prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 18 08:32:19 2021
revi take: plot time series of deal amount for SEI/P2015 clusters (5 or 10) on settelament level
and then on the right a map with corohpleths with mean/median value
for this, i need to prepare the muni shapefile with RC in it.
@autho... | pd.concat(series, axis=1) | pandas.concat |
## for data
import pandas as pd
import numpy as np
import requests
import json
import os
from datetime import datetime, date
from dotenv import load_dotenv
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
## for plotting
import matplotlib.pyplot as plt
import mat... | pd.DataFrame(data=preds, index=ts_test.index, columns=["forecast"]) | pandas.DataFrame |
"""
This module contains methods for generating H2H data for games
"""
import pandas as pd
from scrapenhl2.manipulate import manipulate as manip, add_onice_players as onice
from scrapenhl2.scrape import general_helpers as helpers, parse_toi, parse_pbp, team_info, teams
def get_game_combo_toi(season, game, player_n=2... | pd.concat([homedf, roaddf]) | pandas.concat |
"""Contains functions for preprocessing data
Classes
-------
Person
Functions
----------
recurcive_append
create_pedigree
add_control
prepare_data
"""
import logging
import pandas as pd
import numpy as np
from pysnptools.snpreader import Bed
from bgen_reader import open_bgen, read_bgen
from config... | pd.read_csv(unphased_address+".bim", delim_whitespace=True, header=None) | pandas.read_csv |
'''
OptiSS tool for optimizing spatial joining of big social media data
Arcpy 2.6 and Python 3
Local app that optimize spatial join of social media posts with regions layer.
Check read me file of repository for more details of how it works.
Thanks to the methodological development of Vuokko Heikinheimo in SOME projec... | pd.DataFrame(data[[time_col, long_col, lat_col, userid_col]]) | 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([0.18, 0.5, 1.25]) | pandas.Series |
"""
SCRIPT TO CONVERT WRITE CHARMM RTF AND PRM FILES
FROM BOSS ZMATRIX
Created on Mon Feb 15 15:40:05 2016
@author: <NAME> <EMAIL>
@author: <NAME>
Usage: python OPM_Routines.py -z phenol.z -r PHN
REQUIREMENTS:
BOSS (need to set BOSSdir in bashrc and cshrc)
Preferably Anaconda python with following modules
pandas
ar... | pd.DataFrame(bdat) | pandas.DataFrame |
from pandas import DataFrame
from pandas import Series
from pandas import concat
from pandas import read_csv
from pandas import datetime
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense
from keras.layers impo... | DataFrame() | pandas.DataFrame |
from flask import Flask, render_template, request, redirect, url_for, session
import pandas as pd
import pymysql
import os
import io
#from werkzeug.utils import secure_filename
from pulp import *
import numpy as np
import pymysql
import pymysql.cursors
from pandas.io import sql
#from sqlalchemy import create... | pd.concat([datas['Month'],datas['Factory'],datas['Demand_Forecast']],axis=1) | pandas.concat |
import copy
import re
from textwrap import dedent
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
MultiIndex,
)
import pandas._testing as tm
jinja2 = pytest.importorskip("jinja2")
from pandas.io.formats.style import ( # isort:skip
Styler,
)
from pandas.io.formats.sty... | Styler(mi_df, uuid_len=0) | pandas.io.formats.style.Styler |
import pathlib
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy.optimize import linear_sum_assignment
from dicodile.config import DATA_HOME
from dicodile.utils.viz import display_dictionaries
OUTPUT_DIR = pathlib.Path('benchmarks_results')
DATA_DIR = DATA_HO... | pd.read_pickle(OUTPUT_DIR / result_file) | pandas.read_pickle |
from os.path import join, exists, dirname, basename
from os import makedirs
import sys
import pandas as pd
from glob import glob
import seaborn as sns
import numpy as np
from scipy import stats
import xlsxwriter
import matplotlib.pyplot as plt
from scripts.parse_samplesheet import get_min_coverage, get_role, add_aliass... | pd.read_csv(fp_yielddata, sep="\t") | pandas.read_csv |
"""Unit tests for functions in src/util.py"""
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal, assert_series_equal
from src.util import (
StanInput,
make_columns_lower_case,
one_encode,
stanify_dict,
)
@pytest.mark.parametrize(
"s_in,expected",... | pd.Series([1, 2, 3]) | pandas.Series |
import pandas as pd
import numpy as np
df_1 = pd.read_csv('./data_01.txt',sep="|", header=0, encoding='latin-1')
df_2 = pd.read_csv('./data_02.txt',sep="|", header=0, encoding='latin-1')
df_3 = pd.read_csv('./data_03.txt',sep="|", header=0, encoding='latin-1')
df_4 = | pd.read_csv('./data_04.txt',sep="|", header=0, encoding='latin-1') | pandas.read_csv |
# pylint: disable=E1101
from datetime import datetime, timedelta
from pandas.compat import range, lrange, zip, product
import numpy as np
from pandas import Series, TimeSeries, DataFrame, Panel, isnull, notnull, Timestamp
from pandas.tseries.index import date_range
from pandas.tseries.offsets import Minute, BDay
fr... | tm.assert_frame_equal(result, exp) | pandas.util.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime
import itertools
import numpy as np
import pytest
from pandas.compat import u
import pandas as pd
from pandas import (
DataFrame, Index, MultiIndex, Period, Series, Timedelta, date_range)
from pandas.tests.frame.common ... | assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
#! -*- coding: utf-8 -*-
#%%
from __future__ import print_function, division
from keras import backend as K
from keras.models import Model
from keras.engine.topology import Layer
from keras.layers import Conv1D, Dense, Dropout, Reshape, Flatten, add, MaxPooling1D, Input, UpSampling1D, BatchNormalization, GaussianNoise,... | pd.Series(valid_predictions) | pandas.Series |
import sqlite3
import time
import public_function as pb_fnc
import pandas as pd
import numpy as np
class InfoCheck:
bu_name = ""
db_path = "../data/_DB/"
def __init__(self, bu):
self.__class__.bu_name = bu
# get all master data of single code
def get_single_code_all_master... | pd.DataFrame(index=month_list, data=None) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 13 21:46:26 2021
@author: hk_nien @ Twitter
"""
from pathlib import Path
from time import time
import re
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from tools import set_xaxis_dateformat, set_yaxis_log_minor_labels
import... | pd.Timedelta(0.55, 'd') | pandas.Timedelta |
import os
import glob
import pandas as pd
import streamlit as st
@st.cache
def get_local_feather_files():
list_of_files = glob.glob('*.feather')
files_with_size = [(file_path, os.stat(file_path).st_size) for file_path in list_of_files]
df = pd.DataFrame(files_with_size)
df.columns = ['File Name', 'Fil... | pd.read_feather('crashes.feather') | pandas.read_feather |
'''
dedup.py - Deduplicate reads that are coded with a UMI
=========================================================
:Author: <NAME>, <NAME>
:Release: $Id$
:Date: |today|
:Tags: Python UMI
Purpose
-------
The purpose of this command is to deduplicate BAM files based
on the first mapping co-ordinate and the UMI attac... | pd.DataFrame(stats_post_df_dict) | pandas.DataFrame |
import os
import pandas as pd
import math
import datetime
from tqdm import tqdm
from pathlib import Path
from seg import seglosses
from seg.config import config
from seg.data import DataLoader
from seg.architect.Unet import unet
from seg.utils import time_to_timestr
import tensorflow as tf
from tensorflow.keras.opti... | pd.DataFrame(history.history) | 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([[]], dtype='bool') | pandas.Series |
#!/usr/bin/env python3
import os
import subprocess
import tempfile
from pathlib import Path
import pandas as pd
path_to_file = os.path.realpath(__file__)
repo_root = Path(path_to_file).parent.parent
INPUT_ZIP = repo_root / "downloaded-data" / "fine-grained-refactorings.zip"
SAVE_TO = repo_root / "data" / "fine-grai... | pd.concat(dfs, axis=0, ignore_index=True) | pandas.concat |
from src.sampling import outputs_given_z_c_y
from scipy.spatial import distance
import pandas as pd
import torch
import numpy as np
from torch import nn
def outputs_counterfact_given_y_c(model, y_c_list, center_z=False):
'''
Samples from a list of target and condition given a model
Parameters:
Mod... | pd.DataFrame(ae) | pandas.DataFrame |
import numpy as np
import pandas as pd
from math import ceil
from itertools import combinations, product
from collections import Counter
from scipy.stats import chi2_contingency, pointbiserialr
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import seaborn as sns
from wordcloud import ... | pd.DataFrame(pb_table, columns=num_cols, index=cat_cols) | pandas.DataFrame |
"""Remotely control your Binance account via their API : https://binance-docs.github.io/apidocs/spot/en"""
import re
import json
import hmac
import hashlib
import time
import requests
import base64
import sys
import math
import pandas as pd
import numpy as np
from numpy import floor
from datetime import datetime, time... | pd.DataFrame() | pandas.DataFrame |
import math
from typing import cast
import pandas as pd
import pytest
from ete3 import Tree, ClusterTree
from genomics_data_index.api.query.GenomicsDataIndex import GenomicsDataIndex
from genomics_data_index.api.query.SamplesQuery import SamplesQuery
from genomics_data_index.api.query.impl.DataFrameSamplesQuery impor... | pd.read_csv(snippy_all_dataframes['SampleA'], sep='\t') | pandas.read_csv |
#!/usr/bin/env python3.7
# Copyright [2020] EMBL-European Bioinformatics Institute
#
# 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... | pd.notnull(df) | pandas.notnull |
#
# Copyright 2020 Capital One Services, LLC
#
# 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... | assert_series_equal(expect_out, actual_out, check_names=False) | pandas.util.testing.assert_series_equal |
import pandas as pd
from pandas import Period, offsets
from pandas.util import testing as tm
from pandas.tseries.frequencies import _period_code_map
class TestFreqConversion(tm.TestCase):
"Test frequency conversion of date objects"
def test_asfreq_corner(self):
val = Period(freq='A', year=2007)
... | Period('2007', freq='3A') | pandas.Period |
#This code will take the data scrapped from both Reddit API and Coingecko API,
#transform and process it in order to get a unified dataframe which will be finally
#processed by the LSTM neural network in order to make the predictions.
#Import libraries
import pandas as pd
import transformers
from transfor... | pd.read_csv(
"C:/Users/rober/Desktop/MIS COSAS/DSTI MASTER/SUBJECTS/PYTHON LABS/Crypto Trading Bot/prices.csv") | pandas.read_csv |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for datetime64 and datetime64tz dtypes
from datetime import (
datetime,
time,
timedelta,
)
from itertools import (
product,
starmap,
)
import operator
import warnings
import numpy as np
impo... | tm.assert_index_equal(result, expected) | pandas._testing.assert_index_equal |
""" Contains unit tests for the Metafeatures class. """
import inspect
import json
import jsonschema
import os
import random
import time
import unittest
import pandas as pd
import numpy as np
from metalearn import Metafeatures, METAFEATURES_JSON_SCHEMA_PATH
import metalearn.metafeatures.constants as consts
from tests... | pd.Series([0,1,0], name="target") | pandas.Series |
import pandas as pd
from sqlalchemy import create_engine, text
from datetime import date, datetime, timedelta
import concurrent.futures
import requests as rq
import time
import config
import traceback
pd.set_option('display.max_columns', None)
#pd.set_option('display.max_rows', None)
# Key
key = config.polyg... | pd.read_sql_query('select ticker from companies where active = true', con=engine) | pandas.read_sql_query |
from textwrap import dedent
import numpy as np
import pytest
from pandas import (
DataFrame,
MultiIndex,
option_context,
)
pytest.importorskip("jinja2")
from pandas.io.formats.style import Styler
from pandas.io.formats.style_render import (
_parse_latex_cell_styles,
_parse_latex_css_conversion,
... | option_context(f"styler.latex.{option}", False) | pandas.option_context |
import pandas as pd
import fasttext
import time
import numpy as np
import spacy
import sys
fmodel = fasttext.load_model('/mnt/dhr/CreateChallenge_ICC_0821/lid.176.bin')
def delist_lang(lst):
lang_lst=[]
for i,lang in enumerate(lst):
if not lang:
lang_lst.append(None)
... | pd.read_json(fname, lines=True, chunksize=CHUNKSIZE) | pandas.read_json |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
plt.rcParams["svg.hashsalt"]=0
def mkdirs(pre_path,parm_name):
try:
os.makedirs("../figures/"+pre_path+parm_name)
except:
pass
try:
os.makedirs("../analysed_data/"+pre_path+parm_na... | pd.read_csv('../raw_output/'+pre_path+parm_name+'/'+post_path+string+'.csv') | pandas.read_csv |
from collections import OrderedDict
import pandas as pd
pd.set_option('display.expand_frame_repr', False)
import numpy as np
# ******************************************
# helpers
# ******************************************
def _set_values_series(dfs):
return set(dfs[~ | pd.isnull(dfs) | pandas.isnull |
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.keys import Keys
import requests
import time
from datetime import datetime
import pandas as pd
from urllib import parse
from config import ENV_VARIABLE
from os.path import getsize
fold_path = ... | pd.DataFrame() | pandas.DataFrame |
import types
from functools import wraps
import numpy as np
import datetime
import collections
from pandas.compat import(
zip, builtins, range, long, lzip,
OrderedDict, callable
)
from pandas import compat
from pandas.core.base import PandasObject
from pandas.core.categorical import Categorical
from pandas.co... | Series(values, index=key_index) | pandas.core.series.Series |
#/*##########################################################################
# Copyright (C) 2020-2021 The University of Lorraine - France
#
# This file is part of the PyRecon toolkit developed at the GeoRessources
# Laboratory of the University of Lorraine, France.
#
# Permission is hereby granted, free of charge, to... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
import re
import warnings
from datetime import timedelta
from itertools import product
import pytest
import numpy as np
import pandas as pd
from pandas import (CategoricalIndex, DataFrame, Index, MultiIndex,
compat, date_range, period_range)
from pandas.compat import PY... | u('out') | pandas.compat.u |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
from numpy import nan
import numpy as np
import pandas as pd
from pandas.types.common import is_integer, is_scalar
from pandas import Index, Series, DataFrame, isnull, date_range
from pandas.core.index import MultiIndex
from pa... | Series([0, 0], index=['A', 'C'], name=4) | pandas.Series |
"""
Function and classes used to identify barcodes
"""
from typing import *
import pandas as pd
import numpy as np
import pickle
import logging
from sklearn.neighbors import NearestNeighbors
# from pynndescent import NNDescent
from pathlib import Path
from itertools import groupby
from pysmFISH.logger_utils import sel... | pd.concat([self.counts_df,self.barcoded_spec],axis=1) | pandas.concat |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
##-------- [PPC] Jobshop Scheduling ---------
# * Author: <NAME>
# * Date: Apr 30th, 2020
# * Description:
# Using the event-driven scheuling method
# to solve the JSS prob. Here is a sample
# code with the style of OOP. Feel free to
# modify it a... | pd.DataFrame(columns=["event_type", "time"]) | pandas.DataFrame |
from rdkit import Chem
import numpy as np
import pandas as pd
def mol2bit(MOLS):
BIT = []
FP = []
for i, mol in enumerate(MOLS):
if mol is not None:
bit = {}
fp = Chem.RDKFingerprint(mol, bitInfo=bit)
BIT.append(bit)
FP.append(fp)
else:
... | pd.read_csv(f"{path}/SMILES.csv") | pandas.read_csv |
import logging
import time
import json
import requests
import pytz
from datetime import datetime, timedelta
import tushare as ts
import pandas as pd
from decimal import Decimal
from firestone_engine.Utils import Utils
from bson.objectid import ObjectId
class ConceptPick(object):
_logger = loggin... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
import covasim as cv # Version used in our study is 3.07
import random
from causal_testing.specification.causal_dag import CausalDAG
from causal_testing.specification.scenario import Scenario
from causal_testing.specification.variable import Input, Output
from causal_testing.spec... | pd.DataFrame(results_dict) | pandas.DataFrame |
"""Remove images in blacklist from all other datasets"""
import argparse
from pathlib import Path
from typing import *
import pandas as pd
def parse_arguments():
parser = argparse.ArgumentParser()
parser.add_argument('dataset_dir', type=str, default='data/datasets/')
parser.add_argument('blacklist', type... | pd.DataFrame({'image_name': image_names}) | pandas.DataFrame |
import cv2
from pygame import mixer
import pandas as pd
from config import GameConfig
import time
config = GameConfig()
def play_music(music_file, time=0.0): # music 함수
mixer.init()
mixer.music.load(music_file)
mixer.music.play(1, time)
# clock = pygame.time.Clock()
# clock.tick(10)
def play_s... | pd.read_excel('sunset_glow.xlsx', sheet_name='sunset') | pandas.read_excel |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2020/12/15 15:18
Desc: 东方财富网-数据中心-特色数据-一致行动人
http://data.eastmoney.com/yzxdr/
"""
import demjson
import pandas as pd
import requests
def stock_em_yzxdr(date: str = "20200930") -> pd.DataFrame:
"""
东方财富网-数据中心-特色数据-一致行动人
http://data.eastmoney.com/yzxdr/... | pd.DataFrame(data_json["result"]["data"]) | pandas.DataFrame |
"""The search module of elsapy.
Additional resources:
* https://github.com/ElsevierDev/elsapy
* https://dev.elsevier.com
* https://api.elsevier.com"""
import xmltodict
from . import log_util
from urllib.parse import quote_plus as url_encode
import pandas as pd, json
from .utils import recast_... | pd.DataFrame(self._results) | pandas.DataFrame |
import unittest
import utils
import datetime
import pandas as pd
import numpy as np
from pandas.testing import *
#sample datas and its expected outputs
data1={'meta': {'currency': 'USD', 'symbol': 'TSM', 'exchangeName': 'NYQ', 'instrumentType': 'EQUITY', 'firstTradeDate': 876403800, 'regularMarketTime': 1616529601, 'gm... | pd.to_datetime(output.index, unit="s") | pandas.to_datetime |
# -*- coding: utf-8 -*-
import inspect
import os # noqa: F401
import unittest
import time
import pandas as pd
from configparser import ConfigParser
from GenericsAPI.Utils.NetworkUtil import NetworkUtil
from GenericsAPI.GenericsAPIImpl import GenericsAPI
from GenericsAPI.GenericsAPIServer import MethodContext
from Gen... | pd.DataFrame(data=d) | pandas.DataFrame |
"""
A warehouse for constant values required to initilize the PUDL Database.
This constants module stores and organizes a bunch of constant values which are
used throughout PUDL to populate static lists within the data packages or for
data cleaning purposes.
"""
import pandas as pd
import sqlalchemy as sa
##########... | pd.StringDtype() | pandas.StringDtype |
import re
import pandas as pd
import numpy as np
class Resampler(object):
"""Resamples time-series data from one frequency to another frequency.
"""
min_in_freqs = {
'MIN': 1,
'MINUTE': 1,
'DAILY': 1440,
'D': 1440,
'HOURLY': 60,
'HOUR': 60,
'H': 60,... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pytest
from pandas import (
DataFrame,
MultiIndex,
Series,
concat,
date_range,
)
import pandas._testing as tm
from pandas.api.indexers import (
BaseIndexer,
FixedForwardWindowIndexer,
)
from pandas.core.window.indexers import (
ExpandingIndexer,... | FixedForwardWindowIndexer(window_size=5) | pandas.api.indexers.FixedForwardWindowIndexer |
# import libraries
import streamlit as st
import pandas as pd
import plotly.express as px
import os
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
# set title for the dashboard
st.title('Sentiment Analysis of Tweets about US Airlines')
# set title for the dashboard sidebar
st.sidebar.title... | pd.read_csv(DATA_URL) | pandas.read_csv |
import argparse
import pandas as pd
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--annotations_file", type=str, required=True,
help="CSV file of annotations.")
parser.add_argument("--mrsty_file", type=str, required=True,
help="Pa... | pd.read_csv(annotations_file) | pandas.read_csv |
import numpy as np
from datetime import timedelta
from distutils.version import LooseVersion
import pandas as pd
import pandas.util.testing as tm
from pandas import to_timedelta
from pandas.util.testing import assert_series_equal, assert_frame_equal
from pandas import (Series, Timedelta, DataFrame, Timestamp, Timedelt... | pd.timedelta_range('1 day', '31 day', freq='D', name='idx') | pandas.timedelta_range |
"""Create csv files with emotions in the first column and a column for every
expanded body part.
Usage: python generate_emotion2bodyparts.py <corpus metadata> <dir
with input texts> <output file (.csv)>
In addition to the output.csv, also files for each time period are written.
"""
import os
import argparse
from coll... | pd.DataFrame(columns=heem_body_part_labels, index=heem_emotion_labels) | pandas.DataFrame |
"""
MLTrace: A machine learning progress tracker
====================================================
This module provides some basic functionality to track the process of machine learning model development.
It sets up a SQLite db-file and stores selected models, graphs, and data (for convenience) and recovers them
as... | read_sql("SELECT * FROM weights", self.conn) | pandas.read_sql |
import pandas as pd
import os
import numpy as np
import gc
import copy
import datetime
import warnings
from tqdm import tqdm
from scipy import sparse
from numpy import array
from scipy.sparse import csr_matrix
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decompos... | pd.pivot_table(userSub, values='uId', index=['appId'],columns=['age_group'],aggfunc='count', fill_value=0) | pandas.pivot_table |
from __future__ import print_function
import os
import pandas as pd
import xgboost as xgb
import time
import shutil
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
import numpy as np
from sklearn.utils import shuffle
from sklearn import metrics
def archive_results(filename,resul... | pd.read_csv(train_file,low_memory=False) | pandas.read_csv |
import os
import sys
import pickle
import pandas as pd
import numpy as np
import sys
from sklearn.feature_selection import chi2, SelectKBest, f_regression
from sklearn.decomposition import PCA, TruncatedSVD
from sklearn.manifold import Isomap, LocallyLinearEmbedding
import settings as project_settings
target_data_fold... | pd.DataFrame(data=output_mae, columns=['Fold', 'Algorithm', 'Precision_mae', 'log_Precision_mae']) | pandas.DataFrame |
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from web3 import Web3
import time
import json
import io
plt.rc('figure', titleweight='bold')
plt.rc('axes', grid='True', linewidth=1.2, titlepad=20)
plt.rc('font', weight='bold', size=16)
plt.rc('lines', linewidth=3.5)
eXRD_... | pd.date_range(start='17-11-2020 17:00', periods=180*4, freq='6H') | pandas.date_range |
# Importações
import sqlalchemy
import pandas as pd
import numpy as np
# Criação da engine do sql alchemy para a tabela
db_connection = sqlalchemy.create_engine(
'postgresql+pg8000://postgres:123456@localhost:5433/folhadb',
client_encoding='utf8',
)
# 1. Extract
# Extração da tabela cargos para dataframe do ... | pd.to_datetime(ft_lancamentos_df['dat_admissao']) | pandas.to_datetime |
"""Command line interface."""
from argparse import (
Action,
ArgumentParser,
)
from datetime import datetime
from datetime import date
import pandas
from pandas import DataFrame
from penn_chime.constants import CHANGE_DATE
from penn_chime.model.parameters import Parameters, Disposition
from penn_chime.model... | pandas.concat([head, finfo]) | pandas.concat |
import numpy as np
import pandas as pd
from imblearn.over_sampling import SMOTE
from sklearn import preprocessing
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.neighbors import LocalOutlierFactor
from sklearn.preprocessing import StandardScaler
train_data_path ... | pd.read_csv(labels_path, index_col=False, header=None) | pandas.read_csv |
import pandas as pd
import numpy as np
import tests.mocks.operations as mockops
from trumania.core import operations
from trumania.core.util_functions import build_ids
def test_apply_should_delegate_to_single_col_dataframe_function_correctly():
# some function that expect a dataframe as input => must return
... | pd.DataFrame(columns=[]) | pandas.DataFrame |
import requests
import pandas as pd
from dateutil.parser import parse
# 在Facebook Graph API Exploer取得token
token = '<KEY>'
# 在Facebook Graph API Exploer取得粉絲專頁的id與名稱，並將其包成字典dic
fanpage = {'137698833067234': '資料視覺化 / Data Visualization',
'1703467299932229': 'Data Man 的資料視覺化筆記'}
# 建立一個空的list
information_l... | pd.DataFrame(information_list, columns=['粉絲專頁', '發文內容', '發文時間']) | pandas.DataFrame |
""" test parquet compat """
import datetime
from distutils.version import LooseVersion
import os
from warnings import catch_warnings
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
import pandas._testing as tm
from pandas.io.parquet import (
FastParquetImpl,
Py... | pd.DataFrame({"a": [1, 2, 3], "b": ["q", "r", "s"]}) | pandas.DataFrame |
import composeml as cp
import numpy as np
import pandas as pd
import pytest
from dask import dataframe as dd
from woodwork.column_schema import ColumnSchema
from woodwork.logical_types import NaturalLanguage
from featuretools.computational_backends.calculate_feature_matrix import (
FEATURE_CALCULATION_PERCENTAGE
)... | pd.Series(data=[1.0, 0.5, 0.5, 1.0, 0.5, 1.0]) | pandas.Series |
# -*- coding: utf-8 -*-
#
# License: This module is released under the terms of the LICENSE file
# contained within this applications INSTALL directory
"""
Defines the ForecastModel class, which encapsulates model functions used in
forecast model fitting, as well as their number of parameter... | pd.to_datetime(s_check) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Evaluation of trained models.
"""
import io
import pathlib
from typing import Dict, List, Optional, Union, Tuple
from dateutil.relativedelta import relativedelta
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from PIL import Image, ImageOps
import seaborn as sns
im... | pd.DataFrame(data=evaluations) | pandas.DataFrame |
import os
from pathlib import Path
import flywheel
import numpy as np
import pandas as pd
import pytest
import sys
sys.path.append(str(Path(__file__).parents[2].resolve()))
from tests.BIDS_popup_curation.acquisitions import acquistions_object
from tests.BIDS_popup_curation.sessions import session_object
from utils.bid... | pd.DataFrame.from_records(acquistions_object) | pandas.DataFrame.from_records |
import requests
import pandas as pd
import numpy as np
from credential import API_KEY
target_dir = '../csv_data/'
movies = pd.read_csv(f'{target_dir}movies.csv')
df_genres = pd.read_csv(f'{target_dir}genres.csv')
df_genre_info = pd.read_csv(f'{target_dir}genre_info.csv')
df_companies = pd.read_csv(f'{targe... | pd.read_csv(f'{target_dir}spoken_languages.csv') | pandas.read_csv |
import logging
import math
import pandas as pd
from sklearn.model_selection import train_test_split
from .tokenizer import WordTokenizer
from .utils import load_obj
from typing import Dict, Optional
from overrides import overrides
from nltk.tree import Tree
from allennlp.common.file_utils import cached_path
from all... | pd.read_pickle(all_data_path) | pandas.read_pickle |
"""
This module contains a collection of functions which make plots (saved as png files) using matplotlib, generated from
some model fits and cross-validation evaluation within a MAST-ML run.
This module also contains a method to create python notebooks containing plotted data and the relevant source code from
this mo... | pd.DataFrame.from_dict(data_dict, orient='index') | pandas.DataFrame.from_dict |
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
from sklearn.tree import DecisionTreeRegressor
from sklearn.preprocessing import PolynomialFeatures
from sklearn.ensemble import AdaBoostRegressor
from sklearn.metrics import mean_squared_error as mae
from sklearn.m... | pd.read_csv('C:\\Users\\<NAME>\\Documents\\Research Projects\\Forecast of Rainfall Quantity and its variation using Envrionmental Features\\Data\\Normalized & Combined Data\\All Districts.csv') | pandas.read_csv |
import pandas as pd
import numpy as np
from datetime import datetime
###############
# SELECT DATA #
###############
print("Selecting attributes...")
# GIT_COMMITS
gitCommits = pd.read_csv("../../data/raw/GIT_COMMITS.csv")
attributes = ['projectID', 'commitHash', 'author', 'committer', 'committerDate']
gitCommits =... | pd.concat([sonarIssues_resolved, sonarIssues_notresolved], sort=False) | pandas.concat |
# --- imports from python standard library -------------------------------------
from abc import ABC, abstractmethod
from typing import Any, Generator, List
# --- external imports ---------------------------------------------------------
import pandas as pd
# --- imports own packages and modules -----------------------... | pd.DataFrame(data=data, index=index) | pandas.DataFrame |
# 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([], index=[100]) | pandas.DataFrame |
from collections import defaultdict
import numpy as np
import pandas as pd
import basty.utils.misc as misc
class AnnotationInfo:
def __init__(self):
self._inactive_annotation = None
self._noise_annotation = None
self._arouse_annotation = None
self._label_to_behavior = None
... | pd.DataFrame.from_dict(report_dict) | pandas.DataFrame.from_dict |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 21 11:12:24 2018
@author: benjamin
"""
from fastText import train_supervised, load_model
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.multiclass import unique_labels
from tempfile import NamedTemporaryFile
import n... | pd.DataFrame() | pandas.DataFrame |
# SPDX-License-Identifier: Apache-2.0
# Licensed to the Ed-Fi Alliance under one or more agreements.
# The Ed-Fi Alliance licenses this file to you under the Apache License, Version 2.0.
# See the LICENSE and NOTICES files in the project root for more information.
import os
from typing import Tuple
import pan... | pd.DataFrame(["one"]) | pandas.DataFrame |
"""
Integrated Label Preparation Code
Created on 4/25/2019
@author: RH
"""
#CPTAC initial prep
import pandas as pd
imlist = pd.read_excel('../S043_CPTAC_UCEC_Discovery_Cohort_Study_Specimens_r1_Sept2018.xlsx', header=4)
imlist = imlist[imlist['Group'] == 'Tumor ']
cllist = pd.read_csv('../UCEC_V2.1/waffles_updated.t... | pd.read_csv('../TCGA_Image_meta.tsv', sep='\t', header=0) | pandas.read_csv |
import streamlit as st
import pandas as pd
import seaborn as sns
import numpy as np
from matplotlib import pyplot as plt
from sklearn.neighbors import NearestNeighbors
import random
import missingno as msno
import ppscore as pps
from sklearn.neighbors import NearestNeighbors
from sklearn.preprocessing import StandardSc... | pd.get_dummies(market_pre['natureza_juridica_macro'],drop_first=True) | pandas.get_dummies |
import json
import pandas as pd
from tqdm import tqdm
from curami.commons import file_utils
from curami.preprocess.clean import AttributeCleaner
def generate_features_file(from_file_no, to_file_no):
pd_unique_attributes = | pd.read_csv(file_utils.unique_attributes_file_final) | pandas.read_csv |
import numpy as _np
from scipy.stats import sem as _sem
import pandas as _pd
import matplotlib.pyplot as _plt
from nicepy import format_fig as _ff, format_ax as _fa
class TofData:
"""
General class for TOF data
"""
def __init__(self, filename, params, norm=True, noise_range=(3, 8), bkg_range=(3, 8), ... | _pd.DataFrame({'Time': times, 'Mass': masses, 'Volts': errors}) | pandas.DataFrame |
#!/usr/bin/env python
"""
Name: Filter and Convert Fetched Data from OpenSky Network
Author: <NAME>
Copyright: University of Liverpool © 2021
License: MIT
Version: 1.0
Status: Operational
Description: The source code performs a filter and conversion of data ready for analysis.
"""
import sys, csv, pytz
import panda... | pd.read_csv('test01.csv') | pandas.read_csv |
# Copyright 2018 IBM Corp.
#
# 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, s... | pd.DataFrame.from_dict(agg_dict) | pandas.DataFrame.from_dict |
"""
Pandas(2)
"""
## 5. Data Aggregation(데이터 수집)
import pandas as pd
from numpy.random import seed, rand, randint
import numpy as np
# 넘파이의 무작위 함수
seed(42)
df = pd.DataFrame({
'Weather': ['cold', 'hot', 'cold', 'hot', 'cold', 'hot', 'cold'],
'Food': ['soup', 'soup', 'icecream', '... | pd.to_datetime(['1902-11-12', 'not a date'], errors='coerce') | pandas.to_datetime |
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | pd.get_dummies(data_frame_train) | pandas.get_dummies |
"""
Tests for scalar Timedelta arithmetic ops
"""
from datetime import datetime, timedelta
import operator
import numpy as np
import pytest
import pandas as pd
from pandas import NaT, Timedelta, Timestamp, offsets
import pandas._testing as tm
from pandas.core import ops
class TestTimedeltaAdditionSubtraction:
"... | Timestamp("20121230 9:02") | pandas.Timestamp |
# -*- coding: utf-8 -*-
from warnings import catch_warnings
import numpy as np
from datetime import datetime
from pandas.util import testing as tm
import pandas as pd
from pandas.core import config as cf
from pandas.compat import u
from pandas._libs.tslib import iNaT
from pandas import (NaT, Float64Index, Series,
... | tm.makePeriodFrame() | pandas.util.testing.makePeriodFrame |
"""This file contains utility functions used for numpy data manipulation"""
import json
import logging
try:
import dicom
except:
import pydicom as dicom
import matplotlib.pylab as plt
import numpy as np
import pandas as pd
import os
import SimpleITK as sitk
logging.basicConfig(level=logging.INFO, format='%(asc... | pd.concat([df_all, df]) | pandas.concat |
# Copyright 2019 Elasticsearch BV
#
# 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 applicabl... | pd.get_option("display.max_rows") | pandas.get_option |
import argparse
import requests
## for working with data in lots of formats
## python3 -m pip install pandas
import pandas
ITEMURL = "http://pokeapi.co/api/v2/item/"
def main():
# Make HTTP GET request using requests
# and decode JSON attachment as pythonic data structure
# Also, append the URL ITEMUR... | pandas.DataFrame(matchedwords) | pandas.DataFrame |
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