prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
"""
Survey response summary calculation
Calculation builds up a nested dict structure which makes referencing columns
and specific values easy. This is converted to an array structure for Django
Template Language to be able to put it on the screen.
The results nested dict looks something like
{u'demographics_group/ag... | pandas.DataFrame(responses) | pandas.DataFrame |
"""
.. module:: repository
:platform: Unix, Windows
:synopsis: A module for examining a single git repository
.. moduleauthor:: <NAME> <<EMAIL>>
"""
import os
import sys
import datetime
import time
import numpy as np
import json
import logging
import tempfile
import shutil
from git import Repo, GitCommandErr... | DataFrame(ds, columns=['author', 'committer', 'date', 'message', 'lines', 'insertions', 'deletions', 'net']) | pandas.DataFrame |
# Imports
import streamlit as st
import streamlit.components.v1 as components
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import time
import os.path
# ML dependency imports
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.manif... | pd.get_dummies(masterMerge) | pandas.get_dummies |
#!/usr/bin/env python
'''
The article dictionary ends up in this mongo format
comment_id : [<e|"#comment-68330010">]
author : [<content>]
author_id : [<content>]
reply_count : [<content>]
timestamp : [<content>]
reply_to_author : [<content>]
reply_to_comment : [<content>]
content : [<c... | pd.DataFrame(article['comments']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101
import itertools
import warnings
from warnings import catch_warnings
from datetime import datetime
from pandas.types.common import (is_integer_dtype,
is_float_dtype,
is_scalar)
from pandas.compat... | range(5) | pandas.compat.range |
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 26 15:24:17 2019
@author: <NAME>
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from io import StringIO
import math
df=pd.read_csv('file:///C:/Users/<NAME>/Desktop/Parkinsons/parkinsonsdisease/Data1.csv')
print(df.describe)
... | pd.DataFrame(rmse_val) | pandas.DataFrame |
import csv
import pandas as pd
import sanalytics.algorithms.utils as sau
import sanalytics.estimators.pu_estimators as pu
from sanalytics.estimators.utils import diff_df, join_df
from gensim.models.doc2vec import Doc2Vec
import joblib
from time import time
## Read arguments
while True:
finished = set(['.'.join(i.s... | pd.DataFrame(df_rows, columns=columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import numpy as np
import pytest
from numpy.random import RandomState
from numpy import nan
from datetime import datetime
from itertools import permutations
from pandas import (Series, Categorical, CategoricalIndex,
Timestamp, DatetimeIndex, Index, IntervalIndex)
import pan... | Series([np.nan, 2.0, 1.0], dtype=t) | pandas.Series |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
""" Subway Module
.. moduleauthor:: <NAME> <<EMAIL>>
"""
import io
import re
from bokeh import io as bkio
from bokeh import models as bkm
import geopy.distance as gpd
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import requests
import seaborn a... | pd.to_datetime('09:00:00') | pandas.to_datetime |
"""Expression Atlas."""
import logging
import os
import sys
from collections import OrderedDict
from typing import List, Tuple, Optional
import pandas as pd
from pandas.core.frame import DataFrame
import xmltodict
from pyorient import OrientDB
from tqdm import tqdm
from ebel.constants import DATA_DIR
from ebel.manage... | pd.DataFrame(data, columns=['group_comparison_id', 'group_comparison']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
#
# wxtruss
# License: MIT License
# Author: <NAME>
# E-mail: <EMAIL>
# ~ from __future__ import division
import wx
import wx.grid as grid
import wx.html as html
import numpy as np
import matplotlib
matplotlib.use('WXAgg')
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
fro... | pd.DataFrame(ELEMENTS_CONN, columns=["Ni","Nj"], index=ELEMENTS) | pandas.DataFrame |
import pandas as pd
import numpy as np
from suzieq.utils import SchemaForTable, humanize_timestamp, Schema
from suzieq.engines.base_engine import SqEngineObj
from suzieq.sqobjects import get_sqobject
from suzieq.db import get_sqdb_engine
from suzieq.exceptions import DBReadError, UserQueryError
import dateparser
from d... | pd.DataFrame({column: r}) | pandas.DataFrame |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import operator
import numpy as np
import pytest
import pandas.compat as compat
from pandas.compat import range
import pandas as pd
from pandas import (
Categorical, DataFrame, Index, NaT, Series, bdate_range, date_range, ... | assert_series_equal(res, expected) | pandas.util.testing.assert_series_equal |
import pandas as pd
def add_empty_buildings(missing_list: list, dict_to_add_to: dict):
'''
function adds missing buildings (with empty dict as value) to a dict over buildings
so that jsons being saved always have all buildings
'''
for building in missing_list:
dict_to_add_to[building] = {... | pd.DataFrame(samples[i], index=dates) | pandas.DataFrame |
# -*- 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 os
import logging
from pprint import pprint
from typing import Dict
import scipy.signal
import numpy as np
import pandas as pd
from matplotlib import pyplot
from .log import logger
from .helpers import normalize, get_equidistant_signals
from .abstract_extractor import AbstractExtractor
from .synchronization_er... | pd.DataFrame() | pandas.DataFrame |
import os
import torch
from nltk import sent_tokenize, word_tokenize
from collections import defaultdict
import json
import pandas as pd
import pickle
from nltk.tag.perceptron import PerceptronTagger
from nltk.stem.porter import *
from transformers import BertTokenizer, GPT2Tokenizer
from lemmagen3 import Lemmatizer
im... | pd.DataFrame(all_docs) | pandas.DataFrame |
import pandas as pd
import numpy as np
import re
from unidecode import unidecode
# Map district in Kraków to integers.
# For details see:
# https://en.wikipedia.org/wiki/Districts_of_Krak%C3%B3w
districts = {'stare miasto': 1,
'grzegórzki': 2,
'prądnik czerwony': 3,
'prądnik bi... | pd.isnull(x) | pandas.isnull |
# ********************************************************************************** #
# #
# Project: Data Frame Explorer #
# Author: <NAME> ... | pd.Series(df_var_list) | pandas.Series |
import pandas as pd
import numpy as np
from pandas import DataFrame
from pandas import concat
from sklearn.preprocessing import MinMaxScaler
def getBatteryCapacity(Battery):
cycle = []
capacity = []
i = 1
# print(len(Battery))
# print(len(Battery))
for Bat in Battery:
if Bat['cycle'] ==... | pd.DataFrame(testing_std) | pandas.DataFrame |
import common_python.constants as cn
from common_python.testing import helpers
from common_python.classifier import feature_analyzer
from common_python.classifier.feature_analyzer import FeatureAnalyzer
from common_python.tests.classifier import helpers as test_helpers
import copy
import os
import pandas as pd
import ... | pd.Series() | pandas.Series |
import docx
from docx.shared import Pt
from docx.enum.text import WD_ALIGN_PARAGRAPH, WD_BREAK
from docx.shared import Cm
import os
import math
import pandas as pd
import numpy as np
import re
from datetime import date
import streamlit as st
import json
import glob
from PIL import Image
import smtplib
import docx2pdf
... | pd.Series(data=dia) | pandas.Series |
from __future__ import print_function
from __future__ import division
# load libraries
from builtins import str
from builtins import range
from past.utils import old_div
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import geopandas as gpd
import sys
import os
from matp... | pd.concat(dataframesListmap_df, ignore_index=True) | pandas.concat |
import os
import sys
import numpy as np
import pandas as pd
import xarray as xr
# import analysis_tools.naming_conventions.var_info
from oas_dev.util.filenames import get_filename_pressure_coordinate_field
from oas_dev.util.naming_conventions import var_info
def make_folders(path):
"""
Takes path and create... | pd.read_csv(var_mod_info_filen, index_col=0) | pandas.read_csv |
# being a bit too dynamic
# pylint: disable=E1101
import datetime
import warnings
import re
from math import ceil
from collections import namedtuple
from contextlib import contextmanager
from distutils.version import LooseVersion
import numpy as np
from pandas.util.decorators import cache_readonly, deprecate_kwarg
im... | com.isnull(values) | pandas.core.common.isnull |
import re
from inspect import isclass
import numpy as np
import pandas as pd
import pytest
from mock import patch
import woodwork as ww
from woodwork.accessor_utils import (
_is_dask_dataframe,
_is_dask_series,
_is_koalas_dataframe,
_is_koalas_series,
init_series,
)
from woodwork.exceptions import... | pd.Series(["new", "column", "inserted"], name="test_col") | pandas.Series |
# script for preparing necessary data for single tasks
import os
os.environ["PYTHONWARNINGS"] = "ignore"
import json
import time
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
from utils.data import Dataset, create_adult_dataset, create_compas_dataset, create_titanic_dataset,... | pd.DataFrame(t_data.y, columns=["_TARGET_"]) | pandas.DataFrame |
import pandas as pd
import numpy as np
import fileinput
import json
from scipy.stats import beta
import matplotlib.pyplot as plt
import re
import networkx as nx
import math
from scipy.stats import wilcoxon
from statistics import mean
from scipy.stats import pearsonr
# from cpt_valuation import evaluateP... | pd.to_numeric(elNoMessage["sender_subject_id"]) | pandas.to_numeric |
import numpy as np
import matplotlib.pyplot as plt
from numpy import array,identity,diagonal
import os
import numpy
import pandas as pd
import sys
import random
import math
#from scipy.linalg import svd
from math import sqrt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
f... | pd.read_csv("dolphins_label.csv",delimiter=' ',header=None) | pandas.read_csv |
import pandas as pd
from sklearn import linear_model
import statsmodels.api as sm
import numpy as np
from scipy import stats
df_all = pd.read_csv("/mnt/nadavrap-students/STS/data/imputed_data2.csv")
print(df_all.columns.tolist())
print (df_all.info())
df_all = df_all.replace({'MtOpD':{False:0, True:1}})
df_all = ... | pd.DataFrame() | pandas.DataFrame |
import os
import sys
from numpy.core.numeric import zeros_like
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use("seaborn-poster")
# I hate this too but it allows everything to use the same helper functions.
sys.path.insert(0, "TP_model")
from helper_functions i... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
# import tensorflow as tf
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.optimizers import SGD, Adam
from keras.utils import plot_model
import matplotlib.pyplot as plt
import re
import tensorflowvisu as tfvu
df = pd.read_csv('t... | pd.DataFrame() | pandas.DataFrame |
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import pandas as pd
import numpy as np
import datetime
import os, sys
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import Neare... | pd.to_datetime(df_raw[name_LastVisitDate].loc[inds]) | pandas.to_datetime |
# -*- coding: utf-8 -*-
from typing import Optional, IO
import pandas as pd
import os
from PySDDP.dessem.script.templates.deflant import DeflAntTemplate
MNE = 'DEFANT'
COMENTARIO = '&'
class DeflAnt(DeflAntTemplate):
"""
Classe que contem todos os elementos comuns a qualquer versao do arquivo DeflAnt do De... | pd.DataFrame(self.defluencias_uhe_anteriores) | pandas.DataFrame |
from urllib import response
from pyparsing import col
from IMLearn.utils import split_train_test
from IMLearn.learners.regressors import LinearRegression
from typing import NoReturn
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
from os.path ... | pd.read_csv(filename) | pandas.read_csv |
from http.server import BaseHTTPRequestHandler, HTTPServer
import socketserver
import pickle
import urllib.request
import json
from pprint import pprint
from pandas.io.json import json_normalize
import pandas as pd
from sklearn import preprocessing
from sklearn.preprocessing import PolynomialFeatures
from sklearn impor... | pd.merge(finalDF,reqs_duration_max, left_index=True, right_index=True) | pandas.merge |
import datetime
import numpy as np
import pytest
import pytz
import pandas as pd
from pandas import Timedelta, merge_asof, read_csv, to_datetime
import pandas._testing as tm
from pandas.core.reshape.merge import MergeError
class TestAsOfMerge:
def read_data(self, datapath, name, dedupe=False):
path = da... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
# -*- coding: utf-8 -*-
"""
Created on Sat Dec 15 15:08:00 2018
@author: Mangifera
"""
from datetime import datetime, timezone
from dateutil import tz
import pandas as pd
def formatTime(timestamp, t_format, city_timezone):
utc = datetime.fromtimestamp(timestamp, timezone.utc)
city_timezone = tz.gettz(city_tim... | pd.to_numeric(df["Day"], errors='coerce') | pandas.to_numeric |
from __future__ import print_function
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from pandas import (Series, Index, Int64Index, Timestamp, Period,
DatetimeIndex, PeriodIndex, TimedeltaIndex,
Timedelta, timedelta_range, date_range, Float64Index... | TimedeltaIndex(['1 day', '2 day']) | pandas.TimedeltaIndex |
# -*- 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, ... | Timestamp('20130101T10:00:00', tz='US/Eastern') | pandas.Timestamp |
from collections import defaultdict
from utils import plot_utils
def mean(*lst):
columns = lst.key
return sum(lst) / len(lst)
# if __name__ == '__main__':
#
# ls_dct=[{'Stars':2, 'Cast':0.11},
# {'Stars':3, 'Cast':0.01},
# {'Stars':5, 'Cast':0.01}
# ]
#
# # result =map(mean... | pd.DataFrame(columns=['genres', 'year', 'stars', 'rating'], data=ls_movies) | pandas.DataFrame |
import collections
import os
import geopandas as gpd
import numpy as np
import pandas as pd
import requests
from datetime import datetime, timedelta
from typing import Tuple, Dict, Union
import pytz
from pandas.core.dtypes.common import is_string_dtype, is_numeric_dtype
from hydrodataset.data.data_base import DataSour... | pd.read_csv(file_path, sep="\t") | pandas.read_csv |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('seaborn-notebook')
plt.rcParams['figure.figsize'] = (14, 12)
REGION_ID = 3034
# Load data for new snow line calculated in *new_snow_line.py*
nsl = pd.read_csv(r"C:\Users\kmu\PycharmProjects\APS\aps\scripts\t... | pd.merge(_merged2, aw2, how='left', on='Date', suffixes=['_nsl', '_APS0iso']) | pandas.merge |
# LIBRARIES
# set up backend for ssh -x11 figures
import matplotlib
matplotlib.use('Agg')
# read and write
import os
import sys
import glob
import re
import fnmatch
import csv
import shutil
from datetime import datetime
# maths
import numpy as np
import pandas as pd
import math
import random
# miscellaneous
import ... | pd.read_csv('/n/groups/patel/uk_biobank/project_52887_41230/ukb41230.csv', usecols=usecols) | pandas.read_csv |
# -*- 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... | Timestamp(x) | pandas.Timestamp |
# The xrayvis bokeh app
import os
import numpy as np
import pandas as pd
import requests
import yaml
from tempfile import TemporaryDirectory, NamedTemporaryFile
from base64 import b64decode
import parselmouth
from bokeh_phon.utils import remote_jupyter_proxy_url_callback, set_default_jupyter_url
from bokeh_phon.model... | pd.DataFrame({'x': [], 'y': []}) | pandas.DataFrame |
from superiq import VolumeData
from superiq.pipeline_utils import *
import boto3
import pandas as pd
from datetime import datetime
def collect_brain_age():
bucket = "mjff-ppmi"
version = "simple-v2"
prefix = f"superres-pipeline-{version}/"
objects = list_images(bucket, prefix)
brain_age = [i for i in objects if i... | pd.concat([metadata_df, prodro_df]) | pandas.concat |
import pandas as pd
from pandas.tseries.offsets import DateOffset
import configparser
import fire
import os
import math
import numpy as np
import qlib
from qlib.data import D
import matplotlib
import matplotlib.pyplot as plt
matplotlib.use('Agg')
from sklearn.metrics.pairwise import cosine_similarity
import sys
sys.p... | DateOffset(years=numberOfYears, months=numberOfMonths, days=numberOfDays) | pandas.tseries.offsets.DateOffset |
#!/usr/bin/env python3.6
import os
import statistics
import requests
import datetime
from typing import Dict, List, Tuple, Optional, Union, Iterable, Any
from collections import defaultdict
from shutil import copyfile
import pandas as pd
from urllib import parse
from dataclasses import dataclass, field
from enum impor... | pd.DataFrame(el) | pandas.DataFrame |
import re
import pandas as pd
import numpy as np
from collections import Counter
from tqdm import tqdm
tqdm.pandas()
class TextPreprocessing:
"""
Clean and preprocess your text data
"""
@staticmethod
def text_case(df, columns, case='lower', verbose=True):
"""
Perform string manipu... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
from sklearn.metrics import explained_variance_score
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
from skle... | pd.DataFrame(self.evsd, index=index_as_array_dem) | pandas.DataFrame |
# Copyright (c) 2022 <NAME> <<EMAIL>>
#
# 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(records) | pandas.DataFrame |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | pd.DataFrame(data) | pandas.DataFrame |
import urllib
import requests
import pandas as pd
from bs4 import BeautifulSoup
def get_cols(table):
header = table.find_all("th")
cols = []
for column in header:
try:
col = column.find("a").get_text()
except AttributeError:
col = column.get_text()
cols.ap... | pd.DataFrame(data=table_data, columns=table_cols) | pandas.DataFrame |
'''
Plotter to collect all plotting functionality at one place.
If available, it uses simple plotting functionalities included into the different classes.
Merges them together to create more meaningfull plots.
'''
from __future__ import print_function, division
import numpy as np
import pandas as pd
import math
from w... | pd.Timedelta("48h") | pandas.Timedelta |
from collections import deque
from functools import lru_cache
import pandas as pd
import numpy as np
from pyrich.record import Record
from pyrich import stock
class Portfolio(Record):
currency_mapping = {
'CRYPTO': 'KRW',
'KOR': 'KRW',
'USA': 'USD',
}
def __init__(self, name: str... | pd.DataFrame(currently_owned_stock) | pandas.DataFrame |
import pandas as pd
import numpy as np
from scipy.stats import ttest_ind
'''Assignment 4 - Hypothesis Testing
This assignment requires more individual learning than previous assignments - you are encouraged to check
out the pandas documentation to find functions or methods you might not have used yet, or ask question... | pd.ExcelFile('gdplev.xls') | pandas.ExcelFile |
# -*- coding: utf-8 -*-
import csv
import os
import platform
import codecs
import re
import sys
from datetime import datetime
import pytest
import numpy as np
from pandas._libs.lib import Timestamp
import pandas as pd
import pandas.util.testing as tm
from pandas import DataFrame, Series, Index, MultiIndex
from pand... | StringIO(data) | pandas.compat.StringIO |
## Produce College Rankings
## Based on Earnings Outcomes
## Load Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.formula.api as sm
from scipy import stats
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error
from statsmodels.stat... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
from pandas.api.types import is_scalar as pd_is_scalar
from dask.array import Array
from dask.dataframe.core import Series
from dask.delayed import delayed
from dask.utils import derived_from
__all__ = ("to_numeric",)
@derived_from(pd, ua_args=["downcast"])
def to_numeric(arg, errors="raise", me... | pd.to_numeric(arg._meta) | pandas.to_numeric |
# Author: <NAME>, PhD
#
# Email: <EMAIL>
#
# Organization: National Center for Advancing Translational Sciences (NCATS/NIH)
#
# References
#
# Ref: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.aggregate.html
# Ref: https://stackoverflow.com/questions/27298178/concatenate-strings-from-seve... | pd.DataFrame ({'host_protein':host_proteins, 'activation':activations, 'activation_type':activation_types, 'metadata': md}) | pandas.DataFrame |
import sys
import pandas as pd
import numpy as np
import h5py
import os
import time
import pickle
import multiprocessing as mp
from os import listdir
from os.path import isfile, join, splitext, dirname, abspath
from joblib import Parallel, delayed
from datetime import datetime
from dataset_paths import (
get_balan... | pd.DataFrame(components_dict) | pandas.DataFrame |
import sys
import numpy as np
import pandas as pd
from natsort import natsorted
from pyranges.statistics import StatisticsMethods
from pyranges.genomicfeatures import GenomicFeaturesMethods
from pyranges import PyRanges
from pyranges.helpers import single_value_key, get_key_from_df
def set_dtypes(df, int64):
# ... | pd.Series(s, index=idx) | pandas.Series |
################################################################################
# Module: archetypal.template
# Description:
# License: MIT, see full license in LICENSE.txt
# Web: https://github.com/samuelduchesne/archetypal
################################################################################
import colle... | pd.to_numeric(x, errors="ignore") | pandas.to_numeric |
import json
from datetime import datetime as dt
from datetime import timedelta
from numpy import busday_count
from os import makedirs
from os.path import isdir
from os.path import join
# from os.path import isfile
from os import listdir
import pandas as pd
from tws_futures.helpers import project
def save_as_json(data... | pd.DataFrame(_bars) | pandas.DataFrame |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import logging
import os
import h5py
import numpy as np
import pandas as pd
import torch
from core.config import get_model... | pd.DataFrame(per_grouping_detected) | pandas.DataFrame |
# Mar21, 2022
##
#---------------------------------------------------------------------
# SERVER only input all files (.bam and .fa) output MeH matrix in .csv
# August 3, 2021 clean
# FINAL github
#---------------------------------------------------------------------
import random
import math
import pysam
import csv
... | pd.DataFrame(data=d) | pandas.DataFrame |
import os
import warnings
from collections import OrderedDict
from unittest.mock import patch
import numpy as np
import pandas as pd
import pytest
import woodwork as ww
from sklearn.exceptions import NotFittedError, UndefinedMetricWarning
from sklearn.preprocessing import label_binarize
from evalml.exceptions import ... | pd.Series(y_pred) | pandas.Series |
import glob
import os
import sys
# these imports and usings need to be in the same order
sys.path.insert(0, "../")
sys.path.insert(0, "TP_model")
sys.path.insert(0, "TP_model/fit_and_forecast")
from Reff_functions import *
from Reff_constants import *
from sys import argv
from datetime import timedelta, datetime
from ... | pd.to_datetime(today) | pandas.to_datetime |
# coding: utf-8
# In[1]:
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
from scipy import interp
from itertools import cycle
from sklearn.svm import LinearSVC
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve,auc
from sklearn.naive_bayes import GaussianNB
from sklear... | pd.read_csv('crx.data',header=None,sep = ',') | pandas.read_csv |
import os
import re
import numpy as np
import pandas as pd
import nltk
from nltk import WordNetLemmatizer
from nltk.tokenize import word_tokenize
from argparse import ArgumentParser
import json
from collections import Counter
from nltk.corpus import stopwords
import spacy
nlp = spacy.load("en_core_web_sm")
# paths
... | pd.concat([df_triples["l1"], df_triples["l2"]]) | pandas.concat |
import abc
import logging
import math
import os
import time
import numpy as np
import pandas as pd
from PyDSS.common import PV_LOAD_SHAPE_FILENAME
from PyDSS.reports.reports import ReportBase, ReportGranularity
from PyDSS.utils.dataframe_utils import read_dataframe, write_dataframe
from PyDSS.utils.utils import dump... | pd.DataFrame(data, index=pf1_power.index) | pandas.DataFrame |
import yfinance as yf
import ta
import pandas as pd
from datetime import date, timedelta, datetime
from IPython.display import clear_output
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
ticker = 'FSLY'
start_date = '2019-10-23'
end_date = '2020-10-23'
def get_stock_backtest_data(t... | pd.DataFrame(cum_value, index=bt_df.index, columns=['CUM_RET']) | pandas.DataFrame |
import os
import pandas as pd
import numpy as np
"""
Rewrite DataFrame Keys: ['open', 'close', 'high', 'low', 'volume', 'money'] + 6
[ma_1, ma_2, ......, ma_12] 12
[momentum_1, momentum_2, ......, momentum_12] 12
... | pd.DataFrame(rewrite_data, index=store_indices, columns=store_columns) | pandas.DataFrame |
try:
import spacy
from spacy.gold import offsets_from_biluo_tags as _offsets_from_biluo_tags
from spacy.gold import iob_to_biluo as _iob_to_biluo
import pandas as pd
HAS_SPACY = True
except:
HAS_SPACY = False
from pathlib import Path
import json,random,os,tempfile,logging
__all__=["_from_bio_ta... | pd.Series(out) | pandas.Series |
import pandas as pd
import numpy as np
def merge_all(Curr,Bonds,OilN,NetSp,FundsRates, Jobs, pred_days=100):
Curr.columns=Curr.columns.get_level_values(0)
OilN.columns=OilN.columns.get_level_values(0)
Feedt=pd.merge(Bonds,OilN,how='outer',left_index=True,right_index=True)
Feedt=pd.merge(Feedt,FundsRate... | pd.merge(Feedt,FundsRates,how='outer',left_index=True,right_index=True) | pandas.merge |
import ibeis
import six
import vtool
import utool
import numpy as np
import numpy.linalg as npl # NOQA
import pandas as pd
from vtool import clustering2 as clustertool
from vtool import nearest_neighbors as nntool
from plottool import draw_func2 as df2
np.set_printoptions(precision=2)
pd.set_option('display.max_rows',... | pd.DataFrame(ranked_scores, index=ranked_aids, columns=['score']) | pandas.DataFrame |
import re
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import IntervalArray
class TestSeriesReplace:
def test_replace_explicit_none(self):
# GH#36984 if the user explicitly passes value=None, give it to them
ser = pd.Series([0, 0, ""],... | pd.Interval(1.0, 2.7) | pandas.Interval |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
from numpy import nan
import numpy as np
import pandas as pd
from pandas.types.common import is_integer, is_scalar
from pandas import Index, Series, DataFrame, isnull, date_range
from pandas.core.index import MultiIndex
from pa... | pd.Timestamp('2011-01-01 00:00', tz=tz) | pandas.Timestamp |
## License: ?
## Copyright(c) <NAME>. All Rights Reserved.
## Copyright(c) 2017 Intel Corporation. All Rights Reserved.
# Run this file initially to get the initial position of the player
# Positions(2d, 3d) are stored in pickle files which need to be imported in the main code to get initial positions/angles
# Use for... | pd.DataFrame(distance_data2d ,columns=joints) | pandas.DataFrame |
import pandas as pd
from typing import Optional
import umap
def target_encoding(train_df:pd.DataFrame, test_df:pd.DataFrame, target_key:str, encoding_keys:list, method='mean') -> pd.DataFrame:
"""do target encoding. encoded column name is enc_ + method + '_' + encoding_key
Arguments:
train_df {pd.... | pd.DataFrame(tmp, columns=['dim_x', 'dim_y']) | pandas.DataFrame |
# -*- coding:utf-8 -*-
import datetime
from random import random
import numpy as np
import pandas as pd
def get_random_univariate_forecast_dataset():
X = pd.DataFrame({'ds': | pd.date_range("20130101", periods=100) | pandas.date_range |
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import methfun as mf
import methdata as md
from scipy.interpolate import UnivariateSpline
# to register datetimes in matplotlib
from pandas.plotting import register_matplotlib_converters
register_matplotlib_conv... | pd.to_datetime(tdf0['csv_name'], format='%Y%m%d_%H%M') | pandas.to_datetime |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
import numpy as np
import pandas as pd
from pandas import (Index, Series, DataFrame, isnull)
from pandas.compat import lrange
from pandas import compat
from pandas.util.testing import assert_series_equal
import pandas.util.testing as tm
from .common import TestData
... | assert_series_equal(result, expected) | pandas.util.testing.assert_series_equal |
"""
Routines for casting.
"""
from contextlib import suppress
from datetime import date, datetime, timedelta
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
Optional,
Sequence,
Set,
Sized,
Tuple,
Type,
Union,
)
import numpy as np
from pandas._libs import lib, tslib, t... | is_datetime64_dtype(dtype) | pandas.core.dtypes.common.is_datetime64_dtype |
# version: 0.5
# this for pre-process from raw data and store into csv file format
import os, sys
import pandas as pd
import numpy as np
import ta
def preProcessPATH(pathBase, fileType, countryCode=''):
fileList = []
for dirPath, dirNames, fileNames in os.walk(pathBase):
for i, f in enume... | pd.Series(Score) | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 7 15:38:47 2022
@author: jimmy
"""
# Pandas Data Series
import pandas as pd
"""1.Write a Pandas program to create and display a one-dimensional
array-like object containing an array of data using Pandas module
"""
data = [2,4,8,16]
data_frame ... | pd.Series([2, 4, 6, 8, 10, 0]) | pandas.Series |
import os
import itertools
import collections
import pprint
import numpy as np
import pandas as pd
from scipy import stats as sps
from scipy.interpolate import interp1d
from datetime import datetime
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import ticker
import matplotlib.d... | pd.to_datetime(ts[-1]) | pandas.to_datetime |
"""Track and analyze grocery spending at an item level.
This app allows a user to input a grocery item.
"""
# app.py
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output, State
import plotly.express as ... | pd.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
# -*- coding: utf-8 -*-
import time
import pandas as pd
from .momentum import *
from .overlap import *
from .performance import *
from .statistics import *
from .trend import *
from .volatility import *
from .volume import *
from .utils import verify_series
from pandas.core.base import PandasObject
class BasePanda... | pd.api.extensions.register_dataframe_accessor('ta') | pandas.api.extensions.register_dataframe_accessor |
import os
import pandas as pd
from bs4 import BeautifulSoup
DATA_FOLDER = "../data/Energy_Price"
RESULT_FILENAME = "../data/sm_price/price_time.csv"
# Script to collect data in dataframes and save it in the data folder
def load_xml(data_file):
print(data_file)
with open(data_file, 'r') as src:
soup ... | pd.DataFrame(data_list) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sun Jun 6 00:12:14 2021
@author: charlie.henry
"""
# https://data.austintexas.gov/resource/x44q-icha.csv?match_validity=valid
import pandas as pd
import numpy as np
from sodapy import Socrata
import geopandas
## Include your app token from socrata below
clien... | pd.to_datetime(data['start_time'],format='%Y-%m-%dT%H:%M:%S') | pandas.to_datetime |
import os
import pandas as pd
import numpy as np
import scipy.sparse as sp
from logging import getLogger
from libcity.utils import StandardScaler, NormalScaler, NoneScaler, \
MinMax01Scaler, MinMax11Scaler, LogScaler, ensure_dir
from libcity.data.dataset import AbstractDataset
class ChebConvDataset(AbstractDatas... | pd.read_csv(self.data_path + self.rel_file + '.rel') | pandas.read_csv |
# coding: utf-8
"""tools for analyzing VPs in an individual precipitation event"""
from collections import OrderedDict
from os import path
from datetime import timedelta
import numpy as np
import pandas as pd
import xarray as xr
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy.io import loadmat
fro... | pd.Timedelta(minutes=15) | pandas.Timedelta |
import os
import glob
import click
import pickle
import zipfile
import datetime
import pandas as pd
@click.command()
@click.option('--redmine_instance', help='Path to pickled Redmine API instance')
@click.option('--issue', help='Path to pickled Redmine issue')
@click.option('--work_dir', help='Path to Redmine issue w... | pd.read_csv(csv_file) | pandas.read_csv |
'''
Author:
<NAME> <<EMAIL>>
<NAME> <<EMAIL>>
<NAME> <<EMAIL>>
<NAME> <<EMAIL>>
'''
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from matplotlib import pyplot as plt
import plotly.subplots as tls
import plotly.graph_objs... | pd.Series(mutual_info) | pandas.Series |
"""
Tests the relational_features module.
"""
import re
import unittest
import numpy as np
import pandas as pd
import mock
from .context import relational_features
from .context import config
from .context import util
from .context import test_utils as tu
class RelationalFeaturesTestCase(unittest.TestCase):
def s... | pd.Series([0.0, 0.0, 0.0, 0.0]) | pandas.Series |
from pandas import DataFrame
import pandas as pd
from sklearn.cross_decomposition import PLSRegression, PLSCanonical
def pls_wrapper(pls):
class PLSPandasMixin(pls):
def fit(self, x, y):
self.x = x
self.y = y
return super().fit(x, y)
def transform(self, x... | DataFrame(scores[component]) | pandas.DataFrame |
from flask import Flask, render_template, request, redirect, url_for, session
import pandas as pd
import pymysql
import os
import io
#from werkzeug.utils import secure_filename
from pulp import *
import numpy as np
import pymysql
import pymysql.cursors
from pandas.io import sql
#from sqlalchemy import create... | pd.DataFrame(Xedata) | pandas.DataFrame |
#SPDX-License-Identifier: MIT
""" Helper methods constant across all workers """
import requests
import datetime
import time
import traceback
import json
import os
import sys
import math
import logging
import numpy
import copy
import concurrent
import multiprocessing
import psycopg2
import csv
import io
from logging im... | pd.DataFrame(insert) | pandas.DataFrame |
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