUFIAR1
"""
import pandas as pd
import locale
locale.setlocale(locale.LC_TIME, "en_US.UTF-8")
def plot_finance_data(finance_data):
finance_data['All Data']=finance_data['Material'].astype('str')+','+finance_data['Fiscal year/peri... | pd.to_datetime(forecast_data.index) | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
##########
Processing
##########
*Created on Thu Jun 1 14:15 2017 by <NAME>*
Processing results from the CellPainting Assay in the Jupyter notebook.
This module provides the DataSet class and its methods.
Additional functions in this module act on pandas DataFrames.... | pd.DataFrame(control) | pandas.DataFrame |
import pytest
from pandas._libs.tslibs.frequencies import INVALID_FREQ_ERR_MSG, _period_code_map
from pandas.errors import OutOfBoundsDatetime
from pandas import Period, Timestamp, offsets
class TestFreqConversion:
"""Test frequency conversion of date objects"""
@pytest.mark.parametrize("freq", ["A", "Q", ... | Period(freq="A", year=2007) | pandas.Period |
import json
import pandas as pd
import argparse
import os
import numpy as np
# from pretrained_model_list import MODEL_PATH_LIST
# import promptsource.templates
from tqdm import tqdm
import ipdb
def clean_up_tokenization(out_string: str) -> str:
"""
Clean up a list of simple English tokenization artifacts like... | pd.read_csv(pth, sep="\t") | pandas.read_csv |
import pandas as pd
from sklearn.ensemble import AdaBoostRegressor
from sklearn.model_selection import RepeatedKFold
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import mean_absolute_error as mae
from sklearn.metrics import median_absolute_error as mdae
from sklearn.metrics import ex... | pd.read_csv('C:\\Users\\<NAME>\\Documents\\Research Projects\\Forecast of Rainfall Quantity and its variation using Envrionmental Features\\Data\\Normalized & Combined Data\\All Districts.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import pandas.compat as compat
###############################################################
# Index / Series common tests which may trigger dtype coercions
###############################################... | pd.Timestamp('2011-01-01', tz=tz) | pandas.Timestamp |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 26 15:39:02 2018
@author: joyce
"""
import pandas as pd
import numpy as np
from numpy.matlib import repmat
from stats import get_stockdata_from_sql,get_tradedate,Corr,Delta,Rank,Cross_max,\
Cross_min,Delay,Sum,Mean,STD,TsRank,TsMax,TsMin,DecayLinea... | pd.DataFrame(data['diff']) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 15 16:46:50 2021
@author: <NAME>
"""
#This script prepares phenotypes and covariates for associtatoin analysis
"""Several filtering and QC steps are applied, filter masks and overview plots created. As last steps files are reformatted to BGENIE st... | pd.melt(final_DTI_qc.iloc[:,1:]) | pandas.melt |
# standard imports
import os
import glob
import inspect
from pprint import pprint
import pickle as pkl
import copy
import pandas as pd
import numpy as np
from tqdm import tqdm
import logging
import subprocess
import warnings
import itertools
import matplotlib.pyplot as plt
from astropy.io import fits
from astropy.wcs i... | pd.Index(id) | pandas.Index |
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 14 13:14:38 2022
@author: mauro
"""
def plot_boxplot(input_data, fig_name):
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams.update({'font.size' : 10})
axis_font = {'fontname' : 'Arial', 'size' : '16'}
sns.boxplot(data=inpu... | pd.DataFrame() | pandas.DataFrame |
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