prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pytest
import pandas as pd
import pandas._testing as tm
@pytest.mark.parametrize(
"values, dtype",
[
([1, 2, 3], "int64"),
([1.0, 2.0, 3.0], "float64"),
(["a", "b", "c"], "object"),
(["a", "b", "c"], "string"),
([1, 2, 3], "datetime64[ns]"),
([1, 2, 3], ... | pd.Series(mask, index=ser.index) | pandas.Series |
import numpy as np
import pandas as pd; pd.options.mode.chained_assignment = None
import matplotlib.pyplot as plt
from tqdm import tqdm
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy.optimize import minimize
from scipy.optimize import least_squares
import os
def is_const(x):
... | pd.read_csv(csv2) | pandas.read_csv |
# Copyright (c) 2019, MD2K Center of Excellence
# - <NAME> <<EMAIL>>, <NAME> <<EMAIL>>
# 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 co... | pd.DataFrame(all_dict, columns=[centroid_id_name, 'centroid_latitude', 'centroid_longitude', 'centroid_area']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Functions for cleaning mdredze Sandy Twitter dataset.
"""
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from statsmodels.graphics.tsaplots import plot_acf
from twitterinfrastructure.tools import cross_corr, output, query
def create_timeseries_diff(df, col1, col2... | pd.to_timedelta(s_y1.index.values, unit='h') | pandas.to_timedelta |
import shutil
import random
import tempfile
import pandas as pd
from catalyst.exchange.exchange_bundle import ExchangeBundle
from catalyst.exchange.exchange_bcolz import BcolzExchangeBarWriter, \
BcolzExchangeBarReader
from catalyst.exchange.bundle_utils import get_df_from_arrays
from nose.tools import assert_eq... | pd.to_datetime('2015-04-01 00:00') | pandas.to_datetime |
import pandas
from dmscripts.models.writecsv import csv_path
from dmscripts.models.modeltrawler import ModelTrawler
def base_model(base_model, keys, get_data_kwargs, client, logger=None, limit=None):
"""Fetch all the data for a given Digital Marketplace model from the api.
:param base_model: A Digital Marke... | pandas.DataFrame(columns=keys) | pandas.DataFrame |
import fcntl
import sys
import time
import numpy as np
import pandas as pd
from keras.layers import Dense, BatchNormalization, Dropout
from keras.models import Sequential
from keras.optimizers import SGD
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV
from sklea... | pd.DataFrame(data={'probability': results}) | pandas.DataFrame |
import bedrock.viz
import bedrock.common
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import RFE
from sklearn.naive_bayes import BernoulliNB, MultinomialNB
from tpot import TPOTClassifier
from sklearn.svm import LinearSVC
from sklearn.feature_selection import SelectFwe, f_classif
fro... | pd.DataFrame({'y': y_df, 'y_hat': y_hat, 'group': groups_df[y_df.index]}) | pandas.DataFrame |
import pandas as pd
import numpy as np
import datetime
def min2day_v2(df,lag_ps):
intraday = df;
#preparation
intraday['range1']=intraday['high'].rolling(lag_ps).max()-intraday['close'].rolling(lag_ps).min()
intraday['range2']=intraday['close'].rolling(lag_ps).max()-intraday['low'].rolling(lag_ps).mi... | pd.Series(signals['signals']) | pandas.Series |
# 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(1, index=['a', 'a', 'b', 'b', 'c']) | pandas.Series |
#!/usr/bin/env python
# encoding: utf-8
import os
import numpy as np
import scipy as sp
import matplotlib as mpl
mpl.use("TkAgg")
mpl.rcParams['pdf.fonttype'] = 42
import matplotlib.pylab as plt
import seaborn as sns
import pandas as pd
from IPython import embed as shell
from tqdm import tqdm
from sim_tools import ge... | pd.DataFrame() | pandas.DataFrame |
import glob
import os
from networkx.readwrite import json_graph
import json
import networkx as nx
import pandas as pd
from subs2network.utils import add_prefix_to_dict_keys
from subs2network.imdb_dataset import imdb_data
from subs2network.consts import MOVIE_YEAR
def get_node_features(g):
closeness = nx.closenes... | pd.DataFrame(res) | pandas.DataFrame |
# pylint: disable=too-many-lines
"""Field class."""
import os
import sys
from copy import deepcopy
import weakref
from functools import partial
from string import Template
import logging
import numpy as np
import pandas as pd
import h5py
import pyvista as pv
from anytree import PreOrderIter
from deprecated.sphinx impor... | pd.to_datetime(self.meta['START']) | pandas.to_datetime |
import pytest
from mapping import mappings
from pandas.util.testing import assert_frame_equal, assert_series_equal
import pandas as pd
from pandas import Timestamp as TS
import numpy as np
from pandas.tseries.offsets import BDay
@pytest.fixture
def dates():
return pd.Series(
[ | TS('2016-10-20') | pandas.Timestamp |
import pandas as pd
import numpy as np
import time
import sys
import json
from jsmin import jsmin
from collections import Counter
import os.path
from xlrd.biffh import XLRDError
from aenum import IntEnum
import time
# set up logging (to console)
import logging
logger = logging.getLogger(__name__)
logger.setLevel(loggi... | pd.concat(years_dat, sort=False) | pandas.concat |
"""Amazon Neptune Module."""
import logging
import re
from typing import Any
import pandas as pd
from gremlin_python.process.graph_traversal import GraphTraversalSource, __
from gremlin_python.process.translator import Translator
from gremlin_python.process.traversal import Cardinality, T
from gremlin_python.structur... | pd.concat([df, expanded], axis=1) | pandas.concat |
import io
import os
import re
import sys
import time
import pandas
import datetime
import requests
import mplfinance
from matplotlib import dates
# Basic Data
file_name = __file__[:-3]
absolute_path = os.path.dirname(os.path.abspath(__file__))
# <editor-fold desc='common'>
def load_json_config():
global file_dir... | pandas.concat([stock_open_old, stock_open_new], join='outer') | pandas.concat |
#!/usr/bin/env python
# Author: <NAME> (jsh) [<EMAIL>]
import itertools
import joblib
import logging
import os.path
import pathlib
import random
import shutil
import sys
import numpy as np
import pandas as pd
from pandas.api.types import CategoricalDtype
from sklearn import preprocessing as skpreproc
from keras.layer... | pd.DataFrame(voframe.loc[matchmask]) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 12 17:13:29 2018
@author: pamelaanderson
"""
from difflib import SequenceMatcher
import json
import numpy as np
import os
import operator
import pandas as pd
def load_adverse_events(path, year, q):
""" Loading adverse drug events while perfor... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# QTPyLib: Quantitative Trading Python Library
# https://github.com/ranaroussi/qtpylib
#
# Copyright 2016-2018 <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 ... | pd.to_datetime(df['datetime'], utc=True) | pandas.to_datetime |
import subprocess, gzip, datetime, pickle, glob, os, openpyxl, shutil, math
import pandas as pd
from plotly.subplots import make_subplots
from pathlib import Path
from joblib import Parallel, delayed
import plotly.graph_objects as go
import plotly.express as px
from statistics import mean
from statistics import median
... | pd.DataFrame() | pandas.DataFrame |
""" test fancy indexing & misc """
from datetime import datetime
import re
import weakref
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas.core.dtypes.common import (
is_float_dtype,
is_integer_dtype,
)
import pandas as pd
from pandas import (
DataFrame,
Index,... | Index(["b", "a", "a"]) | pandas.Index |
import pytest
import jax.numpy as np
import pandas as pd
from pzflow import Flow
from pzflow.bijectors import Chain, Reverse, Scale
from pzflow.distributions import *
@pytest.mark.parametrize(
"data_columns,bijector,info,file",
[
(None, None, None, None),
(("x", "y"), None, None, None),
... | pd.DataFrame(xarray, columns=("redshift", "y", "y_err", "redshift_err")) | pandas.DataFrame |
import requests
import pandas as pd
from io import StringIO, BytesIO
from lxml import etree as et
API_KEY = '<GREATSCHOOLS.ORG API KEY GOES HERE>'
def generate_file(name, response):
d = {}
df = | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import random
from rpy2.robjects.packages import importr
utils = importr('utils')
prodlim = importr('prodlim')
survival = importr('survival')
#KMsurv = importr('KMsurv')
#cvAUC = importr('pROC')
#utils.install_packages('pseudo')
#utils.install_packages('prodlim')
#utils... | pd.get_dummies(long_test_clindata, columns=['time_point']) | pandas.get_dummies |
#!/usr/bin/python
import time
import numpy as np
import pandas as pd
import argparse
from math import exp
from math import sqrt
from datetime import datetime
from os import listdir
import sys
# BOKEH
from bokeh import events
from bokeh.io import output_file, show
from bokeh.models import CustomJS, HoverT... | pd.DataFrame() | pandas.DataFrame |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import os
import operator
import unittest
import cStringIO as StringIO
import nose
from numpy import nan
import numpy as np
import numpy.ma as ma
from pandas import Index, Series, TimeSeries, DataFrame, isnull, notnull
from pandas.core.index... | assert_series_equal(result, expected) | pandas.util.testing.assert_series_equal |
from collections import OrderedDict
import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
)
import pandas._testing as tm
from pandas.core.construction import create_series_with_explicit_dtype
class TestFromDict:
# Note: these tests are specif... | DataFrame.from_dict(a, orient="columns") | pandas.DataFrame.from_dict |
# -*- coding: utf-8 -*-
"""project3.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1PW90I5c1X5VipzIvowFpbLOAtjLw7-co
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
"""1.**transforming data csv ... | pd.read_csv("/content/drive/MyDrive/simplilearn/python with data science /project3/Comcast_telecom_complaints_data.csv") | pandas.read_csv |
import pandas as pd
from autor import Author
from excel import ExcelFile
from individuos import Student, Egress
from verifica_autores import em_lista_autores, trata_exceçoes
from valores import ND, quadrennium
from PyscopusModified import ScopusModified
from pprint import pprint
from excecoes import excecoes_artigos_sc... | pd.read_csv("Qualis/QualisCC_2013_2016.csv", sep=";", encoding='iso-8859-1') | pandas.read_csv |
import numpy as np
import pandas as pd
from numba import njit, typeof
from numba.typed import List
from datetime import datetime, timedelta
import pytest
import vectorbt as vbt
from vectorbt.portfolio.enums import *
from vectorbt.generic.enums import drawdown_dt
from vectorbt import settings
from vectorbt.utils.random... | pd.Index(['first', 'second'], dtype='object', name='group') | pandas.Index |
import pandas as pd
import os
import warnings
import pickle
from nltk.corpus import stopwords
from nltk.tokenize import RegexpTokenizer
from collections import namedtuple
Fact = namedtuple("Fact", "uid fact file")
answer_key_map = {"A": 0, "B": 1, "C": 2, "D": 3, "E": 4, "F": 5}
tables_dir = "annotation/expl-tablesto... | pd.isna(s) | pandas.isna |
import streamlit as st
import streamlit.components.v1 as stc
import time
from random import random
import numpy as np
import pandas as pd
import altair as alt
from altair import Chart, X, Y, Axis, SortField, OpacityValue
# 2020-10-25 edit@ from st.annotated_text import annotated_text
from annotated_text import annota... | pd.Series(res) | pandas.Series |
import os
import pandas as pd
BASE_DIR = os.path.abspath(os.path.dirname(__file__))
def open_csv(filepath, header_names=None):
"""Opens CSV file with option to add header names."""
if header_names and hasattr(header_names, "__iter__"):
return | pd.read_csv(filepath, sep=",", header=0, names=header_names) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
@author: <NAME> - https://www.linkedin.com/in/adamrvfisher/
"""
#This is a strategy tester
#pandas_datareader is deprecated, use YahooGrabber
#Import modules
from pandas_datareader import data
import pandas as pd
import numpy as np
#Assign ticker
ticker = '^GSPC'
#Re... | pd.Series(AroonUp, index=s.index) | pandas.Series |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
import numpy as np
import pytest
from pandas.compat import lrange, range
import pandas as pd
from pandas import DataFrame, Index, Series
import pandas.util.testing as tm
from pandas.util.testing import assert_series_equal
def test_get():
# GH 6383
s = Series... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
import argparse
import os
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
def parse_args(args):
"""define arguments"""
parser = argparse.ArgumentParser(description="TATA_enrichment_plots")
parser.add_argument(
"file_names",
type=str,
help="Name of folder ... | pd.read_table(gat_output2, sep="\t", header=0) | pandas.read_table |
"""
PFRA Module for working with HEC-RAS model output files
"""
import gdal
from time import time
import geopandas as gpd
from geopandas.tools import sjoin
from shapely.ops import cascaded_union
from shapely.geometry import Point, LineString, Polygon
import numpy as np
import pandas as pd
import h5py
from matplotlib im... | pd.Index(desired_columns) | pandas.Index |
import pandas as pd
import json
import os
import sys
import datetime
from datetime import time
from src.util import logger
def loadIntradayData(filepath):
data = | pd.read_csv(filepath, parse_dates=[0], names=['datetime', 'value']) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
@file:base_6900.py
@time:2019/7/6 21:49
@author:Tangj
@software:Pycharm
@Desc
"""
import pandas as pd
import numpy as np
from sklearn.model_selection import StratifiedKFold
from lightgbm.sklearn import LGBMClassifier
from sklearn.metrics import mean_squared_error, mean_absolute_error, log_lo... | pd.read_csv('../data/user_behavior_logs.csv', parse_dates=['behavior_time']) | pandas.read_csv |
from ..parsers import get_parsing_function
import pandas as pd
from tqdm import tqdm
from ..DataStructures import reg_fixed_fileds
from ...settings import get_regions_batch_size
# Loading strategy
loading_strategy = "single_core"
batch = get_regions_batch_size()
def load_regions(collected_result):
# get the num... | pd.DataFrame.from_dict(result) | pandas.DataFrame.from_dict |
"""
Module for static data retrieval. These functions were performed once during the initial project creation. Resulting
data is now provided in bulk at the url above.
"""
import datetime
import json
from math import sin, cos, sqrt, atan2, radians
import re
import requests
import pandas as pd
from riverrunner import s... | pd.unique(group.STATION) | pandas.unique |
"""
Machine learning examples with SciPy and scikit-learn.
"""
from pandas import Categorical, DataFrame, Series
from scipy.cluster.hierarchy import fcluster, linkage
from sklearn import linear_model
class Classify:
"""
Train, use, and re-use an automatic classifier.
Input training data, then call with ne... | Series(data, index=clues.index, name="class") | pandas.Series |
# Copyright 2015 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
# Unless required by ... | melt(data, id_vars=['y'], var_name='x') | pandas.melt |
import pandas as pd
import matplotlib.pyplot as plt
def plot_results_for_probability_changes():
df1 = pd.read_csv("base.csv")
df2 = pd.read_csv("base_pc_100_pm_80.csv")
df3 = pd.read_csv("base_pc_80_pm_5.csv")
df_iterations = pd.DataFrame({
"90%% crossover, 40%% mutação": df1["iterations"],
... | pd.read_csv("pmx_pc_100_pm_80_pop_200.csv") | pandas.read_csv |
import pandas as pd
from datetime import date
from pandas.core.indexes import category
import config as config
from sklearn.preprocessing import MinMaxScaler, RobustScaler, StandardScaler, MaxAbsScaler
from main_table import MainInsert
class AlgoInsert:
def __init__(self):
self.category = config.Config.CA... | pd.merge(camping_data, last_df, how="left", left_on = 'place_id', right_on='index') | pandas.merge |
import pandas as pd
import numpy as np
def frequency_encoding(df,feature):
map_dict=df[feature].value_counts().to_dict()
df[feature]=df[feature].map(map_dict)
def target_guided_encoding(df,feature,target):
order=df.groupby([feature])[target].mean().sort_values().index
map_dic={k:i for i,k in enumerate(order,0)... | pd.DataFrame(order) | pandas.DataFrame |
from pathlib import Path
import pandas as pd
import typer
from jinja2 import Environment, FileSystemLoader
from reki.data_finder import find_local_file
from reki_data_tool.postprocess.grid.gfs.ne.config import OUTPUT_DIRECTORY
from reki_data_tool.postprocess.grid.gfs.util import get_random_start_time, get_random_fore... | pd.to_datetime(start_time, format="%Y%m%d%H") | pandas.to_datetime |
# Copyright 2020 Google 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 to in writing, ... | isna(x) | pandas.isna |
#파이썬으로 상관분석 회기분석테스트
import numpy as np
import pandas as pd
#csv 파일 읽어오기
hdr = ['V1','V2','V3','V4','V5','V6','V7','V8','V9']
df = | pd.read_csv('c:/java/phone-02.csv', header=None,names=hdr) | pandas.read_csv |
from caes import ICAES2
import pandas as pd
from joblib import Parallel, delayed, parallel_backend
import time
import os
from datetime import datetime
# =====================
# function to enable sensitivity analysis
# =====================
def sizing_and_sensitivity(wrkdir, xlsx_filename, sheet_name, capacity, durat... | pd.Series() | pandas.Series |
from datetime import (
datetime,
timedelta,
)
from importlib import reload
import string
import sys
import numpy as np
import pytest
from pandas._libs.tslibs import iNaT
import pandas.util._test_decorators as td
from pandas import (
NA,
Categorical,
CategoricalDtype,
Index,
Interval,
... | Series(["2010-01-04 00:00:00-05:00"]) | pandas.Series |
import re
import time
import requests
import pandas as pd
from bs4 import BeautifulSoup
class stackScrape(object):
def __init__(self):
pass
def extractDataFromUrl(self, url):
'''
Returns the scraped data from the target URL in raw format (HTML), which can be stackoverflow or stackexchange
P... | pd.Series(bagOfWordsViews) | pandas.Series |
import csv
import json
import multiprocessing
import os
import queue
import subprocess
import warnings
from datetime import datetime, timedelta
from glob import glob
from time import time
import joblib
import numpy as np
import pandas as pd
import psutil
# import wfdb
from sklearn.model_selection import train_test_sp... | pd.DataFrame({}) | pandas.DataFrame |
import warnings
from copy import deepcopy
from typing import Dict
from typing import List
from typing import Optional
from typing import Union
import numpy as np
import pandas as pd
from sklearn.base import TransformerMixin
from etna.core import StringEnumWithRepr
from etna.transforms.base import Transform
from etna.... | pd.concat([x[segment] for segment in segments]) | pandas.concat |
"""Functions for transofrmation of films and books datasets.
Functions
---------
get_books_ratings - transform books dataset
get_films_ratings - transform films dataset
generate_datasets - generate films and books datasets
"""
from typing import Set
import pandas as pd
from pathlib im... | pd.read_csv(location_1, sep='\t', low_memory=False) | pandas.read_csv |
import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from MulticoreTSNE import MulticoreTSNE as TSNE
import umap
from sklearn.cluster import KMeans
SEED = 100
# @st.cache is speedup opti... | pd.read_csv(uploaded_file) | pandas.read_csv |
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... | DataFrame(output, index=obj.index, columns=columns) | pandas.core.frame.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
from pandas import ExcelFile
from pandas import ExcelWriter
from scipy import ndimage
from scipy.stats import randint as sp_randint
from sklearn.base import BaseEstimator
from sklearn.base import TransformerMixin
from sklearn.ensemble import ExtraTreesClassifier
from s... | pd.DataFrame(Average_output) | pandas.DataFrame |
import pandas as pd
import numpy as np
def get_rating_summary(df,num_users = None, num_items = None):
'''
print summary of user-item matrix
args:
df: data frame which contains userId & itemId columns
'''
if num_users == None:
num_users = len(df['userId'].unique())
if num_items ... | pd.DataFrame({'userId':old_userId,'new_userId':new_userId}) | pandas.DataFrame |
# for each 1-minute window from the training data, apply the ensemble model to get the score. Sort the scores and find out what score s is at a specific percentile p. e.g., if p=10, it means that 10% of scores are <= than s
# --- Imports ---
from sklearn.preprocessing import MinMaxScaler
import scipy.integrate as in... | pd.DataFrame(scores_topk, columns=COL_NAMES_RANKING) | pandas.DataFrame |
import pandas as pd
import numpy as np
from random import gauss, uniform
def get_makespan(curr_plan, num_resources, workflow_inaccur, positive=False, dynamic_res=False):
'''
Calculate makespan
'''
under = False
reactive_resource_usage = [0] * num_resources
resource_usage = [0] * num_resources
... | pd.read_csv('../Data/heft/DynHeteroResources_StHomoCampaignsHEFT.csv') | pandas.read_csv |
from __future__ import absolute_import, division, print_function
import os
import numpy as np
import pandas as pd
import shutil
import requests
import numpy.testing as npt
import pytest
import skimage.io as skio
from .. import argus_shapes as shapes
import pulse2percept.implants as p2pi
try:
FileNotFoundError
e... | pd.DataFrame(data) | pandas.DataFrame |
import sys
import pandas as pd
import numpy as np
from scipy import stats
from itertools import compress
import statsmodels.stats.multitest as smt
import scikits.bootstrap as bootstrap
from sklearn.decomposition import PCA
from .scaler import scaler
from .imputeData import imputeData
class statistics:
usage = """G... | pd.DataFrame({'Percent_Total_Missing': totalMissing}) | pandas.DataFrame |
import codecs
import math
import os
import re
import gensim
import jieba.posseg as jieba
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
# 返回特征词向量
def getWordVecs(wordList, model):
name = []
vecs = []
for word in wordList:
word = word.replace('\n', '')
try:
... | pd.DataFrame(name, columns=['word']) | pandas.DataFrame |
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(data_c1, columns=["bins", "pA_c1"]) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 18 17:54:55 2020
@author: RredRrobin
"""
import os
import tkinter as tk
import tkinter.filedialog as filedialog
import tkinter.ttk as ttk
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
class TextScrollCombo(tk.Fra... | pd.to_timedelta(self.df2['time']) | pandas.to_timedelta |
"""Tests for the sdv.constraints.tabular module."""
import numpy as np
import pandas as pd
import pytest
from sdv.constraints.errors import MissingConstraintColumnError
from sdv.constraints.tabular import (
ColumnFormula, CustomConstraint, GreaterThan, UniqueCombinations)
def dummy_transform():
pass
def d... | pd.to_datetime(['2020-01-01T00:00:01', '2020-01-02T00:00:01']) | pandas.to_datetime |
"""Data abstractions."""
from abc import abstractmethod
from collections import defaultdict, namedtuple
import copy
import os
import time
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset, IterableDataset
TYPE_NORMAL_ATTR = 0
TYPE_INDICATOR = 1
TYPE_FANOUT = 2
def time_this(f... | pd.DataFrame({c.name: c.data for c in self.columns}) | pandas.DataFrame |
"""Build daily-level feature sets, stitching together weather datasets and defining features.
"""
import numpy as np
import pandas as pd
import geopandas as gpd
from dask import dataframe as dd
from loguru import logger
from shapely.ops import nearest_points
from src.data.gfs.utils import grb2gdf
from src.conf import... | pd.NamedAgg(column="t_mean", aggfunc="mean") | pandas.NamedAgg |
import pandas as pd
import numpy as np
# from.tools import *
from Multivariate_Markov_Switching_Model.tools import *
from Multivariate_Markov_Switching_Model.core import *
from Multivariate_Markov_Switching_Model.tools import _2dim
import numpy as np
import os
# os.chdir("Multivariate_Markov_Switching_Model")
"""
tes... | pd.DataFrame(data) | pandas.DataFrame |
# Classification
# SVM
# -*- coding: utf-8 -*-
### 기본 라이브러리 불러오기
import pandas as pd
import seaborn as sns
'''
[Step 1] 데이터 준비/ 기본 설정
'''
# load_dataset 함수를 사용하여 데이터프레임으로 변환
df = sns.load_dataset('titanic')
# IPython 디스플레이 설정 - 출력할 열의 개수 한도 늘리기
| pd.set_option('display.max_columns', 15) | pandas.set_option |
# This script generates the scoring and schema files
# Creates the schema, and holds the init and run functions needed to
# operationalize the chestXray model
import os, sys, pickle, base64
import keras.models
import keras.layers
import keras_contrib.applications.densenet
import pandas as pd
import numpy as np
impor... | pd.DataFrame(data=[[encoded_image]], columns=[as_string_b64encoded_pickled_data_column_name]) | pandas.DataFrame |
import json
import logging
import os
import pandas as pd
import wandb
import yaml
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
from transformers import RobertaConfig, RobertaTokenizerFast, RobertaForSequenceClassification, Trainer, \
TrainingArguments
from ClassificationDat... | pd.read_csv("./data/dev/in.tsv", delimiter='\t', header=None, encoding="utf8", quoting=0) | pandas.read_csv |
################################################################################
### Python port of rlassoEffects.R
### https://github.com/cran/hdm/blob/master/R/rlassoEffects.R
################################################################################
############################################################... | pd.DataFrame(se, index=idx) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Module to parse/process/visualize/export GTFS feed.
@author: ikespand
"""
import pandas as pd
import zipfile
# from keplergl_cli.keplergl_cli import Visualize
from rasta.rasta_kepler import RastaKepler
from shapely.geometry import Point, LineString
import geopandas a... | pd.concat(geo_df) | pandas.concat |
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
DatetimeIndex,
Series,
date_range,
)
import pandas._testing as tm
from pandas.core.api import Int64Index
class TestDataFrameTruncate:
def test_truncate(self, datetime_frame, frame_or_series):
ts = datetim... | tm.assert_equal(result, expected) | pandas._testing.assert_equal |
import os
import pickle
import numpy as np
import torch
import torch.nn.functional as F
from collections import Counter
from collections import OrderedDict
import copy
from sys import argv
import json
import pandas as pd
import argparse
from tqdm.auto import tqdm
from IPython.core.debugger import Pdb
split_files = { ... | pd.DataFrame(df) | pandas.DataFrame |
import pandas as pd
from functools import reduce
def load():
print("Cargando datos")
datos ={}
"""
Seguridad y convivencia
"""
datos['Convivencia'] = data_convivencia = pd.read_excel('./data/datos separados.xlsx', 'Indicadores de convivencia decr')
datos['Seguridad'] = data_seguridad = p... | pd.read_excel('./data/datos separados.xlsx', 'Producción de agua') | pandas.read_excel |
import numpy as np
import pandas as pd
from numba import njit
from datetime import datetime
import pytest
from itertools import product
from sklearn.model_selection import TimeSeriesSplit
import vectorbt as vbt
from vectorbt.generic import nb
seed = 42
day_dt = np.timedelta64(86400000000000)
df = pd.DataFrame({
... | pd.RangeIndex(start=0, stop=4, step=1) | pandas.RangeIndex |
import numpy as np
import pandas as pd
from datetime import datetime
import random as rd
from pandas import DataFrame
from math import sqrt
from scipy.stats import norm
from pandas import DataFrame
from functools import wraps
class create_data():
'''create data
e.g.
s = pd.to_datetime('01-01-2019')
cr... | DataFrame(dates, columns=['Date']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Main module, contains the base object that host all the different analysis
Authors: B.G. 18/11/2018
"""
# This module manages raster I/O operations, based on rasterio (which itself depends on GDAL)
from lsdtopytools import raster_loader as rl
from lsdtopytools import lsdtopytools_utilitie... | pd.DataFrame(Dict_of_ksn) | pandas.DataFrame |
# coding=utf-8
# Author: <NAME>
# Date: Sept 11, 2019
#
# Description: Indexes meta-genes to select core meiotic genes.
# Pipeline: Only mammal (HS & MM) conserved genes that Up/Down Regulated.
#
#
import math
import numpy as np
import pandas as pd
| pd.set_option('display.max_rows', 100) | pandas.set_option |
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import numpy as np
import pandas as pd
from adjustText import adjust_text
from pylab import cm
from matplotlib import colors
def PCA_var_explained_plots(adata):
n_rows = 1
n_cols = 2
fig = plt.figure(figsize=(n_cols*4.5, n... | pd.isnull(s) | pandas.isnull |
"""
Name : c9_44_equal_weighted_vs_value_weighted.py
Book : Python for Finance (2nd ed.)
Publisher: Packt Publishing Ltd.
Author : <NAME>
Date : 6/6/2017
email : <EMAIL>
<EMAIL>
"""
import pandas as pd
import scipy as sp
x=pd.read_pickle("c:/temp/yanMonthly.pkl")
def ret_f(t... | pd.DataFrame(p[1:],index=ddate) | pandas.DataFrame |
from __future__ import print_function
import collections
import os
import re
import sys
import numpy as np
import pandas as pd
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import StandardScaler, MinMaxScaler, MaxAbsScaler
file_path = os.path.dirname(os.path.realpath(__file__))
lib_path = os.... | pd.read_csv(path, engine='c', dtype=np.float32) | pandas.read_csv |
# -*- coding: utf-8 -*-
import unittest
import platform
import pandas as pd
import numpy as np
import pyarrow.parquet as pq
import hpat
from hpat.tests.test_utils import (
count_array_REPs, count_parfor_REPs, count_array_OneDs, get_start_end)
from hpat.tests.gen_test_data import ParquetGenerator
from numba import ... | pd.Series(data) | pandas.Series |
from matplotlib import cm, rcParams
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib as matplotlib
import numpy as np
import math as math
import random as rand
import os, sys, csv
import pandas as pd
#matplotlib.pyplot.xkcd(scale=.5, length=100, randomness=2)
c = ['#aa3863', '#d9702... | pd.Series(phis4_uncorr) | pandas.Series |
from typing import Iterable, Optional
import pandas as pd
import numpy as np
from scipy.special import expit
def get_expanded_df(df, event_type_col='J', duration_col='X', pid_col='pid'):
"""
This function gets a dataframe describing each sample the time of the observed events,
and returns an expanded dat... | pd.concat(temp_series, axis=1) | pandas.concat |
# coding: utf-8
# In[69]:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import random
get_ipython().magic('matplotlib inline')
# In[70]:
PokemonDf=pd.read_csv('pokemon/Pokemon.csv')
# In[71]:
PokemonDf.head(200000)
# In[72]:
PokemonDf.describe()
PokemonDf.Name... | pd.concat([PokemonDf['Attack'],PokemonDf['HP']]) | pandas.concat |
import jieba
import pandas as pd
import wordcloud
# 读取弹幕 txt 文件
with open("dan_mu.txt", encoding="utf-8") as f:
txt = f.read()
danmu_list = txt.split("\n")
# jieba 分词
danmu_cut = [jieba.lcut(item) for item in danmu_list]
# 获取停用词
with open("baidu_stopwords.txt",encoding="utf-8") as f:
stop = f.r... | pd.Series(all_words) | pandas.Series |
from typing import Union, Optional, List, Dict
import faiss
import pickle
import torch.distributed # noqa: WPS301
import numpy as np
import pandas as pd
from time import time
from pathlib import Path
from catalyst.dl import IRunner, CallbackOrder, Callback
from catalyst.utils.torch import get_activation_fn
from s... | pd.read_excel(doev2_xlsx, sheet_name=None) | pandas.read_excel |
from ioUtils import getFile, saveFile
from timeUtils import clock, elapsed
from numpy import isnan
from pandas import DataFrame, Series
def testManualEntries(fast=False, saveit=False):
if fast:
start, cmt = clock("Testing Manual Entries Pickle File")
else:
start, cmt = clock("Testing Manual ... | DataFrame(manualEntries) | pandas.DataFrame |
# Copyright (c) 2019-2020, NVIDIA CORPORATION.
import datetime as dt
import re
import cupy as cp
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from pandas.util.testing import (
assert_frame_equal,
assert_index_equal,
assert_series_equal,
)
import cudf
from cudf.core import Data... | pd.Series([None, None], dtype="datetime64[ns]") | pandas.Series |
import numpy as np
import pytest
import pandas as pd
from pandas.core.sorting import nargsort
import pandas.util.testing as tm
from .base import BaseExtensionTests
class BaseMethodsTests(BaseExtensionTests):
"""Various Series and DataFrame methods."""
@pytest.mark.parametrize('dropna', [True, False])
d... | pd.Series(orig_data2) | pandas.Series |
"""
Getting most discussed stocks from r/wallstreetbets hot
"""
import json
import os
import re
import time
from collections import ChainMap, Counter
from datetime import datetime
import pandas as pd
import requests
from dotenv import load_dotenv
load_dotenv()
CLIENT_ID = os.getenv("CLIENT_ID")
SECRET = os.getenv("S... | pd.DataFrame() | pandas.DataFrame |
from datetime import datetime
import operator
import numpy as np
import pytest
from pandas import DataFrame, Index, Series, bdate_range
import pandas._testing as tm
from pandas.core import ops
class TestSeriesLogicalOps:
@pytest.mark.parametrize("bool_op", [operator.and_, operator.or_, operator.xor])
def te... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
"""
Extracts path data for a user or a set of users and analyses with pathpy.
"""
import csv
import json
import os
import numpy as np
import matplotlib.pyplot as plt
import igraph
import pathpy as pp
from scipy.stats import chi2
from collections import Counter
from pandas import DataFrame
import seaborn as sns
from sci... | DataFrame(eval_list) | pandas.DataFrame |
import matplotlib
matplotlib.use('Agg')
import re
import argparse
from datetime import datetime, timedelta, time
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
import matplotlib.patches as mpatches
import numpy as np
import pandas as pd
from pandas.plotting import register_matplotlib_converters
regis... | pd.read_csv(labelDictCSV, usecols=['annotation', labelDictCol]) | pandas.read_csv |
from mpl_toolkits import mplot3d
import sys, os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from plotnine import *
import copy, math
dist = 10
def find_min_discm_each_hyperparam(df):
x = df.sort_values(by=['Discm_percent', 'Points-Removed']).groupby("Model-count", as_index=False).first(... | pd.concat([df_noremoval, df_nosensitive, df_massaging, df_ps, df_lfr, df_DIR, df_adver, df_our], sort=True) | pandas.concat |
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