prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import collections.abc
from pathlib import Path
import pandas as pd
import xml.etree.ElementTree as ET
from io import BytesIO
from typing import List, Union, Dict, Iterator
from pandas import DataFrame
from .types import UploadException, UploadedFile
from .config import column_names
import logging
logger = logging.... | pd.DataFrame(header_df) | pandas.DataFrame |
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.cluster import KMeans
from yellowbrick.cluster import KElbowVisualizer
def principal_component_analysis(dragon_subset):
'''
:param dragon_subset: Inpu... | pd.DataFrame({'label': kmeans_.labels_}) | pandas.DataFrame |
import tensorflow as tf
import sys
import json
import numpy as np
import pandas as pd
#from hdfs.ext.kerberos import KerberosClient
import os
#import io
#import gzip
#import distkeras
LEARNING_DECAY = 0.0
ADAMBETA1 = 0.9
ADAMBETA2 = 0.999
MOMENTUM = 0.0
model_file_path = sys.argv[1]
loss = sys.argv[2]
optimizer = sys... | pd.read_csv(data_path+'/'+file, sep='|', header=None, dtype=np.float32) | pandas.read_csv |
from typing import List
import pandas as pd
from shapely.geometry import Polygon
import matplotlib.pyplot as plt
import geopy.distance
import pypsa
from epippy.geographics import get_shapes, get_subregions
from epippy.topologies.core.plot import plot_topology
from epippy.technologies import get_costs
def upgrade_... | pd.DataFrame(columns=["x", "y", "country", "onshore_region", "offshore_region"]) | pandas.DataFrame |
# coding: utf-8
# In[1]:
#I import the libraries I may need
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn as skl
import json
import gzip
# #### Loading and preparing the dataset
# In[2]:
f = gzip.open("C:/Users/Marta/Desktop/AppDataScience_data.gz", "rb")
print(type(f))... | pd.Series(usermeanlist) | pandas.Series |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import codecs
import os
import re
from concurrent.futures import ProcessPoolExecutor
import matplotlib.pyplot as plt
import pandas as pd
from pmdarima import arima
from pmdarima.model_selection import train_test_split
from sklearn.metrics import r2_score
def adjust_date... | pd.Timedelta("1D") | pandas.Timedelta |
# import libraries
import sys
from sqlalchemy import create_engine
import pandas as pd
import numpy as np
import re
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
import nltk
nltk.download(['punkt', 'wordnet', 'averaged_perceptron_tagger','stopwords'])
from sklearn.multioutput import ... | pd.Series(X) | pandas.Series |
#!/usr/bin/env python
# coding: utf-8
# ## Compile dataset of clones resistant to other drugs
#
# **<NAME>, 2021**
#
# **<NAME>, 2021**
#
# This script is modified from Greg Way's original scripts of 8.compile-otherclone-dataset.
#
# This dataset includes new batches of 24~27 including WT (10, 12-15, parental) and... | pd.crosstab(full_df_val.Metadata_clone_type_indicator, full_df_val.Metadata_model_split) | pandas.crosstab |
"""
Copyright 2019 <NAME>.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distribut... | pd.date_range("2019-01-01", periods=4, freq="D") | pandas.date_range |
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 1 19:58:26 2021
@author: <NAME> and <NAME>
"""
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pyomo.environ as pyo
import utils as pu
def init():
import ... | pd.concat([base_elec_rate]*self.n_days, ignore_index=True) | pandas.concat |
# Copyright (c) Microsoft Corporation and contributors.
# Licensed under the MIT License.
import logging
import numpy as np
import pandas as pd
import pickle
import scipy.optimize as opt
from sklearn.dummy import DummyClassifier
from time import time
from ._constants import _PRECISION, _INDENTATION, _LINE
from fairl... | pd.Series(dtype="float64") | pandas.Series |
import pytest
import numpy as np
import pandas as pd
import databricks.koalas as ks
from pandas.testing import assert_frame_equal
from gators.feature_generation.polynomial_features import PolynomialFeatures
ks.set_option('compute.default_index_type', 'distributed-sequence')
@pytest.fixture
def data_inter():
X = p... | pd.DataFrame(X_expected.values) | pandas.DataFrame |
import os
import os.path
import random
from operator import add
from datetime import datetime, date, timedelta
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
import shutil
import ema_workbench
import time
## Step 2: Function for initiating the main dictionary of clim... | pd.read_csv(pcpCaseStudy[i]) | pandas.read_csv |
'''
Created on 9 de nov de 2020
@author: klaus
'''
import jsonlines
from folders import DATA_DIR, SUBMISSIONS_DIR
import os
from os import path
import pandas as pd
import numpy as np
import urllib
import igraph as ig
from input.read_input import read_item_data, get_emb
def create_ratio(mode = 'train',CUTOFF=50, whi... | pd.qcut(df['price'].values,100) | pandas.qcut |
# %% imports
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
import pickle
import numpy as np
import pandas as pd
import pandarallel
from pandarallel import pandarallel
from sgt import SGT
import matplotlib.pypl... | pd.concat([heavy_half, light_half_shuffled], axis=1) | pandas.concat |
#This finds address matches between files by looking for exact matches on street number and 'fuzzy' matches on street name
#the goal is to use Open Addresses files to assign geocoordinates
import pandas as pd
from fuzzywuzzy import fuzz
from fuzzywuzzy import process
import time
import sys
import unidecode #to remov... | pd.read_csv(input_dir+database) | pandas.read_csv |
import os
import pathlib
from itertools import chain
import pandas as pd
__all__ = ['ImageDataset', 'ImageClassFolderDataset']
class ImageDataset():
def __init__(self, root, image_format=['png', 'jpg', 'jpeg'], label_func=None):
"""Construct an image dataset label index.
Args:
... | pd.DataFrame() | pandas.DataFrame |
import fnmatch
import functools
import os
import dateutil
import pandas as pd
import pytest
from bs4 import BeautifulSoup
from tika import config
from covid_data_briefing import briefing_atk
from covid_data_briefing import briefing_case_types
from covid_data_briefing import briefing_deaths_provinces
from covid_data_b... | pd.DataFrame(columns=["Date"]) | pandas.DataFrame |
import logging
import os
import time
import numpy as np
import pandas as pd
from flask import Flask, Response, request
import config
import dataset
import torch
import torch.utils.data
from model import BERTBaseUncased
app = Flask(__name__)
MODEL = None
DEVICE = "cpu"
os.environ["TOKENIZERS_PARALLELISM"] = "false"
... | pd.read_json(data) | pandas.read_json |
import hashlib
import re
from typing import List, Tuple, Union
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
import pandas as pd
def md5_hash(text: str) -> str:
"""
Generate MD5 hash of a text.
Args:
text: String
Returns:
MD5 hash
"""
return ... | pd.DataFrame(keywords[top_terms].T, columns=categories) | pandas.DataFrame |
# pylint: disable=E1101
from datetime import datetime, timedelta
from pandas.compat import range, lrange, zip, product
import numpy as np
from pandas import Series, TimeSeries, DataFrame, Panel, isnull, notnull, Timestamp
from pandas.tseries.index import date_range
from pandas.tseries.offsets import Minute, BDay
fr... | Series(arr, index=idx) | pandas.Series |
"""
Say you
Initially This script takes the Options_averages_calls.db & Options_averages_puts.db files created with contracts_avg_volume.py
combines it with the
"""
import sqlite3
import os
import pandas as pd
import time
from sqlalchemy import create_engine
from pandas.io.sql import DatabaseError
os.system('afplay... | pd.DataFrame(data=new_row) | pandas.DataFrame |
from itertools import chain
import operator
import numpy as np
import pytest
from pandas.core.dtypes.common import is_number
from pandas import (
DataFrame,
Index,
Series,
)
import pandas._testing as tm
from pandas.core.groupby.base import maybe_normalize_deprecated_kernels
from pandas.tests.apply.common... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
import numpy as np
import pandas as pd
# メモリ削減関数
def reduce_mem_usage(df, verbose=False):
start_mem_usg = df.memory_usage().sum() / 1024**2
numerics = ['int8', 'int16', 'int32', 'int64', 'float16', 'float32', 'float64']
print("Memory usage of properties dataframe is :", start_mem_usg, " MB")
NAlist = ... | pd.to_datetime(df[cols[i]], format='%Y-%m-%d') | pandas.to_datetime |
import pathlib
import re
import pandas as pd
def lnc_txt2csv():
p_temp = pathlib.Path('ldcc-20140209/text')
article_list = []
# フォルダ内のテキストファイルを全てサーチ
for p in p_temp.glob('**/*.txt'):
# フォルダ名からニュースサイトの名前を取得
media = str(p.parent.stem)
# 拡張子を除くファイル名を取得
file_name = str(p.... | pd.read_csv('ldcc-20140209/csv/lnp.csv') | pandas.read_csv |
import pandas as pd
import math
from csv import reader
"""https://www.easycalculation.com/statistics/standard-deviation.php"""
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
pd.options.display.float_fo... | pd.to_numeric(s, errors="coerce") | pandas.to_numeric |
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 9 11:15:48 2018
@author: bfyang.cephei
"""
import numpy as np
import pandas as pd
#import pandas_datareader.data as web
#import tushare as ts
#import datetime
#==============================================================================
def poss_date(date):
if le... | pd.read_pickle(add_alpha_day_stand + saf) | pandas.read_pickle |
from typing import Sequence
import pandas as pd
import numpy as np
import logging
import sys
import click
import joblib
from pathlib import Path
from collections import OrderedDict
from sklearn.metrics import roc_auc_score, accuracy_score, average_precision_score, f1_score, \
confusion_matrix, classification_repo... | pd.DataFrame.from_dict(model_metrics, orient='index') | pandas.DataFrame.from_dict |
# -*- 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.read_csv('dogshows_bernese_est_2019.csv') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jul 7 21:37:25 2018
@author: jeanfernandes
"""
import pandas as pd
base = pd.read_csv('./bigData/credit-data.csv')
base.describe()
base.loc[base['idade'] < 0]
#apagar a coluna
base.drop('idade', 1, inplace = True)
#apagar somento os reg com problem... | pd.isnull(base['idade']) | pandas.isnull |
import streamlit as st
import pandas as pd
import numpy as np
import os
from update_data import update_data_instant, update_data_anual
import geopy.distance
from geopy.geocoders import Nominatim
from geopy.extra.rate_limiter import RateLimiter
from streamlit_folium import folium_static
import folium
import branca
fro... | pd.DataFrame(P, columns=[f"Prix"]) | pandas.DataFrame |
# Import Libraries
# PyTorch
from torchvision import models
import torch
import torch.nn as nn
import warnings
warnings.filterwarnings('ignore', category=FutureWarning)
# Data science tools
import pandas as pd
# Useful for examining network
from torchsummary import summary
# Visualization loading
from tqdm import ... | pd.DataFrame(history, columns=['train_loss', 'test_loss', 'train_acc', 'test_acc']) | pandas.DataFrame |
#coding=utf-8
import pandas as pd
import numpy as np
import sys
import os
from sklearn import preprocessing
import datetime
import scipy as sc
from sklearn.preprocessing import MinMaxScaler,StandardScaler
from sklearn.externals import joblib
#import joblib
class FEbase(object):
"""description of class"""
def ... | pd.merge(df_data, df_adj_all, how='inner', on=['ts_code','trade_date']) | pandas.merge |
# -*- coding: utf-8 -*-
"""
Created on Sun Jan 10 17:50:04 2021
@author: <NAME>
"""
import pandas as pd
import numpy as np
df = pd.read_csv(r'CoinDatasets\ripple_price.csv')
print(df)
#Dataframe indexlenmesi :
#1)Sütunsal indexleyebilirim ve sütun indexini verip çağırabilirim
#2)dataframe iloc-loc ile ... | pd.DataFrame({'X': [1, 2, 3], 'Y': [4, 5, 6], 'Z': [7, 8, 9]}, index=['A', 'B', 'C']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import pytest
import re
from numpy import nan as NA
import numpy as np
from numpy.random import randint
from pandas.compat import range, u
import pandas.compat as compat
from pandas import Index, Series, DataFrame, isn... | u('c_d,e') | pandas.compat.u |
"""
Implements a series of technical
indicators used in finance and trading.
"""
import pandas as pd
def ADX(data, ma, full_output=False):
"""
Calculate average directional index (ADX) for a given
ohlc dataframe.
Parameters
----------
data: pd.DataFrame
DataFrame containing OHLC dat... | pd.concat([df, full_df], axis=1) | pandas.concat |
"""
Analysis plots of transient sources
$Header: /nfs/slac/g/glast/ground/cvs/pointlike/python/uw/like2/analyze/transientinfo.py,v 1.7 2017/11/18 22:26:38 burnett Exp $
"""
import os, pickle, glob
from astropy.io import fits as pyfits
from uw.like2.analyze import (sourceinfo, associations,)
from uw.like2.tools impo... | pd.DataFrame(months) | pandas.DataFrame |
"""
Particles and Populations
=========================
A particle contains the sampled parameters and simulated data.
A population gathers all particles collected in one SMC
iteration.
"""
from typing import Callable, List, Tuple
import numpy as np
import pandas as pd
from pyabc.parameters import Parameter
import l... | pd.DataFrame([p.parameter for p in particles]) | pandas.DataFrame |
import numpy as np
import pandas as pd
import quandl
import urllib.request
import requests
import json
import os
from pathlib import Path
import threading
import shutil
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.firefox.options import Options
from selenium.com... | pd.DataFrame(data={'id':opt_id, 'date':opt_dt}) | pandas.DataFrame |
########################################################################################################################
# |||||||||||||||||||||||||||||||||||||||||||||||||| AQUITANIA ||||||||||||||||||||||||||||||||||||||||||||||||||||||| #
# ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||... | pd.read_hdf(asset_dir + the_file) | pandas.read_hdf |
import os
import copy
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import matplotlib.dates as mdates
from datetime import date, timedelta, datetime
import seaborn as sns
import geopandas as gpd
import matplotlib.colors as colors
from plotting.colors import load_color_p... | pd.to_datetime(df['date']) | pandas.to_datetime |
# Copyright (c) 2019-2021 - for information on the respective copyright owner
# see the NOTICE file and/or the repository
# https://github.com/boschresearch/pylife
#
# 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 co... | pd.api.extensions.register_dataframe_accessor('test_accessor_one') | pandas.api.extensions.register_dataframe_accessor |
from django.shortcuts import render, redirect
from django.contrib import messages
from sqlalchemy import inspect
import sqlalchemy
import pandas as pd
import ast
import numpy as np
from sqlalchemy.sql import exists
import xgboost as xgb
import plotly.express as px
import plotly.io as pio
import plotly.graph_objs as po
... | pd.read_sql(sql, localengine) | pandas.read_sql |
import codecs
import imageio
import matplotlib.pyplot as plt
from wordcloud import WordCloud, STOPWORDS
import plotly.offline as py
import plotly.graph_objs as go
import pandas as pd
import tweepy
import locale
import emoji
import sys
import re
import string
import os
def get_user_tweets(api, userna... | pd.DataFrame({'retweets': retweets}) | pandas.DataFrame |
import pandas as pd
v_4 = pd.read_csv('50/predictions_dev_queries_50k_normalized_exp.csv')
temp = list(v_4['query_id'])
v_4['query_id'] = list(v_4['reference_id'])
v_4['reference_id'] = temp
v_5 = pd.read_csv('ibn/predictions_dev_queries_50k_normalized_exp.csv')
temp = list(v_5['query_id'])
v_5['query_id'] = list(v_5... | pd.merge(new_456, v_5, on=['query_id','reference_id'], how='inner') | pandas.merge |
import pandas as pd
import numpy as np
import lightgbm as lgb
import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import KFold, RepeatedKFold
from scipy import sparse
# 显示所有列
pd.set_option('display.max_columns', None)
... | pd.isnull(x) | pandas.isnull |
"""
config for drug target challenge.
"""
import pandas as pd
import os
import sys
import evaluation_metrics_python2 as eval
CHALLENGE_SYN_ID = "syn15667962"
CHALLENGE_NAME = "IDG-DREAM Drug-Kinase Binding Prediction Challenge"
ADMIN_USER_IDS = [3360851]
REQUIRED_COLUMNS = [
"Compound_SMILES", "Compound_InchiKeys... | pd.read_csv(submission.filePath) | pandas.read_csv |
import os
import locale
import codecs
import nose
import numpy as np
from numpy.testing import assert_equal
import pandas as pd
from pandas import date_range, Index
import pandas.util.testing as tm
from pandas.tools.util import cartesian_product, to_numeric
CURRENT_LOCALE = locale.getlocale()
LOCALE_OVERRIDE = os.en... | pd.Index(['1.5', '2.7', '3.4'], name='xxx') | pandas.Index |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import nose
import numpy as np
import pandas as pd
from pandas import (Index, Series, DataFrame, Timestamp, isnull, notnull,
bdate_range, date_range, _np_version_under1p7)
import pandas.core.common as com
from pandas.compa... | to_timedelta('00:00:01') | pandas.to_timedelta |
import pandas as pd
import requests
import dropbox
from bs4 import BeautifulSoup
from tqdm import tqdm
from datetime import datetime
import re
from datetime import date
from os.path import join
DATADIR = 'data'
def get_word_parenthesis(s: str) -> str:
return s[s.find("(") + 1:s.find(")")]
def get_features(url:... | pd.DataFrame() | pandas.DataFrame |
from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import Ridge
from sklearn.svm import SVR
from sklearn.model_selection import cross_val_predict
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.preprocessing import scale
from math impo... | pd.DataFrame(rms_err, columns=rmse_cols, index=err_percent) | pandas.DataFrame |
# pylint: disable=E1101
from datetime import datetime, timedelta
from pandas.compat import range, lrange, zip, product
import numpy as np
from pandas import Series, TimeSeries, DataFrame, Panel, isnull, notnull, Timestamp
from pandas.tseries.index import date_range
from pandas.tseries.offsets import Minute, BDay
fr... | date_range('1/1/2000', periods=3, freq='5t') | pandas.tseries.index.date_range |
import csv
import pandas as pd
import threading
from helpers.movie_helper import get_one_movie_resource_pt, get_one_movie_resource_en
def merge_links_movies():
# First we merge links and movies to have access to external TMDB API
links = pd.read_csv("../movie_data/links.csv", dtype=str)
movies = pd.read_... | pd.concat([all_movies, movie_details]) | pandas.concat |
"""
Quantilization functions and related stuff
"""
from functools import partial
import numpy as np
from pandas._libs.lib import infer_dtype
from pandas.core.dtypes.common import (
ensure_int64, is_categorical_dtype, is_datetime64_dtype,
is_datetime64tz_dtype, is_datetime_or_timedelta_dtype, is_integer,
... | is_datetime64_dtype(x) | pandas.core.dtypes.common.is_datetime64_dtype |
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 4 23:37:00 2021
@author: Usuario
"""
from selenium import webdriver
import pandas as pd
import re
from bs4 import BeautifulSoup
import requests
from webdriver_manager.chrome import ChromeDriverManager
driver = webdriver.Chrome(ChromeDriverManager().install())
url = "... | pd.DataFrame() | pandas.DataFrame |
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.Timedelta('8 days 00:00:00') | pandas.Timedelta |
# Author: <NAME>, PhD
# University of Los Angeles California
import os
import sys
import re
import tkinter as tk
from tkinter import ttk
from tkinter import filedialog
import matplotlib
matplotlib.use("TkAgg")
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib import pyplot as plt
import ... | pd.read_excel(self.templateFileName, sheet_name="MAP") | pandas.read_excel |
## 1. Recap ##
import pandas as pd
loans = pd.read_csv("cleaned_loans_2007.csv")
print(loans.info())
## 3. Picking an error metric ##
import pandas as pd
# False positives.
fp_filter = (predictions == 1) & (loans["loan_status"] == 0)
fp = len(predictions[fp_filter])
# True positives.
tp_filter = (predictions == 1) ... | pd.Series(predictions) | pandas.Series |
# v 0.1.5 Oct 1 2020
import pandas as pd
import numpy as np
import shap
import matplotlib.pyplot as plt
import waterfall_chart
class shapwaterfall:
def __init__(self, Model, X_tng, X_sc, ref1, ref2, num_feature=5):
self.Model = Model
self.X_tng = X_tng
self.X_sc = X_sc
self.ref1 =... | pd.DataFrame({"x_values": xlist, 'y_values': ylist}) | pandas.DataFrame |
"""
This module is the main API used to create track collections
"""
# Standard library imports
import copy
import random
import inspect
import logging
import itertools
from typing import Any
from typing import List
from typing import Union
from typing import Tuple
from typing import Callable
from dataclasses impo... | pd.DataFrame(all_tracks) | pandas.DataFrame |
'''
This script is to help
with basic data preparation
with the nuMoM2b dataset
'''
import pandas as pd
import numpy as np
# location of the data in this repository (not saved to Github!)
data_loc = './data/nuMoM2b_Dataset_NICHD Data Challenge.csv'
# This does dummy variables for multiple columns
# Here used for dru... | pd.read_csv(data_loc, usecols=diff, dtype=str) | pandas.read_csv |
import platform
if platform.system() != "Windows":
#Note pysam doesn't support Windows
import numpy as np
import anndata as ad
import pandas as pd
import pysam
from scipy.sparse import lil_matrix
from tqdm import tqdm
def bld_mtx_fly(tsv_file, annotation, csv_file=None, genome=... | pd.read_csv(tsv_file, sep='\t', header=None) | pandas.read_csv |
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.read_csv("/mnt/nadavrap-students/STS/data/data_Shapira_20200911_2014_2015.csv") | pandas.read_csv |
#! /usr/bin/env python3
## pooling_pipeline.py -
## index: A list of operations and functions included in this function
'''
0. import libraries and initialize global variables [Tommer]
1. parses input file [Tommer]
1a. create expression matrix for set of runs [Tommer]
2. single project PCA and elimination [Dan]
3. p... | pd.DataFrame() | pandas.DataFrame |
# -*- 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.Categorical(pred_clusters) | pandas.Categorical |
# creating my first module:
# libraries
import pandas as pd
import numpy as np
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from pandas import read_csv as csv
def Explore(file, column_names=None, title_line_number=100, head_line_number=20):
#df = pd.read_csv(file... | pd.concat([sc_X_train,X_train_cat],axis=1) | pandas.concat |
from clean_helpers import *
import pandas as pd
# Specify here what cleaning functions you want to use
cleaning_actions = ['clean_new_line', 'clean_tags', 'clean_punctuation', \
'remove_numbers']
clean = {
"clean_new_line": clean_new_line,
"lowercase": lowercase,
"lemmatize": lemmatize... | pd.concat([pos_data_full, neg_data_full]) | pandas.concat |
#!/usr/bin/env python
"""Tests for `pubchem_api` package."""
import os
import numpy as np
import pandas as pd
import scipy
from scipy.spatial import distance
import unittest
# from click.testing import CliRunner
# from structure_prediction import cli
class TestDataPreprocessing(unittest.TestCase):
"""Tests for ... | pd.concat([critical_info_to_df_3, adjacency_matrix_df_4], axis=1, join='inner') | pandas.concat |
'''script to calculate adjusted ppl and acc
python -u scripts/adjust_ppl_acc.py -bs 256 --cuda cuda:5 -model_dir model/nodp/20210418/181252/data-wikitext-2-add10b_model-LSTM_ebd-200_hid-200_bi-False_lay-1_tie-False_tok-50258_bs-16_bptt-35_lr-20.0_dp-False_partial-False_0hidden-False.pt_ppl-69.7064935_acc-0.38333_epoch... | pd.DataFrame(records, columns=column_names) | pandas.DataFrame |
from datetime import timedelta
from functools import partial
import itertools
from parameterized import parameterized
import numpy as np
from numpy.testing import assert_array_equal, assert_almost_equal
import pandas as pd
from toolz import merge
from zipline.pipeline import SimplePipelineEngine, Pipeline, CustomFacto... | pd.Timestamp("2015-01-15") | pandas.Timestamp |
'''This module implements the word2vec model service that is responsible
for training the model as well as a backend interface for the API.
'''
from datetime import datetime
import json
import logging
import pandas as pd
from gensim.models.ldamulticore import LdaMulticore
import numpy as np
from wb_nlp.interfaces.mil... | pd.DataFrame(payload) | pandas.DataFrame |
# Imports and cleans viral sequencing data, to throw into Angular app.
# Does a bunch of things:
# 1) standardizes all inputs to conform with schema
# 2) creates a series of Experiment objects to store the experimental data with experiment IDs
# 3) creates a series of Patient objects for patients who are not in the KGH... | pd.read_csv(lassa_MDfile) | pandas.read_csv |
"""
Author: <NAME> <<EMAIL>>
Date: 2019-06-22
Function: Encapsulates RESTful API logic.
"""
import random
import requests
from requests.exceptions import HTTPError
from urllib.parse import urlencode
import numpy as np
import pandas as pd
from io import StringIO
from .common import (
halt,
... | pd.DataFrame({}) | pandas.DataFrame |
import numpy as np
import pandas as pd
from glob import glob
import os
fnames= glob('noaa storm/*.csv')
details_fn= sorted([fn for fn in fnames if 'details' in fn])
timezone_mapper= {
'CST' : 'US/Central',
'CST-6' : 'US/Central',
'EST' : 'US/Eastern',
'EST-5' : 'US/Eastern',
'PST' : 'US/Pac... | pd.isna(x.DESCRIPTION) | pandas.isna |
# pylint: disable-msg=W0612,E1101,W0141
import nose
from numpy.random import randn
import numpy as np
from pandas.core.index import Index, MultiIndex
from pandas import Panel, DataFrame, Series, notnull, isnull
from pandas.util.testing import (assert_almost_equal,
assert_series_equal... | assert_frame_equal(joined, expected, check_names=False) | pandas.util.testing.assert_frame_equal |
from __future__ import division #brings in Python 3.0 mixed type calculation rules
import datetime
import inspect
import numpy as np
import numpy.testing as npt
import os.path
import pandas as pd
import sys
from tabulate import tabulate
import unittest
print("Python version: " + sys.version)
print("Numpy version: " +... | pd.Series([8.33, 7.98, 6.75, np.nan], dtype='float') | pandas.Series |
import json
import logging
import uuid
from abc import ABC, abstractmethod
from pathlib import Path
import numpy as np
import pandas as pd
import pickle5 as pickle
import sklearn
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
import apollo
from apollo import metrics
log... | pd.Index(cols) | pandas.Index |
"""
"""
"""
>>> # ---
>>> # 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.DataFrame() | pandas.DataFrame |
#! /user/bin/env python3
import argparse
import xlrd
from datetime import datetime
import pandas as pd
import os
import shutil
import configparser
config = configparser.ConfigParser()
config.read("config.ini")
unixFilesPath = os.getcwd() + config["FilePaths"]["unixFilesPath"]
unixConvertedPath = os.getcwd() + config... | pd.to_datetime(inputDataDict["Datetime"],format="%Y-%m-%dT%H:%M:%S") | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Keras implementation of Dilated Causal Convolutional Neural Network for Time
Series Predictions based on the following sources:
[1] <NAME> et al., “Wavenet: A generative model for raw audio,” arXiv
preprint arXiv:1609.03499, 2016.
[2] <NAME>, <NAME>, and <NAME... | pd.isna(timeseries) | pandas.isna |
import folium.plugins as plugins
import pandas as pd
import folium
#siteList = ['1418A', '3015A', '3133A', '3014A', '1419A']
siteList = []
siteRecord = {}
df = | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
def create_table(col_names, data_array, type_array):
new_data_array = data_array.copy()
string_to_type = {'int' : int, 'string' : str, 'double' : float}
for j in range(0, len(col_names)):
for i in range(0, len(data_array)):
if type_array[j] ... | pd.DataFrame(new_data_array, columns=col_names) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import re
import warnings
from datetime import timedelta
from itertools import product
import pytest
import numpy as np
import pandas as pd
from pandas import (CategoricalIndex, DataFrame, Index, MultiIndex,
compat, date_range, period_range)
from pandas.compat import PY... | pd.MultiIndex.from_arrays([eidx1, eidx2]) | pandas.MultiIndex.from_arrays |
"""
Market Data Presenter.
This module contains implementations of the DataPresenter abstract class, which
is responsible for presenting data in the form of mxnet tensors. Each
implementation presents a different subset of the available data, allowing
different models to make use of similar data.
"""
from typing impo... | pd.Series.ewm(data['close'], span=12) | pandas.Series.ewm |
#!/usr/bin/env python3
__author__ = "<EMAIL>"
import os
import os.path
import sys
import subprocess
import argparse
import datetime
import epiweeks
import pandas as pd
import numpy as np
def load_data(assemblies_tsv, collab_tsv, min_unambig, min_date, max_date):
df_assemblies = pd.read_csv(assemblies_tsv, sep... | pd.isna(x) | pandas.isna |
# -*- coding: utf-8 -*-
import codecs
import re
import pandas as pd
import argparse
from collections import defaultdict
import sys
import os
# gather arguments
parser = argparse.ArgumentParser(
description="Extract tabular paradigms from annotated templates."
)
parser.add_argument(
"-candidates_dir",
actio... | pd.isnull(word) | pandas.isnull |
""" test partial slicing on Series/Frame """
import pytest
from datetime import datetime, date
import numpy as np
import pandas as pd
import operator as op
from pandas import (DatetimeIndex, Series, DataFrame,
date_range, Index, Timedelta, Timestamp)
from pandas.util import testing as tm
class ... | tm.assert_series_equal(result, expected) | pandas.util.testing.assert_series_equal |
from pathlib import Path
import fiona # noqa
from geopandas import GeoDataFrame
import pandas as pd
from pandas import DataFrame
import pytest
from shapely import wkt
@pytest.fixture(scope='session')
def fixtures_path():
return Path(__file__).parent / 'fixtures'
@pytest.fixture(scope='session')
def counties(f... | pd.read_csv(fixtures_path / 'counties_polygons.csv', parse_dates=['date']) | pandas.read_csv |
## Functions to support SPEI drought index analysis
## Code: EHU | SPEI data: SC
## 12 Sept 2019
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
from datetime import date
import collections
## Constants associated with this analysis
yrs = np.linspace(1900, 2101, num=24... | pd.DataFrame.from_dict(tempdict) | pandas.DataFrame.from_dict |
import os, datetime
import csv
import pycurl
import sys
import shutil
from openpyxl import load_workbook
import pandas as pd
import download.box
from io import BytesIO
import numpy as np
from download.box import LifespanBox
verbose = True
snapshotdate = datetime.datetime.today().strftime('%m_%d_%Y')
box_temp='/home/p... | pd.DataFrame() | pandas.DataFrame |
'''
Created on Jun 8, 2017
@author: husensofteng
'''
import matplotlib
matplotlib.use('Agg')
from matplotlib.backends.backend_pdf import PdfPages
import pybedtools
from pybedtools.bedtool import BedTool
from matplotlib.pyplot import tight_layout
import matplotlib.pyplot as plt
from pylab import gca
import pandas as pd... | pd.read_table(input, sep='\t', header=None, usecols=[x_col_index, y_col_index], names=names) | pandas.read_table |
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import plotly.express as px
from lib.unsupervised.dimension import Pca_vectors
class Anomaly_nature():
def __init__(self, model, X_full, y_full,
left_axes_limit, right_axes_limit,
redu... | pd.concat([df, df_inp], ignore_index=True) | pandas.concat |
import pandas as pd
import numpy as np
import re
from law.utils import *
import jieba.posseg as pseg
import datetime
import mysql.connector
class case_reader:
def __init__(self, user, password, n=1000, preprocessing=False):
'''
n is total types，
preprocessing: whether needs preproc... | pd.DataFrame(newdict) | pandas.DataFrame |
import numpy as np
import pytest
from pandas.errors import UnsupportedFunctionCall
from pandas import (
DataFrame,
DatetimeIndex,
Series,
date_range,
)
import pandas._testing as tm
from pandas.core.window import ExponentialMovingWindow
def test_doc_string():
df = DataFrame({"B": [0, 1, 2, np.na... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
'''Module for ML algorithm'''
import pandas as pd
import numpy as np
from joblib import load
df = pd.read_csv('./data/working_ratings.csv', index_col=0)
nmf = load('./models/nmf.joblib')
def simple_recommender():
pass
def nmf_recommender(user_input, rating_data=df, model=nmf, n_movies:int =5):
user = | pd.DataFrame(user_input, columns=rating_data.columns, index=[rating_data.shape[0]+1]) | pandas.DataFrame |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
pd.set_option('display.max_columns', 500)
def clean_features(data, type):
df = pd.DataFrame(data)
# df = df.drop("PassengerId", axis=1)
df.set_index("PassengerId")
df = df.drop(columns=['Cabin', 'Name', 'Tick... | pd.cut(df["Faily_count"], bins=[-1, 0, 3, 7, 16], labels=["Alone", "Small Family", "Medium Family", "Big Family"]) | pandas.cut |
import tensorflow as tf
import numpy as np
import scipy.io as sio
import pandas as pd
import os
import csv
from feature_encoding import *
from keras.models import load_model
from keras.utils import to_categorical
import Efficient_CapsNet_sORF150
import Efficient_CapsNet_sORF250
import lightgbm as lgb
from sklearn.metri... | pd.read_csv(datapath + 'fruitfly_cds_trainp_framed_3mer_1.csv', header=None, delimiter=',') | pandas.read_csv |
import pandas as pd
import numpy as np
import os
import errno
import re, arrow
import warnings, glob
'''
time_to_numeric: convert time to excel numeric format
read_tecplot: read single zone multi columns tecplot file
read_csv: load csv and change time to numeric on the fly
colum_match: find the common row or merge dat... | pd.merge(left=left, how='inner', right=right, left_on=left_on, right_on=right_on) | pandas.merge |
from SPARQLWrapper import SPARQLWrapper, JSON
import pandas as pd
import pickle, hashlib
class QTLSEARCH:
def __init__(self, search, qtls, go_annotations):
self.qtls = qtls
self.search = search
self.go_annotations = go_annotations
self.p_uniprot_reviewed = 1.0... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
codes = pd.read_csv("./data/London_District_codes.csv")
socio = pd.read_spss("./data/London_ward_data_socioeconomic.sav")
health = pd.read_sas("./data/london_ward_data_health.sas7bdat",
format='sas7bdat', encoding='latin1')
health = health.drop('Population2011Census', axis=1)
... | pd.merge(total_df, health, on='District') | pandas.merge |
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