prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#
# Copyright 2017 Human Longevity, Inc.
#
# 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 ... | pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}) | pandas.DataFrame |
"""Format helpers"""
import math
import pandas as pd
import pandas.lib as lib
import numpy as np
pd_is_datetime_arraylike = None
try:
from pandas.core.common import is_datetime_arraylike as pd_is_datetime_arraylike
except:
pass
from functools import partial
def is_datetime_arraylike(arr):
if isinstance... | pd.DataFrame(values) | pandas.DataFrame |
# *****************************************************************************
# Copyright (c) 2019, Intel Corporation All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# Redistributions of sou... | pandas.Series(self._data % other) | pandas.Series |
"""Performance visualization class"""
import os
from dataclasses import dataclass, field
from typing import Dict, List
import pandas as pd
import seaborn as sns
import scikit_posthocs as sp
from matplotlib.backends.backend_pdf import PdfPages
from matplotlib import pyplot
import matplotlib.pylab as plt
from tqdm import... | pd.DataFrame(columns=self.cv_methods, index=index_fold) | pandas.DataFrame |
from extract_from_html import *
import tensorflow as tf
import nltk
from visualize_data import display_data,compute_accuracy
import pandas as pd
import argparse
stopwords = nltk.corpus.stopwords.words('english')
english_words = set(nltk.corpus.words.words())
def clean_reviews(reviews):
clean_reviews = []
for t... | pd.DataFrame() | pandas.DataFrame |
# -*- 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(['2011-01-02'], tz='US/Eastern') | pandas.DatetimeIndex |
#!/usr/bin/python3
from gooey import *
from Bio import SeqIO
from Bio.Seq import Seq, MutableSeq, reverse_complement
from Bio.Data import IUPACData
import pandas as pd
pd.options.mode.chained_assignment = None
# input parameters
@Gooey(required_cols=2, program_name='CpG island identificator', header_bg_color= '#DCDCDC'... | pd.DataFrame() | pandas.DataFrame |
import urllib3
from bs4 import BeautifulSoup as bs
import pandas as pd
import os.path
import sys
import csv
from pathlib import Path
# Grabs raw web page from basketball reference and converts it into a text file for NLP functionality
class raw_text(object):
def process_raw_text(self, year):
url = 'https... | pd.DataFrame(transaction) | pandas.DataFrame |
import pandas as pd
import numpy as np
import altair as alt
import matplotlib.pyplot as plt
def get_first_row(s):
return s.iloc[0]
#Reads the first line of line of data and determines if data is categorical, quantitative or nominal
def auto_get_data_type(df):
type_dict = dict()
columns = list(df.columns... | pd.DataFrame(summary_dict) | pandas.DataFrame |
import warnings
from decimal import Decimal
from typing import List, Tuple, Dict
from pandas import DataFrame
from pandas.core.common import SettingWithCopyWarning
from model.DomObject import DomObject
from service.i_scraping_service import IScrapingService
from service.ulitity import extract_numbers, regex
def get... | DataFrame.from_records(lens_data_list) | pandas.DataFrame.from_records |
# networkx experimentation and link graph plotting tests
# not in active use for the search engine but left here for reference
import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd
import sqlite3
from nltk import FreqDist
from networkx.drawing.nx_agraph import graphviz_layout
import spacy
nlp = s... | pd.read_csv("data/external_link_status.csv") | pandas.read_csv |
# ----------------
# IMPORT PACKAGES
# ----------------
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
import sklearn.metrics as skm
import numpy as np
import matplotlib.pyplot as plt
# ----------------
# OBTAIN DATA
# ----------------
# Data Source: https://archive.ics.uci.edu... | pd.read_csv("train/X_train.txt", header=None, delim_whitespace=True, index_col=False) | pandas.read_csv |
# laod library
import pandas as pd
# create data frame
df = | pd.DataFrame() | pandas.DataFrame |
from datetime import datetime
import json
from os.path import join, exists
from tempfile import TemporaryDirectory
import numpy as np
import pandas as pd
from delphi_utils import read_params
from delphi_cdc_covidnet.update_sensor import update_sensor
params = read_params()
STATIC_DIR = params["static_file_dir"]
cla... | pd.isna(hosp_df["sample_size"]) | pandas.isna |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
import pydot
from sklearn import preprocessing, model_selection
from sklearn.tree import export_graphviz
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import cross_val_score
from skl... | register_matplotlib_converters() | pandas.plotting.register_matplotlib_converters |
from __future__ import division
import pandas as pd
import SimpleITK as sitk
import numpy as np
import os
import argparse
def resample_img(itk_image, out_spacing=[2.0, 2.0, 2.0], is_label=False):
# resample images to 2mm spacing with simple itk
original_spacing = itk_image.GetSpacing()
original_size = i... | pd.DataFrame(data={'imgs': val_imgs}) | pandas.DataFrame |
__version__ = '0.1.3'
__maintainer__ = '<NAME> 31.12.2019'
__contributors__ = '<NAME>, <NAME>'
__email__ = '<EMAIL>'
__birthdate__ = '31.12.2019'
__status__ = 'dev' # options are: dev, test, prod
#----- imports & packages ------
if __package__ is None or __package__ == '':
import sys
from os import path
... | pd.DataFrame(index=self.data.index) | pandas.DataFrame |
#!/usr/bin/python
# -*- coding: utf-8 -*-
import decimal
import datetime
import pandas as pd
from scipy.optimize import fsolve
from django.http import HttpResponse
from django.shortcuts import render
from .models import Currency, Category, Bank, Account, AccountCategory, AccountRec, Risk, InvProj, InvRec
from . impo... | pd.Series(name=cat.name) | pandas.Series |
import pandas
import numpy as np
from pandas import DataFrame
from sklearn.cross_validation import train_test_split
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
deneme = []
human = []
with open("human.txt","r") as f:
for line in f:
... | pandas.DataFrame(data) | pandas.DataFrame |
import pandas as pd
from functools import reduce
from datetime import datetime
import numpy as np
from EnergyIntensityIndicators.pull_bea_api import BEA_api
from EnergyIntensityIndicators.get_census_data import Econ_census
from EnergyIntensityIndicators.utilities.standard_interpolation \
import standard_interpolat... | pd.concat([fuels, sector_estimates_fuels], axis=0) | pandas.concat |
# Calculating Annual California State Median HIR
import pandas as pd
import numpy as np
import array
# Median Income
ca_med_inc = pd.read_excel('h08.xls', skiprows=4, usecols=([0,1,3,7,9,11,13] + list(range(17,64,2))))[:10] # Source: https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-... | pd.read_csv('county_housing_units_8918.csv') | pandas.read_csv |
""" test the scalar Timestamp """
import pytz
import pytest
import dateutil
import calendar
import locale
import numpy as np
from dateutil.tz import tzutc
from pytz import timezone, utc
from datetime import datetime, timedelta
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from pandas.ts... | Timestamp(t) | pandas.Timestamp |
""" Copyright start
Copyright (C) 2008 - 2022 Fortinet Inc.
All rights reserved.
FORTINET CONFIDENTIAL & FORTINET PROPRIETARY SOURCE CODE
Copyright end """
from asyncore import read
import requests
import pandas as pd
import numpy as np
import csv
from os.path import join
import json
from connectors.core.connec... | pd.concat(chunk) | pandas.concat |
from __future__ import division
from functools import wraps
import pandas as pd
import numpy as np
import time
import csv, sys
import os.path
import logging
from .ted_functions import TedFunctions
from .ted_aggregate_methods import TedAggregateMethods
from base.uber_model import UberModel, ModelSharedInputs
class Te... | pd.Series([], dtype="float", name="mineau_sca_fact") | pandas.Series |
import argparse
import multiprocessing
import os
import random as rn
from typing import List, Tuple, Union
import cv2
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from tqdm import tqdm
import config
import preprocessing.augment_op as aop
from pe_logger import PELogger
# Set seed to get... | pd.concat([df_aug, *df_series_aug], axis=0, ignore_index=True) | pandas.concat |
#from dqn_env import TrainLine
import sys
sys.path.append('.\subway_system')
from subway_env import TrainLine
from RL_brain import DeepQNetwork
import numpy as np
import matplotlib.pyplot as mplt
import tensorflow as tf
import pandas as pd
import TrainAndRoadCharacter as trc
def plot(r,ylabel):
import m... | pd.DataFrame(energy) | pandas.DataFrame |
# Gist example of IB wrapper from here: https://gist.github.com/robcarver17/f50aeebc2ecd084f818706d9f05c1eb4
#
# Download API from http://interactivebrokers.github.io/#
# (must be at least version 9.73)
#
# Install python API code /IBJts/source/pythonclient $ python3 setup.py install
#
# Note: The test cases, and the d... | pd.read_hdf(folder + ticker + '_bid_' + bss + '.h5') | pandas.read_hdf |
'''
<< New Release >>
For stability issues, R packages are replaced by recent python packages (if available) or removed (otherwise).
'''
### SCIKIT-SURVIVAL
from sksurv.linear_model import CoxPHSurvivalAnalysis, CoxnetSurvivalAnalysis
from sksurv.ensemble import RandomSurvivalForest
from lifelines imp... | pd.concat([X, T, Y], axis=1) | pandas.concat |
#!/usr/bin/env python
"""
manta: microbial association network clustering toolbox.
The script takes a weighted and undirected network as input
and uses this to generate network clusters.
Moreover, it can generate a Cytoscape-compatible layout (with optional taxonomy input).
Detailed explanations are available in the h... | pd.DataFrame(properties) | pandas.DataFrame |
import datetime
import glob
import pandas as pd
import xarray as xr
import matplotlib.pyplot as plt
from src.data.observations import OpenAQDownloader
from src.data.utils import Location
from src.constants import ROOT_DIR
from src.workflow import Workflow
from pathlib import Path
variable = "no2"
station_id = "US007"... | pd.concat(predictions) | pandas.concat |
from arche import arche, SH_URL
from arche.arche import Arche
from arche.rules.result import Level
from conftest import create_result
import pandas as pd
import pytest
def test_target_equals_source():
with pytest.raises(ValueError) as excinfo:
Arche(source="0/0/1", target="0/0/1")
assert (
str... | pd.DataFrame(get_cloud_items[:2]) | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib.patches import Patch
from scipy import linalg, stats
from scipy.interpolate import interp1d
from scipy.ndimage import gaussian_filter
from scipy.optimize import minimize
import os
os.makedirs("... | pd.read_csv(filename_pk) | pandas.read_csv |
from distutils.version import LooseVersion
from warnings import catch_warnings
import numpy as np
import pytest
from pandas._libs.tslibs import Timestamp
import pandas as pd
from pandas import (
DataFrame,
HDFStore,
Index,
MultiIndex,
Series,
_testing as tm,
bdate_range,
concat,
d... | ensure_clean_store(setup_path) | pandas.tests.io.pytables.common.ensure_clean_store |
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def object_creation():
s = pd.Series([1, np.nan])
dates = pd.date_range('20130101', periods=2)
df = pd.DataFrame(np.random.randn(2, 3), index=dates, columns=list('ABC'))
df2 = pd.DataFrame({'A': pd.Timestamp('20130102'),
... | pd.date_range('1/1/2000', periods=1000) | pandas.date_range |
#!/usr/bin/python3
import sys
from glob import glob
from pandas.io.parsers import read_csv
import igraph as ig
from leidenalg import find_partition_temporal, ModularityVertexPartition
from re import compile
import numpy as np
if (len(sys.argv) == 1):
print("usage: ./leiden.py path/to/outputdir")
sys.exit()
pt... | read_csv(fl) | pandas.io.parsers.read_csv |
import math
import subprocess
import einops as eo
from loguru import logger
import numpy as np
import pandas as pd
from PIL import Image
from scipy.signal import savgol_filter
import torch
from torch import optim, nn
from collections import Counter
from pytti import (
format_input,
set_t,
print_vram_usag... | pd.concat(frames, ignore_index=False) | pandas.concat |
# Copyright (c) 2018, NVIDIA CORPORATION.
import pickle
import warnings
from numbers import Number
import numpy as np
import pandas as pd
import pyarrow as pa
from numba import cuda, njit
import nvstrings
import rmm
import cudf
import cudf._lib as libcudf
from cudf._lib.stream_compaction import nunique as cpp_uniqu... | pd.api.types.pandas_dtype(dtype) | pandas.api.types.pandas_dtype |
# -*- 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, ... | DataFrame({'a': 0.7}, columns=['a']) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from datetime import timedelta
import itertools
import warnings
import numpy as np
import pandas as pd
import ruptures as rpt
from covsirphy.util.error import SubsetNotFoundError, UnExpectedValueError, deprecate
from covsirphy.util.error import NotRegisteredMainError, NotR... | pd.DataFrame(columns=output_cols) | pandas.DataFrame |
"""
This script visualises the prevention parameters of the first and second COVID-19 waves.
Arguments:
----------
-f:
Filename of samples dictionary to be loaded. Default location is ~/data/interim/model_parameters/COVID19_SEIRD/calibrations/national/
Returns:
--------
Example use:
------------
"""
__author_... | pd.Timestamp('2021-04-05') | pandas.Timestamp |
"""SQL io tests
The SQL tests are broken down in different classes:
- `PandasSQLTest`: base class with common methods for all test classes
- Tests for the public API (only tests with sqlite3)
- `_TestSQLApi` base class
- `TestSQLApi`: test the public API with sqlalchemy engine
- `TestSQLiteFallbackApi`: t... | DataFrame({"col1": [1, 2], "col2": [0.1, 0.2], "col3": ["a", "n"]}) | pandas.DataFrame |
# read inventory of all sites
from hydroDL.data import usgs, gageII
from hydroDL import kPath
import pandas as pd
import numpy as np
import time
import os
import matplotlib.pyplot as plt
# read site inventory
workDir =os.path.join(kPath.dirData,'USGS','inventory')
modelDir = os.path.join(workDir, 'modelUsgs2')
fileInv... | pd.DataFrame.from_dict(dictTab) | pandas.DataFrame.from_dict |
import json
import pandas as pd
import time
import requests
from shapely.geometry import shape
from shapely.geometry import Point
from sqlalchemy import create_engine
path = r'C:\Users\Hamza\OneDrive\startup-where\data\Neighbourhoods.geojson'
yelp_api_key = '<KEY>'
def get_neighbourhoods(path):
'''For each ne... | pd.DataFrame(lst, columns=cols) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
/*------------------------------------------------------*
| Spatial Uncertainty Research Framework |
| |
| Author: <NAME>, UC Berkeley, <EMAIL> |
| |
| Date: 07/11/... | pd.DataFrame(history.history) | pandas.DataFrame |
# --------------
#Importing header files
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#Path of the file
path
data = | pd.read_csv(path) | pandas.read_csv |
import pandas as pd
import glob
def import_all():
path = './data/'
allFiles = glob.glob(path +'/*.csv')
frame = pd.DataFrame()
list_ = []
for file_ in allFiles:
df = pd.read_csv(file_,index_col=None,header=0,low_memory=False)
list_.append(df)
frame = | pd.concat(list_) | pandas.concat |
import base64
import numpy as np
import os
import pandas as pd
import streamlit as st
from streamlit.uploaded_file_manager import UploadedFile
import streamlit.components.v1 as components
import json
from datetime import datetime
from pathlib import Path
from .repo import get_all_commits
DATE_COLUMN = 'last_updated'
... | pd.Series([0],dtype='int') | pandas.Series |
# hackathon T - Hacks 3.0
# flask backend of data-cleaning website
import matplotlib.pyplot as plt
#import tensorflow as tf
#from tensorflow.keras import layers
import pandas as pd
import numpy as np
from flask import *
import os
from datetime import *
from subprocess import Popen, PIPE
from math import floor
import co... | pd.read_csv("static/"+name+".csv") | pandas.read_csv |
import numpy as np
import pandas as pd
from numpy import inf, nan
from numpy.testing import assert_array_almost_equal, assert_array_equal
from pandas import DataFrame, Series, Timestamp
from pandas.testing import assert_frame_equal, assert_series_equal
from shapely.geometry.point import Point
from pymove import MoveDa... | assert_series_equal(window_ends, win_end_expected) | pandas.testing.assert_series_equal |
import datetime
from typing import List
import pandas as pd
import pytest
from ruamel.yaml import YAML
import great_expectations.exceptions as ge_exceptions
from great_expectations.core.batch import (
Batch,
BatchDefinition,
BatchSpec,
RuntimeBatchRequest,
)
from great_expectations.core.batch_spec imp... | pd.DataFrame(data={"col1": [5, 6], "col2": [7, 8]}) | pandas.DataFrame |
# Database Lib
"""
Oracle
PostGresSQL
SQLite
SQLServer
Hive
Spark
"""
import os, datetime, pandas, time, re
from collections import namedtuple, OrderedDict
import jmespath
import sqlalchemy
from multiprocessing import Queue, Process
from xutil.helpers import (
log,
elog,
slog,
get_exception_message,
struct,... | pandas.DataFrame(rows, columns=self._fields) | pandas.DataFrame |
import pandas as pd
import inspect
import functools
# ============================================ DataFrame ============================================ #
# Decorates a generator function that yields rows (v,...)
def pd_dfrows(columns=None):
def dec(fn):
def wrapper(*args,**kwargs):
return pd... | pd.MultiIndex.from_tuples(i,names=by) | pandas.MultiIndex.from_tuples |
"""
Contain codes about parse plate info and generate sample sheet
"""
import pathlib
import re
from collections import OrderedDict
import pandas as pd
import cemba_data
# Load defaults
PACKAGE_DIR = pathlib.Path(cemba_data.__path__[0])
# the Illumina sample sheet header used by Ecker Lab
with open(PACKAGE_DIR / '... | pd.read_csv(barcode_table_path, sep='\t') | pandas.read_csv |
import os
import gzip
import shutil
from typing import Tuple
import wget
import spacy
import numpy as np
import pandas as pd
import nlpaug.augmenter.word as naw
from sklearn.model_selection import train_test_split as splitting
from ..DataAugmenter import AbstractDataAugmenter
class DataAugmenterNLP(AbstractDataAug... | pd.concat([not_to_aug, to_aug]) | pandas.concat |
import pdb
import numpy as np
import pandas as pd
from math import ceil
def score_at_percentage(alpha, df, targets):
segment = ceil(alpha * df.shape[0])
segmented_df = df[0:segment]
targets_seen = 0
for i, row in segmented_df.iterrows():
if row.node in targets:
targets_seen += 1
... | pd.concat(dfs) | pandas.concat |
import pdb # NOQA F401
import copy
import os
import sqlite3
import pandas as pd
__alchemy_installed = True
try:
from sqlalchemy import create_engine, inspect
# from sqlalchemy.engine.reflection import Inspector
except:
__alchemy_installed = False
def db_exists(db='xxx.sqlite'):
return os.path.isfile... | pd.DataFrame(out, columns=['col_nr', 'col_name', 'col_type']) | pandas.DataFrame |
from optparse import OptionParser
import datetime as dt
import pandas as pd
import numpy as np
import blpapi # See our installation guide to learn how to install this library properly
class BBG(object):
"""
This class is a wrapper around the Bloomberg API. To work, it requires an active bloomberg terminal an... | pd.Series() | pandas.Series |
""" This script stores the shared settings for other .py files in the same repository."""
import pandas as pd
from utils.concentration import rainfall_events
# read the discrete storm events
obspath = '../data/obs/'
modpath = '../data/mod/'
outpath = '../output/'
events_name = 'obs_storm_event_common.csv'
obs_events ... | pd.to_datetime(mod_load_flow.index, dayfirst=False) | pandas.to_datetime |
import numpy as np
import pytest
from pandas.compat import IS64
import pandas as pd
import pandas._testing as tm
@pytest.mark.parametrize("ufunc", [np.abs, np.sign])
# np.sign emits a warning with nans, <https://github.com/numpy/numpy/issues/15127>
@pytest.mark.filterwarnings("ignore:invalid value encountered in si... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
# util.py
from __future__ import print_function
from collections import Mapping, OrderedDict
import datetime
import itertools
import random
import warnings
import pandas as pd
np = pd.np
from scipy import integrate
from matplotlib import pyplot as plt
import seaborn
from scipy.optimize import minimize
from scipy.si... | pd.Series(x2, index=t2) | pandas.Series |
import numpy as np
import pandas as pd
import pandas.testing as pdt
import pyarrow as pa
import pytest
from pandas.arrays import SparseArray
from kartothek.core.cube.constants import (
KTK_CUBE_DF_SERIALIZER,
KTK_CUBE_METADATA_DIMENSION_COLUMNS,
KTK_CUBE_METADATA_KEY_IS_SEED,
KTK_CUBE_METADATA_PARTITIO... | pd.DataFrame({"x": [0, 1, 2, 3], "p": [0, 0, 1, 1], "v": [10, 11, 12, 13]}) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
from rulelist.datastructure.attribute.nominal_attribute import activation_nominal, NominalAttribute
class TestNominalAttribute(object):
def test_normal(self):
dictdata = {"column1" : np.array(["below50" if i < 50 else "above49" for i in range(100)]),
... | pd.testing.assert_series_equal(actual_vector, expected_vector, check_exact=True) | pandas.testing.assert_series_equal |
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.concat((idx2_daid, idx2_dfx, idx2_wfx), axis=1, names=['idx']) | pandas.concat |
"""
Utils for time series generation
--------------------------------
"""
import math
from typing import Union
import numpy as np
import pandas as pd
import holidays
from ..timeseries import TimeSeries
from ..logging import raise_if_not, get_logger
logger = get_logger(__name__)
def constant_timeseries(value: floa... | pd.Timestamp('2000-01-01') | pandas.Timestamp |
import pandas as pd
import numpy as np
from pathlib import Path
from datetime import datetime as dt
def mergeManagers(managers, gameLogs):
#Get visiting managers
visitingManagers = gameLogs[['row','Date','Visiting team manager ID']]
visitingManagers['yearID'] = pd.DatetimeIndex(pd.to_datetime(v... | pd.to_datetime(homeTeams['Date']) | pandas.to_datetime |
import sys
import pandas as pd
import numpy as np
def load_data(messages_filepath, categories_filepath):
'''
INPUT
file paths of the message and categories files in cvs format
OUTPUT
a dataframe contains both dataset
'''
messages = pd.read_csv(messages_filepath)
categories = | pd.read_csv(categories_filepath) | pandas.read_csv |
# aisles : aisle_id | aisle
# departments : department_id | department
# orders_products (merge prior + train): order_id | product_id | add_to_cart_order | reordered
# orders : order_id | user_id | eval_set | order_number | order_dow | order_hour_of_day | days_since_prior_order
# products : product_id | product_nam... | pd.DataFrame() | pandas.DataFrame |
from mpl_toolkits.mplot3d import axes3d
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import csv
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
from mpl_toolkits.mplot3d import Axes3D
import plotly.graph_objects as go
import plotly.express as px
... | pd.concat([df1_select,df2_select,df3_select,df4_select], sort=False) | pandas.concat |
#!/usr/bin/env python
import numpy as np
import pandas as pd
import click as ck
from sklearn.metrics import classification_report
from sklearn.metrics.pairwise import cosine_similarity
import sys
from collections import deque
import time
import logging
from sklearn.metrics import roc_curve, auc, matthews_corrcoef
from... | pd.read_pickle(terms_file) | pandas.read_pickle |
#
# Convert API responses to Pandas DataFrames
#
import pandas as pd
def accounts(data):
"""accounts as dataframe"""
return pd.concat(
pd.json_normalize(v["securitiesAccount"]) for v in data.values()
).set_index("accountId")
def transactions(data):
"""transaction information as Dataframe"""... | pd.to_datetime(df[col], unit="ms") | pandas.to_datetime |
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
class Data:
'''Obtains hydro data and preprocesses it.'''
def data(self, test_len):
names = ['date', 'price', 'avg_p', 'bid', 'ask',
'o', 'h', 'l', 'c', 'avgp', 'vol', 'oms', 'num']
... | pd.Series(no_null) | pandas.Series |
from finrl_meta.data_processors.processor_alpaca import AlpacaProcessor as Alpaca
from finrl_meta.data_processors.processor_wrds import WrdsProcessor as Wrds
from finrl_meta.data_processors.processor_yahoofinance import YahooFinanceProcessor as YahooFinance
from finrl_meta.data_processors.processor_binance import Binan... | pd.DataFrame() | pandas.DataFrame |
from autodesk.model import Model
from autodesk.sqlitedatastore import SqliteDataStore
from autodesk.states import UP, DOWN, ACTIVE, INACTIVE
from pandas import Timestamp, Timedelta
from pandas.testing import assert_frame_equal
from tests.stubdatastore import StubDataStore
import pandas as pd
import pytest
def make_sp... | Timestamp(2018, 1, 1) | pandas.Timestamp |
import numpy as np
import pandas as pd
import sys
import time
def make_trip(N=50):
"""
Simulate random selection of coffee cards
Each card starts with N drinks.
Randomly pick a card until one of them runs out.
When a card runs out what are the odds there are drinks
left on the other card.
... | pd.DataFrame({'n':drinks}) | pandas.DataFrame |
import pandas as pd
import xml.etree.ElementTree as ET
import lxml.etree as etree
most_serious_problem = | pd.read_csv(
"../data/processed_data/special_eb/data/3_final/most_serious_problem/special_eb_most_serious_problem_final.csv") | pandas.read_csv |
import os
import re
import string
import random
import numpy as np
import pandas as pd
from pybedtools import BedTool
from Bio import SeqIO
import warnings
import logging.config
warnings.filterwarnings("ignore")
## Intialize logger
logging.config.fileConfig('logging.ini', disable_existing_loggers=False)
logger = logg... | pd.DataFrame(bed) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 21 10:00:33 2018
@author: jdkern
"""
from __future__ import division
from sklearn import linear_model
from statsmodels.tsa.api import VAR
import scipy.stats as st
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
##############... | pd.read_excel('Synthetic_demand_pathflows/46_daily.xlsx',sheet_name='Sheet1',header=0) | pandas.read_excel |
import os
import csv
import sys
import json
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from operator import itemgetter
from datetime import date, datetime
from collections import Counter, defaultdict
from normalize import TextNormalizer
# Constants
BASE = os.path.di... | pd.Series(values, index=dates, name=key) | pandas.Series |
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import roc_curve, precision_recall_curve, auc, make_scorer, recall_score, accuracy_score, p... | pd.read_csv('processedclevelandPrime.csv') | pandas.read_csv |
import pandas as pd
chrom_sizes = pd.Series(
{1: 249250621,
10: 135534747,
11: 135006516,
12: 133851895,
13: 115169878,
14: 107349540,
15: 102531392,
16: 90354753,
17: 81195210,
18: 78077248,
19: 59128983,
2: 243199373,
20: 63025520,
21: 48129895,
... | pd.Series('o', index=df.index) | pandas.Series |
# Created by <NAME>
# email : <EMAIL>
import json
import os
import time
from concurrent import futures
from copy import deepcopy
from pathlib import Path
from typing import IO, Union, List
from collections import defaultdict
import re
from itertools import tee
import logging
# Non standard libraries
import pandas as p... | pd.DataFrame(data) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri May 14 19:56:42 2021
@author: vyass
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.read_csv('eda_data.csv')
# choose relevant columns
df.columns
df_model =df[['avg_salary','Rating','Size','Type of ownership','Indus... | pd.get_dummies(df_model) | pandas.get_dummies |
#%%
import logging
logging.basicConfig(filename='covi19_dashboarder.log',
level=logging.ERROR,
format='%(asctime)s %(message)s')
logger = logging.getLogger("covi19_dashboarder")
class Preprocessor():
def __init__(self):
from pathlib import Path
self.curren... | pd.Series(time_columns) | pandas.Series |
import pandas as pd
########### Add State Info ################
def add_state_abbrev(df, left):
us_state_abbrev = {
'Alabama': 'AL', 'Alaska': 'AK', 'Arizona': 'AZ', 'Arkansas': 'AR', 'California': 'CA', 'Colorado': 'CO',
'Connecticut': 'CT', 'Delaware': 'DE', 'Florida': 'FL', 'Georgia': 'GA', 'Hawaii': 'HI', 'Id... | pd.read_csv('Final_Data/ETL/zillow_house_prices.csv') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 9 17:02:59 2018
@author: bruce
"""
# last version = plot_corr_mx_concate_time_linux_v1.6.0.py
import pandas as pd
import numpy as np
from scipy import fftpack
from scipy import signal
import matplotlib.pyplot as plt
from matplotlib.colors import ... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
'''
Original author: <NAME> (ORNL)
Current version by: <NAME>
'''
from __future__ import print_function
import json
import decimal
import pandas
from journals.utilities import parse_datetime
from journals.databases.icat.sns.communicate import SnsICat
class SnsICatInterface(object):
def ... | pandas.DataFrame.from_dict(data,orient='index') | pandas.DataFrame.from_dict |
# @Date: 2019-11-22T15:19:51+08:00
# @Email: <EMAIL>
# @Filename: ProcessUniProt.py
# @Last modified time: 2019-11-24T22:58:36+08:00
import urllib.parse
import urllib.request
import ftplib
import wget
import pandas as pd
import numpy as np
from random import uniform
from time import sleep
import os, re
from collecti... | pd.read_csv(outputPath, sep='\t', names=new_colNames, skiprows=1, header=None) | pandas.read_csv |
# 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... | pandas.Series([2] + [1] * 5) | pandas.Series |
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
@pytest.mark.parametrize("align_axis", [0, 1, "index", "columns"])
def test_compare_axis(align_axis):
# GH#30429
s1 = pd.Series(["a", "b", "c"])
s2 = pd.Series(["x", "b", "z"])
result = s1.compare(s2, align_axis=align_... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
import re
import json
import requests
from bs4 import BeautifulSoup
import time
import datetime
import pandas as pd
import numpy as np
# Fans page ==================================================================
# Crawl_PagePosts
def Crawl_PagePosts(pageurl, until_date='2019-01-01'):
page_id = pagecrawler.get... | pd.merge(left=content_df, right=feedback_df, how='left', on=['PAGEID', 'POSTID']) | pandas.merge |
import os
import glob
import datetime
import pandas as pd
if __name__ == '__main__':
"""
ASOS ๋ฐ์ดํฐ์
์ ๋ณด
ํ์ผ๋ช
: SURFACE_ASOS_[์ง์ ๋ฒํธ]_HR_[๊ด์ธก๋
๋]_[๊ด์ธก๋
๋]_[๊ด์ธก๋
๋+1].csv
์ปฌ๋ผ : ์ง์ (0), ์ผ์(1), ๊ธฐ์จ(2), ๊ฐ์๋(3), ํ์(4),
ํํฅ(5), ์ต๋(6), ์ฆ๊ธฐ์(7), ์ด์ฌ์ ์จ๋(8), ํ์ง๊ธฐ์(9),
ํด๋ฉด๊ธฐ์(10), ์ผ์กฐ(11), ์ผ์ฌ(12), ์ ์ค(13), 3์๊ฐ์ ์ ... | pd.read_csv(file_name, skiprows=1, header=None, encoding='cp949') | pandas.read_csv |
from unittest import TestCase
import pandas as pd
from moonstone.parsers.transform.cleaning import StringCleaner
class TestStringCleaner(TestCase):
def test_remove_trailing_spaces(self):
df = pd.DataFrame(
[
[1, ' b'],
[4, " a "]
],
co... | pd.testing.assert_frame_equal(transform_cleaning.df, expected_df) | pandas.testing.assert_frame_equal |
import numpy as np
import pandas as pd
from bs4.element import NavigableString, Comment, Doctype
from report_parser.src.text_class import Text
def print_tag(tag):
print('printing tag:', type(tag), tag.name)
if type(tag) not in [NavigableString, Doctype, Comment]:
for child in tag.children:
... | pd.Series(dtype=str) | pandas.Series |
import dash
from datetime import datetime, timedelta
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_daq as daq
import dash_html_components as html
import dash_table
import numpy as np
import pandas as pd
import plotly.graph_objs as go
from dash.dependencies import Input, Output, ... | pd.DataFrame(data) | pandas.DataFrame |
"""
Simple audio clustering
1. Get the embeddings - at an interval of 0.5s each
2. Get the VAD - variable interval
3. Get embeddings for a VAD interval -> Take average of the embeddings
4. Get the ground truth for embedding for each speaker - marked 0.5s interval
5. L2 Normalize the embeddings before taking a distance ... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
class IPA:
def __init__(self, url_file_1, url_file_2):
self.url_file_1 = file_harapan
self.url_fule_2 = file_persepsi
def filtering_column(file_path):
print("run filtering column...")
result = pd.read_excel(file_path).drop(
c... | pd.DataFrame(convert_SP, columns=['X']) | pandas.DataFrame |
import pandas as pd
import numpy as np
import csv
import os
import matplotlib.pyplot as plt
## Written by <NAME>
def topspin_to_pd(input_filename):
###row_dict was written by <NAME> ###
Rows = dict()
with open(input_filename) as p:
reader = csv.reader(p, delimiter=" ")
for row in reader:
... | pd.DataFrame.from_dict(Rows, orient='index',columns = ['1H','13C']) | pandas.DataFrame.from_dict |
#Cleaning data
import pandas as pd
import numpy as np
def clean():
#Reading in features/echonest frames and tracks df's to merge on track_id
features = pd.read_csv('features.csv',skiprows=[2,3])
features.rename(columns={'feature':'track_id'}, inplace=True)
columns = np.array(features.columns)
des... | pd.read_csv('features.csv',skiprows=[0,1,2,3],header=None,names=cols) | pandas.read_csv |
"""Live and historical flood monitoring data from the Environment Agency API"""
import requests
import pandas as pd
import flood_tool.geo as geo
import flood_tool.tool as tool
import numpy as np
import folium
__all__ = []
LIVE_URL = "http://environment.data.gov.uk/flood-monitoring/id/stations"
ARCHIVE_URL = "http://... | pd.to_numeric(DF4['value'], errors='coerce') | pandas.to_numeric |
import pandas as pd
import matplotlib.pyplot as plt
import data
import testing_data
import statistics
import numpy as np
pd.set_option('display.max_columns', None)
def findWaitingTime(arrival_time, processes, total_processes, burst_time, waiting_time, quantum):
rem_bt = [0] * total_processes
for i ... | pd.DataFrame() | pandas.DataFrame |
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