prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sun Jan 20 10:24:34 2019
@author: labadmin
"""
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 02 21:05:32 2019
@author: Hassan
"""
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.linear... | pd.read_csv("F:\\Projects\\Master\\Statistical learning\\project\\standing\\dataset10.csv",skiprows=4) | pandas.read_csv |
import chess
import chess.pgn
import chess.svg
import chess.engine
import re
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from datetime import datetime
from cairosvg import svg2png
#---------------------
STOCKFISH_PATH = '/usr/local/Cellar/sto... | pd.DataFrame(columns=['move','probability'], data=[[0,0.00]] + probArr) | pandas.DataFrame |
import tensorflow as tf
import tensorflow_probability as tfp
# from tensorflow.core.protobuf import config_pb2
import numpy as np
# import os
# from fit_model import load_data
import matplotlib.pyplot as plt
import time
import numbers
import pandas as pd
import tf_keras_tfp_lbfgs as funfac
from dotenv import load_doten... | pd.concat([dat, dat2], axis=0) | pandas.concat |
# EIA_CBECS_Land.py (flowsa)
# !/usr/bin/env python3
# coding=utf-8
"""
2012 Commercial Buildings Energy Consumption Survey (CBECS)
https://www.eia.gov/consumption/commercial/reports/2012/energyusage/index.php
Last updated: Monday, August 17, 2020
"""
import io
import pandas as pd
import numpy as np
from flowsa.locati... | pd.DataFrame(df_raw_data.loc[15:32]) | pandas.DataFrame |
import sys
sys.path.append(r'simulation_tool/') # multi_modal_simulation is found here
import ast
import muse_sc as muse
from multi_modal_simulation import multi_modal_simulator
import pandas as pd
import phenograph
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
import numpy as np
from sklear... | pd.read_csv('/exports/reum/tdmaarseveen/RA_Clustering/data/6_clustering/df_tfidf.csv', sep=',') | pandas.read_csv |
from __future__ import annotations
from pandas._typing import (
FilePath,
ReadBuffer,
)
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.inference import is_integer
from pandas.core.frame import DataFrame
from pandas.io.common import get_handle
from pandas.io.parsers.ba... | import_optional_dependency("pyarrow.csv") | pandas.compat._optional.import_optional_dependency |
from nltk import ngrams
import collections
import string
import tika
tika.initVM()
import re
from tika import parser
import pandas as pd
import PyPDF2
import os
import shutil
import ast
import numpy as np
import jellyfish
from fuzzywuzzy import fuzz
import dill
import click
from report_pattern_analysis import rec_separ... | pd.concat([series, df]) | pandas.concat |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import glob
def ecdf(data):
"""
Computes the empirical cumulative distribution function for a collection of provided data.
Parameters
----------
data : 1d-array, Pandas Series, or list
One-dimensional collection of data for whi... | pd.Series(samp_dict) | pandas.Series |
import os
import h5py
import numpy as np
import pandas as pd
from config import DATA_PATH
class Scaler:
def __init__(self, data):
self.mean = np.mean(data)
self.std = np.std(data)
def transform(self, data):
return (data - self.mean) / self.std
def inverse_transform(self, data):
return d... | pd.DataFrame(data) | pandas.DataFrame |
import streamlit as st
import pandas as pd
import altair as alt
from ml_model import *
#### Title ####
st.title("How To Get Away With Murder: Data Edition")
st.write("If Batman were to study data visualization, it might look something like this.")
st.markdown("<p>Data taken from the <a href='https://data.cityofchicag... | pd.read_json(crime_url) | pandas.read_json |
import pandas as pd
import numpy as np
from financePy import scraper as scr
from financePy import plotter
from scipy.optimize import minimize
from financePy import general_tools as gt
from financePy.estimators import finance_estimates as fe
"""
traili_ret_freq:
d,m,
dividens :
... | pd.Series(self.stocks) | pandas.Series |
import numpy as np
import pandas as pd
from pandas import (
get_dummies,
)
from numpy.linalg import lstsq
import warnings
# before version 0.0.3, still use epsilon when demean
def demean_dataframe(df, consist_var, category_col, epsilon=1e-8, max_iter=1e6):
"""
:param df: Dataframe
:param consist_var: L... | get_dummies(df_copy[min_cat]) | pandas.get_dummies |
"""Pandas/Numpy common recipes."""
import os
import scipy
import numpy as np
import pandas as pd
def rename_duplicates(series, delim="-"):
"""Rename duplicate values to be unique. ['a', 'a'] will become ['a', 'a-1'], for example.
:param series: series with values to rename
:type series: pandas.Series
... | pd.concat([df1, df2], axis=0) | pandas.concat |
import datetime
import numpy as np
import pandas as pd
import pandas.testing as pdt
from cape_privacy.pandas import dtypes
from cape_privacy.pandas.transformations import DateTruncation
from cape_privacy.pandas.transformations import NumericRounding
def _make_apply_numeric_rounding(input, expected_output, ctype, dt... | pd.Timestamp(year=2018, month=10, day=3, hour=9, minute=20, second=25) | pandas.Timestamp |
import re
import datetime
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
# ---------------------------------------------------
# Person data methods
# ---------------------------------------------------
class TransformGenderGetFromName:
"""Gets clients' gen... | pd.isnull(age) | pandas.isnull |
import logging
import pandas as pd
import os
import sys
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from skle... | pd.read_csv(metadata_file, header=0) | pandas.read_csv |
import pandas as pd
import numpy as np
def construct_freq_df(df_copy):
'''
Construct a dataframe such that indices are seperated by delta 1 min from the Market Data
and put it in a format that markov matrices can be obtained by the pd.crosstab() method
'''
#This is here in case user passes the act... | pd.to_datetime('08:00',format='%H:%M') | pandas.to_datetime |
"""
SPDX-FileCopyrightText: 2019 oemof developer group <<EMAIL>>
SPDX-License-Identifier: MIT
"""
import pytest
import pandas as pd
import numpy as np
from pandas.util.testing import assert_series_equal
import windpowerlib.wind_farm as wf
import windpowerlib.wind_turbine as wt
import windpowerlib.wind_turbine_cluster... | assert_series_equal(test_tc_mc.power_output, power_output_exp) | pandas.util.testing.assert_series_equal |
### gcode_reader in code folder
### instructions in SETUP.txt
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
##################################
# University of Wisconsin-Madison
# Author: <NAME>
##################################
"""
Gcode reader for both FDM (regular and Stratasys) and LPBF.
It supports the followi... | pd.Series(self.mesh_lengths) | pandas.Series |
from numpy import dtype
def estado_civil_dummy():
dic_estado={"Separado(a) o divorciado(a)":0,
"Soltero(a)":0,"Casado":1,"En unión libre":1,
"Viudo(a)":0,1.0:1,2.0:1,3.0:0,4.0:0,5.0:0}
return dic_estado
def dic_etnia():
import numpy as np
dic_etnia={"Mestizo":1,'Ninguno de los anterior... | pd.merge(datos_a,Av,on="id_hogar",how="outer") | pandas.merge |
import sys
import numpy as np
import pandas as pd
from ar6_ch6_rcmipfigs.constants import BASE_DIR
from pathlib import Path
path_FaIR_header_general_info = Path(BASE_DIR) / 'misc/badc_header_FaIR_model.csv'
path_FaIR_warming_header_general_info = Path(BASE_DIR) / 'misc/badc_header_FaIR_model_warming.csv'
path_FaIR_h... | pd.read_csv(fp, header=None) | pandas.read_csv |
#!/usr/bin/env python
import argparse
import pandas as pd
import os
import re
def get_immediate_subdirectories(a_dir):
return [name for name in os.listdir(a_dir)
if os.path.isdir(os.path.join(a_dir, name))]
def get_files_with_prefix(dir, prefix):
return [name for name in os.listdir(dir)
... | pd.read_json(eval_path, typ="series") | pandas.read_json |
import base64
import io
import textwrap
import dash
import dash_core_components as dcc
import dash_html_components as html
import gunicorn
import plotly.graph_objs as go
from dash.dependencies import Input, Output, State
import flask
import pandas as pd
import urllib.parse
from sklearn.preprocessing import StandardSca... | pd.DataFrame(data=zero_scale_input_covar, columns=["PC1", "PC2"]) | pandas.DataFrame |
import finterstellar as fs
import pandas as pd
import numpy as np
import datetime as dt
class LoadData:
def read_investing_price(self, path, cd):
file_name = path + cd + ' Historical Data.csv'
df = pd.read_csv(file_name, index_col='Date')
return (df)
def create_portfoli... | pd.to_datetime(i) | pandas.to_datetime |
from interface import *
from steps import *
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from copy import copy
class ADSAApp():
"""The class managing the interface for the project.
:param App app: The curses app wrapper where we will draw the interface.
"""
def __init__(sel... | pd.set_option('display.expand_frame_repr', False) | pandas.set_option |
import warnings
warnings.simplefilter("ignore", category=FutureWarning)
from pmaf.biome.essentials._metakit import (
EssentialFeatureMetabase,
EssentialSampleMetabase,
)
from pmaf.biome.essentials._base import EssentialBackboneBase
from collections import defaultdict
from os import path
import pandas as pd
imp... | pd.api.types.is_numeric_dtype(tmp_dtypes[0]) | pandas.api.types.is_numeric_dtype |
#-*-coding:utf-8-*-
import numpy as np
import pandas as pd
import time
from bayes_smoothing import *
from sklearn.preprocessing import LabelEncoder
import copy
def roll_browse_fetch(df, column_list):
print("==========================roll_browse_fetch ing==============================")
df = df.sort('context_... | pd.merge(data, user_day_hourmin_min, 'left',on=['user_id','day']) | pandas.merge |
from flowsa.common import WITHDRAWN_KEYWORD
from flowsa.flowbyfunctions import assign_fips_location_system
from flowsa.location import US_FIPS
import math
import pandas as pd
import io
from flowsa.settings import log
from string import digits
YEARS_COVERED = {
"asbestos": "2014-2018",
"barite": "2014-2018",
... | pd.DataFrame(df_raw_data.loc[21:22]) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
from zentables.zentables import _do_suppression
@pytest.fixture(scope="function")
def random() -> np.random.Generator:
return np.random.default_rng(123456)
def test_negative_numbers():
"""
Suppression should work on the _absolute value_ of the number... | pd.DataFrame(input_array) | pandas.DataFrame |
from datetime import (
datetime,
timedelta,
timezone,
)
import numpy as np
import pytest
import pytz
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
NaT,
Period,
Series,
Timedelta,
Timestamp,
date_range,
isna,
)
import pandas._testing as tm
class TestS... | Series(idx) | pandas.Series |
import numpy as np
import pdb
import gzip
import matplotlib
import matplotlib.pyplot as plt
import cPickle as pkl
import operator
import scipy.io as sio
import os.path
import pandas as pd
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.dummy import Du... | pd.read_csv(root+'/u.data',sep="\t",names=['uid','iid','rating']) | pandas.read_csv |
"""unit test for loanpy.loanfinder.py (2.0 BETA) for pytest 7.1.1"""
from inspect import ismethod
from os import remove
from pathlib import Path
from unittest.mock import patch, call
from pandas import DataFrame, RangeIndex, Series, read_csv
from pandas.testing import (assert_frame_equal, assert_index_equal,
... | Series(["a", "b", "c"], name="col1", index=[0, 1, 1]) | pandas.Series |
import numpy as np
from numpy import where
from pandas import DataFrame
from src.support import get_samples, display_cross_tab
from src.model import fit_predict, preprocessing_pipeline
from src.plots import create_model_plots, plot_smd
from src.propensity import create_matched_df, calc_smd
class PropensityScorer:
... | DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""Main formatting source code to format modelling results for plotting.
This code was written to process PLEXOS HDF5 outputs to get them ready for plotting.
Once the data is processed it is outputted as an intermediary HDF5 file format so that
it can be read into the marmot_plot_main.py file
... | pd.to_numeric(df[0], downcast='float') | pandas.to_numeric |
from .statistic import StatisticHistogram
import singlecellmultiomics.pyutils as pyutils
import collections
import pandas as pd
import matplotlib.pyplot as plt
class MappingQualityHistogram(StatisticHistogram):
def __init__(self, args):
StatisticHistogram.__init__(self, args)
self.histogram = coll... | pd.DataFrame.from_dict({'mq': self.histogram}) | pandas.DataFrame.from_dict |
import os
import pandas as pd
from datetime import datetime
from maldives.technical_analysis import TA
from maldives.bot.models.dealer import Dealer
from pandas import DataFrame
class Wallet:
cache_file: str = '../data/transactions.csv'
data: DataFrame
assets: {}
def __init__(self):
self.data... | pd.to_datetime(self.data['date']) | pandas.to_datetime |
from transformers import RobertaTokenizer, RobertaForSequenceClassification, AdamW
import torch
import json
from sklearn import metrics
from tqdm import tqdm
import numpy as np
from time import time
from datetime import timedelta
import pandas as pd
from sklearn.model_selection import train_test_split
import argparse
i... | pd.DataFrame.from_dict(test) | pandas.DataFrame.from_dict |
import utils
import numpy as np
from sklearn.model_selection import StratifiedShuffleSplit
import requests
import pandas as pd
import os
BASE_DATA_DIR = "/p/adversarialml/as9rw/datasets/census"
SUPPORTED_PROPERTIES = ["sex", "race", "none"]
PROPERTY_FOCUS = {"sex": "Female", "race": "White"}
# US Income dataset
cla... | pd.concat([df_1, df_2], axis=1, join='inner') | pandas.concat |
from sqlalchemy import true
import FinsterTab.W2020.DataForecast
import datetime as dt
from FinsterTab.W2020.dbEngine import DBEngine
import pandas as pd
import sqlalchemy as sal
import numpy
from datetime import datetime, timedelta, date
import pandas_datareader.data as dr
def get_past_data(self):
"""
Get raw... | pd.read_sql_query(query, self.engine) | pandas.read_sql_query |
import pandas as pd
import os
import matplotlib.pyplot as plt
import random
import numpy as np
def countChannelsInBarcodeList(path_to_decoded_genes: str):
'''
This function focuses on all stats that are purely based on how many times a certain channel was called, in what round.
This can be useful in debugg... | pd.read_csv(path_to_decoded_genes) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# In this Notebook I have implemented Scratch Implementations of Logistic Regression using Gradient Descent Algorithm and also Regularized Logistic Regression. The main motive for including scratch implementations but not scikit libraries were
#
# <ul>
# <li> Understand how g... | pd.get_dummies(churndata[var], prefix=var,drop_first=True) | pandas.get_dummies |
"""Test OMMBV.satellite functions"""
import datetime as dt
import numpy as np
import pandas as pds
import pysat
import OMMBV
class TestSatellite(object):
def setup(self):
"""Setup test environment before each function."""
self.inst = pysat.Instrument('pysat', 'testing', num_samples=32)
... | pds.DataFrame(trace, columns=['x', 'y', 'z']) | pandas.DataFrame |
import os
import pandas as pd
from IPython.core.display import display, HTML
from recordsearch_tools.client import RSSeriesClient
import plotly.offline as py
import plotly.graph_objs as go
from textblob import TextBlob
import nltk
stopwords = nltk.corpus.stopwords.words('english')
py.init_notebook_mode()
def make_summ... | pd.concat(all_years) | pandas.concat |
from pathlib import Path
import pandas as pd
import numpy as np
DATA_DIR = Path(__file__).parents[1] / 'data'
def load_so_cgm():
data_path = str(DATA_DIR / 'private' / 'dexcom_cgm')
dfs = []
for p in Path(data_path).iterdir():
if str(p).endswith('.csv'):
df = | pd.read_csv(p) | pandas.read_csv |
###############
#
# Transform R to Python Copyright (c) 2016 <NAME> Released under the MIT license
#
###############
import os
import numpy as np
import pystan
import pandas
import pickle
import seaborn as sns
import matplotlib.pyplot as plt
import arviz as az
file_beer_sales_3 = pandas.read_csv('3-6-1-beer-sales-3... | pandas.DataFrame(mcmc_sample['sales_pred']) | pandas.DataFrame |
import json
from definitions import *
from cdrkm_model import CDRKM
from kernels import kernel_factory
import argparse
from pathlib import Path
import pandas
import torch
from utils import save_altairplot, load_dataset, merge_two_dicts
import numpy as np
def eval_training(filepath: Path):
sd_mdl = torc... | pandas.DataFrame(std_hs_diffs, columns=algos_names, index=algos_names) | pandas.DataFrame |
# Copyright 2020 AI2Business. All Rights Reserved.
#
# 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.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report,confusion_matrix
import... | pd.read_csv("Classified Data",index_col=0) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# # PCA (Principal Components Analysis)
# ## wine.cvs
# In[16]:
#importing necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn.decomposition as sk
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale... | pd.DataFrame(wine_norm) | pandas.DataFrame |
from __future__ import division
import copy
import bt
from bt.core import Node, StrategyBase, SecurityBase, AlgoStack, Strategy
from bt.core import FixedIncomeStrategy, HedgeSecurity, FixedIncomeSecurity
from bt.core import CouponPayingSecurity, CouponPayingHedgeSecurity
from bt.core import is_zero
import pandas as p... | pd.Series(data=1, index=dts, name='a') | pandas.Series |
import warnings
warnings.filterwarnings("ignore")
import os
import json
import argparse
import time
import datetime
import json
import pickle
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import tensorflow as tf
from scipy.stats import spearmanr, mannwhitneyu
import sci... | pd.DataFrame(data=inv_p, index=test_samples, columns=metab_comp_df.columns) | pandas.DataFrame |
#encoding=utf-8
from nltk.corpus import stopwords
from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import FeatureUnion
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.cross_validation import KFold
f... | pd.read_csv("../input/test_active.csv", nrows=nrows, usecols=used_cols) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""Functions for importing data"""
import io, re, datetime, warnings
import xml.etree.ElementTree
import numpy as np
import pandas as pd
import xarray as xr
class MultipleScansException(Exception):
def __init__(self, value):
self.parameter = value
def __str__(self):
retu... | pd.read_csv(sequences, parse_dates=[3, 4]) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# - Edge weight is inferred by GNNExplainer and node importance is given by five eBbay annotators. Not every annotator has annotated each node.
# - Seed is the txn to explain.
# - id is the community id.
import os
import pickle
import math
from tqdm.auto import tqdm
import rando... | pd.read_csv('../05GNNExplainer-eval-hitrate/input/data-edge-weight.txt') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
@author: hkaneko
"""
import numpy as np
import pandas as pd
import sample_functions
from sklearn import svm
ocsvm_nu = 0.003 # OCSVM における ν。トレーニングデータにおけるサンプル数に対する、サポートベクターの数の下限の割合
ocsvm_gammas = 2 ** np.arange(-20, 11, dtype=float) # γ の候補
dataset = | pd.read_csv('unique_m.csv', index_col=-1) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 30 12:33:58 2018
@author: michaelek
"""
import os
import pandas as pd
from hilltoppy import web_service as ws
from hilltoppy.util import convert_site_names
from pyhydrotel import get_ts_data, get_sites_mtypes
from pdsql import mssql
from time import sleep
import yaml
impo... | pd.concat([tsdata, other_ts, br_ts], axis=1) | pandas.concat |
import pandas as pd
import numpy as np
from keras.models import load_model
from sklearn.metrics import roc_curve, roc_auc_score, auc, precision_recall_curve, average_precision_score
import os
import pickle
from scipy.special import softmax
from prg import prg
class MetricsGenerator(object):
def __init__(self, data... | pd.Series(precision_recall_auc, index=i) | pandas.Series |
#!/usr/bin/env python
import math
from Bio import SeqIO
import pandas as pd
import sys
import matplotlib.pyplot as plt
import logomaker as lm
###
dna = {'A': [1, 0, 0, 0, 0], 'C': [0, 1, 0, 0, 0], 'G': [0, 0, 1, 0, 0], 'T': [0, 0, 0, 1, 0], '-': [0, 0, 0, 0, 1], 'Y': [0, 0.5, 0, 0.5, 0], 'K': [0, 0, 0.5, 0.5, 0], '... | pd.DataFrame({0: [0, 0, 0, 0, 0]}, index=['A','C','G','T','-']) | pandas.DataFrame |
import warnings
import numpy as np
import pandas as pd
import scipy.ndimage
import skimage
import matplotlib._contour
from matplotlib.pyplot import get_cmap as mpl_get_cmap
import bokeh.models
import bokeh.palettes
import bokeh.plotting
import altair as alt
def _outliers(data):
bottom, middle, top = np.percent... | pd.concat(df_list, ignore_index=True) | pandas.concat |
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas as pd
from . import registry
from .. import runs, files
from logging import getLogger
log = getLogger(__name__)
def array(run, channel):
return registry.reader(run, channel).array()
def pandas(run, channel, field=Non... | pd.Timestamp.now('UTC') | pandas.Timestamp.now |
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
Index,
Series,
concat,
date_range,
)
import pandas._testing as tm
class TestEmptyConcat:
def test_handle_empty_objects(self, sort):
df = DataFrame(np.random.randn(10, 4), columns=list("abcd"))
... | Index([0, 1, 2], dtype="O") | pandas.Index |
import pandas as pd
import datetime
def formatTopStocks(top):
top_data = {"code": [], "name": [], "increase": [], "price": [],
"totalCirculationValue": [], "volume": [], "mainNet": [],
"mainBuy": [], "mainSell": [], "concept": []}
for t in top:
top_data['code'].append(t[... | pd.DataFrame(top_data) | pandas.DataFrame |
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# +
# %%capture
# Compile and import local pyrossgeo mo... | pd.read_csv('london_simulation/cnode_parameters.csv') | pandas.read_csv |
import math
import os
import time
from datetime import datetime
from math import inf
from heapq import heappop, heappush
import collections
import functools
from collections import defaultdict
import heapq
import random
import networkx as nx
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import ... | pd.read_csv("../train_dataset/dataset.csv") | pandas.read_csv |
# -*- coding: utf-8 -*
'''问卷数据分析工具包
Created on Tue Nov 8 20:05:36 2016
@author: JSong
1、针对问卷星数据，编写并封装了很多常用算法
2、利用report工具包，能将数据直接导出为PPTX
该工具包支持一下功能：
1、编码问卷星、问卷网等数据
2、封装描述统计和交叉分析函数
3、支持生成一份整体的报告和相关数据
'''
import os
import re
import sys
import math
import time
import pandas as pd
import numpy as np
import matplo... | pd.DataFrame({'name':[qq]}) | pandas.DataFrame |
"""Tests the stored flow mappings to provide quality assurance."""
import unittest
import pandas as pd
import fedelemflowlist
def get_required_flowmapping_fields():
"""Gets required field names for Flow Mappingt:return:list of required fields."""
from fedelemflowlist.globals import flowmapping_fields
requi... | pd.merge(flowmapping_targetinfo,self.flowlist) | pandas.merge |
# # Jupyter Notebook for Counting Building Occupancy from Polaris Traffic Simulation Data
#
# This notebook will load a Polaris SQLlite data file into a Pandas data frame using sqlite3 libraries and count the average number of people in each building in each hour of the simulation.
#
# For help with Jupyter notebook... | pd.read_sql_query("SELECT * FROM Beginning_Location_All", cnx) | pandas.read_sql_query |
# -*- coding: utf-8 -*-
"""
Created on Tue May 14 12:52:08 2019
@author: ScmayorquinS
"""
# Necessary libraries
import requests
from bs4 import BeautifulSoup
import re
import itertools
import pandas as pd
import os
import urllib
import PyPDF2
import time
import glob
#------------------------------------------------
... | pd.DataFrame(dic, columns = ['Planes Nacionales de Desarrollo','Capítulos o tomos','Link']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 14 10:59:05 2021
@author: franc
"""
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
import json
from collections import Counter, OrderedDict
import math
import torchtext
from torchtext.data import get_tokenizer
... | pd.DataFrame({'spanish': ["pejerrey"], 'english': ["silverside"]}) | pandas.DataFrame |
import streamlit as st
import math
from scipy.stats import *
import pandas as pd
import numpy as np
from plotnine import *
def app():
# title of the app
st.subheader("Proportions")
st.sidebar.subheader("Proportion Settings")
prop_choice = st.sidebar.radio("",["One Proportion","Two Proportions"])
... | pd.DataFrame({"x":x,"y":y}) | pandas.DataFrame |
# Copyright 2020 The Johns Hopkins University Applied Physics Laboratory LLC
# All rights reserved.
# Distributed under the terms of the MIT License.
import pandas as pd
def gen_state(demand, prof):
counties = (
pd.DataFrame(demand, index=["demand"])
.T.reset_index()
.rename(columns={"i... | pd.merge(counties, state_demand, on="state", how="left") | pandas.merge |
import re
from datetime import datetime
import nose
import pytz
import platform
from time import sleep
import os
import logging
import numpy as np
from distutils.version import StrictVersion
from pandas import compat
from pandas import NaT
from pandas.compat import u, range
from pandas.core.frame import DataFrame
im... | range(test_size) | pandas.compat.range |
import os
import pandas as pd
import matplotlib.pyplot as plt
from tqdm.auto import tqdm
import re
from ipywidgets import widgets, interact
# Deep Face
from deepface import DeepFace
from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
from deepface.commons import functions
# https://github.com/seren... | pd.DataFrame(self.faces_metadata) | pandas.DataFrame |
import pandas as pd
from numpy.random import randint
data = | pd.read_csv('mubeena1.csv') | pandas.read_csv |
# data loading
__author__ = 'Guen'
import sys,os,glob,fnmatch,datetime,time
import configparser, logging
import numpy as np
import pandas as pd
import json
from .gutil import get_config
from PyQt4 import QtGui
import imp
config = get_config()
_DATA_FOLDER = config.get('Data','DataFolder')
if 'DATA_DIR' in os.environ.... | pd.read_csv(filepath, sep='\t') | pandas.read_csv |
class Deploy:
'''Functionality for deploying a model to a filename'''
def __init__(self, scan_object, model_name, metric, asc=False):
'''Deploy a model to be used later or in a different system.
NOTE: for a metric that is to be minimized, set asc=True or otherwise
you will end up wit... | pd.DataFrame() | pandas.DataFrame |
from nose.tools import eq_
import pandas as pd
from pavooc.preprocessing.generate_pdb_bed import pdb_coordinates
def test_pdb_coordinates_forward_strand():
# SP_BEG is corrected already. In file SP_BEG would be 6
pdb = | pd.Series({'SP_BEG': 5, 'SP_END': 34, 'PDB': 'ABC'}) | pandas.Series |
import numpy as np
import pandas as pd
import pytest
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.feature_selection import (
f_regression,
SelectKBest,
SelectFromModel,
)
from sklearn.linear_model import Lasso
from sklearn.datasets im... | pd.testing.assert_frame_equal(X_train_t, X[[0, 1, 2, 6, 7, 8, 9, 10, 11, 12]]) | pandas.testing.assert_frame_equal |
#%% [markdown]
# # Author : <NAME>
# ***
# ## Capstone Project for Qualifying IBM Data Science Professional Certification
# ***
#%% [markdown]
#
# # Import Packages
#
#%%
import numpy as np # library to handle data in a vectorized manner
import pandas as pd # library for data analsysis
pd.set_option('display.max_co... | pd.set_option('display.max_rows', None) | pandas.set_option |
import pandas as pd
import numpy as np
import re
from nltk import word_tokenize
import nltk
from others.logging_utils import init_logger
from itertools import chain
import geojson
import json
from geopy import distance
from tqdm import tqdm
import os
import gc
def free_space(del_list):
for name in del_list:
... | pd.json_normalize(fermate_json['features']) | pandas.json_normalize |
from collections import OrderedDict
from datetime import timedelta
import numpy as np
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype, DatetimeTZDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Series,
Timedelta,
Timestamp,
_np_version_under1p14,
... | Timestamp("2013-01-03 00:00:00-0500", tz="US/Eastern") | pandas.Timestamp |
"""
content level plays, timespent and ratings by week
"""
import json
import sys, time
import pdb
import os
import pandas as pd
from datetime import datetime, timedelta, date
from pathlib import Path
from string import Template
from azure.common import AzureMissingResourceHttpError
from cassandra.cluster import Clust... | pd.DataFrame() | pandas.DataFrame |
import pandas
import numpy
import similaritymeasures
def stats_between_series(
xaxis_1: pandas.Series,
values_1: pandas.Series,
xaxis_2: pandas.Series,
values_2: pandas.Series,
print_: bool = False,
) -> dict:
"""Dynamic time warping and discret frechet distance for measuring similarity betwee... | pandas.to_numeric(unified["values_2"], errors="coerce", downcast="float") | pandas.to_numeric |
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... | Series([0.1, 0.2], index=idx, name="s") | pandas.Series |
def report_classification(df_features,df_target,algorithms='default',test_size=0.3,scaling=None,
large_data=False,encode='dummy',average='binary',change_data_type = False,
threshold=8,random_state=None):
'''
df_features : Pandas DataFrame
... | pd.concat([encoding,df_num],axis=1) | pandas.concat |
import inspect
import os
import warnings
from unittest.mock import MagicMock, patch
import numpy as np
import pandas as pd
import pytest
import woodwork as ww
from evalml.model_understanding.graphs import visualize_decision_tree
from evalml.pipelines.components import ComponentBase
from evalml.utils.gen_utils import ... | pd.Int64Index([1]) | pandas.Int64Index |
import pandas as pd
import numpy as np
from auto_causality.utils import featurize
def nhefs() -> pd.DataFrame:
"""loads the NHEFS dataset
The dataset describes the impact of quitting smoke on weight gain over a period of 11 years
The data consists of the treatment (quit smoking yes no), the outcome (chang... | pd.read_csv(url) | pandas.read_csv |
from ast import operator
import csv
from datetime import datetime
from operator import index, mod
import os
import sys
import math
import time
import warnings
import itertools
import numpy as np
import pandas as pd
# import scrapbook as sb
import matplotlib.pyplot as plt
from pmdarima.arima import auto_arima
pd.optio... | pd.Series(actuals) | pandas.Series |
from __future__ import division
from functools import wraps
import pandas as pd
import numpy as np
import time
import csv, sys
import os.path
import logging
from .ted_functions import TedFunctions
from .ted_aggregate_methods import TedAggregateMethods
from base.uber_model import UberModel, ModelSharedInputs
class Te... | pd.Series([], dtype="float", name="cbt_inv_bw_grow_loec") | pandas.Series |
import os
from abc import ABC
import json
import numpy as np
import pandas as pd
from odin.classes import DatasetInterface, TaskType
from odin.utils import *
from odin.utils.utils import encode_segmentation, compute_aspect_ratio_of_segmentation
from pycocotools import mask
from pycocotools import coco
logger = get_r... | pd.DataFrame(data["annotations"]) | pandas.DataFrame |
import numpy as np
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
import os
import re
import torch
import pandas as pd
import subprocess
import torch.nn.functional as F
def isEnglish(s):
try:
s.encode(encoding='utf-8').decode('ascii')
except UnicodeDecodeError:
retur... | pd.DataFrame(list_lines_qrels) | pandas.DataFrame |
"""Plot data gathered for success and collision rates"""
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
def plot_success_rate():
# Define data, gathered from various scripts, in tidy data format
data = []
# Neural oCBF/oCLF data generated using eval_turtlebot_neural_cbf_mpc_suc... | pd.DataFrame(data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from pandas.compat import range
import pandas.util.testing as tm
from pandas import read_csv
import os
import nose
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
import pandas.tools.rplot as rplot
def curpath():
pth, _ = os.path.split(os.path.abspath(__file__))... | rplot.TrellisGrid(['sex', '.']) | pandas.tools.rplot.TrellisGrid |
import argparse
import os
import pickle
from datetime import datetime
import numpy as np
import pandas as pd
import seaborn as sns
from scipy.stats import wilcoxon
from statsmodels import robust
import config
import data_loader
import solve.helper
from data_loader import load_stats_log, load_predict_log
def figsize... | pd.read_csv(logfile) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
.. module:: citationanalysis
:synopsis: Set of functions for typical bibliometric citation analysis
.. moduleauthor:: <NAME> <<EMAIL>>
"""
import pandas as pd
import numpy as np
import scipy.sparse as spsparse
from sklearn.metrics import pairwise_distances
from sklearn.preprocessing... | pd.DataFrame(distance_df, columns = ['iFieldId', 'jFieldId', year_col, 'FieldDistance']) | pandas.DataFrame |
"""
Pipeline Evaluation module
This module runs all the steps used and allows you to visualize them.
"""
import datetime
from typing import List, Tuple, Union
import pandas as pd
from sklearn.pipeline import Pipeline
from .evaluation import Evaluator
from .feature_reduction import FeatureReductor
from .labeling imp... | pd.DataFrame(self.y_pred, index=self.X_train.index) | pandas.DataFrame |
import numpy as np
# 2013-10-31 Added MultiRate class, simplified fitting methods, removed full_output parameter
# 2014-12-18 Add loading of Frequency, Integration time and Iterations, calculate lower
# bound on errors from Poisson distribution
# 2015-01-28 Simplified fitting again. Needs more work
# 2015-0... | pd.concat(dfs, ignore_index=True) | pandas.concat |
# pylint: disable=E1101,E1103,W0232
from datetime import datetime, timedelta
from pandas.compat import range, lrange, lzip, u, zip
import operator
import re
import nose
import warnings
import os
import numpy as np
from numpy.testing import assert_array_equal
from pandas import period_range, date_range
from pandas.c... | tm.assertRaisesRegexp(ValueError, label_error) | pandas.util.testing.assertRaisesRegexp |
import datetime
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.animation as animation
sns.set_theme(style="whitegrid")
class Datos():
def __init__(self, ruta):
self.ruta = ruta
def leerCSV(self):
... | pd.read_csv(self.ruta) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Sat May 19 17:14:29 2018
@author: GTayl
"""
################################## Set-up ##########################################
# Import the required packages
import pandas as pd
import time
import os
from pandas import ExcelWriter
from pandas import ExcelFile
# Change the wor... | pd.DataFrame(Master_Direct_Edge_List['To']) | pandas.DataFrame |
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