prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import os, sys, re, getopt, functools, pysam
import pandas as pd
import numpy as np
from plotnine import *
from PIL import Image
from ATACFragQC import __version__
class ArgumentList:
file_bam = ''
file_ref = ''
file_out = False
quality = 50
isize = 147
cn_len = 10
chr_filte... | pd.DataFrame({'V1': factors}) | pandas.DataFrame |
import pandas as pd
import numpy as np
np.random.seed(99)
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import GridSearchCV
from sklearn.multioutput import MultiOutputClassifier, MultiOutputRegressor
from sklearn.multiclass import OneVsRestCl... | pd.DataFrame(model.cv_results_) | pandas.DataFrame |
import pandas as pd
sec_file = 'uniprot_sec_ac.txt'
lines = open(sec_file, 'rt').readlines()
for i, l in enumerate(lines):
if l.startswith('Secondary AC'):
entry_lines = lines[i+2:]
sec_id = []
prim_id = []
for l in entry_lines:
s, p = l.split()
sec_id.append(s)
prim_id.append(p)
d = {'Secondary... | pd.DataFrame(data=d) | pandas.DataFrame |
import json
import numpy as np
import pandas as pd
import os
def scan(file_path):
for file in os.listdir(file_path):
file_real = file_path + "/" + file
if os.path.isdir(file_real):
scan(file_real)
else:
if file_real.endswith("json"):
file_handle(file... | pd.DataFrame(res_data) | pandas.DataFrame |
import pandas as pd
import os
import re
import numpy as np
import argparse
def get_args_from_command_line():
"""Parse the command line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument("--country_code", type=str,
default="US")
parser.add_argument("--method", t... | pd.DataFrame.from_dict(results_dict) | pandas.DataFrame.from_dict |
import sys
sys.path.append('gen')
from collections import defaultdict
from pathlib import Path
import argparse
import datetime
import locale
import logging
from dash import Dash, dcc, html
from sqlitedict import SqliteDict
import grpc
import pandas as pd
from gen import users_pb2
from models import constants as cnst... | pd.set_option('display.max_rows', None) | pandas.set_option |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 12 16:44:53 2018.
@author: dmitriy
"""
import datetime as dt
import os
import time as t
from datetime import datetime
from typing import Any, List, Tuple, Iterable
import pandas as pd
import psycopg2
import requests
# import all the necessary libr... | pd.concat([df2, df1]) | pandas.concat |
import pytest
import numpy as np
import pandas as pd
from ..linkages import sortLinkages
from ..linkages import calcDeltas
### Create test data set
linkage_ids = ["a", "b", "c"]
linkage_lengths = [4, 5, 6]
linkage_members_ids = []
for i, lid in enumerate(linkage_ids):
linkage_members_ids += [lid for j in range(l... | pd.testing.assert_frame_equal(LINKAGE_MEMBERS[["linkage_id", "obs_id"]], linkage_members_sorted) | pandas.testing.assert_frame_equal |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import time
import sys
import json
import numpy as np
import pandas as pd
from pathlib import Path
import subprocess as subp
import traceback
from sklearn.model_selection import train_test_split, TimeSeriesSplit
import sklearn.metrics as skmet
from autogluon.tab... | pd.read_csv(filepath, sep=';', header='infer') | pandas.read_csv |
import matplotlib
matplotlib.use('Agg')
import pdb
import sys
import Pipelines as pl
import pandas as pd
from datetime import datetime
import numpy as np
import time
# saving the models for the iteration tests:
# to save the models for the iteration tests, we will save a dataframe (in the form of the final dataframe f... | pd.DataFrame() | pandas.DataFrame |
from configs import Level, LEVEL_MAP
from db.DBConnector import close_connection
from refactoring_statistics.plot_utils import box_plot_seaborn
from refactoring_statistics.query_utils import get_metrics_refactoring_level, get_metrics_refactorings, retrieve_columns
from utils.log import log_init, log_close, log
import t... | pd.DataFrame() | pandas.DataFrame |
"""
Coding: UTF-8
Author: Randal
Time: 2021/2/20
E-mail: <EMAIL>
Description: This is a simple toolkit for data extraction of text.
The most important function in the script is about word frequency statistics.
Using re, I generalized the process in words counting, regardless of any preset
word segmentation. Besides, ... | pd.DataFrame.from_dict(uni, orient='index') | pandas.DataFrame.from_dict |
# -*- coding: utf-8 -*-
"""System transmission plots.
This code creates transmission line and interface plots.
@author: <NAME>, <NAME>
"""
import os
import logging
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.colors as mcolors
import matplotlib.d... | pd.notna(start_date_range) | pandas.notna |
import pandas as pd
import numpy as np
from copy import deepcopy
from rdkit import Chem
from data import *
from sklearn.externals import joblib
from sklearn.manifold import TSNE
import matplotlib
#matplotlib.use('TkAgg')
import matplotlib.pyplot as plot
def can_smile(smi_list):
can_list = []
for item in smi_l... | pd.Series(data=smi_list, name='SMILES') | pandas.Series |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# QTPyLib: Quantitative Trading Python Library
# https://github.com/ranaroussi/qtpylib
#
# Copyright 2016-2018 <NAME>
#
# 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 ... | pd.ewma(series, span=window, min_periods=min_periods) | pandas.ewma |
from Bio import SeqIO
from src.inputValueException import InputValueException
import os
import pandas as pd
import re
# calculates kmer frequencies
# k: kmer-length
# peak: peak-position, where sequences should be aligned
# selected: input files
# no_sec_peak: status (-1= no structural data available, 0= False, 1= Tr... | pd.DataFrame(x_axis, index=kmer_list, columns=[file_name1]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from __future__ import division
from __future__ import absolute_import
from __future__ import print_function
# Created at UC Berkeley 2015
# Authors: <NAME>
# ==============================================================================
'''This code trai... | pd.DataFrame(data) | pandas.DataFrame |
import pandas as pd
from collections import deque
import sys
def addExtension(tVal):
return str(tVal) + ".png"
if __name__ == '__main__':
dataDir = sys.argv[1]
timestampPath = sys.argv[2]
gyroPath = sys.argv[3]
# ----------#
timestampLabels = ["#timestamp [ns]", "filename"]
timestamps ... | pd.DataFrame(data=data) | pandas.DataFrame |
from pandas import Series, DataFrame
daeshin = {'open': [11650, 11100, 11200, 11100, 11000],
'high': [12100, 11800, 11200, 11100, 11150],
'low' : [11600, 11050, 10900, 10950, 10900],
'close': [11900, 11600, 11000, 11100, 11050]}
#daeshin_day = DataFrame(daeshin)
daeshin_day = | DataFrame(daeshin, columns=['open', 'high', 'low', 'close']) | pandas.DataFrame |
import logging
from concurrent.futures import ThreadPoolExecutor
import numpy as np
import pandas as pd
from msi_recal.join_by_mz import join_by_mz
from msi_recal.math import get_centroid_peaks, is_valid_formula_adduct
from msi_recal.mean_spectrum import hybrid_mean_spectrum
from msi_recal.params import RecalParams
... | pd.DataFrame(spectral_peaks, columns=['hit_index', 'ref_mz', 'ref_ints']) | pandas.DataFrame |
from league import League
import playerID
from authorize import Authorize
from team import Team
from player import Player
from utils.building_utils import getUrl
from itertools import chain
import pandas as pd
import numpy as np
import requests
import math
from tabulate import tabulate as table
import os
import sys
fr... | pd.DataFrame(data=seasonScores) | pandas.DataFrame |
from collections import Counter
from sklearn.cross_validation import cross_val_score
import pandas as pd
import numpy as np
# pandas importando data frame d. f.
df = | pd.read_csv('situacao_cliente.csv') | pandas.read_csv |
import pandas as pd
import numpy as np
import datetime as dt
import math
#输入H 文件名
def cal_riskrt(H,source):
source=source.iloc[:,0:6]
source=source.drop(columns=["Unnamed: 0"])
source=source.set_index('date').dropna(subset=['long_rt','short_rt','long_short_rt'],how='all')
#新建一个数据框记录各种指标
df=pd.Dat... | pd.read_csv("../draw/rollrt2H35.csv") | pandas.read_csv |
####################################################################################################
"""
dashboard.py
This script implements a dashboard-application for the efficient planning of the municipal
enforcement process, based on housing fraud signals, within the municipality of Amsterdam.
<NAME> & <NAME> 20... | pd.read_json(intermediate_value, orient='split') | pandas.read_json |
#
# Copyright 2015 Quantopian, 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 law or agreed to in wr... | pd.Timestamp('2013-7-1', tz='UTC') | pandas.Timestamp |
from numpy.core.fromnumeric import var
import pytest
import pandas as pd
import numpy as np
from dowhy import CausalModel
class TestIDIdentifier(object):
def test_1(self):
treatment = "T"
outcome = "Y"
causal_graph = "digraph{T->Y;}"
columns = list(treatment) + list(outcome)
... | pd.DataFrame(columns=columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
This enables to parameterize a desired scenario to mock a multi-partner ML project.
"""
import datetime
import re
import uuid
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from loguru import logger
from sklearn.preprocessing import LabelEnc... | pd.DataFrame() | pandas.DataFrame |
"""Wraps sklearn Gradient Boosting Regressor to
1) automate modeling similar to gbm library in R
2) overlay data and descriptive statistics in data visualization
of partial dependencies for better inference
author: <NAME>
date created: 2018-06-15
"""
import sklearn_gbm_ots.sklearn_gbm_extend as sklearn_g... | pd.get_dummies(df) | pandas.get_dummies |
#!/usr/bin/env python
from __future__ import print_function
from .tabulate import tabulate as tabulate_
import sys
import pandas as pd
import re
import datetime
def _get_version():
import ph._version
return ph._version.__version__
def print_version():
print(_get_version())
# Command line parsing of (... | pd.DataFrame(d) | pandas.DataFrame |
import logging
pvl_logger = logging.getLogger('pvlib')
import datetime
import numpy as np
import numpy.testing as npt
import pandas as pd
from nose.tools import raises, assert_almost_equals
from nose.plugins.skip import SkipTest
from pandas.util.testing import assert_frame_equal
from pvlib.location import Location
... | assert_frame_equal(frame, result) | pandas.util.testing.assert_frame_equal |
### Load Necessary Libraries
from bs4 import BeautifulSoup as bs
import pandas as pd
import requests
### Loading page content
page=requests.get('https://www.speedtest.net/global-index#mobile')
cont=page.content
print(page.status_code)
soupobj=bs(cont,'html.parser')
#print(soupobj.prettify()) #printing out soup obj... | pd.read_csv('Broadbandtest.csv') | pandas.read_csv |
from tensorflow.python.keras import Sequential
from pandas_datareader import data
import pandas as pd
from Common.StockMarketIndex.AbstractStockMarketIndex import AbstractStockMarketIndex
from Common.StockMarketIndex.Yahoo.SnP500Index import SnP500Index
from Common.StockMarketIndex.Yahoo.VixIndex import VixIndex
from C... | pd.concat((yahooStockOption.DataFrame['Adj Close'], test_data['Adj Close']), axis=0) | pandas.concat |
# Send same show commands to all devices
# Read devices from an Excel file
import pandas as pd
from netmiko import ConnectHandler
# Read Excel file
excel_file = pd.read_excel(
io="Voice-Gateways-Info.xlsx", sheet_name=0, engine="openpyxl"
)
# Converts Excel file to data frame
df = | pd.DataFrame(excel_file) | pandas.DataFrame |
from Tools import *
from Agent import *
import time
import csv
import graphicDisplayGlobalVarAndFunctions as gvf
import commonVar as common
import pandas as pd
import parameters as par
import numpy as np
import warnings
warnings.filterwarnings("ignore")
# to eliminate an annoying warning at time 1 in time series plot
... | pd.DataFrame(columns=['entrepreneurs', 'workers']) | pandas.DataFrame |
#!/usr/bin/env python3
import unittest
import numpy as np
import numpy.testing as nptest
import pandas as pd
import pandas.testing as pdtest
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from datafold.dynfold.transform import (
TSCApplyLambdas,
TSCFeaturePreprocess,
... | pdtest.assert_index_equal(tsc.columns, rbf_coeff_inverse.columns) | pandas.testing.assert_index_equal |
import pandas as pd
import numpy as np
import pytest
from kgextension.endpoints import DBpedia
from kgextension.schema_matching import (
relational_matching,
label_schema_matching,
value_overlap_matching,
string_similarity_matching
)
class TestRelationalMatching:
def test1_default(self):
... | pd.read_csv(path_input) | pandas.read_csv |
# imports
import io
import math
import os
from pathlib import Path
import matplotlib.pyplot as plt
import pandas as pd
import xgboost as xgb
from sklearn.metrics import accuracy_score
# Simple_markings folder. Holds the "events", e.g. 3PM, 2PM, PASS, FOUL, etc...
# Returns a dictionary containing all the players, w... | pd.DataFrame(submission_dict) | pandas.DataFrame |
import pandas as pd
def list_platform_metadata_s4():
s4_dict = {
'COSPAR': '1998-017A',
'NORAD': 25260,
'full_name': 'Satellite Pour l’Observation de la Terre',
'instruments': {'Végétation', 'HRVIR', 'DORIS'},
'constellation': 'SPOT',
'launch': '1998-03-24',
... | pd.DataFrame(d) | pandas.DataFrame |
__author__ = 'lucabasa'
__version__ = '1.0'
__status__ = 'development'
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold, RandomizedSearchCV
import lightgbm as lgb
import xgboost as xgb
from sklearn.ensemble import RandomForestRegressor... | pd.concat([feature_importance_df, fold_importance_df], axis=0) | pandas.concat |
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from bagging import Bagging
from sklearn import svm
from sklearn import preprocessing
import random
from keras.utils import to_categorical
from opts import DLOption
from dbn_tf import DBN
from nn_tf import NN
from sklearn.metric... | pd.concat([lower_data1, data0]) | pandas.concat |
'''
The analysis module
Handles the analyses of the info and data space for experiment evaluation and design.
'''
from slm_lab.agent import AGENT_DATA_NAMES
from slm_lab.env import ENV_DATA_NAMES
from slm_lab.lib import logger, util, viz
import numpy as np
import os
import pandas as pd
import pydash as ps
import shutil... | pd.DataFrame({'epi': x, 'mean_reward': mean_sr}) | pandas.DataFrame |
import logging
import os
from typing import List, Dict, Optional
import numpy as np
import pandas as pd
import shap
from sklearn.cluster import KMeans
from d3m import container, utils
from d3m.metadata import base as metadata_base, hyperparams, params
from d3m.primitive_interfaces import base
from d3m.primitive_interf... | pd.concat(dfs) | pandas.concat |
import numpy as np
import pytest
from pandas import DataFrame, Series, concat, isna, notna
import pandas._testing as tm
import pandas.tseries.offsets as offsets
@pytest.mark.parametrize(
"compare_func, roll_func, kwargs",
[
[np.mean, "mean", {}],
[np.nansum, "sum", {}],
[lambda x: np... | isna(result) | pandas.isna |
# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership. The Modin Development Team licenses this file to you under the
# Apache License, Version 2.0 (the "License"); you may not u... | pandas.concat([df, df2], axis="columns") | pandas.concat |
##
# Many of my features are taken from or inspired by public kernels. The
# following is a probably incomplete list of these kernels:
# - https://www.kaggle.com/ggeo79/j-coupling-lightbgm-gpu-dihedral-angle for
# the idea to use dihedral angles on 3J couplings.
# - https://www.kaggle.com/titericz/giba-r-data-tab... | pd.Series(cos_angles0) | pandas.Series |
"""tests.core.archive.test_archive.py
Copyright Keithley Instruments, LLC.
Licensed under MIT (https://github.com/tektronix/syphon/blob/master/LICENSE)
"""
import os
from typing import List, Optional, Tuple
import pytest
from _pytest.capture import CaptureFixture
from _pytest.fixtures import FixtureRequest
fro... | read_csv(filepath, dtype=str) | pandas.read_csv |
''' IVMS checker program
'''
import datetime
import pandas as pd
import numpy as np
IVMS_file = 'D:\\OneDrive\\Work\\PDO\\IVMS\\Daily Trip Report - IVMS.xls'
vehicle_file = 'D:\\OneDrive\\Work\\PDO\\IVMS\\Lekhwair Vehicles Demob Plan V3.xlsx'
vehicle_ivms_file = 'D:\\OneDrive\\Work\\PDO\\IVMS\\Lekhwair Vehicles - IVMS... | pd.isna([_date]) | pandas.isna |
from abc import abstractmethod
from analizer.abstract.expression import Expression
from analizer.abstract import expression
from enum import Enum
from storage.storageManager import jsonMode
from analizer.typechecker.Metadata import Struct
from analizer.typechecker import Checker
import pandas as pd
from analizer.symbol... | pd.DataFrame(result, columns=newColumns) | pandas.DataFrame |
import argparse
from bs4 import BeautifulSoup
import multiprocessing as mp
from multiprocessing.pool import ThreadPool
import os
import pandas as pd
import pathlib
import requests
import subprocess
from tqdm.auto import tqdm
from utils import load_config
''' load config and secrets '''
# config = load_config(path='... | pd.DataFrame.from_records(urls) | pandas.DataFrame.from_records |
#!/usr/bin/env python
# Copyright (C) 2019 <NAME>
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import pearsonr
from dtrace.DTracePlot import DTracePlot
class Preliminary(DTracePlot):
HIST_KDE_KWS = dict(cumulative=False, cut=0)
@classmethod
def _pairplot_fix... | pd.concat([df, hue_vars], axis=1, sort=False) | pandas.concat |
from lib.timecards import Timecards
from datetime import date, timedelta
import pandas as pd
import pdb
class MonthTimecards:
def __init__(self, year, month):
self.sundays = [sunday for sunday in self.get_sundays_in_month(year, month)]
def get_timecards_in_month(self):
""" get the timecards in... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
from multiprocessing import Pool
import tqdm
import sys
import gzip as gz
from tango.prepare import init_sqlite_taxdb
def translate_taxids_to_names(res_df, reportranks, name_dict):
"""
Takes a pandas dataframe with ranks as columns and contigs as rows and taxids as value... | pd.DataFrame(cl, index=reportranks) | pandas.DataFrame |
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from kneed import KneeLocator
from scipy import stats
from pyod.models.cblof import CBLOF
from pyod.models.feature_bagging import FeatureBagging
from pyod.models.hbos import HBOS
from pyod.models.... | pd.DataFrame(kmeans_sel.labels_) | pandas.DataFrame |
'''
Created on Jul 16, 2019
@author: vincentiusmartin
'''
import pandas as pd
from sitesfinder.imads import iMADS
from sitesfinder.imadsmodel import iMADSModel
from sitesfinder.plotcombiner import PlotCombiner
from sitesfinder.pbmescore import PBMEscore
from sitesfinder.sequence import Sequence
from sitesfinder.pred... | pd.read_csv(slist, sep='\t') | pandas.read_csv |
import pkg_resources
import pandas as pd
from unittest.mock import sentinel
import osmo_jupyter.dataset.parse as module
def test_parses_ysi_csv_correctly(tmpdir):
test_ysi_classic_file_path = pkg_resources.resource_filename(
"osmo_jupyter", "test_fixtures/test_ysi_classic.csv"
)
formatted_ysi_d... | pd.to_datetime("2019-01-01 00:00:02") | pandas.to_datetime |
# -*- coding: utf-8 -*-
import unittest
import pandas
from pipesnake.pipe import SeriesPipe
from pipesnake.transformers.imputer import KnnImputer
from pipesnake.transformers.imputer import ReplaceImputer
from pipesnake.transformers.selector import ColumnSelector
class TestImputer(unittest.TestCase):
def test_r... | pandas.isnull(y_new) | pandas.isnull |
#GiG
import numpy as np
import pandas as pd
from pathlib import Path
from deep_blocker import DeepBlocker
from tuple_embedding_models import AutoEncoderTupleEmbedding, CTTTupleEmbedding, HybridTupleEmbedding, SIFEmbedding
from vector_pairing_models import ExactTopKVectorPairing
import blocking_utils
from configurati... | pd.DataFrame(predictions,columns=['ltable_id','rtable_id','value']) | pandas.DataFrame |
# Arithmetic Operators
num1 = 10
num2 = 20
print(num1 + num2)
print(num1 - num2)
print(num1 * num2)
print(num1 / num2)
print("END")
print()
# RELATIONAL OPERATIONS
print(num1 < num2)
print(num1 > num2)
print(num1 == num2)
print(num1 != num2)
print("END")
print()
# LOGICAL OPERATIONS
log1 = True
log2 = False
print("EN... | pd.read_csv('Book1.csv') | pandas.read_csv |
"""A module to help perform analyses on various observatioanl studies.
This module was implemented following studies of M249, Book 1.
Dependencies:
- **scipy**
- **statsmodels**
- **pandas**
- **numpy**
"""
from __future__ import annotations as _annotations
import math as _math
from scipy import st... | _pd.DataFrame(index=["chisq", "pval"]) | pandas.DataFrame |
import pandas as pd
import numpy as np
import nltk
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from nltk.tokenize import RegexpTokenizer
##### Read data for 5 sample positions ####... | pd.Series(full_df["ID"]) | pandas.Series |
import datetime
from datetime import timedelta
from distutils.version import LooseVersion
from io import BytesIO
import os
import re
from warnings import catch_warnings, simplefilter
import numpy as np
import pytest
from pandas.compat import is_platform_little_endian, is_platform_windows
import pandas.util._test_deco... | tm.makeTimeDataFrame(100064, "S") | pandas.util.testing.makeTimeDataFrame |
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... | notnull(res_r) | pandas.core.common.notnull |
#importing libraries
import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
from sklearn import preprocessing
from sklearn.preprocessing import LabelEncoder
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
#load... | pd.concat([df_frst,df_scnd],ignore_index=True) | pandas.concat |
import numpy as np
import pandas as pd
import io
import urllib.request
import requests
import camelot
from beis_indicators import project_dir
def download_data():
travel_to_work_2016 = 'https://www.ons.gov.uk/file?uri=/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/adhocs/007252averagehometowor... | pd.read_csv(f'{project_dir}/data/aux/equivalents_regions.csv',encoding='cp1252') | pandas.read_csv |
from helper import *
import pandas as pd
import os
import glob
import re
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import numpy as np
from sklearn.decomposition import PCA
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from s... | pd.read_csv(filedir_unseen) | pandas.read_csv |
"""Utilities for solving geodesic equation
"""
import itertools
import typing
from collections import namedtuple
import numpy
import pandas
import sympy
from scipy import integrate
from pystein import metric, curvature, utilities
class Solution:
def __init__(self, soln: typing.List[sympy.Eq], vec_funcs: typing.Li... | pandas.concat(dfs, axis=0) | pandas.concat |
import pandas as pd
import numpy as np
import pdb
import sys
import os
#######################################
# creates validation table in CSV format
#
# this script assumes download of lake_surface_temp_preds.csv from
# the data release (https://www.sciencebase.gov/catalog/item/60341c3ed34eb12031172aa6)
#
####... | pd.DataFrame() | pandas.DataFrame |
import traceback
from pathlib import Path
from operator import itemgetter
import shlex
import os
import sys
import time
import cv2
import numpy as np
import subprocess
import requests
import json
import pydicom
from pydicom.dataset import Dataset
import pytesseract
from PIL import Image, ImageDraw, I... | pd.read_csv('anatomy_classes.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
#
# Copyright (c) 2018 SMHI, Swedish Meteorological and Hydrological Institute
# License: MIT License (see LICENSE.txt or http://opensource.org/licenses/mit).
"""
Created on Thu Aug 30 15:30:28 2018
@author:
"""
import os
import codecs
import datetime
try:
import pandas as pd
except:
... | pd.read_csv(file_path, sep='\t', encoding='cp1252', dtype=str) | pandas.read_csv |
import sys
import os
import datetime
import time
import math
from functions import *
from PyQt5 import QtCore, QtGui, QtWidgets, uic
from PyQt5.QtWidgets import QInputDialog, QLineEdit, QFileDialog, QGridLayout
from PyQt5.QtGui import QIcon
from PyQt5.QtCore import pyqtSignal
#from PyQt5 import QtCore, QtGui, QtWidgets... | pandas.read_sql(queryRef, con=self.conn3) | pandas.read_sql |
import json
import logging
import os
from itertools import compress
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.metrics import (
accuracy_score,
f1_score,
precision_score,
recall_score,
roc_auc_score,
)
from sklearn.model_selection import train_test_split
from tensor... | pd.DataFrame.from_dict(tag_auc, orient="index") | pandas.DataFrame.from_dict |
# This file is intended to provide some "reference information" in a useful form for python.
# The names of each run in a family (as defined in the families described in the simulation releases)
# are provided in a dictionary; their most-likely most-relevant comparison run is included as well.
# The file also provides ... | pd.DataFrame(self.summaries[tablemetrics].loc[self.family[f]]) | pandas.DataFrame |
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
from collections import Counter, OrderedDict
def plot_active_users(df):
dic = df.to_dict()
k = list(dic.keys())
v = list(dic.values())
out_df = pd.DataFrame({'Пользователь': k, 'Число сообщений': v}).head(7)
... | pd.DataFrame({'Дата': k, 'Число сообщений': v}) | pandas.DataFrame |
"""
A warehouse for constant values required to initilize the PUDL Database.
This constants module stores and organizes a bunch of constant values which are
used throughout PUDL to populate static lists within the data packages or for
data cleaning purposes.
"""
import pandas as pd
import sqlalchemy as sa
##########... | pd.StringDtype() | pandas.StringDtype |
#!/usr/bin/env python
#-*- coding:utf-8 -*-
import numpy as np
import pandas as pd
import scipy.ndimage
import skimage.morphology
import sklearn.mixture
class HDoG_CPU(object):
def __init__(self, width=2560, height=2160, depth=None, sigma_xy=(4.0, 6.0), sigma_z=(1.8,2.7),
radius_small=(24,3), ra... | pd.Series(region_size) | pandas.Series |
import glob
import datetime
import os
import pandas as pd
import numpy as np
import re
from tkinter import filedialog
from tkinter import *
from tkinter import ttk
from tkinter import messagebox
# pyinstaller --onefile --noconsole --icon GetCSV.ico Arca_GetCSVConverter_2-0-0.py
#for MMW 18-6 spreadsheets... | pd.Series() | pandas.Series |
import gzip
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
# from sklearn.model_selection import train_test_split
from collections import Counter
import csv
import tensorflow as tf
import os.path
# from os import listdir
from tensorflow import keras
import os
import re
import spacy... | pd.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
import streamlit as st
import plotly_express as px
import pandas as pd
from plotnine import *
from plotly.tools import mpl_to_plotly as ggplotly
import numpy as np
import math
import scipy.stats as ss
from scipy.stats import *
def app():
# add a select widget to the side bar
st.sidebar.subheader("Discrete Pr... | pd.concat([giah,pmf,cdf],axis=1) | pandas.concat |
from datetime import timedelta
from functools import partial
import itertools
from parameterized import parameterized
import numpy as np
from numpy.testing import assert_array_equal, assert_almost_equal
import pandas as pd
from toolz import merge
from zipline.pipeline import SimplePipelineEngine, Pipeline, CustomFacto... | pd.Timestamp("2015-01-08") | pandas.Timestamp |
#!/usr/bin/env python3
import json
import math
import sys
import glob
import argparse
import os
from collections import namedtuple, defaultdict
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.lines import Line2D
from matplotlib.ticker import MaxNLocator
impo... | pandas.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
# -*- coding: utf-8 -*-
"""Untitled0.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1uPsIhY5eetnUG-xeLtHmKvq5K0mIr6wW
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import keras
import tensorflow as tf
dataset = pd.r... | pd.get_dummies(X['Gender'],drop_first=True) | pandas.get_dummies |
from ast import literal_eval
import numpy as np
import pandas as pd
import scipy
from pandas import DataFrame
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.neighbors import BallTree, KDTree, NearestNeighbors
from sklearn.preprocessing import Mu... | DataFrame(X) | pandas.DataFrame |
import pandas as pd
import instances.dinamizators.dinamizators as din
import math
def simplest_test():
'''
Test if the dinamizators are running
'''
df = (
pd.read_pickle('./instances/analysis/df_requests.zip')
.reset_index()
)
din.dinamize_as_berbeglia(df.pickup_location_x_co... | pd.DataFrame([[3, 2, 1], [1, 2, 3]]) | pandas.DataFrame |
import os, os.path, sys
if 'OORB_DATA' not in os.environ:
os.environ['OORB_DATA'] = '/Users/mjuric/projects/lsst_ssp/oorb-lynne/data'
extra_paths = [
'/Users/mjuric/projects/lsst_ssp/oorb-lynne/python',
]
for _p in extra_paths:
if not os.path.isdir(_p):
print(f"{_p} not present. Skipping.")
... | pd.DataFrame({'objId': id}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import click
import logging
from pathlib import Path
# from dotenv import find_dotenv, load_dotenv
import requests
from bs4 import BeautifulSoup
import numpy as np
import pandas as pd
import datetime
import yfinance as yf
from pandas_datareader import data as pdr
from flask import current_app
f... | pd.Series(df['log_ret_1d']) | pandas.Series |
import os
from datetime import date
from dask.dataframe import DataFrame as DaskDataFrame
from numpy import nan, ndarray
from numpy.testing import assert_allclose, assert_array_equal
from pandas import DataFrame, Series, Timedelta, Timestamp
from pandas.testing import assert_frame_equal, assert_series_equal
from pymo... | assert_frame_equal(move_df, expected) | pandas.testing.assert_frame_equal |
# License: Apache-2.0
from gators.feature_generation_str import StringContains
from pandas.testing import assert_frame_equal
import pytest
import numpy as np
import pandas as pd
import databricks.koalas as ks
ks.set_option('compute.default_index_type', 'distributed-sequence')
@pytest.fixture
def data():
X = pd.Da... | assert_frame_equal(X_new, X_expected) | pandas.testing.assert_frame_equal |
"""Tasks to process Alpha Diversity results."""
from pandas import DataFrame
from app.extensions import celery
from app.display_modules.utils import persist_result_helper
from .models import AncestryResult
@celery.task()
def ancestry_reducer(samples):
"""Wrap collated samples as actual Result type."""
fram... | DataFrame(samples) | pandas.DataFrame |
# general utilities used throughout the project
import numpy as np
import pandas as pd
import requests
import const
# convert time string to season
def to_season(time):
datetime = pd.to_datetime(time)
return (datetime.month % 12 + 3) // 3 if datetime is not np.nan else np.nan
# normalize values of data-fram... | pd.isnull(value) | pandas.isnull |
import itertools
import numpy as np
import pytest
import pandas as pd
from pandas.core.internals import ExtensionBlock
from .base import BaseExtensionTests
class BaseReshapingTests(BaseExtensionTests):
"""Tests for reshaping and concatenation."""
@pytest.mark.parametrize('in_frame', [True, False])
def ... | pd.DataFrame({'A': b}, index=[1, 2, 3]) | pandas.DataFrame |
import xml.etree.ElementTree as ET
from pathlib import Path
import pandas as pd
from .utils import remove_duplicate_indices, resample_data
NAMESPACES = {
"default": "http://www.topografix.com/GPX/1/1",
"gpxtpx": "http://www.garmin.com/xmlschemas/TrackPointExtension/v1",
"gpxx": "http://www.garmin.com/xm... | pd.to_numeric(temperature) | pandas.to_numeric |
import numpy as np
import pandas as pd
from pandas import (
Categorical,
DataFrame,
MultiIndex,
Series,
Timestamp,
date_range,
)
from .pandas_vb_common import tm
try:
from pandas.tseries.offsets import (
Hour,
Nano,
)
except ImportError:
# For compatibility with ol... | DataFrame(self.data) | pandas.DataFrame |
import pandas as pd
import matplotlib.pyplot as plt
from data import games #import games from data.py
# Select Attendance
# The games DataFrame contains the attendance for each game. An attendance row looks like this:
# type multi2 multi3 ... year
# info attendance 45342 ... 1946
# We need to select all of these r... | pd.to_numeric(attendance.loc[:, 'attendance']) | pandas.to_numeric |
from __future__ import division, print_function
from builtins import object, zip
import pandas as pd
import xarray as xr
from dask.diagnostics import ProgressBar
from numpy import array
from past.utils import old_div
# This file is to deal with CAMx code - try to make it general for CAMx 4.7.1 --> 5.1
ProgressBar(... | pd.Series(self.dset[varname].dims) | pandas.Series |
import re
from inspect import isclass
import numpy as np
import pandas as pd
import pytest
from mock import patch
import woodwork as ww
from woodwork.accessor_utils import (
_is_dask_dataframe,
_is_dask_series,
_is_koalas_dataframe,
_is_koalas_series,
init_series,
)
from woodwork.exceptions import... | pd.Series([True, pd.NA], dtype="boolean") | pandas.Series |
import math
import warnings
from typing import List, Tuple, Union
import numpy as np
import pandas as pd
from scipy.stats import kurtosis, skew
from sklearn.cluster import KMeans
pi = math.pi
pd.options.display.max_columns = 500
warnings.filterwarnings("ignore")
def range_func(x: List[Union[int, float]]) -> float:
... | pd.read_csv(path + "test_features.csv") | pandas.read_csv |
"""Provides utilities to import the account *.csv files in the folder `csv`.
The csv files have to match a certain naming pattern in order to map them to
different importers. See `_read_account_csvs()`."""
import pathlib
import re
from typing import Iterable
import numpy as np
import pandas as pd
import wealth.config... | pd.DateOffset(hours=1) | pandas.DateOffset |
from typing import Optional
from dataclasses import dataclass
import pandas as pd
from poker.base import unique_values, native_mean, running_mean, running_std, running_median, running_percentile
from poker.document_filter_class import DocumentFilter
pd.set_option('use_inf_as_na', True)
def _ts_concat(dic: dict, inde... | pd.DataFrame(columns=class_cols, index=class_ind) | pandas.DataFrame |
from sales_analysis.data_pipeline import BASEPATH
from sales_analysis.data_pipeline._pipeline import SalesPipeline
import pytest
import os
import pandas as pd
# --------------------------------------------------------------------------
# Fixtures
@pytest.fixture
def pipeline():
FILEPATH = os.path.join(BASEPATH, ... | pd.Timestamp('2019-08-15 00:00:00') | pandas.Timestamp |
# -*- coding: utf-8 -*-
"""main.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1_KzpxPsl8B2T4hE_Z2liSu1xzHxbA5KE
"""
import pandas as pd
import numpy as np
import seaborn as sns
from datetime import datetime
import matplotlib.pyplot as plt
# ===... | pd.to_datetime(df["StartTime(UTC)"], format="%Y-%m-%d %H:%M:%S") | pandas.to_datetime |
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