prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
"""
.. _example5:
Fifth Example: Demultiplexor - multiplexor
-----------------------------------------------
An imaginative layout using a classifier to predict the cluster labels and fitting a separate model for each cluster.
Steps of the **PipeGraph**:
- **scaler**: A :class:`MinMaxScaler` data preprocessor
- **c... | pd.concat([y_first, y_second, y_third], axis=0) | pandas.concat |
import pandas as pd
import numpy as np
from pathlib import Path
from compositions import *
RELMASSS_UNITS = {
'%': 10**-2,
'wt%': 10**-2,
'ppm': 10**-6,
'ppb': 10**-9,
'ppt': 10**-12,
'ppq': 10**-15,
... | pd.isna(self.data.loc[i, 'value']) | pandas.isna |
import matplotlib
import pandas as pd
import numpy as np
import cvxpy as cp
from cvxopt import matrix, solvers
import pickle
import matplotlib.pyplot as plt
import os
from tqdm import tqdm
from colorama import Fore
from config import RISK_FREE_RATE, DATAPATH, EXPECTED_RETURN, STOCKS_NUMBER, MONTO_CARLO_TIMES
... | pd.DataFrame(columns=['HS300', 'Portfolio', "Period"]) | pandas.DataFrame |
"""Tests for piece.py"""
from fractions import Fraction
import pandas as pd
import numpy as np
from harmonic_inference.data.data_types import KeyMode, PitchType
from harmonic_inference.data.piece import Note, Key, Chord, ScorePiece, get_reduction_mask
import harmonic_inference.utils.harmonic_constants as hc
import ha... | pd.Series(key_dict) | pandas.Series |
# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/feature_testing.ipynb (unless otherwise specified).
__all__ = ['get_tabular_object', 'train_predict', 'SPLIT_PARAMS', 'hist_plot_preds', 'BoldlyWrongTimeseries']
# Cell
from loguru import logger
from fastai.tabular.all import *
from ashrae import loading, preprocessing,... | pd.NamedAgg(column='difference', aggfunc=fun) | pandas.NamedAgg |
# coding=utf-8
from datetime import datetime
from wit import Wit
from string import Template
from time import sleep
from collections import namedtuple
from pathlib import Path
import pandas as pd
import deepcut
import os
import glob
import pickle
import config
toq_key = config.toq_key
say_key = config.say_key
sub_key ... | pd.DataFrame({'time': a1, 'name': a2, 'text': a3}) | pandas.DataFrame |
"""
PIData contains a number of auxiliary classes that define common functionality
among :class:`PIPoint` and :class:`PIAFAttribute` objects.
"""
# pragma pylint: disable=unused-import
from __future__ import absolute_import, division, print_function, unicode_literals
from builtins import (
ascii,
bytes,
c... | DataFrame() | pandas.DataFrame |
import pandas as pd
import datetime
from apscheduler.schedulers.background import BackgroundScheduler
def round_datetime_to_minute(dt):
dt = dt - datetime.timedelta(seconds=dt.second, microseconds=dt.microsecond)
return dt
class Occurrences:
def __init__(self):
self.update_graph = (lambda: print... | pd.DataFrame({'Datetime': [now_floored], 'Cnt': [0]}) | pandas.DataFrame |
import streamlit as st
import os
import pandas as pd
import numpy as np
import datetime
import plotly.express as px
import plotly as plty
import seaborn as sns
import country_converter as coco
from bokeh.io import output_file, show, output_notebook, save
from bokeh.plotting import figure
from bokeh.models import Colum... | pd.concat([sets_grouped[0][yesterday], top_death], axis=1, join='inner') | pandas.concat |
#!/home/twixtrom/miniconda3/envs/analogue/bin/python
##############################################################################################
# run_wrf.py - Code for calculating the best member over a date range
#
#
# by <NAME>
# Texas Tech University
# 22 January 2019
#
##########################################... | pd.to_datetime('2016-05-11T12:00:00') | pandas.to_datetime |
# coding: utf-8
# Create input features for the boosted decision tree model.
import os
import sys
import math
import datetime
import pandas as pd
from sklearn.pipeline import Pipeline
from common.features.lag import LagFeaturizer
from common.features.rolling_window import RollingWindowFeaturizer
from common.features... | pd.merge(data_filled, aux_df, how="left", on=["store", "brand", "week"]) | pandas.merge |
#!/usr/bin/python3
import sys
import pandas as pd
import numpy as np
import os
import concurrent.futures
import functools, itertools
import sofa_time
import statistics
import multiprocessing as mp
import socket
import ipaddress
# sys.path.insert(0, '/home/st9540808/Desktop/sofa/bin')
import sofa_models, sofa_preproce... | pd.DataFrame(modified_rows) | pandas.DataFrame |
"""Classes that represent production profiles"""
import numpy as np
import pandas as pd
from palantir.facilities import OilWell
from scipy import optimize
# Initial estimate of Di for newton.optimize
OIL_WELL_INITIAL_DI = 0.000880626223092
GAS_WELL_INITIAL_DI = 0.000880626223092 # TODO check this
def _decline(di, ... | pd.date_range(well.start_date, periods=well.active_period) | pandas.date_range |
import argparse
import logging
import os
import tqdm
import numpy as np
import pandas as pd
from sys import getsizeof
def arg_parser():
description = ("Merge FCAS data in directories to parquet chunks.\n"
+ "Indexed on sorted datetime column to improve Dask speed")
parser = argparse.Argum... | pd.read_csv(path, header=None) | pandas.read_csv |
import pandas as pd
import random
import itertools
def create_net_edges(start_node, end_node):
node1 = random.randint(start_node,end_node)
node2 = random.randint(start_node,end_node)
return node1, node2
def list_edges(n_edges, start_node, end_node):
edges = [(create_net_edges(start_node, end_node)) f... | pd.DataFrame() | pandas.DataFrame |
"""
accounting.py
Accounting and Financial functions.
project : pf
version : 0.0.0
status : development
modifydate :
createdate :
website : https://github.com/tmthydvnprt/pf
author : tmthydvnprt
email : <EMAIL>
maintainer : tmthydvnprt
license : MIT
copyright : Copyright 2016, tmthydvnprt
cr... | pd.concat(balance_sheets, 1) | pandas.concat |
from logging import NullHandler
from numpy.__config__ import show
from pkg_resources import yield_lines
import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import matplotlib.pyplot as plt
from streamlit_ech... | pd.read_csv(path + "/assets/df_druggable.csv") | pandas.read_csv |
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) | pandas.MultiIndex.from_tuples |
"""
A minimalistic version helper in the spirit of versioneer, that is able to run without build step using pkg_resources.
Developed by <NAME>, see https://github.com/flying-sheep/get_version.
"""
# __version__ is defined at the very end of this file.
import re
import os
from pathlib import Path
from subprocess import... | pd.DataFrame(all_dependencies_list[::-1], columns=["package", "version"]) | pandas.DataFrame |
"""
Functions for writing a directory for iModulonDB webpages
"""
import logging
import os
import re
from itertools import chain
from zipfile import ZipFile
import numpy as np
import pandas as pd
from matplotlib.colors import to_hex
from tqdm.notebook import tqdm
from pymodulon.plotting import _broken_line, _get_fit... | pd.concat([cutoff_row, res]) | pandas.concat |
""" breaks down by-cell variants table to by-sample
also condensing ROIs to genes """
import pandas as pd
def driver():
""" loops through variant_df, matches cells to samples, and fills in
samples_x_gene with read count values """
for i in range(0,len(variant_df.index)):# looping over by-cell df
currCell = v... | pd.notna(samples_x_gene_sub['gene']) | pandas.notna |
from __future__ import print_function
import unittest
from unittest import mock
from io import BytesIO, StringIO
import random
import six
import os
import re
import logging
import numpy as np
import pandas as pd
from . import utils as test_utils
import dataprofiler as dp
from dataprofiler.profilers.profile_builder ... | pd.Series(['this', 'is my', '\n\r', 'test']) | pandas.Series |
import pandas as pd
import numpy as np
from datetime import datetime
def consolidate():
#################################################################################
# Read in Data
bene_train = pd.read_csv('./data/Train_Beneficiary.csv')
inpat_train = pd.read_csv('./data/Train_Inpatient.csv')
outpat_train... | pd.read_csv('./data/Train.csv') | pandas.read_csv |
"""
the battery.py document contains the battery class which sets up the test battery and runs the different components
and the pipe class which handles the tracking and support for MP runs
maybe it would make sense to put the folder dict here and pass it on to make it more consistent
currently it's in basetest and sa... | pd.Series() | pandas.Series |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import scipy.special
import scipy.optimize
import scipy.io
import glob
# Import the project utils
import sys
sys.path.insert(0, '../')
import NB_sortseq_utils as utils
# Import matplotlib stuff for plotting
import matplotlib.pyplot as plt
import m... | pd.DataFrame() | pandas.DataFrame |
import unittest
import pathlib
import os
import pandas as pd
from enda.contracts import Contracts
from enda.timeseries import TimeSeries
class TestContracts(unittest.TestCase):
EXAMPLE_A_DIR = os.path.join(pathlib.Path(__file__).parent.absolute(), "example_a")
CONTRACTS_PATH = os.path.join(EXAMPLE_A_DIR, "co... | pd.to_datetime("2020-09-30") | pandas.to_datetime |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.odr
import itertools
def computeModelDetails(frame):
""" Takes a dataframe and computes columns related to the dynamical frb model """
tauwerror_expr = lambda r: 1e3*r['time_res']*np.sqrt(r['max_sigma']**6*r['min_sigma_error']**2*np... | pd.concat([f for f in frames]) | pandas.concat |
import numpy as np
import pandas as pd
# The order of the pixel colors - RGB or GRB. Some NeoPixels have red and green reversed!
# For RGBW NeoPixels, simply change the ORDER to RGBW or GRBW.
# NeoPixels must be connected to D10, D12, D18 or D21 to work.
# %%
class Screen:
def __init__(self,
nr... | pd.DataFrame() | pandas.DataFrame |
import random
import unittest
import numpy as np
import pandas as pd
from haychecker.chc.metrics import deduplication
class TestDeduplication(unittest.TestCase):
def test_singlecolumns_empty(self):
df = pd.DataFrame()
df["c1"] = []
df["c2"] = []
r1, r2 = deduplication(["c1", "c2... | pd.DataFrame() | pandas.DataFrame |
# Copyright (c) 2018-2021, NVIDIA CORPORATION.
import array as arr
import datetime
import io
import operator
import random
import re
import string
import textwrap
from copy import copy
import cupy
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from numba import cuda
import cudf
from cudf.c... | pd.Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) | pandas.Index |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 22 16:30:38 2019
input/output operation.
@author: zoharslong
"""
from base64 import b64encode, b64decode
from numpy import ndarray as typ_np_ndarray
from pandas.core.series import Series as typ_pd_Series # 定义series类型
from pandas.core.... | pd_DataFrame([self.dts]) | pandas.DataFrame |
from __future__ import division
import logging
import os.path
import pandas as pd
import sys
# #find parent directory and import base (travis)
# parentddir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))
# sys.path.append(parentddir)
from base.uber_model import UberModel, ModelSharedInputs
... | pd.Series([], dtype="float", name="out_cf") | pandas.Series |
"""
Test output formatting for Series/DataFrame, including to_string & reprs
"""
from datetime import datetime
from io import StringIO
import itertools
from operator import methodcaller
import os
from pathlib import Path
import re
from shutil import get_terminal_size
import sys
import textwrap
import dateutil
import ... | DataFrame({"x": [12345.6789, 2e6]}) | pandas.DataFrame |
import torch
import pandas as pd
from torch.utils.data import Dataset
import h5pickle as h5py
import io
import os
import numpy as np
from sklearn import preprocessing
import yaml
class ParticleJetDataset(Dataset):
"""CMS Particle Jet dataset."""
def __init__(self, dataPath, yamlPath=None, normalize=True, file... | pd.DataFrame(self.h5File["jets"][:], columns=columns_arr) | pandas.DataFrame |
"""
Tests that work on both the Python and C engines but do not have a
specific classification into the other test modules.
"""
from io import StringIO
import numpy as np
import pytest
from pandas import DataFrame, Series
import pandas._testing as tm
def test_int_conversion(all_parsers):
data = """A,B
1.0,1
2.0... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
import pandas as pd
def get_toy_data_seqclassification():
train_data = {
"sentence1": [
'Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .',
"Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billio... | pd.DataFrame(train_data) | pandas.DataFrame |
import pandas as pd
import numpy as np
import os
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from sklearn.model_selection import train_test_split
from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout, AveragePooling2D
from keras.models import Sequen... | pd.read_pickle('data/raw/train_annotations.pkl') | pandas.read_pickle |
'''
Scrape Robospect output and do some processing of the results
'''
import os
import sys
import glob
import logging
import pandas as pd
import numpy as np
import matplotlib
from astropy.io.fits import getdata
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import matplotlib.pylab as pl
from . import *
class... | pd.read_csv(read_in_filename) | pandas.read_csv |
# -*- coding: utf-8 -*-
import numpy as np
import pickle
from constants import *
import time
import json
import sys
import os
import utils
import base64
import generate_tf_record
import pandas as pd
import lightgbm as lgb
from datetime import datetime
import math
from collections import defaultdict
EVAL_NUM = 5
def ... | pd.concat([df1,df2],axis=1) | pandas.concat |
import sys
import traceback
import json
from copy import deepcopy
from uuid import uuid1
from datetime import datetime, timedelta
from time import sleep
from threading import Thread
from multiprocessing.dummy import Pool
from typing import Dict
import pandas as pd
from vnpy.event import Event
from vnpy.rpc import RpcC... | pd.concat([sz, sh]) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
# Reads in photometry from different sources, normalizes them, and puts them
# onto a BJD time scale
# Created 2021 Dec. 28 by E.S.
import numpy as np
import pandas as pd
from astropy.time import Time
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler... | pd.DataFrame(data = [t%1. for t in df_test2["baseline_div_period"]], columns=["phase"]) | pandas.DataFrame |
from pandas import DataFrame
from random import SystemRandom
def prepare_cards(num_decks=8):
"""
Prepare decks
:return: List of shuffled cards as integers, J, Q, K are represented by
11, 12, 13, respectively.
"""
sys_rand = SystemRandom()
# Init 8 decks
cards = [i for i in r... | DataFrame(data=data, index=titles) | pandas.DataFrame |
"""正式版"""
import pandas as pd
from pyecharts.chart import Chart
from pyecharts.option import get_all_options
def values2keys(dict):
"""取出任意字典中所有值升序排序后对应的所有键的列表形式"""
temp_list = []
temp_dict = {}
for k, v in dict.items():
temp_dict.setdefault(v, []).append(k)
for i in sorted(list(temp_d... | pd.read_excel("test_data.xlsx", header=None) | pandas.read_excel |
# Author: Group 404
# Date: 2020-01-23
#
"""Reads in raw csv data and performs the necessary wrangling and transformations.
Usage: src/EDA.py --path_in=<path_in> --path_out=<path_out>
Options:
--path_in=<path_in> Path (including filename) of where to read source data
--path_out=<path_out> Path (excluding filena... | pd.DataFrame.describe(train) | pandas.DataFrame.describe |
#!/usr/bin/env python
import os
import json
import pandas as pd
import xarray as xr
import abc
from typing import Tuple
from tqdm import tqdm
import numpy as np
from icecube.utils.common_utils import (
measure_time,
NumpyEncoder,
assert_metadata_exists,
)
from icecube.utils.logger import Logger
from icecub... | pd.isnull(row["product_fpath"]) | pandas.isnull |
from enum import Enum
from typing import List
import numpy as np
import pandas as pd
class AggregationMode(str, Enum):
"""Enum for different aggregation modes."""
mean = "mean"
max = "max"
min = "min"
median = "median"
AGGREGATION_FN = {
AggregationMode.mean: np.mean,
AggregationMode.m... | pd.Series(1, index=not_selected_features) | pandas.Series |
# 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', '2/29/2000') | pandas.tseries.index.date_range |
import os, sys, logging, warnings, time
import osmnx
import networkx as nx
import pandas as pd
import geopandas as gpd
import numpy as np
from shapely.geometry import Point
from .core import pandana_snap
from .core import calculate_OD as calc_od
def calculateOD_gdf(G, origins, destinations, fail_value=-1, weight="ti... | pd.read_csv(originCSV) | pandas.read_csv |
#!/usr/bin/env python
# ------------------------------------------------------------------------------------------------------%
# Created by "Thieu" at 14:05, 28/01/2021 %
# ... | to_numeric(df_full["Fit2"]) | pandas.to_numeric |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import json
import folium
import requests
import plotly.graph_objects as go
from sklearn.linear_model import LinearRegression
import streamlit as st
from streamlit_folium import folium_static
import streamlit.components.v1 as components
from bs4 i... | pd.read_csv("ct.csv") | pandas.read_csv |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
NAME:
debug_inp.py
DESCRIPTION:
debugs and fixes with user input .inp format files of CIT (sam file) type data.
SYNTAX:
~$ python debug_inp.py $INP_FILE
FLAGS:
-h, --help:
prints this help message
-dx, --dropbox:
Prioritize user's... | pd.read_csv(inp_file, sep='\t', header=1, dtype=str) | pandas.read_csv |
"""
Genetic algorithm tools
Uses the same conventions as DEAP:
fitness values are stored in
p.fitness.values
p is a list
"""
import os
import numpy as np
import random
from copy import deepcopy
from collections import Sequence
from itertools import repeat
import hashlib
import math
import glob
impo... | pd.read_csv(filename, sep=' ') | pandas.read_csv |
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
import plotly as pl
import re
import requests
from .DataFrameUtil import DataFrameUtil as dfUtil
class CreateDataFrame():
"""Classe de serviços para a criação de dataframes utilizados para a construção dos gr... | pd.merge(dfPre, dfWorldMetersNew, on="Name") | pandas.merge |
import argparse
import glob
import math
import os
import time
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from numba import jit, prange
from sklearn import metrics
from utils import *
@jit(nopython=True, nogil=True, cache=True, parallel=True, fastmath=True)
def compute_tp_tn_fp_fn(y_true,... | pd.MultiIndex.from_arrays(arrays, names=('number', 'name')) | pandas.MultiIndex.from_arrays |
#!/usr/bin/env python
# -- coding: utf-8 --
# PAQUETES PARA CORRER OP.
import netCDF4
import pandas as pd
import numpy as np
import datetime as dt
import json
import wmf.wmf as wmf
import hydroeval
import glob
import MySQLdb
#modulo pa correr modelo
import hidrologia
from sklearn.linear_model import LinearRegression
... | pd.to_datetime(df_nobs0.index) | pandas.to_datetime |
#!/usr/bin/env python
# coding: utf-8
# In[2]:
import pandas as pd
import numpy as np
import glob,os
from glob import iglob
#import scanpy as sc
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import RocCurveDisplay
from sklearn.datasets import load_wine
from skle... | pd.read_csv('../script4paper2/combined_gene_for_machine_learning.csv',index_col=1) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Tests parsers ability to read and parse non-local files
and hence require a network connection to be read.
"""
import os
import nose
import pandas.util.testing as tm
from pandas import DataFrame
from pandas import compat
from pandas.io.parsers import read_csv, read_table
class TestUrlGz... | tm.get_data_path('tips.csv') | pandas.util.testing.get_data_path |
import pandas as pd
import numpy as np
from tqdm import tqdm
from dateutil.relativedelta import relativedelta
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.multioutput import RegressorChain
from sklearn.metrics import fbeta_score, mean_squared_error, r2_score
from sklearn.prepr... | pd.DataFrame() | pandas.DataFrame |
'''
MIT License
Copyright (c) 2020 Minciencia
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, di... | pd.concat([regionTotal, blank_line], axis=0) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Sun May 3 10:34:57 2020
@author: hcji
"""
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 6 15:29:15 2019
@author: hcji
"""
import json
import numpy as np
import pandas as pd
from tqdm import tqdm
from scipy.sparse import load_npz
from DeepEI.utils import get_score, get_fp_sc... | pd.DataFrame(columns=['smiles', 'mass', 'score', 'rank', 'inNIST']) | pandas.DataFrame |
import os
os.chdir('osmFISH_Ziesel/')
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use('qt5agg')
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
import matplotlib.pyplot as plt
import scipy.stats as st
from matplotlib.lines import Line2D
import pickle... | pd.read_csv('Results/gimVI_LeaveOneOut.csv',header=0,index_col=0,sep=',') | pandas.read_csv |
import google_streetview.api
from google_streetview import helpers
import pandas as pd
import numpy as np
import os
import random
import imageio
import math
import h5py
# define parameters
earth_radius = 6271
grid_size = 6720 # m
fov = 120
res = 224
random.seed(42)
def getGridSample(lat, lon, n):
"""
Get a r... | pd.read_csv(r'C:/Users/nsuar/Google Drive/Carbon_emissions/urban_emissions_git/urban_emissions/01_Data/01_Carbon_emissions/Airnow/World_all_locations_2020_avg_clean.csv' ) | pandas.read_csv |
"""
Class Features
Name: drv_dataset_hmc_io_dynamic_forcing
Author(s): <NAME> (<EMAIL>)
Date: '20200401'
Version: '3.0.0'
"""
#######################################################################################
# Library
import logging
import warnings
import os
import re
import datetime... | pd.DatetimeIndex(time_stamp_list) | pandas.DatetimeIndex |
import wandb
from wandb import data_types
import numpy as np
import pytest
import os
import sys
import datetime
from wandb.sdk.data_types._dtypes import *
class_labels = {1: "tree", 2: "car", 3: "road"}
test_folder = os.path.dirname(os.path.realpath(__file__))
im_path = os.path.join(test_folder, "..", "assets", "test... | pd.DataFrame([[42], [42]]) | pandas.DataFrame |
import pandas as pd
from msi_recal.passes.transform import Transform
from msi_recal.plot import save_spectrum_image
class Normalize(Transform):
CACHE_FIELDS = [
'intensity',
'ref_vals',
]
def __init__(self, params, intensity='median', ref='tic'):
try:
self.intensity =... | pd.Series(self.ref_vals) | pandas.Series |
import datetime
import os
from concurrent.futures import ProcessPoolExecutor
from math import ceil
import pandas as pd
# In[] 读入源数据
def get_source_data():
# 源数据路径
DataPath = 'data/'
# 读入源数据
off_train = pd.read_csv(os.path.join(DataPath, 'ccf_offline_stage1_train.csv'),
par... | pd.merge(X, temp, how='left', on='User_id') | pandas.merge |
import pandas as pd
from rake_nltk import Rake
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import CountVectorizer
pd.set_option('display.max_columns', 100)
df = pd.read_csv('movie_metadata.csv')
print(df.head())
print(df.shape)
list(df.columns.values)... | pd.notnull(df) | pandas.notnull |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 2 14:24:25 2017
@author: ajaver
"""
from tierpsy.features.tierpsy_features.helper import get_n_worms_estimate, \
get_delta_in_frames, add_derivatives
from tierpsy.features.tierpsy_features.events import get_event_stats, event_region_labels, ev... | pd.Series() | pandas.Series |
#!/usr/bin/env python3
# Pancancer_Aberrant_Pathway_Activity_Analysis scripts/alternative_genes_pathwaymapper.py
import os
import sys
import pandas as pd
import argparse
import matplotlib.pyplot as plt
import seaborn as sns
import argparse
from sklearn.metrics import roc_auc_score, average_precision_score
sys.path.in... | pd.read_table(genes) | pandas.read_table |
from unittest import TestCase
from nose_parameterized import parameterized
from collections import OrderedDict
import os
import gzip
from pandas import (
Series,
DataFrame,
date_range,
Timestamp,
read_csv
)
from pandas.util.testing import assert_frame_equal
from numpy import (
arange,
zero... | Timestamp('2015-06-08', tz='UTC') | pandas.Timestamp |
# -*- coding: utf-8 -*-
"""EDA with Visualization.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1Anp_qii2EQ2tJDUBUSE4PNXOcLJpaS0v
# **SpaceX Falcon 9 First Stage Landing Prediction**
## Assignment: Exploring and Preparing Data
Estimated time... | pd.get_dummies(features["LandingPad"]) | pandas.get_dummies |
#%%
import os
import sys
os.chdir(os.path.dirname(os.getcwd())) # make directory one step up the current directory
from pymaid_creds import url, name, password, token
import pymaid
rm = pymaid.CatmaidInstance(url, token, name, password)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.py... | pd.DataFrame(fraction_cell_types_1o_us_scatter, columns = ['fraction', 'cell_type']) | pandas.DataFrame |
# coding: utf-8
# # PuLP testing
# In[32]:
import pulp
# Import PuLP modeler functions
from pulp import *
from funcs import store_namespace
from funcs import load_namespace
from funcs import emulate_jmod
import os
import datetime
import time
import pandas as pd
#from multiprocessing import Pool
from mpcpy import ... | pd.Series(0,index=index) | pandas.Series |
import typing
import warnings
import logging
import uuid
import os
from tqdm import tqdm
import pandas as pd
import imgaug
import cv2
from ..hashers import ImageHasher, tools
from ..tools import deduplicate, flatten
from .common import BenchmarkTransforms, BenchmarkDataset, BenchmarkHashes
# pylint: disable=invalid-... | pd.DataFrame.from_records(hash_dicts) | pandas.DataFrame.from_records |
# -*- coding: utf-8 -*-
import pandas as pd
INCIDENCE_BASE = 100000
# https://code.activestate.com/recipes/577775-state-fips-codes-dict/
STATE_TO_FIPS = {
"WA": "53",
"DE": "10",
"DC": "11",
"WI": "55",
"WV": "54",
"HI": "15",
"FL": "12",
"WY": "56",
"PR": "72",
"NJ": "34",
... | pd.isnull(map_df[colname]) | pandas.isnull |
import logging as _logging
import numpy as _np
import pandas as _pd
from gn_lib.gn_const import J2000_ORIGIN as _J2000_ORIGIN, C_LIGHT as _C_LIGHT, SISRE_COEF_DF as _SISRE_COEF_DF
from gn_lib.gn_io.common import path2bytes as _path2bytes
from gn_lib.gn_io.sp3 import diff_sp3_rac as _diff_sp3_rac, read_sp3 as _read_sp... | _pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import json as json
import os
select_columns = ['AGE', 'BBTYPE', 'ETHNICITY', 'GENDER', 'LOCATION', 'SAMPLEID']
def bb_sample_names():
bb_samples_df = pd.read_csv("belly_button_biodiversity_samples.csv")
bb_names_list = list(bb_samples_df.columns.values)[1:]
return bb_names_list
def ... | pd.read_csv("Belly_Button_Biodiversity_Metadata.csv") | pandas.read_csv |
#########################################################
### DNA variant annotation tool
### Version 1.0.0
### By <NAME>
### <EMAIL>
#########################################################
import pandas as pd
import numpy as np
import allel
import argparse
import subprocess
import sys
import os.path
import pickle... | pd.DataFrame() | pandas.DataFrame |
import pandas
import numpy as np
import matplotlib.pyplot as plt
def get_from_pie_plot(df, minimum_emails=25):
df["from"].value_counts()
dict_values = np.array(list(df["from"].value_counts().to_dict().values()))
dict_keys = np.array(list(df["from"].value_counts().to_dict().keys()))
ind = dict_values >... | pandas.to_datetime(df.date) | pandas.to_datetime |
import pandas as pd
import os, sys
from eternabench.stats import calculate_Z_scores
package_list=['vienna_2', 'vienna_2_60C', 'rnastructure', 'rnastructure_60C', 'rnasoft_blstar','contrafold_2','eternafold_B']
external_dataset_types = pd.read_csv(os.environ['ETERNABENCH_PATH']+'/eternabench/external_dataset_metadata... | pd.DataFrame() | pandas.DataFrame |
# -----------------------------------------------------------------------------
# WWW 2019 Debiasing Vandalism Detection Models at Wikidata
#
# Copyright (c) 2019 <NAME>, <NAME>, <NAME>, <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentati... | pd.DataFrame(result) | pandas.DataFrame |
import pandas as pd
import numpy as np
from scipy.stats import pearsonr, spearmanr, mannwhitneyu
from scripts.python.routines.manifest import get_manifest
from scripts.python.EWAS.routines.correction import correct_pvalues
from tqdm import tqdm
path = f"E:/YandexDisk/Work/pydnameth/datasets"
datasets_info = pd.read_e... | pd.read_pickle(f"{tmp_path}/betas.pkl") | pandas.read_pickle |
import unittest
from unittest import mock
from unittest.mock import MagicMock
import numpy as np
import pandas as pd
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from pandas.util.testing import assert_frame_equal
import tests.test_data as td
from shift_detector.checks.statistical_checks impor... | pd.MultiIndex.from_product([df1.columns, metadata_names], names=['column', 'metadata']) | pandas.MultiIndex.from_product |
# Functions and classes for visualization
def plot_by_factor(df, factor, colors, showplot=False):
''' Plot by factor on a already constructed
t-SNE plot.
'''
import matplotlib.pyplot as plt
listof = {} # this gets numbers to get the colors right
listnames = []
for i, j in enumerate(df[... | pd.DataFrame(cluster_dfs[i][j]) | pandas.DataFrame |
# This file is part of the
# Garpar Project (https://github.com/quatrope/garpar).
# Copyright (c) 2021, 2022, <NAME>, <NAME> and QuatroPe
# License: MIT
# Full Text: https://github.com/quatrope/garpar/blob/master/LICENSE
# =============================================================================
# IMPORTS
# =... | pd.Series(data={"stock0": 0.02, "stock1": 0.04}) | pandas.Series |
from MP import MpFunctions
import requests
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import pandas as pd
import plotly.graph_objs as go
import datetime as dt
import numpy as np
import warnings
warnings.filterwarnings('ignore')
app = ... | pd.Timestamp(date_time_close) | pandas.Timestamp |
#!/usr/bin/env python
"""
Represent connectivity pattern using pandas DataFrame.
"""
from collections import OrderedDict
import itertools
import re
from future.utils import iteritems
from past.builtins import basestring
import networkx as nx
import numpy as np
import pandas as pd
from .plsel import Selector, Select... | pd.isnull(row['io_x']) | pandas.isnull |
import pandas as pd
import matplotlib.pyplot as plt
import seaborn
"""
Item #01: Análise monovariada global dos preditores
- plotar histogramas
- calcular média, desvio padrão e assimetria
"""
# setando estilo e outras configs
seaborn.set()
# paths e arquivos
dataset = "datasets/glass.dat"
figpath = "figures/item1/"... | pd.DataFrame(columns=columns) | pandas.DataFrame |
"""
Scripts used to analyse data in the human_data directory and produce the
data files used for plotting.
"""
import argparse
import os.path
import json
import collections
import numpy as np
import pandas as pd
import sys
import tskit
import tqdm
data_prefix = "human-data"
def print_sample_edge_stats(ts):
"""... | pd.read_csv(source_file) | pandas.read_csv |
import logging
from typing import NamedTuple, Dict, List, Set, Union
import d3m
import d3m.metadata.base as mbase
import numpy as np
import pandas as pd
from common_primitives import utils
from d3m.container import DataFrame as d3m_DataFrame
from d3m.metadata import hyperparams as metadata_hyperparams
from d3m.metadat... | pd.isnull(data) | pandas.isnull |
# BUG: DatetimeIndex has become unhashable in 1.3.1? #42844
import random
import pandas as pd
print(pd.__version__)
# Right data
ts_open = pd.DatetimeIndex(
[
pd.Timestamp("2021/01/01 00:37"),
pd.Timestamp("2021/01/01 00:40"),
pd.Timestamp("2021/01/01 01:00"),
pd.Timestamp("2021/... | pd.Timestamp("2021/01/01 00:00") | pandas.Timestamp |
# -*- coding: utf-8 -*-
from datetime import timedelta
import operator
from string import ascii_lowercase
import warnings
import numpy as np
import pytest
from pandas.compat import lrange
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
Categorical, DataFrame, MultiIndex, Serie... | lrange(10) | pandas.compat.lrange |
from itertools import groupby
from sklearn.model_selection import train_test_split
from all_stand_var import conv_dict, vent_cols3
from all_own_funct import extub_group, memory_downscale, age_calc_bron
import all_own_funct as func
import os
from all_stand_var import all_cols
import pandas as pd
import numpy as... | pd.to_numeric(df['mon_hr'], errors='coerce') | pandas.to_numeric |
import os
import numpy as np
import pandas as pd
from sklearn.model_selection import KFold
import argparse
def _save_split(annotation, patients, labels, out_path):
os.makedirs(os.path.dirname(out_path), exist_ok=True)
annotation = annotation[annotation['Patient ID'].isin(patients)]
labels = set(labels)
... | pd.read_csv(path_to_data_entry) | pandas.read_csv |
import pandas as pd
import numpy as np
import seaborn as sns
import warnings
def createRowColorDataFrame( discreteStatesDataFrame, nanColor =(0,0,0), predeterminedColorMapping={} ):
""" Create color dataframe for use with seaborn clustermap
Args:
discreteStatesDataFrame (pd.DataFrame) : Dataframe con... | pd.DataFrame(colorMatrix, index=discreteStatesDataFrame.columns, columns=discreteStatesDataFrame.index ) | pandas.DataFrame |
import argparse
import logging
import pandas as pd
import pathlib
from pyspark.sql import SparkSession
from typing import List
from src import constants
from src.utils.logging import get_logger
from src.processing import recovery_analysis
logger = get_logger(__name__)
logger.setLevel(logging.INFO)
def _get_paisagen... | pd.DataFrame({}) | pandas.DataFrame |
"""
Tests that work on both the Python and C engines but do not have a
specific classification into the other test modules.
"""
import csv
from io import StringIO
from pandas import DataFrame
import pandas._testing as tm
from pandas.io.parsers import TextParser
def test_read_data_list(all_parsers):
parser = all... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
from configparser import ConfigParser
from election_anomaly import db_routines
from election_anomaly import db_routines as dbr
from election_anomaly.db_routines import create_cdf_db as db_cdf
from election_anomaly import munge_routines as mr
import pandas as pd
import numpy as np
import csv
from sqlalchemy.orm import ... | pd.read_excel(f_path,dtype=str,thousands=munger.thousands_separator) | pandas.read_excel |
from __future__ import absolute_import, division, print_function
import datetime
import pandas as pd
from config import *
def _analysis_create_members():
"""
Creates a table with members data
:return:
"""
logger.info("Creating members table")
members_metadata = pd.read_csv(members_metadata_pa... | pd.to_datetime(members_metadata['start_date']) | pandas.to_datetime |
#IMPORTS.......................................................................
import pandas as pd
from numpy import log2 as log
from sklearn.metrics import confusion_matrix
import seaborn as sns
import os # accessing directory structure
import numpy as np
import matplotlib.pyplot as plt
import random
e... | pd.DataFrame() | pandas.DataFrame |
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