prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import dask.dataframe as dd
import numpy as np
import pandas as pd
from pandas.api.types import is_categorical_dtype
DEFAULT_WINDOW = 7
DEFAULT_TAKE_LOGS = True
DEFAULT_CENTER = False
DEFAULT_MIN_PERIODS = 1
def calculate_weekly_incidences_from_results(
results,
outcome,
groupby=None,
):
"""Create th... | pd.concat(period_outcomes) | pandas.concat |
"""Script to add interval on GVA and population file
Run script on 'data' folder in scenarios_not_extracted folder
"""
import os
import pandas as pd
import numpy as np
from energy_demand.basic import lookup_tables
from energy_demand.basic import basic_functions
def run(
path_to_folder,
path_MSOA_basel... | pd.read_csv(file_path) | pandas.read_csv |
""" Model for output of general/metadata data, useful for a batch """
import logging
from pathlib import Path
from typing import List, Optional, Union
import pandas as pd
from pydantic import BaseModel, Field, validator
from nowcasting_dataset.consts import SPATIAL_AND_TEMPORAL_LOCATIONS_OF_EACH_EXAMPLE_FILENAME
fro... | pd.read_csv(filename) | pandas.read_csv |
from datetime import datetime
import pandas as pd
import numpy as np
from extract import PreProcess
from shapely.geometry import Point
from shapely.geometry.polygon import Polygon
import geopandas as gpd
import fun_logger
log = fun_logger.init_log()
prepros = PreProcess()
fmt = '%Y-%m-%d %H:%M:%S'
class AnalyzeDf()... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import astropy.units as u
import pandas as pd
def deg2mas(value):
'''
Converts value from degree to milliarcseconds
value: a value in degree
'''
value_mas = (value * u.degree).to(u.mas).value
return value_mas
def time_diff(catalog):
"""
Calculates the time differ... | pd.DataFrame({'delta_days': delta_days}) | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
from sklearn.base import BaseEstimator
from sklearn.pipeline import Pipeline
from sklearn.metrics import (accuracy_score, precision_score, recall_score, roc_auc_score,
f1_score, roc_curve, ... | pd.DataFrame(y_test.values, columns=["True Label"]) | pandas.DataFrame |
import os
import logging
import copy
import numpy as np
import pandas as pd
from oemof.solph import EnergySystem, Bus, Sink, Source
import oemof.tabular.tools.postprocessing as pp
from oemof.tools.economics import annuity
from oemof_flexmex.helpers import delete_empty_subdirs, load_elements, load_scalar_input_data,\
... | pd.concat(carrier_cost) | pandas.concat |
import streamlit as st
import pandas as pd
import altair as alt
import numpy as np
from datetime import datetime, timedelta
from dateutil import parser
from utils import suffix, custom_strftime
population = 68134973
alt.themes.enable('fivethirtyeight')
latest_date = parser.parse("2021-04-07")
dose1 = pd.read_csv(f"d... | pd.to_datetime(dose1.date) | pandas.to_datetime |
#!/usr/bin/env python3
from pathlib import Path
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from itertools import product
import statsmodels.api as sm
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.arima_process import arma_generate_sample
from rom_plots import detrend
imp... | pd.unique(df.YEAR) | pandas.unique |
"""
Utils to plot graphs with arrows
"""
import matplotlib.transforms
import matplotlib.patches
import matplotlib.colors
import matplotlib.cm
import numpy as np
import pandas as pd
import logging
from tctx.util import plot
def _clip_arrows(arrows, tail_offset, head_offset):
"""
shorten head & tail so the ... | pd.DataFrame.from_dict(pos, orient='index', columns=['x', 'y']) | pandas.DataFrame.from_dict |
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 28 22:50:43 2018
@author: kennedy
"""
"""
Credit:
https://www.quantopian.com/posts/technical-analysis-indicators-without-talib-code
Bug Fix by Kennedy:
Works fine for library import.
returns only column of the indicator r... | pd.Series(DoI) | pandas.Series |
import blpapi
import logging
import datetime
import pandas as pd
import contextlib
from collections import defaultdict
from pandas import DataFrame
@contextlib.contextmanager
def bopen(debug=False):
con = BCon(debug=debug)
con.start()
try:
yield con
finally:
con.stop()
class BCon(obj... | DataFrame(data) | pandas.DataFrame |
from __future__ import annotations
import numbers
from typing import TYPE_CHECKING
import warnings
import numpy as np
from pandas._libs import (
lib,
missing as libmissing,
)
from pandas._typing import (
ArrayLike,
Dtype,
type_t,
)
from pandas.compat.numpy import function as nv
from pandas.core.... | is_bool_dtype(result) | pandas.core.dtypes.common.is_bool_dtype |
import numpy as np
import pandas as pd
import pickle
import scipy.sparse
import tensorflow as tf
from typing import Union, List
import os
from tcellmatch.models.models_ffn import ModelBiRnn, ModelSa, ModelConv, ModelLinear, ModelNoseq
from tcellmatch.models.model_inception import ModelInception
from tcellmatch.estimat... | pd.DataFrame({"label": self.label_ids}) | pandas.DataFrame |
from stockscore.data import Stocks, return_top
import pandas as pd
import pytest
symbols = ["FB", "AAPL", "AMZN", "NFLX", "GOOGL"]
stocks = Stocks(symbols)
tdata = {
"Score": [6, 5, 4, 3, 2],
"Value Score": [1, 2, 3, 1, 0],
"Growth Score": [3, 2, 0, 1, 2],
"Momentum Score": [2, 1, 1, 1, 0],
}
tscores ... | pd.DataFrame(tdata, index=symbols) | pandas.DataFrame |
from xml.parsers.expat import model
import numpy as np
import pandas as pd
import plotly.express as px
import streamlit as st
import os
from joblib import dump, load
import sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
import pickle
# Titulo do app
st.write("""
# Prevendo oco... | pd.read_csv('cardio_app2.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
# (c) <NAME>, see LICENSE.rst.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
from ciso8601 import parse_datetime
from lxml import etree
from pytz import FixedOffset
import num... | pd.DataFrame(meta_data) | pandas.DataFrame |
"""Dynamic file checks."""
from dataclasses import dataclass
from datetime import date, timedelta
from typing import Dict, Set
import re
import pandas as pd
import numpy as np
from .errors import ValidationFailure, APIDataFetchError
from .datafetcher import get_geo_signal_combos, threaded_api_calls
from .utils import r... | pd.isna(frame["ftstat"]) | pandas.isna |
# Importar librerias
import pandas # importar libreria pandas
import time # importar libreria time
import datetime ... | pandas.DataFrame(registros) | pandas.DataFrame |
from ..pyhrp.tools.distancematrices import CorrDistance, PortfolioDistance, LTDCDistance
from math import fabs, sqrt, log
import pandas as pd
import numpy as np
def test_CorrDistance_get_distance_matrix():
d = {'A': [0.5, 1, 0], 'B': [0, -1, 0.5], 'C':[-0.5, 1, 0]}
df = pd.DataFrame(data=d)
corr = df.corr(... | pd.DataFrame(data=d) | pandas.DataFrame |
'''
pyjade
A program to export, curate, and transform data from the MySQL database used by the Jane Addams Digital Edition.
'''
import os
import re
import sys
import json
import string
import datetime
import mysql.connector
from diskcache import Cache
import pandas as pd
import numpy as np
from bs4 import Beautiful... | pd.read_sql(statement,DB) | pandas.read_sql |
import pandas as pd
from assistants.compliance.util import fields
from assistants.compliance.util.input_data_utility import completed_to_due_vector
dt_new_mogl = pd.Timestamp(2020, 9, 15) # September 2020 MOGL changes
def fix_some_dates(data: pd.DataFrame) -> pd.DataFrame:
"""Fix Some Dates.
Make sure if ... | pd.Series(False, index=data.index) | pandas.Series |
#!/usr/bin/env python
# -*- coding: utf-8; -*-
# Copyright (c) 2020, 2022 Oracle and/or its affiliates.
# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
from __future__ import print_function, absolute_import
import os
import re
import warnings
import oci
import... | pd.DataFrame() | pandas.DataFrame |
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import numpy as np
from pylab import rcParams
##########################################################################################
# Designed and developed by <NAME>
# Date : 11 ... | pd.DataFrame(df1) | pandas.DataFrame |
# importação de bibliotecas
import pandas as pd
# carrega um arquivo do HD para a memoria
data = pd.read_csv('data/kc_house_data.csv')
# mostrar na tela as primeiras 6 linhas
# print(data.head())
# fução que converte de object (string) -> date
data['date'] = | pd.to_datetime(data['date']) | pandas.to_datetime |
import sqlite3
import pandas as pd
import pandas.io.sql as psql
import ast
import hashlib
import sys
import random
from sqlalchemy import create_engine
import sqlalchemy.types as dtype
import requests
# message: |channel|user|text| <= ts
# coin : val <= username
slack_columns = ["ts", "text", "user", "channel"]
slack... | pd.DataFrame(data, columns=slack_columns, index=None) | pandas.DataFrame |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.utils import resample
from sklearn.metrics import accuracy... | pd.read_csv('thorp.csv') | pandas.read_csv |
import pandas as pd
import os
def prepare_legends(mean_models, models, interpretability_name):
bars = []
y_pos = []
index_bars = 0
for nb, i in enumerate(mean_models):
if nb % len(models) == int(len(models)/2):
bars.append(interpretability_name[index_bars])
index_bars +=... | pd.DataFrame(columns=self.columns_name_file3) | pandas.DataFrame |
"""
Data: Temperature and Salinity time series from SIO Scripps Pier
Salinity: measured in PSU at the surface (~0.5m) and at depth (~5m)
Temp: measured in degrees C at the surface (~0.5m) and at depth (~5m)
- Timestamp included beginning in 1990
"""
# imports
import sys,os
import pandas as pd
import numpy as np
im... | pd.read_csv('/Users/MMStoll/Python/Data/Ocean569_Data/SIO_Data/SIO_TEMP_1916_201905.txt', sep='\t', skiprows = 26) | pandas.read_csv |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
test_hydrofunctions
----------------------------------
Tests for `hydrofunctions` module.
"""
from __future__ import (
absolute_import,
print_function,
division,
unicode_literals,
)
from unittest import mock
import unittest
import warnings
from pandas... | pd.Timedelta("1 day 1 hour 2 minutes") | pandas.Timedelta |
import pandas as pd
import numpy as np
from preprocess import process_examples, get_requests_from_logs
from scipy.spatial import distance
import logging
log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
def _process(data, max_attributes, restructure):
processed = process_examples(data, max_attributes,... | pd.get_dummies(processed) | pandas.get_dummies |
import pandas as pd
import sys,os,io,re
import numpy as np
path=sys.argv[1]
outName=sys.argv[2]
thresh=int(sys.argv[3])
anno_file=sys.argv[4]
anno_table= | pd.read_csv(anno_file) | pandas.read_csv |
#! /usr/bin/env python
import os
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow.contrib import learn
from bson.objectid import ObjectId
from sentiment_analysis.tweet_preprocessing import preprocess_tweets
from sentiment_analysis.data_helpers import batch_iter
from config import config
f... | pd.DataFrame(posts) | pandas.DataFrame |
import pandas as pd
import numpy as np
import math
from datetime import datetime
from dateutil import parser
import csv
import urllib2
import sys
import pytz
url = 'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=NSE:RELIANCE&interval=1min&datatype=csv&outputsize=full&apikey=<KEY>'
response = ... | pd.read_hdf('StockMarketData_2020-02-14.h5', key='/RELIANCE__EQ__NSE__NSE__MINUTE') | pandas.read_hdf |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 26 14:26:51 2021
@author: michaeltown
"""
## beginning of module 1 MVP data analysis
import numpy as np
import pandas as pd
import os as os
import datetime as dt
import matplotlib.pyplot as plt
import seaborn as sns
## revised EDA project to look... | pd.read_csv(dataFileLoc1) | pandas.read_csv |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import pandas.compat as compat
###############################################################
# Index / Series common tests which may trigger dtype coercions
###############################################... | pd.Timestamp('2011-01-04') | pandas.Timestamp |
import altair as alt
import pandas as pd
import streamlit as st
import coin_metrics
# Get data from coin metrics API
@st.cache
def get_data(asset_id, metrics, asset_name):
rates = coin_metrics.get_reference_rates_pandas(asset_id, metrics)
df = | pd.DataFrame(data=rates) | pandas.DataFrame |
# load in libraries
from bs4 import BeautifulSoup
import pandas as pd
import time
from selenium import webdriver
# %% set up selenium
from selenium import webdriver
driver = webdriver.Firefox()
url1 = 'https://freida.ama-assn.org/search/list?spec=43236&page=1'
driver.get(url1)
# %% define standard parse function
def ... | pd.DataFrame.from_dict(itemdict) | pandas.DataFrame.from_dict |
from enum import Enum
import sys
import os
import re
from typing import Any, Callable, Tuple
from pandas.core.frame import DataFrame
from tqdm import tqdm
import yaml
from icecream import ic
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.dirname(SCRIPT_DIR))
from argpar... | pd.DataFrame.sparse.from_spmatrix(sprs, columns=columns) | pandas.DataFrame.sparse.from_spmatrix |
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
"""
Created by <NAME> on 4/2/18.
Email : <EMAIL>
Website: http://ce.sharif.edu/~naghipourfar
"""
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import keras
from... | pd.get_dummies(all_data) | pandas.get_dummies |
#!/usr/bin/env python
# coding: utf-8
# # ReEDS Scenarios on PV ICE Tool
# To explore different scenarios for furture installation projections of PV (or any technology), ReEDS output data can be useful in providing standard scenarios. ReEDS installation projections are used in this journal as input data to the PV ICE... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
import seaborn as sns
import shap
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import r2_score
# from .utils import Boba_Utils as u
class Boba_Model_Diagn... | pd.qcut(y_temp['predicted'], 10) | pandas.qcut |
# %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data = {
'bids_regdup': pd.read_csv('data/as_bids_REGUP.csv'),
'bids_regdown': pd.read_csv('data/as_bids_REGDOWN.csv'),
'plans': pd.read_csv('data/as_plan.csv'),
'energy_prices': | pd.read_csv('data/energy_price.csv') | pandas.read_csv |
# pylint: disable=E1101,E1103,W0232
from datetime import datetime, timedelta
from pandas.compat import range, lrange, lzip, u, zip
import operator
import re
import nose
import warnings
import os
import numpy as np
from numpy.testing import assert_array_equal
from pandas import period_range, date_range
from pandas.c... | Int64Index([1, 2, 5, 7, 12, 25]) | pandas.core.index.Int64Index |
from datetime import datetime, date
import sys
if sys.version_info >= (2, 7):
from nose.tools import assert_dict_equal
import xlwings as xw
try:
import numpy as np
from numpy.testing import assert_array_equal
def nparray_equal(a, b):
try:
assert_array_equal(a, b)
except Asse... | pd.MultiIndex.from_arrays([['a', 'a', 'b'], [1., 2., 1.]]) | pandas.MultiIndex.from_arrays |
import numpy as np
import pandas as pd
import pytest
import woodwork as ww
from evalml.data_checks import (
ClassImbalanceDataCheck,
DataCheckError,
DataCheckMessageCode,
DataCheckWarning,
)
class_imbalance_data_check_name = ClassImbalanceDataCheck.name
def test_class_imbalance_errors():
X = pd.... | pd.Series([200] * 10) | pandas.Series |
"""
Base class for a runnable script
"""
import pandas as pd
import numpy as np
from .. import api as mhapi
import os
from ..utility import logger
class Processor:
def __init__(self, verbose=True, violate=False, independent=True):
self.verbose = verbose
self.independent = independent
self.violate = violate
... | pd.DataFrame() | pandas.DataFrame |
"""--------------------------------------------------------------------------------------------------------------------
Copyright 2021 Market Maker Lite, LLC (MML)
Licensed under the Apache License, Version 2.0
THIS CODE IS PROVIDED AS IS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND
This file is part of the MML Op... | pd.DataFrame(symbol_df[0:][0]) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
from scipy.stats import entropy
import pickle
import os
import json
from flask import Flask
from flask import request
from jinja2 import Template
import pandas as pd
from sklearn.ensemble import RandomF... | pd.concat([self.all_data, self.labelled]) | pandas.concat |
# -*- coding: utf-8 -*-
import csv
import os
import platform
import codecs
import re
import sys
from datetime import datetime
import pytest
import numpy as np
from pandas._libs.lib import Timestamp
import pandas as pd
import pandas.util.testing as tm
from pandas import DataFrame, Series, Index, MultiIndex
from pand... | tm.assert_frame_equal(df, expected) | pandas.util.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 1 13:37:10 2019
@author:Imarticus Machine Learning Team
"""
import numpy as np
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
color = sns.color_palette()
pd.options.mode.chain... | pd.read_csv("order_products_prior.csv") | pandas.read_csv |
#! /usr/bin/env python
##! /usr/bin/arch -x86_64 /usr/bin/env python
from logging import error
import dash
import dash_table
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
f... | is_numeric_dtype(df[groups[1]]) | pandas.api.types.is_numeric_dtype |
from pymongo import MongoClient
import pandas as pd
from collections import Counter
# NLP libraries
from nltk.tokenize import TweetTokenizer
from nltk.corpus import stopwords
import string
import csv
import json
# from datetime import datetime
import datetime
from collections import deque
import pymongo
"""TIME SERI... | pd.Series(ones, index=idx) | pandas.Series |
#!/usr/bin/python
# <EMAIL>
#====================SET============================#
C_END = "\033[0m"
C_BOLD = "\033[1m"
C_RED = "\033[31m"
#==================================================#
import inspect
import sys
import os
import glob
import pandas as pd
import numpy as np
def DebugPrinter(arg):
... | pd.read_csv(result) | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 10 04:11:27 2017
@author: konodera
nohup python -u 501_concat.py &
"""
import pandas as pd
import numpy as np
from tqdm import tqdm
import multiprocessing as mp
import gc
import utils
utils.start(__file__)
#======================================... | pd.merge(df, organic, on='product_id', how='left') | pandas.merge |
#!/usr/bin/env python
# coding: utf-8
# # OpenMC Program for BurnUp analysis and Benchmarking
#
# In[1]:
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 7 11:40:23 2020
@author: feryantama
"""
# ## 1. Initialization
#
# Initialize package we used in this program
# In[2]:
import numpy ... | pd.DataFrame(data=count_list,columns=['label'],index=Pebbledf.index) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
import click
import datetime
import locale
import numpy as np
import os
from pathlib import Path
import pandas as pd
import plotly.graph_objects as go
CSV_NAME_MAP = {
"pl_PL": {
"p2.5": "dolne 2.5% modelowań",
"p25": "dolne 25% modelowań",
"p75": "gór... | pd.read_csv('https://raw.githubusercontent.com/KITmetricslab/covid19-forecast-hub-de/master/data-truth/MZ/truth_MZ-Incident%20Cases_Poland.csv') | pandas.read_csv |
"""
NAME : Molecular Arrangement and Fringe Identification Analysis from Molecular Dynamics (MAFIA-MD)
AUTHORS : <NAME>, Dr. <NAME> and Dr. <NAME>
MAFIA-MD is a post-processing utility to capture ring structures from molecular trajectory files (.xyz) generated by
reactive molecular dynamics simulation of Hydrocarbon... | pd.DataFrame({'Atoms. Timestep: 0'}, index=[0]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# ---
# 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
# ---
# + [markdown] papermill={"dura... | pd.read_gbq(query, dialect='standard') | pandas.read_gbq |
import pandas as pd
def process(feats, out_overlap, out_weights, out_feats):
#read features
overlap = pd.DataFrame(pd.np.zeros([0, 6]))
overlap.columns = ["drug1", "drug2", "mode", "overlap", "no_feats1", "no_feats2"]
weights = pd.DataFrame( | pd.np.zeros([0, 5]) | pandas.np.zeros |
import sys
import pandas as pd
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.model_selection import ParameterGrid
class EVI(BaseEstimator):
"""Class for evaluating multi-modal data integration approaches for combining unspliced, spliced, and RNA velocity gene expression modalities
... | pd.DataFrame() | pandas.DataFrame |
""" Profile a single GConv layer """
import os
import sys
import argparse
import copy
import time
import shutil
import json
import logging
logging.getLogger().setLevel(logging.DEBUG)
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.back... | pd.DataFrame(data, columns=columns) | pandas.DataFrame |
""" test the scalar Timedelta """
from datetime import timedelta
import numpy as np
import pytest
from pandas._libs import lib
from pandas._libs.tslibs import (
NaT,
iNaT,
)
import pandas as pd
from pandas import (
Timedelta,
TimedeltaIndex,
offsets,
to_timedelta,
)
import pandas._testing as ... | Timedelta("-1 days, 10:11:12") | pandas.Timedelta |
from flask import Flask, render_template, request, session, redirect, url_for
from datetime import datetime, timedelta
import pandas as pd
import sqlite3, hashlib, os, random, os, dotenv
app = Flask(__name__)
app.secret_key = "super secret key"
dotenv.load_dotenv()
MAPBOX_TOKEN = os.getenv('MAPBOX_TOKEN')
conn = sqlit... | pd.read_sql("select * from w_sales_history", conn) | pandas.read_sql |
import numpy as np
import pandas as pd
from settings.config import RECOMMENDATION_LIST_SIZE, KL_LABEL, HE_LABEL, CHI_LABEL, FAIRNESS_METRIC_LABEL, \
VARIANCE_TRADE_OFF_LABEL, \
COUNT_GENRES_TRADE_OFF_LABEL, TRADE_OFF_LABEL, evaluation_label, MACE_LABEL, FIXED_LABEL, MAP_LABEL, \
MRR_LABEL, order_label, MC_... | pd.DataFrame() | pandas.DataFrame |
# %% load in libraries
from bs4 import BeautifulSoup
import pandas as pd
import time
from selenium import webdriver
import random
import numpy as np
# %% set up selenium
from selenium import webdriver
driver = webdriver.Firefox()
# %%
driver.get('https://www.doximity.com/residency/programs/009b631d-3390-4742-b583-820... | pd.read_csv('specialties_doximity.csv') | pandas.read_csv |
import re
import fnmatch
import os, sys, time
import pickle, uuid
from platform import uname
import pandas as pd
import numpy as np
import datetime
from math import sqrt
from datetime import datetime
import missingno as msno
import statsmodels.api as sm
from statsmodels.tsa.seasonal import seasonal_decompose
from stat... | pd.DataFrame.from_dict({'Actual': model.actual, 'Prediction': model.pred}) | pandas.DataFrame.from_dict |
import operator
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import FloatingArray
import pandas.core.ops as ops
# Basic test for the arithmetic array ops
# -----------------------------------------------------------------------------
@pytest.mark.paramet... | pd.Series([1, 2, 3], dtype="Int64") | pandas.Series |
import streamlit as st
import pandas as pd
import yfinance as yf
import datetime
import os
from pathlib import Path
import requests
import hvplot.pandas
import numpy as np
import matplotlib.pyplot as plt
from MCForecastTools_2Mod import MCSimulation
import plotly.express as px
from statsmodels.tsa.arima_model import ... | pd.Series(conf[:, 1], index=empty_df.index) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 1 16:06:36 2017
@author: Gonxo
This file is merges the different grid and solar feed into a single feed. Also
it deseasonalizes the data in hour of the daya, day of the week, and month of
the year. Finally it computes ANOVA tests to check seasonal variations significan... | pd.HDFStore(sourceFile) | pandas.HDFStore |
import os
import unittest
import random
import sys
import site # so that ai4water directory is in path
ai4_dir = os.path.dirname(os.path.dirname(os.path.abspath(sys.argv[0])))
site.addsitedir(ai4_dir)
import scipy
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from ai4wa... | pd.date_range('20110101', periods=35, freq='D') | pandas.date_range |
import numpy as np
import pandas as pd
from lightgbm import LGBMClassifier, LGBMRegressor
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import GroupKFold
from src.python.space_configs import space_lightgbm, tune_model, gp_minimize, forest_minimize
from ml_metrics import rmse
train_df = pd.rea... | pd.concat([importances, imp_df], axis=0, sort=False) | pandas.concat |
# 导入类库
import os.path
import os
import datetime
import lightgbm as lgb
from sklearn.model_selection import train_test_split
from gensim.models import Word2Vec
from pandas import read_csv
import re
import pandas as pd
import numpy as np
import itertools
import sys
def get_dict(file): ... | pd.read_csv('result/' + i) | pandas.read_csv |
# Author: <NAME> and <NAME>
# Plant and Food Research New Zealand and UNSW Sydney
#!/usr/bin/env python3
#! module load pfr-python3/3.6.5
print('Here we go...')
import sys
import pyqrcode
import os, os.path # For PATH etc. https://docs.python.org/2/library/os.html
import sys # For handling command line args
from... | pd.DataFrame() | 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 |
from datetime import (
datetime,
timedelta,
)
import numpy as np
import pytest
from pandas.compat import (
pa_version_under2p0,
pa_version_under4p0,
)
from pandas.errors import PerformanceWarning
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
isna,
)
import pandas._tes... | Series(["foo", "bar"]) | pandas.Series |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import pandas.compat as compat
###############################################################
# Index / Series common tests which may trigger dtype coercions
###############################################... | pd.Timestamp('2012-01-01') | pandas.Timestamp |
from typing import Any
from typing import Dict
from typing import Optional
import pandas
import pytest
from evidently.model_profile.sections.classification_performance_profile_section import \
ClassificationPerformanceProfileSection
from .helpers import calculate_section_results
from .helpers import check_profil... | pandas.DataFrame({'target': [1, 1, 3, 3], 'prediction': [1, 2, 1, 4]}) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: abhijit
"""
#%% preamble
import numpy as np
import pandas as pd
from glob import glob
#%% Tidy data
filenames = glob('data/table*.csv')
filenames = sorted(filenames)
table1, table2, table3, table4a, table4b, table5 = [pd.read_csv(f) for f in filenames] ... | pd.read_csv('data/pew.csv') | pandas.read_csv |
import json
import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score
class SEIssueRF:
def __init__(self, data):
self.data = data
de... | pd.DataFrame({"corpus": fitting_data}) | pandas.DataFrame |
import pandas as pd
import numpy as np
from datetime import timedelta, datetime
from sys import argv
dates=("2020-04-01", "2020-04-08", "2020-04-15", "2020-04-22",
"2020-04-29" ,"2020-05-06", "2020-05-13","2020-05-20", "2020-05-27", "2020-06-03",
"2020-06-10", "2020-06-17", "2020-06-24", "2020-07-01", "2020-07-08",
... | pd.DataFrame.from_dict(sims_dict) | pandas.DataFrame.from_dict |
#!/usr/bin/env python
# coding: utf-8
# <b>Python Scraping of Book Information</b>
# In[1]:
get_ipython().system('pip install bs4')
# In[2]:
get_ipython().system('pip install splinter')
# In[3]:
get_ipython().system('pip install webdriver_manager')
# In[1]:
# Setup splinter
from splinter import Browser
... | pd.read_csv('greek-roman-clean.csv') | pandas.read_csv |
"""MVTecAd Dataset."""
# default packages
import dataclasses as dc
import enum
import logging
import pathlib
import shutil
import sys
import tarfile
import typing as t
import urllib.request as request
# third party packages
import pandas as pd
# my packages
import src.data.dataset as ds
import src.data.utils as ut
#... | pd.read_csv(self.train_list) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 17 14:10:58 2017
@author: tkc
"""
import os
import pandas as pd
from tkinter import filedialog
AESQUANTPARAMFILE='C:\\Users\\tkc\\Documents\\Python_Scripts\\Augerquant\\Params\\AESquantparams.csv'
class AESspectrum():
''' Single instance of AES spectra file created f... | pd.read_csv('Backfitlog.csv', encoding='cp437') | pandas.read_csv |
import pandas as pd
import datetime
import numpy as np
class get_result(object):
def __init__(self, data, material, start_time):
self.data = data
self.material = material
self.start_time = start_time
self.freq = 0.5 # 单位：小时
ele_struct = pd.read_excel("尖峰平谷电费结构.xlsx", ... | pd.DataFrame(index=idx, columns=['ele'], data=ygdn) | pandas.DataFrame |
import re
from datetime import datetime, timedelta
import numpy as np
import pandas.compat as compat
import pandas as pd
from pandas.compat import u, StringIO
from pandas.core.base import FrozenList, FrozenNDArray, DatetimeIndexOpsMixin
from pandas.util.testing import assertRaisesRegexp, assert_isinstance
from pandas i... | tm.assert_series_equal(hist, expected) | pandas.util.testing.assert_series_equal |
import time
import pandas as pd
import numpy as np
CITY_DATA = { 'chicago': 'chicago.csv',
'new york city': 'new_york_city.csv',
'washington': 'washington.csv' }
months = ['all','january','february','march','april','may','june','july','august','september','october','december']
def get_fi... | pd.read_csv(CITY_DATA[city]) | pandas.read_csv |
import pandas as pd
import networkx as nx
import logging
import math
import numpy as np
from statsmodels.stats.outliers_influence import variance_inflation_factor
def get_vif(df: pd.DataFrame, threshold: float = 5.0):
"""
Calculates the variance inflation factor (VIF) for each feature column. A VIF
value... | pd.DataFrame() | pandas.DataFrame |
#
# Copyright (C) 2019 Databricks, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | pd.get_dummies(df.d) | pandas.get_dummies |
"""
This module allows to collect experimental variables from fits
to data that can then be used as input to simulations
"""
# Author: <NAME>, <NAME>, 2019
# License: MIT License
import numpy as np
import pandas as pd
import scipy.optimize
import scipy.stats
import colicycle.time_mat_operations as tmo
import coli... | pd.read_pickle(file_to_load) | pandas.read_pickle |
"""
library for simulating semi-analytic mock maps of CMB secondary anisotropies
"""
__author__ = ["<NAME>", "<NAME>"]
__email__ = ["<EMAIL>", "<EMAIL>"]
import os
import warnings
from sys import getsizeof
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
from warnings ... | pd.DataFrame(catalog) | pandas.DataFrame |
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
from sklearn.model_selection import StratifiedKFold
import numpy as np
import csv
import re
import pickle
import time
from datetime import timedelta
import pandas as pd
from pathlib import Path
import sys
sys.path.insert(0,'/nfs/ghome/live/yas... | pd.DataFrame.from_dict(expdata) | pandas.DataFrame.from_dict |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = 'ipetrash'
# SOURCE: https://stackoverflow.com/questions/7961363/removing-duplicates-in-lists
from collections import OrderedDict
from functools import reduce
# pip install pandas
import pandas as pd
# pip install numpy
import numpy as np
def remove_d... | pd.unique(items) | pandas.unique |
# standard libraries
import enum
import glob
import os
import warnings
import zipfile
# third-party libraries
import matplotlib.pyplot as plt
import natsort
import pandas
def get_align_count_pipelines():
return enum.Enum('align_count_pipeline', 'STAR_HTSeq Kallisto') # SAMstats')
# Below is the complete list ... | pandas.DataFrame(rows_list) | pandas.DataFrame |
from collections import OrderedDict
from datetime import datetime, timedelta
import numpy as np
import numpy.ma as ma
import pytest
from pandas._libs import iNaT, lib
from pandas.core.dtypes.common import is_categorical_dtype, is_datetime64tz_dtype
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
Da... | Series([np.nan, np.nan]) | pandas.Series |
# -*- 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... | pd.Timestamp('2011-01-01') | pandas.Timestamp |
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
import pandas
TEST_DF = | pandas.DataFrame( [1,2,3]) | pandas.DataFrame |
# 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... | assert_index_equal(renamed.columns, modin_renamed.columns) | pandas.testing.assert_index_equal |
import datetime
import inspect
import logging
import numpy.testing as npt
import os.path
import pandas as pd
import pkgutil
import sys
from tabulate import tabulate
import unittest
try:
from StringIO import StringIO
except ImportError:
from io import StringIO, BytesIO
# #find parent directory and import model
... | pd.read_csv(csv_transpose_path_in, index_col=0, engine='python') | pandas.read_csv |
'''
Created on 19 May 2018
@author: Ari-Tensors
Binary classification: Predict if an asset will fail within certain time frame (e.g. cycles)
'''
import keras
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os, traceback
import json
# Setting seed for reproducibility
np.random.seed(1234... | pd.read_csv('./server/Dataset/PM_test.txt', sep=" ", header=None) | pandas.read_csv |
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