prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pandas as pd
from os.path import join
import networkx as nx
import socket
import sys
from scseirx.model_school import SEIRX_school
from scseirx import analysis_functions as af
def compose_agents(measures, simulation_params):
'''
Utility function to compose agent dictionaries as expected by the simulati... | pd.DataFrame() | pandas.DataFrame |
import seaborn as sns
import pandas as pd
from collections import defaultdict
from matplotlib import colors
import matplotlib.pylab as plt
from scipy.stats import zscore
import scanpy as sc
import matplotlib.pyplot as plt
#______ UTILS________
def reorder_from_labels(labels, index):
# order based on labels:
cl... | pd.DataFrame(props,columns=[x_value,x_value+"_proportion",color_value,hue]) | pandas.DataFrame |
import pandas as pd
from scipy.stats import spearmanr
import numpy as np
def find_complexes(tables_containing_list_complexes, protein_table,
feature_count_start_column, feature_count_end_column,
output_table):
tables_containing_list_complexes_df = pd.read_excel(tables_contain... | pd.DataFrame.from_records(series) | pandas.DataFrame.from_records |
"""PyChamberFlux I/O module containing a collection of data parsers."""
import pandas as pd
# A collection of parsers for timestamps stored in multiple columns.
# Supports only the ISO 8601 format (year-month-day).
# Does not support month-first (American) or day-first (European) format.
timestamp_parsers = {
# d... | pd.to_datetime(s, format='%Y %m %d') | pandas.to_datetime |
from uin_fc_lib import ts_forecasts, ml_visualizations
import pandas as pd
import numpy as np
import keras as k
from keras.wrappers.scikit_learn import KerasRegressor
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau, TensorBoard
from sklearn.preprocessing import StandardScaler
from sklearn.... | pd.read_csv('test_data3.csv') | pandas.read_csv |
#!/usr/bin/env python
"""
Script to georeference Nikon D800 images using a GPX track.
Default arguments (filepaths) may have to be edited in the main() function.
REQUIREMENT: Needs to be run on Linux right now and have exiftool installed.
"""
import datetime
import os
import subprocess
import pandas as pd
import g... | pd.Series(data=destination_cam_times.index.values, index=destination_cam_times.values) | pandas.Series |
# -*- coding: utf-8 -*-
# %reset -f
"""
@author: <NAME>
"""
# Demonstration of Bayesian optimization for multiple y variables
import warnings
import matplotlib.figure as figure
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.stats import norm
from sklearn import model_... | pd.read_csv('x_for_prediction.csv', encoding='SHIFT-JIS', index_col=0) | pandas.read_csv |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pandas as pd
from pandas.api.types import is_scalar
from pandas.util._validators import validate_bool_kwarg
from pandas.core.index import _ensure_index_from_sequences
from pandas._libs import lib
from pa... | pd.DataFrame() | pandas.DataFrame |
# coding: utf-8
# * Build baseline model
# * Example of leaky variables:
# * Missing Data
# * Example of New categorical variables
# * Features not available in production, only in training
# * Outliers
# * Blacklist variables
#
#
# * Example of overfitting
# * Multi-collinearity of variables in linear & NN models
# ... | pd.get_dummies(X['Dest'], prefix='dest_') | pandas.get_dummies |
import pandas as pd
import numpy as np
from pathlib import Path
def load(path, dt=False, stats=False):
print("loading data from",path)
dataFrames = {}
dataFrames['gameLogs'] = pd.read_csv(path/'GameLogs.csv', index_col=False)
if dt:
dataFrames['gameLogs']['Date'] = pd.to_datetime(dataFrames['g... | pd.merge(gameLogs[column], identifier, left_on=column, right_on='retroID', how="left") | pandas.merge |
#!/usr/bin/env python
import logging
import os
import importlib
import sys
import pickle
import numpy as np
import pandas as pd
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras.models import load_model
from scipy.stats import spearmanr
from keras.layers import Input
import tensorflow as tf... | pd.read_csv(config.input_dataset) | pandas.read_csv |
import streamlit as st
import pandas as pd
import numpy as np
import base64
import re
import plotly.graph_objects as go
import plotly.express as px
# import seaborn as sns
# import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score, m... | pd.Series(diabetes.target, name='response') | pandas.Series |
import joblib
from ..config import config
from .. import models
import fasttext
import numpy as np
import pandas as pd
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from sklearn.preprocessing import MultiLabelBinarizer
from keras import backend as K
from pathlib i... | pd.read_csv(file_mapping[model_name]) | pandas.read_csv |
import pandas as pd
import os
# Load the data
df = pd.read_pickle('data_frame.pickle')
# Get distinct artist
artists = df['artist']
unique_artists = | pd.unique(artists) | pandas.unique |
from simulationClasses import DCChargingStations, Taxi, Bus, BatterySwappingStation
import numpy as np
import pandas as pd
from scipy import stats, integrate
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
from matplotlib.dates import DateFormatter, HourLocator, MinuteLocator, AutoDateLocato... | pd.DataFrame(taxiSwapperIncome,columns=["time","income"]) | pandas.DataFrame |
#!/usr/bin/env python3
import abc
from functools import partial
from typing import Generator, Optional, Tuple, Union
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn import metrics
from sklearn.model_selection import KFold
from sklearn.preprocessing import MinMaxScaler, Normalizer, ... | pd.DataFrame() | pandas.DataFrame |
"""
Tests that rely on a server running
"""
import base64
import json
import datetime
import os
from unittest import mock
import pytest
from heavydb import connect, ProgrammingError, DatabaseError
from heavydb.cursor import Cursor
from heavydb._parsers import Description, ColumnDetails
from heavydb.thrift.ttypes impor... | is_object_dtype(df["A"]) | pandas.api.types.is_object_dtype |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
# coding: utf-8
# # TorchArrow in 10 minutes
#
# TorchArrow is a torch.Tensor-like Python DataFrame library for data preprocessing in deep learning. It supports multiple execution runtimes and Arrow as a common memory format.
#
# (Remark. In case... | pd.Series([1, 2, None, 4]) | pandas.Series |
from glob import glob
import pandas as pd
from sklearn.ensemble.forest import RandomForestRegressor
from tqdm import tqdm
from util import COUPLING_TYPES
def main():
predictions = []
for coupling_type in COUPLING_TYPES:
predictions.extend(process_coupling_type(coupling_type))
print('writing pred... | pd.read_pickle(path) | pandas.read_pickle |
#
# Copyright (c) 2021, NVIDIA CORPORATION.
#
# 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.Series(to_add) | pandas.Series |
# bsub -q short -W 4:00 -R "rusage[mem=50000]" -oo multiple_dot_lists.out -eo multiple_dot_lists.err 'python multiple_dot_lists.py'
# %matplotlib inline
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
mpl.style.use('seaborn-white')
import multipr... | pd.merge(windows1, windows2, left_index=True, right_index=True, suffixes=('1', '2')) | pandas.merge |
import json
import numpy as np
import pytest
from pandas import DataFrame, Index, json_normalize
import pandas._testing as tm
from pandas.io.json._normalize import nested_to_record
@pytest.fixture
def deep_nested():
# deeply nested data
return [
{
"country": "USA",
... | json_normalize(state_data[0], "counties") | pandas.io.json.json_normalize |
import birankpy
import pandas as pd
import sys
import numpy as np
from scipy import stats
import argparse
def read_data(filepath):
try:
data = pd.read_csv(filepath)
# print("loading data ")
except:
data = pd.read_csv(filepath,sep='\t')
# print("loading data ")
first_colum... | pd.merge(tweet_birank_df,ground_truth_tweet[ground_truth_tweet['num_favorites_retweets']>=0],on='tweet') | pandas.merge |
# -*- coding: utf-8 -*-
import os
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from sklearn.model_selection import ShuffleSplit, cross_validate
def crossvalidate_pipeline_scores(X, y, pipelines, n_splits, rand... | pd.DataFrame(columns=["Model", "MAE", "MSE", "R2"]) | pandas.DataFrame |
# @file riverlog_for_gis.py
# @brief riverlog related library, share with DevZone
# @author <EMAIL>
import requests
import json
import os
import pandas as pd
from datetime import timedelta, datetime,date
import time
from pandas.api.types import is_numeric_dtype
def url_get(filename,url,reload=False):
"""
... | pd.to_datetime(df['timeGMT8']) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 11 20:08:48 2021
@author: jan_c
"""
import pandas as pd
from tkinter import *
from tkinter import filedialog
if __name__ == '__main__':
def frame():
def abrir_archivo():
global archivo
archivo = filedial... | pd.ExcelWriter(archivo[:-5] + "_salida" + ".xlsx") | pandas.ExcelWriter |
from pandas.util.py3compat import StringIO
import unittest
import sqlite3
import sys
import numpy as np
import pandas.io.sql as sql
import pandas.util.testing as tm
from pandas import Series, Index
class TestSQLite(unittest.TestCase):
def setUp(self):
self.db = sqlite3.connect(':memory:')
def test_... | sql.uquery(stmt, con=self.db) | pandas.io.sql.uquery |
import pandas as pd
import urllib.request
import numpy as np
import shapefile
from datetime import datetime
from zipfile import ZipFile
import pandasql as ps
import requests
import json
import pkg_resources
def softmax(x):
if np.max(x) > 1:
e_x = np.exp(x/np.max(x))
else:
e_x = np.exp(x - np.max(x... | pd.DataFrame() | 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, ... | testing.assert_frame_equal(output, expected) | pandas.testing.assert_frame_equal |
from typing import Optional, Union
import numpy as np
import pandas as pd
from bokeh.io import output_notebook, reset_output
from bokeh.models import Legend, Dropdown, ColumnDataSource, CustomJS
from bokeh.plotting import figure, output_file, show
from bokeh.layouts import column
from bokeh.events import MenuItemClic... | pd.DataFrame(reduced_points) | pandas.DataFrame |
# coding: utf-8
# ## Integrating LSTM model with Azure Machine Learning Package for Forecasting
#
# In this notebook, learn how to integrate LSTM model in the framework provided by Azure Machine Learning Package for Forecasting (AMLPF) to quickly build a forecasting model.
# We will use dow jones dataset to build ... | pd.DataFrame(inv_x_y, columns=feat_tgt_cols) | pandas.DataFrame |
import json
from optparse import OptionParser
import sys
import numpy as np
import pandas as pd
from scipy import stats
import tensorflow as tf
import utils
import models
pd.options.display.max_columns = 100
def train_on_data(train_vals, num_feats, passenger, outfile, init_bound, set_vars={}):
"""
Trains o... | pd.read_csv(options.TPM_FILE, sep='\t', index_col=0) | pandas.read_csv |
import glob
import numpy as np
import pandas as pd
import re
from PIL import Image
from torch.utils.data import Dataset
from torchvision.transforms import transforms
from lib.cfg import *
def get_calcification_data_index():
# grep .png files in absolute path
list_image_path = glob.glob(PATH_IMAGE+'*.png')
... | pd.DataFrame({'cal_mask_path': list_cal_mask_path}) | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from astropy.time import Time
def load_omni():
columns = ['date', 'time', 'hgi_lat', 'hgi_lon', 'br', 'bt', 'bn', 'b', 'v', 'v_lat', 'v_lon', 'density', 'temperature']
omni = pd.read_csv('OMNI_COHO1HR_MERGED_MAG_PLASMA_199207.txt', delim_wh... | pd.DataFrame({'cost':costs, 'perfect': 0, 'climatology': 0, 'cmes': 0, 'v': 0, 'b': 0, 'vb': 0}) | pandas.DataFrame |
import json
import pandas as pd
import re
import sys
fdir = '../data/geo/1_separate/chelsa'
base_url = 'https://www.wsl.ch/lud/chelsa/data'
if __name__ == "__main__":
# First part to modify js file so that it dumps the js object as JSON
if sys.argv[1] == 'part1':
path = f'{fdir}/index.js'
f =... | pd.concat([df1, df2, df3, df4, df5]) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Tests the usecols functionality during parsing
for all of the parsers defined in parsers.py
"""
import nose
import numpy as np
import pandas.util.testing as tm
from pandas import DataFrame, Index
from pandas.lib import Timestamp
from pandas.compat import StringIO
class UsecolsTests(obj... | StringIO(data) | pandas.compat.StringIO |
"""
Importing necessary libraires.
"""
import tweepy
import json
import re
import string
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
import matplotlib.pyplot as plt
import pandas as pd
from tensorflow.python.keras.preprocessing.text import Tokenizer
from tensorflow.python.keras.preprocessing.sequen... | pd.Series([tweet.text for tweet in results]) | pandas.Series |
import operator
import re
import warnings
import numpy as np
import pytest
from pandas._libs.sparse import IntIndex
import pandas.util._test_decorators as td
import pandas as pd
from pandas import isna
from pandas.core.sparse.api import SparseArray, SparseDtype, SparseSeries
import pandas.util.testing as tm
from pan... | tm.assert_sp_array_equal(res, exp) | pandas.util.testing.assert_sp_array_equal |
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 20 15:50:32 2022
@author: kkrao
"""
import os
import pandas as pd
import init
csvs = os.listdir(os.path.join(init.dir_root, "data","gee","all_states"))
df = pd.read_csv(os.path.join(init.dir_root, "data","gee",\
"lightnings_22_feb_2022_... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import csv
import copy
import time
import json
import ipaddress
import pickle
import operator
from Policy import Policy
from time import sleep
class Utils(object):
@staticmethod
def search_interval_array(interval_dict, value):
inte... | pd.arrays.IntervalArray.from_arrays(self.src_dist[:-1], self.src_dist[1:]) | pandas.arrays.IntervalArray.from_arrays |
import html5lib
import requests
import lxml
from bs4 import BeautifulSoup
from bs4 import Comment
import pandas as pd
import numpy as np
pd.set_option('mode.chained_assignment', None)
#Getting the teams acronims
teams = | pd.read_csv('mlb_teams_abbreviations.csv') | pandas.read_csv |
"""
Long/Short Cross-Sectional Momentum
Author: <NAME>
This algorithm creates traditional value factors and standardizes
them using a synthetic S&P500. It then uses a 130/30 strategy to trade.
https://www.math.nyu.edu/faculty/avellane/Lo13030.pdf
Please direct any questions, feedback, or corrections to <EMA... | pd.Series(data=df_composite,index=index) | pandas.Series |
#!/usr/bin/env python3
import numpy as np
import pandas as pd
import pytest
from histogrammar.dfinterface.pandas_histogrammar import PandasHistogrammar
from histogrammar.dfinterface.make_histograms import (
get_bin_specs,
get_time_axes,
make_histograms,
)
def test_get_histograms():
pandas_filler = ... | pd.DataFrame(d) | pandas.DataFrame |
import pandas as pd
import tqdm
from pynput import keyboard
import bird_view.utils.bz_utils as bzu
import bird_view.utils.carla_utils as cu
from bird_view.models.common import crop_birdview
from perception.utils.helpers import get_segmentation_tensor
from perception.utils.segmentation_labels import DEFAULT_CLASSES
fr... | pd.DataFrame(summary) | pandas.DataFrame |
#!/usr/bin/env python
from __future__ import print_function
import warnings
import pandas as pd
from tabulate import tabulate
from matplotlib import pyplot as plt
import matplotlib
import numpy as np
import cPickle
######################################
warnings.filterwarnings('ignore')
pd.options.display.max_colum... | pd.get_dummies(combined['Cabin'],prefix='Cabin') | pandas.get_dummies |
#!/usr/bin/env python3
"""Parse Postgres log to retrieve the dataset ids and the IP of the API users."""
import argparse
import glob
import gzip
import ipaddress
import json
import logging
import os
from urllib.parse import unquote
import dateutil
import pandas as pd
import sqlalchemy as sqla
# CONSTANTS
logging.bas... | pd.DataFrame.from_records(dicts) | pandas.DataFrame.from_records |
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 |
import gspread
from oauth2client.service_account import ServiceAccountCredentials
import pandas as pd
import os
import shutil
import time
from PIL import Image
pictures_dir = 'D:/Libraries/Documents/Projects/Jenna Paintings/'
def get_data():
scope = ['https://spreadsheets.google.com/feeds',
'https://w... | pd.DataFrame(data[2:], columns=headers) | pandas.DataFrame |
# Script wh helps to plot Figures 3A and 3B
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# Include all GENES, those containing Indels and SNVS (that's why I repeat this step of loading "alleles" dataframe) This prevents badly groupping in 20210105_plotStacked...INDELS.py
alleles = pd.read_csv... | pd.read_csv('/path/to/phenotypes_20210107.csv',sep='\t') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# ~~~~~~~~~~~~~~IMPORTS~~~~~~~~~~~~~~ #
# Standard library imports
from collections import *
# Third party imports
import pysam
import pandas as pd
from tqdm import tqdm
# Local imports
from NanoCount.Read import Read
from NanoCount.common import *
# ~~~~~~~~~~~~~~MAIN... | pd.DataFrame() | pandas.DataFrame |
"""Provides functions to load entire benchmark result datasets
"""
import os
import io
import glob
import gzip
import tarfile
import warnings
import numpy
import pandas
from .parse import IorOutput, MdWorkbenchOutput
from .contention import validate_contention_dataset, JobOverlapError, ShortJobError
def _load_ior_ou... | pandas.concat((dataframe, subframe)) | pandas.concat |
from collections import OrderedDict
import contextlib
from datetime import datetime, time
from functools import partial
import os
from urllib.error import URLError
import warnings
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, Index, Multi... | pd.ExcelFile("test4" + read_ext) | pandas.ExcelFile |
import dataclasses
import itertools
from typing import Dict, List
import datetime
from typing import Iterable
from typing import Iterator
from typing import Optional
from typing import Union
import pytest
from datapublic.common_fields import CommonFields
import pandas as pd
from datapublic.common_fields import Demogra... | pd.DataFrame(rows) | pandas.DataFrame |
from pylab import *
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime
import requests
import pandas_datareader.data as web
from Create_PDF_Report import portfolio_report
ALPHA_VANTAGE_KEY = 'ENTER_KEY'
RESULT_DETAILED = True
USER_AGENT = {
'User-Agent': (
... | pd.to_datetime(temp_data['x']) | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 24 21:12:53 2020
@author: daniel
"""
## [1]
# @title Imports (run this cell)
from __future__ import print_function
import numpy as np
import pandas as pd
import collections
#from mpl_toolkits.mplot3d import Axes3D
from IPython import display
from ... | pd.DataFrame.from_dict(x) | pandas.DataFrame.from_dict |
#Importing the required packages
from flask import Flask, render_template, request
import os
import pandas as pd
from pandas import ExcelFile
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
from sklearn.preprocessing import StandardScaler, Label... | pd.concat([cat_train,col_dummies],axis=1) | pandas.concat |
import pandas as pd
from pandas_profiling.config import config
from pandas_profiling.report.presentation.frequency_table_utils import freq_table
from pandas_profiling.visualisation.plot import histogram
from pandas_profiling.report.presentation.core import (
Image,
FrequencyTable,
FrequencyTableSmall,
... | pd.Series(summary["category_alias_values"]) | pandas.Series |
# AUTOGENERATED! DO NOT EDIT! File to edit: 01_features.ipynb (unless otherwise specified).
__all__ = ['read_tsv', 'gzip_reading', 'school_plan__features', 'translate_latlng', 'kdtree_neighbors',
'train_plan__latlng', 'train_plan__nbusers', 'train_time_features', 'census_income_median',
'census_i... | pd.to_datetime(data['account_start_date']) | pandas.to_datetime |
import pandas as pd
# from matplotlib import pyplot as plt
# import matplotlib.dates as md
import datetime
import glob
from io import BytesIO
import base64
from pymongo import MongoClient
class Plots():
def test_plot():
plt.plot([1,2,3,4,5,6,7,8,9])
plt.rcParams["figure.figsize"] = (10,5)
... | pd.read_csv(arquivo, sep=';') | pandas.read_csv |
"""Utility functions for logging operations."""
__author__ = "<NAME>"
import logging
import warnings
import pandas as pd
from pathlib import Path
from typing import Union
def remove_inner_brackets(message: str) -> str:
"""Remove the inner brackets i.e., [ or ], from a string, outer brackets are kept.
Para... | pd.to_datetime(df["log_time"]) | pandas.to_datetime |
import os
import json
def format_ts(ts):
return ts.strftime("%Y-%m-%dT%H:%M:%S.%fZ")
def get_sim_folder_path():
#return '/Users/ngoh511/Documents/projects/PycharmProjects/volttron_ep_toolkit/dashboard/src/simulations'
return '/home/vuser/volttron/simulations/'
def get_sim_file_path(bldg, sim, baseline... | pd.to_datetime(df[['Year', 'Month', 'Day', 'Hour', 'Minute']]) | pandas.to_datetime |
# -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
from datetime import datetime, timedelta
import itertools
from numpy import nan
import numpy as np
from pandas import (DataFrame, Series, Timestamp, date_range, compat,
option_context, Categorical)
from pandas.core.arra... | assert_series_equal(result, expected) | pandas.util.testing.assert_series_equal |
"""Failure analysis of national-scale networks
For transport modes at national scale:
- rail
- Can do raod as well
Input data requirements
-----------------------
1. Correct paths to all files and correct input parameters
2. csv sheets with results of flow mapping based on MIN-MAX generalised costs estimates:... | pd.merge(ef_df,e_flow,how='left',on=['edge_id']) | pandas.merge |
"""Metric Functions.
"""
import numpy as np
import pandas as pd
import statsmodels.api as sm
import itertools as it
import scipy.stats as st
from sklearn.preprocessing import PolynomialFeatures as pnf
__all__ = ['deviation',
'vif',
'mean_absolute_percentage_error',
'average_absolut... | pd.DataFrame(index=ind, columns=col) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Dec 3 15:17:10 2017
@author: zeinabhakimi
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_extraction.text import CountVectorizer
from scipy.sparse import lil_matrix
from sklearn.svm... | pd.read_csv('result_train.csv') | pandas.read_csv |
from __future__ import division
import numpy as np
import os.path
import sys
import pandas as pd
from base.uber_model import UberModel, ModelSharedInputs
from .therps_functions import TherpsFunctions
import time
from functools import wraps
def timefn(fn):
@wraps(fn)
def measure_time(*args, **kwargs):
... | pd.Series([], dtype='float', name="out_eec_arq_herp_hm_mean") | pandas.Series |
# -*- coding: utf-8 -*-
"""
@author: <EMAIL>
@site: e-smartdata.org
"""
import numpy as np
import pandas as pd
df1 = pd.DataFrame(np.random.rand(10, 4), columns=list('abcd'))
df2 = pd.DataFrame(np.random.rand(10, 4), columns=list('abcd'))
df3 = pd.DataFrame(np.random.rand(10, 4), columns=list('abcd'))
s = pd.Series... | pd.concat([df1, s]) | pandas.concat |
# -*- coding: UTF-8 -*-
# import matplotlib as mpl
# mpl.use('Agg')
import time
import datetime
from sqlalchemy import create_engine
from configparser import ConfigParser
import pandas as pd
import matplotlib.pyplot as plt
import tushare as ts
import math
import sys
reload(sys) # Python2.5 初始化后会删除 sys.setdefaultencodi... | pd.read_sql(sql, con=engine) | pandas.read_sql |
# -*- coding: utf-8 -*-
import json
from datetime import datetime
import pandas as pd
import numpy as np
from sqlalchemy import func
from findy import findy_config
from findy.interface import Region, Provider
from findy.database.schema.fundamental.dividend_financing import SpoDetail, DividendFinancing
from findy.data... | pd.to_datetime(df['timestamp']) | pandas.to_datetime |
#coding=utf-8
import pandas as pd
import time
import datetime
import matplotlib.pyplot as plt
import xlrd
import numpy as np
from matplotlib.dates import DayLocator, HourLocator, DateFormatter
from luminol.anomaly_detector import AnomalyDetector
import matplotlib.dates as dates
def timestamp_to_datetime(x):
'''
... | pd.DataFrame(temp, columns=['kpi_time', 'kpi_value']) | pandas.DataFrame |
import sys
import pandas as pd
DAILY_LTLA_FILE = "ltla_daily_cases.csv"
SGTF_FILE = "ltla_sgtf.xlsx"
GEOCODE_LOOKUP_FILE = (
"Local_Authority_Districts_(December_2017)_Boundaries_in_Great_Britain.csv"
)
OUTPUT = "uk-ltla.csv"
MERGE_ERROR_MSG = """
Error: Merge happened incorrectly
The newCasesBySpecimenDate colu... | pd.to_datetime(df["date"]) | pandas.to_datetime |
# Copyright 2021 Research Institute of Systems Planning, 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 applica... | pd.DataFrame(columns=columns) | pandas.DataFrame |
#!/usr/bin/env python3
from datetime import datetime, timedelta
import sys
import json
import re
import pandas as pd
LOGFORMAT="/var/log/nsd/nsd-dnstap.log.%Y%m%d-%H"
def read_data(data):
if isinstance(data, list) and not data:
sys.stderr.write("No valid input supplied!\n")
sys.exit(-1)
ids ... | pd.to_datetime(df['time']) | pandas.to_datetime |
import pandas as pd
from pandas.testing import assert_frame_equal
from evaluate.report import (
PrecisionReport,
RecallReport,
Report,
DelimNotFoundError,
ReturnTypeDoesNotMatchError
)
from evaluate.classification import AlignmentAssessment
import pytest
from io import StringIO
import math
from test... | assert_frame_equal(actual, expected, check_dtype=False) | pandas.testing.assert_frame_equal |
"""
2a. Modelling folds
====================
This tutorial will show how Loop Structural improves the modelling of
folds by using an accurate parameterization of folds geometry. This will
be done by: 1. Modelling folded surfaces without structural geology,
i.e. using only data points and adjusting the scalar field... | pd.concat([data[:npoints],data[data['feature_name']=='s1']]) | pandas.concat |
#! python
import os
import pandas as pd
BASEDIR = os.path.dirname(__file__)
WEATHERFILE = os.path.join(BASEDIR, 'onemin-WS_1-2017')
GROUNDFILE = os.path.join(BASEDIR, 'onemin-Ground-2017')
EASTERN_TZ = 'Etc/GMT+5'
# LATITUDE, LONGITUDE = 39.1374, -77.2187 # weather station
# LATITUDE, LONGITUDE = 39.1319, -77.2141 ... | pd.concat(gnd_data) | pandas.concat |
# AUTOGENERATED! DO NOT EDIT! File to edit: 01_vulnerabilidad.ipynb (unless otherwise specified).
__all__ = ['show_feature_importances', 'mostrar_coeficientes_PLS', 'agregar_conteo_pruebas',
'agregar_tasas_municipales', 'caracteristicas_modelos_municipios', 'ajustar_pls_letalidad',
'ajustar_pls_c... | pd.concat(resultados, ignore_index=True) | pandas.concat |
# !/usr/bin/env python3
# -*- coding: utf-8 -*-
from __future__ import division
import math
import numpy as np
import pandas as pd
from scipy.optimize import minimize
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils import validation
from sklearn.utils.multiclass import unique_labels
from s... | pd.DataFrame(self.prototypes_classes) | pandas.DataFrame |
import pytest
from pandas._libs.tslibs.frequencies import INVALID_FREQ_ERR_MSG, _period_code_map
from pandas.errors import OutOfBoundsDatetime
from pandas import Period, Timestamp, offsets
class TestFreqConversion:
"""Test frequency conversion of date objects"""
@pytest.mark.parametrize("freq", ["A", "Q", ... | Period(freq="D", year=2007, month=1, day=1) | pandas.Period |
import logging
import pandas as pd
"""
User başına aramaların spam olarak etiketlenebilmesi
için ön koşul olarak call_count en az 5 belirlenmiştir;
Eğer bu koşul, aşağıdakilerden biri ile birlikte sağlanıyorsa
user spam olarak etiketlenmelidir.
1. call_count'un yuzde 50'sinden azı answered ise;
2. answered başına 5... | pd.DataFrame(call_data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 31 10:28:49 2018
@author: dani
Make tSNE & PCA plots for each combination of 2 channels from same movie
Need to ask <NAME> what moving threshold is for 'time_moving01' etc. parameters
and whether/how I can change that if needed
this should be somewhere in the ind... | pd.read_csv(indir+Ch2,usecols = col) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
"""
import sys
import os
import pandas as pd
import time
from datetime import datetime
def to_list(s):
return list(s)
def generate_rows(s):
return s*[[s,0,0]]
#input_file = sys.argv[1]
#output_file = sys.argv[2]
# input_dir=r"C:\Gamal Elkoumy\PhD\OneDrive - Tartu Ülikool\Secur... | pd.DataFrame.from_records(padded_value) | pandas.DataFrame.from_records |
import tempfile
from . import common
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
def taxa_cols(df):
"""Returns metadata columns from DataFrame object."""
cols = []
for col in df.columns:
if 'Unassigned' in col:
cols.append(col)
elif '__' in col:
... | pd.concat([df, mf], axis=1, join='inner') | pandas.concat |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
from numpy import nan
import numpy as np
import pandas as pd
from pandas.types.common import is_integer, is_scalar
from pandas import Index, Series, DataFrame, isnull, date_range
from pandas.core.index import MultiIndex
from pa... | tm.assert_series_equal(s, expected) | pandas.util.testing.assert_series_equal |
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 24 22:35:51 2021
function to check missing data
input parameter: dataframe
output: missing data values
@author: Ashish
"""
# import required libraries
import re, os, emoji, numpy as np
import pandas as pd
#Count vectorizer for N grams
from sklearn.feature_extraction.text i... | pd.concat([total,percentage],axis=1,keys=['Total','Percentage']) | pandas.concat |
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
def label_rectify(data):
data[1:-10] += 5
data[-10:] += 1
return data
def data_preparation(file):
epoch = []
top1 = []
top5 = []
loss = []
delete_title = list(range(4))
for i in range(4):
delete_title[i] = file.readline()
f... | pd.DataFrame(data=data, index=['epoch', 'top1', 'top5', 'loss']) | 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... | ints_to_pydatetime(self.asi8, self.tz, box="time") | pandas._libs.tslibs.ints_to_pydatetime |
from abc import ABC
from abc import abstractmethod
from typing import List
from typing import Optional
import pandas as pd
from etna.transforms.base import Transform
class WindowStatisticsTransform(Transform, ABC):
"""WindowStatisticsTransform handles computation of statistical features on windows."""
def ... | pd.MultiIndex.from_frame(_idx) | pandas.MultiIndex.from_frame |
# -*- coding: utf-8 -*-
from __future__ import absolute_import
import inspect
import warnings
from ._utils import get_string
is_pandas_installed = True
try:
import pandas as pd
except ImportError:
is_pandas_installed = False
class Iterator(object):
def __init__(self, module, function):
assert i... | pd.DataFrame(data) | pandas.DataFrame |
from __future__ import absolute_import, division, unicode_literals
import unittest
import jsonpickle
from helper import SkippableTest
try:
import pandas as pd
import numpy as np
from pandas.testing import assert_series_equal
from pandas.testing import assert_frame_equal
from pandas.testing import... | assert_index_equal(decoded_idx, idx) | pandas.testing.assert_index_equal |
# -*- coding: utf-8 -*-
"""
Tests dtype specification during parsing
for all of the parsers defined in parsers.py
"""
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas import DataFrame, Series, Index, MultiIndex, Categorical
from pandas.compat import StringIO
from pan... | Categorical(['a', 'a', 'b']) | pandas.Categorical |
import pandas as pd
import numpy as np
import pytest
import re
import tubular
import tubular.testing.helpers as h
import tubular.testing.test_data as data_generators_p
import input_checker
from input_checker._version import __version__
from input_checker.checker import InputChecker
from input_checker.exceptions import... | pd.to_datetime("15/09/2017") | pandas.to_datetime |
# This is a test file intended to be used with pytest
# pytest automatically runs all the function starting with "test_"
# see https://docs.pytest.org for more information
import math
import os
import sys
import numpy as np
import pandas as pd
## Add stuff to the path to enable exec outside of DSS
plugin_root = os.p... | pd.Timestamp('20190131 01:59:00') | pandas.Timestamp |
import pandas as pd
import numpy as np
##This file takes results_hyperParams(output of aucroc_aucpr) as input, retrieves the maxima of AUCROCmax/AUCPRmax/AUCROClast/AUCPRlast values and obtains the Hyperparamaters
##initialize Pandas DataFrame
values_df= | pd.DataFrame(columns=['hiddenSizes', 'lastDropout', 'weightDecay', 'AUCROC_max', 'AUCPR_max','AUCROC_last', 'AUCPR_last']) | pandas.DataFrame |
#!/bin/env python
import boto3
import os
from datetime import datetime, timedelta
from boto3.dynamodb.conditions import Key
from botocore.exceptions import ClientError
from dash.app import stock_cache
from dash.stock import Stock
from dash.userInfo import UserInfo
from decimal import Decimal
import numpy as np
import p... | pd.isna(stock.current_price) | pandas.isna |
import numpy as np
import pandas as pd
import os,re
import multiprocessing
import h5py
import csv
import ujson
from operator import itemgetter
from collections import defaultdict
from io import StringIO
from . import helper
from ..utils import misc
def index(eventalign_result,pos_start,out_paths,locks):
eventali... | pd.read_csv(out_paths['index'],sep=',') | pandas.read_csv |
# AUTOGENERATED! DO NOT EDIT! File to edit: 01_followers.ipynb (unless otherwise specified).
__all__ = ['get_followers', 'get_new_followers', 'get_dif', 'get_followers_change', 'get_ads_status', 'save_ads_status',
'get_updated_followers', 'more_stats', 'update_insights', 'update_dashboard_followers',
... | pd.concat([df, new_followers], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import pytest
import re
from numpy import nan as NA
import numpy as np
from numpy.random import randint
from pandas.compat import range, u
import pandas.compat as compat
from pandas import Index, Series, DataFrame, isn... | Series(mixed) | pandas.Series |
import numpy as np
import pandas as pd
from pandas import Series
from weaverbird.backends.pandas_executor.types import DomainRetriever, PipelineExecutor
from weaverbird.pipeline.steps import AddMissingDatesStep
# cf. https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases
_FREQUENCIES =... | pd.Grouper(key=step.dates_column, freq=_FREQUENCIES[step.dates_granularity]) | pandas.Grouper |
def op_corr(ENc_file_name,RSCU_file_name):
"""
determine the optimal codons using the correlation method described here: https://doi.org/10.1371/journal.pgen.1000556
Args:
ENc_file_name (file): file contains the ENc values for a set of genes
RSCU_file_name (file): file contains the ... | pd.read_csv(RSCU_file_name) | pandas.read_csv |
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