prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#!/usr/bin/env python3
import gc
import os
import pickle
import fire
import h5py
import matplotlib.pyplot as plt
import seaborn as sns
from hyperopt.fmin import generate_trials_to_calculate
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import precision_recall_curve
from numpy import linalg as L... | pd.DataFrame(None) | pandas.DataFrame |
#!/usr/bin/env python3
import sys
import json
import glob
import os.path
import matplotlib as mpl
import pandas as pd
import numpy as np
import scipy.sparse.csgraph as csg
if len(sys.argv) < 2:
print(usage)
sys.exit(1)
folder = sys.argv[1].rstrip('/')
tables = glob.glob('{}/*-analyzed.csv'.format(folder))
pri... | pd.read_csv(t) | pandas.read_csv |
#!/home/jmframe/programs/anaconda3/bin/python3
import mannkendal as mk
"""
Run Mann Kendall test a lot of sites...
"""
import numpy as np
import pandas as pd
from glob import glob
from tqdm import tqdm
att_path = "/home/NearingLab/data/camels_attributes_v2.0/camels_all.txt"
attributes = pd.read_csv(att_path, sep="... | pd.read_csv('results-mk-runoff-ratio.txt', sep=" ") | pandas.read_csv |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import zipfile
import os
import geopy.distance
import random
import pandas as pd
import numpy as np
import csv
from enum import Enum
from yaml import safe_load
from maro.cli.data_pipeline.utils import download_file, StaticParameter
from maro.u... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
# import re
def processFE_df(df):
"""Function to process Pandas dataframe from Funds Explorer site:
'https://www.fundsexplorer.com.br/ranking'
After this function the DataFrame can be filtered to analysis
Args:
df ([type]): pandas.core.frame.DataFrame
Returns:
[... | pd.to_numeric(df['vpaR$'], errors='coerce') | pandas.to_numeric |
# -*- coding: utf-8 -*-
import unittest
import pandas as pd
import pandas.testing as tm
import numpy as np
from pandas_xyz import algorithms as algs
class TestAlgorithms(unittest.TestCase):
def test_displacement(self):
"""Test out my distance algorithm with hand calcs."""
lon = pd.Series([0.0, 0.0, 0.0... | pd.Series([np.nan, np.nan, np.nan]) | pandas.Series |
# *****************************************************************************
# Copyright (c) 2019-2020, Intel Corporation All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# Redistributions o... | pd.Series([1., -1., 0., 0.1, -0.1]) | pandas.Series |
import numpy as np
import pytest
from pandas import (
DataFrame,
Series,
)
import pandas._testing as tm
from pandas.core.base import DataError
# gh-12373 : rolling functions error on float32 data
# make sure rolling functions works for different dtypes
#
# further note that we are only checking rolling for fu... | Series([np.nan, 4, 8, 12, 16]) | pandas.Series |
import pandas as pd
import numpy as np
###SCRIPT DESCRIPTION###
# This script provides statistical analysis for LSTM labeled data.
###SCRIPT INPUT###
# The .csv file given to this script should be equivalent to the labeled output generated by the corresponding
# data-generation.py script. This means a "Format" column ... | pd.DataFrame(rows, columns=cols) | pandas.DataFrame |
from flask import Flask, jsonify, request, json
import joblib
import pandas as pd
app = Flask(__name__)
app.config['SECRET_KEY'] = 'KEY'
#rutas raiz
@app.route('/movies', methods=['POST'])
def recommended():
try:
cosine_sim = joblib.load("./models/modelCosine.pkl")
df_movies = | pd.read_csv('./data/df_movies.csv') | pandas.read_csv |
from backlight.strategies import filter as module
import pytest
import pandas as pd
import numpy as np
import backlight
from backlight.strategies.amount_based import simple_entry_and_exit
@pytest.fixture
def signal():
symbol = "usdjpy"
periods = 22
df = pd.DataFrame(
index= | pd.date_range(start="2018-06-06", freq="1min", periods=periods) | pandas.date_range |
#getting data from the internet
import sys
import csv
import pandas as pd
import requests
from bs4 import BeautifulSoup
pd.set_option('max_columns', 50)
def get_all(weeknum):
print('getting stats')
print('pulling ESPN lines')
get_espn_lines(weeknum)
#print('pulling ESPN team stats')
#get_espn_stats(weeknum)... | pd.DataFrame.from_dict(team_lines, orient='index', columns=['Avg Line']) | pandas.DataFrame.from_dict |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 22 20:22:15 2020
@author:
"""
class Comparison:
def __init__(self):
super().__init__()
#The goal of this function is to execute the models and show the differents results.
#It is the function to call when we want to test ... | pd.read_csv(folder_path+file_, sep = ',', skiprows=6, header = 0, dtype='unicode', error_bad_lines=False) | pandas.read_csv |
#!/usr/bin/env python
#
# Inspired by g_mmpbsa code.
# #
# Copyright (c) 2016-2019,<NAME>.
# All rights reserved.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
# * Redistributions of source code must retain the... | Series.from_array(total_en, index=time) | pandas.Series.from_array |
from typing import *
import pandas as pd
from .api import TBARawAPI
from .client import TBACachedSession, TBAQueryArguments
from .exceptions import *
__all__ = ["query_args", "event_helper"]
class TBABaseHelper:
def __init__(self, session: "TBACachedSession"):
self._api = TBARawAPI(session)
def _g... | pd.DataFrame(schedule_rows, columns=columns) | pandas.DataFrame |
from flask import (
Blueprint, Flask, request, session, g,
redirect, url_for, abort, render_template, flash,
make_response, send_file
)
from flask_login import login_required, current_user
from pfedu.forms import UserForm, MoleculeForm, PasswdForm
from pfedu.models import db, Molecule... | pd.DataFrame(data=data,columns=['temperature', 'delta_g',
'delta_h', 'delta_s', 'k_p']) | pandas.DataFrame |
import logging
from functools import lru_cache
from time import perf_counter as pc
from typing import Tuple, Dict, Union, List
import numpy as np
import pandas as pd
from sortedcontainers import SortedDict
from pandas_ml_utils.constants import *
from pandas_ml_utils.model.features_and_labels.features_and_labels impor... | pd.DataFrame({}, index=df.index) | pandas.DataFrame |
# Functions for performing analysis in the article
# "Material Culture Studies in the Age of Big Data:
# Digital Excavation of Homemade Facemask Production
# during the COVID-19 Pandemic"
#
# Code Written By: <NAME>
#
# For import/use instructions, see README.md
import pandas as pd
import geopandas as gpd
import nltk
... | pd.read_csv(data_path + 'clean_etsy_data.csv') | pandas.read_csv |
"""
Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy of this
software and associated documentation files (the "Software"), to deal in the Software
without restriction, including without limitation the rights to use, cop... | pd.to_datetime(by_timestamp['timestamp'], unit='s') | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
set of functions to drive EasyQuake
"""
print(r"""
____ __
___ ____ ________ __/ __ \__ ______ _/ /_____
/ _ \/ __ `/ ___/ / / / / / / / / / __ `/ //_/ _ \
/ __/ /_/ (__ ) /_/ / /_/ / /_/ / /_/ / ,< / __/
\___/\__,_/____... | pd.DataFrame() | pandas.DataFrame |
#
# 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... | pd.Timestamp(val[0], tz='UTC') | pandas.Timestamp |
from sales_analysis.data_pipeline import BASEPATH
from sales_analysis.data_pipeline._pipeline import SalesPipeline
import pytest
import os
import pandas as pd
# --------------------------------------------------------------------------
# Fixtures
@pytest.fixture
def pipeline():
FILEPATH = os.path.join(BASEPATH, ... | pd.Timestamp('2019-08-20 00:00:00') | pandas.Timestamp |
from __future__ import print_function
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from pandas import (Series, Index, Int64Index, Timestamp, Period,
DatetimeIndex, PeriodIndex, TimedeltaIndex,
Timedelta, timedelta_range, date_range, Float64Index... | PeriodIndex([], freq='D') | pandas.PeriodIndex |
import re
import numpy as np
import pandas as pd
from os.path import join
def read_lastfm(raw_dir, debug=None):
"""
Read the lastfm dataset from .dat file
:param raw_dir: the path to raw files (users.dat, movies.dat, ratings.dat)
:param debug: the portion of ratings userd, float
:return: artists, ... | pd.DataFrame(user_artists) | pandas.DataFrame |
import numpy as np
from pandas import Timedelta, Series
from pandas import to_timedelta
from pandas.tseries.frequencies import to_offset
from scipy import signal
def _noise_limits(y):
"""
Return upper and lower limits of a noise band. Values in this band can be considered as noise.
:param y: The... | Timedelta(p) | pandas.Timedelta |
import pandas as pd
from predict_functions import build_rmsa_map, calculate_tournament_table, sort_table, predict_match
from utils.constants import Maps, Teams, calc_map_type
# Pandas options for better printing
from utils.utils import calc_match_date, calc_season
pd.set_option('display.max_columns', 500)
pd.set_opti... | pd.read_csv('map_data/match_map_stats.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
import operator
import warnings
from functools import wraps, partial
from numbers import Number, Integral
from operator import getitem
from pprint import pformat
import numpy as np
import pandas as pd
from pandas.util import cach... | pd.DataFrame({'idx': [idx], 'value': [value]}) | pandas.DataFrame |
from os.path import exists, join
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from tqdm import trange
from math import ceil
from traitlets import Dict, List
from ctapipe.core import Tool
from targetpipe.fitting.spe_sipm import sipm_spe_fit
from targetpipe.fitting.chec import CHECSSPEFit... | pd.DataFrame(df_list) | pandas.DataFrame |
# To access Earth Engine Python API.
import ee
ee.Authenticate()
ee.Initialize()
# For data manipulation and analysis.
import math
import pandas as pd
import numpy as np
np.set_printoptions(precision=4, suppress=True)
from datetime import datetime
import scipy.signal
# For plotting
import matplotlib.pyplot as plt
fro... | pd.to_datetime(windows['Time']) | pandas.to_datetime |
# -*- coding: utf-8 -*-
from datetime import timedelta
import operator
from string import ascii_lowercase
import warnings
import numpy as np
import pytest
from pandas.compat import lrange
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
Categorical, DataFrame, MultiIndex, Serie... | Series(non_int_round_dict) | pandas.Series |
import pandas as pd
import pandas.testing as pd_testing
import pyfakefs.fake_filesystem_unittest
import unittest
from .main import (
bh_correction,
calculate_enrichment,
count_domains_by_bait,
filter_saint,
fishers_test,
get_background,
map_file_ids,
parse_domains,
read_domains,
read_gene_map,
re... | pd_testing.assert_frame_equal(a, b) | pandas.testing.assert_frame_equal |
__version__ = '0.1.3'
__maintainer__ = '<NAME> 31.12.2019'
__contributors__ = '<NAME>, <NAME>'
__email__ = '<EMAIL>'
__birthdate__ = '31.12.2019'
__status__ = 'dev' # options are: dev, test, prod
#----- imports & packages ------
if __package__ is None or __package__ == '':
import sys
from os import path
... | pd.to_timedelta(data.loc[:, colWeek] * 7, unit='days') | pandas.to_timedelta |
import numpy as np
import pandas as pd
from tqdm import tqdm
import datetime as dt
from collections import defaultdict
from dateutil.relativedelta import relativedelta
def collect_dates_for_cohort(df_pop, control_reservoir, control_dates, col_names=None):
'''
Fill 'control_used' dictionary with the dates (... | pd.isna(d1_dt) | pandas.isna |
# coding: utf-8
# # ASSIGNMENT 1
# In[5]:
import pandas as pd
# In[6]:
from matplotlib import pyplot as plt
get_ipython().magic(u'matplotlib inline')
import numpy as np
# In[7]:
import seaborn as sns
# In[ ]:
#In the above cells, I imported libraries required for this assignment.
# In[9]:
df_math = pd.r... | pd.to_numeric(df['Math']) | pandas.to_numeric |
import time
import logging
from TwitterAPI import TwitterAPI
from twython import Twython
from twython import TwythonError, TwythonRateLimitError, TwythonAuthError
import pandas as pd
from datetime import datetime, timedelta
from spikexplore.NodeInfo import NodeInfo
from spikexplore.graph import add_node_attributes, add... | pd.DataFrame() | pandas.DataFrame |
import os
import tempfile
import dask.dataframe as dd
import numpy as np
import pandas as pd
import pytest
from dask.datasets import timeseries
from dask.distributed import Client
from pandas.testing import assert_frame_equal
@pytest.fixture()
def timeseries_df(c):
pdf = timeseries(freq="1d").compute().reset_ind... | pd.DataFrame({"a": [1, 2, 3], "b": [1.1, 2.2, 3.3]}) | pandas.DataFrame |
from __future__ import division
import matplotlib
matplotlib.use('TkAgg')
import multiprocessing as mp
import itertools
import numpy as np
from scipy import interpolate
from pylab import flipud
import pandas as pd
try:
from pandas import Categorical
except ImportError:
from pandas.core.categorical import Categ... | pd.DataFrame({'x': percentiles, 'y': intensities, 'yerr': yerr, 'bins': bin_polygons}) | pandas.DataFrame |
import pandas as pd
from collections import OrderedDict
import frappe
# TODO
# 1. create a transaction doctype list
# 2. Get all transactions
# 3. Sort all transactios by their posting dates
# 4.
Transaction_Type_List = [
'Purchase Invoice',
'Sales Invoice',
'Stock Entry',
'Delivery Note',
'Purcha... | pd.DataFrame() | pandas.DataFrame |
import contextlib
import os
import traceback
from itertools import chain
from typing import Any, Callable, Dict, Optional, Type
from unittest.mock import MagicMock, _CallList
import pandas as pd
import pytest
from _pytest.doctest import DoctestModule
from _pytest.python import Module
from sklearn.linear_model import L... | pd.DataFrame([[1, 0], [0, 1]], columns=['a', 'b']) | pandas.DataFrame |
import torch
import os
import pandas as pd
import numpy as np
from TLA.Analysis.lang_mapping import mapping
from distutils.sysconfig import get_python_lib
def analysis_table():
lang_dict = mapping()
directory = "analysis"
parent_dir = get_python_lib() + "/TLA/Analysis"
p = os.path.join(parent_dir, dire... | pd.read_csv(f) | pandas.read_csv |
# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership. The Modin Development Team licenses this file to you under the
# Apache License, Version 2.0 (the "License"); you may not u... | is_list_like(func) | pandas.core.dtypes.common.is_list_like |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
# In[2]:
train = pd.read_csv("D:/ML/Dataset/MedicalInsurance/Train-1542865627584.csv")
beneficiary = pd.read_csv("D:/ML/Dataset/MedicalInsurance/Train_Beneficiarydata-1542865627584.csv")
inpatient = pd.read_csv("D:/ML/Dataset/Medica... | pd.to_datetime(beneficiary['DOD'],format = '%Y-%m-%d',errors='ignore') | pandas.to_datetime |
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(data_start, tz=tz) | pandas.Timestamp |
""" ``xrview.handlers`` """
import asyncio
import numpy as np
import pandas as pd
from bokeh.document import without_document_lock
from bokeh.models import ColumnDataSource
from pandas.core.indexes.base import InvalidIndexError
from tornado import gen
from tornado.platform.asyncio import AnyThreadEventLoopPolicy
# TO... | pd.to_datetime(start, unit="ms") | pandas.to_datetime |
# -*- coding: utf-8 -*-
##########################################################################
# NSAp - Copyright (C) CEA, 2020
# Distributed under the terms of the CeCILL-B license, as published by
# the CEA-CNRS-INRIA. Refer to the LICENSE file or to
# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
#... | pd.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
#! python3
# 2019-03-27 by recs
# ===check the current owner of type licenses===
import os
import pandas as pd
from spareparts.lib.settings import temp_jde, tempo_local
index_manual = ["How to fill fileds in the Data Tab", "Unnamed: 1", "Unnamed: 2"]
index_auto = [
"Item Number",
"Number(Drawing)",
"Qu... | pd.read_excel(name_file) | pandas.read_excel |
import pandas as pd
from unittest import TestCase # or `from unittest import ...` if on Python 3.4+
import numpy as np
import category_encoders as encoders
X = pd.DataFrame({
'none': [
'A', 'A', 'B', None, None, 'C', None, 'C', None, 'B',
'A', 'A', 'C', 'B', 'B', 'A', 'A', None, 'B', None
],
... | pd.Series([13, 7]) | pandas.Series |
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from math import ceil, floor
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt... | pd.get_dummies(X_train_processed, columns=X_train_processed.columns) | pandas.get_dummies |
# 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.
# pyre-unsafe
import json
import logging
from typing import List, Optional, Any, Dict, Union, Tuple
import numpy as np
import torch
from k... | pd.to_datetime(time[:, -1]) | pandas.to_datetime |
# -*- 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... | tm.assert_almost_equal(rs, xp) | pandas.util.testing.assert_almost_equal |
import pandas as pd
import numpy as np
import os
import sys
import pdb
from scipy.stats import binom_test
from statsmodels.stats import multitest
from collections import Counter
from GLOBAL_VAR import *
alignmetn_dir = '/work-zfs/abattle4/heyuan/tissue_spec_eQTL_v8/datasets/TFBS_ChIP_seq/STAR_output'
SNP_in_TFBS_di... | pd.read_csv('%s/reads_count/%s' % (outdir, save_read_counts_fn), sep='\t', index_col = [0,1]) | pandas.read_csv |
'''
Created on Dec 14, 2016
Purpose: Given a list of keggKO Results from "Detail Page". Create a map which contains
further information besides protein ID (e.g. HOG membership)
Purpose2: For individual lists containing this secondary information extract all its
genes by id and extract all its annotated genes and pa... | pd.read_csv(filepath, sep=sep, names=columnnames, index_col=False) | pandas.read_csv |
from __future__ import division
__author__ = 'saeedamen' # <NAME> / <EMAIL>
#
# Copyright 2017 Cuemacro Ltd. - http//www.cuemacro.com / @cuemacro
#
# See the License for the specific language governing permissions and limitations under the License.
#
import numpy as np
import pandas as pd
import pytz
import re
fro... | pd.concat([start_df, finish_df]) | pandas.concat |
import json
import os
import tempfile
import shutil
import pandas as pd
from sample_sheet import Sample
from unittest import main, TestCase
from metapool import KLSampleSheet
from metapool.count import (_extract_name_and_lane, _parse_samtools_counts,
_parse_fastp_counts, bcl2fastq_counts,
... | pd.DataFrame(RUN_STATS) | pandas.DataFrame |
import numpy as np
import pandas as pd
import os
from settings import *
"""
Augment the original training examples by adding anti-symmetrical ones in terms of left/right motion.
Doubles the quantity of examples. The new examples have reversed sign for the joint values of
'HeadYaw', 'HipRoll', swapped values for Arms... | pd.read_csv(path, index_col=0) | pandas.read_csv |
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas.core.dtypes.generic import ABCIndexClass
import pandas as pd
import pandas._testing as tm
from pandas.api.types import is_float, is_float_dtype, is_integer, is_scalar
from pandas.core.arrays import IntegerArray, integer_array
from... | tm.assert_frame_equal(result, df) | pandas._testing.assert_frame_equal |
# -*- coding:Utf-8 -*-
"""
This module handles CORMORAN measurement data
CorSer Class
============
.. autoclass:: CorSer
:members:
Notes
-----
Useful members
distdf : distance between radio nodes (122 columns)
devdf : device data frame
"""
#import mayavi.mlab as mlabc
import os
import pdb
import sys
import ... | pd.DataFrame(pos[:,d,:],columns=['x','y','z'],index=t) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import datetime as dt
from argparse import ArgumentTypeError
import japandas as jpd
import pandas as pd
def next_bday(day=None, n=1):
"""Returns the next business day after argument day.
"""
if day is None:
da... | pd.to_datetime(day) | pandas.to_datetime |
import shutil
from pathlib import Path
import itertools
import math
import numpy as np
import pandas as pd
from statistics import mean
from scipy.optimize import minimize_scalar
def removeChars(s):
for c in [' ', '\\', '/', '^']:
s = s.replace(c, '')
return s
def rchop(s, suffix):
if suffix and ... | pd.concat(all_data, ignore_index=True) | pandas.concat |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from kneed import KneeLocator
from jupyter_utils import AllDataset
data_dir = '../drp-data/'
GDSC_GENE_EXPRESSION = 'preprocessed/gdsc_tcga/gdsc_rma_gene_expr.csv'
TCGA_GENE_EXPRESSION = 'preprocessed/gdsc_tcga/tcga_log2_gene_expr.csv'
TCGA_CANCER... | pd.read_csv(data_dir + TCGA_DR, index_col=0) | pandas.read_csv |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
from pandas.compat import range
import pandas as pd
import pandas.util.testing as tm
# -------------------------------------------------------------------
# Comparisons
class TestFrameComparisons(object):
def test_df_boolean_comparison_error(self):
... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
import keras
import numpy as np
import pandas as pd
import tensorflow as tf
from keras.layers import *
from keras.models import Sequential
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
# Wczytanie datasetu
iris = load_iris()
# Stworzenie tabeli danych
data = | pd.DataFrame(data=np.c_[iris['data'], iris['target']], columns=iris['feature_names'] + ['target']) | pandas.DataFrame |
import numpy as np
import pandas as pd
from datetime import datetime
import pytest
import empyrical
import vectorbt as vbt
from vectorbt import settings
from tests.utils import isclose
day_dt = np.timedelta64(86400000000000)
ts = pd.DataFrame({
'a': [1, 2, 3, 4, 5],
'b': [5, 4, 3, 2, 1],
'c': [1, 2, 3, ... | pd.Series([1, 2, 3]) | pandas.Series |
#!/usr/bin/env python
# coding: utf-8
# In[26]:
"""
LICENSE MIT
2021
<NAME>
Website : http://www.covidtracker.fr
Mail : <EMAIL>
README:
This file contains scripts that download data from data.gouv.fr and then process it to build many graphes.
I'm currently cleaning the code, please ask me if something is not clear ... | pd.DataFrame() | pandas.DataFrame |
import act
import requests
import json
import glob
import pandas as pd
import datetime as dt
import numpy as np
import xarray as xr
import dask
import matplotlib.pyplot as plt
import textwrap
import argparse
import importlib
from scipy import stats
from matplotlib.dates import DateFormatter
from matplotlib.dates impor... | pd.Timedelta('1 days') | pandas.Timedelta |
import pandas as pd
def extract_forecast(orders: pd.DataFrame):
df = orders.iloc[:, -12:].copy()
df.drop(df[df.sum(axis=1) == 0].index, inplace=True)
return df
class OrderHistory:
def __init__(self):
self.orders = pd.DataFrame(columns=["date", "product_no", "amount"])
def initialize(sel... | pd.read_csv("C://sl_data//2019_kalan_tuketim.csv", low_memory=False) | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 24 16:22:22 2020
@author: trucdo
"""
#%% Define path, file names, and other parameters
# indicate name (exclude file extension) and path of .csv file containing TFBS hits
in_path = "path_to_directory"
tfbs_file_name = "Burkholderia_cepacia_ATCC_25... | pd.DataFrame(all_tfbs_dict) | pandas.DataFrame |
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-4_2.annotate.csv', sep='\t', index_col=0) | pandas.read_table |
""" ecospold2matrix - Class for recasting ecospold2 dataset in matrix form.
The module provides function to parse ecospold2 data, notably ecoinvent 3, as
Leontief A-matrix and extensions, or alternatively as supply and use tables for
the unallocated version of ecoinvent.
:PythonVersion: 3
:Dependencies: pandas 0.14.... | pd.read_csv(path, sep=sep) | pandas.read_csv |
"""This module contains functions for using LDA topic modeling."""
import os
import datetime
import pandas as pd
import pickle
import json
from gensim.models import Phrases
from gensim.corpora import Dictionary
from gensim.models import TfidfModel, LdaModel
from gensim.models.coherencemodel import CoherenceModel
from ... | pd.Series(docs) | pandas.Series |
from itertools import product
import numpy as np
from numpy import ma
import pandas as pd
import pytest
from scipy import sparse as sp
from scipy.sparse import csr_matrix, issparse
from anndata import AnnData
from anndata.tests.helpers import assert_equal, gen_adata
# some test objects that we use below
adata_dense... | pd.DataFrame(index=adata.var_names) | pandas.DataFrame |
"""
Includes classes and functions to test and select the optimal
betting strategy on historical and current data.
"""
# Author: <NAME> <<EMAIL>>
# License: BSD 3 clause
from argparse import ArgumentParser
from ast import literal_eval
from itertools import product
from os.path import join
from sqlite3 import connect... | pd.read_sql('select * from y', DB_CONNECTION) | pandas.read_sql |
# Custom Modules
import avaxtar
from avaxtar import Avax_NN
from avaxtar import DF_from_DICT
# Py Data Stack
import numpy as np
import pandas as pd
# Neural Network
import torch
import torch.nn as nn
import torch.nn.functional as F
# Feature Engineering
import sent2vec
# File Manipulation
from glob import glob
impo... | pd.DataFrame() | pandas.DataFrame |
import plotly.express as px
import plotly.figure_factory as ff
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from collections import Counter
from nltk import word_tokenize
from nltk.corpus import stopwords
import numpy as np
import pandas as pd
import datetime as dt
import time
import ... | pd.read_csv(query_string) | pandas.read_csv |
# -*- coding: utf-8 -*-
'''
Analysis module for analysis of frequency-dependence ("line shape analysis")
Author:
<NAME>,
Max Planck Institute of Microstructure Physics, Halle
Weinberg 2
06120 Halle
<EMAIL>
'''
''' Input zone '''
# ________________________________________________________________... | pd.read_csv(inputFile.fileDirName,index_col=False) | pandas.read_csv |
import numpy as np
import pytest
from pandas import DataFrame, Index, MultiIndex, Series, concat, date_range
import pandas._testing as tm
import pandas.core.common as com
@pytest.fixture
def four_level_index_dataframe():
arr = np.array(
[
[-0.5109, -2.3358, -0.4645, 0.05076, 0.364],
... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
import numpy as np
import pandas as pd
from tests.datasets import numerical
class TestRandomIntegerGenerator:
def test(self):
output = numerical.RandomIntegerGenerator.generate(10)
assert len(output) == 10
assert output.dtype == int
assert len(pd.unique(output)) > 1
asser... | pd.unique(output) | pandas.unique |
import sys
import os
import codecs
import glob
import configparser
import pandas as pd
from datetime import datetime
from docopt import docopt
from jinja2 import Environment, FileSystemLoader
from lib.Util.util import *
# Type of printing.
OK = 'ok' # [*]
NOTE = 'note' # [+]
FAIL = 'fail' # [-]
WARNING... | pd.read_csv(file, names=self.header_test, sep=',') | pandas.read_csv |
#!/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 |
"""
q1-final.py: for sub-challenge 1
"""
import sklearn.ensemble
import pandas
import step00
if __name__ == "__main__":
# clinical_data
clinical_data = pandas.read_csv("/data/clinical_data.csv")
clinical_data.set_index("patientID", inplace=True)
clinical_data["ECOGPS"] = list(map(lambda x: float(x) if ... | pandas.concat(data_list, axis="columns", join="inner", verify_integrity=True) | pandas.concat |
import sys
import os
import logging
import datetime
import pandas as pd
from job import Job, Trace
from policies import ShortestJobFirst, FirstInFirstOut, ShortestRemainingTimeFirst, QuasiShortestServiceFirst
sys.path.append('..')
def simulate_vc(trace, vc, placement, log_dir, policy, logger, start_ts, *args):
if... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# # Data Preprocessing
# ### Importing the libraries
# In[ ]:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# ### Reading the dataset
# In[ ]:
dataset = pd.read_csv('startups.csv')
dataset
# In[ ]:
dataset.describe()
# In[ ]:
# Separate In... | pd.DataFrame({'y_pred': y_pred, 'y_test': y_test, 'error': err}) | pandas.DataFrame |
import os
import fnmatch
import pandas
def load_config_yml(config_file, individual=False):
# loads a configuration YAML file
#
# input
# config_file: full filepath to YAML (.yml) file
#
# output
# config: Configuration object
import os
import yaml
import yamlordereddictlo... | pd.concat(new_rows) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 23 14:57:38 2021
@author: kenhu
"""
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.ensemble import RandomForestClassif... | pd.factorize(df['Attrition_Flag']) | pandas.factorize |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
import datetime as dtm
pd.options.mode.chained_assignment = None
from scipy.optimize import curve_fit
from .data import _get_connection
from .plotting import _init_plot,... | pd.unique(self.R['isotope']) | pandas.unique |
# SPDX-License-Identifier: Apache-2.0
# Licensed to the Ed-Fi Alliance under one or more agreements.
# The Ed-Fi Alliance licenses this file to you under the Apache License, Version 2.0.
# See the LICENSE and NOTICES files in the project root for more information.
from typing import Tuple
from datetime import datetime... | DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import datetime as dt
import matplotlib.pyplot as plt
from matplotlib import style
import pandas as pd
import numpy as np
import xlrd
from sklearn import linear_model
from sklearn.linear_model import LinearRegression
from sklearn import datasets
import calendar
import j... | pd.Series(X) | pandas.Series |
import collections
import torch
import os
import pandas as pd
import torch.nn as nn
from tqdm import tqdm
import numpy as np
EPS = 1e-12
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
... | pd.Series(y) | pandas.Series |
#
# Analysis of the hvorg_movies
#
import os
import pickle
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import astropy.units as u
from sunpy.time import parse_time
import hvorg_style as hvos
plt.rc('text', usetex=True)
plt.rc('font', size=14)
figsize = (10, 5)
# Read in the data
directory =... | pd.TimeGrouper(freq='D') | pandas.TimeGrouper |
import logging
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_california_housing
from sklearn.datasets import load_boston
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import boston_housing
logger = logging.ge... | pd.DataFrame(housing.data, columns=housing.feature_names) | pandas.DataFrame |
import unittest
from abc import ABC
import numpy as np
import pandas as pd
from toolbox.ml.ml_factor_calculation import ModelWrapper, calc_ml_factor, generate_indexes
from toolbox.utils.slice_holder import SliceHolder
class MyTestCase(unittest.TestCase):
def examples(self):
# index includes non trading... | pd.Timedelta(days=44) | pandas.Timedelta |
import pkg_resources
from unittest.mock import sentinel
import pandas as pd
import pytest
import osmo_jupyter.dataset.combine as module
@pytest.fixture
def test_picolog_file_path():
return pkg_resources.resource_filename(
"osmo_jupyter", "test_fixtures/test_picolog.csv"
)
@pytest.fixture
def test_... | pd.to_datetime("2019") | pandas.to_datetime |
"""
This script contains helper functions to make plots presented in the paper
"""
from itertools import product
from itertools import compress
import copy
from pickle import UnpicklingError
import dill as pickle
from adaptive.saving import *
from IPython.display import display, HTML
import scipy.stats as stats
from g... | pd.DataFrame.copy(df_bias) | pandas.DataFrame.copy |
import pandas as pd
import numpy as np
from salescleanup import convert_currency
from salescleanup import convert_percent
df = pd.read_csv("https://github.com/chris1610/pbpython/blob/master/data/sales_data_types.csv?raw=True")
# Transforming data types
df['Customer Number'].astype('int')
df["Customer Number"] = df['C... | pd.to_numeric(df['Jan Units'], errors='coerce') | pandas.to_numeric |
from rdkit import Chem
import pandas as pd
from pathlib import Path, PosixPath
import pickle
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--workpath", type=PosixPath, help="absolute path for pkl generation", required=True)
parser.add_argument("--sdf", t... | pd.DataFrame({'smiles':smis_woh, 'n_conformers':maxconfs}) | pandas.DataFrame |
__author__ = 'brendan'
import main
import pandas as pd
import numpy as np
from datetime import datetime as dt
from matplotlib import pyplot as plt
import random
import itertools
import time
import dateutil
from datetime import timedelta
cols = ['BoP FA Net', 'BoP FA OI Net', 'BoP FA PI Net', 'CA % GDP']
raw_data = pd... | pd.DataFrame(index=eur_gdp.index) | pandas.DataFrame |
import unittest
import os
from collections import defaultdict
from unittest import mock
import warnings
import pandas as pd
import numpy as np
from dataprofiler.profilers import FloatColumn
from dataprofiler.profilers.profiler_options import FloatOptions
test_root_path = os.path.dirname(os.path.dirname(os.path.real... | pd.Series(['4']) | pandas.Series |
#!/usr/bin/env python
from collections import defaultdict
import math
import numpy as np
import os
import pandas as pd
import pickle
import pysam
import re
import sys
def get_gene_id(row):
# return row["attribute"].split(";")[0].split()[1][1:-1]
if "gene_name" in row["attribute"]:
return row["attri... | pd.Series(CI_new.reverse.values, index=CI_new.refName_ABR1) | pandas.Series |
# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership. The Modin Development Team licenses this file to you under the
# Apache License, Version 2.0 (the "License"); you may not u... | pandas.DataFrame(result) | pandas.DataFrame |
# import libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.metrics import accuracy_score, recall_score, precision_score, roc_auc_score
from sklearn.metrics import confusion_matrix, plot_confusion_ma... | pd.Series(train_roc_auc_scores) | pandas.Series |
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