prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pandas as pd
import numpy as np
import re
from pandas import DataFrame
NWR = pd.read_excel('NWR_ALDT.xls', sheet_name='ICT')
# print(NWR.columns)
# con=(NWR['(1) ROUTE', '(21) ROUTES LOCKED'])
a: DataFrame = pd.DataFrame(NWR[['(1) ROUTE', '(21) ROUTES LOCKED']])
b = pd.DataFrame(NWR['(1) ROUTE'])
e= pd.DataFra... | pd.DataFrame(NWR['(21) ROUTES LOCKED']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import yfinance as yf
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import os
import math
import matplotlib.pylab as plt
import matplotlib
from Machine_Learning_for_Asset_Managers import ch2_fitKDE_find_best_bandwidth as best_bandwidth... | pd.Series(instrument) | pandas.Series |
from datetime import datetime
import re
import unittest
import nose
from nose.tools import assert_equal
import numpy as np
from pandas.tslib import iNaT
from pandas import Series, DataFrame, date_range, DatetimeIndex, Timestamp
from pandas import compat
from pandas.compat import range, long, lrange, lmap, u
from pand... | tm.assert_almost_equal(out, expected) | pandas.util.testing.assert_almost_equal |
import pandas as pd
import math
import matplotlib.pyplot as plt
import seaborn as sn
import matplotlib.patches as mpatches
from matplotlib import rcParams
#from brokenaxes import brokenaxes
from natsort import index_natsorted, order_by_index
#sn.set_context("paper", font_scale = 2)
#AUX FUNC
def Vm_groupby(df, grou... | pd.concat(df_aux_list) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 13 15:21:55 2019
@author: raryapratama
"""
#%%
#Step (1): Import Python libraries, set land conversion scenarios general parameters
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import quad
import seaborn as sns
import pandas as... | pd.read_excel('C:\\Work\\Programming\\Practice\\PF_PO.xlsx', 'PF_PO_Enu') | pandas.read_excel |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
# function for loading data from disk
def load_data():
"""
this function is responsible for loading traing data from disk.
and performs some basic opertaions like
- one-hot encoding
- feature ... | pd.get_dummies(train['label']) | pandas.get_dummies |
import pandas as pd
import numpy as np
def getDailyVol(close,
span0=100):
"""SNIPPET 3.1 DAILY VOLATILITY ESTIMATES
Daily vol reindexed to close
"""
df0=close.index.searchsorted(close.index-pd.Timedelta(days=1))
df0=df0[df0>0]
df0=(pd.Series(close.index[df0-1],
... | pd.DataFrame(index=events_.index) | pandas.DataFrame |
# coding: utf-8
# # Estimating the total biomass of terrestrial protists
# After searching the literature, we could not find a comprehensive account of the biomass of protists in soils. We generated a crude estimate of the total biomass of protists in soil based on estimating the total number of individual protists i... | pd.Series(best_num_CI,index= inter_method_num_CI.index) | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import argparse
import csv
import datetime
import gc
import gzip
import json
import subprocess
import sys
from typing import Union, List
import nibabel as nib
import pandas as pd
import pydicom as dicom
from bids import layout
from matgrab import mat2df
from pyedflib im... | pd.DataFrame() | pandas.DataFrame |
"""max temp before jul 1 or min after"""
import datetime
import psycopg2.extras
import numpy as np
import pandas as pd
from matplotlib.patches import Rectangle
from pyiem.plot.use_agg import plt
from pyiem.util import get_autoplot_context, get_dbconn
from pyiem.exceptions import NoDataFound
PDICT = {'fall': 'Minimum ... | pd.Series(dyear) | pandas.Series |
from contextlib import nullcontext as does_not_raise
from functools import partial
import pandas as pd
from pandas.testing import assert_series_equal
from solarforecastarbiter import datamodel
from solarforecastarbiter.reference_forecasts import persistence
from solarforecastarbiter.conftest import default_observatio... | pd.Timestamp('20190512T1100', tz=tz) | pandas.Timestamp |
import pandas as pd
from collections import Counter
from natsort import index_natsorted
import numpy as np
ids = []
text = []
ab_ids = []
ab_text = []
normal_vocab_freq_dist = Counter()
ab_vocab_freq_dist = Counter()
# keywords that most likely associated with abnormalities
KEYWORDS = ['emphysema', 'cardiomegaly', '... | pd.DataFrame(ab_normal) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# # ReEDS Scenarios on PV ICE Tool STATES
# To explore different scenarios for furture installation projections of PV (or any technology), ReEDS output data can be useful in providing standard scenarios. ReEDS installation projections are used in this journal as input data to the... | pd.concat([materiallist, yearlylist], axis=1) | pandas.concat |
import io
import time
import json
from datetime import datetime
import pandas as pd
from pathlib import Path
import requests
drop_cols = [
'3-day average of daily number of positive tests (may count people more than once)',
'daily total tests completed (may count people more than once)',
'3-day average of ... | pd.read_csv(raw_school, parse_dates=['date']) | pandas.read_csv |
from itertools import product
import pandas as pd
from pandas.testing import assert_series_equal, assert_frame_equal
import pytest
from solarforecastarbiter.validation import quality_mapping
def test_ok_user_flagged():
assert quality_mapping.DESCRIPTION_MASK_MAPPING['OK'] == 0
assert quality_mapping.DESCR... | pd.Series([False, False, False]) | pandas.Series |
# rate_of_rise.py is part of the `ca_img_analyzer' package:
# github.com/DanielSchuette/ca_img_analyzer
#
# this code is MIT licensed
#
# if you find a bug or want to contribute, please
# use the GitHub repository or write an email:
# d.schuette(at)online.de
import re
import matplotlib.pyplot as plt
import numpy as np... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# # Calculating Similarity
#
# create some transformer embedded vectors, then use a cosine similarity
# In[2]:
import h
import pandas as pd
# pd.set_option('display.max_colwidth', None)
# use movies dataset
df = pd.read_csv('../data/imdb_top_1000.csv')#.head(10)
# df[['Series_... | pd.Series(df.index) | pandas.Series |
from nose_parameterized import parameterized
from unittest import TestCase
from pandas import (
Series,
DataFrame,
DatetimeIndex,
date_range,
Timedelta,
read_csv
)
from pandas.util.testing import (assert_frame_equal)
import os
import gzip
from pyfolio.round_trips import (extract_round_trips,... | Timedelta(days=1) | pandas.Timedelta |
import sys
import nltk
nltk.download(['punkt', 'wordnet', 'stopwords'])
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import re
import numpy as np
import pandas as pd
import pickle
import sklearn
from sqlalchemy import create_engine
from sklearn.metr... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
from staircase import Stairs
def s1(closed="left"):
int_seq1 = Stairs(initial_value=0, closed=closed)
int_seq1.layer(1, 10, 2)
int_seq1.layer(-4, 5, -1.75)
int_seq1.layer(3, 5, 2.5)
int_seq1.layer(6, 7, -2.5)
int_seq1.layer(7, 10, -2.5)
... | pd.Interval(6, 8, closed="left") | pandas.Interval |
"""Integration tests for the HyperTransformer."""
import re
from copy import deepcopy
from unittest.mock import patch
import numpy as np
import pandas as pd
import pytest
from rdt import HyperTransformer
from rdt.errors import Error, NotFittedError
from rdt.transformers import (
DEFAULT_TRANSFORMERS, BaseTransfo... | pd.DataFrame({'col1': [1, 2], 'col2': ['a', 'b']}) | pandas.DataFrame |
# Copyright 1999-2020 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | pd.DataFrame(remain_counts, columns=in_data.columns, index=summaries[-1].index) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable=E1101
# flake8: noqa
from datetime import datetime
import csv
import os
import sys
import re
import nose
import platform
from multiprocessing.pool import ThreadPool
from numpy import nan
import numpy as np
from pandas.io.common import DtypeWarning
from pandas import DataFr... | StringIO(text) | pandas.compat.StringIO |
# -*- coding: utf-8 -*-
# pylint: disable=E1101
# flake8: noqa
from datetime import datetime
import csv
import os
import sys
import re
import nose
import platform
from multiprocessing.pool import ThreadPool
from numpy import nan
import numpy as np
from pandas.io.common import DtypeWarning
from pandas import DataFr... | tm.ensure_clean() | pandas.util.testing.ensure_clean |
import os
import numpy as np
import pandas as pd
import streamlit as st
import time
from datetime import datetime
from glob import glob
from omegaconf import OmegaConf
from pandas.api.types import is_numeric_dtype
from streamlit_autorefresh import st_autorefresh
from dataloader import read_csv, clear_data
fr... | pd.DataFrame() | 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... | DataFrame({'A': [1, 2, 3]}) | pandas.DataFrame |
import os
import sys
import datetime
from pkg_resources import resource_filename
import numpy as np
import matplotlib.pyplot as plt
import pandas
import nose.tools as nt
import numpy.testing as nptest
from matplotlib.testing.decorators import image_comparison, cleanup
import pandas.util.testing as pdtest
import wqio... | pandas.Timestamp('2013-05-19 06:10') | pandas.Timestamp |
import itertools
import pandas as pd
from pandas.testing import assert_series_equal
import pytest
from solarforecastarbiter.reference_forecasts import forecast
def assert_none_or_series(out, expected):
assert len(out) == len(expected)
for o, e in zip(out, expected):
if e is None:
assert... | assert_series_equal(out, exp) | pandas.testing.assert_series_equal |
import glob
import os
import sys
import subprocess
from configparser import ConfigParser
import numpy as np
import pandas as pd
from astropy import units as u
from astropy.io import ascii
from astropy.io import fits as pyfits
from radio_beam import Beam, Beams, commonbeam
import fits_magic as fm
def load_config(c... | pd.isnull(bpas) | pandas.isnull |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import timedelta
from numpy import nan
import numpy as np
import pandas as pd
from pandas import (Series, isnull, date_range,
MultiIndex, Index)
from pandas.tseries.index import Timestamp
from pandas.compat import range
from pandas.u... | Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75]) | pandas.Index |
# 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.concat([df1, s3, df2], axis=1) | pandas.concat |
# generate features
import networkx as nx
import pandas as pd
import numpy as np
from networkx.algorithms import node_classification
import time
from collections import Counter
from utils import normalize_features
def dayday_feature(data, n_class=2, label_most_common_1=19, flag_unlabel=0):
t1 = time.time... | pd.merge(left=df, right=features_in_1st, left_on="src_idx", right_on="node_index", how="left") | pandas.merge |
#!/usr/bin/env python
'''
<NAME> October 2018
Scripts for looking at and evaluating input data files for dvmdostem.
Generally data has been prepared by M. Lindgren of SNAP for the IEM project and
consists of directories of well labled .tif images, with one image for each
timestep.
This script has (or will have) a var... | pd.to_datetime(pncar_df['date']) | pandas.to_datetime |
import os
# os.environ["OMP_NUM_THREADS"] = "16"
import logging
logging.basicConfig(filename=snakemake.log[0], level=logging.INFO)
import pandas as pd
import numpy as np
# seak imports
from seak.data_loaders import intersect_ids, EnsemblVEPLoader, VariantLoaderSnpReader, CovariatesLoaderCSV
from seak.scoretest impo... | pd.DataFrame.from_dict(stats) | pandas.DataFrame.from_dict |
# License: BSD 3 clause
"""
In this example, we simulate a unidimensional (ground truth) MHP with a
multimodal Gaussian kernel with three modes.
We estimate the parameters of this MHP using ASLSD, with a SBF Gaussian model
with ten modes.
"""
import os
import sys
# add the path of packages to system path
nb_dir = os... | pd.Series(list_times[0][1:]-list_times[0][:-1]) | pandas.Series |
import pandas as pd
import os
# this file contains variables and names given in turkish words
# blood transfusions related data
writer = pd.ExcelWriter('tümü.xlsx', engine='xlsxwriter')
writer2 = pd.ExcelWriter('ozet.xlsx', engine='xlsxwriter')
writer3 = pd.ExcelWriter('hasta başı toplam transfüzyon sayısı.xlsx', eng... | pd.Timedelta(days=1) | pandas.Timedelta |
import os
import numpy as np
from itertools import product
from collections import defaultdict
import pandas as pd
import json
from nlafl import common
class HeatMapValue:
IsSet = False
def set_dir_version(dir,version):
HeatMapValue.dir = dir
HeatMapValue.version = version
HeatMapValue.... | pd.DataFrame(df) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Bootstrap - Top Gun Stochastic Modelling Class
Created on Tue Sep 8 08:17:30 2020
@author: <NAME>
"""
# %% IMPORTs CELL
# Default Imports
import numpy as np
import pandas as pd
import scipy.linalg as LA
# Plotly for charting
import plotly.express as px
import plotly.graph_objs as go
im... | pd.Series(portstats['mcr'][port], name='mcr') | pandas.Series |
# -*- coding: utf-8 -*-
# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)
# <NAME> (<EMAIL>), Blue Yonder Gmbh, 2016
from unittest import TestCase
import numpy as np
import pandas as pd
import pandas.testing as pdt
from tests.fixtures import DataTestCase
from t... | pd.DataFrame({"val": [5, 6, 7, 8, 12, 13], "id": [4, 4, 3, 3, 2, 2]}) | pandas.DataFrame |
import os
import pandas as pd #for data analysis
import matplotlib.pyplot as plt
import cv2
import numpy as np
import math
import pydicom as pydicom
import tensorflow as tf
import tensorflow_addons as tfa
import sklearn
from sklearn.model_selection import train_test_split
import tensorflow.keras.backend as K
imp... | pd.concat([df_class_0_under, df_class_1], axis=0) | pandas.concat |
import numpy as np
import pandas as pd
from collections import OrderedDict
from .utils import is_list, to_list, is_fitted
class Attributes:
"""
The Attributes class handles checking and setting the attributes
for the InterpretToolkit, GlobalInterpret, and LocalInterpret classes.
Attributes is a base ... | pd.DataFrame(data=X, columns=feature_names) | pandas.DataFrame |
import pandas as pd
import numpy as np
import os
import datetime
def process_diagnostics(save=0):
df_ms1 = pd.read_csv('..\\data\\raw\\transfer_2018-03-08\\diagnostic\\2018-03-02 - MS1 - Database Merge.csv')
df_ms2 = | pd.read_csv('..\\data\\raw\\transfer_2018-03-08\\diagnostic\\2018-03-05 - MS2 - Database Merge.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
"""
import os
from datetime import datetime
from oemof.tabular.datapackage import building
import pandas as pd
def eGo_offshore_wind_profiles(
buses,
weather_year,
scenario_year,
datapackage_dir,
raw_data_path,
correction_factor=0.8,
):
"""
Parameter
--... | pd.read_csv(filepath, index_col=[0], parse_dates=True) | pandas.read_csv |
import datetime as dt
import pandas as pd
from bs4 import BeautifulSoup
import re
import requests
import time
today = dt.date.today()
zenhan = str.maketrans("１２３４５６７８９０","1234567890","")
token = "***<PASSWORD>***"
auth = {"Authorization": token}
query = "unit_id:133089874031904245 全裸 OR 下半身露出 "
limit = "50"
url = "h... | pd.json_normalize(jsonInput) | pandas.json_normalize |
import base64
import io
import textwrap
import dash
import dash_core_components as dcc
import dash_html_components as html
import gunicorn
import plotly.graph_objs as go
from dash.dependencies import Input, Output, State
import flask
import pandas as pd
import urllib.parse
from sklearn.preprocessing import StandardSca... | pd.concat([outlier_names, principalDf_outlier_scale], axis=1) | pandas.concat |
import json
import pickle
import glob
import numpy as np
import pandas as pd
from tabulate import tabulate
from datetime import datetime
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor
from sklearn... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
AIDeveloper
---------
@author: maikherbig
"""
import os,sys,gc
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'#suppress warnings/info from tensorflow
if not sys.platform.startswith("win"):
from multiprocessing import freeze_support
freeze_support()
# Make sure to get the right ... | pd.DataFrame() | pandas.DataFrame |
"""K-Means Classifier"""
import collections
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from sklearn.preprocessing import minmax_scale
from default_clf import DefaultNSL, COL_NAMES, ATTACKS
class KMeansNSL(DefaultNSL):
def __init__(self):
super(KMeansNSL, self).__init__()
... | pd.DataFrame([packet], columns=COL_NAMES) | pandas.DataFrame |
import os
import sys
import keras
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
from keras.callbacks import CSVLogger, History
from keras.layers import BatchNormalization, Dense, Dropout, Input
from keras.models import Model
# from .IntegratedGradient import integrated... | pd.DataFrame(feature_importances) | pandas.DataFrame |
import os, sys, math, random, time
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import pickle as pkl
import scipy.sparse as sp
from typing import List, Dict, Tuple, Iterable, Type, Union, Callable
from tqdm import tqdm
import xclib.evaluation.xc_metrics as xc_metrics
import xclib.data.data... | pd.DataFrame(xc_eval_metrics) | pandas.DataFrame |
import cPickle as pickle
import numpy as np
import pandas as pd
import functools
from scoop import futures
from scipy.interpolate import griddata
from scipy.signal import convolve2d
from sklearn.metrics import average_precision_score, roc_auc_score, precision_recall_curve
def calculate_hessian(model, data, step_size):... | pd.DataFrame(raw_outputs) | pandas.DataFrame |
import os
import pandas
import logging
import datetime
import psycopg2
import functools
from dotenv import load_dotenv
from .utils import classproperty
import urllib.request, urllib.error
logger = logging.getLogger(__name__)
if not hasattr(functools, 'cache'):
# Function below is copied straight
# from Pytho... | pandas.read_sql('SELECT * FROM student_data', temp_connection) | pandas.read_sql |
# ,---------------------------------------------------------------------------,
# | This module is part of the krangpower electrical distribution simulation |
# | suit by <NAME> <<EMAIL>> et al. |
# | Please refer to the license file published together with this code. |
# | All rights not explic... | _DataFrame(data=mtx, index=raw_n_ord, columns=raw_n_ord) | pandas.DataFrame |
import asyncio
import io
import os
import random
import shutil
from collections import defaultdict
import pandas as pd
import pytest
pa = pytest.importorskip("pyarrow")
import dask
import dask.dataframe as dd
from dask.distributed import Worker
from dask.utils import stringify
from distributed.shuffle.shuffle_exten... | pd.Series(worker_for, name="_worker") | pandas.Series |
import pandas as pd
import networkx as nx
import pytest
from kgextension.feature_selection import hill_climbing_filter, hierarchy_based_filter, tree_based_filter
from kgextension.generator import specific_relation_generator, direct_type_generator
class TestHillCLimbingFilter:
def test1_high_beta(self):
i... | pd.read_csv("test/data/feature_selection/hierarchy_based_test9_expected.csv") | pandas.read_csv |
#Copyright 2019 NUS pathogen genomics
#Written by <NAME> (<EMAIL>)
import os
import sys
import gzip
import argparse
import pandas as pd
import statistics
import subprocess
from statistics import mode
from collections import Counter
#function to determine repeat number based on total number of mismatches in primer se... | pd.read_csv(MIRU_table, sep='\t') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Downloads rfr and stores in sqlite database for future reference
"""
import datetime
import os
import zipfile
import pandas as pd
import urllib
from datetime import date
import logging
from solvency2_data.sqlite_handler import EiopaDB
from solvency2_data.util import get_config
from solven... | pd.read_sql(sql, con=db.conn) | pandas.read_sql |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from Dimension_Reduction import Viewer
from Silhouette import Silhouette_viewer
from sklearn.cluster import KMeans
from sklearn.mixture import GaussianMixture as GMM
from sklearn.cluster import DBSCAN
from sklearn.cluster import Agglomer... | pd.DataFrame(labels_hier) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 16 15:46:07 2019
@author:
"""
import logging
import os
import sys
import numpy as np
import pickle
import csv
import datetime as dt
import pandas as pd
import matplotlib.pylab as plt
file_dir = os.path.dirname(__file__)
sys.path.append(file_dir)... | pd.DataFrame.from_dict(dict_emb_concepts[entity_name], orient="index") | pandas.DataFrame.from_dict |
"""
"""
"""
>>> # ---
>>> # SETUP
>>> # ---
>>> import os
>>> import logging
>>> logger = logging.getLogger('PT3S.Rm')
>>> # ---
>>> # path
>>> # ---
>>> if __name__ == "__main__":
... try:
... dummy=__file__
... logger.debug("{0:s}{1:s}{2:s}".format('DOCTEST: __main__ Context: ','path = os.p... | pd.Timedelta('3 Minutes') | pandas.Timedelta |
from abc import ABCMeta, abstractmethod
from abc import ABC
import warnings
from decimal import Decimal
from tqdm import tqdm
import numpy as np
import pandas as pd
import pandas_ta as ta
from ..util import huf, pdiff
warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)
FEE = Decimal(0.6/100)... | pd.read_csv(self._csv_file) | pandas.read_csv |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.11.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
import pandas as pd
impo... | pd.to_datetime(seq1['Sleep End']) | pandas.to_datetime |
import pandas as pd
import numpy as np
from tensorflow.contrib import layers
from tensorflow.contrib import learn
import tensorflow as tf
def input_fn(df):
feature_cols = {}
feature_cols['Weight'] = tf.constant(df['Weight'].values)
feature_cols['Species'] = tf.SparseTensor(
indices=[[i, 0] for i i... | pd.DataFrame({'Species': spec, 'Weight': weight, 'Height': height}) | pandas.DataFrame |
import logging
import os
import re
import shutil
import warnings
from datetime import datetime
from typing import Union
import h5py
import numpy as np
import pandas as pd
from omegaconf import DictConfig
from deepethogram.utils import get_subfiles
from deepethogram.zscore import zscore_video
from . import utils
from ... | pd.read_csv(labelfile, index_col=0) | pandas.read_csv |
import os
import numpy as np
import pandas as pd
from common.data_source_from_bundle import __td__, __ds__
def dataframe_to_ndarray(df):
"""
pd.DataFrame to ndarray, 除去trade_date, wind_code, 其他n列变成n*4000的ndarray
:param df: 数据
:return: ndarray
"""
columns = df.columns
assert ("wind_code" i... | pd.read_csv(infile) | pandas.read_csv |
# encoding: utf-8
from opendatatools.common import RestAgent
from progressbar import ProgressBar
import demjson
import json
import pandas as pd
fund_type = {
"全部开放基金" : {"t": 1, "lx": 1},
"股票型基金" : {"t": 1, "lx": 2},
"混合型基金" : {"t": 1, "lx": 3},
"债券型基金" : {"t": 1, "lx": 4},
"指数型基金" : ... | pd.DataFrame(rsp) | pandas.DataFrame |
# %%%%
import pandas as pd
import numpy as np
import re
# %%%% functions
## Fill missing values
def fillmissing(x,col,index,benchmark):
for i in range(index,len(x)):
# find missing value
if x.loc[i,col] == benchmark:
# if first is missing, fill using the value next to it
if... | pd.to_datetime(csi['Date'], format='%Y-%m-%d') | pandas.to_datetime |
import requests
import json
import pandas as pd
from apscheduler.schedulers.blocking import BlockingScheduler
import apscheduler.schedulers.blocking
from datetime import datetime,timedelta
import time
import sqlalchemy
import sys
import numpy as np
# taxa de periodicidade para realizar a operação
periodicidade = 1
# ... | pd.merge(abertura,fechamento,on='CRIPTOMOEDA') | pandas.merge |
import requests
import base64
import gzip
import bz2
from pathlib import Path
import pandas as pd
from multiprocessing import Pool
magic_dict = {
b"\x1f\x8b\x08": (gzip.open, 'rb'),
b"\x42\x5a\x68": (bz2.BZ2File, 'r'),
}
max_len = max(len(x) for x in magic_dict)
def open_by_magic(filename):
with open... | pd.DataFrame(datadic) | pandas.DataFrame |
'''This file holds all relevant functions necessary for starting the data analysis.
An object class for all account data is established, which will hold the raw data after import,
the processed data and all subdata configuration necessary for plotting.
The account data is provided through the account identification pr... | pd.concat(self.saved_dataframe[account_name]) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Detect audio recordings with rain using MFCC and logistic regression
Assuming that rain events are stable during a period of 60s or more, the detector analyzes
the first 10 seconds of each recording. It computes the MFCC and uses a trained model to
evaluate the probab... | pd.DataFrame(df_pred, index=['proba_rain']) | pandas.DataFrame |
################################################################
# ---------- Network Gene Name Conversion Functions ---------- #
################################################################
import requests
import re
import time
import pandas as pd
# Determine if id to be input is a valid gene name (does not conta... | pd.DataFrame(data=edgelist_filt1) | pandas.DataFrame |
import glob
import numpy as np
import pandas as pd
import re
import sys
# generate aspect classes based on the MethodAspect0 template
def generate_aspects(df):
base_path = "./src/main/java/se/kth/castor/pankti/instrument/plugins/MethodAspect"
found_aspects = sorted(glob.glob(base_path + "*.java"), key=lambda x:flo... | pd.isnull(row['param-list']) | pandas.isnull |
from sklearn.model_selection import StratifiedKFold
from sklearn.pipeline import Pipeline
from sklearn.dummy import DummyClassifier
from pandas import DataFrame
import numpy as np
from sklearn.datasets import load_digits
import sys
from autoclf import auto_utils as au
from autoclf.classification import eval_utils as ... | DataFrame(data=digits.target, columns=[target]) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import unittest
import pandas as pd
import pathlib, pickle, json, copy, yaml
from emhass.retrieve_hass import retrieve_hass
from emhass.forecast import forecast
from emhass.optimization import optimization
from emhass.utils import get_root, get_yaml_parse, get_days_list, ... | pd.DataFrame() | pandas.DataFrame |
from datetime import timedelta
from functools import partial
from operator import attrgetter
import dateutil
import numpy as np
import pytest
import pytz
from pandas._libs.tslibs import OutOfBoundsDatetime, conversion
import pandas as pd
from pandas import (
DatetimeIndex, Index, Timestamp, date_range, datetime,... | Timestamp('2018-01-01', tz=tz) | pandas.Timestamp |
import streamlit as st # streamlit run Location100_RF_streamlit.py
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
import warnings
from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve, cross_val_score
from sklearn.metrics impo... | pd.DataFrame(numer_feature, columns=["POTENTIAL_REV_AMT"]) | pandas.DataFrame |
#views.py
from flask import abort, jsonify, send_from_directory, render_template, request, redirect, url_for, send_file, make_response
from app import app
from models import *
import os
import csv
import json
import uuid
import pandas as pd
import requests
import requests_cache
import metadata_validator
import config... | pd.read_table(config.PATH_TO_PARSED_GLOBAL_OCCURRENCES) | pandas.read_table |
# -*- 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(cued_stim) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
@pytest.fixture
def df_checks():
"""fixture dataframe"""
return pd.DataFrame(
{
"famid": [1, 1, 1, 2, 2, 2, 3, 3, 3],
"birth": [1, 2, 3, 1, 2, 3, 1, 2, 3],
"ht1": [2.... | assert_frame_equal(result, expected_output) | pandas.testing.assert_frame_equal |
#!/usr/bin/env python3
import click
import pandas as pd
from Bio import Phylo
@click.command(context_settings=dict(help_option_names=['-h', '--help']))
@click.option("-i", "--newick-tree-input", type=click.Path(exists=True), required=False, default='')
@click.option("-m", "--metadata-output", type=click.Path(exists=Fa... | pd.read_table(metadata_aa_change) | pandas.read_table |
import datetime as dt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from functools import reduce
def asset_class_heatmap(df, period):
df_period = df[-period:]
mask = np.triu(df_period.corr())
plt.figure(figsize=(12.8, 12.8))
return sns.heatmap(
... | pd.merge(left=x, right=y, on=['JPM Account Id', 'Date'], how='inner') | pandas.merge |
# -*- coding: utf-8 -*-
"""
Created on Sun May 17 14:48:16 2020
@author: <NAME>
"""
import json
import requests
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from fbprophet import Prophet
import math
import time
import calendar
from datetime import date, datetime
def india_world_pred():
... | pd.read_csv("data/world_fitted.csv") | pandas.read_csv |
from dagster_pandas.constraints import (
ColumnAggregateConstraintWithMetadata,
ColumnConstraintWithMetadata,
ColumnRangeConstraintWithMetadata,
ColumnWithMetadataException,
ConstraintWithMetadata,
ConstraintWithMetadataException,
DataFrameWithMetadataException,
MultiAggregateConstraintW... | DataFrame({'foo': [1, 2, 3], 'bar': [3, 2, 1], 'baz': [1, 4, 5]}) | pandas.DataFrame |
#%%
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix
from sklearn import preprocessing
import random
#%%
df_train = | pd.read_csv("data/train_ohe.csv") | pandas.read_csv |
#
# Copyright (C) 2014 Xinguard Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, dis... | pd.read_html(urll) | pandas.read_html |
# Heavily influenced by: https://www.kaggle.com/opanichev/lightgbm-and-tf-idf-starter?login=true#
import pandas as pd
import lightgbm as lgbm
import numpy as np
import os
import scripts.donorchoose_functions as fn
import re
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import StratifiedKFold
... | pd.concat([train, test], axis=0) | pandas.concat |
import logging
import os
import warnings
from pathlib import Path
from typing import Dict, Iterable, Union
import nibabel as nib
import numpy as np
import pandas as pd
import tqdm
from nilearn.image import resample_to_img
from nipype.interfaces.ants import ApplyTransforms
from nipype.interfaces.freesurfer import (
... | pd.DataFrame() | pandas.DataFrame |
'''
The main driving code
1. CML/FL Training
2. Compute/Approximate Cosine Gradient Shapley
3. Calculate and realize the fair gradient reward
'''
import os, sys, json
from os.path import join as oj
import copy
from copy import deepcopy as dcopy
import time, datetime, random, pickle
from collections im... | pd.DataFrame(fed_perfs) | pandas.DataFrame |
import pandas as pd
import pathlib
from utils import load_from_file, morse_potential, fit_morse_potential
import matplotlib.pyplot as plt
import numpy as np
paths = pathlib.Path("/home/mscherbela/runs/forces/atoms/").glob("*/results.bz2")
data = []
for p in paths:
data_content = load_from_file(p)
config = dat... | pd.DataFrame(data) | pandas.DataFrame |
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
from datasets.dataset import Dataset
from backport.utils import RepresentationTranslator
from competitor.actions.feature import CategoricFeature, Feature
class DatasetWrapper(Dataset):
def __init__(self, name, legacy_dataset,... | pd.DataFrame(X) | pandas.DataFrame |
import sys
sys.path.append("..") # Adds higher directory to python modules path.
from img2vec_pytorch import Img2Vec
import pandas as pd
from PIL import Image
from tqdm import tqdm
import numpy as np
import os
def most_similar(train_path, test_path, images_path, results_path, cuda=False):
"""
Nearest Neighbor... | pd.DataFrame.from_dict(sim_test_results, orient="index") | pandas.DataFrame.from_dict |
from pathlib import Path
import numpy as np
import numpy.testing as npt
import pandas as pd
import pytest
from message_ix import Scenario, macro
from message_ix.models import MACRO
from message_ix.testing import SCENARIO, make_westeros
W_DATA_PATH = Path(__file__).parent / "data" / "westeros_macro_input.xlsx"
MR_DAT... | pd.Index(nodes, name="node") | pandas.Index |
import mechanize
import pandas as pd
import bs4
from bs4 import BeautifulSoup
from bs4 import SoupStrainer
from math import ceil
from time import sleep
import re
import os
import sys
import unicodedata
def strip_special_latin_char(string):
"""
Method that either transliterates selected latin characters, or ma... | pd.to_timedelta(df['time']) | pandas.to_timedelta |
import pandas as pd
import lightgbm
from sklearn.linear_model import Lasso
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import mutual_info_regression
from sklearn.preprocessing import StandardScaler
def GBoostingFeatureSelection(X, y, random_state=0):
__trashhold__ = 2
# ... | pd.DataFrame(X_Scaled, columns=X.columns, index=X.index) | pandas.DataFrame |
"""
This script contains a simple (intraday) trend following strategy using a bollinger band.
Strategy -
1) BUY when the price crosses the Upper Band from below.
2) SELL when the price crosses the Lower Band from above.
3) Close the positions at Take Profit or Stop Loss or
when counter positions needed to be taken.
4)... | pd.to_datetime(bar['timestamp']) | pandas.to_datetime |
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
@pytest.mark.parametrize("sort", [True, False])
def test_factorize(index_or_series_obj, sort):
obj = index_or_series_obj
result_codes, result_uniques = obj.factorize(sort=sort)
constructor = pd.Index
if is... | tm.assert_index_equal(result_uniques, expected_uniques) | pandas._testing.assert_index_equal |
import pandas as pd
import numpy as np
import argparse
import time
def getArgs():
parser = argparse.ArgumentParser()
parser.add_argument('-output',
required=False,
default='processed_data/',
help='path of output folder.')
parser.add_a... | pd.read_csv('drugs_mol2vec/' + drug_name + '.csv') | pandas.read_csv |
import numpy as np
import pandas as pd
import pytest
from hypothesis import assume, given
from pandas.testing import assert_frame_equal
from janitor.testing_utils.strategies import (
categoricaldf_strategy,
df_strategy,
)
def test_case_when_1():
"""Test case_when function."""
df = pd.DataFrame(
... | assert_frame_equal(result, expected) | pandas.testing.assert_frame_equal |
import os
import sys
import numpy as np
import pandas as pd
from sklearn.model_selection import KFold, train_test_split, StratifiedKFold
import PIL
from PIL import Image
import io
import cv2
from keras.datasets import mnist
import multiprocessing as mp
from multiprocessing import Pool, Manager, Process
from functool... | pd.Series(self.x_list) | pandas.Series |
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