prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 10 16:45:01 2017
@author: Isaac
"""
def make():
import pandas as pd
import random
import numpy as np
from datetime import datetime
from numpy import genfromtxt
from time import time
from datetime import datetime
from... | pd.read_csv('dataSeed/players.csv') | pandas.read_csv |
# pylint: disable=E1101
from datetime import datetime
import datetime as dt
import os
import warnings
import nose
import struct
import sys
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
from pandas.compat import iterkeys
from pandas.core.frame import DataFrame, Series
from pandas.c... | read_stata(self.dta17_117, convert_missing=True) | pandas.io.stata.read_stata |
from project import logger
from flask_mongoengine import ValidationError
from mongoengine import MultipleObjectsReturned, DoesNotExist
import pandas as pd
def get_user(id_, username=None):
from project.auth.models import User
user_obj = None
try:
if username:
user_obj = User.objects.... | pd.DataFrame(values, index=category) | pandas.DataFrame |
from __future__ import absolute_import
import numpy as np
import pandas as pd
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import StandardScaler, MinMaxScaler, MaxAbsScaler
from keras.utils import np_utils
from nas4candle.candle.common.default_utils import DEFAULT_SEED
from nas4candle.candle... | pd.get_dummies(df_train[class_col]) | pandas.get_dummies |
# -*- coding: utf-8 -*-
import math
import os
import seaborn as sns
import pickle
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import wandb
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
def periods_where_pv_is_null(d... | pd.concat([data_zone[i][j] for i in [0, 1, 2]], axis=0, join='inner') | pandas.concat |
import glob
import json
import logging
import os.path
import re
from datetime import datetime
from os import mkdir
from os.path import exists, isfile, join
import pandas as pd
from bsbetl import calc_helpers, g, helpers
def save_runtime_config():
''' call this after runtime values need to be persisted '''
... | pd.HDFStore(ov_fn) | pandas.HDFStore |
import numpy as np
import pandas as pd
from datetime import datetime
import pytest
import empyrical
from vectorbt import defaults
from vectorbt.records.drawdowns import Drawdowns
from tests.utils import isclose
day_dt = np.timedelta64(86400000000000)
index = pd.DatetimeIndex([
datetime(2018, 1, 1),
datetime... | pd.Series.vbt.returns.from_price(ts['a'], year_freq='365 days') | pandas.Series.vbt.returns.from_price |
"""Geographical extracts of natural increase, nom and nim
"""
from pathlib import Path
import pandas as pd
import data
import file_paths
from data import read_abs_data, read_abs_meta_data
DATA_ABS_PATH = Path.home() / "Documents/Analysis/Australian economy/Data/ABS"
def read_3101():
series_id = data.series_i... | pd.read_parquet(filepath) | pandas.read_parquet |
""" this is a mixture of the best #free twitter sentimentanalysis modules on github.
i took the most usable codes and mixed them into one because all of them
where for a linguistical search not usable and did not show a retweet or a full tweet
no output as csv, only few informations of a tweet, switching la... | pd.DataFrame(data=[tweet.full_text for tweet in tweets], columns=['tweets']) | pandas.DataFrame |
from datetime import datetime
import pandas as pd
import pytest
from dask import dataframe as dd
import featuretools as ft
from featuretools import Relationship
from featuretools.tests.testing_utils import to_pandas
from featuretools.utils.gen_utils import import_or_none
ks = import_or_none('databricks.koalas')
@p... | pd.isnull(v2) | pandas.isnull |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 18 15:52:01 2020
modeling operation.
requirements = [
'matplotlib>=3.1.3',
'sklearn>=0.22.1',
'seaborn>=0.10.0',
'factor_analyzer>=0.3.2',
'joblib>=0.14.1',
]
@author: zoharslong
"""
from numpy import max as np_max, min as np_min, ... | DataFrame(dtf_fct, index=self._x.index) | pandas.DataFrame |
#!/usr/bin/env python
# =============================================================================
# GLOBAL IMPORTS
# =============================================================================
import os
import numpy as np
import pandas as pd
from typeI_analysis import mae, rmse, barplot_with_CI_errorbars
from ty... | pd.read_csv(statistics_filename, index_col=False) | pandas.read_csv |
"""
Market Data Presenter.
This module contains implementations of the DataPresenter abstract class, which
is responsible for presenting data in the form of mxnet tensors. Each
implementation presents a different subset of the available data, allowing
different models to make use of similar data.
"""
from typing impo... | pd.Series.ewm(macd, span=9) | pandas.Series.ewm |
import io
import numpy as np
import pytest
from pandas.compat._optional import VERSIONS
from pandas import (
DataFrame,
date_range,
read_csv,
read_excel,
read_feather,
read_json,
read_parquet,
read_pickle,
read_stata,
read_table,
)
import pandas._testing as tm
from pandas.util... | read_csv("memory://test/test.csv", parse_dates=["dt"]) | pandas.read_csv |
"""
Copyright 2019 <NAME>.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distribut... | pd.Series(actual) | pandas.Series |
import sys
sys.path.append('/pvc/')
import src.evaluation_utils as evaluation_utils
import utils.utils as utils
import datasets
import pandas as pd
import numpy as np
def save_adapter_metrics(data_paths, language, eval_dataset_name, eval_type, eval_model_path, output_path, nsamples):
train_dataset, dev_dataset,... | pd.DataFrame.from_dict(result) | pandas.DataFrame.from_dict |
"""
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.Int64Dtype() | pandas.Int64Dtype |
from __future__ import division
from unittest import TestCase
from nose_parameterized import parameterized
from numpy.testing import assert_allclose, assert_almost_equal
import numpy as np
import pandas as pd
import pandas.util.testing as pdt
from .. import timeseries
from .. import utils
DECIMAL_PLACES = 8
class... | pd.Timestamp('2000-01-22') | pandas.Timestamp |
import numpy as np
import pandas as pd
from pycytominer import aggregate
from pycytominer.cyto_utils import infer_cp_features
# Build data to use in tests
data_df = pd.concat(
[
pd.DataFrame({"g": "a", "Cells_x": [1, 3, 8], "Nuclei_y": [5, 3, 1]}),
pd.DataFrame({"g": "b", "Cells_x": [1, 3, 5], "Nuc... | pd.DataFrame({"g": "b", "Cells_x": [3], "Nuclei_y": [4]}) | pandas.DataFrame |
import unittest
from pandas import DataFrame
from my_lambdata6.assignment import add_state_names_column
class TestMyAssignment(unittest.TestCase):
def test_add_state_names(self):
df = | DataFrame({'abbrev': ['CA', 'CO', 'CT', 'DC', 'TX']}) | pandas.DataFrame |
import warnings
warnings.filterwarnings("ignore")
import sys
import os
import pandas
from gensim.models import Word2Vec
import numpy as np
import torch
import torch.utils.data as Data
from vectorize_patch import PatchVectorizer
from svm_clf import SVM
from transformer_class import Config
from transformer_class import... | pandas.DataFrame(vectors) | pandas.DataFrame |
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
# Data for plotting
df = | pd.read_csv('data/primes.txt', header=None, names=['n', 'prime', 'diff']) | pandas.read_csv |
"""
Growth Curve Collation
===========================
This script reads through all experiments within `code/processing/growth_curves/`
and `code/procssing/diauxic_shifts/` and collates all data from "accepted" experiments.
`collated_experiment_record_OD600_growth_curves.csv`:
This is a long-form tidy CSV file wi... | pd.concat(shift_curves, sort=False) | pandas.concat |
# -*- coding: utf-8 -*-
# pylint: disable=E1101,E1103,W0232
import os
import sys
from datetime import datetime
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
import pandas.compat as compat
import pandas.core.common as com
import pandas.util.testing as tm
from pandas import (Categor... | tm.assert_frame_equal(df, exp_single_cats_value) | pandas.util.testing.assert_frame_equal |
from scipy import stats
import numpy as np
import pandas as pd
import re
class PWR(object):
def __init__(self, weight=1, regress_to=None, values=None):
self.weight = weight
self.regress_to = regress_to
if values is None:
self.values = None
else:
s... | pd.merge(self.combined, system.values, on='Player', suffixes=('','_')) | pandas.merge |
# -*- coding: utf-8 -*-
"""
.. module:: trend
:synopsis: Trend Indicators.
.. moduleauthor:: <NAME> (Bukosabino)
"""
import pandas as pd
import numpy as np
from .utils import *
def macd(close, n_fast=12, n_slow=26, fillna=False):
"""Moving Average Convergence Divergence (MACD)
Is a trend-following mome... | pd.Series(kst_sig, name='kst_sig') | pandas.Series |
# -*- coding: utf-8 -*-
class DictionaryResult:
""" Main class of library """
def __init__(self, results):
self.results = results
def help(self):
print("""
[HELP] PicSureHpdsLib.Client(connection).useResource(uuid).dictionary().find(term)
.count() Returns the num... | pandas.DataFrame(data=ret) | pandas.DataFrame |
#!/usr/bin/env python3
import argparse
import sys
import numpy as np
import pandas as pd
import sklearn.datasets
import sklearn.metrics
import sklearn.model_selection
class DecisionTree:
def __init__(
self,
max_depth=None,
min_to_split=2,
max_leaves=None,
criterion="gini",... | pd.Series(targets) | pandas.Series |
import pandas as pd
import numpy as np
# Loading libraries for modeling
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import confusion_matrix, recall_score
import time
import pickle
import argparse
import sys
import os
import cProfile,... | pd.DataFrame(y_train) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pandas._testing as tm
import ibis
def test_map_length_expr(t):
expr = t.map_of_integers_strings.length()
result = expr.execute()
expected = pd.Series([0, None, 2], name='map_of_integers_strings')
tm.assert_series_equal(result, expected)
def test_map_val... | pd.Series([4, 1, 4], name='dup_strings') | pandas.Series |
"""
Unit test suite for OLS and PanelOLS classes
"""
# pylint: disable-msg=W0212
from __future__ import division
from datetime import datetime
import unittest
import nose
import numpy as np
from pandas import date_range, bdate_range
from pandas.core.panel import Panel
from pandas import DataFrame, Index, Series, no... | ols(y=y, x=data, window=20, min_periods=10) | pandas.stats.api.ols |
import datetime as dt
import unittest
import pandas as pd
import numpy as np
import numpy.testing as npt
import seaice.nasateam as nt
import seaice.tools.plotter.daily_extent as de
class Test_BoundingDateRange(unittest.TestCase):
def test_standard(self):
today = dt.date(2015, 9, 22)
month_bound... | pd.to_datetime('2010-01-15') | pandas.to_datetime |
# vim: fdm=indent
# author: <NAME>
# date: 16/08/17
# content: Dataset functions to reduce dimensionality of gene expression
# and phenotypes.
# Modules
import numpy as np
import pandas as pd
from .plugins import Plugin
from ..utils.cache import method_caches
from ..counts_table.counts_table i... | pd.DataFrame(L, index=X.index, columns=X.columns) | pandas.DataFrame |
import json
import pandas as pd
import geopandas as gp
import requests
from shapely.geometry import Point
def create_folder(path):
"""Create empty directory if outpath does not already exist."""
path.parent.mkdir(parents=True, exist_ok=True)
def get_raw_data(query):
"""Get raw text data of pumps from O... | pd.Series(dtype=str) | pandas.Series |
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.api.types import is_float, is_float_dtype, is_scalar
from pandas.core.arrays import IntegerArray, integer_array
from pandas.tests.extension.base import BaseOpsUtil
class TestArithmeticOps(BaseOpsUtil):
def _check_divmod... | tm.assert_extension_array_equal(result, expected) | pandas._testing.assert_extension_array_equal |
# bca4abm
# See full license in LICENSE.txt.
from builtins import range
import logging
import os.path
import numpy as np
import pandas as pd
import itertools
from activitysim.core import inject
from activitysim.core import config
from activitysim.core import tracing
from bca4abm import bca4abm as bca
logger = log... | pd.concat([base_hhs_df, base_cocs_df], axis=1) | pandas.concat |
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# This file contains dummy data for the model unit tests
import numpy as np
import pandas as pd
AIR_FCST_LINEAR_95 = pd.DataFrame(
{
... | pd.Timestamp("2012-08-19 00:00:00") | pandas.Timestamp |
import logging
logging.basicConfig(level=logging.WARNING)
import pytest
import numpy
import os
import pypipegraph as ppg
import pandas as pd
from pathlib import Path
from pandas.testing import assert_frame_equal
import dppd
import dppd_plotnine # noqa:F401
from mbf_qualitycontrol.testing import assert_image_equal
fro... | pd.DataFrame({"chr": ["1b"], "start": [1200], "stop": [1232]}) | pandas.DataFrame |
import pandas as pd
import numpy as np
import h5py
"""
This module loads h5 files created by a DSM2 hydro or qual run.
All the input, geometry and data tables are available as pandas DataFrame objects
In addition there are convenience methods for retrieving the data tables as
DataFrame that represent time seri... | pd.DataFrame(bf, dtype=np.str) | pandas.DataFrame |
import re
import unicodedata
from collections import Counter
from itertools import product
import pickle
import numpy as np
import pandas as pd
from sklearn.decomposition import TruncatedSVD
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import LabelEncoder
import umap
import pickle
fro... | pd.to_datetime(test.publishedAt) | pandas.to_datetime |
#!/usr/bin/python3
import json, dateutil
import pandas as pd
import coin_wizard.broker_platform_objects as BrokerPlatform
from datetime import datetime
from time import sleep
from oandapyV20 import API
import oandapyV20.endpoints.accounts as accounts
import oandapyV20.endpoints.orders as orders
import oandapyV20.end... | pd.to_datetime(df['timestamp']) | pandas.to_datetime |
import pandas as pd
import numpy as np
from urllib.request import urlopen
import requests
from bs4 import BeautifulSoup
from unidecode import unidecode
from Player import Player
class SeasonStats:
"""
The class scrapes and stores NBA Player Stats for a certain NBA season.
"""
def __init__(self, s... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import ee
import pandas as pd
import datetime
import geopandas
# Filter collection by point and date
def collection_filtering(point, collection_name, year_range, doy_range):
collection = ee.ImageCollection(collection_name)\
.filterBounds(point)\
.filter(ee.Filter.calendarRange(... | pd.DatetimeIndex(data['datetime']) | pandas.DatetimeIndex |
from surfboard.sound import Waveform
# import numpy as np
import pandas as pd
import altair as alt
path = "../resources/no-god.wav"
# Instantiate from a .wav file.
sound = Waveform(path=path, sample_rate=44100)
# OR: instantiate from a numpy array.
# sound = Waveform(signal=np.sin(np.arange(0, 2 * np.pi, 1/24000)),... | pd.DataFrame(f0_contour[0], columns=["pitch"]) | pandas.DataFrame |
#
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | pd.DataFrame.from_dict(report, orient='index') | pandas.DataFrame.from_dict |
import os
from os.path import join
import pandas as pd
import numpy as np
import torch
from Hessian.GAN_hessian_compute import hessian_compute
# from hessian_analysis_tools import scan_hess_npz, plot_spectra, average_H, compute_hess_corr, plot_consistency_example
# from hessian_axis_visualize import vis_eigen_explore,... | pd.DataFrame(SSIM_stat_col, columns=["id", "cc", "logcc", "reg_slop", "reg_intcp", "reg_log_slop", "reg_log_intcp", "H_cc", "logH_cc"]) | pandas.DataFrame |
"""
Author: <NAME>
"""
import numpy as np
import pandas as pd
class Naive_Bayes_Classifier():
def __init__(self):
#save the classes and their data
self.data_class={}
def fit(self,X_train,y_train):
def group_data_to_classes(data_class,X_train,... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: UTF-8 -*-
"""
base class and functions to handle with hmp file and GWAS results
"""
import re
import sys
import numpy as np
import pandas as pd
import os.path as op
from tqdm import tqdm
from pathlib import Path
from subprocess import call
from collections import Counter
from schnablelab.apps.base import... | pd.read_csv(self.fn, delim_whitespace=True, dtype=self.dtype_dict) | pandas.read_csv |
# Author: <NAME>, PhD
# University of Los Angeles California
import os
import sys
import re
import tkinter as tk
from tkinter import ttk
from tkinter import filedialog
import matplotlib
matplotlib.use("TkAgg")
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib import pyplot as plt
import ... | pd.DataFrame(log) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 7 12:04:39 2018
@author: saintlyvi
"""
import time
import pandas as pd
import numpy as np
from sklearn.cluster import MiniBatchKMeans, KMeans
import somoclu
from experiment.algorithms.cluster_prep import xBins, preprocessX, clusterStats, bestClu... | pd.concat([cluster_lbls, best_clusters],axis=1) | pandas.concat |
from abc import ABC, abstractmethod
from enum import Enum, auto
from math import sqrt
from pathlib import Path
from typing import Callable, ClassVar, Dict, Optional, Tuple, Type
import pandas
from pandas import DataFrame, Series
from ..util.integrity import recursive_sha256
from .filetype import Csv, FileType
from .r... | DataFrame(columns=pandas_columns) | pandas.DataFrame |
"""Unit tests for soundings.py."""
import copy
import unittest
import numpy
import pandas
from gewittergefahr.gg_utils import soundings
from gewittergefahr.gg_utils import nwp_model_utils
from gewittergefahr.gg_utils import storm_tracking_utils as tracking_utils
from gewittergefahr.gg_utils import temperature_conversi... | pandas.DataFrame(THIS_MATRIX) | pandas.DataFrame |
import argparse
import logging
import os
import json
import boto3
import subprocess
import sys
from urllib.parse import urlparse
os.system('pip install autogluon')
from autogluon import TabularPrediction as task
import pandas as pd # this should come after the pip install.
logging.basicConfig(level=logging.DEBUG)
... | pd.DataFrame.from_dict({'Predicted': y_pred}) | pandas.DataFrame.from_dict |
#!/usr/bin/env python3
# coding=utf-8
import datetime as dt
import logging
# The arrow library is used to handle datetimes
import arrow
import pandas as pd
from parsers import occtonet
from parsers.lib.config import refetch_frequency
# Abbreviations
# JP-HKD : Hokkaido
# JP-TH : Tohoku
# JP-TK : Tokyo area
# JP-CB... | pd.merge(df, df2, how="inner", on="datetime") | pandas.merge |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import pandas
from pandas.compat import string_types
from pandas.core.dtypes.cast import find_common_type
from pandas.core.dtypes.common import (
is_list_like,
is_numeric_dtype,
... | pandas.Index(final_columns) | pandas.Index |
#import requests
#youtube=requests.get(youtube_trending_url)
#youtube1=youtube.text
#print(youtube.status_code)
#print(len(youtube1))
#from bs4 import BeautifulSoup
#doc = BeautifulSoup(youtube1, 'html.parser')
youtube_trending_url='https://youtube.com/trending'
#response=requests.get(youtube_trending_url)
#with open('... | pd.DataFrame(videos_data) | pandas.DataFrame |
"""
Evaluation
----------
Evaluation metrics and plotting techniques for models.
Based on
Uber.Causal ML: A Python Package for Uplift Modeling and Causal Inference with ML. (2019).
URL:https://github.com/uber/causalml.
<NAME>. & <NAME>. (2011). Real-World Uplift Modelling with Significance-Based Uplift T... | pd.DataFrame(qini_metrics) | pandas.DataFrame |
import pandas as pd
import pytz
import datetime
from sqlalchemy.types import *
def convert_result_to_df(data):
df = | pd.DataFrame(data) | pandas.DataFrame |
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from calh.visualization import Heatmap
from . import CURR_DIR
def test_date_df_for_heatmap_from_ics_input():
hm = Heatmap(input_data=CURR_DIR / "data" / "ics" / "02-04_05-05-2020_urlab.ics")
expected_date_df = pd.DataFrame(
... | pd.to_datetime(expected_date_df["date"], utc=True) | pandas.to_datetime |
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pypatent
def search_patent():
"""
Find Rooster cumstomers in the patent databases
Find all operators in the mesenchymal/exosome sector
Identify operators not citing Rooster
"""
print("running s... | pd.DataFrame() | pandas.DataFrame |
from typing import Tuple, Union
import datetime
import os
from xlrd import XLRDError
import pandas as pd
def load_df(url: str, sheet_name: Union[int, str] = 0) -> Tuple[pd.DataFrame, bool]:
from_html = os.path.splitext(url)[1] in ['.htm', '.html']
# Read from input file
if from_html:
try:
... | pd.Timestamp(x) | pandas.Timestamp |
# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership. The Modin Development Team licenses this file to you under the
# Apache License, Version 2.0 (the "License"); you may not u... | pandas.DataFrame(df.iloc[0]) | pandas.DataFrame |
"""
The data_cleaner module is used to clean missing or NaN values from pandas dataframes (e.g. removing NaN, imputation, etc.)
"""
import pandas as pd
import numpy as np
import logging
from sklearn.preprocessing import Imputer
import os
from scipy.linalg import orth
log = logging.getLogger('mastml')
def flag_outli... | pd.concat([df_hold_out, df_imputed], axis=1) | pandas.concat |
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, MultiIndex, Series
import pandas._testing as tm
class TestDataFrameIsIn:
def test_isin(self):
# GH#4211
df = DataFrame(
{
"vals": [1, 2, 3, 4],
"ids": ["a", "b", "f", "n"... | DataFrame([[1, 1], [1, 0], [0, 0]], columns=["A", "A"]) | pandas.DataFrame |
from datetime import time
import numpy as np
import pytest
from pandas import DataFrame, date_range
import pandas._testing as tm
class TestBetweenTime:
def test_between_time(self, close_open_fixture):
rng = date_range("1/1/2000", "1/5/2000", freq="5min")
ts = DataFrame(np.random.randn(len(rng), ... | DataFrame(rand_data, index=rng, columns=rng) | pandas.DataFrame |
import dash_html_components as html
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash
import plotly.graph_objects as go
import plotly.figure_factory as ff
from dash.dependencies import Input, Output
import calendar
import datetime
from datetime import datetime
import pandas as pd
i... | pd.to_datetime(df.DISCHTIME) | pandas.to_datetime |
import argparse
import logging
import multiprocessing as mp
import os
import pickle
import re
import sys
import warnings
from datetime import datetime
from itertools import product
import pandas as pd
import tabulate
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from greenguard import get... | pd.to_timedelta(orig_rule) | pandas.to_timedelta |
# -*- coding: utf-8 -*-
import warnings
from datetime import datetime, timedelta
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.errors import PerformanceWarning
from pandas import (Timestamp, Timedelta, Series,
DatetimeIndex, TimedeltaIndex,
... | tm.assert_index_equal(rng, expected) | pandas.util.testing.assert_index_equal |
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 28 09:19:10 2022
@author: BM109X32G-10GPU-02
"""
import torch
import pandas as pd
import train
import predict
test = train.train('../dataset/world_wide.txt')
f =pd.read_table('../dataset/zinc15.txt')
#predict = predict.predict('../dataset/world_wide.txt',property=True)
to... | pd.DataFrame(predict) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
from scipy.stats import pearsonr
# from mpl_toolkits.axes_grid1 import host_subplot
# import mpl_toolkits.axisartist as AA
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.tic... | pd.DataFrame(df_UmbralH_Nube, columns=['Umbral']) | pandas.DataFrame |
# coding : utf-8
# created by cjr
import pandas as pd
def trip_id_count(train, test):
"""
每名用户的行程数
:param train:
:param test:
:return:
"""
train_data = train.groupby(["TERMINALNO"])["TRIP_ID"].max()
train_df = | pd.DataFrame(train_data) | pandas.DataFrame |
import sys
import time
import pandas as pd
import numpy as np
from datetime import datetime
def func5(gc, cursor):
wb = gc.open_by_url('https://docs.google.com/spreadsheets/d/1mOa_ipZ8xyzvpDcd3QoyRsows')
nexp = wb.worksheet('Downloads')
print('\nConectado ao Google Sheets:Dados Adobe / Downloads.')
... | pd.DataFrame.from_records(rows, columns=[col[0] for col in cursor.description]) | pandas.DataFrame.from_records |
import pandas as pd
import numpy as np
# list of the data files:
days = ["monday.csv","tuesday.csv","wednesday.csv","thursday.csv","friday.csv"]
# creating an empty dataframe for listing all the customer walks:
customer_walks = | pd.DataFrame(columns=["timestamp","customer_no","location","next_location"]) | pandas.DataFrame |
import pandas as pd
import numpy as np
crime = | pd.read_csv("https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/04_Apply/US_Crime_Rates/US_Crime_Rates_1960_2014.csv") | pandas.read_csv |
from os import listdir
from os.path import isfile, join, abspath
import pandas as pd
import sys
import facial_validation_processor as fvp
import warnings
warnings.filterwarnings("ignore")
def import_file(dataset_path):
#Check format
if(dataset_path.endswith(('xlsx', 'xls','csv','dta')) is False):
retu... | pd.io.stata.StataReader(dataset_path) | pandas.io.stata.StataReader |
from torch.utils.data import DataLoader, Dataset
import cv2
import os
from utils import make_mask,mask2enc,make_mask_
import numpy as np
import pandas as pd
from albumentations import (HorizontalFlip, Normalize, Compose, Resize, RandomRotate90, Flip, RandomCrop, PadIfNeeded)
from albumentations.pytorch import To... | pd.DataFrame(predictions2+predictions, columns=[1, 2, 3, 4]) | pandas.DataFrame |
""" Normalizing flow architecture class definitions for param distributions. """
import numpy as np
import scipy.stats
import matplotlib.pyplot as plt
import seaborn as sns
import os
import pickle
import time
import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow_probability.python.internal impor... | pd.concat(opt_it_dfs, ignore_index=True) | pandas.concat |
# Copyright 2021 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | pd.read_csv(sb_incidents_path) | pandas.read_csv |
import pandas as pd
scores = pd.DataFrame({
'Physics': | pd.Series([15, 12, 8, 8, 7, 7, 7, 6, 5, 3]) | pandas.Series |
import os
import math
from tqdm import tqdm
import textwrap
from PIL import Image
import numpy as np
import pandas as pd
import matplotlib.patches as patches
import matplotlib.pyplot as plt
def compute_nb_days(db, start):
"""
Compute the number of days of the project (days between start and the date of the la... | pd.to_datetime(task_history[-1]['actionDate']) | pandas.to_datetime |
import tempfile
from pathlib import Path
import pandas as pd
import pytest
from hypothesis import settings
from autorad.config import config
from autorad.data.dataset import FeatureDataset
settings.register_profile("fast", max_examples=2)
settings.register_profile("slow", max_examples=10)
prostate_root = Path(confi... | pd.DataFrame() | pandas.DataFrame |
import copy
import os
import warnings
from collections import OrderedDict
import numpy as np
import pandas as pd
import woodwork as ww
from sklearn.exceptions import NotFittedError
from sklearn.inspection import partial_dependence as sk_partial_dependence
from sklearn.inspection._partial_dependence import (
_grid_... | pd.Series(predictions) | pandas.Series |
## Online battery validation
import os
import glob
import pandas as pd
import numpy as np
import pickle
class BESS(object):
def __init__(self, max_energy, max_power, init_soc_proc, efficiency):
self.soc = init_soc_proc
self.max_e_capacity = max_energy
self.efficiency = efficiency
... | pd.Series(frcst_imbs_hh) | pandas.Series |
# -*- coding: utf-8 -*-
import pandas as pd
from sklearn import metrics
import numpy as np
from config import *
def sigmoid(x, a=60, b=30):
return 1.0 / (1 + np.exp(-a * x + b))
def split_map(x, a=0.3, b=0.7):
return 0. if x < a else 1. if x > b else x
def extract_features(infile, degree=0.95):
df = pd.... | pd.DataFrame({coupon_label: [df[coupon_label][0]], 'auc': [auc]}) | pandas.DataFrame |
import os
import unittest
import warnings
from collections import defaultdict
from unittest import mock
import numpy as np
import pandas as pd
import six
from dataprofiler.profilers import TextColumn, utils
from dataprofiler.profilers.profiler_options import TextOptions
from dataprofiler.tests.profilers import utils ... | pd.concat([df1, df2, df3]) | pandas.concat |
import abc
import os
import numpy as np
import pandas as pd
from odin.utils import get_root_logger
from odin.utils.draw_utils import make_multi_category_plot, display_sensitivity_impact_plot, \
plot_categories_curve, plot_class_distribution
logger = get_root_logger()
class AnalyzerInterface(metaclass=abc.ABCMe... | pd.DataFrame(type_dict) | pandas.DataFrame |
import argparse
import itertools
import hdbscan
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.gridspec as gridspec
import numpy as np
import pandas as pd
import seaborn as sns
from scipy.spatial.distance import pdist, squareform
from sklearn.manifold import TSNE, MDS
from sklearn.decomposit... | pd.DataFrame(max_values) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sun Aug 15 14:54:22 2021
@author: 10979
"""
from Bio import SeqIO
from Bio import Seq
import regex as re
import pandas as pd
import numpy as np
def lncRNA_features(fasta):
records = SeqIO.parse(fasta, 'fasta')
orf_length = []
orf_count = []
orf_position = []
... | pd.DataFrame(data) | pandas.DataFrame |
from termcolor import colored
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
############### Show colored text #############
def bg(value, type='num', color='blue'):
value = str('{:,}'.format(value)) if type == 'num' else str(value)
return colored(' '+value+' ', co... | pd.DataFrame({'dtypes': df.dtypes}) | pandas.DataFrame |
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin, clone
from sklearn.utils.validation import check_is_fitted
from ._grouped_utils import _split_groups_and_values
class GroupedTransformer(BaseEstimator, TransformerMixin):
"""
Construct a transformer per data gro... | pd.DataFrame(X_value) | pandas.DataFrame |
# python 2/3 compatibility
from __future__ import division, print_function
import sys
import os.path
import numpy
import pandas
import copy
import difflib
import scipy
import collections
import json
# package imports
import rba
from .rba import RbaModel, ConstraintMatrix, Solver
from .rba_SimulationData import RBA_Simu... | pandas.DataFrame(index=Controller.Problem.Processes) | pandas.DataFrame |
import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os, sys, time, datetime, pathlib, random, math
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms as tvtransforms
from skimage import io, transform
# HELPER FUNCTION
def _check_if_array_3D(source... | pd.DataFrame(sample) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[18]:
#Question 1
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from kmodes.kmodes import KModes as km
import seaborn as sns
import sklearn.cluster as cl
from sklearn.neighbors import NearestNeighbors as NN
import math
import numpy.linalg as linalg
... | pd.DataFrame(c,columns=labels_Cylinders) | pandas.DataFrame |
# coding: utf-8
# In[1]:
import pandas as pd
import os
import wiggum as wg
import numpy as np
import pytest
def test_basic_load_df_wages():
# We'll first load in some data, this has both regression and rate type trends. We will load it two ways and check that the structure is the same
# In[2]:
la... | pd.unique(labeled_df.result_df['comparison_type']) | pandas.unique |
"""Contains methods and classes to collect data from
tushare API
"""
import pandas as pd
import tushare as ts
from tqdm import tqdm
class TushareDownloader :
"""Provides methods for retrieving daily stock data from
tushare API
Attributes
----------
start_date : str
start date of th... | pd.to_datetime(data_df["date"]) | pandas.to_datetime |
# -*- coding:utf-8 -*-
# By：<NAME>
# Create：2019-12-23
# Update：2021-10-20
# For: Scrape data from weibo and a simple and not so rigours sentiment analysis based on sentiment dictionary
import requests
import re
import os
import time
import random
from lxml import etree
from datetime import datetime, tim... | pd.concat([main_post, temp_main], ignore_index=True, axis=0) | pandas.concat |
#################################################################
# #
# Useful python scripts for interfacing #
# with datasets and programs #
# ... | pd.read_csv(data_path) | pandas.read_csv |
'''
MIT License
Copyright (c) 2020 <NAME>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distri... | pd.read_csv('../input/JAC/JAC_localidades.csv') | pandas.read_csv |
import logging
import re
import time
from urllib.parse import parse_qs
from urllib.parse import urlparse
import pandas as pd
import requests
from bs4 import BeautifulSoup
from covidata import config
from covidata.persistencia.dao import persistir_dados_hierarquicos
def pt_PortoAlegre():
url = config.url_pt_Port... | pd.DataFrame(linhas_df, columns=nomes_colunas) | pandas.DataFrame |
import pandas as pd
import yfinance as yf
import altair as alt
from pandas_datareader import data
import streamlit as st
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
plt.style.use("fivethirtyeight")
# For reading stock data from yahoo
from datetime import datetime
st.header("Part ... | pd.to_datetime(['1970-01-01']) | pandas.to_datetime |
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