prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 20 20:51:01 2018
@author: SilverDoe
"""
'''
To Apply our own function or some other library’s function, pandas provide three
important functions namely :
1. Table wise Function Application: pipe()
2. Row or Column Wise Function Application: apply()
... | pd.Series([66,57,75,44,31,67,85,33,42,62,51,47]) | pandas.Series |
# Extract data
import urllib.request
from PyPDF2 import PdfFileReader
import io #input/output
import pandas as pd
import tests
# Set up the URL
url = "https://www.normanok.gov/sites/default/files/documents/2021-03/2021-03-01_daily_incident_summary.pdf"
# Set up the headers
headers = {}
headers['User-Agent'] = "Mozil... | pd.concat([output, df]) | pandas.concat |
# -*- coding:utf-8 -*-
import math
import phate
import anndata
import shutil
import warnings
import pickle
import numpy as np
import pandas as pd
import seaborn as sns
from scipy.spatial.distance import cdist
from scipy.stats import wilcoxon, pearsonr
from scipy.spatial import distance_matrix
from sklearn.decomposition... | pd.read_csv(cluster_marker_genes_fp, sep="\t") | pandas.read_csv |
import numpy as np
import pandas as pd
import datetime as dt
import os
import zipfile
from datetime import datetime, timedelta
from urllib.parse import urlparse
study_prefix = "U01"
def get_user_id_from_filename(f):
#Get user id from from file name
return(f.split(".")[3])
def get_file_names_from_zip(z, file_... | pd.read_csv(catalog_file) | pandas.read_csv |
# -*- coding: utf-8 -*-
# pylint: disable=W0612,E1101
from datetime import datetime
import operator
import nose
from functools import wraps
import numpy as np
import pandas as pd
from pandas import Series, DataFrame, Index, isnull, notnull, pivot, MultiIndex
from pandas.core.datetools import bday
from pandas.core.n... | assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 14 10:59:05 2021
@author: franc
"""
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
import json
from collections import Counter, OrderedDict
import math
import torchtext
from torchtext.data import get_tokenizer
... | pd.DataFrame({len_src: [text],len_dest: "no_translation"}) | pandas.DataFrame |
"""
Implementation of Econometric measures of
connectness and systemic risk in finance and
insurance sectors by M.Billio, M.Getmansky,
<NAME>, L.Pelizzon
"""
import pandas as pd
import numpy as np
from arch import arch_model
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from ty... | pd.DataFrame(data, columns=col_names) | pandas.DataFrame |
import streamlit as st
st.title('inspo-Book')
st.header('Upload an item of clothing to find matching looks')
st.subheader('<NAME>')
st.subheader('Insight Data Science, Los Angeles')
from PIL import *
import cv2
###########################################################
### uploading the image ###
###################... | pd.read_csv('/home/ec2-user/inspo/an_features.csv', index_col='names') | pandas.read_csv |
import re
import dash_core_components as dcc
import dash_html_components as html
import numpy as np
import pandas as pd
import plotly.express as px
# from plotly.subplots import make_subplots
import plotly.graph_objs as go
from dash.dependencies import Input, Output, State
from sklearn.ensemble import RandomForestC... | pd.DataFrame(rows) | pandas.DataFrame |
from __future__ import print_function, absolute_import
import unittest, math
import pandas as pd
import numpy as np
from . import *
class T(base_pandas_extensions_tester.BasePandasExtensionsTester):
def test_concat(self):
df = pd.DataFrame({'c_1':['a', 'b', 'c'], 'c_2': ['d', 'e', 'f']})
df.en... | pd.DataFrame({'n_1': [10, 12, 13, 15, 2, 12, 34], 'n_2': [1, 2, 3, 5, 2, 2, 4]}) | pandas.DataFrame |
# -*- coding:utf-8 -*-
"""
股票技术指标接口
Created on 2018/07/26
@author: Wangzili
@group : **
@contact: <EMAIL>
所有指标中参数df为通过get_k_data获取的股票数据
"""
import pandas as pd
import numpy as np
import itertools
def ma(df, n=10):
"""
移动平均线 Moving Average
MA（N）=（第1日收盘价+第2日收盘价—+……+第N日收盘价）/N
"""
pv = pd.DataFrame(... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import re
'''
Perform "eco-exceedance" analysis for functional flow data on the Merced River and produce figures
'''
def eco_endpoints(ffc_data, rh_data):
# define the eco endpoints. 5-95th of control. table of endpoints for each ffm
for m... | pd.DataFrame(data) | pandas.DataFrame |
#!/usr/bin/env python3
"""
Prep sam compare data for Bayesian Machine
Logic:
* group df by sample,
* only keep samples with more than 2 reps
* summarize columns
* filtering
* calculate:
* total_reads_counted
* both total
* g1_total
* g2 total
* ase total
* for each rep, if APN > input then
* flag_APN = 1 ... | pd.read_table(args.design, header=0) | pandas.read_table |
"""
This modules contains utility functions for data manipulation and plotting of
results and data
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import r2_score
import torch
#######################################################
# Data Utilities ... | pd.DataFrame() | pandas.DataFrame |
import collections
import json
import os
from datetime import time
import random
from tqdm import tqdm
from main import cvtCsvDataframe
import pickle
import numpy as np
import pandas as pd
import random
import networkx as nx
import time
from main import FPGrowth
from shopping import Shopping, Cell
import main
# QoL f... | pd.read_csv("explanations.csv") | pandas.read_csv |
from numpy.linalg.linalg import eig
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
engdata = pd.read_csv("./engdata.txt")
pdata = engdata.loc[:, ["Age", "Salary"]]
pdata = pdata.drop_duplicates()
scaler = StandardScaler()
scaler = scaler.fit(pdata)
transformed = ... | pd.cut(pdata.Age, [0, 10, 20, 30, 40, 50, 60, 70, 80]) | pandas.cut |
# -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime, timedelta
import functools
import itertools
import numpy as np
import numpy.ma as ma
import numpy.ma.mrecords as mrecords
from numpy.random import randn
import pytest
from pandas.compat import (
PY3, PY36, OrderedDict, ... | Series(idx2, name='foo') | pandas.Series |
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
lreshape,
melt,
wide_to_long,
)
import pandas._testing as tm
class TestMelt:
def setup_method(self, method):
self.df = tm.makeTimeDataFrame()[:10]
self.df["id1"] = (self.df["A"] > 0).astype(np.int... | tm.assert_frame_equal(result4, expected4) | pandas._testing.assert_frame_equal |
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... | DataFrame({0: ["foo", "bar"], 1: ["bah", "bas"], 2: [1, 2]}) | pandas.DataFrame |
from sqlalchemy import create_engine
import pandas as pd
import os
csv_data = pd.read_csv('./assets/ks-projects-201801.csv')
df = | pd.DataFrame(csv_data) | pandas.DataFrame |
import pytest, pandas
from os import remove
from datetime import date
from patentpy.utility import get_date_tues
from patentpy.convert_txt import convert_txt_to_df
from patentpy.acquire import get_bulk_patent_data
### TEST_GET_BULK_PATENT_DATA ###
# test generic; should return true and create/append to csv file.
def t... | pandas.read_csv("test.csv") | pandas.read_csv |
import baostock as bs
import pandas as pd
import datetime
import time
from sqlalchemy import create_engine
def download_data(date):
# 获取指定日期的指数、股票数据
stock_rs = bs.query_all_stock(date)
stock_df = stock_rs.get_data()
data_df = | pd.DataFrame() | pandas.DataFrame |
from collections import OrderedDict
from datetime import timedelta
import numpy as np
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype, DatetimeTZDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Series,
Timedelta,
Timestamp,
_np_version_under1p14,
... | tm.assert_frame_equal(ri, ei) | pandas.util.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
# pylint: disable=W0612,E1101
from datetime import datetime
import operator
import nose
from functools import wraps
import numpy as np
import pandas as pd
from pandas import Series, DataFrame, Index, isnull, notnull, pivot, MultiIndex
from pandas.core.datetools import bday
from pandas.core.n... | assert_panel_equal(unshifted, ps) | pandas.util.testing.assert_panel_equal |
# -*- coding: utf-8 -*-
"""
Create the economic tables required to run the MRIA model.
"""
import numpy as np
import pandas as pd
class io_basic(object):
"""
This is the class object **io_basic** which is used to set up the table.
"""
def __init__(self, name, filepath, list_regions):
"""
... | pd.read_excel(self.file, sheet_name="FD", header=None) | pandas.read_excel |
import pandas as pd
import numpy as np
import pytest
from .conftest import DATA_DIR, assert_series_equal
from numpy.testing import assert_allclose
from pvlib import temperature, tools
from pvlib._deprecation import pvlibDeprecationWarning
@pytest.fixture
def sapm_default():
return temperature.TEMPERATURE_MODEL_... | pd.Series([0., 23.06066166, 5.], index=times) | pandas.Series |
from collections import OrderedDict
from datetime import timedelta
import numpy as np
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype, DatetimeTZDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Series,
Timedelta,
Timestamp,
_np_version_under1p14,
... | tm.assert_frame_equal(ri, ei) | pandas.util.testing.assert_frame_equal |
import os
import sys
import glob
import numpy as np
import pandas as pd
from cooler import Cooler
import matplotlib
import matplotlib.pyplot as plt
import h5py
import seaborn as sns
import shelve
from collections import defaultdict, Iterable
# cooler_path = '/net/levsha/share/lab/dekkerU54/new_files/'
# cooler_paths =... | pd.DataFrame(pileup_dict) | pandas.DataFrame |
import pandas as pd
# Baca file sample_csv.csv
df = pd.read_csv("https://storage.googleapis.com/dqlab-dataset/sample_csv.csv")
# Tampilkan tipe data
print("Tipe data df:\n", df.dtypes)
# Ubah tipe data kolom quantity menjadi tipe data numerik float
df["quantity"] = | pd.to_numeric(df["quantity"], downcast="float") | pandas.to_numeric |
import pandas as pd
import math
def combination_generator():
dfA = pd.read_csv('A_feature.csv', header=0)
dfB = pd.read_csv('B_feature.csv', header=0)
dfX = | pd.read_csv('X_feature.csv', header=0) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Small analysis of Estonian kennelshows using Bernese mountain dogs data from kennelliit.ee and CatBoost algorithm
"""
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from catboost import CatBoostRegressor, CatBoostClassifier, Pool, cv
from sklearn.model_selecti... | pd.concat(frames, join='inner') | pandas.concat |
import numpy as np
import pandas as pd
import os, errno
import datetime
import uuid
import itertools
import yaml
import subprocess
import scipy.sparse as sp
from scipy.spatial.distance import squareform
from sklearn.decomposition.nmf import non_negative_factorization
from sklearn.cluster import KMeans
from sklearn.me... | pd.DataFrame(index=tpm.columns) | pandas.DataFrame |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 15 10:31:23 2017
@author: robertmarsland
"""
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import subprocess
import os
import pickle
import datetime
from sklearn.decomposition import PCA
StateData = ['ACI', 'ACII', 'CIATP'... | pd.read_table(folder+name+suffix,index_col=0,usecols=col_ind) | pandas.read_table |
from bs4 import BeautifulSoup as BS
from selenium import webdriver
from selenium.common.exceptions import NoSuchElementException, \
TimeoutException, StaleElementReferenceException
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.common.by import By
from selenium.webdriver.fi... | pd.DataFrame(page_data, columns=COLUMNS) | pandas.DataFrame |
from predict import *
from control import *
from operator import add
import pandas as pd
from statistics import stdev, mean
def plotPredictionMC(runs, episodes, everyVisit, save):
val = np.zeros((4,62,10))
for i in range(runs):
v = MonteCarlo(basicPolicy, episodes, everyVisit)
val += v
plot... | pd.DataFrame() | pandas.DataFrame |
import lightgbm as lgb
import numpy as np
import pandas as pd
import sklearn.ensemble as ensemble
import sklearn.linear_model as linear_model
import sklearn.model_selection as model_selection
import sklearn.svm as svm
import sklearn.tree as tree
import xgboost as xgboost
from utils.misc import get_display_time
# Keep... | pd.read_csv('data.csv') | pandas.read_csv |
import dash
import dash_html_components as html
import dash_core_components as dcc
import plotly.graph_objs as go
import dash_daq as daq
import dash_table
import datetime
from datetime import datetime as dt
from datetime import timedelta
import dateutil.relativedelta
import pandas as pd
import numpy as np
import war... | pd.merge(SignupsPerCountry, TrueCodes, left_on='CountryCode', right_on='rand', how='right') | pandas.merge |
import pandas as pd
import ast
# ==================================== #
# Movie Network Generator #
# ==================================== #
# Movie Features
# 순번 영화명 감독 제작사 수입사 배급사 개봉일 영화유형 영화형태
# 국적 전국스크린수 전국매출액 전국관객수 서울매출액 서울관객수 장르 등급 영화구분
# + 주연, 조연
def load_data():
movies_df = pd.read_exce... | pd.DataFrame(columns=['actor', 'genre'], index=None) | pandas.DataFrame |
import pandas as pd
import numpy as np
import requests
from fake_useragent import UserAgent
import io
import os
import time
import json
import demjson
from datetime import datetime
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
# Main Economic Indicators: https://alfred.stlouisfed.org/re... | pd.read_csv(tmp_url) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 27 05:13:39 2018
@author: IvanA
"""
from selenium import webdriver
from selenium.common.exceptions import NoSuchElementException
from selenium.common.exceptions import ElementNotVisibleException
from selenium.common.exceptions import StaleElementReferenceExcept... | pd.DataFrame(columns=['vuelo','paginaweb']) | pandas.DataFrame |
import json
from typing import Tuple, Union
import pandas as pd
import numpy as np
import re
import os
from tableone import TableOne
from collections import defaultdict
from io import StringIO
from .gene_patterns import *
import plotly.express as px
import pypeta
from pypeta import Peta
from pypeta import filter_descr... | pd.Series([], dtype='float64') | pandas.Series |
# Type: module
# String form: <module 'WindPy' from '/opt/conda/lib/python3.6/WindPy.py'>
# File: /opt/conda/lib/python3.6/WindPy.py
# Source:
from ctypes import *
import threading
import traceback
from datetime import datetime, date, time, timedelta
import time as t
import re
from WindData import *
... | pd.DataFrame(out.Data, columns=out.Codes, index=out.Fields) | pandas.DataFrame |
# Copyright 2022 The Feast Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | pd.DataFrame() | pandas.DataFrame |
# ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# --------------------------------------------... | pd.Index(['ACGT', 'TGCA']) | pandas.Index |
import collections
import fnmatch
import os
from typing import Union
import tarfile
import pandas as pd
import numpy as np
from pandas.core.dtypes.common import is_string_dtype, is_numeric_dtype
from hydrodataset.data.data_base import DataSourceBase
from hydrodataset.data.stat import cal_fdc
from hydrodataset.utils im... | pd.DataFrame({"gauge_id": dirs_}) | pandas.DataFrame |
# coding=utf-8
import pandas as pd
import xgboost as xgb
from sklearn.metrics import f1_score
import param
############################ 定义评估函数 ############################
def micro_avg_f1(preds, dtrain):
y_true = dtrain.get_label()
return 'micro_avg_f1', f1_score(y_true, preds, average='micro')
##########... | pd.DataFrame() | pandas.DataFrame |
# Copyright (C) 2020 <NAME>, <NAME>
# Code -- Study 2 -- What Personal Information Can a Consumer Facial Image Reveal?
# https://github.com/computationalmarketing/facialanalysis/
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
import matplotlib.patches as mpa... | pd.read_csv(PATH+'/data_face.csv') | pandas.read_csv |
import pandas as pd
import pickle
def main():
gene_info = pd.read_csv('./../list/GRCh38_ensembl96_geneset.csv', sep='\t')
gene_info_dict = {}
for n, r in gene_info.iterrows():
gene_info_dict[r['transcript_stable_id']] = [
r['display_label'], r['gene_stable_id']
]
score = ... | pd.concat(df2, axis=0) | pandas.concat |
import pandas as pd
from texthero import preprocessing
from . import PandasTestCase
import unittest
import string
class TestPreprocessing(PandasTestCase):
"""
Remove digits.
"""
def test_remove_digits_only_block(self):
s = pd.Series("remove block of digits 1234 h1n1")
s_true = pd.Ser... | pd.Series("E-I-E-I-O\nAnd on") | pandas.Series |
import glob
import os
import sys
# these imports and usings need to be in the same order
sys.path.insert(0, "../")
sys.path.insert(0, "TP_model")
sys.path.insert(0, "TP_model/fit_and_forecast")
from Reff_functions import *
from Reff_constants import *
from sys import argv
from datetime import timedelta, datetime
from ... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pandas as pd
from zvt.contract.api import df_to_db
from zvt.contract.recorder import Recorder, TimeSeriesDataRecorder
from zvt.recorders.emquantapi.common import mainCallback
from zvt.recorders.joinquant.common import to_entity_id
from zvt.utils.pd_utils import pd_is_not_null
from zvt.ut... | pd.to_datetime(df['timestamp']) | pandas.to_datetime |
#!/usr/bin/env python3
'''
A module for reading Next Gen Stats data
'''
import pandas as pd
import csv
class NextGenStatsReader(object):
'''
A class for reading and manipulating Next Gen Stats data in a DataFrame
'''
def load_ngs_data_into_dataframe(self, file_path):
'''
Load a CSV file... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
import pandas as pd
import seaborn as sns
import pylab as plt
__package__ = "Byron times plot"
__author__ = "<NAME> (<EMAIL>)"
if __name__ == '__main__':
filename = 'byron_times.dat'
data = | pd.read_csv(filename, sep=',', header=0) | pandas.read_csv |
import pandas as pd
import numpy as np
#import psycopg2
import matplotlib.pyplot as plt
from sklearn.model_selection import KFold
import Constants
import sys
from pathlib import Path
output_folder = Path(sys.argv[1])
output_folder.mkdir(parents = True, exist_ok = True)
#conn = psycopg2.connect('dbname=mimic user=haor... | pd.isnull(row['charttime']) | pandas.isnull |
from flask import Flask, request, jsonify, g, render_template
from flask_json import FlaskJSON, JsonError, json_response, as_json
from app.data_process import bp
from datetime import datetime
import pandas as pd
from pathlib import Path
from bs4 import BeautifulSoup
import glob
import os
positivity_replace = {
'ALG':3... | pd.read_csv(file) | pandas.read_csv |
import json
from unittest import TestCase
import pandas
from pandas.io.json import json_normalize
from pandas.util.testing import assert_frame_equal
from gamebench_api_client.api.utilities.dataframe_utilities import \
json_to_normalized_dataframe, session_detail_to_dataframe, to_dataframe
from tests import *
cl... | pandas.DataFrame(data=[self.session_json['response']['app']]) | pandas.DataFrame |
import pandas as pd
import numpy as np
from scipy.spatial.distance import cdist, pdist, squareform
from scipy.cluster import hierarchy
import copy
import sys
sys.path.append('/home/sd375')
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.cluster import hierarchy
from .load_and_save_environment_data i... | pd.read_csv('/n/groups/patel/Alan/Aging/Medical_Images/data/data-features_instances.csv') | pandas.read_csv |
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt, mpld3
from pandas import DataFrame
#----------------------------------------------------------------------------------------------------------------------#
#Load the data into a DF
with open(r"C:/Users/Pathtoyourdata", 'r', encod... | pd.option_context('display.max_rows', None, 'display.max_columns', None) | pandas.option_context |
import logging
import os
import gc
import pandas as pd
from src.data_models.tdidf_model import FrequencyModel
from src.evaluations.statisticalOverview import StatisticalOverview
from src.globalVariable import GlobalVariable
from src.kemures.tecnics.content_based import ContentBased
from src.preprocessing.preprocessin... | pd.DataFrame() | pandas.DataFrame |
from sklearn import tree
from sklearn import preprocessing
from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_absolute_error, f1_score
from itertools import product
from tqdm import tqdm
from functools import partial
import multiprocessing as mp
import numpy as np
import pandas as pd
... | is_timedelta64_dtype(df[y]) | pandas.api.types.is_timedelta64_dtype |
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import pytest
import pandas_datareader.data as web
pytestmark = pytest.mark.stable
class TestEcondb(object):
def test_get_cdh_e_fos(self):
# EUROSTAT
# Employed doctorate holders in non managerial and non professional
... | pd.Timestamp("2005-01-01") | pandas.Timestamp |
import os
import unittest
import pandas as pd
import numpy as np
from pandas.testing import assert_frame_equal, assert_series_equal
from mavedbconvert import empiric, constants
from tests import ProgramTestCase
class TestEmpiricInit(ProgramTestCase):
def setUp(self):
super().setUp()
self.path =... | pd.read_excel(self.excel_path, engine="openpyxl") | pandas.read_excel |
# Utility scripts
#
import sys
import os
import logging
from os.path import abspath, dirname, isdir, join, exists
from collections import defaultdict
from pathlib import Path
import numpy as np
import pandas as pd
from enum import Enum
# Setup log
class colr:
GRN = "\033[92m"
END = "\033[0m"
WARN = "\03... | pd.read_csv(fh, sep="\t", index_col=0, header=0) | pandas.read_csv |
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from recipe_app import read_all_files, path # import the web-extracted data reader
from typing import List, Tuple
d_full = read_all_files(path) #define the path as... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101
import itertools
import warnings
from warnings import catch_warnings
from datetime import datetime
from pandas.types.common import (is_integer_dtype,
is_float_dtype,
is_scalar)
from pandas.compat... | DataFrame({'x': [1.], 'y': [2.], 'z': [3.]}) | pandas.core.api.DataFrame |
# streamlit4.py
import streamlit as st
import pandas as pd
import numpy as np
import time
# import joblib
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
import nltk #Natural language processing tool-kit
from nltk.corpus import stopwords ... | pd.concat([st.session_state.train_text, st.session_state.train_label], axis=1) | pandas.concat |
#!/usr/bin/env python3
"""
Pipeline for PANGAEA data, with custom NETCDF reading.
This script allows for data updates.
@author: giuseppeperonato
"""
import json
import logging
import os
import shutil
import sys
import frictionless
import numpy as np
import pandas as pd
import requests
import utilities
import xarray
... | pd.DataFrame(rasters) | pandas.DataFrame |
import pandas as pd
import numpy as np
from sklearn.base import TransformerMixin, BaseEstimator
from sklearn.feature_extraction import DictVectorizer
from sklearn.preprocessing import FunctionTransformer, StandardScaler, RobustScaler
from sklearn.preprocessing import Imputer, MultiLabelBinarizer
from sklearn.impute imp... | pd.merge(X1, X2, left_index=True, right_index=True) | pandas.merge |
#!/usr/bin/env python
# coding=utf-8
"""
@version: 0.1
@author: li
@file: factor_solvency.py
@time: 2019-01-28 11:33
"""
import gc, six
import json
import numpy as np
import pandas as pd
from pandas.io.json import json_normalize
from utilities.calc_tools import CalcTools
from utilities.singleton import Singleton
# fr... | pd.merge(factor_solvency, management, how='outer', on="security_code") | pandas.merge |
# %%
import pandas as pd
from collections import defaultdict
import pickle
from typing import DefaultDict
cmap_data = pickle.load(open("./cmap_transformer.pkl", "rb"))
mm_data = pickle.load(open("./mm_report_transformer.pkl", "rb"))
# %%
def convert_to_metric_first(data):
rows = defaultdict(dict)
for model, ... | pd.DataFrame(data) | pandas.DataFrame |
# # # # # # # # # # # # # # # # # # # # # # # #
# #
# Module to run real time contingencies #
# By: <NAME> and <NAME> #
# 09-08-2018 #
# Version Aplha-0. 1 #
# ... | pd.DataFrame() | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
from random import seed
RANDOM_SEED = 54321
seed(RANDOM_SEED) # set the random seed so that the random permutations can be reproduced again
np.random.seed(RANDOM_SEED)
def load_spambase_data():
# input vars
data_name = 'sp... | pd.read_csv(raw_data_file) | pandas.read_csv |
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... | offsets.Minute(50) | pandas.tseries.offsets.Minute |
#-*- coding: utf-8 -*-
# 阈值寻优
import numpy as np
import pandas as pd
inputfile = '../data/water_heater.xls' # 输入数据路径,需要使用Excel格式
n = 4 # 使用以后四个点的平均斜率
threshold = pd.Timedelta(minutes=5) # 专家阈值
data = pd.read_excel(inputfile)
data[u'发生时间'] = pd.to_datetime(data[u'发生时间'], format='%Y%m%d%H%M%S')
data = data[data[u'水流... | pd.DataFrame(dt, columns=[u'阈值']) | pandas.DataFrame |
"""This module is dedicated to helpers for the DeepDAO class"""
import pandas as pd
def unpack_dataframe_of_lists(df_in: pd.DataFrame) -> pd.DataFrame:
"""Unpacks a dataframe where all entries are list of dicts
Parameters
----------
df_in: pd.DataFrame
input DataFrame
Returns
... | pd.concat(df_list, keys=df_in.columns, axis=1) | pandas.concat |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for Period dtype
import operator
import numpy as np
import pytest
from pandas._libs.tslibs.period import IncompatibleFrequency
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas impo... | pd.period_range("1/1/2000", freq="Q", periods=3) | pandas.period_range |
#!/usr/bin/env python3
"""Universal kernel blocks"""
import re
import os
import time
import datetime as dt
import numpy as np
import scipy as ss
import pandas as pd
import requests
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
import xgboost as xgb
from catboost import CatBoostRegressor... | pd.DataFrame() | pandas.DataFrame |
from collections import abc, deque
from decimal import Decimal
from io import StringIO
from warnings import catch_warnings
import numpy as np
from numpy.random import randn
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
... | Series(data=[1, None, 1], index=[0, 1, 0]) | pandas.Series |
#!/usr/bin/python3
# coding: utf-8
import sys
import os.path
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
# get_ipython().run_line_magic('matplotlib', 'inline')
# plt.close('all')
# dpi = 300
# figsize = (1920 / dpi, 1080 / dpi)
from p... | pd.read_csv(filepath_or_buffer=path, header=None) | pandas.read_csv |
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
from numba import njit
import vectorbt as vbt
from tests.utils import record_arrays_close
from vectorbt.generic.enums import range_dt, drawdown_dt
from vectorbt.portfolio.enums import order_dt, trade_dt, log_dt
day_dt = np.timedelta64... | pd.Index(['g1', 'g1'], dtype='object') | pandas.Index |
"""
trees_matplotlib_seaborn.py
An extension of trees.py using the matplotlib and seaborn libraries.
"""
import datetime as dt
import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
st.title("SF Trees")
st.write(
"This app analyzes San Francisco's tree data provided by... | pd.to_datetime(trees_df['date']) | pandas.to_datetime |
from dotmap import DotMap
from model.BLRPRx import *
from calendar import month_abbr
from datetime import timedelta as td
from datetime import datetime as dt
from datetime import datetime
from utils.utils import *
from utils.stats_calculation import *
import numpy as np
import pandas as pd
import os
from sampling.merge... | pd.read_csv(args.IO.stats_file_path,index_col=0, header=0) | pandas.read_csv |
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from typing import Any, Dict, Optional, Sequence, Tuple, Union
import matplotlib.pyplot as plt
import numpy as np
import pandas ... | pd.infer_freq(original.index) | pandas.infer_freq |
"""Clean, bundle and create API to load KSSL data
The KSSL database is provided as a Microsoft Access database designed
as an OLTP. The purposes of this module are: (i) to export all tables
as independent .csv files to make it platform independent; (ii) to
make it amenable to multi-dimensional analytical queries (OLAP... | pd.read_csv(in_folder / 'analyte_dim_tbl.csv') | pandas.read_csv |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# four representative days in each season
winter_day = '01-15'
spring_day = '04-15'
summer_day = '07-15'
fall_day = '10-15'
# define a function to plot household profile and battery storage level
def plot_4days(mode, tmy_code, utility, year, c_c... | pd.to_datetime(s.str[0], format="%m/%d") | pandas.to_datetime |
from sklearn.metrics import accuracy_score
import pandas as pd
import joblib
from sklearn.tree import DecisionTreeClassifier
import sys
try:
from StringIO import StringIO ## for Python 2
except ImportError:
from io import StringIO ## for Python 3
def PClassification(name, clf, loadFilename=False):
# Dat... | pd.Series(check_answ, name='reales') | pandas.Series |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
from numpy import nan
import numpy as np
import pandas as pd
from pandas.types.common import is_integer, is_scalar
from pandas import Index, Series, DataFrame, isnull, date_range
from pandas.core.index import MultiIndex
from pa... | Series([True, False, False, True, False], index=s.index) | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Purpose: Uses the statement data to train a classifier and use the classifier
for the prediction of the alternatives in the bluebook from 1988-2008
Here: standard classifier as RF, MN logitic regression, SVM
Status: Draft
Author: olivergiesecke
"""
... | pd.read_csv("../data/statements_text_extraction_cleaned.csv") | pandas.read_csv |
# fmt: off
import numpy as np
import pandas as pd
import h5py
import scipy.signal
import shutil
import skimage as sk
import os
import pickle
import sys
import h5py_cache
import copy
import pickle as pkl
from parse import compile
from time import sleep
from distributed.client import futures_of
import dask.dataframe as ... | pd.DataFrame(pd_output,columns=["fov","row","trench","timepoints","File Index","Image Index","lane orientation","y (local)","x (local)"]) | pandas.DataFrame |
import sys
import os.path
sys.path.insert(1,
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
import numpy as np
import pandas as pd
from urllib.parse import quote
import os
from utils.scraping_utils import get_soup_for_url, get_postcode_prefix, identify_postcode, strip_text
from utils.... | pd.isnull(company_name) | pandas.isnull |
import warnings
warnings.simplefilter(action = 'ignore', category = UserWarning)
# Front matter
import os
import glob
import re
import pandas as pd
import numpy as np
import scipy.constants as constants
import sympy as sp
from sympy import Matrix, Symbol
from sympy.utilities.lambdify import lambdify
import matplotlib
... | pd.DataFrame() | pandas.DataFrame |
from matplotlib.dates import DateFormatter, WeekdayLocator, \
DayLocator, MONDAY
import pandas as pd
import numpy as np
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
#from matplotlib.finance import candlestick_ohlc
from mpl_finance import candlestick_ochl as candlestick
from utilities import l... | pd.Timedelta('730 days') | pandas.Timedelta |
from collections import OrderedDict
from datetime import timedelta
import numpy as np
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype, DatetimeTZDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Series,
Timedelta,
Timestamp,
_np_version_under1p14,
... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
"""
author: zengbin93
email: <EMAIL>
create_dt: 2021/11/4 17:39
describe: A股强势股票传感器
"""
import os
import os.path
import traceback
import inspect
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from datetime import timedelta, datetime
from collections import Counter
from... | pd.DataFrame() | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
from scipy.io.arff import loadarff
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder, LabelEncoder
from sklearn.compose import ColumnTransformer
from collections import defaultdict
def load_kropt():
# Read input dataset
dataset = os.path.join('datas... | pd.DataFrame(raw_data[0]) | pandas.DataFrame |
## Making the code corpus
## This involves
## Hit every directory and read every supported files
## Form a corpus of words without special symbols
## Tokenize Camel case and Hungarian to split out new words
## Any word below 3 letter is not Allowed
import os
import pandas as pd
import sys
import pickle
import configpa... | pd.DataFrame(file_contents) | pandas.DataFrame |
from pathlib import Path
import nibabel as nib
import numpy as np
import pandas as pd
from scipy.stats import ttest_rel
import tqdm
from nipype.interfaces import fsl
from utils.parcellation import (
parcellation_labels,
parcellation_fname,
)
def get_available_parcellations(mother_dir: Path):
parcellations... | pd.DataFrame(columns=["t", "p"], index=before.columns) | pandas.DataFrame |
import sys
sys.path.append('../')
def WriteAriesScenarioToDB(scenarioName, ForecastName, ForecastYear, start_date, end_date, User, Area, GFO = False, CorpID = ['ALL']):
from Model import ImportUtility as i
from Model import BPXDatabase as bpxdb
from Model import ModelLayer as m
import datetime a... | pd.tseries.offsets.MonthEnd(1) | pandas.tseries.offsets.MonthEnd |
import os
import pyproj
import pandas as pd
import numpy as np
ancpth = os.path.join(os.path.dirname(__file__), 'ancillary')
shppth = os.path.join(os.path.dirname(__file__), 'shp')
lcc_wkt = \
"""PROJCS["North_America_Lambert_Conformal_Conic",
GEOGCS["GCS_North_American_1983",
DATUM["North_American_Da... | pd.Timestamp(t) | pandas.Timestamp |
import argparse
from pathlib import Path
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelBinarizer
def clean_data(data, features_to_clean):
for feature in features_to_clean:
data.drop(feature, axis=1, inplace=True)
def fulfill_missing_values(data, metadata=None):
if me... | pd.read_csv(args.input_test_data_path) | pandas.read_csv |
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