prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 22 11:05:21 2018
@author: 028375
"""
from __future__ import unicode_literals, division
import pandas as pd
import os.path
import numpy as np
def Check2(lastmonth,thismonth,collateral):
ContractID=(thismonth['ContractID'].append(lastmonth['ContractID'])).append(coll... | pd.to_datetime('2017-12-22') | pandas.to_datetime |
'''
MIT License
Copyright (c) [2018] [<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 without limitation the
rights to use, copy, modify, merge, publish, di... | pd.unique(feature) | pandas.unique |
# -*- coding: utf-8 -*-
# Example package with a console entry point
"""Reads and formats data from the SWMM 5 output file."""
from __future__ import absolute_import, print_function
import copy
import datetime
import os
import struct
import sys
import warnings
from builtins import object, range, str, zip
import mand... | pd.Series(values, index=dates) | pandas.Series |
''' An experiment testing linearly interpolating the predictions of the MIMIC and HIRID models for fine-tuning'''
import argparse
import ipdb
import random
import os
import os.path
import pickle
import csv
import glob
import sys
import matplotlib
matplotlib.use("Agg")
matplotlib.rcParams['pdf.fonttype'] = 42
import m... | pd.DataFrame(df_out_dict) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pymysql
import pandas as pd
import datetime
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os
import matplotlib.ticker as tck
import matplotlib.font_manager as fm
import math as m
import matplotlib.dates as mdates
im... | pd.Grouper(freq="H") | pandas.Grouper |
from pickle import loads, dumps
import numpy as np
import pandas as pd
from classicML import _cml_precision
from classicML import CLASSICML_LOGGER
from classicML.api.models import BaseModel
from classicML.backend import get_conditional_probability
from classicML.backend import get_dependent_prior_probability
from cla... | pd.Series(y) | pandas.Series |
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 datetime
from pandas.core import series
import pytz
import os
import pathlib
import csv
import math
import urllib.request
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
population_total = 32657400
# Get the current generation time in MYT timezone
timeZ_My = pytz.timezone('Asi... | pd.read_csv(url) | pandas.read_csv |
import os, sys
from numpy.lib.function_base import copy
import cv2
import numpy as np
import pandas as pd
import torch as th
from stable_baselines3.common.utils import get_device
from kairos_minerl.gail_wrapper import (
ActionShaping_FindCave,
ActionShaping_Waterfall,
ActionShaping_Animalpen,
ActionSh... | pd.concat([odometry_log_df, action_log_df], axis=1) | pandas.concat |
import json
import os
import pandas
from tools.dataset_tool import dfs_search
data_path = "../input/"
recursive = False
file_list = []
file_list = file_list + dfs_search(os.path.join(data_path, ''), recursive)
file_list = [file for file in file_list if 'train' in file]
file_list.sort()
rawinput = []
for filename in ... | pandas.DataFrame(columns=["q","a","r"]) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 22 15:48:30 2020
@author: <NAME>
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import random
from ventiliser.BreathVariables import BreathVariables
class Evaluation:
"""
Class to help visualise and evaluate bre... | pd.DataFrame(output) | pandas.DataFrame |
#!/usr/bin/python3
import json
from SPARQLWrapper import SPARQLWrapper, POST
import psycopg2
import pandas
def main():
conn = psycopg2.connect(database='htsworkflow', host='felcat.caltech.edu')
#total = 0
#total += display_subclass_tree('http://purl.obolibrary.org/obo/UBERON_0001134', conn=conn)
#to... | pandas.DataFrame(tables) | pandas.DataFrame |
import pandas as pd
import numpy as np
from pathlib import Path
from datetime import datetime as dt
def mergeManagers(managers, gameLogs):
#Sum up doubled data
managers = managers.groupby(['yearID','playerID'], as_index=False)['Games','Wins','Losses'].sum()
#Get visiting managers
visitingManagers ... | pd.to_datetime(homeManagers['Date']) | pandas.to_datetime |
import pandas as pd
import networkx as nx
import os
import csv
import matplotlib.pyplot as plt
from networkx.algorithms.community import k_clique_communities
import pygraphviz
from networkx.drawing.nx_agraph import graphviz_layout
from itertools import groupby
import numpy as np
from nxviz import CircosPlot
from nxviz.... | pd.read_csv('phys_networks.csv', usecols=[0]) | pandas.read_csv |
# coding: utf-8
import numpy as np
import pandas as pd
import mplleaflet
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import rc
import matplotlib.dates as mdates
from matplotlib.dates import DateFormatter
# from matplotlib.ticker import FixedLocator, LinearLocator, FormatStrFormatter
# impor... | pd.read_csv('fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv') | pandas.read_csv |
import datetime as dt
import pandas as pd
from .. import AShareDataReader, DateUtils, DBInterface, utils
from ..config import get_db_interface
class IndustryComparison(object):
def __init__(self, index: str, industry_provider: str, industry_level: int, db_interface: DBInterface = None):
if not db_interf... | pd.concat([holding_industry, index_industry], axis=1, sort=True) | pandas.concat |
import pandas as pd
import pickle
from sklearn.linear_model import Lasso
from xgboost import XGBRegressor
from sklearn.ensemble import RandomForestRegressor as RFR
from hyperopt import hp, fmin, tpe, STATUS_OK
from sklearn.model_selection import cross_val_score
def lasso_regression(X_train, y_train, X_test, y_test, ... | pd.DataFrame({'predicted_density': y_train_predicted}) | pandas.DataFrame |
import numpy as np
import pandas as pd
import torch
import torch.optim as optim
from train import train, loss_func, test
from model import NN, CNN
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import Ridge, Lasso
from sklearn.ensemble import RandomForestRegre... | pd.read_csv('dataset/%s.csv'%f_name0) | pandas.read_csv |
import os
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from .. import read_sql
@pytest.fixture(scope="module") # type: ignore
def postgres_url() -> str:
conn = os.environ["POSTGRES_URL"]
return conn
@pytest.mark.xfail
def test_on_non_select(postgres_url: str) -> None:
... | assert_frame_equal(df, expected, check_names=True) | pandas.testing.assert_frame_equal |
"""
Contains various methods used by Corpus components
"""
import pandas as pd
def get_utterances_dataframe(obj, selector = lambda utt: True,
exclude_meta: bool = False):
"""
Get a DataFrame of the utterances of a given object with fields and metadata attributes,
with an opt... | pd.DataFrame(ds) | pandas.DataFrame |
#########################################################################################################
# @Author: --
# @Description: Retrieve Overpass data for specific key-values and create GeoJSON files
# @Usage: Create GeoJSON data for specific key value tags from OSM
#############################################... | pd.read_csv('data/osm/osm_key_values_additional.csv') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 4 14:42:02 2018
@author: rwilson
"""
import pandas as pd
import numpy as np
import scipy.linalg as linalg
import random
import os
import h5py
import matplotlib.pyplot as plt
import itertools
from numba import njit
from numba import prange
import o... | pd.DataFrame({'src': src, 'rec': rec}) | pandas.DataFrame |
import pandas as pd
from collections import defaultdict
from urllib.parse import urlparse
import math
df = pd.read_csv('Final_newData_withFeatures.csv')
urls = df['0']
entropies = []
for index, url in enumerate(urls):
domain=""
if url[:4] == 'http':
domain = urlparse(url).netloc
else:
... | pd.Series(entropies) | pandas.Series |
#!/usr/bin/env python
# coding: utf-8
# import libraries
import numpy as np
import pandas as pd
import streamlit as st
import plotly as pt
import matplotlib.pyplot as plt
from collections import Counter
import seaborn as sns
#import pandas_profiling as pf
import plotly.express as px
import plotly.graph_objects as go
sn... | pd.DataFrame(data=data,index=data_index,columns=data_cols) | pandas.DataFrame |
import numpy as np
import pandas as pd
import h5py
import os
import math
import pickle
from datetime import timedelta
from modules.image_processor import cart2polar
def remove_outlier_and_nan(numpy_array, upper_bound=1000):
numpy_array = np.nan_to_num(numpy_array, copy=False)
numpy_array[numpy_array > upper_b... | pd.read_hdf(file_path, key='info', mode='r') | pandas.read_hdf |
from sklearn import svm, datasets
import sklearn.model_selection as model_selection
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
import numpy as np
import matplotlib.pyplot as plt
import glob
import cv2
import os
import seaborn as sns
import pandas as pd
import sys
from skimage.filter... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
import datetime
import sys
import time
import xgboost as xgb
from add_feture import *
FEATURE_EXTRACTION_SLOT = 10
LabelDay = datetime.datetime(2014,12,18,0,0,0)
Data = pd.read_csv("../../../../data/fresh_comp_offline/drop1112_sub_item.csv")
Data['daystime'] = Data['days'].map(lam... | pd.merge(test,d,on=['user_id','item_category'],how='left') | pandas.merge |
import os
from pathlib import Path
import pandas as pd
import requests
class OisManager:
TOIS_CSV_URL = 'https://exofop.ipac.caltech.edu/tess/download_toi.php?sort=toi&output=csv'
CTOIS_CSV_URL = 'https://exofop.ipac.caltech.edu/tess/download_ctoi.php?sort=ctoi&output=csv'
KOIS_LIST_URL = 'https://exofop.... | pd.read_csv(self.tois_csv) | pandas.read_csv |
import numpy as np
import gdax
import json
import logging
from os.path import expanduser
import pandas as pd
from backfire.bots import bot_db
logger = logging.getLogger(__name__)
def load_gdax_auth(test_bool):
home = expanduser("~")
if test_bool == True:
gdax_auth = json.load(open(f'{home}/auth/gdax_s... | pd.merge(fills_df, aff, how='left', left_on='order_id', right_on='order_id') | pandas.merge |
#!/usr/bin/env python3
# coding: utf-8
import argparse
import json
import logging
import numpy as np
import os
import pandas as pd
import time
from dart_id.align import align
from dart_id.converter import process_files
from dart_id.exceptions import ConfigFileError
from dart_id.fido.BayesianNetwork import run_interna... | pd.isnull(df_out['razor_protein_fdr']) | pandas.isnull |
"""
Prepare sample split
Created on 04/10/2020
@author: RH
"""
import os
import pandas as pd
import numpy as np
def set_sep(path, cut=0.3):
trlist = []
telist = []
valist = []
pos = | pd.read_csv('../COVID-CT-MetaInfo.csv', header=0, usecols=['image', 'patient']) | pandas.read_csv |
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import numpy as np
from plotly.subplots import make_subplots
from pathlib import Path
repo_dir = Path(__file__).parent.parent
outputdir = repo_dir/'output'
outputdir.mkdir(parents=True, exist_ok=True)
casos = pd.read_csv('https://raw.git... | pd.read_csv('https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto5/TotalesNacionales_T.csv') | pandas.read_csv |
# ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# --------------------------------------------... | pd.DataFrame({'foo': [22, 22, 0]}) | pandas.DataFrame |
import random
import os
import pandas as pd
from datetime import timedelta
from logbook import TestHandler
from pandas.util.testing import assert_frame_equal
from catalyst import get_calendar
from catalyst.exchange.exchange_asset_finder import ExchangeAssetFinder
from catalyst.exchange.exchange_data_portal import Dat... | pd.set_option('precision', 8) | pandas.set_option |
import numpy as np
import pandas as pd
a = np.arange(4)
print(a)
# [0 1 2 3]
s = pd.Series(a)
print(s)
# 0 0
# 1 1
# 2 2
# 3 3
# dtype: int64
index = ['A', 'B', 'C', 'D']
name = 'sample'
s = pd.Series(data=a, index=index, name=name, dtype='float')
print(s)
# A 0.0
# B 1.0
# C 2.0
# D 3.0
# Na... | pd.DataFrame(a) | pandas.DataFrame |
import numpy as np
import pytest
import pandas as pd
from pandas.core.sparse.api import SparseDtype
@pytest.mark.parametrize("dtype, fill_value", [
('int', 0),
('float', np.nan),
('bool', False),
('object', np.nan),
('datetime64[ns]', pd.NaT),
('timedelta64[ns]', pd.NaT),
])
def test_inferred... | SparseDtype(int, 0.0) | pandas.core.sparse.api.SparseDtype |
import numpy as np
import pytest
from pandas.compat import lrange
import pandas as pd
from pandas import Series, Timestamp
from pandas.util.testing import assert_series_equal
@pytest.mark.parametrize("val,expected", [
(2**63 - 1, 3),
(2**63, 4),
])
def test_loc_uint64(val, expected):
# see gh-19399
... | Timestamp('2011-01-03', tz='US/Eastern') | pandas.Timestamp |
#!/usr/bin/env python
import time
import math
import re
import pandas as pd
from pathlib import Path
import numpy as np
import subprocess
from difflib import unified_diff, Differ
from mirge.libs.miRgeEssential import UID
from mirge.libs.bamFmt import sam_header, bow2bam, createBAM
from mirge.libs.mirge2_tRF_a2i import ... | pd.DataFrame.from_dict(pre_summary) | pandas.DataFrame.from_dict |
#
# DATA EXTRACTED FROM:
#
# FREIRE, F.H.M.A; <NAME>; <NAME>. Projeção populacional municipal
# com estimadores bayesianos, Brasil 2010 - 2030. In: <NAME> (coord.).
# Seguridade Social Municipais. Projeto Brasil 3 Tempos. Secretaria Especial
# de Assuntos Estratégicos da Presidência da República (SAE/SG/PR) , United
#... | pd.concat([female, male], axis=1) | pandas.concat |
# -*- 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... | assert_frame_equal(a.ix[:, 22, [111, 333]], b) | pandas.util.testing.assert_frame_equal |
# The analyser
import pandas as pd
import matplotlib.pyplot as plt
import dill
import os
import numpy as np
from funcs import store_namespace
from funcs import load_namespace
import datetime
from matplotlib.font_manager import FontProperties
from matplotlib import rc
community = 'ResidentialCommunity'
sim_ids = ['M... | pd.DataFrame.from_dict(emutemps[sim_id],orient='index') | pandas.DataFrame.from_dict |
"""Unittests for the `methods` module."""
import unittest
import pandas as pd
from pandas_data_cleaner import strategies
class TestRemoveDuplicates(unittest.TestCase):
"""Unittests for the `RemoveDuplicates` class."""
def test_invalid_options(self):
"""Test that when no options are provided, the `ca... | pd.DataFrame({'a': [1, 2, 3]}) | pandas.DataFrame |
import sys
import time
import requests
import pandas as pd
import os
import numpy as np
name_dicc = {
'208': 'Energy (Kcal)',
'203': 'Protein(g)',
'204': 'Total Lipid (g)',
'255': 'Water (g)',
'307': 'Sodium(mg)',
'269': 'Total Sugar(g)',
'291': 'Fiber(g)',
'301': 'Calcium(mg)',
'3... | pd.DataFrame(arr) | pandas.DataFrame |
# 生成xml标注文件
import pandas as pd
from PIL import Image
data = pd.read_csv('data/train_labels.csv')
del data['AB']
data['temp'] = data['ID']
def save_xml(image_name, name_list, xmin_list, ymin_list, xmax_list, ymax_list):
xml_file = open('data/train_xml/' + image_name.split('.')[-2] + '.xml', 'w')
image_name = ... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Retrieve bikeshare trips data."""
# pylint: disable=invalid-name
import os
import re
from glob import glob
from typing import Dict, List
from zipfile import ZipFile
import pandas as pd
import pandera as pa
import requests
from src.utils import log_prefect
trips_... | pd.read_parquet(parquet_data_filepath) | pandas.read_parquet |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.compose import ColumnTra... | pd.DataFrame(df_prep, index=[0]) | pandas.DataFrame |
# author: <NAME>, <NAME>
# date: 2020-01-22
'''This script reads in 5 .csv files located in the <file_path_data> folder:
1. All accepted vanity plates
2. All rejected vanity plates
3. Combined rejected and undersampled rejected plates
4. Feature training data
5. Targ... | pd.read_csv(file_path_raw + rejected_plates_csv) | pandas.read_csv |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
from pytz import FixedOffset, timezone, utc
from random import randint
from enum import Enum
from sqlalchemy import create_engine, DateTime
from datetime import datet... | pd.concat([df_hourly_copy, df_all_drivers]) | pandas.concat |
import sys
import random as rd
import matplotlib
#matplotlib.use('Agg')
matplotlib.use('TkAgg') # revert above
import matplotlib.pyplot as plt
import os
import numpy as np
import glob
from pathlib import Path
from scipy.interpolate import UnivariateSpline
from scipy.optimize import curve_fit
import pickle
import pandas... | pd.read_pickle(dir_paths[0]) | pandas.read_pickle |
import boto3
import logging, os
import pandas as pd
from botocore.exceptions import ClientError
s3 = boto3.client('s3')
def upload_file(file_name, bucket, object_name=None):
# If S3 object_name was not specified, use file_name
if object_name is None:
object_name = os.path.basename(file_name)
try... | pd.merge(linked_df,linked_table_df,how='left',left_on=linked_field_name,right_on=linked_table_df.columns[1]) | pandas.merge |
from __future__ import division
from functools import wraps
import pandas as pd
import numpy as np
import time
import csv, sys
import os.path
import logging
from .ted_functions import TedFunctions
from .ted_aggregate_methods import TedAggregateMethods
from base.uber_model import UberModel, ModelSharedInputs
class Te... | pd.Series([], dtype="float", name="dbt_mamm_sub_direct_wgt") | pandas.Series |
from __future__ import print_function
import os
import pandas as pd
import xgboost as xgb
import time
import shutil
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
import numpy as np
from sklearn.utils import shuffle
def archive_results(filename,results,algo,script):
"""
... | pd.read_csv('../features/surgical_procedure_type_code_counts_train.csv.gz') | pandas.read_csv |
"""Class to read and store all the data from the bucky input graph."""
import datetime
import logging
import warnings
from functools import partial
import networkx as nx
import pandas as pd
from joblib import Memory
from numpy import RankWarning
from ..numerical_libs import sync_numerical_libs, xp
from ..util.cached_... | pd.DataFrame(df) | pandas.DataFrame |
#
# Copyright © 2021 Uncharted Software 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 l... | pd.DataFrame.sparse.from_spmatrix(word_features) | pandas.DataFrame.sparse.from_spmatrix |
# Import libraries
import os
import sys
import anemoi as an
import pandas as pd
import numpy as np
import pyodbc
from datetime import datetime
import requests
import collections
import json
import urllib3
def return_between_date_query_string(start_date, end_date):
if start_date != None and end_date != None:
... | pd.concat([incoming, outgoing], axis=1) | pandas.concat |
import logging
import numpy as np
import copy
import pandas as pd
from juneau.utils.utils import sigmoid, jaccard_similarity
from juneau.search.search_prov_code import ProvenanceSearch
class Sorted_State:
def __init__(self, query, tables):
self.name = query.name # the query name
self.tables = tabl... | pd.DataFrame([pair[1] for pair in ub1], index = [pair[0] for pair in ub1], columns = ["score"]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
import random
import numpy as np
import pandas as pd
from pandas.compat import lrange
from pandas.api.types import CategoricalDtype
from pandas import (DataFrame, Series, MultiIndex, Timestamp,
date_range, NaT, IntervalIn... | assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
'''
Created on 17.04.2018
@author: malte
'''
import numpy as np
import pandas as pd
class SAGH:
def __init__(self, normalize=False, item_key='track_id', artist_key='artist_id', session_key='playlist_id', return_num_preds=500):
self.item_key = item_key
self.artist_key = artist_key
self.... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2020-05-13 16:48
# @Author : NingAnMe <<EMAIL>>
import os
import sys
import argparse
from numpy import loadtxt
from numpy import cos as np_cos
from numpy import sin as np_sin
from numpy import radians, arcsin, rad2deg, cumsum
from numpy import ones
from numpy... | read_excel(infile) | pandas.read_excel |
"""
Created on June 6, 2016
@author: <NAME> (<EMAIL>)
Updated Nov 21, 2017 by <NAME> (github.com/Spenca)
"""
import csv
import os, sys, io
import re
import pandas as pd
import numpy as np
import requests
import yaml
from string import Template
from collections import OrderedDict
from datetime import date, datetime, ... | pd.DataFrame(newl) | pandas.DataFrame |
from collections import OrderedDict
from datetime import datetime, timedelta
import numpy as np
import numpy.ma as ma
import pytest
from pandas._libs import iNaT, lib
from pandas.core.dtypes.common import is_categorical_dtype, is_datetime64tz_dtype
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
Da... | pd.Series(values, dtype=dtype) | pandas.Series |
# -*- coding: utf-8 -*-
# Copyright (c) 2019 by University of Kassel, T<NAME>, RWTH Aachen University and Fraunhofer
# Institute for Energy Economics and Energy System Technology (IEE) Kassel and individual
# contributors (see AUTHORS file for details). All rights reserved.
import numpy as np
import pandas as pd
impo... | pd.concat([reserved_strings, series], ignore_index=True) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
# ### Import necessary libraries
# In[1]:
# Data representation and computation
import pandas as pd
import numpy as np
pd.options.display.float_format = '{:20,.4f}'.format
# plotting
import matplotlib.pyplot as plt
import seaborn as sns
# Data splitting and pipeline for... | pd.DataFrame(models, index=['Accuracy', 'Precision', 'Recall', 'F1-Score']) | pandas.DataFrame |
import pandas as pd
from pandas._testing import assert_frame_equal
import pytest
import numpy as np
from scripts.normalize_data import (
remove_whitespace_from_column_names,
normalize_expedition_section_cols,
remove_bracket_text,
remove_whitespace,
ddm2dec,
remove_empty_unnamed_columns,
nor... | pd.DataFrame(data) | pandas.DataFrame |
import numpy as np
import pandas as pd
from scipy.integrate import odeint
def append_df(df, ret, t, nivel_isolamento):
"""
Append the dataframe
:param df: dataframe to be appended
:param ret: solution of the SEIR
:param t: time to append
:param nivel_isolamento: string "without is... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import os
dataset_dir = 'D:/Downloads/test'
csv_origin = './example.csv'
csv_unet = './unet.csv'
csv_submit = './rle_submit.csv'
def generate_final_csv(df_with_ship):
print("最终提交版本 : %d instances, %d images" %(df_with_ship.shape[0], len(get_im_list(df_with_ship))))
im_n... | pd.read_csv(csv_origin) | pandas.read_csv |
import pandas as pd
from pandas import DataFrame
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import f_regression
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR, LinearSVR
from metalfi.src.data.dataset ... | pd.concat([X_d, X_f], axis=1) | pandas.concat |
# installed
import pandas as pd
import numpy as np
import talib
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import ParameterGrid
import matplotlib.pyplot as plt
# custom
import data_processing as dp
def load_stocks_calculate_short_corr():
dfs, sh_int, fin_sh = dp.load_stocks(s... | pd.concat(all_sh_stocks) | pandas.concat |
"""
The SamplesFrame class is an extended Pandas DataFrame, offering additional methods
for validation of hydrochemical data, calculation of relevant ratios and classifications.
"""
import logging
import numpy as np
import pandas as pd
from phreeqpython import PhreeqPython
from hgc.constants import constants
from hgc... | pd.Series(index=df.index, dtype='object') | pandas.Series |
# -*- coding: utf-8 -*-
import os
import argparse
import datetime
import pandas as pd
from pyspark.sql import functions as f
from src.spark_session import spark
import setting
from src.utils import log_config, utils
logger = log_config.get_logger(__name__)
def ingest_raw_csv(raw_csv_filename=setting.nyc_raw_csv_filen... | pd.to_datetime(setting.test_date_end) | pandas.to_datetime |
#!/usr/bin/env python3
# add rgb shading value based on the relative abundances of all pb transcripts
# of a gene
# %%
import pandas as pd
import math
import argparse
# examine all pb transcripts of a gene, determine rgb color
def calculate_rgb_shading(grp):
"""
Examine CPM for all PB transc
ripts of a ... | pd.merge(bed, shaded, how='left', on='acc_full') | pandas.merge |
"""
Procedures for fitting marginal regression models to dependent data
using Generalized Estimating Equations.
References
----------
<NAME> and <NAME>. "Longitudinal data analysis using
generalized linear models". Biometrika (1986) 73 (1): 13-22.
<NAME> and <NAME>. "Longitudinal Data Analysis for Discrete and
Contin... | pd.DataFrame(exog_out, columns=xnames) | pandas.DataFrame |
# County Housing Vacancy Raw Numbers
# Source: Census (census.data.gov) advanced search (Topics: 'Housing-Vacancy-Vacancy Rates' ('Vacancy Status' tabl); Geography: All US Counties; Years: 2010-2018 ACS 5-Yr. Estimates)
import pandas as pd
import numpy as np
import os
master_df = | pd.DataFrame() | pandas.DataFrame |
import pickle
from tqdm import tqdm
import numpy as np
import pandas as pd
from nltk.util import ngrams
from nltk import word_tokenize
from nltk.tokenize.treebank import TreebankWordDetokenizer
from nltk.lm.preprocessing import padded_everygram_pipeline, pad_both_ends
from nltk.lm import NgramCounter, Vocabulary, MLE... | pd.DataFrame() | pandas.DataFrame |
from __future__ import division #brings in Python 3.0 mixed type calculation rules
import datetime
import inspect
import numpy as np
import numpy.testing as npt
import os.path
import pandas as pd
import sys
from tabulate import tabulate
import unittest
print("Python version: " + sys.version)
print("Numpy version: " +... | pd.Series([0.34, 0.84, 0.02]) | pandas.Series |
import logging
import os
import sys
import pandas as pd
import lightkurve as lk
import foldedleastsquares as tls
import matplotlib.pyplot as plt
import astropy.units as u
from astropy.coordinates import SkyCoord
from astropy.wcs import WCS
from astroquery.mast import Catalogs, Tesscut
from sherlockpipe.ois.OisManager ... | pd.read_csv(negative_dir + "/" + tic_dir + "/time_series_long.csv") | pandas.read_csv |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
#===========================================================================================================
Copyright 2006-2021 Paseman & Associates (www.paseman.com)
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and ass... | pd.read_csv("SPY-NorgateExtended.txt",header=0, sep='\t', index_col=0, parse_dates=True) | pandas.read_csv |
from os.path import expanduser
from text_extraction import *
import pandas as pd
import argparse
source_path = expanduser('~') + '/Downloads/kanika/source2'
parser = argparse.ArgumentParser()
parser.add_argument("--source_dir", help="1 This should be the source directory of files to be processed", type=str, required=... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 23 14:48:18 2018
@author: RomanGutin
"""
import numpy as np
import pandas as pd
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
### CrossValidation Score Functions###
def concat_train(x): #I wrote this function to convert the list of training dataframes... | pd.concat(concat_set) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 29 17:53:37 2019
@author: kazuki.onodera
"""
import numpy as np
import pandas as pd
import os, gc
from glob import glob
from tqdm import tqdm
import sys
sys.path.append(f'/home/{os.environ.get("USER")}/PythonLibrary')
import lgbextension as ex
imp... | pd.read_csv('../input/train.csv.zip') | pandas.read_csv |
import pandas as pd
from sqlalchemy import create_engine
import sqlite3
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import bar_chart_race as bcr
import streamlit as st
import ffmpeg
import rpy2.robjects as ro
from math import pi
from rpy2.robjects import pandas2ri
from rpy2.robjects.convers... | pd.to_datetime(spotify_track_data.release_date) | pandas.to_datetime |
# -*- coding: utf-8 -*-
# 时间系列模型
# forecast monthlybirths with xgboost
from numpy import asarray
from pandas import read_csv
from pandas import DataFrame
from pandas import concat
from sklearn.metrics import mean_absolute_error
from xgboost import XGBRegressor
from matplotlib import pyplot
# transform a time serie... | read_csv('../data/per_month_sale_and_risk.csv') | pandas.read_csv |
import os
import collections
import pandas
import pandas as pd
import matplotlib, seaborn, numpy
from matplotlib import pyplot
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
from sklearn.model_selection import train_test_split
import sklearn
fro... | pd.DataFrame(list_of_rows,columns=['uniprot_id','start', 'end', 'sequence', 'num_hits']) | pandas.DataFrame |
from datetime import datetime
from io import StringIO
import itertools
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
Period,
Series,
Timedelta,
date_range,
)
import pandas._testing as tm
... | tm.assert_frame_equal(s_unstacked, expected["A"]) | pandas._testing.assert_frame_equal |
from __future__ import annotations
import itertools
from typing import (
TYPE_CHECKING,
Sequence,
cast,
)
import numpy as np
from pandas._libs import (
NaT,
internals as libinternals,
)
from pandas._typing import (
ArrayLike,
DtypeObj,
Manager,
Shape,
)
from pandas.util._decorator... | find_common_type(dtypes) | pandas.core.dtypes.cast.find_common_type |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
def recommendcase(location,quality,compensate,dbcon,number):
"""
location: np.array
quality: np.array
compensate: np.array
dbcon: database connection
number: number of recommended cases
"""
import pandas as pd
caseset = pd.Data... | pd.read_sql_query(sql1,db) | pandas.read_sql_query |
import numpy as np
import pandas as pd
import pyarrow as pa
import fletcher as fr
class ArithmeticOps:
def setup(self):
data = np.random.randint(0, 2 ** 20, size=2 ** 24)
self.pd_int = | pd.Series(data) | pandas.Series |
import math
from functools import partial
from pathlib import Path
import cv2
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.cluster import DBSCAN
from sklearn.neighbors import LocalOutlierFactor
from statsmodels.stats.weightstats import DescrStatsW
from tqdm import tqdm
from ... | pd.read_csv(file) | pandas.read_csv |
import argparse
from sklearn.metrics import roc_curve, auc
import tensorflow as tf
from tensorflow.python.ops.check_ops import assert_greater_equal_v2
import load_data
from tqdm import tqdm
import numpy as np
import pandas as pd
from math import e as e_VALUE
import tensorflow.keras.backend as Keras_backend
from sklear... | pd.DataFrame() | pandas.DataFrame |
# Copyright (c) 2013, ElasticRun and contributors
# For license information, please see license.txt
from __future__ import unicode_literals
import frappe
from croniter import croniter
from datetime import datetime
import pandas as pd
from datetime import timedelta
import pytz
def execute(filters=None, as_df=False):
... | pd.DataFrame.from_records(filtered_jobs, index=['method']) | pandas.DataFrame.from_records |
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from utils import *
import os
import argparse
from pathlib import Path
from collections import Counter
class k... | pd.concat([df_1, df_2, df_3]) | pandas.concat |
# -*- coding: utf-8 -*-
# pylint: disable=E1101
# flake8: noqa
from datetime import datetime
import csv
import os
import sys
import re
import nose
import platform
from multiprocessing.pool import ThreadPool
from numpy import nan
import numpy as np
from pandas.io.common import DtypeWarning
from pandas import DataFr... | DataFrame({'A': [0, 0], 'B': [0, np.nan]}) | pandas.DataFrame |
from datetime import datetime
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas.core.dtypes.base import _registry as ea_registry
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from pandas.core.dtypes.dtypes import (... | DataFrame([[1, 3, 5]] + [[2, 4, 6]] * n, columns=["a", "b", "c"]) | pandas.DataFrame |
from operator import itemgetter
import os
import json
import logging
from datetime import datetime
from typing import Iterable, List, Optional
from marshmallow.fields import Field
from pytz import timezone
from pathlib import Path
from flask_restx.fields import MarshallingError, Raw
from datetime import datetime, time... | pd.concat(dfs) | pandas.concat |
# Kør herfra ved start for at få fat i de nødvendige funktioner og dataframes
import Functions
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
coin_list_NA = ['BTC', 'BCHNA', 'CardonaNA', 'dogecoinNA', 'EOS_RNA', 'ETHNA', 'LTCNA', 'XRP_RNA', 'MoneroNA',
'BNB_RNA',
... | pd.DataFrame() | pandas.DataFrame |
# This example script shows how to utilize idealreport to create various interactive HTML plots.
# The framework generates a "report" that is an HTML file with supporting js files.
#
# These are the steps to generate the example report:
# 1. Use python 2.7 and install the requirements using "pip install htmltag pandas"... | pd.DataFrame(
{"Stat 1": [2.0, 1.6, 0.9, 0.2, -1.3], "Stat 2": [1.1, 0.7, -0.8, -1.4, 0.4], "Value 1": [8, 10, 50, 85, 42], "Value 2": [100, 50, 10, 100, 25]},
index=["Entity 1", "Entity 2", "Entity 3", "Entity 4", "Entity 5"],
) | pandas.DataFrame |
"""
Created on Wed Nov 07 2018
@author: Analytics Club at ETH <EMAIL>
"""
import itertools
import time
from time import localtime, strftime
from os import path, mkdir, rename
import sys
from sklearn.metrics import (accuracy_score, confusion_matrix, classification_report)
from sklearn.model_selection import GridSearch... | pd.DataFrame(weights, index=[0]) | pandas.DataFrame |
import cv2
import os
import time
import face_recognition
import pickle
from mss import mss
from PIL import Image
import pandas as pd
import argparse
import configparser
## Captures the current screen and returns the image ready to be saved
## Optional parameter to set incase there's more than 1 monitor.
## If the val... | pd.DataFrame(columns=['Date', 'ElapsedSeconds', 'Name', 'EmotionScore', 'EyeCount']) | pandas.DataFrame |
from cde.evaluation.empirical_eval.experiment_util import run_benchmark_train_test_fit_cv, run_benchmark_train_test_fit_cv_ml
import cde.evaluation.empirical_eval.datasets as datasets
from ml_logger import logger
import config
import pandas as pd
EXP_PREFIX = 'benchmark_empirical'
class Rule_of_thumb:
def __ini... | pd.concat([result_df, df], ignore_index=True) | pandas.concat |
import sys,os
#os.chdir("/Users/utkarshvirendranigam/Desktop/Homework/Project")
# required_packages=["PyQt5","re", "scipy","itertools","random","matplotlib","pandas","numpy","sklearn","pydotplus","collections","warnings","seaborn"]
#print(os.getcwd())
# for my_package in required_packages:
# try:
# command... | pd.concat([self.list_corr_features, df[features_list[3]]], axis=1) | pandas.concat |
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