prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import csv
from io import StringIO
import os
import numpy as np
import pytest
from pandas.errors import ParserError
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
NaT,
Series,
Timestamp,
date_range,
read_csv,
to_datetime,
)
import pandas._testing as tm
impo... | tm.ensure_clean("__tmp_to_csv_multiindex__") | pandas._testing.ensure_clean |
# 信用卡违约率分析
import pandas as pd
import seaborn as sns
from sklearn.svm import SVC
import matplotlib.pyplot as plt
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.preprocessing imp... | pd.DataFrame({'default.payment.next.month': next_month.index, 'values': next_month.values}) | pandas.DataFrame |
# -*- coding: utf-8 -*
'''问卷数据分析工具包
Created on Tue Nov 8 20:05:36 2016
@author: JSong
1、针对问卷星数据，编写并封装了很多常用算法
2、利用report工具包，能将数据直接导出为PPTX
该工具包支持一下功能：
1、编码问卷星、问卷网等数据
2、封装描述统计和交叉分析函数
3、支持生成一份整体的报告和相关数据
'''
import os
import re
import sys
import math
import time
import pandas as pd
import numpy as np
import matplo... | pd.DataFrame(fo) | pandas.DataFrame |
from unittest import TestCase
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from datasets.formatting import NumpyFormatter, PandasFormatter, PythonFormatter, query_table
from datasets.formatting.formatting import NumpyArrowExtractor, PandasArrowExtractor, PythonArrowExtractor
from datasets... | pd.Series(_COL_C, name="c") | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 25 16:14:12 2019
@author: <NAME>
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#import graphviz
import os
import seaborn as sns
from scipy.stats import chi2_contingency
os.chdir("E:\PYTHON NOTES\projects\cab fare prediction")
d... | pd.concat([dataset_int1,dataset_train["passenger_count"]],axis=1) | pandas.concat |
import pandas as pd
import numpy as np
import click
import h5py
import os
import logging
from array import array
from copy import deepcopy
from tqdm import tqdm
from astropy.io import fits
from fact.credentials import create_factdb_engine
from zfits import FactFits
from scipy.optimize import curve_fit
from joblib imp... | pd.to_datetime("") | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 4 10:30:17 2018
@author: avelinojaver
"""
from tierpsy.features.tierpsy_features.summary_stats import get_summary_stats
from tierpsy.summary.helper import augment_data, add_trajectory_info
from tierpsy.summary.filtering import filter_trajectories
fr... | pd.concat(all_summary, ignore_index=True, sort=False) | pandas.concat |
#
import numpy
import pandas
def _lag_it(frame, n_lags):
frame_ = frame.copy()
if frame_.index.nlevels == 1:
frame_ = frame_.shift(periods=n_lags, axis=0)
elif frame_.index.nlevels == 2:
for ix in frame_.index.levels[0]:
frame_.loc[[ix], :] = frame_.loc[[ix], :].shift(periods=n... | pandas.concat(frames, axis=1) | pandas.concat |
### Model Training and Evaluation ###
# Author: <NAME>
from IPython import get_ipython
get_ipython().magic('reset -sf')
import os, shutil
import re
import csv
from utils import bigrams, trigram, replace_collocation
import timeit
import pandas as pd
import string
from nltk.stem import PorterStemmer
import numpy as np... | pd.DataFrame([]) | pandas.DataFrame |
import batman
import ellc
import torch
import numpy as np
import pickle
import matplotlib.pyplot as plt
import pandas as pd
from time import time
from pytransit import OblateStarModel, QuadraticModel
from data_preparation.data_processing_utils import min_max_norm_vectorized, resize, standardize
R_SUN2JUPYTER = 1.0 / ... | pd.read_csv("TESS_Gravity_Darkening.csv", comment='#', sep=',') | pandas.read_csv |
#!/usr/bin/env python3
# coding: utf-8
"""Global surveillance data for the home page
Author: <NAME> - Vector Engineering Team (<EMAIL>)
"""
import argparse
import datetime
import json
import pandas as pd
import numpy as np
from scipy.stats import linregress
from pathlib import Path
def main():
parser = argpar... | pd.read_json(args.case_data) | pandas.read_json |
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2022, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | pd.testing.assert_frame_equal(obs, df) | pandas.testing.assert_frame_equal |
import numpy as np
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.cm import ScalarMappable
from monty.serialization import loadfn, dumpfn
g_csvpd = loadfn('../data/g_mae_corrsvpd_opts.json')
h_csvpd = loadfn('../data/h_ma... | pd.DataFrame(info_dict) | pandas.DataFrame |
#!/usr/bin/env python
import pandas as pd
from argparse import ArgumentParser
import statsmodels.formula.api as smf
import math
# Function to compute effect sizes
# Based on method in Nakagawa, S. and <NAME>. (2007). Biol. Rev. 82. pp. 591-605.
def cohensd(t, df, n1, n2):
d = ( t * (n1+n2) ) / (math.sqrt(n1*n... | pd.DataFrame(model_es, index=features) | pandas.DataFrame |
import numpy as np
import pandas as pd
from sklearn import metrics
from sklearn.ensemble import IsolationForest
import STRING
from sklearn.preprocessing import StandardScaler
def isolation_forest(x, y, contamination=0.1, n_estimators=50, bootstrap=True, max_features=0.33, validation=[]):
if contaminati... | pd.DataFrame(predict_valid, columns=['outliers']) | pandas.DataFrame |
import pandas as pd
import numpy as np
START_PULL_UPS_UPPER_ANGLE_THRESHOLD = 40
END_PULL_UPS_UPPER_ANGLE_THRESHOLD = 130
TIME_FRAME_LIST = 20
reps_position = []
count_reps = 0
in_reps = 0
precedent_pos = 0
df_reps = pd.DataFrame(columns=['x_Nose','y_Nose','x_Neck','y_Neck','x_RShoulder','y_RShoulder','x_RElbow',
'y... | pd.DataFrame(columns=['x_Nose','y_Nose','x_Neck','y_Neck','x_RShoulder','y_RShoulder','x_RElbow',
'y_RElbow','x_RWrist','y_RWrist','x_LShoulder','y_LShoulder','x_LElbow','y_LElbow','x_LWrist','y_LWrist',
'x_RHip','y_RHip','x_RKnee','y_RKnee','x_RAnkle','y_RAnkle','x_LHip','y_LHip','x_LKnee','y_LKnee','x_LAnkle'... | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
from scipy.stats import pearsonr
# from mpl_toolkits.axes_grid1 import host_subplot
# import mpl_toolkits.axisartist as AA
# import matplotlib
import matplotlib.pyplot as plt
import matplotlib.t... | pd.concat(Appended_data_desp_975) | pandas.concat |
import pytz
import pytest
import dateutil
import warnings
import numpy as np
from datetime import timedelta
from itertools import product
import pandas as pd
import pandas._libs.tslib as tslib
import pandas.util.testing as tm
from pandas.errors import PerformanceWarning
from pandas.core.indexes.datetimes import cdate_... | Timestamp('2013-01-02') | pandas.Timestamp |
# -*- coding: utf-8 -*-
"""
Reading data for WB, PRO,
for kennisimpulse project
to read data from province, water companies, and any other sources
Created on Sun Jul 26 21:55:57 2020
@author: <NAME>
"""
import pytest
import numpy as np
import pandas as pd
from pathlib import Path
import pickle as pckl
from hgc impor... | pd.read_excel(WD, header=None, encoding='ISO-8859-1') | pandas.read_excel |
import numpy as np
import pylab as pl
import seaborn as sns
from remodnav import EyegazeClassifier
from remodnav.tests.test_labeled import load_data as load_anderson
import pdb
#pdb.set_trace() to set breakpoint
import pandas as pd
labeled_files = {
'dots': [
'TH20_trial1_labelled_{}.mat',
'TH38_t... | pd.concat([target_events_df, peaks_amps_df], axis=1) | pandas.concat |
import os
import json
import pandas as pd
import requests
from requests.adapters import HTTPAdapter
from requests.packages.urllib3.util.retry import Retry
import math
import configparser
import logging
import pickle
config = configparser.ConfigParser()
config.read('../config.ini')
logger = logging.getLogger(__name__)... | pd.DataFrame(dict_missed_data) | pandas.DataFrame |
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import cross_validate
from pandas.api.types import is_numeric_dtype
import statsmodels.api as sm
import warnings
import time
from sklearn.linear_model import LinearRegression
from sklearn.preprocess... | pd.read_csv(titanic_csv) | pandas.read_csv |
import pandas as pd
import numpy as np
from scipy import integrate, stats
from numpy import absolute, mean
from itertools import islice
import statsmodels.api as sm
from statsmodels.formula.api import ols
import statsmodels.stats.multicomp
import seaborn as sns
import matplotlib.pyplot as plt
import statsmodels.formu... | pd.DataFrame() | 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.... | assert_frame_equal(X, X_pred) | pandas.testing.assert_frame_equal |
import pandas as pd
from pydatafaker import utilities
def test_create_date():
x = utilities.create_date()
assert type(x) is pd.Timestamp
def test_create_date_ranges():
sep_1 = "2020-09-01"
sep_2 = "2020-09-02"
sep_3 = "2020-09-03"
for _ in range(25):
x = utilities.create_date(sep_1,... | pd.to_datetime(sep_3) | pandas.to_datetime |
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 12})
plt.rcParams["figure.figsize"] = (3.5,3)
METRIC_IDX = 3
NUM_EPOCHS = 20
NUM_EXPS = 5
GRAPH_FORMAT = 'pdf'
# GRAPH_TITLE = 'Piano Playing'
# GRAPH_FILE = 'piano_playing'
GRAPH_TITLE = 'Keyboard Typing... | pd.DataFrame(columns=['acc','rec','pre','f1']) | pandas.DataFrame |
# *****************************************************************************
# Copyright (c) 2020, Intel Corporation All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# Redistributions of sou... | pandas.Series(data=res_data, index=by_index, name=name) | pandas.Series |
from ast import literal_eval
from datetime import timedelta
from faker import Faker
from src.make_feedback_tool_data.make_data_for_feedback_tool import (
create_dataset,
create_phrase_level_columns,
drop_duplicate_rows,
extract_phrase_mentions,
preprocess_filter_comment_text,
save_intermediate_d... | pd.DataFrame([i], columns=["themed_phrase_mentions"]) | pandas.DataFrame |
import copy
import datetime as dt
import logging
import os
import re
import warnings
from datetime import datetime
from unittest.mock import patch
import cftime
import numpy as np
import pandas as pd
import pytest
from numpy import testing as npt
from packaging.version import parse
from pandas.errors import Unsupporte... | pd.Int64Index([0, 2]) | pandas.Int64Index |
#coding=utf-8
import pandas as pd
import numpy as np
import sys
import os
from sklearn import preprocessing
import datetime
import scipy as sc
from sklearn.preprocessing import MinMaxScaler,StandardScaler
from sklearn.externals import joblib
#import joblib
class FEbase(object):
"""description of class"""
def ... | pd.read_csv('real_now.csv',index_col=0,header=0) | pandas.read_csv |
"""
A sbatch wrapper for stampede2
See stampede2 doc:
https://portal.tacc.utexas.edu/user-guides/stampede2#running-sbatch
"""
import pathlib
import re
import shlex
import subprocess
import time
from collections import defaultdict
import pandas as pd
import random
import string
import cemba_data
PACKAGE_DIR = pathlib... | pd.concat(stats) | pandas.concat |
import os
from datetime import datetime, date
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from fbprophet import Prophet
class Detector:
def __init__(
self,
min_time_points: int = 10,
none_zero_ratio: float = 0.0,
min_dataset_size: int = 0... | pd.DataFrame() | pandas.DataFrame |
import configparser
import importlib
import numpy as np
import pandas as pd
###############################################################################
#Non-Standard Import
###############################################################################
try:
from . import model_handler as mh
fr... | pd.DataFrame(params) | pandas.DataFrame |
from datetime import datetime
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas.core.dtypes.base import _registry as ea_registry
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from pandas.core.dtypes.dtypes import (... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
import numpy as np
from scipy.io import loadmat
import pandas as pd
import datetime as date
from dateutil.relativedelta import relativedelta
cols = ['age', 'gender', 'path', 'face_score1', 'face_score2']
imdb_mat = 'imdb_crop/imdb.mat'
wiki_mat = 'wiki_crop/wiki.mat'
imdb_data = loadmat(imdb_mat)
wiki_data = loadmat... | pd.concat((final_imdb_df, final_wiki_df)) | pandas.concat |
import numpy as np
import pandas as pd
import pytest
from rayml.data_checks import (
DataCheckActionCode,
DataCheckActionOption,
DataCheckMessageCode,
DataCheckWarning,
IDColumnsDataCheck,
)
id_data_check_name = IDColumnsDataCheck.name
def test_id_cols_data_check_init():
id_cols_check = IDCo... | pd.DataFrame() | pandas.DataFrame |
from typing import Optional
import numpy as np
import pandas as pd
import pytest
from pytest import approx
from evidently.pipeline import column_mapping
from evidently.analyzers.classification_performance_analyzer import ClassificationPerformanceAnalyzer
from evidently.analyzers.classification_performance_analyzer i... | pd.DataFrame({"target": [1, 0, 0, 1, 1, 1], "prediction": [0, 1, 0, 1, 0, 0]}) | pandas.DataFrame |
import glob
import pandas as pd
files = glob.glob('Corpus_mda/*')
files.sort()
df_agg1 = | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 3 17:14:53 2019
@author: liuhongbing
"""
import pandas as pd
import numpy as np
from scipy import stats
from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix, roc_curve, auc
import tensorflow as tf
from sklearn.mode... | pd.get_dummies(labels) | pandas.get_dummies |
import copy
import math
import sys
import numpy.random as rnd
from datetime import datetime
import pandas as pd
from datetime import timedelta
import traceback
from heuristic.construction.construction import ConstructionHeuristic
from config.construction_config import *
from heuristic.improvement.reopt.reopt_repair_ge... | pd.DataFrame(unassigned_requests) | pandas.DataFrame |
"""This modules contains code to be executed after the anonymization kernel has been run"""
import logging
import datetime
import pandas as pd
from anytree import AnyNode
from tqdm import tqdm
logger = logging.getLogger(__name__)
class PostProcessor():
"""The postprocessor will actually recode sensitive terms ... | pd.DataFrame(columns=df.columns) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jun 9 15:00:37 2019
@author: <NAME>
@contact: <EMAIL>
"""
import numpy as np
import pandas as pd
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, execute
def print_array(A):
data_frame = pd.DataFrame(A).round(3)
| pd.set_option('precision', 3) | pandas.set_option |
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({"x": [1, 2, 3, 4], "y": [1, 0, 1, 0]}) | pandas.DataFrame |
import dash # pip install dash
import dash_html_components as html
import dash_core_components as dcc
from dash.dependencies import Output, Input
from dash_extensions import Lottie # pip install dash-extensions
import dash_bootstrap_components as dbc # pip install dash-bootstrap-com... | pd.to_datetime(df_msg["DATE"]) | pandas.to_datetime |
# vim: set fdm=indent:
'''
___
/ | ____ ___ ____ _____ ____ ____
/ /| | / __ `__ \/ __ `/_ / / __ \/ __ \
/ ___ |/ / / / / / /_/ / / /_/ /_/ / / / /
/_/ |_/_/ /_/ /_/\__,_/ /___/\____/_/ /_/
... | pd.Timestamp(dt_stop) | pandas.Timestamp |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
import nose
import numpy as np
from numpy import nan
import pandas as pd
from distutils.version import LooseVersion
from pandas import (Index, Series, DataFrame, Panel, isnull,
date_range, period_range)
from pandas.core.index import MultiIn... | tm.assertRaises(ValueError) | pandas.util.testing.assertRaises |
"""This code is part of caerus and is not designed for usage of seperate parts."""
#--------------------------------------------------------------------------
# Name : caerus.py
# Author : E.Taskesen
# Contact : <EMAIL>
# Date : May. 2020
#---------------------------------------------------------... | pd.concat((tmpvalue, df.iloc[idx_start:idx_stop])) | pandas.concat |
from context import dero
import pandas as pd
from pandas.util.testing import assert_frame_equal
from pandas import Timestamp
from numpy import nan
import numpy
class DataFrameTest:
df = pd.DataFrame([
(10516, 'a', '1/1/2000', 1.01),
(10516, 'a'... | Timestamp('2000-01-06 00:00:00') | pandas.Timestamp |
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import glob
import json
import collections
TIMEFRAMES = [
"2017-06-12_2017-07-09_organic",
"2017-07-10_2017-08-06_organic",
"2017-08-07_2017-09-03_organic",
"2017-12-03_2017-12-30_organic",
"2018-01-01_2018... | pd.DataFrame(csv_ready_dict_timeframe_one_type) | pandas.DataFrame |
# DAG schedulada para utilização dos dados do Titanic
from airflow import DAG
# Importação de operadores
from airflow.operators.bash_operator import BashOperator
from airflow.operators.python_operator import PythonOperator, BranchPythonOperator
from datetime import datetime, timedelta
import pandas as pd
import zipf... | pd.read_csv(f'{data_path}/microdados_enade_2019/2019/3.DADOS/microdados_enade_2019.txt', sep=';', decimal=',', usecols=cols) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import os
import re
import matplotlib.pyplot as plt
import numpy as np
import json
import plotly.io as pio
import plotly.offline as pl
import plotly.graph_objs as go
import plotly.express as px
from plotly.offline import download_plotlyjs,init_notebo... | pd.set_option('display.max_rows', None) | pandas.set_option |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from pandas.core.base import PandasObject
from scipy.optimize import minimize
from decorator import decorator
from sklearn.covariance import ledoit_wolf
@decorator
def mean_var_weights(func_covar, *args, **kwargs):
"""
Calculates the mean-variance ... | pd.Series(erc_weights, index=returns.columns, name='erc') | pandas.Series |
from flask import Blueprint, jsonify
from numpy import minimum
from datetime import datetime
import requests
import psycopg2
import warnings
import pandas as pd
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error, mean_absolute_error
warnings.filterwarnings("ignore")
btcData = Blueprin... | pd.DataFrame(rows, columns=['time', 'recommendation', 'price']) | pandas.DataFrame |
import pandas as pd
import STRING
import numpy as np
import datetime
from sklearn.cluster import AgglomerativeClustering
from models.cluster_model import cluster_analysis
pd.options.display.max_columns = 500
# SOURCE FILE
offer_df = | pd.read_csv(STRING.path_db + STRING.file_offer, sep=',', encoding='utf-8', quotechar='"') | pandas.read_csv |
"""Test the DropTokensByList pipeline stage."""
import pandas as pd
import pdpipe as pdp
def test_drop_tokens_by_list_short():
data = [[4, ["a", "bad", "cat"]], [5, ["bad", "not", "good"]]]
df = pd.DataFrame(data, [1, 2], ["age", "text"])
filter_tokens = pdp.DropTokensByList('text', ['bad'])
res_df =... | pd.DataFrame(data, [1, 2], ["age", "text"]) | pandas.DataFrame |
import os
import sys
from pandas.core.indexes import base
sys.path.append('..')
import argparse
import datetime as dt
import pickle
import yaml
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.utils.class_weight import compute_class_weight
from src.data.imgproc import tf_read_image
... | pd.DataFrame(x_image_train) | pandas.DataFrame |
import pandas as pd
import numpy as np
import warnings
from numpy import cumsum, log, polyfit, sqrt, std, subtract
from datetime import datetime, timedelta
import scipy.stats as st
import statsmodels.api as sm
import math
import matplotlib
import matplotlib.pyplot as plt
from tqdm import tqdm
from scipy.stats import ... | pd.DataFrame() | pandas.DataFrame |
# Not yet tested
#Import Libraries:
from __future__ import print_function
import keras
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers import Dense, Gl... | pd.DataFrame(hist) | pandas.DataFrame |
import typing
import datetime
import pandas as pd
from .make_df import ComicDataFrame
from lib.aws_util.s3.upload import upload_to_s3
from lib.aws_util.s3.download import download_from_s3
def store(df: ComicDataFrame) -> typing.NoReturn:
dt = datetime.datetime.now()
bucket = 'av-adam-store'
save_dir = '/tmp/'
... | pd.read_csv(author_path) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created in February 2018
@author: <NAME>
file: Method to filter termium from the csv files to represent as dataframe
"""
import pandas as pd
import glob
INPUT = "path to CSV files"
OUTPUT = "path to output file that should then be loaded to a MySQL table"
outputFile = open("outputFile pa... | pd.isnull(row[synonyms]) | pandas.isnull |
import os
from collections import defaultdict
import luigi
import ujson
import numpy as np
from numpy.random import RandomState
import pandas as pd
from .config import INPUT_DIR, OUTPUT_DIR
from .input_data import OrdersInput, OrderProductsInput
class _InputCSV(luigi.ExternalTask):
filename = None
@classme... | pd.concat(df_parts) | pandas.concat |
import os
import pandas as pd
import numpy as np
from scipy.fftpack import fft
from scipy import integrate
from scipy.stats import kurtosis
from notebook.pca_reduction import PCAReduction
from notebook.utils import general_normalization, universal_normalization, trim_or_pad_data, feature_matrix_extractor
from noteboo... | pd.DataFrame(featureMatrixReally) | pandas.DataFrame |
# Preppin' Data 2021 Week 26
import pandas as pd
import numpy as np
from datetime import date, timedelta, datetime
# Load data
rolling = pd.read_csv('unprepped_data\\PD 2021 Wk 26 Input - Sheet1.csv')
# Create a data set that gives 7 rows per date (unless those dates aren't included in the data set).
# - ie 1st Jan... | pd.date_range(sdate,edate,freq='d') | pandas.date_range |
import os
import numpy as np
import pandas as pd
import pytest
from conceptnet5.uri import is_term
from conceptnet5.vectors import get_vector
from conceptnet5.vectors.transforms import (
l1_normalize_columns,
l2_normalize_rows,
make_big_frame,
make_small_frame,
shrink_and_sort,
standardize_row... | pd.DataFrame(data=data, index=index) | pandas.DataFrame |
from datetime import datetime, timedelta
from dateutil import parser
from typing import Any, Dict, Iterable, List
import pandas as pd
from sgqlc.operation import Operation
from ..models.iot import (
MetricField,
MetricWindow,
)
from ..utils import make_logger
from ..utils.config import ContxtEnvironmentConfig... | pd.Series(parsed_data, time_index) | pandas.Series |
"""Tests."""
from math import ceil # type: ignore
import datetime # type: ignore
import pytest # type: ignore
import pandas as pd # type: ignore
import numpy as np # type: ignore
import altair as alt # type: ignore
from src.penn_chime.charts import new_admissions_chart, admitted_patients_chart, chart_descriptio... | pd.read_csv('tests/projection_admits.csv') | pandas.read_csv |
import pandas as pd
import numpy as np
from .QCBase import VarNames
class Exporter(object):
""" Export class which writes parsed data to a certain format"""
valid_formats = ["pdf", "xlsx", "txt", "csv", "dataframe"]
def __init__(self, data=None):
self.data = data
# for later: add pand... | pd.DataFrame(d) | pandas.DataFrame |
import itertools
import pandas as pd
from pandas.testing import assert_series_equal
import pytest
from solarforecastarbiter.reference_forecasts import forecast
def assert_none_or_series(out, expected):
assert len(out) == len(expected)
for o, e in zip(out, expected):
if e is None:
assert... | pd.date_range(start='20190101', freq='1h', periods=2) | pandas.date_range |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import xlrd
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.metrics import roc_curve, auc, accuracy_score
import matplotlib.pyplot as plt
import xgboost as... | DataFrame(X_train,dtype='float') | pandas.core.frame.DataFrame |
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Index,
Series,
)
import pandas._testing as tm
dt_data = [
pd.Timestamp("2011-01-01"),
pd.Timestamp("2011-01-02"),
pd.Timestamp("2011-01-03"),
]
tz_data = [
pd.Timestamp("2011-01-01", tz="U... | pd.Period("2012-01-01", freq="D") | pandas.Period |
#!/usr/bin/env python
# -*-coding:utf-8 -*-
'''
@File : Stress_detection_script.py
@Time : 2022/03/17 09:45:59
@Author : <NAME>
@Contact : <EMAIL>
'''
import os
import logging
import plotly.express as px
import numpy as np
import pandas as pd
import zipfile
import fnmatch
import flirt.reader.empatica
... | pd.to_datetime(eda_df['datetime']) | pandas.to_datetime |
import numpy as np
import pytest
from pandas import (
DataFrame,
MultiIndex,
)
import pandas._testing as tm
class TestReorderLevels:
def test_reorder_levels(self, frame_or_series):
index = MultiIndex(
levels=[["bar"], ["one", "two", "three"], [0, 1]],
codes=[[0, 0, 0, 0, 0... | tm.get_obj(expected, frame_or_series) | pandas._testing.get_obj |
"""
General purpose parser for the output of the MAP operations of GMQL
"""
import pandas as pd
import os
import xml.etree.ElementTree
class OutputGenerator:
def __init__(self,path):
self.path = path
self.data = None
self.meta_data = None
return
def get_sample_name(self, p... | pd.DataFrame() | pandas.DataFrame |
"""
Create by: apenasrr
Source: https://github.com/apenasrr/mass_videojoin
"""
import os
import pandas as pd
import datetime
import logging
from video_tools import change_width_height_mp4, get_video_details, \
join_mp4, split_mp4
from config_handler import handle_config_file
import unid... | pd.concat([df1, df2]) | pandas.concat |
"""
The ``pvsystem`` module contains functions for modeling the output and
performance of PV modules and inverters.
"""
from collections import OrderedDict
import io
import os
from urllib.request import urlopen
import warnings
import numpy as np
import pandas as pd
from pvlib._deprecation import deprecated
from pvli... | pd.DataFrame(out, index=photocurrent.index) | pandas.DataFrame |
from datetime import datetime, timedelta
import itertools
import netCDF4
import numpy as np
import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.model_selection import train_test_split
def valid_maxz(maxz, threshold=0.7):
... | pd.DataFrame.from_dict(past_features[dataset]) | pandas.DataFrame.from_dict |
import pandas as pd
import pandas.testing as pdt
import pytest
from pyspark.sql import functions
from cape_privacy.spark import utils
from cape_privacy.spark.transformations import tokenizer as tkn
def _apply_tokenizer(sess, df, tokenizer, col_to_rename):
df = sess.createDataFrame(df, schema=["name"])
result... | pdt.assert_frame_equal(tokenized, tokenized_expected) | pandas.testing.assert_frame_equal |
import datetime
import numpy as np
import pytest
import pytz
import pandas as pd
from pandas import Timedelta, merge_asof, read_csv, to_datetime
import pandas._testing as tm
from pandas.core.reshape.merge import MergeError
class TestAsOfMerge:
def read_data(self, datapath, name, dedupe=False):
path = da... | pd.date_range("2019-10-01", freq="30min", periods=5, tz="UTC") | pandas.date_range |
#!/usr/bin/env python
# coding: utf-8
import requests as req
import json
import pandas as pd
import warnings
from IPython.display import clear_output
from time import sleep
from abc import *
warnings.filterwarnings("ignore")
class BigwingAPIProcessor(metaclass=ABCMeta) :
''' 빅윙추상클래스 '''
def run(self, limit=T... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
===============================================================================
FINANCIAL IMPACT FILE
===============================================================================
Most recent update:
21 Ja... | pd.DataFrame([general_OM_cost_daily]*(end_year-start_year)*365) | pandas.DataFrame |
from datetime import datetime
import itertools
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from utils.matrix_convert import MatrixConversion
from calculations.AllMetrics import Metrics
from utils.constants import TYPES
from utils.helpers import remove_offset_from_julian_date
from params impor... | pd.DataFrame(cols, index=[0]) | pandas.DataFrame |
""" ### Utilities
A rather bare script, just for labeling new images if you have them.
"""
import os
from skimage import io
import pandas as pd
def label():
"A simple function for adding new data"
files = sorted(os.listdir(config.DATA_DIR))
tot = len(files)
y = []
for i, f in enumerate(files):... | pd.DataFrame({"filenames": files, "target": y}) | pandas.DataFrame |
import csv
import json
from glob import glob
from pprint import pprint
import pandas
from numpy import mean
files = glob('*.json')
results = {}
for file in files:
name = file.split(".")[0].split("_")
name = name[1] + " " + name[2]
data = json.load(open(file))
accuracy = mean([max(run["acc"]) for run i... | pandas.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
from Bio import PDB
repository = PDB.PDBList()
parser = PDB.PDBParser()
repository.retrieve_pdb_file('1TUP', pdir='.', file_format='pdb')
p53_1tup = parser.get_structure('P 53', 'pdb1tup.ent')
my_residues = set()
for residue in p53_1tup.get_residues():
my_residues.add(residu... | pd.DataFrame(my_mass, index=chain_names, columns=['No Water', 'Zincs', 'Water']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from googleapiclient.discovery import build
from google_auth_oauthlib.flow import InstalledAppFlow
from google.auth.transport.requests import Request
import pandas as pd
import numpy as np
import pickle
import os.path
import dateutil.parser
import calendar
from datetime import datetime
# =====... | pd.read_excel(file) | pandas.read_excel |
'''
Created on 14.01.2022
@author: <NAME> @UOL/OFFIS
@review: <NAME> @OFFIS
This set of functions model the different power plants and their outputs
in the different markets
#1 Define Power Plants Scenarios
#2 Define Market Scenarios
#3 Build Energy Systems which consider the different Scenarios
#4 Combine Scenario... | pd.ExcelWriter(data_path, engine='xlsxwriter') | pandas.ExcelWriter |
import sqlite3
import datetime
import os
import pandas as pd
import numpy as np
# --------------------------------------------------------------------------------------------
# DATA QUERY FUNCTIONS
def retrieve_accounts(gnucash_file, build_fullname=False) -> pd.DataFrame:
# get all account data from the sqlite3... | pd.to_datetime(df.tx_date) | pandas.to_datetime |
import numpy as np
import pandas as pd
from gmm_model_fit import gmm_model_fit
def get_fish_info(df):
fishes_IDs = df.index.get_level_values('fish_ID').unique().values
df["distance_to_center"] = np.sqrt(df["bout_x"]**2 + df["bout_y"]**2)
df["correct"] = df["heading_angle_change"].values > 0
extracted... | pd.DataFrame(gmm_fitting_results, columns=["stim", "w_left", "w_center", "w_right", "m_left", "m_center", "m_right", "s_left", "s_center", "s_right"]) | pandas.DataFrame |
from sklearn.model_selection import StratifiedKFold
import pandas as pd
skf = StratifiedKFold(n_splits=10, random_state=48, shuffle=True)
def CV(predictors,target):
for fold, (train_index, test_index) in enumerate(skf.split(predictors, target)):
x_train, x_valid = pd.DataFrame(predictors.iloc[train_i... | pd.DataFrame(predictors.iloc[test_index]) | pandas.DataFrame |
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# This file contains dummy data for the model unit tests
import numpy as np
import pandas as pd
AIR_FCST_LINEAR_95 = pd.DataFrame(
{
... | pd.Timestamp("2012-12-22 00:00:00") | pandas.Timestamp |
# import start
import ast
import asyncio
import calendar
import platform
import subprocess as sp
import time
import traceback
import xml.etree.ElementTree as Et
from collections import defaultdict
from datetime import datetime
import math
import numpy as np
import pandas as pd
from Utility.CDPConfigValues import CDPC... | pd.DataFrame(previous_preprocessed_df) | pandas.DataFrame |
"""This module contains auxiliary functions for RD predictions used in the main notebook."""
import json
import matplotlib as plt
import pandas as pd
import numpy as np
import statsmodels as sm
from auxiliary.auxiliary_predictions import *
from auxiliary.auxiliary_plots import *
from auxiliary.auxiliary_tables import... | pd.concat([trimmed_treat, control], axis=0) | pandas.concat |
#Implementing random forests with feature engineering
# importing numpy, pandas, and matplotlib
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from skbio.stats.composition import clr
import sys
# importing sklearn
from sklearn.model_selection import train_test_split
from skle... | pd.read_csv("./data/feature_sel_LEFSe/selected_microbes.csv",index_col=0) | pandas.read_csv |
from __future__ import print_function
import random
import yfinance as yf
import os
import requests
from bs4 import BeautifulSoup
import pandas as pd
import numpy as np
from IPython.display import clear_output
from tqdm import tqdm
import pandas_datareader.data as web
import datetime
import argparse
import multiprocess... | pd.DataFrame() | pandas.DataFrame |
import pandas
import numpy as np
import requests
from sklearn.model_selection import train_test_split
# my_lambdata/my_script.py
from my_mod import enlarge
print("HELLO WORLD")
df = pandas.DataFrame({"State": ['CT', "CO", "CA", "TX"]})
print(df.head())
print("--------")
x = 5
print("NUMBER", x)
print("ENLARGED NUM... | pandas.read_csv('https://raw.githubusercontent.com/ryanleeallred/datasets/master/Ames%20Housing%20Data/train.csv') | pandas.read_csv |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# x13.py
# @Author : wanhanwan (<EMAIL>)
# @Link : ~
# @Date : 2019/11/24 上午9:53:41
"""
X13季节性调整。
注：
cny.csv文件记录中国历年农历春节的日期，目前截止到2020年春节。
x13as.exe是X13主程序目录。
"""
import os
import pandas as pd
import numpy as np
from pathlib import Path
from statsmodels.tsa.x13 import... | Series() | pandas.Series |
# -----------------------------------------------------------------------------
# Copyright (c) 2014--, The Qiita Development Team.
#
# Distributed under the terms of the BSD 3-clause License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | pd.update_insdc_status('not valid state') | pandas.update_insdc_status |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from finquant.moving_average import compute_ma, sma, ema, sma_std, ema_std
from finquant.moving_average import plot_bollinger_band
def test_sma():
orig = np.array(
[
[np.nan, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5],
... | pd.DataFrame({"0": l1, "1": l2}) | pandas.DataFrame |
"""
Modules that can determine diferent metrics to evaluate an algorithm sucess
"""
import logging
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from datetime import date
logger = logging.getLogger()
sns.set(style="darkgrid")
class ConfusionMatrix:
"""
Class wit... | pd.merge(selected_surveys, selected_users[['user_id', 'company_id']], on="user_id") | pandas.merge |
import numpy as np
import os
import csv
import requests
import pandas as pd
import time
import datetime
from stockstats import StockDataFrame as Sdf
from ta import add_all_ta_features
from ta.utils import dropna
from ta import add_all_ta_features
from ta.utils import dropna
from config import config
def load_dataset(... | pd.DataFrame(temp_rsi) | pandas.DataFrame |
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