prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# -*- coding: utf-8 -*-
import re
import warnings
from datetime import timedelta
from itertools import product
import pytest
import numpy as np
import pandas as pd
from pandas import (CategoricalIndex, DataFrame, Index, MultiIndex,
compat, date_range, period_range)
from pandas.compat import PY... | pd.DatetimeIndex([x[1] for x in result]) | pandas.DatetimeIndex |
import pandas as pd
import time
from collections import defaultdict
import re
import pickle
import argparse
import csv
import sys
import matplotlib.pyplot as plt
import seaborn as sns
import pickle as pkl
import math
import itertools
import os
import scipy
import numpy as np
from datetime import datetime
import copy
fr... | pd.isnull(ing) | pandas.isnull |
""" Fred Model """
__docformat__ = "numpy"
import logging
from typing import Dict, List, Tuple
import fred
import pandas as pd
import requests
from fredapi import Fred
from gamestonk_terminal import config_terminal as cfg
from gamestonk_terminal.decorators import log_start_end
from gamestonk_terminal.helper_funcs im... | pd.DataFrame() | pandas.DataFrame |
import os
import mat73
import json
import numpy as np
import pandas as pd
import cv2
import math
def normalized2KITTI(box):
"""
convert Bbox format
:param box: [X, Y, width, height]
:return: [xmin, ymin, xmax, ymax]
"""
o_x, o_y, o_width, o_height = box
xmin = int(o_x)
ymin = int(o_y)
... | pd.DataFrame(data=numpy_data, columns=cols) | pandas.DataFrame |
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import Index, MultiIndex, Series, date_range, isna
import pandas._testing as tm
@pytest.fixture(
params=[
"linear",
"index",
"values",
"nearest",
"slinear",
... | Series([1, 4, np.nan, 16], index=[1, 2, 3, 4]) | pandas.Series |
#!/usr/bin/env python3
import argparse
import datetime
import logging
import os
import shutil
import time
from subprocess import check_output
import coloredlogs
import netCDF4 as nc
import numpy as np
import pandas as pd
from inicheck.utilities import mk_lst, remove_chars
from spatialnc.topo import get_topo_stats
fro... | pd.to_datetime(dt_str) | pandas.to_datetime |
# python 3.7
# -*- coding: utf-8 -*-
#!/usr/bin/env python
# coding: utf-8
"""
1. using most recent publication of researchers as input to generate user profiles
2. pretrain word2vec model window_5.model.bin and candidate_paper.csv are available via google drive link,
you can download the files and
change the path in ... | pd.DataFrame(sim_scores) | pandas.DataFrame |
import os
import copy
import pytest
import numpy as np
import pandas as pd
import pyarrow as pa
from pyarrow import feather as pf
from pyarrow import parquet as pq
from time_series_transform.io.base import io_base
from time_series_transform.io.numpy import (
from_numpy,
to_numpy
)
from time_series_transfor... | pd.DataFrame(expect_collection_expandFull['pad']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
from pandas.types.dtypes import DatetimeTZDtype, PeriodDtype, CategoricalDtype
from pandas.types.common import pandas_dtype, is_dtype_equal
import pandas.util.testing as tm
class TestPandasDtype(tm.TestCase):
def test_numpy_dtype(self):
for dtyp... | pandas_dtype('datetime64[ns, US/Eastern]') | pandas.types.common.pandas_dtype |
import pandas as pd
import numpy as np
from sklearn import datasets, preprocessing, metrics, model_selection
from ..models.ccf import CanonicalCorrelationForestClassifier
def test_ccf():
data = datasets.load_breast_cancer()
X_train, X_valid, y_train, y_valid = model_selection.train_test_split(preprocessing.scale(da... | pd.DataFrame(X_train, columns=data.feature_names) | pandas.DataFrame |
# http://github.com/timestocome
#
# Build Bayesian using daily BitCoin Closing Price
# Use today and tomorrow's data to see if it can predict
# next few days market movements
from collections import Counter
import pandas as pd
import numpy as np
# http://coindesk.com/price
data_file = 'BitCoin_Daily_Close.csv... | pd.Series(data=edges) | pandas.Series |
import os
import json
import random
import pandas as pd
import numpy as np
import experiments
import utils
import granularity
from granularity import SeqRange, VectorRange, TaggedString
from eval_functions import eval_f1, iou_score_multi, rmse
import merge_functions
def label2tvr(label, default=None):
return defa... | pd.read_json("data/PICO/PICO-annos-crowdsourcing.json", lines=True) | pandas.read_json |
"""
Tests the usecols functionality during parsing
for all of the parsers defined in parsers.py
"""
from io import StringIO
import numpy as np
import pytest
from pandas._libs.tslib import Timestamp
from pandas import DataFrame, Index
import pandas._testing as tm
_msg_validate_usecols_arg = (
"'usecols' must eit... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
import numpy as np
import pandas as pd
from bach import Series, DataFrame
from bach.operations.cut import CutOperation, QCutOperation
from sql_models.util import quote_identifier
from tests.functional.bach.test_data_and_utils import assert_equals_data
PD_TESTING_SETTINGS = {
'check_dtype': False,
'check_exact... | pd.qcut(p_series, q=[0.5]) | pandas.qcut |
# Import Libraries
import time
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
# Import Libraries
from scipy import stats
import matplotlib.pyplot as plt
# import time
# Import Libraries
import math
class YinsDL:
print("... | pd.Series(y_test_hat_) | pandas.Series |
'''
MIT License
Copyright (c) 2019 <NAME>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distri... | pd.DataFrame(facial_ids) | pandas.DataFrame |
from __future__ import division
from functools import wraps
import pandas as pd
import numpy as np
import time
import csv, sys
import os.path
import logging
from .ted_functions import TedFunctions
from .ted_aggregate_methods import TedAggregateMethods
from base.uber_model import UberModel, ModelSharedInputs
class Te... | pd.Series([], dtype="float", name="pt_dicot_post_loec") | pandas.Series |
"""(West) German interest and inflation rate 1972-1998"""
from numpy import recfromtxt, column_stack, array
from pandas import DataFrame
from statsmodels.datasets.utils import Dataset
from os.path import dirname, abspath, pardir, join
__docformat__ = 'restructuredtext'
COPYRIGHT = """...""" # TODO
TITLE = __doc__
... | DataFrame(dataset.data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 2 11:40:15 2019
@author: JUANSE
"""
# importamos las librerias necesarias
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
#establecemos un directorio de trabajo
os.chdir("C:/Users/Usuario/Documents/Sequia/acomo... | pd.read_csv('C:/Users/Usuario/Documents/Sequia/acomodar_estaciones/todas/est_prec_qc.csv',sep=';',parse_dates=["Date"]) | pandas.read_csv |
import pandas as pd
if __name__=='__main__':
for i in range(90001,90011):
prophet_file_path='../data/prophet/prophet_feature_'+str(i)+'.csv'
prophet_data=pd.read_csv(prophet_file_path,index_col=0)
prophet_data.index=pd.to_datetime(prophet_data.index)
train_file_path = '../data/tra... | pd.merge(train_data, prophet_data, left_index=True, right_index=True) | pandas.merge |
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# This file contains dummy data for the model unit tests
import numpy as np
import pandas as pd
AIR_FCST_LINEAR_95 = pd.DataFrame(
{
... | pd.Timestamp("2013-01-05 00:00:00") | pandas.Timestamp |
import pandas as pd
import numpy as np
import datetime as dt
import concurrent.futures
import threading
from unidecode import unidecode
def get_parties_procesos():
_parties_procesos = pd.read_sql(sql=""" select "t1"."CodigoProceso" as "tender/id",
"t1"."UnidadCompra" as "parties/0/name",
"t1"."CodigoUnidadCom... | pd.merge(completo, mapeo_parties4, left_on='parties/0/id', right_on='parties/0/id', how='left') | pandas.merge |
# This file is part of Patsy
# Copyright (C) 2012-2013 <NAME> <<EMAIL>>
# See file LICENSE.txt for license information.
# Exhaustive end-to-end tests of the top-level API.
import sys
import __future__
import six
import numpy as np
from nose.tools import assert_raises
from patsy import PatsyError
from patsy.design_inf... | pandas.DataFrame({"x": [1, 2, 3]}) | pandas.DataFrame |
# standard library
from typing import List, Union, Tuple
# dependent packages
import numpy as np
import pandas as pd
from lmfit.models import LorentzianModel
from scipy.interpolate import interp1d
from scipy.stats import cauchy
# type aliases
ArrayLike = Union[np.ndarray, List[float], List[int], float, int]
# main ... | pd.DataFrame(fit, columns=["Center", "HWHM", "max height", "chi sq"]) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pandas._testing as tm
class TestTranspose:
def test_transpose_tzaware_1col_single_tz(self):
# GH#26825
dti = | pd.date_range("2016-04-05 04:30", periods=3, tz="UTC") | pandas.date_range |
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeig... | pd.concat([whole_score, scores], ignore_index=True) | pandas.concat |
import numpy as np
import matplotlib.pyplot as plt
import psycopg2 as sql
import pandas as pd
db = sql.connect(
database='IMDb',
user='username',
password = 'password'
)
c = db.cursor()
def media_counts(q_tvEpisode, q_short, q_movie, q_video, q_tvMovie, q_tvSeries):
c.execute(q_tvEpisode)... | pd.DataFrame(rows, columns=['year_produced', 'Amount']) | pandas.DataFrame |
# std
import copy
import time
from contextlib import closing
# 3rd
import numpy as np
import pandas as pd
import pathlib
from typing import Union, List, Dict, Optional, Iterable, Sequence, Any
from sqlite3 import Connection
# ours
import ankipandas.raw as raw
from ankipandas.util.dataframe import replace_df_inplace, ... | pd.DataFrame(columns=self.columns, index=all_cids) | pandas.DataFrame |
import time
import numpy as np
import pandas as pd
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
URLS_PATH = "./data/urls_transferwise.csv"
CHROMEDRIVER_PATH = "./drivers/chromedriver"
# connect to chrome webdriver
options = Options()
options.add_argument('--headless')
# option... | pd.read_csv(URLS_PATH) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
# import shapefile
# import finoa
# import shapely
import numpy as np
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
import pandas as pd
# from pyproj import Proj, transform
# import stateplane
# from datetime import datetime
impor... | pd.read_csv("../crashes/crash20" + year + "/CRASH.txt", low_memory=False) | pandas.read_csv |
import numpy as np
#import scipy.io #required to read Matlab *.mat file
from scipy import linalg
import pandas as pd
import networkx as nx
#import pickle
import itertools
from sklearn.covariance import GraphLassoCV, ledoit_wolf, graph_lasso
from statsmodels.stats.correlation_tools import cov_nearest
import networkx as... | pd.read_excel(file, index_col=[0,1]) | pandas.read_excel |
import os
import networkx as nx
import matplotlib.pyplot as plt
import geopandas as gpd
import pandas as pd
import numpy as np
import functools
import operator
import shapely.affinity
from shapely.ops import split
from shapely.geometry import Point, LineString, MultiLineString, GeometryCollection, Polygon
from math imp... | pd.to_numeric(Af_o["dip"], downcast="float") | pandas.to_numeric |
import hdt
import gzip, sys, csv
import pandas as pd
import numpy as np
import kgbench as kg
from tqdm import tqdm
"""
Extracts target labels.
"""
def entity(ent):
"""
Returns the value of an entity separated from its datatype ('represented by a string')
:param ent:
:return:
"""
if ent.sta... | pd.DataFrame(ent_data, columns=['index', 'datatype', 'label']) | pandas.DataFrame |
"""SQL io tests
The SQL tests are broken down in different classes:
- `PandasSQLTest`: base class with common methods for all test classes
- Tests for the public API (only tests with sqlite3)
- `_TestSQLApi` base class
- `TestSQLApi`: test the public API with sqlalchemy engine
- `TestSQLiteFallbackApi`: t... | Series(["00:00:01", "00:00:03"], name="foo") | pandas.Series |
import pandas as pd
import numpy as np
import glob
import sys
import re
from scipy import interpolate
from astropy.cosmology import Planck15 as cosmo
from astropy.cosmology import z_at_value
import astropy.units as u
from cosmic.evolve import Evolve
from cosmic.sample.initialbinarytable import InitialBinaryTable
#----... | pd.DataFrame(columns=columns) | pandas.DataFrame |
# noinspection PyPackageRequirements
import datawrangler as dw
import numpy as np
import pandas as pd
from .common import Manipulator
# noinspection PyShadowingBuiltins
def fitter(data, axis=0):
if axis == 1:
return dw.core.update_dict(fitter(data.T, axis=0), {'transpose': True})
elif axis != 0:
... | pd.Series(index=data.columns) | pandas.Series |
import pandas as pd
import dropbox
from tqdm import tqdm
from dropbox import DropboxOAuth2FlowNoRedirect
'''
This sets up a dropbox OAuthed client
'''
APP_KEY = 'xxx'
APP_SECRET = 'xxx'
auth_flow = DropboxOAuth2FlowNoRedirect(APP_KEY, APP_SECRET)
authorize_url = auth_flow.start()
print("1. Go to: " +... | pd.DataFrame([]) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
import datetime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pytz
import random
# Date and Time
# =============
print(datetime.datetime(2000, 1, 1))
print(datetime.datetime.strptime("2000/1/1", "%Y/%m/%d"))
print(datetime.datetime(2000, 1, 1, 0, ... | pd.date_range(start="2000-01-01", periods=5, freq='1D1h1min10s') | pandas.date_range |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import math
import json
import datetime
import matplotlib.dates as mdates
import os.path
import pickle
sns.set(context='paper', style={'axe... | pd.DataFrame(data=d) | pandas.DataFrame |
from contextlib import nullcontext as does_not_raise
from functools import partial
import pandas as pd
from pandas.testing import assert_series_equal
from solarforecastarbiter import datamodel
from solarforecastarbiter.reference_forecasts import persistence
from solarforecastarbiter.conftest import default_observatio... | pd.Series(obs_values, index=obs_index, dtype=float) | pandas.Series |
# -*- coding: utf-8 -*-
"""FINAL PROJECT.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1bMDq2WSLnIYa4O1Eq-xw65RoksCOSIh6
# FINAL PROJECT
## A. UNDERSTAND DATA
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import s... | pd.read_csv('Data_Negara_HELP.csv') | pandas.read_csv |
import pandas
import sqlite3
import datetime
import requests
import zipfile
import io
import subprocess
from fantasy_machine import config
from fantasy_machine import data_ops
class update_data(object):
def __init__(self):
pass
def main(self):
start = datetime.datetime.now()
print('Beginning Update: {}'.for... | pandas.read_sql_query(query ,con) | pandas.read_sql_query |
import configparser
import os
from os.path import exists
from datetime import datetime, timedelta
import sys
from time import time
import pandas as pd
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtCore import QProcess, Qt, QThread, QTimer
from mainUi import Ui_MainWindow
from pandascontroller import DomainInp... | pd.DataFrame(None, columns=columnHeaders, dtype=object) | pandas.DataFrame |
# coding: utf-8
# In[1]:
get_ipython().run_line_magic('matplotlib', 'inline')
# In[2]:
#-------------------------------------------------------------------------------------------------------------------------------
# By <NAME> (August 2018)
#
# Plot heatmap of gene expression data as environment change from h... | pd.DataFrame(corr_score, index=dataset.index, columns=['Pearson', 'Pvalue']) | pandas.DataFrame |
from pathlib import Path
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 models.OutlierDetector import Detector... | pd.DataFrame() | pandas.DataFrame |
import os
from datetime import date
from altair_saver import save
import altair as alt
import pandas as pd
import modules.c19api as c19api
def tested():
filename = "./graphs/no_tested.png"
if os.path.exists(filename):
os.remove(filename)
data = c19api.timeseries("tested_lab")
df = pd.DataFram... | pd.to_datetime(df["date"]) | pandas.to_datetime |
# Parses a GL5 file and extracts raw data and event information.
import pickle
import sys
import numpy as np
import os
import time
from legacy_codes import *
from message_codes import *
import datetime
import pandas as pd
from Models import *
def typecast(value, dtype):
value = np.array(value)
b = value.tobyt... | pd.to_datetime(start_date, unit='ns') | pandas.to_datetime |
#!/usr/bin/env python3
"""
Combine FoldX AnalyseComplex output from many complexes
"""
import sys
import argparse
import pandas as pd
from pathlib import Path
def import_complex_dir(path):
"""
Import tables from an AnalyseComplex output directory
"""
path = path.rstrip('/')
interactions = pd.read_c... | pd.concat(complex_dfs) | pandas.concat |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import io
import os
import pkgutil
from datetime import datetime
from typing import cast, List
from unittest import TestCase
import matplot... | pd.Series([4]) | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@created: 11.11.19
@author: felix
"""
from typing import Optional
from collections import Counter
from calendar import monthrange
import datetime
import pandas as pd
class Weekday:
MONDAY = 0
TUESDAY = 1
WEDNESDAY = 2
THURSDAY = 3
FRIDAY = 4
S... | pd.to_datetime(df['date']) | pandas.to_datetime |
import funcy
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import re
from dateutil import parser
from tqdm import tqdm
from utils.helpers import *
from utils.plot import plot_joint_distribution
font = {
"size": 30
}
matplotlib.rc("font", **font)
pd.options.mo... | pd.get_dummies(df_final.irc, prefix="irc") | pandas.get_dummies |
import os
from pathlib import Path
import random
import time
import math
import json
import json
import shutil
import inspect
import warnings
import logging
import functools
from concurrent.futures import ThreadPoolExecutor
import dask
from dask.diagnostics import ProgressBar
from copy import deepcopy
from tqdm import... | pd.concat(all_df, ignore_index=True) | pandas.concat |
"""WISDM dataset
URL of dataset: https://www.cis.fordham.edu/wisdm/includes/datasets/latest/WISDM_ar_latest.tar.gz
"""
import re
import numpy as np
import pandas as pd
from pathlib import Path
from typing import Optional, Union, List, Tuple
from ..core import split_using_target, split_using_sliding_window
from .base... | pd.DataFrame(seg.iloc[:, 3:], columns=raw.columns[3:]) | pandas.DataFrame |
# 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"); y... | pandas.util.testing.assert_almost_equal(left_float, right_float) | pandas.util.testing.assert_almost_equal |
import argparse
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.firefox.options import Options
from selenium.webdriver.common.desired_capabilities import DesiredCapabilities
from selenium.webdriver.firefox.firefox_profile import FirefoxProfile
from selenium.webdriv... | pd.Series([url, opening, middle, closing], index=thank_you.columns) | pandas.Series |
# ▣ 2.1 bit.ly의 1.usa.gov 데이터
path = 'PythonForDataAnalysis/ch02/usagov_bitly_data2012-03-16-1331923249.txt'
print(open(path).readline())
# - json 모듈의 loads 함수로 내려받은 샘플 파일을 한 줄씩 읽는다.
import json
path = 'PythonForDataAnalysis/ch02/usagov_bitly_data2012-03-16-1331923249.txt'
records = [json.loads(line) for line in open(... | pd.pivot_table() | pandas.pivot_table |
# encoding: utf-8
from __future__ import division
import sys
import os
import time
import datetime
import pandas as pd
import numpy as np
import math
CURRENT_DIR = os.path.abspath(os.path.dirname(__file__))
ADD_PATH = "%s/../"%(CURRENT_DIR)
sys.path.append(ADD_PATH)
from tools.mail import MyEmail
from tools.html impor... | pd.read_csv(DATA_PATH+'/regist.'+year+'-'+month+'-'+day, encoding = 'utf-8') | pandas.read_csv |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import itertools
#Import Data
mbhmms_card = pd.read_csv("../s01-card/MBhmms/201006-MBhmms_pr_analysis.txt",sep='\t',skiprows=2,usecols=[0,4,5],names=["Family","Precision", "Recall"])
mbhmms_card["Database"] = "Resfams 2.0"
ol... | pd.read_csv("../s02-ncbi/MBHmms/201007-MBHmms_pr_analysis.txt",sep='\t',skiprows=2,usecols=[0,4,5],names=["Family","Precision", "Recall"]) | pandas.read_csv |
# This file can download the rarity data from website rarity.tools
# There is a hidden API - "https://projects.rarity.tools/static/staticdata/<project_name>.json" that we can use to download rarity data in seconds.
# However the data is in raw format, we need to recalculate the scoring using the algorithm below.
# Wit... | pd.DataFrame.from_records(trait_data) | pandas.DataFrame.from_records |
from nltk.corpus import stopwords
import string, re
from collections import Counter
import wordcloud
import seaborn as sns
import regex as re
import numpy as np # linear algebra
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import trai... | pd.DataFrame({'a_name': y_train[1400:], 'b_name': X_train[1400:]}) | pandas.DataFrame |
import pandas as pd
import bioframe
import pyranges as pr
import numpy as np
from io import StringIO
def bioframe_to_pyranges(df):
pydf = df.copy()
pydf.rename(
{"chrom": "Chromosome", "start": "Start", "end": "End"},
axis="columns",
inplace=True,
)
return pr.PyRanges(pydf)
d... | pd.DataFrame([["chr1", 4, 5]], columns=["chrom", "start", "end"]) | pandas.DataFrame |
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,
... | DataFrame([[1, 5, 3, 7], [2, 6, 4, 8]]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Tests that quoting specifications are properly handled
during parsing for all of the parsers defined in parsers.py
"""
import csv
import pytest
from pandas.compat import PY3, StringIO, u
from pandas.errors import ParserError
from pandas import DataFrame
import pandas.util.testing as tm
... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
"""Test evaluator."""
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score
from sktime.benchmarking.evaluation import Evaluator
from sktime.benchmarking.metrics import PairwiseMetric
from sktime.benchmarking.results import RAMResults
from sktime.series_as_features.... | pd.to_datetime(1605268800, unit="ms") | pandas.to_datetime |
# Third party imports
import numpy as np
import pandas as pd
from scipy.optimize import curve_fit
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
from scipy.interpolate import interp1d
from scipy.integrate import odeint
# Local appli... | pd.DataFrame(data) | pandas.DataFrame |
''' Essential packages '''
import os
import pickle as pkl
from datetime import datetime
import numpy as np
import pandas as pd
import pandas_datareader as pdr
class StockMarket:
def __init__(self, start_date=datetime(2010, 1, 1), end_date=datetime.now(), data_dir='data'):
self.start_date = start_date
... | pd.concat([df_return, stock_return], axis=1) | pandas.concat |
from __future__ import print_function
import pandas as pd
import numpy as np
import tensorflow as tf
import os
import shutil
import copy
from time import time
from datetime import timedelta
import h5py
tf.compat.v1.disable_eager_execution()
'''
CHRONOS: population modeling of CRISPR readcount data
<NAME> (<EMAIL>)
T... | pd.DataFrame(self.cell_efficacy) | pandas.DataFrame |
import copy
import logging
from os import posix_fallocate
from typing import Tuple
from d3m.metadata.hyperparams import List
import numpy as np
import pandas as pd
from d3m.container import dataset
from processing import pipeline
from sklearn import metrics
from processing import metrics as processing_metrics
from skle... | pd.to_numeric(result_df[confidence_col]) | pandas.to_numeric |
# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership. The Modin Development Team licenses this file to you under the
# Apache License, Version 2.0 (the "License"); you may not u... | is_list_like(index) | pandas.core.dtypes.common.is_list_like |
import asyncio
import logging
import json
from glob import glob
from pathlib import Path
from dataclasses import dataclass, field, asdict
from typing import Dict, List
import pandas as pd
from magda.pipeline.parallel import init
from ki67.pipelines.config import ConfigPipeline
from ki67.common import Request
loggin... | pd.read_csv('data/experiments/thresholds.csv', index_col=0) | pandas.read_csv |
from ast import literal_eval
from os import listdir
from os.path import isfile, join
from scipy.sparse import save_npz, load_npz
import numpy as np
import os
import pandas as pd
import pickle
import stat
import yaml
def save_dataframe_csv(df, path, name):
df.to_csv(path+name, index=False)
def load_dataframe_cs... | pd.DataFrame(best_settings) | pandas.DataFrame |
import numpy as np
import pandas as pd
from src.create_initial_states.make_educ_group_columns import (
_create_group_id_for_non_participants,
)
from src.create_initial_states.make_educ_group_columns import (
_create_group_id_for_one_strict_assort_by_group,
)
from src.create_initial_states.make_educ_group_colum... | pd.Series([20.0, 21.0, 22.0, 23.0], index=df.index, name="group_id") | pandas.Series |
import os
from glob import glob
import zipfile
import xml.etree.ElementTree as ET
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import register_matplotlib_converters
| register_matplotlib_converters() | pandas.plotting.register_matplotlib_converters |
# -*- coding: utf-8 -*-
"""
Tests for abagen.samples module
"""
import numpy as np
import pandas as pd
import pytest
from abagen import samples
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# generate fake data (based largely on real data) so we know what to expect #
# # # # # # ... | pd.testing.assert_frame_equal(out, expected, check_like=True) | pandas.testing.assert_frame_equal |
import re
import numpy as np
import pandas as pd
import ops.filenames
from ops.constants import *
def parse_czi_export(f):
pat = '.*_s(\d+)c(\d+)m(\d+)_ORG.tif'
scene, channel, m = re.findall(pat, f)[0]
return {WELL: int(scene), CHANNEL: int(channel), SITE: int(m) - 1}
def make_czi_file_table(f... | pd.concat([df1, df2], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 30 12:49:13 2019
@author: andre
"""
import pandas as pd
import datetime
import numpy as np
import matplotlib.pyplot as plt
from sklearn import model_selection
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metr... | pd.DataFrame(data) | pandas.DataFrame |
#!/usr/bin/env python
import os
import glob
import numpy as np
import pandas as pd
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
from .utils import fracPoissonErrors
__all__ = ['Sim', 'CompareSims']
class Sim(object):
"""
Class to describe Simulation with Mass Functions
Parameter... | pd.DataFrame(dfdict) | 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... | Series([2, 3, 4]) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 22 15:07:09 2016
@author: advena
"""
#import re
from datetime import datetime
#import numpy as np
import pandas as pd
import os
import sys
import shutil
from dateutil import parser
########################################################################
#... | pd.DataFrame(lst) | pandas.DataFrame |
# Copyright 2020 <NAME>. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agree... | pd.DataFrame(test_predictions_baseline) | pandas.DataFrame |
from src.typeDefs.section_1_1.section_1_1_volt import ISection_1_1_volt
import datetime as dt
from src.repos.metricsData.metricsDataRepo import MetricsDataRepo
from src.utils.addMonths import addMonths
import pandas as pd
def fetchSection1_1_voltContext(appDbConnStr: str, startDt: dt.datetime, endDt: dt.datetime) -> ... | pd.DataFrame(voltData400) | pandas.DataFrame |
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_table
import pandas as pd
import numpy as np
import plotly.graph_objs as go
import plotly.tools as tools
from dash.dependencies import Input, Output, State
from dateutil.parser import parse
import squarify
import math
from da... | pd.to_datetime(ts) | pandas.to_datetime |
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from generate_paper_outputs import wave_column_headings
def plot_grouped_bar(backend="combined", output_dir="released_outputs/combined", measure="declined", breakdown="high_level_ethnicity"):
''' Plot a chart showing the percent of people of ea... | pd.read_csv(f"released_outputs/{backend}/tables/waves_1_9_declined_{breakdown}.csv", index_col=0) | pandas.read_csv |
import os
import glob
import datetime
from collections import OrderedDict
import pandas as pd
import numpy as np
import pandas_market_calendars as mcal
import matplotlib.pyplot as plt
FILEPATH = '/home/nate/Dropbox/data/sp600/'
CONSTITUENT_FILEPATH = '/home/nate/Dropbox/data/barchart.com/'
WRDS_FILEPATH = '/home/nat... | pd.read_excel(filename, skiprows=3, skipfooter=11) | pandas.read_excel |
import dash
import dash_html_components as html
import dash_core_components as dcc
from dash.dependencies import Input, Output
import plotly.graph_objs as go
import pandas as pd
url_confirmed = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_se... | pd.read_csv(url_recovered) | pandas.read_csv |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="darkgrid")
from sklearn.preprocessing import OneHotEncoder, LabelEncoder, StandardScaler
def concat_df(train_data, test_data):
return pd.concat([train_data, test_data], sort=True).reset_index(drop=True)
... | pd.read_csv('https://storage.googleapis.com/dqlab-dataset/challenge/feature-engineering/titanic_train.csv') | pandas.read_csv |
from functools import partial
from tqdm import tqdm
import multiprocessing as mp
import pandas as pd
import geopandas as gpd
import numpy as np
idx = pd.IndexSlice
def cartesian(s1, s2):
"""Cartesian product of two pd.Series"""
return pd.DataFrame(np.outer(s1, s2), index=s1.index, columns=s2.index)
def rev... | pd.read_excel(fn_transport, "TrRoad_ene", index_col=0) | pandas.read_excel |
import pandas as pd
import numpy as np
from pandas.tseries.holiday import USFederalHolidayCalendar
import seaborn as sns
import matplotlib.pyplot as plt
import glob
import sweetviz as sv
from scipy import stats
import sklearn
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LinearRegress... | pd.read_json(station_info_path) | pandas.read_json |
"""
Multi criteria decision analysis
"""
from __future__ import division
from __future__ import print_function
import json
import os
import pandas as pd
import numpy as np
import cea.config
import cea.inputlocator
from cea.optimization.lca_calculations import lca_calculations
from cea.analysis.multicriteria.optimizat... | pd.DataFrame({"network": dict_network}, index=[individual]) | pandas.DataFrame |
"""
Evaluate prediction model for USDCAD spot rate (moving up or down or flat)
"""
__version__ = '0.2'
__author__ = '<NAME>'
import pandas as pd # Version 0.22.0
import numpy as np # Version 1.14.0
from Sof... | pd.read_csv(input_root+'OoS FX Data.csv', index_col=0) | pandas.read_csv |
import requests
from model.parsers import model as m
import pandas as pd
import datetime
dataset = m.initialize()
unique_dates = list()
raw_data = requests.get('https://api.covid19india.org/states_daily.json')
raw_json = raw_data.json()
for item in raw_json['states_daily']:
if item['date'] not in unique_dates:
... | pd.DataFrame(data) | pandas.DataFrame |
# *****************************************************************************
# Copyright (c) 2019, Intel Corporation All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# Redistributions of sou... | pandas.Series(new_data, new_index) | pandas.Series |
import config
import pandas as pd
path_to_dir= config.path_file_name['path_result']
path_to_file_ori= config.path_file_name['label_raw']
path_to_file_dest= config.path_file_name['label_process']
def pre_process_label(path_to_dir, path_to_file_ori, path_to_file_dest):
out_data = []
pre_df = | pd.read_csv(path_to_dir + path_to_file_ori) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 20 15:09:59 2016
@author: MichaelEK
"""
import numpy as np
import pandas as pd
import os
import geopandas as gpd
import xarray as xr
from niwa import rd_niwa_vcsn
from pdsql import mssql
from hydrointerp import interp2d
from gistools import vector
import seaborn as sns
im... | pd.merge(pts0, pts1, on=['x', 'y']) | pandas.merge |
import numpy
import matplotlib.pyplot as plt
import tellurium as te
from rrplugins import Plugin
auto = Plugin("tel_auto2000")
from te_bifurcation import model2te, run_bf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import ScalarFormatter
sf = ScalarFormatter()
sf.set_sc... | pd.DataFrame(binned_cons) | pandas.DataFrame |
import streamlit as st
col1, col2 = st.beta_columns((2,1))
col1.write("""# Análise de sentimento""")
col2.image('a3.png', width =60)
st.write("""Utilizando modelos de processamento de linguagem natural e dados de texto do Twitter elaboramos uma aplicação que dada uma palavara de interesse é possível medir o senti... | pd.DataFrame(scores) | pandas.DataFrame |
import os
import json
import pickle
from sys import getsizeof
from memory_profiler import profile #, memory_usage
from pprint import pprint
from pandas import DataFrame
from networkx import write_gpickle, read_gpickle
from dotenv import load_dotenv
from conftest import compile_mock_rt_graph
from app import DATA_DIR,... | DataFrame(self.results) | pandas.DataFrame |
"""
Make folds
"""
import argparse
import copy
import json
import math
import os.path
import sys
from pathlib import Path
sys.path.append('/home/user/challenges/lyft/lyft_repo/src')
import cv2
import numpy as np
import pandas as pd
from sklearn.model_selection import StratifiedKFold, train_test_split
... | pd.read_csv('scenes_folds.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 28 13:14:48 2019
@author: RDCRLDDH
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pwlf
from openpyxl import load_workbook
import sys, getopt, ast, os
import warnings
warnings.filterwarnings("ignore")
# suppress divide and invalid warni... | pd.ExcelWriter(out_book,engine='xlsxwriter') | pandas.ExcelWriter |
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 13 18:53:16 2021
@author: <NAME>
https://www.kaggle.com/ash316/eda-to-prediction-dietanic
"""
"""
Part1: Exploratory Data Analysis(EDA):
1)Analysis of the features.
2)Finding any relations or trends considering multiple features.
Part2: Feature Engineering and Data Cl... | pd.Series(model.feature_importances_,X.columns) | pandas.Series |
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