prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# -*- coding: utf-8 -*-
import copy
import os
import shutil
from builtins import range
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
from ..testing_utils import make_ecommerce_entityset
import featuretools as ft
from featuretools import variable_types
from featuretools.entityset... | pd.isnull(y) | pandas.isnull |
# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import pickle
import streamlit as st
import pandas as pd
import numpy as np
sim = pickle.load(open(r"user_sim_df", 'rb'))
user_sim_df=pd.DataFrame(sim)
alist=pickle.load(open(r"anime", 'rb'))
anime=pd.DataFrame(alist)
pv= pick... | pd.DataFrame(pv) | pandas.DataFrame |
import pandas as pd
from collections import deque, namedtuple
class PositionSummary(object):
"""
Takes the trade history for a user's watchlist from the database and it's
ticker. Then applies the FIFO accounting methodology to calculate the
overall positions status i.e. final open lots, average cost a... | pd.concat([df, df_bottom]) | pandas.concat |
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Index,
Series,
)
import pandas._testing as tm
dt_data = [
pd.Timestamp("2011-01-01"),
pd.Timestamp("2011-01-02"),
pd.Timestamp("2011-01-03"),
]
tz_data = [
pd.Timestamp("2011-01-01", tz="U... | pd.concat([s2, s1], ignore_index=True) | pandas.concat |
"""
A collection of Algos used to create Strategy logic.
"""
from __future__ import division
import abc
import random
import re
import numpy as np
import pandas as pd
import sklearn.covariance
from future.utils import iteritems
import bt
from bt.core import Algo, AlgoStack, SecurityBase, is_zero
def run_always(f):... | pd.Timestamp(date_to_compare) | pandas.Timestamp |
import os
from uuid import uuid4
import pytest
from thrift.transport import TSocket, TTransport
from thrift.transport.TSocket import TTransportException
from heavyai import connect
import datetime
import random
import string
import numpy as np
import pandas as pd
heavydb_host = os.environ.get('HEAVYDB_HOST', 'localho... | pd.read_csv("tests/data/polys_10000.zip", header=None) | pandas.read_csv |
import pandas as pd
import time
def patient(rdb):
""" Returns list of patients """
patients = """SELECT "Name" FROM patient ORDER BY index"""
try:
patients = pd.read_sql(patients, rdb)
patients = patients["Name"].values.tolist()
except:
patients = ['Patient']
return patien... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def plot_scatter(latent_code, output_path,
label_file='data/PANCAN/GDC-PANCAN_both_samples_tumour_type.tsv',
colour_file='data/TCGA_colors_obvious.tsv', latent_code_dim=2, have_label=True):
... | pd.read_csv(label_file, sep='\t', index_col=0) | pandas.read_csv |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# finpie - a simple library to download some financial data
# https://github.com/peterlacour/finpie
#
# Copyright (c) 2020 <NAME>
#
# Licensed under the MIT License
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and assoc... | pd.io.parsers.ParserBase({'names':df.columns}) | pandas.io.parsers.ParserBase |
import argparse
from autogluon.tabular import TabularDataset, TabularPredictor
from autogluon.tabular.models import CatBoostModel, KNNModel, LGBModel, XGBoostModel, TabularNeuralNetModel, RFModel
import os
from numpy.core.fromnumeric import trace
import pandas as pd
import traceback
parser = argparse.ArgumentParser()
... | pd.DataFrame(result) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 26 11:57:27 2015
@author: malte
"""
import numpy as np
import pandas as pd
from scipy import sparse
import implicit
class ImplicitNN:
'''
ImplicitNN(factors=100, epochs=15, reg=0.03, steps=None, weighting='same', session_key = 'playlist_id', item_key... | pd.Series() | pandas.Series |
from flask import Flask, Markup, render_template
import pandas as pd
import json
from sentiment_score_calculator import get_and_process_tweets
final_list = get_and_process_tweets()
#print(len(final_list))
list_values = [val for d in final_list for val in d.values()]
list_values = list_values[::-4]
#json_list = []
#... | pd.Series(['variable 1','variable 2', 'variable 3', 'variable 4', 'variable 5']) | pandas.Series |
import functools
import itertools
import itertools as it
import logging
import shutil
import warnings
from pathlib import Path
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Tuple
from typing import Union
import dask.dat... | pd.isna(end) | pandas.isna |
""" test parquet compat """
import datetime
from distutils.version import LooseVersion
import os
from warnings import catch_warnings
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
import pandas._testing as tm
from pandas.io.parquet import (
FastParquetImpl,
Py... | pd.Timestamp("20130101") | pandas.Timestamp |
import matplotlib.pyplot as plt
import pandas as pd
dataset_file = "datasets/stock_data.csv"
def load_data():
data = pd.read_csv(dataset_file)
# print(data)
return data
df = load_data()
plt.figure(figsize=(10, 5))
top = plt.subplot2grid((4, 4), (0, 0), rowspan=3, colspan=4)
bottom = plt.subplot2grid((... | pd.DataFrame({'AAPL': df['Close'], 'SMA 10': sma10, 'SMA 20': sma20, 'SMA 50': sma50}) | pandas.DataFrame |
import argparse
import pandas as pd
import re
from pathlib import Path
import torch
parser = argparse.ArgumentParser()
parser.add_argument('dir', type=str)
parser.add_argument('fmt', default=None,nargs='?')
args = parser.parse_args()
res = {}
root_dir = Path(args.dir)
train_log = root_dir / 'train.log'
config = torc... | pd.DataFrame(res) | pandas.DataFrame |
import pandas as pd
import numpy as np
import sklearn.neighbors
import scipy.sparse as sp
import seaborn as sns
import matplotlib.pyplot as plt
import torch
from torch_geometric.data import Data
def Transfer_pytorch_Data(adata):
G_df = adata.uns['Spatial_Net'].copy()
cells = np.array(adata.obs_nam... | pd.concat(KNN_list) | pandas.concat |
#Compare painted data with observed data - for three different sets of ages
#Works but could do with some tidying up of the code
import numpy as np
import h5py
import pandas as pd
import math
from astropy.io import fits
from astropy.table import Table, join
import matplotlib.pyplot as plt
from matplotlib.colors import ... | pd.isna(apogee_data['rl']) | pandas.isna |
from string import ascii_letters
import struct
from uuid import uuid4
from datashape import var, R, Option, dshape
import numpy as np
from odo import resource, odo
import pandas as pd
import pytest
import sqlalchemy as sa
from warp_prism._warp_prism import (
postgres_signature,
raw_to_arrays,
test_overflo... | pd.DataFrame({'a': data}) | pandas.DataFrame |
# @Time : 4/7/2022 11:15 AM
# @Author : <NAME>
"""
This script performs the data post-processing, before feeding it into any machine learning algorithm
"""
import os
import numpy as np
from numpy import genfromtxt
import math
import statistics as st
import matplotlib.pyplot as plt
from statistics import mean, stdev
im... | pd.read_csv(location + name + stage + topics[0] + '.csv', header=None, index_col=False) | pandas.read_csv |
#!/usr/bin/python
# -*- coding: UTF-8 -*-
"""
程序通用函数库
作者：wking [http://wkings.net]
"""
import os
import statistics
import time
import datetime
import requests
import numpy as np
import pandas as pd
import threading
from queue import Queue
from retry import retry
# from rich.progress import track
# from rich import pri... | pd.concat([df, data], axis=0, ignore_index=True) | pandas.concat |
import datetime
import numpy as np
import pandas as pd
import pytest
from .utils import (
get_extension,
to_json_string,
to_days_since_epoch,
extend_dict,
filter_by_columns,
breakdown_by_month,
breakdown_by_month_sum_days,
to_bin,
)
@pytest.fixture
def issues():
return pd.DataFram... | pd.Timestamp(2018, 1, 1) | pandas.Timestamp |
import numpy as np
import pandas as pd
import joblib, os
class dataset_creator():
def __init__(self, project, data, njobs=1):
self.data = data
self.dates_ts = self.check_dates(data.index)
self.project_name= project['_id']
self.static_data = project['static_data']
self.path... | pd.DateOffset(hours=1) | pandas.DateOffset |
import argparse
import yaml
import os
import shutil
from pathlib import Path
from collections import OrderedDict
import torch
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rcParams["lines.linewidth"] = 0.8
from pysnn.neuron import BaseNeuron
from pysnn.network im... | pd.DataFrame(columns=["u", "v", "lw_raw", "color", "lw"]) | pandas.DataFrame |
import pandas as pd
import re
import numpy as np
def read_single_data(path):
"""
Read data in ndarray type
:param path: path of data file
:return: data: ndarray
"""
normal = pd.read_csv(path, header=None)
normal = filter_data(normal)
return normal.values
def read_origin_data(path):
... | pd.concat([abnormal_sample_data, normal_sample_data]) | pandas.concat |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
# Import python libraries
import sys
import os
import argparse
import pandas as pd
import math
import time
import datetime
try:
from pyrainbowterm import *
except ImportError:
print('Can not import pyrainbowterm!', log_type='e... | pd.DataFrame(unique_values, columns=['label']) | pandas.DataFrame |
'''
For more information and details about the algorithm, please refer to
Pattern classification with Evolving Long-term Cognitive
Networks
<NAME> a,b,⇑, <NAME>˛bska c, <NAME> d
'''
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn import datasets
from sklearn import model_selection
from skle... | pd.DataFrame(data=matrix,dtype=float) | pandas.DataFrame |
import os
from datetime import datetime, timedelta
from http import HTTPStatus
from typing import Any, List, Tuple
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import tinvest as ti
import edhec_risk_kit as erk
class HTTPError(Exception):
pass
class CustomClient(ti.SyncClient):
de... | pd.DataFrame(df) | pandas.DataFrame |
from sklearn import datasets
import pandas as pd
# %matplotlib inline
ds = datasets.load_breast_cancer();
NC = 4
lFeatures = ds.feature_names[0:NC]
df_orig = pd.DataFrame(ds.data[:,0:NC] , columns=lFeatures)
df_orig['TGT'] = ds.target
df_orig.sample(6, random_state=1960)
from sklearn.ensemble import RandomForestCla... | pd.DataFrame() | pandas.DataFrame |
import argparse
import os
import numpy as np
import pandas as pd
def save_exp(exp_dir):
"""
Stores the rewards and corresponding time-steps for each run (since other parts of the
logs are not used in the final table). Also calculates and store the mean and standard
error over all the repetitions. Cha... | pd.DataFrame(all_scalars) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
sns.set_theme(style="ticks", color_codes=True)
# In[2]:
#load data
df = | pd.read_csv('in-vehicle-coupon-recommendation.csv') | pandas.read_csv |
#!/usr/bin/env python3
"""Add domain as nested property to transcript. Same for hugo symbol and exon
info. Output resulting JSON"""
import pandas as pd
import numpy as np
import argparse
def add_nested_hgnc(transcripts):
""" Make nested object HGNC symbols per transcript"""
def get_hgnc_symbol(transcript_id... | pd.isnull(hgnc_symbols.hgnc_symbol) | pandas.isnull |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import re
import bisect
from io import BytesIO
from pathlib import Path
import fire
import requests
import pandas as pd
from lxml import etree
from loguru import logger
NEW_COMPANIES_URL = "http://www.csindex.com.cn/uploads/file/autofile/cons/0... | pd.read_excel(_io, sheet_name=None) | pandas.read_excel |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
from numpy import nan
import numpy as np
import pandas as pd
from pandas.types.common import is_integer, is_scalar
from pandas import Index, Series, DataFrame, isnull, date_range
from pandas.core.index import MultiIndex
from pa... | tm.assert_series_equal(expr, res2l) | pandas.util.testing.assert_series_equal |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
from numpy import nan
import numpy as np
import pandas as pd
from pandas.types.common import is_integer, is_scalar
from pandas import Index, Series, DataFrame, isnull, date_range
from pandas.core.index import MultiIndex
from pa... | pd.Timestamp('2011-01-01', tz=tz) | pandas.Timestamp |
import blpapi
import logging
from .BbgRefDataService import BbgRefDataService
import pandas as pd
import numpy as np
from . import BbgLogger
logger = BbgLogger.logger
SECURITY_DATA = blpapi.Name("securityData")
SECURITY = blpapi.Name("security")
FIELD_DATA = blpapi.Name("fieldData")
FIELD_EXCEPTIONS = blpapi.Name("fi... | pd.DataFrame() | pandas.DataFrame |
from collections import OrderedDict, Counter
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
import pylcs
import config.constants as constants
from config.constants import DOC_LABELS, SUBTYPE_A, SUBTYPE_B
from corpus.tokenizati... | pd.DataFrame(x, columns=columns) | pandas.DataFrame |
import pandas as pd
TRAIN_PATH = 'data/multinli_1.0/multinli_1.0_train.txt'
DEV_PATH = 'data/multinli_1.0/multinli_1.0_dev_matched.txt'
#things get a bit weird here as we use the dev set as the test set
#and make a test set from the train set
train_df = pd.read_csv(TRAIN_PATH, sep='\t', error_bad_lines=False, keep_de... | pd.read_csv(DEV_PATH, sep='\t', keep_default_na=False) | pandas.read_csv |
"""
GIS For Electrification (GISEle)
Developed by the Energy Department of Politecnico di Milano
Supporting Code
Group of supporting functions used inside all the process of GISEle algorithm
"""
import os
import requests
import pandas as pd
import geopandas as gpd
import numpy as np
import json
import shapely.ops
imp... | pd.concat([add_hours, df], ignore_index=True) | pandas.concat |
"""
Generate all plots for the pipeline. For biotype specific plots, all plots are generated as a multi page PDF. There
is a plot for each biotype on its own, and one for the combined results.
"""
import json
import matplotlib
import logging
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.use('Agg')
import itertool... | pd.DataFrame(consensus_data[genome]['Evaluation Improvement']['changes']) | 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.read_csv(path_df, index_col=False, parse_dates=[0]) | pandas.read_csv |
"""ClinVar integration script"""
import fire
import fsspec
import pandas as pd
from datetime import datetime
from pathlib import Path
from prefect import task, context, Flow, Parameter, Task
from prefect.engine.results import LocalResult
from data_source.prefect.tasks import constant
from data_source import catalog
fro... | pd.to_datetime(created) | pandas.to_datetime |
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
from numba import njit
import vectorbt as vbt
from tests.utils import record_arrays_close
from vectorbt.generic.enums import range_dt, drawdown_dt
from vectorbt.portfolio.enums import order_dt, trade_dt, log_dt
day_dt = np.timedelta64... | pd.Timestamp('2020-01-01 00:00:00') | pandas.Timestamp |
#!/bin/env python
# -*- coding: utf-8 -*-
#
# Created on 5/23/19
#
# Created for py_bacy
#
# @author: <NAME>, <EMAIL>
#
# Copyright (C) {2019} {<NAME>}
#
# System modules
import logging
import os
import glob
import time
import warnings
import abc
from typing import Any
import gc
# External modules
import numpy as... | pd.to_timedelta(self.config['obs']['td_start']) | pandas.to_timedelta |
#+ 数据科学常用工具
import matplotlib as mpl
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style
import seaborn as sns
from sklearn.preprocessing import PowerTransformer
import category_encoders as ce
from sklearn.model_selection import StratifiedKFold, KFold
from joblib impo... | pd.qcut(x, n_scatter) | pandas.qcut |
from django.http import JsonResponse
import pandas as pd
import numpy as np
import json
from django.views.decorators.csrf import csrf_protect
import os # os.getcwd()
df_comPreRequisitos = pd.read_csv('data_science/disciplinas_prerequisitosnome.csv')
df_turmas2015 = pd.read_csv('data_science/turmas_new.csv')
def dataF... | pd.concat([df_contagemRep, aprovados]) | pandas.concat |
"""
"""
"""
>>> # ---
>>> # SETUP
>>> # ---
>>> import os
>>> import logging
>>> logger = logging.getLogger('PT3S.Rm')
>>> # ---
>>> # path
>>> # ---
>>> if __name__ == "__main__":
... try:
... dummy=__file__
... logger.debug("{0:s}{1:s}{2:s}".format('DOCTEST: __main__ Context: ','path = os.p... | pd.Series(pFWVBMeasureValue) | pandas.Series |
import pandas as pd
import os
from metaquantome.util.utils import DATA_DIR
import numpy as np
def write_testfile(df, name):
df.to_csv(os.path.join(DATA_DIR, 'test', name), sep='\t', index_label='peptide')
# simple: single intensity
func = pd.DataFrame({'go': ['GO:0008152', 'GO:0022610']}, index=['A', 'B'])
wri... | pd.DataFrame({'ec': ['3.4.11.-', '172.16.58.3']}, index=['A', 'B']) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[5]:
#!/usr/bin/env python
# coding: utf-8
# In[11]:
#!/usr/bin/env python
# coding: utf-8
# In[7]:
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import linear_model
from scipy.optimize import fmin_l_bfgs_b
from sklea... | pd.Series(deSeasoned) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 3 14:47:20 2017
@author: Flamingo
"""
#%%
from bs4 import BeautifulSoup
import urllib
import pandas as pd
import numpy as np
CITY_NAME = | pd.read_csv('CITY_NAME2.csv') | pandas.read_csv |
import numpy as np
import pandas as pd
from scipy.stats import mode
from tqdm import tqdm
from geopy.geocoders import Nominatim
from datetime import datetime
def handle_bornIn(x):
skip_vals = ['16-Mar', '23-May', 'None']
if x not in skip_vals:
return datetime(2012, 1, 1).year - datetime(int(x), 1, 1)... | pd.read_csv(data_content.base_dir + 'temp/tdf.csv') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 2 21:02:58 2020
@author: RMS671214
"""
from faspy.interestrate.fixincome import date_structures, calc_customfix_structures, \
value_customfix_structures
import numpy as np
from faspy.interestrate import rmp_dates as rd
from faspy.interestrate ... | pd.DataFrame(new_structures) | pandas.DataFrame |
import pandas as pd
import streamlit as st
import plotly.express as px
@st.cache
def load_data(file):
data = pd.read_csv(file,
na_filter=True,
na_values=[' -', '-'],
keep_default_na=False)
return data
#####################
### HTML SETTING... | pd.concat([dfp1, dfp2]) | pandas.concat |
# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/05-orchestrator.ipynb (unless otherwise specified).
__all__ = ['retry_request', 'if_possible_parse_local_datetime', 'SP_and_date_request', 'handle_capping',
'date_range_request', 'year_request', 'construct_year_month_pairs', 'year_and_month_request',
... | pd.to_datetime(end_date) | pandas.to_datetime |
"""A collections of functions to facilitate
analysis of HiC data based on the cooler and cooltools
interfaces."""
import warnings
from typing import Tuple, Dict, Callable
import cooltools.expected
import cooltools.snipping
import pandas as pd
import bioframe
import cooler
import pairtools
import numpy as np
... | pd.concat((cis_temp, trans_temp)) | pandas.concat |
import os
import argparse
from configparser import ConfigParser
import time
import sys
import logging
import shutil
import pandas as pd
import numpy as np
import Metrics
parser = argparse.ArgumentParser()
parser.add_argument('--seq_len', type=int, default=6, help='sequence length of values, which should be even nums (... | pd.read_csv(road_path) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# In[15]:
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import datetime
import matplotlib.patches as mpatches
from datetime import datetime
# ## Exploratory data analysis
#
# Aims for the project:
# 1. ... | pd.to_datetime(file['alert_open_date_formula']) | pandas.to_datetime |
# coding: utf-8
# Copyright (c) 2021 AkaiKKRteam.
# Distributed under the terms of the Apache License, Version 2.0.
from copy import deepcopy
import pandas as pd
import numpy as np
from sklearn.metrics import mean_absolute_error
_SUCCESS_ = "O"
_FAILED_ = "X"
_NO_REF_ = "-"
_FILL_STR_ = "-"
_PAD_STR_ = " "
_CURRE... | pd.concat([df_result_totaldos, df_ref_totaldos], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 14 19:18:18 2020
@author: <NAME>
"""
import pandas as pd
import numpy as np
import itertools
from operator import itemgetter
try:
from support_modules import role_discovery as rl
except:
import os
from importlib import util
spec = util.spec_from_file_loca... | pd.DataFrame.from_dict(log) | pandas.DataFrame.from_dict |
from datetime import datetime
from functools import lru_cache
from typing import Union, Callable, Tuple
import dateparser
import pandas as pd
from dateutil.relativedelta import relativedelta
from numpy.distutils.misc_util import as_list
from wetterdienst.dwd.metadata import Parameter, TimeResolution, PeriodType
from ... | pd.to_numeric(df[column], errors="coerce") | pandas.to_numeric |
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(data, columns=["X", "Y"]) | pandas.DataFrame |
# %%
import os
import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder
from sklearn.decomposition import PCA
base_dir = os.getcwd()
# %%
train_op_df = pd.read_csv(base_dir + '/dataset/dataset2/trainset/train_op.csv')
train_trans_df = pd.read_csv(base_dir + '/dataset/dataset2/trainset/tra... | pd.DataFrame.from_dict(data=mp_trans_type2, orient='columns') | pandas.DataFrame.from_dict |
import numpy as np
import pandas as pd
import genetic_algorithm_feature_selection.variable_selection as vs
import genetic_algorithm_feature_selection.genetic_steps as gs
from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_regression
# import matplotlib.pyplot as plt
nCols = 50
nGoods = ... | pd.Series(target, name='target') | pandas.Series |
import sys
from PyQt4.QtGui import QApplication
from PyQt4.QtCore import QUrl
from PyQt4.QtWebKit import QWebPage
import bs4 as bs
import urllib.request
import pandas as pd
import requests
from bs4 import BeautifulSoup
import re
import datetime
import os
today=datetime.date.today()
camera=[]
model1=[]
... | pd.DataFrame(records, columns = ['COUNTRY', 'COMPANY', 'MODEL', 'USP', 'DISPLAY', 'CAMERA', 'MEMORY', 'BATTERY', 'THICKNESS', 'PROCESSOR', 'EXTRAS/ LINKS']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import os
import sys
from typing import List, NamedTuple
from datetime import datetime
from google.cloud import aiplatform, storage
from google.cloud.aiplatform import gapic as aip
from kfp.v2 import compiler, dsl
from kfp.v2.dsl import component, pipeline, Input, Output, Model, Metrics, Datas... | pd.read_csv(df.path) | pandas.read_csv |
import pytest
import unittest
from unittest import mock
from ops.tasks.anomalyDetection import anomalyService
from anomaly.models import Anomaly
from pandas import Timestamp
from decimal import Decimal
from mixer.backend.django import mixer
import pandas as pd
@pytest.mark.django_db(transaction=True)
def test_createAn... | pd.DataFrame(fakedata) | pandas.DataFrame |
import csv
from io import StringIO
import os
import numpy as np
import pytest
from pandas.errors import ParserError
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
NaT,
Series,
Timestamp,
date_range,
read_csv,
to_datetime,
)
import pandas._testing as tm
impo... | tm.ensure_clean("csv_date_format_with_dst") | pandas._testing.ensure_clean |
import os
from dataclasses import dataclass
from typing import Callable, List, Dict
from typing import Optional
import pandas as pd
from PIL.Image import Image as Img
from dacite import from_dict
from wheel5.dataset import LMDBImageDataset, SimpleImageClassificationDataset
from wheel5.dataset import SimpleImageDetect... | pd.DataFrame(entries) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
@pytest.fixture
def df_checks():
"""fixture dataframe"""
return pd.DataFrame(
{
"famid": [1, 1, 1, 2, 2, 2, 3, 3, 3],
"birth": [1, 2, 3, 1, 2, 3, 1, 2, 3],
"ht1": [2.... | assert_frame_equal(result, actual) | pandas.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
from datetime import timedelta
import operator
from string import ascii_lowercase
import warnings
import numpy as np
import pytest
from pandas.compat import lrange
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
Categorical, DataFrame, MultiIndex, Serie... | tm.assert_series_equal(result, expected) | pandas.util.testing.assert_series_equal |
import pandas as pd
import numpy as np
import math
import re
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib as mpl
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib.patches import Rectangle
import io
from ... | pd.to_timedelta(drugitems_continuous['duration'], unit='ms') | pandas.to_timedelta |
# import Ipynb_importer
import pandas as pd
from .public_fun import *
# 全局变量
class glv:
def _init():
global _global_dict
_global_dict = {}
def set_value(key,value):
_global_dict[key] = value
def get_value(key,defValue=None):
try:
return _global_dict[key... | pd.merge(self.ol, self.f_09.ol, left_index=True, right_index=True) | pandas.merge |
#!/usr/bin/env python
# Main script for foul ball risk analysis. Performs web scraping, data ingest
# data cleaning, summarization and statistical analyses.
import warnings
from bs4 import BeautifulSoup
import numpy as np
import nbinom_fit
import pandas as pd
import os
import subprocess
import argparse
import dateti... | pd.read_csv(teams_file_name) | pandas.read_csv |
#!/bin/env python
# -*- coding: utf-8 -*-
"""
A Python package that aids the user in making dynamic cuts to data in various
parameter spaces, using a simple GUI.
.. versioncreated:: 0.1
.. versionchanged:: 0.6
.. codeauthor:: <NAME> <<EMAIL>>
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.... | pd.DataFrame() | pandas.DataFrame |
#############################################################################
# Copyright (C) 2020-2021 German Aerospace Center (DLR-SC)
#
# Authors:
#
# Contact: <NAME> <<EMAIL>>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You... | pd.read_json(self.test_string1) | pandas.read_json |
import os
import sys
import numpy as np
import pandas as pd
import time
import scipy.sparse
import scipy.sparse.linalg
from scipy import stats
from scipy.optimize import minimize
np.set_printoptions(threshold=sys.maxsize)
# Add lib to the python path.
from genTestDat import genTestData2D, prodMats2D
from est2d import... | pd.read_csv(results_file, index_col=0) | pandas.read_csv |
import pandas as pd
import numpy as np
import datetime
import calendar
from math import e
from brightwind.analyse import plot as plt
# noinspection PyProtectedMember
from brightwind.analyse.analyse import dist_by_dir_sector, dist_12x24, coverage, _convert_df_to_series
from ipywidgets import FloatProgress
from IPython.d... | pd.Series([]) | pandas.Series |
import json
from django.http import HttpResponse
from .models import (
Invoice,
Seller,
Receiver,
)
from .serializers import (
InvoiceSerializer,
SellerSerializer,
ReceiverSerializer,
)
import re
from django.views import View
from django.http import Http40... | pd.DataFrame({'date': sf.index, 'total': sf.values}) | pandas.DataFrame |
import os
import ast
import glob
import numpy as np
import pandas as pd
from tqdm import tqdm
from itertools import chain
from astropy.io import ascii
import multiprocessing as mp
from astropy.stats import mad_std
from astropy.timeseries import LombScargle as lomb
from pysyd import __file__
from pysyd.plots import set... | pd.read_csv(files[0]) | pandas.read_csv |
from sklearn.feature_selection import RFE
from sklearn.svm import SVR
import pandas as pd
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
def get_data_RFE():
# from this... | pd.DataFrame(newdata2) | pandas.DataFrame |
"""
usage: parking_utilisation.py [-h] --park PARK [--pfile PFILE]
[--dbname DBNAME] [--dbhost DBHOST]
[--dbuser DBUSER] --dbpwd DBPWD
[--veh_type VEHT] [--granular G]
Script to plot parking utilisation by time of day.
optional ... | pd.to_datetime(park_slots.end_time, format="%H:%M:%S") | pandas.to_datetime |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2020/10/10 13:46
Desc: 东方财富网-数据中心-COMEX库存数据
http://data.eastmoney.com/pmetal/comex/by.html
"""
import demjson
import pandas as pd
import requests
def futures_comex_inventory(symbol: str = "黄金") -> pd.DataFrame:
"""
东方财富网-数据中心-COMEX库存数据
http://data.eas... | pd.DataFrame(data_json["data"]) | pandas.DataFrame |
#!/usr/bin/env python
import os
from collections import defaultdict
import pandas as pd
import click
import numpy as np
from scipy.signal import argrelmax
from HotGauge.thermal.ICE import load_3DICE_grid_file
from HotGauge.utils.io import open_file_or_stdout
##########################################################... | pd.read_pickle(local_max_stats_file) | pandas.read_pickle |
from mbf_genomics.annotator import Annotator, FromFile
import pandas as pd
class Description(Annotator):
"""Add the description for the genes from genome.
@genome may be None (default), then the ddf is queried for a '.genome'
Requires a genome with df_genes_meta - e.g. EnsemblGenomes
"""
columns... | pd.Series(result, index=ddf.df.index) | pandas.Series |
import asyncio
from collections import defaultdict, namedtuple
from dataclasses import dataclass, fields as dataclass_fields
from datetime import date, datetime, timedelta, timezone
from enum import Enum
from itertools import chain, repeat
import logging
import pickle
from typing import Collection, Dict, Generator, Ite... | pd.concat([df] + chunks) | pandas.concat |
import pandas as pd
df_ab = pd.DataFrame({'a': ['a_1', 'a_2', 'a_3'], 'b': ['b_1', 'b_2', 'b_3']})
df_ac = pd.DataFrame({'a': ['a_1', 'a_2', 'a_4'], 'c': ['c_1', 'c_2', 'c_4']})
print(df_ab)
# a b
# 0 a_1 b_1
# 1 a_2 b_2
# 2 a_3 b_3
print(df_ac)
# a c
# 0 a_1 c_1
# 1 a_2 c_2
# 2 a_4 c_4
... | pd.merge(df_abx, df_acx_, left_on=['a', 'x'], right_on=['a', 'x_']) | pandas.merge |
import pandas as pd
import argparse
import os
import sys
script_path = os.path.dirname(os.path.realpath(__file__))
sys.path.append(script_path+'/src')
from utils import *
from loguru import logger
import numpy as np
############### parameters for the program #################
parser = argparse.ArgumentParser()
parser... | pd.read_csv(script_path+'/data/cancer_genes_tad.bed',header=None, sep='\t') | pandas.read_csv |
#Importing the required packages
from flask import Flask, render_template, request
import os
import pandas as pd
from pandas import ExcelFile
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
from sklearn.preprocessing import StandardScaler, Label... | pd.read_excel('trainfile.xlsx') | pandas.read_excel |
"""This script is designed to perform statistics of demographic information
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import pearsonr,spearmanr,kendalltau
import sys
sys.path.append(r'D:\My_Codes\LC_Machine_Learning\lc_rsfmri_tools\lc_rsfmri_tools... | pd.DataFrame(headmotion_name_dataset2, dtype=np.int32) | pandas.DataFrame |
# ---
# jupyter:
# jupytext:
# formats: jupyter_scripts//ipynb,ifis_tools//py
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.3'
# jupytext_version: 1.0.0
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# ... | pd.Timestamp(date2) | pandas.Timestamp |
import sys, re
import pandas as pd, numpy as np
from data_processing import split_wrd, space_fill
def df_format_print(df,file=sys.stdout,index=False,align='c',squeeze=False,uwidth=2,spcwidth=1,kind="simple",margin=None):
lengths = []
if index: df = df.reset_index()
collen = len(df.columns)
delta = uw... | pd.DataFrame(ddfl) | pandas.DataFrame |
import pandas as pd
import networkx as nx
import pytest
from kgextension.feature_selection import hill_climbing_filter, hierarchy_based_filter, tree_based_filter
from kgextension.generator import specific_relation_generator, direct_type_generator
class TestHillCLimbingFilter:
def test1_high_beta(self):
i... | pd.read_csv("test/data/feature_selection/hierarchy_based_test8_expected.csv") | pandas.read_csv |
# internal modules
import os
from typing import Tuple
from app_logic import dataframe_creation
from data_structures.annotation_data import AnnotationData
from data_structures.raw_data import RawData
# python modules
import logging
# dependencies
import numpy as np
import pandas as pd
# DEFINITIONS
from util.definit... | pd.concat([df_signals, df_baselines], axis=1) | pandas.concat |
from typing import List
import datetime
import requests
from matplotlib import pyplot as plt
from matplotlib.ticker import FuncFormatter
from matplotlib.rcsetup import cycler
import pandas as pd
DATA_GOUV_2_OPEN = {
"date": "date",
"granularite": "granularite",
"maille_code": "maille_code",
"maille_no... | pd.read_csv(gouv_file) | pandas.read_csv |
import pandas as pd
from functools import reduce
from fooltrader.contract.files_contract import *
import re
import json
class agg_future_dayk(object):
funcs={}
def __init__(self):
self.funcs['shfeh']=self.getShfeHisData
self.funcs['shfec']=self.getShfeCurrentYearData
self.funcs['ineh']... | pd.DataFrame(data=load_dict['o_curinstrument']) | pandas.DataFrame |
__all__ = [
"tran_shapley_cohort",
"tf_shapley_cohort",
]
from grama import add_pipe, pipe
from itertools import chain, combinations
from numpy import all, number, sum, zeros, empty, NaN
from pandas import concat, DataFrame
from scipy.special import comb
from toolz import curry
## Helper
def powerset(iterabl... | DataFrame() | pandas.DataFrame |
#coding=utf-8
import pandas as pd
import numpy as np
import sys
import os
from sklearn import preprocessing
import datetime
import scipy as sc
from sklearn.preprocessing import MinMaxScaler,StandardScaler
from sklearn.externals import joblib
#import joblib
class FEbase(object):
"""description of class"""
def ... | pd.merge(df_data, df_adj_all, how='left', on=['ts_code','trade_date']) | pandas.merge |
from urllib.request import urlopen
from dateutil.parser import parse
import os
from urllib.error import HTTPError
import pandas as pd
import xarray as xr
import glob
from datetime import timedelta
def _create_unid(x, haz_type):
r"""Creates a unique id for each svrgis report.
The unid format is as follows:
... | pd.read_csv(fname) | pandas.read_csv |
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.api.types import is_float, is_float_dtype, is_scalar
from pandas.core.arrays import IntegerArray, integer_array
from pandas.tests.extension.base import BaseOpsUtil
class TestArithmeticOps(BaseOpsUtil):
def _check_divmod... | pd.Series(data) | pandas.Series |
import unittest
import pdb
import pandas as pd
import numpy as np
from pandas.util.testing import assert_frame_equal, assert_index_equal
from ..models.condition_models import RuleKPI, RuleCondition, RuleConditionalOperator, RuleConditionGroup, RuleConditionGroupOperator
class Test_conditional_operator(unittest.TestC... | pd.Series([4., 3., 4.], index=dataIndex) | pandas.Series |
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