prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
""" test the scalar Timestamp """
import pytz
import pytest
import dateutil
import calendar
import locale
import numpy as np
from dateutil.tz import tzutc
from pytz import timezone, utc
from datetime import datetime, timedelta
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from pandas.ts... | tm.set_locale(time_locale, locale.LC_TIME) | pandas.util.testing.set_locale |
class pydb():
def __init__(self,seed=None):
"""
Initiates the class and creates a Faker() object for later data generation by other methods
seed: User can set a seed parameter to generate deterministic, non-random output
"""
from faker import Faker
import... | pd.Series(lst) | pandas.Series |
from __future__ import annotations
import copy
import itertools
from typing import (
TYPE_CHECKING,
Sequence,
cast,
)
import numpy as np
from pandas._libs import (
NaT,
internals as libinternals,
)
from pandas._libs.missing import NA
from pandas._typing import (
ArrayLike,
DtypeObj,
M... | find_common_type([arr.dtype for arr in to_concat_no_proxy]) | pandas.core.dtypes.cast.find_common_type |
"""Age prediction using MRI, fMRI and MEG data."""
# Author: <NAME> <<EMAIL>>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import pandas as pd
from sklearn.dummy import DummyRegressor
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from joblib impor... | pd.DataFrame(data=data, index=index) | pandas.DataFrame |
import sys
sys.path.append('~/combs/src/')
import combs
import pandas as pd
ifg_dict = {'ASN': 'CB CG OD1 ND2'}
csv_path = 'path_to_asn_comb_csv_file'
an = combs.analyze.Analyze(csv_path)
dist_vdms = an.get_distant_vdms(7)
dist_vdms_hbond = | pd.merge(dist_vdms, an.ifg_hbond_vdm, on=['iFG_count', 'vdM_count']) | pandas.merge |
# -*- coding: utf-8 -*-
"""
Creates textual features from an intput paragraph
"""
# Load Packages
import textstat
from sklearn.preprocessing import label_binarize
from sklearn.decomposition import PCA
import numpy as np
import pandas as pd
import pkg_resources
import ast
import spacy
#from collections import Counter
f... | pd.concat([temp_df,paragraph_features], axis=0, sort=True) | pandas.concat |
#!/usr/bin/env python
import math
import numpy as np
import pandas as pd
import random
import string
from scipy.stats import zipf
from itertools import chain
import json
class contentCatalogue():
def __init__(self, size=1000):
'''
Assigns the size and constructs an empty list of contents. Construc... | pd.DataFrame(r.contentMatrix, index=names, columns=names) | pandas.DataFrame |
import numpy as np
import os
import util
import argparse
import pandas as pd
import matplotlib
from matplotlib import colors
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
if __name__ == "__main__":
post = True
plot_title = True
plot_patrol_effort = True
plot_illegal_activity = ... | pd.DataFrame(columns=['x', 'y', 'value']) | pandas.DataFrame |
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import train_test_split, cross_val_score
from imblearn.under_sampling import RandomUnderSampler
from process_loaded_data import check_if_many_relative_followers_to_friends
from datetime import dateti... | pd.read_csv('data/training_user_tweet_data.csv') | pandas.read_csv |
############################################################################
#Copyright 2019 Google LLC
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LIC... | pd.to_datetime(train[col],infer_datetime_format=True) | pandas.to_datetime |
import random
from concurrent.futures import ThreadPoolExecutor
import concurrent.futures
from io import StringIO
from urllib.parse import urljoin
import numpy as np
import pandas as pd
import requests
from urllib.error import HTTPError
X_SOURCE = 'API de Series de Tiempo: Test de Integración'
def read_source_csv(s... | pd.merge(api_df, original_df, left_index=True, right_index=True) | pandas.merge |
import torch
import numpy as np
import pandas as pd
from tqdm import tqdm
import os
import time
import pickle
import jieba
from collections import Counter
from gensim.models import KeyedVectors
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import StratifiedShuffleSplit, train_... | pd.read_csv(path) | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 10 21:26:32 2019
@author: alexandradarmon
"""
import numpy as np
import pandas as pd
import gutenberg.acquire
import logging
from logs.logger import logging_function
from punctuation.utils.utils import splitter_function
logger = logging.getLogger(_... | pd.DataFrame() | pandas.DataFrame |
from featureEngineering.feature_engineering import DataCleaning,VariableReduction
from modelBuilding.segmentation_algo import DistBasedAlgo
from evaluationMetrices.evaluation_metrices import EMSegmentation
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
fr... | pd.concat([Seg_size.T, Seg_pct.T, Profiling_output], axis=0) | pandas.concat |
import pandas as pd
from sktime.transformers.series_as_features.base import \
BaseSeriesAsFeaturesTransformer
from sktime.utils.data_container import tabularize
from sktime.utils.validation.series_as_features import check_X
__author__ = "<NAME>"
class PAA(BaseSeriesAsFeaturesTransformer):
""" (PAA) Piecewise... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import pickle
import csv
import glob
import errno
import re
from sklearn.preprocessing import Imputer, StandardScaler
from sklearn.cluster import KMeans
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
from keras.layers import Dense, Embedding, Dropout, Reshape,... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
from scipy import interpolate
import pandas as pd
from .checkarrays import checkarrays, checkarrays_tvd, checkarrays_monotonic_tvd
def interpolate_deviation(md, inc, azi, md_step=1):
"""
Interpolate a well deviation to a given step.
Parameters
----------
md: float, measured dept... | pd.DataFrame({'new_tvd':new_tvd,'new_easting':new_easting,'new_northing':new_northing}) | pandas.DataFrame |
import pandas as pd
import numpy as np
import nltk
import os
import cv2
import imutils
import matplotlib.pyplot as plt
import re
from nltk.corpus import stopwords
from IPython.display import clear_output, display
import time
# Montamos el Drive al Notebook
from google.colab import drive
drive.mount('/content/drive', ... | pd.read_csv("./insumos/places.csv", sep="\t") | pandas.read_csv |
def performance_visualizer(trials_obj,n_models,choice=False,**choice_var):
import pandas as pd
performance = [1-t['result']['loss'] for t in trials_obj.trials]
hyperparam= list(trials_obj.trials[0]['misc']['vals'].keys())
values_dict ={}
for i in hyperparam:
... | pd.DataFrame.from_dict(values_dict) | pandas.DataFrame.from_dict |
import pandas as pd
import numpy as np
"""
1. Subset the Census_Crime_All_Right_Sorted_Missing_Census_Filled df to get the fixed and YEAR columns
"""
nat_cen_all_sorted = | pd.read_csv('/Users/salma/Studies/Research/Criminal_Justice/research_projects/US_Crime_Analytics/data/merge_files/census_crime/Census_90-15_Final_Sorted.csv') | pandas.read_csv |
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... | Categorical([0, 0, 1, 1]) | pandas.Categorical |
# =============================================================================
# Created By : <NAME>
# Created Date: 2021-09
# =============================================================================
"""Module containing plotting functions.
"""
# ==================================================================... | pd.ExcelFile('../Datasets/variants_damage.xlsx') | pandas.ExcelFile |
""" 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... | tm.makeDataFrame() | pandas._testing.makeDataFrame |
import gc
import numpy as np
import pandas as pd
import tables
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from icu_benchmarks.common import constants
def gather_cat_values(common_path, cat_values):
# not too many, so read all of them
df_cat = pd.read_... | pd.read_parquet(parts[0]) | pandas.read_parquet |
import os
import json
import numpy as np
try:
import requests
except ImportError:
requests = None
import pandas as pd
from pmagpy import find_pmag_dir
from pmag_env import set_env
DM = []
CRIT_MAP = []
class DataModel():
"""
Contains the MagIC data model and validation information.
self.dm is a ... | pd.DataFrame(full_df['tables'][level]['columns']) | pandas.DataFrame |
import folium
import geopandas as gpd
import pandas as pd
import streamlit as st
from matplotlib import pyplot as plt
from streamlit_folium import folium_static
# Caching allows to store return variables in memory
# Saving execution time for time costly operations
@st.cache
def load_files():
"""
Loads necessa... | pd.read_pickle("../data/gdf_europe.p") | pandas.read_pickle |
import glob
import os
import pandas as pd
import pytz
from dateutil import parser, tz
from matplotlib import pyplot as plt
fp = "C:\\Users\\Robert\\Documents\\Uni\\SOLARNET\\HomogenizationCampaign\\catania\\"
df = [ | pd.read_csv(file, delimiter=" ", names=["file", "date", "time", "tz"]) | pandas.read_csv |
import os
import numpy as np
import pandas as pd
import boto3
import yaml
import utils
from scipy import signal
# import config
with open("02_munge/params_config_munge_noaa_nos.yaml", 'r') as stream:
config = yaml.safe_load(stream)
# check where to read data inputs from
read_location = config['read_location']
# ... | pd.DataFrame(columns=['datetime']) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# Created by <NAME>
from typing import Dict, Optional
from cached_property import cached_property
import pandas as pd
from skbio import TabularMSA, DNA, Sequence
from allfreqs.classes import Reference, MultiAlignment
from allfreqs.constants import AMBIGUOUS_COLS, STANDARD... | pd.read_csv(reference, **kwargs) | pandas.read_csv |
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import sqlite3 as sqlite
import pandas as pd
from scipy import stats
import pylab
from sklearn.neighbors import KernelDensity
from scipy.stats import mode
import json
from json2html import *
from scipy.stats import norm
from sklearn.preprocessing ... | pd.DataFrame(columns=['category', 'variable', 'Stats', 'value']) | pandas.DataFrame |
import unittest
import numpy
import pandas
from helpers.general import add_temporal_noise
from ..features import TimeRange, get_event_series, add_roll, roll
from .. import general
class TimeShiftTests(unittest.TestCase):
@staticmethod
def _get_data(n=100):
X = numpy.asarray(range(n))
return ... | pandas.datetime(year=2016, month=4, day=1) | pandas.datetime |
from process_cuwb_data.uwb_extract_data import extract_by_data_type_and_format
from process_cuwb_data.uwb_motion_features import FeatureExtraction
import numpy as np
import pandas as pd
class TestUWBMotionFeatures:
@classmethod
def prep_test_cuwb_data(cls, cuwb_dataframe):
# Build dataframe with:
... | pd.unique(df_motion_features['device_id']) | pandas.unique |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 17 12:25:20 2018
@author: kazuki.onodera
cd Home-Credit-Default-Risk/py
python run.py 817_cv_LB804_Branden.py
"""
import gc, os
#from tqdm import tqdm
import pandas as pd
import numpy as np
import sys
sys.path.append(f'/home/{os.environ.get("USE... | pd.DataFrame(index=X.index) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
"""Unit tests for (dunder) composition functionality attached to the base class."""
__author__ = ["fkiraly"]
__all__ = []
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sktime.transformations.co... | pd.DataFrame({"a": [1, 2], "b": [3, 4]}) | pandas.DataFrame |
#!/usr/bin/env python
#
# MIT License
# Copyright (c) 2020 Dr. <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, ... | pd.read_csv(data_file_path) | pandas.read_csv |
import unittest
import numpy as np
import pandas as pd
from numpy import testing as nptest
from operational_analysis.methods import plant_analysis
from examples.project_ENGIE import Project_Engie
class TestPandasPrufPlantAnalysis(unittest.TestCase):
def setUp(self):
np.random.seed(42)
# Set ... | pd.to_datetime(['2014-06-01', '2014-12-01', '2015-10-01']) | pandas.to_datetime |
import streamlit as st
import pandas as pd
import altair as alt
import pickle
import numpy as np
from map import create_map
from airdata import AirData
from utils import parse_time, parse_time_hms
from vega_datasets import data
#st.set_page_config(layout="wide")
# Getting data ready, Refresh every hour (same data... | pd.DataFrame(df_pred) | pandas.DataFrame |
import datetime
import logging
import pandas as pd
from django.core.exceptions import ValidationError
from django.db import transaction
from reversion import revisions as reversion
from xlrd import XLRDError
from app.productdb.models import Product, CURRENCY_CHOICES, ProductGroup, ProductMigrationSource, ProductMigrati... | pd.isnull(row[row_key]) | pandas.isnull |
#!/usr/bin/env python
# Author: <NAME> (@Cyb3rPandaH)
###### Importing Python Libraries
import yaml
yaml.Dumper.ignore_aliases = lambda *args : True
import glob
from os import path
# Libraries to manipulate data
import pandas as pd
from pandas import json_normalize
pd.set_option('display.max_columns', None)
# Libr... | json_normalize(attck) | pandas.json_normalize |
import sqlite3
import uuid
import numpy as np
import pandas as pd
import time
import sys
import ast
import os
import re
from random import shuffle as shuffle_list
from datetime import datetime
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
#from tensorflow import set_random_seed #valid fo... | pd.read_sql_query(query, con, params=(uid,)) | pandas.read_sql_query |
from surprise import Dataset, Reader, SVD, dump
from definitions import ROOT_DIR
import logging.config
import pandas as pd
import numpy as np
import helpers
import time
class SurpriseSVD:
logging.config.fileConfig(ROOT_DIR + "/logging.conf", disable_existing_loggers=False)
log = logging.getLogger(__name__)
... | pd.DataFrame(ds[:, 0:3], columns=["userId", "movieId", "rating"]) | pandas.DataFrame |
import pyarrow.parquet as pq
import pandas as pd
import json
from typing import List, Callable, Iterator, Union, Optional
from sportsdataverse.config import WBB_BASE_URL, WBB_TEAM_BOX_URL, WBB_PLAYER_BOX_URL, WBB_TEAM_SCHEDULE_URL
from sportsdataverse.errors import SeasonNotFoundError
from sportsdataverse.dl_utils impo... | pd.DataFrame() | pandas.DataFrame |
from backend.lib import sql_queries
import pandas as pd
from pandas.testing import assert_frame_equal, assert_series_equal
def test_get_user_info_for_existing_user(refresh_db_once, db_connection_sqlalchemy):
engine = db_connection_sqlalchemy
user_id = sql_queries.get_user_id(engine, email='<EMAIL>', password... | assert_frame_equal(df, df_test) | pandas.testing.assert_frame_equal |
import os
import cx_Oracle
import logging
import numpy as np
import pandas as pd
import re
import zipfile
import logging
from datetime import datetime
from glob import glob
from os.path import split, normpath, join, relpath, basename
from pathlib import Path
from piper.decorators import shape
from piper.text import _fi... | pd.read_excel(xl_file) | pandas.read_excel |
import gdalnumeric
import pandas as pd
import numpy as np
import gdal
from sklearn.linear_model import LogisticRegression
######################################################################################
#Write out a raster from a numpy array.
#Template: a raster file on disk to use for pixel size, height/width, ... | pd.DataFrame(array_data) | pandas.DataFrame |
import types
from functools import wraps
import numpy as np
import datetime
import collections
from pandas.compat import(
zip, builtins, range, long, lzip,
OrderedDict, callable
)
from pandas import compat
from pandas.core.base import PandasObject
from pandas.core.categorical import Categorical
from pandas.co... | OrderedDict() | pandas.compat.OrderedDict |
import pandas as pd
import numpy as np
import glob, math
# 合併月營收資料
val = pd.read_excel(r"./Data/營收/上市電子業_營收.xlsx")
val['年份'] = val['年月'].apply(lambda x: x[:4])
val['月份'] = val['年月'].apply(lambda x: int(x[5:]))
val['季'] = val['年月'].apply(lambda x: math.ceil(int(x[5:]) / 3))
val_season = val.groupby(['代號', '年份', '季'])['... | pd.read_excel(x) | pandas.read_excel |
# Library
import pandas as pd
import numpy as np
import datetime as dt
import time,datetime
import math
from math import sin, asin, cos, radians, fabs, sqrt
from geopy.distance import geodesic
from numpy import NaN
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import KFold
from sklear... | pd.merge(gtd_grouped,diff_feature,on='Timestamp',how='right') | pandas.merge |
import pandas as pd
import numpy
class DataSplitter:
@classmethod
def split_to_x_and_y(self, data, timesteps):
x, y = [], []
for i in range(len(data) - timesteps):
x.append(data.iloc[i:(i + timesteps)].drop('date', axis=1).as_matrix())
y.append([data.iloc[i + timesteps]... | pd.to_datetime('2003-01-22') | pandas.to_datetime |
import distutils
import sys
import subprocess
import re
import os
import difflib
from functools import wraps
from pkg_resources import resource_filename
from io import StringIO
from collections import namedtuple
from contextlib import contextmanager
import numpy
import pandas
import pytest
def get_img_tolerance():
... | pandas.DataFrame(index=index, columns=["res"]) | pandas.DataFrame |
import pandas as pd
import numpy as np
df = | pd.read_csv('datasets_672162_1182853_dataset.csv') | pandas.read_csv |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 15 10:31:23 2017
@author: robertmarsland
"""
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import subprocess
import os
import pickle
import datetime
from sklearn.decomposition import PCA
StateData = ['ACI', 'ACII', 'CIATP'... | pd.DataFrame.from_dict(Sdot,orient='index') | pandas.DataFrame.from_dict |
"""
Python script for all analysis
"""
import pandas as pd
from _variable_definitions import *
from _optimization import optimization
from _parameter_calculations import *
from _file_import_optimization import *
import datetime
from _utils import *
# -----------------------------------------------------------------... | pd.read_csv('scenarios/scenario_parameters.csv') | pandas.read_csv |
"""
Views and helper functions for downloading analyses.
"""
import tempfile
import openpyxl
from openpyxl.worksheet.hyperlink import Hyperlink
from openpyxl.styles import PatternFill, Border, Side, Alignment, Protection, Font
from django.http import HttpResponse, HttpResponseForbidden, HttpResponseBadRequest
from dja... | pd.DataFrame({'Property': properties, 'Value': values}) | pandas.DataFrame |
#!/usr/bin/env python
from __future__ import print_function
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
pd.options.mode.chained_assignment = None
import numpy as np
import numpy.ma as ma
import numpy.random as rd
from numpy import inf
from scipy.stats import norm
from scipy.special impo... | pd.read_hdf(inFile, "colourTab") | pandas.read_hdf |
import numpy as np
from pandas.tseries.holiday import USFederalHolidayCalendar
import datetime
import pandas as pd
def mape(y_true, y_pred):
y_true, y_pred = np.array(y_true), np.array(y_pred)
return np.mean(np.abs((y_true - y_pred) / y_true)) * 100
def rmse(y_true, y_pred):
y_true, y_pred = np.array(y_... | pd.concat([DateTime, lm_data], axis=1) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
# In[2]:
df=pd.read_csv('car_data.csv')
# In[3]:
df.head()
# In[5]:
df.shape
# In[6]:
print(df['Seller_Type'].unique())
# In[26]:
print(df['Transmission'].unique())
print(df['Owner'].unique())
print(df['Fuel_Type'].unique())
# I... | pd.get_dummies(final_dataset,drop_first=True) | pandas.get_dummies |
"""Defines the base classes to be extended by specific types of models."""
import sys
from os import makedirs
from os.path import join, exists, dirname, splitext, basename
import logging
from glob import glob
import multiprocessing as mp
from collections import defaultdict
from typing import Any, Dict, Tuple, List
from... | pd.DataFrame(instance_losses) | pandas.DataFrame |
from music21 import *
import music21 as m21
import time
# import requests
# httpx appears to be faster than requests, will fit better with an async version
import httpx
from pathlib import Path
import pandas as pd
import numpy as np
import xml.etree.ElementTree as ET
from itertools import combinations
# Unncessary at... | pd.Series(notesAndRests, name=part_name) | pandas.Series |
import pandas as pd
import re
from sklearn.model_selection import train_test_split
import numpy as np
def df_to_letor(df, queries_df: pd.DataFrame) -> pd.DataFrame:
# ensure that df has qid, docid, pid
expected_cols = ("QID", "DocID", "PassageID")
feat_cols = [col for col in df.columns if col not in expec... | pd.read_csv("data/processed/letor.csv") | pandas.read_csv |
# -*- coding: utf-8 -*-
__all__ = [
'get_dataframe'
]
import builtins
import pandas as pd
import six
from .exceptions import OasisException
def get_dataframe(
src_fp=None,
src_type='csv',
src_buf=None,
src_data=None,
float_precision='high',
lowercase_cols=True,
index_col=True,
... | pd.DataFrame(data=src_data, dtype=object) | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import fftpack
from scipy.integrate import cumtrapz
import numbers
class Quaternion:
def __init__(self, w, x=None, y=None, z=None):
q = []
if isinstance(w, Quaternion):
q = w.q
elif isinstance(w... | pd.DataFrame([x, y, z]) | pandas.DataFrame |
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
from tqdm import tqdm
import yaml
import os
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from joblib import dump, load
from category_encoders import OrdinalEncoder
from src.data.spdat import ... | pd.to_numeric(df['MonthlyAmount']) | pandas.to_numeric |
import os
import numpy as np
import pytest
from pandas.compat import is_platform_little_endian
import pandas as pd
from pandas import DataFrame, HDFStore, Series, _testing as tm, read_hdf
from pandas.tests.io.pytables.common import (
_maybe_remove,
ensure_clean_path,
ensure_clean_store,
tables,
)
fr... | read_hdf(path, "df", mode=mode) | pandas.read_hdf |
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 2 11:37:09 2017
@author: <NAME>
"""
# =============================================================================
# 调用所需的库
# =============================================================================
import pandas as pd
import numpy as np
from sklearn.c... | pd.DataFrame(importance, columns=['feature', 'fscore']) | pandas.DataFrame |
# standard library imports
import os
import datetime
import re
import math
import copy
import collections
from functools import wraps
from itertools import combinations
import warnings
import pytz
import importlib
# anaconda distribution defaults
import dateutil
import numpy as np
import pandas as pd
# anaconda distr... | pd.DataFrame(rcs.iloc[i, :]) | pandas.DataFrame |
import numpy as np
import pandas as pd
from scipy import stats
from scipy.optimize import curve_fit
import os
import re
from fuelcell import utils
from fuelcell.model import Datum
dlm_default = utils.dlm_default
col_default_labels = {'current':'i', 'potential':'v', 'time':'t', 'current_err':'i_sd', 'potential_err':'v... | pd.DataFrame({'real':this_re, 'imag':this_im}) | pandas.DataFrame |
from collections import defaultdict
from datetime import datetime, timedelta, timezone
import pickle
from sqlite3 import OperationalError
import numpy as np
from numpy.testing import assert_array_equal
import pandas as pd
from pandas.testing import assert_frame_equal
import pytest
from sqlalchemy import delete, func, ... | pd.Timestamp("2017-02-01 09:51:10") | pandas.Timestamp |
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import dask.dataframe as dd
from dask.dataframe.utils import (shard_df_on_index, meta_nonempty, make_meta,
raise_on_meta_error)
import pytest
def test_shard_df_on_index():
df = pd.DataFrame({'x': [1, 2, 3, 4... | pd.PeriodIndex(['1970-01-01'], freq='d', name='foo') | pandas.PeriodIndex |
import numpy as np
#np.set_printoptions(precision=2)
import pandas as pd
from typing import Any, Dict, List, Tuple, NoReturn
import argparse
import os
import pickle
import json
from sklearn.mixture import BayesianGaussianMixture
def parse_arguments() -> Any:
"""Parse command line arguments."""
parser = argparse... | pd.DataFrame(data=result, columns=labels) | pandas.DataFrame |
import re
from pathlib import Path
from urllib.parse import urlparse, parse_qsl
import lxml.html
import pandas as pd
import requests
from boatrace.util import Config
config = Config(path=Path(__file__).parent / "params.yaml")
racer_class = config.get_racer_class()
field_name2code = config.get_field_code()
class Ad... | pd.to_datetime(df["date"], format="%y%m%d") | pandas.to_datetime |
#!/usr/bin/env python
# encoding: utf-8
'''
\ \ / /__| | ___ _ _ __ / ___| | | | / \ |_ _|
\ V / _ \ |/ / | | | '_ \ | | | |_| | / _ \ | |
| | __/ <| |_| | | | | | |___| _ |/ ___ \ | |
|_|\___|_|\_\\__,_|_| |_| \____|_| |_/_/ \_\___
===... | pd.read_csv(csv_file) | pandas.read_csv |
#! /usr/bin/env python
import maple
import maple.data as data
import maple.audio as audio
import numpy as np
import joblib
import pandas as pd
import argparse
import datetime
import sounddevice as sd
from scipy import signal
from pathlib import Path
from sklearn import preprocessing
from sklearn.model_selection impo... | pd.DataFrame({}, columns=self.cols) | pandas.DataFrame |
"""
Generating data from the CarRacing gym environment.
!!! DOES NOT WORK ON TITANIC, DO IT AT HOME, THEN SCP !!!
"""
import argparse
from os import makedirs
from os.path import join, exists
import gym
import numpy as np
from utils.misc import sample_continuous_policy
import pandas as pd
import numpy as np
import ma... | pd.DataFrame(combination,columns=["date","tic"]) | pandas.DataFrame |
"""
Module contains tools for processing files into DataFrames or other objects
"""
from collections import abc, defaultdict
import csv
import datetime
from io import StringIO
import itertools
import re
import sys
from textwrap import fill
from typing import (
Any,
Dict,
Iterable,
Iterator,
List,
... | is_integer(self.header) | pandas.core.dtypes.common.is_integer |
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Series, concat
from pandas.core.base import DataError
from pandas.util import testing as tm
def test_rank_apply():
lev1 = tm.rands_array(10, 100)
lev2 = tm.rands_array(10, 130)
lab1 = np.random.randint(0, 100, size=500)
... | DataFrame([2.5, 2.5, 2.5, 2.5], columns=["val"]) | pandas.DataFrame |
import datetime
import math
import sys
import warnings
import numpy as np
import pandas as pd
import pytest
from scipy.stats import randint as sp_randint
from sklearn.metrics import explained_variance_score
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
from sklearn.pipe... | pd.date_range("2018-01-01", periods=1000, freq="D") | pandas.date_range |
import json
import os
import time
import pandas as pd
import sys
sys.path.append("..")
from utils.resource import Resource
from utils.mail import Mail
from utils.handler import xlyHandler
class Monitor(xlyHandler):
"""正在运行任务监控"""
def __init__(self):
self.required_labels = [
'广州-CPU集群', '保... | pd.DataFrame(data) | pandas.DataFrame |
# PriceVelocity was developed by <NAME> and <NAME>
import os
import datetime
import numpy
import sqlalchemy as sa
import pandas as pd
import traceback
# create a parent class for all types of fuel
class Fuel(object):
dburl = os.environ.get('SOME_ENV_VAR')
engine = sa.create_engine(dburl)
restricted = Fals... | pd.DataFrame(data, columns=['rank', 'station_id', 'day_lag', 'price_change']) | pandas.DataFrame |
import os
os.environ['PROJ_LIB'] = '/home/jlee/.conda/envs/mmc_sgp/share/proj'
import glob
import xarray as xr
import wrf
from netCDF4 import Dataset
import numpy as np
import pandas as pd
file_dir = '/projects/wfip2les/cdraxl/2020100300/'
# file_dir = '/home/jlee/wfip/test_case/'
out_dir = '/home/jlee/wfip/'
file_l... | pd.DataFrame(columns=['time', 'wind-speed_62m', 'wind-speed_72m', 'wind-speed_82m', 'wind-speed_92m']) | pandas.DataFrame |
from inspect import isclass
import dask.dataframe as dd
import numpy as np
import pandas as pd
import pytest
from woodwork.column_schema import ColumnSchema
from woodwork.logical_types import Boolean, Datetime
import featuretools as ft
from featuretools.computational_backends.feature_set import FeatureSet
from featur... | pd.Timestamp('2001-01-04') | pandas.Timestamp |
import cv2 as cv
import numpy as np
import pandas as pd
import os
def Microplate(am, title):
def click(event, x, y, flag, param):
ix = x
iy = y
if event == cv.EVENT_LBUTTONDOWN:
for i in circles[0, :]:
menorX = (i[0] - i[2])
maiorX = i[0] + i[2... | pd.DataFrame(columns=['ID1', 'ID2', 'pixel']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from pandas.compat import range
import pandas.util.testing as tm
from pandas import read_csv
import os
import nose
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
import pandas.tools.rplot as rplot
def curpath():
pth, _ = os.path.split(os.path.abspath(__file__))... | read_csv(path, sep=',') | pandas.read_csv |
"""
RF Prediction
"""
# import
import pickle
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
# from sklearn.model_selection import train_test_split
# RF training
def RF_training(num_tree, X, y):
"""
train the RF model with the given dataset.
return a trained mod... | pd.DataFrame(columns=col_names) | pandas.DataFrame |
import matplotlib.pyplot as plt
import numpy
import pandas as pd
import math
import numpy.fft as fourier
import scipy.interpolate as inter
# READ DATA FROM SIMULATION
iT = 0
nT = 3
nend = 30000 #Interrompi risultati qui, perchè dopo non ha più senso
nend = 180000
df1 = pd.read_csv('Bl1outin.txt', header=None)
bl1mom =... | pd.read_csv('t3.T3.out', sep='\t', header=None, skiprows=10) | pandas.read_csv |
# -*- coding: utf-8 -*-
# %%
import pandas as pd
import numpy as np
import tkinter as tk
class package:
def __init__(self):
# elements defined
C = 12
H = 1.007825
N = 14.003074
O = 15.994915
P = 30.973763
S = 31.972072
Na = 22.98977
Cl = 34.9... | pd.Series(end) | pandas.Series |
from datetime import (
datetime,
timedelta,
timezone,
)
import numpy as np
import pytest
import pytz
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
NaT,
Period,
Series,
Timedelta,
Timestamp,
date_range,
isna,
)
import pandas._testing as tm
class TestS... | Timestamp("2011-01-04 10:00") | pandas.Timestamp |
from enum import Enum
import sys
import os
import re
from typing import Any, Callable, Tuple
from pandas.core.frame import DataFrame
from tqdm import tqdm
import yaml
from icecream import ic
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.dirname(SCRIPT_DIR))
from argpar... | pd.DataFrame(test_series) | pandas.DataFrame |
from context import tables
import os
import pandas as pd
def test_tables_fetcher():
try:
tables.fetcher()
tables_dir=os.listdir(tables.TABLES_PATH)
print(f'\n----------------------------------\ntest_tables_fetcher worked,\ncontent of {tables.TABLES_PATH} is:\n{tables_dir}\n---------------... | pd.DataFrame.head(ret) | pandas.DataFrame.head |
import os
import math
import torch
import torch.nn as nn
import traceback
import pandas as pd
import time
import numpy as np
import argparse
from utils.generic_utils import load_config, save_config_file
from utils.generic_utils import set_init_dict
from utils.generic_utils import NoamLR, binary_acc
from utils.gene... | pd.Series(preds, name='Predicted') | pandas.Series |
from typing import List
import torch
import numpy as np
import pandas as pd
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import utils
class Predictor:
result_df = None
def predict_group(self, samples: List[str], group_name: str):
raise NotImplementedError
def save... | pd.DataFrame(columns=["group"]) | pandas.DataFrame |
import torch
from torch import optim
import os
import os.path
import time
import numpy as np
import pandas as pd
from collections import defaultdict
import argparse
import utils
from utils import read_vocab, Tokenizer, vocab_pad_idx, timeSince, try_cuda
from env import R2RBatch, ImageFeatures
from model import Encode... | pd.DataFrame(data_log) | pandas.DataFrame |
import argparse
import json
import os
import pandas as pd
from PIL import Image
def load_json(path):
with open(path, 'r') as f:
labels = json.load(f)['annotations']
return labels
def load_filenames(path, validate=True):
files = os.listdir(path)
if validate:
files = validate_images... | pd.concat([df_train, df_valid]) | pandas.concat |
import pandas as pd
import numpy as np
import datetime
import os
from scipy import array
from scipy.interpolate import interp1d
def subst(x, str_re, loc):
"""
Parameters:
-----------
x : str, the string to be updated
str_re : str, the new string to replace
loc : int or numpy.array, the index ... | pd.to_datetime(concentration['Time'], format=time_format) | pandas.to_datetime |
# _*_ coding: utf-8 _*_
"""
Bill Searcher.
Author: <NAME>
"""
import faiss
import nmslib
import joblib
import numpy as np
import pandas as pd
from typing import List, Tuple
# Own customized variables
from bill_helper.tokenizer import MyTokenizer
from bill_helper.global_variables import (BILL_DATA_FILEPATH,
... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
import os
import argparse
import time
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('clint.mpl')
from pprint import pprint
import scipy.signal as signal
import itertools
from pygama import DataSet
import pygama.utils as pu
import pygama.analysis.histograms ... | pd.read_hdf(f_grid) | pandas.read_hdf |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tests for `perfume` package.
perfume is fairly visualization-heavy and deals with stochastic
events, so end-to-end testing isn't really aimed for here. But we can
test the transformations in analyze somewhat.
"""
import unittest
import numpy as np
import numpy.test... | pd.Timedelta("1.1s") | pandas.Timedelta |
# pylint: disable-msg=W0612,E1101,W0141
import nose
from numpy.random import randn
import numpy as np
from pandas.core.index import Index, MultiIndex
from pandas import Panel, DataFrame, Series, notnull, isnull
from pandas.util.testing import (assert_almost_equal,
assert_series_equal... | assert_frame_equal(unstacked, expected) | pandas.util.testing.assert_frame_equal |
import pandas as pd
import numpy as np
import sklearn as sk
import matplotlib.pyplot as plt
from sklearn import metrics
from json import *
import requests
pd.set_option('display.max_rows', 21000)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 150)
def read_csv():
dataset = | pd.read_csv('earthquakes.csv') | pandas.read_csv |
from os.path import abspath, dirname, join
import h5py
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
from utils import make_dir, numpy_ewma_vectorized_v2, plot_postprocess, print_init, label_converter, series_indexer, \
color4la... | pd.Series(mean_proxy, index=self.params_df.index) | pandas.Series |
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