prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
from typing import Union, Dict, Tuple, List
import os
import tempfile
import zipfile as zf
import logging
import patsy
import pandas as pd
import numpy as np
import scipy.sparse
import xarray as xr
import dask
import dask.array
try:
import anndata
except ImportError:
anndata = None
logger = logging.getLogge... | pd.read_csv(fpath, header=None, sep=delim) | pandas.read_csv |
# Copyright (c) 2020 Civic Knowledge. This file is licensed under the terms of the
# MIT license included in this distribution as LICENSE
import logging
import re
from collections import defaultdict, deque
from pathlib import Path
from time import time
import pandas as pd
from synpums.util import *
''
_logger = logg... | pd.DataFrame(series) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
## Copyright 2015-2021 PyPSA Developers
## You can find the list of PyPSA Developers at
## https://pypsa.readthedocs.io/en/latest/developers.html
## PyPSA is released under the open source MIT License, see
## https://github.com/PyPSA/PyPSA/blob/master/LICENSE.txt
"""
T... | pd.to_numeric(duals['Marginal'], 'coerce') | pandas.to_numeric |
import math
import pandas as pd
import os
from abc import ABC, abstractclassmethod
class Base(ABC):
'''Abstract class'''
def __init__(self, formula, iter = 8, dec_places=4):
self.formula = lambda x: eval(formula)
self.a = int(input('Value of A = '))
self.b = int(input('Value of B = ')... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
plt.style.use('fivethirtyeight')
import warnings
warnings.filterwarnings("ignore")
import os
from stockscraperalpha import getStockInfo
from functions import SMA, EMA,DEMA,MACD,RSI
def show_menu():
return """[1]change stock
[... | pd.DataFrame() | pandas.DataFrame |
"""
This tests whether the Study object was created correctly.
No computation or visualization tests yet.
"""
from __future__ import absolute_import, division, print_function
from __future__ import unicode_literals
from six import iteritems
from collections import Iterable
import itertools
import json
import matplotl... | pdt.assert_equal(study.default_sample_subset, 'all_samples') | pandas.util.testing.assert_equal |
import pandas as pd
import numpy as np
import jinja2
import math
import re
class Plate_Desc:
def __init__(self, title, descrip, serialnum, date_time, datafname):
self.title = title
self.descrip = descrip
self.serialnum = serialnum
self.date_time = date_time
self.datafname =... | pd.Index([]) | pandas.Index |
import numpy as np
import pandas as pd
import os, errno
import datetime
import uuid
import itertools
import yaml
import subprocess
import scipy.sparse as sp
from scipy.spatial.distance import squareform
from sklearn.decomposition.nmf import non_negative_factorization
from sklearn.cluster import KMeans
from sklearn.me... | pd.DataFrame(index=tpm.index) | pandas.DataFrame |
import pandas as pd
import datetime
import numpy as np
from tpau_gtfsutilities.gtfs.gtfssingleton import gtfs as gtfs_singleton
from tpau_gtfsutilities.helpers.datetimehelpers import seconds_since_zero
from tpau_gtfsutilities.helpers.datetimehelpers import seconds_to_military
def get_trip_duration_seconds(gtfs_overr... | pd.concat([min_arrival_times, max_arrival_times], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
from multiprocess import Pool
import simplejson as json
import six
import sys
from cytoolz import compose
import numpy as np
import pandas as pd
import h5py
from ._util import parse_bins, parse_kv_list_param, parse_field_param
fr... | pd.__version__.split('.') | pandas.__version__.split |
# 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 u... | pandas.DataFrame() | pandas.DataFrame |
# Author: <NAME> <<EMAIL>>
#
# License: Apache Software License 2.0
"""Tests for Drift package."""
import numpy as np
import pandas as pd
import plotly.graph_objects
import pytest
from sklearn.impute import SimpleImputer
from nannyml.chunk import CountBasedChunker, DefaultChunker, PeriodBasedChunker, SizeBasedC... | pd.testing.assert_frame_equal(expected_drift, drift.data[['key', 'reconstruction_error']]) | pandas.testing.assert_frame_equal |
# pylint: disable=E1101
from datetime import time, datetime
from datetime import timedelta
import numpy as np
from pandas.core.index import Index, Int64Index
from pandas.tseries.frequencies import infer_freq, to_offset
from pandas.tseries.offsets import DateOffset, generate_range, Tick
from pandas.tseries.tools impo... | tools.to_datetime(data) | pandas.tseries.tools.to_datetime |
import sys
import time
import math
import warnings
import numpy as np
import pandas as pd
from os import path
sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
from fmlc.triggering import triggering
from fmlc.baseclasses import eFMU
from fmlc.stackedclasses import controller_stack
class testcontroll... | pd.isna(df3['b'][0]) | pandas.isna |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for datetime64 and datetime64tz dtypes
from datetime import (
datetime,
time,
timedelta,
)
from itertools import (
product,
starmap,
)
import operator
import warnings
import numpy as np
impo... | pd.array(expected, dtype="bool") | pandas.array |
"""
This creates Figure S1 - Full Cytokine plots
"""
import numpy as np
import pandas as pd
import seaborn as sns
from scipy.stats import pearsonr
from tfac.dataImport import form_tensor, import_cytokines
from .common import getSetup
def fig_S1_setup():
tensor, _, patInfo = form_tensor()
plasma, _ = import_cy... | pd.DataFrame(plasma_slice) | pandas.DataFrame |
#!/usr/bin/env python
from gensim.models.doc2vec import Doc2Vec, TaggedDocument
from scipy.spatial.distance import pdist, squareform
from sklearn import datasets, preprocessing, manifold
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.tokenize import word_tokenize
from glob import glob... | pandas.DataFrame(embedding, index=distance.index, columns=["x", "y"]) | pandas.DataFrame |
import pandas as pd
from pandas import DataFrame
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import f_regression
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR, LinearSVR
from metalfi.src.data.dataset ... | pd.concat(data) | pandas.concat |
# %%
import os
import sys
os.chdir(os.path.dirname(os.getcwd())) # make directory one step up the current directory
from pymaid_creds import url, name, password, token
import pymaid
rm = pymaid.CatmaidInstance(url, token, name, password)
import navis
import numpy as np
import pandas as pd
import seaborn as sns
import ... | pd.DataFrame(inputs.values, index = inputs.index, columns = ['axon_input', 'dendrite_input']) | pandas.DataFrame |
import numpy as np
import pandas as pd;
# python list
data = [1,2,3,4,5];
# numpy
ndata = np.array(data);
# pandas
pdata = pd.Series(data);
print(pdata[0]);
print(pdata.values);
print(pdata.index);
pdata2 = pd.Series(data, index=['A','B','C','D','E']);
print(pdata2);
print(pdata2['C']);
# dic
data2 = {'name':'kim'... | pd.Series(data2) | pandas.Series |
"""Functions related to SCOP classes of structures."""
import os
import pandas as pd
SCOP_CLA_LATEST_FILE = 'atom3d/data/metadata/scop-cla-latest.txt'
PDB_CHAIN_SCOP2_UNIPROT_FILE = 'atom3d/data/metadata/pdb_chain_scop2_uniprot.csv'
PDB_CHAIN_SCOP2B_SF_UNIPROT_FILE = 'atom3d/data/metadata/pdb_chain_scop2b_sf_uniprot... | pd.to_numeric(scop2_uniprot['sf-domid']) | pandas.to_numeric |
"""Excel Model"""
__docformat__ = "numpy"
# pylint: disable=abstract-class-instantiated
import logging
import pandas as pd
from openbb_terminal.decorators import log_start_end
from openbb_terminal.rich_config import console
logger = logging.getLogger(__name__)
@log_start_end(log=logger)
def load_configuration(ex... | pd.ExcelWriter(file) | pandas.ExcelWriter |
from typing import List
import numpy as np
import itertools
import sklearn.neighbors
import functools
import joblib
import psutil
import warnings
import pandas as pd
from numpy.typing import NDArray
from ..base import BaseExplainer
from ._cadex_parallel import compute_criterion # type: ignore
class ECE(BaseExplain... | pd.DataFrame(k_subset, columns=self._col_names) | pandas.DataFrame |
#!/usr/bin/env python
# coding=utf-8
"""
@version: 0.1
@author: li
@file: factor_solvency.py
@time: 2019-01-28 11:33
"""
import gc, six
import json
import numpy as np
import pandas as pd
from pandas.io.json import json_normalize
from utilities.calc_tools import CalcTools
from utilities.singleton import Singleton
# fr... | pd.merge(factor_solvency, cash_flow, how='outer', on="security_code") | pandas.merge |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Description : This code do basic statistical tests (i.e., student t-test, fold change,
Benjamini-Hochberg false discovery rate adjustment) for peak table generated
by MZmine-2.53
Copyright : (c) LemasLab, 02/23/2020
Author : <NAME>
Lic... | pd.isnull(row["row identity (main ID)"]) | pandas.isnull |
import numpy as np
from datetime import timedelta
from distutils.version import LooseVersion
import pandas as pd
import pandas.util.testing as tm
from pandas import to_timedelta
from pandas.util.testing import assert_series_equal, assert_frame_equal
from pandas import (Series, Timedelta, DataFrame, Timestamp, Timedelt... | Timedelta('0 days 01:00:00') | pandas.Timedelta |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
qualify donor data
"""
# %% REQUIRED LIBRARIES
import os
import argparse
import json
import ast
import pandas as pd
import datetime as dt
import numpy as np
# %% USER INPUTS (choices to be made in order to run the code)
codeDescription = "qualify donor data"
parser... | pd.to_datetime(basalData.time) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 25 14:10:00 2020
@author: asweet
"""
import pandas as pd
import numpy as np
from sqlalchemy.types import Integer, Numeric, String, DateTime
from sqlalchemy import create_engine
import urllib.parse
from sys import platform
from abc import ABC, abstractmethod
... | pd.read_csv(url_confirmed) | pandas.read_csv |
from collections import OrderedDict
import datetime
from datetime import timedelta
from io import StringIO
import json
import os
import numpy as np
import pytest
from pandas.compat import is_platform_32bit, is_platform_windows
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame... | pd.Timestamp("20130101") | pandas.Timestamp |
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 23 08:06:31 2021
@author: bcamc
"""
#%% Import Packages
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import InsetPosition, inset_axes
from matplotlib.lines import Line2D
import pandas as pd
import numpy ... | pd.Series(ind, name='ind') | pandas.Series |
# 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.... | compat.iteritems(meta_vals) | pandas.compat.iteritems |
"""
Makes Data Available in Standard Formats. Creates the following data:
df_gene_description DF
cn.GENE_ID (index), cn.GENE_NAME, cn.LENGTH, cn.PRODUCT, cn.START, cn.END, cn.STRAND
Dataframes in dfs_centered_adjusted_read_count
cn.GENE_ID (index), columns: time indices
hypoxia curve DF
cn.SAMPLE, cn.... | pd.read_csv(path) | pandas.read_csv |
import os
import json
import sqlite3 as sql
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
from catboost import CatBoostRegressor
from typing import List
import logging
from constants import (
UTILS_PATH, STREETS_FOR_PREDICTIONS_FILE,
CURRENT_FILENAMES_FILE, SELECT_DATETIME_STR... | pd.to_datetime(df[DATETIME]) | pandas.to_datetime |
import json
from datetime import datetime
import pandas as pd
from collections import namedtuple
from FinanceTools import *
import numpy as np
import sys
class StatusInvest:
def __init__(self, inFile):
self.orders = pd.read_csv(inFile)
self.orders['Category'] = self.orders['Category'].appl... | pd.to_datetime(self.orders['Date']) | pandas.to_datetime |
'''
Importing pandasTools enables several features that allow for using RDKit molecules as columns of a Pandas dataframe.
If the dataframe is containing a molecule format in a column (e.g. smiles), like in this example:
>>> from rdkit.Chem import PandasTools
>>> import pandas as pd
>>> import os
>>> from rdkit import R... | pd.version.version.split('.') | pandas.version.version.split |
from incrementalSearch import incrementalSearch
from bisection import bisection
from newton import newton
from falseRule import falseRule
from fixedPoint import fixedPoint
from multipleRoots import multipleRoots
from gaussPartialPivot import partialPivot
from gaussSimple import gaussSimple
from gaussTotal import gaussT... | pd.DataFrame(table) | pandas.DataFrame |
# Copyright 2020 <NAME>, <NAME>, <NAME>, <NAME>, <NAME>
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required b... | DataFrame(lsParsed[0], columns=["WORD", "TAG", "CHUNK", "ROLE", "PNP"]) | pandas.DataFrame |
from sqlalchemy import func
import pandas as pd
import numpy as np
from cswd.sql.base import session_scope
from cswd.sql.models import (Issue, StockDaily, Adjustment, DealDetail,
SpecialTreatment, SpecialTreatmentType)
DAILY_COLS = ['symbol', 'date',
'open', 'high', 'low', 'c... | pd.DataFrame(columns=ADJUSTMENT_COLS) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable=W0612,E1101
from datetime import datetime
import operator
import nose
from functools import wraps
import numpy as np
import pandas as pd
from pandas import Series, DataFrame, Index, isnull, notnull, pivot, MultiIndex
from pandas.core.datetools import bday
from pandas.core.n... | assert_panel_equal(sorted_panel, self.panel) | pandas.util.testing.assert_panel_equal |
import numpy as np
from fconcrete import config as c
import matplotlib.pyplot as plt
import time
import ezdxf
import pandas as pd
_Q = c._Q
def cond(x, singular=False, order=0):
"""
If It is singular, return 1 if x>0 else 0.
If It is not singular, return x**order if x>0 else 0
"""
if... | pd.DataFrame(array_table) | pandas.DataFrame |
""" Prep a series of graphs for the algorithm diagram figure.
"""
from collections import defaultdict, Counter
from copy import deepcopy
from itertools import combinations
from math import sqrt, factorial # , comb
import numpy
import pandas
def binom_coeff(n, k):
"""
apparently `from math import comb` isn't ... | pandas.DataFrame.from_dict(edges) | pandas.DataFrame.from_dict |
from PhiRelevance.PhiUtils1 import phiControl,phi
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import math
from random import seed
from random import randint
from random import random
class SmoteRRegression:
"""
Class SmoteRRegression takes arguments as follows:
data - Pandas... | pd.concat(frames) | pandas.concat |
import os
import pandas as pd
import numpy as np
import untangle
import requests
import scripts.manipulation as manipulation
mais_path = "../../bd+/mais_projects/data/alesp"
def parse_deputados(download=True): # sourcery no-metrics
if download:
r = requests.get(
"https://www.al.sp.gov.br... | pd.read_csv("../data/servidores/deputados_alesp_aux.csv") | pandas.read_csv |
"""
Tests for zipline/utils/pandas_utils.py
"""
from unittest import skipIf
import pandas as pd
from zipline.testing import parameter_space, ZiplineTestCase
from zipline.testing.predicates import assert_equal
from zipline.utils.pandas_utils import (
categorical_df_concat,
nearest_unequal_elements,
new_pan... | pd.Series([100, 102, 103], dtype='int64') | pandas.Series |
# This file is generated from image_classification/dataset.md automatically through:
# d2lbook build lib
# Don't edit it directly
#@save_all
#@hide_all
import pathlib
import pandas as pd
from matplotlib import pyplot as plt
from typing import Union, Sequence, Callable, Optional
import fnmatch
import numpy as np
imp... | pd.DataFrame(entries) | pandas.DataFrame |
import asyncio
import concurrent.futures
import itertools
import logging
import math
import random
import sys
import dask.array as da
import dask.dataframe as dd
import numpy as np
import pandas as pd
import pytest
import scipy
import toolz
from dask.distributed import Future
from distributed.utils_test import ( # no... | pd.DataFrame(search.history_) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[79]:
import numpy as np
import pandas as pd
import datetime
import csv
# In[45]:
#modifing the text file to make the split of date and temperture pairs.
def textModify():
with open('temperature.txt', 'r') as file :
filedata = file.read()
filedata = filed... | pd.read_csv('temperature.txt',sep="$", header=None) | pandas.read_csv |
from __future__ import division
import pytest
import numpy as np
from datetime import timedelta
from pandas import (
Interval, IntervalIndex, Index, isna, notna, interval_range, Timestamp,
Timedelta, compat, date_range, timedelta_range, DateOffset)
from pandas.compat import lzip
from pandas.tseries.offsets imp... | tm.assert_index_equal(result, expected) | pandas.util.testing.assert_index_equal |
# -*- coding: utf-8 -*-
"""
Created on 12/20/2019
@author: Azhu
"""
import glob
import pdfquery
import os
import tabula as tb
import cv2
import numpy as np
import pandas as pd
import PyPDF2
from PyPDF2 import PdfFileReader, PdfFileWriter
###############################################################################... | pd.DataFrame() | pandas.DataFrame |
import scipy.io as sio
import numpy as np
import pandas as pd
import tables
import pickle
from scipy.interpolate import interp1d
import os
from ismore import settings
from utils.constants import *
pkl_name = os.path.expandvars('$BMI3D/riglib/ismore/traj_reference_interp.pkl')
mat_name = os.path.expandvars('$HOME/Des... | pd.DataFrame(kin, columns=columns) | pandas.DataFrame |
import pytest
from datetime import datetime
import pandas as pd
from tadpole_algorithms.transformations import convert_to_year_month, \
convert_to_year_month_day, map_string_diagnosis
def test_forecastDf_date_conversion():
forecastDf = pd.DataFrame([{'Forecast Date': '2019-07'}])
assert pd.api.types.is... | pd.api.types.is_datetime64_ns_dtype(d4Df_new2['ScanDate']) | pandas.api.types.is_datetime64_ns_dtype |
import pandas as pd
ds = | pd.Series([2, 4, 6, 8, 10, 12, 14, 16, 18, 20]) | pandas.Series |
"""
Copyright (C) 2018 <NAME> (<EMAIL>)
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 writing, sof... | pd.merge(self.eg_edges, events_meta, left_on='source', right_index=True) | pandas.merge |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2021/7/27 15:14
Desc: 东方财富-经济数据-英国
http://data.eastmoney.com/cjsj/foreign_4_0.html
"""
import pandas as pd
import requests
import demjson
# Halifax房价指数月率
def macro_uk_halifax_monthly():
"""
东方财富-经济数据-英国-Halifax 房价指数月率
http://data.eastmoney.com/cjsj/fo... | o_numeric(temp_df["现值"]) | pandas.to_numeric |
import os
from re import X
import sys
import logging
import argparse
import shutil
import numpy as np
from pandas import read_csv, DataFrame
from Bio.SeqIO import parse
from itertools import product
from subprocess import call
from scipy.spatial.distance import pdist, squareform
import plotly.express as px
#from pand... | read_csv('./data/CONCAT_base.tsv', sep='\t', index_col=[0]) | pandas.read_csv |
import os
import shutil
import dash
import base64
import io
import pandas as pd
import geopandas as gpd
from shapely.geometry import Polygon
import numpy as np
import dash_table
import dash_bootstrap_components as dbc
import dash_html_components as html
import dash_core_components as dcc
from dash.dependencies import I... | pd.DataFrame(datatable_load_profile) | pandas.DataFrame |
import pandas
from functools import reduce
from .. import SystemClass
from ..decorators import actions, mode
from typing import Iterable, Tuple, Callable
@mode
def run_static_pf(
distSys: SystemClass,
actions: Iterable[Callable] = (lambda distSys: None,),
tools: Iterable[Callable] = (lambda distSys: None,... | pandas.concat([df1, df2], ignore_index=True) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 4 16:22:57 2020
@author: Natalie
"""
import os
import sys
import click
import pickle
import pandas as pd
import numpy as np
import geopandas as gpd
import imageio
from shapely.geometry import Point
import json
from bokeh.io import output_file
from bokeh.plotting import... | pd.merge(tmp_df, age_count_temp, how='left', left_index=True,right_index=True) | pandas.merge |
# Copyright (c) 2019-2021 - for information on the respective copyright owner
# see the NOTICE file and/or the repository
# https://github.com/boschresearch/pylife
#
# 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 co... | pd.testing.assert_series_equal(expected_obj, obj) | pandas.testing.assert_series_equal |
from __future__ import division
import os
import os.path as op
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import nibabel as nib
from nipype import (Workflow, Node, MapNode, JoinNode,
IdentityInterface, DataSink)
from nipype.interfaces.base import traits, TraitedSpec
fro... | pd.DataFrame(mc_data, columns=cols) | pandas.DataFrame |
import unittest
import xmlrunner
import pandas as pd
import numpy as np
class DummyUnitTest(unittest.TestCase):
def setUp(self):
self.int = 5
self.yes = True
self.no = False
self.float = 0.5
self.pi = 3.141592653589793238462643383279
self.string = "Miguel"
s... | pd.DataFrame(self.dict, index=[0]) | pandas.DataFrame |
import os
import random
import math
import numpy as np
import pandas as pd
import itertools
from functools import lru_cache
##########################
## Compliance functions ##
##########################
def delayed_ramp_fun(Nc_old, Nc_new, t, tau_days, l, t_start):
"""
t : timestamp
current date
... | pd.Timestamp('2022-09-21') | pandas.Timestamp |
import os
from sklearn import metrics
from . import data as D
import itertools
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from . import plotting
SURROGATES = 'surrogates iaaft'.split() + [None]
TESTSET_VALS = (False, True)
LABELS = 'Wake S1 S2 S3 S4 REM'.split()
REDUCED_LABELS = 'Wake Ligh... | pd.Series(data=None, index=mindex) | pandas.Series |
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats as stats
import numpy as np
import seaborn as sns
import os,sys
import itertools
import datetime
import scipy.signal as signal
import matplotlib.dates as mdates
from sklearn.neighbors._kde import Ke... | pd.DataFrame(gps_mean.loc[df_std.index]) | pandas.DataFrame |
import itertools
from collections import defaultdict
import numpy as np
import pandas as pd
import torch
from pytorch_toolbelt.utils.fs import id_from_fname
from pytorch_toolbelt.utils.torch_utils import to_numpy
from sklearn.metrics import cohen_kappa_score
from torch import nn
from torch.utils.data import DataLoader... | pd.DataFrame.from_dict(predictions) | pandas.DataFrame.from_dict |
from .test_dataset import CMD_CREATE_TEST_TABLE
import pytest
import pandas as pd
import numpy as np
import os
from ..dataset import sql_dataset
from .gen_rand_data import rand_df
CMD_DROP_TEST_TABLE_IF_EXISTS = "IF OBJECT_ID('test_table', 'U') IS NOT NULL DROP TABLE test_table;"
CMD_CREATE_TRUNCATED_TEST_TABLE = """... | pd.testing.assert_frame_equal(df_queried, df_orig, check_dtype=False, check_names=False) | pandas.testing.assert_frame_equal |
import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
import statistics
import math
from sklearn.decomposition import PCA
COLUMNS_TO_USE = ["Home_type", "rooms", "home_size_m2", "lotsize_m2",
"expenses_dkk", "floor_as_int", "balcony", "zipcodes",
"m2_pr... | pd.concat([df, zipcodes_onehot], axis=1) | pandas.concat |
""" Format data """
from __future__ import division, print_function
import pandas as pd
import numpy as np
import re
from os.path import dirname, join
from copy import deepcopy
import lawstructural.lawstructural.constants as lc
import lawstructural.lawstructural.utils as lu
#TODO: Take out entrant stuff from lawData
... | pd.merge(self.data, self.data_sets[dset], how='outer') | pandas.merge |
import pandas as pd
import pytest
from pandas.util.testing import assert_frame_equal
from pyspark import sql
from pysparkhelpers import helpers
@pytest.mark.usefixtures("hive_context")
def test_single(hive_context):
df = pd.DataFrame({'user': ['a', 'a', 'a', 'b'],
'values': [1, 1, 1, 4]})
... | assert_frame_equal(expected_value, returned_value) | pandas.util.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 24 20:03:24 2019
@author: RV
"""
# Python(R)
# Modeules/packageslibraries
# OS - submodules/path/join
#eg. (os.path.join)
# pandas
# scipy
# onspy
#%% Setup
import os
projFld = "C:/Users/RV/Documents/Teaching/2019_01_Spring/ADEC7430_Spring2019/Lec... | pd.isnull(rawTrain) | pandas.isnull |
import datetime
from typing import List
import pandas as pd
import pytest
from ruamel.yaml import YAML
import great_expectations.exceptions as ge_exceptions
from great_expectations.core.batch import (
Batch,
BatchDefinition,
BatchSpec,
RuntimeBatchRequest,
)
from great_expectations.core.batch_spec imp... | pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]}) | pandas.DataFrame |
from dataProcessing import *
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import IsolationForest
import pandas as pd
import pickle
from RandomForestCounterFactual import *
def checkSamples(
datasetFileName,
unscaledFactualsFileName,
unscaledCounterFactualsFileName,
... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
.. module:: courtship
:synopsis: Classes for Fly objects.
.. moduleauthor:: <NAME>
"""
import numpy as np
import pandas as pd
from .behavior import Behavior
class Point(object):
"""Stores 2D coordinates as attributes.
Attributes
----------
row : np... | pd.concat(to_concat, axis=1) | pandas.concat |
#!usr/local/bin
import pandas as pd
import numpy as np
from sys import argv
import subprocess
def sequence_splice_site(sp_junc, fasta_file):
part = sp_junc
part = part[['chr','first_base','last_base','motif','STAR_annotation','strand']]
part['first_base'] = part['first_base']
#part['chr'] = 'chr' + pa... | pd.concat([cat1,cat2]) | pandas.concat |
#https://www.youtube.com/watch?v=xKvffLRSyPk&list=PL3JVwFmb_BnSLFyVThMfEavAEZYHBpWEd&index=1
import os, io
from google.cloud import vision
import pandas as pd
#from google.cloud.vision import types -> 버전 업그레이드 되면서 types 사용 안함
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = r'ServiceAccountToken.json'
client ... | pd.DataFrame(columns=['locale', 'description']) | pandas.DataFrame |
"""
This is a place to create a python wrapper for the BASGRA fortran model in fortarn_BASGRA_NZ
Author: <NAME>
Created: 12/08/2020 9:32 AM
"""
import os
import ctypes as ct
import numpy as np
import pandas as pd
from subprocess import Popen
from copy import deepcopy
from input_output_keys import param_keys, out_co... | pd.to_datetime(strs, format='%Y-%j') | pandas.to_datetime |
import os
import uuid
from datetime import datetime
from time import sleep
import fsspec
import pandas as pd
import pytest
import v3iofs
from storey import EmitEveryEvent
import mlrun
import mlrun.feature_store as fs
from mlrun import store_manager
from mlrun.datastore.sources import CSVSource, ParquetSource
from mlr... | pd.Timestamp("2021-01-10 12:00:00") | pandas.Timestamp |
#modules to initialize the API
#the API will run on two endpoints:
# Students - to get the user's course code
# Lessons - to access the timetable details for which the alerts will be sent
#with the two endpoints, if the API is located at www.api.com
# comm. with the Students and Lessons classes will be at
# www.a... | pd.read_json('classCodes.json') | pandas.read_json |
#!/usr/bin/env python
import os
import sys
import pandas as pd
import argparse
import configparser
import multiprocessing
import time
import datetime
from sqlalchemy import create_engine
from sqlalchemy.pool import NullPool
import tqdm
import statsmodels.stats.multitest as multitest
import snps
import genes
import in... | pd.read_sql(sql, con=con) | pandas.read_sql |
################################################################################
# The contents of this file are Teradata Public Content and have been released
# to the Public Domain.
# <NAME> & <NAME> - April 2020 - v.1.1
# Copyright (c) 2020 by Teradata
# Licensed under BSD; see "license.txt" file in the bundle root ... | pd.to_numeric(df['tot_cust_years']) | pandas.to_numeric |
import numpy as np
import pandas as pd
import time
import cv2
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.nn.init
from torch.autograd import Variable
from . models import Spatial_CNN
class TESLA(object):
def __init__(self):
super(TESLA,... | pd.Series(nbs) | pandas.Series |
from collections import abc, deque
from decimal import Decimal
from io import StringIO
from warnings import catch_warnings
import numpy as np
from numpy.random import randn
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
... | pd.MultiIndex.from_tuples(tuples, names=["level2", "level1", None]) | pandas.MultiIndex.from_tuples |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from .plan_losses import PPC, PlanCost,get_leading_hint
from query_representation.utils import deterministic_hash,make_dir
from query_representation.viz import *
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pyplot as plt
i... | pd.DataFrame(cur_costs) | pandas.DataFrame |
import cbsodata
import math
import pandas
import matplotlib.pyplot as plt
from functools import reduce
from pathlib import Path
from sklearn.preprocessing import MinMaxScaler
def download(identifier: str):
"""Download a dataset from the CBS odata portal."""
# Prepare download directory
download_director... | pandas.read_csv(source_path) | pandas.read_csv |
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_array_equal
from pandas.testing import assert_series_equal
from src.shared import _create_group_ids
from src.shared import _determine_number_of_groups
from src.shared import _expand_or_contract_ids
from src.shared import create_group... | pd.Series({1: 20, 2: 5, 5: 2}) | pandas.Series |
#-*- coding:utf-8 -*-
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import numpy as np
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extrac... | pd.read_csv('dataset/raw_training.csv') | pandas.read_csv |
import os
import pandas as pd
import numpy as np
from pandas.api.types import CategoricalDtype
from imblearn.over_sampling import SMOTE
DATA_PATH = '../cell-profiler/measurements'
def load_data(filename, data_path=DATA_PATH):
"""
Read a csv file.
"""
csv_path = os.path.join(data_path, filename)
... | pd.DataFrame(y_sm, columns=['stiffness']) | pandas.DataFrame |
from collections import OrderedDict
from datetime import timedelta
import numpy as np
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype, DatetimeTZDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Series,
Timedelta,
Timestamp,
_np_version_under1p14,
... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
# -*- 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... | StringIO(test) | pandas.compat.StringIO |
# import Ipynb_importer
import pandas as pd
from .public_fun import *
# 全局变量
class glv:
def _init():
global _global_dict
_global_dict = {}
def set_value(key,value):
_global_dict[key] = value
def get_value(key,defValue=None):
try:
return _global_dict[key... | pd.merge(self.o.ol, self.c.ol, left_index=True, right_index=True) | pandas.merge |
import pandas as pd
import pymmwr as pm
import datetime
import warnings
import io
import requests
warnings.simplefilter(action='ignore')
def read_fips_codes(filepath):
# read file
fips_codes = pd.read_csv(filepath)
# take state code from all fips codes
fips_codes['state_abbr'] = fips_codes['location'].str[:2]
... | pd.to_datetime(df_byday['date']) | pandas.to_datetime |
import numpy as np
from numpy.random import randn
import pytest
from pandas import DataFrame, Series
import pandas._testing as tm
@pytest.mark.parametrize("name", ["var", "vol", "mean"])
def test_ewma_series(series, name):
series_result = getattr(series.ewm(com=10), name)()
assert isinstance(series_result, S... | tm.assert_series_equal(a, c) | pandas._testing.assert_series_equal |
import os
import json
import datetime
import multiprocessing
import random
import copy
import time
import warnings
import pandas as pd
import numpy as np
from datetime import date, timedelta
from pathlib import Path
from models.common.mit_buildings import MITBuildings
from models.common.to_precision import sig_fig_f... | pd.unique(self.all_boostrap_campus_daily_sample_occupancy['date']) | pandas.unique |
# last edited: 04/10/2021
#
# The functions pca_initial, pca_initial_, pca_final, and pca_final_ are adapted
# from a post by <NAME> here:
# https://nirpyresearch.com/classification-nir-spectra-principal-component-analysis-python/
#
# Retrieved in December 2020 and is licensed under Creative Commons Attribution 4.0
# I... | pd.DataFrame(data) | pandas.DataFrame |
import os
import shutil
import logging
import pandas as pd
import matplotlib
matplotlib.use("agg") # no need for tk
from supervised.automl import AutoML
from frameworks.shared.callee import call_run, result, output_subdir, utils
log = logging.getLogger(os.path.basename(__file__))
def run(dataset, config):
log... | pd.DataFrame(dataset.test.X, columns=column_names) | pandas.DataFrame |
"""
@author: <NAME>
"""
import os
import re
import numpy as np
import pandas as pd
import json
import pickle
from argparse import ArgumentParser
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, cl... | pd.Series([], dtype=str) | pandas.Series |
import pandas as pd
def break_even_moneyline(probability):
if probability > 0.5:
x = -(100 / (1-probability))+100
else:
x = 100/probability - 100
return x
def predict_probs_and_moneylines(data):
predictions = | pd.DataFrame(columns=["Team1", "Seed1", "Team2", "Seed2", "Win%1", "Win%2", "ML1", "ML2"]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 13 10:15:43 2018
Build Neural network from perceptron，treating biases as part of weights and use
matrix computation to optimize the stochastic gradient descent method.
@author: Feng
"""
#### Libraries
import random
import numpy as np
class NeuralNetw... | pd.get_dummies(car_data['Auction'],prefix='Auction') | pandas.get_dummies |
import numpy as np
import pandas as pd
from sklearn import preprocessing, linear_model, metrics
import gc; gc.enable()
import random
import time, datetime
from sklearn.neural_network import MLPRegressor
from sklearn.linear_model import TheilSenRegressor, BayesianRidge
from sklearn.ensemble import BaggingRegressor
from... | pd.to_datetime(df['date']) | pandas.to_datetime |
import pysubgroup as ps
import pandas as pd
from subgroup_sem import SEMTarget, TestQF
############################################################################################
# imoport and preprocess data
############################################################################################
data = | pd.read_csv('artificial_data.csv') | pandas.read_csv |
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