prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# coding: utf-8
# # Structural durability analyses for carbon/epoxy laminates
#
# ## §3: Experimental
# In[39]:
#Preamble to hide inputs so that massive code scripts are not cluttering the data visualization output
from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (c... | pd.concat([eqsf_df, qsf_df]) | pandas.concat |
import pandas as pd
import json
import requests
import os
from flask import Flask, request, Response
# constants
TOKEN = '<KEY>'
# info about bot
#
#https://api.telegram.org/bot1643763356:AAHDHaS1qGa34XkcOgYWta5cpUY-kzSK7y4/getMe
#
# get updates
#
#https://api.telegram.org/bot1643763356:AAHDHaS1qGa... | pd.read_csv("store.csv") | pandas.read_csv |
from __future__ import print_function, division
import torch
from torch.nn import init
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.optim import lr_scheduler
import numpy as np
from torch.utils.data import DataLoader, Dataset
import torchvision
from torchvision import dat... | pd.DataFrame(dict, index=labels) | pandas.DataFrame |
import os
import pandas as pd
import gavia.time as gavtime
from gavia.version import __version__
import sys
def getlogs(dir,logtype):
'''
get list of logs for camera
'''
files = []
loglist = os.listdir(dir)
for log in loglist:
if logtype in log:
files.ap... | pd.DataFrame(columns=headers) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import requests
import json
import pandas as pd
from io import StringIO
import numpy as np
import time
#
timezones={}
#function = 'TIME_SERIES_INTRADAY'
apii = 'https://www.alphavantage.co/query?function={function}&symbol={symbol}&interval={interval}&outputsize=full&datatype=csv&apikey='
apid =... | pd.read_csv(fixed) | pandas.read_csv |
# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership. The Modin Development Team licenses this file to you under the
# Apache License, Version 2.0 (the "License"); you may not u... | pandas.read_hdf(unique_filename_modin, key="foo", mode="r") | pandas.read_hdf |
import requests
import math
import functools
import os
import argparse
from io import StringIO
import pandas as pd
pd.options.mode.chained_assignment = None
from urllib.request import urlopen
import xml.etree.ElementTree as et
from itertools import combinations, product
from itertools import chain
import ... | pd.concat([sample_accession_new, run_accession_new, read_file_new], axis=1) | pandas.concat |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import pickle
import shutil
import sys
import tempfile
import numpy as np
from numpy import arange, nan
import pandas.testing as pdt
from pandas import DataFrame, MultiIndex, Series, to_datetime
# dependencies testing specific
import pytest
import recordlinka... | pdt.assert_series_equal(result['s_'], expected) | pandas.testing.assert_series_equal |
#%load_ext autoreload
#%autoreload 2
import dataclasses
import glob
import logging
import os
import shutil
import warnings
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Tuple
import numpy as np
import pandas as pd
from scipy.sparse.csr import csr_m... | types.is_string_dtype(patterns) | pandas.api.types.is_string_dtype |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Import OptionMetrics data.
"""
from __future__ import print_function, division
import os
import zipfile
import numpy as np
import pandas as pd
import datetime as dt
from scipy.interpolate import interp1d
from impvol import lfmoneyness, delta, vega
from datastorage.q... | pd.read_hdf(path + 'std_options.h5', 'std_options') | pandas.read_hdf |
from flowsa.common import WITHDRAWN_KEYWORD
from flowsa.flowbyfunctions import assign_fips_location_system
from flowsa.location import US_FIPS
import math
import pandas as pd
import io
from flowsa.settings import log
from string import digits
YEARS_COVERED = {
"asbestos": "2014-2018",
"barite": "2014-2018",
... | pd.DataFrame(df_raw_data_one.loc[7:10]) | pandas.DataFrame |
#############################################################
#
# Robust Synthetic Control Tests (based on ALS)
#
# You need to ensure that this script is called from
# the tslib/ parent directory or tslib/tests/ directory:
#
# 1. python tests/testScriptSynthControlALS.py
# 2. python testScriptSynthControlALS.p... | pd.read_csv(filename) | pandas.read_csv |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import math
import scipy.stats as stats
from matplotlib import gridspec
from matplotlib.lines import Line2D
from .util import *
import seaborn as sns
from matplotlib.ticker import FormatStrFormatter
import matplotlib.pylab as pl
import matplotlib.... | pd.DatetimeIndex([date_string_prior]) | pandas.DatetimeIndex |
from collections import defaultdict
from multiprocessing import Pool
import os.path
import random
import igraph
from numpy import *
import numpy.random as nprandom
import pandas as pd
from sklearn.metrics import adjusted_rand_score
from sklearn import svm
"""
The names of the datasets used for training.
"""
TRA... | pd.match(dataC.id_1,wlC.index) | pandas.match |
import pandas as pd
from typing import Dict, List, Optional, Tuple
def integrate_col(df: pd.DataFrame, val_col: str, class_col: str = 'edc_id') -> Dict[str, float]:
res: Dict[str, float] = dict()
for class_id in df[class_col].unique():
val = 0
prev_t = 0
prev_val = 0
sub_df = d... | pd.DataFrame(columns=['time', consumer_col, pwr_col, 'acc_return', 'acc_energy', 'acc_cost'], index=None) | pandas.DataFrame |
'''
Feature scoring functionality
'''
from operator import itemgetter
import math
import numpy as np
import pandas as pd
from sklearn.ensemble import (ExtraTreesClassifier, ExtraTreesRegressor,
RandomForestClassifier, RandomForestRegressor)
from sklearn.feature_selection import (f_regr... | pd.DataFrame(pvalues) | pandas.DataFrame |
from bs4 import BeautifulSoup
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.chrome.service import Service
import pandas as pd
import requests
import time
import re
import os
def grab_all_url... | pd.concat([final_df, dfs]) | pandas.concat |
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
import plotly as pl
import re
import requests
from .DataFrameUtil import DataFrameUtil as dfUtil
class CreateDataFrame():
"""Classe de serviços para a criação de dataframes utilizados para a construção dos gr... | pd.read_csv(url) | pandas.read_csv |
import argparse
import numpy as np
import pandas as pd
from PIL import Image
from pathlib import Path
from skimage import measure
from mmdet.apis import inference_detector, init_detector
################################################################################
parser = argparse.ArgumentParser()
parser.add_argu... | pd.DataFrame(trace) | pandas.DataFrame |
import os
import pandas
import sqlalchemy as sa
import json
import wx, wx.adv, wx.lib
from datetime import datetime
from wx.lib.wordwrap import wordwrap
from threading import Thread
from pydispatch import dispatcher
from openpyxl import load_workbook
CLOSE_DIALOG_SIGNAL = 'close-notification-dialog'
from components.d... | pandas.read_csv(filepath) | pandas.read_csv |
import numpy as np
from pandas import DataFrame, Series
import pandas as pd
from utilities import (LICENSE_KEY, generate_token, master_player_lookup,
YAHOO_FILE, YAHOO_KEY, YAHOO_SECRET)
import json
from yahoo_oauth import OAuth2
from pathlib import Path
# store credentials if don't already exis... | pd.read_csv('./projects/integration/raw/lookup.csv') | pandas.read_csv |
import spacy
import pandas as pd
import glob
import re
import numpy as np
import os
######## ######## ######## ########
######## NLP INFORMATION EXTRACTION MODULE ########
######## ######## ######## ########
## lots borrowed from git jists in
## https://www.analyticsvidhya.com/blog/2020/06/nlp-project-informati... | pd.set_option('display.width', None) | pandas.set_option |
from statsmodels.compat.numpy import lstsq
from statsmodels.compat.pandas import assert_index_equal
from statsmodels.compat.platform import PLATFORM_WIN
from statsmodels.compat.python import lrange
import os
import warnings
import numpy as np
from numpy.testing import (
assert_,
assert_allclose,
assert_al... | pd.DataFrame({"a": a, "b": b}) | pandas.DataFrame |
"""
Code for loading data
"""
import os, sys
import shutil
import argparse
import functools
import multiprocessing
import gzip
import inspect
import glob
import json
import itertools
import collections
import logging
from typing import *
import torch
from torch.utils.data import Dataset
import numpy as np
import pan... | pd.isnull(aa) | pandas.isnull |
"""
********************************************************************************
* Name: spatial_dataset_mwv_tests.py
* Author: mlebaron
* Created On: August 15, 2019
* Copyright: (c) Aquaveo 2019
********************************************************************************
"""
from unittest import mock
import p... | pd.DataFrame(columns=['Time (min)']) | pandas.DataFrame |
# JACSNET Evaluation
# Author: <NAME> 04.11.19
# get libraries
import sys
import numpy as np
import pandas as pd
import scipy
import csv
import matplotlib.pyplot as plt
import tensorflow as tf
import itertools
import librosa
import librosa.display
import keras
from keras.models import Model
from keras.layers import ... | pd.DataFrame(SAR_array) | pandas.DataFrame |
import numpy as np
import pandas as pd
from numba import njit
from datetime import datetime
import pytest
from itertools import product
from sklearn.model_selection import TimeSeriesSplit
import vectorbt as vbt
from vectorbt.generic import nb
seed = 42
day_dt = np.timedelta64(86400000000000)
df = pd.DataFrame({
... | pd.DatetimeIndex(['2018-01-04'], dtype='datetime64[ns]', name='split_0', freq=None) | pandas.DatetimeIndex |
"""Module containing class to build feature matrices for prediction.
There are two kinds of features:
- either features for direct prediction model
- either features for recursive prediction model
Only the first one is used for now.
"""
from os import path, makedirs
import logging
from datetime import datetime, tim... | pd.concat([self.result_concat, df]) | pandas.concat |
from datetime import datetime, timedelta, timezone
import random
from tabnanny import check
import unittest
import pandas as pd
import pytz
if __name__ == "__main__":
from pathlib import Path
import sys
sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
from datatube.dtype import check_dtypes
... | pd.Series(data) | pandas.Series |
from collections import OrderedDict
import math
import pandas as pd
import pytest
from numpy import nan, round, sqrt, floor, log as ln
from numpy.testing import assert_almost_equal
from pandas.util.testing import assert_frame_equal
from fklearn.training.transformation import (
selector,
capper,
floorer,
... | pd.DataFrame({"feat1": [11, 15], "feat2": [50, None]}) | pandas.DataFrame |
# -- coding: utf-8 --
import os
from time import sleep
import pandas as pd
import numpy as np
# read data
dataset = | pd.read_csv('C:/Users/sch/PycharmProjects/pythonProject3/data/Alibaba_requests_up_5min.csv') | pandas.read_csv |
#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2018, Anaconda, Inc. and Intake contributors
# All rights reserved.
#
# The full license is in the LICENSE file, distributed with this software.
#------------------------------------------------------------------------... | pd.read_csv(file2) | pandas.read_csv |
import os
import copy
import pytest
import numpy as np
import pandas as pd
import pyarrow as pa
from pyarrow import feather as pf
from pyarrow import parquet as pq
from time_series_transform.io.base import io_base
from time_series_transform.io.numpy import (
from_numpy,
to_numpy
)
from time_series_transfor... | pd.testing.assert_frame_equal(testData,expandTime_remove,check_dtype=False) | pandas.testing.assert_frame_equal |
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
from PyQt5.QtGui import *
import pandas as pd
import numpy as np
from dataclasses import dataclass, field, asdict
from node_editor.utils import dumpException
from typing import Dict, List, Union, Iterable, Any, TYPE_CHECKING
if TYPE_CHECKING:
from .datafram... | pd.api.types.is_numeric_dtype(value) | pandas.api.types.is_numeric_dtype |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from filterpy.kalman import KalmanFilter
from filterpy.common import Q_discrete_white_noise
# Saves figures to output folder
def savefig(name):
plt.xlabel('Vreme')
plt.savefig('./out/' + name + '.png')
# Region to watch
REGION = 'Serbia'
... | pd.read_csv(BASE_URL + '/time_series_19-covid-Deaths.csv', error_bad_lines=False) | pandas.read_csv |
#Deliveries and Vitals Analyser
import base64
import datetime
import io
import dash_table
from dash.dependencies import Input,State,Event,Output
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import dash_table_experiments as dt
from app import app
colors = {
'backgrou... | pd.DataFrame(rows, columns=[c['name'] for c in columns]) | pandas.DataFrame |
# coding: utf-8
import pandas as pd
import numpy as np
import dateutil
import requests
import datetime
from matplotlib import pyplot as plt
def smape(actual, predicted):
a = np.abs(np.array(actual) - np.array(predicted))
b = np.array(actual) + np.array(predicted)
return 2 * np.mean(np.divide(a, b, out=np... | pd.read_csv('../image/lw_aq_new.csv') | pandas.read_csv |
# estos csv se guardan en la carpeta csvs, pero no se dejan en github
# estan en https://www.dropbox.com/sh/dpzxb1hwq6n26qh/AAB-HMaoQqEF6l7ZuUtSy5sAa?dl=0
import sqlite3
import pandas as pd
NUM_QUESTIONS = 26
db = sqlite3.connect("../predictor_pol/predictor_prod.db", isolation_level=None)
cur = db.cursor()
sql = "... | pd.DataFrame.from_dict(d) | pandas.DataFrame.from_dict |
import sys
import pandas as pd
import numpy as np
from scipy import stats
def significant(array1,array2):
try:
arr1 = np.array(array1)
arr2 = np.array(array2)
print(stats.ttest_ind(arr1,arr2)[1])
return stats.ttest_ind(arr1,arr2)[1] < 0.1
except:
print('PROBLEM!')
... | pd.notnull(allPrior1) | pandas.notnull |
import boto3
import pandas as pd
import warnings
warnings.filterwarnings('ignore', category=FutureWarning)
warnings.filterwarnings('ignore', category=DeprecationWarning)
import argparse
import os
import warnings
warnings.simplefilter(action='ignore')
import json
print("import your necessary libraries in her... | pd.to_numeric(df.TotalCharges, errors='coerce') | pandas.to_numeric |
# -*- coding: utf-8 -*-
from datetime import timedelta
import operator
from string import ascii_lowercase
import warnings
import numpy as np
import pytest
from pandas.compat import lrange
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
Categorical, DataFrame, MultiIndex, Serie... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
"""SQL io tests
The SQL tests are broken down in different classes:
- `PandasSQLTest`: base class with common methods for all test classes
- Tests for the public API (only tests with sqlite3)
- `_TestSQLApi` base class
- `TestSQLApi`: test the public API with sqlalchemy engine
- `TestSQLiteFallbackApi`: t... | tm.assert_frame_equal(test_frame1, test_frame4) | pandas._testing.assert_frame_equal |
"""
Author: <NAME>
Modified: <NAME>
"""
import os
import warnings
import numpy as np
import pandas as pd
import scipy.stats
import pytest
from numpy.testing import assert_almost_equal, assert_allclose
from statsmodels.tools.sm_exceptions import EstimationWarning
from statsmodels.tsa.holtwinters import (ExponentialSmo... | pd.date_range('2000-1-1', periods=100, freq='MS') | pandas.date_range |
import os
import pandas as pd
import pytest
from dplypy.dplyframe import DplyFrame
from dplypy.pipeline import write_file
def test_write_file():
pandas_df = pd.DataFrame(
data={
"col1": [0, 1, 2, 3],
"col2": [3, 4, 5, 6],
"col3": [6, 7, 8, 9],
"col4": [9, ... | pd.read_csv("df_with_index.csv", sep=",", index_col=0) | pandas.read_csv |
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Index,
Series,
)
import pandas._testing as tm
dt_data = [
pd.Timestamp("2011-01-01"),
pd.Timestamp("2011-01-02"),
pd.Timestamp("2011-01-03"),
]
tz_data = [
pd.Timestamp("2011-01-01", tz="U... | Series(vals3) | pandas.Series |
import tables
import pandas as pd
def h5_gps(fn):
with tables.open_file(fn, "r") as fh:
data = fh.root.gps.read()
return pd.DataFrame(data)
def h5_gps_yo(fn):
data = h5_gps(fn)
return data.to_dict('list')
def h5_ins(fn):
with tables.open_file(fn, "r") as fh:
data = fh.root.ins.rea... | pd.DataFrame(data) | pandas.DataFrame |
from pipelines.base_pipeline import BasePipeline
from pipelines.unet.unet_architecture_paper import color_model
from pipelines.common_utils.lungmap_dataset import LungmapDataSet
from pipelines.unet.data_pipeline import unet_generators
from keras.callbacks import ModelCheckpoint
import os
from keras.models import load_m... | pd.DataFrame(test_data_processed) | pandas.DataFrame |
from __future__ import print_function
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from pandas import (Series, Index, Int64Index, Timestamp, Period,
DatetimeIndex, PeriodIndex, TimedeltaIndex,
Timedelta, timedelta_range, date_range, Float64Index... | pd.DatetimeIndex([]) | pandas.DatetimeIndex |
import matplotlib.pylab as plt
import networkx as nx
import numpy as np
import pandas as pd
from sklearn.metrics.pairwise import distance_metrics
def handle_2d_plot(
embedding,
kind,
color=None,
xlabel=None,
ylabel=None,
show_operations=False,
annot=False,
):
"""
Handles the logic... | pd.DataFrame(dist) | pandas.DataFrame |
'''
Step 2
This file reads over all the scrapped airfoil data in the "scrape" folder and runs xfoil
creates a file called
This file needs to be run in WSL for it to work
'''
import glob
from pandas.core.reshape.concat import concat
from libs.utils import pchip
import os, copy
import os.path as osp
fro... | pd.DataFrame(airfoil_data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 30 17:33:29 2016
@author: betienne
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.utils.extmath import randomized_svd
class MCA(object):
def __init__(self, X, ind=None, supp=None, n_components=None):
... | pd.read_excel('../chiens.xlsx') | pandas.read_excel |
import pytest
import numpy as np
import pandas
import pandas.util.testing as tm
from pandas.tests.frame.common import TestData
import matplotlib
import modin.pandas as pd
from modin.pandas.utils import to_pandas
from numpy.testing import assert_array_equal
from .utils import (
random_state,
RAND_LOW,
RAND_... | pandas.DataFrame(data) | pandas.DataFrame |
import numpy as np
import tensorflow as tf
import pickle
import sys
import pandas as pd
def predictWithTrainedModels(fileName):
with open('../tokenizer/tokenizer.pickle', 'rb') as handle:
tokenizer = pickle.load(handle)
df_temp = | pd.DataFrame({'source_code':[]}) | pandas.DataFrame |
"""
______ _ _ _ _ _ _
| ___ \ | | | | | | | (_) |
| |_/ /___ ___ ___ _ __ ___ _ __ ___ ___ _ __ __| | ___ _ __ | | | | |_ _| |___
| // _ \/ __/ _ \| '_ ` _ \| '_ ` _ \ / _ \ '_ \ / _` |/... | pd.concat(df_list, axis=1) | pandas.concat |
import json
from unittest.mock import MagicMock, patch
import numpy as np
import pandas as pd
import pytest
import woodwork as ww
from evalml.exceptions import PipelineScoreError
from evalml.model_understanding.prediction_explanations.explainers import (
abs_error,
cross_entropy,
explain_prediction,
e... | pd.Series([1]) | pandas.Series |
#Importing Necessary Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import ElasticNet
from pandas import Series, DataFrame
from sklearn.model_selection import train_test_split
#Importing the Training and Test Files
train = pd.read_csv('Train.csv')
test = pd.r... | Series(ENreg.coef_,predictors) | pandas.Series |
import pandas as pd
import numpy as np
class IdleSleepModeConverter:
def __init__(self, init_fill=None, init_is_ism=None):
if init_fill is not None:
self._current_fill = init_fill
else:
self._current_fill = pd.DataFrame()
if init_is_ism is not None:
self... | pd.Timedelta(10, unit='s') | pandas.Timedelta |
from src.lstm import LSTM
from src.attention import Attention
from src.regressor import AttnRegressor
from src.make_data import DataGenerator
from src.optimize import OptimizedRounder
import pandas as pd
import numpy as np
import warnings
import os
import argparse
import joblib
import pickle
import torch
from torch.uti... | pd.read_csv(test_path) | pandas.read_csv |
from statsmodels.compat.numpy import lstsq
from statsmodels.compat.pandas import assert_index_equal
from statsmodels.compat.platform import PLATFORM_WIN
from statsmodels.compat.python import lrange
import os
import warnings
import numpy as np
from numpy.testing import (
assert_,
assert_allclose,
assert_al... | pd.DataFrame({"b": b, "a": a}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from .. import paths
def encode_dates(dt):
'''Encodes datetime values as floats expressed in year fractions.
>>> encode_dates(pd.date_range('2017-11-01', '2018-02-01', freq='M').value... | pd.to_timedelta(dt) | pandas.to_timedelta |
import numpy as np
import pandas as pd
import pytest
from fairlens.metrics.distance import BinomialDistance, MeanDistance
from fairlens.metrics.significance import binominal_proportion_p_value as bin_prop
from fairlens.metrics.significance import bootstrap_binned_statistic as bootstrap_binned
from fairlens.metrics.sig... | pd.Series([-2, -1, 0, 1]) | pandas.Series |
import os
import scipy
import pyccl as ccl
import numpy as np
import pylab as plt
from numpy import linalg
import pandas as pd
import random
from util import *
mode = 'parallel_search'
iter_index = 99 # 0-99
print("Index :", iter_index)
export_dirfilename = "/mnt/zfsusers/sdatta/Desktop/cmb_expts/cmb_sdat/bin/cmb_expo... | pd.read_csv(fnms[pd_df]) | pandas.read_csv |
# Imports
import streamlit as st
import streamlit.components.v1 as components
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import time
import os.path
# ML dependency imports
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.manif... | pd.read_csv(droughtFile) | pandas.read_csv |
import datetime
from numbers import Number
from typing import Any
import numpy as np
import pandas as pd
def convert_indices(df: pd.DataFrame):
"""
extract all indices to columns if all are not numerical
and don't clash with existing column names
"""
if df.index.nlevels > 1:
# always rese... | pd.to_numeric(ser, downcast="unsigned") | pandas.to_numeric |
"""
espnapi.py
classes for scraping, parsing espn football api
this includes fantasy and real nfl data
Usage:
import nflprojections.espnapi as espn
season = 2020
week = 1
s = espn.Scraper(season=season)
p = espn.Parser(season=season)
data = s.playerstats(season)
print(p.weekly_project... | pd.DataFrame(proj) | pandas.DataFrame |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patheffects as mpe
import utils
import pandas as pd
from sklearn.metrics import precision_score, recall_score, roc_auc_score, label_ranking_average_precision_score
from sklearn.metrics import label_ranking_loss, confusion_matrix, average_precision_... | pd.DataFrame() | pandas.DataFrame |
import sys
import numpy as np
import scipy as sp
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib.collections import BrokenBarHCollection, PathCollection, LineCollection
import seaborn as sns
from . import genome
import logging
log = logging.getLogger(__name__)
def scale_colors(minval, maxval,... | pd.DataFrame({"chr": self.genome.chrs, "centro_mid": self.genome.centro_mid}) | pandas.DataFrame |
import sys
sys.path.append('../../../')
import apps.streetdownloader.pkg.streetview as streetview
sys.path.append('/scratch/guxi/gsv_processor')
import urlsigner
import logging
import subprocess
import requests
import multiprocessing
import pandas as pd
import numpy as np
import sqlalchemy as sa
from tqdm import tq... | pd.read_csv('/scratch/guxi/gsv_processor/no_result_loc_info_all.csv') | pandas.read_csv |
import settings
import helpers
import SimpleITK # conda install -c https://conda.anaconda.org/simpleitk SimpleITK
import numpy
import pandas
import ntpath
import cv2 # conda install -c https://conda.anaconda.org/menpo opencv3
import shutil
import random
import math
import multiprocessing
from bs4 import Be... | pandas.DataFrame(all_lines, columns=["patient_id", "anno_index", "coord_x", "coord_y", "coord_z", "diameter", "malscore", "sphericiy", "margin", "spiculation", "texture", "calcification", "internal_structure", "lobulation", "subtlety"]) | pandas.DataFrame |
import logging
import pandas as pd
from .abstract_trainer import AbstractTrainer
from .model_presets.presets import get_preset_models
logger = logging.getLogger(__name__)
# This Trainer handles model training details
class AutoTrainer(AbstractTrainer):
def __init__(self, path, problem_type, scheduler_options=Non... | pd.concat([y_train, y_test], ignore_index=True) | pandas.concat |
from flask import Flask, render_template, request, redirect, make_response
import os
from bokeh.embed import components
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource, HoverTool
from bokeh.models.widgets import Panel, Tabs
import numpy as np
import pandas as pd
from bokeh.palettes import Sp... | pd.DataFrame(data_all) | pandas.DataFrame |
from transformers import pipeline
# from scipy import stats
# import seaborn as sns
import pandas as pd
from collections import defaultdict
# import matplotlib.pylab as plt
# from nrclex import NRCLex
import argparse
from tqdm.notebook import tqdm
from utils import *
from aggregating_nouns_pronouns_names import run_exp... | pd.concat([all_df, new_add]) | pandas.concat |
import warnings
import numpy as np
import pandas
from sklearn.feature_extraction.text import CountVectorizer
# Ingore zero devision errors in cosine and qgram algorithms
# warnings.filterwarnings("ignore")
################################
# STRING SIMILARITY #
################################
def jaro_s... | pandas.isnull(x[0]) | pandas.isnull |
"""
This script visualises the prevention parameters of the first and second COVID-19 waves.
Arguments:
----------
-f:
Filename of samples dictionary to be loaded. Default location is ~/data/interim/model_parameters/COVID19_SEIRD/calibrations/national/
Returns:
--------
Example use:
------------
"""
__author_... | pd.Timestamp('2021-01-04') | pandas.Timestamp |
# -*- coding: utf-8 -*-
from datetime import timedelta
import pandas as pd
import pandas.util.testing as tm
class TestTimedeltaSeriesComparisons(object):
def test_compare_timedelta_series(self):
# regresssion test for GH5963
s = pd.Series([timedelta(days=1), timedelta(days=2)])
actual = s... | pd.Series([4, 2], name='xxx', dtype=object) | pandas.Series |
"""Check notebook execution speed"""
import subprocess
import time
from pathlib import Path
import pandas as pd
def bench_notebook(filename):
cmd = f"jupyter nbconvert --to notebook --ExecutePreprocessor.timeout=-1 --execute {filename}"
print(cmd)
t = time.time()
subprocess.call(cmd, shell=True)
... | pd.DataFrame(timings) | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
from tf_pipeline.conf import SUBMIT_PATH
from typing import Union
from tqdm import tqdm
SUBMIT_PATH = "./data/submission/"
from skopt.plots import plot_objective
horizon = 'validation'
your_submission_path = os.path.join(SUBMIT_PATH, "tf_estim_%s.csv" % horizon)
## fr... | pd.read_csv("data/raw/calendar.csv") | pandas.read_csv |
import pandas as pd
import tqdm
import numpy as np
import json
import datetime
tweets = []
columns_needed = ['covv_collection_date','covv_location','covv_lineage']
for line in open('provision.json', 'r'):
initial = json.loads(line)
final = dict(filter(lambda elem: elem[0] in columns_needed, initial.items()... | pd.DataFrame(tweets) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sat May 9 17:44:14 2020
@author: GE702PL
"""
import xlwings as xw
from functools import wraps
import datetime
import pandas as pd
from calendar import monthrange
# LOGS
def log(*m):
print(" ".join(map(str, m)))
def black(s):
return '\033[1;30m%s\033[m' % s
def green... | pd.DateOffset(months=mths_offset) | pandas.DateOffset |
#!/usr/bin/env python
# coding: utf-8
#%% ---- DEPENDENCIES
import os
import sys
import pandas as pd
import matplotlib.pyplot as plt
import manynames as mn
#%% ---- FUNCTIONS TO RECREATE DISTRIBUTION FIGURE
def statistics_mn_topnames(df, domain_key, print_stats=False):
"""
VG image distribution, MN entry-leve... | pd.DataFrame.from_dict(print_df) | pandas.DataFrame.from_dict |
from typing import Dict, Tuple, List
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import datetime as dt
def plot_score_distribution(reviews_distribution, nb_reviews):
"""
Plot a bar chart showing reviews' score ditribution.
Parameters
---... | pd.DataFrame(columns=columns) | pandas.DataFrame |
#Import dependecies
from flask import Flask, jsonify
import datetime as dt
import sqlalchemy
import pandas as pd
import numpy as np
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func, asc
from sqlalchemy import inspect
#SQLAlchemy
engine = cre... | pd.DataFrame(tobs_only) | pandas.DataFrame |
import pandas as pd
import numpy as np
import re
import datetime
import itertools
import sklearn.linear_model
import sklearn.svm
import sklearn.ensemble
import sklearn.preprocessing
import sklearn.pipeline
import sklearn.model_selection
import sklearn.tree
import numpy.random
import matplotlib.pyplot as plt
import matp... | pd.read_csv("algae.csv") | pandas.read_csv |
"""
The tests in this package are to ensure the proper resultant dtypes of
set operations.
"""
import numpy as np
import pytest
from pandas.core.dtypes.common import is_dtype_equal
import pandas as pd
from pandas import (
CategoricalIndex,
DatetimeIndex,
Float64Index,
Int64Index,
MultiIndex,
R... | tm.assert_index_equal(result, expected) | pandas._testing.assert_index_equal |
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 9 20:13:44 2020
@author: Adam
"""
#%% Heatmap generator "Barcode"
import os
os.chdir(r'C:\Users\Ben\Desktop\T7_primase_Recognition_Adam\adam\paper\code_after_meating_with_danny')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
imp... | pd.read_csv('./data/chip_B_favor.csv') | pandas.read_csv |
# The normal imports
import numpy as np
from numpy.random import randn
import pandas as pd
# Import the stats library from numpy
from scipy import stats
# These are the plotting modules adn libraries we'll use:
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
# Now we'l learn how to com... | Series(dataset, name='My_DATA') | pandas.Series |
from polo2 import PoloDb
import pandas as pd
"""
Consider deleting this; it has been replaced by Elements in the Flask app. However, there may be
wisdom in putting something here for more general purpose use.
"""
class PoloReport():
def __init__(self, config, trial_name='trial1'):
# Set some values
... | pd.read_sql_query(sql, self.model.conn) | pandas.read_sql_query |
from .utilities import format_ft_comp, format_end2end_prompt, split_multi_answer
from .configs import ANSWER_COL, INCORRECT_COL
from datasets import load_metric
import openai
import numpy as np
import pandas as pd
import warnings
from t5.evaluation import metrics
from time import sleep
import logging
logger = logging.... | pd.isnull(frame.loc[idx, ANSWER_COL]) | pandas.isnull |
"""max temp before jul 1 or min after"""
import datetime
import psycopg2.extras
import numpy as np
import pandas as pd
from matplotlib.patches import Rectangle
from pyiem.plot.use_agg import plt
from pyiem.util import get_autoplot_context, get_dbconn
from pyiem.exceptions import NoDataFound
PDICT = {'fall': 'Minimum ... | pd.DataFrame(d) | pandas.DataFrame |
# coding=utf-8
"""
'nn_random_pipeline.py" script is a pipeline of the following jobs:
(1) calls "sampling_main" function for random generation of features
(2) calls "neural_trainer" function for training a first neural network and saving the model
(3) executes a loop in which "sampling_main" and "neural_tr... | pd.DataFrame(dictionary) | pandas.DataFrame |
import numpy as np
import torch
import torch.nn as nn
import os.path as os
import pandas as pd
from sklearn.metrics import confusion_matrix
from collections import deque
class EarlyStopping(object):
"""EarlyStopping handler can be used to stop the training if no improvement after a given number of events
Args... | pd.DataFrame() | pandas.DataFrame |
# coding=utf-8
'''
获取原始数据
'''
import numpy as np
import pandas as pd
ORI_PATH = 'D:/StockData/'
start = '2015-01-01'
end = '2017-12-31'
def preprocess():
stock_list = pd.read_csv(ORI_PATH + 'stock.csv', dtype=object)
for idx in stock_list.index:
code = stock_list.loc[idx]['code']
try:
... | pd.DataFrame(result, index=df.index[0:lenth]) | pandas.DataFrame |
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# 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 a... | pd.testing.assert_frame_equal(pdf, mdf) | pandas.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 16 12:41:09 2019
@author: sdenaro
"""
import matplotlib.pyplot as plt
import pandas as pd
from datetime import datetime as dt
from datetime import timedelta
import numpy as np
from numpy import matlib as matlib
import seaborn as sns
import statsmodels.api... | pd.DataFrame(X_poly, columns=X_poly_feature_name) | pandas.DataFrame |
import requests
import pandas as pd
from datetime import datetime
# generate filepath relative to script location
scriptPath = __file__
path = scriptPath[:-28] + '/data/'
filepath = path + 'frozenDFICake.csv'
# API request for freezer DFI
link='https://poolapi.cakedefi.com/freezer-page'
siteContent = requests.get(... | pd.read_json(siteContent.text, orient='colums') | pandas.read_json |
import pandas as pd
import numpy as np
from matplotlib import colors, pyplot as plt
import os
from PIL import Image, ImageFont, ImageDraw, ImageEnhance
import shutil
import sqlite3
import sys
from os.path import expanduser
# generate a tile for each frame, annotating intersecting precursor cuboids
MZ_MIN = 748 ... | pd.DataFrame(colours_l, columns=['intensity','colour']) | pandas.DataFrame |
"""
This is the dashboard of CEA
"""
from __future__ import division
from __future__ import print_function
import os
import cea.config
import cea.inputlocator
from cea.plots.solar_potential.solar_radiation_curve import solar_radiation_curve
from cea.plots.solar_potential.solar_radiation import solar_radiation_district... | pd.DataFrame(dict_not_aggregated_2) | pandas.DataFrame |
import pandas as pd
import sqlite3 as sql
from asn1crypto._ffi import null
from pandas.tests.frame.test_sort_values_level_as_str import ascending
class FunctionMgr:
def __init__(self,sql_con):
self.sql_con = sql_con
pass
'''
获取大于某换手率的股票列表
'''
def GetTurnoverRateList(self... | pd.merge(left=data_daily, left_on=['ts_code','trade_date'],right=data_turnover, right_on=['ts_code','trade_date']) | pandas.merge |
"""
2020-10-09 MY
Printing plots of forecasts from JRC, Satotilasto and EO predictions.
RUN:
python forecasting.py -i /Users/myliheik/Documents/myCROPYIELD/cropyieldMosaics/results/test1320 -y 2018 -n
python forecasting.py -i /Users/myliheik/Documents/myCROPYIELD/cropyieldMosaics/results/test1320 -y 2018 -n -r -f
#... | pd.Series(farmID3D) | pandas.Series |
import numpy as np
import pandas as pd
from utilities.constants import TREAT, CONC, index_order, column_order
_THRESHOLD_ABOVE = 1
_THRESHOLD_BELOW = 0
def threshold_dict(data, _THRESHOLD):
# TODO -- document
"""COMMENT"""
# std and mean by column
numerical_cols = data._get_numeric_data().columns
... | pd.DataFrame.from_dict(result, orient='index') | pandas.DataFrame.from_dict |
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