prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#!/usr/bin/env/python
import argparse
import numpy as np
import os
import pandas as pd
import yaml
import micro_dl.inference.evaluation_metrics as metrics
import micro_dl.utils.aux_utils as aux_utils
import micro_dl.utils.preprocess_utils as preprocess_utils
import micro_dl.utils.image_utils as image_utils
import mic... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Series, date_range, timedelta_range
import pandas._testing as tm
class TestTimeSeries:
def test_contiguous_boolean_preserve_freq(self):
rng = date_range("1/1/2000", "3/1/2000", freq="B")
mask = np.zeros(len(rng), ... | pd.date_range("2000", periods=2, tz=tz) | pandas.date_range |
import pandas as pd
import numpy as np
from pathlib import Path
from sklearn.manifold import TSNE
from sklearn.cluster import KMeans
import seaborn as sns
import matplotlib.pyplot as plt
import configparser
from dateutil.parser import parse
import os
from sklearn.metrics import roc_auc_score, f1_score, precision_score,... | pd.read_csv(self.train_file) | pandas.read_csv |
import numpy as np
import pytest
from pandas.core.dtypes.generic import ABCIndex
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays.integer import (
Int8Dtype,
UInt32Dtype,
)
def test_dtypes(dtype):
# smoke tests on auto dtype construction
if dtype.is_signed_integer:
a... | pd.Series([1, 2, 3], dtype=dtype) | pandas.Series |
import numpy as np
import pandas as pd
class DictUtil:
ks = []
ds = []
def to_kv(self, src):
for k, v in src.items():
self.ks.append(k)
if type(v) == dict:
self.to_kv(v)
else:
self.ds.append(np.array([".".join(sel... | pd.merge(m_df, right_df, on='key', how='left') | pandas.merge |
import properties
from sklearn.neighbors import NearestNeighbors
from sklearn.metrics.pairwise import cosine_similarity
import json
import pandas as pd
import numpy as np
import utility
import ast
# Feature engineering family history
def create_cols_family_hist(x):
if x["tschq04-1"] == "YES":
if isinstan... | pd.read_pickle(properties.simulate_hearing_file_location) | pandas.read_pickle |
import matplotlib.pyplot as plt
# %matplotlib inline
# from utils import utils
# import utils.utils as utils
from utils.qedr.eval.hinton import hinton
import os
import numpy as np
from utils.qedr.eval.regression import normalize, entropic_scores, print_table_pretty, nrmse
from utils.qedr.zero_shot import get_gap_ids
f... | pd.DataFrame(data=gts) | pandas.DataFrame |
# import tabula
import pandas as pd
import numpy as np
# !pip install tabula-py
import camelot
import os
import string
import pytz
from datetime import datetime, timezone, timedelta
from tzlocal import get_localzone
from StatusMsg import StatusMsg
from tqdm import tqdm
from urllib.error import HTTPError
im... | pd.read_csv(base_csv) | pandas.read_csv |
# load libraries
from sklearn import preprocessing
from sklearn.pipeline import Pipeline
import pandas as pd
raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'],
'age': [42, 52, 36, 24, 73],
'city': ['San Francisco'... | pd.get_dummies(df['city']) | pandas.get_dummies |
import pandas as pd
#set the Quarter, suold be yearQn
Quarter = "2020Q1"
#read CSV file: quote and contract
quotes = | pd.read_csv("./all quotes.csv") | pandas.read_csv |
import argparse, pandas, os, random, seaborn, sys, re
import numpy as np
from unicodedata import name
from numpy import median
import matplotlib.pyplot as plt
names_to_translate = {
'gflop_per_s_per_iter': 'Throughput [Gflop/s]',
'gbyte_per_s_per_iter': 'Bandwidth [GB/s]',
'runtime_problem_sizes_dict': 'P... | pandas.read_json(file) | pandas.read_json |
"""add comment in script explaining what its for
This is where the scripts to prepross the data go
save files in data/targets/
"""
import itertools
import json
import os
import sys
from datetime import datetime
import numpy as np
import pandas as pd
from google_drive_downloader import GoogleDriveDownloader as gdd
fro... | pd.read_csv(icu_dest) | pandas.read_csv |
"""
json 불러와서 캡션 붙이는 것
"""
import json
import pandas as pd
path = './datasets/vqa/v2_OpenEnded_mscoco_train2014_questions.json'
with open(path) as question:
question = json.load(question)
# question['questions'][0]
# question['questions'][1]
# question['questions'][2]
df = pd.DataFrame(question['questions'])
d... | pd.DataFrame(cap) | pandas.DataFrame |
"""
A warehouse for constant values required to initilize the PUDL Database.
This constants module stores and organizes a bunch of constant values which are
used throughout PUDL to populate static lists within the data packages or for
data cleaning purposes.
"""
import importlib.resources
import pandas as pd
import ... | pd.StringDtype() | pandas.StringDtype |
# 导入相关库
import requests
import json
import time
import pandas as pd
# import fool
from PIL import Image,ImageSequence
import numpy as np
from wordcloud import WordCloud,ImageColorGenerator
import matplotlib.pyplot as plt
# 获取微博ID
def getWeibo_id():
content_parameter = [] # 用来存放weibo_id值
# 获取每条微博的id值
url ... | pd.DataFrame(feature, columns=["性别", "年龄", "星座", "国家城市"]) | pandas.DataFrame |
import copy
import os
import pandas as pd
import numpy as np
import tempfile
import skimage.io as io
from toffy import rosetta
import toffy.rosetta_test_cases as test_cases
from ark.utils import test_utils
from ark.utils.load_utils import load_imgs_from_tree
from ark.utils.io_utils import list_folders, list_files
f... | pd.read_csv(rosetta_path, index_col=0) | pandas.read_csv |
"""This module contains the HydroMonitor object for reading groundwater
head measurements from a HydroMonitor csv export file
"""
from collections import OrderedDict
import warnings
import os.path
import errno
import os
import matplotlib as mpl
import matplotlib.pyplot as plt
from pandas import Series, DataFrame
imp... | Series(data=heads,index=datetimes) | pandas.Series |
import pandas as pd
import transformer.result.generator as generator
from transformer.result.result_config import ResultFormatterConfig, ResultFieldFormat
class AbstractResultFormatter:
def run(self, config:dict, frames: dict[str, pd.DataFrame]): pass
class DefaultArrayResultFormatter(AbstractResultFormatter):
... | pd.concat(data, axis=1) | pandas.concat |
#!/usr/bin/env python3
"""Module to calculate reliability of samples of raw accelerometer files."""
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import argparse
import os
def main():
"""
Application entry point responsible for parsing command line requests
"""
parser = argparse... | pd.read_csv(input_file) | pandas.read_csv |
import os
import numpy as np
import pandas as pd
from numpy import abs
from numpy import log
from numpy import sign
from scipy.stats import rankdata
import scipy as sp
import statsmodels.api as sm
from data_source import local_source
from tqdm import tqdm as pb
# region Auxiliary functions
def ts_sum(df, window=10):
... | pd.DataFrame(na_lwma, index=df.index, columns=['CLOSE']) | pandas.DataFrame |
import torch
import json
import pandas as pd
import numpy as np
from tqdm import tqdm
import src.config as config
import src.model_utils as mutils
from src.dataset import CustomDataset
def predict(df, model, device, label_list, description_col=config.TEXT_COLUMN):
test_dataset = CustomDataset(
desc... | pd.read_csv(data) | pandas.read_csv |
from scipy import signal
import numpy as np
import pandas as pd
from scipy.signal import filtfilt, butter
import sympy as sp
import math
def interpolate(data):
print("STATUS: Filling NaNs")
unfixed = 0
for index in range(0, data.shape[0]):
amount_before = data.loc[index, "interval_data"].isnull()... | pd.DataFrame(filtered_interval_data) | pandas.DataFrame |
"""
Provide a generic structure to support window functions,
similar to how we have a Groupby object.
"""
from collections import defaultdict
from datetime import timedelta
from textwrap import dedent
from typing import List, Optional, Set
import warnings
import numpy as np
import pandas._libs.window as libwindow
fro... | Substitution(name="rolling") | pandas.util._decorators.Substitution |
from __future__ import unicode_literals, division, print_function
import os
import unittest
import pandas as pd
import numpy as np
import warnings
from itertools import product
from pymatgen.core.structure import Structure
from pymatgen.util.testing import PymatgenTest
from sklearn.dummy import DummyRegressor, Dummy... | pd.DataFrame({'x': [1, 2, 3]}) | pandas.DataFrame |
#!/usr/bin/python3
# Functions to handle Input
#############################################################################################
def read_csv():
# simple function to read data from a file
data = pd.read_csv('out.csv', sep=';')
return data
def read_sacct():
# function to read the data directly fro... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 21 11:14:57 2021
@author: carlos
"""
import pandas as pd
from wordcloud import WordCloud
import matplotlib.pyplot as plt
from parse_abstracts import *
import seaborn as sns
import numpy as np
import json
import re
import nltk
from nltk.corpus import... | pd.DataFrame(columns=masked_sepex_concepts) | pandas.DataFrame |
import numpy as np
import pandas as pd
import datetime as dt
import pickle
import bz2
from .analyzer import summarize_returns
DATA_PATH = '../backtest/'
class Portfolio():
"""
Portfolio is the core class for event-driven backtesting. It conducts the
backtesting in the following order:
1. Initializati... | pd.Series() | pandas.Series |
# -*- coding: UTF-8 -*-
'''
@author: Andrewzhj
@contact: <EMAIL>
@file: comment_words_cloud.py
@time: 10/16/18 3:53 PM
@desc: 提取评论数据，进行热词展示
@note:
'''
import jieba
from wordcloud import WordCloud, ImageColorGenerator
import pandas as pd
from pymongo import MongoClient
import numpy
from PIL import Image
import matplotl... | pd.DataFrame() | pandas.DataFrame |
# Copyright 2018-2021 The Salish Sea NEMO Project and
# The University of British Columbia
# 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
# U... | pd.merge(Chl2017,staMap2017,how='inner', left_on=['Station'], right_on = ['Station']) | pandas.merge |
# Dashboard Interativo com Streamlit, Folium e Plotly Para Monitoramento de Casos de Covid-19 em Tempo Real
# Execute no terminal: streamlit run Mini-Projeto1.py
# Imports
import json
import folium
import requests
import mimetypes
import http.client
import pandas as pd
import streamlit as st
import plotl... | pd.read_csv('dados/country-coordinates-world.csv') | pandas.read_csv |
#### Master Script 5: Assess CPM_MNLR and CPM_POLR performance ####
#
# <NAME>
# University of Cambridge
# email address: <EMAIL>
#
### Contents:
# I. Initialisation
# II. Create bootstrapping resamples (that will be used for all model performance evaluation)
# III. Prepare compiled CPM_MNLR and CPM_POLR testing set pr... | pd.read_csv('../cross_validation_splits.csv') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Graficar rendimientos de los padres en periodos desconocidos.
"""
import pickle
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from Simulacion import Optimizacion
from Simulacion import Graficos
from Simulacion import Genetico
from Simulacion i... | pd.value_counts(padres[:,i+1]) | pandas.value_counts |
#!/usr/bin/env python3.7
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 23 11:46:57 2020
@author: reideej1
:DESCRIPTION: Evaluate coaching data for the last 50 years of college football
- the goal is to determine how coaches who struggle in their first 3 years
fare over time at the same program
:REQUIRES... | pd.DataFrame() | pandas.DataFrame |
# coding=utf=8
import numpy as np
import pandas as pd
# from pandas.tools.plotting import bootstrap_plot
import matplotlib.pyplot as plt
import dataviz.utils as utils
import matplotlib
matplotlib.style.use('ggplot')
def files2dataframe(root_dir: str, expression: str, offset: int, sort_columns: dict, size: int) -> p... | pd.DataFrame(errors, columns=column) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
qualify donor data
"""
# %% REQUIRED LIBRARIES
import os
import argparse
import json
import ast
import pandas as pd
import datetime as dt
import numpy as np
# %% USER INPUTS (choices to be made in order to run the code)
codeDescription = "qualify donor data"
parser... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Tue May 3 10:49:58 2016
Auger peak finding and quantitative routines ... batch processing
@author: tkc
First get it working for single file.
"""
#%%
import pandas as pd
import numpy as np
import os, sys, shutil, glob, re
if 'C:\\Users\\tkc\\Documents\\Python_Scripts' n... | pd.read_csv('C:\\Users\\tkc\\Documents\\Python_Scripts\\AESquantparams.csv', encoding='utf-8') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""hood_event_scrape_module
Authors:
<NAME> <EMAIL>
<NAME> <EMAIL>
<NAME> <EMAIL>
Imports to:
WhatsUp_main_gui.py
"""
# !pip install rtree
# !pip install geopandas
# !pip install beautifulsoup4
# !pip install censusgeocode
# Import libraries
import rtree
import geopandas as gp
import num... | pd.DataFrame() | pandas.DataFrame |
import datetime
from datetime import timedelta
from distutils.version import LooseVersion
from io import BytesIO
import os
import re
from warnings import catch_warnings, simplefilter
import numpy as np
import pytest
from pandas.compat import is_platform_little_endian, is_platform_windows
import pandas.util._test_deco... | tm.assert_class_equal(result.index, df.index, obj="dataframe index") | pandas.util.testing.assert_class_equal |
#Copyright 2013 <NAME> (<EMAIL>)
#
# 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 t... | pd.DataFrame([data.extension, allcoords.ra, allcoords.dec, allcoords.l, allcoords.b, allcoords.ebv, data.z, data.flag_sdss]) | pandas.DataFrame |
import numpy as np
import pandas as pd
import time
import sys
def bill_calculator(load_profile, tariff):
def pre_processing_load(load_profile):
# placeholder for a quick quality check function for load profile
# make sure it is kwh
# make sure it is one year
# make sure it doesn't... | pd.to_datetime(load_profile_f['READING_DATETIME']) | pandas.to_datetime |
# NB: You have to run main_sampling.py in order for this script to function
import numpy as np
import pandas as pd
import pickle
import datetime
from seir.sampling.model import SamplingNInfectiousModel
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as st
import logging
logging.basicConfi... | pd.read_csv(f'data/sampling-runs/run{run:02}_resample.csv') | pandas.read_csv |
import os
import glob
import pandas as pd
# dict matching target to download link
source_dict = {'Deaths': 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/' \
'csse_covid_19_time_series/time_series_covid19_deaths_global.csv',
'Cases': 'https://raw.... | pd.to_datetime(df.date) | pandas.to_datetime |
import logging
from pathlib import Path
import altair as alt
import pandas as pd
import requests
import streamlit as st
import streamlit.components.v1 as components
st.set_page_config(layout="wide")
st.title("Bundes-Notbremse Ampel")
pd.set_option('precision', 2)
def is_covid_file_up_to_date():
covid_path = Path... | pd.DataFrame({'y': [100]}) | pandas.DataFrame |
"""
inspiration from R Package - PerformanceAnalytics
"""
from collections import OrderedDict
import pandas as pd
import numpy as np
from tia.analysis.util import per_series
PER_YEAR_MAP = {
'BA': 1.,
'BAS': 1.,
'A': 1.,
'AS': 1.,
'BQ': 4.,
'BQS': 4.,
'Q': 4.,
'QS': 4.,
'D': 365.... | pd.expanding_count(below) | pandas.expanding_count |
# %% Import
import numpy as np
import pandas as pd
import requests
import os
from bs4 import BeautifulSoup
"""
Takes a dictionary of relevant brands and their URLs and returns a raw csv file
"""
# %% Functions
def outlets_crawl(brand, url):
"""
Returns a raw, unformatted df of outlets with it's brand from t... | pd.DataFrame(_ls) | pandas.DataFrame |
import pandas as pd
import numpy as np
import seaborn as sb
import base64
from io import BytesIO
from flask import send_file
from flask import request
from napa import player_information as pi
import matplotlib
matplotlib.use('Agg') # required to solve multithreading issues with matplotlib within flask
import matplotli... | pd.DataFrame(perms_joined, columns = ['permutation','r1','r2']) | pandas.DataFrame |
""" Getting final clusters data
:Author: <NAME> <<EMAIL>>
:Date: 2019-09-30
:License: MIT
"""
# Import Libraries
import pandas as pd
import numpy as np
import random
from collections import Counter
def main():
# clusters group user id
clusters_users = pd.read_csv('clusters_final.csv')
# clusters group user id
... | pd.read_csv('clusters_group_names.csv') | pandas.read_csv |
# Making prediction about diagnostic labels of the subjects. Note that this file needs
# the output of 'fit/gql_ml_pred.py'.
from BD.sim.rnn_label_pred import finding_CV
from actionflow.data.data_process import DataProcess
from actionflow.qrl.gql import GQL
from actionflow.qrl.opt_ml import OptML
from actionflow.util ... | pd.DataFrame({'id': ids, 'train': 'train'}) | pandas.DataFrame |
"""The classes for specifying and compiling a declarative visualization."""
from __future__ import annotations
import io
import os
import re
import sys
import inspect
import itertools
import textwrap
from collections import abc
from collections.abc import Callable, Generator, Hashable
from typing import Any
import pa... | pd.option_context("mode.use_inf_as_null", True) | pandas.option_context |
import calendar
from datetime import date, datetime, time
import locale
import unicodedata
import numpy as np
import pytest
import pytz
from pandas._libs.tslibs.timezones import maybe_get_tz
from pandas.core.dtypes.common import is_integer_dtype, is_list_like
import pandas as pd
from pandas import (
DataFrame, ... | tm.assert_index_equal(result, expected) | pandas.util.testing.assert_index_equal |
import pytest
import sys
import numpy as np
import swan_vis as swan
import networkx as nx
import math
import pandas as pd
import anndata
###########################################################################
################# Related to input/error handling #########################
##############################... | pd.DataFrame(data=data, columns=cols) | pandas.DataFrame |
# coding: utf-8
# In[1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import math
# ### 讀入資料
# - 取10～12月資料
# - 將NR轉換成0
# In[2]:
data = pd.read_excel('hsinchu.xls')
#10~12
data = data[data['日期'].between('2017/10/01','2017/12/31 ')]
# NR->0
data.replace('NR',0, inplace=True)
# In[3]:
... | pd.DataFrame(test_data_18) | pandas.DataFrame |
from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
import os
wd = os.chdir('/Users/larslarson/Documents/School/CU/Research/TADD/Data/2022-03-04')
filepaths = [f for f in os.listdir(wd) if f.endswith('.csv')]
df = pd.concat(map(pd.read_csv, filepaths),axis='columns')
file_names = []
data_frame... | pd.concat(data_frames, axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Authors: <NAME>
UNESCO-IHE 2016
Contact: <EMAIL>
Repository: https://github.com/wateraccounting/wa
Module: Sheets/sheet1
"""
import os
import pandas as pd
import time
import xml.etree.ElementTree as ET
import subprocess
def create_sheet3(basin, period, units, data, output, templat... | pd.isnull(lp_r04c07) | pandas.isnull |
from typing import Any
import numpy as np
import numpy.testing as npt
import pandas as pd
import pytest
from sklearn.preprocessing import PowerTransformer
from etna.datasets import TSDataset
from etna.transforms.power import BoxCoxTransform
from etna.transforms.power import YeoJohnsonTransform
@pytest.fixture
def n... | pd.date_range("2021-06-01", "2021-07-01", freq="1d") | pandas.date_range |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for Period dtype
import operator
import numpy as np
import pytest
from pandas._libs.tslibs.period import IncompatibleFrequency
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas impo... | tm.box_expected(pi, box_with_array) | pandas.util.testing.box_expected |
import argparse
import json
import logging
import os
import sys
import warnings
from itertools import product
import numpy as np
import pandas as pd
import torch
from paccmann_chemistry.models import (StackGRUDecoder, StackGRUEncoder, TeacherVAE)
from paccmann_chemistry.utils import get_device
from paccmann_generator... | pd.DataFrame({'loss': rl_losses, 'rewards': rewards}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 18 15:43:55 2021
@author: ZeitgeberH
"""
from pathlib import Path
from PyQt5 import QtGui, QtCore, QtSvg
from pyqtgraph.Qt import QtWidgets
from PyQt5.QtWidgets import QMessageBox, QTableWidgetItem
import pyqtgraph as pg
import pyqtgraph.opengl as gl
from pyqt... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
from datetime import date
"""
dataset split:
(date_received)
dateset3: 20160701~20160731 (113640),features3 from 20160315~20160630 (off_test)
dateset2: 20160515~20160615 (258446),features2 from 20160201~2... | pd.merge(user2_feature,t13,on='user_id',how='left') | pandas.merge |
import requests
from bs4 import BeautifulSoup
import pandas as pd
pages=list(range(0,250,25))
def request_douban(url):
htmls=[]
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0 Win64 x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36 Edg/96.0.1054.62'
}
try:
... | pd.DataFrame(results) | pandas.DataFrame |
# coding: utf8
import os
import numpy as np
from tqdm import tqdm
import pandas as pd
import time
from upsetplot import plot
from matplotlib import pyplot
from scipy import stats
from intervaltree import IntervalTree
def PeakOverlap(genesfile, peaksfile,tssdistance=[0,0],peakname='null'):
LuckPeak, LuckGen, Luck... | pd.merge(peaktable, UTR3table, left_on='Gen_TransID', right_on='Gen_TransID', how='inner') | pandas.merge |
import dataclasses
from collections import namedtuple
from copy import deepcopy, copy
from typing import NoReturn
import numpy as np
import pandas as pd
from numpy import datetime64
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer
from IMLearn import BaseEstimator
from chall... | pd.get_dummies(df, columns=cat_vars) | pandas.get_dummies |
#! /usr/bin/env python3
"""My Podcaster."""
import datetime
import email.utils
from subprocess import call, check_output
import mimetypes
import os
import re
import shutil
import socket
import urllib.error
import urllib.request
import requests
import tqdm
import random
import signal
from Podcast import Podcast
import c... | pandas.DataFrame(columns=["Podcast", "Title"]) | pandas.DataFrame |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# *****************************************************************************/
# * Authors: <NAME>
# *****************************************************************************/
"""transformCSV.py
This module contains the basic functions for creating the content of... | pandas.StringDtype() | pandas.StringDtype |
import sys
import os
import traceback
from shapely.geometry import Point
import core.download as dlf
import pandas as pd
import geopandas as gpd
def err_to_parent(UDF):
def handling(connection, load, message):
try:
UDF(connection, load, message)
except Exception as e:
... | pd.read_pickle(filepath, **message['read_args']) | pandas.read_pickle |
import os
from distutils.util import strtobool
import numpy as np
import pytest
import opendp.smartnoise.core as sn
from tests import (TEST_PUMS_PATH, TEST_PUMS_NAMES)
# Used to skip showing plots, etc.
#
IS_CI_BUILD = strtobool(os.environ.get('IS_CI_BUILD', 'False'))
def test_multilayer_analysis(run=True):
wi... | pd.DataFrame(non_dp_corr) | pandas.DataFrame |
#%%
# A. Importing packages, necessary datasets and concluding to our final dataset
# i. Importing the packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pandas_datareader import wb
import ipywidgets as widgets
#%%
# ii. Dowloading data from the World Bank (Countries, Years and GD... | pd.read_excel(country_codes) | pandas.read_excel |
from collections import namedtuple
import pandas as pd
import numpy as np
Scores = namedtuple('Scores', ['Benign', 'Likely_benign', 'Uncertain_significance', 'not_provided',
'Conflicting_interpretations_of_pathogenicity',
'Likely_pathogenic', 'Pathogenic', ... | pd.read_csv(clinvar_variant_summary_file, sep="\t") | pandas.read_csv |
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | pd.Series([100], index=["A"]) | pandas.Series |
def NMDS_analysis(TaXon_table_xlsx, meta_data_to_test, taxonomic_level, width, height, nmds_s, max_iter_val, n_init_val, path_to_outdirs, template, font_size, color_discrete_sequence, nmds_dissimilarity):
import pandas as pd
import numpy as np
from skbio.diversity import beta_diversity
from sklearn.man... | pd.DataFrame(nmds_results_dict[2]["nmds_results"], index=[samples]) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/1/26 13:10
Desc: 申万指数-申万一级、二级和三级
http://www.swsindex.com/IdxMain.aspx
https://legulegu.com/stockdata/index-composition?industryCode=851921.SI
"""
import time
import json
import pandas as pd
from akshare.utils import demjson
import requests
from bs4 import Bea... | numeric(temp_df["昨收盘"]) | pandas.to_numeric |
import datareader
import dataextractor
import bandreader
import numpy as np
from _bisect import bisect
import matplotlib.pyplot as plt
import matplotlib.ticker as plticker
import pandas as pd
from scipy import stats
from sklearn import metrics
def full_signal_extract(path, ident):
"""Extract breathing and heartbe... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 16 11:36:13 2021
@author: <NAME> (Finnish Meteorological Institute)
"""
import sys
import pandas as pd
import numpy as np
import xarray as xr
from satellitetools.biophys import SNAP_BIO_RMSE
def xr_dataset_to_timeseries(
xr_dataset,
variab... | pd.to_datetime(xr_dataset.time.values) | pandas.to_datetime |
# Module: Bachelor thesis
# Theme: Detect malicious/unusual Login Events
# Author: <NAME> <<EMAIL>>
# Status: 28.07.2021
import datetime
import pandas as pd
import re
import numpy as np
from joblib import dump
def read_features(data_path):
features = pd.read_csv(data_... | pd.DataFrame(columns=features_without_scores.columns) | pandas.DataFrame |
import igraph as Graph
import pandas as pd
import os
import numpy as np
import spacy
from sklearn.cluster import KMeans
from pylab import *
import re
import time
import src.pickle_handler as ph
import src.relation_creator as rc
# the dataframe has been preprocessed by many other functions. However we only need a subs... | pd.concat(frames, sort=False) | pandas.concat |
import pandas as pd
import numpy as np
import re
import math
import codecs
import csv
# 预计剩余电影总量220k到200k
data= | pd.read_csv("Website_ETL.CSV") | pandas.read_csv |
# -*- coding:utf-8 -*-
import re
import logging
import pandas as pd
from contrib.utils.DataCleanCheckTool import DataCleanCheckTool
class CorpusFromEllisQTB(object):
"""
CorpusFromEllis, question_text with blank
整个程序是由大量函数构成的
主要的函数是final_process，其他在final_process中调度
final_process完成之... | pd.merge(data, data_packageid, on="exercise_id", how="left") | pandas.merge |
# -*- coding: utf-8 -*-
"""Copy of rnn.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1hw5VX0w03qnA-pD4YmOck-HAmzP9_fO8
# Recurrent Neural Network
## Part 1 - Data Preprocessing
### Importing the libraries
"""
import numpy as np
import matplo... | pd.read_csv('Google_Stock_Price_Test.csv') | pandas.read_csv |
import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers impo... | pd.read_csv('data/wind_table_07.csv') | pandas.read_csv |
import logging
import textwrap
import pandas
from sqlalchemy import text
from triage.database_reflection import table_exists
from triage.component.catwalk.storage import MatrixStore
class ProtectedGroupsGeneratorNoOp(object):
def generate_all_dates(self, *args, **kwargs):
logging.warning(
"N... | pandas.DataFrame() | pandas.DataFrame |
import os
from typing import cast
import matplotlib.pyplot as plt
import pandas as pd
import pandera as pa
import requests
import seaborn as sns
from dagster_pandera import pandera_schema_to_dagster_type
from pandera.typing import Series
# ****************************************************************************
#... | pd.read_csv(path, parse_dates=["date"]) | pandas.read_csv |
import logging
import pandas as pd
from itertools import tee, izip
from copy import deepcopy
from modules.loggingFunctions import initialize_logging
from modules.amr.aro import ARO_ACCESSIONS
DF_ARO = | pd.DataFrame(ARO_ACCESSIONS) | pandas.DataFrame |
from datetime import datetime
import numpy as np
import pandas as pd
from pandas import (
Period,
Series,
date_range,
period_range,
to_datetime,
)
import pandas._testing as tm
class TestCombineFirst:
def test_combine_first_period_datetime(self):
# GH#3367
didx = date_range(st... | tm.assert_series_equal(ser, result) | pandas._testing.assert_series_equal |
import pandas as pd
import numpy as np
import networkx as nx
from sklearn.preprocessing import StandardScaler, normalize, MinMaxScaler
import matplotlib.pyplot as plt
from collections import defaultdict, Counter
import urllib.request as request
import json
import os
from scipy.sparse import csr_matrix as csr_matrix
fro... | pd.DataFrame(edge_list) | pandas.DataFrame |
"""
Hold pandas dataframe of given excel sheet
Performs various read operations which all return numpy arrays
"""
import numpy as np
import pandas as pd
def clean_vector(x):
return x[~np.isnan(x)]
class DataHandler:
def __init__(self, filepath):
self._path = filepath
self._df = pd.read_exce... | pd.read_excel(self._path) | pandas.read_excel |
'''reports details about a virtual boinc farm'''
# standard library modules
import argparse
import collections
#import contextlib
#from concurrent import futures
#import errno
import datetime
#import getpass
#import json
import logging
#import math
#import os
#import re
#import socket
#import shutil
#import signal
impo... | pd.DataFrame() | pandas.DataFrame |
import pytest
from pandas._libs.tslibs.frequencies import INVALID_FREQ_ERR_MSG, _period_code_map
from pandas.errors import OutOfBoundsDatetime
from pandas import Period, Timestamp, offsets
class TestFreqConversion:
"""Test frequency conversion of date objects"""
@pytest.mark.parametrize("freq", ["A", "Q", ... | Period(freq="A-JAN", year=2008) | pandas.Period |
# Copyright (c) 2019-2020, NVIDIA CORPORATION.
import datetime as dt
import re
import cupy as cp
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from pandas.util.testing import (
assert_frame_equal,
assert_index_equal,
assert_series_equal,
)
import cudf
from cudf.core import Data... | pd.Index([1, 2, 3, 4]) | pandas.Index |
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 25 16:14:12 2019
@author: <NAME>
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#import graphviz
import os
import seaborn as sns
from scipy.stats import chi2_contingency
os.chdir("E:\PYTHON NOTES\projects\cab fare prediction")
d... | pd.concat([dataset_train2,temp],axis=1) | pandas.concat |
import os
from typing import List
try:
from typing import Literal
except ImportError:
from typing_extensions import Literal # type: ignore
from typing import Optional
import numpy as np
import pandas as pd
import scanpy as sc
from anndata import AnnData
from rich import print
WORKING_DIRECTORY = os.path.di... | pd.concat(all_markers) | pandas.concat |
'''
Author: <NAME>
Date: May 1, 2019
Course: ISTA355
Final Project
This file contains all the functions used for my ISTA355 Final Project.
The purpose of the file is to accomplish the task of incorporating question
answering features to a classifier in order to replicate a open ended question
answer model or search e... | pd.DataFrame(data, index=index, columns=cols) | pandas.DataFrame |
import pandas as pd
import os
CUR_PATH = os.path.abspath(os.path.dirname(__file__))
SYR = 2011 # calendar year used to normalize factors
BEN_SYR = 2014 # calendar year used just for the benefit start year
EYR = 2030 # last calendar year we have data for
SOI_YR = 2014 # most recently available SOI estimates
# defi... | pd.DataFrame(total_pop2) | pandas.DataFrame |
#!/usr/bin/env python3.7
# -*- coding: utf-8 -*-
"""
Created on Mon May 10 15:36:24 2021
@author: reideej1
:DESCRIPTION:
Rolls up situational statistics for individual player stats contained in
CFBStats/teamXXX/individual folders.
Totals will be generated for each player on a yearly and a career bas... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow import keras
tf.random.set_seed(2021)
from models import DNMC, NMC, NSurv, MLP, train_model, evaluate_model
FILL_VALUES = {
'alb': 3.5,
'pafi': 333.3,
'bili': 1.01,
'crea': 1.01,
'bun': 6.51,
'wblc': 9.,
'urin... | pd.read_csv('../datasets/support2.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Wed May 24 16:15:24 2017
Sponsors Club messaging functions
@author: tkc
"""
import pandas as pd
import smtplib
import numpy as np
import datetime
import tkinter as tk
import glob
import re
import math
import textwrap
from tkinter import filedialog
from email.mime.multipart impor... | pd.read_csv(cnf._INPUT_DIR+'\\master_signups.csv', encoding='cp437') | pandas.read_csv |
import boto3
import json
import os
import requests
import pandas as pd
import warnings
from pandas import json_normalize
from github import Github
warnings.filterwarnings('ignore')
bucket = 'wmwaredata'
fileName = 'gw_releases.json'
s3 ... | pd.concat(data, ignore_index=True) | pandas.concat |
import sys
# sys.path.append("..")
# sys.path.append("../..")
import os, errno
import core_models.parser_arguments as parser_arguments
import warnings
import numpy as np
import pandas as pd
import core_models.utils as utils
import operator
from sklearn.metrics import roc_auc_score as auc_compute
from sklearn.metric... | pd.DataFrame([], index=['error_repair_dirtycells','error_repair_cleancells'], columns=attributes) | pandas.DataFrame |
from typing import List
import pytest
import numpy as np
import pandas as pd
from obp.dataset import (
linear_reward_function,
logistic_reward_function,
linear_behavior_policy_logit,
SyntheticSlateBanditDataset,
)
from obp.types import BanditFeedback
# n_unique_action, len_list, dim_context, reward_... | pd.DataFrame() | pandas.DataFrame |
import itertools
from sklearn.model_selection import train_test_split
from challenge.agoda_cancellation_estimator import AgodaCancellationEstimator
import matplotlib.pyplot as plt
from sklearn import metrics
import numpy as np
import pandas as pd
import re
PATTERN = re.compile(r"((?P<days1>[1-9]\d*)D(?P<amount1>[1... | pd.read_csv(f'labels//l{i}.csv') | pandas.read_csv |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from linearmodels import PanelOLS
import statsmodels.api as sm
import econtools as econ
import econtools.metrics as mt
import math
from statsmodels.stats.outliers_influence import variance_inflation_factor
from auxiliary.prepare import *
from auxil... | pd.DataFrame((ci_1,ci_2)) | pandas.DataFrame |
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