prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# coding: utf-8
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc
def cut_bins(x, bins=10, method='equal'):
"""
对x进行分bin，返回每个样本的bin值和每个bin的下界
Parameters
----------
x: numpy.ndarray or pandas.Series
变量
bins: int... | pd.DataFrame() | pandas.DataFrame |
'''
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any late... | pd.concat([first_eve_df, last_eve_df], axis=1) | pandas.concat |
'''
tRNA Adaptation Index
'''
import collections
import os
import json
import logging
import pandas as pd
import numpy as np
import scipy.stats.mstats
from sqlalchemy import create_engine
from ..alphabet import CODON_REDUNDANCY
logger = logging.getLogger(__name__)
def main():
logging.basicConfig(level=logging... | pd.isnull(tpl.codon) | pandas.isnull |
import numpy as np
#import skimage.transform as sktransform
import random
import matplotlib.image as mpimg
import os
import pandas as pd
import matplotlib.pyplot as plt
import shutil
new_path = './track1/IMG/'
current_path = './out2rev/IMG/'
if not os.path.exists(new_path):
os.makedirs(new_path)
print('Fold... | pd.DataFrame() | pandas.DataFrame |
import pickle
import numpy as np
import pandas as pd
from sklearn.exceptions import ConvergenceWarning
from sklearn.mixture import BayesianGaussianMixture
from sklearn.preprocessing import OneHotEncoder
from sklearn.utils.testing import ignore_warnings
class DataTransformer(object):
"""Data Transformer.
Mod... | pd.DataFrame(output, columns=column_names) | pandas.DataFrame |
"""
Prepare training and testing datasets as CSV dictionaries
Created on 11/26/2018
@author: RH
"""
import os
import pandas as pd
import sklearn.utils as sku
import numpy as np
# get all full paths of images
def image_ids_in(root_dir, ignore=['.DS_Store','dict.csv', 'all.csv']):
ids = []
for id in os.listdi... | pd.concat(valist) | pandas.concat |
from sympy import *
import pandas as pd
from random import random
def random_optimization(xl, xu, n, function):
x = Symbol('x')
f = parse_expr(function)
iteration = 0
data = | pd.DataFrame(columns=['iteration','xl','xu','x','f(x)','max_x','max_f(x)']) | pandas.DataFrame |
import os
from datetime import date
from dask.dataframe import DataFrame as DaskDataFrame
from numpy import nan, ndarray
from numpy.testing import assert_allclose, assert_array_equal
from pandas import DataFrame, Series, Timedelta, Timestamp
from pandas.testing import assert_frame_equal, assert_series_equal
from pymo... | Timestamp('2008-10-23 05:53:11') | pandas.Timestamp |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
#-- fix for tensorflow 2.0 version ---
# import tensorflow.compat.v1 as tf
# tf.disable_v2_behavior()
import numpy as np
import os
import matplotlib.pyplot as plt
import traceback
import... | pd.plotting.scatter_matrix(df) | pandas.plotting.scatter_matrix |
import streamlit as st
import streamlit.components.v1 as components
from streamlit_folium import folium_static
# import folium
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import immo
import ssl
# to avoid SSLCertVerificationError
ssl._create_default_https_context = ssl._create_unverified_contex... | pd.cut(df['prixm2'], bins=4,labels=['blue','green', 'yellow', 'red']) | pandas.cut |
''' ATP Matches Data Pipeline '''
import datetime as dt, pandas as pd
def run_pipeline(start_year = 1968, end_year = dt.datetime.now().year + 1):
# Extract match data
tour_files = []
for i in range(start_year, end_year):
url_base = r'https://raw.githubusercontent.com/JeffSackmann/tennis_atp/master... | pd.read_csv(tour_files[0]) | pandas.read_csv |
# -*- coding: utf-8 -*-
# pylint: disable=W0612,E1101
from datetime import datetime
import operator
import nose
from functools import wraps
import numpy as np
import pandas as pd
from pandas import Series, DataFrame, Index, isnull, notnull, pivot, MultiIndex
from pandas.core.datetools import bday
from pandas.core.n... | assert_almost_equal(casted2.values, exp_values) | pandas.util.testing.assert_almost_equal |
import pandas as pd
from schoonmaken.common import Person, Task
def save_data(people, save_path):
pass
def retrieve_data(data_path) -> list[tuple]:
'''
Layout of data:
[
(person_name: str, [TaskData, ...]),
...
]
'''
td = []
return td
def retrieve_p... | pd.DataFrame(task_table) | pandas.DataFrame |
import pkg_resources
import os
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from windea_tool import weibull
from windea_tool import plotting
turbines_dict = {'Enercon E-70 (2300 kW)':'Enercon_E-70_2300kW.xlsx',
'Enercon E-115 (3000 kW)':'Enercon_E-115_3000kW.xlsx'}... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
from os.path import join as joinPaths
from os.path import isdir
from os.path import isfile
from os import listdir as ls
from IPython.display import display, Markdown, Latex
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
from matplotlib.pyplot import cm
from mult... | pd.to_datetime(df["time"], unit="s", utc=True) | pandas.to_datetime |
import numpy as np
import pandas as pd
import pytest
from pandas.util import hash_pandas_object
import dask.dataframe as dd
from dask.dataframe import _compat
from dask.dataframe._compat import tm
from dask.dataframe.utils import assert_eq
@pytest.mark.parametrize(
"obj",
[
pd.Series([1, 2, 3]),
... | pd.Series([1000, 2000, 3000, 4000]) | pandas.Series |
"""Tests for the sdv.constraints.tabular module."""
import numpy as np
import pandas as pd
import pytest
from sdv.constraints.errors import MissingConstraintColumnError
from sdv.constraints.tabular import (
ColumnFormula, CustomConstraint, GreaterThan, UniqueCombinations)
def dummy_transform():
pass
def d... | pd.to_datetime(['2020-01-01T00:00:01', '2020-01-02T00:00:01']) | pandas.to_datetime |
import matplotlib.dates as mdates
from tqdm import tqdm as tqdm
import datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from CometTS.CometTS import interpolate_gaps
import argparse
import os
sns.set(color_codes=True)
# Functions for a seasonal auto-regressive integ... | pd.read_csv(CometTSOutputCSV) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
/***************************************************************************
Summarizes raster statistics in parallel from soilgrids to estonian soil polygons
-------------------
copyright : (C) 2018-2020 by <NAME>
email ... | pd.DataFrame(outputs) | pandas.DataFrame |
import pandas as pd
lista_valores = [1,2,3]
lista_indices = ['a', 'b', 'c']
serie = pd.Series(lista_valores, index=lista_indices)
print(serie)
lista_notas = [[6,7,8],[8,9,5],[6,9,7]]
lista_indices2 = ['Matematicas', 'historia', 'fisica']
lista_nombres = ['Antonio', 'Maria', 'Pedro']
dataframe = | pd.DataFrame(lista_notas, index=lista_indices2, columns=lista_nombres) | pandas.DataFrame |
import numpy as np
import pandas as pd
from scipy import signal as ssig
from scipy import stats as spst
import os
import re
import string
from salishsea_tools import geo_tools
import netCDF4 as nc
class Cast:
def __init__(self,fpath):
mSta,mLat,mLon,df=readcnv(fpath)
self.sta=mSta
self.lat=... | pd.DataFrame(p,columns=['depth_m']) | pandas.DataFrame |
import datetime as dt
import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import numerapi
import plotly.express as px
from utils import *
# setup backend
napi = numerapi.SignalsAPI()
leaderboard_df = pd.DataFrame(napi.get_leaderboard(limit = 10_000))
MODELS_TO_CHECK = ... | pd.concat(rep_dfs) | pandas.concat |
from itertools import product as it_product
from typing import List, Dict
import numpy as np
import os
import pandas as pd
from scipy.stats import spearmanr, wilcoxon
from provided_code.constants_class import ModelParameters
from provided_code.data_loader import DataLoader
from provided_code.dose_evaluation_class imp... | pd.read_csv(consolidate_data_paths['weights'], index_col=[0, 1, 2, 3], header=[0, 1]) | pandas.read_csv |
import os
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sbn
import click
# define functions
def getUnique(df):
"""Calcualtes percentage of unique reads"""
return (
df.loc[df[0] == "total_nodups", 1].values[0]
/ df.loc[df[0] == "total_mapped", 1].values[0... | pd.melt(uniqueF) | pandas.melt |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 13 11:22:34 2022
@author: mariaolaru
"""
import os
import pandas as pd
import numpy as np
import statsmodels.api as sm
import scipy.stats as stat
import xarray as xr
from matplotlib import pyplot as plt
import math
from sklearn.preprocessing import ... | pd.Series(diff == 1) | pandas.Series |
from datetime import datetime
import numpy as np
import pytest
from pandas.core.dtypes.cast import find_common_type, is_dtype_equal
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series
import pandas._testing as tm
class TestDataFrameCombineFirst:
def test_combine_first_mixed(self):
... | pd.Timestamp("2011-01-01") | pandas.Timestamp |
from io import StringIO
import pandas as pd
import numpy as np
import pytest
import bioframe
import bioframe.core.checks as checks
# import pyranges as pr
# def bioframe_to_pyranges(df):
# pydf = df.copy()
# pydf.rename(
# {"chrom": "Chromosome", "start": "Start", "end": "End"},
# axis="col... | pd.Int64Dtype() | pandas.Int64Dtype |
# Copyright 2021 <NAME>. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agree... | pd.DataFrame(results) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import string
from collections import OrderedDict
from datetime import date, datetime
import numpy as np
import pandas as pd
import pandas.util.testing as pdt
import pytest
from kartothek.core.common_metadata import make_meta, store_schema_metadata
from kartothek.core.index import ExplicitSe... | pd.DataFrame({"a": [2]}) | pandas.DataFrame |
"""
author: <NAME> & <NAME>
Implementation of the climate data-utils for our training framework (i.e. on
synthetic SDE data).
This is mainly a copy of the data_utils.py file from the official implementation
of GRU-ODE-Bayes: https://github.com/edebrouwer/gru_ode_bayes.
"""
import torch
import pandas as pd
import num... | pd.read_csv(root_dir + "/" + label_file) | pandas.read_csv |
#!/usr/bin/env python3
"""Module to download cryptocurrency ohlc data"""
import time
import datetime as dt
import logging
import json
import requests
import pandas as pd
def from_datetime_to_unix(date):
'''in: datetime, out: unix_timestamp'''
return int(time.mktime(date.timetuple()))
def from_unix_to_date(da... | pd.concat(frames) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 3 09:59:23 2021
@author: <NAME>
Install Packages:
- pip install pandas
Draws a Gantt chart per trial
Each event is on a different y-tick
"""
# eNPHR 1.0 SS_6
#
from ganttChartDrawer import draw_group
import pandas as pd
import numpy as np
# Read and organize... | pd.read_excel('cleversys_events/Trial event export ' + filename + '.xlsx', skiprows=[0,1,2,3,4,5]) | pandas.read_excel |
"""
Name: shread.py
Author: <NAME>, Reclamation Technical Service Center
Description: Utilities for downloading and processing snow products
ADD CLASSES / FUNCTIONS DEFINED
ADD WHA
"""
import ftplib
import os
import tarfile
import gzip
from osgeo import gdal
import csv
import logging
import glob
from osgeo import osr
... | pd.concat(frames, axis=1) | pandas.concat |
import json
import csv
import numpy as np
from stockstats import StockDataFrame
import pandas as pd
import mplfinance as mpf
import seaborn as sn
import matplotlib.pyplot as plt
def load_secrets():
"""
Load data from secret.json as JSON
:return: Dict.
"""
try:
with open('secret.json', 'r')... | pd.DataFrame(data) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Contains the Evaluator class.
Part of symenergy. Copyright 2018 authors listed in AUTHORS.
"""
import os
import sys
import gc
import py_compile
import sympy as sp
import numpy as np
from importlib import reload
from multiprocessing import current_process
import pandas... | pd.Series(True, index=df.index) | pandas.Series |
from __future__ import division
from datetime import datetime
import sys
if sys.version_info < (3, 3):
import mock
else:
from unittest import mock
import pandas as pd
import numpy as np
import random
from nose.tools import assert_almost_equal as aae
import bt
import bt.algos as algos
def test_algo_name():... | pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
from greykite.common.features.timeseries_lags import build_agg_lag_df
from greykite.common.features.timeseries_lags import build_autoreg_df
from greykite.common.features.timeseries_lags import build_autoreg_df_multi
from greykite.common.features.timeseries_lags impo... | pd.isnull(lag_df) | pandas.isnull |
import streamlit as st
import numpy as np
import pandas as pd
from matplotlib.image import imread
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import seaborn as sns
import requests
import joblib
import shap
# import streamlit.components.v1 as components
shap.initjs()
st.set_option('deprecation.sho... | pd.DataFrame(X_train, columns=features_list_after_prepr) | pandas.DataFrame |
# Notebook to transform OSeMOSYS output to same format as EGEDA
# Import relevant packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
from openpyxl import Workbook
import xlsxwriter
import pandas.io.formats.excel
import glob
import re
# Path for OSeMOSYS output
path_output = './d... | pd.DataFrame() | pandas.DataFrame |
import os
from tkinter import *
import pandas as pd
# UI set up
root = Tk()
root.title("Zoom Automator")
scroll = Scrollbar(root)
canvas = Canvas(root, width = 350, height = 350)
canvas.grid(columnspan = 7, rowspan = 7)
left_margin = Label(root, text = " ", padx = 7)
left_margin.grid(row = 0, column = 0)
bottom_... | pd.DataFrame(data=[], columns=colNames) | pandas.DataFrame |
#!/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.DataFrame(ending_timestamp) | pandas.DataFrame |
# Copyright (c) 2013-2015 Siphon Contributors.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""Read data from the National Data Buoy Center."""
from io import StringIO
import warnings
import numpy as np
import pandas as pd
import requests
from ..http_util import ... | pd.to_datetime(df[['year', 'month', 'day', 'hour', 'minute']], utc=True) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Load averaged results from csv, containing scores of all ckpts.
For each exp group, return the best ckpt (ranked by valid performance).
"""
import shutil
import configargparse
import os
import pandas as pd
__author__ = "<NAME>"
__email__ = "<EMAIL>"
dev_test_pairs = [
('kp20k_valid... | pd.concat([selfbest_dev_rows, dev_row]) | pandas.concat |
#!/usr/bin/env python
"""
Fuse csv files into one single file.
"""
## file_fuser.py
### fuse individual files into one giant file
import pandas as pd
import os
import argparse
from abc import ABCMeta, abstractmethod
class CsvFuserAbs(object, metaclass=ABCMeta):
## This object initialized from command line a... | pd.DataFrame() | pandas.DataFrame |
# Adapted from code written by <NAME>
import pandas as pd
from tqdm import tqdm
import numpy as np
import datetime
'''
Sources are currently human, mouse, and rat, in order of descending priority.
'''
sources = [
'ftp://ftp.ncbi.nih.gov/gene/DATA/GENE_INFO/Mammalia/Rattus_norvegicus.gene_info.gz',
'ftp://ftp.... | pd.read_csv(geneid_path, sep='\t', na_filter=False) | pandas.read_csv |
from flask import Flask, request, jsonify
import json
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
# import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# from scipy.spatial import distance
from sklearn.manifold imp... | pd.read_csv(path) | pandas.read_csv |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.9.1
# kernelspec:
# display_name: Python [conda env:core_acc] *
# language: python
# name: conda-env-core_acc-py
# -... | pd.read_csv(pa14_metadata_filename, header=0, index_col=0) | pandas.read_csv |
import warnings
import numpy as np
import pandas as pd
warnings.filterwarnings("ignore")
Data = pd.read_excel(io="Training_Data.xlsx", sheet_name="E0_Mod")
D = pd.DataFrame(Data[['HS','AS','HST','AST','HF','AF','HC','AC','HY','AY','HR','AR']].T)
D2 = pd.DataFrame(Data[['HS','AS','HST','AST','HF','AF','HC','AC',... | pd.DataFrame() | pandas.DataFrame |
"""
Tasks
-------
Search and transform jsonable structures, specifically to make it 'easy' to make tabular/csv output for other consumers.
Example
~~~~~~~~~~~~~
*give me a list of all the fields called 'id' in this stupid, gnarly
thing*
>>> Q('id',gnarly_data)
['id1','id2','id3']
Observations:
--... | u('name') | pandas.compat.u |
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({"tz": s_aware, "notz": s_naive}) | pandas.DataFrame |
#!/usr/bin/env python3
##############################################################
## <NAME> & <NAME> ##
## Copyright (C) 2020-2021 ##
##############################################################
'''
Created on 30 oct. 2020
@author: alba
Modified in Ma... | pd.DataFrame(data=None, columns=["length"]) | pandas.DataFrame |
import os
import ctypes
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.path import Path
import matplotlib.patches as patches
from matplotlib.collections import PolyCollection
import matplotlib.colors as colors
from pywfm import DLL_PATH, D... | pd.DataFrame({"NodeID": node_ids, "X": x, "Y": y}) | pandas.DataFrame |
import numpy
import pandas as pd
import math as m
#Moving Average
def MA(df, n):
MA = pd.Series(df['Close'].rolling(n).mean(), name = 'MA_' + str(n))
df = df.join(MA)
return df
def MACD(df, n_fast, n_slow):
"""Calculate MACD, MACD Signal and MACD difference
:param df: pandas.DataFr... | pd.Series(UpI) | pandas.Series |
from typing import List, Optional, Union
from pandas import DataFrame
from mstrio.api import documents
from mstrio.project_objects.document import Document
from mstrio.server.environment import Environment
from mstrio.utils import helper
from mstrio.connection import Connection
def list_dossiers(connection: Connec... | DataFrame(objects) | pandas.DataFrame |
from collections import deque
import numpy as np
import pandas as pd
from sunpy.util import SunpyUserWarning
__all__ = ['ELO']
class ELO:
"""
Recreating the ELO rating algirithm for Sunspotter.
"""
def __init__(self, score_board: pd.DataFrame, *, k_value=32, default_score=1400,
max... | pd.DataFrame([state_dict_0, state_dict_1]) | pandas.DataFrame |
import librosa
import numpy as np
import pandas as pd
from os import listdir
from os.path import isfile, join
from audioread import NoBackendError
def extract_features(path, label, emotionId, startid):
"""
提取path目录下的音频文件的特征，使用librosa库
:param path: 文件路径
:param label: 情绪类型
:param startid: 开始的序列号
... | pd.Series() | pandas.Series |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sat Jan 26 22:10:18 2019
@author: shashank
Takes a list of entries into a tournament you are running as:
ams.csv
amd.csv
awd.csv
axd.csv
bms.csv
etc.
and uses the rankings from the ranking.csv file to rank every entry in each
bracket against each other. ... | pd.read_csv("sandbagging/amd.csv",names=['Number','player']) | pandas.read_csv |
from difflib import SequenceMatcher
import functools
from typing import Optional
import pandas
__doc__ = """Get specialty codes and consolidate data from different sources in basic_data."""
COLUMNS = ['first_name', 'last_name', 'city', 'postal_code', 'state', 'specialty_code']
GENERIC_OPHTHALMOLOGY_CODE = '207W00000X... | pandas.isnull(value := row[col]) | pandas.isnull |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from pandas.api.types import is_string_dtype
from pandas.api.types import is_numeric_dtype
import re
import warnings
import multiprocessing as mp
import matplotlib.pyplot as plt
import time
import os
import platform
from .condition_fun import *
from .info_... | pd.merge(bins_breakslist[['variable', 'breaks']], vars_class, how='left', on='variable') | pandas.merge |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 18 13:51:22 2020
@author: adiallo
<NAME>
Pans Project 2020
"""
import os #operating system import to get paths
import matplotlib.pyplot as plt #to plot graphs
import pandas as pd #to read/write data
#Reading the dataset
base_path = os.getcwd()
li... | pd.read_csv(path + 'iris.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
bootstrapping based on event rate on one animal
num_bs_replicates=50000 takes too much time, I did 1000 instead.
Results with narrowest CIs: (space: 60%,75%,90% exceedance, 40-90 state threshold )
CASE: SpikerateCoact
animal min of exceedance & state threshold combination win... | pd.DataFrame(bsstats_all) | pandas.DataFrame |
"""
Generate figures for the DeepCytometer paper for v8 of the pipeline.
Environment: cytometer_tensorflow_v2.
We repeat the phenotyping from klf14_b6ntac_exp_0110_paper_figures_v8.py, but change the stratification of the data so
that we have Control (PATs + WT MATs) vs. Het MATs.
The comparisons we do are:
* Cont... | pd.read_pickle(dataframe_areas_filename) | pandas.read_pickle |
# -*- coding: utf-8 -*-
import os
import logging
import tempfile
import uuid
import shutil
import numpy as np
import pandas as pd
from rastertodataframe import util, tiling
log = logging.getLogger(__name__)
def raster_to_dataframe(raster_path, vector_path=None):
"""Convert a raster to a Pandas DataFrame.
... | pd.concat(tile_dfs) | pandas.concat |
# Copyright (c) 2021-2022, NVIDIA CORPORATION.
import numpy as np
import pandas as pd
import pytest
import cudf
from cudf.testing._utils import NUMERIC_TYPES, assert_eq
from cudf.utils.dtypes import np_dtypes_to_pandas_dtypes
def test_can_cast_safely_same_kind():
# 'i' -> 'i'
data = cudf.Series([1, 2, 3], d... | pd.to_numeric(ps, downcast=downcast) | pandas.to_numeric |
from __future__ import print_function, division
import GLM.constants, os, pdb, pandas, numpy, logging, crop_stats
import pygeoutil.util as util
class CropFunctionalTypes:
"""
"""
def __init__(self, res='q'):
"""
:param res: Resolution of output dataset: q=quarter, h=half, o=one
:r... | pandas.read_csv(GLM.constants.FAO_CONCOR) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 5 15:51:36 2018
@author: huangjin
"""
import pandas as pd
from tqdm import tqdm
import os
def gen_data(df, time_start, time_end):
df = df.sort_values(by=['code','pt'])
df = df[(df['pt']<=time_end)&(df['pt']>=time_start)]
col = [c for c in df.columns if c not ... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
from example_data_base import get_historical_data, get_connection, get_query
from sklearn.metrics.pairwise import cosine_similarity
STRONG_SIMILARITY_DIFFERENCE = 5
SMALL_SIMILARITY_DIFFERENCE = 10
FEATURES_AMOUNT = 6
# (strong similar features count, small similar features count)
records_similari... | pd.read_sql(sql, conn) | pandas.read_sql |
'''
This file includes all the locally differentially private mechanisms we designed for the SIGMOD work.
I am aware that this code can be cleaned a bit and there is a redundancy. But this helps keeping the code plug-n-play.
I can simply copy a class and use it in a different context.
http://dimacs.rutgers.edu/~graha... | pd.DataFrame(columns=["irr_l1_std", "mrr_l1_std", "iht_l1_std", "mht_l1_std", "ips_l1_std", "mps_l1_std","iolh_l1_std","icms_l1_std","icmsht_l1_std"]) | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
from scipy import interp
from statsmodels.distributions import ECDF
import matplotlib
matplotlib.use('Agg')
import seaborn as sns
import matplotlib.pyplot as plt
from . import FIGURES_DIR
sns.set(context='notebook', font_scale=3.0, font='sans-serif')
sns.set_palette(s... | pd.DataFrame({'threshold': thresh[0], 'far': far, 'frr': frr}) | pandas.DataFrame |
import pandas as pd
import math
from sklearn.preprocessing import MinMaxScaler
class DataProcessor:
def __init__(self):
self.df_train = None
self.df_test = None
self.df_store = None
self.scale_y = None
'''importing data'''
def load_data(self, path):
... | pd.DataFrame() | pandas.DataFrame |
import os
import six
import inspect
import threading
import pandas as pd
import json
from tornado.gen import coroutine, Return, sleep
from tornado.httpclient import AsyncHTTPClient
from gramex.config import locate, app_log, merge, variables
from sklearn.externals import joblib
from sklearn.preprocessing import Standard... | pd.np.isnan(lo) | pandas.np.isnan |
import datetime
import hashlib
import os
import time
from warnings import (
catch_warnings,
simplefilter,
)
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
DatetimeIndex,
Index,
MultiIndex,
Series,
Timestamp,
concat,
date_range,
timedelt... | HDFStore(path) | pandas.io.pytables.HDFStore |
# -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
import random
import numpy as np
import pandas as pd
from pandas.compat import lrange
from pandas.api.types import CategoricalDtype
from pandas import (DataFrame, Series, MultiIndex, Timestamp,
date_range, NaT, IntervalIn... | Timestamp("2016-01-01") | pandas.Timestamp |
#!/usr/bin/env python
# coding: utf-8
# <h2>Introduction:</h2>
# This is my First kernel, I have attempted to understand which features contribute to the Price of the houses.
# <br> A shoutout to SRK and Anisotropic from whom iv learned a lot about data visualisation</br>
# <h2>Lets import the libraries we need for n... | pd.to_datetime(data['date']) | pandas.to_datetime |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import requests
import numpy as np
import pandas as pd
from io import BytesIO
import PyPDF2
from bs4 import BeautifulSoup
from functools import reduce
from alphacast import Alphacast
from dotenv import dotenv_values
API_KEY = dotenv_values(".env").get("API_KEY")
alphac... | pd.to_numeric(df_aviar[df_aviar.columns[0]], errors='coerce') | pandas.to_numeric |
import pandas as pd
import streamlit as st
import yfinance as yf
@st.experimental_memo(max_entries=1000, show_spinner=False)
def get_asset_splits(ticker, cache_date):
return yf.Ticker(ticker).actions.loc[:, 'Stock Splits']
@st.experimental_memo(max_entries=50, show_spinner=False)
def get_historical_prices(ticke... | pd.Timestamp.now() | pandas.Timestamp.now |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from geoedfframework.utils.GeoEDFError import GeoEDFError
from geoedfframework.GeoEDFPlugin import GeoEDFPlugin
import pandas as pd
""" Module for implementing the DateTimeFilter. This supports a date time string pattern
that specifies the kinds of values that will ... | pd.to_datetime(self.end,format='%m/%d/%Y %H:%M:%S') | pandas.to_datetime |
"""Load remote meet data to DB."""
import copy
import datetime
import json
import logging
import os
import sys
from io import StringIO
from urllib.error import HTTPError
from urllib.request import Request, urlopen
import pandas as pd
from codetiming import Timer
from data.models.meets import Meet
from data.models.syn... | pd.DataFrame([df_meet]) | pandas.DataFrame |
# ---
# jupyter:
# jupytext:
# cell_metadata_filter: -all
# comment_magics: true
# formats: ipynb,py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.13.8
# kernelspec:
# display_name: Python 3 (ipykernel... | pd.read_csv(AltairSaver.path + f"/tables/{filename}.csv") | pandas.read_csv |
import random
from copy import deepcopy
import logging
import json, gzip
from tqdm import tqdm
import pandas as pd
from LeapOfThought.artiset import ArtiSet
from LeapOfThought.resources.teachai_kb import TeachAIKB
from LeapOfThought.common.data_utils import pandas_multi_column_agg
from LeapOfThought.common.file_utils i... | pd.DataFrame(preds) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# 1 Import libraries and Set path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
import scipy.stats as scs
from scipy.stats.mstats import winsorize
from scipy.stats.mstats import gmean
from tabulate import ... | pd.isnull(df48['Market Index']) | pandas.isnull |
#!/usr/bin/env python
# -*- coding: utf-8; -*-
# Copyright (c) 2020, 2022 Oracle and/or its affiliates.
# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
import logging
import time
import sys
import warnings
from abc import ABC, abstractmethod, abstractproperty
i... | pd.DataFrame(info) | pandas.DataFrame |
import itertools
import numpy as np
import pandas as pd
import pytest
from staircase import Stairs
def _expand_interval_definition(start, end=None, value=1):
return start, end, value
def _compare_iterables(it1, it2):
it1 = [i for i in it1 if i is not None]
it2 = [i for i in it2 if i is not None]
i... | pd.Series([1, 0, 1, 0, 1, 0]) | pandas.Series |
"""
Prelim script for looking at netcdf files and producing some trends
Broken into three parts
Part 1 pull out the NDVI from the relevant sites
"""
#==============================================================================
__title__ = "Time Series Chow Test"
__author__ = "<NAME>"
__version__ = "v1.0(27.02.2019... | pd.read_csv(infile+"AnnualMax.csv", index_col="sn") | pandas.read_csv |
# %% [markdown]
#
# # Comprehensive Exam
#
# ## Coding Artifact
#
# <NAME>
#
# Nov 20, 2020
# ## Model Selection
#
# Base selection of regressors is performed by fitting multiple regressors without
# performing any parameter tuning, then comparing the resulting errors across
# functional groups. Models with lower erro... | pd.set_option("display.max_rows", 120) | pandas.set_option |
import pandas as pd
import numpy as np
import git
import os
import sys
from pathlib import Path
import matplotlib.pyplot as plt
#-- Setup paths
# Get parent directory using git
repo = git.Repo("./", search_parent_directories=True)
homedir = repo.working_dir
# Change working directory to parent directory
os.chdir(ho... | pd.read_csv(cluster_ref_fln) | pandas.read_csv |
import numpy as np
import pandas as pd
import nibabel as nib
from nilearn import plotting
class SubjectAnalyzer:
def __init__(self,subject_nii_path,mean_nii_path,sd_nii_path,atlas_nii_path):
'''Get paths for files'''
self.subject_nii_path = subject_nii_path
self.mean_nii_path = mean_nii_p... | pd.Series([4, 2], index=['Values', 'Z-scores']) | pandas.Series |
# -*- coding: utf-8 -*-
# pylint: disable=W0612,E1101
from datetime import datetime
import operator
import nose
from functools import wraps
import numpy as np
import pandas as pd
from pandas import Series, DataFrame, Index, isnull, notnull, pivot, MultiIndex
from pandas.core.datetools import bday
from pandas.core.n... | assert_panel_equal(result, expected) | pandas.util.testing.assert_panel_equal |
# Copyright (c) 2020-2022, NVIDIA CORPORATION.
import datetime
import operator
import re
import cupy as cp
import numpy as np
import pandas as pd
import pytest
import cudf
from cudf.core._compat import PANDAS_GE_120
from cudf.testing import _utils as utils
from cudf.testing._utils import assert_eq, assert_exceptions... | pd.Timedelta(34765, unit="D") | pandas.Timedelta |
import requests
from bs4 import BeautifulSoup
import pandas as pd
import re
from datetime import timedelta
from datetime import date
scraped_job_titles = []
scraped_job_locations = []
scraped_company_names = []
scraped_salaries = []
scraped_ratings = []
scraped_apply_urls = []
scraped_days = []
scraped_d... | pd.DataFrame() | pandas.DataFrame |
"""
Copyright 2020 The Google Earth Engine Community Authors
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
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed ... | pd.DataFrame(ds, columns=pai_names) | pandas.DataFrame |
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | pd.to_numeric(arg, errors=errors) | pandas.to_numeric |
from util import load_csv_as_dataframe
import pandas as pd
from feature_extractor import FeatureExtractor
import numpy as np
import pickle
from dateutil import parser
import monthdelta
import csv
from util import read_csv_file
from dateutil.relativedelta import relativedelta
import timeit
class LeaderBoard():
def... | pd.to_numeric(lb1_lb2['LB2']) | pandas.to_numeric |
# Copyright 2018-2019 QuantumBlack Visual Analytics Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# THE SOFTWARE IS PROVIDED "AS IS"... | assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
import datetime
import os
import re
import requests
import urllib.parse
import time
from bs4 import BeautifulSoup
import html2text
import numpy as np
import pandas
search_key_word = 'climate'
search_key = 's'
url = r'https://thebfd.co.nz/'
link_list_data_file_path = 'url-data.csv'
delay_time_min = 0.
delay_time_ma... | pandas.DataFrame(page_lists) | pandas.DataFrame |
import re
import numpy as np
import pandas as pd
import altair as alt
import streamlit as st
from vega_datasets import data
df = pd.read_csv('suicide_population.csv')
def getCountry(s):
# Get country name from country-year string
country = ""
return country.join(re.findall(r"\D",s))
def getYear(s):
#... | pd.read_csv('suicide_population.csv') | pandas.read_csv |
#Rule 24 - Description and text cannot be same.
def description_text(fle, fleName, target):
import re
import os
import sys
import json
import openpyxl
import pandas as pd
from pandas import ExcelWriter
from pandas import ExcelFile
file_name="Description_text_not_same.py"
configFile = 'https://s3.us-east.clou... | ExcelWriter(target, engine='openpyxl', mode='a') | pandas.ExcelWriter |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import nose
import numpy as np
import pandas as pd
from pandas import (Index, Series, DataFrame, Timestamp, isnull, notnull,
bdate_range, date_range, _np_version_under1p7)
import pandas.core.common as com
from pandas.compa... | ct('10ms') | pandas.tseries.timedeltas._coerce_scalar_to_timedelta_type |
import json
import os
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State, MATCH
import plotly.express as px
import pandas as pd
## DATA FROM https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_... | pd.read_csv(url) | pandas.read_csv |
import logging
import os
import os.path as op
import sys
from copy import copy, deepcopy
import cobra.flux_analysis
import cobra.manipulation
import numpy as np
import pandas as pd
from Bio import SeqIO
from cobra.core import DictList
from slugify import Slugify
import ssbio.core.modelpro
import ssbio.databases.ncbi
... | pd.DataFrame.from_records(appender, columns=cols) | pandas.DataFrame.from_records |
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