prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
##########################################################################
## Summary
##########################################################################
'''
Creates flat table of decisions from our Postgres database and runs the prediction pipeline.
Starting point for running our models.
'''
################... | pandas.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 21 14:21:25 2021
@author: mchini
"""
from scipy.io import loadmat
from scipy.optimize import curve_fit
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
folder2load = 'D:/models_neonates/autocorr_spikes/data/'
# see excel file... | pd.unique(exps['Age'].loc[exps['animal_ID'] == animal]) | pandas.unique |
from collections import OrderedDict
from datetime import timedelta
import numpy as np
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype, DatetimeTZDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Series,
Timedelta,
Timestamp,
_np_version_under1p14,
... | pd.Series(dtype=np.object) | pandas.Series |
#
# Copyright (C) 2019 Databricks, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | pd.Series([10, 20, 30], name="rep") | pandas.Series |
"""
Tests for live trading.
"""
from unittest import TestCase
from datetime import time
from collections import defaultdict
import pandas as pd
import numpy as np
# fix to allow zip_longest on Python 2.X and 3.X
try: # Python 3
from itertools import zip_longest
except ImportErro... | pd.Timedelta('1 min') | pandas.Timedelta |
import unittest
import pandas as pd
import pandas.util.testing as pdtest
import tia.rlab.table as tbl
class TestTable(unittest.TestCase):
def setUp(self):
self.df1 = df1 = pd.DataFrame({'A': [.55, .65], 'B': [1234., -5678.]}, index=['I1', 'I2'])
# Multi-index frame with multi-index
cols ... | pd.DataFrame([['A', 'B'], ['A', 'D']], index=[1, 2], columns=[1, 2]) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
# this definition exposes all python module imports that should be available in all subsequent commands
import json
import numpy as np
import pandas as pd
import datetime as dt
import stumpy
# ...
# global constants
MODEL_DIRECTORY = "/srv/app/model/data/"
... | pd.concat([df, result], axis=1) | pandas.concat |
from collections import OrderedDict
from datetime import datetime, timedelta
import numpy as np
import numpy.ma as ma
import pytest
from pandas._libs import iNaT, lib
from pandas.core.dtypes.common import is_categorical_dtype, is_datetime64tz_dtype
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
Da... | tm.assert_series_equal(ser, expected) | pandas._testing.assert_series_equal |
import argparse
import itertools
from collections import defaultdict
from glob import glob
from shutil import copy2
import multiprocessing as mp
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from qpputils import dataparser as dp
from Timer import Timer
from crossval import InterTopicCrossVali... | pd.to_numeric(df['lambda']) | pandas.to_numeric |
from __future__ import annotations
import numpy as np
import pandas as pd
from sklearn import datasets
from IMLearn.metrics import mean_square_error
from IMLearn.utils import split_train_test
from IMLearn.model_selection import cross_validate
from IMLearn.learners.regressors import PolynomialFitting, LinearRegression, ... | pd.DataFrame(X) | pandas.DataFrame |
from datetime import datetime, timezone
from ast import literal_eval
from collections import OrderedDict, defaultdict
from functools import partial, partialmethod
from math import ceil, floor, fmod
import numpy as np
import os.path
import pandas as pd
import pyqtgraph as pg
from pyqtgraph import QtCore, QtGui
from .. ... | pd.concat(args, axis=1) | pandas.concat |
"""
Results containers and post-estimation diagnostics for IV models
"""
from __future__ import annotations
from linearmodels.compat.statsmodels import Summary
import datetime as dt
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
from numpy import array, asarray, c_, diag, empty, isnan, log, nda... | DataFrame(ci, index=self._vars, columns=["lower", "upper"]) | pandas.DataFrame |
"""
preprocess images nad train lane navigation model
"""
import fnmatch
import os
import pickle
import random
from os import path
from os.path import exists
from os.path import join
import numpy as np
np.set_printoptions(formatter={'float_kind': lambda x: "%.4f" % x})
import pandas as pd
pd.set_option('display.widt... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import quantecon as qe
import requests
import string
"""
Description: Voter analysis for Index Coop DAO Decision Gate 2 Governance Votes
Prereqs:
- Download Snapshot vote reports either manually or using snapshot_report_download.py script
... | pd.read_csv(f'{local_download_folder_path}snapshot-report-{proposal_id}.csv') | pandas.read_csv |
# Reading an xlsx file and creating an matrix multiplication across.
import pandas as pd
inputFileName='./ICC-Test-Championship.xlsx'
outputFileName='./result.csv'
dataIndiaEngland = | pd.read_excel(inputFileName, sheet_name='India-England-Forecast') | pandas.read_excel |
# Copyright 2019 <NAME>, Inc. and the University of Edinburgh. 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
#
# Unle... | pd.concat([merge_df[["eid", "input_text"]], eval_df], axis=1) | pandas.concat |
import pytest
import os
from mapping import util
from pandas.util.testing import assert_frame_equal, assert_series_equal
import pandas as pd
from pandas import Timestamp as TS
import numpy as np
@pytest.fixture
def price_files():
cdir = os.path.dirname(__file__)
path = os.path.join(cdir, 'data/')
files = ... | pd.Series(['USD', 'USD'], index=['A', 'A']) | pandas.Series |
"""
(C) Copyright 2019 IBM Corp.
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
... | pd.concat([X_covariates, X_effmod, X_treatment], axis="columns", ignore_index=False) | pandas.concat |
import xml.etree.ElementTree as ET
import os
import json
import string
import copy
import re
import pandas as pd
import numpy as np
from datetime import datetime
from nltk.corpus import wordnet
import sys
from nltk import Tree
import spacy
from insert_whitespace import append_text
from config import DATA_PATH, TMP_PATH... | pd.DataFrame() | pandas.DataFrame |
"""This is a collection of helper functions"""
import pandas as pd
from datetime import datetime as dt
class NewDataFrame(pd.DataFrame):
"""Class that inherits from pandas DataFrame"""
def null_count(self):
"""Method that returns the numbers of null values in a DataFrame"""
return self.isnull... | pd.Series(list) | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 07 14:42:32 2021
@author: silviapagliarini
"""
import os
import numpy as np
import pandas as pd
import csv
from pydub import AudioSegment
import scipy.io.wavfile as wav
def opensmile_executable(data, baby_id, classes, args):
"""
Generate a ... | pd.read_csv(args.data_dir + '/' + 'baby_list.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
from datetime import timedelta
import pandas as pd
import pandas.util.testing as tm
class TestTimedeltaSeriesComparisons(object):
def test_compare_timedelta_series(self):
# regresssion test for GH5963
s = pd.Series([timedelta(days=1), timedelta(days=2)])
actual = s... | pd.Period('2015-01-10', freq='D') | pandas.Period |
"""
data hash pandas / numpy objects
"""
import itertools
from typing import Optional
import numpy as np
from pandas._libs import Timestamp
import pandas._libs.hashing as hashing
from pandas.core.dtypes.cast import infer_dtype_from_scalar
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_exten... | is_extension_array_dtype(dtype) | pandas.core.dtypes.common.is_extension_array_dtype |
"""
Module containing metrics for the centralized version of hay_checker.
Some functions parameters are unused, they have been kept like this to allow
easier code evolution.
"""
import numpy as np
import pandas as pd
from sklearn.metrics import mutual_info_score
from haychecker.chc import task
def _completeness_todo... | pd.to_numeric(df[cond["column"]], errors="coerce") | pandas.to_numeric |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author : <NAME>
# @Contact : <EMAIL>
import numpy as np
import pandas as pd
from pandas import Index
from autoflow import DataManager
from autoflow import datasets
from autoflow.tests.base import LocalResourceTestCase
from autoflow.utils.dict_ import sort_dict
cla... | pd.Series(data_manager.feature_groups) | pandas.Series |
import joblib
from ..config import config
from .. import models
import fasttext
import numpy as np
import pandas as pd
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from sklearn.preprocessing import MultiLabelBinarizer
from keras import backend as K
from pathlib i... | pd.DataFrame(prob_prediction, columns=config.ASPECT_TARGET) | pandas.DataFrame |
"""Auxiliary file for regressions."""
from collections import OrderedDict
from unittest import mock
import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
from linearmodels.iv.model import IV2SLS
from linearmodels.iv.model import IVLIML
from statsmodels.regression.linear_model import OLS
from . ... | pd.concat((exog, endog_pred), axis=1) | pandas.concat |
"""
Import as:
import im.ib.data.load.test.test_s3_data_loader as tsdloa
"""
import pandas as pd
import pytest
import helpers.hunit_test as hunitest
import im.common.data.types as imcodatyp
import im.ib.data.load.ib_s3_data_loader as imidlisdlo
class TestS3IbDataLoader1(hunitest.TestCase):
"""
Test data lo... | pd.to_datetime("2021-03-05 05:00:00-05:00") | pandas.to_datetime |
#!/usr/bin/python
# -*- coding: utf-8 -*-
from __future__ import print_function
import nltk.tokenize
import psycopg2
import pandas as pd
import sys, re
def clean_str(string):
"""
Tokenization/string cleaning for all datasets
Every dataset is lower cased
Original taken from https://github.com/yoonkim/CN... | pd.DataFrame(output, columns=["author_id", "doc_content"]) | pandas.DataFrame |
import json, datetime, requests, time
import schedule
import pytz
import pandas as pd
def convert_datetime_timezone(dt, tz1, tz2):
tz1 = pytz.timezone(tz1)
tz2 = pytz.timezone(tz2)
dt = datetime.datetime.strptime(dt,"%Y/%m/%d %H:%M:%S")
dt = tz1.localize(dt)
dt = dt.astimezone(tz2)
dt = dt.strf... | pd.read_csv('dolar.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 17 15:42:42 2018
@author: MichaelEK
"""
import numpy as np
import pandas as pd
from pdsql import mssql
import os
import geopandas as gpd
from shapely.geometry import Point
from hydrolm.lm import LM
from hydrolm import util
from seaborn import regplot
import matplotlib.pyp... | pd.to_datetime(man_summ_data.FromDate) | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 25 15:50:20 2019
work flow for ZWD and PW retreival after python copy_gipsyx_post_from_geo.py:
1)save_PPP_field_unselected_data_and_errors(field='ZWD')
2)select_PPP_field_thresh_and_combine_save_all(field='ZWD')
3)use mean_ZWD_over_sound_... | pd.to_datetime(geo_df.starting_date) | pandas.to_datetime |
# coding=utf-8
import pandas as pd
import numpy as np
import re
from matplotlib.ticker import FuncFormatter
def number_formatter(number, pos=None):
"""Convert a number into a human readable format."""
magnitude = 0
while abs(number) >= 1000:
magnitude += 1
number /= 1000.0
return '%.1f... | pd.DataFrame(data=df_resultado) | pandas.DataFrame |
from collections import OrderedDict
import numpy as np
import pandas as pd
from sklearn.ensemble import BaggingClassifier, RandomForestRegressor
from sklearn.tree import DecisionTreeClassifier
from unittest.mock import patch
from zipline.data import bundles
from tests import assert_output, project_test, generate_rand... | pd.Series(targets, index) | pandas.Series |
#!/usr/bin/env python3
#
# Copyright © 2016, Evolved Binary Ltd
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notic... | pd.read_csv(file) | pandas.read_csv |
# coding: utf-8
import glob
import os
import pandas as pd
import numpy as np
import shutil
# BLOCK FOR JFC1 AT FENDT
rootdir = "/home/maik/b2drop/cosmicsense/inbox/fendt/timeseries/crns/JFC-1-sd"
rtdir = "/home/maik/b2drop/cosmicsense/inbox/fendt/timeseries/crns/JFC-1"
trgdir = "/media/x/cosmicsense/data/fendt/crns"
... | pd.read_csv(tmpfile, sep=",", comment="#", header=None, error_bad_lines=False, warn_bad_lines=True) | pandas.read_csv |
import strat_models
import networkx as nx
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
np.random.seed(123)
"""
Cardiovascular disease dataset
data is from https://www.kaggle.com/sulianova... | pd.concat([data, dummies_chol, dummies_gluc], axis=1) | pandas.concat |
"""Exports burst data to other data structures."""
import pandas as pd
import numpy as np
import os
import itertools
import pickle
from itertools import groupby
def df_export(bursts, offsets, from_svo=False):
"""Exports the burst data to a dataframe.
TODO: remove offsets parameter, as it is not used to gener... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 7 12:04:39 2018
@author: saintlyvi
"""
import time
import pandas as pd
import numpy as np
from sklearn.cluster import MiniBatchKMeans, KMeans
import somoclu
from experiment.algorithms.cluster_prep import xBins, preprocessX, clusterStats, bestClu... | pd.DataFrame() | pandas.DataFrame |
from __future__ import division
import inspect
import json
import re
from datetime import datetime
from functools import wraps
import jsonschema
from numbers import Number
import numpy as np
import pandas as pd
from dateutil.parser import parse
from scipy import stats
from six import string_types
from .base import ... | pd.Timedelta(-1) | pandas.Timedelta |
"""
Provide a generic structure to support window functions,
similar to how we have a Groupby object.
"""
from collections import defaultdict
from datetime import timedelta
from textwrap import dedent
from typing import List, Optional, Set
import warnings
import numpy as np
import pandas._libs.window as libwindow
fro... | Substitution(name="expanding") | pandas.util._decorators.Substitution |
from rvranking.logs import hplogger
from rvranking.sampling.main import prep_samples_list, get_train_test
import pandas as pd
from rvranking.globalVars import _FAKE_ELWC, _EVENT_FEATURES, _RV_FEATURES
def get_data():
sample_list_train, sample_list_test = get_train_test()
x_train, y_train, xy_train = x_y_data... | pd.DataFrame(tot_feat_list, columns=all_feat_names, dtype='int') | pandas.DataFrame |
import requests
import time
import pandas
from string import Template
ENDPOINT = 'https://api.portfolio123.com'
AUTH_PATH = '/auth'
SCREEN_ROLLING_BACKTEST_PATH = '/screen/rolling-backtest'
SCREEN_BACKTEST_PATH = '/screen/backtest'
SCREEN_RUN_PATH = '/screen/run'
UNIVERSE_PATH = '/universe'
RANK_PATH = '/rank'
DATA_P... | pandas.DataFrame(data=rows, columns=ret['columns']) | pandas.DataFrame |
import os
import re
import json
import abc
import warnings
from typing import MutableMapping, List, Union
from functools import reduce
from enum import Enum
import pandas as pd
import numpy as np
from scipy import sparse
import loompy as lp
from loomxpy import __DEBUG__
from loomxpy._specifications import (
Proje... | pd.api.types.is_bool_dtype(arr_or_dtype=value) | pandas.api.types.is_bool_dtype |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import time
import re
def extractTradingPartners():
directory = 'Data Sources/Trading Partners (1)/'
files = os.listdir(directory)
for i,file in enumerate(files):
# We are only interested in the csv's and not the sour... | pd.concat([tradingPartners,temp],axis = 0) | pandas.concat |
#SPDX-License-Identifier: MIT
import datetime
import json
import logging
import os
import sys
import warnings
from multiprocessing import Process, Queue
from workers.worker_git_integration import WorkerGitInterfaceable
import numpy as np
import pandas as pd
import requests
import sqlalchemy as s
from s... | pd.to_datetime(df_past['msg_timestamp']) | pandas.to_datetime |
# -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime, timedelta
import functools
import itertools
import numpy as np
import numpy.ma as ma
import numpy.ma.mrecords as mrecords
from numpy.random import randn
import pytest
from pandas.compat import (
PY3, PY36, OrderedDict, ... | DataFrame([[1, 'a'], [2, 'b']], columns=columns) | pandas.DataFrame |
"""
This module contains classes for quantifying the predicted model errors (uncertainty quantification), and preparing
provided residual (true errors) predicted model error data for plotting (e.g. residual vs. error plots), or for
recalibration of model errors using the method of Palmer et al.
ErrorUtils:
Collect... | pd.read_excel(file) | pandas.read_excel |
# -*- coding: utf-8 -*-
"""
Created on Fri May 15 01:55:22 2020
@author: balajiramesh
"""
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 10 00:25:12 2020
@author: balajiramesh
Raw : 16,319230 2,641562
Within study timeline: 14393806 2247749
Within study area and timeline: 7892752 1246896
AFter removing washout pe... | pd.get_dummies(df[i],prefix=i) | pandas.get_dummies |
import pandas as pd
import numpy as np
import pickle
import lap_v2_py3 as lap_v2
reprocess_new_basis = True
#Folder with data:
source_folder = '../Source_Data/'
dest_folder = '../Processed_Data/'
if reprocess_new_basis:
#Load in the conversion table
conv = pd.read_csv(source_folder+'ann.csv',usecols=[1,2],i... | pd.read_csv(source_folder+'keri_ranknorm_data_corr.txt',index_col=None,header=None,sep='\t') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# ================================================================================================ #
# Project : Deep Learning for Conversion Rate Prediction (CVR) #
# Version : 0.1.0 ... | is_numeric_dtype(self._df[column]) | pandas.api.types.is_numeric_dtype |
import pandas as pd
data_av_week = pd.read_csv("data_av_week.csv")
supermarkt_urls = pd.read_csv("supermarkt_urls.csv")
s_details = pd.read_csv("notebooksdetailed_supermarkt_python_mined.csv", header= None)
migros_details = pd.read_csv("notebooksdetailed_Migros_python_mined.csv", header= None)
coop_details = pd.read_c... | pd.merge(supermarkt_details, data_days_urls, how="outer", on="codes") | pandas.merge |
"""
Tests for zipline/utils/pandas_utils.py
"""
from unittest import skipIf
import pandas as pd
from zipline.testing import parameter_space, ZiplineTestCase
from zipline.testing.predicates import assert_equal
from zipline.utils.pandas_utils import (
categorical_df_concat,
nearest_unequal_elements,
new_pan... | pd.Series(['c', 'b', 'd'], dtype='category') | pandas.Series |
import pandas as pd
import numpy as np
import matplotlib as plt
pd.set_option('display.max_columns', None)
df=pd.read_csv('train_HK6lq50.csv')
def train_data_preprocess(df,train,test):
df['trainee_engagement_rating'].fillna(value=1.0,inplace=True)
df['isage_null']=0
df.isage_null[df.age... | pd.crosstab(df.trainee_engagement_rating,df.is_pass) | pandas.crosstab |
"""
TODO Pendletoon, doc this whole module
"""
import logging
import pandas as pd
import capture.devconfig as config
from utils.data_handling import update_sheet_column
from utils import globals
from utils.globals import lab_safeget
modlog = logging.getLogger('capture.prepare.interface')
def _get_reagent_header_ce... | pd.concat([chemical_names_df, nominals_df], axis=1) | pandas.concat |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# QTPyLib: Quantitative Trading Python Library
# https://github.com/ranaroussi/qtpylib
#
# Copyright 2016-2018 <NAME>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a ... | pd.to_datetime(self._raw_bars.index, utc=True) | pandas.to_datetime |
#!/usr/bin/env python3
"""
Class to organize and extract data from a .vmrk file.
Created 8/20/2020 by <NAME>.
Last updated 5/20/2021 by <NAME>.
"""
from pathlib import Path
import pandas
import re
from dataclasses import dataclass
from os import PathLike
from functools import cached_property
from typing import List
... | pandas.DataFrame(split_line_list, columns=self.column_names) | pandas.DataFrame |
# -*- coding: utf-8 -*-
__author__ = '<NAME>'
import re
import os
import pymorphy2
import pandas as pd
import numpy as np
from sklearn.metrics import roc_auc_score
from nltk.corpus import stopwords
from nltk.tokenize import RegexpTokenizer
from gensim import models
from datetime import datetime as dt
def get_similar... | pd.read_csv('data/texts/splits/OUTtrain_2.csv', compression='gzip') | pandas.read_csv |
import sys, os, socket
os.environ["CUDA_VISIBLE_DEVICES"]="0"
hostname = socket.gethostname()
if hostname=='tianx-pc':
homeDir = '/analyse/cdhome/'
projDir = '/analyse/Project0257/'
elif hostname[0:7]=='deepnet':
homeDir = '/home/chrisd/'
projDir = '/analyse/Project0257/'
import keras
keras.backend.c... | pd.concat([colleague0_df, colleague1_df]) | pandas.concat |
import pandas as pd
from functools import reduce
from aggregations import Aggragator, Measure, MeasureF, MeasureF1, MeasureTime
from file_helper import write_file
import sys
import numpy as np
from logger import Logger
import scipy.stats as stats
from collections import namedtuple
TableData = namedtuple('TableData',
... | pd.Series(data=formatted_cols, name=series.name) | pandas.Series |
# -*- coding: utf-8 -*-
import csv
import os
import platform
import codecs
import re
import sys
from datetime import datetime
import pytest
import numpy as np
from pandas._libs.lib import Timestamp
import pandas as pd
import pandas.util.testing as tm
from pandas import DataFrame, Series, Index, MultiIndex
from pand... | StringIO(text) | pandas.compat.StringIO |
""" test the scalar Timestamp """
import pytz
import pytest
import dateutil
import calendar
import locale
import numpy as np
from dateutil.tz import tzutc
from pytz import timezone, utc
from datetime import datetime, timedelta
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from pandas.ts... | Timestamp.utcfromtimestamp(current_time) | pandas.Timestamp.utcfromtimestamp |
# coding: utf-8
"""Main estimation code.
"""
import re
import numpy as np
import pandas as pd
from scipy.stats.mstats import gmean
from statsmodels.base.model import GenericLikelihoodModel
from numba import jit
_norm_pdf_C = np.sqrt(2 * np.pi)
@jit(nopython=True)
def _norm_pdf(x):
return np.exp(-x ** 2 / 2)... | pd.Series(data=1, index=self._data.index, dtype=np.double) | pandas.Series |
from _thread import start_new_thread
from hamcrest import assert_that, equal_to, is_in
from hamcrest.core.core.is_ import is_
from pandas.core.frame import DataFrame
from pytest import fail
from tanuki.data_store.column import Column
class TestColumn:
def test_type_casting(self) -> None:
data = [1, 2, 3... | DataFrame({"test": [False, True, False]}) | pandas.core.frame.DataFrame |
import pandas as pd
import numpy as np
import requests
from bs4 import BeautifulSoup
import re
import ast
import os
import sys
from urllib.request import urlopen
from datetime import datetime, timedelta, date
from traceback import format_exc
import json
import math
import urllib.error
from urllib.parse im... | pd.DataFrame(info) | pandas.DataFrame |
# 1.题出问题
# 什么样的人在泰坦尼克号中更容易存活？
# 2.理解数据
# 2.1 采集数据
# https://www.kaggle.com/c/titanic
# 2.2 导入数据
# 忽略警告提示
import warnings
warnings.filterwarnings('ignore')
# 导入处理数据包
import numpy as np
import pandas as pd
# 导入数据
# 训练数据集
train = pd.read_csv("./train.csv")
# 测试数据集
test = pd.read_csv("./test.csv")
# 显示所有列
| pd.set_option('display.max_columns', None) | pandas.set_option |
from copy import deepcopy
from typing import List
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 AddConstTransform
from etna.t... | pd.testing.assert_index_equal(df_slice.index, expected_index) | pandas.testing.assert_index_equal |
# -*- coding: utf-8 -*-
"""
author: zengbin93
email: <EMAIL>
create_dt: 2021/10/24 16:12
describe: Tushare 数据缓存，这是用pickle缓存数据，是临时性的缓存。单次缓存，多次使用，但是不做增量更新。
"""
import os.path
import shutil
import pandas as pd
from .ts import *
from ..utils import io
class TsDataCache:
"""Tushare 数据缓存"""
def __init__(self, data... | pd.to_datetime(end_date) | pandas.to_datetime |
import unittest
import pandas as pd
from pandas.core.indexes.range import RangeIndex
from pandas.testing import assert_frame_equal
import itertools
from datamatch.indices import MultiIndex, NoopIndex, ColumnsIndex
class BaseIndexTestCase(unittest.TestCase):
def assert_pairs_equal(self, pair_a, pair_b):
d... | pd.DataFrame([[1, 2]], index=["x"], columns=cols) | pandas.DataFrame |
####
#### Feb 22, 2022
####
"""
After creating the first 250 eval/train set
there are inconsistencies between NASA/Landsat
labels and Forecast/Sentinel labels from experts.
Here we are.
"""
import csv
import numpy as np
import pandas as pd
import datetime
from datetime import date
import time
... | register_matplotlib_converters() | pandas.plotting.register_matplotlib_converters |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/5/10 17:00
Desc: 股票数据-总貌-市场总貌
股票数据-总貌-成交概括
http://www.szse.cn/market/overview/index.html
http://www.sse.com.cn/market/stockdata/statistic/
"""
import warnings
from io import BytesIO
import pandas as pd
import requests
from bs4 import BeautifulSoup
def stock... | ric(temp_df["股票交易额"], errors="coerce") | pandas.to_numeric |
from abc import ABC, abstractmethod
import matplotlib.pyplot as plt
from matplotlib import animation
from time import time
from datetime import timedelta
import numpy as np
import torch
import pandas as pd
class Trainer:
def __init__(self, env, env_test, algo, seed=0, num_steps=10**6, eval_interval=10*... | pd.DataFrame(self.returns['return']) | pandas.DataFrame |
#%%
from initial_data_processing import ProcessSoccerData
from scraper import Scrape_Soccer_Data
import pandas as pd
import os
#%%
NO_PREV_MATCHES_TO_CALULATE_AVERAGE_FROM = 5
class Feature_Engineering:
def __init__(self, calc_features=False):
self.soccer_data = ProcessSoccerData()
self.dictionar... | pd.read_csv(path) | pandas.read_csv |
#
# ___ _ ____ ____
# / _ \ _ _ ___ ___| |_| _ \| __ )
# | | | | | | |/ _ \/ __| __| | | | _ \
# | |_| | |_| | __/\__ \ |_| |_| | |_) |
# \__\_\\__,_|\___||___/\__|____/|____/
#
# Copyright (c) 2014-2019 Appsicle
# Copyright (c) 2019-2020 QuestDB
#
# Licensed under the Apache... | pd.set_option('max_columns', 4) | pandas.set_option |
import luigi
import os
import pandas as pd
from db import extract
from db import sql
from forecast import util
import shutil
import luigi.contrib.hadoop
from sqlalchemy import create_engine
from pysandag.database import get_connection_string
from pysandag import database
from db import log
class IncPopulation(luigi.T... | pd.read_sql(in_query2, sql_in_engine, index_col=['age', 'race_ethn', 'sex', 'mildep']) | pandas.read_sql |
# -*- coding: utf-8 -*-
"""
Test data
"""
# Imports
import pandas as pd
from edbo.feature_utils import build_experiment_index
# Build data sets from indices
def aryl_amination(aryl_halide='ohe', additive='ohe', base='ohe', ligand='ohe', subset=1):
"""
Load aryl amination data with different features.
""... | pd.read_csv('data/aryl_amination/base_mordred.csv') | pandas.read_csv |
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
lreshape,
melt,
wide_to_long,
)
import pandas._testing as tm
class TestMelt:
def setup_method(self, method):
self.df = tm.makeTimeDataFrame()[:10]
self.df["id1"] = (self.df["A"] > 0).astype(np.int... | wide_to_long(wide_df, stubnames=["PA"], i=["node_id", "A"], j="time") | pandas.wide_to_long |
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.metrics import f1_score
import pickle
from sklearn.metrics import cla... | pd.concat([current_report, csv_report]) | pandas.concat |
from datetime import datetime
from os import system
import pandas as pd
import json
def merge_mysql_csv():
mysql_gdax = pd.read_csv('/home/bitnami/backfire/data/resources/gdax_mysql.csv')
most_recent_date = datetime.strptime(mysql_gdax.time.max(), '%Y-%m-%d %H:%M:%S')
mysql_gdax = mysql_gdax[pd.to_datetime... | pd.concat([old_gdax, mysql_gdax]) | pandas.concat |
import random
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
DataFrame,
NaT,
Timestamp,
date_range,
)
import pandas._testing as tm
class TestDataFrameSortValues:
def test_sort_values(self):
frame = DataFrame(
[[1, 1, 2], [3, 1, 0], ... | DataFrame({"a": [1, 2, 3]}) | pandas.DataFrame |
import os
import glob
import psycopg2
import psycopg2.extras
import pandas as pd
from sql_queries import *
def process_song_file(cur, filepath):
"""
Reads raw data from the data files to split artist and songs
corresponding tables
:param cur: Postgres cursor
:param filepath: A path to a file to pr... | pd.DataFrame(columns=column_labels) | pandas.DataFrame |
#!/usr/bin/env python3
import pandas as pd
import numpy as np
from sklearn.cluster import DBSCAN
from sklearn.metrics import accuracy_score
def nonconvex_clusters():
return | pd.DataFrame() | pandas.DataFrame |
#############################################################
# Begin defining Dash app layout
# code sections
# 1 Environment setup
# 2 Setup Dataframes
# 3 Define Useful Functions
# 4 Heatmap UI controls
# 5 Curves plot UI controls
# 6 Navbar definition
# 7 Blank figure to display during initial app loading
# 8 Overa... | pd.Timedelta(days=1) | pandas.Timedelta |
## Prep, join and create metrics to model
# Libraries
import os
import pandas as pd
import numpy as np
import seaborn as sns
from datetime import datetime
import matplotlib.pyplot as plt
# Set working directory
os.chdir("---Your working directory path")
print(os.getcwd())
# Set theme for sns plots
sns.set_theme(styl... | pd.read_csv("en_climate_daily__2019_P1D.csv",header=0) | pandas.read_csv |
"""
This module contains a collection of functions which make plots (saved as png files) using matplotlib, generated from
some model fits and cross-validation evaluation within a MAST-ML run.
This module also contains a method to create python notebooks containing plotted data and the relevant source code from
this mo... | pd.DataFrame({'best run pred': best_run['y_test_pred'], 'best run true': best_run['y_test_true']}) | pandas.DataFrame |
import datetime
import glob
import os
from scipy import stats
import numpy as np
from dashboard.models import Location, Report
from dashboard.libraries import constants
import pandas as pd
# 日次実績レポートを更新する
def update_report(row_report_date: datetime.date):
# カラム名を辞書形式で取得
column_names = get_column_names(row_re... | pd.merge(df_sum, df_mean, on=[column_name_province_state, column_name_country_region], how='inner') | pandas.merge |
"""
Classes for pipeline processing
$Header: /nfs/slac/g/glast/ground/cvs/pointlike/python/uw/like2/process.py,v 1.31 2018/01/27 15:37:17 burnett Exp $
"""
import os, sys, time, glob
import cPickle as pickle
import numpy as np
import pandas as pd
from scipy import optimize
from skymaps import SkyDir, Band
from uw.util... | pd.DataFrame(source.sedrec) | pandas.DataFrame |
# CODING-STYLE CHECKS:
# pycodestyle test_decorators.py
import os
import sys
import pytest
import importlib
import numpy as np
from pandas import DataFrame
from pandas.util.testing import assert_frame_equal
import taxcalc
from taxcalc.decorators import *
def test_create_apply_function_string():
ans = create_appl... | DataFrame(data=[2.0] * 5, columns=['var']) | pandas.DataFrame |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pandas
import numpy as np
from .dataframe import DataFrame
from .utils import _reindex_helper
def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
keys=None, levels=N... | pandas.DataFrame(index=idx) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime, timedelta
import functools
import itertools
import numpy as np
import numpy.ma as ma
import numpy.ma.mrecords as mrecords
from numpy.random import randn
import pytest
from pandas.compat import (
PY3, PY36, OrderedDict, ... | DataFrame([arr, s1]) | pandas.DataFrame |
import os
import re
import sys
import warnings
from datetime import timedelta
from runpy import run_path
from time import sleep
import click
import pandas as pd
from six import string_types
import catalyst
from catalyst.data.bundles import load
from catalyst.data.data_portal import DataPortal
from catalyst.exchange.e... | pd.to_datetime('today', utc=True) | pandas.to_datetime |
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Modelagem em tempo real | COVID-19 no Brasil
--------------------------------------------
Ideias e modelagens desenvolvidas pela trinca:
. <NAME>
. <NAME>
. <NAME>
Esta modelagem possui as seguintes características:
a) NÃO seguimos modelos paramétricos => Não existem dur... | pd.Series(uf_mortes) | pandas.Series |
import pytest
import numpy as np
import pandas as pd
from pandas import Categorical, Series, CategoricalIndex
from pandas.core.dtypes.concat import union_categoricals
from pandas.util import testing as tm
class TestUnionCategoricals(object):
def test_union_categorical(self):
# GH 13361
data = [
... | Categorical([np.nan, 'b']) | pandas.Categorical |
import gzip
import pandas as pd
import os
import shutil
from prepare_vcf_files_helpers import update_dict_with_file, change_format, change_info
pd.options.mode.chained_assignment = None
def make_unique_files(input_folder, output_folder):
if not os.path.exists(output_folder):
os.makedirs(output_folder)
... | pd.concat([new_record, new_columns_normal], axis=1) | pandas.concat |
import pandas as pd
import tensorflow as tf
from pathlib import Path
from datetime import datetime
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.models import load_model
#enviroment settings
path = Path(__file__).parent.absolute()/'Deep Training'
name_data = 'none_'#''
metric = 'binary_accu... | pd.read_csv(data_path/'Training'/(name_data+'training_targets.csv'), index_col=targets_index) | pandas.read_csv |
# Packages
import os
import pandas as pd
import spacy
import matplotlib.pyplot as plt
from spacytextblob.spacytextblob import SpacyTextBlob
# Achieving polarity
def polarity(df):
polarity_scores = []
for doc in nlp.pipe(df["headline_text"]):
polarity_scores.append(doc._.sentiment.polarity)
retu... | pd.to_datetime(sample.publish_date, format="%Y%m%d") | pandas.to_datetime |
from __future__ import division
import logging
from time import time
from os import getpid
from timeit import default_timer as timer
import pandas
import numpy as np
import scipy
import statsmodels.api as sm
import traceback
from settings import (
ALGORITHMS,
CONSENSUS,
FULL_DURATION,
MAX_TOLERABLE_BO... | pandas.Series([x[1] for x in timeseries]) | pandas.Series |
import os
import numpy as np
import pandas as pd
import shap
import json
from ngboost import NGBRegressor
from ngboost.distns import Normal
from ngboost.learners import default_tree_learner
from ngboost.scores import MLE, LogScore
from classes.inputs_gatherer import InputsGatherer
class FeaturesAnalyzer:
"""
... | pd.DataFrame({'date': data['dataset']['date'], target_column: data['dataset'][target_column]}) | pandas.DataFrame |
from dataclasses import replace
import datetime as dt
from functools import partial
import inspect
from pathlib import Path
import re
import types
import uuid
import pandas as pd
from pandas.testing import assert_frame_equal
import pytest
from solarforecastarbiter import datamodel
from solarforecastarbiter.io impor... | pd.Timestamp('20190422T1945Z') | pandas.Timestamp |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#%matplotlib inline
import codecs
import lightgbm as lgb
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
# Read data
image_file_path = './simulated_dpc_d... | pd.read_table(file, delimiter=",") | pandas.read_table |
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