prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import re
import os
import xml.etree.ElementTree as ET
import pandas as pd
import boto3
import csv
from urllib.parse import unquote_plus
s3_client = boto3.client('s3')
s3 = boto3.resource('s3')
from xml_2_data import mnfp_2_data
from xml_2_data import mnfp1_2_data
from xml_2_data import mnfp2_2_data
from nmfp_rename_... | pd.DataFrame(columns=holdings_columns) | pandas.DataFrame |
#!/usr/bin/python
# coding=utf-8
# 采用TF-IDF方法提取文本关键词
# http://scikit-learn.org/stable/modules/feature_extraction.html#tfidf-term-weighting
import sys,codecs
import pandas as pd
import numpy as np
import jieba.posseg
import jieba.analyse
# from sklearn import feature_extraction
from sklearn.feature_extraction.... | pd.read_csv(path_in) | pandas.read_csv |
#put the columns two at a time in a dataframe
# dataframe and visualization tools
import pandas as pd
import numpy as np
import matplotlib as mlp
import time
from matplotlib import pyplot as plt
import wx
import os
import numpy.polynomial.polynomial as poly
import statistics as stats
from statistics import mode
from ... | pd.DataFrame() | pandas.DataFrame |
from flask import Flask, render_template, jsonify, request
from flask_pymongo import PyMongo
from flask_cors import CORS, cross_origin
import json
import collections
import numpy as np
import re
from numpy import array
from statistics import mode
import pandas as pd
import warnings
import copy
from joblib import Mem... | pd.DataFrame.from_dict(dicLR) | pandas.DataFrame.from_dict |
# Ab initio Elasticity and Thermodynamics of Minerals
#
# Version 2.5.0 27/10/2021
#
# Comment the following three lines to produce the documentation
# with readthedocs
# from IPython import get_ipython
# get_ipython().magic('cls')
# get_ipython().magic('reset -sf')
import datetime
import os
import sys
import... | pd.set_option('colheader_justify', 'center') | pandas.set_option |
import datetime
from collections import OrderedDict
import pandas as pd
from google.cloud import bigquery
CLIENT = None
PROJECT_ID = None
def insert_date_range(sql, date_range):
start, end = date_range
if start is None and end is None: return sql
if start is None:
return sql + ' WHERE `date` <= ... | pd.to_datetime(df['date_time']) | pandas.to_datetime |
from json import load
from pickle import FALSE
from tools.funclib import table2fasta
import pandas as pd
import numpy as np
import joblib
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
import benchmark_common as bcommon
import config as cfg
import os
#region 获取酶训练的数据集
def get_en... | pd.read_feather(cfg.TRAIN_FEATURE) | pandas.read_feather |
import pandas as pd
import folium
import math
from itertools import combinations
from pyproj import Proj, transform
from tqdm import tqdm
from typing import List
def preprocess_data(path: str) -> pd.DataFrame:
"""
"Note": Modify and use according to your own data.
Or you don't need to use this code, and ... | pd.Series(char_list) | pandas.Series |
"""Predict all plots which have NEON field data"""
from deepforest import deepforest
import os
import rasterstats
import geopandas as gp
import pandas as pd
from crown_maps.LIDAR import non_zero_99_quantile
from crown_maps.predict import predict_tiles, project
def run(eval_path, CHM_dir, min_height=3):
#Predi... | pd.read_csv("Figures/vst_field_data.csv") | pandas.read_csv |
#!/usr/bin/env python3
import sys
import argparse
import loompy
import numpy as np
import pandas as pd
def main():
description = """This script compares two loom files and checks that they contain identical data up to a constant"""
parser = argparse.ArgumentParser(description=description)
parser.add_argu... | pd.DataFrame(data=truth_loom[:, :], index=truth_loom.row_attrs['gene_names'], columns=truth_loom.col_attrs['cell_names']) | pandas.DataFrame |
import argparse
import sys
import random
import csv
import ujson
import re
import pandas as pd
import numpy as np
from collections import Counter
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
import wordvecdata as wvd
from sklearn.metrics import average_precision_score
from date... | pd.DataFrame(a_dict) | pandas.DataFrame |
import ast
import collections
import datetime
import math
import numpy as np
import pandas as pd
from pyspark import Row, SparkContext, SparkConf
from pyspark.sql import SQLContext
from pyspark.sql.functions import col
class Calculator:
def __init__(self):
self.localClusterURL = "local[2]"
self.c... | pd.DataFrame(user_tag_data[1]) | pandas.DataFrame |
import pytest
from grasping_position_inference.training.exceptions import DataSetIsEmpty, ModelIsNotTrained
from grasping_position_inference.training.model import Model
import pandas as pd
from mock import patch
DUMMY_FILENAME = 'cup.n.01,BACK,:BACK :BOTTOM,pr2_left_arm.csv'
@patch('grasping_position_inference.train... | pd.DataFrame() | pandas.DataFrame |
from __future__ import division
##External base packages.
import time
import glob
import os
import pdb
import sys
##External packages.
import pandas as pd
import numpy as np
from sklearn.preprocessing import Imputer
from numpy_sugar.linalg import economic_qs, economic_svd
from limix.stats import effsizes_se, lrt_pvalue... | pd.DataFrame() | pandas.DataFrame |
from collections import defaultdict
import json
import re
import sys
import time
import matplotlib.pyplot as plt
from itertools import permutations
import numpy as np
import pandas as pd
from scipy.cluster.hierarchy import fcluster, linkage
from scipy.spatial.distance import pdist
from scipy.stats import lognorm
impor... | pd.DataFrame(columns=cols) | pandas.DataFrame |
"""
Name : c9_14_get_stock_return_matrix_from_yanMonthly.py
Book : Python for Finance (2nd ed.)
Publisher: Packt Publishing Ltd.
Author : <NAME>
Date : 6/6/2017
email : <EMAIL>
<EMAIL>
"""
import numpy as np
import scipy as sp
import pandas as pd
#
n_stocks=10
x=pd.read_pickle... | pd.DataFrame(ret,index=ddate[1:]) | pandas.DataFrame |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# *****************************************************************************/
# * Authors: <NAME>
# *****************************************************************************/
"""transformCSV.py
This module contains the basic functions for creating the content of... | pandas.StringDtype() | pandas.StringDtype |
import os
import sys
import argparse
import shlex
from pprint import pprint
from copy import deepcopy
import numpy as np
from scipy.sparse import coo_matrix
from sklearn.grid_search import ParameterGrid
from clusterlib.scheduler import queued_or_running_jobs
from clusterlib.scheduler import submit
from clusterlib.st... | pd.DataFrame(results) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""Master_NBA_Predictive_Model.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/16mdsw4rUN3jcKETlA4rHSXlp1Hjr4raK
"""
from argparse import ArgumentParser
import pandas as pd
import random as rnd
import numpy as np
import wa... | pd.merge(addteamData, dfNew, on=['TEAM']) | pandas.merge |
import os
import pytest
import pandas as pd
import numpy as np
from collections import OrderedDict
from ..catalog_matching import (crossmatch,
select_min_dist,
post_k2_clean,
find_campaigns,
... | pd.read_csv('catalog_matching/tests/exfiles/select_min_dist_union_k2.csv') | pandas.read_csv |
import json
import os
from datetime import datetime
import pandas as pd
import pytest
from py.path import local
from pytest import fixture
from socceraction.data.base import MissingDataError
from socceraction.data.opta import (
OptaEventSchema,
OptaGameSchema,
OptaPlayerSchema,
OptaTeamSchema,
)
from ... | pd.DataFrame.from_dict(events, orient="index") | pandas.DataFrame.from_dict |
"""
Example use of vixutil to plot the term structure.
Be sure to run vixutil -r first to download the data.
"""
import vixutil as vutil
import pandas as pd
import logging as logging
import asyncio
import sys
pd.set_option('display.max_rows', 10)
#need over two months
pd.set_option('display.min_rows', 10)
pd.set_op... | pd.concat([s1,s2]) | pandas.concat |
from __future__ import annotations
import os
import hashlib
from collections import OrderedDict
from typing import List, Optional, Callable, Dict, Any, Union
import pandas as pd
import colorama
import pprint
import json
class Parser:
def __init__(self, hashed_resources_folder: str):
self._hashed_resources... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 13 15:21:55 2019
@author: raryapratama
"""
#%%
#Step (1): Import Python libraries, set land conversion scenarios general parameters
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import quad
import seaborn as sns
import pandas as... | pd.read_excel('RIL_StLF.xlsx', 'NonRW_RIL_S2') | pandas.read_excel |
# ActivitySim
# See full license in LICENSE.txt.
import logging
import orca
import numpy as np
import pandas as pd
from activitysim.core.util import reindex
logger = logging.getLogger(__name__)
@orca.table()
def tours(non_mandatory_tours, mandatory_tours, tdd_alts):
non_mandatory_df = non_mandatory_tours.lo... | pd.Series(1, index=tours.index) | pandas.Series |
# -*- coding: utf-8 -*-
"""SOUTH DAKOTA Arrest Analysis.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/10iVfY_TbBf7JUU4Mba4E3dlHlHjy5Sr_
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pylab
from math import... | pd.read_csv(data_path, index_col=0) | pandas.read_csv |
import pytest
from cellrank.tl._colors import _map_names_and_colors, _create_categorical_colors
import numpy as np
import pandas as pd
from pandas.api.types import is_categorical_dtype
from matplotlib.colors import is_color_like
class TestColors:
def test_create_categorical_colors_too_many_colors(self):
... | pd.Series(["bar", "bar", "bar"], dtype="category") | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 2 15:03:42 2019
@author: user
"""
import time
import itertools
import os
import tables
import shutil
from glob import glob
import fastparquet as pq
import numpy as np
import pandas as pd
import psycopg2 as pg
import grimsel
import grimsel.auxilia... | pd.DataFrame(dat, columns=self.columns) | pandas.DataFrame |
import numpy as np
import pytest
from pandas import (
DataFrame,
NaT,
Series,
Timedelta,
Timestamp,
)
import pandas._testing as tm
def test_group_shift_with_null_key():
# This test is designed to replicate the segfault in issue #13813.
n_rows = 1200
# Generate a moderately large data... | Timedelta("6 days") | pandas.Timedelta |
import itertools
import string
import numpy as np
from numpy import random
import pytest
import pandas.util._test_decorators as td
from pandas import DataFrame, MultiIndex, Series, date_range, timedelta_range
import pandas._testing as tm
from pandas.tests.plotting.common import TestPlotBase, _check_plot_works
impor... | tm.assert_produces_warning(UserWarning) | pandas._testing.assert_produces_warning |
import calendar
import pandas as pd
import hashlib
import json
from profootballref.Parsers import PlayerParser
from profootballref.Tools import Loader
from profootballref.Tools import Passhash
from profootballref.Tools import Rechash
from profootballref.Tools import Rushhash
from profootballref.Tools import Kickhash
fr... | pd.read_html(html) | pandas.read_html |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
from tqdm import tqdm as pb
import datetime
import re
import warnings
import matplotlib.pyplot as plt
import pylab as mpl
from docx import Document
from docx.shared import Pt
from data_source import local_source
def concat_ts_codes(df): #拼接df中所有TS_CODE... | pd.merge(stocks_ind, quotations_monthly_ind, on=['TS_CODE','END_DATE'], how="left") | pandas.merge |
"""
Utilities that help with the building of tensorflow keras models
"""
import io
from muti import chu, genu
import tensorflow as tf
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import plotly.io as pio
from plotly.subplots import make_subplots
import warnings
import os
import math
import mul... | pd.concat([samps, samp_num], axis=1) | pandas.concat |
###################################
# CALIBRATION DETECTION AND CORRECTION #
###################################
# This file includes functionality for identification and correction of calibration events.
# Functions include detection based on edges or persistence restricted by day of week and hour of day, identificati... | pd.to_datetime(candidates) | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu June 7 22:33:07 2019
@author: bruce
"""
import pandas as pd
import numpy as np
from scipy import fftpack
from scipy import signal
from scipy import stats
import matplotlib.pyplot as plt
import os
# set saving path
path_result_freq = "/home/bruce/Dro... | pd.DataFrame(matrix_temp_square) | pandas.DataFrame |
import numpy as np
import pandas as pd
import logging
from utils.utils import count_nulls
import etl.processing_raw as processing_raw
import etl.processing_l1 as processing_l1
from io import StringIO
import geopandas as gpd
logger = logging.getLogger(__name__)
def transform_raw(sources: dict, config: dict) -> dict:
... | pd.merge(kpis, lugares, on=['anyo', 'id_barrio'], how='left') | pandas.merge |
import pandas as pd
import os
import importlib
reader = importlib.import_module("read_csv")
index_filename = "Index.csv"
def write_to_index(filename: str,
mean_street_quality: float,
distance: float,
speed: float,
relevant: bool):
... | pd.DataFrame(original_data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# Test some of the basic _core functions
import datetime as dt
from importlib import reload
import logging
import numpy as np
import pandas as pds
import pytest
import xarray as xr
import pysat
import pysat.instruments.pysat_testing
import pysat.instruments.pysat_testing_xarray
import pysat.in... | pds.date_range(start[0], stop[0]) | pandas.date_range |
#!/usr/bin/env python
# ------------------------------------------------------------------------------------------------------%
# Created by "Thieu" at 00:30, 01/06/2021 %
# ... | read_csv(f"{Config.BENCHMARK_BEST_FIT}/{PROBLEM_SIZE}D_{mha}_best_fit.csv", usecols=["function", "time", "trial", "fit"]) | pandas.read_csv |
#!/usr/bin/env bash
from __future__ import absolute_import, print_function, unicode_literals
import argparse
import json
import numpy as np
import os
import pandas as pd
import pickle
import sys
import tensorflow as tf
from itertools import compress
from keras import backend as keras_backend
from thesis.classificati... | pd.concat(features_progression, ignore_index=True) | pandas.concat |
import pandas as pd
import numpy as np
import random
import math
'''
Allocate nodes for each update in different scenarios
'''
# uniform allocation, i.e., scenario 1
def uniform_alloc(data, random_seed, available_num):
random.seed(random_seed)
nodes = []
for i in range(data.shape[0]):
nodes.appe... | pd.DataFrame(current_times[0:test_size]) | pandas.DataFrame |
import datetime
import re
from warnings import (
catch_warnings,
simplefilter,
)
import numpy as np
import pytest
from pandas._libs.tslibs import Timestamp
from pandas.compat import is_platform_windows
import pandas as pd
from pandas import (
DataFrame,
Index,
Series,
_testing as tm,
bdat... | Timestamp("20130102") | pandas._libs.tslibs.Timestamp |
import os
import param
import pandas as pd
import concurrent.futures
from ulmo.usgs import nwis
from functools import partial
from quest import util
from quest.static import ServiceType, GeomType, DataType
from quest.plugins import ProviderBase, TimePeriodServiceBase, load_plugins
BASE_PATH = 'usgs-nwis'
class Nw... | pd.read_csv(url, sep='\t', comment='#') | pandas.read_csv |
from collections import defaultdict
from functools import partial
import itertools
import operator
import re
from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
from pandas._libs import Timedelta, Timestamp, internals as libinternals, lib
from pandas.util._validators import validate_bool_kwar... | lib.get_reverse_indexer(indexer, self.shape[0]) | pandas._libs.lib.get_reverse_indexer |
'''
Run using python from terminal.
Doesn't read from scripts directory (L13) when run from poetry shell.
'''
import pandas as pd
import pandas.testing as pd_testing
import typing as tp
import os
import unittest
from unittest import mock
import datetime
from scripts import influx_metrics_univ3 as imetrics
class Test... | pd_testing.assert_frame_equal(expected_pcs[0], actual_pcs[0]) | pandas.testing.assert_frame_equal |
# 预处理复赛数据
import os
import pandas as pd
import lightgbm as lgb
from sklearn.model_selection import StratifiedKFold
import numpy as np
from sklearn.metrics import f1_score
path = './'
w2v_path = path + '/w2v'
train = pd.read_csv(path + '/train_2.csv')
test = pd.read_csv(path + '/test_2.csv')
train_stacking = pd.rea... | pd.read_csv(w2v_path + '/' + col + '.csv') | pandas.read_csv |
#!/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.Series(labels_24h_clock) | pandas.Series |
from os.path import join
import threading
from pandas import DataFrame
try:
from main import main
from data_access import GetData
from utils import get_folder_path, write_yaml, read_yaml
from configs import conf
from scheduler_service import create_job
except Exception as e:
from .main import m... | DataFrame() | pandas.DataFrame |
# Import pyVPLM packages
from pyvplm.core.definition import PositiveParameter, PositiveParameterSet
from pyvplm.addon import variablepowerlaw as vpl
from pyvplm.addon import pixdoe as doe
from pint import UnitRegistry
import save_load as sl
import pi_format as pif
import csv_export as csv
import constraint_form... | pd.DataFrame(data=doePi_all, columns=columns_2) | pandas.DataFrame |
# Copyright (c) 2017, Apple Inc. All rights reserved.
#
# Use of this source code is governed by a BSD-3-clause license that can be
# found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
import unittest
import tempfile
import os
import pandas as pd
import random
import pytest
from coremlto... | pd.DataFrame(x, columns=column_names) | pandas.DataFrame |
"""Tests for encodings submodule."""
from nxviz import encodings as aes
import pytest
import pandas as pd
from random import choice
import numpy as np
def categorical_series():
"""Generator for categorical series."""
categories = "abc"
return pd.Series([choice(categories) for _ in range(30)])
def conti... | pd.Series(values) | pandas.Series |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
gen_sgRNAs.py generates sgRNAs as part of ExcisionFinder. New Cas enzymes can be added by modifying CAS_LIST.txt.
Written in Python v 3.6.1.
<NAME> et al 2018.
Usage:
gen_sgRNAs.py [-chvrd] <bcf> <annots_file> <locus> <pams_dir> <ref_fasta> <out> <cas_types> <guide... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 16 16:51:36 2021
@author: FELIPE
"""
# Note: Read the header before running
# =============================================================================
# >>> Project: Disaster Response Pipeline (Udacity - Data Science Nanodegree) <<<
# How to execute this file
# Sam... | pd.read_csv(dataset_messages) | pandas.read_csv |
"""
Functions that plot the results from the simulated experiments.
@author: <NAME> <<EMAIL>>
"""
import seaborn as sns
import matplotlib
matplotlib.rcParams['text.usetex'] = True
import matplotlib.pyplot as plt
import pandas as pd
"""
Global dictionaries used to store LaTeX formatting for labels used in plots.
""... | pd.DataFrame(data=formatted_results) | pandas.DataFrame |
# coding=utf-8
import math
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from sklearn import metrics
import tensorflow as tf
from tensorflow.python.data import Dataset
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# ### 设置
tf.logging.set_verbosity(tf.logging.ERROR) # 日... | pd.DataFrame() | pandas.DataFrame |
# License: Apache-2.0
import databricks.koalas as ks
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from gators.encoders import MultiClassEncoder, WOEEncoder
ks.set_option("compute.default_index_type", "distributed-sequence")
@pytest.fixture
def data():
X = pd... | assert_frame_equal(X_new, X_expected) | pandas.testing.assert_frame_equal |
from datetime import datetime, timedelta
from ..utils import process_dataframe_and_series
import rich
from jsonpath import jsonpath
from retry import retry
import pandas as pd
import requests
import multitasking
import signal
from tqdm import tqdm
from typing import (Dict,
List,
... | pd.concat(dfs) | pandas.concat |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
#-------------read csv---------------------
df_2010_2011 = pd.read_csv("/mnt/nadavrap-students/STS/data/data_Shapira_20200911_2010_2011.csv")
df_2012_2013 = pd.read_csv("/mnt/nadavrap-students/STS/data/data_Shapira_20200911_2012_2013.csv")
df_2014_... | pd.merge(df7, df_2018, on='hospid') | pandas.merge |
import itertools
import pandas as pd
from pandas.testing import assert_series_equal
import pytest
from solarforecastarbiter.reference_forecasts import forecast
def assert_none_or_series(out, expected):
assert len(out) == len(expected)
for o, e in zip(out, expected):
if e is None:
assert... | pd.Series([10., 6.75, 350.]) | pandas.Series |
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... | compat.itervalues(self.frame) | pandas.compat.itervalues |
from __future__ import print_function
from __future__ import division
import pandas as pd
import numpy as np
import click
import glob
import os
import sys
READ_CUTOFF = 2
SAMPLE_CUTOFF = 1
CIRC_HEADER = [
'chrom',
'start',
'end',
'name',
'score',
'strand',
'thickStart',
'thickEnd',
... | pd.read_table(exp_table, index_col=0) | pandas.read_table |
from fctest.__EISData__ import EISData
import pandas as pd
import os
import numpy as np
class AutoLEISData(EISData):
ENCODING = "ISO-8859-1"
def __init__(self, data_path, mea_area):
raw_data = pd.read_csv(data_path, sep='\t')
raw_data = raw_data.iloc[:, 0].str.split(',', expand=True)
... | pd.to_numeric(data_section.z_im) | pandas.to_numeric |
"""
step04.py: Clearify and Merge Synapse Data
"""
import argparse
import pandas
import step00
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("clinical", type=str, help="Clinical CSV file")
parser.add_argument("expression", type=str, help="Expression CSV file(s)", nargs=... | pandas.concat(data_list, axis="columns", join="inner", verify_integrity=True) | pandas.concat |
import re
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import IntervalArray
class TestSeriesReplace:
def test_replace_explicit_none(self):
# GH#36984 if the user explicitly passes value=None, give it to them
ser = pd.Series([0, 0, ""],... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
# Import modules
from models import get_model_cnn_crf,get_model,get_model_cnn
import numpy as np
from utils import gen, chunker, WINDOW_SIZE, rescale_array, rescale_wake
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from keras.models import load_model
from sklearn.metrics import f1_sco... | pd.DataFrame(allgt) | pandas.DataFrame |
""" module generates schemas and lookups used in dropdowns """
__author__ = 'etuka'
from django.core.management.base import BaseCommand
import os
import glob
import json
import pandas as pd
import xml.etree.ElementTree as ET
from dal.copo_base_da import DataSchemas
from dal.mongo_util import get_collection_ref
from w... | pd.DataFrame(data) | pandas.DataFrame |
import datetime
from time import sleep
import pandas as pd
from loguru import logger
import ofanalysis.const as const
import ofanalysis.utility as ut
import tushare as ts
class TSDataUpdate:
def __init__(self, ts_pro_token:str):
self.__pro = ts.pro_api(ts_pro_token)
self.__today = datetime.date.... | pd.concat([df_daily, df_batch_daily], ignore_index=True) | pandas.concat |
# -*- coding: utf-8 -*-
# pylint: disable=W0612,E1101
from datetime import datetime
import operator
import nose
from functools import wraps
import numpy as np
import pandas as pd
from pandas import Series, DataFrame, Index, isnull, notnull, pivot, MultiIndex
from pandas.core.datetools import bday
from pandas.core.n... | com.is_float_dtype(res3['ItemE'].values) | pandas.core.common.is_float_dtype |
import datetime
from datetime import date
import pandas as pd
import numpy as np
import requests
from pandas.tseries.offsets import BDay
from mip import Model, xsum, minimize, BINARY, maximize
import re
api_key = '30d9085988663142ce4cb478d09e6d00'
def next_weekday(d, weekday):
days_ahead = weekday - d.weekday()
... | pd.to_datetime('today') | pandas.to_datetime |
'''
Copyright (c) 2020, <NAME>, Sunnybrook Research Institute
Script that iwll run statistical tests for comparing tokenizers with
MannWhitney U-test or classifiers with the McNemar test.
Input: 2 or more .xlsx fiels of different model testing results.
output: NLPRR_ExperimentsSummary.csv containing a comparison of d... | pd.read_excel(file, engine='openpyxl', sheet_name='Summary_Metrics') | pandas.read_excel |
import json
import pandas as pd
with open(r'data\unique_authors_list_full.json') as json_file:
unique_authors = json.load(json_file)
with open(r'data\n_unique_authors_full.json') as json_file:
n_unique_authors = json.load(json_file)
print(f"Current len of unique authors : {len(unique_authors)}")
replacem... | pd.DataFrame(strength_cols,index=labels,columns=labels) | pandas.DataFrame |
# *-* coding: utf-8 *-*
"""Read binary data from the IRIS Instruments Syscal Pro system
TODO: Properly sort out handling of electrode positions and conversion to
electrode numbers.
"""
import struct
from io import StringIO
import logging
import pandas as pd
import numpy as np
from reda.importers.utils.decorators im... | pd.DataFrame(columns=['a', 'b', 'm', 'n', 'r']) | pandas.DataFrame |
"""Runs experiments on CICIDS-2017 dataset."""
import itertools
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import RFE
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
from sklearn import metrics
from sklearn.metrics import f1_score
... | pd.concat([df, df7]) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 9 20:13:44 2020
@author: Adam
"""
#%% Heatmap generator "Barcode"
import os
os.chdir(r'C:\Users\Ben\Desktop\T7_primase_Recognition_Adam\adam\paper\code_after_meating_with_danny')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
imp... | pd.read_csv('./data/chip_B_favor.csv') | pandas.read_csv |
import pickle
import os
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
import multiprocessing
# from sklearn.utils.random import sample_without_replacement
# from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
# from sklearn.ensemble import GradientBoostingClassifi... | pd.DataFrame() | pandas.DataFrame |
'''
A scenario discovery oriented implementation of PRIM.
The implementation of prim provided here is data type aware, so
categorical variables will be handled appropriately. It also uses a
non-standard objective function in the peeling and pasting phase of the
algorithm. This algorithm looks at the increase in the ... | pd.concat(boxes) | pandas.concat |
"""
For working with metabolic models
"""
from __future__ import print_function, division, absolute_import
import os
import json
import pandas as pd
from ..globals import MODEL_DIR
from math import isnan
# ----------------------------------------
# Functions for aggregation (and/or)
# ---------------------------------... | pd.Series(x.meta, name=x.id) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 7 15:19:27 2020
utilities
@author: Merten
"""
import pandas as pd
import numpy as np
import os
import scipy.interpolate as scpinter
from matplotlib import pyplot as plt
from sklearn.utils import shuffle
from sklearn.model_selection import StratifiedKFold
from sklearn.mo... | pd.DataFrame(y_test, columns=y_col_names) | pandas.DataFrame |
import pandas as pd
from sklearn.model_selection import train_test_split
import numpy as np
import xgboost as xgb # use xgboost=1.0.2
import pickle
def read_excel(filePath):
df = pd.read_excel(filePath, sheet_name='Sheet1_user_dt')
df_1 = df.dropna()
drop_colume = ['email',
'sn',
... | pd.DataFrame(data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# author: ysoftman
# python version : 3.x
# desc : pandas test
import numpy as np
import pandas as pd
# dataframe 은 데이터들을 컬럼 모양으로 묶어 표처럼 나탄낸다.
# 시리즈의를 묶어 2차원의 dataframe 구조를 만들 수 있다.
dic1 = {"name": "jane", "fruit": "lemon", "price": 1000}
dic2 = {"name": "bill", "fruit": "orange", "price": 200... | pd.Series(dic2) | pandas.Series |
import argparse
import copy
import itertools
import os
import shutil
import time
import warnings
from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
import torch.multiprocessing as mp
from src.utils.SREA_utils import single_experiment_SREA
from src.utils.g... | pd.set_option('display.max_rows', None) | pandas.set_option |
from DataHandler.DataHandler import DataHandler
import pandas
from Event.EventQueue import EVENT_QUEUE
from Event.Event import Event
import Information.Info as Info
# DEFAULT_COLUMN为默认的读取数据文件的列
DEFAULT_COLUMN = ["Symbol", "Date", "Time",
"Open", "High", "Low", "Close", "Volume", "Turnover"]
def se... | pandas.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 20 21:05:00 2020
Revised on Thur Mar 18 16:04:00 2021
@author: Starlitnightly
New Version 1.2.3
"""
import itertools
import numpy as np
import pandas as pd
from upsetplot import from_memberships
from upsetplot import plot
def FindERG(data, depth=2, sort_num=20, verbose... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
from trackintel.geogr.distances import check_gdf_planar, calculate_haversine_length
def calculate_modal_split(tpls_in, freq=None, metric="count", per_user=False, norm=False):
"""Calculate the modal split of triplegs
Parameters
----------
tpls_in : GeoDataFrame ... | pd.Grouper(freq=freq) | pandas.Grouper |
# -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101
import itertools
import warnings
from warnings import catch_warnings
from datetime import datetime
from pandas.types.common import (is_integer_dtype,
is_float_dtype,
is_scalar)
from pandas.compat... | tm.assert_frame_equal(df, expected) | pandas.util.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import nltk as nl
from difflib import SequenceMatcher
# %% BEST MATCH STRING
def findBestMatchingString(inputTable,compareStringList,old_label_column,new_label_column='MATCHED_STRING', matchingTreshold = 0.6, printMatchingString=True):
#la funzione c... | pd.DataFrame(columns=['word','frequency']) | pandas.DataFrame |
import zimp_clf_client
import mlflow
import pandas as pd
import os
import time
import logging
from zimp_clf_client.rest import ApiException
from experiment.config import Config
from sklearn.metrics import accuracy_score, balanced_accuracy_score, f1_score, precision_score, recall_score
def get_or_create_mlflow_exper... | pd.read_csv(prediction_path) | pandas.read_csv |
"""
Tasks
-------
Search and transform jsonable structures, specifically to make it 'easy' to make tabular/csv output for other consumers.
Example
~~~~~~~~~~~~~
*give me a list of all the fields called 'id' in this stupid, gnarly
thing*
>>> Q('id',gnarly_data)
['id1','id2','id3']
Observations:
--... | u('?20a82645-c095-46ed-80e3-08825760534b?') | pandas.compat.u |
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from rasa_sdk import Action
from rasa_sdk.events import SlotSet
from rasa_sdk.events import Restarted
from rasa_sdk.events import AllSlotsReset
import zomatopy
import json
import smtplib
from email.mime.multi... | pd.DataFrame({'Restaurant Name': name, 'Restaurant locality address': location, 'Average budget for two people': avg_cost, 'Zomato user rating': agg_rating}) | pandas.DataFrame |
import pandas as pd
import pytest
from numpy import inf, nan, testing
from toucan_data_sdk.utils.postprocess import waterfall
@pytest.fixture
def sample_data():
return [
{
'ord': 1,
'category_name': 'Clap',
'category_id': 'clap',
'product_id': 'super clap',... | pd.DataFrame(sample_data) | pandas.DataFrame |
from pandas.core.common import notnull, isnull
import pandas.core.common as common
import numpy as np
def test_notnull():
assert notnull(1.)
assert not notnull(None)
assert not notnull(np.NaN)
assert not notnull(np.inf)
assert not notnull(-np.inf)
def test_isnull():
assert not isnull(1.)
... | common.indent(s, spaces=6) | pandas.core.common.indent |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 4 09:34:08 2017
@author: <NAME>
Answer query script: This script contains functions to query and manipulate DLR survey answer sets. It references datasets that must be stored in a /data/tables subdirectory in the parent directory.
"""
... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
from bld.project_paths import project_paths_join as ppj
# Read the dataset.
adults2005 = pd.read_stata(ppj("IN_DATA", "vp.dta"))
adults2009 = pd.read_stata(ppj("IN_DATA", "zp.dta"))
adults2013 = pd.read_stata(ppj("IN_DATA", "bdp.dta"))
# Extract Column of Big 5 Variables we need for the research... | pd.concat([ids2013, big_adults_2013], axis=1) | pandas.concat |
#!/usr/bin/python3.6
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 2 21:57:56 2017
@author: The Computer
"""
def ModelIt(SubjectIDandOneHotEncoded):
import numpy as np
from sklearn.externals import joblib
SubjectIDandOneHotEncoded.fillna(value=0,inplace=True)
#import the model
clf=joblib.load... | pd.to_timedelta(DF['Duration']) | pandas.to_timedelta |
import gc
from logging import warning
from time import sleep, perf_counter
from typing import Optional, Union, Dict, List, Tuple, Callable
import numpy as np
import pandas as pd
from numpy import ndarray
from rdkit.Chem import AddHs, CanonSmiles, MolToSmiles, MolFromSmiles, MolFromInchi, Kekulize, SanitizeMol
... | pd.DataFrame(data=value, index=index, columns=column, dtype=np.float32) | pandas.DataFrame |
import sys
import pandas as pd
import csv
from ChefRequest import makeRequest
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
starter_problems = {
"0": "CHCHCL",
"1": "TEST",
"2": "INTEST",
"3": "TSORT",
"4": "FCTRL2",
"5": "ATM... | pd.read_csv(csv_file, low_memory=False) | pandas.read_csv |
"""ML-Experiments"""
import os
import pandas
from zipfile import ZipFile
class experiment:
def __init__(self, kaggle_api, dataset, dataset_target,
download_directory):
"""Experiment encapsulates a ML experiment
Arguments:
kaggle_api {KaggleApi} -- Instance of KaggleAp... | pandas.read_csv(self.dataset_file) | pandas.read_csv |
import pandas as pd
import os
#
from .... import global_tools, global_var
from . import paths, transcode
def load(map_code = None):
"""
Loads the production data provided by ENTSO-E
in the given delivery zone.
:param map_code: The bidding zone
:type map_code: string
:ret... | pd.to_datetime(df[global_var.production_dt_UTC]) | pandas.to_datetime |
import logging
import os
import typing as t
from glob import glob
from pathlib import Path
import pandas as pd
from keras.models import load_model
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.externals import joblib
from sklearn.model_selection import train_test_split
from sklearn.pipeline impo... | pd.DataFrame([image_path, class_folder_name]) | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
import re
from sklearn.feature_extraction import DictVectorizer
from sklearn.model_selection import train_test_split
import xgboost as xgb
from sklearn.cluster import MiniBatchKMeans
def process_am... | pd.to_datetime(all_data.last_review) | pandas.to_datetime |
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