prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import dash
import dash_bootstrap_components as dbc
from newsapi import NewsApiClient
from dash import dcc, Input, Output, html, State
from IPython import display
import math
from pprint import pprint
import pandas as pd
import numpy as np
import nltk
import matplotlib.pyplot as plt
import seaborn as sns
import plotly... | pd.DataFrame.from_records(headlinesResults) | pandas.DataFrame.from_records |
#%load_ext autoreload
#%autoreload 2
import dataclasses
import glob
import logging
import os
import shutil
import warnings
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Tuple
import numpy as np
import pandas as pd
from scipy.sparse.csr import csr_m... | pd.concat([results_pd, results_shaps], axis=1) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
import inspect
import json
import os
import urllib.request
from functools import reduce
from glob import glob
from time import sleep
from urllib.parse import quote
import jieba
import numpy as np
import pandas as pd
import seaborn as sns
from icecream import ic
from snorkel.label... | pd.set_option("display.max_rows", 200) | pandas.set_option |
"""
Module contains tools for processing Stata files into DataFrames
The StataReader below was originally written by <NAME> as part of PyDTA.
It has been extended and improved by <NAME> from the Statsmodels
project who also developed the StataWriter and was finally added to pandas in
a once again improved version.
Yo... | Index(columns) | pandas.core.indexes.base.Index |
# coding=utf-8
# Copyright 2018-2020 EVA
#
# 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 ... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
import functions as f
from lightfm import LightFM
from scipy import sparse
import math
import operator
import collections as cl
from scipy.sparse import csr_matrix
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
import time
impo... | pd.read_csv(metadata_file) | pandas.read_csv |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.3.0
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
from google.cloud import ... | pd.io.gbq.read_gbq(successful_selected_unit_concept_ids_by_site_query, dialect='standard') | pandas.io.gbq.read_gbq |
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 13 15:21:55 2019
@author: raryapratama
"""
#%%
#Step (1): Import Python libraries, set land conversion scenarios general parameters
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import quad
import seaborn as sns
import pandas as... | pd.DataFrame.from_dict({'Year':Col1,'kg_CO2':Col2_S1nu,'kg_CH4':Col3_S1nu,'kg_CO2_seq':Col4,'emission_ref':Col5}) | pandas.DataFrame.from_dict |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.neural_network import MLPRegressor, MLPC... | pd.DataFrame(index=index) | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from linearmodels import PanelOLS
import statsmodels.api as sm
import econtools as econ
import econtools.metrics as mt
import math
from statsmodels.stats.outliers_influence import variance_inflation_factor
from auxiliary.prepare import *
from auxil... | pd.DataFrame((ci_1,ci_2)) | pandas.DataFrame |
import altair as alt
import pandas as pd
import seaborn as sns
import six
from .util import build_dataframe, size_chart, vega_palette
from .pyplot import fill_between, plot, scatter as pscatter
__all__ = ["regplot", "lmplot"]
def regplot(
x, y, data=None, x_estimator=None, x_bins=None, x_ci="ci",
x_range=None... | pd.DataFrame({x: [xval, xval], y: cci}) | pandas.DataFrame |
import imgaug.augmenters as iaa
import numpy as np
import torch
from pose_est_nets.utils.io import (
check_if_semi_supervised,
set_or_open_folder,
get_latest_version,
)
from pose_est_nets.models.heatmap_tracker import (
HeatmapTracker,
SemiSupervisedHeatmapTracker,
)
from pose_est_nets.models.regres... | pd.DataFrame(predictions, columns=pdindex) | pandas.DataFrame |
# coding: utf-8
# # Visualize E-GEOD-33245 patterns
# This notebook will examine patterns of generic and experiment-specific genes using E-GEOD-33245 as the template experiment
#
# This experiment contains multiple comparisons/conditions:
#
# * grp_1v2 compares WT vs *crc* mutants
# * grp_1v3 compares WT vs *cbrB* ... | pd.Series(degs_1v3_diff) | pandas.Series |
import os
import pandas as pd
from typing import Any
from django.contrib.gis.geos import LineString, MultiLineString
def mission_planner_convert_log(url: str) -> list:
""" This function takes in a string url of the .waypoints, .txt or .json
file exported from the mission planner flight plan
... | pd.read_csv("me.csv", index_col=0) | pandas.read_csv |
import os
import random
import re
import sys
from shutil import copyfile
import cv2
import numpy as np
import pandas as pd
import pydicom as dicom
import torch
from PIL import Image
import cn.protect.quality as quality
from cn.protect.hierarchy import OrderHierarchy
from torch.utils.data import Dataset
from torchvisio... | pd.read_csv('data/HeartDisease/test.csv') | pandas.read_csv |
import re
from typing import Optional
import warnings
import numpy as np
from pandas.errors import AbstractMethodError
from pandas.util._decorators import cache_readonly
from pandas.core.dtypes.common import (
is_hashable,
is_integer,
is_iterator,
is_list_like,
is_number,
)
from p... | is_list_like(self.title) | pandas.core.dtypes.common.is_list_like |
# -*- coding: utf-8 -*-
from abc import ABC
from pathlib import Path
import pandas as pd
import scrapy
from src.crawl.utils import cleanup
from settings import YEAR, CRAWLING_OUTPUT_FOLDER
BASE_URl = "https://www.helmo.be/Formations/{}"
PROG_DATA_PATH = Path(__file__).parent.absolute().joinpath(
f'../../../../{C... | pd.Series(courses_ids_list, courses_urls_list) | pandas.Series |
# ----------------
# IMPORT PACKAGES
# ----------------
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
import sklearn.metrics as skm
import numpy as np
import matplotlib.pyplot as plt
# ----------------
# OBTAIN DATA
# ----------------
# Data Source: https://archive.ics.uci.edu... | pd.read_csv("train/subject_train.txt", header=None, delim_whitespace=True, index_col=False) | pandas.read_csv |
import json
import pandas as pd
import random
import os
import pyproj
import numpy as np
import geopandas as gpd
from pathlib import Path
from datetime import datetime
from copy import deepcopy
from shapely.geometry import Point
from shapely.ops import transform
from sklearn.preprocessing import OneHotEncoder
# load c... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import html
from bedrock.doc.relation import Relation
from bedrock.doc.annotation import Annotation
from bedrock.doc.token import Token
from bedrock.doc.layer import Layer
from bedrock.common import uima
import logging
from typing import Any
import warnings
class CAS2DataFrameConverter:
def ... | pd.DataFrame(columns=Annotation.COLS) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import csv
import random
from time import time
from decimal import Decimal
#from faker import Faker
import boto3
import string
import random
import os
import os
import re
import collections
import nltk
import pandas as pd
#from nltk.corpus import stopwords
from io import StringIO # python3; pyth... | pd.DataFrame(preprocessed_list) | pandas.DataFrame |
# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/04_evaluation.core.ipynb (unless otherwise specified).
__all__ = ['get_mean_probs', 'find_parens', 'mean_dist_probs', 'token_taxonomy', 'non_wordy', 'get_error_rates',
'get_error_rates_df', 'get_last_token_error_df', 'get_mean_cross_entropy', 'get_mean_probs',... | pd.read_json(bigclone_path / f"bigclone-type-{i}.jsonl", orient="records", lines=True) | pandas.read_json |
import numpy as np
import pandas as pd
from scipy.interpolate import interp1d
from scipy.spatial import distance
from scipy.optimize import differential_evolution
class IntracellAnalysisV2:
# IA constants
FC_UPPER_VOLTAGE = 4.20
FC_LOWER_VOLTAGE = 2.70
NE_UPPER_VOLTAGE = 0.01
NE_LOWER_VOLTAGE = 1.... | pd.DataFrame() | pandas.DataFrame |
# Copyright 2019 Elasticsearch BV
#
# 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 applicabl... | pd.get_option("display.max_rows") | pandas.get_option |
import os
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import matplotlib.dates as mdates
from datetime import date, timedelta, datetime
import seaborn as sns
import geopandas as gpd
from shapely.geometry import mapping, Point, Polygon
mpl.rcParams['pdf.fonttype'] = 42... | pd.merge(left=ll_df, right=ltcf_df, on='Deceased Date', how='outer') | pandas.merge |
import torch, os
import numpy as np
from MiniImagenet_memorization import MiniImagenet as MiniImagenet_fix
from torch.utils.data import DataLoader
import random, argparse
from meta import Meta_mini
from utils import get_config, save_model, name_path, load_model
import time
import pandas as pd
# get arg... | pd.DataFrame(test_zero_text) | pandas.DataFrame |
import pandas as pd
import random
from collections import deque
from .Broker import Broker
from .Order import Order
class BacktestBroker(Broker):
def __init__(self, balance, maxLeverage=1, interest=0, commission=0.001,
liveTrading=False, symbol='BTC'):
self._balance = balance
sel... | pd.to_datetime([]) | pandas.to_datetime |
"""
Estimators for systems of equations
References
----------
Greene, <NAME>. "Econometric analysis 4th edition." International edition,
New Jersey: Prentice Hall (2000).
StataCorp. 2013. Stata 13 Base Reference Manual. College Station, TX: Stata
Press.
<NAME>., & <NAME>. (2007). systemfit: A Package for Estim... | DataFrame(sigma, columns=names, index=names) | pandas.DataFrame |
import os, sys
import numpy as np
import pandas as pd
from datetime import datetime, date
import csv
import ast
import shutil
import requests
import json
from zipfile import ZipFile
from bs4 import BeautifulSoup
__author__ = '<NAME>, <NAME>'
__copyright__ = '© Pandemic Central, 2021'
__license__ = 'MIT'
__status__ = '... | pd.Timedelta(value=1, unit='day') | pandas.Timedelta |
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import os
import pickle
import re
from collections import defaultdict
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix, roc_auc_score... | pd.read_pickle(f) | pandas.read_pickle |
import numpy as np
import pandas as pd
import seaborn as sns
import estimagic.differentiation.finite_differences as fd
from multiprocessing import Pool
from matplotlib import pyplot as plt
from estimagic.differentiation.generate_steps import generate_steps
from estimagic.optimization.utilities import namedtuple_from_kw... | pd.DataFrame(err) | pandas.DataFrame |
#encoding=utf-8
import pandas as pd
from Data import load_file
from sklearn.preprocessing import Imputer
dir='D:/kesci/data/part_data'
test_master_numeric='/test_master_numeric.csv'
test_master_category='/test_master_category.csv'
test_UserUpdate='/test_UserUpdate.csv'
test_LogInfo='/test_LogInfo.csv'
tr... | pd.DataFrame(test) | pandas.DataFrame |
import glob
import numpy as np
import scipy
import os
from sklearn.cluster import KMeans
from sklearn.metrics import adjusted_rand_score
from joblib import dump
import pandas as pd
from multiprocessing.pool import ThreadPool
from pyhydra.utils import check_symmetric, launch_svc
__author__ = "<NAME>"
__copyright__ = "C... | pd.read_csv(cluster_ass2, sep='\t') | pandas.read_csv |
# pip3 install apyori
# importacao das bibliotecas
import pandas as pd
import numpy as np
from apyori import apriori
from matplotlib import pyplot as plt
# Configurando print das colunas e linhas
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
def... | pd.read_csv('_ASSOC_VoleiStars.csv', index_col=None, encoding='iso-8859-1') | pandas.read_csv |
#libraries
import numpy as np
import pandas as pd
from datetime import datetime as dt
import time
import datetime
import os
import warnings
warnings.filterwarnings("ignore")
import logging
logging.basicConfig(filename='log.txt',level=logging.DEBUG, format='%(asctime)s %(message)s')
| pd.set_option('max_colwidth', 500) | pandas.set_option |
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Any
import ujson
import numpy as np
import pandas as pd
from tensorflow import config as tfc
from tensorflow.keras.models import load_model
from tqdm import trange, tqdm
from tifffile import imsave
import matplotlib.py... | pd.read_csv(dm_pattern, header=None) | pandas.read_csv |
import numpy as np
import pandas as pd
class LinearRegression:
def __init__(self, reg: str = None, pen: float = None):
# Sanity check
if reg not in (None, 'l1', 'l2'):
raise ValueError('Regularization not supported')
if reg in ('l1', 'l2'):
try:
asse... | pd.Series(yi, index=[i_data], name=self.target) | pandas.Series |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import scipy.optimize as optimize
from scipy.special import betaln
from pandas.stats.moments import rolling_mean as rolling_m
from pandas.stats.moments import rolling_corr
from warnings import warn
import matplotlib.pyplot as plt
from time import time
from ... | rolling_m(x[col_x] * y[col_y], **kwargs) | pandas.stats.moments.rolling_mean |
import os.path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib import pyplot
from pandas.api.types import is_string_dtype
from sklearn import metrics
from sklearn.decomposition import PCA
from sklearn.ensemble import BaggingClassifier, AdaBoostClassifier, RandomForestClassifier, ... | pd.read_csv("prediction_info.csv") | pandas.read_csv |
# Load datasets
import os
import re
import shutil
import json
import pickle
import logging as log
from pathlib import Path
from typing import *
from collections import Counter
from itertools import combinations, groupby
import tqdm
import pandas as pd
import numpy as np
import takco
from takco.link.profile import pfd... | pd.DataFrame(fkclass_candidates, columns=["ti", "ci", "fkclass", "score"]) | pandas.DataFrame |
#
# Copyright (C) 2019 Databricks, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | pd.MultiIndex.from_tuples([("a", "x", 1), ("b", "y", 2), ("c", "z", 3)]) | pandas.MultiIndex.from_tuples |
from __future__ import annotations
import numpy as np
import pandas as pd
from lamarck.utils import objective_ascending_map
def rank_formatter(name):
def deco(rank_func):
def wrapper(obj, *a, **kw):
return rank_func(obj, *a, **kw).astype(int).rename(name)
return wrapper
return dec... | pd.DataFrame(data_dict, index=df.index) | pandas.DataFrame |
#!/usr/bin/env python
from pandas.io.formats.format import SeriesFormatter
from Bio.SeqUtils import seq1
from Bio import SeqIO
import pandas as pd
import argparse
from pathlib import Path
import numpy as np
from summarise_snpeff import parse_vcf, write_vcf
import csv
import re
from functools import reduce
from binding... | pd.Series(headiter) | pandas.Series |
import os
import logging
import json
import collections
import yaml
import pandas as pd
import graphviz as gv
# import numpy as np
# from matplotlib import pyplot as plt
from kinemparse import assembly as lib_asm
from mathtools import utils
from seqtools import fstutils_openfst as fstutils
import pywrapfst as libfst
... | pd.DataFrame({'start': start_seq, 'end': end_seq, 'label': label_seq}) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import library.areamanager as areamanager
import pandas as pd
import json
import time
import collections
import numpy as np
import pickle
import library.cat_utils as cat_utils
import library.geo_utils as geo_utils
from library.parallel_util import run_parallel
from libr... | pd.merge(df_checkin,df_diff_users_visited,on='poi_id',how='inner') | pandas.merge |
#!/usr/bin/python
#-*- coding: utf-8 -*-
# >.>.>.>.>.>.>.>.>.>.>.>.>.>.>.>.
# Licensed under the Apache License, Version 2.0 (the "License")
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# --- File Name: collect_results.py
# --- Creation Date: 08-09-2020
# --- Last Modified: T... | pd.DataFrame(new_results) | pandas.DataFrame |
"""
Summarize and run basic analysis on MTurk returns
"""
import json
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from factor_analyzer import FactorAnalyzer, ModelSpecificationParser, ConfirmatoryFactorAnalyzer
from joblib import load
from matplotlib.backends.backend_pd... | pd.concat([demos, d]) | pandas.concat |
"""
training of LR_clim_clim_conv baseline
"""
from tensorflow.keras.layers import Input, Dense
from cbrain.layers import *
from tensorflow.keras.models import Model
from tensorflow.keras.losses import mse, binary_crossentropy
from tensorflow.keras.utils import plot_model
from tensorflow.keras import backend as K
from... | ps.read_csv('nn_config/scale_dicts/Scaling_enc_II_range.csv') | pandas.read_csv |
# pylint: disable=W0102
import nose
import numpy as np
from pandas import Index, MultiIndex, DataFrame, Series
from pandas.compat import OrderedDict, lrange
from pandas.sparse.array import SparseArray
from pandas.core.internals import *
import pandas.core.internals as internals
import pandas.util.testing as tm
from ... | randn(N) | pandas.util.testing.randn |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from catboost import CatBoostRegressor
from tqdm import tqdm
import gc
import datetime as dt
print('Loading Properties ...')
properties2016 = pd.read_csv('../input/properties_2016.csv', low_memory = False)
proper... | pd.Timestamp('2017-11-30') | pandas.Timestamp |
# pylint: disable-msg=E1101,W0613,W0603
import os
import copy
from collections import defaultdict
import numpy as np
import pandas.json as _json
from pandas.tslib import iNaT
from pandas.compat import StringIO, long, u
from pandas import compat, isnull
from pandas import Series, DataFrame, to_datetime
from pandas.io.... | AbstractMethodError(self) | pandas.core.common.AbstractMethodError |
import re
import struct
import pandas as pd
import numpy as np
from argparse import ArgumentParser
from pathlib import Path
from tqdm import tqdm
from collections import namedtuple
from datetime import datetime, timedelta
from model_sx_log import ModelSxLog
from kaitaistruct import KaitaiStream, BytesIO, ValidationNotE... | pd.DataFrame(values) | pandas.DataFrame |
import argparse
import tempfile
import os
import pandas as pd
import numpy as np
from conga.tcrdist.make_10x_clones_file import make_10x_clones_file
from conga.preprocess import calc_tcrdist_matrix_cpp
# from hello import say_hello_to, parse_charptr_to_py_int
def covepitope_convert_from_10x():
parser = argparse.A... | pd.DataFrame(D_cpp) | pandas.DataFrame |
import pandas as pd
from sqlalchemy import create_engine
from library import cf
import talib.abstract as ta
import pymysql.cursors
import numpy as np
from library.logging_pack import *
logger.debug("subindex시작!!!!")
pymysql.install_as_MySQLdb()
daily_craw_engine=create_engine(
"mysql+mysql... | pd.DataFrame(th_obvsig9, columns=['obvsig9']) | pandas.DataFrame |
# Fed Interest Rate Data (Dates are approximate- representing the Sunday of the week when the rate was announced)
import pandas as pd
import numpy as np
from datetime import timedelta
file0319 = pd.read_html('https://www.federalreserve.gov/monetarypolicy/openmarket.htm')
file9002 = pd.read_html('https://www.fede... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import time
def patient(rdb):
""" Returns list of patients """
patients = """SELECT "Name" FROM patient ORDER BY index"""
try:
patients = pd.read_sql(patients, rdb)
patients = patients["Name"].values.tolist()
except:
patients = ['Patient']
return patien... | pd.read_sql(sql, rdb) | pandas.read_sql |
# -*- coding: utf-8 -*-
# pylint: disable=E1101
# flake8: noqa
from datetime import datetime
import csv
import os
import sys
import re
import nose
import platform
from multiprocessing.pool import ThreadPool
from numpy import nan
import numpy as np
from pandas.io.common import DtypeWarning
from pandas import DataFr... | StringIO(data) | pandas.compat.StringIO |
import pathlib
import pytest
import pandas as pd
import numpy as np
import gradelib
EXAMPLES_DIRECTORY = pathlib.Path(__file__).parent / "examples"
GRADESCOPE_EXAMPLE = gradelib.Gradebook.from_gradescope(
EXAMPLES_DIRECTORY / "gradescope.csv"
)
CANVAS_EXAMPLE = gradelib.Gradebook.from_canvas(EXAMPLES_DIRECTORY ... | pd.Series([2, 50, 100, 20], index=columns) | pandas.Series |
import unittest
import pandas as pd
import numpy as np
from pandas.testing import assert_frame_equal
from msticpy.analysis.anomalous_sequence import sessionize
class TestSessionize(unittest.TestCase):
def setUp(self):
self.df1 = pd.DataFrame({"UserId": [], "time": [], "operation": []})
self.df1_... | pd.to_timedelta(0, "min") | pandas.to_timedelta |
from tqdm import tqdm
import pandas as pd
import sys, os
import collections
"""
Small script to concat ENCODE files into a single dataframe to process it easily
5 cols = SRS sequencing
12 cols = LRS sequencing
"""
encode_dl_directory = "/gstock/biolo_datasets/ENCODE/DL/"
dict_df = collections.defaultdict(list)
for... | pd.read_csv(encode_dl_directory + file, sep="\t") | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
#Importing all the necessary packages and operators
import os
import pandas as pd
import dask.dataframe as dd
import json
import datetime
from airflow import models
from airflow.providers.google.cloud.operators.bigquery import (
BigQueryCreateEmptyDatasetOperator,
... | pd.to_datetime(transformed_df['price_timestamp']) | pandas.to_datetime |
import pandas as pd
import numpy as np
#series
'''
s = pd.Series(np.random.randn(5))
print(s)
print (s.tail(2))
'''
#dataFrame
d={'name':['anish','harshal','shivam','joyal'],
'age':[20,20,19,19],
'home':['kol','mum','del','ker'],
'rate':[4.4,4.3,5.4,6.5],
'rate2':[1,2,3,4]}
pf= | pd.DataFrame(d,index=[1,2,3,4]) | pandas.DataFrame |
"""Feature extraction of image for training ML models"""
import os
import cv2
import numpy as np
import pandas as pd
#import matplotlib.pyplot as plt
from skimage.filters import sobel, scharr, roberts, prewitt
from skimage.feature import canny
from scipy import ndimage as nd
# Extract the features using different gab... | pd.DataFrame([]) | pandas.DataFrame |
import numpy as np
import scipy
import matplotlib
import pandas as pd
import sklearn
from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf
import keras
import matplotlib.pyplot as plt
from datetime import datetime
from loss_mse import loss_mse_warmup
from custom_generator import batch_generator
#Keras
... | pd.to_datetime(dataset.Data,format='%Y%m%d') | pandas.to_datetime |
# Copyright 2019 QuantRocket LLC - 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.Series(0, index=pair_prices.columns) | pandas.Series |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import re
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import TruncatedSVD
from sklearn import preprocessing, model_select... | pd.DataFrame(enc_mat_test) | pandas.DataFrame |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from vc.definitions import ROOT_DIR
####################################################################
# Common variables.
####################################################################
# Get the the root dir of the module.
# Fol... | pd.DataFrame({"Vitoria": vitoria_df.loc[year]}) | pandas.DataFrame |
# Analysis of *rXiv clusters
# %%
import logging
import re
from datetime import datetime
import altair as alt
import pandas as pd
import statsmodels.api as sm
from numpy.random import choice
from scipy.spatial.distance import cityblock
from statsmodels.api import OLS, Poisson, ZeroInflatedPoisson
from eurito_indicat... | pd.concat(collabs) | pandas.concat |
import torch
from torch.utils import data as D
import os, io
from datetime import datetime
from . import preprocessing
from zeiss_umbrella.fundus.adversarial import get_diff
from zeiss_umbrella.fundus.quality_augmentation.transform import preset_augment
from zeiss_umbrella.fundus.quality_augmentation.make_dataset impor... | pd.DataFrame(distance_dict) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import numpy as np
import pandas as pd
import datetime as dt
from scipy import stats
import pymannkendall as mk
from Modules import Read
from Modules.Utils import Listador, FindOutlier, Cycles
from Modules.Graphs import GraphSerieOutliers, GraphDataFrames, Grap... | pd.DatetimeIndex(Rayar_t.index) | pandas.DatetimeIndex |
import os
import wx
import datetime
from pubsub import pub
import xlwings as xlw
import pandas as pd
import numpy as np
import wx.lib.mixins.listctrl as listmix
import image_viewer
import analyzer
wildcard = "Python source (*.py)|*.py|" \
"Compiled Python (*.pyc)|*.pyc|" \
"Comma sep(csv) (*.c... | pd.Series(input_text) | pandas.Series |
from typing import NoReturn, Tuple, Any, Union, Optional, List, Callable, Dict
from timeatlas.abstract.abstract_base_generator import AbstractBaseGenerator
from timeatlas.time_series import TimeSeries
from timeatlas.time_series_dataset import TimeSeriesDataset
from timeatlas.config.constants import COMPONENT_VALUES
f... | pd.Series(new_data) | pandas.Series |
"""
Create ensemble forecast.
"""
import numpy as np
import pandas as pd
from pywtk.site_lookup import get_3tiersites_from_wkt
from pywtk.wtk_api import get_nc_data, WIND_FCST_DIR
from pywtk import site_lookup
from . import stats
class Ensemble:
"""Creation of ensemble forecasts."""
_allowable_horizons = (1,... | pd.DataFrame(forecasts, index=forecast.index) | pandas.DataFrame |
import os
import pandas as pd
CURRENT_DIR = os.path.dirname(__file__)
INPUT_DIR = os.path.join(CURRENT_DIR, "input")
TMP_DIR = os.path.join(CURRENT_DIR, "tmp")
GRAPHER_DIR = os.path.join(CURRENT_DIR, "grapher")
def main():
# GCP data
gas_gcp = pd.read_excel(
os.path.join(INPUT_DIR, "country_fuel/gas... | pd.melt(coal_gcp, id_vars=["Year"], var_name=["Country"], value_name="Coal") | pandas.melt |
import os
import re
from retry import retry
from typing import List, Union
import pandas as pd
import requests
from tqdm import tqdm
import multitasking
import signal
from .config import EastmoneyFundHeaders
from ..utils import to_numeric
from jsonpath import jsonpath
signal.signal(signal.SIGINT, multitasking.killall)
... | pd.DataFrame(rows) | pandas.DataFrame |
import warnings
from collections import Counter
from typing import Dict
from unittest.mock import patch
import numpy as np
import pandas as pd
import pyarrow
import pytest
from pandas import DataFrame
import ray
from ray.data import Dataset
from ray.data.aggregate import Max
from ray.data.preprocessor import Preproce... | pd.DataFrame.from_dict({"A": col_a, "B": col_b, "C": col_c}) | pandas.DataFrame.from_dict |
#!/conda/bin/python2.7
import sys
import argparse
import vcf
import pandas
from vcf.parser import _vcf_metadata_parser as vcf_parser
class vep_converter():
def __init__(self, input_name, output_name, transcript_file):
self._input_name = input_name
self._output_name = output_name
... | pandas.DataFrame(columns=column_names) | pandas.DataFrame |
__author__ = "<NAME>"
__copyright__ = "BMW Group"
__version__ = "0.0.1"
__maintainer__ = "<NAME>"
__email__ = "<EMAIL>"
__status__ = "Development"
from tsa import Logger
import sys
import numpy as np
import pandas as pd
import datetime
from dateutil.relativedelta import relativedelta
import argparse
import matplotlib... | pd.Series(yhat, index=self._train_dt.index) | pandas.Series |
'''
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py 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, either version 3 of the License, or
(at your option) any late... | pd.Series.to_list(col) | pandas.Series.to_list |
'''
example of loading FinMind api
'''
from Data import Load
import requests
import pandas as pd
url = 'http://finmindapi.servebeer.com/api/data'
list_url = 'http://finmindapi.servebeer.com/api/datalist'
translate_url = 'http://finmindapi.servebeer.com/api/translation'
'''----------------TaiwanStockInfo-------------... | pd.DataFrame(temp['data']) | pandas.DataFrame |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.13.7
# kernelspec:
# display_name: Python 3.8.8 64-bit ('cam')
# language: python
# name: python388jvsc74a57bd0acafb728b15233fa3654ff8b422... | pd.read_csv("example_data.csv", index_col=0) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 12 15:18:57 2018
@author: Denny.Lehman
"""
import pandas as pd
import numpy as np
import datetime
import time
from pandas.tseries.offsets import MonthEnd
def npv(rate, df):
value = 0
for i in range(0, df.size):
value += df.iloc[i] / (1 + rate) ** (i + 1... | pd.to_datetime(df['InService Date']) | pandas.to_datetime |
import unittest
import pandas as pd
import numpy as np
from scipy.sparse.csr import csr_matrix
from string_grouper.string_grouper import DEFAULT_MIN_SIMILARITY, \
DEFAULT_REGEX, DEFAULT_NGRAM_SIZE, DEFAULT_N_PROCESSES, DEFAULT_IGNORE_CASE, \
StringGrouperConfig, StringGrouper, StringGrouperNotFitException, \
... | pd.Series(["hello"]) | pandas.Series |
# -*- coding: utf-8 -*-
# run in py3 !!
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1";
import tensorflow as tf
config = tf.ConfigProto()
# config.gpu_options.per_process_gpu_memory_fraction=0.5
config.gpu_options.allow_growth = True
tf.Session(config=config)
import numpy as np
from sklearn import preprocessing... | pd.DataFrame(df_month) | pandas.DataFrame |
from minder_utils.configurations import feature_config, config
import numpy as np
from .calculation import entropy_rate_from_sequence
from .TimeFunctions import rp_location_delta
from .util import *
from minder_utils.util.util import PBar
import pandas as pd
from typing import Union
import sys
def get_moving_average(... | pd.to_datetime(data.time) | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 14 21:31:56 2017
@author: Franz
"""
import scipy.signal
import numpy as np
import scipy.io as so
import os.path
import re
import matplotlib.pylab as plt
import h5py
import matplotlib.patches as patches
import numpy.random as rand
import seaborn as s... | pd.DataFrame(columns=['Idf', 'Freq', 'Pow', 'Lsr'], data=data) | pandas.DataFrame |
"""
Copyright 2022 <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
distrib... | pd.DataFrame([dimred.components_[ic]],columns=analyte_df.columns.values,index=[gid]) | pandas.DataFrame |
"""
NAD Lab Tools
This program was written for the NAD Lab at the University of Arizona by <NAME>.
It processes intracellular calcium concentration and pH measurements (from the InCytim2 software)
as well as filters the data for outliers and spikes.
The experiment consists of placing fluorescent-stained cells... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import tabula
import numpy as np
from CNAFenums import Approach, Landing, Role
from local_func import getFILES, INTERGER
import uuid, re, os, glob
import PyPDF2
from progress.bar import Bar
def msharp(log_file, aircraft_filter='All', nav = False):
#print(log_file)
msharp_data_raw = pd.read_... | pd.isna(x) | pandas.isna |
import numpy as np
import pandas as pd
import argparse
def check_smiles_match(data,screen):
return (data['SMILES'].values==screen['SMILES'].values).all()
def apply_screen(data,col_name,selection_type,selection_thresh,keep):
data = data.sort_values(col_name,ascending=True)
if selection_type=='Fraction':
... | pd.DataFrame(screen2.columns) | pandas.DataFrame |
import folium
import time
import branca
from tqdm import tqdm
from datetime import datetime
import requests
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import codecs
from folium.features import DivIcon
from nts_data_collect import terminal_intakes
from bokeh.io import save
from bokeh.pl... | pd.DataFrame(terminal_data) | pandas.DataFrame |
'''
Tests for bipartitepandas
DATE: March 2021
'''
import pytest
import numpy as np
import pandas as pd
import bipartitepandas as bpd
import pickle
###################################
##### Tests for BipartiteBase #####
###################################
def test_refactor_1():
# 2 movers between firms 0 and 1, ... | pd.DataFrame(worker, index=[i]) | pandas.DataFrame |
__author__ = "<NAME>"
__copyright__ = "xuanchen yao"
__license__ = "mit"
import mysql.connector
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
def train(i):
try:
rds_host='mybike.c0jxuz6r8olg.us-west-2.rds.amazonaws.com'
name='hibike'
pwd='<PASSWORD>'
db_name='... | pd.read_sql("select * from weather", con=conn) | pandas.read_sql |
import random
import time
import numpy as np
import pandas as pd
from pyziabm.runner2017mpi_r4 import Runner
def participation_to_list(h5in, outlist):
trade_df = pd.read_hdf(h5in, 'trades')
trade_df = trade_df.assign(trader_id = trade_df.resting_order_id.str.split('_').str[0])
lt_df = pd.DataFra... | pd.merge(buy_trades, sell_trades, left_on=['trader_id', 'BuyVol'], right_on=['trader_id', 'SellVol']) | pandas.merge |
import numpy as np
from datetime import timedelta
from distutils.version import LooseVersion
import pandas as pd
import pandas.util.testing as tm
from pandas import to_timedelta
from pandas.util.testing import assert_series_equal, assert_frame_equal
from pandas import (Series, Timedelta, DataFrame, Timestamp, Timedelt... | date_range('20130101', periods=3, name='bar') | pandas.date_range |
import numpy as np
import pandas as pd
from scipy import sparse
def genpoisson_spiketrain(rate, dt, duration):
offset = duration
events = np.cumsum(np.random.exponential(scale = 1 / rate, size = int(duration*rate + offset)))
return np.round(events[np.logical_and(0 < events, events < duration)], -int(np.log... | pd.concat([df, df_seq]) | pandas.concat |
from __future__ import print_function, division
import os
import glob
import re
import copy
import warnings
import numpy as np
import pandas as pd
pd.options.display.max_colwidth = 100
import pyemu
from ..pyemu_warnings import PyemuWarning
from pyemu.pst.pst_controldata import ControlData, SvdData, RegData
from pyemu.... | pd.read_csv(filename, sep=sep, na_values=missing_vals) | pandas.read_csv |
"""
Created on May 21, 2020
@author: <NAME>
start server with the following command:
bokeh serve --show OS_Report
view at: http://localhost:5006/OS_Report
"""
import os, sys
import pandas as pd
import numpy as np
import logging
from bokeh.io import curdoc
from bokeh.models import TextInput, Button, TextAreaInput, ... | pd.to_datetime(self.df.Date,format='%b. %d, %Y') | pandas.to_datetime |
from contextlib import nullcontext
import copy
import numpy as np
import pytest
from pandas._libs.missing import is_matching_na
from pandas.core.dtypes.common import is_float
from pandas import (
Index,
MultiIndex,
Series,
)
import pandas._testing as tm
@pytest.mark.parametrize(
"arr, idx",
[
... | is_float(left) | pandas.core.dtypes.common.is_float |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright 2013-2019 European Commission (JRC);
# Licensed under the EUPL (the 'Licence');
# You may not use this work except in compliance with the Licence.
# You may obtain a copy of the Licence at: http://ec.europa.eu/idabc/eupl
"""*(TO BE DEFUNCT)* The core that acce... | pd.DataFrame() | pandas.DataFrame |
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