prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import datetime, timedelta
import numpy as np
import pytest
from pandas.errors import (
NullFrequencyError, OutOfBoundsDatetime, PerformanceWarning)
import pandas as pd
from pandas import (
DataFrame, ... | tm.box_expected(td1, box_with_array, transpose=False) | pandas.util.testing.box_expected |
import os
import math
import torch
import torch.nn as nn
import traceback
import pandas as pd
import time
import numpy as np
import argparse
from utils.generic_utils import load_config, save_config_file
from utils.generic_utils import set_init_dict
from utils.generic_utils import NoamLR, binary_acc
from utils.gene... | pd.Series(targets, name='Target') | pandas.Series |
import os
import time
import logging
import datetime
import pandas as pd
import pydicom as dicom
from pathlib import Path
from collections import defaultdict
from dicomweb_client.api import DICOMwebClient
from ._utils import *
try:
import progressbar as pg
except ImportError:
pg = None
has_progressbar = bool(p... | pd.Series(dtype=str) | pandas.Series |
import pandas as pd
import numpy as np
import metapy
from ast import literal_eval
class Searcher:
def __init__(self):
self.movies = None
self.filtered_movies = None
self.searched_movies = None
def read_file(self, file_name):
self.movies = | pd.read_csv(file_name, low_memory=False) | pandas.read_csv |
''' Evaluation off calibration metrics'''
import argparse
import os
import os.path
import ipdb
import random
import pickle
import csv
import numpy as np
import pandas as pd
import numpy.random as np_rand
import sklearn.calibration as skcal
import sklearn.metrics as skmetrics
import sklearn.linear_model as sklm
import... | pd.merge(df_feat_valid,shapelet_df,on=["AbsDatetime","PatientID"]) | pandas.merge |
import pytest
import pandas as pd
from pandas.testing import assert_frame_equal
import pypipegraph as ppg
from pathlib import Path
from mbf_genomics import DelayedDataFrame
from mbf_genomics.annotator import Annotator
def DummyAnnotatable(name):
return DelayedDataFrame(
name,
lambda: pd.DataFrame(... | assert_frame_equal(df, a.df, check_less_precise=2, check_dtype=False) | pandas.testing.assert_frame_equal |
""" I/O functions of the aecg package: tools for annotated ECG HL7 XML files
This module implements helper functions to parse and read annotated
electrocardiogram (ECG) stored in XML files following HL7
specification.
See authors, license and disclaimer at the top level directory of this project.
"""
# Imports ====... | pd.DataFrame() | pandas.DataFrame |
import tkinter as tk
import sys
from tkinter import filedialog
import random
import numpy as np
import pandas as pd
import math
import seaborn as sns
sys.path.append('Portplanering')
sys.path.append('Bilbokning/src')
from bilbokning import calculate_carriages
HEURISTICS = ['local_search',
'simulated_an... | pd.read_csv('Portplanering/aip.csv') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 7 10:31:21 2020
Script tp discover if the given df to detect any features for the stiffness
or any other target is feasible
@author: nikorose
"""
import pandas as pd
import numpy as np
import csv
from tpot import TPOTRegressor
from sklearn.model_... | pd.concat([y_test, predicted_regression], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
import numpy as np
from pandas import Series, DataFrame, Index, Float64Index
from pandas.util.testing import assert_series_equal, assert_almost_equal
import pandas.util.testing as tm
class TestFloatIndexers(tm.TestCase):
def check(self, result, original, indexer, getitem):
"""
... | assert_series_equal(result1, result3) | pandas.util.testing.assert_series_equal |
#
# 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.DataFrame({"a": [2], "b": [1]}) | pandas.DataFrame |
'''
Utility functions for running DeepSurv experiments
'''
import h5py
import scipy.stats as st
from collections import defaultdict
import numpy as np
import pandas as pd
import copy
import lasagne
def load_datasets(dataset_file):
datasets = defaultdict(dict)
with h5py.File(dataset_file, 'r') as fp:
... | pd.concat([xdf, dt, censor], axis=1) | pandas.concat |
"""
Plotting for the Huys task. The functions have been written
for the object in resourceAllocator.py (gradient-free
optimization - either CMAES or Bayesian Optimization for
the equal precision model).
Also plots comparison of gradient-based with gradient-free
after the relevant simulation results have been produced... | pd.merge(df, dm, left_index=True, right_index=True) | pandas.merge |
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 27 23:40:50 2018
@author: austin
20181213 add .drop_duplicates() for Dataframe
20181215 add combine csv in funtino
20181220 skip index column when import
20181230 add GetDetail() function for 近30天内成交
"""
import requests
import re
from bs4 import BeautifulSou... | pandas.concat([df,df2],ignore_index=True) | pandas.concat |
# LIBRARIES
import os
import pandas as pd
from siuba import group_by, ungroup, arrange, summarize, _
import numpy as np
import geopandas as gpd
from datetime import datetime
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import contextily as ctx
from shapely.geometry import box,... | pd.read_csv('/home/soniame/shared/spd-sdv-omitnik-waze/corona/geo_partition/figures/coarse_grid_distribution.csv') | pandas.read_csv |
#!/usr/bin/env python3
import sys
import struct
import pandas as pd
import matplotlib
# Must be before importing matplotlib.pyplot or pylab!
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
###############################################
dsize = 16
###################... | pd.DataFrame() | pandas.DataFrame |
import matplotlib
matplotlib.use('Agg')
from Swing.util.BoxPlot import BoxPlot
from matplotlib.backends.backend_pdf import PdfPages
from scipy import stats
import pdb
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
import os
import time
from Swing.util.mplstyle import style1
import s... | pd.DataFrame() | pandas.DataFrame |
import os
import pandas as pd
import numpy as np
import PIL
import matplotlib.pyplot as plt
import matplotlib
import json
matplotlib.use('Agg')
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from torch import optim
from hzhu_gen import *
from hzhu_data import *... | pd.DataFrame(loss_list) | pandas.DataFrame |
from numpy import save
import pandas as pd
import os
from glob import glob
import numpy as np
from shutil import copy
import argparse
def seg_id_extract_pr1954(p):
return "PAIRED_" + "_".join(p.split("_")[:-1])
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--seg... | pd.DataFrame(val_labels, columns=["filename", "words"]) | pandas.DataFrame |
from transformers import AutoTokenizer, AutoModelForMaskedLM, \
GPT2LMHeadModel, GPT2Tokenizer, \
RobertaForMaskedLM, RobertaTokenizer, BertTokenizer, BertForMaskedLM, \
BartForConditionalGeneration, BartTokenizer, XLNetTokenizer, T5Tokenizer
import torch
import json
from utils.constant import CUDA_DEVICE, ... | pd.DataFrame(data) | pandas.DataFrame |
from __future__ import absolute_import, print_function
import os
import pandas as pd
import numpy as np
from .BaseStructProtocol import BaseStructProtocol
from codifyComplexes.CodifyComplexException import CodifyComplexException
from computeFeatures.seqStep.seqToolManager import SeqToolManager
AA_CODE_ELEMENTS= SeqToo... | pd.merge(singleChainFeats, winData, how='inner', on=mergeOn) | pandas.merge |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from pandas.api.types import is_string_dtype
from pandas.api.types import is_numeric_dtype
import re
import warnings
import multiprocessing as mp
import matplotlib.pyplot as plt
import time
import os
import platform
from .condition_fun import *
from .info_... | pd.cut(dtm['value'], brk, right=False, labels=labels) | pandas.cut |
"""
data_curation_functions.py
Extract Kevin's functions for curation of public datasets
Modify them to match Jonathan's curation methods in notebook
01/30/2020
"""
import os
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib_venn import venn3
import seaborn as sns
impor... | pd.concat(lst) | pandas.concat |
# -*- coding: utf-8 -*-
import sys
sys.path.append('../train_code')
import numpy as np
import pandas as pd
from utils.utils import *
from train_config import args
from sklearn.neighbors import NearestNeighbors
import joblib
from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics import silhouette_score... | pd.Series(x) | pandas.Series |
import os
import pandas as pd
import numpy as np
import logging
import wget
import time
import pickle
from src.features import preset
from src.features import featurizer
from src.data.utils import LOG
from matminer.data_retrieval.retrieve_MP import MPDataRetrieval
from tqdm import tqdm
from pathlib import Path
from s... | pd.concat([df,df_portion]) | pandas.concat |
# pylint: disable=E1101,E1103,W0232
from datetime import datetime, timedelta
from pandas.compat import range, lrange, lzip, u, zip
import operator
import re
import nose
import warnings
import os
import numpy as np
from numpy.testing import assert_array_equal
from pandas import period_range, date_range
from pandas.c... | tm.makeCustomDataframe(5, 5) | pandas.util.testing.makeCustomDataframe |
import pandas as pd
import pytest
from pandas.testing import assert_series_equal
from long_duration_mdk import ( # calc_change_in_reserve,
calc_benefit_reserve,
calc_continuance,
calc_discount,
calc_interpolation,
calc_pv,
calc_pvfnb,
)
def test_calc_continuance():
mortality_rate = pd.Se... | pd.Series([0.95, 0.9, 0.85]) | pandas.Series |
"""
@FileName: make_csv.py
@Description: Implement make_csv
@Author: Ryuk
@CreateDate: 2022/01/10
@LastEditTime: 2022/01/10
@LastEditors: Please set LastEditors
@Version: v0.1
"""
import glob
import pandas as pd
import argparse
from sklearn.utils import shuffle
parser = argparse.ArgumentParser()
parser.add_argument(... | pd.DataFrame({"path": target_list, "label":target_label}) | pandas.DataFrame |
from flask import Flask, render_template, request, redirect, url_for, session
import pandas as pd
import pymysql
import os
import io
#from werkzeug.utils import secure_filename
from pulp import *
import numpy as np
import pymysql
import pymysql.cursors
from pandas.io import sql
#from sqlalchemy import create... | pd.concat([ss]) | pandas.concat |
"""
(C) Copyright 2019 IBM Corp.
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
... | pd.DataFrame() | pandas.DataFrame |
from copy import Error
import os
from typing import Type
from ase.parallel import paropen, parprint, world
from ase.db import connect
from ase.io import read
from glob import glob
import numpy as np
from gpaw import restart
import BASIC.optimizer as opt
import sys
from ase.constraints import FixAtoms,FixedLine
import p... | pd.DataFrame(adsorption_energy_dict) | pandas.DataFrame |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import logging
from datetime import datetime
from typing import Any, Optional, Tuple
import matplotlib.pyplot as plt
import num... | pd.concat([zeros, res_anomaly_magnitude_ts_val], copy=False) | pandas.concat |
"""
--------------------------
OFFLINE OPTIMAL BENCHMARK:
---------------------------
It uses IBM CPLEX to maximise the social walfare of a current network structure and task list by solving the current environment given the usual problem restrictions.
This represents the upper bound of the social walfare.
In order ... | pd.DataFrame(name_value_dict) | pandas.DataFrame |
"""
Topic: Claims Grouping Analysis Exercise
Author: <NAME>
Date Created: 05/10/2018
"""
import gc
import os
import sys
import pyodbc
import random
import textwrap
import warnings
import numpy as np
import pandas as pd
from time import time
from time import sleep
from copy import deepcopy
from datetime import timede... | pd.concat(chunkList, axis=0) | pandas.concat |
import numpy as np
import pytest
import pandas as pd
from pandas import (
CategoricalDtype,
CategoricalIndex,
DataFrame,
Index,
IntervalIndex,
MultiIndex,
Series,
Timestamp,
)
import pandas._testing as tm
class TestDataFrameSortIndex:
def test_sort_index_and_reconstruction_doc_exa... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
import argparse
import pandas as pd
import numpy as np
from tqdm import tqdm
import os
import pickle
from sklearn.decomposition import IncrementalPCA, MiniBatchDictionaryLearning
import gc
def load_subject(subject_filename):
with open(subject_filename, 'rb') as f:
subject_data = pickle.load(f)
return ... | pd.read_csv(_p) | pandas.read_csv |
"""
<NAME>
<EMAIL>
<EMAIL>
"""
"""
This is used to generate images containing data from a Slifer Lab NMR cooldown.
The NMR analysis toolsuite produces a file called "global_analysis.csv" which this program needs
in tandem with the raw DAQ .csv to form an image sequence that captures the cooldown datastream.
"""
impo... | pandas.to_datetime(ga_csv['time'], format="%Y-%m-%d %H:%M:%S") | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 14 11:30:55 2019
@author: <NAME>
"""
# import the necessary packages
import cv2
from PIL import Image
import numpy as np
import datetime
import os
import pandas as pd
#%% Set the output file location
run_data = datetime.datetime.now().strftime("%Y_%m_%d"... | pd.DataFrame(data) | pandas.DataFrame |
#%%
import time
from pathlib import Path
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import pandas as pd
import seaborn as sns
from graspologic.plot import pairplot
from graspologic.utils import get_lcc, pass_to_ranks, to_laplace
from sparse_decomposition import SparseMatrixApproximation
... | pd.read_csv(data_dir / "meta_data.csv", index_col=0) | pandas.read_csv |
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
import simplejson as json
import six
import os
from pandas.api.types import is_integer_dtype
from scipy.sparse import coo_matrix
import numpy as np
import pandas as pd
import h5py
from .core import (
get,
region_to_offset... | pd.Categorical.from_codes(chrom_col, chromnames, ordered=True) | pandas.Categorical.from_codes |
import pandas as pd
import numpy as np
import sklearn
import warnings
import sys
# sys.path.append('Feature Comparison/Basic.py')
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.metric... | pd.set_option('display.max_columns', 10000) | pandas.set_option |
"""
All Features CV Analysis
"""
import sys
import pickle
import pandas as pd
import matplotlib.pyplot as plt
import utils as u
#####################################################################################################
def run_module_2():
PATH = "./Metrics - 2/All Features CV Analysis (CC).pkl"
... | pd.DataFrame.from_dict(params[a]) | pandas.DataFrame.from_dict |
import json
import pandas as pd
class Teaproduction:
def __init__(self):
pass
def production(self):
frame_f = self.production_1()
frame_e = self.production_2()
frame = pd.concat([frame_f , frame_e])
frame.columns = ['生產重量']
... | pd.DataFrame(data) | pandas.DataFrame |
# pandas and numpy for data manipulation
import pandas as pd
import numpy as np
import sqlite3
from bokeh.plotting import Figure
from bokeh.models import (
CategoricalColorMapper,
HoverTool,
ColumnDataSource,
Panel,
FuncTickFormatter,
SingleIntervalTicker,
LinearAxis,
Legend,
)
from bok... | pd.to_datetime(vmstat["datetime"]) | pandas.to_datetime |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
"""
@version:
@author: li
@file: factor_operation_capacity.py
@time: 2019-05-30
"""
import gc
import sys
sys.path.append('../')
sys.path.append('../../')
sys.path.append('../../../')
import six, pdb
import pandas as pd
from pandas.io.json import json_normalize
from utili... | pd.merge(factor_derivation, management, how='outer', on="security_code") | pandas.merge |
# -*- coding: utf-8 -*-
"""
Created on Fri May 15 01:55:22 2020
@author: balajiramesh
"""
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 10 00:25:12 2020
@author: balajiramesh
Raw : 16,319230 2,641562
Within study timeline: 14393806 2247749
Within study area and timeline: 7892752 1246896
AFter removing washout pe... | pd.to_numeric(sp.ETHNICITY,errors="coerce") | pandas.to_numeric |
import streamlit as st
import mysql.connector
from fbprophet import Prophet
from fbprophet.plot import plot_plotly
from fbprophet.plot import plot_components_plotly
from fbprophet.diagnostics import cross_validation
from fbprophet.diagnostics import performance_metrics
from plotly import graph_objs as go
import pand... | pd.read_sql_query("SELECT * FROM sales_order WHERE date >= '2021-03-01 00:00:00'", connection) | pandas.read_sql_query |
import collections
from datetime import timedelta
from io import StringIO
import numpy as np
import pytest
from pandas._libs import iNaT
from pandas.compat.numpy import np_array_datetime64_compat
from pandas.core.dtypes.common import needs_i8_conversion
import pandas as pd
from pandas import (
Dat... | TimedeltaIndex(["1 days"], name="dt") | pandas.TimedeltaIndex |
import argparse
import copy
import cPickle
import matplotlib.pyplot as plt
import ntpath
import numpy as np
import pandas as pd
import pylab
#from pylab import plot, show, savefig, xlim, figure, hold, ylim, legend, boxplot, setp, axes, xlabel, ylabel
import scipy
import time
import sys, os, re
from sklearn.decompositi... | pd.concat([dc_data_validation, ndc_data_validation]) | pandas.concat |
# Core Pkg
import streamlit as st
import streamlit.components.v1 as stc
# EDA Pkgs
import pandas as pd
# Data Vis Pkgs
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use("Agg")
# Opening Files/Forensic MetaData Extraction
# For Images
from PIL import Image
import exifread
import... | pd.concat([df_file_details, df_file_details_with_pdf]) | pandas.concat |
import numpy as np
import pandas as pd
returns = prices.pct_change()
returns.dropna()
returns.std()
deviations = (returns - returns.mean())**2
squared_deviations = deviations ** 2
variance = squared_deviations.mean()
volatility = np.sqrt(variance)
me_m = pd.read_csv('./Data/Portfolios_Formed_on_ME_monthly_EW.csv',... | pd.concat(var_list, axis=1) | pandas.concat |
import pandas as pd
import os
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import random
from sklearn import svm
from keras.optimizers import Adam
from keras.layers import LeakyReLU
from nltk.stem import WordNetLemmatizer
import operator
from textblob import TextBl... | pd.read_csv('clean.csv') | pandas.read_csv |
# 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... | ensure_index(columns) | pandas.core.indexes.api.ensure_index |
"""
This example reads texts and return the average glove embeddings for sentence.
>>> get_features(
>>> tweet_samples,
>>> embedding=WordEmbedding(model),
>>> preprocessor=TweetPreprocessor(normalize=['link', 'mention']),
>>> tokenizer=TweetTokenizer()
>>> ).shape
>>> (5, 100)
"""
from typing impo... | pd.Series(texts) | pandas.Series |
from __future__ import print_function
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from pandas import (Series, Index, Int64Index, Timestamp, Period,
DatetimeIndex, PeriodIndex, TimedeltaIndex,
Timedelta, timedelta_range, date_range, Float64Index... | pd.offsets.Minute() | pandas.offsets.Minute |
import pysam
import pandas as pd
import os
input_files = snakemake.input
df = pd.DataFrame(columns=["Sample"])
for sample_file in input_files:
variant_file = pysam.VariantFile(sample_file)
sample_name = os.path.basename(sample_file).split(".")[0]
gene_variant_dict = {"Sample": [sample_name]}
for rec ... | pd.concat([df, sample_df], join="outer", ignore_index=False, sort=False) | pandas.concat |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.3.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %% [markdown]
"""
#... | pd.DataFrame.from_records(data) | pandas.DataFrame.from_records |
import pandas as pd
import numpy as np
import json
import csv
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm_notebook as tqdm
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.tokenize import sent_tokenize
from nltk.stem import WordNetLemmatizer
impor... | pd.DataFrame(data=btgv,columns=['var']) | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import warnings
from sklearn import metrics
from sklearn.exceptions import UndefinedMetricWarning
from sklearn.calibration import CalibratedClassifierCV
from .preprocessing import horizontal_... | pd.DataFrame(index=binned.index) | pandas.DataFrame |
import os
import sys
import cv2
import glob
import hashlib
import numpy as np
import pandas as pd
from filelock import FileLock
from multiprocessing.pool import ThreadPool
import uoimdb as uo
from uoimdb.tagging.image_processing import ImageProcessor
from uoimdb.tagging.app import user_col
import traceback
class ... | pd.read_csv(f, index_col='src') | pandas.read_csv |
## 1. Introduction ##
import pandas as pd
happiness2015 = pd.read_csv("World_Happiness_2015.csv")
happiness2016 = pd.read_csv("World_Happiness_2016.csv")
happiness2017 = pd.read_csv("World_Happiness_2017.csv")
happiness2015['Year'] = 2015
happiness2016['Year'] = 2016
happiness2017['Year'] = 2017
## 2. Combining Data... | pd.merge(left=three_2015, right=three_2016, on='Country') | pandas.merge |
from argparse import ArgumentParser
import os, sys
import cv2
import numpy as np
import pandas as pd
import torch
import pytorch_lightning as pl
from pytorch_lightning import Trainer, loggers
from torchsummary import summary
import torch.nn.functional as F
sys.path.append('../../loaders/pytorch_lightning/')
from data... | pd.to_datetime(train['DateTime']) | pandas.to_datetime |
"""
Estimate results, inc. economic impacts.
Written by <NAME>.
February 2022.
"""
import os
import configparser
import pandas as pd
from tqdm import tqdm
import numpy as np
import geopandas as gpd
import rasterio
import random
from misc import params, technologies, get_countries, get_regions, get_scenarios
CONFI... | pd.read_csv(path) | pandas.read_csv |
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2017, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | pd.to_numeric(x, errors='ignore') | pandas.to_numeric |
import os
import sys
import xarray as xr
import numpy as np
import pandas as pd
from datetime import datetime
from dateutil.relativedelta import relativedelta
pkg_dir = os.path.join(os.path.dirname(__file__),'..')
sys.path.append(pkg_dir)
from silverpieces.functions import *
def fill_time_index(nd_array):
td = ... | pd.to_datetime('2007-12-31') | pandas.to_datetime |
import os
import ast
import math
import json
import logging
import pathlib
import numpy as np
import pandas as pd
import opendssdirect as dss
from .pydss_parameters import *
from jade.utils.timing_utils import track_timing, Timer
from disco import timer_stats_collector
from disco.enums import LoadMult... | pd.DataFrame(final_list) | pandas.DataFrame |
# from folders import dir_isomap_biclasse
from folders import dir_pca_biclasse, output_dir
from parameters import order, alphas
from statistics import Statistics
import pandas as pd
def main():
diag = Statistics()
#df = pd.read_csv('./../output_dir/results_multiclass_PCA.csv')
#diag.calcula_media_folds_multiclass(... | pd.read_csv('./../output_dir/resultado_media_multiclass_PCA.csv') | pandas.read_csv |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import math
import scipy.stats as stats
from matplotlib import gridspec
from matplotlib.lines import Line2D
from .util import *
import seaborn as sns
from matplotlib.ticker import FormatStrFormatter
import matplotlib.pylab as pl
import matplotlib.... | pd.isnull(row[aa]) | pandas.isnull |
import os.path
import json
import zipfile
import numpy as np
import pandas as pd
import requests
from openpyxl import load_workbook
import ukcensusapi.Nomisweb as Api
import ukpopulation.utils as utils
class SNPPData:
"""
Functionality for downloading and collating UK Subnational Population Projection (NPP) dat... | pd.read_csv(wales_raw) | pandas.read_csv |
from collections import defaultdict
import argparse
import sys
import pandas as pd
from sigtestv.database import ResultsDatabase
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--database-file', '-f', type=str, required=True)
parser.add_argument('--model-name', '-m', type=str, requir... | pd.DataFrame(df_data) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
from hypothesis import given, settings
from pandas.testing import assert_frame_equal
from janitor.testing_utils.strategies import (
conditional_df,
conditional_right,
conditional_series,
)
@pytest.mark.xfail(reason="empty object will pass thru")
@given(... | assert_frame_equal(expected, actual) | pandas.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
########################################################################
# NSAp - Copyright (C) CEA, 2021
# Distributed under the terms of the CeCILL-B license, as published by
# the CEA-CNRS-INRIA. Refer to the LICENSE file or to
# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
# f... | pd.read_csv(meta_path, sep="\t") | pandas.read_csv |
## main2a* is for plotting weights
# main2a1 is for preparing this data
#
# uses data from:
# reduced_model_results_sbrc/no_opto
# reduced_model_results_sbrc/no_opto_no_licks1
# reduced_model_results_sbrc_subsampling/no_opto
# DATASET/features
import json
import os
import pandas
import numpy as np
import my.de... | pandas.concat(
icpt_transformed_part_l, axis=1, keys=keys_l, names=['subsampling', 'dataset', 'model']) | pandas.concat |
from .metrics import accuracy
from .metrics import topk_acc
from .metrics import generalized_distance_matrix
from .metrics import generalized_distance_matrix_torch
from .chemspace import get_drug_batch
from typing import Optional, Sequence, Tuple, Union
import scipy.io as sio
import scipy.stats as st
from scipy impor... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
#import matplotlib.pyplot as plt
import pandas as pd
import os
import math
#import beeswarm as bs
import sys
import time
import pydna
import itertools as it
import datetime
import dnaplotlib as dpl
import matplotlib.pyplot as plt
import matplotlib.transforms as mtransforms
import matplotlib.patches a... | pd.DataFrame.append(dfs["parts_1"],dfs["Gibson"]) | pandas.DataFrame.append |
import pandas as pd
import numpy as np
import sys
import traceback
from tqdm.auto import tqdm
import os
import csv
import git
import sys
repo = git.Repo("./", search_parent_directories=True)
homedir = repo.working_dir
def get_date(x):
return '-'.join(x.split('-')[:3])
def get_fips(x):
return x.split('-')[-1]
... | pd.read_csv(submission) | pandas.read_csv |
# -*- coding: utf-8 -*-
import re
import numpy as np
import pandas as pd
def re_split_col(arr):
pattern = re.compile(r'(\d+)')
ret = [pattern.split(string) for string in arr]
data = [[str_list[0], ''.join(str_list[0:3]), ''.join(str_list[3:])] for str_list in ret]
data = np.array(data)
print(dat... | pd.read_csv('./decice_name.csv') | pandas.read_csv |
"""<2018.07.24>"""
import pandas as pd
import numpy as np
s= pd.Series([9904312,3448737,2890451,2466052],index=["Seoul","Busan","Incheon","Daegue"])
#print(s)
#print(s.index)
#print(s.values)
#s.name="인구"
#s.index.name="도시"
#print(s.index.name)
#시리즈에 연산을 하면 value에만 적용된다
#print(s/100000)
#print(s[(250e4<s)&(s<500e4)])
#... | pd.cut(titanic["age"],age_group,labels=level) | pandas.cut |
#!/usr/bin/env python
# coding: utf-8
# # SLIDING WINDOW SPLIT
# ### LOAD LIBRARIES
# In[ ]:
import os
import gc
import warnings
import pandas as pd
from pandas.plotting import register_matplotlib_converters
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pickle
warnings.filterwarn... | pd.to_numeric(df[col], downcast="integer") | pandas.to_numeric |
# encoding: utf-8
from opendatatools.common import RestAgent
from progressbar import ProgressBar
import demjson
import json
import pandas as pd
fund_type = {
"全部开放基金" : {"t": 1, "lx": 1},
"股票型基金" : {"t": 1, "lx": 2},
"混合型基金" : {"t": 1, "lx": 3},
"债券型基金" : {"t": 1, "lx": 4},
"指数型基金" : ... | pd.DataFrame(rsp) | pandas.DataFrame |
""" test scalar indexing, including at and iat """
from datetime import (
datetime,
timedelta,
)
import numpy as np
import pytest
from pandas import (
DataFrame,
Series,
Timedelta,
Timestamp,
date_range,
)
import pandas._testing as tm
from pandas.tests.indexing.common import Base
class T... | DataFrame({"a": [1, 2]}, index=[[1, 2], [3, 4]]) | pandas.DataFrame |
from unittest import TestCase
import pandas as pd
from datamatch.filters import DissimilarFilter, NonOverlappingFilter
class DissimilarFilterTestCase(TestCase):
def test_valid(self):
f = DissimilarFilter('agency')
index = ['agency', 'uid']
self.assertFalse(f.valid(
pd.Series(... | pd.Series(['123', 10, 12], index=index) | pandas.Series |
#----------------------------------------------------------------------------------------------
####################
# IMPORT LIBRARIES #
####################
import streamlit as st
import pandas as pd
import numpy as np
import plotly as dd
import plotly.express as px
import seaborn as sns
import matplotl... | pd.ExcelWriter(output, engine="xlsxwriter") | pandas.ExcelWriter |
from qualipy.exceptions import InvalidColumn
import pandas as pd
import numpy as np
import os
import types
import json
from typing import Any, Dict, Callable, Optional, Union
import importlib
def get_column(data: pd.DataFrame, name: str) -> pd.Series:
if name == "index":
return data.index
... | pd.to_datetime(data.date) | pandas.to_datetime |
import textwrap
import warnings
from functools import reduce, partial
import numpy as np
import pandas as pd
import torch
from hdxrate import k_int_from_sequence
from numpy.lib.recfunctions import append_fields
from scipy import constants
import pyhdx
from pyhdx.alignment import align_dataframes
from pyhdx.fileIO imp... | pd.concat([v.d_exp for v in self], keys=self.timepoints, axis=1) | pandas.concat |
__author__ = 'marcopereira'
import os
from datetime import date
import pandas as pd
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
WORKING_DIR = os.path.join(BASE_DIR, 'workspace')
trim_start = date(2005,1,10)
trim_end = date(2006,1,10)
start = date(2005, 3, 30)
referenceDate = date(2005, 3, 30) # 6 months af... | pd.DataFrame(x0Vas) | pandas.DataFrame |
import sys
import pandas as pd
from sqlalchemy import create_engine
def load_data(messages_filepath, categories_filepath):
"""
" Load the data from 2 csv files
"
" Args:
" messages_filepath: file path of the csv file containing the messages
" categories_filepath: file path of the csv file... | pd.merge(messages, categories, on='id') | pandas.merge |
import pandas as pd
from sklearn.decomposition import TruncatedSVD, NMF
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.manifold import TSNE
imp... | pd.read_csv('Analytics/reviews/reviews0.csv') | pandas.read_csv |
"""
Entry points for compass
"""
from __future__ import absolute_import, print_function, division
import argparse
import os
import multiprocessing
import numpy as np
import pandas as pd
import sys
import subprocess as sp
import logging
import datetime
import json
import gzip
from functools import partial
from tqdm impo... | pd.read_csv(args['latent_space'], sep='\t', index_col=0) | pandas.read_csv |
#Gathers domains that DHS site has membership in
#mm10_domains.csv generated by modification of Genome liftover of mm9_domains from domain paper in domain_id_assignment.py
#DHS_intergenic_#.csv generated by UNKONWN
#Exports DHS_#_with_domain.csv
import pandas as pd
import matplotlib.pyplot as plt
import csv
printe... | pd.read_csv(csv_file, header=None, index_col=False) | pandas.read_csv |
# -*- 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, ... | DataFrame(gen) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# Links:
# - [imports](#imports)
# - [Pytorch Lightning](#pytorch_lightning)
# - [Bouts](#bouts)
# - [Train](#train)
# - [Plot](#plot)
# # Imports <a id='imports'></a>
# In[771]:
import pandas as pd
import ast
import os
from glob import glob
import numpy as np
import scipy
fr... | pd.read_csv('acm_health_sleep_data-main/processed_mesa/MESA_pid_train.csv') | pandas.read_csv |
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
from dask import dataframe as dd
from woodwork.column_schema import ColumnSchema
from woodwork.logical_types import Categorical, Datetime, Double, Integer
import featuretools as ft
from featuretools import Timedelta
from featuretools.c... | pd.Series([0], dtype="Int64") | pandas.Series |
# coding: utf-8
# ## <u> go_chandra - Python </u>
#
# The follwoing code is a script adapted from Gladstone's *go_chandra* IDL script.
#
# The code takes the corrected file from *sso_freeze* (hardwired by user), peforms a corrdinate transformation on the X-ray emission to wrap the PSF around Jupiter and plot... | pd.DataFrame({'time': bigtime, 'x': bigxarr, 'y': bigyarr, 'pha': bigchannel}) | pandas.DataFrame |
import ffn
import pandas as pd
import numpy as np
from numpy.testing import assert_almost_equal as aae
try:
df = pd.read_csv('tests/data/test_data.csv', index_col=0, parse_dates=True)
except FileNotFoundError as e:
try:
df = pd.read_csv('data/test_data.csv', index_col=0, parse_dates=True)
except Fi... | pd.isnull(stats['yearly_sharpe']) | pandas.isnull |
# -*- coding: utf-8 -*-
"""DiamondRegression.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1NPXMsi1hxVlY2f0dRGNVwuoMmMSaeFWP
"""
import pandas as pd
df = pd.read_csv("diamonds.csv")
df.head()
cuts = {'Ideal': 0,'Premium': 1, 'Very Good': 2, "... | pd.DataFrame(X_train) | pandas.DataFrame |
import time
import config
import os
import mysql.connector
import pandas as pd
from WindPy import w
from importlib import resources
from helper.mysql_dbconnection import mysql_dbconnection
from helper.upload_github import upload_github
with resources.path('helper', 'mysql.cfg') as p:
resource_path = str(p)
if os.p... | pd.DataFrame(index=[wind_code]) | pandas.DataFrame |
import zmq
from datetime import datetime
import threading
from posttroll.message import Message
import os
import os.path
import pandas as pd
SDR_PUBLISHER = "tcp://viirscollector:29092"
PICKLE_DIR = "/viirs/pickle"
SDR_PICKLE = os.path.join(PICKLE_DIR, "sdr2.pickle")
class SdrSubscriber(threading.Thread):
def __... | pd.read_pickle(SDR_PICKLE) | pandas.read_pickle |
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
@pytest.fixture
def df_checks():
"""fixture dataframe"""
return pd.DataFrame(
{
"famid": [1, 1, 1, 2, 2, 2, 3, 3, 3],
"birth": [1, 2, 3, 1, 2, 3, 1, 2, 3],
"ht1": [2.... | assert_frame_equal(result, actual) | pandas.testing.assert_frame_equal |
import numpy as np
import pandas as pd
class Tracker2:
def __init__(self, means, T, store_rewards_arm=False):
"""
:param means: means for the different arms.
:param T: horizon.
:param store_rewards_arm: storing the rewards for the different arms.
"""
self.means = me... | pd.DataFrame(self.means, self.time_changes) | pandas.DataFrame |
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