prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
"""Tests for the sdv.constraints.tabular module."""
import uuid
import numpy as np
import pandas as pd
import pytest
from sdv.constraints.errors import MissingConstraintColumnError
from sdv.constraints.tabular import (
Between, ColumnFormula, CustomConstraint, GreaterThan, Negative, OneHotEncoding, Positive,
... | pd.to_datetime(['2020-01-02']) | pandas.to_datetime |
import json
import logging
import timeit
import numpy as np
import pandas as pd
from .mapping import Map
from .mappingprofile import Map_Profile
from juneau.utils.utils import jaccard_similarity
logging.basicConfig(format='%(asctime)s - %(message)s', level=logging.INFO)
registered_attribute = ['last_name', 'firs... | pd.DataFrame.from_dict(dict_df) | pandas.DataFrame.from_dict |
import numpy as np
import pytest
from pandas.errors import UnsupportedFunctionCall
import pandas.util._test_decorators as td
from pandas import DataFrame, Series, Timedelta, concat, date_range
import pandas._testing as tm
from pandas.api.indexers import BaseIndexer
@td.skip_if_no_scipy
def test_constructor(frame_or... | Timedelta("2s") | pandas.Timedelta |
from distutils.version import LooseVersion
from warnings import catch_warnings
import numpy as np
import pytest
from pandas._libs.tslibs import Timestamp
import pandas as pd
from pandas import (
DataFrame,
HDFStore,
Index,
MultiIndex,
Series,
_testing as tm,
bdate_range,
concat,
d... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
import torch
import random
import numpy as np
import sys
import torch.nn as nn
import platalea.basic as basic
import platalea.encoders as encoders
import platalea.attention
import platalea.config
import os.path
import logging
import json
from plotnine import *
import pandas as pd
import ursa.similarity as S
import ur... | pd.read_json("global_diagnostic.json", orient='records') | pandas.read_json |
"""dynaPreprocessing Class"""
#!/usr/bin/env python
import itertools
from optimalflow.funcPP import PPtools
import pandas as pd
import joblib
import datetime
import numpy as np
from time import time
from optimalflow.utilis_func import update_progress,delete_old_log_files
import warnings
import os
path = os.getcwd()
... | pd.concat([pp.num_df,encoded_col],axis = 1) | pandas.concat |
# coding: utf-8
"""基于HDF文件的数据库"""
import pandas as pd
import numpy as np
import os
import warnings
from multiprocessing import Lock
from ..utils.datetime_func import Datetime2DateStr, DateStr2Datetime
from ..utils.tool_funcs import ensure_dir_exists
from ..utils.disk_persist_provider import DiskPersistProvid... | pd.DataFrame(dummy, index=data.index, columns=mapping, dtype='int8') | pandas.DataFrame |
"""Contains a collection of MTR equipment parsing.
These include:
* Version 3/4 (old version) [ ]
* Version 5 (MTRduino) [x]
"""
import pandas as pd
class rcm(object):
r""" Anderaa instruments (RCM 4, 7, 9, 11's
EcoFOCI QC procedure developed by <NAME>. and done within excel spreadsheet
<NAME>. usuall... | pd.read_excel(filename, skiprows=4, parse_dates=["date/time"], index_col="date/time") | pandas.read_excel |
#!/usr/bin/env python
# coding: utf-8
# # <<<<<<<<<<<<<<<<<<<< Tarea Número 4>>>>>>>>>>>>>>>>>>>>>>>>
# ## Estudiante: <NAME>
# ## Ejercicio 1
# In[1]:
import os
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.datasets import make_blobs
from sklearn.cl... | pd.DataFrame() | pandas.DataFrame |
import csv
import pandas as pd
import logging
class OnetSkillImportanceExtractor(object):
"""
An object that creates a skills importance CSV based on ONET data
"""
def __init__(self, onet_source, output_filename, hash_function):
"""
Args:
output_filename: A filename to writ... | pd.DataFrame(onet) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""Unit tests for cartoframes.data.services.Geocode"""
import unittest
import os
import sys
import json
import warnings
import pandas as pd
import geopandas as gpd
from carto.exceptions import CartoException
from cartoframes.data import Dataset
from cartoframes.auth import Credentials
from ca... | pd.DataFrame([['Gran Via 46', 'Madrid'], ['Ebro 1', 'Sevilla']], columns=['address', 'city']) | pandas.DataFrame |
import pandas as pd
def get_concatenated_df(files, separator, fields_to_keep = None):
dfs = [ | pd.read_csv(file, sep=separator) | pandas.read_csv |
#dependencies
from sklearn.cross_decomposition import PLSRegression
from sklearn.model_selection import cross_validate
import pandas as pd
import numpy as np
from scipy.signal import savgol_filter
from sklearn.base import TransformerMixin, RegressorMixin, BaseEstimator
from scipy import sparse, signal
from BaselineRem... | pd.DataFrame(X) | pandas.DataFrame |
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2021, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | pd.Series(['ACGT', 'ACCT', 'ACCA'], index=['f1', 'f2', 'f3']) | pandas.Series |
import os
import sqlite3
from unittest import TestCase
import warnings
from contextlib2 import ExitStack
from logbook import NullHandler, Logger
import numpy as np
import pandas as pd
from six import with_metaclass, iteritems, itervalues
import responses
from toolz import flip, groupby, merge
from trading_calendars im... | pd.Timestamp(cls.EQUITY_MINUTE_BAR_START_DATE) | pandas.Timestamp |
import nltk
import pandas as pd
import text2emotion as te
from nltk.corpus import stopwords
import altair as alt
import re
from nltk.tokenize import sent_tokenize
nltk.download("stopwords")
def counter(text):
"""
Generates a summary dataframe of the input
text which contains counts for characters,
wo... | pd.DataFrame() | pandas.DataFrame |
from __future__ import division
import pytest
import numpy as np
from datetime import timedelta
from pandas import (
Interval, IntervalIndex, Index, isna, notna, interval_range, Timestamp,
Timedelta, compat, date_range, timedelta_range, DateOffset)
from pandas.compat import lzip
from pandas.tseries.offsets imp... | Timedelta(days=1) | pandas.Timedelta |
from selenium.webdriver import Chrome
import pandas as pd
import time as time
webdriver = "webdriver/chromedriver.exe"
driver = Chrome(webdriver)
url = "https://blog.deeplearning.ai/blog"
next_posts_btn_selector = 'next-posts-link'
driver.get(url)
load_more_btn = driver.find_element_by_class_name(next_posts_btn_se... | pd.DataFrame(links, columns=['link']) | pandas.DataFrame |
import os
import sys
import logging
import pandas as pd
import numpy as np
from linker.plugins.base import AlgorithmProvider
from linker.core.union_find import UnionFind
from jellyfish import levenshtein_distance, jaro_winkler
logger = logging.getLogger(__name__)
class Levenshtein(AlgorithmProvider):
name = 'L... | pd.concat([s1, s2], axis=1, ignore_index=True) | pandas.concat |
import json
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
from sklearn.metrics import precision_recall_fscore_support
from statsmodels.stats.inter_rater import fleiss_kappa
__author__ = '<NAME>'
pd.set_option('max_colwidth', 999)
pd.set_option('display.max_rows', 999)
pd.set_o... | pd.DataFrame(data) | pandas.DataFrame |
#
# Build a graph describing the layout of each station based on data
# from the MTA's elevator and escalator equipment file. We also
# incorporate an override file, since some of the MTA descriptions
# too difficult for this simple program to understand. Writes to
# stdout.
#
import argparse
import pandas as pd
import... | pd.concat([equipment, from_to], axis=1, sort=False) | pandas.concat |
import math
import pandas as pd
import numpy as np
def clean_portfolio(portfolio):
""" Clean the portfolio dataset.
"""
portfolio_clean = portfolio.copy()
# Create dummy columns for the channels column
clean_channels = pd.get_dummies(portfolio_clean.channels.apply(pd.Series).stack(),
... | pd.get_dummies(transcript_clean.event, prefix="event") | pandas.get_dummies |
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 24 13:10:27 2020
@author: Oliver
"""
import os
import numpy as np
import scipy.io
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
from scipy.signal import savgol_filter, find_peaks
database = | pd.DataFrame(columns=['condition', 'name', 'ecg']) | pandas.DataFrame |
import pandas as pd
import numpy as np
from collections import defaultdict
from solarnet.preprocessing.masks import MaskMaker, IMAGE_SIZES
class TestMasks:
@staticmethod
def _make_polygon_vertices_pixel_coordinates(polygon_shapes):
# make the fake data
max_vertices = max(polygon_shapes.value... | pd.DataFrame(data=test_data) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Virginia Case Study
"""
import os
import sys
import re
import csv
import json
import random
import math
import numpy as np
from functools import partial
import pandas as pd
import geopandas as gpd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as... | pd.concat(recom_mms) | pandas.concat |
import pytest
import numpy as np
import os
import pandas as pd
import minst.model as model
@pytest.fixture
def rwc_obs():
return dict(index='U1309f091', dataset='uiowa',
audio_file="RWC_I_05/172/172VCSPP.flac",
instrument='piano', source_index='U12345',
start_time... | pd.DataFrame.from_records(test_obs, index=index) | pandas.DataFrame.from_records |
import collections
import numpy as np
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Index,
Series,
isna,
)
import pandas._testing as tm
class TestCategoricalMissing:
def test_isna(self):
exp = np... | CategoricalDtype(categories) | pandas.core.dtypes.dtypes.CategoricalDtype |
#! usr/bin/python
# coding=utf-8
# Convolution using mxnet ### x w
from __future__ import print_function
import mxnet as mx
import numpy as np
import pandas as pd
from mxnet import nd, autograd, gluon
from mxnet.gluon.nn import Dense, ELU, LeakyReLU, LayerNorm, Conv2D, MaxPool2D, Flatten, Activation
from mxnet.gluon ... | pd.Series(keys[pre][:,0]) | pandas.Series |
import codecs
import sys
import matplotlib.pyplot as plt
import pandas as pd
import re
import sklearn
print(sys.path)
# sys.path.append("C:/Program Files/Anaconda/envs/Coursework")
sys.path.append("C:/Program Files/Anaconda/envs/Coursework/Lib/site-packages")
import nltk
from nltk.corpus import stopwords
from nltk.t... | pd.concat([realTargets, fakeTargets, humourTargets]) | pandas.concat |
# Generated by nuclio.export.NuclioExporter
import mlrun
from mlrun.platforms.iguazio import mount_v3io, mount_v3iod
from mlrun.datastore import DataItem
from mlrun.execution import MLClientCtx
import os
from subprocess import run
import pandas as pd
import numpy as np
from pyspark.sql.types import LongType
from pys... | pd.Series(['UNIQUE'], index=['type'], name=column) | pandas.Series |
import numpy as np
import pandas as pd
import inspect, os.path
import matplotlib.pyplot as plt
import seaborn as sns
import re
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.e... | pd.read_csv(path+"/input/gender_submission.csv") | pandas.read_csv |
import pandas as pd
import re, json
import argparse
'''
preprocessing for mimic discharge summary note
1. load NOTEEVENTS.csv
2. get discharge sumamry notes
a) NOTEVENTS.CATEGORY = 'Discharge Summary'
b) NOTEVENTS.DESCRIPTION = 'Report'
c) eliminate a short-note
3. preprocess discharge sumamry notes
... | pd.to_datetime(df.CHARTDATE, format='%Y-%m-%d', errors='raise') | pandas.to_datetime |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import pickle
from glob import glob
import os
from time import sleep
import subprocess
def get_all_file_paths(root_dir):
to_return = []
current_level_dfs = glob(f"{root_dir}/*Df.csv")
if len(current_level_dfs) > 0:
... | pd.read_csv(file_path) | pandas.read_csv |
"""
The double-7s-ave-portfolio stategy.
This is double-7s strategy applied to a portfolio.
The simple double 7's strategy was revealed in the book
'Short Term Strategies that Work: A Quantified Guide to Trading Stocks
and ETFs', by <NAME> and <NAME>. It's a mean reversion
strategy looking to buy dips and sell on stre... | pd.Series(ts.close) | pandas.Series |
#----------------------------------------------------------------------------------------------
####################
# IMPORT LIBRARIES #
####################
import streamlit as st
import pandas as pd
import numpy as np
import plotly as dd
import plotly.express as px
import seaborn as sns
import matplotl... | pd.ExcelWriter(output, engine="xlsxwriter") | pandas.ExcelWriter |
# -*- coding: utf-8 -*-
"""
This module contains all the methods required to request the data from
a particular object, obtain it from the ESA NEOCC portal and parse it
to show it properly. The information of the object is shows in the
ESA NEOCC in different tabs that correspond to the different classes
within this mod... | pd.to_datetime(ephem['Date']) | pandas.to_datetime |
#!/usr/bin/env python
"""
Module implementing the Data class that manages data for
it's associated PandasTable.
Created Jan 2014
Copyright (C) <NAME>
This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by th... | pd.to_datetime(value) | pandas.to_datetime |
import plotly.express as px
import pandas as pd
import numpy as np
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
from app import app
# preprocessing
df = | pd.read_csv('supermarket_sales_preprocessed.csv') | pandas.read_csv |
######### imports #########
from ast import arg
from datetime import timedelta
import sys
sys.path.insert(0, "TP_model")
sys.path.insert(0, "TP_model/fit_and_forecast")
from Reff_constants import *
from Reff_functions import *
import glob
import os
from sys import argv
import arviz as az
import seaborn as sns
import m... | pd.DataFrame() | pandas.DataFrame |
import os
import sys
import subprocess
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patheffects as path_effects
import scipy.cluster.hierarchy
from matplotlib import cm
from decneo.commonFunctions import read, write
import multiprocessing
cwd = '/mnt/gs18/scratch/users/pater... | pd.MultiIndex.from_tuples(df.index.values, names=['ligand', 'receptor']) | pandas.MultiIndex.from_tuples |
# -*- coding: utf-8 -*-
# flake8: noqa
"""Domain module
This module defines preconfigured CORDEX domain from csv tables. The module
also contains some tools to create a domain dataset from a csv tables or simply
from grid information.
Example:
To get a list of available implementations, create cordex domains, wr... | pd.concat(tables) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 8 13:32:06 2021
Revisiting some older SKS, SKKS, SYNTH data to make plots for chapter 2 of my thesis
Chapter 2, i.e., global data collection chapter
Wrangles data and makes SI comp. plot, synthetics SNR v splitting params, |BAZ - SPOL| histograms,... | pd.DataFrame() | pandas.DataFrame |
# utilities
import pickle
import inflection
import warnings
from IPython.display import Image
from tabulate import tabulate
# data manipulation
import pandas as pd
import numpy as np
# plots
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
# for categorical correla... | pd.set_option('display.expand_frame_repr', False) | pandas.set_option |
import pandas as pd
import numpy as np
from tqdm import tqdm
from Bio.PDB import Selection, PDBParser
import os
def extract_beads(pdb_path):
amino_acids = pd.read_csv('/home/hyang/bio/erf/data/amino_acids.csv')
vocab_aa = [x.upper() for x in amino_acids.AA3C]
vocab_dict = {x.upper(): y for x, y in zip(ami... | pd.read_csv(f'{root_dir}/{pdb_id}/flist.txt') | pandas.read_csv |
import sys
import seaborn as sns
import prince
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import scipy.cluster.hierarchy as sch
from sklearn.cluster import KMeans, DBSCAN, Birch, MeanShift, \
SpectralClustering, AffinityPropagation, FeatureAgglomeration, Agglomerati... | pd.DataFrame(data=transformed_data, columns=["feature_1", "feature_2", "feature_3"]) | pandas.DataFrame |
import datetime
import os
from concurrent.futures import ProcessPoolExecutor
from math import ceil
import pandas as pd
# In[] 读入源数据
def get_source_data():
# 源数据路径
DataPath = 'data/'
# 读入源数据
off_train = pd.read_csv(os.path.join(DataPath, 'ccf_offline_stage1_train.csv'),
par... | pd.merge(X, temp, how='left', on='Merchant_id') | pandas.merge |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import dataclasses
from dataclasses import dataclass
import json
from pathlib import Path
import numpy as np
import pandas as pd
from axcell.models.structure.nbsvm import *
from sklearn.metrics import confusion_matrix
from matplotlib import pyplo... | pd.Categorical(df["label"]) | pandas.Categorical |
"""
The grapevine variant pipeline outputs nucleotide mutations (SNPs, indels)
and amino acid substitutions (synonymous and nonsynonymous) in separate files.
However, they do not link the nucleotide mutations, or provide
amino acid indels, so we have to do that ourselves here.
Writes out nucleotide to amino acid links... | pd.read_csv(nuc_mut_tsv, sep="\t", comment="#") | pandas.read_csv |
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
# Load the UK Covid overview data
df1 = pd.read_csv('overview_2021-07-15.csv')
print(df1.head().to_string())
print(df1.tail().to_string())
# Drop columns that don't provide any additional data
df1.drop(['areaCode', 'areaName', 'areaT... | pd.to_datetime(df2['date']) | pandas.to_datetime |
import numpy
import pandas
import scipy
import sklearn.metrics as metrics
from sklearn.model_selection import train_test_split
import statsmodels.api as stats
# The SWEEP Operator
def SWEEPOperator (pDim, inputM, tol):
# pDim: dimension of matrix inputM, positive integer
# inputM: a square and sy... | pandas.get_dummies(thisVar) | pandas.get_dummies |
import ast
import json
import pickle
from typing import Tuple
import numpy as np
import pandas as pd
from tqdm import tqdm
def _concat_browsing_and_search(browsing_df: pd.DataFrame, search_df: pd.DataFrame) -> pd.DataFrame:
browsing_df["is_search"] = False
search_df["is_search"] = True
res = pd.concat([b... | pd.read_csv('../session_rec_sigir_data/train/search_train.csv') | pandas.read_csv |
import requests
import os
import pandas as pd
import json
import matplotlib.pyplot as plt
import re
import numpy as np
from requests.exceptions import HTTPError
import os
def fred_function(**kwargs):
"""
Using this function can collect data from FRED API, check the status of the request the server returns and... | pd.DataFrame(fred_json['seriess']) | pandas.DataFrame |
#! /usr/local/bin/python
# ! -*- encoding:utf-8 -*-
from pathlib import Path
import pandas as pd
import numpy as np
import random
import os
def generate_gt(clusters, dataset):
cci_labels_gt_path = '{}/mouse_small_intestine_1189_cci_labels_gt_{}_{}.csv'
cci_labels_junk_path = '{}/mouse_small_intestine_1189_cci... | pd.read_csv(ligand_receptor_pair_path, header=0, index_col=0) | pandas.read_csv |
"""
Author: <NAME>
Date Created: 11 March 2020
Scripts related to training the VAE including
1. Normalizing gene expression data
2. Wrapper function to input training parameters and run vae
training in `vae.tybalt_2layer_model`
"""
from ponyo import vae, utils
import os
import pickle
import pandas as pd
from sklearn ... | pd.read_csv(input_data_filename, header=0, sep="\t", index_col=0) | pandas.read_csv |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : hotgrid.py
@Author : <NAME>
@Version : 1.0
@Contact : <EMAIL>
@License : Copyright © 2007 Free Software Foundation, Inc
@Desc : None
'''
import numpy as np
import pandas as pd
from .geokit import getlngandlat, haversine
class HotGridGen... | pd.DataFrame(index=self.indexList, columns=self.colList) | pandas.DataFrame |
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import folium
from folium.plugins import HeatMap
###### Auxiliar Functions ##############
def pre_processing(df,nombre):
"""
Recibe un DataFrame y lo entrega
listo para la aplicación.
nom... | pd.DataFrame([]) | pandas.DataFrame |
# coding: utf-8
# In[1]:
# get_ipython().magic(u'matplotlib inline')
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
# import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy.random as npr
from sklearn.cl... | pd.concat(te_list) | pandas.concat |
# SPDX-License-Identifier: Apache-2.0
#
# Copyright (C) 2019, Arm Limited and contributors.
#
# 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
#
# ... | pd.Series(values, index=new_index) | pandas.Series |
# -*- coding: utf-8 -*-
"""
analyze and plot results of experiments
"""
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sb
import yaml
#E2: How large can I make my output domain without loosing skill?
E2_results = pd.read_csv('param_optimization/E2_results_t2m_34_t2m.csv',sep... | pd.concat(df_list) | pandas.concat |
from pydap.client import open_url
from datetime import datetime
from calendar import monthrange, month_name
import os
import numpy as np
import pandas as pd
import netCDF4 as nc
import xarray as xr
import time
import pickle
import cdsapi
import math
from Plot import incidence_and_ml_plot
# Interpolation
from scipy.i... | pd.DataFrame() | pandas.DataFrame |
import os
import pandas as pd
import numpy as np
def read_data():
# Define raw data path
raw_data_path = os.path.join('data', 'raw')
train_file_path = os.path.join(raw_data_path, 'train.csv')
test_file_path = os.path.join(raw_data_path, 'test.csv')
# read data from cvs file
train_df = | pd.read_csv(train_file_path, index_col='PassengerId') | pandas.read_csv |
import pandas as pd
import numpy as np
import requests as rq
import datetime as dt
from .constants import CAPS_INFO
def expand(df):
'''Fill missing dates in an irregular timeline'''
min_date = df['date'].min()
max_date = df['date'].max()
idx = pd.date_range(min_date, max_date)
df.index = pd.... | pd.DatetimeIndex(df.date) | pandas.DatetimeIndex |
#################################################
#created the 04/05/2018 09:52 by <NAME>#
#################################################
#-*- coding: utf-8 -*-
'''
'''
'''
Améliorations possibles:
'''
import warnings
warnings.filterwarnings('ignore')
#################################################
########### ... | pd.DataFrame(pred) | pandas.DataFrame |
import json
import datetime
from datetime import time, timedelta
import pandas as pd
import numpy as np
import plotly.graph_objs as go
from plotly.offline import plot
import pytz
import os
from pyloopkit.dose import DoseType
from pyloopkit.generate_graphs import plot_graph, plot_loop_inspired_glucose_graph
from pyloop... | pd.DataFrame() | pandas.DataFrame |
from collections.abc import MutableSequence
import warnings
import io
import copy
import numpy as np
import pandas as pd
from . import endf
import openmc.checkvalue as cv
from .resonance import Resonances
def _add_file2_contributions(file32params, file2params):
"""Function for aiding in adding resonance paramet... | pd.DataFrame.from_records(records, columns=columns) | pandas.DataFrame.from_records |
'''This script contains functions for evaluating models and calculating and visualizing metrics'''
import pandas as pd
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, StratifiedKFold, KFold, ... | pd.Series(balanced_val) | pandas.Series |
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
SMALL_SIZE = 10
MEDIUM_SIZE = 12
plt.rc('font', size=SMALL_SIZE)
plt.rc('axes', titlesize=MEDIUM_SIZE)
plt.rc('axes', labelsize=MEDIUM_SIZE)
plt.rcParams['figure.dpi']=150
... | pd.get_dummies(data['class']) | pandas.get_dummies |
import math
import numpy as np
import pandas as pd
from typing import Union
from scipy import signal
from sklearn import preprocessing
from scipy.spatial import distance
def asin2(x: float, y: float) -> float:
"""Function to return the inverse sin function across the range (-pi, pi], rather than (-pi/2, pi/2]
... | pd.DataFrame(index=data.index, columns=data.columns) | pandas.DataFrame |
import os
from copy import deepcopy
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
from mpl_toolkits.axes_grid1.inset_locator import InsetPosition
from numpy import logical_not, isnan, array, where, abs, max, min, vstack, hstack
from pandas i... | read_csv(csv) | pandas.read_csv |
import numpy as np
import pandas as pd
import json
import geopandas as gpd
from shapely.geometry import Point, MultiPolygon
blrDF = gpd.read_file("data/base/"+city+"/city.geojson")
blr_quarantined = | pd.read_csv("data/base/"+city+"/BLR_incoming travel.csv") | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
These the test the public routines exposed in types/common.py
related to inference and not otherwise tested in types/test_common.py
"""
from warnings import catch_warnings, simplefilter
import collections
import re
from datetime import datetime, date, timedelta, time
from decimal import De... | inference.is_array_like([1, 2, 3]) | pandas.core.dtypes.inference.is_array_like |
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
from app import helpers
from app.ui import (
header,
contact_modal,
tab_comparison_controls,
tab_comparison_sip_cards,
tab_port_sip_cards,
tab_map_controls,
tab_map_sip_cards,
)
from app im... | pd.read_csv("data/second_tab_dataset.csv") | pandas.read_csv |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.13.0
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %% [markdown]
# # ... | pd.concat([train_qrels, eval_qrels], ignore_index=True) | pandas.concat |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for datetime64 and datetime64tz dtypes
from datetime import (
datetime,
time,
timedelta,
)
from itertools import (
product,
starmap,
)
import operator
import warnings
import numpy as np
impo... | Timestamp("2000-01-01") | pandas.Timestamp |
import mankey.custom_helpers as transformers
import pandas as pd
def test_basic():
assert 1 == 1
def test_ordinal_h():
import pandas as pd
data = {'Pclass': ['First_class', 'Second_Class', 'Third_Class', 'Fourth_Class'],
'level': [1, 2, 3,4],
}
df = pd.DataFrame(data)
l... | pd.testing.assert_frame_equal(df, target_df) | pandas.testing.assert_frame_equal |
import datetime as dt
import pytest
from distutils.version import LooseVersion
import numpy as np
try:
import pandas as pd
from pandas._testing import (
makeCustomDataframe, makeMixedDataFrame, makeTimeDataFrame
)
except ImportError:
pytestmark = pytest.mark.skip('pandas not available')
from... | makeMixedDataFrame() | pandas._testing.makeMixedDataFrame |
import os
import io
import pandas as pd
import sys
import tempfile
import webbrowser
from functools import lru_cache
import zipfile
import requests
import datetime
import bisect
import re
import numpy as np
import plotly.express as pex
import math
try:
import numpy_ext as npext
except ImportError:
npext = None
... | pd.to_datetime(dt) | pandas.to_datetime |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
#
# GUI module generated by PAGE version 5.0.3
# in conjunction with Tcl version 8.6
# Feb 08, 2021 09:54:12 PM +03 platform: Windows NT
# -*- coding: utf-8 -*-
from logging import disable
from selenium import webdriver
from selenium.webdriver.support.ui import Selec... | pd.DataFrame() | pandas.DataFrame |
'''
May 2020 by <NAME>
<EMAIL>
https://www.github.com/sebbarb/
'''
import feather
import pandas as pd
import numpy as np
from hyperparameters import Hyperparameters
from pdb import set_trace as bp
def main():
hp = Hyperparameters()
# Load data
#df = feather.read_dataframe(hp.data_di... | pd.to_datetime(df['out_broad_cvd_adm_date'], format='%Y-%m-%d', errors='coerce') | pandas.to_datetime |
import pandas as pd
import numpy as np
import ml_metrics as metrics
from sklearn.neighbors import KNeighborsClassifier
from sklearn.cross_validation import StratifiedKFold
from sklearn.metrics import log_loss
np.random.seed(131)
path = '../Data/'
NumK = 11
print("read training data")
train = pd.read_csv(pa... | pd.DataFrame(preds, index=ID, columns=sample.columns[1:]) | pandas.DataFrame |
"""Extract information from the GeoLife dataset"""
import numpy as np
import os
import pandas as pd
from dateutil import parser
from time import time
from joblib import Parallel, delayed
def decode_str(s):
return s.decode('utf-8')
class GeoLifeExtractor(object):
def __init__(self, base_path='./geolife-gp... | pd.concat(dfs) | pandas.concat |
'''
This set of functions creates, loads, encodes, and saves DataFrames
of each sequence.
Pos: H(6), K(8), R(14)
Neg: D(2), E(3)
'''
import numpy as np
import pandas as pd
from sklearn import preprocessing
import plot_functions
def ordinal_decode(seq):
'ordinal to amino acid sequence'
AAlist=np.ar... | pd.concat([pos_df,neg_df],ignore_index=True) | pandas.concat |
"""unit test for loanpy.loanfinder.py (2.0 BETA) for pytest 7.1.1"""
from inspect import ismethod
from os import remove
from pathlib import Path
from unittest.mock import patch, call
from pandas import DataFrame, RangeIndex, Series, read_csv
from pandas.testing import (assert_frame_equal, assert_index_equal,
... | assert_series_equal(mocksearch.search_in, srsad) | pandas.testing.assert_series_equal |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from utils import bin
"""
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(fig)
sciblue = '#0C5DA5'
scigreen = '#00B945'
# -... | pd.read_excel(fp4) | pandas.read_excel |
import os
import time
import uuid
import yaml
import logging
import shutil
import numpy as np
import pandas as pd
import multiprocessing as mp
from functools import partial
from astropy.time import Time
from .config import Config
from .config import Configuration
from .clusters import find_clusters, filter_clusters_by... | pd.concat(projected_dfs) | pandas.concat |
import os
import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from datetime import timedelta
import sqlite3
from sqlite3 import Connection
import plotly.express as px
userDir = os.path.expanduser('~')
URI_SQLITE_DB = userDir + '/Bird... | pd.crosstab(df5, df5.index.hour, dropna=False) | pandas.crosstab |
import pandas as pd
import networkx as nx
import numpy as np
from scipy import sparse
import torch, sys
Your_path = '/data/project/yinhuapark/ssl/'
sys.path.append(Your_path+'ssl_make_graphs')
sys.path.append(Your_path+'ssl_graphmodels')
from PairData import PairData
pd.set_option('display.max_columns', None)
import os... | pd.DataFrame(dfs, columns=['word1', 'word2', col_name]) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# QTPy: Light-Weight, Pythonic Algorithmic Trading Library
# https://github.com/ranaroussi/qtpylib
#
# Copyright 2016-2018 <NAME>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You m... | pd.read_csv('https://qtpylib.io/resources/futures_spec.csv.gz') | pandas.read_csv |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from fastai.text import *
from pathlib import Path
import pandas as pd
import numpy as np
import pickle
from .experiment import Labels, label_map
from .ulmfit_experiment import ULMFiTExperiment
import re
from .ulmfit import ULMFiT_SP
from ...pipel... | pd.DataFrame(flat, columns=["paper", "table", "row", "col", "predicted_tags"]) | pandas.DataFrame |
import pandas as pd
import time
# Need to open original file, filter out non class1
phospho_file = input('Enter phospho filepath: (default: Phospho (STY)Sites.txt) ') or 'Phospho (STY)Sites.txt'
PSP_dataset_file = input('Enter PhosphoSite Plus dataset: (default: Phosphorylation_site_dataset.xlsx) ') or 'Phospho... | pd.read_csv(PSP_dataset_file) | pandas.read_csv |
# -*- coding: utf-8 -*-
# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)
# <NAME> (<EMAIL>), Blue Yonder Gmbh, 2016
import numpy as np
import pandas as pd
import pytest
from tsfresh.feature_selection.selection import select_features
class TestSelectFeatures:
... | pd.DataFrame([10, 10], index=[1, 2]) | pandas.DataFrame |
import torch
import torch.nn.functional as F
import os
import wandb
import pandas as pd
import numpy as np
from dataloader.dataloader import data_generator, few_shot_data_generator, generator_percentage_of_data
from configs.data_model_configs import get_dataset_class
from configs.hparams import get_hparams_class
from... | pd.DataFrame(columns=["scenario", "acc", "f1"]) | pandas.DataFrame |
import pandas as pd
import gdal
import numpy as np
import os
import rasterio
import tqdm
class TrainingData:
"""Prepares training datasets using a raster stack, species occurrences and a set of band means and standard
deviations.
:param self: a class instance of TrainingData
:param oh: an Occurrence... | pd.DataFrame(X) | pandas.DataFrame |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sun Jan 20 10:24:34 2019
@author: labadmin
"""
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 02 21:05:32 2019
@author: Hassan
"""
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.linear... | pd.read_csv("F:\\Projects\\Master\\Statistical learning\\project\\walking\\dataset8.csv",skiprows=4) | pandas.read_csv |
import pandas as pd
from plaster.tools.zplots.zplots import ZPlots
from plaster.tools.plots import plots
from plaster.tools.plots import plots_dev
from plaster.tools.ipynb_helpers.displays import hd
from plaster.tools.utils.utils import json_print, munch_abbreviation_string
from IPython.display import display # for d... | pd.set_option("display.max_columns", None) | pandas.set_option |
# -*- coding: utf-8 -*-
from datetime import timedelta
from distutils.version import LooseVersion
import numpy as np
import pytest
import pandas as pd
import pandas.util.testing as tm
from pandas import (
DatetimeIndex, Int64Index, Series, Timedelta, TimedeltaIndex, Timestamp,
date_range, timedelta_range
)
f... | tm.assert_index_equal(result, expected) | pandas.util.testing.assert_index_equal |
import pytz
import pytest
import dateutil
import warnings
import numpy as np
from datetime import timedelta
from itertools import product
import pandas as pd
import pandas._libs.tslib as tslib
import pandas.util.testing as tm
from pandas.errors import PerformanceWarning
from pandas.core.indexes.datetimes import cdate_... | pd.date_range('1/1/2000', freq='D', periods=5, tz=tz) | pandas.date_range |
from copy import copy
import dask
import dask.array as da
import dask.dataframe as dd
import numpy as np
import pandas as pd
import pandas.testing as tm
import pytest
import sklearn.preprocessing as spp
from dask import compute
from dask.array.utils import assert_eq as assert_eq_ar
from dask.dataframe.utils import ass... | is_categorical_dtype(trn["B"]) | pandas.api.types.is_categorical_dtype |
#!/usr/bin/env python3.6
import pandas as pd
from collections import defaultdict, Counter
import argparse
import sys
import os
import subprocess
import re
import numpy as np
from datetime import datetime
from itertools import chain
from pyranges import PyRanges
from SV_modules import *
pd.set_option('display.max_colum... | pd.DataFrame(compoundhet) | pandas.DataFrame |
import pandas as pd
import numpy as np
from datetime import datetime
from datetime import timedelta
import json
import os
import os.path
import pytz
import sys
from helpers import *
# global_dir = "/Volumes/dav/MD2K Processed Data/smoking-lvm-cleaned-data/"
global_dir = "../cleaned-data/"
python_version = int(sys.vers... | pd.read_csv(csv_file, header=None) | pandas.read_csv |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.