prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# _____ _ _ _ ____ _ _ _
# |_ _| |__ (_)___ (_)___ | _ \(_)_ __ _ _| | __ _| |_ ___
# | | | '_ \| / __| | / __| | |_) | | '_ \| | | | |/ _` | __/ _ \
# | | | | | | \__ \ | \__ \ | __/| | |_) | |_| | | (_| | || __/
# |_| |_| |_|_|___/ |_|___/ |_| |_| .__/ \__,_|_|\__... | pd.DataFrame(list_of_tuples, columns=columns) | pandas.DataFrame |
# -*- coding:utf-8 -*-
# Author: <NAME>
# Data: 2/20/2018
# Describe: Build a dictionary based on my own needs.
import os.path
import re
import pandas as pd
import translate as tl
class Dictionary(object):
def __init__(self, dic_name):
if not os.path.isfile(dic_name):
open(dic_name, 'a').cl... | pd.DataFrame(columns=self.columns, dtype=str) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pickle
import time
import random
import os
from sklearn import linear_model, model_selection, ensemble
from sklearn.svm import SVC
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.base import clone
from sklearn import metrics
from sklearn.model_selectio... | pd.concat(perf_dfs) | pandas.concat |
import re
import requests
from bs4 import BeautifulSoup
import json
from collections import OrderedDict
from io import StringIO
import pandas as pd
from astropy.time import Time
from datetime import datetime,date,timedelta
from tns_api_search import search, get, format_to_json, get_file
from astropy.coordinates import ... | pd.DataFrame(df) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 28 17:05:36 2018
@author: kutay.erkan
"""
"""
References:
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html
https://seaborn.pydata.org/generated/sea... | pd.DataFrame(columns=["feature","accuracy"]) | pandas.DataFrame |
# coding: utf-8
# # CareerCon 2019 - Help Navigate Robots
# ## Robots are smart… by design !!
#
# 
#
# ---
#
# Robots are smart… by design. To fully understand and properly navigate a task, however, they need input about their environment.
#
# In this compe... | pd.DataFrame() | pandas.DataFrame |
import viola
import pandas as pd
from io import StringIO
import sys, os
HERE = os.path.abspath(os.path.dirname(__file__))
data_expected = """test1 0 small_del
test2 0 small_del
test3 0 large_del
test4 0 large_del
test5 0 large_del
test6 0 small_dup
test7 0 small_inv
test8 0 others
test9 0 small_inv
viola_breakpoint:0 0... | pd.Series([2, 3, 1, 0, 2, 2, 1]) | pandas.Series |
#!/usr/bin/env python3
# various functions and mixins for downstream genomic and epigenomic anlyses
import os
import glob
import re
import random
from datetime import datetime
import time
from pybedtools import BedTool
import pandas as pd
import numpy as np
from tqdm import tqdm_notebook, tqdm
# Get Current Git Co... | pd.concat([data_df, df.loc[index]]) | pandas.concat |
""" plotting functions for Dataset objects
To Do:
Edit hyp_stats plots to take transitions.HypStats object instead of ioeeg.Dataset object
Remove redundant plotting fns added into EKG classs
Add subsetEEG function to break up concatenated NREM segments for plotting. Will require adjust... | pd.Timestamp(x) | pandas.Timestamp |
import pandas as pd
import numpy as np
from datetime import datetime
from tqdm import tqdm
from tqdm.notebook import tqdm as tqdmn
try:
from trade import Trade
except:
pass
try:
from backtest.trade import Trade
except:
pass
import chart_studio.plotly as py
import plotly.graph_objs as go
from plo... | pd.read_csv('data/datasets/de30eur/de30eur_hour.csv') | pandas.read_csv |
# pylint: disable=E1101
from datetime import datetime
import datetime as dt
import os
import warnings
import nose
import struct
import sys
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
from pandas.compat import iterkeys
from pandas.core.frame import DataFrame, Series
from pandas.c... | DataFrame(columns=['unit']) | pandas.core.frame.DataFrame |
import pandas as pd
from pandas import DataFrame
import sys
#--------
# Imports medi dataset with icd9 and rxcui descriptions to .csv file
# PARAMETERS:
# medi = medi spreadsheet
# icd9_desc = contains icd9 codes and their descriptions
# rxcui_desc = contains rxcui codes and their descriptions
def add_info_to_medi(med... | pd.read_csv(medi_rxcui_icd9) | pandas.read_csv |
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from argparse import ArgumentParser
from os import path
from time import time
from utils import trj2blocks
# MDAnalysis
import MDAnalysis as mda
from MDAnalysis.analysis.hydrogenbonds import hbond_analysis
def parse():
'''Parse command ... | pd.DataFrame(results[0]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable
"""
tests.test_validator
~~~~~~~~~~~~~~~~~~~~
:copyright: (c) 2018 by <NAME>.
:license: MIT, see LICENSE for more details.
"""
import numpy as np
from pandas import Series, date_range, to_datetime
from dfmapper import (
DateRangeValidator,
DtypeValida... | to_datetime(series_2) | pandas.to_datetime |
import pandas as pd
from geopy.geocoders import Nominatim
import os
import pathlib as plib
def add_coords(coords, city, state):
gloc = Nominatim(user_agent='my-application', timeout=3)
loc = gloc.geocode(city + ' ' + state)
if loc is None:
coords[city] = None
else:
coords[city] = [loc.... | pd.DataFrame({'City': cts, 'Latitude': lats, 'Longitude': lons}) | pandas.DataFrame |
'''
PipelineTranscriptDiffExpression.py - Utility functions for
pipeline_transcriptdiffexpression.py
==============================================================
:Author: <NAME>
:Release: $Id$
:Date: |today|
:Tags: Python
Code
----
'''
import cgatpipelines.tasks.expression as Expression
import cgatpipelines.task... | pd.merge(df_kmer, df_agg, left_index=True, right_index=True) | pandas.merge |
"""Transform signaling data to smoothed trajectories."""
import sys
import numpy
import pandas as pd
import geopandas as gpd
import shapely.geometry
import matplotlib.patches
import matplotlib.pyplot as plt
import mobilib.voronoi
SAMPLING = pd.Timedelta('00:01:00')
STD = pd.Timedelta('00:05:00')
def smoothen(arr... | pd.to_datetime(signals['pos_time']) | pandas.to_datetime |
""" Plots the tracker charts. """
import os
from datetime import datetime
from datetime import timedelta
import logging
import shutil
import pathlib
import multiprocessing as mp
import typing
from timeit import default_timer as timer
import pandas as pd
import numpy as np
from scipy.interpolate import interp1d
import... | pd.Timedelta(days=days) | pandas.Timedelta |
import unittest
import numpy as np
import pandas as pd
from sklearn.cluster import DBSCAN, KMeans
from sklearn.covariance import EmpiricalCovariance, MinCovDet
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.mixture import GaussianMixture
from dsbox.ml.outliers import CovarianceOutliers, Ga... | pd.DataFrame([1, 0, 0, 1, 10, 2, 115, 110, 32, 16, 2, 0, 15, 1]) | pandas.DataFrame |
# https://www.udemy.com/course/ai-finance
import os
from glob import glob
from datetime import datetime, date
import random
import pandas as pd
import yfinance as yf
def load_stock_list(market='br', symbols_list='', qty = 100):
"""This function loads the desirable symbols.
Args:
market (str): accepts only 'br' or... | pd.read_csv('./data/interim/lst_stock_symbols.txt', sep=';') | pandas.read_csv |
import os
import numpy as np
import pandas as pd
from pkg_resources import resource_filename
def load_arrests(return_X_y=False, give_pandas=False):
"""
Loads the arrests dataset which can serve as a benchmark for fairness. It is data on
the police treatment of individuals arrested in Toronto for simple po... | pd.DataFrame({"yt": result, "date": stamps}) | pandas.DataFrame |
import argparse
import os
import os.path as path
import pandas as pd
import cv2
import progressbar
from annotations import ImageAnnotation, SUPPORTED_CLASSES
from keypoints_detection.KeypointDetector import KeypointDetector
from keypoints_detection.factory import create_keypoint_detector
allowed_extensions = ['.jpg']... | pd.DataFrame() | pandas.DataFrame |
# Copyright 2021 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... | pd.Index(['Fidelity', 'Vanguard'], name='institution') | pandas.Index |
"""
Transforms the extracted data.
"""
import click
import feather
import numpy as np
import pandas as pd
import re
import tqdm
import yaml
from logging import *
from typing import *
def yes_no(x: str) -> float:
"""
Transforms a yes/no value to a numeric value.
Args:
x: The value to transform.
... | pd.isnull(x) | pandas.isnull |
"""Tests for Table Schema integration."""
import json
from collections import OrderedDict
import numpy as np
import pandas as pd
import pytest
from pandas import DataFrame
from pandas.core.dtypes.dtypes import (
PeriodDtype, CategoricalDtype, DatetimeTZDtype)
from pandas.io.json.table_schema import (
as_json_... | make_field(arr) | pandas.io.json.table_schema.make_field |
import pandas as pd
p1 = | pd.Series({'a':10,'b':20,'c':30}) | pandas.Series |
import csv
import pandas as pd
import numpy as np
######=================================================########
###### Segment A.1 ########
######=================================================########
SimDays = 365
SimHours = SimDays * 24
HorizonHours = 24 ##planning horizo... | pd.read_csv('data_camb_genparams.csv',header=0) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import os
from pathlib import Path
testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'ElectricFutures')
# Another option using relative address; for some operative systems you might need '/' instead of '\'
# testfolder = os.path.abspath(r'..\..\PV_... | pd.DataFrame() | pandas.DataFrame |
# Copyright (c) 2017, Intel Research and Development Ireland Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | pandas.DataFrame() | pandas.DataFrame |
import wiggum as wg
from itertools import combinations
import pandas as pd
import sys
import logging
from sklearn import mixture
import numpy as np
import json
def updateMetaData(labeled_df, meta):
"""
Update Meta Data
Parameters
-----------
labeled_df : DataFrame
LabeledDataFrame
meta ... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
from __future__ import division # so that 1/3=0.333 instead of 1/3=0
from psychopy import visual, core, data, event, logging, gui
from psychopy.constants import * # things like STARTED, FINISHED
import pandas as pd
import numpy as np # whole numpy lib is available, prep... | pd.DataFrame(X) | pandas.DataFrame |
import numpy as np; np.set_printoptions(precision=4, linewidth=200)
import pandas as pd; pd.set_option('display.width', 200)
import os
import logging
import scipy.stats as stats
from tqdm import tqdm
from polyfun import configure_logger, check_package_versions
from polyfun_utils import set_snpid_index
from pyar... | pd.read_table(args.regions_file) | pandas.read_table |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author : Mike
# @Contact : <EMAIL>
# @Time : 2020/1/6 22:49
# @File : base.py
import lightgbm as lgb
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from scipy import sparse
fro... | pd.merge(f1, i, on='instance_id', how='left') | pandas.merge |
import pandas as pd
import streamlit as st
import folium
from streamlit_folium import folium_static
from folium.plugins import MarkerCluster
import plotly.express as px
import geopandas
from PIL import Image
st.set_page_config(layout='wide')
@st.cache(allow_output_mutation=True)
def get_data(path):
data = pd.read... | pd.to_datetime(data['date']) | pandas.to_datetime |
"""Xray object detection dataset class."""
from typing import Dict, Tuple
import cv2
import numpy as np
import os
import pandas as pd
import torch
from albumentations.core.composition import Compose
from hydra.utils import to_absolute_path
from omegaconf import DictConfig, OmegaConf
from sklearn.model_selection impo... | pd.DataFrame(test_ids, columns=["image_id"]) | pandas.DataFrame |
import numpy as np
import pandas as pd
from scipy.stats import norm
import unittest
import networkx as nx
from context import grama as gr
from context import models
## FD stepsize
h = 1e-8
## Core function tests
##################################################
class TestModel(unittest.TestCase):
"""Test implem... | pd.DataFrame({"x": [0]}) | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import analyze
from utils import plot_collections, bin, modify, plotting
"""
Blue: #0C5DA5
Green: #00B945
"""
plt.style.use(['science', 'ieee', 'std-colors'])
fig, ax = plt.subplots()
size_x_inches, size_y_inches = fig.get_size_inches()
plt.close... | pd.read_excel(read_dir + fn1 + '.xlsx') | pandas.read_excel |
from django.shortcuts import render
from django.http import HttpResponse
from datetime import datetime
import psycopg2
import math
import pandas as pd
from openpyxl import Workbook
import csv
import random
def psql_pdc(query):
#credenciales PostgreSQL produccion
connP_P = {
'host' : '10.150.1.74',
'p... | pd.DataFrame(anwrD) | pandas.DataFrame |
from __future__ import print_function, division
"""
.. note::
These are the spectral modeling functions for SPLAT
"""
# imports: internal
import bz2
import copy
import glob
import gzip
import os
import requests
import shutil
import sys
import time
# imports: external
#import corner
import matplotlib; matplo... | pandas.DataFrame(toplot) | pandas.DataFrame |
import xgboost as xgb
import graphviz
import numpy as np
import pandas as pd
import random
import matplotlib
import textwrap
import scipy.spatial.distance as ssd
from scipy.stats import ks_2samp
from scipy.stats import entropy
import warnings
from sklearn import tree
from sklearn.manifold import TSNE
from sklearn.ense... | pd.DataFrame(data = principalComponents, columns = ['pc1', 'pc2','pc3','pc4','pc5']) | pandas.DataFrame |
import pandas as pd
import numpy as np
import math
import os
from scipy.interpolate import interp1d
import time
from sklearn.ensemble import RandomForestRegressor
import xgboost as xgb
from lightgbm import LGBMRegressor
from catboost import CatBoostRegressor
from information_measures import *
from joblib import Para... | pd.DataFrame([0],columns=['wap_std2_1']) | pandas.DataFrame |
import numpy as np
import pandas as pd
from ..mean_characters_per_word import MeanCharactersPerWord
from ..utils import PrimitiveT, find_applicable_primitives, valid_dfs
class TestMeanCharactersPerWord(PrimitiveT):
primitive = MeanCharactersPerWord
def test_sentences(self):
x = pd.Series(['This is a... | pd.Series([3.0, 4.0, 8.0, 10.5, 4.0]) | pandas.Series |
'''This module implements the word2vec model service that is responsible
for training the model as well as a backend interface for the API.
'''
from datetime import datetime
import json
import logging
import pandas as pd
from gensim.models.ldamulticore import LdaMulticore
import numpy as np
from wb_nlp.interfaces.mil... | pd.DataFrame(payload) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
from hypothesis import given, settings
from pandas.testing import assert_frame_equal
from janitor.testing_utils.strategies import (
conditional_df,
conditional_right,
conditional_series,
)
@pytest.mark.xfail(reason="empty object will pass thru")
@given(... | pd.Int64Dtype() | pandas.Int64Dtype |
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 14 12:04:33 2018
@author: gurunath.lv
"""
try :
import base64
import datetime
import io
import dash
from dash.dependencies import Input, Output
import dash_core_components as dcc
import dash_html_components as html
import dash... | pd.Series(labels) | pandas.Series |
# -*- coding: utf-8 -*-
""" Calculate isotopic interference and standard ratios. """
import pandas as pd
import itertools
from interference_calculator.molecule import Molecule, mass_electron, periodic_table
"""xmin = 0.0
xmax = 0.0"""
def interference(atoms, target, targetrange=0.3, maxsize=5, charge=[1],
... | pd.concat(data_w_charge) | pandas.concat |
import string
from typing import Any, Dict, List, Optional, Tuple, Mapping, Callable, Union
import pandas as pd
import numpy as np
import pytest
def _resolve_random_state(random_state: Union[int, np.random.RandomState]) -> np.random.RandomState:
""" Return a RandomState based on Input Integer (as seed) or RandomS... | pd.Series(["a", "b"] * (nrows // 2), name="category_no_miss", dtype="category") | pandas.Series |
# Preparation for Theme3 Cell of Origin using Panoptes
import pandas as pd
# train = pd.read_csv('../Theme3/train.csv', header=0)
# validation = pd.read_csv('../Theme3/val.csv', header=0)
# test = pd.read_csv('../Theme3/test.csv', header=0)
#
# cancer_dict = {'HNSCC': 0, 'CCRCC': 1, 'CO': 2, 'BRCA': 3, 'LUAD': 4, 'LSC... | pd.read_csv('../DLCCA/test.csv', header=0) | pandas.read_csv |
from src.network import Network
import src.VisualizeNN as VisNN
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
def split_dataset(train_df: pd.DataFrame,
fraction: float = 0.2):
"""
Split train data into train and validation.
"""
#permute all samples
... | pd.get_dummies(valid_df['cls'], dtype=float) | pandas.get_dummies |
import os
import re
import pandas as pd
import metis_cut as metis
import kahip_cut as kahip
import inertialflow_cut as inertialflow
import flowcutter_cut as flowcutter
import inertialflowcutter_cut as ifc
experiments_folder = ""
graphs = ["col", "cal", "europe", "usa"]
partitioners = ["metis", "kahip_v2_11", "inertial... | pd.DataFrame(rows) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import operator as op
import seaborn as sns
# http://data8.org/datascience/_modules/datascience/tables.html
#####################
# Frame Manipulation
def relabel(df, OriginalName, NewName):
return df.rename(index=str, columns={OriginalN... | pd.DataFrame(df) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 4 20:48:37 2018
@author: elcok
"""
import os
import sys
import numpy as np
import geopandas as gpd
import pandas as pd
sys.path.append(os.path.join( '..'))
from scripts.functions import region_exposure,region_losses,poly_files,load_sample
from scripts.utils import load_... | pd.concat(country_table) | pandas.concat |
import numpy as np
import random
from flask import Flask, request, render_template
from model.simple_recommender_model import simple_recommend
import pandas as pd
from tensorflow import keras
from rake_nltk import Rake
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.... | pd.read_csv(r'C:\Users\Lenovo PC\Desktop\Summary Work - Sheet1.csv', encoding='latin-1') | pandas.read_csv |
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats
from sklearn import linear_model
import statsmodels.api as sm
from scipy import stats
###################
yaara="723"
daniel = "957"
hilla="355"
generic_path = "/tmp/pycharm_project_"+hilla+"/"
#Full data
dfOp = pd.read_csv("/mnt/nadavrap-student... | pd.get_dummies(dfOp["Gender"]) | pandas.get_dummies |
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_frame_equal(expected, output) | pandas.testing.assert_frame_equal |
# %%%%
import pandas as pd
import numpy as np
import re
# %%%% functions
## Fill missing values
def fillmissing(x,col,index,benchmark):
for i in range(index,len(x)):
# find missing value
if x.loc[i,col] == benchmark:
# if first is missing, fill using the value next to it
if... | pd.to_datetime(xr['DATE'], format='%Y-%m-%d') | pandas.to_datetime |
import pymortar
import pandas as pd
import pendulum
import toml
from flask import Flask
from flask import jsonify, send_from_directory
from flask import request
from flask import current_app
from flask import make_response
from flask import render_template
from collections import defaultdict
from functools import updat... | pd.DataFrame(readings,index=times,columns=['readings']) | pandas.DataFrame |
""" test get/set & misc """
from datetime import timedelta
import re
import numpy as np
import pytest
from pandas import (
DataFrame,
IndexSlice,
MultiIndex,
Series,
Timedelta,
Timestamp,
date_range,
period_range,
timedelta_range,
)
import pandas._testing as tm
def test_basic_ind... | Series({1: [1, 2, 3], 2: [1, 2, 2, 3]}) | pandas.Series |
from time import sleep
from os import getcwd, makedirs
from datetime import datetime
from pandas import DataFrame, concat
from config.config import CONFIG
from thread_runner.runner import ThreadRunner
from models.house import House
from models.room import Room
from models.datetime import Datetime
COLUMNS = [
'ti... | concat((self.room_dataframes[room_id], latest_data), ignore_index=True) | pandas.concat |
# import pandas and numpy, and load the nls data
import pandas as pd
pd.set_option('display.width', 80)
| pd.set_option('display.max_columns', 7) | pandas.set_option |
import ast
import os
import pandas as pd
data_folder = "../../data"
num_of_workpiece = 20
workpiece_list = [f"wp_{idx + 1}" for idx in range(num_of_workpiece)]
workcell_list = [f"wc_{idx + 1}" for idx in range(17)]
input_filename = "input_3"
score_type = "independent_qc"
threshold = 0.9
input_folder = f"{data_folder}... | pd.read_csv(f"{input_folder}/{folder}/{filename}") | pandas.read_csv |
#!/usr/bin/env python
import os
import logging
import argparse
import numpy as np
import pandas as pd
import logging.handlers
from .__init__ import __version__
from .plot import make_color_dict, plot_legend, plot_passages, plot_appearance
from .colorlog import ColorFormatter
logger = logging.getLogger('evol')
de... | pd.concat([app, no_app]) | pandas.concat |
import hydra
from ncmw import community
from omegaconf import DictConfig, OmegaConf
import cobra
import logging
import socket
import time
import random
import numpy as np
import pandas as pd
from copy import deepcopy
import sys, os
import json, pickle
file_dir = os.path.dirname(os.path.dirname(__file__))
sys.path.... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
from . import net_feature_extract, snp_feature_extract, trace_feature_extract, utils
import podspy.log as logpkg
import podspy.petrinet as petripkg
import os, sys
import pandas as pd
import numpy as np
__all__ = [
'extract_feature_df'
]
@utils.timeit(on=True, verbose=False)
def extract_... | pd.Series(feature_dict) | pandas.Series |
"""Integer optimization of livestock and water services."""
import os
import sys
import shutil
from osgeo import gdal
import re
import pandas
import numpy as np
import pygeoprocessing.geoprocessing
import marginal_value as mv
def integer_optim_Peru(objective_list, suf):
"""Calculate optimal intervention portfol... | pandas.read_csv(agreement_summary) | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Date : Feb-03-20 23:44
# @Author : <NAME> (<EMAIL>)
# @Link : http://example.org
import time
import os
import json
import random
import pickle
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.optimizers import ... | pd.DataFrame(data=table, columns=columns) | pandas.DataFrame |
import logging
from unittest.mock import Mock
import pandas as pd
import pytest
from numpy import nan
from pdlog.logging import log_change_index
from pdlog.logging import log_fillna
from pdlog.logging import log_filter
from pdlog.logging import log_rename
from pdlog.logging import log_reshape
@pytest.fixture
def ca... | pd.DataFrame(index=after_index, columns=after_columns) | pandas.DataFrame |
import numpy as np
import pytest
from pandas._libs import join as _join
from pandas import Categorical, DataFrame, Index, merge
import pandas._testing as tm
class TestIndexer:
@pytest.mark.parametrize(
"dtype", ["int32", "int64", "float32", "float64", "object"]
)
def test_outer_join... | Categorical(["a", "b", "a", "c", "a", "b"], ["a", "b", "c"]) | pandas.Categorical |
# Import required modules
import requests
import pandas as pd
import json
import subprocess
from tqdm import tqdm
import re
# Set pandas to show full rows and columns
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwi... | pd.DataFrame(icdata) | pandas.DataFrame |
"""
This module implements several methods for calculating and outputting solutions of the unionfind_cluster_editing() algorithm.
It contains some methods to print solutions
and, more importantly, methods to merge solutions into one better solution.
There are 3 main algorithms: merge, repair and undo.
Two repair algori... | pd.unique(best_fits) | pandas.unique |
"""
Open Power System Data
Household Datapackage
validation.py : fix possible errors and wrongly measured data.
"""
import logging
logger = logging.getLogger(__name__)
import os
import yaml
import pytz
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
from .tools import update_progres... | pd.concat([feeds_output, feed_output], axis=1) | pandas.concat |
from elasticsearch import Elasticsearch, helpers
import pandas
from utils.log import *
class ES(object):
def __init__(self, conf):
self.es = Elasticsearch([conf['es_host']])
self.winlogbeat = conf['winlogbeat_index']
def insert_behaviors(self, _index, data):
records = []
for _... | pandas.DataFrame(records) | pandas.DataFrame |
import pandas as pd
# from WindPy import *
import sympy as smp
import scipy as scp
import scipy.stats as sss
import scipy.optimize as sop
import numpy as np
import pandas as pd
from datetime import datetime, date, timedelta
import matplotlib as mpl
import matplotlib.pyplot as plt
from fh_tools.fh_utils impor... | pd.DataFrame(simu, columns=wind_code_list) | pandas.DataFrame |
__all__ = [
"read_clock_paramaters",
"read_weather_inputs",
"read_model_parameters",
"read_irrigation_management",
"read_field_management",
"read_groundwater_table",
"compute_variables",
"compute_crop_calander",
"calculate_HIGC",
"calculate_HI_linear",
"read_model_initial_con... | pd.date_range(freq="D", start=SimStartTime, end=SimEndTime) | pandas.date_range |
# -*- coding: utf-8 -*-
"""Data structure for validating and efficiently slicing
fixed-length segments of typically multichannel time-series data.
"""
import numpy as np
import pandas as pd
from .errors import FitGridError
from . import tools
class Epochs:
"""Container class used for storing epochs tables and ... | pd.Index([time for time, _ in snapshots], name=time) | pandas.Index |
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
# =====================================================================
# Copyright (C) 2018-2019 by Owl Data
# author: <NAME>
# =====================================================================
import requests
import json
import time
import pandas as pd
from panda... | terEnd(1) | pandas.tseries.offsets.QuarterEnd |
import pandas as pd
import numpy as np
import seaborn as sns
from scipy import stats
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklea... | pd.DataFrame(sj_prediction) | pandas.DataFrame |
import os
import pathlib
import sys
import febrl_data_transform as transform
import pandas as pd
OUTPUT_DATA_DIR = pathlib.Path(__file__).parent / "holdout"
ORIGINALS_DATA_DIR = pathlib.Path(__file__).parent / "holdout" / "originals"
def main():
# Read in FEBRL data with dupes and separate into A/B/true links.... | pd.concat([df_B, df_B_extra]) | pandas.concat |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
## Import packages
import pandas as pd ## necessary data analysis package
import pyam
import nomenclature as nc
import fileinput
import yaml
import os
import sys
if len(sys.argv) <3:
print('python imputdir outputdir')
else:
inputdir=sys.argv[1]
outputfi... | pd.read_csv(inputdir+'\\'+file) | pandas.read_csv |
#!/usr/bin/env python
import os
import pdb
import glob
import sys
import shutil
import json
import re
import nibabel as nib
from argparse import ArgumentParser
import pandas as pd
import numpy as np
import nilearn.plotting as plotting
import itertools
import matplotlib.colors as colors
import seaborn as sns
impor... | pd.DataFrame(columns=["BETAS_est", "DELTAS_est", "Onset_age", "Age", "Education", "C(Gender)"], index=["CSF_AB42", "CSF_Tau", "CSF_pTau"]) | pandas.DataFrame |
#!/usr/bin/env python
import numpy as np
import pandas as pd
import pytest
from modnet.preprocessing import get_cross_nmi
from modnet.preprocessing import nmi_target
def test_nmi_target():
# Test with linear data (should get 1.0 mutual information, or very close due to algorithm used
# in mutual_info_regre... | pd.DataFrame({'x': x, 'y': y}) | pandas.DataFrame |
import datetime
from datetime import timedelta
from distutils.version import LooseVersion
from io import BytesIO
import os
import re
from warnings import catch_warnings, simplefilter
import numpy as np
import pytest
from pandas.compat import is_platform_little_endian, is_platform_windows
import pandas.util._test_deco... | tm.assert_frame_equal(expected, result) | pandas.util.testing.assert_frame_equal |
import os
from pathlib import Path
import pandas as pd
import requests
class OisManager:
TOIS_CSV_URL = 'https://exofop.ipac.caltech.edu/tess/download_toi.php?sort=toi&output=csv'
CTOIS_CSV_URL = 'https://exofop.ipac.caltech.edu/tess/download_ctoi.php?sort=ctoi&output=csv'
KOIS_LIST_URL = 'https://exofop.... | pd.read_csv(self.ctois_csv) | pandas.read_csv |
"""
Generic data algorithms. This module is experimental at the moment and not
intended for public consumption
"""
from __future__ import annotations
import operator
from textwrap import dedent
from typing import (
TYPE_CHECKING,
Literal,
Union,
cast,
final,
)
from warnings import warn
import nump... | ensure_platform_int(new_codes) | pandas.core.dtypes.common.ensure_platform_int |
import pandas as pd
import numpy as np
import datetime as dt
import pickle
import os
import shutil
import sys
from joblib import Parallel, delayed, cpu_count
import subprocess
from tqdm import tqdm
from copy import deepcopy
from blechpy.utils import print_tools as pt, write_tools as wt, userIO
from blechpy.utils.decora... | pd.DataFrame() | pandas.DataFrame |
import os
import unittest
import numpy as np
import pandas as pd
from cgnal.core.data.model.ml import (
LazyDataset,
IterGenerator,
MultiFeatureSample,
Sample,
PandasDataset,
PandasTimeIndexedDataset,
CachedDataset,
features_and_labels_to_dataset,
)
from typing import Iterator, Generat... | pd.Series([1, 2, 3, 4], name="feat2") | pandas.Series |
import pandas as pd
import datetime
from copy import deepcopy
from rgtfs import io, tables
def calculate_exits(row, calendar_dates_by_trip_id):
dow = {
0: "monday",
1: "tuesday",
2: "wednesday",
3: "thursday",
4: "friday",
5: "saturday",
6: "sunday",
}... | pd.concat(realized_trips) | pandas.concat |
#!/usr/bin/env python3
import itertools
import string
from elasticsearch import Elasticsearch,helpers
import sys
import os
from glob import glob
import pandas as pd
import json
host = sys.argv[1]
port = int(sys.argv[2])
alias = sys.argv[3]
print(host)
print(port)
print(alias)
es = Elasticsearch([{'host':... | pd.read_csv(file, sep=None, engine='python') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""CARND3
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1a44b45RBXDba9Nj99y_VLiS6lOwt5uKL
"""
import os
import csv
import cv2
import glob
from PIL import Image
import numpy as np
import sklearn
import random
import pandas as p... | pd.DataFrame(right_turn, columns=["center_image", "left_image", "right_image", "steering"]) | pandas.DataFrame |
"""
Applying Box-Jenkins Forecasting Methodology
to Predict Massachusetts Cannabis Data
Copyright (c) 2021 Cannlytics and the Cannabis Data Science Meetup Group
Authors: <NAME> <<EMAIL>>
Created: 10/6/2021
Updated: 11/10/2021
License: MIT License <https://opensource.org/licenses/MIT>
References:
- Time Serie... | pd.to_datetime('2019-01-01') | pandas.to_datetime |
"""Unit tests for engine module utility functions."""
import numpy as np
import pandas as pd
import pytest
from pandera.engines import utils
@pytest.mark.parametrize(
"data_container, data_type, expected_failure_cases",
[
[pd.Series(list("ab1cd3")), int, [False, False, True] * 2],
[pd.Series... | pd.Series([1, 2, "foo", "bar"]) | pandas.Series |
# coding=utf-8
import pandas as pd
import xgboost as xgb
from sklearn.metrics import f1_score
import param
############################ 定义评估函数 ############################
def micro_avg_f1(preds, dtrain):
y_true = dtrain.get_label()
return 'micro_avg_f1', f1_score(y_true, preds, average='micro')
##########... | pd.read_csv(param.data_path + '/output/feature/tfidf/mnb_prob_12w.csv') | pandas.read_csv |
from datetime import datetime, timedelta
from importlib import reload
import string
import sys
import numpy as np
import pytest
from pandas._libs.tslibs import iNaT
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Index,
Series,
... | date_range("20130101", periods=3) | pandas.date_range |
import os
from pathlib import Path
import joblib
import pandas as pd
import numpy as np
from multiprocessing import Pool
from collections import defaultdict
import functools
import re
import sys
sys.path.insert(0, './code')
from utils import DataLogger # noqa: E402
class DataNotFoundException(Exception):
pa... | pd.read_csv(self.output_path / data_name, **kwargs) | pandas.read_csv |
import time
import pandas as pd
import A01_process_route_seq
import A02_process_package_data
import A03_process_route_data
import numpy as np
import os
import json
def haversine_np(lon1, lat1, lon2, lat2):
"""
Calculate the great circle distance between two points
on the earth (specified in d... | pd.DataFrame(stop_tt) | pandas.DataFrame |
#import urllib2
import csv
import sys
import re
from datetime import datetime
import time
import pandas as pd
import configparser
import hashlib
import os
import rdflib
import logging
logging.getLogger().disabled = True
if sys.version_info[0] == 3:
from importlib import reload
reload(sys)
if sys.version_info[0] == ... | pd.notnull(row.End) | pandas.notnull |
import pandas as pd
import pathlib
from scripts.python.routines.mvals import logit2
import numpy as np
path_global = f"E:/YandexDisk/Work/pydnameth/datasets"
folder_name = f"GPL13534_Blood_ICD10-V"
path = f"{path_global}/meta/tasks/GPL13534_Blood_ICD10-V"
pathlib.Path(f"{path}/R/one_by_one").mkdir(parents=True, exist... | pd.read_pickle(f"{path}/train_val/betas.pkl") | pandas.read_pickle |
import math
import warnings
import numpy as np
import pandas as pd
import scipy.signal
import matplotlib.pyplot as plt
from typing import Optional, Union, List
from tqdm import tqdm
from signalanalysis.signalanalysis import general
from signalanalysis import signalplot
from signalanalysis import tools
class Egm(gen... | pd.isna(window_end.loc[i_row, key]) | pandas.isna |
import streamlit as st
import pandas as pd, seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import joblib
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import scipy.sparse as sp
import pytesseract
pytesserac... | pd.read_csv(r"D:\CoderSchool_ML30\FINAL PROJECT\Data\OCR_additives.csv") | pandas.read_csv |
"""AWS Glue Catalog Module."""
# pylint: disable=redefined-outer-name
import itertools
import logging
import re
import unicodedata
from typing import Any, Dict, Iterator, List, Optional, Tuple
from urllib.parse import quote_plus
import boto3 # type: ignore
import pandas as pd # type: ignore
import sqlalchemy # typ... | pd.DataFrame(data=df_dict) | pandas.DataFrame |
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