prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# -*- coding: utf-8 -*-
## Copyright 2015-2021 PyPSA Developers
## You can find the list of PyPSA Developers at
## https://pypsa.readthedocs.io/en/latest/developers.html
## PyPSA is released under the open source MIT License, see
## https://github.com/PyPSA/PyPSA/blob/master/LICENSE.txt
"""
Power flow functionality... | pd.Index(snapshots) | pandas.Index |
# coding: utf-8
# In[15]:
import sys, os, time, pickle
from timeit import default_timer as timer
from humanfriendly import format_timespan
# In[16]:
import pandas as pd
import numpy as np
# In[17]:
from dotenv import load_dotenv
load_dotenv('admin.env')
# In[18]:
from db_connect_mag import Session, Pape... | pd.read_pickle('data/collect_haystack_2127048411_seed-1/test_papers.pickle') | pandas.read_pickle |
#
# Prepare the hvorg_movies
#
import os
import datetime
import pickle
import json
import numpy as np
import pandas as pd
from sunpy.time import parse_time
# The sources ids
get_sources_ids = 'getDataSources.json'
# Save the data
save_directory = os.path.expanduser('~/Data/hvanalysis/derived')
# Read in the data
di... | pd.DataFrame(0, index=df.index, columns=all_sources) | pandas.DataFrame |
"""
try to classify reddit posts.
"""
import os
import glob
from collections import defaultdict
from pprint import pprint
import time
from datetime import datetime
import pandas as pd
from sklearn_pandas import DataFrameMapper, cross_val_score
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
f... | pd.concat([merged_result_df, long_df]) | pandas.concat |
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# DATA COMPARISONS OF NEW hurs AND OLD hur DOWNSCALED DATA
# <NAME> (<EMAIL>) September 2018
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
def open_raster( fn, band=1 ):
with rasterio.open( fn ) as rst:
arr = rst.read( band )
... | pd.concat(groups) | pandas.concat |
### Notes:
### What does this function return ?
# 1. A dataframe with daywise summary for the spends, leads, appointments and surgeries
# 2. Added columns of CPL, CPA and CPS
# 3. Split of Total leads, appointment and surgeries from Facebook and Google
### What does this function take as input?:
# 1. Nothi... | pd.merge(f2f_sch_daily, f2f_comp_daily, on = ["Date", "Dept", "Service"], how = "outer") | pandas.merge |
from keras.models import load_model
from random import seed
from random import randint
import numpy as np
import pandas as pd
import sys
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
def position_3D_approximation(result, strategy):
# result => predicted
#yclone = np.copy(y)
... | pd.concat([df1,pred],axis=1) | pandas.concat |
import os
import time
import shutil
import sys
sys.path.append('C:/prismx/')
import h5py as h5
import pandas as pd
import numpy as np
import random
import show_h5 as ph5
import seaborn as sns
import matplotlib.patches as mpatches
from scipy import stats
import matplotlib.pyplot as plt
import prismx as px
f100 = pd... | pd.read_csv("logs/validationscore50.tsv", sep="\t") | pandas.read_csv |
import pandas as pd
df_raw = | pd.read_csv("/data/wifi-analysis/competition/train.csv") | pandas.read_csv |
import numpy as np
import pytest
import pandas as pd
from pandas import CategoricalIndex, Index
import pandas._testing as tm
class TestMap:
@pytest.mark.parametrize(
"data, categories",
[
(list("abcbca"), list("cab")),
(pd.interval_range(0, 3).repeat(3), pd.interval_range(... | pd.Series([False, False]) | pandas.Series |
from collections import OrderedDict
import contextlib
from datetime import datetime, time
from functools import partial
import os
from urllib.error import URLError
import warnings
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, Index, Multi... | tm.assert_frame_equal(url_table, local_table) | pandas.util.testing.assert_frame_equal |
import pandas as pd
import numpy as np
from tensorflow import keras
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
# instance of the neural network to predit future prices
class Neural_Network:
def neural_network(self, n_df):
df = n_df.copy()
... | pd.Series(y_test) | pandas.Series |
#!/usr/bin/env python3
"""A module for analyzing answer agreement.
The experiment is this:
* Each member of a group records the answers from a respondent.
* All members of all groups submit their surveys to form a dataset.
This dataset is analyzed with this module.
A dataset is all submissions for a survey. A colum... | pd.read_csv(df_path) | pandas.read_csv |
import logging
import numpy as np
import pandas as pd
import re
from os import PathLike
from pathlib import Path
from scipy.ndimage import maximum_filter
from typing import (
Generator,
List,
Optional,
Sequence,
Tuple,
Union,
)
from steinbock import io
try:
from readimc import MCDFile, TX... | pd.StringDtype() | pandas.StringDtype |
import requests
import pandas as pd
import numpy as np
import arviz as az
idx = pd.IndexSlice
def get_raw_covidtracking_data():
""" Gets the current daily CSV from COVIDTracking """
url = "https://covidtracking.com/api/v1/states/daily.csv"
data = pd.read_csv(url)
return data
def process_covidtracki... | pd.Timestamp("2020-06-26") | pandas.Timestamp |
import gym
import numpy as np
import torch
import torch.nn as nn
import tqdm
import wandb
import inspect
from typing import List, Tuple, Optional, Dict
from dataclasses import dataclass
from gym import spaces
from numpy import floor, inf
from sequoia.methods import Method
from sequoia.settings import (Actions, Environm... | pd.DataFrame(data) | pandas.DataFrame |
import warnings
import numpy as np
import pandas as pd
def isNumberAndIsNaN(obj):
return obj != obj
def scale_range(x, new_min=0.0, new_max=1.0, old_min=None, old_max=None, squash_outside_range=True, squash_inf=False, ):
"""
Scales a sequence to fit within a new range.
If squash_inf is set, then i... | pd.DataFrame() | pandas.DataFrame |
import json
import os
import sqlite3
import pyAesCrypt
import pandas
from os import stat
from datetime import datetime
import time
import numpy
# Global variables for use by this file
bufferSize = 64*1024
password = os.environ.get('ENCRYPTIONPASSWORD')
# py -c 'import databaseAccess; databaseAccess.reset()'
def reset... | pandas.read_sql_query("SELECT COUNT(*) AS cnt, CAST(CAST(nearest_5miles AS INT) AS VARCHAR(1000)) || ' < ' || CAST(CAST(nearest_5miles + 5 AS INT) AS VARCHAR(1000)) AS nearest_5miles FROM (SELECT id, ROUND((distance* 0.000621371)/5,0)*5 AS nearest_5miles FROM activities) a GROUP BY nearest_5miles", conn) | pandas.read_sql_query |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Atividade para trabalhar o pré-processamento dos dados.
Criação de modelo preditivo para diabetes e envio para verificação de peformance
no servidor.
@author: <NAME> <<EMAIL>>
"""
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
... | pd.Series(y_pred) | pandas.Series |
""" Finnhub View """
__docformat__ = "numpy"
import os
from tabulate import tabulate
import pandas as pd
from matplotlib import pyplot as plt
from pandas.plotting import register_matplotlib_converters
from gamestonk_terminal.stocks.due_diligence import finnhub_model
from gamestonk_terminal.helper_funcs import plot_aut... | pd.to_datetime(rot["period"]) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib.ticker import MultipleLocator
import lasio
import math
from datetime import datetime
import matplotlib.lines as mlin... | pd.read_csv('WestAfricaLogs/YoyoLOT_md.txt', delimiter='\t') | pandas.read_csv |
from typing import List, Text, Dict
from dataclasses import dataclass
import ssl
import urllib.request
from io import BytesIO
from zipfile import ZipFile
from urllib.parse import urljoin
from logging import exception
import os
from re import findall
from datetime import datetime, timedelta
import lxml.html... | pd.to_numeric(dfAmbima[column]) | pandas.to_numeric |
import json
import os
from math import ceil, isnan
import openpyxl
import pandas as pd
from Entities.SigfoxProfile import Sigfox
def extract_data(file, output):
| pd.set_option('display.max_columns', None) | pandas.set_option |
import sys
import pandas as pd
from sqlalchemy import create_engine
def load_data(messages_filepath, categories_filepath):
"""
Load & merge messages and categories ds
messages_filepath: path for csv with messages
categories_filepath: path for csv with cats
Returns:
df: dataframe
... | pd.concat([df,categories],axis=1) | pandas.concat |
#############################################################
# ActivitySim verification against TM1
# <NAME>, <EMAIL>, 02/22/19
# C:\projects\activitysim\verification>python compare_results.py
#############################################################
import pandas as pd
import openmatrix as omx
################... | pd.read_csv(asim_per_filename) | pandas.read_csv |
# ActivitySim
# See full license in LICENSE.txt.
import logging
import os
import numpy as np
import pandas as pd
logger = logging.getLogger(__name__)
def undupe_column_names(df, template="{} ({})"):
"""
rename df column names so there are no duplicates (in place)
e.g. if there are two columns named "... | pd.concat([df[trace_rows], trace_results], axis=1) | pandas.concat |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
"""
This file contains training and testing settings to be used in this benchmark,
mainly:
TRAIN_BASE_END: Base training end date common across all rounds
TRAIN_ROUNDS_ENDS: a set of dates denoting end of training period for each
... | pd.to_datetime(("2017-02-01", "2017-03-01")) | pandas.to_datetime |
# flu prediction
import os
import pandas as pd
import feather
from utils.fastai.structured import *
from utils.fastai.column_data import *
from sklearn import preprocessing
from sklearn.metrics import classification_report, confusion_matrix
import keras
from keras.layers import Input, Embedding, Dense, Dropout
from ke... | pd.concat([train_x, train_y], axis=1) | pandas.concat |
from drain.aggregation import SimpleAggregation, SpacetimeAggregation, AggregationJoin, SpacetimeAggregationJoin
from drain.aggregate import Count
from drain import step
from datetime import date
import pandas as pd
import numpy as np
class SimpleCrimeAggregation(SimpleAggregation):
@property
def aggregates(se... | pd.DataFrame({'District':[1,2], 'Community Area':[1,100]}) | pandas.DataFrame |
"""Bloch wave solver"""
import importlib as imp
import numpy as np,pandas as pd,pickle5,os,glob,tifffile
from typing import TYPE_CHECKING, Dict, Iterable, Optional, Sequence, Union
from subprocess import check_output#Popen,PIPE
from crystals import Crystal
from utils import glob_colors as colors,handler3D as h3d
from u... | pd.DataFrame.from_dict(d) | pandas.DataFrame.from_dict |
'''
'''
import spacy
import numpy as np
import pandas as pd
from pprint import pprint
import scipy.spatial.distance
from sqlalchemy.orm import sessionmaker
from sqlalchemy import create_engine
import json
import re
import os
def normal(token):
# Should the token be kept? (=is normal)
# Spacy... | pd.DataFrame({'social_tag': socialtagsorder, 'vector': socialvectors}) | pandas.DataFrame |
import os
import math
import warnings
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import shutil as sh
from glob import glob
from PIL import Image
from copy import copy
from tqdm ... | pd.read_csv(automatic_path) | pandas.read_csv |
#distance to com plots
#first run dataprocessing.m
#then run this file
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import seaborn as sns
# %matplotlib inline
import scipy.io as sio
sns.set(style="white")
from pylab import rcParams
from matplotlib import rc
# rcPa... | pd.DataFrame(data=annulus,columns=['x1','x2','x3','x4','x5']) | pandas.DataFrame |
import pathlib
import shutil
from loguru import logger
import types
import pathlib
import importlib
import pandas as pd
import datamol
def _introspect(module, parent_lib):
data = []
for attr_str in dir(module):
if attr_str.startswith("_"):
continue
# Get the attribute
... | pd.DataFrame(data) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import polling2
import requests
import json
from web3 import Web3
import pandas as pd
from decouple import config
from datetime import datetime
import logging
from collections import defaultdict
import time
from sqlalchemy import create_engine, desc
from sqlalchemy.orm im... | pd.DataFrame.from_dict(block_result['data']['blocks']) | pandas.DataFrame.from_dict |
# utils for plotting behavior
# this should be renamed to plotting/figures
from scipy.stats import norm, sem
from scipy.optimize import minimize
from scipy import interpolate
from statsmodels.stats.proportion import proportion_confint
from utilsJ.regularimports import groupby_binom_ci
from mpl_toolkits.axes_grid1 impor... | pd.DataFrame({"target": target, "coh": coherence}) | pandas.DataFrame |
import logging
import pickle
import os
import time
import threading
import urllib
from base64 import b64encode
from collections import defaultdict
from io import BytesIO
import matplotlib as mpl
mpl.use('Agg') # noqa
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import pyinotify
from jinj... | pd.to_datetime(age_data.Date) | pandas.to_datetime |
import datetime as dt
import glob
import os
import shutil
import unittest
import numpy as np
import pandas as pd
import devicely
class EverionTestCase(unittest.TestCase):
READ_PATH = 'tests/Everion_test_data'
BROKEN_READ_PATH = 'tests/Everion_test_data_broken' #for testing with missing files
WRITE_PATH =... | pd.testing.assert_index_equal(old_joined_data_time_col + timedelta, new_joined_data_time_col) | pandas.testing.assert_index_equal |
"""Live and historical flood monitoring data from the Environment Agency API"""
import requests
import pandas as pd
import flood_tool.geo as geo
import flood_tool.tool as tool
import numpy as np
import folium
__all__ = []
LIVE_URL = "http://environment.data.gov.uk/flood-monitoring/id/stations"
ARCHIVE_URL = "http://... | pd.DataFrame(daily_rainfall) | pandas.DataFrame |
import types
from functools import wraps
import numpy as np
import datetime
import collections
from pandas.compat import(
zip, builtins, range, long, lzip,
OrderedDict, callable
)
from pandas import compat
from pandas.core.base import PandasObject
from pandas.core.categorical import Categorical
from pandas.co... | is_categorical_dtype(items) | pandas.core.common.is_categorical_dtype |
from collections import OrderedDict
import datetime
from datetime import timedelta
from io import StringIO
import json
import os
import numpy as np
import pytest
from pandas.compat import is_platform_32bit, is_platform_windows
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame... | pd.DataFrame({"a": [1, 2], "b": [3.0, 4.0], "c": ["5", "6"]}) | pandas.DataFrame |
from urllib.request import urlopen
import requests
import datetime
import pandas as pd
import pandas.io.sql as pd_sql
import json
import telepot
import sqlite3
# OPENDART 고유번호 저장
from io import BytesIO
from zipfile import ZipFile
import xml.etree.ElementTree as ET
crtfc_key = '개인이 각자 받은 API인증키' # 자신의 API키를 넣어야 됨.
# ... | pd.read_sql("SELECT 보고서번호 from DART WHERE 종목명='%s'"%(stock), con=con) | pandas.read_sql |
import streamlit as st
from PIL import Image
import pandas as pd
import subprocess
import os
import base64
import pickle
# Molecular descriptor calculator
def desc_calc():
# Performs the descriptor calculation
bashCommand = "java -Xms2G -Xmx2G -Djava.awt.headless=true -jar ./PaDEL-Descriptor/PaDEL-Descriptor.j... | pd.read_csv('descriptor_list.csv') | pandas.read_csv |
#aggregation script
from distributed import wait
import pandas as pd
import geopandas as gpd
from panoptes_client import Panoptes
from shapely.geometry import box, Point
import json
import numpy as np
import os
from datetime import datetime
import utils
import extract
import start_cluster
def download_data(everglades_... | pd.DataFrame(rows) | pandas.DataFrame |
#!/usr/bin/env python3
"""
https://mor.nlm.nih.gov/download/rxnav/RxNormAPIs.html
https://www.nlm.nih.gov/research/umls/rxnorm/docs/
"""
###
import sys,os,re,json,logging,urllib.parse,tqdm
import pandas as pd
from ..util import rest
#
API_HOST='rxnav.nlm.nih.gov'
API_BASE_PATH='/REST'
BASE_URL='https://'+API_HOST+API... | pd.DataFrame() | pandas.DataFrame |
# *****************************************************************************
# 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 - other) | pandas.Series |
# download.py : download am arff file from within a zipped file from a given url
# author: <NAME>, <NAME> and <NAME>
# date: 2020-01-15
"""Downloads .zip url to current folder, unzips arff file, loads data, splits data and saves original CSV, as well as train/test splits into a data folder. Currently supports only .zi... | pd.DataFrame(data[0], dtype='str') | pandas.DataFrame |
import pandas as pd
import os
'''
Label files from original dataset have following structure:
DepthVideoName, EnteringNumber, ExitingNumber, VideoType
DepthVideoName: the depth video name
EnteringNumber: the number of people entering the bus
ExitingNumber: the number of people exiting the bus
VideoType: the video t... | pd.read_csv(top_path + 'pcds_dataset_labels_united.csv', names=HEADER) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# # 1 Compiling notebook 2 outputs
# In[1]:
import configparser
import glob
import json
import math
import numpy as np
import pandas as pd
import re
from utils.misc.regex_block import MutationFinder, TmVar, CustomWBregex, normalize_mutations
with open("data/model_output/proc... | pd.read_csv("data/model_output/processed/final.csv") | pandas.read_csv |
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow import keras
tf.random.set_seed(2021)
from models import DNMC, NMC, NSurv, MLP, train_model, evaluate_model
FILL_VALUES = {
'alb': 3.5,
'pafi': 333.3,
'bili': 1.01,
'crea': 1.01,
'bun': 6.51,
'wblc': 9.,
'urin... | pd.DataFrame(all_results) | pandas.DataFrame |
# third-party libraries
import pandas as pd
import pytest
# local imports
from .. import lstm_preprocessing
class TestSpatialGrouping:
"""Tests the output of a single location for the record"""
def test_selection_1(self):
"""Select the default, which is choosing the location from dataset 1"""
... | pd.to_datetime(target['time']) | pandas.to_datetime |
import pandas as pd
import datetime
# create a variable with dates, and from that extract the weekday
# I create a list of dates with 20 days difference from today
# and then transform it into a dataframe
df_base = datetime.datetime.today()
df_date_list = [df_base - datetime.timedelta(days=x) for x in range(0, 20)]
df... | pd.DataFrame(df_date_list) | pandas.DataFrame |
"""
Module: Build_csv_files
=============================
A module for building the csv-files for GEOSeMOSYS https://github.com/KTH-dESA/GEOSeMOSYS to run that code
In this module the logic around electrified and un-electrified cells are implemented for the 378 cells
--------------------------------------------------... | pd.concat(outwind, axis=1) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
# ### For futher info on BorutaPy package see: <br><br>https://github.com/scikit-learn-contrib/boruta_py
# ##### Articles:
# #### BorutaPy: <br> https://www.jstatsoft.org/article/view/v036i11
# #### Robustness of RF-based feature selection: <br> https://bmcbioinformatics.biomedce... | pd.concat([pid_train, y, X_filtered], axis=1) | pandas.concat |
from python_back_end.utilities.custom_multiprocessing import DebuggablePool
import numpy as np
import pandas as pd
from python_back_end.triangle_formatting.date_sorter import DateSorter
from python_back_end.data_cleaning.date_col_identifier import DateColIdentifier
from python_back_end.data_cleaning.type_col_extracter ... | pd.Index(id_col) | pandas.Index |
'''
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 |
#!/usr/bin/env python
#CITATION https://www.biorxiv.org/content/10.1101/540229v1
#####IMPORT ALL NECESSARY MODULES#####
import sys, ast, json
import os
import argparse
import itertools
import ntpath
import numbers
import decimal
import sys, os
import pymol
from pymol import cmd
from pymol.cgo import *
... | Series(Data.Label.values, index=Data.PDB_Position) | pandas.Series |
''' CODE TO CLEAN AND STANDARDIZE ALL DATA '''
import glob
import os
import pandas as pd
# filepath expressions for data
data_path = 'data'
#PATH_ALL_AGE = 'data/clean/*_age.csv'
PATH_ALL_AGE = os.path.join(data_path, 'clean', '*_age.csv')
#PATH_ALL_SEX = 'data/clean/*_sexrace.csv'
PATH_ALL_SEX = os.path.join(data_p... | pd.read_csv(file_path, header=0, names=SEX_COLUMNS) | pandas.read_csv |
import os
from pathlib import Path
import sys
from time import strptime
import path_config
import requests
from bs4 import BeautifulSoup
import pandas as pd
class EspnTournament():
def __init__(self) -> None:
self.tournament_info = {
"tournament_id":"",
"tournament_name":"",
... | pd.to_numeric(df["win_total"], downcast="integer") | pandas.to_numeric |
"""
This script uses geopandas to place all addresses into CSDs.
This uses the digital boundary files, which extend into the water. This is deliberate so that addresses on coastlines are not accidentally dropped.
"""
import pandas as pd
import geopandas as gpd
from hashlib import blake2b
import sys
name_in = sys.arg... | pd.DataFrame(gdf_csd) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
#########################################################################################
# Name: <NAME>
# Student ID: 64180008
# Department: Computer Engineering
# Assignment ID: A3
#######################################################################################... | pd.DataFrame({'temp':temp}) | pandas.DataFrame |
from bookcut.mirror_checker import main as mirror_checker
from bookcut.downloader import filename_refubrished
from bookcut.settings import path_checker
from bs4 import BeautifulSoup as Soup
import mechanize
import pandas as pd
import os
import requests
from tqdm import tqdm
RESULT_ERROR = "\nNo results found or bad In... | pd.DataFrame(table_data) | pandas.DataFrame |
import util
import pandas as pd
import re
import pygsheets
import hashlib
import datetime
class SheetCreator: # refactor! its sheet creator
"""
Pandas Form with init
Try to solve problem with add formula
Now call add_objects and object come with default formula (what of protected range?/)
"""
d... | pd.DataFrame(data=[formula], columns=self.header) | pandas.DataFrame |
import operator
import re
import warnings
import numpy as np
import pytest
from pandas._libs.sparse import IntIndex
import pandas.util._test_decorators as td
import pandas as pd
from pandas import isna
from pandas.core.sparse.api import SparseArray, SparseDtype, SparseSeries
import pandas.util.testing as tm
from pan... | tm.assert_produces_warning(FutureWarning) | pandas.util.testing.assert_produces_warning |
from contextlib import contextmanager
from unittest.mock import patch
from zipfile import ZipFile
from pandas import DataFrame, read_csv
from pandas.util.testing import assert_frame_equal
from pytest import raises, fixture, warns, mark
from IPython import get_ipython
from data_vault import Vault, parse_arguments, Vau... | read_csv(f, sep='|', index_col=0) | pandas.read_csv |
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import numpy as np
from pylab import rcParams
##########################################################################################
# Designed and developed by <NAME>
# Da... | pd.to_numeric(batsman['Runs']) | pandas.to_numeric |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import pytest
import re
from numpy import nan as NA
import numpy as np
from numpy.random import randint
from pandas.compat import range, u
import pandas.compat as compat
from pandas import Index, Series, DataFrame, isn... | tm.assert_raises_regex(AttributeError, message) | pandas.util.testing.assert_raises_regex |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from backtesting.backtester.BackTest.backtest import Strategy, Portfolio
import backtesting.backtester.fuzzySystem.membership as fuzz
import backtesting.backtester.fuzzySystem.control as ctrl
from pandas import to_datetime
class FuzzyMovingAverageCr... | pd.DataFrame(index=self.bars.index) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 14 14:55:21 2018
@author: <NAME>
"""
import pandas as pd
import scipy.sparse
import pytest
from sklearn.base import clone
from sklearn.exceptions import NotFittedError
from tests.helpers.testing_help import rec_assert_equal
from aikit.tools.data_structure_helper impor... | pd.DataFrame() | pandas.DataFrame |
"""unit test for loanpy.loanfinder.py (2.0 BETA) for pytest 7.1.1"""
from inspect import ismethod
from os import remove
from pathlib import Path
from unittest.mock import patch, call
from pandas import DataFrame, RangeIndex, Series, read_csv
from pandas.testing import (assert_frame_equal, assert_index_equal,
... | assert_series_equal(msdict[i], expsrs) | pandas.testing.assert_series_equal |
import pandas as pd
from sklearn import preprocessing
from sklearn.svm import SVC
import evaluateTask1
# import csv data
data = pd.read_csv('insurance-train.csv')
data_test = | pd.read_csv('insurance-test.csv') | pandas.read_csv |
from __future__ import division
import numpy as np
import datetime
import pandas as pd
from os.path import join, basename, exists
from os import makedirs
import matplotlib.pyplot as plt
from nilearn import input_data
from nilearn import datasets
import pandas as pd
from nilearn import plotting
from nilearn.image import... | pd.DataFrame(region_correlation_matrix, index=labels.index, columns=labels.index) | pandas.DataFrame |
# Build default model and do permutation feature importance (PFI)
import warnings
import pandas as pd
import numpy as np
from sklearn import ensemble, model_selection, metrics, inspection
from skopt import BayesSearchCV, space
import shap
import load_data
import misc_util
RANDOM_SEED = 11798
# A very repetitive Bay... | pd.read_csv(fset + '/train_30m.csv') | pandas.read_csv |
from os import path
import pandas as pd
path = "../../data/processed/"
accounts_features = pd.read_csv(path+"accounts_features_2021.txt")
accounts_created_features = pd.read_csv(path+"Accounts2021_Created_Features.csv", nrows=37083)
accounts_labels = | pd.read_csv(path+"accounts_labels_2021.txt", nrows=37083) | pandas.read_csv |
# Copyright 2021 VicEdTools 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 to in writi... | pd.concat([self.data, temp.data], ignore_index=True) | pandas.concat |
"""
test date_range, bdate_range construction from the convenience range functions
"""
from datetime import datetime, time, timedelta
import numpy as np
import pytest
import pytz
from pytz import timezone
from pandas._libs.tslibs import timezones
from pandas._libs.tslibs.offsets import BDay, CDay, DateOffset, MonthE... | tm.assert_index_equal(result, expected) | pandas._testing.assert_index_equal |
import os
import pandas as pd
from pandas import DataFrame
from tqdm.autonotebook import tqdm
def group_seeds(dirname):
seeds = []
for f in os.listdir(dirname):
num, _ = f.split('.csv')
try:
num = int(num)
seeds.append(num)
except Exception:
pass
... | pd.read_csv(f'{dirname}/{seed}.csv') | pandas.read_csv |
import time
import numpy as np
import pandas as pd
import geopandas as gpd
import numpy as np
from sqlalchemy import extract, select, func
from sqlalchemy.sql import or_, and_
import datetime
from shapely.geometry import Point
from src.data.processing_func import (get_direction, extract_geo_sections)
def extract_jps(... | pd.read_sql(query_all, meta.bind) | pandas.read_sql |
#----------------------------------------------------------------------------------------------
####################
# IMPORT LIBRARIES #
####################
import streamlit as st
import pandas as pd
import numpy as np
import plotly as dd
import plotly.express as px
import seaborn as sns
import matplotl... | pd.DataFrame() | pandas.DataFrame |
# Load libraries
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn import metrics
import numpy as np
from sklearn import tree
from sklearn import preprocessing
from sklearn.tree import export_graphviz
import matplotlib.pyplot as plt
#r... | pd.DataFrame(SizesAndAccurcy, columns=['Test Size', 'Itreation 1', 'Itreation 2', 'Itreation 3']) | pandas.DataFrame |
from datetime import timedelta
from math import ceil
import pandas as pd
from pyparsing import col
import scipy.sparse
from sklearn.decomposition import PCA
from sklearn.feature_extraction import DictVectorizer
from sklearn.neighbors import LocalOutlierFactor
from sklearn.preprocessing import StandardScaler, MinMaxScal... | pd.read_json(data_link, encoding='utf-8') | pandas.read_json |
# -*- coding: utf-8 -*-
"""
Created on Sun May 22 10:30:01 2016
SC process signups functions
@author: tkc
"""
#%%
import pandas as pd
import numpy as np
from datetime import datetime, date
import re, glob, math
from openpyxl import load_workbook # writing to Excel
from PIL import Image, ImageDraw, ImageFont
import tkin... | pd.merge(Recruits, famcontact,how='inner', on='Famkey', suffixes=('','_r')) | pandas.merge |
import pandas as pd
import numpy as np
import talib
class Indicators(object):
"""
Input: Price DataFrame, Moving average/lookback period and standard deviation multiplier
This function returns a dataframe with 5 columns
Output: Prices, Moving Average, Upper BB, Lower BB and BB Val
"""
def bb... | pd.DataFrame(columns=l_sym, index=df_high.index) | pandas.DataFrame |
# Databricks notebook source
import pandas as pd
import numpy as np
import networkx as nx
from nodevectors import Node2Vec as NVVV
from sklearn.decomposition import PCA
import os
import itertools
import pickle
spark.conf.set("spark.databricks.delta.properties.defaults.autoOptimize.optimizeWrite", "true")
spark.conf.se... | pd.DataFrame(principalComponents) | pandas.DataFrame |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data=pd.read_table("GSE5583.txt",header=0,index_col=0)
print("Previous 5:\n",data.head())
number_of_genes=len(data.index)
print("Gene Number:",number_of_genes)
# normalization
data2=np.log2(data+0.0001)
print("Previous 5:\n",da... | pd.DataFrame({'pvalue':gene_array,'FoldChange':fold}) | pandas.DataFrame |
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import re
from math import ceil
import pandas as pd
from sklearn.metrics import classification_report
from scipy.stats import shapiro, boxcox, yeojohnson
from scipy.stats import probplot
from sklearn.preprocessing import LabelEncoder, PowerTransfo... | pd.DataFrame(x, columns=x_labels) | pandas.DataFrame |
import pandas as pd
def subm_to_df(subm):
data = []
for key in sorted(subm.keys()):
top_imgs = subm[key]
for img_id, score in top_imgs.items():
row = [key, img_id, score]
data.append(row)
return | pd.DataFrame(columns=["topic_id", "image_id", "confidence_score"], data=data) | pandas.DataFrame |
"""
2 - Jan - 2018 / <NAME> / <EMAIL>
datred.py is a module created as part of the FUSS package to help with the data reduction of spectropolarimetric
data (at the present time only used with FORS2 data)
Pre-requisites
--------------
os, astropy.io, numpy, math, matplotlib.pyplot, pysynphot, scipy.special, pandas
Va... | pd.DataFrame(columns=['wl', 'f', 'f_r'], dtype='float64') | pandas.DataFrame |
# -*- coding: utf-8 -*-
import click
import logging
from pathlib import Path
# from dotenv import find_dotenv, load_dotenv
import requests
from bs4 import BeautifulSoup
import numpy as np
import pandas as pd
import datetime
import yfinance as yf
from pandas_datareader import data as pdr
from flask import current_app
f... | pd.Series(df['intraday_volumes']) | pandas.Series |
import json
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.normalization import BatchNormalization
from keras.optimizers import SGD
from scipy.optimize import basinhopp... | pd.DataFrame(data) | pandas.DataFrame |
import pandas as pd
import numpy as np
import math
from functools import reduce
from scipy.stats.stats import pearsonr
from matplotlib import pyplot as plt
data_path=r'./SWI closing price.xlsx'
#columns_list=['801040.SWI','801180.SWI','801710.SWI']
data=pd.read_excel(data_path)
columns_list=list(data.head(0)... | pd.DataFrame(data) | pandas.DataFrame |
import logging
import os
from pathlib import Path
import click
import pandas as pd
from scipy import stats
from tqdm import tqdm
logging.basicConfig(level=logging.INFO)
CORRECT_NER_ENTAILS = "Entails"
CORRECT_NER_NOT_ENTAILS = "Not Entails/Error"
CORRECT_NER_VALS = [CORRECT_NER_ENTAILS, CORRECT_NER_NOT_ENTAILS]
AGG... | pd.DataFrame(newrows) | pandas.DataFrame |
import numpy as np
import pandas as pd
# 1. load dataset
ratings = pd.read_csv('chapter02/data/movie_rating.csv')
movie_ratings = pd.pivot_table(
ratings,
values='rating',
index='title',
columns='critic'
)
# 2. calculate similarity
def calcualte_norm(u):
norm_u = 0.0
for ui in u:
if... | pd.isna(ratings_critic.rating) | pandas.isna |
import os
import warnings
import itertools
import pandas
import time
class SlurmJobArray():
""" Selects a single condition from an array of parameters using the SLURM_ARRAY_TASK_ID environment variable.
The parameters need to be supplied as a dictionary. if the task is not in a slurm environment,
... | pandas.Series(self.slurm_variables) | pandas.Series |
### import used modules first
from TPM.localization import select_folder
from glob import glob
import random
import string
import numpy as np
import os
import datetime
import pandas as pd
import scipy.linalg as la
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.axes3d i... | pd.read_excel(path, sheet_name=sheet_name) | pandas.read_excel |
# -*- coding: utf-8 -*-
import numpy as np
from numpy.linalg import cholesky
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import tensorflow as tf
from random import choice, shuffle
from numpy import array
############<NAME>的基于tensorflow写的一个kmeans模板###############
def KMeansCluster(vectors, ... | pd.DataFrame(res) | pandas.DataFrame |
"""
Copyright (c) 2020 <NAME>
This software is released under the MIT License.
https://opensource.org/licenses/MIT
"""
import pandas as pd
import json
import os
class Fhirndjson(object):
def __init__(self):
self._df = | pd.DataFrame(columns=[]) | pandas.DataFrame |
from collections import (
abc,
deque,
)
from decimal import Decimal
from warnings import catch_warnings
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
PeriodIndex,
Series,
concat,
date_range,
)
import pandas._testing as tm
fr... | concat([s1, df, s2], axis=1) | pandas.concat |
import dotenv
import os
from pyairtable import Table
import pandas as pd
def load_airtable(key, base_id, table_name):
at = Table(key, base_id, table_name)
return at
def get_info(airtable_tab):
yt_links, emails = [], []
for record in range(len(airtable_tab.all())):
talk = airtable_tab.all()[... | pd.merge(df, df_new, how='right') | pandas.merge |
"""
A simple library of functions that provide scikit-learn-esque
feature engineering and pre-processing tools.
MIT License
<NAME>, https://www.linkedin.com/in/tjpell
Target encoding inspired by the following Kaggle kernel:
https://www.kaggle.com/tnarik/likelihood-encoding-of-categorical-features
"""
import numpy a... | pd.DataFrame([x, y], columns=["x", "y"]) | pandas.DataFrame |
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