prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# encoding=utf-8
from nltk.corpus import stopwords
from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import FeatureUnion
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.cross_validation import KFold
from sk... | pd.DataFrame(test_features, columns=[f'image_quality']) | pandas.DataFrame |
""" Core functions of the aecg package: tools for annotated ECG HL7 XML files
This submodule implements helper functions to validate 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.
"""
# Impor... | pd.DataFrame() | pandas.DataFrame |
"""
Script to use the static chunks found by `StaticFinder` to calibrate accelerometer raw data so that gravity drift is minimized.
Prerequiste:
Run `StaticFinder` before using this script
Usage:
pad -p <PID> -r <root> process -p <PATTERN> --par AccelerometerCalibrator <options>
options:
--static_chunks <pat... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
from matplotlib import pyplot as plt
import numpy as np
from sklearn.preprocessing import MinMaxScaler
import random
MAXLIFE = 120
SCALE = 1
RESCALE = 1
true_rul = []
test_engine_id = 0
training_engine_id = 0
def kink_RUL(cycle_list, max_cycle):
'''
Piecewise linear functi... | pd.concat(frames) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
#<NAME>/ <EMAIL>
import pandas as pd
import numpy as np
import datetime
import matplotlib.pyplot as plt
from scipy import stats
df = pd.read_csv("chocolateWeightMMT3field.txt", parse_dates = ['Reading'],
na_values=['-999'], delim_whitespace=True)
df.columns = ['... | pd.concat([time_array,df],axis=1) | pandas.concat |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import os
import operator
import unittest
import numpy as np
from pandas.core.api import (Index, Series, TimeSeries, DataFrame, isnull)
import pandas.core.datetools as datetools
from pandas.util.testing import assert_series_equal
import panda... | common.randn(20) | pandas.util.testing.randn |
#-------------------------------------------------------------------------------
# Name: GIS Viewer Attribution Evaluation
# Version: V_2.0
# Purpose: Produce report for installation geodatabase detailing data attribution
#
# Author: <NAME>
#
# Created: 2018/01/26
# Last Update: 2018/03/22
# Des... | pandas.DataFrame(otherCntByFC) | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Plots model, forecast, and residual plots for the
# given df model.
# Should have columns:
# ts - time series data
# model - model fit on training data
# forecast - forecasted values
# Date - dates
def plot_forecast(df, t... | pd.notnull(df["model"]) | pandas.notnull |
import warnings
import logging
warnings.filterwarnings('ignore', category=FutureWarning)
from .index import build as build_index
from .index import build_from_matrix, LookUpBySurface, LookUpBySurfaceAndContext
from .embeddings.base import load_embeddings, EmbedWithContext
from .ground_truth.data_processor import Wiki... | pd.concat(df_sentence) | pandas.concat |
#%%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.integrate
import growth.model
import growth.viz
colors, palette = growth.viz.matplotlib_style()
const = growth.model.load_constants()
# Set the constants
gamma_max = const['gamma_max']
nu_max = 4.5
Kd_cpc = const['Kd_cpc']
... | pd.concat(dfs, sort=False) | pandas.concat |
# coding: utf-8
# ---
#
# _You are currently looking at **version 1.2** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-data-analysis/resources/0dhYG) course resource._
#
... | pd.concat([ls,simp],axis=0) | pandas.concat |
"""
Author: <NAME>
Created: 14/08/2020 11:04 AM
"""
import os
import numpy as np
import pandas as pd
from basgra_python import run_basgra_nz, _trans_manual_harv, get_month_day_to_nonleap_doy
from input_output_keys import matrix_weather_keys_pet
from check_basgra_python.support_for_tests import establish_org_input, g... | pd.read_csv(data_path, index_col=0) | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Analyze CSV file into scores.
Created on Sat Feb 12 22:15:29 2022 // @hk_nien
"""
from pathlib import Path
import os
import re
import sys
import pandas as pd
import numpy as np
PCODES = dict([
# Regio Noord
(1011, 'Amsterdam'),
(1625, 'Hoorn|Zwaag'),
... | pd.to_datetime(df['Timestamp']) | pandas.to_datetime |
#!/usr/bin/env python
# coding: utf-8
import os
import glob
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from pandas.plotting import register_matplotlib_converters
| register_matplotlib_converters() | pandas.plotting.register_matplotlib_converters |
import numpy as np
import os
import pandas as pd
import argparse
import glob
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
from scipy.special import softmax
TEMPERATURE = 1.548991 # optimized temperature for calibration of Catnet
parser = argparse.ArgumentParser()
parser.add_argument(
'-... | pd.concat(all_df) | pandas.concat |
from abc import abstractmethod
from hashlib import new
from analizer.abstract.expression import Expression
from analizer.abstract import expression
from enum import Enum
import sys
sys.path.append("../../..")
from storage.storageManager import jsonMode
from analizer.typechecker.Metadata import Struct
from analizer.typ... | pd.DataFrame(result, columns=newColumns) | pandas.DataFrame |
from pathlib import Path
from src import utils
from src.data import DataLoaders
import numpy as np
import pandas as pd
from xgboost import XGBClassifier
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import log_loss
from sklearn.metrics import ro... | pd.read_csv( data_path/'Master Project Data'/'Tract Rurality Data.csv', dtype = {'Tract':'object'},encoding = 'latin-1' ) | pandas.read_csv |
#!/usr/bin/env python3
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
def split_date(df):
# Remove the empty lines
df = df.dropna(how="all")
# Create a new dateframe for only the date and time
date = df.Päivämäärä.str.split(expand=True)
# Change... | pd.concat([date, pruned], axis=1) | pandas.concat |
'''
AAA lllllll lllllll iiii
A:::A l:::::l l:::::l i::::i
A:::::A l:::::l l:::::l iiii
A:::::::A l:::::l l:::::l
... | pd.read_csv('test.csv') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 26 17:19:41 2020
@author: <NAME>
"""
import pandas as pd
def int_br(x):
return int(x.replace('.',''))
def float_br(x):
return float(x.replace('.', '').replace(',','.'))
dia = '2805'
file_HU = '~/ownCloud/sesab/exporta_bole... | pd.concat([df0, dff], sort=False) | pandas.concat |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from shrike import compliant_logging
from shrike.compliant_logging.constants import DataCategory
from shrike.compliant_logging.logging import (
CompliantLogger,
get_aml_context,
)
from shrike.compliant_logging.exceptions import PublicRunt... | pd.Series([1, 2, 3, 4, 5]) | pandas.Series |
import copy
from datetime import datetime
import warnings
import numpy as np
from numpy.random import randn
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, DatetimeIndex, Index, Series, isna, notna
import pandas._testing as tm
from pandas.core.window.common i... | Index([]) | pandas.Index |
import os
import glob
import json
import argparse
import numpy as np
import pandas as pd
import joblib
from azureml.core.model import Model
from azureml.core import Run
current_run = None
model = None
def init():
print("Started batch scoring by running init()")
parser = argparse.ArgumentParser()
parser.... | pd.read_csv(filename) | pandas.read_csv |
# -*- encoding: utf-8 -*-
"""
===============================
Test and Train data with Pandas
===============================
*auto-sklearn* can automatically encode categorical columns using a label/ordinal encoder.
This example highlights how to properly set the dtype in a DataFrame for this to happen,
and showcase ... | pd.DataFrame(y, dtype='category') | pandas.DataFrame |
import argparse
import os
import sys
import time
from datetime import datetime, timedelta
from pathlib import Path
from pprint import pprint
import pandas as pd
import schedule
sys.path.insert(1, str(Path('src/marktech').resolve()))
import scrape_static
scraper = scrape_static.StaticPageScraper(verbose=0)
de... | pd.DataFrame(row) | pandas.DataFrame |
# -*- encoding:utf-8 -*-
"""
中间层，从上层拿到x，y，df
拥有create estimator
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import os
import functools
from enum import Enum
import numpy as np
import pandas as pd
from sklearn.base import TransformerM... | pd.get_dummies(raw_df['Embarked'], prefix='Embarked') | pandas.get_dummies |
"""
Data Set Information: Dataset named “Online Retail II” includes UK based online store between 01/12/2009 - 09/12/2011 which included the sales. Souvenirs included in the product catalog of this company and these can be considered as promotional items Also known that most of that company’s customers are wholesaler... | pd.qcut(rfm["Frequency"],5,labels=[1,2,3,4,5]) | pandas.qcut |
#%%
import json
import matplotlib
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib.colors as colors
from matplotlib import ticker
from utils.libfunctions import *
def replace_at_index1(tup, ix, val):
lst = list(tup)
... | pd.to_datetime(data[dates[0]]['time']) | pandas.to_datetime |
from functools import lru_cache
from os.path import join
from pathlib import Path
import mne
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from alice_ml.utils import get_epochs_from_df
class IC:
"""
A wrapper that represents the independent component. Contains the signal, weights of ... | pd.read_csv(path/'ics.csv') | pandas.read_csv |
import numpy as np
import pandas as pd
from analysis.transform_fast import load_raw_cohort, transform
def test_immuno_group():
raw_cohort = load_raw_cohort("tests/input.csv")
cohort = transform(raw_cohort)
for ix, row in cohort.iterrows():
# IF IMMRX_DAT <> NULL | Select | Next
if pd... | pd.notnull(row["cns_cov_dat"]) | pandas.notnull |
import numpy as np
np.random.seed(42)
import chainer
from chainer import functions as F
from chainer import links as L
from chainer import initializer
from chainer.initializers import Normal
from time import time
import pandas as pd
eps = 1e-8
def phi(obs):
"""
Feature extraction function
"""
xp = c... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# # Medical Cost Personal Datasets.
# ## Objectives.
# 1. Preprocess and clean the data.
# 2. Perform Statistical Analysis of the data.
# 3. Perform Linear Regression to predict charges.
# 4. Perform Logistic Analysis to predict if a person is a smoker or not.
# 5. Perform SVM and... | pd.read_csv("../../../input/mirichoi0218_insurance/insurance.csv") | pandas.read_csv |
import pickle
import pandas as pd
import numpy as np
def load_data():
train_data = {}
file_path = '../data/tiny_train_input.csv'
data = pd.read_csv(file_path, header=None)
data.columns = ['c' + str(i) for i in range(data.shape[1])]
label = data.c0.values
label = label.reshape(len(label), 1)
... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sep
@author: CocoLiao
Topic: NEC_system_PathDist_module
Input ex:
Run_TotalSites('D:\\nec-backend\\dist\\docs\\mrData.xlsx',
'D:\\nec-backend\\dist\\docs\\workerData.xlsx',
'D:\\nec-backend\\dist\\docs\\officeAddress.xlsx',
'D:\\nec-backend\\dist\... | pd.read_excel(custDist_file, index_col=0) | pandas.read_excel |
# -*- 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) | pandas.DataFrame |
# <NAME> (<EMAIL>)
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
import scipy.stats as ss
import mlpaper.boot_util as bu
from mlpaper.constants import METHOD, METRIC, PAIRWISE_DEFAULT, STAT, STD_STATS
from mlpaper.util import clip_chk
N_BOOT = 1000 # Default... | pd.MultiIndex.from_product([metrics, STD_STATS], names=[METRIC, STAT]) | pandas.MultiIndex.from_product |
# Copyright (C) 2014-2017 <NAME>, <NAME>, <NAME>, <NAME> (in alphabetic order)
#
# This file is part of OpenModal.
#
# OpenModal is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, version 3 of the License.
#
... | pd.DataFrame(columns=['model_id', 'uffidcs', 'node_nums', 'thx', 'thy', 'thz']) | pandas.DataFrame |
'''
Simple vanilla LSTM multiclass classifier for raw EEG data
'''
import scipy.io as spio
import numpy as np
from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
import pandas as pd
import matplotli... | pd.DataFrame(SubjectData['EEG_Data']['activeEEG']) | pandas.DataFrame |
from datetime import datetime
import numpy as np
import pandas as pd
from evidently import ColumnMapping
from evidently.analyzers.data_quality_analyzer import DataQualityAnalyzer
from evidently.analyzers.data_quality_analyzer import FeatureQualityStats
from evidently.analyzers.utils import process_columns
import pyt... | pd.DataFrame({"category_feature": []}) | pandas.DataFrame |
import pickle
from pathlib import Path
from flask import Flask, render_template
from flask_bootstrap import Bootstrap
from flask_wtf import FlaskForm
from wtforms import SelectField, SubmitField, SelectMultipleField
from wtforms.widgets import CheckboxInput, ListWidget
from wtforms.validators import DataRequired
import... | pd.Series(index=_FACTIONS, data=0) | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# ============================================================================================= #
# DS_generator.py #
# Author: <NAME> ... | pd.read_csv('original_data/bio-decagon-ppi.csv',sep=',') | pandas.read_csv |
import pandas as pd
import numpy as np
import random
import networkx as nx
import math
import time, math
import json
import glob
import os
import pickle
from datetime import datetime, timedelta, date
from collections import Counter
import networkx as nx
"""Helper Functions"""
def convert_datetime(dataset, verbose):
... | pd.to_datetime(dataset['nodeTime']) | pandas.to_datetime |
# -*- coding: utf-8 -*-
import unittest
import pandas as pd
import pandas.testing as tm
import numpy as np
from pandas_xyz import algorithms as algs
class TestAlgorithms(unittest.TestCase):
def test_displacement(self):
"""Test out my distance algorithm with hand calcs."""
lon = | pd.Series([0.0, 0.0, 0.0]) | pandas.Series |
import torch
import pandas as pd
import numpy as np
import time
import traceback
import torch.utils.data
from pathlib import Path
import os,sys
import cv2
import yaml
from imutils.paths import list_images
from tqdm import tqdm
import argparse
import albumentations as A
try:
import pretty_errors
pretty_errors.c... | pd.DataFrame(columns=('filename','pred','score')) | pandas.DataFrame |
import os
import urllib
import json
import time
import arrow
import numpy as np
import pandas as pd
from pymongo import MongoClient, UpdateOne
MONGO_URI = os.environ.get('MONGO_URI')
DARKSKY_KEY = os.environ.get('DARKSKY_KEY')
FARM_LIST = ['BLUFF1', 'CATHROCK', 'CLEMGPWF', 'HALLWF2', 'HDWF2',
'LKBONNY2... | pd.isnull(v_minus) | pandas.isnull |
import numpy as np
import pytest
import pandas as pd
from pandas import CategoricalIndex, Index
import pandas._testing as tm
class TestMap:
@pytest.mark.parametrize(
"data, categories",
[
(list("abcbca"), list("cab")),
(pd.interval_range(0, 3).repeat(3), pd.interval_range(... | pd.Series([False, False, False]) | pandas.Series |
'''
Created on May 16, 2018
@author: cef
significant scripts for calculating damage within the ABMRI framework
for secondary data loader scripts, see fdmg.datos.py
'''
#===============================================================================
# IMPORT STANDARD MODS ----------------------------------------... | pd.isnull(df2['tailpath']) | pandas.isnull |
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import scikitplot
from sklearn.metrics import classification_report
from sklearn.utils import class_weight
import argparse
from tensorflow.keras.models import Sequential
from tensorflow.keras.l... | pd.melt(df_loss) | pandas.melt |
import matplotlib
# Force matplotlib to not use any Xwindows backend.
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import datetime
import pandas as pd
import subprocess
import pydot
import numpy as np
from os.path import join
from sklearn.tree import export_graphviz
from io import StringIO
def plot_blocks(d... | pd.merge(smoothed_data, data, how='left', on="Datetime") | pandas.merge |
from atmPy.aerosols.instruments import POPS
import icarus
import pathlib
import numpy as np
import xarray as xr
import pandas as pd
from ipywidgets import widgets
from IPython.display import display
import matplotlib.pylab as plt
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
from nsasci import database... | pd.DataFrame(dffi, index=[path.name]) | pandas.DataFrame |
import unittest
import numpy as np
import pandas as pd
from numpy import testing as nptest
from operational_analysis.types import plant
from operational_analysis.methods import plant_analysis
from examples.operational_AEP_analysis.project_EIA import Project_EIA
class TestPandasPrufPlantAnalysis(unittest.TestCase):
... | pd.Series([0.017261, 0.006928, 0.024606]) | pandas.Series |
__author__ = 'heroico'
import os
import io
import json
import re
import logging
import gzip
from . import Exceptions
import pandas
import numpy
VALID_ALLELES = ["A", "T", "C", "G"]
def hapName(name):
return name + ".hap.gz"
def legendName(name):
return name + ".legend.gz"
def dosageName(name):
return n... | pandas.to_numeric(x, errors=to_numeric) | pandas.to_numeric |
# Copyright 2017 Google 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 writing, ... | assert_series_equal(actual, expected) | pandas.util.testing.assert_series_equal |
"""
Desafio 2
Escreva uma classe utilizando a linguagem python que faça a conexão com o banco de dados Postgres,
utilizando a biblioteca Pandas:
a) Criar uma tabela;
b) Inserir uma linha contendo uma coluna indexada, uma coluna texto, uma coluna numérica,
uma coluna boolena e uma coluna datetime;
c) Faça o versionament... | pd.DataFrame(registros, columns=['id', 'texto', 'numero', 'opcao', 'data']) | pandas.DataFrame |
import json
from collections import OrderedDict
from itertools import repeat
from pathlib import Path
import pandas as pd
import torch
ROOT_PATH = Path(__file__).absolute().resolve().parent.parent.parent
def ensure_dir(dir_name):
dir_name = Path(dir_name)
if not dir_name.is_dir():
dir_name.mkdir(par... | pd.DataFrame(index=keys, columns=["total", "counts", "average"]) | pandas.DataFrame |
import pandas as pd
import re
from datetime import date
import config as config
class MainInsert:
def __init__(self):
self.camp=config.Config.CAMP
self.festival=config.Config.FESTIVAL
self.tour=config.Config.TOUR
self.camp_details=config.Config.CAMP_DETAILS
self.sigungu=conf... | pd.concat([dataset, festival], 0) | pandas.concat |
#!/bin/python
# <NAME>
# last updated: 06 December 2021
# version 1.1.0
import os
import argparse
import pandas as pd
class CustomFormatter(argparse.ArgumentDefaultsHelpFormatter,
argparse.RawDescriptionHelpFormatter):
pass
desc = 'Clean your file up and add taxonomy info... | pd.merge(tax_df, csv_df, on=['Species_merged'], how='right') | pandas.merge |
import datetime
from datetime import timedelta
from distutils.version import LooseVersion
from io import BytesIO
import os
import re
from warnings import catch_warnings, simplefilter
import numpy as np
import pytest
from pandas.compat import is_platform_little_endian, is_platform_windows
import pandas.util._test_deco... | concat(results) | pandas.concat |
"""This module contains PlainFrame and PlainColumn tests.
"""
import collections
import datetime
import pytest
import numpy as np
import pandas as pd
from numpy.testing import assert_equal as np_assert_equal
from pywrangler.util.testing.plainframe import (
NULL,
ConverterFromPandas,
NaN,
PlainColumn... | types.is_object_dtype(df["str"]) | pandas.api.types.is_object_dtype |
# Copyright (c) 2020 Huawei Technologies Co., Ltd.
# <EMAIL>
#
# 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 a... | pd.DataFrame(data) | pandas.DataFrame |
# import cassandra requires pip3 install cassandra-driver
from cassandra.cluster import Cluster
import pandas as pd
def pandas_factory(colnames, rows):
return pd.DataFrame(rows, columns=colnames)
class CassandraWrapper:
def __init__(self):
self._client = Cluster(['localhost'], port=9042)
prin... | pd.DataFrame() | pandas.DataFrame |
import requests
import re
from bs4 import BeautifulSoup
import pandas as pd
import sys
import bs4 as bs
import urllib.request
import datetime
import os
today=datetime.date.today()
display_list = []
display_list1 = []
memory_list = []
processor_list = []
camera_list = []
battery_list = []
thickness_list ... | pd.DataFrame(records, columns = ['COUNTRY', 'COMPANY', 'MODEL', 'USP', 'DISPLAY', 'CAMERA', 'MEMORY', 'BATTERY', 'THICKNESS', 'PROCESSOR', 'EXTRAS/ LINKS']) | pandas.DataFrame |
import datetime
from datetime import timedelta
from distutils.version import LooseVersion
from io import BytesIO
import os
import re
from warnings import catch_warnings, simplefilter
import numpy as np
import pytest
from pandas.compat import is_platform_little_endian, is_platform_windows
import pandas.util._test_deco... | Term() | pandas.io.pytables.Term |
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 08 13:41:45 2018
@author: behzad
"""
import numpy as np
import pandas as pd
A1=np.array([2,5.2,1.8,5])
S1 = pd.Series([2,5.2,1.8,5],["a","b","c","d"])
S2 = | pd.Series([2,5.2,1.8,5],index= ["a","b","c","d"]) | pandas.Series |
"""
Tests encoding functionality during parsing
for all of the parsers defined in parsers.py
"""
from io import BytesIO
import os
import tempfile
import numpy as np
import pytest
from pandas import DataFrame
import pandas._testing as tm
def test_bytes_io_input(all_parsers):
encoding = "cp1255"
parser = all... | DataFrame({"a": [np.nan, 1]}) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 1 19:18:20 2019
@author: <NAME>
"""
from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint
from keras import optimizers
from keras.models import Model
from keras.callbacks import History
from keras.applications import vgg16, ... | pd.DataFrame(history.history) | pandas.DataFrame |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2021/7/8 22:08
Desc: 金十数据中心-经济指标-美国
https://datacenter.jin10.com/economic
"""
import json
import time
import pandas as pd
import demjson
import requests
from akshare.economic.cons import (
JS_USA_NON_FARM_URL,
JS_USA_UNEMPLOYMENT_RATE_URL,
JS_USA_EIA_... | pd.DataFrame(json_data["values"]) | pandas.DataFrame |
# Copyright Contributors to the Amundsen project.
# SPDX-License-Identifier: Apache-2.0
import csv
import logging
import os
import shutil
from csv import DictWriter
from typing import (
Any, Dict, FrozenSet,
)
from pyhocon import ConfigFactory, ConfigTree
from databuilder.job.base_job import Job
from databuilder... | pd.read_csv('s3://' + _s3_bucket_info + '/' + _node_s3_prefix + file_suffix+'.csv') | pandas.read_csv |
# BUG: Cannot calculate quantiles from Int64Dtype Series when results are floats #42626
import pandas as pd
print(pd.__version__)
result = | pd.Series([1, 2, 3], dtype="Int64") | pandas.Series |
import os.path
my_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
filepath = os.path.join(my_path, 'documents/Leadss.csv')
fpath = os.path.join(my_path, 'static/images/outliers')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pandas.tools.plotting import tab... | pd.read_csv(filepath) | pandas.read_csv |
"""
Game scraping functions - ties together the other scraping modules.
"""
import logging
import pandas as pd
from pandas import DataFrame
from hockeydata.constants import PBP_COLUMNS_ENHANCED
from hockeydata.scrape.json_schedule import get_date, get_schedule_game_ids
from hockeydata.scrape.players import get_player... | pd.concat(pbps) | pandas.concat |
import pandas as pd
def _reversion(bfq_data, xdxr_data, type_):
"""使用数据库数据进行复权"""
info = xdxr_data.query('category==1')
bfq_data = bfq_data.assign(if_trade=1)
if len(info) > 0:
# 有除权数据
data = pd.concat([bfq_data, info.loc[bfq_data.index[0]:bfq_data.index[-1], ['category']]], axis=1)
... | pd.concat([data, info.loc[bfq_data.index[0]:bfq_data.index[-1], ['fenhong', 'peigu', 'peigujia', 'songzhuangu']]], axis=1) | pandas.concat |
import os
import time
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.legend_handler import HandlerTuple
import datetime
import utils
import thesismain
MESSAGES_GENERATED = 'generated'
MESSAGES_PROCESSED = 'processed'
class Plot:
def __init__(self, config_name, network_nam... | pd.to_numeric(generated[node]) | pandas.to_numeric |
"""This module imports other modules to train the vgg16 model."""
from __future__ import print_function
from crop_resize_transform import model_data
from test import test
import matplotlib.pyplot as plt
import random
from scipy.io import loadmat
import numpy as np
import pandas as pd
import cv2 as cv
import glob
fr... | pd.read_csv('FLIC-full/test_joints.csv', header=None) | pandas.read_csv |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# *****************************************************************************/
# * Authors: <NAME>
# *****************************************************************************/
"""transformCSV.py
This module contains the basic functions for creating the content of... | pandas.StringDtype() | pandas.StringDtype |
from __future__ import annotations
import numpy as np
from numpy.linalg import lstsq
from numpy.random import RandomState, standard_normal
from numpy.testing import assert_allclose
from pandas import Categorical, DataFrame, date_range, get_dummies
from pandas.testing import assert_frame_equal, assert_series_equal
fro... | assert_series_equal(res1.pvalues.iloc[:n], res2.pvalues.iloc[:n]) | pandas.testing.assert_series_equal |
from __future__ import division
import pytest
import numpy as np
from datetime import timedelta
from pandas import (
Interval, IntervalIndex, Index, isna, notna, interval_range, Timestamp,
Timedelta, compat, date_range, timedelta_range, DateOffset)
from pandas.compat import lzip
from pandas.tseries.offsets imp... | IntervalIndex.from_intervals(index.values, copy=False) | pandas.IntervalIndex.from_intervals |
import skimage.feature
import skimage.transform
import skimage.filters
import scipy.interpolate
import scipy.ndimage
import scipy.spatial
import scipy.optimize
import numpy as np
import pandas
import plot
class ParticleFinder:
def __init__(self, image):
"""
Class for finding circular particles
... | pandas.DataFrame(columns=['r', 'y', 'x', 'dev']) | pandas.DataFrame |
# -*- coding:utf-8 -*-
"""
Binance API wrapper over Pandas lib.
"""
import inspect
import os
import sys
import time as tm
import warnings
from collections import Iterable
from functools import partial
import ccxt
import numpy as np
import pandas as pd
import requests as req
from ccxt.base import errors as apierr
from ... | pd.DataFrame([[r['p'], r['q'], r['f'], r['l'], r['T']] for r in raw], columns=cols) | pandas.DataFrame |
"""
This module contains functions for preparing data that was extracted from the FPLManagerBase API for the calculations to follow.
"""
import datetime as dt
import numpy as np
import pandas as pd
from typing import Dict
from .common import Context, POSITION_BY_TYPE, STATS_TYPES
import collections
# Define type alia... | pd.to_numeric(df['ICT Index']) | pandas.to_numeric |
from multiprocessing.sharedctypes import Value
from numpy import isin
import pandas as pd
import os, json, re, tempfile, logging, typing
from typing import Tuple
from jsonschema import Draft4Validator, ValidationError
from .. import db
from ..models import RawMetadataModel
from ..metadata.metadata_util import check_for... | pd.DataFrame(formatted_csv_data) | pandas.DataFrame |
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2022, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | pd.Index(['id1', 'id2', 'id3'], name='id') | pandas.Index |
import pandas as pd
import numpy as np
def build_items(master_red: pd.DataFrame, master_ubicaciones: pd.DataFrame, master_demanda, master_producto):
"""
Crea un df de items con 5 columnas donde se especifica tiempo, producto, nodo, tipo, y valor. Estamos
ignorando material importado, ya que toca hacer cam... | pd.concat([cond1, cond2, cond3, cond4, cond5, cond6], ignore_index=True) | pandas.concat |
"""
Class Features
Name: driver_data_io_source
Author(s): <NAME> (<EMAIL>)
Date: '20200515'
Version: '1.0.0'
"""
######################################################################################
# Library
import logging
import os
import numpy as np
import pandas as pd
import glob
fro... | pd.DataFrame(index=file_time_discharge) | pandas.DataFrame |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
#-------------read csv---------------------
df_2010_2011 = pd.read_csv("/mnt/nadavrap-students/STS/data/data_Shapira_20200911_2010_2011.csv")
df_2012_2013 = pd.read_csv("/mnt/nadavrap-students/STS/data/data_Shapira_20200911_2012_2013.csv")
df_2014_... | pd.merge(df3, df2014, on='surgid') | pandas.merge |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 4 07:59:39 2021
@author: suriyaprakashjambunathan
"""
#Regressors
from sklearn.ensemble.forest import RandomForestRegressor
from sklearn.ensemble.forest import ExtraTreesRegressor
from sklearn.ensemble.bagging import BaggingRegressor
from sklearn.... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
import logging
import warnings
import os
import pandas_datareader as pdr
from collections import Counter
from scipy import stats
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_percentage... | pd.Series([1 if (x >= start) & (x <= end) else 0 for x in self.current_dates]) | pandas.Series |
import calendar
import datetime
import numpy as np
import pandas as pd
from pandas.util.testing import (assert_frame_equal, assert_series_equal,
assert_index_equal)
from numpy.testing import assert_allclose
import pytest
from pvlib.location import Location
from pvlib import solarposi... | assert_frame_equal(expected_solpos, ephem_data[expected_solpos.columns]) | pandas.util.testing.assert_frame_equal |
# Copyright 1999-2018 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | pd.DataFrame(count) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Authors: <NAME>, <NAME>, <NAME>, and
<NAME>
IHE Delft 2017
Contact: <EMAIL>
Repository: https://github.com/gespinoza/hants
Module: hants
"""
from __future__ import division
import netCDF4
import pandas as pd
import math
from .davgis.functions import (Spatial_Reference, Lis... | pd.np.sum(p == 0) | pandas.np.sum |
import pandas as pd
import numpy as np
from collections import Counter
test = | pd.read_csv('./robust_log_test.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 12 11:00:56 2017
@author: 028375
"""
import pandas as pd
import numpy as np
begindate='20171001'
spotdate='20171018'
lastdate='20171017'
path0='F:\月结表\境内TRS\S201710\\'.decode('utf-8')
def TestTemplate(Status,Collateral,Position):
path1=('股衍境内TRS检验... | pd.ExcelWriter(path0+path1) | pandas.ExcelWriter |
# Ab initio Elasticity and Thermodynamics of Minerals
#
# Version 2.5.0 27/10/2021
#
# Comment the following three lines to produce the documentation
# with readthedocs
# from IPython import get_ipython
# get_ipython().magic('cls')
# get_ipython().magic('reset -sf')
import datetime
import os
import sys
import... | pd.DataFrame(exp_serie,\
index=['Temp','Cp exp','Cp calc','Del Cp','S exp','S calc','Del S']) | pandas.DataFrame |
#%%
# Our numerical workhorses
import numpy as np
import pandas as pd
import itertools
# Import libraries to parallelize processes
from joblib import Parallel, delayed
# Import matplotlib stuff for plotting
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib as mpl
# Seaborn, useful for grap... | pd.DataFrame(ccaps, columns=names) | pandas.DataFrame |
import sys
import time
import math
import warnings
import numpy as np
import pandas as pd
from os import path
sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
from fmlc.triggering import triggering
from fmlc.baseclasses import eFMU
from fmlc.stackedclasses import controller_stack
class testcontroll... | pd.isna(df3['b'][0]) | pandas.isna |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import json
import time
import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import datetime as dt
from numpy import newaxis
from keras.layers import Dense, Activation, Dropout, LSTM
from keras.models import Sequential, load_model
from kera... | pd.read_csv("/Users/william/Downloads/DP-LSTM-Differential-Privacy-inspired-LSTM-for-Stock-Prediction-Using-Financial-News-master/data/source_price_noise0.csv",index_col=0) | pandas.read_csv |
# importing libraries
import pandas as pd
import numpy as np
import cv2
from constants import *
from sklearn.model_selection import train_test_split
from keras.models import Sequential,load_model
from keras.layers import Convolution2D,MaxPooling2D,Flatten,Dense
from keras.callbacks import ModelCheckpoint
... | pd.get_dummies(dataset['emotion']) | pandas.get_dummies |
__author__ = "<NAME>, <NAME>"
__copyright__ = "Copyright 2018, University of Technology Graz"
__credits__ = ["<NAME>", "<NAME>"]
__license__ = "MIT"
__version__ = "1.0.0"
__maintainer__ = "<NAME>, <NAME>"
import pandas as pd
def time_delta_table(date_time_index, timedelta=pd.Timedelta(minutes=1), monotonic=False):
... | pd.Timedelta(minutes=0) | pandas.Timedelta |
import pandas as pd
import numpy as np
import os
import cvxpy as cvx
from matplotlib import pyplot as plt
from datetime import datetime
from column_names import ColumnNames
ENCODING = 'iso-8859-8'
# FOLDER_PATH = r'C:\Users\Asus\Google Drive\Votes Migration 2020'
FOLDER_PATH = 'data files'
KNESSET_SIZ... | pd.read_csv(file_path, encoding=ENCODING) | pandas.read_csv |
from itertools import chain
import operator
import numpy as np
import pytest
from pandas.core.dtypes.common import is_number
from pandas import (
DataFrame,
Index,
Series,
)
import pandas._testing as tm
from pandas.core.groupby.base import maybe_normalize_deprecated_kernels
from pandas.tests.apply.common... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.