prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import requests
import os
import pandas as pd
from datetime import datetime
import csv
def norm(qty, asset):
dps = 6 if (asset == "USDC" or asset == "USDT") else 18
tmp = qty.rjust(dps + 1, "0")
return (tmp[0:-dps] + "." + tmp[-dps:]).rstrip("0").rstrip(".")
def main(in_class="mined", out_class="remove fu... | pd.DataFrame(all_trx) | pandas.DataFrame |
from tqdm import tqdm
from functools import partial
import multiprocessing
import pandas as pd
import numpy as np
import argparse
import os
def step0_extract(filename):
df = pd.read_csv(filename, low_memory=False)
print('Total size:\t\t\t\t\t', len(df))
df = df[df['TransactionType'] == 'FI-InvoicedDocum... | pd.offsets.MonthEnd(0) | pandas.offsets.MonthEnd |
"""
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... | Series([], dtype="timedelta64[ns]") | pandas.Series |
import matplotlib.pyplot as plt
#import numpy
import pandas as pd
#This is the evaluation methods used for extracting the data for the test-cases.
#-Hopefully the names of the functions is self-explanatory
all_cost = []
last_costs = []
acceptance = []
temp = []
seconds = []
usages = []
requirements = []
RB_usages = ... | pd.DataFrame(usages) | pandas.DataFrame |
"""
Created on Mon Feb 22 15:52:51 2021
@author: <NAME>
"""
import pandas as pd
import numpy as np
import os
import pickle
import calendar
import time
import warnings
from pyproj import Transformer
import networkx as nx
import matplotlib as mpl
import matplotlib.pyplot as plt
from requests import get
import datafram... | pd.read_csv(f'./data/{year:d}{month:02d}-bergen.csv') | pandas.read_csv |
from datetime import datetime, timedelta
import inspect
import numpy as np
import pytest
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
Index,
IntervalIndex,
MultiIndex... | date_range("1/1/2011", periods=5, freq="D", tz=tz, name="idx") | pandas.date_range |
## python 101917081.py "C:\Users\hp\Desktop\Assignment-4\Input files for Assignment04\data.csv" "1,1,1,1,1" "+,-,+,-,+" "abcd-result.csv"
from argparse import ArgumentParser
from pathlib import Path
import pandas as pd
import sys
def main():
# total arguments
n = len(sys.argv)
if n<5 :
exit('Ente... | pd.to_numeric(s, errors='coerce') | pandas.to_numeric |
# Copyright WillianFuks
#
# 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 to in writing, softw... | pd.concat([pre_data.iloc[:, 0], post_data.iloc[:, 0]]) | pandas.concat |
# -*- coding: utf-8 -*-
import warnings
from datetime import datetime, timedelta
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.errors import PerformanceWarning
from pandas import (Timestamp, Timedelta, Series,
DatetimeIndex, TimedeltaIndex,
... | tm.assert_raises_regex(TypeError, msg) | pandas.util.testing.assert_raises_regex |
# ActivitySim
# See full license in LICENSE.txt.
import logging
import numpy as np
import pandas as pd
from activitysim.core import config
from activitysim.core import inject
from activitysim.core import pipeline
from activitysim.core import simulate
from activitysim.core import tracing
from activitysim.core import l... | pd.concat(sample_list) | pandas.concat |
import unittest
import numpy as np
import pandas as pd
from haychecker.chc.metrics import rule
class TestRule(unittest.TestCase):
def test_empty(self):
df = | pd.DataFrame() | pandas.DataFrame |
# Copyright 2014 Google Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agree... | pd.Timestamp('20180110') | pandas.Timestamp |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2021/7/8 22:08
Desc: 金十数据中心-经济指标-美国
https://datacenter.jin10.com/economic
"""
import json
import time
import pandas as pd
import demjson
import requests
from akshare.economic.cons import (
JS_USA_NON_FARM_URL,
JS_USA_UNEMPLOYMENT_RATE_URL,
JS_USA_EIA_... | pd.to_datetime(temp_se.iloc[:, 0]) | pandas.to_datetime |
import logging
import pandas as pd
import numpy as np
from binance.client import Client
_logger = logging.getLogger(__name__)
def get_metadata(sid_map):
client = Client("", "")
metadata = pd.DataFrame(
np.empty(
len(sid_map),
dtype=[
('symbol', 'str'),
... | pd.concat([cache[key], res]) | pandas.concat |
import time
import csv
import gensim
import nltk
import numpy as np
import pandas as pd
from datetime import datetime
from gensim.models import Word2Vec
from gensim.models.callbacks import CallbackAny2Vec
from nltk.sentiment.vader import SentimentIntensityAnalyzer
from progress.bar import Bar
from scipy impo... | pd.to_datetime(tmp_df["created"], unit='s') | pandas.to_datetime |
import spacy
import pandas as pd
from warnings import filterwarnings
from pathlib import Path
from os.path import isfile
filterwarnings('ignore')
nlp = spacy.load('en_core_web_md')
def form_similar_sequences(text: str, n: int) -> list:
tokens = nlp(text)
token_tuples = [(tokens[i].text, i) for i in range(len... | pd.concat([sarcasm_labels, clean_data], axis=1) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
# # Generate tessellation diagram
#
# Computational notebook 01 for **Morphological tessellation as a way of partitioning space: Improving consistency in urban morphology at the plot scale**.
#
#
# <NAME>., <NAME>., <NAME>. and <NAME>. (2020) _‘Morphological tessellation as a w... | pd.DataFrame() | pandas.DataFrame |
import os
from getpass import getpass
import pandas as pd
import numpy as np
import lib.galaxy_utilities as gu
from panoptes_client import Panoptes, Project, Subject
def find_duplicates():
Panoptes.connect(username='tingard', password=getpass())
gzb_project = Project.find(slug='tingard/galaxy-builder')
s... | pd.DataFrame(pairings, columns=('subject_id', 'dr7objid')) | pandas.DataFrame |
import glob
import tempfile
import pandas as pd
import pytest
from pandas.api.types import is_integer_dtype
from pandas.testing import assert_frame_equal, assert_series_equal
import grblogtools as glt
@pytest.fixture(scope="module")
def glass4_summary():
"""Summary data from API call."""
return glt.parse("d... | is_integer_dtype(seeds) | pandas.api.types.is_integer_dtype |
import csv as cs
import os
from pathlib import Path
from app import app
import pandas as pd
from app.routes import app
def fileread(filename):
Time_f = 's'
# Open csv file
File_name = str(filename)
file_to_open = os.path.join(app.config['USER_FOLDER'],File_name)
csvFile = open(file_to_open, "r")
... | pd.DataFrame(columns=Columns) | pandas.DataFrame |
import numpy as np
import pandas as pd
df = pd.DataFrame(
[
["A", "group_1", pd.Timestamp(2019, 1, 1, 9)],
["B", "group_1", pd.Timestamp(2019, 1, 2, 9)],
["C", "group_2", pd.Timestamp(2019, 1, 3, 9)],
["D", "group_1", pd.Timestamp(2019, 1, 6, 9)],
["E", "group_1", pd.Timest... | pd.Timestamp("2019-04-08 09:00:00") | pandas.Timestamp |
from os.path import join, realpath, dirname, exists, basename
from os import makedirs
import pandas as pd
from pandas import CategoricalDtype
from tqdm.auto import tqdm
from .coalitions import coalitions
def generate_number_of_tweets_per_day(df, output_dir):
# exclude retweets
df = df[df.user_rt.isnull()]
... | pd.to_datetime(df['date']) | pandas.to_datetime |
#------------------------------------------------------------------------------
# Libraries
#------------------------------------------------------------------------------
import pandas as pd
import numpy as np
from sklearn.base import is_regressor
from collections.abc import Sequence
#---------------------------------... | pd.api.types.is_numeric_dtype(Y.dtype) | pandas.api.types.is_numeric_dtype |
from functools import partial
import pandas as pd
# the following functions should be easy:
# a function that just gets all the component collections it requires.
# useful for pandas.
# a function that gets the component collections, filtered by the
# intersection of entity_id. Useful for Python, not useful for pand... | pd.DataFrame(components, index=entity_ids) | pandas.DataFrame |
"""Tests suite for Period handling.
Parts derived from scikits.timeseries code, original authors:
- <NAME> & <NAME>
- pierregm_at_uga_dot_edu - mattknow_ca_at_hotmail_dot_com
"""
from unittest import TestCase
from datetime import datetime, timedelta
from numpy.ma.testutils import assert_equal
from pandas.tseries.p... | Period(freq='D', year=2006, month=12, day=29) | pandas.tseries.period.Period |
"""
An improved version of your marketsim code that accepts
a "trades" data frame (instead of a file).
More info on the trades data frame below.
"""
import warnings
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
import pandas as pd
import numpy as np
import datetime as dt
from util import g... | pd.date_range(start_date, end_date) | pandas.date_range |
# Copyright (c) 2019-2020, NVIDIA CORPORATION.
"""
Test related to MultiIndex
"""
import re
import cupy as cp
import numpy as np
import pandas as pd
import pytest
import cudf
from cudf.core.column import as_column
from cudf.core.index import as_index
from cudf.tests.utils import assert_eq, assert_neq
def test_mult... | pd.MultiIndex(levels, codes) | pandas.MultiIndex |
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 30 02:13:48 2016
@author: Евгений
"""
import pandas as pd
from folders import ParsedCSV
from row_parser import get_colname_dtypes
file = ParsedCSV(2015).filepath()
chunksize = 100*1000
chunks = pd.read_csv(file, dtype=get_colname_dtypes(), chunksize=chunksize, iterator... | pd.DataFrame() | pandas.DataFrame |
"""Tests for irradiance quality control functions."""
from datetime import datetime
import pytz
import pandas as pd
import numpy as np
import pytest
from pandas.util.testing import assert_series_equal
from pvanalytics.quality import irradiance
@pytest.fixture
def irradiance_qcrad():
"""Synthetic irradiance data... | assert_series_equal(ghi_out, ghi_out_expected, check_names=False) | pandas.util.testing.assert_series_equal |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from hash import *
class simulation:
def __init__(self, length=12096, mu=0, sigma=0.001117728,
b_target=10, block_reward=12.5, hash_ubd=55,
hash_slope=3, hash_center=1.5, prev_data=pd.DataFrame(),
... | pd.to_datetime(prev_data['time']) | pandas.to_datetime |
import os
import json
from typing import Union
from scipy.spatial import distance
import numpy as np
import pandas as pd
import bottleneck
from .fileformat import WordVecSpaceFile
from .base import WordVecSpaceBase
np.set_printoptions(precision=4)
check_equal = np.testing.assert_array_almost_equal
# export data dire... | pd.Series(p) | pandas.Series |
import io
import os
import pandas as pd
import numpy as np
from datetime import date
from .io import ms_file_to_df
MINT_ROOT = os.path.dirname(__file__)
PEAKLIST_COLUMNS = ['peak_label', 'mz_mean', 'mz_width',
'rt_min', 'rt_max', 'intensity_threshold', 'peaklist']
def example_peaklist():
re... | pd.concat(peaklist) | pandas.concat |
import numpy as np
from numpy.testing import assert_equal, assert_, assert_raises
import pandas as pd
import pandas.util.testing as tm
import pytest
from statsmodels.base import data as sm_data
from statsmodels.formula import handle_formula_data
from statsmodels.regression.linear_model import OLS
from statsmodels.genm... | tm.assert_frame_equal(self.data.orig_endog, self.endog) | pandas.util.testing.assert_frame_equal |
import pickle
import pandas as pd
import numpy as np
from random import sample
from collections import Counter
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.ioff()
def sub_sampling(table, sampling_depth = 50):
'''
subsamping a table to get same sampling depth for all samples.
... | pd.to_datetime(ISM_df['date']) | pandas.to_datetime |
# Diffusion Maps Framework implementation as part of MSc Data Science Project of student
# <NAME> at University of Southampton, MSc Data Science course
# Script 9: Network example taken on 20/08/2016 from
# https://networkx.readthedocs.io/en/stable/examples/drawing/weighted_graph.html
import openpyxl
import numpy ... | pd.ExcelFile(dtsource) | pandas.ExcelFile |
from flask import Flask, request, render_template, Response
from flask import make_response, jsonify
import sys
import os
import requests
import json
import threading
import time
import pandas as pd
import tempfile
import datetime
from collections import defaultdict
import namegenerator
sys.path.append(os.path.absp... | pd.DataFrame(columns=['id', 'comment', 'score', 'sensitivity']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
@author:
<NAME>
Aarhus University
TECOLOGY.xyz
Production of collages of crops of flower and insect detections to be used on Zooniverse.
This script takes raw images and detection info (bounding box coordinates) for flowers and insects as input.
It crops detections and produces collages ... | pd.DataFrame(manifest, columns = ["ID", "!TL_Path", "!TL_Coordinates", "!TL_KnownInsect","!TR_Path","!TR_Coordinates", "!TR_KnownInsect","!BL_Path","!BL_Coordinates", "!BL_KnownInsect", "!BR_Path", "!BR_Coordinates", "!BR_KnownInsect", "Folder_Name"]) | pandas.DataFrame |
from datetime import datetime
import numpy as np
import pytest
import pytz
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from pandas import (
DataFrame,
DatetimeIndex,
Index,
IntervalIndex,
Series,
Timestamp,
cut,
date_... | cut(df.A, 5) | pandas.cut |
#!/usr/bin/env python
# coding: utf-8
import streamlit as st
import streamlit.components.v1 as components
import matplotlib.pyplot as plt
import kayak
from PIL import Image
import numpy as np
filename_airport = './assets/airports.csv'
filename_aircraft = './assets/aircraft.csv'
output = './assets/output.xlsx'
blan... | pd.read_csv(filename_airport) | pandas.read_csv |
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn import svm
from sklearn import preprocessing
from sklearn.metrics import accuracy_score, precision_score, recall_score, roc_curve, auc
import random
import warnings
warnings.filterwarnings("ignore")
def main():
tr_x, tr_y, ts_... | pd.concat([data[906:1813], data[1813:3207]]) | pandas.concat |
import ast
import csv
import sys, os
from pandas import DataFrame, to_datetime
from PyQt5 import uic
from PyQt5.QtChart import QChartView, QValueAxis, QBarCategoryAxis, QBarSet, QBarSeries, QChart
from PyQt5.QtCore import QFile, QTextStream, Qt
from PyQt5.QtGui import QPainter
from PyQt5.QtWidgets import QApplication,... | DataFrame(export_data, columns=export_dataframe) | pandas.DataFrame |
from copy import deepcopy
import os
import pandas as pd
from pandas.util.testing import assert_frame_equal
import pytest
import cdpybio as cpb
FL = [os.path.join(cpb._root, 'tests', 'express', 'results.{}.xprs'.format(x))
for x in ['a','b','c']]
TG = os.path.join(cpb._root, 'tests', 'express', 'tg.tsv')
T... | assert_frame_equal(df, df2) | pandas.util.testing.assert_frame_equal |
from pandas_datareader import data as web
import pandas as pd
import datetime as dt
import numpy as np
import requests
http_header = {
"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36",
"X-Requested-With": "XMLHttpRequest"
}
cl... | pd.isna(dtframe.loc[date][field]) | pandas.isna |
# -*- coding: utf-8 -*-
"""
@author: mje
@emai: <EMAIL>
"""
import numpy as np
import mne
import matplotlib.pyplot as plt
import pandas as pd
import itertools
from my_settings import (tf_folder, subjects_test, subjects_ctl, subjects_dir)
plt.style.use("ggplot")
b_df = pd.read_csv("/Volumes/My_Passport/agency_connec... | pd.DataFrame() | pandas.DataFrame |
import datetime
import numpy as np
import pandas as pd
import pytest
from sklearn.exceptions import NotFittedError
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
from testfixtures import LogCapture
import greykite.common.constants as cst
from greykite.algo.forecast.silv... | pd.date_range("2018-01-01", periods=periods, freq="D") | pandas.date_range |
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 28 09:27:49 2020
@author: <NAME>
"""
import pickle
import pandas as pd
import numpy as np
from country import country
from scipy.integrate import solve_ivp
from scipy.optimize import minimize
from scipy.optimize import dual_annealing
from scipy.optimize i... | pd.DataFrame.from_dict(data, orient='index', columns=data_col) | pandas.DataFrame.from_dict |
# CPTAC Images Join
import pandas as pd
import numpy as np
imglist = | pd.read_csv('../CPTAC-LUAD-HEslide-filename-mapping_Jan2019.csv', header=0) | pandas.read_csv |
import streamlit as st
import pandas as pd
import numpy as np
import sklearn.neighbors
import pydeck as pdk
import seaborn as sns
from util import config
from util import mapping
from util import trip_data
@st.cache(suppress_st_warning=True)
def load_data():
st.write('Loading data...')
trips = pd.read_feath... | pd.read_feather(config.MODEL_PATH + 'grid_points_500.feather') | pandas.read_feather |
"""
Utilities
"""
from microbepy.common import combination_iterator
from microbepy.common import config
from microbepy.common import constants as cn
from microbepy.common.equivalence_class import EquivalenceClass
from microbepy.common import schema
import collections
import math
import matplotlib.cm as cm
import nump... | pd.DataFrame(values) | pandas.DataFrame |
import matplotlib
#matplotlib.use('agg')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import cmocean
#####
# The following section is a little brittle due to hardcoded names, but we'll fix
# that later. Code copy pasted from jupyter notebook.
#####
def all_plots(df):
'''Create and outpu... | pd.DataFrame() | pandas.DataFrame |
"""
Obtains category distributions for included and excluded patients.
"""
from click import *
from logging import *
import pandas as pd
@command()
@option("--all-input", required=True, help="the CSV file to read all diagnoses from")
@option(
"--included-input",
required=True,
help="the CSV file to read... | pd.read_csv(all_input, index_col="subject_id") | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Mon May 21 21:08:09 2018
@author: <NAME>
"""
# System Utilities
import os
import io
import sys
import gc
import traceback
# Email and Text processing
import email
from email.header import decode_header
import re
import uuid # unique ID
# Data handling and analytics tools
impo... | pd.DataFrame() | pandas.DataFrame |
"""
Author: <NAME>
Created: 27/08/2020 11:13 AM
"""
import pandas as pd
import os
import numpy as np
from supporting_functions.conversions import convert_RH_vpa
from supporting_functions.woodward_2020_params import get_woodward_mean_full_params
test_dir = os.path.join(os.path.dirname(__file__), 'test_data')
def e... | pd.merge(matrix_weather, pet, how='outer', left_index=True, right_index=True) | pandas.merge |
"""
The training function used in the finetuning task.
"""
import csv
import logging
import os
import pickle
import time
from argparse import Namespace
from logging import Logger
from typing import List
import numpy as np
import pandas as pd
import torch
from torch.optim.lr_scheduler import ExponentialLR
from torch.ut... | pd.DataFrame(data, index=test_smiles, columns=ind) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import demjson
import logging
import pandas as pd
import requests
from zvt.api.common import generate_kdata_id
from zvt.recorders.consts import EASTMONEY_ETF_NET_VALUE_HEADER
from zvt.api.technical import get_kdata
from zvt.domain import Index, Provider, SecurityType, StoreCategory, TradingLev... | pd.concat([df, response_df]) | pandas.concat |
import warnings
warnings.simplefilter('ignore')
import numpy as np
import pandas as pd
import multiprocessing as mp
import pandas.io.data as web
import matplotlib.pyplot as plt
def compTrade(dt):
d=0.001
dt['reg']=np.where(dt['dmacd']>d,1,0)
dt['reg']=np.where(dt['dmacd']<-d,-1,dt['reg'])
dt['strat... | pd.DataFrame() | pandas.DataFrame |
"""
"""
__author__ = ""
__version__ = ""
import os
import json
from uuid import UUID
from flask import g
import pandas as pd
# Function to save the recieved JSON file to disk
def jsonDump(name, struct, dir = os.getcwd() + "\\"):
print('JSON dump')
# Open a file for writing, filename will always be unique so ap... | pd.pivot_table(df, values=values, index=index, columns=columns, aggfunc=aggFunc) | pandas.pivot_table |
import pandas as pd
import numpy as np
import re
## Different than what was expected, creating a unique for for every DF column
## performed a slower execution than having different fors for each DF column
def cleanInvalidDFEntries(id_key,stamp,actor_column,verb_column,object_column):
day = [] # Check which day ... | pd.DataFrame(data={'id':id_key,'timestamp':stamp,'weekday':day,'dayshift':day_shift,'actor':actor,'verb':action,'object':object_aim,'language':lang}) | pandas.DataFrame |
from openff.toolkit.typing.engines.smirnoff import ForceField
from openff.toolkit.topology import Molecule, Topology
from biopandas.pdb import PandasPdb
import matplotlib.pyplot as plt
from operator import itemgetter
from mendeleev import element
from simtk.openmm import app
from scipy import optimize
import subprocess... | pd.concat([df_1, df_2, df_3], axis=1) | pandas.concat |
import unittest
import numpy as np
import pandas as pd
from numpy import testing as nptest
from operational_analysis.toolkits import power_curve
from operational_analysis.toolkits.power_curve.parametric_forms import *
noise = 0.1
class TestPowerCurveFunctions(unittest.TestCase):
def setUp(self):
np.rand... | pd.Series([1., 2., 3.]) | pandas.Series |
from argparse import ArgumentParser
from pathlib import Path
from typing import Tuple
import pandas as pd
from sklearn.model_selection import train_test_split
def split_metadata(original_metadata: pd.DataFrame, train_fraction: float, random_state: int) -> Tuple[pd.DataFrame, pd.DataFrame]:
image_ids = | pd.unique(original_metadata['image_id']) | pandas.unique |
from warnings import catch_warnings, simplefilter
import numpy as np
from numpy.random import randn
import pytest
import pandas as pd
from pandas import (
DataFrame, MultiIndex, Series, Timestamp, date_range, isna, notna)
from pandas.util import testing as tm
@pytest.mark.filterwarnings("ignore:\\n.ix:Deprecati... | Timestamp('2015-01-01') | pandas.Timestamp |
import os
import glob
import numpy as np
import pandas as pd
from keras import backend as K
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers.advanced_activations import PReLU
from keras.layers.core import Dense, Dropout
from keras.layers.normalization import BatchNormalization
from keras.mo... | pd.read_csv(TRAIN_FILE_PATH) | pandas.read_csv |
# -*- coding: utf-8 -*-
import os
import logging
import tempfile
import uuid
import shutil
import numpy as np
import pandas as pd
from rastertodataframe import util, tiling
log = logging.getLogger(__name__)
def raster_to_dataframe(raster_path, vector_path=None):
"""Convert a raster to a Pandas DataFrame.
... | pd.Series(mask_values) | pandas.Series |
# heartparser.py
# Author: <NAME>
#
# This script parses the Apple Health export xml file for Heart Rate and
# Blood Pressure data and produces graphs of the data for given date ranges.
import numpy as np
from datetime import date, datetime, timedelta as td
from matplotlib import pyplot, dates as mdates
from mat... | DataFrame(list_weekhr) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from __future__ import print_function
from numpy import nan
import numpy as np
from pandas import compat
from pandas import (DataFrame, Series, MultiIndex, Timestamp,
date_range)
import pandas.util.testing as tm
from pandas.tests.frame.common import TestData
class Test... | compat.iteritems(test_data) | pandas.compat.iteritems |
from scipy.spatial.distance import cosine
from scipy.stats import pearsonr
import numpy as np
import pandas as pd
def metric_for_similarity_f(b, metric="euclidean"):
if metric == "euclidean":
return lambda a:np.sqrt(np.sum((a-b)**2))#L2
elif metric == "cosine":
return lambda a:cosine(a,b)#cosin... | pd.DataFrame(distances, columns=["id","dist"]) | pandas.DataFrame |
# The test is referenced from https://hdbscan.readthedocs.io/en/latest/performance_and_scalability.html
import time
import hdbscan
import warnings
import sklearn.cluster
import scipy.cluster
import sklearn.datasets
import numpy as np
import pandas as pd
import seaborn as sns
from numpy.linalg import norm
from classix.a... | pd.read_csv("results/exp1/gs_hdbscan_ar.csv") | pandas.read_csv |
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib
import warnings
import sklearn
#import gensim
import scipy
import numpy
import json
import nltk
from nltk.stem import PorterStemmer
from nltk.tokenize import sent_tokenize, word_tokenize
import sys
import csv
... | pd.concat([ids,results], axis=1) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
# #### functions in this script are created searching for keywords related measures
import pandas as pd
from nltk import tokenize
from iteration_utilities import deepflatten
import re
import statistics as stat
def hasNumbers(inputString):
'''check if string has numbers... | pd.Series([x['defect'][1] for x in measures]) | 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([], columns=['movieId']) | pandas.DataFrame |
# Jun. 28th, 2020
# Score: 0.14508
import torch
from torch import nn
from torch.nn import init
import torch.utils.data as Data
import pandas as pd
from modules import base
def get_net(feature_num):
hidden_num = 32, 8
drop_prob = .001
net = nn.Sequential(
nn.Linear(feature_num, hidden_num[0]),
... | pd.get_dummies(all_features, dummy_na=True) | pandas.get_dummies |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 26 15:39:02 2018
@author: joyce
"""
import pandas as pd
import numpy as np
from numpy.matlib import repmat
from stats import get_stockdata_from_sql,get_tradedate,Corr,Delta,Rank,Cross_max,\
Cross_min,Delay,Sum,Mean,STD,TsRank,TsMax,TsMin,DecayLinea... | pd.concat([Open,close,low,high,close_delay,open_delay,low_delay], axis =1 ,join = 'inner') | pandas.concat |
import os
import pickle
from functools import reduce
from tqdm import tqdm
from datetime import datetime
from dateutil.relativedelta import relativedelta
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn import metr... | pd.concat([y_predict, y_true], axis=1) | pandas.concat |
# -*- 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.compat import long
from pandas.core import ops
from pan... | tm.box_expected(td1, box) | pandas.util.testing.box_expected |
"""
Original code based on Kaggle competition
Modified to take 3-channel input
"""
from __future__ import division
import numpy as np
from keras.models import Model
from keras.layers import Input, merge, Convolution2D, MaxPooling2D, UpSampling2D, Cropping2D
from keras import backend as K
import keras
... | pd.DataFrame(history.history) | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib
import os
from glob import glob
import sys
import gc
from scipy.optimize import curve_fit
from astropy.table import Table
import astropy.io.fits as fits
from astropy.timeseries import LombScargle, BoxLe... | pd.Series(files_i) | pandas.Series |
# -*- coding: utf-8 -*-
# author: <NAME>
# Email: <EMAIL>
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import generators
from __future__ import with_statement
import re
from bs4 import BeautifulSoup... | pd.read_excel(filename, sheet_name="统计", index_col=0) | pandas.read_excel |
import fileinput
import pandas as pd
import numpy as np
import drep
import os
import shutil
import json
import re
from PyPDF2 import PdfFileReader
from .dprint import dprint
from .config import _globals
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 100... | pd.concat(df_stats_l) | pandas.concat |
# column deletion using del operator and pop method of pandas dataframe
import pandas as pd
import numpy as np
d={'one': | pd.Series([1,2,3],index=['a','b','c']) | pandas.Series |
import numpy as np
import matplotlib.pyplot as plt
from glob import glob
from skimage.io import imread, imsave
from skimage.transform import resize
import pandas as pd
import os
from tqdm import tqdm
root_dir = "./birds"
save_dir = "./birds_preprocessed/"
IMG_SIZE = 64
# utility functions -> from STACK... | pd.merge(df, df_bbox, on="id") | pandas.merge |
import hashlib
import pandas as pd
from pandas import DataFrame
from pathlib import Path
from struct import calcsize
from struct import unpack
from tqdm import tqdm
from jotdx.consts import MARKET_BJ
from jotdx.consts import MARKET_SH
from jotdx.consts import MARKET_SZ
from jotdx.logger import logger
def get_stock_m... | pd.DataFrame(data=[v]) | pandas.DataFrame |
import numpy
import matplotlib.pyplot as plt
import pandas
from pandas import DataFrame
import math
import yfinance as yf
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
from sklearn.preprocessing import MinMaxScaler
from sklea... | DataFrame(tickerdata) | pandas.DataFrame |
import pandas as pd
import scipy
import numpy as np
import seaborn as sns
import matplotlib as mpl
#from sinaplot import sinaplot
import scanpy as sc
from matplotlib import pyplot as plt
import scanpy.external as sce
import os
import scipy.spatial.distance
cwd = os.getcwd()
print(cwd)
def getLineagesFromChangeo(chang... | pd.melt(point_plot_df, value_vars = point_plot_df.columns[1:], id_vars = point_plot_df.columns[0]) | pandas.melt |
import PyPDF2
import csv
from pathlib import Path
import io
import pandas
import numpy
from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
from pdfminer.converter import TextConverter
from pdfminer.layout import LAParams
from pdfminer.pdfpage import PDFPage
# def Cpk(usl, lsl, avg, sigma , c... | pandas.set_option('display.expand_frame_repr', False) | pandas.set_option |
import numpy as np
from datetime import timedelta
from distutils.version import LooseVersion
import pandas as pd
import pandas.util.testing as tm
from pandas import to_timedelta
from pandas.util.testing import assert_series_equal, assert_frame_equal
from pandas import (Series, Timedelta, DataFrame, Timestamp, Timedelt... | DataFrame(['00:00:02']) | pandas.DataFrame |
from __future__ import print_function
import zeep
import numpy as np
import pandas as pd
import warnings
_INFO = """PyIress documentation (GitHub):
https://github.com/ceaza/pyiress"""
WSDL_URL_GENERIC='http://127.0.0.1:51234/wsdl.aspx?un={username}&cp={companyname}&svc={service}&svr=&pw={password}'
class PyIressExc... | pd.DataFrame() | pandas.DataFrame |
from datetime import datetime, timedelta
import warnings
import operator
from textwrap import dedent
import numpy as np
from pandas._libs import (lib, index as libindex, tslib as libts,
algos as libalgos, join as libjoin,
Timedelta)
from pandas._libs.lib import is_da... | is_bool(other) | pandas.core.dtypes.common.is_bool |
# -*- coding: utf-8 -*-
"""
@author: <NAME>
"""
import numpy as np
import pandas as pd
def func_WVF(close, low, Lookback=22):
WVF = np.zeros((len(close),1))
for i in range(Lookback, len(close)):
highest_close_temp = close[i-22:i].max()
WVF[i] = (1 - low.values[i] / highest_close_temp) * 1... | pd.DataFrame(WVF, index=close.index) | pandas.DataFrame |
import pandas as pd
import numpy as np
from matplotlib import colors, cm, text, pyplot as plt
import matplotlib.patches as patches
import os
import time
from cmcrameri import cm
from PIL import Image, ImageFont, ImageDraw, ImageEnhance
from cmcrameri import cm
import sqlite3
import glob
import tempfile
import zipfile
i... | pd.merge(pixel_intensity_df, colours_df, how='left', left_on=['intensity'], right_on=['intensity']) | pandas.merge |
# Given an ensemble of models, evolve a random sequence to fulfill an objective.
# cf. https://blog.keras.io/how-convolutional-neural-networks-see-the-world.html
import keras
from keras import backend as K
from functools import partial
import sys
import os
import pandas
import numpy as np
import random
from numpy.ran... | pandas.DataFrame(ans) | pandas.DataFrame |
import json
import csv
import glob
import pandas as pd
from pandas.core.series import Series
from pandas.core.frame import DataFrame
import matplotlib.pyplot as plt
from matplotlib import cm
# 日本語フォント設定
from matplotlib import rc
jp_font = "Yu Gothic"
rc('font', family=jp_font)
def delete_duplicaion_index(input_list... | pd.concat(temp_list, ignore_index=False) | pandas.concat |
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import re
import seaborn as sns; sns.set()
import warnings; warnings.filterwarnings("ignore", category=UserWarning, module="matplotlib")
from matplotlib.colors import ListedColormap
from matplotlib.ticker import MaxNLocator
from pylab imp... | pd.Series(kw_occ_traj_df.stripped_kw_occ_matrix.values,
index=kw_occ_traj_df.user_id) | pandas.Series |
from sklearn.metrics import roc_auc_score, accuracy_score, r2_score, mean_squared_error, mean_absolute_error
from amlpp.conveyor import Conveyor
from datetime import datetime
from typing import List
import pandas as pd
import pickle
import os
###########################################################################... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 01 10:00:58 2021
@author: <NAME>
"""
#------------------------------------------------------------------#
# # # # # Imports # # # # #
#------------------------------------------------------------------#
from math import e
import numpy as np
import... | pd.DataFrame(data={'ismatched': ismatched, 'idx': idx, 'd2d': d2d}) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
import os
import datetime
import time
import random
import pandas as pd
import numpy as np
import re
import torch
from torch.utils.data import Dataset, DataLoader, RandomSampler, SequentialSampler
from tqdm import tqdm
from transformers import (
AdamW,
GPT2LMHeadModel,
GPT2... | pd.read_csv('Datasets/dart_dev.csv') | pandas.read_csv |
#!/usr/bin/env python3
"""
Create pandas dataframe from downloaded csv files and categorize sectors
"""
import csv
import pandas as pd
import sys
import json
import pdb
def main():
frl = []
for i in range(2, len(sys.argv)-1):
df = pd.read_csv(sys.argv[i], encoding = "ISO-8859-1")
frl.append(d... | pd.concat(frl, sort=False) | pandas.concat |
from redisclustergrid import StrictRedisCluster
import pandas as pd
host = "10.11.153.125"
startup_nodes = [
{"host": host, "port": "7000"},
{"host": host, "port": "7001"},
{"host": host, "port": "7002"},
{"host": host, "port": "7003"},
{"host": host, "port": "7004"},
{"host": host, "port": "70... | pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) | pandas.DataFrame |
# -----------------------------------------------------------
# <NAME>
# -----------------------------------------------------------
import streamlit as st
import pandas as pd
import numpy as np
from sodapy import Socrata
import pydeck as pdk
import plotly.express as px
import requests
# from IPytho... | pd.read_html(html) | pandas.read_html |
from datetime import datetime
from functools import reduce
import logging
from multiprocessing import cpu_count
from multiprocessing import Pool
import os
from bs4 import BeautifulSoup
import urllib
import pandas as pd
import numpy as np
NCORES = cpu_count()
logging.basicConfig(format='%(asctime)s - %(message)s', lev... | pd.concat([x, y], sort=True) | pandas.concat |
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