prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 8 22:58:48 2021
@author: laura.gf
"""
import requests
from requests.exceptions import HTTPError
import time
from dateutil.relativedelta import relativedelta
from datetime import datetime
import pandas as pd
import sys
def query_entry_pt(url):
... | pd.json_normalize(json_resp,record_path=record_path_field) | pandas.json_normalize |
from datetime import datetime, time, timedelta
from pandas.compat import range
import sys
import os
import nose
import numpy as np
from pandas import Index, DatetimeIndex, Timestamp, Series, date_range, period_range
import pandas.tseries.frequencies as frequencies
from pandas.tseries.tools import to_datetime
impor... | frequencies.get_freq_code((1000, 1)) | pandas.tseries.frequencies.get_freq_code |
import pandas as pd
import numpy as np
from itertools import chain
import requests
def read_cnv(inputfile):
"""Function to read CNV input file.
input: _somatic_cnv.tsv
output: dataframe"""
def convert_to_int(row):
if row['chr'].lower() in ["x", "y"]:
return row["chr"]
elif... | pd.DataFrame(reshaped_data) | pandas.DataFrame |
import pandas as pd
import numpy as np
from xgboost import XGBRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn import metrics
import os
import sys
import itertools
from pathlib import Path
import... | pd.DataFrame(training_overhead) | pandas.DataFrame |
## Packages.
import pandas, os, tqdm
## Group of table of data.
group = []
for mode in ['train', 'test']:
if(mode=='train'):
## Load table.
table = pandas.read_csv("../DATA/BMSMT/TRAIN/CSV/LABEL.csv")
table['mode'] = 'train'
## Information.
fol... | pandas.read_csv("../DATA/BMSMT/TEST/CSV/LABEL.csv") | pandas.read_csv |
import types
from functools import wraps
import numpy as np
import datetime
import collections
from pandas.compat import(
zip, builtins, range, long, lzip,
OrderedDict, callable
)
from pandas import compat
from pandas.core.base import PandasObject
from pandas.core.categorical import Categorical
from pandas.co... | _possibly_downcast_to_dtype(result, dtype) | pandas.core.common._possibly_downcast_to_dtype |
"""
Todo:
* Implement correct cfgstrs based on algorithm input
for cached computations.
* Go pandas all the way
Issues:
* errors when there is a word without any database vectors.
currently a weight of zero is hacked in
"""
from __future__ import absolute_import, division, print_function
import ib... | pd.DataFrame(kpts_list, index=aid_series, columns=['kpts']) | pandas.DataFrame |
import pandas as pd
import pickle
from sklearn.cluster import KMeans
import math
latlong_df = pd.read_csv("./latlongfinal.csv")
features = latlong_df.iloc[:, [2,3]]
new_features = | pd.DataFrame() | pandas.DataFrame |
#
# Copyright (C) 2019 Databricks, 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 applicable law or agreed to i... | pd.Series([1, 2, 3, 4, 3, 4, 3, 4]) | pandas.Series |
import os
import dash
from dash.dependencies import Input, Output, State
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
import plotly.graph_objects as go
from plotly import express as px
import pandas as pd
from layout import layout_1, layout_2, navbar, ... | pd.to_datetime(date) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 13 17:52:00 2021
@author: SimenLab
"""
import pandas as pd
def Data_getter(file_location):
"""A function which gets and prepares data from CSV files, as well as
returning some additional params like an number of ROIs and their
corresponding labels.
Pa... | pd.DataFrame.to_numpy(averages_dataframe) | pandas.DataFrame.to_numpy |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 15 22:15:22 2018
@author: tknapen https://github.com/tknapen/hedfpy/blob/master/hedfpy/EyeSignalOperator.py
- Wrapper sacc detection
- sacc detection algorithm
- interpolate gaze function (for pl)
"""
import numpy as np
from scipy.interpolate im... | pd.DataFrame(vel_data) | pandas.DataFrame |
from wf_core_data_dashboard import core
import wf_core_data
import mefs_utils
import pandas as pd
import inflection
import urllib.parse
import os
def generate_mefs_table_data(
test_events_path,
student_info_path,
student_assignments_path
):
test_events = pd.read_pickle(test_events_path)
student_in... | pd.read_pickle(student_assignments_path) | pandas.read_pickle |
# To add a new cell, type '#%%'
# To add a new markdown cell, type '#%% [markdown]'
#%%
from IPython import get_ipython
#%%
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import io
import base64
from matplotlib import animation
from matplotlib import cm
from matplotlib.pyplot import... | pd.read_csv('train.csv') | pandas.read_csv |
# 爬取智联 武汉地区 所有.net 相关的 职位信息
import requests
from bs4 import BeautifulSoup
import pandas
pos=[]
headers={
'Accept':'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8',
'Accept-Encoding':'gzip, deflate',
'Accept-Language':'zh-CN,zh;q=0.9,en;q=0.8',
'Connection':'keep-... | pandas.DataFrame(pos) | pandas.DataFrame |
import pandas as pd
import pdb
import openpyxl
import html
import sys
import datetime
def main():
data = pd.read_excel('cancer_genes_with_pcgp_210921.xlsx',sheet_name='final',index_col=0)
# check args
try:
if sys.argv[1] == 'pcgp':
filterField = 'PCGP category (PMID 26580448)'
... | pd.ExcelWriter(directory + '/' + filename + '.xlsx') | pandas.ExcelWriter |
"""Tests for Table Schema integration."""
import json
from collections import OrderedDict
import numpy as np
import pandas as pd
import pytest
from pandas import DataFrame
from pandas.core.dtypes.dtypes import (
PeriodDtype, CategoricalDtype, DatetimeTZDtype)
from pandas.io.json.table_schema import (
as_json_... | pd.to_datetime(data, utc=True) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 7 11:48:59 2020
@author: mazal
"""
"""
=========================================
Support functions of pydicom (Not sourced)
=========================================
Purpose: Create support functions for the pydicom project
"""
"""
Test mode 1 | Basics... | pd.read_csv(path_ProductType+filename) | pandas.read_csv |
# -*- coding: utf-8 -*-
from datetime import timedelta, time
import numpy as np
from pandas import (DatetimeIndex, Float64Index, Index, Int64Index,
NaT, Period, PeriodIndex, Series, Timedelta,
TimedeltaIndex, date_range, period_range,
timedelta_range, notnu... | Index(rng.asi8) | pandas.Index |
import streamlit as st
import pandas as pd
import numpy as np
import datetime
import plotly.express as px
import base64
def app():
LOGO_IMAGE_IBM = "apps/ibm.png"
LOGO_IMAGE_U_OF_F = "apps/u_of_f.svg.png"
LOGO_IMAGE_BRIGHTER = "apps/brighter_potential_logo.png"
st.markdown(
"""
<style>... | pd.to_datetime(area_stats['date_time']) | pandas.to_datetime |
# encoding=utf-8
""" gs_data centralizes all data import functions such as reading csv's
"""
import pandas as pd
import datetime as dt
from gs_datadict import *
def do_regions(ctydf: pd.DataFrame, mergef: str):
"""
do_regions assigns FIPS missing for multi-county regions, primarily occuring in UT where covid
data ... | pd.notnull(excl.iat[x, 5]) | pandas.notnull |
import collections
import logging
import multiprocessing
import os
import re
import warnings
import numpy as np
import pandas as pd
import tables
from trafficgraphnn.utils import (E1IterParseWrapper, E2IterParseWrapper,
TLSSwitchIterParseWrapper, _col_dtype_key,
... | pd.DataFrame(data) | pandas.DataFrame |
from unittest import TestCase
import pandas as pd
from pandas.api.types import is_numeric_dtype
import numpy as np
from scripts.utils import di
class TestDi(TestCase):
"""Test di() in utils.py"""
def test_di_nan_row(self):
"""Tests that correct distance is computed if NaNs occur in a row of a colum... | pd.Series({2: 0, 3: 0.05 * 0.05, 4: 0.1*0.1, 5: 0.0}) | pandas.Series |
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import config_local
import logging
_logger = logging.getLogger(__name__)
class PrepareData:
def __init__(self):
path_str = './data/' + config_local.data_clean['data_file_name'] + '.csv'
self.ds = pd.read_csv(path_s... | pd.to_datetime(self.ds['incident_date']) | pandas.to_datetime |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
#-------------read csv---------------------
df_2010_2011 = pd.read_csv("/mnt/nadavrap-students/STS/data/data_Shapira_20200911_2010_2011.csv")
df_2012_2013 = pd.read_csv("/mnt/nadavrap-students/STS/data/data_Shapira_20200911_2012_2013.csv")
df_2014... | pd.merge(df5, df2016, on='siteid', how='outer') | pandas.merge |
# coding: utf-8
# In[ ]:
from __future__ import division
import numpy as np
import pandas as pd
from sklearn.model_selection import StratifiedKFold
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.preprocessing import OneHotEncoder,LabelEncoder
from sklearn.model_selection import train_test_... | pd.concat([df_majority_downsampled, df_minority]) | pandas.concat |
from numbers import Number
from collections import Iterable
import re
import pandas as pd
from pandas.io.stata import StataReader
import numpy as np
pd.set_option('expand_frame_repr', False)
class hhkit(object):
def __init__(self, *args, **kwargs):
# if input data frame is specified as a stata data file or text ... | pd.crosstab(df[columns[0]], df[columns[1]], dropna=dropna) | pandas.crosstab |
'''
ASTGCN
'''
import sys
import math
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import pandas as pd
from Param import *
from Param_ASTGCN import *
from scipy.sparse.linalg import eigs
from torchsummary import su... | pd.merge(result1,sensor_ids2,on='to') | pandas.merge |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 14 14:45:58 2018
@author: yasir
"""
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
from sklearn.compose import make_column_transformer
from sklea... | pd.read_csv("Churn_Modelling.csv") | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
@author: dani
stable version as per 13 March, 2019
# Once per computer, Before the first run, install heteromotility by copying the following line into the console (and hit enter).
! pip install heteromotility
# Minor error is the vertical position of the text in the boxplots, which is c... | pd.read_csv(HM_outdir + HM_data) | pandas.read_csv |
import pandas as pd
import os
import dcase_util
import random
import tempfile
WORKSPACE = "/home/ccyoung/Downloads/dcase2018_task1-master"
def generate_new_meta_csv():
meta_csv_path = os.path.join(WORKSPACE, 'appendixes', 'meta.csv')
df = pd.read_csv(meta_csv_path, sep='\t')
data = df.groupby(['scene_la... | pd.concat([new_df, v]) | pandas.concat |
import json
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import cross_val_score,train_test_split
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.naive_bayes i... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 21 10:24:15 2016
@author: <NAME>
In this file, you will find the main filters
and basics function for time series operations
"""
# Filters
# Every filter has to produce a filtered time serie of prices from a sequence of price relatives
"""
Documentation for all the fol... | pd.DataFrame() | pandas.DataFrame |
import re
import tempfile
from dataclasses import fields
from urllib.error import HTTPError
import click
import pandas as pd
from loguru import logger
from ..etl import collections
from ..etl.core import get_etl_sources
from .scrape import downloaded_pdf, extract_pdf_urls, get_driver
from .utils import RichClickComma... | pd.to_datetime(dt) | pandas.to_datetime |
# # # # # # # # # # # # # # # # # # # # # # # #
# #
# Module to run real time contingencies #
# By: <NAME> and <NAME> #
# 09-08-2018 #
# Version Aplha-0. 1 #
# ... | pd.concat([net.res_sgen.Type, net.res_storage.Type,net.res_ext_grid.Type], ignore_index=True) | pandas.concat |
from datetime import datetime, timedelta
import time
import pandas as pd
from email.mime.text import MIMEText
from smtplib import SMTP
import pytz
from utility import run_function_till_success
| pd.set_option('expand_frame_repr', False) | pandas.set_option |
# Importing necessary packages
import pandas as pd
import numpy as np
import datetime
import geocoder
from geopy.geocoders import Nominatim
from darksky.api import DarkSky, DarkSkyAsync
from darksky.types import languages, units, weather
# Reading monthly yellow taxi trip data for 2019
df1 = pd.read_csv("yellow_tripda... | pd.concat([df1, df2, df3, df4, df5, df6, df7, df8, df9, df10, df11, df12]) | pandas.concat |
import numpy as np
import pandas as pd
import os.path
import random
import collections
from bisect import bisect_right
from bisect import bisect_left
from .. import multimatch_gaze as mp
dtype = [
("onset", "<f8"),
("duration", "<f8"),
("label", "<U10"),
("start_x", "<f8"),
("start_y", "<f8"),
... | pd.DataFrame(data) | pandas.DataFrame |
import os
import sys
import pandas_alive
import pytest
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from PIL import Image
myPath = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, "../..")
@pytest.fixture(scope="function")
def example_dataframe():
test_data = [
... | pd.DataFrame(data=test_data, columns=test_columns, index=test_index) | pandas.DataFrame |
import datetime
import numpy as np
import pytest
import pytz
import pandas as pd
from pandas import Timedelta, merge_asof, read_csv, to_datetime
import pandas._testing as tm
from pandas.core.reshape.merge import MergeError
class TestAsOfMerge:
def read_data(self, datapath, name, dedupe=False):
path = da... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
import unittest
import pandas as pd
import numpy as np
from econ_watcher_reader.reader import EconomyWatcherReader
import logging
logging.basicConfig()
logging.getLogger("econ_watcher_reader.reader").setLevel(level=logging.DEBUG)
class TestReaderCurrent(unittest.TestCase):
@classmethod
def setUpClass(cls):
... | pd.datetime(2018, 1, 1) | pandas.datetime |
from scapy.utils import RawPcapReader
from scapy.all import PcapReader, Packet
from scapy.layers.l2 import Ether
from scapy.layers.inet import IP, TCP
import scapy.contrib.modbus as mb
from scapy.fields import (
ConditionalField,
Emph,
)
from scapy.config import conf, _version_checker
from scapy.base_classes im... | pd.read_csv(file + "_1") | pandas.read_csv |
import pandas as pd
def generate_demand_csv(input_fn: str, user_data_dir: str):
# Demand
demand = pd.read_excel(input_fn, sheet_name='2.3 EUD', index_col=0, header=1, usecols=range(5))
demand.columns = [x.strip() for x in demand.columns]
demand.index = [x.strip() for x in demand.index]
# Add add... | pd.concat((header, updated_time_series)) | pandas.concat |
"""
Functions for building bokeh figure objects from dataframes.
"""
import datetime
import logging
import math
from typing import Optional, Tuple, Union
import numpy as np
import pandas as pd
from bokeh.layouts import Column, Row, gridplot
from bokeh.models import ColumnDataSource, LabelSet, Legend, LegendItem
from b... | pd.concat([df_bottom, df_top], ignore_index=True) | pandas.concat |
# Copyright (c) 2016 <NAME>
import numpy as np
import pandas as pd
from sklearn import decomposition
import json
import math
import pickle
### Load data
loadPrefix = "import/input/"
# Bins 1, 2, 3 of Up are to be removed later on
dirmagUpA = np.genfromtxt(loadPrefix+"MLM_adcpU_dirmag.csv", skip_header=3, delimite... | pd.DataFrame(data=dirmagDownA[:,1:(1+nBinsUnfilteredDown)], index=dirmagDownIndex) | pandas.DataFrame |
import numpy as np
np.random.seed(0)
import pandas as pd
import matplotlib.pyplot as plt
import gym
env = gym.make('Taxi-v3')
env.seed(0)
print('观察空间 = {}'.format(env.observation_space))
print('动作空间 = {}'.format(env.action_space))
print('状态数量 = {}'.format(env.observation_space.n))
print('动作数量 = {}'.format(env.action_s... | pd.DataFrame(agent.q) | pandas.DataFrame |
import pandas as pd
import pandas.testing as pdt
import qiime2
from qiime2.plugin.testing import TestPluginBase
from q2_types.feature_data import DNAFASTAFormat
from genome_sampler.subsample_diversity import subsample_diversity
class TestSubsampleDiversity(TestPluginBase):
package = 'genome_sampler.tests'
... | pdt.assert_series_equal(sel.inclusion, exp_inclusion) | pandas.testing.assert_series_equal |
from connections.mysql_connector import MySQL_Connector
from models.topic_modeling import Topic_Modeling
from connections.neo4j_connector import Neo4j_Connector
import os
from datetime import datetime
from gensim import corpora, models, similarities
from models.graph_generator import Graph_Generator
from models.tuple_e... | pd.DataFrame() | pandas.DataFrame |
"""Code for the bootstrap uncertainty quantification (BUQ) algorithm."""
import time
import logging
import numpy as np
import pandas as pd
import buq
import models
import tests
def import_time_series_data():
"""Import time series data for model, without any time slicing."""
ts_data = pd.read_csv('data/deman... | pd.to_datetime(ts_data.index) | pandas.to_datetime |
#!python
##################################################
# ACCESS QC Module
# Innovation Laboratory
# Center For Molecular Oncology
# Memorial Sloan Kettering Cancer Research Center
# maintainer: <NAME> (<EMAIL>)
#
#
# This module functions as an aggregation step to combine QC metrics
# across Waltz runs on differe... | pd.concat([gc_cov_int_table, unfilt[2], simplex[2], duplex[2]]) | pandas.concat |
import pytest
from pandas import (
DataFrame,
Index,
Series,
)
import pandas._testing as tm
@pytest.mark.parametrize("n, frac", [(2, None), (None, 0.2)])
def test_groupby_sample_balanced_groups_shape(n, frac):
values = [1] * 10 + [2] * 10
df = DataFrame({"a": values, "b": values})
... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor
from sklearn.feature_selection import SelectFromModel
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preproce... | pd.read_csv(POSTPROCESSED_DATAPATH, sep=",", header="infer") | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 19 16:00:06 2020
@author: <NAME>, FINTECH CONSULTANCY
license: Apache 2.0,
Note: only tested on windows 10
"""
import pandas as pd
import sys
import re
import os
import win32com.client
from docx import *
# Hardcoded for now. Wondering where configurations should go for... | pd.isna(data['Revised Definition'].iloc[i]) | pandas.isna |
# Copyright 2019 DeepMind Technologies Limited
#
# 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 ... | pd.DataFrame(res, columns=names) | pandas.DataFrame |
# Related third party imports
import pandas as pd
import numpy as np
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, roc_auc_score
import... | pd.DataFrame() | pandas.DataFrame |
import logging
from pathlib import Path
import pandas as pd
from ..gov import Gov, Matcher
from ..const import FILENAME_GOV_TEST_SET
GOV_URL = "http://wiki-de.genealogy.net/Verlustlisten_Erster_Weltkrieg/Projekt/Ortsnamen"
logger = logging.getLogger(__name__)
class GovTestData:
def __init__(self, gov: Gov, url:... | pd.concat(correction_tables) | pandas.concat |
"""The noisemodels module contains all noisemodels available in Pastas.
Supported Noise Models
----------------------
.. autosummary::
:nosignatures:
:toctree: ./generated
NoiseModel
NoiseModel2
Examples
--------
By default, a noise model is added to Pastas. It is possible to replace the
default mod... | Series(index=res.index, data=a, name="Noise") | pandas.Series |
"""
Tasks
-------
Search and transform jsonable structures, specifically to make it 'easy' to make tabular/csv output for other consumers.
Example
~~~~~~~~~~~~~
*give me a list of all the fields called 'id' in this stupid, gnarly
thing*
>>> Q('id',gnarly_data)
['id1','id2','id3']
Observations:
--... | u('fxVersion') | pandas.compat.u |
import pytest
from pandas import Categorical, DataFrame, Series
import pandas.util.testing as tm
def _assert_series_equal_both(a, b, **kwargs):
"""
Check that two Series equal.
This check is performed commutatively.
Parameters
----------
a : Series
The first Series to compare.
b... | Series([1, 2, 3]) | pandas.Series |
#!/usr/bin/env python
#-*- coding:utf-8 -*-
"""Overview:
Classify cell candidates and determine true cells
Usage:
HDoG_classifier.py PARAM_FILE
Options:
-h --help Show this screen.
--version Show version.
"""
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import numpy ... | pd.Series(preds) | pandas.Series |
import os
import copy
import pytest
import numpy as np
import pandas as pd
import pyarrow as pa
from pyarrow import feather as pf
from pyarrow import parquet as pq
from time_series_transform.io.base import io_base
from time_series_transform.io.numpy import (
from_numpy,
to_numpy
)
from time_series_transfor... | pd.DataFrame(data) | pandas.DataFrame |
import os
from datetime import datetime, timedelta, timezone
import pandas as pd
from pandas.core.frame import DataFrame
from sklearn.linear_model import LinearRegression
def demand(exp_id, directory, threshold, warmup_sec):
raw_runs = []
# Compute SLI, i.e., lag trend, for each tested configuration
filen... | pd.DataFrame(raw_runs) | pandas.DataFrame |
#from scipy.stats import chi2
import argparse
import sys
import numpy as np
import pandas as pd
import itertools
#ARGS = None
pnames = ["PRIOR-0", "PRIOR-1", "LIK-0", "LIK-1", "LIK", "POST-0", "POST-1"]
def sigmoid(x, derivative=False):
return x*(1-x) if derivative else 1/(1+np.exp(-x))
def main(args):
par... | pd.read_csv(param_file, sep="\t") | pandas.read_csv |
from __future__ import absolute_import, print_function, division
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib import rc
import pandas as pd
import logging
import json
import numpy as np
import datetime
from better.tools.indicator import max_drawdown, sharpe, positive_count, negative... | pd.DataFrame(results, index=labels) | pandas.DataFrame |
import pandas as pd
import numpy as np
from sklearn.model_selection import GridSearchCV, learning_curve,ShuffleSplit, train_test_split
import os
import time
import shap
import xgboost as xgb
areas = ['CE']
data_version = '2021-07-14_3'
#targets = ['g1','g2','q','r','D','mu_w_0','mu_a_0','RoCof','nadir','MeanDevInFirst... | pd.read_hdf(res_folder+'y_pred_cont.h5') | pandas.read_hdf |
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 20 11:41:36 2021
@author: Koustav
"""
import os
import glob
import matplotlib.pyplot as plt
import seaborn as sea
import numpy as np
import pandas as pan
import math
import matplotlib.ticker as mtick
from scipy.optimize import curve_fit
def expo(x, a, b, c):
return ... | pan.DataFrame(blind, columns= ["p", "A", "SD(A)", "B", "SD(B)", "Decay Rate", "SD(C)"]) | pandas.DataFrame |
# The file will produce result based on closing rank
# -*- coding: utf-8 -*-
"""//@<NAME>
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/github/priyanshgupta1998/Machine_learning/blob/master/Krisko_Assignmnet/coding.ipynb
"""
"""#Krisko Assi... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
from estimagic.benchmarking.process_benchmark_results import _clip_histories
from estimagic.benchmarking.process_benchmark_results import _find_first_converged
from estimagic.benchmarking.process_benchmark_results import (
_get_history_as_stacked_sr_from_results,... | pd.DataFrame() | pandas.DataFrame |
from pyspark.sql.functions import expr, col, lit, year
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def violations_in_year(nyc_data, vyear):
return nyc_data.select('issue_date').filter(year('issue_date') == vyear).count()
def reduction_in_violations(nyc_data, enable_plot=True):
viola... | pd.DataFrame(seasonwise_violations, columns = ['Violation Code', 'Frequency', 'Season']) | pandas.DataFrame |
import os
import datetime
import random
import pandas as pd
import numpy as np
from calendar import monthrange
from dateutil.easter import easter
from utilities import get_path, get_config
DAYS = {"MON": 0, "Mon": 0, "Mo": 0, "Montag": 0, "Monday": 0,
"TUE": 1, "Tue": 1, "Di": 1, "Dienstag": 1, "Tuesday": 1,
... | pd.DataFrame(index=idx) | pandas.DataFrame |
import numpy as np
import networkx as nx
import pandas as pd
import random
import string
import scipy.stats
import network_prop
import sys
# for parallel processing
#from joblib import Parallel, delayed
#import multiprocessing
def main(num_reps=10, seed_gene_file='HC_genes/example_seed.tsv',int_file='../interactomes/... | pd.DataFrame({'min_degree':min_degree,'max_degree':max_degree,'genes_binned':genes_binned}) | pandas.DataFrame |
import numpy as np
import pandas as pd
from .base_test_class import DartsBaseTestClass
from darts.timeseries import TimeSeries
from darts.utils import timeseries_generation as tg
from darts.metrics import mape
from darts.models import (
NaiveSeasonal,
ExponentialSmoothing,
ARIMA,
Theta,
FourTheta,
... | pd.DataFrame({"V1": values}) | pandas.DataFrame |
import numpy as np
import pandas as pd
from cascade_at.core.log import get_loggers
from cascade_at.dismod.api.fill_extract_helpers import utils
from cascade_at.dismod.constants import DensityEnum, IntegrandEnum, \
INTEGRAND_TO_WEIGHT
LOG = get_loggers(__name__)
DEFAULT_DENSITY = ["uniform", 0, -np.inf, np.inf]
... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
from pathlib import Path
from datetime import datetime
import random
import sys
from sklearn.model_selection import ParameterSampler
from scipy.stats import randint as sp_randint
from scipy.stats import uniform
from functions import (
under_over_sampler,
classifier_train... | pd.DataFrame.from_dict(classifier_parameters, orient="index") | pandas.DataFrame.from_dict |
# -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime
import itertools
import numpy as np
import pytest
from pandas.compat import u
import pandas as pd
from pandas import (
DataFrame, Index, MultiIndex, Period, Series, Timedelta, date_range)
from pandas.tests.frame.common ... | assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from time import time
from sklearn import metrics
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale, LabelEncoder
from sklearn.linear_model import LinearRegression
###################... | pd.DataFrame(kmeans_out, index=companies, columns=['ClusterID']) | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
def data_filling():
train_data = pd.read_csv("./data/train.csv")
test_data = pd.read_csv("./data/test.csv")
train_data = fill_holiday(train_data)
test_data = f... | pd.get_dummies(all_data['holiday']) | pandas.get_dummies |
import pandas as pd
from pandas import DataFrame
import pandas._testing as tm
class TestConcatSort:
def test_concat_sorts_columns(self, sort):
# GH-4588
df1 = DataFrame({"a": [1, 2], "b": [1, 2]}, columns=["b", "a"])
df2 = DataFrame({"a": [3, 4], "c": [5, 6]})
# for sort=True/None... | tm.assert_produces_warning(None) | pandas._testing.assert_produces_warning |
import os.path as osp
import matplotlib.pyplot as plt
# from bokeh.palettes import Category20
from sklearn.manifold import TSNE
import pandas as pd
def tsne(feature_map, results, component_num, dir_path):
# fig, ax = plt.subplots()
# y_pred, y, conf, img_name = results
y_pred, y = results
model_tsne ... | pd.DataFrame() | pandas.DataFrame |
from datetime import datetime
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
CategoricalIndex,
DataFrame,
Index,
MultiIndex,
Series,
qcut,
)
import pandas._testing as tm
def cartesian_product_for_groupers(result, args, names, fill... | Index([0, 2], dtype="int64") | pandas.Index |
import pandas as pd
import numpy as np
from multiprocessing import Pool
import tqdm
import sys
import gzip as gz
from tango.prepare import init_sqlite_taxdb
def translate_taxids_to_names(res_df, reportranks, name_dict):
"""
Takes a pandas dataframe with ranks as columns and contigs as rows and taxids as value... | pd.DataFrame(taxidmap, index=["staxids"]) | pandas.DataFrame |
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout
from sklearn.metrics import confusion_matri... | pd.DataFrame.from_dict(trainingDict, orient='index', columns=['DataSize']) | pandas.DataFrame.from_dict |
import pandas as pd
import numpy as np
from ..siu import create_sym_call, Symbolic
from functools import singledispatch
# TODO: move into siu
def register_symbolic(f):
@f.register(Symbolic)
def _dispatch_symbol(__data, *args, **kwargs):
return create_sym_call(f, __data.source, *args, **kwargs)
ret... | pd.Categorical.from_codes(new_codes, new_cats) | pandas.Categorical.from_codes |
from datetime import datetime
import numpy as np
from pandas import (
DataFrame,
Index,
MultiIndex,
Period,
Series,
period_range,
to_datetime,
)
import pandas._testing as tm
def test_multiindex_period_datetime():
# GH4861, using datetime in period of multiindex raise... | to_datetime("03/01/2020") | pandas.to_datetime |
#%%
path = '../../dataAndModel/data/o2o/'
import os, sys, pickle
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import date
from sklearn.linear_model import SGDClassifier, LogisticRegression
dfoff = pd.read_csv(path+'ccf_offline_stage1_train.csv')
dftest = pd.read_csv(path+'ccf_... | pd.to_datetime(row['Date_received'], format='%Y%m%d') | pandas.to_datetime |
import os
import random
from io import BytesIO
from tempfile import TemporaryDirectory
import tensorflow as tf
from PIL import Image
from google.cloud import storage
import numpy as np
import glob
from tqdm import tqdm
import h5py
import json
from data.thor_constants import THOR_AFFORDANCES, THOR_OBJECT_TYPES, THOR_AC... | pd.DataFrame(df_rows) | pandas.DataFrame |
import functools
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.transforms as transforms
import matplotlib.pyplot as plt
import scipy.interpolate as interp
import scipy.optimize as opt
from .stats import poisson_interval
__all__ = [
"cms_label", "legend_data_mc", "data_mc", "data... | pd.DataFrame(outdata) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Combine and normalize tweet.json files into a DataFrame dumped to csv
- Find json files (recursively) within the curent path
- Load those that look like tweets dumped by tweetget
- Expand columns that contain arrays, e.g. geo.coordinates -> geo.coordinates.lat and .lon
... | pd.json.load(fin) | pandas.json.load |
import pandas as pd
class TECRDB_compounds_data(object):
def __init__(self):
"""
A module that processes information of compounds in TECRDB
"""
self.TECRDB_compounds_data_dict = {}
self.TECRDB_compounds_pH7_species_id_dict = {}
self.TECRDB_compounds_least_H_sid_dict ... | pd.read_csv('data/TECRDB_compounds_data.csv') | pandas.read_csv |
# ClinVarome annotation functions
# Gather all genes annotations : gene, gene_id,
# (AF, FAF,) diseases, clinical features, mecanismes counts, nhomalt.
# Give score for genes according their confidence criteria
# Commented code is the lines needed to make the AgglomerativeClustering
import pandas as pd
import numpy as ... | pd.read_csv(compare_gene, sep="\t", compression="gzip") | pandas.read_csv |
from unittest import TestCase # or `from unittest import ...` if on Python 3.4+
from category_encoders.utils import convert_input_vector, convert_inputs
import pandas as pd
import numpy as np
class TestUtils(TestCase):
def test_convert_input_vector(self):
index = [2, 3, 4]
result = convert_input... | pd.Series(alist, aindex) | pandas.Series |
"""
Technical Analysis Library
Library of functions to compute various technical indicators.
@author: eyu
"""
import logging
import numpy as np
import pandas as pd
import math as math
import statistics as stats
import datetime
import constants as c
# create logger
logger = logging.getLogger("algo-trader")
def co... | pd.DateOffset(months=3) | pandas.DateOffset |
# -*- 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.DataFrame(value_list) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
单变量分析中常用工具，主要包含以下几类工具：
1、自动分箱（降基）模块：包括卡方分箱、Best-ks分箱
2、基本分析模块，单变量分析工具，以及woe编码工具，以及所有变量的分析报告
3、单变量分析绘图工具，如AUC，KS，分布相关的图
"""
# Author: <NAME>
import numpy as np
import pandas as pd
from abc import abstractmethod
from abc import ABCMeta
from sklearn.utils.multiclass import type_of_target
fro... | pd.concat(datas, axis=1) | pandas.concat |
import pandas as pd
import sqlite3
import sys
import datetime
_db_path = 'Database\Database.db'
class GetData(object):
@staticmethod
def Equity(Ticker, Start=None, End = None):
connection = sqlite3.connect(_db_path)
cursor = connection.cursor()
if (Start == None) & (End ==... | pd.read_csv(path) | pandas.read_csv |
import numpy as np
import pandas as pd
from pandas import DataFrame
import matplotlib.pyplot as plt
import seaborn as sns
from os.path import exists
from pathlib import Path
from scipy.constants import value
def change_width(ax, new_value) :
for patch in ax.patches :
current_width = patch.get_width()
... | pd.read_csv(stat_realization_csv_file) | pandas.read_csv |
# -*- coding: utf-8 -*-
# pylint: disable=E1101
# flake8: noqa
from datetime import datetime
import csv
import os
import sys
import re
import nose
import platform
from multiprocessing.pool import ThreadPool
from numpy import nan
import numpy as np
from pandas.io.common import DtypeWarning
from pandas import DataFr... | tm.assert_frame_equal(df, expected) | pandas.util.testing.assert_frame_equal |
#Merges two CSV files and saves the final result
import pandas as pd
import sys
df1 = pd.read_csv(sys.argv[1])
df2 = pd.read_csv(sys.argv[2])
df = | pd.concat([df1, df2], ignore_index=True) | pandas.concat |
import pandas as pd
import numpy as np
# personal csv reader module
import reader
def count_number(array, number):
"""
Counts the occurrence of number in array.
"""
count = 0
for entry in array:
if entry == number:
count += 1
return count
def count_numbers(array, numbers... | pd.DataFrame(data=out, dtype=np.int16) | pandas.DataFrame |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for datetime64 and datetime64tz dtypes
from datetime import (
datetime,
time,
timedelta,
)
from itertools import (
product,
starmap,
)
import operator
import warnings
import numpy as np
impo... | Timestamp("2000-02-15", tz="US/Central") | pandas.Timestamp |
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