prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
"""Run the mni to bold transformation on an fmriprep output"""
import os
def get_parser():
"""Build parser object."""
from argparse import ArgumentParser
from argparse import RawTextHelpFormatter, RawDescriptionHelpFormatter
parser = ArgumentParser(
description="""NiWorkflows Utilities""", for... | pd.DataFrame(data) | pandas.DataFrame |
from os import path
import sys
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
import data
def lng_lat_to_x_y(lng, lat, origin_lng, origin_lat) -> tuple:
EARTH_RADIUS = 6371.000
x = np.deg2rad(lng - origin_lng) * EARTH_RADIUS * np.cos(np.deg2rad(lat... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable=E1101
# flake8: noqa
from datetime import datetime
import csv
import os
import sys
import re
import nose
import platform
from multiprocessing.pool import ThreadPool
from numpy import nan
import numpy as np
from pandas.io.common import DtypeWarning
from pandas import DataFr... | StringIO(self.data1) | pandas.compat.StringIO |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
#########################################################################################
# Name: <NAME>
# Student ID: 64180008
# Department: Computer Engineering
# Assignment ID: A3
#######################################################################################... | pd.Series([7,11,13,17]) | pandas.Series |
import os
import pandas as pd
base_dir = "extracted_data"
files_list_dir = base_dir + "/files.xlsx"
files_dir = base_dir + "/files"
txt_files_dir = files_dir + "/txt"
pdf_files_dir = files_dir + "/pdf"
if not os.path.isdir(base_dir):
os.mkdir(base_dir)
if not os.path.isdir(files_dir):
os.mkdir(files_dir)
i... | pd.DataFrame({'type': [], 'year': [], 'number': [], 'title': [], 'note': []}) | pandas.DataFrame |
#!/home/ubuntu/anaconda3/bin//python
'''
MIT License
Copyright (c) 2018 <NAME> <<EMAIL>>
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 the righ... | pd.DataFrame(columns=('congress', 'speech_id', 'speaker_party', 'spoken_party', 'sentence')) | pandas.DataFrame |
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.model_selection import train_test_split
################Stage-1: Sentence Level Classification
df_t... | pd.read_csv('kannadaW.txt',header=None) | pandas.read_csv |
import redis # import redis module
import pyarrow as pa
import pandas as pd
import time
import json
def init_connt(host='localhost',port=6379):
return
class twitter_cache():
def __init__(self,host='localhost',port=6379):
self.r = redis.Redis(host=host, port=port, decode_responses=False)
def ... | pd.DataFrame({'A':[4,5,6]}) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 4 22:33:07 2018
@author: bruce
"""
import pandas as pd
import numpy as np
from scipy import fftpack
from scipy import signal
import matplotlib.pyplot as plt
import os
# set saving path
path_result_freq = "/home/bruce/Dropbox/Project/5.Result/5.R... | pd.DataFrame() | pandas.DataFrame |
import numpy as np; import pandas as pd
from pyg_timeseries._math import stdev_calculation_ewm, skew_calculation, cor_calculation_ewm, covariance_calculation, corr_calculation_ewm, LR_calculation_ewm, variance_calculation_ewm, _w
from pyg_timeseries._decorators import compiled, first_, _data_state
from pyg_base import ... | pd.Series(res[:,0,1], index) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 10 21:11:50 2020
@author: huiyeon
"""
##################
# RNN 실행해보기 #
##################
# import package ------------------------------
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import font_manager, rc
font_... | pd.isnull(train) | pandas.isnull |
import pandas as pd
from matplotlib import pyplot as plt
import numpy as np
from matplotlib.widgets import CheckButtons
import utils
def visualize(file_name):
# Enter CSV to process:
word_count = | pd.read_csv(file_name) | pandas.read_csv |
import argparse
import configparser
import json
import numpy as np
import os
import pandas as pd
from pathlib import Path
import skimage.io
import skimage.transform
import sys
import time
import torch
import torch.utils.data
import torchvision
from tqdm import tqdm
sys.path.insert(1, '/home/xview3/src') # use an appro... | pd.concat(preds) | pandas.concat |
#!/usr/bin/env python
######################################################################################
# AUTHOR: <NAME> <<EMAIL>>
# CONTRIBUTORS: <NAME> <<EMAIL>>, <NAME> <<EMAIL>>
# DESCRIPTION: gaitGM module with functions for KEGG PEA Tools
######################################################################... | pd.read_table(args.metDataset, sep="\t", header=0) | pandas.read_table |
"""
File: train_baseline
Description: This file trains a MLP on the wine dataset, with varied amounts of
labels available in order to establish a baseline for the model.
Author <NAME> <<EMAIL>>
License: Mit License
"""
from datetime import datetime
import numpy as np
import tensorflow as tf
import pandas as pd
import ... | pd.DataFrame.to_numpy(train_y) | pandas.DataFrame.to_numpy |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 27 19:43:48 2020
@author: tommasobassignana
"""
import pandas as pd
import numpy as np
from datetime import timedelta, datetime
from sklearn import preprocessing
del(xml_file, xroot, xtree)
data = df
del(df)
def resample(data, freq):
"""
:... | pd.DatetimeIndex(data.loc[ph + hist - 1:, "datetime"].values) | pandas.DatetimeIndex |
from __future__ import annotations
import threading
import time
import numpy as np
import pandas as pd
from aistac.components.abstract_component import AbstractComponent
from ds_discovery import EventBookPortfolio
from ds_discovery.components.commons import Commons
from ds_discovery.managers.controller_property_manage... | pd.json_normalize(explode['run_book']) | pandas.json_normalize |
import numpy as np
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Index,
Series,
Timestamp,
)
import pandas._testing as tm
from pandas.core.arrays import IntervalArray
class TestGetNumericData:
def test_get_numeric_data_preserve_dtype(self):
# get the numeric data
... | DataFrame({"A": [1, "2", 3.0]}) | pandas.DataFrame |
import networkx as nx
import pandas as pd
import numpy as np
import copy
import time
# class for features
class Features:
def __init__(self, reducible_pairs, tree_set, str_features=None, root=2, distances=True, comb_measure=True):
if str_features is None:
self.str_features = ["cherry_height", ... | pd.DataFrame() | pandas.DataFrame |
# importing libraries
from tkinter import *
from tkinter import ttk, filedialog, messagebox
import pandas as pd
import random
from tkcalendar import *
from datetime import date
from captcha.image import ImageCaptcha
import pyttsx3
import pyaudio
import speech_recognition as sr
engine = pyttsx3.init("sapi5... | pd.DataFrame(columns=['Srno', 'Task No.', 'Application Name', 'BAU- Project', 'Package Type', 'Request Type', 'BOPO', 'Package Complexity', 'Start Date', 'End Date', 'H&M Billing Cycle', 'Standard SLA Working Days', 'SLA Measurement in Working Days', 'SLA Met?', 'Remarks']) | pandas.DataFrame |
import matplotlib
import matplotlib.pylab as plt
import os
from matplotlib.pyplot import legend, title
from numpy.core.defchararray import array
from numpy.lib.shape_base import column_stack
import seaborn as sns
import pandas as pd
import itertools
import numpy as np
def plot_graph(data, plot_name, figsize, legend... | pd.set_option('display.width', None) | pandas.set_option |
# coding: utf-8
# In[106]:
from flask import Flask
from flask import request
from flask import jsonify
import pprint
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.neighbors import NearestNeighbors
app = Flask(__name__)
#... | pd.concat([w_pref1_trans,w_pref2_trans],axis=1) | pandas.concat |
'''
Module with auxiliary functions.
'''
from .xml_reader import read_xml
from gensim.parsing.preprocessing import strip_non_alphanum
from gensim.parsing.preprocessing import strip_multiple_whitespaces
from gensim.matutils import Sparse2Corpus
from string import punctuation
from nltk.corpus import stopwords
import pa... | pd.DataFrame(features) | pandas.DataFrame |
import numpy as np
import pandas as pd
from numpy.random import default_rng
#///////////////////// miscellaneous functions starts here
def cAngle(i):
x=i % 360
return x
def weib(x,A,k): #A is the scale and k is the shape factor
return (k / A) * (x / A)**(k - 1) * np.exp(-(x / A)**k) #This function show the p... | pd.DataFrame(statistics_dictionary) | pandas.DataFrame |
import pandas as pd
from pandas import HDFStore
import numpy as np
import subprocess
import io
import matplotlib.pyplot as plt
import gc
import os
from scipy.stats import ks_2samp
from functools import lru_cache
'''
Analyze wsprspots logs (prepared by WSPRLog2Pandas)
All manipulations are performed against an HDF5 ... | pd.Series([]) | pandas.Series |
from src.utils import get_files, get_platform_selector, read_file_per_line
from os import path
import re
import pandas as pd
dirname = path.dirname(__file__)
results_dir = path.join(dirname, "../tests/boot/output")
output_dir = path.join(dirname, "extracted")
output_file = "boot_times.csv"
results = get_files(results... | pd.DataFrame(data=data) | pandas.DataFrame |
# Modified version for Erie County, New York
# Contact: <EMAIL>
from functools import reduce
from typing import Generator, Tuple, Dict, Any, Optional
import os
import pandas as pd
import streamlit as st
import numpy as np
import matplotlib
from bs4 import BeautifulSoup
import requests
import ipyvuetify as v
from trait... | pd.to_datetime(erie_df['Date']) | pandas.to_datetime |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
from pandas.plotting import scatter_matrix
from sklearn import model_selection, preprocessing, svm
from sklearn.linear_model import LinearRegression
from sklearn.metrics import classification_report
from sklearn.metrics ... | pd.read_csv(r'tests\SP500.csv', parse_dates=True, index_col=0) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 4 10:27:55 2021
@author: Raj
"""
import numpy as np
from .mechanical_drive import MechanicalDrive
from .utils.load import params_from_experiment as load_parm
from .utils.load import simulation_configuration as load_sim_config
from ffta.pixel_utils.load import configura... | pd.DataFrame(index=taus, data=tfps) | pandas.DataFrame |
from __future__ import print_function, division
import pdb
import unittest
import random
from collections import Counter
import pandas as pd
import numpy as np
from scipy.spatial import distance as dist
from scipy.spatial import distance
from sklearn.neighbors import NearestNeighbors as NN
def get_ngbr(df... | pd.DataFrame(total_data) | pandas.DataFrame |
import logging
logger = logging.getLogger(__name__)
import autosklearn.metrics
import copy
import joblib
import numpy as np
import pandas as pd
import mlxtend.feature_selection
import networkx as nx
import sklearn.pipeline
import sklearn.preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn.... | pd.DataFrame() | pandas.DataFrame |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from subprocess import check_output
from keras.models import Model
from keras.layers import Dense, Embedding, Input , Activation
from keras.layers import LSTM, Bidirectional, GlobalMaxPool1D, Dropout, GRU
from ker... | pd.read_csv("data/train.csv") | pandas.read_csv |
# pylint: disable-msg=E1101,W0613,W0603
import os
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas_datareader.io import read_jsdmx
class TestJSDMX(object):
def setup_method(self, method):
self.dirpath = tm.get_data_path()
def test_tourism(self):
# OECD -... | pd.DataFrame(values, index=exp_idx, columns=exp_col) | pandas.DataFrame |
"""
Written by <NAME>, 22-10-2018
This script contains functions for data formatting and accuracy assessment of keras models
"""
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
import keras.backend as K
from math import sqrt
import numpy as ... | pd.DataFrame(df_t18, index=None, columns=["obs", "pred"]) | pandas.DataFrame |
import os
import json
import numpy as np
import pandas as pd
from copy import copy
import matplotlib.pyplot as plt
from abc import abstractmethod
from IPython.display import display, display_markdown
from .utils import load_parquet, Position
from common_utils_dev import make_dirs
from collections import OrderedDict, de... | pd.Series(self.historical_trade_returns) | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 9 12:07:56 2020
@author: B.Mika-Gospdoorz
Input files: .tsv quantification table with combined results from all samples
.tsv file with annotations extracted from gff using extract_annotations_from_gff.py
Output file: *combined_quant_ge... | pd.read_csv(annotations_table,sep="\t",index_col=0, dtype='str') | pandas.read_csv |
######## EPAUNI
with open (fn, 'r') as file:
list_lines = [line for line in file.readlines() if line.strip()]
# %%
list_time_ix=[]
regex = '[+-]?[0-9]+\.?[0-9]*'
for ix, line in enumerate(list_lines):
if 'TIME' in line:
list_time_ix.append((ix, int(float(re.findall(regex, line)[0]) ) ) )
# %% helper fu... | pd.DataFrame( {'state': ['Ohio', 'Color', 'Utah', 'ny'], 'one': [0, 4, 8, 12], 'two': [1, 5, 9, 13] } ) | pandas.DataFrame |
import pandas as pd
import requests
from bs4 import BeautifulSoup, Comment
import json
import re
from datetime import datetime
import numpy as np
comm = re.compile("<!--|-->")
class Team: #change team player object
def __init__(self, team, year, player=None):
self.year = year
self.team = team
... | pd.to_numeric(df_totals[column]) | pandas.to_numeric |
import streamlit as st
from bs4 import BeautifulSoup
import requests
import pandas as pd
import re
import ast
import base64
def local_css(file_name):
with open(file_name) as f:
st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
local_css("style.css")
st.write("""
# Steam Community Market -... | pd.to_numeric(final_df['quantity_sold']) | pandas.to_numeric |
# -*- coding: utf-8 -*-
import pandas as pd
import plotly.graph_objs as go
import requests
from base64 import b64encode as be
from dash_html_components import Th, Tr, Td, A
from datetime import datetime, timedelta
from flask import request
from folium import Map
from operator import itemgetter
from os.path import join... | pd.DataFrame(j['records']) | pandas.DataFrame |
import numpy as np
import pandas as pd
from scipy import stats as sps
from . import normalizers as norm
from . import weigtings as weight
class DataMatrix:
""" Load and Prepare data matrix """
def __init__(self, path, delimiter=",", idx_col=0):
self.data = | pd.read_csv(path, delimiter=delimiter, index_col=idx_col) | pandas.read_csv |
import datetime as dt
import unittest
from typing import Any, Callable, Dict, Union, cast
from unittest.mock import MagicMock, patch
import lmfit
import numpy as np
import pandas as pd
from numpy.testing import assert_allclose
from pandas.testing import assert_frame_equal
from darkgreybox.base_model import DarkGreyMo... | pd.Series([10, 20]) | pandas.Series |
"""Transformation of the FERC Form 714 data."""
import logging
import pathlib
import re
import geopandas
import numpy as np
import pandas as pd
import pudl
import pudl.constants as pc
logger = logging.getLogger(__name__)
##############################################################################
# Constants req... | pd.Timedelta(-10, unit="hours") | pandas.Timedelta |
from typing import List
import numpy as np
import pandas as pd
import scipy.io
from graphysio.plotwidgets.curves import CurveItem
def curves_to_matlab(
curves: List[CurveItem], filepath: str, index_label: str = 'timens'
) -> None:
sers = [c.series for c in curves]
data = | pd.concat(sers, axis=1) | pandas.concat |
import requests
import pandas as pd
import urllib.error
import os
''' This module provides a class to work with the downloaded db
of Gwas Catalog. I had to write this because I couldn't access
(for whatever reason) the Gwas Catalog Rest API documentation.
The class is initialitiated by downloading the GWAS Catalog
... | pd.DataFrame(series) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon May 27 11:13:15 2019
@author: jkern
"""
from __future__ import division
import pandas as pd
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt
def hydro(sim_years):
#################################################################... | pd.read_csv(File_name,delimiter=' ',header=None) | pandas.read_csv |
# Copyright 2016 <NAME> and The Novo Nordisk Foundation Center for Biosustainability, DTU.
# 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
# Unle... | DataFrame(columns=["name", "molecular_weight", "formula", "uipac_name", "create_date", "compound_id"]) | pandas.DataFrame |
import pyupbit
import time
from datetime import datetime
from pytz import timezone
import pandas as pd
import telegram # pip install python-telegram-bot
import json
from dotenv import load_dotenv # pip install python-dotenv
import os
def cal_target(ticker): # 변동성 돌파 전략으로 매수 목표가 설정
# time.sleep(0.1)
... | pd.read_csv('saved_data.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
# Copyright (c) 2021, libracore AG and contributors
# For license information, please see license.txt
from __future__ import unicode_literals
import frappe
import pandas as pd
from frappe.utils.data import add_days, getdate, get_datetime, now_datetime
# Header mapping (ERPNext <> MVD)
hm = {
... | pd.set_option('display.max_rows', None, 'display.max_columns', None) | pandas.set_option |
# @author <NAME>
# This code is licensed under the MIT license (see LICENSE.txt for details).
"""
Custom data structures for paperfetcher.
"""
import contextlib
import csv
import logging
import pandas as pd
import rispy
from rispy.config import LIST_TYPE_TAGS, TAG_KEY_MAPPING
from paperfetcher.exceptions import Datas... | pd.DataFrame(self._items, columns=['DOI']) | pandas.DataFrame |
__docformat__ = "numpy"
import argparse
import pandas as pd
import matplotlib.pyplot as plt
from prompt_toolkit.completion import NestedCompleter
from gamestonk_terminal import feature_flags as gtff
from gamestonk_terminal.helper_funcs import get_flair
from gamestonk_terminal.menu import session
from gamestonk_termina... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
import json
import os
import pandas as pd
from pandas import Series
try:
import requests
except ImportError:
requests = None
from . import find_pmag_dir
from . import data_model3 as data_model
from pmag_env import set_env
pmag_dir = find_pmag_dir.get_pmag_dir()
data_model_dir = os.path.j... | pd.concat([all_codes, df]) | pandas.concat |
# -*- coding: utf-8 -*-
# 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
# "... | pd.Series(result) | pandas.Series |
import numpy as np
import pytest
from pandas.errors import UnsupportedFunctionCall
from pandas import (
DataFrame,
DatetimeIndex,
Series,
date_range,
)
import pandas._testing as tm
from pandas.core.window import ExponentialMovingWindow
def test_doc_string():
df = DataFrame({"B": [0, 1, 2, np.na... | Series([np.nan] * 2 + [1.0] * 4) | pandas.Series |
from building_data_requests import get_value, get_bulk
from main import HeatmapMain
import pandas as pd
import numbers
import requests
from tempfile import mkstemp
from shutil import move
from os import fdopen, remove
current_air_data = None
rooms_and_sensors = None
def init_data_tools(rooms_and_sensors_path):
g... | pd.DataFrame.from_dict(df_dictionary) | pandas.DataFrame.from_dict |
import pytest
import pandas as pd
import numpy as np
from shapely import wkt
from tenzing.core.model_implementations import *
_test_suite = [
pd.Series([1, 2, 3], name='int_series'),
pd.Series([1, 2, 3], name='categorical_int_series', dtype='category'),
pd.Series([1, 2, np.nan], name='int_nan_series'),
... | pd.Series([1.0, 2.0, 3.1], dtype='category', name='categorical_float_series') | pandas.Series |
'''
<NAME> (05-05-20)
Quick tutorial on importing data into pandas. See associated readme file for more information.
'''
# import statements:
import pandas as pd # the "as pd" component just allows you to reference pandas functions with the shortcut "pd."
import os # used to specify filepaths
'''
This first part set... | pd.read_csv(data_filepath, dtype={"CBSA Code": str, 'countyFIPS': str}) | pandas.read_csv |
import warnings
import pandas as pd
import os
import shutil
from itertools import product
import glob
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '4'
import tensorflow as tf
sess = tf.compat.v1.Session()
from tensorflow.keras.layers import Dense, LSTM, Input, BatchNormalization
from tensorflow.keras.optimizers import Adam
f... | pd.concat([self.results, self.engaged_customers_results]) | pandas.concat |
import pandas as pd
import numpy as np
import pickle
import os
from librosa.core import load
from librosa.feature import melspectrogram
from librosa import power_to_db
from config import RAW_DATAPATH
class Data():
def __init__(self, genres, datapath):
self.raw_data = None
self.GENRES = ge... | pd.DataFrame.from_records(records, columns=['spectrogram', 'genre']) | pandas.DataFrame.from_records |
#!/usr/bin/env python
r"""Test :py:class:`~solarwindpy.core.ions.Ion`.
"""
import pdb
# import re as re
import numpy as np
import pandas as pd
import unittest
# import sys
# import itertools
# from numbers import Number
# from pandas import MultiIndex as MI
# import numpy.testing as npt
import pandas.testing as pdt... | pdt.assert_series_equal(ani, ot.anisotropy) | pandas.testing.assert_series_equal |
from multiprocessing.pool import Pool
from typing import Tuple
from pandas import DataFrame
from sklearn.model_selection import train_test_split
from tensorflow.keras import Input
from tensorflow.keras.layers import Embedding, Lambda, Flatten, Concatenate
from tensorflow.keras.models import Model
from tensorflow.keras... | pd.DataFrame(columns=['CUSTOMER_ID', 'pPRODUCT_ID', 'nPRODUCT_ID']) | pandas.DataFrame |
import pytest
import numpy as np
from datetime import date, timedelta, time, datetime
import dateutil
import pandas as pd
import pandas.util.testing as tm
from pandas.compat import lrange
from pandas.compat.numpy import np_datetime64_compat
from pandas import (DatetimeIndex, Index, date_range, DataFrame,
... | tm.assert_numpy_array_equal(arr, exp_arr) | pandas.util.testing.assert_numpy_array_equal |
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 12 08:47:38 2018
@author: cenv0574
"""
import os
import json
import pandas as pd
import geopandas as gpd
from itertools import product
def load_config():
# Define current directory and data directory
config_path = os.path.realpath(
os.path.join(os.path... | pd.DataFrame(valueA) | pandas.DataFrame |
# ------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# -------------------------------------------------------------------... | pd.concat(new_df, axis=0) | pandas.concat |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/6/16 15:28
Desc: 东方财富网-数据中心-特色数据-千股千评
http://data.eastmoney.com/stockcomment/
"""
from datetime import datetime
import pandas as pd
import requests
from tqdm import tqdm
def stock_comment_em() -> pd.DataFrame:
"""
东方财富网-数据中心-特色数据-千股千评
http://dat... | o_numeric(temp_df["大户"]) | pandas.to_numeric |
import networkx as nx
import pandas as pd
import numpy as np
import networkx as nx
from nltk.tokenize import word_tokenize
from .build_network import *
def character_density(book_path):
'''
number of central characters divided by the total number of words in a novel
Parameters
----------
book_pat... | pd.DataFrame(comparison) | pandas.DataFrame |
import numpy as np
np.random.seed(0)
import pandas as pd
def highlight_nan(data: pd.DataFrame, color: str) -> pd.DataFrame:
attr = f'background-color: {color}'
is_nan = pd.isna(data)
return pd.DataFrame(
np.where(is_nan, attr, ''),
index=data.index,
columns=data.columns
)
def... | pd.date_range("21/4/2021", periods=num_samples) | pandas.date_range |
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from dask.utils import raises
import dask.dataframe as dd
from dask.dataframe.utils import eq, assert_dask_graph
def groupby_internal_repr():
pdf = pd.DataFrame({'x': [1, 2, 3, 4, 6, 7, 8, 9, 10],
'y': list('abcbabbcd... | tm.assertRaisesRegexp(ValueError, msg) | pandas.util.testing.assertRaisesRegexp |
''' ALL DIFFERENT MILP MODELS IN ONE FILE
1. MILP WITHOUT MG -> Doesn't consider the Microgrid option and works with only 1 type of cable. However, it considers reliability.
2. MILP2 -> Consider
3. MILP3
4. MILP4
5. MILP5
'''
from __future__ import division
from pyomo.opt import SolverFactory
from pyomo.core import A... | pd.DataFrame(columns=[['index', 'voltage [p.u]']]) | pandas.DataFrame |
import numpy as np
from pandas import DataFrame, Series, DatetimeIndex, date_range
try:
from pandas.plotting import andrews_curves
except ImportError:
from pandas.tools.plotting import andrews_curves
import matplotlib
matplotlib.use('Agg')
class Plotting(object):
def setup(self):
self.s = Series(... | DataFrame({'col': self.s}) | pandas.DataFrame |
import pandas as pd
import numpy as np
from statsmodels.formula.api import ols
from swstats import *
from scipy.stats import ttest_ind
import xlsxwriter
from statsmodels.stats.multitest import multipletests
from statsmodels.stats.proportion import proportions_ztest
debugging = False
def pToSign(pval):
if pval < .... | pd.DataFrame(resultTables[0]) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 6 14:51:54 2020
A collection of cleanup functions that should just run.
Just keep them in one place and clean up the file structure a bit.
Expects that the entire pipeline up until now has been completed.
Hopefully this all works because will be ... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import FunctionTransformer
from sktime.pipeline import Pipeline
from sktime.tests.test_pipeline import X_train, y_train, X_test, y_test
from sktime.transformers.compose import ColumnTransformer, Tabula... | pd.concat([X_train, X_train], axis=1) | pandas.concat |
import numpy as np
import pandas as pd
import pytest
from samplics.utils.formats import (
numpy_array,
array_to_dict,
dataframe_to_array,
sample_size_dict,
dict_to_dataframe,
sample_units,
convert_numbers_to_dicts,
)
df = | pd.DataFrame({"one": [1, 2, 2, 3, 0], "two": [4, 9, 5, 6, 6]}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri May 29 18:41:43 2020
@author: Cliente
"""
import pandas as pd
import statistics
from imblearn.pipeline import make_pipeline
from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import NearMiss,RandomUnderSampler
import glob
from sklearn.model_selection impor... | pd.concat([df_main, df]) | pandas.concat |
import random
import requests
import pandas as pd
from xgboost import XGBClassifier
URL = "https://aydanomachado.com/mlclass/03_Validation.php"
DEV_KEY = "Café com leite"
def replace_sex(data):
data[['sex']] = data[['sex']].replace(
{'I': 0, 'F': 1, 'M': 2}).astype(int)
return data
train = pd.read... | pd.Series(y_pred) | pandas.Series |
import os
import pandas as pd
import gluonts
import numpy as np
import argparse
import json
import pathlib
from mxnet import gpu, cpu
from mxnet.context import num_gpus
import matplotlib.pyplot as plt
from gluonts.dataset.util import to_pandas
from gluonts.mx.distribution import DistributionOutput, StudentTOutput, N... | pd.DataFrame(data['related_values'], index=data['timestamp']) | pandas.DataFrame |
"""
Procedures needed for Common support estimation.
Created on Thu Dec 8 15:48:57 2020.
@author: MLechner
# -*- coding: utf-8 -*-
"""
import copy
import numpy as np
import pandas as pd
from mcf import mcf_data_functions as mcf_data
from mcf import general_purpose as gp
from mcf import general_purpose_estimation as... | pd.concat([x_total, x_dummies], axis=1) | pandas.concat |
"""
A set of functions needed for DataCarousel app
"""
import random
import logging
import json
import time
import datetime
import numpy as np
import pandas as pd
from sklearn.preprocessing import scale
import urllib.request as urllibr
from urllib.error import HTTPError
import cx_Oracle
from django.core.cache imp... | pd.concat([result, dfadd]) | pandas.concat |
#!/usr/bin/env python
# ----------------------------------------------------------------------------
# Copyright (c) 2016--, Biota Technology.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# -------------------------------------... | pd.util.testing.assert_frame_equal(obs, sources) | pandas.util.testing.assert_frame_equal |
# streamlit run ./files.py/old_testing.py
import streamlit as st
import yfinance as yf
import pandas as pd
import numpy as np
import math
# programmatic calculations
import get12Data as g12d
import getAnalytics as gAna
def colorHeader(fontcolor = '#33ff33', fontsze = 30, msg="Enter some Text"):
st.markdown(f'<h... | pd.to_numeric(new_df["High"]) | pandas.to_numeric |
import unittest
import pandas as pd
import numpy as np
from scipy.sparse.csr import csr_matrix
from string_grouper.string_grouper import DEFAULT_MIN_SIMILARITY, \
DEFAULT_REGEX, DEFAULT_NGRAM_SIZE, DEFAULT_N_PROCESSES, DEFAULT_IGNORE_CASE, \
StringGrouperConfig, StringGrouper, StringGrouperNotFitException, \
... | pd.Series(['FOO', 'bar', 'bop']) | pandas.Series |
# Based on SMS_Spam_Detection
# edited to run on local PC without GPU setup
import io
import re
import stanza
import pandas as pd
import tensorflow as tf
import stopwordsiso as stopwords
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feat... | pd.concat([x, train_tmp], axis=1) | pandas.concat |
###############################################################################
from functools import partial
from math import sqrt
from copy import deepcopy
import operator, sys
import json
import pandas as pd
import numpy as np
from scipy.io import arff
from sklearn.model_selection import train_test_split
from skle... | pd.to_pickle(df, '../metrics/metrics_summary.pkl') | pandas.to_pickle |
# coding: utf-8
# In[1]:
# import librerie
import os
import tweepy
import facebook
import requests
import datetime
import pandas as pd
import numpy as np
from sqlalchemy import create_engine
import json
import requests
# In[2]:
# configuration file
config = {}
config_path = os.path.join(os.path.abspath('../../'... | pd.DataFrame(l[3]) | pandas.DataFrame |
import sys
import os
import pytest
import mock
from keras.models import Sequential
from keras.layers import Dense
import sklearn.datasets as datasets
import pandas as pd
import numpy as np
import yaml
import tensorflow as tf
import mlflow
import mlflow.keras
import mlflow.pyfunc.scoring_server as pyfunc_scoring_serve... | pd.DataFrame([[1.0, 2.1], [True, False]], columns=["col1", "col2"]) | pandas.DataFrame |
from collections import deque
from datetime import datetime
import operator
import numpy as np
import pytest
import pytz
import pandas as pd
import pandas._testing as tm
from pandas.tests.frame.common import _check_mixed_float, _check_mixed_int
# -------------------------------------------------------------------
# ... | pd.Timedelta(days=1) | pandas.Timedelta |
import pandas as pd
import os
from os.path import isfile, join
import time
from util import Log
class transform_data:
def __init__(self, dir_name='workspace/data/', length=10800):
self.dir_name = dir_name
self.length = length
self.df = None
self.columns = ['date', 'pair', 'change'... | pd.to_datetime(old['date']) | pandas.to_datetime |
import pandas as pd
import pandasql as ps
import plotly.graph_objs as go
from django.shortcuts import render, redirect
from django.contrib.auth.models import User
from .models import Expense, ExpenseType
from .forms import (
ExpenseForm,
AuthenticationFormWithCaptchaField,
DateRangeForm
)
from django.contr... | pd.to_numeric(df['amount'], downcast='float') | pandas.to_numeric |
import pandas as pd
import requests
from tqdm import tqdm
from bs4 import BeautifulSoup
from datetime import datetime
import os
import io
import sys
import tkinter as tk
x = datetime.now()
DateTimeSTR = '{}{}{}'.format(
x.year,
str(x.month).zfill(2) if len(str(x.month)) < 2 else str(x.month),
str(x.day).zf... | pd.DataFrame(allCTData) | pandas.DataFrame |
import xml.etree.ElementTree as ET
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import calendar
import time
from datetime import datetime
import pytz
from scipy import stats
from os.path import exists
# an instance of apple Health
# fname is the name of data file to be pa... | pd.to_datetime(self.record_data[col], format=format) | pandas.to_datetime |
# pragma pylint: disable=W0603
"""
Cryptocurrency Exchanges support
"""
import asyncio
import inspect
import logging
from copy import deepcopy
from datetime import datetime, timezone
from math import ceil
from typing import Any, Dict, List, Optional, Tuple
import arrow
import ccxt
import ccxt.async_support as ccxt_asy... | DataFrame() | pandas.DataFrame |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from typing import NamedTuple
import numpy as np
import pandas as pd
from .nmc import obtain_posterior
logger = logging.g... | pd.DataFrame(items) | pandas.DataFrame |
"""
Routines for casting.
"""
from contextlib import suppress
from datetime import date, datetime, timedelta
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
Optional,
Sequence,
Set,
Sized,
Tuple,
Type,
Union,
)
import numpy as np
from pandas._libs import lib, tslib, t... | notna(result) | pandas.core.dtypes.missing.notna |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Monday 3 December 2018
@author: <NAME>
"""
import os
import pandas as pd
import numpy as np
import feather
import time
from datetime import date
import sys
from sklearn.cluster import MiniBatchKMeans, KMeans
from sklearn.metrics import silhouette_score
fr... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
def fix_datasets():
dati = pd.read_csv("dati_regioni.csv")
regioni = pd.read_csv("regioni.csv")
## Devo mergiare i dati del trentino
dati.drop(columns = ["casi_da_sospetto_diagnostico", "casi_da_screening"], axis = 1, inplace = True)
df_r = dati.loc[(dati['denominazione_region... | pd.read_csv("dati_province.csv") | pandas.read_csv |
import csv
import logging
import os
import re
import time
import traceback
import pandas as pd
import requests
import xlrd
import settings
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
class ExcelConverter(object):
"""
Conversion of multiple excel sheets to csv files
Adapted... | pd.read_csv(f) | pandas.read_csv |
# -*- 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... | pd.crosstab(df.a, df.b, margins=True, dropna=True) | pandas.crosstab |
#For the computation of average temperatures using GHCN data
import ulmo, pandas as pd, matplotlib.pyplot as plt, numpy as np, csv, pickle
#Grab weather stations that meet criteria (from previous work) and assign lists
st = ulmo.ncdc.ghcn_daily.get_stations(country ='US',elements=['TMAX'],end_year=1950, as_dataframe... | pd.isnull(nanminjan) | pandas.isnull |
import pandas as pd
import numpy as np
def frequency_encoding(df,feature):
map_dict=df[feature].value_counts().to_dict()
df[feature]=df[feature].map(map_dict)
def target_guided_encoding(df,feature,target):
order=df.groupby([feature])[target].mean().sort_values().index
map_dic={k:i for i,k in enumerate(order,0)... | pd.concat([df,dummies],axis=1) | pandas.concat |
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