prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#
# Copyright (C) 2019 Databricks, Inc.
#
# 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 i... | pd.DataFrame({("X", "A"): [0, 1, 2, 3, 4], ("X", "B"): [100, 200, 300, 400, 500]}) | pandas.DataFrame |
import numpy as np
import pytest
from pandas import (
Categorical,
CategoricalDtype,
CategoricalIndex,
DataFrame,
Index,
MultiIndex,
Series,
Timestamp,
concat,
get_dummies,
period_range,
)
import pandas._testing as tm
from pandas.core.arrays import SparseArray
class TestGe... | tm.assert_series_equal(ts, df.iloc[:, 0]) | pandas._testing.assert_series_equal |
# /usr/bin/python3
import numpy as np
import pandas as pd
import data.data_input as di
import package.solution as sol
import package.instance as inst
import pytups.tuplist as tl
import pytups.superdict as sd
import os
import random as rn
class Experiment(object):
"""
This object represents the unification of ... | pd.DataFrame(statesMissions, columns=['resource', 'period', 'status']) | pandas.DataFrame |
import re
import pandas as pd
import spacy as sp
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
from foobar.data_loader import load_all_stock_tags
def clean_text_col(df, col):
def text_processing(text):
text = str(text) # remove handlers
text = re.sub(r"@[^\s]+", "", text)... | pd.to_datetime(df[col], unit="s") | pandas.to_datetime |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from subprocess import call
from orca import *
from orca.data import *
climate_indices = True
climate_forecasts = True
run_projection = True
consolidate_outputs = True
consolidate_inputs = False
#need climate data folders for this, which are too la... | pd.read_csv('orca/data/scenario_runs/%s/orca-data-climate-forecasted-%s.csv'%(sc,sc), parse_dates = True, index_col = 0) | pandas.read_csv |
# Volatility Futures vs Equity Index Futures
import numpy as np
import pandas as pd
# import matplotlib.pyplot as plt
# import statsmodels.formula.api as sm
# import statsmodels.tsa.stattools as ts
# import statsmodels.tsa.vector_ar.vecm as vm
entryThreshold = 0.1
onewaytcost = 1 / 10000
# VX futures
vx = pd.read_c... | pd.to_datetime(vix['Date'], format='%Y-%m-%d') | pandas.to_datetime |
import os
from datetime import date
from dask.dataframe import DataFrame as DaskDataFrame
from numpy import nan, ndarray
from numpy.testing import assert_allclose, assert_array_equal
from pandas import DataFrame, Series, Timedelta, Timestamp
from pandas.testing import assert_frame_equal, assert_series_equal
from pymo... | Timestamp('2008-10-23 05:53:06') | pandas.Timestamp |
import numpy as np
import scipy
import matplotlib
import pandas as pd
import sklearn
from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf
import keras
import matplotlib.pyplot as plt
from datetime import datetime
from loss_mse import loss_mse_warmup
from custom_generator import batch_generator
#Keras
... | pd.DataFrame(data=y_train[1:,0]) | pandas.DataFrame |
"""
Copyright 2019 <NAME>.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distribut... | pd.Series(actual) | pandas.Series |
import numpy as np
import pandas as pd
def compute_date_difference(df: pd.DataFrame) -> pd.DataFrame:
df.construction_year = pd.to_datetime(df.construction_year, format='%Y')
df.date_recorded = pd.to_datetime(df.date_recorded, format='%Y/%m/%d')
df['date_diff'] = (df.date_recorded - df.construction_year).... | pd.get_dummies(df, columns=one_hot_features) | pandas.get_dummies |
import datetime
import pathlib
import pickle
from io import BytesIO
from unittest.mock import MagicMock, patch
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pytest
import yaml
from sklearn.base import BaseEstimator, RegressorMixin
from sklearn.dummy import DummyClassifier
from sklearn.e... | pd.testing.assert_frame_equal(data.test_x, test.test_x) | pandas.testing.assert_frame_equal |
import json
import numpy as np
import pandas as pd
import xarray as xr
import cubepy
from pyplan_engine.classes.evaluators.BaseEvaluator import BaseEvaluator
from pyplan_engine.common.classes.filterChoices import filterChoices
from pyplan_engine.common.classes.indexValuesReq import IndexValuesReq
from cubepy.cube imp... | pd.isnull(finalValues) | pandas.isnull |
"""
Testing the ``modelchain`` module.
SPDX-FileCopyrightText: 2019 oemof developer group <<EMAIL>>
SPDX-License-Identifier: MIT
"""
import pandas as pd
import numpy as np
import pytest
from pandas.util.testing import assert_series_equal
import windpowerlib.wind_turbine as wt
import windpowerlib.modelchain as mc
cl... | pd.Series(data=[1.304071, 1.297581]) | pandas.Series |
import pandas as pd
import numpy as np
import pymc3 as pm
import matplotlib.pyplot as plt
import seaborn as sns
from mpl_toolkits.mplot3d import Axes3D
import theano.tensor as tt
def fit_spindle_density_prior():
#data from purcell
data = [[85, 177],
[89, 148],
[93, 115],
[9... | pd.read_pickle('../data/raw/refractory_prior_samples.pkl') | pandas.read_pickle |
"""Live or test trading account"""
import re
import requests
import numpy as np
import pandas as pd
from binance.client import Client
from models.exchange.binance import AuthAPI as BAuthAPI, PublicAPI as BPublicAPI
from models.exchange.coinbase_pro import AuthAPI as CBAuthAPI
class TradingAccount():
def __init... | pd.DataFrame() | pandas.DataFrame |
from pathlib import Path
from typing import List
import pandas as pd
from settings.conf import (LOCAL_DATASETS_DIR, LOCAL_DIR, blacklisted,
false_positives)
from strategies.ppb import extract
from utils import list_directory
from utils.pages import check_page_orientation
def validate_file... | pd.concat(liabs_list) | pandas.concat |
# Import modules
import abc
import random
import numpy as np
import pandas as pd
from tqdm import tqdm
from math import floor
from itertools import chain
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.layers import *
from tensorflow.keras import Sequential
from tensorflow.keras impor... | pd.DataFrame(cache) | pandas.DataFrame |
import nose
import os
import string
from distutils.version import LooseVersion
from datetime import datetime, date, timedelta
from pandas import Series, DataFrame, MultiIndex, PeriodIndex, date_range
from pandas.compat import range, lrange, StringIO, lmap, lzip, u, zip
import pandas.util.testing as tm
from pandas.uti... | tm.choice(['Male', 'Female'], size=n) | pandas.util.testing.choice |
import numpy as np
import pandas as pd
import itertools
import operator
import copy
import matplotlib.pyplot as plt
import seaborn as sns
get_ipython().magic('matplotlib inline')
sns.set(style="white", color_codes=True)
# imported ARIMA from statsmodels pkg
from statsmodels.tsa.arima_model import ARIMA
# hel... | pd.concat([y_truth, y_forecasted], axis=1, keys=['original', 'predicted']) | pandas.concat |
import numpy
import pandas
import spacy
in_data = pandas.read_excel('./data/source.xlsx')
array = in_data['Text'].values
nlp = spacy.load('en_core_web_sm')
# Step 2. Make our data (with the vocabulary navigating columns)
start = True
start_len = 0
j = 0
result = []
columns = []
for y in array:
doc = nlp(y)
... | pandas.DataFrame(data=result, columns=columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
from tqdm import tqdm as pb
import datetime
import re
import warnings
import matplotlib.pyplot as plt
import pylab as mpl
from docx import Document
from docx.shared import Pt
from data_source import local_source
def concat_ts_codes(df): #拼接df中所有TS_CODE... | pd.merge(stocks_ind, stock_indicators_daily_ind, on=['TS_CODE','END_DATE'], how="left") | pandas.merge |
from IMLearn.utils import split_train_test
from IMLearn.learners.regressors import LinearRegression
from IMLearn.metrics.loss_functions import mean_square_error
from typing import NoReturn
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
pio.... | pd.read_csv(filename) | pandas.read_csv |
import html2text
import requests
import pandas as pd
import os
from Property import Property
class DataSource:
def __init__(self, region='Auckland', district='Auckland-City', suburb='Parnell'):
self.region = region.lower()
self.district = district.lower()
self.suburb = suburb.lower()
... | pd.DataFrame(apts_map) | pandas.DataFrame |
import os
import pandas as pd
import re
from io import BytesIO
from urllib.request import urlopen
from zipfile import ZipFile
from requests_html import HTMLSession
def get_fia_data(force: bool = False):
if not os.path.exists('data/fia'):
os.makedirs('data/fia')
html_list = ["https://www.fia.com/docum... | pd.read_csv('data/ergast/races.csv') | pandas.read_csv |
import os
import spotipy
import spotipy.util as util
import pandas as pd
def load_environment():
from dotenv import load_dotenv
load_dotenv()
username = os.getenv("USR")
client_id = os.getenv("ID")
client_secret = os.getenv("SECRET")
redirect_uri = os.getenv("URI")
return username, client_... | pd.read_csv(infile) | pandas.read_csv |
import warnings
import anndata
import numpy as np
from packaging import version
import pandas as pd
import scipy as sp
import traceback
from scipy import sparse
from sklearn.preprocessing import StandardScaler
import igraph as ig
import leidenalg
import time
from sklearn.decomposition import PCA
import os
import gc
fro... | pd.Index(cids[filt]) | pandas.Index |
# -*- coding: utf-8 -*-
"""Datareader for cell testers and potentiostats.
This module is used for loading data and databases created by different cell
testers. Currently it only accepts arbin-type res-files (access) data as
raw data files, but we intend to implement more types soon. It also creates
processed files in ... | pd.concat([cycle_df, c], axis=0) | pandas.concat |
import numpy as np
import pandas as pd
import pickle
import scipy.sparse
import tensorflow as tf
from typing import Union, List
import os
from tcellmatch.models.models_ffn import ModelBiRnn, ModelSa, ModelConv, ModelLinear, ModelNoseq
from tcellmatch.models.model_inception import ModelInception
from tcellmatch.estimat... | pd.DataFrame({"antigen": self.peptide_seqs_train}) | pandas.DataFrame |
"""Module to run demo on streamlit"""
import cv2
import time
import beepy
import threading
import numpy as np
import pandas as pd
import streamlit as st
from datetime import date
import face_recognition as fr
class Camera:
'''
Camera object to get video from remote source
use read() method ... | pd.DataFrame() | pandas.DataFrame |
import os
import pandas as pd
import numpy as np
import nips15
folds_dir = 'models/jmlr/folds'
demographic = ['female', 'afram']
molecular = ['aca', 'scl']
pfvc_spec = {'t':'years_seen_full', 'y':'pfvc', 'x1':demographic, 'x2':demographic + molecular}
pfvc = | pd.read_csv('data/benchmark_pfvc.csv') | pandas.read_csv |
#
# Copyright 2018 Quantopian, Inc.
#
# 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 wr... | pd.Timestamp('2006-01-04', tz='UTC') | pandas.Timestamp |
import pandas as pd
class CryptoDataDownload:
def __init__(self):
self.url = "https://www.cryptodatadownload.com/cdd/"
def fetch_default(self, exchange_name, base_symbol, quote_symbol, timeframe, include_all_volumes=False):
filename = "{}_{}{}_{}.csv".format(exchange_name, quote_symbol, ba... | pd.to_datetime(df["date"]) | pandas.to_datetime |
from typing import Tuple, Optional, List, Union, Dict
from typing import Any # pylint: disable=unused-import
from collections import OrderedDict # pylint: disable=unused-import
from datetime import datetime
import logging
import xmltodict
import pandas as pd
import numpy as np
from toolz import get_in
from .utils ... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
import json
import re
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from pymongo import ASCENDING, DESCENDING
from src.data import conn
from src.data.setting import TRADE_BEGIN_DATE
from src.data.future.setting import NAME2CODE_MAP, COLUMNS_MAP
from src.data.... | pd.read_html(text, header=0) | pandas.read_html |
import json
import logging
import math
import os
import ntpath
import random
import sys
import time
from itertools import product, chain
from collections import defaultdict, Iterable
import glob
import numpy as np
import pandas as pd
import torch
import yaml
import imgaug as ia
from PIL import Image
from attrdict impo... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
import requests
import json
import pandas as pd
import tweepy
import os
import config as cfg
from datetime import datetime, timedelta
from pytz import timezone
def main():
# get data
nys_data = get_nys_data()
nys = get_nys_appt(nys_data, cfg.config["nys_sites"])
alb = get_nys_app... | pd.DataFrame() | pandas.DataFrame |
# ~~~~~~~~~~~~ Author: <NAME> ~~~~~~~~~~~~~~~
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import os
class Plot_helper(object):
def __init__(self, MainDir):
""" Function used for initializing the Plot_helper object
... | pd.DataFrame(np.nan, index=bus_list, columns=[toname+'_'+'1', toname+'_'+'2', toname+'_'+'3']) | pandas.DataFrame |
from __future__ import print_function
import os
import datetime
import sys
import pandas as pd
import numpy as np
import requests
import copy
# import pytz
import seaborn as sns
from urllib.parse import quote
import monetio.obs.obs_util as obs_util
"""
NAME: cems_api.py
PGRMMER: <NAME> ORG: ARL
This code written at... | pd.DataFrame() | pandas.DataFrame |
import os
os.chdir(os.path.split(os.path.realpath(__file__))[0])
import torch
import pickle
import dgl
import pandas as pd
import numpy as np
from scipy import sparse
import constants
def array_norm(array,clip=100):
data=array
upper=np.percentile(data,clip)
data_clip=np.clip(data,0,upper... | pd.read_csv(filepath) | pandas.read_csv |
import argparse
from seqeval.metrics import classification_report
from seqeval.metrics import accuracy_score
from collections import defaultdict # available in Python 2.5 and newer
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
def read_conllu(... | pd.DataFrame(cm, index=labels, columns=labels) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import unittest
import platform
import pandas as pd
import numpy as np
import pyarrow.parquet as pq
import hpat
from hpat.tests.test_utils import (
count_array_REPs, count_parfor_REPs, count_array_OneDs, get_start_end)
from hpat.tests.gen_test_data import ParquetGenerator
from numba import ... | pd.testing.assert_series_equal(S1, S2) | pandas.testing.assert_series_equal |
""""
This does not work
"""
import pandas as pd
import numpy as np
import os
import math
def sigmoid(x):
return 1 / (1 + math.exp(-x))
inputdir='../blend/'
preds0=pd.read_csv(inputdir+'vw_nn.csv.gz')
preds1=pd.read_csv(inputdir+'Nolearn_score_0.800750.csv.gz')
preds2=pd.read_csv(inputdir+'Nolearn_score_0.802373.cs... | pd.read_csv(inputdir+'XGBOOST_Best_score_0.820948.csv.gz') | pandas.read_csv |
# Copyright IBM All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Common functions for generating Dash components
"""
from typing import Optional, NamedTuple, List, Dict
import pandas as pd
import dash_bootstrap_components as l
from dash import html
# import dash_html_components as html
import dash_pivot... | pd.DataFrame(data=data) | pandas.DataFrame |
# Copyright 2017 Google Inc.
#
# 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, ... | assert_series_equal(actual_1, expected_1) | pandas.util.testing.assert_series_equal |
import streamlit as st
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import altair as alt
from requests import get
import re
import os
from bs4 import BeautifulSoup
from urllib.request import Request, urlopen
import datetime
import time
import matplotlib.pyplo... | pd.concat([eda_df, goals_df], axis=1) | pandas.concat |
import pytest
def test_concat_with_duplicate_columns():
import captivity
import pandas as pd
with pytest.raises(captivity.CaptivityException):
pd.concat(
[pd.DataFrame({"a": [1], "b": [2]}), pd.DataFrame({"c": [0], "b": [3]}),],
axis=1,
)
def test_concat_mismatch... | pd.DataFrame({"c": [0], "b": [3]}) | pandas.DataFrame |
"""
Author: <NAME>
Created: 14/08/2020 11:04 AM
"""
import os
import numpy as np
import pandas as pd
from basgra_python import run_basgra_nz, _trans_manual_harv, get_month_day_to_nonleap_doy
from input_output_keys import matrix_weather_keys_pet
from check_basgra_python.support_for_tests import establish_org_input, g... | pd.read_csv(external_data_path) | pandas.read_csv |
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
from xgboost.sklearn import XGBClassifier
def xtrain_and_test(df_all):
'''
得到训练数据和测试数据
'''
df_label = pd.read_csv('../data/public/train.csv')
df_test_label = | pd.read_csv('../data/public/evaluation_public.csv') | pandas.read_csv |
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
import pandas as pd
import sys
import os
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
from nltk.corpus import twitter_samples
from sklearn.model_selection import train_test_split
# directory to sentiment data
dir_na... | pd.read_csv(path, names=['dates', 'news']) | pandas.read_csv |
import glob
import numpy as np
import pandas as pd
from statsmodels.stats.multicomp import pairwise_tukeyhsd
# from statsmodels.stats.multicomp import MultiComparison
from statsmodels.stats.libqsturng import psturng
from scipy.interpolate import UnivariateSpline, interp1d
def get_segments_mean(a, n):
'''
Calcu... | pd.read_csv(totalAreaFile, header=0, sep='\t') | pandas.read_csv |
import os
import argparse
import numpy as np
import pandas as pd
from time import time
from scipy.stats import norm
from scipy.spatial.distance import euclidean
from editing_dist_n_lcs_dp import edit_distance
from editing_dist_n_lcs_dp import lcs
#global variables
# BREAK_POINTS = []
# LOOKUP_TABLE = []
... | pd.DataFrame() | pandas.DataFrame |
'''A double-bar plot of April's maximum and Minimum temperatures of each day'''
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import glob
Min = [100]*30 #a bviously big number to make sure others will be smaller
Max = [-100]*30
for fname in glob.glob("./input/Montreal*"): # For loop for... | pd.read_csv(fname, header=0) | pandas.read_csv |
"""Download population projections from https://github.com/nismod/population/blob/master/README.md
Info
-----
https://github.com/virgesmith/UKCensusAPI
https://www.nomisweb.co.uk/myaccount/webservice.asp
https://github.com/nismod/population
https://github.com/virgesmith/UKCensusAPI
Steps
------
1. optain nomis key
... | pd.DataFrame() | pandas.DataFrame |
import argparse
import pandas as pd
import numpy as np
import param
import os
def preprocess_sam(r1_sam, r2_sam):
"""
preprocess sam files
"""
#if not os.path.isfile(r1_sam) or not os.path.isfile(r2_sam):
# print("file doesn't exist")
# exit(0)
dir_name = os.path.dirname(r1_sa... | pd.read_table(hDB, sep="\t") | pandas.read_table |
__all__ = ['ZeroBasedSkill']
import attr
import pandas as pd
from sklearn.utils.validation import check_is_fitted
from .. import annotations
from ..annotations import Annotation, manage_docstring
from ..base import BaseClassificationAggregator
from .majority_vote import MajorityVote
from ..utils import get_accuracy, ... | pd.Series(skill_value, index=skill_index) | pandas.Series |
#!/usr/bin/env python -W ignore::DeprecationWarning
import os
import ast
import pathlib
import pandas as pd
import numpy as np
import random
import itertools
from tqdm import tqdm
from skimage import measure
from scipy import stats
def warn(*args, **kwargs):
pass
import warnings
warnings.warn = warn
import log... | pd.DataFrame() | pandas.DataFrame |
# -- coding: utf-8 --
import pandas as pd
import numpy as np
data = pd.read_csv('train.csv')
data['datatime'] = | pd.to_datetime(data.date) | pandas.to_datetime |
import pandas as pd
from flask import Flask, jsonify, request
from tensorflow.keras.models import load_model
import pickle
import numpy as np
UP_Wheat = load_model('UP_Wheat')
october = pickle.load(open('UP_Wheat/october.pkl','rb'))
november = pickle.load(open('UP_Wheat/november.pkl','rb'))
december = pickle.load(op... | pd.DataFrame({'october': result1, 'november': result2, 'december': result3, 'january': result4, 'february': result5}) | pandas.DataFrame |
##### file path
# input
path_df_D = "tianchi_fresh_comp_train_user.csv"
path_df_part_1 = "df_part_1.csv"
path_df_part_2 = "df_part_2.csv"
path_df_part_3 = "df_part_3.csv"
path_df_part_1_tar = "df_part_1_tar.csv"
path_df_part_2_tar = "df_part_2_tar.csv"
path_df_part_1_uic_label = "df_part_1_uic_label.csv"
... | pd.to_datetime('2014-12-19') | pandas.to_datetime |
import pandas as pd
import networkx as nx
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
#funtions
def degree(G,f):
"""
Adds a column to the dataframe f with the degree of each node.
G: a networkx graph.
f: a pandas dataframe.
"""
if not(set(f.name) == set(G.nodes()... | pd.merge(f, communities_df, on='name') | pandas.merge |
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from config import LEGENDS, HEATMAP_LIST, GLUCOSE_LIST, GLUCOSE_LIST_AUC, NORMAL_LIST
from pandas.plotting import parallel_coordinates
def curveplots(df, parameter=None):
"""
Will plot the curves for OGTT.
Paramet... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
#----------------#
#--- run_sens ---#
#----------------#
#--- This script was developed to run a local sensitivity analysis for
#--- JULES-crop for the specific sites flagged with run_jules=TRUE in the
#--- sensitivity_run_setup.csv file. The file sensitivity_p... | pd.to_datetime(val_date) | pandas.to_datetime |
# -*- coding: utf-8 -*-
# pylint: disable=E1101
# flake8: noqa
from datetime import datetime
import csv
import os
import sys
import re
import nose
import platform
from multiprocessing.pool import ThreadPool
from numpy import nan
import numpy as np
from pandas.io.common import DtypeWarning
from pandas import DataFr... | range(nv) | pandas.compat.range |
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([80, 100, 85, 70, 85]) | pandas.Series |
"""判断趋势示例"""
import datetime
import talib as ta
import pandas as pd
from core.back_test import BackTest
class MyBackTest(BackTest):
def sizer(self):
pass
def strategy(self):
date_now = self.data["trade_date"].iloc[-1]
sma_data_20 = ta.MA(self.data["close"], timeperiod=20, matype=0)
... | pd.read_csv("./point_data_used_by_trend_hs300.csv", index_col=[0], parse_dates=[2]) | pandas.read_csv |
# -*- coding: utf-8 -*-
# Copyright (c) 2018-2021, earthobservations developers.
# Distributed under the MIT License. See LICENSE for more info.
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
import pytz
from freezegun import freeze_time
from pandas import Timestamp
from pandas._tes... | Timestamp("2020-12-01 00:00:00+0000", tz="UTC") | pandas.Timestamp |
from .models import *
import pandas as pd
import numpy as np
from copy import deepcopy
from scipy.stats import mode
TIME_UNITS = 'time'
SAMP_UNITS = 'samples'
def extract_event_ranges(samples, events_dataframe, start_offset=0,
end_offset=0, round_indices=True, borrow_attributes=[]):
""" ... | pd.DataFrame() | pandas.DataFrame |
"""Performs attention intervention on Winobias samples and saves results to JSON file."""
import json
import fire
from pandas import DataFrame
from transformers import (
GPT2Tokenizer, TransfoXLTokenizer, XLNetTokenizer,
BertTokenizer, DistilBertTokenizer, RobertaTokenizer
)
import winobias
from attention_ut... | DataFrame(results) | pandas.DataFrame |
import pytest
import pandas as pd
from data_dashboard.features import NumericalFeature, CategoricalFeature, Features
from data_dashboard.descriptor import FeatureDescriptor
@pytest.mark.parametrize(
("column_name",),
(
("AgeGroup",),
("bool",),
("Product",),
("S... | pd.concat([X, y], axis=1) | pandas.concat |
import os
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from .. import read_sql
@pytest.fixture(scope="module") # type: ignore
def mssql_url() -> str:
conn = os.environ["MSSQL_URL"]
return conn
@pytest.mark.xfail
def test_on_non_select(mssql_url: str) -> None:
query ... | pd.Series([0], dtype="int64") | pandas.Series |
"""Model the behavioral data."""
# %%
# Imports
import itertools
import json
import sys
import warnings
from functools import partial
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pingouin
import scipy.stats
import seaborn as sns
from scipy.optimize import Bounds, minimize
from tqdm.aut... | pd.read_csv(tsv, sep="\t") | pandas.read_csv |
import requests
import json
from datetime import datetime
import os
import sys
import pandas as pd
import numpy as np
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.dirname(SCRIPT_DIR))
from global_variables import config as g
ROOT_DIR = g.ROOT_DIR
processed_data_dir = g.processed_dat... | pd.to_datetime(df_pv_forecast['Time'], format='%d-%m-%Y %H:%M') | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 17 14:47:03 2019
@author: olivergiesecke
"""
###############################################################################
### Import packages
import pandas as pd
import re
import os
from io import StringIO
import numpy as np
import matplotlib.pyp... | pd.to_datetime(df_output['meeting_date']) | pandas.to_datetime |
"""
Module to perform recursive feature elimination
Author: <NAME>
Email: <EMAIL>
"""
import os
import pandas as pd
import joblib
import s... | pd.DataFrame({'features': self.refined_features, 'importance_score':RFECV_importance}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import re
import numpy as np
import pytest
from pandas.core.dtypes.common import (
is_bool_dtype, is_categorical, is_categorical_dtype,
is_datetime64_any_dtype, is_datetime64_dtype, is_datetime64_ns_dtype,
is_datetime64tz_dtype, is_datetimetz, is_dtype_equal, is_interval_dtype,
... | is_datetime64_dtype('datetime64[ns, US/Eastern]') | pandas.core.dtypes.common.is_datetime64_dtype |
#!/usr/bin/python
# extract gtf-like annotations and intersect gene names
import argparse
import os
import re
import subprocess as sp
from time import time
import warnings
import matplotlib.pyplot as plt
# from numba import njit, prange, set_num_threads
import pandas as pd
from tqdm import tqdm
from upsetplot import fr... | pd.Series(data.index) | pandas.Series |
import os, functools
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import skfuzzy as fuzz
from kneed import KneeLocator
from sklearn.decomposition import PCA
from GEN_Utils import FileHandling
from loguru import logger
logger.info("Import ok")
def multiple_PCAs(test_di... | pd.merge(clustered, pca_data, left_index=True, right_index=True) | pandas.merge |
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import scipy
import matplotlib.pyplot as plt
from sklearn.neighbors import LocalOutlierFactor
df = | pd.read_csv('german_data-numeric',delim_whitespace=True,header=None) | pandas.read_csv |
'''
Code written by <NAME> (August 2019)
(415)-845-2118
DESCRIPTION: Takes dataframe with list of epitopes
and UniProt Protein Names (Obtained from SwissProt);
Runs query on db2db to obtain matching gene name
for each entry in the dataframe.
'''
###IMPORT AND CLEAN-UP UNIPROT PROTEIN NAMES FOR QUERY
import pandas as ... | pd.io.json.json_normalize(data) | pandas.io.json.json_normalize |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 21 23:24:11 2021
@author: rayin
"""
import os, sys
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import math
import re
import random
from collections import Counter
from pprint import pprint
os.chdir("/Users/rayin/Google ... | pd.Series(aa) | pandas.Series |
from concurrent.futures import ProcessPoolExecutor, as_completed
from itertools import combinations
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import pandas as pd
from networkx.algorithms.centrality import edge_betweenness_centrality
from numpy import log
from scipy.special import betaln
... | pd.Series(best_pattern_final) | pandas.Series |
#!/usr/bin/python3
# Module with dataframe operations.
# -
# append to a dataframe a.append(pd.DataFrame({'close':99.99},index=[datetime.datetime.now()])
import pandas as pd
from scipy import signal
import numpy
from numpy import NaN
import matplotlib.pyplot as plt
import datetime
from scipy.stats import linregress
#... | pd.DataFrame() | pandas.DataFrame |
'''
AAA lllllll lllllll iiii
A:::A l:::::l l:::::l i::::i
A:::::A l:::::l l:::::l iiii
A:::::::A l:::::l l:::::l
... | pd.read_csv('test.csv') | pandas.read_csv |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# ====================================================================
# @authors: <NAME>, <NAME>
# @since: 07/21/2018
# @summary: Functions for plotting radiance curves and errors.
# ====================================================================
import os
import csv
impo... | pd.read_csv(args.datasetpath) | pandas.read_csv |
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2022, 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.Index(['id1', 'id2'], name='feature ID') | pandas.Index |
import datetime
import json
import pandas as pd
from dateutil import relativedelta
from rest_framework.generics import ListCreateAPIView, get_object_or_404
from rest_framework.response import Response
from rest_framework.views import APIView
from analytics.events.utils.dataframe_builders import ProductivityLogEventsD... | pd.to_numeric(supplement_series) | pandas.to_numeric |
import numpy as np
import pandas as pd
from collections import defaultdict
import re
import csv
from bs4 import BeautifulSoup
import sys
import os
import multiprocessing as mp
os.environ['KERAS_BACKEND']='theano'
import keras
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_se... | pd.merge(data1, data2, on="Event") | pandas.merge |
"""
Copyright (c) 2021, Electric Power Research Institute
All rights reserved.
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 li... | pd.concat([pro_forma, hp_proforma], axis=1) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''
pyplr.oceanops
==============
A module to help with measurents for Ocean Optics spectrometers.
'''
from time import sleep
import numpy as np
import pandas as pd
import spectres
from seabreeze.spectrometers import Spectrometer
class OceanOptics(Spectrometer):
... | pd.DataFrame(info) | pandas.DataFrame |
import nltk
nltk.download('punkt')
nltk.download('stopwords')
import re
from bs4 import BeautifulSoup
import unicodedata
import contractions
import spacy
import nltk
import pandas as pd
import numpy as np
nlp = spacy.load('en_core_web_sm')
ps = nltk.porter.PorterStemmer()
# Links removal
def remove_links(text):
... | pd.DataFrame.from_dict(example, orient='index') | pandas.DataFrame.from_dict |
import numpy as np
import pandas as pd
import argparse
def check_smiles_match(data,screen):
return (data['SMILES'].values==screen['SMILES'].values).all()
def apply_screen(data,col_name,selection_type,selection_thresh,keep):
data = data.sort_values(col_name,ascending=True)
if selection_type=='Fraction':
... | pd.read_csv(args.screen_file2) | pandas.read_csv |
# -*- coding: utf-8 -*-
# pylint: disable=E1101
# flake8: noqa
from datetime import datetime
import csv
import os
import sys
import re
import nose
import platform
from multiprocessing.pool import ThreadPool
from numpy import nan
import numpy as np
from pandas.io.common import DtypeWarning
from pandas import DataFr... | read_csv(*args, **kwds) | pandas.io.parsers.read_csv |
"""This module contains code to recode values to achieve k-anonymity"""
import math
import pandas as pd
from pandas.api.types import (is_categorical_dtype, is_datetime64_any_dtype, is_numeric_dtype)
from kernel.util import is_token_list, must_be_flattened, flatten_set_valued_series, next_string_to_reduce, reduce_stri... | is_categorical_dtype(series) | pandas.api.types.is_categorical_dtype |
from logging import log
import numpy as np
import pandas as pd
from tqdm import tqdm
import scipy.sparse as sp
from sklearn.utils import check_array
from sklearn.feature_extraction.text import (
CountVectorizer,
TfidfTransformer,
TfidfVectorizer
)
from sklearn.metrics.pairwise import cosine_similarity
from ... | pd.Series([words[idx] for idx in keywords_idx]) | pandas.Series |
import pandas as pd
import os
import xgboost as xgb
import operator
from matplotlib import pylab as plt
from sklearn import preprocessing
# import data
train = | pd.read_csv("../input/train.csv") | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 28 17:01:23 2021
@author: sercan
"""
#Import libraries--------------------------------------------------------------
import streamlit as st
import numpy as np
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
import pandas as pd
import xl... | pd.DataFrame(data=d) | pandas.DataFrame |
import pandas as pd
import logging
import os
from collections import defaultdict
from annotation.utility import Utility
_logger = logging.getLogger(__name__)
TYPE_MAP_DICT = {"string": "String", "number": "Quantity", "year": "Time", "month": "Time", "day": "Time",
"date": "Time", "entity": 'WikibaseIt... | pd.DataFrame(columns=['column', 'row', 'value', 'context', "item"]) | pandas.DataFrame |
#Setting up the data for chapter
#Import the required packages
import pandas as pd
#Read in the data
df = pd.read_csv('all_stocks_5yr.csv')
#Convert the date column into datetime data type
df['date'] = pd.to_datetime(df['date'])
#Filter the data for Apple stocks only
df_apple = df[df['Name'] == 'AAL']
#Import... | pd.read_csv("all_stocks_5yr.csv") | pandas.read_csv |
import pytest
from pandas import DataFrame
@pytest.fixture(scope='module')
def model():
from model import Model
from config import DATA_FILES, DATA_MERGE_KEYS
try:
model = Model( DATA_FILES['companies'], DATA_FILES['users'], DATA_MERGE_KEYS['companies'], DATA_MERGE_KEYS['users'] )
model.... | DataFrame({'col1':[1,2,3,4],'col2':['val1','val2','val3','val4']}) | pandas.DataFrame |
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... | Timestamp('2013-02-28', tz='Asia/Tokyo') | pandas.Timestamp |
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