prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import numpy as np
import matplotlib.pyplot as plt
import h5py
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import os
import datetime as dt
import pandas as pd
import imageio
def average_grid(val_data, val_long, val_lat, long, lat, flipped=True, cropped=True):
count = np.zeros((lat.shape[0] - 1,... | pd.to_datetime(df["acq_date"], format=date_format) | pandas.to_datetime |
import glob
import random
import numpy as np
import pandas as pd
from sklearn import preprocessing
import torch
from torch.utils.data.sampler import Sampler
from torch.utils.data import DataLoader,Dataset
import torch.nn as nn
min_max_scaler = preprocessing.MinMaxScaler()
class GTExTaskMem(object):
# This class i... | pd.read_csv(type_data,sep="\t",index_col=0, header=None) | pandas.read_csv |
"""
OBJECT RECOGNITION USING A SPIKING NEURAL NETWORK.
* The data preparation module.
@author: atenagm1375
"""
import os
import numpy as np
import pandas as pd
from torch.utils.data import Dataset
import cv2
class CaltechDataset(Dataset):
"""
CaltechDataset class.
Attributes
----------
calt... | pd.DataFrame({"x": x, "y": y}, columns=["x", "y"]) | pandas.DataFrame |
'''
@author : <NAME>
ML model for foreign exchange prediction
'''
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
import joblib
def getFxRatesForPairs(pairName):
df = pd.read_csv("C:\\Users\\Srivastava_Am\\PycharmProjects\\exchange-rate-prediction\\data_source\\fx_rates_a... | pd.read_csv("C:\\Users\\Srivastava_Am\\PycharmProjects\\exchange-rate-prediction\\data_source\\USA-CPI.csv") | pandas.read_csv |
import numpy as np
import monai
import porchio
from porchio import Queue
from torchvision import datasets, transforms, models
from torch.utils.data import DataLoader, Dataset
from PIL import Image
import pandas as pd
import os
import argparse
import torchvision
import torch
import torch.nn as nn
import torch.nn.functio... | pd.read_csv(dataset_csv) | pandas.read_csv |
from Heuristic import CPH
from joblib import Parallel, delayed
from datetime import datetime
import pandas as pd
import numpy as np
import pickle
import csv
def run_heuristic(tree_set=None, tree_set_newick=None, inst_num=0, lengths=True, repeats=1, time_limit=None,
progress=True, reduce_trivial=Fa... | pd.Series(seq_ra) | pandas.Series |
# pylint: disable=E1101
from datetime import datetime, timedelta
from pandas.compat import range, lrange, zip, product
import numpy as np
from pandas import Series, TimeSeries, DataFrame, Panel, isnull, notnull, Timestamp
from pandas.tseries.index import date_range
from pandas.tseries.offsets import Minute, BDay
fr... | tm.assert_frame_equal(result, exp) | pandas.util.testing.assert_frame_equal |
from sklearn.tree import DecisionTreeClassifier
import pytest
import numpy as np
import pandas as pd
from probatus.interpret import ShapModelInterpreter
from unittest.mock import patch
@pytest.fixture(scope='function')
def X_train():
return pd.DataFrame({'col_1': [1, 1, 1, 1],
'col_2': [0,... | pd.testing.assert_frame_equal(expected_feature_importance, importance_df) | pandas.testing.assert_frame_equal |
from autodesk.states import INACTIVE, ACTIVE, DOWN
from pandas import Timedelta
import numpy as np
import pandas as pd
def enumerate_hours(start, end):
time = start
while time < end:
yield (time.weekday(), time.hour)
time = time + | Timedelta(hours=1) | pandas.Timedelta |
import os
import pandas as pd
import numpy as np
import datetime
from sklearn import linear_model
from scipy.special import erfinv
import scipy as sp
import matplotlib.pyplot as plt
from tqdm import tqdm
import warnings
warnings.filterwarnings("ignore")
np.random.seed(7)
def load_data():
'''Load the input Excel fi... | pd.read_excel(File_name, parse_dates=Date_col,date_parser=dateparse) | pandas.read_excel |
import pandas as pd
coverage = {'source': [], 'count': [], 'percentage': []}
coverage_df = | pd.DataFrame(coverage) | pandas.DataFrame |
import time
from collections import Counter
import warnings; warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from algorithms import ShapeletTransformer
from extractors.extractor import GeneticExtractor, MultiGeneticExtractor, SAXExtractor, LearningExtractor
fro... | pd.read_csv(train_path) | pandas.read_csv |
import os
from datetime import datetime
import pandas as pd
from read import clean_read
def relate_gauges_to_storms(storm_file, storm_effect_folder, ext='.txt'):
"""
Finds what dates correspond to a hurricane landfall for gauges
Args:
storm_file: a csv that relates storm names to landfall dates... | pd.read_csv(storm_file) | pandas.read_csv |
import pandas as pd
import numpy as np
from sklearn.metrics import roc_curve, auc, confusion_matrix, precision_score, recall_score, f1_score
from sklearn.metrics import average_precision_score, precision_recall_curve
from ._woe_binning import woe_binning, woe_binning_2, woe_binning_3
class Metrics:
def __init__(s... | pd.concat(val_dfs, axis=0) | pandas.concat |
# %%
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
from pymatgen.core import Composition
from scipy.stats import sem
plt.rcParams.update({"font.size": 20})
plt.rcParams["axes.linewidth"] = 2.5
plt.rcParams["lines.linewidth"]... | pd.concat(df_hull_list) | pandas.concat |
import pandas as pd
import pvlib
import re
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, linear_model
from Find_Panels_DB import panels_iguais_df
def module_name(mod):
part = mod.split('_')
name = {}
for i in range(len(part)):
if part[i].isdi... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
"""
06/08/18
* Determine a null distributiom in order to set an appropriate p-value threshold
Steps
-----
For each mutant line
get n number of homs
relabel n wildtypes as mutants
run organ volume LM organ_volume ~ genotype + 'staging metric'
Notes
-----
The p and t-values th... | pd.DataFrame(row) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 12 10:31:02 2020
@author: mazal
"""
"""
=========================================
Support functions of imageio
=========================================
Purpose: Create support functions for the pydicom project.
"""
"""
Test mode 1 | Basics
testMode = ... | pd.read_csv(path_output_train+filename) | pandas.read_csv |
import fast_to_sql.fast_to_sql as fts
from fast_to_sql import errors
import datetime
import pandas as pd
import unittest
import pyodbc
import numpy as np
# Tests
class FastToSQLTests(unittest.TestCase):
conn = None
# Intentionally included weird column names
TEST_DF = pd.DataFrame({
"T1;'":... | pd.DataFrame({"A":[1,2,3],"B":["a","b","c"],"C":[True,False,True]}) | pandas.DataFrame |
# 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.DataFrame({"f": [10.2, 11.2332, 0.22, 3.3, 44.23, 10.0]}) | pandas.DataFrame |
import os
import torch
import torch.nn as nn
import numpy as np
import torch.optim as optim
import torch.nn.functional as F
import matplotlib.pyplot as plt
import pandas as pd
from mmd_metric import polynomial_mmd
import argparse
# Hinge Loss
def loss_hinge_dis_real(dis_real):
loss_real = torch.mean(F.relu(1. - d... | pd.DataFrame() | pandas.DataFrame |
# Script to plot Figures 4 (A, B and C)
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
# Prepare the dataframe containing all variation data. MERGED_prio1_prio2.csv is a dataframe with all germline variation found in actionable genes (known and novel)
df = pd.read_csv... | pd.concat([dfaux,dff, dff2]) | pandas.concat |
from mlxtend.frequent_patterns import apriori, fpgrowth, association_rules
from mlxtend.preprocessing import TransactionEncoder
import pandas as pd
from functools import wraps
from constants import *
from helpers import DataCleaner, ResponseParser
def _can_export(f):
"""
Decorator for AssociationMiner method... | pd.concat(filter_partials) | pandas.concat |
# -*- 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.DataFrame(history.history) | pandas.DataFrame |
# %%
import os
import pandas as pd
import numpy as np
import datetime
from googletrans import Translator
from vininfo import Vin
# %%
motocicleta_p2 = | pd.read_excel(r'D:\Basededatos\Origen\MOTOCICLETAS-COLOMBIA\MOTOCICLETA_P2.xlsx', engine='openpyxl') | pandas.read_excel |
# =================================================================
# IMPORT REQUIRED LIBRARIES
# =================================================================
import os
import pandas as pd
# =================================================================
# READ DATA
# ===========================================... | pd.DataFrame(seatData, columns=['row', 'col']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# Arithmetc tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import timedelta
import operator
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.core import ops
from pandas.errors import NullFrequency... | Series([2, 3, 4]) | pandas.Series |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.2'
# jupytext_version: 1.1.2
# kernelspec:
# display_name: PyCharm (ocean_alzheimers_demo)
# language: python
# name: pycharm-55ce45ad
# ---
# %% {"_uuid": "8f283... | pd.read_csv('./input/oasis_longitudinal.csv') | pandas.read_csv |
### Librerias necesarias
import luigi
import luigi.contrib.s3
from luigi import Event, Task, build # Utilidades para acciones tras un task exitoso o fallido
from luigi.contrib.postgres import CopyToTable, PostgresQuery
import boto3
from datetime import date, datetime
import getpass # Usada para obtener el usuario
from... | pd.read_csv(dir_name + item, low_memory=False) | pandas.read_csv |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import xgboost as xgb
from sklearn.model_selection import train_test_split
import statsmodels.api as sm
# all data
prec = pd.read_csv('../data/MH25_vaisalawxt520prec_2017.csv')
prec['time'] = pd.to_datetime(prec['time'])
wind = pd.read_csv('../data... | pd.to_datetime(radio['time']) | pandas.to_datetime |
from pandas import DataFrame, read_csv, to_datetime
from datetime import datetime
import time, sys, os, argparse, pendulum
# Get report related arguments from the command line
parser = argparse.ArgumentParser()
parser.add_argument("-sd","--start_date", help="Enter date in YYYYMMDD format ONLY!", type=str)
parser.add_a... | read_csv(last_record_file) | pandas.read_csv |
import json
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import plotly.graph_objs as go
def update_graph(
graph_id,
graph_title,
y_train_index,
y_val_index,
run_log_json,
yaxis_title,
):
def smooth(scalars, weight=0.6):
last = scalars[... | pd.read_json(run_log_json, orient="split") | pandas.read_json |
# hst.py
import os
import numpy as np
import pandas as pd
from ..io.read_hst import read_hst
class Hst:
def read_hst(self, savdir=None, merge_mhd=True, force_override=False):
"""Function to read hst and convert quantities to convenient units
"""
# Create savdir if it doesn't exist
... | pd.DataFrame() | pandas.DataFrame |
import itertools
import pandas as pd
from twobitreader import TwoBitFile
from typing import Union
from sys import stdout
import numpy as np
from .utils import compl, get_true_snps_from_maf, get_dnps_from_maf
from .context import context96, context1536, context78, context83, context_composite, context_polymerase, contex... | pd.concat([sbs_df,dbs_df,id_df]) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
# # ReEDS Scenarios on PV ICE Tool STATES
# To explore different scenarios for furture installation projections of PV (or any technology), ReEDS output data can be useful in providing standard scenarios. ReEDS installation projections are used in this journal as input data to the... | pd.concat([materiallist, yearlylist], axis=1) | pandas.concat |
# Module for plotting and fitting EIS data
# (C) <NAME> 2020
import os
import glob
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.optimize import minimize, basinhopping, differential_evolution, curve_fit, least_squares
from datetime import datetime, timedelta
import itertools
import r... | pd.read_csv(file,sep='\t',skiprows=skiprows,skipfooter=skipfooter,header=None,names=header,usecols=usecols,engine='python') | pandas.read_csv |
import json
from tabulate import tabulate
import pandas as pd
fmt="psql" #"pipe" #"psql"
with open('out.json') as f:
d = json.load(f) # --> dict
print("# Moves\n")
for _d in d['moves']:
df = pd.DataFrame(_d)
a = tabulate(df,headers="keys", tablefmt=fmt)
print(a, end="\n\n")
f... | pd.DataFrame(_d) | pandas.DataFrame |
import os
import uuid
from datetime import datetime
from time import sleep
import fsspec
import pandas as pd
import pytest
import v3iofs
from storey import EmitEveryEvent
import mlrun
import mlrun.feature_store as fs
from mlrun import store_manager
from mlrun.datastore.sources import CSVSource, ParquetSource
from mlr... | pd.Timestamp("2021-01-10 10:00:00") | pandas.Timestamp |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author : <NAME>
# @Contact : <EMAIL>
import category_encoders.utils as util
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin
from autoflow.utils.logging_ import get_logger
class BaseImputer(BaseEstimator, TransformerM... | pd.isna(X) | pandas.isna |
# import app components
from app import app, data
from flask_cors import CORS
CORS(app) # enable CORS for all routes
# import libraries
from flask import request
import pandas as pd
import re
from datetime import datetime
from functools import reduce
# define functions
## process date args
def date_arg(arg):
try... | pd.read_csv(data.ccodwg[data_other[stat]]) | pandas.read_csv |
"""
This module implements dynamic visualizations for EOPatch
Credits:
Copyright (c) 2017-2022 <NAME>, <NAME>, <NAME>, <NAME>, <NAME> (Sinergise)
Copyright (c) 2017-2022 <NAME>, <NAME>, <NAME>, <NAME>, <NAME> (Sinergise)
Copyright (c) 2017-2022 <NAME> (Sinergise)
Copyright (c) 2017-2019 <NAME>, <NAME> (Sinergise)
Thi... | pd.concat((vector, temp_df), ignore_index=True) | pandas.concat |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.9.1+dev
# kernelspec:
# display_name: Python [conda env:annorxiver]
# language: python
# name: conda-env-annorxiver-... | pd.DataFrame(embedding, columns=["tsne1", "tsne2"]) | pandas.DataFrame |
#%%
"""
Analyze model:
Meant to analyze each models and their performance
"""
import h5py
import matplotlib.pyplot as plt
import seaborn
import numpy as np
import pandas as pd
import os
from keras.models import load_model
path = os.path.abspath(os.curdir)
from sklearn.model_selection import train_test_split
from sklea... | pd.DataFrame(counts, columns=['genre', '#movies']) | pandas.DataFrame |
"""
Base and utility classes for tseries type pandas objects.
"""
from __future__ import annotations
from datetime import datetime
from typing import (
TYPE_CHECKING,
Any,
Callable,
Sequence,
TypeVar,
cast,
final,
)
import warnings
import numpy as np
from pandas._libs import (
NaT,
... | RangeIndex(rng) | pandas.core.indexes.range.RangeIndex |
import pandas as pd
import matplotlib.pyplot as plt
import data
import testing_data
import statistics
import numpy as np
pd.set_option('display.max_columns', None)
def findWaitingTime(arrival_time, processes, total_processes, burst_time, waiting_time, quantum):
rem_bt = [0] * total_processes
for i ... | pd.DataFrame(data_boxplot_bt) | pandas.DataFrame |
import pandas as pd
from sklearn.tree import DecisionTreeRegressor
from sklearn.preprocessing import LabelEncoder
#import data set file
data= | pd.read_csv('500_Person_Gender_Height_Weight_Index.csv') | pandas.read_csv |
###############################################################################################
#### Initialization
import pandas as pd
import numpy as np
df = pd.read_csv(filename, header=None, names=col_names, na_values={'col_name':['-1']}, \
parse_dates=[[0, 1, 2]], index_col='Date')
# if the first 3 columns a... | pd.merge(df_bronze, df_gold, on=['NOC', 'Country'], suffixes=['_bronze', '_gold']) | pandas.merge |
from __future__ import division
from functools import wraps
import pandas as pd
import numpy as np
import time
import csv, sys
import os.path
import logging
from .ted_functions import TedFunctions
from .ted_aggregate_methods import TedAggregateMethods
from base.uber_model import UberModel, ModelSharedInputs
class Te... | pd.Series([], dtype="float", name="cbt_inv_bw_sub_indirect") | pandas.Series |
# Copyright (c) 2018-2021, NVIDIA CORPORATION.
import os
from string import ascii_letters
import numpy as np
import pandas as pd
import pytest
import cudf
from cudf.testing._utils import DATETIME_TYPES, NUMERIC_TYPES, assert_eq
try:
import tables # noqa F401
except ImportError:
pytest.skip(
"PyTabl... | pd.read_hdf(gdf_series_fname) | pandas.read_hdf |
import pickle
import sys
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
from matplotlib import rc
import seaborn as sns
import os
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import LeaveOneOut
from sklearn.neighbors import KernelDensity
from scip... | pd.DataFrame.from_dict({'basis': basis, 'MAE (kcal/mol)': error, 'method':method}) | pandas.DataFrame.from_dict |
import os
import glob
import pathlib
import re
import base64
import pandas as pd
from datetime import datetime, timedelta
# https://www.pythonanywhere.com/forums/topic/29390/ for measuring the RAM usage on pythonanywhere
class defichainAnalyticsModelClass:
def __init__(self):
workDir = os.path.ab... | pd.to_datetime(newestDate) | pandas.to_datetime |
import numpy as np
import pandas as pd
from scipy.stats import gamma # type: ignore
def _sigmoid(x: float) -> float:
"""Helper function to apply sigmoid to a float.
Args:
x: a float to apply the sigmoid function to.
Returns:
(float): x after applying the sigmoid function.
"""
ret... | pd.concat([data, features], axis=1) | pandas.concat |
import pandas as pd
import re, json
import argparse
'''
preprocessing for mimic discharge summary note
1. load NOTEEVENTS.csv
2. get discharge sumamry notes
a) NOTEVENTS.CATEGORY = 'Discharge Summary'
b) NOTEVENTS.DESCRIPTION = 'Report'
c) eliminate a short-note
3. preprocess discharge sumamry notes
... | pd.to_datetime(df.STORETIME) | pandas.to_datetime |
import util
import argparse
from model import *
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
parser = argparse.ArgumentParser()
parser.add_argument('--device',type=str,default='cuda:3',help='')
parser.add_argument('--data',type=str,default='data/METR-LA',help='data path'... | pd.DataFrame({'real1': y1, 'pred1':yhat1 , 'real12':y12,'pred12':yhat12}) | pandas.DataFrame |
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import mplcursors
data = {'jenis': ['set1', 'set2', 'set3', 'set4', 'set5'],
'data1': [0.80443, 0.84176, 0.84278, 0.82316, 0.82260],
'data2': [0.71956, 0.77691, 0.77279, 0.74522, 0.74747],
'data3': [0.84256, 0.83268, 0.84152... | pd.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
"""
Tests dtype specification during parsing
for all of the parsers defined in parsers.py
"""
from io import StringIO
import os
import numpy as np
import pytest
from pandas.errors import ParserWarning
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import Categorical, DataFram... | Categorical([]) | pandas.Categorical |
#===============================================================================
# Copyright 2020 BenchmarkXPRT Development Community
#
# 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
#
# h... | pd.concat(sets) | pandas.concat |
import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
import pandas as pd
from scipy.optimize import minimize
import matplotlib.gridspec as gridspec
from datetime import date, timedelta
import geopandas as gpd
#import today date
date_today = date.today()
year_t,month_t,date_t=str(date_t... | pd.read_excel('input/input.xlsx') | pandas.read_excel |
#!/usr/bin/python3 `
""" The api.py module contains the classes and functions.
class tsSLD implements the Supervices Learning Data concept for modelled time series.
Auxuliary functions imports neural net models and AR models objects from predictor package/
"""
import copy
from os import getcwd,path
import sys
from p... | pd.to_datetime(df[dt_col_name], dayfirst=True) | pandas.to_datetime |
import torch
import sys
import importlib
import os
from sklearn.neighbors import NearestNeighbors
import transform as t
import ShapeNetDataLoader as dset
import numpy as np
sys.path.append("/content/treelearning/python")
import cloud
position_path = "/content/drive/MyDrive/Colab/tree_learning/data/positions_attempt2.j... | pd.DataFrame(stack, columns=["x", "y", "z", "pred", "target", "hash"]) | pandas.DataFrame |
"""
Main pipeline helpers
=====================
"""
import os
import sys
import json
import time
import uuid
import logging
import itertools
from dataclasses import asdict
from datetime import datetime
import multiprocessing
from collections import Counter
from pprint import pprint
import pandas as pd
import numpy as... | pd.DataFrame({i: result[i] for i in keys}) | pandas.DataFrame |
import pandas as pd
from .datastore import merge_postcodes
from .types import ErrorDefinition
from .utils import add_col_to_tables_CONTINUOUSLY_LOOKED_AFTER as add_CLA_column # Check 'Episodes' present before use!
def validate_165():
error = ErrorDefinition(
code = '165',
description = 'Data entry for moth... | pd.to_datetime(epi['DEC'], format='%d/%m/%Y', errors='coerce') | pandas.to_datetime |
import IMLearn.learners.regressors.linear_regression
from IMLearn.learners.regressors import PolynomialFitting
from IMLearn.utils import split_train_test
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.io as pio
import plotly.graph_objects as go
import matplotlib.pyplot as plt
pio.tem... | pd.concat([df, dummies], axis='columns') | pandas.concat |
#!/usr/bin/env python
"""
Parses SPINS' EA log files into BIDS tsvs
usage:
parse_ea_task.py <log_file>
arguments:
<log_file> The location of the EA file to parse
Details:
insert these later
Requires:
insert these later
"""
import pandas as pd
import numpy as np
from docopt import docopt
import re
impo... | pd.read_csv(timing_path) | pandas.read_csv |
"""Preprocessing code for Sumo outputs.
Use to put data into hdf stores with A, X, Y arrays.
"""
import logging
import multiprocessing
import os
import re
import time
from collections import OrderedDict
from itertools import repeat
import networkx as nx
import pandas as pd
import six
from trafficgraphnn.load_data im... | pd.concat(A_dfs, axis=1) | pandas.concat |
import pandas as pd
import os
from collections import namedtuple
from strategy.strategy import Exposures, Portfolio
from strategy.rebalance import get_relative_to_expiry_instrument_weights, \
get_relative_to_expiry_rebalance_dates, get_fixed_frequency_rebalance_dates
from strategy.calendar import get_mtm_dates
de... | pd.Timestamp(ed) | pandas.Timestamp |
# *****************************************************************************
# © Copyright IBM Corp. 2018. All Rights Reserved.
#
# This program and the accompanying materials
# are made available under the terms of the Apache V2.0
# which accompanies this distribution, and is available at
# http://www.apache.org/... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import math
import os
import sys
import time
import re
from datetime import date
import logging
from django.conf import settings
import sqlite3
import scipy.spatial
from stations import IDS_AND_DAS, STATIONS_DF
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
# This BASE... | pd.read_sql_query(query, con=conn, params=(station, 'Q', 'H')) | pandas.read_sql_query |
"""
Code for scraping Reddit data using PushShift.io instead of normal praw (Python Reddit API wrapper) due to size
constraints imposed by Reddit after they moved off of cloudsearch
"""
import datetime as dt
import re
import time
import pandas as pd
import requests
from nltk.stem import WordNetLemmatizer
# Define fu... | pd.concat(mylist, sort=False) | pandas.concat |
import pandas as pd
import numpy as np
import pytest
from kgextension.endpoints import DBpedia
from kgextension.schema_matching import (
relational_matching,
label_schema_matching,
value_overlap_matching,
string_similarity_matching
)
class TestRelationalMatching:
def test1_default(self):
... | pd.read_csv(path_input) | pandas.read_csv |
import os
import numpy as np
import matplotlib as mpl
mpl.use("pgf")
general_fontsize = 16
custon_pgf_rcparams = {
'font.family': 'serif',
'font.serif': 'cm',
'font.size': general_fontsize,
'xtick.labelsize': general_fontsize,
'ytick.labelsize': general_fontsize,
'axes.labelsize': general_font... | pd.Series(stlsum) | pandas.Series |
"""
provider_JST_macrohistory.py
JORDÀ-SCHULARICK-TAYLOR MACROHISTORY DATABASE
Note: these data are in an Excel spreadsheet (XLSX); the user needs to download and place it in the appropriate
directory (based on config settings). The code assumes that there is only one spreadsheet in the directory.
Description from t... | pandas.Series(df[c]) | pandas.Series |
import time
import numpy as np
import pandas as pd
from sklearn.datasets import fetch_openml
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.linear_model import LogisticRegression
from sklearn.decomposition import TruncatedSVD
from sklearn.metrics import accuracy_score
from sklearn.model_sel... | pd.DataFrame({'target': target}) | pandas.DataFrame |
"""Hardware FonduerModel."""
import pickle
import numpy as np
from emmental.data import EmmentalDataLoader
from pandas import DataFrame
from fonduer.learning.dataset import FonduerDataset
from fonduer.packaging import FonduerModel
from fonduer.parser.models import Document
from tests.shared.hardware_lfs import TRUE
f... | DataFrame([entity_relation], columns=["doc", "part", "val"]) | pandas.DataFrame |
import numpy as np
import pandas as pd
import scipy.stats as sp
# file path
DATA_DIR = "./data"
ORI_DATA_PATH = DATA_DIR + "/diabetic_data.csv"
MAP_PATH = DATA_DIR + "/IDs_mapping.csv"
OUTPUT_DATA_PATH = DATA_DIR + "/preprocessed_data.csv"
# load data
dataframe_ori = pd.read_csv(ORI_DATA_PATH)
NUM_RECORDS = dataframe... | pd.to_numeric(df['diag_3'], errors='coerce') | pandas.to_numeric |
# This script preps the county level COVID data for he ultraCOVID project
# Importing required modules
import pandas as pd
import numpy as np
import datetime
# Specifying the path to the data -- update this accordingly!
username = ''
filepath = 'C:/Users/' + username + '/Documents/Data/ultraCOVID/'
# ... | pd.concat([counties, states, fips, lat, lon, pop, dates, case_vals, death_vals], axis = 1) | pandas.concat |
import pandas as pd
import numpy as np
def label_to_pos_map(all_codes):
label_to_pos = dict([(code,pos) for code, pos in zip(sorted(all_codes),range(len(all_codes)))])
pos_to_label = dict([(pos,code) for code, pos in zip(sorted(all_codes),range(len(all_codes)))])
return label_to_pos, pos_to_la... | pd.read_csv('dataset_creation/input_files/ids_codie_test.csv') | pandas.read_csv |
from __future__ import division #brings in Python 3.0 mixed type calculation rules
import datetime
import inspect
import numpy as np
import numpy.testing as npt
import os.path
import pandas as pd
import sys
from tabulate import tabulate
import unittest
##find parent directory and import model
#parentddir = os.path.ab... | pd.Series([], dtype='float') | pandas.Series |
import os
import sys
from numpy.core.numeric import zeros_like
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn-poster')
# I hate this too but it allows everything to use the same helper functions.
sys.path.insert(0, 'model')
from helper_functions impor... | pd.read_csv(local, parse_dates=['date']) | pandas.read_csv |
import numpy as np
import pandas as pd
import numba
from vtools.functions.filter import cosine_lanczos
def get_smoothed_resampled(df, cutoff_period='2H', resample_period='1T', interpolate_method='pchip'):
"""Resample the dataframe (indexed by time) to the regular period of resample_period using the interpolate me... | pd.to_datetime(zc) | pandas.to_datetime |
#%%
import os
from pathlib import Path
import colorcet as cc
import matplotlib.colors as mplc
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import pandas as pd
import seaborn as sns
from joblib import Parallel, delayed
from scipy.sparse import csr_matrix, lil_matrix
from scipy.sparse.csgraph... | pd.DataFrame(data=mds_embed) | pandas.DataFrame |
import pandas as pd
import numpy as np
import re
from transformers import pipeline
nlp = pipeline("zero-shot-classification")
sr = pd.read_csv('/Back End Code/sports_reference_teams.csv')
df = pd.read_csv('/Data/MTeams.csv')
df.TeamName = [re.sub(r'Univ$', "University", team.replace(' St', ' State')) for team in df.... | pd.DataFrame(fixed_list) | pandas.DataFrame |
import numpy as np
import pandas as pd
import hydrostats.data as hd
import hydrostats.visual as hv
import HydroErr as he
import matplotlib.pyplot as plt
import os
from netCDF4 import Dataset
# *****************************************************************************************************
# ****************ERA 5*... | pd.DataFrame(data=Q[counter, :], index=dates, columns=['flowrate (cms)']) | pandas.DataFrame |
"""
Various tools to process WAIS data
Author: <NAME> <<EMAIL>>
"""
import fnmatch
import numpy as np
import icecap as icp
import inspect
import os
import rsr.run as run
import rsr.fit as fit
import rsr.utils as utils
import rsr.invert as invert
#import string
import subradar as sr
import time
import pandas as pd
impo... | pd.DataFrame() | pandas.DataFrame |
# Module deals with creation of ligand and receptor scores, and creation of scConnect tables etc.
import scConnect as cn
import scanpy as sc
version = cn.database.version
organism = cn.database.organism
# Scoring logic for ligands
def ligandScore(ligand, genes):
"""calculate ligand score for given ligand and gen... | pd.DataFrame(target.uns["ligands"]) | pandas.DataFrame |
import time, os, pickle
import numpy as np
import pandas as pd
from flask import Flask, request, jsonify, make_response, Response
from flask_restplus import Api, fields, Resource
from flask_cors import CORS, cross_origin
from werkzeug.utils import secure_filename
from werkzeug.datastructures import FileStorage
app = F... | pd.read_csv(file) | pandas.read_csv |
'''
Open Power System Data
Time series Datapackage
read.py : read time series files
'''
import pytz
import yaml
import os
import sys
import numpy as np
import pandas as pd
import logging
from datetime import datetime, date, time, timedelta
import xlrd
from xml.sax import ContentHandler, parse
from .excel_parser impo... | pd.to_datetime(df.index.values[0]) | pandas.to_datetime |
import pandas as pd
import numpy as np
from pandas.api.types import is_numeric_dtype, is_categorical, infer_dtype
def dataset_profile(data: pd.DataFrame):
"""A simple function to get you a simple dataset variables overview
Args:
data (pd.DataFrame): the dataset to be profiled
Returns:
pd.... | infer_dtype(data[col]) | pandas.api.types.infer_dtype |
import numpy as np
import pandas as pd
df1 = pd.DataFrame(np.ones((3,4))*0,columns=['a','b','c','d'])
df2 = pd.DataFrame(np.ones((3,4))*1,columns=['a','b','c','d'])
df3 = pd.DataFrame(np.ones((3,4))*2,columns=['a','b','c','d'])
print(df1)
print(df2)
print(df3)
#ignore_index 会将index进行重新重排
res = pd.concat([df1,df2... | pd.concat([df1,df2,df3],axis=1,ignore_index=True) | pandas.concat |
# External Libraries
from datetime import date
import pandas as pd
pd.options.mode.chained_assignment = None
import os
from pathlib import Path
import logging, coloredlogs
# Internal Libraries
import dicts_and_lists as dal
import Helper
# ------ Logger ------- #
logger = logging.getLogger('get_past_datasets.py')
color... | pd.read_html(url) | pandas.read_html |
# pip install pytest
# pytest tests\test_bn.py
from pgmpy.factors.discrete import TabularCPD
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pgmpy.estimators import TreeSearch
from pgmpy.models import BayesianModel
import networkx as nx
from pgmpy.inference import VariableElimination
from p... | pd.DataFrame(edge_properties2) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 13 18:53:16 2021
@author: <NAME>
https://www.kaggle.com/ash316/eda-to-prediction-dietanic
"""
"""
Part1: Exploratory Data Analysis(EDA):
1)Analysis of the features.
2)Finding any relations or trends considering multiple features.
Part2: Feature Engineering and Data Cl... | pd.read_csv('D:\\AI\\Kaggle\\EDA To Prediction(DieTanic)\\train.csv') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 28 17:32:32 2020
@author: hugokleikamp
"""
#%% clear variables and console
try:
from IPython import get_ipython
get_ipython().magic('clear')
get_ipython().magic('reset -f')
except:
pass
""""""""""""""""""""""""""""""""""""""""""""... | pd.DataFrame(fun_parameters,columns=["Name","Value"]) | pandas.DataFrame |
# Python 3 Required. Tested in rh-python36 #
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import traceback
import time
import datetime
import multiprocessing
from matplotlib.dates import DateFormatter
from tcp_latency import measure_latency
# IP List for Reference
# 172.16.58.3 ... | pd.to_datetime(x_raw_selected) | pandas.to_datetime |
import pandas
import pytest
import modin.pandas as pd
import numpy as np
from .utils import test_data_values, test_data_keys, df_equals
@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
def test_isna(data):
pandas_df = pandas.DataFrame(data)
modin_df = pd.DataFrame(data)
pandas_resu... | pandas.Series([1, np.nan, 2]) | pandas.Series |
import pandas as pd
import numpy as np
import os
import random
import json
import argparse
from random import shuffle
random.seed(42)
from configs.config import Config
def main():
parser = argparse.ArgumentParser()
parser.add_argument('config_path')
args = parser.parse_args()
# ge... | pd.Series(folds, name='fold') | pandas.Series |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Author: <NAME>
date: 2020/1/9 22:52
contact: <EMAIL>
desc: 金十数据中心-经济指标-央行利率-主要央行利率
https://datacenter.jin10.com/economic
美联储利率决议报告
欧洲央行决议报告
新西兰联储决议报告
中国央行决议报告
瑞士央行决议报告
英国央行决议报告
澳洲联储决议报告
日本央行决议报告
俄罗斯央行决议报告
印度央行决议报告
巴西央行决议报告
"""
import json
import time
import pandas as pd... | pd.DataFrame(value_list) | pandas.DataFrame |
# coding: utf-8
# In[42]:
import pandas as pd
import numpy as np
store = pd.read_csv("C:/Users/Administrator/Desktop/Jupiter notebooks/Store.csv", header = 0, encoding="latin")
store.head(n=5)
# In[16]:
#1.How many unique cities are the orders being delivered to
cities = store.City.unique()
print(len(cities))
... | pd.to_datetime(store['Order Date']) | pandas.to_datetime |
"""
Analysis for Thermal Field Double state VQE experiment
"""
import os
import matplotlib.pylab as pl
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import numpy as np
import pycqed.analysis_v2.base_analysis as ba
from pycqed.analysis.analysis_toolbox import get_datafilepath_fro... | pd.DataFrame.from_dict({T:self.proc_data_dict[T]['fidelity'] for T in self.proc_data_dict['T']}, orient='index',columns=['F']) | pandas.DataFrame.from_dict |
from datetime import datetime, timedelta, timezone
import random
from tabnanny import check
import unittest
import pandas as pd
import pytz
if __name__ == "__main__":
from pathlib import Path
import sys
sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
from datatube.dtype import check_dtypes
... | pd.DataFrame(with_na) | pandas.DataFrame |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.