prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pytest
import pandas as pd
import pandas._testing as tm
@pytest.mark.parametrize(
"values, dtype",
[
([], "object"),
([1, 2, 3], "int64"),
([1.0, 2.0, 3.0], "float64"),
(["a", "b", "c"], "object"),
(["a", "b", "c"], "string"),
([1, 2, 3], "datetime64[ns]... | pd.DataFrame(dtype=dtype) | pandas.DataFrame |
'''
Plots all the results of the dwglasso analysis on a map of Canada.
NOTE: This file is intended to be executed by make from the top
level of the project directory hierarchy. We rely on os.getcwd()
and it will not work if run directly as a script from this directory.
'''
from dwglasso import cross_validate, dwglass... | pd.HDFStore(HDF_FINAL_FILE, mode='r') | pandas.HDFStore |
# Copyright (c) 2020 Huawei Technologies Co., Ltd.
# <EMAIL>
#
# 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 a... | pd.DataFrame(data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
@author: <NAME>
Harmonize the features between the target and the source data so that:
- same feature space is considered between the source and the target.
- features are odered in the same way, avoiding permutation issue.
"""
import numpy as np
import pandas as pd
def harmonize_feature... | pd.read_csv(gene_lookup_file, delimiter=',') | pandas.read_csv |
import warnings
warnings.filterwarnings("ignore")
import os
import math
import numpy as np
import tensorflow as tf
import pandas as pd
import argparse
import json
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.manifold import TSNE
from scipy.stats imp... | pd.DataFrame(data=gen_decoded, index=sample_list, columns=gene_list) | pandas.DataFrame |
# Package imports
import pandas as pd
import requests
import datetime
from unidecode import unidecode as UnicodeFormatter
import os
import bcolors
# Local imports
import path_configuration
import url_configuration
import progress_calculator
class Season_Info(object):
Url = None
Path = None
Requests = Non... | pd.DataFrame(data=Circuit_Data) | pandas.DataFrame |
import tweepy
import pandas as pd
from langdetect import detect
from .sentiment import analyse_per_language
from .datahandler import DataHandler
import gc
class GlobalStreamListener(tweepy.StreamListener):
"""
Twitter listener. collects tweets and stores it to a data-handler
"""
def __init__(self, lan... | pd.DataFrame.from_dict(buffered_data) | pandas.DataFrame.from_dict |
# -*- coding: utf-8 -*-
# -*- coding: utf-8 -*-
"""
Simulate elections.
Elements of an election
1. Create voter preferences
- Create voter preference distributions
- Create voter preference tolerance distribution
2. Create candidate preferences
3. Simulate voter behavior, strategy
4. Transform voter preference... | pd.Series(self._output_history[index]) | pandas.Series |
import os
import random
import math
import numpy as np
import pandas as pd
import itertools
from functools import lru_cache
##########################
## Compliance functions ##
##########################
def delayed_ramp_fun(Nc_old, Nc_new, t, tau_days, l, t_start):
"""
t : timestamp
current date
... | pd.Timestamp('2021-07-01') | pandas.Timestamp |
import pandas as pd
import numpy as np
from datetime import datetime
from multiprocessing import Pool
from functools import partial
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
from datetime import datetime
import seaborn as sns
import matplotlib.dates as dates
import calendar
from itertools import *
... | pd.DataFrame(data) | pandas.DataFrame |
# Standard library imports
from sqlalchemy.inspection import inspect
from datetime import datetime, timedelta
from pandas import isnull
# project imports
from PhosQuest_app.data_access.db_sessions import import_session_maker
from PhosQuest_app.data_access.class_functions import get_classes_key_attrs
# define null-typ... | isnull(row[df_heading]) | pandas.isnull |
import pandas as pd
import numpy as np
import git
import os
import sys
from pathlib import Path
import matplotlib.pyplot as plt
#-- Setup paths
# Get parent directory using git
repo = git.Repo("./", search_parent_directories=True)
homedir = repo.working_dir
# Change working directory to parent directory
os.chdir(ho... | pd.to_datetime('2020 Jan 21') | pandas.to_datetime |
# IMAGE CLASSIFIER COMMAND LINE APPLICATION
# predict.py
#
# USAGE:
# python predict.py
# --data_dir Path to the folder of the flower images
# --save_dir Path to save the model checkpoints
# --path_to_image Path to an image file
# --c... | pd.Series(data=probs, dtype='float64') | pandas.Series |
#!/usr/bin/env python
# coding: utf-8
# # [Memanggil Library Pandas](https://academy.dqlab.id/main/livecode/178/346/1682)
# In[1]:
import pandas as pd
import numpy as np
# # [DataFrame & Series](https://academy.dqlab.id/main/livecode/178/346/1683)
# In[2]:
import pandas as pd
# Series
number_list = pd.Series([... | pd.read_csv("https://dqlab-dataset.s3-ap-southeast-1.amazonaws.com/sample_tsv.tsv", sep="\t", nrows=10) | pandas.read_csv |
# import modules ----------------------
import nba_py
import nba_py.game
import nba_py.player
import nba_py.team
import pandas as pd
import numpy as np
import datetime
import pytz
old_settings = np.seterr(all='print')
np.geterr()
print('modules imported')
# define functions ----------------------
def get_games(... | pd.merge(players, all_players[['PERSON_ID', 'DISPLAY_FIRST_LAST', 'TEAM_ABBREVIATION']], on='PERSON_ID') | pandas.merge |
"""
Attribution
"""
import datetime
import pandas as pd
import numpy as np
import win32com.client
import matplotlib
import matplotlib.pyplot as plt
import attribution.extraction
from dateutil.relativedelta import relativedelta
start_date = datetime.datetime(2020, 1, 31)
end_date = datetime.datetime(2020, 3, 31)
input... | pd.melt(df_market_values, id_vars=['Manager'], value_name='Market Value') | pandas.melt |
import os
import time
from datetime import timedelta
import pandas as pd
import pytest
from peakina.cache import InMemoryCache
from peakina.datasource import DataSource, read_pandas
from peakina.helpers import TypeEnum
from peakina.io import MatchEnum
@pytest.fixture
def read_csv_spy(mocker):
read_csv = mocker.... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import pickle
import csv
import glob
import errno
import re
from sklearn.preprocessing import Imputer, StandardScaler
from sklearn.cluster import KMeans
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
from keras.layers import Dense, Embedding, Dropout, Reshape,... | pd.concat([self.prep_data, dfFalse], ignore_index=True) | pandas.concat |
import pytest
import pandas as pd
from cr.sparse import io
import jax.numpy as jnp
def test_print_dataframe_as_list_table():
d = {
"one": pd.Series([1.0, 2.0, 3.0], index=["a", "b", "c"]),
"two": pd.Series([1.0, 2.0, 3.0], index=["a", "b", "c"]),
"three": | pd.Series([1, 2, 3], index=["a", "b", "c"]) | pandas.Series |
from numpy import isnan
from pandas import read_csv, DataFrame
from sklearn.impute import SimpleImputer
# Load the data
df = read_csv('https://raw.githubusercontent.com/jbrownlee/Datasets/master/horse-colic.csv',
header=None,
na_values='?',)
# Show the first 5 rows of the data
df.head()
#... | DataFrame(data=Xtrans) | pandas.DataFrame |
import matplotlib.pyplot as plt
from pathlib import Path
import pandas as pd
import os
import numpy as np
def get_file_paths(file_directory):
file_paths = os.listdir(file_directory)
file_paths = list(filter(lambda f_path: os.path.isdir(file_directory / f_path), file_paths))
return file_paths
def plot_da... | pd.datetime(2017, 8, 7) | pandas.datetime |
import numpy as np
import pandas as pd
import statsmodels.api as sm
tsa = sm.tsa # as shorthand
mdata = sm.datasets.macrodata.load().data
type(mdata)
endog = np.log(mdata['m1'])
exog = np.column_stack([np.log(mdata['realgdp']), np.log(mdata['cpi'])])
exog = sm.add_constant(exog, prepend=True)
exog
res1 = sm.OLS(endo... | pd.DataFrame([iprod_m,gdp_m],index=['IPROD','GDP MONTHLY']) | pandas.DataFrame |
from collections import namedtuple
import numpy as np
import pandas as pd
import pytest
import statsmodels.api as sm
from estimagic.config import EXAMPLE_DIR
from estimagic.visualization.estimation_table import _apply_number_format
from estimagic.visualization.estimation_table import _check_order_of_model_name... | afe(res[1], exp[1]) | pandas.testing.assert_frame_equal |
import pandas as pd
import io
import requests
import json
import wbdata
class ProductoInternoBruto:
def __init__(self):
pass
def getPreciosCorrientesBase2004(self, periodo = "Anual"):
"""
El PIB es el valor total de bienes y servicios FINALES producidos en
un pais dur... | pd.to_datetime(df_temp['indice_tiempo'], format='%Y-%m-%d', errors='ignore') | pandas.to_datetime |
""" plotting functions for Dataset objects
To Do:
Edit hyp_stats plots to take transitions.HypStats object instead of ioeeg.Dataset object
Remove redundant plotting fns added into EKG classs
Add subsetEEG function to break up concatenated NREM segments for plotting. Will require adjust... | pd.Series(index=norm_dat.index) | pandas.Series |
# -*- coding: utf-8 -*-
# Run this app with `python app.py` and
# visit http://127.0.0.1:8050/ in your web browser.
#AppAutomater.py has App graphs and data
#Graphs.py has all graphs
#Data.py has all data processing stuff
#Downloader.py is used to download files daily
import dash
import dash_core_components... | pd.DataFrame(g.grouped_daily_regions["data"]) | pandas.DataFrame |
#!/usr/bin/env python
__author__ = '<NAME>'
import argparse
import multiprocessing as mp
from collections import OrderedDict
import pandas as pd
from RouToolPa.Parsers.Sequence import CollectionSequence
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input_file_list", action="store", dest="input_fi... | pd.DataFrame() | pandas.DataFrame |
import argparse
import mplfinance as mpf
import numba as nb
import os
import pandas as pd
from pandas_datareader import data, wb
from pandas_datareader.nasdaq_trader import get_nasdaq_symbols
from pandas.tseries.holiday import USFederalHolidayCalendar
from pandas.tseries.frequencies import to_offset
import matplotlib.p... | pd.Timedelta("1d") | pandas.Timedelta |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
from matplotlib.lines import Line2D
from sklearn.metrics import confusion_matrix
import seaborn as sn
import sys
import re
import csv
from itertools import chain
def visualizeData(file, compfile, source, class_):
... | pd.to_numeric(data[score],downcast='float') | pandas.to_numeric |
#%%
import os
import sys
try:
os.chdir('/Volumes/GoogleDrive/My Drive/python_code/connectome_tools/')
print(os.getcwd())
except:
pass
from pymaid_creds import url, name, password, token
import pymaid
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# allows text... | pd.DataFrame(inputs.values, index = inputs.index, columns = ['axon_input', 'dendrite_input']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import warnings
from datetime import datetime, timedelta
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.errors import PerformanceWarning
from pandas import (Timestamp, Timedelta, Series,
DatetimeIndex, TimedeltaIndex,
... | Timestamp('2000-2-29') | pandas.Timestamp |
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from keras.models import load_model
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.utils import Sequence, to_categorical
from sklearn.model_selection import tra... | pd.read_csv(file, sep="\t", header=0) | pandas.read_csv |
import unittest
import pandas as pd
from mavedbconvert import validators, constants, exceptions
class TestHGVSPatternsBackend(unittest.TestCase):
def setUp(self):
self.backend = validators.HGVSPatternsBackend()
def test_validate_hgvs_raise_HGVSValidationError(self):
with self.assertRaises(e... | pd.DataFrame({constants.nt_variant_col: ["a", "b", "a"]}) | pandas.DataFrame |
from __future__ import print_function, division
import os
import re
import datetime
import sys
from os.path import join, isdir, isfile, dirname, abspath
import pandas as pd
import yaml
import psycopg2 as db
from nilmtk.measurement import measurement_columns
from nilmtk.measurement import LEVEL_NAMES
from nilmtk.datasto... | pd.read_sql(sql_query, conn) | pandas.read_sql |
import requests
import json
import arrow
from datetime import datetime
from requests.auth import HTTPBasicAuth
import numpy as np
import pandas as pd
from datetime import date, datetime, timedelta as td
#######################
#### aTimeLogger #####
######################
# Modified from https://github.com/YujiShe... | pd.to_datetime(start_date) | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This file makes the Supplementary Figure 5, it needs the filter_SRAG.py
results to run.
"""
import pandas as pd
import numpy as np
import datetime
import matplotlib.pyplot as plt
data_init = pd.read_csv('../Data/SRAG_filtered_morb.csv')
data_init = data_init[(data_i... | pd.DataFrame(s) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
import woodwork as ww
from evalml.automl import get_default_primary_search_objective
from evalml.data_checks import (
DataCheckAction,
DataCheckActionCode,
DataCheckError,
DataCheckMessageCode,
DataChecks,
DataCheckWarning,
InvalidTargetD... | pd.Series() | pandas.Series |
from __future__ import absolute_import
import random
import time
import logbook
import pandas as pd
import requests
from cnswd.websource.base import friendly_download
from cnswd.websource._selenium import make_headless_browser
log = logbook.Logger('提取成交明细网页数据')
BASE_URL_FMT = 'http://vip.stock.finance.sina.com.cn/... | pd.Timedelta(days=20) | pandas.Timedelta |
# coding: utf-8
# # Laterality Curves
# ### import modules
#
## In[1]:
#
#get_ipython().magic(u'matplotlib inline')
#
#
# In[2]:
from nilearn import input_data, image, plotting
import os
import sys
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# ### get absolute ... | pd.concat([threshDataLeft,threshDataRight],axis=1) | pandas.concat |
# coding=utf-8
import numpy as np
from scipy.stats import norm
import pandas as pd
def LHS_norm(N, mean, cv):
"""
:param std:数据标准差
:param mean:数据均值
:param N:拉丁超立方层数
:return:样本数据
"""
result = np.empty([N])
d = 1.0 / N
for j in range(N):
result[j] = np.random.uniform(low=j * ... | pd.Series(job) | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@title: Non-Exhaustive Gaussian Mixture Generative Adversarial Networks (NE-GM-GAN)
@topic: Generate qualified dataset from raw data
@author: <NAME>, <NAME>
@run: python gen_Data.py KDD99 ../data/
"""
import os
import sys
import numpy as np
import pandas as pd
from ut... | pd.read_csv(data_path+"train.csv") | pandas.read_csv |
########################################################################
# Copyright 2020 Battelle Energy Alliance, LLC ALL RIGHTS RESERVED #
# Mobility Systems & Analytics Group, Idaho National Laboratory #
########################################################################
import pyodbc
import ... | pd.DataFrame(cfg.hdrLocationInfoCSV) | pandas.DataFrame |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import timedelta
from numpy import nan
import numpy as np
import pandas as pd
from pandas import (Series, isnull, date_range,
MultiIndex, Index)
from pandas.tseries.index import Timestamp
from pandas.compat import range
from pandas.u... | Series([1, 3, np.nan, np.nan, np.nan, 11]) | pandas.Series |
# -*- coding:utf-8 -*-
# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
# This program is free software; you can redistribute it and/or modify
# it under the terms of the MIT License.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the ... | pd.DataFrame([[performance]], columns=["PSNR"]) | pandas.DataFrame |
__title__ = "playground"
__author__ = "murlux"
__copyright__ = "Copyright 2019, " + __author__
__credits__ = (__author__, )
__license__ = "MIT"
__email__ = "<EMAIL>"
from queue import Queue
import threading
import time
import pandas as pd
from datetime import datetime as dt
from typing import Any, Dict, List, Callable... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import os
import json
import csv
from simple_salesforce import Salesforce # imported salesforce
from config import *
# Login to Salesforce
print("---- logging into Salesforce ----")
sf = Salesforce(username=username, password=password, security_token=token, domain='test')
print("--- login success... | pd.DataFrame(chunkINFO) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 3 14:18:10 2017
@author: massimo
Straight import of exiobase data
"""
import pandas as pd
import numpy as np
def importing(filename, celltype):
'''
Args:
'filename' [string] name of the file...
'celltype' [type of file], three... | pd.read_csv(filename, header = [0,1], index_col = [0,1,2], sep = ";", dtype = object) | pandas.read_csv |
"""
Runs ramulator on specified trace files individually in order to gather basic miss/hit information beforehand
-> stats for each run are stored in BASE_STATS_DIR, where a trace file named 'trace_name' is saved as trace_name_stats.txt
Note: Expects trace files to not be in archives
Usage: python get_trace_stats.py ... | pd.DataFrame.from_dict(trace_stat_group_dict, orient='index') | pandas.DataFrame.from_dict |
import plotly.graph_objects as go
import pandas as pd
import plotly.express as px
from datetime import datetime, timedelta
import requests
import json
import time
def read():
df1 = pd.read_csv("CSV/ETH_BTC_USD_2015-08-09_2020-04-04-CoinDesk.csv")
df1.columns = ['date', 'ETH', 'BTC']
df1.date = pd.to_dateti... | pd.read_csv("ICO_coins/cardano/ADA_USD_2018-06-06_2020-04-02-CoinDesk.csv") | pandas.read_csv |
from config.logger import logger
import pandas as pd # must be replaced with internal python tools!
import datetime
from docker import DockerClient
from docker.errors import DockerException, APIError, ContainerError, ImageNotFound
import os
import time
class Operator():
def __init__(self):
try:
... | pd.DataFrame(data=hist_test_df['y'].values, columns=['y']) | pandas.DataFrame |
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from datetime import datetime
import json
from bs4 import BeautifulSoup
import requests
from tqdm import tqdm
def timestamp2date(timestamp):
# function converts a Uniloc timestamp into Gregorian date
return datetime.fromtimestamp(int(... | pd.to_datetime(df.date) | pandas.to_datetime |
"""
Created on Wed Feb 27 15:12:14 2019
@author: cwhanse
"""
import numpy as np
import pandas as pd
from pandas.testing import assert_series_equal
from datetime import datetime
import pytz
import pytest
from solarforecastarbiter.validation import validator
import pvlib
from pvlib.location import Location
@pytest.fi... | assert_series_equal(expected, result) | pandas.testing.assert_series_equal |
"""
Tasks
-------
Search and transform jsonable structures, specifically to make it 'easy' to make tabular/csv output for other consumers.
Example
~~~~~~~~~~~~~
*give me a list of all the fields called 'id' in this stupid, gnarly
thing*
>>> Q('id',gnarly_data)
['id1','id2','id3']
Observations:
--... | u('value') | pandas.compat.u |
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Index,
Series,
)
import pandas._testing as tm
dt_data = [
pd.Timestamp("2011-01-01"),
pd.Timestamp("2011-01-02"),
pd.Timestamp("2011-01-03"),
]
tz_data = [
pd.Timestamp("2011-01-01", tz="U... | Index(vals1, name="x") | pandas.Index |
"""Predict lexical norms, either to evaluate word vectors, or to get norms for unnormed words."""
import numpy as np
import pandas as pd
import sklearn.linear_model
import sklearn.model_selection
import sklearn.preprocessing
import sklearn.utils
import argparse
import os
from .vecs import Vectors
from .utensils import ... | pd.DataFrame(scores) | pandas.DataFrame |
# @name: metadata.py
# @summary: pulls metadata from an FCS experiment
# @description:
# @sources:
# @depends:
# @author: <NAME>
# @email: <EMAIL>
# @license: Apache-2.0
# @date: 23 April 2018
# [Import dependencies] ---------------------------------------------------------------------... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import ast
import sys
import os.path
from pandas.core.algorithms import isin
sys.path.insert(1,
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
import dateutil.parser as parser
from utils.mysql_utils import separator
from utils.io import read_json
from utils.scr... | pd.read_csv(connections_filename, index_col=0) | pandas.read_csv |
"""
This module contains functions related to ML-matcher, that is common across
all the ML-matchers.
"""
import logging
# import numpy as np
import pandas as pd
# import dask
import dask
from dask import delayed
from dask.diagnostics import ProgressBar
import py_entitymatching.catalog.catalog_manager as cm
from py_e... | pd.np.delete(y, 0, 1) | pandas.np.delete |
# -*- coding: utf-8 -*-
"""
Created on Sat Jan 26 15:13:19 2019
@author: kennedy
"""
__author__ = "<NAME>"
__email__ = "<EMAIL>"
__version__ = '1.0'
seed = 1333
from numpy.random import seed
seed(19)
from tensorflow import set_random_seed
set_random_seed(19)
import os
from STOCK import stock, loc
import pandas as p... | pd.to_datetime(data.index) | pandas.to_datetime |
from datetime import datetime
import numpy as np
from pandas.tseries.frequencies import get_freq_code as _gfc
from pandas.tseries.index import DatetimeIndex, Int64Index
from pandas.tseries.tools import parse_time_string
import pandas.tseries.frequencies as _freq_mod
import pandas.core.common as com
import pandas.core... | _freq_mod._period_group(self.freq) | pandas.tseries.frequencies._period_group |
import pandas as pd
import os
import sys # ----------------------------- To handle system paths
sys.path.append('.') # ------------------- All paths till the current folder have alias as '.'
class Aircraft():
'''Class defining the object
Aircrafts. It will hold all
the aircraft data and provide
relevan... | pd.read_csv(path, encoding='utf-8') | pandas.read_csv |
import time
import pandas as pd
# Read original data
df_train = pd.read_json('./data/train.json')
df_test = | pd.read_json("./data/test.json") | pandas.read_json |
import pandas as pd
from pandas.testing import assert_frame_equal
from unittest import TestCase
from src.executor.utils import calc_target_positions
df_blended_list1 = [
pd.DataFrame([
['ETH', 0.2],
['BTC', 0.1],
], columns=['symbol', 'position']).set_index(['symbol']),
pd.DataFrame([
... | assert_frame_equal(result, expected) | pandas.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 28 22:33:19 2021
@author: zishi
"""
import pandas as pd
def ensemble_t(df_list,mode='soft'):
df = pd.concat(df_list)
df_mean = df.groupby(df.index).mean()
if mode == 'soft':
result = pd.DataFrame(df_mean['proba'],index=df_list[0].index)
... | pd.DataFrame(df_mean['label'],index=df_list[0].index) | pandas.DataFrame |
from Model.BERT_BILSTM_CRF import BERTBILSTMCRF
from Model.BILSTM_Attetion_CRF import BILSTMAttentionCRF
from Model.BILSTM_CRF import BILSTMCRF
from Model.IDCNN_CRF import IDCNNCRF
from Model.IDCNN5_CRF import IDCNNCRF2
from sklearn.metrics import f1_score, recall_score
import numpy as np
import pandas as pd
from Pub... | pd.DataFrame(columns=columns) | pandas.DataFrame |
import altair as alt
import pandas as pd
import numpy as np
from datetime import date, timedelta
from os import path
from io import StringIO
from flask import current_app as app
import redis, requests, time, pyarrow
def connect():
return redis.Redis( host=app.config['REDIS_HOST'], port=app.config['REDIS_PORT'] )
... | pd.to_datetime(dt['date'].values[0]) | pandas.to_datetime |
############################################################################################
# FileName [ comut_plot_analysis.py ]
# PackageName [ lib/analysis ]
# Synopsis [ Implement CoMut analysis. ]
# Author [ <NAME> ]
# Copyright [ 2021 9 ]
########################################################... | pd.read_csv(tsv_info, sep='\t') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 1 16:28:33 2021
@author: yeabinmoon
"""
import pandas as pd
from safegraph_py_functions import safegraph_py_functions as sgpy
import time
df = | pd.read_pickle('/Users/yeabinmoon/Documents/JMP/data/SafeGraph/POI/temp/temp2.pickle.gz') | pandas.read_pickle |
#!/usr/bin/env python3
"""
Normalize UCI computer hardware dataset (http://archive.ics.uci.edu/ml/datasets/Computer+Hardware)
1. vendor name: 30
(adviser, amdahl,apollo, basf, bti, burroughs, c.r.d, cambex, cdc, dec,
dg, formation, four-phase, gould, honeywell, hp, ibm, ipl, magnuson,
microdata, nas, ncr, nixdo... | pd.read_csv(data_path, header='infer') | pandas.read_csv |
import pandas as pd
import numpy as np
from keras.models import load_model
from sklearn.metrics import roc_curve, roc_auc_score, auc, precision_recall_curve, average_precision_score
import os
import pickle
from scipy.special import softmax
from prg import prg
class MetricsGenerator(object):
def __init__(self, data... | pd.Series(average_precision, index=i) | pandas.Series |
import numpy as np
import pandas as pd
from EvaluationFunctions.LoadFrameworkDesignsFilenames import load_framework_designs_filenames
from EvaluationFunctions.LoadCompetitorsFilenames import load_competitors_filenames
from EvaluationFunctions.Load_withIterations_Results import load_with_iterations_results
from Evaluati... | pd.DataFrame(data=times_per_example, index=experiments) | pandas.DataFrame |
"""
FyleExtractConnector(): Connection between Fyle and Database
"""
import logging
from os import path
from typing import List
import pandas as pd
class FyleExtractConnector:
"""
- Extract Data from Fyle and load to Database
"""
def __init__(self, fyle_sdk_connection, dbconn):
self.__dbconn ... | pd.DataFrame(expenses) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 24 14:43:54 2019
@author: Gary
This script is used to download a new raw set, save it if the
day of the week is in the list, and look for new events.
It will also record how many records are in each new event, runs
tripwire and uploads a webpage summary for general acce... | pd.read_csv(datefn,quotechar='$') | pandas.read_csv |
import pandas as pd
import matplotlib as mpl
def create_stim_artists(app):
pattern = mpl.patches.Circle((0, 0),
app.p.stim_size / 2,
fc="firebrick", lw=0,
alpha=.5,
animated=True)
... | pd.read_json(trial_data, typ="series") | pandas.read_json |
import ipdb
import fnmatch
import pandas as pd
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--path',type = str)
parser.add_argument('--savepath',type = str)
args = parser.parse_args()
path = args.path
savepath = args.savepath
# Preprocess the b... | pd.read_csv(path,sep = '\t',header=None) | pandas.read_csv |
import pandas as pd
def run(labels, model_class, **kwargs):
"""Run test.
Parameters
----------
labels: torch.LongTensor
Tensor of target (label) data.
model_class
The class of the model on which prediction will be performed.
It must implement the fit, the predict_proba an... | pd.DataFrame({"prediction": predictions}) | pandas.DataFrame |
import pandas as pd
import numpy as np
import datetime
import time
import math
from pypfopt import risk_models
from pypfopt import expected_returns
from pypfopt import black_litterman
from pypfopt.efficient_frontier import EfficientFrontier
from pypfopt.black_litterman import BlackLittermanModel
from statsmodels.tsa.ar... | pd.read_csv("newfund.csv", header=0, encoding="UTF-8") | pandas.read_csv |
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Index, Series, date_range, offsets
import pandas._testing as tm
class TestDataFrameShift:
def test_shift(self, datetime_frame, int_frame):
# naive shift
shiftedFrame = datetime_frame.shift(5)
tm.assert_inde... | tm.assert_frame_equal(shiftedFrame, shiftedFrame2) | pandas._testing.assert_frame_equal |
import numpy as np
import pytest
from pandas import (
DataFrame,
NaT,
Series,
Timedelta,
Timestamp,
)
import pandas._testing as tm
def test_group_shift_with_null_key():
# This test is designed to replicate the segfault in issue #13813.
n_rows = 1200
# Generate a moderately large data... | DataFrame({"a": [1, 2, 2], "b": data}) | pandas.DataFrame |
from pandas import DataFrame, Index, Series
from numpy import ndarray
from lasso.dyna import Binout
from plotly.graph_objects import (
Figure,
Scatter,
Layout
)
class Extended_Binout(Binout):
def read(self, *args):
super().read.__doc__
# automatically sort returned lists for readabil... | Index(time_array, name='time') | pandas.Index |
from matplotlib.axes import Axes
from mpl_format.axes.axis_utils import new_axes
from mpl_format.text.text_utils import wrap_text
from nltk import RegexpTokenizer, WordNetLemmatizer
from nltk.corpus import stopwords
from nltk.tokenize.api import TokenizerI
from pandas import Series, DataFrame, concat
from typing import... | concat([self.data, other.data]) | pandas.concat |
from math import pi
import numpy as np
import sklearn as sk
import scipy as sp
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.base import BaseEstimator, TransformerMixin
from scipy.spatial import distance
import emm
def compute_probs(data, b... | pd.DataFrame({"feature": X}) | pandas.DataFrame |
"""
Copyright 2016 <NAME>, <NAME>, <NAME>, BlackRock 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.isnull(cost) | pandas.isnull |
import pandas as pd
import os
class InputFileGenerator:
def __init__(self, path_to_file):
self.path_to_file = path_to_file
def __fill_in_missing_values(self, df):
list_of_years_to_calculate_for = sorted(
[name for name in list(df.columns) if name.isnumeric()]
)[1:-1]
... | pd.DataFrame([result]) | pandas.DataFrame |
import os
import time
import shutil
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
from torch.nn.utils import clip_grad_norm_
import numpy as np
from config import parser
args = parser.parse_args()
import pickle
from network import Two_Stream_RNN
from... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
from .._utils import color_digits, color_background
from ..data import Data, DataSamples
#from ..woe import WOE
import pandas as pd
#import math as m
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
from matplotlib.col... | pd.read_excel(input_conditions) | pandas.read_excel |
import streamlit as st
import random
import psycopg2
import pandas as pd
import numpy as np
from sqlalchemy import create_engine
from sqlalchemy.types import Integer
from streamlit.report_thread import get_report_ctx
#from streamlit.report_thread import add_script_run_ctx
import pydeck as pdk
from datasets import load_... | pd.DataFrame(data) | pandas.DataFrame |
import argparse
import datetime
import os
import shutil
import unittest
from unittest import mock
import pandas
from matrix.common import date
from matrix.common.request.request_tracker import Subtask
from matrix.common.query.cell_query_results_reader import CellQueryResultsReader
from matrix.common.query.feature_que... | pandas.DataFrame() | pandas.DataFrame |
from scipy.signal import butter, lfilter, resample, firwin, decimate
from sklearn.decomposition import FastICA, PCA
from sklearn import preprocessing
import numpy as np
import pandas as np
import matplotlib.pyplot as plt
import scipy
import pandas as pd
class SpectrogramImage:
"""
Plot spectrogram for each ch... | np.alltrue(ch == 0.0) | pandas.alltrue |
from __future__ import division
from datetime import timedelta
from functools import partial
import itertools
from nose.tools import assert_true
from parameterized import parameterized
import numpy as np
from numpy.testing import assert_array_equal, assert_almost_equal
import pandas as pd
from toolz import merge
fro... | pd.Timestamp('2015-01-20') | pandas.Timestamp |
"""
Functions to create candidate data DataFrames
"""
import pandas as pd
pd.options.mode.chained_assignment = None
def create_df(dictionary):
'''
Functions that converts dictionary into pandas DataFrame
Args:
dictionary: dictionary to be converted into pandas DataFrame
Returns:
crea... | pd.DataFrame.from_dict(dictionary, orient='columns') | pandas.DataFrame.from_dict |
from copy import deepcopy as _deepcopy
import numpy as _np
import pandas as _pd
from scipy import integrate as _integrate
from atmPy.aerosols.size_distribution import moments as _sizedist_moment_conversion
from atmPy.general import timeseries as _timeseries
from atmPy.general import vertical_profile as _vertical_prof... | _pd.Series(extinction_crossection, index=diam) | pandas.Series |
from io import StringIO
from pathlib import Path
import pytest
import pandas as pd
from pandas import DataFrame, read_json
import pandas._testing as tm
from pandas.io.json._json import JsonReader
@pytest.fixture
def lines_json_df():
df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
return ... | DataFrame([[1, 2], [1, 2]], columns=["a", "b"]) | pandas.DataFrame |
import pandas as pd
import numpy as np
from sklearn.ensemble.forest import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, median_absolute_error
from sklearn.preprocessing import MinMaxScaler, StandardScaler
impo... | pd.merge(data_groupby_day_out_Q1,data_groupby_day_in_Q1,on=['Station Name','month','day','hour'],how='outer') | pandas.merge |
import pandas as pd
import numpy as np
from utils import is_number
import settings as conf
class EFO:
UKB_MAP = pd.read_csv(conf.OMIM_SILVER_STANDARD_UKB_EFO_MAP_FILE, sep='\t')
def __init__(self, efo_file):
"""
efo_file in CSV format downloaded from: https://bioportal.bioontology.org/on... | pd.Series(efo_terms) | pandas.Series |
import pandas as pd
import numpy as np
import datetime as dt
from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
import matplotlib.pyplot as plt
import os
import logging
import json
# data directory containing the raw NMIR files
NMIR_DATA_DIR = "data/NMIR"
# savepath for training job outputs... | pd.DataFrame(daywise_rowdata_list, columns=header) | pandas.DataFrame |
#Tools for reading and analysis of data from Schlumberger CTD Divers
from pandas import read_csv
from pandas import concat
from pandas import DataFrame
"""
Functions to read Schlumberger diver logger files.
"""
#read in the CSV file from a CTD diver and return a pandas DataFrame
def readCTD(csvfile):
"""
Rea... | concat(dflist) | pandas.concat |
"""
Test suite for forecasting module.
"""
import pandas as pd
from forecasting import pandas_to_patients
from hospital.people import Patient
PATIENT_LIST = [
Patient(
name="John",
sex="male",
weight=70,
department="surgery",
age=37,
specialty="trauma_and_orthopaedi... | pd.DataFrame(PATIENT_JSON) | pandas.DataFrame |
# coding: utf-8
import pandas as pd
import numpy as np
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource
from bokeh.models import HoverTool, PanTool, WheelZoomTool, BoxSelectTool, TapTool, OpenURL
from bokeh.models import GMapPlot, GMapOptions, Circle, DataRange1d, Range1d
from bokeh.io impor... | pd.read_csv(MAP_DATA_FILE, index_col=None) | pandas.read_csv |
import os
import json
import datetime
import csv
import string
import gensim
from gensim import corpora
from gensim.models.coherencemodel import CoherenceModel
import nltk
from nltk.corpus import words, stopwords, wordnet
from nltk.tokenize import RegexpTokenizer
from nltk.stem import PorterStemmer, WordNetLemmatizer
... | pd.read_csv(file_path, encoding = "utf-8", header=None, sep=delimiter, lineterminator='\n') | pandas.read_csv |
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