prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
########################## Fuzzy Discernibility Matrix: Reduct ###############################
####################### Dr. <NAME> 25-01-21, version: 1.0 ###########################
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
from sklearn import preprocessing
from sklearn.preprocessing impor... | pd.DataFrame(F) | pandas.DataFrame |
#!/usr/bin/env python
import json
import pandas
import os
series_description_map = {
'TORAX AP': 'AP',
'PORTATIL': 'AP',
'CHEST': 'UNK',
'W034 TÓRAX LAT.': 'LAT',
'AP HORIZONTAL': 'AP SUPINE',
'TÓRAX PA H': 'PA',
'BUCKY PA': 'PA',
'ESCAPULA Y': 'UNK',
... | pandas.DataFrame.from_dict(data, orient='index') | pandas.DataFrame.from_dict |
import torch, sys, math, pickle, datetime
import numpy as np
import numpy.random as npr
from collections import OrderedDict
plot_path = './'
use_cuda = torch.cuda.is_available()
npr.seed(1234)
if use_cuda :
torch.set_default_tensor_type('torch.cuda.DoubleTensor')
torch.cuda.manual_seed(1234)
else :
torch.set_def... | pd.DataFrame(dpvi_times) | pandas.DataFrame |
import psycopg2
import psycopg2
import sqlalchemy as salc
import numpy as np
import warnings
import datetime
import pandas as pd
import json
from math import pi
from flask import request, send_file, Response
# import visualization libraries
from bokeh.io import export_png
from bokeh.embed import json_item
from bokeh.p... | pd.Series(x) | pandas.Series |
from .._common import *
import pandas as pd
import numpy as np
class ToDataframe(yo_fluq.agg.PushQueryElement):
def __init__(self, **kwargs):
self.kwargs = kwargs
def on_enter(factory,instance):
instance.lst = []
def on_process(factory, instance, element):
instance.lst.append(elem... | pd.DataFrame(instance.lst,**factory.kwargs) | pandas.DataFrame |
import pandas as pd
import numpy as np
class PreviousValuesGenerator:
transactions = None
def __init__(self, transactions_path):
print(f'leyendo fichero {transactions_path}')
self.transactions = pd.read_csv(transactions_path, sep=';')
print(f'Existen {self.transactions.shape[0]} re... | pd.isna(w8) | pandas.isna |
from pandas import DataFrame
# State abbreviation -> Full Name and visa versa. FL -> Florida, etc.
# (Handle Washington DC and territories like Puerto Rico etc.)
def add_state_names(my_df):
new_df = my_df.copy()
names_map = {"CA":"Cali", "CO":"Colo", "CT":"Conn"}
new_df["name"] = new_df["abbrev"].map(names_... | DataFrame({"abbrev":["CA","CO","CT","DC","TX"]}) | pandas.DataFrame |
"""
General utility functions that are used in a variety of contexts.
The functions in this module are used in various stages of the ETL and post-etl
processes. They are usually not dataset specific, but not always. If a function
is designed to be used as a general purpose tool, applicable in multiple
scenarios, it sh... | pd.to_datetime(partition, format='%Y') | pandas.to_datetime |
import numpy as np
import pandas as pd
import os
import csv
import scipy
import torch
import torch.nn as nn
from torch_geometric.data import Data, Batch
from torch_geometric.nn import graclus, max_pool
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
def get_genes... | pd.read_csv('./data/9606.protein.links.detailed.v11.0.txt', sep=' ') | pandas.read_csv |
# Data source: College Scorecard
import ssl
import pandas as pd
from ._data_processing import DataProcessor, MisValueFiller
class Dataset:
def __init__(self, path='https://raw.githubusercontent.com/alisoltanirad/'
'CDA/main/cda/college_scorecard/'
'colleg... | pd.DataFrame(data) | pandas.DataFrame |
import pandas as pd
import numpy as np
from sklearn.ensemble.forest import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, median_absolute_error
from sklearn.preprocessing import MinMaxScaler, StandardScaler
impo... | pd.DataFrame([(month,day,i,0)], columns=['month','day','hour','Number Of Outgoing Trips']) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""DeepPrecip Module
<NAME> 2022
This is the alternate main executable for DeepPrecip that includes the code necessary for running the model on GraphCore IPUs.
You can adjust model hyperparams in the global variable definition section.
For more information ... | pd.Series(self.y_test) | pandas.Series |
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
from elecsim.constants import ROOT_DIR, KW_TO_MW
import numpy as np
from scipy.optimize import root
KTOE_TO_MWH = 11630
investment_mechanism = "future_price_fit"
# investment_mechanism = "projection_fit"
potential_pl... | pd.concat([historical_fuel_prices_mw, fuel_prices], axis=1) | pandas.concat |
from collections import OrderedDict
import math
from auto_ml import utils
import pandas as pd
from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier
from sklearn.metrics import mean_squared_error, make_scorer, brier_score_loss, accuracy_score, explained_variance_score, mean_absolute_error, ... | pd.Series(actuals,name='actuals') | pandas.Series |
from pandas import DataFrame, Series
def avg_medal_count():
"""
Compute the average number of bronze medals earned by countries who
earned at least one gold medal.
Save this to a variable named avg_bronze_at_least_one_gold. You do not
need to call the function in your code when running it in t... | Series(silver) | pandas.Series |
import pandas as pd
import numpy as np
import pytest
from kgextension.caching_helper import freeze_unhashable, unfreeze_unhashable
class TestFreezeUnfreezeUnhashable:
def test1_arg_series(self):
@freeze_unhashable(freeze_by="argument", freeze_argument="the_arg")
def test_fun(a, b, c=12, the_arg=[... | pd.DataFrame({"a": [1,2,3,np.nan], "b": ["x", "y", "z", np.nan]}) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# ## This is a YOLOv4 training pipeline with Pytorch.
# I use coco pre-trained weights.
# Have fun and feel free to leave any comment!
# ## Reference
# https://github.com/Tianxiaomo/pytorch-YOLOv4
# https://www.kaggle.com/orkatz2/yolov5-train
# In[20]:
# !pip install torch==1.... | pd.read_csv(data_dict['train']['csv_path'], index_col=0) | pandas.read_csv |
# Functions to estimate cost for each lambda, by voxel:
from __future__ import division
from numpy.linalg import inv, svd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.linear_model import Ridge, RidgeCV
import time
import scipy as sp
from skle... | pd.isna(behavior_data) | pandas.isna |
"""プロットサンプルページの管理データと挙動を実装するクラス."""
import numpy as np
import pandas as pd
from use_cases.linear_function_interactor import LinearFunctionInteractor
from view_models.plot_sample_view_model import PlotSampleViewModel
class PlotSampleController:
"""サンプルプロットページ制御クラス."""
def __init__(self, use_case: LinearFunct... | pd.DataFrame(data) | pandas.DataFrame |
import unittest
import qteasy as qt
import pandas as pd
from pandas import Timestamp
import numpy as np
import math
from numpy import int64
import itertools
import datetime
from qteasy.utilfuncs import list_to_str_format, regulate_date_format, time_str_format, str_to_list
from qteasy.utilfuncs import maybe_trade_day, ... | Timestamp('2028-12-31') | pandas.Timestamp |
""" I/O functions of the aecg package: tools for annotated ECG HL7 XML files
This module implements helper functions to parse and read annotated
electrocardiogram (ECG) stored in XML files following HL7
specification.
See authors, license and disclaimer at the top level directory of this project.
"""
# Imports ====... | pd.DataFrame([valrow], columns=VALICOLS) | pandas.DataFrame |
""" Module for data preprocessing.
"""
import datetime
import warnings
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Set
from typing import Union
import numpy as np
import pandas as pd
from sklearn.base import BaseEstim... | pd.concat([X, X_dif], axis=1) | pandas.concat |
#codes to for analyse the model.
import re
import os
from astropy import units as u
from tardis import constants
import numpy as np
import pandas as pd
class LastLineInteraction(object):
@classmethod
def from_model(cls, model):
return cls(model.runner.last_line_interaction_in_id,
... | pd.Panel(ion_populations_dict) | pandas.Panel |
"""
This module contains the classes for testing the exodata of mpcpy.
"""
from mpcpy import exodata
from mpcpy import utility
from mpcpy import units
from mpcpy import variables
from testing import TestCaseMPCPy
import unittest
import numpy as np
import pickle
import copy
import os
import pandas as pd
import datetim... | pd.to_datetime(self.df['Time']) | pandas.to_datetime |
from IMLearn.utils import split_train_test
from IMLearn.learners.regressors import LinearRegression
from typing import NoReturn
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
pio.templates.default = "simple_white"
def load_data(filename: ... | pd.get_dummies(df, prefix='zipcode', columns=['zipcode']) | pandas.get_dummies |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import os
import operator
import unittest
import cStringIO as StringIO
import nose
from numpy import nan
import numpy as np
import numpy.ma as ma
from pandas import Index, Series, TimeSeries, DataFrame, isnull, notnull
from pandas.core.index... | Series([1., 2, 3], index=[0, 1, 2]) | pandas.Series |
import pandas as pd
import yaml
import datetime
from workalendar.europe import Belgium
meta = | pd.read_csv('jouleboulevard_metadata.csv') | pandas.read_csv |
import pandas as pd
import numpy as np
from sklearn.impute import SimpleImputer
def mean():
df = pd.read_csv('../train_cuting/train_cutting2_lstm.csv')
df['Timestamp'] = pd.to_datetime(df['Timestamp'])
hour = pd.Timedelta('1h')
dt = df['Timestamp']
in_block = (dt.diff() == hour)
in_block[0] = T... | pd.Timedelta('1h') | pandas.Timedelta |
import pandas as pd
import functools
# TODO: figure out if array return hundredths or tenths of inches; apply appropriate functions
def format_df(file, col_name, cb):
df = pd.read_csv(file,
names=['station_id', 'month', 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32... | pd.merge(max_temps, min_temps, on=merge_criteria) | pandas.merge |
import sys
assert sys.version_info >= (3, 5) # make sure we have Python 3.5+
import pandas as pd
import numpy as np
from pathlib import Path
# init input df - fishing gear
def init_fishing_df(path):
fishing_df = pd.read_csv('../data/' + path)
# comment out for real life data--------------
fishing_df = fi... | pd.to_datetime(df["adjust_time_date"]) | pandas.to_datetime |
import pandas as pd
import pytest
import woodwork as ww
from woodwork.logical_types import Boolean, Double, Integer
from rayml.exceptions import MethodPropertyNotFoundError
from rayml.pipelines.components import (
ComponentBase,
FeatureSelector,
RFClassifierSelectFromModel,
RFRegressorSelectFromModel,
... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
from sklearn.utils import resample
from sklearn.metrics import roc_auc_score, f1_score, balanced_accuracy_score, accuracy_score
from sklearn.metrics import precision_score, recall_score, confusion_matrix
import numpy as np
from scipy import stats
def bootstrap_data(dataset):
internal_val = | pd.read_csv('../../results/validation/internal/SSI_%s_y_vals.csv' % dataset) | pandas.read_csv |
from typing import Tuple
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from etna.datasets import generate_ar_df
from etna.datasets.tsdataset import TSDataset
from etna.transforms import DateFlagsTransform
from etna.transforms import TimeSeriesImputerTransform
@py... | pd.concat([df1, df2], ignore_index=True) | pandas.concat |
import ipyleaflet
import ipywidgets
import pandas as pd
import geopandas as gpd
from shapely.geometry import Polygon, Point
import datetime
import requests
import xml.etree.ElementTree as ET
import calendar
import numpy as np
import pathlib
import os
import bqplot as bq
from functools import reduce
class ANA_interact... | pd.date_range(start='2000-01-01',end='2020-01-01', freq='M') | pandas.date_range |
import numpy as np
import pytest
from anndata import AnnData
from pandas import DataFrame
from pandas.testing import assert_frame_equal
from ehrapy.api.anndata_ext import ObsEmptyError, anndata_to_df, df_to_anndata
class TestAnndataExt:
def test_df_to_anndata_simple(self):
df, col1_val, col2_val, col3_va... | DataFrame({"col1": col1_val, "col2": col2_val, "col3": col3_val}, dtype="object") | pandas.DataFrame |
from press_start.pipelines.data_split.nodes import category_encoder
import pandas as pd
import numpy as np
def test_category_encoder(df_categorical):
enc, df_numeric = category_encoder(
df_categorical,
{"_run": True},
{
"columns_categorical": ["buying", "maint"],
"c... | pd.testing.assert_frame_equal(df_exp, df_numeric, check_like=True) | pandas.testing.assert_frame_equal |
import librosa
import numpy as np
import pandas as pd
from os import listdir
from os.path import isfile, join
from audioread import NoBackendError
def extract_features(path, label, emotionId, startid):
"""
提取path目录下的音频文件的特征，使用librosa库
:param path: 文件路径
:param label: 情绪类型
:param startid: 开始的序列号
... | pd.Series() | pandas.Series |
import operator
import re
import warnings
import numpy as np
import pytest
from pandas._libs.sparse import IntIndex
import pandas.util._test_decorators as td
import pandas as pd
from pandas import isna
from pandas.core.sparse.api import SparseArray, SparseDtype, SparseSeries
import pandas.util.testing as tm
from pan... | tm.assert_sp_array_equal(result, expected) | pandas.util.testing.assert_sp_array_equal |
import sklearn.neighbors._base
import sys
sys.modules['sklearn.neighbors.base'] = sklearn.neighbors._base
import pandas as pd
from sklearn.base import TransformerMixin
import numpy as np
from sklearn.impute import SimpleImputer, KNNImputer
from missingpy import MissForest
class prepross(TransformerMixin):
def __in... | pd.DataFrame() | pandas.DataFrame |
import warnings
from datetime import datetime
from functools import partial
import numpy as np
import pandas as pd
import pandas.api.types as pdtypes
from featuretools import variable_types
from featuretools.entityset.relationship import RelationshipPath
from featuretools.exceptions import UnknownFeature
from feature... | pd.Series(values, index=variable_data[0].index) | pandas.Series |
import numpy as np
import os
import pandas as pd
import sqlite3
from datetime import date
from dotenv import load_dotenv
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from pretty_html_table import build_table
from smtplib import SMTP
load_dotenv()
SQLITE_DB_PATH = ... | pd.DataFrame(query_results, columns=column_names) | pandas.DataFrame |
from django.db import models
# Create your models here.
class Stock(models.Model):
stock_id = models.CharField(max_length=1000)
stock_value = models.CharField(max_length=100)
# checkbox
# enter_your_portfolio = models.BooleanField()
def get_stock_id():
return stock_id
def get_stock... | pd.DataFrame(json_prices[stock_symbol]['prices']) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 21 14:08:43 2019
to produce X and y use combine_pos_neg_from_nc_file or
prepare_X_y_for_holdout_test
@author: ziskin
"""
from PW_paths import savefig_path
from PW_paths import work_yuval
from pathlib import Path
cwd = Path().cwd()
hydro_path = work_... | pd.concat([df, height_df], axis=1) | pandas.concat |
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | pd.Timestamp("2021-11-20") | pandas.Timestamp |
""" County info extractor
TODO describe
"""
import glob
from multiprocessing import Pool
import pandas as pd
import matplotlib.pyplot as plt
import lasio
from tqdm import tqdm
import geopandas as gpd
# Unused
##import numpy as np
##from textwrap import wrap # for making pretty well names
##from functools import part... | pd.read_csv("fips.csv") | pandas.read_csv |
import re
import numpy as np
import pandas as pd
import pytest
from woodwork import DataTable
from woodwork.logical_types import (
URL,
Boolean,
Categorical,
CountryCode,
Datetime,
Double,
Filepath,
FullName,
Integer,
IPAddress,
LatLong,
NaturalLanguage,
Ordinal,
... | pd.DataFrame(series) | pandas.DataFrame |
# http://www.vdh.virginia.gov/coronavirus/
from bs4 import BeautifulSoup
import csv
from datetime import datetime
from io import StringIO
import os
import requests
import pandas as pd
# Remove empty rows
def filtered(rows):
return [x for x in rows if "".join([(x[y] or "").strip() for y in x]) != ""]
def run_VA(a... | pd.read_csv(data_name % link[0]) | pandas.read_csv |
import sys
sys.path.append("./log_helper")
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import PercentFormatter
import math
import random
import argparse
import time
import logging
import glob
from os.path import isfile, join, splitext
from dat... | pd.to_datetime(action_df['date']) | pandas.to_datetime |
import ast
import collections
import glob
import inspect
import math
import os
import random
import shutil
import subprocess
import time
import warnings
from concurrent.futures import ThreadPoolExecutor
from contextlib import suppress
from datetime import datetime
from typing import Any, Dict, Tuple, Sequence, List, Op... | pd.DataFrame(infos) | pandas.DataFrame |
#!/usr/bin/python
import argparse
import pandas as pd
import logging
from pandas.io.json import json_normalize
import os
f = '%(asctime)s %(name)-12s %(levelname)-8s %(message)s'
logging.basicConfig(filename = "conversion.log", filemode='a', level=logging.DEBUG, format=f)
console = logging.StreamHandler()
formatter ... | json_normalize(df[column_name][0]) | pandas.io.json.json_normalize |
from typing import Union, Optional, List, Dict, Tuple, Any
import pandas as pd
import numpy as np
from .common.validators import validate_integer
from .macro import Inflation
from .common.helpers import Float, Frame, Date, Index
from .settings import default_ticker, PeriodLength, _MONTHS_PER_YEAR
from .api.data_queri... | pd.concat([df, new], axis=1, join="inner", copy="false") | pandas.concat |
"""
library for simulating semi-analytic mock maps of CMB secondary anisotropies
"""
__author__ = ["<NAME>", "<NAME>"]
__email__ = ["<EMAIL>", "<EMAIL>"]
import os
import warnings
from sys import getsizeof
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
from warnings ... | pd.DataFrame(catalog) | pandas.DataFrame |
from __future__ import print_function
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from pandas import (Series, Index, Int64Index, Timestamp, Period,
DatetimeIndex, PeriodIndex, TimedeltaIndex,
Timedelta, timedelta_range, date_range, Float64Index... | pd.TimedeltaIndex(['2 hours', '3 hours', '6 hours'], name='xxx') | pandas.TimedeltaIndex |
"""
Test output formatting for Series/DataFrame, including to_string & reprs
"""
from datetime import datetime
from io import StringIO
import itertools
from operator import methodcaller
import os
from pathlib import Path
import re
from shutil import get_terminal_size
import sys
import textwrap
import dateutil
import ... | tm.assert_produces_warning(FutureWarning) | pandas._testing.assert_produces_warning |
"""
calculate the option dividend yield per atm strikes per day
- winsorize each strike
- average the strike yield
- boxplot each day yield range
"""
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import helpers.step_functions as sf
from scipy.stats import mstats
# import call and put mids
de... | pd.DataFrame(dy_dict, index=df.index.values) | pandas.DataFrame |
import datetime
from typing import Any, Dict
import pandas as pd
import pytest
from ruamel.yaml import YAML
from great_expectations.execution_engine.execution_engine import MetricDomainTypes
from great_expectations.rule_based_profiler import RuleBasedProfiler
from great_expectations.rule_based_profiler.config.base im... | pd.to_datetime(df["Date"]) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Created on 2018-09-13
@author: <NAME>
"""
import numpy as np
import pandas as pd
CURRENT_ROUND = 38
# Load data from all 2018 rounds
# Data from https://github.com/henriquepgomide/caRtola
rounds = []
rounds.append(pd.read_csv('data/rodada-1.csv'))
rounds.append(pd.read_csv('2018/data/rod... | pd.read_csv('2018/data/rodada-26.csv') | pandas.read_csv |
import numpy as np
import pandas as pd
import pickle
def create_distance_matrix():
distance_path = 'distance.csv'
# ids_path = os.path.join(data_path, dataset_name, 'graph_sensor_ids.txt')
# nodes and indexs
# with open(ids_path) as f:
# ids = f.read().strip().split(',')
# # print(ids)... | pd.DataFrame(adj, index=ids, columns=ids) | pandas.DataFrame |
"""
Sleep features.
This file calculates a set of features from the PSG sleep data.
These include:
- Spectral power (with and without adjustement for 1/f)
- Spindles and slow-waves detection
- Slow-waves / spindles phase-amplitude coupling
- Entropy and fractal dimension
Author: Dr <NAME> <<EMAIL>>, UC Berkeley.
Da... | pd.Series(hypno) | pandas.Series |
import pandas as pd
from openpyxl import Workbook
import cx_Oracle
import sys
from sqlalchemy import create_engine
from PyQt6 import QtCore, QtGui, QtWidgets
import ctypes
import time
import threading
import qdarktheme
import cgitb
cgitb.enable(format = 'text')
dsn_tns = cx_Oracle.makedsn('ip-banco-oracle', 'porta',... | pd.DataFrame(comparaNfCigam, columns=['UN', 'SERIE', 'NOTA', 'DATA', 'SITUACAO', 'TEM']) | pandas.DataFrame |
import biom
import skbio
import numpy as np
import pandas as pd
from deicode.matrix_completion import MatrixCompletion
from deicode.preprocessing import rclr
from deicode._rpca_defaults import (DEFAULT_RANK, DEFAULT_MSC, DEFAULT_MFC,
DEFAULT_ITERATIONS)
from scipy.linalg import svd
... | pd.DataFrame(u, index=table.index, columns=rename_cols) | pandas.DataFrame |
from flask import Flask, render_template, jsonify, request
import pandas as pd
import pickle
import os
import sklearn
from sklearn import preprocessing
app = Flask(__name__)
@app.route("/")
def home():
return render_template("index.html")
@app.route('/predict', methods=['POST'])
def predict():
json = request... | pd.DataFrame.from_dict(json, orient='index') | pandas.DataFrame.from_dict |
import pandas
from enum import Enum
# urls of CSV, from which the tickers will be extracted
_NYSE_URL = 'https://old.nasdaq.com/screening/companies-by-name.aspx?letter=0&exchange=nyse&render=download'
_NASDAQ_URL = 'https://old.nasdaq.com/screening/companies-by-name.aspx?letter=0&exchange=nasdaq&render=download'
_AMEX... | pandas.read_csv(url) | pandas.read_csv |
import json
import multiprocessing
import warnings
from pathlib import PurePosixPath, Path
from typing import Optional, List, Tuple, Dict, Union
import numpy as np
import pandas as pd
from joblib._multiprocessing_helpers import mp
from rdkit import Chem
from rdkit.Chem import AllChem, Mol, MACCSkeys
from sklearn.featu... | pd.DataFrame() | pandas.DataFrame |
import unittest
import qteasy as qt
import pandas as pd
from pandas import Timestamp
import numpy as np
from numpy import int64
import itertools
import datetime
from qteasy.utilfuncs import list_to_str_format, regulate_date_format, time_str_format, str_to_list
from qteasy.utilfuncs import maybe_trade_day, is_market_tr... | pd.Timedelta(7, 'd') | pandas.Timedelta |
# Copyright 2016 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 writ... | pd.isnull(last_max) | pandas.isnull |
import collections
import gc
import os
from typing import List, Optional, Union, Tuple, Dict, Any
import albumentations
import numpy as np
import pandas as pd
import torch
from hydra.utils import instantiate
from pytorch_toolbelt.inference import (
ApplySigmoidTo,
ApplySoftmaxTo,
Ensembler,
Generalized... | pd.DataFrame.from_dict(predictions) | pandas.DataFrame.from_dict |
import numpy as np
import pandas as pd
import theano.tensor as T
from random import shuffle
from theano import shared, function
from patsy import dmatrix
from collections import defaultdict
class MainClauseModel(object):
def __init__(self, nlatfeats=8, alpha=1., discount=None, beta=0.5, gamma=0.9,
... | pd.concat(reps) | pandas.concat |
"""<NAME>0.
MLearner Machine Learning Library Extensions
Author:<NAME><www.linkedin.com/in/jaisenbe>
License: MIT
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import datetime
import time
import joblib
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.model_s... | pd.concat([X_train, X_test], axis=0) | pandas.concat |
import os
import csv
import pandas as pd
# Receives the STS-B and creates gender-occupation datasets
# Inspired by counterfactual data augmentation method as introduced here: https://arxiv.org/pdf/1807.11714.pdf
class CreateGenderStsb():
def __init__(self, lang=None, data_dir=None, occupation=None, multilingual=... | pd.concat([women, women2]) | pandas.concat |
from os.path import join, exists
from os import mkdir, remove
from io import StringIO
from subprocess import call, run as r, DEVNULL, STDOUT
from Bio import SeqIO
from Bio.SeqRecord import SeqRecord
import pandas as pd
from .plotting import plot_alignment, plot_genbank
class Insertion():
def __init__(self, args):... | pd.DataFrame(columns=['chromosome', 'position', 'length']) | pandas.DataFrame |
import math
import shapely
import param
import panel as pn
from holoviews import streams
import geopandas as gpd
import geoviews as gv
import pandas as pd
from pydsm.hydroh5 import HydroH5
import datetime
import holoviews as hv
from holoviews import opts
import hvplot.pandas
hv.extension('bokeh')
gv.extension('bokeh'... | pd.Series(self.dataw.columns) | pandas.Series |
import pandas as pd
from skimage import io
import json
import numpy as np
def createCountMatrix(assigned_genes:str):
original_df = pd.read_csv(assigned_genes)
original_df = original_df[original_df.Cell_Label != 0]
df1 = pd.crosstab(original_df.Gene,original_df.Cell_Label,original_df.Cell_Label,aggfunc='cou... | pd.read_csv(decoded_df_csv) | pandas.read_csv |
#Lib for Streamlit
# Copyright(c) 2021 - AilluminateX LLC
# This is main Sofware... Screening and Tirage
# Customized to general Major Activities
# Make all the School Activities- st.write(DataFrame) ==> (outputs) Commented...
# The reason, since still we need the major calculations.
# Also the Computing is n... | pd.read_html(results_as_html, header=0, index_col=0) | pandas.read_html |
from numpy import *
import nlopt
import numpy as np
import matplotlib.pyplot as plt
import numbers
import math
import pandas as pd
import random
import autograd.numpy as ag
from autograd import grad
from mpl_toolkits.mplot3d import Axes3D
from numpy.lib.function_base import vectorize
from autograd import value_and_grad... | pd.DataFrame(data) | pandas.DataFrame |
# pylint: disable=too-many-lines
"""Statistical functions in ArviZ."""
import warnings
import logging
from collections import OrderedDict
import numpy as np
import pandas as pd
import scipy.stats as st
from scipy.optimize import minimize
import xarray as xr
from ..data import convert_to_inference_data, convert_to_dat... | pd.DataFrame.from_dict(data_dict, orient="index") | pandas.DataFrame.from_dict |
"""
Computes the fingerprint similarity of molecules in the validation and test set to
molecules in the training set.
"""
import numpy as np
import pandas as pd
from syn_net.utils.data_utils import *
from rdkit import Chem
from rdkit.Chem import AllChem
import multiprocessing as mp
from scripts._mp_search_similar impor... | pd.DataFrame({'smiles': data_valid, 'split': 'valid', 'most similar': indices, 'similarity': similaritys}) | pandas.DataFrame |
######################################################################
# This file contains utility functions to load test data from file, #
# and invoke DeepAR predictor and plot the observed and target data. #
######################################################################
import io
import os
import j... | pd.Timedelta(1, unit=self.__freq) | pandas.Timedelta |
#!/usr/bin/env python3
# coding: utf-8
import csv
import numpy as np
import pandas as pd
## I/O configuration
# column delimiters for input and output files
input_sep = '\t'
output_sep = ','
output_type = '_peptides.csv'
# print row names/indices?
write_row_names=False
# print the column titles?
write_header=True
... | pd.isnull(df['Proteins']) | pandas.isnull |
# -*- coding: utf-8 -*-
"""
Autor: <NAME>
Revisó: <NAME>
Aprobó: <NAME>
versión 0.0
"""
import powerfactory as pf
import pandas as pd
import numpy as np
from xlsxwriter.utility import xl_rowcol_to_cell
##### Inicia la aplicación #####
app=pf.GetApplication()
app.ClearOutputWindow()
app.EchoOff()
... | pd.concat([data1,dato2,dato3,dato4], axis=1) | pandas.concat |
import codecs
import datetime
import functools
import json
import os
import re
import shutil
import pandas as pd
from dateutil.relativedelta import relativedelta
from requests.exceptions import ConnectionError
from utils_pandas import add_data
from utils_pandas import cut_ages
from utils_pandas import export
from uti... | pd.crosstab(cases_risks['Date'], cases_risks["risk_group"]) | pandas.crosstab |
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 20 00:24:43 2020
@author: Ray
@email: <EMAIL>
@wechat: RayTing0305
"""
import pandas as pd
import numpy as np
import re
'''
Quiz
'''
index1 = ['James', 'Mike', 'Sally']
col1 = ['Business', 'Law', 'Engineering']
student_df = pd.DataFrame(col1, index1)... | pd.read_csv('assets/world_bank.csv',skiprows=4) | pandas.read_csv |
import pytest
import pandas as pd
import pickle
from hashlib import sha256
from tempfile import NamedTemporaryFile
from ketl.loader.Loader import (
BaseLoader, DatabaseLoader, HashLoader, DelimitedFileLoader, ParquetLoader,
LocalFileLoader, PickleLoader
)
from ketl.db.settings import get_engine
@pytest.fixt... | pd.read_parquet(pq_file2) | pandas.read_parquet |
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import datetime
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
import numpy as np
import operator
import pandas as pd
from Abstract import Conference
# ================================== read c... | pd.to_datetime(df['date']) | pandas.to_datetime |
from tea.ast import ( Node, Variable, Literal,
Equal, NotEqual, LessThan,
LessThanEqual, GreaterThan, GreaterThanEqual,
Relate, PositiveRelationship
)
from tea.runtimeDataStructures.dataset import Dataset
from tea.runtimeDat... | pd.Series(lhs.dataframe, p_ids) | pandas.Series |
from __future__ import division
from datetime import datetime
import sys
if sys.version_info < (3, 3):
import mock
else:
from unittest import mock
import pandas as pd
import numpy as np
from nose.tools import assert_almost_equal as aae
import bt
import bt.algos as algos
def test_algo_name():
class Tes... | pd.to_datetime('2010-01-02') | pandas.to_datetime |
# Databricks notebook source
# MAGIC %md
# MAGIC
# MAGIC # Databricks - Credit Scoring
# MAGIC
# MAGIC ## Introduction
# MAGIC
# MAGIC Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions. For markets and society to function, indi... | pd.set_option('display.max_colwidth', -1) | pandas.set_option |
# -*- coding: utf-8 -*-
from datetime import timedelta
import operator
import numpy as np
import pytest
import pandas as pd
from pandas import Series, compat
from pandas.core.indexes.period import IncompatibleFrequency
import pandas.util.testing as tm
def _permute(obj):
return obj.take(np.random.permutation(len... | pd.Timestamp('20120104') | pandas.Timestamp |
import array
import os
import pandas as pd
import pymongo
import json
import pandas_ta as ta
from bson import json_util, ObjectId
from bson.json_util import loads
from Sma2019 import data
myclient = pymongo.MongoClient("mongodb://localhost:27017/")
df=pd.Series(data)
pd.ewma(df, span=5)
| pd.ewma(df, span=5, min_periods=5) | pandas.ewma |
from collections import OrderedDict
import datetime
from datetime import timedelta
from io import StringIO
import json
import os
import numpy as np
import pytest
from pandas.compat import is_platform_32bit, is_platform_windows
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame... | pd.DataFrame([["foo", "bar"], ["baz", "qux"]], columns=["a", "b"]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
@authors: <NAME> and <NAME>
Functions for Generative Language Model Project
"""
#####################################
# imports
#####################################
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from keras.models import Sequ... | pd.read_sql(sample_parents_sql, db) | pandas.read_sql |
import numpy as np
import sklearn
import pandas as pd
import scipy.spatial.distance as ssd
from scipy.cluster import hierarchy
from scipy.stats import chi2_contingency
from sklearn.base import BaseEstimator
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import CountVectorizer
f... | pd.crosstab(x, y) | pandas.crosstab |
## ~~~~~ Imports ~~~~~
## Data Manipulation
import pandas as pd
import numpy as np
## Plotting
import seaborn as sns
import matplotlib.pyplot as plt
## Scraping
import requests
import xmltodict
## OS Related
import os
from os import listdir
from os.path import isfile, join
## Datetime Handling
from datetime import... | pd.to_datetime(end_date) | pandas.to_datetime |
from pymongo import *
from url import URL
import statistics as stat
from time import strptime, mktime
import pandas as pd
import sys
import re
client = MongoClient(URL)
db = client.crypto_wallet
def checkLen(a, b):
if len(a) == len(b):
return True
else:
return f'DB Objs:{len(a)} < Clean Arr It... | pd.DataFrame(BTC_Data) | pandas.DataFrame |
import geopandas
import pandas as pd
import requests
class WrakkenBankData:
"""
"""
def __init__(self):
url = 'https://wrakkendatabank.api.afdelingkust.be/v1/wrecks'
response = requests.get(url)
if response.status_code == 200:
wrecks_json = response.json()['wrecks']
... | pd.DataFrame(wrecks_json) | pandas.DataFrame |
import argparse
import json
import logging
import os
import pickle
import subprocess
import sys
import tarfile
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])
os.system("conda install -c sebp scik... | pd.read_csv(test_features_data, header=0) | pandas.read_csv |
"""
Tests for Timestamp timezone-related methods
"""
from datetime import (
date,
datetime,
timedelta,
)
import dateutil
from dateutil.tz import (
gettz,
tzoffset,
)
import pytest
import pytz
from pytz.exceptions import (
AmbiguousTimeError,
NonExistentTimeError,
)
fro... | Timestamp("2017-03-26 01:00") | pandas.Timestamp |
from pathlib import Path
import pandas as pd
# Directory of this file
this_dir = Path(__file__).resolve().parent
# Read in all Excel files from all subfolders of sales_data
parts = []
for path in (this_dir / "sales_data").rglob("*.xls*"):
print(f'Reading {path.name}')
part = pd.read_excel(path, index_col="t... | pd.concat(parts) | pandas.concat |
# Import your libraries
import pandas as pd
import numpy as np
# Start writing code
max_users = len(list(set(list(facebook_friends.user2.unique() ) + list(facebook_friends.user1.unique()))))
revert = facebook_friends.rename(columns = {'user1' : 'user2', 'user2':'user1'})
Grouped = | pd.concat([revert, facebook_friends]) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 15 20:41:19 2021
@author: DELL
"""
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load... | pd.DataFrame(dic) | pandas.DataFrame |
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