prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import datetime
import os.path
import numpy as np
import pandas as pd
import pandas.tseries.offsets as offsets
import seaborn as sns
class StatusTypes:
backlog = "backlog"
accepted = "accepted"
complete = "complete"
def extend_dict(d, e):
r = d.copy()
r.update(e)
return r
def to_json_stri... | pd.date_range(first_month, last_month, freq="MS") | pandas.date_range |
import pandas as pd
from scripts.python.routines.manifest import get_manifest
import numpy as np
import os
from scripts.python.pheno.datasets.filter import filter_pheno, get_passed_fields
from scipy.stats import spearmanr
import matplotlib.pyplot as plt
from scripts.python.pheno.datasets.features import get_column_name... | pd.read_excel(f"{path}/{platform}/{dataset}/data/age_sex_L_H_A_Q_I_S_T.xlsx", index_col='Code') | pandas.read_excel |
# This source code file is a part of SigProfilerTopography
# SigProfilerTopography is a tool included as part of the SigProfiler
# computational framework for comprehensive analysis of mutational
# signatures from next-generation sequencing of cancer genomes.
# SigProfilerTopography provides the downstream data analysi... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 5 00:04:41 2020
@author: shashanknigam
web parser for amazon:
Things to be extracted: 1. Title of the product span id = "productTitle"
2. Number of rating : span id = acrCustomerReviewText
3. Average rating given:span class a-icon-alt... | pd.DataFrame(productInformation) | pandas.DataFrame |
# importiamo i pacchetti necessari
import pandas as pd
import matplotlib.pyplot as plt
# l'indirizzo da cui vogliamo scaricare la tabella
pageURL = 'https://it.wikipedia.org/wiki/Leone_d%27oro_al_miglior_film'
# facciamo scaricare la pagina direttamente a pandas, dando indizi su qual e' la tabella che ci interessa
... | pd.read_html(pageURL, match='Anno', header=0) | pandas.read_html |
import conn,numpy as np
import pandas as pd
from flask import jsonify,json
db = conn.cursor
def add_examen(db,titel,vak,klas):
sql = "INSERT INTO examen(examen_titel,vak,klas) value('" + titel + "','" + vak + "','" + klas + "')"
db.execute(sql)
conn.db.commit()
def import_vragen(path)... | pd.read_csv(file) | pandas.read_csv |
""" Module contains functions to retrieve
and process data from the database folder"""
import os
import numpy as np
import shutil
import csv
import pandas as pd
import pkg_resources
pd.options.mode.chained_assignment = None # default='warn'
ROOT = pkg_resources.resource_filename('optimol', '')
DATABASE =... | pd.DataFrame(raw.iloc[i+atom:i+atom+bond_amount]) | pandas.DataFrame |
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import sys
import argparse
import os
import pandas as pd
def plot(directory,title,xlabel,ylabel):
root = os.path.expanduser(directory)
frames = []
for filename in os.listdir(root):
name, extension = os.path.... | pd.concat(frames,axis=1) | pandas.concat |
import plotly
import plotly.express as px
import plotly.graph_objects as go
import dash
import dash_table
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input,Output,State
from dash.exceptions import PreventUpdate
import os
import json
import urllib
import requ... | pd.DataFrame({'id':states,'Demand':demand_list}) | pandas.DataFrame |
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from collections import OrderedDict
import gc
from current_clamp import *
from current_clamp_features import extract_istep_features
from visualization.feature_annotations import feature_name_dict
from read_metadata i... | pd.isnull(animal_info['comment']) | pandas.isnull |
import argparse
from tqdm import tqdm
import re
import os
import json
import pandas as pd
from collections import Counter
pd.set_option('display.max_rows', 800)
| pd.set_option('display.max_columns', 800) | pandas.set_option |
from datetime import timedelta
from functools import partial
import itertools
from parameterized import parameterized
import numpy as np
from numpy.testing import assert_array_equal, assert_almost_equal
import pandas as pd
from toolz import merge
from zipline.pipeline import SimplePipelineEngine, Pipeline, CustomFacto... | pd.Timestamp("2015-01-12") | pandas.Timestamp |
"""
Copyright 2021, Institute e-Austria, Timisoara, Romania
http://www.ieat.ro/
Developers:
* <NAME>, <EMAIL>
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at:
http://www.apache.org/licenses... | pd.factorize(y) | pandas.factorize |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import copy
import torch
import numpy as np
import pandas as pd
from qlib.data.dataset import DatasetH
device = "cuda" if torch.cuda.is_available() else "cpu"
def _to_tensor(x):
if not isinstance(x, torch.Tensor):
return torch.te... | pd.Series(0, index=index) | pandas.Series |
# -*- 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.concat([a, a]) | pandas.concat |
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.DatetimeIndex(['20190514T0900Z'], freq='1h') | pandas.DatetimeIndex |
##############################
## COVID_common.py ##
## <NAME> ##
## Version 2021.09.05 ##
##############################
import os
import sys
import warnings
import collections as clt
import calendar as cld
import datetime as dtt
import copy
import json
import numpy as... | pd.DataFrame(stock2, columns=data.columns) | pandas.DataFrame |
"""
Provide a generic structure to support window functions,
similar to how we have a Groupby object.
"""
from collections import defaultdict
from datetime import timedelta
from textwrap import dedent
from typing import List, Optional, Set
import warnings
import numpy as np
import pandas._libs.window as libwindow
fro... | Appender(_shared_docs["quantile"]) | pandas.util._decorators.Appender |
import numpy as np
from datetime import timedelta
import pandas as pd
import pandas.tslib as tslib
import pandas.util.testing as tm
import pandas.tseries.period as period
from pandas import (DatetimeIndex, PeriodIndex, period_range, Series, Period,
_np_version_under1p10, Index, Timedelta, offsets)
... | tm.assert_index_equal(idx - pd.NaT, exp) | pandas.util.testing.assert_index_equal |
import argparse
import pandas
def mean_std_table(
datasets,
dataset_labels,
metrics,
metric_labels,
model_order,
model_labels,
all_data,
output_file):
# set output_file
output_file = open(output_file, "w")
# stats
... | pandas.read_csv(input_file) | pandas.read_csv |
import dhlab.nbtext as nb
import requests
import pandas as pd
from IPython.display import HTML
# HMMM
# extra function for word frequencies
def word_frequencies(word_list):
""" Find frequency of words global for digibok """
params = {'words':word_list}
r = requests.post("https://api.nb.no/ngram/word_frequ... | pd.DataFrame.from_dict(something, orient='index') | pandas.DataFrame.from_dict |
import pandas as pd
from tqdm import tqdm
from config import visit_plan_raw_data_path, agent_replacements_raw_data_path
from config import date_analysis_raw_data_drop_cols, credit_requests_raw_data_drop_cols
from config import sr_loading_raw_data_drop_cols, sr_unloading_raw_data_drop_cols
from config import take... | pd.read_pickle(pre_easter_effect_data_path) | pandas.read_pickle |
import re
from datetime import datetime, timedelta
import numpy as np
import pandas.compat as compat
import pandas as pd
from pandas.compat import u, StringIO
from pandas.core.base import FrozenList, FrozenNDArray, DatetimeIndexOpsMixin
from pandas.util.testing import assertRaisesRegexp, assert_isinstance
from pandas i... | Series({0.998: 0.5, 1.5: 0.25, 2.0: 0.0, 2.5: 0.25}, index=[0.998, 2.5, 1.5, 2.0]) | pandas.Series |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import argparse
from misc import *
import pandas as pd
DEFPATH = "/home/bakirillov/HDD/weights/fasttext/aligned/wiki.en.align.vec"
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"-v", "--vectors",
dest="vec... | pd.DataFrame(data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 27, 2022
SHREAD Dash Snow Plot
Script for running the snow plot in the dashboard (shread_dash.py)
@author: buriona, tclarkin (2020-2022)
"""
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plot_lib.utils import import_snotel,import_csas_l... | pd.date_range(start_date, end_date, freq="D", tz='UTC') | pandas.date_range |
"""
.. module:: reporters
:platform: Unix, Windows
:synopsis: a module for defining OpenMM reporter classes.
.. moduleauthor:: <NAME> <<EMAIL>>
.. _pandas.DataFrame: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html
.. _StateDataReporter: http://docs.openmm.org/latest/api-python/gener... | pd.DataFrame(index=names, data=values) | pandas.DataFrame |
import dash
from dash.dependencies import Input, Output, State
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objects as go
import dash_color_picker as dcp
import dash_daq as daq
import pandas as pd
import numpy as np
import h5py
import dash_colorscales as dcs
imp... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
from pandas import Period, offsets
from pandas.util import testing as tm
from pandas.tseries.frequencies import _period_code_map
class TestFreqConversion(tm.TestCase):
"Test frequency conversion of date objects"
def test_asfreq_corner(self):
val = Period(freq='A', year=2007)
... | Period(freq="Q-JAN", year=2007, quarter=4) | pandas.Period |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 3 14:18:10 2017
@author: massimo
Straight import of exiobase data
"""
import pandas as pd
import numpy as np
def importing(filename, celltype):
'''
Args:
'filename' [string] name of the file...
'celltype' [type of file], three... | pd.read_csv(filename, header=0, index_col=0, sep=';') | pandas.read_csv |
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin
#from .functions import tokenize
class LengthExtractor(BaseEstimator, TransformerMixin):
def get_length(self, text):
return len(text)
def fit(self, X, y=None):
return self
def transform(self, X):
X_w_len... | pd.DataFrame(X_w_length) | pandas.DataFrame |
def Cosiner(params : dict):
def Column_correction(table):
drop_col = [i for i in table.columns if "Unnamed" in i]
table.drop(drop_col, axis = 1, inplace = True)
return table
def Samplewise_export(neg_csv_file, pos_csv_file, out_path, merged_edge_table, merged_node_table) :
... | pd.Series(samples) | pandas.Series |
# -*- coding: utf-8 -*-
"""Functions for the input and output of data and results.
todo: This file will be removed in version 0.10 and functionality moved to
datasets/_data_io.py
"""
import itertools
import os
import textwrap
from warnings import warn
import numpy as np
import pandas as pd
from sktime.datatypes._pa... | pd.Series(dtype=np.float32) | pandas.Series |
# This Source Code Form is subject to the terms of the MPL
# License. If a copy of the same was not distributed with this
# file, You can obtain one at
# https://github.com/akhilpandey95/altpred/blob/master/LICENSE.
import sys
import numpy as np
import pandas as pd
from tqdm import tqdm
from datetime import datetime
f... | pd.read_csv(file_path, low_memory=False) | pandas.read_csv |
import copy
import gc
import os
from datetime import datetime
import numpy as np
import pandas as pd
import tifffile as tif
from tifffile import TiffWriter
from .adaptive_estimation import AdaptiveShiftEstimation
from .image_positions import load_necessary_xml_tags, get_image_sizes_scan_auto, get_image_sizes_scan_man... | pd.DataFrame(y_size) | pandas.DataFrame |
import pandas as pd
import numpy as np
import streamlit as st
import math
from utilityfunctions import loadPowerCurve, binWindResourceData, searchSorted, preProcessing, getAEP, checkConstraints
from shapely.geometry import Point # Imported for constraint checking
from shapely.geometry.polygon import Po... | pd.DataFrame(data_dict) | pandas.DataFrame |
"""
This function get all the featueres to online processing.
"""
import re
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
import pandas as pd
from gensim.corpora.dictionary import Dictionary
from gensim.models.ldamodel import LdaModel
"""
Common text processing functionalities.
"""... | pd.read_csv(input_file) | pandas.read_csv |
from __future__ import division #brings in Python 3.0 mixed type calculation rules
import logging
import numpy as np
import pandas as pd
class ScreenipFunctions(object):
"""
Function class for screenip.
"""
def __init__(self):
"""Class representing the functions for screenip"""
super(... | pd.Series([msg_pass if boo else msg_fail for boo in boo_ratios]) | pandas.Series |
# -*- coding: utf-8 -*-
"""
@author: <NAME>
"""
from pathlib import Path
import pandas as pd, numpy as np
from itertools import combinations
from scipy.spatial.distance import pdist, squareform
from skbio import DistanceMatrix
from skbio.stats.distance import permanova
script_folder = Path.cwd()
outputs_folder = scri... | pd.concat(frames, axis='columns') | pandas.concat |
import os
import json
import requests
import thingspeak
import datetime
import pandas as pd
from functools import reduce
from itertools import tee
def pairwise(iterable):
"s -> (s0,s1), (s1,s2), (s2, s3), ..."
a, b = tee(iterable)
next(b, None)
return zip(a, b)
def get_block(start, end):
params... | pd.read_csv('pa_sensor_list.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
import numpy as np
import pytest
from pandas import DataFrame, Index, Series, Timestamp
from pandas.util.testing import assert_almost_equal
def _assert_almost_equal_both(a, b, **kwargs):
"""
Check that two objects are approximately equal.
This check is performed commutatively.
... | assert_almost_equal(1, 2) | pandas.util.testing.assert_almost_equal |
from . import __VERSION__
from .cc_metrics import CC_METRICS
from .season import SORT_BY_COLUMNS
from .season import SPECIAL_REPORTS
import argparse
import os
import tbapy
import pandas as pd
import numpy as np
from numpy.linalg import linalg
from numpy.linalg import LinAlgError
def get_opr_df(oprs_raw):
teams ... | pd.DataFrame(index=teams) | pandas.DataFrame |
"""arbin res-type data files"""
import os
import sys
import tempfile
import shutil
import logging
import platform
import warnings
import time
import numpy as np
import pandas as pd
from cellpy.readers.core import (
FileID,
Cell,
check64bit,
humanize_bytes,
xldate_as_datetime,
)
from cellpy.paramet... | pd.read_sql_query(sql, conn) | pandas.read_sql_query |
import pickle
from copy import deepcopy
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from scipy.stats import pearsonr
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from tqdm import tqdm
from transformers import BertModel
from trans... | pd.Series(prediction_scores) | pandas.Series |
# coding: utf-8
# # Create figures for manuscript
#
# Generate figures for manuscript
# In[1]:
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().run_line_magic('load_ext', 'rpy2.ipython')
import rpy2
from rpy2.robjects.packages import importr
im... | pd.Categorical(all_corrected_data_df['num_experiments'], categories=['1', 'multiple']) | pandas.Categorical |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# *****************************************************************************/
# * Authors: <NAME>
# *****************************************************************************/
"""transformCSV.py
This module contains the basic functions for creating the content of... | pandas.StringDtype() | pandas.StringDtype |
# %%
from functools import reduce
import numpy as np
import pandas as pd
from pandas.tseries.offsets import DateOffset
pd.set_option("display.max_rows", None)
pd.set_option("display.max_columns", None)
# %%
def build_gvkeys(prc, fund):
gvkeys_fund = fund.gvkey.unique()
gvkeys_prc = prc[prc.close > 5].gvkey.un... | pd.to_datetime(last_date) | pandas.to_datetime |
import os
from datetime import date
from dask.dataframe import DataFrame as DaskDataFrame
from numpy import nan, ndarray
from numpy.testing import assert_allclose, assert_array_equal
from pandas import DataFrame, Series, Timedelta, Timestamp
from pandas.testing import assert_frame_equal, assert_series_equal
from pymo... | Timestamp('2008-10-23 05:53:11') | pandas.Timestamp |
import os
import ssl
from datetime import date
import json
import pandas as pd
from azure.storage.blob import BlobServiceClient
from azure.core.exceptions import ResourceExistsError, ResourceNotFoundError
ssl._create_default_https_context = ssl._create_unverified_context
def set_cwd_to_script():
dname = os.path.d... | pd.read_json(current["data"], convert_dates=["d"]) | pandas.read_json |
from os import name
from pathlib import Path
import pandas as pd
import numpy as np
import gffpandas.gffpandas as gffpd
from Bio import SeqIO, pairwise2
from Bio.SeqRecord import SeqRecord
from Bio.SeqUtils import seq3
from BCBio import GFF
from Bio.Seq import MutableSeq, Seq
from dna_features_viewer import BiopythonT... | pd.concat(dfs2concat, axis=0, ignore_index=True) | pandas.concat |
import csv
import logging
from pathlib import Path
import tarfile
from typing import Dict
import pandas as pd
from guesslangtools.common import (
Config, File, cached, download_file, CSV_FIELD_LIMIT
)
LOGGER = logging.getLogger(__name__)
# Open source projects dataset: https://zenodo.org/record/3626071/
DATASE... | pd.concat([other_df, df]) | pandas.concat |
from datetime import timedelta
from functools import partial
import itertools
from parameterized import parameterized
import numpy as np
from numpy.testing import assert_array_equal, assert_almost_equal
import pandas as pd
from toolz import merge
from zipline.pipeline import SimplePipelineEngine, Pipeline, CustomFacto... | pd.Timestamp("2015-01-09") | pandas.Timestamp |
import pandas as pd
import numpy as np
import scipy.sparse as spl
from concurrent.futures import ProcessPoolExecutor
import sys
threads = 4
all_tasks = [
[5, 8000, ['5t', '5nt'], 0.352],
[10, 12000, ['10t', '10nt'], 0.38],
[25, 40000, ['25f'], 0.43386578246281293],
[25, 9000, ['25r'], 0.4],
[100, 4... | pd.read_csv('data/million_playlist_dataset/playlist_meta.csv') | pandas.read_csv |
#!/usr/bin/python
# encoding: utf-8
"""
@author: xuk1
@license: (C) Copyright 2013-2017
@contact: <EMAIL>
@file: cluster.py
@time: 8/15/2017 10:38
@desc:
"""
import os
from datetime import datetime
from multiprocessing import Pool, Process
import pandas as pd
from component.factory import Attri... | pd.DataFrame() | pandas.DataFrame |
from datetime import (
datetime,
timedelta,
)
import re
import numpy as np
import pytest
from pandas._libs import iNaT
from pandas.errors import InvalidIndexError
import pandas.util._test_decorators as td
from pandas.core.dtypes.common import is_integer
import pandas as pd
from pandas import (
Categoric... | Categorical(["a", "a", "b", "a", "a", "a", "a"], categories=["a", "b"]) | pandas.Categorical |
# Example data analysis in Pandas
# Data from Kaggle https://www.kaggle.com/mchirico/montcoalert
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
df = | pd.read_csv('911.csv') | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# ## Brain Tumor Classification
# In[3]:
pwd
# In[4]:
path='E:\\DataScience\\MachineLearning\\Brain_Tumor_Data'
# In[5]:
import os
os.listdir(path)
# In[6]:
#importing lib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns... | pd.DataFrame(X.columns) | pandas.DataFrame |
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
from dash.dependencies import Input, Output
import dash_table
app=dash.Dash(__name__)
titulo=html.H1("Modelo de Jerarquรญa Analรญtica AHP",style={'text-align':'center','font-family':'Arial Black','color':'blue'})
sub... | pd.DataFrame(AHPStv_c2paso2_c) | pandas.DataFrame |
import os
import re
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import scikit_posthocs as sp
from pandas import DataFrame
from decimal import Decimal
import scipy.stats as ss
from sklearn.preprocessing import StandardScaler
from metalfi.src.data.memory import Memory
class Visualization:
... | pd.concat(data) | pandas.concat |
import json, os, sys
from pprint import pprint as print
from datetime import datetime
from datetime import date
from collections import Counter
from collections import OrderedDict
import pandas as pd
import lh3.api as lh3
client = lh3.Client()
chats = client.chats()
FRENCH_QUEUES = [
"algoma-fr",
"clavardez... | pd.DataFrame(report) | pandas.DataFrame |
#Sample Lightcurve class with Kapernka model
from scipy.optimize import curve_fit, minimize
import numpy as np
import matplotlib.pyplot as plt
import os
from JSON_to_DF import JSON_to_DataFrame
import ntpath
import json
import pandas as pd
import celerite
import pickle
#Create Kernels for Gaussian Process
#Real t... | pd.isnull(row[1]['band']) | pandas.isnull |
#!/usr/bin/env python
# coding: utf-8
# # ReEDS Scenarios on PV ICE Tool
# To explore different scenarios for furture installation projections of PV (or any technology), ReEDS output data can be useful in providing standard scenarios. ReEDS installation projections are used in this journal as input data to the PV ICE... | pd.DataFrame(df) | pandas.DataFrame |
# -*- 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(d4) | pandas.core.panel.Panel.from_dict |
from linkml_runtime import SchemaView
import pandas as pd
# meta_view = SchemaView("https://raw.githubusercontent.com/linkml/linkml-model/main/linkml_model/model/schema/meta.yaml")
# sis = meta_view.class_induced_slots('slot_definition')
# for i in sis:
# print(i.name)
schema_file = "../artifacts/nmdc_dh.yaml"
se... | pd.DataFrame(lod) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable=E1101
import string
from collections import OrderedDict
import numpy as np
import pandas as pd
import pandas.util.testing as pdt
import pytest
from kartothek.core.dataset import DatasetMetadata
from kartothek.core.index import ExplicitSecondaryIndex
from kartothek.core.uuid... | pd.Series([2], dtype=np.int64) | pandas.Series |
import numpy as np
import pandas as pd
from tqdm import tqdm
import holoviews as hv
hv.extension('bokeh')
import datetime
import argparse
def negativeFields(fields, df):
"""
Function to filter anomalous records based on negative (therefore meaningless) values of the `field`
args:
fields: list con... | pd.to_datetime(df['when_captured']) | pandas.to_datetime |
from dataclasses import replace
import datetime as dt
from functools import partial
import inspect
from pathlib import Path
import re
import types
import uuid
import pandas as pd
from pandas.testing import assert_frame_equal
import pytest
from solarforecastarbiter import datamodel
from solarforecastarbiter.io impor... | pd.Timedelta('1h') | pandas.Timedelta |
#!/usr/bin/env python
### Up to date as of 10/2019 ###
'''Section 0: Import python libraries
This code has a number of dependencies, listed below.
They can be installed using the virtual environment "slab23"
that is setup using script 'library/setup3env.sh'.
Additional functions are housed in file ... | pd.DataFrame() | pandas.DataFrame |
# Import modules
import pickle
import pandas as pd
from psychopy import visual, monitors
from psychopy import core, event
import numpy as np
from titta import Titta, helpers_tobii as helpers
#%% Monitor/geometry participant screen
MY_MONITOR = 'testMonitor' # needs to exists in PsychoPy monitor center... | pd.DataFrame(gaze_data, columns=tracker.header) | pandas.DataFrame |
"""
๊ตญํ ๊ตํต๋ถ Open API
molit(Ministry of Land, Infrastructure and Transport)
1. Transaction ํด๋์ค: ๋ถ๋์ฐ ์ค๊ฑฐ๋๊ฐ ์กฐํ
- AptTrade: ์ํํธ๋งค๋งค ์ค๊ฑฐ๋์๋ฃ ์กฐํ
- AptTradeDetail: ์ํํธ๋งค๋งค ์ค๊ฑฐ๋ ์์ธ ์๋ฃ ์กฐํ
- AptRent: ์ํํธ ์ ์์ธ ์๋ฃ ์กฐํ
- AptOwnership: ์ํํธ ๋ถ์๊ถ์ ๋งค ์ ๊ณ ์๋ฃ ์กฐํ
- OffiTrade: ์คํผ์คํ
๋งค๋งค ์ ๊ณ ์กฐํ
- OffiRent: ์คํผ์คํ
์ ์์ธ ์ ๊ณ ์กฐํ
- RHTrad... | pd.concat([df, data]) | pandas.concat |
import pandas as pd
import joblib
from sklearn.pipeline import Pipeline
from lr_customer_value.config import config
from lr_customer_value import __version__ as _version
import logging
_logger = logging.getLogger(__name__)
def load_dataset(*, files_list: str) -> pd.DataFrame:
data = | pd.DataFrame() | pandas.DataFrame |
# Copyright (c) 2019-2020, RTE (https://www.rte-france.com)
# See AUTHORS.txt
# This Source Code Form is subject to the terms of the Apache License, version 2.0.
# If a copy of the Apache License, version 2.0 was not distributed with this file, you can obtain one at http://www.apache.org/licenses/LICENSE-2.0.
# SP... | pd.testing.assert_frame_equal(exp, conv, check_dtype=False) | pandas.testing.assert_frame_equal |
import time
import os
import io
import json
import shutil
import zipfile
import pathlib
import pandas as pd
import boto3
import datetime
import botocore
from dateutil.parser import parse
s3 = boto3.client('s3')
lookoutmetrics_client = boto3.client( "lookoutmetrics")
def lambda_handler(event, context):
#Functi... | pd.DataFrame(data=data) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# Prerequisite:
# 1. the database contains the whole week's data of last week until last Sunday.
# e.g. if today is 9/26/18 Wed, it must contains the data until 9/23/18 Sunday
#
# The program uses ISO Calendar:
# 1. first day and last day of the week are respectively Monday(1) ... | pd.to_datetime(df["closeddate"], errors="coerce") | pandas.to_datetime |
# Get Open Data resource
# Load required packages
import re
import requests
import pandas as pd
import io
# Open Data user agent
def opendata_ua():
"""
"This is used internally to return a standard useragent, supplying a user agent means requests using the package
can be tracked more easily"
:return:... | pd.DataFrame(data_raw) | pandas.DataFrame |
import numpy as np
import pandas as pd
import sys, os, getopt
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
from PIL import Image
import argparse
parser = argparse.ArgumentParser(description='TMA patches extractor')
parser.add_argument('-a','--APPROACH', type=str, default='ssl', help='teacher/st... | pd.read_csv(new_csv_filename,header=None) | pandas.read_csv |
import datasets
import pandas as pd
from model_code.generator_bart_qa_answer import qa_s2s_generate_answers
from model_code.generator_bart_qa_train import load_support_doc, make_qa_s2s_model
eli5c = datasets.load_dataset('jsgao/eli5_category')
eli5c_train_docs = load_support_doc('support_docs/eli5c_train_docs.dat')
e... | pd.DataFrame(qa_results) | pandas.DataFrame |
import os
import copy
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import matplotlib.dates as mdates
from datetime import date, timedelta, datetime
import seaborn as sns
import geopandas as gpd
import matplotlib.colors as colors
from plotting.colors import load_color_p... | pd.concat([df, df2]) | pandas.concat |
import json
import websocket
import time
import pandas as pd
from minio import Minio
SOCKET = "wss://api2.poloniex.com"
PARAMETERS = {
"command": "subscribe",
"channel": 1002
}
cripto_list = []
usdt_btc_oneminute = []
timestamps = []
rawdata_dict = {}
fullrawdata_header = ["currency pair id", "last trade pri... | pd.DataFrame(fullrawdata_dict, columns=fullrawdata_header) | pandas.DataFrame |
import os
import pandas as pd
import logging
FORMAT = ">>> %(filename)s, ln %(lineno)s - %(funcName)s: %(message)s"
logging.basicConfig(format=FORMAT, level=logging.INFO)
review_folder = 'Z:\\LYR\\LYR_2017studies\\LYR17_2Dmodelling\\LYR17_1_EDDPD\\review\\133'
# initializing csv file lists
hpc_files = []
... | pd.read_csv(f) | pandas.read_csv |
import itertools
import pytest
import pandas as pd
import numpy as np
columns = ["ref", "x1", "x2"]
def gen_value(column, line, id=0):
if column == "ref":
return id * 1e5 + line
else:
return np.random.randint(0, 1000)
def gen_df(columns, date_range, id=0, seed=1):
np.random.seed(seed)
... | pd.concat(dfs) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Fri May 1 18:42:31 2020
@author: maxim
"""
import pandas as pd
x=pd.read_excel('data.xlsx')
x=x.iloc[:,1:].T
#ๆฐๆฎๅๅผๅๅค็
x_mean=x.mean(axis=1)
for i in range(x.index.size):
x.iloc[i,:] = x.iloc[i,:]/x_mean[i]
#ๆๅๅ่้ๅๅๆฏ่พ้ๅ
ck=x.iloc[0,:]
cp=x.iloc[1:,:]
#... | pd.DataFrame() | pandas.DataFrame |
"""Plotting functions for AnnData.
"""
import collections.abc as cabc
from typing import Optional, Union
from typing import Tuple, Sequence, Collection, Iterable
import numpy as np
import pandas as pd
from anndata import AnnData
from cycler import Cycler
from matplotlib.axes import Axes
from pandas.api.types import is... | is_categorical_dtype(adata.obs[groupby]) | pandas.api.types.is_categorical_dtype |
import os
n_threads = 1
os.environ["NUMBA_NUM_THREADS"] = f"{n_threads}"
os.environ["MKL_NUM_THREADS"] = f"{n_threads}"
os.environ["OMP_NUM_THREADS"] = f"{n_threads}"
os.environ["NUMEXPR_NUM_THREADS"] = f"{n_threads}"
import respy as rp
from estimagic.differentiation.differentiation import jacobian
from estimagic.inf... | pd.to_pickle(cov, cov_path) | pandas.to_pickle |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# turn off pink warning boxes
import warnings
warnings.filterwarnings("ignore")
#-----------------------------------------------------------------------------
def clean_flood(flood):
'''Drops unneeded columns from the m... | pd.read_csv('downtown_weather.csv') | pandas.read_csv |
## import packages
import pandas as pd
import numpy as np
from glob import glob
import warnings
pd.options.mode.chained_assignment = None # default='warn'
def read_all_csvs(csv_locations):
'''
Read csvs from all locations and return them as a dict, where keys are previous folder locations and values are data... | pd.read_csv(l) | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: zohaib
This script merges Pangolin report (assigned lineages) with the
metadata file which allows data extraction and filtering based on
lineage information in nf-ncov-voc workflow.
"""
import argparse
import pandas as pd
import csv
def parse_args():
... | pd.read_csv(args.pangolin) | pandas.read_csv |
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import os
from copy import deepcopy
import numpy as np
import pandas as pd
from pandas import DataFrame, Series
import unittest
import nose
from numpy.testing import assert_almost_equal, assert_allcl... | assert_frame_equal(without_subnet, f_expected_without_subnet, check_dtype=False) | pandas.util.testing.assert_frame_equal |
"""
module for testing plot_corr(df, x, y) function.
"""
import random
from time import time
import dask.array as da
import dask.dataframe as dd
import numpy as np
import pandas as pd
import pytest
from ...eda.correlation import compute_correlation, plot_correlation
from ...eda.correlation.compute import (
ke... | pd.DataFrame(data=array) | pandas.DataFrame |
"""
Utilities for public_data.
"""
import gzip
try:
import ujson as json
except ImportError:
import json
import numpy as np
import pandas as pd
import warnings
def read_json(filename):
"""
Read a JSON file.
Parameters
----------
filename : str
Filename. Must be of type .json or .json.gz.
"""
if... | pd.isnull(value) | pandas.isnull |
__author__ = 'saeedamen'
#
# Copyright 2016 Cuemacro
#
# 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 ... | pandas.DataFrame() | pandas.DataFrame |
"""
The data_cleaner module is used to clean missing or NaN values from pandas dataframes (e.g. removing NaN, imputation, etc.)
"""
import pandas as pd
import numpy as np
import logging
from sklearn.preprocessing import Imputer
import os
from scipy.linalg import orth
log = logging.getLogger('mastml')
def flag_outli... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
# In[2]:
pd.set_option('display.max_rows', 1000)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
# In[3]:
df = pd.read_csv('data.csv')
# ## Gro... | pd.to_numeric(row['Value'][1:-1]) | pandas.to_numeric |
##########################################################################
## Summary
##########################################################################
'''
Creates flat table of decisions from our Postgres database and runs the prediction pipeline.
Starting point for running our models.
'''
################... | pandas.read_sql(query1, database_connection) | pandas.read_sql |
#Scrape Trustee details
#<NAME>, <NAME>
#11/10/18
#This file scrapes trustee information from the Charity Commission for Northern Ireland website.
################################# Import packages #################################
from urllib.request import urlopen as uReq
from bs4 import BeautifulSoup as soup
from lx... | pd.DataFrame(dicto) | pandas.DataFrame |
import datetime
import logging
import pathlib
import typing
import xml.parsers.expat
from dataclasses import dataclass
from multiprocessing.dummy import Pool as ThreadPool
import pandas as pd
import pyetrade
import pytz
import requests.exceptions
from tenacity import (
retry,
stop_after_attempt,
wait_expon... | pd.DataFrame() | pandas.DataFrame |
from functools import reduce
from datetime import datetime as dt
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
matplotlib.use("agg")
COLOR_DEATHS = "#dd6600"
COLOR_RECOVERED = "#dbcd00"
COLOR_ACTIVE = "#2792cb"
C... | pd.merge(left, right, on="date") | pandas.merge |
#!/usr/bin/env python3
import sys
import pandas as pd
import numpy as np
def clean_data(filename):
class1 = set()
class2 = set()
class3 = set()
class4 = set()
class5 = set()
class6 = set()
with open("class1_out.txt", "r") as fin:
In = fin.read()
In = In.split(',')
f... | pd.Series(target_values) | pandas.Series |
"""
This pipeline first saves individual image maps to the database
- this is an issue because of storage space
1. Select images
2. Apply pre-processing corrections
a. Limb-Brightening
b. Inter-Instrument Transformation
3. Coronal Hole Detection
4. Convert to Map
5. Combine Maps
... | pd.DataFrame(data=None, columns=df_cols) | pandas.DataFrame |
import numpy as np
import pandas as pd
from unittest import TestCase
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.datasets import load_iris
from utilities.preprocessing import standard_scale, min_max_scale
from pytest import raises
class RollingStatsTests:
def test_scaled_values(se... | pd.DataFrame(predictors) | pandas.DataFrame |
import pytest
import numpy as np
import pandas
import pandas.util.testing as tm
from pandas.tests.frame.common import TestData
import matplotlib
import modin.pandas as pd
from modin.pandas.utils import to_pandas
from numpy.testing import assert_array_equal
from .utils import (
random_state,
RAND_LOW,
RAND_... | pandas.DataFrame(data) | pandas.DataFrame |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.