prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pandas as pd
from State import *
def trajectory2df(t_list, state_list, alpha_list):
x_list = []
vx_list = []
y_list = []
vy_list = []
theta_list = []
for state in state_list:
x_list.append(state.x)
vx_list.append(state.vx)
y_list.append(state.y)
vy_list.ap... | pd.read_csv(file_path) | pandas.read_csv |
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.externals import joblib
import warnings
warnings.filterwarnings("ignore")
# Choose GBDT Regression mode... | pd.date_range(start=start_point, periods=96, freq='15T') | pandas.date_range |
import pandas as pd
import numpy as np
import json
from tqdm import tqdm
from utils import odds, clean_sheet, time_decay, score_mtx, get_next_gw
from ranked_probability_score import ranked_probability_score, match_outcome
import pymc3 as pm
import theano.tensor as tt
class Bayesian:
""" Model scored goals at ho... | pd.DataFrame() | pandas.DataFrame |
""" Research results class """
import os
from collections import OrderedDict
import glob
import json
import dill
import pandas as pd
class Results:
""" Class for dealing with results of research
Parameters
----------
path : str
path to root folder of research
names : str, list or None
... | pd.concat(all_results, sort=False) | pandas.concat |
# This script runs expanded econometric models using both old and new data
# Import required modules
import pandas as pd
import numpy as np
import statsmodels.api as stats
from ToTeX import restab
# Reading in the data
data = | pd.read_csv('C:/Users/User/Documents/Data/demoforestation_differenced_spatial.csv', encoding = 'cp1252') | pandas.read_csv |
'''
Esta clase permite automatizar el proceso de exportacion de datos de un CSV a base de datos
'''
import pandas as pd
from pathlib import Path
import re
import numpy as np
class DataExportManager:
@staticmethod
def exportAttributes(MyConnection):
base_path = Path(__file__).parent
file_path =... | pd.read_csv(file_path,encoding='utf-8') | pandas.read_csv |
#!/usr/bin/env python3
import pytest
import os
import pathlib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import logging
import math
import torch
from neuralprophet import NeuralProphet, set_random_seed
from neuralprophet import df_utils
log = logging.getLogger("NP.test")
log.setLevel("WAR... | pd.read_csv(PEYTON_FILE, nrows=NROWS + 50) | pandas.read_csv |
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import numpy as np
import torch
import pandas as pd
def plot_confusion_matrix(y_true, y_pred, classes, normalize=False, cmap=plt.cm.YlOrBr):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by ... | pd.DataFrame(top_pred, columns=['pred']) | pandas.DataFrame |
import argparse
import numpy as np
import pandas as pd
import os
import sys
import time
from lightgbm import LGBMClassifier
from sklearn.preprocessing import LabelEncoder
import cleanlab
from cleanlab.pruning import get_noise_indices
model = 'clean_embed_all-mpnet-base-v2.csv'
df = pd.read_csv('/global/project/hpcg16... | pd.read_csv('clean.csv') | pandas.read_csv |
# coding:utf-8
#
# The MIT License (MIT)
#
# Copyright (c) 2016-2019 yutiansut/QUANTAXIS
#
# 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 th... | pd.Series(res, index=SeriesA.index) | pandas.Series |
import os, re, getopt, sys
import numpy as np
import pandas as pd
from matplotlib import pyplot
from pathlib import Path
#####################################################################################
## small utils
from matplotlib import rcParams
rcParams.update({'figure.autolayout': True})
def shortenGraphN... | pd.DataFrame() | pandas.DataFrame |
# Copyright 2021 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
# Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software.
"""This module contains utilities for loading and saving SampleSet data files."""
import copy
import logging
import os
... | pd.read_hdf(file_name, "features") | pandas.read_hdf |
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
import glob
import os
import sys
import datetime
import urllib.request
import sys
from sklearn import datasets, linear_model
import csv
from scipy import stats
import pylab
Calculated_GDD=[]
df = pd.DataFrame()
df2... | pd.DataFrame() | pandas.DataFrame |
from sklearn.ensemble import RandomForestClassifier
import numpy as np
from sklearn.metrics import classification_report, accuracy_score # calculating measures for accuracy assessment
from osgeo import gdal
import joblib
import sys
# sys.path.append(r"F:\Work\Maptor\venv\Model")
from ReportModule import ReportModule
im... | pd.DataFrame() | pandas.DataFrame |
#%%%%%%%%%%%%%%%%%%%%%%% Prepare for testing %%%%%%%%%%%%%%
import os
import backtest_pkg.backtest_portfolio as bt
import pandas as pd
from IPython.display import display
import importlib
os.chdir(r'M:\Share\Colleagues\Andy\Python Project\Backtest Module')
price_data = pd.read_csv('pkg_test/Adjusted_Price.csv', in... | pd.DataFrame(data=1, index=[rebalance_date], columns=small_price_data.columns) | pandas.DataFrame |
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# 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 a... | pd.Series(['1.0', 2, -3, '2.0']) | pandas.Series |
import pandas as pd
def create_script(func_str, output_script_file_path=r"./machine_induced_script.py"):
output_script = \
"""import sys
input_file_path = sys.argv[1]
output_file_path = sys.argv[2]
log_lines = open(input_file_path, "r").readlines()
{}
open(output_file_path, "w").write("\\n".join(output_list))
p... | pd.Series(union_list) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 13 15:21:55 2019
@author: raryapratama
"""
#%%
#Step (1): Import Python libraries, set land conversion scenarios general parameters
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import quad
import seaborn as sns
import pandas as... | pd.read_excel('C:\\Work\\Programming\\Practice\\RIL_EC.xlsx', 'NonRW_RIL_S1') | pandas.read_excel |
import pandas as pd
data = | pd.read_csv("data/2016.csv") | pandas.read_csv |
# get all your fuckin imports
import numpy as np
import pandas as pd
from pandas_datareader import data as wb
import matplotlib.pyplot as plt
#get your portfolio tickets
# I am going to get my ticker for stock and mutual funds seperate to compare and see what is performing well
stck_tickers = ['AAPL', 'ENB', 'MDT', '... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import numpy.random as nr
import math
import os
from datetime import datetime
from sklearn.linear_model import LinearRegression, SGDRegressor
import sys
import time
import imp
from sklearn.ensemble import ExtraTreesRegressor
fr... | pd.read_csv(self.testfile,parse_dates=['date'],date_parser=parser) | pandas.read_csv |
# coding: utf-8
# # Notebook to generate a dataframe that captures data reliability
# Perform a series of tests/questions on each row and score the result based on 0 (missing), 1 (ambiguous), 2 (present)
# - is the plot number recorded? If not, this makes it very difficult to identify the plot as unique vs others (2... | pd.notnull(x[0]) | pandas.notnull |
import pandas as pd
import instances.dinamizators.dinamizators as din
import math
def simplest_test():
'''
Test if the dinamizators are running
'''
df = (
pd.read_pickle('./instances/analysis/df_requests.zip')
.reset_index()
)
din.dinamize_as_berbeglia(df.pickup_location_x_co... | pd.DataFrame([[3, 2, 1], [1, 2, 3]]) | pandas.DataFrame |
# AUTOGENERATED! DO NOT EDIT! File to edit: utilities.ipynb (unless otherwise specified).
__all__ = ['make_codes', 'make_data', 'get_rows', 'extract_codes', 'Info', 'memory', 'listify', 'reverse_dict',
'del_dot', 'del_zero', 'expand_hyphen', 'expand_star', 'expand_colon', 'expand_regex', 'expand_code',
... | pd.Series(count) | pandas.Series |
import pandas as pd
import numpy as np
import sklearn.feature_selection
import sklearn.preprocessing
import sklearn.model_selection
import mlr
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
import statistics
# sorting variables
def sort_... | pd.DataFrame(X_test, columns=ind) | pandas.DataFrame |
# ---
# jupyter:
# jupytext:
# cell_metadata_filter: -all
# comment_magics: true
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.11.4
# kernelspec:
# display_name: Python 3 (ipykernel)
# language: python
# n... | pd.notnull(count_subregion_df.index) | pandas.notnull |
""" this is a mixture of the best #free twitter sentimentanalysis modules on github.
i took the most usable codes and mixed them into one because all of them
where for a linguistical search not usable and did not show a retweet or a full tweet
no output as csv, only few informations of a tweet, switching la... | pd.set_option('display.width', 100000000000) | pandas.set_option |
# Downstream: crime prediction (also applicable to Fire calls prediction)
# two modes:
# --- No exogenous data
# --- Oracle network
# The model consists of a 3d cnn network that uses
# historical ST data to predict next time step
# users can choose not to use any features, or
# to use arbitrary number of 1D or 2D feat... | pd.read_csv('../auxillary_data/whole_grid_32_20_demo_1000_intersect_geodf_2018_corrected.csv', index_col = 0) | pandas.read_csv |
# coding: utf-8
# In[37]:
import pandas as pd
from sklearn import preprocessing
import numpy as np
import os
import h5py
import json
import h5py
# In[17]:
distance_data_path = "data.csv"
hnsw_result_path = "/home/lab4/code/HNSW/KNN-Evaluate/hnsw_result1111.h5py"
test_file_path = "test_image_feature.csv"
train_f... | pd.read_csv(test_file_path, sep="\t", converters={1: json.loads}) | pandas.read_csv |
import numpy as np
import pandas as pd
from numba import njit, typeof
from numba.typed import List
from datetime import datetime, timedelta
import pytest
import vectorbt as vbt
from vectorbt.portfolio.enums import *
from vectorbt.generic.enums import drawdown_dt
from vectorbt import settings
from vectorbt.utils.random... | pd.Timedelta('1 days 00:00:00') | pandas.Timedelta |
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd
import os
from sklearn.decomposition import PCA
from sklearn.metrics import silhouette_score
from os.path import join as pjoin
from cplvm import CPLVM
import matplotlib
font = {"size": 30}
matplotlib.rc("font", **font)
matpl... | pd.read_csv(Y_fname, index_col=0) | pandas.read_csv |
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import tensorflow_datasets as tfds
tfds.disable_progress_bar()
def prepare_titanic(test_size=0.3, random_state=123):
print('Download or read from disk.')
ds = tfds.load('titanic', split='train')
# Turn DataSe... | pd.Series(y, name='survived') | pandas.Series |
'''
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any late... | pd.to_datetime(dt1, utc=True) | pandas.to_datetime |
import numpy as np
import re as re
from scipy import stats
import gnc
import netCDF4 as nc
import copy as pcopy
import pdb
import pb
import pandas as pa
def Dic_DataFrame_to_Excel(excel_file,dic_df,multisheet=False,keyname=True,na_rep='', cols=None, header=True, index=True, index_label=None):
"""
Write a dicti... | pa.ExcelWriter(excel_file) | pandas.ExcelWriter |
# coding: utf-8
# In[2]:
#Spam filtering
import numpy as np
import pandas as pd
import os
import email
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cross_validation import StratifiedKFold
from sklearn.naive_bayes import Multinom... | pd.DataFrame(rows, index=index) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# PAQUETES PARA CORRER OP.
import numpy as np
import pandas as pd
import datetime as dt
import json
import wmf.wmf as wmf
import hydroeval
import glob
import SHop
import hidrologia
import os
import seaborn as sns
sns.set(style="whitegrid")
sns.set_context('notebook', font... | pd.to_datetime(date) | pandas.to_datetime |
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import itertools
# def plot_confusion_matrix(cm, classes,
# normalize=False,
# title='Confusion mat... | pd.factorize(data['age']) | pandas.factorize |
from keyword import kwlist
import re
from pandas import DataFrame, Series, Index, MultiIndex
from typing import Union, List, Dict, Iterable
def reindex_series(series: Series, target_series: Series, source_levels: List[int] = None,
target_levels: List[int] = None, fill_value: Union[int, float] = Non... | MultiIndex.from_arrays(arrays) | pandas.MultiIndex.from_arrays |
### Report Rebalance& Grid !!!!! ####
# import neccessary package
import ccxt
import json
import numpy as np
import pandas as pd
import time
import decimal
from datetime import datetime
import pytz
import csv
import sys
# Api and secret
api_key = ""
api_secret = ""
subaccount = ""
# Set y... | pd.DataFrame(trade_history) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 22 14:25:57 2019
@author: skoebric
"""
"""
TODO:
- confirm how net metering is read in
- create agent csv with data we already have
- move agent creation into dgen_model based on params in config?
"""
# --- Python Battery Imports ---
... | pd.DataFrame() | pandas.DataFrame |
import io
import copy
import os
from os.path import join as opj
from PIL import Image
from sqlalchemy import create_engine
import matplotlib.pylab as plt
from matplotlib import patches
from matplotlib.colors import ListedColormap
from pandas import read_sql_query
from pandas import DataFrame, concat, Series
import nump... | read_sql_query(f"""
SELECT "fovname", "participants_{evalset}" AS "participants"
FROM "fov_meta"
WHERE "participants_{evalset}" NOT NULL
;""", dbcon_anchors) | pandas.read_sql_query |
"""
This script analyzes Python imports.
It accepts
* path to csv file with FQ names.
* path to the folder where to save the stats.
* path to the csv file with labeled projects by python version.
For each unique import name, the number of projects in which it occurs is counted.
It is also possible to grou... | pd.read_csv(input_path, keep_default_na=False) | pandas.read_csv |
import pandas as pd
import numpy as np
import os
from scipy.stats import skew
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
import warnings
warnings.filterwarnings('ignore')
class TitanicData:
def __init__(self, file_path):
self.da... | pd.concat([x_train, x_test],axis=0) | pandas.concat |
import argparse
import os
import pickle
from pathlib import Path
import gym
import gym_chrome_dino
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
from network.pg import PG
from utils.show_img import show_img
class GameSession:
def __init__(
self, session_env, in... | pd.DataFrame(columns=['scores']) | pandas.DataFrame |
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | pd.date_range("20130101", periods=1) | pandas.date_range |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, time, timedelta, date
import sys
import os
import operator
from distutils.version import LooseVersion
import nose
import numpy as np
randn = np.random.randn
from pandas import (Index, Series, TimeSeries, DataFrame,
isnull, date_ran... | date_range('1/1/2000', '1/1/2010') | pandas.date_range |
import numpy as np
import pandas as pd
from gym.utils import seeding
import gym
from gym import spaces
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from dl_for_env import call_model
# Global variables
# HMAX_NORMALIZE = 10
# INITIAL_ENERGY = 1000
PLANT_DIM = 1
EFF_PUMP = 0.9
EFF_ERD = 0.8
... | pd.DataFrame(self.rewards_memory) | pandas.DataFrame |
import pandas as pd
import numpy as np
import random
def generate_variants(seq):
# generate a list of all possible variants of a sequence
variants = []
variant_nt = []
variant_pos = []
nts = ['A', 'C', 'T', 'G']
for i, seq_nt in enumerate(seq):
for N in nts:
if seq_nt != N:
new_seq = seq[... | pd.DataFrame({'sequence': sequence_df.sequence, 'prediction': sequence_predictions}) | pandas.DataFrame |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import operator
from itertools import product, starmap
from numpy import nan, inf
import numpy as np
import pandas as pd
from pandas import (Index, Series, DataFrame, isnull, bdate_range,
NaT, date_range, ti... | zip(args, intervals) | pandas.compat.zip |
import numpy as np
from datetime import timedelta
from distutils.version import LooseVersion
import pandas as pd
import pandas.util.testing as tm
from pandas import to_timedelta
from pandas.util.testing import assert_series_equal, assert_frame_equal
from pandas import (Series, Timedelta, DataFrame, Timestamp, Timedelt... | tm.assert_index_equal(result, expected) | pandas.util.testing.assert_index_equal |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
"""
This file contains utility functions for creating features for time
series forecasting applications. All functions defined assume that
there is no missing data.
"""
import calendar
import itertools
import pandas as pd
import numpy as np
from... | pd.DataFrame({"Datetime": datetime_col, "value": value_col}) | pandas.DataFrame |
import requests
import pandas as pd
import numpy as np
from tempfile import NamedTemporaryFile
import os
import subprocess
from astropy.io import fits
import matplotlib.pyplot as plt
from . import spacegeometry
def getChandraObs(
obsID,
fileList
):
pass
def getHeaderInfo(
key,
... | pd.read_csv(f.name, sep='\t', comment='#') | pandas.read_csv |
import logging
from abc import ABCMeta, abstractmethod
from contextlib import contextmanager, asynccontextmanager
from typing import (
Union, Sequence, List, Tuple,
Type, ContextManager, AsyncContextManager,
Iterator, AsyncIterator,
)
import pandas as pd
import aioodbc
import MySQLdb.connections
logger = ... | pd.DataFrame(columns=self.headers) | pandas.DataFrame |
from kafka import KafkaConsumer
from pathlib import Path
from requests import post, exceptions, put
from requests.auth import HTTPBasicAuth
from logger import log
import tensorflow as tf
import pandas as pd
import json
import os
HOME_SERVICE_AUTH = HTTPBasicAuth('model-builder', 'secret')
MODELS_BASE_PATH = '/models' ... | pd.concat([Y_data, sensors_df.iloc[0:1]], ignore_index=True) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Save atlases propagation results using registration with dense displacement fields predicted from networks.
@author: <NAME>
@version: 0.1
"""
from __future__ import print_function, division, absolute_import, unicode_literals
from core import model_ddf_mvmm_label_base as model
f... | pd.ExcelWriter(metrics_path) | pandas.ExcelWriter |
import os
import requests
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import sqlalchemy
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics impo... | pd.read_csv(raw_data) | pandas.read_csv |
import argparse
import numpy as np
import pandas as pd
from settings import experiments, lambdas, functions, TRANSIENT_VALUE, RESULT_DIR
from statistics import response_time_blockchain, number_users_system, calculate_transient, mean_error, \
bar_plot_metrics, bar_plot_one_metric, plot_transient, new_plot, new_plo... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
import json
import math
import sys
import glob
import argparse
import os
from collections import namedtuple, defaultdict
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.lines import Line2D
from matplotlib.ticker import MaxNLocator
impo... | pandas.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
"""
Functions for radiosonde related calculations.
"""
import warnings
import numpy as np
import pandas as pd
import xarray as xr
from act.utils.data_utils import convert_to_potential_temp
try:
from pkg_resources import DistributionNotFound
import metpy.calc as mpcalc
METPY_AVAILABLE = True
except Import... | pd.Series(obj[height].values) | pandas.Series |
import pytest
import collections
from pathlib import Path
import pandas as pd
from mbf_genomics import DelayedDataFrame
from mbf_genomics.annotator import Constant, Annotator
import pypipegraph as ppg
from pypipegraph.testing import run_pipegraph, force_load
from pandas.testing import assert_frame_equal
from mbf_genomi... | pd.DataFrame({"A": [1, 2], "B": ["c", "d"]}) | pandas.DataFrame |
from email import header
import select
from bs4 import BeautifulSoup
from selenium import webdriver
import pandas as pd
from selenium.common.exceptions import NoSuchElementException
import pickle
from connect_to_db import DatabaseConnection
from selenium.webdriver.support.ui import Select
# from cleaning_pickle... | pd.concat([data.tables[self.name], df_stats], ignore_index=True) | pandas.concat |
"""Tests for the sdv.constraints.tabular module."""
import uuid
import numpy as np
import pandas as pd
import pytest
from sdv.constraints.errors import MissingConstraintColumnError
from sdv.constraints.tabular import (
Between, ColumnFormula, CustomConstraint, GreaterThan, Negative, OneHotEncoding, Positive,
... | pd.testing.assert_frame_equal(expected_out, out) | pandas.testing.assert_frame_equal |
from __future__ import division
import copy
import bt
from bt.core import Node, StrategyBase, SecurityBase, AlgoStack, Strategy
from bt.core import FixedIncomeStrategy, HedgeSecurity, FixedIncomeSecurity
from bt.core import CouponPayingSecurity, CouponPayingHedgeSecurity
from bt.core import is_zero
import pandas as p... | pd.date_range('2010-01-01', periods=3) | pandas.date_range |
import pandas as pd
import BeautifulSoup as bs
import requests
import pickle
import os
import os.path
import datetime
import time
def promt_time_stamp():
return str(datetime.datetime.fromtimestamp(time.time()).strftime('[%H:%M:%S] '))
def get_index_tickers(list_indexes=list(), load_all=False):
tickers_all =... | pd.read_html('https://en.wikipedia.org/wiki/FTSE_100_Index') | pandas.read_html |
import datetime
import fileinput
import glob
import gzip
import multiprocessing
import os
import random # for log file names
import re
import shutil
import subprocess
import sys
import time
import urllib as ul # for removing url style encoding from gff text notes
from pathlib import Path
import configargparse
import ... | pd.ExcelWriter('merged_result.xlsx', engine='xlsxwriter') | pandas.ExcelWriter |
import sys
import pandas as pd
import numpy as np
import catboost
DUR_RU = 'Длительность разговора с оператором, сек'
DUR_EN = 'oper_duration'
RU_COLS = [
'Время начала вызова', 'Время окончания вызова', 'Время постановки в очередь',
'Время переключения на оператора', 'Время окончания разговора с оператором'... | pd.read_csv(input_csv, index_col='id') | pandas.read_csv |
from __future__ import division # brings in Python 3.0 mixed type calculation rules
import datetime
import inspect
import numpy.testing as npt
import os.path
import pandas as pd
import pkgutil
import sys
from tabulate import tabulate
import unittest
try:
from StringIO import StringIO
except ImportError:
from i... | pd.concat([result,expected], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Sat Feb 16 09:50:42 2019
@author: michaelek
"""
import os
import numpy as np
import pandas as pd
import yaml
from allotools.data_io import get_permit_data, get_usage_data, allo_filter
from allotools.allocation_ts import allo_ts
from allotools.utils import grp_ts_agg
# from alloto... | pd.DataFrame([{'wap': s['ref'], 'lon': s['geometry']['coordinates'][0], 'lat': s['geometry']['coordinates'][1]} for s in stns_waps]) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'chengzhi'
"""
tqsdk.ta 模块包含了一批常用的技术指标计算函数
"""
import numpy as np
import pandas as pd
import numba
from tqsdk import ta_func
def ATR(df, n):
"""平均真实波幅"""
new_df = pd.DataFrame()
pre_close = df["close"].shift(1)
new_df["tr"] = np.where(df["h... | pd.Series(dmf) | pandas.Series |
#!/usr/bin/env python3.5
""" Predict GE using trained GNN model """
import argparse
import subprocess
import os, sys
import numpy as np
import pandas as pd
_script_dir = os.path.dirname(os.path.realpath(__file__))
def get_arg_parser():
""" Build command line parser
Returns:
command line parser
"""... | pd.read_csv(input_data_filename) | pandas.read_csv |
import unittest
import pandas as pd
import numpy as np
from scipy.sparse.csr import csr_matrix
from string_grouper.string_grouper import DEFAULT_MIN_SIMILARITY, \
DEFAULT_REGEX, DEFAULT_NGRAM_SIZE, DEFAULT_N_PROCESSES, DEFAULT_IGNORE_CASE, \
StringGrouperConfig, StringGrouper, StringGrouperNotFitException, \
... | pd.Series(['foo', 'bar', 'bop']) | pandas.Series |
# Rutina que preprocesa y transforma los datos para series de tiempo
# <NAME>
# <NAME>
# ------------------------------------------------------------------
# Entrada: 2 o mas archivos .csv asincronos.
# Salida: Archivo binario hdf5 con chunks de datos sincronizados
#
# Cada archivo csv debe tener una columna temporal,... | pandas.Interval(timeArray[leftIdx], timeArray[rightIdx]) | pandas.Interval |
import pathlib
import pytest
import pandas as pd
import numpy as np
import gradelib
EXAMPLES_DIRECTORY = pathlib.Path(__file__).parent / "examples"
GRADESCOPE_EXAMPLE = gradelib.Gradebook.from_gradescope(
EXAMPLES_DIRECTORY / "gradescope.csv"
)
CANVAS_EXAMPLE = gradelib.Gradebook.from_canvas(EXAMPLES_DIRECTORY ... | pd.Series(data=[2, 7, 15, 20], index=columns, name="A2") | pandas.Series |
from copy import deepcopy
import inspect
import pydoc
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas.util._test_decorators import (
async_mark,
skip_if_no,
)
import pandas as pd
from pandas import (
DataFrame,
Series,
date_range,
timedelta_range,
)
impo... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
import unittest
from unittest import mock
import pandas as pd
from matplotlib import pyplot as plt
import dataprofiler as dp
from dataprofiler.profilers import IntColumn
from dataprofiler.reports import graphs
@mock.patch("dataprofiler.reports.graphs.plt.show")
@mock.patch("dataprofiler.reports.graphs.plot_col_hist... | pd.Series([], dtype=str) | pandas.Series |
import numpy as np
import pandas as pd
import pickle as pkl
import tensorflow as tf
from optparse import OptionParser
import config
from inputs.data import load_question, load_train, load_test
from inputs.data import init_embedding_matrix
from models.model_library import get_model
from utils import log_utils, os_ut... | pd.read_csv(config.DATA_DIR + "/" + "dev_aug.csv") | pandas.read_csv |
import argparse
import numpy as np
import pandas as pd
from bashplotlib.histogram import plot_hist
from scipy.stats import gamma, beta, norm, randint, bernoulli
from eemeter.location import zipcode_to_station
from eemeter.weather import TMY3WeatherSource
from eemeter.weather import GSODWeatherSource
from eemeter.mode... | pd.DataFrame(consumption_rows) | pandas.DataFrame |
"""
Created by: <NAME>
Sep 7
IEEE Fraud Detection Model
- Add back ids
- Add V Features
"""
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
import sys
import matplotlib.pylab as plt
from sklearn.model_selection import KFold
from datetime import d... | pd.read_parquet('../input/test.parquet') | pandas.read_parquet |
"""
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('2020-12-18') | pandas.to_datetime |
from datetime import datetime
import gzip
import joblib
import linecache
import numpy as np
import os
import pandas as pd
import pyBigWig
import time
import torch
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
def extract_weights(net):
list_dicts = []
... | pd.unique(trainiddf["Organ"]) | pandas.unique |
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 15 17:07:38 2021
@author: perger
"""
# import packages
import pandas as pd
from datetime import timedelta, datetime
import pyam
import FRESH_clustering
from pathlib import Path
import glob
# Model name and version, scenario, region
model_name = 'FRESH:COM v2.0'
scenari... | pd.DataFrame() | pandas.DataFrame |
# License: Apache-2.0
import databricks.koalas as ks
import pandas as pd
import numpy as np
import pytest
from pandas.testing import assert_frame_equal
from gators.imputers.numerics_imputer import NumericsImputer
from gators.imputers.int_imputer import IntImputer
from gators.imputers.float_imputer import FloatImputer
f... | assert_frame_equal(X_new, X_expected_dict['float']) | pandas.testing.assert_frame_equal |
__all__ = ['class_error', 'groupScatter', 'linear_spline', 'lm', 'mae',
'plotPrediction', 'plot_hist', 'r2', 'statx', 'winsorize',]
import riptable as rt
import numpy as np
from .rt_enum import TypeRegister
from .rt_fastarray import FastArray
from .rt_numpy import zeros
# extra classes
import p... | pd.DataFrame({'X': X[goodFilt], 'Y': Y[goodFilt], 'Yhat': Yhat[goodFilt]}) | pandas.DataFrame |
import pandas as pd
import numpy as np
import datetime
from django.core.files import File
from django.core.exceptions import ObjectDoesNotExist
from fixtures_functions import *
from main.functions import max_num_asiento, crea_asiento_simple, extraer_asientos, crear_asientos, valida_simple, valida_compleja
class Test... | pd.DataFrame(asiento_dict) | pandas.DataFrame |
"""Combine demand, hydro, wind, and solar traces into a single DataFrame"""
import os
import time
import pandas as pd
import matplotlib.pyplot as plt
def _pad_column(col, direction):
"""Pad values forwards or backwards to a specified date"""
# Drop missing values
df = col.dropna()
# C... | pd.date_range(start='2016-01-01 01:00:00', end='2051-01-01 00:00:00', freq='1H') | pandas.date_range |
import pandas as pd
import cx_Oracle
import time
import os
from datetime import date
import omdt as odt
import xlwings
import wait_handdle as wth
pt = os.getcwd()
today = date.today()
omdb = os.getcwd() + "\\" + "OMDB.csv"
# lambda <args> : <return Value> if <condition > ( <return value > if <condition> else <return ... | pd.concat(df_cnct) | pandas.concat |
# -*- coding: utf-8 -*-
import warnings
from datetime import datetime, timedelta
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.errors import PerformanceWarning
from pandas import (Timestamp, Timedelta, Series,
DatetimeIndex, TimedeltaIndex,
... | pd.to_datetime(['now', pd.Timestamp.min]) | pandas.to_datetime |
from flask import Flask, render_template, jsonify, request
from flask_pymongo import PyMongo
from flask_cors import CORS, cross_origin
import json
import collections
import numpy as np
import re
from numpy import array
from statistics import mode
import pandas as pd
import warnings
import copy
from joblib import Mem... | pd.DataFrame.from_dict(dicQDA) | pandas.DataFrame.from_dict |
from dataclasses import replace
import datetime as dt
from functools import partial
import inspect
from pathlib import Path
import re
import types
import uuid
import pandas as pd
from pandas.testing import assert_frame_equal
import pytest
from solarforecastarbiter import datamodel
from solarforecastarbiter.io impor... | pd.Timestamp('2020-05-20T15:00Z') | pandas.Timestamp |
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 12 15:18:57 2018
@author: Denny.Lehman
"""
import pandas as pd
import numpy as np
import datetime
import time
from pandas.tseries.offsets import MonthEnd
def npv(rate, df):
value = 0
for i in range(0, df.size):
value += df.iloc[i] / (1 + rate) ** (i + 1... | pd.read_csv(filepath, sep=',', skiprows=0, header=2) | pandas.read_csv |
import unittest
import pandas
from data_set_info_data_class.data_class.data_set_info import DataSetInfo
from data_set_remover.classes.data_class.data_for_criteria_remove import DataForCriteriaRemove
from data_set_remover.depedency_injector.container import Container
from data_set_remover.exceptions.remover_exceptions... | pandas.DataFrame([[1]], columns=["Test"]) | pandas.DataFrame |
import pandas as pd
import numpy as np
import pdb
import sys
sys.path.append('../data')
from pytorch_data_operations import buildLakeDataForRNN_manylakes_finetune2, parseMatricesFromSeqs
import torch
import torch.nn as nn
import torch.utils.data
from torch.utils.data import Dataset, DataLoader
from torch.nn.init import... | pd.read_csv('../../metadata/pball_site_ids.csv', header=None) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 27 13:26:04 2020
@author: alex1
"""
import math
import numpy as np
import pandas as pd
# # debug
# mp = MpFunctions(data=df, freq=2, style='tpo', avglen=8, ticksize=24, session_hr=24)
# mplist = mp.get_context()
# #mplist[1]
# meandict = mp.get_mean()
# #meandict['volu... | pd.Series(bel4) | pandas.Series |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import pickle
import shutil
import sys
import tempfile
import numpy as np
from numpy import arange, nan
import pandas.testing as pdt
from pandas import DataFrame, MultiIndex, Series, to_datetime
# dependencies testing specific
import pytest
import recordlinka... | pdt.assert_series_equal(result, expected) | pandas.testing.assert_series_equal |
from __future__ import division
import copy
import bt
from bt.core import Node, StrategyBase, SecurityBase, AlgoStack, Strategy
import pandas as pd
import numpy as np
from nose.tools import assert_almost_equal as aae
import sys
if sys.version_info < (3, 3):
import mock
else:
from unittest import mock
def te... | pd.date_range('2010-01-01', periods=3) | pandas.date_range |
import functools
import numpy as np
import scipy
import scipy.linalg
import scipy
import scipy.sparse as sps
import scipy.sparse.linalg as spsl
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import logging
import tables as tb
import os
import sandy
import py... | pd.DataFrame(S) | pandas.DataFrame |
import os
from PIL import Image
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.gridspec as gridspec
from matplotlib.ticker import MultipleLocator
import seaborn as sns
import motmetrics as mm
from algorithms.aaa_util import convert_df
fro... | pd.read_csv(weight_path, header=None) | pandas.read_csv |
import datetime
import string
import matplotlib.dates
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from nltk import WordNetLemmatizer, LancasterStemmer, pos_tag, sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.sentiment import SentimentIntensityA... | pd.to_datetime(date) | pandas.to_datetime |
__author__ = "<NAME>, <NAME>"
__credits__ = ["<NAME>", "<NAME>"]
__maintainer__ = "<NAME>, <NAME>"
__email__ = "<EMAIL>"
__version__ = "0.1"
__license__ = "MIT"
import matplotlib.pyplot as plt
import numpy as np
import pandas
import pandas as pd
from matplotlib.ticker import NullFormatter
from idf_analysis import In... | pd.Timedelta(minutes=max_dur) | pandas.Timedelta |
from datetime import datetime
import warnings
import numpy as np
import pytest
from pandas.core.dtypes.generic import ABCDateOffset
import pandas as pd
from pandas import (
DatetimeIndex,
Index,
PeriodIndex,
Series,
Timestamp,
bdate_range,
date_range,
)
from pandas.tests.test_base import ... | pd.Series(idx2) | pandas.Series |
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