prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# Copyright (c) 2018, deepakn94. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | pd.to_datetime(ratings['timestamp'], unit='s') | pandas.to_datetime |
import numpy as np
import pandas as pd
import datetime
import chinese_calendar
from sklearn.preprocessing import OrdinalEncoder
class offsets_pool:
neighbor = [-1, 1]
second = [-1, 1, -60 * 4, -60 * 3, -60 * 2, -60 * 1, 60 * 1, 60 * 2, 60 * 3, 60 * 4]
minute = [-1, 1, -60 * 4, -60 * 3, -60 * 2, -60 * 1, ... | pd.concat([lower_forecast, lower], axis=0) | pandas.concat |
##
import pandas, os
##
path = {
'train':{
'csv':{
'label':"../DATA/BMSMT/TRAIN/CSV/LABEL.csv"
}
},
'test':{
'csv':{
'label':"../DATA/BMSMT/TEST/CSV/LABEL.csv"
}
}
}
##
table = {
'train':{
"label": | pandas.read_csv(path['train']['csv']['label']) | pandas.read_csv |
import os
import time
import psycopg2
import base64
import random
import pandas as pd
from sqlalchemy import create_engine
from google.cloud import secretmanager
from google.cloud import pubsub_v1
from google.cloud import storage
from google.cloud import tasks_v2
import json
from datetime import datetime
import gcsfs
... | pd.read_csv(gcloud_path) | pandas.read_csv |
import fact.io
import os
import pytest
from irf import gadf
import astropy.units as u
import pandas as pd
import numpy as np
FIXTURE_DIR = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
'test_files',
)
@pytest.fixture
def events():
return fact.io.read_data(
os.path.join(FIXTURE_DIR, ... | pd.Series(['01-01-2013', '01-02-2013'], name='foo') | pandas.Series |
"""Locator functions to interact with geographic data"""
import numpy as np
import pandas as pd
import flood_tool.geo as geo
__all__ = ['Tool']
def clean_postcodes(postcodes):
"""
Takes list or array of postcodes, and returns it in a cleaned numpy array
"""
postcode_df = pd.DataFrame({'Postcode':post... | pd.DataFrame({'Postcode':postcodes, 'Flood Risk':flood_risk}) | pandas.DataFrame |
import sys
import numpy as np
import pytest
from pandas.compat import (
IS64,
PYPY,
)
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_dtype_equal,
is_object_dtype,
)
import pandas as pd
from pandas import (
Index,
Series,
)
import pandas._testing as tm
def test_isnull_... | Series([1, 2, 3], dtype="int64", index=["a", "b", "c"]) | pandas.Series |
"""
Author: <NAME>
GitHub: phideltaee
Description: Custom training model for Detectron2 using a modified version of the TACO dataset and the ARC Litter Dataset.
------------------------------------------------------
------------------------------------------------------
NOTES on Implementation:
# Training on TACO... | pd.concat([results_df, row], 0) | pandas.concat |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import nose
import numpy as np
import pandas as pd
from pandas import (Index, Series, DataFrame, Timestamp, isnull, notnull,
bdate_range, date_range, _np_version_under1p7)
import pandas.core.common as com
from pandas.compa... | ct('100ms') | pandas.tseries.timedeltas._coerce_scalar_to_timedelta_type |
import pandas as pd
import numpy as np
#census_data = pd.read_csv('processed.csv')
# read the csv file from the data store: flatten-form-data.csv
flatten_data = | pd.read_csv('flatten-form-data.csv') | pandas.read_csv |
import pandas as pd
import os
import glob
import re
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import numpy as np
from sklearn.decomposition import PCA
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble impor... | pd.read_csv(filedir) | pandas.read_csv |
# pylint: disable=E1101
from datetime import datetime
import datetime as dt
import os
import warnings
import nose
import struct
import sys
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
from pandas.compat import iterkeys
from pandas.core.frame import DataFrame, Series
from pandas.c... | pd.to_datetime(expected['date_td'], coerce=True) | pandas.to_datetime |
# -*- coding: utf-8 -*-
# @Time : 2018/11/8 15:08
# @Author : MengnanChen
# @FileName: audio_process.py
# @Software: PyCharm
import os
import subprocess
from six.moves import cPickle as pickle
import numpy as np
import pandas as pd
import librosa
class AudioProcess(object):
def __init__(self):
self... | pd.DataFrame(data=frame_dict) | pandas.DataFrame |
# Requirments
# pandas==1.1.5
# To download the dataset: wget https://www.cse.msu.edu/computervision/SVW.zip && unzip SVW.zip
# To create the data directories: mkdir -p /mydata/CSQ/Hadamard-Matrix-for-hashing/video/dataset/SVW/raw/data && cd /mydata/Videos && mv * /mydata/CSQ/Hadamard-Matrix-for-hashing/video/dataset/... | pd.read_csv(ANN_FILE) | pandas.read_csv |
# -*- 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... | Index([('a', '_', 'b_c'), ('c', '_', 'd_e'), ('f', '_', 'g_h')]) | pandas.Index |
import re
import numpy as np
import pandas as pd
from nltk.util import ngrams
from blingfire import text_to_words
from unidecode import unidecode
STOPWORDS = {
'i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves',
'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself',
'yoursel... | pd.isnull(s) | pandas.isnull |
import math
import numpy as np
import pandas as pd
import sys
import importlib
from sklearn.decomposition import PCA
import networkx as nx
import pickle
from src.analyze import analyze_utils as au
import argparse
import os
from time import time
def get_parser():
parser = argparse.ArgumentParser(description='Compu... | pd.DataFrame(index=graphs, columns=columns) | pandas.DataFrame |
import sys
import pandas as pd
from sqlalchemy import create_engine
import sqlite3
def load_data(messages_filepath, categories_filepath):
"""
Load messages and categories datasets
Merge these datasets into a dataframe
Args:
messages_filepath : filepath of messages.read_csv
categories_fi... | pd.read_csv(categories_filepath) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Tue May 3 10:49:58 2016
Auger peak finding and quantitative routines ... batch processing
@author: tkc
First get it working for single file.
"""
#%%
import pandas as pd
import numpy as np
import os, sys, shutil, glob, re
if 'C:\\Users\\tkc\\Documents\\Python_Scripts' n... | pd.merge(Tidata, Ti2data, how='inner',on=['Filenumber','Area'], suffixes=('','_2')) | pandas.merge |
import nose
import unittest
import os
import sys
import warnings
from datetime import datetime
import numpy as np
from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range,
date_range, Index)
from pandas.io.pytables import HDFStore, get_store, Term, IncompatibilityWarning
import pandas... | Series() | pandas.Series |
"""
This module provides Preparer classes to
prepare the raw files and make them ready
to be stored in the database.
"""
import numpy as np
import pandas as pd
def _types_to_native(values):
"""
Converts numpy types to native types.
"""
native_values = values.apply(
lambda x: x.items() if isins... | pd.DataFrame(values) | pandas.DataFrame |
import math
import pandas as pd
from copy import copy
from pbu import JSON
from datetime import datetime, timedelta
DEFAULT_DATE_FORMAT = "%Y-%m-%d %H:%M:%S"
class TimeSeries:
"""
Helper class to manage time series with multiple data points. It offers the ability to align dates of different time
series, ... | pd.to_numeric(df[col], errors='coerce') | pandas.to_numeric |
from datetime import datetime
from dateutil.tz import tzlocal, tzutc
import pandas as pd
import numpy as np
from hdmf.backends.hdf5 import HDF5IO
from hdmf.common import DynamicTable
from pynwb import NWBFile, TimeSeries, NWBHDF5IO, get_manager
from pynwb.file import Subject
from pynwb.epoch import TimeIntervals
from... | pd.testing.assert_frame_equal(df_exp, df_obt, check_like=True, check_dtype=False) | pandas.testing.assert_frame_equal |
import pandas as pd
import pandas as pd
sample1 = pd.read_table('MUT-1_2.annotate.csv', sep='\t', index_col=0)["score"]
sample2 = pd.read_table('MUT-2_2.annotate.csv', sep='\t', index_col=0)["score"]
sample3 = pd.read_table('MUT-4_2.annotate.csv', sep='\t', index_col=0)["score"]
sample4 = pd.read_table('MUT-5_2.annot... | pd.read_table('WT-5_2.annotate.csv', sep='\t', index_col=0) | pandas.read_table |
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils.validation import check_is_fitted
from src.processing.errors import InvalidModelInputError
#.- HELPERS
def _define_variables(variables):
# Check that variable names are passed in a list.
# Can ... | pd.concat([X, y], axis=1) | pandas.concat |
from text_analysis import Analysis
import pandas as pd
# initialise lists
positive_scores = []
negative_scores = []
polarity_scores = []
subjective_scores = []
average_sentence_lengths = []
complex_words_percentages = []
fog_indexes = []
average_words_per_sentences = []
complex_words_counts = []
words_counts = []
syl... | pd.read_excel('files/output.xlsx') | pandas.read_excel |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 4 2018
File to pull fluids out of raw mimic data cut for use in downstream RL modeling.
Takes data from the raw mimic csv's in raw-data:
path on odyssey: /n/dtak/mimic-iii-v1-4/raw-data
Most of what we need is in INPUTEVENTS_CV & INPUTEVENT... | pd.set_option("display.max_columns",101) | pandas.set_option |
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 10 12:34:45 2020
@author: gweiss01
"""
import sys
import numpy as np
import pandas as pd
import cv2
import os
import pdb
import tkinter
from tkinter.filedialog import askdirectory,askopenfilename
from tkinter.simpledialog import askstring
#tkinter.Tk().withdraw() # we ... | pd.Series([]) | pandas.Series |
import os
import numpy as np
import pandas as pd
from sklearn.model_selection import KFold
from torchvision import datasets
import argparse
def _get_target_indexes(dataset, target_label):
target_indexes = []
for index, (_, label) in enumerate(dataset):
if label == target_label:
target_inde... | pd.DataFrame(columns=['class', 'class_name', 'valid_class', 'valid_class_name']) | pandas.DataFrame |
"""
Module contains tools for processing Stata files into DataFrames
The StataReader below was originally written by <NAME> as part of PyDTA.
It has been extended and improved by <NAME> from the Statsmodels
project who also developed the StataWriter and was finally added to pandas in
a once again improved version.
Yo... | ensure_object(column._values) | pandas.core.dtypes.common.ensure_object |
"""Generate HVTN505 dataset for Michael on statsrv"""
import pandas as pd
import numpy as np
import re
import itertools
__all__ = ['parseProcessed',
'parseRaw',
'unstackIR',
'compressSubsets',
'subset2vec',
'vec2subset',
'itersubsets',
'subse... | pd.read_csv(fn, dtype={'ptid':str, 'Ptid':str}, skipinitialspace=True, sep=sep) | pandas.read_csv |
""" Test cases for DataFrame.plot """
import string
import warnings
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
DataFrame,
Series,
date_range,
)
import pandas._testing as tm
from pandas.tests.plotting.common import TestPlotBase
fro... | Series([300, 500]) | pandas.Series |
from googleapiclient.discovery import build
from datetime import datetime, timedelta
from pandas import DataFrame, Timedelta, to_timedelta
from structures import Structure
from networkdays import networkdays
from calendar import monthrange
class Timesheet:
def __init__(self, credentials, sheetid):
# The I... | DataFrame(self.values) | pandas.DataFrame |
import argparse
import numpy as np
import os
import pandas as pd
import random
import sys
import time
import torch
import scan
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
class RNN(nn.Module):
def __init__(self,
input_size,
hidden_size,
... | pd.DataFrame(epoch_latency) | pandas.DataFrame |
import os
import re
import sys
import time
import pandas as pd
from typing import Union
from rich import console
from selenium.common.exceptions import NoSuchElementException
from selenium.webdriver import Firefox, FirefoxOptions
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support.... | pd.DataFrame(all_users, columns=('user', 'about', 'profile')) | pandas.DataFrame |
import matplotlib.pyplot as plt
import pandas as pd
from numpy import object
def update_plot_params():
params = {'legend.fontsize': 'x-large',
'figure.figsize': (10, 8),
'axes.labelsize': 'x-large',
'axes.titlesize': 'x-large',
'xtick.labelsize': 'x-large',
... | pd.DataFrame(cv_score) | pandas.DataFrame |
#!/usr/bin/env python
# ----------------------------------------------------------------------------
# Copyright (c) 2016--, Biota Technology.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# -------------------------------------... | pd.Index(['s1', 's2', 's3', 's4'], dtype='object') | pandas.Index |
import pandas as pd, sqlite3 as sql
import datetime as dt, re, time, holidays
from dateutil.relativedelta import relativedelta
# Shift nontrading days data to next available trading day
def next_business_day(date):
ONE_DAY = dt.relativedelta(days=1)
HOLIDAYS_US = holidays.US()
next_day = date + ONE_DAY
... | pd.DataFrame() | pandas.DataFrame |
from django.shortcuts import render
from django.http import HttpResponse
from django.views.generic.edit import CreateView, DeleteView, UpdateView
from . import models, serializers, utils
from django.db.models import Avg
from rest_framework import generics, status
from rest_framework.response import Response
from rest_f... | pd.Series(new_df.ind.values, index=new_df.index.levels[1]) | pandas.Series |
import numpy as np
import pandas as pd
import pytest
from lookback import models
class TestChangeDatesGeneralCase:
@pytest.fixture
def shape_data(self):
shape_df = pd.DataFrame(
data={
'shape_key': ['uts_co_S1', 'uts_co_S2', 'uts_co_S3', 'uts_co_S4'],
'STA... | pd.to_datetime(district_df['StartDate']) | pandas.to_datetime |
import pandas as pd
from pandas.io.json import json_normalize
def venues_explore(client,lat,lng, limit=100, verbose=0, sort='popular', radius=2000, offset=1, day='any',query=''):
'''funtion to get n-places using explore in foursquare, where n is the limit when calling the function.
This returns a pandas datafr... | pd.DataFrame() | pandas.DataFrame |
import pathlib
import os.path as osp
import pandas as pd
import numpy as np
from ast import literal_eval
from .vocabulary import build_vocab, Vocabulary
from ..utils import read_lines, unpickle_data
from ..data_generation.nr3d import decode_stimulus_string
def scannet_official_train_val(valid_views=None, verbose=Tru... | pd.read_csv(args.augment_with_sr3d) | pandas.read_csv |
import argparse
import numpy as np
import csv
import pandas as pd
import json
import scipy.sparse as sp
from sparsebm import (
SBM,
LBM,
ModelSelection,
generate_LBM_dataset,
generate_SBM_dataset,
)
from sparsebm.utils import reorder_rows, ARI
import logging
logger = logging.getLogger(__name__)
tr... | pd.Series(g) | pandas.Series |
# Copyright 2020 (c) Cognizant Digital Business, Evolutionary AI. All rights reserved. Issued under the Apache 2.0 License.
import numpy as np
import pandas as pd
ID_COLS = ['CountryName',
'RegionName',
'Date']
NPI_COLUMNS = ['C1_School closing',
'C2_Workplace closing',
... | pd.DataFrame(future_rows, columns=ips_df.columns) | pandas.DataFrame |
#Importing the required packages
from flask import Flask, render_template, request
import os
import pandas as pd
from pandas import ExcelFile
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
from sklearn.preprocessing import StandardScaler, Label... | pd.read_excel(abc) | pandas.read_excel |
# complete
# The primary (as of the current moment) feature selection method.
from cabi.prepare_data.utils import bal, get_and_adjust_data
import datetime
import numpy as np
import pandas as pd
from pandas.tseries.offsets import Hour
def complete(
db_engine, station_id, start, end, sample_size=int(1.0e5),
... | pd.isnull(temp) | pandas.isnull |
"""
Base class for a runnable script
"""
import pandas as pd
import numpy as np
from .. import api as mhapi
import os
from ..utility import logger
class Processor:
def __init__(self, verbose=True, violate=False, independent=True):
self.verbose = verbose
self.independent = independent
self.violate = violate
... | 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: df, 2: df2}) | pandas.concat |
import pandas as pd
from pandas._testing import assert_frame_equal
#from fopy.database._handle_input_formulas_dtype import _Handle_input_dtype
from fopy import Formulas
d_list = ['d = v*t', 'f = m*a']
d_tuple = tuple(d_list)
d_set = set(d_list)
d_dict_fos = {'Formula': d_list}
d_dict_fos_id = {'ID':[1,2], **d_dict_fos... | assert_frame_equal(h_dict_num.data, good_df) | pandas._testing.assert_frame_equal |
'''
Python reducer function
Copyright 2016 Amazon.com, Inc. or its affiliates. All Rights Reserved.
SPDX-License-Identifier: MIT-0
'''
'''
Modified by <EMAIL> for AWS lambda map-reduce test.
This reducer function takes in multiple files which are mapper phase outputs , writes back to one parquet file in s3
'''
import... | pd.to_numeric(df['RatecodeID']) | pandas.to_numeric |
from datetime import date, timedelta
import pandas as pd
from point import Point
import os
from urllib.error import HTTPError
import datetime
import numpy as np
class County:
def __init__(self, county_name): #( county_list, data_list, label_list):
self.name = county_name
def g... | pd.read_csv(url, error_bad_lines=False) | pandas.read_csv |
import pandas as pd
from hooqu.analyzers.analyzer import COUNT_COL
from hooqu.analyzers.grouping_analyzers import FrequencyBasedAnalyzer
class TestBaseGroupingAnalyzer:
def test_frequency_based_asnalyzers_computes_correct_frequencies(self,):
df = pd.DataFrame({"att1": ["A", "B", "B"]})
state = F... | pd.testing.assert_frame_equal(expected, state.frequencies) | pandas.testing.assert_frame_equal |
import pandas as pd
import numpy as np
import re
import math
import codecs
import csv
# 预计剩余电影总量220k到200k
data=pd.read_csv("Website_ETL.CSV")
data=np.array(data)
dic={}
dic["Jan"]="1"
dic["Feb"]="2"
dic["Mar"]="3"
dic["Apr"]="4"
dic["May"]="5"
dic["Jun"]="6"
dic["Jul"]="7"
dic["Aug"]="8"
dic["Sep"]=... | pd.isna(i) | pandas.isna |
import datetime
import json
import os
import pathlib
import tempfile
from unittest import mock
import numpy as np
import pandas as pd
import pytest
from etna.datasets import TSDataset
from etna.loggers import LocalFileLogger
from etna.loggers import S3FileLogger
from etna.loggers import tslogger
from etna.metrics imp... | pd.DataFrame({"keys": [1, 2, 3], "values": ["first", "second", "third"]}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
""" # CRÉDITOS
Software desarrllado en el laboratorio de biología de plantas ubicado en el campus Antumapu perteneciente a la Universidad de Chile.
- Autores:
- <NAME>.
- <NAME>.
- Contacto:
- <EMAIL>
- <EMAIL> """
#package imports
import pandas as pd
import os
... | pd.read_csv('documents\dwc_terms\GeologicalContext.csv',header=0,sep=';',encoding = 'unicode_escape') | pandas.read_csv |
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Series
import pandas._testing as tm
class TestSeriesCombine:
def test_combine_scalar(self):
# GH 21248
# Note - combine() with another Series is tested elsewhere because
# it is used when testing operators
... | pd.Series([10.0, 61.0, 12.0]) | pandas.Series |
"""Console script for scribbles."""
import os
import sys
import click
import numpy as np
import pandas as pd
import tensorflow.compat.v1 as tf
import tensorflow_probability as tfp
from collections import defaultdict
from pathlib import Path
from scribbles.datasets.synthetic import synthetic_sinusoidal, make_regress... | pd.DataFrame(a) | pandas.DataFrame |
"""
Summary: Pandas extension for converting 15-character Salesforce IDs to 18-character Salesforce IDs
Date: 2020-10-12
Contributor(s):
<NAME>
"""
from functools import lru_cache
from pandas import DataFrame
from pandas.api.extensions import register_series_accessor
@ | register_series_accessor("sf") | pandas.api.extensions.register_series_accessor |
#
# Copyright 2020 Capital One Services, LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | pd.DataFrame([{"a": "hi", "b": 2}, {"a": "bye", "b": 2}]) | pandas.DataFrame |
from multiprocessing import Pool
import requests
import re
from bs4 import BeautifulSoup
from itertools import chain
from collections import Counter
from timeit import default_timer as timer
import pandas as pd
from datetime import datetime
def get_table_rows(fname="stats.html"):
"""
Extract the table rows fr... | pd.DataFrame(results) | pandas.DataFrame |
import os
import pandas as pd
from datetime import datetime, timedelta
# Global variable
PIE_PATH="/Users/fabrice/Documents/chartJS/tutoChartJS/chart/datas"
# Get Pie data
def get_data(path=PIE_PATH, filename="sample-pie-data.csv", separator=','):
csv_path = os.path.join(path, filename)
return pd.read_... | pd.to_datetime(data['Date']) | pandas.to_datetime |
import numpy as np
from scipy.special import expit as sigmoid
import numpyro.handlers as numpyro
import pandas as pd
import pytest
import torch
from jax import random
import pyro.poutine as poutine
from brmp import define_model, brm, makedesc
from brmp.backend import data_from_numpy
from brmp.design import (Categorica... | pd.Categorical(['b1', 'b2', 'b2']) | pandas.Categorical |
import re
import warnings
from datetime import datetime, timedelta
from unittest.mock import patch
import numpy as np
import pandas as pd
import pytest
from pandas.testing import (
assert_frame_equal,
assert_index_equal,
assert_series_equal,
)
from woodwork.logical_types import Double, Integer
from rayml.... | assert_index_equal(X_t.index, X.index) | pandas.testing.assert_index_equal |
#!C:\Users\willi\AppData\Local\Programs\Python\Python38-32\python.exe
#!/usr/bin/python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import psycopg2
import time
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.holtwinters import ExponentialSmoothing as HWES
impor... | pd.DataFrame(rowsHwes,columns = ['Month','Value']) | pandas.DataFrame |
# 读取 Northwind.txt 文本数据到 DataFrame。
# （1） 查询出在1997年3月份销售过的产品名称。
# （2） 求解销售相关性最强的两个产品。
# （3） 求解销售业绩波动最小的产品。
import numpy as np
import pandas as pd
data = pd.read_table('Northwind.txt', sep=',')
# （1） 查询出在1997年3月份销售过的产品名称。
data1 = data[((data.OrderYear == 1997) & \
(data.OrderMonth == 3))]
pri... | pd.DataFrame(g2) | pandas.DataFrame |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import os
import operator
import unittest
import numpy as np
from pandas.core.api import (Index, Series, TimeSeries, DataFrame, isnull)
import pandas.core.datetools as datetools
from pandas.util.testing import assert_series_equal
import panda... | Series([1.], index=[1]) | pandas.core.api.Series |
from flowsa.common import WITHDRAWN_KEYWORD
from flowsa.flowbyfunctions import assign_fips_location_system
from flowsa.location import US_FIPS
import math
import pandas as pd
import io
from flowsa.settings import log
from string import digits
YEARS_COVERED = {
"asbestos": "2014-2018",
"barite": "2014-2018",
... | pd.DataFrame() | pandas.DataFrame |
import sys
sys.path.append('../')
#code below used to deal with special characters on the file path during read_csv()
sys._enablelegacywindowsfsencoding()
import numpy as np
import seaborn as sns
import pandas as pd
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt #MatPlotLi... | pd.Series(y) | pandas.Series |
import logging
from datetime import datetime
from timeit import default_timer as timer
from io import StringIO
import pandas as pd
import pytz
import requests
from celery.schedules import crontab
from celery.task import Task
from api.models import ConfirmedData, DeadData, RecoveredData, CovidData, \
ImportsUpdate... | pd.read_csv(data_content) | pandas.read_csv |
import os
import pandas as pd
from ... import fileleaf as fl
class DatabaseSources:
"""
This Class is used to handle all the data sources and retrieve the applicable data
It keeps track of all sources and handles the fast library functionality
"""
__default_supported_extensions = {
'.csv... | pd.DataFrame(tables) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import pandas.compat as compat
###############################################################
# Index / Series common tests which may trigger dtype coercions
###############################################... | pd.Timestamp('2011-01-03', tz=tz) | pandas.Timestamp |
'''
This code will clean the OB datasets and combine all the cleaned data into one
Dataset name: O-27-Da Yan
semi-automate code, needs some hands work. LOL But God is so good to me.
1. 9 different buildings in this dataset, and each building has different rooms
3. each room has different window, door, ac, indoor, out... | pd.concat([template_window, combined_window], ignore_index=True) | pandas.concat |
import csv
import re
import string
import math
import warnings
import pandas as pd
import numpy as np
import ipywidgets as wg
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.ticker as mtick
from itertools import product
from scipy.optimize import curve_fit
from plate_mapping imp... | pd.read_csv(csv_file, sep=',', index_col=0, engine='python', skiprows=item[0], nrows=item[1], encoding='utf-8') | pandas.read_csv |
############################################# IMPORT STUFF #############################################
import pandas as pd
import numpy as np
import importlib.util
from spellchecker import SpellChecker
# helper function to help load things from BERT folder
def module_from_file(module_name, file_path):
spec = i... | pd.read_csv(PROBABILITY_PATHS[i], sep="\t") | pandas.read_csv |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import random
import unittest.mock as mock
from datetime import datetime, timedelta
from unittest import TestCase
import numpy as np
import... | pd.Series(data_time) | pandas.Series |
from keras.models import Sequential
from keras.optimizers import SGD,adam
from keras.layers import Input, Dense, Convolution2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, merge, Reshape, Activation, LeakyReLU
from sklearn.metrics import log_loss
import numpy as np
import json
import matplotlib.py... | pd.DataFrame(train.T, columns=['TRUE', 'MODEL', 'RMSLE_cal']) | pandas.DataFrame |
"""
Filter and combine various peptide/MHC datasets to derive a composite training set,
optionally including eluted peptides identified by mass-spec.
"""
import sys
import argparse
import os
import json
import collections
from six.moves import StringIO
import pandas
from mhcflurry.common import normalize_allele_name
... | pandas.read_csv(blood_filename, sep="\t", index_col=0) | pandas.read_csv |
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.keys import Keys
import requests
import time
from datetime import datetime
import pandas as pd
from urllib import parse
from config import ENV_VARIABLE
from os.path import getsize
fold_path = ... | pd.concat([dfAll, df]) | pandas.concat |
"""
Tests for CBMonthEnd CBMonthBegin, SemiMonthEnd, and SemiMonthBegin in offsets
"""
from datetime import (
date,
datetime,
)
import numpy as np
import pytest
from pandas._libs.tslibs import Timestamp
from pandas._libs.tslibs.offsets import (
CBMonthBegin,
CBMonthEnd,
CDay,
SemiMonthBegin,
... | Timestamp("2000-01-15 00:15:00", tz="US/Central") | pandas._libs.tslibs.Timestamp |
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_daq as daq
from dash.dependencies import Input, Output, State
import plotly.graph_objs as go
import sqlite3
import pandas as pd
from flask_caching import Cache
import pyarrow as pa
import pyarrow.plasma as plasma
import numpy... | pd.read_sql(query, con) | pandas.read_sql |
import pandas as pd
import numpy as np
from app.db.db_connection import get_db
def import_abandoned_vehicles(input_file: str) -> None:
""" Import the requests for abandoned vehicles to the database.
:param input_file: The file from which to load the requests for abandoned vehicles.
"""
print("Gettin... | pd.read_csv(input_file, sep=',') | pandas.read_csv |
# 2. Use the best model
from keras.models import load_model
from sklearn import preprocessing
import numpy as np
import pandas as pd
# data set
ud = | pd.read_csv('../dataset/ginseng-example.csv') | pandas.read_csv |
# coding: utf-8
# In[1]:
import numpy as np
import pandas as pd
import scipy.integrate as integrate
from scipy.optimize import brentq as root
import math
import numpy as np
import scipy.special as scp
from scipy.special import iv
# In[2]:
def rvonmises(n, mu, kappa):
vm = np.zeros(n)
a = 1 + (1 + 4 * (k... | pd.isnull(kappa) | pandas.isnull |
# feature importance
# local score 0.0449
# kaggle score .14106
# minimize score
import os
import sys # noqa
from time import time
from pprint import pprint # noqa
import lightgbm as lgb
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.feature_selection import Varian... | pd.read_csv(f'../input/{train_file}.csv{zipext}') | pandas.read_csv |
# -*- coding: utf-8 -*-
#
# Copyright 2017-2020 Data61, CSIRO
#
# 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 ... | pd.api.types.is_numeric_dtype(weight_col) | pandas.api.types.is_numeric_dtype |
#
# Copyright 2018 Quantopian, 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 in wr... | tm.assert_series_equal(ans_minutes, cal_minutes, check_freq=False) | pandas.testing.assert_series_equal |
import copy
import io
import json
import os
import string
from collections import OrderedDict
from datetime import datetime
from unittest import TestCase
import numpy as np
import pandas as pd
import pytest
import pytz
from hypothesis import (
given,
settings,
)
from hypothesis.strategies import (
dateti... | pd.DataFrame(data) | pandas.DataFrame |
#!/usr/bin/python
# -*- coding: utf-8 -*-
#
"""
cd /Users/brunoflaven/Documents/02_copy/_000_IA_bruno_light/_my_article_python-explorations/git_repo_python_explorations_nlp/article_1_keyword_extraction_nlp/
python 09_article_1_keyword_extraction_nlp.py
"""
## settings
path="/Users/brunoflaven/Documents/02_copy/_0... | pandas.DataFrame(top2_words) | pandas.DataFrame |
import time
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, learning_curve, ShuffleSplit
from sklearn import metrics
from sklearn.naive_bayes import GaussianNB, MultinomialNB
import paho.mqtt.client as mqtt
# df_raw_normal = pd.re... | pd.concat([df_raw_0, df_raw_1, df_raw_1_1]) | pandas.concat |
# Setup
import pandas as pd
# Load All Files
### Get filenames from repo
# We first retrieve the filenames of all files listed in the repository:
import requests
user = "ard-data"
repo = "2020-rki-archive"
url = "https://api.github.com/repos/{}/{}/git/trees/master?recursive=1".format(user, repo)
r = requests.get... | pd.concat([df_all, df_cum]) | pandas.concat |
# -*- 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(mixed) | pandas.Series |
from django.test import TestCase
import pandas as pd
from .models import join_files
from pandas._testing import assert_frame_equal
class JoinFilesTestCase(TestCase):
def test_two_files_can_be_joined(self):
csv1 = pd.DataFrame(data={'user_id': [1, 2], 'age': [43, 28]})
csv2 = pd.DataFrame(data={'u... | pd.DataFrame(data={'user_id': [1, 2], 'name': ['A', 'B']}) | pandas.DataFrame |
import os.path
from surprise import SVDpp
import pandas as pd
import numpy as np
from surprise import BaselineOnly
from surprise import NormalPredictor
from surprise import Dataset
from surprise.model_selection import cross_validate
from surprise.model_selection import KFold
from surprise import Reader
from surprise i... | pd.read_csv("../Data/train-prep.csv") | pandas.read_csv |
import os
from unittest import TestCase
# most of the features of this script are already tested indirectly when
# running vensim and xmile integration tests
_root = os.path.dirname(__file__)
class TestErrors(TestCase):
def test_canonical_file_not_found(self):
from pysd.tools.benchmarking import runner... | pd.DataFrame({'a': [1, 2]}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 12 11:56:58 2022
@author: lawashburn
"""
import os
import csv
import pandas as pd
import numpy as np
from datetime import datetime
now = datetime.now()
fragment_matches = pd.read_csv(r"C:\Users\lawashburn\Documents\Nhu_Prescursor_Matching\20220417_oldprecu... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
from datetime import datetime
from dateutil.relativedelta import relativedelta
class stock:
now = datetime.now()
def __init__(self, stock_code, from_month) -> None:
self.stock_code = stock_code
self.from_month = from_month
if self.now.month< from_month:
s... | pd.to_numeric(data[i], errors='ignore') | pandas.to_numeric |
import numpy as np
from datetime import timedelta
from distutils.version import LooseVersion
import pandas as pd
import pandas.util.testing as tm
from pandas import to_timedelta
from pandas.util.testing import assert_series_equal, assert_frame_equal
from pandas import (Series, Timedelta, DataFrame, Timestamp, Timedelt... | tm.assertRaises(TypeError) | pandas.util.testing.assertRaises |
import os
import numpy as np
import pandas as pd
from collections import defaultdict
from .io import save_data, load_data, exists_data, save_results
from . import RAW_DATA_DIR
DATASETS = ['password', 'keypad', 'fixed_text', 'free_text', 'mobile']
MOBILE_SENSORS = ['pressure', 'tool_major', 'x', 'x_acceleration', 'x_... | pd.read_csv(fname1_in, index_col=[0, 1]) | pandas.read_csv |
# License: Apache-2.0
import databricks.koalas as ks
import pandas as pd
import numpy as np
import pytest
from pandas.testing import assert_frame_equal
from gators.imputers.numerics_imputer import NumericsImputer
from gators.imputers.int_imputer import IntImputer
from gators.imputers.float_imputer import FloatImputer
f... | pd.DataFrame(X_new_np, columns=X_dict['float'].columns) | pandas.DataFrame |
import os
import pandas as pd
import numpy as np
from joblib import load
import MLPipeline
import AppConfig as app_config
import ml_pipeline.utils.Helper as helper
DATA_FLD_NAME = app_config.TSG_FLD_NAME
class TestSetPreprocessing:
def __init__(self, ml_pipeline: MLPipeline):
self.ml_pipeline = ml_pi... | pd.read_csv(boruta_train_path) | pandas.read_csv |
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