prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
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#
# 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 Apache License, Version 2.0
# (the "License"); you may not us... | PandasMPLPlot._plot(ax, ind, y, style=style, **kwds) | pandas.plotting._matplotlib.core.MPLPlot._plot |
import pandas as pd
import numpy as np
import unittest
from dstools.preprocessing.OneHotEncoder import OneHotEncoder
class TestOneHotEncoder(unittest.TestCase):
def compare_DataFrame(self, df_transformed, df_transformed_correct):
"""
helper function to compare the values of the transformed DataFr... | pd.DataFrame({'x1':[1,2], 'x2':['a','b']}) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# Created by <NAME>
import unittest
import pandas as pd
import pandas.testing as pdtest
from allfreqs import AlleleFreqs
from allfreqs.classes import Reference, MultiAlignment
from allfreqs.tests.constants import (
REAL_ALG_X_FASTA, REAL_ALG_X_NOREF_FASTA, REAL_RSRS_F... | pd.read_csv(REAL_ALG_X_DF, index_col=0) | pandas.read_csv |
import pandas as pd
import numpy as np
from matplotlib import pylab
from textwrap import fill
from . import univariate
def cross_table(variables, category1, category2, data, use_names=True):
"""
Gives a cross table of category1 and category2
Args:
variables: Variables class
category1: name o... | pd.DataFrame(columns=["odds_ratio", "ci_lower", "ci_upper"]) | pandas.DataFrame |
from configparser import ConfigParser
import os
import cv2
import numpy as np
import pandas as pd
import warnings
import glob
def roiByDefinition(inifile):
global ix, iy
global topLeftStatus
global overlay
global topLeftX, topLeftY, bottomRightX, bottomRightY
global ix, iy
global ... | pd.DataFrame(columns=['Video', "Shape_type", "Name", "centerX", "centerY", "radius"]) | pandas.DataFrame |
import os
if not os.path.exists("temp"):
os.mkdir("temp")
def add_pi_obj_func_test():
import os
import pyemu
pst = os.path.join("utils","dewater_pest.pst")
pst = pyemu.optimization.add_pi_obj_func(pst,out_pst_name=os.path.join("temp","dewater_pest.piobj.pst"))
print(pst.prior_information.loc["... | pd.read_csv("sfr_seg_pars.dat",delim_whitespace=False,index_col=0) | pandas.read_csv |
"""
visdex: Summary heatmap
Shows a simple correlation heatmap between numerical fields in the
loaded and filtered data file
"""
import itertools
import logging
import numpy as np
import pandas as pd
import scipy.stats as stats
from sklearn.cluster import AgglomerativeClustering
from dash.dependencies import Input, ... | pd.DataFrame(data=clx, index=corr.index, columns=["column_names"]) | pandas.DataFrame |
import torch
import torch.nn as nn
from torch.nn import functional as F
import math
from torch.utils.data import Dataset
import os
import pandas as pd
import pdb
import numpy as np
import math
import pickle
import random
from sklearn.utils import shuffle
class FinalTCGAPCAWG(Dataset):
def __init... | pd.read_csv(filename,index_col=0) | pandas.read_csv |
import sys, os
sys.path.append('yolov3_detector')
from yolov3_custom_helper import yolo_detector
from darknet import Darknet
sys.path.append('pytorch-YOLOv4')
from tool.darknet2pytorch import Darknet as DarknetYolov4
import argparse
import cv2,time
import numpy as np
from tool.plateprocessing import find_coordinates, p... | pd.read_csv(fp1, sep='\t', header=0) | pandas.read_csv |
import inspect
import os
import datetime
from collections import OrderedDict
import numpy as np
from numpy import nan, array
import pandas as pd
import pytest
from pandas.util.testing import assert_series_equal, assert_frame_equal
from numpy.testing import assert_allclose
from pvlib import tmy
from pvlib import pvsy... | pd.DatetimeIndex(start='2015-01-01', periods=5, freq='12H') | pandas.DatetimeIndex |
import warnings
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pydotplus
import graphviz
import os
if __name__... | pd.read_csv("BRFSS_core_cleaned.csv", decimal=',') | pandas.read_csv |
from bs4 import BeautifulSoup
import pandas as pd
from pprint import pprint
import re
import demjson
from utils import Apps
class DetailPage:
def __init__(self, app, page_source):
self.df_change_log = | pd.DataFrame() | pandas.DataFrame |
from kfp.components import InputPath, OutputPath
from kfp.v2.dsl import (Artifact,
Dataset,
Input,
Model,
Output,
Metrics,
ClassificationMetrics)
def get_full_tech_indi(
# ... | pd.read_pickle(tech_indi_dataset07.path) | pandas.read_pickle |
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.7.1
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# + [markdown] papermill={"dura... | pd.read_gbq(query, dialect='standard') | pandas.read_gbq |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 11 12:08:22 2017
@author: yazar
"""
import numpy as np
import pandas as pd
from scipy import linalg
from sklearn import preprocessing
from matplotlib import pyplot as plt
from scipy import optimize
def sigmoid(x):
return 1 / (1 + np.exp(-x))
... | pd.DataFrame(X) | 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(tuples[0], name=names[0]) | pandas.Index |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import re
import sys
import traceback
from bs4 import BeautifulSoup
from selenium import webdriver
from selenium.webdriver.firefox.options import Options
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as ... | pd.DataFrame.from_dict(cols) | pandas.DataFrame.from_dict |
'''
Naming Conventions for Features:
c_ = categorical
i_ = categoricals as indexes
n_ = numerical
b_ = binary
d_ = date
'''
from . import series, dataframe,\
dataframe_engineer, dataframe_format_convert
import pandas as pd
from . import misc
def _extend_df(name, function):
df = | pd.DataFrame({}) | pandas.DataFrame |
import requests
import pandas as pd
import holoviews as hv
# Instead of using hv.extension, grab a bokeh renderer
renderer = hv.renderer('bokeh')
data = requests.get("https://squash-api.lsst.codes/measurements").json()
meas_df = | pd.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
import copy
import re
from textwrap import dedent
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
MultiIndex,
)
import pandas._testing as tm
jinja2 = pytest.importorskip("jinja2")
from pandas.io.formats.style import ( # isort:skip
Styler,
)
from pandas.io.formats.sty... | pd.Series(["a:v;", ""], index=["X", "Z"]) | pandas.Series |
from __future__ import annotations
import pandas as pd
import geopandas as gpd
from pathlib import Path
from tqdm import tqdm
import pg_data_etl as pg
from network_routing import pg_db_connection
from network_routing.accessibility.logic_analyze import get_unique_ids
class IsochroneGenerator:
"""
- This cla... | pd.concat(gdfs) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
# # Generate Generative Model Figures
# In[1]:
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().run_line_magic('matplotlib', 'inline')
import os
import glob
from collections import OrderedDict
import matplotli... | pd.DataFrame([], columns=['aupr', 'auroc', 'lf_num', 'predicted', 'lf_source']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 14 17:14:29 2020
@author: p000526841
"""
from pathlib import Path
import numpy as np
import pandas as pd
from datetime import datetime
import inspect
#from matplotlib_venn import venn2
from utils import *
plt.rcParams['font.family'] = 'IPAexGothic'
@contextmanager... | pd.concat([df_train, df_test]) | pandas.concat |
import numpy as np
import os
import pandas as pd
import pyro
import torch
from pyro.distributions import Gamma, Normal
from tqdm import tqdm
from deepscm.datasets.morphomnist import load_morphomnist_like, save_morphomnist_like
from deepscm.datasets.morphomnist.transforms import SetThickness, SetSlant, ImageMorphology... | pd.DataFrame(data={'thickness': thickness, 'slant': slant}) | pandas.DataFrame |
from evalutils.exceptions import ValidationError
from evalutils.io import CSVLoader, FileLoader, ImageLoader
import json
import nibabel as nib
import numpy as np
import os.path
from pathlib import Path
from pandas import DataFrame, MultiIndex
import scipy.ndimage
from scipy.ndimage.interpolation import map_coordinates,... | DataFrame(cases, index=index) | pandas.DataFrame |
import os
import sys
import argparse
import pandas as pd
import numpy as np
### Version 3, created 12 August 2020 by <NAME> ###
### Reformats concatenated, headerless MELT vcf files, into the relevant information columns, with extraneous information/columns removed, ready to use in the duplicate-removal scripts
### T... | pd.read_csv(SPLIT_HITS, sep='\t', names=HEADERS1) | pandas.read_csv |
"""
plotting functions for N2 experiments.
"""
# std lib
import logging
logger = logging.getLogger(__name__)
## local
from elchempy.plotters.plot_helpers import PlotterMixin
## for developing and testing
# from elchempy.experiments._dev_datafiles._dev_fetcher import get_files
## constants
from elchempy.constants... | pd.concat([Cdl_an_slice, Cdl_cath_slice], sort=False, axis=0) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 22 10:16:42 2021
@author: tungbioinfo
"""
import argparse
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm
import time
from sklearn.model_selection import train_test_split
from skle... | pd.concat([c, e, a, f["All"]], axis=1) | pandas.concat |
import matplotlib
matplotlib.use('Agg')
from Swing.util.BoxPlot import BoxPlot
from matplotlib.backends.backend_pdf import PdfPages
from scipy import stats
import pdb
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
import os
import time
from Swing.util.mplstyle import style1
import s... | pd.read_pickle("merged_window_scan_comparisons_network1.pkl") | pandas.read_pickle |
import datetime
from datetime import timedelta
from distutils.version import LooseVersion
from io import BytesIO
import os
import re
from warnings import catch_warnings, simplefilter
import numpy as np
import pytest
from pandas.compat import is_platform_little_endian, is_platform_windows
import pandas.util._test_deco... | ensure_clean_store(setup_path) | pandas.tests.io.pytables.common.ensure_clean_store |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import multiprocessing
import random
from threading import Thread
import botocore
from django.contrib import auth
from django.contrib.auth import authenticate
from django.shortcuts import render
from django.template import RequestContext
from django.util... | pd.DataFrame(num_references, columns=['References']) | pandas.DataFrame |
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import tensorflow as tf
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
sess = tf.Session(config=tf_config)
import sys
if not '../' in sys.path: sys.path.append('../')
import pandas as pd
from... | pd.concat([train_data['answer'], val_data['answer'], test_data['answer']]) | pandas.concat |
#!/usr/bin/env python3
import pytest
import os
import pathlib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import logging
import math
import torch
from neuralprophet import NeuralProphet, set_random_seed
from neuralprophet import df_utils
log = logging.getLogger("NP.test")
log.setLevel("WAR... | pd.read_csv(PEYTON_FILE, nrows=512) | pandas.read_csv |
# 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 Apache License, Version 2.0 (the
# "License"); you may not u... | pd.read_csv(data, encoding="latin-1") | pandas.read_csv |
# This code extract the features from the raw joined dataset (data.csv)
# and save it in the LibSVM format.
# Usage: python construct_features.py
import pandas as pd
import numpy as np
from sklearn.datasets import dump_svmlight_file
df = pd.read_csv("data.csv", low_memory=False)
# NPU
NPU = df.NPU.copy()
NPU[NPU ==... | pd.get_dummies(Multiple_Violations, prefix="Multiple_Violations") | pandas.get_dummies |
from numpy import NaN, nan
import pandas as pd
from amparos.pesquisa import Pesquisa_Sem_Driver, Pesquisa_Com_Driver
# Verifica se o arquico .xlsx e um arquivo valido
def VerificarXlsx(local):
"""
Parameters:
local: Arquivo .xlsx para ser analisado
Returns:
return 'ERRO: Caminho ... | pd.read_excel(local_xlsx) | pandas.read_excel |
import os
import pandas as pd
path = './csv'
files = os.listdir(path)
df1 = pd.read_csv(path + '/' + files[0], encoding='utf_8_sig')
for file in files[1:]:
df2 = pd.read_csv(path + '/' + file, encoding='utf_8_sig')
df1 = | pd.concat([df1, df2], axis=0, ignore_index=True) | pandas.concat |
"""GitHub Model"""
__docformat__ = "numpy"
# pylint: disable=C0201,W1401
import logging
from typing import Any, Dict
import math
from datetime import datetime
import requests
import pandas as pd
from openbb_terminal import config_terminal as cfg
from openbb_terminal.decorators import log_start_end
from openbb_termina... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable=E1101
import string
from collections import OrderedDict
import numpy as np
import pandas as pd
import pandas.util.testing as pdt
import pytest
from kartothek.core.dataset import DatasetMetadata
from kartothek.core.index import ExplicitSecondaryIndex
from kartothek.core.uuid... | pd.DataFrame({"x": [1], "y": [1]}) | pandas.DataFrame |
import os
import sys
import numpy as np
import pandas as pd
import time
import scipy.sparse
import scipy.sparse.linalg
from scipy import stats
from scipy.optimize import minimize
np.set_printoptions(threshold=sys.maxsize)
# Add lib to the python path.
from genTestDat import genTestData2D, prodMats2D
from est2d import... | pd.DataFrame(index=row, columns=col) | pandas.DataFrame |
"""
Tests for scalar Timedelta arithmetic ops
"""
from datetime import datetime, timedelta
import operator
import numpy as np
import pytest
import pandas as pd
from pandas import NaT, Timedelta, Timestamp, offsets
import pandas._testing as tm
from pandas.core import ops
class TestTimedeltaAdditionSubtraction:
"... | Timedelta(hours=3, minutes=4) | pandas.Timedelta |
import web
import pandas as pd
import numpy as np
import common
import os
import click
def hydro_op_chars_inputs_(webdb, project,
hydro_op_chars_sid,
balancing_type_project):
rows = webdb.where("inputs_project_hydro_operational_chars",
proj... | pd.DataFrame(rows) | pandas.DataFrame |
import logging
import os
import pandas as pd
from glob import glob
from pathlib import Path, PosixPath, WindowsPath
from ekorpkit.utils.func import elapsed_timer
log = logging.getLogger(__name__)
def get_filepaths(
filename_patterns, base_dir=None, recursive=True, verbose=True, **kwargs
):
if isinstance(fil... | pd.read_parquet(filepath, engine=engine) | pandas.read_parquet |
""" parquet compat """
from __future__ import annotations
from distutils.version import LooseVersion
import io
import os
from typing import Any, AnyStr, Dict, List, Optional, Tuple
from warnings import catch_warnings
from pandas._typing import FilePathOrBuffer, StorageOptions
from pandas.compat._optional import impor... | stringify_path(path) | pandas.io.common.stringify_path |
# link: https://github.com/liulingbo918/ATFM/tree/master/data/TaxiNYC
import h5py
import pandas as pd
import numpy as np
import json
import util
outputdir = 'output/NYCTAXI20140112'
util.ensure_dir(outputdir)
dataurl = 'input/NYCTAXI20140112/'
dataname = outputdir+'/NYCTAXI20140112'
f = h5py.File(dataurl + 'NYC2014.... | pd.DataFrame() | pandas.DataFrame |
# %% Dependencies and variables' definitions.
import pandas as pd
import geopandas as gpd
from osmi_helpers import data_gathering as osmi_dg
# Define Data Sources
ARBRAT_VIARI_URL = "https://opendata-ajuntament.barcelona.cat/data/dataset/27b3f8a7-e536-4eea-b025-ce094817b2bd/resource/28034af4-b636-48e7-b3df-fa1c422e6... | pd.concat([df_aviari, df_azona]) | pandas.concat |
from keras.layers.core import Dense, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf
import datetime
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
def data_preparation(company_input_dat... | pd.read_csv(train_data) | pandas.read_csv |
#!/usr/bin/python
print('financials_update_quarterly - initiating. Printing Stock and % Progress.')
import os
import pandas as pd
from datetime import date
pd.set_option('display.max_columns', None)
pd.options.display.float_format = '{:20,.2f}'.format
pd.options.mode.use_inf_as_na = True
cwd = os.getcwd()
input_fol... | pd.merge(to_merge, df_info, how='left', left_on=['symbol'], right_on=['symbol'], suffixes=('', '_drop')) | pandas.merge |
import datetime
import logging
from pathlib import Path
import numpy as np
import pandas as pd
import plotly.graph_objs as go
from numpy.linalg import inv
from scipy.linalg import sqrtm
from sklearn import covariance
from sklearn.base import BaseEstimator
from sklearn.covariance import EmpiricalCovariance
from sklearn... | pd.DataFrame(1 + Y, columns=mean.index, index=S.index) | pandas.DataFrame |
import unittest
import pandas as pd
import numpy as np
from pandas.testing import assert_frame_equal
from msticpy.analysis.anomalous_sequence import sessionize
class TestSessionize(unittest.TestCase):
def setUp(self):
self.df1 = pd.DataFrame({"UserId": [], "time": [], "operation": []})
self.df1_... | pd.to_datetime("2020-01-05 00:00:00") | pandas.to_datetime |
#!/usr/bin/env python
# coding: utf-8
# # ReEDS Scenarios on PV ICE Tool STATES
# To explore different scenarios for furture installation projections of PV (or any technology), ReEDS output data can be useful in providing standard scenarios. ReEDS installation projections are used in this journal as input data to the... | pd.DataFrame() | pandas.DataFrame |
import ast
import datetime
import time
import math
import pypandoc
import os
import matplotlib.pyplot as plt
import numpy as np
import numpy.ma as ma
import pandas as pd
import statsmodels.api as sm
from library.api import API_HOST, fetch_objects, fetch_objects_by_id, get_token
from library.settings import MIN_VIDEO_... | pd.DataFrame(columns=header) | pandas.DataFrame |
import logging
import numpy as np
import pandas as pd
from pathlib import Path
import PyCrowdTangle as pct
import time
import glob
import os
from tqdm import tqdm
from ratelimiter import RateLimiter
from .utils import Utils
logger = logging.getLogger(__name__)
class CrowdTangle:
"""Descripción de la clase.
... | pd.DataFrame(data['result']['posts']) | pandas.DataFrame |
import requests
import pandas as pd
import json
import datetime as dt
import time
#=========================================================================================
# Automatic CSV File Generator for Meetup.com API Data
# Created by: <NAME>
# Date: Jan 17, 2018
#===============================================... | pd.DataFrame(raw_data[i]) | pandas.DataFrame |
''' Get Per Season Level data from the Player Page '''
import requests, pandas
from bs4 import BeautifulSoup, Comment
def getpp(player_id):
baseurl = "http://www.basketball-reference.com/players/{firstletter}/{playerid}.html"
return requests.get(baseurl.format(firstletter=player_id[:1],playerid=player_id))
def tes... | pandas.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
from pathlib import Path
import os
import argparse
import logging
from camel_tools.utils.charsets import UNICODE_PUNCT_CHARSET
import pandas as pd
import re
from funcy import log_durations
from camel_tools.utils.normalize import normalize_unicode
# punctuation set used in tokenize_hyph, which s... | pd.DataFrame(record_list) | pandas.DataFrame |
# ********************************************************************************** #
# #
# Project: FastClassAI workbecnch #
# ... | pd.Series(y_true) | pandas.Series |
import pytest
import copy
import numpy as np
import pandas as pd
from sklearn.compose import TransformedTargetRegressor
from sklearn.preprocessing import QuantileTransformer
from sklearn.linear_model import Ridge
from sklearn.tree import DecisionTreeRegressor
from sklearn.svm import SVR, LinearSVR
from sklearn.metrics ... | pd.DataFrame(_, index=[9999]) | pandas.DataFrame |
import urllib
from io import StringIO
from io import BytesIO
import csv
import numpy as np
from datetime import datetime
import matplotlib.pylab as plt
import pandas as pd
import scipy.signal as signal
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
datos=pd.read_c... | pd.read_csv('https://raw.githubusercontent.com/ComputoCienciasUniandes/FISI2029-201910/master/Seccion_1/Fourier/Datos/transacciones2010.txt',sep=";",header=None, decimal=",") | pandas.read_csv |
from __future__ import division
import argparse
import mayavi.mlab as mlab
from CameraNetwork.visualization import calcSeaMask
import matplotlib.mlab as ml
import datetime
import glob
import json
import moviepy.editor as mpy
import numpy as np
import os
import pandas as pd
import pymap3d
FLIGHT_PATH = r"data\2017_04_... | pd.DataFrame(data=data, index=indices, columns=COLUMNS) | pandas.DataFrame |
"""Tools to visualize the JHU CSSE COVID-19 Data and the forecasts made
with it using the model module.
"""
import numpy as np
import pandas as pd
from babel.dates import format_date
from babel.numbers import format_decimal
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import matplotlib.dates as ... | pd.concat([cases[-8:], cases_forecast['yhat']]) | pandas.concat |
#!/usr/bin/env python
# Copyright 2016 DIANA-HEP
#
# 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 a... | pd.to_datetime(x) | pandas.to_datetime |
import xarray as xr
import numpy as np
import pandas as pd
import glob
import os
import warnings
warnings.filterwarnings('ignore')
def get_ds_latlon(infile):
ds = xr.open_dataset(infile)
vars_needed = ['StdPressureLev:ascending_TqJoint', 'SurfPres_Forecast_TqJ_A', 'SurfPres_Forecast_TqJ_D',
... | pd.read_csv(station_file) | pandas.read_csv |
from anndata import AnnData
import numpy as np
import pandas as pd
import warnings
from ... import logging as logg
from .._distributed import materialize_as_ndarray
from .._utils import _get_mean_var
from scipy.sparse import issparse
def filter_genes_dispersion(data,
flavor='seurat',
... | pd.cut(df['mean'], bins=n_bins) | pandas.cut |
#!/usr/bin/env python
# coding: utf-8
# In[95]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import math
# ## Step 1: collecting data
# In[96]:
#Reading data
titanic_data = pd.read_csv('titanic_train_clean.csv')
titanic_data.head(10)
# In[97]:
#Get the total ... | pd.isnull(Age) | pandas.isnull |
import sys, warnings, operator
import json
import time
import types
import numbers
import inspect
import itertools
import string
import unicodedata
import datetime as dt
from collections import defaultdict, OrderedDict
from contextlib import contextmanager
from distutils.version import LooseVersion as _LooseVersion
fr... | pd.isna(val) | pandas.isna |
from __future__ import division
from functools import wraps
import pandas as pd
import numpy as np
import time
import csv, sys
import os.path
import logging
from .ted_functions import TedFunctions
from .ted_aggregate_methods import TedAggregateMethods
from base.uber_model import UberModel, ModelSharedInputs
class Te... | pd.Series([], dtype="float", name="cbt_bird_1inten_mort") | pandas.Series |
'''
Created with love by Sigmoid
@Author - <NAME> - <EMAIL>
'''
# Importing all libraries
import numpy as np
import pandas as pd
import random
import sys
from math import floor
from .erorrs import NotBinaryData, NoSuchColumn
def warn(*args, **kwargs):
pass
import warnings
warnings.warn = warn
cla... | pd.concat([self.df, self.synthetic_df], axis=0) | pandas.concat |
from requests_html import HTMLSession
from requests.exceptions import ConnectionError
from retry import retry
from typing import List
from time import sleep
import pandas as pd
from numpy import nan
import pickle
from logging import getLogger, StreamHandler, Formatter, DEBUG
logger = getLogger(__name__)
logge... | pd.DataFrame(columns=["code", "kind", "title", "article"]) | pandas.DataFrame |
""" Characteristic the heuristic algorithm. """
import argparse
import os
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from gumi.pruning.mask_utils import group_sort, run_mbm
from gumi.model_runner import utils
# Plotting
import matplotlib as mpl
mpl.use("Agg")
import matplotlib.pypl... | pd.DataFrame(results) | pandas.DataFrame |
import subete
import pandas as pd
import matplotlib.pyplot as plt
repo = subete.load()
data = {}
data["language"] = [lang for lang in repo.language_collections().keys()]
data["total_programs"] = [lang.total_programs() for lang in repo.language_collections().values()]
data["total_size"] = [lang.total_size() for lang i... | pd.DataFrame(data) | pandas.DataFrame |
import pandas as pd
from options_parser import arguments
options, args = arguments()
def brca_data(train_test=False, full_data=False):
# input cells over which the predsictive model is built
input_cells = open(options.cell_list, "r").read().splitlines()
input_cells = [ic.replace("-", "").upper() for ic in... | pd.read_csv(options.test_set, index_col=0) | pandas.read_csv |
# +
"""
Functions/classes/variables for interacting between a pandas DataFrame
and postgres/mysql/sqlite (and potentially other databases).
"""
import json
import pandas as pd
import logging
import re
from copy import deepcopy
from math import floor
from sqlalchemy import JSON, MetaData, select
from sqlalchemy.sql impo... | pd.api.types.is_list_like(val) | pandas.api.types.is_list_like |
import argparse
import matplotlib.pyplot as plt
import matplotlib
import pandas as pd
import numpy as np
import glob
import os
from ML.DDModel import DDModel
from sklearn.metrics import auc
from sklearn.metrics import roc_curve
parser = argparse.ArgumentParser()
parser.add_argument('-pr','--project',required=True,hel... | pd.merge(ID_labels, train_pd, how='inner',on=['ZINC_ID']) | pandas.merge |
import argparse
import os
import boto3
import pandas as pd
from io import StringIO
# Parse Command Line Arguments
parser = argparse.ArgumentParser(description='Add some integers.')
parser.add_argument('startitem', metavar='s', type=int,
help='What item of the OnlineRetail.csv should I start at')
pa... | pd.DataFrame(stats_list, columns =['Item_Number', 'Item', 'Total_Units_Sold', 'Average_Price_Of_Unit']) | pandas.DataFrame |
import gym
import pandas as pd
import numpy as np
from numpy import inf
from gym import spaces
from sklearn import preprocessing
from statsmodels.tsa.statespace.sarimax import SARIMAX
from empyrical import sortino_ratio, calmar_ratio, omega_ratio
from render.BitcoinTradingGraph import BitcoinTradingGraph
from util.sta... | pd.DataFrame(scaled, columns=features.columns) | pandas.DataFrame |
from __future__ import division
from functools import wraps
import pandas as pd
import numpy as np
import time
import csv, sys
import os.path
import logging
from .ted_functions import TedFunctions
from .ted_aggregate_methods import TedAggregateMethods
from base.uber_model import UberModel, ModelSharedInputs
class Te... | pd.Series([], dtype="float", name="cbt_reptile_1inmill_mort") | pandas.Series |
#!/usr/bin/env python3
from argparse import ArgumentParser
import matplotlib
matplotlib.rcParams['text.usetex'] = True
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
def plot(combined_df):
systems = [
"Weak Spec.", "Weak Spec. + Search", "Expert + Search",... | pd.read_csv(path) | pandas.read_csv |
from __future__ import annotations
from typing import Any, cast, Generator, Iterable, Optional, TYPE_CHECKING, Union
import numpy as np
import pandas as pd
from pandas.core.frame import DataFrame
from pandas.core.series import Series
from tanuki.data_store.data_type import DataType
from tanuki.data_store.index.index... | DataFrame(data) | pandas.core.frame.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 16 12:00:00 2018
@author: <NAME>
"""
import pandas as pd
import os
import psycopg2
import networkx as nx
import csv
import itertools
import operator
import ast
from sqlalchemy import create_engine
import numpy as np
import igraph as ig
import copy
from collections import ... | pd.to_numeric(all_edge_fail_scenarios['probability']) | pandas.to_numeric |
import os
import collections
import argparse
import numpy as np
import pandas as pd
import statistics as stat
from datetime import datetime, timedelta, date
import plotly.graph_objects as go
import dash # (version 1.12.0) pip install dash
import dash_table
from dash_table.Format import Format, Scheme
from dash_table... | pd.DataFrame(insert, columns=[column['id'] for column in ticker_df_columns]) | pandas.DataFrame |
from __future__ import division #brings in Python 3.0 mixed type calculation rules
import datetime
import inspect
import numpy as np
import numpy.testing as npt
import os.path
import pandas as pd
import sys
from tabulate import tabulate
import unittest
print("Python version: " + sys.version)
print("Numpy version: " +... | pd.Series([15., 20., 30.], dtype='float') | pandas.Series |
import os
from math import floor, ceil
from pprint import pprint
import csv
import argparse
import simuran
import pandas as pd
import matplotlib.pyplot as plt
import astropy.units as u
from neurochat.nc_lfp import NLfp
import numpy as np
from scipy.signal import coherence
from skm_pyutils.py_table import list_to_df, d... | pd.DataFrame(results, columns=headers) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""Main module."""
### Libraries ###
import pandas as pd
from datetime import datetime
import croissance
from croissance import process_curve
from croissance.estimation.outliers import remove_outliers
import re
import os
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
from... | pd.DataFrame() | pandas.DataFrame |
# =============================================================================
# File: get_fees.py
# Author: <NAME>
# Created: 12 Jun 2017
# Last Modified: 12 Jun 2017
# Description: description
# =============================================================================
import requests
impo... | pd.DataFrame() | pandas.DataFrame |
import datetime
import fiona
import geopandas as gpd
import jinja2
import logging
import numpy as np
import pandas as pd
import random
import requests
import sqlite3
import sys
import time
import yaml
from collections import ChainMap, defaultdict
from operator import attrgetter, itemgetter
from osgeo import ogr, osr
fr... | pd.DataFrame(strplaname) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed May 13 13:59:31 2020
@author: <NAME>
"""
import sys, os
sys.path.append('H:/cloud/cloud_data/Projects/DL/Code/src')
sys.path.append('H:/cloud/cloud_data/Projects/DL/Code/src/ct')
import pandas as pd
import ntpath
import datetime
from openpyxl.worksheet.datavalidation import ... | pd.concat([df_data_old, df_dicom[idx==False]], axis=0) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 26 15:39:02 2018
@author: joyce
"""
import pandas as pd
import numpy as np
from numpy.matlib import repmat
from stats import get_stockdata_from_sql,get_tradedate,Corr,Delta,Rank,Cross_max,\
Cross_min,Delay,Sum,Mean,STD,TsRank,TsMax,TsMin,DecayLinea... | pd.DataFrame(data2['open'] - data2['open_min']) | pandas.DataFrame |
import pandas as pd
import numpy as np
""" LOCAL IMPORTS """
from src.data_preprocessing import remove_misc, randomize_units
from src.common import Common
from src.common import get_max_len, create_final_data
from src.data_creation.laptop_data_classes import populate_spec
from src.data_creation.general_cpu_data_creati... | pd.read_csv('data/train/spec_train_data_new.csv') | pandas.read_csv |
import numpy as np
import pandas as pd
import pandas._testing as tm
def test_data_frame_value_counts_unsorted():
df = pd.DataFrame(
{"num_legs": [2, 4, 4, 6], "num_wings": [2, 0, 0, 0]},
index=["falcon", "dog", "cat", "ant"],
)
result = df.value_counts(sort=False)
expect... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
import copy
import gc
import numpy
from numpy.linalg import LinAlgError
import joblib
import pandas
import psutil
import pygmo
from scipy.optimize import minimize
from scipy.optimize import differential_evolution
import time
from typing import Dict, List, Tuple
import warnings
from .constants import Cons... | pandas.DataFrame(repeated_estimates) | pandas.DataFrame |
import json
from datetime import datetime
import numpy as np
import pandas as pd
from joblib import dump, load
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.preprocessing import OneHotEncoder
from sklearn.impute impo... | pd.json_normalize(df_dicts[col]) | pandas.json_normalize |
import os
import param
import pandas as pd
from .tasks import add_async
from .projects import _get_project_dir
from .collections import get_collections
from .metadata import get_metadata, update_metadata
from .. import util
from .. import static
from ..plugins import load_providers, load_plugins, list_plugins
from ..... | pd.DataFrame(datasets) | pandas.DataFrame |
from __future__ import division
from datetime import datetime
import sys
if sys.version_info < (3, 3):
import mock
else:
from unittest import mock
import pandas as pd
import numpy as np
import random
from nose.tools import assert_almost_equal as aae
import bt
import bt.algos as algos
def test_algo_name():... | pd.to_datetime('2018-01-02') | pandas.to_datetime |
""" test the scalar Timestamp """
import pytz
import pytest
import dateutil
import calendar
import locale
import numpy as np
from dateutil.tz import tzutc
from pytz import timezone, utc
from datetime import datetime, timedelta
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from pandas.ts... | conversion.pydt_to_i8(result) | pandas._libs.tslibs.conversion.pydt_to_i8 |
# encoding: utf-8
import tkinter.messagebox
import webbrowser
from tkinter import *
import jieba
import pandas as pd
import pymongo
from pyecharts import options as opts
from pyecharts.charts import Bar, Page, Pie, WordCloud, Line, Map
class App:
def __init__(self, master):
self.master = ... | pd.DataFrame(data) | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import mplfinance as mpl
plt.rcParams['legend.facecolor'] = 'darkgray'
############################## RETAIL SALES ##############################
def process_retailsales(path):
data = pd.read_csv(path, index_col=0, parse_... | pd.read_csv(path, index_col=0, parse_dates=True) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Wed May 19 18:24:25 2021
@author: HASANUL
"""
import pandas as pd
from scipy import sparse
ratings = pd.read_csv('ratings.csv')
movies = | pd.read_csv('movies.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
# Copyright (c) 2016-2021 by University of Kassel and Fraunhofer Institute for Energy Economics
# and Energy System Technology (IEE), Kassel. All rights reserved.
import pandas as pd
from pandapower.plotting.generic_geodata import create_generic_coordinates
from pandapower.plotting.plotly.tr... | pd.Series(index=hover_index, data=hoverinfo) | pandas.Series |
from kmeaningful import __version__
from kmeaningful.preprocess import preprocess
from sklearn.datasets import make_blobs
import pandas as pd
import numpy as np
import pytest
def test_version():
assert __version__ == '0.1.0'
def test_preprocess():
""" Performs tests for preprocess function """
# empty... | pd.DataFrame({}) | pandas.DataFrame |
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