prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import os
import time
import scipy
import random
import pickle
import torch
import json
import numpy as np
import pandas as pd
from urllib import request
pd.set_option('display.width', 1000)
def adj_to_tensor(adj):
if type(adj) != scipy.sparse.coo.coo_matrix:
adj = adj.tocoo()
sparse_row = torch.Long... | pd.DataFrame(result_dict, index=[index]) | pandas.DataFrame |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import operator
from itertools import product, starmap
from numpy import nan, inf
import numpy as np
import pandas as pd
from pandas import (Index, Series, DataFrame, isnull, bdate_range,
NaT, date_range, ti... | assert_series_equal(ts + s, expected) | pandas.util.testing.assert_series_equal |
from typing import Optional
import numpy as np
import pandas as pd
from pandas import DatetimeIndex
from stateful.representable import Representable
from stateful.storage.tree import DateTree
from stateful.utils import list_of_instance, cast_output
from pandas.api.types import infer_dtype
class Stream(Representable)... | infer_dtype([state]) | pandas.api.types.infer_dtype |
# Copyright 2018 <NAME>, <NAME>.
# (Strongly inspired by original Google BERT code and Hugging Face's code)
""" Fine-tuning on A Classification Task with pretrained Transformer """
import itertools
import csv
import fire
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
import toke... | pd.read_csv(path, sep='\t', dtype=str) | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 5 16:32:56 2019
@author: daniele
"""
#%% IMPORTS
from dataset import DatabaseManager, loadData, splitData
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from scipy.stats import poisson, skellam
import statsmodels.api ... | pd.DataFrame(result) | pandas.DataFrame |
"""Collect model input data"""
import os
from dataclasses import dataclass
import pandas as pd
@dataclass
class ModelData:
# Directory containing core data files
data_dir: str = os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir, os.path.pardir, 'data')
# Directory co... | pd.read_excel(path, index_col='DUID', sheet_name='Generators and Scheduled Loads') | pandas.read_excel |
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 25 11:24:51 2020
@author: <NAME>
@project: Classe que trata o topic modeling
"""
from spacy.tokens import Doc
import numpy
from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
from neo4j import GraphDatabase
import pandas as pd
import os
#import subprocess
#import requ... | pd.DataFrame(dict_models) | pandas.DataFrame |
import pandas as pd
import xarray as xr
import numpy as np
import sys
from config import *
from sklearn.ensemble import RandomForestRegressor
from joblib import dump, load
from sklearn.model_selection import PredefinedSplit,RandomizedSearchCV
def inputs():
msg = "You must specify whether to retrain the model (Tr... | pd.read_pickle(clean_data) | pandas.read_pickle |
# -*- coding: utf-8 -*-
# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)
# <NAME> (<EMAIL>), Blue Yonder Gmbh, 2016
import pandas as pd
import numpy as np
from sklearn import model_selection
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeli... | pd.DataFrame() | pandas.DataFrame |
import os
import gzip
import warnings
import pandas as pd
warnings.simplefilter("ignore")
import pickle
def outlier_analysis(df, model_dir):
_df = df[df["is_rescurable_homopolymer"]].reset_index(drop=True)
if not len(_df):
return df
__df = df[~df["is_rescurable_homopolymer"]].reset_index(drop=T... | pd.concat([__df, at_ins_df, at_del_df, gc_ins_df, gc_del_df], axis=0) | pandas.concat |
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import geopandas as gpd
from energy_demand.read_write import data_loader, read_data
from energy_demand.basic import date_prop
from energy_demand.basic import basic_functions
from energy_demand.basic import lookup_tables
from energy_demand... | pd.DataFrame([line_entries_05], columns=scenarios) | pandas.DataFrame |
import pandas as pd
import os
import cv2
import numpy as np
import random
def get_diff_scaled(img1,img2,scale):
return ((np.clip((img1.astype('int32') - img2.astype('int32')) * scale, -255, 255) + np.ones_like(
img1) * 255) / 2).astype('uint8')
def get_diff(img1,img2):
return ((img1.astype('int32')-im... | pd.Index(col_names, name="columns") | pandas.Index |
import itertools
import logging
import json
import networkit as nk
import pandas as pd
from src.generators.graphs.SBM import SBM
from src.generators.graphs.ErdosRenyi import ErdosRenyi
from src.generators.graphs.BarabasiAlbert import BarabasiAlbert
from src.measures.FairHarmonicCentrality import FairGroupHarmonicCentra... | pd.DataFrame(lista_to_csv) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
:Purpose: Perform automated testing on pdvalidate.
:Platform: Linux/Windows | Python 3.5
:Developer: <NAME>
:Email: <EMAIL>
"""
# pylint: disable=protected-access
# pylint: disable=wrong-import-position
import os
import sys
sys.path.insert(0, os.path.dirname(o... | pd.datetime(2014, 1, 7) | pandas.datetime |
import pandas as pd
from pandas.tseries.offsets import DateOffset
FOUR_MINUTE_OFFSET = DateOffset(minutes=4)
HOUR_MINUTE_OFFSET = | DateOffset(hours=1) | pandas.tseries.offsets.DateOffset |
# -*- 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... | StringIO(data) | pandas.compat.StringIO |
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from argparse import ArgumentParser
from os import path
from time import time
from utils import trj2blocks
# MDAnalysis
import MDAnalysis as mda
from MDAnalysis.analysis.hydrogenbonds import hbond_analysis
def parse():
'''Parse command ... | pd.DataFrame(results[1]) | pandas.DataFrame |
# Copyright (c) Facebook, Inc. and its affiliates.
from factor_learning.utils import utils
from factor_learning.dataio.DigitImageTfDataset import DigitImageTfDataset
from factor_learning.dataio.DigitImageTfPairsDataset import DigitImageTfPairsDataset
from subprocess import call
import os
from scipy import linalg
impo... | pd.DataFrame(data) | pandas.DataFrame |
import pandas as pd
import ast
import sys
import os.path
from pandas.core.algorithms import isin
sys.path.insert(1,
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
import dateutil.parser as parser
from utils.mysql_utils import separator
from utils.io import read_json
from utils.scr... | pd.isnull(row[k]) | pandas.isnull |
"""
Author : <NAME>\n
email : <EMAIL>\n
LICENSE : MIT License
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap
import seaborn as sns
import time
from scipy.signal import butter, sosfiltfilt, sosfreqz
from scipy.signal import spectrogram as spe... | pd.Series(sr) | pandas.Series |
# -*- coding: utf-8 -*-
# pylint: disable=E1101,E1103,W0232
import os
import sys
from datetime import datetime
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
import pandas.compat as compat
import pandas.core.common as com
import pandas.util.testing as tm
from pandas import (Categor... | pd.timedelta_range('1 days', periods=5) | pandas.timedelta_range |
import pandas as pd
import numpy as np
import os
from numba import types
from numba.typed import Dict
from numba import njit
from openbatlib import model
from openbatlib import view
class Error(Exception):
pass
class InputError(Error):
def __init__(self, expression):
self.expressio... | pd.DataFrame.from_dict(E_ideal, orient='index', columns=['ideal / MWh']) | pandas.DataFrame.from_dict |
"""Step 1: Solving the problem in a deterministic manner."""
import cvxpy as cp
import fledge
import numpy as np
import os
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import shutil
def main():
# Settings.
scenario_name = 'course_project_step_1'
results_path = os.pat... | pd.DataFrame(0.0, index=der_model_set.timesteps, columns=der_model_set.outputs) | pandas.DataFrame |
import logging
import pandas as pd
import dataiku
from dataiku.runnables import ResultTable
import datetime
import adal
import requests
import re
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO,
format='jira plugin %(levelname)s - %(message)s')
class AzureClient(objec... | pd.DataFrame(columns=["date", "user", "type", "message"]) | pandas.DataFrame |
import pandas
import glob
import urllib.request
url = 'http://example.com/'
open_data_ms = pandas.read_csv(urllib.request.urlopen("https://raw.githubusercontent.com/od-ms/resources/master/coronavirus-fallzahlen-regierungsbezirk-muenster.csv"))
open_data_ms['Datum'] = pandas.to_datetime(open_data_ms['Datum'], format='... | pandas.Timestamp(day) | pandas.Timestamp |
import os
import pickle
import sys
from pathlib import Path
from typing import Union
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from Bio import pairwise2
from scipy import interp
from scipy.stats import linregress
from sklearn.metrics import roc_curve, auc, precision_recall_curve
import th... | pd.read_excel(indiv_validation_data_xlsx, sheet_name="mean_o_minus_r_by_sample", index_col=0) | pandas.read_excel |
import numpy
import matplotlib.pyplot as plt
import tellurium as te
from rrplugins import Plugin
auto = Plugin("tel_auto2000")
from te_bifurcation import model2te, run_bf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import ScalarFormatter
sf = ScalarFormatter()
sf.set_sc... | pd.DataFrame.from_dict({astr:a1sp[bi], bstr:b1sp[bi], gstr:gsp[bi], Kstr: Ksp[bi],'Category': cat_strs[0]}) | pandas.DataFrame.from_dict |
from typing import Any, Dict, List, Tuple
import numpy as np
import pandas as pd
from hydra.utils import to_absolute_path
from joblib import Parallel, delayed
from sklearn.cluster import KMeans
from tqdm import tqdm
data_dir = to_absolute_path("../../input/optiver-realized-volatility-prediction/") + "/"
# Function ... | pd.merge(test, mat2[selected_cols], how="left", on="time_id") | pandas.merge |
from __future__ import division
import math
import sys
from random import randint
from random import random as rnd
from reoccuring_drift_stream import ReoccuringDriftStream
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.optimize import minimize
from scipy.spatial.distance import cd... | pd.DataFrame(self.c_w_) | pandas.DataFrame |
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.dates as mdates
import numpy as np
import pandas as pd
import string
from matplotlib.dates import MonthLocator, DayLocator, WeekdayLocator, MO, TU, WE, TH, FR, SA, SU
def plot_rca_timeseries_oneradar(
rca_file, output_directory, baseline_d... | pd.to_datetime(start_date, format='%Y-%m-%d') | pandas.to_datetime |
from typing import Tuple, Union, Optional
import os
import pytest
from PIL import Image
from pathlib import Path
import scanpy as sc
import scvelo as scv
import cellrank as cr
from anndata import AnnData
from cellrank.tl.kernels import VelocityKernel, PrecomputedKernel, ConnectivityKernel
import numpy as np
import p... | pd.isnull(expected) | pandas.isnull |
from __future__ import absolute_import, division, print_function
import pytest
pytest.importorskip('flask')
pytest.importorskip('flask.ext.cors')
from base64 import b64encode
from copy import copy
import datashape
from datashape.util.testing import assert_dshape_equal
import numpy as np
from odo import odo, convert
... | pd.Series(exp_res) | pandas.Series |
import requests
import time
import json
import os
from tqdm import tqdm
import pandas as pd
def searchPlace():
'''Get all places' name and vicinity around
GPS location point from list of location point.
List of location point consists of strings
in form [latitude,longitude]. (without square brackets)... | pd.DataFrame(locationList, columns=['vicinity', 'name']) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
import tweepy
import tqdm
import csv
import json
import time
from tqdm import tqdm_notebook as tqdm
def makeAuthConnection():
consumerApiKey = 'XXXXXXX'
consumerApiSecret = 'XXXXXXX'
acessToken = 'XXXXXX'
acessTokenSecret = 'XXXXXX'
auth = tweepy.OAuthHa... | pd.concat([X_train, y_train], axis=1) | pandas.concat |
import pandas as pd
from sklearn.decomposition import PCA
x_train = pd.read_csv('X_train.csv', index_col=0).drop(['member_id', 'id', 'pymnt_plan', 'policy_code', 'url'], axis=1)
# last_pymnt_d : 81 => 13 dim
# issue_d : 78 => 13 dim
# last_credit_pull_d : 81 => 13 dim
# earliest_cr_line : 758 => 13 dim
X_tra... | pd.read_csv('X_test.csv', index_col=0) | pandas.read_csv |
import numpy as np
import pytest
from pandas import (
DatetimeIndex,
IntervalIndex,
NaT,
Period,
Series,
Timestamp,
)
import pandas._testing as tm
class TestDropna:
def test_dropna_empty(self):
ser = Series([], dtype=object)
assert len(ser.dropna()) == 0
return_va... | Period("NaT", freq="M") | pandas.Period |
import sys
import os
import logging
import datetime
import pandas as pd
from job import Job, Trace
from policies import ShortestJobFirst, FirstInFirstOut, ShortestRemainingTimeFirst, QuasiShortestServiceFirst
sys.path.append('..')
def simulate_vc(trace, vc, placement, log_dir, policy, logger, start_ts, *args):
if... | pd.Timestamp(x) | pandas.Timestamp |
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 24 09:54:59 2021
@author: Gary
This set of routines is used to assist in the curation of IngredientName.
"""
import numpy as np
import pandas as pd
import difflib as dl
import build_common
sources = build_common.get_transformed_dir()
# nonspdf = pd.read_csv('./sources... | pd.merge(t,gbalt,on='IngredientName',how='left') | pandas.merge |
# use for environment variables
import os
# use if needed to pass args to external modules
import sys
# used for math functions
import math
# used to create threads & dynamic loading of modules
import threading
import multiprocessing
import importlib
# used for directory handling
import glob
#discord needs import ... | pd.DataFrame(macd, columns=['time', 'open', 'high', 'low', 'close', 'volume']) | pandas.DataFrame |
import boto3, argparse, subprocess, sys
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
def pip_install(package):
subprocess.call([sys.executable, "-m", "pip", "install", package])
pip_install('sagemaker')
import sagemaker
from sagemaker.feature_store.feature_grou... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import requests
import numpy as np
import pandas as pd
from alphacast import Alphacast
from dotenv import dotenv_values
API_KEY = dotenv_values(".env").get("API_KEY")
alphacast = Alphacast(API_KEY)
# In[2]:
BEA_API_KEY = dotenv_values(".env").get("BEA_API_KEY")
# ... | pd.to_datetime(df['TimePeriod'], format='%Y-%m-%d') | pandas.to_datetime |
import numpy as np
from datetime import timedelta
from distutils.version import LooseVersion
import pandas as pd
import pandas.util.testing as tm
from pandas import to_timedelta
from pandas.util.testing import assert_series_equal, assert_frame_equal
from pandas import (Series, Timedelta, DataFrame, Timestamp, Timedelt... | tm.assertRaisesRegexp(ValueError, errmsg, np.argmax, td, out=0) | pandas.util.testing.assertRaisesRegexp |
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 17 11:04:59 2020
@author: <NAME>
"""
import pandas as pd
import janitor
import datetime
import pickle
from pathlib import Path
#from builder import *
from fars_cleaner.builder import get_renaming
import fars_cleaner.extra_info as ei
from fars_cleaner.fars_utils impo... | pd.concat(accidents) | pandas.concat |
#
# 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
# distributed under ... | pd.to_datetime(exec_detail.m_time, utc=True) | pandas.to_datetime |
#!/usr/bin/env python
# coding: utf-8
# # BCG Gamma Challenge
# # Libraries
# In[1]:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import stats
# In[2]:
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
# # Dataset
# In... | pd.read_csv('../bcggammachallenge/municipios/municipios20160101.csv') | pandas.read_csv |
# Copyright 2017-2021 QuantRocket LLC - All Rights Reserved
#
# 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 applicabl... | pd.concat((base_currencies,trade_currencies), axis=1) | pandas.concat |
# # -*- coding: utf-8 -*-
import argparse
import itertools
import logging.config
import os
import sys
from collections import Counter
from multiprocessing import Pool, cpu_count
import numpy as np
import pandas as pd
from pandas import DataFrame
src = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.p... | pd.concat(lst) | pandas.concat |
import os
import openmatrix as omx
import pandas as pd
import geopandas as gpd
from shapely import wkt
import numpy as np
import logging
import requests
from tqdm import tqdm
import time
from pilates.utils.geog import get_block_geoms, \
map_block_to_taz, get_zone_from_points, get_taz_geoms
logger = logging.getLog... | pd.concat(enroll_list, axis=1) | pandas.concat |
import argparse
import os
import numpy as np
import torch
import torch.utils.data
from PIL import Image
import pandas as pd
import cv2
import json
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
from torchvision.transforms import functional as F
from torchvision.models.detection im... | pd.DataFrame() | pandas.DataFrame |
"""
test date_range, bdate_range construction from the convenience range functions
"""
from datetime import datetime, time, timedelta
import numpy as np
import pytest
import pytz
from pytz import timezone
from pandas._libs.tslibs import timezones
from pandas._libs.tslibs.offsets import BDay, CDay, DateOffset, MonthE... | tm.assert_index_equal(rng, values) | pandas._testing.assert_index_equal |
from datetime import (
datetime,
timedelta,
)
import re
import numpy as np
import pytest
from pandas._libs import iNaT
from pandas.errors import InvalidIndexError
import pandas.util._test_decorators as td
from pandas.core.dtypes.common import is_integer
import pandas as pd
from pandas import (
Categoric... | tm.assert_frame_equal(df, expected) | pandas._testing.assert_frame_equal |
# install pattern
# install gensim
# install nltk
# install pyspellchecker
import re
import pandas as pd
import numpy as np
import gensim
from collections import Counter
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import RegexpTokenizer
from nltk.stem impor... | pd.DataFrame() | pandas.DataFrame |
# coding:utf-8
import os
import base64
import configparser
import json
import urllib
import pandas as pd
import requests
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
from QUANTAXIS.QAMarket.QABroker import QA_Broker
from QUANTAX... | pd.DataFrame(data=result) | pandas.DataFrame |
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
from message_ix import Scenario, macro
from message_ix.models import MACRO
from message_ix.testing import SCENARIO, make_westeros
# tons of deprecation warnings come from reading excel (xlrd library), ignore
# them for now
pytestmark = pyt... | pd.testing.assert_series_equal(obs, exp) | pandas.testing.assert_series_equal |
#%%
#### Processes the raw data json using pandas to get
#### dataframes that can be exported directly to Postgres as normalized tables
import sys
import inspect
import os
import json
import pandas as pd
class DataProcessing:
def __init__(self):
self.product_data_path = self.data_path = '../data/product_d... | pd.DataFrame() | pandas.DataFrame |
from datetime import datetime
import pandas as pd
from featuretools.primitives import IsNull, Max
from featuretools.primitives.base import PrimitiveBase, make_agg_primitive
from featuretools.variable_types import DatetimeTimeIndex, Numeric
def test_call_agg():
primitive = Max()
# the assert is run twice on... | pd.Series([0, 1]) | pandas.Series |
import argparse
import json
import math
import random
import string
import pandas as pd
from faker import Faker
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config', help='Path to json config file', dest='config_file_path', required=True)
parser.add_argument('--output', ... | pd.DataFrame() | pandas.DataFrame |
"""
Coding: utf-8
Author: Jesse
Code Goal:
完成一级指标：政策主体的指标构建，使用颁布主题清单。
Code Logic:
分成两个部分：颁布主体行政级别以及是否联合发布
颁布主体行政级别部分
"""
import pandas as pd
import numpy as np
import xlwings as xw
from PolicyAnalysis import cptj as cj
"""
————————————————————
以下是使用 re 检索+ DFC 映射的数据处理写法
————————————————————
"""
class supervisors_r... | pd.DataFrame(df_indi) | pandas.DataFrame |
"""
Created on Mon May 30 2020
@author: evadatinez
"""
from pathlib import Path
import pandas as pd
def complaintsData(fname, data):
"""This function updates a dataframe with the CSV data
with complaints
Params:
fname: path to FILE
data: pandas dataframe to store data
"""
pa... | pd.read_csv(path) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# # 02__trans_motifs
#
# in this notebook, i find motifs that are associated w/ trans effects using linear models and our RNA-seq data
# In[1]:
import warnings
warnings.filterwarnings('ignore')
import itertools
import pandas as pd
import numpy as np
import matplotlib as mpl
i... | pd.DataFrame(scaled_features, index=data_indiv.index, columns=["logFC_trans", "gc", "cpg"]) | pandas.DataFrame |
import pandas as pd
import numpy as np
ht_fail = pd.read_csv('/content/sample_data/heart failur classification dataset.csv')
ht_fail.head(5)
ht_fail.shape
ht_fail.isnull()
ht_fail.isnull().sum()
#Imputing missing values
from sklearn.impute import SimpleImputer
impute = SimpleImputer(missing_values=np.nan, st... | pd.concat([principal_df, htfail_df[["target"]]], axis=1) | pandas.concat |
import pandas as pd
from koapy import KiwoomOpenApiContext
from koapy.backend.cybos.CybosPlusComObject import CybosPlusComObject
kiwoom = KiwoomOpenApiContext()
cybos = CybosPlusComObject()
kiwoom.EnsureConnected()
cybos.EnsureConnected()
kiwoom_codes = kiwoom.GetCommonCodeList()
cybos_codes = cybos.GetCommonCodeLi... | pd.DataFrame(kiwoom_codes, columns=['code']) | pandas.DataFrame |
import scrapy
from bs4 import BeautifulSoup
import pandas as pd
import shelve
import os
import Notification
import json
def scrapHTML(html):
soup=BeautifulSoup(html,"html.parser")
tableRows=soup.find_all("tr")
# print(tableRows[1])
dataframe=[]
# [1:]
tableRows=tableRows[1:]
for tr in tabl... | pd.DataFrame(scrapped, columns=cols) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# File : utils.py
# Modified : 17.02.2022
# By : <NAME> <<EMAIL>>
from collections import OrderedDict
import numpy as np
import os
from typing import List
import random
import cv2
from PIL import Image
import torch
import torchvision
from pathlib impor... | pd.concat(benign_groups_list[250:800]) | pandas.concat |
import dash
from dash import dcc
import dash_bootstrap_components as dbc
from dash import html
from dash.dependencies import Input, Output, State
import pandas as pd
import random
import re
#######################
# Helper functions
#######################
# # convert a dataframe into a dict where each item is anoth... | pd.read_json(players_json) | pandas.read_json |
from typing import Any, Dict, Tuple, Union, Mapping, Optional, Sequence
from typing_extensions import Literal
from enum import auto
from types import MappingProxyType
from pathlib import Path
from datetime import datetime
from anndata import AnnData
from cellrank import logging as logg
from cellrank._key import Key
f... | pd.Series(g.coarse_grained_input_distribution, index=names) | pandas.Series |
import json
import os
import warnings
import random
import string
import csv
import time
import datetime
import io
import pandas as pd
from flask import (
Blueprint, flash, Flask, g, redirect, render_template, request, url_for, jsonify, Response
)
from flask_wtf import FlaskForm
from wtforms import StringField, P... | pd.DataFrame(data=[job_config]) | pandas.DataFrame |
# What is different in this kernel:
# - data preprocessing was modularised and hopefully made more clear, as repetitative actions were moved into a separate function
# - LightGBM hyperparameters were taken from my another kernel, where they were tuned to the `application` data subset only:
# https://www.kaggle.com/m... | pd.factorize(data[f_]) | pandas.factorize |
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import calendar
import ccdproc
import collections
import datetime
import glob
import logging
import math
import numpy as np
import os
import pandas
import random
import re
import subprocess
import sys
import ti... | pandas.DataFrame(spec_mode, columns=columns) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster.bicluster import SpectralCoclustering
from bokeh.plotting import figure, output_file, show
from bokeh.models import HoverTool, ColumnDataSource
from itertools import product
######... | pd.DataFrame.corr(flavors) | pandas.DataFrame.corr |
import glob
import os
import sys
utils_path = os.path.join(os.path.abspath(os.getenv('PROCESSING_DIR')),'utils')
if utils_path not in sys.path:
sys.path.append(utils_path)
import util_files
import util_cloud
import util_carto
import logging
from ftplib import FTP
import urllib
import numpy as np
import pandas as pd... | pd.read_csv(file) | pandas.read_csv |
import numpy as np
import gegenbauer
import compute_NTK_spectrum
import matplotlib.pyplot as plt
import approx_learning_curves
import csv
import numba
from numba import jit
from numba import prange
import time
import pandas as pd
import argparse
def SGD(X, Y, Theta, r, num_iter, readout_only=False):
P = X.shape[0]... | pd.DataFrame(training_errs) | pandas.DataFrame |
import operator
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.arrays import FloatingArray
@pytest.fixture
def data():
return pd.array(
[True, False] * 4 + [np.nan] + [True, False] * 44 + [np.nan] + [True, False],
dtype="boolean",
... | pd.array([True, False, None] * 3, dtype="boolean") | pandas.array |
#!/usr/bin/python
'''
This file holds the functions necessary to process the data behind the scenes
'''
import config
import json
import pandas as pd
import requests
import spotipy
from datetime import datetime
from flask import Flask
from flask import request
from numpy import nan
from spotipy.oauth2 import Spotif... | pd.DataFrame(df_dict) | pandas.DataFrame |
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd
import numpy.linalg as LA
from scipy.sparse import csr_matrix
from sklearn.preprocessing import MinMaxScaler
def show_mtrx(m, title = None):
fig, ax = plt.subplots(figsize = (10, 5))
min_val = int(m.min())
max_val... | pd.DataFrame(mad) | pandas.DataFrame |
import csv
import pandas as pd
import os
import numpy as np
BASE_DIR = os.getcwd()
def merge_dev_data(result_filename, file_pos, file_neg):
"""
Description: function that merges dev data from both
sentiments into a single data structure
Input:
-result_filename: str, n... | pd.DataFrame() | pandas.DataFrame |
from aridanalysis import aridanalysis as aa
import pytest
import pandas as pd
import numpy as np
import sklearn
from vega_datasets import data
import altair as alt
import statsmodels
# import warnings
import sys
import os
myPath = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, myPath + "/../aridanalysi... | pd.DataFrame(data, columns=["Age", "Sex", "Weight", "Target"]) | pandas.DataFrame |
#!/usr/bin/env python3
import argparse
import collections
import copy
import datetime
import functools
import glob
import json
import logging
import math
import operator
import os
import os.path
import re
import sys
import typing
import warnings
import matplotlib
import matplotlib.cm
import matplotlib.dates
import ma... | pandas.read_hdf(compare_instances_path) | pandas.read_hdf |
from sklearn.metrics import f1_score,accuracy_score
import numpy as np
from utilities.tools import load_model
import pandas as pd
def predict_MSRP_test_data(n_models,nb_words,nlp_f,test_data_1,test_data_2,test_labels):
models=[]
n_h_features=nlp_f.shape[1]
print('loading the models...')
for i in range... | pd.DataFrame({"Quality": final_labels}) | pandas.DataFrame |
import contextlib
import json
import gzip
import io
import logging
import os.path
import pickle
import random
import shutil
import sys
import tempfile
import traceback
import unittest
import pandas
COMMON_PRIMITIVES_DIR = os.path.join(os.path.dirname(__file__), 'common-primitives')
# NOTE: This insertion should appea... | pandas.read_csv(scores_path) | pandas.read_csv |
"""
Core implementation of :mod:`sklearndf.transformation.wrapper`
"""
import logging
from abc import ABCMeta, abstractmethod
from typing import Any, Generic, List, Optional, TypeVar, Union
import numpy as np
import pandas as pd
from sklearn.base import TransformerMixin
from sklearn.compose import ColumnTransformer
f... | pd.Series(index=column_names, data=column_names) | pandas.Series |
"""Plotting functions for AnnData.
"""
import os
import numpy as np
import pandas as pd
from pandas.api.types import is_categorical_dtype
from matplotlib import pyplot as pl
from matplotlib import rcParams
from matplotlib.colors import is_color_like
import seaborn as sns
from .. import settings
from .. import logging... | pd.DataFrame(X, index=adata.obs_names, columns=adata.var_names) | pandas.DataFrame |
import json
import networkx as nx
import numpy as np
import os
import pandas as pd
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm
from config import logger, config
def read_profile_data():... | pd.read_csv('/home/ubuntu/projects/kddcup2019track1/build/feature/od_coord_feature.csv') | pandas.read_csv |
# %% imports
import numpy as np
import pandas as pd
import config as cfg
from src.utils.data_processing import hours_in_year, medea_path
# --------------------------------------------------------------------------- #
# %% settings and initializing
# -------------------------------------------------------------------... | pd.DataFrame(data=0, index=cfg.zones, columns=dict_sets['k'].index) | pandas.DataFrame |
from english_words import english_words_set
import pandas as pd
import numpy as np
# make a list of 5 lenght words without non-alphas, and no proper nouns
words5 = []
for word in english_words_set:
if len(word) == 5 and word[0].islower() and word.isalpha():
words5.append(word)
df_words = pd.DataFrame(wor... | pd.DataFrame({'word' : [],'value':[]}) | pandas.DataFrame |
# Let's start off by loading in Jeff's CDR3's
import numpy as np
import pandas
def getBunker():
total_Abs=pandas.read_csv('app_data/mouse_IgA.dat',sep='\s+',header=None,names=['cdrL1_aa','cdrL2_aa','cdrL3_aa','cdrH1_aa','cdrH2_aa','cdrH3_aa','react'])
total_abs1 = total_Abs.where((pandas.notnull(total_Abs)), '... | pandas.concat([my_light,my_heavy,poly_YN],axis=1) | pandas.concat |
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import power_transform
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import os
import datetime
import numpy as np
import seaborn as sns
from sklearn.preprocessing import LabelEncoder... | pd.DataFrame(y_pred, columns=['predictions','isFraud']) | pandas.DataFrame |
def get_files_in_path(path, ext="wav"):
"""
Get files in a path
exampe : files = get_files_in_path("./audioFiles")
"""
import os, glob
path = os.path.join(path, "*."+ext)
theFiles = glob.glob(path, recursive=True)
return theFiles
def find_last_slash_pos_in_path(path):
"""
Find l... | pd.read_csv(fullPath) | pandas.read_csv |
'''
OOPitch brings Object-Oriented programming to football analytics. It is based on the most common data-analysis
libraries -- numpy (scipy), pandas and matplotlib -- and it extends the computational geometry library shapely
to account for the necessities of football analytics.
'''
import numpy as np
from scipy.sign... | pd.isna(temp['player']) | pandas.isna |
import os
import sys
import math
import pandas as pd
import numpy as np
from sklearn.datasets import make_classification
from keras import backend as K
from keras import initializers, layers
from keras.utils import to_categorical
from keras.constraints import non_neg, max_norm
from keras.initializers import Zeros
from... | pd.concat([hist_df, pd_temp]) | pandas.concat |
# -*- 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... | tm.assert_equal(expected.C.dtype, 'float') | pandas.util.testing.assert_equal |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib.colors import LinearSegmentedColormap
import scipy as sp
from scipy import interpolate
import patsy
import logging
from time import time
import warnings
from scipy import optimize
from scipy import linalg
from scipy import stats
from... | pd.DataFrame(results) | pandas.DataFrame |
import pandas as pd
from evaluate.calculator import (
RecallCalculator,
PrecisionCalculator,
EmptyReportError,
)
import pytest
from unittest.mock import patch, Mock
from evaluate.report import (
Report,
PrecisionReport,
RecallReport
)
from tests.common import create_precision_report_row
from io ... | pd.DataFrame() | pandas.DataFrame |
#/Library/Frameworks/Python.framework/Versions/3.6/bin/python3
#
# Author: <NAME>
# Date: 2018-09-26
#
# This script runs all the models on Baxter Dataset subset of onlt cancer and normal samples to predict diagnosis based on OTU data only. This script only evaluates generalization performance of the model.
#
#######... | pd.read_table("data/metadata.tsv") | pandas.read_table |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 3 12:15:41 2018
@author: nmei
"""
import pandas as pd
from statsmodels.formula.api import ols
from statsmodels.stats.anova import anova_lm
from statsmodels.graphics.factorplots import interaction_plot
import matplotlib.pyplot as plt
from scipy impo... | pd.read_csv('../results/ATT_control.csv') | pandas.read_csv |
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.keys import Keys
import requests
import time
from datetime import datetime
import pandas as pd
from urllib import parse
from config import ENV_VARIABLE
from os.path import getsize
fold_path = ... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
import argparse
from collections import OrderedDict
from copy import deepcopy
from glob import glob
import itertools
import os.path as op
from os import environ
import sys
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import numpy as np
import pandas as pd
from scipy.signal... | pd.concat(dfs) | pandas.concat |
"""
Experiment 1: swarm tec correlation
- for various background estimation sizes and artifact keys:
- collect random days
- get dtec prof
- interpolate swarm dne at the profile points
- estimate mean and covariance between the two
"""
import numpy as np
import pandas
from ttools im... | pandas.DataFrame(data=data) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[2]:
get_ipython().system('pip install numpy')
get_ipython().system('pip install pandas')
get_ipython().system('pip install matplotlib')
get_ipython().system('pip install seaborn')
get_ipython().system('pip install pandas-profiling')
get_ipython().getoutput('pip install scik... | scatter_matrix(data[features_mean],c=colors,alpha=0.9,figsize=(20,20)) | pandas.plotting.scatter_matrix |
# -*- coding: utf-8 -*-
# *****************************************************************************
# 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:
#
# ... | pd.Series(data_right, index=index_data) | pandas.Series |
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