prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import argparse
import os
import pandas as pd
from af_dataset_builder import AFDatasetBuilder
import plots
import utils
CLASS_NAMES = ['normal', 'af', 'other', 'noise']
SEED = 3
def summarize_metrics(models_path, test_set_path, target_record_len, batch_size, model_name=None, output_path=None):
all_metrics = | pd.DataFrame() | pandas.DataFrame |
import bs4 as bs
import urllib.request
import pandas as pd
import random
import pathlib
import progressbar
import numpy as np
def schedule(names):
schedule = [['YourScore', "OppScore","FGA","FGP","3PA","3PP","FTA","FTP","TRB","STL","BLK","TOV","OppFGA","OppFGP","Opp3PA","Opp3PP","OppFTA","OppFTP","OppTRB"... | pd.read_csv(url, names=names,encoding='utf-8') | pandas.read_csv |
import argparse
import pandas as pd
import os
from random import shuffle
def parse_args():
parser = argparse.ArgumentParser(description="Takes the meta_data, l4, and l4_no_pca files for the train, val and test sets "
"of students and returns them in libsvc format.... | pd.DataFrame([]) | pandas.DataFrame |
import climetlab as cml
from . import DATA_VERSION, PATTERN_GRIB, PATTERN_NCDF
class Info:
def __init__(self, dataset):
import os
import yaml
self.dataset = dataset
filename = self.dataset.replace("-", "_") + ".yaml"
path = os.path.join(os.path.dirname(os.path.abspath(__... | pd.to_datetime(only_one_date) | pandas.to_datetime |
import pandas as pd
import numpy as np
import math
from openpyxl import load_workbook
# Dictionary of expiry dates hard coded as historical expiry dates are not readily available
expdct = {'10APR20': 1586505600000,
'17APR20': 1587110400000,
'24APR20': 1587715200000
}
# Arbit... | pd.ExcelWriter(data_destination, engine="openpyxl", mode="a") | pandas.ExcelWriter |
""" This script aggregates zugdata on a daily basis and uploads it in /live/aggdata """
import os
import re
import pandas as pd
from datetime import datetime, date, timedelta
# compatibility with ipython
#os.chdir(os.path.dirname(__file__))
import json
import boto3
from pathlib import Path
from coords_to_kreis import c... | pd.DataFrame(json_content) | pandas.DataFrame |
from functools import lru_cache
import datetime
from typing import Tuple, List, Callable, NamedTuple
from collections import namedtuple
import sqlalchemy
import pandas as pd
def get_securities():
return pd.read_sql('securities', con=sqlalchemy.create_engine('sqlite:///../data/jq.db'))
@lru_cache(maxsize=1)
def... | pd.DataFrame(data, columns=['ts_code', 'mg', 'mg_rank', 'ms', 'ms_rank']) | pandas.DataFrame |
# %%
import os
import pandas as pd
import numpy as np
import threading
import time
base_dir = os.getcwd()
# %%
# 初始化表头
header = ['user', 'n_op', 'n_trans', 'op_type_0', 'op_type_1', 'op_type_2', 'op_type_3', 'op_type_4', 'op_type_5',
'op_type_6', 'op_type_7', 'op_type_8', 'op_type_9', 'op_type_perc', 'op_ty... | pd.read_csv(base_dir + '/dataset/dataset2/testset/test_a_op.csv') | pandas.read_csv |
import os
import multiprocessing as mp
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import tqdm
import cv2
# Specify HSV color range for detection
lower = (25, 40, 200)
upper = (30, 100, 255)
data_dir = 'data/pufferfish-struggle'
num_images = len([name for name in os.listdir(data_dir) if ... | pd.DataFrame(progress) | pandas.DataFrame |
import nose
import os
import string
from distutils.version import LooseVersion
from datetime import datetime, date, timedelta
from pandas import Series, DataFrame, MultiIndex, PeriodIndex, date_range
from pandas.compat import range, lrange, StringIO, lmap, lzip, u, zip
import pandas.util.testing as tm
from pandas.uti... | tm.close() | pandas.util.testing.close |
# -*- coding: utf-8 -*-
import argparse
import json
import os
import re
from io import StringIO
from pathlib import Path
import dotenv
import pandas as pd
import requests
from utils import get_gene_id2length
DOTENV_KEY2VAL = dotenv.dotenv_values()
def make_tissue2subtissue2sample_id(rawdir: str) -> pd.DataFrame:
... | pd.MultiIndex.from_frame(sample_id2tissue_type_subtype_df) | pandas.MultiIndex.from_frame |
import unittest
import pandas as pd
import numpy as np
from autopandas_v2.ml.featurization.featurizer import RelationGraph
from autopandas_v2.ml.featurization.graph import GraphEdge, GraphEdgeType, GraphNodeType, GraphNode
from autopandas_v2.ml.featurization.options import GraphOptions
get_node_type = GraphNodeType.g... | pd.MultiIndex.from_tuples(tuples) | pandas.MultiIndex.from_tuples |
# coding: utf-8
# CS FutureMobility Tool
# See full license in LICENSE.txt.
import numpy as np
import pandas as pd
#import openmatrix as omx
from IPython.display import display
from openpyxl import load_workbook,Workbook
from time import strftime
import os.path
import mode_choice.model_defs as md
import mode_choice.ma... | pd.concat([town_definition,zone_vmt_daily_o],axis=1,join='inner') | pandas.concat |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/5/11 17:52
Desc: 加密货币
https://cn.investing.com/crypto/currencies
高频数据
https://bitcoincharts.com/about/markets-api/
"""
import math
import pandas as pd
import requests
from tqdm import tqdm
from akshare.datasets import get_crypto_info_csv
def crypto_name_ur... | pd.DataFrame() | pandas.DataFrame |
import inspect
from datetime import datetime
from tralo.utils import filter_args, sha1_hash_object, valid_run, AttributeDict, get_attribute
import yaml
import os
import json
import re
import torch
from os.path import join, isfile, expanduser, realpath
from tralo.log import log
def load_model(checkpoint_id, weights_fi... | DataFrame(table) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from unittest import TestCase
from parameterized import parameterized
import pandas as pd
import numpy as np
from numpy.testing.utils import assert_array_equal
from pandas import (MultiIndex,
Index)
from pandas.util.testing import assert_frame_equal, assert_series_equal
from... | pd.DataFrame({'001': [1, 2, 3], '002': [2, 3, 4]}, index=['2014', '2015', '2016']) | pandas.DataFrame |
import torch
from torch.utils.data import Dataset
import pandas as pd
import numpy as np
class DeepInflammationDataset(Dataset):
def __init__(self, c1_data, c2_data):
"""
- c1_data: pandas dataframe of the cell 1 data
- c2_data: pandas dataframe of the cell 2 data
"""
supe... | pd.concat([self.c1_data['expr'], self.c2_data['expr']], axis=0, ignore_index=True) | pandas.concat |
import textattack
import textattack.datasets as datasets
import random
import pandas as pd
from textattack.transformations.word_swap_embedding import WordSwapEmbedding as WordSwapEmbedding
from textattack.constraints.semantics.word_embedding_distance import WordEmbeddingDistance as WordEmbeddingDistance
NUM_NEAREST = ... | pd.DataFrame() | pandas.DataFrame |
#!/usr/local/bin/python
import argparse
import os
import sys
import pandas as pd
import numpy as np
import time
pd.options.mode.chained_assignment = None
parser = argparse.ArgumentParser(prog='snvScore')
parser.add_argument('SampleBED',type=str,help='Path to the mosdepth per-base BED output')
parser.add_argument('SNVG... | pd.read_csv(args.SNVGermlineTXT,sep='\t') | pandas.read_csv |
#!/usr/bin/python3
#By <NAME>
import os
import re
import argparse
import time
import timeit
import numpy as np
import pandas as pd
import pipeline_base
def merge_window(intervals,vcf,ref_sequence,log_file=False,fullcheck=True,ignored=True,info_just_indel=False,drop_info=False,intervals_alignment_bool=False,interv... | pd.read_csv(interval,sep='\t') | pandas.read_csv |
from __future__ import annotations
import os
import pandas as pd
import streamlit as st
st.set_page_config(layout="wide")
class Pager:
"""Generates cycled stepper indices."""
def __init__(self, count) -> None:
self.count = count
self.current = 0
@property
def next(self) -> int:
"""Fetches next index."... | pd.DataFrame(data) | pandas.DataFrame |
####################################################
## TO TRAIN AND TEST CLASSIFIER
## train : to train classifier with train dataset
## test : to predict labels of validation dataset
## submit: to predict labels of test dataset
####################################################
import time
import numpy a... | pd.concat([results_df, row_df]) | pandas.concat |
from flask import Flask, render_template, request, redirect, url_for, session
import pandas as pd
import pymysql
import os
import io
#from werkzeug.utils import secure_filename
from pulp import *
import numpy as np
import pymysql
import pymysql.cursors
from pandas.io import sql
#from sqlalchemy import create... | pd.DataFrame(data['TotalDemand']) | pandas.DataFrame |
from rdkit import Chem
from rdkit.Chem import rdmolops, rdMolDescriptors, Crippen, GraphDescriptors
import numpy as np
import pandas as pd
import pkg_resources
import sys
def crippenHContribs(mol,contribs):
"""Adds Crippen molar refractivity atomic contributions from attached H atoms to a heavy atom's contribution... | pd.read_csv(stream) | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""This is to find out why my R peaks at a value around 2021-07-01, that is
much higher than RIVM's.
Created on Fri Jul 23 12:52:53 2021
@author: hk_nien
"""
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import tools
import nlcovidstats as nlcs
... | pd.Timedelta(4, 'd') | pandas.Timedelta |
'''
This is a script for editing OS's ITN Road Network shapefiles so that:
- the attributeds include an id for the 'to' and 'from' nodes
- line strings are duplicated along links that are bidirectional
Purpose of this script is to use the extracted orientation information from the gml data to edit the roads linesting ... | pd.concat([gdfORLink, gdfORLinkReversed]) | pandas.concat |
from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor,RandomForestClassifier
import pickle
import scikitplot as skplt
from sklearn import tree
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix,precision_score,rec... | pd.read_csv('Classification_winter_data.csv') | pandas.read_csv |
import os
import cv2
import pandas as pd
import dataset_settings
from util import insert_into_df, write_info, resize_image
def prepare_subset_data(data_path, train_csv_path, test_csv_path, source_url):
count = {'normal': 0, 'pneumonia': 0, 'covid-19': 0}
train_csv = pd.read_csv(train_csv_path, nrows=None)
... | pd.read_csv(test_csv_path, nrows=None) | pandas.read_csv |
import plotly.graph_objs as go
import math
import pandas as pd
color_palette = ['#586BA4',
'#324376',
'#F5DD90',
'#F68E5F',
'#F76C5E']
def create_timedelta_graph(events_df):
if events_df.empty:
x_values = list()
y_values = list()... | pd.to_datetime(events_df['timestamp']) | pandas.to_datetime |
# coding: utf-8
# In[1]:
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout
from sklearn import preprocessing
from keras.optimizers import SGD
import pandas as pd
# In[4]:
def xtrain_and_test(df_all):
'''
得到训练数据和测试数据
'''
df_label = pd.read_csv('../data... | pd.read_csv('../data/public/evaluation_public.csv') | pandas.read_csv |
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error
from sklearn.externals import joblib
from collections import OrderedDict
import json
import argparse
import os
# 加上48小时前obs 信息
# 处理 RAIN 值 去除 35以上数值
target_list=['t2m','rh2m','w10m']
from datetime import timedelta
from datetime import... | pd.to_datetime(opt.last_day) | pandas.to_datetime |
import os
import sys
sys.path.append(os.path.abspath(os.path.dirname(__file__) + '/' + '../..'))
import numpy as np
import pandas as pd
from python.tools import (
clean_folder
)
# Formatters for LaTeX output
def f1(x):
return '%1.0f' % x
def f2(x):
return '%1.2f' % x
################
## Parameters ##
#... | pd.DataFrame(res) | pandas.DataFrame |
import pandas as pd
import matplotlib.pyplot as plt
import glob
import numpy
# open btc csv clean and get ready
btc = pd.read_csv('btc_dataset.csv')
btc['Date'] = pd.to_datetime(btc['Date'])#
btc= btc.set_index('Date')
Btc = | pd.DataFrame(btc.ix['2015-01-01':, "Avg_price"]) | pandas.DataFrame |
# coding=utf-8
import pandas as pd
from mock import MagicMock
from sparkmagic.livyclientlib.exceptions import BadUserDataException
from nose.tools import assert_raises, assert_equals
from sparkmagic.livyclientlib.command import Command
import sparkmagic.utils.constants as constants
from sparkmagic.livyclientlib.sendpa... | pd.DataFrame({"A": [1], "B": [2]}) | pandas.DataFrame |
import pandas as pd
# bookings_to_arr
#
# Accepts a pandas dataframe containing bookings data and returns a pandas
# dataframe containing changes in ARR with the following columns:
# - date - the date of the change
# - type - the type of the change (new, upsell, downsell, and churn)
# - customer_id - the id o... | pd.Timestamp(ts_input="9/25/2020", tz="UTC") | pandas.Timestamp |
# coding: utf-8
import tweepy
import json
import os
from datetime import datetime
import pandas as pd
import credentials.credentials_twitter as cred
class Twitter_Analysis:
""" copyright© 2019 — <NAME> - License MIT """
__consumer_key = cred.CONSUMER_KEY
__token = cred.TOKEN
__api = None
def __in... | pd.DataFrame(new) | pandas.DataFrame |
from copy import deepcopy
from sklearn.model_selection import KFold
import numpy as np
import pandas as pd
from .data_augmentation import DACombine
from core.models.metrics import avg_loss, mse, rejection_ratio, avg_win_loss, avg_loss_ratio, loss_sum, invariance
benchmark_functions = [avg_loss, mse, rejection_ratio,... | pd.DataFrame(results) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 24 00:52:56 2016
@author: ARM
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.models import Sequential, Graph
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD, Adam
from sklearn.cross_validati... | pd.read_csv(name5,' ') | pandas.read_csv |
import pandas as pd
import numpy as np
from sklearn.metrics import roc_curve, auc, confusion_matrix, precision_score, recall_score, f1_score
from sklearn.metrics import average_precision_score, precision_recall_curve
from ._woe_binning import woe_binning, woe_binning_2, woe_binning_3
class Metrics:
def __init__(s... | pd.merge(dev, val, how='left', on=['var_name', 'var_cuts'], suffixes=['_dev','_val']) | pandas.merge |
import unittest
import qteasy as qt
import pandas as pd
from pandas import Timestamp
import numpy as np
from numpy import int64
import itertools
import datetime
from qteasy.utilfuncs import list_to_str_format, regulate_date_format, time_str_format, str_to_list
from qteasy.utilfuncs import maybe_trade_day, is_market_tr... | pd.to_datetime(prev_seems_trade_day) | pandas.to_datetime |
#!/usr/bin/env python3
# requirement: a unique train path for each given bitmap day and UID
import app.solr as solr
import json
import pandas as pd
DAY = pd.offsets.Day()
MONDAY = pd.offsets.Week(weekday=0)
WEEK = 7 * DAY
N = 0
def days_str(n):
return '{:b}'.format(n).zfill(7)
def day_int(bitmap):
return i... | pd.DataFrame(UPDATE) | pandas.DataFrame |
# import libraries
import requests
import numpy as np
import pandas as pd
from bs4 import BeautifulSoup as bs
import time
import random
import re
import os
# Important Note ---
# change the value for which you want to scrape the data defaults to 2008-2019
year_list = [year for year in range(2019, 2007, -1)]
# proje... | pd.to_numeric(df["Mat"], errors="coerce") | pandas.to_numeric |
# -*- coding: utf-8 -*-
"""Provides programs to process and analyze GOES X-ray data."""
from __future__ import absolute_import
import datetime
import matplotlib.dates
from matplotlib import pyplot as plt
from astropy.io import fits as pyfits
from numpy import nan
from numpy import floor
from pandas import DataFrame
f... | DataFrame({'xrsa': newxrsa, 'xrsb': newxrsb}, index=times) | 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 = """vcf1_test1 0 small_del
vcf2_test1 0 small_del
vcf1_test2 0 small_del
vcf2_test2 0 small_del
vcf1_test3 0 large_del
vcf2_test3 0 large_del
vcf1_test4 0 large_del
vcf2_test4 0 large... | pd.testing.assert_frame_equal(manual_sv_type, manual_sv_type_expected, check_like=True) | pandas.testing.assert_frame_equal |
from os.path import abspath, dirname, join, isfile, normpath, relpath
from pandas.testing import assert_frame_equal
from numpy.testing import assert_allclose
from scipy.interpolate import interp1d
import matplotlib.pylab as plt
from datetime import datetime
import mhkit.wave as wave
from io import StringIO
import panda... | pd.Series(data['freqBinWidth']) | pandas.Series |
import re
import pandas as pd
from .soup import get_soup, table_to_df
TICKER_IN_PARENTHESIS_RE = re.compile(r'(?P<company_name>.+) \((?P<ticker>[A-Z]+)\)')
def get_wiki_table_df(url, index_col=None, columns=None):
"""Returns the first table of a Wikipedia page as a DataFrame"""
soup = get_soup(url)
tabl... | pd.DataFrame(d) | pandas.DataFrame |
import io
import os
from random import choice
import pandas as pd
import torch
import torch.nn as nn
from PIL import Image
from torch.utils.data import DataLoader
from torchvision import transforms as T
from torchvision.datasets import ImageFolder
from torchvision.models.resnet import BasicBlock, ResNet
SANITY_DIR = ... | pd.DataFrame([{'accuracy': acc, 'loss': loss}]) | pandas.DataFrame |
import calendar
import datetime
import numpy as np
import pandas as pd
from pandas.util.testing import (assert_frame_equal, assert_series_equal,
assert_index_equal)
from numpy.testing import assert_allclose
import pytest
from pvlib.location import Location
from pvlib import solarposi... | assert_frame_equal(expected_solpos, ephem_data[expected_solpos.columns]) | pandas.util.testing.assert_frame_equal |
from datetime import datetime, timedelta
import dateutil
import numpy as np
import pytest
import pytz
from pandas._libs.tslibs.ccalendar import DAYS, MONTHS
from pandas._libs.tslibs.period import IncompatibleFrequency
from pandas.compat import lrange, range, zip
import pandas as pd
from pandas import DataFrame, Seri... | pd.DatetimeIndex([1457537600000000000, 1458059600000000000]) | pandas.DatetimeIndex |
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import random
import pickle
import missingno as msno
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import IterativeImputer
from sklearn.preprocessing import StandardScaler
from sklearn.decomposi... | pd.DataFrame(y_test) | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from scipy.stats import multivariate_normal as mvn
import seaborn as sn
import math
import gc
import tensorflow as tf
from tensorflow.keras.models import Sequ... | pd.DataFrame(data=X_test_data, columns=X_ID2) | pandas.DataFrame |
import matplotlib
matplotlib.use('Agg')
import tessreduce as tr
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import lightkurve as lk
from astropy.coordinates import SkyCoord
from astropy import units as u
import os
dirname = os.path.dirname(__file__)
#where we're going we dont need warnings... | pd.DataFrame(columns=['mjd','flux','err','trend1','trend2','zp','sector']) | pandas.DataFrame |
import numpy as np
import pandas as pd
from insolver.frame import InsolverDataFrame
from insolver.transforms import InsolverTransform, AutoFillNATransforms
def test_fillna_numerical():
df_test = InsolverDataFrame(pd.DataFrame(data={'col1': [1, 2, np.nan]}))
df_transformed = InsolverTransform(df_test, [
... | pd.DataFrame(data={'col1': [np.nan, np.nan, np.nan]}) | pandas.DataFrame |
"""
This module defines geometric methods that work in 3D and allow receiverpoints and observation objects to interact with a map
"""
# rays,to_crs used in observations
# fresnel,to_crs,is_outside,ground_level used in sim
# map_to_crs is a standalone map method
from itertools import chain, compress, cycle, repeat
f... | pd.Series((point.z for point in points), index=points.index) | pandas.Series |
import json
import pandas as pd
from pandas import DataFrame
import matplotlib.pyplot as plt
import os
import collections
import nltk.classify
import nltk.metrics
import numpy as np
import csv
"""
read all business id
"""
business=[]
users=[]
scores=[]
rates=[]
t=0
userdd= | pd.read_csv('users.tsv', sep="\t") | pandas.read_csv |
import unittest
from zeppos_bcpy.sql_statement import SqlStatement
import pandas as pd
import os
class TestTheProjectMethods(unittest.TestCase):
def test_get_table_create_statement_method(self):
df = | pd.DataFrame({'seconds': [3600], 'minutes': [10]}, columns=['seconds', 'minutes']) | pandas.DataFrame |
import shelve
import numpy as np
import re
import pandas as pd
import json
import pickle
import pdb
from copy import copy
with open('metadata/bacnet_devices.json', 'r') as fp:
sensor_dict = json.load(fp)
nae_dict = dict()
nae_dict['bonner'] = ["607", "608", "609", "557", "610"]
nae_dict['ap_m'] = ['514', '513','6... | pd.read_csv('metadata/bacnettype_mapping.csv') | pandas.read_csv |
# Librairies
print("Load Libraries")
import os
import hashlib
import numpy as np
import pandas as pd
import tensorflow.keras.preprocessing.image as kpi
import tensorflow.keras.models as km
from tensorflow.python.client import device_lib
MODE = "GPU" if "GPU" in [k.device_type for k in device_lib.list_local_devices(... | pd.DataFrame(array, columns=["filename","probabilities","classes"]) | pandas.DataFrame |
'''
This program will calculate a timeseries of active users across the lifetime of a project (or a workflow id/version for a project).
The inputs needed are:
the classification export file (request & download from the Project Builder)
[optional] the workflow id
[optional] the workflow version (only the major (... | pd.to_datetime(t_temp, format='%Y-%m-%d %H:%M:%S') | pandas.to_datetime |
# ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# --------------------------------------------... | pd.Index([]) | pandas.Index |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
i... | pd.get_dummies(df['Gender'], drop_first=True) | pandas.get_dummies |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 26 15:39:02 2018
@author: joyce
"""
import pandas as pd
import numpy as np
from numpy.matlib import repmat
from stats import get_stockdata_from_sql,get_tradedate,Corr,Delta,Rank,Cross_max,\
Cross_min,Delay,Sum,Mean,STD,TsRank,TsMax,TsMin,DecayLinea... | pd.DataFrame(r['r1'] + r['r2']) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import xlrd
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.metrics import roc_curve, auc, accuracy_score
import matplotlib.pyplot as plt
import xgboost as... | DataFrame(X_test) | pandas.core.frame.DataFrame |
__author__ = "<NAME>"
__license__ = "Apache 2"
__version__ = "1.0.0"
__maintainer__ = "<NAME>"
__website__ = "https://llp.berkeley.edu/asgari/"
__git__ = "https://github.com/ehsanasgari/"
__email__ = "<EMAIL>"
__project__ = "1000Langs -- Super parallel project at CIS LMU"
import requests
from bs4 import BeautifulSoup
... | pd.read_table('../meta/massive_par_stat.tsv', sep='\t') | pandas.read_table |
# -*- coding: utf-8 -*-
"""Creates folders and files with simulated data for various characterization techniques
Notes
-----
All data is made up and does not correspond to the materials listed.
The data is meant to simply emulate real data and allow for basic analysis.
@author: <NAME>
Created on Jun 15, 2020
"""
fr... | pd.DataFrame(data) | pandas.DataFrame |
import pandas as pd
import numpy as np
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
import itertools
from dateutil.relativedelta import relativedelta
import sklearn.tree as tree
from sklearn.neural_network import MLPClassifier
from imblearn.over_sampling import SMOTE, ADASY... | pd.DataFrame() | pandas.DataFrame |
from __future__ import annotations
from typing import Optional, List, Dict, Tuple
import logging
import textwrap
import pandas as pd
import numpy as np
import h5py
from tqdm import tqdm
from .catmaid_interface import Catmaid, Bbox, ConnectorDetail
from .utils import CoordZYX
logger = logging.getLogger(__name__)
de... | pd.HDFStore(fpath, "r") | pandas.HDFStore |
import pandas as pd
from collections import Counter
import sklearn.preprocessing as preprocessing
import numpy as np
import os
from pandas import Series
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import SelectKBest
column_names = ['age','workclass... | pd.read_csv(data,sep='\s*,\s*',encoding='ascii',names = column_names,engine='python') | pandas.read_csv |
"""
Functions and objects to work with mzML data and tabular data obtained from
third party software used to process Mass Spectrometry data.
Objects
-------
MSData: reads raw MS data in the mzML format. Manages Chromatograms and
MSSpectrum creation. Performs feature detection on centroid data.
Functions
---... | pd.Series(df.columns) | pandas.Series |
import os
import argparse
import itertools
import numpy as np
import pandas as pd
import scipy.sparse as sp
from tqdm import tqdm
from shqod import (
read_trajec_csv,
trajec,
read_level_grid,
od_matrix,
calculate_field,
field_to_dict,
mobility_functional,
fractalD,
trajectory_lengt... | pd.concat(dfs) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 31 15:16:47 2017
@author: wasifaahmed
"""
from flask import Flask, flash,render_template, request, Response, redirect, url_for, send_from_directory,jsonify,session
import json as json
from datetime import datetime,timedelta,date
from sklearn.cluste... | pd.Series(Tfirt_x) | pandas.Series |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.4.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %% [markdown]
# # P1 REST API
#
# - This Jupyter no... | pd.DataFrame.from_records(data["payload_data"]) | pandas.DataFrame.from_records |
from six import string_types, text_type, PY2
from docassemble.webapp.core.models import MachineLearning
from docassemble.base.core import DAObject, DAList, DADict
from docassemble.webapp.db_object import db
from sqlalchemy import or_, and_
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForest... | pd.DataFrame(data) | pandas.DataFrame |
import os
import warnings
import contextlib
import tempfile
import zipfile
from typing import Iterable
import logging
import pandas as pd
from .base import BaseEndpoint
from ..models.odata import Series, Task
logger = logging.getLogger(__name__)
@contextlib.contextmanager
def _with_dir_path(dir_path=None):
# p... | pd.DataFrame(df_data) | pandas.DataFrame |
"""
This package will create the simplified comix matrix needed by simple_network_sim basing itself in the following data:
- The CoMix matrix: https://cmmid.github.io/topics/covid19/reports/20200327_comix_social_contacts.xlsx
- The Scottish demographics (NRS): ftp://boydorr.gla.ac.uk/scrc/human/demographics/scotland/d... | pd.DataFrame(rows) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 12 08:47:38 2018
@author: cenv0574
"""
import os
import json
import pandas as pd
import geopandas as gpd
from itertools import product
def load_config():
# Define current directory and data directory
config_path = os.path.realpath(
os.path.join(os.path... | pd.DataFrame(tax_sub) | pandas.DataFrame |
#!python3
# -*- coding:utf-8 -*-
import os
import numpy as np
import pandas as pd
'''
This source code is a sample of using pandas library.
Series and DataFrame.
'''
# Series Object: It's one dimension object.
# It's not ndarray, list, and other sequense object.
print("make instance of Series")
ser = | pd.Series([10,20,30,40]) | pandas.Series |
"""
A class to carry localization data.
"""
import copy
import logging
import time
import warnings
from itertools import accumulate
import numpy as np
import pandas as pd
from google.protobuf import json_format, text_format
try:
from scipy.spatial import QhullError
except ImportError:
from scipy.spatial.qhu... | pd.concat([self.dataframe, new_df], axis=1) | pandas.concat |
import baostock as bs
import pandas as pd
import numpy as np
from IPython import embed
class Data_Reader():
"""
reading the data from the file
"""
def __init__(self, file="stock.csv"):
self.file = file
self.code_list = []
self.data = None
def read_data(self, file="stock.c... | pd.read_csv(file, encoding="gbk") | pandas.read_csv |
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
def pyscript_diseases():
# measels
measlesdf = pd.read_csv('https://docs.google.com/spreadsheets/d/1ogMiFRnX-N4lp1cqI0N22F9K9fFVVFfCWxw4T6W2iVw/export?format=csv&id')
measlesdf['Total Measles Cases'] = measlesdf... | pd.read_csv("Data/COVID-19.csv") | pandas.read_csv |
import os
import unittest
from builtins import range
import matplotlib
import mock
import numpy as np
import pandas as pd
import root_numpy
from mock import MagicMock, patch, mock_open
import six
from numpy.testing import assert_array_equal
from pandas.util.testing import assert_frame_equal
import ROOT
from PyAnalysi... | pd.DataFrame({'var1': [1., 2.]}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
HornMT_Dataset_Preparation
Created on Mon Dec 12 01:25:16 2021
@author: <NAME>
"""
# Import libs
import pandas as pd
# Load HornMT dataset
file_path = '/data/HornMT.xlsx'
HornMT = pd.read_excel(file_path)
#HornMT.head(1)
# Preprocess the dataframe
eng = pd.DataFrame(HornMT... | pd.DataFrame(HornMT['tir']) | pandas.DataFrame |
from datetime import timedelta
from functools import partial
from operator import attrgetter
import dateutil
import numpy as np
import pytest
import pytz
from pandas._libs.tslibs import OutOfBoundsDatetime, conversion
import pandas as pd
from pandas import (
DatetimeIndex, Index, Timestamp, date_range, datetime,... | DatetimeIndex(['1-1-2000 00:00:01']) | pandas.DatetimeIndex |
'''
MIT License
Copyright (c) 2020 <NAME>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distri... | pd.read_csv(fte) | pandas.read_csv |
# coding: utf-8
# In[6]:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# # 权利数据5right.csv提取特征
# 1. 企业拥有权利的个数，RIGHT_CNT
# 2. 企业拥有权利类型的个数，RIGHT_TYPE_CNT
# 3. 企业拥有权利类型的比例，RIGHT_TYPE_RATE
# 4. 第一个获得的权利的类型，RIGHT_FIRST_TYPECODE
# 5. 最后一个获得的权利的类型，RIGHT_EN... | pd.Series(row,columns) | pandas.Series |
# Copyright (c) 2016. Mount Sinai School of Medicine
#
# 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 o... | pandas.Panel(dataframes) | pandas.Panel |
import datetime as dt
import os
import unittest
import numpy as np
import pandas as pd
import devicely
class SpacelabsTestCase(unittest.TestCase):
READ_PATH = "tests/SpaceLabs_test_data/spacelabs.abp"
WRITE_PATH = "tests/SpaceLabs_test_data/spacelabs_written.abp"
def __init__(self, *args, **kwargs):
... | pd.to_datetime("1.1.99 17:05:00") | pandas.to_datetime |
import os
import types
import pandas as pd
import data_go_kr as dgk
def category_from_url(url:str) -> str:
return os.path.basename( os.path.dirname(url) )
for k,v in dgk.api.__dict__.items():
if isinstance(v, types.ModuleType):
print(k,v)
lst_name = []
lst_desc = []
lst_url = []
lst_cat = []
lst_fl... | pd.set_option('display.colheader_justify', 'left') | pandas.set_option |
#!/usr/bin/env python3
"""tests.compare.compare.py: Auxiliary variables for tests.compare"""
import pandas as pd
from exfi.io.read_bed import read_bed3
from exfi.io.bed import BED3_COLS, BED3_DTYPES
from exfi.compare import \
TP_DF_COLS, TP_DF_DTYPES, \
STATS_COLS, STATS_DTYPES
BED3_EMPTY_FN = "tests/compa... | pd.DataFrame(columns=BED3_COLS) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 10 11:56:35 2017
@author: Madhav.L
"""
import pandas as pd
from sklearn import tree
from sklearn import model_selection
import io
import pydot
import os
os.environ["PATH"] += os.pathsep + 'D:/datascience/graphviz-2.38/release/bin/'
#returns current workin... | pd.read_csv("test.csv") | pandas.read_csv |
import csv
import os
import re
import numpy as np
import pandas as pd
from functools import reduce
from modules.classes.item_id_parser import ItemIDParser
def map_dict(elem, dictionary):
if elem in dictionary:
return dictionary[elem]
else:
return np.nan
def create_time_feature(series, wind... | pd.concat([df_shard[columns], gender_dummied], axis=1) | pandas.concat |
import json
from pathlib import Path
from itertools import repeat
from collections import OrderedDict
import pandas as pd
import os
import warnings
def check_input(text_list):
"""
检查预测的输入list
:param text_list:
:return:
"""
# 一个str的话，转list
if isinstance(text_list, str):
text_list ... | pd.DataFrame(index=keys, columns=['total', 'counts', 'average']) | pandas.DataFrame |
# %%
from datetime import datetime, timedelta
from pathlib import Path
import random
import pandas as pd
# %%
data = pd.read_csv("../data/base2020.csv", sep=";")
# %%
def report(state, date, last_date, last_state, age, sex):
if last_state is not None:
events.append(dict(
from_state=last_state,... | pd.isna(confirm_date) | pandas.isna |
# Author: <NAME>, PhD
#
# Email: <EMAIL>
#
#
# Ref: https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
# Ref: https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.jaccard.html#scipy.spatial.distance.jaccard
# Ref: https://docs.scipy.org/doc/scipy/reference/generated/scip... | pd.DataFrame ({'ID': ids, 'Dim_1': X_embedded[:,0], 'Dim_2': X_embedded[:,1]}) | pandas.DataFrame |
from matplotlib import pyplot as plt
import matplotlib.ticker as mtick
import pandas as pd
import json
from matplotlib import rcParams
# Plots the qualifier statistics for the LC-QuAD 2.0 dataset and Wikidata
line_width = 2
font_size = 15
rcParams.update({"figure.autolayout": True})
with open("./results/datasets_s... | pd.DataFrame(panda_data, index=["LC-QuAD 2.0"]) | pandas.DataFrame |
#!/usr/bin/env python
#ADAPTED FROM
#https://github.com/bio-ontology-research-group/deepgoplus/blob/master/evaluate_deepgoplus.py
import numpy as np
import pandas as pd
import click as ck
from sklearn.metrics import classification_report
from sklearn.metrics.pairwise import cosine_similarity
import sys
from collection... | pd.read_pickle(terms_file) | pandas.read_pickle |
"""
This module contains transformers that apply string functions.
"""
import pandas as pd
from tubular.base import BaseTransformer
class SeriesStrMethodTransformer(BaseTransformer):
"""Tranformer that applies a pandas.Series.str method.
Transformer assigns the output of the method to a new column. It is p... | pd.Series(["a"]) | pandas.Series |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
INPUT_DIR = "~/data/query-result/"
OUTPUT_DIR = "~/data/summary-stats/"
RES_LIST = ['cpu', 'mem', 'net_send', 'net_receive', 'disk_read', 'disk_write']
METRIC_LIST = ['_util_per_instance_95p', '_util_per_instance_max', '_util_per_pool', '_util_per_... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
from geopy.distance import great_circle
#filter and drop duplcates rows
data = pd.read_csv("AllStudent.csv")
data = data.filter(['N°Ins','Adresse']).drop_duplicates()
data.fillna("other", inplace=True)
| pd.DataFrame(data) | pandas.DataFrame |
import unittest
import numpy as np
import pandas as pd
from pyalink.alink import *
class TestPinyi(unittest.TestCase):
def run_segment(self):
# -*- coding=UTF-8 -*-
data = np.array([
[0, u'二手旧书:医学电磁成像'],
[1, u'二手美国文学选读（ 下册 ）李宜燮南开大学出版社 9787310003969'],
[2, u'... | pd.DataFrame({"id": data[:, 0], "text": data[:, 1]}) | pandas.DataFrame |
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