prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import numpy as np
import pandas as pd
from bach import Series, DataFrame
from bach.operations.cut import CutOperation, QCutOperation
from sql_models.util import quote_identifier
from tests.functional.bach.test_data_and_utils import assert_equals_data
PD_TESTING_SETTINGS = {
'check_dtype': False,
'check_exact... | pd.Series(data=[1, 1, 2, 3, 6, 7, 8], name='a') | pandas.Series |
import datetime, os, pathlib, platform, pprint, sys
import fastai
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
#import sdv
import sklearn
import yellowbrick as yb
import imblearn
from imblearn.over_sampling import SMOTE
from fastai.tabular.data... | pd.set_option('display.max_rows', 100) | pandas.set_option |
"""
:Authors: <NAME>
:Date: 11/24/2016
:TL;DR: this module is responsible for categorical and numerical columns transformations
"""
from collections import defaultdict
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
class TrainTransformations:
col_to_scaler, c... | pd.DataFrame() | pandas.DataFrame |
import operator
import re
import warnings
import numpy as np
import pytest
from pandas._libs.sparse import IntIndex
import pandas.util._test_decorators as td
import pandas as pd
from pandas import isna
from pandas.core.sparse.api import SparseArray, SparseDtype, SparseSeries
import pandas.util.testing as tm
from pan... | tm.assert_produces_warning(FutureWarning) | pandas.util.testing.assert_produces_warning |
# imports
import csv
import functools
import hashlib
import logging
import warnings
from os.path import isfile as isfile
import click
import fbprophet
import mlflow
import mlflow.pyfunc
import numpy as np
import pandas as pd
from elasticsearch import Elasticsearch
from elasticsearch_dsl import Search
from fbprophet im... | pd.concat([prediction, actual], axis=1) | pandas.concat |
import anndata
import gzip
import os
import pandas as pd
import scipy.io
import tarfile
def load(data_dir, **kwargs):
fn = os.path.join(data_dir, "GSE164378_RAW.tar")
adatas = []
with tarfile.open(fn) as tar:
samples = ['GSM5008737_RNA_3P', 'GSM5008738_ADT_3P']
for sample in samples:
... | pd.DataFrame(protein.X.A, columns=protein.var_names, index=protein.obs_names) | pandas.DataFrame |
import string
from concurrent.futures import ThreadPoolExecutor
from datetime import datetime
import pandas as pd
import requests as req
from bs4 import BeautifulSoup as bs
from tqdm import tqdm
BASE_LINK = 'https://www.nasdaq.com/screening/companies-by-name.aspx?{}&pagesize=200&page={}'
# can get all stocks at once... | pd.DataFrame() | pandas.DataFrame |
import argparse
import os
from dataclasses import dataclass
from functools import lru_cache
import socket
from urllib.parse import parse_qsl, urlencode, urlparse
import flask
from cached_property import cached_property
from pathlib import Path
from typing import Dict, List, Optional, Union
import cv2
import pandas as... | pd.DataFrame(entries) | pandas.DataFrame |
import pandas as pd
import numpy as np
import tensorflow as tf
import functools
'''
DATA FORMAT
- Dates: YEAR-MONTH-DAY
'''
# Define the unique key for all dataset entries
dataset_key = 'object_id'
# Rename labels for a selected dataframe aka columns
def rename_id_label(dataframe, old_label,new_label):
dataf... | pd.read_csv("../datasets/CrunchBase_MegaDataset/milestones.csv") | pandas.read_csv |
#!/usr/bin/env python3
import argparse
import math
import pandas as pd
import sys
from collections import namedtuple
from datetime import date
from enum import Enum
from pathlib import Path
from time import localtime, strftime
class Verbosity(Enum):
LOW=1
HIGH=2
Settings=namedtuple('Settings', ['datapath','... | pd.read_csv(previous_income_file, index_col=0) | pandas.read_csv |
import os
import fnmatch
import calendar
import numpy as np
import pandas as pd
import xarray as xr
from itertools import product
from util import month_num_to_string
import xesmf as xe
"""
Module contains several functions for preprocessing S2S hindcasts.
Author: <NAME>, NCAR (<EMAIL>)
Contributions from <NAME>, N... | pd.Timestamp(ds.time.values) | pandas.Timestamp |
import tensorflow as tf
import pandas as pd
import tensorflow_hub as hub
import os
import re
import numpy as np
from bert.tokenization import FullTokenizer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from tqdm import tqdm
from tensorflow.keras import backend as K... | pd.read_csv('data/test.csv') | pandas.read_csv |
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.decomposition import NMF
from sklearn.preprocessing import MinMaxScaler
def add_team_postfix(input_df):
output_df = input_df.copy()
top = output_df['inning'].str.contains('表')
output_df.loc[top, 'batter'] = ... | pd.pivot_table(input_df, index='subGameID', columns='outCount', values=value_col, aggfunc=np.median) | pandas.pivot_table |
import pandas as pd
import csv
from itertools import zip_longest
import os
import math
def readFiles(tpath):
txtLists = os.listdir(tpath)
return txtLists
def atan(x):
b = []
for i in x:
bb = ( math.atan(i) * 2 / math.pi)
b.append(bb)
b = pd.Series(b)
return b
def log(x,maxx):... | pd.read_csv(add,index_col='Class') | pandas.read_csv |
#%%
import os
import glob
import itertools
import re
import numpy as np
import pandas as pd
import collections
import skbio
import git
#%%
# Find project parental directory
repo = git.Repo("./", search_parent_directories=True)
homedir = repo.working_dir
# Define data directory
datadir = f"{homedir}/data/processed_seq... | pd.DataFrame(columns=names) | pandas.DataFrame |
import pandas as pd
from datetime import datetime
from sapextractor.utils import constants
def apply(dataframe, dt_column, tm_column, target_column):
try:
if str(dataframe[dt_column].dtype) != "object":
print("a")
dataframe[dt_column] = dataframe[dt_column].apply(lambda x: x.strfti... | pd.to_datetime(dataframe[tm_column], format=constants.HOUR_FORMAT_INTERNAL) | pandas.to_datetime |
import re
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import IntervalArray
class TestSeriesReplace:
def test_replace_explicit_none(self):
# GH#36984 if the user explicitly passes value=None, give it to them
ser = pd.Series([0, 0, ""],... | pd.Interval(2.8, 3.1) | pandas.Interval |
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import os
import glob
import subprocess
from libraries.lib_percentiles import *
from libraries.lib_gtap_to_final import gtap_to_final
from libraries.lib_common_plotting_functions import greys, quint_colors, quint_labels
from libraries.lib_country_... | pd.read_csv(out_dir+'carbon_cost/CC_per_hh_indirect_'+pais+'_allGHG.csv') | pandas.read_csv |
"""
Tests shared for DatetimeIndex/TimedeltaIndex/PeriodIndex
"""
from datetime import datetime, timedelta
import numpy as np
import pytest
import pandas as pd
from pandas import (
CategoricalIndex,
DatetimeIndex,
Index,
PeriodIndex,
TimedeltaIndex,
date_range,
period_range... | pd.Series(idx) | pandas.Series |
import pandas as pd
import datetime as dtt
import numpy as np
import matplotlib.pyplot as plt
import copy
excel_path = "E:\\Desktop\\PyCode\\data.xlsx"
clean_excel_path = "E:\\Desktop\\uads\\AnalysisReport\\cleandata_basis.xlsx"
clean_excel_path2 = "E:\\Desktop\\PyCode\\cleandata_basis2.xlsx"
cornData_excel_path = "E:... | pd.notnull(df.iloc[i,0]) | pandas.notnull |
'''Unit tests for functions in cross_correlate.py'''
import numpy as np
import pandas as pd
import pytest
from cross_correlate import get_cross_cor
def test_get_cross_cor():
"""
Tests the ability of get_cross_cor to properly correlate two arrays. Identical arrays must give zero, opposite arrays must give one... | pd.DataFrame(bad_array, columns=['f_lambda']) | pandas.DataFrame |
# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership. The Modin Development Team licenses this file to you under the
# Apache License, Version 2.0 (the "License"); you may not u... | pandas.DataFrame(data) | pandas.DataFrame |
import torch
import os
import numpy as np
from PIL import Image
import Constants
from data import cxr_process as preprocess
import pandas as pd
from torchvision import transforms
import pickle
from pathlib import Path
from torch.utils.data import Dataset, ConcatDataset
def get_dfs(envs = [], split = None, only_fronta... | pd.read_csv(paths[i]) | pandas.read_csv |
import glob
import os
from functools import wraps
from shutil import rmtree
# import matplotlib
# matplotlib.use('Pdf') # noqa
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.lines import Line2D
if os.getenv("FLEE_TYPE_CHECK") is not None and os.environ["FLEE_TYPE_CHECK"].lower(... | pd.read_csv(filename, index_col=None, header=0) | pandas.read_csv |
"""
Monthly Class
Meteorological data provided by Meteostat (https://dev.meteostat.net)
under the terms of the Creative Commons Attribution-NonCommercial
4.0 International Public License.
The code is licensed under the MIT license.
"""
from datetime import datetime
from typing import Union
import numpy as np
import ... | pd.Grouper(level='time', freq=self._freq) | pandas.Grouper |
# -*- coding:utf-8 -*-
# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
# This program is free software; you can redistribute it and/or modify
# it under the terms of the MIT License.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the ... | pd.read_csv(path) | pandas.read_csv |
import os
import sys
import time
import asyncio
import matplotlib.pyplot as plt
import bar_chart_race as bcr
import pandas
from datetime import datetime
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from utils.setup import stats, DbStatsManager, DbConnection # noqa: E402
""" Sc... | pandas.DataFrame(columns, index=indexes) | pandas.DataFrame |
from matplotlib.pylab import rcParams
import requests
import pandas as pd
import numpy as np
from pandas import DataFrame
from io import StringIO
import time
import json
from datetime import date
from statsmodels.tsa.stattools import adfuller, acf, pacf
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa... | pd.read_csv("SeaPlaneTravel.csv") | pandas.read_csv |
# Generic ultratils utility functions
import os, sys
import errno
from datetime import datetime
from dateutil.tz import tzlocal
import numpy as np
import pandas as pd
try:
import ultratils.acq
except:
pass
import audiolabel
from ultratils.pysonix.bprreader import BprReader
def make_acqdir(datadir):
"""Mak... | pd.DataFrame.from_records(rows) | pandas.DataFrame.from_records |
"""
This script is designed to perform table statistics
"""
import pandas as pd
import numpy as np
import sys
sys.path.append(r'D:\My_Codes\LC_Machine_Learning\lc_rsfmri_tools\lc_rsfmri_tools_python')
import os
from Utils.lc_read_write_mat import read_mat
#%% ----------------------------------Our center 550----------... | pd.merge(allsubjname, scale_data, left_on=0, right_on=0, how='inner') | pandas.merge |
"""
<NAME> VR437255
"""
import matplotlib.pyplot as plt
import pandas as pd
import os
import warnings
from tqdm import tqdm
from utils.forecast import *
warnings.filterwarnings("ignore")
FREQ = "W"
SEASONAL = False
SEASONAL_PERIOD = {
'W': 52,
'M': 12
}
# EXECUTION SETTINGS
EXECUTE_NAIVE = True
EXECUTE_ARIMA... | pd.DataFrame(index=ts_forecast_index, columns=['naive', 'arima', 'stlarima']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.6.0
# kernelspec:
# display_name: Python [conda env:fine-dev-py36]
# language: python
... | pd.Series([400, 200], index=['regionN', 'regionS']) | pandas.Series |
from __future__ import annotations
from ..watcher import Watcher as W
import pandas as pd
import numpy as np
import scipy
from sklearn.utils import shuffle
from sklearn.linear_model import LogisticRegression, RidgeClassifier, PassiveAggressiveClassifier, LinearRegression
from sklearn.discriminant_analysis import Quad... | pd.DataFrame(self.results) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# + {}
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import networkx as nx
import matplotlib as mpl
import numba
import squarify
import numpy as np
from math import pi
from sklearn.decomposition import PCA
from sklearn.mixture import GaussianMixture as GMM
from umap i... | pd.read_csv("denoised_coli.csv") | pandas.read_csv |
from piper.custom import ratio
import datetime
import numpy as np
import pandas as pd
import pytest
from time import strptime
from piper.custom import add_xl_formula
from piper.factory import sample_data
from piper.factory import generate_periods, make_null_dates
from piper.custom import from_julian
from pipe... | pd.Series([10, 20, 30]) | pandas.Series |
import numpy as np
import cv2
import csv
import os
import pandas as pd
import time
def calcuNearestPtsDis2(ptList1):
''' Find the nearest point of each point in ptList1 & return the mean min_distance
Parameters
----------
ptList1: numpy array
points' array, shape:(x,2)
Return
... | pd.read_csv(csv_dir + '/' + picID + 'positive_tumour' + '_pts.csv', usecols=['x_cord', 'y_cord']) | pandas.read_csv |
# miscellaneous tools
import os
import subprocess
import sys
import pandas as pd
from collections import defaultdict
import gzip
from numpy import unique
import numpy as np
import pickle
#import HTSeq
#import pysam
#PATH = './'
PATH = os.path.dirname(__file__)
HOME = os.path.expanduser('~')
STAR_PATH = os.path.joi... | pd.DataFrame() | pandas.DataFrame |
# -*- 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_test1,dataset_test["pickup_datetime"]],axis=1) | pandas.concat |
import wandb
import pandas as pd
import logging
logger = logging.getLogger('export')
logging.basicConfig()
api = wandb.Api()
"""
These can be replaced, but make sure you also correct the names in `probing.py` and `training.py` scripts.
"""
WANDB_USERNAME = '<ANONYMIZED>'
MODEL_TRAINING_PROJECT_NAME = 'bias-probing'
ON... | pd.DataFrame({'name': name_list}) | pandas.DataFrame |
import os
import copy
import pickle
import numpy as np
import matplotlib.animation as animation
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import torch
from tqdm import tqdm
from behavenet import get_user_dir
from behavenet import make_dir_if_not_exists
from behavenet.data.utils import b... | pd.concat(metrics_dfs_frame, sort=False) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 21 14:08:43 2019
to produce X and y use combine_pos_neg_from_nc_file or
prepare_X_y_for_holdout_test
@author: ziskin
"""
from PW_paths import savefig_path
from PW_paths import work_yuval
from pathlib import Path
cwd = Path().cwd()
hydro_path = work_... | pd.DataFrame(y_attrs) | pandas.DataFrame |
import pandas as pd
import numpy as np
import altair as alt
import altair_saver
import glob
import os
import copy
import collections
import traceback
import json
# ---------------- Plot themes ------------------------
def personal():
return {
'config': {
'font': 'sans-serif',
'vie... | pd.concat(results, sort=False) | pandas.concat |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import os
import operator
import unittest
import cStringIO as StringIO
import nose
from numpy import nan
import numpy as np
import numpy.ma as ma
from pandas import Index, Series, TimeSeries, DataFrame, isnull, notnull
from pandas.core.index... | assert_series_equal(empty, empty2) | pandas.util.testing.assert_series_equal |
# BUG: Regression on DataFrame.from_records #42456
from numpy import (
array,
empty,
)
import pandas as pd
print(pd.__version__)
structured_dtype = [("prop", int)]
# Does NOT work any more
result = empty((0, len(structured_dtype)))
structured_array = array(result, dtype=structured_dtype)
result = | pd.DataFrame.from_records(structured_array) | pandas.DataFrame.from_records |
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 22 14:50:25 2021
@author: <NAME>
"""
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from ke... | pd.DataFrame(trY8) | pandas.DataFrame |
from dask import delayed
from dask.distributed import Client, LocalCluster
from dask_jobqueue import SLURMCluster
import glob
import pickle
import numpy as np
import scipy.stats
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
from metric_hse import HSEMetric
cluster = SLURMCluster(memory='2g'... | pd.DataFrame(stacked_entropies_PI, columns=["Seed", "Vehicle_A", "Min(Hx, Hy)", "Hx+Hy", "Hx", "Hy", "Hxy", "Hy_given_x", "Hx_given_y", "PI_xy", "PI_xy_Normalized"]) | pandas.DataFrame |
# Author : <NAME>
# Date : 23-26 Dec, 2021
# Based on plotly Dash interface for plotting in html https://dash.plotly.com/
# Binance python library https://python-binance.readthedocs.io from Binance API https://binance-docs.github.io
# issues : slow, may overrequest should be converted to websocket api for smooth re... | pd.DataFrame(depth['asks'],columns=['price','qty']) | pandas.DataFrame |
from datetime import timedelta
import numpy as np
from pandas.core.dtypes.dtypes import DatetimeTZDtype
import pandas as pd
from pandas import (
DataFrame,
Series,
date_range,
option_context,
)
import pandas._testing as tm
def _check_cast(df, v):
"""
Check if all dtypes of df are equal to v... | DataFrame([["foo"]]) | pandas.DataFrame |
import streamlit as st
from google.cloud import storage, bigquery
from google.cloud.bigquery.schema import SchemaField
from google.oauth2 import service_account
from PIL import Image
import json
import io
import os
import pandas as pd
SEND_FEEDBACK = True
class GCP_USER:
def __init__(self, credentials):
... | pd.DataFrame({'PATH': [img_file], 'CAPTION': [caption], 'ID': new_id}) | pandas.DataFrame |
import pandas as pd
import datetime
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.widgets import CheckButtons
from pandas.plotting import scatter_matrix
import ta
import talib
#https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html#momentum-i... | pd.DataFrame(xli_data, columns = ['ticker', 'descr', 'date', 'low', 'high', 'close', 'vol', 'ret', 'bid', 'ask', 'retx']) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.ticker as tck
import matplotlib.font_manager as fm
from mpl_toolkits.basemap import Basemap, addcyclic, ... | pd.DataFrame({'COD_max':COD_max, 'COD_min':COD_min, 'COD_mean':COD_mean}, index= fechas_horas_COD) | pandas.DataFrame |
import math
import os
import timeit
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy
import tensorflow as tf
import random
from matplotlib.lines import Line2D
from data import Dataset
from prediction import train_model, test_model
from prediction import load_encoder_and_predictor_w... | pd.Series(entry_train) | pandas.Series |
"""
descriptive analysis - Utility functions to work with:
- mongoDB's result cursors
- pandas' DataFrames
Also contains as set of auxiliary internal functions and external routines
Members:
# time_serie... | pd.DataFrame(per_month) | pandas.DataFrame |
# simple feature engineering from A_First_Model notebook in script form
import cudf
def see_percent_missing_values(df):
"""
reads in a dataframe and returns the percentage of missing data
Args:
df (dataframe): the dataframe that we are analysing
Returns:
percent_missing (dataframe): a... | dd.get_dummies(unified, columns=dummy_cols, dtype='int64') | pandas.get_dummies |
# -*- coding: utf-8 -*-
"""
The data module contains tools for preprocessing data. It allows users to merge timeseries, compute
daily and monthly summary statistics, and get seasonal periods of a time series.
"""
from __future__ import division
import pandas as pd
from numpy import inf, nan
__all__ = ['julian_to_gre... | pd.DataFrame.join(sim_df_copy, obs_df_copy) | pandas.DataFrame.join |
###############################################################################
##
## Copyright (C) 2020-2022, New York University.
## All rights reserved.
## Contact: <EMAIL>
##
## This file is part of BugDoc.
##
## "Redistribution and use in source and binary forms, with or without
## modification, are permitted prov... | pd.read_csv(bad_dataset) | pandas.read_csv |
"""Combine demand, hydro, wind, and solar traces into a single DataFrame"""
import os
import time
import pandas as pd
import matplotlib.pyplot as plt
def _pad_column(col, direction):
"""Pad values forwards or backwards to a specified date"""
# Drop missing values
df = col.dropna()
# C... | pd.MultiIndex.from_product([['WIND'], df.columns]) | pandas.MultiIndex.from_product |
"""
CMO-PMO Dashbaord report generation.
Reads daily metric data from blob storage and uploads
"""
import sys, time
import os
from datetime import datetime, date, timedelta
from pathlib import Path
import argparse
import pandas as pd
util_path = os.path.abspath(os.path.join(__file__, '..', '..', '..', 'util'))
sys.pa... | pd.read_csv(result_loc_) | pandas.read_csv |
import os
from datetime import datetime
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from keras.callbacks import EarlyStopping
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout... | pd.concat([X_train, y_train], axis=1) | pandas.concat |
'''
This file contains the ML-algorithms used to
operate on the data provided by the user
'''
import pandas as pd
import numpy as np
from flask import current_app
import os
from sklearn.tree import DecisionTreeClassifier as DTC
from sklearn.naive_bayes import GaussianNB as GNB
from sklearn.svm import SVC
from sklearn.... | pd.DataFrame(reconstructed_data, index=None) | pandas.DataFrame |
import pandas as pd
def unfold(df,s):
df=df[s].values
lst=[]
for i in df:
dic={}
for j in range(len(i)):
dic[j]=i[j]
lst.append(dic)
return pd.DataFrame(lst)
def load_raw_data(file_path,vectorizer,dataset_index):
if dataset_index==2:
df = pd.read_pickl... | pd.concat([p_train,n_train],ignore_index=True) | pandas.concat |
from flask import Flask, render_template, request, flash, redirect, url_for, send_file
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from mrcnn.config import Config
from mrcnn.model import MaskRCNN
from matplotlib import pyplot
from matplotlib.patches import Rectangle... | pd.DataFrame(dict) | pandas.DataFrame |
'''
Train and evaluate binary classifier
Produce a human-readable HTML report with performance plots and metrics
Usage
-----
```
python eval_classifier.py {classifier_name} --output_dir /path/ \
{clf_specific_kwargs} {data_kwargs} {protocol_kwargs}
```
For detailed help message:
```
python eval_classifier.py {cl... | pd.DataFrame(cv_te_perf_df) | pandas.DataFrame |
import numpy as np
import pytest
from pandas.errors import UnsupportedFunctionCall
from pandas import (
DataFrame,
DatetimeIndex,
Series,
date_range,
)
import pandas._testing as tm
from pandas.core.window import ExponentialMovingWindow
def test_doc_string():
df = DataFrame({"B": [0, 1, 2, np.na... | date_range("2000", freq="D", periods=10) | pandas.date_range |
''' Descrição: Solução do desafio-05 - osprogramadores.com '''
import json
import pandas as pd
def main():
'''Faz a leitura do arquivo JSON'''
with open('funcionarios.json') as file:
data = json.load(file)
df_funcionario = | pd.DataFrame.from_dict(data['funcionarios']) | pandas.DataFrame.from_dict |
import cv2
import os
import copy
import numpy as np
import pandas as pd
from classix import CLASSIX
import matplotlib.pyplot as plt
from collections import OrderedDict
def order_pics(figs):
images = list()
labels = list()
for i in range(40):
num = i + 1
for img in figs:
try:
... | pd.Series(labels) | pandas.Series |
import os
from numpy import mean, std, sqrt
from algorithms.common.stopping_criterion import MaxGenerationsCriterion, ErrorDeviationVariationCriterion, TrainingImprovementEffectivenessCriterion
from data.io_plm import _get_path_to_data_dir
import numpy as np
import pandas as pd
def _metric_in_dict(metric, d):
r... | pd.DataFrame.from_dict(se_dict) | pandas.DataFrame.from_dict |
import numpy as np
import pandas as pd
import gpflow
from gpflow.utilities import print_summary
def make_subset_simplified(cmip6, start, end, column_name, mean_center=False):
Xmake_all = []
Ymake_all = []
dataset_names = cmip6.name.unique()
for n in dataset_names:
p = cmip6[cmip6.name == n]
... | pd.DataFrame(d, columns=['mean', 'var', 'obs']) | pandas.DataFrame |
from pathlib import Path
import os
import re
import pandas as pd
import numpy as np
import random
from math import ceil
import cv2
import glob
import shutil
import experiment_code.constants as consts
from experiment_code.targetfile_utils import Utils
# import experiment_code.targetfile_utils as utils
# create inst... | pd.read_csv(fpath) | pandas.read_csv |
import datetime as dt
import os.path
import re
import numpy as np
import pandas as pd
import pandas.testing as pdt
import pint.errors
import pytest
import scmdata.processing
from scmdata import ScmRun
from scmdata.errors import MissingRequiredColumnError, NonUniqueMetadataError
from scmdata.testing import _check_pand... | pdt.assert_series_equal(res, exp) | pandas.testing.assert_series_equal |
import os
import pandas as pd
import csv
from sklearn.model_selection import train_test_split
import numpy as np
import random
import tensorflow as tf
import torch
#directory of tasks dataset
os.chdir("original_data")
#destination path to create tsv files, dipends on data cutting
path_0 = "mttransformer/... | pd.concat([labeled2, unlabeled2]) | pandas.concat |
# CCI (Commodity Channel Index)
# http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:commodity_channel_index_cci
# Bir menkul kıymetin fiyat değişikliği ile ortalama fiyat değişikliği
# arasındaki farkı ölçer. Yüksek pozitif okumalar, fiyatların ortalamalarının
# oldukça üzerinde olduğu... | pd.Series(cci_series, name="cci") | pandas.Series |
'''
This script tests the function from shapiro_wilk.py
Parameters
----------
None
Returns
-------
Assertion errors if tests fail
'''
# dependencies
import pytest
import numpy as np
import pandas as pd
from normtestPY.shapiro_wilk import shapiro_wilk
# Sample data
data_df = pd.DataFrame({'data' : [41.5,38.7,44.5,4... | pd.Series([41.5,38.7,44.5,43.8,46.0]) | pandas.Series |
import numpy as np
import pytest
from pandas._libs.tslibs import IncompatibleFrequency
from pandas import (
DatetimeIndex,
Series,
Timestamp,
date_range,
isna,
notna,
offsets,
)
import pandas._testing as tm
class TestSeriesAsof:
def test_asof_nanosecond_index_access(self):
ts... | Series(np.nan, index=rng) | pandas.Series |
# -*- coding: utf-8 -*-
import json
import base64
import datetime
import requests
import pathlib
import math
import pandas as pd
import flask
import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.plotly as py
import plotly.graph_objs as go
from dash.dependencies import Input,... | pd.Series(PP - df["high"] + df["low"]) | pandas.Series |
# *****************************************************************************
# Copyright (c) 2019, 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(local_data) | pandas.Series |
import pandas as pd
from fbprophet import Prophet
from fbprophet.plot import add_changepoints_to_plot
from fbprophet.diagnostics import cross_validation
from fbprophet.diagnostics import performance_metrics
from fbprophet.plot import plot_cross_validation_metric
from time import gmtime, strftime
import matplotlib.pyplo... | pd.to_datetime(cell_df['START_TIME']) | pandas.to_datetime |
# -*- coding: utf-8 -*-
from datetime import timedelta
from distutils.version import LooseVersion
import numpy as np
import pytest
import pandas as pd
import pandas.util.testing as tm
from pandas import (
DatetimeIndex, Int64Index, Series, Timedelta, TimedeltaIndex, Timestamp,
date_range, timedelta_range
)
f... | timedelta_range('9H', freq='H', periods=3) | pandas.timedelta_range |
"""Alpha Vantage Model"""
__docformat__ = "numpy"
import logging
from typing import Dict, List, Tuple
import numpy as np
import pandas as pd
import requests
from alpha_vantage.fundamentaldata import FundamentalData
from gamestonk_terminal import config_terminal as cfg
from gamestonk_terminal.decorators import log_st... | pd.DataFrame() | pandas.DataFrame |
import scanpy as sc
import pandas as pd
import numpy as np
import anndata as ad
import matplotlib.pyplot as plt
import seaborn as sns
import sys
import gseapy as gp
import math
import os
def check_filter_single_cluster(adata,key):
vc = adata.obs[key].value_counts()
exclude_clusters= vc.loc[vc==1].index
tru... | pd.Series(key_size_dict) | pandas.Series |
import gc
import numpy as np
import pandas as pd
from itertools import chain
from sklearn.decomposition import IncrementalPCA
import sklearn.linear_model
import sklearn.naive_bayes
import sklearn.ensemble
import sklearn.gaussian_process
from sklearn import metrics
from ..utils import map_fn
from ..config import cfg
fro... | pd.concat([outputs_df, dec_ser], axis=1) | pandas.concat |
from selenium import webdriver
import datetime as dt
import pandas as pd
import os
import time as time
import platform
import getpass
class Focus(object):
"""
Classe para puxar os dados do PIB total e IPCA do focus.
"""
indicator_dict = {'ipca': '5', 'pib': '9'}
metric_dict = {'mean': '2', 'media... | pd.read_excel(file_path, skiprows=1, na_values=[' ']) | pandas.read_excel |
"""
Receipts endpoint wrapper class
Possible requests:
* get_by_query: get receipts that respect passed in query parameters
* get_by_id: get receipt with a given ID
* get_by_date: get receipts for a given date
* get_by_dates: get receipts between two dates
"""
import pandas as pd
from datetime import datetime, timez... | pd.DataFrame({key: receipt[key] for key in fields.receipt}, index=[0]) | pandas.DataFrame |
"""
This file contains various generic utility methods that do not fall within
data or input-output methods.
"""
import math
import numpy as np
from datetime import datetime
from typing import Dict, Tuple
from collections import defaultdict
import pandas as pd
def timestamp() -> str:
return datetime.strftime(dat... | pd.DataFrame.from_dict(results) | pandas.DataFrame.from_dict |
import numpy as np
import pandas as pd
import datetime as dt
def make_column_index(df:pd.DataFrame, column_label:str) -> None:
df.index = df[column_label]
df.drop(column_label, axis=1, inplace=True)
df.index.name = None
def rename_column(df:pd.DataFrame, column_label:str, new_name:str) -> None:
df.ren... | pd.offsets.MonthBegin(1) | pandas.offsets.MonthBegin |
# Copyright 2019 Nokia
# Licensed under the BSD 3 Clause Clear license
# SPDX-License-Identifier: BSD-3-Clause-Clear
import pandas as pd
import numpy as np
from datetime import datetime
import math
# increments = 0
# search_range = 0
# P7_NUM = 0
# current_date = 0
# qq_plot_start = 5
# qq_plot_end = ... | pd.to_datetime(feature_data['Month_Ending'], format='%d/%m/%Y') | pandas.to_datetime |
# Written by: <NAME>, @dataoutsider
# Viz: "Party Lines", enjoy!
import pandas as pd
import os
import math
df = pd.read_csv(os.path.dirname(__file__) + '/1976-2016-president.csv', engine='python') # test with , nrows=20
df['term'] = df['year']
df2016 = df.loc[df['year'] == 2016]
df2016 = df2016.groupby('state').agg(... | pd.DataFrame(data, columns=df3.columns) | pandas.DataFrame |
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.DataFrame(df_list.iloc[i, 0]) | pandas.DataFrame |
from datetime import timedelta
from io import StringIO
import pandas as pd
from abide.schedule import ScheduledJobDefinition, ScheduledJobState, RunState, Scheduler, \
read_job_definitions
def test_basic():
s = Scheduler(pd.to_datetime('9/28/2020 13:06:03'),
{'A': ScheduledJobDefinition("* ... | pd.to_datetime('9/28/2020 00:00:00') | pandas.to_datetime |
# Copyright (C) 2016 <NAME> <<EMAIL>>
# All rights reserved.
# This file is part of the Python Automatic Forecasting (PyAF) library and is made available under
# the terms of the 3 Clause BSD license
import pandas as pd
import numpy as np
from . import Time as tsti
from . import DateTime_Functions as dtfunc
from . i... | pd.DataFrame() | pandas.DataFrame |
"""Tests for dynamic validator."""
from datetime import date, datetime
import numpy as np
import pandas as pd
from delphi_utils.validator.report import ValidationReport
from delphi_utils.validator.dynamic import DynamicValidator
class TestReferencePadding:
params = {
"common": {
"data_source":... | pd.date_range(start="2020-09-24", end="2020-10-23") | pandas.date_range |
import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
from Augmenter import Augmenter
from DataLoader import DataLoader
from cnn_classifier import ClassifierCNN
def main():
# unbalanced data = ['insect', 'ecg200', 'gunpoint']
data_name = 'insect'
path = 'C:/Users/letiz/D... | pd.DataFrame(fake) | pandas.DataFrame |
import datetime
import re
from warnings import (
catch_warnings,
simplefilter,
)
import numpy as np
import pytest
from pandas._libs.tslibs import Timestamp
import pandas as pd
from pandas import (
DataFrame,
HDFStore,
Index,
Int64Index,
MultiIndex,
RangeIndex,
... | _maybe_remove(store, "f") | pandas.tests.io.pytables.common._maybe_remove |
import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.cluster import KMeans
from sklearn import preprocessing
from matplotlib import animation
from mpl_toolkits.mplot3d import Axes3D, proj3d
def extract_close(data_frame):
close_df = data_frame.drop([data_frame.columns[... | pd.read_csv("data_proj_414.csv") | pandas.read_csv |
"""<NAME>-2020.
MLearner Machine Learning Library Extensions
Author:<NAME><www.linkedin.com/in/jaisenbe>
License: MIT
"""
import pandas as pd
import numpy as np
import pytest
from mlearner.preprocessing import DataAnalyst
import matplotlib
matplotlib.use('Template')
data = | pd.DataFrame({"a": [0., 1., 1., 0., 1., 1.], "b": [10, 11, 12, 13, 11, 100], "c": ["OK", "OK", "NOK", "OK", "OK", "NOK"]}) | pandas.DataFrame |
# Author: <NAME>
import os
import time
import requests
import pandas as pd
import geopandas as gpd
import numpy as np
import subprocess
import sqlalchemy
import datetime
import multiprocessing as mp
from datetime import datetime
from io import StringIO
pd.set_option('display.max_columns', None) # DEBUG
# Get DB conn... | pd.read_sql(df_query, engine) | pandas.read_sql |
######### imports #########
from ast import arg
from datetime import timedelta
import sys
sys.path.insert(0, "TP_model")
sys.path.insert(0, "TP_model/fit_and_forecast")
from Reff_constants import *
from Reff_functions import *
import glob
import os
from sys import argv
import arviz as az
import seaborn as sns
import m... | pd.read_csv(file, parse_dates=["date"]) | pandas.read_csv |
import pandas as pd
import networkx as nx
import numpy as np
import os
import random
'''
code main goal: make a graph with labels and make a knowledge-graph to the classes.
~_~_~ Graph ~_~_~
Graph nodes: movies
Graph edges: given 2 movies, an edge determined if a cast member play in both of the movies.
Label: the genre... | pd.read_csv(self.data_paths['cast']) | pandas.read_csv |
import streamlit as st
import requests
from bs4 import BeautifulSoup as bs
import time
import pandas as pd
import random
import re
import urllib.request
from PIL import Image
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.image as mpim
import numpy as np
from mpl_toolkits import mplot3d
imp... | pd.DataFrame(player_images) | pandas.DataFrame |
import pandas as pd
import pytest
import woodwork as ww
from pandas.testing import (
assert_frame_equal,
assert_index_equal,
assert_series_equal,
)
from evalml.pipelines.components import LabelEncoder
def test_label_encoder_init():
encoder = LabelEncoder()
assert encoder.parameters == {"positive_... | pd.Series(["a", "b"]) | pandas.Series |
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