prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# pylint: disable-msg=E1101,W0613,W0603
import os
import copy
from collections import defaultdict
import numpy as np
import pandas.json as _json
from pandas.tslib import iNaT
from pandas.compat import StringIO, long, u
from pandas import compat, isnull
from pandas import Series, DataFrame, to_datetime
from pandas.io.... | DataFrame(dtype=None, **decoded) | pandas.DataFrame |
from __future__ import print_function
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from pandas import (Series, Index, Int64Index, Timestamp, Period,
DatetimeIndex, PeriodIndex, TimedeltaIndex,
Timedelta, timedelta_range, date_range, Float64Index... | Timedelta('1 days') | pandas.Timedelta |
# -*- coding: utf-8 -*-
"""
This module contains the classes for testing the model module of mpcpy.
"""
import unittest
from mpcpy import models
from mpcpy import exodata
from mpcpy import utility
from mpcpy import systems
from mpcpy import units
from mpcpy import variables
from testing import TestCaseMPCPy
import pan... | pd.read_csv('mpcpy_simulation_inputs_model.csv', index_col='Time') | pandas.read_csv |
# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/01_parsing_roll_call_votes.ipynb (unless otherwise specified).
__all__ = ['get_ix', 'useful_string', 'SummaryParser', 'VotesParser', 'get_all_issues']
# Cell
import PyPDF2 as pdf
from pathlib import Path
import typing
import re
import pandas as pd
import collections
imp... | pd.concat(all_corrections) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
import os
from tqdm import trange
# In[2]:
df = | pd.read_excel("https://censusindia.gov.in/2011Census/Language-2011/DDW-C19-0000.xlsx") | pandas.read_excel |
#!/usr/bin/env python3
# std
from math import ceil
import logging
from typing import List
# 3d party
import matplotlib.pyplot as plt
import matplotlib
# noinspection PyUnresolvedReferences
from mpl_toolkits.mplot3d import Axes3D # NOTE BELOW (*)
import numpy as np
import pandas as pd
# ours
from clusterking.util.l... | pd.DataFrame([]) | pandas.DataFrame |
# Copyright (c) 2016. Mount Sinai School of Medicine
#
# 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 o... | DataFrame(dummy_binding_data) | pandas.DataFrame |
# Copyright (c) 2020 Intel 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 License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | pd.DataFrame(stats_dicts) | pandas.DataFrame |
from RecSearch.DataWorkers.Abstract import DataWorkers
from RecSearch.ExperimentSupport.ExperimentData import ExperimentData
import pandas as pd
class Recommenders(DataWorkers):
"""
Recommenders class creates recommender data.
"""
# Configs inline with [[NAME]]
@classmethod
def set_config(cls)... | pd.DataFrame(columns=[ckey := 'R__' + column_name]) | pandas.DataFrame |
import unittest
from triple_walk import utils
from triple_walk import rw
from triple_walk.model import CBOWTriple, SkipGramTriple
import torch
import numpy as np
import pandas as pd
class ModelTest(unittest.TestCase):
def test_model_cbow(self):
# triples
triples_list = [
("A"... | pd.DataFrame(data=triples_list,columns=["head","relation","tail"]) | pandas.DataFrame |
# from dotenv import load_dotenv
import os
import psycopg2
from psycopg2.extensions import register_adapter, AsIs
import pandas as pd
import json
from dotenv import load_dotenv
import logging
import random
from fastapi import APIRouter
import pandas as pd
from pydantic import BaseModel, Field, validator
log = loggin... | pd.DataFrame(result, columns=columns) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from itertools import combinations, permutations
import logging
import networkx as nx
import numpy as np
import pandas as pd
# +
# generate a random adjacency matrix
# traces: Number or Domino Traces
# If traces>1 the output will be a data frame of list
# nodes: Number ... | pd.DataFrame(data=l_pval) | pandas.DataFrame |
def meanOrderFrequency(path_to_dataset):
"""
Displays the mean order frequency by utilizing the orders table.
:param path_to_dataset: this path should have all the .csv files for the dataset
:type path_to_dataset: str
"""
assert isinstance(path_to_dataset, str)
import pandas as pd
order_... | pd.CategoricalIndex(grouped_data.index, categories=[0,1,2,3,4,5,6]) | pandas.CategoricalIndex |
__all__ = ["spectrometer_sensitivity"]
# standard library
from typing import List, Union
# dependent packages
import numpy as np
import pandas as pd
from .atmosphere import eta_atm_func
from .instruments import eta_Al_ohmic_850, photon_NEP_kid, window_trans
from .physics import johnson_nyquist_psd, rad_trans, T_fro... | pd.Series(spectral_NEFD, name="NEFD_line") | pandas.Series |
from datetime import datetime
from functools import lru_cache
from typing import Union, Callable, Tuple
import dateparser
import pandas as pd
from dateutil.relativedelta import relativedelta
from numpy.distutils.misc_util import as_list
from wetterdienst.dwd.metadata import Parameter, TimeResolution, PeriodType
from ... | pd.to_datetime(date_to) | pandas.to_datetime |
import pytest
from pandas import Series
import pandas._testing as tm
class TestSeriesUnaryOps:
# __neg__, __pos__, __inv__
def test_neg(self):
ser = tm.makeStringSeries()
ser.name = "series"
| tm.assert_series_equal(-ser, -1 * ser) | pandas._testing.assert_series_equal |
#!/home/wli/env python3
from __future__ import print_function
from __future__ import absolute_import
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os.path as osp
import openslide
from pathlib import Path
from skimage.filters import threshold_otsu
import glob
#before importing HDFStore, ... | pd.concat([training_patches_tumor, training_patches_normal]) | pandas.concat |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
from numpy import nan
import numpy as np
import pandas as pd
from pandas.types.common import is_integer, is_scalar
from pandas import Index, Series, DataFrame, isnull, date_range
from pandas.core.index import MultiIndex
from pa... | assert_series_equal(result, expected) | pandas.util.testing.assert_series_equal |
# http://github.com/timestocome
# take a look at the differences in daily returns for recent bull and bear markets
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
# pandas display options
pd.options.display.max_rows = 1000
pd.options.display.max_columns = 25
pd.optio... | pd.to_datetime('01-13-2000') | pandas.to_datetime |
# -*- coding: utf-8 -*-
import sys
import io
import os
from pathlib import Path
import numpy as np
import pandas as pd
from sklearn.compose import ColumnTransformer
import yaml
# Test if the required parameters are received
if len(sys.argv) != 3:
sys.stderr.write("Arguments error. Usage:\n")
sys.stderr.write(
... | pd.DataFrame(x_train_out, index=train_in_index, columns=cols) | pandas.DataFrame |
"""A module to describe information coming from ensemble averaging
"""
class Population():
max_moments = 2
@classmethod
def load(cls, filename):
from numpy import load
try:
pop_dict = load(filename)
except ValueError:
pop_dict = load(filename, allow_pickle... | DF() | pandas.DataFrame |
#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2018, Anaconda, Inc. and Intake contributors
# All rights reserved.
#
# The full license is in the LICENSE file, distributed with this software.
#------------------------------------------------------------------------... | pd.Timestamp('1970-01-01 00:00:00') | pandas.Timestamp |
import glob
import os
import pandas as pd
def retrieve(csv, csv2, structures_paths):
row = []
for folder in sorted(structures_paths):
print(folder)
input_met = os.path.join(folder, "metrics.out")
input_clu = os.path.join(folder, "cluster.out")
if not os.path.exists(input_met):... | pd.read_csv(csv) | pandas.read_csv |
import xml.etree.ElementTree
import pandas as pd
import dateutil.parser
import re
def process_user_data(input_file, output_file):
root = xml.etree.ElementTree.parse(input_file).getroot()
user_list = []
for user in root.getchildren():
user_list.append(user.attrib)
user_data = | pd.DataFrame.from_dict(user_list) | pandas.DataFrame.from_dict |
import pytest
def test_concat_with_duplicate_columns():
import captivity
import pandas as pd
with pytest.raises(captivity.CaptivityException):
pd.concat(
[pd.DataFrame({"a": [1], "b": [2]}), pd.DataFrame({"c": [0], "b": [3]}),],
axis=1,
)
def test_concat_mismatch... | pd.DataFrame({"a": [1], "b": [2]}) | pandas.DataFrame |
import numpy as np
import pandas as pd
from TACT.computation.adjustments import Adjustments, empirical_stdAdjustment
def perform_G_SFc_adjustment(inputdata):
"""
simple filtered regression results from phase 2 averages used
"""
results = pd.DataFrame(
columns=[
"sensor",
... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 26 11:57:27 2015
@author: malte
"""
import numpy as np
import pandas as pd
from scipy import sparse
import implicit
import time
class ColdImplicit:
'''
ColdImplicit(n_factors = 100, n_iterations = 10, learning_rate = 0.01, lambda_session = 0.0, la... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 6 11:49:36 2019
@author: MAGESHWARAN
"""
import pandas as pd
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import f1_score
# impo... | pd.DataFrame(store, columns=index_) | pandas.DataFrame |
import random
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
DataFrame,
NaT,
Timestamp,
date_range,
)
import pandas._testing as tm
class TestDataFrameSortValues:
def test_sort_values(self):
frame = DataFrame(
[[1, 1, 2], [3, 1, 0], ... | tm.assert_frame_equal(df_sorted, df_reversed) | pandas._testing.assert_frame_equal |
import pandas as pd
pd.options.mode.chained_assignment = None # default='warn'
discrete_props = ['Direction'] # demonstrate which column of data is discrete feature (indicating others are linear)
def fillnan(df, col_name):
num=0
for i in df[col_name].notnull():
if i is False or df[col_name][num]=='N... | pd.isna(df[col_name][num+j]) | pandas.isna |
import io
import os
import re
import sys
import time
import pandas
import datetime
import requests
import mplfinance
from matplotlib import dates
# Basic Data
file_name = __file__[:-3]
absolute_path = os.path.dirname(os.path.abspath(__file__))
# <editor-fold desc='common'>
def load_json_config():
global file_dir... | pandas.concat([stock_close_old, stock_close_new], join='outer') | pandas.concat |
# -*- coding: utf-8 -*-
r"""
general helper functions
"""
# Import standard library
import os
import logging
import itertools
from pathlib import Path
from glob import glob
from operator import concat
from functools import reduce
from os.path import join, exists
from pprint import pprint
# Import from module
# from ... | pd.read_csv(dl_link) | pandas.read_csv |
# -*- coding: utf-8 -*-
# Arithmetc tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import timedelta
import operator
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.compat import long
from pandas.core import ops
from pan... | Timedelta(days=1) | pandas.Timedelta |
import numpy as np
import pytest
import pandas as pd
from pandas.util import testing as tm
pyreadstat = pytest.importorskip("pyreadstat")
def test_spss_labelled_num(datapath):
# test file from the Haven project (https://haven.tidyverse.org/)
fname = datapath("io", "data", "labelled-num.sav")
df = pd.re... | pd.Categorical(expected["var1"]) | pandas.Categorical |
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.assert_frame_equal(expected, result) | pandas.util.testing.assert_frame_equal |
import streamlit as st
import pandas as pd
import requests
import plotly.graph_objects as go
from plotly.subplots import make_subplots
@st.cache
def get_countries():
api_uri = "https://covid19-eu-data-api-gamma.now.sh/api/countryLookup"
data = requests.get(api_uri).json()
countries = data["countries"]
... | pd.DataFrame(data_records) | pandas.DataFrame |
import pandas as pd
import numpy as np
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, LabelEncoder
from sklearn.pipeline import Pipeline
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing... | pd.DataFrame(encoded_feat, columns=cols) | pandas.DataFrame |
# Copyright (c) 2019 Princeton University
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Standard
from datetime import datetime
import json
from json.decoder import JSONDecodeError
import os
from os.path import isfile, join
import pandas ... | pd.DataFrame(workload['instances']) | pandas.DataFrame |
import os
import pprint
from collections import OrderedDict
import numpy as np
import pandas as pd
from sklearn.metrics import roc_auc_score
import common
def main():
train_df = common.load_data('train')
path = [common.OUTPUT_DIR]
for name in os.listdir(os.path.join(*path)):
if not os.path.isdir... | pd.DataFrame(results) | pandas.DataFrame |
from tensorflow.keras.models import Sequential, model_from_json
from tensorflow.keras.layers import Conv3D, Conv2D
from tensorflow.keras.layers import ConvLSTM2D
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras import losses
import numpy as np
import pandas as pd
import random
import... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
modules of info class, including cashinfo, indexinfo and fundinfo class
"""
import os
import csv
import datetime as dt
import json
import re
import logging
from functools import lru_cache
import pandas as pd
from bs4 import BeautifulSoup
from sqlalchemy import exc
import xalpha.remain as ... | pd.DataFrame(data=dd) | pandas.DataFrame |
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import re
import unittest
import pkgutil
import io
from datetime import timedelta
from unittest import TestCase
import numpy as np
im... | pd.to_datetime("2020-01-01") | pandas.to_datetime |
# Copyright (c) 2018-2022, NVIDIA CORPORATION.
import numpy as np
import pandas as pd
import pytest
from pandas.api import types as ptypes
import cudf
from cudf.api import types as types
@pytest.mark.parametrize(
"obj, expect",
(
# Base Python objects.
(bool(), False),
(int(), False)... | pd.Series(dtype="float") | pandas.Series |
from pathlib import Path
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import us
def create_prevalence_df(file_path, population_group):
"""
Creates a data frame tha... | pd.concat(all_df, axis=0, ignore_index=True, sort=True) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 9 17:02:59 2018
@author: bruce
"""
# last version = plot_corr_mx_concate_time_linux_v1.6.0.py
import pandas as pd
import numpy as np
from scipy import fftpack
from scipy import signal
import matplotlib.pyplot as plt
from matplotlib.colors import ... | pd.concat([df_EFR_85_vsc_a,df_EFR_85_vsc_e], axis=1) | pandas.concat |
from numpy.core.fromnumeric import shape
import pytest
import pandas as pd
import datetime
from fast_trade.build_data_frame import (
build_data_frame,
detect_time_unit,
load_basic_df_from_csv,
apply_transformers_to_dataframe,
apply_charting_to_df,
prepare_df,
process_res_df,
)
def test_de... | pd.to_datetime(mock_df.index, unit="s") | pandas.to_datetime |
import pandas as pd
import xml.etree.ElementTree as ET
import lxml.etree as etree
most_serious_problem = pd.read_csv(
"../data/processed_data/special_eb/data/3_final/most_serious_problem/special_eb_most_serious_problem_final.csv")
personally_taken_action = pd.read_csv(
"../data/processed_data/special_eb/data/3... | pd.concat(data) | pandas.concat |
"""
Functions used to compile water quality data from files that have already undergone basic formatting to have the same
column headers and units. List of data sources is available in readme.md file.
Functions:
* format_lake_data: Create additional columns for date and sampling frequency and round to daily means
* ca... | pd.concat([springsummer_gw_data, chla_temp], axis=0) | pandas.concat |
# -*- coding: utf-8 -*-
# @author: <NAME>
# @date: 2020-11
'''
This file will help you get infomation you need from baidu map or amap by official API
- Baidu: http://lbsyun.baidu.com/index.php?title=webapi
- Amap: https://lbs.amap.com/api/webservice/summary
Continuing updating.....
'''
import osmnx as ... | pd.read_csv(filedir,engine='python') | pandas.read_csv |
import pandas as pd
import re
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
import mapping_career_causeways.text_cleaning_utils as text_cleaning_utils
def tfidf_keywords(p, dataframe, text_field, stopwords, N=10):
"""
Fast method to generate keywords characterising each cluster... | pd.DataFrame(Data, columns=names) | pandas.DataFrame |
#june 2014
#determine genes in copy number variants for TNBC
#genes lost at this stage are not relevant to triple negative
import csv
import math
import numpy as np
import scipy
from scipy import stats
from scipy import misc
import matplotlib.pyplot as plt
import math
import itertools
from itertools import zip_longes... | pd.concat([path,CNVs],axis=1,join='inner') | pandas.concat |
import re
import numpy as np
import pandas as pd
import random as rd
from sklearn import preprocessing
from sklearn.cluster import KMeans
from sklearn.ensemble import RandomForestRegressor
from sklearn.decomposition import PCA
# Print options
np.set_printoptions(precision=4, threshold=10000, linewidth=160, edgeitems=9... | pd.set_option('expand_frame_repr', False) | pandas.set_option |
import re
import datetime as dt
from ftplib import FTP
import gzip
from zipfile import ZipFile
from pandas.compat import StringIO
from pandas import read_csv, DataFrame, to_datetime
from pandas_datareader.base import _BaseReader
from pandas_datareader._utils import RemoteDataError
from pandas_datareader.compat import... | to_datetime(end) | pandas.to_datetime |
##### file path
### input
# data_set keys and lebels
path_df_part_1_uic_label = "df_part_1_uic_label.csv"
path_df_part_2_uic_label = "df_part_2_uic_label.csv"
path_df_part_3_uic = "df_part_3_uic.csv"
# data_set features
path_df_part_1_U = "df_part_1_U.csv"
path_df_part_1_I = "df_part_1_I.csv"
path_df_part_1_... | pd.merge(train_data_df_part_2, df_part_2_UC, how='left', on=['user_id', 'item_category']) | pandas.merge |
from pathlib import Path
import os
import pandas as pd
import tensorflow as tf
from six import StringIO
from tensor2tensor.data_generators import generator_utils
from tensor2tensor.data_generators import text_problems
from tensor2tensor.data_generators.function_docstring import GithubFunctionDocstring
from tensor2ten... | pd.read_json(train_filename) | pandas.read_json |
# %% Imports
import pandas
import altair
import datetime
import boto3
from plot_shared import get_chrome_driver
from data_shared import get_s3_csv_or_empty_df, get_ni_pop_pyramid
# %%
age_bands = pandas.read_excel('https://www.health-ni.gov.uk/sites/default/files/publications/health/doh-dd-030921.xlsx', sheet_name='I... | pandas.Series(newind) | pandas.Series |
"""Calculate weighted distances between samples in a given timepoint and both other samples in that timepoint and samples from a timepoint at a given delta time in the future.
"""
import argparse
from collections import defaultdict
import csv
import numpy as np
import pandas as pd
import sys
def get_distances_by_samp... | pd.DateOffset(months=args.delta_months) | pandas.DateOffset |
'''
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, math_util, util, viz
from slm_lab.spec import spec_util
import numpy as np
import os
import ... | pd.DataFrame(data=[mean_sr]) | pandas.DataFrame |
"""ETS Prediction View"""
__docformat__ = "numpy"
import datetime
import os
import warnings
from typing import Union
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
import pandas as pd
from pandas.plotting import register_matplotlib_converters
from gamestonk_terminal import feat... | pd.Series(forecast, index=l_pred_days, name="Price") | pandas.Series |
from constants_and_util import *
from scipy.stats import norm, pearsonr, spearmanr
import pandas as pd
import copy
import numpy as np
import random
import matplotlib.pyplot as plt
import statsmodels.api as sm
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from scipy.stats impo... | pd.concat(new_df) | pandas.concat |
import os
import sys
import numpy as np
import pandas as pd
import geopandas as gpd
import argparse
import torch
import tqdm
import segmentation_models_pytorch as smp
from torch.utils.data import DataLoader, Dataset
import cv2
from shapely.wkt import loads as wkt_loads
import shapely.wkt
import rasterio
import shapely
... | pd.read_csv(spacenet_out_dir + '/SAR_orientations.txt', header=None, sep=" ") | pandas.read_csv |
"""This module runs unit tests over functions in the get_sentiment_score
and analyze_comments_as_tblob modules"""
import os
import unittest
import pandas as pd
import movie_analysis as mv
class TestSentiment(unittest.TestCase):
"""This class runs unit tests over functions in the get_sentiment_score
and anal... | pd.DataFrame(columns=['movie_id', 'sentiment_score']) | pandas.DataFrame |
from datetime import datetime
from typing import Any, List, Union
import pandas as pd
from binance.client import Client
from binance.exceptions import BinanceAPIException
from yacht.data.markets.base import H5Market
from yacht.logger import Logger
class Binance(H5Market):
def __init__(
self,
... | pd.to_numeric(df['Close']) | pandas.to_numeric |
# import Asclepius dependencies
from asclepius.instelling import GGZ, ZKH, HardCodedParameters
# import other dependencies
from pandas import read_excel, merge, isnull, DataFrame
from typing import Union
class TestFuncties:
def __init__(self):
pass
# DAILY AUDIT FUNCTIES
def wrangle_da(self... | isnull(prestatiekaart['norm_p'][i]) | pandas.isnull |
# Copyright (c) 2016 <NAME>
import numpy as np
import pandas as pd
from sklearn import decomposition
import json
import math
import pickle
### Load data
loadPrefix = "import/input/"
# Bins 1, 2, 3 of Up are to be removed later on
dirmagUpA = np.genfromtxt(loadPrefix+"MLM_adcpU_dirmag.csv", skip_header=3, delimite... | pd.Series(sigma0A[:,1], index=sigma0Index) | pandas.Series |
import json
import matplotlib.pyplot as plt
import pandas as pd
with open('benchmark_results.json') as f:
data = json.load(f)
df = | pd.json_normalize(data['benchmarks']) | pandas.json_normalize |
from scipy import stats
import random
import numpy as np
import pandas as pd
import CleanData
import timeit
import PullDataPostgreSQL
# Conditional Parameter Aggregation (CPA) is one of the most important parts
# of the entire SDV paper. It is what allows the user to synthesize an entire
# database instead of a single... | pd.DataFrame(child[child[df.columns[0]] == ID]) | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
from solartf.core.pipeline import TFPipelineBase
from .generator import ClassifierDirectoryGenerator
class ClassificationPipeline(TFPipelineBase):
def inference(self, dataset_type='test'):
self.load_model().load_dataset()
results = []
for ... | pd.DataFrame(results) | pandas.DataFrame |
import typing
import collections
import pandas as pd
import glob
import re
from itertools import product
from pycoingecko import CoinGeckoAPI
from datetime import datetime, timedelta, timezone
from utils import load_json, save_json, json_serialize_datetime
cg = CoinGeckoAPI()
symbol_id_map = {}
def initialize_coingeck... | pd.DataFrame(prices) | pandas.DataFrame |
"""
ABSOLUTELY NOT TESTED
"""
import time
import os
import datetime
from collections import namedtuple
import numpy as np
import pandas as pd
import sklearn.preprocessing
import torch
import torch.nn as nn
import torch.optim as optim
from dateutil.relativedelta import relativedelta
from simple_ts_forecast.models i... | pd.to_datetime(date_test_start) | pandas.to_datetime |
from aide_design.shared.units import unit_registry as u
from datetime import datetime, timedelta
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
from pathlib import Path
def ftime(data_file_path, start, end=-1):
"""This function extracts the column of times from a ProCoDA data fil... | pd.read_csv(data_file, delimiter='\t') | pandas.read_csv |
from datetime import datetime
from decimal import Decimal
from io import StringIO
import numpy as np
import pytest
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series, Timestamp, date_range, read_csv
import pandas._testing as tm
from pa... | pd.Index(["a", "a"]) | pandas.Index |
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 31 19:28:58 2020
@author: hcb
"""
import pandas as pd
import numpy as np
import lightgbm as lgb
import os
from tqdm import tqdm
from sklearn.model_selection import KFold
from sklearn.metrics import f1_score
from config import config
import warnings
from sklearn.feature_ex... | pd.concat((df_train, df_test)) | pandas.concat |
"""This function will load the given data and continuosly interpet selected patients"""
import argparse
import pickle as pickle
import numpy as np
import pandas as pd
import tensorflow as tf
import keras.backend as K
from keras.models import load_model, Model
from keras.preprocessing import sequence
from keras.constrai... | pd.read_pickle(path_data) | pandas.read_pickle |
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score
import yaml
from math import ceil
import collections
from ... | pd.DataFrame({protected_variable: X[protected_variable], 'y_val': y, 'y_pred': pred}) | pandas.DataFrame |
import glob
import itertools
import os
from configparser import ConfigParser, MissingSectionHeaderError, NoSectionError, NoOptionError
from datetime import datetime
import numpy as np
import pandas as pd
from shapely import geometry
from shapely.geometry import Point
from simba.drop_bp_cords import getBpHeader... | pd.read_hdf(ROIcoordinatesPath, key='rectangles') | pandas.read_hdf |
__author__ = "<NAME>"
import json
import pandas as pd
import sqlite3
import argparse
import os
def BrowserHistoryParse(f):
conn = sqlite3.connect(f)
cursor = conn.cursor()
BrowserHistoryTable = pd.read_sql_query("SELECT events_persisted.sid, events_persisted.payload from events_persisted inner... | pd.read_sql_query("""SELECT events_persisted.payload from events_persisted inner join event_tags on events_persisted.full_event_name_hash = event_tags.full_event_name_hash inner join tag_descriptions on event_tags.tag_id = tag_descriptions.tag_id where (tag_descriptions.tag_id = 11 and events_persisted.full_event_name ... | pandas.read_sql_query |
# ********************************************************************************** #
# #
# Project: FastClassAI workbecnch #
# ... | pd.Series(["raw"]*img_filenames.shape[0]) | pandas.Series |
#!/usr/bin/env python
# coding: utf-8
import numpy as np
import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.compose import make_column_tr... | pd.Series(testx) | pandas.Series |
import numpy as np
from datetime import timedelta
import pandas as pd
import pandas.tslib as tslib
import pandas.util.testing as tm
import pandas.tseries.period as period
from pandas import (DatetimeIndex, PeriodIndex, period_range, Series, Period,
_np_version_under1p10, Index, Timedelta, offsets)
... | pd.PeriodIndex(['2011-01-01', 'NaT'], freq='D') | pandas.PeriodIndex |
# coding: utf8
import torch
import pandas as pd
import numpy as np
from os import path
from torch.utils.data import Dataset
import torchvision.transforms as transforms
import abc
from clinicadl.tools.inputs.filename_types import FILENAME_TYPE
import os
import nibabel as nib
import torch.nn.functional as F
from scipy i... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Update acled
------------
"""
from datetime import timedelta
import pandas as pd
import dateutil.relativedelta
from hdx.data.resource import Resource
from os.path import join
from hdx.location.country import Country
from src.helpers import OutputError, hxlate, drop_colu... | pd.DataFrame({cannon_column_name: valid_names}) | pandas.DataFrame |
# Package imports
import pandas as pd
import requests
import datetime
from unidecode import unidecode as UnicodeFormatter
import os
import bcolors
# Local imports
import path_configuration
import url_configuration
import progress_calculator
class GrandPrix(object):
Url = None
Path = None
Requests = None
... | pd.DataFrame(data=TrackStatusDict) | pandas.DataFrame |
import pandas as pd
import glob as glob
# **Introduction**
# <NAME>
#
# The dataset from MS Birka Stockholm is in .xls Excel-97 format.
# And the data was gathered in several steps during three different trips.
# Some of the data is overlapping in time-index, and same headers (data points) exist in several files.
# So... | pd.read_excel(xls_data_path+'2014_fw_gw_distance.xlsx',index_col=0) | pandas.read_excel |
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 16 00:05:11 2017
@author: kbui1993
"""
import sys
import queue
import os
from copy import deepcopy
import pandas as pd
from matplotlib.dates import strpdate2num
#CHANGE DIRECTORIES HERE
base_directory = "C:/Users/kbui1993/Desktop/New Results/Cap_and_Delay/base(cap_and_d... | pd.read_csv(file+"RawOutput_yMELD.csv") | pandas.read_csv |
import numpy as np
import pandas as pd
def make_onehot(sequences, seq_length):
"""
Converts a sequence string into a one-hot encoded array
"""
fd = {'A': [1, 0, 0, 0], 'T': [0, 1, 0, 0], 'G': [0, 0, 1, 0],
'C': [0, 0, 0, 1], 'N': [0, 0, 0, 0]}
onehot = [fd[base] for seq in sequences for ... | pd.concat(chromatin_data, axis=1) | pandas.concat |
import os, sys, re, copy
import pandas as pd
import rdkit
from rdkit import Chem, RDLogger
from rdkit.Chem import rdChemReactions
RDLogger.DisableLog('rdApp.*')
sys.path.append('../')
from LocalTemplate.template_extractor import extract_from_reaction
from Extract_from_train_data import build_template_extractor, ... | pd.read_csv('../data/%s/template_infos.csv' % args['dataset']) | pandas.read_csv |
import os
import glob
import psycopg2
import pandas as pd
import json
from io import StringIO
import logging
import datetime
from postgre import Postgre
logger = logging.getLogger()
logger.setLevel(logging.INFO)
logger.addHandler(logging.StreamHandler())
def get_json_data(filepath):
for root, dirs, files in os.w... | pd.DataFrame(time_dict) | pandas.DataFrame |
import operator
import numpy as np
import pytest
from pandas import (
DataFrame,
MultiIndex,
Series,
)
import pandas._testing as tm
from pandas.tests.apply.common import frame_transform_kernels
from pandas.tests.frame.common import zip_frames
def unpack_obj(obj, klass, axis):
"""
Helper to ensur... | tm.assert_produces_warning(FutureWarning, match=match) | pandas._testing.assert_produces_warning |
import unittest
import pickle as pkl
import numpy as np
import pandas as pd
import os
from keras.preprocessing import image
from keras.applications import vgg16
from src.server.context import Context
from src.model.prediction import PredictionHandler
from src.model.encoder import Encoder
DATA_DIR = "tests/data"
EXPE... | pd.DataFrame(np_zeros_ones_array_att, columns=columns) | pandas.DataFrame |
#!/usr/bin/env python
import pandas as pd
import sys
import os
from os.path import basename as bn
from snakemake.io import expand
# configuration
def prepost_string(config):
"""
Generate preprocess string based on configuration
:param config: Snakemake config dictionary
:return: PREPROCESS, POSTPRO... | pd.concat([df, _df]) | pandas.concat |
"""Main flashbang class
The Simulation object represents a single 1D FLASH model.
It can load model datafiles, manipulate/extract that data,
and plot it across various axes.
Expected model directory structure
----------------------------------
$FLASH_MODELS
│
└───<model_set>
| |
| └───<model>
| │ │ <run>.da... | pd.DataFrame() | pandas.DataFrame |
import sys, os
import argparse
import numpy as np
import pandas as pd
import json
import time
import torch
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import (roc_curve, accuracy_score, log_loss,
balanced_accura... | pd.read_csv(args.test_vitals_csv) | pandas.read_csv |
#!/usr/bin/env python
from __future__ import print_function
import argparse
from collections import Counter
from datetime import datetime
import logging
import re, sys
import os, pycurl, tarfile, zipfile, gzip, shutil
from pkg_resources import resource_filename
from sistr.version import __version__
from sistr.src.bl... | pd.DataFrame(genome_marker_cgmlst_result) | pandas.DataFrame |
import sqlite3
import pandas as pd
import geopandas as gpd
import os
def fix_badtext_in_litholog(filename,stateID = 'SA'):
"""fixes up bad text in the South Australia, QLD 'NGIS_LithologyLog.csv' file
filename: string.csv name of bad file eg 'NGIS_LithologyLog.csv'
Do not use on NT lithology.csv file... | pd.DataFrame(meta) | pandas.DataFrame |
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,
... | offsets.Nano() | pandas.offsets.Nano |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 10 17:22:51 2019
Work flow: to obtain the TD products for use with ZWD (after download):
1)use fill_fix_all_10mins_IMS_stations() after copying the downloaded TD
2)use IMS_interpolating_to_GNSS_stations_israel(dt=None, start_year=2019(latest)... | pd.DataFrame(T_lats, index=tdf.columns) | pandas.DataFrame |
import pandas as pd
import numpy as np
from concurrent.futures import ProcessPoolExecutor
import re
import os
import time
import warnings
from pandas.core.common import SettingWithCopyWarning
warnings.simplefilter(action="ignore", category=SettingWithCopyWarning)
def gender_age_percentage (df_name,df):
df=df.rena... | pd.DataFrame(columns=new_headers) | pandas.DataFrame |
"""Purpose: generate profiles for obs and models at multiple leadtimes.
Author: <NAME>
Date: 04/05/2022.
"""
# Standard library
from pprint import pprint
# Third-party
import matplotlib.pyplot as plt
import pandas as pd
# First-party
from plot_profile.utils.stations import sdf
from plot_profile.utils.utils import ... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[6]:
import pandas as pd
import io
import requests
import time
import random
# In[3]:
# gets the hidden API keys
api_key = pd.read_csv('secrets.csv').api_key.to_string().split()[1]
# In[124]:
# gets data using user's parameters
def get_data(symbol, interval):
"""... | pd.read_csv('data/stocks_etfs_list.csv') | pandas.read_csv |
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