prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import rba
import copy
import pandas
import time
import numpy
import seaborn
import matplotlib.pyplot as plt
from .rba_Session import RBA_Session
from sklearn.linear_model import LinearRegression
# import matplotlib.pyplot as plt
def find_ribosomal_proteins(rba_session, model_processes=['TranslationC', 'TranslationM... | pandas.isna(mean_val) | pandas.isna |
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 4 15:42:23 2020
@author: MichaelEK
"""
import pandas as pd
import numpy as np
import json
from pdsf import sflake as sf
from utils import split_months
def agg_allo(param, allo, use_mapping):
"""
"""
run_time_start = | pd.Timestamp.today() | pandas.Timestamp.today |
# -*- coding: utf-8 -*-
"""
.. module:: skimpy
:platform: Unix, Windows
:synopsis: Simple Kinetic Models in Python
.. moduleauthor:: SKiMPy team
[---------]
Copyright 2017 Laboratory of Computational Systems Biotechnology (LCSB),
Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland
Licens... | pd.DataFrame(columns=['solution_id', 'time']+sol_cols) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
test_preprocessing
----------------------------------
Tests for `preprocessing` module.
"""
import pytest
from sktutor.preprocessing import (GroupByImputer, MissingValueFiller,
OverMissingThresholdDropper,
... | pd.DataFrame(expected, index=missing_data.index) | pandas.DataFrame |
import pandas as pd
import numpy as np
import scipy.sparse as spl
from concurrent.futures import ProcessPoolExecutor
import sys
threads = 4
all_tasks = [
[5, 8000, ['5t', '5nt'], 0.352],
[10, 12000, ['10t', '10nt'], 0.38],
[25, 40000, ['25f'], 0.43386578246281293],
[25, 9000, ['25r'], 0.4],
[100, 4... | pd.concat([playlist_meta, playlist_meta_c], axis=0, ignore_index=True) | pandas.concat |
import tensorflow as tf
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
tf.keras.backend.set_session(tf.Session(config=tf_config))
from tensorflow.python.keras.models import load_model
import numpy as np
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, cohen_k... | pd.DataFrame(creport) | pandas.DataFrame |
""" Test cases for DataFrame.plot """
import warnings
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame
import pandas._testing as tm
from pandas.tests.plotting.common import TestPlotBase, _check_plot_works
@td.skip_if_no_mpl
class TestDataF... | tm.close() | pandas._testing.close |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Copyright 2014-2019 OpenEEmeter contributors
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/LIC... | pd.Timestamp("2016-12-19 06:00:00+00:00", tz="UTC") | pandas.Timestamp |
#---------------------------------------------------------------
#__main__.py
#this script collates measurements from individual csv outputs of
#the morphometriX GUI
#the csvs can be saved either all in one folder or within each individual
#animals folder.
#this version includes a safety net that recalculates the measu... | pd.read_csv(f,sep='^',header=None,prefix='X',engine = 'python',quoting=3, na_values = ['""','"'],encoding_errors = "ignore") | pandas.read_csv |
# -*- coding: utf-8 -*-
# pylint: disable=W0612,E1101
from collections import OrderedDict
from datetime import datetime
import numpy as np
import pytest
from pandas.compat import lrange
from pandas import DataFrame, MultiIndex, Series, date_range, notna
import pandas.core.panel as panelm
from pandas.core.panel impor... | DataFrame({'i1': [1., 2], 'i2': [1., 2]}, index=exp_idx) | pandas.DataFrame |
# Core Pkg
import streamlit as st
import pandas as pd
import numpy as np
import pickle # loading model
import base64 # enable file download
#function to load and cache(faster) the dataset and set mutation to True
@st.cache(allow_output_mutation=True)
def load_data(dataset):
df = | pd.read_csv(dataset) | pandas.read_csv |
import pandas as pd
class TripleBarrier:
def __init__(self, price, vol_span=50, barrier_horizon=5, factors=None, label=0):
"""
Labels the Data with the Triple Barrier Method
:param price: closing price
:param vol_span: look back to dertermine volatility increment threshold
... | pd.Series(dtype=events.index.dtype) | pandas.Series |
import funcy
import functools
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import shap
from copy import deepcopy
font = {
"size": 30
}
matplotlib.rc("font", **font)
def plot_results_2x2(summaries, save_fpath, fformat="pdf", dpi=300):
fig = plt... | pd.DataFrame(data_tmp) | pandas.DataFrame |
import pandas as pd
def llr(k):
'''
Compute loglikelihood ratio see
http://tdunning.blogspot.de/2008/03/surprise-and-coincidence.html
And
https://github.com/apache/mahout/blob/4f2108c576daaa3198671568eaa619266e787b1a/math/src/main/java/org/apache/mahout/math/stats/LogLikelihood.jav... | pd.DataFrame([[1000, 1000], [1000, 99000]]) | pandas.DataFrame |
import requests
import pandas as pd
import numpy as np
from pandas import json_normalize
from scipy.optimize import curve_fit
from time import gmtime, strftime
import streamlit as st
base_url = 'http://corona-api.com/countries'
def getcountrylist():
response = requests.get(base_url).json()
countrylistcode =... | json_normalize(response['data']) | pandas.json_normalize |
import pandas as pd
import numpy as np
import lightgbm as lgb
import time
train_1 = | pd.read_csv("dataset/validation_2/train_complete.csv") | pandas.read_csv |
import sys
import re
import json
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.preprocessing import MultiLabelBinarizer
from scipy.spatial.distance import cdist
from colorama import Fore, Style
from kneed impo... | pd.Series(cluster_data) | pandas.Series |
from __future__ import print_function
from __future__ import division
import source.cymdist_tool.tool as cymdist
import v2gsim
import pandas
import datetime
import random
import numpy
import matplotlib.pyplot as plt
import progressbar
import traceback
try:
import cympy
except:
pass
class EVForecast(object):
... | pandas.read_pickle(row.occupancy_filename) | pandas.read_pickle |
# -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
from datetime import datetime, timedelta
import itertools
from numpy import nan
import numpy as np
from pandas import (DataFrame, Series, Timestamp, date_range, compat,
option_context, Categorical)
from pandas.core.arra... | pd.Index(['a', 'b', 'e']) | pandas.Index |
"""Implementation of prototype set models with sklearn compatible interface.
Copyright by <NAME>
Released under the MIT license - see LICENSE file for details
This submodule creates a logger named like itself that logs to a NullHandler and tracks progress on model fitting at log
level INFO. The invoking applica... | pd.concat(result, axis=0) | pandas.concat |
#!/usr/bin/env python3
from argparse import ArgumentParser
from pathlib import Path
import anndata
import h5py
import numpy as np
import pandas as pd
import scipy.io
import scipy.sparse
def main(
umap_coords_csv: Path,
cell_by_gene_raw_mtx: Path,
cell_by_gene_smoothed_hdf5: Path,
cell_by_bin_mtx: Pat... | pd.DataFrame(index=genes) | pandas.DataFrame |
import os
import time
import pandas as pd
import numpy as np
import json
from hydroDL import kPath
from hydroDL.data import usgs, gageII, gridMET, ntn
from hydroDL.post import axplot, figplot
import matplotlib.pyplot as plt
dirInv = os.path.join(kPath.dirData, 'USGS', 'inventory')
fileSiteNo = os.path.join(dirInv, 'si... | pd.date_range(start='1979-01-01', end='2019-12-30', freq='W-TUE') | pandas.date_range |
import pandas as pd
import numpy as np
import requests
import time
import argparse
from tqdm import tqdm
from pyarrow import feather
def get_edit_history(
userid=None, user=None, latest_timestamp=None, earliest_timestamp=None, limit=None
):
"""For a particular user, pull their whole history of edits.
Arg... | pd.concat(histories) | pandas.concat |
from django.core.files import temp
from django.shortcuts import render
from django.conf import settings
from django.http import HttpResponse
from django.core.files.storage import FileSystemStorage
from django.http import FileResponse
from django.views.static import serve
import xlsxwriter
import pdfkit
import csv
impor... | pd.read_excel(inpath_SA_mm) | pandas.read_excel |
# coding: utf-8
"""
.. _l-estim-sird-theory:
Estimation des paramètres d'un modèle SIRD
==========================================
On part d'un modèle :class:`CovidSIRD <aftercovid.models.CovidSIRD>`
qu'on utilise pour simuler des données. On regarde s'il est possible
de réestimer les paramètres du modèle à partir de... | pandas.DataFrame(data) | pandas.DataFrame |
import pandas as pd
import numpy as np
import math
from scipy.stats import hypergeom
from prettytable import PrettyTable
from scipy.special import betainc
class DISA:
"""
A class to analyse the subspaces inputted for their analysis
Parameters
----------
data : pandas.Dataframe
... | pd.isna(self.data.at[row, column]) | pandas.isna |
import Functions
import pandas as pd
import matplotlib.pyplot as plt
def group_sentiment(dfSentiment):
dfSentiment['datetime'] = pd.to_datetime(dfSentiment['created_utc'], unit='s')
dfSentiment['date'] = pd.DatetimeIndex(dfSentiment['datetime']).date
dfSentiment = dfSentiment[
['created_utc', 'ne... | pd.read_csv(r'Data\Bots.csv', index_col=0, sep=';') | pandas.read_csv |
import pandas as pd
pd.options.mode.chained_assignment = None # default='warn'
import numpy as np
import os
from py2neo import Graph, Node, Relationship, NodeMatcher, RelationshipMatcher
# from neo4j import GraphDatabase
# import neo4j
import networkx as nx
import json
import datetime
import matplotlib.pyplot as plt
#... | pd.merge(full_df, df, on="smiles_str", how='left') | pandas.merge |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix, roc_curve, auc
def multiple_histograms_plot(data, x, hue, density=False, bins=10,
alpha=0.5, colors=None, hue_order=None,
... | pd.pivot_table(data=df, values=hue, index=[x], aggfunc='mean') | pandas.pivot_table |
import datetime as dt
import io
import unittest
from unittest.mock import patch
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal, assert_series_equal
from spaced_repetition.domain.problem import Difficulty, ProblemCreator
from spaced_repetition.domain.problem_log import ProblemLogC... | assert_frame_equal(expected_df, formatted_df) | pandas.testing.assert_frame_equal |
import enum
from functools import lru_cache
from typing import List
import dataclasses
import pathlib
import pandas as pd
import numpy as np
from covidactnow.datapublic.common_fields import CommonFields
from covidactnow.datapublic.common_fields import FieldName
from covidactnow.datapublic.common_fields import GetByVal... | pd.isna(row[NYTimesFields.END_DATE]) | pandas.isna |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import sys
import matplotlib.pyplot as plt
sys.path.insert(1, '../MLA')
import imp
import numpy as np
import xgboost_wrapper as xw
import regression_wrappers as rw
from sklearn.model_selection import train_test_split
import warnings
warnings.filterw... | pd.read_excel('results_10Nov/coefficients_GLM_ICU_1_britbaseline.xls') | pandas.read_excel |
import numpy as np
import pandas as pd
from typing import List, Tuple, Dict
from sklearn.preprocessing import MinMaxScaler
from data_mining import ColorizedLogger
logger = ColorizedLogger('NullsFixer', 'yellow')
class NullsFixer:
__slots__ = ('sort_col', 'group_col')
sort_col: str
group_col: str
col... | pd.isna(row['total_vaccinations']) | pandas.isna |
import pathlib
import subprocess
import pandas as pd
from papermill import execute_notebook, PapermillExecutionError
from .m3c import m3c_mapping_stats, m3c_additional_cols
from .mc import mc_mapping_stats, mc_additional_cols
from .mct import mct_mapping_stats, mct_additional_cols
from ._4m import _4m_mapping_stats, ... | pd.read_csv(path, index_col=0) | pandas.read_csv |
# ============= COMP90024 - Assignment 2 ============= #
#
# The University of Melbourne
# Team 37
#
# ** Authors: **
#
# <NAME> 1048105
# <NAME> 694209
# <NAME> 980433
# <NAME> 640975
# <NAME> 1024577
#
# Location: Melbourne
# ==... | pd.DataFrame(columns=['tweet id','user id','text','lang','user location','user geo_enabled','coordinates','created_at','latlong','search_radius']) | pandas.DataFrame |
"""
Functions to prepare the data for
the components
"""
from collections import Counter
from datetime import datetime, timedelta
from typing import Any, Callable, Dict, List, Optional, Tuple
import pandas as pd
from dateutil import parser
from loguru import logger
from pandas import DataFrame
from openstats.client i... | pd.DataFrame({**my_activity, **activity}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 7 11:41:44 2018
@author: MichaelEK
"""
import types
import pandas as pd
import numpy as np
import json
from pdsf import sflake as sf
from utils import split_months
def process_allo(param):
"""
"""
run_time_start = pd.Timestamp.today().strftime('%Y-%m-%d %H... | pd.to_datetime(permits2['ToDate'], infer_datetime_format=True, errors='coerce') | pandas.to_datetime |
import os
import logging
import pickle
from abc import ABC, abstractmethod
import pandas as pd
import numpy as np
from . import dtutil
from . import arguments
from amulog import common
from amulog import config
_logger = logging.getLogger(__package__)
SRCCLS_LOG = "log"
SRCCLS_SNMP = "snmp"
class EventDefinition(A... | pd.concat(l_df, axis=1) | pandas.concat |
import pytest
from pandas import (
DataFrame,
Index,
Series,
)
import pandas._testing as tm
@pytest.mark.parametrize("n, frac", [(2, None), (None, 0.2)])
def test_groupby_sample_balanced_groups_shape(n, frac):
values = [1] * 10 + [2] * 10
df = DataFrame({"a": values, "b": values})
... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
import argparse
import sys, os
import numpy as np
import pandas as pd
import datetime, time
import logging
import traceback
from sqlalchemy import select, Table, Column
from semutils.logging.setup_logger import setup_logger
setup_logger('download_data.log')
from semutils.messaging.Slack import Slack
from semutils.db_a... | pd.read_hdf(filepath, 'table') | pandas.read_hdf |
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from sklearn.model_selection import train_test_split, KFold, cross_val_score
from sklearn.metrics import r2_score, confusion_matrix, classification_... | pd.read_csv('../data/HR_comma_sep.csv') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 8 11:39:33 2020
@author: cristian
"""
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import os
from gurobipy import *
from matplotlib import cm
from time import time
from scripts.utils import save_solution... | pd.DataFrame(data=dfw, columns=['i', 'j', 'k', 'w_final']) | pandas.DataFrame |
#!/usr/bin/env python
import os
import sys
import pandas as pd
from datetime import datetime
from distutils.dir_util import mkpath
import shutil
from collections import defaultdict
sys.path.append("..")
from model_utils.model import DeepSpeech2Model
from utils.yaml_loader import load_yaml_config
import model_utils.n... | pd.DataFrame.from_dict(outputs) | pandas.DataFrame.from_dict |
import operator
import re
import warnings
import numpy as np
import pytest
from pandas._libs.sparse import IntIndex
import pandas.util._test_decorators as td
import pandas as pd
from pandas import isna
from pandas.core.sparse.api import SparseArray, SparseDtype, SparseSeries
import pandas.util.testing as tm
from pan... | SparseArray(self.arr_data) | pandas.core.sparse.api.SparseArray |
import logging
logging.basicConfig(level=logging.WARNING)
import pytest
import numpy
import os
import pypipegraph as ppg
import pandas as pd
from pathlib import Path
from pandas.testing import assert_frame_equal
import dppd
import dppd_plotnine # noqa:F401
from mbf_qualitycontrol.testing import assert_image_equal
fro... | pd.read_csv(self.sample_filename, sep="\t") | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 11 22:14:51 2021
@author: Allectus
"""
import os
import re
import copy
import pandas as pd
import tkinter as tk
import plotly.io as pio
import plotly.express as px
from tkinter import filedialog
from lxml import etree
#=================================================... | pd.merge(vro_results, base_results, how='left', on=['race', 'size', 'type', 'mk'], suffixes=['_vro', '_base']) | pandas.merge |
"""
Sumarize results for the train/valid/test splits.
# PROGRAM : metrics.py
# POURPOSE : compute model metrics on the test datasete
# AUTHOR : <NAME>
# EMAIL : <EMAIL>
# V1.0 : 05/05/2020 [<NAME>]
"""
import argparse
import numpy as np
import tensorflow as tf
import pandas as pd
im... | pd.DataFrame([X], columns=cols) | pandas.DataFrame |
# -*- coding: utf-8 -*-
""" dati_selezione.ipynb
Extraction of data from ISS weekly covid-19 reports
https://www.epicentro.iss.it/coronavirus/aggiornamenti
See example pdf:
https://www.epicentro.iss.it/coronavirus/bollettino/Bollettino-sorveglianza-integrata-COVID-19_12-gennaio-2022.pdf
Requirements:
Python 3.6+, Gh... | pd.DataFrame(results_) | pandas.DataFrame |
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal, assert_series_equal
from blocktorch.pipelines.components.transformers.preprocessing import (
DropRowsTransformer,
)
def test_drop_rows_transformer_init():
drop_rows_transformer = DropRowsTransformer()
assert drop_rows_transf... | assert_series_equal(y, transformed[1]) | pandas.testing.assert_series_equal |
#!/usr/bin/python
# -*- coding: UTF-8 -*-
"""
程序通用函数库
作者：wking [http://wkings.net]
"""
import os
import statistics
import time
import datetime
import requests
import numpy as np
import pandas as pd
import threading
from queue import Queue
from retry import retry
# from rich.progress import track
# from rich import pri... | pd.read_csv(ucfg.tdx['csv_gbbq'] + '/gbbq.csv', encoding='gbk', dtype={'code': str}) | pandas.read_csv |
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2022, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | pd.Index(['id1', 'id2', 'id3'], name='id') | pandas.Index |
"""This is test module for knoema client with test credentials"""
import unittest
import knoema
import pandas
class TestKnoemaClient(unittest.TestCase):
"""This is class with knoema client unit tests with test credentials"""
base_host = 'knoema.com'
def setUp(self):
apicfg = knoema.A... | pandas.to_datetime('2017-01-01') | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 14 21:31:56 2017
@author: Franz
"""
import scipy.signal
import numpy as np
import scipy.io as so
import os.path
import re
import matplotlib.pylab as plt
import h5py
import matplotlib.patches as patches
import numpy.random as rand
import seaborn as s... | pd.DataFrame(columns=columns, data=vals) | pandas.DataFrame |
import numpy as np
import argparse
from pathlib import Path
import re
import glob
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True, style="white", context="talk", font_scale=1)
PALETTE = sns.color_palette("Set1")
name_dict = {
"Gradients 0": "Gradient 1",
"Gr... | pd.concat([df, pvalues]) | pandas.concat |
'''
__author__=<NAME>
MIT License
Copyright (c) 2020 crewml
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, mer... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
import h5py
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pprint import pprint
def main():
show_h5()
# show_evts()
# test_bool()
write_h5()
def show_h5():
"""
simple LEGEND data viewer function.
shows the group structure, attri... | pd.Series(data[c][...], name=c) | pandas.Series |
#!/usr/bin/python
from xml.dom.minidom import parse
import numpy as np
import zipfile
import tempfile
import sys
if sys.version_info.major == 3:
import urllib.request as request
else:
import urllib2 as request
import io
import os.path
from impactutils.extern.openquake.geodetic import geodetic_distance
import p... | pd.DataFrame.from_dict(mydict) | pandas.DataFrame.from_dict |
#!/usr/bin/env python
# ----------------------------------------------------------------------------
# Copyright (c) 2016--, Biota Technology.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# -------------------------------------... | pd.util.testing.assert_frame_equal(obs_sources, exp_sources) | pandas.util.testing.assert_frame_equal |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
def hello():
return 'hello world'
def sample_from_finite_probability_space(finite_prob_space):
"""
Produces a random outcome from a given finite probability space.
Input
-----
- finite_prob_space: finite probability space e... | pd.DataFrame(array, row_labels, col_labels) | pandas.DataFrame |
# Copyright (C) 2022 National Center for Atmospheric Research and National Oceanic and Atmospheric Administration
# SPDX-License-Identifier: Apache-2.0
#
#Code to create plots for surface observations
import os
import monetio as mio
import monet as monet
import seaborn as sns
from monet.util.tools import calc_8hr_roll... | pd.DataFrame() | pandas.DataFrame |
import csv
import itertools
import math
import re
from pathlib import Path
from typing import *
import pandas
from loguru import logger
NumericType = Union[int, float]
IterableValues = Union[List[NumericType], pandas.Series]
NUMERIC_REGEX = re.compile("^.?(?P<number>[\d]+)")
def _coerce_to_series(item:Any)->pandas.S... | pandas.Series(item) | pandas.Series |
#!/usr/bin/python3
# coding: utf-8
import sys
import os.path
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
# get_ipython().run_line_magic('matplotlib', 'inline')
# plt.close('all')
# dpi = 300
# figsize = (1920 / dpi, 1080 / dpi)
from p... | pd.merge(exDf[['id', 'class']], maDf[['id', 'label']], on='id', how='left') | pandas.merge |
import io
import os
import time
import re
import string
from PIL import Image, ImageFilter
import requests
import numpy as np
import pandas as pd
from scipy.fftpack import fft
from sklearn.cluster import KMeans
from sklearn.neighbors import NearestNeighbors
from sklearn.preprocessing import StandardScaler
from sklear... | pd.Series(target_data.index[indices[:, 1]]) | pandas.Series |
#
# Copyright 2018 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... | tm.assert_index_equal(ans_dti, cal_dti) | pandas.testing.assert_index_equal |
from __future__ import annotations
from datetime import timedelta
import operator
from sys import getsizeof
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
List,
cast,
)
import warnings
import numpy as np
from pandas._libs import index as libindex
from pandas._libs.lib import no_... | is_integer(x) | pandas.core.dtypes.common.is_integer |
# -*- coding: utf-8 -*-
# Author: <NAME> <<EMAIL>>
# License: BSD 3 clause
"""
Unitary tests for bigfish.stack.utils module.
"""
import os
import pytest
import tempfile
import bigfish.stack as stack
import numpy as np
import pandas as pd
from bigfish.stack.utils import fit_recipe
from bigfish.stack.utils import ge... | pd.DataFrame() | pandas.DataFrame |
"""
本地数据查询及预处理，适用于zipline ingest写入
读取本地数据
1. 元数据所涉及的时间列 其tz为UTC
2. 数据框datetime-index.tz为None
注：只选A股股票。注意股票总体在`ingest`及`fundamental`必须保持一致。
"""
import re
import warnings
from concurrent.futures.thread import ThreadPoolExecutor
from functools import lru_cache, partial
from trading_calendars import get_calendar
import... | pd.DataFrame() | pandas.DataFrame |
from directional import *
import pandas as pd
import numpy as np
demo_sin_cos_matrix = pd.read_csv("sample_data/sin-cos.csv")
demo_sin_cos_mean = pd.read_csv("sample_data/sin-cos-mean.csv")
demo_angle_matrix = pd.read_csv("sample_data/degrees.csv")
demo_radian_matrix = pd.read_csv("sample_data/radians.csv")
demo_radia... | pd.read_csv("sample_data/radians-mean.csv") | pandas.read_csv |
import pandas as pd
from pandas.io.json import json_normalize
def venues_explore(client,lat,lng, limit=100, verbose=0, sort='popular', radius=2000, offset=1, day='any',query=''):
'''funtion to get n-places using explore in foursquare, where n is the limit when calling the function.
This returns a pandas datafr... | json_normalize(new_cats.iloc[i_sub,0]) | pandas.io.json.json_normalize |
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas.core.dtypes.generic import ABCIndexClass
import pandas as pd
import pandas._testing as tm
from pandas.api.types import is_float, is_float_dtype, is_integer, is_scalar
from pandas.core.arrays import IntegerArray, integer_array
from... | pd.Series([2, 1], index=[1, 2], dtype="Int64") | pandas.Series |
# This chart export activity series in pandas format
import pandas as pd
import datetime
import numpy as np
act = GC.activity()
dd = {}
for k, v in act.items():
dd[k] = np.array(v)
df = | pd.DataFrame(dd) | pandas.DataFrame |
import pandas as pd
import os
import pickle
import logging
from tqdm import tqdm
import sys
from flashtext import KeywordProcessor
import joblib
import multiprocessing
import numpy as np
import urllib.request
import zipfile
import numpy as np
import hashlib
import json
from .flags import flags
logging.basicConfig(lev... | pd.read_csv(feature_code_path, sep='\t', names=['feature_code', 'description-short', 'description-long']) | pandas.read_csv |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import datetime, timedelta
import numpy as np
import pytest
from pandas.errors import (
NullFrequencyError, OutOfBoundsDatetime, PerformanceWarning)
import pandas as pd
from pandas import (
DataFrame, ... | tm.box_expected(tdi, box_with_array) | pandas.util.testing.box_expected |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@version: 1.4.0
@file: GSP_main.py
@time: 2021/1/26 10:50
@functions: graph signal processing main script
@update: support Yeo-ICN definition
@update: support ICN-level brain activity and connecitivty strength saving
"""
import numpy as np
import glob
import os
impor... | pd.DataFrame(data = s_deCoupIdx_individual.T, columns = network_assign_csv.loc[:,'LABEL']) | pandas.DataFrame |
import json
import logging
import os
import pandas as pd
from .TfidfModel import TFIDFModel
logging.basicConfig(format='%(filename)s:%(lineno)d %(message)s')
log = logging.getLogger(__name__)
log.setLevel('INFO')
# print(CONFIG['dataset'])
if 'DATA_DIR' in os.environ.keys():
CONFIG = json.load(open('../config.jso... | pd.read_csv(cluster_file, index_col=0) | pandas.read_csv |
# -- coding: utf-8 --'
import pandas as pd
import numpy as np
import os
import textwrap
import string
import unicodedata
import sys
import sqlite3
import easygui
import re
import copy
import json
import xlsxwriter
# import pyanx
MAX_TAM_LABEL = 100 # nro máximo de caracteres nos labels
PALETA = {'vermelho':'#e82f4c... | pd.merge(df_consolida, df_oco2, how="left", on="Indexador") | pandas.merge |
"""
Utilities that help with the building of tensorflow keras models
"""
import io
from muti import chu, genu
import tensorflow as tf
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import plotly.io as pio
from plotly.subplots import make_subplots
import warnings
import os
import math
import mul... | pd.merge(samps, to_join[target], on='grp') | pandas.merge |
# coding: utf8
"""
Utils to convert AIBL dataset in BIDS
"""
def listdir_nohidden(path):
"""
This method lists all the subdirectories of path except the hidden
folders'
:param path: path whose subdirectories are needed
:return: list of all the subdirectories of path
"""
f... | pd.Series(field_col_values) | pandas.Series |
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | pd.Timestamp("2026-07-11") | pandas.Timestamp |
#!/usr/bin/env python
""" fnPersistence, a class which provides a storage layer for meta-data and snv distances from the
findneighbour4 system in mongodb
A component of the findNeighbour4 system for bacterial relatedness monitoring
Copyright (C) 2021 <NAME> <EMAIL>
repo: https://github.com/davidhwyllie/findNeighbour4
... | pd.DataFrame.from_records(contents) | pandas.DataFrame.from_records |
import abc
import asyncio
import concurrent.futures
import datetime
import glob
import json
import logging
import os
import shutil
import socket
import time
from concurrent.futures import ThreadPoolExecutor
from contextlib import suppress
from pathlib import Path
from typing import Any, Callable, Coroutine, Dict, List,... | pd.DataFrame(infos) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# ================================================================================
# ACUMOS
# ================================================================================
# Copyright © 2017 AT&T Intellectual Property & Tech Mahindra. All rights reserved.
# ==============================... | pd.DataFrame() | 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.assert_frame_equal(chunks[0], df[1:3]) | pandas.util.testing.assert_frame_equal |
import numpy as np
import pandas as pd
def set_order(df, row):
if | pd.isnull(row['order']) | pandas.isnull |
import os
import pickle
import re
from pathlib import Path
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from numpy import interp
import thoipapy
from thoipapy.utils import make_sure_path_exists
def validate_multiple_predictors_and_subsets_auboc(s, df_set, logging):
logging.info("s... | pd.concat([df_o_minus_r_mean_df, df_o_minus_r_mean], axis=1, join="outer") | pandas.concat |
import unittest
from unittest.mock import patch
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
from road_data_scraper.steps.metadata import (
create_sensor_metadata_tuples,
direction_string_cleaner,
get_sensor_urls,
get_sites_by_sensor,
name_string_cleaner,
)
... | pd.DataFrame(data_midas, columns=headers) | pandas.DataFrame |
import os
import sys
import pickle
import pandas as pd
import numpy as np
import sys
from sklearn.feature_selection import chi2, SelectKBest, f_regression
from sklearn.decomposition import PCA, TruncatedSVD
from sklearn.manifold import Isomap, LocallyLinearEmbedding
import settings as project_settings
target_data_fold... | pd.read_csv(f"{features_data_folder}test_{no_fold}_fused.csv",sep='\t', index_col=0) | pandas.read_csv |
"""
August 2020
<NAME>, Data Science Campus
Processes the raw JSON Play Store review file
Returned JSON from the API is in nested JSON, with some optional values.
See the following link for the schema:
https://developers.google.com/android-publisher/api-ref/rest/v3/reviews
Access to the API is controlled through oa... | pd.to_datetime(df['user_comment_last_modified_seconds'],errors='coerce', unit='s') | pandas.to_datetime |
"""
Analyze results and plot figures
"""
# Imports
#==============#
import pandas as pd
import numpy as np
import scipy
import random
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
import bioinformatics as bioinf
# Plots for HMM method 10-fold cross ... | pd.read_csv('results_final/ml_rf_pred/position_rules.csv', index_col=0) | pandas.read_csv |
from unittest.mock import patch
import numpy as np
import pandas as pd
import pytest
import woodwork as ww
from pandas.testing import assert_frame_equal
from pytest import importorskip
from woodwork.logical_types import (
Boolean,
Categorical,
Datetime,
Double,
Integer,
NaturalLanguage
)
from ... | pd.Series([2, 1, 1, 1, 1], dtype="Int64") | pandas.Series |
# transform pairs table to contact counts between binned coordinates
# pos-pos -> bin-bin / point -> pixel
import pandas as pd, numpy as np
import datashader as ds
import datashader.transfer_functions as tf
import pickle as pkl
import xarray as xr
from pkgutil import get_data
from io import StringIO
from . import ref
... | pd.DataFrame.sparse.from_spmatrix(mat) | pandas.DataFrame.sparse.from_spmatrix |
#!/usr/bin/env python
### Up to date as of 10/2019 ###
'''Section 0: Import python libraries
This code has a number of dependencies, listed below.
They can be installed using the virtual environment "slab23"
that is setup using script 'library/setup3env.sh'.
Additional functions are housed in file ... | pd.read_csv('library/avprofiles/global_as_av2.csv') | pandas.read_csv |
import re
import numpy as np
import pandas as pd
from dateutil.tz import tzutc
from dateutil.parser import parse as parse_date
from datetime import datetime, timedelta, timezone
from qset.utils.numeric import custom_round
# NOTE: slow
def parse_human_timestamp_re(hts, min_date_str="2000"):
"""
:param hts: Hum... | pd.Series(lst) | pandas.Series |
import os
import tqdm
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from collections import Counter
from sklearn import model_selection
def load_data():
fp = os.path.dirname(__file__)
# Sensor data
data = pd.read_csv(fp + '/PdM... | pd.get_dummies(data.failure) | pandas.get_dummies |
import pandas as pd
from xml.etree import ElementTree as etree
pd.set_option('display.max_columns', 500)
class DataFrame:
def __init__(self, doc, allElements):
'''doc = .eaf file; allElements = line element and its children'''
self.doc = doc
self.allElements = allElements
self.tbl ... | pd.DataFrame({"START": startTimes, "END": endTimes}) | pandas.DataFrame |
import pandas as pd
import numpy as np
import random
from human_ISH_config import *
import math
import os
import sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import f1_score
from sklearn.metrics import roc_auc_score
from sklearn... | pd.read_csv(path_to_sz_info) | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 25 21:13:58 2020
@author: sarakohnke
"""
#Set working directory
import os
path="/Users/sarakohnke/Desktop/data_type_you/interim-tocsv"
os.chdir(path)
os.getcwd()
#Import required packages
import pandas as pd
import numpy as np
import matplotlib
im... | pd.read_csv('GHB_D_NHANES_A1C_2005.csv') | pandas.read_csv |
import functools
import json
import warnings
from abc import ABC, abstractmethod, abstractproperty
from collections.abc import Iterable
from typing import Dict, List, Optional, Tuple, Union
import pandas as pd
import pastas as ps
from numpy import isin
from pastas.io.pas import PastasEncoder
from tqdm import tqdm
fro... | pd.to_datetime(s.index, unit='ms') | pandas.to_datetime |
import pandas as pd
from pandas._testing import assert_frame_equal
import pytest
import numpy as np
from scripts.normalize_data import (
remove_whitespace_from_column_names,
normalize_expedition_section_cols,
remove_bracket_text,
remove_whitespace,
ddm2dec,
remove_empty_unnamed_columns,
nor... | pd.DataFrame(data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
@author: <EMAIL>
@site: e-smartdata.org
"""
import numpy as np
import pandas as pd
import seaborn as sns
sns.set()
dft = pd.DataFrame({'price': np.random.randn(97)},
index=pd.date_range('20190101 09:00:00', periods=97,
freq='5min'... | pd.concat([fake_price, fake_price_mean], axis=1) | pandas.concat |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.