prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pandas as pd
import glob
from string import ascii_uppercase
from functools import partial
## Environmental Variables + Medical condition
from ..processing.base_processing import read_ethnicity_data
from ..environment_processing.base_processing import path_features , path_predictions, path_inputs_env, path_targe... | pd.read_csv('/n/groups/patel/samuel/EWAS/AutomaticClusters/%s_%s.csv' % (env_dataset, target_dataset)) | pandas.read_csv |
import pytest
import os
from mapping import util
from pandas.util.testing import assert_frame_equal, assert_series_equal
import pandas as pd
from pandas import Timestamp as TS
import numpy as np
@pytest.fixture
def price_files():
cdir = os.path.dirname(__file__)
path = os.path.join(cdir, 'data/')
files = ... | TS('2015-01-03') | pandas.Timestamp |
from sacred.observers import MongoObserver, FileStorageObserver
from pandas import Series
from pymongo import MongoClient
from gridfs import GridFS
from tensorflow.python.summary.summary_iterator import summary_iterator
import xview.settings as settings
from xview.datasets import get_dataset
from bson.json_util import ... | Series(value, index=step) | pandas.Series |
# -*- coding: utf-8 -*-
"""
@file:maketrain.py
@time:2019/5/6 16:42
@author:Tangj
@software:Pycharm
@Desc
"""
import pandas as pd
import numpy as np
import gc
import time
name = ['log_0_1999', 'log_2000_3999', 'log_4000_5999','log_6000_7999', 'log_8000_9999', 'log_10000_19999',
'log_20000_29999', 'log_30000_39... | pd.concat([Train, train2]) | pandas.concat |
import random
import itertools as it
import pandas as pd
import pandera as pa
import numpy as np
import plotly.graph_objects as pg
import textwrap as tw
def create_transactions(
date1: str,
date2: str,
freq: str="12H",
income_categories: list[str]=None,
expense_categories: list[str]=None,
) -> pd... | pd.Grouper(freq=freq, label="left", closed="left") | pandas.Grouper |
"""
helper functions for `goenrich`
"""
import pandas as pd
import numpy as np
def generate_background(annotations, df, go_id, entry_id):
""" generate the backgound from pandas datasets
>>> O = ontology(...)
>>> annotations = goenrich.read.gene2go(...)
>>> background = generate_background(annotations,... | pd.merge(annotations, df[[entry_id]]) | pandas.merge |
import os
import pandas as pd
# https://github.com/CSSEGISandData/COVID-19.git
REPOSITORY = "https://raw.githubusercontent.com/CSSEGISandData"
MAIN_FOLDER = "COVID-19/master/csse_covid_19_data/csse_covid_19_time_series"
CONFIRMED_FILE = "time_series_covid19_confirmed_global.csv"
DEATHS_FILE = "time_series_covid19_d... | pd.read_csv(url_stable, sep=";") | pandas.read_csv |
'''this module for the data was emported from excel sheet analysis'''
import pandas as pd
import openpyxl as xl
from openpyxl import load_workbook
import numpy as np
import os
from copy import copy
import random
from random import randint,seed
from openpyxl.chart import BarChart, Reference, Series,LineChart
from .col... | pd.read_excel(self.readfile,"input") | pandas.read_excel |
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from scipy.stats import kurtosis
import matplotlib.pyplot as plt
from scipy.stats import skew
def Outliers_StdDev(data: pd.Series, distance_threshold: int) -> list:
"""
Returns the outliers in a pandas series with the spec... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# Created by <NAME>
import unittest
import pandas as pd
import pandas.testing as pdtest
from allfreqs import AlleleFreqs
from allfreqs.classes import Reference, MultiAlignment
from allfreqs.tests.constants import (
REAL_ALG_X_FASTA, REAL_ALG_X_NOREF_FASTA, REAL_RSRS_F... | pd.read_csv(TEST_CSV) | pandas.read_csv |
import pandas as pd
import numpy as np
def clean_static_df(static_df):
static_df_clean = static_df
static_df_clean = pd.get_dummies(data=static_df_clean, columns=[
'sex']).drop('sex_MALE', axis=1)
static_df_clean.drop('discharge_destination', axis=1, inplace=True)
... | pd.to_timedelta(treat_df['hours_in']) | pandas.to_timedelta |
import pandas as pd
import numpy as np
DATA_PATH = 'rawdata/' #where the raw data files are
ALT_OUTPUT_PATH = 'alt_output/' #there will be many files produced -- the files that are not "the main ones"
# are placed in this directory
feasDF = pd.read_csv(DATA_PATH+"mipdev_feasibility.csv") #read ... | pd.read_csv(DATA_PATH+"RedCost_integral_data.csv") | pandas.read_csv |
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import re
from math import ceil
import pandas as pd
from sklearn.metrics import classification_report
from scipy.stats import shapiro, boxcox, yeojohnson
from scipy.stats import probplot
from sklearn.preprocessing import LabelEncoder, PowerTransfo... | pd.DataFrame(x, columns=x_labels) | pandas.DataFrame |
#!/usr/bin/env python3
#################################################
# Title: ADSB Plot
# Project: ADSB
# Version: 0.0.1
# Date: Jan, 2020
# Author: <NAME>, KJ4QLP
# Comment:
# - learning plotly
#################################################
import math
import string
import time
import sys
import os
impor... | pd.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
import pytest
import numpy as np
from datetime import date, timedelta, time, datetime
import dateutil
import pandas as pd
import pandas.util.testing as tm
from pandas.compat import lrange
from pandas.compat.numpy import np_datetime64_compat
from pandas import (DatetimeIndex, Index, date_range, DataFrame,
... | tm.assert_index_equal(idx, exp_idx) | pandas.util.testing.assert_index_equal |
import pandas as pd
import numpy as np
from tqdm import tqdm
import os
# import emoji
import gc
from utils.definitions import ROOT_DIR
from collections import OrderedDict
from utils.datareader import Datareader
def check_conditions( df, mean, std, error=(1.5,1.5)):
"""
checks if the dataframe given is near has... | pd.concat([df_test_pl, df]) | pandas.concat |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# *****************************************************************************/
# * Authors: <NAME>
# *****************************************************************************/
"""transformCSV.py
This module contains the basic functions for creating the content of... | pandas.StringDtype() | pandas.StringDtype |
"""
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.concat([p_balance, net]) | pandas.concat |
import operator
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays.numpy_ import PandasDtype
from .base import BaseExtensionTests
class BaseSetitemTests(BaseExtensionTests):
def test_setitem_scalar_series(self, data, box_in_series):
i... | pd.DataFrame(index=df.index) | pandas.DataFrame |
# %% [markdown]
# This python script takes audio files from "filedata" from sonicboom, runs each audio file through
# Fast Fourier Transform, plots the FFT image, splits the FFT'd images into train, test & validation
# and paste them in their respective folders
# Import Dependencies
import numpy as np
import pandas... | pd.concat([test, testTemp]) | pandas.concat |
import sys
from pathlib import Path
from pprint import pprint
import pandas as pd
from train_model import train_model_pipe
parent_dir = str(Path(__file__).parent.parent.resolve())
sys.path.append(parent_dir)
if __name__ == '__main__':
from features.build_features import clean_for_reg
raw_d = Path('../../dat... | pd.read_csv(raw_d / 'train.csv') | pandas.read_csv |
import tarfile
with tarfile.open('data/aclImdb_v1.tar.gz', 'r:gz') as tar:
tar.extractall()
import pyprind
import pandas as pd
import os
basepath = 'aclImdb'
labels = {'pos':1, 'neg':0}
pbar = pyprind.ProgBar(50000)
df = pd.DataFrame()
for s in ('test','train'):
for l in ('pos','neg'):
path = os.p... | pd.read_csv('data/imdb.csv', encoding='utf-8') | pandas.read_csv |
# SCOTT ST. LOUIS, Michigan Publishing, Fall 2020
# Preliminary Metadata Curation: ACLS Humanities E-Book Collection on Fulcrum
import csv
import pandas as pd
def read_csv_with_pandas(filename):
"""
Function written by <NAME>, Digital Publishing Coordinator,
during troubleshooting with Scot... | pd.read_csv(filename,dtype=str) | pandas.read_csv |
import random
import pandas as pd
from detoxai.preprocess import *
from sklearn import model_selection as ms
import os
import math
import json
class LoadData:
def __init__(self, task='all'):
with open('config.json', 'r') as f:
config = json.load(f)
self.paths = config['paths']
... | pd.concat([x_sample_0, x_sample_1]) | pandas.concat |
from datasets import load_dataset
import streamlit as st
import pandas as pd
from googletrans import Translator
import session_state
import time
from fuzzywuzzy import fuzz,process
# Security
#passlib,hashlib,bcrypt,scrypt
import hashlib
# DB Management
import sqlite3
import os
import psycopg2
# impo... | pd.DataFrame(user_result, columns=["Username", "FullName", "Password"]) | pandas.DataFrame |
from datetime import datetime, timedelta
import numpy as np
import pytest
from pandas._libs import iNaT
from pandas._libs.algos import Infinity, NegInfinity
import pandas.util._test_decorators as td
from pandas import DataFrame, Series
import pandas._testing as tm
class TestRank:
s = Series([1, 3, 4, 2, np.nan... | DataFrame([[2012, 66, 3], [2012, 65, 2], [2012, 65, 1]]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
import nose
import numpy as np
from numpy import nan
import pandas as pd
from distutils.version import LooseVersion
from pandas import (Index, Series, DataFrame, Panel, isnull,
date_range, period_range)
from pandas.core.index import MultiIn... | Series([1., 2., 3., 4., 5., 6., 6.]) | pandas.Series |
import os
import argparse
import pandas as pd
import numpy as np
import xgboost as xgb
from math import ceil
from operator import itemgetter
from itertools import product
from scipy.stats import pearsonr
import matplotlib.pyplot as plt
def evalerror_pearson(preds, dtrain):
labels = dtrain.get_label()
return 'pearso... | pd.DataFrame(columns=cols) | pandas.DataFrame |
import pandas as pd
import glob
import os
import numpy as np
import time
import fastparquet
import argparse
from multiprocessing import Pool
import multiprocessing as mp
from os.path import isfile
parser = argparse.ArgumentParser(description='Program to run google compounder for a particular file and setting')
parse... | pd.DataFrame() | pandas.DataFrame |
import itertools
import json
import os
import os.path
from collections import Counter
import numpy as np
# from torchtext import data
import pandas as pd
import torch
from nltk.stem import WordNetLemmatizer
from torch.autograd import Variable
from tqdm import tqdm
from wiki_util import tokenizeText
... | pd.read_pickle(self.test_df_file) | pandas.read_pickle |
import os
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as tck
from typing import Mapping, List
class Results:
def __init__(self, main_folder: str, combine_models: bool = False) -> None:
self.main_folder = main_folder
self.combine_model... | pd.MultiIndex.from_product([[""], models.columns]) | pandas.MultiIndex.from_product |
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | pandas.Series([None, None, None], dtype="dbtime") | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 11 10:17:13 2018
@author: David
"""
# Built-in libraries
#import argparse
#import collections
#import multiprocessing
import os
#import pickle
#import time
# External libraries
#import rasterio
#import gdal
import matplotlib.pyplot as plt
from matplot... | pd.DataFrame(hd_compare_all_array, columns=hd_compare_all_cns) | pandas.DataFrame |
"""
Lotka's Law
===============================================================================
>>> from techminer2 import *
>>> directory = "data/"
>>> file_name = "sphinx/images/lotka.png"
>>> lotka_law(directory=directory).savefig(file_name)
.. image:: images/lotka.png
:width: 700px
:align: center
>>> l... | pd.isna(author) | pandas.isna |
import requests
import pandas as pd
output_csv = 'data/chapter_info.csv'
url = 'https://harrypotter.fandom.com/wiki/List_of_chapters_in_the_Harry_Potter_novels'
book_names = ["Harry Potter and the Philosopher's Stone",
'Harry Potter and the Chamber of Secrets',
'Harry Potter and the Prison... | pd.concat(clean_chapter_dfs) | pandas.concat |
"""
send a gRPC command that has streaming results
capture the results in the db as StoredResponse objects
"""
import copy
import uuid
from random import random
import math
import numpy as np
from django.conf import settings
from django.http import JsonResponse
from tworaven_apps.utils.static_keys import KEY_SUCCESS... | pd.Series.mode(x) | pandas.Series.mode |
import pandas as pd
from bld.project_paths import project_paths_join as ppj
# Read the dataset.
adults2005 = pd.read_stata(ppj("IN_DATA", "vp.dta"))
adults2009 = pd.read_stata(ppj("IN_DATA", "zp.dta"))
adults2013 = pd.read_stata(ppj("IN_DATA", "bdp.dta"))
# Extract Column of Big 5 Variables we need for the research... | pd.concat([data_adults_replace, trait], axis=1) | pandas.concat |
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# This file contains dummy data for the model unit tests
import numpy as np
import pandas as pd
AIR_FCST_LINEAR_95 = pd.DataFrame(
{
... | pd.Timestamp("2012-12-12 00:00:00") | pandas.Timestamp |
import pandas as pd
import numpy as np
import json
import csv
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm_notebook as tqdm
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.tokenize import sent_tokenize
from nltk.stem import WordNetLemmatizer
impor... | pd.read_csv('./testbadwordsn.csv') | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# In[3]:
import pandas as pd
from tqdm import tqdm_notebook
prefix = '../data/domlin_fever/'
# In[4]:
train_labels = pd.read_table(prefix + 'train-domlin.label', header=None)
train_hypothesis = pd.read_table(prefix + 'train-domlin.hypothesis', header=None)
train_premise = p... | pd.concat([df_class_0_under, df_class_1_under, df_class_2], axis=0) | pandas.concat |
#
# Copyright 2020 Capital One Services, LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | assert_series_equal(expect_out, actual_out, check_names=False) | pandas.util.testing.assert_series_equal |
import pandas as pd
import seaborn as sb
from sklearn.linear_model import LinearRegression
from statsmodels.graphics.tsaplots import plot_acf
from statsmodels.graphics.tsaplots import plot_pacf
from statsmodels.tsa.seasonal import seasonal_decompose
import numpy as np
import pmdarima
import matplotlib.pyplot as plt
imp... | pd.concat([H1,H2]) | pandas.concat |
import numpy as np
import pandas as pd
import fiona
import io
from shapely import geometry
import click
from wit_tooling import query_wit_data
def shape_list(key, values, shapefile):
"""
Get a generator of shapes from the given shapefile
key: the key to match in 'properties' in the shape file
... | pd.read_csv(input_file, header=None) | pandas.read_csv |
#from _typeshed import NoneType
from bs4 import BeautifulSoup
import requests
import lxml,json
import pandas as pd
def export_data(df):
df.to_csv(r'Data\data_extra.csv',encoding='utf-8',mode='a', index=False)
print("done")
df = | pd.DataFrame(columns = ['Image_url', 'Name', 'Publisher','Release','Rating','Genre','URL', 'Metascore','Userscore','Platform', 'Summary']) | pandas.DataFrame |
import cv2
import os
import re
import numpy as np
import pandas as pd
import random
"""
This program is used to augment uv data by taking only the sick cells and create a mosaic of them and augmenting them
"""
# Constants
HOME = os.path.expanduser("~")
DATA_DIR = os.path.join(HOME, "Downloads", "AllUVScopePreProcData/... | pd.read_excel(xls_file_name, sheetname=None, ignore_index=True) | pandas.read_excel |
from collections import defaultdict
from sklearn import preprocessing
import signal
import influxdb_client
from influxdb_client import InfluxDBClient
from datetime import datetime
from sklearn.preprocessing import KBinsDiscretizer
import argparse
import ntopng_constants as ntopng_c
import numpy as np
import pandas as p... | pd.Timestamp.utcnow() | pandas.Timestamp.utcnow |
from sklearn.datasets import load_breast_cancer, fetch_california_housing
import pandas as pd
import numpy as np
import pickle
import os
import collections
from sklearn.model_selection import StratifiedShuffleSplit, ShuffleSplit
def handle_categorical_feat(X_df):
''' It moves the categorical features to the last ... | pd.concat([dataset['full']['X'], increment_X], axis=0) | pandas.concat |
# -*- coding: utf-8 -*-
# @Time : 2019/10/25 10:46
# @Author : <NAME>
# @FileName: parse_eggNOG.py
# @Usage: """non-module organisms are always lack of KEGG and GO information. A convenient method is use eggNOG server
# to annotate. This script is used for parse the eggNOG result to a user friendly format wh... | pd.read_csv(input_file, sep="\t", comment="#", usecols=[0, 6, 8], names=['Query', 'GOs', "KEGG_ko"]) | pandas.read_csv |
import pandas as pd
import tensorflow as tf
# from IPython.display import clear_output
from matplotlib import pyplot as plt
from sklearn.metrics import roc_curve
# Load dataset.
dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')
dfeval = pd.read_csv('https://storage.googleapis.com/tf... | pd.Series([pred['probabilities'][1] for pred in pred_dicts]) | pandas.Series |
import os
import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn import model_selection
from time import time
import argparse
import pickle
GAS_STATIONS_PATH = os.path.join('..', '..', 'data', 'raw', 'input_data', 'Eingabedaten', 'Tankstellen.csv')
GAS_PRICE_PATH = os.path.join('..', '..', 'data',... | pd.Series(gas_station_df.index[1:] - gas_station_df.index[:-1]) | pandas.Series |
# Import Library
import pandas as pd
import numpy as np
import os
from pathlib import Path
import sys
import multiprocessing
from manage_path import *
def read_data(file_name,low_memory=False,memory_map=True,engine='c'):
"""Read FINRA TRACE data and perform date conversion and data merging with Mergent FISD"""
... | pd.to_datetime(x, format='%Y%m%d %H%M%S', errors='coerce') | pandas.to_datetime |
#!/bin/env python3
"""
Copyright (C) 2021 - University of Mons and University Antwerpen
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 requir... | pandas.to_numeric(time[column]) | pandas.to_numeric |
# -*- coding: utf-8 -*-
try:
import json
except ImportError:
import simplejson as json
import math
import pytz
import locale
import pytest
import time
import datetime
import calendar
import re
import decimal
import dateutil
from functools import partial
from pandas.compat import range, StringIO, u
from pandas.... | ujson.encode(i, orient="split") | pandas._libs.json.encode |
from collections import deque
import jax
import numpy as np
import pandas as pd
from skfda import FDataGrid
from skfda.representation.basis import Fourier
from tensorly.decomposition import tucker
from tensorly.tenalg import mode_dot
from finger_sense.utility import KL_divergence_normal, normalize
class Processor:... | pd.unique(self.core['material']) | pandas.unique |
from torch.utils.data import Dataset
from cmapPy.pandasGEXpress.parse import parse
from torch.utils.data import DataLoader
from torch.utils.data.sampler import Sampler
import numpy as np
import os
import ai.causalcell.utils.register as register
from rdkit import Chem
import pandas as pd
from rdkit.Chem import AllChem
i... | pd.DataFrame(index=sig_info.index, columns=["env_repr"]) | pandas.DataFrame |
import glob
import json
import os
from wsgiref.util import FileWrapper
import shutil
import pandas as pd
from django.conf import settings
from django.contrib.auth.decorators import login_required
from django.http.response import HttpResponse
from django.shortcuts import render
from interface.models import DbQuery
fro... | pd.to_datetime(table["start"]) | pandas.to_datetime |
#!/usr/bin/env python
# coding: utf-8
# #### Author : <NAME>
# #### Topic : Multiple Linear Regression : Car Price Prediction
# #### Email : <EMAIL>
# It is the extension of simple linear regression that predicts a response using two or more features. Mathematically we can explain it as follows −
#
# Consider a data... | pd.concat([df,dummies],axis='columns') | pandas.concat |
import json
import os
import sys
import tensorflow as tf
from keras import backend as K
from keras import optimizers, utils
from keras.callbacks import CSVLogger
from keras.engine import Model
from keras.layers import Dropout, Flatten, Dense
from keras.layers import GlobalAveragePooling2D, GlobalMaxPooling2D
from src... | pd.DataFrame({"patientId": valid_ids, "y_pred": y_pred}) | pandas.DataFrame |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for datetime64 and datetime64tz dtypes
from datetime import (
datetime,
time,
timedelta,
)
from itertools import (
product,
starmap,
)
import operator
import warnings
import numpy as np
impo... | Timestamp("2000-01-31 00:23:00") | pandas.Timestamp |
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
from numba import njit
import vectorbt as vbt
from tests.utils import record_arrays_close
from vectorbt.generic.enums import range_dt, drawdown_dt
from vectorbt.portfolio.enums import order_dt, trade_dt, log_dt
day_dt = np.timedelta64... | pd.Timedelta('1 days 16:00:00') | pandas.Timedelta |
import sys
import numpy as np
import pandas as pd
# data_dir = 'data/train_data_comp/'
# data_dir = 'data/train_data'
data_dir = 'data/'
class Student:
free_students = set()
def __init__(self, sid: int, pref_list: list, math_grade, cs_grade, utils):
self.sid = sid
self.math_grade = math_gra... | pd.read_csv(f'{data_dir}/grades_{n}.csv', index_col='student_id') | pandas.read_csv |
# -*- codeing = utf-8 -*-
# @Time : 2021-07-09 2:08
# @Author : cAMP-Cascade-DNN
# @File : idSteal.py
# @Software : Pycharm
# @Contact: qq:1071747983
# mail:<EMAIL>
# -*- 功能说明 -*-
# 设定爬虫结构 进行爬虫数据本地保存与数据库上传
# -*- 功能说明 -*-
from queue import Queue
from Selenium import Selenium
from Sql import DbConnect
import... | pd.Series(self.userList) | pandas.Series |
#!/usr/bin/env python2
# -*- coding: utf-8 -*
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
import pylab
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sea
import pandas as pd
from utils import mkdir_p
from feature_selection import kolmogorov_smirnov_two_sample_test
sea... | pd.DataFrame(data=x12_to_x17,columns=["X18","X19","X20","X21","X22","X23"]) | pandas.DataFrame |
from typing import Optional
import numpy as np
import pandas as pd
from pandas import DatetimeIndex
from stateful.representable import Representable
from stateful.storage.tree import DateTree
from stateful.utils import list_of_instance, cast_output
from pandas.api.types import infer_dtype
class Stream(Representable)... | pd.DataFrame(values, index=index) | pandas.DataFrame |
# decision tree model
import pandas as pd
x = pd.read_csv("../../dataset/input_data.csv").to_numpy()
resp = pd.read_csv("../../dataset/output_data.csv").to_numpy()
##
from sklearn.decomposition import PCA
import numpy as np
pca = PCA(n_components = 1)
resp_pca = pca.fit_transform(resp)
y = (resp_pca > 0).astype("int"... | pd.DataFrame.from_dict(clf.cv_results_) | pandas.DataFrame.from_dict |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jun 16 20:50:39 2019
@author: mwu
"""
from __future__ import absolute_import
import networkx as nx
import pandas as pd
from community import community_louvain
import snf
import numpy as np
import warnings
import networkx.algorithms.traversal as nextra
f... | pd.merge(link_1, link_2, on=['edge'], how='outer' if method == 'union' else 'inner') | pandas.merge |
"""Module with the tests for the pileup creation realted tasks."""
import os
import unittest
from unittest.mock import patch
from unittest.mock import MagicMock, PropertyMock
import pandas as pd
from pandas.testing import assert_frame_equal
import numpy as np
from hicognition.test_helpers import LoginTestCase, TempDirT... | assert_frame_equal(expected, calculated) | pandas.testing.assert_frame_equal |
import ast
import json
import os
import sys
import uuid
import lxml
import networkx as nx
import pandas as pd
import geopandas as gpd
import pytest
from pandas.testing import assert_frame_equal, assert_series_equal
from shapely.geometry import LineString, Polygon, Point
from genet.core import Network
from genet.input... | assert_frame_equal(n.change_log[cols_to_compare], correct_change_log_df[cols_to_compare], check_dtype=False) | pandas.testing.assert_frame_equal |
"""
Use generated models to perform predictions.
@author: eyu
"""
import os
import logging
import click
import glob
import pandas as pd
from datetime import date
from keras.models import load_model
from data_reader_stooq import StooqDataReader
from data_reader_yahoo import YahooDataReader
import talib as talib
impo... | pd.set_option('display.width', 1000) | pandas.set_option |
import numpy as np
from at import *
from at.load import load_mat
from matplotlib import pyplot as plt
import matplotlib.pyplot as plt
import at.plot
import numpy as np
from pylab import *
import pandas as pd
import csv
from random import random
def defineMatrices( Nm, C0x, C0y, C0xy, C0yx, Cxx_err, Cyy_err, ... | pd.DataFrame(twiss1) | pandas.DataFrame |
# %%
'''
'''
## Se importan las librerias necesarias
import pandas as pd
import numpy as np
import datetime as dt
from datetime import timedelta
pd.options.display.max_columns = None
pd.options.display.max_rows = None
import glob as glob
import datetime
import re
import jenkspy
import tkinter as tk
... | pd.crosstab(grupo_2['Califica_suscr_class'],grupo_2['Efectivo Pago']) | pandas.crosstab |
# -*- coding: utf-8 -*-
"""
@author: Diego
"""
import zipfile
import pandas as pd
from bs4 import BeautifulSoup
import requests
import io
import sqlite3
import os
import datetime
cwd = os.getcwd()
pd.set_option("display.width", 400)
pd.set_option("display.max_columns", 10)
pd.options.mode.chained_assignment = None
c... | pd.to_datetime(t.text[23:39]) | pandas.to_datetime |
import json
import pathlib
from xml.sax.saxutils import escape
from scipy import sparse
import regex
import sklearn
from sklearn.utils.extmath import safe_sparse_dot
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.preprocessing import normalize
import numpy as np
import pandas as pd
from lxml ... | pd.Series(frequencies, index=vocabulary) | pandas.Series |
import numpy as np
import pandas as pd
import ipaddress
from .engineobj import SqPandasEngine
class EvpnvniObj(SqPandasEngine):
@staticmethod
def table_name():
return 'evpnVni'
def get(self, **kwargs) -> pd.DataFrame:
"""Class-specific to extract info from addnl tables"""
drop_... | pd.DataFrame(columns=assert_cols) | pandas.DataFrame |
import pandas as pd
import json
from os import mkdir, rmdir
from . import utils
from synapseclient import File, Activity
import numpy as np
class NumpyEncoder(json.JSONEncoder):
""" Special json encoder for numpy types """
def default(self, obj):
if isinstance(obj, np.integer):
return int(o... | pd.isna(value) | pandas.isna |
from igraph import *
import leidenalg as la
import pandas as pd
import OmicsIntegrator as oi
import networkx as nx
import numpy as np
import pickle
import matplotlib
import matplotlib.pyplot as plot
matplotlib.rcParams['pdf.fonttype'] = 42
#import plotly.express as px
class hyphalNetwork:
"""
The hypha class... | pd.DataFrame(stat_list) | pandas.DataFrame |
"""tests.core.archive.test_archive.py
Copyright Keithley Instruments, LLC.
Licensed under MIT (https://github.com/tektronix/syphon/blob/master/LICENSE)
"""
import os
from typing import List, Optional, Tuple
import pytest
from _pytest.capture import CaptureFixture
from _pytest.fixtures import FixtureRequest
fro... | read_csv(filepath, dtype=str) | pandas.read_csv |
"""Tests for the sdv.constraints.tabular module."""
import uuid
from datetime import datetime
from unittest.mock import Mock
import numpy as np
import pandas as pd
import pytest
from sdv.constraints.errors import MissingConstraintColumnError
from sdv.constraints.tabular import (
Between, ColumnFormula, CustomCon... | pd.Series([True, False, True, False, True], index=[2, 1, 3, 5, 4]) | pandas.Series |
"""
Unit tests for base boost capability.
"""
# Author: <NAME>
# License: MIT
import unittest
import sklearn.ensemble
import pandas as pd
from sklearn.datasets import load_boston, load_linnerud
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from physlearn import ModifiedPip... | pd.DataFrame(X) | pandas.DataFrame |
from linearmodels.compat.statsmodels import Summary
import warnings
import numpy as np
from numpy.linalg import pinv
from numpy.testing import assert_allclose, assert_equal
import pandas as pd
from pandas.testing import assert_series_equal
import pytest
import scipy.linalg
from statsmodels.tools.tools import add_cons... | pd.DataFrame(instr) | pandas.DataFrame |
from main_app_v3 import forecast_cases, forecast_cases_active
import pandas as pd
import numpy as np
import os
import warnings
warnings.filterwarnings("ignore")
from bs4 import BeautifulSoup as bs
from datetime import date
from selenium import webdriver
def update(region_dict, driver):
df = pd.r... | pd.Series(forecast_active_high) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Generalized Additive Models
Author: <NAME>
Author: <NAME>
created on 08/07/2015
"""
from collections.abc import Iterable
import copy # check if needed when dropping python 2.7
import numpy as np
from scipy import optimize
import pandas as pd
import statsmodels.base.wrapper as wrap
fro... | pd.DataFrame(predict_results, index=exog_index) | pandas.DataFrame |
import os,glob
import pandas as pd
path = "C:/Users/Fayr/Documents/GitHub/spotifyML/datasets"
files = glob.glob(os.path.join(path, '*.csv'))
df_from_each_file = (pd.read_csv(f, sep=',').iloc[0:30, :] for f in files)
df_merged = | pd.concat(df_from_each_file, ignore_index=True) | pandas.concat |
import json
import requests
import re
import os.path
import datetime
import configparser
from unicodedata import normalize
import pandas as pd
import numpy as np
from pandas.io.json import json_normalize
"""
Get your API access token
1. Create an Yelp account.
2. Create an app (https://www.yelp.com/developers/v3/man... | pd.read_csv(YELP_DATASET_PATH) | pandas.read_csv |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import nose
import numpy as np
import pandas as pd
from pandas import (Index, Series, DataFrame, Timestamp, isnull, notnull,
bdate_range, date_range, _np_version_under1p7)
import pandas.core.common as com
from pandas.compa... | pd.to_timedelta(pd.NaT) | pandas.to_timedelta |
import os.path
import pickle
import sys
import pandas as pd
import numpy as np
from tqdm import tqdm
import behavelet
import utils2p
import utils2p.synchronization
import flydf
root_dir=os.path.abspath("../..")+'/'
# print(root_dir)
# root_dir="/mnt/data/CLC/"
annotation_dir=root_dir+"Ascending_neuron_screen_analy... | pd.DataFrame() | pandas.DataFrame |
import os
import joblib
import numpy as np
import pandas as pd
from joblib import Parallel
from joblib import delayed
from pytz import timezone
from sklearn.decomposition import KernelPCA
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import MinMaxScaler
from Fuzzy_clustering.version2.com... | pd.DateOffset(hours=25) | pandas.DateOffset |
import importlib
from hydroDL.master import basins
from hydroDL.app import waterQuality
from hydroDL import kPath, utils
from hydroDL.model import trainTS
from hydroDL.data import gageII, usgs
from hydroDL.post import axplot, figplot
from sklearn.linear_model import LinearRegression
from hydroDL.data import usgs, gageI... | pd.DataFrame(index=df.index, columns=varC) | pandas.DataFrame |
import pickle
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler , Normalizer
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from scipy.stats import norm
from scipy import stats
from sklearn import metri... | pd.read_csv("./Cleaned-Data.csv") | pandas.read_csv |
from gams import *
import pandas as pd
import DataBase
import COE
import regex_gms
from DB2Gams import *
class am_base(gams_model_py):
def __init__(self,tree,gams_settings=None):
self.tree = tree
super().__init__(self.tree.database,gsettings=gams_settings, blocks_text = None, functions = None, groups = {}, excep... | pd.Series(0.5,index=self.database[self.tree.mapname],name=mu) | pandas.Series |
#!/usr/bin/env python3
#
# Copyright 2019 <NAME> <<EMAIL>>
#
# This file is part of Salus
# (see https://github.com/SymbioticLab/Salus).
#
# 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
#
#... | pd.to_numeric(df.Duration) | pandas.to_numeric |
from flask import Flask, render_template, jsonify, request
from flask_pymongo import PyMongo
from flask_cors import CORS, cross_origin
import json
import copy
import warnings
import re
import pandas as pd
pd.set_option('use_inf_as_na', True)
import numpy as np
from joblib import Memory
from xgboost import XGBClassi... | pd.DataFrame(targetRows3Arr) | pandas.DataFrame |
"""
This module contains all US-specific data loading and data cleaning routines.
"""
import requests
import pandas as pd
import numpy as np
idx = pd.IndexSlice
def get_raw_covidtracking_data():
""" Gets the current daily CSV from COVIDTracking """
url = "https://covidtracking.com/api/v1/states/daily.csv"
... | pd.Timestamp("2020-06-26") | pandas.Timestamp |
# pylint: disable-msg=E1101,W0612
from __future__ import with_statement # for Python 2.5
from datetime import datetime, time, timedelta
import sys
import os
import unittest
import nose
import numpy as np
from pandas import (Index, Series, TimeSeries, DataFrame, isnull,
date_range, Timestamp)
fro... | date_range('1/1/2011', periods=100, freq='H') | pandas.tseries.index.date_range |
# Copyright (c) 2018-2021, NVIDIA CORPORATION.
import array as arr
import datetime
import io
import operator
import random
import re
import string
import textwrap
from copy import copy
import cupy
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from numba import cuda
import cudf
from cudf.c... | pd.Series(["a", "b", "c"], dtype="category") | pandas.Series |
from __future__ import print_function
import sys
from collections import defaultdict
try:
import mkl
mkl.set_num_threads(1)
except ImportError:
pass
import pandas as pd
import numpy as np
import pyranges as pr
import pkg_resources
from pyranges.pyranges import PyRanges
from pyranges import data
from ... | pd.DataFrame(d) | pandas.DataFrame |
from collections import abc, deque
from decimal import Decimal
from io import StringIO
from warnings import catch_warnings
import numpy as np
from numpy.random import randn
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
# author: <NAME>
# date: 2020-01-23
#
# This script will perform preprocessing on both training and test data, and create various models to predict the grades of Portuguese subject.
# It will output the best hyperparameters for each model's cross validation, and score the predictions of the different models
# Models us... | pd.Series(optimizer_lmridge.max['params']) | pandas.Series |
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
from numba import njit
import vectorbt as vbt
from tests.utils import record_arrays_close
from vectorbt.generic.enums import range_dt, drawdown_dt
from vectorbt.portfolio.enums import order_dt, trade_dt, log_dt
day_dt = np.timedelta64... | pd.testing.assert_index_equal(stats_df.columns, stats_index) | pandas.testing.assert_index_equal |
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import json
import pandas as pd
import numpy as np
import pathlib
import streamlit as st
import getYfData as yfd
from time import sleep
import time
from datetime import datetime
from datetime import timedelta
from dateutil.r... | pd.DataFrame(meta_data, index=[0]) | pandas.DataFrame |
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