prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# IMPORTATION STANDARD
# IMPORTATION THIRDPARTY
import pandas as pd
import pytest
# IMPORTATION INTERNAL
from openbb_terminal.stocks.discovery import ark_view
@pytest.fixture(scope="module")
def vcr_config():
return {
"filter_headers": [("User-Agent", None)],
"filter_query_parameters": [
... | pd.DataFrame() | pandas.DataFrame |
##### file path
# input
path_df_D = "tianchi_fresh_comp_train_user.csv"
path_df_part_1 = "df_part_1.csv"
path_df_part_2 = "df_part_2.csv"
path_df_part_3 = "df_part_3.csv"
path_df_part_1_tar = "df_part_1_tar.csv"
path_df_part_2_tar = "df_part_2_tar.csv"
path_df_part_1_uic_label = "df_part_1_uic_label.csv"
... | pd.get_dummies(df_part_3_c_b_count_in_6['behavior_type']) | pandas.get_dummies |
# coding: utf-8
# # Content
# __1. Exploratory Visualization__
# __2. Data Cleaning__
# __3. Feature Engineering__
# __4. Modeling & Evaluation__
# __5. Ensemble Methods__
# In[1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filte... | pd.DataFrame(grid_search.cv_results_) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 6 12:22:30 2019
@author: nk7g14
Currently, this only queries objects found in the XMM-Newton Serendipitous
Source Catalog (XMMSSC) https://heasarc.gsfc.nasa.gov/W3Browse/xmm-newton/xmmssc.html
We hope to however extended it to all observations as... | pd.concat((start_end, flux_df), axis=1) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 27 12:01:19 2021
@author: leila
"""
import numpy as np
import pandas as pd
import random
#import matplotlib.pyplot as plt
#import csv
import datetime
from sklearn.model_selection import train_test_split
#from sklearn.model_selection import KFold
#f... | pd.DataFrame(Xtest, columns=cols_rest) | pandas.DataFrame |
import pandas as pd
from lyrics_function import get_genres, get_missing_genres
from lyrics_function import get_song_lyrics
import pandas as pd
import os
import unicodedata
from tqdm import tqdm
GENIUS_API_TOKEN = '<KEY>'
#====================================#
# CLEANING & FORMATTIING FUNCTIONS #
#==================... | pd.DataFrame() | pandas.DataFrame |
"""
Lasso_regulation_program
- train_data = 20대 총선 자료,
d = 더불어 민주당
s = 새누리당
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import font_manager, rc
from pandas import Series
from sklearn.model_selection import train_test_split
from sklearn.linear_model ... | Series(lassoReg.coef_, predictors) | pandas.Series |
# coding=utf-8
# Author: <NAME>
# Date: Jan 13, 2020
#
# Description: Reads all available gene information (network, FPKM, DGE, etc) and extracts features for ML.
#
#
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 10... | pd.isnull(x) | pandas.isnull |
from argh import arg
import os
from functools import partial
import pandas as pd
from typing import List
import hashlib
from functools import partial
from tqdm import tqdm
tqdm.pandas()
def calculate_improvement(df, current_row):
ensemble_size = current_row["ensemble_size"]
image = current_row["image"]
... | pd.concat(results) | pandas.concat |
import numpy as np
import pandas as pd
from numba import njit, typeof
from numba.typed import List
from datetime import datetime, timedelta
import pytest
from copy import deepcopy
import vectorbt as vbt
from vectorbt.portfolio.enums import *
from vectorbt.generic.enums import drawdown_dt
from vectorbt.utils.random_ im... | pd.Int64Index([0, 1], dtype='int64') | pandas.Int64Index |
import json
import random
from collections import OrderedDict, Counter
from itertools import groupby
import copy
import pandas as pd
from django.shortcuts import render
from django.db.models import Count
from django.db.models.functions import Concat
from django.http import JsonResponse
from django.core.exceptions impo... | pd.DataFrame(algs_w_concordances) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)
# <NAME> (<EMAIL>), Blue Yonder Gmbh, 2016
import numpy as np
from unittest import TestCase
import pandas as pd
from tsfresh.feature_selection.selection import select_features
class Sele... | pd.DataFrame([1, 2], index=[1, 2]) | pandas.DataFrame |
#!/usr/bin/python
import pandas as pd
from scipy.signal import savgol_filter
import json
import time
import darts
from darts import TimeSeries
from darts.models import RNNModel
from sktime.performance_metrics.forecasting import mean_absolute_percentage_error
import dysts
from dysts.flows import *
from dysts.base i... | pd.DataFrame(y_train) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
These the test the public routines exposed in types/common.py
related to inference and not otherwise tested in types/test_common.py
"""
from warnings import catch_warnings, simplefilter
import collections
import re
from datetime import datetime, date, timedelta, time
from decimal import De... | is_number(1.1) | pandas.core.dtypes.common.is_number |
import os
import yaml
import json
import pandas as pd
import matplotlib.pyplot as plt
from pylab import rcParams
import seaborn as sns
import numpy as np
from sklearn.linear_model import LinearRegression
import glob
import time
###############################################################################... | pd.DataFrame() | pandas.DataFrame |
import sys
from sqlalchemy import create_engine
import pandas as pd
def load_data(messages_filepath, categories_filepath):
"""
Load messages and categroies from CSV files to Pandas df
:param messages_filepath: str, filepath of messages
:param categories_filepath: str, filepath of categories
:retur... | pd.read_csv(categories_filepath) | pandas.read_csv |
#!/usr/bin/env python
'''
Author: <NAME>
This program will read subnet planning and port matrix from two different spreadsheets and by use of Jinja2
will create a configuration file for a device or devices.
At the same time the program will create a YAML file with the device(s) configuration and also will create... | excel.ExcelFile(inputSubPlan) | pandas.io.excel.ExcelFile |
"""
Thi script will compate variables to FRI to recaalculate the results
"""
#==============================================================================
__title__ = "FRI vs variables"
__author__ = "<NAME>"
__version__ = "v1.0(21.08.2019)"
__email__ = "<EMAIL>"
#====================================================... | pd.Timestamp.now() | pandas.Timestamp.now |
"""
Module contains tools for processing files into DataFrames or other objects
"""
from collections import abc, defaultdict
import csv
import datetime
from io import StringIO
import itertools
import re
import sys
from textwrap import fill
from typing import (
Any,
Dict,
Iterable,
Iterator,
List,
... | is_scalar(parse_dates) | pandas.core.dtypes.common.is_scalar |
# -*- 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... | Timestamp('20010102') | pandas.Timestamp |
import pandas as pd
def read_local_data(data_dir):
static_vars = pd.read_csv(data_dir + 'static_vars.csv')
dynamic_vars = pd.read_csv(data_dir + 'dynamic_vars.csv')
outcome_vars = | pd.read_csv(data_dir + 'outcome_vars.csv') | pandas.read_csv |
import datetime
from collections import OrderedDict
import warnings
import numpy as np
from numpy import array, nan
import pandas as pd
import pytest
from numpy.testing import assert_almost_equal, assert_allclose
from conftest import assert_frame_equal, assert_series_equal
from pvlib import irradiance
from conftes... | pd.DatetimeIndex(['2016-07-19 06:11:00'], tz='America/Phoenix') | pandas.DatetimeIndex |
# Copyright (c) 2018, Faststream Technologies
# Author: <NAME>
import numpy as np
import pandas as pd
import os
# Import to show plots in seperate Windows
# from IPython import get_ipython
# get_ipython().run_line_magic('matplotlib', 'qt5')
# CURR and PARENT directory constants
CURR_DIR = os.path.dirname(os.path.abs... | pd.Series(target_bools) | pandas.Series |
# -----------------------------------------------------------------------------
# WSDM Cup 2017 Classification and Evaluation
#
# Copyright (c) 2017 <NAME>, <NAME>, <NAME>, <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the ... | pd.Series() | pandas.Series |
import numpy as np
import pandas as pd
import scipy.sparse as sp
import sklearn.preprocessing as pp
from math import exp
from heapq import heappush, heappop
# conventional i2i
class CosineSimilarity():
# expects DataFrame, loaded from ratings.csv
def __init__(self, df, limit=20):
self.limit = limit
... | pd.DataFrame(self.recs[movie_id], columns=['movieId', 'score']) | pandas.DataFrame |
import pandas as pd
import numpy as np
from sklearn import metrics
import pickle
from sklearn.preprocessing import label_binarize
import os
import argparse
def get_thres_fold(gold_labels_train_folds, results_softmax, folds=5):
'''
find the threshold that equates the label cardinality of the dev set
to that... | pd.DataFrame(metrics) | pandas.DataFrame |
import imgaug as ia
ia.seed(1)
# imgaug uses matplotlib backend for displaying images
#%matplotlib inline
from imgaug.augmentables.bbs import BoundingBox, BoundingBoxesOnImage
from imgaug import augmenters as iaa
# imageio library will be used for image input/output
import imageio
import pandas as pd
import numpy as np... | pd.concat([aug_bbs_xy, aug_df]) | pandas.concat |
from glob import glob
import pandas as pd
import numpy as np # linear algebra
from tensorflow.keras.applications.imagenet_utils import preprocess_input
from tensorflow.keras.callbacks import ModelCheckpoint
from sklearn.model_selection import train_test_split
from models import get_model_classif_nasnet
from utils im... | pd.DataFrame({'id': test_files, 'label': preds}) | pandas.DataFrame |
import pandas as pd
import json
import numpy as np
from dataclasses import dataclass
import os
from os.path import join, splitext
import unidecode
import pickle as pkl
import sys
from sklearn.model_selection import KFold
import functools
import rampwf
from sklearn.base import is_classifier
from sklearn.metrics import ... | pd.read_csv("data/acteurs.csv") | pandas.read_csv |
import os
from os.path import expanduser
import altair as alt
import numpy as np
import pandas as pd
from scipy.stats.stats import pearsonr
import sqlite3
from util import to_day, to_month, to_year, to_local, allocate_ys, save_plot
from config import dummy_start_date, dummy_end_date, cutoff_date
# %matplotlib inline... | pd.to_numeric(x, errors='coerce', downcast='integer') | pandas.to_numeric |
from tqdm.notebook import trange, tqdm
import pandas as pd
import matplotlib
import numpy as np
# import csv
from itertools import product
from functools import reduce
import pickle as pkl
from warnings import catch_warnings
from warnings import filterwarnings
import time
import datetime
from multiprocessing import cp... | pd.merge(left,right,left_index=True,right_index=True) | pandas.merge |
# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/02_data_process.ipynb (unless otherwise specified).
__all__ = ['imgids_from_directory', 'imgids_testing', 'read_img', 'load_RGBY_image', 'save_image', 'CellSegmentator',
'load_segmentator', 'get_cellmask', 'encode_binary_mask', 'coco_rle_encode', 'rle_encode',... | pd.read_csv(pth_csv) | pandas.read_csv |
import os
import pandas as pd
import numpy as np
from collections import Counter
from imblearn.datasets import make_imbalance
from imblearn.over_sampling import SMOTE, ADASYN
from sklearn.utils import shuffle
os.chdir('/content/gdrive/My Drive/training_testing_data/')
train = pd.read_csv('train_data_rp_3_... | pd.DataFrame(X_train_ADASYN) | pandas.DataFrame |
import pandas as pd
from pathlib import Path
import os
from xlrd import open_workbook, XLRDError
class Ballistics:
def __init__(self, csv='./ballistics.csv', min_range=-1, max_range=-1, step=-1, range_col='Range', cols=[]):
csv_file = Path(csv)
if csv_file.is_file():
#print("File Foun... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
from pathlib import Path
import numpy as np
import pylab as pl
from scipy.signal import find_peaks
from my_general_helpers import butter_lowpass_filter
def angle_between_points_signcorrect(x1, y1, x2, y2, x3, y3):
ang1 = np.degrees(np.arctan2(y1 - y2, x1 - x2))
ang2 = np.degrees(np.arctan2(... | pd.read_hdf(root_path / "all_data_deepposekit.h5", key="raw_data") | pandas.read_hdf |
import re
import datetime
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
# ---------------------------------------------------
# Person data methods
# ---------------------------------------------------
class TransformGenderGetFromName:
"""Gets clients' gen... | pd.isnull(veh_issue_year) | pandas.isnull |
from __future__ import absolute_import, division, unicode_literals
import datetime
import pytest
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
except ImportError... | pd.DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-05']) | pandas.DatetimeIndex |
# coding:utf-8
#
# The MIT License (MIT)
#
# Copyright (c) 2016-2018 yutiansut/QUANTAXIS
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... | pd.merge(df, profit, left_on=['ts_code', 'season'], right_on=['ts_code', 'end_date'],how = 'left') | pandas.merge |
""" A set of helping functions used by the main functions """
import re
import urllib
import zipfile
from typing import List, Tuple
from io import TextIOWrapper, BytesIO
from pathlib import Path, PurePosixPath
import pandas as pd
from multiprocessing import Pool
import ftplib
from python_dwd.constants.column_name_mapp... | pd.DataFrame(None, columns=METADATA_COLUMNS) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed May 03 15:01:31 2017
@author: jdkern
"""
import pandas as pd
import numpy as np
def setup(year,hist,hist_year,operating_horizon,perfect_foresight):
# year = 0
# hist = 0
# hist_year = 2010
#read generator parameters into DataFrame
df_gen = pd.read_csv('CA_... | pd.read_csv('Path_setup/CA_path_mins46.csv', header=0) | pandas.read_csv |
import requests
import dateutil
import datetime
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import warnings
import statsmodels.api as sm
import time
tsa = sm.tsa
# Read recession data. First try to parse html table at nber.org
try:
# Read HTML table at nber.org
tables = pd.... | pd.to_datetime('today') | pandas.to_datetime |
from sklearn import metrics
import random
from sklearn import metrics
from scipy.stats import wasserstein_distance
import datatable as dt
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_selection import mutual_info_classif
from sklearn.model_select... | pd.read_excel(f"./data/source_data/LIWC_5k_final_leadership_values.xlsx", converters={'id': str}) | pandas.read_excel |
#!/bin/python
# Copyright 2018 <NAME>
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, di... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import random
import tensorflow.keras as keras
from sklearn.model_selection import train_test_split
def read_data(random_state=42,
otu_filename='../../Datasets/otu_table_all_80.csv',
metadata_filename='../../Datasets/metadata_table_all_80.csv'):
... | pd.get_dummies(domain['soil_type'], prefix='soil_type') | pandas.get_dummies |
# -*- coding: utf-8 -*-
import os
import pandas as pd
from collections import defaultdict
os.chdir("/home/jana/Documents/PhD/CompBio/")
herds = pd.read_table("/home/jana/Documents/PhD/CompBio/TestingGBLUP/PedCows_HERDS.txt", sep=" ")
IndGeno = pd.read_table("/home/jana/Documents/PhD/CompBio/IndForGeno_5gen.txt", heade... | pd.DataFrame.from_dict(NapAmean, orient="index") | pandas.DataFrame.from_dict |
# -*- coding: utf-8 -*-
"""
uGrid "Macro" Code
@author: Phy
"""
from __future__ import division
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from technical_tools_PC_3 import Tech_total
from economic_tools_PC_3 import Econ_total
import time
if __name__ == "__main__":
cl... | pd.DataFrame(data = data_plot_variables ,columns=['Batt_SOC', 'Charge', 'LoadkW', 'genLoad', 'Batt_Power_to_Load', 'Batt_Power_to_Load_neg', 'PV_Power', 'PV_Batt_Change_Power', 'dumpload', 'Batt_frac', 'Gen_Batt_Charge_Power', 'Genset_fuel', 'Fuel_kW']) | pandas.DataFrame |
import os, datetime
from glob import glob
import pandas as pd
import numpy as np
from datetime import timedelta
pd.options.mode.chained_assignment = None # default='warn'
PROB_WEAR = 'PROB_WEAR'
PROB_SLEEP = 'PROB_SLEEP'
PROB_NWEAR = 'PROB_NWEAR'
MHEALTH_TIMESTAMP_FORMAT = "%Y-%m-%d %H:%M:%S"
def mhealth_timestam... | pd.DataFrame(ff_obout_array, columns=['START_IND', 'STOP_IND']) | pandas.DataFrame |
# -*- 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... | tm.assert_almost_equal(result, exp) | pandas.util.testing.assert_almost_equal |
"""
BootstrapChainLadder implementation.
"""
import functools
import warnings
import numpy as np
import pandas as pd
from numpy.random import RandomState
from scipy import stats
from .base import BaseRangeEstimator, BaseRangeEstimatorResult
class BootstrapChainLadder(BaseRangeEstimator):
"""
The purpose of th... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""System operating cost plots.
This module plots figures related to the cost of operating the power system.
Plots can be broken down by cost categories, generator types etc.
@author: <NAME>
"""
import logging
import pandas as pd
import marmot.config.mconfig as mconfig
from marmot.plottingm... | pd.notna(custom_data_file_path) | pandas.notna |
import pandas as pd
import numpy as np
from random import randrange
from datetime import date,timedelta
def random_date(start, end):
"""
This function returns a random datetime between two datetime
objects
"""
delta = end - start
int_delta = (delta.days * 24 * 60 * 60) + delta.seconds
rand... | pd.concat([scores, metrics], axis=1) | pandas.concat |
import pandas as pd
data_from_db = '../data/from_db/'
cleaned_data_path = '../data/cleaned/'
def print_summary(name, df):
print(f'\n\n=============={name}==============\n\n')
print(df.head())
print(f'\nWymiary df: {df.shape}')
print(f'Rozmiar danych:')
df.info(memory_usage='deep')
def data_mining... | pd.read_pickle(cleaned_data_path + 'commits.pkl') | pandas.read_pickle |
from kivy.config import Config
Config.set('input', 'mouse', 'mouse,multitouch_on_demand')
from kivy.app import App
from kivy.uix.gridlayout import GridLayout
from kivy.uix.popup import Popup
from kivy.uix.label import Label
import matplotlib.pyplot as plt
import pandas as pd
from multiprocessing import Process
class ... | pd.read_csv(filename, sep=',', engine='python', header=None) | pandas.read_csv |
"""
Limited dependent variable and qualitative variables.
Includes binary outcomes, count data, (ordered) ordinal data and limited
dependent variables.
General References
--------------------
<NAME> and <NAME>. `Regression Analysis of Count Data`.
Cambridge, 1998
<NAME>. `Limited-Dependent and Qualitative Vari... | get_dummies(endog, drop_first=False) | pandas.get_dummies |
#get ap original information which will be exported to apinfo.csv
#get name and serial infomation, add nessisary columns which renaming workflow needs, also change the ap_name as site+"AP"+model+number, the info will be exported to csv_file.csv.
import http.client
import pandas as pd
import json
import pprint as... | pd.DataFrame(data_json) | pandas.DataFrame |
import pandas as pd
from statsmodels.distributions.empirical_distribution import ECDF
from statsmodels.stats.multitest import multipletests
if __name__ == '__main__':
cov_sig = | pd.read_csv(snakemake.input[0], sep="\t", index_col=0) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""Imersao_dados.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/11hUX4kVtP3llYH7c83fiSCeCtP8MGTm0
"""
import pandas as pd
import matplotlib.pyplot as plt
url_date = "https://github.com/alura-cursos/imersaodados3/blob/mai... | pd.crosstab([dados['dose'], dados['tempo']], dados['tratamento'], normalize='columns', values=dados['g0'], aggfunc='mean') | pandas.crosstab |
"""
test date_range, bdate_range construction from the convenience range functions
"""
from datetime import datetime, time, timedelta
import numpy as np
import pytest
import pytz
from pytz import timezone
from pandas._libs.tslibs import timezones
from pandas._libs.tslibs.offsets import BDay, CDay, DateOffset, MonthE... | CDay() | pandas._libs.tslibs.offsets.CDay |
import os
import sys
from enum import Enum
from pathlib import Path
import tkinter as tk
from tkinter import filedialog
import csv
import pandas as pd
import warnings
file_dir = os.path.dirname(__file__)
sys.path.append(file_dir)
root = tk.Tk()
root.withdraw()
def get_root_folder():
path = Path(os.getcwd())
... | pd.DataFrame(all_results) | pandas.DataFrame |
import warnings
import numpy as np
import pandas as pd
from pandas.api.types import (
is_categorical_dtype,
is_datetime64tz_dtype,
is_interval_dtype,
is_period_dtype,
is_scalar,
is_sparse,
union_categoricals,
)
from ..utils import is_arraylike, typename
from ._compat import PANDAS_GT_100
f... | pd.Categorical(data, categories=cats, ordered=s.cat.ordered) | pandas.Categorical |
import pymongo
import logging
import numpy as np
import pandas as pd
from scipy.stats import entropy
from config import Configuration
from utils.bot_utils import is_bot
from tasks.collectors.edit_type import CollectEditTypes
from utils.date_utils import parse_timestamp
from tasks.collectors.revision import CollectRevis... | pd.DataFrame(data=data, columns=cols) | pandas.DataFrame |
#!/usr/bin/env python3
# Process cleaned data set into separate Q-n-A pairs, with each Q-n-A pair as one row in a CSV file
import pandas as pd
def qna_pairs(row):
'''
For argument row of pandas dataframe, parse column 'FAQ' into heading and
question-and-answer pairs, storing in columns 'heading' and 'qna... | pd.Series(x['qna']) | pandas.Series |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author: MingZ
# @Date created: 21 Sep 2017
# @Date last modified: 21 Sep 2017
# Python Version: 2.7
# historical data from Google/yahoo finace
# http://www.google.com/finance/historical?q=JNUG&startdate=20170101&enddate=20170707&output=csv
# start = datetime.datetime(2... | pd.DataFrame() | pandas.DataFrame |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.4.1
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Kaggle Titanic Data -... | pd.cut(data_train['Age'],10) | pandas.cut |
"""
Tests for Series cumulative operations.
See also
--------
tests.frame.test_cumulative
"""
from itertools import product
import numpy as np
import pytest
import pandas as pd
from pandas import _is_numpy_dev
import pandas._testing as tm
def _check_accum_op(name, series, check_dtype=True):
f... | pd.Series([0, 1, np.nan, 1], dtype=object) | pandas.Series |
# coding: utf-8
# In[1]:
# Load dependencies
from scipy.stats import gmean
import pandas as pd
import numpy as np
import sys
sys.path.insert(0, '../../statistics_helper')
from CI_helper import *
from fraction_helper import *
pd.options.display.float_format = '{:,.1f}'.format
# # Estimating the biomass of soil mic... | pd.concat([xu_upper_CI,xu_lower_CI],axis=1) | pandas.concat |
import pandas as pd
from sodapy import Socrata
import datetime
import definitions
# global variables for main data:
hhs_data, test_data, nyt_data_us, nyt_data_state, max_hosp_date = [],[],[],[],[]
"""
get_data()
Fetches data from API, filters, cleans, and combines with provisional.
After running, global variables are... | pd.Timestamp(max_date) | pandas.Timestamp |
import numpy as np
import pandas as pd
import os
import trace_analysis
import sys
import scipy
import scipy.stats
def compute_kolmogorov_smirnov_2_samp(packets_node, window_size, experiment):
# Perform a Kolmogorov Smirnov Test on each node of the network
ks_2_samp = None
for node_id in packets_node:
... | pd.to_numeric(stats["packet_loss"], downcast='float') | pandas.to_numeric |
# Load dependencies
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from matplotlib import *
import matplotlib.pyplot as plt
from matplotlib.cm import register_cmap
from scipy import stats
from sklearn.decomposition import PCA
import seaborn
class Wrangle:
def __init__(self... | pd.Categorical(df["den"]) | pandas.Categorical |
# Copyright (c) 2018 Via Technology Ltd. All Rights Reserved.
# Consult your license regarding permissions and restrictions.
"""
Functions to find trajectory sector intersection data.
"""
import numpy as np
import pandas as pd
from via_sphere import global_Point3d
from .AirspaceVolume import AirspaceVolume
from .gis_d... | pd.DataFrame() | pandas.DataFrame |
"""
SARIMAX parameters class.
Author: <NAME>
License: BSD-3
"""
import numpy as np
import pandas as pd
from numpy.polynomial import Polynomial
from statsmodels.tsa.statespace.tools import is_invertible
from statsmodels.tsa.arima.tools import validate_basic
class SARIMAXParams(object):
"""
SARIMAX parameters... | pd.Series(self.params, index=self.param_names) | pandas.Series |
"""
Generates choropleth charts that are displayed in a web browser.
Takes data from simulation and displays a single language distribution across a
global map. Uses plotly's gapminder dataset as a base for world data.
For more information on choropleth charts see https://en.wikipedia.org/wiki/C... | pd.merge(gapminder, df_map, on="iso_alpha") | pandas.merge |
# pylint: disable=C0103,E0401
"""
Template for SNAP Dash apps.
"""
import copy, math, os
import dash
import luts
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import plotly.express as px
from dash.dependencies import Input, Output
from gui import layout, path_prefix
from plotly.subplots import ... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 10 00:05:49 2021
@author: <NAME>
"""
import requests
import json
import time
from datetime import date, timedelta
import itertools
from ftfy import fix_encoding
import unidecode
import pandas as pd
class admetricks_api:
"""
A class to generate requests to the... | pd.DataFrame.from_dict(data['data']) | pandas.DataFrame.from_dict |
import tkinter as tk
import os
import sys
import pandas as pd
import numpy as np
from PIL import Image, ImageTk
import matplotlib
matplotlib.use("TkAgg")
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import requests
from bs4 import B... | pd.Categorical(df_cat['category'], self.myscale['cat_order']) | pandas.Categorical |
# all domains
# merge/split common boundary x = max(3bin,0.1 TAD Length)
# region < agrs.remote
# less complex
# zoom
# to filter the strength first
import pandas as pd
import numpy as np
#from tqdm import tqdm
import argparse
import os
# import warnings
# warnings.filterwarnings('ignore')
# the arguments from command... | pd.concat([single,note_tad,note_cross],axis=0,ignore_index = True) | pandas.concat |
# pip install git+https://github.com/alberanid/imdbpy
# pip install imdbpy
from imdb import IMDb, IMDbDataAccessError
import pandas as pd
import time
import requests
from bs4 import BeautifulSoup
from tqdm import tqdm
import ast
from collections import defaultdict
import multiprocessing
dct_no_entries = defaultdict(int... | pd.read_csv('../data/generated/df_joined_partly.csv') | pandas.read_csv |
"""
Note, this contains both the older V1 processing code
as well as the V2 code. The V1 code isn't tested to
work for a full processing cycle, and may need
some adjustments.
"""
# pylint: disable=all
import pandas as pd
import dask.dataframe as dd
import os
from datetime import datetime
from luts import speed_rang... | pd.read_csv("WRF_hwe_perc.csv") | pandas.read_csv |
from sales_analysis.data_pipeline import BASEPATH
from sales_analysis.data_pipeline._pipeline import SalesPipeline
import pytest
import os
import pandas as pd
# --------------------------------------------------------------------------
# Fixtures
@pytest.fixture
def pipeline():
FILEPATH = os.path.join(BASEPATH, ... | pd.Timestamp('2019-08-14 00:00:00') | pandas.Timestamp |
# -*- coding: utf-8 -*-
################ imports ###################
import pandas as pd
import numpy as np
import itertools
# import matplotlib.pyplot as plt
# %matplotlib inline
import welly
from welly import Well
import lasio
import glob
from sklearn import neighbors
import pickle
import math
import dask
import d... | pd.concat([train_or_test_y, df_result], axis=1) | pandas.concat |
import os
import pandas as pd
from sta_core.handler.db_handler import DataBaseHandler
from sta_core.handler.shelve_handler import ShelveHandler
from sta_api.module.load_helper import global_dict
from sta_api.module.load_helper import tester
from sta_api.module.load_helper import db_exists
from flask import Blueprin... | pd.to_datetime(df["updated_at"], unit="ms") | pandas.to_datetime |
"""Eto SDK Fluent API for managing datasets"""
import os
import uuid
from itertools import islice
from typing import Optional, Union
import pandas as pd
from rikai.io import _normalize_uri
from rikai.parquet.dataset import Dataset as RikaiDataset
from eto.config import Config
from eto.fluent.client import get_api
fr... | pd.DataFrame(rows) | pandas.DataFrame |
"""
Outil de lecture des fichiers IPE
"""
import logging
import zipfile
from pathlib import Path
from typing import IO
from typing import List
from typing import Optional
from typing import Union
import pandas as pd
import tqdm
from .. import pathtools as pth
from .. import misc
logger = logging.getLogger(__name__)
... | pd.concat(dfs) | pandas.concat |
import MetaTrader5 as mt5
from datetime import datetime
import pandas as pd
import pytz
# display data on the MetaTrader 5 package
print("MetaTrader5 package author: ", mt5.__author__)
print("MetaTrader5 package version: ", mt5.__version__)
print("Connecting.....")
# establish MetaTrader 5 connection to... | pd.set_option('display.max_columns', 30) | pandas.set_option |
"""Analyze waterfloods with capacitance-resistance models. # noqa: D401,D400
Classes
-------
CRM : standard capacitance resistance modeling
CrmCompensated : including pressure
Methods
-------
q_primary : primary production
q_CRM_perpair : production due to injection (injector-producer pairs)
q_CRM_perproducer : produ... | pd.DataFrame(self.tau) | pandas.DataFrame |
import math
from collections import Iterable
from typing import List, Literal, Optional, Tuple, Union
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from lazy_object_proxy.utils import cached_property
from sklearn import metrics
from sklearn.cluster import KMeans
class StraightLine:
def _... | pd.concat(df_list) | pandas.concat |
# -*- coding: utf-8 -*-
import sys
import pandas
import numpy
import json
import os
sys.path.append('../')
from core_functions import remove_unannotated
from core_functions import construct_graph_from_mongo
from core_functions import get_mapping_from_mongo
import core_classes
if __name__ == '__main__':
main... | pandas.DataFrame(agg_ic_matrix, columns=terms, index=terms) | pandas.DataFrame |
import logging
import numpy as np
import pandas as pd
import pandas.testing as pdt
import pytest
import sentry_sdk
from solarforecastarbiter import utils
def _make_aggobs(obsid, ef=pd.Timestamp('20191001T1100Z'),
eu=None, oda=None):
return {
'observation_id': obsid,
'effective... | pd.MultiIndex.from_product([[0], ['a', 'b']]) | pandas.MultiIndex.from_product |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 29 13:13:47 2019
Implement a Naive Bayes Classifier
@author: liang257
"""
import pandas as pd
import numpy as np
'''read data'''
train_data = | pd.read_csv("trainingSet.csv") | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Sat Jan 13 22:45:00 2018
@author: benmo
"""
import pandas as pd, numpy as np, dask.dataframe as ddf
import quandl
import sys, os, socket
import pickle
from dask import delayed
from difflib import SequenceMatcher
from matplotlib.dates import bytespdate2num, num2date
from matplotl... | pd.read_csv("C:/users/benmo/desktop/fedReserve.csv") | pandas.read_csv |
from typing import Dict
from typing import Union
import numpy as np
import pandas as pd
import pytest
from etna.datasets import TSDataset
from etna.transforms import ResampleWithDistributionTransform
DistributionDict = Dict[str, pd.DataFrame]
@pytest.fixture
def daily_exog_ts() -> Dict[str, Union[TSDataset, Distri... | pd.concat([df1, df2], ignore_index=True) | pandas.concat |
import asyncio
from .integration_test_utils import setup_teardown_test, _generate_table_name, V3ioHeaders, V3ioError
from storey import build_flow, ReadCSV, WriteToCSV, Source, Reduce, Map, FlatMap, AsyncSource, WriteToParquet
import pandas as pd
import aiohttp
import pytest
import v3io
import uuid
@pytest.fixture()
... | pd.read_parquet(out_dir, columns=columns) | pandas.read_parquet |
# -*- coding: utf-8 -*-
"""
Created on Sun May 2 22:57:59 2021
@author: <NAME> -Spatial structure index value distribution of urban streetscape
"""
from multiprocessing import Pool
from polar_metrics_pool import polar_metrics_single
from tqdm import tqdm
import glob,os
import pandas as pd
#packages\pylandstats\lands... | pd.DataFrame(columns=columns) | pandas.DataFrame |
import pandas as pd
df1 = pd.read_csv("student1.csv")
df2 = pd.read_csv("student2.csv")
result = | pd.concat([df1, df2]) | pandas.concat |
import pandas as pd
import numpy as np
import os
import csv
data_path='/Users/paulsharp/Documents/Dissertation_studies/data/QC_Applied'
output_path='/Users/paulsharp/Documents/Dissertation_studies/data'
self_report_path='/Users/paulsharp/Documents/Dissertation_studies/data'
os.chdir(self_report_path)
self_report_dat... | pd.concat([self_report_data,mast_csv_diff_right],axis=1) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import glob
from shutil import copyfile
import hashlib
import json
import sys
import subprocess
import logging
from multiprocessing import Pool
import pdb
import time
import pickle
import numpy as np
import pandas as pd
import pydicom as dicom
import png
#pydi... | pd.DataFrame(headerlist) | pandas.DataFrame |
# %% Imports
import os
import sys
import pandas as pd
import numpy as np
# %% Setup paths
HomeDIR='Tentin-Quarantino'
wd=os.path.dirname(os.path.realpath(__file__))
DIR=wd[:wd.find(HomeDIR)+len(HomeDIR)]
os.chdir(DIR)
homedir = DIR
datadir = f"{homedir}/data/us/"
sys.path.append(os.getcwd())
# %% load mobility dat... | pd.to_datetime('2020 Jan 21') | pandas.to_datetime |
### This python script is used to perform the keyword search in several steps, allocate the remaining rows to the specified domains & perform a post-processing task based on manually selected similarity scores. ###
import pandas as pd
import os
import progressbar
from urllib.request import urlopen, Request
from bs4 im... | pd.DataFrame({'index': jaccard_score.index, 'jaccard': jaccard_score.values}) | pandas.DataFrame |
import pandas as pd
from Bio import SeqIO
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.cluster import MeanShift
from sklearn import preprocessing
import matplotlib.pyplot as plt
import... | pd.DataFrame([d]) | pandas.DataFrame |
##########################################################################
#
# Functions for calculating signals from share-prices and financial data.
#
##########################################################################
# SimFin - Simple financial data for Python.
# www.simfin.com - www.github.com/simfin/simfin... | pd.DataFrame(index=df_prices.index) | pandas.DataFrame |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.