prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pandas as pd
import matplotlib.pyplot as plt
from alpha_vantage.timeseries import TimeSeries
#defining alpha-vantage API key
api = '888888888'
#collecting data from API/url
deaths = pd.read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_serie... | pd.to_datetime(silver['timestamp']) | pandas.to_datetime |
from scipy import misc
import numpy as np
import pandas as pd
import cv2
import sys, getopt
def main(argv):
# Getting arguments
inputFile = ''
inputSharpness = 0
try:
opts, args = getopt.getopt(argv,"hi:ms:",["input-file=","min-sharpness="])
except getopt.GetoptError:
print ('bestm... | pd.DataFrame(bestMoments, columns=['resized_frame', 'smiles', 'sharpness']) | pandas.DataFrame |
"""Main class and helper functions.
"""
import os
from enum import Enum
from collections import OrderedDict
from functools import reduce
from pathlib import Path
from typing import Any, Union, Optional
from typing import Iterable, Sized, Sequence, Mapping, MutableMapping
from typing import Tuple, List, Dict, KeysView
f... | pd.DataFrame(index=X.index) | pandas.DataFrame |
from __future__ import division
from unittest import TestCase
from nose_parameterized import parameterized
from pandas import (
Series,
DataFrame,
date_range,
datetime,
Panel
)
from pandas.util.testing import (assert_frame_equal,
assert_series_equal)
from pyfolio.c... | assert_frame_equal(dtlp, expected) | pandas.util.testing.assert_frame_equal |
# import module and libraries
import sys
sys.path.append('../')
from text_classification import text_classification as tc # noqa: E402
# ignoring E402 because need import sys and sys.path to access submodule
import pandas as pd # noqa: E402
import unittest # noqa: E402
from sklearn.feature_extraction.text import Co... | pd.read_csv("sample_yelp_data.csv") | pandas.read_csv |
# coding: utf-8
"""
Classifiers.
Based on sklearn doc:
"http://scikit-learn.org/dev/developers/contributing.html\
#rolling-your-own-estimator"
"""
from itertools import product
import numpy as np
import pandas as pd
from scipy.optimize import LinearConstraint, minimize
from sklearn.base import BaseEstimator, Classifi... | pd.DataFrame(dist_classes) | pandas.DataFrame |
## License: ?
## Copyright(c) <NAME>. All Rights Reserved.
## Copyright(c) 2017 Intel Corporation. All Rights Reserved.
import cmath
import math
import os
from utils import calculateAngle2d, calculateAngle3d, calculateAngleFromSlope, direction_string_generator, forwards_string_generator, is_reach_out_left, is_reach_ou... | pd.DataFrame(angle_data,columns=['Forward angle','Sway_angle']) | pandas.DataFrame |
#Descriptions: An inefficient script that scrubs unwanted streams and variables. Also reassigns node names to simplified names.
#Author: iblack
#Last updated: 2020-05-06
import os
import requests
import pandas as pd
import numpy as np
from pandas.io.json import json_normalize
os.chdir(r'')
master = pd.read_csv(r'')
... | pd.concat([dpt_df,t]) | pandas.concat |
import logging
import re
from datetime import datetime as dt
from datetime import timedelta as delta
import exchangelib as ex
import pandas as pd
from exchangelib import (DELEGATE, Account, Configuration, Credentials,
FaultTolerance)
from smseventlog import functions as f
from smseventlog imp... | pd.read_csv(data, header=header) | pandas.read_csv |
"""
assign cell identity based on SNR and UMI_min
"""
from celescope.__init__ import ROOT_PATH
from celescope.tools.step import Step, s_common
import celescope.tools.utils as utils
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import subprocess
import matplotlib
matplotlib.use('Agg')
def g... | pd.read_csv(self.tsne_file, sep="\t", index_col=0) | pandas.read_csv |
import load
import tokenizer
import pickle
import numpy as np
from collections import Counter
import pandas
import os
tags = ["eou", "eot"]
word_counts_path = "dumps/word_counts.pkl"
word_indices_parth = "dumps/word_indices.pkl"
_unk = "<UNK>"
_pad = "<PAD>"
def construct_indices_from_count():
""... | pandas.DataFrame(subtrains[i]) | pandas.DataFrame |
import numpy as np
import imageio
import os
import pandas as pd
from glob import glob
import matplotlib.pyplot as plt
from brainio_base.stimuli import StimulusSet
class Stimulus:
def __init__(self, size_px=[448, 448], bit_depth=8,
stim_id=1000, save_dir='images', type_name='stimulus',
... | pd.Series(all_p) | pandas.Series |
__version__ = 'v1'
__author__ = 'Vizerfur'
__function__ = ['del_unique_col','del_none_col','find_mul_class_col','translate',
'none_values_description','one_hot_encoder','data_info_desc']
__last_edit_time__ = 2/23/2020
import numpy
import random
import re
import pandas
import SDV.support
... | pandas.concat([df,oh],axis = 1) | pandas.concat |
# -*- coding: utf-8 -*-
"""
author: zengbin93
email: <EMAIL>
create_dt: 2021/11/17 22:11
describe: 配合 CzscAdvancedTrader 进行使用的掘金工具
"""
import os
import dill
import inspect
import czsc
import traceback
import pandas as pd
from gm.api import *
from datetime import datetime, timedelta, timezone
from collections import Ord... | pd.to_datetime(context.backtest_end_time) | pandas.to_datetime |
"""
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... | lib.is_interval(val) | pandas._libs.lib.is_interval |
import unittest
import pandas as pd
import numpy as np
from econ_watcher_reader.reader import EconomyWatcherReader
import logging
logging.basicConfig()
logging.getLogger("econ_watcher_reader.reader").setLevel(level=logging.DEBUG)
class TestReaderCurrent(unittest.TestCase):
@classmethod
def setUpClass(cls):
... | pd.datetime(2100, 1, 1) | pandas.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({'Value': [7]}, index=['count(X)']) | pandas.DataFrame |
import pandas as pd
from scipy.stats import chi2_contingency
import matplotlib.pyplot as plt
# Include all GENES, those containing Indels and SNVS (that's why I repeat this step of loading "alleles" dataframe) This prevents badly groupping in 20210105_plotStacked...INDELS.py
alleles = pd.read_csv('/path/to/Alleles_202... | pd.crosstab(dff_aux['from_general'], dff_aux[gene]) | pandas.crosstab |
import datetime
import re
from warnings import (
catch_warnings,
simplefilter,
)
import numpy as np
import pytest
from pandas._libs.tslibs import Timestamp
import pandas as pd
from pandas import (
DataFrame,
HDFStore,
Index,
Int64Index,
MultiIndex,
RangeIndex,
... | _maybe_remove(store, "df") | pandas.tests.io.pytables.common._maybe_remove |
import datetime as dt
import numpy as np
import pandas as pd
from tqdm import tqdm
from .. import utils
from ..ashare_data_reader import AShareDataReader
from ..data_source.data_source import DataSource
from ..database_interface import DBInterface
from ..factor import CompactFactor
from ..tickers import FundTickers, ... | pd.MultiIndex.from_tuples([(date, self.policy.ticker)], names=['DateTime', 'ID']) | pandas.MultiIndex.from_tuples |
#!/usr/bin/env python3
import sys
import pandas as pd
import numpy as np
import json
from datetime import datetime
from hashlib import md5
import os.path as path
import argparse
import os.path as path
import pysolr
from uuid import uuid1
DEBUG = True
filename = 'output/PATH_005'
filename = 'output/PATH_147'
filename... | pd.to_timedelta(df2['Schedule']) | pandas.to_timedelta |
import numpy as np
import pandas as pd
from nwp_cali import PrepareData
from sklearn.model_selection import train_test_split
from sklearn.decomposition import NMF
from sklearn.svm import SVR
from sklearn.pipeline import make_pipeline
from joblib import dump
import datetime
date = datetime.datetime.now().strftime('%Y%... | pd.concat([y_df, tmp_df], axis=1, join='outer') | pandas.concat |
"""<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.qcut(data,4,labels=["Q1","Q2","Q3","Q4"]) | pandas.qcut |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This python file evaluates the Machine learning models from the SKlearn libraries
using cross-validation method and output the test score to select top 5 models.
"""
import pandas
import csv
import numpy as np
import time
import signal
import warnings
from sklearn.mode... | pandas.read_csv('../out/train/X_PT_train.csv', delimiter=',', encoding='latin-1') | pandas.read_csv |
# write_Crosswalk_USGS_NWIS_WU.py (scripts)
# !/usr/bin/env python3
# coding=utf-8
# <EMAIL>
"""
Create a crosswalk linking the downloaded USGS_NWIS_WU to NAICS_12. Created by selecting unique Activity Names and
manually assigning to NAICS
"""
import pandas as pd
from flowsa.common import datapath
from scripts.common... | pd.DataFrame([['Industrial', '5111']], columns=['Activity', 'Sector']) | pandas.DataFrame |
"""
Test our groupby support based on the pandas groupby tests.
"""
#
# This file is licensed under the Pandas 3 clause BSD license.
#
from sparklingpandas.test.sp_test_case import \
SparklingPandasTestCase
from pandas import bdate_range
from pandas.core.index import Index, MultiIndex
from pandas.core.api import D... | assert_frame_equal(last, expected) | pandas.util.testing.assert_frame_equal |
import sys
import pandas as pd
import numpy as np
from sqlalchemy import create_engine
'''
run this file from root folder:
python3 datasets/process_data.py datasets/messages.csv datasets/categories.csv datasets/DisasterResponse.db
'''
def load_data(messages_filepath, categories_filepath):
"""
PARAMETER:
m... | pd.concat([messages, categories], axis=1) | pandas.concat |
import pickle
from pprint import pprint
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from functools import reduce
import sys
import time
from sklearn.decomposition import PCA
from sklearn import cluster as sklearn_clustering
from sklearn.neural_network import MLPClassifier
from sklearn.metric... | pd.DataFrame(M) | pandas.DataFrame |
from matplotlib import style
style.use('fivethirtyeight')
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fivethirtyeight')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import datetime as dt
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalche... | pd.DataFrame(last_year_tobs) | pandas.DataFrame |
"""
画K线文件，反应策略买入卖出节点。
"""
import os
import sys
import time
import threading
from multiprocessing import Pool, RLock, freeze_support
import numpy as np
import pandas as pd
from tqdm import tqdm
from rich import print as print
import CeLue # 个人策略文件，不分享
import func_TDX
import user_config as ucfg
from pyecharts.charts im... | pd.isna(row['低点价格']) | pandas.isna |
import numpy as np
import pandas as pd
import timeit
import resource
rsrc = resource.RLIMIT_DATA
limit = int(1e9)
resource.setrlimit(rsrc, (limit, limit))
import opt_einsum as oe
| pd.set_option('display.width', 200) | pandas.set_option |
import pandas as pd
from scipy import stats
import numpy as np
import math
import os
import sys
import json, csv
import itertools as it
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import scikit_posthocs
from statsmodels.sandbox.stats.multicomp import multiple... | pd.DataFrame() | pandas.DataFrame |
from datetime import datetime, timedelta
import numpy as np
import pytest
from pandas._libs.tslibs import period as libperiod
import pandas as pd
from pandas import DatetimeIndex, Period, PeriodIndex, Series, notna, period_range
import pandas._testing as tm
class TestGetItem:
def test_ellipsis(self):
#... | pd.Period("2017-09-02") | pandas.Period |
# TO DO
# 1. Fair probability
# 2. Hedge opportunities
# 3. Datapane map
# 4. Change since prior poll
# Import modules
import json
import requests
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import pandas as pd
pd.set_option('display.max_rows', None) #print all rows without truncatin... | pd.merge(df, recent_pres_polling, on=['state', 'answer'], how='left') | pandas.merge |
import numpy as np
import pandas as pd
import random
import plotly.express as px
from datetime import datetime
rows_to_keep = 43
sheet_data = pd.read_excel("https://docs.google.com/spreadsheets/d/1DuYUj2ODS8D3PWK42ZopUD1dqcg89ckI6vPn71LidGo/export?format=xlsx")
sheet_data = sheet_data.iloc[:rows_to_keep].dro... | pd.to_numeric(sheet_data["Starting Weight"]) | pandas.to_numeric |
from hydroDL import kPath, utils
from hydroDL.app import waterQuality
from hydroDL.data import gageII, usgs, gridMET
from hydroDL.master import basins
from hydroDL.post import axplot, figplot
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import os
import time
# read NTN
dirNTN = os.path.join(k... | pd.read_csv(fileSiteNo, header=None, dtype=str) | pandas.read_csv |
# ===============================================================================
# Copyright 2018 dgketchum
#
# 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... | read_csv(c) | pandas.read_csv |
'''Functions used for the primary analysis'''
import pandas as pd
import numpy as np
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import StratifiedKFold, cross_val_predict
from scipy.stats import binom, chi2, norm
from copy import deepcopy
from multiprocessing import Pool
def threshold(... | pd.DataFrame(cis, columns=['lower', 'upper']) | pandas.DataFrame |
import pandas as pd
import numpy as np
import argparse
import random
def create_context_to_id_map(df, df_sent):
context_to_id = {}
c_context_id = 0
context_ids = []
relevant_sentence_ids_arr = []
df = df.reset_index()
for index, row in df.iterrows():
# add the relevant sentences to the ... | pd.DataFrame() | pandas.DataFrame |
import asyncio
import copy
import logging
import talib as ta
from .exceptions import NotImplementedException
from sklearn.cluster import KMeans, DBSCAN, MeanShift
from sklearn.metrics import silhouette_score
import pandas as pd
import numpy as np
from itertools import groupby
from operator import itemgetter
from .utils... | pd.Series(fractal_line['bearish']) | pandas.Series |
from analytic_types.segment import Segment
import utils
import unittest
import numpy as np
import pandas as pd
import math
import random
RELATIVE_TOLERANCE = 1e-1
class TestUtils(unittest.TestCase):
#example test for test's workflow purposes
def test_segment_parsion(self):
self.assertTrue(True)
... | pd.Series(data) | pandas.Series |
import os
import sys
import sklearn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import re
import preprocessor as p
from sklearn.feature_extraction.text import CountVectorizer
from sklearn import svm
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import accuracy_score
from... | pd.DataFrame({'Tweets':X, 'Gender':y}) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2019/5/27 9:55 AM
# @Author : R
# @File : TMDB_Predict_Finally.py
# @Software: PyCharm
# coding: utf-8
# # Kaggle for TMDB
# In[1]:
import numpy as np
import pandas as pd
import warnings
from tqdm import tqdm
from datetime import datetime
from sklearn... | pd.merge(test, release_dates, how='left', on=['id']) | pandas.merge |
# -*- coding: utf-8 -*-
"""
Poop analysis
Created 2020
@author: PClough
"""
import pandas as pd
import numpy as np
import chart_studio
import plotly.graph_objects as go
from plotly.offline import plot
from plotly.subplots import make_subplots
from scipy import stats
import datetime as dt
from time i... | Timedelta(0, unit='h') | pandas.Timedelta |
import pandas as pd
from pandas.testing import assert_frame_equal
from evaluate.report import (
PrecisionReport,
RecallReport,
Report,
DelimNotFoundError,
ReturnTypeDoesNotMatchError
)
from evaluate.classification import AlignmentAssessment
import pytest
from io import StringIO
import math
from test... | assert_frame_equal(actual, expected, check_dtype=False) | pandas.testing.assert_frame_equal |
import pandas as pd
from pandas.testing import assert_frame_equal
from evaluate.report import (
PrecisionReport,
RecallReport,
Report,
DelimNotFoundError,
ReturnTypeDoesNotMatchError
)
from evaluate.classification import AlignmentAssessment
import pytest
from io import StringIO
import math
from test... | pd.read_csv(contents_1_input, sep="\t", keep_default_na=False) | pandas.read_csv |
import os
import sys
from os import path
import argparse
import subprocess
#import logging
import threading
import time
from datetime import datetime
import shutil
import numpy as np
import pandas as pd
import win32com.client as win32
import pythoncom
from file_read_backwards import FileReadBackwards
import EFT_Tool... | pd.concat([outputBus, outputCoa], axis=0) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 10 04:11:27 2017
@author: konodera
nohup python -u 501_concat.py &
"""
import pandas as pd
import numpy as np
from tqdm import tqdm
import multiprocessing as mp
import gc
import utils
utils.start(__file__)
#======================================... | pd.read_pickle('../input/mk/timezone.p') | pandas.read_pickle |
import copy
import inspect
import json
import os
import numpy as np
import pandas as pd
import pytest
from solarforecastarbiter.datamodel import Site, Observation
TEST_DATA_DIR = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
def site_dicts():
return [copy.deepcopy(site) for ... | pd.Timestamp('20190101T1200Z') | pandas.Timestamp |
import time
import os
import sys
import scipy
import math
import laspy
import psutil
import pickle
import logging
import numpy as np
import pandas as ps
import scipy.linalg
import datetime
import multiprocessing
import matplotlib as plt
from scipy import spatial
from sklearn import metrics
from numpy import linalg as L... | ps.DataFrame(data=col) | pandas.DataFrame |
import streamlit as st
from collections import defaultdict
from kafka import KafkaConsumer
from json import loads
import time
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
import PIL
from PIL import Imag... | pd.DataFrame({'lat': [], 'lon': [], 'z': [], 'mag': [], 'time': [], 'size': []}) | pandas.DataFrame |
import pandas as pd
from pandas.io.json import json_normalize
from TweetsToDB.TweetModel import Tweet
import json
#Need to create a dataframe in order to compute stats
def statTweets(jsonTweet):
options = ['tweetLikes', 'tweetRe', 'tweetTextCount']
formatted_options = ['Likes','Retweets', 'Character Count']
... | json_normalize(jsonTweet) | pandas.io.json.json_normalize |
"""
Author: <NAME>, Phd Student @ Ishida Laboratory, Department of Computer Science, Tokyo Institute of Technology
Created on: February 21st, 2020
Description: This file contains necessary functions for the generation and splitting of the raw original dataset.
"""
import os
import random
import numpy as np
impor... | pd.read_csv(args.dataset_config.raw_dataset) | pandas.read_csv |
import pandas as pd
import ast
import json
from psutil import test
from torch.utils import data
from transformers import BertTokenizerFast as fast_tokenizer
from transformers import AutoTokenizer
import torch
import numpy as np
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
i... | pd.read_csv(f'/{path}/{language}_train_data_cutoff.csv') | pandas.read_csv |
import numpy as np
from .base import EvaluationMethod
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
class TemporalMetric(EvaluationMethod):
def __init__(self, metric, label=None):
super(TemporalMetric, self).__init__()
self.metric = metric
self.ts = []
... | pd.DataFrame.from_dict({name: data[name][..., c] for name in data}) | pandas.DataFrame.from_dict |
# pylint: disable=W0612,E1101
from datetime import datetime
import os
import operator
import unittest
import numpy as np
from pandas.core.api import DataFrame, Index, notnull
from pandas.core.datetools import bday
from pandas.core.frame import group_agg
from pandas.core.panel import WidePanel, LongPanel, pivot
impo... | LongPanel.fromRecords(series, 'f0', 'f1', exclude=['f2']) | pandas.core.panel.LongPanel.fromRecords |
import json
import django
import sys
import os
os.environ['DJANGO_SETTINGS_MODULE'] = 'carebackend.settings'
sys.path.append(os.path.dirname(__file__) + '/..')
django.setup()
from places.models import Neighborhood, NeighborhoodEntry, Place, Area
from django.contrib.gis.geos import Polygon
import pandas as pd
from shape... | pd.read_csv(fl) | pandas.read_csv |
from __future__ import division
from datetime import datetime
import sys
if sys.version_info < (3, 3):
import mock
else:
from unittest import mock
import pandas as pd
import numpy as np
from nose.tools import assert_almost_equal as aae
import bt
import bt.algos as algos
def test_algo_name():
class Tes... | pd.date_range('2010-01-01', periods=3) | pandas.date_range |
#*- coding: utf-8 -*-
"""
Created on Sun Oct 9 17:37:42 2016
@author: noore
"""
from bigg import BiGG
from kegg import KEGG
import settings
import cache
import colorsys
import sys
from distutils.util import strtobool
import pandas as pd
import os
import json
import seaborn as sns
import numpy as np
from scipy.stats ... | pd.DataFrame.from_csv(settings.ECOLI_METAB_FNAME) | pandas.DataFrame.from_csv |
import nose
import unittest
from numpy import nan
from pandas.core.daterange import DateRange
from pandas.core.index import Index, MultiIndex
from pandas.core.common import rands, groupby
from pandas.core.frame import DataFrame
from pandas.core.series import Series
from pandas.util.testing import (assert_panel_equal,... | assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
import itertools
import numpy as np
import pandas as pd
def F_score(v, y_label):
x_0 = 0
x_1 = 0
v_pos = v[y_label > 0]
v_neg = v[y_label <= 0]
v_ave = np.mean(v)
v_pos_ave = np.mean(v_pos)
v_neg_ave = np.mean(v_neg)
len_pos = len(v_pos)
len_neg = len(v_neg)
... | pd.DataFrame(tes_positive_seq) | pandas.DataFrame |
"""Functions to generate metafeatures using heuristics."""
import re
import numpy as np
import pandas as pd
from pandas.api import types
def _raise_if_not_pd_series(obj):
if not isinstance(obj, pd.Series):
raise TypeError(
f"Expecting `pd.Series type as input, instead of {type(obj)} type."
... | pd.to_numeric(row, errors="coerce") | pandas.to_numeric |
import json
import os
import albumentations as alb
import numpy as np
import pandas as pd
import pytorch_lightning as pl
import torch
from albumentations.pytorch import ToTensorV2
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MultiLabelBinarizer
from torch... | pd.DataFrame(result, columns=mlb.classes_) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*
import sys
sys.path.append('../') # or just install the module
sys.path.append('../../fuzzy-tools') # or just install the module
sys.path.append('../../astro-lightcurves-handler') # or just install the module
sys.path.append('../../astro-lightcurves-fats') # or just install... | pd.concat([train_df_y_r], axis='rows') | pandas.concat |
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.DataFrame(index=dts, columns=['c1', 'c2'], data=100) | pandas.DataFrame |
##############################
##### DO NOT TOUCH BELOW #####
##############################
# Import packages and start CAS session
import swat, sys
conn = swat.CAS()
table = sys.argv[1]
nodeid = sys.argv[2]
caslib = sys.argv[3]
# Bring data locally
df = conn.CASTable(caslib = caslib, name = table).to_frame()
####... | pd.get_dummies(df[nominals]) | pandas.get_dummies |
import pandas as pd
import numpy as np
from multiprocessing import Pool
import tqdm
import sys
import gzip as gz
from tango.prepare import init_sqlite_taxdb
def translate_taxids_to_names(res_df, reportranks, name_dict):
"""
Takes a pandas dataframe with ranks as columns and contigs as rows and taxids as value... | pd.to_numeric(lineage_df[rank]) | pandas.to_numeric |
#!/usr/bin/env python
# coding: utf-8
# ## Predictive Analysis on Bank Marketing Dataset :
#
# ### Bank Marketing Dataset contains both type variables 'Categorical' and 'Numerical'.
#
# ### Categorical Variable :
#
# * Marital - (Married , Single , Divorced)",
# * Job - (Management,BlueCollar,Technician,en... | pd.crosstab(data.t_e_min, data.deposit) | pandas.crosstab |
#!/usr/bin/env python
r"""Aggregate, create, and save spiral plots.
"""
import pdb # noqa: F401
import logging
import numpy as np
import pandas as pd
import matplotlib as mpl
from datetime import datetime
from numbers import Number
from collections import namedtuple
from numba import njit, prange
from matplotlib i... | pd.DataFrame({dt_key: dt, "N Divisions": n_replaced}, index=index) | pandas.DataFrame |
import pandas as pd
import os.path
frames=[]
sheet=[0,1,1,2,6,6,8,4,4,8,8,8,9,9,1,2,1,4,6,4,10,34,34,8,1,34,34,34,34,7]
total=0
for i in range(1,len(sheet)):
for j in range(1,sheet[i]+1):
total+=1
print (total)
now=0.0
for i in range(1,len(sheet)):
for j in range(1,sheet[i]+1):
if os.path.isfile('SektorRiil%d_... | pd.concat(frames,ignore_index=True,axis=0) | pandas.concat |
# external libraries
import pandas as pd
import numpy as np
from collections import Counter
from ast import literal_eval
import time
import sys
from shutil import copyfile
# tensorflow and keras
import keras.optimizers
from keras.datasets import imdb
from keras.models import Model, Sequential
from keras.layers import I... | pd.read_csv('../../../data/processed/tok_phase1-games-hidden.csv') | 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.Index(['first', 'second'], dtype='object', name='group') | pandas.Index |
from typing import Dict, Iterable, Tuple, Union
from pathlib import Path
import lmfit
import pandas as pd
def get_data_path(sub_path: str) -> Path:
"""
Returns the Path object of a path in data and
creates the parent folders if they don't exist already
Parameters
----------
sub_path : str
... | pd.read_csv(translate_path) | pandas.read_csv |
#
# Copyright (c) 2015 - 2022, Intel Corporation
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions a... | pandas.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 7 21:33:48 2021
@author: David
"""
import sys
sys.path.append('.')
# import os
# import inspect
from datetime import date
from pathlib import Path
import locale
import pandas as pd
import numpy as np
import scipy.signal as sig
import matplotlib.pyplot as plt
import m... | pd.read_csv(r'../data/LUT/Bundeslaender2.tsv', sep='\t', comment='#', index_col='Gebiet') | pandas.read_csv |
"""
<NAME>, <EMAIL>
<NAME>, <EMAIL>
seoulai.com
2018
"""
import pandas as pd
from seoulai_gym.envs.traders.base import Constants
import os
class Price(Constants):
def __init__(
self,
price_list_size: int=1000, # trading game size
tick: int=0,
):
"""Price constructor.
... | pd.read_csv(price_file) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 9 15:33:46 2018
@author: <NAME>
"""
import cantera as ct
from .. import simulation as sim
from ...cti_core import cti_processor as ctp
import pandas as pd
import numpy as np
import time
import copy
import re
class JSR_steadystate(sim.Simulation):
'''Child clas... | pd.DataFrame(columns=columnNames) | pandas.DataFrame |
"""
Tasks
-------
Search and transform jsonable structures, specifically to make it 'easy' to make tabular/csv output for other consumers.
Example
~~~~~~~~~~~~~
*give me a list of all the fields called 'id' in this stupid, gnarly
thing*
>>> Q('id',gnarly_data)
['id1','id2','id3']
Observations:
--... | u('value') | pandas.compat.u |
import numpy as np, pandas as pd, os
from ..measure.bootstrap import *
from ..measure.filter_topological_events import *
from ..measure.compute_forces_at_annihilation import *
# from ..utils.utils_traj import get_tips_in_range
import random
#####################################################
# Methods conditioned on... | pd.DataFrame() | pandas.DataFrame |
import json
import pickle
import matlab
import scipy
import numpy as np
import os
import yaml
from EDL.dialogue.MatEngine_object import eng1
from EDL.dialogue.func_helpers import CalculateFuncs, ScorecardDataFrameFuncs, get_variable_info, correlation_multiprocessing
from EDL.models import EDLContextScorecards
from d... | pd.DataFrame(corr_matrix, index=list_metrics_arm, columns=list_metrics_arm) | pandas.DataFrame |
# Dec 21 to mod for optional outputting original counts
##
#---------------------------------------------------------------------
# SERVER only input all files (.bam and .fa) output MeH matrix in .csv
# Oct 19, 2021 ML after imputation test
# github
#--------------------------------------------------------------------... | pd.DataFrame(columns=['Qname']) | pandas.DataFrame |
import pandas
import scipy.interpolate
import numpy as np
from ..j_utils.string import str2time, time2str
from ..j_utils.path import format_filepath
from collections import OrderedDict
class History:
"""
Store dataseries by iteration and epoch.
Data are index through timestamp: the number of iteration sin... | pandas.concat((df, mini_count), axis=1, copy=False) | pandas.concat |
import logging
import os
import time
import warnings
from datetime import date, datetime, timedelta
from io import StringIO
from typing import Dict, Iterable, List, Optional, Union
from urllib.parse import urljoin
import numpy as np
import pandas as pd
import requests
import tables
from pvoutput.consts import (
B... | pd.Timestamp.now() | pandas.Timestamp.now |
# Importas bibliotecas necessarias
import streamlit as st
import pandas as pd
import numpy as np
import quandl as q
import base64
import plotly.express as px
from graf import plot, plotC
from datetime import date, datetime
# Listas para as tabelas e os dataframes
API = ['CEPEA/CALF','CEPEA/CALF_C','CEPEA/CATTLE','CE... | pd.DataFrame(df) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import pytest
import re
from numpy import nan as NA
import numpy as np
from numpy.random import randint
from pandas.compat import range, u
import pandas.compat as compat
from pandas import Index, Series, DataFrame, isn... | Series(strs) | pandas.Series |
# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/05_search.ipynb (unless otherwise specified).
__all__ = ['compare_frags', 'ppm_to_dalton', 'get_idxs', 'compare_spectrum_parallel', 'query_data_to_features',
'get_psms', 'frag_delta', 'intensity_fraction', 'add_column', 'remove_column', 'get_hits', 'score',
... | pd.DataFrame(psms) | pandas.DataFrame |
import numpy as np
from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import preprocessing, svm
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn ... | pd.read_csv('autumn_data.csv') | pandas.read_csv |
import numpy as np
import pandas as pd
import pytest
import woodwork as ww
from evalml.pipelines.components import LogTransformer
def test_log_transformer_init():
log_ = LogTransformer()
assert log_.parameters == {}
def test_log_transformer_no_y(X_y_regression):
X, y = X_y_regression
y = None
... | pd.Series(y_) | pandas.Series |
import os
from glob import glob
from tqdm import tqdm as print_progress
from datetime import datetime, timedelta, date
import dateutil
import math
import numpy as np
import pandas as pd
import featuretools as ft
from featuretools.variable_types import Id, Numeric, Categorical, Datetime
import ai.src.utils as utils
f... | pd.to_datetime(input_date, format='%Y-%m-%d') | pandas.to_datetime |
import calendar
import pandas as pd
from colourutils import extend_colour_map
def extend_data_range(data):
"""
Extends the index of the given Series so that it has daily values, starting from the 1st of the earliest month and
ending on the last day of the latest month.
:param data: The Series to be ... | pd.Timestamp(year=earliest_date.year, month=earliest_date.month, day=1) | pandas.Timestamp |
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(PerClassMetric) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import time
from datetime import datetime
import warnings
from textwrap import dedent, fill
import numpy as np
import pandas as pd
from numpy.linalg import norm, inv
from scipy.linalg import solve as spsolve, LinAlgError
from scipy.integrate import trapz
from scipy import stats
from lifelines.... | pd.DataFrame(schoenfeld_residuals[E, :], columns=self.params_.index, index=index[E]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.11.3
# kernelspec:
# display_name: Python 3
# name: python3
# ---
# + [markdown] id="view-in-github" colab_type="text"
... | pd.Series(trace_noncentered['mu_alpha'], name='mu') | pandas.Series |
import pandas as pd
import os
import shutil
import collections
def allFile(path):
res = []
for root, dirs, files in os.walk(path):
for file in files:
res.append(os.path.join(root, file))
return res
def allDir(path):
res = []
for root, dirs, files in os.walk(path):
for Dir in dir... | pd.DataFrame(columns = ['대화번호','미션제목','발화자 구분','한국어','영어','파일명']) | pandas.DataFrame |
import operator
import numpy as np
import pytest
import pytz
from pandas._libs.tslibs import IncompatibleFrequency
import pandas as pd
from pandas import Series, date_range
import pandas._testing as tm
def _permute(obj):
return obj.take(np.random.permutation(len(obj)))
class TestSeriesFlexArithmetic:
@py... | Series(expected_value) | pandas.Series |
import pandas as pd
import acquire as a
import matplotlib.pyplot as plt
import seaborn as sns
##########################################################################################
# My Prepare Functions
##########################################################################################
def set_index(df, ... | pd.to_datetime(df.sale_date, format='%a, %d %b %Y %H:%M:%S %Z') | pandas.to_datetime |
import numpy as np
import pandas as pd
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score
from sklearn.metrics import accuracy_score
# Import the data
train = pd.read_csv('./data/train.csv')
test = pd.read_csv('./data/test.csv')
# Process the data
... | pd.DataFrame(tlabels) | pandas.DataFrame |
import random
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
DataFrame,
NaT,
Timestamp,
date_range,
)
import pandas._testing as tm
class TestDataFrameSortValues:
def test_sort_values(self):
frame = DataFrame(
[[1, 1, 2], [3, 1, 0], ... | Timestamp(x) | pandas.Timestamp |
# pylint: disable=E1101,E1103,W0232
from datetime import datetime, timedelta
import numpy as np
import warnings
from pandas.core import common as com
from pandas.types.common import (is_integer,
is_float,
is_object_dtype,
... | is_period_dtype(dtype) | pandas.types.common.is_period_dtype |
import numpy as np
from pandas import (
DataFrame,
Index,
RangeIndex,
Series,
)
import pandas._testing as tm
# -----------------------------------------------------------------------------
# Copy/view behaviour for the values that are set in a DataFrame
def test_set_column_with_array():
# Case: ... | Series([7, 8, 9], name="c") | pandas.Series |
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