prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import collections.abc as cabc
from copy import copy
from typing import Union, Optional, Sequence, Any, Mapping, List, Tuple
import numpy as np
import pandas as pd
from anndata import AnnData
from cycler import Cycler
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from pandas.api.types import is... | pd.isnull(color_source_vector) | pandas.isnull |
#!/usr/bin/env python3
"""
This module prepares a table comparing mass spec MM peptide results from gencode
against the fasta sequences of various orf calling methods
Inputs:
------------------------------------------------------------------------------------------
1. gene isoname file: map transcript n... | pd.Series(cpat.prot_seq.values, index=cpat.gene) | pandas.Series |
import pandas as pd
def putdateon(df):
"""Puts a date on the dataframe, and the year."""
return (
df
.assign(release_date = pd.to_datetime(df.release_date))
.pipe(lambda x: x.assign(year = x.release_date.dt.year))
)
movies = putdateon(pd.read_csv('~/data/tmdb/movies.csv'))
cast = p... | pd.read_csv('~/data/tmdb/crew.csv') | pandas.read_csv |
import logging
import copy
import pandas as pd
import numpy as np
from datetime import date
from spaceone.core import cache
from spaceone.core.manager import BaseManager
from spaceone.cost_analysis.error import *
from spaceone.cost_analysis.manager.identity_manager import IdentityManager
from spaceone.cost_analysis.mo... | pd.merge(cost_df, project_df, on=['project_id'], how='left') | pandas.merge |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import neighbors
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report, accuracy_score, confusion_matrix
from sklearn.model_selection i... | pd.read_csv('diabetes.csv') | pandas.read_csv |
import tensorflow.keras.backend as K
import tensorflow as tf
from tensorflow.keras.utils import to_categorical
import numpy as np
import pandas as pd
import warnings
from scipy import stats
from scipy.stats import entropy
T = 50
class Predictor:
f = None
def __init__(self, model):
#self.f = K.fun... | pd.DataFrame() | pandas.DataFrame |
# This file is part of the mt5se package
# mt5se home: https://github.com/paulo-al-castro/mt5se
# Author: <NAME>
# Date: 2020-11-17
## chamadas que sao enviadas para a corretora (brokerage), logo exigem comunicação com a mesma
# broker module
import MetaTrader5 as mt5
import pandas as pd
import numpy... | pd.DataFrame(rates) | pandas.DataFrame |
"""
This script does the following:
Loads various word2vec models with different hyperparameters, then obtains the word embeddings for common words
in its vocabulary (>5 frequency). Then performs KMeans clustering with N=3 clusters on the word vectors.
Finally performs PCA (Principal Component Analysis) on the word vec... | pd.DataFrame(X) | pandas.DataFrame |
import csv
from io import StringIO
import os
import numpy as np
import pytest
from pandas.errors import ParserError
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
NaT,
Series,
Timestamp,
date_range,
read_csv,
to_datetime,
)
import pandas._testing as tm
impo... | read_csv(path, header=[0, 1], index_col=[0]) | pandas.read_csv |
# -*- coding: utf-8 -*-
from datetime import timedelta
from distutils.version import LooseVersion
import numpy as np
import pytest
import pandas as pd
import pandas.util.testing as tm
from pandas import (
DatetimeIndex, Int64Index, Series, Timedelta, TimedeltaIndex, Timestamp,
date_range, timedelta_range
)
f... | Series(['00:00:02']) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 7 11:05:09 2018
@author: abaena
"""
#******************************************************************************
#Add logmapper-agent directory to python path for module execution
#******************************************************************************
if __na... | pd.read_sql_query("SELECT * FROM lmp_measure_type", connDbMaster) | pandas.read_sql_query |
from datetime import datetime, timedelta, timezone
import numpy as np
from numpy.testing import assert_array_equal
import pandas as pd
import pytest
from athenian.api.controllers.features.entries import MetricEntriesCalculator
from athenian.api.controllers.features.github.deployment_metrics import \
group_deploym... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
from warnings import catch_warnings
import numpy as np
from datetime import datetime
from pandas.util import testing as tm
import pandas as pd
from pandas.core import config as cf
from pandas.compat import u
from pandas._libs.tslib import iNaT
from pandas import (NaT, Float64Index, Series,
... | isnull([[False]]) | pandas.core.dtypes.missing.isnull |
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 9 14:59:45 2020
@author: wonwoo
"""
from sklearn.pipeline import Pipeline
from sklearn.svm import LinearSVR
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.model_sele... | pd.to_datetime('2015-8-01 01:00:00') | pandas.to_datetime |
import sqlite3
import json
import pandas as pd
class MamphiDataFetcher:
mamphi_db = ""
def __init__(self, mamphi_db=mamphi_db):
self.mamphi_db = mamphi_db
def fetch_center(self):
conn = sqlite3.connect(self.mamphi_db)
conn.row_factory = sqlite3.Row
cursor = conn.cursor()... | pd.date_range(start='6/1/2019', periods=5, freq='3M') | pandas.date_range |
"""Test functions in owid.datautils.dataframes module.
"""
import numpy as np
import pandas as pd
from pytest import warns
from typing import Any, Dict
from owid.datautils import dataframes
class TestCompareDataFrames:
def test_with_large_absolute_tolerance_all_equal(self):
assert dataframes.compare(
... | pd.Series(["country_01", "country_02", "country_03"]) | pandas.Series |
from eflow.utils.sys_utils import dict_to_json_file,json_file_to_dict
from eflow.utils.language_processing_utils import get_synonyms
from eflow._hidden.custom_exceptions import UnsatisfiedRequirments
from eflow._hidden.constants import BOOL_STRINGS
import copy
import numpy as np
import pandas as pd
from dateutil impo... | pd.DataFrame({'Data Types': feature_types}) | pandas.DataFrame |
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.testing.assert_frame_equal(output_df, expected_df, check_like=True) | pandas.testing.assert_frame_equal |
from os import path
from app.api import fill_missing_dates
from app.api.gsheets import csv_url_for_sheets_url, save_to_sheet
import pandas as pd
def get_all_state_urls():
# TODO: move this out into something like config.py so it's not buried here
url_link = 'https://docs.google.com/spreadsheets/d/1kBL149bp8P... | pd.isnull(df['Date']) | pandas.isnull |
import time
import numpy as np
import pandas as pd
from sklearn.metrics import log_loss
from sklearn.preprocessing import scale
from sklearn.decomposition import pca
import fancyimpute
from sklearn.preprocessing import StandardScaler
import xgbfir
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 12, 4... | pd.read_csv(train_client_path, header=0) | pandas.read_csv |
import os
from pathlib import Path
import joblib
import pandas as pd
import numpy as np
from multiprocessing import Pool
from collections import defaultdict
import functools
import re
import sys
sys.path.insert(0, './code')
from utils import DataLogger # noqa: E402
class DataNotFoundException(Exception):
pa... | pd.concat(feats + feat_group, axis=1) | pandas.concat |
import contextlib
import json
import gzip
import io
import logging
import os.path
import pickle
import random
import shutil
import sys
import tempfile
import traceback
import unittest
import pandas
COMMON_PRIMITIVES_DIR = os.path.join(os.path.dirname(__file__), 'common-primitives')
# NOTE: This insertion should appea... | pandas.read_csv(scores_path) | pandas.read_csv |
import json
import math
import os
import random
import sys
import time
import warnings
from functools import reduce
from itertools import combinations, product
from operator import add
from typing import List, Sequence, Union
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pretty_errors
i... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
import os
import logging
import argparse
import numpy as np
from io import StringIO
import pandas as pd
from model.vanilla import classification_model as cm
from sklearn.metrics import classification_report as class_report
from data_utils.testset import load_test_set
from data_uti... | pd.DataFrame(report) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
OVERVIEW: DEBRIS THICKNESS ESTIMATES BASED ON DEM DIFFERENCING BANDS
Objective: derive debris thickness using an iterative approach that solves for when the modeled melt rate agrees with
the DEM differencing
If using these methods, cite the following paper:
<NAM... | pd.to_datetime(ds_lr.time.values) | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 25 15:50:20 2019
work flow for ZWD and PW retreival after python copy_gipsyx_post_from_geo.py:
1)save_PPP_field_unselected_data_and_errors(field='ZWD')
2)select_PPP_field_thresh_and_combine_save_all(field='ZWD')
3)use mean_ZWD_over_sound_... | pd.pivot_table(cnt, index='year', columns='month') | pandas.pivot_table |
#
# Copyright 2016 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... | DataFrame(index=ix, columns=[1, 2]) | pandas.DataFrame |
"""Provide ground truth."""
import logging
import os
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from tqdm import tqdm
logger = logging.getLogger(__name__)
def provide_ground_truth(main_dir, date, xml):
ind = xml.find('T')
time = xml[ind+1:ind+7]
overpass_time = datet... | pd.read_csv('../data/ground_truth/' + file, sep=',',index_col=0) | pandas.read_csv |
from matplotlib.pyplot import title
import requests
import json
import pandas as pd
import mplfinance as mpl
def plot_candlestick_graph(df):
df.date = | pd.to_datetime(df.date) | pandas.to_datetime |
import os
import numpy as np
import pytest
from pandas.compat import is_platform_little_endian
import pandas as pd
from pandas import DataFrame, HDFStore, Series, _testing as tm, read_hdf
from pandas.tests.io.pytables.common import (
_maybe_remove,
ensure_clean_path,
ensure_clean_store,
tables,
)
fr... | HDFStore(path, mode="a") | pandas.HDFStore |
from pandas._testing import assert_series_equal, assert_frame_equal
import pandas as pd
def test_types_assert_series_equal() -> None:
s1 = pd.Series([0, 1, 1, 0])
s2 = pd.Series([0, 1, 1, 0])
assert_series_equal(left=s1, right=s2)
assert_series_equal(s1, s2, check_freq=False, check_categorical=True, ... | assert_frame_equal(df1, df2) | pandas._testing.assert_frame_equal |
import pandas as pd
from pathlib import Path
from pandarallel import pandarallel
from functools import partial
from .utils import LookupTable
from .language_model import SRILM
pandarallel.initialize(verbose=0)
class Corpus:
def __init__(self, root):
self.root = Path(root)
def load_data_frame(self, ... | pd.merge(ali, cls, how="left", on="classlabel") | pandas.merge |
import datetime
import glob
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import csv
from matplotlib.dates import num2date, date2num
from mplfinance.original_flavor import candlestick_ochl
import sqlalchemy
from sqlalchemy import MetaData, Table, Column, Integer, String, Float, DateTi... | pd.read_hdf(path + hdf_file, 'table') | pandas.read_hdf |
"""Tools for generating and forecasting with ensembles of models."""
import datetime
import numpy as np
import pandas as pd
import json
from autots.models.base import PredictionObject
def BestNEnsemble(
ensemble_params,
forecasts_list,
forecasts,
lower_forecasts,
upper_forecasts,
forecasts_run... | pd.Series() | pandas.Series |
# %%
import pandas as pd
import numpy as np
import pathlib
import matplotlib
import matplotlib.pyplot as plt
from our_plot_config import derived_dir, fig_dir, raw_dir, setplotstyle
# Call function that sets the plot style
setplotstyle()
# %%
# Input file
f_betas = derived_dir / '13f_sp500_unfiltered.parquet'
f_scrap... | pd.read_parquet(f_betas) | pandas.read_parquet |
import collections
import copy
import hashlib
import json
import os
import pickle
import pandas as pd
import random
import time
from collections import defaultdict
from os.path import join
from shutil import rmtree
import numpy as np
import torch
import yaml
from data_helper import Task
dir_path = os.path.dirname(os.... | pd.DataFrame.from_dict(data=obj) | pandas.DataFrame.from_dict |
"""Authors: Salah&Yassir"""
import functools
import numpy as np
import pandas as pd
import pickle as pk
import ABONO as abono
# dir xs list dial les colonnes li bghit tapliqui 3lihom
xs = ['eeg_{i}'.format(i=i) for i in range(0, 2000)]
# defini fonction:
def f(objs):
s = 0
for x in xs:
v = objs[x]
... | pd.DataFrame(rslt[0]) | pandas.DataFrame |
import os
import shutil
import time
import numpy as np
import pandas as pd
from jina import Document, DocumentArray, Flow
from sklearn.datasets import make_blobs
from sklearn.model_selection import train_test_split
from executor.executor import AnnLiteIndexer
Nq = 1
D = 128
top_k = 10
R = 5
n_cells = 64
n_subvectors... | pd.DataFrame({'results': results_current}) | pandas.DataFrame |
# License: Apache-2.0
import databricks.koalas as ks
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from pyspark.ml.classification import RandomForestClassifier as RFCSpark
from xgboost import XGBClassifier
from gators.feature_selection.select_from_model import Selec... | assert_frame_equal(X_new, X_expected) | pandas.testing.assert_frame_equal |
"""
COLLECTION OF FUNCTIONS FOR PROTEIN SEQUENCE FEATURE CONSTRUCTION & BLAST PREDICTION
Created on Thu Nov 9 13:29:44 2017
@author: dimiboeckaerts
Some of the code below is taken from the following Github repo:
https://github.com/Superzchen/iFeature
(Chen et al., 2018. Bioinformatics.)
"""
# IMPORT LIBRARIES
# --... | pd.DataFrame.from_dict(codontable) | pandas.DataFrame.from_dict |
import os
import json
import datetime
import argparse
import pandas as pd
import config, utils
import inf_outf
def bitcoin_data():
"Gets the OHLCV for Bitcoin over the period"
pair = "xbtusd_bitmex" # the pair we want to look at
url = "https://web3api.io/api/v2/market/ohlcv/"+pai... | pd.DataFrame(payload["data"]["bitmex"], columns=payload["metadata"]["columns"]) | pandas.DataFrame |
from operator import mul
import sys
import matplotlib.pyplot as plt
import numpy as np
from holoviews import opts
from scipy.signal.ltisys import dfreqresp
from scipy.spatial import Voronoi
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from skl... | pd.DataFrame(data = principalComponents, columns = ['principal component 1', 'principal component 2']) | pandas.DataFrame |
import pandas as pd
import numpy as np
from pyshop.shopcore.shop_api import get_attribute_value, get_time_resolution, set_attribute
class ShopApiMock:
mock_dict = {
'GetIntValue': 11,
'GetIntArray': [11, 22],
'GetDoubleValue': 1.1,
'GetDoubleArray': [1.1, 2.2],
'GetStringV... | pd.Timestamp(self.shop_api['GetTxySeriesStartTime']) | pandas.Timestamp |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/2/2 23:26
Desc: 东方财富网-行情首页-沪深京 A 股
"""
import requests
import pandas as pd
def stock_zh_a_spot_em() -> pd.DataFrame:
"""
东方财富网-沪深京 A 股-实时行情
http://quote.eastmoney.com/center/gridlist.html#hs_a_board
:return: 实时行情
:rtype: pandas.DataFrame
... | o_numeric(temp_df["收盘"]) | pandas.to_numeric |
"""Tests suite for Period handling.
Parts derived from scikits.timeseries code, original authors:
- <NAME> & <NAME>
- pierregm_at_uga_dot_edu - mattknow_ca_at_hotmail_dot_com
"""
from unittest import TestCase
from datetime import datetime, timedelta
from numpy.ma.testutils import assert_equal
from pandas.tseries.p... | Period('1Q2005') | pandas.tseries.period.Period |
import numpy as np
import pytest
from pandas._libs import iNaT
from pandas.core.dtypes.common import (
is_datetime64tz_dtype,
needs_i8_conversion,
)
import pandas as pd
from pandas import NumericIndex
import pandas._testing as tm
from pandas.tests.base.common import allow_na_ops
def test_unique(index_or_se... | allow_na_ops(obj) | pandas.tests.base.common.allow_na_ops |
# -*- coding: utf-8 -*-
"""
Created on Fri May 21 14:50:55 2021
@author: Oswin
"""
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import itertools
from sklearn.metrics import accuracy_score, recall_score, precision_score
from sklearn.compose import ColumnTransformer
from sklearn.pipeline impor... | pd.DataFrame() | pandas.DataFrame |
import os
import random
import pyperclip
import string
from datetime import datetime
import pandas as pd
def generator():
length = 16
password = []
punctuation = "-+?_!&"
password.append(random.choice(string.ascii_lowercase))
password.append(random.choice(string.ascii_uppercase))
password.ap... | pd.read_csv("data.csv") | pandas.read_csv |
__author__ = "<NAME>"
__license__ = "GPL"
__credits__ = ["<NAME>", "<NAME>", "<NAME>",
"<NAME>"]
__maintainer__ = "Md. <NAME>"
__email__ = "<EMAIL>"
__status__ = "Prototype"
# Importing libraries
import os
import glob
import pandas as pd
import numpy as np
from datetime import datetime
import aggregator... | pd.to_timedelta(raw_dataset['PreOp Time']) | pandas.to_timedelta |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Patch
from pandas import Timestamp
##### DATA #####
data = {'Task': {0: 'TSK M',
1: 'TSK N',
2: 'TSK L',
3: 'TSK K',
4: 'TSK J',
5: ... | Timestamp('2022-02-19 00:00:00') | pandas.Timestamp |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 15 15:08:28 2019
@author: binbin
"""
## import some libriaries ##
import pandas as pd
import seaborn as sns
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.prepr... | pd.DataFrame() | pandas.DataFrame |
from warnings import catch_warnings
import numpy as np
import pytest
from pandas import DataFrame, MultiIndex, Series
from pandas.util import testing as tm
@pytest.fixture
def single_level_multiindex():
"""single level MultiIndex"""
return MultiIndex(levels=[['foo', 'bar', 'baz', 'qux']],
... | DataFrame(df, columns=index) | pandas.DataFrame |
def DeleteDuplicatedElementFromList(list):
resultList = []
for item in list:
if not item in resultList and str(item)!="nan":
resultList.append(item)
return resultList
import pandas as pd
#coding:utf-8
import matplotlib.pyplot as plt
import numpy
plt.rcParams['font.sans-serif']=['SimHei']... | pd.DataFrame({u'未逾期客户':Y3,u'逾期客户':Y4}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""coronasense_analysis.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1SptFyUf_Y4y1APZxBY-ZteB3q3mcQkPE
"""
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import matplotlib
from sklearn.linea... | pd.Timedelta('0.5 day') | pandas.Timedelta |
#!/usr/bin/env python
# coding: utf-8
# # import required library
# In[1]:
# Import numpy, pandas for data manipulation
import numpy as np
import pandas as pd
# Import matplotlib, seaborn for visualization
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
# I... | pd.concat([actual_df,predicted_df],axis=1) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 9 10:50:38 2021
@author: github.com/sahandv
take ideas from:
https://towardsdatascience.com/multi-class-text-classification-with-lstm-1590bee1bd17
https://github.com/susanli2016/NLP-with-Python/blob/master/Multi-Class%20Text%20Classificati... | pd.read_csv(label_address) | pandas.read_csv |
import pandas._libs.tslibs.nattype
from sklearn import linear_model
from sklearn.metrics import r2_score
import numpy as np
import pandas as pd
from math import log, isnan
from statistics import stdev
from numpy import repeat
from strategy import *
def calc_features(ivv_hist, bonds_hist, n_vol):
# Takes in:
# ... | pd.DataFrame([1 / 12, 2 / 12, 3 / 12, 6 / 12, 1, 2]) | pandas.DataFrame |
from typing import List
import matplotlib.pyplot as plt
import numbers
import numpy as np
import pandas as pd
from scipy import stats
from sklearn.metrics import auc, plot_roc_curve, roc_curve, RocCurveDisplay
from sklearn.model_selection import KFold, LeaveOneOut, GroupKFold, LeaveOneGroupOut
from sklearn.preprocessin... | pd.DataFrame(X[:, x_chart_indices], columns=x_chart) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 20 10:52:09 2019
@author: <NAME>
"""
import requests, smtplib, os, datetime
import pandas as pd
from bs4 import *
import urllib.request as ur
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from matplotlib import pyplot as... | pd.to_datetime(destination['departd']) | pandas.to_datetime |
"""Tests suite for Period handling.
Parts derived from scikits.timeseries code, original authors:
- <NAME> & <NAME>
- pierregm_at_uga_dot_edu - mattknow_ca_at_hotmail_dot_com
"""
from unittest import TestCase
from datetime import datetime, timedelta
from numpy.ma.testutils import assert_equal
from pandas.tseries.p... | PeriodIndex(start=start, end=end_intv) | pandas.tseries.period.PeriodIndex |
'''
/*******************************************************************************
* Copyright 2016-2019 Exactpro (Exactpro Systems Limited)
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
... | pandas.to_datetime(frame['Created_tr']) | pandas.to_datetime |
from __future__ import print_function
import argparse
import math
import numpy as np
import pandas as pd
import tensorflow.compat.v1 as tf
from cnvrg import Experiment
from sklearn.metrics import mean_squared_error
tf.disable_v2_behavior()
import psutil
import time
tic = time.time()
parser = argparse.ArgumentParser... | pd.read_csv(test_file) | pandas.read_csv |
# Copyright 2019 Toyota Research Institute. All rights reserved.
"""Unit tests related to batch validation"""
import json
import os
import unittest
import pandas as pd
import numpy as np
import boto3
from botocore.exceptions import NoRegionError, NoCredentialsError
from monty.tempfile import ScratchDir
from beep.val... | pd.read_csv(path, index_col=0) | pandas.read_csv |
import os
import numpy as np
import random
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
def read_data(data_dir, symbol, dates):
df = pd.DataFrame(index=dates)
new_df = pd.read_csv(data_dir+ "hkex_" + symbol +".csv", index_col... | pd.read_csv(sentiment_path,index_col='dates',parse_dates=['dates'], na_values=['nan']) | pandas.read_csv |
'''
the 'load' module provides common access to the 'sim' and 'hsr' modules, including
automated batch routines and plotting. all parsing logic for sim files/reports should
be accomplished in 'sim.py' or 'hsr.py'; this is mainly an API for script-running.
'''
import os
import xlwings as xw
import shutil
... | pd.DataFrame(dflist) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 29 12:05:23 2020
@author: haukeh
"""
# import tkinter as tk
# from tkinter import filedialog
import numpy as np
import pandas as pd
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
# root = tk... | pd.DataFrame({'fuel_name':['BFI','BFX','BMI','BMX','COI','COX','GOX','HFI','NGI','NGX','OII','OIX','URI','WSX'],'fuel_abr':['biofuel','biofuel','biomass','biomass','coal','coal','geo','oil','gas','gas','oil','oil','nuclear','waste']}, columns = ['fuel_name','fuel_abr']) | pandas.DataFrame |
#! /usr/bin/env python3.6
'''
Author : Coslate
Date : 2018/07/07
Description :
This program will examine the input excel whether have the job number, and
concatenate it to the total check excel output. It will also highlight the
one that has repeated job number in multipl... | pd.read_excel(org_checked_file) | pandas.read_excel |
from sklearn.svm import SVR
from sklearn.dummy import DummyRegressor
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import r2_score
from sklearn import model_selection
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import numpy as ... | pd.read_csv(WALKLETS_EMBEDDINGS_256, sep=',') | pandas.read_csv |
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
lreshape,
melt,
wide_to_long,
)
import pandas._testing as tm
class TestMelt:
def setup_method(self, method):
self.df = tm.makeTimeDataFrame()[:10]
self.df["id1"] = (self.df["A"] > 0).astype(np.int... | DataFrame.from_dict(wide_data) | pandas.DataFrame.from_dict |
"""
Routines for casting.
"""
from contextlib import suppress
from datetime import date, datetime, timedelta
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
Optional,
Sequence,
Set,
Sized,
Tuple,
Type,
Union,
)
import numpy as np
from pandas._libs import lib, tslib, t... | is_scalar(result) | pandas.core.dtypes.common.is_scalar |
import argparse
import sys
import time
sys.path.insert(0, 'catboost/catboost/python-package')
import ml_dataset_loader.datasets as data_loader
import numpy as np
import pandas as pd
import xgboost as xgb
from sklearn.metrics import mean_squared_error, accuracy_score
from sklearn.model_selection import train_test_split... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
import datetime
import numpy as np
import os
import pandas as pd
import pandas.testing as tm
from fastparquet import ParquetFile
from fastparquet import write, parquet_thrift
from fastparquet import writer, encoding
from pandas.testing import assert_frame_equal
from pandas.api.types import Categ... | pd.testing.assert_frame_equal(df, out, check_dtype=False) | pandas.testing.assert_frame_equal |
"""Base Constraint class."""
import copy
import importlib
import inspect
import logging
import pandas as pd
from copulas.multivariate.gaussian import GaussianMultivariate
from rdt import HyperTransformer
from sdv.constraints.errors import MissingConstraintColumnError
LOGGER = logging.getLogger(__name__)
def _get_... | pd.concat(all_sampled_rows, ignore_index=True) | pandas.concat |
import pandas as pd
import numpy as np
import copy
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV
from sklearn.feature_selection import mutual_info_classif, SelectKBest
import matplotlib.pyplot as plt
from sklearn import svm
from sk... | pd.read_csv(f"musikreviews_balanced_authors.csv", sep=',', encoding="utf-8") | pandas.read_csv |
from polo2 import PoloDb
import pandas as pd
import numpy as np
import sqlite3
class Corpus(object):
def __init__(self, config):
corpus_db_file = self.config.generate_corpus_db_file_path()
self.corpus = PoloDb(corpus_db_file)
class Elements(object):
def __init__(self, config, trial_name='tri... | pd.read_sql_query(sql, self.model.conn, params=(topic_id,)) | pandas.read_sql_query |
"""
Tax-Calculator tax-filing-unit Records class.
"""
# CODING-STYLE CHECKS:
# pycodestyle records.py
# pylint --disable=locally-disabled records.py
import os
import json
import six
import numpy as np
import pandas as pd
from taxcalc.growfactors import GrowFactors
from taxcalc.utils import read_egg_csv, read_egg_json
... | pd.read_csv(benefits_path) | pandas.read_csv |
import re
import numpy as np
import pandas as pd
import pytest
from woodwork import DataTable
from woodwork.logical_types import (
URL,
Boolean,
Categorical,
CountryCode,
Datetime,
Double,
Filepath,
FullName,
Integer,
IPAddress,
LatLong,
NaturalLanguage,
Ordinal,
... | pd.Series(['2020-01-01', '2020-01-02', '2020-01-03'], name=column_name) | pandas.Series |
import datetime as dt
from numpy import nan
from numpy.testing import assert_equal
from pandas import DataFrame, Timestamp
from pandas.testing import assert_frame_equal
from pymove import MoveDataFrame, datetime
from pymove.utils.constants import (
COUNT,
LOCAL_LABEL,
MAX,
MEAN,
MIN,
PREV_LOCA... | assert_frame_equal(df, expected) | pandas.testing.assert_frame_equal |
import os
import requests
from time import sleep, time
import pandas as pd
from polygon import RESTClient
from dotenv import load_dotenv, find_dotenv
from FileOps import FileReader, FileWriter
from TimeMachine import TimeTraveller
from Constants import PathFinder
import Constants as C
class MarketData:
... | pd.DataFrame() | pandas.DataFrame |
"""
Tests for zipline/utils/pandas_utils.py
"""
from unittest import skipIf
import pandas as pd
from zipline.testing import parameter_space, ZiplineTestCase
from zipline.testing.predicates import assert_equal
from zipline.utils.pandas_utils import (
categorical_df_concat,
nearest_unequal_elements,
new_pan... | pd.to_datetime(['2014', '2014']) | pandas.to_datetime |
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.keys import Keys
import requests
import time
from datetime import datetime
import pandas as pd
from urllib import parse
from config import ENV_VARIABLE
from os.path import getsize
fold_path = ... | pd.DataFrame() | pandas.DataFrame |
from kfp.v2.dsl import (Artifact,
Dataset,
Input,
Model,
Output,
Metrics,
ClassificationMetrics)
def get_ml_op(
start_date : str,
pre_processed_dataset : Input[Dataset... | pd.DataFrame() | pandas.DataFrame |
# import necessary libraries
import pandas as pd
import os
import matplotlib.pyplot as plt
from itertools import combinations
from collections import Counter
def get_city(address):
return address.split(',')[1]
def get_state(address):
return address.split(',')[2].split(' ')[1]
# plt.style.use('fivethirtyeigh... | pd.to_numeric(all_data['Price Each']) | pandas.to_numeric |
"""
This script creates a boolean mask based on rules
1. is it boreal forest zone
2. In 2000, was there sufficent forest
"""
#==============================================================================
__title__ = "FRI calculator for the other datasets"
__author__ = "<NAME>"
__version__ = "v1.0(21.08.2019)"
__emai... | pd.to_datetime(tm) | pandas.to_datetime |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | pd.DataFrame(data) | pandas.DataFrame |
import pandas as pd
import numpy as np
import sys
import argparse
import time
from scipy.special import gamma
import os
import pickle
import torch
import NMF_functions
from ARD_NMF import ARD_NMF
import pyarrow.feather as feather
from ARD_NMF import run_method_engine
import torch.nn as nn
import torch.multiprocessing ... | pd.read_csv(args.data, sep='\t', header=0, index_col=0) | pandas.read_csv |
# coding=utf-8
from __future__ import absolute_import, print_function
import os
import pandas as pd
from suanpan.app.arguments import Csv
from suanpan.app import app
from suanpan.storage import storage
from suanpan.utils import image
from suanpan import path
from text.opencv_dnn_detect import angle_detect
from utils.f... | pd.DataFrame(outputData) | pandas.DataFrame |
import pandas as pd
import numpy as np
from tqdm import tqdm
import os
# import emoji
import gc
from utils.definitions import ROOT_DIR
from collections import OrderedDict
from utils.datareader import Datareader
def check_conditions( df, mean, std, error=(1.5,1.5)):
"""
checks if the dataframe given is near has... | pd.read_csv(ROOT_DIR+"/data/original/train_playlists.csv", delimiter='\t') | pandas.read_csv |
import pandas as pd
import numpy as np
from pandas import Int8Dtype
@pd.api.extensions.register_extension_dtype
class Bool(Int8Dtype):
name = "Bool"
# TODO: overload dtype Int8 name...
x = pd.Series([True, False, False, np.nan] * 100000, dtype="Bool")
print(x.memory_usage(deep=True), x.dtype)
z = pd.Series... | pd.Series([True, False, False, False] * 100000, dtype="bool") | pandas.Series |
import os
import pandas as pd
import numpy as np
import logging
import wget
import time
import pickle
from src.features import preset
from src.features import featurizer
from src.data.utils import LOG
from matminer.data_retrieval.retrieve_MP import MPDataRetrieval
from tqdm import tqdm
from pathlib import Path
from s... | pd.DataFrame({}) | pandas.DataFrame |
import time
import numpy as np
import pandas as pd
def add_new_category(x):
"""
Aimed at 'trafficSource.keyword' to tidy things up a little
"""
x = str(x).lower()
if x == 'nan':
return 'nan'
x = ''.join(x.split())
if r'provided' in x:
return 'not_provided'
if r'youtube... | pd.DatetimeIndex(merged_df['formated_date']) | pandas.DatetimeIndex |
from django.shortcuts import render
from django.http import HttpResponse
from datetime import datetime
import psycopg2
import math
import pandas as pd
from openpyxl import Workbook
import csv
import random
def psql_pdc(query):
#credenciales PostgreSQL produccion
connP_P = {
'host' : '10.150.1.74',
'p... | pd.DataFrame(anwr) | pandas.DataFrame |
# Authors: <NAME> <<EMAIL>>
# License: BSD 3 clause
from typing import List, Union
import pandas as pd
from feature_engine.encoding.base_encoder import BaseCategoricalTransformer
from feature_engine.variable_manipulation import _check_input_parameter_variables
class MeanEncoder(BaseCategoricalTransformer):
"""... | pd.Series(y) | pandas.Series |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Importing the required modules
import pandas as pd
import numpy as np
import time
import sys
import warnings
from collections import defaultdict
from operator import itemgetter
# To make sure warnings are filtered out
warnings.filterwarnings("ignore")
col_name = ['user_id'... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 15 17:14:55 2021
@author: sergiomarconi
"""
import numpy as np
import pandas as pd
import pickle
from sklearn.ensemble import RandomForestClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from sklearn.preproce... | pd.merge(X_res, ave_coords, how='left', left_on=['latitude', 'longitude'], right_on = ['latitude', 'longitude']) | pandas.merge |
#!/usr/bin/env python3
'''
Script to update all the data of a real estate agent database with new data.
Can be run as a script directly or via the use of the function 'update_real_estate_data'
'''
import os
import numpy
import pandas
from load_and_display_database import export_data_frame_to_excel, HOUSE_DATA_FILE_NAM... | pandas.read_pickle(path) | pandas.read_pickle |
import pandas as pd
import datetime
import dateutil.parser
import Utils
#
# given a synthea object, covert it to it's equivalent omop objects
#
class SyntheaToOmop6:
#
# Check the model matches
#
def __init__(self, model_schema, utils):
self.model_schema = model_schema
self.utils = utils
... | pd.merge(df, visitmap, left_on='ENCOUNTER', right_on='synthea_encounter_id', how='left') | pandas.merge |
# 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.DataFrame(data, index=["count", "mean", "std", "min"]) | pandas.DataFrame |
# -*- 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.decode(encoded) | pandas._libs.json.decode |
# -*- coding: utf-8 -*-
# author: <NAME>
# Email: <EMAIL>
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import generators
from __future__ import with_statement
import re
from bs4 import BeautifulSoup... | pd.DataFrame() | pandas.DataFrame |
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