prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import json
import os
import pandas as pd
from ..version import __version__
from .description import describe_label_times
from .plots import LabelPlots
SCHEMA_VERSION = "0.1.0"
class LabelTimes(pd.DataFrame):
"""The data frame that contains labels and cutoff times for the target entity."""
def __init__(
... | pd.concat(sample_per_label, axis=0, sort=False) | pandas.concat |
import numpy as np
import pandas as pd
from .util import cartesian
class Namespace:
"""Holds all Variables that are defined
"""
def __init__(self):
pass
def _add(self, name, obj):
setattr(self, name, obj)
NS = Namespace()
class Variable:
"""A discrete variable, with a distrib... | pd.DataFrame.from_records(recs, columns=cols) | pandas.DataFrame.from_records |
from collections import OrderedDict
import datetime
from datetime import timedelta
from io import StringIO
import json
import os
import numpy as np
import pytest
from pandas.compat import is_platform_32bit, is_platform_windows
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
import pandas as pd
import numpy as np
import statsmodels.api as sm
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from scipy import stats
import plotly.graph_objs as go
import cufflinks
cufflinks.go_offline()
def make_hist(df, x, category=None):
"""
Make ... | pd.DataFrame({"param": params, "value": values}) | pandas.DataFrame |
import duckdb
import pandas as pd
import numpy
import pytest
from datetime import date, timedelta
class TestMap(object):
def test_map(self, duckdb_cursor):
testrel = duckdb.values([1, 2])
conn = duckdb.connect()
conn.execute('CREATE TABLE t (a integer)')
empty_rel = conn.table('t')
... | pd.to_datetime(y[0]) | pandas.to_datetime |
import streamlit as st
import pandas as pd
import requests
import os
from dotenv import load_dotenv
from nomics import Nomics
import json
import plotly
import yfinance as yf
import matplotlib.pyplot as plt
from PIL import Image
from fbprophet import Prophet
import hvplot as hv
import hvplot.pandas
import datetime as d... | pd.read_json(nomics_currency_url) | pandas.read_json |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import pytest
import re
from numpy import nan as NA
import numpy as np
from numpy.random import randint
from pandas.compat import range, u
import pandas.compat as compat
from pandas import Index, Series, DataFrame, isn... | Series(['fooBAD__barBAD', NA]) | pandas.Series |
# feature generation & selection
# sample
# full
# kaggle 0.14481
# minimize score
import os
import json
import sys # pylint: disable=unused-import
from time import time
import csv
from pprint import pprint # pylint: disable=unused-import
from timeit import default_timer as timer
import lightgbm as lgb
import numpy ... | pd.read_csv('../input/test.csv') | pandas.read_csv |
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
# data = pd.read_csv('data_subset.csv')
#
# # data formatted to be able to use surprise package
# data_surprise = data[['customer_id', 'product_id','star_rating']].\
# rename(columns={'customer_id': 'userID', 'product_id': 'itemID', 'star_ratin... | pd.read_csv(_file_path, sep='\t', nrows=1) | pandas.read_csv |
import sys
sys.path.append('.')
# stdlib
import os
from glob import glob
from tqdm.auto import tqdm
import json
import pickle
from collections import defaultdict
import time
import argparse
# numlib
import numpy as np
import pandas as pd
from ensemble_boxes import nms, weighted_boxes_fusion
#from include import *
f... | pd.merge(df, df_study_none, on='id', how='left') | pandas.merge |
import os
import pandas
from c3x.data_loaders import configfileparser, nextgen_loaders
from c3x.data_statistics import statistics as stats
# Reads a config file to produce a dictionary that can be handed over to functions
config = configfileparser.ConfigFileParser("config/config_nextGen_stats.ini")
data_paths = conf... | pandas.DataFrame(samples) | pandas.DataFrame |
import json
import pandas as pd
from collections import OrderedDict
from datetime import datetime
from contextlib import closing
import os
import errno
import logging
from airflow.hooks.http_hook import HttpHook
from airflow.hooks.postgres_hook import PostgresHook
def upsert_rows(hook, table, rows, on_c... | pd.to_datetime(df_taxis_connexions.timestampUTC, format='%Y-%m-%dT%H:%M:%S.%fZ') | pandas.to_datetime |
import requests as re
import pandas as pd
from datetime import datetime, timedelta
from typing import Callable
import time
def format_url(coin: str="DOGE") -> str:
url = "https://production.api.coindesk.com/v2/price/values/"
start_time = (datetime.now() - timedelta(minutes=10)).isoformat(timespec="minutes")
... | pd.DataFrame(prices, columns=["time", "price"]) | pandas.DataFrame |
from itertools import compress
import pandas as pd
import numpy as np
from abc import ABCMeta, abstractmethod
from surveyhelper.scale import QuestionScale, LikertScale, NominalScale, OrdinalScale
from scipy.stats import ttest_ind, f_oneway, chisquare
class MatrixQuestion:
__metaclass__ = ABCMeta
def __init__(... | pd.DataFrame(data) | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler, PolynomialF... | pd.concat([X_val_genre,X_val_con_scaled_df],axis=1) | pandas.concat |
from datetime import timedelta
from functools import partial
from itertools import permutations
import dask.bag as db
import numpy as np
import pandas as pd
import pandas.testing as pdt
import pytest
from hypothesis import given, settings
from hypothesis import strategies as st
from kartothek.core.cube.conditions imp... | pdt.assert_frame_equal(df_actual, df_expected, check_like=True) | pandas.testing.assert_frame_equal |
##########################################
# Share issuance as factor
# December 2018
# <NAME>
##########################################
import pandas as pd
import numpy as np
import os
from pandas.tseries.offsets import *
# Note that ccm, comp and crsp_m are WRDS datasets. However, the code is useful for
# other d... | pd.merge(ccm5[['gvkey', 'jdate', 'shs_iss']], nyse, how='inner', on=['gvkey', 'jdate', 'shs_iss']) | pandas.merge |
from datetime import datetime
from typing import Dict
import pandas as pd
import abc
import threading
from loguru import logger as log
from pathlib import Path
class Writer(object):
# TODO: get chunksize from config
def __init__(self):
self.schema = [
'source',
'created_at',
... | pd.concat([self.buffer, df], ignore_index=True, sort=False) | pandas.concat |
# #-- -- -- -- Merging DataFrames with pandas
# # Used for Data Scientist Training Path
# #FYI it's a compilation of how to work
# #with different commands.
# ### --------------------------------------------------------
# # # # ------>>>> Reading DataFrames from multiple files
# Import pandas
import pand... | pd.read_csv('GDP.csv', parse_dates=True, index_col='DATE') | pandas.read_csv |
import logging
import time
import traceback
from collections import Counter
import discord
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import numpy as np
import pandas as pd
from bot import SentdeBot
from discord.ext import commands, tasks
from discord.ext.comm... | pd.to_datetime(df_msgs['time'], unit='s') | pandas.to_datetime |
"""Query DB for analyses."""
import csv
import pandas as pd
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from sqlalchemy import func, and_
from sqlalchemy.sql.expression import distinct
from ideotype.sql_declarative import (IdeotypeBase,
WeaData,
... | pd.DataFrame(results, columns=columns) | pandas.DataFrame |
"""Postprocesses data across dates and simulation runs before aggregating at geographic levels (ADM0, ADM1, or ADM2)."""
import concurrent.futures
import gc
import queue
import shutil
import threading
import numpy as np
import pandas as pd
import tqdm
from fastparquet import ParquetFile
from loguru import logger
from... | pd.DataFrame(q_dict) | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import style
import time as time
import pickle
from astropy.table import Table
from astropy import coordinates as coords
import astropy.units as u
from astroquery.sdss import SDSS
def download_spectra(coord_list_url, from_sp, to_sp... | pd.DataFrame(df) | pandas.DataFrame |
import pandas as pd
import numpy as np
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
from PyQt5.QtGui import *
import sys
class temsilci(QItemDelegate):
def __init__(self, parent=None):
super().__init__()
def olustur(self, parent, option, index):
olustur = QLineEdit(parent)
... | pd.Series(data=["Müşteri Bilgisi Giriniz",0,"Tarih Belirt","Durum Ne","Nakit"], index=["Müşteri Adı-Soyad","Borç Tutarı","Son Ödeme tarihi","Ödendi/Ödenmedi","Ödeme Tipi"]) | pandas.Series |
from __future__ import division
from textwrap import dedent
import numpy.testing as npt
import pandas.util.testing as pdtest
import numpy
from numpy.testing import assert_equal
import pandas
import pytest
from statsmodels.imputation import ros
from statsmodels.compat.python import StringIO
if | pandas.__version__.split('.') | pandas.__version__.split |
import pandas as pd
import numpy as np
import os
from sklearn.linear_model import LogisticRegression
from math import exp
import pickle
def logistic_regression(data_set_path):
X,y = prepare_data(data_set_path)
retain_reg = LogisticRegression(penalty='l1', solver='liblinear', fit_intercept=True)
retain_reg.... | pd.DataFrame(predictions,index=X.index,columns=['churn_prob','retain_prob']) | pandas.DataFrame |
import pandas as pd
import os
import glob
from pathlib import Path
import json
from mlsriracha.interfaces.process import ProcessInterface
from mlsriracha.plugins.kubernetes.common.helper import s3_download, s3_upload, azblob_download
class KubernetesProcess(ProcessInterface):
def __init__(self):
print('... | pd.read_csv(f) | pandas.read_csv |
#----------------------------------------------------------------------- NEEDED PACKAGES ---------------------------------------------------------------------
import pandas as pd
from glob import glob
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import re
import seaborn as sn
import tensor... | pd.DataFrame(mat, index=lab, columns=lab) | pandas.DataFrame |
import os, sys
import numpy as np
import pandas as pd
import time
import pydicom
from glob import glob
def computeSliceSpacing(alldcm):
try:
if len(alldcm)>1:
ds0 = pydicom.dcmread(alldcm[0], force = False, defer_size = 256, specific_tags = ['SliceLocation'], stop_before_pixels = True)
... | pd.DataFrame(columns=specific_tags) | pandas.DataFrame |
from datetime import datetime, timedelta
import numpy as np
import pytest
from pandas._libs.tslibs import period as libperiod
import pandas as pd
from pandas import DatetimeIndex, Period, PeriodIndex, Series, notna, period_range
import pandas._testing as tm
class TestGetItem:
def test_ellipsis(self):
#... | pd.PeriodIndex([p0, p1, p2]) | pandas.PeriodIndex |
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import Index, MultiIndex, Series, date_range, isna
import pandas._testing as tm
@pytest.fixture(
params=[
"linear",
"index",
"values",
"nearest",
"slinear",
... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
import os, sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.optimize
from pyddem.volint_tools import neff_circ, std_err
import functools
import matplotlib.ticker as mtick
from mpl_toolkits.axes_grid.inset_locator import inset_axes
plt.rcParams.update({'font.size': 5})
plt.rcPara... | pd.DataFrame() | pandas.DataFrame |
import keras
import tensorflow as tf
import math
import logging
import numpy as np
import pandas as pd
from keras.callbacks import EarlyStopping
from sacred import Ingredient
from sklearn import preprocessing
from sklearn.cross_validation import KFold
from pypagai.util.class_loader import ClassLoader
tb_callback = ... | pd.DataFrame() | pandas.DataFrame |
"""
Parallel HTTP transport
IMPORT from multiple independent processes running in parallel
"""
import pyexasol
import _config as config
import multiprocessing
import pyexasol.callback as cb
import pandas
import pprint
printer = pprint.PrettyPrinter(indent=4, width=140)
class ImportProc(multiprocessing.Process):
... | pandas.DataFrame(data, columns=['user_id', 'user_name', 'shard_id']) | pandas.DataFrame |
from utils import mol2fp
import os
import pandas as pd
import numpy as np
from rdkit import Chem
import configparser
import argparse
if __name__ == '__main__':
# argments
parser = argparse.ArgumentParser()
parser.add_argument('conf')
args = parser.parse_args()
# load config file
conf_file ... | pd.read_csv(csv_path) | pandas.read_csv |
from bittrex import Bittrex
import requests
import pandas as pd
import os
import bittrex_test as btt
import quandl_api_test as qat
from scrape_coinmarketcap import scrape_data
API_K = os.environ.get('bittrex_api')
API_S = os.environ.get('bittrex_sec')
if API_K is None:
API_K = os.environ.get('btx_key')
API_S =... | pd.io.json.json_normalize(hist['result']) | pandas.io.json.json_normalize |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for Period dtype
import operator
import numpy as np
import pytest
from pandas._libs.tslibs.period import IncompatibleFrequency
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas impo... | tm.box_expected(exp, xbox) | pandas.util.testing.box_expected |
import wf_core_data
import pandas as pd
from collections import OrderedDict
import datetime
import logging
logger = logging.getLogger(__name__)
class FamilySurveyAirtableClient(wf_core_data.AirtableClient):
def fetch_school_inputs(
self,
pull_datetime=None,
params=None,
base_id=wf... | pd.to_datetime(field_name_inputs_df['field_name_input_created_datetime_at']) | pandas.to_datetime |
# (C) Copyright 2021 IBM Corp.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing... | pd.DataFrame(matches_dict) | pandas.DataFrame |
from clean2 import*
import pandas as pd
import matplotlib.pyplot as plt
import math
import datetime
import time
def main():
loop_set=[3,5]
set3=[] #labels scaled at different window sizes
set4=[] #labels without scaling
for i in range(0,len(loop_set)):
set3.extend(['totalmovavg_predictclose... | pd.read_csv('folder address'+'true_features.txt') | pandas.read_csv |
from pathlib import Path
from typing import Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
from omegaconf.dictconfig import DictConfig
from sklearn.model_selection import train_test_split
from torch.nn impo... | pd.concat(val_parts, axis=0, ignore_index=True) | pandas.concat |
import copy
from datetime import datetime
import warnings
import numpy as np
from numpy.random import randn
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, DatetimeIndex, Index, Series, isna, notna
import pandas._testing as tm
from pandas.core.window.common i... | tm.assert_series_equal(expected, x) | pandas._testing.assert_series_equal |
# import des librairies et des datasets
import streamlit as st
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.backends.backend_agg import RendererAgg
import seaborn as sns
import os
# import et fusion des datasets
ptbdb_normal = pd.read_csv("data/ptbdb_normal.csv", header=... | pd.concat([mitbih_test, mitbih_train]) | pandas.concat |
"""
Utilities for time series preprocessing
"""
import numpy as np
import pandas as pd
def get_timeseries_at_node(node_ind, node2feature, ts_code):
"""
Return ts_code time series at node_ind
output shape : (T, )
"""
return node2feature[node_ind][ts_code]
def merge_timeseries(node_indices, node2fe... | pd.to_timedelta(timedelta, unit='m') | pandas.to_timedelta |
import numpy as np
import pandas as pd
import neurokit2 as nk
# =============================================================================
# Example 1
# =============================================================================
# Generate synthetic signals
ecg = nk.ecg_simulate(duration=10, heart_rate=70)
emg ... | pd.DataFrame({"ECG": ecg, "EMG": emg}) | pandas.DataFrame |
#!/usr/bin/env python
# inst: university of bristol
# auth: <NAME>
# mail: <EMAIL> / <EMAIL>
import os
import shutil
from glob import glob
import zipfile
import numpy as np
import pandas as pd
import gdalutils
from osgeo import osr
def _secs_to_time(df, date1):
df = df.copy()
conversion = 86400 # 86400s =... | pd.read_csv(filename, delim_whitespace=True) | pandas.read_csv |
# 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("2012-10-21 00:00:00") | pandas.Timestamp |
import numpy as np
import pandas as pd
import matplotlib.cm as cm
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
from scipy import stats
import warnings
import os
from itertools import combinations
import multiprocessing
from multiprocessing import Pool
from ... | pd.concat([df_fmt, df]) | pandas.concat |
from collections import deque
from datetime import datetime
import operator
import numpy as np
import pytest
import pytz
import pandas as pd
import pandas._testing as tm
from pandas.tests.frame.common import _check_mixed_float, _check_mixed_int
# -------------------------------------------------------------------
# ... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2020/3/24 15:00
Desc: 生意社网站采集大宗商品现货价格及相应基差数据, 数据时间段从 20110104-至今
备注：现期差 = 现货价格 - 期货价格(这里的期货价格为结算价)
黄金为 元/克, 白银为 元/千克, 玻璃现货为 元/平方米, 鸡蛋现货为 元/公斤, 鸡蛋期货为 元/500千克, 其余为 元/吨.
焦炭现货规格是: 一级冶金焦; 焦炭期货规格: 介于一级和二级之间, 焦炭现期差仅供参考.
铁矿石现货价格是: 湿吨, 铁矿石期货价格是: 干吨
网页地址: http://www.100ppi.c... | pd.DataFrame(df_data[df_data["symbol"] == string]) | pandas.DataFrame |
from scipy.optimize import leastsq, curve_fit, minimize, OptimizeResult
import matplotlib
from matplotlib import axes
import matplotlib.pyplot as plt
import numpy as np
import math
from typing import Callable
import datetime
import pandas as pd
from io import StringIO
from numpy import mean, std, median
def f_logis... | pd.read_csv(tempFile, sep='\t', header=None) | pandas.read_csv |
"""
Methods used by Block.replace and related methods.
"""
import operator
import re
from typing import Optional, Pattern, Union
import numpy as np
from pandas._typing import ArrayLike, Scalar
from pandas.core.dtypes.common import (
is_datetimelike_v_numeric,
is_numeric_v_string_like,
is_re,
is_scala... | isna(value) | pandas.core.dtypes.missing.isna |
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
class BaseStrategy:
def __init__(self, df, mv_type):
self.df = df
self.mvType = mv_type
# calculates profit for the specific algorithm
def calculate_profit(self):
# daily profit
self.df["daily_profit"]... | pd.DataFrame(index=self.df.index) | pandas.DataFrame |
import numpy as np
import pytest
from pandas._libs import join as _join
from pandas import Categorical, DataFrame, Index, merge
import pandas._testing as tm
class TestIndexer:
@pytest.mark.parametrize(
"dtype", ["int32", "int64", "float32", "float64", "object"]
)
def test_outer_join... | Index([1, 1, 2, 5]) | pandas.Index |
"""
Various processing utility functions
Usage:
import only
"""
import os
import pandas as pd
from pycytominer.cyto_utils import infer_cp_features
def get_recode_cols():
return_dict = {}
return_dict["recode_cols"] = {
"Metadata_CellLine": "Metadata_clone_number",
"Metadata_Dosage": "Metadat... | pd.read_csv(count_file, sep="\t") | pandas.read_csv |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
# Modifications copyright (C) 2019 <NAME>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in complianc... | pd.DataFrame(result_list) | pandas.DataFrame |
#%%
from pymaid_creds import url, name, password, token
import pymaid
rm = pymaid.CatmaidInstance(url, token, name, password)
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
import numpy.random as random
import gzip
import csv
import connectome_tools.celltype as ct
import... | pd.DataFrame(path_counts_data, columns=['count', 'condition']) | pandas.DataFrame |
"""Construct the clean data set"""
import pandas as pd
from pathlib import PurePath
import numpy as np
import datetime as dt
from pandas.tseries.holiday import USFederalHolidayCalendar
from scipy.interpolate import interp1d
from sklearn.svm import SVR
#================================================================... | pd.notnull(x) | pandas.notnull |
import sys
import timeit
import warnings
import numpy as np
import pandas as pd
from abc import abstractmethod
from math import ceil
from dask import dataframe as dd
from tqdm.auto import tqdm
from .tqdm_dask_progressbar import TQDMDaskProgressBar
from .base import (
_SwifterBaseObject,
suppress_stdout_stder... | pd.Series(tmp_df.values[:, 0]) | pandas.Series |
import pandas as pd
import pytest
from rdtools.normalization import normalize_with_expected_power
from pandas import Timestamp
import numpy as np
@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_... | Timestamp('2020-01-01 12:45:00', freq='15T') | pandas.Timestamp |
from datetime import datetime
import numpy as np
import pytest
from pandas.core.dtypes.cast import find_common_type, is_dtype_equal
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series
import pandas._testing as tm
class TestDataFrameCombineFirst:
def test_combine_first_mixed(self):
... | pd.PeriodIndex(["2012-01-01", "2012-01-02", "2012-01-03"], freq="D") | pandas.PeriodIndex |
from datetime import timedelta
from functools import partial
import itertools
from parameterized import parameterized
import numpy as np
from numpy.testing import assert_array_equal, assert_almost_equal
import pandas as pd
from toolz import merge
from zipline.pipeline import SimplePipelineEngine, Pipeline, CustomFacto... | pd.Timestamp("2015-01-20") | pandas.Timestamp |
""" Test cases for DataFrame.plot """
import warnings
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame
import pandas._testing as tm
from pandas.tests.plotting.common import TestPlotBase, _check_plot_works
@td.skip_if_no_mpl
class TestDataF... | DataFrame(random_array, columns=["A label", "B label", "C label"]) | pandas.DataFrame |
import logging
import os
import numpy as np
import pandas as pd
from opencell.database import utils, constants
logger = logging.getLogger(__name__)
def parseFloat(val):
try:
val = float(val)
except ValueError:
val = float(str(val).replace(',', ''))
return val
def load_library_snapshot(... | pd.read_csv(filename) | pandas.read_csv |
#
# bow_module.py
#
# Copyright (c) 2017 <NAME>
#
# This software is released under the MIT License.
# http://opensource.org/licenses/mit-license.php
#
#
# Includes pandas
#-----------------------------------------------------------------------------
# Copyright (c) 2012, PyData Development Team
# All rights reserved.
... | pd.DataFrame(frequency_matrix, columns=word_list) | pandas.DataFrame |
# Adapted from https://github.com/BinPro/CONCOCT/blob/develop/scripts/fasta_to_features.py
from itertools import product
from collections import OrderedDict
from .fasta import fasta_iter
def generate_feature_mapping(kmer_len):
BASE_COMPLEMENT = {"A": "T", "T": "A", "G": "C", "C": "G"}
kmer_hash = {}
count... | pd.DataFrame.from_dict(composition, orient='index', dtype=float) | pandas.DataFrame.from_dict |
"""
Provide the groupby split-apply-combine paradigm. Define the GroupBy
class providing the base-class of operations.
The SeriesGroupBy and DataFrameGroupBy sub-class
(defined in pandas.core.groupby.generic)
expose these user-facing objects to provide specific functionality.
"""
from contextlib import contextmanager... | Substitution(name="groupby") | pandas.util._decorators.Substitution |
#!/usr/bin/env python
# coding: utf-8
# # Create datasets
# ## Scalling, Reduction and Feature Selection
# The original dataset and/or the ballanced ones will be first splitted into separated files as training and test subsets using a **seed**. All the scalling and feature selection will be apply **only on training s... | pd.DataFrame(X_test_transf, columns=X.columns) | pandas.DataFrame |
import matplotlib.pyplot as plt
import pandas as pd
df = | pd.DataFrame([[1,2,3],[7,0,3],[1,2,2]],columns=['col1','col2','col3'])
df.plot() | pandas.DataFrame |
# Implementation of Multiplicative Marketing Mix Model, Adstock and Diminishing Return
# Author: <NAME>
# Pystan Installation Tips (mac, anaconda3)
# 1. In bash:
# (create a stan environment, install pystan, current version is 2.19)
# conda create -n stan_env python=3.7 -c conda-forge
# conda activate stan_env
# c... | pd.concat([X_media2, X_ctrl2], axis=1) | pandas.concat |
from pandas import DataFrame
import pandas as pd
import numpy as np
import numpy
def method(arr):
index = 0;
ag = arr
for r in arr:
if not (str(r).replace(" ","")== ""):
temp = "$" + str(ag[index])
ag[index] = temp
if r is None:
ag[index] = " "
ind... | pd.Series(pubPriv, index=df2.index) | pandas.Series |
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import itertools
import statsmodels.api as sm
import sklearn
import sklearn.ensemble
from sklearn.model_selection import StratifiedKFold, cross_val_score, LeaveOneOut, LeavePOut, GridSearchCV
import sklearn.linear_model
import ... | pd.concat(probs) | pandas.concat |
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import skimage.io
import functools
from skimage import measure
from scipy.spatial import distance
from sklearn.metrics import pairwise_distances_argmin_min
from loguru import logger
import numpy as np
import matplotli... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 5 13:24:18 2020
@author: earne
"""
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
import seaborn as sns
from sipperplots import (
get_any_idi,
get_side_idi,
get_content_idi,... | pd.DataFrame() | pandas.DataFrame |
# flake8: noqa: F841
import tempfile
from typing import Any, Dict, List, Union
from pandas.io.parsers import TextFileReader
import numpy as np
import pandas as pd
from . import check_series_result, check_dataframe_result
def test_types_to_datetime() -> None:
df = pd.DataFrame({"year": [2015, 2016], "month": [2... | pd.concat({1: s, 2: s2}, axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
import pandas as pd
import pandas.types.concat as _concat
import pandas.util.testing as tm
class TestConcatCompat(tm.TestCase):
def check_concat(self, to_concat, exp):
for klass in [pd.Index, pd.Series]:
to_concat_klass = [klass(c) for c in to_concat]
res ... | pd.DatetimeIndex(['2011-01-01'], tz='Asia/Tokyo') | pandas.DatetimeIndex |
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import pandas.tseries.period as period
from pandas import period_range, PeriodIndex, Index, date_range
def _permute(obj):
return obj.take(np.random.permutation(len(obj)))
class TestPeriodIndex(tm.TestCase):
def setUp(self):
pa... | period_range('7/1/2000', '7/31/2000', freq='D', name='idx') | pandas.period_range |
# -*- coding: utf-8 -*-
""" Librairie personnelle pour manipulation les modèles de machine learning
"""
# ====================================================================
# Outils ML - projet 4 Openclassrooms
# Version : 0.0.0 - CRE LR 23/03/2021
# ==========================================================... | pd.DataFrame({'true': test, 'pred': predict}) | pandas.DataFrame |
"""
Tests the usecols functionality during parsing
for all of the parsers defined in parsers.py
"""
from io import StringIO
import numpy as np
import pytest
from pandas._libs.tslib import Timestamp
from pandas import DataFrame, Index
import pandas._testing as tm
_msg_validate_usecols_arg = (
"'usecols' must eit... | DataFrame(cols, columns=["c_d", "a"]) | pandas.DataFrame |
import math
import matplotlib.pyplot as plt
import numpy as np
from os import listdir
import pandas as pd
import random
import re
from scipy.stats import norm as std_norm
import seaborn as sns
import tqdm
# plots a heat map of chromosome interaction intensities
def interaction_heat_map(all_files, data_path):
bigg... | pd.concat([all_freqs, file_freqs]) | pandas.concat |
import pandas as pd
import matplotlib.pyplot as plt
from graphviz import Digraph
from simrd.telemetry import Telemetry
TRACE_STATS = [
'time', 'pinned_memory', 'locked_memory', 'evictable_memory', 'total_memory', 'memory_pressure'
]
class State:
def __init__(self, telemetry : Telemetry):
self.material = set... | pd.DataFrame(pinned_data, columns=Telemetry.TENSOR_STATS) | pandas.DataFrame |
import pytest
import pandas as pd
import numpy as np
from numpy import pi, sqrt
import matplotlib.pyplot as plt
import os
from numpy.testing import assert_almost_equal, assert_allclose
from rolldecayestimators.ikeda import Ikeda, IkedaR
from rolldecayestimators import lambdas
import rolldecayestimators
import pyscores... | pd.Series(data=data, index=w_hat) | pandas.Series |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Author: <NAME>
date: 2020/1/23 9:07
contact: <EMAIL>
desc: 新增-事件接口
新增-事件接口新型冠状病毒-网易
新增-事件接口新型冠状病毒-丁香园
新增-事件接口新型冠状病毒-百度
"""
import json
import time
from io import BytesIO
import demjson
import pandas as pd
import requests
from PIL import Image
from bs4 import BeautifulSo... | pd.DataFrame([item["today"] for item in data_json["data"]["chinaDayList"]], index=[item["date"] for item in data_json["data"]["chinaDayList"]]) | pandas.DataFrame |
import re
import pandas as pd
from config import Config
class Dataset(Config):
"""
Attributes
----------
ukbb_vars: list
Variable names based on user selections as coded in the Biobank.
recoded_vars: list
Variable names based on user selections as will be recoded.
... | pd.concat(list_series, axis=1) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
import requests
# For storing png files in memory
import io
# For generating GIF
import imageio
###########################################################
########## Globals....
######################... | pd.DataFrame(_tmp_case_list) | pandas.DataFrame |
import numpy as np
import pandas as pd
from functools import reduce
import seaborn as sns
from matplotlib import pyplot
def hap_load_and_process(url_or_path_to_csv_file, rename_dict,final_list):
# Method Chain 1 (Load data and deal with missing data)
df1 = (
pd.read_csv(url_or_path_to_csv_file)
... | pd.read_csv(url_or_path_to_csv_file) | pandas.read_csv |
import itertools
import numpy as np
import pandas as pd
import pytest
import xarray as xr
from xarray.core.missing import (
NumpyInterpolator,
ScipyInterpolator,
SplineInterpolator,
_get_nan_block_lengths,
get_clean_interp_index,
)
from xarray.core.pycompat import dask_array_type
from xarray.tests... | pd.date_range("2001-01-01", freq="H", periods=11) | pandas.date_range |
# This script uses OSMnx to generate the road network data for US municipalities
# Importing required modules
import pandas as pd
import osmnx as ox
import networkx as nx
import matplotlib.cm as cm
import matplotlib.colors as colors
# Defining username + directories
username = ''
direc = 'C:/Users/' + ... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
import re
import warnings
from datetime import timedelta
from itertools import product
import pytest
import numpy as np
import pandas as pd
from pandas import (CategoricalIndex, DataFrame, Index, MultiIndex,
compat, date_range, period_range)
from pandas.compat import PY... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
import logging
import os
import re
import shutil
from datetime import datetime
from itertools import combinations
from random import randint
import numpy as np
import pandas as pd
import psutil
import pytest
from dask import dataframe as dd
from distributed.utils_test import cluster
from tqdm import tqdm
import featu... | pd.Timestamp('2011-04-09 11:00:00') | pandas.Timestamp |
"""
Functions for implementing 'astype' methods according to pandas conventions,
particularly ones that differ from numpy.
"""
from __future__ import annotations
import inspect
from typing import (
TYPE_CHECKING,
cast,
overload,
)
import warnings
import numpy as np
from pandas._libs import lib
from panda... | find_stack_level() | pandas.util._exceptions.find_stack_level |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 5 11:35:21 2021
@author: mariaolaru
"""
import numpy as np
import pandas as pd
from preproc.preprocess_funcs import *
from plts.plot_funcs import *
from proc.process_funcs import *
parent_dir = "/Users/mariaolaru/Box/RC-S_Studies_Regulatory_and_Da... | pd.concat([phs_final, df_phs]) | pandas.concat |
import pandas as pd
from string import punctuation
import nltk
from IPython.core.display import display
nltk.download('tagsets')
from nltk.data import load
nltk.download('averaged_perceptron_tagger')
from nltk import pos_tag
from nltk import word_tokenize
from collections import Counter
def get_tagsets():
tagdi... | pd.testing.assert_frame_equal(test_feature_df, result_df, check_names=False, check_like=True) | pandas.testing.assert_frame_equal |
# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# %%
DATA_ROOT = '../../data/raw'
# %% [markdown]
# ## LOADING DATA
# %%
print('Loading raw datasets...', flush=True)
GIT_COMMITS_PATH = f"{DATA_ROOT}/GIT... | pd.merge(git_commits, sonar_measures, how='inner', on='commitHash') | pandas.merge |
"""
.. _twitter:
Twitter Data API
================
"""
import logging
from functools import wraps
from twython import Twython
import pandas as pd
from pandas.io.json import json_normalize
TWITTER_LOG_FMT = ('%(asctime)s | %(levelname)s | %(filename)s:%(lineno)d '
'| %(funcName)s | %(message)s')
... | json_normalize(df['tweet_entities']) | pandas.io.json.json_normalize |
import pandas as pd
from rake_nltk import Rake
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import CountVectorizer
pd.set_option('display.max_columns', 100)
df = pd.read_csv('movie_metadata.csv')
print(df.head())
print(df.shape)
list(df.columns.values)... | pd.Series(cosine_sim[idx]) | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import dash
import pandas
import dash_html_components as html
from app import app
import cfg
tableColors = ['rgb(255, 255 ,255)', 'rgb(220, 220, 220)']
@app.callback(
dash.dependencies.Output('detailMainDiv', component_property = 'children'),
[dash.dependencies.... | pandas.DataFrame() | pandas.DataFrame |
from __future__ import division
import pytest
import numpy as np
from pandas import (Interval, IntervalIndex, Index, isna,
interval_range, Timestamp, Timedelta,
compat)
from pandas._libs.interval import IntervalTree
from pandas.tests.indexes.common import Base
import pandas.uti... | Index([1, 2]) | pandas.Index |
import os
import pandas
import numpy
import tensorflow
from tensorflow import Tensor
from typing import Tuple
from src.variants.variant import Variant
from src.structs import DistanceStruct
class SSIMVariant(Variant):
name = "Structural Similarity Index Measure"
def __init__(self, fasta_file: str, sequence... | pandas.DataFrame(index=indexes, columns=indexes) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import re
import warnings
from datetime import timedelta
from itertools import product
import pytest
import numpy as np
import pandas as pd
from pandas import (CategoricalIndex, DataFrame, Index, MultiIndex,
compat, date_range, period_range)
from pandas.compat import PY... | lrange(4) | pandas.compat.lrange |
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