prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pandas as pd
from neuralprophet import NeuralProphet, set_random_seed
from src.demand_prediction.events_models import save_events_model, load_events_model
from src.config import SEED
def NeuralProphetEvents(future_events, past_events, events_name, train, test, leaf_name, model_name,
sta... | pd.DataFrame(test_df, index=test_df.index, columns=['Quantity']) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 22 10:16:42 2021
@author: tungbioinfo
"""
import argparse
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm
import time
from sklearn.model_selection import train_test_split
from skle... | pd.DataFrame(all_F) | pandas.DataFrame |
import pandas as pd
import numpy as np
import yfinance as yf
from pandas import Series
from prettytable import PrettyTable
from Common.Readers.Engine.AbstractEngine import AbstractEngine
from Common.StockType.AbstractStock import AbstractStock
from Common.StockType.Equities.AbstractStockEquity import AbstractStockEquit... | pd.DataFrame() | pandas.DataFrame |
# Inspired by https://www.quantopian.com/posts/grahamfundmantals-algo-simple-screening-on-benjamin-graham-number-fundamentals
# Trading Strategy using Fundamental Data
# 1. Filter the top 50 companies by market cap
# 2. Find the top two sectors that have the highest average PE ratio
# 3. Every month exit al... | pd.DataFrame.from_dict(fundamentals) | pandas.DataFrame.from_dict |
import argparse
import os
import sys
from collections import defaultdict
import pandas as pd
from maggot import Experiment
from maggot.containers import NestedContainer
from maggot.utils import bold, green, red, blue
| pd.set_option("display.max_colwidth", 500) | pandas.set_option |
import os as os
from lib import ReadCsv
from lib import ReadConfig
from lib import ReadData
from lib import NetworkModel
from lib import ModelMetrics
from lib import SeriesPlot
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from lib import modwt
import keras
from datetime import date,datetime... | pd.concat([subset, datesDf], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 8 22:46:34 2019
@author: Samuel
"""
import pandas as pd
import numpy as np
import pickle
import six
import warnings
from itertools import cycle
from collections import OrderedDict
from scipy.sparse import csr_matrix
from sklearn.base import BaseEstimator, TransformerMixi... | pd.DataFrame([[1,2,3,'a',2.0],[1,np.nan,4,'6',3.0],[2,3,4,5,6],
[2.0,3,4,5,np.nan]],columns=['x','y','z','j','k']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 3 13:35:46 2022
@author: user
"""
import os
import numpy as np
import sklearn
from sklearn import metrics
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.ticker as ticker
from mpl_toolkits.axisartist.parasite_axes import SubplotHost
fr... | pd.DataFrame(morph_dist_calyx_r_new) | pandas.DataFrame |
from datetime import datetime
import operator
import numpy as np
import pytest
from pandas import DataFrame, Index, Series, bdate_range
import pandas._testing as tm
from pandas.core import ops
class TestSeriesLogicalOps:
@pytest.mark.parametrize("bool_op", [operator.and_, operator.or_, operator.xor])
def te... | Index(right) | pandas.Index |
import sys
import os
import torch
import numpy as np
import torch_geometric.datasets
import pyximport
from torch_geometric.data import InMemoryDataset, download_url
import pandas as pd
from sklearn import preprocessing
pyximport.install(setup_args={'include_dirs': np.get_include()})
import os.path as osp
from torch_geo... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
from numpy import float64, ceil
from statsmodels.compat.pandas import assert_series_equal, assert_frame_equal
from models.Trading import TechnicalAnalysis
def test_should_calculate_addChangePct():
"""
Adds the close percentage to the DataFrame : close_pc
Adds the cumulative returns... | pd.to_datetime(df['date'], format="%Y-%d-%m %H:%M:%S") | pandas.to_datetime |
import datetime
import glob
import json
import multiprocessing
import os
import pickle
import sys, re
import warnings
from collections import Counter, defaultdict
from itertools import cycle
from string import digits
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from gensim.models import Keyed... | pd.read_json(fact_file) | pandas.read_json |
import sys, os
sys.path.append('./src/common/image_processor/feature_extractor')
import cv2
import numpy as np
import pandas as pd
from operator import itemgetter
from collections import defaultdict
from feature_extractor_utils import (show_image,
smooth_contour,
... | pd.DataFrame(dict_results) | pandas.DataFrame |
import pandas as pd
import numpy as np
import statsmodels.api as sm
from numpy import NaN
import seaborn as sns
#from sklearn.linear_model import LinearRegression
df = pd.read_csv("C:/Users/LIUM3478/OneDrive Corp/OneDrive - Atkins Ltd/Work_Atkins/Docker/hjulanalys/wheel_prediction_data.csv", encoding = 'ISO 8859-1', ... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import ray.tune
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning.utilities.cloud_io import load as pl_load
import json
import pytorch_lightning as pl
import pandas as pd
import sklearn
from ray import tune
import numpy as np
import seaborn
import matplotlib.pyplot as pl... | pd.DataFrame(cm, index=[0, 1], columns=[0, 1]) | pandas.DataFrame |
import pandas as pd
import numpy as np
import os
from datetime import datetime
import plotly.graph_objects as go
import time
def timer():
return '['+datetime.now().strftime("%d/%m/%Y %H:%M:%S")+']'
#Cases where the client does not have access to the links Cargo
try:
from confidential.secrets import url_cargo,... | pd.to_datetime(elem[-10:-4], format='%d%m%y') | pandas.to_datetime |
# =======================================
# PACKAGE IMPORTS
# =======================================
# python built-in packages
import bisect
import os
# 3rd party packages
import h5py
import numpy as np
import pandas as pd
# local
from custom_errors import (
MissingFilesException,
CorruptHDF5Exception,
... | pd.read_csv(csv_path) | pandas.read_csv |
import pandas as pd
import numpy as np
import math
import os
from scipy.interpolate import interp1d
import time
from sklearn.ensemble import RandomForestRegressor
import xgboost as xgb
from lightgbm import LGBMRegressor
from catboost import CatBoostRegressor
from information_measures import *
from joblib import Para... | pd.concat(list_trades1) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 22 12:05:22 2017
@author: rgryan
"""
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import glob
import datetime
import os
from decimal import Decimal
#from os import path
sh = True # Plotting the scale height info?
zc = False ... | pd.DataFrame() | pandas.DataFrame |
"""
Functions for writing to .csv
September 2020
Written by <NAME>
"""
import os
import pandas as pd
import datetime
def define_deciles(regions):
"""
Allocate deciles to regions.
"""
regions = regions.sort_values(by='population_km2', ascending=True)
regions['decile'] = regions.groupby([
... | pd.DataFrame(regional_results) | pandas.DataFrame |
import sys
sys.path.insert(0, "../")
import xalpha as xa
from xalpha.exceptions import FundTypeError
import pandas as pd
import pytest
ioconf = {"save": True, "fetch": True, "path": "pytest", "form": "csv"}
ca = xa.cashinfo(interest=0.0002, start="2015-01-01")
zzhb = xa.indexinfo("0000827", **ioconf)
hs300 = xa.fundi... | pd.Timestamp("2011-01-03") | pandas.Timestamp |
"""Tools for generating and forecasting with ensembles of models."""
import datetime
import numpy as np
import pandas as pd
import json
from autots.models.base import PredictionObject
from autots.models.model_list import no_shared
from autots.tools.impute import fill_median
horizontal_aliases = ['horizontal', 'probab... | pd.Series() | pandas.Series |
import numpy as np
import pandas as pd
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow as tf
import tensorflow.keras.backend as K
from tensorflow.keras.models import Model
from tensorflow.keras.losses import Loss
from sklearn import preprocessing
import matplotlib.pyplot as plt
impo... | pd.read_feather(path) | pandas.read_feather |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 5 21:12:17 2020
@author: sergiomarconi
"""
import numpy as np
import pandas as pd
import pickle
from sklearn.ensemble import RandomForestClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from sklearn.preproce... | pd.DataFrame(predict_an) | pandas.DataFrame |
import pandas as pd
import sys
import os
from functools import reduce
from utils.misc_utils import pandas_to_db
class TemporalFeatureFactory(object):
def __init__(self, time_granularity, start_date, end_date):
'''
Level of Aggregation in space depends on the mapping table
... | pd.date_range(start_date, end_date, freq=time_granularity) | pandas.date_range |
from src.BandC.Parser import Parser
import arff
import pandas as pd
from pandas.core.frame import DataFrame
class Arff(Parser):
"""
An Arff Parser that can automatically detect the correct format.
"""
def parse_file(self):
column_names = [attribute[0] for attribute in self.attributes]
... | pd.DataFrame.from_records(self.data, columns=column_names) | pandas.DataFrame.from_records |
#!/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 LIMBR import simulations
import pandas as pd
sims = {}
for i in range(1,21):
analysis = simulations.analyze('standard_' + str(i) + '_true_classes.txt')
analysis.add_data('standard_' + str(i) + '_LIMBR_processed__jtkout_GammaP.txt','LIMBR')
analysis.add_data('standard_' + str(i) + '_old_processed__jtk... | pd.concat([data, temp_data]) | pandas.concat |
#!/usr/bin/env python
import json
import os
import pandas as pd
from pandas import Series
try:
import requests
except ImportError:
requests = None
from . import find_pmag_dir
from . import data_model3 as data_model
from pmag_env import set_env
pmag_dir = find_pmag_dir.get_pmag_dir()
data_model_dir = os.path.j... | pd.concat([big_series, little_series]) | pandas.concat |
import pytest
from constants import (
HISTONE_QC_FIELDS,
HISTONE_PEAK_FILES_QUERY,
EXPERIMENT_FIELDS_QUERY,
LIMIT_ALL_JSON,
REPORT_TYPES,
REPORT_TYPE_DETAILS
)
from general_qc_report import (
parse_json,
make_url,
get_data,
get_experiments_and_files,
build_rows_from_experimen... | assert_frame_equal(test_rna_mapping_df, df) | pandas.util.testing.assert_frame_equal |
from os import listdir, sep
from os.path import isfile, join
import re
from bs4 import BeautifulSoup
# from DbManager import DatabaseManager
import json
from selenium import webdriver
# from SoccerMatch import SoccerMatch
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.by import By
... | pd.read_excel('./data/info/v_teams.xlsx') | pandas.read_excel |
"""Linear electric grid models module."""
from multimethod import multimethod
import numpy as np
import pandas as pd
import pyomo.core
import pyomo.environ as pyo
import scipy.sparse
import scipy.sparse.linalg
import fledge.config
import fledge.electric_grid_models
import fledge.power_flow_solvers
import fledge.utils... | pd.DataFrame(columns=self.electric_grid_model.nodes, index=timesteps, dtype=np.float) | pandas.DataFrame |
from __future__ import division
import numpy as np
import os.path
import sys
import pandas as pd
from base.uber_model import UberModel, ModelSharedInputs
from .therps_functions import TherpsFunctions
import time
from functools import wraps
def timefn(fn):
@wraps(fn)
def measure_time(*args, **kwargs):
... | pd.Series([], dtype="float", name="noael_bird") | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 2 21:01:59 2019
@author: innerm
"""
import json
import pandas as pd
file_en='stage41.csv'
file_ru='stage42.csv'
df=pd.DataFrame()
df1=pd.read_csv(file_en)
df2= | pd.read_csv(file_ru) | pandas.read_csv |
import pandas as pd
from fbprophet import Prophet
import matplotlib.pyplot as plt
from covid_data import country_list, feature_list, PANDAMIC_FORCAST_DIR_PATH, VACCINATION_WITH_PANDEMIC, DATA_FOR_LSP_PATH
def get_calculated_data(country: str, col_name: str):
filename = country + '.csv'
file_pandemic_bef_vacc... | pd.read_csv(vacc_with_pan_file) | pandas.read_csv |
import logging as log
from datetime import datetime as dt
from multiprocessing import Pool
import gym
import numpy as np
import pandas as pd
from numpy.random import RandomState
class RandomPolicy(object):
def __init__(self, possible_actions=range(6), random_seed=0):
"""
A policy that will take ... | pd.DataFrame(index=seeds, data=df_data) | pandas.DataFrame |
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
import sklearn as sk
import matplotlib.pyplot as plt
import gc
train = pd.read_csv("train.csv",parse_dates=["activation_date"])
test = pd.read_csv("test.csv",parse_dates=["activation_date"])
y_psudo_labels = train["deal_probability"] > 0
ytrain = tra... | pd.read_csv("lda_features.csv") | pandas.read_csv |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sbn
from datetime import date
import scipy.stats as stats
import math
from clean import clean_df
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier
from sklearn.linear_model import L... | pd.read_csv('~/Downloads/2018 DA Take Home Challenge/listings.csv') | pandas.read_csv |
import pyproj
import numpy as np
import pandas as pd
def find_closest_node(G, point):
"""find the closest node on the graph from a given point"""
distance = np.full((len(G.nodes)), fill_value=np.nan)
for ii, n in enumerate(G.nodes):
distance[ii] = point.distance(G.nodes[n]['geometry'])
name_n... | pd.DataFrame.from_dict(self.energy_use) | pandas.DataFrame.from_dict |
import torch
import numpy as np
import pandas as pd
import os
import sys
from torchsummary import summary
import torch.nn as nn
from collections import defaultdict
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
matplotlib.style.use('ggplot')
from matplotlib import cm
import seaborn as sns
sns.s... | pd.DataFrame() | pandas.DataFrame |
import asyncio
import datetime
import logging
from typing import List, Tuple, Union
import pandas as pd
import pytest
import core.signal_processing as csigproc
import helpers.hasyncio as hasynci
import helpers.hdbg as hdbg
import helpers.hunit_test as hunitest
import market_data as mdata
import oms.oms_db as oomsdb
i... | pd.Timestamp("2000-01-01 09:50:00-05:00", tz="America/New_York") | pandas.Timestamp |
# -*- coding: utf-8 -*-
"""Generator capacity factor plots .
This module contain methods that are related to the capacity factor
of generators and average output plots
"""
import logging
import numpy as np
import pandas as pd
import marmot.config.mconfig as mconfig
from marmot.plottingmodules.plotutils.plot_data_h... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 9 11:38:55 2019
@author: <NAME>
"""
import numpy as np
import rdkit
from rdkit import Chem
from rdkit.Chem import Descriptors
import pandas as pd
def generate(smiles, verbose=False):
moldata= []
for elem in smiles:
mol=Chem.MolF... | pd.DataFrame(data=baseData,columns=columnNames) | pandas.DataFrame |
from strategy.rebalance import get_relative_to_expiry_rebalance_dates, \
get_fixed_frequency_rebalance_dates, \
get_relative_to_expiry_instrument_weights
from strategy.calendar import get_mtm_dates
import pandas as pd
import pytest
from pandas.util.testing import assert_index_equal, assert_frame_equal
def ass... | pd.Timestamp("2015-03-18") | pandas.Timestamp |
import calendar
from ..utils import search_quote
from datetime import datetime, timedelta
from ..utils import process_dataframe_and_series
import rich
from jsonpath import jsonpath
from retry import retry
import pandas as pd
import requests
import multitasking
import signal
from tqdm import tqdm
from typing import (Dic... | meric(df['股票权重'], errors='coerce') | pandas.to_numeric |
# Generate content tables
# Run from the root of the repo:
# python3 vanda_jobs/scripts/content-table-generations.py -i objects -j ./GITIGNORE_DATA/elastic_export/objects/custom -g -o ./GITIGNORE_DATA/hc_import/content
# python3 vanda_jobs/scripts/content-table-generations.py -i persons -j ./GITIGNORE_DATA/elastic_expo... | pd.read_json(data_path, lines=True, nrows=max_records) | pandas.read_json |
import os
import pandas as pd
import json
import cv2
def CSV_300W_LP(data_dir):
folders = [folder for folder in os.listdir(data_dir) if os.path.isdir(os.path.join(data_dir, folder))]
images = []
for idx, folder in enumerate(folders):
folder_path = os.path.join(data_dir, folder)
folder_ima... | pd.DataFrame(images) | pandas.DataFrame |
from datetime import datetime
import numpy as np
import pytest
from pandas.core.dtypes.cast import find_common_type, is_dtype_equal
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series
import pandas._testing as tm
class TestDataFrameCombineFirst:
def test_combine_first_mixed(self):
... | Series(["a", "b", "c", "f"], index=idx) | pandas.Series |
import glob
import itertools
import json
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
from class_tree import EmbeddedClassTree
from dataset import EmbeddedDataset
from dataset_descriptor import DatasetDescriptor
from embedding import Embedding
from utils import get_times... | pd.read_csv(most_recent) | pandas.read_csv |
# -*- coding: utf-8 -*-
from warnings import catch_warnings
import numpy as np
from datetime import datetime
from pandas.util import testing as tm
import pandas as pd
from pandas.core import config as cf
from pandas.compat import u
from pandas._libs.tslib import iNaT
from pandas import (NaT, Float64Index, Series,
... | isnull(values) | pandas.core.dtypes.missing.isnull |
from skbio import read
import os
import numpy as np
from typing import Dict
from collections import defaultdict
import pandas as pd
import matplotlib.pyplot as plt
from pysam import AlignmentFile, VariantFile
from tqdm import tqdm
from covid_bronx.quality import sam_files, fasta_files, variant_files
import skbio
def c... | pd.DataFrame(vardf_all) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# Copyright (c) 2018-2020 <NAME>
"""Module implementing WorkerThread."""
import logging
import os
from typing import Optional
import pandas as pd
from PyQt5.QtCore import QCoreApplication, QThread
from easyp2p.excel_writer import write_results
from easyp2p.p2p_credentials import get_credent... | pd.DataFrame() | pandas.DataFrame |
import asyncio
import json
from PoEQuery.official_api_result import OfficialApiResult
from PoEQuery.official_api import search_and_fetch_async
from PoEQuery.official_api_query import StatFilters, OfficialApiQuery
from PoEQuery.affix_finder import find_affixes
from tqdm import tqdm
def estimate_price_in_chaos(price):
... | DataFrame() | pandas.DataFrame |
import os
import glob
import psycopg2
import pandas as pd
from sql_queries import *
#load data from a song_data to song, artist tbls
def process_song_file(cur, conn, filepath):
all_files=[]
for root, dirs, files in os.walk(filepath):
files = glob.glob(os.path.join(root, '*.json'))
for f in file... | pd.to_datetime(df['ts']) | pandas.to_datetime |
from datetime import datetime, timedelta
import dateutil
import numpy as np
import pytest
import pytz
from pandas._libs.tslibs.ccalendar import DAYS, MONTHS
from pandas._libs.tslibs.period import IncompatibleFrequency
from pandas.compat import lrange, range, zip
import pandas as pd
from pandas import DataFrame, Seri... | assert_series_equal(result, expected) | pandas.util.testing.assert_series_equal |
from pyqmc.accumulators import SqAccumulator
from pyqmc.coord import PeriodicConfigs
import numpy as np
import pandas as pd
def test_config():
a = 1
Lvecs = np.eye(3) * a
configs = np.array(
[
[-0.1592848, -0.15798219, 0.04790482],
[0.03967904, 0.50691437, 0.40398405],
... | pd.DataFrame(df) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from io import StringIO
import pandas as pd
import numpy as np
import operator
import math
import os
from .config import max_filesize
from .FlajoletMartin import FMEstimator
"""
dfsummarizer.funcs: Core functions of the dfsummarizer package.
analyse_df( pandas_dataframe): return ... | pd.read_csv(path_to_file, encoding='latin1', sep='\t', low_memory=False) | pandas.read_csv |
import pandas as pd
from plotly import graph_objects as go
from plotly.subplots import make_subplots
import os
import plotly
import re
benchmarks = ['Celecoxib rediscovery', 'Troglitazone rediscovery', 'Thiothixene rediscovery',
'Aripiprazole similarity', 'Albuterol similarity', 'Mestranol similarity', ... | pd.concat([df, current_df], axis=0) | pandas.concat |
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 = [
... | pd.date_range('2014-01-01', '2014-01-05', tz='US/Central') | pandas.date_range |
import math
import numpy as np
import pandas as pd
from scipy import sparse
from tqdm import tqdm
def convert_List_to_Dict(adjList):
"""
Convert adjacency list in the form:
[(source, target, time), (source, target time), ...]
to an adjacency dictionary, with timestamps as keys:
{ t: (source, t... | pd.to_datetime(t2) | pandas.to_datetime |
# Modifications and additions to code written by brooksandrew
import osmnx as ox
import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt
import gpxpy
from collections import Counter
def circuit_path_string_to_int(circuit_rpp):
"""
Converts nodes in path lists from strings to integers
Arg... | pd.DataFrame(rpplist) | pandas.DataFrame |
from pathlib import Path
import pandas as pd
import numpy as np
from matplotlib.font_manager import FontProperties
import os, sys, inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
grandpadir = os.path.dirname(os.path.dirname(currentdir))
sys.path.insert(0, grandpadir)
from ... | pd.DataFrame() | pandas.DataFrame |
#%%
# Import everything we need
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
from datetime import datetime
from datetime import datetime as dt
from sklearn.model_selection import cross_val_score, TimeSeriesSplit, RandomizedSearchCV, GridSearchC... | pd.to_datetime(data_GT['date_time'], format='%Y-%m-%d') | pandas.to_datetime |
import time
import queue
import pandas as pd
import numpy as np
from ..utils import skills_util
from ..inferencing.multi_thread_inference import InferenceThread
def inference(
conversation,
workspace_id,
test_data,
max_retries=10,
max_thread=5,
verbose=False,
user_id="256",
):
"""
... | pd.DataFrame(data=wrongs) | pandas.DataFrame |
import cairo
import pycha.pie
import pandas
from datos import data
d=data('mtcars')
ps = | pandas.Series([i for i in d.cyl]) | pandas.Series |
#
# Copyright 2015 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 wr... | pd.Timestamp("2015-06-05", tz="UTC") | pandas.Timestamp |
# ___________________________________________________________________________
#
# Prescient
# Copyright 2020 National Technology & Engineering Solutions of Sandia, LLC
# (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S.
# Government retains certain rights in this software.
# This software is ... | pd.date_range(start_date, end_date, freq='H') | pandas.date_range |
# -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function, division
import csv, json, shutil
import numpy as np
import pandas as pd
from flask import Flask, request, jsonify
from flask_cors import CORS
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa.arima_model import ARIMAResu... | pd.to_numeric(combined_fish_cpi["CPI"]) | pandas.to_numeric |
from mock import patch, MagicMock
import six
import cufflinks.datagen as cfdg
import pandas as pd
from datetime import datetime, timedelta
from sklearn.datasets import make_classification
from crowdsource.types.utils import _metric, checkAnswer, fetchDataset, answerPrototype
from crowdsource.persistence.models import ... | pd.DataFrame([1]) | pandas.DataFrame |
import datetime as dt
import os
import unittest
import numpy as np
import pandas as pd
import devicely
class SpacelabsTestCase(unittest.TestCase):
READ_PATH = "tests/SpaceLabs_test_data/spacelabs.abp"
WRITE_PATH = "tests/SpaceLabs_test_data/spacelabs_written.abp"
def __init__(self, *args, **kwargs):
... | pd.to_datetime("1.1.99 17:05:00") | pandas.to_datetime |
import pandas as pd
import argparse
import numpy as np
import seaborn as sns
import matplotlib.pylab as plt
titles = ["Evaluation on all documents", "Evaluation on tweets only"]
def visualize_days(path, count):
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 3))
for enum, algo in enumerate(["louvain_macro_t... | pd.concat([res, results]) | pandas.concat |
import argparse
import shelve
import dbm
import os.path
import pandas as pd
import tikzplotlib
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.ticker import FuncFormatter
import pprint
from datetime import timedelta
from pathlib import Path
import numpy as ... | pd.read_csv(path_ + runs["ps50"] + "metrics.csv") | pandas.read_csv |
from scipy.sparse import csc_matrix
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
class Dispersion(object):
def __init__(self, corpus=None, term_doc_mat=None):
"""
From https://www.researchgate.net/publication/332120488_Analyzing_dispersion
<NAME>. ... | pd.DataFrame(df_content, index=terms) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 6 11:33:59 2017
解析天软数据格式
@author: ws
"""
import pandas as pd
_max_iter_stocks = 100
def _int2date(int_date):
if int_date < 10000000:
return pd.NaT
return | pd.datetime(int_date//10000, int_date%10000//100, int_date%100) | pandas.datetime |
# ActivitySim
# See full license in LICENSE.txt.
import logging
import pandas as pd
import numpy as np
from activitysim.core import assign
from activitysim.core import tracing
from activitysim.core import config
from activitysim.core import inject
from activitysim.core import pipeline
from activitysim.core import mem... | pd.concat([od_df[trace_od_rows], trace_results], axis=1) | pandas.concat |
#!/usr/bin/env python3
import os
import sys
import numpy as np
import pandas as pd
np.set_printoptions(edgeitems=3)
np.core.arrayprint._line_width = 80
fname = "res/output_360_merged_2.50.vcf.gz_summary.bin"
# fname = "res/output_360_merged_2.50.vcf.gz_chromosomes.bin"
def readIbrowserBinary(infile):... | pd.DataFrame({reg['serial']: reg['data']}, copy=False) | pandas.DataFrame |
'''
This code will clean the OB datasets and combine all the cleaned data into one
Dataset name: O-21-<NAME>
1. this dataset was first cleaned in excel
2. but the character symbol '-' was found in all the datasets
3. this code will replace all the symbols with -999
'''
import os
import glob
import string
import date... | pd.read_csv(data_path + 'Window_Status.csv') | pandas.read_csv |
import pandas as pd
from business_rules.operators import (DataframeType, StringType,
NumericType, BooleanType, SelectType,
SelectMultipleType, GenericType)
from . import TestCase
from decimal import Decimal
import sys
import pandas
class Str... | pandas.Series([False, False, False, False]) | pandas.Series |
""" test parquet compat """
import datetime
from distutils.version import LooseVersion
import os
from warnings import catch_warnings
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
import pandas._testing as tm
from pandas.io.parquet import (
FastParquetImpl,
Py... | pd.DataFrame({"A": [1, 2, 3]}) | pandas.DataFrame |
#!/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 |
import pandas as pd
import io
import requests
from datetime import datetime
#Import data file if it already exists
try:
past_data = pd.read_excel("Utah_Data.xlsx")
past_dates = past_data["Date"].tolist()
except:
past_data = pd.DataFrame({})
past_dates = []
#Get today's date and then generate a list of dates start... | pd.DataFrame(full_csv.loc["Utah, Utah, US"]) | pandas.DataFrame |
import pandas as pd
from pathlib import Path
from utils.aioLogger import aioLogger
from typing import List
from config.aioConfig import CESDataConfig
from utils.aioError import aioPreprocessError
import re
import matplotlib.pyplot as plt
class CESCsvReader:
"""read data from csv file df, save it in #* ... | pd.DataFrame(data=new_ds) | pandas.DataFrame |
import pandas as pd
import lightgbm as lgb
from sklearn.model_selection import KFold,StratifiedKFold
import warnings
import gc
import time
import sys
import datetime
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error,roc_auc_score,roc_curve
import seaborn,numpy as np
warnings.simplefilter(ac... | pd.concat([features_importance_df,fold_importance_df],axis=0) | pandas.concat |
import pandas as pd
import datetime
import dateutil.parser
import Utils
#
# given a synthea object, covert it to it's equivalent omop objects
#
class SyntheaToOmop6:
#
# Check the model matches
#
def __init__(self, model_schema, utils):
self.model_schema = model_schema
self.utils = utils
... | pd.merge(df, personmap, left_on='PATIENT', right_on='synthea_patient_id', how='left') | pandas.merge |
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 2 10:02:48 2020
@author: Matteo
"""
import numpy as np
import matplotlib.pyplot as plt
import math
from pynverse import inversefunc
from IPython import get_ipython
get_ipython().magic('reset -sf')
import pandas as pd
from scipy.optimize import leastsq, lea... | pd.Series(self.tau) | pandas.Series |
import bz2
import numpy as np
import pandas as pd
import pickle
import requests
import re
import os
import shutil
import tarfile
from zipfile import ZipFile
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.datasets import load_iris, load_digits, load_svmlight_file
from sklearn.datasets import f... | pd.DataFrame(vals) | pandas.DataFrame |
from __future__ import division #brings in Python 3.0 mixed type calculation rules
import datetime
import inspect
import numpy as np
import numpy.testing as npt
import os.path
import pandas as pd
import sys
from tabulate import tabulate
import unittest
print("Python version: " + sys.version)
print("Numpy version: " +... | pd.Series([1015., 1020., 1030.], dtype='float') | pandas.Series |
# Copyright 2022 Google LLC
#
# 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, ... | pd.concat([clean_csv, df], ignore_index=True) | pandas.concat |
import os
import gc
import sys
import time
import click
import random
import sklearn
import numpy as np
import pandas as pd
import lightgbm as lgb
from tqdm import tqdm
from pprint import pprint
from functools import reduce
from lightgbm import LGBMClassifier
from sklearn.metrics import roc_auc_score, roc_curve
from c... | pd.read_pickle(f"misc/null_imp_df_run{nb_runs}time.pkl") | pandas.read_pickle |
#Import the libraries
import pandas as pd
import numpy as np
import requests
import matplotlib.pyplot as plt
import yfinance as yf
import datetime
import math
from datetime import timedelta
from pypfopt.efficient_frontier import EfficientFrontier
from pypfopt import risk_models
from pypfopt import expected_returns
fr... | pd.DateOffset(years=1) | pandas.DateOffset |
# Machine Learning Project 1 - House Price Prediction
import pandas as pd
df1 = pd.read_csv('bengaluru_house_prices.csv')
df1.head()
df1.info()
df1.shape
df2 = df1.drop(['area_type', 'society', 'balcony'], axis=1)
df2.head()
df2.isnull().sum()
df3 = df2.dropna()
df3.isnull().sum()
df3.head()
df3['availabili... | pd.get_dummies(df8['availability'], drop_first=True) | pandas.get_dummies |
import glob
import os
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from matplotlib.lines import Line2D
def plot_metrics(names='default', logs='training', save_name='default', timesteps=1000000, ci='sd', rolling=10):
name = save_name
names_print = names
if lo... | pd.read_csv('./logs/mbo/' + name + '/iterations/front.csv', header=None) | pandas.read_csv |
from argparse import ArgumentParser
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from utils import resample
def get_volumes(trade, underlying):
strikes = trade.index.get_level_values('Strike')
timestamps = trade.index.get_level_values('Time')
underlying_a... | pd.read_parquet(args.underlying_filename) | pandas.read_parquet |
from typing import List, Tuple
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
import time
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
CLAS... | pd.DataFrame(scaled, columns=df.columns) | pandas.DataFrame |
from cbs import cbs
import pandas as pd
import pytest
#Get the CBS K dataframe
@pytest.fixture(scope="module")
def DEF():
return cbs.Cbs().parser('DEF')
def test_cbs_def_columns(DEF):
assert DEF.columns.tolist() == ['Name', 'def_int', 'def_saftey', 'def_sk', 'tackles', 'fum_rec',
'forced_fumbles', 'de... | pd.to_numeric(DEF.iloc[30].def_td, errors='ignore') | pandas.to_numeric |
# -*- coding: utf-8 -*-
"""
Script to play around with Dash
"""
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import numpy as np
import datetime as dt
import plotly.offline as pyo
import plotly.graph_objs as go
#PLOTLY
#import data
df = pd.read_csv('../Data/w... | pd.to_datetime(df['datetime'], format='%d/%m/%Y') | pandas.to_datetime |
import re
import pandas as pd
import numpy as np
from gensim import corpora, models, similarities
from difflib import SequenceMatcher
from build_tfidf import split
def ratio(w1, w2):
'''
Calculate the matching ratio between 2 words.
Only account for word pairs with at least 90% similarity
'''
m = Sequence... | pd.DataFrame(trainData, columns=['qt', 'qd', 'qa', 'mt', 'md', 'ma', 'ql']) | pandas.DataFrame |
""" manage PyTables query interface via Expressions """
import ast
from functools import partial
import numpy as np
from pandas._libs.tslibs import Timedelta, Timestamp
from pandas.compat.chainmap import DeepChainMap
from pandas.core.dtypes.common import is_list_like
import pandas as pd
from pandas.core.base impor... | is_term(right) | pandas.core.computation.ops.is_term |
# Copyright (c) 2022 RWTH Aachen - Werkzeugmaschinenlabor (WZL)
# Contact: <NAME>, <EMAIL>
from sklearn.preprocessing import Normalizer,MinMaxScaler,MaxAbsScaler,StandardScaler,RobustScaler,QuantileTransformer,PowerTransformer
import pandas as pd
import os
from absl import logging
def scale(dataframe,method,scaler,co... | pd.DataFrame(dataframe_inverse_scaled,columns=dataframe_head) | pandas.DataFrame |
from pathlib import Path
import epimargin.plots as plt
import flat_table
import numpy as np
import pandas as pd
import seaborn as sns
from epimargin.estimators import analytical_MPVS
from epimargin.etl.commons import download_data
from epimargin.etl.covid19india import state_code_lookup
from epimargin.models import SI... | pd.read_csv(data/"india_pop.csv", names = ["state", "population"], index_col = "state") | pandas.read_csv |
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