prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import os
import sys
import pickle
import pandas as pd
import numpy as np
import word2vec as wv
from tqdm import tqdm
def get_new_dataframe_names(df_path):
file_name = df_path.rsplit('/')[-1]
return 'sentence_' + file_name
def create_sentence_df(df):
sentences = {}
indices = {}
classes = {}
... | pd.Series(noun_class) | pandas.Series |
import detailed_table
import pandas as pd
from locale import atof
import json
# KAP 19
COL_PROFIT_STOCKS = 'Aktien G/V'
COL_DIVIDENDS_STOCKS = 'Aktien Dividende'
COL_PROFIT_CFDS = 'CFD G/V'
COL_FEES_CFDS = 'CFD Gebühren'
# KAP 20
COL_PROFIT_ON_SALE_STOCKS = 'Aktien - Enthaltene Gewinne aus Aktienveräußerungen'
COL_PR... | pd.DataFrame(columns=resultColumns) | pandas.DataFrame |
import pandas as pd
import random
from src.func import tweet_utils
from src.func import regex
from src.func import labmtgen
from src.scripts.process_tweets import *
from labMTsimple.storyLab import *
def get_tweets_timestamp(park_user_tweets):
"""
Take a list of lists (tweets by user) and assigns a random c... | pd.isnull(tweet['ParkID']) | pandas.isnull |
"""Class for creating a Parallel Pipeline."""
from iguanas.exceptions.exceptions import DataFrameSizeError, NoRulesError
from iguanas.pipeline._base_pipeline import _BasePipeline
from iguanas.utils.typing import PandasDataFrameType, PandasSeriesType
from iguanas.utils.types import PandasDataFrame, PandasSeries, Diction... | pd.DataFrame() | pandas.DataFrame |
"""
This script visualises the prevention parameters of the first and second COVID-19 waves.
Arguments:
----------
-f:
Filename of samples dictionary to be loaded. Default location is ~/data/interim/model_parameters/COVID19_SEIRD/calibrations/national/
Returns:
--------
Example use:
------------
"""
__author_... | pd.to_datetime(end_calibration) | pandas.to_datetime |
import pandas as pd
import numpy as np
import lightgbm as lgb
import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import KFold, RepeatedKFold
from scipy import sparse
# 显示所有列
| pd.set_option('display.max_columns', None) | pandas.set_option |
import pandas as pd
import os
import itertools
from collections import defaultdict
"""
Description: This script performs automatic filtering/aggregation of Qualys scans intended for analysis. Reads in a csv file and outputs a csv file.
"""
def main():
root = os.path.dirname(os.path.abspath(__file__))
... | pd.isna(y) | pandas.isna |
# https://github.com/bokeh/bokeh/issues/5701
# https://groups.google.com/a/continuum.io/forum/#!searchin/bokeh/selected/bokeh/ft2U4nX4fVo/srBMki9FAQAJ
import pandas as pd
import sys
import io
import os.path as op
import MetaVisLauncherConfig as config
from bokeh.io import show, output_file, save
from bokeh.embed import... | pd.DataFrame({"Columns from Col Metadata": colmd_names}) | pandas.DataFrame |
try:
from TACT import logger
except ImportError:
pass
from TACT.readers.config import Config
from future.utils import itervalues, iteritems
import pandas as pd
import re
import sys
from string import printable
import numpy as np
class Data(Config):
"""Class to hold data, derivative features, and metadata ... | pd.read_excel(self.input_filename) | pandas.read_excel |
"""
Script for the calculation of A_uv, A_dv & A_g, and the python graph for the xPDFs at initial scale.
The parametrizations were taken from: <NAME>., & <NAME>. (2019). A new simple PDF parametrization: improved description
of the HERA data. The European Physical Journal Plus, 134(10), 531.... | pd.DataFrame({'x$u_{v}$': x_uv, 'x$d_{v}$': x_dv, 'x$\\overline{u}$': x_ubar, 'x$\\overline{d}$': x_dbar, 'x$gl$': xg}, index=x) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 14 16:13:16 2021
@author: nicolasnavarre
"""
import pandas as pd
import math
data = 'data/'
def crop_yield(POM_data, fish_products, meat_products, feed_list, crop_proxie, diet_div_crop, diet_source_crop):
POM_crop_data = POM_data[~POM_data.Ite... | pd.merge(Weighted_item_tot, Weighted_item_ext['% of ext'], left_index = True, right_index = True) | pandas.merge |
'''
__author__=<NAME>
MIT License
Copyright (c) 2020 crewml
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 without limitation the rights
to use, copy, modify, mer... | pd.to_datetime(df['DTY_REP_TM_UTC'], utc=True) | pandas.to_datetime |
from flask import Flask, session, jsonify, request
import pandas as pd
import numpy as np
import pickle
import os
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import json
import glob
#################Load config.json and ... | pd.concat(df_from_each_file, ignore_index=True) | pandas.concat |
from dataExtractor import reviewToList
from dataExtractor200 import dataExtractor200
import numpy as np
import random
"""IMPORTING FILES"""
reviewList_p = dataExtractor200("positive.review")
reviewList_n = dataExtractor200("negative.review")
X_pos_test = reviewList_p[1055:]
X_neg_test = reviewList_n[665:]
"""Y_trai... | pd.DataFrame(cnf_matrix) | pandas.DataFrame |
import numpy as np
import pandas as pd
from sklearn.ensemble import ExtraTreesClassifier
from cause.plotter import Plotter
from cause.predictor import ClassificationSet
class Breakdown():
def __init__(self, data, weights, algos, name):
self.__data = data
self.__weights = weights
self.__... | pd.DataFrame(columns=["order", "value", "name", "error"]) | pandas.DataFrame |
'''Python script to generate Revenue Analysis given ARR by Customer'''
'''Authors - <NAME>
'''
import numpy as np
import pandas as pd
from datetime import datetime
import collections
from .helpers import *
class RevAnalysis:
def __init__(self, json):
print("INIT REV ANALYSIS")
self.arr = pd.Data... | pd.to_datetime(self.rev_cohorts['Cohort']) | pandas.to_datetime |
import pandas as pd
import numpy as np
from datetime import timedelta
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
class spatial_mapping():
def __init__(self, data, gps, gps_utc=0):
df=pd.DataFrame(data)
df[0]=pd.to_datetime(df[0]-693962,unit='D',origin=pd.Timestamp('1900-01-01'),ut... | pd.to_datetime(slice_df['End_time'],utc=True) | pandas.to_datetime |
#!/usr/bin/env python
# -- coding: utf-8 --
# PAQUETES PARA CORRER OP.
import netCDF4
import pandas as pd
import numpy as np
import datetime as dt
import json
import wmf.wmf as wmf
import hydroeval
import glob
import MySQLdb
#modulo pa correr modelo
import hidrologia
from sklearn.linear_model import LinearRegression
... | pd.Timedelta(stepback_start) | pandas.Timedelta |
import pgpasslib
from sqlalchemy import create_engine
import pandas as pd
from pymedextcore.document import Document
from .med import MedicationAnnotator
def get_engine():
password = pgpasslib.getpass('10.172.28.101', 5432, '<PASSWORD>', '<PASSWORD>')
return create_engine(f'postgresql+psycopg2://coronascien... | pd.DataFrame.from_records(omop) | pandas.DataFrame.from_records |
import argparse
import six
from tqdm import tqdm
import pandas as pd
import string, re
from nltk.translate.bleu_score import corpus_bleu
from typing import List, Tuple, Dict, Set, Union
def compute_corpus_level_bleu_score(references: List[str], hypotheses: List[str]) -> float:
""" Given decoding results and refere... | pd.DataFrame(data_list) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 04 18:33:27 2018
@author: Prodipta
"""
import pandas as pd
import datetime as dt
import numpy as np
from pyfolio.utils import extract_rets_pos_txn_from_zipline
from pyfolio.timeseries import perf_stats
from empyrical.stats import cum_returns_final, aggregate_returns
impo... | pd.concat(frames) | pandas.concat |
import numpy as np
import pandas as pd
import os
import argparse
import json
import tensorflow.keras as k
def readData(tumorFileName, normalFileName):
x_true = pd.read_csv(tumorFileName, sep='\t', header=0, index_col=0).T
x_false = pd.read_csv(normalFileName, sep='\t', header=0, index_col=0).T
# if this d... | pd.concat([x_true, x_false]) | pandas.concat |
from surf.script_tab import keytab
from surf.surf_tool import regex2pairs
import os, json, time, re, codecs, glob, shutil
import matplotlib.pyplot as plt
import matplotlib as mpl
import logging.handlers
import pandas as pd
import itertools
import numpy as np
import random
import tensorflow as tf
from sklearn.model_sele... | pd.Series(index=pricepd.index) | pandas.Series |
import pandas as pd
import numpy as np
from scipy.sparse.linalg import svds
from skbio import OrdinationResults
from skbio.stats.composition import clr
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use("ggplot")
def apca(df):
"""Performs Aitchison PCA on a feature table.
Parameters
-----... | pd.Series(p.T, index=cols) | pandas.Series |
import numpy as np
import pandas as pd
from collections import namedtuple
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.cluster import KMeans
from fcutils.maths.geometry import calc_distance_from_point
from fcutils.maths.geometry import calc_distance_between_points_in_a_vector_2d, calc... | pd.DataFrame(bts) | pandas.DataFrame |
from typing import Dict
from typing import Union
import numpy as np
import pandas as pd
import pytest
from etna.datasets import TSDataset
from etna.transforms import ResampleWithDistributionTransform
DistributionDict = Dict[str, pd.DataFrame]
@pytest.fixture
def daily_exog_ts() -> Dict[str, Union[TSDataset, Distri... | pd.date_range(start="2020-01-05", freq="D", periods=3) | pandas.date_range |
#
# Copyright 2021 Grupo de Sistemas Inteligentes, DIT, Universidad Politecnica de Madrid (UPM)
#
# 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... | pd.DataFrame(columns=['id', 'text', 'polarity']) | pandas.DataFrame |
### preprocessing
"""
code is taken from
tunguz - Surprise Me 2!
https://www.kaggle.com/tunguz/surprise-me-2/code
"""
import glob, re
import numpy as np
import pandas as pd
from sklearn import *
from datetime import datetime
import matplotlib.pyplot as plt
data = {
'tra': pd.read_csv('../input/air_visit_data.csv'... | pd.read_csv('../input/sample_submission.csv') | pandas.read_csv |
from IMLearn.utils import split_train_test
from IMLearn.learners.regressors import LinearRegression
from typing import NoReturn
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
pio.templates.default = "simple_white"
def load_data(filename: ... | pd.to_datetime(df.date, errors='coerce') | pandas.to_datetime |
# -*- coding: utf-8 -*-
# pylint: disable=W0612,E1101
from datetime import datetime
import operator
import nose
from functools import wraps
import numpy as np
import pandas as pd
from pandas import Series, DataFrame, Index, isnull, notnull, pivot, MultiIndex
from pandas.core.datetools import bday
from pandas.core.n... | Panel({'Item1': df}) | pandas.core.panel.Panel |
# %% import packages
import numpy as np
import pandas as pd
import itertools
import warnings
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.colors import Normalize
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa.stattools import acf, pacf
from statsmodels.tsa.stattools i... | CategoricalDtype(categories=cats, ordered=True) | pandas.api.types.CategoricalDtype |
"""
GridFrame -- subclass of wx.Frame. Contains grid and buttons to manipulate it.
GridBuilder -- data methods for GridFrame (add data to frame, save it, etc.)
"""
import wx
import pandas as pd
import numpy as np
from dialogs import drop_down_menus3 as drop_down_menus
from dialogs import pmag_widgets as pw
from dialog... | pd.DataFrame.to_clipboard(self.df_slice, header=False, index=False) | pandas.DataFrame.to_clipboard |
# coding=utf-8
"""
PAT - the name of the current project.
instrument.py - the name of the new file which you specify in the New File
dialog box during the file creation.
Hossein - the login name of the current user.
6 / 15 / 18 - the current system date.
8: 03 AM - the current system time.
PyCharm - the name of the IDE... | pandas.DataFrame(new_row) | pandas.DataFrame |
from typing import List
import pandas as pd
import plotly.graph_objs as go
from dash import Dash
import dash_core_components as dcc
import dash_html_components as html
import dash_table
from dash.dependencies import Input, Output
GOOGLE_SHEETS_URL = "https://docs.google.com/spreadsheets/d/e/{}&single=true... | pd.merge(points_data, schedule, on=[id_col, player_col], how="left") | pandas.merge |
# -*- coding: utf-8 -*-
"""
Created on Sat Dec 2 23:17:22 2017
@author: roshi
"""
import pandas as pd
import matplotlib.pyplot as plt
import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objs as go
from app import app
data = | pd.read_csv('./data/youth_tobacco_analysis.csv') | pandas.read_csv |
import pandas as pd
import pyomo.environ as pe
import os
import shutil
class invsys:
def __init__(self,inp_folder='',dshed_cost=1000000,rshed_cost=500,vmin=0.8,vmax=1.2,sbase=100,ref_bus=0):
"""Initialise the investment problem.
:param str inp_folder: The input directory for the data. It expects... | pd.DataFrame(mat) | pandas.DataFrame |
import pytest
from pandas import (
Index,
MultiIndex,
Series,
)
import pandas._testing as tm
class TestSeriesRenameAxis:
def test_rename_axis_mapper(self):
# GH 19978
mi = | MultiIndex.from_product([["a", "b", "c"], [1, 2]], names=["ll", "nn"]) | pandas.MultiIndex.from_product |
"""
inserindo dados com pandas
C - CREATE
R - READ
U - UPDATE
D - DELETE
"""
import pandas as pd
BASE_PATH = 'base.csv'
# CREATE
def post(dados: dict):
df_antigo = pd.DataFrame(get())
df_novo = pd.DataFrame(dados, index=[0])
df = df_antigo.append(df_novo)
df.to_csv(BASE_PATH, sep=',', index=False)
... | pd.DataFrame(lista_dados_novos) | pandas.DataFrame |
import numpy as np
import pandas as pd
from analysis.transform_fast import load_raw_cohort, transform
def test_immuno_group():
raw_cohort = load_raw_cohort("tests/input.csv")
cohort = transform(raw_cohort)
for ix, row in cohort.iterrows():
# IF IMMRX_DAT <> NULL | Select | Next
if pd... | pd.notnull(row["learndis_dat"]) | pandas.notnull |
import json
import datetime
import re
import sys
import ipdb
import pandas as pd
def logging(message):
sys.stderr.write('\r')
sys.stderr.write(message)
sys.stderr.flush()
def clean_company_name(name):
company_token = [
'^\"', '\"$', 'Inc\W', 'Inc$', 'Co\W', 'Co$',
'Corp\W', 'Corp$', '... | pd.read_csv('webhose_data.csv') | pandas.read_csv |
from os import path
import os.path
from datetime import datetime as dt
import datetime
# import plotly.express as px
# from dash.dependencies import Input, Output, State
# import dash_html_components as html
# import dash_core_components as dcc
import json
import pandas as pd
import numpy as np
# from jupyter_dash impo... | pd.DataFrame(df, columns=['code', 'nom', 'color', 'custom_data']) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
# In[2]:
pd.set_option('display.max_rows', 1000)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
# In[3]:
df = pd.read_csv('data.csv')
# ## Gro... | pd.DataFrame(data=top_2_rated_players, columns=cols) | pandas.DataFrame |
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(font_scale=2.2)
plt.style.use("seaborn")
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler, OneHotEncoder
from sklearn.model_selection import StratifiedKFold, train_test_split, ShuffleSplit... | pd.merge(df_train, agg_train, on="idhogar") | pandas.merge |
# -*- coding: utf-8 -*-
"""
These the test the public routines exposed in types/common.py
related to inference and not otherwise tested in types/test_common.py
"""
from warnings import catch_warnings, simplefilter
import collections
import re
from datetime import datetime, date, timedelta, time
from decimal import De... | pd.Timestamp('2011-01') | pandas.Timestamp |
#!/usr/bin/env python
from scipy import interpolate
import numpy as np
from numpy.lib.recfunctions import append_fields
import scipy.signal as sig
import scipy.stats as st
import time, os
import pandas as pd
import math
#import report_ctd
import ctdcal.report_ctd as report_ctd
import warnings
import ctdcal.fit_ctd as f... | pd.Series(ssscc) | pandas.Series |
import numpy as np
import pandas as pd
import logging
DISTANCE_THRESHOLD = 1.4 #: max threshold for distnr
SCORE_THRESHOLD = 0.4 #: max threshold for sgscore
CHINR_THRESHOLD = 2 #: max threshold for chinr
SHARPNR_MAX = 0.1 #: max value for sharpnr
SHARPNR_MIN = -0.13 #: min value for sharpnr
ZERO_MAG = 100. #: d... | pd.DataFrame(frame) | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sys
from sklearn.metrics import mean_squared_error
from math import sqrt
from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt
# 1. 抽取2012年8月至2013年12月的数据，总共14个月
# Index 11856 marks the end of year 2013
df = pd.r... | pd.to_datetime(train.Datetime,format='%d-%m-%Y %H:%M') | pandas.to_datetime |
from collections import OrderedDict
import pydoc
import warnings
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
Index,
Series,
TimedeltaIndex,
date_range,
period_range,
timedelta_range,
)
from pandas.core.arrays impo... | tm.makeIntIndex(10) | pandas.util.testing.makeIntIndex |
""" This file originated from the online analysis project at:
https://github.com/OlafHaag/UCM-WebApp
"""
import itertools
import pandas as pd
import pingouin as pg
import numpy as np
from scipy.stats import wilcoxon
from sklearn.decomposition import PCA
from sklearn.covariance import EllipticEnvelope
... | pd.concat((df_stats, cov, length, df_synergies), axis='columns') | pandas.concat |
from datetime import datetime, timedelta
import dateutil
import numpy as np
import pytest
import pytz
from pandas._libs.tslibs.ccalendar import DAYS, MONTHS
from pandas._libs.tslibs.period import IncompatibleFrequency
from pandas.compat import lrange, range, zip
import pandas as pd
from pandas import DataFrame, Seri... | Series(1, index=expected_index) | pandas.Series |
###########################################################################
# Librairies
import pandas as pd
import os
import unicodedata
###########################################################################
# Fonctions
def remove_accents(input_str):
nfkd_form = unicodedata.normalize('NFKD', input_str)
o... | pd.DataFrame(col_lab,columns=["col_lab7"]) | pandas.DataFrame |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import os
import operator
import unittest
import cStringIO as StringIO
import nose
from numpy import nan
import numpy as np
import numpy.ma as ma
from pandas import Index, Series, TimeSeries, DataFrame, isnull, notnull
from pandas.core.index... | assert_series_equal(aa, ea) | pandas.util.testing.assert_series_equal |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import pytest
import re
from numpy import nan as NA
import numpy as np
from numpy.random import randint
from pandas.compat import range, u
import pandas.compat as compat
from pandas import Index, Series, DataFrame, isn... | tm.assert_frame_equal(result, exp) | pandas.util.testing.assert_frame_equal |
#definition of add_dataset that creates the meta-dataset
import pandas as pd
from pandas.core.dtypes.common import is_numeric_dtype
from scipy.stats import pearsonr
from sklearn.model_selection import train_test_split
from supervised.automl import AutoML
import os
import pandas as pd
from sklearn.preprocessing import L... | is_numeric_dtype(x) | pandas.core.dtypes.common.is_numeric_dtype |
import gzip
import os
import sys
import logging
from collections import defaultdict
import pandas as pd
from dae.utils.regions import Region
logger = logging.getLogger(__name__)
#
# Exon
#
class Exon:
def __init__(
self,
start=None,
stop=None,
frame=None,
number=None,
... | pd.read_csv(filename, sep="\t") | pandas.read_csv |
# Copyright (c) 2018-2021, NVIDIA CORPORATION.
import array as arr
import datetime
import io
import operator
import random
import re
import string
import textwrap
from copy import copy
import cupy
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from numba import cuda
import cudf
from cudf.c... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Quantify the variability of behavioral metrics within and between labs of mouse behavior.
This script doesn't perform any analysis but plots summary statistics over labs.
<NAME>
16 Jan 2020
"""
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
fr... | pd.DataFrame() | pandas.DataFrame |
import itertools
from typing import List, Optional, Union
import numpy as np
import pandas._libs.algos as libalgos
import pandas._libs.reshape as libreshape
from pandas._libs.sparse import IntIndex
from pandas.util._decorators import cache_readonly
from pandas.core.dtypes.cast import maybe_promote
from pandas.core.d... | is_object_dtype(dtype) | pandas.core.dtypes.common.is_object_dtype |
### Old
import csv
import pandas as pd
import numpy as np
import zipfile
import os
from datetime import datetime, timedelta
import pickle
import gzip
import time
import timeit
def find_between( s, first, last ):
try:
start = s.index( first ) + len( first )
end = s.index( last, start )
retur... | pd.DataFrame(daily_data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# Copyright (c) 2015-2020, Exa Analytics Development Team
# Distributed under the terms of the Apache License 2.0
from unittest import TestCase
import h5py
import numpy as np
import pandas as pd
from exatomic import Universe
from exatomic.base import resource
from exatomic.molcas.output import O... | pd.DataFrame(self.uo2sp.atom) | pandas.DataFrame |
"""
Module: libfmp.b.b_annotation
Author: <NAME>, <NAME>
License: The MIT license, https://opensource.org/licenses/MIT
This file is part of the FMP Notebooks (https://www.audiolabs-erlangen.de/FMP)
"""
import numpy as np
import pandas as pd
import librosa
import libfmp.b
def read_csv(fn, header=True, add_label=Fal... | pd.read_csv(fn_in, sep=',', keep_default_na=False, header=None) | pandas.read_csv |
import datetime as dt
import matplotlib.pyplot as plt
import lifetimes
import numpy as np
import os
import pandas as pd
import seaborn as sns
def numcard(x):
return x.nunique(), len(x)
def todateclean(x):
return | pd.to_datetime(x, errors='coerce') | pandas.to_datetime |
"""
PL Modeling Program
-> Python file 1/3: InteractivePLFittingGUI.py (implements the GUI)
Python file 2/3: PLModeling.py (implements the PL emission models)
Python file 3/3: InterferenceFunction.py (implements the interference function models)
Author: <NAME>
Date: March 2021
"""
import matplotlib
matplotlib.use('TkA... | pd.DataFrame(output_dict) | pandas.DataFrame |
#!/usr/bin python3
import pandas as pd
import numpy as np
import datetime as datetime
def changeDate():
base_CSV = | pd.read_csv('../dataset/lpv_d.csv') | pandas.read_csv |
import os
import glob
import datetime as dt
import pandas as pd
from forest import geo
import bokeh.models
class View(object):
def __init__(self, loader):
self.loader = loader
self.source = bokeh.models.ColumnDataSource({
"x": [],
"y": [],
"date": [],
... | pd.concat(frames, ignore_index=True) | pandas.concat |
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import SGDClassifier
import argparse
rate = "0.5" # 默认为6：4的正负样本比例，若要改为1：1则取rate=“0.5”
class SGD:
def __init__(self, trainfile, validfile, testfile):
super(SGD, self).__in... | pd.read_csv(trainfile) | pandas.read_csv |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import pytest
import re
from numpy import nan as NA
import numpy as np
from numpy.random import randint
from pandas.compat import range, u
import pandas.compat as compat
from pandas import Index, Series, DataFrame, isn... | tm.assert_frame_equal(result, exp) | pandas.util.testing.assert_frame_equal |
import pandas as pd
import os
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
cwd = os.getcwd()
print(cwd)
df = | pd.read_csv(cwd + '/' + 'HFI2021.csv') | pandas.read_csv |
"""Functions for plotting system resource use."""
import logging
from datetime import datetime, timedelta
from pathlib import Path
from typing import Optional, Union
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib import gridspec
async def _rea... | pd.concat([user_dataframes[a_user], missing_times_df], ignore_index=True) | pandas.concat |
import pandas as pd
import math
import numpy as np
def matchCheck(colNo, weightl, impactl):
# print(colNo)
# print(weightl)
# print(impactl)
if colNo != weightl or colNo != impactl or impactl != weightl:
raise Exception("Number of weights, number of impacts and number of columns (from 2nd to ... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable=E1101,E1103,W0232
import os
import sys
from datetime import datetime
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
import pandas.compat as compat
import pandas.core.common as com
import pandas.util.testing as tm
from pandas import (Categor... | pd.Categorical(idx) | pandas.Categorical |
"""Covid Model"""
__docformat__ = "numpy"
import warnings
import pandas as pd
import numpy as np
global_cases_time_series = (
"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_"
"covid_19_time_series/time_series_covid19_confirmed_global.csv"
)
global_deaths_time_series... | pd.read_csv(global_cases_time_series) | pandas.read_csv |
from bs4 import BeautifulSoup
import requests
import pandas as pd
import re
def ensure_string_columns(df):
newcols = []
for col in df.columns:
strcol = str(col)
if strcol[0]=="(": #if it's a tuple, instead of a string
a = strcol
b = a[a.find("("):a.find(",")]
c = b[1:]
d = c.replace("'","")
strco... | pd.DataFrame(table) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 30 01:01:33 2017
@author: Flamingo
"""
#%%
from bs4 import BeautifulSoup
import urllib
import pandas as pd
import numpy as np
CITY_NAME = pd.read_csv('CITY_NAME2.csv')
PORT_NAME = CITY_NAME[['AIRPORT_CODE','PORT']].groupby('AIRPORT_CODE',as_index=False).count()
for ind,v... | pd.DataFrame(Port, columns=['Port']) | pandas.DataFrame |
from collections import (
abc,
deque,
)
from decimal import Decimal
from warnings import catch_warnings
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
PeriodIndex,
Series,
concat,
date_range,
)
import pandas._testing as tm
fr... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
import pandas as pd
from argparse import ArgumentParser
def save_new_labels(df_labels: pd.DataFrame, filename="labels_concatenated.csv"):
df_labels.to_csv(filename, index_label="img_name")
def main(args):
first_labels_path = args.first_labels_path
second_labels_path = args.second_labels_path
df_fir... | pd.read_csv(first_labels_path, index_col="img_name") | pandas.read_csv |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from plotly.offline import plot,iplot
from scipy.stats import norm, kurtosis
import os
from scipy.signal import butter, lfilter, freqz
from scipy import signal
from ... | pd.concat([acc_df,gyro_df],1) | pandas.concat |
"""
Copyright (c) 2021, Electric Power Research Institute
All rights reserved.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice,
this li... | pd.DataFrame(index=new_index) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import pytest
import re
from numpy import nan as NA
import numpy as np
from numpy.random import randint
from pandas.compat import range, u
import pandas.compat as compat
from pandas import Index, Series, DataFrame, isn... | u(' bb') | pandas.compat.u |
"""
Usage:
aggregate-makespan.py -i FOLDER [--output FOLDER] [--start-run INT] [--end-run INT]
Required Options:
-i FOLDER --input FOLDER where the experiments are
Options:
-o FOLDER --output FOLDER where the output should go
[default: input]
--start-run INT ... | pd.DataFrame() | pandas.DataFrame |
import pandas as p#导入目前所需要的库并给与简称
data_train = '../homework/train.csv' #查看基本数据
data_train = p.read_csv(data_train)#导入训练模型
print(data_train.info())#查看数据类型
print(data_train.describe())#粗略查看基本数据
###导入并且查看原始数据
import matplotlib.pyplot as pt
import numpy as n
pt.rcParams['font.sans-serif']=['Simhei'] #解决... | p.get_dummies(data_test[['Cabin','Sex','Embarked','Pclass']]) | pandas.get_dummies |
"""Miscellaneous internal PyJanitor helper functions."""
import fnmatch
import functools
import os
import re
import socket
import sys
import warnings
from collections.abc import Callable as dispatch_callable
from itertools import chain, combinations
from typing import (
Callable,
Dict,
Iterable,
List,
... | pd.MultiIndex.from_arrays([mapping, outcome]) | pandas.MultiIndex.from_arrays |
"""
Module parse to/from Excel
"""
# ---------------------------------------------------------------------
# ExcelFile class
import abc
from datetime import date, datetime, time, timedelta
from distutils.version import LooseVersion
from io import UnsupportedOperation
import os
from textwrap import fill
import warnings... | is_integer(sheet_name) | pandas.core.dtypes.common.is_integer |
import time
import os
import shutil
import sys
from sys import argv
import pickle
import csv
from collections import defaultdict
import cProfile
import pstats
import pandas as pd
import numpy as np
from sklearn.metrics import roc_auc_score
from sklearn.pipeline import FeatureUnion, Pipeline
from sklearn.preprocessin... | pd.DataFrame(dt_train_encoded) | pandas.DataFrame |
import timeit
import pandas as pd
import numpy as np
from typing import Dict,List
loops=1000
inputfile:List[List[int]] = [[1,2,3,4,5,6] for x in range(0,1000)]
# input arrives as a list of row lists
# need to make columns
#######################
# zip
# 60us
def i1() -> List[int]:
return list(map(list, zip(*inpu... | pd.DataFrame(y2) | pandas.DataFrame |
import logging
import math
import os
import sys
import geopandas as gpd
import numpy as np
import pandas as pd
from _helpers import _sets_path_to_root
from _helpers import _to_csv_nafix
from _helpers import configure_logging
# from shapely.geometry import LineString, Point, Polygon
# from osm_data_config import AFRIC... | pd.concat([df_lines, df_cables]) | pandas.concat |
#!/usr/bin/env python3
#
# Copyright, <NAME> 2020
#
# MIT License
# 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 without limitation the rights
# to use, copy, ... | pd.Series([truth_path, pred_path] + pc_iou, index=iou_df.columns) | pandas.Series |
'''
Author: <NAME>
Utilities to summarize the outputs produced by the model i.e. the results.txt files spitted out by the
evaluation scripts.
'''
import os
from sys import path
import re
import pandas as pd
import numpy as np
from scipy.stats import ttest_rel
# res will need to be passed to the last function, as an... | pd.Index(index_list_verbose, name='labels') | pandas.Index |
from functools import lru_cache
from pyiso import client_factory
from datetime import datetime, timedelta
from funcy import compose, identity, retry
from itertools import repeat
from urllib.error import HTTPError
import pandas as pd
import numpy as np
from app.model import RENEWABLES, NON_RENEWABLES
from app.util impo... | pd.Timedelta("1s") | pandas.Timedelta |
'''
File: HAPT_Dataset.py
Author: <NAME>
Date: 03/10/2019
Version: 1.0
Description:
utility functions to load the
Human Activities and Postural Transitions (HAPT) dataset
'''
import numpy as np
import pandas as pd
from os.path import expanduser
from keras.utils import to_categorical
from multiprocessing import P... | pd.concat([filtered_df,data_uuid], ignore_index=True) | pandas.concat |
import os
import pandas
import numpy
from ... import normalize
from ... import convert
def hellinger(aves, stds):
"""
Computes pairwise Hellinger distances between Gaussian distributions
from lists of the means and standard deviations.
Args:
aves (numpy array): list of means (length n)
... | pandas.DataFrame() | pandas.DataFrame |
"""Tradingview model"""
__docformat__ = "numpy"
import requests
from tradingview_ta import TA_Handler
import pandas as pd
from gamestonk_terminal import config_terminal as cfg
INTERVALS = {
"1m": "1 min",
"5m": "5 min",
"15m": "15 min",
"1h": "1 hour",
"4h": "4 hours",
"1d": "1 day",
"1W"... | pd.DataFrame() | pandas.DataFrame |
import datetime
import logging
import json
import requests
from pandas import json_normalize
import pandas as pd
from google.cloud import storage
from anyway.parsers.waze.waze_db_functions import (
insert_waze_alerts,
insert_waze_traffic_jams,
enrich_waze_alerts_ended_at_timestamp,
enrich_waze_traffic_... | json_normalize(waze_jams) | pandas.json_normalize |
from __future__ import division, print_function, absolute_import
import numpy as np
import matplotlib.pyplot as plt
import streamlit as st
import pandas as pd
import seaborn as sns
import random
from sklearn.model_selection import RepeatedKFold, train_test_split, cross_val_score, StratifiedKFold, RepeatedStratifiedKFo... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
"""
coding=utf-8
Code Template
"""
from emblaze import app
import logging
import os
import pandas
import textract
from emblaze.ResumeParser.bin import lib
from emblaze.ResumeParser.bin import field_extraction
import spacy
def main():
"""
Main function documentation template
:retu... | pandas.DataFrame(data=candidate_file_agg, columns=['file_path']) | pandas.DataFrame |
import scipy.signal
import pandas as pd
import numpy as np
import peakutils
from lmfit import models
import chachifuncs as ccf
import os
import glob
################################
# OVERALL Wrapper Function
################################
def ML_generate(import_filepath):
"""Generates a dataframe containing c... | pd.concat([desc, desc_df], ignore_index=True) | pandas.concat |
import json
from datetime import datetime
import pandas as pd
def get_forecast():
first_row = True
nrow = 0
with open('data/tmp/forecast_weather.json') as f:
for line in f:
print(nrow)
nrow += 1
res = json.loads(line)
timezone_offset = res['city']['timezone']
first_line = Tru... | pd.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
# Input arguments flag
import sys
sys.path.append('..')
_, *flag = sys.argv
# Parse arguments
import argparse
parser = argparse.ArgumentParser(prog='hs_check', description='Check phase synchronization for selected BPMs and plane.')
parser.add_argument('-p', '--plane', choices=('x', 'y'), help='data plane', default='x'... | pandas.DataFrame() | pandas.DataFrame |
"""Utilities for parsing corpus tsv files into pandas DataFrames."""
import logging
from glob import glob
from pathlib import Path
from typing import Dict, Iterable, Union
import pandas as pd
from tqdm import tqdm
import harmonic_inference.utils.corpus_utils as cu
from harmonic_inference.data.corpus_constants import ... | pd.DataFrame(files_dict) | pandas.DataFrame |
import pysam
import pandas as pd
import numpy as np
import re
import os
import sys
import collections
import scipy
from scipy import stats
import statsmodels
from statsmodels.stats.multitest import fdrcorrection
try:
from . import global_para
except ImportError:
import global_para
try:
from .consensus_seq ... | pd.concat(list_df_transcript_merge) | pandas.concat |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.