prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
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# Copyright 2019-2020 The Lux 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | pd.read_csv("https://github.com/lux-org/lux-datasets/blob/master/data/airbnb_nyc.csv?raw=true") | pandas.read_csv |
from random import randint
import pandas as pd
import pytest
import janitor # noqa: F401
import janitor.timeseries # noqa: F401
@pytest.fixture
def timeseries_dataframe() -> pd.DataFrame:
"""
Returns a time series dataframe
"""
ts_index = | pd.date_range("1/1/2019", periods=1000, freq="1H") | pandas.date_range |
# Copyright 2017-present, Bill & <NAME> Foundation
#
# 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 ... | pd.Series(data=["var", "var"], name='var_label') | pandas.Series |
from datetime import date, datetime, timedelta
from dateutil import tz
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Index, Series, Timestamp, date_range
import pandas._testing as tm
class TestDatetimeIndex:
def test_setitem_with_datetime_tz(self):
# 168... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
# -*- 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... | Series(['foo', 'bar', NA, 'baz']) | pandas.Series |
import datetime as dt
import os
from datetime import datetime
from typing import List, Tuple
import numpy as np
import pandas as pd
from domain.demand_prediction_mode import DemandPredictionMode
# random.seed(1234)
np.random.seed(1234)
# torch.manual_seed(1234)
# torch.cuda.manual_seed_all(1234)
# torch.backends.cu... | pd.concat(node_list) | pandas.concat |
"""
execution environment: cdips, + pipe-trex .pth file in
/home/lbouma/miniconda3/envs/cdips/lib/python3.7/site-packages
python -u paper_plot_all_figures.py &> logs/paper_plot_all.log &
"""
from glob import glob
import datetime, os, pickle, shutil, subprocess
import numpy as np, pandas as pd
import matplotlib.pyplot ... | pd.read_csv(dfpath, sep=',') | pandas.read_csv |
from datetime import datetime
import pytest
from pandas import (
DatetimeIndex,
offsets,
to_datetime,
)
import pandas._testing as tm
from pandas.tseries.holiday import (
AbstractHolidayCalendar,
Holiday,
Timestamp,
USFederalHolidayCalendar,
USLaborDay,
USThanksgivingDay,
get_c... | get_calendar("USFederalHolidayCalendar") | pandas.tseries.holiday.get_calendar |
# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership. The Modin Development Team licenses this file to you under the
# Apache License, Version 2.0 (the "License"); you may not u... | pandas.DataFrame(result) | pandas.DataFrame |
# coding=utf-8
# Author: <NAME>
# Date: June 17, 2020
#
# Description: Calculates entropy-based on network PCA
#
#
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 500)
| pd.set_option('display.width', 1000) | pandas.set_option |
from qd.cae.dyna import D3plot
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
# import holoviews as hv
# from holoviews import dim, opts
#
# hv.extension('matplotlib')
# ===============================================================... | pd.DataFrame(initial_shape, columns=['x', 'y', 'z']) | pandas.DataFrame |
# Neural network for pop assignment
# Load packages
import tensorflow.keras as tf
from kerastuner.tuners import RandomSearch
from kerastuner import HyperModel
import numpy as np
import pandas as pd
import allel
import zarr
import h5py
from sklearn.model_selection import RepeatedStratifiedKFold, train_test_split
from s... | pd.DataFrame(top_freqs["freq"]) | pandas.DataFrame |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sys
import mlflow
import mlflow.sklearn
def eval_metrics(actual, pred):
rmse = np.sqrt(mean_squared_error(actual, pred))
mae = mean_absolute_error(actual, pred)
r2 = r2_score(actual, pred)
... | pd.get_dummies(ndf) | pandas.get_dummies |
import numpy as np
import pandas as pd
from hotspot import sim_data
from hotspot import Hotspot
def test_models():
"""
Ensure each model runs
"""
# Simulate some data
N_CELLS = 100
N_DIM = 10
N_GENES = 10
latent = sim_data.sim_latent(N_CELLS, N_DIM)
latent = pd.DataFrame(
... | pd.Series(umi_counts) | pandas.Series |
# -*- coding: utf-8 -*-
"""Make a curation sheet for the bioregistry."""
import pandas as pd
import bioregistry
from bioregistry.constants import BIOREGISTRY_MODULE
def descriptions():
"""Make a curation sheet for descriptions."""
columns = [
"prefix",
"name",
"homepage",
"d... | pd.DataFrame(rows, columns=columns) | pandas.DataFrame |
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.to_datetime(df_forecast2_state_R_L.date) | pandas.to_datetime |
from .get_tmy_epw_file import get_tmy_epw_file
from .get_noaa_isd_lite_file import get_noaa_isd_lite_file
from .meteorology import Meteorology
from .analyze_noaa_isd_lite_file import analyze_noaa_isd_lite_file
import tempfile
import pandas as pd
import numpy as np
import os
import pkg_resources
from typing import Tupl... | pd.Timedelta("1h") | pandas.Timedelta |
'''Perform clustering of single particle images based on their latent representations generated by cryoDRGN or cryoSPARC.'''
import sys
import os
import pickle
import numpy as np
import pandas as pd
import cryopicls
def main():
args = cryopicls.args.clustering.parse_args()
# Load particle metadata
if ... | pd.Series(data=cluster_labels, name='cluster') | pandas.Series |
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(['a', 'b', 'c', 'd'], dtype='object') | pandas.Index |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import json
import pandas as pd
import traceback as tb
def json_to_eventlog(file_path):
with open(file_path, "r", encoding = "utf-8") as f:
data = json.load(f)
f.close()
game = []
player = []
color = []
move = []
timestamp_1 = []... | pd.DataFrame({"game":game, "player":player, "color":color, "move":move, "turn":timestamp_1}) | pandas.DataFrame |
import datetime as dt
from functools import wraps
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import numpy.testing as npt
import pandas as pd
from pandas.util.testing import assert_frame_equal
from pandas.util.testing import assert_series_equal
import seaice.nasateam as nt
import ... | pd.Period('1978-11-01', freq='D') | pandas.Period |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# viewer.py - View aggregated i2p network statistics.
# Author: <NAME> <<EMAIL>>
# License: This is free and unencumbered software released into the public domain.
#
# NOTE: This file should never write to the database, only read.
import argparse
import datetime
import m... | pd.read_sql_query(query, conn) | pandas.read_sql_query |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import pickle
import shutil
import sys
import tempfile
import numpy as np
from numpy import arange, nan
import pandas.testing as pdt
from pandas import DataFrame, MultiIndex, Series, to_datetime
# dependencies testing specific
import pytest
import recordlinka... | DataFrame({'col': [1, 1, 1, nan, nan]}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
'''
General toolboxs
'''
import sys
import time
import inspect
import numpy as np
import pandas as pd
from functools import reduce, wraps
from random import randint, random, uniform
from dramkit.logtools.utils_logger import logger_show
from dramkit.speedup.multi_thread import SingleThread
PY... | pd.DataFrame({'v': series}) | pandas.DataFrame |
# coding=utf-8
"""
Module to apply a previously trained model to estimate the epigenome
for a specific cell type in a different species
"""
import os as os
import pandas as pd
import numpy as np
import numpy.random as rng
import operator as op
import multiprocessing as mp
import json as json
import pickle as pck
fro... | pd.HDFStore(fpath, 'r') | pandas.HDFStore |
# -*- 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... | Series(['a;b', 'a', 7]) | pandas.Series |
from __future__ import print_function
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from pandas import (Series, Index, Int64Index, Timestamp, Period,
DatetimeIndex, PeriodIndex, TimedeltaIndex,
Timedelta, timedelta_range, date_range, Float64Index... | tm.assert_index_equal(result, expected) | pandas.util.testing.assert_index_equal |
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
from config import test_snr_dB
import pandas as pd
from scipy.stats import ttest_1samp
def plot_paper_results(folder_envtfs, folder_stft):
sns.set(style="whitegrid")
df_env = pd.read_csv('models\\' + folder_envtfs + ... | pd.concat([df_env1, df_env2, df_env3, df_env4, df_env5, df_env6]) | pandas.concat |
import numpy
import pandas
import operator
import ema_workbench.analysis.prim
from ..scope.box import Box, Bounds, Boxes
from ..scope.scope import Scope
from .discovery import ScenarioDiscoveryMixin
from plotly import graph_objects as go
class Prim(ema_workbench.analysis.prim.Prim, ScenarioDiscoveryMixin):
def fi... | pandas.DataFrame( index=uncs, columns=['min','max']) | pandas.DataFrame |
import matplotlib.pyplot as plt, numpy as np, pandas as pd
from matplotlib.ticker import FuncFormatter # used in formatting log scales
import mpl_toolkits.basemap.pyproj as pyproj
import hydro
#%matplotlib inline
data = | pd.read_csv("stream.csv") | pandas.read_csv |
from collections import deque
from datetime import datetime
import operator
import re
import numpy as np
import pytest
import pytz
import pandas as pd
from pandas import DataFrame, MultiIndex, Series
import pandas._testing as tm
import pandas.core.common as com
from pandas.core.computation.expressions import _MIN_ELE... | DataFrame({"A": [np.nan, 3, np.nan]}, index=base) | pandas.DataFrame |
# Imports for python2 implementation.
from __future__ import print_function, unicode_literals
from __future__ import absolute_import, division
import sys, os
import numpy as np
import pandas as pd
import MDSplus as mds
import matplotlib as mpl
import matplotlib.pyplot as plt
fro... | pd.DataFrame(ne_filt, columns=self.temp_df.columns, index=self.temp_df.index) | pandas.DataFrame |
import os
from useful_scit.util.make_folders import make_folders
import pandas as pd
#####################################################################
# FILL IN FILEPATHS:
#####################################################################
# fill in path to project location (not including /OAS-DEV)
project_base_p... | pd.DataFrame.from_dict(_dic) | pandas.DataFrame.from_dict |
# Copyright 2017 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | pd.Series([18704962.0, 3075662.0, 1973955.0]) | pandas.Series |
#!/usr/bin/env python
# coding: utf-8
import yaml
import os, os.path
import requests
import pandas as pd
from app.util import StateAbbrLookup
ASTHMA_YAML_PATH = os.path.join(os.getcwd(), 'data/raw/asthma.yaml')
ASTHMA_CSV_PATH = os.path.join(os.getcwd(), 'data/cleaned/asthma/all.csv')
lookup = StateAbbrLookup()
def ... | pd.DataFrame(columns=columns) | 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... | DataFrame({0: ['some_equal', 'with_no'], 1: ['splits', 'nans']}) | pandas.DataFrame |
import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
isna,
notna,
)
import pandas._testing as tm
def test_expanding_corr(series):
A = series.dropna()
B = (A + np.random.randn(len(A)))[:-5]
result = A.expanding().corr(B)
... | tm.assert_frame_equal(df1_result, df1_expected) | pandas._testing.assert_frame_equal |
"""Thermal grid models module."""
import itertools
from multimethod import multimethod
import numpy as np
import pandas as pd
import scipy.constants
import scipy.sparse as sp
import scipy.sparse.linalg
import typing
import mesmo.config
import mesmo.data_interface
import mesmo.der_models
import mesmo.solutions
import ... | pd.MultiIndex.from_frame(thermal_grid_data.thermal_grid_ders[["der_type", "der_name"]]) | pandas.MultiIndex.from_frame |
'''
Created on 17 Nov 2017
@author: husensofteng
'''
import matplotlib
matplotlib.use('Agg')
from matplotlib.pyplot import tight_layout
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import sys
import pandas as pd
import numpy as np
import seaborn as sns
import psycopg2
sns.set_style("white")
#... | pd.DataFrame(query_results, columns=cols) | pandas.DataFrame |
from collections import OrderedDict
from datetime import datetime, timedelta
import numpy as np
import numpy.ma as ma
import pytest
from pandas._libs import iNaT, lib
from pandas.core.dtypes.common import is_categorical_dtype, is_datetime64tz_dtype
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
Da... | Series(data) | pandas.Series |
# Data Science with SQL Server Quick Start Guide
# Chapter 03
# This is a comment
print("Hello World!")
# This line is ignored - it is a comment again
print('Another string.')
print('O"Brien') # In-line comment
print("O'Brien")
# Simple expressions
3 + 2
print("The result of 5 + 30 / 6 is:", 5 + 30 / 6)... | pd.read_sql(query, con) | pandas.read_sql |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import re
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import TruncatedSVD
from sklearn import preprocessing, model_select... | pd.DataFrame(enc_mat_test) | pandas.DataFrame |
import glob
from astropy.io import ascii
from astropy.table import Table
from astropy.io import fits
from astropy.time import Time
import os
import numpy as np
import matplotlib
matplotlib.use('Qt5Agg')
import matplotlib.pyplot as plt
import pandas as pd
# Create the dataframe to be filled later :
Sourc... | pd.DataFrame.from_dict(items) | pandas.DataFrame.from_dict |
import sys
import pandas as pd
not_included_gene_file = './not_included_genes.txt'
gene_file = '../../ecoli_refgene/ecoli_refgene.txt'
raw_file = './raw_data.txt'
# load data
with open(not_included_gene_file) as f:
not_included_genes = f.readlines()
not_included_genes = [row.strip() for row in not_included_genes]... | pd.read_csv(raw_file, sep='\t') | pandas.read_csv |
#------------------------------------------------------------------------------
# Libraries
#------------------------------------------------------------------------------
# Standard
import numpy as np
import pandas as pd
import cvxpy as cp
from sklearn.preprocessing import PolynomialFeatures
from statsmodels.tools.too... | pd.Series(data=W,name="W") | pandas.Series |
import numpy as np
import pandas as pd
import yfinance as yf
from yahoo_earnings_calendar import YahooEarningsCalendar
import mktanalytics as ma
from tqdm.notebook import tqdm
def nearest(items, pivot):
return min(items, key=lambda x: abs(x - pivot))
def get_atm_vol(undl_list, weeks=1, calc_strangle=False, target_pr... | pd.Timestamp(x) | pandas.Timestamp |
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import decomposition
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score
def run_train(fold):
df = pd.read_csv('../input/train_folds.csv')
df.review = df.review.apply(str)
... | pd.concat(dfs) | pandas.concat |
import argparse
import numpy as np
import pandas as pd
from scipy import stats
EXPRESSION_MATRIX_METADATA = ['Genotype', 'Genotype_Group', 'Replicate', 'Condition', 'tenXBarcode']
RANDOM_SEED = 42
def main():
ap = argparse.ArgumentParser(description="Create a synthetic UMI count table")
ap.add_argument("-d", ... | pd.read_csv(single_cell_file_name, sep="\t", header=0, index_col=0) | pandas.read_csv |
import numpy as np
from scipy.io import loadmat
import os
from pathlib import Path
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
# plotting parameters
sns.set(font_scale=1.1)
sns.set_context("talk")
sns.set_palette(['#701f57', '#ad1759', '#e13342', '#f37651'])
transparent = False
marke... | pd.DataFrame() | pandas.DataFrame |
######### imports #########
from ast import arg
from datetime import timedelta
import sys
sys.path.insert(0, "TP_model")
sys.path.insert(0, "TP_model/fit_and_forecast")
from Reff_constants import *
from Reff_functions import *
import glob
import os
from sys import argv
import arviz as az
import seaborn as sns
import m... | pd.date_range(start="2020-03-01", end=first_end_date) | pandas.date_range |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import scipy.integrate
import scipy.special
import collections
import fisx
import logging
from contextlib import contextmanager
from ..utils import instance
from ..utils import cache
from ..utils import listtools
from ..math import fit1d
from ..math.utils... | pd.concat(probs, sort=True) | pandas.concat |
# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %%
from dataclasses import dataclass
from typing import List, Tuple, Dict, Optional
from datetime import date
from datetime import timedelta
from pathlib import Path
import click
import pandas as pd
import numpy as np
from scipy.st... | pd.DataFrame(comms) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
单变量分析中常用工具，主要包含以下几类工具：
1、自动分箱（降基）模块：包括卡方分箱、Best-ks分箱
2、基本分析模块，单变量分析工具，以及woe编码工具，以及所有变量的分析报告
3、单变量分析绘图工具，如AUC，KS，分布相关的图
"""
# Author: <NAME>
import numpy as np
import pandas as pd
from abc import abstractmethod
from abc import ABCMeta
from sklearn.utils.multiclass import type_of_target
fro... | pd.set_option("display.max_columns", None) | pandas.set_option |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.10.3
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Processing workflow for th... | pd.Series({91497: 1}) | pandas.Series |
#Author: <NAME>
#Created: July 13th 2018
import pandas as pd
import numpy as np
import nltk
import string
import csv
from sklearn.model_selection import train_test_split
from sklearn.ensemble import VotingClassifier
from mlxtend.classifier import StackingCVClassifier
#read in each of the feature csv files
class_label... | pd.read_csv('char_bigram_features.csv',encoding='utf-8') | pandas.read_csv |
#!/bin/env python3
"""create_csv_of_kp_predicate_triples.py
Creates a CSV of all predicate triples of the form (node type, edge type, node type) for KG1, KG2, and BTE (ARAX's current knowledge providers).
Resulting columns are: subject_type, edge_type, object_type
Usage: python create_csv_of_kp_predicate_triples.py
... | pd.DataFrame(labels_dict) | pandas.DataFrame |
import sys
from collections import deque
import numpy as np
import pandas as pd
import os
from sqlalchemy import create_engine
import re
import nltk
nltk.download('punkt')
nltk.download('stopwords')
from sklearn.multioutput import MultiOutputClassifier
from sklearn.linear_model import *
from sklearn.pipeline import P... | pd.read_sql_table('P1Data', engine) | pandas.read_sql_table |
#
# Copyright 2015 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | pd.Timestamp('2014-01-01') | pandas.Timestamp |
import numpy as np
import pandas as pd
from pandas.api.types import is_string_dtype
from pathlib import Path
import re
from typing import Hashable, List, Tuple, Union
import zipfile
EMME_ENG_UNITS = {
'p': 1E-12,
'n': 1E-9,
'u': 1E-6,
'm': 0.001,
'k': 1000.0,
'M': 1E6,
'G': 1E9,
'T': 1E... | is_string_dtype(df[col]) | pandas.api.types.is_string_dtype |
"""Author: <NAME>
This contains the main Spomato class to be used to access the Spotify API and create new playlists based on the user's
defined criteria.
"""
import os
import pandas as pd
import spotipy
class Spomato():
"""Object used to access spotify API through spotipy and generate playlists.
This can ... | pd.concat(series_list, axis=1) | pandas.concat |
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from cde.density_estimator import LSConditionalDensityEstimation, NeighborKernelDensityEstimation, KernelMixtureNetwork
from matplotlib.lines import Line2D
import pandas as pd
from cde.density_simulation import GaussianMixture, EconDensity
from cd... | pd.DataFrame(d) | pandas.DataFrame |
import numpy as np
import pandas as pd
from numba import njit, typeof
from numba.typed import List
from datetime import datetime, timedelta
import pytest
from copy import deepcopy
import vectorbt as vbt
from vectorbt.portfolio.enums import *
from vectorbt.generic.enums import drawdown_dt
from vectorbt.utils.random_ im... | pd.Series([0.0], index=['Max Winning Streak'], name='a') | pandas.Series |
# -*- 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(p, p_orig) | pandas.util.testing.assert_panel_equal |
from typing import List
import numpy as np
import pandas as pd # type: ignore
import copy
import pdb
from sklearn.model_selection import TimeSeriesSplit # type: ignore
import dask
import dask.dataframe as dd
##### This function loads a time series data and sets the index as a time series
def load_ts_data(f... | pd.to_datetime(ts_index) | pandas.to_datetime |
# coding=utf-8
# Copyright 2018 The TF-Agents 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | pd.DataFrame(agg_data, columns=columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pandas as pd
from zvt.contract.api import df_to_db
from zvt.contract.recorder import Recorder
from zvt.domain.quotes.bond import Bond1dKdata
from zvt.utils.pd_utils import pd_is_not_null
from zvt.utils.time_utils import to_time_str, now_pd_timestamp, TIME_FORMAT_DAY
try:
from EmQuantA... | pd.to_datetime(df['timestamp']) | pandas.to_datetime |
# coding=utf-8
import torch
import re
import pandas as pd
import json
from torch.nn.utils.rnn import pad_sequence
from seqeval.metrics import precision_score, recall_score, f1_score
from torch.utils.data import Dataset
def evaluate_(output, labels, ignore_idx):
### ignore index 0 (padding) when calculating accura... | pd.DataFrame(data={'sents': sents, 'relations': relations}) | pandas.DataFrame |
# the goal of the file is to develop the ada_boost algorithm
import pandas as pd
import numpy as np
import os
import time
import matplotlib.pyplot as plt
import multiprocessing
from joblib import Parallel, delayed
t0 = time.time()
def beta_cal(epsolon):
beta = 1/((1-epsolon)/epsolon)
return beta
def weight_... | pd.Series(Distribution) | pandas.Series |
import collections
from datetime import timedelta
from io import StringIO
import numpy as np
import pytest
from pandas._libs import iNaT
from pandas.compat.numpy import np_array_datetime64_compat
from pandas.core.dtypes.common import needs_i8_conversion
import pandas as pd
from pandas import (
Dat... | Interval(0.997, 3.0) | pandas.Interval |
# -*- coding: utf-8 -*-
'''
This code generates Fig. 1
Trend of global mean surface temperature and anthropogenic aerosol emissions
by <NAME> (<EMAIL>)
'''
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import _env
import seaborn.apionly as sns
import matplotlib
from scipy import stats
... | pd.read_excel(if_temp_pi,index_col=0) | pandas.read_excel |
# encoding: utf-8
# (c) 2017-2019 Open Risk (https://www.openriskmanagement.com)
#
# TransitionMatrix is licensed under the Apache 2.0 license a copy of which is included
# in the source distribution of TransitionMatrix. This is notwithstanding any licenses of
# third-party software included in this distribution. You ... | pd.to_datetime(start_date) | pandas.to_datetime |
# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership. The Modin Development Team licenses this file to you under the
# Apache License, Version 2.0 (the "License"); you may not u... | pandas.Series(pandas_series, index=[1, 2]) | pandas.Series |
# Third party modules
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def sir_step(S, I, R, beta, gamma, N):
Sn = (-beta * S * I) + S
In = (beta * S * I - gamma * I) + I
Rn = gamma * I + R
Sn, Rn, In = (0 if x < 0 else x for x in [Sn, Rn, In])
scale = N / ... | pd.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
# pylint: disable=C0103, C0303
from __future__ import absolute_import
#from builtins import (bytes, str, open, super, range,
# zip, round, input, int, pow, object)
import os
import io
import csv
import gzip
import zipfile
import re
import datetime
import pytz
import arrow
import iso8601
import trac... | pd.DataFrame() | pandas.DataFrame |
import pytest
import pandas as pd
import goldenowl.asset.asset as at
def get_prdata():
date_range =[elem for elem in | pd.date_range(start="1990-01-01",end="2000-01-01", freq='1D') | pandas.date_range |
# -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <codecell>
import sys
sys.path.append('/home/will/PatientPicker/')
# <codecell>
import LoadingTools
# <codecell>
redcap_data = LoadingTools.load_redcap_data().groupby(['Patient ID', 'VisitNum']).first()
# <codecell>
cols = ['Date Of Visit']+[col for col in re... | pd.concat(res, axis=0, ignore_index=True) | pandas.concat |
import sys
import os
import numpy as np
import subprocess as sp
import multiprocessing as mp
import pandas as pd
from pyhdf.SD import SD, SDC
from itertools import repeat
import pickle
import datetime
from time import time
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from mpl_toolkits.mplot3d... | pd.concat(list_of_df, ignore_index=True) | pandas.concat |
'''
@lptMusketeers 2017.10.20
'''
import pandas as pd
import datetime
from functools import reduce
import codecs
import csv
from decimal import *
import numpy as np
class FeatureEngineering(object):
def nondrop_precent(self,source_path,target_path):
print("nondrop_precent...")
df1 = p... | pd.merge(df1,df2,on="enrollment_id",how="left") | pandas.merge |
import os
import time
import numpy as np
import pandas as pd
import scipy.sparse as ssp
import scipy.stats as stats
import statsmodels.sandbox.stats.multicomp
from ete3 import Tree
from matplotlib import pyplot as plt
from numpy.lib.twodim_base import tril_indices
from scipy.cluster import hierarchy
# from plotnine i... | pd.DataFrame(temp_dict) | pandas.DataFrame |
from typing import List
from typing import Union
import pandas as pd
import pystac
import shapely
from google.cloud import bigquery
from google.oauth2 import service_account
from pystac.extensions.eo import AssetEOExtension
from pystac.extensions.eo import EOExtension
from pystac.extensions.projection import Projectio... | pd.concat(dfs) | pandas.concat |
import sys, os
import django
import csv
import calendar
import datetime
import re
import argparse
import openpyxl
import pandas as pd
from typing import TYPE_CHECKING, Dict, List, Optional
from pandas._typing import FilePathOrBuffer, Scalar
from django.core.mail import send_mail
from django.db import transaction
os.e... | pd.DataFrame(data, columns=column_names) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Generates the data needed for Supplementary Figure 3.
The figure is generated by the routine fig_2d_age_bdi.py
"""
import pandas as pd
import numpy as np
import datetime
import matplotlib.pyplot as plt
from scipy.stats import median_test
ref = datetime.date(2019, 12... | pd.DataFrame(saida_H) | pandas.DataFrame |
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([0.34, 0.84, 0.02], dtype='float') | pandas.Series |
import os
import glob
import pandas as pd
classes = os.listdir(os.getcwd())
for classf in classes:
#if os.path.isfile(classf) or classf == 'LAST':
#continue
PWD = os.getcwd() + "/" + classf + "/"
currentdname = os.path.basename(os.getcwd())
csvfiles=glob.glob(PWD + "/*.csv")
df = pd.DataFrame(columns=['im... | pd.read_csv(csvfile, index_col=0) | pandas.read_csv |
import requests
import pandas as pd
def sheet_to_df(access_token, sheet_id):
"""
Converts raw Smartsheet Sheet objects into a nice and tidy pandas DataFrame, just like mum used to make
For more detail, see: https://dataideas.blog/2018/11/13/loading-json-it-looks-simple-part-4/
:param access_to... | pd.DataFrame(columns=col_list) | pandas.DataFrame |
# Author: <NAME>
# Python Version: 3.6
## Copyright 2019 <NAME>
##
## This program is free software: you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 3 of the License, or
## (at your option) any la... | pd.concat([self.dep_tree, _dep_tree]) | pandas.concat |
import warnings
from pkg_resources import resource_filename
from tqdm import tqdm
import numpy as np
import pandas as pd
from tensorflow.keras.models import load_model
from sklearn.externals import joblib
# import concise
from mmsplice.utils import logit, predict_deltaLogitPsi, \
predict_pathogenicity, predict_spli... | pd.DataFrame(X_alt, columns=mmsplice_alt_modules) | 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_numpy_array_equal(df.values, expected) | pandas.util.testing.assert_numpy_array_equal |
#!/usr/bin/env python
# coding: utf-8
# # Data analyses with Python & Jupyter
# ## Introduction
#
# You can do complex biological data manipulation and analyses using the `pandas` python package (or by switching kernels, using `R`!)
#
# We will look at pandas here, which provides `R`-like functions for data manipu... | pd.read_csv('../data/testcsv.csv', sep=',') | pandas.read_csv |
import os
import time
from warnings import simplefilter
simplefilter("ignore")
import glob
import codecs
import pandas as pd
import numpy as np
import requests
import matplotlib.pyplot as plt
import json
import requests
from sklearn.externals import joblib
from matplotlib.gridspec import GridSpec
def refineBGInfo(bg... | pd.DataFrame(charlist) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from __future__ import division
from functools import wraps
import numpy as np
from pandas import DataFrame, Series
#from pandas.stats import moments
import pandas as pd
def simple_moving_average(prices, period=26):
"""
:param df: pandas dataframe object
:param period: periods fo... | pd.rolling_min(s, n) | pandas.rolling_min |
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
@pytest.fixture
def df_checks():
"""fixture dataframe"""
return pd.DataFrame(
{
"famid": [1, 1, 1, 2, 2, 2, 3, 3, 3],
"birth": [1, 2, 3, 1, 2, 3, 1, 2, 3],
"ht1": [2.... | pd.wide_to_long(single_val, ["x"], i="id", j="num") | pandas.wide_to_long |
#!/usr/bin/env python3
import pandas as pd
from pykakasi import kakasi
kakasi=kakasi()
kakasi.setMode('H', 'a')
kakasi.setMode('K', 'a')
kakasi.setMode('J', 'a')
conv = kakasi.getConverter()
pd.set_option('display.max_rows',1000)
listdf=pd.read_csv('crawl.txt', comment='#') #取得対象の読み込み
obsdf=pd.read_csv('obs.txt') ... | pd.merge(listdf,obsdf,on='観測所番号',how='left') | pandas.merge |
import os
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from .. import read_sql
@pytest.fixture(scope="module") # type: ignore
def mssql_url() -> str:
conn = os.environ["MSSQL_URL"]
return conn
@pytest.mark.xfail
def test_on_non_select(mssql_url: str) -> None:
query ... | pd.Series([0, 1, 2, 3, 4, 1314], dtype="int64") | pandas.Series |
# Some utilites functions for loading the data, adding features
import numpy as np
import pandas as pd
from functools import reduce
from sklearn.preprocessing import MinMaxScaler
def load_csv(path):
"""Load dataframe from a csv file
Args:
path (STR): File path
"""
# Load the file
df ... | pd.Series.autocorr(x, 24*7) | pandas.Series.autocorr |
#!/usr/bin/env python
"""
Author: <NAME>
Mail: <EMAIL>
Last updated: 24/04/2020
Takes a dataset and an optional tolerance as arguments
Produces a report file containing the confusion matrix, ACC, MCC and selected
threshold for 10 randomised cross validation runs on a 80/20 split of the
dataset.
It produces also a roc... | pd.concat(df_list) | pandas.concat |
"""
Original data:公司股市代號對照表.csv
Conditions:
1.單月營收歷月排名 1高
from 月營收創新高.xlsx
2.負債比 < 40%
季度
https://goodinfo.tw/StockInfo/StockList.asp?RPT_TIME=&MARKET_CAT=%E7%86%B1%E9%96%80%E6%8E%92%E8%A1%8C&INDUSTRY_CAT=%E8%B2%A0%E5%82%B5%E7%B8%BD%E9%A1%8D%E4%BD%94%E7%B8%BD%E8%B3%87%E7%94%A2%E6%AF%94%E6%9C%80%E9%AB%98%4... | pd.read_excel(file_path, sheet_name=f"{last_month}月") | pandas.read_excel |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
from src.metrics_helpers import score_predictions
def fit_predict(X, y, index):
"""Train and make in-sample prediction."""
... | pd.concat(dfs_model_eval, ignore_index=True) | pandas.concat |
import pandas as pd
import json
def get_dict_index():
""" Read the Title column of the excel file and assign it
to two index for each sheet and return the indices
"""
file1 = "data/dictionary/POS Dictionary.xlsx"
df1 = pd.read_excel(file1,
"Sheet2") # Read sheet2 first si... | pd.isnull(index2) | pandas.isnull |
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
@pytest.mark.parametrize("sort", [True, False])
def test_factorize(index_or_series_obj, sort):
obj = index_or_series_obj
result_codes, result_uniques = obj.factorize(sort=sort)
constructor = pd.Index
if is... | pd.Index([1.0, 2.0, np.nan]) | pandas.Index |
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