prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
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('2015-01-04') | pandas.Timestamp |
# importing the necessory library
import numpy as np
import pandas as pd
# defining the function to read the box boundry
def dimension(file):
f = open(file,'r')
content = f.readlines()
# stroring the each vertext point on the data list
data = []
v_info = []
vertices_data =[]
# ... | pd.DataFrame (data, columns = ['Line_no','x','y','z']) | pandas.DataFrame |
import datetime
import pandas as pd
from src.models.model import *
from hyperopt import Trials, STATUS_OK, tpe, fmin, hp
from hyperas.utils import eval_hyperopt_space
from keras.optimizers import SGD
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import roc_auc_score, recall_score, precision_s... | pd.DataFrame(y_train, columns=['LABEL']) | pandas.DataFrame |
import pandas as pd
from collections import Counter
def df_to_experiment_annotator_table(df, experiment_col, annotator_col, class_col):
"""
:param df: A Dataframe we wish to transform with that contains the response of an annotator to an experiment
| | document_id | annotator_id | annota... | pd.DataFrame.from_dict(vbu_table_dict, orient="index") | pandas.DataFrame.from_dict |
# %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# this module has basic operations
data = pd.read_csv('pandas_help/winter.csv')
dataset = pd.read_csv('pandas_help/wine_data.csv', sep=';')
# if you have na values you can mention in pd.read_csv()
# if suppose in A column of data na_values ar... | pd.Series(['apple', '1.0', '2', -3]) | pandas.Series |
import argparse
import os
import numpy as np
import torch
import torch.utils.data
from PIL import Image
import pandas as pd
import cv2
import json
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
from torchvision.transforms import functional as F
from torchvision.models.detection im... | pd.DataFrame(boxes, columns=['x1','y1','x2','y2']) | pandas.DataFrame |
import pandas as pd
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
import miditoolkit
import os
def getStats(folder_name,num_notes_dict={},channel=0):
if num_notes_dict=={}:
num_notes_dict=numNotes(folder_name,channel)
df= | pd.DataFrame.from_dict(num_notes_dict, orient='index',columns=["Notes"]) | pandas.DataFrame.from_dict |
__author__ = '<NAME>'
import os
import numpy as np
import pandas as pd
import ctypes
pd.options.mode.chained_assignment = None
from sklearn import cross_validation
from sklearn.metrics import mean_absolute_error
import matplotlib.pyplot as plt
import matplotlib
from sklearn.metrics import accuracy_score
matplotlib.st... | pd.DataFrame(index=test_empty_rows_ids, columns=['Expected'], data=empty_test_y) | pandas.DataFrame |
import pandas as pd
from sklearn import linear_model
import statsmodels.api as sm
import numpy as np
from scipy import stats
df_all = pd.read_csv("/mnt/nadavrap-students/STS/data/imputed_data2.csv")
print(df_all.columns.tolist())
print (df_all.info())
df_all = df_all.replace({'MtOpD':{False:0, True:1}})
df_all = ... | pd.merge(d6, df_17, on='HospID', how='outer') | pandas.merge |
#!/usr/bin/env python3
import csv
import os
import time
from datetime import date, datetime, timedelta
from pprint import pprint
import numpy as np
import pandas as pd
import pymongo
import requests
import yfinance as yf
LIMIT = 1000
sp500_file = "constituents.csv"
date_format = "%Y-%m-%d %H:%M:%S"
sp500_list = []
s... | pd.date_range(last_year, today) | pandas.date_range |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# This file is part of CbM (https://github.com/ec-jrc/cbm).
# Author : <NAME>
# Credits : GTCAP Team
# Copyright : 2021 European Commission, Joint Research Centre
# License : 3-Clause BSD
from ipywidgets import (HBox, VBox, Dropdown, Button, Output, Checkbox)
fr... | pd.set_option('display.max_columns', 20) | pandas.set_option |
import re
import string
import logging
import pandas as pd
import numpy as np
import text
import super_pool
logger = logging.getLogger()
cleanup = text.SimpleCleanup()
emoji = text.Emoji()
def hash_(x):
return hash(x)
def run(df=None):
if df is None:
df = pd.read_csv(
"../input/train.... | pd.concat([df, df_test], axis=0) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
# # 5m - Df unification (10 calib. fn-s)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os
from os.path import join
import pickle
from copy import copy
def get_data_name(file):
if "resnet110" in file:
return "resnet110"
elif ... | pd.concat(dfs) | pandas.concat |
import pandas as pd
import numpy as np
import pytest
from sklearn.exceptions import ConvergenceWarning
def test_interpolate_data():
from mspypeline.modules.Normalization import interpolate_data
assert interpolate_data(pd.DataFrame()).equals(pd.DataFrame())
data = pd.DataFrame(np.random.random((100, 100)))... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
from sklearn.base import TransformerMixin, BaseEstimator
from sklearn.feature_extraction import DictVectorizer
from sklearn.preprocessing import FunctionTransformer, StandardScaler, RobustScaler
from sklearn.preprocessing import Imputer, MultiLabelBinarizer
from sklearn.impute imp... | pd.DataFrame(xmlb, index=x.index, columns=cols) | pandas.DataFrame |
from datetime import datetime
from decimal import Decimal
from io import StringIO
import numpy as np
import pytest
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series, Timestamp, date_range, read_csv
import pandas._testing as tm
from pa... | tm.makeTimeDataFrame() | pandas._testing.makeTimeDataFrame |
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('2015-01-02') | pandas.Timestamp |
from SentinelTime.data_preprocessing import *
from SentinelTime.mask_stack import *
import rasterio.mask
import matplotlib.pyplot as plt
import pandas as pd
def extract_dates(directory, allowed_orbits):
"""
Extracts dates from list of preprocessed S-1 GRD files (need to be in standard pyroSAR exported naming ... | pd.set_option('display.max_columns', None) | pandas.set_option |
import pandas as __pd
import datetime as __dt
from dateutil import relativedelta as __rd
from multiprocessing import Pool as __Pool
import multiprocessing as __mp
import requests as __requests
from seffaflik.__ortak.__araclar import make_requests as __make_requests
from seffaflik.__ortak import __dogrulama as __dogrul... | __pd.to_datetime(tarih) | pandas.to_datetime |
import numpy as np
from ..util.math import range_step
from ..util.functions import composer
from pandas import Series
_distribution_samples = {
'float': {
'normal': lambda **kw: composer(
lambda **kw: np.random.normal(kw['mean'], kw['std'], kw['size']),
**kw
),
'uni... | Series(nums) | pandas.Series |
#!python3
import argparse
import pandas as pd
import numpy as np
from scipy.optimize import brentq
from plot_module import *
if __name__ == '__main__':
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-o', '--output', default="theoretical_eq", type=s... | pd.DataFrame(dict_df) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pickle as pk
import glob
import os
county_list = os.listdir('data/set_features')
print(county_list)
to_remove = []
for id, file_name_ in enumerate(county_list[0:1]):
print(id)
s = pd.read_parquet('data/set_features/' + file_name_, engine='pyarrow')
# for i i... | pd.to_numeric(s[c], errors='coerce') | pandas.to_numeric |
# coding: utf-8
# In[ ]:
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # ... | pd.read_csv("../input/test.csv", names=['Store','Dept','Date','isHoliday'],sep=',', header=0) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# In[25]:
# import tabula
import pandas as pd
import requests
from urllib.request import urlopen
from lxml import etree
from collections import OrderedDict
from datetime import datetime
from alphacast import Alphacast
from dotenv import dotenv_values
API_KEY = dotenv_values(".... | pd.read_excel(file_url, sheet_name="Total aglos 1.1", skiprows=3,header=[0,1]) | pandas.read_excel |
print('Chapter 03: Scraping Extraction')
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print('setup.py')
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
BASE_DIR = ".."
def figNum():
figNum.counter += 1
return "{0:02d}".format(figNum.counter)
figNum.counter = 0
FIGPREFIX = 'ch03_fig'
print('\n')... | pd.DataFrame() | pandas.DataFrame |
import csv
from datetime import date, timedelta
from os import path
import pandas as pd
from nba_api.stats.endpoints import leaguegamefinder, scoreboardv2
basepath = path.dirname(path.dirname(path.abspath(__file__)))
data_path = path.join(basepath, 'data', 'irl')
def write_data_file_for_date(date_param):
date_ap... | pd.concat([df[4], df[5]]) | pandas.concat |
import numpy
import pandas
#this is the file that contains our dot product code
import Daphnis.distance_methods.methods
#input parameters
cfmid_csv_address='/home/rictuar/coding_projects/fiehn_work/text_files/_cfmid_4_point_0_spectra_for_experimental_comparison/cfmid_output_csv_nist20_only_adduct_[M+H]+_msrb_relaced.c... | pandas.read_csv(classyfire_results_address,sep='\t',header=0,usecols=['InChIKey','Superclass']) | pandas.read_csv |
# coding: utf-8
# # Classification des Iris en utilisant tensorflow
# # I - Introduction
#
# ---
# #### Objectif
# <div style="text-align:justify;">L'objectif est de suivre un projet de Machine du concept à son intégration. Nous allons donc partir d'une base de données simple existant déjà sur internet. Nous allons... | get_dummies(y) | pandas.get_dummies |
r"""Exp 4:
- Fix:
- n=53, f=?
- Number of iterations = 600
- Not *Long tail* (alpha=1)
- Always NonIID
- Number of runs = 3
- LR = 0.01
- Attack: IPM epsilon=0.1
- Aggregator: CP
- Varies:
- momentum=0, 0.9
- Bucketing: ?
Experiment:
- Fix f=5 varying s:
- s=0,2,5
... | pd.DataFrame(results) | pandas.DataFrame |
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import kendalltau
import pandas as pd
import seaborn as sns
import argparse
import sys, os
import fnmatch
parser = argparse.ArgumentParser()
parser.add_argument('-es', help='<Required> give csv with es generations', required=True)
parser.ad... | pd.concat([de_df,es_df]) | pandas.concat |
import random
import timeit
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from algorithms.sort import (quick_sort, merge_sort, pigeonhole_sort, counting_sort, radix_sort, cocktail_shaker_sort,
shell_sort, max_heap_sort, min_heap_sort, bucket_sort, cycle_sort, c... | pd.DataFrame(data=benchmark_row, columns=["Name", "Sample_size", "Duration"]) | pandas.DataFrame |
"""
accounting.py
Accounting and Financial functions.
project : pf
version : 0.0.0
status : development
modifydate :
createdate :
website : https://github.com/tmthydvnprt/pf
author : tmthydvnprt
email : <EMAIL>
maintainer : tmthydvnprt
license : MIT
copyright : Copyright 2016, tmthydvnprt
cr... | pd.MultiIndex.from_tuples([(x0, x1, 'Total') for x0, x1 in l1_totals.index]) | pandas.MultiIndex.from_tuples |
# -*- coding: utf-8 -*-
# @Author: jerry
# @Date: 2017-09-09 21:03:21
# @Last Modified by: jerry
# @Last Modified time: 2017-09-23 17:09:41
import pandas as pd
from log_lib import log
def get_csv(filename, path=None):
df = | pd.read_csv(filename) | pandas.read_csv |
# Obtaining and processing CVE json **files**
# The code is to download nvdcve zip files from NIST since 2002 to the current year,
# unzip and append all the JSON files together,
# and extracts all the entries from json files of the projects.
# 获取和处理CVE json **文件**
# 代码是从NIST下载nvdcve zip文件从2002年到今年，
# 解压并附加所有JSON文件，
#... | json_normalize(df_in['CVE_Items']) | pandas.json_normalize |
import numpy as np
import pandas as pd
from analysis.transform_fast import load_raw_cohort, transform
def test_immuno_group():
raw_cohort = load_raw_cohort("tests/input.csv")
cohort = transform(raw_cohort)
for ix, row in cohort.iterrows():
# IF IMMRX_DAT <> NULL | Select | Next
if pd... | pd.notnull(row["vld2rx_dat"]) | pandas.notnull |
"""Tests for Table Schema integration."""
import json
from collections import OrderedDict
import numpy as np
import pandas as pd
import pytest
from pandas import DataFrame
from pandas.core.dtypes.dtypes import (
PeriodDtype, CategoricalDtype, DatetimeTZDtype)
from pandas.io.json.table_schema import (
as_json_... | make_field(kind) | pandas.io.json.table_schema.make_field |
import numpy as np
import pytest
from pandas import DataFrame, SparseArray, SparseDataFrame, bdate_range
data = {
"A": [np.nan, np.nan, np.nan, 0, 1, 2, 3, 4, 5, 6],
"B": [0, 1, 2, np.nan, np.nan, np.nan, 3, 4, 5, 6],
"C": np.arange(10, dtype=np.float64),
"D": [0, 1, 2, 3, 4, 5, np.nan, np.nan, np.nan... | SparseDataFrame(data, index=dates) | pandas.SparseDataFrame |
"""Module and script to combine IDs with molreports to form graphs and masks.
This module provides functions and a script to extract bond and atom identifier
information, combine these IDs with bonds from the molreport file, and output:
1) An atom-level node list
2) An atom-level covalent bond list
3) A mask of atoms... | pd.concat([chain_atoms, missing_atoms]) | pandas.concat |
#!/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.to_datetime(df_descr_speech_speakermap['date'], format='%Y%m%d') | pandas.to_datetime |
import sys
import logging
import pandas as pd
import pytz
import bt
try:
from . import module_loader
except:
import module_loader
sys.dont_write_bytecode = True
class AlgoRunner(object):
def __init__(self, stock_data_provider, capital_base, parameters):
self.stock_data_provider_ = stock_data_pr... | pd.to_datetime(end_date) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 20 14:08:35 2019
@author: Team BTC - <NAME>, <NAME>, <NAME>, <NAME>, <NAME>
"""
#sorry the code isnt very efficient. because of time constraints and the number of people working on the project, we couldnt do all the automatizations we would have liked to do. ... | pd.concat([ad,ab,ac],axis=1) | pandas.concat |
import warnings
import pandas as pd
import numpy as np
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger
import torch
from pytorch_forecasting import Baseline, TemporalFusionTransformer, TimeSeriesDataSet
... | pd.read_csv("data/poc.csv") | pandas.read_csv |
'''
Project: WGU Data Management/Analytics Undergraduate Capstone
<NAME>
August 2021
GDELTbase.py
Class for creating/maintaining data directory structure, bulk downloading of
GDELT files with column reduction, parsing/cleaning to JSON format, and export
of cleaned records to MongoDB.
Basic use should ... | pd.StringDtype() | pandas.StringDtype |
import pandas as pd
import numpy as np
import knackpy as kp
import fulcrum as fc
import requests
import pdb
import json
from datetime import datetime, timedelta
from pypgrest import Postgrest
from tdutils import argutil
from config.secrets import *
form_id = "44359e32-1a7f-41bd-b53e-3ebc039bd21a"
key = FULCRUM_CRED.... | pd.DataFrame(results) | pandas.DataFrame |
"""
Module contains miscellaneous functions used for reading data, printing logo etc.
"""
import pickle
from random import sample
import networkx as nx
import pandas as pd
def read_testcase(FOLDER):
"""
Reads the GTFS network and preprocessed dict. If the dicts are not present, dict_builder_functions are cal... | pd.to_timedelta(1, unit="seconds") | pandas.to_timedelta |
import re
import warnings
import numpy as np
import pandas as pd
import scipy
from pandas import DataFrame
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.neighbors import BallTree, KDTree, NearestNeighbors
from sklearn.preprocessing import MultiLabelBinarizer, Normalizer
from tqdm import tqd... | DataFrame(X) | pandas.DataFrame |
import os
pat = "/storage/research/Intern19_v2/AutomatedDetectionWSI/LiverImages/"
#pat_1 = "/storage/research/Intern19_v2/AutomatedDetectionWSI/level_1/"
#pat_2 = "/storage/research/Intern19_v2/AutomatedDetectionWSI/level_2/"
a= os.walk(pat)
a = list(a)
l = []
for i in a[0][2]:
if '.xml' in i or 'svs' in i or 'SV... | pd.DataFrame(whole) | pandas.DataFrame |
"""
Created on Wed Nov 18 14:20:22 2020
@author: MAGESHWARI
"""
import os
from tkinter import *
from tkinter import messagebox as mb
from tkinter import filedialog
import re
import csv
import pandas as pd
def center_window(w=200, h=500):
# get screen width and height
ws = root.winfo_screenwidt... | pd.read_csv(basefilepath) | pandas.read_csv |
from typing import Union
import numpy as np
import pandas as pd
import modin.pandas as mpd
from datetime import datetime, timedelta
import calendar
def convert_date(date: Union[datetime, str, pd.Series, np.ndarray]) -> np.ndarray:
"""Receives `date` from a variety of datatypes and converts it into a numeric value... | pd.to_datetime(date) | pandas.to_datetime |
import json
import os
from typing import Union
import numpy as np
import pandas as pd
from mlflow.exceptions import MlflowException
from mlflow.types.utils import TensorsNotSupportedException
from mlflow.utils.proto_json_utils import NumpyEncoder
ModelInputExample = Union[pd.DataFrame, np.ndarray, dict, list]
clas... | pd.DataFrame(input_example) | pandas.DataFrame |
import numpy as np
import pandas as pd
from numpy import inf, nan
from numpy.testing import assert_array_almost_equal, assert_array_equal
from pandas import DataFrame, Series, Timestamp
from pandas.testing import assert_frame_equal, assert_series_equal
from shapely.geometry.point import Point
from pymove import MoveDa... | Timestamp('2008-10-23 11:58:33') | pandas.Timestamp |
import cProfile
import os
import pstats
import sys
import warnings
from datetime import datetime
from functools import partial
import numpy as np
import pandas as pd
import pandas.api.types as pdtypes
from .base_backend import ComputationalBackend
from .feature_tree import FeatureTree
from featuretools import variab... | pd.Series(d) | pandas.Series |
"""
SIR 3S Logfile Utilities (short: Lx)
"""
__version__='192.168.3.11.dev1'
import os
import sys
import logging
logger = logging.getLogger(__name__)
import argparse
import unittest
import doctest
import nbformat
from nbconvert.preprocessors import ExecutePreprocessor
from nbconvert.preprocessor... | pd.concat(TCsdfLDSResLst) | pandas.concat |
from bs4 import BeautifulSoup
import chardet
from datetime import datetime
import json
import lxml
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
from serpapi import GoogleSearch
import statistics
import re
import requests
import time
from a0001_admin import clean_dataf... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
# @Time : 09.04.21 09:54
# @Author : sing_sd
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import src.common_functions as cf
import csv
import ais
from datetime import datetime, timedelta, timezone
import re
vb_dir = os.path.dir... | pd.Series(to_append, index=data.columns) | pandas.Series |
#!/usr/bin/env python3
import argparse
import collections
import copy
import datetime
import functools
import glob
import json
import logging
import math
import operator
import os
import os.path
import re
import sys
import typing
import warnings
import matplotlib
import matplotlib.cm
import matplotlib.dates
import ma... | pandas.DataFrame(data=records) | pandas.DataFrame |
#dependencies
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.model_selection im... | pd.read_csv('criminal_train.csv') | pandas.read_csv |
""" LSTM MODEL STUFF """
import numpy as np
import scipy.io as sio
import json
import tensorflow as tf
from pandas import DataFrame, Series, concat
from tensorflow.python.keras.layers import Input, Dense, LSTM
from tensorflow.python.keras.models import Sequential
from random import randrange
from sklearn.preprocessing... | concat(cols, axis=1) | pandas.concat |
from __future__ import print_function
# from builtins import str
# from builtins import object
import pandas as pd
from openpyxl import load_workbook
import numpy as np
import os
from .data_utils import make_dir
class XlsxRecorder(object):
"""
xlsx recorder for results
including two recorder: one for curre... | pd.Index(self.name_list_buffer+['average']) | pandas.Index |
"""
SPDX-FileCopyrightText: 2019 oemof developer group <<EMAIL>>
SPDX-License-Identifier: MIT
"""
import pytest
import pandas as pd
import numpy as np
from pandas.util.testing import assert_series_equal
import windpowerlib.wind_farm as wf
import windpowerlib.wind_turbine as wt
import windpowerlib.wind_turbine_cluster... | assert_series_equal(test_tc_mc.power_output, power_output_exp) | pandas.util.testing.assert_series_equal |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Author: Ivar
"""
import sys
import os
#from scipy import interp
import pandas as pd
import numpy as np
from sklearn import svm
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report, plot_confusion_matrix
from sklearn.prepro... | pd.DataFrame({"FPR":fpr, "TPR":tpr}) | pandas.DataFrame |
#!/usr/bin/env python3
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
import pandas.testing as pdtest
from pandas.api.types import is_datetime64_dtype
from sklearn.base import TransformerMixin
from sklearn.exceptions import NotFittedError
from sklearn.utils.valid... | pd.DataFrame(X, index=idx, columns=col) | pandas.DataFrame |
import pymongo
import numpy as np
import pandas as pd
from sys import argv
# Set up mongodb database
myclient = pymongo.MongoClient("mongodb://localhost:27017/")
mydb = myclient["product_Durability_db"]
mycol = mydb[argv[1]]
# myquery = {"address": "Park Lane 38"}
# mydoc = mycol.find(myquery)
# Set up matrix for dis... | pd.DataFrame(d, columns=continents, index=continents) | pandas.DataFrame |
import tehran_stocks.config as db
import matplotlib.pyplot as plt
from tehran_stocks import Stocks
import pandas as pd
import matplotlib.ticker as mtick
from bidi.algorithm import get_display
import arabic_reshaper
import pathlib
def histogram_value(history_len):
q = f"select date_shamsi,SUM(value) as value from... | pd.merge(total, data, on='date_shamsi') | pandas.merge |
# NOTE: It is the historian's job to make sure that keywords are not repetitive (they are
# otherwise double-counted into counts).
from collections import defaultdict
from collections import OrderedDict
import os
import pandas as pd
import re
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word... | pd.isnull(f) | pandas.isnull |
"""
kkpy.io
========================
Functions to read and write files
.. currentmodule:: io
.. autosummary::
kkpy.io.read_aws
kkpy.io.read_2dvd_rho
kkpy.io.read_mxpol_rhi_with_hc
kkpy.io.read_dem
"""
import numpy as np
import pandas as pd
import datetime
import glob
import os
import sys
def read_a... | pd.to_datetime(_df[['year','month','day','hour','minute']]) | pandas.to_datetime |
"""
Utilities for dealing with PCTS cases.
"""
import dataclasses
import re
import typing
import pandas
GENERAL_PCTS_RE = re.compile("([A-Z]+)-([0-9X]{4})-([0-9]+)((?:-[A-Z0-9]+)*)$")
MISSING_YEAR_RE = re.compile("([A-Z]+)-([0-9]+)((?:-[A-Z0-9]+)*)$")
VALID_PCTS_PREFIX = {
"AA",
"ADM",
"AP... | pandas.to_datetime(end_date) | pandas.to_datetime |
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.DateOffset(years=18) | pandas.DateOffset |
# Make a stackplot and a stackplot where total = 100% of agegroups
# <NAME> (@rcsmit) - MIT Licence
# IN: https://data.rivm.nl/covid-19/COVID-19_ziekenhuis_ic_opnames_per_leeftijdsgroep.csv
# OUT : Stackplots
#
# TODO : Legend DONE
# Nice colors DONE
# Restrictions ??
# Set a date-period DONE
# ... | pd.to_datetime(show_from) | pandas.to_datetime |
import numpy as np
from scipy.spatial import distance_matrix, distance
from visualizations.iVisualization import VisualizationInterface
from controls.controllers import TimeSeriesController
import panel as pn
import holoviews as hv
from holoviews.streams import Pipe, Buffer
import pandas as pd
import time
from threadin... | pd.DataFrame([(i, u)], columns=['time', 'neurons']) | pandas.DataFrame |
'''''
Authors: <NAME> (@anabab1999) and <NAME> (@felipezara2013)
'''
from calendars import DayCounts
import pandas as pd
from pandas.tseries.offsets import DateOffset
from bloomberg import BBG
import numpy as np
bbg = BBG()
#Puxando os tickers para a curva zero
tickers_zero_curve = ['S0023Z 1Y BLC2 Curncy',
... | DateOffset(months=periodcupons) | pandas.tseries.offsets.DateOffset |
# This Python file uses the following encoding: utf-8
# <NAME> <<EMAIL>>, september 2020
import os
import pandas as pd
import numpy as np
from datetime import date
from qcodes.instrument.base import Instrument
class BlueFors(Instrument):
"""
This is the QCoDeS python driver to extract the temperature and pres... | pd.to_datetime(df['date']+'-'+df['time'], format='%d-%m-%y-%H:%M:%S') | pandas.to_datetime |
#!/usr/bin/env python3
### Burak Less Data Experiment Utils
### GENERIC
import copy
import datetime
import io
import os
from os import listdir
from os.path import isfile, join, isdir
import sys
from functools import partial
### DATA PROCESS
import pandas as pd
import numpy as np
import ast
from sklearn.metrics impor... | pd.DataFrame() | pandas.DataFrame |
from __future__ import print_function, division
from nilmtk.disaggregate import Disaggregator
from keras.layers import Conv1D, Dense, Dropout, Flatten
import pandas as pd
import numpy as np
from collections import OrderedDict
from keras.models import Sequential
from sklearn.model_selection import train_test_split
clas... | pd.concat(app_df, axis=0) | pandas.concat |
# index page
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash.dependencies import Input,Output,State
import users_mgt as um
from server import app, server
from flask_login import logout_user, current_user
from views import success, login, login_fd,... | pd.read_sql_table('python_data_analysis', con='sqlite:///users.db') | pandas.read_sql_table |
"""
Tools to clean Balancing area data.
A data cleaning step is performed by an object that subclasses
the `BaDataCleaner` class.
"""
import os
import logging
import time
import re
from gridemissions.load import BaData
from gridemissions.eia_api import SRC, KEYS
import pandas as pd
import numpy as np
from collections i... | pd.concat(r_list, axis=1) | pandas.concat |
import os
import sys
import json
import yaml
import pandas as pd
from ananke.graphs import ADMG
from networkx import DiGraph
from optparse import OptionParser
sys.path.append(os.getcwd())
sys.path.append('/root')
from src.causal_model import CausalModel
from src.generate_params import GenerateParams
def config_optio... | pd.DataFrame([config]) | pandas.DataFrame |
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import requests
import spotipy
from typing import List
from os import listdir
import json
import sys
from tqdm import tqdm
"""
Credentials to :
https://toward... | pd.read_json(file_path) | pandas.read_json |
# Long Author List formatting tool
# <NAME> (<EMAIL> 2020)
# Usage: python3 lal.py
# Input: lal_data2.txt with one author per row and up to 5 affiliations
# <First>;<Last>;<Email>;<Group1>;<Group2>;<Group3>;<Group4>;<Group5>
# Example: Heiko;Goelzer;<EMAIL>;IMAU,UU;ULB;nil;nil;nil
# Use 'nil','nan','0' or '-' to fi... | pd.DataFrame() | pandas.DataFrame |
import nltk
import sklearn_crfsuite
from sklearn_crfsuite import metrics
import pandas as pd
from sklearn.preprocessing import label_binarize
import string
# nltk.download('conll2002')
flatten = lambda l: [item for sublist in l for item in sublist]
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
fr... | pd.DataFrame(hyper_parm_turning) | pandas.DataFrame |
import pandas as pd
import numpy as np
__all__=['xgb_parse']
def _xgb_tree_leaf_parse(xgbtree,nodeid_leaf):
'''给定叶子节点，查找 xgbtree 树的路径
'''
leaf_ind=list(nodeid_leaf)
result=xgbtree.loc[(xgbtree.ID.isin(leaf_ind)),:]
result['Tag']='Leaf'
node_id=list(result.ID)
while len(node_id)>0:
... | pd.DataFrame({'GainTotal':model.feature_importances_,'Feature':feature_names}) | pandas.DataFrame |
from urllib.request import urlretrieve
import pandas as pd
import os
FREMONT_URL = 'https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD'
def get_fremont_data(filename = "fremont.csv", url=FREMONT_URL, force_download=False):
'''
This function is used to prepare the data:
a) download t... | pd.to_datetime(data.index) | pandas.to_datetime |
import pandas as pd
import requests
import datetime
import numpy as np
from numpy import array
import matplotlib.pyplot as plt
from numpy import hstack
import seaborn as sns
import random
from functools import reduce
from keras.models import load_model
from keras.models import Sequential
from keras.layers import LSTM,... | pd.read_csv("repo_reviews_all.csv") | pandas.read_csv |
# -*- 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, ... | Index(['a', 'b']) | pandas.Index |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.5.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
# remove_cell
import sys
sys.path.insert(0, '/home/... | pd.DataFrame(data_dct) | pandas.DataFrame |
import numpy as np
import random
from sklearn.neighbors import NearestNeighbors
import matplotlib.pyplot as plt
import queue
import collections
import pandas as pd
INPUT_FILE="blobs.txt"
ITERATIONS=10
#Define label for differnt point group
NOISE = 0
UNASSIGNED = 0
core=-1
edge=-2
dataset = []
def read_dataset():
"... | pd.DataFrame(clusters, columns=['cluster']) | pandas.DataFrame |
import pandas as pd
test_data_set = pd.read_csv('test.csv')
train_data_set = pd.read_csv('train.csv')
gen_sub_set = pd.read_csv('gender_submission.csv')
test_set = gen_sub_set.merge(test_data_set,how='left')
Data_Set = | pd.concat([train_data_set,test_set],axis=0) | pandas.concat |
# Copyright (C) 2019-2020 Zilliz. All rights reserved.
#
# 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... | pandas.Series(arr1) | pandas.Series |
#!usr/bin/env python
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
import pickle
from dataProcessor import processor
df_psy = pd.read_csv("Dataset/Youtube01-Psy.csv")
df_katyperry = pd.read_csv("Dataset/Youtube02-KatyPerry.csv")
df_lmfao = pd.read_csv("Dataset/Youtube03-LMFAO.csv")
df_eminem... | pd.concat([df_psy, df_katyperry, df_lmfao, df_eminem, df_shakira]) | pandas.concat |
#coding=utf-8
#键盘分析
#（1）分别读取csdn和yahoo数据库中的passwd
#（2）自定义了常见的14种键盘密码字符串
#（3）将从数据库中读取的passwd与定义的字符串进行子串匹配（忽略单个的字母和数字）
#（4）只选择相对高频的密码，生成保存频率最高的密码和对应频率的csv
import pandas as pd
import numpy as np
import csv
np.set_printoptions(suppress=True)
##############################################
#（1）读取数据
#########################... | pd.DataFrame({'password' : yahoo_output.index , 'numbers' : yahoo_output.values , 'probability' : None}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
This module contains all the remote tests. The data for these
tests is requested to ESA NEOCC portal.
* Project: NEOCC portal Python interface
* Property: European Space Agency (ESA)
* Developed by: Elecnor Deimos
* Author: <NAME>
* Date: 02-11-2021
© Copyright [European Space Agency][2021... | ptypes.is_datetime64_ns_dtype(new_list['Date']) | pandas.api.types.is_datetime64_ns_dtype |
from unittest import TestCase, main
import os
import pandas as pd
import numpy as np
import numpy.testing as npt
from io import StringIO
from metapool.metapool import (read_plate_map_csv, read_pico_csv,
calculate_norm_vol,
format_dna_norm_picklist, assign_index, format_index_picklist,
... | pd.testing.assert_frame_equal(combined_df, exp_df, check_like=True) | pandas.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
"""
Tests dtype specification during parsing
for all of the parsers defined in parsers.py
"""
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas import DataFrame, Series, Index, MultiIndex, Categorical
from pandas.compat import StringIO
from pan... | tm.assert_frame_equal(actual, expected) | pandas.util.testing.assert_frame_equal |
# @Author: <NAME>
# @Date: Mon, May 4th 2020, 8:36 pm
# @Email: <EMAIL>
# @Filename: migrate_db.py
'''
Functions for duplicating, archiving, and converting database assets, including raw source files as well as SQLite db files.
'''
from tqdm import tqdm
from os.path import isfile
import shutil
import pandas as p... | pd.concat(file_not_found,axis=1) | pandas.concat |
import numpy as np
import pandas as pd
from io import StringIO
import re
import csv
from csv import reader, writer
import sys
import os
import glob
import fnmatch
from os import path
import matplotlib
from matplotlib import pyplot as plt
print("You are using Zorbit Analyzer v0.1")
directory_path = input... | pd.unique(all_merge_just_inter['SeqID']) | pandas.unique |
# Copyright 2019-2020 The Lux Authors.
#
# 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 agreed... | pd.DatetimeIndex(ldf["Year"]) | pandas.DatetimeIndex |
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 27 13:08:45 2021
@author: MalvikaS
Build classifier on RNA seq data
"""
# Import
import os
import pandas as pd
import glob
import random
from imblearn.ensemble import BalancedRandomForestClassifier
from imblearn.ensemble import BalancedBaggingClassifier
from imblearn.e... | pd.concat(data) | pandas.concat |
import os
import glob
import psycopg2
import pandas as pd
from sql_queries import *
def process_song_file(cur, filepath):
"""Reads songs log file row by row, selects needed fields and inserts them into song and artist tables.
Parameters:
cur (psycopg2.cursor()): Cursor of the sparkifydb databa... | pd.to_datetime(df['ts'], unit='ms') | pandas.to_datetime |
#!/usr/bin/env python
import argparse
import pandas as pd
import re
import sys
import collections
#Read arguments
parser = argparse.ArgumentParser(description="Generate input for exint plotter")
parser.add_argument("--annotation", "-a", required=True)
parser.add_argument("--overlap", "-o", required=True)
parser.add_a... | pd.Series(my_final_phases.Phase.values, index=my_final_phases.Coords) | pandas.Series |
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
from numba import njit
import vectorbt as vbt
from tests.utils import record_arrays_close
from vectorbt.generic.enums import range_dt, drawdown_dt
from vectorbt.portfolio.enums import order_dt, trade_dt, log_dt
day_dt = np.timedelta64... | pd.Index(['b'], dtype='object') | pandas.Index |
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