prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
"""Module providing functions to plot data collected during sleep studies."""
import datetime
from typing import Dict, Iterable, List, Optional, Sequence, Tuple, Union
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import matplotlib.ticker as mticks
import pandas as pd
import seaborn as sns
from fau... | pd.to_datetime(sleep_endpoints["sleep_onset"]) | pandas.to_datetime |
from sklearn.linear_model import LogisticRegression
import pandas as pd
import numpy as np
from classifiers.gaussian_bayesian import BayesGaussian
from classifiers.parzen_bayesian import KDEClassifier
from classifiers.ensemble import Ensemble
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selecti... | pd.DataFrame(fcm_results) | pandas.DataFrame |
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
from numpy import linalg
from src import configs
def do_preprocessing(debug=False, save=True):
train = | pd.read_csv(configs.train, index_col='id') | pandas.read_csv |
# -*- coding: utf-8 -*-
import os
import pandas as pd
from .material_properties import MaterialProperties
from .material_transport_properties import MaterialTransportProperties
from .time_series import TimeSeries
__all__ = ['write_hot_start_file', 'read_bc_file',
'write_bc_file']
def write_hot_start_file(... | pd.DataFrame.from_records(temp_data['nb_sdr_list'], columns=labels) | pandas.DataFrame.from_records |
# generator functions to simplify and streamline signal injection and recovery of filterbanks
import setigen as stg
import numpy as np
import pandas as pd
import os
import astropy.units as u
from turbo_seti.find_doppler.find_doppler import FindDoppler
from turbo_seti.find_event.find_event import read_dat
from blimpy im... | pd.concat(results, ignore_index=True) | pandas.concat |
import datetime as dt
import numpy as np
import pandas as pd
import pytest
from dutil.transform import ht
@pytest.mark.parametrize(
"data, expected",
[
((0, 1, 3, 5, -1), (0, 1, 5, -1)),
([0, 1, 3, 5, -1], [0, 1, 5, -1]),
([0, 1.0, 3232.22, 5.0, -1.0, np.nan], [0, 1.0, -1.0, np.nan])... | pd.Series([0, 1.0, 3232.22, -1.0, np.nan]) | pandas.Series |
# -*- coding: utf-8 -*-
import pytest
import os
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal, assert_series_equal
import numpy.testing as npt
from numpy.linalg import norm, lstsq
from numpy.random import randn
from flaky import flaky
from lifelines import CoxPHFitter, WeibullA... | assert_frame_equal(df, expected, check_like=True) | pandas.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
"""Add model years to an existing Scenario."""
# Sections of the code:
#
# I. Required python packages are imported
# II. Generic utilities for dataframe manipulation
# III. The main function, add_year()
# IV. Function add_year_set() for adding and modifying the sets
# V. Function add_year... | pd.isna(df_yrs[yr_diff_new[0]]) | pandas.isna |
# -*- coding: utf-8 -*-
"""
Created on Wed May 20 17:30:42 2020
@author: bruger
his module grab the age distribution and saves the dataframe
"""
import pandas as pd
import requests
from pathlib import Path
try:
0/0 # uncomment to force read from github
agedistribution_df = pd.read_excel('data/agedistri... | pd.DataFrame(agedic) | pandas.DataFrame |
# GNU Lesser General Public License v3.0 only
# Copyright (C) 2020 Artefact
# <EMAIL>
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation; either
# version 3 of the License, or (at your opti... | pd.DataFrame({text_column: ["This is a text", "This is another text"]}) | pandas.DataFrame |
from json_extract import flatten_json
import requests
from tabulate import tabulate
import math
import numpy
import pandas
import urllib.request, urllib.parse
import json
source_csv = pandas.read_csv("~/Downloads/ncvoter_statewide_latsandlongs_copy.csv", sep="\t")
count = 0
missing = 0
successful = 0
source_csv['l... | pandas.isna(source_csv.latitude) | pandas.isna |
""" test the scalar Timestamp """
import pytz
import pytest
import dateutil
import calendar
import locale
import numpy as np
from dateutil.tz import tzutc
from pytz import timezone, utc
from datetime import datetime, timedelta
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from pandas.ts... | Timestamp(2015, 11, 12) | pandas.Timestamp |
import numpy as np
import pandas as pd
import pickle5 as pkl
def read(model, class_name, method):
with open(f"/disks/bigger/xai_methods/distances/dataframes/{class_name}/{model}/{method}.pkl", "rb") as f:
data = pkl.load(f).values
return data
def main():
hermitries = []
for model in ["de... | pd.DataFrame(hermitries) | pandas.DataFrame |
from unittest import TestCase, main
import os
import dask.dataframe as dd
import numpy as np
import numpy.testing as npt
import pandas as pd
import pandas.testing as pdt
import skbio
from qiime2 import Metadata
from qiime2.plugin.testing import TestPluginBase
from q2_sidle import (KmerMapFormat,
... | pdt.assert_frame_equal(known, test) | pandas.testing.assert_frame_equal |
# Copyright 2020 (c) Cognizant Digital Business, Evolutionary AI. All rights reserved. Issued under the Apache 2.0 License.
import argparse
import os
import pandas as pd
import numpy as np
import pickle
from econ.econ_predictor import econ_predictor
# import econ.econ_utils as econ_utils
#
# import os,sys,inspect
# c... | pd.tseries.offsets.QuarterEnd() | pandas.tseries.offsets.QuarterEnd |
# READ/WRITE REPORTS AS JSON
import json
import pandas as pd
from pandas.io.json import json_normalize
from swmmio.utils import spatial
from swmmio.graphics import swmm_graphics as sg
def decode_report(rpt_path):
#read report from json into a dict
with open(rpt_path, 'r') as f:
read_rpt = json.loads(f... | pd.notnull(rpt.flood_comparison.Category) | pandas.notnull |
# -*- coding: utf-8 -*-
from typing import List, Union, Mapping, Dict, Tuple, Callable
import yaml
import os, sys, time
from shutil import copyfile, copy
import glob
import numpy as np
import pandas as pd
from ...model.core_model import AbstractCoreModel
from ...scope.scope import Scope
from ...database.database impor... | pd.DataFrame(measures_dictionary, index=[experiment_id]) | pandas.DataFrame |
"""
ReadData
========
Converts the data from matlab to a HDF5 data structure.
Data is stored in row-major order- where each row is a next sample.
"""
import deepdish as dd
import numpy as np
import scipy.io as sio
import glob
import os
from collections import Counter
import pandas as pd
def Load_Rest():
"""Load... | pd.read_csv('/Users/ryszardcetnarski/Desktop/Nencki/Badanie_NFB/Dane/channels.csv') | pandas.read_csv |
# -*- coding: UTF-8 -*-
# create_paper_figures.py
import numpy as np
import pandas as pd
import statsmodels.formula.api as sm
import os
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
from matplotlib.lines import Line2D
try:
import cantera as ct
except:
raise Exception("I am not... | pd.Series() | pandas.Series |
from model_lstm.utils import data_management as dm
from model_lstm.config import config
import numpy as np
import pandas as pd
import logging
logger = logging.getLogger(__name__)
lstm_pipeline = dm.load_fitted_pipeline()
def predict_many(X):
df = pd.DataFrame({"text":X})
pred = lstm_pipeline.predict(df)
... | pd.DataFrame({"text":[X]}) | pandas.DataFrame |
import pandas as pd
import numpy as np
from sklearn import datasets, linear_model
from __future__ import division
class LRPI:
def __init__(self, normalize=False, n_jobs=1, t_value = 2.13144955):
self.normalize = normalize
self.n_jobs = n_jobs
self.LR = linear_model.LinearRegression(normaliz... | pd.DataFrame(X_train.values) | pandas.DataFrame |
import numpy as np
import pandas as pd
# Compute moving averages across a defined window. Used to compute regimes
# INTERPRETATION: The regime is the short MAV minus the long MAV. A positive value indicates
# a bullish trend, so we want to buy as soon as the regime turns positive.
# Therefore, we want to identify in o... | pd.to_datetime(transdat.index) | pandas.to_datetime |
import requests
import time
import pandas as pd
states_list = ['Alaska', 'Alabama', 'Arkansas', 'Arizona', 'California',
'Colorado', 'Connecticut', 'Delaware', 'Florida', 'Georgia',
'Hawaii', 'Iowa', 'Idaho', 'Illinois', 'Indiana', 'Kansas', 'Kentucky',
'Louisiana', 'Massachusetts', 'Maryland', 'Maine', 'Michigan... | pd.DataFrame.from_records(race_records) | pandas.DataFrame.from_records |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
INPUT_DIR = "~/data/query-result/"
OUTPUT_DIR = "~/data/summary-stats/"
RES_LIST = ['cpu', 'mem', 'net_send', 'net_receive', 'disk_read', 'disk_write']
METRIC_LIST = ['_util_per_instance_95p', '_util_per_instance_max', '_util_per_pool', '_util_per_... | pd.read_csv(csv_num, sep=',') | pandas.read_csv |
"""
Code for transforming EIA data that pertains to more than one EIA Form.
This module helps normalize EIA datasets and infers additonal connections
between EIA entities (i.e. utilities, plants, units, generators...). This
includes:
- compiling a master list of plant, utility, boiler, and generator IDs that
appear... | pd.StringDtype() | pandas.StringDtype |
import pandas as pd
from sklearn.preprocessing import LabelEncoder
import pickle
import nltk
import string
import re
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.naive_bayes import Berno... | pd.read_excel('/Users/dyuwan/Downloads/song_dataset_lyrics.xlsx') | pandas.read_excel |
import yfinance as yf
from datetime import date, datetime, timedelta
from pandas.tseries.offsets import BDay
import pandas_market_calendars as mcal
import pandas as pd
import numpy as np
import requests
from lxml import html
import ssl
def get_df_list(sym):
def get_page(url):
headers = {
... | pd.DataFrame(dfttm1, columns=['TTM']) | pandas.DataFrame |
import os
import pickle
import sys
from pathlib import Path
from typing import Union
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from Bio import pairwise2
from scipy import interp
from scipy.stats import linregress
from sklearn.metrics import roc_curve, auc, precision_recall_curve
import th... | pd.Series(d) | pandas.Series |
import re
import pandas as pd
def match_variables(variables, pattern, columns, value_name="capacity"):
"""Search through dictionary of variables, extracting data frame of values.
:param dict variables: dictionary, keys are strings, values are dictionaries
with key "Value" and float value.
:param... | pd.Series(dc_branch_ids, dtype=str) | pandas.Series |
#Project: GBS Tool
# Author: Dr. <NAME>, <EMAIL>, denamics GmbH
# Date: January 16, 2018
# License: MIT License (see LICENSE file of this package for more information)
# Contains the main flow of the optimization as it is to be called from the GBSController.
import os
import time
import numpy as np
import pandas as ... | pd.Series(genList) | pandas.Series |
import os
import gc
import argparse
import pandas as pd
import numpy as np
import keras.backend as K
from keras.preprocessing.image import Iterator
from src.data.category_idx import map_categories
from keras.layers.embeddings import Embedding
from keras.layers import Flatten
from keras.layers import Input
from keras.la... | pd.read_csv(category_idx_csv) | pandas.read_csv |
#!/usr/bin/python3
from argparse import ArgumentParser
import urllib.request
import pandas as pd
import re
import os
import datetime
from time import sleep
def make_dir():
if not os.path.exists('DATABASE'):
os.makedirs('DATABASE')
def get_content(url, year):
try:
request = u... | pd.DataFrame(items1, columns=['html', 'title']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
This script runs the data extraction, data cleaning, calculations and visualization
scripts used in "Regionalized footprints of battery electric vehicles in Europe"
Users can run two types of experiments concurrently; the electricity sampling period
('el_experiments'), and vehicle parameter... | pd.ExcelWriter(SI_fp, engine="openpyxl") | pandas.ExcelWriter |
# This file is called separately from the rest of the program. This file takes the original data and creates cleaner csvs for app.py to use
import gsw
import numpy as np
import pandas as pd
# all of the parameters from the full data: 'yyyy-mm-ddThh:mm:ss.sss', 'Longitude [degrees_east]', 'Latitude [degrees_north]',
#... | pd.concat(data, axis=1, keys=headers) | pandas.concat |
import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
Timestamp,
date_range,
)
import pandas._testing as tm
@pytest.mark.parametrize("bad_raw", [None, 1, 0])
def test_rolling_apply_invalid_raw(bad_raw):
with pytest.raises(ValueError, m... | Series([None, None, None]) | pandas.Series |
import requests
import pandas as pd
import json
# Assumes you've gotten a HUD census key at ./config/hudkey
# from https://www.huduser.gov/hudapi/public/register?comingfrom=1
# Supports Zip->Tract or Tract->Zip
request_url = 'https://www.huduser.gov/hudapi/public/register?comingfrom=1'
class HUDCall():
def __in... | pd.DataFrame.from_dict(_dict, orient='index') | pandas.DataFrame.from_dict |
# Python 3 server example
from http.server import BaseHTTPRequestHandler, HTTPServer
from keras.models import Sequential
from keras import layers
import time
import math
import keras
import json
import pandas as pd
from sklearn.model_selection import train_test_split
import numpy as np
from keras.preprocessing.sequence... | pd.DataFrame(data=d) | pandas.DataFrame |
"""
This module tests high level dataset API functions which require entire datasets, indices, etc
"""
from collections import OrderedDict
import pandas as pd
import pandas.testing as pdt
from kartothek.core.dataset import DatasetMetadata
from kartothek.core.index import ExplicitSecondaryIndex
def test_dataset_ge... | pdt.assert_frame_equal(result, expected) | pandas.testing.assert_frame_equal |
"""
"""
import numpy as np
import pandas as pd
import itertools
from scipy.sparse import csr_matrix
import networkx as nx
def make_mapping(unique_individuals):
"""
Create a mapping between a set of unique indivdual id's and indices into
co-occurrence and distance matrices.
parameters
----------
... | pd.isnull(diff) | pandas.isnull |
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 14 19:28:11 2020
@author: Ray
@email: <EMAIL>
@wechat: RayTing0305
"""
###chapter5
import pandas as pd
from pandas import Series, DataFrame
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(12345)
plt.rc('figure', figsize=(10, 6))
PREV... | pd.DataFrame(pdata) | pandas.DataFrame |
#!/usr/bin/python3
#-*- coding: UTF-8 -*-
#import os
import sys
import logging
import argparse
import time
import re
import json
import xml.etree.ElementTree
from xml.dom import minidom
import numpy as np
import pandas as pd
def parse_args():
"""Parse the command line for options."""
parser = argparse.ArgumentPars... | pd.DataFrame(data=tab2content) | pandas.DataFrame |
import robinhoodwrapper
import logging
import inspect
import pandas as pd
import commonqueries
import numpy as np
from datetime import datetime
from dateutil.relativedelta import relativedelta
import pandas_market_calendars as mcal
import pytz
import os
import configwrapper
class TradeRobinhood():
def __init__(self,c... | pd.notnull(best_put_to_sell) | pandas.notnull |
import re
import numpy as np
import pandas as pd
import pytest
import woodwork as ww
from woodwork import DataColumn, DataTable
from woodwork.datatable import _check_unique_column_names
from woodwork.logical_types import (
URL,
Boolean,
Categorical,
CountryCode,
Datetime,
Double,
EmailAddr... | pd.Timestamp('2020-02-02 18:00:00') | pandas.Timestamp |
import pandas
import lib.file as file
import lib.text as text
def getCombined(corpora, targetCorpus, shouldEnhance=False):
"""
@param corpora:
@param targetCorpus:
@param shouldEnhance:
@return:
@rtype: DataFrame
"""
metadataList = []
for corpus in corpora:
if ((targetCor... | pandas.concat(metadataList) | pandas.concat |
import pandas as pd
from rdflib import URIRef, BNode, Literal, Graph
from rdflib.namespace import RDF, RDFS, FOAF, XSD
from rdflib import Namespace
import numpy as np
import math
import sys
import argparse
import json
import html
def read_excel(path):
df = pd.read_excel(path, sheet_name=0, header=None, index_col=N... | pd.isnull(df.iloc[j, 17]) | pandas.isnull |
import pandas as pd
import numpy as np
df= pd.read_csv('..//Datos//Premios2020.csv', encoding='ISO-8859-1')
opciones = | pd.value_counts(df['genre1']) | pandas.value_counts |
import sys, os
sys.path.append("../ern/")
sys.path.append("../..dies/dies/")
sys.path.append(os.path.expanduser("~/workspace/prophesy_code/"))
import pandas as pd
import numpy as np
import glob, argparse, copy, tqdm
from ern.shift_features import ShiftFeatures
from ern.utils import to_short_name
import pathlib
from er... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import utils.gen_cutouts as gc
from sklearn import metrics
import pandas as pd
import ipdb
import matplotlib
from matplotlib import pyplot as plt
matplotlib.rcParams['mathtext.fontset'] = 'stixsans'
matplotlib.rcParams['font.family'] = 'STIXGeneral'
MEAN_TEMP = 2.726 * (10**6)
DEFAULT_FONT = 24
i... | pd.DataFrame(result['val']) | pandas.DataFrame |
'''
LICENSE: MIT license
This module can help us know about who can ask when
we have troubles in some buggy codes while solving problems.
'''
from asyncio import gather, get_event_loop
from pandas import DataFrame, set_option
from online_judge import Online_Judge
loop = get_event_loop()
set_option('display.max_col... | DataFrame() | pandas.DataFrame |
from bs4 import BeautifulSoup
import requests
import pandas as pd
from datetime import datetime
import time
#cahnge url
url = "https://www.sec.gov/cgi-bin/current?q1=0&q2=0&q3=4"
#url = 'https://www.sec.gov/edgar/searchedgar/companysearch.html'
page = requests.get(url)
data = page.text
soup = BeautifulSoup(data... | pd.read_html(index) | pandas.read_html |
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 2 17:10:19 2016
@author: tkc
"""
import pandas as pd
import numpy as np
import sys, glob
import scipy.stats
import matplotlib.pyplot as plt
import os
if 'C:\\Users\\tkc\\Documents\\Python_Scripts\\Augerquant\\Modules' not in sys.path:
sys.path.append('C:... | pd.read_csv('Augerparamlog.csv', encoding='cp437') | pandas.read_csv |
# Pylint is complaining about duplicated lines, but they are all imports
# pylint: disable=duplicate-code
import shutil
from pathlib import Path
from data_pipeline_api import standard_api
from simple_network_sim import inference
import pandas as pd
import pytest
# Path to directory containing test files for fixtures... | pd.DataFrame([{"Date": "2020-01-01", "Value": 0.5}]) | pandas.DataFrame |
import matplotlib as mpl
import warnings
warnings.simplefilter(action='ignore', category=mpl.MatplotlibDeprecationWarning)
################################################################################
# System dependencies
################################################################################
import power... | pd.Series(estimator.feature_importances_,index=features[:-1]) | pandas.Series |
import sys
import pandas as pd
from sqlalchemy import create_engine
def load_data(messages_filepath, categories_filepath):
'''
Loads files with Tweeter messages and categories and returns dataframe
Input:
message_filepath: CSV file with messages
categories_filepath: CSV file with categoriess... | pd.read_csv(categories_filepath) | pandas.read_csv |
import os
import glob
import pandas as pd
import csv
from collections import defaultdict
import pyrosetta
pyrosetta.init()
def emboss_needle_search(target_seq_path, template_seq_path):
for template_seq in template_seq_path:
target_seq_id = os.path.basename(target_seq_path).split('.')[0]
template_seq_id = os.pat... | pd.DataFrame(columns = ('query', 'template', 'length', 'identity', 'similarity', 'gaps', 'score')) | pandas.DataFrame |
import re
import warnings
from datetime import datetime, timedelta
from unittest.mock import patch
import numpy as np
import pandas as pd
import pytest
from pandas.testing import (
assert_frame_equal,
assert_index_equal,
assert_series_equal,
)
from woodwork.logical_types import Double, Integer
from rayml.... | pd.DataFrame(X) | pandas.DataFrame |
#=======================================================================================================================
#
# ALLSorts v2 - The STAR aligner counts creator thingy!
# Note: Only for hg19
#
# Author: <NAME>
# License: MIT
#
# Input: user --help for all parameters
# Output: Counts formatted for ALLSo... | pd.concat(progress, join="inner", axis=1) | pandas.concat |
import subprocess, os
import matplotlib.pyplot as plt
import pandas as pd
def get_performance(cmd):
command = subprocess.run(cmd.split(), stdout=subprocess.PIPE)
return command.stdout.decode('utf-8').split()[-1]
cmd = "./model/{} ../sample_inputs/{} {} {} {} {} {}"
def search_sync():
file_name = "glife_k... | pd.DataFrame(result) | pandas.DataFrame |
import csv
import numpy as np
import pandas as pd
df1 = pd.read_table('./train1_robert_result.txt',header=None)
df2 = pd.read_table('./train1_nezha_result.txt',header=None)
df3 = | pd.read_table('./train1_skep_result.txt',header=None) | pandas.read_table |
import collections
import json
import re
from collections import defaultdict
from io import StringIO
import numpy as np
import pandas as pd
import plotly.offline as opy
from clustergrammer import Network
from django.conf import settings
from django.urls import reverse
from django.utils import timezone
from loguru impo... | pd.DataFrame(analysis_data.json_data) | pandas.DataFrame |
# --------------
#Importing header files
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv(path)
#Code starts here
# data['Rating'].hist()
x = data['Rating'] <= 5
data = data[x]
data.hist()
#Code ends here
# --------------
# code starts here
total_null = data.isnull().sum... | pd.to_numeric(data['Price'], downcast='float') | pandas.to_numeric |
"""
Pull data from CA open data related to medical surge facilities
and hospital data
"""
import pandas as pd
from processing_utils import default_parameters
"""
The catalog file seems to throw up an error
because the dataset IDs disappear and appear at
different times. Let's stick with the download URL for now.
i... | pd.read_csv(HOSPITAL_DATA_URL) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
@author: <EMAIL>
@site: e-smartdata.org
"""
import numpy as np
import pandas as pd
import seaborn as sns
sns.set()
# %%
pd.set_option('display.max_rows', 999)
pd.set_option('precision', 3)
pd.describe_option('precision')
| pd.get_option('expand_frame_repr') | pandas.get_option |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""distance_from_median_pis.py
This script investigates the L1 distance between each of the images to the
median vectorial representation of a persistence image within a diagnostic
category.
"""
__author__ = "<NAME>"
__email__ = "<EMAIL>"
import matplotlib.pyplot as p... | pd.DataFrame(diffs, columns=["H_0", "H_1", "H_2"]) | pandas.DataFrame |
import os
import time
import pytest
import pandas as pd
import numpy as np
import ray
from ray.data.dataset_pipeline import DatasetPipeline
from ray.tests.conftest import * # noqa
def test_pipeline_actors(shutdown_only):
ray.init(num_cpus=2, num_gpus=1)
pipe = ray.data.range(3) \
.repeat(10) \
... | pd.concat([df1, df2]) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
# (file-types:notebooks)=
# # Jupyter Notebook files
#
# You can create content with Jupyter notebooks.
# For example, the content for the current page is contained in {download}`this notebook file <./notebooks.ipynb>`.
#
# ```{margin}
# If you'd like to write in plain-text file... | pd.DataFrame([['hi', 'there'], ['this', 'is'], ['a', 'DataFrame']], columns=['Word A', 'Word B']) | pandas.DataFrame |
import logging
import pickle
import warnings
import pandas as pd
import numpy as np
import plotly.express as px
import streamlit as st
from sklearn.feature_selection import chi2
from utils import seed_everything, INDEX2LEVEL, LEVEL2INDEX
from preprocess import word_tokenize, clean_dialogue
seed_everything(seed=914)
l... | pd.read_csv('./data/esaleshub.csv') | pandas.read_csv |
from __future__ import print_function
import os
import pandas as pd
from ..base import BASE
##################################################################### 1 Enter Data
# input
class read_table(BASE):
def fit(self):
# step1: check inputs
# step2: assign inputs to parameters if necessary ... | pd.read_table(**self.parameters) | pandas.read_table |
import sys
import pandas as pd
import numpy as np
from sqlalchemy import create_engine
'''
run this file from root folder:
python3 datasets/process_data.py datasets/messages.csv datasets/categories.csv datasets/DisasterResponse.db
'''
def load_data(messages_filepath, categories_filepath):
"""
PARAMETER:
m... | pd.read_csv(messages_filepath) | pandas.read_csv |
"""
Spatial based Segregation Metrics
"""
__author__ = "<NAME> <<EMAIL>>, <NAME> <<EMAIL>> and <NAME> <<EMAIL>>"
import numpy as np
import pandas as pd
import geopandas as gpd
import warnings
import pysal.lib
from pysal.lib.weights import Queen, Kernel, lag_spatial
from pysal.lib.weights.util import fill_diagonal
fr... | pd.concat((merged, islands)) | pandas.concat |
#!/usr/bin/env python
# Python Script for Kaggle Competition
# BNP Paribas Cardif claim management
# Doesn't work!
# Import Library & Modules
from sklearn.ensemble import RandomForestClassifier
from sklearn import preprocessing
import numpy as np # linear algebraic manipulation
import pandas as pd # data process... | pd.read_csv('../../dataset/test_splitted.csv') | pandas.read_csv |
#%% Loading irish data
import pandas as pd
data1 = pd.read_fwf('bible.txt', header=None)
data2 = pd.read_fwf('blogs.txt', header=None)
data3 = pd.read_fwf('legal.txt', header=None)
data4 = pd.read_fwf('news.txt', header=None)
data5 = pd.read_fwf('wiki.txt', header=None)
data = data1[0]+data2[0]+data3[0]+data4[0]+data5[... | pd.Series(final, copy=False) | pandas.Series |
# --------------
#Importing header files
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy
#Code starts here
data = | pd.read_csv(path) | pandas.read_csv |
from typing import Union, Optional, List, Dict, Tuple, Any
import pandas as pd
import numpy as np
from .common.validators import validate_integer
from .macro import Inflation
from .common.helpers import Float, Frame, Date, Index
from .settings import default_ticker, PeriodLength, _MONTHS_PER_YEAR
from .api.data_queri... | pd.concat([df, s2], axis=1, copy="false") | pandas.concat |
from Bio import SeqIO, GenBank
from Bio.Graphics import GenomeDiagram
from Bio.SeqFeature import FeatureLocation
import seaborn as sns
import argparse, os, sys, math, random
from ete3 import Tree, TreeStyle, NodeStyle
import matplotlib.colors as colors
from reportlab.lib import colors as rcolors
import numpy as np
impo... | pd.DataFrame(dat) | pandas.DataFrame |
import pandas as __pd
import datetime as __dt
from dateutil import relativedelta as __rd
from multiprocessing import Pool as __Pool
import multiprocessing as __mp
from seffaflik.__ortak.__araclar import make_requests as __make_requests
from seffaflik.__ortak import __dogrulama as __dogrulama
__first_part_url = "produ... | __pd.concat(df_list, sort=False) | pandas.concat |
import logging
import itertools
import sys
import pandas as pd
import numpy as np
from capture.generate import calcs
from capture.models import chemical
from capture.generate.wolframsampler import WolframSampler
from capture.generate.qrandom import get_unique_chemical_names, build_reagent_vectors
import capture.devco... | pd.concat([finalmmoldf, mmoldf], axis=1) | pandas.concat |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import re
import os
def get_plot_data(path, span=100):
df = pd.DataFrame()
with open(path + 'test.txt') as file:
data = | pd.read_csv(file, index_col=None) | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# This file is part of CbM (https://github.com/ec-jrc/cbm).
# Author : <NAME>
# Credits : GTCAP Team
# Copyright : 2021 European Commission, Joint Research Centre
# License : 3-Clause BSD
import pandas as pd
import matplotlib.dates as mdates
from matplotlib impor... | pd.to_datetime(s1_bs_profile.acq_date) | pandas.to_datetime |
# -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
import operator
from collections import OrderedDict
from datetime import datetime
from itertools import chain
import warnings
import numpy as np
from pandas import (notna, DataFrame, Series, MultiIndex, date_range,
Time... | assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
# ------------------------------------------------------------------------------
# Copyright IBM Corp. 2018
#
# 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/licens... | pd.read_parquet(tempfilename) | pandas.read_parquet |
#!/usr/bin/env python
# Author: <NAME> (jsh) [<EMAIL>]
import argparse
import logging
import pathlib
import sys
import pandas as pd
import gamma_lib as gl
import model_lib as ml
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)s %(message)s')
_PACKAGEDIR = pathlib.Path(__... | pd.DataFrame(anno) | pandas.DataFrame |
import pandas as pd
def to_pandas_Xy(dataset):
"""
Extracts `data` and `target` from a scikit-learn dataset and returns them as a pandas DataFrame
and Series.
"""
X = pd.DataFrame(dataset.data, columns=dataset.feature_names)
y = | pd.Series(dataset.target, name="target") | pandas.Series |
#!/usr/bin/env python
# coding: utf-8
# Loading the json with the grade data:
# In[1]:
import json
with open('grades.json', 'rb') as f:
data = json.load(f)
# Extracting the relevant information out of the json for one course:
# In[2]:
build_dict = lambda course: {
'id': course['content']['achievementDt... | pd.to_numeric(df['grade']) | pandas.to_numeric |
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from pytz import timezone, utc
from scipy import stats
from time import gmtime, strftime, mktime
def data_sampler_renamer_parser(path='weather-data.txt'):
# Take columns that are useful, rename them, parse the timestamp string
... | pd.DataFrame(wind) | pandas.DataFrame |
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... | Series(1, index=idx) | pandas.Series |
# *- coding: utf-8 -*
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoMinorLocator, MultipleLocator
# from model.ESPNet_v2.SegmentationModel import EESPNet_Seg
# from model.CGNet import CGNet
# from model.ContextNet import ContextNet
# from model.DABNet import DABNet
# fr... | pd.set_option('display.max_rows', 500) | pandas.set_option |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import pytest
import re
from numpy import nan as NA
import numpy as np
from numpy.random import randint
from pandas.compat import range, u
import pandas.compat as compat
from pandas import Index, Series, DataFrame, isn... | DataFrame([['a', 'b'], ['c', 'd']]) | pandas.DataFrame |
import math
import os
import shutil
from copy import deepcopy
from shutil import copyfile
import numpy as np
import pandas
import tifffile
import yaml
from pathlib import Path
from speedrun import BaseExperiment, locate
from speedrun.yaml_utils import recursive_update
from .cellpose_training.start_training import sta... | pandas.read_csv(input_images_csv_path, index_col=None) | pandas.read_csv |
# -*- coding:utf-8 -*-
import math
import phate
import anndata
import shutil
import warnings
import pickle
import numpy as np
import pandas as pd
import seaborn as sns
from scipy.spatial.distance import cdist
from scipy.stats import wilcoxon, pearsonr
from scipy.spatial import distance_matrix
from sklearn.decomposition... | pd.read_csv(fp, header=None) | pandas.read_csv |
#!/usr/bin/local/python3
"""
NOTE: When including text files as command line arguments, their names must not begin with a hyphen or they will be ignored.
"""
from data_manager import add_text_sample, DATA_FOLDER, get_all_samples, DATAFRAME_DEST, abs_path, clear_all_samples
from txt_learn import arr_for_string
import ... | pd.Series([english, arr], index=[e_key, v_key]) | pandas.Series |
from flask import Flask, render_template, jsonify, request
from flask_pymongo import PyMongo
from flask_cors import CORS, cross_origin
import json
import copy
import warnings
import re
import pandas as pd
pd.set_option('use_inf_as_na', True)
import numpy as np
from joblib import Memory
from xgboost import XGBClassi... | pd.Series() | pandas.Series |
import unittest
from pydre import project
from pydre import core
from pydre import filters
from pydre import metrics
import os
import glob
import contextlib
import io
from tests.sample_pydre import project as samplePD
from tests.sample_pydre import core as c
import pandas
import numpy as np
from datetime import timedel... | pandas.DataFrame(data=d) | 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.to_numeric(df.index) | pandas.to_numeric |
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 5 15:33:50 2019
@author: luc
"""
#%% Import Libraries
import numpy as np
import pandas as pd
import itertools
from stimuli_dictionary import cued_stim, free_stim, cued_stim_prac, free_stim_prac
def randomize(ID, Age, Gender, Handedness):
'''
Create a rand... | pd.DataFrame(free_stim_prac) | pandas.DataFrame |
"""
Behaiviour_Recognizer Toolbox
© <NAME>
@author: <NAME>
This script is for making prediction for any desire validation set. In k-fold validation,
data is devided into k portion. For each validation set the network has used k-1 portion
for training the network and have saved the corresponding weights. Here we use ... | pd.DataFrame(Indexes[kth_Validation-1], columns = ['Index']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sun June 16 15:30:27 2019
@author: <NAME>
"""
#IMPORTING NECESSARY LIBRARIES
import pandas as pd
import csv
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import webbrowsert
from scipy.stats import norm
from sklearn.preprocessing import ... | pd.DataFrame(Data2,columns=['not qualified']) | pandas.DataFrame |
"""Jリーグ各節の試合情報を読み込み、CSVとして取得、保存
"""
import os
from datetime import datetime, time, timedelta
from typing import List, Set, Dict, Any
import re
from glob import glob
import argparse
import pandas as pd
from bs4 import BeautifulSoup
import requests
PREFERENCE = {}
PREFERENCE['debug'] = False
DATE_FORMAT = '%Y%m%d'
SEASO... | pd.to_datetime(all_matches['match_date']) | pandas.to_datetime |
import pandas as pd
import numpy as np
from scipy import stats
from ast import literal_eval
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.metrics.pairwise import linear_kernel, cosine_similarity
from surprise import Reader, Dataset, SVD, evaluate
from imdbToId import convert... | pd.read_csv('data/ratings_small.csv') | pandas.read_csv |
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import random
import seaborn as sns
import gc
import calendar
import pickle
import os
from sklearn.preprocessing import StandardScaler
from os.path import join
from sklearn.metrics import confu... | pd.concat([total, percent], axis=1, keys=['Total', 'Percent']) | pandas.concat |
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