prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# coding: utf-8
# # Convert downloaded TCGA datasets into sample × gene matrices
#
# This notebook is updated to include the data from the [TCGA PanCanAtlas April 2018 updates](http://www.cell.com/pb-assets/consortium/pancanceratlas/pancan/index.html).
# In[1]:
import collections
import os
import pandas
# ## R... | pandas.read_csv(path, dtype='str') | pandas.read_csv |
# -*- coding: utf-8 -*-
import re
import warnings
from datetime import timedelta
from itertools import product
import pytest
import numpy as np
import pandas as pd
from pandas import (CategoricalIndex, DataFrame, Index, MultiIndex,
compat, date_range, period_range)
from pandas.compat import PY... | pd.read_pickle(path) | pandas.read_pickle |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import pandas.compat as compat
###############################################################
# Index / Series common tests which may trigger dtype coercions
###############################################... | pd.Timestamp('2011-01-01') | pandas.Timestamp |
import pyvinecopulib as pv
import numpy as np
import pandas as pd
from experiments_utils import random_bicop, get_pvcopfamily, beta_copula_cdf, emp_cdf, gaussian_mixture_copula
from models.igc import ImplicitGenerativeCopula
from datetime import datetime
import pickle
import matplotlib.pyplot as plt
import seaborn as s... | pd.DataFrame(l2) | pandas.DataFrame |
"""
Unit and regression test for the kissim.comparison.FeatureDistances class.
"""
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
from kissim.comparison import FeatureDistances
PATH_TEST_DATA = Path(__name__).parent / "kissim" / "tests" / "data"
class TestsFeatureDistances:
"""
... | pd.Series([1, 1, 1, 1, np.nan]) | pandas.Series |
#!/usr/bin/env python3
import os
import re
import cv2
import keras
import numpy as np
import pandas as pd
DATA_PATH = 'cage/images/'
LEFT_PATH = 'data/left.h5'
RIGHT_PATH = 'data/right.h5'
NUM_PATH = 'data/numbers.csv'
DataSet = (np.ndarray, np.ndarray, np.ndarray)
def extract() -> (list, list):
l_data, r_data... | pd.DataFrame([nums]) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 26 15:39:02 2018
@author: joyce
"""
import pandas as pd
import numpy as np
from numpy.matlib import repmat
from stats import get_stockdata_from_sql,get_tradedate,Corr,Delta,Rank,Cross_max,\
Cross_min,Delay,Sum,Mean,STD,TsRank,TsMax,TsMin,DecayLinea... | pd.DataFrame((data['close'] - data['close_mean'])/data['close_mean']) | pandas.DataFrame |
import json
import pandas as pd
from tqdm import tqdm
def read_var(file='parameters.xlsx', scenario='base'):
parameter_frame = | pd.read_excel(file) | pandas.read_excel |
import pandas as pd
def load_data(portfolio_data_absolute_path="/home/chris/Dropbox/Finance/data/portfolio_trades.ods",
stock_data_absolute_path="/home/chris/Dropbox/Finance/data/stock_trades.ods",
income_data_absolute_path="/home/chris/Dropbox/Finance/data/income.ods",
etf_ma... | pd.offsets.MonthBegin() | pandas.offsets.MonthBegin |
import http.client
from datetime import datetime
import json
import pandas as pd
API = '<KEY>'
def str2time(strng):
return datetime.strptime(strng, '%H:%M:%S').time()
def str2date(strng):
return datetime.strptime(strng, '%Y-%m-%d').date()
def create_db_connection():
DB_USER = 'root'
DB_PASS = 'pas... | pd.read_csv(fl) | pandas.read_csv |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
import logging
import warnings
import os
import pandas_datareader as pdr
from collections import Counter
from scipy import stats
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_percentage... | pd.read_html(results_as_html, header=0, index_col=0) | pandas.read_html |
from __future__ import print_function
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from pandas import (Series, Index, Int64Index, Timestamp, Period,
DatetimeIndex, PeriodIndex, TimedeltaIndex,
Timedelta, timedelta_range, date_range, Float64Index... | pd.Period('2011-01-01') | pandas.Period |
# --------------
import pandas as pd
from sklearn import preprocessing
import seaborn as sns
import numpy as np
from matplotlib import pyplot as plt
from sklearn import cross_validation
from sklearn.preprocessing import StandardScaler
from sklearn.feature_selection import SelectPercentile
from sklearn.feature_selection... | pd.read_csv(path) | pandas.read_csv |
import numpy as np
import pandas as pd
from . import util as DataUtil
from . import cols as DataCol
"""
The main data loader.
TODO: population & common special dates
"""
class DataCenter:
def __init__(self):
self.__kabko = None
self.__dates_global = pd.DataFrame([], columns=DataCol.DATES_GLOBAL)
... | pd.read_excel(path, sheet_name="covid_indo") | pandas.read_excel |
# coding: utf-8
"""
Loads data from :epkg:`INSEE`.
"""
from pandas import to_datetime
from .pandas_cache import read_csv_cache, geo_read_csv_cache
def data_france_departments(cache='dep_france', metropole=False):
"""
Retrieves data from
`Contours géographiques des départements
<https://www.data.gouv.f... | to_datetime(df['jour']) | pandas.to_datetime |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
import json
import logging
import itertools
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from .visualization_utilize import VisualizationUtilize
from ..data.base import UniformScene
from .... | pd.DataFrame(scene_obj_num_bc) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Scientific Computing and Visualization with Spyder
Created on Thu May 20 10:17:27 2021
@author: <NAME>
"""
# %% Import libraries
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats as stats
import seaborn as sns
# %% Load raw data (parquet)
data... | pd.Series(ca_test.q255, dtype="int") | pandas.Series |
"""
July 2021
This code retrieves the calculation of sand use for concrete and glass production in the building sector in 26 global regions. For the original code & latest updates, see: https://github.com/
The dynamic material model is based on the BUMA model developed by <NAME>, Leiden University, the Netherlan... | pd.DataFrame(avg_m2_cap_rur2.iloc[3].values * people_hig_rur.values, columns=people_hig_rur.columns, index=people_hig_rur.index) | pandas.DataFrame |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
folder_path_txt = "hidden/box_folder_path.txt"
with open(folder_path_txt) as f:
content = f.readlines()
content = [x.strip() for x in content]
box_folder_path = content[0]
file_path = "/data/d_traj.csv"
df = pd.read_csv(box_folder_path + file_p... | pd.to_numeric(df['load'], errors='coerce') | pandas.to_numeric |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Simulation framework for testing LDSC
Models for SNP effects:
- Infinitesimal (can simulate n correlated traits)
- Spike & slab (can simulate up to 2 correlated traits)
- Annotation-informed
Features:
- Field aggregation tools for annotation-inform... | pd.DataFrame([0]*M, columns=['beta']) | pandas.DataFrame |
import pandas as pd
from .video import Video
def get_videos_pages(cursor):
"""
Get the set of pages by load_video event
:param cursor:
:return:
"""
request = """
select *
from load_video
"""
cursor.execute(request)
data = cursor.fetchall()
colu... | pd.DataFrame(data=data, columns=columns_names) | pandas.DataFrame |
from PIL import Image, ImageDraw, ImageFont
import io
import numpy as np
import pandas as pd
import folium
from matplotlib.colors import LinearSegmentedColormap, rgb_to_hsv, hsv_to_rgb
import scipy.ndimage.filters
from pathlib import Path
pd.options.display.max_columns = 50
def main(dir):
# Loading Data Set
p... | pd.to_datetime(RentalData["EndDate"]) | pandas.to_datetime |
import snowflake.connector as sf
import pandas as pd
import matplotlib.pyplot as plt
from config import config
import numpy as np
# Connection String
conn = sf.connect(
user=config.username,
password=config.password,
account=config.account
)
def test_connection(connect, query):
cursor = connect.cursor... | pd.merge(df5, df3, how='left', on='PROFUNCTIONID') | pandas.merge |
#------------------------------------------------------------------------------------------------------------------------------
# By <NAME>
# (updated October 2018)
#
# Define offset vectors
# An offset vector represents the difference in gene expression profiles between two states (ex. two different conditions like ... | pd.read_table(non_target_gene_file, header=0, index_col=0) | pandas.read_table |
from sklearn.datasets import fetch_openml
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
import fairlearn.metrics as fm
import fairsd as dsd
#Import dataset, training the classifier, producing y_pred
d = fetch_openml(data_id=1590, as_frame=True)
dataset =... | pd.get_dummies(dataset) | pandas.get_dummies |
'''
/*******************************************************************************
* Copyright 2016-2019 Exactpro (Exactpro Systems Limited)
*
* 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
... | pandas.to_datetime(frame['Created_tr']) | pandas.to_datetime |
#!/usr/bin/env python3
import pandas as pd
import numpy as np
# import click #command line interface
#import tkinter for simple gui
from tkinter import filedialog, Tk
#automate the boring stuff
import time, os, sys, re, warnings, shutil
#define localfile system
if not 'nb_dir' in globals():
nb_dir = os.getcwd()
d... | pd.read_excel(dict_dir) | pandas.read_excel |
import argparse
import itertools
import multiprocessing as mp
import os
from inspect import signature
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from Timer import Timer, timer
import qpputils as dp
try:
from crossval import InterTopicCrossValidation, IntraTopicCrossValidation
from... | pd.DataFrame(results) | pandas.DataFrame |
#%%
from pymaid_creds import url, name, password, token
import pymaid
rm = pymaid.CatmaidInstance(url, token, name, password)
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
import numpy.random as random
import gzip
import csv
import connectome_tools.celltype as ct
import... | pd.DataFrame(path_counts_length_data, columns=['path_length', 'condition', 'N']) | pandas.DataFrame |
import sys
import os
import math
import datetime
import itertools
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from statsmodels.tsa.stattools import grangercausalitytests
import scipy.stats as stats
from mesa.batchrunner import BatchRunner, BatchRunnerMP
from mesa.datacol... | pd.merge(df_overall, df_testing, on=['province', 'date'], how='outer') | pandas.merge |
import pandas as pd
import numpy as np
import datetime
name = ['IP', 'app', 'daytime', 'platform', 'channel_type', 'channel', 'user_id',
'device_id', 'system_version', 'brand', 'model', 'version', 'event_id', 'para']
# 如果不是csv(默认逗号分隔)的文件 就需要加sep指定分隔符，否则会分割出\t, 要设定header=None,否则默认使用第一行的数据当做列名
f1 = pd.DataFrame(pd.re... | pd.to_datetime(df['daytime']) | pandas.to_datetime |
# -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
import operator
from collections import OrderedDict
from datetime import datetime
from itertools import chain
import warnings
import numpy as np
from pandas import (notna, DataFrame, Series, MultiIndex, date_range,
Time... | assert_frame_equal(result, df) | pandas.util.testing.assert_frame_equal |
from cgitb import enable
import os
import json
from lightgbm import early_stopping
from tabulate import tabulate
from functools import partial
from IPython.display import display
from tqdm.auto import tqdm
import numpy as np
import xgboost as xgb
from .logger import logger
from sklearn.preprocessing import LabelEncod... | pd.DataFrame.from_dict(data=d, orient='index') | pandas.DataFrame.from_dict |
import numpy as np
import matplotlib.pylab as plt
import pandas as pd
import scipy.signal as signal
#Concatenación de los datos
data1 = pd.read_csv("transacciones2008.txt",sep = ";",names=['Fecha','Hora','Conversion','Monto'],decimal =",")
data2 = pd.read_csv("transacciones2009.txt",sep = ";",names=['Fecha','Hora'... | pd.to_datetime(data["Fecha"],format='%d/%m/%Y %H:%M:%S') | pandas.to_datetime |
import unittest
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
from pandas_extras import (
concatenate_columns, expand_list, expand_lists,
extract_dict_key, extract_dictionary, merge_columns,
)
class TransformationsTestCase(unittest.TestCase):
def test_... | assert_frame_equal(dataframe, expected_result, check_like=True, check_dtype=False) | pandas.testing.assert_frame_equal |
from __future__ import division #brings in Python 3.0 mixed type calculation rules
import datetime
import inspect
import numpy as np
import numpy.testing as npt
import os.path
import pandas as pd
import sys
from tabulate import tabulate
import unittest
print("Python version: " + sys.version)
print("Numpy version: " +... | pd.Series([175., 100., 200.], dtype='float') | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 4 13:21:36 2019
@author: mt01034
"""
import pandas as pd
import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import LeaveOneOut
from sklearn.metrics import confusion_matrix
from KNNImplement import MyKNeighborsClassifi... | pd.read_csv("output_filename.csv") | pandas.read_csv |
# -*- coding: utf-8 -*-
"""Run the MRIA Model for a given set of disruptions.
"""
import os
import numpy as np
import pandas as pd
from vtra.mria.disruption import create_disruption
from vtra.mria.model import MRIA_IO as MRIA
from vtra.mria.table import io_basic
from vtra.utils import load_config
def estimate_losses... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 5 15:33:50 2019
@author: luc
"""
#%% Import Libraries
import numpy as np
import pandas as pd
import itertools
from stimuli_dictionary import cued_stim, free_stim, cued_stim_prac, free_stim_prac
def randomize(ID, Age, Gender, Handedness):
'''
Create a rand... | pd.DataFrame() | pandas.DataFrame |
import os
import ubelt as ub
import numpy as np
import netharn as nh
import torch
import torchvision
import itertools as it
import utool as ut
import glob
from collections import OrderedDict
import parse
def _auto_argparse(func):
"""
Transform a function with a Google Style Docstring into an
`argparse.Argum... | pd.set_option("display.max_rows", None) | pandas.set_option |
import pkg_resources
import pandas as pd
from unittest.mock import sentinel
import osmo_jupyter.dataset.parse as module
def test_parses_ysi_csv_correctly(tmpdir):
test_ysi_classic_file_path = pkg_resources.resource_filename(
"osmo_jupyter", "test_fixtures/test_ysi_classic.csv"
)
formatted_ysi_d... | pd.to_datetime("2019-01-01 00:00:04") | pandas.to_datetime |
import datetime
import re
from warnings import (
catch_warnings,
simplefilter,
)
import numpy as np
import pytest
from pandas._libs.tslibs import Timestamp
from pandas.compat import is_platform_windows
import pandas as pd
from pandas import (
DataFrame,
Index,
Series,
_testing as tm,
bdat... | read_hdf(path, "df") | pandas.read_hdf |
__author__ = "<NAME>"
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
path = "../data/localData/"
nc = | pd.read_csv(path+"newCasesWithClass.csv") | pandas.read_csv |
import os
import tempfile
import pandas as pd
import pytest
from pandas.util import testing as pdt
from .. import simulation as sim
from ...utils.testing import assert_frames_equal
def setup_function(func):
sim.clear_sim()
sim.enable_cache()
def teardown_function(func):
sim.clear_sim()
sim.enable_... | pdt.assert_frame_equal(store['table'], df) | pandas.util.testing.assert_frame_equal |
import nltk.data
from gensim.models import word2vec
from gensim.models.word2vec import LineSentence
from sklearn.cluster import KMeans
from sklearn.neighbors import KDTree
import pandas as pd
import numpy as np;import os
import re
import logging
import sqlite3
import time
import sys
import multiprocessing
... | pd.set_option('display.max_columns', None) | pandas.set_option |
# 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... | tm.box_expected(idx, box_with_array) | pandas._testing.box_expected |
import unittest
import numpy as np
import pandas as pd
import scipy.stats as st
from os import path, getcwd
from ..graphs import GraphGroupScatter
from ..data import Vector
from ..analysis.exc import NoDataError
from ..data import UnequalVectorLengthError
class MyTestCase(unittest.TestCase):
@property
def s... | pd.DataFrame({'a': cs_x, 'b': cs_y, 'c': grp}) | pandas.DataFrame |
# coding: utf-8
from __future__ import unicode_literals, print_function
import matplotlib
import matplotlib.dates
import matplotlib.patches as mpatch
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import random
from . import core
from .. import metrics
available_s... | pd.to_datetime(df['submission_time'], unit='s') | pandas.to_datetime |
"""
**pyPheWAS Core version 2 (main pyPheWAS code)**
Contains all functions that drive the core PheWAS & ProWAS analysis tools.
"""
from collections import Counter
import getopt
import math
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import statsmodels.discrete.discrete_model as s... | pd.read_csv(wholefname,dtype={'ICD_CODE':str}) | pandas.read_csv |
import os
from datetime import date
from dask.dataframe import DataFrame as DaskDataFrame
from numpy import nan, ndarray
from numpy.testing import assert_allclose, assert_array_equal
from pandas import DataFrame, Series, Timedelta, Timestamp
from pandas.testing import assert_frame_equal, assert_series_equal
from pymo... | Timestamp('2008-10-23 05:53:06') | pandas.Timestamp |
# -*- coding: utf-8 -*-
from sklearn.neighbors import KNeighborsRegressor
import numpy as np
import pandas as pd
def return_alike_axis(X,Y):
idx = [x for x in X.index if x in Y.index]
X = X.loc[idx]
Y = Y.loc[idx]
return (X,Y)
def get_data(series, steps, forward = False):
if forward:
fb =... | pd.DataFrame(error_dict) | pandas.DataFrame |
import torch
import pandas as pd
from fast_radiology.metrics import dice as dice3D
from artificial_contrast.const import (
DICE_NAME,
PATH_NAME,
PATIENT_NAME,
PREDICTIONS_NAME,
TARGETS_NAME,
)
def evaluate_patients(learn, patients, img_size):
results = []
preds, targets = learn.get_preds... | pd.DataFrame(results) | pandas.DataFrame |
import ast
import datetime
import time
import math
import pypandoc
import os
import matplotlib.pyplot as plt
import numpy as np
import numpy.ma as ma
import pandas as pd
import statsmodels.api as sm
from library.api import API_HOST, fetch_objects, fetch_objects_by_id, get_token
from library.settings import MIN_VIDEO_... | pd.DataFrame(columns=['start', 'peak', 'end', 'rise_rate']) | pandas.DataFrame |
##Exec Dashboard Project
# PACKAGES and MODULES----------------------------------------------------------
import os
import operator
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as tck
# FUNCTIONS----------------------------------------------------------------------
def to_usd(my_pr... | pd.to_numeric(master_data['yearmon']) | pandas.to_numeric |
import os
import pathlib
import spacy
import re
import pandas as pd
import matplotlib.pyplot as plt
from gensim.models.phrases import Phrases, Phraser
ROOT_DIR = pathlib.Path(__file__).parent.parent
# Set directories and create them if necessary
plain_text_dir = pathlib.Path().joinpath(ROOT_DIR,"data","plaintext")
s... | pd.Series(trigrams) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 5 12:51:54 2016
@author: tkc
"""
import os, re, glob
import pandas as pd
import numpy as np
from math import factorial # used by Savgol matrix
from io import StringIO
import datetime
from scipy import optimize
def rangefromstring(x):
result = []
for part in x.s... | pd.DataFrame(columns=mycols3) | pandas.DataFrame |
"""
Evaluate vega expressions language
"""
import datetime as dt
from functools import reduce, wraps
import itertools
import math
import operator
import random
import sys
import time as timemod
from typing import Any, Callable, Dict, Optional, List, Union, overload
import numpy as np
import pandas as pd
from dateutil ... | pd.to_datetime(value) | pandas.to_datetime |
"""
Tests the financial data structures
"""
import unittest
import os
import numpy as np
import pandas as pd
from mlfinlab.data_structures import imbalance_data_structures as ds
class TestDataStructures(unittest.TestCase):
"""
Test the various financial data structures:
1. Imbalance Dollar bars
2. I... | pd.read_csv('test.csv') | pandas.read_csv |
from glob import glob
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.patches import Polygon
import matplotlib
from utils import load_obj
show_plot = False
cols = ["dataset", "period", "clf", "magic", "model_params", "k", "bot_thresh",
"top_thresh", "mode", "trade_frequ... | pd.read_csv('../sp500.csv') | pandas.read_csv |
import teneto
import tvc_benchmarker
import numpy as np
import pandas as pd
def dfc_calc(data,methods=['SW','TSW','SD','JC','TD'],sw_window=63,taper_name='norm',taper_properties=[0,10],sd_distance='euclidean',mtd_window=7,mi='alpha',colind=None):
"""
Required parameters for the various differnet methods:
I... | pd.DataFrame(data=dfc, index=data.index) | pandas.DataFrame |
""" io_utils.py
Utilities for reading and writing logs.
"""
import os
import statistics
import re
import csv
import numpy as np
import pandas as pd
import scipy as sc
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import torch
import networkx as nx
import tensorboardX
import cv2
import ... | pd.DataFrame(collector) | pandas.DataFrame |
import pandas as pd
import numpy as np
from datetime import date
"""
dataset split:
(date_received)
dateset3: 20160701~20160731 (113640),features3 from 20160315~20160630 (off_test)
dateset2: 20160515~20160615 (258446),features2 from 20160201~2... | pd.merge(t,t1,on='user_id',how='left') | pandas.merge |
from abc import ABC
import numpy as np
import pandas as pd
from fastnumbers import isintlike, isreal, fast_forceint, fast_float
from optimus.engines.base.functions import BaseFunctions
from optimus.helpers.logger import logger
from optimus.infer import is_int_like, is_list_or_tuple
class PandasBaseFunctions(BaseFun... | pd.to_numeric(series, errors='coerce') | pandas.to_numeric |
"""
Simple Streamlit webserver application for serving developed embedding
a dashboard visualisation in streamlit.
"""
# Streamlit dependencies
import streamlit as st
st.beta_set_page_config(layout="wide", page_icon="pear")
#import joblib,os
# Data dependencies
import numpy as np
import random
import matplot... | pd.Series(cluster_proportion_df['cluster_centers'].values, index=cluster_proportion_df['index'].values) | pandas.Series |
#
# Example of solving the inventory control with lost sales problem
#
# Env: https://github.com/paulhendricks/gym-inventory/blob/master/gym_inventory/envs/inventory_env.py
#
# Author: <NAME>, NUS/ISS
#
import gym
import pyogmaneo
import gym_inventory # workaround for registration issue
import matplotlib.pyplot as plt... | pd.Series(history) | pandas.Series |
import numpy as np
import matplotlib.pyplot as plt
import itertools
import os
from multiprocessing import Pool
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.integrate import solve_ivp
import scipy.integrate
from sklearn.metrics import mean_squared_... | pd.to_datetime(county_data["date"].values) | pandas.to_datetime |
import os
from uuid import uuid4
import pytest
from thrift.transport import TSocket, TTransport
from thrift.transport.TSocket import TTransportException
from heavyai import connect
import datetime
import random
import string
import numpy as np
import pandas as pd
heavydb_host = os.environ.get('HEAVYDB_HOST', 'localho... | pd.read_csv("tests/data/lines_10000.zip", header=None) | pandas.read_csv |
# -*- coding: utf-8 -*-
import numpy as np, pandas as pd, torch, cv2, os, argparse, math
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
from matplotlib import cm
from pydub import AudioSegment, effects, scipy_effects
from nnAudio import Spectrogram
from yolo... | pd.DataFrame() | pandas.DataFrame |
from ast import literal_eval as make_tuple
from itertools import groupby
import pandas as pd
from pm4py.objects.log.log import Trace
from src.encoding.declare.declare_mining import filter_candidates_by_support, generate_train_candidate_constraints, \
transform_results_to_numpy
from src.encoding.declare.declare_te... | pd.DataFrame(data, columns=featurenames) | pandas.DataFrame |
import numpy as np
import pandas as pd
class HMM:
"""
Implementation of Filtering, Smoothing, Decoding(Viterbi) and Prediction
for Hidden Markov Models
"""
def __init__(self, T:np.ndarray, M:np.ndarray, state_list:list, obs_dict:dict):
"""
Parameters:
-------... | pd.DataFrame() | pandas.DataFrame |
def update_rel_frame_time(org_frame_time, duration):
return round(org_frame_time - duration, 7)
def replace_src_with_dst(col_name):
if 'src' in col_name:
col_name = col_name.replace('src', 'dst')
else:
if 'dst' in col_name:
col_name = col_name.replace('dst', 'src')
return ... | pd.read_csv(csv_file_path) | pandas.read_csv |
import datetime
import logging
import os
import pandas as pd
from ..models.order import Order
from ..models.price import Price
from ..models.dealer import Dealer
from yahooquery import Ticker
from pandas import DataFrame
class YQBroker(Dealer):
cache_file: str = '../data/yq_broker_data.csv'
ticker: Ticker
... | pd.to_datetime(self.historical_data['date']) | pandas.to_datetime |
import ccxt
import config
import schedule
import pandas as pd
import numbers
pd.set_option('display.max_rows', None)
import warnings
warnings.filterwarnings('ignore')
from datetime import datetime
import time
from stockstats import StockDataFrame as Sdf
# how much quote currency example [DOGE] you want to spend on... | pd.read_csv("trades.csv") | pandas.read_csv |
# gpu_id = None
# if len(sys.argv) == 2:
# gpu_id = sys.argv[1]
# if not gpu_id:
# raise Exception('insert gpu_id')
# import os
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
#
# os.environ["CUDA_VISIBLE_DEVICES"] = gpu_id
import sys
sys.path.append('../')
from wrappers.bioc_wrapper import bioc_t... | pd.read_excel('metrics/results_'+model_name+'.xlsx') | pandas.read_excel |
"""Functions to calculate mean squared displacements from trajectory data
This module includes functions to calculate mean squared displacements and
additional measures from input trajectory datasets as calculated by the
Trackmate ImageJ plugin.
"""
import warnings
import random as rand
import pandas as pd
import nu... | pd.DataFrame(data=data1) | pandas.DataFrame |
import datetime
import json
import os.path
import pandas as pd
import numpy as np
import folium
from folium import plugins
from branca.element import MacroElement
from jinja2 import Template
from flask import Flask, Response
app = Flask(__name__)
app.config.from_object(__name__)
class FloatMacro(MacroElement):
... | pd.DataFrame(raw['locations']) | pandas.DataFrame |
import pandas as pd
import requests
import sys
import os
import urllib3
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime
import utils.general_utils as general_utils
# import strava_analysis.utils.general_utils as general_utils
def get_updated_access_token(refre... | pd.json_normalize(my_dataset) | pandas.json_normalize |
# -*- coding: utf-8 -*-
import numpy as np
import pytest
from pandas.compat import lrange, lzip, range
import pandas as pd
from pandas import Index, MultiIndex, Series
import pandas.util.testing as tm
def test_equals(idx):
assert idx.equals(idx)
assert idx.equals(idx.copy())
assert idx.equals(idx.astyp... | tm.assert_numpy_array_equal(index_a == index_a, expected1) | pandas.util.testing.assert_numpy_array_equal |
import pandas as pd
import numpy as np
import datetime
import os
def construct_weather_data(response, station, cols) -> pd.DataFrame:
timestamps = sorted(response.data.keys())
d = {}
d["time"] = timestamps
for col in cols:
print(col)
values = []
for t in timestamps:
... | pd.DataFrame(config) | pandas.DataFrame |
from __future__ import division
import json
import numpy as np
import pandas as pd
from scipy import stats
from visigoth.stimuli import Point, Points, PointCue, Pattern
from visigoth import (AcquireFixation, AcquireTarget,
flexible_values, limited_repeat_sequence)
def define_cmdline_params(sel... | pd.Series(res) | pandas.Series |
import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from scipy.stats import ttest_ind
from sklearn.preprocessing import LabelEncoder
def load_data():
questionnaire = pd.read_excel('XAutoML.xlsx')
encoder = LabelEncoder()
encoder.classes_ = np.array([... | pd.read_excel('task_results.ods', sheet_name=0) | pandas.read_excel |
#!/usr/bin/env python
# coding: utf-8
# Author : <NAME>
# Initial Date: Feb 17, 2020
# About: strymread class to read CAN data from CSV file captured using
# libpanda (https://jmscslgroup.github.io/libpanda/) or from `strym` class.
# Read associated README for full description
# License: MIT License
# Permission is... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
import argparse
import pandas as pd
import re
#read arguments
parser = argparse.ArgumentParser(description="Recluster the gene clusters by species pairs based on orthopairs")
parser.add_argument("--orthopairs", "-op", required=True)
parser.add_argument("--orthogroups", "-og", required=True)
pa... | pd.Series(orthogroups_df.Species.values, index=orthogroups_df.GeneID) | pandas.Series |
from sklearn.linear_model import LogisticRegression
import argparse
import os
import numpy as np
from sklearn.metrics import mean_squared_error
import joblib
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
from azureml.core.run import Run
from azu... | pd.get_dummies(x_df.education, prefix="education") | pandas.get_dummies |
import argparse
import os
from typing import List
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from molgym.tools.analysis import parse_json_lines_file, parse_results_filename, collect_results_paths
# Styling
fig_width = 3.3
fig_height = 2.1
plt.style.use('ggplot')
plt.rcParams.update({'fon... | pd.concat(frames) | pandas.concat |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | pd.DataFrame(raw) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101
import itertools
import warnings
from warnings import catch_warnings
from datetime import datetime
from pandas.types.common import (is_integer_dtype,
is_float_dtype,
is_scalar)
from pandas.compat... | tm.assert_frame_equal(df, expected) | pandas.util.testing.assert_frame_equal |
from datetime import datetime
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
CategoricalIndex,
DataFrame,
Index,
MultiIndex,
Series,
qcut,
)
import pandas._testing as tm
def cartesian_product_for_groupers(result, args, names, fill... | Index([1, 2], name="person_id") | pandas.Index |
import argparse
import logging
from decimal import getcontext, Decimal, ROUND_UP
from pathlib import Path
from typing import Dict, Set
from junitparser import JUnitXml
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
EWM_ALPHA = 0.1
EWM_ADJUST = False
HEATMAP_FIGSIZE = (100... | pd.Timedelta(days=days * window_count) | pandas.Timedelta |
from scipy import stats
import numpy as np
import pandas as pd
from itertools import combinations
from sklearn.metrics import precision_score,recall_score,accuracy_score,roc_auc_score,f1_score,roc_curve,precision_recall_curve
from static_data import *
import pickle
def normalize(arr):
return arr
# return arr/ar... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'han'
import os
import h5py
import math
import torch
import torch.utils.data
from torch.utils.data.sampler import Sampler, SequentialSampler
import logging
import pandas as pd
from dataset.preprocess_data import PreprocessData
from utils.functions import *
l... | pd.DataFrame(data=lengths, columns=['length']) | pandas.DataFrame |
from __future__ import division
import time
from datetime import datetime
import sys
import numpy as np
import faiss
import pandas as pd
import os
'''
* Create a GitHub repo to house the code and results.
* Show results with different:
X vector length - 96, 300, 4096
* dataset vector count
* batch size
... | pd.read_csv('benchmark_tests.csv') | pandas.read_csv |
import pandas as pd
import numpy as np
np.random.seed(99)
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import GridSearchCV
from sklearn.multioutput import MultiOutputClassifier, MultiOutputRegressor
from sklearn.multiclass import OneV... | pd.DataFrame(feat_imp2) | pandas.DataFrame |
# coding: utf-8
# # Online Retail
#
# - http://archive.ics.uci.edu/ml/datasets/online+retail#
#
#
# ## Data Set Information:
#
# This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly... | pd.to_datetime(start_date) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Created on Sun Jul 8 10:39:33 2018
@author: jimmybow
"""
from dash import Dash
from dash.dependencies import Input, State, Output
from .Dash_fun import apply_layout_with_auth
import dash_core_components as dcc
import dash_html_components as html
from flask_login import current_user
import ... | pd.read_sql('SELECT * FROM META_STATION_BASSIN', conn) | pandas.read_sql |
# -*- coding: utf-8 -*-
# pylint: disable=E1101,E1103,W0232
import os
import sys
from datetime import datetime
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
import pandas.compat as compat
import pandas.core.common as com
import pandas.util.testing as tm
from pandas import (Categor... | option_context("display.width", None) | pandas.core.config.option_context |
import datetime as dt
import os
from os.path import join, normpath
import pandas as pd
class OutputProcessor(object):
def __init__(self, output_dir: str, output_name: str) -> None:
"""
Output processor manages output data
"""
self.output_dir = output_dir
self.output_file... | pd.concat([self.df, df_temp], axis=0, sort=True) | pandas.concat |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'chengzhi'
"""
tqsdk.ta 模块包含了一批常用的技术指标计算函数
"""
import numpy as np
import pandas as pd
import numba
from tqsdk import ta_func
def ATR(df, n):
"""平均真实波幅"""
new_df = pd.DataFrame()
pre_close = df["close"].shift(1)
new_df["tr"] = np.where(df["h... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
i... | pd.get_dummies(testData['City Group']) | pandas.get_dummies |
import pytest
import os
import pandas as pd
from playgen import playsampler
from playgen.exceptions import InsufficientDataException
# @pytest.fixture
# def full_pbp_df():
# dirname = os.path.dirname(__file__)
# filename = os.path.join(dirname, 'data/testdata.csv')
# return pd.read_csv(filename)
@pyte... | pd.DataFrame(data, columns=columns) | pandas.DataFrame |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.