prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pandas as pd
def run_fengxian(path_1, path_2):
"""
name：“求风险值函数”
function: 将分组求和后的销项发票信息和进项发票信息合并; 求销售净利率
path_1：销项发票
path_2：进项发票
"""
df_1 = pd.read_csv(path_1, encoding='UTF-8') # 销项
df_2 = pd.read_csv(path_2, encoding='UTF-8') # 进项
# 删除没用的列
del df_1['... | pd.read_csv(path, encoding='GBK') | pandas.read_csv |
import pandas as pd
import pytest
import plotly.graph_objects as go
from easyplotly import Sankey
@pytest.fixture()
def sankey_a_b_target():
return go.Sankey(
node=dict(label=['Source A', 'Source B', 'Target']),
link=dict(
source=[0, 1],
target=[2, 2],
value=[1,... | pd.Series({('B', 'C'): 1}) | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import pandas as pd
import os
import xgboost as xgb
from constants import VARS, IDS
from sklearn.metrics import mean_absolute_error
from utils import train_test_split
from src.constants import DATA_DIR, LOGS_DIR
def best_params(path):
if os.path.exists(path):
... | pd.concat(arrays, axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
from warnings import catch_warnings
import numpy as np
from datetime import datetime
from pandas.util import testing as tm
import pandas as pd
from pandas.core import config as cf
from pandas.compat import u
from pandas._libs.tslib import iNaT
from pandas import (NaT, Float64Index, Series,
... | notnull(values) | pandas.core.dtypes.missing.notnull |
import os
import pandas as pd
import numpy as np
import uproot
import h5py
from twaml.data import dataset
from twaml.data import scale_weight_sum
from twaml.data import from_root, from_pytables, from_h5
branches = ["pT_lep1", "pT_lep2", "eta_lep1", "eta_lep2"]
ds = from_root(
["tests/data/test_file.root"], name="m... | pd.concat([ds1.df, ds2.df]) | pandas.concat |
#kMeans
import random
import pandas as pd
import numpy as np
from pandas.util.testing import assert_frame_equal
import matplotlib as plt
def random_sample(df,k):
rindex = np.array(random.sample(xrange(len(df)), k))
return df.ix[rindex]
def distance(e1,e2):
return np.linalg.norm(e1-e2)
def create_clusters(cen... | assert_frame_equal(f1, f2) | pandas.util.testing.assert_frame_equal |
import pandas as pd
#Summary null values
def summary(X):
''' This Function will return the columns names as index,null_value_count,any unique character we specify & its percentage of occurance per column.'''
null_values = X.apply(lambda x:X.isnull().sum())
blank_char = X.apply(lambda x:X.isin(['?']).sum()... | pd.to_datetime(X[date]) | pandas.to_datetime |
import pandas as pd,requests, plotly.graph_objects as go, plotly.express as px, os
from dotenv import load_dotenv
from plotly.subplots import make_subplots
# Using requests library to create urls
def req(series: str, start: str, end: str, json: str):
'''
{param} series: The series we are looking at (PAYEMS, GD... | pd.json_normalize(RPM, record_path=['observations']) | pandas.json_normalize |
import torch
from torchtext.legacy import data
from torchtext.legacy.data import Field, BucketIterator
import pandas as pd
import os
from .NLPClassificationDataset import NLPClassificationDataset
class SSTDataset(NLPClassificationDataset):
def __init__(self, data_path, seed, batch_size, device, split_ratio=[0.7,... | pd.merge(sst_sents, sst_phrases, how='inner', left_on=['sentence'], right_on=['phrase']) | pandas.merge |
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... | read_hdf(path, "dfq", where="A>0 or C>0") | pandas.io.pytables.read_hdf |
import numpy as np
import pandas as pd
from astropy.table import Table
from astropy.io.fits import getdata
from astropy.time import Time
from astropy.io import fits
import sys
from astroquery.simbad import Simbad
from astropy.coordinates import SkyCoord
import astropy.units as u
# Read base CSV from the Google driv... | pd.to_numeric(df['DEC']) | pandas.to_numeric |
#test dataset model
from deepforest import get_data
from deepforest import dataset
from deepforest import utilities
import os
import pytest
import torch
import pandas as pd
import numpy as np
import tempfile
def single_class():
csv_file = get_data("example.csv")
return csv_file
def multi_class():
csv... | pd.read_csv(csv_file2) | pandas.read_csv |
#coding=utf-8
from sklearn.metrics import roc_auc_score
import pandas as pd
import os
val = pd.read_csv('../data/validation/validation_set.csv')
"""
for i in range(30):
xgb = pd.read_csv('./val/svm_{0}.csv'.format(i))
tmp = pd.merge(xgb,val,on='Idx')
auc = roc_auc_score(tmp.target.values,tmp.score.va... | pd.DataFrame(Idx,columns=['Idx']) | pandas.DataFrame |
import logging
import os
import sys
import pandas as pd
import pytest
import handy as hd
log: logging.Logger
@pytest.fixture
def setup_logging():
logging.basicConfig(level=logging.INFO, stream=sys.stdout)
global log
log = logging.getLogger('handy test')
log.setLevel(logging.INFO)
... | pd.Timestamp('2020-08-31 00:00:00') | pandas.Timestamp |
# *****************************************************************************
# © Copyright IBM Corp. 2018. All Rights Reserved.
#
# This program and the accompanying materials
# are made available under the terms of the Apache V2.0 license
# which accompanies this distribution, and is available at
# http://www.apac... | pd.api.types.is_numeric_dtype(df_copy[feature].dtype) | pandas.api.types.is_numeric_dtype |
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
import xarray as xr
from pandas.api.types import (
is_datetime64_any_dtype,
is_numeric_dtype,
is_string_dtype,
is_timedelta64_dtype,
)
def to_1d(value, unique=False, flat=True, get=None):
# pd.Series converts datetime... | pd.unique(array) | pandas.unique |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
from tqdm import tqdm as pb
import datetime
import re
import warnings
import matplotlib.pyplot as plt
import pylab as mpl
from docx import Document
from docx.shared import Pt
from data_source import local_source
def concat_ts_codes(df): #拼接df中所有TS_CODE... | pd.DataFrame(df_sub.iloc[0,:]) | pandas.DataFrame |
# Copyright (C) 2014-2017 <NAME>, <NAME>, <NAME>, <NAME> (in alphabetic order)
#
# This file is part of OpenModal.
#
# OpenModal is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, version 3 of the License.
#
... | pd.DataFrame(index=df_elem_index, columns=self.modaldata.tables['elements_values'].columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from __future__ import absolute_import
import pandas as pd
import cobra
from cobra_utils.query.met_info import classify_metabolites_by_type
def rxn_info_from_metabolites(model, metabolites, verbose=True):
'''
This function looks for all the reactions where the metabolites in the list... | pd.DataFrame.from_records(rxn_gene_association, columns=labels) | pandas.DataFrame.from_records |
import os
import gc
import re
import json
import random
import numpy as np
import pandas as pd
import scipy.io as sio
from tqdm import tqdm
import matplotlib.pyplot as plt
from daisy.utils.data import incorporate_in_ml100k
from scipy.sparse import csr_matrix
from collections import defaultdict
from IPython import embe... | pd.to_datetime(df['timestamp']) | pandas.to_datetime |
from sklearn.cluster import MeanShift, estimate_bandwidth
import pandas as pd
import numpy as np
from clusteredmvfts.partitioner import KMeansPartitioner
from pyFTS.benchmarks import Measures
from clusteredmvfts.fts import cmvhofts
#Set target and input variables
target_station = 'DHHL_3'
#All neighbor stations wit... | pd.read_pickle("../../notebooks/df_oahu.pkl") | pandas.read_pickle |
# -*- encoding:utf-8 -*-
"""
中间层，从上层拿到x，y，df
拥有create estimator
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import os
import functools
from enum import Enum
import numpy as np
import pandas as pd
from sklearn.base import TransformerM... | pd.get_dummies(raw_df['Sex'], prefix='Sex') | pandas.get_dummies |
import os
import pickle
from pathlib import Path
from typing import Union
import joblib
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from numpy import interp
from sklearn.metrics import roc_curve, auc
import thoipapy.common
import thoipapy.figs
import thoipapy.utils
import thoipapy.vali... | pd.read_csv(testdata_combined_file, sep=',', engine='python', index_col=0) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
@file
@brief Defines a streaming dataframe.
"""
import pickle
import os
from io import StringIO, BytesIO
from inspect import isfunction
import numpy
import numpy.random as nrandom
import pandas
from pandas.testing import assert_frame_equal
from pandas.io.json import json_normalize
from .data... | assert_frame_equal(a, b) | pandas.testing.assert_frame_equal |
"""Transform signaling data to smoothed trajectories."""
import sys
import numpy
import pandas as pd
import geopandas as gpd
import shapely.geometry
import matplotlib.patches
import matplotlib.pyplot as plt
import mobilib.voronoi
SAMPLING = pd.Timedelta('00:01:00')
STD = | pd.Timedelta('00:05:00') | pandas.Timedelta |
import pandas as pd
import os
import re
import pprint
import shutil
# Clean all the obvious typos
corrections ={'BAUGHWJV':'BAUGHMAN',
'BOHNE':'BOEHNE',
'EISEMENGER':'EISENMENGER',
'GEITHER':'GEITHNER',
'KIMBREL':'KIMEREL',
'MATTINGLY': 'MATTLIN... | pd.DataFrame(columns=interjection_df.columns) | pandas.DataFrame |
from EL.models import resnet
import os
from EL import CONSTS
import torch.nn as nn
from torchvision import transforms
import torch
from sacred import Experiment
import argparse
import numpy as np
from EL.data.data import ChexpertDataset
from EL.models.models import SenderChexpert, ReceiverChexpert
from EL.utils.utils i... | pd.DataFrame({'ID': test_dataset.img_paths, 'Ground Truth': lbls, 'Predictions': predictions, 'Message': msgs}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# /home/smokybobo/opt/repos/git/personal/loadlimit/test/unit/stat/test_tmp.py
# Copyright (C) 2016 authors and contributors (see AUTHORS file)
#
# This module is released under the MIT License.
"""Tempy"""
# ============================================================================
# Imports... | DataFrame(vals, index=dfindex) | pandas.DataFrame |
import functools
import numpy as np
import scipy
import scipy.linalg
import scipy
import scipy.sparse as sps
import scipy.sparse.linalg as spsl
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import logging
import tables as tb
import os
import sandy
import py... | pd.concat(pivot_matrix) | pandas.concat |
# -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101
import itertools
import warnings
from warnings import catch_warnings
from datetime import datetime
from pandas.types.common import (is_integer_dtype,
is_float_dtype,
is_scalar)
from pandas.compat... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
'''
calculate intrinsic economic value of a property based on buy or rent decision indifference (arbitrage)
Rent = Price*mortgage_rate
+ DEPRECIATION_RATE*min(Building, Price)
- growth * Land
+ Price*tax
- (Price - 24k) * tax_braket
+ Price * mortgage_insurance
'''
### PATH... | pd.to_datetime(table['date']) | pandas.to_datetime |
import math
import numpy as np
import datetime as dt
import pandas_datareader.data as web
import pandas as pd
pd.core.common.is_list_like = pd.api.types.is_list_like
# Z-score normalization
def scale(data):
col = data.columns[0]
return (data[col] - data[col].mean()) / data[col].std()
# 전일 Close price와 금일 Clos... | pd.DataFrame() | pandas.DataFrame |
from __future__ import annotations
import re
import warnings
from enum import Enum, auto
from typing import Dict, List, Union, Tuple, Optional
import numpy as np
import pandas as pd
import torch
from ..exceptions import TsFileParseException
from ..utils import stack_pad
class TsTagValuePattern(Enum):
"""
E... | pd.Series(dtype="object") | pandas.Series |
import datetime
import apimoex
import pandas as pd
import requests
from tqdm import tqdm
def get_board_tickers(board={"board": "TQBR", "shares": "shares"}):
"""This function returns list with tickers available on a specific board.
:Input:
:board : dict like {'board': 'TQBR', 'shares': 'shares'},
:Ou... | pd.concat([data, data1], join="outer", axis=1) | pandas.concat |
import pandas as pd
from xml.etree import ElementTree as etree
from pprint import pprint
from yattag import *
import pdb
#------------------------------------------------------------------------------------------------------------------------
class Line:
tierInfo = []
spokenTextID = ""
rootElement = None
t... | pd.DataFrame(e.attrib for e in lineElements) | pandas.DataFrame |
import pandas as pd
import os
import numpy as np
SUMMARY_RESULTS='summaryResults/'
NUM_BINS = 100
BITS_IN_BYTE = 8.0
MILLISEC_IN_SEC = 1000.0
M_IN_B = 1000000.0
VIDEO_LEN = 44
K_IN_M = 1000.0
K_IN_B=1000.0
REBUF_P = 4.3
SMOOTH_P = 1
POWER_RANGE= 648 #Difference between max and min avg power
BASE_POWER_XCOVER=1800.... | pd.read_csv(energy_g_dir) | pandas.read_csv |
import numpy as np
import pandas as pd
import random
from rpy2.robjects.packages import importr
utils = importr('utils')
prodlim = importr('prodlim')
survival = importr('survival')
#KMsurv = importr('KMsurv')
#cvAUC = importr('pROC')
#utils.install_packages('pseudo')
#utils.install_packages('prodlim')
#utils... | pd.get_dummies(long_df, columns=['time_point']) | pandas.get_dummies |
from Bio import AlignIO
import pandas as pd
import os
import sys
# This script makes the file with allele ID similar to bionumerics output
script=sys.argv[0]
base_dir=sys.argv[1]+"/prod_fasta/"
allele_dir=base_dir+"../../all_alleles/"
os.chdir(allele_dir)
dic1={}
def Parse(filename,seqs):
file = open... | pd.read_csv("newTtable1_nogap.csv",index_col=0) | pandas.read_csv |
from datetime import datetime, timedelta
import sys
import fnmatch
import os
import numpy as np
from scipy import io as sio
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
import zarr
from numcodecs import Blosc
from mat_files import masc_mat_file_to_dict,masc_mat_triplet_to_dict,triplet_images... | pd.read_pickle(trainingset_pkl_path+'melting_trainingset_'+cam+'.pkl') | pandas.read_pickle |
"""
Tests the coalescence tree object.
"""
import os
import random
import shutil
import sqlite3
import sys
import unittest
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
from setup_tests import setUpAll, tearDownAll, skipLongTest
from pycoalescence import Simulation
from pycoales... | pd.DataFrame(expected_metacommunity_parameters_list) | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from empiricaldist import Pmf
from scipy.stats import gaussian_kde
from scipy.stats import binom
from scipy.stats import gamma
from scipy.stats import poisson
def values(series):
"""Make a series of values and the number of times they appear... | pd.DataFrame(pmf_seq) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Copyright © Spyder Project Contributors
#
# Licensed under the terms of the MIT License
# (see spyder/__init__.py for details)
# ----------------------------------------------------------------------------
"""
Tes... | pandas.DataFrame(['foo', 'bar']) | pandas.DataFrame |
#!/home/caoy7/anaconda2/envs/py37/bin/python3
#--coding:utf-8--
"""
tracPre.py
Pre-processing code for Hi-Trac data, implemented with cLoops2, from fastq to bedpe files and qc report.
2020-02-27: finished and well tested.
2020-06-30: add linker filter, new stat, and changing mapping to end-to-end
"""
__author__ = "<NA... | pd.DataFrame(data) | pandas.DataFrame |
import copy
import logging
import pandas as pd
import numpy as np
from collections import Counter
from sklearn import preprocessing, utils
import sklearn.model_selection as ms
from scipy.sparse import isspmatrix
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import os
import s... | pd.DataFrame(validate_x) | pandas.DataFrame |
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm
from IPython.core.display import HTML
from fbprophet import Prophet
from fbprophet.plot import plot_plotly
import plotly.offline as py
import plotly.graph_objs as go
import plotly.express as px
class... | pd.Timestamp(self.test_date) | pandas.Timestamp |
# -*- coding: utf-8 -*-
# pylint: disable=E1101,E1103,W0232
import os
import sys
from datetime import datetime
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
import pandas.compat as compat
import pandas.core.common as com
import pandas.util.testing as tm
from pandas import (Categor... | com.is_float_dtype(cat.categories) | pandas.core.common.is_float_dtype |
from __future__ import print_function
import csv
import os
import copy
import numpy as np
import os, sys
from dotenv import load_dotenv, find_dotenv
load_dotenv(find_dotenv())
sys.path.append(os.environ.get("PROJECT_ROOT"))
sys.path.append(os.path.join(os.environ.get("PROJECT_ROOT"), 'test'))
import GPy_1_0_5
import sc... | read_csv(f, sep='\t', header=0, compression='gzip', index_col=0) | pandas.read_csv |
#!python3
"""Module for working with student records and making Students tab"""
import numpy as np
import pandas as pd
from reports_modules.excel_base import safe_write, write_array
from reports_modules.excel_base import make_excel_indices
DEFAULT_FROM_TARGET = 0.2 # default prediction below target grad rate
... | pd.isnull(strat) | pandas.isnull |
import pytest
import pandas as pd
from opendc_eemm.preprocess import aggregate_predictions
from opendc_eemm.visualization import plot_power_draw
@pytest.mark.parametrize('value, expected', [(1, 1)])
def test_test(value, expected):
assert value == expected
def test_aggregate_predictions():
with pytest.raises(V... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""Generate data for examples"""
# author: <NAME>, <NAME>, Duke University; <NAME>, <NAME>
# Copyright Duke University 2020
# License: MIT
import pandas as pd
import numpy as np
def generate_uniform_given_importance(num_control=1000, num_treated=1000,
num... | pd.concat([df2, df1]) | pandas.concat |
import numpy as np
import pandas as pd
from woodwork.column_schema import ColumnSchema
from woodwork.logical_types import Datetime, Double
from featuretools.primitives.base.transform_primitive_base import (
TransformPrimitive
)
from featuretools.primitives.utils import (
_apply_roll_with_offset_gap,
_roll_... | pd.Series(1, index=datetime) | pandas.Series |
#Download and clean nest label series from Zooniverse
import pandas as pd
import geopandas as gpd
from panoptes_client import Panoptes
from shapely.geometry import box, Point
import json
import numpy as np
import os
from datetime import datetime
import utils
def species_from_label(value):
label_dict = {}
label... | pd.DataFrame(rows) | pandas.DataFrame |
#%%
from pathlib import Path
import graspy
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from graspy.embed import *
from graspy.plot import gridplot, heatmap, pairplot
from graspy.utils import *
import pickle
data_dir = Path(".")
data_dir = data_dir / "Cook et al revise... | pd.DataFrame(columns=df.columns) | pandas.DataFrame |
import numpy as nmp
import numpy.random as rnd
import pandas as pnd
import clonosGP.aux as aux
import clonosGP.stats as sts
##
def get_1sample(sampleid = 'S0', weights=(0.65, 0.25, 0.10), z=None, phi=(1.0, 0.5, 0.25), nmuts=100, rho=0.9, mean_depth=1000):
CNm = nmp.ones(nmuts, dtype='int')
CNt = nmp.repeat(2... | pnd.concat({'r': r, 'R': R, 'PHI': phi}, axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import SVR
from sklearn.metrics import r2_score
import statsmodels.api as sm
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model ... | pd.read_csv('xtr.csv') | pandas.read_csv |
import pandas as pd
import requests
import os
import beis_indicators
from beis_indicators.utils.dir_file_management import make_indicator,save_indicator
PROJECT_DIR = beis_indicators.project_dir
TARGET_PATH = f"{PROJECT_DIR}/data/processed/housing"
INTERIM_PATH = f"{PROJECT_DIR}/data/interim/ashe_mean_salary"
# Get... | pd.read_csv(
"https://opendata.arcgis.com/datasets/9b4c94e915c844adb11e15a4b1e1294d_0.csv") | pandas.read_csv |
# load dependencies
import requests
import pandas as pd
import matplotlib.pyplot as plt
from bs4 import BeautifulSoup
from datetime import date, datetime, timedelta
headers = {"User-Agent": "<NAME>. <<EMAIL>>"}
def fetch_ags() -> pd.DataFrame:
"""
Fetch Amtliche Gemeindeschlüssel for Thüringen and return the... | pd.DataFrame.from_records(data=incidences) | pandas.DataFrame.from_records |
#!/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.DataFrame(df_EFR_sorted.iloc[1056:, :]) | pandas.DataFrame |
import numpy as np
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import Categorical, DataFrame, Index, Series
import pandas._testing as tm
class TestDataFrameIndexingCategorical:
def test_assignment(self):
# assignment
df = DataFrame(
... | tm.assert_frame_equal(df, exp_parts_cats_col) | pandas._testing.assert_frame_equal |
# Copyright (c) 2019-2020, NVIDIA CORPORATION.
import datetime as dt
import re
import cupy as cp
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from pandas.util.testing import (
assert_frame_equal,
assert_index_equal,
assert_series_equal,
)
import cudf
from cudf.core import Data... | pd.date_range("2010-01-01", "2010-02-01") | pandas.date_range |
import h5py
import typing
import datetime
import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.preprocessing import OneHotEncoder
from numpy.core._multiarray_umath import ndarray
from model_logging import get_logger
import glob
class DataLoader():
def __init__(
self,
... | pd.DataFrame(year_cycle_y) | pandas.DataFrame |
# Copyright 2020 trueto
# 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 writi... | pd.DataFrame(data=entity_data, columns=['entity', 'label_type']) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
# %load ./imports.py
# %load /Users/bartev/dev/github-bv/sporty/notebooks/imports.py
## Where am I
get_ipython().system('echo $VIRTUAL_ENV')
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:95% !important; }</style>"))
# magics
g... | pd.to_datetime(x['game_date_est']) | pandas.to_datetime |
'''
Run using python from terminal.
Doesn't read from scripts directory (L13) when run from poetry shell.
'''
import pandas as pd
import pandas.testing as pd_testing
import typing as tp
import os
import unittest
from unittest import mock
import datetime
from scripts import influx_metrics_univ3 as imetrics
class Test... | pd.to_datetime(df._time) | pandas.to_datetime |
"""
Copyright 2020 Google LLC
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
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
di... | pd.Series(trace) | pandas.Series |
import pandas as pd
from ismore import brainamp_channel_lists
from ismore.common_state_lists import *
from utils.constants import *
#### BrainAmp-related settings ####
VERIFY_BRAINAMP_DATA_ARRIVAL = True
# print warning if EMG data doesn't arrive or stops arriving for this long
VERIFY_BRAINAMP_DATA_ARRIVAL_TIME = 1 ... | pd.Series(0.0, ismore_pos_states) | pandas.Series |
#!/usr/bin/env python
# coding: utf-8
# ## Problem 2 - Plotting temperatures
#
# In this problem we will plot monthly mean temperatures from the Helsinki-Vantaa airpot for the past 30 years.
#
# ## Input data
#
# File `data/helsinki-vantaa.csv` monthly average temperatures from Helsinki Vantaa airport. Column des... | pd.DataFrame() | pandas.DataFrame |
import cv2
from datetime import datetime
import pandas
#First frame
first_frame = None
status_list = [None, None]
times=[]
df= | pandas.DataFrame(columns= ["Start","End"]) | pandas.DataFrame |
import pdb
import glob
import copy
import os
import pickle
import joblib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats
import sklearn.feature_selection
from random import choices
class FeatureColumn:
def __init__(self, category, field, preprocessors, args=None, cost=Non... | pd.concat(df_proc, axis=1) | pandas.concat |
import os
import sys
import inspect
from copy import deepcopy
import numpy as np
import pandas as pd
from ucimlr.helpers import (download_file, download_unzip, one_hot_encode_df_, xy_split,
normalize_df_, split_normalize_sequence, split_df, get_split, split_df_on_column)
from ucimlr.datase... | pd.read_csv(file_path, keep_default_na=False, header=None) | pandas.read_csv |
#!/usr/bin/env python3
import os
import sys
import pandas as pd
import numpy as np
import argparse
import matplotlib.pyplot as plt
import seaborn as sns
from functools import reduce
from multiprocessing import Pool
from os.path import isfile, join
import shutil
import warnings
from pathlib import Path
import time
warn... | pd.read_csv(recurr_5_yr_filename,dtype={'feature_id': str}) | pandas.read_csv |
"""Tests for the sdv.constraints.tabular module."""
import uuid
from datetime import datetime
from unittest.mock import Mock
import numpy as np
import pandas as pd
import pytest
from sdv.constraints.errors import MissingConstraintColumnError
from sdv.constraints.tabular import (
Between, ColumnFormula, CustomCon... | pd.to_datetime(['2020-01-01T00:00:00', '2020-01-02T00:00:00']) | pandas.to_datetime |
from typing import List
import plotly.graph_objects as go
from fhir.resources.resource import Resource
import pandas as pd
def plot_resource_field(resources: List[Resource], field: str, title: str = None, plot_type: str = "bar",
show: bool = True) -> go.Figure:
"""
Plot a field of a re... | pd.Series(values) | pandas.Series |
from django.shortcuts import render
from django.http import HttpResponse
from django.views import View
import pytz
import numpy as np
from datetime import datetime, time
import pandas as pd
import os, subprocess, psutil
from django.conf.urls.static import static
from . forms import SubmitTickerSymbolForm
... | pd.read_csv(csvPathForex) | pandas.read_csv |
# -*- coding: utf-8 -*-
import click
import json
import shutil
import logging
from pathlib import Path
from functools import partial
from dotenv import find_dotenv, load_dotenv
import pandas as pd
@click.group()
def main():
pass
@main.command()
@click.argument('input_filepath', type=click.Path(exists=True, dir... | pd.read_csv(input_filepath, header=None, names=columns) | pandas.read_csv |
import Bio.SeqIO, os
import pandas as pd
import sys, time, regex
from tqdm import tqdm
start = time.time()
def main():
sAnalysis_Tag = '63_GS_PE off-target_283T_2_1rxn_220118'
BaseDIR = r'C:\Users\home\Desktop\220128_miniseq'
FASTQ_file = r'%s\%s\%s.fastq' % (BaseDIR, sAnalysis_Tag, sAnalysis_Tag.split(... | pd.Series(val) | pandas.Series |
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# 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 a... | pd.testing.assert_series_equal(result, expected) | pandas.testing.assert_series_equal |
"""
This script is for exploring the implementation of DeepAR in GluonTS
"""
import json, itertools, os
import streamlit as st
import mxnet as mx
from mxnet import gluon
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from gluonts.transform import FieldName
from gluonts.dataset.common import List... | pd.DataFrame() | pandas.DataFrame |
""" a light-weight aligner"""
import os
import sys
from pathlib import Path
import tkinter as tk
from tkinter import messagebox
# pylint: disable=unused-import
_ = """
from jinja2 import ( # type: ignore # noqa: F401
PackageLoader,
Environment,
ChoiceLoader,
FileSystemLoader,
)
# """ # pyinstaller... | pd.DataFrame(slist) | pandas.DataFrame |
import time
import random
import numpy as np
import pandas as pd
import hdbscan
import sklearn.datasets
from sklearn import metrics
from classix import CLASSIX
from sklearn.cluster import KMeans
from sklearn.cluster import DBSCAN
from sklearn import preprocessing
from tqdm import tqdm
from sklearn.cluster import MeanSh... | pd.DataFrame(kamil_timing) | pandas.DataFrame |
# Robust Bayesian Binary logistic regression in 1d for iris flowers
# Code is based on
# https://github.com/aloctavodia/BAP/blob/master/code/Chp4/04_Generalizing_linear_models.ipynb
import superimport
import pymc3 as pm
import numpy as np
import pandas as pd
import theano.tensor as tt
#import seaborn as sns
import ... | pd.Categorical(df['species']) | pandas.Categorical |
import pandas as pd
import numpy as np
import requests
import random
import urllib
import json
import time
import sys
import datetime
from datetime import date
from bs4 import BeautifulSoup
from selenium import webdriver
from discourse_ordering import DiscourseOrderingClass
from twitter_api import TwitterClass
import o... | pd.merge(df_selecionados, df_estatisticas, how='left', on='cidade') | pandas.merge |
import glob
import os
import re
import pandas as pd
import numpy as np
from collections import Counter
path = "files/"
files = glob.glob(path+"*.txt")
# first we need a list of all words in all files.
finalDataframe = pd.DataFrame()
for file in files:
with open(file, mode="r") as f:
data = f.read()
... | pd.crosstab(finalDataframe['values'], finalDataframe['filename'], margins=True) | pandas.crosstab |
# ---------------------------------------------------------------------------------------------
# MIT License
# Copyright (c) 2020, Solace Corporation, <NAME> (<EMAIL>)
# ---------------------------------------------------------------------------------------------
import array as arr
import json
from .broker_series i... | pd.DataFrame(data={"sample":value}) | pandas.DataFrame |
import json
import numpy as np
import os
import pandas as pd
import urllib2
def collectData():
# connect to poloniex's API
url = 'https://poloniex.com/public?command=returnChartData¤cyPair=USDT_BTC&start=1518393227&end=9999999999&resolution=auto'
# parse json returned from the API to Pandas DF
openUrl =... | pd.DataFrame(d) | pandas.DataFrame |
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... | Timestamp('2017-12-31') | pandas.Timestamp |
"""
Code for the optimization and gaming component of the Baselining work.
@author: <NAME>, <NAME>
@date Mar 2, 2016
"""
import numpy as np
import pandas as pd
import logging
from gurobipy import GRB, Model, quicksum, LinExpr
from pandas.tseries.holiday import USFederalHolidayCalendar
from datetime import datetime
f... | pd.Series(dcharges, index=indx) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 22 15:36:17 2019
@author: fgw
"""
import pandas as pd
from collections import deque
from sklearn.preprocessing import MinMaxScaler
class Data_Process(object):
def __init__(self, data_path):
data = | pd.read_csv(data_path, sep=',') | pandas.read_csv |
from sklearn.naive_bayes import GaussianNB
from sklearn import svm
from sklearn.neighbors import KNeighborsClassifier
from .naive_bayes import NaiveBayes
import pandas as pd
import numpy as np
from scipy.stats import mode
from matplotlib import pyplot
from mpl_toolkits.mplot3d import Axes3D
import random
np.seterr... | pd.DataFrame(data) | pandas.DataFrame |
from collections import OrderedDict
import datetime as dt
import itertools
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sys
import xarray as xr
def _raiseException(prefix, msg):
sys.tracebacklimit = None
raise(Exception('[OSMPythonTools.' + prefix + '] ' + msg))
def dictRange(... | pd.concat(arg2, axis=1) | pandas.concat |
from __future__ import annotations
import copy
import itertools
from typing import (
TYPE_CHECKING,
Sequence,
cast,
)
import numpy as np
from pandas._libs import (
NaT,
internals as libinternals,
)
from pandas._libs.missing import NA
from pandas._typing import (
ArrayLike,
DtypeObj,
M... | is_1d_only_ea_dtype(empty_dtype) | pandas.core.dtypes.common.is_1d_only_ea_dtype |
import vectorbt as vbt
import numpy as np
import pandas as pd
from numba import njit
from datetime import datetime
import pytest
from vectorbt.signals import nb
seed = 42
day_dt = np.timedelta64(86400000000000)
index = pd.Index([
datetime(2020, 1, 1),
datetime(2020, 1, 2),
datetime(2020, 1, 3),
date... | pd.Series.vbt.signals.generate((5, 2), choice_func_nb, 1) | pandas.Series.vbt.signals.generate |
from Bio import Entrez
import numpy
import pandas
from urllib.error import HTTPError
import os
import re
import lxml.etree
import datetime
import time
def check_config_dir(args):
files = os.listdir(args.config_dir)
asserted_files = [
'group_attribute.config',
'group_tissue.config',
'... | pandas.DataFrame() | pandas.DataFrame |
import igraph as Graph
import pandas as pd
import os
import numpy as np
import spacy
from sklearn.cluster import KMeans
from pylab import *
import re
import time
import src.pickle_handler as ph
import src.relation_creator as rc
# the dataframe has been preprocessed by many other functions. However we only need a subs... | pd.read_csv(file) | pandas.read_csv |
# License: BSD_3_clause
#
# Copyright (c) 2015, <NAME>, <NAME>, <NAME>
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# Redistributions of source code must retain the above copyright
# notice, this list of conditio... | pd.to_datetime(data_observations_cf.Time) | pandas.to_datetime |
# This code extract the features from the raw joined dataset (data.csv)
# and save it in the LibSVM format.
# Usage: python construct_features.py
import pandas as pd
import numpy as np
from sklearn.datasets import dump_svmlight_file
df = pd.read_csv("data.csv", low_memory=False)
# NPU
NPU = df.NPU.copy()
NPU[NPU ==... | pd.concat([LotSize, LotSize_zero], axis=1) | pandas.concat |
import pandas as pd
import numpy as np
from pandas._testing import assert_frame_equal
from NEMPRO import planner, units
def test_start_off_with_initial_down_time_of_zero():
forward_data = pd.DataFrame({
'interval': [0, 1, 2],
'nsw-energy': [200, 200, 200]})
p = planner.DispatchPlanner(dispatc... | assert_frame_equal(expect_dispatch, dispatch) | pandas._testing.assert_frame_equal |
import warnings
import numpy as np
import pytest
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
Index,
Series,
TimedeltaIndex,
Timestamp,
date_range,
period_range,
timedelta_range,
)
import pandas._testing as tm
from pandas.core.arrays import PeriodArray
from panda... | timedelta_range("1 days", "10 days") | pandas.timedelta_range |
# -*- 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(result, expected) | pandas.util.testing.assert_panel_equal |
from datetime import timedelta
from functools import partial
import itertools
from parameterized import parameterized
import numpy as np
from numpy.testing import assert_array_equal, assert_almost_equal
import pandas as pd
from toolz import merge
from zipline.pipeline import SimplePipelineEngine, Pipeline, CustomFacto... | pd.Timestamp("2015-01-01", tz="UTC") | pandas.Timestamp |
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