prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import abc
import sys
import copy
import time
import datetime
import importlib
from abc import ABC
from pathlib import Path
from typing import Iterable, Type
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import fire
impo... | pd.concat(_res, sort=False) | pandas.concat |
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from pandas.plotting import autocorrelation_plot
from keras import Sequential
from tensorflow.python.keras.layers.recurrent import LSTM
df = | pd.read_csv(r'C:\Users\Michael\Desktop\pwrball_rand\pwr_ball - Copy.csv') | pandas.read_csv |
"""
Provide classes to perform the groupby aggregate operations.
These are not exposed to the user and provide implementations of the grouping
operations, primarily in cython. These classes (BaseGrouper and BinGrouper)
are contained *in* the SeriesGroupBy and DataFrameGroupBy objects.
"""
from __future__ import annota... | libreduction.apply_frame_axis0(sdata, f, names, starts, ends) | pandas._libs.reduction.apply_frame_axis0 |
from datetime import datetime
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
CategoricalIndex,
DataFrame,
Index,
MultiIndex,
Series,
qcut,
)
import pandas._testing as tm
def cartesian_product_for_groupers(result, args, names, fill... | Series([np.nan, np.nan, 1, 1, 2, 2, 3, 3, 4, 4]) | pandas.Series |
import hashlib
import json
import logging
import random
import os
import signal
import numpy as np
import torch
from requests.exceptions import ConnectionError
from torch import multiprocessing as mp
import mlflow
from copy import deepcopy
import pandas as pd
from tqdm import tqdm
from farm.visual.ascii.images import... | pd.DataFrame(records, columns=["qid", "text", "pid", "text_b", "label"]) | pandas.DataFrame |
import sbatch_prepare as sp
import path_manipulate as pm
import os
import time
import traceback
from pandas import DataFrame as df
from pandas import Series
from mpi4py.futures import MPIPoolExecutor
res_columns = ['obj','std',
'k1.value','k1.grad','k1.std',
'k2.value','k2.grad','k2.std'... | df(columns=res_columns) | pandas.DataFrame |
from __future__ import print_function, division
# MIMIC IIIv14 on postgres 9.4
import os, psycopg2, re, sys, time, numpy as np, pandas as pd
from sklearn import metrics
from datetime import datetime
from datetime import timedelta
from os.path import isfile, isdir, splitext
import argparse
import pickle as cPickle
imp... | pd.read_sql_query(query, con) | pandas.read_sql_query |
import os
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from scipy.optimize import curve_fit
import shutil
from . import C_preprocessing as preproc
class ODE_POSTPROC:
'''
In this class the output of the ODE is post-processed and the output is written as require... | pd.Series(self.REACNAME,index=[self.REACNAME]) | pandas.Series |
#!/usr/bin/env python3
import requests
import json
import pandas as pd
import numpy as np
import os
import sys
import time
from datetime import datetime, date
from strava_logging import logger
from db_connection import connect, sql
from location_data import lookup_location
class Athlete:
def __init__(self, **kwa... | pd.DataFrame(activity_data['splits_metric']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable=E1101,E1103,W0232
import os
import sys
from datetime import datetime
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
import pandas.compat as compat
import pandas.core.common as com
import pandas.util.testing as tm
from pandas import (Categor... | tm.assert_series_equal(result, expected) | pandas.util.testing.assert_series_equal |
import task_submit
from task_submit import VGGTask,RESTask,RETask,DENTask,XCETask
import random
import kubernetes
import influxdb
import kubernetes
import signal
#from TimeoutException import TimeoutError,Myhandler
import yaml
import requests
from multiprocessing import Process
import multiprocessing
import urllib
impo... | pd.DataFrame(self.cpu_per) | pandas.DataFrame |
from datetime import timedelta
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
Series,
)
import pandas._testing as tm
from pandas.core.indexes.timedeltas import timedelta_range
def test_asfreq_bug():
df = DataFrame(data=[1, 3], index=[timedelta(), time... | timedelta_range("00:00:00", "00:10:00", freq="2T") | pandas.core.indexes.timedeltas.timedelta_range |
'''
Created on April 15, 2012
Last update on July 18, 2015
@author: <NAME>
@author: <NAME>
@author: <NAME>
'''
import pandas as pd
class Columns(object):
OPEN='Open'
HIGH='High'
LOW='Low'
CLOSE='Close'
VOLUME='Volume'
# def get(df, col):
# return(df[col])
# df['Close'] =... | pd.rolling_mean(EoM, n) | pandas.rolling_mean |
#!/usr/bin/env python
r"""Test :py:class:`~solarwindpy.core.vector.Vector` and :py:class:`~solarwindpy.core.tensor.Tensor`.
"""
import pdb
# import re as re
import numpy as np
import pandas as pd
import unittest
import sys
import pandas.testing as pdt
from unittest import TestCase
from abc import ABC, abstractpropert... | pdt.assert_series_equal(t.par, self.object_testing.par) | pandas.testing.assert_series_equal |
#1. 데이터를 db에 넣기
#sklearn에서 dataset 가져와서
from sklearn import datasets
boston = datasets.load_boston()
#dataset을 pandas로 변형
import pandas as pd
df = | pd.DataFrame(boston['data'],columns=boston['feature_names']) | pandas.DataFrame |
import bz2
import copy
from functools import partial
import gzip
import io
from inspect import signature
import json
from logging import getLogger, INFO
import lzma
import multiprocessing as mp
import pickle
import re
import sys
import traceback as trc
# to accept all typing.*
from typing import *
import warnings
impo... | pd.read_table(file_to_read, **kwargs) | pandas.read_table |
# -------------------------------------------------------------
#
# 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 unde... | pd.concat([combined_dataset_frame, converted_one_hot_column], axis=1, join="outer", sort=False) | pandas.concat |
import numpy as np
import pandas as pd
class DataGenerator:
def __init__(self, file_path, names=None, features=None, labels=None):
raw_data = | pd.read_csv(file_path, names=names) | pandas.read_csv |
# Collection of functions to process and laod tables for visualisation
# for a set of schools whose data has been updated through syncthing_data
from math import ceil
from scripts.clix_platform_data_processing.get_static_vis_data import get_log_level_data, get_engagement_metrics
from scripts.clix_platform_data_process... | pandas.read_csv(state_tools_logs_file) | pandas.read_csv |
import pandas as pd
from datetime import timedelta, datetime
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
import warnings
warnings.filterwarnings("ignore")
from acquire import get_store_data
# plotting defaults
plt.rc('figure', figsize=(13, 7))
plt.style.use('... | pd.read_csv("https://raw.githubusercontent.com/jenfly/opsd/master/opsd_germany_daily.csv") | pandas.read_csv |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import pytest
import re
from numpy import nan as NA
import numpy as np
from numpy.random import randint
from pandas.compat import range, u
import pandas.compat as compat
from pandas import Index, Series, DataFrame, isn... | Series(['fooBAD__barBAD', NA, 'foo']) | pandas.Series |
from abc import ABC, abstractmethod
import logging
import os
import tempfile
import pandas as pd
import tensorflow as tf
from .. import normalisation
from ..vector_model import VectorRegressionModel
log = logging.getLogger(__name__)
class TensorFlowSession:
session = None
_isKerasSessionSet = False
@c... | pd.DataFrame(Y, columns=self.outputScaler.dimensionNames) | pandas.DataFrame |
""" test feather-format compat """
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.io.feather_format import read_feather, to_feather # isort:skip
pyarrow = pytest.importorskip("pyarrow", minversion="1.0.1")
filter_sparse = pytest.mark.filterwarnings("ignore:The Sparse... | pd.MultiIndex.from_tuples([("a", 1)]) | pandas.MultiIndex.from_tuples |
from tkinter import *
import pandas
import random
BACKGROUND_COLOR = "#B1DDC6"
FONT_NAME = "COMIC SANS MS"
current_card = {}
to_learn = {}
# --------------------------------------- FETCH DATA FROM CSV ---------------------------------------- #
try:
data = | pandas.read_csv("data/words_to_learn.csv") | pandas.read_csv |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 8 19:49:40 2017
print Baidu Map
@author: luminous
"""
import pandas as pd
"""implore data"""
res_file = open("k_means_res.txt", "r")
#res_file = open("dbscan_res.txt", "r")
k = int(res_file.readline())
str_label = res_file.readline()
res_file.close(... | pd.DataFrame(gps_data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# Arithmetc tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import timedelta
import operator
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.core import ops
from pandas.errors import NullFrequency... | tm.box_expected(idx, box) | pandas.util.testing.box_expected |
import copy
import re
from textwrap import dedent
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
MultiIndex,
)
import pandas._testing as tm
jinja2 = pytest.importorskip("jinja2")
from pandas.io.formats.style import ( # isort:skip
Styler,
)
from pandas.io.formats.sty... | MultiIndex.from_arrays([[1, 1, 2, 1], ["a", "b", "b", "d"]]) | pandas.MultiIndex.from_arrays |
# coding=utf-8
# Author: <NAME>
# Date: Jul 17, 2019
#
# Description: Merges DM Selected Gens with DM Screening Data
#
#
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
from utils import ensurePathExists
def ma... | pd.read_csv('../2-core_genes/results/all3-pooling-DM/DM_meiotic_genes.csv', index_col='id_string', usecols=['id_gene', 'id_string', 'gene']) | pandas.read_csv |
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
CategoricalIndex,
DataFrame,
Index,
NaT,
Series,
date_range,
offsets,
)
import pandas._testing as tm
class TestDataFrameShift:
@pytest.mark.parametrize(
"input_... | pd.concat([df1, df2], axis=1) | pandas.concat |
#!/usr/bin/env python3
"""
script for calculating genome coverage
"""
import os
import sys
import argparse
import pandas as pd
from ctbBio.fasta import iterate_fasta as parse_fasta
def parse_cov(cov_table, scaffold2genome):
"""
calculate genome coverage from scaffold coverage table
"""
size = {} # ... | pd.DataFrame(coverage) | pandas.DataFrame |
from glob import glob
from astropy.io import fits
import pandas as pd
import numpy as np
from progressbar import ProgressBar
phoenix_bibtex = """
@ARTICLE{2013A&A...553A...6H,
author = {{<NAME>. and {<NAME>}, S. and {Dreizler}, S. and
{Homeier}, D. and {Reiners}, A. and {Barman}, T. and {Hauschildt}, P.~H.
},
... | pd.Series(phoenix_meta) | pandas.Series |
from piper.custom import ratio
import datetime
import numpy as np
import pandas as pd
import pytest
from time import strptime
from piper.custom import add_xl_formula
from piper.factory import sample_data
from piper.factory import generate_periods, make_null_dates
from piper.custom import from_julian
from pipe... | pd.Timestamp('2020-05-11 00:00:00') | pandas.Timestamp |
from PyQt5.QtWidgets import QDialog
from PyQt5.QtWidgets import QVBoxLayout
from PyQt5.QtWidgets import QGridLayout
from PyQt5.QtWidgets import QTabWidget
from PyQt5.QtWidgets import QWidget
from PyQt5.QtWidgets import QLabel
from PyQt5.QtWidgets import QLineEdit
from PyQt5.QtWidgets import QPushButton
from PyQt5.QtWid... | pd.read_csv('../../data/visual_set.csv') | pandas.read_csv |
import numpy as np
import pandas as pd
import streamlit as st
import base64
import altair as alt
import datetime
from streamlit_option_menu import option_menu
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
import sklearn.metrics as metri... | pd.to_datetime(df['Time'], errors='coerce') | pandas.to_datetime |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from ...pvtpy.black_oil import Pvt,Oil,Water,Gas
from scipy.optimize import root_scalar
from .inflow import OilInflow, GasInflow
from ...utils import intercept_curves
from typing import Union
## Incompressible pressure drop
def potential_energy_ch... | pd.DataFrame(arr,columns=['pwf','thp','di'],index=gas_arr) | pandas.DataFrame |
# Copyright (c) 2018-2022, NVIDIA CORPORATION.
import numpy as np
import pandas as pd
import pytest
from pandas.api import types as ptypes
import cudf
from cudf.api import types as types
@pytest.mark.parametrize(
"obj, expect",
(
# Base Python objects.
(bool(), False),
(int(), False)... | pd.Series(dtype="timedelta64[s]") | pandas.Series |
import pandas as pd
# static variables
tool1 = 'Polyphen2'
tool2 = 'PROVEAN'
tool3 = 'SIFT'
# clean all the redundant whitespace in the generated file, then output it
def clean_pph2_data():
with open('pph2-full.txt', 'r') as file:
for line in file:
if line.startswith('##'): # ignore the comm... | pd.read_csv("DataAnalysis/provean3.tsv", sep='\t', usecols=col_names) | pandas.read_csv |
import os
import numpy as np
import pandas as pd
from glob import glob
from typing import Any, List, Dict, Optional, Tuple
def load_single_feed(fullpath: str):
df = pd.read_csv(fullpath)
df["first_seen"] = (
pd.to_datetime(df["first_seen"]).values.astype(np.int64) // 10 ** 9
)
df["last_seen"] ... | pd.DataFrame(df["feeds"]) | pandas.DataFrame |
# python 2
try:
from urllib.request import Request, urlopen
# Python 3
except ImportError:
from urllib2 import Request, urlopen
import pandas as pd
import time
import datetime
import numpy as np
import re
import json
from bs4 import BeautifulSoup
from pytrends.request import TrendReq
cla... | pd.to_datetime(output['date'], unit='s') | pandas.to_datetime |
"""stuff to help with computing the noise ceiling
"""
import matplotlib as mpl
# we do this because sometimes we run this without an X-server, and this backend doesn't need
# one. We set warn=False because the notebook uses a different backend and will spout out a big
# warning to that effect; that's unnecessarily alar... | pd.DataFrame(metadata, index=[0]) | pandas.DataFrame |
"""
=======================================
Clustering text documents using k-means
=======================================
This is an example showing how the scikit-learn API can be used to cluster
documents by topics using a `Bag of Words approach
<https://en.wikipedia.org/wiki/Bag-of-words_model>`_.
Two algorithms... | pd.DataFrame(evaluations[::-1]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
@author: <NAME>
"""
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR, OneClassSVM
from sklearn.model_selection import KFold, cross_val_predict, GridSearchCV
from sklearn.gaussian_p... | pd.DataFrame(ad_index_prediction, index=x_prediction.index, columns=['ocsvm_data_density']) | pandas.DataFrame |
import numpy as np
import pandas as pd
from datetime import datetime
import pytest
import empyrical
import vectorbt as vbt
from vectorbt import settings
from tests.utils import isclose
day_dt = np.timedelta64(86400000000000)
ts = pd.DataFrame({
'a': [1, 2, 3, 4, 5],
'b': [5, 4, 3, 2, 1],
'c': [1, 2, 3, ... | pd.Series([res_a, res_b, res_c], index=ret.columns) | pandas.Series |
from collections import OrderedDict
import numpy as np
from numpy import nan, array
import pandas as pd
import pytest
from .conftest import (
assert_series_equal, assert_frame_equal, fail_on_pvlib_version)
from numpy.testing import assert_allclose
import unittest.mock as mock
from pvlib import inverter, pvsystem... | pd.Series(index=dr, data=expected) | pandas.Series |
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([1,2,3]) | pandas.Series |
#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.read_csv(filename,index_col=False) | pandas.read_csv |
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import re
from math import ceil
import pandas as pd
from sklearn.metrics import classification_report
from scipy.stats import shapiro, boxcox, yeojohnson
from scipy.stats import probplot
from sklearn.preprocessing import LabelEncoder, PowerTransfo... | pd.DataFrame(s, columns=[self.target]) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 29 11:19:12 2019
@author: salilsharma
"""
#Identify trucks; genrate data for Biogeme
import json
import requests
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import time
from localfunctions import*
from clusterAlgorithm i... | pd.read_pickle(Masterpath + 'Recursive_logit/ODP/'+Path+'/' + Path+ "_masterList.pkl") | pandas.read_pickle |
import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from sklearn.linear_model import LinearRegression
import ipywidgets as widgets
st.write("""
# Hackaton Navi-Capital
Ferramenta para ajudar investidores a avaliar a pontuação ESG de empresas
... | pd.to_datetime(df_companies_financials.ref_date) | pandas.to_datetime |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
sys.path.append("../")
from DAL import labConn2
from LOG import logs_APP as log
import plotly.express as px
# Com o pandas monsta um dataFrame com os dados do Excel
_arq = | pd.read_excel(r"C:\Claro\Desenvolvimento\Python\server.xlsx") | pandas.read_excel |
import pandas as pd
import logging
modlog = logging.getLogger('capture.generate.calcs')
def mmolextension(reagentdf, rdict, experiment, reagent):
"""TODO Pendltoonize this docc"""
mmoldf = (pd.DataFrame(reagentdf))
portionmmoldf = pd.DataFrame()
for chemlistlocator, conc in (rdict['%s' %reagent].con... | pd.DataFrame() | pandas.DataFrame |
# Notebook to transform OSeMOSYS output to same format as EGEDA
# Import relevant packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
from openpyxl import Workbook
import xlsxwriter
import pandas.io.formats.excel
import glob
import re
# Path for OSeMOSYS output
path_output = './d... | pd.DataFrame() | pandas.DataFrame |
'''
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(dd_df['calc_price']) | pandas.isnull |
#!/usr/bin/env python
# coding: utf-8
import numpy as np
import pandas as pd
from time import time
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-q', action="store", dest="qrel_file", help="qrel train file")
parser.add_argument('-t', action="store", dest="top1000_file", help="top1000 train f... | pd.read_csv(qrel_file, delimiter='\t', header=None) | pandas.read_csv |
import os
from typing import Any, Callable
import flask
import joblib
import pandas as pd
from sklearn.pipeline import Pipeline
def create_predict_handler(
path: str = os.getenv("MODEL_PATH", "data/pipeline.pkl"),
) -> Callable[[flask.Request], flask.Response]:
"""
This function loads a previously traine... | pd.DataFrame.from_records([request_json]) | pandas.DataFrame.from_records |
#!/usr/bin/env python3
import atddm
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import pytz
# from datetime import time
from constants import COLORS, TZONES, CODES, BEGDT, ENDDT
import rpy2.robjects as robjects
from rpy2.robjects.packages import importr
from rpy2.robje... | pd.Timestamp('08:00:00') | pandas.Timestamp |
import flask
from flask import request, jsonify
import numpy as np
import pandas as pd
import json
import nltk
nltk.download('vader_lexicon')
from nltk.sentiment.vader import SentimentIntensityAnalyzer
from newsapi import NewsApiClient
api = NewsApiClient(api_key='0924f039000046a99a08757a5b122a4c')
app = flask.Flask... | pd.DataFrame(scores) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 12 12:29:19 2019
@author: sdenaro
"""
import matplotlib.pyplot as plt
import pandas as pd
from datetime import datetime as dt
from datetime import timedelta
import numpy as np
import numpy.matlib as matlib
import seaborn as sns
from sklearn import linear_... | pd.concat([MidC,CAISO], axis=1) | pandas.concat |
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_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
import subprocess
import os
import re
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin
from attrdict import AttrDict
from tqdm import tqdm
import argparse
import collections
import logging
import json
import re
import torch
from torch.utils.data import TensorDataset, Dat... | pd.DataFrame(bert_output.loc[idx,"features"]) | pandas.DataFrame |
"""
KAMA: Kaufmans Adaptive Moving Average.
"""
import pyximport; pyximport.install()
from datautils import gen_closes
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pandas import Series
def kama(x, n=10, pow1=2, pow2=30):
"""KAMA: Kaufmans Adaptive Moving Average.
Params:
... | pd.concat([closes, kama10], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
from pandas.compat import range
import pandas as pd
import pandas.util.testing as tm
# -------------------------------------------------------------------
# Comparisons
class TestFrameComparisons(object):
def test_df_boolean_comparison_error(self):
... | pd.DataFrame(['ax', np.nan, 'ax']) | pandas.DataFrame |
from os import listdir
from os.path import isfile, join
import re
import nltk
from nltk.corpus import stopwords
from string import punctuation
import pymorphy2
import pandas
from collections import Counter
from collections import defaultdict, OrderedDict
import math
import numpy
# nltk.download("stopwords") # used onl... | pandas.DataFrame(tfDictionaries) | pandas.DataFrame |
from time import time
from os import path, listdir
from datetime import timedelta
from datetime import date as dt_date
from datetime import datetime as dt
from numpy import cumprod
from pandas import DataFrame, read_sql_query, read_csv, concat
from functions import psqlEngine
class Investments():
def __init__(se... | concat([domestic_bonds, self.domestic_stocks[columns], self.international_stocks[columns], self.crypto[columns], self.domestic_funds[columns], self.domestic_options[columns]]) | pandas.concat |
#! /bin/python3
# compute_PCs.py takes
# RUN ON MASTODON--doesn't have memory issues there
# want local mean with #of non-missing values in either direction
# local mean with max number of positions to search in either direction
# global mean
# global mean by category
# filter out cases where there is no methylation (... | pd.read_csv(args.filter_file) | pandas.read_csv |
# Name : <NAME>
# Roll Number : 101903508
import pandas as pd
import os
import sys
def main():
if len(sys.argv) != 5:
print("ERROR : incorrect number of parameters")
sys.exit(1)
elif not os.path.isfile(sys.argv[1]):
print(f"ERROR : {sys.argv[1]} Don't exist!!")
sys.exit(1)
... | pd.to_numeric(dataset.iloc[:, i], errors='coerce') | pandas.to_numeric |
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 |
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
from sklearn import svm, tree, linear_model, neighbors, naive_bayes, ensemble, discriminant_analysis, gaussian_process
from xgboost import XGBClassifier
from sklearn.model_sele... | pd.merge(features, match_results[['match.matchId', 'result']], on='match.matchId') | pandas.merge |
"""Load the processed QCMR data."""
from pathlib import Path
from typing import Iterator, Tuple, Type
import numpy as np
import pandas as pd
from pydantic import validate_arguments
from ..utils.misc import fiscal_year_quarter_from_path
from . import cash, obligations, personal_services, positions
from .base import E... | pd.read_csv(f, dtype={"dept_code": str}) | pandas.read_csv |
#!/usr/bin/env python
import rospy
from std_msgs.msg import Empty
import os
import csv
import time
import pandas as pd
import matplotlib
matplotlib.use('Agg')
#import matplotlib.pyplot as plt
#import sys
class Plots:
def __init__(self, path, param):
self.path = path
self.param = param
rospy... | pd.merge(df, reference_, on='Tiempo', how='inner') | pandas.merge |
# Adapted from
# https://github.com/CODAIT/text-extensions-for-pandas/blob/dc03278689fe1c5f131573658ae19815ba25f33e/text_extensions_for_pandas/array/tensor.py
# and
# https://github.com/CODAIT/text-extensions-for-pandas/blob/dc03278689fe1c5f131573658ae19815ba25f33e/text_extensions_for_pandas/array/arrow_conversion.py
... | pd.api.types.is_object_dtype(dtype) | pandas.api.types.is_object_dtype |
from torch.utils.data.sampler import WeightedRandomSampler
from sklearn.metrics import accuracy_score
from torch.utils.data import DataLoader
from multiprocessing import cpu_count
import pytorch_lightning as pl
import torch
import functools
import traceback
import psutil
import pandas as pd
class GloveFinetuner(pl.Li... | pd.DataFrame(columns=['valid_batch_loss','valid_batch_acc']) | pandas.DataFrame |
# -*- coding:/ utf-8 -*-
"""
Created on Tue Jul 23 12:07:20 2019
This piece of software is bound by The MIT License (MIT)
Copyright (c) 2019 <NAME>
Code written by : <NAME>
User name - ADM-PKA187
Email ID : <EMAIL>
Created on - Mon Jul 29 09:17:59 2019
version : 1.1
"""
# Importing the required libraries... | pd.concat([df_inter_1, df_inter_2], axis=1, sort=False) | pandas.concat |
from datetime import datetime
import numpy as np
import pytest
from pandas import DataFrame, Index, MultiIndex, RangeIndex, Series
import pandas.util.testing as tm
class TestSeriesAlterAxes:
def test_setindex(self, string_series):
# wrong type
msg = (
r"Index\(\.\.\.\) must be called... | Series({1: 10, 2: 20}) | pandas.Series |
import streamlit as st
import pandas as pd
import plotly.express as px
import datetime
# Setting Dashboard Interface
st.set_page_config(layout="wide")
st.title('🦠COVID-19 Dashboard')
st.markdown(
'''A collaborative work of building an interactive Covid-19 dashboard to provide insights about COVID globally. [GitHub P... | pd.to_datetime(data_to_work['Date'], format='%m/%d/%y') | pandas.to_datetime |
# BSD 2-CLAUSE LICENSE
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
# Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
# Redistributions i... | pd.concat([df, time_df], axis=1) | pandas.concat |
from copy import deepcopy
from distutils.version import LooseVersion
from operator import methodcaller
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, MultiIndex, Series, date_range
import pandas.util.testing as tm
from pandas.util.testing ... | td.skip_if_no('xarray', min_version='0.7.0') | pandas.util._test_decorators.skip_if_no |
import os
import pytest
import yaml
import numpy as np
import pandas as pd
from collections import namedtuple
from datetime import datetime, timedelta, date
from unittest import mock
from prophet import Prophet
import mlflow
import mlflow.prophet
import mlflow.utils
import mlflow.pyfunc.scoring_server as pyfunc_scori... | pd.to_numeric(example_["y"]) | pandas.to_numeric |
# %% Packages
import os
import shutil
import glob
import numpy as np
from PIL import Image
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from pyhocon import ConfigTree
from typing import List, Tuple
from sklearn.utils.class_weight import compute_sample_weight
from src.base_classes.task imp... | pd.Series(multi_label) | pandas.Series |
import yfinance as yf
import datetime as dt
import pandas as pd
import time
from yahoo_fin import stock_info as si
pd.set_option('display.max_columns', None)
mylist = []
today = dt.date.today()
mylist.append(today)
today = mylist[0]
#Asks for stock ticker
stocks = si.tickers_sp500()
stocks = [item.replace(".", "-") ... | pd.to_datetime(df.index) | pandas.to_datetime |
import pandas as pd
def to_df(figure):
"""
Extracts the data from a Plotly Figure
Parameters
----------
figure : plotly_figure
Figure from which data will be
extracted
Returns a DataFrame or list of DataFrame
"""
dfs=[]
for trace in figure... | pd.concat(dfs,axis=1) | pandas.concat |
import os
os.environ['CUDA_VISIBLE_DEVICES']='4'
import argparse
import logging
import torch
from torchvision import transforms
from seq2seq.trainer.supervised_trainer import SupervisedTrainer
from seq2seq.models.DecoderRNN import DecoderRNN
from seq2seq.models.EncoderRNN import EncoderRNN
from seq2seq.models.seq2seq i... | pd.to_datetime(train_data.index) | pandas.to_datetime |
import os
import streamlit as st
import pandas as pd
import plotly.express as px
from PIL import Image
favicon = Image.open("media/favicon.ico")
st.set_page_config(
page_title = "AICS Results",
page_icon = favicon,
menu_items={
'Get Help': 'https://github.com/All-IISER-Cubing-Society/Results',
... | pd.concat(frames) | pandas.concat |
from collections import abc, deque
from decimal import Decimal
from io import StringIO
from warnings import catch_warnings
import numpy as np
from numpy.random import randn
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
... | Series([], dtype="M8[ns]") | pandas.Series |
'''
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py 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, either version 3 of the License, or
(at your option) any late... | pd.merge(df_1_act, df_2_act, how='outer', on='var') | pandas.merge |
from __future__ import division
from datetime import timedelta
from functools import partial
import itertools
from nose.tools import assert_true
from parameterized import parameterized
import numpy as np
from numpy.testing import assert_array_equal, assert_almost_equal
import pandas as pd
from toolz import merge
fro... | pd.Timestamp('2015-01-07') | pandas.Timestamp |
# Copyright 2017-2020 Lawrence Livermore National Security, LLC and other
# CallFlow Project Developers. See the top-level LICENSE file for details.
#
# SPDX-License-Identifier: MIT
import pandas as pd
class RuntimeScatterplot:
def __init__(self, state, module):
self.graph = state.new_gf.graph
se... | pd.DataFrame(ret) | pandas.DataFrame |
"""Unit tests for Model class
"""
import unittest
import pandas as pd
import torch
from stock_trading_backend.agent import Model
class TestModel(unittest.TestCase):
"""Unit tests for Model class.
"""
def test_initializes(self):
"""Checks if model initializes properly.
"""
model =... | pd.Series([1, 2, 3], ["balance", "net_worth", "owned"]) | pandas.Series |
from collections import OrderedDict
import contextlib
from datetime import datetime, time
from functools import partial
import os
from urllib.error import URLError
import warnings
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, Index, Multi... | tm.assert_frame_equal(actual, expected) | pandas.util.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
"""Converter for miRBase Families."""
from typing import Iterable
import pandas as pd
from tqdm import tqdm
from .mirbase_constants import (
get_premature_df,
get_premature_family_df,
get_premature_to_prefamily_df,
)
from ..struct import Obo, Reference, Term, has_member
__all__ ... | pd.merge(intermediate_df, premature_df, left_on="premature_key", right_on="premature_key") | pandas.merge |
"""Dynamic file checks."""
from dataclasses import dataclass
from datetime import date, timedelta
from typing import Dict, Set
import re
import pandas as pd
import numpy as np
from .errors import ValidationFailure, APIDataFetchError
from .datafetcher import get_geo_signal_combos, threaded_api_calls
from .utils import r... | pd.isna(frame["ststat"]) | pandas.isna |
# -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101
import itertools
import warnings
from warnings import catch_warnings
from datetime import datetime
from pandas.types.common import (is_integer_dtype,
is_float_dtype,
is_scalar)
from pandas.compat... | Series([0.2, 0.2, 0.1], index=[2, 2, 1]) | pandas.core.api.Series |
import warnings
import pandas as pd
import numpy as np
__all__ = ['Pandas2numpy']
def assert_list_contains_all(l, l_subset):
"Raise a warning if some columns from `l_subset` do not exist in `l`."
non_existing_columns = set(l_subset).difference(l)
if len(non_existing_columns) > 0:
non_existing_colu... | pd.Categorical.from_codes(codes, categories=categories) | pandas.Categorical.from_codes |
import numpy as np
import pandas as pd
import sys
import pickle
import matplotlib.pyplot as plt
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
import pyqtgraph
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtTest import *
from Model_modul... | pd.DataFrame(compared_db) | pandas.DataFrame |
from datetime import datetime as dt
import os
import pandas as pd
import ntpath
import numpy as np
import math
from distutils.dir_util import copy_tree
from shutil import rmtree
import sqlite3
# 'cleanData' is taking the data that was imported from 'http://football-data.co.uk/'
# and 'cleaning' the data so that only ... | pd.read_csv(file_path) | pandas.read_csv |
"""
.. module:: linregress
:platform: Unix
:synopsis: Contains methods for doing linear regression.
.. moduleauthor:: <NAME> <<EMAIL>>
.. moduleauthor:: <NAME> <<EMAIL>>
"""
from disaggregator import GreenButtonDatasetAdapter as gbda
import pandas as pd
import numpy as np
import json
import matplotlib.pyplot as... | pd.merge(df_trace,df_temps_dropped,left_index=True,right_index=True) | pandas.merge |
from six import string_types, text_type, PY2
from docassemble.webapp.core.models import MachineLearning
from docassemble.base.core import DAObject, DAList, DADict
from docassemble.webapp.db_object import db
from sqlalchemy import or_, and_
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForest... | pd.Series(df[key]) | pandas.Series |
import copy
import os
import re
from functools import reduce
from os.path import join
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
from custom.const import get_fig_folder
db_order = [
'Traumabase',
'UKBB',
'MIMIC',
'NHIS',
]
markers_db = {
... | pd.isna(df) | pandas.isna |
import os
import matplotlib
matplotlib.use("agg")
from matplotlib import pyplot as plt
import seaborn as sns
import datetime
import pandas as pd
import matplotlib.dates as mdates
import common
infile = snakemake.input[0]
outfile = snakemake.output[0]
df = pd.read_table(infile)
df["time"] = | pd.to_datetime(df["time"]) | pandas.to_datetime |
# coding: utf-8
import numpy as np
import tensorflow as tf
import cv2 as cv
import time
import base64
import pandas as pd
from utils.visualization_utils import visualize_boxes_and_labels_on_image_array # Taken from Google Research GitHub
from utils.mscoco_label_map import category_index
############################# ... | pd.concat([timestamp_df, boxes_df, classes_df, classes_str_df, score_df], axis=1) | pandas.concat |
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