prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import os
import random
from typing import Iterable, Dict, Any
import pandas
from IPython.display import display
from duorat.datasets.spider import SpiderItem, SpiderDataset
from duorat.asdl.lang.spider.spider import SpiderGrammar
from duorat.utils.evaluation import load_from_lines
def show_question(ex):
print(... | pandas.DataFrame(all_metadata) | pandas.DataFrame |
# coding: utf-8
# In[11]:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# In[12]:
#need to add city after finishing
df_city = pd.read_csv("cityresults.dat", header=None)
df_bayview = pd.read_csv("bayviewresults.dat", header=None)
df_ingleside = pd.read_csv("Ingleside_results.dat", he... | pd.read_csv("Central_results.dat", header=None) | pandas.read_csv |
import os
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from .. import read_sql
@pytest.fixture(scope="module") # type: ignore
def mssql_url() -> str:
conn = os.environ["MSSQL_URL"]
return conn
@pytest.mark.xfail
def test_on_non_select(mssql_url: str) -> None:
query ... | assert_frame_equal(df, expected, check_names=True) | pandas.testing.assert_frame_equal |
# Packages
# Basic packages
import numpy as np
from scipy import integrate, stats, spatial
from scipy.special import expit, binom
import pandas as pd
import xlrd # help read excel files directly from source into pandas
import copy
import warnings
# Building parameter/computation graph
import inspect
from collection... | pd.to_datetime("2025-03-23", format="%Y-%m-%d") | pandas.to_datetime |
# -*- coding: utf-8 -*-
import sys
import os
from pandas.io import pickle
# import pandas as pd
PROJECT_ID = "dots-stock" # @param {type:"string"}
REGION = "us-central1" # @param {type:"string"}
USER = "shkim01" # <---CHANGE THIS
BUCKET_NAME = "gs://pipeline-dots-stock" # @param {type:"string"}
PIPELINE_ROOT = f"... | pd.read_pickle(bros_dataset.path) | pandas.read_pickle |
import pytest
from pandas import (
DataFrame,
Index,
Series,
)
import pandas._testing as tm
@pytest.mark.parametrize("n, frac", [(2, None), (None, 0.2)])
def test_groupby_sample_balanced_groups_shape(n, frac):
values = [1] * 10 + [2] * 10
df = DataFrame({"a": values, "b": values})
... | DataFrame({"a": values, "b": values}, index=[1, 2, 2, 2, 2, 2]) | pandas.DataFrame |
from datetime import datetime
from decimal import Decimal
from io import StringIO
import numpy as np
import pytest
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series, Timestamp, date_range, read_csv
import pandas._testing as tm
from pa... | pd.Timestamp.utcnow() | pandas.Timestamp.utcnow |
import numpy as np
import operator
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
from matplotlib.colors import LinearSegmentedColormap
import seaborn as sns
import math
from tkinter import *
# Functions age_encode, race_encode, state_encode, and self_core_dict used to create the cor... | pd.Series(clean_percentage, name=' ') | pandas.Series |
# -*- coding: utf-8 -*-
import numpy as np
import pytest
from numpy.random import RandomState
from numpy import nan
from datetime import datetime
from itertools import permutations
from pandas import (Series, Categorical, CategoricalIndex,
Timestamp, DatetimeIndex, Index, IntervalIndex)
import pan... | algos.duplicated(case, keep=False) | pandas.core.algorithms.duplicated |
from xml.etree import ElementTree
from ..windows import BaseWindow
from ..utils.authorization import Authorization
#from ..utils.resources import Resources
from urllib.parse import urlparse
from base64 import b16encode, b64encode
from esppy.espapi.eventsources import EventSources
import pandas as pd
import esppy.espapi... | pd.to_datetime(k,unit="us") | pandas.to_datetime |
import numpy as np
import pandas as pd
import yaml
from tqdm import tqdm
import logging
import math
import random
import argparse
import collections
import sys
from pathlib import Path
import os
import copy
import re
from lib.constants import *
from lib.TSP import TSP
from lib.TSPObjective import TSPObjective
from l... | pd.read_json(string) | pandas.read_json |
import pandas as pd
import pickle as pkl
from glob import glob
import numpy as np
from sklearn.metrics import roc_auc_score, accuracy_score, recall_score, precision_score, f1_score
import pandas as pd
inner_fold = 5
label_file = "/mnt/data3/pnaylor/CellularHeatmaps/outputs/label_nature.csv"
y_interest = "Residual"
... | pd.concat(validation_predictions, axis=0) | pandas.concat |
from __future__ import print_function, absolute_import, unicode_literals, division
import glob
import itertools
import json
import os
from collections import OrderedDict
import pandas as pd
import numpy as np
# from amt.settings import PATH_visible_not_visible_actions_csv
def robust_decode(bs):
'''Takes a byte... | pd.read_csv(path_context_csv) | pandas.read_csv |
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | pd.Series(dtype=np.float64) | pandas.Series |
import operator
import re
import warnings
import numpy as np
import pytest
from pandas._libs.sparse import IntIndex
import pandas.util._test_decorators as td
import pandas as pd
from pandas import isna
from pandas.core.sparse.api import SparseArray, SparseDtype, SparseSeries
import pandas.util.testing as tm
from pan... | SparseArray([np.nan, 1, 2, np.nan]) | pandas.core.sparse.api.SparseArray |
from datetime import timedelta,datetime
from processor.processor import Processor as p
import pandas as pd
from tqdm import tqdm
class Backtester(object):
def __init__(self,strat):
self.strat = strat
def equity_timeseries_backtest(self,start_date,end_date,seats):
trades = []
sim = ... | pd.DataFrame(blacklist) | pandas.DataFrame |
from warnings import catch_warnings, simplefilter
import numpy as np
from numpy.random import randn
import pytest
import pandas as pd
from pandas import (
DataFrame, MultiIndex, Series, Timestamp, date_range, isna, notna)
from pandas.util import testing as tm
@pytest.mark.filterwarnings("ignore:\\n.ix:Deprecati... | tm.assert_frame_equal(df, result) | pandas.util.testing.assert_frame_equal |
###############################################################################
# Building the Model
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.externals import joblib
# import pickle
# opening the databases
train_df = | pd.read_csv('data/train_data_modified.csv') | pandas.read_csv |
# <NAME> (Ausar Geophysical)
# 2017/01/31
import numpy as np
import scipy.signal
import pandas as pd
from sklearn import preprocessing, metrics
from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
from sklearn.base import clone
from matplotlib import pyplot as plt
import scipy.optimize
from scipy.op... | pd.concat([train_data, validation_data]) | pandas.concat |
import pandas as pd
import numpy as np
import time
import datetime
from keras.models import Sequential
from keras.layers import Dense
from keras.layers.core import Dropout
from keras.utils import to_categorical
from keras.regularizers import l2
from keras.models import load_model
class CompleteCode:
def __init__(sel... | pd.read_csv(csvfile) | pandas.read_csv |
"""
๊ตญํ ๊ตํต๋ถ Open API
molit(Ministry of Land, Infrastructure and Transport)
1. Transaction ํด๋์ค: ๋ถ๋์ฐ ์ค๊ฑฐ๋๊ฐ ์กฐํ
- AptTrade: ์ํํธ๋งค๋งค ์ค๊ฑฐ๋์๋ฃ ์กฐํ
- AptTradeDetail: ์ํํธ๋งค๋งค ์ค๊ฑฐ๋ ์์ธ ์๋ฃ ์กฐํ
- AptRent: ์ํํธ ์ ์์ธ ์๋ฃ ์กฐํ
- AptOwnership: ์ํํธ ๋ถ์๊ถ์ ๋งค ์ ๊ณ ์๋ฃ ์กฐํ
- OffiTrade: ์คํผ์คํ
๋งค๋งค ์ ๊ณ ์กฐํ
- OffiRent: ์คํผ์คํ
์ ์์ธ ์ ๊ณ ์กฐํ
- RHTrad... | pd.DataFrame() | pandas.DataFrame |
import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.linear_model import SGDClassifier
from sklearn.svm import SVC
df = | pd.read_csv('https://raw.githubusercontent.com/toshihiroryuu/Machine_learning/master/ML_001_Heart_faliure/Dataset/heart_failure_clinical_records.csv') | pandas.read_csv |
import pandas as pd
import numpy as np
import matplotlib as mpl
#mpl.use('Agg')
import matplotlib.pyplot as plt
import os
import seaborn as sns
import matplotlib.dates as mdates
import sys
sys.path.append('../')
from processing_helpers import *
from load_paths import load_box_paths
mpl.rcParams['pdf.fonttype'] = 42
... | pd.merge(pop_df_i, pop_df_ii) | pandas.merge |
import pandas as pd
from SALib.sample.radial.radial_sobol import sample
from .settings import * # import project-specific settings
# read in previous sample set for a single climate scenario
# we use this as a template
df = pd.read_csv(indir+'example_sample.csv', index_col=0)
is_perturbed = (df != df.iloc[0]).any(... | pd.DataFrame(data=samples, columns=perturbed_cols) | pandas.DataFrame |
import numpy as np
import pandas as pd
from bokeh.plotting import figure
from bokeh.models import Span, Range1d
import random
from math import pi
def calculate_diff(b, m, stats_df, group_names, groups_dict, mean_or_med=0):
"""
calculate difference from reference group
:param b: name of the population (bub... | pd.DataFrame() | pandas.DataFrame |
"""
Prepare training and testing datasets as CSV dictionaries
Created on 11/26/2018
@author: RH
"""
import os
import pandas as pd
import sklearn.utils as sku
import numpy as np
# get all full paths of images
def image_ids_in(root_dir, ignore=['.DS_Store','dict.csv', 'all.csv']):
ids = []
for id in os.listdi... | pd.DataFrame(validation_tiles_list, columns=['slide', 'level', 'path', 'label']) | pandas.DataFrame |
import pybitflyer2 as PBF
import pandas as pd
import datetime
import calendar
import time
import pickle
import traceback
pbf = PBF.API()
def main():
today = datetime.datetime.now()
yesterday = today - datetime.timedelta(days = 1)
#tday = today
tday = yesterday
start_time = datetime.datetime(tday.y... | pd.DataFrame(data) | pandas.DataFrame |
import os
import pandas as pd
PATH = 'C:\\Users\\jmedel\\Desktop\\CARDBOARD'
URL_prefix = 'https://sustaynaianotations.blob.core.windows.net/sustaynmechanical/'
filename_lst = os.listdir(PATH)
df = | pd.DataFrame(columns=['image_url']) | pandas.DataFrame |
import os
from numpy.core.numeric import full
import pandas as pd
from feature_computation import Feature
import json
import librosa
from collections import defaultdict
import sys
dataset_mode = sys.argv[1]
print("Dataset mode: {}".format(dataset_mode))
# ***************** PATH CONFIGURATION *****************
# Conf... | pd.read_csv(csv_features, index_col='song_id') | pandas.read_csv |
import os
from cleverhans.attacks import FastGradientMethod
from io import BytesIO
import IPython.display
import numpy as np
import pandas as pd
from PIL import Image
from scipy.misc import imread
from scipy.misc import imsave
import tensorflow as tf
from tensorflow.contrib.slim.nets import inception
sli... | pd.DataFrame({"CategoryId": true_classes}) | pandas.DataFrame |
from typing import Tuple
from argparse import Namespace as APNamespace, _SubParsersAction,ArgumentParser
from train_help import *
from pathlib import Path
import os
import platform
import time
import pandas as pd
import numpy as np
import global_vars as GLOBALS
from ptflops import get_model_complexity_info
import copy
... | pd.DataFrame(columns=cell_list_columns) | pandas.DataFrame |
from pymongo import MongoClient
import json
import requests, zipfile, io, os, re
import pandas as pd
import geopandas, astral
import time
from astral.sun import sun
METEO_FOLDER = r"C:/Users/48604/Documents/semestr5/PAG/pag2/Meteo/"
ZAPIS_ZIP = METEO_FOLDER + r"Meteo_"
url = "https://dane.imgw.pl/datastore... | pd.merge(sun_info[key], astral_info[key], left_index=True, right_index=True) | pandas.merge |
import numpy as np
import pandas as pd
import warnings
from ecomplexity.calc_proximity import calc_discrete_proximity
from ecomplexity.calc_proximity import calc_continuous_proximity
from ecomplexity.ComplexityData import ComplexityData
from ecomplexity.density import calc_density
from ecomplexity.coicog import calc_co... | pd.concat(cdata.output_list) | pandas.concat |
import Functions
import pandas as pd
import matplotlib.pyplot as plt
def group_sentiment(dfSentiment):
dfSentiment['datetime'] = pd.to_datetime(dfSentiment['created_utc'], unit='s')
dfSentiment['date'] = pd.DatetimeIndex(dfSentiment['datetime']).date
dfSentiment = dfSentiment[
['created_utc', 'ne... | pd.DatetimeIndex(dfSentiment['Date']) | pandas.DatetimeIndex |
from datetime import datetime, timedelta
import unittest
from pandas.core.datetools import (
bday, BDay, BQuarterEnd, BMonthEnd, BYearEnd, MonthEnd,
DateOffset, Week, YearBegin, YearEnd, Hour, Minute, Second,
format, ole2datetime, to_datetime, normalize_date,
getOffset, getOffsetName, inferTimeR... | MonthEnd() | pandas.core.datetools.MonthEnd |
import os; os.environ['OMP_NUM_THREADS'] = '3'
from sklearn.ensemble import ExtraTreesRegressor
import nltk
nltk.data.path.append("/media/sayantan/Personal/nltk_data")
from nltk.stem.snowball import RussianStemmer
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer as Tfidf
fro... | pd.read_feather('../train_imagetop_targetenc.pkl') | pandas.read_feather |
from datetime import datetime
import warnings
import numpy as np
import pytest
from pandas.core.dtypes.generic import ABCDateOffset
import pandas as pd
from pandas import (
DatetimeIndex,
Index,
PeriodIndex,
Series,
Timestamp,
bdate_range,
date_range,
)
from pandas.tests.test_base import ... | bdate_range(START, END, freq="C") | pandas.bdate_range |
# -*- coding: utf-8 -*-
"""Interface for flopy's implementation for MODFLOW."""
__all__ = ["MfSfrNetwork"]
import pickle
from itertools import combinations, zip_longest
from textwrap import dedent
import geopandas
import numpy as np
import pandas as pd
from shapely import wkt
from shapely.geometry import LineString,... | pd.Series(dtype=bool) | pandas.Series |
# -*- coding: utf-8 -*-
"""One line description.
Authors:
<NAME> - <EMAIL>
Todo:
* Docstring
* Put all hyper to arguments
"""
import logging
import os
import time
from pathlib import Path
import click
import numpy as np
import pandas as pd
import wandb
from sklearn.model_selection import train_test_split
... | pd.read_csv(path_data_all) | pandas.read_csv |
from sklearn.model_selection import train_test_split
import os
from functools import reduce
import matplotlib.colors as mcolors
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
def read_data(filepath):
p... | pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S.%f') | pandas.datetime.strptime |
# ndp d2 app for smoooth rdm...
import streamlit as st
import pandas as pd
import numpy as np
from st_aggrid import AgGrid
import plotly.express as px
from apis import pno_data
from apis import mtk_rak_pno
from apis import pno_hist
# page setup
st.set_page_config(page_title="NDP App d2", layout="wide")
padding = 2
s... | pd.DataFrame(selected_row) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""Benchmark the speed for generating new datasets by remixing old ones."""
import itertools as itt
import logging
import os
import time
from datetime import datetime
import click
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import torch
from humanize import intwo... | pd.DataFrame(dataset_rows, columns=columns) | pandas.DataFrame |
"""Console script for koapy."""
import os
import locale
import logging
import click
import koapy
from koapy.utils.logging import set_verbosity
CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help'])
client_check_timeout = 3
def fail_with_usage(message=None):
ctx = click.get_current_context()
if messa... | pd.Series(dic) | pandas.Series |
import pandas as pd
from sqlalchemy import create_engine
from dbnd import log_metric, log_dataframe
QUERY = ""
DB_CONNECTION = ""
def track_database():
engine = create_engine(DB_CONNECTION)
log_metric("query executed", QUERY)
with engine.connect() as connection:
result = connection.execute(QUERY... | pd.DataFrame(data, columns=header) | pandas.DataFrame |
##
drive_path = 'c:/'
import numpy as np
import pandas as pd
import os
import sys
import matplotlib.pyplot as plt
from scipy.stats import ks_2samp
from scipy.stats import anderson_ksamp
from scipy.stats import kruskal
from scipy.stats import variation
from scipy import signal as sps
import seaborn as sns
import glob
im... | pd.DataFrame([]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 19 22:51:03 2018
@author: <NAME>
"""
import os
import time
import pdb
import shutil
import sys
import argparse
import logging
import tempfile
import multiprocessing as mp
import platform
import pytest
import numpy as np
import pandas as pd
import sandy
from sandy.setti... | pd.concat(dfperts) | pandas.concat |
import argparse
import datetime
import logging
import os
import pickle
from random import Random
import numpy as np
import pandas as pd
from dltranz.data_preprocessing.util import pd_hist
logger = logging.getLogger(__name__)
def parse_args(args=None):
parser = argparse.ArgumentParser()
parser.add_argument... | pd.merge(df, df_event_time, on=cols_event_time) | pandas.merge |
import pandas as pd
import numpy as np
import pickle
import json
def save_arguments(path="", args=None):
print(vars(args))
if args!=None:
file = open("{}/arguments.json".format(path), "w", encoding="utf8")
json.dump(vars(args), file, indent=4, sort_keys=True)
file.close()
def load_... | pd.DataFrame(rewards, columns=seeds) | pandas.DataFrame |
import numpy as np
import pandas as pd
import xarray as xr
import copy
import warnings
try:
from plotly import graph_objs as go
plotly_installed = True
except:
plotly_installed = False
# warnings.warn("PLOTLY not installed so interactive plots are not available. This may result in unexpected funtionali... | pd.Series(y[x], index=x) | pandas.Series |
# -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
import operator
from collections import OrderedDict
from datetime import datetime
from itertools import chain
import warnings
import numpy as np
from pandas import (notna, DataFrame, Series, MultiIndex, date_range,
Time... | assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
# utils.py
"""
Utils
-----
Utility functions for the whole project.
"""
import collections
from copy import deepcopy
import logging.config
import os
from pathlib import Path
from typing import List, Optional, Tuple
import numpy as np
import pandas as pd
from pandas.tseries import offsets
from soam.constants import DS... | pd.Timestamp(datetime_start) | pandas.Timestamp |
# -*- coding: utf-8 -*-
"""
Xarray Stacked Images Writer.
Create 3D datasets, allows setting spatial and temporal subset (images and time
series)
"""
#TODO. File locking as option for multiple processes?
# todo: Add Point data results manager (for ismn based results)
import xarray as xr
import numpy as np
import pand... | pd.to_datetime(z.values) | pandas.to_datetime |
import pandas as pd
import dateutil
import datetime
class api_IEX:
"""A class to work with the IEX API @ api.iextrading.com """
baseURL = "https://api.iextrading.com/1.0/stock/"
dfResponse = "nothing queried"
def __init__(self, ticker):
self.symbol=ticker
self.baseURL = self.b... | pd.read_json(apiPath, typ='series') | pandas.read_json |
import sys
from typing import List, Tuple
import numpy as np
import pandas as pd
def get_valid_gene_info(
genes: List[str],
release=102,
species='homo sapiens'
) -> Tuple[List[str], List[int], List[int], List[int]]:
"""Returns gene locations for all genes in ensembl release 93 --S Markson 3 June 202... | pd.DataFrame(loom.ca[ca1], copy=True) | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
import boto3
from tqdm import tqdm
import yaml
from ._01_ETL import Boba_ETL as etl
from ._02_Preprocessing import Boba_Preprocessing as pp
from ._03_Modeling import Boba_Modeling as m
class BobaModeling(etl,pp,m)... | pd.merge(id_map,fantrax, how='left',left_on='FANTRAXNAME', right_on='Player' ) | pandas.merge |
import os
import numpy as np
import pandas as pd
from pathlib import Path
from tqdm import tqdm
import json
# import sys
# sys.path.insert(0, './data')
# sys.path.insert(0, './utils')
# sys.path.insert(0, './common')
import os,sys,inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentfra... | pd.DataFrame(data=data, columns=['euler', 'descriptions', 'quaternion', 'fke', 'rifke']) | pandas.DataFrame |
import os
import pandas as pd
INDEXES = ["deaths", "confirmed", "hospitalized", "intensive care", "intubated", "released"]
def read_inputs():
text = input("Insert data to append as: [date: MM/DD/YY], [deaths], [confirmed], [hospitalized], [intensive care], "
"[intubated], [released]\n")
d... | pd.read_csv(filepath_overall) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
"""
make_herbarium_2022_catalog_df.py
"""
#
# Description:
#
# Created On: Sunday Feb 27th, 2022
# Created By: <NAME>
# ### Key constants
# DATASETS_ROOT = "/media/data_cifs/projects/prj_fossils/data/processed_data/leavesdb-v1_1/images"
# EXTANT_ROOT = "/media/data_cifs/pr... | pd.DataFrame(train_data['institutions']) | pandas.DataFrame |
#!/usr/bin/env python3
import glob
import math
import sqlite3
import sys
from itertools import product
import logzero
import pandas as pd
from logzero import logger
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.statespace.sarimax import SARI... | pd.DataFrame(results) | pandas.DataFrame |
# https://www.kaggle.com/shivank856/gtsrb-cnn-98-test-accuracy
import PIL
import numpy as np
import pandas as pd
import os
import cv2
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from PIL import Image
from sklearn.model_selection import train_test_split
from tensorflow.keras.prep... | pd.read_csv(data_dir + '/Test.csv') | pandas.read_csv |
import pandas as pd
from cellphonedb.src.core.methods import cpdb_statistical_analysis_helper
from cellphonedb.src.core.core_logger import core_logger
from cellphonedb.src.core.models.interaction import interaction_filter
def call(meta: pd.DataFrame,
counts: pd.DataFrame,
interactions: pd.DataFrame... | pd.DataFrame() | pandas.DataFrame |
# Mar21, 2022
##
#---------------------------------------------------------------------
# SERVER only input all files (.bam and .fa) output MeH matrix in .csv
# August 3, 2021 clean
# FINAL github
#---------------------------------------------------------------------
import random
import math
import pysam
import csv
... | pd.DataFrame(data=d) | pandas.DataFrame |
import os
import numpy as np
import json
import requests
try:
import modin.pandas as pd
except ImportError:
import pandas as pd
import galaxy_utilities as gu
from tqdm import tqdm
import make_cutouts as mkct
from astropy.wcs import WCS, FITSFixedWarning
import warnings
warnings.simplefilter('ignore', FITSFixed... | pd.Series(sid_list, index=sid_list) | pandas.Series |
import numpy as np
import pandas as pd
import numba
from vtools.functions.filter import cosine_lanczos
def get_smoothed_resampled(df, cutoff_period='2H', resample_period='1T', interpolate_method='pchip'):
"""Resample the dataframe (indexed by time) to the regular period of resample_period using the interpolate me... | pd.to_timedelta(tdelta) | pandas.to_timedelta |
# coding=utf-8
# Copyright 2018-2020 EVA
#
# 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 ... | pd.DataFrame(lvalues != rvalues) | pandas.DataFrame |
"""
Description : This file implements the Drain algorithm for log parsing
Author : LogPAI team
License : MIT
"""
import hashlib
import os
import re
import pandas as pd
from datetime import datetime
from typing import List
from .log_signature import calc_signature
# ไธไธชๅถๅญ่็นๅฐฑๆฏไธไธชLogCluster
clas... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sat May 9 19:30:38 2020
@author: aletu
"""
import numpy as np
import pandas as pd
import random
import datetime
def generateWarehouseData(num_SKUs = 100,
nodecode = 1,
idwh = ['LOGICAL_WH1', 'LOGICAL_WH2', 'FAKE'],
whsubarea = ['AREA 1'],
num_corsie = 5,
nu... | pd.DataFrame() | pandas.DataFrame |
import os, datetime, pymongo, configparser
import pandas as pd
from bson import json_util
global_config = None
global_client = None
global_stocklist = None
def getConfig(root_path):
global global_config
if global_config is None:
#print("initial Config...")
global_config = configparser.ConfigPa... | pd.read_json(result['data'], orient='records') | pandas.read_json |
#!/home/admin/anaconda3/envs/TF/bin/ python3.5
# -*- coding: utf-8 -*-
'''
Created on 2018ๅนด6ๆ11ๆฅ
@author: <NAME>
Jiangxi university of finance and economics
'''
from pandas import DataFrame
from pandas import concat
import pandas as pd
time_ser_process=pd.read_csv('pricedetails_plus.csv')#ๅๅค่ฟ่กๆถ้ดๅบๅๅค็
time_... | concat(cols, axis=1) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
# pip install factor_analyzer
# In[2]:
#All the header files required for the code
import numpy as np
import pandas as pd
from factor_analyzer import FactorAnalyzer
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn import metrics
import... | pd.DataFrame(a) | pandas.DataFrame |
# Filename: reference.py
"""
Data provided for free by IEX (https://iextrading.com/developer/).
See https://iextrading.com/api-exhibit-a/ for more information.
"""
from iex.base import _Base, IEXAPIError
import pandas as pd
class Reference(_Base):
"""https://iextrading.com/developer/docs/#reference-data"""
_ENDP... | pd.DataFrame(data) | pandas.DataFrame |
import pandas as pd
import numpy as np
import os
import sys
import tensorflow as tf
import json
import joblib
import time
from tensorflow import keras
from keras import optimizers
from datetime import datetime,timedelta
from sklearn.preprocessing import MinMaxScaler
from datetime import datetime
pd.set_option('display.... | pd.DatetimeIndex(df_power['DATE']) | pandas.DatetimeIndex |
"""
This notebook plots DFT results for thermoelectric properties of several
candidate materials identified via random forest regression and portfolio-like
risk management. See src/notsbooks/screen/random_forest.py for details.
"""
# %%
import pandas as pd
from matplotlib import pyplot as plt
from thermo.utils impor... | pd.to_numeric(zT_el_greedy_gurobi[0], errors="coerce") | pandas.to_numeric |
# from google.colab import drive
# drive.mount('/content/drive')
# !pip install shap
# !pip install pyitlib
# import os
# os.path.abspath(os.getcwd())
# os.chdir('/content/drive/My Drive/Protein project')
# os.path.abspath(os.getcwd())
#!/usr/bin/env python
#-*- coding:utf-8 -*-
"""
Created on Mar 1, 2020
@author: <NA... | pd.DataFrame(self.X_1_true_) | pandas.DataFrame |
from io import StringIO
import subprocess
import pandas as pd
import os
# Time columns in job records
# If we exclude PENDING jobs (that we do in slurm_raw_processing), all time columns should have a time stamp,
# except RUNNING jobs that do not have the 'End' stamp.
time_columns = ['Eligible','Submit','Start','End']
... | pd.DataFrame() | pandas.DataFrame |
from __future__ import division
import os, gc, copy, json
import pandas as pd
import numpy as np
import ipywidgets as widgets
from IPython.display import display, clear_output
import matplotlib as mpl
import matplotlib.pyplot as plt#, mpld3
import seaborn as sns
import warnings
#mpld3 hack
# class NumpyEncoder(json.J... | pd.DataFrame() | pandas.DataFrame |
# Copyright 2021, <NAME>.
#
# Developed as a thesis project at the TORSEC research group of the Polytechnic of Turin (Italy) under the supervision
# of professor <NAME> and engineer <NAME> and with the support of engineer <NAME>.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this f... | pd.DataFrame(results, index=query_shas) | pandas.DataFrame |
import os
import re
import copy
import json
import tqdm
import pprint
import sklearn
import pandas as pd
from sklearn.model_selection import train_test_split
for lib in ["emoji", "fasttext", "google_trans_new"]:
try:
exec(f"import {lib}")
except ImportError:
os.system(f"pip install {lib}")
... | pd.DataFrame(train) | pandas.DataFrame |
import os
import sys
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
from joblib import Memory
matplotlib.use('TkAgg')
# Auto-detect terminal width.
pd.options.display.width = None
pd.options.display.max_rows = 1000
pd.options.display.max_colwidth = 200
# Initialize a persistent memcache.
mem_... | pd.to_datetime('09:30:00.000001') | pandas.to_datetime |
#
# Copyright 2019 <NAME> <<EMAIL>>
#
# This file is part of Salus
# (see https://github.com/SymbioticLab/Salus).
#
# 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.o... | pd.Timedelta(microseconds=1) | pandas.Timedelta |
"""
Misc tools for implementing data structures
"""
import re
import collections
import numbers
from datetime import datetime, timedelta
from functools import partial
import numpy as np
import pandas as pd
import pandas.algos as algos
import pandas.lib as lib
import pandas.tslib as tslib
from pandas import compat
fro... | lib.checknull_old(obj) | pandas.lib.checknull_old |
#j Import Dependencies
import requests
import pandas as pd
import matplot.lib.pyplot as plt
import hvplot.panda
import plotly.express as px
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
## Create a dataframe
### Import csv... | pd.DataFrame(data=PCA_data, columns=['PC1', 'PC2', 'PC3'], index=Cryptocurrency_DF.index) | pandas.DataFrame |
import numpy as np
import pandas as pd
import joblib, os
class dataset_creator():
def __init__(self, project, data, njobs=1):
self.data = data
self.dates_ts = self.check_dates(data.index)
self.project_name= project['_id']
self.static_data = project['static_data']
self.path... | pd.DateOffset(hours=48) | pandas.DateOffset |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scheduler.GOBI import GOBIScheduler
plt.style.use(['science'])
plt.rcParams["text.usetex"] = False
class Stats():
def __init__(self, Environment, WorkloadModel, Datacenter, Scheduler):
self.env = Environment
self.env.stats... | pd.DataFrame(metric_with_interval) | pandas.DataFrame |
import spacy
import json
import numpy as np
import itertools
import multiprocessing as mp
from pandas import pandas
from collections import Counter
from keras.preprocessing.text import Tokenizer
from keras.utils import Sequence
from keras.preprocessing.sequence import pad_sequences
from utils import preprocess
from u... | pandas.DataFrame({"sentences": txt}) | pandas.pandas.DataFrame |
"""
Classes that represent a collection of points/structures that will define a labelmap or similar for image analysis purposes.
Currently the parent object is GeometryTopologyData, that can contain objects of type Point and/or BoundingBox.
The structure of the object is defined in the GeometryTopologyData.xsd schema.
... | pd.DataFrame(columns=columns) | pandas.DataFrame |
import numpy as np
import pandas as pd
from munch import Munch
from plaster.run.priors import ParamsAndPriors, Prior, Priors
from plaster.tools.aaseq.aaseq import aa_str_to_list
from plaster.tools.schema import check
from plaster.tools.schema.schema import Schema as s
from plaster.tools.utils import utils
from plaster.... | pd.DataFrame(self.dyes) | pandas.DataFrame |
"""Utilities for read counting operations.
"""
import warnings
from collections import defaultdict
from collections import Counter
from collections import OrderedDict
from functools import reduce
import os
import subprocess
import sys
import numpy as np
import pandas as pd
import pybedtools
import pysam
import six
i... | pd.read_table(gene_coverages, compression="gzip") | pandas.read_table |
"""
WSP Cleaning:
Takes a csv file from WSP's collision analysis tool and returns a new csv file
in a format that can be merged with Weather Underground data, ultimately being
used in a visualization tool
"""
import numpy as np
import pandas as pd
from IPython.core.interactiveshell import InteractiveShell
from pypro... | pd.DataFrame(x_new) | pandas.DataFrame |
"""Scraper for https://projects.fivethirtyeight.com/soccer-predictions."""
import itertools
import json
from pathlib import Path
from typing import Callable, Dict, List, Optional, Union
import pandas as pd
from ._common import BaseRequestsReader, make_game_id, standardize_colnames
from ._config import DATA_DIR, NOCAC... | pd.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
import pandas as pd
import os
from datetime import date
import logging
from tqdm import tqdm
# logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", datefmt='%Y-%m-%d %H:%M:%S')
def getTrialStats(today:date):
os.chdir(os.path.realpath('../'))
diseases... | pd.DataFrame.from_records(sitesDiseaseCount) | pandas.DataFrame.from_records |
from datetime import datetime
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
_testing as tm,
)
def test_split(any_string_dtype):
values = Series(["a_b_c", "c_d_e", np.nan, "f_g_h"], dtype=any_string_dtype)
... | tm.assert_series_equal(result, exp) | pandas._testing.assert_series_equal |
import requests,json,os,re,argparse
import pandas as pd
from time import sleep
parser=argparse.ArgumentParser()
parser.add_argument('-i','--input_file', required=True, help='Input csv file with user name and orcid id')
parser.add_argument('-o','--output_xml', required=True, help='Output xml file')
args=parser.parse_ar... | pd.DataFrame() | pandas.DataFrame |
import argparse
import collections
import pandas
import numpy as np
import os
import gym
from keras.layers import Activation, Dense, Flatten
from keras.models import Sequential
from keras.optimizers import Adam
import tensorflow as tf
from rl.agents import SARSAAgent
from rl.core import Processor
from rl.policy impor... | pandas.DataFrame(history_normal.history) | pandas.DataFrame |
#!/usr/bin/env python
import pandas as pd
pd.set_option('display.max_rows', 15500)
pd.set_option('display.max_columns', 55500)
pd.set_option('display.width', 551000)
data = pd.read_csv("crimemar14.csv")
data.head()
newdata = data["Datetime"].str.split("@", n = 1, expand = True)
data["date"] = newdata[0]
data["time... | pd.to_datetime(data["time"],format=' %I:%M %p' ) | pandas.to_datetime |
import decimal
import numpy as np
from numpy import iinfo
import pytest
import pandas as pd
from pandas import to_numeric
from pandas.util import testing as tm
class TestToNumeric(object):
def test_empty(self):
# see gh-16302
s = pd.Series([], dtype=object)
res = to_numeric(s)
... | pd.to_numeric(data, downcast=downcast) | pandas.to_numeric |
# -*- coding: utf-8 -*-
"""
These the test the public routines exposed in types/common.py
related to inference and not otherwise tested in types/test_common.py
"""
from warnings import catch_warnings, simplefilter
import collections
import re
from datetime import datetime, date, timedelta, time
from decimal import De... | pd.Period('2011-01', freq='D') | pandas.Period |
import xenaPython as xena
import pandas as pd
GENES = ['FOXM1', 'TP53']
def get_codes(host, dataset, fields, data):
"get codes for enumerations"
codes = xena.field_codes(host, dataset, fields)
codes_idx = dict([(x['name'],
x['code'].split('\t')) for x in codes if x['code'] is not N... | pd.DataFrame(lst[1]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from __future__ import print_function
from numpy import nan
import numpy as np
from pandas import compat
from pandas import (DataFrame, Series, MultiIndex, Timestamp,
date_range)
import pandas.util.testing as tm
from pandas.tests.frame.common import TestData
class Test... | DataFrame.from_records([headers]) | pandas.DataFrame.from_records |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from mabwiser.mab import MAB, LearningPolicy, NeighborhoodPolicy
from tests.test_base import BaseTest
class MABTest(BaseTest):
#################################################
# Test context fr... | pd.Series([1, 1, 1, 2, 2, 3, 3, 3, 3, 3]) | pandas.Series |
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