prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
'''
Load data template. Includes load(), metadata, split_stations(),
remove_upcast() and locals().update().
Inputs:
load() - data/ctd/<DATE>.cnv
metadata() - data/csv/coordenadas_<DATE>.csv
'''
# Dependencies
import pandas as pd
from code.functions import *
saida1 = 'data/ctd/stations_25-01-2017_processed.cnv'
said... | pd.Series(top) | pandas.Series |
import pytest
import numpy as np
import pandas as pd
from pandas import Categorical, Series, CategoricalIndex
from pandas.core.dtypes.concat import union_categoricals
from pandas.util import testing as tm
class TestUnionCategoricals(object):
def test_union_categorical(self):
# GH 13361
data = [
... | Categorical([]) | pandas.Categorical |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 20 16:00:06 2018
@author: nmei
"""
if __name__ == "__main__":
import os
import pandas as pd
import numpy as np
import utils
import seaborn as sns
sns.set_style('whitegrid')
sns.set_context('poster')
from matplotlib i... | pd.concat([df1_transition,df2_transition,df3_transition]) | pandas.concat |
from __future__ import division # brings in Python 3.0 mixed type calculation rules
import datetime
import inspect
import numpy.testing as npt
import os.path
import pandas as pd
import pkgutil
import sys
from tabulate import tabulate
import unittest
try:
from StringIO import StringIO
except ImportError:
from i... | pd.read_csv(data_inputs, index_col=0, engine='python') | pandas.read_csv |
from warnings import catch_warnings, simplefilter
import numpy as np
from numpy.random import randn
import pytest
import pandas as pd
from pandas import (
DataFrame, MultiIndex, Series, Timestamp, date_range, isna, notna)
from pandas.util import testing as tm
@pytest.mark.filterwarnings("ignore:\\n.ix:Deprecati... | tm.assert_almost_equal(df.values, values) | pandas.util.testing.assert_almost_equal |
# -*- 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.core import ops
from pandas.errors import NullFrequency... | TimedeltaIndex(['1 Day', '12 Hours']) | pandas.TimedeltaIndex |
import pandas as pd
from isitfit.utils import logger
class TagsImplierHelper:
def __init__(self, names_df):
self.names_df = names_df
self.names_original = names_df.Name.tolist()
def freq_list(self):
logger.info("Step 1: calculate word frequencies")
# lower-case
self.names_lower = [x.lower() ... | pd.concat([df_freq_1w,df_freq_2w], axis=0) | pandas.concat |
import pytest
import numpy as np
import pandas as pd
from pandas import Categorical, Series, CategoricalIndex
from pandas.core.dtypes.concat import union_categoricals
from pandas.util import testing as tm
class TestUnionCategoricals(object):
def test_union_categorical(self):
# GH 13361
data = [
... | pd.Timestamp('2011-01-01') | pandas.Timestamp |
import pandas as pd
import numpy as np
import math
import cmath
import pickle
joints = ['Nose','Neck','Right_shoulder','Right_elbow','Right_wrist','Left_shoulder',
'Left_elbow','Left_wrist','Right_hip','Right_knee','Right_ankle','Left_hip',
'Left_knee','Left_ankle','Right_eye','Left_eye','Right_ear','L... | pd.read_csv("C:\\Users\\Testing\\Downloads\\reachstepout_position2d_new.csv") | pandas.read_csv |
import numpy as np
import pandas as pd
import random
from rpy2.robjects.packages import importr
utils = importr('utils')
#utils.install_packages('prodlim')
prodlim = importr('prodlim')
eventglm = importr('eventglm')
#utils.install_packages('eventglm')
import rpy2.robjects as robjects
from rpy2.robjects impo... | pd.get_dummies(df,columns= ['race' ,'ethnicity' ,'pathologic_stage' ,'molecular_subtype'],dtype=float) | pandas.get_dummies |
"""Tests suite for Period handling.
Parts derived from scikits.timeseries code, original authors:
- <NAME> & <NAME>
- pierregm_at_uga_dot_edu - mattknow_ca_at_hotmail_dot_com
"""
from unittest import TestCase
from datetime import datetime, timedelta
from numpy.ma.testutils import assert_equal
from pandas.tseries.p... | Period('9/1/2005', freq='Q') | pandas.tseries.period.Period |
import requests
from bs4 import BeautifulSoup
import multiprocessing as mp
import os
import pandas as pd
import time
folder = './adj_temp'
os.makedirs(folder, exist_ok=True)
def _try_request( url, params, max_tries = 10, pause = 0.01 ):
res = None
n_tries = 1
while True:
try:
res = ... | pd.DataFrame(fhdata) | pandas.DataFrame |
'''
MIT License
Copyright (c) 2020 <NAME>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distri... | pd.concat(data) | pandas.concat |
"""This module contains the Model class in Pastas.
"""
from collections import OrderedDict
from logging import getLogger
from os import getlogin
import numpy as np
from pandas import date_range, Series, Timedelta, DataFrame, Timestamp
from .decorators import get_stressmodel
from .io.base import dump, _load_model
fr... | Timestamp(tmin) | pandas.Timestamp |
#!/usr/bin/env python
# coding: utf-8
# # >>>>>>>>>>>>>>>>>>>>Tarea número 3 <<<<<<<<<<<<<<<<<<<<<<<<
# # Estudiante: <NAME>
# # Ejercicio #1
# In[2]:
import os
import pandas as pd
import numpy as np
from math import pi
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
from scipy.cluster... | pd.Series(col, copy=True) | pandas.Series |
import pytest
import sys, os
import pandas as pd
import pyDSlib
def test_count_subgroups_in_group():
df = {}
df['subgroup'] = []
df['group'] = []
for color in ['R','G','B']:
slice_ = [i for i in range(3)]
df['subgroup'] = df['subgroup']+ slice_+slice_
df['group'] = df['group'] ... | pd.DataFrame.from_dict(df) | pandas.DataFrame.from_dict |
import numpy as np
import pandas as pd
import datetime as dt
import time
import matplotlib.pyplot as plt
import seaborn as sns
import vnpy.analyze.data.data_prepare as dp
from jqdatasdk import *
from vnpy.trader.database import database_manager
from mpl_toolkits.axisartist.parasite_axes import HostAxes, ParasiteAxes
im... | pd.DataFrame(columns=('date', 'invest')) | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import FinanceDataReader as fdr
from pykrx import stock
import datetime
import requests
# from datetime import timedelta # 마이크로초 전, 마이크로초 후 를 구하고 싶다면 timedelta
from dateutil.relativedelta import relativedelta # 몇달 전, 몇달 후, 몇년 전, 몇년 후 를 구하고 싶다면 relat... | pd.DataFrame(columns=['종목명', '종목코드', '수량(주)', '투자금액(원)', '투자비중']) | pandas.DataFrame |
import pandas as pd
import os
os.chdir('db')
from pathlib import Path
import sys
#Package to pre process
import gensim
from gensim.utils import simple_preprocess
from gensim.models import ldamodel
from gensim.test.utils import datapath
import numpy as np
from gensim.models import Word2Vec
from shorttext.utils import st... | pd.concat([test_set, test['response_round_score']], axis=1) | pandas.concat |
# The script is used to perform analysis of XRF spectra measured by
# Olympus Delta XRF (https://www.olympus-ims.com/en/xrf-xrd/delta-handheld/delta-prof/).
# The measurement is done for powder samples which are fixed on the XRF
# device using a custom 3D printed plastic holder(s). Several holders can be used in one
# ... | pd.read_csv(args.spectra_path, encoding=args.encoding, delimiter=',') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""CICID1.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1q-T0VLplhSabpHZXApgXDZsoW7aG3Hnw
"""
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.... | pd.read_csv("/content/drive/My Drive/Colab Notebooks/kshield_project/dataset/cicids2017/MachineLearningCSV/MachineLearningCVE/Tuesday-WorkingHours.pcap_ISCX.csv") | pandas.read_csv |
#!/usr/bin/env python3
# See the README.md file
import json
import logging
import sys
import os
from datetime import datetime
import numpy
from PIL import Image
import pandas as pd
import requests
from skimage import exposure
# The site file, of the format: site_tag,latitude,longitude,start_date,end_date,kmAboveBelo... | pd.read_csv(csv) | pandas.read_csv |
import pandas as pd
import mdtraj as md
__all__ = ["load_dataframe", "load_trajectory", "plumed_to_pandas"]
def is_plumed_file(filename):
"""
Check if given file is in PLUMED format.
Parameters
----------
filename : string, optional
PLUMED output file
Returns
-------
bool
... | pd.read_csv(filename, sep=" ", skipinitialspace=True, nrows=0) | pandas.read_csv |
import matplotlib.pyplot as plt
import matplotlib
import scipy.stats as stats
from statsmodels.graphics.mosaicplot import mosaic
import statsmodels.api as sm
from statsmodels.formula.api import ols
import pandas as pd
import numpy as np
import scipy
class InteractionAnalytics():
@staticmethod
d... | pd.DataFrame.from_dict(cramer_dict, orient='index') | pandas.DataFrame.from_dict |
#%%
import os
from pyteomics import mzid, mzml
import pandas as pd
import numpy as np
import glob
"""
Files are downloaded and manually randomly divided into different folders
the following code is repeated but has the same effect, it is applied to various folders to
generate pandas data frames and to store all the d... | pd.DataFrame({'file':file_location,'id':spectrum_ids,'seq':seq}) | pandas.DataFrame |
# load
import pandas as pd
# import lightgbm
data = pd.read_csv("X_train.csv", index_col=0)
data["mark"] = | pd.read_csv("y_train.csv", index_col=0) | pandas.read_csv |
import pandas as pd
import numpy as np
import re
pd.options.mode.chained_assignment = None
class Validate:
def Member(self, data, current_org, missing_output, output_ID = None, personIDs = None):
key = 'Member'
bad_data_count = 0
bad_data_locations= []
last_row = 0
for row... | pd.isna(val) | pandas.isna |
# python 2/3 compatibility
from __future__ import division, print_function
import sys
import os.path
import numpy
import pandas
import copy
import json
import jxmlease
import xml.etree.ElementTree as ET
import csv
from sbtab import SBtab
# package imports
import rba
from .data_block import DataBlock
class RBA_Simula... | pandas.isna(j) | pandas.isna |
"""
Copyright 2019 Samsung SDS
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 ... | pd.Series(arr) | pandas.Series |
import glob
from datetime import datetime, timezone
import pytz
from tzlocal import get_localzone
import pandas as pd
import streamlit as st
from google.oauth2 import service_account
from gspread_pandas import Spread, Client
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Standar... | pd.concat([master_df, tmp_df], ignore_index=True) | pandas.concat |
from __future__ import division
from datetime import datetime
import sys
if sys.version_info < (3, 3):
import mock
else:
from unittest import mock
import pandas as pd
import numpy as np
import random
from nose.tools import assert_almost_equal as aae
import bt
import bt.algos as algos
def test_algo_name():... | pd.date_range('2010-01-01', periods=3) | pandas.date_range |
import pandas as pd
from loguru import logger
import arrow
import time
import json
import requests
import tqdm
from retrying import retry
headers = {'Accept': 'application/json', 'Content-Type': 'application/json', 'Cookie': 'G_zj_gsid=08890c40500a4a8ab21e0b2b9e9e47b1-gsid-', 'User-Agent': 'Mozilla/5.0 (Macintosh; Int... | pd.DataFrame(res['data']['list']) | pandas.DataFrame |
'''
This sample shows how to set Column Format with DataFrame and from_df, to_df functions.
Make sure you've installed pandas. To install the module,
open the Script Window (Shift+Alt+3), type the following and press Enter:
pip install pandas
The following will check and install:
pip -chk pandas
'''
import originpr... | pd.to_datetime(df['Date']) | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun May 5 23:37:07 2019
@author: manuel
"""
# Exercise 12
# Implement k-means soft clustering with online update, adopting the Euclidean
# distance as dissimilarity metric. Given the dataset data3.csv, apply the
# algorithm using $k = 3$ and $\eta = 0.1... | pd.Series(tmplist) | pandas.Series |
#!/usr/bin/env python3
# Author:: <NAME> (mailto:<EMAIL>)
"""
Python Class Check Yellowstone Campground Booking API for Availability
"""
from datetime import datetime, timedelta
from json import loads
import logging
from random import choice
from typing import List, Optional
from urllib import parse
from pandas... | DataFrame(data=available_rooms) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 11 13:38:13 2020
@author: zhanghai
"""
'''
Input parameters： ticker，interval, test start date, test end date, model name
Output ： dataframe： initial deposit， gross profit，gross loss， total net profit，profit factor，
expected payoff， absolute drawdown， m... | pd.DataFrame({'position size':0,'total':self.total_value,'profit':profit},index=[cur_date]) | pandas.DataFrame |
#!/usr/bin/env python3
from logFormat import C
from util import Cred
from typing import List
import lxml.html, lxml.cssselect, os, pandas, requests
csssel = lxml.cssselect.CSSSelector
listText = lxml.etree.XPath('text()') # [Using XPath to find text](https://lxml.de/tutorial.html#using-xpath-to-find-text)
headers = ... | pandas.DataFrame({'user':users,'artistsInCommon':artists}) | pandas.DataFrame |
""" function to read data from dwd server """
from itertools import zip_longest, groupby
from pathlib import Path
from typing import List, Tuple, Optional, Union
import re
from io import BytesIO
import pandas as pd
from python_dwd.additionals.functions import retrieve_parameter_from_filename, retrieve_period_type_from... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/1/26 13:10
Desc: 申万指数-申万一级、二级和三级
http://www.swsindex.com/IdxMain.aspx
https://legulegu.com/stockdata/index-composition?industryCode=851921.SI
"""
import time
import json
import pandas as pd
from akshare.utils import demjson
import requests
from bs4 import Bea... | numeric(temp_df["市净率"], errors="coerce") | pandas.to_numeric |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import csv
import pandas as pd
# In[2]:
df = | pd.read_csv("C:\\Users\\user\\Downloads\\moreno_highschool\\out.moreno_highschool_highschool", sep=" ", header=None, skiprows=2, names=["ndidfr", "ndidto", "weight"]) | pandas.read_csv |
########
# LocusExtractor
# Created by <NAME> for the Meningitis lab in the CDC, under contract with IHRC. Inc.
# Version 0.8, 20 Mar 2015
#
# The organization of this script is horrible. Sorry. - ACR
#pylint: disable=global-statement, broad-except
script_version = 1.5
script_subversion = 7 ##Added notes about composi... | pd.read_table(outfile,names=_outfmt_head) | pandas.read_table |
from datetime import datetime
import warnings
import numpy as np
from numpy.random import randn
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, DatetimeIndex, Index, Series
import pandas._testing as tm
from pandas.core.window.common import flex_binary_moment
... | DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
'''
FILE: nav_manager.py
DESCRIPTION: Contains the various classes used by the r2rNavManagerPy programs.
BUGS:
NOTES:
AUTHOR: <NAME>
COMPANY: OceanDataTools
VERSION: 0.3
CREATED: 2021-04-15
REVISION: 2021-05-11
LICENSE INFO: This code is l... | pd.isnull(row['deltaT']) | pandas.isnull |
from sklearn.svm import SVR
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.externals import joblib
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
'''
this file is to use 10 fold cross-validation to select ... | pd.read_csv('kratosbat/Data/DataForSVR/VC_PCA.csv') | pandas.read_csv |
import pandas as pd
import variables
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
import sys
import matplotlib.ticker as mtick
import numpy as np
from matplotlib.patches import Patch
from matplotlib.lines import Line2D
from matplotlib import collections as matcoll
from textwrap impor... | pd.DataFrame(notable_divergencies) | pandas.DataFrame |
'''
Created on Jul 5, 2018
@author: cef
'''
import os, sys, copy, logging, time
#weakref
from collections import OrderedDict
from weakref import WeakValueDictionary as wdict
import pandas as pd
import numpy as np
import model.sofda.hp.basic as hp_basic
import model.sofda.hp.pd as hp_pd
#import hp.plot
import model.... | pd.isnull(self.data) | pandas.isnull |
# importing numpy, pandas, and matplotlib
import numpy as np
import pandas as pd
import matplotlib
import multiprocessing
matplotlib.use('agg')
import matplotlib.pyplot as plt
# importing sklearn
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedKFold
from sklearn.decom... | pd.DataFrame(dm.X_train, index=dm.train_indices) | pandas.DataFrame |
"""Loader of raw data into deepchem dataset after featurization.
Qest loader creates datasets of featurized molecules.
QesTS loader creates datasets of featurized reactions.
Double loader creates dataset of featurized reactants and products,
makes a prediction with Qest, and uses this to produce a dataset of
featurize... | pd.concat(dfs) | pandas.concat |
#!/usr/bin/env python3
# author : <NAME>
# date : 10.01.2019
# license : BSD-3
# ==============================================================================
import os.path
import sys
import time
import argparse
import numpy as np
import pandas as pd
import sqlite3 as sql
from collections... | pd.merge(df_sub_act, df_ph, on=['mol_name', 'conf_id'], how='inner') | pandas.merge |
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 18 13:54:04 2019
@author: Tobias
"""
import os
import pickle
import numpy as np
import datetime as dt
import pandas as pd
import pandas_datareader as web
import sys
import pdb
import gensim
import gensim.corpora as corpora
from gensim.utils import simple_preprocess
from ... | pd.DataFrame(resOut) | pandas.DataFrame |
# coding: utf-8
# ### Import
# In[5]:
import numpy as np
import pandas as pd
import xgboost
import xgboost as xgb
from xgboost.sklearn import XGBClassifier
from sklearn.metrics import *
from IPython.core.display import Image
from sklearn.datasets import make_classification
from sklearn.ensemble import ExtraTreesC... | pd.concat([pre_age_dum, pre_age_sub], axis=1) | pandas.concat |
from abc import ABC, abstractmethod
from pclima.http_util import PClimaURL
import json
import pandas as pd
import requests
import io
import xarray as xr
import numpy as np
class RequestFactory:
def get_order(self, type_of_order,token,json):
if type_of_order == "NetCDF":
return Netcdf(token, jso... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
from cabi.cabi import _get_station_dataframe
if __name__ == "__main__":
df = _get_station_dataframe()
datalist = df.iloc[0, 0]
df = | pd.DataFrame(datalist) | pandas.DataFrame |
import multiprocessing.dummy as mp
import time
from exceptions import TestException
from functools import wraps
from sys import stdout, stderr
import numpy as np
import pandas as pd
import tweepy
from sqlalchemy.exc import IntegrityError, ProgrammingError
from database_handler import DataBaseHandler
from helpers impo... | pd.read_sql(query, db_connection) | pandas.read_sql |
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 8 08:56:36 2016
@author: davidangeles
"""
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import tissue_enrichment_analysis as tea
import os
import mpl_toolkits.mplot3d
import pyrnaseq_graphics as ... | pd.read_csv('../input/neuropeptides.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 20 12:46:41 2018
@author: MichaelEK
"""
import numpy as np
from os import path
import pandas as pd
from pdsql.mssql import rd_sql
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import datetime
import matplotlib.ticker... | pd.concat([restr_all1, restr1]) | pandas.concat |
import os
from pyspark.sql import SparkSession
import pyspark
import multiprocessing
import pretty_midi
from legacy.transcription import encode
import pandas as pd
# Functions for transcripting dataset stored in midi files using spark
# Load midi from 'processed_dir'
# Save resulted transcriptions to 'processed_dir'
... | pd.Series([], dtype='str') | pandas.Series |
from reframed import CBModel, Compartment, Metabolite, CBReaction, save_cbmodel
from reframed.io.sbml import parse_gpr_rule
from ..reconstruction.utils import to_rdf_annotation
import pandas as pd
import requests
import sys
UNIVERSE_URL = 'http://bigg.ucsd.edu/static/namespace/universal_model.json'
COMPARTMENTS_URL = ... | pd.read_csv(cpd_annotation, sep="\t", index_col=0) | pandas.read_csv |
import numpy as np
import pandas as pd
from rdt import HyperTransformer
from rdt.transformers import OneHotEncodingTransformer
def get_input_data_with_nan():
data = pd.DataFrame({
'integer': [1, 2, 1, 3, 1],
'float': [0.1, 0.2, 0.1, np.nan, 0.1],
'categorical': ['a', 'b', np.nan, 'b', 'a'... | pd.testing.assert_frame_equal(df, rever) | pandas.testing.assert_frame_equal |
"""
Functions to add a model version to the ModMon database.
Run this script once, the first time an analyst submits a model file (including for a
new version of a model)
"""
import argparse
import json
import os
import sys
import pandas as pd
from ..db.connect import get_session
from ..db.utils import get_unique_id... | pd.read_csv(training_metrics_csv) | pandas.read_csv |
"""SQL io tests
The SQL tests are broken down in different classes:
- `PandasSQLTest`: base class with common methods for all test classes
- Tests for the public API (only tests with sqlite3)
- `_TestSQLApi` base class
- `TestSQLApi`: test the public API with sqlalchemy engine
- `TestSQLiteFallbackApi`: t... | DataFrame({"col1": [1, 2], "col2": [0.1, 0.2], "col3": ["a", "n"]}) | pandas.DataFrame |
#
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
#
import numpy as np
import pandas as pd
from mlos.Logger import create_logger
from mlos.Optimizers.ExperimentDesigner.UtilityFunctions.UtilityFunction import UtilityFunction
from mlos.Optimizers.ParetoFrontier import ParetoFrontier
fro... | pd.to_numeric(arg=batched_poi_df['utility'], errors='raise') | pandas.to_numeric |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from django.db.models import Q
from django_pandas.io import read_frame
from shuup.core.models import OrderLine, OrderStatus
from shuup_recommender.models import ProductView
from ._base import BaseRecommender
from ._consts import EVERYTHING
def distance(... | pd.merge(sold_items_rank, viewed_products_rank, how="outer", left_index=True, right_index=True) | pandas.merge |
import math
import pandas as pd
from scipy import stats
import streamlit as st
st.title("Udacity A/B Testing Final Project")
"""
I recently completed Google and Udacity's introduction to A/B testing, which was pretty interesting! This is my take on the final project.
The problem definition below comes almost verbati... | pd.read_csv("control.csv") | pandas.read_csv |
# -*- coding:utf-8 -*-
import pandas as pd
import numpy as np
import warnings
def test(x):
print('类型：\n{}\n'.format(type(x)))
if isinstance(x, pd.Series):
print('竖标：\n{}\n'.format(x.index))
else:
print('竖标：\n{}\n'.format(x.index))
print('横标：\n{}\n'.format(x.columns))
... | pd.concat([c, product_trans], axis=1) | pandas.concat |
# Recurrent Neural Network
# Part 1 - Data Preprocessing
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the training set
dataset_train = pd.read_csv('Google_Stock_Price_Train.csv')
training_set = dataset_train.iloc[:,1:2].values
# Feature Sc... | pd.read_csv('Google_Stock_Price_Test.csv') | pandas.read_csv |
import copy
import time
from functools import partial
import matplotlib
import pint
import os
from pint.quantity import _Quantity
from eam_core.YamlLoader import YamlLoader
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from eam_core import Q_, FormulaProcess, collect_process_variables, SimulationControl
imp... | pd.DataFrame.from_dict(raw) | pandas.DataFrame.from_dict |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 2 12:49:47 2021
@author: madeline
"""
import argparse
import pandas as pd
import os
def parse_args():
parser = argparse.ArgumentParser(
description='Creates two dataframes from a surveillance report TSV')
parser.add_argumen... | pd.read_csv(args.tsv, sep='\t', header=0) | pandas.read_csv |
import logging
import os
import numpy as np
from torch_geometric.graphgym.config import cfg
from torch_geometric.graphgym.utils.io import (dict_list_to_json,
dict_list_to_tb, dict_to_json,
json_to_dict_list,
... | pd.DataFrame(results[key]) | pandas.DataFrame |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import timedelta
from numpy import nan
import numpy as np
import pandas as pd
from pandas import (Series, isnull, date_range,
MultiIndex, Index)
from pandas.tseries.index import Timestamp
from pandas.compat import range
from pandas.u... | Timestamp('20130104') | pandas.tseries.index.Timestamp |
import kaggle
import argparse
import pandas as pd
import os
import json
import requests
import traceback
from requests.exceptions import ConnectionError, ChunkedEncodingError
import errno
from multiprocessing import Pool, cpu_count, Queue
from bs4 import BeautifulSoup
from urllib.parse import urlparse, parse_qs
... | pd.DataFrame(version_metadata) | pandas.DataFrame |
import math
import warnings
from typing import List, Union
import matplotlib
import numpy as np
import pandas as pd
matplotlib.rcParams["text.usetex"] = True
from pykelihood import kernels
from pykelihood.distributions import Exponential, MixtureExponentialModel
from pykelihood.stats_utils import Profiler
try:
... | pd.Series(local_count_hp, index=h_range) | pandas.Series |
import matplotlib.image as mpimg
import matplotlib.style as style
import matplotlib.pyplot as plt
from matplotlib import rcParams
from simtk.openmm.app import *
from simtk.openmm import *
from simtk.unit import *
from sys import stdout
import seaborn as sns
from math import exp
import pandas as pd
import mdtraj as md
i... | pd.DataFrame(index_indces_c1, columns=["index"]) | pandas.DataFrame |
import sys
import numpy as np
import pandas as pd
from scipy.stats import mannwhitneyu, norm, rankdata, tiecorrect
from statsmodels.stats.multitest import multipletests
from tqdm import tqdm_notebook as tqdm
from . import config
from .utils import precheck_align
try:
import cupy as cp
from cupyx.scipy.specia... | pd.DataFrame(pvals, index=a_names, columns=b_names) | pandas.DataFrame |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import timedelta
from numpy import nan
import numpy as np
import pandas as pd
from pandas import (Series, isnull, date_range,
MultiIndex, Index)
from pandas.tseries.index import Timestamp
from pandas.compat import range
from pandas.u... | assert_series_equal(result, expected) | pandas.util.testing.assert_series_equal |
import datetime
from dateutil.relativedelta import *
from fuzzywuzzy import fuzz
import argparse
import glob
import numpy as np
import pandas as pd
from scipy.stats import ttest_1samp
import sys
import xarray as xr
from paths_bra import *
sys.path.append('./..')
from refuelplot import *
setup()
from utils import *
... | pd.read_csv(bra_path+ '/labels_turbine_data_gwa' + GWA + '.csv',index_col=0) | pandas.read_csv |
import pandas as pd
import numpy as np
def btk_data_decoy_old():
df = pd.read_csv('btk_active_decoy/BTK_2810_old.csv')
df_decoy = pd.read_csv('btk_active_decoy/btk_finddecoy.csv')
df_decoy = pd.DataFrame(df_decoy['smile'])
df_decoy['label'] = 0
df_active = df[df['target2']<300]
df_active['tar... | pd.read_csv('btk_active_decoy/btk_our_decoy.csv') | pandas.read_csv |
from matplotlib import pyplot as plt
from matplotlib.ticker import FormatStrFormatter
import math
import numpy as np
import pandas as pd
from matplotlib import colors
from matplotlib.pyplot import cm
import sys
try:
# case = int(sys.argv[1])
infile = str(sys.argv[1])
except IndexError as err:
print("Not e... | pd.concat(data_frames, join='outer', axis=1) | pandas.concat |
'''
CIS 419/519 project: Using decision tree ensembles to infer the pathological
cause of age-related neurodegenerative changes based on clinical assessment
nadfahors: <NAME>, <NAME>, & <NAME>
This file contains code for preparing NACC data for analysis, including:
* synthesis of pathology data to create pat... | pd.DataFrame(Xbool) | pandas.DataFrame |
import hvplot.pandas
import pandas as pd
import panel as pn
def _get_chart_data() -> pd.DataFrame:
"""## Chart Data
Returns:
pd.DataFrame -- A DataFrame with dummy data and columns=["Day", "Orders"]
"""
chart_data = {
"Day": ["Sunday", "Monday", "Tuesday", "Wednesday", "... | pd.DataFrame(chart_data) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 11 20:21:34 2020
@author: nickcostanzino
"""
def NN_structure(layers, perceptrons):
A = list()
for n in range(layers):
A.append(perceptrons)
return tuple(A)
def NN_structures(layers, perceptrons):
A = list()
for i ... | pd.DataFrame(S.X) | pandas.DataFrame |
'''
Created on Sep 2, 2016
@author: Gully
'''
from __future__ import print_function, division
import argparse
import argparse_config
import codecs
import os
import numpy as np
import pandas as pd
import warnings
from sets import Set
import re
from sets import Set
import re
from bokeh.plotting import figure, show,... | pd.DataFrame.from_records(gantt_rows2, columns=['fig_ref','clause_id']) | pandas.DataFrame.from_records |
import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os
from pandas.core.frame import DataFrame
from torch.utils.data import Dataset, DataLoader
import torch
import pickle
import datetime
class data_loader(Dataset):
def __init__(self, df_feature, df_label, df_label_reg, t=No... | pd.to_datetime(start_date) | pandas.to_datetime |
#!/usr/bin/env python
"""Calculate regionprops of segments.
"""
import sys
import argparse
# conda install cython
# conda install pytest
# conda install pandas
# pip install ~/workspace/scikit-image/ # scikit-image==0.16.dev0
import os
import re
import glob
import pickle
import numpy as np
import pandas as pd
fro... | pd.DataFrame(rpt) | pandas.DataFrame |
#!/usr/bin/env python3
#SBATCH --partition=mcs.default.q
#SBATCH --output=openme.out
# coding: utf-8
# In[1]:
import pandas as pd
import datetime as dt
import numpy as np
import time
from sklearn.feature_extraction.text import CountVectorizer
word_vectorizer = CountVectorizer(ngram_range=(1,2), analyzer='word')
im... | pd.read_csv('./rawdata/BPI2016_Questions.csv', sep=';', encoding='latin-1', keep_default_na=False) | pandas.read_csv |
from flask import Flask,render_template,request,send_file
from flask_sqlalchemy import SQLAlchemy
import os
import pandas as pd
from openpyxl import load_workbook
import sqlalchemy as db
######### function#######################
def transform(df):
#count the number of columns in the data frame
col=len(df.colu... | pd.ExcelFile(file) | pandas.ExcelFile |
#! /usr/bin/env python3
import os
import sys
import json
import numpy as np
import pandas as pd
from glob import glob
from enum import Enum
from dateutil import tz
from datetime import datetime, timedelta
map_station = {
1:"Castello, <NAME>", 2:"Hotel Carlton", 3:"Via del Podestà", 4:"Corso di P.Reno / Via Ragno" ,... | pd.read_csv(filein, sep=';', parse_dates=['time'], index_col='time') | pandas.read_csv |
# -*- coding: utf-8 -*-
from collections import OrderedDict
from datetime import date, datetime, timedelta
import numpy as np
import pytest
from pandas.compat import product, range
import pandas as pd
from pandas import (
Categorical, DataFrame, Grouper, Index, MultiIndex, Series, concat,
date_range)
from p... | tm.assert_frame_equal(test_case, norm_sum) | pandas.util.testing.assert_frame_equal |
import os
import joblib
import numpy as np
import pandas as pd
from joblib import Parallel
from joblib import delayed
from Fuzzy_clustering.version2.common_utils.logging import create_logger
from Fuzzy_clustering.version2.dataset_manager.common_utils import check_empty_nwp
from Fuzzy_clustering.version2.dataset_manag... | pd.DateOffset(hours=1) | pandas.DateOffset |
import logging
import re
import tempfile
from os.path import exists, join
from unittest.mock import MagicMock, patch
import netCDF4 as nc
import numpy as np
import numpy.testing as npt
import packaging.version
import pandas as pd
import pytest
import xarray as xr
from scmdata import ScmRun
from scmdata.netcdf import ... | pd.concat(big_df) | pandas.concat |
import pandas as pd
import os
def read_file(filename:str,return_type:str='dataframe'):
from .file_utils import FileHandler
file:FileHandler = FileHandler(filename)
return file.get_data(return_type=return_type)
# def calculate_top_marginal_roi(data_frame,colname:str,top_n:int=10):
# lookup_cols = [... | pd.concat([actor_1,actor_2,actor_3]) | pandas.concat |
# coding: utf-8
from functools import wraps
from fastcache import clru_cache
from collections import Iterable
from datetime import datetime as pdDateTime
from FactorLib.data_source.trade_calendar import tc
from xlrd.xldate import xldate_as_datetime
import pandas as pd
import numpy as np
# 日期字符串(20120202)转成... | pd.DatetimeIndex([dates]) | pandas.DatetimeIndex |
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# import seaborn as sns
from aif360.datasets import AdultDataset
from aif360.datasets import GermanDataset
from aif360.datasets import MEPSDataset19
from torch.utils.data import Dataset
from sklearn.model_selection import train_... | pd.read_excel("Results/ger_sex.xlsx", index_col=0) | pandas.read_excel |
# https://projects.datacamp.com/projects/441
# A Visual History of Nobel Prize Winners
## Task 1
# Loading in required libraries
import pandas as pd
import seaborn as sns
import numpy as np
import os as os
# Reading in the Nobel Prize data
fullnobelearly = os.path.abspath(os.path.join('dc','441_nobel_prize_winners',... | pd.read_csv(fullnobelearly) | pandas.read_csv |
# -*- coding: UTF-8 -*-
from __future__ import division
import re
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# 思路分析:
# # 问题: 这里是要求各个国家的GDP和Energy Supply之类的情况.
# # 数据源:
# ## Energy Indicators.xls: 国家及地区名: Energy Supply和Energy Supply每人;
# ## world_bank.csv: 国家及地区名: 历年GDP;
# ## scimagojr-3.... | pd.merge(energy, GDP, how='outer', left_index=True, right_index=True) | pandas.merge |
import pandas as pd
import numpy as np
from src.configs import *
from src.features.transform import categorical_to_ordinal
import collections
class HousePriceData:
'''
Load House Price data for Kaggle competition
'''
def __init__(self, train_path, test_path):
self.trainset = pd.read_csv(train_... | pd.concat([self.trainset[ORIGINAL_FEATURE_COLS], self.testset[ORIGINAL_FEATURE_COLS]], axis=0) | pandas.concat |
"""
Tasks
-------
Search and transform jsonable structures, specifically to make it 'easy' to make tabular/csv output for other consumers.
Example
~~~~~~~~~~~~~
*give me a list of all the fields called 'id' in this stupid, gnarly
thing*
>>> Q('id',gnarly_data)
['id1','id2','id3']
Observations:
--... | u('value') | pandas.compat.u |
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/... | pd.to_datetime(df[constants.DATE]) | pandas.to_datetime |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pandas.core.groupby
import pandas as pd
from pandas.core.dtypes.common import is_list_like
import ray
from .utils import _map_partitions
from .utils import _inherit_docstrings
@_inherit_docstrings(pan... | pd.DataFrame(df) | pandas.DataFrame |
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
print("Python version: " + sys.version)
print("Numpy version: " +... | pd.Series([[0.34], [0.78, 11.34, 3.54, 1.54], [2.34, 1.384]], dtype='object') | pandas.Series |
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