prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
__author__ = 'lucabasa'
__version__ = '1.1.0'
__status__ = 'development'
import numpy as np
import pandas as pd
from sklearn.model_selection import StratifiedKFold
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, NuSVC
from sklearn.decomposition import PCA
from sklearn.discriminant_... | pd.DataFrame(train2[cols]) | pandas.DataFrame |
import dash
import numpy as np
from dash.dependencies import Input, Output, State
import dash_core_components as dcc
import dash_bootstrap_components as dbc
import dash_html_components as html
import dash_table
from app import app
import plotly.graph_objs as go
import json, codecs
from scipy.integrate import simps
impo... | pd.DataFrame(data) | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
"""
for class_archivo in archivos:
f = open(os.path.abspath(os.path.join(path,class_archivo)),'r')
lineas = f.read().splitlines()
#print(lineas,"\n")
f.close()
"""
def leer_predicciones(archivos):
lista_archivos = []
for file_archivos in archivos:... | pd.merge(df, df_iou, on='imagen') | pandas.merge |
#!/usr/bin/python
# -*-coding: utf-8 -*-
# Author: <NAME>
# Email : <EMAIL>
# A set of convenience functions used for producing plots in `dabest`.
from .misc_tools import merge_two_dicts
def halfviolin(v, half='right', fill_color='k', alpha=1,
line_color='k', line_width=0):
import numpy as np
... | pd.unique(data[x]) | pandas.unique |
import pandas as pd
from pathlib import Path
import numpy as np
def getTeams(teamColumn ,gameLogs):
teams = {}
for team in gameLogs[teamColumn].unique():
teams[team] = gameLogs[gameLogs[teamColumn]==team]
return teams
def getWinRatio(teamType, team, window=10):
if teamType=="Home":
ret... | pd.read_csv(path+r'\Filtered\_mlb_filtered_GameLogs.csv', index_col=False) | pandas.read_csv |
import os
os.chdir("D:/George/Projects/PaperTrends/src")
import tweepy
from tqdm import tqdm
import pandas as pd
import sys
disableTQDM = False
class TwitterParser():
def __init__(self, user='arxivtrends'):
print("> Twitter Parser initialized")
keys = self._readAPIKeys("env.json", user)
a... | pd.DataFrame(dfList, columns=['key', 'id', 'user', 'favorited', 'retweeted', 'created_at', 'url', 'text']) | pandas.DataFrame |
import numpy as np
import pandas as pd
import collections
from datetime import datetime
from datetime import timedelta
import os
''' THIS CLASS HAS MULTIPLE FUNCTIONS FOR DATA LOADING AND STORING '''
class DataHandler(object):
''' This function splits the data in train/test/dev sets and slices it into "game ... | pd.read_csv('data/data_prices_daycat_2.csv', sep=None, decimal='.', engine='python') | pandas.read_csv |
from src.config.logger import AppLogger
from scipy import stats
import pandas as pd
import numpy as np
class OutlierRemoval(AppLogger):
def __init__(self):
super(OutlierRemoval, self).__init__()
self.cur_file_path = self.get_working_file_location()(__file__)
def log_transformation(self, dat... | pd.Series() | pandas.Series |
import xarray as xr
import pandas as pd
import numpy as np
import cdsapi
import xarray as xr
from pathlib import Path
from typing import List
import logging
def find_nearest_datapoint(lat, lon, ds):
"""Find the point in the dataset closest to the given latitude and longitude"""
datapoint_lats = ds.coords.inde... | pd.DataFrame() | pandas.DataFrame |
import functools
import unittest
from typing import Sequence, Optional
import numpy as np
import pandas as pd
from parameterized import parameterized
import torch
from scipy.special import expit
from torch_hlm.mixed_effects_model import MixedEffectsModel
from torch_hlm.simulate import simulate_raneffects
SEED = 20... | pd.concat(df_raneff_est) | pandas.concat |
import pickle
import os
import cv2
import numpy as np
import pandas as pd
from tqdm import tqdm
from gazenet.utils.registrar import *
from gazenet.utils.helpers import extract_width_height_thumbnail_from_image
from gazenet.utils.sample_processors import SampleReader, SampleProcessor, ImageCapture
# TODO (fabawi): s... | pd.read_csv(csv_file, names=self.columns, header=0) | pandas.read_csv |
import numpy as np
import pandas as pd
import pytest
from scipy import sparse
import sklearn.datasets
import sklearn.model_selection
from autoPyTorch.data.tabular_validator import TabularInputValidator
@pytest.mark.parametrize('openmlid', [2, 40975, 40984])
@pytest.mark.parametrize('as_frame', [True, False])
def... | pd.isnull(X_train_t) | pandas.isnull |
import pandas as pd
import numpy as np
import sys, os, pytest
path = "../pytrendseries/"
sys.path.append(path)
path2 = "../pytrendseries/tests/resource"
sys.path.append(path2)
import detecttrend
import maxtrend
import vizplot
class TestClass():
def __init__(self):
self.year = 2020
se... | pd.DataFrame([1,2,3],columns=["date"]) | pandas.DataFrame |
"""Runs experiments on CICIDS-2017 dataset."""
import itertools
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import RFE
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
from sklearn import metrics
from sklearn.metrics import f1_score
... | pd.read_csv("Friday-WorkingHours-Morning.pcap_ISCX.csv") | pandas.read_csv |
import pandas as pd
import numpy as np
import os
'''首先读入grid2loc.csv确定每个基站数据放入一维向量的位置'''
coordi = pd.read_csv('grid2loc.csv',index_col=0)
#print(coordi.shape)
gridlocdict = {} #字典：基站坐标--1d向量位置
for i in range(len(coordi)):
grid = coordi.index[i]
# 二维展成一维后的坐标， x_cor是列数，y_cor是行数
loc1d = coordi.at[grid, 'x_... | pd.Series(zerocoordi,name='zerocoordinates') | pandas.Series |
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from builtins import object
from past.utils import old_div
import os
import numpy as np
import pandas as pd
from threeML.io.rich_display import display
from threeML.io.file_utils import sanitize_filename
from ... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
train = | pd.read_csv('../data/train_mapped.tsv', sep='\t', header=0) | pandas.read_csv |
#!/usr/bin/env python3
import pandas as pd
import sys
import Bio
from Bio import SeqIO
import tagmatch
import os
from collections import defaultdict
import sqlite3
import gzip
enzymes =os.environ['mn_enzymes'].split(';')
specificity =os.environ['mn_specificity']
max_mc = int(os.environ['mn_max_missed_cleavages'])
f ... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import os,sys
# In[2]:
sys.path.insert(0,"./../") #so we can import our modules properly
# In[3]:
get_ipython().run_line_magic('matplotlib', 'notebook')
#auto reload changed modules
from IPython import get_ipython
ipython = get_ipython()
ipython.magic("pylab")
... | pd.read_hdf(pathD5min, 'data') | pandas.read_hdf |
import numpy as np
import pandas as pd
import xarray as xr
import matplotlib.pyplot as plt
import geocat.viz.util as gvutil
path = r'H:\Python project 2021\climate_data_analysis_with_python\data\sst.mnmean.nc'
ds= xr.open_dataset(path)
# time slicing
sst = ds.sst.sel(time=slice('1920-01-01','2020-12-01'))
# anomaly wi... | pd.Timestamp(year=yend, month=1, day=1) | pandas.Timestamp |
#!/usr/bin/env python
###
# File Created: Wednesday, February 6th 2019, 8:23:06 pm
# Author: <NAME> <EMAIL>
# Modified By: <NAME>
# Last Modified: Friday, February 8th 2019, 3:37:43 pm
###
import sys
import os
import csv
from os.path import isfile, join, split, exists
import glob
import ast
import pandas as pd
impor... | pd.DataFrame(avg_default, index=[0]) | pandas.DataFrame |
import pandas as pd
import statsmodels.formula.api as api
from sklearn.preprocessing import scale, StandardScaler
from sklearn.linear_model import RidgeCV
from plotnine import *
import torch
import numpy as np
def sumcode(col):
return (col * 2 - 1).astype(int)
def massage(dat, scaleall=False):
dat['durations... | pd.read_csv("data/out/pairwise_similarities.csv") | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: olivergiesecke
1) Collect the data on the speakers and text for each alternative.
2) Do the regular pre-processing for each text entry.
3) Apply standard LDA
4) Provide summary statics how the probability mass lines up with the different alternatives.
5) Check... | pd.DataFrame() | pandas.DataFrame |
import pystan
import os
import pickle as pkl
import numpy as np
import pandas as pd
from .utils import do_ols
__dir__ = os.path.abspath(os.path.dirname(__file__))
class HierarchicalModel(object):
def __init__(self, X, subject_ids, subjectwise_errors=False, cauchy_priors=False):
self.X = pd.DataFrame(X)... | pd.DataFrame(traces, columns=columns) | pandas.DataFrame |
"""Volume Technical Analysis"""
__docformat__ = "numpy"
import pandas as pd
import pandas_ta as ta
def ad(df_stock: pd.DataFrame, use_open: bool) -> pd.DataFrame:
"""Calculate AD technical indicator
Parameters
----------
df_stock : pd.DataFrame
Dataframe of prices
use_open : bool
... | pd.DataFrame(df_ta) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import csv
import os
import platform
import codecs
import re
import sys
from datetime import datetime
import pytest
import numpy as np
from pandas._libs.lib import Timestamp
import pandas as pd
import pandas.util.testing as tm
from pandas import DataFrame, Series, Index, MultiIndex
from pand... | tm.assert_frame_equal(out, expected) | pandas.util.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
# Copyright (C) 2018-2022, earthobservations developers.
# Distributed under the MIT License. See LICENSE for more info.
import pandas as pd
from pandas._testing import assert_series_equal
from wetterdienst.core.scalar.values import ScalarValuesCore
def test_coerce_strings():
series = Sca... | pd.StringDtype() | pandas.StringDtype |
"""Get data into JVM for prediction and out again as Spark Dataframe"""
import logging
logger = logging.getLogger('nlu')
import pyspark
from pyspark.sql.functions import monotonically_increasing_id
import numpy as np
import pandas as pd
from pyspark.sql.types import StringType, StructType, StructField
class DataConv... | pd.Series(data) | pandas.Series |
import argparse
from collections import namedtuple
from datetime import datetime
import logging
import re
import struct
import time
import json
import pandas as pd
import numpy as np
import requests
# Datafeed functions
from . import iex
from . import portcalc
logger = logging.getLogger(__name__)
# pylint: disab... | pd.DataFrame(columns=Book) | pandas.DataFrame |
from pyexpat import model
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score
from tqdm import tqdm
from fastinference.models import Ensemble, Tree
def create_mini_batches(inputs, targets, batch_size, shuffle=False):
""" Create an mini-batch like iterator for the given inputs / targ... | pd.get_dummies(df) | pandas.get_dummies |
import pandas as pd
import matplotlib.pyplot as pyplot
import os
from fctest.__PolCurve__ import PolCurve
class ScribPolCurve(PolCurve):
# mea_active_area = 0.21
def __init__(self, path, mea_active_area):
path = os.path.normpath(path)
raw_data = pd.read_csv(path, sep='\t', skiprows=41) # d... | pd.to_numeric(data_part.iloc[:, 3].values) | pandas.to_numeric |
from airflow import DAG
from airflow.operators.python import PythonOperator, ShortCircuitOperator
from KafkaClient import KafkaClient
from AWSClient import AWSClient
from logger_creator import CreateLogger
from datetime import datetime
import pandas as pd
from io import StringIO
# Configuration Variables
csv_file_na... | pd.DataFrame(fetched_data) | pandas.DataFrame |
from dash import html, dcc
import pandas as pd
from adasher.elements import number, number_with_diff, CardHeaderStyles
from adasher.cards import card, container, stats_from_df
from adasher.templates import pie_plot, bar_plot, scatter_plot
from adasher import templates
from adasher.advanced import auto_analytics, ass... | pd.DataFrame({'name': ['A', 'B', 'A', 'B'], 'value': [3, 4, 5, 6], 'group': ['X', 'X', 'Y', 'Y']}) | pandas.DataFrame |
""" Fred Model """
__docformat__ = "numpy"
import logging
from typing import Dict, List, Tuple
import fred
import pandas as pd
import requests
from fredapi import Fred
from gamestonk_terminal import config_terminal as cfg
from gamestonk_terminal.decorators import log_start_end
from gamestonk_terminal.helper_funcs im... | pd.DataFrame(d_series["seriess"]) | pandas.DataFrame |
import pandas as pd
import os
import numpy as np
from matplotlib import pyplot as plt
import matplotlib.dates as mdates
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
root = '/Users/Gabe/Downloads/thesis spreadies'
# sg_1k_1k = pd.read_csv(os.path.join(root,'we_depletions_s... | pd.to_datetime(sg_600_600['date']) | pandas.to_datetime |
import pandas as pd
import numpy as np
import pycountry_convert as pc
import pycountry
import os
from iso3166 import countries
PATH_AS_RELATIONSHIPS = '../Datasets/AS-relationships/20210701.as-rel2.txt'
NODE2VEC_EMBEDDINGS = '../Check_for_improvements/Embeddings/Node2Vec_embeddings.emb'
DEEPWALK_EMBEDDINGS_128 = '../... | pd.read_csv(NODE2VEC_WL5_E3_GLOBAL, sep=',') | pandas.read_csv |
import sys
import pandas as pd
import numpy as np
from random import getrandbits
from collections import OrderedDict
from argparse import ArgumentParser
from datetime import datetime
import ruamel.yaml as yaml
from faker import Factory
from faker.providers.date_time import Provider as date_provider
from faker.providers... | pd.DataFrame(data) | pandas.DataFrame |
import streamlit as st
import numpy as np
import pandas as pd
import requests
from bs4 import BeautifulSoup
import re
from nltk.tokenize import sent_tokenize
from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans
'''
# BERTerReads
---
'''
@st.cache(allow_output_mutation=True)
d... | pd.Series(ips) | pandas.Series |
import pandas as pd
class RecHash:
def __init__(self):
# Combinations of header labels
self.base = ['Rk', 'Date', 'G#', 'Age', 'Tm', 'Home', 'Opp', 'Result', 'GS']
self.receiving = ['Rec_Tgt', 'Rec_Rec', 'Rec_Yds', 'Rec_Y/R', 'Rec_TD', 'Rec_Ctch%', 'Rec_Y/Tgt']
self.rushing = ['rus... | pd.DataFrame(columns=self.kick_rt + self.scoring2p) | pandas.DataFrame |
from __future__ import division, unicode_literals, print_function # for compatibility with Python 2 and 3
import numpy as np
import pandas as pd
import pims
import trackpy as tp
import ipywidgets as widgets
import matplotlib as mpl
import matplotlib.pyplot as plt
from taxispy.detect_peaks import detect_peaks
import ma... | pd.DataFrame(np.nan, index=vel.index, columns=vel.columns) | pandas.DataFrame |
"""
废弃
新浪网设置了访问频次限制。
新浪有许多以列表形式提供的汇总列，每天访问也仅仅一次。
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
from datetime import date
from urllib.error import HTTPError
import pandas as pd
import requests
from bs4 import BeautifulSoup
import logbook
f... | pd.read_html(url, attrs={'id': 'comInfo1'}) | pandas.read_html |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import tensorflow as tf
from numpy.random import uniform, seed
# from scipy.interpolate import griddata
tf.random.set_seed(123)
data_path = "../../../data"
train_file_path = "%s/titanic/train.csv" % data_path
test_file_path ... | pd.read_csv(test_file_path) | pandas.read_csv |
import re
import json
import datetime
from datetime import datetime
from datetime import timedelta
import pandas as pd
from pandas.io.json import json_normalize
import numpy as np
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import argparse
import os
import csv
class ProcessTweets(object):
def __in... | pd.DataFrame.from_dict(sentiments) | pandas.DataFrame.from_dict |
import pandas as pd
import logging
_log = logging.getLogger(__name__)
COUNTRIES = [
'australia',
'brazil',
'canada',
'china',
'denmark',
'finland',
'france',
'germany',
'hong kong',
'india',
'indonesia',
'italy',
'japan',
'malaysia',
'mexico',
'netherla... | pd.concat(all_data, axis=0) | pandas.concat |
# ------------------------------------------------------------------------------
# Copyright IBM Corp. 2020
#
# 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/licens... | pd.read_csv(file_content, nrows=10) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
=================================
myInvestor-toolkit startup script
=================================
"""
import datetime as dt
import os
import pandas as pd
from fundamental import DividendYield
from source import YahooFinanceSource
class StockAnalysis:
"""
Stock analysis.
... | pd.Series(stock_summary_data[ticker]) | pandas.Series |
import datetime as dt
import pandas as pd
import pytest
from intake_google_analytics.utils import as_day, is_dt
def test_is_dt():
assert is_dt(dt.date(2020, 3, 19))
assert is_dt(dt.datetime(2020, 3, 19, 16, 20, 0))
assert is_dt(pd.to_datetime('2020-03-19'))
assert is_dt(pd.Timestamp(2020, 3, 19))
... | pd.DateOffset(days=1) | pandas.DateOffset |
import multiprocessing, logging
import pandas as pd
from os import listdir
from os.path import isfile, join
from pandas import DataFrame
from . import load_pointer
from ..savers import save_pointer
from .. import s3_utils, multiprocessing_utils
from .load_s3 import list_bucket_prefix_suffix_s3
logger = logging.getLog... | pd.read_parquet(path, columns=columns_to_keep, engine='pyarrow') | pandas.read_parquet |
"""
Pre-trained model obtained from:
https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki.ru.zip
https://gist.github.com/brandonrobertz/49424db4164edb0d8ab34f16a3b742d5
"""
import pandas as pd
import numpy as np
import text
import super_pool
from tqdm import tqdm
cleanup = text.SimpleCleanup()
pool = super_poo... | pd.concat([df, df_test], axis=0) | pandas.concat |
#%%
import os
import sys
import pandas as pd
import numpy as np
from tqdm import tqdm
from sklearn.metrics import roc_auc_score
import torch
from utils import *
HOME = os.path.dirname(os.path.abspath(__file__))
# DATA_DIR = '/home/scao/Documents/kaggle-riiid-test/data/'
# MODEL_DIR = f'/home/scao/Documents/kaggle-riii... | pd.read_pickle(DATA_DIR+'cv2_valid.pickle') | pandas.read_pickle |
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 |
"""
Extract summary unit data created using tabulate_area.py and postprocess to join
into vector tiles.
The following code compacts values in a few ways. These were tested against
versions of the vector tiles that retained individual integer columns, and the
compacted version here ended up being smaller.
Blueprint a... | pd.read_feather(working_dir / "blueprint.feather") | pandas.read_feather |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Use a machine learning approach to identify which acoustic index is more important to
discriminate between landscape cover.
env_cover ~ acoustic_indices
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import glob
#%% Load data
df_env = | pd.read_csv('../../env_data/ANH_to_GXX.csv') | pandas.read_csv |
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import csv
import os
import math
import matplotlib.lines as mlines
import numpy as np
import seaborn as sns
import pandas as pd
from sklearn.decomposition import PCA
from itertools import chain
import logging
from matplotlib.patches import Patch
import... | pd.read_csv("./maskandclassloss/"+f) | pandas.read_csv |
#!/usr/bin/env python3
"""
LINCS REST API client
New (2019) iLINCS:
http://www.ilincs.org/ilincs/APIinfo
http://www.ilincs.org/ilincs/APIdocumentation
(http://lincsportal.ccs.miami.edu/dcic/api/ DEPRECATED?)
"""
###
import sys,os,re,json,logging
import urllib,urllib.parse
import pandas as pd
#
from ..util import rest
... | pd.DataFrame() | pandas.DataFrame |
from typing import Any
import pandas as pd
from sklearn.model_selection import train_test_split
from error_consistency.consistency import (
ErrorConsistencyKFoldHoldout,
ErrorConsistencyKFoldInternal,
)
from error_consistency.testing.loading import CLASSIFIERS, DATA, OUTDIR
def test_classifiers_holdout(caps... | pd.DataFrame() | pandas.DataFrame |
"""
GIS For Electrification (GISEle)
Developed by the Energy Department of Politecnico di Milano
Initialization Code
Code for importing input GIS files, perform the weighting strategy and creating
the initial Point geodataframe.
"""
import os
import math
import pandas as pd
import geopandas as gpd
from shapely.geomet... | pd.read_csv('Landcover.csv') | pandas.read_csv |
import streamlit as st
from PIL import Image
import cv2
import numpy as np
from matplotlib import pyplot as plt
from skimage import data
from skimage.color import rgb2gray
from skimage.feature import corner_harris, corner_subpix, corner_peaks, hessian_matrix_det
from skimage.filters import difference_of_gaussians
impo... | pd.DataFrame(s, columns=['Edge','s1','s2',"s2-s1"]) | pandas.DataFrame |
import unittest
import pandas as pd
import pytest
import riptable as rt
# N.B. TL;DR We have to import the actual implementation module to override the module global
# variable "tm.N" and "tm.K".
# In pandas 1.0 they move the code from pandas/util/testing.py to pandas/_testing.py.
# The "import ... | tm.makeTimeDataFrame() | pandas.util.testing.makeTimeDataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
调用wset函数的部分
下载数据的方法
1.在时间上使用折半可以最少的下载数据，但已经下了一部分，要补下时如果挪了一位，又得全重下
2.在文件上，三个文件一组，三组一样，删中间一个，直到不能删了，退出
"""
import os
import pandas as pd
from .utils import asDateTime
def download_sectorconstituent(w, date, sector, windcode, field='wind_code'):
"""
板块成份
中信证... | pd.DataFrame(w_wset_data.Data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
import prep_for_model_runs as prep
import build_models as build
import modify_contact as mod
import model_params_class as mp
import calc_summary_stat as summ
"""Build model for policy intervention 1
This function ... | pd.DataFrame(recovered_rows, columns=Group_Names) | pandas.DataFrame |
# %% Imports
import pandas as pd
import re
def flatten_columns(df):
df.columns = ["_".join(df) for df in df.columns.ravel()]
df.columns = [re.sub(r'_$', '', col) for col in df.columns]
return df
def rename_cols_3m(df):
df.columns = [f"{col}_3m" if col not in INDEX else col for col in df.columns]
r... | pd.to_datetime(df_rte.date_last_opened) | pandas.to_datetime |
import pandas as pd
def fix_datasets():
dati = pd.read_csv("dati_regioni.csv")
regioni = pd.read_csv("regioni.csv")
## Devo mergiare i dati del trentino
dati.drop(columns = ["casi_da_sospetto_diagnostico", "casi_da_screening"], axis = 1, inplace = True)
df_r = dati.loc[(dati['denominazione_region... | pd.concat([dati, df_trentino], sort=False) | pandas.concat |
import datetime
import pandas as pd
import numpy as np
import numpy.ma as ma
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
def plot_team(team):
years = [2012,2013,2014,2015,2016,2017]
g = pd.read_csv("audl_elo.csv")
dates = pd.to_datetime(g[(g["team_id"] == team)]["date"])
elo = g... | pd.read_csv("audl_elo.csv") | pandas.read_csv |
import praw
import pandas as pd
from praw.models import MoreComments
import datetime
reddit = praw.Reddit(client_id='pm9diOFYiSsXHw',
client_secret='<KEY>',
user_agent='webscraper',
username='yash3277',
password='<PASSWORD>')
... | pd.DataFrame(posts,columns=['title', 'score', 'id', 'subreddit', 'url', 'num_comments', 'body', 'created', 'date']) | pandas.DataFrame |
import requests
import zipfile
import io
import pandas as pd
from datetime import datetime, timedelta
pd.set_option('display.width', None)
class DBManager:
"""Constructs and manages a sqlite database for accessing historical inputs for NEM spot market dispatch.
Constructs a database if none exists, otherwis... | pd.read_sql_query(query, con=self.con) | pandas.read_sql_query |
import datetime
import dill
import tqdm.auto
import pathlib
import zipfile
import numpy as np
import pandas as pd
def parse_interaction_events(data_path, first_day_date, from_date_incl, to_date_excl, num_timesteps=48, bidirectional=True):
dfs = []
for filename in tqdm.auto.tqdm(sorted(list(pathlib.Path(data_... | pd.read_csv(alive_path, parse_dates=['annotated_tagged_date', 'inferred_death_date']) | pandas.read_csv |
import matplotlib.pyplot as plt # type: ignore
import numpy as np # type: ignore
import pandas as pd # type: ignore
class CalculationsMixin(object):
__perf_charts = False # TODO move
def _constructDf(self, dfs):
# join along time axis
if dfs:
df = pd.concat(dfs, sort=True)
... | pd.DataFrame(position.instrumentPriceHistory, columns=[price_col, 'when']) | pandas.DataFrame |
"""
"""
"""
>>> # ---
>>> # SETUP
>>> # ---
>>> import os
>>> import logging
>>> logger = logging.getLogger('PT3S.Rm')
>>> # ---
>>> # path
>>> # ---
>>> if __name__ == "__main__":
... try:
... dummy=__file__
... logger.debug("{0:s}{1:s}{2:s}".format('DOCTEST: __main__ Context: ','path = os.p... | pd.Timedelta('0 seconds') | pandas.Timedelta |
import re
import pandas as pd
import numpy as np
from glob import glob
import os
from tqdm import tqdm
import sys
from itertools import combinations
from p_tqdm import p_map, p_umap
from scipy import sparse
from src.utils import UniqueIdAssigner
class SmaliApp():
LINE_PATTERN = re.compile('^(\.method.*)|^(\.end ... | pd.Series(self.API_uid.value_by_id, name='api') | pandas.Series |
# -*- coding: utf-8 -*-
# @Author: liuyulin
# @Date: 2018-10-08 15:33:11
# @Last Modified by: liuyulin
# @Last Modified time: 2018-10-08 15:37:06
import numpy as np
import pandas as pd
def generate_testing_set(actual_track_datapath = '../../DATA/DeepTP/processed_flight_tracks.csv',
flight... | pd.read_csv(flight_plan_datapath) | pandas.read_csv |
##? not sure what this is ...
from numpy.core.numeric import True_
import pandas as pd
import numpy as np
## this function gives detailed info on NaN values of input df
from data_clean import perc_null
#these functionas add a date column (x2) and correct mp season format
from data_fix_dates import game_add_mp_date... | pd.read_excel(io = betting_path+'nhl odds 2010-11.xlsx') | pandas.read_excel |
import os.path
import json
import pandas as pd
import xgboost as xgb
import joblib
from IPython import get_ipython
from sklearn.preprocessing import scale
from sklearn.model_selection import KFold
from time import time
from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix, \
classifi... | pd.concat([df, newdf], ignore_index=True, sort=False) | pandas.concat |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import copy
from datetime import timedelta
from math import log10, floor
import warnings
import numpy as np
import pandas as pd
import ruptures as rpt
from sklearn.model_selection import train_test_split
from sklearn import linear_model
from covsirphy.util.error import dep... | pd.DataFrame.from_dict(_dict, orient="index") | pandas.DataFrame.from_dict |
import psycopg2
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import display
from wordcloud import WordCloud, ImageColorGenerator
from sklearn.feature_extraction import text
from sklearn.decomposition import LatentDirichletAllocation as LDA
from sklear... | pd.DataFrame(rows, columns=['topic', 'probability', 'statement']) | pandas.DataFrame |
import os
import html5lib
import pandas as pd
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from sel... | pd.read_html(retable) | pandas.read_html |
# Copyright 1999-2020 Alibaba Group Holding Ltd.
#
# 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 a... | pd.MultiIndex.from_tuples([index], names=empty_df.index.names) | pandas.MultiIndex.from_tuples |
import pandas as pd
import numpy as np
import os
script_dir = os.path.dirname(os.path.abspath(__file__))
# https://www.nomisweb.co.uk/query
###
# Assemble joint distribution of: region - sex - age - ethnicity
###
census_11_male_white = pd.read_csv(script_dir + '/male_white.csv')
census_11_male_asian = pd.read_csv(scr... | pd.read_csv(script_dir + '/male_other.csv') | pandas.read_csv |
import pytplot
import pandas as pd
import copy
def spec_mult(tvar,new_tvar=None):
"""
Multiplies the data by the stored spectrogram bins and created a new tplot variable
.. note::
This analysis routine assumes the data is no more than 2 dimensions. If there are more, they may become flattened!
... | pd.DataFrame(dataframe*specframe, columns=d.columns, index=d.index) | pandas.DataFrame |
# Import the class
import kmapper as km
import pandas
import sklearn
import numpy
import matplotlib.pyplot as plt
#========== Define Data and Labels here==========
b_data=pandas.read_csv("./../Results/bronchieactasis_data.csv",index_col=0)
c_data=pandas.read_csv("./../Results/COPD.csv",index_col=0)
#=======Data crea... | pandas.Series(graph['nodes']) | pandas.Series |
"""
Genereate ablated modality images. One time use code.
Modality ablation experiment. Generate and save the ablated brats images
Generate dataset with
Save in the directory: Path(brats_path).parent / "ablated_brats", and can be loaded with the script:
T1 = os.path.join(image_path_list[0], bratsID, bratsID+... | pd.read_csv(fl) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
This module contains all classes and functions dedicated to the processing and
analysis of a decay data.
"""
import logging
import os # used in docstrings
import pytest # used in docstrings
import tempfile # used in docstrings
import yaml # used in docstrings
import h5py
import copy
from... | pd.DataFrame(items) | pandas.DataFrame |
'''
Tools for simple baseline/benchmark forecasts
These methods might serve as the forecast themselves, but are more likely
to be used as a baseline to evaluate if more complex models offer a sufficient
increase in accuracy to justify their use.
Naive1:
Carry last value forward across forecast horizon (random wal... | pd.DataFrame(train) | pandas.DataFrame |
from collections import defaultdict
import copy
import json
import numpy as np
import pandas as pd
import pickle
import scipy
import seaborn as sb
import torch
from allennlp.common.util import prepare_environment, Params
from matplotlib import pyplot as plt
from pytorch_pretrained_bert import BertTokenizer, BertModel
... | pd.DataFrame(data) | pandas.DataFrame |
import os
os.system('apt-get clean')
os.system('mv /var/lib/apt/lists /var/lib/apt/lists.old')
os.system('mkdir -p /var/lib/apt/lists/partial')
os.system('apt-get clean')
os.system('apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 04EE7237B7D453EC')
os.system('apt-key adv --keyserver keyserver.ubuntu.com --rec... | pd.Series(result) | pandas.Series |
import os
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import tushare as ts
import datetime
#ts.set_token('09f77414f088aad7959f5eecba391fe685ea50462e208ce451b1b6a6')
pro = ts.pro_api('09f77414f088aad7959f5eecba391fe685ea50462e208ce451b1b6a6')
StockBasic = pro.query('stock_basic', list_st... | pd.DataFrame(columns=['ts_code', 'HighPoint2015']) | pandas.DataFrame |
#!/usr/bin/env python3
import os
import sys
import random
import time
from random import seed, randint
import argparse
import platform
from datetime import datetime
import imp
import subprocess
import glob
import re
from helperFunctions.myFunctions_helper import *
import numpy as np
import pandas as pd
import fileinput... | pd.read_table(location+f"qn", names=["qn"]) | pandas.read_table |
"""General utility functions that are used in a variety of contexts.
The functions in this module are used in various stages of the ETL and post-etl
processes. They are usually not dataset specific, but not always. If a function
is designed to be used as a general purpose tool, applicable in multiple
scenarios, it sho... | pd.StringDtype() | pandas.StringDtype |
import os
from nose.tools import *
import unittest
import pandas as pd
import numpy as np
import py_entitymatching as em
from py_entitymatching.utils.generic_helper import get_install_path
import py_entitymatching.catalog.catalog_manager as cm
import py_entitymatching.utils.catalog_helper as ch
from py_entitymatching.... | pd.DataFrame(A) | pandas.DataFrame |
import numpy as np
import pandas as pd
import altair as alt
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Plot a 3d
def Vis3d(X,Y,Z):
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, color='y')
ax.set_xlabel('X')
ax.set... | pd.DataFrame(item_embedding) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
from datetime import datetime, timedelta
import itertools
from numpy import nan
import numpy as np
from pandas import (DataFrame, Series, Timestamp, date_range, compat,
option_context, Categorical)
from pandas.core.arra... | pd.isna(Y['g']['c']) | pandas.isna |
"""
Use the ``MNLDiscreteChoiceModel`` class to train a choice module using
multinomial logit and make subsequent choice predictions.
"""
from __future__ import print_function, division
import abc
import logging
import numpy as np
import pandas as pd
from patsy import dmatrix
from prettytable import PrettyTable
from... | pd.Series() | pandas.Series |
#%%
import pandas as pd
import numpy as np
import holoviews as hv
import hvplot.pandas
from scipy.sparse.linalg import svds
from scipy.stats import chisquare, chi2_contingency
from sklearn.decomposition import TruncatedSVD
from umoja.ca import CA
import logging
from pathlib import Path
import numpy as np
import hvplot... | pd.to_datetime(X_mode.date) | pandas.to_datetime |
"""Expression Atlas."""
import logging
import os
import sys
from collections import OrderedDict
from typing import List, Tuple, Optional
import pandas as pd
from pandas.core.frame import DataFrame
import xmltodict
from pyorient import OrientDB
from tqdm import tqdm
from ebel.constants import DATA_DIR
from ebel.manage... | pd.read_csv(file_path, sep="\t", header=None, names=names) | pandas.read_csv |
import os
from pathlib import Path
from random import shuffle
from itertools import product
import dotenv
import tensorflow as tf
import h5py
import pandas as pd
from src.models.fetch_data_from_hdf5 import get_tf_data
from src.models.models_2d import unet_model, CustomModel, custom_loss
from src.models.losses_2d impo... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
import miditoolkit
import os
def getStats(folder_name,num_notes_dict={},channel=0):
if num_notes_dict=={}:
num_notes_dict=numNotes(folder_name,channel)
df=pd.DataFrame.from_dict(num_notes_dict, orient='index',c... | pd.DataFrame(columns=["Metric","Value"]) | pandas.DataFrame |
"""Requires installation of requirements-extras.txt"""
import pandas as pd
import os
import seaborn as sns
from absl import logging
from ._nlp_constants import PROMPTS_PATHS, PERSPECTIVE_API_MODELS
from credoai.data.utils import get_data_path
from credoai.modules.credo_module import CredoModule
from credoai.utils.com... | pd.DataFrame(responses) | pandas.DataFrame |
#!/usr/bin/env python3
"""
Process raw data to get related disease pairs from Disease Ontology
"""
__author__ = "<NAME>"
__version__ = "0.1.0"
__license__ = "MIT"
import argparse
import logging
from funcs import utils
import pandas as pd
import numpy as np
from tqdm.autonotebook import trange
import random
from os.pa... | pd.read_csv(args.in_diseases_path, sep='\t', index_col='diseaseId') | pandas.read_csv |
from Calculatefunction import k,dl,seita
import csv
import pandas as pd
import numpy as np
sourcenamelist=csv.reader(open('/Users/dingding/Desktop/sample5.9.csv','r'))
GRBname=[column[0]for column in sourcenamelist]
Znamelist=csv.reader(open('/Users/dingding/Desktop/sample5.9.csv','r'))
z=[column[1]for column in Zname... | pd.DataFrame(seitalist) | pandas.DataFrame |
import pytest
from pandas.tests.series.common import TestData
@pytest.fixture(scope="module")
def test_data():
return | TestData() | pandas.tests.series.common.TestData |
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