prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# being a bit too dynamic
# pylint: disable=E1101
import datetime
import warnings
import re
from math import ceil
from collections import namedtuple
from contextlib import contextmanager
from distutils.version import LooseVersion
import numpy as np
from pandas.util.decorators import cache_readonly, deprecate_kwarg
im... | Appender(_shared_docs['plot'] % _shared_doc_series_kwargs) | pandas.util.decorators.Appender |
import streamlit as st
from alphapept.gui.utils import (
check_process,
init_process,
start_process,
escape_markdown,
)
from alphapept.paths import PROCESSED_PATH, PROCESS_FILE, QUEUE_PATH, FAILED_PATH
from alphapept.settings import load_settings_as_template, save_settings
import os
import psutil
import... | pd.DataFrame(queue_files, columns=["File"]) | pandas.DataFrame |
import pandas as pd
import numpy as np
from collections import Counter
import os
import sys
data_path = "data\iris.data"
column_names = ["sepal_length", "sepal_width",
"petal_length", "petal_width", "class"]
def is_float_list(iterable):
"""Checks if all elements of an iterable are floats
"""
... | pd.read_csv(data_path, names=column_names) | pandas.read_csv |
from abc import ABC, abstractmethod
from collections import defaultdict
from datetime import datetime
from functools import cached_property
from typing import List, Dict, Union, Optional, Iterable
import numpy as np
import pandas as pd
from gym import Space, spaces
from pandas import Interval
from torch.utils.data imp... | pd.Series() | pandas.Series |
from collections import deque
from datetime import datetime
import operator
import numpy as np
import pytest
import pytz
import pandas as pd
import pandas._testing as tm
from pandas.tests.frame.common import _check_mixed_float, _check_mixed_int
# -------------------------------------------------------------------
# ... | pd.DataFrame({"A": [1.1, 3.3], "B": [2.5, -3.9]}) | pandas.DataFrame |
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
import copy
import seaborn as sn
from sklearn.naive_bayes import GaussianNB, MultinomialNB, CategoricalNB
from DataLoad import dataload
from Classifier.Bayes.NaiveBayes import NaiveBayes
from sklearn.neighbors import KNeighborsClassifier... | pd.DataFrame(train_ordinal) | pandas.DataFrame |
"""
Lineplot from a wide-form dataset
=================================
_thumb: .52, .5
"""
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(style="whitegrid")
rs = np.random.RandomState(365)
values = rs.randn(365, 4).cumsum(axis=0)
dates = pd.date_range("1 1 2016", periods=365, freq="D")
data = | pd.DataFrame(values, dates, columns=["A", "B", "C", "D"]) | pandas.DataFrame |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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 cop... | pd.read_csv(args.test_data_file) | pandas.read_csv |
"""
Utility functions for ARNA campaign/project work
"""
import os
import sys
import glob
import gc
import numpy as np
import pandas as pd
import xarray as xr
import xesmf as xe
import AC_tools as AC
from netCDF4 import Dataset
from datetime import datetime as datetime_
import datetime as datetime
import time
from time... | pd.DataFrame() | pandas.DataFrame |
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
from scipy.signal import periodogram
from .misc import get_equivalent_days
import re
#%% plotting functions
def adjust_bright(color, amount=1.2):
"""
Adjust color brightness in plots for use.
Inpu... | pd.DataFrame(resid, columns=cols) | pandas.DataFrame |
import numpy as np
import pandas as pd
from analysis.transform_fast import load_raw_cohort, transform
def test_immuno_group():
raw_cohort = load_raw_cohort("tests/input.csv")
cohort = transform(raw_cohort)
for ix, row in cohort.iterrows():
# IF IMMRX_DAT <> NULL | Select | Next
if pd... | pd.notnull(row["shield_dat"]) | pandas.notnull |
import os
import math
import copy
import random
import calendar
import csv
import pandas as pd
import numpy as np
import networkx as nx
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.ticker as ticker
import sqlite3
import seaborn as sns
#from atnresilience import ... | pd.concat([IAPL_df_all,IAPL_df_airline],ignore_index = True) | pandas.concat |
import pandas as pd
from pandas import HDFStore
import numpy as np
import subprocess
import io
import matplotlib.pyplot as plt
import gc
import os
from scipy.stats import ks_2samp
from functools import lru_cache
'''
Analyze wsprspots logs (prepared by WSPRLog2Pandas)
All manipulations are performed against an HDF5 ... | pd.Series([]) | pandas.Series |
import cv2
import os
import pandas as pd
import pickle
import random
import zipfile
from ml.repository import TextDataset, ClassificationDataset
from ml.utils import LogMixin
from ml.utils.io import download_url
class BBCNews(LogMixin):
"""Internal class to handle the download, unpack and merging of the bbc
... | pd.DataFrame(data, columns=[self._FEATURE_LABEL, self._TARGET_LABEL]) | pandas.DataFrame |
"""Road network risks and adaptation maps
"""
import os
import sys
from collections import OrderedDict
import ast
import numpy as np
import geopandas as gpd
import pandas as pd
import cartopy.crs as ccrs
import cartopy.io.shapereader as shpreader
import matplotlib.pyplot as plt
from shapely.geometry import LineString
... | pd.read_csv(flow_file_path) | pandas.read_csv |
# vim: fdm=indent
'''
author: <NAME>
date: 01/11/17
content: Try to see where in the sorting plots are successful and failed
cells for different colon cell types (after RNA-Seq annotation).
'''
# Modules
import os
import sys
import argparse
import numpy as np
import pandas as pd
import matplot... | pd.read_csv(fn_index, sep=',', index_col='Index') | pandas.read_csv |
# -*- coding: utf-8 -*-
import logging
import os
from collections import Counter
from multiprocessing.dummy import Pool as ThreadPool
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from src.globalVariable import GlobalVariable
pd.options.display.float_format = '{0:.3}'.format
class Preferen... | pd.concat(users_relevance_df, sort=False) | pandas.concat |
import pandas
#DataFrame is an object that holds data, this is also called data structure..
df1=pandas.DataFrame([[2,4,6],[10,20,30]])
print(df1)
print("\n")
#Adding column names to data frame
df1=pandas.DataFrame([[2,4,6],[10,20,30]],columns=["Price","Age","Value"])
print(df1)
print("\n")
#Adding index name... | pandas.DataFrame([[2,4,6],[10,20,30]], columns=["Price","Age","Value"], index=["First","Second"])
print(df1) | pandas.DataFrame |
import urllib
import pytest
import pandas as pd
from pandas import testing as pdt
from anonympy import __version__
from anonympy.pandas import dfAnonymizer
from anonympy.pandas.utils_pandas import load_dataset
@pytest.fixture(scope="module")
def anonym_small():
df = load_dataset('small')
anonym = dfAnonymize... | pdt.assert_series_equal(expected, output, check_names=False) | pandas.testing.assert_series_equal |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 8 16:10:09 2019
@author: andreypoletaev
"""
import numpy as np
import pandas as pd
import freud
from scipy.spatial import Voronoi
from matplotlib import pyplot as plt
import matplotlib as mpl
from colorsys import rgb_to_hls, hls_to_rgb
from scip... | pd.DataFrame({'total':total_BR/total_time, new_r_col:r, 'site':'BR'}) | pandas.DataFrame |
import datetime
import dspl2
from flask import (
Flask, render_template, request, Response)
from functools import lru_cache
from icu import SimpleDateFormat
from io import StringIO
import json
import os.path
import pandas as pd
from urllib.parse import urlparse
app = Flask(__name__)
@app.route('/')
def main():
... | pd.DataFrame(ret) | pandas.DataFrame |
import pandas as pd
from plotly import graph_objs as go
import os
import glob
import shapefile
import datetime as dt
def generate_figure(figure_title, time_series):
"""
Generate a figure from a list of time series Pandas DataFrames.
Args:
figure_title(str): Title of the figure.
time_seri... | pd.to_datetime(df.iloc[:, 0], unit='ms') | pandas.to_datetime |
import time
import sys
import pandas as pd
from pandas import DataFrame as df
from daqmx_session import DAQmxSession
# Configure Testing Parameters Here
samples = 10000 # samples per trial
trials = 100
device = 'Dev1' # device alias as listed in NI MAX
channel = 'ao1' # for digital tasks use 'port#/line#', for ana... | df(results_no_cfg, columns=['Time']) | pandas.DataFrame |
import numpy as np
import pandas as pd
import scipy.stats
import h5py
import pkg_resources
import pybedtools
from DIGDriver.data_tools import mutation_tools
from DIGDriver.sequence_model import nb_model
def load_pretrained_model(h5, key='genic_model', restrict_cols=True):
""" Load a pretrained gene model
"""
... | pd.read_table('/data/cb/maxas/data/projects/cancer_mutations/DRIVER_DBs/OncoKB_cancerGeneList.txt') | pandas.read_table |
import pytest
import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.preprocessing import StandardScaler
from time_series_experiments.pipeline import Pipeline, ColumnsProcessor, Step
from time_series_experiments.pipeline.dataset import DatasetConfig, VarType
from time_series_experiments.pipeline.v... | pd.DataFrame({"target": y, "date": dates}) | pandas.DataFrame |
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# --------------------------------------------------------------------------------------------
import unittest
import numpy as np... | pd.Series(data, name="ts") | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 25 23:16:15 2020
@author: Eli
"""
from sklearn.model_selection import cross_validate
from sklearn.model_selection import GroupKFold
from sklearn.model_selection import cross_val_predict
from sklearn.neighbors import KNeighborsClassifier
from sklear... | pd.DataFrame({col:unique_vals,'accuracy':accuracy}) | pandas.DataFrame |
from typing import Any, Dict, Type # NOQA
import logging
from easydict import EasyDict
from kedro.utils import load_obj
import numpy as np
import pandas as pd
import sklearn # NOQA
from sklearn.metrics import (
accuracy_score,
confusion_matrix,
f1_score,
precision_score,
recall_score,
roc_auc... | pd.DataFrame() | pandas.DataFrame |
"""
Module to generate counterfactual explanations from a KD-Tree
This code is similar to 'Interpretable Counterfactual Explanations Guided by Prototypes': https://arxiv.org/pdf/1907.02584.pdf
"""
from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
import numpy as np
import timeit
from sklearn.neighbo... | pd.get_dummies(query_instance_df) | pandas.get_dummies |
# -*- coding: utf-8 -*-
# Copyright 2020 <NAME>
# Permission is hereby granted, free of charge, to any person
# obtaining a copy of this software and associated documentation files
# (the "Software"), to deal in the Software without restriction,
# including without limitation the rights to use, copy, modify, merge,
#... | pd.read_excel(path, *args, **kwargs) | pandas.read_excel |
# -*- coding: utf-8 -*-
"""System operating cost plots.
This module plots figures related to the cost of operating the power system.
Plots can be broken down by cost categories, generator types etc.
@author: <NAME>
"""
import logging
import pandas as pd
import marmot.config.mconfig as mconfig
from marmot.plottingm... | pd.concat(total_cost_chunk, axis=0, sort=False) | pandas.concat |
"""
Tests for DatetimeIndex timezone-related methods
"""
from datetime import date, datetime, time, timedelta, tzinfo
import dateutil
from dateutil.tz import gettz, tzlocal
import numpy as np
import pytest
import pytz
from pandas._libs.tslibs import conversion, timezones
import pandas.util._test_decorators as td
imp... | tm.assert_almost_equal(result, exp) | pandas._testing.assert_almost_equal |
import importlib
from hydroDL.data import gridMET
from hydroDL import kPath
import numpy as np
import pandas as pd
import os
import time
import argparse
"""
convert raw data to tab format of each sites
"""
workDir = kPath.dirWQ
dataFolder = os.path.join(kPath.dirData, 'gridMET')
maskFolder = os.path.join(kPath.dirData... | pd.read_csv(fileName, index_col=0) | pandas.read_csv |
"""Tests for model_selection.py."""
import numpy as np
import pandas as pd
import pytest
from fclearn.model_selection import create_rolling_forward_indices, train_test_split
groupby = ["SKUID", "ForecastGroupID"]
class TestTrainTestSplit:
"""Test train_test_split()."""
def test_one(self, demand_df):
... | pd.to_datetime("2017-01-16") | pandas.to_datetime |
import numpy as np
import pytest
from pandas import Series, Timestamp, isna
import pandas._testing as tm
class TestSeriesArgsort:
def _check_accum_op(self, name, ser, check_dtype=True):
func = getattr(np, name)
tm.assert_numpy_array_equal(
func(ser).values, func(np.array(ser)), check_... | tm.assert_numpy_array_equal(qindexer, mindexer) | pandas._testing.assert_numpy_array_equal |
"""
Responsible for production of data visualisations and rendering this data as inline
base64 data for various django templates to use.
"""
from datetime import datetime, timedelta
from collections import Counter, defaultdict
from typing import Iterable, Callable
import numpy as np
import pandas as pd
import matplotli... | pd.date_range(start, end, freq="BMS") | pandas.date_range |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# moldynplot.dataset.TimeSeriesYSpecDataset.py
#
# Copyright (C) 2015-2017 <NAME>
# All rights reserved.
#
# This software may be modified and distributed under the terms of the
# BSD license. See the LICENSE file for details.
"""
Processes and represents data that is... | pd.concat([mean_df, errors]) | pandas.concat |
import json
import pandas as pd
import requests
import logging
log = logging.getLogger(__name__)
def get_titled_players(chess_title: str) -> list:
"""
Returns a list of player names
:param chess_title:
:return: None
"""
url = f'https://api.chess.com/pub/titled/{chess_title}'
log.info(f"... | pd.DataFrame(player_stats) | pandas.DataFrame |
# Copyright (c) 2018-2021, NVIDIA CORPORATION.
import operator
import string
import numpy as np
import pandas as pd
import pytest
import cudf
from cudf.core._compat import PANDAS_GE_110
from cudf.testing._utils import (
NUMERIC_TYPES,
assert_eq,
assert_exceptions_equal,
)
@pytest.fixture
def pd_str_cat... | pd.CategoricalDtype(categories=["aa", "bb", "c"]) | pandas.CategoricalDtype |
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 1 00:49:21 2018
@author: teo
"""
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 8 10:32:18 2018
@author: teo
"""
import pandas as pd
from plotly import tools
import numpy as np
import matplotlib.pyplot as plt
import plotly.plotly as py
import... | pd.DataFrame() | pandas.DataFrame |
import os
import glob
import pandas as pd
import numpy as np
from datetime import datetime
import plotly.express as px
import plotly.figure_factory as ff
import plotly.graph_objs as go
from plotly.subplots import make_subplots
from preprocess import (sleep_preprocess, heart_preprocess, exercise_preprocess, stepcount_... | pd.date_range("00:00", "23:59", freq="min") | pandas.date_range |
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 2 22:43:29 2020
@author: Lyy
"""
import pandas as pd
import numpy as np
import re
import random
import matplotlib.patches as patches
import matplotlib.pyplot as plt
class Node(object):
idcase = {}
def __init__(self, nid, ntype, x, y):
self.id = nid
... | pd.DataFrame() | pandas.DataFrame |
# <NAME> (<EMAIL>)
from __future__ import absolute_import, division, print_function
from builtins import range
import numpy as np
import pandas as pd
RANDOM = "random"
ORDRED = "ordered"
LINEAR = "linear"
ORDERED = ORDRED # Alias with extra char but correct spelling
SFT_FMT = "L%d"
INDEX = None # Dummy variable t... | pd.concat(D, axis=1, names=["lag"]) | pandas.concat |
import logging
import numpy as np
import pandas as pd
import sklearn
from sklearn.model_selection import train_test_split
from tensorflow import keras
from tensorflow.keras import layers
logger = logging.getLogger("ACE")
class TestAskJunoACE:
def __init__(self):
self.k_fold_count = 4
self.num_ep... | pd.merge(data, y_test[['preds']], how='left', left_index=True, right_index=True) | pandas.merge |
# -*- encoding:utf-8 -*-
import pandas as pd
import numpy as np
from datetime import datetime
# 提交处理
# rule_data1.fft+ever_1
# rule_data2.规则2+fft+ever_1
# rule_data3.规则4+规则2+fft+ever_1
# rule_data4.规则4
# rule_data5.规则5
dire = '../../data/'
train = pd.read_csv(dire + 'train5.csv', encoding='utf-8')
test = pd.read_csv(di... | pd.read_csv(dire + 'backup/LOVECT/rule_data8.csv', encoding='utf-8') | pandas.read_csv |
import os
import pandas as pd
from random import randint
# Class responsible for: calculating where to paste detection images on
# background, pasting images on backgrounds, formatting and outputting CSV
# files
class DataGenerator:
def __init__(self, image_set, background_set, num_samples, cutoff):
self... | pd.DataFrame(train_list, columns=column_name) | pandas.DataFrame |
import pandas as pd
typeDict = {'Types': ['yearly', 'monthly', 'daily', 'hourly']}
aDict = {'AUS_Dep_Results A': [3.1, 4.6, 7.9, 8.4]}
a1Dict = {'AUS_With_Results A': [3.1, 4.6, 7.9, 8.4]}
bDict = {'AUS_Dep_Results B': [5.4, 9.3, 1.2, 6.6]}
b1Dict = {'AUS_With_Results B': [5.4, 9.3, 1.2, 6.6]}
cDict = {'HUN_Dep_Result... | pd.DataFrame(table) | pandas.DataFrame |
import csv
from io import StringIO
import os
import numpy as np
import pytest
from pandas.errors import ParserError
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
NaT,
Series,
Timestamp,
date_range,
read_csv,
to_datetime,
)
import pandas._testing as tm
impo... | tm.assert_frame_equal(df[cols], rs_c, check_names=False) | pandas._testing.assert_frame_equal |
# create dataframes based on the available data
import json
import os
import geopandas as gpd
import pandas as pd
import rasterio
import rasterstats
import time
import random
def generate_dataframe(shapefile, raster):
# Read the shapefile and convert its crs
districts = gpd.read_file(shapefile)
s = sha... | pd.DataFrame(lis1) | pandas.DataFrame |
from bs4 import BeautifulSoup as BS
import requests
import pandas as pd
import os
round = 1
data = []
while True:
a = requests.get(f"https://www.sololearn.com/codes?page={round}").content
html = BS(a, "html.parser")
code_boxes = html.find_all("div", class_="code")
print(round)
if len(code_boxes... | pd.DataFrame(data) | pandas.DataFrame |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
#-------------read csv---------------------
df_2010_2011 = pd.read_csv("/mnt/nadavrap-students/STS/data/data_Shapira_20200911_2010_2011.csv")
df_2012_2013 = pd.read_csv("/mnt/nadavrap-students/STS/data/data_Shapira_20200911_2012_2013.csv")
df_2014... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 21 10:04:20 2022
@author: wyattpetryshen
"""
#Data Source: https://climate.weather.gc.ca/climate_data/hourly_data_e.html?hlyRange=2014-10-23%7C2022-01-20&dlyRange=2018-10-29%7C2022-01-20&mlyRange=%7C&StationID=52959&Prov=BC&urlExtension=_e.html&searchType=stnProx&optLimit... | pd.concat(y2021,ignore_index=True) | pandas.concat |
import pandas as pd
import seaborn as sns
import numpy as np
def plot_mri_settings_scatter(df, path, subject):
"""
function to group data by mri_settings and plot data
returns data for each mri setting as dataframe and plot as linegraph
inclding scatterplot
"""
df_base = None
df_tr1 = None... | pd.DataFrame(group) | pandas.DataFrame |
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
def preprocess(df):
returns = df[['beat0', 'beat1', 'beat2', 'beat3']].copy()
tickers = df[['ticker']].copy()
df = df.drop(columns=['ticker', "longName", "logo_url", "close_price", 'beat0', 'beat1', 'beat2', 'beat3'])
# standard... | pd.concat([tickers, std, returns], axis=1) | pandas.concat |
import os
import urllib
import json
import time
import arrow
import numpy as np
import pandas as pd
from pymongo import MongoClient, UpdateOne
MONGO_URI = os.environ.get('MONGO_URI')
DARKSKY_KEY = os.environ.get('DARKSKY_KEY')
FARM_LIST = ['BLUFF1', 'CATHROCK', 'CLEMGPWF', 'HALLWF2', 'HDWF2',
'LKBONNY2... | pd.concat([weather, df], axis=0, sort=True) | pandas.concat |
"""
This file is part of the accompanying code to our paper
<NAME>., <NAME>., <NAME>., & <NAME>. (2021). Uncovering flooding mecha-
nisms across the contiguous United States through interpretive deep learning on
representative catchments. Water Resources Research, 57, e2021WR030185.
https://doi.org/10.1029/2021WR030185... | pd.to_datetime(peak_date) | pandas.to_datetime |
# -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime, timedelta
import functools
import itertools
import numpy as np
import numpy.ma as ma
import numpy.ma.mrecords as mrecords
from numpy.random import randn
import pytest
from pandas.compat import (
PY3, PY36, OrderedDict, ... | DataFrame(ad) | pandas.DataFrame |
#!/usr/bin/env python3
"""Make silly table showing distribution over block sizes for extended blocks
world run."""
import os
import re
import click
import pandas as pd
NAME_RE = re.compile(
# success-blocks-nblk35-seed2107726020-seq42
r'\b(?P<succ_fail>success|failure)-blocks-nblk(?P<nblk>\d+)(-ntow'
r'... | pd.DataFrame.from_records(result_dicts) | pandas.DataFrame.from_records |
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Loads the episode lengths from the csv files into a dictionary and return the dictionary
def load_data(algpath, name='episodes'):
Data = []
dirFiles = os.listdir(algpath)
# Files = np.array([i for i in dirFiles if 'episodes'... | pd.DataFrame({'failures': failureTimesteps}) | pandas.DataFrame |
from collections import deque
from datetime import datetime
import operator
import re
import numpy as np
import pytest
import pytz
import pandas as pd
from pandas import DataFrame, MultiIndex, Series
import pandas._testing as tm
import pandas.core.common as com
from pandas.core.computation.expressions import _MIN_ELE... | pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) | pandas.DataFrame |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
import warnings
def _validate_axis(data, axis):
ndim = data.ndim
if not -ndim <= axis < ndim:
raise IndexError('axis %r out of bounds [-%r, %r)'
... | pd.isnull(values) | pandas.isnull |
def Cosiner(params : dict):
def Column_correction(table):
drop_col = [i for i in table.columns if "Unnamed" in i]
table.drop(drop_col, axis = 1, inplace = True)
return table
def Samplewise_export(neg_csv_file, pos_csv_file, out_path, merged_edge_table, merged_node_table) :
... | pd.DataFrame() | pandas.DataFrame |
from datetime import datetime
from random import randint
from time import sleep
import numpy as np
import pandas as pd
import requests
from bs4 import BeautifulSoup
class BooksScraper:
"""Automated data collection tool (web-scraper) that is specifically
tailored to scrape data on Bookdepository based on spec... | pd.DataFrame(nested_book_details, columns) | pandas.DataFrame |
from airflow.decorators import dag, task
from airflow.utils.dates import days_ago
from airflow.operators.bash import BashOperator
from airflow.providers.postgres.operators.postgres import PostgresOperator
from airflow.hooks.postgres_hook import PostgresHook
from airflow.models import Variable
from datetime import datet... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import networkx as nx
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
#funtions
def degree(G,f):
"""
Adds a column to the dataframe f with the degree of each node.
G: a networkx graph.
f: a pandas dataframe.
"""
if not(set(f.name) == set(G.nodes()... | pd.merge(f, p_df, on='name') | pandas.merge |
#-*- coding: utf-8 -*-
"""
revision process for:
"tertiaryElectricityConsumption_1092915978"
"tertiaryElectricityConsumption_7104124143"
"gasConsumption_1092915978"
"gasConsumption_5052736858"
"gasConsumption_3230658933"
"gasConsumption_7104124143"
"gasConsumption_8801761586"
"""
import calendar
from d... | pd.to_numeric(df.value) | pandas.to_numeric |
import pandas as pd
df4 = pd.read_csv('../data/readme_train.csv', sep=';')
df5 = | pd.read_csv('../data/abstracts.csv', sep=';') | pandas.read_csv |
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2021, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | pdt.assert_series_equal(obs, exp) | pandas.testing.assert_series_equal |
# Copyright (c) 2020-2022, NVIDIA CORPORATION.
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
import cudf
from cudf.core._compat import PANDAS_GE_130
from cudf.core.column import ColumnBase
from cudf.core.dtypes import (
CategoricalDtype,
Decimal32Dtype,
Decimal64Dtype,
Deci... | pd.core.arrays._arrow_utils.ArrowIntervalType(subtype, closed) | pandas.core.arrays._arrow_utils.ArrowIntervalType |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# linkedin_jog_scraping.py
import os
import pandas as pd
from parsel import Selector
from time import sleep
from selenium import webdriver
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.comm... | pd.DataFrame(dict) | pandas.DataFrame |
from openff.toolkit.typing.engines.smirnoff import ForceField
from openff.toolkit.topology import Molecule, Topology
from biopandas.pdb import PandasPdb
import matplotlib.pyplot as plt
from operator import itemgetter
from mendeleev import element
from simtk.openmm import app
from scipy import optimize
import subprocess... | pd.concat([df_energy_xml, df_energy_prmtop], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
import re
import warnings
from datetime import timedelta
from itertools import product
import pytest
import numpy as np
import pandas as pd
from pandas import (CategoricalIndex, DataFrame, Index, MultiIndex,
compat, date_range, period_range)
from pandas.compat import PY... | u('x') | pandas.compat.u |
import pandas as pd
from sodapy import Socrata
import datetime
import definitions
# global variables for main data:
hhs_data, test_data, nyt_data_us, nyt_data_state, max_hosp_date = [],[],[],[],[]
"""
get_data()
Fetches data from API, filters, cleans, and combines with provisional.
After running, global variables are... | pd.DataFrame(lst) | pandas.DataFrame |
'''
Utility scripts
'''
import argparse
import copy
import logging
import sys
import typing
import pandas as pd
_logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.DEBUG)
def time_granularity_value_to_stringfy_time_format(granularity_int: int) -> str:
try:
granularity_int = int(granu... | pd.to_datetime(time_column) | pandas.to_datetime |
import copy
from typing import Optional, Collection, Any, Dict, Tuple
from causalpy.bayesian_graphs.scm import (
SCM,
NoiseGenerator,
Assignment,
IdentityAssignment,
MaxAssignment,
SignSqrtAssignment,
SinAssignment,
)
import networkx as nx
import pandas as pd
import numpy as np
class SumA... | pd.concat(obs, sort=True) | pandas.concat |
import pandas as pd
from settings import settings
from gensim.models.word2vec import Word2Vec
from utils import get_sequence, get_blocks, check_path
def generate_embeddings(ast_path, pairs_path, size=settings.vec_size):
source = pd.read_pickle(ast_path)
pairs = | pd.read_pickle(pairs_path) | pandas.read_pickle |
"""Construct the clean data set"""
import pandas as pd
from pathlib import PurePath
import numpy as np
import datetime as dt
from pandas.tseries.holiday import USFederalHolidayCalendar
from scipy.interpolate import interp1d
from sklearn.svm import SVR
#================================================================... | pd.to_datetime(yields['Date'], format="%Y-%m-%d") | pandas.to_datetime |
import json
import signal
from functools import wraps
from time import time
import warnings
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
warnings.simplefilter("ignore")
class TimeoutException(Exception):
pass
def timeout_handler(signum, frame):
raise TimeoutException
signal.si... | pd.concat((test_df_calls.iloc[:100], test_df_puts.iloc[:100]), 0) | pandas.concat |
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.to_datetime(i) | pandas.to_datetime |
# Choose a Top Performer of ETF from previous week
## https://www.etf.com/etfanalytics/etf-finder
etf_list = ['QQQ', 'QLD', 'TQQQ', 'GDXD', 'SPY']
# Get best ticker performance of past 1 week
# def best_etf(etf_list):
# best_ticker_performance = 0
# best_ticker = ''
# for ticker in etf_list:... | pd.DataFrame(json_dump[symbol]) | pandas.DataFrame |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, time, timedelta, date
import sys
import os
import operator
from distutils.version import LooseVersion
import nose
import numpy as np
randn = np.random.randn
from pandas import (Index, Series, TimeSeries, DataFrame,
isnull, date_ran... | range(n) | pandas.compat.range |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 8 08:53:30 2019
@author: rhou
"""
import warnings
warnings.filterwarnings("ignore")
import os, sys
import argparse
import matplotlib
matplotlib.use('agg')
import pandas as pd
import numpy as np
try:
import seaborn as sns
except ImportError:
... | pd.read_csv(clusterMapFilename, index_col=None, header=0) | pandas.read_csv |
from bapiw.api import API
from datetime import datetime, date
import pandas as pd
import numpy as np
bapiw = API()
class DataParser:
# intervals used when calling kline data
# https://github.com/binance-exchange/binance-official-api-docs/blob/master/rest-api.md#enum-definitions
INTERVAL_1MIN = '1m'
... | pd.DataFrame({'askPrice0': askprice, 'askQuantity0': askquantity}) | pandas.DataFrame |
#!/opt/conda/envs/feature-detection/bin/python
# main.py
# 1. load point cloud in modelnet40 normal format
# 2. calculate ISS keypoints
# 3. calculate FPFH or SHOT for detected keypoints
# 3. visualize the results
import os
import sys
import copy
ROOT = os.path.dirname(os.path.abspath(__file__))
sys.p... | pd.concat(df_signature_visualization, ignore_index=True) | pandas.concat |
import torch
import os, sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tqdm
from sklearn.metrics import roc_auc_score, roc_curve, precision_recall_curve, accuracy_score
#####################################################################################
experiment_name = "Jun1"
mapp... | pd.read_csv(labels_location) | pandas.read_csv |
#!pip install plotnine
import numpy as np
import pandas as pd
from plotnine import *
def plot_factor_spatial(adata, fact, cluster_names,
fact_ind=[0], trans="log",
sample_name=None, samples_col='sample',
obs_x='imagecol', obs_y='imagerow',
... | pd.to_numeric(for_plot['imagecol']) | pandas.to_numeric |
import numpy as np
import pandas as pd
import sys
init_path = sys.argv[1]
og_path = sys.argv[1].split('/')
file = og_path.pop()
pre_dir = og_path
pdy_data = np.load(init_path, allow_pickle=True)
# csv_data = pd.read_csv(init_path, header=None, index_col=False)
data = | pd.DataFrame(pdy_data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Tests that quoting specifications are properly handled
during parsing for all of the parsers defined in parsers.py
"""
import csv
import pytest
from pandas.compat import PY3, StringIO, u
from pandas.errors import ParserError
from pandas import DataFrame
import pandas.util.testing as tm
... | StringIO(data) | pandas.compat.StringIO |
import pandas as pd
import numpy as np
import statsmodels as sm
import statsmodels.api as smapi
import math
from pyqstrat.pq_utils import monotonically_increasing, infer_frequency
from pyqstrat.plot import TimeSeries, DateLine, Subplot, HorizontalLine, BucketedValues, Plot
import matplotlib as mpl
import matplotlib.fig... | pd.DataFrame({'ret': returns, 'timestamp': timestamps}) | pandas.DataFrame |
import pandas as pd
# import matplotlib.pyplot as plt
# import seaborn as sns
import numpy as np
# import copy
# from scipy.stats import norm
# from sklearn import preprocessing
fileName = '/home/kazim/Desktop/projects/IE490/input/tubitak_data2_processesed2.csv'
df = pd.read_csv(fileName, sep = ',')
#pr... | pd.get_dummies(df, columns=["ilce_kod"]) | pandas.get_dummies |
from contextlib import contextmanager
import struct
import tracemalloc
import numpy as np
import pytest
from pandas._libs import hashtable as ht
import pandas as pd
import pandas._testing as tm
from pandas.core.algorithms import isin
@contextmanager
def activated_tracemalloc():
tracemalloc.start()
try:
... | ht.mode(values, False) | pandas._libs.hashtable.mode |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import pandas as pd
import powerlaw
import sys
from matplotlib.ticker import MaxNLocator
from data import prepare_data
from synthetic_data import SyntheticGraphGenerator
from basic_algorithms import get_connected_components, comp... | pd.DataFrame(degree_sequence, columns=['degree']) | pandas.DataFrame |
import pandas as pd
from time import sleep
import csv
from datetime import datetime
import time
ra= 10
Entree420mA = '4-20mA'
EntréeTension = '0-20V'
EntréeAutre = 'Autre'
BrandAdafruit = 'Adafruit'
ProductRefVMSB = 'VM-SB'
sleepmillisecond=0.1
sleepsecond=1
sleep10second=10
sleep30second=30
sleepminut=60
sleephour=36... | pd.DataFrame(columns=['Type','Value','Input Type','Product Ref','Brand','Name','Date']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
from pandas.compat import range
import pandas as pd
import pandas.util.testing as tm
# -------------------------------------------------------------------
# Comparisons
class TestFrameComparisons(object):
def test_df_boolean_comparison_error(self):
... | pd.Series([0, 0, 0, 0], dtype='float64') | pandas.Series |
import pandas as pd
from pathlib import Path
import numpy as np
import glob
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error as mse, mean_absolute_error as mae
from scipy.fft import fft, ifft
### Lale dependencies
import lale
from lale.lib.lale import NoOp, Hyperopt
fr... | pd.read_csv(fname) | pandas.read_csv |
from keras.layers import Input, Dense, concatenate
from keras.layers.recurrent import GRU
from keras.utils import plot_model
from keras.models import Model, load_model
from keras.callbacks import ModelCheckpoint
import keras
import pandas as pd
import numpy as np
import keras.backend as K
from keras.utils import to_cat... | pd.read_csv('../../data/' + dataset + 'interim/interactions.csv', header=0, sep='\t') | pandas.read_csv |
from typing import Dict, Optional, Union, cast
import numpy as np
import pandas as pd
from fseval.pipeline.estimator import Estimator
from fseval.types import AbstractEstimator, AbstractMetric, Callback
class UploadFeatureImportances(AbstractMetric):
def _build_table(self, feature_vector: np.ndarray):
"... | pd.DataFrame() | pandas.DataFrame |
import logging
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
import locan.data.metadata_pb2
from locan import ROOT_DIR, LocData
from locan.dependencies import HAS_DEPENDENCY
from locan.locan_io.locdata.io_locdata import load_rapidSTORM_file, load_txt_file
logger = logging.getLogger(__... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
import re
import os
import gc
import glob
import keras
import numbers
import tldextract
import numpy as np
import pandas as pd
from tqdm import tqdm
import tensorflow as tf
from itertools import chain
from keras.models import Model
from keras.models import load_model
import matpl... | pd.read_csv('/dlabdata1/harshdee/tag_counts.csv', header=None) | 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(h, "/key") | pandas.read_hdf |
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