prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# -*- coding: utf-8 -*-
"""System unserved energy plots.
This module creates unserved energy timeseries line plots and total bar
plots and is called from marmot_plot_main.py
@author: <NAME>
"""
import logging
import pandas as pd
import marmot.config.mconfig as mconfig
from marmot.plottingmodules.plotutils.plot_lib... | pd.DataFrame() | pandas.DataFrame |
# coding=utf-8
import os
import sys
# === import project path ===
curPath = os.path.abspath(os.path.dirname(__file__))
rootPath = os.path.split(curPath)[0]
sys.path.append(rootPath)
# ===========================
import base.default_excutable_argument as dea
import pandas as pd
from PIL import Image
import os... | pd.concat(sample_pd_collection, ignore_index=True) | pandas.concat |
import numpy as np
import pandas as pd
import argparse
import os
# Takes in list from decode.py
# Generates data required for next round of translation
parser = argparse.ArgumentParser()
parser.add_argument('--input', type=str, required=True) # New pairs from decode.py
parser.add_argument('--output', type=str, requir... | pd.read_csv(args.old_pairs,delimiter=' ',header=None) | pandas.read_csv |
#python script to convert a croesus rebalancing output to a national bank independent network
#mutual fund trade list.
import pandas as pd
import numpy as np
import argparse
import sys
import datetime as dt
import os
from enum import Enum
from pathlib import Path, PureWindowsPath
pd.set_option('display.min_rows', 100... | pd.to_numeric(df[mvsc]) | pandas.to_numeric |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 28 14:35:23 2018
@author: kazuki.onodera
"""
import os
import pandas as pd
import gc
from multiprocessing import Pool
from glob import glob
import utils
utils.start(__file__)
#========================================================================... | pd.merge(train, base, on=KEY, how='left') | pandas.merge |
"""This module is the **core** of `FinQuant`. It provides
- a public class ``Stock`` that holds and calculates quantities of a single stock,
- a public class ``Portfolio`` that holds and calculates quantities of a financial
portfolio, which is a collection of Stock instances.
- a public function ``build_portfolio()`... | pd.DataFrame() | pandas.DataFrame |
# Copyright (C) 2019-2020 Zilliz. All rights reserved.
#
# 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... | pd.DataFrame([(buffer,)],["buffer"]) | pandas.DataFrame |
import pandas as pd
import geopandas as gpd
def query_df(df, att, val):
val = '\''+val+'\'' if isinstance(val, str) else val
return df.query( f" {att} == {val} " )
def gdf_concat(lst):
return gpd.GeoDataFrame( | pd.concat(lst) | pandas.concat |
#!/usr/bin/env python
"""
Copyright 2018 by <NAME> (alohawild) and <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
Unle... | pd.read_csv('cities.csv') | pandas.read_csv |
from collections import Counter
import pandas as pd
from aa.unit import Army
from .battle import Battle, LandBattle
from .utils import battle_factory
new_battle = battle_factory(army_cls=Army, battle_cls=Battle)
new_land_battle = battle_factory(army_cls=Army, battle_cls=LandBattle)
def simulate_battles(battle_confi... | pd.DataFrame(stats) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import re
import pandas as pd
import scrapy
from scrapy import Request
from scrapy import Selector
from scrapy import signals
from fooltrader.api.quote import get_security_list
from fooltrader.consts import DEFAULT_KDATA_HEADER
from fooltrader.contract.files_contract import get_finance_report... | pd.Timestamp(report_event_date) | pandas.Timestamp |
#---------------------------------------------------------------------
# File Name : LogisticRegression2.py
# Author : <NAME>.
# Description : Implementing Logistic Regression
# Date: : 12 Nov. 2020
# Version : V1.0
# Ref No : DS_Code_P_K07
#--------------------------------------------------------... | pd.crosstab(y_pred,Y) | pandas.crosstab |
# -*- coding: utf-8 -*-
"""
Created on Sun Jan 16 10:27:53 2022
@author: dariu
"""
import numpy as np
import pandas as pd
import os
from tqdm import tqdm
import pacmap
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
import umap
from sklearn.cluster import KMeans
from sklearn.cluster import DBSCAN... | pd.concat(dfs) | pandas.concat |
import pytest
from pandas._libs.tslibs.frequencies import INVALID_FREQ_ERR_MSG, _period_code_map
from pandas.errors import OutOfBoundsDatetime
from pandas import Period, Timestamp, offsets
class TestFreqConversion:
"""Test frequency conversion of date objects"""
@pytest.mark.parametrize("freq", ["A", "Q", ... | Period(freq="Min", year=2007, month=1, day=1, hour=0, minute=0) | pandas.Period |
# FLOWDO
# FlowDo is an application created for the purpose of managing business activities like Inventory Maintenance, Billing, Sales analysis and other business functions.
# Developed by:
# <NAME> (@Moulishankar10)
# <NAME> (@ToastCoder)
# REQUIRED MODULES
import numpy as np
import pandas as pd
from datetime impor... | pd.read_csv('data/revenue.csv') | pandas.read_csv |
from bs4 import BeautifulSoup, element as bs4_element
import numpy as np
import pandas as pd
import re
import requests
from typing import Optional
from .readers import parse_oakland_excel
from ..caada_typing import stringlike
from ..caada_errors import HTMLParsingError, HTMLRequestError
from ..caada_logging import l... | pd.DataFrame(df_dict, index=index) | pandas.DataFrame |
import os
import configparser
import pandas as pd
import numpy as np
import psycopg2
import psycopg2.extras
# Set up GCP API
from google.cloud import bigquery
# Construct a BigQuery client object.
client = bigquery.Client()
import sql_queries as sql_q
def convert_int_zipcode_to_str(df, col):
"""
Converts i... | pd.read_gbq(acs_data_query) | pandas.read_gbq |
import copy
import json
import numpy as np
import pandas as pd
import re
import sklearn.dummy
import sklearn.ensemble
import sklearn.linear_model
import sklearn.model_selection
import sklearn.neighbors
import sklearn.neural_network
import sklearn.pipeline
import sklearn.preprocessing
import sklearn.svm
import sklearn.u... | pd.DataFrame(X, index=rownames, columns=colnames) | pandas.DataFrame |
import operator
import numpy as np
import pandas as pd
import staircase as sc
from staircase.core.ops import docstrings
from staircase.core.ops.common import _combine_stairs_via_values, requires_closed_match
from staircase.util import _sanitize_binary_operands
from staircase.util._decorators import Appender
def _ma... | pd.api.types.is_float_dtype(s2) | pandas.api.types.is_float_dtype |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2017 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-... | pd.DataFrame() | pandas.DataFrame |
import os
import pandas as pd
import json
import csv
from torch.utils.data import Dataset, DataLoader
import nlpaug.augmenter.word as naw
def create_en_dataset(txt_path, save_path, language='en'):
patient = []
doctor = []
if language == 'en':
with open(txt_path, 'r') as f:
lines = f.... | pd.read_csv(csv) | pandas.read_csv |
from collections import OrderedDict
import pydoc
import warnings
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
Index,
Series,
TimedeltaIndex,
date_range,
period_range,
timedelta_range,
)
from pandas.core.arrays impo... | Series([1, 2, np.nan]) | pandas.Series |
import pytest
from pandas import Interval, DataFrame
from pandas.testing import assert_frame_equal
from datar.base.funs import *
from datar.base import table, pi, paste0
from datar.stats import rnorm
from .conftest import assert_iterable_equal
def test_cut():
z = rnorm(10000)
tab = table(cut(z, breaks=range(-... | Interval(0, 1, closed='right') | pandas.Interval |
# coding: utf-8
import os
import pandas as pd
from tqdm import tqdm
from czsc.objects import RawBar, Freq
from czsc.utils.bar_generator import BarGenerator, freq_end_time
from test.test_analyze import read_1min
cur_path = os.path.split(os.path.realpath(__file__))[0]
kline = read_1min()
def test_freq_end_time():
... | pd.to_datetime("2021-11-11 09:43") | pandas.to_datetime |
from sklearn.model_selection import train_test_split
import os
import shutil
import zipfile
import logging
import pandas as pd
import glob
from tqdm import tqdm
from pathlib import Path
from ..common import Common
from ..util import read_img, get_img_dim
from .dataset import Dataset
# All dataset pars... | pd.read_csv(self.annot_file, sep=",", header=0, usecols=wanted_cols) | pandas.read_csv |
import math
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime
#from mpmath import mp
'''
cw.hist_nco(220,1,1e-2,0,90,-90,90,1,3000)
xh,yh,zh -> x(t) homogeneo,y(t) homogeneo,z(t) homogeneo, (Tempo de simulacao[s])
vxh,vyh,vzh -> vxh(t) homogeneo,vyh(t) homogeneo,vzh(t) homogen... | pd.DataFrame({'TEMPO': tempo, 'X[t]': xt, 'Y[t]': yt, 'Z[t]': zt, 'VXT': vxt, 'VYT': vyt, 'VZT': vzt, 'R[t]': r, 'V[t]': v}) | pandas.DataFrame |
"""Base classes for data management."""
# Authors: <NAME> <<EMAIL>>
# <NAME>
# License: MIT
import numpy as np
import pandas as pd
from .extraction import activity_power_profile
from .io import bikeread
from .utils import validate_filenames
class Rider(object):
"""User interface for a rider.
User... | pd.DateOffset(1) | pandas.DateOffset |
# *****************************************************************************
#
# Copyright (c) 2020, the pyEX authors.
#
# This file is part of the pyEX library, distributed under the terms of
# the Apache License 2.0. The full license can be found in the LICENSE file.
#
from functools import wraps
import numpy as... | pd.DataFrame(e) | pandas.DataFrame |
#-*- coding: utf-8 -*-
import sys
import random
import numpy as np
import pandas as pd
import utility_1
import h5py
import json
eps=1e-12
def countCG(strs):
strs = strs.upper()
return float((strs.count("C")+strs.count("G")))/(len(strs))
def countCG_N(strs):
strs = strs.upper()
return float((strs.count("C")+strs... | pd.read_csv(filename1,sep='\t',header=header) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""Cross references from cbms2019.
.. seealso:: https://github.com/pantapps/cbms2019
"""
import pandas as pd
from pyobo.constants import (
PROVENANCE,
SOURCE_ID,
SOURCE_PREFIX,
TARGET_ID,
TARGET_PREFIX,
XREF_COLUMNS,
)
__all__ = [
"get_cbms2019_xrefs_df",
]
#: C... | pd.read_csv(all_to_all, sep="\t", usecols=["SNOMEDCT_ID", "ICD10CM_ID", "MESH_ID"]) | pandas.read_csv |
import dataiku
from dataiku.customrecipe import *
from dataiku import pandasutils as pdu
import pandas as pd, numpy as np
import cvxpy
# Retrieve input and output dataset names
input_dataset_name = get_input_names_for_role('input_dataset')[0]
output_dataset_name = get_output_names_for_role('output_dataset')[0]
# Retr... | pd.DataFrame(output_data, columns=output_cols) | pandas.DataFrame |
import sys
import warnings
warnings.filterwarnings("ignore")
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
import pandas as pd
from mgefinder import bowtie2tools
from mgefinder.inferseq import InferSequence
import click
from o... | pd.DataFrame(columns=['pair_id', 'method', 'loc', 'inferred_seq_length', 'inferred_seq']) | pandas.DataFrame |
import os, copy
import numpy
import pandas as pd
import utils.data_connection.constant_variables_db as cons
from utils.data_connection.api_data_manager import APISourcesFetcher
from datetime import datetime, timedelta
from unittest import TestCase, mock
from active_companies.src.models.train.active_companies_algorithm... | pd.DataFrame.equals(self.classifier.users, self.classifier.viable_users) | pandas.DataFrame.equals |
# -*- 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(df, expected) | pandas.util.testing.assert_frame_equal |
import numpy as np
import pandas as pd
array1 = [1,2,3,4,5,6,7,8]
array2 = [1,3,5,7,9,11,13,15]
ds_array1 = pd.Series(array1)
ds_array2 = | pd.Series(array2) | pandas.Series |
# -*- coding: utf-8 -*-
import pandas as pd
import itertools
import argparse
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument("--outdir", nargs='?', type=str, default="tmp_results", help="input output directory")
parser.add_argument("--input", nargs='... | pd.concat([depth0, depth1], axis=0) | pandas.concat |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sn
# 设置最大显示行数为777
pd.options.display.max_columns = 777
# 读取Excel
students_one = | pd.read_excel('./TestExcel/AlbertData.xlsx',sheet_name='Sheet1',index_col='Index') | pandas.read_excel |
# ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# --------------------------------------------... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import pickle
import time
import random
import os
from sklearn import linear_model, model_selection, ensemble
from sklearn.svm import SVC
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.base import clone
from sklearn import metrics
from sklearn.model_selectio... | pd.concat(perm_fimps_dfs) | pandas.concat |
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsRegressor
datos_originales = pd.read_csv('bones_mineral_density.csv')
datos = datos_originales[['age', 'gender', 'spnbmd']]
datos_ma... | pd.DataFrame(x_female) | pandas.DataFrame |
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer, SnowballStemmer
from nltk.stem.porter import *
import string
from operator import itemgetter
# Importing Gensim
import gensim
from gensim import corpora, models
from gensim.models.coherencemodel import CoherenceModel
import pandas as pd
clas... | pd.DataFrame(topics, columns = ["topic","word","prop"]) | pandas.DataFrame |
import pandas as pd
import pytest
from rdtools.normalization import normalize_with_expected_power
from pandas import Timestamp
import numpy as np
@pytest.fixture()
def times_15():
return pd.date_range(start='20200101 12:00', end='20200101 13:00', freq='15T')
@pytest.fixture()
def times_30():
return pd.date_... | Timestamp('2020-01-01 12:15:00', freq='15T') | pandas.Timestamp |
"""
Load the data from different cohorts separately.
"""
import logging
import torch
import numpy as np
import pandas as pd
from sklearn import preprocessing
from sklearn.metrics.pairwise import cosine_similarity
from statsmodels import robust
from torch_geometric.utils import dense_to_sparse, to_dense_batch
from torc... | pd.read_table(test_file, sep=' ', header=0) | pandas.read_table |
import pandas as pd
import time
from bs4 import BeautifulSoup
import requests
import sys
import random
def get_headers():
"""
Genera un diccionario con los datos del header. Incluye una lista de diferentes user agent de la cual elige uno
de manera aleatoria.
"""
uastrings = [
"Mozilla/5.0 ... | pd.Series(elem) | pandas.Series |
import streamlit as st
from ml_api.ml import QuestionGenerationAPI
import spacy
from spacy.pipeline import EntityRuler
from spacy import displacy
from collections import defaultdict
import pandas as pd
# https://qiita.com/irisu-inwl/items/9d49a14c1c67391565f8
@st.cache(allow_output_mutation=True)
def load_ml(ml):
... | pd.DataFrame(ner_questions, columns=['ent', 'label', 'start', 'end', 'question']) | pandas.DataFrame |
import pandas as pd
import numpy as np
import os
from scipy.spatial import distance
import networkx as nx
import math
import scipy.sparse as sp
from glob import glob
import argparse
import time
parser = argparse.ArgumentParser(description='Main Entrance of MP_MIM_RESEPT')
parser.add_argument('--sampleName'... | pd.DataFrame({'embedding_name':embedding_name_list,'embedding_MP_MIM':embedding_MIrow_max_list}) | pandas.DataFrame |
import time
import warnings
import pandas as pd
import numpy as np
from collections import defaultdict
from sklearn.dummy import DummyClassifier, DummyRegressor
from sklearn.preprocessing import QuantileTransformer
from sklearn.compose import TransformedTargetRegressor
from sklearn.metrics import make_scorer
from sklea... | pd.concat([models, dummy], axis=0) | pandas.concat |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author : Mike
# @Contact : <EMAIL>
# @Time : 2020/1/6 22:46
# @File : cross_feature.py
"""
top100的特征强制相除交叉
"""
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
import lightgbm as lgb
import nu... | pd.read_csv('../data/trend_feature.csv') | pandas.read_csv |
import pandas as pd
from Data_BCG.Download_Data import scraping_Functions as sf
# performance = sf.get_aggregated_season_data(1980)
per_game_data = sf.get_game_data(2009)
birthplaces = sf.get_birthplaces()
high_schools = sf.get_high_school_cities()
player_id = sf.get_players_id()
# Standarizing each database... | pd.merge(per_game_data, birthplaces, how="inner", on="player") | pandas.merge |
# -*- coding: utf-8 -*-
from numpy import where as npWhere
from pandas import DataFrame
from pandas_ta.overlap import hlc3, ma
from pandas_ta.utils import get_drift, get_offset, non_zero_range, verify_series
def kvo(high, low, close, volume, fast=None, slow=None, length_sig=None, mamode=None, drift=None, offset=None,... | DataFrame(data) | pandas.DataFrame |
#params aaaaa
import numpy as np
from copy import copy , deepcopy
from collections import Iterable, Collection
import pandas as pd
import random
DEBUG = False
class InvalidArguments(Exception) :
def __init__(self, err="invalid arguments") :
self.err = err
def __str__(self) :
return self.err
c... | pd.Series(self.chromosome) | pandas.Series |
import datetime
import pandas as pd
from pandas import DataFrame, Series
from pandas.api.extensions import ExtensionArray, ExtensionDtype
from pandas.api.extensions import register_extension_dtype
from qapandas.base import QAPandasBase
from enum import Enum
import numpy as np
class QACode(Enum):
orig = 0
au... | Series.__repr__(self) | pandas.Series.__repr__ |
import pandas as pd
import numpy as np
import matplotlib.colors
import matplotlib.pyplot as plt
import seaborn as sns
def get_substrate_info(substrate_string, colname, carbo_df):
"""Get values in a column of the carbohydrates spreadsheet based on a string-list of substrates.
Parameters:
substrate_stri... | pd.isna(substrate_string) | pandas.isna |
# -*- coding: utf-8 -*-
"""Aggregating feed-in time series for the model regions.
SPDX-FileCopyrightText: 2016-2019 <NAME> <<EMAIL>>
SPDX-License-Identifier: MIT
"""
__copyright__ = "<NAME> <<EMAIL>>"
__license__ = "MIT"
# Python libraries
import logging
import os
import datetime
# External libraries
import pandas... | pd.DataFrame(columns=end_energy_table.columns) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 5 10:15:25 2021
@author: lenakilian
"""
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import geopandas as gpd
ghg_year = 2015 # 2017
wd = r'/Users/lenakilian/Documents/Ausbildung/UoLeeds/PhD/Analysi... | pd.read_csv(wd + 'data/raw/Income_Data/equivalised_income_2017-18.csv', header=4, encoding='latin1') | pandas.read_csv |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
def main():
df = | pd.read_csv('../../data/complete_df_7.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
# 検体検査結果データ（患者ごと）の読み込みと検体検査結果データ（検査項目ごと）の出力
# └→RS_Base_laboファイル
#
# 入力ファイル
# └→患者マスターファイル ：name.csv
# └→検体検査結果データファイル：患者ID.txt（例：101.txt,102.txt,103.txt・・・）
#
# Create 2017/07/09 : Update 2017/07/09
# Auther Katsumi.Oshiro
import csv # csvモジュールの読み込み（CSVファイルの読み書き）
import glob... | pd.to_datetime('today') | pandas.to_datetime |
__author__ = 'qchasserieau'
import json
import time
import warnings
from random import random
import numpy as np
import pandas as pd
import pyproj
import requests
import shapely
from tqdm import tqdm
try:
from geopy.distance import geodesic # works for geopy version >=2
except ImportError:
warnings.warn('Yo... | pd.DataFrame(cluster_series) | pandas.DataFrame |
import collections
import pandas as pd
big_list = [[{'автопродление': 1},
{'аккаунт': 1},
{'акция': 2},
{'безумный': 1},
{'бесплатно': 1},
{'бесплатнои': 1},
{'бесплатныи': 1},
{'бесплатный': 1},
{'бесценок': 1},
{'билет': 2},
{'бритва': 1},
{'бритвеныи': 1},
{'важный': 2},
{'вводить': 1},
{... | pd.DataFrame(counter, columns=["Word", "Count"]) | pandas.DataFrame |
import pandas as pd
import xlsxwriter
with open("authors_qcr.txt", encoding='utf-8') as f:
x = f.readlines()
s = []
for i in x:
s.append(i)
#clean_file.write(j)
print(s)
data = pd.DataFrame(s)
data2excel = | pd.ExcelWriter("wordcloud_test.xlsx", engine='xlsxwriter') | pandas.ExcelWriter |
import os
import shutil
from attrdict import AttrDict
import numpy as np
import pandas as pd
from scipy.stats import gmean
from deepsense import neptune
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split, KFold, StratifiedKFold
from . import pipeline_config as cfg
from .pip... | pd.read_csv(params.POS_CASH_balance_filepath, nrows=nrows_pos_cash_balance) | pandas.read_csv |
import streamlit as st
import pandas as pd
import base64
import os
import datetime
import sqlalchemy as sa
from pathlib import Path
import psycopg2
#creating sql alchemy engine
engine = sa.create_engine('postgresql://xiamtznyktfwmk:<EMAIL>:5432/dekfhtva5ndr6b',echo=False)
def check_if_weekend(today):
try:
... | pd.DataFrame(input_data) | pandas.DataFrame |
import random
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
DataFrame,
NaT,
Timestamp,
date_range,
)
import pandas._testing as tm
class TestDataFrameSortValues:
def test_sort_values(self):
frame = DataFrame(
[[1, 1, 2], [3, 1, 0], ... | tm.assert_frame_equal(df, expected) | pandas._testing.assert_frame_equal |
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import numpy as np
import os
import pandas as pd
import seaborn as sns
from sklearn.metrics import confusion_matrix
def CM(y, y_pred, labels, save_path=None, verbose=True):
cm = confusion_matrix(y, y_pred)
if verbose... | pd.concat([tagore0, tagore1]) | pandas.concat |
import matplotlib.pyplot as plt
import pandas as pd
def plot_transactions_by_hour(transactions_by_hour):
"""
Generates a bar plot for transactions by hour
"""
plt.figure()
avg_block_sizes_df = | pd.Series(transactions_by_hour) | pandas.Series |
import pandas as pd
import pickle
import argparse
import numpy as np
from plotnine import ggplot,theme_bw,scale_alpha_manual,guides,scale_size_manual,guide_legend,element_rect,element_line, ggsave,scale_color_brewer,annotate,element_blank, element_text, scale_x_discrete,scale_y_continuous, aes,theme, facet_grid, labs, ... | pd.DataFrame({'name': ['APS', 'APS', 'APS'], 'Coverage': [lower_quantiles_mean[0, 0], lower_quantiles_mean[1, 0], lower_quantiles_mean[2, 0]], 'Position': [' ', ' ', ' '], 'Model': ['DenseNet', 'ResNet', 'VGG']}) | pandas.DataFrame |
from linescanning.plotting import LazyPlot
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy.interpolate import interp1d
import seaborn as sns
from nilearn.glm.first_level import first_level
from nilearn.glm.first_level import hemodynamic_models
from nilearn import plotting
im... | pd.concat([X_matrix, regressors_df], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
"""
These the test the public routines exposed in types/common.py
related to inference and not otherwise tested in types/test_common.py
"""
from warnings import catch_warnings, simplefilter
import collections
import re
from datetime import datetime, date, timedelta, time
from decimal import De... | is_scalar(zerodim) | pandas.core.dtypes.common.is_scalar |
import pandas as pd
import numpy as np
import datetime
import calendar
from math import e
from brightwind.analyse import plot as plt
# noinspection PyProtectedMember
from brightwind.analyse.analyse import dist_by_dir_sector, dist_12x24, coverage, _convert_df_to_series
from ipywidgets import FloatProgress
from IPython.d... | pd.Series([]) | pandas.Series |
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
plt.rcParams['font.size'] = 6
import os
root_path = os.path.dirname(os.path.abspath('__file__'))
graphs_path = root_path+'/boundary_effect/graph/'
if not os.path.exists(graphs_path):
os.makedirs(graphs_path)
time = pd.read_csv(root_p... | pd.read_csv(root_path+"/Huaxian_ssa/data/SSA_FULL.csv") | pandas.read_csv |
# -*- 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,
... | isnull(idx) | pandas.core.dtypes.missing.isnull |
import pandas as pd
from abc import ABC
from geopandas import GeoDataFrame
from carto.do_dataset import DODataset
from . import subscriptions
from ....utils.geom_utils import set_geometry
from ....utils.logger import log
_DATASET_READ_MSG = '''To load it as a DataFrame you can do:
df = pandas.read_csv('{}')
'''... | pd.DataFrame([item.data for item in self]) | pandas.DataFrame |
import math
import queue
from datetime import datetime, timedelta, timezone
import pandas as pd
from storey import build_flow, SyncEmitSource, Reduce, Table, AggregateByKey, FieldAggregator, NoopDriver, \
DataframeSource
from storey.dtypes import SlidingWindows, FixedWindows, EmitAfterMaxEvent, EmitEveryEvent
tes... | pd.Timestamp('2021-05-30 17:09:15.806000+0000', tz='UTC') | pandas.Timestamp |
__author__ = "<NAME>, <EMAIL>, <EMAIL>"
__date__ = "January 1, 2021 10:00:00 AM"
import os
import subprocess
from time import strftime
import numpy as np
import pandas as pd
from PIL import Image
import matplotlib.pyplot as plt
from scipy import stats
from keras.models import load_model
from keras.preprocessing import... | pd.read_csv(class_file,sep='\t') | pandas.read_csv |
import tkinter as tk
from tkinter import *
from tkinter import ttk
import data
from tkinter import messagebox
import pandas as pd
from random import randint
## Cores
color1 = '#ffffff'
color2 = '#7CEBEA'
color3 = '#D6EB4D'
color4 = '#AB4DEB'
color5 = '#EB8F59'
selected ='#66d1d0'
## lista da combobox de mercados
me... | pd.DataFrame(condor, columns=["Código", "Produto", "Média", "Qtd"]) | pandas.DataFrame |
from __future__ import print_function
_README_ = '''
-------------------------------------------------------------------------
Generate JSON files for GBE decomposition page.
-p option outputs python numpy npz file (compressed format) for python
Author: <NAME> (<EMAIL>)
Date: 2017/12/01
------------------------------... | pd.DataFrame(contribution_var) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Covid-19 em São Paulo
Gera gráficos para acompanhamento da pandemia de Covid-19
na cidade e no estado de São Paulo.
@author: https://github.com/DaviSRodrigues
"""
from datetime import datetime, timedelta
from io import StringIO
import locale
import math
from tableauscraper import TableauS... | pd.read_csv(URL, sep=';', decimal=',') | pandas.read_csv |
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimator
class PropensityScoreEstimator(CausalEstimator):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# We need to initialize the model when we create any propensity score estima... | pd.get_dummies(self._observed_common_causes, drop_first=True) | pandas.get_dummies |
# %% [markdown]
# pip install -r pykrx
# %%
from datetime import datetime, timedelta
import FinanceDataReader as fdr
import yfinance as yf
import numpy as np
import pandas as pd
from pykrx import stock
import time
import bt
import warnings
# from tqdm import tqdm
warnings.filterwarnings(action='ignore')
# pd.options.d... | pd.DateOffset(years=1) | pandas.DateOffset |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import operator
from itertools import product, starmap
from numpy import nan, inf
import numpy as np
import pandas as pd
from pandas import (Index, Series, DataFrame, isnull, bdate_range,
NaT, date_range, ti... | Series([20, 30, 40]) | pandas.Series |
#!/usr/bin/env python3
# coding: utf-8
import sys
import pickle
import sklearn
import numpy as np
import pandas as pd
def load_sepsis_model():
with open('nbclf.pkl','rb') as f:
clf = pickle.load(f)
return clf
def get_sepsis_score(data_mat, clf):
# convert d to dataframe from numpy
varofint ... | pd.DataFrame(columns=['sumHR','sumO2','sumTemp','sumSP','sumMAP','sumDP', 'varHR','varO2','varTemp','varSP','varMAP','varDP','maxHR','maxO2','maxTemp','maxSP','maxMAP','maxDP', 'minHR','minO2','minTemp','minSP','minMAP','minDP']) | pandas.DataFrame |
# Import libraries
import json
import matplotlib.pyplot as plt, numpy as np, pandas as pd
from sklearn.neighbors import radius_neighbors_graph
from scipy.sparse.csgraph import connected_components
# Contact spacing
dist1 = 0.2
dist2 = 0.5
# Get connected components and distances
data = pd.read_csv("output/dispcont.cs... | pd.DataFrame(refpts) | pandas.DataFrame |
import pandas as pd
DELIMITER = '|'
cols = ['Base', 'E_nX', 'E_X', 'C_nX', 'C_X']
connectives = ['I repeat', 'again', 'in short', 'therefore', 'that is', 'thus']
expanders = {
'{Prep}': ['Near', 'By', 'Nearby'], # ['near', 'nearby', 'by']
'{E/D}': ['Here is', 'This is'], # ['Here is', 'There is', 'That is'... | pd.DataFrame.from_records(unexpanded_sentences, columns=cols[1:]) | pandas.DataFrame.from_records |
from __future__ import division
from datetime import datetime
import sys
if sys.version_info < (3, 3):
import mock
else:
from unittest import mock
import pandas as pd
import numpy as np
import random
from nose.tools import assert_almost_equal as aae
import bt
import bt.algos as algos
def test_algo_name():... | pd.date_range('2010-01-01', periods=3) | pandas.date_range |
# coding=utf-8
'''
Use coreNLP to lexical analysis for short text
'''
import sys
reload(sys)
sys.setdefaultencoding('utf8')
import pandas as pd
from stanfordcorenlp import StanfordCoreNLP
nlp = StanfordCoreNLP(r'/home/dl/Downloads/stanford-corenlp-full-2018-01-31/', lang='zh')
pos= | pd.read_excel('./data/pos.xls',header=None,index=None,encoding='utf-8') | pandas.read_excel |
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Series
import pandas._testing as tm
class TestSeriesCombine:
def test_combine_scalar(self):
# GH 21248
# Note - combine() with another Series is tested elsewhere because
# it is used when testing operators
... | pd.Series([10, 61, 12]) | pandas.Series |
"""
Copyright 2019 <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 by applicable law or agreed to in writing,
software distribut... | pd.Series(actual) | pandas.Series |
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, "data") | pandas.io.pytables.read_hdf |
import sys
import time
import numpy as np
import pandas as pd
from scipy.special import softmax
# from sklearn.feature_selection import SelectKBest
# from sklearn.feature_selection import VarianceThreshold
np.seterr(divide='ignore', invalid='ignore')
st = time.time()
mode = sys.argv[1]
train_path = sys.argv[2]
test_... | pd.get_dummies(data, columns=cols, drop_first=True) | pandas.get_dummies |
# -*- coding: utf-8 -*-
'''
clf.py
'''
import os
import logging
import pandas as pd
import numpy as np
from PIL import Image
from chainer.datasets import ImageDataset, LabeledImageDataset, split_dataset
def _read_image_as_array(path, dtype, img_size, img_type):
image = Image.open(path)
width, height = image.... | pd.read_csv(train_list_path, sep='\t', usecols=['file_name', 'category_id']) | pandas.read_csv |
# # **********************************************************************************************************
# # Important (task is often ignored when doing data science)
# # !!! Clean up project by removing any assets that are no longer needed !!!
# # Remove zip file which has downloaded and the directory to which ... | pd.read_csv(file, names=['name', 'sex', 'births']) | pandas.read_csv |
'''
@Description: code
@Author: MiCi
@Date: 2020-03-12 08:55:59
@LastEditTime: 2020-03-12 23:20:24
@LastEditors: MiCi
'''
import pandas as pd
import numpy as np
class Basic1(object):
def __init__(self):
return
def basic_use(self):
# 数据导入
filename, query, connection_object, json_str... | pd.DataFrame() | pandas.DataFrame |
import sys
import pandas as pd
import matplotlib
import numpy as np
import scipy as sp
import IPython
import sklearn
import mglearn
# !! This script is not optimized.
print(f"Python version {sys.version}")
print(f"pandes version {pd.__version__}")
print(f"matplotlib version {matplotlib.__version__}")
print(f"numpy ve... | pd.DataFrame(X_train, columns=iris_dataset.feature_names) | pandas.DataFrame |
import datareader
import dataextractor
import bandreader
import numpy as np
from _bisect import bisect
import matplotlib.pyplot as plt
import matplotlib.ticker as plticker
import pandas as pd
from scipy import stats
from sklearn import metrics
def full_signal_extract(path, ident):
"""Extract breathing and heartbe... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[48]:
import pandas as pd
import urllib
import numpy as np
import json
from tqdm.autonotebook import tqdm
#%matplotlib inline
tqdm.pandas()
import dask.dataframe as dd
from dask.multiprocessing import get
from dask.diagnostics import ProgressBar
from datetime import ... | pd.concat(res, sort=False) | pandas.concat |
# ENRICHMENT SCRIPT
import random
import numpy as np
import pandas as pd
import seaborn as sns
from scipy.special import comb
from collections import Counter
import matplotlib.pyplot as plt
from scipy.stats import hypergeom
from pyclustering.cluster.kmedoids import kmedoids
from statsmodels.stats.multitest import mult... | pd.DataFrame(columns=["K_Option", "Total_enriched"]) | pandas.DataFrame |
import pandas as pd
import numpy as np
import holidays
from config import log
from datetime import date
from pandas.tseries.offsets import BDay
from collections import defaultdict
from xbbg import blp
logger = log.get_logger()
class clean_trade_file():
def __init__(self, trade_file, reuse_ticker_dict):
se... | pd.Timestamp(effect_date) | pandas.Timestamp |
import logging
from copy import deepcopy
import numpy as np
import pandas as pd
from reamber.osu.OsuMap import OsuMap
from reamber.osu.OsuSample import OsuSample
log = logging.getLogger(__name__)
def hitsound_copy(m_from: OsuMap, m_to: OsuMap, inplace: bool = False) -> OsuMap:
""" Copies the hitsound from mFrom... | pd.concat([i.df for i in m_from.notes], sort=False) | pandas.concat |
import sys
import re
import os
import csv
import shutil
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import seaborn as sns
import scipy.stats as stats
from tabulate import tabulate
from NotSoFastQC.modules import module_dict as md
from NotSoFastQC... | pd.DataFrame(self.data[ROWS], columns=["Tile", "Position in read (bp)", "value"]) | pandas.DataFrame |
import copy
import os
import numpy as np
import pandas as pd
import h5py
import pickle as pkl
# import glob
from pathlib import Path
# %% Hyperparameters
DATA_FILE_NAME = 'model_data'
RUN_NAME = 'run'
# data_filetype = 'pkl'
# %% Helper functions
def __unique_to_set(a, b):
"""
Return elements that are unique... | pd.DataFrame(table_params, index=[run_id], dtype=str) | pandas.DataFrame |
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