prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pandas as pd
'''
Data pipeline for ingestion of 311-data datasets
General sections:
1. ACQUIRE: Download data from source
2. CLEAN: Perform data cleaning and organization before entering into SQL
3. INGEST: Add data set to SQL database
These workflows can be abstracted/encapsulated in order to better gener... | pd.Timedelta(days=1) | pandas.Timedelta |
import argparse
import warnings
import logging
import flywheel
import pandas as pd
from fw_heudiconv.backend_funcs.query import get_seq_info
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger('fw-heudiconv-tabulator')
def tabulate_bids(client, project_label, path=".", subject_labels=None,
... | pd.DataFrame.from_dict(seq_info_dicts) | pandas.DataFrame.from_dict |
"""
Copyright (C) 2021 <NAME>
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 writing, software
dist... | pd.Series(data.index.day.values, index=data.index) | pandas.Series |
import pandas as pd
import numpy as np
from datetime import date
"""
dataset split:
(date_received)
dateset3: 20160701~20160731 (113640),features3 from 20160315~20160630 (off_test)
dateset2: 20160515~20160615 (258446),features2 from 20160201~2... | pd.merge(user_merchant3,t1,on=['user_id','merchant_id'],how='left') | pandas.merge |
import numpy as np
from datetime import timedelta
import pandas as pd
import pandas.tslib as tslib
import pandas.util.testing as tm
import pandas.tseries.period as period
from pandas import (DatetimeIndex, PeriodIndex, period_range, Series, Period,
_np_version_under1p10, Index, Timedelta, offsets)
... | tm.assertRaises(TypeError) | pandas.util.testing.assertRaises |
#
# 人脸检测和属性分析 WebAPI 接口调用示例
# 运行前：请先填写Appid、APIKey、APISecret以及图片路径
# 运行方法：直接运行 main 即可
# 结果： 控制台输出结果信息
#
# 接口文档（必看）：https://www.xfyun.cn/doc/face/xf-face-detect/API.html
#
from datetime import datetime
from wsgiref.handlers import format_date_time
from time import mktime
import hashlib
import base64
import hmac
from... | pd.DataFrame(res, index=[0]) | pandas.DataFrame |
# -*- 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... | strings.str_contains(values, pat) | pandas.core.strings.str_contains |
"""
utils4text.py is the script file storing many useful functions for processing the comment dataframes from the subreddits.
That is, it is mainly used for text EDA.
Made by <NAME>.
"""
import numpy as np
import pandas as pd
import multiprocess as mp
import re
import nltk
import contractions
import string
from emoji... | pd.DataFrame(turn_dist) | pandas.DataFrame |
import datetime
import functools
import os
from urllib.parse import urljoin
import arcgis
import geopandas
import numpy
import pandas
import requests
from airflow import DAG
from airflow.hooks.base_hook import BaseHook
from airflow.models import Variable
from airflow.operators.python_operator import PythonOperator
fro... | pandas.DataFrame() | pandas.DataFrame |
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import lib_plot
from lib_db import DBClient, NodeClassification
from lib_fmt import fmt_barplot, fmt_thousands
from lib_agent import agent_name, go_ipfs_version, go_ipfs_v08_version
def main(client: DBClient):
sns.set_theme()
def plot... | pd.DataFrame(results, columns=['agent_version', 'count']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import csv
import os
import platform
import codecs
import re
import sys
from datetime import datetime
import pytest
import numpy as np
from pandas._libs.lib import Timestamp
import pandas as pd
import pandas.util.testing as tm
from pandas import DataFrame, Series, Index, MultiIndex
from pand... | StringIO(data) | pandas.compat.StringIO |
import os
import sys
import argparse
import pandas as pd
from scipy import stats
sys.path.append(os.path.abspath(".."))
from survey._app import CODE_DIR, app
from core.models.metrics import gain_mean, rejection_ratio, gain
from utils import get_con_and_dfs, get_all_con_and_dfs
import metrics
STATS_FUNCTIONS = {}
... | pd.notnull(v) | pandas.notnull |
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | pd.Series([False, False, True]) | pandas.Series |
import datetime
import logging
import random
import re
import time
from typing import Iterator, List, Union, Dict
from urllib.parse import quote
import pandas as pd
import requests
from bs4 import BeautifulSoup
from .conn_postgresql import ConnPostgreSQL
log = logging.getLogger(__name__)
class HhParser:
"""Пар... | pd.merge(pg_unique_jobs, self.__df[['date', 'href']], on='href', how='outer') | pandas.merge |
import argparse
import os
import pandas as pd
import matplotlib.pyplot as plt
import sys
sys.path.append('../')
from load_paths import load_box_paths
import matplotlib as mpl
import matplotlib.dates as mdates
from datetime import date, timedelta, datetime
import seaborn as sns
from processing_helpers import *
#from plo... | pd.to_datetime(today) | pandas.to_datetime |
from copy import deepcopy
import numpy as np
import pandas as pd
import torch as t
import torch.nn as nn
from scipy import constants, linalg
from pyhdx.fileIO import dataframe_to_file
from pyhdx.models import Protein
from pyhdx.config import cfg
# TORCH_DTYPE = t.double
# TORCH_DEVICE = t.device('cpu')
class Delta... | pd.concat([deltaG, hdxm.coverage['exchanges']], axis=1, keys=['dG', 'ex']) | pandas.concat |
"""Exemplo de python."""
from matplotlib import pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
def plot_matplotlib(var):
fig = plt.figure(figsize=(16, 9))
plt.hist(var, bins=100)
plt.show()
def plot_pandas(var):
fig = plt.figure(figsize=(16, 9))
ax = fig.add_subplot(1... | pd.DataFrame(x, columns=["x"]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from warnings import catch_warnings
import numpy as np
from datetime import datetime
from pandas.util import testing as tm
import pandas as pd
from pandas.core import config as cf
from pandas.compat import u
from pandas._libs.tslib import iNaT
from pandas import (NaT, Float64Index, Series,
... | notnull(values) | pandas.core.dtypes.missing.notnull |
# cancer without number
import pandas as pd
import scipy.stats as ss
from add_weights_helpers import rev_comp, change_in_motifs, iupac_dict, motifs, \
motif_check, mirna_weight_calc
# from add_weights_helpers import get_whole_sequence
path = '/media/martyna/Pliki2/praca/IChB/ZGM/repos/'
df_all_mut = pd.read_csv... | pd.DataFrame() | pandas.DataFrame |
"""
"""
"""
>>> # ---
>>> # SETUP
>>> # ---
>>> import os
>>> import logging
>>> logger = logging.getLogger('PT3S.Rm')
>>> # ---
>>> # path
>>> # ---
>>> if __name__ == "__main__":
... try:
... dummy=__file__
... logger.debug("{0:s}{1:s}{2:s}".format('DOCTEST: __main__ Context: ','path = os.p... | pd.DataFrame() | pandas.DataFrame |
import sys
sys.path.append("../")
import argparse
from augur.utils import json_to_tree
import Bio
import Bio.Phylo
import json
import pandas as pd
import sys
from Helpers import get_y_positions
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("tree", help="auspice tree JSON"... | pd.DataFrame(records) | pandas.DataFrame |
import os
import sys
import argparse
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import (MultipleLocator, NullFormatter, ScalarFormatter)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description = "Build haplotypes and make scatter plot for vizualiz... | pd.merge(hapCntFile, byChrDf, left_on='start', right_on='start', how='left') | pandas.merge |
import pandas as pd
import numpy as np
import os
from datetime import datetime
from IPython.display import IFrame,clear_output
# for PDF reading
import textract
import re
import sys
import docx
from difflib import SequenceMatcher
##################################################################################... | pd.read_csv(filename, delimiter=delimiter, encoding='utf-8',engine='c',header=header) | pandas.read_csv |
import os
import pandas as pd
import filedialog
class playerinfo():
''' loads all dataframes with player info, teams, etc. '''
def __init__(self, *args, **kwargs):
self.path = filedialog.askdirectory()
# open files
self.players=None
self.famcontact=None
self.m... | pd.DataFrame() | pandas.DataFrame |
import os.path
import pandas as pd
import numpy as np
import scipy
import scipy.signal as sig
import scipy.interpolate as inter
"""
This module encapsulates functions related to correlation, causation and data formatting
"""
b = 1.5
c = 4
q1 = 1.540793
q2 = 0.8622731
z = lambda x: (x-x.mean())/np.std(x, ddof=1)
g = ... | pd.infer_freq(idx) | pandas.infer_freq |
# Copyright (c) 2018, NVIDIA CORPORATION.
import numpy as np
import pandas as pd
import pytest
import cudf as gd
from cudf.tests.utils import assert_eq
def make_frames(index=None, nulls="none"):
df = pd.DataFrame(
{
"x": range(10),
"y": list(map(float, range(10))),
"z... | pd.concat([df, df2, df, df_empty1]) | pandas.concat |
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from collections import OrderedDict
import gc
from current_clamp import *
from current_clamp_features import extract_istep_features
from visualization.feature_annotations import feature_name_dict
from read_metadata i... | pd.isnull(row['fill']) | pandas.isnull |
# Load modules
from __future__ import print_function
import os
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
#Read dataset into a pandas.DataFrame
beer_df = pd.read_csv('datasets/quarterly-beer-production-in-aus-March 1956-June 1994.csv')
#Display shape of the dataset
print('Shape of th... | pd.isnull(beer_df['Beer_Prod']) | pandas.isnull |
import copy
from collections import OrderedDict, UserString, UserDict
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from scipy.stats import lognorm
from .control import Control, Controls
from .likelihood import Likelihood
from .impact import Impact
from .vulnerability import Vulnerabilit... | pd.set_option("display.float_format", lambda x: "%.2f" % x) | pandas.set_option |
import sys
import ast
import pandas as pd
from flask import Flask, jsonify, request
from flasgger import Swagger
from flasgger.utils import swag_from
from resources import constants
from utils import api_utils
SWAGGER_CONFIG = {
"headers": [
],
"specs": [
{
"version": "0.1",
... | pd.DataFrame(group) | pandas.DataFrame |
import sys
import pandas as pd
import nltk
nltk.download('punkt')
nltk.download('wordnet')
nltk.download('stopwords')
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
import re
from sqlalchemy import create_engine
def tokenize(text):
"""
Functio... | pd.concat([df,categories],axis=1) | pandas.concat |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import datetime, timedelta
import numpy as np
import pytest
from pandas.errors import (
NullFrequencyError, OutOfBoundsDatetime, PerformanceWarning)
import pandas as pd
from pandas import (
DataFrame, ... | tm.assert_equal(result, expected) | pandas.util.testing.assert_equal |
##
drive_path = 'c:/'
import numpy as np
import pandas as pd
import os
import sys
import matplotlib.pyplot as plt
from scipy.stats import ks_2samp
from scipy.stats import anderson_ksamp
from scipy.stats import kruskal
from scipy.stats import variation
from scipy import signal as sps
import seaborn as sns
import glob
im... | pd.concat([group, temp], axis=1) | pandas.concat |
from itertools import product
from pathlib import Path
from warnings import warn
import numpy as np
import pandas as pd
import sep
from astropy.io import fits
from astropy.modeling.functional_models import Gaussian2D
from astropy.nddata import CCDData, Cutout2D, VarianceUncertainty
from astropy.stats import sigma_clip... | pd.DataFrame.from_dict(self.phots) | pandas.DataFrame.from_dict |
import datetime
import numpy as np
import pandas as pd
import plotly.graph_objs as go
def last_commits_prep(payload):
commits = | pd.DataFrame.from_dict(payload['commits']) | pandas.DataFrame.from_dict |
import pandas as pd
import os
os.chdir("/Users/laurahughes/GitHub/cvisb_data/sample-viewer-api/src/static/data")
import helpers
# Comparing two versions of survivor roster.
v1_file = "/Users/laurahughes/GitHub/cvisb_data/sample-viewer-api/src/static/data/input_data/patient_rosters/survivor_IDdict_v1_2019-02-27_PRIVA... | pd.read_excel(v1_file, converters={'Study Specific #': str, 'ID': str}) | pandas.read_excel |
#!/usr/bin/env python
# coding: utf-8
# ## Damage and Loss Assessment (12-story RC frame)
#
# This example continues the example2 to conduct damage and loss assessment using the PLoM model and compare the results against the results based on MSA
# ### Run example2
import numpy as np
import random
import time
from m... | pd.DataFrame() | pandas.DataFrame |
from io import StringIO
from typing import Iterable, _GenericAlias
from urllib.parse import urljoin
import json
import logging
import pytest
from pandas.api.types import is_object_dtype, is_categorical_dtype
import numpy as np
import pandas as pd
from omnipath import options
from omnipath.requests import Enzsub, Com... | pd.Series(["foo:123", "bar:45;baz", None, "bar:67;baz:67", "foo"]) | pandas.Series |
#' ---
#' title: Greater Seattle Area Housing--Sales Price Prediction
#' author: <NAME>
#' date: 2017-02-27
#' abstract: |
#' The goal of this project is to predict the sale price of a property
#' by employing various predictive machine learning models in an ensemble
#' given housing data such as the number... | pd.to_datetime(training[col], infer_datetime_format=True) | pandas.to_datetime |
import warnings
import pandas as pd
import numpy as np
import copy
from .syntax import Preprocessor, Regressor, Evaluator
from ..base import BASE
from ...utils import value
##################################################################### 2 Prepare Data
class automatic_run(BASE):
def fit(self):
sel... | pd.DataFrame(scores) | pandas.DataFrame |
# %%
import pandas as pd
sp_dir = '/Users/rwang/RMI/Climate Action Engine - Documents/OCI Phase 2'
up_mid_down = pd.read_csv(sp_dir + '/Upstream/upstream_data_pipeline_sp/Postprocessed_outputs_2/downstream_postprocessed_scenarios_fix.csv')
up_mid_down = up_mid_down[up_mid_down['gwp']==20]
def prep_for_webtool(up_mid_do... | pd.DataFrame() | pandas.DataFrame |
"""Tools for pre/post processing inputs/outputs from neural networks
Unlike pytorch's builtin tools, this code allows building pytorch modules from
scikit-learn estimators.
"""
import pandas as pd
import numpy as np
import xarray as xr
import torch
from torch import nn
from uwnet.thermo import compute_apparent_source... | pd.DataFrame(inputs, columns=idx) | pandas.DataFrame |
import pandas as pd
import io
import requests
from datetime import datetime
#Import data file if it already exists
try:
past_data = pd.read_excel("Utah_Data.xlsx")
past_dates = past_data["Date"].tolist()
except:
past_data = | pd.DataFrame({}) | pandas.DataFrame |
# Spectral_Analysis_Amp_and_Phase.py
import os
import numpy as np
import pandas as pd
import scipy.linalg as la
import matplotlib.pyplot as plt
# Import time from the data or define it
t = np.arange(0.015, 0.021, 10**-7)
dt = 10**-7
# Define trainsize and number of modes
trainsize = 20000 # Number of snapshots u... | pd.DataFrame(FOM_) | pandas.DataFrame |
import pandas
import os
from locale import *
import locale
locale.setlocale(LC_NUMERIC, '')
fs = pandas.read_csv('./Ares.csv', sep=';', encoding='cp1252', parse_dates=[1,3,5,7,9,11], dayfirst=True)
# Separar por pares de columnas
materia_organica = pandas.DataFrame(fs[[fs.columns[0], fs.columns[1]]])
con... | pandas.Series(temperatura[temperatura.columns[1]].values, temperatura[temperatura.columns[0]]) | pandas.Series |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
""" test get/set & misc """
import pytest
from datetime import timedelta
import numpy as np
import pandas as pd
from pandas.core.dtypes.common import is_scalar
from pandas import (Series, DataFrame, MultiIndex,
Timestamp, Timedelta, Categorical)
... | Series([]) | pandas.Series |
# coding: utf-8
import json
import pandas as pd
import numpy as np
import glob
import ast
from modlamp.descriptors import *
import re
import cfg
import os
def not_in_range(seq):
if seq is None or len(seq) < 1 or len(seq) > 80:
return True
return False
def bad_terminus(peptide):
if peptide.nTermi... | pd.DataFrame.from_dict(ampeps) | pandas.DataFrame.from_dict |
import argparse
import sys
import os
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import glob
from sklearn import metrics
from scipy.stats import pearsonr, spearmanr
from scipy.optimize import curve_fit
from collections import Counter
import pickle
impor... | pd.read_csv(args.tophits_neff_bench4[0]) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 16 15:11:22 2020
@author: 81701
"""
from datetime import datetime, timedelta
import gc
import numpy as np, pandas as pd
import lightgbm as lgb
h = 28
max_lag = 0
tr_last = 1913
fday = datetime(2016,4, 25)
CAL_DTYPES={"event_name_1": "category", "event_name_2": "cat... | pd.to_datetime(cal["date"]) | pandas.to_datetime |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import random
df_motor = pd.read_csv('/home/ubuntu/bagfiles/3r/r2_motor.csv', header=0)
df_odom1 = pd.read_csv('/home/ubuntu/bagfiles/net_arch/PPO3_odom.csv', header=0)
df_odom2 = pd.read_csv('/home/ubuntu/bagfiles/net_arch/PP... | pd.DataFrame(time) | pandas.DataFrame |
import argparse
import subprocess
import sys
import numpy as np
import pandas as pd
from scipy.stats import ks_2samp
from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve
from sklearn.model_selection import train_test_split
from parallelm.mlops import S... | pd.DataFrame({'v1': v1, 'rank1': rank1}) | pandas.DataFrame |
import gc
import json
import logging
import os
import warnings
from datetime import datetime
import numpy as np
from enum import auto, Enum
from multiprocessing import Process, Event, JoinableQueue
from multiprocessing.managers import SyncManager
from multiprocessing.queues import Empty, Full
from tqdm import tqdm
... | pd.read_hdf(endpoint_file, self.hdf5_group, mode='r', **self.read_kwargs) | pandas.read_hdf |
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import sys as sys
import pandas as pd
sys.path.insert(1, './../..')
from Dispersion_NN import Dispersion_NN
#************Start of of user block******************
output_csv_file='./Fig2_ab_NN.csv'
read_csv=True #change to True if one want to r... | pd.read_csv(read_output_csv_file) | pandas.read_csv |
#Importamos librerias
import pandas as pd
import datetime as dt
import requests
#Importamos Panel Lider (web scraping)
def iol_scraping_panel_lider():
df = pd.read_html(
"https://iol.invertironline.com/Mercado/Cotizaciones",
decimal=',', thousands='.')
df = df[0]
df = df.iloc[:, 0:13]
df.rename(columns... | pd.DataFrame(response) | pandas.DataFrame |
# Libraries
import random
from math import pi
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
COLOR = ['#B6BFF2', '#04C4D9', '#F2C12E', '#F26363', '#BF7E04', '#7F2F56', '#E8B9B5', '#63CAF3', '#F27405', '#68BD44']
MARKER = ['D', '^', 'o', 'H', '+', 'x', 's', 'p', '*', '3']
def cross_methods_pl... | pd.DataFrame(raw_data, columns=df_col) | pandas.DataFrame |
import pandas as pd
from .indicator import Indicator
class RSI(Indicator):
_NAME = 'rsi'
def __init__(self, currency_pair='btc_jpy', period='1d', length=14):
super().__init__(currency_pair, period)
self._length = self._bounded_length(length)
def request_data(self, count=100, to_epoch_tim... | pd.concat([candlesticks['time'], rsi], axis=1) | pandas.concat |
#!/Tsan/bin/python
# -*- coding: utf-8 -*-
# Libraries to use
from __future__ import division
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
import json
import mysql.connector
# 读取数据库的指针设置
with o... | pd.DataFrame(result) | pandas.DataFrame |
import pytest
from matplotcheck.base import PlotTester
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from scipy import stats
"""Fixtures"""
@pytest.fixture
def pd_df_reg_data():
"""Create a pandas dataframe with points that are roughly along the same
line."""
data = {
... | pd.DataFrame(data) | pandas.DataFrame |
import datetime
import logging
import pandas as pd
from django.core.exceptions import ValidationError
from django.db import transaction
from reversion import revisions as reversion
from xlrd import XLRDError
from app.productdb.models import Product, CURRENCY_CHOICES, ProductGroup, ProductMigrationSource, ProductMigrati... | pd.isnull(row[row_key]) | pandas.isnull |
"""
GUI code modified based on https://github.com/miili/StreamPick
For earthquake PKiKP coda quality evaluation and stack
"""
import os
import pickle
import pandas as pd
import numpy as np
# GUI import
import PyQt5
from PyQt5 import QtGui
from PyQt5 import QtWidgets
from PyQt5.QtWidgets import *
from PyQt5.QtGui im... | pd.DataFrame() | pandas.DataFrame |
# Copyright 2020 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | pd.DataFrame({'Y-weighted var(X)': [2., 1]}, index=['A', 'B']) | pandas.DataFrame |
#! /usr/bin/env python
# <NAME>
# February 22, 2016
# Vanderbilt University
"""
Tools for converting pandas DataFrames to .hdf5 files, and converting from
one type of hdf5 file to `pandas_hdf5` file format
"""
from __future__ import print_function, division, absolute_import
__author__ =['<NAME>']
__copyright__ ... | pd.HDFStore(hdf5_file) | pandas.HDFStore |
import unittest
import pandas as pd
import numpy as np
from tickcounter.questionnaire import Encoder
from pandas.testing import assert_frame_equal, assert_series_equal
class TestEncoder(unittest.TestCase):
@classmethod
def setUpClass(cls):
super(TestEncoder, cls).setUpClass()
cls.or... | assert_frame_equal(result_2, expected_2, check_dtype=False) | pandas.testing.assert_frame_equal |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''
# Created on Feb-24-19 22:06
# pca.py
# @author: <NAME>
'''
import pandas as pd
def pca(df, num, **kw):
max_value = df.max()[0]
min_value = df.min()[0]
window_size = int(kw['algorithm_param']['PCA']['window_size'])
dl_out = []
loc = 0
while ... | pd.DataFrame(x) | pandas.DataFrame |
import pandas as pd
ser = pd.Series(["NTU", "NCKU", "NCU", "NYCU"])
# Using drop() function with list method.
ser = ser.drop(3)
print(ser)
# Using drop() with argument index.
ser = | pd.Series(["NTU", "NCKU", "NCU", "NYCU"], index=["Bes", "Dec", "Thr", "Flo"]) | pandas.Series |
"""
@brief test log(time=400s)
"""
import os
import unittest
from logging import getLogger
from pandas import DataFrame
from pyquickhelper.loghelper import fLOG
from pyquickhelper.pycode import (
get_temp_folder, ExtTestCase, skipif_appveyor)
from sklearn.ensemble import AdaBoostRegressor
from sklearn.gaussian... | DataFrame(rows) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 4 2021, last edited 27 Oct 2021
Fiber flow emissions calculations module - class version
Inputs:
Excel file with old PPI market & emissions data ('FiberModelAll_Python_v3-yields.xlsx')
Outputs:
Dict of keys 'old','new','forest','trade' with emissions calcs
... | pd.read_excel(x, 'EmTables', usecols="L:P", skiprows=46, nrows=11, index_col=0) | pandas.read_excel |
import numpy as np
import pandas as pd
import random
c_u = 100
m_u = 1
c_p = 0.1
m_p = 0.01
censor_mean = 103
censor_sig = 0
possible_xes = [0,1,2,3,4,5,6,7]
nrows = 100000
x = np.random.choice(possible_xes, nrows)
u = c_u + m_u*x
p = c_p + m_p*x
true_y = np.random.normal(u,u*p,nrows)
censor_y = np.random.nor... | pd.DataFrame(dfDict) | pandas.DataFrame |
from flask import Flask, redirect, request, url_for,render_template
from application import app, db
from application.models import Products,Orders,Customers #,SummaryOrder,OrdersSummary,ItemTable,OrdersTable,,CustomersTable
import sqlalchemy as sql
import pandas as pd
from datetime import datetime
@app.route('/')
def ... | pd.read_sql_table('customers', sql_engine) | pandas.read_sql_table |
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
import pandas as pd
import numpy as np
import argparse
import requests
import tempfile
import logging
import sklearn
import os
lo... | pd.DataFrame(test) | pandas.DataFrame |
# -*- coding: utf-8 -*-
""" Simple multi-area model for Nordic electricity market
Created on Wed Jan 16 11:31:07 2019
@author: elisn
Notes:
1 - For conversion between dates (YYYYMMDD:HH) and weeks (YYYY:WW) weeks are counted as starting during the first hour
in a year and lasting 7 days, except for the last week wh... | pd.DataFrame(dtype=float,index=self.timerange_p1,columns=self.reservoir.columns) | pandas.DataFrame |
import pandas as pd
import toffee
class SpectralLibrary():
"""
SpectralLibrary data type.
This is essentially just a wrapper around a pandas dataframes, `data`. It provides convinient inits from one
file type, common operations and a standard format with which to pass to procantoolbox figure factorie... | pd.read_table(srl_fname) | pandas.read_table |
import argparse
import math
import sys
import pandas as pd
from scipy import stats
def calc_interval(df: pd.DataFrame) -> pd.DataFrame:
means = []
deltas = []
for _, items in df.items():
n = len(items)
mean = items.mean()
var = items.var()
if var == 0:
means.ap... | pd.read_csv(args.data[0]) | pandas.read_csv |
# This program loads the HILT data and parses it into a nice format
import argparse
import pathlib
import zipfile
import re
from datetime import datetime, date
import pandas as pd
import numpy as np
from sampex_microburst_widths import config
class Load_SAMPEX_HILT:
def __init__(self, load_date, extract=False, ... | pd.DataFrame(data={'counts':self.counts}, index=self.times) | pandas.DataFrame |
# -*- 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, ... | is_platform_little_endian() | pandas.compat.is_platform_little_endian |
import unittest
from .. import simulate_endToEnd
from Bio.Seq import MutableSeq
from Bio import SeqIO
from Bio.Alphabet import generic_dna
import pandas as pd
import numpy as np
import mock
import os
class TestSimulateNormal(unittest.TestCase):
def setUp(self):
self.genome = {"chr1": MutableSeq("NNNNAGAGC... | pd.DataFrame(lists) | pandas.DataFrame |
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.backends.backend_pdf import PdfPages
import math
from config import site, dates, folders
from os import listdir
from os.path import isfile, join
dir = "/hom... | pd.to_datetime(df_th["Label"], format="%Y-%m-%d %H") | pandas.to_datetime |
import re
from decimal import Decimal
import math
import pandas as pd
import numpy as np
from scipy.optimize import curve_fit
from scipy.interpolate import UnivariateSpline
from scipy.special import lambertw
from lmfit import Model, Parameters
from uncertainties import ufloat
def subsetDf(df, start, end):
result ... | pd.DataFrame() | pandas.DataFrame |
from datetime import datetime
import numpy as np
import pytest
from pandas import Series, _testing as tm
def test_title():
values = Series(["FOO", "BAR", np.nan, "Blah", "blurg"])
result = values.str.title()
exp = Series(["Foo", "Bar", np.nan, "Blah", "Blurg"])
tm.assert_series_equal(result, exp)
... | Series(mixed) | pandas.Series |
## usage
# at a level above emmer/
# python3 -m emmer.test.test_bifurication
from ..bake import BakeCommonArgs
from ..posthoc.stats.bifurication import BifuricationArgs, linearRegressionPVal, DifferentiatingFeatures
from ..posthoc.visual.viewer import Projection
from ..troubleshoot.err.error import ErrorCode12, ErrorC... | pandas.DataFrame(C, columns=['x1','x2'], index=['A__s1','A__s2','A__s3','B__s4','B__s5','B__s6']) | pandas.DataFrame |
# Written by i3s
import os
import numpy as np
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import seaborn as sns
import time
from sklearn.model_selection import KFold
from matplotlib import pyplot as plt
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier, AdaBoostC... | pd.DataFrame(topGenes2, columns=col) | pandas.DataFrame |
import utils as dutil
import numpy as np
import pandas as pd
import astropy.units as u
from astropy.time import Time
import astropy.constants as const
import astropy.coordinates as coords
from astropy.coordinates import SkyCoord
from scipy.interpolate import interp1d, UnivariateSpline
from scipy.optimize import curve_... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
# Copyright (c) 2016 by University of Kassel and Fraunhofer Institute for Wind Energy and Energy
# System Technology (IWES), Kassel. All rights reserved. Use of this source code is governed by a
# BSD-style license that can be found in the LICENSE file.
import os
import pickle
import pandas as... | pd.DataFrame(net.std_types["line"]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# !/usr/bin/env python
#
# @file multi_md_analysis.py
# @brief multi_md_analysis object
# @author <NAME>
#
# <!--------------------------------------------------------------------------
# Copyright (c) 2016-2019,<NAME>.
# All rights reserved.
# Redistribution and use in source and bina... | pd.DataFrame(T) | pandas.DataFrame |
# Note
#
# 2D Numpy Array can hold values only of one dayatype. Pandas does not have that
# restriction and is ideal data structure for tabular data which has columnn values
# with multiple data types
#
import pandas as pd
# Create Panda dataframe using dictionary
dict = {
"country" : ["Brazil", "Russia", "In... | pd.DataFrame(dict) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable=W0612,E1101
from datetime import datetime
import operator
import nose
from functools import wraps
import numpy as np
import pandas as pd
from pandas import Series, DataFrame, Index, isnull, notnull, pivot, MultiIndex
from pandas.core.datetools import bday
from pandas.core.n... | Panel.from_dict(data, orient='minor') | pandas.core.panel.Panel.from_dict |
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
def get_edges_df_from_obabel(obabel_edges_df, structures_df):
"""
Given obabel edge data, convert it to format of edges_df so that all other code remains the same.
"""
obabel_edges_df = obabel_edges_df[[
'mo... | pd.concat([edge_df, e_df], ignore_index=True) | pandas.concat |
""" Behind the scenes work of querying a tweet and producing graphs relating to the sentiment analysis. """
from afinn import Afinn
from matplotlib.figure import Figure
from matplotlib import rcParams
from pandas import DataFrame
from sqlite3 import connect
from twitterscraper.query import query_tweets
rcParams.update... | DataFrame(tweet.__dict__ for tweet in tweets) | pandas.DataFrame |
import json
import sys
import warnings
from pathlib import Path
# import matplotlib.pyplot as plt
import pandas as pd
import requests
class Timeseries:
def __init__(self, dataset, id_timeseries="", name_timeseries=""):
self.ds = dataset
self._id_ds = self.ds._id
self._id_proj = self.ds.c... | pd.DataFrame(json_["data"]) | pandas.DataFrame |
import pyprind
import pandas as pd
import os
xxxhmiac = 'imported done'
print(xxxhmiac)
labels = {'pos': 1, 'neg': 0}
pbar = pyprind.ProgBar(50000)
df = pd.DataFrame()
for s in ('test', 'train'):
for l in ('pos', 'neg'):
path = r'C:\Users\<NAME>\Documents\aclImdb/%s/%s' % (s, l)
for file in os.listdir(path):... | pd.read_csv(r'C:\Users\<NAME>\Documents\SAfiles/movie_data.csv') | pandas.read_csv |
import pickle
from pathlib import Path
import pandas as pd
import data.utils.web_scrappers as ws
DATA_DIR = Path("data/data")
COM_DATA_DIR = DATA_DIR / "DAX30"
PKL_DIR = DATA_DIR / "PKL_DIR"
DAX_DATA_PKL = PKL_DIR / "DAX30.data.pkl"
DAX_DATA_CSV = DATA_DIR / "DAX30.csv"
def path_to_string(path):
return "/".joi... | pd.read_pickle(path) | pandas.read_pickle |
import boto3
import base64
import os
from botocore.exceptions import ClientError
import json
import psycopg2
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import sys
import traceback
class DB:
"""database interface class"""
@staticmethod
def connect(params: dict) -> [ps... | pd.read_sql_query(sql_query, database) | pandas.read_sql_query |
import pandas as pd
import os
import matplotlib.pyplot as plt
def plot_mortality_vs_excess(csv, owid_excess_mortality):
"""
Description: plots a comparison between official death count and p-score death count
in a two-ax chart for countries with p-score > 1
:param owid_data: OWID main coronavirus data... | pd.read_csv(owid_excess_mortality) | pandas.read_csv |
import os
import tempfile
import unittest
import numpy as np
import pandas as pd
from sqlalchemy import create_engine
from tests.settings import POSTGRESQL_ENGINE, SQLITE_ENGINE
from tests.utils import get_repository_path, DBTest
from ukbrest.common.pheno2sql import Pheno2SQL
class Pheno2SQLTest(DBTest):
@unitt... | pd.isnull(query_result.loc[1000061, 'c50_0_0']) | pandas.isnull |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# <NAME>
#
import sys
import os
import argparse
import pandas as pd
from decimal import Decimal
from collections import OrderedDict
import scipy.stats as st
import numpy as np
def main():
# Parse args
args = parse_args()
# Load top loci
top_loci = pd.r... | pd.merge(top_loci, study, on='study_id', how='left') | pandas.merge |
# -*- coding: utf-8 -*-
# @Time : 09.04.21 09:54
# @Author : sing_sd
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import src.common_functions as cf
import csv
import ais
from datetime import datetime, timedelta, timezone
import re
vb_dir = os.path.dir... | pd.read_csv(f) | pandas.read_csv |
import os
import joblib
import numpy as np
import pandas as pd
from joblib import Parallel
from joblib import delayed
from pytz import timezone
from sklearn.decomposition import KernelPCA
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import MinMaxScaler
from Fuzzy_clustering.version2.com... | pd.DateOffset(hours=25) | pandas.DateOffset |
import gc
from pathlib import Path
from tqdm import tqdm
import skvideo
import skvideo.io
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from moviepy.editor import *
import torch
import torch.optim as optim
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch... | pd.DataFrame(audio_dataframe) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
this script enables to transform SNP data in vcf files to fasta format.
it is thought to be used for the Mytilus dataset.
"""
__author__ = '<NAME>'
__mail__ = '<EMAIL>'
#import os
import pandas as pd
import argparse
### use a single vcf field and choose a base due ... | pd.Series(data=basealign) | pandas.Series |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import math
import scipy.stats as stats
from matplotlib import gridspec
from matplotlib.lines import Line2D
from .util import *
import seaborn as sns
from matplotlib.ticker import FormatStrFormatter
import matplotlib.pylab as pl
import matplotlib.... | pd.DatetimeIndex([date_string_current]) | pandas.DatetimeIndex |
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