prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import os
import json
import pprint
import numpy as np
import pandas as pd
import tensorflow as tf
from commonmodels2.models.model import ModelBase
from commonmodels2.log.logger import Logger
class DataContainer():
def __init__(self):
self._keystore = {}
def __str__(self):
return pprint.pforma... | pd.DataFrame(data=out_preds, columns=pred_cols) | pandas.DataFrame |
"""
concavity_automator comports multiple scripts automating concavity constraining method for landscape
"""
import lsdtopytools as lsd
import numpy as np
import numba as nb
import pandas as pd
from matplotlib import pyplot as plt
import sys
import matplotlib
from matplotlib.patches import Polygon
from matplotlib.colle... | pd.read_csv(prefix + "all_raster_names.csv") | pandas.read_csv |
import numpy as np
import pandas as pd
import glob
from pmdarima.arima import ndiffs
from pandas.tseries.offsets import QuarterBegin, QuarterEnd
from .hand_select import hand_select
import pandas_datareader.data as web
import xlrd, csv
from openpyxl.workbook import Workbook
from openpyxl.reader.excel import load_workbo... | pd.to_datetime(df['date']) | pandas.to_datetime |
#!/usr/bin/env python
# coding: utf-8
# # Predicting Student Admissions with Neural Networks in Keras
# In this notebook, we predict student admissions to graduate school at UCLA based on three pieces of data:
# - GRE Scores (Test)
# - GPA Scores (Grades)
# - Class rank (1-4)
#
# The dataset originally came from here... | pd.read_csv('student_data.csv') | pandas.read_csv |
# -*- encoding: utf-8 -*-
# @Time : 2020/12/17
# @Author : <NAME>
# @email : <EMAIL>
# UPDATE
# @Time : 2020/12/17
# @Author : <NAME>
# @email : <EMAIL>
import json
import math
import re
import shutil
from collections import OrderedDict
from typing import Union, Tuple
import torch
from .metr... | pd.MultiIndex.from_tuples(df.columns) | pandas.MultiIndex.from_tuples |
import pandas as pd # Пакет для работы с таблицами
import numpy as np # Пакет для работы с векторами и матрицами
# Из библиотеки для работы с текстами вытащим
# методы для предобработки и модели
from gensim import corpora, models
from gensim.models.callbacks import PerplexityMetric
# Пара... | pd.DataFrame(comments) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import sys
import os
import argparse
import toml
import librosa
import pandas as pd
import numpy as np
from tqdm import tqdm
from joblib import Parallel, delayed
sys.path.append(os.getcwd())
from audio.metrics import SI_SDR, STOI, WB_PESQ, NB_PESQ, REGISTERED_METRICS
def calculate_metric(noi... | pd.read_csv(train_path) | pandas.read_csv |
import pandas as pd
import numpy as np
from statsmodels.formula.api import ols
import plotly_express
import plotly.graph_objs as go
from plotly.subplots import make_subplots
# Read in data
batter_data = pd.read_csv("~/Desktop/MLB_FA/Data/fg_bat_data.csv")
del batter_data['Age']
print(len(batter_data))
print(batter_dat... | pd.to_numeric(df['WAR']) | pandas.to_numeric |
import os
import sys
import time
import subprocess
import webbrowser
from collections import defaultdict
import pandas as pd
import numpy as np
from numpy import floor, ceil
path = os.path.dirname(os.path.realpath(__file__))
sys.path.append(path)
pd.set_option('display.float_format', lambda x: '%.3f' % x)
from valida... | pd.concat([failures, row.loc[add_related]]) | pandas.concat |
"""Module to read, check and write a HDSR meetpuntconfiguratie."""
__title__ = "histTags2mpt"
__description__ = "to evaluate a HDSR FEWS-config with a csv with CAW histTags"
__version__ = "0.1.0"
__author__ = "<NAME>"
__author_email__ = "<EMAIL>"
__license__ = "MIT License"
from meetpuntconfig.fews_utilities import Fe... | pd.DataFrame(ex_loc_errors) | pandas.DataFrame |
from multiprocessing import cpu_count
import numba as nb
import numexpr as ne
import numpy as np
import pandas as pd
from typing import Tuple, Union, List, Callable, Iterable
EPS = 1.0e-7
def matrix_balancing_1d(m: np.ndarray, a: np.ndarray, axis: int) -> np.ndarray:
"""Balances a matrix using a single constrain... | pd.DataFrame(intrazonal_scalar * intrzonal_matrix, index=new_zones, columns=new_zones) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 11 20:08:48 2021
@author: jan_c
"""
import pandas as pd
from tkinter import *
from tkinter import filedialog
if __name__ == '__main__':
def frame():
def abrir_archivo():
global archivo
archivo = filedial... | pd.DataFrame(datos["Muestra"]) | pandas.DataFrame |
import os
import pandas as pd
# https://github.com/CSSEGISandData/COVID-19.git
REPOSITORY = "https://raw.githubusercontent.com/CSSEGISandData"
MAIN_FOLDER = "COVID-19/master/csse_covid_19_data/csse_covid_19_time_series"
CONFIRMED_FILE = "time_series_covid19_confirmed_global.csv"
DEATHS_FILE = "time_series_covid19_d... | pd.period_range(df.columns[0], df.columns[-1], freq="D") | pandas.period_range |
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import xarray as xr
def plot_obs_preds(pred_file, obs_file, site_id, start_date, end_date,
outfile=None, info_dict=None):
df_pred = | pd.read_feather(pred_file) | pandas.read_feather |
from sqlalchemy import true
import FinsterTab.W2020.DataForecast
import datetime as dt
from FinsterTab.W2020.dbEngine import DBEngine
import pandas as pd
import sqlalchemy as sal
import numpy
from datetime import datetime, timedelta, date
import pandas_datareader.data as dr
def get_past_data(self):
"""
Get raw... | pd.read_sql_query(query, self.engine) | pandas.read_sql_query |
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import os
import sys
import glob
import pandas as pd
import numpy as np
import gym
from gym import spaces
import sim_analysis
import tqdm
from pprint import pprint
import config
from scipy.spatial.distance import cdist
import sim_utils
... | pd.read_csv(x) | pandas.read_csv |
from datetime import datetime
import warnings
import numpy as np
import pytest
from pandas.core.dtypes.generic import ABCDateOffset
import pandas as pd
from pandas import (
DatetimeIndex,
Index,
PeriodIndex,
Series,
Timestamp,
bdate_range,
date_range,
)
from pandas.tests.test_base import ... | tm.assert_index_equal(idx, result) | pandas.util.testing.assert_index_equal |
import pandas as pd
def dataframe_column_to_str(dataframe, col_name, inplace=False,
return_col=False):
"""Convert columun in the dataframe into string type while preserving NaN
values.
This method is useful when performing join over numeric columns. Currently,
the join m... | pd.isnull(val) | pandas.isnull |
import numpy as np
import pandas as pd
from nilearn import image
import json
import pytest
from neuroquery_image_search import _searching, _datasets
def test_image_search(tmp_path, fake_img):
img_path = str(tmp_path / "img.nii.gz")
fake_img.to_filename(img_path)
results_path = tmp_path / "results.json"... | pd.DataFrame(loaded["b"]) | pandas.DataFrame |
"""
This script loads Google and Apple Mobility reports, builds cleaned reports in different formats and builds merged files from both sources.
Original data:
- Google Community Mobility reports: https://www.google.com/covid19/mobility/
- Apple Mobility Trends reports: https://www.apple.com/covid19/mobili... | pd.read_csv(google_source, low_memory=False) | pandas.read_csv |
import numpy as np
import sys
import pandas as pd
import os
import datetime
from fnmatch import fnmatch
from splitDate import splitDate
from ObligorReminder import ObligorReminder
def UpdatePeopleState(ifEmail):
PeopleExpenditure()
PeopleAccount()
if ifEmail == 'yes':
key = raw_input("Shall people with negat... | pd.read_csv('PeopleList') | pandas.read_csv |
import pandas as pd
import numpy as np
import xml.etree.ElementTree as ET
from math import radians, cos, sin, asin, sqrt
def parse_gpx(filename):
"""Parse data from a GPX file and return a Pandas Dataframe"""
tree = ET.parse(filename)
root = tree.getroot()
# define a namespace dictionary to make ele... | pd.DataFrame(data, index=times) | pandas.DataFrame |
import builtins
from io import StringIO
import numpy as np
import pytest
from pandas.errors import UnsupportedFunctionCall
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series, Timestamp, date_range, isna
import pandas._testing as tm
import pandas.core.nanops as nanops
from pandas.util import ... | Series([1.0, 2.0, np.nan, 3.0]) | pandas.Series |
from datasets import load_dataset
import streamlit as st
import pandas as pd
from googletrans import Translator
import session_state
import time
from fuzzywuzzy import fuzz,process
# Security
#passlib,hashlib,bcrypt,scrypt
import hashlib
# DB Management
import sqlite3
import os
import psycopg2
# impo... | pd.read_csv("mcq.tsv",sep="\t", lineterminator='\n') | pandas.read_csv |
import click
import pandas as pd
from Bio.SeqIO import parse, write
from random import randint, choice
TENMIL = 10 * 1000 * 1000
REGION_SIZES = [1000, 2 * 1000, 4 * 1000, 8 * 1000, 16 * 1000, 32 * 1000]
def insert_repetitive_regions(seq_rec, window_size=TENMIL, region_sizes=REGION_SIZES):
"""Insert repetitive r... | pd.DataFrame.from_dict(regions, orient='index') | pandas.DataFrame.from_dict |
import sys
import numpy as np
import pandas as pd
import sqlalchemy
from sqlalchemy import create_engine
# import sqlite3
def load_data(messages_filepath, categories_filepath):
'''
Function to load data and merge them into one file
Args:
messages_filepath: Filepath to load the messages.csv
catego... | pd.read_csv(messages_filepath) | pandas.read_csv |
import datetime
from datetime import datetime
from functools import reduce
from pkg_resources import normalize_path
import streamlit as st
import pandas as pd
import altair as alt
import plotly.express as px
import plotly.graph_objects as go
import pydeck as pdk
import os
import matplotlib.pyplot as plt
import numpy as... | pd.read_csv(url_deaths, index_col=0) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# # Дружественные числа. Исследование
# ### #Занимательная Математика
#
# #### Весь код на Github, ссылка в конце статьи!
# Импорт библиотек
# In[1]:
from IPython.display import Image
from IPython.core.display import HTML
from IPython.core.interactiveshell import InteractiveS... | pd.DataFrame([(220,284),(1184,1210),(2620,2924),(5020,5564),(6232,6368),(10744,10856),(12285,14595),(17296,18416),(63020,76084),(66928,66992)]) | pandas.DataFrame |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
import logging, os
import h5py
import glob
import numpy as np
import io3d.datareader as DR
import io3d.datawriter as DW
import argparse
import pandas as pd
import imtools.misc as misc
logger = logging.getLogger(__name__)
def sliver_preparation(datadir... | pd.DataFrame.from_dict(dt) | pandas.DataFrame.from_dict |
import itertools
import numpy as np
import pytest
from pandas import (
DataFrame,
Series,
notna,
)
# create the data only once as we are not setting it
def _create_consistency_data():
def create_series():
return [
Series(dtype=np.float64, name="a"),
Series([np.nan] * ... | notna(values) | pandas.notna |
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(q1) | pandas.DataFrame |
import json
import pandas as pd
from vvc.utils import json_utils
def to_df(json_file):
count_summary = {}
time_summary = {}
with open(json_file) as json_data:
data = json.load(json_data)
for frame_id, objects in data['frames'].items():
# Extract counts
if frame_... | pd.DataFrame.from_dict(time_summary, orient='index') | pandas.DataFrame.from_dict |
import os
import copy
import numpy as np
import pandas as pd
import itertools
from tqdm import tqdm
from abc import ABC, abstractmethod
from collections.abc import Iterable, Mapping
from sklearn.model_selection import KFold, GroupKFold, StratifiedKFold
from sklearn.preprocessing import LabelEncoder
from sklearn.utils i... | pd.Series(data[args['stratify_on']]) | pandas.Series |
from functools import partial
from pathlib import Path
import multiprocessing
import glob
import tqdm
import pandas as pd
import numpy as np
import torch
import torchaudio
# fastai2_audio
# add flac to supported audio types
import mimetypes
mimetypes.types_map[".flac"] = "audio/flac"
from fastai2_audio.core.all imp... | pd.read_csv(fname) | pandas.read_csv |
"""
<NAME>
<EMAIL>
<EMAIL>
"""
"""
This is used to generate images containing data from a Slifer Lab NMR cooldown.
The NMR analysis toolsuite produces a file called "global_analysis.csv" which this program needs
in tandem with the raw DAQ .csv to form an image sequence that captures the cooldown datastream.
"""
impo... | pandas.to_datetime(primary_df[variablenames.vd_GA_timecol], format="%Y-%m-%d %H:%M:%S") | pandas.to_datetime |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import time
from sklearn.model_selection import train_test_split
import string
import nltk
from nltk.corpus import stopwords
plt.style.use(style='seaborn')
#%matplotlib inline
df=pd.read_csv('all-data.csv',encoding = "ISO-88... | pd.DataFrame(x_test) | pandas.DataFrame |
"""Test the enrichment of the entire dataset, or specific clusters against gene ontologies associated with complexes"""
import re
import os
import pandas as pd
import numpy as np
from collections import defaultdict
from scipy import stats
from utilities.database_map_and_filter import ortholog_map, uniprot_go_genes
fr... | pd.read_excel(f'{ontology_path}') | pandas.read_excel |
#can choose to import in global namespace
from classes import INSTINCT_process,Split_process,SplitRun_process,Unify_process,INSTINCT_userprocess
from getglobals import PARAMSET_GLOBALS
from misc import get_param_names
from .misc import file_peek,get_difftime
import hashlib
import pandas as pd
import os
from pipe_shap... | pd.read_csv(inFile, dtype=DETdict,compression='gzip') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 9 12:44:52 2019
@author: Jarvis
AQ Map fuctions libary for compter project
"""
#All the imports
#pip install folium
#pip install vincent
#pip install mpld3
import folium
from folium import plugins
#needed to get plot in popup
import vincent
import json... | pd.to_datetime(df.index[0]) | pandas.to_datetime |
import numpy as np
import pandas as pd
from shapely.geometry import Point, Polygon, GeometryCollection
import geopandas
from geopandas import GeoDataFrame, GeoSeries, base, read_file, sjoin
from pandas.util.testing import assert_frame_equal
import pytest
@pytest.fixture()
def dfs(request):
polys1 = GeoSeries(
... | pd.Series(name="index_right", dtype="int64") | pandas.Series |
import argparse
import os
import logging
from netCDF4 import Dataset
import numpy as np
import pandas as pd
def nc2csv_obs_and_M(src_file_path, dst_dir):
with Dataset(src_file_path) as nc:
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
stations = nc.variables['station'][:]
da... | pd.Series(data=id_list, name='Time') | pandas.Series |
from __future__ import absolute_import, division, print_function
import os
import numpy as np
import pandas as pd
import nibabel as nib
from scipy.stats import zscore, gaussian_kde
from sklearn.decomposition import PCA
from matplotlib import pyplot as plt
from matplotlib.cm import ScalarMappable
from matplotlib.colors ... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
from datetime import datetime
from multiprocessing import Pool
from functools import partial
from plots import *
from matplotlib import rcParams
rcParams.update({'figure.autolayout': True})
'''
Notice: This computer software was prepared by Battelle Memorial Institute, hereinaf... | pd.to_datetime(df['time']) | pandas.to_datetime |
import pandas as pd
import numpy as np
import random
from scipy.optimize import minimize
import networkx as nx
import math
from gurobipy import * # Liscence needed, free academic liscence available at https://www.gurobi.com/
# =====================================#
# Component Based Event Simulation
# Stoch... | pd.read_csv(links_file) | pandas.read_csv |
from __future__ import division
import pytest
import numpy as np
from datetime import timedelta
from pandas import (
Interval, IntervalIndex, Index, isna, notna, interval_range, Timestamp,
Timedelta, compat, date_range, timedelta_range, DateOffset)
from pandas.compat import lzip
from pandas.tseries.offsets imp... | Interval(0, 1) | pandas.Interval |
#!/usr/local/bin/python3
# <NAME> - <EMAIL>
# Create Markdown of Spotify Play History
from spotipy import Spotify
from spotipy import util
import pandas as pd
# Get authorization token for this user - resfreshes or asks for permission as needed
my_token = util.prompt_for_user_token(username="1238655357", # Michelle's... | pd.DataFrame() | pandas.DataFrame |
"""
Code was adapted from https://github.com/Britefury/self-ensemble-visual-domain-adapt
"""
"""
Incorporates mean teacher, from:
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
<NAME>, <NAME>
https://arxiv.org/abs/1703.01780
"""
from bayes_opt... | pd.Series(tgt_tea_scores_dict) | pandas.Series |
import cv2
import face_recognition
import pickle
import os
import numpy as np
import pandas as pd
from datetime import datetime
from scipy.spatial import distance as dist
class Attendance(object):
def __init__(self):
self.ENCODINGS_PATH = "encodings.pkl"
self.ATTENDANCE_FILE = "./Attendance.xlsx"
... | pd.isna(temp.iloc[-1]['Check-Out']) | pandas.isna |
# -*- coding: utf-8 -*-
import logging
import numpy as np
import pandas as pd
import scipy.stats
import statsmodels.stats.proportion
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import KBinsDiscretizer
from dku_data_drift.preprocessing import ... | pd.DataFrame(new, columns=['val_new', 'new_density']) | pandas.DataFrame |
"""
Data: Temeprature and Salinity time series from SIO Scripps Pier
Salinity: measured in PSU at the surface (~0.5m) and at depth (~5m)
Temp: measured in degrees C at the surface (~0.5m) and at depth (~5m)
- Timestamp included beginning in 1990
"""
# imports
import sys,os
import pandas as pd
import numpy as np
im... | pd.to_datetime(PDO_data['Date'], format='%Y%m') | pandas.to_datetime |
import pandas as pd
import numpy as np
from sklearn import svm
from sklearn import model_selection
from sklearn.model_selection import learning_curve
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import cross_val_score
from skle... | pd.read_csv(readFile_testfeatures[i]) | pandas.read_csv |
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LogisticRegression
import pdb
from sklearn.metrics import *
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.preproces... | pd.DataFrame(events_data) | pandas.DataFrame |
# this functino is to run the mlp on the 0.5s binned data created by Shashiks
# features: downloaded bytes amount is the feature to be updated.
import pandas as pd
import numpy as np
import os
import math
import argparse
from keras import Sequential
from keras.layers import Dense, BatchNormalization, Dropout, Conv1D, ... | pd.read_csv(v_type_path_synth + '/' + 'V360.csv') | pandas.read_csv |
# Created by rahman at 14:51 2020-03-05 using PyCharm
import os
import random
import pandas as pd
import scipy
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier
from sklearn.linear_model import LogisticRegression
city = 'ny' #'ny'
DATAPATH = '../data/' + city + "/"
... | pd.np.random.permutation(pair_n.index) | pandas.np.random.permutation |
"""
Misc tools for implementing data structures
"""
try:
import cPickle as pickle
except ImportError: # pragma: no cover
import pickle
import itertools
from numpy.lib.format import read_array, write_array
import numpy as np
import pandas.algos as algos
import pandas.lib as lib
import pandas.tslib as tslib
... | Series(result, index=obj.index, copy=False) | pandas.Series |
import datetime
from datetime import timedelta
from distutils.version import LooseVersion
from io import BytesIO
import os
import re
from warnings import catch_warnings, simplefilter
import numpy as np
import pytest
from pandas.compat import is_platform_little_endian, is_platform_windows
import pandas.util._test_deco... | ensure_clean_store(setup_path) | pandas.tests.io.pytables.common.ensure_clean_store |
import time
import numpy as np
import pandas as pd
from molecules import mol_from_smiles
from molecules import add_property
from molecules import (
add_atom_counts, add_bond_counts, add_ring_counts)
from .config import get_dataset_info
from .filesystem import load_dataset
SCORES = ["validity", "novelty", "unique... | pd.read_csv(filename, index_col=0) | pandas.read_csv |
import sys
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
from sqlalchemy import create_engine
def load_data(messages_filepath, categories_filepath):
"""
Loading Messages and Categories from Destination Database
Arguments:
messages_filepath -... | pd.read_csv(messages_filepath) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 28 09:33:11 2022
@author: rossgra
"""
import pandas as pd
import numpy as np
import csv
import glob
import os
Phase = "1H"
Computer = "personal"
if Computer == "work":
USB = "D"
os.chdir("C:/Users/rossgra/Box/OSU, CSC, CQC Project files/"+ Phase +"/Compiler_1_... | pd.read_csv(file, skiprows=data_start) | pandas.read_csv |
from .data import CovidData
import datetime as dt
from matplotlib.offsetbox import AnchoredText
import pandas as pd
import seaborn as sns
import geopandas as gpd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
def pan_duration(date):
"""Return the duration in days of the pandemic.
As... | pd.to_datetime(data.index) | pandas.to_datetime |
import os
import subprocess
import pickle
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy as sc
import pathlib
import threading
import concurrent.futures as cf
from scipy.signal import medfilt
import csv
import tikzplotlib
import encoders_comparison_tool as enc
impo... | pd.concat(rdf_list) | pandas.concat |
"""
Copyright 2019 <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.Series([5, 1, 2], index=[0.75, 0.25, 0.5]) | pandas.Series |
import pandas as pd
def date_formatter(time_stamp,ldf):
"""
Given a numpy timestamp and ldf, inspects which date granularity is appropriate and reformats timestamp accordingly
Example
----------
For changing granularity the results differ as so.
days: '2020-01-01' -> '2020-1-1'
months: '2020-01-01' -> '2020-1'... | pd.DatetimeIndex(date_column) | pandas.DatetimeIndex |
import streamlit as st
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import altair as alt
from requests import get
import re
import os
from bs4 import BeautifulSoup
from urllib.request import Request, urlopen
import datetime
import time
import matplotlib.pyplo... | pd.read_csv('master_df.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
@file
@brief Defines a streaming dataframe.
"""
import pickle
import os
from io import StringIO, BytesIO
from inspect import isfunction
import numpy
import numpy.random as nrandom
import pandas
from pandas.testing import assert_frame_equal
from pandas.io.json import json_normalize
from .data... | pandas.concat(self, axis=0) | pandas.concat |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from tqdm import tqdm
from multiprocessing import Pool
class NoiseGenerator:
def __init__(self, n_frequencies, f_interval):
self.f_interval = f_interval
self.t_end = 1 / self.f_interval
self.n_frequencies = n_frequencie... | pd.Series(self.psd, index=self.fft_frequencies) | pandas.Series |
import numpy as np
import pandas as pd
import os
import librosa
from multiprocessing import Pool
SEED = int(1e9+7e7+17)
np.random.seed(SEED)
default_labels = ['blues']*100 + ['classical']*100 + ['country']*100 + ['disco']*100 + ['hiphop']*100 + ['jazz']*99 + ['metal']*100 + ['pop']*100 + ['reggae']*100 + ['roc... | pd.concat((X,tmpX), axis=0, ignore_index=True) | pandas.concat |
from smach_based_introspection_framework.offline_part.model_training import train_anomaly_classifier
from smach_based_introspection_framework._constant import (
anomaly_classification_feature_selection_folder,
)
from smach_based_introspection_framework.configurables import model_type, model_config, score_metric
fro... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 26 06:04:34 2017
A set of functions to analyze autosal conductivity files/data
@author: <NAME>
"""
# break into two
#docstrings
# keyword argument in calibration default = worm
import csv
import numpy as np
import pandas as pd
import sys
import os... | pd.read_csv(i) | pandas.read_csv |
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('2015-01-04') | pandas.Timestamp |
# -*- coding: utf-8 -*-
"""
Pipeline-GUI for Analysis with MNE-Python
@author: <NAME>
@email: <EMAIL>
@github: https://github.com/marsipu/mne_pipeline_hd
License: BSD (3-clause)
Written on top of MNE-Python
Copyright © 2011-2020, authors of MNE-Python (https://doi.org/10.3389/fnins.2013.00267)
inspired by <NAME>. (2018... | pd.read_csv(pd_funcs_path, sep=';', index_col=0) | pandas.read_csv |
from .context import lux
import pytest
import pandas as pd
import numpy as np
from lux.utils import date_utils
from lux.executor.PandasExecutor import PandasExecutor
def test_dateformatter():
ldf = pd.read_csv("lux/data/car.csv")
ldf["Year"] = pd.to_datetime(ldf["Year"], format='%Y') # change pandas dtype for the c... | pd.DatetimeIndex(ldf["Year"]) | pandas.DatetimeIndex |
#!/usr/bin/env python3
from shapely.geometry import box
import datetime
import numpy as np
import pandas as pd
import geopandas as gpd
import argparse
from datetime import datetime
from make_boxes_from_bounds import find_hucs_of_bounding_boxes
import requests
from concurrent.futures import ThreadPoolExecutor,as_comple... | pd.DataFrame() | pandas.DataFrame |
"""
Functions to facilitate maintenance of apero sheet.
Primarly designed for automated updates with update_sheet.py, but also useful
for interactive editing.
@author: vandalt
"""
import glob
import os
import re
import numpy as np
import pandas as pd
import tqdm
from astropy.io import fits
from astropy.io.votable.tre... | pd.DataFrame(rej_names) | pandas.DataFrame |
'''
Data pre process
@author:
<NAME> (<EMAIL>)
@ created:
25/8/2017
@references:
'''
import os
import json
import pandas as pd
# import pickle
import numpy as np
import dill as pickle
dataset_name = "movies"
TPS_DIR = '../data2014/%s' % dataset_name
# TP_file = os.path.join(TPS_DIR, 'Musical_Instruments_5.json')
# T... | pd.Series(items_id) | pandas.Series |
import requests
import re
from bs4 import BeautifulSoup
import pandas as pd
import sys
from PyQt4.QtGui import QApplication
from PyQt4.QtCore import QUrl
from PyQt4.QtWebKit import QWebPage
import bs4 as bs
import urllib.request
import time
import random
import urllib3
import os
urllib3.disable_warnings(u... | pd.DataFrame(records, columns = ['COMPANY', 'MODEL', 'PRICE','LAUNCH DATE', 'USP', 'DISPLAY', 'CAMERA', 'MEMORY', 'BATTERY', 'THICKNESS', 'PROCESSOR', 'EXTRAS/ LINKS']) | pandas.DataFrame |
# This is a early version of Enumerat.py
import sys
import os
import copy
import time
import json
import pandas as pd
sys.path.append(os.path.abspath('..\\game'))
class Tree(object):
def __init__(self):
self.up = None # tree structure
self.down = None # tree structure
self.layer = None ... | pd.DataFrame.to_csv(P, 'assets//data.csv', encoding='utf-8') | pandas.DataFrame.to_csv |
'''
@Description: code
@Author: MiCi
@Date: 2020-03-13 17:17:47
@LastEditTime: 2020-03-14 08:47:08
@LastEditors: MiCi
'''
import pandas as pd
# import numpy as np
class Basic4(object):
def __init__(self):
return
def basic_use(self):
df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
... | pd.concat([df1, df2], axis=1) | pandas.concat |
from collections import deque
from datetime import datetime
import operator
import re
import numpy as np
import pytest
import pytz
import pandas as pd
from pandas import DataFrame, MultiIndex, Series
import pandas._testing as tm
import pandas.core.common as com
from pandas.core.computation.expressions import _MIN_ELE... | pd.DataFrame((df.values.T + val1).T, index=df.index, columns=df.columns) | pandas.DataFrame |
import pandas as pd
import matplotlib
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
import numpy as np
import seaborn as sns; sns.set()
import csv
from scipy.stats import ranksums
"""
Load data song data
"""
# load in song data
data_path = "C:/Users/abiga/Box " \
"Sync/Ab... | pd.merge(data_subset, time_data, on='CatalogNo') | pandas.merge |
import copy
import itertools
import multiprocessing
import string
import traceback
import warnings
from multiprocessing import Pool
from operator import itemgetter
import jellyfish as jf
import numpy as np
import pandas as pd
from scipy.optimize import linear_sum_assignment
from scipy.stats import wasserstein_distance... | pd.DataFrame.from_dict(daily_windows, orient='index') | pandas.DataFrame.from_dict |
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector
import argparse, sys, glob, os
import pandas as pd
import numpy as np
from PIL import Image
from tensorflow.python.ops import data_flow_ops
import validate_on_lfw
import lfw
import facenet
def parse_arguments(argv):
parser = argpa... | pd.read_csv(args.schema_dir) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 24 18:15:35 2022
Used for plottinf future H2 scenarios for Section 3.4
@author: <NAME>
"""
# Standard Library imports
import argparse
import gzip
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import netCDF4
import numpy as np
import... | pd.to_datetime(date) | pandas.to_datetime |
#! /usr/bin/env python
from __future__ import print_function
import pandas as pd
import numpy as np
import argparse
def generate_csv(num_rows, num_cols, num_distinct_vals, fname):
cols = [str('A' + str(i)) for i in range(num_cols)]
data = []
if type(num_distinct_vals) is list or type(num_distinct_vals) ... | pd.DataFrame(data=data, columns=cols) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: <NAME>
NHK COVID-19 Dataset
Data link: https://www3.nhk.or.jp/n-data/opendata/coronavirus/nhk_news_covid19_prefectures_daily_data.csv
Q: How it works?
A: It gets NHK COVID-19 dataset automatically and saves as working csv, then plots them.
Q: How to use th... | pd.DataFrame(index=[], columns=[]) | pandas.DataFrame |
import coloredlogs
import datetime
import errno
import ipaddress
import logging
import maxminddb
import os
from numpy import source
import pandas as pd
import getpass
import pyesedb as esedb
import sqlite3
import sys
import traceback
import uuid
import binascii
import struct
import time
from argparse import ArgumentPar... | pd.DataFrame() | pandas.DataFrame |
import os
import json
import pandas as pd
import zipfile
from werkzeug.utils import secure_filename
import shutil
import time
from random import randint
from datetime import timedelta
import tempfile
import sys
from elasticsearch import Elasticsearch
##
##
# dataframes
from dataframes import dataframe
# functions
from ... | pd.DataFrame(columns=['name', 'file', 'type', 'error', 'project']) | pandas.DataFrame |
from __future__ import print_function
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from pandas import (Series, Index, Int64Index, Timestamp, Period,
DatetimeIndex, PeriodIndex, TimedeltaIndex,
Timedelta, timedelta_range, date_range, Float64Index... | pd.period_range('2013Q1', periods=1, freq="Q") | pandas.period_range |
import datetime
import os
import time
import pandas as pd
import requests
HOST = 'https://wsn.latice.eu'
#HOST = 'http://localhost:8000' # For the developer
# Prepare the session
TOKEN = os.getenv('WSN_TOKEN') # export WSN_TOKEN=xxx
session = requests.Session()
session.headers.update({'Authorization': f'Token {TOK... | pd.DataFrame(data['rows'], columns=data['columns']) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: andreypoletaev
"""
# =============================================================================
# %% Block 1: initial imports
# =============================================================================
import os, sys, re, glob
if os.path.join(os.path... | pd.DataFrame({'time':a2.index.values[1:-1],'cdt':cdt}) | pandas.DataFrame |
import numpy as np
import pandas as pd
from powersimdata.input.input_data import InputData
from powersimdata.tests.mock_scenario import MockScenario
from postreise.analyze.transmission import congestion
mock_plant = {
"plant_id": ["A", "B", "C", "D"],
"bus_id": [1, 1, 2, 3],
}
mock_bus = {
"bus_id": [1, 2... | pd.DataFrame({"UTC": ["t1"], 1: [410], 2: [0]}) | pandas.DataFrame |
import numpy as np
import pandas as pd
class RegressionTree:
def __init__(self, col_names):
self.train_data, self.test_data = RegressionTree.get_data(col_names)
full_data = | pd.concat([self.train_data, self.test_data]) | pandas.concat |
from IMLearn.utils import split_train_test
from IMLearn.learners.regressors import LinearRegression
from IMLearn.metrics import loss_functions as loss
from typing import NoReturn
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
pio... | pd.get_dummies(X, columns=["zipcode"]) | pandas.get_dummies |
"""
事前準備に
$ pip install pandas
$ pip install openpyxl
$ pip install xlrd
が必要
リファレンス
https://pandas.pydata.org/pandas-docs/stable/reference/index.html
"""
import pandas as pd
import openpyxl
excel_in_path1 = './data/excel_in_header_2sheet.xlsx'
print("********何も指定せず読み込み********")
# 何も指定しない場合は最初のシートになる
df = pd.rea... | pd.read_excel(excel_in_path1, sheet_name="Member") | pandas.read_excel |
import re
import pandas as pd
import numpy as np
from datasets.constants import signal_types
from datasets.sources.source_base import SourceBase
import logging
logger = logging.getLogger(__name__)
class EverionSource(SourceBase):
FILES = {
'signals': r'^CsvData_signals_EV-[A-Z0-9-]{14}\.csv$',
'... | pd.concat([df, chunk[subset]], sort=False) | pandas.concat |
import itertools
import warnings
from typing import Callable
from typing import Optional
import numpy as np
import pandas as pd
from sid.shared import boolean_choices
from sid.validation import validate_return_is_series_or_ndarray
def perform_rapid_tests(
date: pd.Timestamp,
states: pd.DataFrame,
params:... | pd.Series(np.nan, index=states.index) | pandas.Series |
import json
import requests
import pandas as pd
def get_collection(code):
url = 'http://sweetgum.nybg.org/science/api/v1/institutions/' + code
collection = requests.get(url)
if collection.status_code == 200:
collections = json.loads(collection.text)
collections = {'code' : collections['... | pd.DataFrame(collections, index=[0]) | pandas.DataFrame |
#
# Copyright 2020 Capital One Services, 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... | assert_series_equal(expect_out, actual_out, check_names=False) | pandas.util.testing.assert_series_equal |
# -*- coding: utf-8 -*-
"""
author: zengbin93
email: <EMAIL>
create_dt: 2021/12/13 17:39
describe: 事件性能分析
"""
import os
import os.path
import traceback
import pandas as pd
import matplotlib.pyplot as plt
from datetime import timedelta, datetime
from tqdm import tqdm
from typing import Callable, List
from czsc.objects i... | pd.DataFrame(results) | pandas.DataFrame |
import heapq
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
def calculate_rating(a, b, c, b1, c1):
rating = np.mean(a)
user1 = calculate_consin(a, b) * (b1 - rating)
user2 = calculate_consin(a, c) * (c1 - rating)
rating = rating + ((user1 + user2) / (calculate_consin(a, b) ... | pd.read_csv(path) | pandas.read_csv |
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