prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 13 14:18:46 2018
@author: rick
Module with functions for importing and integrating biostratigraphic
age-depth data from DSDP, ODP, and IODP into a single, standardized csv file.
Age-depth data are not available for Chikyu expeditions.
IODP: Must ... | pd.read_csv('hole_metadata.csv', sep='\t', index_col=0) | pandas.read_csv |
import numpy as np
import pandas as pd
from datetime import date
df = | pd.read_csv('supermarket.csv') | pandas.read_csv |
import numpy as np
import pandas as pd
import time
import cv2
import pylab
import os
import sys
from .dl_face_detector import get_face_from_img
def resource_path(relative_path):
""" Get absolute path to resource, works for dev and for PyInstaller """
try:
# PyInstaller creates a temp folder and store... | pd.DataFrame(columns=['x', 'y', 'h', 'w']) | pandas.DataFrame |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
from xarray import Dataset, DataArray, Variable
from xarray.core import indexing
from . import TestCase, ReturnItem
class TestIndexers(TestCase):
def set_to_zero(sel... | pd.Index([1, 2, 3]) | pandas.Index |
import types
from typing import List, Optional, Iterable
import numpy as np
import pandas as pd
import sqlalchemy as sa
from boadata.core import DataObject
from boadata.core.data_conversion import DataConversion, MethodConversion
from .. import wrap
from .mixins import (
AsArrayMixin,
CopyableMixin,
GetI... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/python
import click
import pandas as pd
import datetime
import time
import os
timestr = time.strftime("%Y%m%d-%H%M%S")
from click_help_colors import HelpColorsGroup, HelpColorsCommand
from pyfiglet import Figlet
# DEFAULT URLS FOR DATASOURCE Original Deprecated
# confirmed_cases_url_deprecated = "https://... | pd.read_csv(data_url) | pandas.read_csv |
'''Python script to generate CAC'''
'''Authors - <NAME>
'''
import numpy as np
import pandas as pd
from datetime import datetime
import collections
from .helpers import *
class CAC:
def __init__(self, fin_perf, oper_metrics, oth_metrics):
print("INIT CAC")
self.fin_perf = pd.DataFrame(fin_perf)
... | pd.Series(index, name="") | pandas.Series |
import os
import random
import sys
import joblib
import math
import lightgbm as lgb
import xgboost as xgb
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.svm as svm
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.linear_model import LinearRegress... | pd.DataFrame() | pandas.DataFrame |
import os
import unittest
from builtins import range
import matplotlib
import mock
import numpy as np
import pandas as pd
import root_numpy
from mock import MagicMock, patch, mock_open
import six
from numpy.testing import assert_array_equal
from pandas.util.testing import assert_frame_equal
import ROOT
from PyAnalysi... | pd.DataFrame({'var1': [3., 4.]}) | pandas.DataFrame |
""" test fancy indexing & misc """
from datetime import datetime
import re
import weakref
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas.core.dtypes.common import (
is_float_dtype,
is_integer_dtype,
)
import pandas as pd
from pandas import (
DataFrame,
Index,... | pd.array([1, 3], dtype="Int64") | pandas.array |
"""
test date_range, bdate_range construction from the convenience range functions
"""
from datetime import datetime, time, timedelta
import numpy as np
import pytest
import pytz
from pytz import timezone
from pandas._libs.tslibs import timezones
from pandas._libs.tslibs.offsets import BDay, CDay, DateOffset, MonthE... | date_range(start=pd.NaT, end="2016-01-01", freq="D") | pandas.date_range |
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# 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 Licen... | pd.Timestamp(x) | pandas.Timestamp |
# -*- coding: utf-8 -*-
from datetime import timedelta, time
import numpy as np
from pandas import (DatetimeIndex, Float64Index, Index, Int64Index,
NaT, Period, PeriodIndex, Series, Timedelta,
TimedeltaIndex, date_range, period_range,
timedelta_range, notnu... | pd.Timedelta('1 day') | pandas.Timedelta |
import pandas as pd
import datetime as dt
import string
import time
## Carregando dados
df = pd.read_csv("apache.log", sep=" ", names=['host', 'delete', 'logname', 'user', 'time', 'request', 'response', 'bytes', 'url', 'browserLog', 'browser', 'networkClass' ])
df.drop('delete', axis=1, inplace=True)
## Pré processa... | pd.concat([dfResult, dfData]) | pandas.concat |
# coding: utf-8
# In[19]:
from keras.models import model_from_json
import os
import cv2
import glob
import h5py
import pandas as pd
from sklearn.metrics import mean_absolute_error
import scipy.io as io
from PIL import Image
import numpy as np
# In[20]:
def load_model():
json_file = open('models/Model.j... | pd.DataFrame({'name': name,'y_pred': y_pred,'y_true': y_true}) | pandas.DataFrame |
import warnings
import logging
import pandas as pd
from functools import partial
from collections import defaultdict
from dae.utils.helpers import str2bool
from dae.variants.attributes import Role, Sex, Status
from dae.backends.raw.loader import CLILoader, CLIArgument
from dae.pedigrees.family import FamiliesData, P... | pd.isna(r.sampleId) | pandas.isna |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from ampligraph.datasets import load_from_csv
from ampligraph.discovery import find_clusters
from ampligraph.evaluation import train_test_split_no_unseen
from ampligraph.utils import restore_model
from sklearn.cluster import KM... | pd.Series(clusters) | pandas.Series |
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
from numba import njit
import vectorbt as vbt
from tests.utils import record_arrays_close
from vectorbt.generic.enums import range_dt, drawdown_dt
from vectorbt.portfolio.enums import order_dt, trade_dt, log_dt
day_dt = np.timedelta64... | pd.testing.assert_index_equal(stats_df.index, exit_trades.wrapper.columns) | pandas.testing.assert_index_equal |
# -*- 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('C:\\Work\\Programming\\Practice\\PF_SF_EC.xlsx', 'PF_SF_E') | pandas.read_excel |
import csv
import datetime
import random
from operator import itemgetter
import lightgbm as lgb
import numpy as np
import pandas as pd
from catboost import CatBoostClassifier, CatBoostRegressor
from sklearn.ensemble import (
AdaBoostClassifier,
AdaBoostRegressor,
BaggingClassifier,
BaggingRegressor,
... | pd.DataFrame(lst_dict) | pandas.DataFrame |
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("2015-01-12") | pandas.Timestamp |
"""
Module for header classes and metadata interpreters. This includes interpreting data file headers or dedicated files
to describing data.
"""
from os.path import basename
import pandas as pd
import pytz
from .data import SiteData
from .db import get_table_attributes
from .interpretation import *
from .projection ... | pd.unique(temp['orientation']) | pandas.unique |
#!/usr/bin/env python
# coding: utf-8
# In[2]:
#TEST 01
#trying to write to csv file
#training the above code
import numpy as np
import pandas as pd
#import matplotlib.pyplot as plt
import csv
import cv2 as cv2 #importing opevcv
import os
import numpy as np
#training the model with above generated csv file
#... | pd.read_csv("dr_features_output_main.csv") | pandas.read_csv |
import unittest
import os
import tempfile
from collections import namedtuple
from blotter import blotter
from pandas.util.testing import assert_frame_equal, assert_series_equal, \
assert_dict_equal
import pandas as pd
import numpy as np
class TestBlotter(unittest.TestCase):
def setUp(self):
cdir = os... | pd.Timestamp('2015-08-04T00:00:00') | pandas.Timestamp |
from time import time
from keras import Sequential
import numpy as np
import pandas as pd
from keras.layers import Embedding, LSTM, Dense
from keras.preprocessing import sequence
from matplotlib import pyplot
from gensim.models import Word2Vec
from sklearn.decomposition import PCA
from sklearn.metrics import mean_squ... | pd.read_csv(DEBATE_DATA_PATH) | pandas.read_csv |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
class RedRio:
def __init__(self,codigo = None,**kwargs):
self.info = pd.Series()
self.codigo = codigo
self.info.slug = None
self.fecha = '2006-06-06 06:06'
self.workspace = '/media/'
self.seccion... | pd.DataFrame() | pandas.DataFrame |
import os
import csv
import pandas
from sklearn.svm import LinearSVC
from sklearn import linear_model, metrics
from sklearn.model_selection import train_test_split
from scipy.sparse import csr_matrix
from questionparser import QuestionParser
CORPUS_DIR = os.path.join(os.path.dirname(__file__), 'corpus')
def compare_mo... | pandas.read_csv(test_file) | pandas.read_csv |
from __future__ import print_function
import os
import csv
import numpy as np
import pandas as pd
from inferelator_ng import single_cell_workflow
from inferelator_ng import results_processor
from inferelator_ng import utils
from inferelator_ng import default
from inferelator_ng import bbsr_python
from inferelator_ng... | pd.Series(True, index=self.meta_data.index) | pandas.Series |
import numpy as np
import pandas as pd
pd.options.display.max_rows = 20;
pd.options.display.expand_frame_repr = True
import sys
sys.path.insert(1,'/home/arya/workspace/bio')
import UTILS.Util as utl
import multiprocessing
from UTILS.BED import BED
from UTILS.Util import mask
from time import time
CHROMS=['2L', '2R'... | pd.read_pickle(utl.PATH.data + "GO/GO.fly.df") | pandas.read_pickle |
#@author: bfoster2
# -*- coding: utf-8 -*-
"""
Created on Tue May 28 10:05:23 2019
@author: bfoster2
"""
import os
#os.system("!pip install gensim --upgrade")
#os.system("pip install keras --upgrade")
#os.system("pip install pandas --upgrade")
# DataFrame
import pandas as pd
# Matplot
import matplotlib.p... | pd.DataFrame([x], index=['string_values']) | pandas.DataFrame |
import pandas as pd
import string
crime_2019 = pd.read_csv('./crime_feat_2019.csv')
crime_2018 = pd.read_csv('./crime_feat_2018.csv')
crime_2017 = pd.read_csv('./crime_feat_2017.csv')
crime_2016 = pd.read_csv('./crime_feat_2016.csv')
crime_2015 = pd.read_csv('./crime_feat_2015.csv')
crime_2014 = pd.read_csv('./crime_... | pd.concat(frames) | pandas.concat |
"""Analyzes Terms in terms of the underlying gene structure and comparisons with other terms."""
"""
A term ontology is a classification of genes. Examples include: GO (gene ontology),
KO (KEGG Orthology), KEGG Pathway, and EC (Enzyme Commission). A term ontology
is a many-to-many relationship between genes and terms.... | pd.concat([df_term, df_expressed_excluded]) | pandas.concat |
from argparse import ArgumentParser
import numpy as np
import pandas as pd
import statsmodels.api as sm
from arch.bootstrap import StationaryBootstrap
from statsmodels.nonparametric.kernel_regression import KernelReg
from utils import resample
from align_settings import STARTTIME, ENDTIME
SESSIONSTART = pd.to_datet... | pd.read_parquet(args.trade_filename) | pandas.read_parquet |
# -*- coding: utf-8 -*-
import sys
import dnaio
import numpy as np
import pandas as pd
from xopen import xopen
from .protocol import BarcodePattern, MisSeq
from .report import Reporter
from .utils import getlogger, CommandWrapper
logger = getlogger(__name__)
logger.setLevel(10)
def barcode(
ctx,
fq... | pd.DataFrame(cell_umi_base_array) | pandas.DataFrame |
"""Helper classes and functions with RTOG studies.
"""
import random
import pandas as pd
import numpy as np
import pickle
from collections import Counter
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from tqdm import tqdm
import pint
# Constants defining variable and fi... | pd.merge(self.df, self.df_rt[['cn_deidentified', 'pelvic_rt']], on=['cn_deidentified'], how='left') | pandas.merge |
import pandas as pd
from .datastore import merge_postcodes
from .types import ErrorDefinition
from .utils import add_col_to_tables_CONTINUOUSLY_LOOKED_AFTER as add_CLA_column # Check 'Episodes' present before use!
def validate_165():
error = ErrorDefinition(
code = '165',
description = 'Data entry for moth... | pd.to_numeric(episodes['PL_DISTANCE'], errors='coerce') | pandas.to_numeric |
import pandas as pd
import json
import numpy as np
import ast
from tqdm import tqdm_notebook
#Reading lasVegas.csv into Pandas Dataframe df
df=pd.DataFrame.from_csv('/home/rim/INF-Project/preprocessed_lasVegas.csv')
df.shape
#Select required columns and rename columns to standard names
df=df[['business_id','name'... | pd.concat([df_1, df_3], axis=1) | pandas.concat |
import numpy as np
import pandas as pd
files = ['1.1.csv', '1.2.csv', '1.3.csv', '1.4.csv',
'2.1.csv', '2.2.csv', '2.3.csv', '2.4.csv',
'3.1.csv', '3.2.csv', '3.3.csv', '3.4.csv']
data = []
for fname in files:
data.append(pd.read_csv(fname))
data[2]['Location'][118] = '23:E'
data[2]['Location'][... | pd.concat([data[4], data[5], data[6], data[7]]) | pandas.concat |
import json
import io
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import dash
from dash import html
from dash import dcc
import dash_bootstrap_components as dbc
import pandas as pd
import numpy as np
import plotly.express as px
from dash.dependencies import Output, Input, State
from date... | pd.ExcelWriter(output, engine='xlsxwriter') | pandas.ExcelWriter |
import pandas as pd
import numpy as np
import copy
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV
from sklearn.feature_selection import mutual_info_classif, SelectKBest
import matplotlib.pyplot as plt
from sklearn import svm
from sk... | pd.DataFrame(dic_results) | pandas.DataFrame |
import pandas as pd
import sys
# from urllib import urlopen # python2
from urllib.request import urlopen
#try:
# from rpy2.robjects.packages import importr
# try:
# biomaRt = importr("biomaRt")
# except:
# print "rpy2 could be loaded but 'biomaRt' could not be found.\nIf you want to use 'biomaRt'... | pd.DataFrame(enz) | pandas.DataFrame |
# 数据处理
import numpy as np
import pandas as pd
# 绘图
import seaborn as sns
import matplotlib.pyplot as plt
# %matplotlib inline
# 各种模型、数据处理方法
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, StratifiedKFold, learning_curve
from sklearn.lin... | pd.get_dummies(combine_df['Pclass'], prefix='Pclass') | pandas.get_dummies |
from functools import reduce
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
# set jupyter's max row display
pd.set_option("display.max_row", 1000)
# set jupyter's max column width to 50
pd.set_option("display.max_... | pd.merge(left, right, on="date") | pandas.merge |
from datetime import datetime
import pandas as pd
from botocore.exceptions import ClientError
from fbprophet import Prophet
from flask import request
from flask_restx import Namespace, Resource, fields
from core.data import ReturnDocument
from db import Expense, RepositoryException, User
from db.factory import create... | pd.to_datetime(df['date']) | pandas.to_datetime |
"""
Removes non-linear ground reaction force signal drift in a stepwise manner. It is intended
for running ground reaction force data commonly analyzed in the field of Biomechanics. The aerial phase before and after
a given stance phase are used to tare the signal instead of assuming an overall linear trend or signal ... | pd.Series(drift_signal) | pandas.Series |
import os, sys
import numpy as np
import pandas as pd
import pickle
from tqdm import tqdm
import argparse
from sklearn.utils import shuffle
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer
#from nltk.stem import PorterStemmer
from pyspark.sql.types import *
fro... | pd.read_csv(test_fn, header=None) | pandas.read_csv |
import lehd
import pandas as pd
import geopandas as gpd
import urllib.request
import gzip
from shapely import wkt
class to_geo:
"""
Takes downloaded LEHD data and converts it to GeoDataFrames which can be used for spatial analysis and visualization
"""
def od(df):
gtype = lehd.utils.infer... | pd.concat(gdfo) | pandas.concat |
# Packages
# Basic packages
import numpy as np
from scipy import integrate, stats, spatial
from scipy.special import expit, binom
import pandas as pd
import xlrd # help read excel files directly from source into pandas
import copy
import warnings
# Building parameter/computation graph
import inspect
from collection... | pd.read_csv('../data/socialcontactdata_UK_Mossong2008_social_contact_matrix_with_distancing.csv', sep=',') | pandas.read_csv |
from __future__ import print_function
# from: https://github.com/asap-report/carla/blob/racetrack/PythonClient/racetrack/client_controller.py
import os
import argparse
import logging
import random
import time
import pandas as pd
import numpy as np
from scipy.interpolate import splprep, splev
# I need to prepend `sys... | pd.DataFrame(log_dicts) | pandas.DataFrame |
#Creates a BPT diagram for all objects, and a second figure that shows objects for which single lines are low
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 collections
#Folder to save the figures
figout = '/... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
import os
import numpy as np
import statsmodels.api as sm # recommended import according to the docs
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats.mstats as mstats
from common import globals as glob
from datetime import datetime, timedelta
import seaborn as sb
sb.set... | pd.Series(ts_log.ix[0], index=ts_log.index) | pandas.Series |
import os
import sys
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, compat
from pandas.util import testing as tm
class TestToCSV:
@pytest.mark.xfail((3, 6, 5) > sys.version_info >= (3, 5),
reason=("Python csv library bug "
... | tm.convert_rows_list_to_csv_str(exp_rows) | pandas.util.testing.convert_rows_list_to_csv_str |
# -*- coding: utf-8 -*-
###########################################################################
# we have searched for keywords in the original news
# for stemmed keywords in the stemmed news
# for lemmatized keywords int the lemmatized news
# now, want to merge... | pd.read_csv('output/file1output_origin_news.csv') | pandas.read_csv |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Race-car Data Creation Class.
This script contains all utilities to create proper dataset.
Revision History:
2020-05-10 (Animesh): Baseline Software.
2020-08-22 (Animesh): Updated Docstring.
Example:
from _data_handler import DataHandler
"""... | pd.DataFrame(data_17, columns=["image"]) | pandas.DataFrame |
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from datetime import time
import joblib
import pickle
def time_to_seconds(time):
return time.hour * 3600 + time.minute * 60 + time.second
d... | pd.read_csv('./data.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 26 22:55:37 2020
@author: <NAME> <EMAIL>
Data and Model from:
A conceptual model for the coronavirus disease 2019 (COVID-19)
outbreak in Wuhan, China with individual reaction and
governmental action
DOI:https://doi.org/10.1016/j.ijid.2020.02.058
https://ww... | pd.DataFrame(Tdata.T, columns=[*indexesb]) | pandas.DataFrame |
#!/usr/bin/env python
import unittest
import os
import logging
import numpy as np
import filecmp
import pandas as pd
from vaws.model.house import House
from vaws.model.config import Config
# from model import zone
# from model import engine
def check_file_consistency(file1, file2, **kwargs):
try:
ident... | pd.read_hdf(file2, 'di') | pandas.read_hdf |
import os
import sys
import requests
import logging
import json
import pandas as pd
from bs4 import BeautifulSoup
import pickle
from git import Git
class FPL_Review_Scraper:
""" Scrape FPL Review website """
def __init__(self, logger, season_data, team_id):
"""
Args:
logger (log... | pd.DataFrame(columns=csv_cols) | pandas.DataFrame |
import os
import argparse
from typing import List, Dict, Tuple, Optional, Iterable, Any, Union
from enum import Enum
import numpy as np
import pandas as pd
from . import BaseAddOn
from .. import GutenTAG
from ..generator import Overview, TimeSeries
from ..utils.global_variables import SUPERVISED_FILENAME, UNSUPERVISED... | pd.DataFrame(columns=columns) | pandas.DataFrame |
# import modules
import bcolz
import pickle
import random
import argparse
import numpy as np
import pandas as pd
from os.path import dirname, realpath, join
from IPython.terminal.debugger import set_trace as keyboard
# function for tokenizing
def corpus_indexify(corpus_dict, word2idx):
# initialize corpus array
... | pd.read_csv(f) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""Device curtailment plots.
This module creates plots are related to the curtailment of generators.
@author: <NAME>
"""
import os
import logging
import pandas as pd
from collections import OrderedDict
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.dates as mdates
... | pd.DataFrame(vre_collection,index=[scenario]) | pandas.DataFrame |
"""
Multi criteria decision analysis
"""
from __future__ import division
from __future__ import print_function
import json
import os
import pandas as pd
import numpy as np
import cea.config
import cea.inputlocator
from cea.optimization.lca_calculations import lca_calculations
from cea.analysis.multicriteria.optimizat... | pd.DataFrame(dict_capacities, index=[individual]) | pandas.DataFrame |
"""
Provide a generic structure to support window functions,
similar to how we have a Groupby object.
"""
from collections import defaultdict
from datetime import timedelta
from textwrap import dedent
from typing import List, Optional, Set
import warnings
import numpy as np
import pandas._libs.window as libwindow
fro... | nv.validate_window_func("var", args, kwargs) | pandas.compat.numpy.function.validate_window_func |
import pandas as pd
from .datastore import merge_postcodes
from .types import ErrorDefinition
from .utils import add_col_to_tables_CONTINUOUSLY_LOOKED_AFTER as add_CLA_column # Check 'Episodes' present before use!
def validate_165():
error = ErrorDefinition(
code = '165',
description = 'Data entry for moth... | pd.to_datetime(mis['MIS_START'], format='%d/%m/%Y', errors='coerce') | pandas.to_datetime |
import numpy as np
import cv2
import os
import pandas as pd
import cv2
import progressbar
from utilities.generators import VideoSequenceGenerator
from pathlib import Path
from itertools import islice
from utilities.preprocessing import VideoVGG16FeatureExtractor
from utilities.preprocessing import VideoScorerPreproces... | pd.read_csv(video_csv_path) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""Constants and functions in common across modules."""
# standard library imports
import contextlib
import mmap
import os
import sys
import tempfile
from pathlib import Path
# third-party imports
import numpy as np
import pandas as pd
import xxhash
from loguru import logger as loguru_logger
fr... | pd.CategoricalDtype() | pandas.CategoricalDtype |
"""
EC Models
=============================
**Author:** `ichbinkk`
"""
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pypl... | pd.DataFrame(data) | pandas.DataFrame |
"""Module grouping tests for the pydov.util.query module."""
import pandas as pd
import numpy as np
import pytest
from pydov.util.dovutil import build_dov_url
from pydov.util.query import (
PropertyInList,
Join,
)
class TestPropertyInList(object):
"""Test the PropertyInList query expression."""
def t... | pd.Series(l) | pandas.Series |
import pytest
import numpy as np
from datetime import date, timedelta, time, datetime
import dateutil
import pandas as pd
import pandas.util.testing as tm
from pandas.compat import lrange
from pandas.compat.numpy import np_datetime64_compat
from pandas import (DatetimeIndex, Index, date_range, DataFrame,
... | pd.date_range('1/1/2000', freq='12H', periods=10) | pandas.date_range |
"""
Go from the RVs <NAME> sent (with delta Pav as the
template) to RVs that can be input to radvel.
"""
import os
import pandas as pd, numpy as np
from astrobase.lcmath import find_lc_timegroups
from numpy import array as nparr
from timmy.paths import DATADIR
rvdir = os.path.join(DATADIR, 'spectra', 'Veloce', 'RVs')... | pd.read_csv(rvpath, names=['time','rv','rv_err'], sep=' ') | pandas.read_csv |
# coding: utf-8
# ***Visualization(Exploratory data analysis) - Phase 1 ***
# * ***Major questions to answer(A/B Testing):***
# 1. Does the installment amount affect loan status ?
# 2. Does the installment grade affect loan status ?
# 3. Which grade has highest default rate ?
# 4. Does annual income/home-ownership a... | pd.DataFrame(new_data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import click
import logging
from pathlib import Path
import pandas as pd
from src.utils.config import Config
from src.features import build_features
from dotenv import find_dotenv, load_dotenv
from sklearn.manifold import TSNE
import umap
from sklearn.decomposition import PCA
import numpy as np... | pd.DataFrame(X_tsne,columns=components) | 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... | Series(["FOO", "BAR", NA, "Blah", "blurg"]) | pandas.Series |
import numpy as np
import cvxpy as cp
from tqdm import tqdm
import random
import time
import torch
import torch.nn as nn
import torch.autograd as autograd
import torch.optim as optim
import torch.nn.functional as F
import matplotlib.pyplot as plt
from numpy import linalg
from itertools import accumulate
import pandas ... | pd.read_excel('dataset/input_data_pool.xlsx',sheet_name='theta_amb') | pandas.read_excel |
def process_result_file_into_dataframe(p_file):
import numpy as np
from pandas import DataFrame
l_go_entries = []
with open(p_file, 'r') as f:
flag_start_p, flag_start = False, False
for line in f.readlines():
if line.startswith('Finding terms for P'):
fl... | DataFrame(l_go_entries, columns=['GOID', 'TERM', 'CORRECTED P-VALUE', 'UNCORRECTED P-VALUE', 'FDR RATE', 'NBR GENE INTER', 'NBR GENE NET', 'NBR GENE GO', 'FOLD ENRICHMENT', 'NUM ANNOTATION', 'GENES']) | pandas.DataFrame |
"""
Tests the coalescence tree object.
"""
import os
import random
import shutil
import sqlite3
import sys
import unittest
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
from setup_tests import setUpAll, tearDownAll, skipLongTest
from pycoalescence import Simulation
from pycoales... | assert_frame_equal(df, actual_df, check_like=True) | pandas.testing.assert_frame_equal |
#########################
# generate-gafs.py
# Author: <NAME>
##########################
# Create our labelled image data for AI training
#########################
import pandas as pd
import time
import numpy as np
import matplotlib.pyplot as plt
from pyts.image import GramianAngularField
from pathlib import Path
impo... | pd.DataFrame(gafDataRow) | pandas.DataFrame |
import pandas as pd
from IPython.core.display_functions import display
raw_csv_data = pd.read_csv("Absenteeism-data.csv")
type(raw_csv_data)
raw_csv_data
# Eyeballed the data to check the data for errors
df = raw_csv_data.copy()
pd.options.display.max_columns = None
pd.options.display.max_rows = 50
df.info()
# This ... | pd.concat([df, reason_type_1, reason_type_2, reason_type_3, reason_type_4], axis=1) | pandas.concat |
# python vaccination_adaptive_hybrid_autosearch_conform.py MSA_NAME VACCINATION_TIME VACCINATION_RATIO consider_hesitancy ACCEPTANCE_SCENARIO w1 w2 w3 w4 w5 quick_test
# python vaccination_adaptive_hybrid_autosearch_conform.py Atlanta 15 0.1 True cf18 1 1 1 1 1 False
import setproctitle
setproctitle.setproctitle(... | pd.merge(cbg_ids_msa, cbg_occupation, on='census_block_group', how='left') | pandas.merge |
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from lmfit import Model, Parameters, minimize, report_fit
from scipy.optimize import curve_fit
from scipy import stats
from utilities.statistical_tests import r_squared_calculator
from GEN_Utils import FileHandling... | pd.merge(clusters, fitting_parameters, on=info_cols, how='inner') | pandas.merge |
import pandas as pd
import logging
import heapq
import significance_tests as st
class InsightExtractor:
def __init__(self, data, dimensions, measure, agg):
"""
input:
data: pandas dataframe
dimensions: array of strings of dimension names
measure: string measur... | pd.read_csv(filename, encoding='mac_roman') | pandas.read_csv |
import pandas as pd
from unittest2 import TestCase # or `from unittest import ...` if on Python 3.4+
import numpy as np
import category_encoders.tests.helpers as th
import category_encoders as encoders
np_X = th.create_array(n_rows=100)
np_X_t = th.create_array(n_rows=50, extras=True)
np_y = np.random.randn(np_X.sh... | pd.DataFrame({'city': ['chicago', np.nan, np.nan]}) | pandas.DataFrame |
import matplotlib.pyplot as plt
import seaborn as sns
from datos import data
import pandas
sns.set(style="white")
d=data('mtcars')
colors = sns.husl_palette(3)
d=data('mtcars')
ps = | pandas.Series([i for i in d.cyl]) | pandas.Series |
from datetime import datetime
import pandas as pd
import pytest
from dask import dataframe as dd
import featuretools as ft
from featuretools import Relationship
from featuretools.tests.testing_utils import to_pandas
from featuretools.utils.gen_utils import import_or_none
ks = import_or_none('databricks.koalas')
@p... | pd.isnull(v2) | pandas.isnull |
import re
import numpy as np
import pytest
from pandas import Categorical, CategoricalIndex, DataFrame, Index, Series
import pandas._testing as tm
from pandas.core.arrays.categorical import recode_for_categories
from pandas.tests.arrays.categorical.common import TestCategorical
class TestCategoricalAPI:
... | tm.assert_frame_equal(desc, expected) | pandas._testing.assert_frame_equal |
from collections import OrderedDict
import os
import torch
import numpy as np
import pandas as pd
from tqdm import tqdm
from utils.measure import Measure, psnr
from utils.imresize import imresize
from utils.util import patchify, fiFindByWildcard, t, rgb, imread, imwrite, impad
from models.modules.flow import Gaussian... | pd.DataFrame([meas]) | pandas.DataFrame |
# Dash dependencies import
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_uploader as du
import uuid
import pathlib
import dash_bootstrap_components as dbc
import plotly.figure_factory as ff
from dash.dependencies import Input, Output,State
import plotly.express as px
imp... | pd.read_json(jsonified_global_dataframe, orient='split') | pandas.read_json |
import pandas
import numpy
class ScriptSetting:
csv_file_name = 'telecom.csv'
csv_separator = ','
csv_null_values = 'null'
csv_true_values = 'true'
csv_false_values = 'false'
columns_out_of_prediction = ['customerId']
missing_column = 'customerAge'
missing_column_range = [14, 18, 28, 3... | pandas.isnull(age_range) | pandas.isnull |
import numpy as np
import pandas as pd
import tensorflow as tf
Data = | pd.read_csv('ratings.csv', sep=';', names=['user', 'item', 'rating', 'timestamp'], header=None) | pandas.read_csv |
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from labels import *
def transformDataHotEncoding(df, labels=None):
if labels == None:
labels = df.columns
for col in labels:
if df[col].dtypes == "object":
if len(df[col].unique()) == 2:
df[col] =... | pd.get_dummies(df[col], prefix=col) | pandas.get_dummies |
import os
from pathlib import Path
from .. import api
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
description = """
Parse the Reports directory from bcl2fastq.
This command will parse, and extract various statistics from, HTML files in
the Reports directory created by the bcl2fastq or ... | pd.read_html(html_file) | pandas.read_html |
from DIS import DIS
from itertools import chain
from scipy.stats import norm
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
plt.ion()
import tensorflow as tf
import tensorflow_probability as tfp
tfb = tfp.bijectors
tfd = tfp.distributions
tfe = tf.contrib.eager
tf.enable_ea... | pd.to_pickle(output, "mg1_comparison.pkl") | pandas.to_pickle |
import numpy as np
import pandas as pd
from numpy.testing import assert_array_equal
from pandas.testing import assert_frame_equal
from nose.tools import (assert_equal,
assert_almost_equal,
raises,
ok_,
eq_)
from rsmtool.p... | assert_frame_equal(df_new, df) | pandas.testing.assert_frame_equal |
# Copyright (c) Microsoft Corporation and Fairlearn contributors.
# Licensed under the MIT License.
import numpy as np
import pandas as pd
import pytest
import sklearn.metrics as skm
import fairlearn.metrics as metrics
from .data_for_test import y_t, y_p, g_1, g_2, g_3, g_4
from test.unit.input_convertors import co... | pd.DataFrame(data=g_4, columns=['My feature']) | pandas.DataFrame |
import argparse
from functools import partial
import pandas as pd
class Converter(object):
def __init__(self, input_file, output_file):
self.output_file = output_file
self.input_file = input_file
self.file = None
self.package_size = 5 # 5 bytes
self.columns = ["Time", "P... | pd.DataFrame(readings, columns=converter.columns) | 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... | tm.get_data_path('tips.csv') | pandas.util.testing.get_data_path |
# -*- coding: utf-8 -*-
import io
import pandas as pd
import requests
from jqdatasdk import auth, get_price, logout
from zvt.api.common import generate_kdata_id, to_jq_security_id
from zvt.api.technical import get_kdata
from zvt.domain import TradingLevel, SecurityType, Provider, Stock1DKdata, StoreCategory, Stock
fr... | pd.to_datetime(df['timestamp']) | pandas.to_datetime |
from datetime import datetime
import numpy as np
import pytest
from pandas.core.dtypes.cast import find_common_type, is_dtype_equal
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series
import pandas._testing as tm
class TestDataFrameCombineFirst:
def test_combine_first_mixed(self):
... | DataFrame({"DATE": exp_dts}) | pandas.DataFrame |
from __future__ import division
import numpy as np
import pandas as pd
import pickle
import os
from math import ceil
import matplotlib
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
from sklearn.metrics import r2_score
warnings.simplefilter("ignore")
# col... | pd.read_csv(file_name) | pandas.read_csv |
from __future__ import unicode_literals, division, print_function
import numpy as np
import pandas as pd
from pymatgen.core import Structure, Lattice
from pymatgen.util.testing import PymatgenTest
from pymatgen.analysis.local_env import VoronoiNN, JmolNN, CrystalNN
from matminer.featurizers.site import AGNIFingerprin... | pd.DataFrame({'struct': [self.sc], 'site': [0]}) | pandas.DataFrame |
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