prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This module is for visualizing the results
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from sklearn.manifold import TSNE
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
import... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 15 11:51:39 2020
This is best run inside Spyder, not as standalone script.
Author: @hk_nien on Twitter.
"""
import re
import sys
import io
import urllib
import urllib.request
from pathlib import Path
import time
import locale
import json
import pan... | pd.isna(res_t_end) | pandas.isna |
"""A collections of functions to facilitate
analysis of HiC data based on the cooler and cooltools
interfaces."""
import warnings
from typing import Tuple, Dict, Callable
import cooltools.expected
import cooltools.snipping
import pandas as pd
import bioframe
import cooler
import pairtools
import numpy as np
... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""model_bnb_h.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1LubfQy8-34FekTlgdarShQ5MUnXa328i
"""
import pandas as pd
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import statsmodels.api as ... | pd.Series(DoI) | pandas.Series |
# -- coding: utf-8 --
import io
import cv2
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import pandas as pd
def pie(predict_data):
plt.rcParams['font.sans-serif'] = ['Microsoft Yahei']
# 指定饼图的每个切片名称
labels = '弱火', '正常', '过火'
colors = ['r', 'y', 'b']
# 指定每个切片的数值，从而决定了百分... | pd.DataFrame(predict_data, columns=['结果', '概率', '弱火', '正常', '过火'], dtype=float) | pandas.DataFrame |
# -*- encoding: utf-8 -*-
import time
import json
import pandas as pd
class Hmm:
def __init__(self):
self.trans_p = {'S': {}, 'B': {}, 'M': {}, 'E': {}}
self.emit_p = {'S': {}, 'B': {}, 'M': {}, 'E': {}}
self.start_p = {'S': 0, 'B': 0, 'M': 0, 'E': 0}
self.state_num = {'S': 0, 'B':... | pd.DataFrame(index=self.state_list) | pandas.DataFrame |
import pandas as pd
from web3 import Web3
def get_cleaned_poap_data():
###__getting all info about POAP events__###
poap_events = pd.read_json("datasets/event_data.json")
# renaming event columns for merging with poap dataset
new_event_columns_names = {}
for col in poap_events.columns:
i... | pd.read_json("datasets/dao_member_daohaus.json") | pandas.read_json |
import numpy as np
import pandas as pd
from numba import njit, typeof
from numba.typed import List
from datetime import datetime, timedelta
import pytest
import vectorbt as vbt
from vectorbt.portfolio.enums import *
from vectorbt.generic.enums import drawdown_dt
from vectorbt import settings
from vectorbt.utils.random... | pd.DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05']) | pandas.DatetimeIndex |
import sys
from PyQt5.QtCore import QSize
from PyQt5.QtGui import QPixmap, QImage, QPalette, QBrush
from PyQt5.QtWidgets import *
from PyQt5 import uic
import pandas as pd
form_class = uic.loadUiType('./ui/Title_.ui')[0]
cam = True
class Title(QWidget, form_class):
def __init__(self):
super().__init__()
... | pd.read_csv('./file/friend.csv') | pandas.read_csv |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from scipy.stats import multivariate_normal as mvn
import seaborn as sn
import math
import gc
import tensorflow as tf
from tensorflow.keras.models import Sequ... | pd.DataFrame(data=X_valid_data, columns=X_ID2) | pandas.DataFrame |
from datetime import datetime as dt
import os
import pandas as pd
import ntpath
import numpy as np
import math
from distutils.dir_util import copy_tree
from shutil import rmtree
import sqlite3
# 'cleanData' is taking the data that was imported from 'http://football-data.co.uk/'
# and 'cleaning' the data so that only ... | pd.read_csv(final_path) | pandas.read_csv |
import numpy as np
import pandas as pd
from scipy.spatial import distance
from seaborn import clustermap
Air = ["taxonomic_profile_4.txt" ,"taxonomic_profile_7.txt" ,"taxonomic_profile_8.txt" ,"taxonomic_profile_9.txt" ,"taxonomic_profile_10.txt" ,"taxonomic_profile_11.txt" ,"taxonomic_profile_12.txt" ,"taxonomic_prof... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
def compare_df(df_list,column,name_ext):
"""Creates DataFrame from same column of multiple DataFrames (df_list) and
resample it linear in time.
It needs:
df_list ... 1D list with pandas.DataFrames which have the common column(s)
<column(s)>
column ... column or list of... | pd.tseries.offsets.DateOffset(seconds=1) | pandas.tseries.offsets.DateOffset |
"""
Common functions used in flux calculation
(c) 2016-2017 <NAME> <<EMAIL>>
"""
from collections import namedtuple
import warnings
import numpy as np
from scipy import optimize
import scipy.constants.constants as sci_const
import pandas as pd
# Physical constants
# Do not modify unless you are in a different univ... | pd.Timestamp(chamber_config[sch_id]['schedule_end']) | pandas.Timestamp |
from discord.ext import commands
import discord
import math
import pandas as pd
from numpy import nan
import datetime as dt
import plotly.graph_objects as go
import tweepy
from wol_bot_static import token, teams, ha, pred_cols, twitter_apikey, twitter_secret_apikey, \
twitter_access_token, twitter_secret_access_tok... | pd.read_csv('data_wol/polls.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
import csv
import os
import platform
import codecs
import re
import sys
from datetime import datetime
import pytest
import numpy as np
from pandas._libs.lib import Timestamp
import pandas as pd
import pandas.util.testing as tm
from pandas import DataFrame, Series, Index, MultiIndex
from pand... | tm.get_data_path() | pandas.util.testing.get_data_path |
from project import logger
from flask_mongoengine import ValidationError
from mongoengine import MultipleObjectsReturned, DoesNotExist
import pandas as pd
def get_user(id_, username=None):
from project.auth.models import User
user_obj = None
try:
if username:
user_obj = User.objects.... | pd.Series(type_) | pandas.Series |
#Ref: <NAME>
"""
Code tested on Tensorflow: 2.2.0
Keras: 2.4.3
dataset: https://finance.yahoo.com/quote/GE/history/
Also try S&P: https://finance.yahoo.com/quote/%5EGSPC/history?p=%5EGSPC
"""
import numpy as np
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense, Dro... | pd.to_datetime(df['Date']) | pandas.to_datetime |
import pandas as pd
import numpy as np
import scipy.stats as scs
import keras
from keras.models import Sequential, Model, Input
from keras.layers import Dense, Dropout, Activation
import tensorflow as tf
import requests
import json
from IPython.display import display, Image
import urllib.request
from PIL.ExifTags im... | pd.DataFrame({'imgfile':images,'simscore':sims}) | pandas.DataFrame |
import math
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
import json
with open('prescraped/artist_result.csv') as c:
table = pd.read_csv(c, header=None)
popular = table[table.iloc[:, 4] >= 65]
candidates = table[table.iloc[:,4]<65]
popular_ids = set()
for pid in popular.iloc[:,0]:
... | pd.DataFrame(columns=['id', 'cluster']) | pandas.DataFrame |
# coding:utf-8
#
# The MIT License (MIT)
#
# Copyright (c) 2018-2020 azai/Rgveda/GolemQuant
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation... | pd.to_datetime(data_realtime['datetime']) | pandas.to_datetime |
#!/usr/bin/env python
# coding: utf-8
# In[24]:
import numpy
import pandas as pd
import tensorflow as tf
from PyEMD import CEEMDAN
import warnings
warnings.filterwarnings("ignore")
### import the libraries
from tensorflow import keras
from tensorflow.keras import layers
from keras.models import Sequential
from ke... | pd.DataFrame(testX) | pandas.DataFrame |
"""Contains the code for ICAPAI'21 paper "Counterfactual Explanations for Multivariate Time Series"
Authors:
<NAME> (1), <NAME> (1), <NAME> (2), <NAME> (1)
Affiliations:
(1) Department of Electrical and Computer Engineering, Boston University
(2) Sandia National Laboratories
This work has been partially f... | pd.DataFrame() | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
import json
import lib.galaxy_utilities as gu
from astropy.io import fits
from tqdm import tqdm
aggregated_models = pd.read_pickle('lib/models.pickle')['tuned_aggregate']
def get_n_arms(gal):
keys = (
't11_arms_number_a31_1_debiased',
't11_arms_nu... | pd.DataFrame([], columns=('hart_pa', 'winding', 'n_arms')) | pandas.DataFrame |
# 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_excel(io=filename, **excel_to_df_kwargs) | pandas.read_excel |
# Command to run bokeh server
# bokeh serve --show example_data_visualization_with_bokeh.py
# Import the necessary modules
from bokeh.io import curdoc, show
from bokeh.models import ColumnDataSource, Slider, CategoricalColorMapper, HoverTool, Select
from bokeh.plotting import figure
from bokeh.palettes import S... | pandas.read_csv("C:\\Users\\olive\\Documents\\Class\\Data Mining\\Bokeh\\airline_data.csv") | pandas.read_csv |
#!/usr/bin/python
"""functions to create the figures for publication
"""
import seaborn as sns
import math
import pyrtools as pt
import neuropythy as ny
import os.path as op
import warnings
import torch
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from mpl_toolkits.axes_grid1.anchored_art... | pd.concat(data) | pandas.concat |
import argparse
import glob
import itertools
import os
import random
import numpy as np
import pandas as pd
from scipy.stats import ttest_ind, kendalltau
def parse_argument() -> argparse.Namespace:
"""
Parse input arguments.
"""
parser = argparse.ArgumentParser()
parser.add_argument(
'--... | pd.read_csv(filename, sep='\t', names=names) | pandas.read_csv |
import pandas as pd
import numpy as np
import tkinter as tk
from tkinter import filedialog
Response=pd.read_json("1.json",encoding="UTF-8")
carList=Response["response"]["classifieds"]
df=pd.DataFrame(carList)
for each in range(2,295):
try:
Response=pd.read_json(str(each)+".json",en... | pd.DataFrame(df) | pandas.DataFrame |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pa
# local_conn = mu.get_conn()
# local_conn = create_engine('mysql+pymysql://root:root@localhost:3306/test?charset=utf8')
# 显示所有列
pa.set_option('display.max_columns', None)
# 显示所有行
pa.set_option('display.max_rows', None)
path = r'C:\Users\AL\Deskt... | pa.DataFrame(text_df, columns=col_n) | pandas.DataFrame |
#!/usr/bin/env python
import argparse
import logging
from glob import glob
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from baselines.eval_task import construct_parser as eval_construct_parser
from baselines.eval_task import main as eval_main
def plot_log_file(log_fi... | pd.DataFrame(v) | pandas.DataFrame |
##############################################################################
# Usage: python extract_QCT.py Proj_path Demo_path Proj
# ex) python extract_QCT.py
# data/sample_Proj/Proj_Subj
# data/sample_demo.csv
# ENV18PM
#
# Run Time: ~1 min
# Ref: ENV18PM.drawio
# ################################... | pd.concat([final_df, df], ignore_index=True) | pandas.concat |
# Substitute for psvl
# Front matter
##############
import os
from os import fdopen, remove
from tempfile import mkstemp
from shutil import move
import glob
import re
import time
import pandas as pd
import numpy as np
from scipy import constants
from scipy.optimize import curve_fit, fsolve
import matplotlib
import mat... | pd.read_csv(K_bcc_path, engine='python') | pandas.read_csv |
#!/usr/bin/env python
"""Tests for `arcos_py` package."""
from numpy import int64
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from arcos4py import ARCOS
from arcos4py.tools._errors import noDataError
@pytest.fixture
def no_bin_data():
"""
pytest fixture t... | pd.read_csv('tests/testdata/2objMergeSplitCommon_in.csv') | pandas.read_csv |
# libraries
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import os, sys
import matplotlib.dates as mdates
import matplotlib as mpl
from matplotlib.colors import ListedColormap
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from matplotlib.offsetbox import An... | pd.DataFrame(melt_rate, columns=["Site", "hour", "melted"]) | pandas.DataFrame |
from io import StringIO
from typing import Dict
import unittest
import pandas as pd
from data_manager.base_manager import DataParam
from data_manager.time_series_manager import TimeSeriesDataManager
from proto.aiengine.v1 import aiengine_pb2
def get_test_fields(fill_method) -> Dict[str, aiengine_pb2.FieldData]:
... | pd.to_datetime(10, unit="s") | pandas.to_datetime |
"""
A module used to work
with animations
"""
import abc
from enum import Enum
import json
from typing import Optional, List, Union
import pandas as pd
from pandas.api.types import is_numeric_dtype
from ipyvizzu.json import RawJavaScript, RawJavaScriptEncoder
from ipyvizzu.schema import DataSchema
class Animation:... | pd.DataFrame(data_frame) | pandas.DataFrame |
import numpy as np
import pandas as pd
import xarray as xr
def convert_datetime64(obj, tz_from, tz_to, **kwargs):
"""Convert a numpy datetime object to a different timezone.
Numpy datetime objects do not have native support for timezones anymore.
Therefore pandas is used to convert between different timezones.
... | pd.notnull(x) | pandas.notnull |
SECONDS_IN_ONE_DAY = 60*60*24 # 86400 # used for granularity (daily)
import logging
logger = logging.getLogger('isitfit')
# Exception classes
class NoCloudtrailException(Exception):
pass
class DdgNoData(ValueError):
pass
class HostNotFoundInDdg(DdgNoData):
pass
class DataNotFoundForHostInDdg(DdgNoData):... | pd.DataFrame({'a2': s2}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101
import itertools
import warnings
from warnings import catch_warnings
from datetime import datetime
from pandas.types.common import (is_integer_dtype,
is_float_dtype,
is_scalar)
from pandas.compat... | tm.assert_frame_equal(start_dataframe, expected_dataframe) | pandas.util.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import pytest
import re
from numpy import nan as NA
import numpy as np
from numpy.random import randint
from pandas.compat import range, u
import pandas.compat as compat
from pandas import Index, Series, DataFrame, isn... | u('c') | pandas.compat.u |
# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from pathlib import Path
import pandas as pd
import re
from tabulate import tabulate
from colo... | pd.DataFrame(rows) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
'''
'''
import time
import pandas as pd
import datarobot as dr
from datarobot.models.modeljob import wait_for_async_model_creation
import numpy as np
import re
import os
from datarobot.errors import JobAlreadyRequested
token_id = ""
ts_setting = {"project_name":"fake_job_postin... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import lightgbm as lgb
from sklearn.metrics import roc_auc_score
import numpy as np
print("Loading resampled train data")
train_X = pd.read_csv("../input/AllData_v4_os.train")
train_X.pop("Unnamed: 0")
print("Loading resampled train labels")
train_y = pd.read_csv("../input/AllData_v4_os.label")
tr... | pd.DataFrame(valid_preds) | pandas.DataFrame |
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
def test_over_with_sorting(c, user_table_1):
df = c.sql(
"""
SELECT
user_id,
ROW_NUMBER() OVER (ORDER BY user_id, b) AS R
FROM user_table_1
"""
)
df = df.compute()
expected_df = pd... | pd.DataFrame({"user_id": user_table_2.user_id, "R": [1, 1, 1, 1]}) | pandas.DataFrame |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2020/3/23 19:12
Desc: 东方财富网-数据中心-沪深港通持股
http://data.eastmoney.com/hsgtcg/
http://finance.eastmoney.com/news/1622,20161118685370149.html
"""
import requests
import json
import demjson
import pandas as pd
from bs4 import BeautifulSoup
def stock_em_hsgt_north_net_fl... | pd.DataFrame(data_json["data"]["sh2hk"]) | pandas.DataFrame |
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
def pyscript_diseases():
# measels
measlesdf = | pd.read_csv('https://docs.google.com/spreadsheets/d/1ogMiFRnX-N4lp1cqI0N22F9K9fFVVFfCWxw4T6W2iVw/export?format=csv&id') | pandas.read_csv |
"""Internal utilties; not for external use
"""
import contextlib
import functools
import itertools
import os.path
import re
import warnings
from collections import OrderedDict
from typing import (
AbstractSet, Any, Callable, Container, Dict, Hashable, Iterable, Iterator,
Mapping, MutableMapping, MutableSet, Opt... | pd.isnull(first) | pandas.isnull |
import gzip
import pickle5 as pickle
# import pickle
from collections import defaultdict
import numpy as np
import pandas as pd
import os
from copy import deepcopy
import datetime
import neat
from tensorflow.python.framework.ops import default_session
from scipy.optimize import curve_fit
from ongoing.prescriptors.ba... | pd.DataFrame(df_dict) | pandas.DataFrame |
import datetime
import os
import geopandas as gpd
import numpy as np
import pandas as pd
import pytest
from shapely.geometry import Point
from sklearn.cluster import DBSCAN
from geopandas.testing import assert_geodataframe_equal
import trackintel as ti
from trackintel.geogr.distances import calculate_distance_matrix
... | pd.Timestamp("1971-01-02 09:00:00", tz="utc") | pandas.Timestamp |
import io
import os
import json
import gc
import pandas as pd
import numpy as np
from datetime import date, timedelta
from fastapi import FastAPI, File, HTTPException
import lightgbm as lgb
from lightgbm import LGBMClassifier
import matplotlib.pyplot as plt
import joblib
app = FastAPI(
title="Home Credit Default... | pd.DataFrame(ext_source_2_data_repaid) | pandas.DataFrame |
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
import copy
import seaborn as sn
from sklearn.naive_bayes import GaussianNB, MultinomialNB, CategoricalNB
from DataLoad import dataload
from Classifier.Bayes.NaiveBayes import NaiveBayes
from sklearn.neighbors import KNeighborsClassifier... | pd.unique(train_label) | pandas.unique |
import sqlite3
from sqlite3 import Error
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
import folium
conn = sqlite3.connect('../data/rodents_data.db')
sql_statement = """SELECT latitude,longitude,count(inspection_date) as recurrence_index FROM 'rodent_incidents' where inspection_dat... | pd.read_sql_query(sql_statement, conn) | pandas.read_sql_query |
"""
Script to run MCCE simulation at different charges for water molecules.
"""
import os
import sys
import numpy as np
from scipy import stats
from pymcce.automated_mcce import MCCEParams
from pymcce.mcce_simulation import Simulation
from pymcce.utils import write_watpdb_from_coords, get_last_prot_at_index
import ma... | pd.DataFrame({'x': dipole_x, 'y': dipole_y, 'z': dipole_z, 'count': numbers}) | pandas.DataFrame |
# %%
import pandas as pd
import numpy as np
import time
import datetime
from datetime import datetime as dt
from datetime import timezone
from spacepy import coordinates as coord
from spacepy.time import Ticktock
from astropy.constants import R_earth
import plotly.graph_objects as go
from plotly.subplots imp... | pd.unique(agroup[sat]) | pandas.unique |
"""
Unit test suite for OLS and PanelOLS classes
"""
# pylint: disable-msg=W0212
from __future__ import division
from datetime import datetime
import unittest
import nose
import numpy as np
from pandas import date_range, bdate_range
from pandas.core.panel import Panel
from pandas import DataFrame, Index, Series, no... | assert_almost_equal(exp_x_filtered, result._x_filtered.values) | pandas.util.testing.assert_almost_equal |
# -*- coding: utf-8 -*-
from collections import OrderedDict
from datetime import date, datetime, timedelta
import numpy as np
import pytest
from pandas.compat import product, range
import pandas as pd
from pandas import (
Categorical, DataFrame, Grouper, Index, MultiIndex, Series, concat,
date_range)
from p... | Grouper(freq='6MS', level='foo') | pandas.Grouper |
# -*- coding: utf-8 -*-
import nose
import numpy as np
from datetime import datetime
from pandas.util import testing as tm
from pandas.core import config as cf
from pandas.compat import u
from pandas.tslib import iNaT
from pandas import (NaT, Float64Index, Series,
DatetimeIndex, TimedeltaIndex, da... | DatetimeIndex([0, np.nan], tz='US/Eastern') | pandas.DatetimeIndex |
import unittest
import os
import shutil
import numpy as np
import pandas as pd
from aistac import ConnectorContract
from ds_discovery import Wrangle, SyntheticBuilder
from ds_discovery.intent.wrangle_intent import WrangleIntentModel
from aistac.properties.property_manager import PropertyManager
class WrangleIntentCo... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 25 14:09:46 2018
@author: JSong
"""
import numpy as np
import pandas as pd
from sklearn import metrics
import matplotlib.pyplot as plt
#%maplotlib inline
import seaborn as sns
#from tqdm import tqdm
import warnings
warnings.filterwarnings("ignore")
#import pysnooper
__... | pd.crosstab(y_true,y_pred) | pandas.crosstab |
import os
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, date, timedelta
import calendar
from bota import constant
import re
import discord
def findDay(date):
born = datetime.strptime(date, '%Y-%m-%d').weekday()
return (calendar.day_name[born])
class LogStat():
def __... | pd.Series(temp_rows, index=temp_dates) | pandas.Series |
import pandas
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
def evaluate_components(clf, x, y, n_iterations=500, check = 100,
evaluate = True, plot = True, thr = 0.95,
metric=None, random_state=123):
if type(... | pandas.DataFrame(X.loc[n_ind],copy=True) | pandas.DataFrame |
import numpy as np
import cv2
import csv
import os
import pandas as pd
import time
def calcuNearestPtsDis2(ptList1):
''' Find the nearest point of each point in ptList1 & return the mean min_distance
Parameters
----------
ptList1: numpy array
points' array, shape:(x,2)
Return
... | pd.read_csv(csv_dir+'/'+ picID +'positive_lymph_pts.csv',usecols=['x_cord', 'y_cord']) | pandas.read_csv |
from utils import load_yaml
import pandas as pd
import click
from datetime import datetime, timedelta
import numpy as np
import os
cli = click.Group()
@cli.command()
@click.option('--lan', default='en')
@click.option('--config', default="configs/configuration.yaml")
def dump(lan, config, country_code):
# load th... | pd.to_datetime(tweets.created_at, format='%a %b %d %H:%M:%S +0000 %Y') | pandas.to_datetime |
import numpy as np
import pytest
from pandas.compat import range, u, zip
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series
import pandas.core.common as com
from pandas.core.indexing import IndexingError
from pandas.util import testing as tm
@pytest.fixture
def frame_random_data_integer_mul... | tm.assert_series_equal(result, expected) | pandas.util.testing.assert_series_equal |
import os, re, subprocess, matplotlib, seaborn, pandas
from . import utils, FEATURETABLE, GENOME, CODONTABLE, TYPEPOS, SEQTYPES
from time import time
from Bio import SeqIO, AlignIO
def rm_genome_w_stopm(vtab):
"""Return a dataframe of genomes with nonsense variants given a
dataframe of genomes with their shared & u... | pandas.DataFrame(sites_out,columns=['protein','site','syn', 'nonsyn', 'post_prob']) | pandas.DataFrame |
import pandas as pd
import numpy as np
from bokeh.io import curdoc
from bokeh.layouts import row, column
from bokeh.models import ColumnDataSource
from bokeh.models.widgets import Slider, TextInput
from bokeh.plotting import figure
from bokeh.palettes import Spectral5, Spectral11
from bokeh.driving import count
# ... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Dec 22 11:30:32 2018
@author: jkp
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data= | pd.read_csv("/home/sysadm/Desktop/JKP BSPro/Used_startup_funding.csv") | pandas.read_csv |
import matplotlib.pyplot as plt
import numpy as np
import os,glob,sys,importlib,pickle#,scipy,coolbox,pybedtools,
# from tqdm import tqdm
from scipy.stats import rankdata
import pandas as pd
import networkx as nx
import seaborn as sns
from joblib import delayed, wrap_non_picklable_objects
from pathlib import Path
impor... | pd.DataFrame(columns=['N','Q','I','type']) | pandas.DataFrame |
import sys
import pandas as pd
def combine_express_output(fnL,
column='eff_counts',
names=None,
tg=None,
define_sample_name=None,
debug=False):
"""
Combine eXpress output file... | pd.DataFrame(transcriptL) | pandas.DataFrame |
#!/usr/bin/env python
import pandas as pd
import numpy as np
from collections import defaultdict
from itertools import combinations
from itertools import chain
import pickle
from pas_utils import *
from feature import *
if __name__=="__main__":
OUTPUT_DIR="./APA_ML/processed"
if not os.path.exists(OUTPUT_DIR):... | pd.Series(bl_signal,index=sorted_index) | pandas.Series |
import os
import sys
import time
import argparse
import pandas as pd
import numpy as np
from scipy import interp
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.ticker import FormatStrFormatter
plt.style.use('ggplot')
sns.set(color_codes=True)
sns.set(f... | pd.read_csv(filename, sep='\t', nrows=1) | pandas.read_csv |
# all domains
# merge/split common boundary x = max(3bin,0.1 TAD Length)
# region < agrs.remote
# less complex
# zoom
# to filter the strength first
import pandas as pd
import numpy as np
#from tqdm import tqdm
import argparse
import os
# import warnings
# warnings.filterwarnings('ignore')
# the arguments from command... | pd.DataFrame(columns=colnames) | pandas.DataFrame |
from collections import namedtuple
import numpy as np
import pandas as pd
import random
from scipy.special import gammaln
from scipy.integrate import solve_ivp
from scipy.optimize import minimize
from scipy.linalg import expm
from tqdm import tqdm
from matplotlib import pyplot as plt
from tqdm import tqdm
from eda im... | pd.date_range(start=start, end=today) | pandas.date_range |
# -*- coding: utf8 -*-
# My imports
from __future__ import division
import numpy as np
import os
import pandas as pd
from astropy.io import fits
def save_synth_spec(x, y, initial=None, **options):
'''Save synthetic spectrum of all intervals
Input
----
x : ndarray
Wavelength
y : ndarray
... | pd.DataFrame([]) | pandas.DataFrame |
print('Chapter 04: Data Preparation')
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print('setup.py')
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
BASE_DIR = ".."
def figNum():
figNum.counter += 1
return "{0:02d}".format(figNum.counter)
figNum.counter = 0
FIGPREFIX = 'ch04_fig'
print('~~~~~~~... | pd.Series(data, name='text') | pandas.Series |
import ast
import os
import re
import uuid
import pandas as pd
import configuration as cf
from guesslang import Guess
from pydriller import Repository
from utils import log_commit_urls
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
fixes_columns = [
'cve_id',
'hash',
'repo_url',
]
commit_columns = [
'has... | pd.DataFrame.from_dict(repo_methods) | pandas.DataFrame.from_dict |
from collections import OrderedDict
import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
)
import pandas._testing as tm
from pandas.core.construction import create_series_with_explicit_dtype
class TestFromDict:
# Note: these tests are specif... | Series(dtype=object) | pandas.Series |
import os
import pandas as pd
import numpy as np
from sklearn.model_selection import StratifiedShuffleSplit
def read_meta(meta_csv):
df = pd.read_csv(meta_csv, sep=',')
df = pd.DataFrame(df)
audio_names = []
set_categories = []
cycle_labels = []
for row in df.iterrows():
audio_name = ... | pd.DataFrame(data={'audio_name': audio_test, 'cycle_label': label_test}) | pandas.DataFrame |
""" self-contained to write legacy pickle files """
from __future__ import print_function
def _create_sp_series():
import numpy as np
from pandas import SparseSeries
nan = np.nan
# nan-based
arr = np.arange(15, dtype=np.float64)
index = np.arange(15)
arr[7:12] = nan
arr[-1:] = nan
... | SparseTimeSeries(arr, index=date_index, kind='block') | pandas.SparseTimeSeries |
"""This module contains classes and functions specific to SAMPL6 data files"""
import pandas as pd
import numpy as np
from titrato.titrato import TitrationCurve, free_energy_from_population
from titrato.titrato import data_dir
from titrato.stats import (
area_between_curves,
BootstrapDistribution,
array_rms... | pd.DataFrame(columns=["Molecule", "Δ"]) | pandas.DataFrame |
# <NAME> & LYDIA SCHWEITZER Assignment 3
# Yelp Data visualization using Streamlit
# code referenced from demo-uper-nyc-pickups
# https://github.com/streamlit/demo-uber-nyc-pickups/blob/master/streamlit_app.py
# IMPORTS **********************************************************************
import streamlit as s... | pd.to_datetime(data[dateCol]) | pandas.to_datetime |
import logging
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from math import pi
from wordcloud import (WordCloud, get_single_color_func)
import numpy as np
from PIL import Image
import squarify
import os
logger = logging.getLogger('nodes.data_viz')
class SimpleGroupedColorFunc(object):
... | pd.read_sql_query(query, client.engine) | pandas.read_sql_query |
import pandas as pd
import requests
from bs4 import BeautifulSoup
import numpy as np
from time import sleep
website = lambda start, end: [f"https://en.wikipedia.org/wiki/UFC_{i}" for i in range(start, end)]
def get_top_level_data(end, start=20, get_fight_card_stats=False, both=True, avg_pause=.6):
"""
@para... | pd.read_html(j) | pandas.read_html |
from typing import List, Optional
import numpy as np
import pandas as pd
from pandas import Series
from snorkel.labeling.model import LabelModel
from bohr.config.pathconfig import PathConfig
from bohr.datamodel.dataset import Dataset
from bohr.datamodel.task import Task
def label_dataset(
task: Task,
datase... | Series(probs[:, 0]) | pandas.Series |
'''Python script to generate Revenue Analysis given ARR by Customer'''
'''Authors - <NAME>
'''
import numpy as np
import pandas as pd
from datetime import datetime
import collections
from .helpers import *
class RevAnalysis:
def __init__(self, json):
print("INIT REV ANALYSIS")
self.arr = pd.Data... | pd.to_datetime(mrr_ttm.columns[-1]) | pandas.to_datetime |
# This script performs the statistical analysis for the pollution growth paper
# Importing required modules
import pandas as pd
import numpy as np
import statsmodels.api as stats
from ToTeX import restab
# Reading in the data
data = pd.read_csv('C:/Users/User/Documents/Data/Pollution/pollution_data_kp.cs... | pd.get_dummies(ghg_data['Country']) | pandas.get_dummies |
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# 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.testing.assert_frame_equal(expected, result) | pandas.testing.assert_frame_equal |
from datetime import datetime, timedelta
import pandas as pd
from driver_repo import driver, driver_stats_fv
from feast import FeatureStore
def main():
pd.set_option("display.max_columns", None)
| pd.set_option("display.width", 1000) | pandas.set_option |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import timedelta
from numpy import nan
import numpy as np
import pandas as pd
from pandas import (Series, isnull, date_range,
MultiIndex, Index)
from pandas.tseries.index import Timestamp
from pandas.compat import range
from pandas.u... | assert_series_equal(result, s) | pandas.util.testing.assert_series_equal |
import pytest
import os
from mapping import util
from pandas.util.testing import assert_frame_equal, assert_series_equal
import pandas as pd
from pandas import Timestamp as TS
import numpy as np
@pytest.fixture
def price_files():
cdir = os.path.dirname(__file__)
path = os.path.join(cdir, 'data/')
files = ... | TS('2015-01-03') | pandas.Timestamp |
#!/usr/bin/env python
# coding: utf-8
# # Attrition Rate Analytics
# Customer Attrition is a tendency of customers to abandon a brand and stop being a paying client of a particular business. The percentage of customers that discontinue using a company’s products or services during a particular time period is called C... | pd.get_dummies(train_df, columns=cat_features, drop_first=True) | pandas.get_dummies |
# -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime, timedelta
import functools
import itertools
import numpy as np
import numpy.ma as ma
import numpy.ma.mrecords as mrecords
from numpy.random import randn
import pytest
from pandas.compat import (
PY3, PY36, OrderedDict, ... | DataFrame(data) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 8 12:39:17 2019
@author: nmei
This script systematically test the encoding model (Ridge regression) with different
embedding features predicting the BOLD signal, within each small ROIs (15 in total)
"""
import os
import numpy as np
import pand... | pd.read_csv(f) | pandas.read_csv |
#%%
import os
import sys
try:
os.chdir('/Volumes/GoogleDrive/My Drive/python_code/connectome_tools/')
print(os.getcwd())
except:
pass
from pymaid_creds import url, name, password, token
import pymaid
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# allows text... | pd.read_csv('VNC_interaction/data/brA1_axon-dendrite.csv', header = 0, index_col = 0) | pandas.read_csv |
from multiprocessing import Process, Queue
import matplotlib.pyplot as plt
from PyQt5.QtWidgets import QMainWindow, QApplication, QPushButton, QWidget
from PyQt5.QtWidgets import QAction, QTabWidget,QVBoxLayout, QFileDialog
import os
from pysilcam.config import PySilcamSettings
import pysilcam.oilgas as scog
import nu... | pd.DataFrame(data=[vd], columns=dias) | pandas.DataFrame |
"""
Movie Recommendation Skill.
- movies like <movie-name>
"""
import numpy as np
import pandas as pd
from nltk import edit_distance
# Local Imports.
from backend.config import cosine_sim_scores_path, movie_data_path
def find_nearest_title(user_input_title):
"""
Checks for nearest movie title in dataset
... | pd.Series(movie_data.index, index=movie_data["title"]) | pandas.Series |
import re
import io
import bs4
import csv
import copy
import urllib
import pandas as pd
import numpy as np
from .utils import *
from .web import *
from pyhelpers.dir import validate_input_data_dir
from pyhelpers.ops import confirmed, download_file_from_url, fake_requests_headers,update_nested_dict
from pyhelpers.store ... | pd.concat([cities_coords, coordinates], axis=1) | pandas.concat |
from abc import abstractmethod
from datetime import timedelta
import numpy as np
import pandas as pd
from src import constants
from src.data_generator.day_ahead_extractors.base_day_ahead_extractor import BaseDayAheadExtractor
from src.data_generator.day_ahead_extractors.utils.mappings import ACTUAL_MAPPING, FORECAST_... | pd.concat([data, adjacent_data]) | pandas.concat |
import errno
import json
import logging
import os
import shutil
import uuid
import zipfile
import re
import subprocess
import pandas as pd
import plotly.express as px
from plotly.offline import plot
import plotly.graph_objs as go
from installed_clients.DataFileUtilClient import DataFileUtil
from installed_clients.KB... | pd.DataFrame(matrix_tab, index=row_ids, columns=col_ids) | pandas.DataFrame |
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