prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import pandas as pd
from pandas import Timestamp
def create_dataframe(tuple_data):
"""Create pandas df from tuple data with a header."""
return pd.DataFrame.from_records(tuple_data[1:], columns=tuple_data[0])
### REUSABLE FIXTURES --------------------... | Timestamp('2013-07-01 00:00:00') | pandas.Timestamp |
# Databricks notebook source
# MAGIC %md-sandbox
# MAGIC
# MAGIC <div style="text-align: center; line-height: 0; padding-top: 9px;">
# MAGIC <img src="https://databricks.com/wp-content/uploads/2018/03/db-academy-rgb-1200px.png" alt="Databricks Learning" style="width: 600px">
# MAGIC </div>
# COMMAND ----------
# M... | pd.DataFrame(data=y_test, columns=["label"]) | pandas.DataFrame |
import factal.schema as schema
from arcgis.features import GeoAccessor
from arcgis.gis import GIS
from datetime import datetime, timedelta
import pandas as pd
import requests
import time
class Extractor(object):
def __init__(self, token):
self.token = token
self.urls = self.get_urls()
se... | pd.DataFrame(topic_data) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Aug 5 14:19:54 2018
@author: canf
"""
import pandas as pd
from sklearn import ensemble
from sklearn.model_selection import cross_validate
from sklearn import metrics
from sklearn.naive_bayes import MultinomialNB,BernoulliNB,GaussianNB
import gzip
imp... | pd.read_csv("./input/train.csv") | pandas.read_csv |
import nose
import unittest
import os
import sys
import warnings
from datetime import datetime
import numpy as np
from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range,
date_range, Index)
from pandas.io.pytables import HDFStore, get_store, Term, IncompatibilityWarning
import pandas... | date_range('1/1/2000', periods=3) | pandas.date_range |
# -*- coding: utf-8 -*-
import os
import sys
from typing import List, NamedTuple
from datetime import datetime
from google.cloud import aiplatform, storage
from google.cloud.aiplatform import gapic as aip
from kfp.v2 import compiler, dsl
from kfp.v2.dsl import component, pipeline, Input, Output, Model, Metrics, Datas... | pd.read_csv(X_train.path) | pandas.read_csv |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.13.1
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %% _uuid="8f2839f25d086af736a60e9eeb907d3b93b6e0e5... | pd.DataFrame(X_test) | pandas.DataFrame |
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... | timezones.tz_standardize(dtype.tz) | pandas._libs.tslibs.timezones.tz_standardize |
import os
import unittest
import numpy as np
import pandas as pd
from cgnal.core.data.model.ml import (
LazyDataset,
IterGenerator,
MultiFeatureSample,
Sample,
PandasDataset,
PandasTimeIndexedDataset,
CachedDataset,
features_and_labels_to_dataset,
)
from typing import Iterator, Generat... | pd.Series([0, 0, 0, 1], name="Label") | pandas.Series |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Test functions for tools.tools
"""
import warnings
from six.moves import range
import numpy as np
from numpy.testing import (assert_equal, assert_array_equal,
assert_almost_equal,
assert_string_equal)
import pandas... | tm.assert_series_equal(expected, output['const']) | pandas.util.testing.assert_series_equal |
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 5 13:24:18 2020
@author: earne
"""
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
import seaborn as sns
from sipperplots import (
get_any_idi,
get_side_idi,
get_content_idi,... | pd.DataFrame({c:bar_h}, index=bar_x) | pandas.DataFrame |
import pandas as pd
import gensim
import csv
import random
def kw_bigram_score(concept, segment):
"""
Rank the segment using the key word search algorithm
:param segment (list): a list of the tokens in the segment
:param concept (str): the concept
:return: a numeric score of the number of occurenc... | pd.DataFrame(data=data, index=index) | pandas.DataFrame |
"""
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([5, 1, 2], index=idx, name='impliedVolatility') | pandas.Series |
# Copyright (c) 2018-2021, NVIDIA CORPORATION.
import array as arr
import datetime
import io
import operator
import random
import re
import string
import textwrap
from copy import copy
import cupy
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from numba import cuda
import cudf
from cudf.c... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import pickle
import os
import yaml
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import r2_score
# from .utils import Boba_Utils as u
# from ._03_Modeling ... | pd.read_csv(path,index_col=0) | pandas.read_csv |
from ipywidgets import Button, Text, VBox, HBox, Layout, Dropdown, Checkbox, \
DatePicker, Select, SelectMultiple, Tab, BoundedFloatText, Label, Output, interactive
from IPython.display import display
import traitlets
import itertools
import pandas as pd
import copy
from tkinter import Tk, filedialog
from datetime ... | pd.ExcelWriter('Output_files/' + model_file_name + '.xlsx', engine='xlsxwriter') | pandas.ExcelWriter |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import pytest
from ..testing_utils import make_ecommerce_entityset
from featuretools import Timedelta
from featuretools.computational_backends import PandasBackend
from featuretools.primitives import (
Absolute,
Add,
Count,
CumCount,
... | pd.isnull(t) | pandas.isnull |
import pandas as pd
import numpy as np
from .QCBase import VarNames
class Exporter(object):
""" Export class which writes parsed data to a certain format"""
valid_formats = ["pdf", "xlsx", "txt", "csv", "dataframe"]
def __init__(self, data=None):
self.data = data
# for later: add pand... | pd.DataFrame(d) | pandas.DataFrame |
import numpy as np
import pandas as pd
from datetime import datetime
def string_date(mnthDay, year):
"""Return a string date as 'mm/dd/yyyy'.
Argument format:
'mm/dd' string
'yyyy'"""
return(mnthDay + '/' + str(year))
class TouRate(object):
"""Object for Utility Time Of Use Tariff.... | pd.DataFrame() | pandas.DataFrame |
# Copyright (C) 2018 GuQiangJs.
# Licensed under Apache License 2.0 <see LICENSE file>
import pandas as pd
from pandas import read_excel
def get_stock_holdings(index: str):
""" 从 中证指数有限公司 获取指数的成分列表
Args:
index: 指数代码
Returns:
``pandas.DataFrame``:
Examples:
.. code-block:: p... | read_excel(url, convert_float=False, dtype=object, usecols=[4, 5, 8]) | pandas.read_excel |
from __future__ import print_function
import os
import sys
import logging
import pandas as pd
import numpy as np
file_path = os.path.dirname(os.path.realpath(__file__))
lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common'))
sys.path.append(lib_path2)
import candle
logger = logging.getLogger(__n... | pd.read_hdf(train_file, 'x_test_0') | pandas.read_hdf |
#!/usr/bin/env python
__author__ = '<NAME>'
import os
import pandas as pd
import argparse
from copy import deepcopy
from _collections import OrderedDict
import pandas as pd
from BCBio import GFF
from RouToolPa.Collections.General import SynDict, IdList
from RouToolPa.Parsers.VCF import CollectionVCF
from MACE.Routines... | pd.read_csv(args.scaffold_length_file, sep='\t', header=None, names=("scaffold", "length"), index_col=0) | pandas.read_csv |
from datetime import datetime
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
CategoricalIndex,
DataFrame,
Index,
MultiIndex,
Series,
qcut,
)
import pandas._testing as tm
def cartesian_product_for_groupers(result, args, names, fill... | Categorical(["c", "d", "c", "d"], categories=["c", "d", "y"], ordered=True) | pandas.Categorical |
import pandas as pd
import sys
import numpy as np
def procesar(model, load, dates, results):
#Listas para guardar los valores
pv_result = []
dg_result = []
#Ebat_c_result = []
#Ebat_d_result = []
p_gf_result = []
LPSP_result = []
SOC_result = []
#Ciclo que obtiene el val... | pd.DataFrame(p_gf_result) | pandas.DataFrame |
"""
Copyright (c) 2021, Electric Power Research Institute
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 source code must retain the above copyright notice,
this li... | pd.period_range(start=start_year, end=end_year, freq='y') | pandas.period_range |
import zlib
import base64
import json
import re
import fnmatch
import pendulum
import requests
from redis import Redis
import pandas as pd
from pymongo import MongoClient
import pymongo.errors as merr
from ..constants import YEAR
from .orm import Competition
def _val(v, s=None):
if s is None:
s = {"raw... | pd.DataFrame(l[k], columns=cols) | pandas.DataFrame |
# Visualize streamflow time series and fill missing data
# Script written in Python 3.7
import config as config
import numpy as np
import pandas as pd
import tempfile
import datetime
from sklearn.svm import SVR
import geopandas as gpd
from sklearn.metrics import mean_squared_error as mse
import matplotlib.pyplot as pl... | pd.DataFrame(index=rng) | pandas.DataFrame |
# coding:utf-8
# This file is part of Alkemiems.
#
# Alkemiems is free software: you can redistribute it and/or modify
# it under the terms of the MIT License.
__author__ = '<NAME>'
__version__ = 1.0
__maintainer__ = '<NAME>'
__email__ = "<EMAIL>"
__date__ = '2021/06/10 16:29:05'
import numpy as n... | pd.DataFrame(data.iloc[train_index].values, columns=data.columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# ------------------------------------------------------------------------------
# Created by <NAME> (<EMAIL>)
# Created On: 2020-2-24
# ------------------------------------------------------------------------------
import cv2
import random
import json
import numpy as np
import os
import os.path... | pd.DataFrame(data=loc_mat) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""Revolving credit.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1g-iUOJyARAnpOuEepyI7-N48uzW1oHYL
# Financial Project
## The Data
Revolving credit
### Business Objective:
Revolving credit means you're borrowing a... | pd.concat([df_1,dummies_1],axis=1) | pandas.concat |
import numpy as np
import pandas as pd
import pytest
from whylogs.core.types import TypedDataConverter
_TEST_NULL_DATA = [
([None, np.nan, None] * 3, 9),
([pd.Series(data={"a": None, "b": None}, index=["x", "y"]), | pd.Series(data={"c": None, "d": 1}, index=["x", "y"]) | pandas.Series |
# -*- coding: UTF-8 -*-
"""
collector.aggregation - 聚合数据采集
聚合数据采集是指一次性采集模型分析所需要的数据
====================================================================
"""
import os
import traceback
from tqdm import tqdm
import pandas as pd
import tma
# tma.DEBUG = True
from tma.utils import debug_print
from tma.collector.ts import ... | pd.read_csv(FILE_CACHE, encoding='utf-8', dtype={"code": str}) | pandas.read_csv |
"""
Base and utility classes for pandas objects.
"""
import textwrap
import warnings
import numpy as np
import pandas._libs.lib as lib
import pandas.compat as compat
from pandas.compat import PYPY, OrderedDict, builtins, map, range
from pandas.compat.numpy import function as nv
from pandas.errors import AbstractMetho... | Series(mapper) | pandas.Series |
from datetime import timezone
from functools import lru_cache, wraps
from typing import List, Optional
import numpy as np
from pandas import Index, MultiIndex, Series, set_option
from pandas.core import algorithms
from pandas.core.arrays import DatetimeArray, datetimes
from pandas.core.arrays.datetimelike import Datet... | is_array_like(self.left_join_keys[i]) | pandas.core.dtypes.inference.is_array_like |
"""
Prelim script for looking at netcdf files and producing some trends
These estimates can also be used for P03 climate estimation
"""
#==============================================================================
__title__ = "Global Climate Trends"
__author__ = "<NAME>"
__version__ = "v1.0(13.02.2019)"
__email__ ... | pd.Timestamp.now() | pandas.Timestamp.now |
#!/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
from mpl_toolkits.basemap import Basemap, addcyclic, ... | pd.DataFrame(df_UmbralH_Nube, columns=['Umbral']) | pandas.DataFrame |
import numpy as np
import pandas as pd
from scipy.spatial.transform import Rotation as R
import os
# We use only folders 1-20. This code creates a robot state csv for a case when one camera is used for testing and 5 for training
# save the dataset size of first 20 folders
path1 = '/home/kiyanoush/UoLincoln/Projects/D... | pd.read_csv(path1, header=None) | pandas.read_csv |
from fbprophet import Prophet
import numpy as np
import os
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from sklearn import linear_model
from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor, AdaBoostRegressor
from sklearn.model_selection import train_test_split
fr... | pd.to_datetime(df_groupby_daily['published_date']) | pandas.to_datetime |
# -*- coding: utf-8 -*-
import csv
import os
import platform
import codecs
import re
import sys
from datetime import datetime
import pytest
import numpy as np
from pandas._libs.lib import Timestamp
import pandas as pd
import pandas.util.testing as tm
from pandas import DataFrame, Series, Index, MultiIndex
from pand... | StringIO(data) | pandas.compat.StringIO |
from pathlib import Path
from sparta.ab.portfolio_metrics import portfolio_metrics, yearly_returns
import pandas as pd
from sparta.tomer.alpha_go.consts import LOCAL_PATH
import pdb
class ReportBuilder(object):
def __init__(self):
self.returns = {}
def set_args(self, year, predictions, portfolio_size... | pd.concat(self.returns['btm']) | pandas.concat |
import datetime
import backtrader as bt
import pandas as pd
class MyStrategy(bt.Strategy):
def __init__(self):
print('init')
def start(self):
print('start')
def prenext(self):
print('prenext')
def nextstart(self):
print('next start')
def next(self):
print... | pd.to_datetime(df['Date']) | pandas.to_datetime |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.preprocessing import Normalizer, StandardScaler, MinMaxScaler, RobustScaler
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import mea... | pd.read_csv(path_BTC_Data, sep=',', index_col='Date') | pandas.read_csv |
import numpy as np
import pandas as pd
import pystan as ps
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
import plotly.express as px
import glob
import arviz
from tqdm import tqdm
import matplotlib
import os
import sys
import datetime
# load the 10xv3 results with 30x sampling for each cell/de... | pd.DataFrame(s['summary'], columns=s['summary_colnames'], index=s['summary_rownames']) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/2/14 18:19
Desc: 新浪财经-股票期权
https://stock.finance.sina.com.cn/option/quotes.html
期权-中金所-沪深 300 指数
https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php
期权-上交所-50ETF
期权-上交所-300ETF
https://stock.finance.sina.com.cn/option/quotes.html
"""
import json
i... | pd.DataFrame(temp_df) | pandas.DataFrame |
import geopandas as gpd
import matplotlib.pyplot as plt
import matplotlib
from pandas import Series
import numpy as np
import os
from cargador_datos_csv_population import *
from cargador_datos_csv_area import *
municipiosLetMeHelp=["25041",\
"25068",\
"25048",\
"25052",\
"25068",\
"25093",\
"25099",\
"25113",\
"25122"... | Series(colors, dtype="str", index=data.index) | pandas.Series |
from __future__ import division #brings in Python 3.0 mixed type calculation rules
import datetime
import inspect
import numpy as np
import numpy.testing as npt
import os.path
import pandas as pd
import sys
from tabulate import tabulate
import unittest
##find parent directory and import model
#parentddir = os.path.ab... | pd.Series([], dtype='float') | pandas.Series |
# -*- coding: utf-8 -*-
from datetime import timedelta
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas import (Timedelta,
period_range, Period, PeriodIndex,
_np_version_under1p10)
import pandas.core.indexes.period as period
cla... | pd.offsets.MonthBegin(1) | pandas.offsets.MonthBegin |
__author__ = 'thor'
import pandas as pd
import numpy as np
import ut.daf.manip as daf_manip
import ut.daf.ch as daf_ch
# from ut.pstr.trans import toascii as strip_accents
from sklearn.feature_extraction.text import strip_accents_unicode as strip_accents
def to_lower_ascii(d):
if isinstance(d, pd.DataFrame):
... | pd.concat([result, tok_lists[too_small_lidx]]) | pandas.concat |
"""Analyze and plot cell motility from tracks"""
from collections import OrderedDict
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d as p3
import matplotlib.animation as animation
from mpl_toolkits.mplot3d import Axes3D
from sklearn.cluste... | pd.set_option("display.max_rows", 1000) | pandas.set_option |
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn import metrics
import pandas as pd
import numpy as np
def get_logreg_output(features_df, target_df, active_norm):
non_num_features = [col for col,... | pd.get_dummies(features_df[non_num_features]) | pandas.get_dummies |
import itertools
from logging import log
import os
import json
import numpy as np
# import snowballstemmer
# import requests
# response = requests.get(url)
# response.raise_for_status() # raises exception when not a 2xx response
from streamlit_lottie import st_lottie
from io import StringIO
import spacy
from spac... | pd.DataFrame(categories_output) | pandas.DataFrame |
import pandas as pd
class Shape:
def __init__(self, parent, x_coords, y_coords, color, plot_style="o-"):
self.parent = parent
self.x_coords = x_coords
self.y_coords = y_coords
self.color = [color]
self.plot_style = [plot_style]
self.graph = [[self.x_coords, self.y_... | pd.DataFrame({"X": self.x_coords, "Y": self.y_coords}) | pandas.DataFrame |
# Copyright 2021 The TensorFlow Probability 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 o... | pd.DateOffset(days=1) | pandas.DateOffset |
#!/usr/bin/env python
"""Tests for `qnorm` package."""
import unittest
import numpy as np
import pandas as pd
import qnorm
import tracemalloc
tracemalloc.start()
df1 = pd.DataFrame(
{
"C1": {"A": 5.0, "B": 2.0, "C": 3.0, "D": 4.0},
"C2": {"A": 4.0, "B": 1.0, "C": 4.0, "D": 2.0},
"C3": {... | pd.read_csv("test_large_out.csv", index_col=0, header=0) | pandas.read_csv |
import time
import logging
import asyncio
import pandas as pd
from collections import defaultdict
from github import Github
from github.GithubException import RateLimitExceededException, UnknownObjectException
from ghutil import get_tokens, get_issues_in_text
async def get_issue(gh: Github, repo: str, numbe... | pd.read_excel("data/prs.xlsx") | pandas.read_excel |
import threading
import time
import datetime
import pandas as pd
from functools import reduce, wraps
from datetime import datetime, timedelta
import numpy as np
from scipy.stats import zscore
import model.queries as qrs
from model.NodesMetaData import NodesMetaData
import utils.helpers as hp
from utils.helpers import... | pd.merge(self.thp, self.rtm, how='outer') | pandas.merge |
'''
This class uses scikit-learn to vectorize a corpus of text and
allow comparison of new documents to the existing corpus matrix
'''
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
class CosineMatcher(object):
... | pd.notnull(best_matches) | pandas.notnull |
import loader
import numpy as np
import pandas as pd
from pathlib import Path
from definitions import *
def get_features():
def generate_user_features(df):
print('Generate user features')
if Path(name__features_user).exists():
print('- {} is existed already. Let\'s load it'.format(nam... | pd.read_pickle(name__features_test) | pandas.read_pickle |
import pandas as pd
data_av_week = pd.read_csv("data_av_week.csv")
supermarkt_urls = pd.read_csv("supermarkt_urls.csv")
s_details = pd.read_csv("notebooksdetailed_supermarkt_python_mined.csv", header= None)
migros_details = pd.read_csv("notebooksdetailed_Migros_python_mined.csv", header= None)
coop_details = pd.read_c... | pd.read_csv("data_av_day.csv") | pandas.read_csv |
import pandas as pd
import numpy as np
import pickle
import pyranges as pr
import pathlib
path = pathlib.Path.cwd()
if path.stem == 'ATGC':
cwd = path
else:
cwd = list(path.parents)[::-1][path.parts.index('ATGC')]
##your path to the files directory
file_path = cwd / 'files/'
usecols = ['Hugo_Symbol', 'Chromoso... | pd.merge(pcawg_maf, result.iloc[:, 3:], how='left', on='index') | pandas.merge |
import argparse
import pandas as pd
import numpy as np
import sys
p = str(Path(__file__).resolve().parents[2]) # directory two levels up from this file
sys.path.append(p)
from realism.realism_utils import make_orderbook_for_analysis
def create_orderbooks(exchange_path, ob_path):
MID_PRICE_CUTOFF = 10000
pro... | pd.Timestamp(date) | pandas.Timestamp |
"""Summarize upstream catchment information.
Description
----------
Module that helps support network summarization of
information from "local" segments of the network.
Methods require information pre-summarized to local segments.
Methods currently support calculations for sum, min, max and
... | pd.DataFrame(seg_summaries) | pandas.DataFrame |
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2021, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | pd.Index(['feat1', 'feat2'], name='id') | pandas.Index |
#!/usr/bin/env python3
import os
import re
from collections import defaultdict
from datetime import datetime
from robobrowser import RoboBrowser
from ccf.config import LoadSettings
import pandas as pd
browser = RoboBrowser(history=True, timeout=6000, parser="lxml")
config = LoadSettings()["KSADS"]
download_dir = conf... | pd.concat(dfs, sort=False) | pandas.concat |
# -*- coding: utf-8 -*-
# Arithmetc tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import timedelta
import operator
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.compat import long
from pandas.core import ops
from pan... | tm.assert_index_equal(result, expected) | pandas.util.testing.assert_index_equal |
import pandas as pd
import numpy as np
import copy
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV
from sklearn.feature_selection import mutual_info_classif, SelectKBest
import matplotlib.pyplot as plt
from sklearn import svm
from sk... | pd.concat([df_percent, df_gnb, df_svc, df_knn], axis=1) | pandas.concat |
import pandas as pd
import numpy as np
X = np.load("all_scores_mag_compo60.npy", allow_pickle=True).item()
df = | pd.DataFrame(X) | pandas.DataFrame |
"""
Module to test differing featuresets.
"""
import os
import itertools
import pandas as pd
class Ablation_Experiment:
# public
def __init__(self, config_obj, app_obj, util_obj):
self.config_obj = config_obj
self.app_obj = app_obj
self.util_obj = util_obj
def run_experiment(self... | pd.DataFrame(rows, columns=cols) | pandas.DataFrame |
from flask import Blueprint, redirect, url_for, render_template, request, session
from src.constants.model_params import Ridge_Params, Lasso_Params, ElasticNet_Params, RandomForestRegressor_Params, \
SVR_params, AdabootRegressor_Params, \
GradientBoostRegressor_Params
from src.constants.model_params import Kmea... | pd.read_csv(file_path) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
:Module: khorosjx.utils.df_utils
:Synopsis: Useful tools and utilities to assist in importing, manipulating and exporting pandas dataframes
:Usage: ``from khorosjx import df_utils``
:Example: TBD
:Created By: <NAME>
:Last Modified: <NAME>
:Modified Date: 18 Dec 2... | pd.read_excel(file_path, sheet_name=excel_sheet, header=None) | pandas.read_excel |
from directional import *
import pandas as pd
import numpy as np
demo_sin_cos_matrix = pd.read_csv("sample_data/sin-cos.csv")
demo_sin_cos_mean = pd.read_csv("sample_data/sin-cos-mean.csv")
demo_angle_matrix = pd.read_csv("sample_data/degrees.csv")
demo_radian_matrix = | pd.read_csv("sample_data/radians.csv") | pandas.read_csv |
from .gamedata import getPlayers, getPointLog, getMatches, getUnplayed, getDisqualified
from .pwr import PWRsystems
from .regression import Regression
from .simulate import simulateBracket, simulateMatch, simulateGamelog
from .players import Player, Players
from .tiebreak import getPlayoffSeeding
from .util impor... | pd.merge(self.standings, self.seeding, on='Player', suffixes=('', '_')) | pandas.merge |
#-*- coding:utf-8 -*-
from __future__ import print_function
import os,sys,sip,time
from datetime import datetime,timedelta
from qtpy.QtWidgets import QTreeWidgetItem,QMenu,QApplication,QAction,QMainWindow
from qtpy import QtGui,QtWidgets
from qtpy.QtCore import Qt,QUrl,QDate
from Graph import graphpage
from layout impo... | pd.DataFrame(series) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import numpy as np
import pytest
from numpy.random import RandomState
from numpy import nan
from datetime import datetime
from itertools import permutations
from pandas import (Series, Categorical, CategoricalIndex,
Timestamp, DatetimeIndex, Index, IntervalIndex)
import pan... | Series([10.3, 5., 5., None]) | pandas.Series |
import datetime
import hashlib
import os
import time
from warnings import (
catch_warnings,
simplefilter,
)
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
DatetimeIndex,
Index,
MultiIndex,
Series,
Timestamp,
concat,
date_range,
timedelt... | timedelta_range(start="0s", periods=10, freq="1s", name="example") | pandas.timedelta_range |
from pandas import read_csv, DataFrame
from numpy import asarray, transpose, array, linalg, abs, cov, reshape
from sklearn.externals import joblib
from sklearn import mixture
from sklearn.metrics import silhouette_score
from operator import itemgetter
import sympy as sp
def get_dataset(path):
data = | read_csv(path) | pandas.read_csv |
import base64
import json
import pandas as pd
import streamlit as st
st.set_page_config(layout='wide')
def download_link(object_to_download, download_filename, download_link_text):
"""
Generates a link to download the given object_to_download.
object_to_download (str, pd.DataFrame): The object to be do... | pd.DataFrame(selected_lists) | pandas.DataFrame |
# %%
# Artificial Neural Network for RPM and FCR Prediction
# <NAME>, Ph.D. Candidate
# %%
# Load required libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import keras
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from skl... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 26 15:39:02 2018
@author: joyce
"""
import pandas as pd
import numpy as np
from numpy.matlib import repmat
from stats import get_stockdata_from_sql,get_tradedate,Corr,Delta,Rank,Cross_max,\
Cross_min,Delay,Sum,Mean,STD,TsRank,TsMax,TsMin,DecayLinea... | pd.DataFrame(r['r1'] * r['r2']) | 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(cats) | pandas.MultiIndex.from_tuples |
# 有三種網頁轉換方法，必放
from django.shortcuts import render # 呼叫模板，合成後送往瀏覽器
from django.http import HttpResponse, request # 程式送往瀏覽器
from django.shortcuts import redirect # 程式送往程式
import pymysql
import re
import pandas as pd
from datetime import datetime
from sql_account import sql_account
'''思考一下
1. 能否依照權限顯示資料 - ok
2. 是否可以刪... | pd.DataFrame(columns=columns) | pandas.DataFrame |
"""
E2E Tests for Generating Data. These tests make use of pre-created models that can
be downloaded from S3. We utilize a generation utility that will automatically determine
if we are using a simple model or a DF Batch model.
When adding a new model to test, the model filename should conform to:
description-MODE-... | pd.DataFrame(seed) | pandas.DataFrame |
# Copyright 2016 <NAME> and The Novo Nordisk Foundation Center for Biosustainability, DTU.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unle... | DataFrame(columns=["ub", "lb", "value", "strain"]) | pandas.DataFrame |
from collections import deque
from datetime import datetime
import operator
import re
import numpy as np
import pytest
import pytz
import pandas as pd
from pandas import DataFrame, MultiIndex, Series
import pandas._testing as tm
import pandas.core.common as com
from pandas.core.computation.expressions import _MIN_ELE... | pd.DataFrame(False, index=df.index, columns=df.columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101
import itertools
import warnings
from warnings import catch_warnings
from datetime import datetime
from pandas.types.common import (is_integer_dtype,
is_float_dtype,
is_scalar)
from pandas.compat... | DataFrame({'foo': start_data}) | pandas.core.api.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 26 15:39:02 2018
@author: joyce
"""
import pandas as pd
import numpy as np
from numpy.matlib import repmat
from stats import get_stockdata_from_sql,get_tradedate,Corr,Delta,Rank,Cross_max,\
Cross_min,Delay,Sum,Mean,STD,TsRank,TsMax,TsMin,DecayLinea... | pd.DataFrame(data_temp['temp'] - data_temp['temp_delay']) | pandas.DataFrame |
#!/usr/bin/python
# coding: utf-8
import json
import pickle
import re
import jieba
import numpy as np
import pandas as pd
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import QuantileTransformer
def max_min_scaler(x):
return... | pd.DataFrame(list_text_b, columns=["col_raw"]) | pandas.DataFrame |
import pandas as pd
import sqlite3
def load_coded_as_dicts(link_codes_file, twitter_user_codes_file):
"""
Loads two dictionaries
link: code_str
twitter_screen_name: code_str
"""
try:
link_codes_df = pd.read_csv(link_codes_file)
link_codes = pd.Series(link_codes_df.code_str.valu... | pd.concat([df, newdf], sort=True) | pandas.concat |
from pathlib import Path
from typing import Callable, List, Optional, Dict
import cv2
import torch
import pandas as pd
from torch.utils.data import Dataset
from transforms import tensor_transform
N_CLASSES = 1103
DATA_ROOT = Path('./data')
def build_dataframe_from_folder(root: Path, class_map: Optional[Dict] = No... | pd.DataFrame(tmp, columns=["image_path", "label"]) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[6]:
# import of standard python libraries
import numpy as np
import os
import time
import corner
import astropy.io.fits as pyfits
import sys
import argparse
#from tqdm import tqdm
import pandas as pd
import gc
#sys.path.insert(0, '../lenstronomy/lenstronomy/')
import matplo... | pd.read_csv('merged_agn_lc.csv') | pandas.read_csv |
from django.test import TestCase
from transform_layer.services.data_service import DataService, KEY_SERVICE, KEY_MEMBER, KEY_FAMILY
from transform_layer.calculations import CalculationDispatcher
from django.db import connections
import pandas
from pandas.testing import assert_frame_equal, assert_series_equal
import un... | pandas.DataFrame(d1) | pandas.DataFrame |
import matplotlib.pyplot as plt
import os
import pandas as pd
import numpy as np
from qutip import *
from scipy.optimize import curve_fit
from scipy.interpolate import interp1d
import scipy
from .loading import load_settings
from .fitting import decay_gen
from ..tools.tools import metastable_calc_optimization, prob_obj... | pd.concat([self.rates, rates], sort=True) | pandas.concat |
import pandas as pd
import numpy as np
import json
from bs4 import BeautifulSoup
import requests
import matplotlib.pyplot as plt
# save data
import pickle
def save(data,fileName):
with open(fileName+'.dat', 'wb') as f:
pickle.dump(data, f)
def load(fileName):
with open(fileName+'.dat', ... | pd.Series(cumsum) | pandas.Series |
# Este script toma todas las cuentas de POS y crea un nuevo dataset de ellas.
import pandas as pd
import os
datasets = [file for file in os.listdir(os.path.join("..","data","processed")) if "POS" in file]
filepath_in = os.path.join("..","data","processed", datasets[0])
data = pd.DataFrame()
for data_file in datase... | pd.read_csv(filepath_in) | pandas.read_csv |
#!/usr/bin/env python
# coding=utf-8
"""
@version: 0.1
@author: li
@file: factor_revenue_quality.py
@time: 2019-01-28 11:33
"""
import gc, six
import sys
sys.path.append("../")
sys.path.append("../../")
sys.path.append("../../../")
import numpy as np
import pandas as pd
import json
from pandas.io.json import json_norma... | pd.merge(revenue_quality, earning, how='outer', on="security_code") | pandas.merge |
# import packages
import pandas as pd
import numpy as np
import seaborn as sns
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import time
import datetime as dt
import json
from path... | pd.get_dummies(X_train.parent_name, prefix='parent_name') | pandas.get_dummies |
# -*- coding: utf-8 -*-
from __future__ import division, absolute_import, print_function
import numpy as np
from numpy.testing import assert_allclose
import pandas as pd
from scipy import signal
def cont2discrete(sys, dt, method='bilinear'):
discrete_sys = signal.cont2discrete(sys, dt, method=method)[:-1]
if l... | pd.DataFrame({'x': x, 'y': y}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import os
import shutil
import ipdb
import numpy as np
import string
from collections import Counter
import pandas as pd
from tqdm import tqdm
import random
import time
from functools import wraps
import collections
import sklearn
import utils
# from utils import log_time_delta
from tqdm import... | pd.read_csv(filename,header = None,sep="\t",names=["text","label"]) | pandas.read_csv |
import pandas as pd
import numpy as np
import copy
import re
import string
# Note: this requires nltk.download() first as described in the README.
# from nltk.book import *
from nltk.corpus import stopwords
from nltk.tokenize import TreebankWordTokenizer
from collections import Counter, OrderedDict
from sklearn.model_... | pd.merge(tweets_by_user_df, user_class_df, left_on='username', right_on='username') | pandas.merge |
import pandas as pd
pd.options.display.max_rows=9999
pd.options.display.max_columns=15
| pd.set_option("display.max_columns", 100) | pandas.set_option |
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