prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import os
import numpy as np
import pandas as pd
from collections import Counter
from sklearn.datasets import load_svmlight_file
def sparsity(X):
number_of_nan = np.count_nonzero(np.isnan(X))
number_of_zeros = np.count_nonzero(np.abs(X) < 1e-6)
return (number_of_nan + number_of_zeros) / float(X.shape[0] *... | pd.DataFrame(all) | pandas.DataFrame |
import pandas as pd
import graphlab as gl
orderData = | pd.read_csv("Data/orders.csv") | pandas.read_csv |
import os
import pandas as pd
statdir = '/u/58/wittkes3/unix/Documents/bdeo/stats/18'
csvname = '/u/58/wittkes3/unix/Documents/bdeo/s1_VVVH_18.csv'
attributes = '/media/wittkes3/satdat6/bigdataeo_LUKE/original/feb20/reference-zone1-2017.csv'
datelist=[]
fulldf=None
for x in os.listdir(statdir):
xpa = os.path.jo... | pd.merge(fulldf,dfa, on='parcelID' ) | pandas.merge |
import requests
import pandas as pd
import os
import sys
import io
utils_path = os.path.join(os.path.abspath(os.getenv('PROCESSING_DIR')),'utils')
if utils_path not in sys.path:
sys.path.append(utils_path)
import util_files
import util_cloud
import util_carto
from zipfile import ZipFile
import glob
import shutil
im... | pd.to_numeric(df['yr_data'], errors='coerce') | pandas.to_numeric |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import easygui
import pandas as pd
from PyQt5 import QtWidgets as qtw
from PyQt5 import QtGui as qtg
from PyQt5 import QtCore as qtc
from GUI.MainWindow import Ui_MainWindow
from GUI.RefreshDataBasePopButton import Ui_RefreshDataBasePopButton
from GUI.StatsPopBu... | pd.read_csv(f"{DestinyPathWay}/csv/full_df.csv",header=0,sep=',') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 27 13:30:31 2020
@author: User
"""
import sys
import datetime as dt
from collections import Counter
import pprint
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
from matplotlib import cm
from matplotlib import gridspe... | pd.DataFrame() | pandas.DataFrame |
from nltk import ngrams
import collections
import string
import tika
tika.initVM()
import re
from tika import parser
import pandas as pd
import PyPDF2
import os
import shutil
import ast
import numpy as np
import jellyfish
from fuzzywuzzy import fuzz
import dill
import click
from report_pattern_analysis import rec_separ... | pd.read_csv('learned_patterns.csv', index_col=0) | pandas.read_csv |
import requests
import re
import bs4
import pandas as pd
import time
import pandas as pd
url = 'https://funddb.cn/tool/energy'
header={"User-Agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"}
req = requests.get(url,headers=header)
html=req.cont... | pd.DataFrame(res) | pandas.DataFrame |
def GetRoutes(area):
query = 'select distinct Route from [dimensions].[wells] WITH (NOLOCK) where [Area] = \'' + area + '\''
return query
def GetWells(area, route):
query = 'select distinct WellName from [dimensions].[wells] WITH (NOLOCK) where [Route] = \'' + route + '\' and [Area] = \'' + area ... | pd.to_datetime(date) | pandas.to_datetime |
import torch
import pathlib
import pandas as pd
import pytorch_lightning as pl
from datetime import datetime
from collections import OrderedDict
class CSVLogger(pl.Callback):
"""Custom metric logger and model checkpoint."""
def __init__(self, output_path=None):
super(CSVLogger, self).__init__()
... | pd.concat([self.epoch_metrics, new_metrics]) | pandas.concat |
import os
import re
from os import path
import numpy as np
import pandas as pd
from scipy.stats import norm
data_dir = path.abspath(path.join(path.dirname(__file__), "..", "data"))
def _shift_turbine_curve(turbine_curve, hub_height, maxspd, new_curve_res):
"""Shift a turbine curve based on a given hub height.
... | pd.read_csv(form_860_path, skiprows=1) | pandas.read_csv |
#----------------------------------------------------------
#importing Neccessary libraries
import pandas as pd
import os.path
from os import path
from datetime import date
#----------------------------------------------------------
#Important functions
def enter_record():
n='y'
while n=='y':
... | pd.DataFrame({ "date" : data[0],"value":data[1]},index=[0]) | pandas.DataFrame |
import streamlit as st
import pandas as pd
import altair as alt
def clean_summary_data(file_str:str, name:str):
input_df = pd.read_csv(
file_str,
names=['1', '2','3','type','ministry','source','amount'],
thousands=',')
input_df[['amount']] = input_df[['amount']].fillna(value='EM... | pd.concat([pastoral_ministry, admin, custodian]) | pandas.concat |
import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
Series,
)
import pandas._testing as tm
import pandas.core.common as com
class TestSample:
@pytest.fixture(params=[Series, DataFrame])
def obj(self, request):
klass = request.param
if klass is Series:
... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
# coding: utf8
from collections import deque
from collections import Counter
# noinspection PyPackageRequirements
import pytest
from pandas import DataFrame
# noinspection PyProtectedMember
from dfqueue.core.dfqueue import QueuesHandler, QueueHandlerItem, QueueBehaviour
def test_singleton():
handler_a = QueuesHa... | DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import numpy as np
from sklearn.impute import SimpleImputer
from sklearn import svm
from sklearn import linear_model
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from scipy.stats import uniform
import warnings
from sklearn.exceptions import ConvergenceWarni... | pd.DataFrame(columns=['solution'], data=b) | pandas.DataFrame |
import os
import sys
import multiprocessing as mp
import string
import platform
import shutil
import os
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from mpl_toolkits.axes_grid1 import make_axes_locatable
import calendar
import p... | pd.to_datetime(m.start_datetime) | pandas.to_datetime |
import pandas as pd
import requests
import ratelimit
from ratelimit import limits
from ratelimit import sleep_and_retry
def id_to_name(x):
"""
Converts from LittleSis ID number to name.
Parameters
----------
x : LittleSis ID number
Example
-------
>>> id_to_name(96583)
'<... | pd.DataFrame(relationships['attributes']) | pandas.DataFrame |
# plot_helper.py (python3)
# utilities for graphic display of training and evaluation of CNNs
# experiments in knowledge documentation; with an application to AI for ethnobotany
# March 2020
#-------------------------------------------------------------------------------
import os, sys, glob
from pyt_utilities import *... | pandas.set_option('display.width', 1000) | pandas.set_option |
# -*- coding: utf-8 -*-
"""
Created on 15/05/2020
@author: yhagos
"""
import pandas as pd
import os
import numpy as np
import itertools
from scipy.spatial.distance import cdist
import multiprocessing as mp
pd.options.mode.chained_assignment = None
class IdentifyMarkersCoExpression:
def __init__(self, combined_cell_p... | pd.DataFrame() | pandas.DataFrame |
# ๅ่: https://www.python.ambitious-engineer.com/archives/1630
# ๅ่: https://note.com/kamakiriphysics/n/n2aec5611af2a
# ๅ่: https://qiita.com/Gen6/items/2979b84797c702c858b1
import os
from datetime import datetime
from flask import Flask, render_template, request, redirect, url_for, send_from_directory, g, flash... | pd.read_csv(dtct_lbl+'/'+file_name) | pandas.read_csv |
import pandas as pd
import matplotlib.pyplot as plt
from xgboost import cv
import xgboost as xgb
import joblib
import numpy as np
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
import seaborn as sns
def plot_roc(fpr, tpr, roc_auc):
""" Plot ROC curve. """
#fig ... | pd.concat([df_rna, df_gt[column_name]], axis=1) | pandas.concat |
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm
from IPython.core.display import HTML
from fbprophet import Prophet
from fbprophet.plot import plot_plotly
import plotly.offline as py
import plotly.graph_objs as go
import plotly.express as px
class... | pd.Series(local_predictions['error_quantities'].values, name='error_quantities\nin%') | pandas.Series |
from opentrons import robot, containers, instruments
from datetime import datetime
import numpy as np
import pandas as pd
import getch
import shutil
import os
import sys
def initialize_pipettes(p10_tipracks,p10s_tipracks,p200_tipracks,trash):
# Declare all of the pipettes
p10 = instruments.Pipette(
axi... | pd.read_sql_query(query_outcomes, con=engine) | pandas.read_sql_query |
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 06 14:25:48 2016
@author: vskritsk
"""
import pandas as pd
import numpy as np
import os
pd.set_option('display.expand_frame_repr', False)
| pd.set_option('max_rows', 280) | pandas.set_option |
import pandas as pd
import networkx as nx
import warnings
import seaborn as sns
import numpy as np
import matplotlib.patches as mpatches
import microbe_directory as md
from capalyzer.packet_parser import DataTableFactory, NCBITaxaTree, annotate_taxa, TaxaTree
from capalyzer.packet_parser.data_utils import group_small_... | pd.Categorical(city['variable'], categories=top_taxa_stat) | pandas.Categorical |
#
# Copyright 2020 Capital One Services, LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | pd.DataFrame([{"a": 1, "b": "A"}, {"a": 2, "b": "a"}]) | pandas.DataFrame |
import pandas as pd
import yfinance as yf
def Coletar_Fundamentos(Tickers):
"""
Coleta Indicadores fundamentalistas por leitura das tabelas hmtl do site
Fundamentus.
Argumentos:
Tickers = String ou lista de Tickers
"""
df3 = pd.DataFrame(index=['P/L', 'P/VP', 'P/EBIT', 'PSR', 'P/A... | pd.read_html(f"http://www.fundamentus.com.br/detalhes.php?papel={Ticker}") | pandas.read_html |
# RHR Online Anomaly Detection & Alert Monitoring
######################################################
# Author: <NAME> #
# Email: <EMAIL> #
# Location: Dept.of Genetics, Stanford University #
# Date: Oct 29 2020 #
###################... | pd.DataFrame(data_test) | pandas.DataFrame |
################################################################################
# This module retrieves synonyms from Wordnet as a part of NLTK module and its
# corpus. The module recognizes each input pandas.DataFrame record as a unit of
# assessment content (i.e. a single passage section, an item stem,
# or an ite... | pd.__version__.split('.') | pandas.__version__.split |
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 16 09:15:54 2016
@author: <NAME>
"""
import pandas as pd
import numpy as np
###### Import packages needed for the make_vars functions
from scipy.interpolate import interp1d
import pywt
from skimage.filters.rank import entropy
from skimage.morphology import rectangle
fro... | pd.rolling_max(arg=temp_data, window=window, min_periods=1, center=True) | pandas.rolling_max |
import os
from typing import List
import numpy as np
import pandas as pd
import pytest
from pandas import DataFrame
from data_domain import CategoricalDataDomain, RealDataDomain
from privacy_budget import PrivacyBudget
from private_table import PrivateTable
from utils import check_absolute_error
@pytest.fixture
def... | pd.DataFrame(data) | pandas.DataFrame |
from __future__ import absolute_import, division, print_function
import datetime
import pandas as pd
from config import *
def _drop_in_time_slice(m2m, m2b, m5cb, time_slice, to_drop):
"""Drops certain members from data structures, only in a given time slice.
This can be useful for removing people who weren't... | pd.HDFStore(clean_store_path) | pandas.HDFStore |
import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeClassifier as DT
from sklearn.ensemble import RandomForestClassifier as RF
from sklearn.ensemble import GradientBoostingClassifier as GB
from sklearn.feature_selection import f_classif as ANOVA
from matplotlib import pyplot as plt
from sklearn... | pd.read_csv('phish1_2500.csv', index_col=0) | pandas.read_csv |
from download_gps_data import download_data
from extract_stations import extract_stations, output_extracted_stations
import pandas as pd
from plot_extracted_stations import plot_extracted_stations
## PARAMETERS
minLatitude = -90
maxLatitude = 90
minLongitude = -180
maxLongitude = 180
sttime = "2017-01-01" #starttime... | pd.DataFrame(columns=['StnCode','Latitude','Longitude','Elev']) | pandas.DataFrame |
#!/usr/bin/env python3
# Licensed under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
from xdsl.xdsl_opt_main import xDSLOptMain
from io import IOBase
from src.ibis_frontend import ibis_to_xdsl
from d... | pd.DataFrame({"a": ["AS", "EU", "NA"]}) | pandas.DataFrame |
import pandas as pd
import numpy as np
from scipy import interpolate
import os, sys
def pseudo_wells_model(zmin, zmax, sr, no_wells, zones={}, zones_ss={}, depth='Depth', zone_idx='Zone_idx', zone_col='Zone'):
depth_log = np.arange(zmin, zmax, sr)
pseudo_wells = pd.DataFrame(np.zeros((len(depth_log), no_wel... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/python3
import pandas as pd
import subprocess
import os
import matplotlib.pyplot as plt
import numpy as np
import time
import glob
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
# Set up a bunch of settings to test, more than will be plotted to ensure that I can change t... | pd.Series(dtype='int32') | pandas.Series |
import os
import sys
import pickle
import numpy as np
import pandas as pd
import scipy.sparse as sp
from pathlib import Path
reaction_num = int(sys.argv[1])
with open('data/candidates_single.txt') as f:
candidates_smis = [s.rstrip() for s in f.readlines()]
n_candidates = len(candidates_smis)
candidates_smis = np.... | pd.read_pickle('data/preprocessed_liu_dataset/test_sampled.pickle') | pandas.read_pickle |
import numpy as np
import pandas as pd
import us
import os
import gc
from datetime import timedelta
from numpy import linalg as la
from statsmodels.formula.api import ols
from cmdstanpy import CmdStanModel
import matplotlib.pyplot as plt
# os.chdir("/home/admin/gรถzdeproject/")
class ELECTION_2016:
def __init__... | pd.read_csv("data/abramowitz_data.csv") | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Wed May 13 13:59:31 2020
@author: bernifoellmer
"""
import sys, os
import pandas as pd
import openpyxl
import ntpath
import datetime
from openpyxl.worksheet.datavalidation import DataValidation
from openpyxl.styles import Font, Color, Border, Side
from openpyxl.styles import col... | pd.read_excel(filepath_phase_exclude_stenosis) | pandas.read_excel |
import inspect
import json
import logging
import random
import re
import sys
from collections import defaultdict
from contextlib import redirect_stdout
from datetime import datetime, timedelta
from io import StringIO
from itertools import product
from os import getenv
from os.path import dirname, realpath
from pathlib ... | pd.DataFrame(columns=["author", "name"]) | pandas.DataFrame |
import csv
import httplib2
from apiclient.discovery import build
import urllib
import json
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import plotly
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.tools import FigureFactory... | pd.to_numeric(pivot_cost['2011']) | pandas.to_numeric |
# ----------------------------------------------------------------------------
# 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.Index(['id1', 'id2', 'id3'], name='id') | pandas.Index |
import pandas as pd
from pandas._testing import assert_frame_equal
import pytest
import numpy as np
from scripts.my_normalize_data import (
normalize_expedition_section_cols,
remove_bracket_text,
remove_whitespace,
normalize_columns
)
class XTestNormalizeColumns:
def test_replace_column_name_with... | assert_frame_equal(df, expected) | pandas._testing.assert_frame_equal |
from __future__ import division
from matplotlib import pyplot as plt
import matplotlib.colors as colors
from matplotlib.pylab import *
from heapq import heappush, heappop
from itertools import count
import os
import pandas as pd
import numpy as np
import networkx as nx
import geopandas as gp
import ema_workbench
f... | pd.merge(gdf,betweenness_df,on='FromTo',how='outer') | pandas.merge |
# --------------
#Importing header files
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
#Code starts here
data = pd.read_csv(path)
#plt.hist(data['Rating'])
#plt.show()
data = data[data.Rating < 6]
plt.hist(data['Rating'])
plt.show()
#Code ends here
# --------------
# code starts here
t... | pd.concat([total_null_1, percent_null_1], axis=1, keys=['Total','Percent']) | pandas.concat |
'''
Using dataset from smart intersection, time table with TOD labels is estimated by K-Means method
* Unit: 30 minute
* Single intersection
* Go-direction traffic includes right-turn traffic
* Input dataset:
- ORT_CCTV_5MIN_LOG
- ORT_CCTV_MST
* Output:
- TOD table
- Traffic analysis according to each TOD period (Tra... | pd.DatetimeIndex(cctv_log['REG_DT']) | pandas.DatetimeIndex |
#!/usr/bin/env python
# coding: utf-8
def haversine_vectorize(lon1, lat1, lon2, lat2):
import numpy as np
lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])
newlon = lon2 - lon1
newlat = lat2 - lat1
haver_formula = np.sin(newlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.s... | pd.merge(new_hospitals,nodes,right_on='nodeID',left_on='nearest_node') | pandas.merge |
#Miscellaneous Functions for Fetch! Dog Adoption, not utilized
from scipy.spatial import distance
import pandas as pd
from numpy import inner
from numpy.linalg import norm
def cosine_similarity(user_predict, adoptable_dogs, images):
'''
Calculating cosine similarity between user submitted picture and adoptab... | pd.DataFrame({'imgFile':images, 'SimScore':sim_score}) | pandas.DataFrame |
"""
hhpy.ipython.py
~~~~~~~~~~~~~~~
Contains convenience wrappers for ipython
"""
# ---- imports
# --- standard imports
import pandas as pd
# --- third party imports
from IPython.display import display, HTML
# --- local imports
from hhpy.main import export, assert_list, list_exclude
# ---- functions
# --- export
@e... | pd.reset_option('display.float_format') | pandas.reset_option |
import tarfile
import anndata
import os
import pandas as pd
import scipy.sparse
import h5py
def load(data_dir, sample_fn, **kwargs):
fn = os.path.join(data_dir, 'GSE122960_RAW.tar')
with tarfile.open(fn) as tar:
f = h5py.File(tar.extractfile(f'{sample_fn}_filtered_gene_bc_matrices_h5.h5'), 'r')['GRC... | pd.DataFrame({'feature_id': f['genes'], 'feature_symbol': f['gene_names']}) | pandas.DataFrame |
import numpy as np
import matplotlib.pyplot as plt
import mpl_finance as mpf
import pandas as pd
def plot_Self(file1, file2):
# data1 = pd.read_csv(file1, header=None).to_numpy()
# data2 = pd.read_csv(file2, header=None).to_numpy()
data1 = np.loadtxt(file1)
data2 = np.loadtxt(file2)
label1 = range(d... | pd.DataFrame(data) | pandas.DataFrame |
###########################################################
# Encode
###########################################################
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn import preprocessing,model_selection, ensemble
from sklearn.preprocess... | pd.concat((enc_mat,train_df), axis=1) | pandas.concat |
import numpy as np
import glob
import pandas as pd
import os
import time
from tqdm.auto import tqdm
from .misc import dfMirror
########################################################################################################################
# it's best to use asciiToDfMulti() (which exploits this asciiToDf())... | pd.DataFrame() | pandas.DataFrame |
# Copyright (c) 2020, <NAME>.
# Distributed under the MIT License. See LICENSE for more info.
"""
Scree plot
==========
This example will show the eigenvalues of principal components
from a
`principal component analysis
<https://en.wikipedia.org/wiki/Principal_component_analysis>`_.
"""
from matplotlib import pyplot a... | pd.DataFrame(data_set['data'], columns=data_set['feature_names']) | pandas.DataFrame |
"""
<NAME>
Test 2
Exploratory data analysis for the admissions dataset
"""
import pandas as pd
import matplotlib.pyplot as plt
from mlxtend.plotting import scatterplotmatrix
import numpy as np
from mlxtend.plotting import heatmap
from sklearn.preprocessing import OneHotEncoder
import sys
#read the data into a pandas ... | pd.read_csv('Admission_Predict.csv') | pandas.read_csv |
import json
import os
import re
from typing import List
import numpy as np
import pandas as pd
from common import camel_case_to_snake_case, load_institutions, isnumber
def convert_initial_to_row(data: dict, rank: int, document_specific=False) -> List:
row = [rank, data["name"], data["country"]]
if document_... | pd.to_numeric(a.iloc[:, 3]) | pandas.to_numeric |
import gensim
import numpy as np
import pandas as pd
import psycopg2
import re
import os
import time
import warnings
warnings.filterwarnings('ignore')
my_time = time.time() # global time setter for timer_func() debugging purposes
def fill_id(id):
"""Adds leading zeroes back if necessary. This makes the id match ... | pd.read_csv('title_basics_small.csv') | pandas.read_csv |
from suzieq.gui.guiutils import display_help_icon
from suzieq.gui.guiutils import (gui_get_df, get_base_url, get_session_id,
SuzieqMainPages)
from suzieq.sqobjects.path import PathObj
from copy import copy
from urllib.parse import quote
from typing import Tuple
import graphviz as graphv... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as st
import joblib
import pickle
'''
ๅฎไนๅ
จๅฑๅ้
'''
# df_hx = pd.read_csv('./data/hx_js.csv', header=0)
# df_xfx = pd.read_csv('./data/xfx_js.csv', header=0)
def get_time_tuple(df):
res = []
get_operate_date =... | pd.Timedelta(days=1) | pandas.Timedelta |
import os
import yaml
import argparse
import numpy as np
import pandas as pd
from pycytominer import audit
from scripts.viz_utils import plot_replicate_correlation, plot_replicate_density
parser = argparse.ArgumentParser()
parser.add_argument("--config", help="configuration yaml file for batch information")
parser.a... | pd.read_csv(audit_output_file) | pandas.read_csv |
import requests
import bs4
import sqlite3
import pandas as pd
hr_db_filename = 'C:/Users/Jeff/Google Drive/research/Hampton Roads Data/Time Series/' \
'hampt_rd_data.sqlite'
def get_id(typ, data):
"""
gets either the siteid or variableid from the db
:param typ: String. Either "Site" or "... | pd.to_numeric(df['Value']) | pandas.to_numeric |
"""
Experimental manager based on storing a collection of 1D arrays
"""
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Callable,
TypeVar,
)
import numpy as np
from pandas._libs import (
NaT,
lib,
)
from pandas._typing import (
ArrayLike,
Hashable,
)
from p... | external_values(self.array) | pandas.core.internals.blocks.external_values |
import glob
import sys
from pprint import pprint
import pandas as pd
import os
import dateutil
import json
import numpy as np
from datetime import timedelta
SETTLEMENT_DATE = 'Settlement Date'
ACCOUNT_TYPE = 'Account Type'
RRSP_ACCOUNT_TYPE = 'Individual RRSP'
TFSA_ACCOUNT_TYPE = 'Individual TFSA'
ACTIVITY_TYPE = '... | pd.read_excel(fpath) | pandas.read_excel |
"""
Parse FGDC metadata
"""
import re
from pathlib import Path
import geopandas as gpd
import pandas as pd
from bs4 import BeautifulSoup
from shapely.geometry import box
def parse_xml(xml, fields):
soup = BeautifulSoup(xml)
# Field names must be unique within the FGDC metadata
data = {}
for field in... | pd.to_numeric(df['x']) | pandas.to_numeric |
# ---
# jupyter:
# jupytext:
# notebook_metadata_filter: all,-language_info,-toc,-latex_envs
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.6.1-dev
# kernelspec:
# display_name: Python 3
# language: python
# na... | pd.read_csv('http://landsat-pds.s3.amazonaws.com/c1/L8/scene_list.gz', compression='gzip') | pandas.read_csv |
"""Profile Settings Page."""
import dash_html_components as html
import dash_table
import pandas as pd
from dash.dependencies import Input, Output
from dash_charts import appUtils
from icecream import ic
from .plaidWrapper import PlaidDashWrapper
class TabProfile(appUtils.TabBase):
"""Profile Page."""
NAME... | pd.DataFrame(rows, columns=[c['name'] for c in columns]) | pandas.DataFrame |
import json
import os
from imblearn.over_sampling import ADASYN, RandomOverSampler
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier, export_graphviz
import pandas as pd
import numpy as np
import rando... | pd.cut(df_test['Age'], bins) | pandas.cut |
# -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime
import itertools
import numpy as np
import pytest
from pandas.compat import u
import pandas as pd
from pandas import (
DataFrame, Index, MultiIndex, Period, Series, Timedelta, date_range)
from pandas.tests.frame.common ... | assert_series_equal(result, expected) | pandas.util.testing.assert_series_equal |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 26 14:55:32 2018
@author: kazuki.onodera
check all feature
"""
import gc, os
from tqdm import tqdm
import pandas as pd
import numpy as np
import sys
sys.path.append(f'/home/{os.environ.get("USER")}/PythonLibrary')
import lgbextension as ex
import... | pd.read_feather(f) | pandas.read_feather |
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 26 14:29:57 2020
@author: Shane
"""
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import scipy
import scipy.stats
import operator
from operator import truediv
import glob
import statsmodels.stats.api as sms
#import matplotlib... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
"""Download files from Brazilian Flora 2020 Web Service."""
import argparse
import json
import os
import random
import socket
import sys
import textwrap
import time
import urllib.request
from urllib.error import HTTPError
import pandas as pd
from selenium import webdriver
from selenium.webdrive... | pd.DataFrame(data['result']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from .._utils import color_digits, color_background
from ..data import Data, DataSamples
#from ..woe import WOE
import pandas as pd
#import math as m
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
from matplotlib.col... | pd.DataFrame(vifs, index=[iteration]) | pandas.DataFrame |
import numpy as np
import pandas as pd
import scipy
from sklearn import metrics
from FPMax import FPMax
from Apriori import Apriori
from MASPC import MASPC
import csv
from scipy.cluster.hierarchy import fcluster
from scipy.cluster.hierarchy import linkage
from optbinning import ContinuousOptimalBinning
# pd.set_option... | pd.read_csv(self.sortedInputFile, dtype=str) | pandas.read_csv |
from flask import Flask, render_template, jsonify, request
from flask_pymongo import PyMongo
from flask_cors import CORS, cross_origin
import json
import collections
import numpy as np
import re
from numpy import array
from statistics import mode
import pandas as pd
import warnings
import copy
from joblib import Mem... | pd.DataFrame.from_dict(dicGausNB) | pandas.DataFrame.from_dict |
import pandas as pd
import numpy as np
import os
from tqdm import tqdm
from vtkplotter import ProgressBar, shapes, merge, load
from vtkplotter.mesh import Mesh as Actor
from morphapi.morphology.morphology import Neuron
import brainrender
from brainrender.Utils.data_io import load_mesh_from_file, load_json
from brain... | pd.DataFrame(summary_structures) | pandas.DataFrame |
import unittest
from enda.timeseries import TimeSeries
import pandas as pd
import pytz
class TestTimeSeries(unittest.TestCase):
def test_collapse_dt_series_into_periods(self):
# periods is a list of (start, end) pairs.
periods = [
(pd.to_datetime('2018-01-01 00:15:00+01:00'), pd.to_d... | pd.to_datetime('2018-01-04') | pandas.to_datetime |
# 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("2013-05-29 00:00:00") | pandas.Timestamp |
from collections import namedtuple
import pathlib
import timeit
import textwrap
import pytest
import hypothesis as hyp
import hypothesis.strategies as hyp_st
import hypothesis.extra.numpy as hyp_np
import numpy as np
import pandas as pd
from endaq.calc import psd, stats, utils
@hyp.given(
df=hyp_np.arrays(
... | pd.DataFrame([0, 0, 1, 0, 0, 0, 0, 0]) | pandas.DataFrame |
import wandb
from wandb import data_types
import numpy as np
import pytest
import os
import sys
import datetime
from wandb.sdk.data_types._dtypes import *
class_labels = {1: "tree", 2: "car", 3: "road"}
test_folder = os.path.dirname(os.path.realpath(__file__))
im_path = os.path.join(test_folder, "..", "assets", "test... | pd.DataFrame([[42], [42]]) | pandas.DataFrame |
from __future__ import annotations
from datetime import (
datetime,
time,
timedelta,
tzinfo,
)
from typing import (
TYPE_CHECKING,
Literal,
overload,
)
import warnings
import numpy as np
from pandas._libs import (
lib,
tslib,
)
from pandas._libs.arrays import NDArrayBacked
from pa... | extract_array(data, extract_numpy=True) | pandas.core.construction.extract_array |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True, categories=False, auxcats=False):
"""
Frame a time series as a supervised learning dataset.
Arguments:
data: Sequence of observations as a list, df, or NumPy array.
... | pd.DataFrame(data) | pandas.DataFrame |
import pandas as pd
from tqdm import trange
import os, sys
def download(refresh=False):
# requirements
import requests, zipfile, io, os
# we can override re-downloading to the data folder if we want
if not refresh:
return
print('Scraping for all downloads. . .')
# scrape thge websit... | pd.read_csv(csv, nrows=nrows) | pandas.read_csv |
# %% Imports
import os
import glob
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.patheffects as path_effects
import seaborn as sns
from sklearn.linear_model import LinearRegression
from scipy.optimize import least_squares
from ruamel_yaml import Y... | pd.read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv", header=0, index_col=0) | pandas.read_csv |
import json
import io
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import dash
from dash import html
from dash import dcc
import dash_bootstrap_components as dbc
import pandas as pd
import numpy as np
import plotly.express as px
from dash.dependencies import Output, Input, State
from date... | pd.read_sql(f"""select substr(REQUESTTIME,1,7) as month,BACTERIA as ่,count(1) as num from BACTERIA where BACTERIA in ('ๅคง่ ๅๅธ่', '้ฒๆผไธๅจๆ่', '่บ็ๅ
้ทไผฏ่', '้้ป่ฒ่ก่็่', '้็ปฟๅๅ่่', 'ๅฑ่ ็่', '็ฒช่ ็่')
and substr(REQUESTTIME,1,7)>='{btime}' and substr(REQUESTTIME,1,7)<='{etime}'
group ... | pandas.read_sql |
import os
import os.path as path
import sys
sys.path.append(path.dirname(path.abspath(__file__)))
import numpy as np
import pandas as pd
import concurrent.futures
import argparse
import json
import traceback
import tracemalloc
from functools import reduce
import pyFigure
def computeGasPhaseO2Conc(df):
#df is a g... | pd.merge(df_min_max,df_transverse_data) | pandas.merge |
"""
Copyright 2021 Novartis Institutes for BioMedical Research Inc.
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 applicabl... | pd.read_csv(summary_table, index_col=0, header=0) | pandas.read_csv |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import operator
from itertools import product, starmap
from numpy import nan, inf
import numpy as np
import pandas as pd
from pandas import (Index, Series, DataFrame, isnull, bdate_range,
NaT, date_range, ti... | assert_series_equal(result, expected) | pandas.util.testing.assert_series_equal |
# -*- coding: utf-8 -*-
import shlex
import subprocess
from unittest import TestCase
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from tstoolbox import tstoolbox, tsutils
output_peak_detection = tsutils.read_iso_ts(
b"""Datetime,0,0::peak,0::valley
2000-01-0... | assert_frame_equal(out, output_peak_detection) | pandas.testing.assert_frame_equal |
import tsfel
import numpy as np
import pandas as pd
from tsfresh import extract_features
from tsfresh import select_features
from tsfresh.utilities.dataframe_functions import impute
import pickle
import numpy, scipy.io
acc_data = np.loadtxt(open("../original_data/acc_data.csv", "rb"), delimiter=",", skiprow... | pd.DataFrame(acc_data[:,0:3], columns=["acc_x", "acc_y", "acc_z"]) | pandas.DataFrame |
import numpy as np
import pandas as pd
import datetime
from downscale.utils.decorators import timer_decorator
def select_range(month_begin, month_end, year_begin, year_end, date_begin, date_end):
import pandas as pd
if (month_end != month_begin) or (year_begin != year_end):
dates = pd.date_range(date... | pd.to_datetime(end) | pandas.to_datetime |
"""
This module enables construction of observed over expected pixels tables and
storing them inside a cooler.
It includes 2 functions.
expected_full - is a convenience function that calculates cis and trans-expected
and "stitches" them togeter. Such a stitched expected that "covers"
entire Hi-C heatmap can be... | pd.concat([cvd, cpb], ignore_index=True) | pandas.concat |
import pandas as pd
import os
import matplotlib.pyplot as plt
plt.rc('font', size=14)
import numpy as np
import seaborn as sns
sns.set(style='white')
sns.set(style='whitegrid', color_codes=True)
#
working_dir = '/Users/ljyi/Desktop/capstone/capstone8'
os.chdir(working_dir)
#
raw_data = pd.read_csv('moss_plos_one_dat... | pd.DataFrame(X_test, columns=X_train_df.columns) | pandas.DataFrame |
# Copyright 2020 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... | pd.date_range(start='2020-01-01', end='2020-02-01', freq='D') | pandas.date_range |
#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='w') | pandas.ExcelWriter |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import matplotlib.pyplot as plt
import tweepy
import re
import sys,csv
import pandas as pd
import numpy as np
import os
import nltk
import pycountry
import string
# In[2]:
from textblob import TextBlob
class SentimentAnalysis:
def __init__(self):
self.... | pd.DataFrame(columns=["Date","User","IsVerified","Tweet","Likes","RT",'User_location'])
print(df) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
from dku_timeseries import WindowAggregator
from recipe_config_loading import get_windowing_params
@pytest.fixture
def columns():
class COLUMNS:
date = "Date"
category = "country"
aggregation = "value1_avg"
return COLUMNS
@pytest.f... | pd.date_range("1-1-2020", periods=2, freq="M") | pandas.date_range |
# Copyright (c) 2018-2021, NVIDIA CORPORATION.
import array as arr
import datetime
import io
import operator
import random
import re
import string
import textwrap
from copy import copy
import cupy
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from numba import cuda
import cudf
from cudf.c... | pd.Series([0, 15, 10], index=[0, None, 9]) | pandas.Series |
# standard library imports
import os
import datetime
import re
import math
import copy
import collections
from functools import wraps
from itertools import combinations
import warnings
import pytz
import importlib
# anaconda distribution defaults
import dateutil
import numpy as np
import pandas as pd
# anaconda distr... | pd.to_datetime(test_date) | pandas.to_datetime |
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