prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#
# Copyright (c) 2021 salesforce.com, inc.
# All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
# For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
#
import logging
import os
import requests
from tqdm import tqdm
import pandas as pd
from ts_datasets... | pd.DataFrame({"trainval": sequence.index < sequence.index[ntrain]}, index=sequence.index) | pandas.DataFrame |
"""
Fractional differentiation is a technique to make a time series stationary but also
retain as much memory as possible. This is done by differencing by a positive real
number. Fractionally differenced series can be used as a feature in machine learning
process.
"""
import numpy as np
import pandas as pd
class Fr... | pd.Series(index=series.index) | pandas.Series |
import numpy
import matplotlib.pyplot as plt
import tellurium as te
from rrplugins import Plugin
auto = Plugin("tel_auto2000")
from te_bifurcation import model2te, run_bf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import ScalarFormatter
sf = ScalarFormatter()
sf.set_sc... | pd.DataFrame(binned_Rts) | pandas.DataFrame |
# coding: utf-8
# ---
#
# _You are currently looking at **version 1.2** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-social-network-analysis/resources/yPcBs) course reso... | pd.isnull(df['Future Connection']) | pandas.isnull |
"""
XGBoost regressor for construction machine price prediction
"""
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import xgboost as xgb
from sklearn.model_selection import train_test_split
from pandas_profiling import ProfileReport
pd.set_option('display.max_rows', 500)
plt.style.use('ggplo... | pd.api.types.is_string_dtype(content) | pandas.api.types.is_string_dtype |
import requests
import pandas as pd
import numpy as np
import pathlib
import zipline as zl
import logbook
import datetime
import os
from azul import price_manager_registry, BasePriceManager
log = logbook.Logger('PolygonPriceManager')
@price_manager_registry.register('polygon')
class PolygonPriceManager(BasePriceMana... | pd.DataFrame() | pandas.DataFrame |
# --------------
import pandas as pd
from sklearn import preprocessing
#path : File path
# Code starts here
dataset = | pd.read_csv(path) | pandas.read_csv |
#======================================================
# Model Utility Functions
#======================================================
'''
Info: Utility functions for model building.
Version: 2.0
Author: <NAME>
Created: Saturday, 13 April 2019
'''
# Import modules
import os
import uuid
import copy
im... | pd.concat(dfs, axis=0) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Wed May 30 14:47:20 2018
@author: Greydon
"""
import os
import re
import numpy as np
import pandas as pd
from scipy.signal import welch, hanning, butter, lfilter, resample
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
import matplotlib.ticker as... | pd.DataFrame([]) | pandas.DataFrame |
import mongomanager
import intriniowrapper
import logging
import inspect
import copy
import pandas as pd
import commonqueries
from datetime import datetime
from dateutil.relativedelta import relativedelta
import os
import configwrapper
class IntrinioUpdater():
def __init__(self,config_file,proxies=None,timeout=300,ma... | pd.DataFrame() | pandas.DataFrame |
import unittest
import numpy as np
import pandas as pd
import scipy.stats as st
from ..analysis import determine_analysis_type
from ..analysis.exc import NoDataError
from ..data import Vector, Categorical
class MyTestCase(unittest.TestCase):
def test_small_float_array(self):
np.random.seed(123456789)
... | pd.Series(['a']) | pandas.Series |
# GetData.py
# <NAME>
# 29 November 2019
#
# This program takes as input an output filename and a starting date and
# returns song/artist names to the specified file.
import csv
import sys
import time
import pandas as pd
from selenium import webdriver
from datetime import datetime, timedelta
# get the list of songs/... | pd.read_csv(fname) | pandas.read_csv |
#!/usr/bin/env python
"""Script for generating figures of catalog statistics. Run `QCreport.py -h`
for command line usage.
"""
import os
import sys
import errno
import argparse
from datetime import date, datetime
from math import sqrt, radians, cos
import markdown
import numpy as np
import pandas as pd
import cartopy.... | pd.Timedelta(seconds=16) | pandas.Timedelta |
import os
try:
import fool
except:
print("缺少fool工具")
import math
import pandas as pd
import numpy as np
import random
import tensorflow as tf
import re
np.random.seed(1)
def add2vocab(path,word):
vocab_data=pd.read_csv(path)
idx_to_chars=list(vocab_data['vocabulary'])+[word]
df_data = pd... | pd.DataFrame(datas) | pandas.DataFrame |
"""
Функции и классы для проведения WoE-преобразований
"""
import math
import warnings
import numpy as np
import pandas as pd
import sklearn as sk
from IPython.display import display
from matplotlib import pyplot as plt
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.model_selection import trai... | pd.concat([X_woe_num, X_woe_cat, X_woe_oth]) | pandas.concat |
import unittest
import pandas as pd
from pandas.core.dtypes.common import is_numeric_dtype, is_string_dtype
from pandas.util.testing import assert_frame_equal
from shift_detector.precalculations.store import InsufficientDataError, Store
from shift_detector.utils.column_management import ColumnType
class TestStore(u... | pd.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pickle
met_df = pd.read_csv('../datasets/cleaned_dataset.csv', index_col=0)
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
le = LabelEncoder()
ohe = OneHotEncoder(sparse=False)
val = le.fit_transform(met_df['rest_type']).res... | pd.concat([node,res], axis=1) | pandas.concat |
import builtins
from io import StringIO
from itertools import product
from string import ascii_lowercase
import numpy as np
import pytest
from pandas.errors import UnsupportedFunctionCall
import pandas as pd
from pandas import (
DataFrame, Index, MultiIndex, Series, Timestamp, date_range, isna)
import pandas.cor... | tm.assert_series_equal(result, expected) | pandas.util.testing.assert_series_equal |
# -*- coding: utf-8 -*-
"""Interface for flopy's implementation for MODFLOW."""
__all__ = ["MfSfrNetwork"]
import pickle
from itertools import combinations, zip_longest
from textwrap import dedent
import geopandas
import numpy as np
import pandas as pd
from shapely import wkt
from shapely.geometry import LineString,... | pd.Series(dtype=int) | pandas.Series |
import numpy as np
import random
import pandas as pd
import korbinian
import sys
##########parameters#############
seq_len = 10000
number_seq = 50
number_mutations = 2000
subset_num = 12
ident = 100 * (seq_len - number_mutations) / seq_len
List_rand_TM = r"D:\Databases\summaries\01\List01_rand\List01_rand_TM.csv"
#L... | pd.Series.from_csv(List_rand_TM, sep="\t") | pandas.Series.from_csv |
"""Legacy feature computation from depart."""
import itertools
import re
import numpy as np
import pandas as pd
from Bio.SeqUtils.ProtParam import ProteinAnalysis
from Bio.SeqUtils.ProtParamData import kd
from pyteomics import parser
from sklearn.preprocessing import PolynomialFeatures
from xirt import sequences
d... | pd.DataFrame() | pandas.DataFrame |
import unittest
from yauber_algo.errors import *
class IIFTestCase(unittest.TestCase):
def test_iif(self):
import yauber_algo.sanitychecks as sc
from numpy import array, nan, inf
import os
import sys
import pandas as pd
import numpy as np
from yauber_algo.... | pd.Series(arr_false) | pandas.Series |
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
#
# Read in data frame
#
df = | pd.read_csv("mcs_interestrate_change.csv", skiprows=1) | pandas.read_csv |
import argparse
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
parser = argparse.ArgumentParser(description="TF_diversity_rw")
parser.add_argument(
"file_names",
type=str,
help="Name of folder and filenames for the promoters extracted",
)
parser.add_... | pd.merge(select_genes, df, on="AGI", how="left") | pandas.merge |
"""Script of my solution to DrivenData Modeling Women's Health Care Decisions
Use this script in the following way:
python solution.py <name-of-submission>
Argument is optional, the script will assign default name.
"""
from __future__ import division
import sys
import pdb
import numpy as np
import pandas as pd
fr... | pd.read_csv('data/processed_train.csv') | 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(data) | pandas.compat.StringIO |
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 17 17:28:40 2019
@author: Administrator
"""
import pdblp
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
con = pdblp.BCon(debug=False, port=8194, timeout=5000)
con.start()
from datetime import date
start = '... | pd.concat( [swap_spread_de, swap_spread_uk, swap_spread_jp, swap_spread_ch, swap_spread_tr, swap_spread_br, swap_spread_ru, swap_spread_sa ],axis=1) | pandas.concat |
import pandas as pd
def full_describe(series: pd.Series, verbose=True):
"""
Calculates a pandas describe of series, plus a count of unique and NaN
:param verbose: printing some other info
:param series: Pandas Series
:return: df with stats as cols
"""
stats_df = | pd.DataFrame() | pandas.DataFrame |
# Pre-Process Text Data
# Import Modules
import os
import pandas as pd
import numpy as np
import csv
import matplotlib.pyplot as plt
import nltk
import string
import re
import datetime
from tqdm import tqdm
from textblob import TextBlob
from nltk.corpus import wordnet as wn
from nltk.stem.wordnet import WordNetLemmati... | pd.concat([posts,toplevelcomments,subcomments]) | pandas.concat |
"""
Test output formatting for Series/DataFrame, including to_string & reprs
"""
from datetime import datetime
from io import StringIO
import itertools
from operator import methodcaller
import os
from pathlib import Path
import re
from shutil import get_terminal_size
import sys
import textwrap
import dateutil
import ... | option_context("display.precision", 5) | pandas.option_context |
import pytrec_eval
from repro_eval.Evaluator import RplEvaluator
from repro_eval.util import trim
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
sns.set()
sns.set_style('whitegrid')
palette = sns.color_palette("GnBu_d")
sns.set_palette(palette)
colors = sns.color_palette()
ORIG_B = './d... | pd.DataFrame(df_content, index=['tf_1', 'tf_2', 'tf_3', 'tf_4', 'tf_5']) | pandas.DataFrame |
import os, datetime, pymongo, configparser
import pandas as pd
from bson import json_util
global_config = None
global_client = None
global_stocklist = None
def getConfig(root_path):
global global_config
if global_config is None:
#print("initial Config...")
global_config = configparser.ConfigPa... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sun May 3 18:37:20 2020
@author: Blackr
The following is a script I wrote to automatically find the tafel data
a .csv file with the appropriate I, Ewe, and time columns
time = s
I = mA
E = V
This is mostly used as a coding excerise for post processing. Re-evaluate ... | pd.Series(unique) | pandas.Series |
# pylint: disable=redefined-outer-name
import filecmp
from io import StringIO
from pathlib import Path
from tempfile import TemporaryDirectory
import pandas as pd
import pytest
from courier.config import get_config
from courier.elements import CourierIssue, IssueStatistics, export_articles
from courier.elements.expor... | pd.DataFrame(stats) | pandas.DataFrame |
"""
To test the quality of the estimators, we generate data
both from a semilinear Choo and Siow model
and from a semilinear nested logit model.
We use both the Poisson estimator and the minimum-distance estimator
on the former model, and only the minimum-distance estimator on the latter.
"""
from typing import List... | pd.read_csv(choo_siow_results_file) | pandas.read_csv |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2020 qizai <<EMAIL>>
#
# Distributed under terms of the MIT license.
"""
This script will search for all lp{0, 1, ..., N}_{right, left, middle}_100_region.csv
file. Each line in the csv file is a complex, with at least two fragment interse... | pd.DataFrame() | pandas.DataFrame |
import io
import textwrap
from collections import namedtuple
import numpy as np
import pandas as pd
import statsmodels.api as sm
from estimagic.config import EXAMPLE_DIR
from estimagic.visualization.estimation_table import _convert_model_to_series
from estimagic.visualization.estimation_table import _create_statistics... | ase(exp, res) | pandas.testing.assert_series_equal |
# Streamlit live coding script
import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import plotly.express as px
import plotly.graph_objects as go
df = pd.read_csv('src/data/marketing_campaign_cleaned.csv', index_col=[0])
st.title("Customer personality ana... | pd.DataFrame(d) | pandas.DataFrame |
import os
import pandas as pd
import datetime
import numpy as np
from talib import abstract
from .crawler import check_monthly_revenue
class Data():
def __init__(self):
self.date = datetime.datetime.now().date()
self.warrning = False
self.col2table = {}
t... | pd.to_numeric(s, errors='coerce') | pandas.to_numeric |
from bs4 import BeautifulSoup as BS
from selenium import webdriver
from functools import reduce
import pandas as pd
import time
import xport
import pandas as pd
def render_page(url):
driver = webdriver.Chrome('/Users/cp/Downloads/chromedriver')
driver.get(url)
time.sleep(3)
r = driver.p... | pd.merge(left, right, left_index=True, right_index=True) | pandas.merge |
import pytest
from datetime import datetime, timedelta
import pytz
import numpy as np
from pandas import (NaT, Index, Timestamp, Timedelta, Period,
DatetimeIndex, PeriodIndex,
TimedeltaIndex, Series, isna)
from pandas.util import testing as tm
from pandas._libs.tslib import iNa... | Timestamp('NaT') | pandas.Timestamp |
"""
Testing that functions from rpy work as expected
"""
import pandas as pd
import numpy as np
import unittest
import nose
import pandas.util.testing as tm
try:
import pandas.rpy.common as com
from rpy2.robjects import r
import rpy2.robjects as robj
except ImportError:
raise nose.SkipTest('R not inst... | com.load_data(name) | pandas.rpy.common.load_data |
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(args.smi) | pandas.read_csv |
import numpy as np
from datetime import timedelta
from distutils.version import LooseVersion
import pandas as pd
import pandas.util.testing as tm
from pandas import to_timedelta
from pandas.util.testing import assert_series_equal, assert_frame_equal
from pandas import (Series, Timedelta, DataFrame, Timestamp, Timedelt... | Timestamp('20130102') | pandas.Timestamp |
# -*- 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... | Categorical([], ["a", "b", "c"]) | pandas.Categorical |
""" 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,... | Timedelta(10.0, unit='d') | pandas.Timedelta |
#coding=utf-8
#键盘分析
#（1）分别读取csdn和yahoo数据库中的passwd
#（2）自定义了常见的14种键盘密码字符串
#（3）将从数据库中读取的passwd与定义的字符串进行子串匹配（忽略单个的字母和数字）
#（4）只选择相对高频的密码，生成保存频率最高的密码和对应频率的csv
import pandas as pd
import numpy as np
import csv
np.set_printoptions(suppress=True)
##############################################
#（1）读取数据
#########################... | pd.Series(yahoo_data['passwd'].values) | pandas.Series |
"""Functions for transofrmation of films and books datasets.
Functions
---------
get_books_ratings - transform books dataset
get_films_ratings - transform films dataset
generate_datasets - generate films and books datasets
"""
from typing import Set
import pandas as pd
from pathlib im... | pd.read_csv(location_2, sep='\t', low_memory=False) | pandas.read_csv |
import empyrical
import pandas as pd
def main(payload):
port_vals_df = _convert_port_vals_to_df(payload["portVals"])
# Calculates per data point returns
port_vals_returns = port_vals_df["value"].pct_change()
cum_returns = empyrical.cum_returns(port_vals_returns, starting_value=0)
# aggregate_retu... | pd.DataFrame.from_dict(port_vals, orient="columns") | pandas.DataFrame.from_dict |
import pandas as pd
from traja.dataset import dataset
def test_category_wise_sampling_few_categories():
data = list()
num_categories = 5
for category in range(num_categories):
for sequence in range(40 + int(category / 14)):
data.append([sequence, sequence, category])
df = pd.Dat... | pd.DataFrame(data, columns=['x', 'y', 'ID']) | pandas.DataFrame |
import pandas as pd
from bs4 import BeautifulSoup
import requests
import re
from tqdm import tqdm
def get_fund_holding(symbol):
url = 'http://finance.sina.com.cn/fund/quotes/{}/bc.shtml'.format(symbol)
html = requests.get(url)
bs = BeautifulSoup(html.content, features="lxml")
tbl = bs.find('tabl... | pd.concat(res, ignore_index=True) | pandas.concat |
import pandas as pd
from Datasets.utils import read_parquet, get_bib_info, clean
import json
import sys
import time
import csv
import random
from tqdm import tqdm
sys.path.append("Models/")
from Models import *
from position_rank import get_weights
def mask(text1, text2):
"""
a simple vectorization function
... | pd.DataFrame(all_scores_adjust) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
""" Computes broadband power, offset and slope of power spectrum
Based on selected epochs (e.g. ASCIIS) in the list of files a power spectrum
is computed. Based on this power spectrum the broadband power is calculated,
followed by the offset and slope using the FOOOF algo... | pd.DataFrame(mean_pxx[index,:,:]) | pandas.DataFrame |
# 均分321个区间，统计落入格区间的数目
import pandas as pd
import numpy as np
df = | pd.read_csv('submit-final.csv', index_col=0) | pandas.read_csv |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
"""
This file contains training and testing settings to be used in this benchmark,
mainly:
TRAIN_BASE_END: Base training end date common across all rounds
TRAIN_ROUNDS_ENDS: a set of dates denoting end of training period for each
... | pd.to_datetime(("2017-04-01", "2017-05-01")) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
This script can be used to convert a .mat simulation result file into a
.csv file with a subset of model variables, as defined in the "outputs" list.
The .csv file is saved in the same directory as the .mat file, and is based
on the case, climate, and scenario.
"""
from buildingspy.io.out... | pd.DataFrame(index=time,data=values,columns=[variable]) | pandas.DataFrame |
"""
Module contains tools for processing Stata files into DataFrames
The StataReader below was originally written by <NAME> as part of PyDTA.
It has been extended and improved by <NAME> from the Statsmodels
project who also developed the StataWriter and was finally added to pandas in
a once again improved version.
Yo... | DataFrame.from_records(data) | pandas.core.frame.DataFrame.from_records |
# pylint: disable=E1101,E1103,W0232
from datetime import datetime, timedelta
from pandas.compat import range, lrange, lzip, u, zip
import operator
import re
import nose
import warnings
import os
import numpy as np
from numpy.testing import assert_array_equal
from pandas import period_range, date_range
from pandas.c... | Float64Index([1.0, np.nan]) | pandas.core.index.Float64Index |
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, **base_kwds) | pandas.read_csv |
import ipyleaflet
import ipywidgets
import pandas as pd
import geopandas as gpd
from shapely.geometry import Polygon, Point
import datetime
import requests
import xml.etree.ElementTree as ET
import calendar
import numpy as np
import pathlib
import os
class ANA_interactive_map:
def __init__(self, path_inventario)... | pd.date_range(start='2000-01-01',end='2020-01-01', freq='M') | pandas.date_range |
import warnings
warnings.simplefilter(action = 'ignore', category = UserWarning)
# Front matter
import os
import glob
import re
import pandas as pd
import numpy as np
import scipy.constants as constants
# Find the filepath of all .res NRIXS files
resfilepath_list = [filepath for filepath in glob.glob('*/*.res')]
# C... | pd.DataFrame({'Date': [month+' '+year], 'Folder': [folder], 'Index': [filename]}) | pandas.DataFrame |
def test_get_number_rows_cols_for_fig():
from mspypeline.helpers import get_number_rows_cols_for_fig
assert get_number_rows_cols_for_fig([1, 1, 1, 1]) == (2, 2)
assert get_number_rows_cols_for_fig(4) == (2, 2)
def test_fill_dict():
from mspypeline.helpers import fill_dict
def test_default_to_regular... | pd.DataFrame() | pandas.DataFrame |
# This script helps to create TAble 1 (phenotypes per country)
import pandas as pd
from scipy.stats import chi2_contingency
import matplotlib.pyplot as plt
import numpy as np
# Include all GENES, those containing Indels and SNVS (that's why I repeat this step of loading "alleles" dataframe) This prevents badly grouppi... | pd.read_csv('/path/to/phenotypes_20210107.csv',sep='\t') | pandas.read_csv |
#%%
import os
try:
os.chdir('/Volumes/GoogleDrive/My Drive/python_code/connectome_tools/')
print(os.getcwd())
except:
pass
#%%
import sys
sys.path.append("/Volumes/GoogleDrive/My Drive/python_code/connectome_tools/")
import pandas as pd
import numpy as np
import connectome_tools.process_matrix as promat... | pd.to_numeric(matrix_ad.columns) | pandas.to_numeric |
"""
Classes for comparing outputs of two RSMTool experiments.
:author: <NAME> (<EMAIL>)
:author: <NAME> (<EMAIL>)
:author: <NAME> (<EMAIL>)
:organization: ETS
"""
import warnings
from collections import defaultdict
from copy import deepcopy
from os.path import exists, join
import numpy as np
import pandas as pd
fro... | pd.concat(correlation_list, sort=True) | pandas.concat |
from datetime import (
datetime,
timedelta,
timezone,
)
import numpy as np
import pytest
import pytz
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
NaT,
Period,
Series,
Timedelta,
Timestamp,
date_range,
isna,
)
import pandas._testing as tm
class TestS... | Timestamp("2012-11-11 00:00:00+01:00") | pandas.Timestamp |
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
import smtplib
import os
from os.path import join
import json
import pandas as pd
import subprocess
USER_FIELDS = [
"created_at",
#"description",
#"entities",
"id",
"location",
"name",
#"pinned_tweet_id",
... | pd.concat([hour_tweets, tmp]) | pandas.concat |
### Filename = test_sfw_categorical_autotest_aggregated_functions.py
## ##
# #
# THIS TEST WAS AUTOGENERATED BY generator_categorical_unit_test.py #
# ... | pd.DataFrame(test_class.data) | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import argparse
from scipy.signal import butter, lfilter
from scipy.signal import freqs
def exponential_smooth(data, smooth_fac):
"""
:param data(np.array)
:param smooth_fac(int): span_interval
:return:
"""
ser = pd.Series(... | pd.read_csv(args.file[0]) | pandas.read_csv |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
import numpy as np
import pytest
from pandas.compat import lrange, range
import pandas as pd
from pandas import DataFrame, Index, Series
import pandas.util.testing as tm
from pandas.util.testing import assert_series_equal
def test_get():
# GH 6383
s = Series... | Series([2, np.nan], index=idx) | pandas.Series |
import pandas as pd
import numpy as np
from datetime import datetime
def transformData(RideWaits):
RideWaits["RideId"] = pd.Categorical(RideWaits["RideId"])
#RideWaits["Status"] = pd.Categorical(RideWaits["Status"])
RideWaits["ParkId"] = pd.Categorical(RideWaits["ParkId"])
RideWaits["Tier"] = pd.Catego... | pd.Categorical(RideWaits["MagicHourType"]) | pandas.Categorical |
import json
from types import SimpleNamespace
import pandas as pd
from hana_ml.dataframe import ConnectionContext
from hana_ml.model_storage import ModelStorage
from typing import List
from hana_automl.algorithms.base_algo import BaseAlgorithm
from hana_automl.algorithms.ensembles.blendcls import BlendingCls
from han... | pd.DataFrame(res, columns=col_names) | pandas.DataFrame |
import pandas as pd
import numpy as np
import psycopg2
from sklearn.model_selection import KFold
import Constants
import sys
from pathlib import Path
output_folder = Path(sys.argv[1])
output_folder.mkdir(parents=True, exist_ok=True)
# update database credentials if MIMIC data stored in postgres database
conn = psycop... | pd.merge(left=df, right=icds, on='hadm_id') | pandas.merge |
# -*- coding: utf-8 -*-
# Copyright (c) 2018-2021, earthobservations developers.
# Distributed under the MIT License. See LICENSE for more info.
import numpy as np
import pandas as pd
import pytest
from pandas._testing import assert_frame_equal
from wetterdienst import Settings
from wetterdienst.exceptions import Inva... | assert_frame_equal(df, expected_df, check_categorical=False) | pandas._testing.assert_frame_equal |
import unittest
from enda.timeseries import TimeSeries
import pandas as pd
import pytz
class TestTimeSeries(unittest.TestCase):
def test_collapse_dt_series_into_periods(self):
# periods is a list of (start, end) pairs.
periods = [
(pd.to_datetime('2018-01-01 00:15:00+01:00'), pd.to_d... | pd.to_datetime('2018-01-01 00:00:00+01:00') | pandas.to_datetime |
import re
import numpy as np
import pytest
from pandas.core.dtypes.common import pandas_dtype
from pandas import (
Float64Index,
Index,
Int64Index,
)
import pandas._testing as tm
class TestAstype:
def test_astype_float64_to_object(self):
float_index = Float64Index([0.0, 2.5... | Float64Index([1, 2, non_finite]) | pandas.Float64Index |
import csv
import json
import os
import re
from collections import OrderedDict
from io import StringIO
import pandas as pd
import requests
from django.core.management.base import BaseCommand
from django.forms.models import model_to_dict
from va_explorer.va_data_management.models import CauseCodingIssue
from va_explor... | pd.DataFrame.from_records(va_data) | pandas.DataFrame.from_records |
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedKFold, KFold
def cv_index(n_fold, feature, label):
skf = KFold(n_fold, shuffle=True, random_state=7840)
index_list = []
for i, j in skf.split(feature, label):
i... | pd.get_dummies(for_dummy, prefix=col) | pandas.get_dummies |
import pandas as pd
import numpy as np
import scipy.sparse as spl
from concurrent.futures import ProcessPoolExecutor
import sys
threads = 4
all_tasks = [
[5, 8000, ['5t', '5nt'], 0.352],
[10, 12000, ['10t', '10nt'], 0.38],
[25, 40000, ['25f'], 0.43386578246281293],
[25, 9000, ['25r'], 0.4],
[100, 4... | pd.read_csv('data/challenge_set/playlists.csv') | pandas.read_csv |
import nltk
nltk.download('punkt')
from newspaper import Article, Config
from pygooglenews import GoogleNews
import requests
import pandas as pd
from bs4 import BeautifulSoup
import re
from urllib.parse import urlparse
from pathlib import Path
class Newspaper_agent:
TMP_DIRECTORY = Path("./tmp_data")
if not T... | pd.read_csv("data_news/Custom_Websites_Tags.csv") | pandas.read_csv |
"""
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_timedelta64_ns_dtype(arr.dtype) | pandas.core.dtypes.common.is_timedelta64_ns_dtype |
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import numpy as np
from pylab import rcParams
##########################################################################################
# Designed and developed by <NAME>
# Date : 11 ... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 26 17:19:41 2020
@author: <NAME>
"""
import pandas as pd
def int_br(x):
return int(x.replace('.',''))
def float_br(x):
return float(x.replace('.', '').replace(',','.'))
dia = '2805'
file_HU = '~/ownCloud/sesab/exporta_bole... | pd.DataFrame(columns=colsutils) | pandas.DataFrame |
import pandas as pd
import numpy as np
from multiprocessing import cpu_count
from functools import partial
from scipy.optimize import minimize
from trading.accountcurve import accountCurve
from core.utility import draw_sample, weight_forecast
from multiprocessing_on_dill import Pool
from contextlib import closing
"""... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
def get_market_list(client, *args):
marketList = pd.DataFrame(client.get_products()['data'])
if len(args)>0:
quoteBase = args[0]
marketList = marketList[marketList['quoteAsset']==quoteBase]
marketList['volume_24h'] = marketList['tradedMoney']
marke... | pd.to_numeric(klines[10]) | pandas.to_numeric |
import pymorphy2
import re
import string
import os
import time
import collections as cl
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from collections import defaultdict
from matplotlib.backends.backend_pdf import PdfPages
def numnum(y):
retu... | pd.DataFrame(Ue, columns=['кол', 'п', 'чр']) | pandas.DataFrame |
#
# Copyright (c) 2018 <NAME> <<EMAIL>>
#
# See the file LICENSE for your rights.
#
"""
Methods for processing VERIFICATION data.
"""
import os
import re
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
import pickle
import requests
from collections import OrderedDict
... | pd.Timestamp(date.year,date.month,date.day,hour2) | pandas.Timestamp |
# Core functions
#
# this file contains reusable core functions like filtering on university
# and adding year and month name info
# these are functions which are generally used in every product
# roadmap: I want to push all functions from loose function
# to functions combined in classgroups
from nlp_functions impo... | pd.read_csv(path_deals) | pandas.read_csv |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# ========================================================================
# the metircs for docking power(AUC, Rp, success rate) just for ML-based models
# ========================================================================
from warnings import simplefilter
simplefi... | pd.concat([df, df_ref.loc[df.index][['rmsd']]], axis=1) | pandas.concat |
# 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(input_obj, dtype=np_dtypes_to_pandas_dtypes[dtype]) | pandas.Series |
import pandas as pd
import glob
import os
import numpy as np
import time
import fastparquet
import argparse
from multiprocessing import Pool
import multiprocessing as mp
from os.path import isfile
parser = argparse.ArgumentParser(description='Program to run google compounder for a particular file and setting')
parse... | pd.DataFrame() | pandas.DataFrame |
from __future__ import print_function, division
from warnings import warn
from nilmtk.disaggregate import Disaggregator
from keras.layers import Conv1D, Dense, Dropout, Reshape, Flatten
import os
import pickle
import pandas as pd
import numpy as np
from collections import OrderedDict
from keras.optimizers import SGD
fr... | pd.concat(train_main,axis=0) | pandas.concat |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2019 <NAME> <<EMAIL>>
# and the Talkowski Laboratory
# Distributed under terms of the MIT license.
"""
Collect features per gene for an input gtf
"""
import pybedtools as pbt
import numpy as np
import pandas as pd
from pysam import faidx
import csv
fro... | pd.read_csv(exac_cnv_tsv, delimiter='\t') | pandas.read_csv |
import os
import re
import sys
import warnings
from argparse import ArgumentParser
warnings.filterwarnings('ignore', category=FutureWarning,
module='rpy2.robjects.pandas2ri')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pandas.api.types import is_object_dtype, is... | is_object_dtype(x) | pandas.api.types.is_object_dtype |
import collections
import dask
from dask import delayed
from dask.diagnostics import ProgressBar
import logging
import multiprocessing
import pandas as pd
import numpy as np
import re
import six
import string
import py_stringsimjoin as ssj
from py_stringsimjoin.filter.overlap_filter import OverlapFilter
from py_string... | pd.concat([ret_candset, missing_value_pairs], ignore_index=True, sort=False) | pandas.concat |
import sys
import pandas
import numpy
import math
import numpy as np
import networkx as nx
from sklearn.preprocessing import normalize
def ComputeRankWeightage(row):
return (1+ row['max_actor_rank'] - row['actor_movie_rank'])/(1+ row['max_actor_rank'] - row['min_actor_rank'])
def ComputeTimestampWeights(row, min_ti... | pandas.read_csv("movie-actor.csv") | pandas.read_csv |
# This script gets the amount of funding gained within 9 months
# of the first date
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from tqdm import tqdm
# Need to merge two patreon stat CSV files
df_patreon1 = pd.read_csv('files/20190714_github_patreon_stats.csv')
df_patreon2 = pd.read_... | pd.concat([df_patreon1, df_patreon2], ignore_index=True) | pandas.concat |
import os
import numpy as np
import pandas as pd
from datetime import datetime
from datetime import timedelta
from dateutil import parser
# from scipy.interpolate import NearestNDInterpolator
import matplotlib.pyplot as plt
# from emtracks.mapinterp import get_df_interp_func
# copied interpolation here. FIXME!
from ma... | pd.DataFrame(rows_list) | pandas.DataFrame |
from typing import Tuple, Sequence, Mapping, Optional, Union
try:
from typing import Literal
except ImportError:
from typing_extensions import Literal
from anndata import AnnData
from copy import copy
import pandas as pd
import ntpath
import numpy as np
import matplotlib.pyplot as plt
import scanpy as sc
impor... | pd.read_table(psms, low_memory=False) | pandas.read_table |
import collections
import os
import sys
import joblib
import numpy as np
import pandas as pd
import torch
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import tra... | pd.DataFrame(columns=cols) | pandas.DataFrame |
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