prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import warnings
import logging
warnings.filterwarnings('ignore', category=FutureWarning)
from .index import build as build_index
from .index import build_from_matrix, LookUpBySurface, LookUpBySurfaceAndContext
from .embeddings.base import load_embeddings, EmbedWithContext
from .ground_truth.data_processor import Wiki... | pd.read_pickle(context_matrix_file) | pandas.read_pickle |
import numpy as np
import nibabel as nib
import os.path as op
import pandas as pd
from glob import glob
from scipy import ndimage
from nilearn.datasets import fetch_atlas_harvard_oxford, load_mni152_template
from nilearn.image import coord_transform
def extract_roi_info(statfile, stat_name=None, out_dir=None, unilate... | pd.Series([np.nan]) | pandas.Series |
import pandas as pd
import numpy as np
#modify file name you want to split into train and test set
data = | pd.read_csv('./gravel_clay_class3_total.csv') | pandas.read_csv |
import time # 引入time模块
import pandas as pd
import re
import sqlparse
attributeNameArray = ['tableName', 'createTime', 'lastModifyTime', 'owner', 'rowNumber', 'columnNumber',
'primaryKey', 'uniqueKey', 'foreignKey', 'notNullColumn', 'indexColumn', 'columnDataType']
remarksList = ['表名', '创建时间', '最... | pd.Series(attributeNameArray, index=tempArray, name="attribute") | pandas.Series |
# ----------------------------------------------------------------------------
# Copyright (c) 2020, <NAME>.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# -----------------------------------------------------------------------... | assert_frame_equal(md_abx_filt_mm, test_md_abx_mm) | pandas.testing.assert_frame_equal |
import glob
import pandas as pd
import os
def merge():
if os.path.isfile('data.csv'):
os.remove('data.csv')
files = glob.glob("*.csv")
columns = ['Bus Body','Date','Packet','Slot','Latitude','Longitude','Place']
df = []
for file in files:
data = | pd.read_csv(file) | pandas.read_csv |
from pymongo import MongoClient
import pandas as pd
from collections import Counter
# NLP libraries
from nltk.tokenize import TweetTokenizer
from nltk.corpus import stopwords
import string
import csv
import json
# from datetime import datetime
import datetime
from collections import deque
import pymongo
"""TIME SERI... | pd.DatetimeIndex(datesQuery) | pandas.DatetimeIndex |
"""
Authors: <NAME> @dshemetov, <NAME> @jsharpna
"""
from io import BytesIO
from os.path import join, isfile
from zipfile import ZipFile
import requests
import pandas as pd
import numpy as np
# Source files
INPUT_DIR = "./old_source_files"
OUTPUT_DIR = "../../delphi_utils/data"
FIPS_BY_ZIP_POP_URL = (
"https://... | pd.concat([census_pop_pr, territories_pop]) | pandas.concat |
#Cntrl+C #Cntrl+V #PYCODING
#1
dict = {"country": ["Brazil", "Russia", "India", "China", "South Africa"],
"capital": ["Brasilia", "Moscow", "New Dehli", "Beijing", "Pretoria"],
"area": [8.516, 17.10, 3.286, 9.597, 1.221],
"population": [200.4, 143.5, 1252, 1357, 52.98] }
import pandas a... | pd.date_range("20210101", periods=12) | pandas.date_range |
from contextlib import nullcontext as does_not_raise
from functools import partial
import pandas as pd
from pandas.testing import assert_series_equal
from solarforecastarbiter import datamodel
from solarforecastarbiter.reference_forecasts import persistence
from solarforecastarbiter.conftest import default_observatio... | pd.Timestamp('20190513 1200', tz=tz) | pandas.Timestamp |
import plotly.graph_objects as go
import pandas as pd
import plotly.express as px
from datetime import datetime, timedelta
import requests
import json
import time
def read():
df1 = pd.read_csv("CSV/ETH_BTC_USD_2015-08-09_2020-04-04-CoinDesk.csv")
df1.columns = ['date', 'ETH', 'BTC']
df1.date = pd.to_dateti... | pd.read_csv("CSV/XAU-GOLD_USD_Historical Data_2018-06-06--2020-04-04.csv") | pandas.read_csv |
import nose
import warnings
import os
import datetime
import numpy as np
import sys
from distutils.version import LooseVersion
from pandas import compat
from pandas.compat import u, PY3
from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range,
date_range, period_range, Index, Categori... | assert_series_equal(i, i_rec) | pandas.util.testing.assert_series_equal |
import os
import re
import sys
import time
import math
from collections import Counter
from functools import partial
from tempfile import mkdtemp, NamedTemporaryFile
import logging
import multiprocessing as mp
# "hidden" features, in development
try:
import MOODS.tools
import MOODS.parsers
import MOODS.sca... | pd.concat((self._threshold, df), axis=1) | pandas.concat |
import itertools as itt
import pathlib as pl
from configparser import ConfigParser
import joblib as jl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.stats as sst
import seaborn as sns
from statannot import add_stat_annotation
from src.visualization import fancy_plots as fplt
from... | pd.concat((pops.loc[:, ['region', 'value']], sing_pivot.loc[:, 'max']), axis=1) | pandas.concat |
import pandas as pd
import numpy as np
from scipy.special import boxcox1p
from scipy.stats import boxcox_normmax
from scipy.stats import skew
from sklearn.preprocessing import RobustScaler
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import Ridge, Lasso, ElasticNet
from mlxtend.regressor impor... | pd.get_dummies(all_data) | pandas.get_dummies |
import pandas as pd
import pytest
pd.set_option("display.max_rows", 500)
pd.set_option("display.max_columns", 500)
| pd.set_option("display.width", 1000) | pandas.set_option |
from datetime import (
datetime,
timedelta,
timezone,
)
import numpy as np
import pytest
import pytz
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
NaT,
Period,
Series,
Timedelta,
Timestamp,
date_range,
isna,
)
import pandas._testing as tm
class TestS... | Timestamp("2011-01-02 10:00", tz=tz) | pandas.Timestamp |
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 1 18:10:18 2019
@author: <NAME>
Code will plot the keypoint coordinates vs time in order to assign the maximum
value from this plot to the real-world distance measurement. This will be
the label.
Coding Improvement Note: Make use of functions for things lik... | pd.DataFrame({"Height": [69]*50 + [69.5]*10 + [67]*10}) | pandas.DataFrame |
"""
Tests encoding functionality during parsing
for all of the parsers defined in parsers.py
"""
from io import BytesIO
import os
import tempfile
import numpy as np
import pytest
from pandas import DataFrame
import pandas._testing as tm
def test_bytes_io_input(all_parsers):
encoding = "cp1255"
parser = all... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
import base64
import io
import textwrap
import dash
import dash_core_components as dcc
import dash_html_components as html
import gunicorn
import plotly.graph_objs as go
from dash.dependencies import Input, Output, State
import flask
import pandas as pd
import urllib.parse
from sklearn.preprocessing import StandardSca... | pd.DataFrame(data=features_input_outlier, columns=['Features']) | pandas.DataFrame |
from datetime import datetime
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import accuracy_score, f1_score, make_scorer, r2_score, mean_squared_error
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
import re
import numpy as np
import pandas as pd
... | pd.to_datetime(df['host_since']) | pandas.to_datetime |
import sys, os, argparse, pickle, json, hashlib, copy
import pandas as pd
import numpy as np
from pathlib import Path
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler, MinMaxScaler
project_root = os.getcwd()
sys.path.append(project_root)
import demand.models.utils_general as ug
from de... | pd.concat(all_cons) | pandas.concat |
"""Tests for arithmetic.py"""
import pytest
import pandas as pd
from pandas.testing import assert_frame_equal
from timeflux.core.io import Port
from timeflux_example.nodes.arithmetic import Add, MatrixAdd
def test_add():
node = Add(1)
node.i = Port()
node.i.data = pd.DataFrame([[1, 1], [1, 1]])
node.u... | pd.DataFrame([[3, 3], [3, 3]]) | pandas.DataFrame |
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
# Series Problem
s1 = pd.Series(-3, index=range(2, 11, 2))
s2 = pd.Series({'Bill':31, 'Sarah':28, 'Jane':34, 'Joe':26})
# Random Walk Problem
# five random walks of length 100 plotted together
N = 100
for i in xrange(5):
s1 = np.zeros(N)
... | pd.read_csv("crime_data.txt", header=1, skiprows=0, index_col=0) | pandas.read_csv |
import torch
import numpy as np
import matplotlib.pyplot as plt
from yasa import get_bool_vector, spindles_detect
from EEG.templates import get_templates
import pandas as pd
import pickle as pkl
import glob
def plot_cam(saved_model_name, signal_name, plot_inds, test_loader, model, cam_target, label='normal',
... | pd.DataFrame(rep2label_perf) | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from astropy.time import Time
def open_avro(fname):
with open(fname,'rb') as f:
freader = fastavro.reader(f)
schema = freader.writer_schema
for packet in freader:
return packet
def make_dataframe(packet):
... | pd.DataFrame(packet['candidate'], index=[0]) | pandas.DataFrame |
import os
import statistics
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import torch
FILE_DIRS = []
FILE_LISTS = []
PLOT_NAMES = []
TITLES = []
MEAN_TRAIN_STEPS = []
STD_TRAIN_STEPS = []
"""
0.pt -> rewards train: real
2.pt -> rewards train: synth., HPs: varied
1.pt -... | pd.DataFrame(data=data_dict) | pandas.DataFrame |
"""
2018 <NAME>
9.tcga-classify/classify-with-raw-expression.py
Predict if specific genes are mutated across TCGA tumors based on raw RNAseq gene
expression features. Also make predictions on cancer types using raw gene expression.
Usage:
python classify-with-raw-expression.py
Output:
Gene specific DataFrames s... | pd.read_table(file) | pandas.read_table |
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays.sparse import SparseArray
class TestSparseArrayConcat:
@pytest.mark.parametrize("kind", ["integer", "block"])
def test_basic(self, kind):
a = SparseArray([1, 0, 0, 2], kind=kind)
b = Spar... | SparseArray._concat_same_type([a, b]) | pandas.core.arrays.sparse.SparseArray._concat_same_type |
import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
Timestamp,
date_range,
to_datetime,
)
import pandas._testing as tm
import pandas.tseries.offsets as offsets
class TestRollingTS:
# rolling time-series friendly
# xref GH13327
def set... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
import settings # Import related setting constants from settings.py
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import pandas as pd
import plotly.graph_objs as go
import settings
import itertools
import math
import base64
from flask imp... | pd.read_sql(query, engine) | pandas.read_sql |
import pickle
import pandas as pd
import time as time
def merge_with_metatable(from_sp, to_sp, df_spectra, save=False):
"""
merge_with_metatable()
Parameters
----------
from_sp : string
The number from which to merge spectra with meta-data. String, beceause it
must match the filename in folder data/sdss/sp... | pd.merge(df_spectra, df_meta_data, on=['objid']) | pandas.merge |
from sklearn import tree
from sklearn.metrics import accuracy_score
import pandas as pd
import os
from sklearn.ensemble import RandomForestClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
import itertools
import math
from TMDataset import TMDataset
import const
import util
clas... | pd.DataFrame({'sensor': sensor, 'accuracy': accuracy, 'dev_standard': std}) | pandas.DataFrame |
# import Ipynb_importer
import pandas as pd
from .public_fun import *
# 全局变量
class glv:
def _init():
global _global_dict
_global_dict = {}
def set_value(key,value):
_global_dict[key] = value
def get_value(key,defValue=None):
try:
return _global_dict[key... | pd.merge(self.pl, self.f_08.pl, left_index=True, right_index=True) | pandas.merge |
import pandas as pd
import matplotlib.pyplot as plt
# import seaborn as sns
# sns.set(rc={'figure.figsize':(11, 4)})
dataDirectory = '../data/'
graphsDirectory = 'graphs/'
def visDay(dfs,sensors,day):
plt.clf()
fig, axs = plt.subplots(len(dfs),sharex=True,sharey=True,gridspec_kw={'hspace': 0.5},figsize=(20, 10... | pd.Grouper(freq='60s') | pandas.Grouper |
import pandas as pd
import re
filename="/Users/laurahughes/GitHub/cvisb_data/sample-viewer-api/src/static/data/input_data/expt_summary_data/viral_seq/LASV_all_metadata_Raphaelle_2019-07-23.xlsx"
lsv_file = "/Users/laurahughes/GitHub/cvisb_data/sample-viewer-api/src/static/data/input_data/patient_rosters/acuteLassa_me... | pd.merge(kgh, lsv_geo, how="left", left_on="gID", right_on="gID", indicator=True) | pandas.merge |
import os
import sys
import json
import copy
import numpy as np
import pandas as pd
import random
import tensorflow as tf
# import PIL
seed_value = 123
os.environ['PYTHONHASHSEED']=str(seed_value)
random.seed(seed_value)
np.random.seed(seed_value)
tf.set_random_seed(seed_value)
from keras.utils import to_categorical
... | pd.Series(y) | pandas.Series |
import requests
from typing import List
import re
# from nciRetriever.updateFC import updateFC
# from nciRetriever.csvToArcgisPro import csvToArcgisPro
# from nciRetriever.geocode import geocodeSites
# from nciRetriever.createRelationships import createRelationships
# from nciRetriever.zipGdb import zipGdb
# from nciRe... | pd.concat([mainToSubTypeRelsDf, mainToSubTypeRelDf], ignore_index=True, verify_integrity=True) | pandas.concat |
import os
import pandas as pd
import cv2
import scipy.stats as stat
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
from .matplotlibstyle import *
import datetime
class Datahandler():
'Matches EL images paths to... | pd.DataFrame(images) | pandas.DataFrame |
import time
import gc
import cv2
import numpy as np
import pandas as pd
import tensorflow as tf
from traffic_analysis.d00_utils.bbox_helpers import (bboxcv2_to_bboxcvlib,
bboxcvlib_to_bboxcv2,
display_bboxes_on_f... | pd.concat(video_info_list) | pandas.concat |
"""
Module for implementation of BHPS education data to daedalus frame.
"""
import pandas as pd
from vivarium.framework.utilities import rate_to_probability
from pathlib import Path
import random
import os
import subprocess # For running R scripts in shell.
class Employment:
""" Main class for application of empl... | pd.DataFrame(index=pop.index) | pandas.DataFrame |
import sys
import json
import csv
import pandas as pd
from datetime import datetime
def find_news(lang):
response = []
x =[]
values = []
df = | pd.DataFrame() | pandas.DataFrame |
# Copyright (c) 2020 Huawei Technologies Co., Ltd.
# <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 a... | pd.read_csv(f, error_bad_lines=False, index_col=False) | pandas.read_csv |
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# This file contains dummy data for the model unit tests
import numpy as np
import pandas as pd
AIR_FCST_LINEAR_95 = pd.DataFrame(
{
... | pd.Timestamp("2013-05-22 00:00:00") | pandas.Timestamp |
import numpy as np
import pandas as pd
import matplotlib.image as mpimg
from typing import Tuple
from .config import _dir
_data_dir = '%s/input' % _dir
cell_types = ['HEPG2', 'HUVEC', 'RPE', 'U2OS']
positive_control = 1108
negative_control = 1138
nsirna = 1108 # excluding 30 positive_control + 1 negative_control
pl... | pd.concat([df, df_controls], sort=False) | pandas.concat |
# -*- 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('2011-01-01', tz=tz) | pandas.Timestamp |
"""
Normals Interface Class
Meteorological data provided by Meteostat (https://dev.meteostat.net)
under the terms of the Creative Commons Attribution-NonCommercial
4.0 International Public License.
The code is licensed under the MIT license.
"""
from copy import copy
from typing import Union
from datetime import dat... | pd.Index([self._end]) | pandas.Index |
import streamlit as st
import requests
import numpy as np
import pandas as pd
import os
import json
import re
from datetime import datetime
class UserData:
def getUserInfo():
st.title('Instagram Dashboard')
with st.form(key='my_form'):
#gets a text input
... | pd.DataFrame(x) | pandas.DataFrame |
import matplotlib.pyplot as plt
import numpy as np
import pandas
import math
import sys
import numbers
import argparse
from sklearn.cluster import KMeans
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.svm import ... | pandas.Series(countryList) | pandas.Series |
import os
import pickle
import serpent
from threading import Lock
from tempfile import NamedTemporaryFile
from contextlib import suppress
import numpy as np
import pandas as pd
from astroquery.utils.tap.core import TapPlus
from astropy.coordinates import SkyCoord
import Pyro5.server
from Pyro5.api import Proxy, regis... | pd.concat([result, df[is_new]], ignore_index=False) | pandas.concat |
#!/usr/bin/env python
# -*-coding:utf-8 -*-
'''
@File : Stress_detection_script.py
@Time : 2022/03/17 09:45:59
@Author : <NAME>
@Contact : <EMAIL>
'''
import os
import logging
import plotly.express as px
import numpy as np
import pandas as pd
import zipfile
import fnmatch
import flirt.reader.empatica
... | pd.DataFrame(starting_timestamp) | pandas.DataFrame |
from __future__ import print_function
import caffe
import sys
import os
import random
import numpy as np
import pandas as pd
import cv2
import pickle
# If you get "No module named _caffe", either you have not built pycaffe or you have the wrong path.
model_root = "/datasets_1/sagarj/BellLabs/caffe_models/places/"
... | pd.DataFrame(data=d) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 8 15:49:09 2019
@author: d
"""
print("Running 'wrangle.py'...")
import numpy as np
import pandas as pd
np.random.seed(0)
print('Beginning wrangling of training and test set')
# Loading data
df_train = pd.read_csv('../../data/raw/train_users_2.csv')
df_test = pd.read... | pd.to_datetime(df_all['date_account_created']) | pandas.to_datetime |
#!/usr/bin/env python
"""
@author: cdeline
bifacial_radiance.py - module to develop radiance bifacial scenes, including gendaylit and gencumulativesky
7/5/2016 - test script based on G173_journal_height
5/1/2017 - standalone module
Pre-requisites:
This software is written for Python >3.6 leveraging many Anaconda... | pd.Timedelta('1h') | pandas.Timedelta |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#pct change of returns
def returns(dataset):
returns = dataset.pct_change()
returns = returns*100
returns = returns.dropna()
print(returns)
#compounded percentage product of returns
def product(dataset):
returns = dataset.pct_chang... | pd.to_datetime(dataset.index, format="%Y%m") | pandas.to_datetime |
import requests
from bs4 import BeautifulSoup
import numpy as np
import pandas as pd
from dateutil import relativedelta
import dateparser
import datetime
import warnings
warnings.filterwarnings('ignore')
URL = "http://www.tns-sofres.com/cotes-de-popularites"
resultats = requests.get(URL)
page = BeautifulSoup(resulta... | pd.DataFrame(columns=["President", "Confiance", "Pas Confiance"]) | pandas.DataFrame |
import json
import pandas as pd
import csv
NUM_PLAYER_LIST = ["6p", "9p"]
def transformHand(handsStr):
handList = handsStr.split(",")
hand1 = handList[0].strip()
hand2 = handList[1].strip()
hand1Num = hand1[0]
hand1Suit = hand1[1]
hand2Num = hand2[0]
hand2Suit = hand2[1]
# Data is ... | pd.DataFrame(data=mergedResultDict) | pandas.DataFrame |
import sys
import re
from pathlib import Path
import logging
from typing import Optional, Union
import pandas as pd
logger = logging.getLogger(__name__)
class GradsCtl(object):
def __init__(self):
self.dset = None # data file path
self.dset_template = False
self.title = ''
sel... | pd.Timedelta(days=v) | pandas.Timedelta |
import asyncio
import queue
import uuid
from datetime import datetime
import pandas as pd
from storey import build_flow, Source, Map, Filter, FlatMap, Reduce, FlowError, MapWithState, ReadCSV, Complete, AsyncSource, Choice, \
Event, Batch, Table, NoopDriver, WriteToCSV, DataframeSource, MapClass, JoinWithTable, R... | pd.DataFrame(expected2) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# @Time : 2021/11/13 10:31
# @Author : <NAME>
# @FileName: plugins.py
# @Usage:
# @Note:
# @E-mail: <EMAIL>
import os
import numpy as np
import pandas as pd
from Bio import SeqIO
from Bio.SeqRecord import SeqRecord
from Bio.Blast.Applications import NcbimakeblastdbCommandline
from Bio.Restrict... | pd.DataFrame(_combined_list) | pandas.DataFrame |
import logging
import os
import re
import warnings
from multiprocessing import Pool
from contextlib import ExitStack
import numpy as np
import pandas as pd
import tables
from tables import open_file
from tqdm import tqdm
import astropy.units as u
from astropy.table import Table, vstack, QTable
from ctapipe.container... | pd.concat([data, data_srcdep], axis=1) | pandas.concat |
#I. cleangot(): clean dfgot from wikiling.de
#1. insert links()
#2. every lemma() to own row
#3. occurences() to own col
#4. certainty() to own col
#5. reconstructedness() to own col
#6.a clean col lemma
#6.b clean col lemma
#6. translations()
#7.a activate got-ipa transcription ... | pd.read_csv(path,encoding="utf-8") | pandas.read_csv |
import pandas as pd
import numpy as np
import datetime
import pytrends
import os
from pytrends.request_1 import TrendReq
pytrend = TrendReq()
country = pd.read_csv(r"C:\Users\Dell\Desktop\livinglabcountries.csv")
country_list = list(country['living lab countries'])
city = | pd.DataFrame() | pandas.DataFrame |
import datetime
from datetime import timedelta
from distutils.version import LooseVersion
from io import BytesIO
import os
import re
from warnings import catch_warnings, simplefilter
import numpy as np
import pytest
from pandas.compat import is_platform_little_endian, is_platform_windows
import pandas.util._test_deco... | _maybe_remove(store, "df") | pandas.tests.io.pytables.common._maybe_remove |
import base64
from pathlib import Path
import pandas as pd
import streamlit
import os
from pathlib import Path
import numpy as np
import pydeck as pdk
import random
from send_email import send_message
import streamlit as st
from PIL import Image
file_dir = Path(os.path.dirname(os.path.abspath(__file__)))
DATE_TIME =... | pd.read_csv(DATA_URL, nrows=nrows) | pandas.read_csv |
"""
Get Massachusetts Data | Cannlytics
Authors: <NAME> <<EMAIL>>
Created: 9/20/2021
Updated: 9/30/2021
License: MIT License <https://opensource.org/licenses/MIT>
Data Sources:
MA Cannabis Control Commission
- Retail Sales by Date and Product Type: https://dev.socrata.com/foundry/opendata.mass-cannabis-contr... | pd.DataFrame(new_row) | pandas.DataFrame |
from collections import defaultdict
import arrow
import numpy as np
import pandas as pd
train = pd.read_csv("../../../data/train.csv")
train["src"] = "train"
train["is_test"] = 0
test = | pd.read_csv("../../../data/test.csv") | pandas.read_csv |
# IMPORTATION STANDARD
import os
# IMPORTATION THIRDPARTY
import pandas as pd
import pytest
# IMPORTATION INTERNAL
from openbb_terminal.stocks.backtesting import bt_controller
# pylint: disable=E1101
# pylint: disable=W0603
# pylint: disable=E1111
EMPTY_DF = pd.DataFrame()
@pytest.mark.vcr(record_mode="none")
@py... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
import json
import csv
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm_notebook as tqdm
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.tokenize import sent_tokenize
from nltk.stem import WordNetLemmatizer
impor... | pd.read_csv('./testbadwordsc.csv') | pandas.read_csv |
import concurrent.futures
import csv
import itertools
import os
import time
from datetime import datetime, timezone
from pprint import pprint
import pandas as pd
import praw
import yaml
import utils
from args import args
# https://www.reddit.com/r/redditdev/comments/7muatr/praw_rate_limit_headers/drww... | pd.DataFrame.from_dict(comments_df_dict) | pandas.DataFrame.from_dict |
from scipy.stats import norm
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
import pandas as pd
import numpy as np
import glob
import functools
import os
output_folder = 'Experiment_X-description/python_results'
plot_folder = f'{output_folder}/dwell_analysis_figs'
if not os.path.exists(p... | pd.concat(filt) | pandas.concat |
"""
Detection Recipe - 192.168.3.11
References:
(1) 'Asteroseismic detection predictions: TESS' by Chaplin (2015)
(2) 'On the use of empirical bolometric corrections for stars' by Torres (2010)
(3) 'The amplitude of solar oscillations using stellar techniques' by Kjeldson (2008)
(4) 'An absolutely calibrated Teff ... | pd.to_numeric(data[:, 113]) | pandas.to_numeric |
#!/usr/bin/env python
# coding: utf-8
# In[66]:
import requests
import json
import pandas as pd
# In[67]:
client_id = '07bd2676-f950-48c0-8b12-ebd5e8b1491d'
client_secret = '<KEY>'
owner = "gitfeedV3"
thing = "github"
nodes = ['pulls', 'issues', 'commits']
# nodes = ['pulls', 'issues']
start_date = '2020-09-01T0... | pd.DataFrame.from_dict(code_data) | pandas.DataFrame.from_dict |
from __future__ import print_function, absolute_import, unicode_literals, division
import csv
import random
from collections import OrderedDict
import pandas as pd
import nltk
import numpy as np
from keras_preprocessing.sequence import pad_sequences
from nltk import word_tokenize
import json
from sklearn import pre... | pd.read_table("data/glove_vectors.txt", sep=" ", index_col=0, header=None, quoting=csv.QUOTE_NONE) | pandas.read_table |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import pickle
import shutil
import sys
import tempfile
import numpy as np
from numpy import arange, nan
import pandas.testing as pdt
from pandas import DataFrame, MultiIndex, Series, to_datetime
# dependencies testing specific
import pytest
import recordlinka... | MultiIndex.from_arrays([A.index.values, B.index.values]) | pandas.MultiIndex.from_arrays |
from snapedautility.detect_outliers import detect_outliers
import pandas as pd
import pytest
@pytest.fixture
def simple_series():
return | pd.Series([1, 2, 1, 2, 1, 1000]) | pandas.Series |
import pandas as pd
def main(type):
df = | pd.read_csv('./data/servant_data_'+type+'.csv') | pandas.read_csv |
import math
import operator
import pandas as pd
from scipy.stats import pearsonr,spearmanr,kendalltau,rankdata
import itertools
import numpy as np
import numexpr as ne
### Basic correlation measures ###
def corr_pearson(top_list_prev, top_list, k=None):
"""Compute Pearson correlation (based on Scipy)
NOTE: L... | pd.concat([data_table,con_dis_data], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
"""
.. moduleauthor:: <NAME> (<EMAIL>, <EMAIL>)
"""
import fnmatch
import os
import random
import shutil
import time
from collections import OrderedDict
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from scipy import stats
from scipy.stats import s... | pd.read_csv(datafile) | pandas.read_csv |
"""This is a finantial library useful to convert Candle Data from
financial time series datasets (Open,Close, High, Low, Volume).
It is built on Pandas and Numpy.
.. moduleauthor:: <NAME>
"""
import pandas as pd
from datetime import datetime
def convertcandle(
time: pd.Series,
open: pd.Series,
... | pd.Series(new_time_lst) | pandas.Series |
# -*- coding: utf-8 -*-
# pylint: disable=W0612,E1101
from datetime import datetime
import operator
import nose
from functools import wraps
import numpy as np
import pandas as pd
from pandas import Series, DataFrame, Index, isnull, notnull, pivot, MultiIndex
from pandas.core.datetools import bday
from pandas.core.n... | Panel.from_dict(d3) | pandas.core.panel.Panel.from_dict |
import csv
import logging
from datetime import datetime
from pathlib import Path
import extract_data as ex
import pandas as pd
logger = logging.getLogger(__name__)
def read_dat_as_DataFrame(input_filepath):
logger.info(f"reading {input_filepath}")
converted_count = 0
start_ts = datetime.now()
record... | pd.DataFrame.from_records(records) | pandas.DataFrame.from_records |
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 15 10:59:14 2020
@author: <NAME>
"""
#reproducability
from numpy.random import seed
seed(1+347823)
import tensorflow as tf
tf.random.set_seed(1+63493)
import numpy as np
from bayes_opt import BayesianOptimization
from bayes_opt.logger import JSONLogger
from bayes_opt.eve... | pd.to_datetime('28122015', format='%d%m%Y') | pandas.to_datetime |
from datetime import time
import numpy as np
import pytest
from pandas import DataFrame, date_range
import pandas._testing as tm
class TestBetweenTime:
def test_between_time(self, close_open_fixture):
rng = date_range("1/1/2000", "1/5/2000", freq="5min")
ts = DataFrame(np.random.randn(len(rng), ... | DataFrame([[1, 2, 3], [4, 5, 6]]) | pandas.DataFrame |
from flask import Blueprint, request, jsonify, make_response, url_for
from flask.views import MethodView
from io import StringIO
from marshmallow import ValidationError
import pandas as pd
from sfa_api import spec
from sfa_api.utils import storage
from sfa_api.schema import (ObservationSchema, ObservationLinksSchema,... | pd.read_csv(raw_data, comment='#') | pandas.read_csv |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.11.1
# kernelspec:
# display_name: Python 3 (ipykernel)
# language: python
# name: python3
# ---
# %%
imp... | pd.read_sql_query("select * from Categories", con) | pandas.read_sql_query |
import os
import pickle
import numpy as np
import xgboost as xgb
import pandas as pd
from bayes_opt import BayesianOptimization
from .xgb_callbacks import callback_overtraining, early_stop
from .xgboost2tmva import convert_model
import warnings
# Effective RMS evaluation function for xgboost
def evaleffrms(preds, dt... | pd.read_csv(summary_file) | pandas.read_csv |
#realtor_graph.py
#from neo4j_connect_2 import NeoSandboxApp
#import neo4j_connect_2 as neo
#import GoogleServices as google
#from pyspark.sql import SparkSession
#from pyspark.sql.functions import struct
from cgitb import lookup
import code
from dbm import dumb
from doctest import master
from hmac import trans_36
im... | pd.concat([master_subject_table,unique_dataframe]) | pandas.concat |
"""Compare different GNSS site velocity Where datasets
Description:
------------
A dictionary with datasets is used as input for this writer. The keys of the dictionary are station names.
Example:
--------
from where import data
from where import writers
# Read a dataset
dset = data.Dataset(rundat... | pd.DataFrame() | pandas.DataFrame |
"""
국토교통부 Open API
molit(Ministry of Land, Infrastructure and Transport)
1. Transaction 클래스: 부동산 실거래가 조회
- AptTrade: 아파트매매 실거래자료 조회
- AptTradeDetail: 아파트매매 실거래 상세 자료 조회
- AptRent: 아파트 전월세 자료 조회
- AptOwnership: 아파트 분양권전매 신고 자료 조회
- OffiTrade: 오피스텔 매매 신고 조회
- OffiRent: 오피스텔 전월세 신고 조회
- RHTrad... | pd.DataFrame() | pandas.DataFrame |
'''
Copyright (c) 2021 <NAME>, <NAME>, Technical University of Denmark
'''
# Import modules
import os
import pandas as pd
import freesasa as fs
from Bio.PDB import PDBParser
import pkg_resources
import json
from natsort import natsort_keygen
# Path to resource files
naccess_config = pkg_resources.resource_filename(__... | pd.DataFrame(area_list, columns=('Chain', 'Number', 'Wild', 'RSA')) | pandas.DataFrame |
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from scipy import stats
from sklearn.metrics import mean_squared_error
import numpy as np
import torch
import torch.nn as nn
from copy import deepcopy
from numpy import inf
from math import exp, gamma
... | pd.DataFrame(pdata, columns=['Indicator']+params) | pandas.DataFrame |
from __future__ import division
from datetime import datetime
import sys
if sys.version_info < (3, 3):
import mock
else:
from unittest import mock
import pandas as pd
import numpy as np
import random
from nose.tools import assert_almost_equal as aae
import bt
import bt.algos as algos
def test_algo_name():... | pd.to_datetime('2010-01-05') | pandas.to_datetime |
# coding: utf-8
'''
from: examples/tutorial/fifth.cc
to: fifth.py
time: 20101110.1948.
//
// node 0 node 1
// +----------------+ +----------------+
// | ns-3 TCP | | ns-3 TCP |
// +----------------+ +----------------+
// | 10.1.1.1 | | 10.1.1.2 |... | pd.to_numeric(size_ns3_df["Size"]) | pandas.to_numeric |
# -*- coding: utf-8 -*-
try:
import json
except ImportError:
import simplejson as json
import math
import pytz
import locale
import pytest
import time
import datetime
import calendar
import re
import decimal
import dateutil
from functools import partial
from pandas.compat import range, StringIO, u
from pandas.... | ujson.dump([], "") | pandas._libs.json.dump |
#!/usr/bin/env python3
import sys
sys.path.extend(['.', '..'])
import argparse
import os
import gensim
import numpy as np
import pandas as pd
from scipy import stats
from Bio import pairwise2
import matplotlib.pyplot as plt
plt.style.use('seaborn-colorblind')
from dna2vec.multi_k_model import MultiKModel
# Helper ... | pd.DataFrame(matches) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable=E1101,E1103,W0232
import os
import sys
from datetime import datetime
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
import pandas.compat as compat
import pandas.core.common as com
import pandas.util.testing as tm
from pandas import (Categor... | pd.date_range('20000101', periods=3) | pandas.date_range |
# This is a test file intended to be used with pytest
# pytest automatically runs all the function starting with "test_"
# see https://docs.pytest.org for more information
import os
import sys
import numpy as np
import pandas as pd
## Add stuff to the path to enable exec outside of DSS
plugin_root = os.path.dirname(... | pd.concat(df_list, axis=0) | pandas.concat |
# -*- coding: utf-8 -*-
"""Find hydrated waters in structure."""
# standard library imports
from pathlib import Path
from typing import List
from typing import Optional
from typing import Tuple
# 3rd-party imports
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from loguru... | pd.read_csv(res_file_path, sep="\t") | pandas.read_csv |
from unittest.case import TestCase
from pandas import Series
from probability.distributions import Multinomial, Binomial
class TestMultinomial(TestCase):
def setUp(self) -> None:
self.p = Series({'a': 0.4, 'b': 0.3, 'c': 0.2, 'd': 0.1})
self.m_array = Multinomial(n=10, p=self.p.values)
... | Series({'p1': 0.4, 'p2': 0.3, 'p3': 0.2, 'p4': 0.1}) | pandas.Series |
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