prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import logging
import os
import sys
import pandas as pd
import pytest
import handy as hd
log: logging.Logger
@pytest.fixture
def setup_logging():
logging.basicConfig(level=logging.INFO, stream=sys.stdout)
global log
log = logging.getLogger('handy test')
log.setLevel(logging.INFO)
... | pd.to_datetime(days[2]) | pandas.to_datetime |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from . import _unittest as unittest
try:
import pandas
except ImportError:
pandas = None
from datatest._compatibility.collections.abc import Iterator
from datatest._utils import IterItems
from datatest._vendor.repeatingcontainer import RepeatingContainer
class T... | pandas.testing.assert_frame_equal(df2, expected) | pandas.testing.assert_frame_equal |
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output, State
import plotly.graph_objs as go
import plotly.express as px
import numpy as np
import pandas as pd
# adding an CSS stylesheet
... | pd.read_csv('restaurants_zomato.csv', encoding='ISO-8859-1') | pandas.read_csv |
from flask import Flask, render_template,request, url_for, redirect
import plotly
import plotly.graph_objs as go
import pandas as pd
import numpy as np
import json
import functions
with open('data/users.json', 'r', errors='ignore') as f:
data = json.load(f)
users = pd.DataFrame(data)
with open('data/problems.j... | pd.DataFrame(data) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_series_equal
from src.policies.single_policy_functions import _interpolate_activity_level
from src.policies.single_policy_functions import reduce_recurrent_model
from src.policies.single_policy_functions import reduce_work_model
fro... | pd.Series(0, index=["a", "b", "c"]) | pandas.Series |
import copy
import csv
import gzip
import logging
import os
import re
import subprocess
import tempfile
from collections import defaultdict
from multiprocessing import Pool
from pathlib import Path
import numpy as np
import pandas as pd
import tqdm
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord impor... | pd.isnull(item) | pandas.isnull |
# coding: utf-8
# ### Import
# In[1]:
from bs4 import BeautifulSoup
import requests
import numpy as np
import pandas as pd
import xgboost
import xgboost as xgb
from xgboost.sklearn import XGBClassifier
from sklearn.metrics import *
from IPython.core.display import Image
from sklearn.datasets import make_classifi... | pd.concat(holiday_ls) | pandas.concat |
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
plt.rcParams['font.size'] = 6
import os
root_path = os.path.dirname(os.path.abspath('__file__'))
graphs_path = root_path+'/boundary_effect/graph/'
if not os.path.exists(graphs_path):
os.makedirs(graphs_path)
time = pd.read_csv(root_p... | pd.read_csv(root_path+"/Huaxian_eemd/data/EEMD_TRAIN.csv") | pandas.read_csv |
# coding: utf-8
# # Dataset Statistics for Compound Gene Sentences
# This notebook is designed to show statistics on the data extracted from pubmed. The following cells below here are needed to set up the environment.
# In[1]:
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('au... | pd.read_csv("../datafile/results/compound_binds_gene.tsv.xz") | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 4 10:30:55 2018
@author: niels-peter
"""
import numpy as np
from numpy import math
import pandas as pd
import pickle
from sklearn.externals import joblib
import re
from italy_transformation import *
clf_EW_italy = joblib.load('/home/niels-pete... | pd.ExcelFile('/home/niels-peter/Dokumenter/ITALY_100_FINANCIAL_STATEMENT.xlsx') | pandas.ExcelFile |
#!/usr/bin/env python
# coding: utf-8
# # <font color='yellow'>How can we predict not just the hourly PM2.5 concentration at the site of one EPA sensor, but predict the hourly PM2.5 concentration anywhere?</font>
#
# Here, you build a new model for any given hour on any given day. This will leverage readings across a... | pd.read_csv("NOAA_Data_MultiPointModel.csv") | pandas.read_csv |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.6.0
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# ## Telecom Churn Case Study
#... | pd.DataFrame( columns = ['prob','accuracy','sensi','speci']) | pandas.DataFrame |
"""Prediction result visualization"""
import pandas as pd
import matplotlib.pyplot as plt
def visualize(result, y_test, num_test, rmse):
"""
:param result: RUL prediction results
:param y_test: true RUL of testing set
:param num_test: number of samples
:param rmse: RMSE of prediction results
... | pd.DataFrame(result) | pandas.DataFrame |
import imagehash
import pandas as pd
import numpy as np
import os
import sys
import math
hashesPath = sys.argv[1]
outPath = sys.argv[2]
df1 = | pd.read_csv(hashesPath) | pandas.read_csv |
from __future__ import annotations
import os
import numpy as np
import pandas as pd
import scipy.optimize as so
import scipy.special as sp
from pymwm.utils.cutoff_utils import f_fp_fpp_cython
class Cutoff:
"""A callable class that calculates the values of u at cutoff frequencies for coaxial waveguides made of ... | pd.DataFrame() | pandas.DataFrame |
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score
import random
import numpy as np
import hydra
from omegaconf import DictConfig
from pytorch_lightning import (
LightningDataModule,
Trainer,
seed_everyth... | pd.Series(y, name="target") | pandas.Series |
import numpy as np
import pandas as pd
import scanpy as sc
from scipy import sparse
from sklearn.linear_model import LinearRegression
from ..utils import check_adata, check_batch
def pcr_comparison(
adata_pre,
adata_post,
covariate,
embed=None,
n_comps=50,
scale=True,
... | pd.get_dummies(covariate) | pandas.get_dummies |
import glob
import os
import sys
import copy
from joblib import Parallel, delayed
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pyabf
from ipfx import feature_extractor
from ipfx import subthresh_features as subt
print("feature extractor loaded")
from .abf_ipfx_dataframes import _build... | pd.DataFrame() | pandas.DataFrame |
"""Contains methods and classes to collect data from
tushare API
"""
import pandas as pd
import tushare as ts
from tqdm import tqdm
class TushareDownloader :
"""Provides methods for retrieving daily stock data from
tushare API
Attributes
----------
start_date : str
start date of th... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
import os
from os import listdir
from os.path import isfile, join
from datetime import datetime
import logging
logger = logging.getLogger(__name__)
def load_csvs(paths):
"""Creates a dataframe dictionary from the csv files in /data : dict_df
Arguments
---------
... | pd.to_datetime(capacityfactor_windcop['date'], errors='coerce', format='%Y/%m/%d %H:%M') | pandas.to_datetime |
import re
import string
from math import ceil
from operator import itemgetter
from random import randrange
import lime
import lime.lime_tabular
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import shap
from gensim.corpora import Dictionary
from gensim.models import CoherenceModel, nmf
from skl... | pd.DataFrame(X_test.iloc[user_idx]) | pandas.DataFrame |
import pandas as pd
import numpy as np
from pandas.tseries.offsets import *
import scipy.optimize as opt
import scipy.cluster.hierarchy as sch
from scipy import stats
class FHBacktestAncilliaryFunctions(object):
"""
This class contains a set of ancilliary supporting functions for performing backtests.
The... | pd.DataFrame(index=self.ts.index,columns=self.ts.columns,data=0) | pandas.DataFrame |
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from nntransfer.analysis.results.base import Analyzer
from nntransfer.analysis.plot import plot
class BiasTransferAnalyzer(Analyzer):
def generate_table(
self,
objective=("Test", "img_classification", "accuracy"),
l... | pd.DataFrame(row_list) | pandas.DataFrame |
import json
import os
from collections import defaultdict
from typing import Dict
import numpy as np
import pandas as pd
from scipy.optimize import linear_sum_assignment
from scipy.spatial import KDTree, distance_matrix
from .constants import PIX_TO_M, MAX_OBJECT_LENGTH_M
__all__ = ["drop_low_confidence_preds", "off... | pd.DataFrame() | pandas.DataFrame |
"""
Functions useful in finance related applications
"""
import numpy as np
import pandas as pd
import datetime
import dateutil.relativedelta as relativedelta
def project_to_first(dt):
return datetime.datetime(dt.year, dt.month, 1)
def multiple_returns_from_levels_vec(df_in, period=1):
df_out = df = (df... | pd.DataFrame({out_name: l_monthly_returns}, index=l_dates) | pandas.DataFrame |
#!/usr/bin/env python3
"""
Tests the integration between:
- grand_trade_auto.model.model_meta
- grand_trade_auto.orm.orm_meta
While unit tests already test this integration to some degree, this is to a more
exhaustive degree. Those unit tests are mostly using integrative approaches due
to minimizing mock complexity a... | pd.DataFrame(results) | pandas.DataFrame |
# Import standard python libraries.
import pandas as pd
import numpy as np
import pathlib
import warnings
import sys
# Import the functions used throughout this project from the function dictionary library file
fileDir = pathlib.Path(__file__).parents[2]
code_library_folder = fileDir / 'Code' / 'function_dictionary_li... | pd.merge(COALQUAL, Mining_Volume, on='County_Name_State') | pandas.merge |
from os.path import join
import numpy as np
import streamlit as st
import pandas as pd
import datetime
import plotly.express as px
import plotly.graph_objects as go
import requests
from streamlit import caching
st.set_page_config(page_title="Covid Dashboard", page_icon="🕸", layout='wide', initial_sidebar_state='exp... | pd.read_csv(url) | pandas.read_csv |
# %% [markdown]
# This notebook is a VSCode notebook version of:
# https://www.kaggle.com/georsara1/lightgbm-all-tables-included-0-778
#
# You could find the data from:
# https://www.kaggle.com/c/home-credit-default-risk/data
## All the data files should be in the same directory with this file!
#%% Importing necessar... | pd.read_csv('application_train.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 7 09:40:49 2018
@author: yuwei
"""
import pandas as pd
import numpy as np
import math
import random
import time
import scipy as sp
import xgboost as xgb
def loadData():
"下载数据"
trainSet = pd.read_table('round1_ijcai_18_train_20180301.txt',sep=' ')
testSet ... | pd.merge(data,user_user_occupation,on=['user_id','user_occupation_id'],how='left') | pandas.merge |
import pandas as pd
import numpy as np
import itertools
import warnings
import scipy.cluster.hierarchy as sch
from scipy.spatial import distance
from joblib import Parallel, delayed
__all__ = ['hcluster_tally',
'neighborhood_tally',
'running_neighborhood_tally',
'any_cluster_tally']
"""TO... | pd.DataFrame(res) | pandas.DataFrame |
# 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... | tm.assert_equal(result, expected) | pandas._testing.assert_equal |
import operator
import warnings
import numpy as np
import pandas as pd
from pandas import DataFrame, Series, Timestamp, date_range, to_timedelta
import pandas._testing as tm
from pandas.core.algorithms import checked_add_with_arr
from .pandas_vb_common import numeric_dtypes
try:
import pandas.core.computation.e... | pd.offsets.Day() | pandas.offsets.Day |
# how to run locally
# python text_loc_data.py $(cat ../../debugcommand.txt)
import base64
import io
import json
import math
import os
import re
import sys
import warnings
from urllib.parse import quote
import cv2
import numpy as np
import pandas as pd
from deskew import determine_skew
from google.cloud import vision
f... | pd.DataFrame(merged_dicts_w) | pandas.DataFrame |
from datetime import datetime, timedelta
from io import StringIO
import re
import sys
import numpy as np
import pytest
from pandas._libs.tslib import iNaT
from pandas.compat import PYPY
from pandas.compat.numpy import np_array_datetime64_compat
from pandas.core.dtypes.common import (
is_datetime64_dtype,
is_... | Timestamp("2011-01-02", tz="US/Eastern") | pandas.Timestamp |
# -*- coding: utf-8 -*-
from __future__ import division
from functools import wraps
import numpy as np
from pandas import DataFrame, Series
#from pandas.stats import moments
import pandas as pd
def simple_moving_average(prices, period=26):
"""
:param df: pandas dataframe object
:param period: periods fo... | pd.rolling_sum(bp, n1) | pandas.rolling_sum |
# Boston housing demo
import superimport
import numpy as np
import matplotlib.pyplot as plt
import os
figdir = "../figures"
def save_fig(fname): plt.savefig(os.path.join(figdir, fname))
import pandas as pd
import sklearn.datasets
import sklearn.linear_model as lm
from sklearn.model_selection import train_test_split... | pd.DataFrame(X) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from mabwiser.mab import MAB, LearningPolicy, NeighborhoodPolicy
from tests.test_base import BaseTest
class MABTest(BaseTest):
#################################################
# Test context fr... | pd.Series([0, 0, 0, 0, 0, 0, 1, 1, 1]) | pandas.Series |
# This code extract the features from the raw joined dataset (data.csv)
# and save it in the LibSVM format.
# Usage: python construct_features.py
import pandas as pd
import numpy as np
from sklearn.datasets import dump_svmlight_file
df = pd.read_csv("data.csv", low_memory=False)
# NPU
NPU = df.NPU.copy()
NPU[NPU ==... | pd.concat([yearbuilt, yearbuilt_zero], axis=1) | pandas.concat |
import numpy as np
import pandas as pd
from numba import njit, typeof
from numba.typed import List
from datetime import datetime, timedelta
import pytest
from copy import deepcopy
import vectorbt as vbt
from vectorbt.portfolio.enums import *
from vectorbt.generic.enums import drawdown_dt
from vectorbt.utils.random_ im... | pd.Timestamp('2020-01-05 00:00:00') | pandas.Timestamp |
import pandas as pd
all_genes = pd.read_csv("https://raw.githubusercontent.com/s-a-nersisyan/HSE_bioinformatics_2021/master/seminar13/all_genes.txt", header=None)[0]
df = | pd.DataFrame(index=all_genes) | pandas.DataFrame |
import warnings
warnings.simplefilter(action='ignore', category=Warning)
from IMLearn import BaseEstimator
from challenge.agoda_cancellation_estimator import AgodaCancellationEstimator
import numpy as np
import pandas as pd
from sklearn import metrics
from sklearn.preprocessing import MinMaxScaler
# conversions from ... | pd.to_datetime(df['checkin_date']) | pandas.to_datetime |
from read_data import read_data
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.patches as ptc
import matplotlib.dates as mdt
import datetime as dt
import numpy as np
import math
#This visualization shows time series of raw volume recorded in each time step
# as well as a color coded rectangle to... | pd.DataFrame(index=tdc[0], columns=['vol']) | pandas.DataFrame |
'''Train CIFAR10 with PyTorch.'''
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import argparse
from tqdm import trange
import pandas as pd
from PIL import Ima... | pd.read_csv(anno_path) | pandas.read_csv |
import unittest
import numpy as np
import pandas as pd
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
import matplotlib as mpl
import os
from openiec.property.coherentenergy_OC import CoherentGibbsEnergy_OC
from openiec.calculate.calcsigma_OC import SigmaCoherent_OC
from pyOC import opencalphad as... | pd.DataFrame(columns=['X_U', 'n_phase1', 'n_phase2', 'mu_U', 'mu_O']) | pandas.DataFrame |
import pandas as pd
from scripts.python.routines.manifest import get_manifest
from tqdm import tqdm
from scripts.python.EWAS.routines.correction import correct_pvalues
import plotly.graph_objects as go
from scripts.python.routines.plot.save import save_figure
from scripts.python.routines.plot.scatter import add_scatter... | pd.read_pickle(f"{path}/{platform}/{dataset}/pheno_xtd.pkl") | pandas.read_pickle |
import numpy as np
import pandas as pd
import pytest
import woodwork as ww
from pandas.testing import assert_frame_equal
from woodwork.logical_types import (
Boolean,
Categorical,
Double,
Integer,
NaturalLanguage
)
from evalml.pipelines.components import Imputer
@pytest.fixture
def imputer_test_d... | pd.Series([0, 0, 1, 0, 1]) | pandas.Series |
#!/usr/bin/env python
# Copyright 2021 Owkin, 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... | pd.DataFrame() | pandas.DataFrame |
import os, sys
import shutil
from pathlib import Path
import pandas as pd
import urllib
import configparser
try:
from bing import Bing
except ImportError: # Python 3
from .bing import Bing
def download(query, limit=100, output_dir='dataset', adult_filter_off=False,
force_replace=False, timeout=60, verbose... | pd.read_excel(task_filename) | pandas.read_excel |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from mabwiser.mab import MAB, LearningPolicy, NeighborhoodPolicy
from tests.test_base import BaseTest
class MABTest(BaseTest):
#################################################
# Test context fr... | pd.Series([0, 1, 1, 0, 0, 0, 0, 1, 1, 1]) | pandas.Series |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.metrics import precision_score, recall_score, f1_score
from sklearn.metrics import roc_curve, ... | pd.DataFrame(df_test['y']) | pandas.DataFrame |
# coding: utf-8
# In[ ]:
from __future__ import division
import os as os
from IPython.display import HTML
import pandas as pd
import numpy as np
import os as os
from matplotlib import pyplot as plt
import seaborn as sns
from numpy import random as random
from matplotlib.colors import ListedColormap
plt.rcParams... | pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used']) | pandas.Series |
import sys
from datetime import datetime
import statistics
import pandas
from scipy.stats import linregress
import src.point as point
def main():
if len(sys.argv) not in (3, 4):
exit("Invalid number of arguments. Input and output .csv files' names required, may be followed by cities csv")
input_fil... | pandas.Series(regression, index=["Regression"]) | pandas.Series |
import numpy as np
import re
from enum import Enum
import pandas as pd
ORIGINAL_DATA_DIR = "./original_transactions/"
CLEAN_DATA_DIR = "./clean_transactions/"
BOA_COLS = ["Posted Date", "Payee", "Amount"]
CHASE_COLS = ["Transaction Date", "Description", "Amount"]
CITI_COLS = ["Date", "Description", "Amount"]
GENERIC_... | pd.isnull(row.Debit) | pandas.isnull |
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Series, concat
from pandas.core.base import DataError
from pandas.util import testing as tm
def test_rank_apply():
lev1 = tm.rands_array(10, 100)
lev2 = tm.rands_array(10, 130)
lab1 = np.random.randint(0, 100, size=500)
... | pd.Timestamp("2018-01-08") | pandas.Timestamp |
import sys
import os
import math
import datetime
import itertools
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from statsmodels.tsa.stattools import grangercausalitytests
import scipy.stats as stats
from mesa.batchrunner import BatchRunner, BatchRunnerMP
from mesa.datacol... | pd.set_option('display.max_rows', 500) | pandas.set_option |
import os
from hilbertcurve.hilbertcurve import HilbertCurve
from pyqtree import Index
import pickle
import sys
import math
import json
import pandas
from epivizfileserver.parser import BigWig
import struct
class QuadTreeManager(object):
def __init__(self, genome, max_items = 128, base_path = os.getcwd()):
... | pandas.DataFrame(matches, columns=["start", "end", "offset", "size", "fileid"]) | pandas.DataFrame |
from snsql import *
import pandas as pd
import numpy as np
privacy = Privacy(epsilon=3.0, delta=0.1)
class TestPreAggregatedSuccess:
# Test input checks for pre_aggregated
def test_list_success(self, test_databases):
# pass in properly formatted list
pre_aggregated = [
('keycount'... | pd.DataFrame(data=pre_aggregated[1:], index=None) | pandas.DataFrame |
import datasets
import training
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
import time
from itertools import combinations
def run_linear_regression_model(train_query_x, test_query_x... | pd.DataFrame.to_numpy(real_query_attributes[['query_ratio', 'mean_accuracy']]) | pandas.DataFrame.to_numpy |
import numpy as np
import pandas as pd
from classifier2 import what1, what
import matplotlib.pyplot as plt
"""
dataframe that provides very good info about individual classes!
"""
#v = what['value(in%)'].cumsum()
what2 = what1.join(what['value(in%)'])
what3 = what2.sort_values(by='value(in%)',ascending= False)
what... | pd.DataFrame(classAA) | pandas.DataFrame |
import argparse
import os
import warnings
import boto3, time, json, warnings, os, re
import urllib.request
from datetime import date, timedelta
import numpy as np
import pandas as pd
import geopandas as gpd
from multiprocessing import Pool
# the train test split date is used to split each time series into train and t... | pd.to_datetime(max_time) | pandas.to_datetime |
import pytest
from mapping import mappings
from pandas.util.testing import assert_frame_equal, assert_series_equal
import pandas as pd
from pandas import Timestamp as TS
import numpy as np
from pandas.tseries.offsets import BDay
@pytest.fixture
def dates():
return pd.Series(
[TS('2016-10-20'), TS('2016-11... | pd.Series([10, 20, 11], index=["CLX16", "CLZ16", "CLF17"]) | pandas.Series |
import time
import copy
from lxml import html
import numpy as np
import pandas as pd
import requests
from bs4 import BeautifulSoup
from datetime import datetime
from selenium import webdriver
def get_benzinga_data(stock, days_to_look_back):
ffox_options = webdriver.FirefoxOptions()
minimum_date = pd.Timestamp(dateti... | pd.Timedelta(timeperiod) | pandas.Timedelta |
"""
This module merges temperature, humidity, and influenza data together
"""
import pandas as pd
import ast
__author__ = '<NAME>'
__license__ = 'MIT'
__status__ = 'release'
__url__ = 'https://github.com/caominhduy/TH-Flu-Modulation'
__version__ = '1.0.0'
def merge_flu(path='data/epidemiology/processed_CDC_2008_2021... | pd.DataFrame(frames) | pandas.DataFrame |
#!/usr/bin/python2
from __future__ import nested_scopes, generators, division, absolute_import, with_statement, print_function, unicode_literals
import pandas as pd
from sklearn.model_selection import train_test_split
RANDOM_STATE = 3
all_cols = 'linenum text id subreddit meta time author ups downs authorlinkkarma au... | pd.concat(training) | pandas.concat |
from typing import Dict, List, Tuple, Union
import geopandas
import numpy as np
import pandas as pd
from .matches import iter_matches
from .static import ADMINISTRATIVE_DIVISIONS, POSTCODE_MUNICIPALITY_LOOKUP
from .static import df as STATIC_DF
INDEX_COLS = ["municipality", "postcode", "street_nominative", "house_nr... | pd.DataFrame(vecs, columns=self.df.index.names) | pandas.DataFrame |
#python3 LED_inference.py arg1 where arg1 is the dataset -> transformers or 'others'
#if the dataset is 'others' it must exist a csv on 'datasets' folder with the name introduced
from datasets import Dataset
import pandas as pd
import torch
import os
import csv
import sys
import gcc
from transformers import LEDForCo... | pd.DataFrame(text_column, columns=["Text"]) | pandas.DataFrame |
import matplotlib
#matplotlib.use('TkAgg')
from config import *
from plot_utils import *
from shared_utils import *
import pickle as pkl
import numpy as np
from collections import OrderedDict
from matplotlib import pyplot as plt
from pymc3.stats import quantiles
import os
import pandas as pd
from pathlib import Path
#... | pd.Timedelta(days=5) | pandas.Timedelta |
# -*- coding: utf-8 -*-
"""
Created on Sun May 8 18:29:53 2016
@author: bmanubay
"""
# Check what moelcules we have appear in Chris's list
import pandas as pd
# read in ; delimited csv of comp/mix counts created in thermomlcnts.py
a0 = pd.read_csv("/home/bmanubay/.thermoml/tables/Ken/allcomp_counts_all.csv", sep='... | pd.read_csv("/home/bmanubay/.thermoml/tables/Ken/mix_counts_all.csv", sep=';') | pandas.read_csv |
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2022, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | pd.Index(['id.1', 'id.2'], name='sample_name') | pandas.Index |
#!/usr/bin/env python
# coding: utf-8
'''
'''
import time
import pandas as pd
import datarobot as dr
from datarobot.models.modeljob import wait_for_async_model_creation
import numpy as np
import re
import os
from datarobot.errors import JobAlreadyRequested
token_id = ""
ts_setting = {"project_name":"fake_job_postin... | pd.DataFrame() | pandas.DataFrame |
"""
Test model.py module.
"""
import numpy as np
import pandas as pd
import pytest
from src import model
@pytest.fixture
def dummy_df():
"""Example data to test modeling utility functions"""
dummy_df = pd.DataFrame(data={"col1": list(range(100)), "target": list(range(100))})
return dummy_df
def test_pa... | pd.DataFrame(data=[sample_data], columns=colnames) | pandas.DataFrame |
'''
Created on April 15, 2012
Last update on July 18, 2015
@author: <NAME>
@author: <NAME>
@author: <NAME>
'''
import pandas as pd
class Columns(object):
OPEN='Open'
HIGH='High'
LOW='Low'
CLOSE='Close'
VOLUME='Volume'
# def get(df, col):
# return(df[col])
# df['Close'] =... | pd.ewma(EMA1, span=s, min_periods=s - 1) | pandas.ewma |
import matplotlib.pyplot as plt
import pandas as pd
from oneibl.one import ONE
from ibllib.time import isostr2date
# import sys
# sys.path.extend('/home/owinter/PycharmProjects/WGs/BehaviourAnaysis/python')
from load_mouse_data import get_behavior
from behavior_plots import plot_psychometric
one = ONE()
# https://al... | pd.DataFrame(subject_details['weighings']) | pandas.DataFrame |
import pandas as pd
import requests
# class name，必須跟檔案名一致，例如 class demo，檔名也是 demo.py
class demo:
def __init__(self,
stock_price,
**kwargs, ):
# -------------------------------------------------------------------
# 此區塊請勿更動
stock_price = stock_price.sort_val... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import matplotlib.pyplot as plt
import sys
import os
from scipy.signal import find_peaks
from scipy.signal import butter, lfilter, freqz
import matplotlib.pyplot as plt
from get_peaks import load_dat_file
def get_mcell_observables_counts(dir):
counts = {}
seed_dirs = os.listdir(dir)
... | pd.DataFrame() | pandas.DataFrame |
# Copyright (c) 2020 Huawei Technologies Co., Ltd.
# <EMAIL>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | pd.DataFrame(columns=setting.month_column_name) | pandas.DataFrame |
from typing import Tuple
import numpy as np
import pandas as pd
class DecisionStump:
def __init__(self, epsilon: float = 1e-6):
r"""A depth-1 decision tree classifier
Args:
epsilon: float
To classify all the points in the training set as +1,
the model ... | pd.Series(err, name=f"{feature}-inverse") | pandas.Series |
#%%
# CARGO LOS DATASETS
import pandas as pd
import numpy as np
from shapely.geometry import Point
import shapely as shp
import geopandas as gpd
from geopandas.array import points_from_xy
path = "merged1_listas.pkl"
df_merge1 = pd.read_pickle(path)
df_merge1.reset_index(inplace=True)
#%%
#region PART 1... | pd.read_csv('/home/ingrid/Documents/labodatos/TP_final/df_principal/romi_completos.csv') | pandas.read_csv |
import psycopg2
import pandas as pd
import db.db_access as access
CONST_SQL_GET_TWITTER_DET = 'SELECT id, main_company_id, twitter_keyword, twitter_cashtag, twitter_url, is_parent_company FROM company'
CONST_SQL_GET_MAIN_COMPANY = 'SELECT * FROM maincompany'
CONST_SQL_GET_COMPANY_DETAIL = 'SELECT * FROM compan... | pd.DataFrame.from_records(result, columns=[x[0] for x in cur.description]) | pandas.DataFrame.from_records |
# -*- 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... | tm.assert_produces_warning(FutureWarning) | pandas.util.testing.assert_produces_warning |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
#
# Laps.py
# Interact with the RaceMonitor lap timing system
# TODO:
# When in live race mode timestamps are tagging with an offset different than the historical view.
# If this time offset can be adjusted it would be preferable to store the data in live view format over... | pandas.set_option("display.max_rows", 1024) | pandas.set_option |
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('2019-08-05 00:00:00') | pandas.Timestamp |
import numpy as np
import pandas as pd
#import scipy.stats as stats
import matplotlib.pyplot as plt
#factores (constantes) para grupos de 4 observaciones
factorA2 = 0.729
factorD4 = 2.282
factorD3 = 0
#6 observaciones, 24 instancias
arrayDatos = np.array([[1010,991,985,986],
[995,996,1009,1001],
[990,1003,994,9... | pd.DataFrame(arrayDatos) | pandas.DataFrame |
# Zip lists: zipped_lists
zipped_lists = zip(feature_names, row_vals)
# Create a dictionary: rs_dict
rs_dict = dict(zipped_lists)
# Print the dictionary
print(rs_dict)
# Define lists2dict()
def lists2dict(list1, list2):
"""Return a dictionary where list1 provides
the keys and list2 provides the values."""
... | pd.read_csv('ind_pop_data.csv', chunksize=1000) | pandas.read_csv |
#!/usr/bin/env python
import argparse
import glob
import os
from abc import abstractmethod, ABC
from collections import defaultdict
import logging
import numpy as np
import pandas as pd
from sklearn.model_selection import RepeatedKFold
from qpputils import dataparser as dp
# TODO: change the functions to work with ... | pd.read_json(self.folds_file) | pandas.read_json |
# -*- coding: utf-8 -*-
import pandas as pd
import pymysql
import pymysql.cursors
from functools import reduce
import numpy as np
import pandas as pd
import uuid
import datetime
from sklearn.feature_extraction import DictVectorizer
from sklearn.metrics.pairwise import pairwise_distances
import json
import common.commo... | pd.read_sql_query(m_sql, conn) | pandas.read_sql_query |
"""
Tests for the pandas.io.common functionalities
"""
import mmap
import os
import re
import pytest
from pandas.compat import FileNotFoundError, StringIO, is_platform_windows
import pandas.util._test_decorators as td
import pandas as pd
import pandas.util.testing as tm
import pandas.io.common as icom
class Custo... | tm.ensure_clean('fspath') | pandas.util.testing.ensure_clean |
import spotipy
import pandas as pd
from spotipy.oauth2 import SpotifyClientCredentials
#-- IMPORTANT --#
''' for this script to work you have to have a credentials.py file in the same directory
with the following variables
cid = 'YOUR_SPOTIFY_API_CLIENT_ID'
secret = 'YOUR_SPOTIFY_API_CLIENT_SECRET'
... | pd.read_csv("datasets/tcc_ceds_music.csv", delimiter=',', encoding=None) | pandas.read_csv |
#%%
# from libs.Grafana.config import Config
# from libs.Grafana.dbase import Database
import datetime
import pandas as pd
import numpy as np
import datetime
import time
import logging
import pprint
from time import time
import requests
from influxdb import InfluxDBClient
from influxdb.client import InfluxDBClien... | pd.to_datetime(d) | pandas.to_datetime |
# -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime, timedelta
import functools
import itertools
import numpy as np
import numpy.ma as ma
import numpy.ma.mrecords as mrecords
from numpy.random import randn
import pytest
from pandas.compat import (
PY3, PY36, OrderedDict, ... | Index([], name='id') | pandas.Index |
import sys,os
import pathlib
import joblib
import pandas as pd
import numpy as np
import spacy
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from pickle import dump, load
import string
def punct_space(token):
""... | pd.DataFrame(arr) | pandas.DataFrame |
# coding: utf-8
# In[ ]:
from __future__ import division
import numpy as np
import pandas as pd
from sklearn.model_selection import StratifiedKFold
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.preprocessing import OneHotEncoder,LabelEncoder
from sklearn.model_selection import train_test_... | pd.get_dummies(df[column_name], prefix=column_name) | pandas.get_dummies |
# -*- coding: utf-8 -*-
import base64
import logging
from pathlib import Path
from zipfile import ZipFile
import numpy as np
import pandas as pd
import streamlit as st
from PIL import Image
def create_dataframe(tissue_final: float, fibrosis_final: float, csv_filename: str) -> None:
data = [[tissue_final, fibrosi... | pd.DataFrame(data, columns=["tissue_percentage", "fibrosis_percentage"]) | pandas.DataFrame |
# RHR Online Anomaly Detection & Alert Monitoring
######################################################
# Author: <NAME> #
# Email: <EMAIL> #
# Location: Dept.of Genetics, Stanford University #
# Date: Oct 29 2020 #
###################... | pd.to_datetime(df_hr.index) | pandas.to_datetime |
### HI_Waterbird_Repro_DataJoinMerge_v3.py
### Version: 5/7/2020
### Author: <NAME>, <EMAIL>, (503) 231-6839
### Abstract: This Python 3 script pulls data from the HI Waterbirds Reproductive Success ArcGIS Online feature service and performs joins and merges to result in a combined CSV dataset.
import arcpy
import pan... | pd.DataFrame(naNestVisitData) | pandas.DataFrame |
import enum
import json
from glob import glob
from typing import Dict, List, Tuple
import re
import datetime as dt
from collections import Counter
import os
from numpy.random.mtrand import sample
from tqdm.auto import tqdm
import numpy as np
from numpy.random.mtrand import sample
import pandas as pd
import torch
from ... | pd.isnull(dose) | pandas.isnull |
import pandas as pd
import datetime as dt
from ._db_data import DBData
class RDA(DBData):
"""A class that contains all the Rapid Diagnostic Analytics tests"""
def __init__(self):
super().__init__()
db_obj = DBData()
# assign class variables
self.df_ta = db_obj.retrieve_data('c... | pd.to_datetime(x) | pandas.to_datetime |
import pandas as pd
from pandas_datareader.base import _BaseReader
from pandas_datareader.exceptions import DEP_ERROR_MSG, ImmediateDeprecationError
class RobinhoodQuoteReader(_BaseReader):
"""
Read quotes from Robinhood
DEPRECATED 1/2019 - Robinhood ended support for the endpoints used by this
read... | pd.to_datetime(vals["begins_at"]) | pandas.to_datetime |
"""
Same basic parameters for the Baselining work.
@author: <NAME>, <NAME>
@date Aug 30, 2016
"""
import numpy as np
import pandas as pd
import os
from itertools import chain, combinations
from scipy.signal import cont2discrete
from datetime import datetime
from pytz import timezone
from pandas.tseries.holiday impor... | pd.date_range(start=tsstart, end=tsend, freq='15Min') | pandas.date_range |
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