prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import re
import socket
from datetime import datetime
from urlextract import URLExtract
import urllib.parse as urlparse
from urllib.parse import parse_qs
import click
import argparse
import csv
import os
from dateutil.parser import parse
import pandas as pd
from urllib.parse import unquote
import hashlib
... | pd.isnull(request) | pandas.isnull |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import pandas as pd
from pandas import Timestamp
def create_dataframe(tuple_data):
"""Create pandas df from tuple data with a header."""
return pd.DataFrame.from_records(tuple_data[1:], columns=tuple_data[0])
### REUSABLE FIXTURES --------------------... | Timestamp('2012-08-01 00:00:00') | pandas.Timestamp |
# pip install bs4 lxml
import time
import re
import json
import os
from bs4 import BeautifulSoup
import pandas as pd
import functions as func
from settings import Settings as st
class Songs:
def __init__(self, keyword,limit):
# 初始歌单
self.only_lyric = []
self.plist = None
self.ke... | pd.DataFrame(columns=['id','name','url']) | pandas.DataFrame |
# -*- 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... | u(' a ') | pandas.compat.u |
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 23 09:05:39 2015
@author: efouche
"""
from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
from future import standard_library
standard_library.install_aliases()
from ibmdbpy.i... | pd.Series(entropy_dict) | pandas.Series |
import pandas as pd
import numpy as np
#######################################################################################
# Return recommendations based on reviews
#######################################################################################
def find_reviews(query,reviews, n_results... | pd.Series(inner_product) | pandas.Series |
import os
data_path = os.path.abspath(os.path.join('other','aml','w1','datasets'))
### This cell imports the necessary modules and sets a few plotting parameters for display
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (20.0, 10.0)
### GRADED
### Code a fun... | pd.DataFrame() | pandas.DataFrame |
import sys
import os
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.dirname(SCRIPT_DIR))
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import Models
from Models.Models import away_features, home_features, features, ... | pd.DataFrame(data) | pandas.DataFrame |
"""Pytest unit tests for the core module of GuideMaker
"""
import os
import pytest
from Bio.Seq import Seq
from Bio import SeqIO
from Bio.SeqRecord import SeqRecord
from Bio.Alphabet import IUPAC
import numpy as np
import pandas as pd
from typing import List, Dict, Tuple, TypeVar, Generator
from Bio import Seq
import a... | pd.DataFrame(tardict) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# IMPORTS
import numpy as np
import pandas as pd
from tqdm import tqdm
from scipy import ndimage
import matplotlib.pyplot as plt
from skimage.io import imread
from rnaloc import toolbox
# Function definition
def process_folder(path_scan,
region_label,
... | pd.DataFrame({'bins_center': bins_center}) | pandas.DataFrame |
from datetime import datetime, timedelta
import inspect
import numpy as np
import pytest
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
Index,
IntervalIndex,
MultiIndex... | tm.assert_index_equal(renamed.columns, new_columns) | pandas.util.testing.assert_index_equal |
# Copyright 2016 Feather Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | pd.DataFrame({'strings': values * repeats}) | pandas.DataFrame |
"""
"""
"""
>>> # ---
>>> # SETUP
>>> # ---
>>> import os
>>> import logging
>>> logger = logging.getLogger('PT3S.Rm')
>>> # ---
>>> # path
>>> # ---
>>> if __name__ == "__main__":
... try:
... dummy=__file__
... logger.debug("{0:s}{1:s}{2:s}".format('DOCTEST: __main__ Context: ','path = os.p... | pd.DataFrame() | pandas.DataFrame |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O
import seaborn as sns
import matplotlib.pyplot as plt
import gif
plt.style.use('fivethirtyeight')
#Data Source from KAggle: https://www.kaggle.com/jeanmidev/smart-meters-in-london
df=pd.read_csv('london_weather_hourly_darksky.c... | pd.Timestamp(date) | pandas.Timestamp |
# -*- coding: utf-8 -*-
"""SonDenemeler.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/19x1FeWR8BZ3sWyZqbRuUR8msEuL1RXzm
"""
from google.colab import drive
drive.mount("/content/drive")
"""# Model 1"""
from __future__ import print_function
imp... | pd.read_csv('/content/drive/MyDrive/Plant_Pathology_2020/train.csv') | pandas.read_csv |
from data import *
import numpy as np
import pdb
import pandas as pd
def kppv(k,test,data_train,train_label):
predict = [] #liste des prédictions
for indx in test.index:
test_line = test.loc[indx]
neighbors = find_kppv_neighbors(k,test_line,data_train) #on récupère les kppv
neighbors['label'] = train_label.lo... | pd.Series(IRIS_TEST_LABEL) | pandas.Series |
import pandas as pd
period = pd.Period('2020-06', freq='M')
print(period)
print(period.asfreq('D', 'start'))
print(period.asfreq('D', 'end'))
# Can perform period arithmetic - increment month
print(period + 1)
# Can create period range per month in a year
monthly_period_range = pd.period_range('2020-01', '2021-12', ... | pd.period_range('2015', '2021', freq='A-DEC') | pandas.period_range |
import unittest
from setup.settings import *
from numpy.testing import *
from pandas.util.testing import *
import numpy as np
import dolphindb_numpy as dnp
import pandas as pd
import orca
class FunctionLogicalXorTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
# connect to a DolphinDB server... | pd.Series([1, 2, 4]) | pandas.Series |
import numpy as np
import pandas as pd
from pathlib import Path
from typing import Dict, List, Union
from collections import OrderedDict
from pathos.multiprocessing import ThreadPool as Pool
from tqdm import tqdm
from src.utils import remap_label, get_type_instances
from .metrics import PQ, AJI, AJI_plus, DICE2, split... | pd.DataFrame.from_records(metrics) | pandas.DataFrame.from_records |
import sys
import os
import libsbml
from tqdm import tqdm
import pandas as pd
from itertools import product
from bioservices.kegg import KEGG
from requests.exceptions import HTTPError, RequestException
import helper_functions as hf
'''
Usage: check+annotate_metabolites.py <path_input_sbml-file> <outfile-csv> <program_... | pd.read_csv("Databases/SEED/compounds.tsv", header=0, sep="\t") | pandas.read_csv |
"""This module performs estimation of image overlap or shift
using phase correlation from OpenCV.
It contains class AdaptiveShiftEstimation
that can handle manual and automatically scanned image data sets.
"""
from copy import deepcopy
from itertools import chain
from typing import List, Tuple, Union
import cv2 as c... | pd.DataFrame(self._micro_ids) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 01 00:39:44 2018
@author: punck
"""
import numpy as np
import scipy.stats as ss
import pandas as pd
class Generator:
"""A random dataset generator class"""
def Binomial(self, n, p, size):
"""
Dataset of random binomial variables with probab... | pd.DataFrame(y, columns=columns) | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
from sklearn.metrics import classification_report, accuracy_score
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
from torchvision import datasets
from tqdm import tqdm
from models import BadNet
from utils import print_model_perform
de... | pd.DataFrame(train_process, columns=("dataname", "batch_size", "trigger_label", "learning_rate", "epoch", "loss", "train_acc", "test_ori_acc", "test_tri_acc")) | pandas.DataFrame |
import re
import time
import math
import sys
import os
import psutil
from abc import ABCMeta, abstractmethod
from pathlib import Path
from contextlib import contextmanager
import pandas as pd
import numpy as np
def reduce_mem_usage(df):
start_mem = df.memory_usage().sum() / 1024**2
print('Memory usage of data... | pd.concat(dfs, axis=1) | pandas.concat |
import numpy as np
import pandas as pd
from main.data import SETTINGS, IN_PAPER_NAMES
from framework.util import get_average_result_from_df, save_tsv, no_zeros_formatter, load_tsv
import datetime
import scipy.stats as st
directions=['be2vad', 'vad2be']
models=['baseline', 'reference_LM', 'Reference_KNN', 'my_model']... | pd.DataFrame(columns=directions) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# Imports
import random
import pandas as pd
import numpy as np
from pyclustering.cluster.kmedoids import kmedoids
from pyclustering.cluster import cluster_visualizer
from pyclustering.utils.metric import distance_metric, type_metric
from sklearn.cluster import KMeans
from sklearn.metrics impor... | pd.concat([closest, cluster_i.iloc[closest_i]], sort=False) | pandas.concat |
# Author: <NAME>
# tomoyuki (at) genemagic.com
import sys
import argparse
import csv
import time
import datetime
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--source', type=str, help="CSV data url or f... | time(df['公表_年月日'], format="%Y-%m-%d") | pandas.to_datetime |
"""Module to provide generic utilities for other accelerometer modules."""
from collections import OrderedDict
import datetime
import glob
import json
import math
import os
import pandas as pd
import re
DAYS = ['mon', 'tue', 'wed', 'thur', 'fri', 'sat', 'sun']
TIME_SERIES_COL = 'time'
def formatNum(num, decimalPlac... | pd.read_csv(summaryCsv) | pandas.read_csv |
import pandas as pd
import pytest
from woodwork.logical_types import Datetime, Double, Integer, NaturalLanguage
from featuretools.entityset import EntitySet
from featuretools.tests.testing_utils import get_df_tags
from featuretools.utils.gen_utils import Library, import_or_none
from featuretools.utils.koalas_utils imp... | pd.to_datetime('2019-01-10') | pandas.to_datetime |
'''
Author:<NAME>
<EMAIL>'''
# Import required libraries
import pathlib
import dash
import numpy as np
from dash.dependencies import Input, Output, State, ClientsideFunction
import dash_core_components as dcc
import dash_html_components as html
import plotly.figure_factory as ff
import plotly.graph_objec... | pd.to_datetime("12-01-2018 00:00") | pandas.to_datetime |
"""Tests for the sdv.constraints.tabular module."""
import uuid
from datetime import datetime
from unittest.mock import Mock
import numpy as np
import pandas as pd
import pytest
from sdv.constraints.errors import MissingConstraintColumnError
from sdv.constraints.tabular import (
Between, ColumnFormula, CustomCon... | pd.testing.assert_frame_equal(expected_out, out) | pandas.testing.assert_frame_equal |
# Copyright (c) Facebook, Inc. and its affiliates.
import unittest
from typing import List, Optional
# Skipping analyzing 'numpy': found module but no type hints or library stubs
import numpy as np # type: ignore
import numpy.ma as ma # type: ignore
# Skipping analyzing 'pandas': found module but no type hints or l... | pd.DataFrame({"a": [1, 2, 3]}) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 21 07:16:35 2018
@author: MiguelArturo
"""
__author__ = "<NAME>"
__copyright__ = "Copyright 2018, <NAME>"
__credits__ = ["<NAME>"]
__license__ = "MIT"
__version__ = "0.0.1"
__maintainer__ = "<NAME>"
__email__ = "<EMAIL>"
__status__ = "Development"
... | pd.DataFrame(unique_days_list.index,columns=columns_ud) | pandas.DataFrame |
import pandas as pd
import pytest
from feature_engine.creation import CyclicalFeatures
@pytest.fixture
def df_cyclical():
df = {
"day": [6, 7, 5, 3, 1, 2, 4],
"months": [3, 7, 9, 12, 4, 6, 12],
}
df = pd.DataFrame(df)
return df
def test_general_transformation_without_dropping_variab... | pd.testing.assert_frame_equal(X, transf_df) | pandas.testing.assert_frame_equal |
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-n_it','--n_iteration',required=True)
parser.add_argument('-protein','--protein',required=True)
parser.add_argument('-file_path','--file_path',required=True)
parser.add_argument('-mdd','--morgan_directory',required=True)
io_args = parser.parse_ar... | pd.DataFrame(data=test_pd.index) | pandas.DataFrame |
import os
import glob
import requests
import pandas as pd
from credential import API_KEY
"""
Notice:
This script assume that you have unnormalised csv files under ../csv_data/
after you run ./data_creation.py.
"""
##########
# Rename table names
##########
target_dir = '../csv_data/'
change_dict = {
... | pd.read_csv(f'{target_dir}movies.csv') | pandas.read_csv |
# EcoFOCI
"""Contains a collection of ADCP equipment parsing.
These include:
* LR-ADCP
* Teledyne ADCP
* RCM ADCP
"""
import numpy as np
import pandas as pd
class adcp(object):
"""
"""
def __init__(self,serialno=None,depdir=None):
if depdir:
self.depdir = depdir + serialno
... | pd.to_datetime(self.vel_df.date+' '+self.vel_df.time,format="%y/%m/%d %H:%M:%S") | pandas.to_datetime |
import sys
import pandas as pd
from sqlalchemy import create_engine
def load_data(messages_file_path, categories_file_path):
"""
Load two files into dataframes and merget them.
Args:
messages_file_path(str): The file path of Messages file
categories_file_path(str): The file path of Categories file
... | pd.read_csv(categories_file_path) | pandas.read_csv |
import torch
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import sys
from os.path import join as pjoin
import scanpy as sc
import anndata
from sklearn.metrics import r2_score, mean_squared_error
from gpsa import VariationalGPSA, rbf_kernel
from gpsa.plotting import call... | pd.melt(results_df) | pandas.melt |
import numpy as np
import pandas as pd
from pandas.tseries import converter
from pathlib import Path
from tqdm import tqdm
from datetime import datetime
from calendar import monthrange
from calendar import month_name
import matplotlib.pyplot as plt
import seaborn as sns
import swifter
import calendar
import pytz
c... | pd.DataFrame(result) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable=E1101
# flake8: noqa
from datetime import datetime
import csv
import os
import sys
import re
import nose
import platform
from multiprocessing.pool import ThreadPool
from numpy import nan
import numpy as np
from pandas.io.common import DtypeWarning
from pandas import DataFr... | StringIO(data) | pandas.compat.StringIO |
import numpy as np
import pandas as pd
import pathlib
import os
###############################################################################
current_week = "30"
output_week = "/Users/christianhilscher/desktop/dynsim/output/week" + str(current_week) + "/"
pathlib.Path(output_week).mkdir(parents=True, exist_ok=True)... | pd.read_pickle(output_path + "doc_full2.pkl") | pandas.read_pickle |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import pprint
import torch
import pandas as pd
import datetime
import numpy as np
import wandb
from sklearn import metrics
from pathlib import Path
from easydict import EasyDict as ed... | pd.read_csv(test_path) | pandas.read_csv |
#####################################################
## PROGRAM TO IMPLEMENT KINESIS PRODUCER THAT FETCHES WEATHER INFORMATION
## AND STREAMS THE DATA INTO KINESIS STREAM
####################################################
# necessary imports
import boto3
import datetime as dt
import pandas as pd
import time
from pa... | pd.DataFrame(temp["results"]) | pandas.DataFrame |
import itertools
from collections.abc import Iterable, Sequence, Mapping
import numpy as np
import pandas as pd
class _VectorPlotter:
"""Base class for objects underlying *plot functions."""
semantics = ["x", "y"]
def establish_variables(self, data=None, **kwargs):
"""Define plot variables."""
... | pd.Series(val) | pandas.Series |
import logging
import os
import sys
from datetime import datetime
import numpy as np
import pandas as pd
import pytz
import requests
import config
import imgkit
import seaborn as sns
import telegram
from requests_toolbelt import sessions
logging.basicConfig(
filename=config.LOG_DIR,
format="%(asctime)s - [%(... | pd.DataFrame.from_dict(countries) | pandas.DataFrame.from_dict |
name = 'nfl_data_py'
import pandas
import numpy
import datetime
def import_pbp_data(years, columns=None, downcast=True):
"""Imports play-by-play data
Args:
years (List[int]): years to get PBP data for
columns (List[str]): only return these columns
downcast (bool): convert float64... | pandas.read_csv(r'https://raw.githubusercontent.com/nflverse/nfldata/master/data/win_totals.csv') | pandas.read_csv |
"""
system.py
Handles the system class for openMM
"""
# Global imports
import openmm
import openmm.app
from simtk import unit
import numpy as np
import pandas
import sklearn.decomposition
import configparser
import prody
import scipy.spatial.distance as sdist
from . import utils
__author__ = '<NAME>'
__version__ = ... | pandas.DataFrame() | pandas.DataFrame |
from utils.qSLP import qSLP
from qiskit.utils import QuantumInstance
from qiskit import Aer, QuantumCircuit
from utils.data_visualization import *
from utils.Utils_pad import padding
from utils.import_data import get_dataset
from qiskit.circuit.library import ZZFeatureMap, ZFeatureMap
from qiskit.circuit.library import... | pd.DataFrame(result) | pandas.DataFrame |
#Find which objects are bad and low based on various cuts through the data. Output this as a dataframe containing True False for every object in every line
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import ascii
import sys, os, string
import pandas as pd
from astropy.io import fits
import collec... | pd.merge(fluxdata,mdata) | pandas.merge |
import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from math_helpers.constants import *
from traj import lambert
from traj.meeus_alg import meeus
from traj.conics import get_rv_frm_elements
from traj.bplane import bplane_vinf
import pandas as pd
from math_helpers.tim... | pd.DataFrame(index=windows[5], columns=windows[4]) | pandas.DataFrame |
# BookNLP LitBank
import pandas as pd
import csv
import os
# import own script
from hyphens import *
from check_inconsistencies import *
books_mapping = {'AliceInWonderland': '11_alices_adventures_in_wonderland',
'DavidCopperfield': '766_david_copperfield',
'Dracula': '345_dracula',... | pd.read_csv(booknlp_filepath, sep='\t', quoting=csv.QUOTE_NONE, usecols=["originalWord","ner"]) | pandas.read_csv |
import pandas as pd
from koapy import KiwoomOpenApiContext
from koapy.backend.cybos.CybosPlusComObject import CybosPlusComObject
kiwoom = KiwoomOpenApiContext()
cybos = CybosPlusComObject()
kiwoom.EnsureConnected()
cybos.EnsureConnected()
kiwoom_codes = kiwoom.GetCommonCodeList()
cybos_codes = cybos.GetCommonCodeLi... | pd.DataFrame(cybos_codes, columns=['code']) | pandas.DataFrame |
""" Functions used in create-documentation notebook"""
import os
import pandas as pd
import numpy as np
button = ":raw-html:`❏`"
csv_header = "\n{}`\n"
csv_entry = ".. csv-table::"
csv_columns = " :header: {}{}\n"
csv_delim = " :delim: |"
csv_row = " {} | {}"
csv_singlerow = " {}"
bool_e... | pd.isna(df.loc[df.index[0], q_categories]) | pandas.isna |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scheduler.GOBI import GOBIScheduler
plt.style.use(['science'])
plt.rcParams["text.usetex"] = False
class Stats():
def __init__(self, Environment, WorkloadModel, Datacenter, Scheduler):
self.env = Environment
self.env.stats... | pd.DataFrame(metric2_with_interval) | 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,
... | tm.assert_frame_equal(res, exp) | pandas._testing.assert_frame_equal |
from Grid_Generator import gen_grid
from A_Star import a_star
import pandas as pd
df = pd.DataFrame(columns=['p','Solvable']) #made use of pandas library to store data
p=0.01
while p < 1: #recording values between 0.01 <= p < 1
for i in range(100): #recording 100 values for each density value
grid=gen_gr... | pd.DataFrame([[p, result]],columns=['p', 'Solvable']) | pandas.DataFrame |
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
from joblib import dump, load
import pandas as pd
# for tree models
def lable_encoding(
X_train: pd.DataFrame,
X_test: pd.DataFrame,
attrs: [str] = None
):
print('Lable encodind')
attributes = attrs if attrs is not None else X_train.c... | pd.concat(train_encods, axis=1) | pandas.concat |
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sn
import pickle
from siuba import *
from datetime import datetime as dt
# Opening SHAP results with pickle
infile = open("lgbm_dict", "rb")
lgbm_dict = pickle.load(infile)
asdas=pickle.load(infile)
df_r2 = pd.DataFrame(columns=["ga... | pd.DataFrame({"game_id": [name], "year": [values[4]], "test": [values[0]], "train": [values[2]]}) | pandas.DataFrame |
from collections import OrderedDict
import george
from george import kernels
import lightgbm as lgb
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import pickle
from astropy.cosmology import FlatLambdaCDM
from scipy.optimize import minimize
from sklearn.model_selection import Stratifie... | pd.DataFrame(all_features, columns=feature_labels) | pandas.DataFrame |
import time
import numpy as np
import pandas as pd
from pandas.api.types import is_categorical_dtype
from scipy.sparse import csr_matrix
from statsmodels.stats.multitest import fdrcorrection as fdr
from joblib import Parallel, delayed, parallel_backend
from typing import List, Tuple, Dict, Union, Optional
import log... | is_categorical_dtype(cluster_labels) | pandas.api.types.is_categorical_dtype |
import os
import sys
import torch
import pickle
import argparse
import warnings
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn as skl
import tensorflow as tf
from scipy.stats import gamma
from callbacks import RegressionCallback
from regression_data import generate_toy_data
from ... | pd.DataFrame(columns=self.cols_data) | pandas.DataFrame |
__author__ = 'thor'
import pandas as pd
import ut.util.ulist as util_ulist
import re
import ut.pcoll.order_conserving as colloc
def incremental_merge(left, right, **kwargs):
"""
as pandas.merge, but can handle the case when left dataframe is empty or None
"""
if left is None or left.shape != (0, 0):
... | pd.merge(left, right, **kwargs) | pandas.merge |
''' Starting with Commonwealth_Connect_Service_Requests.csv, meaning
the tickets feature. See more info in notebook #2
'''
import pandas as pd
import numpy as np
from geopy.distance import geodesic
def find_nearest_building(df,latI,lonI):
minDist = 4000
flag = True
for i in range(0,df.shape[0]):
la... | pd.DataFrame() | pandas.DataFrame |
# coding: utf-8
import pandas as pd
import torch
from sklearn.model_selection import train_test_split
from torch import nn
from torch import optim
from torch.nn.modules import loss
from torch.utils.data import Subset, DataLoader
from torchvision import transforms
from torchvision.datasets import ImageFolder
from datas... | pd.DataFrame(results) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""This functions are based on my own technical analysis library:
https://github.com/bukosabino/ta
You should check it if you need documentation of this functions.
"""
import pandas as pd
import numpy as np
"""
Volatility Indicators
"""
def bollinger_hband(close, n=20, ndev=2):
mavg = ... | pd.Series(hband, name='dchband') | pandas.Series |
from dataclasses import dataclass
import traceback
import random
import itertools
import constraint
@dataclass
class Player:
name: str
hand: list
def take_turn(self):
print('this is your hand:', self.hand)
while True:
command = input('What do you want to do? [suggest, accuse]... | pd.DataFrame(ex) | pandas.DataFrame |
from __future__ import print_function
import baker
import logging
import core.io
from core.cascade import group_offsets
def truncate_data(x, y, qid, docno, k):
"""Truncate each ranked list down to at most k documents"""
import numpy as np
idx = np.concatenate([np.arange(a, min(a + k, b)) for a, b in gr... | pd.read_csv(fname, sep=',', header=None) | pandas.read_csv |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# This file is part of the
# Apode Project (https://github.com/mchalela/apode).
# Copyright (c) 2020, <NAME> and <NAME>
# License: MIT
# Full Text: https://github.com/ngrion/apode/blob/master/LICENSE.txt
from apode import datasets
from apode.basic import ApodeData
imp... | pd.DataFrame({"x": y}) | pandas.DataFrame |
from __future__ import absolute_import
from __future__ import print_function
import pandas as pd
import numpy as np
import itertools
import morphs
import click
import sklearn
import sklearn.linear_model
from sklearn.linear_model import LogisticRegression
from joblib import Parallel, delayed
from six.moves import range
... | pd.DataFrame(stim_ids, columns=["motif"]) | pandas.DataFrame |
#!/usr/bin/python
#
# Copyright 2018-2021 Polyaxon, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | pd.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
import json
import dml
import prov.model
import datetime
import uuid
import pandas as pd
class topCertifiedCompanies(dml.Algorithm):
contributor = 'ashwini_gdukuray_justini_utdesai'
reads = ['ashwini_gdukuray_justini_utdesai.topCompanies', 'ashwini_gdukuray_justini_utdesai.masterList']
writes = ['ashwini_... | pd.Series(businessIDs, index=topCompaniesDF.index) | pandas.Series |
"""
This script is the entry point of a SageMaker TrainingJob for TFIDF
"""
from typing import Optional
from datetime import datetime
import pandas as pd
from sklearn.model_selection import GridSearchCV
from sklearn.multiclass import OneVsRestClassifier
from sklearn.linear_model import LogisticRegression
from trainin... | pd.DataFrame(plr.cv_results_) | pandas.DataFrame |
# Utility functions supporting experiments
import os
import warnings
import numpy as np
import pandas as pd
import netCDF4
# import xarray as xr
import subprocess
from datetime import datetime, timedelta
import collections
import itertools
import time
import sys
from filelock import FileLock
from functools import parti... | pd.read_hdf(file_name) | pandas.read_hdf |
# my_script.py
from pandas import DataFrame
# from my_mod import enlarge
from my_mod import enlarge # this works
print('Hello')
df = | DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) | pandas.DataFrame |
import numpy as np
import pandas as pd
import os
from operator import itemgetter
from abc import ABCMeta, abstractmethod
from flow_equation_parser import FlowEquationParser
class IntermediateVectorManager:
def __init__(self, couplings):
self.couplings = couplings
self.num_intermediate_vectors ... | pd.unique(constant_expressions) | pandas.unique |
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 23 13:23:20 2022
@author: lawashburn
"""
import os
import csv
import pandas as pd
import numpy as np
from datetime import datetime
now = datetime.now()
spectra_import = r"C:\Users\lawashburn\Documents\HyPep1.0\HyPep_Simple_ASMS_Results\Raw_Files\Formatted_MS... | pd.read_csv(spectra_import, sep=",",skiprows=[0], names= ["m/z", "resolution", "charge", "intensity","MS2",'scan_number','empty']) | pandas.read_csv |
#!/usr/bin/env python3
import pytest
import os
import pathlib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import logging
import math
import torch
from neuralprophet import NeuralProphet, set_random_seed
from neuralprophet import df_utils
log = logging.getLogger("NP.test")
log.setLevel("WAR... | pd.read_csv(PEYTON_FILE, nrows=512) | pandas.read_csv |
import pandas as pd
from app import db
from app.fetcher.fetcher import Fetcher
from app.models import Umrti
class DeathsFetcher(Fetcher):
"""
Class for updating deaths table.
"""
DEATHS_CSV = 'https://onemocneni-aktualne.mzcr.cz/api/v2/covid-19/umrti.csv'
def __init__(self):
super().__i... | pd.merge(df, merged, how='left') | pandas.merge |
import pandas as pd
import country_converter as coco
cc = coco.CountryConverter()
def convert_country(country):
return cc.convert(names=[country], to="ISO3")
# read data
happiness_df = pd.read_excel("data/raw/Chapter2OnlineData.xlsx",
sheet_name="Figure2.6")
happiness_names = li... | pd.merge(super_df, science_df, left_on="ISO3", right_on="ISO3") | pandas.merge |
from __future__ import print_function, division, absolute_import
try:
import typing
except ImportError:
import collections as typing
import numpy as np
import pandas as pd
import matplotlib
from matplotlib import pyplot as plt
from matplotlib import colors
from matplotlib import patches
from matplotlib.tight_... | pd.Series(df_packed) | pandas.Series |
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from sklearn.pipeline import Pipeline
from hcrystalball.feature_extraction import HolidayTransformer
@pytest.mark.parametrize(
"X_y_with_freq, country_code, country_code_column, country_code_column_value, extected_error",
[
... | pd.date_range(start="2020-04-10", periods=10) | pandas.date_range |
# SLA Predictor application
# CLASS Project: https://class-project.eu/
#
# 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/LICENSE-2.0
# Unless required by... | pd.DataFrame() | pandas.DataFrame |
from os import link
import flask
from flask.globals import request
from flask import Flask, render_template
# library used for prediction
import numpy as np
import pandas as pd
import pickle
# library used for insights
import json
import plotly
import plotly.express as px
app = Flask(__name__, template_folder = 'templ... | pd.Series([Weekend]) | pandas.Series |
import time
import datetime
import numpy as np
import pandas as pd
import lightgbm as lgb
from dateutil.parser import parse
from sklearn.cross_validation import KFold
from sklearn.metrics import mean_squared_error
import warnings
warnings.filterwarnings("ignore")
train = pd.read_csv('../raw_data/d_train.csv',encoding... | pd.read_csv("../raw_data/fea_test_3.csv") | pandas.read_csv |
#!/usr/bin/env python3
'''compute the running exhaust emissions using perDistance rates'''
import sys
import pandas as pd
from smart_open import open
import geopandas as gpd
from joblib import Parallel,delayed
import yaml
from argparse import ArgumentParser
def groupRates(rates,vmx,srcTypeGroup,countyID,
... | pd.read_csv('links.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Evaluate intra-class correlation coefficients (ICC) for each radiomics feature by comparing extractions from
each set of segmentations (e.g. normal, eroded, dilated segmentations).
Not for clinical use.
SPDX-FileCopyrightText: 2021 Medical Physics Unit, McGill University, Montreal, CA... | pd.DataFrame(global_featureDIL) | pandas.DataFrame |
import os
import inspect
import config
from case_trends_finder import geo_transmission_analyzer
from simulation import Simulation
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from sklearn.metrics import mean_squared_error
import pickle
import skopt
import skopt.plots
import matpl... | pd.read_csv(state_cases) | pandas.read_csv |
#Import modules
import os
import pandas as pd
import numpy as np
from pandas import DatetimeIndex
import dask
import scipy
from scipy.optimize import minimize, LinearConstraint
import time
from sklearn.preprocessing import MinMaxScaler, StandardScaler
import pickle
#Define Column Name
indexName = 'date'
... | pd.DataFrame(testFwd.values, index=testFwd.index) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import pandas as pd
from pandas import Timestamp
def create_dataframe(tuple_data):
"""Create pandas df from tuple data with a header."""
return pd.DataFrame.from_records(tuple_data[1:], columns=tuple_data[0])
### REUSABLE FIXTURES --------------------... | Timestamp('2013-11-01 00:00:00') | pandas.Timestamp |
import nose
import warnings
import os
import datetime
import numpy as np
import sys
from distutils.version import LooseVersion
from pandas import compat
from pandas.compat import u, PY3
from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range,
date_range, period_range, Index, Categori... | tm.assert_almost_equal(x, x_rec) | pandas.util.testing.assert_almost_equal |
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
from tqdm import tqdm
import pandas as pd
import numpy as np
import json
import os
path = os.path.dirname(os.path.abspath(__file__))
list_dir = os.listdir(path + '/results/')
stage = [mpimg.imread(path+'/media/stage_{}.png'.format(i)) for i in rang... | pd.DataFrame(data['agent_0/y']) | pandas.DataFrame |
#!/usr/bin/env python3
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
import os
import json
import urllib.request
# 取得する時刻（Noneとすれば、最新のものを取得）
latest = None
#latest = "2021-05-02T14:30:00+09:00"
# データ取得部分
class AmedasStation():
def __init__(self, latest=None):
url = "https://w... | pd.to_datetime(latest) | pandas.to_datetime |
import requests
from bs4 import BeautifulSoup
import pandas as pd
import math
class Scraper:
"""
The class Scraper scrapes all apartments for sale from website www.domoplius.lt
"""
def __init__(self, header: dict = {"User-Agent": "Mozilla/5.0"}):
"""
Inits Scraper Class
... | pd.DataFrame(all_information) | pandas.DataFrame |
"""
########################################################################
The azmet_maricopa.py module contains the AzmetMaricopa class, which
inherits from the pyfao56 Weather class in weather.py. AzmetMaricopa
provides specific I/O functionality for obtaining required weather input
data from the Arizona Meteorolog... | pd.DataFrame(future) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8; -*-
# Copyright (c) 2021, 2022 Oracle and/or its affiliates.
# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
"""
APIs to interact with Oracle's Model Deployment service.
There are three main classes: ModelDeployment, M... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pytest
from unittest.mock import MagicMock
from copy import deepcopy
import pandas
from .utils import load_data
from tests.utils.df_handler import transform_df
def set_list_tables_mock(client):
list_tables_response = load_data("redshift-data-list-tables-response.json")
list_... | pandas.DataFrame([[1, 1]]) | pandas.DataFrame |
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.... | pd.to_datetime(data_df['reservation_status_date']) | pandas.to_datetime |
# overlap coefficient join
from joblib import delayed, Parallel
from six import iteritems
import pandas as pd
import pyprind
from py_stringsimjoin.filter.overlap_filter import OverlapFilter
from py_stringsimjoin.index.inverted_index import InvertedIndex
from py_stringsimjoin.utils.generic_helper import convert_datafra... | pd.DataFrame(output_rows, columns=output_header) | pandas.DataFrame |
#Rule 9 - PROCESS_AGENT_ID should be alphanumberic and PROCESS_ID should be a number
def process_id(fle, fleName, target):
import re
import os
import sys
import json
import openpyxl
import pandas as pd
from pandas import ExcelWriter
from pandas import ExcelFile
from dateutil.parser import parse
import validat... | ExcelWriter(target, engine='openpyxl', mode='a') | pandas.ExcelWriter |
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