prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
from collections import OrderedDict
import numpy as np
import pytest
from pandas._libs.tslib import Timestamp
from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
import pandas as pd
from pandas import Index, MultiIndex, date_range
import pandas.util.testing as tm
def test_constructor_singl... | pd.Categorical(['a', 'a', 'b', 'b', 'c', 'c'], ordered=True) | pandas.Categorical |
"""
Create DASS features for both the ground truth dataset.
"""
# region PREPARE WORKSPACE
# Import dependencies
import os
import pandas as pd
from joblib import load
import sklearn
# Get current working directory
my_path = os.getcwd()
# Load the ground truth datasets
truth_depression = pd.read_csv(my_path + '/dat... | pd.DataFrame(y_depression) | pandas.DataFrame |
#Importing the required packages
from flask import Flask, render_template, request
import os
import pandas as pd
from pandas import ExcelFile
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
from sklearn.preprocessing import StandardScaler, Label... | pd.read_excel('trainfile.xlsx') | pandas.read_excel |
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 25 17:40:53 2021
@author: ali_d
"""
#Pandas
import pandas as pd
import numpy as np
#data
numbers = [20,30,40,50]
print("----")
leters = ["a","b","c","d",40]
pandas_pd = pd.Series(numbers)
pandas_pd1 = pd.Series(leters)
print(pandas_pd)
print(type(pandas_pd))
print(p... | pd.concat([df_customersA,df_ordersB]) | pandas.concat |
import pandas as pd
# setting display options for df
| pd.set_option('display.max_rows', 500) | pandas.set_option |
from datetime import datetime
import pandas as pd
from iexfinance.base import _IEXBase
class APIReader(_IEXBase):
@property
def url(self):
return "status"
def fetch(self):
return super(APIReader, self).fetch()
def _convert_output(self, out):
converted_date = datetime.fromti... | pd.DataFrame(out, index=[converted_date]) | pandas.DataFrame |
import numpy as np
import pandas as pd
from pandas import DataFrame, MultiIndex, Index, Series, isnull
from pandas.compat import lrange
from pandas.util.testing import assert_frame_equal, assert_series_equal
from .common import MixIn
class TestNth(MixIn):
def test_first_last_nth(self):
# tests for first... | assert_frame_equal(last, expected) | pandas.util.testing.assert_frame_equal |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, time, timedelta, date
import sys
import os
import operator
from distutils.version import LooseVersion
import nose
import numpy as np
randn = np.random.randn
from pandas import (Index, Series, TimeSeries, DataFrame,
isnull, date_ran... | offsets.Hour() | pandas.tseries.offsets.Hour |
#!/usr/bin/env python
import click
import os
import codecs
import json
import pandas as pd
from nlppln.utils import create_dirs, get_files
@click.command()
@click.argument('in_dir', type=click.Path(exists=True))
@click.option('--out_dir', '-o', default=os.getcwd(), type=click.Path())
@click.option('--name', '-n', de... | pd.concat(frames, ignore_index=True) | pandas.concat |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import random
from sklearn.model_selection import ShuffleSplit
from datetime import datetime
from sklearn.preprocessing import FunctionTransformer
import scipy.io as sio
datasets = ['bugzilla', 'columba', 'jdt', 'mozilla', 'platform', 'postgres']
key ... | pd.to_datetime(self.data.index, format='%Y/%m/%d %H:%M') | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 3 13:46:06 2021
@author: Sebastian
"""
import sys
sys.path.append('..\\src')
import unittest
import common.globalcontainer as glob
from dataobjects.stock import Stock
import engines.scaffold
import engines.analysis
import pandas as pd
import datetime
import logging
i... | pd.DataFrame(d, index=idx) | pandas.DataFrame |
import pandas as pd
import numpy as np
#from sklearn.feature_selection import VarianceThreshold
from sklearn.feature_selection import mutual_info_classif,chi2
from sklearn.feature_selection import SelectKBest, SelectPercentile
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.metrics i... | pd.Series(mse_values) | pandas.Series |
"""
lib/vector.py
FIT3162 - Team 10 - Final Year Computer Science Project
Copyright <NAME>, <NAME>, <NAME> 2019
Script containing the class to process vector files to get environment data
"""
from osgeo import ogr, osr, gdal
from pathlib import Path
import pandas as pd
def conv_to_point(row):
"""
M... | pd.DataFrame() | pandas.DataFrame |
# Test for evaluering af hvert forecast og sammenligning mellem forecast
import pandas as pd
import numpy as np
from numpy.random import rand
from numpy import ix_
from itertools import product
import chart_studio.plotly as py
import chart_studio
import plotly.graph_objs as go
import statsmodels.api as sm
chart_studio... | pd.DataFrame(f_dm, columns=['f_dm']) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# file_name : process_csv.py
# time : 3/08/2019 14:10
# author : ruiyang
# email : <EMAIL>
import sys
import numpy as np
import pandas as pd
from Bio.PDB.PDBParser import PDBParser
def split_csv(path):
"""
:function: 将csv原文件分列，返回标准由DataFrame组成的csv,并... | pd.read_csv(file) | pandas.read_csv |
import pandas as pd
import numpy as np
#***********************From dict of Series or dicts********************
#dictionary takes key:value
dict = {"Name":pd.Series(["Nahid", "Rafi", "Meem"]),
"Age":pd.Series([21,22,21]),
"Weight":pd.Series([48,75,76]),
"Height":pd.Series([5.3, 5.8, 5.6])}
df =... | pd.DataFrame(dict) | pandas.DataFrame |
# Global Summary
# Infections / Deaths
# Administered / Fully Vaccinated (%)
# Daily Changes / Daily Changes Per 100K (%)
# Infections, Deaths, Administered, Fully Vaccinated
# Country + State
# Line Graphs / Heatmap
# pylint: disable=unused-variable
# pylint: disable=anomalous-backslash-in-string
import generic
im... | pd.merge(dataset[idx][['Date','adm0_a3','Country/Region',stat_key]],candidates[['index',stat_key]],how='inner',left_on='adm0_a3',right_on=stat_key) | pandas.merge |
import os
import numpy as np
import pandas as pd
import pytest
from featuretools import list_primitives
from featuretools.primitives import (
Age,
Count,
Day,
GreaterThan,
Haversine,
Last,
Max,
Mean,
Min,
Mode,
Month,
NumCharacters,
NumUnique,
NumWords,
Perc... | pd.testing.assert_series_equal(expected_rolling_numeric, expected_rolling_offset) | pandas.testing.assert_series_equal |
"""
k-NN module.
**Available routines:**
- class ``KNN``: Builds K-Nearest Neighbours model using cross validation.
Credits
-------
::
Authors:
- Diptesh
- Madhu
Date: Sep 25, 2021
"""
# pylint: disable=invalid-name
# pylint: disable=R0902,R0903,R0913,C0413
from typing import List, Dict, ... | pd.DataFrame(columns=self.x_var) | pandas.DataFrame |
# Source
# Portfolio optimization in finance is the technique of creating a portfolio of assets, for which your investment has the maximum return and minimum risk.
# https://pythoninvest.com/long-read/practical-portfolio-optimisation
# https://github.com/realmistic/PythonInvest-basic-fin-analysis
###################... | pd.set_option('display.max_colwidth', None) | pandas.set_option |
import os
import pandas as pd
import numpy as np
from scipy import stats
from scipy.stats import norm, skewnorm
from datetime import datetime, date, timedelta, timezone
from dateutil import parser
import pytz
from sklearn.model_selection import ParameterGrid
import matplotlib.pyplot as plt
import seaborn as sns
from... | pd.to_datetime(dates["date"]) | pandas.to_datetime |
import numpy as np
from scipy.io import loadmat
import os
from pathlib import Path
# from mpl_toolkits.mplot3d import Axes3D
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
# plotting parameters
sns.set(font_scale=1.1)
sns.set_context("talk")
sns.set_palette(['#701f57', '#ad1759', '#e1... | pd.concat([res_data,res],axis=0) | pandas.concat |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def synthetic_example(mu=0, sigma = 1,N=400, c_boundary=False,y_ints=[5,6,7], SEED=4):
np.random.seed(SEED)
plt.figure(figsize=(9,9))
plt.title("Synthetic Data Example", fontsize=20)
c1 = np.ones( (2,N)) + np.random.normal(0,sig... | pd.DataFrame(X, columns=['Feature1', 'Feature2', 'Target']) | pandas.DataFrame |
import warnings
warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',
FutureWarning)
warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',
FutureWarning)
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_erro... | pd.to_datetime(test.index) | pandas.to_datetime |
import zlib
import base64
import json
import re
import fnmatch
import pendulum
import requests
from redis import Redis
import pandas as pd
from pymongo import MongoClient
import pymongo.errors as merr
from ..constants import YEAR
from .orm import Competition
def _val(v, s=None):
if s is None:
s = {"raw... | pd.DataFrame(l, columns=["player", "team", "g", "p"]) | pandas.DataFrame |
import numpy as np
import pandas as pd
from numba import njit
import pytest
import os
from collections import namedtuple
from itertools import product, combinations
from vectorbt import settings
from vectorbt.utils import checks, config, decorators, math, array, random, enum, data, params
from tests.utils import hash... | pd.Series([1, 2, 3]) | pandas.Series |
# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/loading.ipynb (unless otherwise specified).
__all__ = ['DATA_PATH', 'N_TRAIN', 'N_TEST', 'get_csvs', 'CSV_NAMES_MAP', 'get_meter_data', 'get_nan_stats',
'show_nans', 'test_meter_train_and_test_set', 'get_building_data', 'test_building', 'get_weather_data',
... | pd.read_csv(path) | pandas.read_csv |
"""
Define the SeriesGroupBy and DataFrameGroupBy
classes that hold the groupby interfaces (and some implementations).
These are user facing as the result of the ``df.groupby(...)`` operations,
which here returns a DataFrameGroupBy object.
"""
from __future__ import annotations
from collections import abc
from functo... | doc(Series.nlargest) | pandas.util._decorators.doc |
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 22 08:32:48 2016
@module: choice_tools.py
@name: Helpful Tools for Choice Model Estimation
@author: <NAME>
@summary: Contains functions that help prepare one's data for choice model
estimation or helps speed the estimation process (the 'mappings')... | pd.read_csv(data) | pandas.read_csv |
from abc import abstractmethod
from collections import OrderedDict
import os
import pickle
import re
from typing import Tuple, Union
import pandas as pd
import numpy as np
import gym
from gridworld.log import logger
from gridworld import ComponentEnv
from gridworld.utils import to_scaled, to_raw, maybe_rescale_box_s... | pd.DataFrame(data, columns=["temp_lb", "temp_ub"], index=self.df.index) | pandas.DataFrame |
"""
Tasks
-------
Search and transform jsonable structures, specifically to make it 'easy' to make tabular/csv output for other consumers.
Example
~~~~~~~~~~~~~
*give me a list of all the fields called 'id' in this stupid, gnarly
thing*
>>> Q('id',gnarly_data)
['id1','id2','id3']
Observations:
--... | u('beta') | pandas.compat.u |
import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
import yfinance as yf
from pandas_datareader import data as web
import datetime as dt
from empyrical import*
import quantstats as qs
from darts.models import*
from darts import TimeSeries
from darts.utils.missing_values import... | pd.Series() | pandas.Series |
from itertools import product
import pandas as pd
from sklearn.datasets import load_boston
from vivid.core import AbstractFeature
from vivid.out_of_fold import EnsembleFeature
from vivid.out_of_fold.boosting import XGBoostRegressorOutOfFold, OptunaXGBRegressionOutOfFold, LGBMRegressorOutOfFold
from vivid.out_of_fold.... | pd.DataFrame() | pandas.DataFrame |
import datetime
import hashlib
import os
import time
from warnings import (
catch_warnings,
simplefilter,
)
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
DatetimeIndex,
Index,
MultiIndex,
Series,
Timestamp,
concat,
date_range,
timedelt... | read_hdf(store, "df") | pandas.io.pytables.read_hdf |
"""
Functions for converting object to other types
"""
import numpy as np
import pandas as pd
from pandas.core.common import (_possibly_cast_to_datetime, is_object_dtype,
isnull)
import pandas.lib as lib
# TODO: Remove in 0.18 or 2017, which ever is sooner
def _possibly_convert_objec... | pd.to_datetime(values, errors='coerce', box=False) | pandas.to_datetime |
from datetime import datetime
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
CategoricalIndex,
DataFrame,
Index,
MultiIndex,
Series,
qcut,
)
import pandas._testing as tm
def cartesian_product_for_groupers(result, args, names, fill... | DataFrame({"cat": cat, "ser": ser}) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import time
import warnings
warnings.filterwarnings('ignore')
import pandas as pd, numpy as np
import math, json, gc, random, os, sys
import torch
import logging
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
from sklearn.model_selecti... | pd.read_csv('/kaggle/input/stanford-covid-vaccine/sample_submission.csv') | pandas.read_csv |
import copy
import io
import json
import os
import string
from collections import OrderedDict
from datetime import datetime
from unittest import TestCase
import numpy as np
import pandas as pd
import pytest
import pytz
from hypothesis import (
given,
settings,
)
from hypothesis.strategies import (
dateti... | pd.read_csv(fp.name, dtype=dtypes) | pandas.read_csv |
"""
Copyright 2022 <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
distrib... | pd.DataFrame(A) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import pandas.compat as compat
###############################################################
# Index / Series common tests which may trigger dtype coercions
###############################################... | pd.Series(self.rep[to_key], index=index, name='yyy') | pandas.Series |
import pandas as pd
from pandas import DataFrame
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import f_regression
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR, LinearSVR
from metalfi.src.data.dataset ... | DataFrame(data=data[dmf], columns=dmf) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import numpy as np
import pytest
from numpy.random import RandomState
from numpy import nan
from datetime import datetime
from itertools import permutations
from pandas import (Series, Categorical, CategoricalIndex,
Timestamp, DatetimeIndex, Index, IntervalIndex)
import pan... | tm.assert_numpy_array_equal(result, expected) | pandas.util.testing.assert_numpy_array_equal |
# coding: utf-8
# # Generates the table of the ontological issues.
#
# ### Note this code assumes that you've already computed psx -- the predicted probabilities for all examples in the training set using four-fold cross-validation. If you have no done that you will need to use `imagenet_train_crossval.py` to do thi... | pd.DataFrame(edges) | pandas.DataFrame |
"""
=======================
Visualizing the Results
=======================
Auto-Pytorch uses SMAC to fit individual machine learning algorithms
and then ensembles them together using `Ensemble Selection
<https://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml04.icdm06long.pdf>`_.
The following examples shows ... | pd.DataFrame(estimator.ensemble_performance_history) | pandas.DataFrame |
from json import load
from matplotlib.pyplot import title
from database.database import DbClient
from discord import Embed
import pandas as pd
from util.data import load_data
class Analytics:
def __init__(self, server_id: str, db):
self.server_id = server_id
self.db = db
@staticmethod
de... | pd.value_counts(df["hours"]) | pandas.value_counts |
# coding: utf-8
# # Val Strategy
# > A good validation strategy is key to winning a competition
# >
# > — @CPMP
#
# The val strategy should be trusted in and utilized for all feature engineering tasks and for hyper parameter tuning.
#
# Generally, the best val strategy will somehow mimic the train-test (submission... | pd.read_csv('input/test_identity.csv.zip') | pandas.read_csv |
"""Interface for running a registry of models on a registry of validations."""
from typing import Optional, Tuple
from kotsu.typing import Model, Results, Validation
import functools
import logging
import os
import time
import pandas as pd
from kotsu import store
from kotsu.registration import ModelRegistry, ModelSp... | pd.DataFrame.from_records(results_list) | pandas.DataFrame.from_records |
import importlib
from hydroDL.master import basins
from hydroDL.app import waterQuality
from hydroDL import kPath, utils
from hydroDL.model import trainTS
from hydroDL.data import gageII, usgs
from hydroDL.post import axplot, figplot
from sklearn.linear_model import LinearRegression
from hydroDL.data import usgs, gageI... | pd.datetime(1979, 1, 1) | pandas.datetime |
# 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 u... | assert_frame_equal(df, rdf) | pandas.util.testing.assert_frame_equal |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from stockstats import StockDataFrame
import warnings
import traceback
warnings.filterwarnings('ignore')
import argparse
import re
import sys, os
sys.path.append(os.getcwd())
import os
import requests
from req... | pd.to_timedelta(364 + i, unit='d') | pandas.to_timedelta |
import sys
sys.path.append("../ern/")
sys.path.append("../dies/")
import copy
import torch
import numpy as np
import pandas as pd
from dies.utils import listify
from sklearn.metrics import mean_squared_error as mse
from torch.utils.data.dataloader import DataLoader
from fastai.basic_data import DataBunch
from fastai.b... | pd.DataFrame({"RMSE": res_rmses, "ParkId": park_ids}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
:Author: <NAME>
:Date: 2018. 6. 19.
"""
import numpy as np
import pandas as pd
from pandas.io.excel import ExcelFile
from preprocess.core.columns import *
KIND = 'Kind'
SYMBOL = 'Symbol'
NAME = 'name'
ITEM_NAME = 'Item Name '
ITEM = 'Item'
FREQUENCY = 'Frequency'
SYMBOL_NAME = 'Symbol Nam... | pd.concat([melted_benchmarks, melted_risk_free]) | pandas.concat |
import os
import numpy as np
import pandas as pd
import consts
first_period='01-2017'
last_period='02-2020'
# Coleta a substring referente ao ano e a transforma no tipo int
first_year = int(first_period[3:])
# Coleta a substring referente ao ano e a transforma no tipo int
last_year = int(last_period[3:])
# Coleta... | pd.merge(df, df_mes[['CNES', f'{ano}-{mes}']], how='outer', left_on='CNES', right_on='CNES') | pandas.merge |
import pandas as pd
import numpy as np
import os
path='D:\sufe\A'
files=os.listdir(path)
train_data=pd.read_csv('D:\sufe\A\data_train_changed.csv')
data1=pd.read_csv('D:\sufe\A\contest_ext_crd_cd_ln.tsv',sep='\t')
data2=pd.read_csv('D:\sufe\A\contest_ext_crd_cd_ln_spl.tsv',sep='\t')
p=pd.merge(train_data,data1,on='REP... | pd.merge(p,data2,on='REPORT_ID',how='left') | pandas.merge |
#%%
import pandas as pd
import json
import os
class Preprocessing:
optional = object()
"""
Helper class for Preprocessing Data
"""
@staticmethod
def extract_str_dict_df(df, column):
"""
To parse data that have rows that look like:
"{"/m/01jfsb": "Thriller", "/m/06n90... | pd.concat(df_1[co_to_keep], df_2[co_to_keep]) | pandas.concat |
import Orange
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from parameters import output_dir, rank_dir, input_dir
from classifiers import classifiers_list
from datasets import dataset_biclass, dataset_multiclass
# geometry
order = ['area',
'volume',
'area_volume_ratio',
... | pd.concat([df_B1, df_B2, df_GEO, df_SMOTE, df_SMOTEsvm, df_original, df_dto]) | pandas.concat |
import unittest
import numpy as np
import pandas as pd
from haychecker.chc.metrics import constraint
class TestConstraint(unittest.TestCase):
def test_empty(self):
df = pd.DataFrame()
df["c1"] = []
df["c2"] = []
condition1 = {"column": "c1", "operator": "lt", "value": 1000}
... | pd.DataFrame() | pandas.DataFrame |
import os
from multiprocessing import Pool, cpu_count
from itertools import repeat
import pandas as pd
from solvers.solvers import SOLVER_MAP
from problem_classes.random_qp import RandomQPExample
from problem_classes.eq_qp import EqQPExample
from problem_classes.portfolio import PortfolioExample
from problem_classes.l... | pd.concat(n_results) | pandas.concat |
# -*- coding: utf-8 -*-
from datetime import timedelta, time
import numpy as np
from pandas import (DatetimeIndex, Float64Index, Index, Int64Index,
NaT, Period, PeriodIndex, Series, Timedelta,
TimedeltaIndex, date_range, period_range,
timedelta_range, notnu... | pd.TimedeltaIndex(['1 day', '2 day', '3 day']) | pandas.TimedeltaIndex |
# pylint: disable=redefined-outer-name
import itertools
import time
import pytest
import math
import flask
import pandas as pd
import numpy as np
import json
import psutil # noqa # pylint: disable=unused-import
from bentoml.utils.dataframe_util import _csv_split, _guess_orient
from bentoml.adapters import DataframeI... | pd.concat(dfs) | pandas.concat |
import pandas as pd
import numpy as np
from os.path import join
import sys
sys.path.append('../utils')
import preproc_utils
class CsvLoaderMain:
def __init__(self, data_path):
self.data_path = data_path
def LoadCsv2HDF5(self, tbl_name, write_path = './'):
if tbl_name in ['monva... | pd.to_datetime(chunk.Datetime) | pandas.to_datetime |
"""
Nothing but variate many functions
"""
# from config import *
import matplotlib.pyplot as plt
from pathlib import Path
import numpy.ma as ma
import math
import pandas as pd
import skgstat as skg
import numpy as np
import glob
import geopandas
import os
from skimage.graph import route_through_array
from sklearn.mode... | pd.DataFrame() | pandas.DataFrame |
# Copyright 2019 <NAME> GmbH
# Copyright 2020-2021 <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 ... | pd.DataFrame.from_dict(self._timers) | pandas.DataFrame.from_dict |
# -*- coding: utf-8 -*-
import re,pandas as pd,numpy as np
from pandas import DataFrame
import os
pathDir=os.listdir(r'C:\Users\aklasim\Desktop\Py6.11 Pdf\t1')
pt=(r'C:\Users\aklasim\Desktop\Py6.11 Pdf\t1')
cols=['工单编号','上级工单编号','项目编号','工单描述','上级工单描述','施工单位','合同号','计划服务费','开工日期','完工日期','作业类型','通知单创建','通知单批准','计划','... | eries(cert) | pandas.Series |
from datetime import datetime
from sqlite3 import connect
from typing import Dict, NamedTuple, Optional, Mapping
import json
from black import line_to_string
import kfp.dsl as dsl
import kfp
from kfp.components import func_to_container_op, InputPath, OutputPath
import kfp.compiler as compiler
from kfp.dsl.types import... | pd.read_sql_query(f"select * from drug_classification_staging", con=engine) | pandas.read_sql_query |
"""Process the USCRN station table
ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/stations.tsv
"""
import pandas as pd
from pyiem.util import get_dbconn
def main():
"""Go"""
pgconn = get_dbconn('mesosite', user='mesonet')
cursor = pgconn.cursor()
df = pd.read_csv('stations.tsv', sep=r'\t', engine='... | pd.isnull(station) | pandas.isnull |
# -*- coding: utf-8 -*-
from datetime import datetime
import pandas as pd
import numpy as np
from findy.database.schema.fundamental.finance import BalanceSheet
from findy.database.plugins.eastmoney.common import to_report_period_type
from findy.database.plugins.eastmoney.finance.base_china_stock_finance_recorder impo... | pd.to_datetime(df['timestamp']) | pandas.to_datetime |
from collections import OrderedDict
from datetime import timedelta
import numpy as np
import pytest
from pandas.core.dtypes.dtypes import DatetimeTZDtype
import pandas as pd
from pandas import DataFrame, Series, Timestamp, date_range, option_context
import pandas._testing as tm
def _check_cast(df, v):
"""
... | date_range("20130101", periods=3) | pandas.date_range |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.ticker import FuncFormatter
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
params = {
"axes.titlesize": 14,
"axes.labelsize": 14,
"font.size": 14,
"xtick.labelsize": 14,
... | pd.read_csv(outfile) | pandas.read_csv |
# Imports
import pandas as pd
from edbo.utils import Data
# import pdb
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import matplotlib
from sklearn.cluster import KMeans
import numpy as np
from sklearn import metrics
from edbo.bro import BO_express
from gpytorch.priors import GammaPrior
import rand... | pd.concat([full_yield_df, value]) | pandas.concat |
import requests
import json
import os
from dotenv import load_dotenv
import pandas as pd
import pickle
import time
# Getting the api key from .env
load_dotenv()
API_KEY = os.getenv("RIOT_API_KEY")
# Getting the data from a json file to a string
while True:
try:
matchesID = pickle.load(open("matchesId.p", ... | pd.DataFrame() | pandas.DataFrame |
from datetime import datetime, timedelta
import unittest
from pandas.core.datetools import (
bday, BDay, BQuarterEnd, BMonthEnd, BYearEnd, MonthEnd,
DateOffset, Week, YearBegin, YearEnd, Hour, Minute, Second,
format, ole2datetime, to_datetime, normalize_date,
getOffset, getOffsetName, inferTimeR... | Week(weekday=0) | pandas.core.datetools.Week |
# -*- coding: utf-8 -*-
import logging
from dotenv import find_dotenv, load_dotenv
import pickle
import os
import numpy as np
import pandas as pd
from goactiwe import GoActiwe
from goactiwe.steps import remove_drops
import dask.dataframe as dd
from fastparquet import write, ParquetFile
def fill_df_with_datetime_vars... | pd.TimeGrouper('15min') | pandas.TimeGrouper |
import os
from nose.tools import *
import unittest
import pandas as pd
from py_entitymatching.utils.generic_helper import get_install_path
import py_entitymatching.catalog.catalog_manager as cm
import py_entitymatching.utils.catalog_helper as ch
from py_entitymatching.io.parsers import read_csv_metadata
datasets_path... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# # Vatsal's Code
# This notebook shows you how to build a model for predicting degradation at various locations along RNA sequence.
# * We will first pre-process and tokenize the sequence, secondary structure and loop type.
# * Then, we will use all the information to train a ... | pd.concat(oof_preds_all) | pandas.concat |
import biom
import skbio
import numpy as np
import pandas as pd
from deicode.matrix_completion import MatrixCompletion
from deicode.preprocessing import rclr
from deicode._rpca_defaults import (DEFAULT_RANK, DEFAULT_MSC, DEFAULT_MFC,
DEFAULT_ITERATIONS)
from scipy.linalg import svd
... | pd.Series(s, index=rename_cols) | pandas.Series |
# 指标计算器
import pandas as pd
import talib
def talib_OBV(DataFrame):
res = talib.OBV(DataFrame.close.values, DataFrame.volume.values)
return pd.DataFrame({'OBV': res}, index=DataFrame.index)
def talib_DEMA(DataFrame, N=30):
res = talib.DEMA(DataFrame.close.values, timeperiod=N)
return pd.DataFrame({'... | pd.DataFrame({'TRANGE': res}, index=DataFrame.index) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import re
import warnings
from datetime import timedelta
from itertools import product
import pytest
import numpy as np
import pandas as pd
from pandas import (CategoricalIndex, DataFrame, Index, MultiIndex,
compat, date_range, period_range)
from pandas.compat import PY... | lrange(4) | pandas.compat.lrange |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import os
from RLC.real_chess import agent, environment, learn, tree
import chess
from chess.pgn import Game
opponent = agent.GreedyAgent()
env = environment.Board(opponent, FEN... | pd.DataFrame(learner.reward_trace) | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
from time import time
from lr.models.transformers.RobertaWrapper import RobertaWrapper
from lr.models.transformers.processor import clean_df
from lr.training.util import filter_df_by_label
from tqdm import tqdm
import glob
import argparse
import logging
# Help Function... | pd.DataFrame(dict_) | pandas.DataFrame |
import numpy as np
import pandas as pd
class NB:
def __init__(self):
self.target = "" # name of the label
self.columns = pd.Index([]) # name of the features
self.num_cols = pd.Index([]) # name of numerical features
self.cat_cols = pd.Index([]) # name of categorical features
... | pd.Series(_ll) | pandas.Series |
import pandas as pd
import numpy as np
import category_encoders as ce
from sklearn.preprocessing import normalize
from sklearn.utils import resample
from imblearn.over_sampling import SMOTE, RandomOverSampler
from imblearn.under_sampling import NearMiss
from scipy.stats import skew, kurtosis
from src.utils.common.commo... | pd.concat([df_minority_upsampled, df_majority]) | pandas.concat |
"""
Code for "How Is Earnings News Transmitted to Stock Prices?" by
<NAME> and <NAME>.
Python 2
The main function takes the TAS (Time and Sales) file for one exchange on one
month and extracts only the trades from daily files, creating trade files.
"""
from os import listdir
import os
import pandas as p... | pd.DataFrame(ls, columns=['Filename']) | pandas.DataFrame |
# Regular Imports
from geojson.feature import *
from src.h3_utils import *
import geopandas as gpd
import pandas as pd
def generate_hourly_charges(charges):
# Create a unique identifier
charges['ID'] = [i for i in range(0, charges.shape[0])]
# Create dataframe by minutes in this datetime range
start =... | pd.datetime(year=x.year, month=x.month, day=x.day, hour=x.hour) | pandas.datetime |
import pandas as pd
class PassHash:
def __init__(self):
# Combinations of header labels
self.base = ['Rk', 'Date', 'G#', 'Age', 'Tm', 'Home', 'Opp', 'Result', 'GS']
self.passing = ['pass_cmp', 'pass_att', 'Cmp%', 'pass_yds', 'pass_td', 'Int', 'Rate', 'Sk', 'Sk-Yds',
... | pd.DataFrame(columns=self.punting + self.scoring2p) | pandas.DataFrame |
import pandas as pd
class FeatureExtractor():
def __init__(self):
pass
def fit(self, X_df, y):
pass
def transform(self, X_df):
X_df.index = range(len(X_df))
X_df_new = pd.concat(
[X_df.get(['instant_t', 'windspeed', 'latitude', 'longitude',
... | pd.get_dummies(X_df.nature, prefix='nature', drop_first=True) | pandas.get_dummies |
# ___________________________________________________________________________
#
# Prescient
# Copyright 2020 National Technology & Engineering Solutions of Sandia, LLC
# (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S.
# Government retains certain rights in this software.
# This software is ... | pd.DataFrame({'datetime':dt, 'forecasts':forecast[site], 'actuals':actual[site]}) | pandas.DataFrame |
# coding: utf-8
# ---
#
# _You are currently looking at **version 1.5** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-data-analysis/resources/0dhYG) course resource._
#
# ... | pd.merge(energy,GDP,on='Country') | pandas.merge |
import io
import numpy as np
import pytest
from pandas.compat._optional import VERSIONS
from pandas import (
DataFrame,
date_range,
read_csv,
read_excel,
read_feather,
read_json,
read_parquet,
read_pickle,
read_stata,
read_table,
)
import pandas._testing as tm
from pandas.util... | read_csv("s3://pandas-test/tips.csv.gz", storage_options=s3so) | pandas.read_csv |
#! /usr/bin/env python
from datetime import datetime, timedelta
import hb_config
import mwapi
from mwapi.errors import APIError
import requests
from requests_oauthlib import OAuth1
import pandas as pd
import json
#TODO
#encapsulate what's in MAIN
#pull hard-coded vals to hb_config
#docstrings
#rmv my dumb API functio... | pd.DataFrame(mems) | pandas.DataFrame |
"""
this is compilation of functions to analyse BEAM-related data for NYC simulation
"""
from urllib.error import HTTPError
import matplotlib.pyplot as plt
import numpy as np
import time
import datetime as dt
import pandas as pd
from shapely.geometry import Point
from shapely.geometry.polygon import Polygon
from io ... | pd.concat([run, pc], axis=0) | pandas.concat |
# encoding: utf-8
# (c) 2017-2021 Open Risk (https://www.openriskmanagement.com)
#
# TransitionMatrix is licensed under the Apache 2.0 license a copy of which is included
# in the source distribution of TransitionMatrix. This is notwithstanding any licenses of
# third-party software included in this distribution. You ... | pd.read_csv('../../datasets/rating_data_raw.csv') | pandas.read_csv |
from flask import Flask, render_template, request, redirect, url_for, session
import pandas as pd
import pymysql
import os
import io
#from werkzeug.utils import secure_filename
from pulp import *
import numpy as np
import pymysql
import pymysql.cursors
from pandas.io import sql
#from sqlalchemy import create... | pd.DataFrame(csdata) | pandas.DataFrame |
# Copyright 2021 The Funnel Rocket Maintainers
#
# 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 o... | Series(data=str_user_ids) | pandas.Series |
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import os
from PIL import Image
import numpy as np
# import torch
import json
import sys
from tqdm import tqdm, trange
from pycocotools.coco import COCO
import skimage.io as io
import pylab
from convert_fat_coco import *
from mpl_toolkits.axes_grid1 impo... | pd.concat(li, axis=0, ignore_index=False) | pandas.concat |
import numpy as np
import pandas as pd
import os.path
from glob import glob
import scipy.stats as ss
from sklearn.metrics import r2_score, roc_auc_score, average_precision_score
COLORS = {
'orange': '#f0593e',
'dark_red': '#7c2712',
'red': '#ed1d25',
'yellow': '#ed9f22',
'light_green': '#67bec5... | pd.DataFrame(columns=['tf', 'comp_group', 'p_score']) | pandas.DataFrame |
import pandas as pd
from sklearn.base import BaseEstimator
import numpy as np
import warnings
class TopCause(BaseEstimator):
'''TopCause finds the single largest action to improve a performance metric.
Parameters
----------
max_p : float
maximum allowed probability of error (default: 0.05)
... | pd.DataFrame(results) | pandas.DataFrame |
import pytest
import os
from mapping import util
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
@pytest.fixture
def price_files():
cdir = os.path.dirname(__file__)
path = os.path.join(cdir, 'data/')
files = ... | TS('2015-01-03') | pandas.Timestamp |
from __future__ import absolute_import, division, print_function
import datetime
import pandas as pd
from config import *
def _drop_in_time_slice(m2m, m2b, m5cb, time_slice, to_drop):
"""Drops certain members from data structures, only in a given time slice.
This can be useful for removing people who weren't... | pd.Timestamp(period1_end, tz=time_zone) | pandas.Timestamp |
"""
Info about all of noaa data can be found at:
http://www.ndbc.noaa.gov/docs/ndbc_web_data_guide.pdf
What all the values mean:
http://www.ndbc.noaa.gov/measdes.shtml
WDIR Wind direction (degrees clockwise from true N).
WSPD Wind speed (m/s) averaged over an eight-minute period.
GST P... | pd.DataFrame() | pandas.DataFrame |
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