prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#!/usr/bin/env python3
import warnings
import os
import subprocess
import multiprocessing
import yaml
import sys
import csv
import glob
import numpy as np
import matplotlib.pyplot as plt
from matplotlib_venn import venn2
import pandas as pd
import seaborn as sns
from tqdm.auto import tqdm
import gseapy as gp
import ha... | pd.read_csv(count_file, sep="\t") | pandas.read_csv |
# summarizeLib.py
# <NAME>
# 3.28.19
#
# module of functions that allow you to create per-cell / per-sample summary tables
import pandas as pd
import numpy as np
import math
def get_laud_db(database_):
""" returns the COSMIC database after lung and fathmm filter """
pSiteList = database_.index[database_['Pr... | pd.isnull(currFus) | pandas.isnull |
"""
Routines for analysing output data.
:Author:
<NAME>
"""
import warnings
from typing import Tuple
import numpy as np
import pandas as pd
from scipy.optimize import curve_fit
def fit_function(x_data, *params):
p, d = x_data
p_th, nu, A, B, C = params
x = (p - p_th)*d**(1/nu)
return A + B*x + C*x... | pd.isna(f_0) | pandas.isna |
'''
Run this to get html files
This file contains code to obtain html data from oslo bors and yahoo finance
'''
import argparse
import re
import threading
import time
from pprint import pprint
from typing import List
import sys
import pathlib
import os
import numpy as np
import pandas as pd
import pypatconsole as ppc... | pd.merge(df_osebx, df_yahoo, on=cng.MERGE_DFS_ON, suffixes=('_osebx', '_yahoo')) | pandas.merge |
import pandas as pd
if __name__ == '__main__':
tennet_delta_df = pd.read_csv('../data/tennet_balans_delta/tennet_balans_delta_okt_2020_nov_2021.csv')
tennet_delta_df.index = | pd.to_datetime(tennet_delta_df['time'], errors='coerce') | pandas.to_datetime |
import pandas as pd
import numpy as np
import time
from scipy import stats
import matplotlib as mpl
import matplotlib.pyplot as plt
import io
import base64
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
from flask import render_template
from lol_onlin... | pd.cut(win.duration, bins=bins, right=False) | pandas.cut |
import os
import csv
import pandas as pd
from os.path import join, basename
import pathlib
import sys
import seaborn as sns
import numpy as np
sns.set_theme()
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
#pd.set_option("display.max_rows", None, "display.max_columns", None)
MO_FACTOR=1.... | pd.read_csv(experiment) | pandas.read_csv |
# pylint: disable=E1101
from datetime import datetime, timedelta
from pandas.compat import range, lrange, zip, product
import numpy as np
from pandas import Series, TimeSeries, DataFrame, Panel, isnull, notnull, Timestamp
from pandas.tseries.index import date_range
from pandas.tseries.offsets import Minute, BDay
fr... | pd.date_range('01-Jan-2014','05-Jan-2014', freq='D') | pandas.date_range |
import numpy as np
import pandas as pd
from utils import data_generator
## data dimension
N_train = 110 # sum of training and valiadation set
dim = 24
## initialize parameters
c1_value = round(np.random.uniform(0, 20),2)
c2_value = round(np.random.uniform(0, 20),2)
duration = round(np.random.uniform(1, 4))
eta = roun... | pd.DataFrame([[c1_value, c2_value, duration, eta]],columns=("c1", "c2", "P", "eta")) | pandas.DataFrame |
"""
@author: <NAME>
@name: Bootstrap Estimation Procedures
@summary: This module provides functions that will perform the MLE for each
of the bootstrap samples.
"""
import numpy as np
import pandas as pd
from . import pylogit as pl
from .display_names import model_type_to_display_name
def extra... | pd.Series(mnl_point["x"], index=mnl_obj.ind_var_names) | pandas.Series |
import faiss
import pandas as pd
import time
import numpy as np
import torch
import os
from scipy import stats as s
class knn:
def __init__(self, datafile, savefile=None, knn_size=10, save_to_file=True, resume=True):
self.knn_size = knn_size
self.x_data = None
self.y_data = None
... | pd.Series(self.y_data) | 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_index_equal(result, expected) | pandas.testing.assert_index_equal |
# -*- coding: utf-8 -*-
import pandas
import numpy
import sys
import unittest
from datetime import datetime
from pandas.testing import assert_frame_equal, assert_series_equal
import os
import copy
sys.path.append("..")
import warnings
import nPYc
from nPYc.enumerations import SampleType
from nPYc.enumerations import As... | assert_series_equal(msData.sampleMetadata['Instrument'], instrument) | pandas.testing.assert_series_equal |
#!/usr/bin/env python3
# coding: utf-8
import requests
import sys
import pandas as pd
from requests.auth import HTTPBasicAuth
name = 'INSERT OWN API NAME HERE'
password = '<PASSWORD> OWN API PASSWORD HERE'
#set initial values
uploads = pd.DataFrame() #empty dataframe
start = 0
end = 100
def transid_dt(transid):
'... | pd.to_datetime(transid[0:8]) | pandas.to_datetime |
import typing
import datetime
import pandas as pd
from .make_df import ComicDataFrame
from lib.aws_util.s3.upload import upload_to_s3
from lib.aws_util.s3.download import download_from_s3
def store(df: ComicDataFrame) -> typing.NoReturn:
dt = datetime.datetime.now()
bucket = 'av-adam-store'
save_dir = '/tmp/'
... | pd.read_csv(tag_path) | pandas.read_csv |
import pandas as pd
from SALib.analyze.radial_ee import analyze as ee_analyze
from SALib.analyze.sobol_jansen import analyze as jansen_analyze
from SALib.plotting.bar import plot as barplot
# results produced with
# python launch.py --specific_inputs oat_mc_10_samples.csv --num_cores 48
# python launch.py --specific_... | pd.DataFrame(extreme_results, index=perturbed_cols) | pandas.DataFrame |
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal, assert_series_equal
from evalml.pipelines import BaselineBinaryPipeline, BaselineMulticlassPipeline
from evalml.utils import get_random_state
def test_baseline_binary_random(X_y_binary):
X, y = X_y_binary
values = np.unique... | pd.Series([10, 11, 10]) | pandas.Series |
""" test fancy indexing & misc """
from datetime import datetime
import re
import weakref
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas.core.dtypes.common import (
is_float_dtype,
is_integer_dtype,
)
import pandas as pd
from pandas import (
DataFrame,
Index,... | DataFrame({"A": [1, 2, 3, 4]}) | pandas.DataFrame |
"""
Copyright 2018 <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... | assert_series_equal(coord_data_result, bond_expected_series) | pandas.util.testing.assert_series_equal |
import os
import pandas as pd
from datetime import datetime, timedelta
from embrace import get_date_from_garmin
import collections
folders = ['01-09-TR1', '10-20-TR2', '21-30-TR3']
def timestamp2datetime2minutes(file_path):
df = pd.read_csv(file_path, header=1)
df.Timestamp = df.Timestamp.map(lambda ts: date... | pd.DataFrame.from_dict(dic_avg) | pandas.DataFrame.from_dict |
#-*-coding: utf-8
"""
Created on Sat Dec 01 2018
@author: JeongChanwoo
"""
import pandas as pd
import numpy as np
import re
from os import listdir
class DataReader(object):
def __init__(self):
self.data_path =None
self.data_list = None
self.total_data = None
self.user_lecture_data =... | pd.read_json(self.data_path + k, encoding='utf-8') | pandas.read_json |
import os
import json
import itertools
import numpy as np
import pandas as pd
from tqdm import tqdm
from pathlib import Path
import subprocess
import matplotlib.pyplot as plt
import geopandas as gpd
import rasterio as rio
from rasterio.windows import (
Window,
transform
)
from rasterio import features
import ... | pd.DataFrame(subdata) | pandas.DataFrame |
################################################################################
"""
DJ JOE Website Playlist File Generator
--------------------------------------
(c) 2021 - Stanley Solutions - <NAME>
This application serves an interface to allow the recording of Apple Music or
Spotify playlists.
"""
################... | pd.DataFrame(table_list, columns=["Title", "Artist(s)"]) | pandas.DataFrame |
"""Format helpers"""
import math
import pandas as pd
import pandas.lib as lib
import numpy as np
pd_is_datetime_arraylike = None
try:
from pandas.core.common import is_datetime_arraylike as pd_is_datetime_arraylike
except:
pass
from functools import partial
def is_datetime_arraylike(arr):
if isinstance... | pd.to_datetime(value) | pandas.to_datetime |
#!/usr/bin/python3
import sys
import pandas as pd
import numpy as np
import os
import concurrent.futures
import functools, itertools
import sofa_time
import statistics
import multiprocessing as mp
import socket
import ipaddress
# sys.path.insert(0, '/home/st9540808/Desktop/sofa/bin')
import sofa_models, sofa_preproce... | pd.DataFrame(traces_inros) | pandas.DataFrame |
import urllib
import pytest
import pandas as pd
from pandas import testing as pdt
from anonympy import __version__
from anonympy.pandas import dfAnonymizer
from anonympy.pandas.utils_pandas import load_dataset
@pytest.fixture(scope="module")
def anonym_small():
df = load_dataset('small')
anonym = dfAnonymize... | pdt.assert_frame_equal(expected, output) | pandas.testing.assert_frame_equal |
#!/usr/bin/env python
import argparse
import numpy as np
import pandas as pd
from scipy import linalg
from tqdm import tqdm
import os
import logging
def get_args():
parser = argparse.ArgumentParser(description="calculate splicing scores per gene/cell")
parser.add_argument("--input", help="Name of the input file... | pd.Series(new_zs[i].values,index=new_zs.index) | pandas.Series |
import os
import tempfile
import torch,torchvision
import torch.distributed as dist
import torch.nn as nn
import torch.optim as optim
import argparse
import torch.multiprocessing as mp
import torchvision.transforms as transforms
import torchvision.models as models
import time
import pandas as pd
def setup(rank, worl... | pd.DataFrame(columns=['epoch','batch','batch_size','gpu_number','time']) | pandas.DataFrame |
import pandas as pd
import os
from pipeline_ie.config import Config
from pathlib import Path
class DataLoader:
def __init__(self, input_data):
self.config = Config().config
self.input = input_data
def check_input(self):
try:
if self.input == "csv":
return ... | pd.concat(list_df, axis=0, ignore_index=True) | pandas.concat |
import requests
import json
from flask import Flask, request
from json import dumps
#from flask.ext.jsonpify import jsonify
from flask_cors import CORS
from datetime import datetime
import pandas as pd
import difflib
import numpy as np
import pickle as pkl
import sys
username = "aditya1495"
apiKey = "3fe2254bb42e851ae... | pd.DataFrame.from_csv('airlines.csv') | pandas.DataFrame.from_csv |
import itertools
import pandas as pd
from pandas.testing import assert_series_equal
import pytest
from solarforecastarbiter.reference_forecasts import forecast
def assert_none_or_series(out, expected):
assert len(out) == len(expected)
for o, e in zip(out, expected):
if e is None:
assert... | pd.date_range(start='20190101 03Z', freq='3h', periods=npts) | pandas.date_range |
# -*- coding: utf-8 -*-
# https://zhuanlan.zhihu.com/p/142685333
import pandas as pd
import datetime
import tushare as ts
import numpy as np
from math import log,sqrt,exp
from scipy import stats
import plotly.graph_objects as go
import plotly
import plotly.express as px
pro = ts.pro_api()
plotly.offline.init_noteboo... | pd.merge(df_basic,df_daily,how='left',on=['ts_code']) | pandas.merge |
from datetime import timedelta
import numpy as np
import pytest
from pandas import Categorical, DataFrame, NaT, Period, Series, Timedelta, Timestamp
import pandas._testing as tm
class TestSeriesFillNA:
def test_fillna_pytimedelta(self):
# GH#8209
ser = Series([np.nan, Timedelta("1 days")], index... | Categorical(data, categories=["a", "b"]) | pandas.Categorical |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
from numpy import nan
import numpy as np
import pandas as pd
from pandas.types.common import is_integer, is_scalar
from pandas import Index, Series, DataFrame, isnull, date_range
from pandas.core.index import MultiIndex
from pa... | Series([np.nan, np.nan, 3, 4]) | pandas.Series |
import requests
import bs4
import pandas as pd
def get_meetup_events(group):
"""Returns a list of events and their details for a given meetup group."""
url = 'https://api.meetup.com/{group}/events?&sign=true&photo-host=public&page=200&status=past'.format(group=group)
r = requests.get(url)
events = r.js... | pd.DataFrame(events) | pandas.DataFrame |
import unittest
import copy
import numpy as np
import numpy.testing as np_test
import pandas as pd
import pandas.testing as pd_test
import warnings
from pyblackscholesanalytics.market.market import MarketEnvironment
from pyblackscholesanalytics.options.options import PlainVanillaOption, DigitalOption
from pyblackschol... | pd_test.assert_frame_equal(test_put, expected_put) | pandas.testing.assert_frame_equal |
import numpy as np
import pandas as pd
import seaborn as sns
import random
from traitlets import Int, List, Bool, CFloat, Unicode
from sepal_ui.model import Model
import component.parameter.app as cp
import component.scripts as cs
import component.parameter as param
import ee
import json
from geopandas import GeoDa... | pd.factorize(df.basin) | pandas.factorize |
"""
2018 <NAME>
2.ensemble-z-analysis/scripts/train_models_given_z.py
This script will train various compression models given a specific z dimension.
Each model will train several times with different initializations.
The script pulls hyperparameters from a parameter file that was determined
after initial hyperparame... | pd.read_table(param_config, index_col=0) | pandas.read_table |
from requests import get, exceptions
from bs4 import BeautifulSoup
from datetime import datetime
from pandas import DataFrame, read_excel
from time import sleep
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
def get_label(soup):
artist = soup.find("span", attrs={"itemprop": "byArtist"})... | DataFrame(diff) | pandas.DataFrame |
import matplotlib.cm as cm
import pandas as pd
import seaborn as sns
import matplotlib.dates as mdates
from matplotlib.dates import DateFormatter
import matplotlib.pyplot as plt
import numpy as np
###############################################################################################################
# IMPORTA... | pd.read_csv(FILENAME_TWEET, dtype='str') | pandas.read_csv |
"""
分析模块
"""
import warnings
from typing import Tuple, Union
import re
import numpy as np
import pandas as pd
from scipy import stats
from statsmodels.api import OLS, add_constant
from QUANTAXIS.QAFactor import utils
from QUANTAXIS.QAFactor.parameters import DAYS_PER_MONTH, DAYS_PER_QUARTER, DAYS_PER_YEA... | pd.Timedelta(period) | pandas.Timedelta |
""" test feather-format compat """
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.io.feather_format import read_feather, to_feather # isort:skip
pyarrow = pytest.importorskip("pyarrow", minversion="1.0.1")
filter_sparse = pytest.mark.filterwarnings("ignore:The Sparse... | tm.assert_frame_equal(df, result) | pandas._testing.assert_frame_equal |
"""
Wrappers around native scikit-learn estimators.
`sklearndf` wrappers accept and return data frames (while scikit-learn transformers
usually return a numpy arrays, and may not accept data frames as input).
Otherwise the wrappers are designed to precisely mirror the API and behavior of the
native estimators they wra... | pd.Series(data=y, index=X.index, name=classes[1]) | pandas.Series |
# pylint: disable-msg=E1101,W0612
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.core.sparse.api import SparseDtype
class TestSparseSeriesIndexing(object):
def setup_method(self, method):
self.orig = pd.Series([1, np.nan, np.nan, 3, np.nan])
sel... | SparseDtype(np.int64) | pandas.core.sparse.api.SparseDtype |
# -*- coding: utf-8 -*-
from fastapi import FastAPI
from fastapi.openapi.utils import get_openapi
from elasticsearch import helpers, Elasticsearch
from covid19dh import covid19, cite
from config import search_host, search_username, search_password, search_port, search_index_name, covid19datahub_title, covid19datahub_ci... | pd.DataFrame(filesindex) | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import tushare as ts
import matplotlib.pyplot as plt
import datetime
#pd.set_option('display.max_rows', None)
#pd.set_option('display.max_columns', None)
tspro = ts.pro_api('09f77414f088aad7959f5eecba391fe685ea50462e208ce451b1b6a6')... | pd.read_pickle('retrieve_2015_data/HighPoint2015010120151231.pkl') | pandas.read_pickle |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import io
import os
import pkgutil
from datetime import datetime
from typing import cast, List
from unittest import TestCase
import matplot... | pd.DataFrame([[100, 200]], columns=["y1", "y2"]) | pandas.DataFrame |
from unittest.mock import patch
import pandas as pd
import pytest
from pandas._testing import assert_frame_equal
from src.app_visualization.data_viz_components.metrics_and_KPIs import get_airline_turnover, add_cost_20min_delay, \
add_cost_10min_delay, cost_of_delay, get_airport_delay_cost, get_number_of_indemniti... | assert_frame_equal(actual, expected) | pandas._testing.assert_frame_equal |
import os
from turtle import pd
from . import app
from pandas import DataFrame
from scipy.spatial import distance
import pandas as pd
import math
from math import sqrt
from math import atan2
from numpy.linalg import norm, det
from numpy import cross, dot
from numpy import radians
from numpy import array, zeros
from num... | pd.DataFrame(data=data_structure) | pandas.DataFrame |
from bedrock.annotator.annotator import Annotator
from bedrock.doc.doc import Doc
from bedrock.doc.token import Token
from bedrock.doc.annotation import Annotation
from bedrock.doc.relation import Relation
from bedrock.doc.layer import Layer
from typing import List
import pandas as pd
from fuzzywuzzy import process
fro... | pd.DataFrame(columns=[Annotation.BEGIN, Annotation.END, self.QUERY]) | pandas.DataFrame |
"""
Eo-tilematcher package.
"""
import geopandas as gpd
import pandas as pd
import pygeos
from pathlib import Path
DATA_DIR = Path(__file__).parent / "data"
def _db_loader(file_name):
subdir = file_name.split("_")[0]
tiles_db = gpd.read_file(DATA_DIR / subdir / file_name, driver="ESRI Shapefile")
return... | pd.concat(intersects_, ignore_index=True) | pandas.concat |
"""
Note: for naming purposes, most tests are title with as e.g. "test_nlargest_foo"
but are implicitly also testing nsmallest_foo.
"""
from itertools import product
import numpy as np
import pytest
import pandas as pd
from pandas import Series
import pandas._testing as tm
main_dtypes = [
"datetime",
"dateti... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
import os
import urllib
import pandas as pd
from gensim.models import Word2Vec
from SPARQLWrapper import SPARQLWrapper, JSON
# DISCLAIMER
# File modified from https://github.com/mariaangelapellegrino/Evaluation-Framework
class data_manager:
def __init__(self, gold_standard_file, vectors_file, w2v_model_name):
... | pd.io.json.json_normalize(results["results"]["bindings"]) | pandas.io.json.json_normalize |
from sklearn.metrics import mean_absolute_error, mean_squared_error
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from neupre.instructions.base import mape
from neupre.misc.dataops import load_data_point_online
plt.style.use('ggplot')
class BaseBackend(object):
def __init__(self, buffsize... | pd.Series(data=p3, index=p3_index) | pandas.Series |
import multiprocessing as mp
import os
import string
import warnings
import numpy as np
import pandas as pd
import uncertainties as un
from nptdms import TdmsFile
from numpy import NaN, sqrt
from scipy.stats import t
from tables import NoSuchNodeError
from uncertainties import unumpy as unp
from . import diodes
from ... | pd.DataFrame(columns=["shot", "schlieren"]) | pandas.DataFrame |
import joblib
import pandas as pd
class DecisionTreeClassifier:
def __init__(self):
path_to_artifacts = "../../research/"
self.value_fill_missing = joblib.load(path_to_artifacts + "pi_train_mode.joblib")
self.model = joblib.load(path_to_artifacts + "pi_decision_tree.joblib")
def preprocessing(self, input_data... | pd.DataFrame(input_data, index=[0]) | pandas.DataFrame |
import os
import numpy as np
import tensorflow as tf
from matplotlib import image
import cv2
import pandas as pd
import debiasmedimg.settings as settings
from copy import deepcopy
def get_filenames(csv_file, domains, merge=False):
"""
Extract the filenames of all images in the folders for all domains
:par... | pd.DataFrame.from_dict(val_dict) | pandas.DataFrame.from_dict |
import time
import traceback
from abc import abstractmethod
from datetime import datetime
from itertools import product
from pathlib import Path
import feather
import pandas as pd
from ..options import ModelOptions
from ..plotting import plot
from ..utils import ModelHandler, common_utils, file_utils
from . import EV... | pd.read_csv(preds_stats_path) | pandas.read_csv |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/2/14 18:19
Desc: 新浪财经-股票期权
https://stock.finance.sina.com.cn/option/quotes.html
期权-中金所-沪深 300 指数
https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php
期权-上交所-50ETF
期权-上交所-300ETF
https://stock.finance.sina.com.cn/option/quotes.html
"""
import json
i... | o_numeric(temp_df['最低']) | pandas.to_numeric |
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('2020-08-07') | pandas.Timestamp |
import pandas as pd
def subset_grm(grm, grm_indiv, target_indiv):
set_target_indiv = set(target_indiv)
isin = np.array([ g in set_target_indiv for g in grm_indiv ])
grm = grm[:, isin][isin, :]
grm_indiv = list(np.array(grm_indiv)[isin])
return grm, grm_indiv
def subset_y(df, indiv):
df_indiv =... | pd.concat(res, axis=0) | pandas.concat |
#!/usr/bin/env python
__author__ = '<NAME>'
import os
import pandas as pd
import argparse
from copy import deepcopy
from _collections import OrderedDict
import pandas as pd
from BCBio import GFF
from RouToolPa.Collections.General import SynDict, IdList
from RouToolPa.Parsers.VCF import CollectionVCF
from MACE.Routines... | pd.DataFrame.from_dict(len_dict, orient="index") | pandas.DataFrame.from_dict |
#!/usr/bin/env python3
import gzip
import json
import os
import pandas as pd
pd.set_option('display.max_colwidth', None)
| pd.set_option('display.max_columns', None) | pandas.set_option |
import numpy as np
import pandas as pd
import bottleneck
from scipy import sparse
import gc
from .utils import *
def MetaNeighbor(
adata,
study_col,
ct_col,
genesets,
node_degree_normalization=True,
save_uns=True,
fast_version=False,
fast_hi_mem=False,
mn_key="MetaNeighbor",
):
... | pd.DataFrame(res) | pandas.DataFrame |
#!/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import shutil
import csv
import zipfile
import tarfile
import configparser
import collections
import statistics
import pandas as pd
import matplotlib.pyplot as plt
import networkx as nx
from datetime import datetime
# Type of printing.
OK ... | pd.Series(train[WEB][1]) | pandas.Series |
#!/usr/bin/env python
"""
Merge biotex results from 30k tweets per files
"""
import pandas as pd
from pathlib import Path
import json
# SentiWordNet
from nltk.corpus import wordnet as wn
from nltk.corpus import sentiwordnet as swn
from nltk.stem import PorterStemmer, WordNetLemmatizer
from nltk import pos_tag, word_to... | pd.concat([dfcompare, dfextractMulti], axis=1) | pandas.concat |
# auto-verify-links.py
#
# This script crawls candidate URLs for municipalities websites and
# checks if they are active and likely to be the city hall or
# city council portals.
#
# Este script navega nas URLs candidatas a sites dos municípios e
# verifica se elas estão ativas e são prováveis portais das prefeituras
... | pd.DataFrame(columns=candidates.columns) | pandas.DataFrame |
#####################################
# DataReader.py
#####################################
# Description:
# * Convert data in format into pandas DataFrame.
import dateutil.parser as dtparser
import numpy as np
from pandas import DataFrame, isnull, read_csv, read_excel
import re
import os
from DynamicETL_Dashboard.Uti... | isnull(series) | pandas.isnull |
import pandas as pd
import numpy as np
import time
import datetime
import random
from sklearn.preprocessing import Imputer
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn import preprocessing
from sklearn.decomposition import PCA
from sklearn.model_selec... | pd.read_csv("../data/adult.csv") | pandas.read_csv |
"""
Simplify Python Client of Google Cloud Speech-to-Text
Need to install (and restart first) with `import kora.install.speech`
Then
```
from kora.speech import Recognizer
sp = Recognizer(sa_file, lang='th', output_dir=None)
op = sp.open(uri)
op.to_df()
```
"""
import pandas as pd
import os.path
from pathlib import Pa... | pd.IntervalIndex.from_arrays(left, right, closed='left', name='time') | pandas.IntervalIndex.from_arrays |
"""Implements the utilities to generate general multi-objective mixed-integer linear program instances
Referenced articles:
@article{mavrotas2005multi,
title={Multi-criteria branch and bound: A vector maximization algorithm for mixed 0-1 multiple objective linear programming},
author={<NAME> and <NAME>},
journ... | pd.Series(binary_var_sum_rhs) | pandas.Series |
import numpy as np
import pandas as pd
from numba import njit, typeof
from numba.typed import List
from datetime import datetime, timedelta
import pytest
from copy import deepcopy
import vectorbt as vbt
from vectorbt.portfolio.enums import *
from vectorbt.generic.enums import drawdown_dt
from vectorbt.utils.random_ im... | pd.DataFrame([[-25, -25], [np.inf, np.inf]]) | pandas.DataFrame |
import pandas as pd
import numpy as np
import pickle
from sklearn.decomposition import NMF
MOVIES = pd.read_csv('movies.csv', header=0)
ratings = pd.read_csv('ratings.csv', header=0)
tags = pd.read_csv('tags.csv', header=0)
links = pd.read_csv('links.csv', header=0)
ratings = ratings[['userId', 'movieId', 'rating']]... | pd.concat(data_dfs, join='outer', sort=True) | pandas.concat |
"""Pipeline to train model, find best parameters, give results."""
import logging
import os
import time
from os.path import join, relpath
import pandas as pd
from sklearn.ensemble import BaggingClassifier, BaggingRegressor
from sklearn.inspection import permutation_importance
from sklearn.model_selection import Shuffl... | pd.Series(X_test.index) | pandas.Series |
import requests
from bs4 import BeautifulSoup
import pandas as pd
# This functio Displays ASCII art banner at the start of the program
def display_banner():
print(r'''
_ _ _ _____
| \ | | | | / ___|
| \| |_ _ _ __ ___ | |__ ___ _ __ \ `... | pd.DataFrame(results) | pandas.DataFrame |
import pandas as pd
import plotly.express as px
import panel as pn
data = {
"Day": ["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday",],
"Orders": [15539, 21345, 18483, 24003, 23489, 24092, 12034],
}
dataframe = | pd.DataFrame(data) | pandas.DataFrame |
"""
accounting.py
Accounting and Financial functions.
project : pf
version : 0.0.0
status : development
modifydate :
createdate :
website : https://github.com/tmthydvnprt/pf
author : tmthydvnprt
email : <EMAIL>
maintainer : tmthydvnprt
license : MIT
copyright : Copyright 2016, tmthydvnprt
cr... | pd.tseries.offsets.MonthEnd(-3 * 12) | pandas.tseries.offsets.MonthEnd |
import argparse
from statistics import median_high, median_low
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from qpputils import dataparser as dt
# Define the Font for the plots
# plt.rcParams.update({'font.size': 35, 'font.family': 'serif', 'font.weight': 'normal'})
# Define the Font for ... | pd.merge(qdf, apdb.data_df, left_on='qid', right_index=True) | pandas.merge |
import sys
import requests
import ConfigParser
#from multiprocessing.dummy import Pool as ThreadPool
from IPython import embed
import pandas as pd
import numpy as np
from genda import calculate_minor_allele_frequency, calculate_ld
from genda.AEI import AEI, dosage_round
pd.options.mode.chained_assignment = None
de... | pd.Index(snpid) | pandas.Index |
# In python and other programming languages developers (Like Phil and Jaleh) use other people's code to accomplish what they wish.
# When a developer wants to let other people use their code they create a package for their code called a module then they let others download that module on to their computer.
# When a mod... | pd.Categorical(games['type'].iloc[:]) | pandas.Categorical |
"""Yahoo Finance Mutual Fund Model"""
__docformat__ = "numpy"
import logging
import os
import matplotlib.pyplot as plt
import pandas as pd
from openbb_terminal import feature_flags as obbff
from openbb_terminal.config_plot import PLOT_DPI
from openbb_terminal.decorators import log_start_end
from openbb_terminal.help... | pd.DataFrame.from_dict(weights, orient="index") | pandas.DataFrame.from_dict |
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_obj(t) | pandas.core.dtypes.common.is_1d_only_ea_obj |
import pytest
import pytz
import dateutil
import numpy as np
from datetime import datetime
from dateutil.tz import tzlocal
import pandas as pd
import pandas.util.testing as tm
from pandas import (DatetimeIndex, date_range, Series, NaT, Index, Timestamp,
Int64Index, Period)
class TestDatetimeInd... | tm.assert_index_equal(result, expected) | pandas.util.testing.assert_index_equal |
"""Tests the interval operations in the hicognition library"""
import unittest
import pandas as pd
from pandas.testing import assert_frame_equal
from hicognition import interval_operations
class TestChunkIntervals(unittest.TestCase):
"""Tests for chunk_intervals"""
@classmethod
def setUp(cls):
cl... | assert_frame_equal(actual_df, expected_df) | pandas.testing.assert_frame_equal |
import os
from requests.exceptions import HTTPError
import pandas as pd
import numpy as np
from polygon import RESTClient
import alpaca_trade_api as ati
data_columns = ['bid','ask']
def get_data_for_symbol(symbol, client, date, stop_time=None, start_time=None, limit=200):
"""
Fetches full volume quote data ... | pd.Timedelta(seconds=1) | pandas.Timedelta |
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Index,
Series,
)
import pandas._testing as tm
dt_data = [
pd.Timestamp("2011-01-01"),
pd.Timestamp("2011-01-02"),
pd.Timestamp("2011-01-03"),
]
tz_data = [
pd.Timestamp("2011-01-01", tz="U... | Series(vals2) | pandas.Series |
from datetime import datetime
from stateful.storage.stream_controller import StreamController
from stateful.representable import Representable
import pandas as pd
from stateful.utils import list_of_instance
class Space(Representable):
def __init__(self, primary_key, primary_value, time_key, graph, configuration... | pd.to_datetime(item, utc=True) | pandas.to_datetime |
import os
import df2img
import disnake
import pandas as pd
import bots.config_discordbot as cfg
from bots.config_discordbot import gst_imgur, logger
from bots.helpers import save_image
from bots.menus.menu import Menu
from gamestonk_terminal.stocks.insider import finviz_model
def lins_command(ticker: str = "", num:... | pd.DataFrame.from_dict(d_finviz_insider) | pandas.DataFrame.from_dict |
"""
Created on Wed Nov 18 14:20:22 2020
@author: MAGESHWARI
"""
import os
from tkinter import *
from tkinter import messagebox as mb
from tkinter import filedialog
import re
import csv
import pandas as pd
def center_window(w=200, h=500):
# get screen width and height
ws = root.winfo_screenwidt... | pd.concat([df2, df1]) | pandas.concat |
"""Treatment estimation functions"""
from linearmodels.iv import IV2SLS
from linearmodels.system.model import SUR
from statsmodels.api import add_constant
from statsmodels.multivariate.multivariate_ols import _MultivariateOLS
import numpy as np
import pandas as pd
def estimate_treatment_effect(aps = None, Y = None, Z ... | pd.DataFrame(cols) | pandas.DataFrame |
"""
서울 열린데이터 광장 Open API
1. TransInfo 클래스: 서울시 교통 관련 정보 조회
"""
import datetime
import numpy as np
import pandas as pd
import requests
from bs4 import BeautifulSoup
class TransInfo:
def __init__(self, serviceKey):
"""
서울 열린데이터 광장에서 발급받은 Service Key를 입력받아 초기화합니다.
"""
# Open API 서비... | pd.to_numeric(df["ALIGHT_PASGR_NUM"]) | pandas.to_numeric |
import argparse
from typing import Final
# 进行数值计算
import numpy as np
# 用于读取csv文件和方便地进行方差、均值计算
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
# 使用方法: main.py --dataset <数据集路径>
class KMeans:
def __init__(self, feats: pd.DataFrame, k: int):
self.tries = 0
self.feats = feat... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import matplotlib.pyplot as plt
from matplotlib import pyplot
import plotly.graph_objs as go
import streamlit as st
import warnings
from sklearn.preprocessing import StandardScaler
from sklearn.decomposi... | pd.Series(principalDf['anomaly1'].values, index=mv_data.index) | pandas.Series |
import time
import pandas as pd
import copy
import numpy as np
from shapely import affinity
from shapely.geometry import Polygon
import geopandas as gpd
def cal_arc(p1, p2, degree=False):
dx, dy = p2[0] - p1[0], p2[1] - p1[1]
arc = np.pi - np.arctan2(dy, dx)
return arc / np.pi * 180 if degree else arc
def... | pd.Series([y - yl for y, yl in df_line[['y', 'y_l']].values]) | pandas.Series |
# 1. Import packages
from __future__ import unicode_literals
import numpy as np
import pandas as pd
import sys
import os
import gensim
from tqdm import tqdm
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Activation
import logging
import re
from utils.stemm... | pd.read_csv("data/train_labels.csv", delimiter=',') | pandas.read_csv |
import sys
import os
#handling the paths and the model
cwd = os.getcwd()
sys.path.append(cwd)
from pathlib import Path
from pysd.py_backend.functions import Model
import pandas as pd
import varcontrol
import time
#handling the paths and the model
def run_model_web(switch0=0,start0=0,end0=0,effectiveness0=0,switch1... | pd.concat([base_df,pol_df],axis=1,keys=['base','policy']) | pandas.concat |
# coding: utf-8
# # ------------- Logistics -------------
# In[1]:
from __future__ import division
import numpy
import os
import pandas
import sklearn
import sys
import sqlite3
import pickle
from operator import itemgetter
from collections import Counter
import itertools
import matplotlib
import matplotlib.pyplot a... | pandas.Series(data=model.feature_importances_, index=data.columns) | pandas.Series |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['font.size']=6
# plt.rcParams['lines.markersize']=7
plt.rcParams['lines.linewidth'] = 0.8
from sklearn import decomposition
import os
root_path = os.path.dirname(os.path.abspath('__file__'))
import sys
sys.path.append(root_path)
def cu... | pd.DataFrame(ini_pcs_dict) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Poop analysis
Created 2020
@author: PClough
"""
import pandas as pd
import numpy as np
import chart_studio
import plotly.graph_objects as go
from plotly.offline import plot
from plotly.subplots import make_subplots
from scipy import stats
import datetime as dt
from time i... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
get_ipython().run_line_magic('matplotlib', 'notebook')
import matplotlib
import seaborn as sb
from matplotlib import pyplot as plt
from matplotlib import colors as mpcolors
import numpy as np
from scipy.optimize import linear_sum_assignment
import pandas as pd
# Jupyt... | pd.DataFrame(columns=['type','minc','mins','ncomp','clustered','unclustered','validity','validitysc','score1','score2']) | pandas.DataFrame |
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