prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pandas as pd
from sklearn.model_selection import train_test_split
# load data sets
# 3' utrs composition
utrs = pd.read_csv("../../data/19-01-17-Get-ORFS-UTRS-codon-composition/sequence-data/zfish_3utr6mer_composition.csv")
utrs = utrs.rename(columns={'ensembl_gene_id': 'Gene_ID'}).drop('3utr', axis=1)
# optima... | pd.read_csv("../19-02-24-OverlapPathwaysFig3/results_data/regulatory_pathways_matrix.csv") | pandas.read_csv |
import pandas as pd
from fairlens.metrics.correlation import distance_cn_correlation, distance_nn_correlation
from fairlens.sensitive.correlation import find_column_correlation, find_sensitive_correlations
pair_race = "race", "Ethnicity"
pair_age = "age", "Age"
pair_marital = "marital", "Family Status"
pair_gender = ... | pd.Series([15, 45, 14, 16, 44, 46]) | pandas.Series |
import sys
import argparse
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as st
import pandas as pd
from scipy import interpolate
from scipy.spatial import Delaunay
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from ATE import UniformSamplingStrat... | pd.DataFrame() | pandas.DataFrame |
###################
#
# File handling the processing of cvs and calculations for
# the Naive Bayes probability
#
# NOTES:
# 1- Need to loop for looking for the historical files
# 2- Need to create accessible function from outside to get:
# a- begin and end lon/lats
# b- sigmoid and 1-5 mapping
# ... | pd.DataFrame([[st, total, num_event, mon, blat, blon, elat, elon, rclass]], columns=['STATE','COUNT','NUM_EVENTS', 'MONTH_NAME','BEGIN_LAT','BEGIN_LON','END_LAT','END_LON', 'CLASS']) | pandas.DataFrame |
__version__ = '0.1.3'
__maintainer__ = '<NAME>'
__contributors__ = '<NAME>, <NAME>'
__email__ = '<EMAIL>'
__birthdate__ = '31.12.2019'
__status__ = 'prod' # options are: dev, test, prod
__license__ = 'BSD-3-Clause'
import pandas as pd
import yaml
from pathlib import Path
def loadConfigDict(configNames: tuple):
... | pd.DataFrame(profileDictOut) | pandas.DataFrame |
from IMLearn.utils import split_train_test
from IMLearn.learners.regressors import LinearRegression
from typing import NoReturn
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
pio.templates.default = "simple_white"
import time
def load_data(... | pd.DataFrame({'x': X[feature], 'y': y}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import datetime
import pandas as pd
from gmsdk import md, to_dict
md.init('13382753152', '940809')
CFFEX = ['IF', 'IH', 'IC', 'T', 'TF']
CZCE = ['CF', 'FG', 'MA', 'RM', 'SR', 'TA', 'ZC']
SHFE = ['AL', 'BU', 'CU', 'HC', 'NI', 'RB', 'RU', 'SN', 'ZN']
DCE = ['C', 'CS', 'I', 'J', 'JD', 'JM', 'L',... | pd.DataFrame(ret) | pandas.DataFrame |
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... | tm.assert_produces_warning(warn, match="concatenating bool-dtype") | pandas._testing.assert_produces_warning |
#
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
#
import numpy as np
import pandas as pd
from sklearn.metrics.pairwise import euclidean_distances
from mlos.Optimizers.ExperimentDesigner.UtilityFunctionOptimizers.UtilityFunctionOptimizer import UtilityFunctionOptimizer
from mlos.Opti... | pd.concat([features_for_top_utility, top_utility_values], axis=1) | pandas.concat |
from os.path import exists
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from k_choice.graphical.two_choice.graphs.hypercube import HyperCube
from k_choice.graphical.two_choice.graphs.random_regular_graph import RandomRegularGraph
from k_choice.graphical.two_choice.strategies.greedy_strategy ... | pd.read_csv("data/32_32_random_regular_greedy_analysis.csv") | pandas.read_csv |
# Preppin' Data 2021 Week 42
import pandas as pd
import numpy as np
# Input the data
df = pd.read_csv('unprepped_data\\PD 2021 Wk 42 Input.csv')
# Create new rows for any date missing between the first and last date in the data set provided
# build a data frame of all dates from min to max
min_date = min(df['Date'])
... | pd.date_range(min_date, max_date) | pandas.date_range |
import pandas as pd
from sklearn import preprocessing
from scipy.sparse import coo_matrix
import numpy as np
def quora_leaky_extracting(concat):
tid1 = concat['q1_id'].values
tid2 = concat['q2_id'].values
doc_number = np.max((tid1.max(), tid2.max())) + 1
adj = coo_matrix((np.ones(len(tid1) * 2), (np.c... | pd.read_csv(path + '/test.tsv', delimiter='\t', header=None) | pandas.read_csv |
""" test parquet compat """
import datetime
from distutils.version import LooseVersion
import os
from warnings import catch_warnings
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
import pandas._testing as tm
from pandas.io.parquet import (
FastParquetImpl,
Py... | tm.ensure_clean_dir() | pandas._testing.ensure_clean_dir |
from qfengine.data.price.price_handler import PriceHandler
from qfengine.asset.universe.static import StaticUniverse
import functools
from qfengine import settings
import numpy as np
import pandas as pd
import pytz
from typing import List
class BacktestPriceHandler(PriceHandler):
def __init__(
self,
... | pd.DataFrame() | pandas.DataFrame |
import geopandas
import pandas as pd
import math
def build_ncov_geodf(day_df):
world_lines = geopandas.read_file('zip://./shapefiles/ne_50m_admin_0_countries.zip')
world = world_lines[(world_lines['POP_EST'] > 0) & (world_lines['ADMIN'] != 'Antarctica')]
world = world.rename(columns={'ADMIN': 'name'})
... | pd.isna(row['Province/State']) | pandas.isna |
import pandas as pd
import json
import numpy as np
from collections import Counter
from operator import itemgetter
def create_edgelist(transactions_file, clients_file, companies_file, atms_file):
transactions = pd.read_csv(transactions_file)
clients = | pd.read_csv(clients_file) | pandas.read_csv |
from pathlib import Path
import os
import sys
os.environ['DISPLAY'] = ':1'
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.stats.multitest import multipletests
import scipy.stats as stats
import seaborn as ... | pd.DataFrame(columns=['lr', 'pval', 'pval_ast']) | pandas.DataFrame |
import os
import tempfile
import path
import functools
from itertools import islice
import pandas as pd
import numpy as np
from trumania.core.random_generators import SequencialGenerator, NumpyRandomGenerator, ConstantGenerator, seed_provider
from trumania.core.random_generators import DependentTriggerGenerator, Fake... | pd.Series([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]) | pandas.Series |
import pandas as pd
import numpy as np
from datetime import datetime
from multiprocessing import Pool
from functools import partial
from pathos import pools as pp
import pickle as pkl
from UserCentricMeasurements import *
from RepoCentricMeasurements import *
from CommunityCentricMeasurements import *
from TEMeasuremen... | pd.to_datetime(df['created_at']) | pandas.to_datetime |
"""
Clean a DataFrame column containing text data.
"""
import re
import string
from functools import partial, update_wrapper
from typing import Any, Callable, Dict, List, Optional, Set, Union
from unicodedata import normalize
import dask.dataframe as dd
import numpy as np
import pandas as pd
from ..assets.english_sto... | pd.notna(text) | pandas.notna |
"""
A warehouse for constant values required to initilize the PUDL Database.
This constants module stores and organizes a bunch of constant values which are
used throughout PUDL to populate static lists within the data packages or for
data cleaning purposes.
"""
import importlib.resources
import pandas as pd
import ... | pd.StringDtype() | pandas.StringDtype |
# -*- coding: utf-8 -*-
"""
Spyder Editor
"""
# =============================================================================
# # imports and prep
# =============================================================================
# imports
from pathlib import Path
import pandas as pd
import numpy as np
# set path | ... | pd.read_csv(listings_file) | pandas.read_csv |
import numpy as np
import pandas as pd
import torch
from scipy.optimize import minimize
from scipy.special import loggamma, expit
from torch.nn.functional import log_softmax
from sim.Sample import get_batches
from sim.best_models import extract_best_run
from sim.EBayDataset import EBayDataset
from analyze.util import s... | pd.concat(elem, axis=1) | pandas.concat |
import pandas as pd
import ast
import sys
import os.path
from pandas.core.algorithms import isin
sys.path.insert(1,
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
import dateutil.parser as parser
from utils.mysql_utils import separator
from utils.io import read_json
from utils.scr... | pd.isnull(longitude) | pandas.isnull |
# -*- coding: utf-8 -*-
from datetime import timedelta
import operator
import numpy as np
import pytest
import pandas as pd
from pandas import Series, compat
from pandas.core.indexes.period import IncompatibleFrequency
import pandas.util.testing as tm
def _permute(obj):
return obj.take(np.random.permutation(len... | Series([2, 3, 4]) | pandas.Series |
#!/usr/bin/python3
#!/usr/bin/env python3
import re
import csv
import xlsxwriter
import pandas as pd
SR= open("shortreadExactMatchTranscripts.txt")
file1=SR.readlines()
# TO Count number of lines in the file
countFile1=0
for i in file1:
if i.strip():
countFile1 +=1
print("Number of lines in file1: ")
pri... | pd.DataFrame(finalDictionary) | pandas.DataFrame |
"""Main module."""
import json
from collections import defaultdict
import numpy as np
import pandas as pd
from copy import deepcopy
from math import nan, isnan
from .constants import IMAGING_PARAMS
DIRECT_IMAGING_PARAMS = IMAGING_PARAMS - set(["NSliceTimes"])
def check_merging_operations(action_csv, raise_on_error=Fa... | pd.read_csv(files_csv) | pandas.read_csv |
from copy import deepcopy
import requests
import os
import bs4
from openpyxl import load_workbook
import pandas as pd
from ..helpers.db_funcs import get_ep_id_by_number, get_season_id_by_number_type
from ..helpers.extract_helpers import search_for_new_seasons
import glob
import re
import numpy as np
DOCS_URL_TEMPLAT... | pd.DataFrame() | pandas.DataFrame |
import sklearn.cluster
from scipy.stats import zscore
from matplotlib.patches import Patch
import gseapy as gp
import numpy as np
import pandas as pd
import sys
import scanpy as sc
def get_genelist_references(reference_file_path = "../../Data/",gene_sets=["GO_Biological_Process_2021"]):
genelist_references ... | pd.DataFrame(labels, index=genes) | pandas.DataFrame |
#!/usr/bin/env python3
from asyncore import loop
import math
import datetime
import argparse
from pkgutil import get_data
import shutil
from webbrowser import get
from numpy import fft
import urllib3
import argparse
import requests
import re
import os
import pandas as pd
import urllib3
from alive_progress import alive_... | pd.read_csv('DataFrames/Enr022-ATZ.csv', index_col=0) | pandas.read_csv |
from io import StringIO
import pandas as pd
import numpy as np
import pytest
import bioframe
import bioframe.core.checks as checks
# import pyranges as pr
# def bioframe_to_pyranges(df):
# pydf = df.copy()
# pydf.rename(
# {"chrom": "Chromosome", "start": "Start", "end": "End"},
# axis="col... | pd.DataFrame([["chrX", 3, 8]], columns=["chrom", "start", "end"]) | pandas.DataFrame |
import requests
import pandas as pd
import util_functions as uf
import geopandas as gpd
from shapely.geometry import Point, Polygon
def extract_json(json_id):
# Loop through each feature in GeoJson and pull our metadata and polygon
url = "https://opendata.arcgis.com/datasets/{}.geojson".format(json_id)
re... | pd.concat(feature_df_list, axis=0) | pandas.concat |
from time import time
import os
import sys
import shutil
import click
from pathlib import Path
import config
from openslide import OpenSlide
import pandas as pd
from tqdm import tqdm
from skimage.morphology import remove_small_objects
from skimage.io import imread
from skimage.transform import rescale
import random
imp... | pd.read_feather(destination_file) | pandas.read_feather |
# -*- coding: utf-8 -*-
from datetime import timedelta
import operator
from string import ascii_lowercase
import warnings
import numpy as np
import pytest
from pandas.compat import lrange
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
Categorical, DataFrame, MultiIndex, Serie... | tm.assert_frame_equal(expected, result) | pandas.util.testing.assert_frame_equal |
import nibabel as nib
import os
import sys
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import pearsonr
from scipy.io import loadmat
# threshold of surface coverage 40 %
percentage = 40
# define species-specific settings
tracts_df = | pd.DataFrame(columns=('species', 'hemi', 'tract', 'tract_name')) | pandas.DataFrame |
# AUTOGENERATED! DO NOT EDIT! File to edit: notebooks/01_data_provider.ipynb (unless otherwise specified).
__all__ = ['DataProvider', 'get_efficiently']
# Cell
from bs4 import BeautifulSoup as bs
import numpy as np
import os
import pandas as pd
from fastcore.foundation import patch
# Cell
class DataProvider():
d... | pd.concat([mov_berlin,mov_dresden,mov_mannheim]) | pandas.concat |
import os
import pytest
import pandas
from pandas import DataFrame, read_csv
import piperoni as hep
from piperoni.operators.transform.featurize.featurizer import (
CustomFeaturizer,
)
"""
This module implements tests for the CustomFeaturizer.
"""
def multiply_column_by_two(df: DataFrame, col_name: str):
... | DataFrame() | pandas.DataFrame |
import pandas as pd
def read_all_scenes_file() -> list[str]:
with open("resources/all_scenes.txt") as file:
return file.readlines()
def write_actors(actors: list[dict]):
_write_actors("output/data/actors.csv", actors)
def write_transition_actors(transition_actors: list[dict]):
_write_actors("o... | pd.DataFrame(actors) | pandas.DataFrame |
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
from amber.plots._plotsV1 import sma
def num_of_val_pos(wd):
managers = [x for x in os.listdir(wd) if x.startswith("manager")]
manager_pos_cnt = {}
for m in managers:
trials = os.listdir(os.path.join(wd, m, "weight... | pd.read_table(config_fp) | pandas.read_table |
# -*- coding: utf-8 -*-
"""
@author: <NAME>
"""
import pandas as pd
from rdkit import Chem
from rdkit.Chem import BRICS
number_of_generating_structures = 100 # 繰り返し 1 回あたり生成する化学構造の数
number_of_iterations = 10 # 繰り返し回数。(number_of_generating_structures × number_of_iterations) 個の化学構造が生成されます
dataset = pd.re... | pd.DataFrame(generated_structures, columns=['SMILES']) | pandas.DataFrame |
### preprocessing
"""
code is taken from
tunguz - Surprise Me 2!
https://www.kaggle.com/tunguz/surprise-me-2/code
"""
import glob, re
import numpy as np
import pandas as pd
from sklearn import *
from datetime import datetime
import matplotlib.pyplot as plt
data = {
'tra': pd.read_csv('../input/air_visit_data.csv'... | pd.merge(train, data[df], how='left', on=['air_store_id','visit_date']) | pandas.merge |
from PyQt5 import QtWidgets as Qtw
from PyQt5 import QtCore as Qtc
from PyQt5 import QtGui as Qtg
from datetime import datetime, timedelta
from bu_data_model import BU366
import sys
import socket
import time
import pandas as pd
from openpyxl.chart import ScatterChart, Reference, Series
class CheckingThrea... | pd.Series(record, name=except_disconnection_start) | pandas.Series |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pandas
from pandas.api.types import is_scalar
from pandas.compat import to_str, string_types, numpy as numpy_compat, cPickle as pkl
import pandas.core.common as com
from pandas.core.dtypes.common import ... | is_numeric_dtype(t) | pandas.core.dtypes.common.is_numeric_dtype |
# -*- coding: utf-8 -*-
"""
Code for interfacing with the Exoplanet Archive catalogs.
"""
from __future__ import division, print_function
import os
import logging
from pkg_resources import resource_filename
import pandas as pd
from six.moves import urllib
from .settings import PEERLESS_DATA_DIR
__all__ = [
"... | pd.read_csv(file_handle) | pandas.read_csv |
#!/usr/bin/env python3
#Author: <NAME>
#Contact: <EMAIL>
from __future__ import print_function
from . import SigProfilerMatrixGenerator as matGen
import os
import SigProfilerMatrixGenerator as sig
import re
import sys
import pandas as pd
import datetime
from SigProfilerMatrixGenerator.scripts import convert_input_t... | pd.DataFrame(0, index=indel_types_simple, columns=samples) | pandas.DataFrame |
#!/usr/bin/python3
import json
import requests
from requests import urllib3
import time
import pprint as pp
import csv
import pandas as pd
from docx import Document
from docx.shared import Inches, Pt
from docx.enum.section import WD_ORIENT, WD_SECTION
from docx.enum.text import WD_ALIGN_PARAGRAPH
from ..models import M... | pd.DataFrame.from_dict(org_data, orient='index') | pandas.DataFrame.from_dict |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 3 17:28:04 2020
@author: shlomi
"""
from PW_paths import work_yuval
from matplotlib import rcParams
import seaborn as sns
from pathlib import Path
import matplotlib.pyplot as plt
from PW_paths import savefig_path
import matplotlib.ticker as ticker
... | pd.DataFrame(records) | pandas.DataFrame |
"""
A warehouse for constant values required to initilize the PUDL Database.
This constants module stores and organizes a bunch of constant values which are
used throughout PUDL to populate static lists within the data packages or for
data cleaning purposes.
"""
import importlib.resources
import pandas as pd
import ... | pd.StringDtype() | pandas.StringDtype |
import requests
import base64
import re
import os
import datetime
import random
import tensorflow as tf
import tensorflow_text
import numpy as np
import pandas as pd
import tweepy
import plotly.graph_objects as go
print("Imports done")
auth = tweepy.OAuthHandler(os.environ.get('API_KEY'), os.environ.get('API_SECRET... | pd.read_csv(file_url) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# In[2]:
import os
import pandas as pd
import numpy as np
import string
# from operator import itemgetter
from collections import Counter, OrderedDict
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.stem import SnowballStemmer
from nltk.corpus import stopwords
... | pd.concat(taste_dataframes, axis=1) | pandas.concat |
import pytest
import numpy as np
import pandas as pd
import databricks.koalas as ks
from pandas.testing import assert_frame_equal
from gators.feature_generation.polynomial_features import PolynomialFeatures
ks.set_option('compute.default_index_type', 'distributed-sequence')
@pytest.fixture
def data_inter():
X = p... | assert_frame_equal(X_new, X_expected) | pandas.testing.assert_frame_equal |
#Library of functions called by SimpleBuildingEngine
import pandas as pd
import numpy as np
def WALLS(Btest=None):
#Building height
h_building = 2.7#[m]
h_m_building = h_building / 2
h_cl = 2.7# heigth of a storey
#number of walls
n_walls = 7
A_fl = 48
#WALLS CHARACTERISTICS
#Orie... | pd.Series([0, 0, 0, 0, 0, 0, 0]) | pandas.Series |
#!/usr/bin/python
from selenium import webdriver
from selenium.common.exceptions import NoSuchElementException
from selenium.webdriver.common.action_chains import ActionChains
from bs4 import BeautifulSoup
import pandas as pd
import time
row_list = list()
columns = ['date','rank','team','points']
driver = webdriver.... | pd.DataFrame(row_list,columns=columns) | pandas.DataFrame |
import logging
import numpy as np
import pandas as pd
from msiwarp.util.warp import to_mz, to_height, to_mx_peaks, generate_mean_spectrum
from msi_recal.join_by_mz import join_by_mz
from msi_recal.math import (
mass_accuracy_bounds,
weighted_stddev,
peak_width,
mass_accuracy_bound_indices,
)
from msi_... | pd.concat(bins) | pandas.concat |
import pandas as pd
import numpy as np
import os
import subprocess
def boaDataMake(X, Y, xlabels, ylabel):
wd = os.getcwd()
if not os.path.isdir('temp'):
os.mkdir('temp')
os.chdir('temp')
f = open('X.txt', 'w+')
xdic = {}
X = pd.DataFrame(X, columns = xlabels)
for label in X:
for cat in set(X[label]):
... | pd.DataFrame.from_dict(xdic) | pandas.DataFrame.from_dict |
"""Handle the raw data input/output and interface with external formats."""
from obspy.core import read
from obspy.core.utcdatetime import UTCDateTime
import pandas as pd
import datetime as dt
def load_stream(path):
"""Loads a Stream object from the file at path.
Args:
path: path to the input file, ... | pd.Series([lat for lat in latitudes]) | pandas.Series |
import sys
import pandas as pd
import scipy
import numpy as np
from datetime import datetime, timedelta
co_df = | pd.read_csv(sys.argv[1]) | pandas.read_csv |
import nose
import pandas
from pandas.compat import u
from pandas.util.testing import network
from pandas.util.testing import assert_frame_equal
from numpy.testing.decorators import slow
from pandas.io.wb import search, download, get_countries
import pandas.util.testing as tm
class TestWB(tm.TestCase):
@slow
... | pandas.DataFrame(expected) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
This module contains the ReadSets class that is in charge
of reading the sets files, reshaping them to be used in
the build class, creating and reading the parameter files and
checking the errors in the definition of the sets and parameters
"""
import itertools as it
from openpyxl import lo... | pd.Index(self.main_years, name="Years") | pandas.Index |
from boonai.project.site.helper import extract_section, get_html_pagination_params
from flask import Blueprint, render_template, request, redirect, current_app
from flask import url_for, session, flash
from flask_uploads import UploadSet
from flask_user import login_required, current_user
from wtforms import StringF... | pd.read_excel(file_path, encoding='utf-8') | pandas.read_excel |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import tqdm
import mut.thermo
import mut.bayes
constants = mut.thermo.load_constants()
# Load the raw data
data = pd.read_csv('../../data/Chure2019_compiled_data.csv', comment='#')
# Segregate the data by classifier
DNA_data = data[data['class']=='DNA'].c... | pd.concat(mutant_dfs, sort=False) | pandas.concat |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from distutils.version import LooseVersion
from scipy.stats import norm
from sklearn.neighbors import KernelDensity
from datetime import datetime
plt.rcParams['font.size'] = 6
import os
root_path = os.path.dirname(os.path.abspath('__file__'))
graphs... | pd.read_csv(root_path+"/boundary_effect/vmd-decompositions-huaxian/x_1_552_imf.csv") | pandas.read_csv |
# Importing the Keras libraries and packages
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.metrics as metrics
from keras.models import Sequential
from keras.layers import Conv1D, Dropout
from keras.layers import MaxPooling1D
from keras.layers import Flatten
from keras.layers impo... | pd.DataFrame(X_test_split) | pandas.DataFrame |
"""The user interface for accessing the Bader calulation.
Contains the Bader class, dictionaries containing the attributes of the Bader
class along with their types and a config file converter.
"""
from ast import literal_eval
from configparser import ConfigParser
from inspect import getmembers, ismodule
from pickle i... | pd.Series(self.atoms_surface_distance, name='Distance') | pandas.Series |
import datetime
import re
import time
from decimal import Decimal
from functools import reduce
from typing import Iterable
import fitz
import pandas
import requests
from lxml import html
from requests.adapters import HTTPAdapter
from requests.cookies import cookiejar_from_dict
from bank_archive import Extractor, Down... | pandas.isna(debit) | pandas.isna |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
from sklearn.externals import joblib
from mpeds.open_ended_coders import *
from pkg_resources import resource_filename
class MPEDS:
def __init__(self):
''' Constructor. '''
self.hay_clf = None
self.hay_vect = None
... | pd.Series(text) | pandas.Series |
from pathlib import Path
from typing import Optional, List
from pandas import DataFrame, to_datetime, read_csv
from pandas._libs.tslibs.timestamps import Timestamp
from timeseries_generator.external_factors.external_factor import ExternalFactor
MIN_DATE = | Timestamp("01-01-1960") | pandas._libs.tslibs.timestamps.Timestamp |
import math
import sys
import pandas as pd
import plotly.express as px
import os
import json
if __name__ == '__main__':
rootpath = ""
while not os.path.isdir(rootpath):
rootpath = input("Enter root of discord data: ") + "/messages"
timezone = input("Enter time Zone, empty for UTC (this wont be chec... | pd.read_csv(csvfile, parse_dates=[1]) | pandas.read_csv |
# coding: utf-8
"""Mapping of production and consumption mixes in Europe and their effect on
the carbon footprint of electric vehicles
This code performs the following:
- Import data from ENTSO-E (production quantities, trades relationships)
- Calculates the production and consumption electricity mixes for Europ... | pd.read_excel(vehicle_fp, sheet_name='veh_emiss', index_col=[0, 1, 2], usecols='A:G') | pandas.read_excel |
# Feb 2019
# <NAME>, <NAME>, <NAME>, <NAME>
#
# This script is the summary function in the
import numpy as np
import pandas as pd
from KMediansPy.distance import distance
def summary(x:np.ndarray, medians:np.ndarray, labels:np.ndarray) -> pd.DataFrame:
"""
Generates a table to display the cluster labels, t... | pd.DataFrame(data=df_data) | pandas.DataFrame |
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from collections import defaultdict
import random as r
import math as m
import numpy as np
from keras import backend as K
from random import Random
import pandas as pd
from keras.preprocessing import sequence
from kera... | pd.Series(y_train) | pandas.Series |
#!/usr/bin/env python3
# python3.6
# ref link: https://www.jianshu.com/p/91c98585b79b
import matplotlib.pyplot as plt
import os
import numpy as np
import pandas as pd
from scipy import stats
import seaborn as sns
import argparse
def DE(fi, wt, ko):
prefix = fi.split('.')[0]
data = pd.read_table(fi, header=0, index... | pd.DataFrame({'pvalue': pvalue_arr, 'FoldChange': foldchange}) | pandas.DataFrame |
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import make_regression
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
import numpy as np, tensorflow as tf
from sklearn.preprocessing import OneHotEncoder
impor... | pd.DataFrame({'rf_base_rmse': [rf_base_rmse], 'xgbt_base_rmse': [xgbt_base_rmse], 'gp_base_rmse': [gp_base_rmse], 'enet_base_rmse': [enet_base_rmse], 'arimax_base_rmse': [arimax_base_rmse]}) | pandas.DataFrame |
#!/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 |
#/usr/bin/env python
# Script to read the result of the benchmark program and plot the results.
# Options:
# `-i arg` : input file (benchmark result)
# `-o arg` : html output for the plot
# Notes: After the script runs the plot will automatically be shown in a browser.
# Tested with python 3 only.
import a... | pd.DataFrame(d, columns=column_labels[1:], index=columns[0]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
These test the private routines in types/cast.py
"""
import pytest
from datetime import datetime, timedelta, date
import numpy as np
import pandas as pd
from pandas import (Timedelta, Timestamp, DatetimeIndex,
DataFrame, NaT, Period, Series)
from pandas.core.dtypes.c... | tm.assert_numpy_array_equal(arr, exp) | pandas.util.testing.assert_numpy_array_equal |
#izvor: https://github.com/asetkn/Tutorial-Image-and-Multiple-Bounding-Boxes-Augmentation-for-Deep-Learning-in-4-Steps/blob/master/Tutorial-Image-and-Multiple-Bounding-Boxes-Augmentation-for-Deep-Learning-in-4-Steps.ipynb
import imgaug as ia
ia.seed(1)
from imgaug.augmentables.bbs import BoundingBox, BoundingBoxesOn... | pd.concat([aug_bbs_xy, aug_df]) | pandas.concat |
import numpy as np
from unet import utils
from unet.sim_measures import jaccard_pixelwise, jaccard_roiwise
from helper_fxns import ijroi
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from shapely.geometry import Polygon, MultiPolygon
from shapely.ops import cascaded_union
from keras.preproce... | pd.DataFrame(columns=ch_keys, index=ch_keys) | pandas.DataFrame |
import unittest
import numpy as np
import pandas as pd
import warnings
from pandas.testing import assert_frame_equal
from typing import Dict
from io import BytesIO, StringIO
from zipfile import ZipFile, ZIP_DEFLATED
import sys
import os
import boto3
# Define type aliases
DF = pd.DataFrame
# Add project root directory... | assert_frame_equal(df, actual_dfs[df_name], check_dtype=False, check_column_type=False) | pandas.testing.assert_frame_equal |
import sqlite3
import pandas as pd
conn = sqlite3.connect('rpg_db.sqlite3')
cur = conn.cursor()
# How many total Characters are there?
query1 = """
SELECT COUNT(character_id)
FROM charactercreator_character cc
"""
cur.execute(query1)
result_list = cur.fetchall()
cols = [ii[0] for ii in cur.description]
df1 = pd.D... | pd.read_sql(quer5, conn) | pandas.read_sql |
#!/usr/bin/python3
from sys import argv
import sys
#from PyQt5 import QtCore, QtGui, uic, QtWidgets
#from PyQt5.QtWebEngineWidgets import *
#from PyQt5.QtCore import QUrl
import numpy as np
from jupyter_dash import JupyterDash
import pandas as pd
from pandas.plotting import scatter_matrix
import matplotlib.pyplot as pl... | pd.read_json(df_qpcr_json) | pandas.read_json |
import numpy as np
import itertools
import pandas as pd
from keras.optimizers import Adam, SGD
from environment import Env
from agents import AgentReinforce
from train import train_reinforce
from simulate import portfolio_safe, portfolio_myopic, portfolio_risky, \
portfolio_reinforce
from helpers import all_close
... | pd.DataFrame(train_actions) | pandas.DataFrame |
# # # # # # # # # # # # # # # # # # # # # # # #
# #
# Module to plot results of #
# real time contingencies assessmemnt #
# By: <NAME> #
# 09-08-2018 #
# Version Aplha-0.1 ... | pd.DataFrame.from_dict(data_dic) | pandas.DataFrame.from_dict |
from unittest import TestCase
import pandas as pd
import numpy as np
from moonstone.normalization.counts.geometric_mean import (
GeometricMeanNormalization
)
class TestGeometricMeanNormalization(TestCase):
def setUp(self):
data = [
[255, 26, 48, 75],
[366, 46, 78, 0],
... | pd.DataFrame(data, columns=column_names, index=ind) | pandas.DataFrame |
"""BLEU SCORE
@author: vasudevgupta
"""
import nltk
import numpy as np
import pandas as pd
class Bleu:
def __init__(self, N=4):
"""GET THE BLEU SCORE
INPUT THE TARGET AND PREDICTION
"""
self.N = N
def get_score(self, target, pred):
ngrams_prec = []
for n in ... | pd.Series(ls) | pandas.Series |
"""Utils module."""
import click
import os.path
import pandas as pd
from tensorflow.keras.models import load_model
from tensorflow.keras.regularizers import l1_l2
from tensorflow.keras.callbacks import CSVLogger, ModelCheckpoint, TensorBoard
from zalando_classification.models import build_model
def get_basename(na... | pd.read_csv(csv_path) | pandas.read_csv |
"""Tests suite for Period handling.
Parts derived from scikits.timeseries code, original authors:
- <NAME> & <NAME>
- pierregm_at_uga_dot_edu - mattknow_ca_at_hotmail_dot_com
"""
from unittest import TestCase
from datetime import datetime, timedelta
from numpy.ma.testutils import assert_equal
from pandas.tseries.p... | PeriodIndex(start='1/1/10', end='12/31/12', freq='2M') | pandas.tseries.period.PeriodIndex |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
from scipy.stats import randint as sp_randint
from sklearn.model_selection import RandomizedSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
path_train ... | pd.crosstab(test['attack'], preds_rf, rownames=['actual'], colnames=['preds']) | pandas.crosstab |
#mcandrew
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sys.path.append("../")
from mods.datahelp import grabData, grabJHUData, grabDHSdata
class reportBuilder(object):
def __init__(self,gd):
self.predictions = gd.predictions()
self.qData ... | pd.to_datetime(mostRecentdata.name) | pandas.to_datetime |
import pandas as pd
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
df_train = | pd.read_csv("DATASET/train.csv") | pandas.read_csv |
__author__ = '<NAME>'
from pandas import DataFrame, read_csv, concat
from os import path
import numpy as np
from datetime import timedelta
from enum import Enum
class OrderEvent(Enum):
SUBMISSION = 1
CANCELLATION = 2
DELETION = 3
EXECUTION = 4
HIDDEN_EXECUTION =5
CROSS_TRADE = 6
TRADING_HA... | DataFrame() | pandas.DataFrame |
from datetime import (
datetime,
timedelta,
)
import numpy as np
import pytest
from pandas.compat import (
pa_version_under2p0,
pa_version_under4p0,
)
from pandas.errors import PerformanceWarning
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
isna,
)
import pandas._tes... | Series(expected, dtype=any_string_dtype) | pandas.Series |
import pandas as pd
from abb_deeplearning.abb_data_pipeline import abb_clouddrl_read_pipeline as abb_rp
from abb_deeplearning.abb_data_pipeline import abb_clouddrl_constants as abb_c
import datetime as dt
import os
df_master = pd.read_hdf('/media/data/Daten/data_C_int/master_index_cav.h5')
file_filter={"_sp_256",... | pd.to_datetime(image_keys) | pandas.to_datetime |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib as mpl
import netCDF4 as nc
import datetime as dt
from salishsea_tools import evaltools as et, places, viz_tools, visualisations, geo_tools
import xarray as xr
import pandas as pd
import pickle
import os
import gsw
#... | pd.concat((bio_time_df,upper_3m_phyto,upper_3m_no3), axis=1) | pandas.concat |
#!/usr/bin/env python3
"""
https://www.ebi.ac.uk/gwas/rest/docs/api
"""
###
import sys,os,re,json,time,logging,tqdm
import urllib.parse
import pandas as pd
#
from ..util import rest
#
API_HOST='www.ebi.ac.uk'
API_BASE_PATH='/gwas/rest/api'
BASE_URL='https://'+API_HOST+API_BASE_PATH
#
NCHUNK=100;
#
#####################... | pd.concat([df_snp, df_gc, df_gcloc, df_gene], axis=1) | pandas.concat |
"""Probabilistic autoregressive model."""
import logging
import numpy as np
import pandas as pd
import torch
from tqdm import tqdm
from deepecho.models.base import DeepEcho
LOGGER = logging.getLogger(__name__)
class PARNet(torch.nn.Module):
"""PARModel ANN model."""
def __init__(self, data_size, context_... | pd.isnull(context[key]) | pandas.isnull |
import locale
import numpy as np
import pytest
from pandas.compat import (
is_platform_windows,
np_version_under1p19,
)
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import FloatingArray
from pandas.core.arrays.floating import (
Float32Dtype,
Float64Dtype,
)
def test_uses... | pd.array(a, dtype="Float64") | pandas.array |
from unittest import TestCase
import pandas as pd
import numpy as np
from moonstone.normalization.counts.geometric_mean import (
GeometricMeanNormalization
)
class TestGeometricMeanNormalization(TestCase):
def setUp(self):
data = [
[255, 26, 48, 75],
[366, 46, 78, 0],
... | pd.Series(data, index=ind) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 4 14:06:09 2018
@author: ashkrelja
"""
#import data
import pandas as pd
import numpy as np
path = 'C:/Users/ashkrelja/Documents/Wall_Street_Lending/Technology/Analytics/Operations_Analytics/2019/Operations Analytics_03_2018.csv'
df = pd.read_csv(path, usecols = ['Stat... | pd.DataFrame(residual_array) | pandas.DataFrame |
import pandas as pd
import re
import sys
from pymicruler.utils import util
from pymicruler.bp.BlockProcessor import BlockProcessor as BP
from pymicruler.bp.BlockInterpreter import BlockInterpreter as BI
from pymicruler.bp.NoteAnalysis import NoteAnalysis as NA
from pymicruler.bp.TaxonomyHandler import TaxonomyHandler ... | pd.DataFrame() | pandas.DataFrame |
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