prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#!/usr/bin/env python
"""Calculate regionprops of segments.
"""
import sys
import argparse
# conda install cython
# conda install pytest
# conda install pandas
# pip install ~/workspace/scikit-image/ # scikit-image==0.16.dev0
import os
import re
import glob
import pickle
import numpy as np
import pandas as pd
fro... | pd.DataFrame(index=df1.index) | pandas.DataFrame |
import os
import random
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn
import torch
from sklearn.metrics import pairwise_distances
from sklearn.model_selection import train_test_split
from torch.utils.data import TensorDataset
import matplotlib.pyplot as plt
from scripts.ssc.evaluation.ml... | pd.DataFrame({'Distances on $\mathcal{M}$': pwd_Ztrue[ind], 'Distances in $\mathcal{Z}$': pwd_Z[ind]}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Monday 18 may 2020
All the thesis code, no code excecution!
@author: Dainean
"""
#Prepare the python system
import pandas as pd #Dataframes
import numpy as np #Numpy
# Reading and saving fits files
import os #Move around in our... | pd.read_hdf('Parts_DB.h5', 'Spectral') | pandas.read_hdf |
from __future__ import print_function
from collections import defaultdict
import pandas as pd
import re
import click
codons = {'AAA': 'Lys',
'AAC': 'Asn',
'AAG': 'Lys',
'AAU': 'Asn',
'ACA': 'Thr',
'ACC': 'Thr',
'ACG': 'Thr',
'ACU': 'Thr',
'AGA': 'Arg',
'AGC': 'Ser',
'AGG': 'Arg',
'AGU': 'Ser',
'AUA': 'Ile... | pd.DataFrame(big_dict[i]) | pandas.DataFrame |
# column deletion using del operator and pop method of pandas dataframe
import pandas as pd
import numpy as np
d={'one':pd.Series([1,2,3],index=['a','b','c']),
'two':pd.Series([1,2,3,4],index=['a','b','c','d']),
'three': | pd.Series([10,20,30],index=['a','b','c']) | pandas.Series |
#!/usr/bin/env python
# coding: utf-8
# # EXPANDING WINDOW SPLIT
# ### LOAD LIBRARIES
# In[ ]:
import os
import gc
import warnings
import pandas as pd
import numpy as np
import pickle
warnings.filterwarnings("ignore")
| pd.set_option("display.max_columns", 500) | pandas.set_option |
import os
import sys
import time
import shutil
import random
import numpy as np
import pandas as pd
import geopandas as gpd
from map2loop.topology import Topology
from map2loop import m2l_utils
from map2loop import m2l_geometry
from map2loop import m2l_interpolation
from map2loop import m2l_map_checker
from map2loop.m... | pd.concat([all_sorts, expected_extra_cols], axis=1) | pandas.concat |
import os
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
import lightgbm as lgb
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from scipy.stats import randint as sp_randint
from scipy.stats import uniform as sp_... | pd.concat([power, meteo], axis=1, join="inner") | pandas.concat |
from os.path import exists, join
import pandas as pd
import torch
import logging
from transformers import AutoModelForSequenceClassification
from train_bert import compute_negative_entropy, LMForSequenceClassification
from dataset import get_dataset_by_name, TokenizerDataModule
from torch.utils.data import DataLoader
... | pd.DataFrame(stds) | pandas.DataFrame |
import pandas as pd
import matplotlib.pyplot as plt
tamano_muestra = 120 #N
bandera_paso = False
iter = 0
lsupAnterior = -5
linfAnterior = -5
licentAnterior = -5
datos = | pd.read_csv('data.csv', header=None) | pandas.read_csv |
from __future__ import division
from datetime import datetime
import sys
if sys.version_info < (3, 3):
import mock
else:
from unittest import mock
import pandas as pd
import numpy as np
import random
from nose.tools import assert_almost_equal as aae
import bt
import bt.algos as algos
def test_algo_name():... | pd.date_range('2010-01-01', periods=35) | pandas.date_range |
import sys,os
import numpy as np
import pandas as pd
import h5py
import math
from scipy.stats import entropy
from collections import Counter
import pickle
# Get Euclidean Norm minus One
def get_ENMO(x,y,z):
enorm = np.sqrt(x*x + y*y + z*z)
ENMO = np.maximum(enorm-1.0, 0.0)
return ENMO
# Get tilt angles
def get_... | pd.DataFrame(features[st_idx:end_idx], columns=columns) | pandas.DataFrame |
''' Starting with Commonwealth_Connect_Service_Requests.csv, meaning
the tickets feature. See more info in notebook #2
'''
import pandas as pd
import numpy as np
from geopy.distance import geodesic
def find_nearest_building(df,latI,lonI):
minDist = 4000
flag = True
for i in range(0,df.shape[0]):
la... | pd.read_csv('/Users/nbechor/Insight/SlipperySlope/data/external/Unshoveled_Icy_Sidewalk_Complaints.csv') | pandas.read_csv |
import pandas as pd
import numpy as np
from adv_finance.multiprocess import mp_pandas_obj
def mp_sample_tw(t1, num_co_events, molecule):
"""
Snippet 4.2 (page 62) Estimating The Average Uniqueness Of A Label
:param timestamps: (Series): Used for assigning weight. Larger value, larger weight e.g, log ret... | pd.Series(index=molecule) | pandas.Series |
#!/usr/bin/env python
# coding: utf-8
# ## Compare compressed vs. raw results
#
# In this notebook, we want to compare mutation status classification results with varying numbers of PCA components as predictors against results with raw features (CpG beta values for methylation data, standardized per-gene expression v... | pd.DataFrame({'x': x, 'y': y, 'gene': gene, 'sig': sig}) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
#without the help of my intern, this option data scraper would never exist
#thank you, Olivia, much appreciated for the data etl
# In[1]:
import requests
import pandas as pd
import os
os.chdir('H:/')
# In[2]:
#scraping function
def scrape(url):
session=requests.S... | pd.DataFrame.from_dict(commoditygroup['children'].iloc[i]) | pandas.DataFrame.from_dict |
import math
import pandas as pd
import csv
import pathlib
import wx
import matplotlib
import matplotlib.pylab as pL
import matplotlib.pyplot as plt
import matplotlib.backends.backend_wxagg as wxagg
import re
import numpy as np
import scipy
import scipy.interpolate
import sys
#from mpl_toolkits.mplot3d import Axes3D
#i... | pd.DataFrame(columns=["NoElectrodes",'A(x)', 'A(z)', 'B(x)', 'B(z)', 'M(x)', 'M(z)', 'N(x)', 'N(z)', 'Resistance']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import os
import logging
import pandas as pd
import pytz
from tzlocal import windows_tz
import appdirs
import ws
LOG = logging.getLogger(__name__)
_TYPE_MAP = {'integer': int,
'unicode': str,
'string': str,
'boolean': bool,
'datetime': '... | pd.core.datetools.to_offset(expiration) | pandas.core.datetools.to_offset |
import numpy as np
from datetime import timedelta
from distutils.version import LooseVersion
import pandas as pd
import pandas.util.testing as tm
from pandas import to_timedelta
from pandas.util.testing import assert_series_equal, assert_frame_equal
from pandas import (Series, Timedelta, DataFrame, Timestamp, Timedelt... | Timedelta(-239, unit='h') | pandas.Timedelta |
import csv
import snscrape.modules.twitter as sntwitter
import pandas as pd
import os.path
def get_company_twitter_posts(account_df1):
# Check if file exist
if os.path.isfile("output/twitter_sentiment_companies.csv"):
print("File already exist - skipping company data extraction.")
return | pd.read_csv("output/twitter_sentiment_companies.csv") | pandas.read_csv |
# -*- coding: utf-8 -*-
import click
import logging
from pathlib import Path
from dotenv import find_dotenv, load_dotenv
from gensim.utils import tokenize
from gensim.parsing.preprocessing import remove_stopwords
from gensim.test.utils import common_texts
from gensim.models.doc2vec import Doc2Vec, TaggedDocument
impor... | pd.DataFrame(te_vecs, index=x_te.index, columns=cols) | pandas.DataFrame |
from rest_framework import generics, status, permissions, mixins, views, viewsets
from rest_framework.response import Response
from rest_framework.parsers import MultiPartParser, FormParser, JSONParser
from rest_framework.decorators import permission_classes, action
from rest_framework.exceptions import ParseError, Val... | pd.read_csv(file, encoding='utf-8') | pandas.read_csv |
# converts warc file into a pandas dataframe type csv: html_and_text_big.csv
# each row of dataframe contains url,html,text for a specific html file
# also saves a csv of filtered text, containing justext extracted text: text_filtered_big.csv
# stripped of non-ascii and filtered for relevance (Parkland shooting)
# de... | pd.read_csv(data_path/'text_filtered_big.csv',index_col=0, header=None) | pandas.read_csv |
import copy
import numpy as np
import pandas as pd
class CustomGeneticAlgorithm():
def server_present(self, server, time):
server_start_time = server[1]
server_duration = server[2]
server_end_time = server_start_time + server_duration
if (time >= server_start_time) and (time < se... | pd.DataFrame(fitness) | pandas.DataFrame |
import dask.dataframe as dd
import pandas as pd
import pytest
import featuretools as ft
from featuretools.entityset import EntitySet, Relationship
def test_create_entity_from_dask_df(pd_es):
dask_es = EntitySet(id="dask_es")
log_dask = dd.from_pandas(pd_es["log"].df, npartitions=2)
dask_es = dask_es.enti... | pd.to_datetime('2019-01-10') | pandas.to_datetime |
# -*- coding: utf-8 -*-
import pandas
import numpy
import sys
import unittest
from datetime import datetime
from pandas.testing import assert_frame_equal, assert_series_equal
import os
import copy
sys.path.append("..")
import warnings
import nPYc
from nPYc.enumerations import SampleType
from nPYc.enumerations import As... | assert_series_equal(msData.sampleMetadata['Sample Base Name'], basename) | pandas.testing.assert_series_equal |
# basics
from typing import Callable
import pandas as pd
import os
from pandas.core.frame import DataFrame
# segnlp
from segnlp import utils
from segnlp import metrics
from segnlp.utils.baselines import MajorityBaseline
from segnlp.utils.baselines import RandomBaseline
from segnlp.utils.baselines import Sentenc... | pd.concat(score_dfs) | pandas.concat |
import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
Timestamp,
date_range,
to_datetime,
)
import pandas._testing as tm
import pandas.tseries.offsets as offsets
class TestRollingTS:
# rolling time-series friendly
# xref GH13327
def set... | Timestamp("20130101 09:00:02") | pandas.Timestamp |
#%%
from pymaid_creds import url, name, password, token
import pymaid
rm = pymaid.CatmaidInstance(url, token, name, password)
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
import numpy.random as random
import gzip
import csv
import connectome_tools.celltype as ct
import... | pd.read_csv('interhemisphere/csv/paths/random-ipsi-contra-edges_left-paths/processed/excised_graph-to-dVNC-right_random-ipsi-contra_500-removed_path-lengths.csv') | pandas.read_csv |
"""
Code for "How Is Earnings News Transmitted to Stock Prices?" by
<NAME> and <NAME>.
Python 2
The main function takes the TAS (Time and Sales) file for one exchange on one
month and extracts only the trades from daily files, creating trade files.
"""
from os import listdir
import os
import pandas as p... | pd.concat(dfs) | pandas.concat |
import time
import numpy as np
import pandas as pd
from scipy.io import arff
from bitmap_mapper.bitmap_mapper_interface import BitmapMapperInterface
from feature_extractor.feature_extractor import FeatureExtractor
class CommonData:
def __init__(self, feature_extractor: FeatureExtractor, bitmap_mapper: BitmapMap... | pd.DataFrame(data[0]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import click
import logging
from pathlib import Path
# from dotenv import find_dotenv, load_dotenv
import requests
from bs4 import BeautifulSoup
import numpy as np
import pandas as pd
import datetime
import yfinance as yf
from pandas_datareader import data as pdr
from flask import current_app
f... | pd.Series(df['log_ret_1d']) | pandas.Series |
#!/usr/bin/env python
# coding: utf-8
# # Generate Generative Model Figures
# In[1]:
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().run_line_magic('matplotlib', 'inline')
import os
import glob
from collections import OrderedDict
import matplotli... | pd.np.array([102, 166, 30, 255]) | pandas.np.array |
#!/usr/bin/python
'''
Tracks colonies through time in a single imaging field
'''
import cv2
import numpy as np
import glob
import os
import warnings
import pandas as pd
from PIL import Image
from string import punctuation
def _convert_to_number(val_str):
'''
Converts val_str to an int or float or logical (in that ... | pd.concat(param_df_list) | pandas.concat |
import sys
import pytz
import hashlib
import numpy as np
import pandas as pd
from datetime import datetime
def edit_form_link(link_text='Submit edits'):
"""Return HTML for link to form for edits"""
return f'<a href="https://docs.google.com/forms/d/e/1FAIpQLScw8EUGIOtUj994IYEM1W7PfBGV0anXjEmz_YKiKJc4fm-tTg/... | pd.read_csv('data/districts.csv') | pandas.read_csv |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.7.1
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # QA queries on new CDR row suppression
#
# Verify al... | pd.read_gbq(query, dialect='standard') | pandas.read_gbq |
#preprocessing script to binned and imputed final data to apply on simple baselines..
import pandas as pd
import numpy as np
import sys
def bin_and_impute(data, bin_width=60, variable_start_index=5):
result = [] #list of patients dataframes
#set of variables to process:
variables = np.array(list(data.il... | pd.to_timedelta(pat_i.index, unit='h') | pandas.to_timedelta |
import pandas as pd
from pandas.testing import assert_frame_equal
from sklearn.pipeline import make_pipeline
import pytest
from sklego.preprocessing import ColumnSelector
@pytest.fixture()
def df():
return pd.DataFrame(
{
"a": [1, 2, 3, 4, 5, 6],
"b": [10, 9, 8, 7, 6, 5],
... | assert_frame_equal(result_df, expected_df) | pandas.testing.assert_frame_equal |
from pycox.datasets import metabric, nwtco, support, gbsg, flchain
from sklearn.preprocessing import KBinsDiscretizer, LabelEncoder, StandardScaler
import numpy as np
import pandas as pd
import pdb
from .utils import LabelTransform
def load_data(config):
'''load data, return updated configuration.
'''
dat... | pd.DataFrame({"duration":df["duration"][df_train.index]}) | pandas.DataFrame |
import os
import locale
import codecs
import nose
import numpy as np
from numpy.testing import assert_equal
import pandas as pd
from pandas import date_range, Index
import pandas.util.testing as tm
from pandas.tools.util import cartesian_product, to_numeric
CURRENT_LOCALE = locale.getlocale()
LOCALE_OVERRIDE = os.en... | tm.assert_numpy_array_equal(res, exp) | pandas.util.testing.assert_numpy_array_equal |
import pandas as pd
import os
from os.path import join, abspath, dirname, isfile
from google_trans_new import google_translator
from sklearn.model_selection import train_test_split
import nlpaug.augmenter.word as naw
import spacy
from nltk.stem import SnowballStemmer
from utils import downloader, exploration, normaliza... | pd.read_csv(csv_path, encoding="windows-1252") | pandas.read_csv |
from datetime import datetime
import pytest
from pandas import DataFrame
from evidently import ColumnMapping
from evidently.analyzers.data_quality_analyzer import DataQualityAnalyzer
from evidently.dashboard.widgets.data_quality_features_widget import DataQualityFeaturesWidget
from evidently.options import OptionsPro... | DataFrame(reference) | pandas.DataFrame |
import os
import torch
from torch.utils.tensorboard import SummaryWriter
import pandas as pd
import numpy as np
# Timing utility
from timeit import default_timer as timer
from utils.utilities import parse_args, parse_yaml, make_dir
import data_loader as dl
from transformations import transforms as trfs
from models.mo... | pd.DataFrame(scores_dict_train) | pandas.DataFrame |
import os
from datetime import date
from dask.dataframe import DataFrame as DaskDataFrame
from numpy import nan, ndarray
from numpy.testing import assert_allclose, assert_array_equal
from pandas import DataFrame, Series, Timedelta, Timestamp
from pandas.testing import assert_frame_equal, assert_series_equal
from pymo... | Timestamp('2008-10-23 05:53:11') | pandas.Timestamp |
#!/usr/bin/python
"""functions to create the figures for publication
"""
import seaborn as sns
import math
import pyrtools as pt
import neuropythy as ny
import os.path as op
import warnings
import torch
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from mpl_toolkits.axes_grid1.anchored_art... | pd.concat(data) | pandas.concat |
# This code reads a results of WQ samplings from a lake at various time and locations by
# several researchers
# The code does the following steps#
# 1) reads the .csv files
# 2) calculates the mean and standard deviations of samples at taken a particular date (by all researcher)
# 3) fills the gap between dates t... | pd.DataFrame({"level":x_data[mask],"TDS":y_data[mask]}) | pandas.DataFrame |
"""
Created on Jun 11, 2013
@author: agross
"""
import pandas as pd
import numpy as np
def to_date(s):
"""
Pulls year, month, and day columns from clinical files and
formats into proper date-time field.
"""
try:
return pd.datetime(int(s['yearofformcompletion']),
... | pd.read_table(f, index_col=0, low_memory=False) | pandas.read_table |
import plotly.express as px
import pandas as pd
import datetime as dt
from utils.gurobi_model import GRBModel
# from configs import output_file_name, days, move_hours, switch_hours
from configs import (output_file_name,
start_year, start_month, start_day,
days,
... | pd.TimedeltaIndex(self.done_orders_info['start'], unit='m') | pandas.TimedeltaIndex |
import pandas as pd
import datetime
import sasoptpy as so
from swat import CAS
from collections import namedtuple
import os
supplier = 'R2R'
def prep_data(car_type='diesel'):
# Data in this repository is randomly populated, original data is provided by Rome2Rio.com
travel_data = | pd.read_csv('../data/all_methods_random.csv') | pandas.read_csv |
import filecmp
import pandas as pd
def merge_col(filepath1: str, filepath2: str) -> pd.DataFrame:
df1 = pd.read_table(filepath1, header=None)
df2 = | pd.read_table(filepath2, header=None) | pandas.read_table |
import sys
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtWidgets import QApplication, QWidget, QTableWidget, QTableWidgetItem, QVBoxLayout
from PyQt5 import QtGui
import sys
import pandas as pd
import ast
import random
true_false_df = pd.read_excel('true_false.xlsx')
true_false_df = true_false_df.reset_ind... | pd.DataFrame() | pandas.DataFrame |
import unittest
import pandas as pd
import numpy as np
from pandas.util.testing import assert_frame_equal
from pdblp import pdblp
import os
IP_PORT = 8194
class TestBCon(unittest.TestCase):
def setUp(self):
self.con = pdblp.BCon(port=IP_PORT, timeout=5000)
self.con.start()
cdir = os.pat... | pd.DataFrame(data=data, index=idx) | pandas.DataFrame |
# %%
import pandas as pd
import requests
from bs4 import BeautifulSoup
from concurrent.futures import ThreadPoolExecutor
# %%
# 메뉴 데이터를 불러옵니다.
data = pd.read_csv("All Menu (Various Versions)/국방부메뉴_v2.1.csv", index_col=0)
data
# %%
# 요청해야하는 URL주소를 가져옵니다 (네이버).
urls = []
for name in data['메뉴이름']:
url = 'https://sea... | pd.DataFrame(columns=['메뉴이름', '다른메뉴', '조합점수']) | pandas.DataFrame |
import sys
from transformers.modeling_openai import OpenAIGPTLMHeadAgenModel
import numpy as np
from transformers import *
import torch
from torch.utils.data import DataLoader
from generate_ivp import sample_sequence_ivp
import pandas as pd
from utils import *
from utils_g import *
from utils_ivp import agen_vector
imp... | pd.Series(data=test_dataset) | pandas.Series |
import re
from unittest.mock import Mock, patch
import numpy as np
import pandas as pd
import pytest
from rdt.transformers import (
CategoricalTransformer, LabelEncodingTransformer, OneHotEncodingTransformer)
RE_SSN = re.compile(r'\d\d\d-\d\d-\d\d\d\d')
class TestCategoricalTransformer:
def test___init__(... | pd.Series(['a', 'a', 'a']) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 24 20:03:24 2019
@author: RV
"""
# Python(R)
# Modeules/packageslibraries
# OS - submodules/path/join
#eg. (os.path.join)
# pandas
# scipy
# onspy
#%% Setup
import os
projFld = "C:/Users/RV/Documents/Teaching/2019_01_Spring/ADEC7430_Spring2019/Lec... | pd.crosstab(rTrain['Age_imputed'], agecond) | pandas.crosstab |
from datetime import datetime, timedelta
import numpy as np
import pytest
from pandas._libs.tslibs import period as libperiod
import pandas as pd
from pandas import DatetimeIndex, Period, PeriodIndex, Series, notna, period_range
import pandas._testing as tm
class TestGetItem:
def test_ellipsis(self):
#... | Period("2012-01-02", freq="D") | pandas.Period |
#!/usr/bin/python
import finaExp as fe
import os, pandas, urllib
from ofxparse import OfxParser as ofp
from ofxparse.ofxparse import OfxParserException as ofpe
def importOFX(fileName):
'''importOFX brings in the OFX transaction objects to be analyzed'''
if not(os.path.exists("data")):
os.makedirs("data")
... | pandas.DataFrame.from_records(transList, columns=['id', 'date', 'payee','cat', 'amount', 'type', 'memo', 'checknum', 'sic']) | pandas.DataFrame.from_records |
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# This file contains dummy data for the model unit tests
import numpy as np
import pandas as pd
AIR_FCST_LINEAR_95 = pd.DataFrame(
{
... | pd.Timestamp("2012-08-05 00:00:00") | pandas.Timestamp |
import pandas as pd
import pytest
@pytest.mark.functions
def test_convert_matlab_date():
mlab = [
733_301.0,
729_159.0,
734_471.0,
737_299.563_296_356_5,
737_300.000_000_000_0,
]
df = | pd.DataFrame(mlab, columns=["dates"]) | pandas.DataFrame |
#!/usr/bin/env python3
#
# SPDX-License-Identifier: BSD-3-Clause
# Copyright 2020-2021, Intel Corporation
#
#
# csv_compare.py -- compare CSV files (EXPERIMENTAL)
#
# In order to compare all CSV are plotted on the same chart.
# XXX include hostname for easier reporting.
#
import argparse
import os
import pandas as pd... | pd.read_csv(csv_file) | pandas.read_csv |
"""Collect specific gene ontologies, and additional background/complex information """
import os
import re
import functools
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sa
import statsmodels.formula.api as sfa
from GEN_Utils import FileHandling
f... | pd.DataFrame(dna_genes) | pandas.DataFrame |
import csv
import httplib2
from apiclient.discovery import build
import urllib
import json
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import plotly
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.tools import FigureFactory... | pd.to_numeric(pivot_cost['2014']) | pandas.to_numeric |
import pandas as pd
from pandas.util.testing import assert_frame_equal
import numpy as np
import os
from dataactbroker.helpers import validation_helper
from dataactvalidator.app import ValidationManager, ValidationError
from dataactvalidator.filestreaming.csvReader import CsvReader
from dataactcore.models.validationMo... | assert_frame_equal(df_under_test, expected_df) | pandas.util.testing.assert_frame_equal |
from typing import Dict, List
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import numpy as np
import pandas as pd
import seaborn as sns
from tqdm import tqdm
import wandb
api = wandb.Api()
entity = "proteins"
import matplotlib.ticker as ticker
class StupidLogFormatter(ticker.LogFormatter):
... | pd.DataFrame({"run_id": id_list}) | pandas.DataFrame |
import datetime
from unittest import TestCase
import numpy as np
import pandas as pd
from mlnext import pipeline
class TestColumnSelector(TestCase):
def setUp(self):
data = np.arange(8).reshape(-1, 2)
cols = ['a', 'b']
self.df = pd.DataFrame(data, columns=cols)
def test_select_col... | pd.DataFrame([[0, 1]], columns=['1', '2'], dtype=object) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from datetime import timedelta
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas import (Timedelta,
period_range, Period, PeriodIndex,
_np_version_under1p10)
import pandas.core.indexes.period as period
cla... | pd.Period('NaT', freq='M') | pandas.Period |
import mysql.connector
import datetime
import pandas as pd
def is_empty(df):
if df.empty:
print('Empty table')
db_user = input('Enter database user : ')
db_password = input('Enter database password : ')
connection = mysql.connector.connect(host='localhost', user = db_user, password = db_passwo... | pd.DataFrame(result) | pandas.DataFrame |
import os, csv
import numpy as np
import pandas as pd
from pathlib import Path
from sklearn.model_selection import train_test_split
from scipy import signal
class ProcessSignalData(object):
def __init__(self):
# path to video data from signal_output.py
self.dir = './processed_new/videos'
s... | pd.DataFrame(self.fake_data_std) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Module for model evaluation
"""
# Built-in
from copy import deepcopy
from typing import Any, Iterable, List, Tuple
# Other
from joblib import delayed, Parallel
import numpy as np
import pandas as pd
def rmse(y_true: np.ndarray, y_pred: np.ndarray) -> float:
"""
Calculates root m... | pd.DataFrame(cv_scores) | pandas.DataFrame |
############################### LightBGM Voting #######################################
import numpy as np
import pandas as pd
import logging
#from sklearn.externals import joblib
import joblib
np.warnings.filterwarnings('ignore')
from sklearn.model_selection import KFold
from sklearn.feature_extraction.text import T... | pd.read_csv('./test_a.csv', sep='\t', nrows=None) | pandas.read_csv |
# -*- coding: utf-8 -*-
import chainer
# v0.7.0 で動作確認
import chainerrl
from chainerrl import replay_buffer
from chainerrl import experiments
from chainerrl import links
from chainerrl import explorers
from chainerrl.q_functions import DistributionalDuelingDQN
import gym
import random
import cv2
import datetime as dt
i... | pd.DataFrame([], columns=trade_cols) | pandas.DataFrame |
from functools import reduce
import re
import numpy as np
import pandas as pd
from avaml import _NONE
from avaml.aggregatedata.__init__ import DatasetMissingLabel
from avaml.score.overlap import calc_overlap
__author__ = 'arwi'
VECTOR_WETNESS_LOOSE = {
_NONE: (0, 0),
"new-loose": (0, 1),
"wet-loose": (1... | pd.MultiIndex.from_product([["global"], ["danger_level", "emergency_warning"]]) | pandas.MultiIndex.from_product |
# -*- coding:utf-8 -*-
import pandas as pd
import math
import csv
import random
import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
base_elo = 1600
team_elos = {}
team_stats = {}
X = []
y = []
folder = 'data'
# calculate elo values for each team
d... | pd.read_csv(folder + '/15-16Miscellaneous_Stat.csv') | pandas.read_csv |
## Basketball Reference Game Log Scraping ####################################################################################
# Georgia Tech: Daily Fantasy Sports Project
# authors: <NAME> & <NAME>
#### Process Outline #################################################################################################... | pd.merge_asof(rslts_df, conversion_df, on='Percentile', direction='nearest') | pandas.merge_asof |
"""
This file contains several helper functions to calculate spectral power from
1D and 2D EEG data.
"""
import mne
import logging
import numpy as np
import pandas as pd
from scipy import signal
from scipy.integrate import simps
from scipy.interpolate import RectBivariateSpline
logger = logging.getLogger('yasa')
__al... | pd.DataFrame(bp, columns=labels) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import os
import re
import ipaddress
import codecs
import time
import pandas as pd
import urllib3
from urllib3 import util
from classifier4gyoithon.GyoiClassifier import DeepClassifier
from classifier4gyoithon.GyoiExploit import Metasploit
from classifier4gyoitho... | pd.Series(['-']) | pandas.Series |
"""Store the data in a nice big dataframe"""
import sys
from datetime import datetime, timedelta
import pandas as pd
import geopandas as gpd
import numpy as np
class Combine:
"""Combine defined countries together"""
THE_EU = [ 'Austria', 'Italy', 'Belgium', 'Latvia',
'Bulgaria', 'L... | pd.DataFrame(transform) | pandas.DataFrame |
import requests
import deeptrade
import pandas as pd
class StockPrice():
def __init__(self):
self.head = {'Authorization': "Token %s" %deeptrade.api_key}
def by_date(self,date,dataframe=False):
"""
:parameters:
- date: a day date in the format %YYYY-%MM-%DD
- datafram... | pd.DataFrame(g) | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from .. import read_sql
@pytest.fixture(scope="module") # type: ignore
def sqlite_db() -> str:
conn = os.environ["SQLITE_URL"]
return conn
def test_read_sql_without_partition(sqlite_db: str) -> No... | pd.Series([], dtype="object") | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 20 14:08:35 2019
@author: Team BTC - <NAME>, <NAME>, <NAME>, <NAME>, <NAME>
"""
#sorry the code isnt very efficient. because of time constraints and the number of people working on the project, we couldnt do all the automatizations we would have liked to do. ... | pd.read_csv('l2_lexicon.csv',sep=';') | pandas.read_csv |
import utils
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from tqdm import tqdm
import numpy as np
import pandas as pd
from itertools import combinations, permutations
import heapq
# Here we created a class in order to store the index... | pd.DataFrame([dataframe.loc[j][['Title','Intro','Url']] for j in [a[1] for a in hp_res]]) | pandas.DataFrame |
## Analysis of Study
################################################################################
### Setup -- Data Loading and Cleaning
################################################################################
###############
### Imports
###############
# Warning Supression
import warnings
warnings.simp... | pd.DataFrame(stage_2_results) | pandas.DataFrame |
from data_get import *
from baseline_functions import *
from calendar_date import *
import global_vars
global_vars.init()
if global_vars.GRAPHFLAG > 0:
from graph_functions import *
from error_graphs import *
import mysql.connector
import pandas as pd
import datetime
import time
# main()
# This function goes th... | pd.DataFrame(columns=storage_df_columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 1 15:28:19 2021
@author: ashum
"""
from skimage import io
import pandas as pd
import os
#get a list of files from file path
pathforall= r'C:\Users\ashum\OneDrive\Desktop\Leukemia\archive\C-NMC_Leukemia\training_data\fold_0\all'
dir_list=os.listdir(p... | pd.concat([df, df2]) | pandas.concat |
from datetime import datetime, timedelta
import logging
import re
import pandas as pd
from scheduler.impala_api_client import ImpalaApiResource
from scheduler.constants import NativeQueryInfoColumn, FormativeQueryInfoColumn
from scheduler.global_utils import convert_mem_unit, spend_time
MEM_LIMIT_REGEX = re.compile(r... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 10 10:56:14 2019
@author: Wignand
"""
from scipy import stats
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pylab as plt
import matplotlib as mpl
import matplotlib.cm as cm
from matplotlib.backends.backend_agg import Fig... | pd.DataFrame({"x":x, "y":y}) | pandas.DataFrame |
# Copyright 2021 The HuggingFace Team. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
import pandas as pd
import numpy as np
from typing import List
TRAIN_DATA_PATH = "train.csv"
TEST_DATA_PATH = "test.csv"
def substrings_in_string(big_string: str, substrings: List[str]):
for substring in substrings:
if big_string.find(substring) != -1:
return substring
... | pd.read_csv(path) | pandas.read_csv |
import os
import pandas as pd
import geopandas as gpd
import numpy as np
import sys
wd = '/disk/bulkw/karger/census_bulk/citylonglat/'
os.chdir(wd)
sys.path.append(wd + 'programs/02_geocode')
import matching_functions as mf
# define dictionary with townvariables by decade here:
townvars_dict = {1790: ['township'],
... | pd.notnull(row['lat']) | pandas.notnull |
import pandas as pd
# Data Import
url = 'https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv'
df = pd.read_csv(url)
# Data selection
df = df[['iso_code', 'continent', 'location', 'date', 'total_cases', 'new_cases', 'total_deaths', 'new_deaths',
'total_cases_per_million... | pd.to_datetime(df['date']) | pandas.to_datetime |
# -----------------------------------------------------------
# <NAME>
# -----------------------------------------------------------
import streamlit as st
import pandas as pd
import numpy as np
from sodapy import Socrata
import pydeck as pdk
import plotly.express as px
import requests
# from IPytho... | pd.read_csv('kkr_schedule.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
# 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
# "... | pd.Categorical([5, 5]) | pandas.Categorical |
# -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101
import itertools
import warnings
from warnings import catch_warnings
from datetime import datetime
from pandas.types.common import (is_integer_dtype,
is_float_dtype,
is_scalar)
from pandas.compat... | Index([1, 2, 3], dtype='int64', name='idx') | pandas.core.api.Index |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import random
import time
import warnings
warnings.filterwarnings('ignore')
sns.set(style='darkgrid', palette='deep')
#Analysing dataset with padas profiling
#from pandas_profiling import ProfileReport
#profile = ProfileReport... | pd.cut(df.age, bins) | pandas.cut |
# -*- 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.DataFrame() | pandas.DataFrame |
from sympy import *
import pandas as pd
from random import random
def random_optimization(xl, xu, n, function):
x = Symbol('x')
f = parse_expr(function)
iteration = 0
data = pd.DataFrame(columns=['iteration','xl','xu','x','f(x)','max_x','max_f(x)'])
max_f = -1E9
for i in range(n):
r = ... | pd.DataFrame({'iteration':[iteration], 'xl':[xl], 'xu':[xu], 'x':[x0], 'yl':[yl], 'yu':[yu], 'y':[y0], 'f(x,y)':[fxy], 'max_x':[max_x], 'max_y':[max_y], 'max_f(x,y)':[max_f]}) | pandas.DataFrame |
# <NAME>
# 5/12/20
import pandas as pd
def save_files(outputfolder, merged):
"""
:param outputfolder: The folder where all of the merged files will be saved
:param merged: The merged dictionaries
:return: None
"""
keys = list(merged.keys())
for i in range(len(keys)):
df = | pd.DataFrame(merged[keys[i]]) | 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
... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
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([np.nan, np.nan], dtype="category") | pandas.Series |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.