prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pytest
import pytz
import dateutil
import numpy as np
from datetime import datetime
from dateutil.tz import tzlocal
import pandas as pd
import pandas.util.testing as tm
from pandas import (DatetimeIndex, date_range, Series, NaT, Index, Timestamp,
Int64Index, Period)
class TestDatetimeInd... | Index(['2016-05-16', 'NaT', 'NaT', 'NaT'], dtype=object) | pandas.Index |
import pandas as pd
import numpy as np
import pycountry_convert as pc
import pycountry
import os
from iso3166 import countries
PATH_AS_RELATIONSHIPS = '../Datasets/AS-relationships/20210701.as-rel2.txt'
NODE2VEC_EMBEDDINGS = '../Check_for_improvements/Embeddings/Node2Vec_embeddings.emb'
DEEPWALK_EMBEDDINGS_128 = '../... | pd.read_csv(NODE2VEC_GLOBAL_EMBEDDINGS_64, sep=',') | pandas.read_csv |
# This script runs the RDD models for a paper on the impact of COVID-19 on academic publishing
# Importing required modules
import pandas as pd
import datetime
import numpy as np
import statsmodels.api as stats
from matplotlib import pyplot as plt
import gender_guesser.detector as gender
from ToTeX import r... | pd.get_dummies(df['Nationality']) | pandas.get_dummies |
# data from https://www.ssa.gov/oact/babynames/limits.html
import os
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
# add your names to plot here in tic marks or quotes, seperated by commas: 'Joe', 'Joseph'
name_to_plot = ['Sarah', 'Sara']
# M, F, or B for both
sex = "F"
#Change to ... | pd.read_csv("name_data.csv") | pandas.read_csv |
import pandas as pd
import requests
from pathlib import Path
from tqdm.auto import tqdm
tqdm.pandas()
rki_to_iso = {0: 'DE',
1: 'DE-SH',
2: 'DE-HH',
3: 'DE-NI',
4: 'DE-HB',
5: 'DE-NW',
6: 'DE-HE',
7: 'DE-RP',
... | pd.DataFrame({'filename':files}) | pandas.DataFrame |
import pandas as pd
import requests
import os.path
import bs4
import requests
import urllib3
import csv
from os import path
#Data loader functions belong here. This is where
# information about the data files is found.
def load_proteomics(version='current', level='protein',
prefix="", suffix="To... | pd.read_csv(file, sep='\t', header=0, index_col=0, usecols=['Protein IDs','Gene names','Fasta headers']) | pandas.read_csv |
# -*- coding: utf-8 -*-
import time
import hashlib
import traceback
import pandas as pd
# 配置
mapping = {
"cy": [(1, 7), (4, 5)], # 餐饮（测试）
# "cy": [(1, 187432), (4, 220703)], # 餐饮
"cs": [(2, 48732), (5, 22389), (7, 72084), (8, -1)], # 催收
"ys": [(3, 193302), (6, -1)] # 疑似催收
}
def format_tel(tel):
... | pd.read_csv(cuishou_file, header=None, names=['tel', 'type']) | pandas.read_csv |
'''
Created on April 15, 2012
Last update on July 18, 2015
@author: <NAME>
@author: <NAME>
@author: <NAME>
'''
import pandas as pd
import numpy as np
class Columns(object):
OPEN='Open'
HIGH='High'
LOW='Low'
CLOSE='Close'
VOLUME='Volume'
indicators=["MA", "EMA", "MOM", "ROC", "ATR", "BBANDS", "P... | pd.Series((df['Close'] - df['Low']) / (df['High'] - df['Low']), name='SO%k') | pandas.Series |
"""Analysis tools."""
import ast
import json
import os
from typing import Any, Dict, Optional, Union
import matplotlib.dates as dates
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def analyze() -> None:
"""Return info messages as dict, plot prize timeline and save both.
Notes
--... | pd.DatetimeIndex(df["date"]) | pandas.DatetimeIndex |
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | pd.date_range("2020-1-1", periods=0) | pandas.date_range |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import base64
from dash import Dash, dcc, html, callback_context
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output, State
from datetime import date, datetime
import io
import numpy as np
import pandas as pd
import plotly.express as px
i... | pd.Grouper(freq="M") | pandas.Grouper |
import pandas as pd
class LSPCResultsFile(object):
"""
A light weight LSPC results parser.
"""
def __init__(self, results_path, summary_path, summary_EOF=-2):
"""
---------
Requires:
- results_path: str, path to the results.csv results file.
- summary_path: str, ... | pd.read_csv(self.results_path) | pandas.read_csv |
# -*- coding: utf-8 -*-
from argparse import ArgumentParser
import etherscan as eth
import pandas as pd
import numpy as np
from cassandra.cluster import Cluster
from random import sample, choice
from time import sleep
import logging
logging.basicConfig(format="%(asctime)s %(levelname)-8s %(message)s", level=logging.I... | pd.DataFrame.from_dict(r, dtype=object) | pandas.DataFrame.from_dict |
from scipy import sparse
from numpy import array
from scipy.sparse import csr_matrix
import os
import copy
import datetime
import warnings
from matplotlib import pyplot as plt
import matplotlib as mpl
import seaborn as sns
import pandas as pd
import numpy as np
import math
from datetime import datetime... | pd.merge(age_train_usage[['uId']],full_tfidf, how='inner', on='uId') | pandas.merge |
import os
os.environ["MKL_NUM_THREADS"]="1"
print(os.environ["MKL_NUM_THREADS"])
import numpy as np
import turbofats
import pickle
import sys
from pathlib import Path
import pandas as pd
#from joblib import Parallel, delayed, dump
#result = Parallel(n_jobs=10)(delayed(compute_fats_features)(batch_names) for batch_nam... | pd.concat(features, axis=1, sort=True) | 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([1.1, 2.2, None], dtype="float") | pandas.Series |
# %%
import warnings
warnings.filterwarnings("ignore")
from folktables import (
ACSDataSource,
ACSIncome,
ACSEmployment,
ACSMobility,
ACSPublicCoverage,
ACSTravelTime,
)
import pandas as pd
from collections import defaultdict
from scipy.stats import kstest, wasserstein_distance
import seaborn ... | pd.DataFrame(train_shap) | pandas.DataFrame |
from random import shuffle
import numpy as np
import torch.nn.functional as F
import torch
import pathlib
import pandas as pd
from torch.autograd import Variable
from networks.net_api.losses import CombinedLoss
from torch.optim import lr_scheduler
import os
from tqdm import tqdm
def per_class_dice(y_pred, y_true, num_c... | pd.DataFrame(data=d) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from __future__ import print_function
import nose
from numpy import nan
from pandas import Timestamp
from pandas.core.index import MultiIndex
from pandas.core.api import DataFrame
from pandas.core.series import Series
from pandas.util.testing import (assert_frame_equal, assert_series_equal
... | assert_series_equal(actual, expected) | pandas.util.testing.assert_series_equal |
import os
import pandas as pd
import pytest
@pytest.mark.skipif(
os.name == "nt", reason="Skip *nix-specific tests on Windows"
)
def test_convert_unix_date():
unix = [
"1284101485",
1_284_101_486,
"1284101487000",
1_284_101_488_000,
"1284101489",
"1284101490",
... | pd.DataFrame(unix, columns=["dates"]) | pandas.DataFrame |
from datetime import datetime
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas import (
DataFrame,
DatetimeIndex,
Series,
concat,
isna,
notna,
)
import pandas._testing as tm
import pandas.tseries.offsets as offsets
@pytest.mark.parametrize(
"compar... | Series(dtype=np.float64) | pandas.Series |
# -*- coding: utf-8 -*-
# Copyright (c) 2015-2020, Exa Analytics Development Team
# Distributed under the terms of the Apache License 2.0
from unittest import TestCase
import h5py
import numpy as np
import pandas as pd
from exatomic import Universe
from exatomic.base import resource
from exatomic.molcas.output import O... | pd.DataFrame(mamsphr.momatrix) | pandas.DataFrame |
import pandas as pd
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import warnings
import itertools
import datetime
import os
from math import sqrt
#import seaborn as sns
class ContagionAnalysis():
def __init__(self, world):
self.world = world
# time as lable to write fil... | pd.to_datetime(op_nodes.time) | pandas.to_datetime |
import Dataset
from Estimators import XGB
from Utils import Profiler
import pandas as pd
from IPython.display import display
import xgboost as xgb
import gc
profile = Profiler()
profile.Start()
# Gather Data
train_X, test_X, train_Y = Dataset.Load('AllData_v3')
# Convert data to DMatrix
dtrain = xgb.D... | pd.concat([gs_summary, gs_results], ignore_index=True) | pandas.concat |
# *****************************************************************************
# Copyright (c) 2020, Intel Corporation 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 sou... | pandas.Series(self < other._data, index=other._index, name=other._name) | pandas.Series |
#!/usr/bin/env python3
"""
Copyright 2020 <NAME>
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the
following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following
disclaime... | pd.DataFrame(earning_deduction_dict_list) | 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... | lrange(0, 8, 2) | pandas.compat.lrange |
from pandas import read_csv, DataFrame, concat, Series, set_option, reset_option, option_context
from os import path
import matplotlib.pyplot as plt
from time import time
start_total = time()
df = read_csv(path.join("Output", "url_frequency.csv"), names=["url", "frequency"])
category_urls = read_csv("url_categories_co... | Series() | pandas.Series |
#!/user/bin/env/python
'''
Input a list and change to a series. Finally concat to a new Data Frame
'''
import pandas as pd
class ConcatFeature:
'''
Parameters:
-------------------------
feature: Takes in a list or series
df: Takes in a dictionary or dataframe
Returns:
--------------------... | pd.concat([df, feature_series], axis=1) | pandas.concat |
"""
general utilities for re-use
"""
from configparser import ConfigParser
import os
import pandas as pd
import pickle
from timelogging.timeLog import log
from typing import List, Tuple, Union, Optional
config_parser = ConfigParser()
def assertDirExistent(path):
if not os.path.exists(path):
raise IOError... | pd.read_csv(in_path, escapechar="\\") | pandas.read_csv |
import requests
from bs4 import BeautifulSoup
import pandas as pd
# TODO: staticmethod 제거
class GetDaumNews:
def __init__(self):
pass
@staticmethod
def get_url(page, date):
return 'http://media.daum.net/breakingnews/politics?page={}®Date={}'.format(page, date)
@staticmethod
de... | pd.DataFrame(columns=['sentence']) | pandas.DataFrame |
from pynwb import NWBFile, NWBHDF5IO, TimeSeries, ProcessingModule
from pynwb.core import MultiContainerInterface, NWBDataInterface
from scipy.stats import mode
from glob import glob
import numpy as np
import pandas as pd
import scipy.signal as signal
import scipy.interpolate as interpolate
import multiprocessing
impo... | pd.Series(onset, index=onset_index) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 13 16:29:34 2020
@author: <NAME>
"""
from sqlalchemy import create_engine
import pandas as pd
import os
import sys
################################################################################
# > QUERY TO POSTGRESQL DATABASE
#######################################... | pd.merge(left=commonnamegroup, right=commonnamegroup_indication, left_on='commonnamegroupid', right_on='commonnamegroupid') | pandas.merge |
# Reviews Counts for Non-US Regions (raw & normalized)
import pandas as pd
import numpy as np
import os
folder = 'non_us_reviews/'
files = os.listdir(folder)
master = | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
from dplypy.dplyframe import DplyFrame
from dplypy.pipeline import join
def test_join():
df_l = DplyFrame(
pd.DataFrame(
data={
"common": [1, 2, 3, 4],
"left_index": ["a", "b", "c", "d"],
"left_key": [3, 4, 7, 6],
... | pd.testing.assert_frame_equal(output5.pandas_df, expected5) | pandas.testing.assert_frame_equal |
import os, unittest, pandas as pd, numpy as np
from saspt.trajectory_group import TrajectoryGroup
from saspt.constants import TRACK, FRAME, PY, PX, TRACK_LENGTH, JUMPS_PER_TRACK, DFRAMES, DR2, DY, DX, RBME
from saspt.utils import track_length
from saspt.io import is_detections
TEST_DIR = os.path.dirname(os.path.abspa... | pd.read_csv(self.track_csv) | pandas.read_csv |
import pandas as pd
import numpy as np
from sklearn import svm
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import ElasticNetCV
from sklearn.model_selection import KFold
from sklearn.cross_validation import KFold as kfo
import... | pd.read_csv(readFile_testfeatures[car_index]) | pandas.read_csv |
from Kernel import Kernel
from agent.ExchangeAgent import ExchangeAgent
from agent.NoiseAgent import NoiseAgent
from agent.ValueAgent import ValueAgent
from agent.market_makers.MarketMakerAgent import MarketMakerAgent
from util.order import LimitOrder
from util.oracle.SparseMeanRevertingOracle import SparseMeanRevertin... | pd.to_timedelta('09:30:00') | pandas.to_timedelta |
import random
import pandas as pd
from queue import CircularQueue
import nltk
#nltk.download()
from nltk.tree import ParentedTree as Tree
import en
class negation:
def __init__(self,max_length):
df = pd.read_csv(r'snli_1.0_train.txt', delimiter='\t')
self.df = df[df['sentence1'].apply(lambda x: le... | pd.DataFrame({'sentence1': [p], 'sentence2': [h], 'index': [i],'sentence1_negation':[neg_p],'sentence2_negation':[neg_h]}) | pandas.DataFrame |
import pandas as pd
import woodwork as ww
from sklearn.datasets import load_diabetes as load_diabetes_sk
def load_diabetes(return_pandas=False):
"""Load diabetes dataset. Regression problem
Returns:
Union[(ww.DataTable, ww.DataColumn), (pd.Dataframe, pd.Series)]: X and y
"""
data = load_diabe... | pd.DataFrame(data.data, columns=data.feature_names) | pandas.DataFrame |
from __future__ import absolute_import
import functools as ft
import warnings
from logging_helpers import _L
from lxml.etree import QName, Element
import lxml.etree
import networkx as nx
import numpy as np
import pandas as pd
from .core import ureg
from .load import draw, load
from six.moves import zip
... | pd.DataFrame() | pandas.DataFrame |
import unittest
import logging
import summer2020py.setup_logger as setup_logger
import summer2020py.make_genebody_coverage_graphs.make_genebody_coverage_graphs as mgcg
import pandas
import tempfile
import os
temp_wkdir_prefix = "TestMakeGeneBodyCoverageGraphs"
logger = logging.getLogger(setup_logger.LOGGER_NAME)
# ... | pandas.DataFrame(data = {"cov_diff_pct":[0.810320,0.867145], "label":["FAKE 0.81", "FACE 0.87"]}, index = ["FAKE", "FACE"]) | pandas.DataFrame |
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import struct
import h5py
import time
import os
class Database():
"""Connection to an HDF5 database storing message and order book data.
Parameters
----------
path : string
Specifies location of the HDF5 file
name... | pd.concat([df_time, df_price], axis=1) | pandas.concat |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import scipy.stats as stats
from scipy.optimize import brentq
from concentration import *
from uniform_concentration import *
import pdb
def plot_upper_tail(ns,s,ms,delta,maxiter):
plt.figure()
# Plot upper tail
fo... | pd.concat(concat_list, ignore_index=True) | pandas.concat |
from collections import OrderedDict
from functools import partial
import matplotlib.pyplot as plt
from scipy.linalg import toeplitz
import scipy.sparse as sps
import numpy as np
import pandas as pd
import bioframe
import cooler
from .lib.numutils import LazyToeplitz
def make_bin_aligned_windows(binsize, chroms, cen... | pd.DataFrame(index=index) | pandas.DataFrame |
from datetime import datetime, timedelta
import inspect
import numpy as np
import pytest
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
Index,
IntervalIndex,
MultiIndex... | is_interval_dtype(df["D"].cat.categories) | pandas.core.dtypes.common.is_interval_dtype |
import logging
import joblib
import seaborn
import scipy.stats
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.utils import shuffle
all_feature_names = [
'slope',
'slope0'... | pd.read_csv(align_metrics_data_url + sas, compression='gzip') | pandas.read_csv |
#
# Copyright 2015 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('2013-12-08 9:31AM', tz='UTC') | pandas.Timestamp |
from datetime import datetime
import numpy as np
import pytest
from pandas.core.dtypes.cast import find_common_type, is_dtype_equal
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series
import pandas._testing as tm
class TestDataFrameCombineFirst:
def test_combine_first_mixed(self):
... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
from __future__ import absolute_import, division, unicode_literals
import unittest
import jsonpickle
from helper import SkippableTest
try:
import pandas as pd
import numpy as np
from pandas.testing import assert_series_equal
from pandas.testing import assert_frame_equal
from pandas.testing import... | pd.Series(0, index=[[1], [2], [3]]) | pandas.Series |
"""Take Excel file from plate reader and conver to fraction infectivity."""
import argparse
import itertools
import os
import numpy as np
import pandas as pd
def parse_args():
"""Parse command line arguments."""
parser = argparse.ArgumentParser(description='Convert plate reader '
... | pd.DataFrame.from_dict(fract_infect_dict) | pandas.DataFrame.from_dict |
# Copyright 2021 Rosalind Franklin Institute
#
# 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 ... | pd.DataFrame(columns=self.meta.columns) | pandas.DataFrame |
from datetime import datetime
import numpy as np
import pytest
import pandas as pd
from pandas import Series, date_range
import pandas._testing as tm
from pandas.tseries.offsets import BDay
class TestTruncate:
def test_truncate(self, datetime_series):
offset = BDay()
ts = datetime_series[::3]
... | Series([1, 2, 3], index=idx[1:4]) | pandas.Series |
import numpy as np
import pandas as pd
import os
import sys
import matplotlib.pyplot as plt
import matplotlib
import sklearn.datasets, sklearn.decomposition
from sklearn.cluster import KMeans
from sklearn_extra.cluster import KMedoids
from sklearn.decomposition import PCA
from sklearn.preprocessing import Sta... | pd.DataFrame(data_represent_days_modified) | pandas.DataFrame |
#Import libraries
from sklearn.model_selection import train_test_split
import sys, os, re, csv, codecs, numpy as np, pandas as pd
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Dense, Input, LSTM, Embedding, Dropout, Activation
from keras.l... | pd.read_csv('data/Clean_Disasters_T_79187_.csv',delimiter = ',' ,converters={'text': str}, encoding = "ISO-8859-1") | pandas.read_csv |
from io import StringIO
import operator
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series, date_range
import pandas._testing as tm
from pandas.core.computation.check import _NUMEXPR_INSTALLED
PARSERS = "python", "pa... | tm.assert_frame_equal(res, expec) | pandas._testing.assert_frame_equal |
import os
import numpy as np
import pandas as pd
def create_list_simu_by_degree(degree, input_dir):
"""Create two list containing names for topographies and simulatins"""
degree_str = str(degree) + 'degree/'
# path to topographies files
topo_dir = input_dir + "dem/" + degree_str
# path to wind fi... | pd.DataFrame(all_info, columns=['degree', 'xi', 'degree_xi', 'topo_name', 'wind_name']) | pandas.DataFrame |
import numpy as np
import pandas as pd
data=pd.read_csv('iris.csv')
data=np.array(data)
data=np.mat(data[:,0:4])
#数据长度
length=len(data)
#通过核函数在输入空间计算核矩阵
k=np.mat(np.zeros((length,length)))
for i in range(0,length):
for j in range(i,length):
k[i,j]=(np.dot(data[i],data[j].T))**2
k[j,i]=k[i,j]
name=... | pd.DataFrame(columns=name,data=normalized_centered_k) | pandas.DataFrame |
from flask import abort, jsonify
from config import db
from models import (
Product,
ProductSchema,
Article,
Inventory
)
import pandas as pd
# Handler function for GET Products endpoint
def read_all_products():
# Query the db to return all products
products = Product.query.order_by(Product.... | pd.DataFrame(data=results) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 9 20:13:44 2020
@author: Adam
"""
#%% Heatmap generator "Barcode"
import os
os.chdir(r'C:\Users\Ben\Desktop\T7_primase_Recognition_Adam\adam\paper\code_after_meating_with_danny')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
imp... | pd.DataFrame() | pandas.DataFrame |
from datetime import datetime
import warnings
import numpy as np
from numpy.random import randn
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, DatetimeIndex, Index, Series
import pandas._testing as tm
from pandas.core.window.common import flex_binary_moment
... | Series([1] * 5) | pandas.Series |
# %%
import pandas as pd
from zhconv import convert
from scipy import stats
from fuzzywuzzy import fuzz
from time import perf_counter
import searchconsole
from datetime import datetime
from datetime import timedelta
# --------------DATA RETRIVING---------------
# no credentials saved, do not save credentials
#account ... | pd.merge(df, similar, how='left', on='modified_query') | pandas.merge |
from __future__ import print_function
import os
import pandas as pd
import xgboost as xgb
import time
import shutil
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
import numpy as np
from sklearn.utils import shuffle
from sklearn import metrics
import sys
def archive_results(fi... | pd.merge(test,diagnosis, on='patient_id',how='left') | pandas.merge |
import os
import sys
import time
import sqlite3
import pyupbit
import pandas as pd
from PyQt5.QtCore import QThread
from pyupbit import WebSocketManager
sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
from utility.setting import *
from utility.static import now, timedelta_sec, strf_time, ti... | pd.DataFrame([[tdct, tbg, tsg, tsig, tssg, sp, sg]], columns=columns_tt, index=[self.str_today]) | pandas.DataFrame |
# --------------
#Importing header files
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data= | pd.read_csv(path) | pandas.read_csv |
# Copyright (c) 2020, eQualit.ie inc.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import datetime
import json
import os
import time
import traceback
import pandas as pd
from baskerville.util.helpers import ... | pd.Grouper(freq=time_window) | pandas.Grouper |
"""PandasMoveDataFrame class."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Callable
import numpy as np
from pandas import DataFrame, DateOffset, Series, Timedelta
from pymove.core.dataframe import MoveDataFrame
from pymove.core.grid import Grid
from pymove.utils.constants import (
... | DataFrame(data) | pandas.DataFrame |
import os
os.environ["OMP_NUM_THREADS"] = "32"
from contextlib import contextmanager
import argparse
import os.path
import csv
import time
import sys
from functools import partial
import shutil as sh
import dill
from graph_tool.all import *
import pandas as pd
import numpy as np
import scipy as sp
from sklearn.covar... | pd.DataFrame(columns=('Nested_Level', 'Block', 'File', 'N_genes', 'Internal_degree', 'Assortatitvity')) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 by <NAME> (www.robo.guru)
# All rights reserved.
# This file is part of Agenoria and is released under the MIT License.
# Please see the LICENSE file that should have been included as part of
# this package.
import datetime as dt
from dateutil.relativedel... | pd.date_range(start_date, end_date) | pandas.date_range |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
READ IN:
1) <NAME> Data "../../../AKJ_Replication/Replication/data/data_replication.csv"
2) Alternative data "../output/alternativedata.csv"
EXPORT:
"../output/alternativedata.csv"
@author: olivergiesecke
"""
import pandas as pd
import numpy ... | pd.to_datetime(econ_df["date"]) | pandas.to_datetime |
import pytest
import numpy as np
from scipy import linalg
import pandas as pd
from linkalman.core.utils import *
# Test mask_nan
def test_mask_nan():
"""
Test when input is a matrix with column size > 1
"""
mat = np.ones((4,4))
is_nan = np.array([True, False, True, False])
expected_result = np... | pd.DataFrame({'a': [1., 2., 3.], 'b': [2., 3., 4.]}) | pandas.DataFrame |
from cytopy.data import gate
from cytopy.data.geometry import *
from scipy.spatial.distance import euclidean
from shapely.geometry import Polygon
from sklearn.datasets import make_blobs
from KDEpy import FFTKDE
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pytest
np.random.seed(42)
de... | pd.DataFrame({"X": data[:, 0], "Y": data[:, 1]}) | pandas.DataFrame |
import os
import shutil
import numpy as np
import pandas as pd
import scipy.integrate, scipy.stats, scipy.optimize, scipy.signal
from scipy.stats import mannwhitneyu
import statsmodels.formula.api as smf
import pystan
def clean_folder(folder):
"""Create a new folder, or if the folder already exists,
delete a... | pd.Series(y) | pandas.Series |
"""
This example shows how to join multiple pandas Series to a DataFrame
For further information take a look at the pandas documentation:
https://pandas.pydata.org/pandas-docs/stable/merging.html
"""
import wapi
import pandas as pd
import matplotlib.pyplot as plt
############################################
# Insert ... | pd.concat([df2,s], axis=1) | pandas.concat |
# Copyright (c) 2021-2022, NVIDIA CORPORATION.
import numpy as np
import pandas as pd
import pytest
import cudf
from cudf.testing._utils import NUMERIC_TYPES, assert_eq
from cudf.utils.dtypes import np_dtypes_to_pandas_dtypes
def test_can_cast_safely_same_kind():
# 'i' -> 'i'
data = cudf.Series([1, 2, 3], d... | pd.CategoricalDtype(categories=["1", "2", "3"]) | pandas.CategoricalDtype |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FFMpegWriter
import copy
from . import otherfunctions
from pathlib import Path
import warnings
import os
from skimage import feature
# Implement the data structure
class BaseMeasurement:
# Store... | pd.DataFrame(set_vals, index=old.index, columns=old.columns) | pandas.DataFrame |
import json
import os
import geopandas
import numpy as np
import pandas as pd
import cea.plots.cache
from cea.constants import HOURS_IN_YEAR
from cea.plots.variable_naming import get_color_array
from cea.utilities.standardize_coordinates import get_geographic_coordinate_system
"""
Implements py:class:`cea.plots.... | pd.DataFrame(hourly_pressure_loss) | pandas.DataFrame |
# *****************************************************************************
# Copyright (c) 2019, Intel Corporation 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 sou... | pd.DatetimeIndex(df['str_date']) | pandas.DatetimeIndex |
# -*- coding: utf-8 -*-
# Loading libraries
import os
import sys
import time
from networkx.algorithms.centrality import group
import pandas as pd
import re
import csv
from swmmtoolbox import swmmtoolbox as swmm
from datetime import datetime
from os import listdir
from concurrent import futures
from sqlalchemy import cr... | pd.DataFrame() | pandas.DataFrame |
from typing import List, Tuple
import pandas as pd
from src.preprocessing.config import RANKING_COLS, RESULTS_COLS
from src.db.manager import DBManager
from src.db.data import Results, GeneralRanking, HomeRanking, AwayRanking
class DataRetriever:
def __init__(self, db_config : str) -> None:
self._db_manag... | pd.DataFrame(data, columns=RESULTS_COLS) | pandas.DataFrame |
"""
Use the ``MNLDiscreteChoiceModel`` class to train a choice module using
multinomial logit and make subsequent choice predictions.
"""
from __future__ import print_function, division
import abc
import logging
import numpy as np
import pandas as pd
from patsy import dmatrix
from prettytable import PrettyTable
from... | pd.concat(ch) | pandas.concat |
# Function 0
def cleaning_func_0(loan):
# core cleaning code
import numpy as np
import pandas as pd
# loan = pd.read_csv('../input/loan.csv', low_memory=False)
loan['90day_worse_rating'] = np.where(loan['mths_since_last_major_derog'].isnull(), 0, 1)
return loan
#=============
# Function 1
def cleaning_func_1(loa... | pd.DataFrame.from_dict(statePop, orient='index') | pandas.DataFrame.from_dict |
# coding=utf-8
# Copyright 2016-2018 <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 ... | pd.concat(df_e3_list, axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
__author__ = '<NAME>'
"""
This python file contains the class BioforskStation which is a class
to handle the time series and general info of each station.
A BioforskStation can be generated with the use of BioforskStation('Aas'),
where 'Aas' is the name of one of the Bioforsk station... | pd.DataFrame(flagged.values, flagged.index) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 10 16:42:22 2018
@author: Ifrana
"""
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.... | pd.read_csv('D:\Data TimeSeries (Hourly)\Classification/for_RF_model.csv', delimiter=',') | pandas.read_csv |
#%%
import pandas as pd
dtypes = {"placekey":"object", "safegraph_place_id":"object", "parent_placekey":"object", "parent_safegraph_place_id":"object",
"location_name":"category", "street_address":"category", "city":"category", "region":"category", "postal_code":"int32",
"safegraph_brand_ids":"object", "brands":"cate... | pd.DataFrame(data[data['latitude'] == lat]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon 9/2/14
Using python pandas to post process csv output from Pacejka Tire model
@author: <NAME>, 2014
"""
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import pylab as py
class PacTire_panda:
'''
@class: loads, manages and plots various output... | pd.DataFrame(self._m_df_T, columns = ['alpha','Mz','Mzc','MP_z','M_zr','t','s'] ) | pandas.DataFrame |
import pandas as pd
from pandas._testing import assert_frame_equal
import pytest
import numpy as np
from scripts.normalize_data import (
remove_whitespace_from_column_names,
normalize_expedition_section_cols,
remove_bracket_text,
remove_whitespace,
ddm2dec,
remove_empty_unnamed_columns,
nor... | assert_frame_equal(df, expected) | pandas._testing.assert_frame_equal |
#!/usr/bin/env python
import json
import logging
import sys
import pandas as pd
import numpy as np
from functools import reduce
# from typing import Optional
from cascade_at.executor.args.arg_utils import ArgumentList
from cascade_at.core.log import get_loggers, LEVELS
from cascade_at.executor.args.args import ModelV... | pd.merge(x, y) | pandas.merge |
"""
.. module:: repeats
:synopsis: Repeats (transposon) related stuffs
.. moduleauthor:: <NAME> <<EMAIL>>
"""
import csv
import subprocess
import os
import gzip
import glob
import logging
logging.basicConfig(level=logging.DEBUG)
LOG = logging.getLogger(__name__)
import uuid
import pandas as PD
import numpy a... | PD.concat(outs, ignore_index=True) | pandas.concat |
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings("ignore")
result_pred = pd.read_csv("./sub_xgb_16_2_h.csv")
print("begin process gt10..")
gt10_prob = pd.read_csv("../classification/result/gt10.csv")
gt10_prob.sort_values(by='gt10_prob',inplace=True)
gt10_25 = gt10_prob.tail(25)
result_p... | pd.merge(result_pred,gt10_25,on='id',how='left') | pandas.merge |
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.Series(index=indexes, data=std_series, name='STD '+series.name) | pandas.Series |
"""
<NAME>017
PanCancer Classifier
scripts/pancancer_classifier.py
Usage: Run in command line with required command argument:
python pancancer_classifier.py --genes $GENES
Where GENES is a comma separated string. There are also optional arguments:
--diseases comma separated string of disease ty... | pd.DataFrame.from_dict(val_x_type) | pandas.DataFrame.from_dict |
import itertools
import time
import glob as gb
import librosa
import matplotlib.pyplot as plt
import librosa.display
import pickle
import pandas as pd
from sklearn.metrics import confusion_matrix, accuracy_score
import os
import soundfile as sf
import sys
import warnings
from keras.utils.vis_utils import plot_model
fro... | pd.DataFrame({'Predicted Values': predictions}) | pandas.DataFrame |
import pandas as pd
import numpy as np
import multiprocessing
from functools import partial
def _df_split(tup_arg, **kwargs):
split_ind, df_split, df_f_name = tup_arg
return (split_ind, getattr(df_split, df_f_name)(**kwargs))
def df_multicores(df, df_f_name, subset=None, njobs=-1, **kwargs):
'''
process ope... | pd.concat([split[1] for split in results]) | pandas.concat |
import logging
import os
import pandas as pd
import sys
from . import settings
# Logging
logging.basicConfig(stream=sys.stdout)
logger = logging.getLogger('avocado')
# Exceptions
class AvocadoException(Exception):
"""The base class for all exceptions raised in avocado."""
pass
def _verify_hdf_chunks(sto... | pd.read_hdf(store, 'chunk_info') | pandas.read_hdf |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.3.4
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
import matplotlib.pyplot as plt
import pandas as pd
imp... | pd.DataFrame({'src':all_sequences,'trg':all_sequences}) | pandas.DataFrame |
import pandas as pd
file1 = r'1_search_standard_box_spacer_0_16_greedy.csv'
file2 = r'2_search_specific_box_spacer_0_16_greedy.csv'
file3 = r'3_search_Epsilonproteobacteria_box_spacer_0_16_greedy.csv'
with open(file1, 'r') as f1:
data1 = pd.read_csv(f1)
with open(file2, 'r') as f2:
data2 = pd.read_... | pd.read_csv(f3) | pandas.read_csv |
# -*- coding:utf-8 -*-
"""
AHMath module.
Project: alphahunter
Author: HJQuant
Description: Asynchronous driven quantitative trading framework
"""
import copy
import collections
import warnings
import math
import numpy as np
import pandas as pd
import statsmodels.api as sm
from scipy.stats import norm
class AHMath... | pd.isnull(a[0]) | pandas.isnull |
# -*- coding: utf-8 -*-
# pylint: disable=W0612,E1101
from datetime import datetime
import operator
import nose
from functools import wraps
import numpy as np
import pandas as pd
from pandas import Series, DataFrame, Index, isnull, notnull, pivot, MultiIndex
from pandas.core.datetools import bday
from pandas.core.n... | Panel(d) | pandas.core.panel.Panel |
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