prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import os
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from results.test_dicts import marker_styles, draw_order, metric_ylim, best_function, is_metric_increasing, \
metric_short_name, metric_en_name
excluded_methods = ['Simple split']
RESULTS_ROOT_DIR = 'detailed_r... | pd.concat(metrics_dfs) | pandas.concat |
import sys
from intopt_energy_mlp import intopt_energy
sys.path.insert(0,'../..')
sys.path.insert(0,"../../Interior")
sys.path.insert(0,"../../EnergyCost")
from intopt_energy_mlp import *
from KnapsackSolving import *
from get_energy import *
from ICON import *
import itertools
import scipy as sp
import numpy as np
im... | pd.DataFrame([two_stage_rslt, spo_rslt,qpt_rslt,intopt_rslt]) | pandas.DataFrame |
import json
import pandas as pd
import time
"""
需要一下文件：
1、预测的json：bbox_level{}_test_results.json
2、test集的json：test.json
3、sample_submission.csv
"""
LABLE_LEVEL = 4
SCORE_THRESHOLD = 0.001
def json_to_dict(json_file_dir):
with open(json_file_dir, "r") as json_file:
json_dict = json.load(json_file)
... | pd.Series(series_imageid) | pandas.Series |
"""
Copyright 2019 <NAME>.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distribut... | pd.Timestamp(state) | pandas.Timestamp |
'''
Created on Jan 11, 2016
@author: jch
'''
import numpy
import pandas
from collections import Mapping, OrderedDict
from blocks.log.log import TrainingLogBase
class _TimeSlice(Mapping):
def __init__(self, time, log):
self._time = time
self._columns = log._columns
assert isinstance(self... | pandas.Series(data, index=col['idx'], dtype=dtype) | pandas.Series |
### Twitter Data Tools
## <NAME>
## Created: 8/15/2018
## Updated: 8/23/2018
import os
import re
import sys
import math
import nltk
import errno
import tarfile
import unidecode
import numpy as np
import pandas as pd
import subprocess as sb
def get_id_sets(data):
parent = list(data['tweet']['tweet_id']['parent'].keys... | pd.read_csv(pathway_T,compression='gzip',sep=',',index_col=0,header=0,dtype=str) | pandas.read_csv |
import requests
from bs4 import BeautifulSoup
from datetime import datetime
import pandas as pd
import datetime
import os
def crawling(id_, page, lastupdate=None):
headers = {
'authority': 'feedback.aliexpress.com',
'cache-control': 'max-age=0',
'upgrade-insecure-requests': '1',
'origin': 'https://f... | pd.date_range(df.index[0], df.index[-1]) | pandas.date_range |
# Press Shift+F10 to execute it or replace it with your code.
# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
import numpy as np
import pandas as pd
import warnings
from sklearn.linear_model import LinearRegression
import scipy.cluster.hierarchy as sch
import datetime
... | pd.DataFrame([volume], columns=data.columns) | pandas.DataFrame |
import numpy as np
import pandas as pd
import random
import time
from sklearn.utils import shuffle
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import random
from torch.utils.data import DataLoader
from torch.nn.functional import relu,leaky_relu
from torch.nn import Lin... | pd.DataFrame(scores) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
qualifier.py
@author <NAME>
<EMAIL>
* qualifier * -> mpi_prep -> mpi_run -> mpi_read -> jackknifer
Program Use Instructions:
Instructions are found in qualifier.ini, as well as space for user input.
Program Description:
Prepares the random and data catal... | pd.DataFrame() | pandas.DataFrame |
import operator
import re
import numpy as np
import pandas as pd
import utils
def get_sites_from_kd_dict(transcript_id, sequence, kd_dict, overlap_dist):
if len(sequence) < 9:
return pd.DataFrame(None)
mir_info = {
'prev_loc': -100,
'prev_seq': '',
'prev_kd': 100,
'k... | pd.DataFrame({'loc': locs}) | pandas.DataFrame |
from dotenv import load_dotenv
load_dotenv()
import os, re, json, unicodedata
import tweepy
from tweepy import Stream,OAuthHandler
from datetime import datetime, timedelta
import nltk
# nltk.download('stopwords')
from nltk.corpus import stopwords
stop_words = stopwords.words('spanish')
from nltk.tokenize import Twe... | pd.DataFrame(topic_weights) | pandas.DataFrame |
from .indicator import Indicator
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Moving Average Crossover
-
Buy when short-term moving average > long-term moving average,
sell when short-term moving average < long-term moving average.
"""
class MovingAverageCrossover(Indicator):
... | pd.DataFrame(index=self.df.index) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Get historical information of a series from /mints. Eg: https://cryptoslam.io/cryptopunks/mints
@author: HP
"""
from selenium import webdriver
from selenium.webdriver.support.ui import Select
import pandas
import time
import requests
from bs4 import BeautifulSoup
from selenium.common.excepti... | pandas.read_html(browser.page_source) | pandas.read_html |
import pandas as pd
import numpy as np
from functools import wraps
import copy
# Pass through pd.DataFrame methods for a (1,1,o,d) shaped triangle:
df_passthru = ['to_clipboard', 'to_csv', 'to_pickle', 'to_excel', 'to_json',
'to_html', 'to_dict', 'unstack', 'pivot', 'drop_duplicates',
'de... | pd.DataFrame(val_array) | pandas.DataFrame |
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from collections import Counter
from itertools import islice
from gensim.models.doc2vec import TaggedDocument, Doc2Vec
from gensim.parsing.preprocessing import preprocess_string
from sklearn.base import BaseEstimator
from sklearn import utils as ... | pd.read_csv('./whole.csv', index_col=False, header=0) | pandas.read_csv |
from Gridworld import Gridworld
from MonteCarlo import MonteCarlo
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import csv
env = Gridworld(shape=[5,5], initialState=25)
print("------------------------------epsilon=0.01-------------------------------------")
MC_1 = MonteCarlo(grid_world = env,... | pd.concat(frames) | pandas.concat |
# -*- coding: utf-8 -*-
import pandas as pd
from flask import Flask, jsonify, render_template
from yahoofinancials import YahooFinancials
import numpy as np
from datetime import date
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn import preprocessing... | pd.DataFrame(data=result[ticker]['prices']) | pandas.DataFrame |
"""Основные метрики доходности на базе ML-модели"""
from functools import lru_cache
import numpy as np
import pandas as pd
from local import moex
from metrics.portfolio import CASH
from metrics.portfolio import PORTFOLIO
from metrics.portfolio import Portfolio
from metrics.returns_metrics import AbstractReturnsMetric... | pd.Timestamp(portfolio.date) | pandas.Timestamp |
from collections import namedtuple
import re
import time
import warnings
from geopy.distance import geodesic
from geopy.exc import GeocoderUnavailable
from geopy.geocoders import Nominatim
import pandas
import requests
Location = namedtuple("Location", ["latitude", "longitude"])
def clean_address(problem_address):... | pandas.isnull(in_row["latitude"]) | pandas.isnull |
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
df=pd.read_csv('TrainingData.csv')
#Change strings to numbers
from sklearn import preprocessing
lb = preprocessing.LabelBinarizer()
wi=lb.fit_transform(np.array(df.loc[:,['Working Ion']]))
cs=lb.fit_transform(np.array(df.loc[:,['Crystal Sys... | pd.DataFrame(newdata) | pandas.DataFrame |
'''
Download PDF files for a series of law chapter numbers
found in our training CSV file provided by partners.
Then extract the chapter texts from those PDFs.
Finally insert the chapter text into a new column 'Text' of our training CSV.
Note: each PDF contains a bit of the previous chapter and following one.
'''
impor... | pd.isna(first_line) | pandas.isna |
from __future__ import print_function
import pickle
import os.path
from googleapiclient.discovery import build
from google_auth_oauthlib.flow import InstalledAppFlow
from google.auth.transport.requests import Request
from collections import Counter
import pandas as pd
from datetime import datetime
import numpy as np
im... | pd.DataFrame(newValList, columns=headers) | pandas.DataFrame |
import pandas as pd
import numpy as np
from custom_stuff import Alone
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selec... | pd.read_csv('./train.csv', index_col='PassengerId') | pandas.read_csv |
from __future__ import absolute_import
import datetime
from copy import deepcopy
import numpy as np
import pandas as pd
from scipy.stats import norm
import expan.core.statistics as statx
from expan.core.debugging import Dbg
from expan.core.version import __version__
class Results(object):
"""
A Results ins... | pd.Timestamp(v) | pandas.Timestamp |
"""
This file tests the utilities stored in cassiopeia/data/utilities.py
"""
import unittest
from typing import Dict, Optional
import networkx as nx
import numpy as np
import pandas as pd
from cassiopeia.data import CassiopeiaTree
from cassiopeia.data import utilities as data_utilities
from cassiopeia.preprocess imp... | pd.testing.assert_frame_equal(weight_matrix, expected_weight_matrix) | pandas.testing.assert_frame_equal |
from datetime import timedelta,datetime
import pandas as pd
from database.market import Market
class Analyzer(object):
@classmethod
def pv_analysis(self,portfolio):
stuff = []
total_cash = 100
trades = portfolio.trades
trades = trades[(trades["date"] >= portfolio.start) & (trade... | pd.DataFrame([{"message":"no trades..."}]) | pandas.DataFrame |
import numpy as np
from graspologic.utils import largest_connected_component
import pandas as pd
def get_paired_inds(meta, check_in=True, pair_key="pair", pair_id_key="pair_id"):
pair_meta = meta.copy()
pair_meta["_inds"] = range(len(pair_meta))
# remove any center neurons
pair_meta = pair_meta[pair_... | pd.DataFrame(node_rows) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
from folium import Map, FeatureGroup, Marker, Popup
from folium.utilities import (
validate_location,
validate_locations,
if_pandas_df_convert_to_numpy,
camelize,
deep_copy,
get_obj_in_upper_tree,
parse_options,
)
@pytest.mark.parametri... | pd.Series([5, 3]) | pandas.Series |
from sys import path
from os.path import expanduser
#path.append('/home/ubuntu/StatisticalClearSky/')
path.append('/Users/bennetmeyers/Documents/ClearSky/StatisticalClearSky/')
from statistical_clear_sky.algorithm.iterative_fitting import IterativeFitting
from solardatatools import standardize_time_axis, make_2d, fix_t... | pandas.read_csv(base + 'sys_meta.csv') | pandas.read_csv |
import os
import pathlib
import sys
import warnings
from functools import partial
from io import StringIO
from typing import Optional, TextIO
import click
import numpy as np # type: ignore
import pandas # type: ignore
import tomlkit as toml # type: ignore
from .compaction import compact as _compact
out = partial(... | pandas.read_csv(src, names=("dz", "porosity"), dtype=float, comment="#") | pandas.read_csv |
#!/home/mario/anaconda3/envs/project2_venv/bin python
"""
DESCRIPTION:
An script to retrieve the information generated during
the resquiggling from the fastq files.
"""
import h5py
import os
import pandas as pd
import csv
import numpy as np
from pytictoc import TicToc
from tombo import tombo_helper, tombo_stats, resq... | pd.DataFrame(reads_data, columns=columns) | pandas.DataFrame |
import pandas as pd
import numpy as np
import json
from tqdm import tqdm
from scipy.optimize import minimize
from utils import get_next_gw, time_decay
from ranked_probability_score import ranked_probability_score, match_outcome
class Bradley_Terry:
""" Model game outcomes using logistic distribution """
de... | pd.merge(self.test_games, idx, left_on="team1", right_on="team") | pandas.merge |
#!/usr/bin/env python
import os
import bisect
import sys
import logging
import math
import yaml
import numpy as np
import pandas as pd
import configparser
import shapefile
from collections import defaultdict
from shapely import geometry
from geopy.distance import geodesic
from scipy import stats
from wistl.constants ... | pd.DataFrame(None) | pandas.DataFrame |
import pandas as pd
from utils.save_data import write_csv
def filter_runs_not_us(data_subject):
data_subject['residence'] = data_subject['Current Country of Residence']
runs_not_us = data_subject.loc[
data_subject['residence'] != 'United States', 'run_id']
print(f"""{len(runs_not_us)} runs do n... | pd.isna(data_subject['logK']) | pandas.isna |
# Charting OSeMOSYS transformation data
# These charts won't necessarily need to be mapped back to EGEDA historical.
# Will effectively be base year and out
# But will be good to incorporate some historical generation before the base year eventually
import pandas as pd
import numpy as np
import matplotlib.pyplot as pl... | pd.Categorical(ref_powcap_df1['TECHNOLOGY'], prod_agg_tech[:-1]) | pandas.Categorical |
# Copyright 2021 The ProLoaF Authors. All Rights Reserved.
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the... | pd.get_dummies(df.index.month, prefix='month') | pandas.get_dummies |
# -*- coding: utf-8 -*-
"""
Created on Tue May 25 14:13:52 2021
@author: <NAME>
"""
import solarenergy as se
import geocoder
from datetime import datetime
from dateutil.relativedelta import relativedelta
import numpy as np
import timezonefinder
import pandas as pd
import streamlit as st
def getDaysList(start_date, t... | pd.DataFrame(data, index=[0]) | pandas.DataFrame |
import os, glob
import pandas as pd
from datetime import datetime as dt
from pathlib import Path
from emotion_recognition import EmotionRecognizer
from pylab import *
import numpy as np
import seaborn as sn
from progressbar import *
import pickle
import ntpath
from pathlib import Path
import shutil
from sklearn.svm imp... | pd.to_datetime(prob_table_by_sessionNmodel_df['Date'], format="%d/%m/%Y") | pandas.to_datetime |
from unittest import TestCase
import pandas as pd
from datamatch.filters import DissimilarFilter, NonOverlappingFilter
class DissimilarFilterTestCase(TestCase):
def test_valid(self):
f = DissimilarFilter('agency')
index = ['agency', 'uid']
self.assertFalse(f.valid(
pd.Series(... | pd.Series(['123', 0, 4], index=index) | pandas.Series |
import numpy as np
from numpy.core.numeric import zeros_like
import pandas as pd
# [TODO] This code was made in a hurry.
# It can be improved, someday I will. Please excuse me
data = {
"a3": [1.0, 6.0, 5.0, 4.0, 7.0, 3.0,8.0,7.0,5.0],
"class": ["CP", "CP", "CN", "CP", "CN", "CN", "CN", "CP", "CN"]
}
divisio... | pd.crosstab(dfi["a3"], dfi["class"], margins=True, margins_name="Total") | pandas.crosstab |
import pandas as pd
confirmed = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data' \
'/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv '
recovered = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data' \
'/cs... | pd.to_datetime(df.index) | pandas.to_datetime |
import pandas as pd
import numpy as np
from sklearn.ensemble import IsolationForest
import matplotlib.pyplot as plt
from scipy import stats
from sklearn.preprocessing import MinMaxScaler
#read data
dataset = | pd.read_csv('raw_dataset.csv',engine='python') | pandas.read_csv |
from datetime import date as dt
import numpy as np
import pandas as pd
import pytest
import talib
import os
from finance_tools_py.simulation import Simulation
from finance_tools_py.simulation.callbacks import talib as cb_talib
from finance_tools_py.simulation import callbacks
@pytest.fixture
def init_global_data():
... | pd.Series.equals(mean, s.data[col_mean]) | pandas.Series.equals |
import pandas
import urllib
import json
import datetime
import pandas
import urllib2
import json
import pymongo
import time
import sys
import datetime
from datetime import date, timedelta
import pymongo
import collections
from bson.json_util import loads
import pandas as pd
import math as m
import nump... | pd.ewma(dataset['price'], span=12) | pandas.ewma |
# -*- coding: utf-8 -*-
"""
Classe for technical analysis of assets.
Created on Sat Oct 31 19:35:28 2020
@author: ryanar
"""
import math
import matplotlib.pyplot as plt
from stock_analysis import StockReader, StockVisualizer, Technical, AssetGroupVisualizer, StockAnalyzer, AssetGroupAnalyzer, StockModeler
from stock... | pd.Series(data) | pandas.Series |
import pandas as pd
import sys
from datetime import datetime
from dotenv import load_dotenv
from os import getcwd, getenv, startfile
from tqdm import tqdm
from tweepy import API, Cursor, OAuthHandler, TweepyException
# Loads .env file
load_dotenv()
cwd = getcwd()
today = datetime.now()
# Gets API Credentials for Tw... | pd.DataFrame(accounts_dict) | pandas.DataFrame |
# -*- coding: utf-8 -*-
'''
Created on Thu Jan 01 16:00:32 2015
@author: LukasHalim
Forked by @edridgedsouza
'''
import sqlite3
import os
import pandas as pd
from contextlib import closing
class Database():
def __init__(self, path='Godwin.db'):
self.path = os.path.abspath(path)
if... | pd.DataFrame(res) | pandas.DataFrame |
import pandas as pd
from statsmodels.stats.diagnostic import acorr_ljungbox
from statsmodels.stats.stattools import jarque_bera
from sklearn.metrics import (
mean_absolute_error,
r2_score,
median_absolute_error,
mean_squared_error,
mean_squared_log_error,
)
class AnnualTimeSeriesSplit():
"""... | pd.DataFrame.from_dict(some_dict[test_dict_name]) | pandas.DataFrame.from_dict |
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.makeTimeDataFrame(100064, "S") | pandas._testing.makeTimeDataFrame |
from unittest import TestCase
import pandas as pd
import numpy as np
from moonstone.analysis.statistical_test import (
statistical_test_groups_comparison,
_compute_best_bins_values
)
class TestStatisticalTestFunction(TestCase):
def setUp(self):
self.test_df = pd.Series({
'sample1': ... | pd.testing.assert_frame_equal(matrix, expected_df, check_dtype=False) | pandas.testing.assert_frame_equal |
import csv
import requests
import pandas as pd
FRED_UNEMPLOY = 'https://www.quandl.com/api/v3/datasets/UNEMPLOY/GDPDEF/data.csv?api_key=<KEY>'
with requests.Session() as s:
download = s.get(FRED_UNEMPLOY)
decoded_content = download.content.decode('utf-8')
cr = csv.reader(decoded_content.splitlines(), de... | pd.DataFrame(UNEMPLOY_list) | pandas.DataFrame |
# Simplified and slightly modularized from:
# https://github.com/LeonardoL87/SARS-CoV-2-Model-with-and-without-temperature-dependence
import pickle
import datetime
import math as m
import numpy as np
import pandas as pd
from tqdm.auto import tqdm
from typing import Callable
from functools import partial
from scipy i... | pd.Series(TC) | pandas.Series |
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 argparse
import os
import tempfile
import mlflow
import mlflow.pytorch
import numpy as np
import optuna
import pandas as pd
import torch
import torch.optim as optim
import torch.utils.data as data
import yaml
from dlkit import models
from dlkit.criterions import Criterion
from estimator impo... | pd.DataFrame(value_list, columns=columns) | pandas.DataFrame |
# coding: utf-8
# # Bike Sharing Dataset Linear Modeling
#
# + Based on Bike Sharing dataset from [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset)
# + This notebook is based upon the hourly data file, i.e. hour.csv
# + This notebook showcases linear modeling using linea... | pd.DataFrame(feature_arr, columns=feature_labels) | pandas.DataFrame |
import pandas as pd
def comparacao(a, b):
if (a == b):
return 'a linha E igual'
else:
return 'a linha NAO e igual'
df = | pd.read_csv('arq.csv', ';', header=0, usecols=["Titulo", "titulo"]) | pandas.read_csv |
# coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# 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 applicab... | pd.DataFrame(labels) | pandas.DataFrame |
import pandas as pd
import numpy as np
import os
import itertools
def dados_teses():
diretorio = "/media/hdvm02/bd/007/002/007/002"
teses_anos = sorted(os.listdir(diretorio))
lista_dfs = []
for tese_ano in teses_anos:
csv = os.path.join(diretorio,tese_ano)
teses = pd.read_csv(csv, se... | pd.read_csv(csv_1987, sep=";", encoding='latin-1', on_bad_lines='skip', low_memory=False) | pandas.read_csv |
from collections import OrderedDict
from datetime import datetime, timedelta
import numpy as np
import numpy.ma as ma
import pytest
from pandas._libs import iNaT, lib
from pandas.core.dtypes.common import is_categorical_dtype, is_datetime64tz_dtype
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
Da... | tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False) | pandas._testing.assert_produces_warning |
# -*- coding: utf-8 -*-
from datetime import timedelta
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas import (Timedelta,
period_range, Period, PeriodIndex,
_np_version_under1p10)
import pandas.core.indexes.period as period
cla... | pd.offsets.QuarterEnd(n=1, startingMonth=12) | pandas.offsets.QuarterEnd |
import pandas as pd
from product.anaiproduct import AnAIProduct
from datetime import timedelta
import pytz
from tqdm import tqdm
pd.options.mode.chained_assignment = None
from modeler.modeler import Modeler as m
from datetime import datetime, timedelta, timezone
import numpy as np
import math
import pickle
from sklearn... | pd.DataFrame([{}]) | pandas.DataFrame |
import os
import json
import pandas as pd
import numpy as np
from scipy.cluster.hierarchy import linkage, leaves_list
from installed_clients.DataFileUtilClient import DataFileUtil
from installed_clients.WorkspaceClient import Workspace
def get_statistics(df_metadata, result, wgs_dict, dist_col="containment_index",upa... | pd.concat(all_df) | pandas.concat |
from scipy.spatial import distance
import numpy as np
import pandas as pd
import scipy.stats
from utils import *
def alignstrategy(str1,str2,flag):
str1t = strpreprocess(str1,'intlist')
str2t = strpreprocess(str2,'intlist')
str1b = strpreprocess(str1,'bytelist')
str2b = strpreprocess(str2,'bytelist')
... | pd.Series(str2t) | pandas.Series |
# -*- coding: utf-8 -*-
###########################################################################
# we have searched for keywords in the original news
# for stemmed keywords in the stemmed news
# for lemmatized keywords int the lemmatized news
# now, want to merge... | pd.merge(selected_news_with_categories2,news[["message_header","trimmed_tag","doc_idx"]], how="left", on='doc_idx') | pandas.merge |
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
def plot_feature_importance(X, Y):
clf = DecisionTreeClassifier()
clf.fit(X, Y)
features = X.columns.values
importances = clf.feature_importances_
df_plot = | pd.DataFrame() | pandas.DataFrame |
import os
import pandas as pd
pd.options.mode.chained_assignment = None
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.colors as mcolors
import numpy as np
import folium
import difflib
import geopandas as gpd
import unicodedata
#function to remove accents from states and munici... | pd.to_numeric(demand_melt_df['hour']) | pandas.to_numeric |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from multiprocessing import cpu_count
import pandas as pd
from joblib import Parallel, delayed
from src.geo_mapping_helpers import map_district_to_community_area
def filter_single_year_range_departure_ca_format_datetimes(df):
"""
Replace single station name, ... | pd.to_datetime(df[c]) | pandas.to_datetime |
import numpy as np
import pytest
from pandas._libs import iNaT
from pandas.core.dtypes.common import (
is_datetime64tz_dtype,
needs_i8_conversion,
)
import pandas as pd
from pandas import NumericIndex
import pandas._testing as tm
from pandas.tests.base.common import allow_na_ops
def test_unique(index_or_se... | tm.assert_index_equal(result, expected, exact=True) | pandas._testing.assert_index_equal |
from typing import Optional
import numpy as np
import pandas as pd
import pytest
from pandas import testing as pdt
from rle_array.autoconversion import auto_convert_to_rle, decompress
from rle_array.dtype import RLEDtype
pytestmark = pytest.mark.filterwarnings("ignore:performance")
@pytest.mark.parametrize(
"o... | pd.Series([1], dtype=np.int32) | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 4 00:13:06 2020
@author: sahand
"""
from rake_nltk import Rake
import pandas as pd
import re
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
import nltk
from nltk.corpus import stopwords
nltk.download('stopwords')
st... | pd.read_csv(path+'Corpus/AI 4k/embeddings/clustering/k10/Doc2Vec patent_wos_ai corpus DEC 200,500,10 k10 labels') | pandas.read_csv |
import requests
import pandas as pd
from datetime import datetime
def get_irradiance_next_hour():
# API parametros
baseurl = 'http://dataservice.accuweather.com/forecasts/v1/hourly/1hour/'
location_key = '310683'
apikey = '<KEY>'
parameters = {
'apikey': apikey,
'details': 'true'... | pd.DataFrame(new_row) | pandas.DataFrame |
#using TA-Lib to create technical analysis / charts / patterns
#package imports
import pandas as pd
import numpy as np
from pandas_datareader import DataReader
import math
import os
import path
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import style
import time
import datetim... | pd.read_csv('TWTR.csv', parse_dates=True, index_col=0) | pandas.read_csv |
"""Test ir_dist._util utility functions"""
from scirpy.ir_dist._util import (
DoubleLookupNeighborFinder,
reduce_and,
reduce_or,
merge_coo_matrices,
)
import pytest
import numpy as np
import scipy.sparse as sp
import pandas as pd
import numpy.testing as npt
@pytest.fixture
def dlnf_square():
clon... | pd.DataFrame() | pandas.DataFrame |
from __future__ import division
from contextlib import contextmanager
from datetime import datetime
from functools import wraps
import locale
import os
import re
from shutil import rmtree
import string
import subprocess
import sys
import tempfile
import traceback
import warnings
import numpy as np
from numpy.random i... | Index([False, True] + [False] * (k - 2), name=name) | pandas.Index |
"""analysis.py: Collection of classes for performing analysis on Corpus"""
# <NAME> (<EMAIL>)
# DS 5001
# 6 May 2021
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas.core.algorithms import mode
import plotly.express as px
import scipy.cluster.hierarchy as sch
from gensim.models import... | pd.MultiIndex.from_product([work_ids, work_ids]) | pandas.MultiIndex.from_product |
import pandas as pd
import numpy as np
#import sys
#sys.path.append("F:\3RDSEM\DM\Assignment_1\DM-Project\Assignment-1\Code")
from Utility import getDataFrame
fileNames = ["./../DataFolder/CGMSeriesLunchPat1.csv", "./../DataFolder/CGMSeriesLunchPat2.csv",
"./../DataFolder/CGMSeriesLunchPat3.csv", "... | pd.concat([df, feature_1_df, feature_2_df, feature_3_df, feature_4_df, feature_5_df, feature_6_df], axis=1) | pandas.concat |
"""
Makes a figure providing an overview of our dataset with a focus on lineages
laid out as follows:
a - Patient metadata
b - Donut plot of our lineage distributions vs the world
c - Timeline of patient sampling vs lineages identified
d - Choropleth of lineages by region
"""
import matplotlib.pyplot as plt
import n... | pd.read_csv("data/external/pangolin2.csv") | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import pysam
import os
import pandas as pd
import numpy as np
import time
import argparse
import sys
from multiprocessing import Pool
# In[ ]:
# ##arguments for testing
# bam_file_path = '/fh/scratch/delete90/ha_g/realigned_bams/cfDNA_MBC_ULP_hg38/realign_bam_pa... | pd.DataFrame() | pandas.DataFrame |
import pytest
import numpy as np
import pandas as pd
import databricks.koalas as ks
from pandas.testing import assert_frame_equal
from gators.feature_generation.polynomial_features import PolynomialFeatures
ks.set_option('compute.default_index_type', 'distributed-sequence')
@pytest.fixture
def data_inter():
X = p... | assert_frame_equal(X_new, X_expected) | pandas.testing.assert_frame_equal |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 14 10:52:33 2022
COVID-19 DEATHS IN US - COUNTY
Author: <NAME> (<EMAIL>)
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
muertes_us... | pd.to_datetime(muertes_us.index,dayfirst=False,yearfirst=False) | pandas.to_datetime |
import time
import numpy as np
import pandas as pd
import logging
from scipy.sparse import issparse, csr_matrix
from scipy.stats import chi2
from sklearn.neighbors import NearestNeighbors
from anndata import AnnData
from joblib import effective_n_jobs
from typing import List, Tuple
from pegasus.tools import update_re... | pd.Series(attr_values[knn_indices[i, :]]) | pandas.Series |
import os
import string
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from common.utils import DATA_DIR
pos_tags_features = [
'noun',
'verb',
'adjective',
'adverb'
]
cos_sim_features = [
'cos_sim'
]
sentiment_features = [
'positive_count',
'negative_count... | pd.concat([features, cols], axis=1) | pandas.concat |
"""
:noindex:
preprocess.py
====================================
Script to convert provider datasets individual record dictionaries
Data Sources:
`https://www.acaps.org/covid-19-government-measures-dataset <https://www.acaps.org/covid-19-government-measures-dataset>`_
`https://www.cdc.gov/mmwr/preview/mmwrhtml/0000159... | pd.to_datetime(cdc["Date Entered"], format='%d/%m/%Y') | pandas.to_datetime |
import numpy as np
import pandas as pd
from sklearn.model_selection import cross_validate, train_test_split
from classification.utils import print_params
from sklearn import metrics
from config import CHANNEL_NAMES
from data.utils import prepare_dfs
# @print_params
def predict(lab, ba, cols, estimator, metapkl, gs=... | pd.DataFrame(y[y_pred != y]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Functions to process raw respondent data from new survey
Convert that to habits
Find top n best matches from 'archetypes' of original survey of 10K people.
Generate habit lists to see overlap with best cluster, unique habits, etc.
Clean habit naming for consisntency.
"""
import sys
sys.pat... | pd.set_option('display.expand_frame_repr', False) | pandas.set_option |
"""
The :mod:`mlshells.model_selection.search` includes utilities to
optimize hyper-parameters.
:class:`mlshell.model_selection.Optimizer` class proposes unified interface to
arbitrary optimizer. Intended to be used in :class:`mlshell.Workflow` . For new
optimizer formats no need to edit `Workflow` class, just adapt i... | pd.DataFrame(optimizer.cv_results_) | pandas.DataFrame |
import pandas as pd
import pytest
| pd.set_option("display.max_rows", 500) | pandas.set_option |
import pandas as pd
import numpy as np
from tqdm import tqdm
from lib import config
from lib.logger import Log
logger = Log()
# Historical data columns
_DATE_COLUMN = 'DATE'
_N1 = 'N1'
_N2 = 'N2'
_N3 = 'N3'
_N4 = 'N4'
_N5 = 'N5'
_N6 = 'N6'
# Result columns
_DRAW_COLUMN = 'draw'
_MAX_SUCCESS_COLUMN = 'max_success'
_... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[2]:
import os
import sys
import pandas as pd
import numpy as np
# In[3]:
# In[4]:
#file 불러오기
#filepath = sys.argv[1]
#filename = sys.argv[2]
filepath = "/home/data/projects/rda/workspace/rda/files/"
filename = "input3.csv"
data = pd.read_csv(filepath + "/" + filena... | pd.Series(silhouette) | pandas.Series |
'''Module to assemble custom tensorflow procedures.'''
from dataclasses import dataclass, field
import tensorflow_datasets as tfds
import pandas as pd
from pyspark.sql import functions as F
from pyspark.sql import (
SparkSession,
functions as F,
DataFrame
)
from pyspark.sql.types import *
@dataclass
... | pd.DataFrame.from_dict(ex['data']) | pandas.DataFrame.from_dict |
import os
import numpy as np
import pandas as pd
import z5py
from mobie import add_segmentation
from mobie.metadata.image_dict import load_image_dict
ROOT = '/g/kreshuk/pape/Work/data/mito_em/data'
RESOLUTION = [.03, .008, .008]
SCALE_FACTORS = [[1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2]]
def compute_object_scores(s... | pd.DataFrame(data, columns=columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import pytest
from ..testing_utils import make_ecommerce_entityset
from featuretools import Timedelta
from featuretools.computational_backends import PandasBackend
from featuretools.primitives import (
Absolute,
Add,
Count,
CumCount,
... | pd.isnull(a) | pandas.isnull |
# Copyright (c) 2016 <NAME> <<EMAIL>>
#
# This module is free software. You can redistribute it and/or modify it under
# the terms of the MIT License, see the file COPYING included with this
# distribution.
""" Module for motif activity prediction """
def warn(*args, **kwargs):
pass
import warnings
warnings.wa... | pd.read_feather(inputfile) | pandas.read_feather |
import numpy as np
import pandas as pd
import pickle
from sklearn.feature_selection import SelectKBest,f_regression
from sklearn.ensemble import RandomForestRegressor
import matplotlib.pyplot as plt
from sklearn import metrics
###########################
# Folder Name Setting
###########################
folder = 'J:... | pd.DataFrame(RESULT,columns=['ID','NUM_FEATURES','N_ESTIMATORS','MAX_FEATURES','MIN_SAMPLES_SPLIT','CV_FOLDS','AUC']) | pandas.DataFrame |
"""
SparseArray data structure
"""
from __future__ import division
import numbers
import operator
import re
from typing import Any, Callable, Union
import warnings
import numpy as np
from pandas._libs import index as libindex, lib
import pandas._libs.sparse as splib
from pandas._libs.sparse import BlockIndex, IntInd... | is_dtype_equal(ltype, rtype) | pandas.core.dtypes.common.is_dtype_equal |
#+ 数据科学常用工具
import matplotlib as mpl
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style
import seaborn as sns
from sklearn.preprocessing import PowerTransformer
import category_encoders as ce
from sklearn.model_selection import StratifiedKFold, KFold
from joblib impo... | pd.concat(retLst, axis=1) | pandas.concat |
import warnings
import numpy as np
import pytest
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas import Index, MultiIndex
import pandas._testing as tm
def test_drop(idx):
dropped = idx.drop([("foo", "two"), ("qux", "one")])
index = MultiIndex.from_tuples([("foo", "two"), ("qux... | tm.assert_produces_warning(PerformanceWarning) | pandas._testing.assert_produces_warning |
import xml.etree.ElementTree as ET
import openpyxl
from openpyxl import Workbook, load_workbook
import pandas as pd
from pandas import ExcelWriter
import csv
import numpy as np
def get_armor(xml_file):
# CREATES TREE AND ROOT
tree = ET.parse(xml_file)
root = tree.getroot()
# head_armor = 'Head Armo... | pd.read_excel('MB2.xlsx', sheet_name='shoulder') | pandas.read_excel |
"""
Functions having to do with loading data from output of
files downloaded in scripts/download_data_glue.py
"""
import codecs
import csv
import json
import numpy as np
import pandas as pd
from allennlp.data import vocabulary
from jiant.utils.tokenizers import get_tokenizer
from jiant.utils.retokenize import realign... | pd.read_json(file_name, lines=True) | pandas.read_json |
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