prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pandas as pd
from py2neo import Graph
from py2neo import ClientError
import sroka.config.config as config
def neo4j_query_data(cypher, parameters=None, **kwparameters):
if type(cypher) != str:
print('Cypher query needs to be a string')
return | pd.DataFrame([]) | pandas.DataFrame |
#!/usr/bin/env python3
"""
File: datasets.py
Author: <NAME>
Email: <EMAIL>
Github: https://github.com/lgalke
Description: Parsing and loading for all the data sets.
"""
import pandas as pd
import numpy as np
import os
from html.parser import HTMLParser
from abc import abstractmethod, ABC
from collections import defaul... | pd.read_pickle(cache) | pandas.read_pickle |
# -*- coding: utf-8 -*-
# Arithmetc tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import timedelta
import operator
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.compat import long
from pandas.core import ops
from pan... | tm.box_expected(rng, box) | pandas.util.testing.box_expected |
import numpy as np
import pandas as pd
from numpy import inf, nan
from numpy.testing import assert_array_almost_equal, assert_array_equal
from pandas import DataFrame, Series, Timestamp
from pandas.testing import assert_frame_equal, assert_series_equal
from shapely.geometry.point import Point
from pymove import MoveDa... | Timestamp('2008-10-23 11:58:33') | pandas.Timestamp |
import os
import pandas as pd
import numpy as np
from autumn.settings import PROJECTS_PATH
from autumn.settings import INPUT_DATA_PATH
from autumn.tools.utils.utils import update_timeseries
from autumn.models.covid_19.constants import COVID_BASE_DATETIME
from autumn.tools.utils.utils import create_date_index
from autu... | pd.to_datetime("today") | pandas.to_datetime |
import os
import glob
import torch
import numpy as np
import pandas as pd
import librosa as lr
import soundfile as sf
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader, ConcatDataset, random_split
from asteroid.data import TimitDataset, TimitLegacyDataset
from asteroid.data.utils import CachedW... | pd.Series(denoised_file_paths) | pandas.Series |
import os
import shutil
import pandas as pd
from numpy import linspace
from functions.helpers import _format_header, _process_data_transposed, _process_data
#IMPORT_FOLDER = '20201224/matrix_1mic/'
#EXPORT_FOLDER = '20201224/matrix_1mic_exported/' #'exported'
IMPORT_FOLDER = '20201224/matrix_beamforming/'
EXPORT_FOLDE... | pd.ExcelWriter(EXPORT_FOLDER +'/' + fname+'_verification.xlsx') | pandas.ExcelWriter |
import numpy as np
from numpy import where
from flask import Flask, request, jsonify, render_template
import pandas as pd
from sklearn.ensemble import IsolationForest
from pyod.models.knn import KNN
import json
from flask import send_from_directory
from flask import current_app
app = Flask(__name__)
class Detect:
... | pd.DataFrame(self.file) | pandas.DataFrame |
from flowsa.common import WITHDRAWN_KEYWORD
from flowsa.flowbyfunctions import assign_fips_location_system
from flowsa.location import US_FIPS
import math
import pandas as pd
import io
from flowsa.settings import log
from string import digits
YEARS_COVERED = {
"asbestos": "2014-2018",
"barite": "2014-2018",
... | pd.DataFrame(df_raw_data.loc[27:28]) | pandas.DataFrame |
import numpy as np
import pytest
from pandas._libs.tslibs.period import IncompatibleFrequency
from pandas.core.dtypes.dtypes import PeriodDtype
import pandas as pd
from pandas import Index, Period, PeriodIndex, Series, date_range, offsets, period_range
import pandas.core.indexes.period as period
import pandas.util.t... | tm.assert_index_equal(res, exp) | pandas.util.testing.assert_index_equal |
from __future__ import division
from builtins import str
from builtins import range
from builtins import object
__copyright__ = "Copyright 2015 Contributing Entities"
__license__ = """
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the Lice... | pd.to_timedelta(pathset_links_df[Assignment.SIM_COL_PAX_LINK_TIME]) | pandas.to_timedelta |
# coding: utf8
import torch
import pandas as pd
import numpy as np
from os import path
from torch.utils.data import Dataset
import torchvision.transforms as transforms
import abc
from clinicadl.tools.inputs.filename_types import FILENAME_TYPE
import os
import nibabel as nib
import torch.nn.functional as F
from scipy i... | pd.concat([valid_df, valid_diagnosis_df]) | pandas.concat |
# -*- coding: utf-8 -*-
"""
code to combine data from different algorithms with different degrees and
coefficient
"""
import pandas as pd
print('Reading data...')
xgb1 = | pd.read_csv("../output/xgboost_1.csv") | pandas.read_csv |
import typing as t
import numpy as np
import pandas as pd
from .report import Report
def plot_performance(freq: str = '1h', **kwargs: t.Union[pd.Series, Report]) -> None:
comparison = pd.DataFrame(dtype=np.float64)
price = min([x.initial_aum for x in kwargs.values() if isinstance(x, Report)])
report_cou... | pd.DataFrame({'Cost': report.costs, 'Proceeds': report.proceeds}) | pandas.DataFrame |
"""
Spatial DataFrame Object developed off of the Panda's Dataframe object
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import warnings
import arcgis
from six import string_types, integer_types
HAS_PANDAS = True
try:
import pandas as pd
from... | pd.DataFrame(self) | pandas.DataFrame |
import wandb
from wandb import data_types
import numpy as np
import pytest
import os
import sys
import datetime
from wandb.sdk.data_types._dtypes import *
class_labels = {1: "tree", 2: "car", 3: "road"}
test_folder = os.path.dirname(os.path.realpath(__file__))
im_path = os.path.join(test_folder, "..", "assets", "test... | pd.DataFrame([[42], [42]]) | pandas.DataFrame |
#!/usr/bin/env python
from sklearn.externals import joblib
import numpy as np
import pandas as pd
def get_sepsis_score(data, model):
num_rows = len(data)
M1 = joblib.load('model-saved.pkl')
s_m = np.load('septic_mean.npy', allow_pickle=True)
ns_m = np.load('Nonseptic_mean.npy', allow_pickle=True)
A... | pd.DataFrame.from_records(data) | pandas.DataFrame.from_records |
#!/usr/bin/env python
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
import sys
import numpy as np
np.random.seed(5)
np.set_printoptions(threshold=sys.maxsize)
import pandas as pd
import glob
import os
import time
import json
from sklearn import preprocessing
from sklearn import ensemble
fro... | pd.DataFrame(results) | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LogisticRegression
# from sklearn.tree import DecisionTreeClassifier
# from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_... | pd.DataFrame(log_predBio.A, index=pokemon, columns=typeList) | pandas.DataFrame |
"""
Import as:
import core.artificial_signal_generators as carsigen
"""
import datetime
import logging
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
import scipy as sp
# import statsmodels as sm
import statsmodels.api as sm
import helpers.hdbg... | pd.date_range(**date_range_kwargs) | pandas.date_range |
import os
import numpy as np
import pandas as pd
import tarfile
import urllib.request
from experimentgenerator.experiment_generator import ExperimentGenerator
from experimentgenerator.parameters_distribution import ParametersDistribution
from autoscalingsim.utils.error_check import ErrorChecker
from autoscalingsim.ut... | pd.concat([selected_workloads_data, selected_part]) | pandas.concat |
#!/usr/bin/env python3
import numpy as np
import pandas as pd
from datetime import datetime
def loadprices_df(csvfile, startdate=None, enddate=None):
df = | pd.read_csv(csvfile, header=0, usecols=['Date', 'Close']) | pandas.read_csv |
import pandas as pd
from autogluon.utils.tabular.utils.savers import save_pd
from .constants import *
from . import evaluate_utils
from.preprocess import preprocess_utils
def evaluate(results_raw, frameworks=None, banned_datasets=None, folds_to_keep=None, columns_to_agg_extra=None, frameworks_compare_vs_all=None, o... | pd.DataFrame(data=results_list, columns=[FRAMEWORK, '> ' + framework_2, '< ' + framework_2, '= ' + framework_2]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
These test the private routines in types/cast.py
"""
import pytest
from datetime import datetime, timedelta, date
import numpy as np
import pandas as pd
from pandas import (Timedelta, Timestamp, DatetimeIndex,
DataFrame, NaT, Period, Series)
from pandas.core.dtypes.c... | maybe_convert_scalar(1) | pandas.core.dtypes.cast.maybe_convert_scalar |
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.patches import Arc
from matplotlib.path import Path
import networkx as nx
import numpy as np
import pandas as pd
from cell2cell.plotting.aesthetics import get_colors_from_labels, generate_legend
def circos_... | pd.DataFrame(index=cells) | pandas.DataFrame |
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
data_base=pd.read_csv("train_data.csv") #####importing train case
test=pd.read_csv("test_data.csv") #####importing test case
features=['Retweet count','Likes count','Tweet value'] ######features for training
y=data_base.User
x=da... | pd.DataFrame(arr) | pandas.DataFrame |
from datetime import datetime
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
_testing as tm,
)
def test_split(any_string_dtype):
values = Series(["a_b_c", "c_d_e", np.nan, "f_g_h"], dtype=any_string_dtype)
... | Series(["a b c", "a b", "", " "], name="test", dtype=any_string_dtype) | pandas.Series |
'''Train CIFAR10 with PyTorch.'''
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import argparse
from tqdm import trange
import pandas as pd
from PIL import Ima... | pd.DataFrame(data_list) | pandas.DataFrame |
# pylint:disable=unsupported-assignment-operation
# pylint:disable=unsubscriptable-object
"""Module containing different I/O functions to load data recorded by Withings Sleep Analyzer."""
import datetime
import re
from ast import literal_eval
from pathlib import Path
from typing import Dict, Optional, Sequence, Union
... | pd.to_timedelta(data["sleep_onset_latency"], unit="seconds") | pandas.to_timedelta |
"""Python library for GCCR002"""
from contextlib import contextmanager
from datetime import datetime
import hashlib
from io import StringIO
from IPython.display import display as _display
from itertools import chain, product, combinations_with_replacement
import joblib
import json
import logging
import matplotlib.pypl... | pd.read_csv('data/processed/author_roles.csv', encoding='latin1') | pandas.read_csv |
# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %%
# Setting up the environment.
import numpy as np
import pandas as pd
# %%
# Load the data from the John Hopkins github repo
df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/c... | pd.concat(frames) | pandas.concat |
import pickle
import numpy as np
import pandas as pd
## plot conf
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 7})
width = 8.5/2.54
height = width*(3/4)
###
import os
script_dir = os.path.dirname(os.path.abspath(__file__))
plot_path = './'
male_rarities, female_rarities = pickle.load(open(script_... | pd.DataFrame(female_nrun_coefs.values, columns=female_names) | pandas.DataFrame |
#General guide: https://github.com/googleads/google-ads-python
#When I use this script, it runs on a cron job every hour. The dataframe is uploaded to SQL (this code is not provided)
# and if the pct_of_budget exceeds a given value, it sends me an email with a list of campaigns to check on
import pandas as pd
import i... | pd.read_csv(output,low_memory=False, dtype= types, na_values=[' --']) | pandas.read_csv |
# https://blog.csdn.net/a19990412/article/details/85139058
# LSTM实现股票预测--pytorch版本【120+行代码】
'''
模型假设
我这里认为每天的沪深300的最高价格，是依赖于当天的前n天的沪深300的最高价。
然后用RNN的LSTM模型来估计（捕捉到时序信息）。
让模型学会用前n天的最高价，来判断当天的最高价。
'''
# depends
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import torch
import torch.nn as nn
import... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
import numpy as np
import pytest
from pandas._libs.tslib import iNaT
import pandas.compat as compat
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
CategoricalIndex, DatetimeIndex, Float64Index, Index, Int64Index,
IntervalIndex, MultiIn... | tm.assert_index_equal(result, expected) | pandas.util.testing.assert_index_equal |
import geopandas as gp
import pandas as pd
import numpy as np
import networkx as nx
import os
from shapely.geometry import Point, Polygon, LineString, mapping
from shapely import geometry
from simpledbf import Dbf5
import warnings
warnings.filterwarnings('ignore')
# GTFS directories, service ids, and years
GTFS = [[r'... | pd.DataFrame() | pandas.DataFrame |
import os
from pathlib import Path
import time
from datetime import datetime
import json
import traceback
import uuid
import pandas as pd
import dash
from dash.dependencies import Input, Output, State
from dash_extensions.enrich import ServersideOutput
import dash_html_components as html
import dash_bootstrap_component... | pd.DataFrame.from_dict(df_dict[ticker_allcaps]['fin_report_dict']) | pandas.DataFrame.from_dict |
import unittest
import pandas as pd
from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType, StructField, StructType, IntegerType, FloatType
from haychecker.dhc.metrics import entropy
replace_empty_with_null = udf(lambda x: None if x == "" else x, StringTyp... | pd.DataFrame() | pandas.DataFrame |
# all_in_one is a fuction, created for splitiing of a dataset inot 3 parts and to do the repatative tasks namely, draw learning curves, ROC curves and model classification analysis(Error Analysis).
# Import basic libraries
import numpy as np
import pandas as pd
import seaborn as sns
from pandas.tools.plotting import ... | pd.DataFrame([[name, acc,prec,rec, f1,roc, t]],columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 ','ROC','Time']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sat May 23 11:28:30 2020
@author: rener
"""
import numpy as np
import pandas as pd
import os
from datetime import date
import time
import sys
dir_path = os.path.dirname(os.path.realpath(__file__))
os.chdir(dir_path)
#%% For the various companies we have data going back differen... | pd.concat(frames) | pandas.concat |
import copy
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk
import matplotlib.pyplot as plt
import pandas as pd
import subprocess
from template import Template
from themeclasses import *
import time
class DERVET(Template):
def __init__(self, parent, controller, bd):
Template._... | pd.to_datetime(self.tsresults['Start Datetime (hb)'], format='%Y-%m-%d %H:%M:%S') | pandas.to_datetime |
import numpy as np
import pandas as pd
import remixt.bamreader
import os
empty_data = {
'fragments': remixt.bamreader.create_fragment_table(0),
'alleles': remixt.bamreader.create_allele_table(0),
}
def _get_key(record_type, chromosome):
return '/{}/chromosome_{}'.format(record_type, chromosome)
def ... | pd.HDFStore(seqdata_filename, 'r') | pandas.HDFStore |
# -*- coding:utf-8 -*-
"""
AHMath module.
Project: alphahunter
Author: HJQuant
Description: Asynchronous driven quantitative trading framework
"""
import copy
import collections
import warnings
import math
import numpy as np
import pandas as pd
import statsmodels.api as sm
from scipy.stats import norm
class AHMath... | pd.isnull(a[i]) | pandas.isnull |
# @name: ont_struct.py
# @title: Imports ontology tree and calculates hierarchical level per ontology term
# @description: Pulls ontology tree from EBI's Ontology Lookup Service (OLS) API (https://www.ebi.ac.uk/ols/index);
# then parses into individual terms and creates the parents for each individual te... | pd.DataFrame({'id': node_id, 'ancestors': [np.NaN], 'node_level': [np.NaN]}) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 2 01:29:34 2021
@author: <NAME>
Predict the infection ending:
SEIRAH time series proceeding from the last situation of SEIRAH_main.py,
until the condition of E+A+I=0.
"""
import networkx as nx
import random
from random import sample
import... | pd.read_csv('new_cases_cr2020.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
""" Geo layer object classes and methods
Toolset for working with static geo layer elements (networks, buffers,
areas such as administrative boundaries, road/electrical networks,
waterways, restricted areas, etc.)
"""
import copy
import math
import os
import random
import warnings
from functo... | concat(df, ignore_index=True) | pandas.concat |
import os
import sqlite3
from unittest import TestCase
import warnings
from contextlib2 import ExitStack
from logbook import NullHandler, Logger
import numpy as np
import pandas as pd
from six import with_metaclass, iteritems, itervalues
import responses
from toolz import flip, groupby, merge
from trading_calendars im... | pd.Timestamp(cls.FUTURE_DAILY_BAR_START_DATE) | pandas.Timestamp |
#!/usr/bin/env python
import pandas as pd
import seaborn as sns
import pylab as plt
__package__ = "Byron times plot"
__author__ = "<NAME> (<EMAIL>)"
if __name__ == '__main__':
filename = 'byron_times.dat'
data = pd.read_csv(filename, sep=',', header=0)
n_version = len(data.Method.unique())
... | pd.concat([ref]*n_version) | pandas.concat |
# this functino is to run the mlp on the 0.5s binned data created by Shashiks
# features: downloaded bytes amount is the feature to be updated.
import pandas as pd
import numpy as np
import os
import math
import argparse
from keras import Sequential
from keras.layers import Dense, BatchNormalization, Dropout, Conv1D, ... | pd.concat(temp_df_list) | pandas.concat |
##Creates the sequence export sheet #just a utility for LocusExtractor
import pandas as pd
import re
import utilities
from seq_utilities import trim_at_first_stop
class SequenceExporter:
def __init__(self,templateFile,locusList,genome_frame):
#Read in teh template
templateFrame = | pd.read_csv(templateFile,header=0) | pandas.read_csv |
from typing import List
import pandas as pd
from utils import request_to_json, get_repo_names
from github_pr import GitHubPR
from github_users import GitHubUsers
# temporary - to minimize the number of requests
REPO_NAMES = [
"dyvenia",
"elt_workshop",
"git-workshop",
"gitflow",
"notebooks",
"... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 19 13:38:04 2018
@author: nmei
"""
import pandas as pd
import os
working_dir = ''
batch_dir = 'batch'
if not os.path.exists(batch_dir):
os.mkdir(batch_dir)
content = '''
#!/bin/bash
# This is a script to qsub jobs
#$ -cwd
#$ -o test_run/out_q... | pd.unique(df['participant']) | pandas.unique |
# -*- coding: utf-8 -*-
"""Interface for flopy's implementation for MODFLOW."""
__all__ = ["MfSfrNetwork"]
import pickle
from itertools import combinations, zip_longest
from textwrap import dedent
import geopandas
import numpy as np
import pandas as pd
from shapely import wkt
from shapely.geometry import LineString,... | pd.DataFrame(index=inflow.index) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from datetime import timedelta
import operator
from string import ascii_lowercase
import warnings
import numpy as np
import pytest
from pandas.compat import lrange
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
Categorical, DataFrame, MultiIndex, Serie... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
from bs4.element import ProcessingInstruction
import requests
from bs4 import BeautifulSoup
import pandas
print()
listadenoticias = []
# recebo da página
retorno = requests.get('https://g1.globo.com/')
# separo o conteudo da página
conteudo = retorno.content
# transformo o conteudo num objeto BeautifulSoup
site = Beau... | pandas.DataFrame(listadenoticias, columns=['Título', 'Subtítulo', 'Links']) | pandas.DataFrame |
####################################
# author: <NAME>
# course: Python for Data Science and Machine Learning Bootcamp
# purpose: lecture notes
# description: Section 06 - Python for Data Analysis, Pandas
# other: N/A
####################################
# PANDAS
# To know: Pandas will try to turn all numeric data into... | pd.concat([df1,df2,df3]) | pandas.concat |
from alphaVantageAPI.alphavantage import AlphaVantage
from unittest import TestCase
from unittest.mock import patch
from pandas import DataFrame, read_csv
from .utils import Path
from .utils import Constant as C
from .utils import load_json, _mock_response
## Python 3.7 + Pandas DeprecationWarning
# /alphaVantageAPI... | read_csv(cls.test_data_path / "mock_ipos_cal.csv") | pandas.read_csv |
from urllib.request import urlretrieve
import os # we want python to be able to read what we have in our hard drive
from statsmodels.tsa.arima.model import ARIMA
import numpy as np
import pandas as pd
from pmdarima import auto_arima
from matplotlib import cm
import matplotlib.pyplot as plt
import seaborn as sns
cl... | pd.to_datetime(self.df["year"], format="%Y") | pandas.to_datetime |
from nose.tools import assert_equal, assert_raises, assert_almost_equal
from unittest.mock import Mock, call, patch
from skillmodels import SkillModel as smo
import numpy as np
from pandas import DataFrame
from numpy.testing import assert_array_equal as aae
from numpy.testing import assert_array_almost_equal as aaae
im... | pd.Series([0.8333333, 0.333333], index=['a', 'b']) | pandas.Series |
"""
This module organizes all output data each decade. In other words, it
concatenates all the 'similarity_scores_{str(year)}-{str(year+9)}.tsv' files into
a single file ('total_data.tsv') and adds a column with the appropriate decade for each row.
The concatenated data will be used to generate time plots in plots.R.
"... | pd.read_csv("inputs/DO-slim-to-mesh.tsv", sep="\t") | pandas.read_csv |
import io
import pytest
import pandas as pd
from doltpy.cli.dolt import Dolt
from doltpy.cli.write import CREATE, UPDATE
from doltpy.cli.read import read_pandas
from doltpy.etl import (get_df_table_writer,
insert_unique_key,
get_unique_key_table_writer,
... | pd.DataFrame({'name': ['Novak'], 'major_count': [16]}) | pandas.DataFrame |
"""Test utils_data."""
import tempfile
from pathlib import Path
import pandas as pd
from dash_charts import utils_data
def test_enable_verbose_pandas():
"""Test enable_verbose_pandas."""
pd.set_option('display.max_columns', 0)
utils_data.enable_verbose_pandas() # act
| pd.get_option('display.max_columns') | pandas.get_option |
#!/bin/python
import pandas as pd
import nltk
import time
import os
import numpy as np
import sys
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.mixture import GaussianMixture
from data_io import *
from speech import *
if __name__ == "__main__":
# Loa... | pd.Series(centroid_predictions) | pandas.Series |
import unittest
import warnings
import pandas as pd
import rowgenerators as rg
from synpums import *
from synpums.util import *
warnings.filterwarnings("ignore")
state = 'RI'
year = 2018
release = 5
def fetch(url):
return rg.dataframe(url).drop(columns=['stusab', 'county', 'name'])
class TestACSIncome(unitt... | pd.get_dummies(dfh_g['b19025']) | pandas.get_dummies |
import numpy as np
import pandas as pd
from astropy import constants as c
from werkzeug.contrib.cache import SimpleCache
cache = SimpleCache()
colors = {
'Blue': '#1f77b4',
'Orange': '#ff7f0e',
'Green': '#2ca02c',
'Red': '#d62728',
'Purple': '#9467bd',
}
def readExoplanetEU():
"""Read the exo... | pd.read_csv('data/exoplanetEU.csv', engine='c') | pandas.read_csv |
# -*- coding: utf-8 -*-
#
# Scikit Learn Machine Learning Process Flow;
# Version 1.0
# Author : <NAME>
#
#
# First edited : 27 September 2018
# Last edited :
#
# Description : Scikit Learn Machine Learning Basics Blog WorkFlow
#
# Required input file details :
# 1. __Placeholder__
#
# Output from the cod... | pd.DataFrame(data_dict) | pandas.DataFrame |
#%%
import numpy as np
import pandas as pd
import networkx as nx
from collections import Counter
#%%
fname = "./processed_data/rideaustin_productivity.csv"
data = pd.read_csv(fname, dtype={"timebin": int}, parse_dates=["completed_on"])
# for interactive env can do next two lines
# data.sort_values('end_taz', inplace... | pd.read_csv("processed_data/splits_qua.csv") | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# In[2]:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# In[78]:
# load Data
# R2 comparison for train set sizes
RFR_score = | pd.read_csv('Generated Data/RFR_score.csv') | pandas.read_csv |
from datetime import (
datetime,
time,
)
import numpy as np
import pytest
from pandas._libs.tslibs import timezones
import pandas.util._test_decorators as td
from pandas import (
DataFrame,
Series,
date_range,
)
import pandas._testing as tm
class TestBetweenTime:
@td.skip_if_has_locale
... | date_range("1/1/2000", "1/5/2000", freq="5min") | pandas.date_range |
from datetime import datetime, timedelta
import pandas as pd
def summary(data, time):
data['Date'] = | pd.to_datetime(data['Date']) | pandas.to_datetime |
from numpy import linalg, zeros, ones, hstack, asarray, vstack, array, mean, std
import itertools
import matplotlib.pyplot as plt
from datetime import datetime
import pandas as pd
import numpy as np
import matplotlib.dates as mdates
from sklearn.metrics import mean_squared_error
from math import sqrt
import warnings
im... | pd.DataFrame(index=df_indices) | pandas.DataFrame |
import requests
from bs4 import BeautifulSoup as bs
from selenium import webdriver
from fake_useragent import UserAgent
from selenium.webdriver.common.desired_capabilities import DesiredCapabilities
import pandas as pd
import numpy as np
import re
import os
import pickle as pk
from collections import deque
import strin... | pd.DataFrame({'name':names}) | pandas.DataFrame |
import os
import pickle
import numpy as np
import pandas as pd
import gzip
import fcsparser
# Load Kuzushiji Japanese Handwritten dataset
def load_kmnist(path, dtype="kmnist", kind='train'):
images_path = os.path.join(path, f'{dtype}-{kind}-imgs.npz')
labels_path = os.path.join(path, f'{dtype}-{kind}-labels.np... | pd.read_csv(label_path) | pandas.read_csv |
from contextlib import nullcontext
import copy
import numpy as np
import pytest
from pandas._libs.missing import is_matching_na
from pandas.core.dtypes.common import is_float
from pandas import (
Index,
MultiIndex,
Series,
)
import pandas._testing as tm
@pytest.mark.parametrize(
"arr, idx",
[
... | Index(ser2, dtype=ser2.dtype) | pandas.Index |
# 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.concat([volume] * data.shape[0]) | pandas.concat |
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from sklearn import datasets, linear_model
from difflib import SequenceMatcher
import seaborn as sns
from statistics import mean
from ast import literal_eval
from scipy import stats
from sklearn.linear_model import LinearRegression
from s... | pd.Series(telo_data.iloc[:,0]) | pandas.Series |
import os
import pandas as pd
import sys
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import random
import statistics
import itertools
JtokWh = 2.7778e-7
weight_factor = [1.50558832,0.35786005,1.0]
path_test = os.path.join(sys.path[0])
representative_days_path= ... | pd.DataFrame(statistics_table) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import json
from datetime import datetime, timedelta
def get_data():
# Load json data
with open('../data/json_file.json') as data_file:
patients = json.load(data_file)
print("JSON file loaded")
# Features computation
print("Fe... | pd.DataFrame({'Y': Y, 'delta': delta}, index=encounter_nums) | pandas.DataFrame |
from keras.layers import Bidirectional, Input, LSTM, Dense, Activation, Conv1D, Flatten, Embedding, MaxPooling1D, Dropout
#from keras.layers.embeddings import Embedding
from keras.preprocessing.sequence import pad_sequences
from keras import optimizers
from keras.models import Sequential, Model
import pandas as pd
impo... | pd.read_csv(TRAIN_FILE_PATH) | pandas.read_csv |
"""
Perform a simple random sample of your words and run optimus on the sample.
Then use a knn to put it all back together at the end.
"""
#-- Imports ---------------------------------------------------------------------
# third party
import fastText as ft
import pandas as pd
from optimus import Optimus
f... | pd.concat([df_sample, df_unsampled]) | pandas.concat |
import sys
sys.path.append("../")
from settings import *
import re
import pandas as pd
import numpy as np
import os
stopwords = {
'max>\'ax>\'ax>\'ax>\'ax>\'ax>\'ax>\'ax>\'ax>\'ax>\'ax>\'ax>\'ax>\'ax>\'ax': 1,
'edu': 1,
'subject': 1,
'com': 1,
'r<g': 1,
'_?w': 1,
'isc': 1,
'cx^': 1,
... | pd.read_csv(path + 'overall_stop.csv', header=0, dtype={'label': int}) | pandas.read_csv |
import unittest
import pandas as pd
import numpy as np
from math import sqrt
import numba
import hpat
from hpat.tests.test_utils import (count_array_REPs, count_parfor_REPs,
count_parfor_OneDs, count_array_OneDs,
count_parfor_OneD_Vars, count_array_O... | pd.DatetimeIndex(df['str_date']) | pandas.DatetimeIndex |
#!/Users/amos/anaconda3/bin/python
# Pythono3 code to extract multiple space delimited txt files into pandas and then manipulate it into a single excel file
# importing pandas and os module
import os
import pandas as pd
#set working directory to where text files are stored
os.chdir("/Volumes/DANIEL/dti_freesurf_MCI... | pd.read_table('lh.hippoSfVolumes-T1.long.v21.txt', delim_whitespace=True,names=['loacation','volume']) | pandas.read_table |
# -*- coding: utf-8 -*-
# Arithmetc tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import timedelta
import operator
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.compat import long
from pandas.core import ops
from pan... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
from __future__ import print_function
import numpy as np
import time, os, sys
import matplotlib.pyplot as plt
from scipy import ndimage as ndi
from skimage import color, feature, filters, io, measure, morphology, segmentation, img_as_ubyte, transform
import warnings
import math
import pandas as pd
import argparse
impor... | pd.DataFrame() | pandas.DataFrame |
# Name: ZStandardizeFields.py
# Purpose: Will add selected fields as standarized Z scores by extending a numpy array to the feature class.
# Author: <NAME>
# Last Modified: 4/16/2021
# Copyright: <NAME>
# Python Version: 2.7-3.1
# ArcGIS Version: 10.4 (Pro)
# --------------------------------
# Copyright 2016 <NAME>
#... | pd.merge(scored_df, field_series, how="outer", left_index=True, right_index=True) | pandas.merge |
#!/usr/bin/env python3
# This script assumes that the non-numerical column headers
# in train and predi files are identical.
# Thus the sm header(s) in the train file must be numeric (day/month/year).
import sys
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA #TruncatedSVD as SVD
from skl... | pd.concat([pre_base, post_model], axis=1) | pandas.concat |
import numpy as np
import pandas as pd
import matplotlib.pyplot as pl
import seaborn as sns
import tensorflow as tf
import re
import json
from functools import partial
from itertools import filterfalse
from wordcloud import WordCloud
from tensorflow i... | pd.value_counts(all_words) | pandas.value_counts |
import pandas as pd
import numpy as np
from datetime import datetime
from tqdm import tqdm
from tqdm.notebook import tqdm as tqdmn
try:
from trade import Trade
except:
pass
try:
from backtest.trade import Trade
except:
pass
import chart_studio.plotly as py
import plotly.graph_objs as go
from plo... | pd.Series([self.InitBalance]) | pandas.Series |
from time import time
import pandas as pd
from numpy import arange
results_df = pd.read_csv('../data/botbrnlys-rand.csv')
def extract_best_vals_index(results_df, df, classifier, hp):
final_df = | pd.DataFrame() | pandas.DataFrame |
import argparse
from umap import UMAP
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
def main():
parser = argparse.ArgumentParser(description='Visualize DAE compressed output using UMAP algorithm.')
parser.add_argument('csv_output', type=str, help='Output CSV file generated from D... | pd.read_csv(args.csv_output, header=None) | pandas.read_csv |
# encoding: utf-8
from opendatatools.common import RestAgent
from bs4 import BeautifulSoup
from progressbar import ProgressBar
import pandas as pd
import re
lianjia_city_map = {
'北京' : 'bj',
'上海' : 'sh',
'成都' : 'cd',
'杭州' : 'hz',
'广州' : 'gz',
'深圳' : 'sz',
'厦门' : 'xm',
'苏州' : 'su',
... | pd.DataFrame(result_list) | pandas.DataFrame |
#!/usr/bin/env python3
#-*- coding: utf8 -*-
"""Scrape products from Woolworths
Returns:
(dict): Prices, product name, datetime
References:
[1] https://github.com/nguyenhailong253/grosaleries-web-scrapers
"""
import argparse
import re
import subprocess
import sys
import traceback
import warnings
from abc im... | pd.DataFrame.from_dict(self.quote) | pandas.DataFrame.from_dict |
"""
Authors:
ITryagain <<EMAIL>>
Reference:
https://www.ibm.com/developerworks/community/blogs/jfp/entry/Fast_Computation_of_AUC_ROC_score?lang=en
https://www.kaggle.com/uberkinder/efficient-metric
https://www.kaggle.com/artgor
introduce:
this file contains the use of models suc... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 25 21:53:10 2018
改自selectSubjID_inScale_V2
根据给定的条件筛选大表的item和subjects' folder
inputs:
file_all：大表
column_basic1=[0,11,19,20,21,22,23,27,28,29,30]：基本信息列
column_basic2=['学历（年）','中国人利手量表']：基本信息名
column_hamd17=np.arange(104,126,1),
col... | pd.DataFrame(screened_ind) | pandas.DataFrame |
import json
import requests
import streamlit as st
from pandas import DataFrame
from web3 import Web3
from opensea_api_client import Client
# page init
client = Client()
def render_asset(asset):
if asset['name'] is not None:
st.subheader(asset['name'])
else:
st.subheader(f"{asset['collection'... | DataFrame(event_list, columns=['time', 'bidder', 'bid_amount', 'collection', 'token_id']) | pandas.DataFrame |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# *****************************************************************************/
# * Authors: <NAME>
# *****************************************************************************/
"""transformCSV.py
This module contains the basic functions for creating the content of... | pandas.StringDtype() | pandas.StringDtype |
import numpy as np
import pandas as pd
from os import path
from PIL import Image
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
import matplotlib.pyplot as plt
def get_youtube_search(query,order,regionCode,channel_id = ''):
import os
import google_auth_oauthlib.flow
import googleapiclien... | pd.DataFrame.from_dict(search_result_1['items']) | pandas.DataFrame.from_dict |
from io import BytesIO
import pytest
import pandas.util._test_decorators as td
import pandas as pd
import pandas._testing as tm
def test_compression_roundtrip(compression):
df = pd.DataFrame(
[[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
index=["A", "B"],
columns=["X... | pd.read_json(path, lines=True, compression=compression) | pandas.read_json |
import json
import geopandas as gp
import numpy as np
import pandas as pd
import pygeos as pg
from pyproj.transformer import Transformer
from shapely.wkb import loads
def to_crs(geometries, src_crs, target_crs):
"""Convert coordinates from one CRS to another CRS
Parameters
----------
geometries : nd... | pd.Series(values, index=index, name="index_right") | pandas.Series |
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