prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
from datetime import datetime
from io import StringIO
import itertools
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
Period,
Series,
Timedelta,
date_range,
)
import pandas._testing as tm
... | Index(["a", "b"], name="third") | pandas.Index |
#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 |
## Bu bölümde Frekans , Tfidf , Rowsum , VectorNorm hesapları yapılmaktadır.
import pandas as pd
import numpy as np
from TurkishStemmer import TurkishStemmer ##elasticSearch
from math import sqrt
kok = TurkishStemmer()
count = []
vectorNorm = []
class getFrequency:
def __init__(self,spor,saglik,magaz... | pd.DataFrame(data=data,columns=features) | pandas.DataFrame |
# Copyright 2020 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... | pd.to_datetime(data['date']) | pandas.to_datetime |
from binance.client import Client
import pandas as pd
from utils import configure_logging
from multiprocessing import Process, freeze_support, Pool, cpu_count
import os
try:
from credentials import API_KEY, API_SECRET
except ImportError:
API_KEY = API_SECRET = None
exit("CAN'T RUN SCRIPT WITHOUT BINANCE AP... | pd.to_datetime(df['time'] * 1000000, format='%Y-%m-%d %H:%M:%S') | pandas.to_datetime |
#%%
import os
import sys
try:
os.chdir('/Volumes/GoogleDrive/My Drive/python_code/connectome_tools/')
print(os.getcwd())
except:
pass
# %%
import sys
sys.path.append('/Volumes/GoogleDrive/My Drive/python_code/maggot_models/')
sys.path.append('/Volumes/GoogleDrive/My Drive/python_code/connectome_tools/')
fr... | pd.DataFrame(sumMat, columns = interlaced_mat.columns[oddCols], index = interlaced_mat.index[oddRows]) | pandas.DataFrame |
import numpy as np
import scipy.sparse
import pandas as pd
import logging
import rpy2.robjects as ro
import rpy2.rinterface_lib.callbacks
import anndata2ri
import scanpy as sc
from scIB.utils import checkAdata, checkBatch
from .utils import diffusion_conn, diffusion_nn
rpy2.rinterface_lib.callbacks.logger.setLevel(lo... | pd.DataFrame.from_dict(kBET_scores) | pandas.DataFrame.from_dict |
## 1. Count the level of gauges
## 2. Sout the gauges by level
## 3. Automatic bias correction from upstreams to downstreams at monthly scale
## 4. Take each gauge delta Q added to downstreams
## 5. Bias correction for the ungauged river at monthly scale
## 6. Bias scale mapping at daily scle
## Input: fast_connecti... | pd.read_csv(gauge_file) | pandas.read_csv |
#!/usr/bin/env python3
import sys, math, gzip
import numpy as np
import pandas as pd
from time import time
# calculate Wen/Stephens shrinkage LD estimate
gmapfile = sys.argv[1] # genetic map
indfile = sys.argv[2] #list of individuals
# NE = 11418.0
NE = float(sys.argv[3])
# CUTOFF = 1e-7
CUTOFF = float(sys.argv[4])
... | pd.DataFrame.from_records(records) | pandas.DataFrame.from_records |
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 17 19:51:21 2018
@author: Bob
"""
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import DBSCAN
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer
from nltk.corpus import stopwords
from sqlalchemy import create_engine
from c... | pd.DataFrame({'word': text}) | pandas.DataFrame |
# Copyright © 2019 <NAME>
"""
Test for the ``preprocess._aggregate_columns._difference`` module.
"""
from pandas import DataFrame
from pandas.util.testing import assert_frame_equal
import unittest
# Tests for:
from ...clean_variables import VariableCleaner
class PreprocessConstantDifferenceTests(unittest.TestCase)... | assert_frame_equal(_expected, _vc.frame) | pandas.util.testing.assert_frame_equal |
#! /usr/bin/env python3
from argparse import ArgumentParser
from collections import defaultdict
from IPython import embed
import itertools
import json
from enum import Enum
from math import sqrt
from multiprocessing import Pool, cpu_count
import pandas as pd
import numpy as np
from pathlib import Path
from pprint impor... | pd.DataFrame([stat_means, stat_stdev, stat_ci, stat_nums]) | pandas.DataFrame |
# coding: utf-8
# In[1]:
import pandas as pd ## biblioteca de estruturação e analise de dados
import numpy as np ## biblioteca de algebra linear entre outras utilidades
## --------------------- ##
## plotly/dash libraries ##
## --------------------- ##
import dash
import plotly.graph_objs as go
from dash.dependen... | pd.to_datetime('2017-01-01') | pandas.to_datetime |
import logging
from tools.EventGeneration import convert_date, generate_random_time, generate_random_node_id
logger = logging.getLogger(__name__.split('.')[-1])
from features.ResponseTypeFeature import ResponseTypeFeature
from features.ReplayTimeSeriesFeature import ReplayTimeSeriesFeature
import tools.Cache as Cach... | pd.DataFrame(res) | pandas.DataFrame |
import tensorflow as tf
import numpy as np
import os
import time
from utils import Feeder, normalize, similarity, loss_cal, optim, test_batch
from configuration import get_config
import sys
sys.path.append(os.getcwd())
from tacotron.models.modules import ReferenceEncoder
from tacotron.utils import ValueWindow
from tens... | pd.DataFrame([]) | pandas.DataFrame |
"""
Tests that work on both the Python and C engines but do not have a
specific classification into the other test modules.
"""
import codecs
import csv
from datetime import datetime
from io import StringIO
import os
import platform
from tempfile import TemporaryFile
from urllib.error import URLError
impo... | concat(reader) | pandas.concat |
"""Functions for downloading data from API."""
import datetime as dt
import logging
from typing import Dict, Tuple
import pandas as pd
from constants import URLS, REGION_TO_POPULATION
logger = logging.getLogger("data_client")
logger.setLevel(logging.INFO)
class DataCache:
def __init__(self):
self.cach... | pd.to_datetime(ans.index) | pandas.to_datetime |
import pandas as pd
import pytest
from rdtools.normalization import normalize_with_expected_power
@pytest.fixture()
def times_15():
return pd.date_range(start='20200101 12:00', end='20200101 13:00', freq='15T')
@pytest.fixture()
def times_30():
return | pd.date_range(start='20200101 12:00', end='20200101 13:00', freq='30T') | pandas.date_range |
"""title
https://adventofcode.com/2021/day/19
"""
import numpy as np
import pandas as pd
import itertools
import re
SMALL_INPUT = open('small_input.txt').read()
ORIENTATIONS = """
x, y, z
x, z,-y
x,-y,-z
x,-z, y
y,-x, z
y, z, x
y, x,-z
y,-z,-x
z, y,-x
z,-x,-y
z,-y, x
z, x, y
-x, y,-z
-x, z, y
-x,-y, z
-... | pd.read_csv(fn) | pandas.read_csv |
"""Tests suite for Period handling.
Parts derived from scikits.timeseries code, original authors:
- <NAME> & <NAME>
- pierregm_at_uga_dot_edu - mattknow_ca_at_hotmail_dot_com
"""
from unittest import TestCase
from datetime import datetime, timedelta
from numpy.ma.testutils import assert_equal
from pandas.tseries.p... | Period.now('q') | pandas.tseries.period.Period.now |
""" I/O functions of the aecg package: tools for annotated ECG HL7 XML files
This module implements helper functions to parse and read annotated
electrocardiogram (ECG) stored in XML files following HL7
specification.
See authors, license and disclaimer at the top level directory of this project.
"""
# Imports ====... | pd.DataFrame([valrow2], columns=VALICOLS) | pandas.DataFrame |
# import sys
import os
import os.path as path
import shutil
import fnmatch as fm
import numpy as np
import pandas as pd
from scipy.io import loadmat
from PyQt4.QtCore import QThread
from PyQt4.QtCore import QObject, pyqtSignal
class DataProcessor(QObject):
print_out = pyqtSignal(str)
prog_out = pyqtSignal(int... | pd.read_csv(matCsv_f, index_col=False, encoding='iso-8859-1', skipinitialspace=True) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 9 08:04:31 2020
@author: <NAME>
Functions to run the station characterization notebook on exploredata.
"""
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import math
import numpy as np
from netCDF4 import Dataset
import textwrap
import datetime... | pd.read_csv(url) | pandas.read_csv |
import pandas as pd
from dateutil import parser
from pm4pymdl.objects.mdl.exporter import exporter as mdl_exporter
from pm4pymdl.objects.mdl.importer import importer as mdl_importer
import os
def execute_script():
stream1 = [{"event_id": "1", "event_activity": "A", "event_timestamp": parser.parse("1970-01-01 00:0... | pd.DataFrame(stream1) | pandas.DataFrame |
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, MultiIndex, Series
import pandas._testing as tm
class TestDataFrameIsIn:
def test_isin(self):
# GH#4211
df = DataFrame(
{
"vals": [1, 2, 3, 4],
"ids": ["a", "b", "f", "n"... | pd.Timedelta(1, "s") | pandas.Timedelta |
import re
import os
import string
import ipdb
import pickle
import matplotlib
matplotlib.use('Agg')
from matplotlib import rcParams
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectori... | pd.Series(data=train_pnl_err) | pandas.Series |
import os
import torch
import numpy as np
import pandas as pd
from PIL import Image
from tqdm import tqdm
from collections import defaultdict
from torchvision.datasets.folder import default_loader
from torchvision.datasets.utils import download_url
from torch.utils.data import Dataset
from torchvision import transforms... | pd.read_csv(self._labelmap_path, sep=' ', names=['label', 'name']) | pandas.read_csv |
"""
test date_range, bdate_range construction from the convenience range functions
"""
from datetime import datetime, time, timedelta
import numpy as np
import pytest
import pytz
from pytz import timezone
from pandas._libs.tslibs import timezones
from pandas._libs.tslibs.offsets import BDay, CDay, DateOffset, MonthE... | Timestamp("20180103", tz="US/Eastern") | pandas.Timestamp |
import numpy as np
import pytest
from pandas._libs.tslibs import iNaT
from pandas._libs.tslibs.period import IncompatibleFrequency
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import PeriodArray, period_array
@pytest.mark.parametrize(
"data, freq, expected",
[
([pd.Period... | pd.tseries.offsets.Day() | pandas.tseries.offsets.Day |
import parms
import pandas as pd
def do(pd_series, sheet_title, default):
description = str(pd_series[parms.COLUMN_DESCRIPTION()])
if sheet_title == "Example":
return description
elif sheet_title == "Example2":
description = adopt_text(pd_series["Next Location"])
else:
return ... | pd.Series(["Description Example"], index=["Description"]) | pandas.Series |
import argparse
import logging
import logging.config
import os
from os.path import dirname, exists, join
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score, roc_auc_score, f1_score
from sklearn.preprocessing import StandardScaler
from qac.simq import simq_features
from qac.evaluation im... | pd.DataFrame.from_dict({'id': pred[:, 0], 'y_pred': y_pred, 'y_pred_proba': pred[:, 2]}) | pandas.DataFrame.from_dict |
from __future__ import absolute_import, division, print_function
import matplotlib.pylab as plt
import tensorflow as tf
import tensorflow_hub as hub
from tensorflow.python.keras import layers
import numpy as np
import os
tf.VERSION
import cv2
import sys
import json
import pandas as pd
from sklearn.metrics import precis... | pd.Series(rollovers) | pandas.Series |
import csv
from collections import defaultdict, Counter
import hashlib
import tempfile
import os
from os.path import join
import subprocess
import shutil
import logging
import socket
from traceback import format_exc
import sys
import click
import numpy.random
import numpy
import biom
import skbio.io
from pandas import... | Series(svs, index=hashes) | pandas.Series |
# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership. The Modin Development Team licenses this file to you under the
# Apache License, Version 2.0 (the "License"); you may not u... | is_list_like(mask) | pandas.core.dtypes.common.is_list_like |
#!/usr/bin/env python
# coding: utf-8
from numbers import Number
from typing import Dict
from typing import Callable
from typing import Optional
from typing import Union
from dataclasses import dataclass, fields
import numpy as np
import pandas as pd
from scipy.stats import chi2_contingency
from evidently import Colu... | pd.api.types.is_numeric_dtype(reference_data[target_name]) | pandas.api.types.is_numeric_dtype |
import os
import pandas as pd
from datetime import datetime, timedelta
from embrace import get_date_from_garmin
import collections
folders = ['01-09-TR1', '10-20-TR2', '21-30-TR3']
def timestamp2datetime2minutes(file_path):
df = | pd.read_csv(file_path, header=1) | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# =========================================================================== #
# Project : ML Studio #
# Version : 0.1.14 #
# File : test_objectives.py ... | pd.DataFrame(data=data['X']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
@author: oustry
"""
from FDFDRadiowaveSimulator import FDFDRadiowaveSimulator
from pandas import DataFrame,read_csv
import time
def FirstExample():
"""
First example of use of the FDFDRadiowaveSimulator class. Generate a .png file
Returns
-------
None.
"""
map... | DataFrame(gain) | pandas.DataFrame |
__author__ = '<NAME>'
__email__ = '<EMAIL>'
# todo: Clean this up! Make it into a real module
import os, sys, itertools
import networkx as nx
import pandas as pd
from statsmodels.tsa.stattools import ccf
import matplotlib.pyplot as plt
import numpy as np
from collections import Counter
import matplotlib as mpl
mpl.rc... | pd.merge(edge_lag, lag_results, how='outer', on='Edge') | pandas.merge |
# coding: utf-8
# In[1]:
import pandas as pd
import os
import matplotlib.pyplot as plt
import re
import numpy as np
import pandas as pd
from scipy.stats import mode
from nltk import skipgrams
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import itertools
import lightgbm as lgb
from l... | pd.read_csv('Devex_train.csv', encoding="latin-1") | pandas.read_csv |
"""Estimate direct damages to physical assets exposed to hazards
"""
import sys
import os
import pandas as pd
import geopandas as gpd
from shapely import wkb
import numpy as np
from analysis_utils import *
from tqdm import tqdm
tqdm.pandas()
def main(config):
incoming_data_path = config['paths']['incoming_data'... | pd.read_csv(damage_file) | pandas.read_csv |
"""Compare different GNSS SPV Where datasets
Description:
------------
A dictionary with datasets is used as input for this writer. The keys of the dictionary are station names.
Example:
--------
from where import data
from where import writers
# Read a dataset
dset = data.Dataset(rundate=rundate,... | pd.concat([dfs_day[field], df_day[field]], axis=1) | pandas.concat |
import os
import json
import pickle
import sys
import traceback
import datetime as dt
import numpy as np
import pandas as pd
import mlflow
import mlflow.pytorch
import torch
from torch.utils.data import Dataset
from MultVAE_Dataset import BasicHotelDataset
from scipy import sparse
import src.modules.letor_metrics as... | pd.DataFrame({'ndcg_score':ndcg_list}) | pandas.DataFrame |
import operator
from enum import Enum
from typing import Union, Any, Optional, Hashable
import numpy as np
import pandas as pd
import pandas_flavor as pf
from pandas.core.construction import extract_array
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_datetime64_dtype,
is_dtype_equal,
... | extract_array(left_c, extract_numpy=True) | pandas.core.construction.extract_array |
import numpy as np
arr = np.arange(0,11)
print(arr)
print("----------------------------")
import pandas
df = | pandas.DataFrame([[1,2,3,4]], columns = ["A", "B", "C", "D"]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 7 11:41:44 2018
@author: MichaelEK
"""
import os
import argparse
import types
import pandas as pd
import numpy as np
from pdsql import mssql
from datetime import datetime
import yaml
import itertools
import lowflows as lf
import util
pd.options.display.max_columns = 10
... | pd.to_numeric(lc1['CombinedAnnualVolume'], errors='coerce') | pandas.to_numeric |
"""
Fetch GPU load data from a remote server using SSH.
"""
import argparse
import configparser
import io
from fabric import Connection
import pandas as pd
import invoke
def get_username(pid, hostname, user):
"""Get the corresponding username for a PID"""
SSH_CMD = 'ps -o user= {}'.format(pid)
# some... | pd.read_csv(csv) | pandas.read_csv |
'''
Created on: 14/12/2016
@author: <NAME>
@description: Extract the O/D weight matrix from a network diagram
'''
import argparse as arg
import os
import sys
import math
from lxml import etree
import numpy as np
import pandas as pd
from collections import defaultdict
# -----------------------------------------------... | pd.DataFrame(od_nodes) | pandas.DataFrame |
# Import Libraries, some are uncessary right now
import configparser
import pandas as pd
import numpy as np
import sys
import os
import random
import copy
import math
import scanpy as sc
from matplotlib import pyplot as plt
import matplotlib as mpl
import seaborn as sns
# null distribution fitting
from scipy.stats imp... | pd.merge(adata.obs[label], df[gene], left_index=True, right_index=True) | pandas.merge |
import re
import gensim
import torch
import transformers
import pandas as pd
import numpy as np
from os.path import dirname
from pathlib import Path
from tqdm.auto import tqdm
from collections import Counter
import os
import sys
CURRENT_DIR = os.getcwd()
sys.path.append(CURRENT_DIR)
MODULE_PATH = Path(dirname(__file_... | pd.to_datetime(news_df.index) | pandas.to_datetime |
from django.http import HttpResponse, JsonResponse
from django.views.decorators.csrf import csrf_exempt
import csv
import json
from app.models import Dataset, Record, Attribute
from api.models import Result, ExecutionLog
from sklearn.cluster import KMeans
from sklearn import metrics
from scipy.spatial.distance import c... | pd.DataFrame(datasetDf, columns=columns) | pandas.DataFrame |
import os
import tensorflow as tf
import pandas as pd
from addressnet.predict import predict_one, predict
def get_gnaf_dataset_labels():
labels_list = [
'building_name', # 1
'level_number_prefix', # 2
'level_number', # 3
'level_number_suffix', # 4
'level_type', # 5
... | pd.DataFrame() | pandas.DataFrame |
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver import ActionChains
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.common.exceptions ... | pd.DataFrame(data=None, columns=datafreempje.columns) | pandas.DataFrame |
"""
The ``python_function`` model flavor serves as a default model interface for MLflow Python models.
Any MLflow Python model is expected to be loadable as a ``python_function`` model.
In addition, the ``mlflow.pyfunc`` module defines a generic :ref:`filesystem format
<pyfunc-filesystem-format>` for Python models and... | pandas.DataFrame(pdf) | pandas.DataFrame |
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
import xgboost as xgb
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.advanced_activations import PReLU
from keras.models import Sequential
from keras.utils import np... | pd.read_csv(testFilePath) | pandas.read_csv |
from dataapi import SGS
from bloomberg import BBG
import numpy as np
import pandas as pd
from sklearn import preprocessing
getdata = SGS()
bbg = BBG()
start_date = pd.to_datetime("01-01-2001")
end_date = pd.to_datetime("07-01-2019")
#fetching Brazil FGV Consumer Confidence Index SA Sep 2005=100 Original Date: '30-se... | pd.DataFrame(x_scaled, index=df_gr.index, columns=['GDP Growth Normalized']) | pandas.DataFrame |
import argparse
import datetime
import logging
import os
import synapseclient
import genie
import pandas as pd
logging.basicConfig()
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
def get_center_data_completion(center, df):
'''
Get center data completion. Calulates the percentile of
... | pd.DataFrame(center_decrease_mapping) | pandas.DataFrame |
from datetime import (
datetime,
timedelta,
)
import re
import numpy as np
import pytest
from pandas._libs import iNaT
from pandas.errors import InvalidIndexError
import pandas.util._test_decorators as td
from pandas.core.dtypes.common import is_integer
import pandas as pd
from pandas import (
Categoric... | DataFrame([[1, 2], [3, 4]], columns=["a", "b"]) | pandas.DataFrame |
"""
Extracts and plots data from an hdf5 file containing peridynamic node information.
The hdf5 (h5) fields are assumed to be formatted as numpy arrays with dimensions of
[timestep, node], with higher-dimensional data having additional array dimensions. The
available datasets and how to access them are defined in OUTPU... | pd.concat([coords, disp, output], axis=1) | pandas.concat |
from datetime import datetime, time, date
from functools import partial
from dateutil import relativedelta
import calendar
from pandas import DateOffset, datetools, DataFrame, Series, Panel
from pandas.tseries.index import DatetimeIndex
from pandas.tseries.resample import _get_range_edges
from pandas.core.groupby impo... | DatetimeIndex(start=start, end=end, freq=freq) | pandas.tseries.index.DatetimeIndex |
# -*- coding: utf-8 -*-
# Version 1.0
# Date: Jan 2 2020
from bokeh.plotting import figure, curdoc
from bokeh.models import ColumnDataSource, HoverTool, ColorBar, LinearColorMapper, Legend, BasicTickFormatter, \
LegendItem, Span, BasicTicker, LabelSet, Panel, Tabs
from bokeh.models.widgets import DataTable, Select... | pd.DataFrame() | pandas.DataFrame |
import os, re, json, datetime, random, csv
import tensorflow as tf
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
import pandas as pd
import numpy as np
import seaborn as sn
from string import ascii_uppercase
import utils.dataGenerator as datagen
import utils.dataGenerator4D as datagen4D
... | pd.read_csv(kFoldFolder + '/' + folder + '/confusion-matrix.csv', header=None) | pandas.read_csv |
from datetime import timedelta
import pytest
from pandas import PeriodIndex, Series, Timedelta, date_range, period_range, to_datetime
import pandas._testing as tm
class TestToTimestamp:
def test_to_timestamp(self):
index = period_range(freq="A", start="1/1/2001", end="12/1/2009")
series = Series... | date_range("1/1/2001", end="1/1/2009", freq="AS-JAN") | pandas.date_range |
# Written by i3s
import os
import numpy as np
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import seaborn as sns
import time
from sklearn.model_selection import KFold
from matplotlib import pyplot as plt
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier, AdaBoostC... | pd.DataFrame(accuracy_test_comp, index=ind_df_comp, columns=alglist) | pandas.DataFrame |
import plotly.express as px
import pandas as pd
import sys
from functools import reduce
data = | pd.read_csv("../data/RKI_COVID19.csv") | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 29 14:00:18 2020
updated on Thu Oct 15 18:07:45 2020
@author: <NAME>
"""
#reproducability
from numpy.random import seed
seed(1)
import tensorflow as tf
tf.random.set_seed(1)
import numpy as np
from bayes_opt import BayesianOptimization
from bayes_opt.logger import JSONL... | pd.DataFrame(data['GWL']) | pandas.DataFrame |
import datetime
import os
from typing import List, Dict, Optional
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
import pandas as pd
from pydantic import BaseModel
API_URL = os.environ.get("API_URL", None)
if API_URL is None:
raise ValueError("API_URL not known")
app = FastAPI()
a... | pd.to_datetime(df["start_date"]) | pandas.to_datetime |
from pippin.classifiers.classifier import Classifier
from pippin.config import mkdirs
from pippin.dataprep import DataPrep
from pippin.snana_fit import SNANALightCurveFit
from pippin.snana_sim import SNANASimulation
from pippin.task import Task
import pandas as pd
import os
from astropy.io import fits
import numpy as n... | pd.merge(df, dataframe, on=self.id, how="outer") | pandas.merge |
#!/usr/bin/env python
# coding: utf-8
import json
from typing import Optional
import pandas as pd
import plotly.graph_objs as go
from evidently import ColumnMapping
from evidently.analyzers.classification_performance_analyzer import ClassificationPerformanceAnalyzer
from evidently.model.widget import BaseWidgetInfo
... | pd.DataFrame(result_metrics.metrics_matrix) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 18 21:53:21 2018
@author: jsulloa
"""
import numpy as np
from scipy.stats import randint, uniform
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier
from sklearn import svm
from sklearn.ensemble impo... | pd.read_csv(path_pred) | pandas.read_csv |
# *-* coding: utf-8 *-*
"""Read binary data from the IRIS Instruments Syscal Pro system
TODO: Properly sort out handling of electrode positions and conversion to
electrode numbers.
"""
import struct
from io import StringIO
import logging
import pandas as pd
import numpy as np
from reda.importers.utils.decorators im... | pd.DataFrame() | pandas.DataFrame |
#%% md
## Read from MIMIC csv files
#%%
import pandas as pd
# files can be downloaded from https://mimic.physionet.org/gettingstarted/dbsetup/
med_file = 'PRESCRIPTIONS.csv'
diag_file = 'DIAGNOSES_ICD.csv'
procedure_file = 'PROCEDURES_ICD.csv'
# drug code mapping files (already in ./data/)
ndc2atc_file = 'ndc2atc_... | pd.read_csv(procedure_file, dtype={'ICD9_CODE': 'category'}) | pandas.read_csv |
import re
import math
import pandas as pd
import numpy as np
import nltk
import heapq
import pickle
import datetime
from nltk.corpus import stopwords
from operator import itemgetter
# Loading the dictionary
with open('dictionary.pkl', 'rb') as f:
data = pickle.load(f)
# Loading the dictionary with term count
with... | pd.set_option('display.max_colwidth', -1) | pandas.set_option |
from datetime import datetime
import numpy as np
import pandas as pd
from sklearn.utils import shuffle
from data_process import data_process_utils
from data_process.census_process.census_data_creation_config import census_data_creation
from data_process.census_process.census_degree_process_utils import consistentize_... | pd.read_csv(from_dir + 'master_census9495' + appendix, skipinitialspace=True) | pandas.read_csv |
import pandas as pd
import geopandas
import json
import altair as alt
def make_metrics_df():
GEOJSON = 'geojson/wi_map_plan_{}.geojson'
mm_gaps = []
sl_indices = []
efficiency_gaps = []
plan_number = [i for i in range(1,84)]
for i in range(1,84):
plan = geopandas.read_file(GEOJSON.forma... | pd.DataFrame(metrics_dict, columns = ['plan_number','mm_gap','sl_index','efficiency_gap']) | pandas.DataFrame |
from datetime import datetime
import inspect
import numpy as np
import pytest
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
Index,
IntervalIndex,
Series,
Timestamp... | tm.assert_frame_equal(res1, res) | pandas._testing.assert_frame_equal |
## The packages.
from selenium import webdriver
from selenium.webdriver import chrome
from selenium.webdriver.common.by import By
import pandas, os, tqdm, time
## The goal.
'''
根據 query 從 PubMed 搜尋引擎下載對應的文章摘要，輸出成表格。
'''
## The arguments.
keyword = "Athlete's foot"
platform = "pubmed"
site = "https://pubmed.n... | pandas.DataFrame({"link":group['link']}) | pandas.DataFrame |
# Exercise 4 : Manipulating Geospatial Data
import math
import pandas as pd
import geopandas as gpd
from learntools.geospatial.tools import geocode
import folium
from folium import Marker
from folium.plugins import MarkerCluster
from learntools.core import binder
binder.bind(globals())
from learntools.geospatial.ex4 im... | pd.read_csv("../input/geospatial-learn-course-data/starbucks_locations.csv") | pandas.read_csv |
import numpy as np
from scipy import stats
import pandas as pd
from sklearn.svm import SVC
from dask.distributed import Client
import dask_ml.model_selection as dms
def test_search_basic(xy_classification):
X, y = xy_classification
param_grid = {"class_weight": [None, "balanced"]}
a = dms.GridSearchCV(... | pd.DataFrame(data=arr) | pandas.DataFrame |
import pandas as pd
import pytest
from dateutil.relativedelta import relativedelta
import featuretools as ft
from featuretools.entityset import Timedelta
from featuretools.primitives import Count # , SlidingMean
from featuretools.utils.wrangle import _check_timedelta
def test_timedelta_equality():
assert Timede... | pd.DateOffset(months=2, days=3) | pandas.DateOffset |
# -*- coding: utf-8 -*-
import time
from datetime import datetime
import warnings
from textwrap import dedent, fill
import numpy as np
import pandas as pd
from numpy.linalg import norm, inv
from scipy.linalg import solve as spsolve, LinAlgError
from scipy.integrate import trapz
from scipy import stats
from lifelines.... | pd.Series(params_, index=X.columns, name="coef") | pandas.Series |
import numpy as np
import pandas as pd
import pytest
import xarray as xr
from sklearn.utils.estimator_checks import parametrize_with_checks
from skdownscale.pointwise_models import (
AnalogRegression,
BcsdPrecipitation,
BcsdTemperature,
CunnaneTransformer,
EquidistantCdfMatcher,
LinearTrendTran... | pd.date_range('2019-01-01', periods=n) | pandas.date_range |
import pandas as pd
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from matplotlib import dates
import hashlib
import json
def load_rawdata(filepath):
data = {'date': [], 'prod. pod': [],
'train. pod': [],
'config': []}
... | pd.to_datetime(df['date'], unit='s') | pandas.to_datetime |
import io
import pickle
from unittest.mock import MagicMock, Mock, mock_open, patch
import numpy as np
import pandas as pd
import pytest
from sdv.lite import TabularPreset
from sdv.metadata import Table
from sdv.tabular import GaussianCopula
from tests.utils import DataFrameMatcher
class TestTabularPreset:
def... | pd.DataFrame() | pandas.DataFrame |
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompan... | pd.read_csv(input_data_path) | pandas.read_csv |
import os, re, sys, time
import datetime as dt
import pandas as pd
import numpy as np
from .bot import Bot
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support.ui import WebDriverWait
class FacebookBot(Bot):
LOGIN_URL = 'https://www.facebook.com/'
SHARE_URL = "https://www.facebo... | pd.DataFrame.from_dict(shared_posts_dict) | pandas.DataFrame.from_dict |
from os import sep
import pandas as pd
encryptionkey = | pd.read_csv(r"C:\Users\cjwhi\OneDrive\Computer\Documents\Coding\Programs\Small Coding Projects\Hash.csv", sep = ',', names = ['Character', 'Byte'], header = None, skiprows = [0]) | pandas.read_csv |
# Copyright 2021 The Funnel Rocket Maintainers
#
# 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... | Series(data=float_32_values, dtype='float32') | pandas.Series |
import builtins
from io import StringIO
import numpy as np
import pytest
from pandas.errors import UnsupportedFunctionCall
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series, Timestamp, date_range, isna
import pandas._testing as tm
import pandas.core.nanops as nanops
from pandas.util import ... | tm.assert_index_equal(result.columns, expected_columns) | pandas._testing.assert_index_equal |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Yahoo! Finance market data downloader (+fix for Pandas Datareader)
# https://github.com/ranaroussi/yfinance
#
# Copyright 2017-2019 <NAME>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Licen... | _pd.DataFrame(data) | pandas.DataFrame |
#!/bin/env python
# coding=utf8
import os
import sys
import json
import functools
import gzip
from collections import defaultdict
from itertools import groupby
import numpy as np
import pandas as pd
import subprocess
from scipy.io import mmwrite
from scipy.sparse import csr_matrix, coo_matrix
import pysam
from celesco... | pd.Series.sum(x[x > 1]) | pandas.Series.sum |
#!/usr/bin/env python3
"""
Plotting routines dedicated to time-series or temporal trends
"""
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import cm
# import seaborn as sns
#--------------------------------------
# Time-Series Plots
#--------------------------... | pd.concat(dates, axis=1, keys=terms) | pandas.concat |
import matplotlib
matplotlib.use("TkAgg")
import matplotlib.pyplot as plt
plt.style.use("./Styles/Scientific.mplstyle")
import numpy as np
import pandas as pd
from plotting import plot_3D_scatter
def get_features(data):
features = {}
for key, values in data.items():
timestamps, counts = np.unique(val... | pd.read_csv(paths["Raw"]) | pandas.read_csv |
import vectorbt as vbt
import numpy as np
import pandas as pd
from numba import njit
from datetime import datetime
import pytest
from vectorbt.generic import nb as generic_nb
from vectorbt.generic.enums import range_dt
from tests.utils import record_arrays_close
seed = 42
day_dt = np.timedelta64(86400000000000)
ma... | pd.Timedelta('3 days 00:00:00') | pandas.Timedelta |
"""
Module of utility methods.
"""
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import re
import os
import sys
import time
import pickle
import random
import scipy.sparse
import numpy as np
import pandas as pd
import xgboost as xgb
import lightgbm as lgb
import termcolor
import sklearn.metric... | pd.DataFrame(preds, columns=columns) | pandas.DataFrame |
from matplotlib import pyplot as plt
import matplotlib.ticker as mticker
from matplotlib import patches
import matplotlib
SMALL_SIZE = 10
MEDIUM_SIZE = 12
BIGGER_SIZE = 14
plt.rc('font', size=SMALL_SIZE) # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title
plt.rc(... | pd.read_csv(fp_medal_patience10) | pandas.read_csv |
import pandas as pd
import numpy as np
from math import sqrt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import OneHotEncoder
from sklearn.pipeline import make_pipeline
from sklearn.metrics import mean_squared_error
from xgboost import XGB... | pd.get_dummies(train_x) | pandas.get_dummies |
# -*- 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.period_range('2014-04-28', '2014-05-12', freq='D') | pandas.period_range |
# Copyright (c) 2018-2021, NVIDIA CORPORATION.
import gzip
import os
import re
import shutil
from collections import OrderedDict
from io import BytesIO, StringIO
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
import cudf
from cudf import read_csv
from cudf.tests.utils import assert_eq,... | pd.DataFrame(data=values, dtype=pdf_dtype, columns=["hex_int"]) | pandas.DataFrame |
from datetime import datetime, timedelta
from io import StringIO
import re
import sys
import numpy as np
import pytest
from pandas._libs.tslib import iNaT
from pandas.compat import PYPY
from pandas.compat.numpy import np_array_datetime64_compat
from pandas.core.dtypes.common import (
is_datetime64_dtype,
is_... | tm.makeIntIndex(10, name="a") | pandas.util.testing.makeIntIndex |
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