prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pandas as pd
from datetime import datetime, timedelta
from typing import Optional, Union
def check_prices(**kwargs) -> bool:
'''checks if one or more series of prices are of correct types'''
for key, value in kwargs.items():
if not isinstance(value, pd.Series):
print(f'{key} must be ... | pd.tseries.offsets.BQuarterBegin(startingMonth=1) | pandas.tseries.offsets.BQuarterBegin |
import json
import numpy as np
import pytest
from pandas import DataFrame, Index, json_normalize
import pandas._testing as tm
from pandas.io.json._normalize import nested_to_record
@pytest.fixture
def deep_nested():
# deeply nested data
return [
{
"country": "USA",
... | json_normalize({"A": {"A": 1, "B": 2}}) | pandas.io.json.json_normalize |
import requests
from bs4 import BeautifulSoup
import json
import pandas as pd
def get_list_of_youtube_channels(term,n):
# initialize list of links
links = []
# get a list of links for channels while searching for a given term
for i in range(0,n,10):
r = requests.get("https://www.bing.com/sea... | pd.DataFrame() | pandas.DataFrame |
from jug import TaskGenerator, CachedFunction
import os
from os import path
from jug.hooks.exit_checks import exit_if_file_exists, exit_env_vars
exit_env_vars()
exit_if_file_exists('jug.exit.marker')
def get_sample(f):
return path.split(f)[-1].split('_')[0]
BASE = '/g/scb2/bork/ralves/projects/genecat/outputs/'... | pd.Series(gi2func) | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
replacement_comparison.py
Functions to compare openSMILE outputs for various noise replacement methods
for each waveform in the sample.
Authors:
– <NAME>, 2017 (<EMAIL>)
© 2017, Child Mind Institute, Apache v2.0 License
@author: jon.clucas
"""
import numpy as ... | pd.DataFrame() | pandas.DataFrame |
""" Functions that load downloaded emoji data and prepare train/dev/test sets for NNs """
from math import ceil
import os
import string
import pandas as pd
import numpy as np
# import emoji
CHARACTERS = """ '",.\\/|?:;@'~#[]{}-=_+!"£$%^&*()abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ01234567890"""
def rea... | pd.Series(y_tuple) | pandas.Series |
import os, glob, sys, io
import numpy as np
import pandas as pd # Timeseries data
import datetime as dt # Time manipulation
import yaml
from matplotlib.dates import date2num # Convert dates to matplotlib axis coords
from matplotlib import dates
from scipy import fftpack
from scipy import stats
fro... | pd.date_range("2018-01-01 0:0", "2018-12-31 23:0", freq='H') | pandas.date_range |
"""
Functions for comparing and visualizing model performance. Most of these functions rely on ATOM's model tracker and
datastore services, which are not part of the standard AMPL installation, but a few functions will work on collections of
models saved as local files.
"""
import os
import sys
import pdb
import panda... | pd.DataFrame(np.nan, index=nai, columns=tempdf.columns) | pandas.DataFrame |
import argparse
import pandas as pd
import numpy as np
GENE = 'Hugo_Symbol'
PROTEIN = 'Protein_Change'
CHROMOSOME = 'Chromosome'
ALT = 'Alteration'
START_POSITION = 'Start_position'
END_POSITION = 'End_position'
REF_ALLELE = 'Reference_Allele'
ALT_ALLELE = 'Tumor_Seq_Allele2'
REF_COUNT = 't_ref_count'
ALT_COUNT = 't_a... | 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... | pd.Series([10, 9, 8]) | pandas.Series |
import json
import os
import re
from typing import Any, Dict, Optional
import pandas as pd
from pandas import DataFrame
import geopandas as gpd
from network_wrangler import ProjectCard
from network_wrangler import RoadwayNetwork
from .transit import CubeTransit, StandardTransit
from .logger import WranglerLogger
fro... | pd.concat([link_df, node_df], ignore_index=True, sort=False) | pandas.concat |
"""
Train for manipulate files only
"""
import argparse
import data_loader
import models
import numpy as np
import utils
from torch import nn
from torch.nn import CrossEntropyLoss
from torch.optim import SGD
from pathlib import Path
import transforms as albu_trans
from torchvision.transforms import ToTensor, Normaliz... | pd.DataFrame({'file_name': flickr_file_names}) | pandas.DataFrame |
import unittest
import backtest_pkg as bt
import pandas as pd
import numpy as np
from math import sqrt, log
from pandas.util.testing import assert_frame_equal
def cal_std(data):
if len(data)<=1:
return np.nan
data_mean = sum(data)/len(data)
data_var = sum((i-data_mean)**2 for i in data)/(len(data... | pd.DataFrame(1., index=[self.index[-1]], columns=self.ticker) | pandas.DataFrame |
import pandas as pd
import string
import matplotlib.pyplot as plt
import numpy as np
import scipy as sp
from scipy import signal
from scipy import constants
from scipy.integrate import cumtrapz
from numba import vectorize, jit
import os
import sys
import seaborn as sns
rc = {'legend.frameon': True, 'legend.... | pd.io.parsers.read_csv(file_path) | pandas.io.parsers.read_csv |
"""
This script calls the networkSimulator to create voxelwise synapse counts and
can be used as starting point to integrate existing inference algorithms.
"""
################################################################################
### LOAD LIBRARIES
###################################################... | pd.read_csv("synapses.csv") | pandas.read_csv |
"""
Tests that work on both the Python and C engines but do not have a
specific classification into the other test modules.
"""
import codecs
import csv
from datetime import datetime
from io import StringIO
import os
import platform
from tempfile import TemporaryFile
from urllib.error import URLError
impo... | CParserWrapper._set_noconvert_columns(self) | pandas.io.parsers.CParserWrapper._set_noconvert_columns |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for datetime64 and datetime64tz dtypes
from datetime import (
datetime,
time,
timedelta,
)
from itertools import (
product,
starmap,
)
import operator
import warnings
import numpy as np
impo... | date_range("20130101", periods=3, tz=tz_naive_fixture) | pandas.date_range |
# -*- coding: utf-8 -*-
"""
Reading data for WB, PRO,
for kennisimpulse project
to read data from province, water companies, and any other sources
Created on Sun Jul 26 21:55:57 2020
@author: <NAME>
"""
import pytest
import numpy as np
import pandas as pd
from pathlib import Path
import pickle as pckl
from hgc impor... | pd.ExcelWriter(r'C:\Users\beta6\Documents\Dropbox\008KWR\0081Projects\kennisimpulse'+r'/provincie_processed.xlsx') | pandas.ExcelWriter |
################################################################################################
# NOTE: I started this code to get better matching results than matching by address,
# but I never finished and thus this code hasn't actually been used yet.
#################################################################... | pd.isnull(ev['street_name']) | pandas.isnull |
'''
Scripts for loading various experimental datasets.
Created on Jul 6, 2017
@author: <NAME>
'''
import os
import pandas as pd
import numpy as np
from evaluation.experiment import data_root_dir
all_root_dir = data_root_dir#os.path.expanduser('~/data/bayesian_sequence_combination')
data_root_dir = os.path.join(all... | pd.concat([all_data, doc_data], axis=0) | pandas.concat |
"""Load a model and evaluate its performance against an unknown test set"""
import glob
import logging
import os
import re
import sqlite3
from pathlib import Path
import configargparse
import keras.models
import numpy as np
import pandas
from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics imp... | pandas.DataFrame(per_image_conf) | pandas.DataFrame |
#!/usr/bin/env python3
import argparse
import datetime
import concurrent
import concurrent.futures
import itertools
import logging
import os
import warnings
import rows.forecast.visit
import rows.forecast.cluster
import rows.forecast.forecast
import numpy
import pandas
import fbprophet
import fbprophet.plot
import... | pandas.DataFrame.from_records(records, columns=record_columns) | pandas.DataFrame.from_records |
import pandas as pd
import numpy as np
import h5py
import geopandas as gp
import os
import datetime
import dask
import dask.dataframe as dd
from tqdm import tqdm
def latlon_iter(latdf, londf, valdf, date):
out_df = pd.concat([latdf, londf, valdf], axis = 1, keys = ['lat', 'lon', 'sst']).stack().reset_index().drop(... | pd.DataFrame(ds['geophysical_data']['par']) | pandas.DataFrame |
from nltk.sentiment.vader import SentimentIntensityAnalyzer
from nltk import tokenize
import nltk
import pandas as pd
import json
from nltk.stem.snowball import SnowballStemmer
import itertools
from scipy.cluster.hierarchy import ward, dendrogram
import matplotlib.pyplot as plt
import random
from wordcloud import WordC... | pd.DataFrame([[sentence,article_pos, article_neg, article_neu, article_compound]], columns=('Sentance','Positive', 'Negative', 'Neutral', 'Compound')) | pandas.DataFrame |
import streamlit as st
import numpy as np
import pandas as pd
import altair as alt
from cohort import CohortTable
# Common functions
color = '#800000ff'
def bar_chart(df_melted, y_axis, title):
chart = alt.Chart(df_melted).mark_bar(color=color, size=40).encode(
x = alt.X('Year:Q', axis=alt.Axis(tickCount=f... | pd.DataFrame([hires_per_year], columns=columns_years, index=['No. Hires']) | pandas.DataFrame |
import argparse
import os
import seaborn as sns
import pandas as pd
import glob
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Agg')
import numpy as np
def plot_primary(args):
"""
Plots the jointplot of model performance (predicted v. true) on held out
test or validation sets from the pr... | pd.DataFrame() | pandas.DataFrame |
import os
import sys
import glob
import numpy as np
import pandas as pd
import shutil
import itertools
import random
import multiprocessing
from sklearn import metrics
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import ParameterGrid... | pd.DataFrame(grid_search.cv_results_) | pandas.DataFrame |
import pandas as pd
import datetime as dt
from typing import Dict
from typing import List
from src.typeDefs.pxiDamRecord import IPxiDamDataRecord
def getPxiDamData(targetFilePath: str) -> List[IPxiDamDataRecord]:
dataSheetDf = | pd.read_csv(targetFilePath) | pandas.read_csv |
__author__ = 'saeedamen' # <NAME>
#
# Copyright 2016-2020 Cuemacro - https://www.cuemacro.com / @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/LICENS... | pd.DataFrame(df_cuemacro_tot_1M[cross + '-forward-tot-1M-cuemacro.close']) | pandas.DataFrame |
import re
from copy import deepcopy
from contextlib import suppress
from collections.abc import Iterable
import numpy as np
import pandas as pd
__all__ = ['aes']
all_aesthetics = {
'alpha', 'angle', 'color', 'colour', 'fill', 'group', 'intercept',
'label', 'lineheight', 'linetype', 'lower', 'middle', 'radius... | pd.Series(y) | pandas.Series |
import datetime
import matplotlib
import numpy as np
import pandas as pd
import pytz
from finrl.config import config
from finrl.marketdata.utils import fetch_and_store, load
from finrl.preprocessing.preprocessors import FeatureEngineer
from finrl.preprocessing.data import calculate_split, data_split
from finrl.env.en... | pd.DataFrame(e_trade_gym.positions, columns=['date', 'cash'] + config.CRYPTO_TICKER) | pandas.DataFrame |
import os
import pandas as pd
import csv
from sklearn.model_selection import train_test_split
import numpy as np
import random
import tensorflow as tf
import torch
#directory of tasks dataset
os.chdir("original_data")
#destination path to create tsv files, dipends on data cutting
path_0 = "mttransformer/... | pd.DataFrame(columns=['id', 'misogynous', 'text']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# Measurements, COrrelation and sanitY of data
import argparse
import os
import sys
import csv
from typing import Callable, Any
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import keras
import tensorflow
from sklearn.model_selection import train_test_split
import ERI... | pd.DataFrame(tmp_mag) | pandas.DataFrame |
# 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... | ensure_index(index) | pandas.core.indexes.api.ensure_index |
from IMLearn.utils import split_train_test
from IMLearn.learners.regressors import LinearRegression
from typing import NoReturn
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import seaborn as sns
import plotly.io as pio
pio.templat... | pd.get_dummies(house_df, prefix="built_decade", columns=["built_decade"]) | pandas.get_dummies |
#%%[markdown]
## Carregar base de dados para o SQLite
#Definir localização da base de dados
#%%
path_to_database='data/raw/elo7_recruitment_dataset.csv'
#%%[markdown]
#Definir localização onde SQLite vai ser guardado, é recomendavel usar uma partição
#mapeada em RAM para aumentar a performance (exemplo /dev/shm)
#%%
p... | pd.read_sql_query("""SELECT price_group,
MIN(price),
MAX(price)
FROM query_elo7
GROUP BY price_group
ORDER BY MIN(price),MAX(price)
""",conn) | pandas.read_sql_query |
import sys
import argparse
import pandas as pd
import numpy as np
import pyfsdb
import dnssplitter
splitter = dnssplitter.DNSSplitter()
splitter.init_tree()
def get_psl(x):
noval = [np.NaN, np.NaN, np.NaN]
try:
ret = splitter.search_tree(x)
if not ret or len(ret) != 3:
return noval... | pd.DataFrame(listvals, index=dfn.index, columns=pslcols) | pandas.DataFrame |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 14 18:21:32 2020
@author: dhbubu18
"""
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
import os
plt.rcParams['savefig.dpi'] = 300
plt.rcParams['font.size'] = 16
climate = 'LA'
file1 = '{0}... | pd.concat([df_ts,df_2],axis=1) | pandas.concat |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import random
import unittest.mock as mock
from datetime import datetime, timedelta
from unittest import TestCase
import numpy as np
import... | pd.date_range(start="2018-02-05", freq="D", periods=500) | pandas.date_range |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib as mpl
import netCDF4 as nc
import datetime as dt
from salishsea_tools import evaltools as et, places, viz_tools, visualisations, geo_tools
import xarray as xr
import pandas as pd
import pickle
import os
import gsw
#... | pd.DataFrame(x) | pandas.DataFrame |
from datetime import datetime
import os
import re
import numpy as np
import pandas as pd
from fetcher.extras.common import MaRawData, zipContextManager
from fetcher.utils import Fields, extract_arcgis_attributes
NULL_DATE = datetime(2020, 1, 1)
DATE = Fields.DATE.name
TS = Fields.TIMESTAMP.name
DATE_USED = Fields.DA... | pd.to_datetime(df.index, errors='coerce') | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Importing modules
import os
import sys
import numpy as np
import pandas as pd
import tqdm
import gc
import csv
import argparse
import scipy
import scipy.stats
# This function is to read the file and to get the DataFrame with unweighted counts
# Input:
# - input_file... | pd.DataFrame() | pandas.DataFrame |
# coding=utf-8
import unittest
import numpy as np
import pandas as pd
from clustermatch.utils.data import merge_sources
from .utils import get_data_file
class ReadTomateTest(unittest.TestCase):
def test_merge_sources_using_ps(self):
## Preparar
data_file = get_data_file('ps_2011_2012.csv')
... | pd.isnull(procesado.loc['Glucoheptonic acid-1.4-lactone', '560']) | pandas.isnull |
'''Assignment 4 - Understanding and Predicting Property Maintenance Fines
This assignment is based on a data challenge from the Michigan Data Science Team (MDST).
The Michigan Data Science Team (MDST) and the Michigan Student Symposium for Interdisciplinary Statistical
Sciences (MSSISS) have partnered with the City of... | pd.get_dummies(train) | pandas.get_dummies |
'''
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN... | pd.read_csv(settings.PATH_LIVEDATA) | pandas.read_csv |
import pandas as pd
from dateutil.relativedelta import relativedelta
from datacode.typing import StrList
def expand_entity_date_selections(full_df: pd.DataFrame, selections_df: pd.DataFrame, cols: StrList = None,
num_firms: int = 3, expand_months: int = 3,
... | pd.DataFrame() | pandas.DataFrame |
import copy
import os
import warnings
from collections import OrderedDict
import numpy as np
import pandas as pd
import woodwork as ww
from sklearn.exceptions import NotFittedError
from sklearn.inspection import partial_dependence as sk_partial_dependence
from sklearn.inspection._partial_dependence import (
_grid_... | pd.DataFrame(label) | pandas.DataFrame |
import pandas as pd
### デスクトップアプリ作成課題
def kimetsu_search(path, word):
# 検索対象取得
df=pd.read_csv(path)
source=list(df["name"])
# 検索
if word in source:
return True
else:
return False
def add_to_kimetsu(path, word):
# 検索対象取得
df= | pd.read_csv("./source.csv") | pandas.read_csv |
# Import Statements
import matplotlib.pyplot as plt
import pandas as pd
import torch
import json
from PIL import Image
import numpy as np
from torch import nn
from torch import optim
from torchvision import datasets, transforms, models
from collections import OrderedDict
# Load Data Function
def LoadData(data_dir,... | pd.Series(data=probs, dtype='float64') | pandas.Series |
def censor_diagnosis(path,genotype_file,phenotype_file,final_pfile, final_gfile, field ='na',type='ICD',ad=1,start_time=float('nan'),end_time=float('nan')):
import pandas as pd
import numpy as np
genotypes = | pd.read_csv(path+genotype_file) | pandas.read_csv |
import matplotlib
import pandas as pd
import numpy as np
import cvxpy as cp
from cvxopt import matrix, solvers
import pickle
import matplotlib.pyplot as plt
import os
from tqdm import tqdm
from colorama import Fore
from config import RISK_FREE_RATE, DATAPATH, EXPECTED_RETURN, STOCKS_NUMBER, MONTO_CARLO_TIMES
... | pd.DataFrame(x_matrix.index, columns=['time']) | pandas.DataFrame |
##############################################################
# #
# <NAME> (2021) #
# Textmining medical notes for cognition #
# ParseDataset #
# ... | pd.DataFrame(items, index=[self.i]) | pandas.DataFrame |
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from builtins import object
from past.utils import old_div
import os
import numpy as np
import pandas as pd
from threeML.io.rich_display import display
from threeML.io.file_utils import sanitize_filename
from ... | pd.Series() | pandas.Series |
from functools import reduce
import numpy as np
import pandas as pd
import pyprind
from .enums import *
class Backtest:
"""Backtest runner class."""
def __init__(self, allocation, initial_capital=1_000_000, shares_per_contract=100):
assets = ('stocks', 'options', 'cash')
total_allocation = s... | pd.DataFrame.from_dict({'cost': total_costs, 'qty': qty, 'date': date}) | pandas.DataFrame.from_dict |
#TODO:
import tensorflow as tf
import os
import argparse
import sys
import random
import math
import logging
import operator
import itertools
import datetime
import numpy as np
import pandas as pd
from csv import reader
from random import randrange
FLAGS = None
#FORMAT = '%(asctime)s %(levelname)s %(message)s'
#lo... | pd.merge(largest, products, how="left", on="product_id") | pandas.merge |
# coding: utf-8
# We are going to try and predict the if a loan will be late or default using the below data. The do the preprocessing and to explore the data.
# ### Import Libraries
# In[ ]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
get_ipython().run_line_magi... | pd.reset_option('display.max_columns') | pandas.reset_option |
import pandas as pd
import pytest
import woodwork as ww
from pandas.testing import (
assert_frame_equal,
assert_index_equal,
assert_series_equal,
)
from evalml.pipelines.components import LabelEncoder
def test_label_encoder_init():
encoder = LabelEncoder()
assert encoder.parameters == {"positive_... | pd.Series([0, 1, 1, 0]) | pandas.Series |
"""
Fields
------
In this module the Fields are defined, which are the main containers for
the elements.
"""
from dataclasses import dataclass
from typing import Sequence, Tuple, Optional
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt, ticker
from matplotlib.axes import Axes
from matplotl... | pd.DataFrame(False, index=charges_strings, columns=charges_strings, dtype=bool) | pandas.DataFrame |
from collections import deque
from datetime import datetime
import operator
import numpy as np
import pytest
import pytz
import pandas as pd
import pandas._testing as tm
from pandas.tests.frame.common import _check_mixed_float, _check_mixed_int
# -------------------------------------------------------------------
# ... | pd.DataFrame({0: dti, 1: tdi}) | pandas.DataFrame |
import pandas as pd
import numpy as np
# from pandas.core.tools.datetimes import normalize_date
from pandas._libs import tslib
from backend.robinhood_api import RobinhoodAPI
class RobinhoodData:
"""
Wrapper to download orders and dividends from Robinhood accounts
Downloads two dataframes and saves to data... | pd.read_hdf('../data/data.h5', 'orders') | pandas.read_hdf |
from datetime import date, datetime, timedelta
from dateutil import tz
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Index, Series, Timestamp, date_range
import pandas._testing as tm
class TestDatetimeIndex:
def test_setitem_with_datetime_tz(self):
# 168... | pd.concat([df, df]) | pandas.concat |
import cPickle
from collections import defaultdict
from helpers import functions as helpers
from view import View
import pandas as pd
import copy
class Chain(defaultdict):
"""
Container class that holds ordered Link defintions and associated Views.
The Chain object is a subclassed dict of list ... | pd.concat(views_on_var, axis=0) | pandas.concat |
from decimal import *
from slugify import slugify # awesome-slugify, from requirements
import configuration # configuration.py, with user-defined variables.
from pandas import DataFrame, read_csv
from pandas import datetime as dt
import pandas as pd #this is how I usually import pandas
import sys #only needed to de... | pd.read_csv(filename, dtype='object', sep=',', encoding="utf8") | pandas.read_csv |
import string
import pandas as pd
import sqlite3
import re
from urllib.request import urlopen
from datetime import datetime
from bs4 import BeautifulSoup
from fundamentus import get_data
from tqdm import tqdm
from exception_util import exception, create_logger, retry
# Create instances of loggers
cvm_logger = create_... | pd.DataFrame(cvm_symbol, columns=['cvm_code', 'symbol', 'date']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
def plot_feature_diff(diff, xaxis_labels=None, title="", file_path_out=None):
data = {i+1: diff[:,i] for i in range(diff.shape[1])}
df = | pd.DataFrame(data) | pandas.DataFrame |
import re
import numpy as np
import pandas as pd
from typing import List, Tuple
def prepare_data(link: str) -> Tuple[pd.DataFrame, List[str]]:
""" Load and prepare/preprocess the data
Parameters:
-----------
link : str
Link to the dataset, which should be in excel and of the following format... | pd.read_excel(link) | pandas.read_excel |
# 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("2013-05-11 00:00:00") | pandas.Timestamp |
# Importing default django methods:
from django.shortcuts import render, redirect
from django.db.models import Count
from django.core.paginator import Paginator
from django.db.models.functions import TruncDay
from django.http import HttpResponseRedirect
# Importing plotly methods:
import plotly.graph_objs as go
from p... | pd.Series(data=0, index=heatmap_datetime_index) | pandas.Series |
import numpy as np
import random
import pandas as pd
from itertools import combinations
items_set = ['beer','burger','milk','onion','potato']
max_trn = 20
data=np.random.randint(2, size=(random.randint(1,max_trn),len(items_set)))
df = pd.DataFrame(data)
df.columns = items_set
print(df)
def candidat... | pd.DataFrame(candidate_set, columns=["Candidate", "Support","Support %"]) | pandas.DataFrame |
import pandas as pd
import numpy as np
import requests
from termcolor import colored as cl
from math import floor
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (20, 10)
plt.style.use('fivethirtyeight')
# EXTRACTING STOCK DATA
def get_historical_data(symbol, start_date):
api_key = 'YOUR API KEY... | pd.DataFrame(stoch_signal) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author:
<NAME>, PhD, MSB, BCBA-D
https://www.researchgate.net/profile/David_Cox26
twitter: @davidjcox_
LinkedIn: https://www.linkedin.com/in/coxdavidj/
Website: https://davidjcox.xyz
"""
#Set current working directory to the folder that contains y... | pd.concat([data, raw_sent_df, lemmed_sent_df], axis=1) | pandas.concat |
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource, Panel, Slider, RangeSlider, Span
from bokeh.models import HoverTool
from bokeh.layouts import column, row
import pandas as pd
import numpy as np
class Residual_model:
def __init__(self, data):
#self.source = ColumnDataSource(data=... | pd.Series(resids, index=res_indices) | pandas.Series |
# Core imports
import os
import time
from datetime import datetime
import random
# Third party imports
import geopandas as gpd
import pandas as pd
import yaml
#import gptables as gpt
# Module imports
import geospatial_mods as gs
import data_ingest as di
import data_transform as dt
import ftp_get_files_logic as fpts
... | pd.concat([non_disab_servd_df_out, disab_servd_df_out]) | pandas.concat |
import os
from common.score import scorePredict
import pandas as pd
import numpy as np
from sklearn.metrics import accuracy_score
from simpletransformers.classification.classification_model import ClassificationModel
def train_predict_model(df_train, df_test, is_predict, use_cuda):
labels_test = pd.Series(df_test... | pd.concat([text_a, text_b], axis=1) | pandas.concat |
# tests.test_features.test_jointplot
# Test the JointPlot Visualizer
#
# Author: <NAME>
# Created: Mon Apr 10 21:00:54 2017 -0400
#
# Copyright (C) 2017 The scikit-yb developers.
# For license information, see LICENSE.txt
#
# ID: test_jointplot.py [9e008b0] <EMAIL> $
"""
Test joint plot visualization methods.
Thes... | pd.Series(self.continuous.y) | pandas.Series |
import pandas as pd
# The ndarrays must all be the same length. If an index is passed, it must clearly also be
# the same length as the arrays. If no index is passed, the result will be range(n), where
# n is the array length.
data = {'Username': ['foo', 'bar', 'buz'],
'Email': ['<EMAIL>', '<EMAIL>', '<EMAIL>... | pd.DataFrame(data=data) | pandas.DataFrame |
#!/usr/bin/env python
# ----------------------------------------------------------------
# Copyright 2016 Cisco Systems
#
# 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.apac... | pd.isnull(row["csv-deviceIP"]) | pandas.isnull |
# coding: utf-8
# In[4]:
from math import sqrt
from numpy import concatenate
from matplotlib import pyplot
import pandas as pd
from datetime import datetime
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import mean_squared_error
from keras.models ... | pd.to_datetime(data['Date/time'][1]) | pandas.to_datetime |
"""
This file contains methods to visualize EKG data, clean EKG data and run EKG analyses.
Classes
-------
EKG
Notes
-----
All R peak detections should be manually inspected with EKG.plotpeaks method and
false detections manually removed with rm_peak method. After rpeak examination,
NaN data can be accounted for by ... | pd.DataFrame(self.rpeaks) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""Calculate the mobility demand.
SPDX-FileCopyrightText: 2016-2019 <NAME> <<EMAIL>>
SPDX-License-Identifier: MIT
"""
__copyright__ = "<NAME> <<EMAIL>>"
__license__ = "MIT"
import os
import pandas as pd
from collections import namedtuple
from reegis import geometries, config as cfg, tools,... | pd.read_excel(filename, sheet, skiprows=7, header=[0, 1]) | pandas.read_excel |
import logging
from abc import ABC, abstractmethod
import numpy as np
import pandas as pd
from hdrbp._util import (
basic_repr,
basic_str,
compute_correlation,
compute_diversification_ratio,
compute_drawdowns,
compute_gini,
compute_prices,
compute_risk_contributions,
compute_turnov... | pd.to_datetime(result["date"].values) | pandas.to_datetime |
from __future__ import annotations
from io import (
BytesIO,
StringIO,
)
import os
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas import (
NA,
DataFrame,
Index,
)
import pandas._testing as tm
import pandas.io.common as icom
from pandas.io.common import ge... | tm.ensure_clean("test.xml") | pandas._testing.ensure_clean |
"""
Prepare training and testing datasets as CSV dictionaries 2.0
Created on 04/26/2019; modified on 11/06/2019
@author: RH
"""
import os
import pandas as pd
import sklearn.utils as sku
import numpy as np
import re
# get all full paths of images
def image_ids_in(root_dir, ignore=['.DS_Store','dict.csv', 'all.csv'])... | pd.concat([validation_tiles, tile_ids]) | pandas.concat |
import keras.models
from keras.layers import Dense, Dropout, Activation, Input, Concatenate
import keras.backend as K
import numpy as np
import pandas as pd
from sklearn.model_selection import KFold
from sklearn.base import clone
from EmoMap.coling18.framework import util
import os
import math
class Model():
def __... | pd.DataFrame(labels_train) | pandas.DataFrame |
import os
import pandas as pd
from settings.config import DATASET_USAGE, K_FOLDS_VALUES, item_label, title_label, genre_label, algorithm_label, \
FAIRNESS_METRIC_LABEL, LAMBDA_LABEL, EVALUATION_METRIC_LABEL, EVALUATION_VALUE_LABEL, evaluation_label, \
LAMBDA_VALUE_LABEL, results_path, N_CORES
from conversion... | pd.DataFrame() | pandas.DataFrame |
from logic.helpers import *
import pandas as pd
from sklearn import svm, preprocessing
from scipy.stats import mode
from sklearn.model_selection import cross_validate
CLASS_LABEL = "class_label"
TIMESTAMP = "timestamp"
GROUP_INDEX = "group_index"
class ClassificationManager:
def __init__(self, windowSize=20):
... | pd.DataFrame() | pandas.DataFrame |
import argparse
import mplfinance as mpf
import numba as nb
import os
import pandas as pd
from pandas_datareader import data, wb
from pandas_datareader.nasdaq_trader import get_nasdaq_symbols
from pandas.tseries.holiday import USFederalHolidayCalendar
from pandas.tseries.frequencies import to_offset
import matplotlib.p... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env runaiida
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import print_function
import sys
import os
import shutil
import matplotlib
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from aiida.orm import load_node... | pd.DataFrame(story) | pandas.DataFrame |
#
# Data for analyzing causality.
# By <NAME>
#
# Classes:
# ccm
# embed
#
# Paper:
# Detecting Causality in Complex Ecosystems
# Ge<NAME> et al. 2012
#
# Thanks to <NAME> and <NAME>
#
# Notes:
# Originally I thought this can be made way faster by only calculting the
# distances once and then chopping it to a specif... | pd.DataFrame(mi,columns=cols) | pandas.DataFrame |
import datetime as dt
import os
import pickle
from typing import Dict, List
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras import activations
from dl_portfolio.logger import LOGGER
from dl_portfolio.data import get_features
from dl_portfolio.pca_ae import build_model
from dl_port... | pd.DataFrame(pred, columns=assets, index=index) | pandas.DataFrame |
"""
July 2021
This code retrieves the calculation of sand use for concrete and glass production in the building sector in 26 global regions. For the original code & latest updates, see: https://github.com/
The dynamic material model is based on the BUMA model developed by <NAME>, Leiden University, the Netherlan... | pd.DataFrame(housing_type_rur3.iloc[1].values*people_rur.values, columns=people_rur.columns, index=people_rur.index) | pandas.DataFrame |
import pytest
import jax.numpy as np
import pandas as pd
from pzflow import Flow
from pzflow.bijectors import Chain, Reverse, Scale
from pzflow.distributions import *
@pytest.mark.parametrize(
"data_columns,bijector,info,file",
[
(None, None, None, None),
(("x", "y"), None, None, None),
... | pd.DataFrame(xarray, columns=columns) | pandas.DataFrame |
import json
import os
import random
from random import sample
import numpy as np
import numpy.random
import re
from collections import Counter
import inspect
import pandas as pd
import matplotlib.pyplot as plt
import requests
from IPython.display import HTML
import seaborn as sns
import networkx as nx
from pylab impor... | pd.DataFrame(something) | pandas.DataFrame |
import pandas as pd
import scipy.stats
import numpy as np
import datetime
pd.set_option('display.width', 1000)
pd.set_option('max.columns', 100)
class HistoricGames(object):
def __init__(self, league, season, bookmaker='BbAv'):
"""
:param league: The league for which historical games should be re... | pd.read_csv(url) | pandas.read_csv |
import numpy as np
from numpy.linalg import inv
import matplotlib.pyplot as graph #matlab versiyasi pythonun
from mpl_toolkits.mplot3d import Axes3D
import pandas as pd #csv faylini read etmek ucun
import csv
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
... | pd.read_csv("turboazmodified.csv") | pandas.read_csv |
import rba
import copy
import pandas
import time
import numpy
import seaborn
import matplotlib.pyplot as plt
from .rba_Session import RBA_Session
from sklearn.linear_model import LinearRegression
# import matplotlib.pyplot as plt
def find_ribosomal_proteins(rba_session, model_processes=['TranslationC', 'TranslationM... | pandas.concat(annotations_list, axis=0) | pandas.concat |
""" Official evaluation script for v1.0 of the ComplexWebQuestions dataset. """
import unicodedata
import re
import json
import pandas as pd
def proprocess(answer):
proc_answer = unicodedata.normalize('NFKD', answer).encode('ascii', 'ignore').decode(encoding='UTF-8')
# removing common endings such as "f.c."
... | pd.Series(spans) | pandas.Series |
# -*- coding: utf-8 -*-
"""
These test the private routines in types/cast.py
"""
import pytest
from datetime import datetime, timedelta, date
import numpy as np
import pandas as pd
from pandas import (Timedelta, Timestamp, DatetimeIndex,
DataFrame, NaT, Period, Series)
from pandas.core.dtypes.c... | Period('2011-01-01', freq=freq) | pandas.Period |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from catboost import CatBoostRegressor
from tqdm import tqdm
import gc
import datetime as dt
print('Loading Properties ...')
properties2016 = pd.read_csv('../input/properties_2016.csv', low_memory = False)
proper... | pd.read_csv('../input/train_2016_v2.csv', parse_dates=['transactiondate'], low_memory=False) | pandas.read_csv |
"""
Extract sampled paramaters of selected traces and prepare simulation input files with fitted parameters
Outputs:
- 2 csvs with fitting paramerers for a) single best fit and b) n best fits
- 2 csv with samples parameters that can be used as input csv for subsequent simulation (for a and b as above)
- 1 emodl with fi... | pd.merge(how='left', left=rank_export_df[['scen_num','norm_rank']], left_on=['scen_num'], right=df_samples_sub, right_on=['scen_num']) | pandas.merge |
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