prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
"""NEDSS data duplicate person-record detector.
This program automates the process of identifying potential duplicate person-records
in NEDSS data. The program takes two command line arguments (1) the filepath
for NEDSS data; (2) the Identifier of the first person-record considered new.
This application will then comp... | pd.read_excel(path) | pandas.read_excel |
import pandas as pd
import numpy as np
import os
from sklearn.metrics import roc_auc_score, accuracy_score
from sklearn import metrics
from scipy.stats import rankdata
import math
import argparse
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--enspath", type=str, default="./da... | pd.read_csv(data_path + csv) | pandas.read_csv |
import pandas as pd
from autox.autox_competition.util import log
def fe_time(df, time_col):
log('[+] fe_time')
result = pd.DataFrame()
prefix = time_col + "_"
df[time_col] = | pd.to_datetime(df[time_col]) | pandas.to_datetime |
'''
MIT License
Copyright (c) 2020 Minciencia
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.read_csv('../input/Vacunacion/vacunacion_region.csv') | pandas.read_csv |
import matplotlib.pyplot as plt
import pandas as pd
class ProvData:
def __init__(self, prov):
self.prov = prov
self.price_list = []
self.quality_list = []
self.size_list = []
self.size_frac_list = []
self.pop_funds_list = []
self.mk_funds_list = []
... | pd.DataFrame(self.pop_ipc_list) | pandas.DataFrame |
import pandas as pd
ratings = pd.read_csv('dataset/ratings.csv')
movies = pd.read_csv('dataset/movies.csv')
all_movie = movies['title'].values
new_movie = []
for movie in all_movie:
split_movie = movie.split()
split_movie.pop()
string = ' '.join(split_movie)
new_movie.append(string)
movies['movie'] ... | pd.merge(movies,ratings) | pandas.merge |
#%%
from pymaid_creds import url, name, password, token
from data_settings import pairs_path, data_date
import pymaid
rm = pymaid.CatmaidInstance(url, token, name, password)
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
import cmasher as cmr
from contools import Cascade... | pd.DataFrame([i_left, b_left, c_left, c_right, b_right, i_right], index = ['Ipsi(L)', 'Bilateral(L)', 'Contra(L)', 'Contra(R)', 'Bilateral(R)', 'Ipsi(R)']) | pandas.DataFrame |
import datetime
import os
import pandas as pd
import pygsheets
import telegram
TELEGRAM_API_TOKEN = os.environ["TELEGRAM_API_TOKEN_MARATHON"]
bot = telegram.Bot(token=TELEGRAM_API_TOKEN)
chat_id = -408362490
def authenticate_google_sheets():
client = pygsheets.authorize(service_account_file="servic... | pd.to_datetime(workout_df.index, format="%d %b %Y") | pandas.to_datetime |
import warnings
import numpy as np
from pandas import Categorical, DataFrame, Series
from .pandas_vb_common import tm
class Construction:
params = ["str", "string"]
param_names = ["dtype"]
def setup(self, dtype):
self.series_arr = tm.rands_array(nchars=10, size=10 ** 5)
self.frame_arr... | DataFrame(self.frame_cat_arr, dtype=dtype) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
from covsirphy.util.error import UnExecutedError
from covsirphy.cleaning.term import Term
from covsirphy.ode.mbase import ModelBase
from covsirphy.simulation.estimator import Estimator
from covsirphy.simulation.simulator import ODESimulator
class Phas... | pd.DataFrame.from_dict(summary_dict, orient="index") | pandas.DataFrame.from_dict |
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 1 10:59:51 2020
Modified on ... look at git commit log, you lazy bum
@author: <NAME>, Assistant Research Professor, CEE WSU
@author: <NAME>, Ecoinformaticist, USDA-ARS
contact: <EMAIL>
Library of functions for the Azure Data Lake download codeset; see the readme within th... | pd.isna(df) | pandas.isna |
# Copyright 2018-2019 QuantumBlack Visual Analytics Limited
#
# 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
#
# THE SOFTWARE IS PROVIDED "AS IS"... | pd.DataFrame(search_cv.cv_results_) | pandas.DataFrame |
import logging
import numpy as np
import pandas as pd
from pytest import approx
from lenskit.metrics.topn import recall
from lenskit.util.test import demo_recs
from lenskit import topn
_log = logging.getLogger(__name__)
def _test_recall(items, rel, **kwargs):
recs = pd.DataFrame({'item': items})
truth = pd... | pd.Series([1, 3]) | pandas.Series |
#!/usr/bin/env python
from __future__ import division
import numpy as np
import pandas as pd
import warnings
from .helpers import *
def analyze_chunk(data, subjgroup=None, subjname='Subject', listgroup=None, listname='List', analysis=None, analysis_type=None, pass_features=False, **kwargs):
"""
Private functio... | pd.concat(analyzed_data) | pandas.concat |
"""
Fetch meteorological data from the SMEAR website and bind them as a CSV table.
Hyytiälä COS campaign, April-November 2016
(c) 2016-2017 <NAME> <<EMAIL>>
"""
import io
import argparse
import copy
import datetime
import requests
import numpy as np
import pandas as pd
import preproc_config
def timestamp_parser(*a... | pd.Timestamp('%d-01-01' % start_year) | pandas.Timestamp |
import argparse
import collections
import pandas
import numpy as np
import os
import gym
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
import tensorflow as tf
from rl.agents.cem import CEMAgent
from rl.memory import EpisodeParameterMemory
from noise_estimator import CartpoleP... | pandas.DataFrame(history_noisy.history) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 01 10:00:58 2021
@author: <NAME>
"""
#------------------------------------------------------------------#
# # # # # Imports # # # # #
#------------------------------------------------------------------#
from math import e
import numpy as np
import... | pd.DataFrame(data={'ismatched': ismatched, 'idx': idx, 'd2d': d2d}) | pandas.DataFrame |
"""
Copyright 2019 <NAME>.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distribut... | pd.DataFrame(response) | pandas.DataFrame |
from pathlib import Path
import os
import pandas as pd
import numpy as np
def get_country_geolocation():
dir_path = os.path.dirname(os.path.realpath(__file__))
country_mapping = pd.read_csv(
dir_path + '/data_files/country_centroids_az8.csv', dtype=str)
country_mapping = country_mapping.iloc[:, [... | pd.read_csv(csv_file) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
SCRIPT # 3
Created on Fri Jul 31 01:40:28 2020
@author: omid
"""
import numpy as np
import pandas as pd
import glob
from khayyam import *
allStocks = pd.read_pickle("./allStocks.pkl")
bookvalues = pd.read_pickle("./bookvalues.pkl")
############################ compute a retur... | pd.DataFrame(columns = [1,2,3,4,5,6], index = allStocks.index) | pandas.DataFrame |
# Modified the provided preprocessing code
# @author <NAME>
# Original license
# Copyright 2020 (c) Cognizant Digital Business, Evolutionary AI. All rights reserved. Issued under the Apache 2.0 License.
import os
# noinspection PyPep8Naming
import numpy as np
import pandas as pd
import tensorflow as tf
from .xpriz... | pd.read_csv(ADDITIONAL_BRAZIL_CONTEXT) | pandas.read_csv |
# Control de datos
from io import BytesIO
from dateutil import tz
from pathlib import Path
from zipfile import ZipFile
from json import loads as loads_json
from datetime import datetime, timedelta
from requests import get as get_request
# Ingeniería de variables
from geopandas import read_file
from pandas import DataF... | concat([acum, new], ignore_index=True) | pandas.concat |
from typing import Any, Literal
from pandas import DataFrame, concat
from weaverbird.backends.pandas_executor.types import DomainRetriever, PipelineExecutor
from weaverbird.pipeline.steps import AggregateStep
AggregateFn = Literal[
'avg',
'sum',
'min',
'max',
'count',
'count distinct',
'f... | concat(aggregated_cols, axis=1) | pandas.concat |
from slytherin.hash import hash_object
from slytherin.functions import get_function_arguments
from ravenclaw.preprocessing import Polynomial, Normalizer
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestClassifier
from pandas import DataFrame, concat
from random import randint... | DataFrame.from_records([x]) | pandas.DataFrame.from_records |
import itertools
import json
import os
import csv
import errno
import random
from random import shuffle
from typing import List
import spacy
from tqdm import tqdm
import pandas as pd
import codecs
import nltk
import glob
import xml.etree.ElementTree as ET
from datasets import load_dataset
import statistics
import json
... | pd.read_json(filename) | pandas.read_json |
import sys
import os
import numpy as np
from tqdm import tqdm
import json
import time as timemodu
from numba import jit, prange
import h5py
import fnmatch
import pandas as pd
import astropy
import astropy as ap
from astropy.io import fits
from astropy.coordinates import SkyCoord
from astropy.io import fits
impor... | pd.read_csv(path) | pandas.read_csv |
import difflib
import glob
import json
import os
import numpy as np
import pandas as pd
from daps.utils.extra import levenshtein_distance
ACTIVITYNET_ANNOTATION_FILE = 'activity_net.v1-2.gt.json'
ANET_SIMILAR_CLASS_IDS_WITH_THUMOS14 = [159, 82, 233, 224, 195,
116, 80, 106, 169... | pd.read_csv(i, header=None, sep=' ') | pandas.read_csv |
import numpy as np
import pandas as pd
from collections import namedtuple
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import pickle
import config as cf
EpisodeStats = namedtuple("EpisodeStats",["episode_lengths", "episode_rewards", "episode_runtime"])
TimeStats = namedtuple("TimeStats"... | pd.Series(stats2.episode_rewards) | pandas.Series |
"""Unit tests for the :mod:`pudl.helpers` module."""
import pandas as pd
from pandas.testing import assert_frame_equal
from pudl.helpers import (convert_df_to_excel_file, convert_to_date,
fix_eia_na, fix_leading_zero_gen_ids)
def test_convert_to_date():
"""Test automated cleanup of EIA... | assert_frame_equal(out_df, expected_df) | pandas.testing.assert_frame_equal |
"""
Script to plot the time series data for solar data
starting with the data found in 1601_18.46_-66.11_2016.csv
hatfieldm
links:
- https://openei.org/datasets/dataset?sectors=buildings&tags=renewable+energy
- https://openei.org/datasets/dataset/rooftop-solar-challenge-rsc-database/resource/2a27dca6-5d04-48... | pd.read_csv(data_file, header=2) | pandas.read_csv |
# Copyright 2019, Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing... | pd.Series(series_data) | pandas.Series |
import os,sys
from pathlib import Path
sys.path.append(str(Path(os.path.realpath(__file__)).parent.parent.absolute()))
#https://stackoverflow.com/questions/19451767/datetime-between-statement-not-working-in-sql-server
from sqlalchemy import create_engine
import time
import pandas as pd
import datetime as dt
nw = dt.... | pd.to_datetime(x[k]) | pandas.to_datetime |
"""
30 May 2020
Author: <NAME>
After we have cleaned all the datasets, we will now combine everything into a
single dataframe.
Saving it as csv for the moment as we are still trying to figure out how we can
best share this data.
"""
import pandas as pd
#Simple calling of all the cleaned csv files with the file pa... | pd.read_csv(r"file_path\API_Pahang_2018_cleaned.csv") | pandas.read_csv |
# util.py (lciafmt)
# !/usr/bin/env python3
# coding=utf-8
"""
This module contains common functions for processing LCIA methods
"""
import uuid
import os
from os.path import join
import lciafmt
import logging as log
import pandas as pd
import numpy as np
import yaml
import pkg_resources
import subprocess
from esupy.pr... | pd.read_csv(datapath+"/"+source+"_"+name+".csv") | pandas.read_csv |
import numpy as np
import scipy as sp
import pandas as pd
import ast
import gensim
from gensim.corpora import Dictionary
import networkx as nx
import network_utils as nu
import matplotlib.pyplot as plt
import matplotlib.cm as cm
# -------------------------------------------------------------------------------------... | pd.read_csv('../data/sessionData.csv') | pandas.read_csv |
#! /usr/bin/env python
import os
import tempfile
import shutil
import warnings
warnings.filterwarnings("ignore")
from unittest import TestCase
from pandashells.lib import plot_lib, arg_lib
import argparse
from mock import patch, MagicMock
import matplotlib as mpl
import pylab as pl
import pandas as pd
from dateutil.par... | pd.DataFrame([[1, 1], [2, 2]], columns=['x', 'y']) | pandas.DataFrame |
import pandas as pd
import numpy as np
import random
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import TfidfVectorizer
import sklearn.preprocessing as pp
from datetime import datetime
from sklearn.linear_model import SGDClassifier
from sklearn.preprocessing import Standa... | pd.Series(99.0, index=[text_id]) | pandas.Series |
# AUTOGENERATED! DO NOT EDIT! File to edit: 00_utils.ipynb (unless otherwise specified).
__all__ = ['logger', 'set_seed', 'set_session_options', 'setup_logging', 'setup_parser', 'timecode',
'print_device_info', 'dump_tensors', 'Monitor', 'show_gpu', 'round_t', 'merge_dicts', 'display_all',
'unpac... | pd.read_csv(path, nrows=1) | pandas.read_csv |
# import libraries
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pylab as plt
def load_and_process(zomato_file_path, forex_file_path, countrycode_file_path):
"""
This method takes the loads, processes, and formats the zomato.csv file to be returned as a dataframe.
Argu... | pd.read_excel(countrycode_file_path) | pandas.read_excel |
import pandas as pd
import numpy as np
import os
from datetime import datetime
from IPython.display import IFrame,clear_output
# for PDF reading
import textract
import re
import sys
import docx
from difflib import SequenceMatcher
##################################################################################... | pd.to_timedelta('1 day') | pandas.to_timedelta |
import pytest
import pandas as pd
from pandas.testing import assert_frame_equal
from pathlib import Path
from data_check.sql import DataCheckSql, LoadMode # noqa E402
from data_check.config import DataCheckConfig # noqa E402
@pytest.fixture(scope="module", params=["csv", "xlsx"])
def file_type(request):
retur... | assert_frame_equal(data, df) | pandas.testing.assert_frame_equal |
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... | pd.concat([s1, s2], ignore_index=True) | pandas.concat |
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
... | Series([1.0, 50.0, 100.0]) | pandas.Series |
import json
import os
import pandas as pd
from .utils import list_to_md_table
SCHEMA_TO_PANDAS_TYPES = {
"integer": "int64",
"number": "float",
"string": "string",
"any": "object",
"boolean": "bool",
}
FORMAT_TO_REGEX = {
# https://emailregex.com/
"email": r"^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-... | pd.DataFrame(config["resources"]) | pandas.DataFrame |
#!/usr/bin/env python
import requests
import os
import string
import random
import json
import datetime
import pandas as pd
import numpy as np
import moment
from operator import itemgetter
class IdsrAppServer:
def __init__(self):
self.dataStore = "ugxzr_idsr_app"
self.period = "LAST_7_DAYS"
self.ALPHABET = '0... | pd.DataFrame() | pandas.DataFrame |
import os
import copy
import time
import numpy as np
import pandas as pd
import torch
from tqdm import tqdm
import typing as Dict
def init_log_loss(last_log_loss_csv, num_models=None):
last_best_avg_loss_all = np.inf
if last_log_loss_csv is None:
if num_models is None:
raise ValueError('Mi... | pd.read_csv(last_log_loss_csv) | pandas.read_csv |
# <NAME>
# Last Modified: 5/22/2020
# Verify fluid properties for Flinak
# Reference: "Annals of Nuclear Energy", Romatoski and Hu
# Note:
#Temperature is in Kelvin
import os
import sys
sys.path.insert(0,'..') #This adds the ability to call flinak prop from the main folder
sys.path.insert(0,'./Flinak') #Looking for ... | pd.DataFrame(viscositydataframes) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Results, graphs
@author: a.stratigakos
"""
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os, sys
import pickle
from sklearn.ensemble import RandomForestRegressor
from scipy import interpolate, stats
import cvxpy as cp
import matplotlib.patches as patches
# ... | pd.read_csv(directory+'\\load_scenarios.csv', index_col=0) | pandas.read_csv |
import requests
import pandas as pd
import re
from bs4 import BeautifulSoup
url=requests.get("http://www.worldometers.info/world-population/india-population/")
t=url.text
so=BeautifulSoup(t,'html.parser')
all_t=so.findAll('table', class_="table table-striped table-bordered table-hover table-condensed table-list"... | pd.Series.tolist(bv[0:7][4]) | pandas.Series.tolist |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn import preprocessing, model_selection, metrics
import lightgbm as lgb
pd.options.mode.chained_assignment = None
pd.options.display.max_columns = 999
train_df = pd.read_csv("C:\\Users\\jowet\\Downloads\... | pd.read_csv("C:\\Users\\jowet\\Downloads\\kaggle\\avito\\test.csv", parse_dates=["activation_date"]) | pandas.read_csv |
# pylint: disable-msg=E1101,W0612
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.core.sparse.api import SparseDtype
class TestSparseSeriesIndexing(object):
def setup_method(self, method):
self.orig = pd.Series([1, np.nan, np.nan, 3, np.nan])
sel... | tm.assert_sp_series_equal(s.iloc[indexer], exp) | pandas.util.testing.assert_sp_series_equal |
import json
import pandas as pd
from sklearn.model_selection._search import ParameterGrid
from logging_ import Logger
from adv_lib.attacks import fmn, alma, apgd
from adv_lib.attacks.auto_pgd import minimal_apgd
#from robustbench.utils import load_model
from tracking import PyTorchModelTracker
from torchvision import t... | pd.read_csv(path) | pandas.read_csv |
def calculateAnyProfile(profileType, df_labs, df_meds, df_procedures, df_diagnoses, df_phenotypes):
"""Calculate a single profile based on the type provided and data cleaned from getSubdemographicsTables
Arguments:
profileType -- which individual profile type you would like generated, this will be the ... | pd.to_datetime(x.ADMIT_DATE) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Created on Sun May 21 13:13:26 2017
@author: ning
"""
import pandas as pd
import os
import numpy as np
import matplotlib.pyplot as plt
import pickle
try:
function_dir = 'D:\\NING - spindle\\Spindle_by_Graphical_Features'
os.chdir(function_dir)
except:
function_dir = 'C:\\Users\... | pd.concat(df_graph) | pandas.concat |
"""Extended DataFrame functionality."""
from typing import Any, Iterator, Optional, Sequence, Text, Tuple, Union, cast
import numpy as np
import pandas as pd
from snmp_fetch.utils import cuint8_to_int
from .types import ip_address as ip
def column_names(
n: Optional[int] = None, alphabet: Sequence[Text] = ... | pd.api.extensions.register_dataframe_accessor('inet') | pandas.api.extensions.register_dataframe_accessor |
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 10 00:10:23 2022
@author: <NAME>
Adapted from <NAME>
"""
r"""
Forward Model
"""
# Standard Library imports
import gzip
import numpy as np
import pandas as pd
import xarray as xr
# Third party imports
from collections import OrderedDict
# Semi-... | pd.Series(chi0_0.iloc[-1], index=timestamps) | pandas.Series |
import pandas as pd
from business_rules.operators import (DataframeType, StringType,
NumericType, BooleanType, SelectType,
SelectMultipleType, GenericType)
from . import TestCase
from decimal import Decimal
import sys
import pandas
class Str... | pandas.Series([True, False, False]) | pandas.Series |
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# plt.close("all")
CURRENT_DIRECTORY = Path(__file__).parent
def plot_01():
ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
ts = ts.cumsum()
ts.plot()
# Display plo... | pd.date_range('1/1/2000', periods=1000) | pandas.date_range |
#!/usr/bin/env python
# coding: utf-8
# In[4]:
# -*- coding: utf-8 -*-
"""
Created on Tue May 4 17:39:59 2021
Collection of custom evaluation functions for embedding
@author: marathomas
"""
import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors
from sklearn.metrics import silhouett... | pd.DataFrame(stats_tab) | pandas.DataFrame |
#!/usr/bin/env python
# stdlib imports
import os.path
import argparse
from collections import OrderedDict
import sys
import warnings
import textwrap
import logging
# third party imports
import pandas as pd
# local imports
from gmprocess.io.read import _get_format, read_data
from gmprocess.utils.args import add_share... | pd.set_option('display.max_columns', 10000) | pandas.set_option |
# -*- coding: utf-8 -*-
"""
Created on Sun Dec 2 21:53:00 2018
@author: RomanGutin
"""
import numpy as np
import pandas as pd
plot_data={}
#####AM_Tuning With Wavelet
def AM_W(x,first,last,steps):
sweep = list(np.linspace(first,last,(last-first)/steps)) #the first amino acid
for acid in count_df.index:
... | pd.DataFrame([sweep,CrossValidation_Scores]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime, timedelta
import functools
import itertools
import numpy as np
import numpy.ma as ma
import numpy.ma.mrecords as mrecords
from numpy.random import randn
import pytest
from pandas.compat import (
PY3, PY36, OrderedDict, ... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
from collections import abc, deque
from decimal import Decimal
from io import StringIO
from warnings import catch_warnings
import numpy as np
from numpy.random import randn
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
... | tm.assert_frame_equal(res, exp) | pandas._testing.assert_frame_equal |
"""figures of merit is a collection of financial calculations for energy.
This module contains financial calculations based on solar power and batteries
in a given network. The networks used are defined as network objects (see evolve parsers).
TODO: Add inverters: Inverters are not considered at the momen... | pd.concat([solar_power, meas_dict[key][meas].iloc[:,column]]) | pandas.concat |
# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership. The Modin Development Team licenses this file to you under the
# Apache License, Version 2.0 (the "License"); you may not u... | is_list_like(by) | pandas.core.dtypes.common.is_list_like |
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import os
import time
# prepare data
(X_train_full, y_train_full),(X_test, y_test) = keras.datasets.fashion_mnist.load_data()
num_valid = 5000
X_valid = X_train_full[:num_valid] / 255.
X_train = ... | pd.DataFrame(history.history) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import yfinance as yf
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import os
import math
import matplotlib.pylab as plt
import matplotlib
from Machine_Learning_for_Asset_Managers import ch2_fitKDE_find_best_bandwidth as best_bandwidth... | pd.Series(pho[-50:]) | pandas.Series |
import pandas as pd
import config
import numpy as np
import os
import datetime
class AttendanceMarker:
def __init__(self):
# current datetime to put attendance
now = datetime.datetime.now()
self.time = now.strftime(config.DATE_TIME_FORMAT)
def _create_new_csv(self):
names = o... | pd.DataFrame(data=names,columns=[config.CSV_COL_NAME]) | pandas.DataFrame |
# Import required Libraries
import csv
from bs4 import BeautifulSoup
from selenium import webdriver
from openpyxl import Workbook
import pandas as pd
# Function to get the search term
def get_url(search_term):
"""Generate a URL from search term"""
template = 'https://www.amazon.com/s?k={}'
search_term = se... | pd.DataFrame({'Product Name':records}) | pandas.DataFrame |
from __future__ import absolute_import
# PopulationSim
# See full license in LICENSE.txt.
from builtins import object
import logging
import os
import numpy as np
import pandas as pd
from activitysim.core.config import setting
from .lp import get_single_integerizer
from .lp import STATUS_SUCCESS
from .lp import STAT... | pd.Series(relaxed_control_totals, index=incidence_table.columns.values) | pandas.Series |
from rest_framework import permissions, status
from rest_framework.decorators import api_view, authentication_classes, permission_classes
from rest_framework.response import Response
from rest_framework.views import APIView
from datetime import date, datetime, timedelta
from django.forms.models import model_to_dict
fro... | pd.DataFrame(web_activity_data) | pandas.DataFrame |
#!/usr/bin/env python3
"""
Gaussian mixture fitting with Nested Sampling. This module was tested in the
main `nestfit` repo on bare arrays and Gaussian components -- without a
spectral axis, units, or other necessary complications.
The `.wrapped` references a Cython implementation of the Gaussian model class.
"""
imp... | pd.DataFrame(margs) | pandas.DataFrame |
import pandas as pd
import bentoml
from bentoml.artifact import PickleArtifact
from bentoml.handlers import DataframeHandler
from data_preprocess import Posts
from word_embedding_vectorizer import WordEmbeddingVectorizer
from gensim.models import Word2Vec
@bentoml.artifacts([PickleArtifact('word_vectorizer'),
... | pd.DataFrame({'text': series, 'confidence_score': confidence_score, 'labels': pred_labels}) | pandas.DataFrame |
#!/usr/bin/env python3
import websocket
import config
import json
import pandas as pd
import numpy as np
from src.data_methods import get_gestures
from src.leap_methods import collect_frame
import src.features as features
import random
import time
import argparse
parser = argparse.ArgumentParser()
parser.add_argumen... | pd.DataFrame(frames) | pandas.DataFrame |
# Author: <NAME>
# Email: <EMAIL>
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None # default='warn'
import matplotlib.pyplot as plt
import logging
class TrajDataset:
def __init__(self):
"""
data might include the following columns:
"scene_id", "frame_id", "... | pd.unique(self.data["frame_id"]) | pandas.unique |
import pandas as pd
def generate_train(playlists):
# define category range
cates = {'cat1': (10, 50), 'cat2': (10, 78), 'cat3': (10, 100), 'cat4': (40, 100), 'cat5': (40, 100),
'cat6': (40, 100),'cat7': (101, 250), 'cat8': (101, 250), 'cat9': (150, 250), 'cat10': (150, 250)}
cat_pids = {}
... | pd.merge(df_eval_itr, df, on='tid') | pandas.merge |
#!/usr/bin/env python
# coding: utf-8
# In[66]:
#置入所需套件
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
# Load in the data
df = | pd.read_csv("InterestsSurvey.csv") | pandas.read_csv |
import time
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm as cm
import seaborn as sns
sns.set_style("whitegrid")
import sys
import os
from pathlib import Path
from sklearn import metrics
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection i... | pd.Series(train_scores_std, name='training_score_std') | pandas.Series |
# encoding: utf-8
'''
组合策略测试
'''
import sys
sys.path.append('../../')
from vnpy.app.cta_strategy.strategies.strategyMulti import MultiStrategy
import argparse
import pandas as pd
import numpy as np
from datetime import datetime
from setup_logger import setup_logger
setup_logger(filename='logsBackTest/vnpy_{0}.log'.fo... | pd.DataFrame([i.__dict__ for i in engine.history_data]) | pandas.DataFrame |
import numpy as np
import pytest
from pandas import Categorical, Series
import pandas._testing as tm
@pytest.mark.parametrize(
"keep, expected",
[
("first", Series([False, False, False, False, True, True, False])),
("last", Series([False, True, True, False, False, False, False])),
(Fa... | tm.assert_series_equal(sc, tc[~expected]) | pandas._testing.assert_series_equal |
# External Libraries
from datetime import date
import pandas as pd
pd.options.mode.chained_assignment = None
import os
from pathlib import Path
import logging, coloredlogs
# Internal Libraries
import dicts_and_lists as dal
import Helper
# ------ Logger ------- #
logger = logging.getLogger('get_past_datasets.py')
color... | pd.read_html(url, match='Basic') | pandas.read_html |
"""
Name: foneutil
Version: 0.4.4
Info: Python based script in order to record customer interactions, allowing
the user to record relevant information from customer interaction. The
script allows for the user to edit already entered in real time.
Requirements: Pandas, pyfiglet and termcolor modules
Created ... | pd.read_csv(filename) | pandas.read_csv |
import matplotlib.pyplot as pl
import numpy as np
import pandas as pd
from pyitab.analysis.results.base import filter_dataframe
from pyitab.analysis.results.dataframe import apply_function
import seaborn as sns
from matplotlib.colors import LinearSegmentedColormap
def find_distance_boundaries(data):
scene_center ... | pd.concat(full_dataset) | pandas.concat |
# -*- coding: utf-8 -*-
#%%
from datetime import datetime
startTime =datetime.now()
import pandas as pd
"""
import numpy as np
import glob
import matplotlib.pyplot as plt
import sys
import time
import datetime
from datetime import timedelta
import matplotlib.gridspec as gridspec
import matplotlib.cm... | pd.concat([d1,d2,d3,d4,d5,d6,d7,d8,d9,d10]) | pandas.concat |
"""
This is used for visualizing -- not really needed otherwise
"""
import os
os.environ["DISPLAY"] = ""
import argparse
import h5py
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm
import numpy as np
import json
import pandas as pd
from matplotlib.animation import FuncAnimation
COLORS = n... | pd.DataFrame(df) | pandas.DataFrame |
from sklearn.metrics import pairwise_distances
import pandas as pd
import geopandas as gpd
import lib.helpers as helpers
def zone_distances(zones):
"""
:param zones
GeoDataFrame [*index, zone, geometry]
Must be in a CRS of unit: metre
"""
for ax in zones.crs.axis_info:
assert ax.unit_n... | pd.Timedelta(timethreshold_hours, "hours") | pandas.Timedelta |
__version__ = '0.1.3'
__maintainer__ = '<NAME> 31.12.2019'
__contributors__ = '<NAME>, <NAME>'
__email__ = '<EMAIL>'
__birthdate__ = '31.12.2019'
__status__ = 'dev' # options are: dev, test, prod
#----- imports & packages ------
if __package__ is None or __package__ == '':
import sys
from os import path
... | pd.to_datetime(self.data['tripEndClock'], format='%H:%M') | pandas.to_datetime |
import pandas as pd
from datetime import datetime as dt
# Here we should fetch our data from the Twitter API but since now we have to
# apply for getting API's credentials we pass this step for the sake of the tutorial.
# We use data.csv as source of tweets.
LOCAL_DIR = '/tmp/'
def main():
# Create the datafram... | pd.read_csv('~/airflow/dags/data/data.csv', encoding='latin1') | pandas.read_csv |
import pandas as pd
import numpy as np
index = | pd.date_range('1/1/2000', periods=8) | pandas.date_range |
import os
from pathlib import Path
import json
import pandas as pd
from google.cloud import bigquery
from datetime import datetime, timedelta, timezone
JST = timezone(timedelta(hours=+9), 'JST')
class Database:
def __init__(self):
super().__init__()
self._usr_table = pd.DataFrame()
... | pd.DataFrame({'uid': uid_list, 'uname': uname_list}) | pandas.DataFrame |
import warnings
from typing import Union
import re
import pandas as pd
import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer, make_column_selector
from sklearn import metrics
from feature_engine.imputation import (
CategoricalImputer,
AddMissingIndicator,
Mea... | pd.concat([df, nan_num_ind]) | pandas.concat |
# -*- coding:utf-8 -*-
##############################################################
# Created Date: Wednesday, September 2nd 2020
# Contact Info: <EMAIL>
# Author/Copyright: Mr. <NAME>
##############################################################
import random, urllib3, json, requests, math, plotly
import pandas a... | pd.to_timedelta(Line_Curr_CV_Time*60, unit='s') | pandas.to_timedelta |
import csv
import math
from absl import app, flags
import matplotlib
matplotlib.use("agg")
import matplotlib.pyplot as plt
import numpy as np
from pylot_utils import ProfileEvent, ProfileEvents, fix_pylot_profile
from utils import setup_plot
import pandas as pd
import seaborn as sns
from utils import *
from p... | pd.concat(runtimes_dfs) | pandas.concat |
# Created on 2020/7/16
# This module is for functions generating random time series.
# Standard library imports
from datetime import datetime
from typing import Union
# Third party imports
import numpy as np
import pandas as pd
from typeguard import typechecked
# Local application imports
from .. import timeseries ... | pd.date_range(start=start_date, end=end_date, freq=frequency) | pandas.date_range |
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
from tqdm import tqdm
import yaml
import os
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from joblib import dump, load
from category_encoders import OrdinalEncoder
from src.data.spdat import ... | pd.to_datetime(cfg['DATA']['GROUND_TRUTH_DATE']) | pandas.to_datetime |
#!/usr/bin/env python
# coding: utf-8
#get_ipython().magic('load_ext autoreload')
#get_ipython().magic('reload_ext autoreload')
import requests
import lxml.html as hl
from xml.etree import ElementTree
import pandas as pd
import numpy as np
from bs4 import BeautifulSoup
from queue import Queue, Empty
from urlli... | pd.merge(article_df[['ArticleID','Title']],nobel_author_df[['ArticleID','AuthorOrder','AuthorDAIS','FullName','LastName']],on=['ArticleID'],how='inner') | pandas.merge |
import toml
import pandas as pd
from pathlib import Path
def excel_mqtt_topics_to_toml(
excelFile="metadata.xlsx", tomlDestination="src/backend/.config/topics.toml"
):
df = pd.read_excel(excelFile, sheet_name="mqtt_topics")
d = df.to_dict()
topics = [d["topic"][row] for row in d["topic"]]
qos = [d... | pd.read_excel(excelFile, sheet_name="3D_cages") | pandas.read_excel |
import operator
import functools as f
import json
from pkg_resources import resource_filename
import pandas as pd
def _events_cleaning_map():
with open(resource_filename(__name__, 'events_cleaning_map.json')) as json_map:
return(json.load(json_map))
def _find(element_path):
return lambda json: f.reduce(... | pd.concat([events_without_generic_cols, details], axis=1) | pandas.concat |
"""
Tests for the blaze interface to the pipeline api.
"""
from __future__ import division
from collections import OrderedDict
from datetime import timedelta, time
from unittest import TestCase
import warnings
import blaze as bz
from datashape import dshape, var, Record
from nose_parameterized import parameterized
im... | pd.Timestamp('2014-01-01') | pandas.Timestamp |
from numbers import Number
from typing import List
import pandas as pd
from pandas.api.types import is_integer_dtype, is_float_dtype, is_string_dtype, is_numeric_dtype
from sklearn.base import TransformerMixin
from sklearn.impute import KNNImputer
from sklearn.preprocessing import LabelEncoder
from model_config impor... | is_float_dtype(train_df[col]) | pandas.api.types.is_float_dtype |
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