prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import itertools
import os
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import gridspec
import warnings
import itertools
import re
from matplotlib import pyplot as plt
from natsort import natsorted
from scipy import optimize as optimization
from sklearn.metrics import roc_auc_score
from ... | pd.DataFrame({'False positive rate': fprs, 'tpr': tprs}) | pandas.DataFrame |
from . import common
import pandas as pd
import matplotlib.pyplot as plt
from skbio.stats.ordination import OrdinationResults
from qiime2 import Artifact
def beta_3d_plot(
artifact, metadata=None, hue=None, azim=-60, elev=30, s=80, ax=None,
figsize=None, hue_order=None
):
"""
Create a 3D scatter plot ... | pd.concat([df, mf], axis=1, join='inner') | pandas.concat |
# Download Census population data by tract
## Upload population data and census boundary files to S3
import numpy as np
import pandas as pd
import geopandas as gpd
import intake
import boto3
import census
from us import states
# Set env
# Can't figure out how to read the API key from env
c = census.... | pd.DataFrame(centroids) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import time, os
import matplotlib.pylab as plt
import matplotlib
LINEWIDTH=0.5
c1 = plt.rcParams['axes.color_cycle'][0]
c2 = plt.rcParams['axes.color_cycle'][1]
matplotlib.rcParams.update({
'font.family' :'Myriad Pro',
'font.size' :7,
... | pd.Series(colors) | pandas.Series |
# coding: utf-8
# # CareerCon 2019 - Help Navigate Robots
# ## Robots are smart… by design !!
#
# 
#
# ---
#
# Robots are smart… by design. To fully understand and properly navigate a task, however, they need input about their environment.
#
# In this compe... | pd.read_csv('../input/robots-best-submission/sub_0.72_2.csv') | pandas.read_csv |
import numpy as np
import pandas as pd
import json
from collections import defaultdict
import spacy
def sentence_segment(text):
nlp = spacy.load("en_core_web_sm")
nlp_text = nlp(text)
return {'named_ents': [ent.string.strip() for ent in nlp_text.ents], 'sents': [sent.text for sent in nlp_text.sents]}
de... | pd.DataFrame(questions) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.ticker as tck
import matplotlib.font_manager as fm
import math as m
import matplotlib.dates as mdates
im... | pd.to_datetime(df_cloud_TS.index, format="%Y-%m-%d %H:%M", errors='coerce') | pandas.to_datetime |
"""
Functions for writing to .csv
September 2020
Written by <NAME>
"""
import os
import pandas as pd
import datetime
def define_deciles(regions):
"""
Allocate deciles to regions.
"""
regions = regions.sort_values(by='population_km2', ascending=True)
regions['decile'] = regions.groupby([
... | pd.DataFrame(regional_results) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 30 13:43:12 2021
@author: @hk_nien
"""
from multiprocessing import Pool, cpu_count
import random
import pandas as pd
import matplotlib.pyplot as plt
from scipy.signal import savgol_filter
import numpy as np
from nl_regions import get_holiday_regions... | pd.DataFrame() | pandas.DataFrame |
#! /usr/bin/env python
#pylint: disable=invalid-name,too-many-arguments,too-many-locals; extension-pkg-whitelist=lxml
"""
Functions used by the other parts of the package
"""
from __future__ import print_function
from io import BytesIO
import base64
import os
import sys
import psutil
import matplotlib#pylint: disa... | pd.read_csv(input_file_name, sep=sep, dtype=dtypes) | pandas.read_csv |
from contextlib import contextmanager
import pandas as pd
from dataviper.logger import IndentLogger
from dataviper.report.profile import Profile
from dataviper.source.datasource import DataSource
import pymysql
class MySQL(DataSource):
"""
class MySQL is a connection provider for MySQL
and query builder ... | pd.read_sql(query, self.__conn) | pandas.read_sql |
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# --------------------------------------------------------------------------------------------
import unittest
import numpy as np... | pd.DataFrame(test_data) | pandas.DataFrame |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 17 10:51:50 2018
@author: nmei
"""
import pandas as pd
import numpy as np
figure_dir = '../figures'
save_dir = '../results'
from utils import resample_ttest_2sample,MCPConverter
# exps
pos = pd.read_csv('../results/Pos.csv')
att = pd.read_csv('..... | pd.concat(temp) | pandas.concat |
from __future__ import annotations
from datetime import (
datetime,
time,
timedelta,
tzinfo,
)
from typing import (
TYPE_CHECKING,
Literal,
overload,
)
import warnings
import numpy as np
from pandas._libs import (
lib,
tslib,
)
from pandas._libs.arrays import NDArrayBacked
from pa... | fields.get_date_name_field(values, "day_name", locale=locale) | pandas._libs.tslibs.fields.get_date_name_field |
import sys
import os
import pandas as pd
from numpy import floor, log10, isnan, nan, isinf
from PyQt5.QtWidgets import QWidget, QVBoxLayout, QHBoxLayout, QGridLayout, QLabel, QComboBox, QLineEdit, QPushButton, QCheckBox
from PyQt5.QtCore import pyqtSignal
from PyQt5 import QtCore
from PyQt5 import QtGui
from PyQt5.Qt... | pd.DataFrame(columns=['wave', 'name']) | pandas.DataFrame |
# ###########################################################################
import os
import json
import requests
import pandas as pd
def load_california_electricity_demand(
filepath='data/demand.json',
api_key_env='EIA_API_KEY',
train_only=False):
data = read_or_download_data(filepat... | pd.DataFrame(data['series'][0]['data']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 22 04:30:07 2019
@author: akris
"""
import urllib.request
from bs4 import BeautifulSoup
import time
import pandas as pd
with open('PageCount-test.csv') as csv_file:
df_pageCount = pd.read_csv(csv_file)
words = []
links = []
for index, row in df_pageCount.... | pd.DataFrame(words,columns=['Words']) | pandas.DataFrame |
# Copyright (c) 2018 Copyright holder of the paper Generative Adversarial Model Learning
# submitted to NeurIPS 2019 for review
# All rights reserved.
import argparse
import pandas as pd
import warnings
import os
import seaborn as sns
import matplotlib.pyplot as plt
import math
import numpy as np
from pathlib import P... | pd.read_csv(experiment_master_folder_name + "results_plot.csv", sep=',', encoding='utf-8') | pandas.read_csv |
import numpy as np
import pandas as pd
import os
import torch
import torch.nn as nn
import torch.optim as optim
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
import matplotlib.pyplot as plt
class Decomposer(nn.Module):
def __init__(self, n_farms, n_crops, n_dims):
super().__init_... | pd.read_csv(filepath, index_col=0) | pandas.read_csv |
"""
query reddit ES index
"""
import datetime
import sys
import os
import re
import logging
import pandas as pd
# import pytz
# from dateutil import parser
sys.path.insert(1, "/home/yongfeng/wissee_ai_projects/")
from common.utils import log_util
from common.utils.elasticsearch_helper import scroll_search
from common.d... | pd.DataFrame(records) | pandas.DataFrame |
import os
import json
from time import sleep
import warnings
import numpy as np
import pandas as pd
from scipy.optimize import minimize, basinhopping
from scipy.special import gamma
from tqdm import tqdm
try:
import cupy as _p
from cupy import asnumpy
from cupyx.scipy.ndimage.filters import convolve as cuda... | pd.Series(x, index=p0.index) | pandas.Series |
#!/usr/bin/env python
# Imports
import gzip
import os
import numpy as np
import matplotlib
matplotlib.use('Qt4Agg')
import matplotlib.pyplot as plt
plt.switch_backend('agg')
import pickle
import time
import math
from collections import Counter, defaultdict
# Keras imports
from keras.utils.np_utils import to_categ... | pd.read_csv(filepath_or_buffer=output_dir_test+'/CV_results.csv', delimiter='\t') | pandas.read_csv |
# -*- coding: utf-8 -*-
from datetime import timedelta
import operator
from string import ascii_lowercase
import warnings
import numpy as np
import pytest
from pandas.compat import lrange
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
Categorical, DataFrame, MultiIndex, Serie... | tm.assert_frame_equal(out, expected) | pandas.util.testing.assert_frame_equal |
from datetime import date as dt
import pandas as pd
import requests
import bandl.common
#default periods
DEFAULT_DAYS = 250
def is_ind_index(symbol):
is_it = symbol in bandl.common.IND_INDICES
return is_it
def get_formated_date(date=None,format=None,dayfirst=False):
"""string date to format date
"... | pd.to_datetime(date,dayfirst=dayfirst) | pandas.to_datetime |
from __future__ import print_function
import argparse
import joblib
import os
import pandas as pd
import logging
import numpy as np
from sklearn.linear_model import Ridge, RidgeCV, LassoCV, Lasso
from sklearn.model_selection import cross_val_score
def train(args, train_X, train_y, model):
'''
아래 모델의 타입에 따라 ... | pd.read_csv(file, header=None, engine="python") | pandas.read_csv |
from glob import glob
import os
import numpy as np
import scipy.io as sio
import lmfit
from lmfit import Model, models
from sklearn import linear_model as lm
from fragmenter import RegionExtractor as re
from niio import loaded, write
import plotnine
from plotnine import ggplot, geom_point, geom_density_2d, geom_lin... | pd.DataFrame(data_dict) | pandas.DataFrame |
# coding: utf-8
# Author: <NAME>
import os
import sys
import traceback
from datetime import datetime
import pandas as pd
import numpy as np
import woe_tools as woe
usage = '''
################################### Summarize #######################################
此工具包用于数据预处理，包含以下内容：
1.Cap
2.Floor
3.MissingImpute
4.Woe... | pd.isnull(impute_value) | pandas.isnull |
import os, time, torch, sys, inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0, parentdir)
import numpy as np
import pandas as pd
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
from pathlib ... | pd.date_range(start=panel_last_ds[i], periods=output_size+1, freq=self.mc.frequency) | pandas.date_range |
from os import abort
from requests import get
from bs4 import BeautifulSoup
from pandas import read_html, concat, DataFrame, read_csv
from .utils import url_daerah, total_page, _baseurl_
def get_daerah() -> list:
page = get(_baseurl_)
data = []
soup = BeautifulSoup(page.text, 'lxml')
table = soup.find_all('td'... | concat([df1, data1]) | pandas.concat |
#functions to be used in the data preparation process
import pandas as pd
import numpy as np
import sklearn.metrics as metric
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
def market_columns(df):
"""Function that maps multiple entries in a column into indivi... | pd.concat([X_nonobj_df, X_obj_ohe_df], axis=1) | pandas.concat |
"""
Validates the exported json files with some data from SQL database.
"""
import json
import os
import pandas as pd
from multiprocessing import Pool, RLock
from tqdm import tqdm
from projects.data_cleaning import *
def validate_data(output_folder, patientunitstayid):
query_schema, conn = connect_to_database... | pd.DataFrame(json_dict[table_name]) | pandas.DataFrame |
# coding=utf-8
# Copyright 2018-2020 EVA
#
# 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 ... | pd.DataFrame({'label': labels}) | pandas.DataFrame |
import unittest
from unittest import mock
import networkx as nx
import numpy as np
import pandas as pd
import cassiopeia as cas
from cassiopeia.plotting import local
class TestLocalPlotting(unittest.TestCase):
def setUp(self):
self.allele_table = pd.DataFrame.from_dict(
{
1: ... | pd.DataFrame(columns=["color"]) | pandas.DataFrame |
#!/usr/bin/env python
import pandas as pd
import numpy as np
def locate_na(data: pd.DataFrame) -> dict:
"""
Locate and return the indices to all missing values within an inputted dataframe.
Each element of the returned dictionary will be a column in a dataframe, which will
contain the row indices of t... | pd.isna(data[i][j]) | pandas.isna |
# coding=utf-8
# Author: <NAME> & <NAME>
# Date: Jan 06, 2021
#
# Description: Parse Epilepsy Foundation Forums and extract dictionary matches
#
import os
import sys
#
#include_path = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir, 'include'))
include_path = '/nfs/nfs7/home/rionbr/myaura/i... | pd.read_sql(sql, con=engine) | pandas.read_sql |
# Poslanci a Osoby
# Agenda eviduje osoby, jejich zařazení do orgánů a jejich funkce v orgánech a orgány jako takové.
# Informace viz https://www.psp.cz/sqw/hp.sqw?k=1301.
from os import path
import pandas as pd
import numpy as np
from parlamentikon.utility import *
from parlamentikon.Snemovna import *
from parlame... | pd.merge(self.tbl['poslanci'], kluby[['id_osoba', 'id_klub', 'nazev_klub_cz', 'zkratka_klub', 'od_klub', 'do_klub']], on='id_osoba', how="left") | pandas.merge |
import pandas as pd
import pickle
import gensim
import gensim.corpora as corpora
from gensim.utils import simple_preprocess
import numpy as np
import datetime as dt
from LDA import remove_stopwords, lemmatization, make_bigrams, sent_to_words
import warnings
warnings.filterwarnings("ignore",category=DeprecationWarning... | pd.to_datetime(users['dob'], format='%d/%m/%Y', errors='coerce') | pandas.to_datetime |
# --------------
# import the libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
import warnings
warnings.filterwarnings('ignore')
# Code starts here
# Load dataset
df = pd.read_json(path, lines=True)
df.columns ... | pd.get_dummies(data=X_test, columns = ['category','cup_size','length']) | pandas.get_dummies |
import json
import pandas as pd
import time
"""
需要一下文件：
1、预测的json：bbox_level{}_test_results.json
2、test集的json：test.json
3、sample_submission.csv
"""
LABLE_LEVEL = 4
SCORE_THRESHOLD = 0.001
def json_to_dict(json_file_dir):
with open(json_file_dir, "r") as json_file:
json_dict = json.load(json_file)
... | pd.concat([sample_csv, df], ignore_index=True) | pandas.concat |
import os
import copy
import pytest
import numpy as np
import pandas as pd
import pyarrow as pa
from pyarrow import feather as pf
from pyarrow import parquet as pq
from time_series_transform.io.base import io_base
from time_series_transform.io.numpy import (
from_numpy,
to_numpy
)
from time_series_transfor... | pd.DataFrame(expect_collection_noExpand['pad']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on 26 Aug 2021
@author: <NAME>
"""
import numpy as np
import pandas as pd
import warnings
from multisim import matprops as mp
from multisim import ut as ut
class Meters:
def __init__(self, simenv, start_time):
self._simenv = simenv # save for attribute lookup
... | pd.DataFrame(index=self._time_index) | pandas.DataFrame |
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 = ... | pd.Series([30.19, 2 * 30.5], index=['CLZ6', 'COZ6']) | pandas.Series |
import os
import pickle
import random
import numpy as np
import pandas as pd
from sklearn.neighbors import KDTree
import gputransform
import config as cfg
#####For training and test data split#####
cfg.SAMPLE_INTERVAL_TEST = 1.5
x_width = 150
y_width = 150
# For Oxford
p1 = [5735712.768124,620084.402381]
p2 = [5735... | pd.DataFrame(columns=['file','northing','easting','yaw']) | pandas.DataFrame |
#========================================================================
# Python library imports
#========================================================================
import math
import pandas as pd
import operator as op
from functools import reduce
#=============================================================... | pd.DataFrame() | pandas.DataFrame |
from typing import List
import numpy as np
import pandas as pd
import stockstats
import talib
import copy
class BasicProcessor:
def __init__(self, data_source: str, start_date, end_date, time_interval, **kwargs):
assert data_source in {
"alpaca",
"baostock",
"ccxt",
... | pd.DataFrame() | pandas.DataFrame |
import requests
import pandas as pd
import re
from bs4 import BeautifulSoup
url=requests.get("http://www.worldometers.info/world-population/india-population/")
t=url.text
so=BeautifulSoup(t,'html.parser')
all_t=so.findAll('table', class_="table table-striped table-bordered table-hover table-condensed table-list"... | pd.Series.tolist(bv[0:7][8]) | pandas.Series.tolist |
import numpy as np
from scipy import sparse
import pandas as pd
import networkx as nx
from cidre import utils
def detect(
A, threshold, is_excessive, min_group_edge_num=0,
):
"""
CIDRE algorithm
Parameters
-----------
A : scipy sparse matrix
Adjacency matrix
threshold : float
... | pd.concat(df_Ul_list, ignore_index=True) | pandas.concat |
import pandas as pd
import numpy as np
from skmob import TrajDataFrame
import datasheets
from human_id import generate_id
import functools
class WeClockExport:
def __init__(self, identifier, filename_or_file):
self.identifier = identifier
self.filename_or_file = filename_or_file
self.type =... | pd.to_numeric(x.value1, errors='coerce') | pandas.to_numeric |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 7 17:35:50 2018
@author: amal
"""
from __future__ import division
import os.path
import pandas as pd
import numpy as np
from datetime import datetime
from geopy.distance import vincenty
import matplotlib.pyplot as plt
import seaborn as sns
import ... | pd.to_numeric(one_week_data['schedule_realtionship']) | pandas.to_numeric |
# Common imports
import numpy as np
import pandas as pd
def FrankeFunction(x,y):
term1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2))
term2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1))
term3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))
term4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)
return term1 + t... | pd.DataFrame(X) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 5 21:51:52 2018
@author: dayvsonsales
"""
import numpy as np
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
import requests
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler... | pd.Series(y_pred) | pandas.Series |
import pandas as pd
import logging
import click
from pathlib import Path
log_fmt = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
logging.basicConfig(level=logging.INFO, format=log_fmt)
logger = logging.getLogger('building_info')
def generate_csv(csv_folder, csv_res):
dfs = [pd.read_csv(csv_file) for csv... | pd.read_csv(csv_res) | pandas.read_csv |
import argparse
import os
import pandas as pd
from sklearn.utils import shuffle
def split_species(specie_set, test_count, valid_count, seed=42):
# Create empty dataframes
train_set = pd.DataFrame(columns=specie_set.columns)
test_set = pd.DataFrame(columns=specie_set.columns)
valid_set = pd.DataFrame(... | pd.read_csv(path_csv) | pandas.read_csv |
# Generates orders items and clickstream orders succeed
import json
import common_functions
import random
import numpy as np
import multiprocessing as mp
import pandas as pd
# reading config
with open('../config.json') as data:
config = json.load(data)
# general config
machine_cores = int(config["n_cores"])
out_p... | pd.DataFrame(clickstream_succeed) | pandas.DataFrame |
from numpy.core.numeric import NaN
import pandas as pd
import math
from scipy.spatial import distance
import streamlit as st
# get 'players.csv' and 'appearances.csv' from Kaggle:
# https://www.kaggle.com/davidcariboo/player-scores
# DATA WRANGLING #
df = pd.read_csv('players.csv', encoding='utf-8')
# I manu... | pd.read_csv('appearances.csv', encoding='utf-8') | pandas.read_csv |
import pandas as pd
import glob
from concurrent.futures import ThreadPoolExecutor
import numpy as np
def create_obs(ticker):
print(ticker)
quote = pd.read_csv(glob.glob(ticker+'/*-quote.csv')[0], index_col=0)
order = pd.read_csv(glob.glob(ticker+'/*-order.csv')[0], index_col=0)
date = glob.glob(ticke... | pd.DataFrame([]) | pandas.DataFrame |
#Genero el dataset de febrero para el approach de boosting. Este approach tiene algunas variables mas incluyendo sumas y promedios de valores pasados
import gc
gc.collect()
import pandas as pd
import seaborn as sns
import numpy as np
#%% Cargo los datos, Con el dataset de boosting no hice las pruebas de quita... | pd.merge(final, subtest4, left_index=True, right_index=True) | pandas.merge |
import matplotlib as mpl
# This line allows mpl to run with no DISPLAY defined
mpl.use('Agg')
from keras.layers import Dense, Flatten, Input, merge, Dropout
from keras.models import Model
from keras.optimizers import Adam
from keras.regularizers import l1, l1l2
import keras.backend as K
import pandas as pd
import num... | pd.DataFrame(history.history) | pandas.DataFrame |
import sys
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from get_data_brasil import run_crear_excel_brasil
from get_data_brasil_wcota import run_crear_excel_brasil_wcota
from get_data_pernambuco import run_crear_excel_recif... | ExcelWriter(save_path_xlsx + last_day + '_' + argv_1 + '_report_EPG.xlsx') | pandas.ExcelWriter |
import pandas as pd
import requests
import pandas_datareader as web
import datetime as dt
import numpy as np
import matplotlib.pyplot as plt
from math import floor
from termcolor import colored as cl
plt.rcParams['figure.figsize'] = (20, 10)
plt.style.use('fivethirtyeight')
# EXTRACTING STOCK DATA
def get_historical... | pd.DataFrame(raw['Technical Analysis: CCI']) | pandas.DataFrame |
import numpy as np
import pandas as pd
import os
import sys
import matplotlib.pyplot as plt
import matplotlib
import datetime
import sklearn.datasets, sklearn.decomposition
from sklearn.cluster import KMeans
from sklearn_extra.cluster import KMedoids
from sklearn.preprocessing import StandardScaler
import sk... | pd.DataFrame(data_represent_days_modified) | pandas.DataFrame |
# %%
# Copyright (c) Microsoft Corporation and Fairlearn contributors.
# Licensed under the MIT License.
"""
===========================
GridSearch with Census Data
===========================
"""
# %%
# This notebook shows how to use Fairlearn to generate predictors for the Census dataset.
# This dataset is a classif... | pd.Series(Y_train) | pandas.Series |
# Copyright 2021 <NAME>, spideynolove @ GitHub
# See LICENSE for details.
__author__ = '<NAME> @spideynolove in GitHub'
__version__ = '0.0.1'
# mimic pro code
# from .technical import technical_indicators, moving_averages, pivot_points
import investpy as iv
import os
import numpy as np
import pandas as pd
import dat... | pd.DataFrame() | pandas.DataFrame |
import ast, json, logging, os, sys, time, traceback, requests
from datetime import datetime
from multiprocessing import Process, Queue
from urllib.parse import urlparse
import pandas as pd
import sqlalchemy as s
from sqlalchemy import MetaData
from sqlalchemy.ext.automap import automap_base
from workers.standard_method... | pd.read_sql(repo_url_SQL, self.db, params={}) | pandas.read_sql |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, time, timedelta, date
import sys
import os
import operator
from distutils.version import LooseVersion
import nose
import numpy as np
randn = np.random.randn
from pandas import (Index, Series, TimeSeries, DataFrame,
isnull, date_ran... | date_range('1/1/2012', periods=4, freq='3H') | pandas.date_range |
import os
import shutil
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from scipy.stats import ttest_ind
from utils.file import get_file_paths
class DataPreprocess:
def __init__(self, data_dir: str, top_n_gene: list, gene_limit):
# Data folder path which includes tra... | pd.read_csv(self.file_paths['train']) | pandas.read_csv |
# -*- coding: utf-8 -*-
import dash
from dash.dependencies import Input, Output, State, Event
from dash.exceptions import PreventUpdate
import dash_html_components as html
import dash_core_components as dcc
import dash_table_experiments as dt
from flask_caching import Cache
import numpy as np
import os
import pandas as... | pd.read_json(tweets_json, orient='split') | pandas.read_json |
# Copyright 2019 Toyota Research Institute. All rights reserved.
"""
Module and scripts for training and predicting with models,
given a matching descriptor set.
Usage:
run_model [INPUT_JSON] [--fit]
Options:
-h --help Show this screen
--fit <true_or_false> [default: False] Fit model
... | pd.DataFrame([features.y]) | pandas.DataFrame |
import pandas as pd
import os
import json
import numpy as np
import pandas as pd
from pandas.io.json import json_normalize
import gc
# Helper function to load full dataset with json columns and generate the train features out of it
def load_df(csv_path, json_columns, features):
ans = | pd.DataFrame() | pandas.DataFrame |
import logging
from typing import Optional
import numpy as np
import pandas as pd
from sklearn import utils
from lob_data_utils import lob, model
from sklearn.decomposition import PCA
from sklearn.svm import SVC
logger = logging.getLogger(__name__)
class SvmGdfResults(object):
def __init__(self, stock, r=1.0, ... | pd.DataFrame() | pandas.DataFrame |
import os
import warnings
import argparse
from pathlib import Path
import netCDF4
import pandas as pd
import numpy as np
from geotiff import GeoTiff
from tqdm import tqdm
from sklearn.metrics import pairwise_distances
from sklearn.model_selection import GroupShuffleSplit
from tools.settings import CLIMATE_OPT, CAT_OPT... | pd.Timedelta(days=365.24) | pandas.Timedelta |
from __future__ import absolute_import, division, print_function
import sys
import os
curr_path = os.path.abspath(os.path.dirname(__file__))
sys.path = [os.path.dirname(os.path.dirname(curr_path)), curr_path] + sys.path
curr_path = None
try:
import cPickle as pickle
except:
import pickle
import logging
import c... | pd.read_hdf(file_path, key=key, mode=mode) | pandas.read_hdf |
from albumentations.augmentations.transforms import Normalize
import torch.nn as nn
import torchvision.models as models
from torch.utils.data import Dataset
import torch
import albumentations as A
from albumentations.pytorch import ToTensorV2
from pathlib import Path
import numpy as np
import re
import umap
import pand... | pd.DataFrame(data=dl_features, columns=columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import skimage.measure as measure
import skimage.morphology as morphology
from pcnaDeep.data.utils import filter_edge
def split_frame(frame, n=4):
"""Split frame into several quadrants.
Args:
frame (numpy.ndarray): single frame slice to s... | pd.DataFrame(columns=table.columns) | pandas.DataFrame |
import random
import cv2
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset
from sklearn.model_selection import train_test_split
from .keypoint_encoder import KeypointEncoder
class FashionAIKeypoints(Dataset):
def __init__(self, opt, phase='train'):
... | pd.read_csv(data_dir0 / 'Annotations/annotations.csv') | pandas.read_csv |
"""Support Disperse I/O.
The reader is based on a description of the structure using Kaitai (https://kaitai.io/).
"""
import numpy as np
import pandas as pd
from ..utilities.decorators import read_files
from ..utilities.types import FloatArrayType, PathType
from .disperse_reader import DisperseReader
class Dispers... | pd.concat((_v1, _v2), axis=1) | pandas.concat |
from this import d
import keras.losses
import matplotlib.pyplot as plt
import streamlit as st
import tensorflow as tf
from keras import layers
from keras.models import Sequential
from random import randint
import visualkeras
import pandas as pd
from streamlit_drawable_canvas import st_canvas
from cv2 import resize
from... | pd.DataFrame(act_func_map[act_func], x) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# ## DS/CMPSC 410 Sparing 2021
# ## Instructor: Professor <NAME>
# ## TA: <NAME> and <NAME>
# ## Lab 6: Movie Recommendations Using Alternative Least Square
# ## The goals of this lab are for you to be able to
# ### - Use Alternating Least Squares (ALS) for recommending movies bas... | pd.DataFrame( columns = ['k', 'regularization', 'iterations', 'validation RMS', 'testing RMS'] ) | pandas.DataFrame |
import pandas as pd
from flask import Blueprint, jsonify, request, render_template, flash, redirect
from web_app.models import Strain, db, migrate
from web_app.services.strain_service import strains
import os
from dotenv import load_dotenv
load_dotenv()
API_KEY = os.getenv("API_KEY")
strain_routes = Blueprint("strai... | pd.read_csv(url) | pandas.read_csv |
from datetime import datetime, timedelta
import inspect
import numpy as np
import pytest
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
Index,
IntervalIndex,
MultiIndex... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
import nose
import numpy as np
from datetime import datetime
from pandas.util import testing as tm
from pandas.core import config as cf
from pandas.compat import u
from pandas.tslib import iNaT
from pandas import (NaT, Float64Index, Series,
DatetimeIndex, TimedeltaIndex, da... | tm.makeObjectSeries() | pandas.util.testing.makeObjectSeries |
import pytest
from mapping import mappings
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
from pandas.tseries.offsets import BDay
@pytest.fixture
def dates():
return pd.Series(
[TS('2016-10-20'), TS('2016-11... | assert_frame_equal(wts, wts_exp) | pandas.util.testing.assert_frame_equal |
"""Base class for any input"""
from abc import ABC, abstractmethod
from typing import List
import pandas as pd
import babao.config as conf
import babao.utils.date as du
import babao.utils.file as fu
import babao.utils.log as log
INPUTS = [] # type: List[ABCInput]
LAST_WRITE = 0 # TODO: this is a stupid idea, bug... | pd.DataFrame(columns=self.raw_columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sun Jun 10 15:13:29 2018
@author: Branson
"""
import BATrader as ba
from collections import defaultdict
import pandas as pd
import numpy as np
import threading
# =============================================================================
# Good implementation of products
... | pd.to_datetime(df['Date'], format="%Y%m%d") | pandas.to_datetime |
"""
test pandabase against supported databases through fixtures:
sqlite: automatic - because SQLite is filesystem- or memory-based, sqlite does not require any setup
postgres: not automatic; execute these with pytest --run-postgres. postgresql requires:
postgres service to be running in background
a database ... | set_option('expand_frame_repr', True) | pandas.set_option |
def search(name=None, source=None, id_No=None, markdown=False):
"""
Search function that interacts directly with the Global Lake Level Database API.
Arguments:
name (str): Name of Lake or Reservoir. Be sure to use proper spelling. Wildcards (%) are allowed,as is any MySQL 5.7 syntax
source (... | pd.option_context('display.max_rows', 5, 'display.max_columns', None) | pandas.option_context |
import numpy as np
from datetime import timedelta
import pandas as pd
import pandas.tslib as tslib
import pandas.util.testing as tm
import pandas.tseries.period as period
from pandas import (DatetimeIndex, PeriodIndex, period_range, Series, Period,
_np_version_under1p10, Index, Timedelta, offsets)
... | pd.Period('2012-01', freq='M') | pandas.Period |
import matplotlib.pylab as plt
import pandas as pd
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa.arima_model import ARMA
from statsmodels.tsa.stattools import adfuller
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from pandas import Series
from pandas import DataFrame
from sklearn.e... | pd.to_datetime(df['report_date'], errors='coerce') | pandas.to_datetime |
from datetime import datetime
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas import DataFrame, DatetimeIndex, Index, MultiIndex, Series
import pandas._testing as tm
from pandas.core.window.common import flex_binary_moment
def _rolling_consistency_cases():
for window in [... | tm.assert_equal(rolling_f_result, rolling_apply_f_result) | pandas._testing.assert_equal |
from typing import Union
from collections import OrderedDict
import numpy as np
import pandas as pd
import plotly.offline as opy
import plotly.graph_objs as go
import plotly.figure_factory as ff
class PySingleSiteSimpleSchedule:
def __init__(
self,
objectives: dict,
campaig... | pd.DataFrame.from_records(batches_table) | pandas.DataFrame.from_records |
# Based on Code of <NAME>, added various modifications
#
# Copyright 2021 <NAME>
#
# 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 requ... | pd.DataFrame(columns=['image', 'hash_bin', 'hash_hex']) | pandas.DataFrame |
from ast import literal_eval
import numpy as np
import pandas as pd
import scipy
from pandas import DataFrame
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.neighbors import BallTree, KDTree, NearestNeighbors
from sklearn.preprocessing import Mu... | DataFrame.from_dict(parse_data) | pandas.DataFrame.from_dict |
"""
Copyright 2019 <NAME>.
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 writing,
software distribut... | pd.Series([1, 2, 3], index=_index * 3, name='spreadOptionVol') | pandas.Series |
from unittest import TestCase
from quick_pandas import monkey
monkey.patch_all()
from quick_pandas.wrappers.numpy_wrapper import ndarray_wrapper
class TestPandas(TestCase):
def test_dataframe_ndarray(self):
import pandas as pd
import numpy as np
data = np.random.randint(0, 10000, 1000)
... | pd.DataFrame(data) | pandas.DataFrame |
#!/usr/bin/env python
from __future__ import absolute_import
from __future__ import print_function
import numpy as np
import pandas as pd
import re
import util
import os
import xml.etree.ElementTree as ET
import datetime as dt
from scipy.sparse import dok_matrix
import hashlib
import six
from six.moves import range
fro... | pd.DataFrame(nodes) | pandas.DataFrame |
import io
import os
import re
import sys
import time
import pandas
import datetime
import requests
import mplfinance
from matplotlib import dates
# Basic Data
file_name = __file__[:-3]
absolute_path = os.path.dirname(os.path.abspath(__file__))
# <editor-fold desc='common'>
def load_json_config():
global file_dir... | pandas.concat([stock_low_old, stock_low_new], join='outer') | pandas.concat |
""" this will read the goes_r data"""
import pandas as pd
import xarray as xr
try:
import s3fs
has_s3fs = True
except ImportError:
print(
"Please install s3fs if retrieving from the Amazon S3 Servers. Otherwise continue with local data"
)
has_s3fs = False
try:
import h5py # noqa: F4... | pd.Timestamp(date) | pandas.Timestamp |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
import warnings
from .condition_fun import *
def eva_dfkslift(df, groupnum=None):
if groupnum is None: groupnum=len(df.index)
# good bad func
def n0(x): return sum(x==0)
def n1(x): return sum(x=... | pd.DataFrame({'label':label, 'pred':pred}) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
description: cleaning tools for tidals (tidepool data analytics tools)
created: 2018-07
author: <NAME>
license: BSD-2-Clause
"""
import pandas as pd
import numpy as np
def remove_duplicates(df, criteriaDF):
nBefore = len(df)
df = df.loc[~(criteriaDF.duplicat... | pd.to_datetime(df.time) | pandas.to_datetime |
"""
Utils for time series generation
--------------------------------
"""
import math
from typing import Union
import numpy as np
import pandas as pd
import holidays
from ..timeseries import TimeSeries
from ..logging import raise_if_not, get_logger
logger = get_logger(__name__)
def constant_timeseries(value: floa... | pd.Timedelta(days=1) | pandas.Timedelta |
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