prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import numpy as np
from numpy.core.numeric import _rollaxis_dispatcher
import pandas as pd
from pymbar import BAR as BAR_
from pymbar import MBAR as MBAR_
from alchemlyb.estimators import MBAR
from sklearn.base import BaseEstimator
import copy
import re
import itertools
import logging
logger = logging.getLogger(__name_... | pd.DataFrame(dGF[1:]) | pandas.DataFrame |
import string
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import tikzplotlib
import utils
import networkx as nx
import extensionanalysis as extanalysis
from plotfig import PlotFig
class EvaluationWar:
output_dir = "results/war/plots"
plot_color_dark = "#003f5c"
plot_color_less_dark = "#... | pd.concat([dfa, df]) | pandas.concat |
"""
Written by <NAME>, 22-10-2018
This script contains functions for data formatting and accuracy assessment of keras models
"""
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
import keras.backend as K
from math import sqrt
import numpy as ... | pd.DateOffset(hours=1) | pandas.DateOffset |
from tqdm import tqdm
import pandas as pd
import numpy as np
from pathlib import Path
from hashlib import md5
from sklearn.feature_extraction.text import TfidfVectorizer
from scipy import sparse as sp
import argparse
def break_text(raw):
return np.array([ i for i, t in enumerate(raw) if t == '¶' ][::2])
def ma... | pd.Series(raw_text) | pandas.Series |
import requests
import pandas as pd
import time
import json
import pymysql
pd.set_option('max_rows',500)
headers = {
'user-agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36'
}
url = 'https://c.m.163.com/ug/api/wuhan/app/data/list-tot... | pd.DataFrame([province['total'] for province in data_province]) | pandas.DataFrame |
'''
Created on April 15, 2012
Last update on July 18, 2015
@author: <NAME>
@author: <NAME>
@author: <NAME>
'''
import pandas as pd
import numpy as np
class Columns(object):
OPEN='Open'
HIGH='High'
LOW='Low'
CLOSE='Close'
VOLUME='Volume'
indicators=["MA", "EMA", "MOM", "ROC", "ATR", "BBANDS", "P... | pd.DataFrame([KelChM, KelChU, KelChD]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from unittest import TestCase
import pandas as pd
import numpy as np
from alphaware.base import (Factor,
FactorContainer)
from alphaware.analyzer import FactorSimpleRank
from pandas.util.testing import assert_series_equal
from datetime import datetime as dt
class T... | pd.DataFrame(index=index, data=[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) | 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, actual) | pandas.testing.assert_frame_equal |
# Importing packages
import pandas as pd
def reformatData(df, feat, hasCombinations=True):
"""
Reformats data from stacked data to pandas dataframes.
"""
# Initializing variables
new_data = list()
new_index = list()
# Stacking data
stacked = df.T.stack(level=0)
# Iteratting over ... | pd.Series(data=new_data, index=new_index, name=feat) | pandas.Series |
# 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.
import re
from collections.abc import Iterable
from datetime import datetime, timedelta
from operator import attrgetter
from unittest import Tes... | pd.DataFrame({"time": previous_seq, "value": previous_values}) | pandas.DataFrame |
# -*- 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... | pd.period_range('2011-01', freq='M', periods=5) | pandas.period_range |
from nemosis import data_fetch_methods, defaults
import pandas as pd
aemo_price_names = {'energy': 'RRP',
'raise_regulation': 'RAISEREGRRP',
'raise_6_second': 'RAISE6SECRRP',
'raise_60_second': 'RAISE60SECRRP',
'raise_5_minute': 'RAISE5MIN... | pd.to_numeric(regional_demand['TOTALDEMAND']) | pandas.to_numeric |
import pandas as pd
class RawReader:
"""
Reads and consumes raw data files (stored as raw.jsonl) from the Music Enabled Running project.
The state can be updated by feeding it additional lines (msg) from the data file.
You can then extract the data of the different sensors and modalities as Pandas da... | pd.Timestamp(msg["t"], unit="s") | pandas.Timestamp |
from collections import OrderedDict
import numpy as np
import pytest
from pandas._libs.tslib import Timestamp
from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
import pandas as pd
from pandas import Index, MultiIndex, date_range
import pandas.util.testing as tm
def test_constructor_singl... | MultiIndex.from_tuples([]) | pandas.MultiIndex.from_tuples |
from contextlib import contextmanager
import pandas as pd
from dataviper.logger import IndentLogger
from dataviper.report.profile import Profile
from dataviper.source.datasource import DataSource
import pymysql
class MySQL(DataSource):
"""
class MySQL is a connection provider for MySQL
and query builder ... | pd.read_sql(query, self.__conn) | pandas.read_sql |
#
# Copyright 2018 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | pd.Timestamp('2020-09-04', tz='utc') | pandas.Timestamp |
import time
import pandas as pd
from googlegeocoder import GoogleGeocoder
geocoder = GoogleGeocoder()
def geocode(row):
"""
Accepts a row from our fatalities list. Returns it with geocoded coordinates.
"""
# If it's already been geocoded, it's already mapped and just return the row.
if hasattr(row... | pd.isnull(row.geocoder_x) | pandas.isnull |
import numpy as np
import pandas as pd
import pdb
from dku_data_processing.filtering import filter_dataframe
def generate_sample_df():
data = {
'id': {0: 2539, 1: 2595, 2: 3647},
'name': {0: 'Clean & quiet apt home by the park', 1: 'Skylit Midtown Castle', 2: 'THE VILLAGE OF HARLEM....NEW YORK !'... | pd.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
import pandas as pd
import ast
from collections import Counter
data = pd.read_csv('../reddit_data_preprocessing/data/curated_pattern_lists.csv')
data.pattern.str.count("QLTY").sum()
qualities_list = []
for idx, row in data.iterrows():
qualities_list += ast.literal_eval(row['QLTY'])
counter = Counter(q... | pd.DataFrame.from_dict(counter, orient='index') | pandas.DataFrame.from_dict |
# -*- coding: utf-8 -*-
"""
Created on Sat Jan 25 17:54:30 2020
@author: Administrator
"""
import pandas as pd
import numpy as np
fns = [#'../checkpoints/eval_resnet50_singleview-Loss-ce-tta-0-test.csv',
# '../checkpoints/eval_resnet50_singleview-Loss-ce-tta-1-test.csv',
# #'../checkpoints/eval_resnet50_... | pd.DataFrame(data = y_pred_total,index =kl1[:n_samp,0], columns = [ 'MEL', 'NV','BCC', 'AKIEC', 'BKL', 'DF','VASC']) | pandas.DataFrame |
from . import pyheclib
import pandas as pd
import numpy as np
import os
import time
import warnings
# some static functions
def set_message_level(level):
"""
set the verbosity level of the HEC-DSS library
level ranges from "bort" only (level 0) to "internal" (level >10)
"""
pyheclib.hec_... | pd.to_timedelta(ibdate,'D') | pandas.to_timedelta |
import librosa
import sys
import argparse
import numpy as np
import pandas as pd
import matplotlib as plt
from pipeline.common.file_utils import ensure_destination_exists
def get_librosa_features(src_audio: str, dst_csv: str):
"""Extract basic audio features from an audio file for HRI with Librosa
TODO: Allo... | pd.DataFrame(v) | pandas.DataFrame |
# -*- coding: utf-8 -*-
#author: kai.zhang
import pandas as pd
import numpy as np
from pandas.core.frame import DataFrame
'''
币安历史数据处理
'''
class HisDataHandler(object):
def __init__(self):
self.data = open('../data/his_data.csv').readlines()
def handler(self):
klink_data = eval(self.data[0])
... | DataFrame(klink_data) | pandas.core.frame.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 2 09:04:41 2019
@author: michaelek
"""
import io
import numpy as np
import requests
from gistools import vector
from allotools import AlloUsage
from hydrolm import LM
from tethysts import Tethys
from tethysts import utils
import os
import sys
import yaml
import pandas as... | pd.DataFrame(stns_list3) | pandas.DataFrame |
import Functions
import pandas as pd
from datetime import datetime
from datetime import timedelta
import matplotlib.pyplot as plt
coin_list_NA = ['BTC', 'BCHNA', 'CardonaNA', 'dogecoinNA', 'EOS_RNA', 'ETHNA', 'LTCNA', 'XRP_RNA', 'MoneroNA',
'BNB_RNA',
'IOTANA', 'TEZOSNA', ]
coin_list =... | pd.DataFrame() | pandas.DataFrame |
import wavefront_api_client as wave_api
from utils.converterutils import addHeader
import datetime as dt
from datetime import datetime
import numpy as np
import pandas as pd
import time
from dateutil.parser import parse
APP_PLACEHOLDER = '[APP]'
SEVEN_DAY = 24*7*60*60*1000
ONE_MINUTE = 60*1000
def retrieveQueryUrl(a... | pd.Series(data[:,0]) | pandas.Series |
"""
Gather data about tweet engagement over time.
"""
# Copyright (c) 2020 <NAME>. All rights reserved.
from typing import List, Tuple
from datetime import datetime
import json
import os
import pickle
import dateutil
import pytz
import pandas as pd
import tweepy
import plot
CREDS_FILENAME = "creds.json"
USER_ID... | pd.concat(dfs) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 20 22:28:42 2018
@author: Erkin
"""
#%%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
def warn(*args, *... | pd.DataFrame(supports, columns=['support_hold','support_buy']) | pandas.DataFrame |
from sklearn.dummy import DummyClassifier
from sklearn.metrics import roc_auc_score
from bac.models.model_base import ModelBase
import pandas as pd
import logging
import sys
logger = logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
class DummyModel(ModelBase):
def __ini... | pd.Series(scores, index=X.index) | pandas.Series |
from model.toolkits.parse_conf import parse_config_vina, parse_protein_vina, parse_ligand_vina
import os
import pandas as pd
import numpy as np
from pathlib import Path
import argparse
import rdkit
from rdkit import Chem, DataStructs
from rdkit.Chem import Descriptors, rdMolDescriptors, AllChem, QED
try:
from openb... | pd.read_csv(dataset) | pandas.read_csv |
import os
import pandas as pd
import argparse
from argparse import ArgumentParser
from datetime import timedelta
from datetime import datetime
from sklearn.model_selection import train_test_split
ARG_PARSER = ArgumentParser()
ARG_PARSER.add_argument("--test_size", default=0.1, type=float)
ARG_PARSER.add_argument("--v... | pd.concat([final, temp]) | pandas.concat |
import time
import pandas as pd
import copy
import numpy as np
from shapely import affinity
from shapely.geometry import Polygon
import geopandas as gpd
def cal_arc(p1, p2, degree=False):
dx, dy = p2[0] - p1[0], p2[1] - p1[1]
arc = np.pi - np.arctan2(dy, dx)
return arc / np.pi * 180 if degree else arc
def... | pd.Series([(dx ** 2 + dy ** 2) ** 0.5 for dx, dy in df_line[['dx', 'dy']].values]) | pandas.Series |
#!/usr/bin/env python
# coding: utf-8
import argparse
import os
import glob
import itertools
from pathlib import Path
from typing import Dict, List, Tuple
from collections import defaultdict
import json
import time
import logging
import random
import pandas as pd
import numpy as np
import re
import torch
from torch... | pd.read_csv('Datasets/dart_train.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 25 12:51:40 2019
@author: 561719
"""
##########################Data Normalization#######################################################
import pandas as pd
import numpy as np
R1=pd.read_csv("C:\\Users\\561719\\Documents\\Imarticus_MLP\\NYC_property_sales\\... | pd.to_datetime(R2['SALE DATE']) | pandas.to_datetime |
"""Test cases for Streamlit app functionality."""
import sqlite3
import unittest
import pandas as pd
from mock import patch
from strigiform.app.streamlit import add_line_break
from strigiform.app.streamlit import get_data
from strigiform.app.streamlit import get_period_stats
class Streamlit(unittest.TestCase):
... | pd.to_datetime("2021-01-01") | pandas.to_datetime |
# -*- coding: utf-8 -*-
try:
import json
except ImportError:
import simplejson as json
import math
import pytz
import locale
import pytest
import time
import datetime
import calendar
import re
import decimal
import dateutil
from functools import partial
from pandas.compat import range, StringIO, u
from pandas.... | ujson.dumps(labelled_input) | pandas._libs.json.dumps |
import preprocessor as p
import re
import wordninja
import csv
import pandas as pd
# Data Loading
def load_data(filename):
filename = [filename]
concat_text = | pd.DataFrame() | pandas.DataFrame |
from unittest import TestCase
import pandas as pd
import numpy as np
import pandas_validator as pv
from pandas_validator.core.exceptions import ValidationError
class BaseSeriesValidatorTest(TestCase):
def setUp(self):
self.validator = pv.BaseSeriesValidator(series_type=np.int64)
def test_is_valid_wh... | pd.Series([0., 1., 2.]) | pandas.Series |
#!/usr/bin/env python3
# Converts PLINK covariate and fam file into a covariate file for Gemma
import sys
import pandas as pd
import argparse
import numpy as np
EOL=chr(10)
def parseArguments():
parser = argparse.ArgumentParser(description='fill in missing bim values')
parser.add_argument('--inp_fam',type=... | pd.read_csv(args.data,delim_whitespace=True,usecols=usecols) | pandas.read_csv |
import pandas as pd
import yaml
import os
from . import DATA_FOLDER, SCHEMA, SYNONYM_RULES
def run(
rule_file: str = SYNONYM_RULES,
schema_file: str = SCHEMA,
data_folder: str = DATA_FOLDER,
):
"""Add rules to capture more terms as synonyms during named entity
recognition (NER)
:param rule_fi... | pd.concat([prefix_df, row]) | pandas.concat |
'''
Scripts for loading various experimental datasets.
Created on Jul 6, 2017
@author: <NAME>
'''
import os
import re
import sys
import pandas as pd
import numpy as np
import glob
from sklearn.feature_extraction.text import CountVectorizer
from evaluation.experiment import Experiment
def convert_argmin(x):
... | pd.read_csv(savepath + '/task1_test_doc_start.csv', skip_blank_lines=False, header=None) | pandas.read_csv |
"""
Produces a tsv file to study all the nii files and perform the quality check.
"""
import os
from os import path
from pathlib import Path
import nibabel as nib
import numpy as np
import pandas as pd
from clinica.utils.inputs import RemoteFileStructure, fetch_file
def extract_metrics(caps_dir, output_dir, group_la... | pd.concat([results_df, row_df]) | pandas.concat |
import datetime as dt
import numpy as np
import pandas as pd
from pandas.testing import assert_series_equal, assert_frame_equal
import pytest
from solarforecastarbiter.datamodel import Observation
from solarforecastarbiter.validation import tasks, validator
from solarforecastarbiter.validation.quality_mapping import ... | pd.Series([10, 1000, -100, 500, 500], index=default_index) | pandas.Series |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Copyright 2014-2019 OpenEEmeter contributors
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/LIC... | pd.read_csv(filepath_or_buffer, **read_csv_kwargs) | pandas.read_csv |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from typing import List, Union, Tuple
from macrosynergy.management.simulate_quantamental_data import make_qdf
from macrosynergy.management.shape_dfs import reduce_df
class NaivePnL:
"""Computes and collects illustrativ... | pd.DataFrame(columns=dfw.columns, index=stats) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from datetime import timedelta
import operator
from string import ascii_lowercase
import warnings
import numpy as np
import pytest
from pandas.compat import lrange
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
Categorical, DataFrame, MultiIndex, Serie... | lrange(5) | pandas.compat.lrange |
import datetime as dtm
import itertools
import pandas as pd
import numpy as np
from sklearn.metrics import r2_score
from sklearn.base import clone
import sugartime.core as core
class Patient:
"""
Object containing data for an
individual patient.
"""
def __init__(self):
self.carbs_per_in... | pd.date_range(start=start_time, end=max_time, freq="5T") | pandas.date_range |
import argparse
import torch
import numpy as np
import pandas as pd
import pickle as pkl
from tqdm import tqdm
from torch.utils.data import DataLoader
from sklearn.model_selection import train_test_split, KFold
from dataset_graph import construct_dataset, mol_collate_func
from transformer_graph import make_mod... | pd.DataFrame.from_dict({'smile': best_valid_result['smile'], 'actual': best_valid_result['label'], 'predict': best_valid_result['prediction']}) | pandas.DataFrame.from_dict |
#!/usr/bin/env python
import datetime
import json
import logging
import os
import traceback
from functools import partial
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List
import yaml
from pandas import DataFrame, Int32Dtype, concat, isna, read_csv
# ROOT directory
ROOT = Path(os.path.d... | read_csv(url, dtype=str, skiprows=1) | pandas.read_csv |
'''
Title: Git Data Commit
Description: This script is used to collect data from the COVID-19 Hub Feature layers hosted on ArcGIS Online into local machine and
then run the Git Commands to commit data in CSV format to this repository.
'''
# Import the required libraries
import pandas as pd
# from arcgis.features imp... | pd.DataFrame.spatial.from_layer(infections_layer) | pandas.DataFrame.spatial.from_layer |
import re
import pandas as pd
import numpy as np
from gensim import corpora, models, similarities
from difflib import SequenceMatcher
from build_tfidf import split
def ratio(w1, w2):
'''
Calculate the matching ratio between 2 words.
Only account for word pairs with at least 90% similarity
'''
m = Sequence... | pd.merge(df_test, df_desc, how='left', on='product_uid') | pandas.merge |
import datetime as dt
import numpy as np
import pandas as pd
from pandas.testing import assert_series_equal, assert_frame_equal
import pytest
from solarforecastarbiter.datamodel import Observation
from solarforecastarbiter.validation import tasks, validator
from solarforecastarbiter.validation.quality_mapping import ... | assert_frame_equal(post_mock.call_args_list[1][0][1], out[-1:]) | pandas.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
from statsmodels.compat.pandas import Appender, Substitution, to_numpy
from collections.abc import Iterable
import datetime as dt
from types import SimpleNamespace
import warnings
import numpy as np
import pandas as pd
from scipy.stats import gaussian_kde, norm
from statsmodels.tsa.base.predi... | pd.date_range(index[0], freq=freq, periods=end) | pandas.date_range |
from datetime import datetime, timedelta
from io import StringIO
import re
import sys
import numpy as np
import pytest
from pandas._libs.tslib import iNaT
from pandas.compat import PYPY
from pandas.compat.numpy import np_array_datetime64_compat
from pandas.core.dtypes.common import (
is_datetime64_dtype,
is_... | Series(arr, index=self.bool_index, name="a") | pandas.Series |
# Copyright (c) 2021-2022, NVIDIA CORPORATION.
import numpy as np
import pandas as pd
import pytest
import cudf
from cudf.testing._utils import NUMERIC_TYPES, assert_eq
from cudf.utils.dtypes import np_dtypes_to_pandas_dtypes
def test_can_cast_safely_same_kind():
# 'i' -> 'i'
data = cudf.Series([1, 2, 3], d... | pd.Series(["1", "a", "3"]) | pandas.Series |
import numpy as np
import pandas as pd
import dask
from dask.delayed import tokenize
from ... import delayed
from .. import methods
from .io import from_delayed, from_pandas
def read_sql_table(
table,
uri,
index_col,
divisions=None,
npartitions=None,
limits=None,
columns=None,
bytes_... | pd.read_sql(q, engine, **kwargs) | pandas.read_sql |
#! /usr/bin/env python3
import os
import string
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from nltk import word_tokenize, pos_tag
from collections import Counter, defaultdict
from tqdm import tqdm
def visualize_class_balance(data_path):
train_fileid = os.listdir(data_path + '/sampled_... | pd.read_csv(data_path + '/annotations_metadata.csv') | pandas.read_csv |
from datetime import timedelta
import operator
import numpy as np
import pytest
import pytz
from pandas._libs.tslibs import IncompatibleFrequency
from pandas.core.dtypes.common import is_datetime64_dtype, is_datetime64tz_dtype
import pandas as pd
from pandas import (
Categorical,
Index,
IntervalIndex,
... | Series(dti) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Tests the TextReader class in parsers.pyx, which
is integral to the C engine in parsers.py
"""
import os
import numpy as np
from numpy import nan
import pytest
import pandas._libs.parsers as parser
from pandas._libs.parsers import TextReader
import pandas.compat as compat
from pandas.com... | StringIO(data) | pandas.compat.StringIO |
"""
Description: Processes model results for visualization
Uses methods:
- :func:`hists`: Processes a model histories for each scenario into results histories by comparing the states over time in each scenario with the states in the nominal scenario.
- :func:`hist`: Compa... | pd.DataFrame(endclasses) | pandas.DataFrame |
import sys
import os
import numpy as np
import pandas as pd
import dill
import torch
def devide_by_steps(data):
# find first/last frame
min_frame = min([x['frame']["id"][0] for x in data])
max_frame = max([max(x['frame']["id"]) for x in data])
#
new_data = []
for n in range(min_frame, max_fra... | pd.to_numeric(data['frame_id'], downcast='integer') | pandas.to_numeric |
import urllib.request
import xmltodict, json
#import pygrib
import numpy as np
import pandas as pd
from datetime import datetime
import time
# Query to extract parameter forecasts for one particular place (point)
#
# http://data.fmi.fi/fmi-apikey/f96cb70b-64d1-4bbc-9044-283f62a8c734/wfs?
# request=getFeature&storedq... | pd.DataFrame(columns=['Measurement_Number', 'Name', 'DateTime', 'Lat', 'Long', 'Value']) | pandas.DataFrame |
# coding=utf-8
# Copyright 2021 The Google Research 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 applicab... | pd.read_csv(train_url, header=0) | pandas.read_csv |
# -*- 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('a,b,c\n1,2,3') | pandas.compat.StringIO |
### Twitter Data Tools
## <NAME>
## Created: 8/15/2018
## Updated: 8/23/2018
import os
import re
import sys
import math
import nltk
import errno
import tarfile
import unidecode
import numpy as np
import pandas as pd
import subprocess as sb
def get_id_sets(data):
parent = list(data['tweet']['tweet_id']['parent'].keys... | pd.read_csv(pathway_U,compression='gzip',sep=',',index_col=0,header=0,dtype=str) | pandas.read_csv |
# -*- coding: utf-8 -*-
# Copyright © 2017 Apple Inc. All rights reserved.
#
# Use of this source code is governed by a BSD-3-clause license that can
# be found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
# This software may be modified and distributed under the terms
# of the BSD license... | pd.Timestamp('2013-05-07T10:04:10') | pandas.Timestamp |
# Copyright (C) 2021 ServiceNow, Inc.
import pytest
import pandas as pd
import re
from nrcan_p2.data_processing.preprocessing_dfcol import (
rm_dbl_space,
rm_cid,
rm_dbl_punct,
convert_to_ascii,
lower,
rm_punct,
rm_newline,
rm_triple_chars,
rm_mid_num_punct,
rm_word_all_punct,
... | pd.DataFrame({'text': text_col}) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pytest
import pandas as pd
import pandas_should # noqa
class TestEqualAccessorMixin(object):
def test_equal_true(self):
df1 = pd.DataFrame([1, 2, 3], columns=['id'])
df2 = pd.DataFrame([1, 2, 3], columns=['id'])
assert df1.should.eq... | pd.DataFrame(data1, columns=['id', 'name', 'age']) | pandas.DataFrame |
import argparse
import os
import shutil
import zipfile
import pathlib
import re
from datetime import datetime
import collections
import pandas as pd
import geohash
import math
import helpers
import plotly.express as px
ControlInfo = collections.namedtuple("ControlInfo", ["num_tracks", "date", "duration"])
def parse_... | pd.concat(d) | pandas.concat |
"""
Data structures for sparse float data. Life is made simpler by dealing only
with float64 data
"""
# pylint: disable=E1101,E1103,W0231
from pandas.compat import range, lrange, zip
from pandas import compat
import numpy as np
from pandas.core.index import Index, MultiIndex, _ensure_index
from pandas.core.frame imp... | com._all_none(items, major, minor) | pandas.core.common._all_none |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu May 14 08:13:14 2020
@author: abhijit
"""
#%% preamble
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
#%% gapminder data
gapminder = pd.read_csv('data/gapminder.tsv', sep='\t')
gapminder[:5]
gapminder.hea... | pd.read_csv('data/weather.csv') | pandas.read_csv |
"""
Copyright 2019 <NAME>.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distribut... | pd.Series(actual) | pandas.Series |
from __future__ import division
import pytest
import numpy as np
from datetime import timedelta
from pandas import (
Interval, IntervalIndex, Index, isna, notna, interval_range, Timestamp,
Timedelta, compat, date_range, timedelta_range, DateOffset)
from pandas.compat import lzip
from pandas.tseries.offsets imp... | IntervalIndex.from_intervals(ivs, name=name) | pandas.IntervalIndex.from_intervals |
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
from numba import njit
import vectorbt as vbt
from tests.utils import record_arrays_close
from vectorbt.generic.enums import range_dt, drawdown_dt
from vectorbt.portfolio.enums import order_dt, trade_dt, log_dt
day_dt = np.timedelta64... | pd.Timestamp('2020-01-06 00:00:00') | pandas.Timestamp |
from builtins import print
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
matplotlib.rcParams['font.family'] = 'sans-serif'
matplotlib.rcParams['font.sans-serif'] = 'Arial'
import os
import operator
import utils
from utils.constants import UNIVARIATE_D... | pd.read_csv(root_dir + filename, index_col=0) | pandas.read_csv |
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# This file contains dummy data for the model unit tests
import numpy as np
import pandas as pd
AIR_FCST_LINEAR_95 = pd.DataFrame(
{
... | pd.Timestamp("2012-05-15 00:00:00") | pandas.Timestamp |
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import json
sns.set_context('paper')
sns.set(font_scale=1)
palette = sns.color_palette("mako_r", 10)
def compare_plots_best_performing(csv):
compare_plot_df = {'model': [], 'conds': [], 'rate': [], 'distortion': []}
... | pd.read_csv(csv['csv_path']) | pandas.read_csv |
import numpy as np
import pandas as pd
import dask
from dask.distributed import Client, progress
import itertools
from maxnorm.maxnorm_completion import *
from maxnorm.tenalg import *
from maxnorm.graphs import *
def generate_data(obs_mask, U, sigma):
data = obs_mask.copy()
clean_data = kr_get_items(U, data.co... | pd.DataFrame(params, columns=['n', 't', 'r', 'sigma', 'r_fit', 'rep', 'd']) | pandas.DataFrame |
# libraries
import pandas as pd
from pandas.api.types import CategoricalDtype, is_categorical_dtype
import numpy as np
import string
import types
import scanpy.api as sc
import anndata as ad
from plotnine import *
import plotnine
import scipy
from scipy import sparse, stats
from scipy.cluster import hierarchy
import gl... | pd.DataFrame({'gene_symbols':gene_names}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from datetime import datetime
from pandas.compat import range, lrange
import operator
import pytest
from warnings import catch_warnings
import numpy as np
from pandas import Series, Index, isna, notna
from pandas.core.dtypes.common import is_float_dtype
from pandas.core.dtypes.missing import re... | assert_almost_equal(casted2.values, exp_values) | pandas.util.testing.assert_almost_equal |
import requests
from opencage.geocoder import OpenCageGeocode
import pandas as pd
# from pprint import pprint
app = flask.Flask(__name__)
# @app.route('/',methods=['GET', 'POST', 'PUT'])
# def pass_val():
# # search_address = request.args.get('search_address')
# # print('search_address', search_address)... | pd.DataFrame(dict) | pandas.DataFrame |
import pandas
from collections import Counter
from tqdm import tqdm
user_df = pandas.read_csv('processed_data/prj_user.csv')
tweets_df = pandas.read_csv('original_data/prj_tweet.csv')
ids = user_df["id"]
ids = list(ids.values)
hobby_1_list = []
hobby_2_list = []
def get_users_most_popular_hashtags_list(tweets_df, u... | pandas.Series(hobby_2_list, index=hobby_df.index) | pandas.Series |
# ===== 라이브러리 ===== #
from random import shuffle
import datetime
import pandas as pd
# ===== 상수 ===== #
EASY, HARD, HISTORY, EXIT = map(str, range(1, 5)) # command용 상수
STRIKE_SCORE, BALL_SCORE = 0.1, 0.05 # 스트라이크/볼 점수
TRY_LIMIT = 30 # 시도 횟수 제한
DATA_FILE = "data.csv" # 점수 기록 파일
RANKING_COUNT = 3 # 상위 몇 개의 기록을 보여줄 지
EN... | pd.read_csv(DATA_FILE) | pandas.read_csv |
import multiprocessing
import operator
import os
from six.moves import xrange
import pandas as pd
COMP_OP_MAP = {'>=': operator.ge,
'>': operator.gt,
'<=': operator.le,
'<': operator.lt,
'=': operator.eq,
'!=': operator.ne}
def get_output_... | pd.isnull(row[join_attr_index]) | pandas.isnull |
import joblib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from reports import mdl_results
from rolldecayestimators import logarithmic_decrement
from rolldecayestimators import lambdas
from sklearn.pipeline import Pipeline
from rolldecayestimators import measure
from rolldecayestimators.direc... | pd.DataFrame() | pandas.DataFrame |
# license: Creative Commons License
# Title: Big data strategies seminar. Challenge 1. www.iaac.net
# Created by: <NAME>
#
# is licensed under a license Creative Commons Attribution 4.0 International License.
# http://creativecommons.org/licenses/by/4.0/
# This script uses pandas for data management for more informatio... | pd.read_csv('../data/opendatabcn/2009_distribucio_territorial_renda_familiar.csv') | pandas.read_csv |
#%% [markdown]
# # Basic of Beamforming and Source Localization with Steered response Power
# ## Motivation
# Beamforming is a technique to spatially filter out desired signal and surpress noise. This is applied in many different domains, like for example radar, mobile radio, hearing aids, speech enabled IoT devices.
#... | pd.DataFrame(phi_xx[:,:,50]) | pandas.DataFrame |
__author__ = "unknow"
__copyright__ = "Sprace.org.br"
__version__ = "1.0.0"
import pandas as pd
import sys
from math import sqrt
import sys
import os
import ntpath
import scipy.stats
import seaborn as sns
from matplotlib import pyplot as plt
#sys.path.append('/home/silvio/git/track-ml-1/utils')
#sys.path.append('.... | pd.read_csv(original_tracks) | pandas.read_csv |
import simpledf as sdf
import pandas as pd
import numpy as np
from unittest import TestCase
def f(y):
''' A custom function that changes the shape of dataframes '''
y['Mean'] = np.mean(y['Data'].values)
return y
class TestApply(TestCase):
def test_apply_basic(self):
x = | pd.DataFrame({'Data': [1, 2, 3], 'Group': ['A', 'B', 'B']}) | pandas.DataFrame |
'''GDELTeda.py
Project: WGU Data Management/Analytics Undergraduate Capstone
<NAME>
August 2021
Class for collecting Pymongo and Pandas operations to automate EDA on
subsets of GDELT records (Events/Mentions, GKG, or joins).
Basic use should be by import and implementation within an IDE, or by editing
se... | pd.StringDtype() | pandas.StringDtype |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
scenario_filenames = ["OUTPUT_110011_20201117123025"]
scenario_labels =["Lockdown enabled,Self Isolation,Mask Compliance (0.5)"]
MAX_DAY = 250#250#120
POPULATION = 10000.0
FIGSIZE = [20,10]
plt.rcParams.update({'font.size': 22})
#### compari... | pd.read_csv(simulation_file) | pandas.read_csv |
import numpy as np
import pandas as pd
from sqlalchemy import create_engine
from pycytominer import aggregate, normalize
from pycytominer.cyto_utils import (
output,
check_compartments,
check_aggregate_operation,
infer_cp_features,
get_default_linking_cols,
get_default_compartments,
assert_l... | pd.read_sql(sql=image_query, con=self.conn) | pandas.read_sql |
#Aug 2015
#compress CNV data for BRCA dataset for multiple isoforms of genes with different gene coordinates
#genes lost at this stage are those which appear in known common CNVs-removed in the 'no_cnv' files
import pandas as pd
import csv
#read CNV data
print('processing CNVs_genes file...')
#this will handle th... | pd.DataFrame(CNV_genes.iloc[:,-2:]) | pandas.DataFrame |
# Copyright 2022 Accenture Global Solutions 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | pd.UInt8Dtype() | pandas.UInt8Dtype |
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Index,
Series,
)
import pandas._testing as tm
dt_data = [
pd.Timestamp("2011-01-01"),
pd.Timestamp("2011-01-02"),
pd.Timestamp("2011-01-03"),
]
tz_data = [
pd.Timestamp("2011-01-01", tz="U... | Series(vals2) | pandas.Series |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os
import matplotlib.ticker as tck
import matplotlib.font_manager as fm
import math as m
import matplotlib.dates as... | pd.read_table('/home/nacorreasa/Maestria/Datos_Tesis/Piranometro/60012018.txt', parse_dates=[2]) | pandas.read_table |
"""
We made this file to create the datasets. By Reversed engineering, we knew how the datasets were made.
Below are the functions to create the datasets. These are called when the 'recreated_data == yes' parameter and if the
datasets are yet not recreated.
"""
import os
import pandas as pd
import numpy as np
from s... | pd.concat([data, targets], axis=1, join='inner') | pandas.concat |
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from lmfit import Model, Parameters, minimize, report_fit
from scipy.optimize import curve_fit
from scipy import stats
from utilities.statistical_tests import r_squared_calculator
from GEN_Utils import FileHandling... | pd.DataFrame(sigmoid_fitted_vals) | pandas.DataFrame |
# Copyright 2017 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | pd.DataFrame(data=[{col_name: place_type}]) | pandas.DataFrame |
import os
import sys
import tensorflow as tf
import random
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import config as cf
class UTILS(object):
######################################
# load all data files #
####################################... | pd.read_csv(self.testPath) | pandas.read_csv |
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