prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import os
import time
import math
import json
import hashlib
import datetime
import pandas as pd
import numpy as np
from run_pyspark import PySparkMgr
graph_type = "loan_agent/"
def make_md5(x):
md5 = hashlib.md5()
md5.update(x.encode('utf-8'))
return md5.hexdigest()
def make... | pd.isnull(df.apply_id) | pandas.isnull |
import pytest
from SCNIC.general import simulate_correls
from SCNIC.correlation_analysis import df_to_correls, between_correls_from_tables, calculate_correlations, \
fastspar_correlation
import pandas as pd
from scipy.stats import pearsonr
from biom import load_table
from os import path
from numpy.testing import a... | pd.MultiIndex.from_tuples(index) | pandas.MultiIndex.from_tuples |
import configparser
import csv
import glob
import hashlib
import json
import logging
import os
import sys
from datetime import datetime, timedelta, timezone
import numpy as np
import pandas as pd
from flask import flash, url_for
from flask_login import current_user
from flask_mail import Message
from thewarden import... | pd.DataFrame({'trade_asset_ticker': list_of_tickers}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from sklearn.utils import shuffle as sk_shuffle
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import StandardScaler
from .rfpimp import oob_importances
class Data(object):
def __init__(self, shuffle=True, st... | pd.Series(y) | pandas.Series |
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
from numba import njit
import vectorbt as vbt
from tests.utils import record_arrays_close
from vectorbt.generic.enums import range_dt, drawdown_dt
from vectorbt.portfolio.enums import order_dt, trade_dt, log_dt
day_dt = np.timedelta64... | pd.Timedelta('3 days 00:00:00') | pandas.Timedelta |
import json
import numpy as np
import pandas as pd
from numba import jit, njit, prange
from sklearn.preprocessing import scale
def load_clades(clades_path="data/clades.json", size=30):
with open(clades_path, "r") as f:
clades = json.load(f)
clade_dict = {}
clade_sizes = {}
for k, v in clades... | pd.read_table(f, delimiter="\t", index_col=0) | pandas.read_table |
import streamlit as st
import cv2
import numpy as np
from lxml import etree
import pytesseract
from pytesseract import Output
import pandas as pd
from mmdetection.mmdet.apis import inference_detector, show_result, init_detector
# import mmcv
# import os
# import numpy as np
# from PIL import Image
# from mmdet.apis im... | pd.DataFrame.from_dict(d) | pandas.DataFrame.from_dict |
import numpy as np
import sys, os, glob, pathlib, csv, importlib
import pandas as pd
#from Geomodel_parameters import egen_project
def egen_paths(geomodeller, model, data=None):
"""define paths for different parts of the process"""
# arg path inputs need to be raw string to avoid escape issue eg. "\U" in C:\Us... | pd.DataFrame([]) | pandas.DataFrame |
# pylint: disable-msg=E1101,E1103
from datetime import datetime
import operator
import numpy as np
from pandas.core.index import Index
import pandas.core.datetools as datetools
#-------------------------------------------------------------------------------
# XDateRange class
class XDateRange(object):
"""
... | datetools.getOffset(timeRule) | pandas.core.datetools.getOffset |
import re
import pandas as pd
# import matplotlib
# matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
import matplotlib.ticker as ticker
import matplotlib.dates as mdates
import numpy as np
import seaborn as sns; sns.set()
from scipy.spatial.distance import squareform
from scip... | pd.Series([today, c, 'Today', msg, 1], index=df_events_owd.columns) | pandas.Series |
import pandas as pd
import numpy as np
def handle_missing_values(df, prop_required_row = 0.75, prop_required_col = 0.75):
''' function which takes in a dataframe, required notnull proportions of non-null rows and columns.
drop the columns and rows columns based on theshold:'''
#drop columns with nul... | pd.concat([zero_val, null_count, mis_val_percent], axis=1) | pandas.concat |
from __future__ import print_function, division, absolute_import
import collections
import functools as ft
import json
import operator as op
import os.path
import re
import pandas as pd
from pandas.core.dtypes.api import is_scalar
def escape_parameters(params):
if isinstance(params, dict):
return {k: es... | is_scalar(obj) | pandas.core.dtypes.api.is_scalar |
# Example of CBF for research-paper domain
# <NAME>
from nltk.stem.snowball import SnowballStemmer
import pandas as pd
from nltk.corpus import stopwords
# --------------------------------------------------------
user_input_data = "It is known that the performance of an optimal control strategy obtained from an off-li... | pd.concat([metadata, user_data], sort=True) | pandas.concat |
import pandas, numpy
from pandas.util import hash_pandas_object
from .warnings import ignore_warnings
from sklearn.metrics import r2_score, make_scorer
from sklearn.exceptions import DataConversionWarning
from sklearn.model_selection import cross_val_score, cross_val_predict, cross_validate
from sklearn.model_select... | hash_pandas_object(S) | pandas.util.hash_pandas_object |
# AUTOGENERATED! DO NOT EDIT! File to edit: 00_core.ipynb (unless otherwise specified).
__all__ = ['makeMixedDataFrame', 'getCrashes', 'is_numeric', 'drop_singletons', 'discretize']
# Cell
import pandas as pd
from pandas.api.types import is_numeric_dtype as isnum
#from matplotlib.pyplot import rcParams
# Cell
def ... | pd.Timestamp('2009-01-02 00:00:00') | pandas.Timestamp |
import openpyxl
import pandas as pd
REQUIRED_COLUMNS = ['<NAME>', 'Name', 'M/F', 'Field of Study', 'Nationality']
teaming_columns = ['1st', '2nd', 'Partner']
# Source: https://sashat.me/2017/01/11/list-of-20-simple-distinct-colors/
_colors = ['#e6194B', '#3cb44b', '#ffe119', '#4363d8', '#f58231', '#911eb4',
... | pd.ExcelWriter(f'{filename}.xlsx', engine='xlsxwriter') | pandas.ExcelWriter |
import ast
import time
import numpy as np
import pandas as pd
from copy import deepcopy
from typing import Any
from matplotlib import dates as mdates
from scipy import stats
from aistac.components.aistac_commons import DataAnalytics
from ds_discovery.components.transitioning import Transition
from ds_discovery.compone... | pd.to_datetime(max_date, errors='coerce', infer_datetime_format=True, utc=True) | pandas.to_datetime |
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(sorted_df, expected) | pandas._testing.assert_frame_equal |
import os
import unittest
import warnings
from collections import defaultdict
from unittest import mock
import numpy as np
import pandas as pd
import six
from dataprofiler.profilers import TextColumn, utils
from dataprofiler.profilers.profiler_options import TextOptions
from dataprofiler.tests.profilers import utils ... | pd.Series(["a", "aa", "a", "a"]) | pandas.Series |
#Move all functions to this file
import pandas as pd
import numpy as np
from urllib import request
import json
import csv
import re
import time
import random
global api_key
api_key = '<KEY>'
def clean_movie_name(movie_name):
value = movie_name.strip().replace(' ','+')
return value
def get_tmdb_movie_id(mo... | pd.read_csv("C:/Users/tam74426/MADS/SIADS 591/Project/data/ml-25m/movies.csv") | pandas.read_csv |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os
import matplotlib.ticker as tck
import matplotlib.font_manager as fm
import math as m
import matplotlib.dates as... | pd.concat([df_P348_Ref_Morning, df_P348_Ref_Afternoon]) | pandas.concat |
import tempfile
import copy
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
try:
from scipy.spatial import distance
from scipy.cluster import hierarchy
_no_scipy = False
except ImportError:
_no_scipy = True
try:
import fastcluster
assert fastclu... | pd.DataFrame(self.x_norm) | pandas.DataFrame |
import warnings
warnings.simplefilter("ignore", category=FutureWarning)
from pmaf.biome.essentials._metakit import EssentialFeatureMetabase
from pmaf.biome.essentials._base import EssentialBackboneBase
from pmaf.internal._constants import (
AVAIL_TAXONOMY_NOTATIONS,
jRegexGG,
jRegexQIIME,
BIOM_TAXONOMY... | pd.read_csv(filepath, **kwargs) | pandas.read_csv |
import os
import sys
import warnings
sys.path.append(os.path.abspath('../'))
import numpy as np
from tqdm import tqdm
from imageio import mimwrite
from skimage import img_as_float, img_as_uint
from skimage.io import imread, imsave
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from natsort ... | pd.Series(u_data['u'].values, index=tu, name=i) | pandas.Series |
"""
Core classes and functions of the pybps package
"""
# Common imports
import os
import sys
import re
import sqlite3
from copy import deepcopy
from multiprocessing import Pool, cpu_count, freeze_support
from time import time, sleep
from random import uniform
from shutil import copy, copytree
from string import Templ... | sql.read_frame(sql_query, cnx) | pandas.io.sql.read_frame |
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
def sigmoid(x):
return 1 / (1 + np.exp(-0.005 * x))
def sigmoid_derivative(x):
return 0.005 * x * (1 - x)
def read_and_divide_into_train_and_test(csv_file):
# Reading csv file here
df = pd.read_csv(csv_file)
# Dropping unne... | pd.to_numeric(df['Bare_Nuclei'], errors='coerce') | pandas.to_numeric |
#v1.0
#v0.9 - All research graph via menu & mouse click
#v0.8 - Candlestick graphs
#v0.7 - Base version with all graphs and bug fixes
#v0.6
import pandas as pd
from pandas import DataFrame
from alpha_vantage.timeseries import TimeSeries
from alpha_vantage.techindicators import TechIndicators
class PrepareTes... | pd.to_datetime(csvdf.index) | pandas.to_datetime |
"""
In the memento task, the behavioral responses of participants were written to
log files.
However, different participants played different versions of the task, and
different versions of the task saved a different amount of variables as a
Matlab struct into the log file.
This file contains information on the variabl... | pd.concat([df_disps, df_onsets, df_probs], axis=1) | pandas.concat |
import os
import pickle
import argparse
import pandas as pd
from gensim.models import (Word2Vec, KeyedVectors)
from gensim.models.fasttext import FastText
from util.params import Params
"""
Possibly useful resources:
https://radimrehurek.com/gensim/scripts/glove2word2vec.html
https://rare-te... | pd.DataFrame(train_set) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""Simply downloads email attachments.
Uses this handy package: https://pypi.org/project/imap-tools/
"""
import io
from os.path import join
import os
from datetime import datetime, timedelta
import pandas as pd
import numpy as np
from imap_tools import MailBox, A, AND
def get_from_email(star... | pd.Timedelta(days=90) | pandas.Timedelta |
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... | tm.assert_numpy_array_equal(l < pd.NaT, expected) | pandas.util.testing.assert_numpy_array_equal |
from functools import partial
import pandas as pd
import sqlalchemy as sa
from airflow.operators.python_operator import PythonOperator
from dataflow.dags import _PipelineDAG
from dataflow.operators.common import fetch_from_api_endpoint, fetch_from_hosted_csv
from dataflow.operators.covid19 import fetch_apple_mobility... | pd.to_datetime(df['Date']) | pandas.to_datetime |
# Copyright 2020 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | pd.DataFrame([[0.75]], columns=['ctr']) | pandas.DataFrame |
import os
import glob
import json
import logging
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from core.utils import Directories
from core.viz import plot_class_dist
class DataHandling(object):
def __init__(self):
pass
def drop_unique_cols(self, train,... | pd.concat([train, test]) | pandas.concat |
from sklearn.inspection import permutation_importance
from typing import List, Callable
from datetime import datetime
import matplotlib.pyplot as plt
import lightgbm as lgb
import pandas as pd
import numpy as np
import warnings
import optuna
import pickle
import tqdm
import shap
import time
import gc
from ..fit_mo... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 16 09:04:46 2017
@author: <NAME>
pygemfxns_plotting.py produces figures of simulation results
"""
# Built-in Libraries
import os
import collections
# External Libraries
import numpy as np
import pandas as pd
#import netCDF4 as nc
import matplotlib as mp... | pd.concat([main_glac_hyps_all, main_glac_hyps_region], sort=False) | pandas.concat |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.font_manager import FontProperties
from statsmodels.tsa import stattools
from statsmodels.graphics import tsaplots
class Chp023(object):
def __init__(self):
self.name = 'Chp022'
... | pd.to_datetime(sh_index.index) | pandas.to_datetime |
from __future__ import annotations
import random
import unittest
from dataclasses import dataclass
from typing import Dict, Iterator, List, Optional
import numpy as np
import pandas as pd
from gabriel_lego.cv.colors import LEGOColorID
class _NotEnoughBricks(Exception):
pass
class _NotEnoughSpace(Exception):
... | pd.set_option('display.max_columns', None) | pandas.set_option |
import re
import io
import demjson
import requests
import numpy as np
import pandas as pd
from fake_useragent import UserAgent
# TODO need add comments
url = {
"eastmoney": "http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx",
"fred_econ": "https://fred.stlouisfed.org/graph/fredgraph.csv?",
"OECD": ... | pd.to_datetime(df["Date"], format="%Y-%m-%d") | pandas.to_datetime |
import pandas as pd
import json
import os
from sklearn import preprocessing
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
base_folder = [".","output","experiments","attacks"]
default_targets = ["s","n","p","r","k","K","d","D","A","e","E"]
def heat_pivot(df, columns=["Source", "Target", "Va... | pd.DataFrame(x_scaled, index=table.index, columns=table.columns) | pandas.DataFrame |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import io
import os
import copy
import math
import json
import collections
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.n... | pd.concat(prob_all, axis=0) | pandas.concat |
import collections
import csv
import enum
import os
from typing import MutableMapping, Text, Tuple, Iterable, List
import pandas as pd
from absl import logging
from tapas_file_utils import (list_directory, make_directories)
from tapas_text_utils import (wtq_normalize)
_TABLE_DIR_NAME = 'table_csv' # Name that the t... | pd.DataFrame(data=sqa_data, columns=df_columns, dtype=str) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jan 9 13:55:53 2021
@author: Clement
"""
import pandas
import geopandas as gpd
import numpy
import os
import sys
import datetime
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
from gen_fct import file_fct
from gen_fct im... | pandas.to_datetime(df.index) | pandas.to_datetime |
#code will get the proper values like emyield, marketcap, cacl, etc, and supply a string and value to put back into the dataframe.
import pandas as pd
import numpy as np
import logging
import inspect
from scipy import stats
from dateutil.relativedelta import relativedelta
from datetime import datetime
from scipy import... | pd.Series(grossmargins) | pandas.Series |
# import libraries
import sys
import re
import nltk
nltk.download(['stopwords','punkt','wordnet'])
from nltk import word_tokenize, sent_tokenize
from nltk.corpus import stopwords, wordnet
from nltk.stem.porter import PorterStemmer
from nltk.stem.wordnet import WordNetLemmatizer
from sklearn.metrics import classificati... | pd.DataFrame(list_of_reports) | pandas.DataFrame |
# coding: utf-8
# ### Importing libraries and magics
# In[1]:
import sys
import os
sys.path.append(os.getcwd()+"/tools/")
#from tester import test_classifier
# In[2]:
#Importing libraries and magics
import sys
from matplotlib.colors import ListedColormap
import matplotlib.patches as mpatches
import matplotl... | pd.DataFrame({'Index_added':idx_added,'DS':DS,'Card':Card}) | pandas.DataFrame |
__author__ = "<NAME>"
__license__ = "Apache 2"
__version__ = "1.0.0"
__maintainer__ = "<NAME>"
__website__ = "https://llp.berkeley.edu/asgari/"
__git__ = "https://github.com/ehsanasgari/"
__email__ = "<EMAIL>"
__project__ = "1000Langs -- Super parallel project at CIS LMU"
import sys
import pandas as pd
sys.path.append... | Series(self.df['language_name'].values, index=self.df['trans_ID']) | pandas.Series |
import numpy as np
import pandas as pd
import pytest
import scipy.stats
from pyextremes import EVA, get_model
@pytest.fixture(scope="function")
def eva_model(battery_wl_preprocessed) -> EVA:
return EVA(data=battery_wl_preprocessed)
@pytest.fixture(scope="function")
def eva_model_bm(battery_wl_preprocessed) -> ... | pd.Series(data=[1, 2, 3], index=["2020", "2021", "2022"]) | pandas.Series |
import unittest
import attrdict as ad
import pandas as pd
# our imports
import emission.core.wrapper.motionactivity as ecwm
import emission.analysis.intake.segmentation.section_segmentation_methods.flip_flop_detection as eaissf
# Test imports
import emission.tests.common as etc
class TestFlipFlopDetection(unittest.T... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
from catboost import Pool
import shap
import numpy as np
import sys
from plotly.offline import init_notebook_mode
from IPython.core.display import display, HTML
import plotly.express as px
from catboost import CatBoostRegressor
import math
from sklearn.metrics import mean_absolute_error
import plotl... | pd.concat([total, percent], axis=1, keys=['Total_count', 'Percent']) | pandas.concat |
# -*- coding: utf-8 -*-
# Arithmetc tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import timedelta
import operator
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.core import ops
from pandas.errors import NullFrequency... | Timedelta('5m4s') | pandas.Timedelta |
from typing import List
import logging
import numpy
import pandas as pd
from libs.datasets.timeseries import TimeseriesDataset
from libs.datasets.population import PopulationDataset
from libs.datasets import data_source
from libs.datasets import dataset_utils
_logger = logging.getLogger(__name__)
def fill_missing_co... | pd.isnull(row.county) | pandas.isnull |
# -*- coding: utf-8 -*-
'''
:author <NAME>
:licence MIT
'''
import pandas as pd
import time
def raw2meta_extract(fn):
"""
Reasds raw2 files including GPS and enginerring information
Parameters
----------
fn : string
Path and filenmae of *.raw2 file
Returns
-------
data : pandas DataFrame
CTD (Salinity, ... | pd.to_timedelta(delta_t, unit='hours') | pandas.to_timedelta |
'''
example of loading FinMind api
'''
from FinMind.Data import Load
import requests
import pandas as pd
url = 'http://finmindapi.servebeer.com/api/data'
list_url = 'http://finmindapi.servebeer.com/api/datalist'
translate_url = 'http://finmindapi.servebeer.com/api/translation'
'''----------------TaiwanStockInfo-----... | pd.DataFrame(temp['data']) | pandas.DataFrame |
import ibis
from pandas import read_csv
from pandas.core.frame import DataFrame
import pytest
from sql_to_ibis import register_temp_table, remove_temp_table
from sql_to_ibis.tests.utils import (
DATA_PATH,
MULTI_LOOKUP,
MULTI_MAIN,
MULTI_PROMOTION,
MULTI_PROMOTION_NO_OVERLAP,
MULTI_RELATIONSHIP... | read_csv(DATA_PATH / "time_data.csv") | pandas.read_csv |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.ticker as tck
import matplotlib.cm as cm
import matplotlib.font_manager as fm
import math as m
import m... | pd.read_csv('/Users/cmcuervol/Dropbox/Codes/NathyTesis/Panel348.txt', sep=',', index_col =0) | pandas.read_csv |
from collections import OrderedDict
import numpy as np
import os
import pandas as pd
import warnings
from tqdm import tqdm
from . import quality_metrics
# from .wrappers import * # Except calculate_pc_metrics and calculate_metrics - they will be replaced below
def calculate_isi_violations(spike_times, spike_clust... | pd.concat([metrics, metrics3], axis=1) | pandas.concat |
import pandas as pd
from sklearn import linear_model
import statsmodels.api as sm
import numpy as np
from scipy import stats
df_all = pd.read_csv("/mnt/nadavrap-students/STS/data/imputed_data2.csv")
# df_all = pd.read_csv("/tmp/pycharm_project_723/new data sum info surg and Hosp numeric values.csv")
# # print(df_... | pd.merge(d3, dfmortf, on='HospID', how='outer') | pandas.merge |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# *****************************************************************************/
# * Authors: <NAME>
# *****************************************************************************/
"""transformCSV.py
This module contains the basic functions for creating the content of... | pandas.StringDtype() | pandas.StringDtype |
import numpy as np
import pandas as pd
import sys
import pickle
import matplotlib.pyplot as plt
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
import pyqtgraph
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtTest import *
from Model_modul... | pd.DataFrame(compared_db) | 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.
"""
This file implements the Bayesian Online Changepoint Detection
algorithm as a DetectorModel, to provide a common interface.
"""
import ... | pd.Series(change_prob) | pandas.Series |
# -*- coding: utf-8 -*-
"""DataFrame client for InfluxDB."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import math
from collections import defaultdict
import pandas as pd
from .client import InfluxDBClient
fro... | pd.concat(data) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 5 02:12:12 2022
@author: Kraken
Project: MHP Hackathon
"""
import os
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 16})
WORKING_DIR = "model_14"
WORKING_DIR2 = "model_12"
# "model_8": dqn with fixed weights
# "model_4": dqn
MVG_... | pd.Series(data) | pandas.Series |
# jupyter nbconvert ouxml/OU_XML2md_Converter.ipynb --to script --template cleaner_py.tpl
# black ouxml/*.py#!/usr/bin/env python
# coding: utf-8
# #!pip3 install markdownify
from bs4 import BeautifulSoup
from markdownify import markdownify as md
from pkg_resources import resource_string
import lxml.html
from lxml ... | pd.read_sql(q, DB.conn) | pandas.read_sql |
import logging
from datetime import date, timedelta
from typing import Dict
import pandas as pd
from databand import parameters
from dbnd import PipelineTask, output, parameter, task
from dbnd.testing.helpers_pytest import assert_run_task
from dbnd_test_scenarios.test_common.task.factories import TTask
class Dummy... | pd.DataFrame(data=[[1, 1]] * 5, columns=["c1", "c2"]) | pandas.DataFrame |
import pytest
import os
from mapping import util
from pandas.util.testing import assert_frame_equal, assert_series_equal
import pandas as pd
from pandas import Timestamp as TS
import numpy as np
@pytest.fixture
def price_files():
cdir = os.path.dirname(__file__)
path = os.path.join(cdir, 'data/')
files = ... | TS('2015-01-05') | pandas.Timestamp |
import csv
import sys
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import json
from os import listdir
from os.path import isfile, join
import re
monnomdistances={'C':0,'I':0,'D':1,'J':1,'K':2,'L':1,'M':2,'S':1,'T':2}
markersize=8
linewidth... | pd.read_csv(path,index_col=0) | pandas.read_csv |
import string
import numpy as np
import re
import random
import pandas as pd
import os
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
label_encoder = LabelEncoder()
... | pd.DataFrame(loaded['i']) | pandas.DataFrame |
from .base import Transformer
import pandas as pd
import numpy as np
import os
ISO_COUNTRY_CODES = os.path.join(os.path.dirname(__file__), 'countrycodes.csv')
class UNTransformer(Transformer):
""" Data source specific transformers """
def __init__(self, source, target):
super().__init__(source, targ... | pd.concat([self.growth_rate_df, growth_rate_copy], axis=1, sort=False) | pandas.concat |
"""
This script generates a train/test database on the basis of the given percentage
it takes the images and the annotations written on the same folder, it shuffles them,
then copy into the upper train/test folders and create a relative csv file to manipulate
with TensorFlow.
More details are coming with the code.
"""
... | pd.DataFrame(xml, columns=column_name) | pandas.DataFrame |
"""
Module to generate learning curves.
"""
import os
import pandas as pd
class Learning_Experiment:
# public
def __init__(self, config_obj, app_obj, util_obj):
self.config_obj = config_obj
self.app_obj = app_obj
self.util_obj = util_obj
def run_experiment(self, test_start=200, t... | pd.DataFrame(rows, columns=cols) | pandas.DataFrame |
import pytest
import os
import sys
from typing import List, Tuple
import pandas as pd
import torch
from torch import Tensor
sys.path.append(os.path.join(os.getcwd(), 'phishGNN'))
from dataset import PhishingDataset
def dataframe_mock(rows: List[Tuple[str, List, str]]):
refs = [[{"url": ref, "nb_edges": 1} for ... | pd.DataFrame(data=data) | pandas.DataFrame |
# Calculate parameters from counts
# Draw FD by using the special points of fundamental diagrams
import pandas as pd
import geopandas as gpd
import numpy as np
import pickle
import matplotlib.pyplot as plt
from tqdm import tqdm
from tqdm.contrib import tenumerate
import collections
import time
import copy
from scipy.op... | pd.DataFrame(parameters_arterial) | pandas.DataFrame |
#!/usr/bin/env python
"""regression_models.py: module is dedicated to produce the regression models."""
__author__ = "<NAME>."
__copyright__ = "Copyright 2020, SuperDARN@VT"
__credits__ = []
__license__ = "MIT"
__version__ = "1.0."
__maintainer__ = "<NAME>."
__email__ = "<EMAIL>"
__status__ = "Research"
import matpl... | pd.read_csv(pfname, parse_dates=["time"]) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# In[47]:
import requests # Include HTTP Requests module
from bs4 import BeautifulSoup # Include BS web scraping module
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import re
# In[48]:
gameID = 'loyola-university-chi... | pd.to_timedelta('00:25:00') | pandas.to_timedelta |
"""
Routines for casting.
"""
from contextlib import suppress
from datetime import date, datetime, timedelta
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
Optional,
Sequence,
Set,
Sized,
Tuple,
Type,
Union,
)
import numpy as np
from pandas._libs import lib, tslib, t... | tslibs.Timedelta(value) | pandas._libs.tslibs.Timedelta |
from __future__ import division
import logging
from os import path
import time
from ast import literal_eval
import traceback
from flask import request
from sqlalchemy.sql import select
from sqlalchemy.sql import text
import settings
import skyline_version
from skyline_functions import (
mkdir_p,
get_redis_con... | pd.DataFrame(yesterday_data) | pandas.DataFrame |
from neurovault.apps.statmaps.tasks import save_resampled_transformation_single
from neurovault.apps.statmaps.tests.utils import (clearDB, save_statmap_form)
from neurovault.apps.statmaps.models import (Collection)
from django.contrib.auth.models import User
from django.test import TestCase, Client
import pandas as pd
... | pd.DataFrame(response["data"], columns=response["columns"]) | pandas.DataFrame |
#!/usr/bin/env python3
"""
Created: March 10th, 2020
@author: <NAME>
PlotDecomposition works with matrix formats SigProfiler SBS-96, SBS-1536, DBS-78,
and ID-83. This program is intended to take two matrices.
(1) Sample matrix - A SigProfiler formatted SBS-96, SBS-1536, DBS-78, or ID-83
matrix.
(2) Basis matrix - A ... | pd.Series(recon_plot, name=denovo_name) | pandas.Series |
import wandb
from wandb import data_types
import numpy as np
import pytest
import os
import sys
import datetime
from wandb.sdk.data_types._dtypes import *
class_labels = {1: "tree", 2: "car", 3: "road"}
test_folder = os.path.dirname(os.path.realpath(__file__))
im_path = os.path.join(test_folder, "..", "assets", "test... | pd.DataFrame([[42], [42]]) | pandas.DataFrame |
"""
<NAME>017
Variational Autoencoder - Pan Cancer
scripts/vae_pancancer.py
Usage:
Run in command line with required command arguments:
python scripts/vae_pancancer.py --learning_rate
--batch_size
--epochs
... | pd.DataFrame(x, index=rnaseq_df.index, columns=rnaseq_df.columns) | pandas.DataFrame |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def __unitsFormat(unitsInput):
if unitsInput != "":
unitsOutput = " ("+unitsInput+")"
else:
unitsOutput = unitsInput
return unitsOutput
# solveData = pd.DataFrame(data = [[1,2,4,8,16,32,64,128],[1,1,2,3,4,3,2,1]], co... | pd.concat([self.currentData, newData], axis=1, sort=False) | pandas.concat |
"""Implements Survey class describing a single SEG-Y file"""
import os
import warnings
from copy import copy, deepcopy
from textwrap import dedent
import segyio
import numpy as np
import pandas as pd
from tqdm.auto import tqdm
from scipy.interpolate import interp1d
from .gather import Gather
from .utils import to_li... | pd.DataFrame(headers) | pandas.DataFrame |
import tkinter as tk
from IPython.display import display
from tkinter import filedialog
import pandas as pd
from pymongo import MongoClient
#conectando DB
client = MongoClient("mongodb+srv://jsoeiro:<EMAIL>/myFirstDatabase?retryWrites=true&w=majority")
print('conectado com o banco')
db = client['dbycar']
collection ... | pd.DataFrame(data_dict) | pandas.DataFrame |
import datetime
from collections import OrderedDict
import warnings
import numpy as np
from numpy import array, nan
import pandas as pd
import pytest
from numpy.testing import assert_almost_equal, assert_allclose
from conftest import assert_frame_equal, assert_series_equal
from pvlib import irradiance
from conftes... | pd.Series([0, 0, 1038.62, 254.53], index=times) | pandas.Series |
import pandas as pd
import numpy as np
en_plt = False
en_tabulate = False
try:
import matplotlib.pyplot as plt
en_plt = True
except Exception as err:
print("not able to load matplotlib.pyplot")
pass
try:
from tabulate import tabulate
en_tabulate = True
except Exception as err:
print("not abl... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
import covasim as cv # Version used in our study is 3.07
import random
from causal_testing.specification.causal_dag import CausalDAG
from causal_testing.specification.scenario import Scenario
from causal_testing.specification.variable import Input, Output
from causal_testing.spec... | pd.concat(simulations_results_dfs, ignore_index=True) | pandas.concat |
# -*- coding: utf-8 -*-
from __future__ import print_function
from distutils.version import LooseVersion
from numpy import nan, random
import numpy as np
from pandas.compat import lrange
from pandas import (DataFrame, Series, Timestamp,
date_range)
import pandas as pd
from pandas.util.testing im... | assert_frame_equal(inp, 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(result, df) | pandas._testing.assert_frame_equal |
#Calculate the Linear Regression between Market Caps
import pandas as pd
import numpy as np
import datetime as date
today = date.datetime.now().strftime('%Y-%m-%d')
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import plotly.io as pio
pio.renderers.default = "browser"
from checkonchain.g... | pd.merge_asof(BTC_data,LTC2,on='date') | pandas.merge_asof |
# -*- coding: utf-8 -*-
# @Time : 2018/10/3 下午2:36
# @Author : yidxue
import pandas as pd
from common.util_function import *
df1 = pd.DataFrame(data={'name': ['a', 'b', 'c', 'd'], 'gender': ['male', 'male', 'female', 'female']})
df2 = pd.DataFrame(data={'name': ['a', 'b', 'c', 'e'], 'age': [21, 22, 23, 20]})
pri... | pd.merge(df1, df2, on=['name'], how='outer') | pandas.merge |
import numpy as np
import pandas as pd
import pytest
@pytest.fixture(scope="module")
def df_vartypes():
data = {
"Name": ["tom", "nick", "krish", "jack"],
"City": ["London", "Manchester", "Liverpool", "Bristol"],
"Age": [20, 21, 19, 18],
"Marks": [0.9, 0.8, 0.7, 0.6],
"dob"... | pd.DataFrame(data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101
import itertools
import warnings
from warnings import catch_warnings
from datetime import datetime
from pandas.types.common import (is_integer_dtype,
is_float_dtype,
is_scalar)
from pandas.compat... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
#!/usr/bin/env python
# coding: utf-8
# # Benchmark Results
# This notebook visualizes the output from the different models on different classification problems
# In[1]:
import collections
import glob
import json
import os
import numpy as np
import pandas as pd
from plotnine import *
from saged.utils import split... | pd.concat([tissue_metrics, new_df]) | pandas.concat |
import pandas as pd
import pandas.testing as pdt
import pytest
from cape_privacy.pandas.transformations import ReversibleTokenizer
from cape_privacy.pandas.transformations import Tokenizer
from cape_privacy.pandas.transformations import TokenReverser
def test_tokenizer():
transform = Tokenizer(key="secret_key")
... | pdt.assert_frame_equal(df, expected) | pandas.testing.assert_frame_equal |
import pytest
import numpy as np
import pandas as pd
from delphi_jhu.geo import geo_map, add_county_pop, INCIDENCE_BASE
from delphi_utils import GeoMapper
from delphi_jhu.geo import geo_map, INCIDENCE_BASE
class TestGeoMap:
def test_incorrect_geo(self, jhu_confirmed_test_data):
df = jhu_confirmed_test_da... | pd.testing.assert_frame_equal(test_df, expected_df) | pandas.testing.assert_frame_equal |
#!/usr/bin/env python
# coding: utf-8
import os
import sys
import git
import shutil
import logging
import argparse
parser = argparse.ArgumentParser()
import time
from datetime import timedelta, datetime
from dateutil import tz
from jinja2 import Environment, FileSystemLoader
import yaml
import json
import pandas
import... | pandas.DataFrame(geojsonlayer) | 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... | tm.assertRaisesRegexp(ValueError, msg) | pandas.util.testing.assertRaisesRegexp |
# import pandas, numpy, and matplotlib
import pandas as pd
from feature_engine.encoding import OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.feature_selection import SelectKBest, chi2, mutual_info_classif
from feature_engine.discretisati... | pd.DataFrame({'score': ksel.scores_,
'feature': X_train_enc.columns},
columns=['feature','score']) | pandas.DataFrame |
"""
@author: <NAME>
file: main_queue.py
"""
from __future__ import print_function
from scoop import futures
import multiprocessing
import numpy as np
import pandas as pd
import timeit
import ZIPapliences as A_ZIP
class load_generation:
""" Class prepares the system for generating load
Attributes
--... | pd.read_csv(IF+'ZIP_spring.csv') | pandas.read_csv |
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