prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import json
import numpy as np
import pandas as pd
import pickle
import sklearn
def process_input(request_data: str) -> pd.DataFrame:
"""
asserts that the request data is correct.
:param request_data: data gotten from the request made to the API
:return: the values from the dataframe
"""
... | pd.DataFrame(data) | pandas.DataFrame |
from typing import Union
import pandas as pd
import numpy as np
from pandas import Series, DataFrame
from pandas.core.arrays import ExtensionArray
from sklearn import preprocessing
import time
def convert_date_2_timestamp(date_str):
time_array = time.strptime(date_str, "%Y%m%d")
return int(time.mktime(time_ar... | pd.DataFrame(columns=['userID', 'itemID', 'rating', 'timestamp']) | pandas.DataFrame |
# pylint: disable=E1101,E1103,W0232
from datetime import datetime, timedelta
from pandas.compat import range, lrange, lzip, u, zip
import operator
import re
import nose
import warnings
import os
import numpy as np
from numpy.testing import assert_array_equal
from pandas import period_range, date_range
from pandas.c... | MultiIndex.from_tuples(obj.values) | pandas.core.index.MultiIndex.from_tuples |
import csv
import os
# vymery k jednotlivym zakazkam, podle kterych se bude provaddet alokace
RR_soubor='Rentroll_podklad.csv'
#-----------------------------------------------------------------------------'
def uprava_cisla(hodnota):
try:
return int(hodnota)
except ValueError:
return float(hodnota... | pandas.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
import random
import numpy as np
import pandas as pd
from pandas.compat import lrange
from pandas.api.types import CategoricalDtype
from pandas import (DataFrame, Series, MultiIndex, Timestamp,
date_range, NaT, IntervalIn... | assert_frame_equal(df, expected) | pandas.util.testing.assert_frame_equal |
'''
This program will simulate leveling a DnD character, showing their ending HP, and stats.
'''
import argparse
import csv
import json
import re
import time
from openpyxl import load_workbook
from pandas import DataFrame
from src import classes, util
def import_race_data(file_path):
'''
This method imports d... | DataFrame(workbook[name].values) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
import orca
from urbansim_templates import utils
def test_parse_version():
assert utils.parse_version('0.1.0.dev0') == (0, 1, 0, 0)
assert utils.parse_version('0.115.3') == (0, 115, 3, None)
assert utils.parse_version('3.1.dev7') == (3, 1, 0, 7)
a... | pd.DataFrame(d) | pandas.DataFrame |
import base64
import io
import textwrap
import dash
import dash_core_components as dcc
import dash_html_components as html
import gunicorn
import plotly.graph_objs as go
from dash.dependencies import Input, Output, State
import flask
import pandas as pd
import urllib.parse
from sklearn.preprocessing import StandardSca... | pd.DataFrame(data=eigenvalues_outlier_covar, columns=['Eigenvalues']) | pandas.DataFrame |
import pickle
from pathlib import Path
from typing import Optional, List, Iterable, Dict, Any
import click
import pandas as pd
import torch
from tqdm import tqdm
from generation.generation import gpt2, gpt3, gpt2_affect, gpt2_ctrl, \
openai_gpt, ctrl, pplm, gpt2mcm
from utils.constants import PERSPECTIVE_API_ATTR... | pd.Series('<|endoftext|>') | pandas.Series |
#Helper
##############
from Helper import split_sequence
from Helper import layer_maker
##Startup
##############
# Library Imports
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
plt.style.use("ggplot")
from keras.models import Sequential
from kera... | pd.to_datetime(df.index) | pandas.to_datetime |
import pandas as pd
from pandas.plotting import lag_plot
import numpy as np
import matplotlib as mlp
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.ticker as ticker
import seaborn as sns
from scipy import stats
import statsmodels.api as sm
from statsmodels.formula.api import ols
imp... | pd.Series([x.year for x in df.index]) | pandas.Series |
#%%
import pandas as pd
from src.models.data_modules import *
from biasbalancer.utils import label_case
# %%
# Train sizes
def get_size(dm, which):
attrname = which+'_idx'
idx = getattr(dm, attrname)
return len(idx)
def get_dataset_info(dm):
index = [0]
if hasattr(dm, 'fold'):
n_folds =... | pd.read_csv(hyperpath) | pandas.read_csv |
"""A set of unit tests for the helper functions."""
import pandas as pd
from pandas._testing import assert_frame_equal
import pytest
from precon.helpers import axis_vals_as_frame
from test.conftest import create_dataframe
class TestAxisValsAsFrame:
"""Tests for the axis_vals_as_frame function.
Uses one inpu... | assert_frame_equal(true_output, expout_index_level_1_all_caps) | pandas._testing.assert_frame_equal |
import pandas as pd
import sqlite3
from datetime import datetime, timedelta, date
from sklearn import preprocessing
import matplotlib.pyplot as plt
import seaborn as sns
import selectStock_datetime
def scaler(result_df:pd.DataFrame) -> pd.DataFrame:
"""
date를 제외한 나머지 컬럼 0과 1사이로 정규화하는 함수
result_d... | pd.merge(news_result_df,stock_result_df, how='outer',on='date') | pandas.merge |
#!/usr/bin/env python
# coding: utf-8
# ## HUDBDC overtime analysis
# In[1]:
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
from glob import glob
from datetime import datetime as dt
#Suppress warning
pd.set_option('mode.chained_assignment', None)
# Read all xls files from desired... | pd.merge(df_abn_final, df_abn_total_hours, on='Month', suffixes=('', ' Total')) | pandas.merge |
from collections import OrderedDict
from datetime import datetime, timedelta
import numpy as np
import numpy.ma as ma
import pytest
from pandas._libs import iNaT, lib
from pandas.core.dtypes.common import is_categorical_dtype, is_datetime64tz_dtype
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
Da... | tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False) | pandas._testing.assert_produces_warning |
import torch
from torch.nn.utils import clip_grad_norm_
torch.multiprocessing.set_sharing_strategy('file_system')
import pandas as pd
import numpy as np
from tqdm import tqdm
import heapq
from pathlib import Path
class Learning():
def __init__(self,
optimizer,
loss_fn,
... | pd.read_csv(self.summary_file) | pandas.read_csv |
import os
import copy
import glob
import h5py
import numpy as np
from matplotlib import pylab as plt
import pandas as pd
#import ebf
import astropy.units as units
from astropy.coordinates import SkyCoord
try:
from dustmaps.bayestar import BayestarWebQuery
except:
try:
from dustmaps.dustmaps.bayestar im... | pd.DataFrame({'ra': [ra], 'dec': [dec]}) | pandas.DataFrame |
import requests
import pandas as pd
import html
from bs4 import BeautifulSoup
class DblpApi:
def __init__(self):
self.session = requests.Session()
self.author_url = 'http://dblp.org/search/author/api'
self.pub_url = 'http://dblp.org/search/publ/api'
def get_pub_list_by_url(self, url... | pd.DataFrame(author_not_found) | pandas.DataFrame |
#
# 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... | pd.DataFrame([{"a": 1, "b": 2}, {"a": 2, "b": 2}]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from __future__ import absolute_import
import sys, os
import re
import datetime as dt
import lxml.html
import requests
import itertools
import glob
import codecs
import html
from . import patterns
from . import fixes
from . import passage
flatten = lambda x: list(itertools.chain.from_iterable(x)... | pd.DataFrame(passages) | pandas.DataFrame |
# TODO move away from this test generator style since its we need to manage the generator file,
# which is no longer in this project workspace, as well as the output test file.
## ##
# #
# THI... | pd.DataFrame(test_class.data) | pandas.DataFrame |
import pandas as pd
#importing all the data from CSV files
master_df = pd.read_csv('People.csv', usecols=['playerID', 'nameFirst', 'nameLast', 'bats', 'throws', 'debut', 'finalGame'])
fielding_df = pd.read_csv('Fielding.csv',usecols=['playerID','yearID','stint','teamID','lgID','POS','G','GS','InnOuts','PO','A','E','DP... | pd.read_csv('Appearances.csv') | pandas.read_csv |
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas.core.dtypes.generic import ABCIndexClass
import pandas as pd
import pandas._testing as tm
from pandas.api.types import is_float, is_float_dtype, is_integer, is_scalar
from pandas.core.arrays import IntegerArray, integer_array
from... | pd.array([-1, 0, 1, None, 2], dtype="Int64") | pandas.array |
# -*- coding: utf-8 -*-
import sys, os
import pandas as pd
import numpy as np
from data_factory.temperature_spider import getTemperatureData
def loadNTL(path):
lineloss = pd.read_csv(path)
lineloss['Date'] = pd.to_datetime(lineloss['Date'])
lineloss = lineloss.sort_values(['AreaID', 'Date'])
linelos... | pd.concat(userdata, ignore_index=True) | pandas.concat |
# Module: internal.ensemble
# Provides an Ensemble Forecaster supporting voting, mean and median methods.
# This is a reimplementation from Sktime original EnsembleForecaster.
# This Ensemble is only to be used internally.
import pandas as pd
import numpy as np
import warnings
from sktime.forecasting.base._base impo... | pd.Series(pred_w, index=pred_forecasters.index) | pandas.Series |
from os import makedirs, path
from typing import Union
import pandas as pd
from .filetype import FileType
class DataReader(object):
def __init__(self):
"""
Stores all dataframes and provides methods to feed data into the dataframes.
"""
self.bus_lines = pd.DataFrame(columns=['id', 'name', 'color', 'card_on... | pd.read_json(file) | pandas.read_json |
from datetime import datetime, timedelta
import warnings
import operator
from textwrap import dedent
import numpy as np
from pandas._libs import (lib, index as libindex, tslib as libts,
algos as libalgos, join as libjoin,
Timedelta)
from pandas._libs.lib import is_da... | com._asarray_tuplesafe(keyarr) | pandas.core.common._asarray_tuplesafe |
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 21 14:48:57 2021
@author: <NAME>
"""
import pandas as pd, numpy as np, os, igraph as ig, leidenalg as la
import cvxpy as cp
from sklearn.neighbors import NearestNeighbors, radius_neighbors_graph
from kneed import KneeLocator
from sklearn.utils.validation import check_sym... | pd.DataFrame(performance_results) | pandas.DataFrame |
import copy
import warnings
import pprint
import numpy as np
import pandas as pd
from chemml.wrapper.database import sklearn_db
from chemml.wrapper.database import chemml_db
from chemml.wrapper.database import pandas_db
# todo: decorate some of the steps in the wrapeprs. e.g. sending out ouputs by finding all the c... | pd.DataFrame(df) | pandas.DataFrame |
import decimal
import numpy as np
from numpy import iinfo
import pytest
import pandas as pd
from pandas import to_numeric
from pandas.util import testing as tm
class TestToNumeric(object):
def test_empty(self):
# see gh-16302
s = pd.Series([], dtype=object)
res = to_numeric(s)
... | to_numeric('XX', errors='ignore') | pandas.to_numeric |
import datetime
from unittest.mock import patch, Mock
import numpy as np
import pandas as pd
from numpy.testing import assert_array_equal
from pandas._testing import assert_series_equal, assert_frame_equal
from src.features.feature_engineering import delete_irrelevant_columns, scale_features, scale_feature_in_df, \
... | assert_series_equal(df[is_night_flight_feature], expected_df_feature) | pandas._testing.assert_series_equal |
#! /usr/bin/env python
from unittest import TestCase
import pandas as pd
import numpy as np
from pandashells.lib.lomb_scargle_lib import (
_next_power_two,
_compute_pad,
_compute_params,
lomb_scargle,
)
class NextPowerTwoTest(TestCase):
def test_proper_return(self):
past_100 = _next_powe... | pd.DataFrame({'t': t, 'y': y}) | pandas.DataFrame |
import pandas as pd
import numpy as np
import logging
logger = logging.getLogger(__name__)
MOVE_SPEED_THRESHOLD = 5
STOP_SPEED_THRESHOLD = 0.5
PREVIOUS_OBSERVATIONS_TIME_FRAME = 5 # store N minutues of observations
def filter_previous_observations_by_timestamp(df):
if len(df) > 0:
return df[lambda x: x['... | pd.DataFrame(columns=ais.columns) | pandas.DataFrame |
# coding=utf-8
import pandas as pd
import numpy as np
import re
from matplotlib.ticker import FuncFormatter
def number_formatter(number, pos=None):
"""Convert a number into a human readable format."""
magnitude = 0
while abs(number) >= 1000:
magnitude += 1
number /= 1000.0
return '%.1f... | pd.DataFrame(data=datos_dataframe_profiling_txt) | pandas.DataFrame |
import logging
import pandas as pd
import glob
import os
import sys
utils_path = os.path.join(os.path.abspath(os.getenv('PROCESSING_DIR')),'utils')
if utils_path not in sys.path:
sys.path.append(utils_path)
import util_files
import util_cloud
import util_carto
import requests
from zipfile import ZipFile
import urll... | pd.to_datetime(df.year, format='%Y') | pandas.to_datetime |
import pandas as pd
from pandas_datareader import data
start_date = '2014-01-01'
end_date = '2018-01-01'
SRC_DATA_FILENAME = 'goog_data.pkl'
try:
goog_data2 = pd.read_pickle(SRC_DATA_FILENAME)
except FileNotFoundError:
goog_data2 = data.DataReader('GOOG', 'yahoo', start_date, end_date)
goog_data2.to_pickle(SRC... | pd.Series(macd_signal_values, index=goog_data.index) | pandas.Series |
# -*- coding:utf-8 -*-
# !/usr/bin/env python
"""
Date: 2022/5/2 15:58
Desc: 东方财富-股票-财务分析
"""
import pandas as pd
import requests
from tqdm import tqdm
def stock_balance_sheet_by_report_em(symbol: str = "SH600519") -> pd.DataFrame:
"""
东方财富-股票-财务分析-资产负债表-按报告期
https://emweb.securities.eastmoney.com/PC_HSF1... | pd.DataFrame(data_json["data"]) | pandas.DataFrame |
import itertools
import json
import os
import gym
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib.ticker import MultipleLocator
import envs
CIFAR10_CLASSES = [
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog"... | pd.DataFrame.from_records(action_stats) | pandas.DataFrame.from_records |
from time import time
from typing import Tuple, Mapping, Optional, Sequence, TYPE_CHECKING
from itertools import product
import sys
import pytest
from scanpy import settings as s
from anndata import AnnData
from scanpy.datasets import blobs
import scanpy as sc
from pandas.testing import assert_frame_equal
import nump... | assert_frame_equal(res[key], bdata.uns["foo"][key]) | pandas.testing.assert_frame_equal |
import pandas as pd
import logging
logger = logging.getLogger(f'cibi.{__file__}')
def make_dataframe(columns, dtypes, index_column=None):
# Stackoverflow-driven development (SDD) powered by
# https://stackoverflow.com/questions/36462257/create-empty-dataframe-in-pandas-specifying-column-types
assert len... | pd.Series(dtype=d) | pandas.Series |
import pandas as pd
def merger(input, output):
print("Merging kmercount files, this may take a while \n")
samples = [ | pd.read_hdf(x, index_col=0) | pandas.read_hdf |
# Standard packages
import numpy as np
import pandas as pd
import pytz
from datetime import datetime, timedelta
# sktime forecasting models
from sktime.forecasting.naive import NaiveForecaster
from sktime.forecasting.exp_smoothing import ExponentialSmoothing
from sktime.forecasting.ets import AutoETS
from sktime.forec... | pd.DataFrame(forecasts) | pandas.DataFrame |
import numpy as np
import pandas as pd
from sklearn import metrics
from sklearn.ensemble import IsolationForest
import STRING
from sklearn.preprocessing import StandardScaler
def isolation_forest(x, y, contamination=0.1, n_estimators=50, bootstrap=True, max_features=0.33, validation=[]):
if contaminati... | pd.read_csv('normal.csv', sep=';', encoding='latin1') | pandas.read_csv |
"""Tests for the sdv.constraints.base module."""
import warnings
from unittest.mock import Mock, patch
import pandas as pd
import pytest
from copulas.multivariate.gaussian import GaussianMultivariate
from copulas.univariate import GaussianUnivariate
from rdt.hyper_transformer import HyperTransformer
from sdv.constrai... | pd.testing.assert_frame_equal(expected_out, out) | pandas.testing.assert_frame_equal |
#import stuff
import numpy as np
import pandas as pd
from kneed import KneeLocator
def confusionMatrix(predicted_clone, real_label):
conf_df = pd.DataFrame(data={'vireo': predicted_clone, 'real_label': real_label})
confusion_matrix = pd.crosstab(conf_df['vireo'], conf_df['real_label'], rownames=['Predicted']... | pd.read_csv('test/BIC_params.csv') | pandas.read_csv |
from typing import (
Any,
Dict,
List,
Tuple,
Union,
Mapping,
TypeVar,
Callable,
Optional,
Sequence,
)
from copy import copy
from pathlib import Path
from itertools import combinations
from collections import namedtuple, defaultdict
from anndata import AnnData
from cellrank impo... | infer_dtype(adata.obs[key]) | pandas.api.types.infer_dtype |
from numpy.random import default_rng
import numpy as np
import emcee
import pandas as pd
from tqdm.auto import tqdm
from sklearn.preprocessing import StandardScaler
import copy
from scipy.stats import norm, ortho_group
import random
import math
import scipy.stats as ss
"""
A collection of synthetic data generators, i... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import pytest
from rdtools.normalization import normalize_with_expected_power
@pytest.fixture()
def times_15():
return pd.date_range(start='20200101 12:00', end='20200101 13:00', freq='15T')
@pytest.fixture()
def times_30():
return pd.date_range(start='20200101 12:00', end='20200101 13:0... | pd.Series([1.0, 3.0, 2.1], index=times_30) | pandas.Series |
# %% [markdown]
# This notebook is a -modified- VSCode notebook version of:
# https://www.kaggle.com/sheriytm/brewed-tpot-for-nyc-with-love-lb0-37
#
# You could find the train data from:
# https://www.kaggle.com/c/nyc-taxi-trip-duration/data
# You could find the fastest routes data from:
# https://www.kaggle.com/oscar... | pd.merge(coord_speed, coord_count, on=gby_cols) | pandas.merge |
import argparse
from itertools import product
from experiment import *
import pandas as pd
from params_helpers import *
# Search parameters for ILP formulation
def search_ilp(insdir, out, lp1, up1, lp2, up2):
try:
os.mkdir(out)
except OSError:
print("Creation of the directory failed or directo... | pd.DataFrame(Results) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime, timedelta
import functools
import itertools
import numpy as np
import numpy.ma as ma
import numpy.ma.mrecords as mrecords
from numpy.random import randn
import pytest
from pandas.compat import (
PY3, PY36, OrderedDict, ... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
# Packages are imported.
import pandas as pd
import requests as req
import numpy as np
import datetime as dt
import time
import multiprocessing as mp
import os
import random
import sys
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
import statsmodels.stats.multitest as statsmodels
import ... | pd.read_pickle('data_combined_filtered/' + job + '/' + job + '_filtered.pkl') | pandas.read_pickle |
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 23 11:37:16 2019
@author: Lieke
"""
import numpy as np
from numpy import linalg as LA
import pandas as pd
from sklearn import svm
from sklearn.decomposition import PCA
from sklearn.utils._testing import ignore_warnings
from sklearn.exceptions import ConvergenceWarning
fr... | pd.DataFrame(data) | pandas.DataFrame |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.4.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# ## Script is used to de... | pd.io.gbq.read_gbq(find_ancestor_lab, dialect='standard') | pandas.io.gbq.read_gbq |
# -*- coding: utf-8 -*-
# pylint: disable=E1101
# flake8: noqa
from datetime import datetime
import csv
import os
import sys
import re
import nose
import platform
from multiprocessing.pool import ThreadPool
from numpy import nan
import numpy as np
from pandas.io.common import DtypeWarning
from pandas import DataFr... | StringIO(data) | pandas.compat.StringIO |
import pandas as pd
from statsmodels.tsa.api import VAR
def time_series(data, future_forcast, location):
#[[people, violations, time, location],[people, violations, time, location],[people, violations, time, location]]
columns = ["people", "violations", "time", "location"]
df = pd.DataFrame(data=data, col... | pd.to_datetime(df['time']) | pandas.to_datetime |
import pandas as pd
import numpy as np
index = ['Mory', 'Ann']
columns = ['Windy', 'Sunny', 'Snowy', 'Thundery', 'Soild', 'Lighting']
data = {
'Mory': [2.0, 4.0, 6.0, 7.0, 6.0, 5.0],
'Ann': [1.0, 5.0, 1.0, 1.0, 1.0, 1.0],
}
df = pd.DataFrame(index=index, columns=columns, dtype=np.float64)
for (k, v) in data... | pd.get_dummies(data.weekday, prefix='weekday') | pandas.get_dummies |
# -*- coding: utf-8 -*-
import os
import datetime
import pandas as pd
from toolz import merge
from argcheck import expect_types
from WindAdapter.factor_loader import FactorLoader
from WindAdapter.utils import save_data_to_file
from WindAdapter.utils import print_table
from WindAdapter.utils import handle_wind_query_ex... | pd.DataFrame() | pandas.DataFrame |
# coding: utf-8
# Import libraries
import pandas as pd
from pandas import ExcelWriter
import numpy as np
import pickle
from sklearn.linear_model import LinearRegression
from sklearn import preprocessing
from sklearn.linear_model import LassoCV
from mlxtend.feature_selection import SequentialFeatureSelector as SFS
de... | pd.DataFrame(matrix_scaled, index=to_normalize.index, columns=to_normalize.columns) | pandas.DataFrame |
import datetime
import re
from warnings import (
catch_warnings,
simplefilter,
)
import numpy as np
import pytest
from pandas._libs.tslibs import Timestamp
import pandas as pd
from pandas import (
DataFrame,
HDFStore,
Index,
Int64Index,
MultiIndex,
RangeIndex,
... | pd.set_option("io.hdf.default_format", None) | pandas.set_option |
# encoding: utf-8
import re
import collections
import operator
import random
import numpy as np
from PIL import Image
from pathlib import Path
import csv
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import jieba
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
from palettabl... | pd.read_csv(csv_file, delimiter='\t', encoding='utf-8') | pandas.read_csv |
#from ai4good.models.cm.initialise_parameters import params, control_data, categories, calculated_categories, change_in_categories
from ai4good.models.cm.initialise_parameters import Parameters
from math import exp, ceil, log, floor, sqrt
import numpy as np
from scipy.integrate import ode
from scipy.stats import norm,... | pd.DataFrame(csv_sol) | pandas.DataFrame |
"""
Pipeline Evaluation module
This module runs all the steps used and allows you to visualize them.
"""
import datetime
from typing import List, Tuple, Union
import pandas as pd
from sklearn.pipeline import Pipeline
from .evaluation import Evaluator
from .feature_reduction import FeatureReductor
from .labeling imp... | pd.Series(self.y_pred, index=self.X_train.index) | pandas.Series |
import pandas as pd
from glob import glob
def process_stream_sessions(raw_dir='../data/raw/Stream Session*.csv',
save_dir=None):
ss_files = glob(raw_dir)
ds = []
for s_id, file in enumerate(ss_files):
fn = file.split('/')[-1]
d = pd.read_csv(file)
d['file... | pd.read_csv(file) | pandas.read_csv |
import pandas as pd
import os
# this file contains variables and names given in turkish words
# blood transfusions related data
writer = pd.ExcelWriter('tümü.xlsx', engine='xlsxwriter')
writer2 = pd.ExcelWriter('ozet.xlsx', engine='xlsxwriter')
writer3 = pd.ExcelWriter('hasta başı toplam transfüzyon sayısı.xlsx', eng... | pd.Timedelta(days=1) | pandas.Timedelta |
import os
import glob
import pandas as pd
import sys
os.chdir(".")
pattern = sys.argv[1]+"*.csv"
all_filenames = [i for i in glob.glob(pattern)]
#combine all files in the list
#combined_csv = pd.concat([pd.read_csv(f, index_col=0) for f in all_filenames ], axis=0, join='outer', ignore_index=False, sort=False)
#export... | pd.DataFrame(combined_csv) | pandas.DataFrame |
""":func:`~pandas.eval` parsers
"""
import ast
import operator
import sys
import inspect
import tokenize
import datetime
import struct
from functools import partial
import pandas as pd
from pandas import compat
from pandas.compat import StringIO, zip, reduce, string_types
from pandas.core.base import StringMixin
fro... | com.flatten(self.terms) | pandas.core.common.flatten |
# 1584927559
import task_submit
# import task_submit_optimus
import task_submit_raw
from task_submit_raw import VGGTask,RESTask,RETask,DENTask,XCETask
import random
import kubernetes
import influxdb
import kubernetes
import signal
from TimeoutException import TimeoutError,Myhandler
import yaml
import requests
from mult... | pd.DataFrame(self.memory_per) | pandas.DataFrame |
"""
Module report
================
A module with helper functions for computing pre-defined plots for the analysis
of fragment combinations.
"""
import warnings
import logging
import argparse
import sys
from shutil import rmtree
from datetime import datetime
import re
from pathlib import Path
from collections import ... | pd.read_csv(c, sep="@", header=None) | pandas.read_csv |
from cplvm import CPLVM
from cplvm import CPLVMLogNormalApprox
from pcpca import CPCA, PCPCA
import functools
import warnings
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd
import tensorflow.compat.v2 as tf
import tensorflow_probability as tfp
from tensorflow_probabilit... | pd.DataFrame(Y.T) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable=W0612,E1101
from collections import OrderedDict
from datetime import datetime
import numpy as np
import pytest
from pandas.compat import lrange
from pandas import DataFrame, MultiIndex, Series, date_range, notna
import pandas.core.panel as panelm
from pandas.core.panel impor... | Series([0.0] * 5) | pandas.Series |
import math
import matplotlib.pyplot as plt
import seaborn as sns
from numpy import ndarray
from pandas import DataFrame, np, Series
from Common.Comparators.Portfolio.AbstractPortfolioComparator import AbstractPortfolioComparator
from Common.Measures.Portfolio.PortfolioBasics import PortfolioBasics
from Common.Measur... | DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 15 14:33:01 2018
@author: AyushRastogi
"""
# Extracting the cumulative 60, 90, 180, 365 and 730 day production for Oil, Gas and Water
import pandas as pd
import os
os.getcwd() # Get the default working directory
path = r'C:\Users\ayush\Desktop\Meetup2_All ... | pd.to_numeric(df['180_Interpol_WATER'], errors='coerce') | pandas.to_numeric |
"""
Written by <NAME>, 22-10-2018
This script contains functions for data formatting and accuracy assessment of keras models
"""
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
import keras.backend as K
from math import sqrt
import numpy as ... | pd.concat(cols, axis=1) | pandas.concat |
import numpy as np
import pylab as pl
from itertools import product
from lib_predict_io import find_matching_trials, load_experiment_data, load_simulation_data
from motionstruct.functions import dist_mod2pi
def score_sep(vb, vn):
"""We combine var_bias and Sig_noise in a score,
ranging from 0 (only bias) to... | pd.Series(dtype=d) | pandas.Series |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 22 16:30:38 2019
input/output operation.
@author: zoharslong
"""
from base64 import b64encode, b64decode
from numpy import ndarray as typ_np_ndarray
from pandas.core.series import Series as typ_pd_Series # 定义series类型
from pandas.core.... | pd_DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
from numpy.random import randint
import os
import netCDF4
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from mpl_toolkits.axes_grid1 import make_axes_locatable
from tensorflow.keras.optimizers import Adam
import logging
logger = logging.getLogger(__nam... | pd.DataFrame(spearman_tmp[24:-25, 7:-6,c]) | pandas.DataFrame |
import json
import os
import glob
import random
from typing import Union
try:
import xarray as xr
except ModuleNotFoundError:
xr = None
import numpy as np
import pandas as pd
from .datasets import Datasets
from .utils import check_attributes, download, sanity_check
from ai4water.utils.utils import dateandtim... | pd.read_csv(f, sep=' ', index_col='gauge_id', nrows=1) | pandas.read_csv |
import pandas as pd
#import numpy as np
from sklearn.ensemble import RandomForestClassifier #, RandomForestRegressor
from sklearn.metrics import roc_auc_score #, mean_squared_error
# 2018.11.28 Created by Eamon.Zhang
def feature_shuffle_rf(X_train,y_train,max_depth=None,class_weight=None,top_n=15,n_estimators=50,r... | pd.Series(feature_dict) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Use this URL for a Google Colab Demo of this class and its usage:
https://colab.research.google.com/drive/154_2tvDn_36pZzU_XkSv9Xvd3KjQCw1U
"""
from datetime import timedelta, datetime, timezone
import sys, os, time, random
import pandas as pd
import json
import csv
import sqlite3
from ... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Grading
"""
#These are the packages you need to install, this will try to install them, otherwise use pip to install
try:
import requests
except:
import pip
pip.main(['install', 'requests'])
import requests
try:
import pandas as pd
except:
import pip
pip.m... | pd.DataFrame(rubric_return['assessments']) | pandas.DataFrame |
import pytest
import os
from mapping import util
from pandas.util.testing import assert_frame_equal, assert_series_equal
import pandas as pd
from pandas import Timestamp as TS
import numpy as np
@pytest.fixture
def price_files():
cdir = os.path.dirname(__file__)
path = os.path.join(cdir, 'data/')
files = ... | pd.Index(["CO1"], name="generic") | pandas.Index |
import pandas as pd
from functools import reduce
from fooltrader.contract.files_contract import *
import re
import json
class agg_future_dayk(object):
funcs={}
def __init__(self):
self.funcs['shfeh']=self.getShfeHisData
self.funcs['shfec']=self.getShfeCurrentYearData
self.funcs['ineh']... | pd.concat(pds) | pandas.concat |
from typing import Sequence
import pandas as pd
import numpy as np
from datetime import date
def replace_values_having_less_count(dataframe: pd.DataFrame, target_cols: Sequence[str], threshold: int = 100, replace_with="OTHER") -> pd.DataFrame:
for c in target_cols:
vc = dataframe[c].value_counts()
... | pd.get_dummies(data_frame[column], drop_first=True) | pandas.get_dummies |
import pandas as pd
import re
import numpy as np
import os
import sys
from collections import OrderedDict, defaultdict
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats, integrate
# load msncodes
msncodes = pd.read_csv("data/csv/original/msncodes.csv")
# load state ... | pd.read_csv("data/csv/state_data/ca_data.csv", engine='c', low_memory=True) | pandas.read_csv |
import pandas as pd
from unittest import TestCase # or `from unittest import ...` if on Python 3.4+
import tests.helpers as th
import numpy as np
import category_encoders as encoders
class TestLeaveOneOutEncoder(TestCase):
def test_leave_one_out(self):
np_X = th.create_array(n_rows=100)
np_X_t ... | pd.DataFrame(np_y_t) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 17 13:21:08 2019
@author: tatumhennig
"""
import numpy as np
import pandas as pd
## ROT rotates and flips a quadrant appropriately.
# Parameters:
# Input, integer N, the length of a side of the square.
# N must be a power of 2.
# Input/... | pd.read_csv(name + '_phipsi.csv') | pandas.read_csv |
from typing import Optional, Union, Tuple
import numpy as np
import pandas as pd
import okama.common.helpers.ratios as ratios
from .common.helpers.helpers import Frame, Float, Date, Index
from .common.make_asset_list import ListMaker
class AssetList(ListMaker):
"""
The list of financial assets implementati... | pd.concat([df, s2], axis=1, copy="false") | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 28 17:32:38 2019
@author: Saint8312
"""
import numpy as np
import pandas as pd
import sys, os
import time
import multiprocessing
import itertools
import pickle
'''
math functions
'''
f_euclid_dist = lambda a,b: np.linalg.norm(a-b)
def f_h_step(x, a):
return 1 if (x... | pd.DataFrame(l) | pandas.DataFrame |
#!/usr/bin/env python
"""
CEP to coordinates (latitude and longitude).
This script receives list of brazilian postal code and returns the
latitude and longitude coordinates related to this postal code, based
on information provided by Open Street Map (OSM).
"""
import sys # basic system library
import urllib # to o... | pd.read_excel(filename) | pandas.read_excel |
# -*- coding: utf-8 -*-
import logging
import traceback
import numpy as np
import pandas as pd
from scipy.optimize import minimize
from sklearn import linear_model
from Eturb import Eturb
from Bturb import Bturb
from turbine_optimizer import objective, contraint, optimizer
# 汽轮发电机组停开机状态判断进汽量
ETURB_M1_MACHINE_STATUS =... | pd.read_csv("/mnt/e/dev/data-analysis/turbine_model/data/1109/result-1109_dropna.csv") | pandas.read_csv |
import os
from glob import glob
import numpy as np, pandas as pd, matplotlib.pyplot as plt
from astropy.io import ascii as ap_ascii
from numpy import array as nparr
from astrobase.services.gaia import objectid_search
from mpl_toolkits.axes_grid1 import make_axes_locatable
from stringcheese import pipeline_utils as pu
... | pd.concat((_hr, _rv)) | pandas.concat |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pickle
from sklearn.metrics import accuracy_score, roc_curve, auc
#constants calculated from eda & feature engineering
lead_time_mean = float(np.load('lead_time_mean.npy'))
potential_issue_probability_matrix = | pd.read_csv('potential_issue_probability_matrix.csv') | 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
from copy import deepcopy
import vectorbt as vbt
from vectorbt.portfolio.enums import *
from vectorbt.generic.enums import drawdown_dt
from vectorbt.utils.random_ im... | pd.Series([1, 100]) | pandas.Series |
from keras.layers import Input, Embedding, LSTM, Dense, concatenate, Bidirectional
from keras.models import Model, Sequential
import numpy as np
import pandas as pd
from sklearn.metrics import classification_report, \
confusion_matrix, auc, roc_curve, zero_one_loss, accuracy_score
from keras.preprocessing.text impo... | pd.DataFrame(xbo) | pandas.DataFrame |
"""
Provide classes to perform the groupby aggregate operations.
These are not exposed to the user and provide implementations of the grouping
operations, primarily in cython. These classes (BaseGrouper and BinGrouper)
are contained *in* the SeriesGroupBy and DataFrameGroupBy objects.
"""
from __future__ import annota... | is_float_dtype(values.dtype) | pandas.core.dtypes.common.is_float_dtype |
import sys
import dask
import dask.dataframe as dd
from distributed import Executor
from distributed.utils_test import cluster, loop, gen_cluster
from distributed.collections import (_futures_to_dask_dataframe,
futures_to_dask_dataframe, _futures_to_dask_array,
futures_to_dask_array, _stack, stack)
imp... | tm.assert_index_equal(a, b) | pandas.util.testing.assert_index_equal |
import datetime
from abc import abstractmethod, ABC
from typing import List, Tuple, Dict
import numpy as np
import pandas as pd
from minotor.constants import DATETIME_ID_FORMAT
from minotor.data_managers.data_types import DataType
class PreprocessorABC(ABC):
def __init__(self):
self.current_ids = None
... | pd.isna(x) | pandas.isna |
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# 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 a... | pd.testing.assert_frame_equal(result, expected) | pandas.testing.assert_frame_equal |
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