prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import warnings
import numpy as np
import pandas as pd
from lhorizon.constants import LUNAR_RADIUS
from lhorizon.lhorizon_utils import make_raveled_meshgrid
from lhorizon.solutions import make_ray_sphere_lambdas
from lhorizon.target import Targeter
from lhorizon.tests.data.test_cases import TEST_CASES
from lhorizon.k... | pd.read_csv(path + "_CENTER.csv") | pandas.read_csv |
from datetime import datetime
import operator
import numpy as np
import pytest
from pandas import DataFrame, Index, Series, bdate_range
import pandas._testing as tm
from pandas.core import ops
class TestSeriesLogicalOps:
@pytest.mark.parametrize("bool_op", [operator.and_, operator.or_, operator.xor])
def te... | Index([1, 0, 1, 0]) | pandas.Index |
"""Move Mouse Pointer."""
"""
Copyright (c) 2018 Intel Corporation.
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, m... | pd.DataFrame.from_dict(runtime, orient='index', columns=["Total runtime"]) | pandas.DataFrame.from_dict |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = "<NAME>"
__copyright__ = "Copyright 2020, University of Copenhagen"
__email__ = "<EMAIL>"
__license__ = "MIT"
import json
import sys
import click
import pandas as pd
from scipy.stats.distributions import chi2
ANCESTRIES = ["ALL", "ANA", "CHG", "WHG", "EHG"]... | pd.read_table(info_tsv) | pandas.read_table |
import numpy as np
import pandas as pd
import scipy.sparse as sps
import matplotlib.pyplot as plt
from mlhub.pkg import mlask, mlcat
from IPython.display import display
from collections import Counter
from relm.mechanisms import LaplaceMechanism
mlcat("Differentially Private Release Mechanism", """\
This demo is ba... | pd.Timestamp('2020-01-01') | pandas.Timestamp |
import pandas as pd
import numpy as np
import yfinance as yf #Yahoo Finance API
from datetime import datetime as dt, date
import time
df = pd.DataFrame()
tickers = ["^KS11", "^GSPC", "^N225", "^HSI", "^N100", "^FTSE", "^DJI"]
start_day = dt(2019, 12, 1)
today = str(date.today())
kospi = yf.download('^KS11', start=dt(... | pd.read_csv(world_aggregated) | pandas.read_csv |
import requests
import pandas as pd
import numpy as np
import configparser
from datetime import datetime
from dateutil import relativedelta, parser, rrule
from dateutil.rrule import WEEKLY
class WhoopClient:
'''A class to allow a user to login and store their authorization code,
then perform pulls using t... | pd.isna(x) | pandas.isna |
#!/usr/bin/env python
import pandas as pd
pd.options.mode.chained_assignment = None
import json
import os
import yaml
try: modulepath = os.path.dirname(os.path.realpath(__file__)).replace('\\', '/') + '/'
except NameError: modulepath = 'stewi/'
output_dir = modulepath + 'output/'
data_dir = modulepath + 'data/'
rel... | pd.DataFrame() | pandas.DataFrame |
"""Multiple Factor Analysis (MFA)"""
import itertools
import numpy as np
import pandas as pd
from sklearn import utils
from . import mca
from . import pca
class MFA(pca.PCA):
def __init__(self, groups=None, rescale_with_mean=True, rescale_with_std=True, n_components=2,
n_iter=10, copy=True, ran... | pd.api.types.is_numeric_dtype(X[c]) | pandas.api.types.is_numeric_dtype |
import inspect
import json
import os
import re
from urllib.parse import quote
from urllib.request import urlopen
import pandas as pd
import param
from .configuration import DEFAULTS
class TutorialData(param.Parameterized):
label = param.String(allow_None=True)
raw = param.Boolean()
verbose = param.Bool... | pd.read_csv(self._data_url, **kwds) | pandas.read_csv |
import numpy as np
import pandas as pd
def main_post(s_name,orig_data):
D = 20
print("Max Moment Order", D)
d = np.genfromtxt("moments.txt", delimiter = "\t")[:,:-1]
frame = []
cell = []
moment = []
for i in range(len(d)):
f = d[i][0]
c = d[i][1]
m =... | pd.concat([df, df_2],axis=1) | pandas.concat |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for Period dtype
import operator
import numpy as np
import pytest
from pandas._libs.tslibs.period import IncompatibleFrequency
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas impo... | tm.box_expected(expected, box, transpose=transpose) | pandas.util.testing.box_expected |
"""
This network uses the last 26 observations of gwl, tide, and rain to predict the next 18
values of gwl for well MMPS-175
"""
import pandas as pd
from pandas import DataFrame
from pandas import concat
from pandas import read_csv
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing imp... | DataFrame(df_t1, index=None, columns=["obs", "pred"]) | pandas.DataFrame |
import pickle
from ds import *
import pandas as pd
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler
from sklearn import metrics
import numpy as np
from sklearn.impute i... | pd.concat([df, dfDummies], axis=1) | pandas.concat |
import numpy as np
import pandas as pd
import datetime as dt
import os
import zipfile
from datetime import datetime, timedelta
from urllib.parse import urlparse
study_prefix = "U01"
def get_user_id_from_filename(f):
#Get user id from from file name
return(f.split(".")[3])
def get_file_names_from_zip(z, file_... | pd.concat(dfs) | pandas.concat |
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# This file contains dummy data for the model unit tests
import numpy as np
import pandas as pd
AIR_FCST_LINEAR_95 = pd.DataFrame(
{
... | pd.Timestamp("2013-04-28 00:00:00") | pandas.Timestamp |
# Define functions used in the landscape-area-measurements notebook
import numpy as np
import json
import requests
import pandas as pd
import geopandas as gpd
import numpy.ma as ma
import xarray as xr
import rioxarray as rxr
import rasterio as rio
from rasterio.crs import CRS
from shapely.geometry import Polygon, shape... | pd.concat(parcel_gdf_list) | pandas.concat |
# -*- coding: utf-8 -*-
__author__ = "<NAME> (Srce Cde)"
__license__ = "GPL 3.0"
__email__ = "<EMAIL>"
__maintainer__ = "<NAME> (Srce Cde)"
from collections import defaultdict
import json
import pandas as pd
from ..helper import openURL
from ..config import YOUTUBE_COMMENT_URL, SAVE_PATH
class VideoComment:
def ... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow import keras
import os
base_dir = "../input/"
train_dir = os.path.join(base_dir,"train/train")
testing_dir = os.path.join(base_dir, "test")
train = pd.read_csv("../input/train.csv")
train_dataframe = pd.read_csv("../input/train.csv")
train... | pd.DataFrame(data) | pandas.DataFrame |
from collections import defaultdict
import copy
import json
import numpy as np
import pandas as pd
import pickle
import scipy
import seaborn as sb
import torch
from allennlp.common.util import prepare_environment, Params
from matplotlib import pyplot as plt
from pytorch_pretrained_bert import BertTokenizer, BertModel
... | pd.DataFrame(data) | pandas.DataFrame |
"""Automated data download and IO."""
# Builtins
import glob
import os
import gzip
import bz2
import hashlib
import shutil
import zipfile
import sys
import math
import logging
from functools import partial, wraps
import time
import fnmatch
import urllib.request
import urllib.error
from urllib.parse import urlparse
imp... | pd.read_hdf(fp, key=rgi_reg) | pandas.read_hdf |
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from gensim.corpora.dictionary import Dictionary
from gensim.models import LdaModel
from shorttext.utils import standard_text_preprocessor_1
import pandas as pd
import os
dir = os.get... | pd.read_csv('train_set.csv') | pandas.read_csv |
# Modified from
# https://github.com/bhattbhavesh91/cowin-vaccination-slot-availability
import datetime
import json
import numpy as np
import requests
import pandas as pd
import streamlit as st
from copy import deepcopy
# Faking chrome browser
browser_header = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; ... | pd.concat([df_18,df_45]) | pandas.concat |
"""
@authors: <NAME> / <NAME>
goal: edf annotation reader
Modified: <NAME>, Stanford University, 2018
"""
import re
import numpy as np
import pandas as pd
import xmltodict
def read_edf_annotations(fname, annotation_format="edf/edf+"):
"""read_edf_annotations
Parameters:
-----------
fnam... | pd.DataFrame() | pandas.DataFrame |
import json
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
from sklearn.metrics import precision_recall_fscore_support
from statsmodels.stats.inter_rater import fleiss_kappa
__author__ = '<NAME>'
pd.set_option('max_colwidth', 999)
pd.set_option('display.max_rows', 999)
pd.set_o... | pd.Series(data) | pandas.Series |
"""Functions for modeling the avalanche risk levels
"""
import sys
sys.path.append("/home/daniel/Schreibtisch/Projekte/avalanche-risk")
import pickle
import numpy as np
import pandas as pd
import seaborn as sns
import re
from eda.functions_eda import plot_correlations, plot_missing_values
from imblearn.over_samplin... | pd.Series() | pandas.Series |
import collections
import csv
import datetime
import fuzzywuzzy.fuzz
import fuzzywuzzy.process
import itertools
import joblib
import libsbml
import lxml
import lxml.etree
import networkx
import numpy
import os
import operator
import pickle
import re
import simstring
import sys
#########################################... | pandas.DataFrame(data) | pandas.DataFrame |
"""
calcimpy
Input impedance calculation program for air column ( wind instruments ).
"""
import argparse
import sys
import os.path
import numpy as np
import pandas as pd
import xmensur as xmn
import imped
__version__ = '1.1.0'
def main():
parser = argparse.ArgumentParser(description='calcimpy : input impedance... | pd.DataFrame() | pandas.DataFrame |
from collections import defaultdict
import glob
import os
import pickle
import re
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from common.utils import METHOD_NAME, get_latest_folder, load_compressed_pickle, mysavefig
from games.maze.maze_game import MazeGame
from games.maze.maze_level i... | pd.DataFrame(all_metrics) | pandas.DataFrame |
import tempfile
import unittest
import numpy as np
import pandas as pd
from airflow import DAG
from datetime import datetime
from mock import MagicMock, patch
import dd.api.workflow.dataset
from dd import DB
from dd.api.workflow.actions import Action
from dd.api.workflow.sql import SQLOperator
dd.api.workflow.datase... | pd.DataFrame([[np.nan, 2], [1, 2]], columns=["n1", "n2"]) | pandas.DataFrame |
# coding:utf-8
#
# The MIT License (MIT)
#
# Copyright (c) 2016-2020
#
# 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, c... | pd.read_csv(filename, names=columns) | pandas.read_csv |
"""
Common routines to work with raw MS data from metabolomics experiments.
Functions
---------
detect_features(path_list) : Perform feature detection on several samples.
feature_correspondence(feature_data) : Match features across different samples
using a combination of clustering algorithms.
"""
import pandas as ... | pd.concat(ft_table_list) | pandas.concat |
import utility_funcs as uf
import ProjectOverlayDataProcess as data
import pandas as pd
import numpy as np
import code
number_of_groups=5
def import_data(only_relevant_groups=True):
if only_relevant_groups:
members = data.get_group_membership()
relevantgroups = data.import_dataframe("relevantgroup... | pd.DataFrame(similarity_matrix) | pandas.DataFrame |
from __future__ import absolute_import, division, print_function
import argparse
import logging
import sys
import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors
from sklearn.preprocessing import StandardScaler
from sklearn.utils import check_random_state
logger = logging.getLogger('ca... | pd.concat([n_row, t1], axis=0) | pandas.concat |
from sklearn.feature_extraction import DictVectorizer
import pandas as pd
import numpy as np
class LinearModel(object):
@staticmethod
def validate_options(opts):
if opts['loss'] == 'quantile':
raise NotImplementedError("Loss function 'quantile' is not implemented yet")
# if opts[... | pd.DataFrame.from_records(data, columns=[feature_column, weight_column]) | pandas.DataFrame.from_records |
import requests
import json
import traceback
import sqlite3
import server.app.decode_fbs as decode_fbs
import scanpy as sc
import anndata as ad
import pandas as pd
import numpy as np
import diffxpy.api as de
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
import seaborn as sns
import matplo... | pd.read_sql_query(sql,conn,params=data['compSel']+data['genes']+data['compSel']) | pandas.read_sql_query |
# Package import
from __future__ import print_function, division
from warnings import warn
from nilmtk.disaggregate import Disaggregator
import pandas as pd
import numpy as np
from collections import OrderedDict
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from statistics impor... | pd.DataFrame(mains) | pandas.DataFrame |
import csv
import pandas as pd
import random
import numpy as np
from sklearn.decomposition import PCA
from sklearn import svm
#from sklearn.neural_network import MLPClassifier
#from sklearn import tree
from sklearn.metrics import accuracy_score
df= | pd.read_csv('C:\\Users\\Admin\\Desktop\\BE Proj\\HighFrequency.txt') | pandas.read_csv |
# 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... | DataFrame() | pandas.DataFrame |
import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
Timestamp,
date_range,
)
import pandas._testing as tm
@pytest.mark.parametrize("bad_raw", [None, 1, 0])
def test_rolling_apply_invalid_raw(bad_raw):
with pytest.raises(ValueError, m... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
from __future__ import division
import pytest
import numpy as np
from pandas import (Interval, IntervalIndex, Index, isna,
interval_range, Timestamp, Timedelta,
compat)
from pandas._libs.interval import IntervalTree
from pandas.tests.indexes.common import Base
import pandas.uti... | Interval(1, 2) | pandas.Interval |
import os
import pandas as pd
import numpy as np
import h5py
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from collections import OrderedDict
test_path = "/Users/marina/Documents/PhD/research/astro_research/data/testing/"
dpath = test_path + "PROCESSED_DATA/"
def pretti... | pd.DataFrame(labels, columns=[label]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
:Author: <NAME>
:Date: 2018. 1. 24.
"""
import numpy as np
import pandas as pd
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.linear_model import Ridge, LogisticRegression, Lasso
from s... | pd.concat([y_val, y_prediction], axis=1) | pandas.concat |
# test vector generation module
__doc__ = """
Test vector generation block for mProbo. We use three sampling schemes:
- Orthogonal arrays with the strength of two in a OA table;
- LatinHyperCube sampling if proper OA doesn't exist;
- Random sampling.
"""
import numpy as np
import os
from BitVector import BitVect... | pd.DataFrame(d_vector) | pandas.DataFrame |
# coding: utf-8
# In[1]:
import sys
sys.path.append("../")
# In[2]:
get_ipython().run_line_magic('load_ext', 'watermark')
get_ipython().run_line_magic('watermark', '-p torch,pandas,numpy -m')
# In[3]:
from pathlib import Path
import itertools
from collections import Counter
from functools import partial, re... | pd.Series(ys) | pandas.Series |
######################################################################
## DeepBiome
## - Main code
##
## July 10. 2019
## Youngwon (<EMAIL>)
##
## Reference
## - Keras (https://github.com/keras-team/keras)
######################################################################
import os
import sys
import json
import ti... | pd.read_csv(path_info['data_info']['count_list_path'], header=None) | pandas.read_csv |
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.preprocessing as preprocessing
from sklearn import linear_model
from sklearn import model_selection
from sklearn.ensemble import RandomForestRegressor
print(os.getcwd())
# data_path = r'C:\Users\ArseneLupin\Desktop\OrderT... | pd.get_dummies(data_train['Embarked'], prefix='Embarked') | pandas.get_dummies |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Feb 2 18:48:59 2019
@author: Kazuki
AvSigVersion を datetime とみなし、日毎の EngineVersion 等のシェアを計る
"""
import numpy as np
import pandas as pd
import os, gc
from glob import glob
from multiprocessing import cpu_count, Pool
import utils
utils.start(__file__... | pd.read_feather(f) | pandas.read_feather |
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_columns', None)
train = pd.read_csv('./train.csv', encoding='utf-8')
train.head()
test = pd.read_csv('./test.csv', encoding='utf-8')
test.head()
## 결측치를 확인하고 결측치 채우기 (simple imputer 이용)
train.info(... | pd.DataFrame(best_param_lgb_gs.feature_importances_, index = X_train.columns, columns = ['value']) | pandas.DataFrame |
import pandas as pd
import re
from functools import wraps
from lxml.etree import ParserError, XMLSyntaxError
from pyquery import PyQuery as pq
from urllib.error import HTTPError
from .. import utils
from .constants import (NATIONALITY,
PLAYER_ELEMENT_INDEX,
PLAYER_SCHEME,... | pd.DataFrame(rows, index=[indices]) | pandas.DataFrame |
import numpy as np
import pandas as pd
shirley_1015_bs_name = np.load(r'D:\voice2face\shirley_1015\shirley_1015_bs_name.npy')
shirley_1119_bs_name = np.load(r'D:\voice2face\shirley_1015\shirley_1119_bs_name.npy')
shirley_1119_bs_name316 = np.load(r'D:\voice2face\shirley_1119\shirley_1119_bs_name316.npy')
bs_value_1114... | pd.DataFrame(weights1,columns=shirley_1119_bs_name) | pandas.DataFrame |
# --------------
# import packages
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# Load Offers
offers= | pd.read_excel(path,sheet_name=0) | pandas.read_excel |
import os
import json
import traceback
import numpy as np
import pickle
import pandas as pd
import csv
#Run this code file from console to create the pickle file
pklfile = "taxa_mapping.pkl"
root_path = "<add root path here>"
image_location = root_path + "result-img\\"
taxa_file_path = root_path + "\\data\\taxa.csv"
i... | pd.read_csv(taxa_file_path) | pandas.read_csv |
#!/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 |
import numpy as np
import pandas as pd
import scipy.stats
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.ticker as ticker
import matplotlib.colors as colors
from matplotlib.colors import hsv_to_rgb
import seaborn as sns
import scipy.cluster.hierarchy as hierarchy
from cycler impo... | pd.concat([eqtl_df, label_s], axis=1, sort=False) | pandas.concat |
import importlib
import copy
import io, time
from io import BytesIO
import chardet
import os
import collections
from itertools import combinations, cycle, product
import math
import numpy as np
import pandas as pd
import pickle
import tarfile
import random
import re
import requests
from nltk.corpus import stopwords
fro... | pd.DataFrame(causalrep_res, index=[0]) | pandas.DataFrame |
import pandas as pd
import click
from hgvs_helpers import var_c_p_prep, rev_comp, tryconvert
def hgvs_nomenclature(output_folder, weight_filter):
table = | pd.read_csv(output_folder + '/all_mutations_with_weights.csv') | pandas.read_csv |
import pandas as pd
import os
data=pd.read_csv('./data/name/namecode.csv')
result=pd.DataFrame()
re=0
for i,d in enumerate(zip(data['ts_code'],data['name'],data['industry'])):
temp=pd.DataFrame()
try:
temp= | pd.read_csv('./data/stock/'+d[0]+'_'+d[1]+'_'+d[2]+'.csv') | pandas.read_csv |
# Copyright 2017 Sidewalk Labs | https://www.apache.org/licenses/LICENSE-2.0
from __future__ import (
absolute_import, division, print_function, unicode_literals
)
from collections import defaultdict, namedtuple
import numpy as np
import pandas
from doppelganger.listbalancer import (
balance_multi_cvx, discr... | pandas.concat([households] * n_tracts) | pandas.concat |
import pandas as pd
import numpy as np
import holidays
import statsmodels.formula.api as sm
import time
from Helper import helper
import datetime
class DR(object):
def __init__(self, dataframe):
df = dataframe.copy()
self.lm_data = helper.DR_Temp_data_cleaning(df)
self.name = 'DR'
de... | pd.to_datetime(self.date) | pandas.to_datetime |
"""
Main experimentation pipeline for measuring robustness of explainers.
Unlike the other pipelines, we just want to compare the original LIME with its robustified version,
so we do not require a list of configs to run through.
We mainly run three experiments:
* Robustness of original LIME against Fooling LIME attac... | pd.DataFrame(X, columns=data['feature_names']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import pandas as pd
from pandas import Timestamp
def create_dataframe(tuple_data):
"""Create pandas df from tuple data with a header."""
return pd.DataFrame.from_records(tuple_data[1:], columns=tuple_data[0])
### REUSABLE FIXTURES --------------------... | Timestamp('2012-08-01 00:00:00') | pandas.Timestamp |
# Voronoi-CNN-ch2Dxysec.py
# 2021 <NAME> (UCLA, <EMAIL>)
## Voronoi CNN for channel flow data.
## Authors:
# <NAME> (UCLA), <NAME> (Argonne National Lab.), <NAME> (Argonne National Lab.), <NAME> (Keio University), <NAME> (UCLA)
## We provide no guarantees for this code. Use as-is and for academic research use only; ... | pd.read_csv('./record_x.csv',header=None,delim_whitespace=False) | pandas.read_csv |
from flask import Flask, flash, current_app, session, render_template, request, redirect, jsonify, abort, send_file
from flask_calendar.calendar_data import CalendarData
from flask_calendar.gregorian_calendar import GregorianCalendar
from flask_calendar.db_setup import init_db, db_session
from flask_calendar.models imp... | pd.ExcelWriter(output, engine='xlsxwriter') | pandas.ExcelWriter |
"""
Support function for mod handling
Author:
<NAME> <<EMAIL>>
"""
import pandas as pd
import numpy as np
from pandas.io.parsers import read_csv
import itertools as iter
# from lol_file
def get_modularity_value_from_lol_file(lol_file):
"""get_modularity_value_from_lol_file"""
with open(lol_file, 'r') as f... | read_csv(info_nodes_file, sep="\t") | pandas.io.parsers.read_csv |
# -*- encoding:utf-8 -*-
import pandas as pd
import numpy as np
import datetime
# from datetime import datetime
dire = '../../data/'
start = datetime.datetime.now()
orderHistory_train = pd.read_csv(dire + 'train/orderHistory_train.csv', encoding='utf-8')
orderFuture_train = | pd.read_csv(dire + 'train/orderFuture_train6.csv', encoding='utf-8') | pandas.read_csv |
import datetime
import numpy as np
import pandas as pd
import pandas.testing as pdt
import pytest
from plateau.io.eager import (
read_dataset_as_dataframes,
read_table,
store_dataframes_as_dataset,
)
from plateau.io.testing.read import * # noqa
@pytest.fixture(
params=["dataframe", "table"],
id... | pdt.assert_frame_equal(df, expected_df, check_dtype=False, check_like=True) | pandas.testing.assert_frame_equal |
"""Backtester"""
from copy import deepcopy
import unittest
import pandas as pd
import pytest
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.preprocessing import StandardScaler
from soam.constants import (
ANOMALY_PLOT,
DS_COL,
FIG_SIZE,
MONTHLY_TIME_GRANULARITY,
P... | pd.Timestamp('2013-02-01 00:00:00') | pandas.Timestamp |
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels
from sklearn.metrics import roc_curve, auc, precision_recall_curve
from sklearn.model_selection import learning_curve
from sklearn.model_selection import ShuffleSp... | pd.get_dummies(y_truth) | pandas.get_dummies |
# Copyright (c) 2018 The Regents of the University of Michigan
# and the University of Pennsylvania
#
# 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 li... | pd.read_csv(feat_csv_path, dtype=object) | pandas.read_csv |
import logging
import os
import shutil
import warnings
warnings.simplefilter("ignore")
import matplotlib
import pandas as pd
matplotlib.use('agg') # no need for tk
from autogluon.task.tabular_prediction.tabular_prediction import TabularPrediction as task
from autogluon.utils.tabular.utils.savers import save_pd, sav... | pd.option_context('display.max_rows', None, 'display.max_columns', None, 'display.width', 1000) | pandas.option_context |
#!/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 |
#
# 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 us... | is_datetime64_dtype(dt) | pandas.api.types.is_datetime64_dtype |
# -*- coding: utf-8 -*-
from sklearn.base import TransformerMixin
#from category_encoders.ordinal import OrdinalEncoder
#import numpy as np
import pandas as pd
import copy
from pandas.api.types import is_numeric_dtype,is_string_dtype
from joblib import Parallel,delayed,effective_n_jobs
import numpy as np
from BDMLtools... | pd.cut(col,[-np.inf]+breaks_cut+[np.inf],labels=woe+[woe_sp],right=False,ordered=False).astype(dtype) | pandas.cut |
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from numpy import dtype
from matplotlib.pyplot import ylabel
from matplotlib.cm import ScalarMappable
from matplotlib.pyplot import savefig
import math
from getCpuUsageForStage import *
import sys
from argparse import ArgumentParser
parser = Argu... | pd.set_option('display.max_columns', 500) | pandas.set_option |
import itertools
from collections import deque
import networkx as nx
import numpy as np
import pandas as pd
import scanpy as sc
from .._util import CapitalData
class Tree_Alignment:
def __init__(self):
self.__successors1 = None
self.__postorder1 = None
self.__tree1 = None
self.__s... | pd.DataFrame(index=forest1, columns=forest2) | pandas.DataFrame |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import os
import operator
import unittest
import cStringIO as StringIO
import nose
from numpy import nan
import numpy as np
import numpy.ma as ma
from pandas import Index, Series, TimeSeries, DataFrame, isnull, notnull
from pandas.core.index... | assert_series_equal(result, exp) | pandas.util.testing.assert_series_equal |
import json
from elasticsearch import Elasticsearch
from elasticsearch import logger as es_logger
from collections import defaultdict, Counter
import re
import os
from pathlib import Path
from datetime import datetime, date
# Preprocess terms for TF-IDF
import numpy as np
from nltk.corpus import stopwords
from nltk.tok... | pd.read_csv("elasticsearch/analyse/TFIDFadaptativeBiggestScore.csv", index_col=0) | pandas.read_csv |
## Script to add load, generators, missing lines and transformers to SciGRID
#
#
## WARNING: This script is no longer supported, since the libraries and data no longer exist in their former versions
#
## It is kept here for interest's sake
#
## See https://github.com/PyPSA/pypsa-eur for a newer model that covers all of... | pd.Series(data=distance_km[:,0],index=[(u,v) for v in vs.index]) | pandas.Series |
import sys
import os
import numpy as np
import scipy.io
import scipy.sparse
import numba
import random
import multiprocessing as mp
import subprocess
import cytoolz as toolz
import collections
from itertools import chain
import regex as re
import yaml
import logging
import time
import gzip
import pandas as pd
from func... | pd.Series(map_info) | pandas.Series |
""" test the scalar Timedelta """
import numpy as np
from datetime import timedelta
import pandas as pd
import pandas.util.testing as tm
from pandas.tseries.timedeltas import _coerce_scalar_to_timedelta_type as ct
from pandas import (Timedelta, TimedeltaIndex, timedelta_range, Series,
to_timedelta,... | ct('100s') | pandas.tseries.timedeltas._coerce_scalar_to_timedelta_type |
import pandas as pd
import numpy as np
import os
import glob
import shutil
import json
import statistics
from PIL import Image
import random
import matplotlib.pyplot as plt
from collections import Counter
from sklearn.metrics import jaccard_score
class AdjacencyMatrices():
def __init__(self) -> None:
self.... | pd.DataFrame(adj_matrix, columns=self.diseaselist) | pandas.DataFrame |
"""Unit tests for cartoframes.data.utils"""
import unittest
import pandas as pd
from shapely.geometry import Point
from shapely.geos import lgeos
from geopandas.geoseries import GeoSeries
from cartoframes.data import Dataset
from cartoframes.auth import Credentials
from cartoframes.data.utils import compute_query, co... | pd.DataFrame({'geom': self.geom}) | pandas.DataFrame |
# user define imports
from my_package.analysis_info import AnalysisInfo, DataInfo, ResultsInfo
from my_package.data_cleaner import DataCleaner
from my_package import visualizer as visualizer
# python imports
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
class DataProcess... | pd.to_numeric(dataset.loc[:, 'population']) | pandas.to_numeric |
"""
Miscellaneous functions useful for Threat Hunting and cybersecurity data analytics
"""
from __future__ import division
from builtins import input
import getpass
import math
from jellyfish import levenshtein_distance, damerau_levenshtein_distance, hamming_distance, jaro_similarity, jaro_winkler_similarity
import sy... | is_list_like(numbers) | pandas.api.types.is_list_like |
# Copyright 2019, The TensorFlow Federated Authors.
#
# 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 o... | pd.Series(hparam_dict) | pandas.Series |
# pylint: disable-msg=W0612,E1101,W0141
import nose
from numpy.random import randn
import numpy as np
from pandas.core.index import Index, MultiIndex
from pandas import Panel, DataFrame, Series, notnull, isnull
from pandas.util.testing import (assert_almost_equal,
assert_series_equal... | assert_frame_equal(unstacked, expected) | pandas.util.testing.assert_frame_equal |
import pandas
from my_lambdata.my_mod import enlarge
df = | pandas.DataFrame({"x":[1,2,3], "y":[4,5,6]}) | pandas.DataFrame |
# feature selection
import numpy as np
import pandas as pd
from statsmodels.stats.outliers_influence import variance_inflation_factor as vif
from sklearn.feature_selection import f_regression
np.seterr(divide='ignore', invalid='ignore') # hide error warning for vif
from sklearn.feature_selection import f_regression, R... | pd.DataFrame(vif_results) | pandas.DataFrame |
# import start
import ast
import asyncio
import calendar
import platform
import subprocess as sp
import time
import traceback
import xml.etree.ElementTree as Et
from collections import defaultdict
from datetime import datetime
import math
import numpy as np
import pandas as pd
from Utility.CDPConfigValues import CDPC... | pd.Timestamp(x) | pandas.Timestamp |
# coding: utf-8
# # Numpy Introduction
# ## numpy arrays
# In[91]:
import numpy as np
arr = np.array([1,3,4,5,6])
arr
# In[8]:
arr.shape
# In[9]:
arr.dtype
# In[10]:
arr = np.array([1,'st','er',3])
arr.dtype
# In[5]:
np.sum(arr)
# ### Creating arrays
# In[11]:
arr = np.array([[1,2,3],[2,4,6],[8,8,8]... | pd.read_csv(filepath_or_buffer='simplemaps-worldcities-basic.csv') | pandas.read_csv |
#!/usr/bin/python
# encoding: utf-8
"""
@author: Ian
@file: test.py
@time: 2019-05-15 15:09
"""
import pandas as pd
if __name__ == '__main__':
mode = 1
if mode == 1:
df = pd.read_excel('zy_all.xlsx', converters={'出险人客户号': str})
df1 = pd.read_csv('../data/zy_all.csv')
df1['出险人客户号_完整'] ... | pd.read_excel('/Users/luoyonggui/Documents/datasets/work/3/82200946506.xlsx', converters={'出险人客户号': str}) | pandas.read_excel |
"""
use cross validation to plot mean ROC curve, show std
ref:
https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc_crossval.html#sphx-glr-auto-examples-model-selection-plot-roc-crossval-py
Note that you have to tune the parameters yourself
"""
from scipy import interp
import argparse... | pd.melt(df) | pandas.melt |
import datetime
import hashlib
import os
import time
from warnings import (
catch_warnings,
simplefilter,
)
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
DatetimeIndex,
Index,
MultiIndex,
Series,
Timestamp,
concat,
date_range,
timedelt... | concat([df, df2]) | pandas.concat |
import argparse
import re
import itertools
import functools
import operator
import os
import glob
import pandas as pd
from scipy.stats import gmean
trace_file_pat = (
re.compile(r'^CPU (?P<index>\d+) runs (?P<tracename>[-./\w\d]+)$'),
lambda match: os.path.basename(match['tracename']),
functoo... | pd.DataFrame.from_records(results) | pandas.DataFrame.from_records |
"""
caproj.datagen
~~~~~~~~~~~~~~
This module contains functions for generating the interval metrics used in modeling
for each unique capital project
**Module variables:**
.. autosummary::
endstate_columns
endstate_column_rename_dict
info_columns
info_column_rename_dict
**Module functions:**
.. autosu... | pd.to_datetime(df[col]) | pandas.to_datetime |
#%%
import requests
from bs4 import BeautifulSoup
import pandas as pd
import time
import traceback
url = "https://www.ceniniger.org/presidentielle"
communes = pd.read_csv("../data/communes.csv")
#%%
def parse_results_table(results_page):
results_table = results_page.find(id="resultat-grid_").find(id="tbody").find... | pd.DataFrame(data) | pandas.DataFrame |
import requests
import json
import pandas as pd
#initializing variables and data structures
teamDict = {1: "ARS", 2: "AVL", 3: "BRE", 4: "BRI", 5: "BUR", 6: "CHE", 7: "CRY", 8: "EVE", 9: "LEE", 10: "LEI", 11: "LIV", 12: "MCI", 13: "MUN", 14: "NEW", 15: "NOR", 16: "SOU", 17: "TOT", 18: "WAT", 19: "WHU", 20: "WOL... | pd.DataFrame(columns=playerColumns) | pandas.DataFrame |
"""
====================
The qc.passqc module
====================
The qc.passqc module contains functions for determining which NPs pass
a set of quality control conditions.
"""
import pandas as pd
def get_reference(condition_ref_string, stats_df):
"""
If condition_ref_string matches a column in stats_df,... | pd.concat(conditions, axis=1) | pandas.concat |
import operator
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import FloatingArray
import pandas.core.ops as ops
# Basic test for the arithmetic array ops
# -----------------------------------------------------------------------------
@pytest.mark.paramet... | pd.Series([2, np.nan, np.nan], dtype="Int64") | pandas.Series |
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