prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
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# extension: .py
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# language: python
# name: conda-env-holovi... | pd.DataFrame(wrfout_vars) | pandas.DataFrame |
import requests
from pandas import DataFrame, Series
import pandas as pd
from utilities import (LICENSE_KEY, generate_token, master_player_lookup)
import numpy as np
pd.options.mode.chained_assignment = None
######################
# top level functions:
######################
def get_league_rosters(lookup, league_id... | pd.concat([starter_df2, bench_df], ignore_index=True) | pandas.concat |
import pandas as pd
import numpy as np
import random
# report confusion matrix with labels
def confusion_matrix(predicted, true):
if len(predicted) != len(true):
print("Error: lengths of labels do not match")
else:
d = {'predicted': predicted, 'true': true}
df = pd.DataFrame(data=d)... | pd.DataFrame(data=d) | pandas.DataFrame |
#!/usr/bin/env python
import sys, time
import numpy as np
from io import StringIO
import pickle as pickle
from pandas import DataFrame
from pandas import concat
from pandas import read_pickle
from pandas import cut
from pandas import concat
from sklearn.externals import joblib
from sklearn.cross_validation... | concat(blunder_cv_results, axis=1) | pandas.concat |
"""Tools for creating and manipulating neighborhood datasets."""
import os
import pathlib
from warnings import warn
import geopandas as gpd
import pandas as pd
from appdirs import user_data_dir
appname = "geosnap"
appauthor = "geosnap"
data_dir = user_data_dir(appname, appauthor)
def _fetcher(local_path, remote_pat... | pd.concat(blocks, sort=True) | pandas.concat |
import matplotlib.pyplot as plt
import cantools
import pandas as pd
import cv2
import numpy as np
import os
import glob
import re
import subprocess
import json
LOG_FOLDER = "/media/andrei/Samsung_T51/nemodrive/25_nov/session_2/1543155398_log"
CAN_FILE_PATH = os.path.join(LOG_FOLDER, "can_raw.log")
DBC_FILE = "logan.db... | pd.read_csv(OBD_SPEED_FILE, header=None) | pandas.read_csv |
from collections import OrderedDict
import timeit
import numpy as np
import pandas as pd
from randomstate.prng import (mt19937, sfmt, dsfmt, xoroshiro128plus,
xorshift1024, pcg64)
REPS = 3
SIZE = 100000
SETUP = """
import numpy
from numpy import array, random
from randomstate.prng impor... | pd.DataFrame(results) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 19 23:34:57 2019
@author: reynaldo.espana.rey
Web scrapping algorithm to build data set for text generator
source: https://towardsdatascience.com/how-to-web-scrape-with-python-in-4-minutes-bc49186a8460
"""
# ============================================================... | pd.DataFrame({'poem': links}) | pandas.DataFrame |
import copy
import itertools
import os
import numpy as np
import pandas as pd
from pathlib import Path
from sklearn.preprocessing import PowerTransformer
from scipy.stats import yeojohnson
from tqdm import tqdm
import tensorflow as tf
import warnings
warnings.simplefilter("ignore")
n_wavelengths = 5... | pd.DataFrame(temp_storage_float) | pandas.DataFrame |
import random
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Explanation "Where": Plot for explanation in fundamentals chapter
# 1. Generate data with gaussian, uniform and mixed distribution
n = 3000
var = 0.12
# Dimension 1
dim1_sequence_100percent_gaussian = np.rand... | pd.Series(dim1_sequence_100percent_gaussian) | pandas.Series |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
调用wset函数的部分
下载数据的方法
1.在时间上使用折半可以最少的下载数据，但已经下了一部分，要补下时如果挪了一位，又得全重下
2.在文件上，三个文件一组，三组一样，删中间一个，直到不能删了，退出
"""
import os
import pandas as pd
from .utils import asDateTime
def download_sectorconstituent(w, date, sector, windcode, field='wind_code'):
"""
板块成份
中信证... | pd.concat([df, curr_df]) | pandas.concat |
def report_classification(df_features,df_target,algorithms='default',test_size=0.3,scaling=None,
large_data=False,encode='dummy',average='binary',change_data_type = False,
threshold=8,random_state=None):
'''
df_features : Pandas DataFrame
... | pd.DataFrame(columns=["Algorithm_name",'R-Squared','Adj. R-Squared','Train-RMSE','Test-RMSE']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import os
import pandas as pd
from pandas.testing import assert_frame_equal
import camelot
from camelot.core import Table, TableList
from camelot.__version__ import generate_version
from .data import *
testdir = os.path.dirname(os.path.abspath(__file__))
testdir = os.path.join(testdir, "fil... | pd.DataFrame(data_stream_strip_text) | pandas.DataFrame |
# This script performs the statistical analysis for the pollution growth paper
# Importing required modules
import pandas as pd
import numpy as np
import statsmodels.api as stats
from ToTeX import restab
# Reading in the data
data = pd.read_csv('C:/Users/User/Documents/Data/Pollution/pollution_data.csv')... | pd.get_dummies(imp_ch4_rob['Year']) | pandas.get_dummies |
#!/usr/bin/env python
"""
coding=utf-8
Build model for a dataset by identifying type of column along with its
respective parameters.
"""
from __future__ import print_function
from __future__ import division
import copy
import random
import re
from collections import OrderedDict, defaultdict
import warnings
import pic... | pd.DataFrame(data, dtype=object) | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.colors import same_color, to_rgb, to_rgba
import pytest
from numpy.testing import assert_array_equal
from seaborn.external.version import Version
from seaborn._core.rules import categorical_order
from seaborn._core.scales import Nominal,... | pd.Series(["a", "b", "c"]) | pandas.Series |
import pytest
import numpy as np
from datetime import date, timedelta, time, datetime
import dateutil
import pandas as pd
import pandas.util.testing as tm
from pandas.compat import lrange
from pandas.compat.numpy import np_datetime64_compat
from pandas import (DatetimeIndex, Index, date_range, DataFrame,
... | DatetimeIndex(['2000-01-04', '2000-01-01', '2000-01-02']) | pandas.DatetimeIndex |
import itertools
import os
import random
import tempfile
from unittest import mock
import pandas as pd
import pytest
import pickle
import numpy as np
import string
import multiprocessing as mp
from copy import copy
import dask
import dask.dataframe as dd
from dask.dataframe._compat import tm, assert_categorical_equal... | pd.DataFrame({"a": [9, 8, 7], "b": [6, 5, 4], "c": [3, 2, 1]}) | pandas.DataFrame |
import json
import numpy
import pandas
import re
from datetime import timedelta
from GoogleSheetIOStream import GoogleSheetIOStream
class BonusProcessor (object):
def __init__(self, iostream, config_dir='config/', working_folder='Chouta Stand Payroll', input_folder='Input', config_folder='Config'):
self.i... | pandas.to_datetime(hours['Clock Out']) | pandas.to_datetime |
import os
import logging
import pandas as pd
from slackbot import licence_plate
log = logging.getLogger(__name__)
class CarOwners:
# Source data:
# https://intranet.xebia.com/display/XNL/Xebia+Group+Kenteken+Registratie
def __init__(self, csv_path='/data/car-owners.csv'):
self.csv_path = csv_pa... | pd.notnull(self.owners_df) | pandas.notnull |
import re
import numpy as np
import pandas as pd
import random as rd
from sklearn import preprocessing
from sklearn.cluster import KMeans
from sklearn.ensemble import RandomForestRegressor
from sklearn.decomposition import PCA
# Print options
np.set_printoptions(precision=4, threshold=10000, linewidth=160, edgeitems=9... | pd.get_dummies(df_titanic_data['TicketPrefix']) | pandas.get_dummies |
"""This module contains nodes for signal filtering."""
import numpy as np
import pandas as pd
from scipy import signal
from timeflux.core.branch import Branch
from timeflux.core.node import Node
from timeflux.nodes.window import Window
from timeflux_dsp.utils.filters import (
construct_fir_filter,
construct_i... | pd.concat([self._previous, self.i.data], axis=0) | pandas.concat |
""" test the scalar Timedelta """
from datetime import timedelta
import numpy as np
import pytest
from pandas._libs import lib
from pandas._libs.tslibs import (
NaT,
iNaT,
)
import pandas as pd
from pandas import (
Timedelta,
TimedeltaIndex,
offsets,
to_timedelta,
)
import pandas._testing as ... | Timedelta(" 10000D ") | pandas.Timedelta |
"""Contains plotting code used by the web server."""
from datetime import timedelta
from bokeh.models import (
ColumnDataSource,
CustomJS,
DataRange1d,
Range1d,
Whisker,
LabelSet,
HoverTool,
)
from bokeh.models.formatters import DatetimeTickFormatter
from bokeh.layouts import row, Row
from ... | pd.DataFrame(measurement_pairs) | pandas.DataFrame |
#==============================================================================
# Import packages
#==============================================================================
import numpy as np
import pandas as pd
# Utilities
from sklearn.utils import resample
# Transformer to select a subset of the Pandas DataFram... | pd.read_csv(DATAFILE, index_col=ID, header=0, nrows=NTRAINROWS) | pandas.read_csv |
from os.path import join
import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
from src import utils as cutil
def convert_non_monotonic_to_nan(array):
"""Converts a numpy array to a monotonically increasing one.
Args:
array (numpy.ndarray [N,]): input array
... | pd.MultiIndex.from_tuples([("date", "")]) | pandas.MultiIndex.from_tuples |
"""SQL io tests
The SQL tests are broken down in different classes:
- `PandasSQLTest`: base class with common methods for all test classes
- Tests for the public API (only tests with sqlite3)
- `_TestSQLApi` base class
- `TestSQLApi`: test the public API with sqlalchemy engine
- `TestSQLiteFallbackApi`: t... | sql.read_sql_query("SELECT * FROM iris_view", self.conn) | pandas.io.sql.read_sql_query |
# -*- coding: utf-8 -*-
"""Device curtailment plots.
This module creates plots are related to the curtailment of generators.
@author: <NAME>
"""
import os
import logging
import pandas as pd
from collections import OrderedDict
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.dates as mdates
... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
from run import prediction
import tensorflow as tf
import time
import os
np.random.seed(12345)
def top_k_movies(users,ratings_df,k):
"""
Returns top k movies for respective user
INPUTS :
users : list of numbers or number , list of user ids
rating... | pd.read_pickle("user_item_table.pkl") | pandas.read_pickle |
from config import engine
import pandas as pd
import numpy as np
from datetime import datetime
from collections import Counter
def date_difference(my_date, max_date):
'''
This function takes in a single date from the donations dataframe (per row) and compares the difference between that date and the date in w... | pd.DataFrame(df, columns=['matching_id', 'amount', 'close_date']) | pandas.DataFrame |
import seaborn as sns
import matplotlib
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.ticker import ScalarFormatter
from matplotlib import lines
import pandas as pd
import numpy as np
from pathlib import Path
import os
import sys
import csv
im... | pd.read_csv(filename, delimiter='\t') | pandas.read_csv |
from itertools import permutations
from typing import List, Dict
import pandas as pd
import scipy.stats
import numpy as np
import json
import sys, os
from statsmodels import api as sm
from src.constants import AVG_SEED_WINS, ESPN_SCORES
from scipy import stats
myPath = os.path.dirname(os.path.abspath(__file__))
sys.p... | pd.DataFrame(self.simulation_results) | pandas.DataFrame |
import pandas as pd
def _performer_list():
performers = [
['FULL_NAME', 'SHORT_NAME'],
#['Test and Evaluation Team', 'te'],
['Accenture', 'acc'],
['ARA','ara'],
['Astra', 'ast'],
['BlackSky', 'bla'],
['Kitware', 'kit'],
['STR', 'str'],
]
retur... | pd.DataFrame(ssh_site_list) | pandas.DataFrame |
import numpy as np
import arviz as az
import pandas as pd
import seaborn as sns
import matplotlib.pylab as plt
from math import *
import json
import itertools
import os
import re
sns.set_style("whitegrid")
import tools
from tools import toVec
def jSonIterator(j):
yield j
if isinstance(j,dict):
... | pd.merge(data,xs,left_index=True,right_index=True) | pandas.merge |
import sys
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
plt.rcParams['font.size'] = 6
root_path = os.path.dirname(os.path.abspath('__file__'))
# root_path = os.path.abspath(os.path.join(root_path,os.path.pardir))
graphs_path = root_pat... | pd.read_csv(root_path+'/Huaxian/projects/lstm/7_ahead/optimal/opt_pred.csv') | pandas.read_csv |
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
import sklearn as sk
import matplotlib.pyplot as plt
import gc
train = pd.read_csv("train.csv",parse_dates=["activation_date"])
test = | pd.read_csv("test.csv",parse_dates=["activation_date"]) | pandas.read_csv |
import numpy as np
import pandas as pd
from sklearn.model_selection import GroupShuffleSplit as sklearnGroupShuffleSplit
class Split():
def __init__(self, dataset=None):
self.dataset = dataset
def UnSplit(self):
"""Unsplit the dataset by setting all values of the split column to null."""
... | pd.DataFrame(group) | pandas.DataFrame |
from bs4 import BeautifulSoup
from datetime import datetime, timedelta
import warnings
import requests
import pandas as pd
import re
class RegionMatcher:
"""
Ironing out disrepances between RosPotrebNadzor labels and iso_alpha3 codes.
"""
def get_simplified_region(self, x):
x = x.lower()
... | pd.read_csv(self.regions_fname) | pandas.read_csv |
# Copyright 2019 WISE-PaaS/AFS
# 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... | pd.DataFrame(data=data) | pandas.DataFrame |
# =============================================================================
# Pelote Network to Tabular Unit Tests
# =============================================================================
import networkx as nx
import pandas as pd
from pytest import raises
from pelote.exceptions import MissingPandasException... | pd.DataFrame(data={"source": [1], "target": [2], "age": [47]}) | pandas.DataFrame |
"""
Author: <NAME>
Modified: <NAME>
"""
import os
import warnings
import numpy as np
import pandas as pd
import scipy.stats
import pytest
from numpy.testing import assert_almost_equal, assert_allclose
from statsmodels.tools.sm_exceptions import EstimationWarning
from statsmodels.tsa.holtwinters import (ExponentialSmo... | pd.Series(data, index) | pandas.Series |
# coding: utf-8
import os
import re
import numpy as np
import pandas as pd
import ujson as json
patient_ids = []
for filename in os.listdir('./raw'):
# the patient data in PhysioNet contains 6-digits
match = re.search('\d{6}', filename)
if match:
id_ = match.group()
patient_ids.append(id_... | pd.DataFrame(values) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
v17.csv (final submission) ... averaging model of v9s, v13 and v16
"""
from logging import getLogger, Formatter, StreamHandler, INFO, FileHandler
import subprocess
import importlib
import math
from pathlib import Path
import json
import re
import warnings
import tqdm
import click
import tab... | pd.read_csv(fn_valtest, index_col='ImageId') | pandas.read_csv |
import numpy as np
from numpy import nan
import pytest
from pandas._libs import groupby, lib, reduction
from pandas.core.dtypes.common import ensure_int64
from pandas import Index, isna
from pandas.core.groupby.ops import generate_bins_generic
import pandas.util.testing as tm
from pandas.util.testing import assert_a... | generate_bins_generic(values, [4], "right") | pandas.core.groupby.ops.generate_bins_generic |
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
import matplotlib as mpl
from sklearn.model_selection import TimeSeriesSplit
import matplotlib.pyplot as plt
from numpy import nan
cv_splits = 3 # time series cross validator
'''
Load dataset
'''
def load_data(path):
raw_data =... | pd.to_datetime(df_uni.index) | pandas.to_datetime |
#import serial
import keras
import pandas as pd
#import serial.tools.list_ports
import os
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
#from com_serial import *
#from filter import *
from model import *
from sklearn.metrics import confusion_matrix,classification_report
rawdata = []
pipi =... | pd.get_dummies(df['EMOSI']) | pandas.get_dummies |
#code will get the proper values like emyield, marketcap, cacl, etc, and supply a string and value to put back into the dataframe.
import pandas as pd
import numpy as np
import logging
import inspect
from scipy import stats
from dateutil.relativedelta import relativedelta
from datetime import datetime
from scipy import... | pd.isnull([sgaexpense1,sgaexpense2,totalrevenue1,totalrevenue2]) | pandas.isnull |
"""
sess_load_util.py
This module contains functions for loading data from files generated by the
Allen Institute OpenScope experiments for the Credit Assignment Project.
Authors: <NAME>
Date: August, 2018
Note: this code uses python 3.7.
"""
import copy
import logging
from pathlib import Path
import cv2
import... | pd.DataFrame() | pandas.DataFrame |
from config_chbp_eeg import bids_root, deriv_root, N_JOBS
import pandas as pd
from joblib import Parallel, delayed
import mne
import coffeine
from config_chbp_eeg import bids_root
subjects_list = list(bids_root.glob('*'))
subjects_list = list(bids_root.glob('*'))
subjects_df = | pd.read_csv(bids_root / "participants.tsv", sep='\t') | pandas.read_csv |
import os
import json
import pandas as pd
import datetime
import numpy as np
import itertools
from pprint import pprint
from tqdm import tqdm as pbar
from textblob import TextBlob
pd.plotting.register_matplotlib_converters()
#Input: N/A
#Return: A list of strings containing all chat names
def get_chats_names():
ch... | pd.io.json.json_normalize(messages) | pandas.io.json.json_normalize |
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from decouple import config
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
def pandas_df(path_to_jsons):
json_files = [pos_json for pos_json in os.listdir(path_to_jsons) if pos_json.endswith('.json')]
js... | pd.read_json(json_file, lines=True) | pandas.read_json |
from utils.support_functions import calculate_rate_exact_day, calculate_rate_exact_day_cop, \
calculate_rate_exact_day_cop_inversed
from decimal import Decimal
import os
import pandas as pd
from settings import CURRENCIES, calc_categories
from statement_parser.preproccessing import get_category
def conversion(x, ... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
import os
import click
import logging
from pathlib import Path
from dotenv import find_dotenv, load_dotenv
import pandas as pd
import networkx as nx
from src.utils.utils_features import NetworkFeatureComputation
from src.data.financial_network import (
IndustryNetworkCreation,
Industry... | pd.DataFrame(adjacency_matrix, index=node_index, columns=node_index) | pandas.DataFrame |
# 국경일 : getHoliDeInfo
# 공휴일 : getRestDeInfo
import requests
from bs4 import BeautifulSoup
import csv
import json
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# holiday_csv 파일 만드는 함수
def make_holiday_csv(years_lst=[2018, 2019, 2020, 2021], filename='C:/Users/km_mz/Desktop/dacon/daconcup/Data... | pd.pivot_table(df, index='year_month', columns='day', values=df.columns[1]) | pandas.pivot_table |
from mpl_toolkits import mplot3d
import sys, os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from plotnine import *
import copy, math
dist = 10
def find_min_discm_each_hyperparam(df):
x = df.sort_values(by=['Discm_percent', 'Points-Removed']).groupby("Model-count", as_index=False).first(... | pd.read_csv(f"{dataset}/results_{dataset}_method1.csv") | pandas.read_csv |
# AUTOGENERATED! DO NOT EDIT! File to edit: 02_process_duplicates_image_level.ipynb (unless otherwise specified).
__all__ = ['create_vocab', 'convert_category_lists_to_probability_vectors', 'get_test_csvs', 'get_train_csv',
'get_image_level_csvs']
# Cell
from fastcore.all import *
from .find_duplicates imp... | pd.DataFrame(labeled_test, columns=["image_name", "probability"]) | pandas.DataFrame |
import time
import sys
import datetime
import shutil
import os
import py7zr
from ftplib import FTP
import numpy as np
import pandas as pd
from stock.globalvar import *
from stock.utils.symbol_util import symbol_to_exsymbol, is_symbol_kc
from stock.utils.calc_price import get_zt_price
pd.set_option('max_columns', None)... | pd.DataFrame(columns=["max_matched", "max_unmatched", "zt_seconds", "open_incr"]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pandas as pd
def traceZig(bars):
# size = len(bars.index)
last_bar = bars.iloc[0]
go_up = True
go_down = True
zigzags = []
# print(bars)
for t, bar in bars[1:].iterrows():
# 最低价是否低于last_bar True 继续 | False last_bar为zig
if go_down:
if... | pd.DataFrame(zigzags) | pandas.DataFrame |
import typing as T
from pathlib import Path
import defopt
import pandas as pd
import seaborn as sb
import matplotlib.pyplot as plt
import sklearn.metrics as metrics
from sklearn.calibration import calibration_curve
def plot_calibration_curves(dset, ax=None):
if ax is None:
_, ax = plt.subplots()
ax... | pd.Series(scores) | pandas.Series |
#
# Copyright 2015 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 to in wr... | assert_frame_equal(adjustment_dfs[action_name], exp) | pandas.testing.assert_frame_equal |
# import all the required files i.e. numpy , pandas and math library
from graphlib.financialGraph import Data
import numpy as np
import pandas as pd
from pandas import DataFrame , Series
import math
# All the indicators are defined and arranged in Alphabetical order
# ------------------> A <------------------------
... | pd.Series(s2, name="s2") | pandas.Series |
import pandas as pd
import pyomo.environ as pe
import pyomo.gdp as pyogdp
import os
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from itertools import product
class TheatreScheduler:
def __init__(self, case_file_path, session_file_path):
"""
Read case and session data into Pandas Da... | pd.DataFrame(results) | pandas.DataFrame |
from googleapiclient.discovery import build
from datetime import datetime, timedelta
from pandas import DataFrame, Timedelta, to_timedelta
from structures import Structure
from networkdays import networkdays
from calendar import monthrange
class Timesheet:
def __init__(self, credentials, sheetid):
# The I... | Timedelta("00:00:00") | pandas.Timedelta |
"""
Used examples from SO:
https://stackoverflow.com/questions/22780563/group-labels-in-matplotlib-barchart-using-pandas-multiindex
"""
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from itertools import groupby
import numpy as np
def add_line(ax, xpos, ypos):
line = plt.Line... | pd.DataFrame(index=index) | pandas.DataFrame |
# In[2]:
"""
Basic Configurations
"""
configs = {}
configs['cdsign'] = '\\'
configs['path_root'] = 'C:\\VQA_Project\\'
configs['path_datasets'] = configs['path_root'] + 'Datasets' ... | pd.DataFrame(temp) | pandas.DataFrame |
"""
Copyright 2018 <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 distribut... | pd.to_datetime('2019-01-20T01:08:00Z') | pandas.to_datetime |
import os
import pathlib
import pickle
import random
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from S2S_load_sensor_data import read_data_datefolder_hourfile
from S2S_settings import settings
FPS = settings["FPS"]
FRAME_INTERVAL = settings["FRAME_INTERVAL"]
sample_counts = settings... | pd.concat(df_list, axis=1) | pandas.concat |
import os
import json
import requests
from pathlib import Path
import pandas as pd
from .formatUtil import formatTopStocks
from .crawler import Crawler
from datetime import datetime
import tushare as ts
class Plate(Crawler):
def __init__(self):
super().__init__()
self.__fileBasePath = str(os.path.a... | pd.read_csv(filePath) | pandas.read_csv |
"""
/*
* Copyright (C) 2019-2021 University of South Florida
*
* 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 b... | pd.DataFrame.from_dict(matches_dict) | pandas.DataFrame.from_dict |
from __future__ import absolute_import, division, print_function
import logging
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import torch
from torch.distributions import constraints
import pyro
import pyro.distributions as dist
from pyro.infer import Empirica... | pd.DataFrame() | pandas.DataFrame |
"""
@<NAME>
==============================================
Training the different models by multiple sequential trials
==============================================
How to train mixtures and HMMs with various observation models on the same dataset.
"""
import bnpy
import numpy as np
import os
from matplotlib import ... | pd.read_csv(folders + '/' + dat_file, sep='\s+', header=None, skiprows=1) | pandas.read_csv |
from collections import OrderedDict
import datetime
from datetime import timedelta
from io import StringIO
import json
import os
import numpy as np
import pytest
from pandas.compat import is_platform_32bit, is_platform_windows
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame... | read_json(path, encoding=encoding) | pandas.read_json |
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['tag']) | pandas.isnull |
# coding: utf-8
# # JDE ETL Source Design
# ## Goal: Generate source SQL with friendly names and built-in data Conversion
# 1. Pull *ALL* Field metadata based on QA 9.3: Name, Datatype, Decimals
# 2. Pull *Specific* Table fields
# 3. Create SQL mapiing pull with data-conversion
# In[254]:
import numpy as np
impor... | pd.read_sql_query(sql_table_fields, engine) | pandas.read_sql_query |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import spearmanr as sr
from scipy.cluster import hierarchy as hc
from typing import List, Any, Union, Tuple, Optional, Dict
import random, math
# TODO: Remove Dependencies, starting with Sklearn
from sklearn.metrics import roc_curve... | pd.DataFrame(probs) | pandas.DataFrame |
#!/usr/bin/env python
import argparse
import pandas as pd
import numpy as np
import umap
import warnings
import sys
warnings.filterwarnings('ignore')
from bokeh.plotting import figure, output_file, show
from bokeh.layouts import column
from bokeh.core.properties import value
from bokeh.palettes import all_palettes
fro... | pd.concat([raw_data[['FID','IID']], umaped_df], axis=1) | pandas.concat |
from pycountry_convert import country_alpha2_to_continent_code, country_name_to_country_alpha2
import pandas as pd
from sklearn.linear_model import LinearRegression as LR
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
import numpy as np
producers = [
# ' Qatar',
' United States (USA)... | pd.read_csv('./Data/SpotEur.csv', index_col='Date') | pandas.read_csv |
from src.report_generators.base_report_generator import BaseReportGenerator
from src.helpers.preprocess_text import extract_links_from_html
from src.helpers.preprocess_text import extract_from_path
from src.helpers.preprocess_text import extract_subtext
import os
import ast
import re
from bs4 import BeautifulSoup
imp... | pd.isna(content_item['details']) | pandas.isna |
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
housing = fe... | pd.DataFrame(history.history) | pandas.DataFrame |
import asyncio
import threading
import logging
import logging.handlers
import numpy as np
import os
import time
import tqdm
from datetime import datetime
from itertools import zip_longest
from collections import OrderedDict
import pandas as pd
import IPython
from ophyd import Device
from ophyd.status import Status
f... | pd.DataFrame(df_data) | pandas.DataFrame |
from flask import Flask
from flask_restful import Resource, Api, reqparse
import pandas as pd
import ast
app = Flask(__name__)
api = Api(app)
class Users(Resource):
def get(self):
data = pd.read_csv('users.csv') # read local CSV
data = data.to_dict() # convert dataframe to dict
return {... | pd.read_csv('locations.csv') | pandas.read_csv |
from selenium import webdriver
import pandas as pd
from bs4 import BeautifulSoup
import hashlib
import datetime
import multiprocessing as mp
import concurrent as cc
df = pd.DataFrame(columns=['Title','Location','Company','Salary','Sponsored','Description','Time'])
class Indeed():
@staticmethod
... | pd.DataFrame(columns=['Title','Location','Company','Salary','Sponsored','Description','Time']) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 5 22:54:58 2020
@author: arti
"""
import pandas as pd
import numpy as np
student1 = pd.Series({'Korean': np.nan, 'English':80, 'Math':90})
student2 = | pd.Series({'Korean':80, 'Math':90}) | pandas.Series |
'''
This script provides code for training a neural network with entity embeddings
of the 'cat' variables. For more details on entity embedding, see:
https://github.com/entron/entity-embedding-rossmann
8-Fold training with 3 averaged runs per fold. Results may improve with more folds & runs.
'''
i... | pd.DataFrame(full_val_preds) | pandas.DataFrame |
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from causalml.inference.tree import UpliftTreeClassifier
from causalml.inference.tree import UpliftRandomForestClassifier
from causalml.metrics import get_cumgain
from .const import RANDOM_SEED, N_SAMPLE, CONTROL_NAME, TREATME... | pd.DataFrame(y_pred) | 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | pd.DataFrame({'variable': [0, 1, 2, 3]}, index=[0, 1, 2, 3]) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pymatgen as mg
# %% define filepath constant
TABLE_PATH = "./torrance_tabulated.xlsx"
# %% read in the tables
# read in the tabulated data of closed_shell oxides in table 2 in the original paper
closed_shell_oxides = pd.read_excel(TABLE_PATH, sheet_name="table_2")
# renam... | pd.read_excel(TABLE_PATH, sheet_name="table_3") | pandas.read_excel |
import argparse
import json
import os
import math
import heapq
from sklearn.cluster import KMeans
import pandas as pd
import matplotlib.pyplot as plt
import random
import time
from rich.progress import track
# --roadnetFile Shuanglong.json --dir .\tools\generator
# --roadnetFile roadnet_10_10.json --dir .\tools\genera... | pd.DataFrame(data=intersections, columns=['point']) | pandas.DataFrame |
"""Compilation of functions used for data processing."""
import os
import yaml
from itertools import compress
from datetime import datetime
import pandas as pd
import numpy as np
from ideotype.utils import get_filelist
from ideotype import DATA_PATH
def read_sims(path):
"""
Read and condense all maizsim raw... | pd.Series(issues, dtype='str') | pandas.Series |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Home Broker API - Market data downloader
# https://github.com/crapher/pyhomebroker.git
#
# Copyright 2020 <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 cop... | pd.DataFrame(columns=result_columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# @Time : 2018/10/3 下午2:36
# @Author : yidxue
import pandas as pd
from common.util_function import *
"""
http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.concat.html
http://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html
"""
df1 = | pd.DataFrame({'a': ['a', 'c', 'd'], 'b': [4, 6, 7]}) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
import requests
import pandas as pd
from bs4 import BeautifulSoup
import numpy as np
from alphacast import Alphacast
from dotenv import dotenv_values
API_KEY = dotenv_values(".env").get("API_KEY")
alphacast = Alphacast(API_KEY)
page = requests.get('https://www.indec.gob.ar/Nive... | pd.read_excel(file_xls.content, sheet_name='Cuadro 18', skiprows=3) | pandas.read_excel |
import requests
import pandas as pd
import json
import os
from pandas.io.json import json_normalize #package for flattening json in pandas df
import flatjson
import warnings
warnings.filterwarnings('ignore')
def show_table():
jtoken = os.getenv('GITHUB_TOKEN', '')
ztoken ... | pd.concat([new, bak, prog, peer, gw]) | pandas.concat |
import numpy as np
import pandas as pd
import random
from rpy2.robjects.packages import importr
utils = importr('utils')
prodlim = importr('prodlim')
survival = importr('survival')
#KMsurv = importr('KMsurv')
#cvAUC = importr('pROC')
#utils.install_packages('pseudo')
#utils.install_packages('prodlim')
#utils... | pd.merge(left=long_test_df, right=test_clindata_all, how='left',left_on='ID' ,right_on='ID') | pandas.merge |
import math
from tqdm import tqdm
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder, QuantileTransformer
from sklearn.neighbors import NearestNeighbors
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
... | pd.read_excel(data) | pandas.read_excel |
# RESpost.py
import json
from numpy import product
import pandas as pd
import os
import glob
wd = os.getcwd()
simulations_raw = glob.glob(wd+'/cache/2030*.json')
extractor = lambda x: x.split('\\')[-1].replace(".json", '').replace("2030RES_", '')
simulations_keys = [extractor(x) for x in simulations_raw]
#%%
# sim_l... | pd.DataFrame(predf, index=simulations_keys) | pandas.DataFrame |
import argparse
import os
import shutil
import zipfile
import pathlib
import re
from datetime import datetime
import collections
import pandas as pd
import geohash
import math
import helpers
import plotly.express as px
ControlInfo = collections.namedtuple("ControlInfo", ["num_tracks", "date", "duration"])
def parse_... | pd.concat([pdf, edf]) | pandas.concat |
import pandas as pd
from sankeyview.sankey_definition import SankeyDefinition, Ordering, ProcessGroup, Waypoint, Bundle
from sankeyview.sankey_view import sankey_view
from sankeyview.partition import Partition
from sankeyview.dataset import Dataset
def test_sankey_view_accepts_dataframe_as_dataset():
nodes = {
... | pd.DataFrame({'id': ['a1', 'b1']}) | pandas.DataFrame |
import datetime
import pandas as pd
from local_group_support.config.config import get_config
from rebel_management_utilities.action_network import get_forms, query, query_all
FORMATION_DATE = datetime.date(2018, 4, 1)
def get_form(submission):
form_id = submission['action_network:form_id']
has_website = 'a... | pd.to_datetime(submission['created_date']) | pandas.to_datetime |
import wx.grid as gridlib
import pandas as pd
import numpy as np
import copy
import ciw
import re
import math
import statistics
import random
import imp
adapt = imp.load_source('adapt', 'src/adapt.py')
summary = imp.load_source('summary', 'src/Summary.py')
cluster = imp.load_source('cluster', 'src/clustering.py')
tra... | pd.DataFrame({'Activity': letters}) | pandas.DataFrame |
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