prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
from .base import AbstractStatistics
from ..compat import pickle
from ..price_parser import PriceParser
import datetime
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
class SimpleStatistics(AbstractStatistics):
"""
Simple Statistics fornisce un semplice... | pd.Series(self.drawdowns, index=timeseries) | pandas.Series |
import datetime
import random
import sys
import time
import unittest
import matplotlib as mpl
#from pandas import Series
import pandas as pd
import numpy as np
from pandas_datareader import data as wb
import matplotlib.pyplot as plt
'''
这个文件的代码 都是 实验性质的。 scribble code！
'''
#from QUANTAXIS.QAFetch import (QATdx );... | pd.Series([-2.1, 3.6, -1.5, 4, 3.1], index=['a', 'c', 'e', 'f', 'g']) | pandas.Series |
import pandas as pd
from dateutil.parser import parse
from datetime import timedelta
import requests
def download_file(url, filename):
"""
Helper method handling downloading large files from `url` to `filename`. Returns a pointer to `filename`.
"""
chunkSize = 1024 ** 2
r = requests.get(url, stre... | pd.to_datetime(fin) | pandas.to_datetime |
import os
import sys
import math
from neuralprophet.df_utils import join_dataframes
import numpy as np
import pandas as pd
import torch
from collections import OrderedDict
from neuralprophet import hdays as hdays_part2
import holidays as pyholidays
import warnings
import logging
log = logging.getLogger("NP.utils")
d... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# # Polifact_Analysis
#
# ### @Author : <NAME>
# In[1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
import plotly.express as px
from scipy import signal
import warnings
warnings.filterwarnings("ignore... | pd.to_datetime(df_new.date,infer_datetime_format=True) | pandas.to_datetime |
import builtins
from io import StringIO
from itertools import product
from string import ascii_lowercase
import numpy as np
import pytest
from pandas.errors import UnsupportedFunctionCall
import pandas as pd
from pandas import (
DataFrame, Index, MultiIndex, Series, Timestamp, date_range, isna)
import pandas.cor... | tm.assert_index_equal(result.columns, expected_columns_numeric) | pandas.util.testing.assert_index_equal |
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 4 15:34:25 2020
@author: diego
"""
import pandas as pd
import os
import sqlite3
from pandas_datareader import DataReader
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from fuzzywuzzy import process
import update_db
pd.set_option('display.widt... | pd.to_datetime(returns.index) | pandas.to_datetime |
# To add a new cell, type '#%%'
# To add a new markdown cell, type '#%% [markdown]'
# %%
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import dm6103 as dm
#%% [markdown]
# The dataset is obtained from
# https://gssdataexplorer.norc.org
# for you here. But ... | pd.to_numeric(dfhappy['hrs1'], errors='coerce') | pandas.to_numeric |
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from builtins import open as io_open
from builtins import str
from future import standard_library
standard_library.install_aliases()
__all__ = ... | pd.set_option('display.width', 1000) | pandas.set_option |
import pandas as pd
import numpy as np
import warnings; warnings.simplefilter('ignore')
from ast import literal_eval
df1 = pd.read_csv("merged_cleaned.csv")
df2 = | pd.read_csv("ratings_combined.csv") | pandas.read_csv |
import pandas as pd
porfolio1 = pd.DataFrame({'Asset': ['FX', 'FX', 'IR'],
'Instrument': ['Option', 'Swap', 'Option'],
'Number': [1, 2, 3]})
porfolio2 = pd.DataFrame({'Asset': ['FX', 'FX', 'FX', 'IR'],
'Instrument': ['Option', 'Option', 'Sw... | pd.merge(porfolio1, porfolio2, on='Asset') | pandas.merge |
import pandas as pd
import numpy as np
from load_data import load_csv
import constants as cst
import sys
file_path = "./data/bitstampUSD_1-min_data_2012-01-01_to_2018-06-27.csv"
def select_data(dataframe, start=None, stop=None):
"""
:param dataframe: df pandas
:param start: str, min date to considerate... | pd.to_datetime(stop, format="%Y-%m-%d") | pandas.to_datetime |
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 |
"""Represent SQL tokens as Pandas operations.
"""
from sqlalchemy.sql import operators
from sqlalchemy import sql
from sqlalchemy import util
from sqlalchemy import types as sqltypes
import functools
import pandas as pd
import numpy as np
import collections
from . import dbapi
from sqlalchemy.sql.functions import Gen... | pd.concat(non_empty) | pandas.concat |
from tensorflow.python.ops.functional_ops import While
import tensorflow as tf
import numpy as np
import pandas as pd
import waktu as wk
import time
from datetime import datetime
from datetime import date
import schedule
import pyrebase
import json
import firebase_admin
from firebase_admin import credentials
from fireb... | pd.DataFrame(data, columns=['waktu', 'hari', 'idrelay', 'status']) | pandas.DataFrame |
from mlapp.managers import DataManager, pipeline
from mlapp.utils.exceptions.base_exceptions import DataManagerException
import pandas as pd
import numpy as np
class FlowRegressionDataManager(DataManager):
@pipeline
def load_train_data(self,*args):
print(args)
return
@pipeline
def cle... | pd.concat([feature_data, features[feature_name]]) | pandas.concat |
from __future__ import division
import pytest
import numpy as np
from pandas import (Interval, IntervalIndex, Index, isna,
interval_range, Timestamp, Timedelta,
compat)
from pandas._libs.interval import IntervalTree
from pandas.tests.indexes.common import Base
import pandas.uti... | tm.assert_index_equal(result, expected) | pandas.util.testing.assert_index_equal |
import pandas as pd
from .datastore import merge_postcodes
from .types import ErrorDefinition
from .utils import add_col_to_tables_CONTINUOUSLY_LOOKED_AFTER as add_CLA_column # Check 'Episodes' present before use!
def validate_165():
error = ErrorDefinition(
code = '165',
description = 'Data entry for moth... | pd.to_datetime(mis['MIS_END'], format='%d/%m/%Y', errors='coerce') | pandas.to_datetime |
import kabuki
import hddm
import numpy as np
import pandas as pd
from numpy.random import rand
from scipy.stats import uniform, norm
from copy import copy
def gen_single_params_set(include=()):
"""Returns a dict of DDM parameters with random values for a singel conditin
the function is used by gen_rand_par... | pd.DataFrame(data=d) | pandas.DataFrame |
################################################################################################
# NOTE: I started this code to get better matching results than matching by address,
# but I never finished and thus this code hasn't actually been used yet.
#################################################################... | pd.isnull(ev['street_type']) | pandas.isnull |
import pandas as pd
from time import sleep
from selenium import webdriver
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.support.select import Select
url = 'https://www.agmarknet.gov.in/SearchCmmMkt.aspx?Tx_Commodity=78&Tx_State=... | pd.concat([temp_df, final_df], axis=1) | pandas.concat |
import ast
import pandas as pd
from pandas.api.types import CategoricalDtype
import os
import numpy as np
import tensorflow as tf
from PIL import Image
import shutil
from tqdm import tqdm
from subprocess import Popen, PIPE, STDOUT
root_path = os.path.join(os.path.dirname(__file__), os.path.pardir)
def load(filepath)... | pd.read_csv(filepath, index_col=0, header=[0, 1]) | pandas.read_csv |
"""
Function related to the loading and processing of CPC instruments from TSI
version: 0.0
date: 2016-09-09
"""
import os
import sys
import pandas as pd
import glob
import pickle
import numpy as np
import re
import matplotlib.pyplot as plt
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirnam... | pd.concat([data,data_temp]) | pandas.concat |
# !pip install git+https://github.com/huggingface/transformers
# !pip install textacy
import sys
from custom_svo_extractor import find_svo
experiment_data = 'squad'
experiment_model = 'Bert'
extractor = 'spacy'
if len(sys.argv) > 1:
experiment_data = str(sys.argv[1])
experiment_model = str(sys.argv[2])
e... | pd.DataFrame(labels, columns=['source', 'edge']) | pandas.DataFrame |
# --------------
#Importing header files
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
#Code starts here
data = pd.read_csv(path)
data['Rating'].hist()
data = data[data['Rating']<=5]
data['Rating'].hist()
#Code ends here
# --------------
# code starts here
total_null = data.isnull().s... | pd.concat([total_null_1,percent_null_1],keys=['Total','Percent'],axis=1) | pandas.concat |
# coding: utf-8
# CS FutureMobility Tool
# See full license in LICENSE.txt.
import numpy as np
import pandas as pd
#import openmatrix as omx
from IPython.display import display
from openpyxl import load_workbook,Workbook
from time import strftime
import os.path
import mode_choice.model_defs as md
import mode_choice.ma... | pd.concat([town_definition,zone_pmt_daily_o],axis=1,join='inner') | pandas.concat |
import pandas as pd
import numpy as np
# import nltk
# import re
# from nltk.corpus import stopwords
# from nltk.tokenize import word_tokenize
import math
class NaiveBayesModel:
def WordGivenNoPI(self, tempNegDocVector, uniqueWords):
data = np.zeros([1, len(uniqueWords)])
wordGivenNoPI = pd.DataF... | pd.DataFrame() | pandas.DataFrame |
"""
The :mod:`codeless.fs` module includes methods to
select best features/most relevant fetures.
"""
# For univariate Selection
from sklearn.feature_selection import SelectKBest as _skb# SelectKBest selects the k best features
from sklearn.feature_selection import chi2 as _chi#This is used for applying the s... | _pd.DataFrame(mutual_info, index=X.columns, columns=['Score']) | pandas.DataFrame |
"""File for saving and loading. Assumes an experiment_folder path in which can data can be freely written and modified. For specific processes it also requires
a process_id. Nothing will be stored above the experiment_folder path.
Every method should call one of these methods for any saving/loading tasks."""
import os... | pd.DataFrame(config_dir) | pandas.DataFrame |
import torch
import numpy as np
import matplotlib.pyplot as plt
import pandas
import os
import seaborn as sns
from argparse import ArgumentParser
from models.utils.continual_model import ContinualModel
from datasets.utils.continual_dataset import ContinualDataset
from typing import Tuple
from utils.conf import base_pa... | pandas.concat([all_df, sub_df]) | pandas.concat |
# -*- coding: UTF-8 -*-
"""
This module contains functions for calculating evaluation metrics for the generated service recommendations.
"""
import numpy
import pandas
runtime_metrics = ["Training time", "Overall testing time", "Individual testing time"]
quality_metrics = ["Recall", "Precision", "F1", "# of recommend... | pandas.DataFrame(true_positives, columns=["TP"]) | pandas.DataFrame |
"""
Packages to use :
tsfresh
tsfel https://tsfel.readthedocs.io/en/latest/
sktime
feature tools : https://docs.featuretools.com/en/stable/automated_feature_engineering/handling_time.html
Cesium http://cesium-ml.org/docs/feature_table.html
Feature Tools for advacned fewatures `https://github.com/Featuretools/pr... | pd.merge(out_df, dept_day_year_lag, left_on="dept_id", right_index=True, how="left") | pandas.merge |
import os
import pandas as pd
import mygene
from util_path import get_path
from util_dei import filter_dei
res_dir = get_path("resource/Entrez")
gene_dir = get_path("vertex/gene")
mg = mygene.MyGeneInfo()
def read_gene2ensembl():
global res_dir
g2e_df = pd.read_csv(os.path.join(res_dir, "gene2ensembl_9606.t... | pd.isnull(x) | pandas.isnull |
"""Parse Tecan files, group lists and fit titrations.
(Titrations are described in list.pH or list.cl file.
Builds 96 titrations and export them in txt files. In the case of 2 labelblocks
performs a global fit saving a png and printing the fitting results.)
:ref:`prtecan parse`:
* Labelblock
* Tecanfile
:ref:`prte... | pd.read_excel(path) | pandas.read_excel |
from datetime import timedelta
from operator import methodcaller
import itertools
import math
import pytest
sa = pytest.importorskip('sqlalchemy')
pytest.importorskip('psycopg2')
import os
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from datashape import dshape
from odo import odo, drop... | tm.assert_series_equal(result, expected) | pandas.util.testing.assert_series_equal |
# -*- coding:utf-8 -*-
# !/usr/bin/env python
"""
Date: 2022/1/12 14:55
Desc: 东方财富网-数据中心-股东分析
https://data.eastmoney.com/gdfx/
"""
import pandas as pd
import requests
from tqdm import tqdm
def stock_gdfx_free_holding_statistics_em(date: str = "20210930") -> pd.DataFrame:
"""
东方财富网-数据中心-股东分析-股东持股统计-十大流通股东
... | numeric(temp_df["持股数"]) | pandas.to_numeric |
# *****************************************************************************
# Copyright (c) 2019, Intel Corporation All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# Redistributions of sou... | pandas.Series(self._data[mask], index[mask], self._name) | pandas.Series |
import pandas as pd
import textacy
import textblob
import en_core_web_sm
nlp = en_core_web_sm.load()
# Multiprocessing Imports
from dask import dataframe as dd
from dask.multiprocessing import get
from multiprocessing import cpu_count
# Sentiment Imports
from vaderSentiment.vaderSentiment import SentimentIntensityAn... | pd.concat([df, sentiment_df], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# formats: ipynb,py
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.9.1+dev
# kernelspec:
# display_name: Python [conda env:core_acc] *
# language: python
# nam... | pd.read_csv(pa14_compendium_filename, sep="\t", header=0, index_col=0) | pandas.read_csv |
from __future__ import annotations
from typing import Optional, Union, cast
import numpy as np
from numpy.linalg import inv, matrix_rank
import pandas as pd
from linearmodels.typing import ArraySequence, Float64Array
def blocked_column_product(x: ArraySequence, s: Float64Array) -> Float64Array:
"""
Paramet... | pd.Series(q, index=r_pd.index) | pandas.Series |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
__doc__="""
ATLAS Higgs Machine Learning Challenge 2014
Read CERN Open Data Portal Dataset http://opendata.cern.ch/record/328
and manipulate it
- Label is changd f... | pd.read_csv(filename, sep='\t', nrows=nrows, header=None, usecols=RESTRICTED_COLUMNS) | pandas.read_csv |
import numpy as np
import pandas as pd
import pytest
from src.policies.single_policy_functions import (
_identify_who_attends_because_of_a_b_schooling,
)
from src.policies.single_policy_functions import mixed_educ_policy
@pytest.fixture
def fake_states():
states = pd.DataFrame(index=np.arange(10))
states... | pd.Series([False, False, False, True, False]) | pandas.Series |
#https://docs.google.com/document/d/1Y31Nt05peNIPwLo9O_TsTRAcy0GbDqGvRcHvJ4qtgzk/edit
###################### Indíce #######################
#---- Instruções
#---- Imports
#---- Funções
#---- Leitura de Ficheiros
#---- Correcções à Base de Dados
#---- Tabelas Descritivas
# ---- Nrº Ciclistas por País
# ... | pd.to_numeric(sport_events["Time"],errors='coerce') | pandas.to_numeric |
#' Download StatsCan Metadata from Product Cube
#'
#' This function allows you to download product metadata from
#' Statistics Canada.
#' @param productId The Statistics Canada Product ID.
#' @keywords productId, product, metadata
#' @importFrom httr POST content content_type
#' @export
#' @examples
#' get_product_meta... | pd.DataFrame(coord_data[0]['object']) | pandas.DataFrame |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import statsmodels
from matplotlib import pyplot
from scipy import stats
import statsmodels.api as sm
import warnings
from itertools import product
import datetime as dt
from stat... | pd.DataFrame() | pandas.DataFrame |
"""
Description : This file implements the Drain algorithm for log parsing
Author : LogPAI team
License : MIT
"""
import hashlib
import os
import re
import pandas as pd
from datetime import datetime
from typing import List
from .log_signature import calc_signature
# 一个叶子节点就是一个LogCluster
clas... | pd.DataFrame(log_messages, columns=headers) | pandas.DataFrame |
import requests
import time
import string
import html5lib
import re
from bs4 import BeautifulSoup
import numpy as np
import pandas as pd
def player_scrape():
start = time.time()
abc = list(string.ascii_uppercase)
players_df = pd.DataFrame()
try:
for z in abc:
url = f"https://ww... | pd.DataFrame(rows, columns=columns) | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from .. import read_sql
@pytest.fixture(scope="module") # type: ignore
def redshift_url() -> str:
conn = os.environ["REDSHIFT_URL"]
return conn
@pytest.mark.skipif(not os.environ.get("REDSHIFT_URL... | pd.Series([3.1, None, 2.2, 3, 7.8, -10], dtype="float64") | pandas.Series |
# make a bootstrapped dataframe from the bootstrap index file
import pandas as pd
import os
import glob
np = pd.np
cwd = os.getcwd()
#os.chdir(read_path)
path =r'/Users/dingwenna/AlpineOrigin/' # use your path
allFiles = glob.glob(path + "/*events.csv.gz")
frame = | pd.DataFrame() | pandas.DataFrame |
import os
from glob import glob
import pandas as pd
def get_options():
import argparse
parser = argparse.ArgumentParser(
description='takes a folder with ')
parser.add_argument('--path', required=True,
metavar="str", type=str,
help='folder where the... | pd.concat(tab_global, axis=0) | pandas.concat |
import pandas as pd
from pandas.util.testing import assert_series_equal, assert_frame_equal
from cellgrid.core import Schema, ModelBlueprint, DataMapper
from cellgrid.ensemble.classifier import DataFrame, Series
class ModeTestClass:
def __init__(self, bp):
self.name = bp.name
self.parent = bp.pare... | assert_frame_equal(df_loc2.df, df_pd) | pandas.util.testing.assert_frame_equal |
#!/usr/bin/env python
"""
BSD 2-Clause License
Copyright (c) 2021 (<EMAIL>)
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, thi... | pd.DataFrame(columns=['basecaller', 'genome', 'match', 'mismatch', 'deletion', 'insertion', 'unaligned','identity', 'error', 'mqual', 'relative read length', 'aligned \% of read']) | pandas.DataFrame |
import os
import numpy as np
import torch
from torchvision import models
from torchvision import transforms
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset
from sklearn.model_selection import train_test_split
import pandas as pd
from itertools import chain
from sklearn import prepro... | pd.Series(labels_list) | pandas.Series |
"""
Module: Instagram Scrapper
Author: <NAME>
Version: 1.0.2
Last Modified: 28/11/2018 (Wednesday)
"""
from bs4 import BeautifulSoup
from SeleniumHelper import SeleniumBrowserHelper
from PostScrapper import PostScrapper
from PostScrapper import PostDetails
import pandas as pd
import json
import time
impo... | pd.DataFrame(self.data) | pandas.DataFrame |
import pandas as pd
import pytest
from pandera import Column, DataFrameSchema, Check
from pandera import dtypes
from pandera.errors import SchemaError
def test_numeric_dtypes():
for dtype in [
dtypes.Float,
dtypes.Float16,
dtypes.Float32,
dtypes.Float64]:
s... | pd.to_datetime(["2010/01/01"]) | pandas.to_datetime |
import glob
from astropy.io import fits
import pandas as pd
class HeaderSummary:
'''
HeaderSummary does retrieving information from fits files' headers. path_list provides the search paths applied by glob.glob(). For each file, fits.open() is used to open the file. Header info is retrieved as specified in keywo... | pd.DataFrame(out,columns=self.colname,) | pandas.DataFrame |
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
def read_file(filename):
labels = ["futures", "title", "wait", "exec", "duration", "us_future", "queue", "numa_sensitive", "num_threads", "info_string", "libcds"]
data = | pd.read_csv(filename, sep=',', header=None) | pandas.read_csv |
from pathlib import Path
import numpy as np
import pandas as pd
from functools import reduce
from loguru import logger
from datetime import datetime, timedelta
from siuba import _, filter, gather, group_by, ungroup, mutate, summarize, arrange
# plots
import matplotlib.pyplot as plt
import plotnine as p9
p9.theme_set... | pd.DataFrame() | pandas.DataFrame |
import pandas
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn import preprocessing
from setlist import setlist
import sys
import os
path=os.getcw... | pandas.DataFrame(test_sets_targets[i],columns=['label']) | pandas.DataFrame |
from matplotlib import colors
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import pandas as pd
import numpy as np
import matplotlib.gridspec as gridspec
import matplotlib.offsetbox as offsetbox
import palettable
from collections import defaultdict
class CoMut:
'''A user-created :class: `C... | pd.to_numeric(data['value'], 'coerce') | pandas.to_numeric |
#
# Copyright © 2021 Uncharted Software Inc.
#
# 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 l... | pd.to_datetime(df) | pandas.to_datetime |
# -*- coding: utf-8 -*-
# Copyright StateOfTheArt.quant.
#
# * Commercial Usage: please contact <EMAIL>
# * Non-Commercial Usage:
# 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
#
... | pd.DataFrame(close_np) | pandas.DataFrame |
import json
from random import shuffle
from pathlib import Path
from tqdm import tqdm
import numpy as np
import pandas as pd
import torch
from torch_scatter import scatter
from torch_geometric.data import Data, InMemoryDataset, Batch
from sklearn.utils.class_weight import compute_class_weight
from utils impor... | pd.read_csv(data_path, sep=' ') | pandas.read_csv |
"""
Preprocess sites scripts.
Written by <NAME>.
Winter 2020
"""
import os
import configparser
import json
import csv
import math
import glob
import pandas as pd
import geopandas as gpd
import pyproj
from shapely.geometry import Polygon, MultiPolygon, mapping, shape, MultiLineString, LineString
from shapely.ops impo... | pd.DataFrame(stats) | pandas.DataFrame |
from typing import List, Union, Dict, Any, Tuple
import os
import json
from glob import glob
from dataclasses import dataclass
import functools
import argparse
from sklearn import metrics
import torch
import pandas as pd
import numpy as np
from tqdm import tqdm
from sklearn.metrics import precision_recall_fscore_suppo... | pd.concat(reports) | pandas.concat |
from sklearn.metrics import mutual_info_score
import matplotlib.pyplot as plt
import networkx as nx
from math import log
import numpy as np
import pandas as pd
import os
from helpers.timer import Timer
def mim(size, bins, data):
"""
Calculates the mutual information matrix.
Input:
The number of genes... | pd.DataFrame({"reg": reg, "tar": tar}) | pandas.DataFrame |
from IPython.core.error import UsageError
from mock import MagicMock
import numpy as np
from nose.tools import assert_equals, assert_is
import pandas as pd
from pandas.testing import assert_frame_equal
from sparkmagic.livyclientlib.exceptions import BadUserDataException
from sparkmagic.utils.utils import parse_argstri... | assert_frame_equal(expected, df) | pandas.testing.assert_frame_equal |
from collections import abc, deque
from decimal import Decimal
from io import StringIO
from warnings import catch_warnings
import numpy as np
from numpy.random import randn
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
... | concat([a, b], keys=["key0", "key1"], names=["lvl0"]) | pandas.concat |
# -*- coding: utf-8 -*-
import io
import json
import pandas as pd
import ijson
import codecs
import warnings
from commons import read_csv_with_encoding, read_json_with_encoding, distance
TRANSMILENIO_FILENAME = "bogota/transmilenio"
BOGOTA_INTEREST_POINTS = "bogota/bogota_interest_points.json"
def nearestBusStop... | pd.DataFrame({'name': x[0], 'amenity': x[1], 'lat': x[2], "lon": x[3], "geometry": x[4]}) | pandas.DataFrame |
import datetime
import itertools
import json
import logging
import os
import sqlite3
from sqlite3 import DatabaseError
from typing import Optional, List, Dict, Tuple
import networkx as nx
import numpy as np
import pandas as pd
from ipyleaflet import Map, ScaleControl, FullScreenControl, Polyline, Icon, Marker, Circle,... | pd.read_sql_query("SELECT * from gnss_clock_measurement_table", db_con) | pandas.read_sql_query |
import json
import math
import numpy as np
import os.path
import pandas as pd
import skimage.io
import sys
from xview3.utils.grid_index import GridIndex
distance_thresh = 10
def merge(preds):
for i, pred in enumerate(preds):
if 'input_idx' in pred.columns:
pred = pred.drop(columns=['input_idx... | pd.read_csv(in_path) | pandas.read_csv |
# -*- coding: utf-8 -*-
import sys, os
import pandas as pd
import openpyxl
from openpyxl.styles import PatternFill
import numpy as np
from collections import defaultdict
from scanner_map import searchKey, CertifiedManufacturerModelNameCTDict, CertifiedManufacturerCTDict, TrueManufacturerModelNameCTDict, TrueManufacture... | pd.isnull(row[k]) | pandas.isnull |
from src.config import CENSUS_KEY
import json
import requests
import pandas as pd
from typing import Dict, List, Tuple, Optional, Collection
from src import data
def download_census_data(geo_ls=["zip", "county"]) -> data.Wrapper:
'''
Top level function to run the queries
'''
# Census tables
detai... | pd.notnull(pct_df) | pandas.notnull |
# IRS CA in-migration from other states (2012-2018)
import pandas as pd
import numpy as np
import os
import re
inmig_12_18 = os.listdir('CA In 12_18')
outmig_12_18 = os.listdir('CA Out 12_18')
master_df = pd.DataFrame()
years = []
folders = ['CA In 12_18/', 'CA Out 12_18/']
types = ['In', 'Out']
counter = 0
for fold... | pd.concat([master_df, temp_df]) | pandas.concat |
"""
__author__ = <NAME>
"""
import attr
import numpy as np
import pandas as pd
from attr.validators import instance_of
from pysight.nd_hist_generator.movie import Movie, FrameChunk
from collections import deque, namedtuple
from typing import Tuple, Union
from numba import jit, uint8, int64
@attr.s(slots=True)
class C... | pd.concat([self.raw["Laser"], last_laser_row]) | pandas.concat |
import calendar
import math
import re
from datetime import datetime, timedelta, date
import pandas as pd
import pytz
from catalyst.exchange.exchange_errors import InvalidHistoryFrequencyError, \
InvalidHistoryFrequencyAlias
def get_date_from_ms(ms):
"""
The date from the number of miliseco... | pd.to_datetime('1970-1-1', utc=True) | pandas.to_datetime |
#!/usr/bin/env python
#
# parse multiple HDF5 files and print graph
#
import pandas as pd
import matplotlib
matplotlib.use('AGG')
import matplotlib.pyplot as plt
from pandas import ExcelWriter
print('Import data from HDF5 files')
store1 = pd.HDFStore("E:\\home\\2014\\07\\logfile_20140701.hdf5", mode='r')
print(sto... | ExcelWriter('output.xlsx') | pandas.ExcelWriter |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from distutils.version import LooseVersion
from scipy.stats import norm
from sklearn.neighbors import KernelDensity
from datetime import datetime
plt.rcParams['font.size'] = 6
import os
root_path = os.path.dirname(os.path.abspath('__file__'))
graphs... | pd.read_csv(root_path+'/boundary_effect/vmd-decompositions-huaxian/x_1_791_imf.csv') | pandas.read_csv |
import argparse
import logging
import sys
from typing import List
import pandas as pd
from analysis.src.python.data_analysis.model.column_name import IssuesColumns, SubmissionColumns
from analysis.src.python.data_analysis.utils.df_utils import merge_dfs
from analysis.src.python.data_analysis.utils.statistics_utils im... | pd.read_csv(issues_path) | pandas.read_csv |
from results_2013_2014.state_legislature_scrape_2013_2014 import scrape_results as sr1314
from results_2015.state_legislature_scrape_2015 import scrape_results as sr15
from results_2016.state_legislature_scrape_2016 import scrape_results as sr16
from results_2016.state_legislature_scrape_2016_ny import scrape_results a... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# HEREHEREHERE
#############################################################################
#
# /home/git/clones/external/SAS_3DSpectrographs/py/gratingequation.py
# ; read-quoted-char-radix
#emacs helpers
# (insert (format "\n# " (buffer-file-name)))
#
# (set-input-me... | pd.set_option('display.max_rows', None) | pandas.set_option |
# -*- coding: utf-8 -*-
# Load All Packages
import numpy as np, pandas as pd
import xgboost as xgb
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
from keras.models import Sequential
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from ke... | pd.read_csv(self.PrhdaPath, error_bad_lines=False, delimiter='\t') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Zerodha Kite Connect - candlestick pattern scanner
@author: <NAME> (http://rasuquant.com/wp/)
"""
from kiteconnect import KiteConnect
import pandas as pd
import datetime as dt
import os
import time
import numpy as np
from technicalta import *
#cwd = os.chdir("D:\\Udemy\\Zerodha KiteConnect... | pd.DataFrame(tech) | pandas.DataFrame |
import pandas as pd
from .datastore import merge_postcodes
from .types import ErrorDefinition
from .utils import add_col_to_tables_CONTINUOUSLY_LOOKED_AFTER as add_CLA_column # Check 'Episodes' present before use!
def validate_165():
error = ErrorDefinition(
code = '165',
description = 'Data entry for moth... | pd.offsets.DateOffset(years=18) | pandas.offsets.DateOffset |
#!/usr/bin/env python
# coding: utf-8
import simpy
import datetime
import pandas as pd
import logging
from enum import Enum
import random
from itertools import repeat
from ruamel.yaml import YAML
from datetime import timedelta
log_filename = "logs-10.log"
mainLogger = logging.getLogger()
fhandler = logging.FileHandle... | pd.DataFrame(self.start_data) | pandas.DataFrame |
import pandas as pd
from sklearn import metrics
from sklearn.linear_model import LogisticRegression
import time
import multiprocessing as mp
start_time=time.time()
def svm(location1,location2):
data=pd.read_csv(location1)
data_columns=data.columns
xtrain = data[data_columns[data_columns != 'typeoffraud']... | pd.read_csv(location2) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 14 19:01:45 2021
@author: David
"""
from pathlib import Path
from datetime import datetime as dt
import zipfile
import os.path
import numpy as np
import scipy.signal as sig
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLoc... | pd.to_numeric(band_data_min.iloc[-10:-4, -1]) | pandas.to_numeric |
from datetime import timedelta
import numpy as np
import pytest
from pandas import Categorical, DataFrame, NaT, Period, Series, Timedelta, Timestamp
import pandas._testing as tm
class TestSeriesFillNA:
def test_fillna_pytimedelta(self):
# GH#8209
ser = Series([np.nan, Timedelta("1 days")], index... | Timestamp("20130101") | pandas.Timestamp |
"""dev_env_for_beta_app"""
code='dev_env_for_beta_app'
from elasticsearch import Elasticsearch
es = Elasticsearch([{'host': 'elastic-helm-elasticsearch-coordinating-only'}])
sample_user_id='607077a405164b0001e72f69'
log='https://apibeta.bighaat.com/crop/api/logerror/create-recommendation-error-log?message={}&api-versio... | pd.concat([posts,posts_crop_doc]) | pandas.concat |
import numpy as np
import pandas as pd
from keras import backend as K
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Activation, BatchNormalization, Dense, Input
from keras.models import Model
from sklearn.decomposition import TruncatedSVD
from sklearn.ensemble import ExtraTreesRegr... | pd.read_csv("../input/commonlitstackingcsv/attention_head_itpt.csv") | pandas.read_csv |
import pandas as pd
import bioframe
import pyranges as pr
import numpy as np
from io import StringIO
def bioframe_to_pyranges(df):
pydf = df.copy()
pydf.rename(
{"chrom": "Chromosome", "start": "Start", "end": "End"},
axis="columns",
inplace=True,
)
return pr.PyRanges(pydf)
d... | pd.DataFrame([["chr1", 2, 10]], columns=["chrom", "start", "end"]) | pandas.DataFrame |
import pandas as pd
import numpy as np
import sqlite3
from retrobiocat_web.retro.generation.node_analysis import rdkit_smile
def convert_to_rdkit(smi):
try:
new_smi = rdkit_smile(smi)
return new_smi
except:
return None
def load_data(path, cols, sep, smi_col):
print(f'Load path: {pa... | pd.read_csv(path, sep=sep) | pandas.read_csv |
import os
import json
from dotenv import load_dotenv
import pandas as pd
from web3 import Web3
from pathlib import Path
class BlockheadMarketPlace:
"""
Attributes:
nft_contract: string
Contract's Application Binary Interface (ABI) represented
in JSON format for the NFT contra... | pd.DataFrame(data) | pandas.DataFrame |
import numpy as np
import os
import csv
import requests
import pandas as pd
import time
import datetime
from stockstats import StockDataFrame as Sdf
from ta import add_all_ta_features
from ta.utils import dropna
from ta import add_all_ta_features
from ta.utils import dropna
from config import config
def load_dataset(... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
from trading_calendars import get_calendar
def get_benchmark_returns(symbol, first_date, last_date):
cal = get_calendar('NYSE')
dates = cal.sessions_in_range(first_date, last_date)
data = | pd.DataFrame(0.0, index=dates, columns=['close']) | pandas.DataFrame |
import os
import h5py
import matplotlib.pyplot as plt
from pathlib import Path
from time import time, strftime
import pandas as pd
import numpy as np
import scipy.ndimage as ndi
import argparse
from rabbitccs.data.utilities import load, save, print_orthogonal
from rabbitccs.inference.thickness_analysis import _local_... | pd.DataFrame(results) | pandas.DataFrame |
import requests
from lxml import etree
from urllib.parse import urljoin
from pandas import DataFrame, read_html, concat
from bs4 import BeautifulSoup
import re
from tqdm import tqdm
def _getLinksFromPage(url, textcrib=None, hrefcrib=True):
page = requests.get(url)
#The file we have grabbed in this case is ... | DataFrame() | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
import InterruptionAnalysis as ia
readpath = './data/edgedir-sim'
data = pd.read_csv('./data/timeseries.csv', index_col = 0)
votedata = pd.read_csv('./data/vote-data.csv')
votedata.set_index('pID', inplace = True)
... | pd.unique(data['gID']) | pandas.unique |
# pylint: disable=W0201
from statsmodels.compat.python import iteritems, string_types, range
import numpy as np
from statsmodels.tools.decorators import cache_readonly
import pandas as pd
from . import var_model as _model
from . import util
from . import plotting
FULL_SAMPLE = 0
ROLLING = 1
EXPANDING = 2
def _get... | pd.DataFrame(data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable=E1101,E1103,W0232
import os
import sys
from datetime import datetime
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
import pandas.compat as compat
import pandas.core.common as com
import pandas.util.testing as tm
from pandas import (Categor... | Categorical(["a", "b", "c", "a"], ordered=True) | pandas.Categorical |
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