prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pandas as pd
import numpy as np
import csv
import json
from math import radians, cos, sin, asin, sqrt
import os
class Peaje:
def __init__(self, name, code):
self.code = code
self.name = name
self.trafico = {}
self.recaudo = {}
print(os.path.dirname(__file__))
home = os.getcw... | pd.read_csv(f, sep=",") | pandas.read_csv |
# -*- coding: utf-8 -*-
# Arithmetc tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import timedelta
import operator
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.compat import long
from pandas.core import ops
from pan... | tm.box_expected(expected, box) | pandas.util.testing.box_expected |
import numpy, pandas
from sklearn.base import TransformerMixin
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_regression, mutual_info_regression
class SelectNAndKBest(
TransformerMixin,
):
"""
Selects the first N features plus the K best other features.
"""
def __init_... | pandas.concat([X.iloc[:,:self._n], X2], axis=1) | pandas.concat |
from stix_shifter.stix_transmission.src.modules.cloudsql import cloudsql_connector
from stix_shifter.stix_transmission.src.modules.base.base_status_connector import Status
import pandas as pd
from unittest.mock import patch
import json
import unittest
@patch('ibmcloudsql.SQLQuery.__init__', autospec=True)
@patch('ibm... | pd.DataFrame(columns=['Deleted Object']) | pandas.DataFrame |
"""
Procedures for fitting marginal regression models to dependent data
using Generalized Estimating Equations.
References
----------
<NAME> and <NAME>. "Longitudinal data analysis using
generalized linear models". Biometrika (1986) 73 (1): 13-22.
<NAME> and <NAME>. "Longitudinal Data Analysis for Discrete and
Contin... | DataFrame(table, columns=names, index=var_names) | pandas.DataFrame |
import csv
from io import StringIO
import os
import numpy as np
import pytest
from pandas.errors import ParserError
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
NaT,
Series,
Timestamp,
date_range,
read_csv,
to_datetime,
)
import pandas._testing as tm
impo... | read_csv(filename, index_col=0) | pandas.read_csv |
import os
import sys
from datetime import datetime
from collections import OrderedDict
import pandas as pd
from loguru import logger
DATA_INDEX = 0
TRANSLATION_INDEX = 1
def convert_dataframe_to_matrix(data_frame_list):
converted_data = OrderedDict()
converted_data.setdefault('peer_id', [])
for value ... | pd.DataFrame(data_list) | pandas.DataFrame |
import math
import os
import pathlib
from functools import reduce
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
from experiment_definitions import ExperimentDefinitions
from data_collectors import MemtierCollector, MiddlewareCollector
class ... | pd.merge(throughput_get, keysize_get) | pandas.merge |
import asyncio
import itertools
from datetime import timedelta
from typing import Coroutine, Dict, List, Union
import pandas as pd
from celery.utils.log import get_task_logger
from celery.utils.time import humanize_seconds
import calc.prod # noqa
import config as conf
import cq.signals # noqa
import cq.util
import ... | pd.DataFrame(index=obj_df.index, columns=obj_df.columns) | pandas.DataFrame |
from fastai.conv_learner import *
from fastai.dataset import *
from tensorboard_cb_old import *
import cv2
import pandas as pd
import numpy as np
import os
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
import scipy.optimize as opt
import torch
import torch.nn as nn
import t... | pd.DataFrame({'Id': sample_list, 'Predicted': pred_list_cor}) | pandas.DataFrame |
import sqlite3
import pandas as pd
conn = sqlite3.connect('rpg_db.sqlite3')
cur = conn.cursor()
# How many total Characters are there?
query1 = """
SELECT COUNT(character_id)
FROM charactercreator_character cc
"""
cur.execute(query1)
result_list = cur.fetchall()
cols = [ii[0] for ii in cur.description]
df1 = pd.D... | pd.read_sql(quer6, conn) | pandas.read_sql |
from context import dero
import pandas as pd
from pandas.util.testing import assert_frame_equal
from pandas import Timestamp
from numpy import nan
import numpy
class DataFrameTest:
df = pd.DataFrame([
(10516, 'a', '1/1/2000', 1.01),
(10516, 'a'... | Timestamp('2012-07-01 00:00:00') | pandas.Timestamp |
'''
https://note.youdao.com/share/?id=50ade2586b4ccbfc5da4c5d6199db863&type=note#/
标题：Python 爬取淘宝商品数据挖掘分析实战
项目内容：
本案例选择>> 商品类目：沙发；
筛选条件：天猫、销量从高到低、价格500元以上；
数量：共100页 4400个商品。
分析目的：
1. 对商品标题进行文本分析 词云可视化
2. 不同关键词word对应的sales的统计分析
3. 商品的价格分布情况分析
4. 商品的销量分布情况分析
5. 不同价格区间的商品的平均销量分布
6. 商品价格对销量的影响分析
7. 商品价格对销售额的影响分析
8. 不同省... | pd.concat([datatmsp,table],axis=0,ignore_index=True) | pandas.concat |
from experiments.utils import save_to_HDF5, update_experiment_run_log
from radcad import Model, Simulation, Experiment
from radcad.engine import Engine, Backend
from models.system_model_v3.model.partial_state_update_blocks import partial_state_update_blocks
from models.system_model_v3.model.params.init import params
... | pd.DataFrame(experiment.results) | pandas.DataFrame |
import sys
import pandas as pd
def optimize_dataframe(df, down_int='integer'):
# down_int can also be 'unsigned'
converted_df = | pd.DataFrame() | pandas.DataFrame |
from .Forg import forg
import time
import pandas as pd
#from statsmodels.iolib.tableformatting import (gen_fmt, fmt_2)
from itertools import zip_longest
from .TableFormat import gen_fmt, fmt_2
from statsmodels.iolib.table import SimpleTable
from statsmodels.compat.python import lrange, lmap, lzip
from scipy.stats impor... | pd.ExcelWriter(file) | pandas.ExcelWriter |
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
train = pd.read_csv("data/train.csv")
test... | pd.get_dummies(locations) | pandas.get_dummies |
import addfips
import os
import pandas as pd
import datetime
variables = {
'hash': 'hash',
'date': 'date_stamp',
'dateChecked': 'datetime_checked',
'state': 'us_state_postal',
'fips': 'us_state_fips',
'positive': 'cnt_tested_pos',
'positiveIncrease': 'cnt_tested_pos_new',
'negative': 'cnt_tested_neg',
'negati... | pd.Int32Dtype() | pandas.Int32Dtype |
from http.server import BaseHTTPRequestHandler, HTTPServer
import socketserver
import pickle
import urllib.request
import json
from pprint import pprint
from pandas.io.json import json_normalize
import pandas as pd
from sklearn import preprocessing
from sklearn.preprocessing import PolynomialFeatures
from sklearn impor... | pd.to_datetime(mem_points_request['time']) | pandas.to_datetime |
import numpy
import random
from glob import glob
from scipy import interpolate
from scipy.special import softmax
from scipy.stats import ttest_ind
from sklearn.model_selection import KFold
import sys
from scipy.stats import skew, kurtosis
import itertools
import collections
import errno
import os.path as osp
import pi... | pd.Series(1 - fpr, index=i) | pandas.Series |
import pytest
import sys
import numpy as np
import swan_vis as swan
import networkx as nx
import math
import pandas as pd
###########################################################################
##################### Related to adding metadata ##########################
#############################################... | pd.read_csv('files/test_preexisting_result_t_df.tsv', sep='\t') | pandas.read_csv |
import csv
import os
import pickle
import re
import emoji
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import MinMaxScaler
from textblob import TextBlob
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
from emotion.EmojiEmotionFeature i... | pd.DataFrame.from_dict(textblob_score) | pandas.DataFrame.from_dict |
__all__ = ["spectrometer_sensitivity"]
# standard library
from typing import List, Union
# dependent packages
import numpy as np
import pandas as pd
from .atmosphere import eta_atm_func
from .instruments import eta_Al_ohmic_850, photon_NEP_kid, window_trans
from .physics import johnson_nyquist_psd, rad_trans, T_fro... | pd.Series(Pkid_cold, name="Pkid_cold") | pandas.Series |
from sklearn.covariance import EmpiricalCovariance, LedoitWolf, OAS
from sklearn.model_selection import GroupShuffleSplit
from scipy.spatial.distance import mahalanobis
import numpy as np
import pandas as pd
from multiprocessing import Pool
from contextlib import closing
from functools import partial
def similarity(X... | pd.concat(res) | pandas.concat |
# Copyright 2017-2021 QuantRocket LLC - All Rights Reserved
#
# 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 applicabl... | pd.MultiIndex.from_product([fields, dt_idx], names=["Field", "Date"]) | pandas.MultiIndex.from_product |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 11 19:07:28 2018
@author: deadpool
"""
import pandas as pd
from sklearn.manifold import Isomap
from scipy import misc
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import os
plt.style.use('ggplot')
folder = 'Datasets/AL... | pd.DataFrame(samples) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
module for implementation of indicator class, which is designed as MinIn for systems with netvalues
"""
import pandas as pd
from pyecharts.globals import CurrentConfig, NotebookType
from pyecharts import options as opts
from pyecharts.charts import Kline, Line, Bar, Grid
from pyecharts.comm... | pd.DataFrame(data={"bcmk": bcmk, "bt": bt}) | pandas.DataFrame |
import ast
import json
import os
from io import BytesIO
from sys import argv
import cv2
import numpy as np
import pandas as pd
import requests
from PIL import Image
def get_box_centers_all_emotions(sample_row):
"""
input: A dictionary of the format
{
emotion1: [{geometry_box1: [list of 4 corner c... | pd.read_csv(path_to_coords) | pandas.read_csv |
import numpy as np
import pandas as pd
from bach import Series, DataFrame
from bach.operations.cut import CutOperation, QCutOperation
from sql_models.util import quote_identifier
from tests.functional.bach.test_data_and_utils import assert_equals_data
PD_TESTING_SETTINGS = {
'check_dtype': False,
'check_exact... | pd.Interval(39.6, 49.5, closed='right') | pandas.Interval |
'''This script runs a couple of loops to find the best-performing combinations of
symptoms for predicting COVID. It makes heavy use of the multiprocessing
module, and the second loop--triggered by the RUN_META parameter--is best run
on a scientific workstation or HPC cluster.
'''
import numpy as np
import pandas as pd... | pd.ExcelWriter(file_dir + 'combo_stats.xlsx') | pandas.ExcelWriter |
# The published output of this file currently lives here:
# http://share.streamlit.io/0.23.0-2EMF1/index.html?id=8hMSF5ZV3Wmbg5sA3UH3gW
import keras
import math
import numpy as np
import pandas as pd
import streamlit as st
from scipy.sparse.linalg import svds
from sklearn.metrics import mean_squared_error
from sklearn... | pd.DataFrame(all_rating) | pandas.DataFrame |
import random
from os import path
import pandas as pd
from IPython.core.display import display, clear_output
from ipywidgets import widgets, Button
from SmartAnno.gui.PreviousNextWidgets import PreviousNext
from SmartAnno.gui.Workflow import Step
import xml.etree.ElementTree as ET
from dateutil.parser import parse
... | pd.DataFrame(columns=['BUNCH_ID', 'DOC_NAME', 'TEXT', 'DATE', 'REF_DATE']) | pandas.DataFrame |
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_table
import pandas as pd
import numpy as np
import plotly.graph_objs as go
import plotly.tools as tools
from dash.dependencies import Input, Output, State
from dateutil.parser import parse
import squarify
import math
from da... | pd.to_datetime(df_val_per_month.Month) | pandas.to_datetime |
import unittest
import pandas as pd
import argopandas.path as path
class TestPath(unittest.TestCase):
def test_path_info(self):
info = path.info('R2901633_052.nc')
self.assertEqual(list(path.info(['R2901633_052.nc'])), [info])
self.assertIsInstance(path.info(pd.Series(['R2901633_052.nc'])... | pd.DataFrame({'file': ['a']}) | pandas.DataFrame |
"""
TODO Pendletoon, doc this whole module
"""
import logging
import pandas as pd
import capture.devconfig as config
from utils.data_handling import update_sheet_column
from utils import globals
from utils.globals import lab_safeget
modlog = logging.getLogger('capture.prepare.interface')
def _get_reagent_header_ce... | pd.DataFrame() | pandas.DataFrame |
import os
from os.path import join, isfile
import subprocess
import json
import pandas as pd
from abc import ABC, abstractmethod
from typing import List, Dict, Tuple, Optional, Union, Any
import random
from nerblackbox.modules.utils.util_functions import get_dataset_path
from nerblackbox.modules.utils.env_variable imp... | pd.read_csv(formatted_file_path, sep="\t", header=None) | pandas.read_csv |
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
import time
from tqdm import tqdm
import os
import glob
import fnmatch
from src.data.config import site, dat... | pd.concat(li_B, axis=0, ignore_index=True) | pandas.concat |
import os
import pandas as pd
UPLOAD_FOLDER = 'files'
INPUT_FILENAME = 'ab.xlsx'
OUTPUT_FILENAME = 'c.xlsx'
input_file_path = os.path.join(UPLOAD_FOLDER, INPUT_FILENAME)
output_file_path = os.path.join(UPLOAD_FOLDER, OUTPUT_FILENAME)
def test():
# read values from input file
df_ab = | pd.read_excel(input_file_path) | pandas.read_excel |
# -*- coding: UTF-8 -*-
# **********************************************************************************#
# File: Report file
# **********************************************************************************#
import pandas as pd
from copy import deepcopy, copy
from . risk_metrics import *
from .. utils.datet... | pd.DataFrame(output_dict, index=self.trade_date) | pandas.DataFrame |
#
# 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.DataFrame({"x1": ["a", "b", "c", "d"], "x2": ["a", "b", "c", "d"]}, index=pidx) | pandas.DataFrame |
import math
import logging
from itertools import groupby
from datetime import date
from dateutil.relativedelta import relativedelta
from calendar import monthrange
import pandas as pd
from statsmodels.tsa.api import ExponentialSmoothing
from dispatch.incident_type.models import IncidentType
from .models import Inci... | pd.date_range(dataframe.index[0], dataframe.index[-1], freq="M") | pandas.date_range |
import copy
import re
import pandas as pd
from num2words import num2words
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
import nltk
from src import db
import src.param as pm
from pandas.api.types import CategoricalDtype
from src.param import no_of_movie_per_genre, no_imdb_genres
from .search_fea... | CategoricalDtype(rank_ids, ordered=True) | pandas.api.types.CategoricalDtype |
import os
import numpy as np
import pandas as pd
from numpy import abs
from numpy import log
from numpy import sign
from scipy.stats import rankdata
import scipy as sp
import statsmodels.api as sm
from data_source import local_source
from tqdm import tqdm as pb
# region Auxiliary functions
def ts_sum(df, window=10):
... | pd.Series(np.nan, index=y.index) | pandas.Series |
#:!/usr/bin/env python
#: -*- coding: utf-8 -*-
__author__ = 'mayanqiong'
from collections import namedtuple
from datetime import datetime
from typing import Callable, Tuple
import aiohttp
from pandas import DataFrame, Series
from sgqlc.operation import Operation
from tqsdk.backtest import TqBacktest
from tqsdk.dat... | Series(data=[q.expire_datetime for q in quotes]) | pandas.Series |
from posixpath import join
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
def get_csv(dir_name,case_name):
file_name = []
# find .csv file
base_name = os.path.join(os.path.dirname( __file__ ),"..")
join_name = os.path.join(base_name, dir_name, cas... | pd.DataFrame(data=ppo_data) | pandas.DataFrame |
from __future__ import division
from datetime import timedelta
from functools import partial
import itertools
from nose.tools import assert_true
from parameterized import parameterized
import numpy as np
from numpy.testing import assert_array_equal, assert_almost_equal
import pandas as pd
from toolz import merge
fro... | pd.Timestamp('2015-01-09') | pandas.Timestamp |
# -*- coding: utf-8 -*-
from logging import getLogger, Formatter, StreamHandler, INFO, FileHandler
from collections import Counter
from pathlib import Path
import subprocess
import importlib
import math
import sys
import glob
import json
import pickle
import re
import warnings
from sklearn.datasets.base import Bunch
f... | pd.DataFrame(rows) | pandas.DataFrame |
#!/usr/bin/env python3
"""Code that makes use of other people's data.
"""
import arviz as az
import pandas as pd
import numpy as np
import jax.numpy as jnp
import numpyro
import numpyro.distributions as dist
from jax.random import PRNGKey
from .mcmc import _compute_hdi
def assemble_dacey_dataset(df):
"""Assemble... | pd.concat(df) | pandas.concat |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Description : This code do basic statistical tests (i.e., student t-test, fold change,
Benjamini-Hochberg false discovery rate adjustment) for peak table generated
by MZmine-2.53
Copyright : (c) LemasLab, 02/23/2020
Author : <NAME>
Lic... | pd.read_csv(data_file) | pandas.read_csv |
from tpq_base import *
from scipy.io import savemat
from ssmtoybox.ssmod import ConstantVelocity, Radar2DMeasurement
from ssmtoybox.ssinf import StudentProcessKalman, StudentProcessStudent, GaussianProcessKalman, UnscentedKalman, \
CubatureKalman, FullySymmetricStudent
"""
Tracking of an object behaving according ... | pd.DataFrame(vel_data, f_label, m_label) | pandas.DataFrame |
# coding=utf-8
""" Charts from Google Earth Engine data. Inpired by this question
https://gis.stackexchange.com/questions/291823/ui-charts-for-indices-time-series-in-python-api-of-google-earth-engine
and https://youtu.be/FytuB8nFHPQ, but at the moment relaying on `pygal`
library because it's the easiest to integrate w... | pd.concat(dataframes, axis=1, sort=False) | pandas.concat |
import os
import logging
import pandas as pd
import numpy as np
import urllib.request
import requests
import re
import io
import zipfile
import json
from pyseir import OUTPUT_DIR
DATA_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'data')
def load_zip_get_file(url, file, decoder='utf-8'):
... | pd.to_datetime(county_case_data['date']) | pandas.to_datetime |
import pandas as pd
def read_csv(pd,link2015,link2016,link2017,link2018,link2019):
"""
Our Project works on data from 2015 to 2019
The data is taken from OFLC (link in the notebook)
Each year the column names vary this has been fixed in the code below
the dataframe for all years are concaten... | pd.read_csv('/content/gdrive/My Drive/H1B_project/H-1B_Disclosure_Data_FY2018_EOY.csv',encoding='latin-1', low_memory=False) | pandas.read_csv |
import numpy as np
import pandas as pd
from pathlib import Path
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
from com_cheese_api.cmm.utl.file import FileReader
from com_cheese_api.ext.db import url, db, openSession, engine
from konlpy.tag import Okt
from collections import Counter
from wordclou... | pd.merge(category_count, item_size, on='sub1_category', how='right') | pandas.merge |
def hover(x):
index=x.find(".")
if index==-1: return x
else: return x[:index]
def morph(x):
index=x.find(".")
if index==-1: return ""
else: return "+"+x[index+1:]
def stransform(inputw):
if inputw.startswith("["):
return " ʔăḏōnāy"
elif len(inputw)>1 and inputw[0]==inputw[1]:
... | pd.read_csv("_data/indexv.csv",sep="\t",header=None) | pandas.read_csv |
import pandas as pd
medals = r'medals.csv'
medals_r = pd.read_csv(medals, header=0)
r = | pd.isnull(medals_r) | pandas.isnull |
"""Test if functions in ``executers.py`` are properly connecting to the database."""
import pandas as pd
from sqltools import executers
def test_run_query() -> None:
"""Test if ``run_query`` connects to database."""
expected_data = pd.DataFrame({"test": [1]})
query = "SELECT 1 test"
actual_data = exe... | pd.testing.assert_frame_equal(expected_data, actual_data) | pandas.testing.assert_frame_equal |
import pandas as pd
import altair as alt
import ci_mapping
from ci_mapping import logger
from ci_mapping.utils.utils import flatten_lists
def annual_publication_increase(data, filename="annual_publication_increase"):
"""Annual increase of publications.
Args:
data (`pd.DataFrame`): MAG paper data.
... | pd.DataFrame(frame) | pandas.DataFrame |
import numpy as np
import pandas as pd
from scipy.optimize import minimize
__all__ = ["NelsonSiegel", "Vasicek"]
def nelson_siegel(theta0, theta1, theta2, kappa, maturities):
inverse_maturities = 1.0 / maturities
inverse_maturities[inverse_maturities == np.inf] = 0
yields = np.zeros(maturities.shape)
... | pd.DataFrame() | pandas.DataFrame |
# CacheIntervals: Memoization with interval parameters
#
# Copyright (C) <NAME>
#
# This file is part of CacheIntervals.
#
# @author = '<NAME>'
# @email = '<EMAIL>'
import logging
from functools import reduce
import loguru
import numpy as np
import pandas as pd
import pendulum as pdl
import sqlite3
import time
import... | pd.Timestamp(2021, 2, 1) | pandas.Timestamp |
import pandas as pd
import abc
from typing import Iterable
from datetime import datetime
class TableWriter(abc.ABC):
@abc.abstractmethod
def __init__(self, rows: Iterable[Iterable]) -> None:
pass
@abc.abstractmethod
def set_rows(self, rows: Iterable[Iterable]) -> None:
pass
@abc.... | pd.DataFrame(data=rows) | pandas.DataFrame |
# pylint: skip-file
# Pylint said Maximum recursion depth exceeded
import sys
import click
from pathlib import Path
@click.command()
@click.argument('input-path', type=click.Path(exists=True))
@click.argument('checkpoint-path', type=click.Path(exists=True, dir_okay=False))
@click.argument('output-dir', type=click.Pa... | pd.DataFrame.from_records(records) | pandas.DataFrame.from_records |
# -*- coding: utf-8 -*-
"""
These the test the public routines exposed in types/common.py
related to inference and not otherwise tested in types/test_common.py
"""
from warnings import catch_warnings, simplefilter
import collections
import re
from datetime import datetime, date, timedelta, time
from decimal import De... | DataFrame([[1]]) | pandas.DataFrame |
import streamlit as st
import plotly_express as px
import pandas as pd
from plotnine import *
from plotly.tools import mpl_to_plotly as ggplotly
import numpy as np
import math
import scipy.stats as ss
from scipy.stats import *
def app():
# add a select widget to the side bar
st.sidebar.subheader("Discrete Pr... | pd.DataFrame(pmf) | pandas.DataFrame |
from datetime import datetime
import numpy as np
import pytest
import pandas as pd
from pandas import (
Series,
Timestamp,
isna,
notna,
)
import pandas._testing as tm
class TestSeriesClip:
def test_clip(self, datetime_series):
val = datetime_series.median()
assert datetime_serie... | Series([1, 1, 1]) | pandas.Series |
import os
import re
import json
import numpy as np
import pandas as pd
import operator
import base64
os.environ['DJANGO_SETTINGS_MODULE'] = 'zazz_site.settings'
import django
django.setup()
from django.core.exceptions import ObjectDoesNotExist
from django.core import serializers
from zazz import models
from time i... | pd.read_excel(filename) | pandas.read_excel |
import re
import os
import sys
import datetime
import numpy as np
import pandas as pd
from pandas.tseries.offsets import BDay
import stock.utils.symbol_util
from stock.marketdata.storefactory import get_store
from stock.globalvar import *
from config import store_type
from stock.utils.calc_price import get_zt_price
d... | pd.datetime.strptime(sys.argv[1], "%Y-%m-%d") | pandas.datetime.strptime |
#Fuel Consumption Rate Functions for PuMA
#By <NAME>
import pandas as pd
def gallons_consumed_per_month(df):
gpm = df.groupby(df.index.to_period('M')).agg({'gallons': 'sum'})
return gpm
def gallonsPerHour(fuelConsumption):
'''
calculates gallons consumed for each hour monitored
:param fuelConsump... | pd.Grouper(freq='M') | pandas.Grouper |
# -*- coding: utf-8 -*-
from datetime import timedelta
import numpy as np
import pandas as pd
battery_names = ["bl", "vb", "bv"]
def twdb_dot(df_row, dual_well=False, drop_dcp_metadata=True):
"""Parser for twdb DOT dataloggers."""
return _twdb_stevens_or_dot(
df_row, reverse=False, dual_well=dual_we... | pd.concat(data) | pandas.concat |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import pandas as pd
def main():
list_x = [5, 7, 5, 3]
list_y = [7, 5, 3, 5]
list_label = ["north", "east", "south", "west"]
list_edge = ["north", "east", "south", "west"]
data = {
'list_x': list_x,
'list_y': list_y,
'list_label':... | pd.DataFrame(data) | pandas.DataFrame |
'''Trains a simple deep NN on the MNIST dataset.
Gets to 98.40% test accuracy after 20 epochs
(there is *a lot* of margin for parameter tuning).
2 seconds per epoch on a K520 GPU.
'''
from __future__ import print_function
import numpy as np
import pandas as pd
import csv
from keras.layers import ELU
np.random.seed(1... | pd.concat([X_train, X_test]) | pandas.concat |
"""Personal Challenge_Draft.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1-25-B3CO6yVCH9u2vgbhIjyyFeU3tJ3w
"""
# Working environment set up
import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.feature_extraction.text imp... | pd.DataFrame(X_input.iloc[:50000, :], columns=X_input.columns) | pandas.DataFrame |
import pandas as pd
import os
import numpy as np
import gc
import copy
import datetime
import warnings
from tqdm import tqdm
from scipy import sparse
from numpy import array
from scipy.sparse import csr_matrix
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decompos... | pd.merge(age_train, user_app, how='left', on='uId') | pandas.merge |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns # used for plot interactive graph. I like it most for plot
from sklearn.model_selection import train_test_split # to split the data into two parts
from sklearn import preprocessing
from sklearn import metrics
from sklearn.ensem... | pd.read_csv("./data/data.csv", header=0) | pandas.read_csv |
#
# Copyright 2016 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | pd.concat(dfs) | pandas.concat |
# coding: utf-8
# ## Lending Club - classification of loans
#
# This project aims to analyze data for loans through 2007-2015 from Lending Club available on Kaggle. Dataset contains over 887 thousand observations and 74 variables among which one is describing the loan status. The goal is to create machine learning m... | pd.read_csv('../input/loan.csv',parse_dates=True) | pandas.read_csv |
"""
This code implements a support vector classifier using the sklearn package to learn a classification model for a chessboard-like dataset.
Written using Python 3.7
"""
# builtin modules
import os
import psutil
import requests
import sys
import math
# 3rd party modules
import pandas as pd
import numpy as np
impo... | pd.DataFrame(b) | pandas.DataFrame |
import base64
import os, shutil, io, zipfile
from re import L, match
import json
from datetime import datetime, timedelta
from urllib.parse import urljoin
import requests
import pandas as pd
import pint
import numpy as np
#import geopandas as gpd
from django.views.decorators.csrf import csrf_protect, csrf_exempt
from ... | pd.isnull(row['carrier']) | pandas.isnull |
from pathlib import Path
import torch
import sgad
import numpy as np
import copy
import time
from torch.utils.data import DataLoader
from yacs.config import CfgNode as CN
from tqdm import tqdm
from torch import nn
from torchvision.utils import save_image
import torch.nn.functional as F
import pandas
import os
import ti... | pandas.DataFrame.from_dict(losses_all) | pandas.DataFrame.from_dict |
import os
import pandas as pd
import cv2
import scipy.stats as stat
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
from .matplotlibstyle import *
import datetime
class Datahandler():
'Matches EL images paths to... | pd.read_csv(self.pathDic['IVfile'], sep=sep) | pandas.read_csv |
# -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
import operator
from collections import OrderedDict
from datetime import datetime
from itertools import chain
import warnings
import numpy as np
from pandas import (notna, DataFrame, Series, MultiIndex, date_range,
Time... | tm.assert_produces_warning(None) | pandas.util.testing.assert_produces_warning |
import pytz
import pytest
import dateutil
import warnings
import numpy as np
from datetime import timedelta
from itertools import product
import pandas as pd
import pandas._libs.tslib as tslib
import pandas.util.testing as tm
from pandas.errors import PerformanceWarning
from pandas.core.indexes.datetimes import cdate_... | tm.assert_index_equal(result, expected) | pandas.util.testing.assert_index_equal |
from flask import render_template, request, redirect, url_for, session
from app import app
from model import *
from model.main import *
import json
import pandas as pd
import numpy as np
class DataStore():
model=None
model_month=None
sale_model=None
data = DataStore()
@app.route('/', methods=["GET"])
def... | pd.DataFrame(fitted_seri_date) | pandas.DataFrame |
import pandas
import numpy
from itertools import islice
from CommonDef.DefStr import *
from Statistics_TechIndicators.CalcTechIndictors import *
def AddAdjOHLbyAdjC(srcData):
dstData = srcData.copy()
adjClose_offset = srcData[strAdjClose] - srcData[strClose]
dstData[strAdjOpen] = pandas.Series(srcData... | pandas.concat([pDI, nDI, ADX, ADXR], axis=1) | pandas.concat |
import json
from unittest.mock import MagicMock, patch
import numpy as np
import pandas as pd
import pytest
import woodwork as ww
from evalml.exceptions import PipelineScoreError
from evalml.model_understanding.prediction_explanations.explainers import (
abs_error,
cross_entropy,
explain_prediction,
e... | pd.Series([2, 1]) | pandas.Series |
import pandas as _pd
# from atmPy.tools import thermodynamics
from atmPy.general import timeseries as _timeseries
import numpy as _np
from atmPy.aerosols.physics import sampling_efficiency as _sampeff
from atmPy.tools import pandas_tools as _pandas_tools
_date_time_alts = ['uas_datetime']
_pressure_alt = ['StaticP', '... | _pd.to_datetime(df.DateTime, format='%Y-%m-%d %H:%M:%S') | pandas.to_datetime |
from datetime import datetime
import numpy as np
import pytest
from pandas._libs.tslibs import ccalendar
@pytest.mark.parametrize(
"date_tuple,expected",
[
((2001, 3, 1), 60),
((2004, 3, 1), 61),
((1907, 12, 31), 365), # End-of-year, non-leap year.
((2004, 12, 31), 366), # ... | ccalendar.get_day_of_year(dt.year, dt.month, dt.day) | pandas._libs.tslibs.ccalendar.get_day_of_year |
import pandas as pd
import numpy as np
# TODO: fix 'skips', add remaining rows once scrape completes
df_list = []
# 87 turned out weird, figure out what happened here
skips = [87, 101, 144, 215, 347, 350, 360,374]
for i in range(600):
if i in skips:
print('skipping {}'.format(i))
pass
else:
df1 = pd.read_csv... | pd.concat(df_list, 0) | pandas.concat |
from main_app_v3 import forecast_cases, forecast_cases_active
import pandas as pd
import numpy as np
import os
import warnings
warnings.filterwarnings("ignore")
from bs4 import BeautifulSoup as bs
from datetime import date
from selenium import webdriver
def update(region_dict, driver):
df = pd.r... | pd.Series(forecast_active_low) | pandas.Series |
import pandas as pd
import numpy as np
import json
import pycountry_convert as pc
from ai4netmon.Analysis.aggregate_data import data_collectors as dc
from ai4netmon.Analysis.aggregate_data import graph_methods as gm
FILES_LOCATION = 'https://raw.githubusercontent.com/sermpezis/ai4netmon/main/data/misc/'
PATH_AS_RANK ... | pd.DataFrame() | pandas.DataFrame |
from __future__ import annotations
from textwrap import dedent
from typing import TYPE_CHECKING
import numpy as np
import pandas as pd
from pandas.util._decorators import doc
from sklearn.pipeline import FeatureUnion as SKFeatureUnion
from sklearn.preprocessing import MinMaxScaler as SKMinMaxScaler
from sklearn.prepr... | doc(SKOneHotEncoder.__init__) | pandas.util._decorators.doc |
import os
import requests
from bs4 import BeautifulSoup
import pandas as pd
from functools import partial, reduce
import time
import multiprocessing
from collections import defaultdict
from gatheringMethods import *
from time import localtime, strftime
import jinja2
import ftplib
import random
import config_local as cf... | pd.read_pickle(DATABASE_FILE) | pandas.read_pickle |
import logging
import os
# import pathlib
import random
import sys
import time
from itertools import chain
from collections import Iterable
# from deepsense import neptune
import numpy as np
import pandas as pd
import torch
from PIL import Image
import yaml
from imgaug import augmenters as iaa
import imgaug as ia
d... | pd.DataFrame(output, columns=["id", "rle_mask"]) | pandas.DataFrame |
"""General data-related utilities."""
import functools
import operator
import pandas as pd
def cartesian(ranges, names=None):
"""Generates a data frame that is a cartesian product of ranges."""
if names is None:
names = range(len(ranges))
if not ranges:
return pd.DataFrame()
if len(ran... | pd.DataFrame({names[0]: [n] * remaining_size}) | pandas.DataFrame |
import os
import pandas as pd
from unittest import TestCase, main
from metapool.sample_sheet import KLSampleSheet, sample_sheet_to_dataframe
from metapool.prep import (preparations_for_run, remove_qiita_id,
get_run_prefix, is_nonempty_gz_file,
get_machine_code, get... | pd.DataFrame(columns=columns, data=data) | pandas.DataFrame |
#%%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import prot.viz
import prot.estimate
colors = prot.viz.plotting_style()
constants = prot.estimate.load_constants()
dataset_colors = prot.viz.dataset_colors()
from scipy.optimize import curve_fit
def func(x, a, c, d):
return a*np.exp(-c*x)+d... | pd.read_csv('../../data/dai2016_raw_data/dai2016_summary.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 29 11:29:34 2020
@author: Pavan
"""
import pandas as pd
pd.set_option('mode.chained_assignment', None)
import numpy as np
import math
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
mpl.rcParams['font.family'] = 'serif'
import ... | pd.to_numeric(df[col],errors='coerce') | pandas.to_numeric |
"""NLP utils & functions for Glass description processing and salient term extraction."""
from itertools import combinations, chain
from typing import Any, Dict, Iterable, List, Optional
import re
import string
import nltk
import pandas as pd
import toolz.curried as t
from gensim import models
from Levenshtein import ... | pd.DataFrame(subcorpus_freqs) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 3 18:39:39 2019
"""
import re
import pandas as pd # pandas for data handling
import pkg_resources
from acccmip5.utilities.util import _fetch_url, _choose_server
class CMIP5DB:
_Turl = "https://rawgit.com/WCRP-CMIP/CMIP6_CVs/master/src/CMIP6_source_i... | pd.DataFrame(did,columns=['Long_name']) | pandas.DataFrame |
import pandas as pd
import argparse
def extract_simple_repeats(row, current_repeat_table):
current_repeat_table_entries = current_repeat_table.loc[(current_repeat_table[2] <= int(row["POS"])) & (current_repeat_table[3] >= int(row["POS"]))]
occurences = len(current_repeat_table_entries)
if occurences == ... | pd.concat(data_grouped) | pandas.concat |
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