prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# File System
import os
import json
from pathlib import Path
from zipfile import ZipFile
import pickle
import gc
import numpy as np
import pandas as pd
from sympy.geometry import *
DATA_PATH = '../data/' # Point this constant to the location of your data archive files
EXPECTED_DATASETS = {'Colorado': [
'county_... | pd.read_pickle(f'../data/covidTemperature.{state}.pkl') | pandas.read_pickle |
"""
Functions to process model inputs and outputs.
This module provides functions that classify 5 minutes PPG time-series or PPG images
into Reliable or Unreliable for each HR-HRV features.
Copyright 2020, <NAME>
Licence: MIT, see LICENCE for more details.
"""
from __future__ import absolute_import, division, prin... | pd.DataFrame(scores, index=model_names, columns=names) | pandas.DataFrame |
"""
Tests for live trading.
"""
from unittest import TestCase
from datetime import time
from collections import defaultdict
import pandas as pd
import numpy as np
# fix to allow zip_longest on Python 2.X and 3.X
try: # Python 3
from itertools import zip_longest
except ImportErro... | pd.Timedelta('10s') | pandas.Timedelta |
import glob
import os
import pandas
WHICH_IMAGING = "CQ1-ctf011-t24"
DO_I_HAVE_TO_MERGE_FILES_FIRST = True
NAME_OF_COMPOUND_WHICH_IS_CONTROL = "DMSO"
def gather_csv_data_into_one_file(path_to_csv_files, output_filename = "output"):
filenames = glob.glob(f"{path_to_csv_files}/*Stats*.csv")
print(filenames)
... | pandas.isna(y) | pandas.isna |
from sklearn.base import TransformerMixin
from suricate.preutils import concatixnames
import pandas as pd
class ConnectorMixin(TransformerMixin):
def __init__(self, ixname='ix', source_suffix='source', target_suffix='target'):
"""
Args:
ixname: 'ix'
source_suffix: 'source'... | pd.DataFrame(index=on_ix) | pandas.DataFrame |
import functools
from threading import Thread
from contextlib import contextmanager
import signal
from scipy.stats._continuous_distns import _distn_names
import scipy
import importlib
from hydroDL.master import basins
from hydroDL.app import waterQuality
from hydroDL import kPath, utils
from hydroDL.model import trainT... | pd.to_datetime(dfP.index) | pandas.to_datetime |
"""
inspiration from R Package - PerformanceAnalytics
"""
from collections import OrderedDict
import pandas as pd
import numpy as np
from tia.analysis.util import per_series
PER_YEAR_MAP = {
'BA': 1.,
'BAS': 1.,
'A': 1.,
'AS': 1.,
'BQ': 4.,
'BQS': 4.,
'Q': 4.,
'QS': 4.,
'D': 365.... | pd.DataFrame(vals, columns=cols, index=dd.columns) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
KAGGLE = False
if KAGGLE:
get_ipython().system('cp ../input/gdcm-conda-install/gdcm.tar .')
get_ipython().system('tar -xvzf gdcm.tar')
get_ipython().system('conda install --offline ./gdcm/gdcm-2.8.9-py37h71b2a6d_0.tar.bz2')
get_ipython().system('pip inst... | pd.read_csv(test_csv_path) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""Console script for pyvirchow."""
import os
import six
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
# os.environ["CUDA_VISIBLE_DEVICES"] = ""
from pyvirchow.io.operations import get_annotation_bounding_boxes
from pyvirchow.io.operations import get_annotation_polygons
f... | pd.concat([modified_df, summary_df]) | pandas.concat |
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
number_cells = 5
df = | pd.DataFrame() | pandas.DataFrame |
"""Module providing various functions for processing more complex structured data (e.g., collected during a study)."""
import warnings
from typing import Any, Dict, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
from scipy import interpolate
from biopsykit.utils._datatype_validation_helper im... | pd.concat(result_data, names=[dict_levels[0]]) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Mar 8 2019
@author: <NAME>
email : <EMAIL>
"""
################################################################################
# THIS SCRIPT IS FOR ANALYZE THE RISK FACTORS WITH SVMs
# Tested with Python 2.7 and Python 3.5 on Ubuntu Mate Release 16.04.5 LTS (Xenial Xerus) 64-bit
... | pd.read_csv(read_samples_path+'setA_df_%s.csv'%setA[0]) | pandas.read_csv |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from functools import reduce
import pickle
import os
import pymssql
from virgo import market
startDate_default = '20060101'
endDate_default = (datetime.now() + timedelta(days=-1)).strftime('%Y%m%d')
# endDate_defau... | pd.read_sql_query(sql, conn243) | pandas.read_sql_query |
# coding: utf8
# part of pybacktest package: https://github.com/ematvey/pybacktest
""" Functions for calculating performance statistics and reporting """
import pandas as pd
import numpy as np
start = lambda eqd: eqd.index[0]
end = lambda eqd: eqd.index[-1]
days = lambda eqd: (eqd.index[-1] - eqd.index[0]).days
tr... | pd.Series(maxdds) | pandas.Series |
import numpy as np
import pandas as pd
from pathlib import Path
def imgs_to_df (imgs, fps=None):
imgs = [ img_to_df(img=i, frame_id=frame) for frame,i in enumerate(imgs) ]
df = pd.concat(imgs, ignore_index=True)
if not fps is None:
df['time'] = df['frame'] * (1/fps)
return df
def img_to_df (im... | pd.Series([ j=='' for j in df[i] ]) | pandas.Series |
#!/usr/bin/env python
'''
Compare the waveforms taken by the MSO5240 scope.
'''
import os
import pandas as pd
import seaborn as sns
import sys
class ScopeWaveform:
def __init__(self, infpn):
columns = ['info_name', 'value', 'units', 'time', 'waveform_value']
self.df = | pd.read_csv(infpn, names=columns) | pandas.read_csv |
import datetime as dt
import numpy as np
import pandas as pd
from pandas.testing import assert_series_equal, assert_frame_equal
import pytest
from solarforecastarbiter.datamodel import Observation
from solarforecastarbiter.validation import tasks, validator
from solarforecastarbiter.validation.quality_mapping import ... | assert_frame_equal(post_mock.call_args_list[1][0][1], out[-1:]) | pandas.testing.assert_frame_equal |
import json
import pandas as pd
from scipy.stats.stats import pearsonr, spearmanr
import numpy as np
from scipy import stats
import sys
import matplotlib.pyplot as plt
import os
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import OneHotEncoder
import argparse
def parse_args(args):
p... | pd.read_json(args['metrics_outputs']) | pandas.read_json |
import jieba
import jieba.analyse as analyse
import jieba.posseg # 输出带词性
import copy
import wordcloud
import streamlit as st
import pandas as pd
import numpy as np
import re
import matplotlib.pyplot as plt
import matplotlib
from wordcloud import WordCloud # 词云包
from sklearn.feature_extraction.text import CountVectori... | pd.DataFrame(keyci, columns=['ci']) | pandas.DataFrame |
import pandas as pd
import numpy as np
import re
def process_brand(x):
if | pd.isnull(x) | pandas.isnull |
#
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
#
from enum import Enum
from typing import List
import pandas as pd
class Prediction:
""" General Prediction class used to capture output from surrogate model .predict() methods
PredictionSchema defines the known universe of... | pd.read_json(json_string, orient='index') | pandas.read_json |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import warnings
warnings.filterwarnings('ignore')
# In[ ]:
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
print(os.listdir("../../../input/mathijs_weather-data-in-new-york-city-2016"))
import seab... | pd.read_csv("../../../input/mathijs_weather-data-in-new-york-city-2016/weather_data_nyc_centralpark_2016(1).csv") | pandas.read_csv |
# -*- coding:utf-8 -*-
import numpy as np
import pandas as pd
import xgboost as xgb
import operator
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.feature_selection import RFE
from sklearn.svm import SVR
from sklearn.preprocessing import Imputer
from sklearn.ensemble import RandomForestR... | pd.DataFrame({'Id': df_test.index, 'SalePrice': preds}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 16 19:07:55 2019
@author: aman
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
gridsizes = [1,2,3,4,6,8,12,16,24,32,48,64]
overlap = [2,4,8,16,32,64,128,256]
playbackrates = [5,10,15,30,40,60,80,100]
def load(fname,threshold=50):
data = p... | pd.DataFrame([t[-1] for t in trials]) | pandas.DataFrame |
from pyg_base import loop, eq, drange, Dict
import pandas as pd; import numpy as np
import pytest
from numpy import array
SP = lambda a, b: Dict(s = a+b, p = a*b)
AB = lambda a, b: a+b
def S(v):
if isinstance(v, list):
return [S(w) for w in v]
else:
return v.s
def test_loop_dict():
f = lo... | pd.Series([2,3], ['a','b']) | pandas.Series |
import pandas as pd
import numpy as np
from sklearn.compose import TransformedTargetRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import FunctionTransformer
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline
from IPython.display imp... | pd.get_dummies(cat_df, drop_first=True) | pandas.get_dummies |
#!/usr/bin/env python
# coding: utf-8
import geopandas as gpd
import pandas as pd
import numpy as np
from datetime import datetime, timedelta, date
import requests
import json
from rasterstats import point_query
from shapely import geometry as sgeom
import ulmo
from collections import OrderedDict
import math
from rand... | pd.to_datetime(values_df['datetime']) | pandas.to_datetime |
# coding=utf-8
# !/usr/bin/env python3
import os, re
import numpy as np
import pandas as pd
from SimpleCalculate import simpleStatistics
from ReadUtils import readFile,svType,svLen,svEnd,processBar
def judgeIfOverlap(start_1,end_1,start_2,end_2,sv_type,refdist,overlap_rate=0.5):
#start_1 < end_1 && star... | pd.DataFrame(columns=data_1.columns) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# **Teeme läbi väikesed harjutused, et hiljem oleks lihtsam kodutööd teha.**
#
#
# In[ ]:
import numpy as np
import pandas as pd
df = pd.read_csv("../input/cwurData.csv")
# 1) Leia kaggle’st dataset ‘World University Rankings’
#
# 2) Tee uus kernel (notebook)
#
# 3) Loe... | pd.DataFrame(data=info[0:], index=["publications", "citations"]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
This is a module for extending pandas dataframes with the modelflow toolbox
Created on Sat March 2019
@author: hanseni
"""
import pandas as pd
from collections import namedtuple
import inspect
from modelclass import model
import modelvis as mv
if not hasattr(pd.DataFrame,'mf'):
... | pd.api.extensions.register_dataframe_accessor("mf") | pandas.api.extensions.register_dataframe_accessor |
"""
Filter and combine various peptide/MHC datasets to derive a composite training set,
optionally including eluted peptides identified by mass-spec.
"""
import sys
import argparse
import os
import json
import collections
from six.moves import StringIO
import pandas
from mhcflurry.common import normalize_allele_name
... | pandas.concat(dfs, ignore_index=True) | pandas.concat |
import logging
import traceback
import pandas as pd
import numpy as np
import seaborn as sns
from collections import defaultdict
import matplotlib
matplotlib.use('Agg')
matplotlib.rcParams['pdf.fonttype'] = 42
import matplotlib.ticker as ticker
from matplotlib import pyplot as plt
import matplotlib.patches as mpatche... | pd.DataFrame(table) | pandas.DataFrame |
import os
import datetime
import numpy as np
import pandas as pd
import scanpy as sc
import matplotlib.pyplot as plt
import seaborn as sns
from ._differential import compute_levelWise_differential_analysis
from ._pseudo import createBins, createSuperbins
from ._visualize import heatmap
from ._enrich import module_e... | pd.DataFrame() | pandas.DataFrame |
import torch
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import sys
from os.path import join as pjoin
import scanpy as sc
import anndata
import time
# sys.path.append("../../..")
sys.path.append("../../../data")
from st.load_st_data import load_st_data
sys.path.append... | pd.read_csv("./out/aligned_coords_st_3d.csv", index_col=0) | pandas.read_csv |
from datetime import timedelta
from functools import partial
from operator import attrgetter
import dateutil
import numpy as np
import pytest
import pytz
from pandas._libs.tslibs import OutOfBoundsDatetime, conversion
import pandas as pd
from pandas import (
DatetimeIndex, Index, Timestamp, date_range, datetime,... | date_range('1/1/2000', periods=10) | pandas.date_range |
import os, glob, sys, io
import numpy as np
import pandas as pd # Timeseries data
import datetime as dt # Time manipulation
import yaml
from matplotlib.dates import date2num # Convert dates to matplotlib axis coords
from matplotlib import dates
from scipy import fftpack
from scipy import stats
fro... | pd.concat((clim.iloc[:(31+28)*24],clim.iloc[(31+29)*24:])) | pandas.concat |
"""
"""
import importlib
import os
import pydoc
import shutil
import subprocess
from datetime import datetime
from json.decoder import JSONDecodeError
from multiprocessing import Pool
import click
import terra.database as tdb
from terra import Task, _get_task_dir
from terra.settings import TERRA_CONFIG
from terra.uti... | pd.DataFrame([run.__dict__ for run in runs]) | pandas.DataFrame |
import time
import requests
from bs4 import BeautifulSoup
import pandas as pd
from bdshare.util import vars as vs
def get_current_trade_data(symbol=None, retry_count=1, pause=0.001):
"""
get last stock price.
:param symbol: str, Instrument symbol e.g.: 'ACI' or 'aci'
:return: dataframecd
... | pd.DataFrame(quotes) | pandas.DataFrame |
import logging
import random
import os
import pickle
import pandas as pd
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union
import torch.utils.data as data
from tqdm import tqdm
@dataclass(frozen=True)
class InputFeatures:
"""
A single set of features of ... | pd.DataFrame(examples) | pandas.DataFrame |
#!/env/bin/python
from tensorflow import keras
from complete_preprocess_script import do_preprocessing
from complete_feature_extraction_script import do_feature_extraction
from Scripts.Feature_extraction.feature_extraction_utilities import dataset_path, dict_path, temp_output_path, output_path
import dask.dataframe as... | pd.get_dummies(test,columns=["mapped_tweet_type","mapped_language_id"]) | pandas.get_dummies |
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
class Data:
'''Obtains hydro data and preprocesses it.'''
def data(self, test_len):
names = ['date', 'price', 'avg_p', 'bid', 'ask',
'o', 'h', 'l', 'c', 'avgp', 'vol', 'oms', 'num']
... | pd.concat([df, df2], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 24 15:37:55 2021
@author: Gary
"""
import pandas as pd
import numpy as np
import build_common
trans_dir = build_common.get_transformed_dir()
lower_tolerance = 95
upper_tolerance = 105
density_min = 6.0
density_max = 13.0
# Normally set to True
remove_dropped_keys = Tr... | pd.concat([self.curdf,t],sort=True) | pandas.concat |
#june 2014
#dget RNA data for candidate CNV genes
import csv
import math
import numpy as np
import scipy
from scipy import stats
import matplotlib.pyplot as plt
import math
import itertools
from itertools import zip_longest
import pandas as pd
import timeit
#function to transpose
def transpose(mylist):
return [... | pd.concat([sub,rest]) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 7 09:40:49 2018
@author: yuwei
"""
import pandas as pd
import numpy as np
import math
import random
import time
import scipy as sp
import xgboost as xgb
def loadData():
"下载数据"
trainSet = pd.read_table('round1_ijcai_18_train_20180301.txt',sep=' ')
testSet ... | pd.merge(result,feat,on=['shop_id'],how='left') | pandas.merge |
import numpy as np
import pandas as pd
from collections import defaultdict
import time
import matplotlib.pyplot as plt
import optuna
import shap
from optuna.integration import LightGBMPruningCallback, XGBoostPruningCallback
from sklearn.base import BaseEstimator, TransformerMixin, is_classifier
from sklearn.pipeline im... | pd.qcut(df[target], quartile_list, labels) | pandas.qcut |
from pathlib import Path
import pandas as pd
import numpy as np
import re
from collections import Counter
import logging
LOGGER = logging.getLogger(__name__)
LOGGER.setLevel( logging.INFO )
__all__ = ['read_geo', 'detect_header_pattern']
''' circular imports problems --- https://stackabuse.com/python-circular-import... | pd.read_csv(filepath, **kwargs) | pandas.read_csv |
import pandas as pd
import numpy as np
import os
import requests
import logging
import argparse
import re
import pathlib
API_KEY = '<KEY>'
MAX_VARS = 50
STATE_CODES = {'Alabama': ('AL', '01'), 'Alaska': ('AK', '02'),
'Arizona': ('AZ', '04'), 'Arkansas': ('AR', '05'),
'Cal... | pd.merge(state_data, vars_data, on='geoid') | pandas.merge |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pandas
from pandas.api.types import is_scalar
from pandas.compat import to_str, string_types, numpy as numpy_compat, cPickle as pkl
import pandas.core.common as com
from pandas.core.dtypes.common import ... | validate_bool_kwarg(inplace, "inplace") | pandas.util._validators.validate_bool_kwarg |
# *****************************************************************************
# Copyright (c) 2019, Intel Corporation All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# Redistributions of sou... | pandas.Series(output_arr) | pandas.Series |
#%%
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn import manifold
from sklearn.cluster import KMeans
from sklearn.cluster import MeanShift
from sklearn.cluster import AgglomerativeClustering
from time import time
#%%
data = pd.read_csv('data/ml_requests.csv')
data.head()
#%%
ite... | pd.DataFrame(index=items.index, columns=features_names) | pandas.DataFrame |
from glob import glob
import pandas as pd
from os import path
datadir = '/Volumes/T7/BEST-AIR/data/ConcExpRisk_tract_poll_CA/'
parts = [path for path in glob(datadir + 'part*')]
csvs = [path for path in glob(datadir + 'part*/*.csv')]
datadict = {}
for pathname in csvs:
print(f"Reading '{pathname}'")
df = pd.... | pd.concat(df_parts, axis='rows') | pandas.concat |
# Common functions for this project
import os, time, datetime
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl
from scipy.stats import zscore
from copy import deepcopy
def ctime():
t = time.time()
f = '%Y-%m-%d %H:%M:%S '
return datetime.datetime.fromtimestamp(t).strf... | pd.ExcelWriter(path) | pandas.ExcelWriter |
# ---
# jupyter:
# jupytext:
# cell_metadata_filter: all
# notebook_metadata_filter: all,-language_info
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.11.2
# kernelspec:
# display_name: Python 3
# language: pytho... | pd.set_option('mode.chained_assignment', None) | pandas.set_option |
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 19 15:07:04 2019
@author: ning
"""
import pandas as pd
import os
from glob import glob
import seaborn as sns
sns.set_style('whitegrid')
sns.set_context('poster')
from matplotlib import pyplot as plt
from utils import resample_ttest_2sample,MCPConverter
working_dir = '..... | pd.read_csv(f) | pandas.read_csv |
import math
import queue
from datetime import datetime, timedelta, timezone
import pandas as pd
from storey import build_flow, SyncEmitSource, Reduce, Table, AggregateByKey, FieldAggregator, NoopDriver, \
DataframeSource
from storey.dtypes import SlidingWindows, FixedWindows, EmitAfterMaxEvent, EmitEveryEvent
tes... | pd.Timestamp('2021-05-30 17:15:15.808000+0000', tz='UTC') | pandas.Timestamp |
"""analisis de malware y benigno con dataset y naive
preprocesado por chinos con binario
paper 244802 en df 293333"""
import pandas as pd
import matplotlib.pyplot as plt
"""matplotlib inline"""
plt.rcParams['figure.figsize'] = (16, 9)
plt.style.use('ggplot')
from sklearn import datasets, metrics
from sklearn.mo... | pd.read_csv(filename) | pandas.read_csv |
from collections import defaultdict
import numpy as np
import pandas as pd
import scipy.stats
from matplotlib import gridspec
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib import cm
from conf import *
cmap = cm.get_cmap('tab10')
colors = [cmap(0), cmap(0), cmap(1), cmap(1), cmap... | pd.read_csv(PFAM_files, sep="\t", names=["ENSEMBL_GENE", "ENSEMBL_TRANSCRIPT", "START", "END", "DOMAIN"]) | pandas.read_csv |
import os
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["OPENBLAS_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["VECLIB_MAXIMUM_THREADS"] = "1"
os.environ["NUMEXPR_NUM_THREADS"] = "1"
import copy
import logging
import pandas as pd
import multiprocessing as mp
from ..orbit import TestOrbit
from ... | pd.concat(ephemeris_dfs) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
#%%
import nltk
from nltk.corpus import stopwords
from nltk.stem.wordnet import WordNetLemmatizer
import string
import gensim
from gensim import corpora, models
import pandas as pd
from nltk import FreqDist
import re
import spacy
# librar... | pd.read_csv('Kindle_review.csv') | pandas.read_csv |
# MIT-License
#
# Copyright 2020 World Infectious Disease Monitoring Foundation
#
# 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 ... | pd.to_datetime(data_new['formatted_date'], format='%Y%W%w') | pandas.to_datetime |
import json
import pandas as pd
pd.set_option('display.max_rows', 30)
pd.set_option('display.max_columns', 50)
pd.set_option('display.width', 1200)
import matplotlib.pyplot as plt
import seaborn as sns # used for plot interactive graph.
import warnings
warnings.filterwarnings('ignore')
def load_tmdb_movies(path):
... | pd.read_csv(path) | pandas.read_csv |
"""
Custom excel types for pandas objects (eg dataframes).
For information about custom types in PyXLL see:
https://www.pyxll.com/docs/udfs.html#custom-types
For information about pandas see:
http://pandas.pydata.org/
Including this module in your pyxll config adds the following custom types that can
be used as retu... | pa.Series(values, index=keys) | pandas.Series |
import pandas as pd
def clean_impex_dataset(file_location, sheet):
df = | pd.read_excel(file_location, skiprows=5, sheet_name=sheet) | pandas.read_excel |
# -*- coding:utf-8 -*-
import pandas as pd
import time,datetime
import matplotlib.pyplot as plt
import random
pd.set_option('display.height',1000)
pd.set_option('display.max_rows',500)
pd.set_option('display.max_columns',50)
pd.set_option('display.width',1000)
class report(object):
def __init__(self... | pd.DataFrame(data=[[result[i][0],result[i][1]] for i in date], index=date,columns=["a","b"]) | pandas.DataFrame |
"""Visualizes burst data."""
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
def to_pandas(ebursts, offsets, svo, unit='s'):
"""Exports burst and offset data to dataframes for a single term.
ebursts is an edgebust dict from the SVO object
of... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
from pathlib import Path
def load(path, dt=False, stats=False):
print("loading data from",path)
dataFrames = {}
dataFrames['gameLogs'] = pd.read_csv(path/'GameLogs.csv', index_col=False)
if dt:
dataFrames['gameLogs']['Date'] = pd.to_datetime(dataFrames['g... | pd.merge(predictors[['Row']], gameLogs, on='Row', how="left") | pandas.merge |
#### Healthy Neighborhoods Project: Using Ecological Data to Improve Community Health
### Neville Subproject: Using Random Forestes, Factor Analysis, and Recursive Feature Selection to Screen Variables for Imapcts on Public Health
## Florida Charts Diabetes Mortality by Census Tract: Pyhton Computing Language Code Scri... | pd.read_csv("_data/neville_dm2_acs.csv", encoding = "ISO-8859-1", low_memory= False) | pandas.read_csv |
# Copyright 2021 Prayas Energy Group(https://www.prayaspune.org/peg/)
#
# 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... | pd.DataFrame({'GDP': [1, 2, 4, 8, 4]*2}, index=index) | pandas.DataFrame |
'''
Number: 4
This file models sequences of words using the statistical properties of n-grams.
I follow the Markov assumption (or independence assumption).
As for probabilities, I use and implement the Kneser-Ney Smoothing method.
'''
import pandas as pd
# --> Unigrams Probabilities (something wrong with this)
def kn... | pd.merge(count_prob, num_w1_wn__1, how='left', left_on=aggregate_on, right_on=aggregate_on) | pandas.merge |
import matplotlib
import pandas as pd
CSV_FILE = 'data.csv'
class DataProcessing:
def __init__(self):
self.df = pd.read_csv(CSV_FILE, parse_dates=['Data'])
self.last_date = self.df['Data'].max().date()
self.today = | pd.Timestamp.today() | pandas.Timestamp.today |
import pandas as pd
import textacy
import textblob
import en_core_web_sm
nlp = en_core_web_sm.load()
# Multiprocessing Imports
from dask import dataframe as dd
from dask.multiprocessing import get
from multiprocessing import cpu_count
# Sentiment Imports
from vaderSentiment.vaderSentiment import SentimentIntensityAn... | pd.concat(sentiment_rows) | pandas.concat |
from codonPython.tolerance import check_tolerance
import numpy as np
import pandas as pd
import pandas.util.testing as pdt
import pytest
## TODO migrate from numpy arrays to pandas series/dataframes
testdata = [
pd.Series([1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242]),
| pd.Series([1, 2, 3, 4, 5, 5.5, 6, 6.5, 7]) | pandas.Series |
"""
Calculate transition matrix for each section
in the supermarket
"""
import datetime
import pandas as pd
# correct data (customers with no marked checkout)
def missing_checkout(data):
"""fixes data quality issue:
last customers of the day are missing from checkout """
data["timestamp"] = | pd.to_datetime(data["timestamp"]) | pandas.to_datetime |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This module is for visualizing the results
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from sklearn.manifold import TSNE
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
import... | pd.concat(frames) | pandas.concat |
from datetime import datetime
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
_testing as tm,
)
def test_split(any_string_dtype):
values = Series(["a_b_c", "c_d_e", np.nan, "f_g_h"], dtype=any_string_dtype)
... | Series([(1, 2, 3), [1, 2, 3], {1, 2, 3}, {1: "a", 2: "b", 3: "c"}]) | pandas.Series |
# -*- coding: utf-8 -*-
"""gender_detection.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1bu4brssep0L-q5nEmT9OBRykyBbvdu6S
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import fea... | pd.read_pickle("/content/drive/My Drive/gender_detection/gender_speech_english.pkl") | pandas.read_pickle |
# -*- coding: utf-8 -*-
"""
Created on Sat Dec 8 12:17:34 2018
@author: Chandar_S
"""
import pandas as pd
import os
from scipy.misc import imread
import numpy as np
import h5py
from urllib.request import urlopen
#from tensorflow.examples.tutorials.mnist import input_data
class nn_utilities:
data_path = None
... | pd.get_dummies(test.iloc[:, 0]) | pandas.get_dummies |
import multiprocessing as mp
import os
import tempfile
import shutil
import dask.dataframe as dd
import dask.diagnostics
import genomepy
from gimmemotifs.scanner import scan_regionfile_to_table
from gimmemotifs.utils import pfmfile_location
from loguru import logger
import numpy as np
import pandas as pd
import pickle... | pd.DataFrame(data=data) | pandas.DataFrame |
# etl.py - module to clean up incoming covid 19 datasets for ingenstion
__version__ = '0.1'
__all__ = ['FetchData', 'GetCTPData', 'ProcessCTPData', 'FormatDates']
""" @TODO - should have own getargs.py """
import csv
import os
import datetime
import pandas as pd
from common import utils
from Datasets.__meta__.state... | pd.to_datetime(df.iloc[:,0]) | pandas.to_datetime |
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import plotly.graph_objs as go
from dash.dependencies import Input, Output, State
from keras.models import load_model
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas_datareader as web
i... | pd.concat((data['Adj Close'], test_data['Adj Close']), axis=0) | pandas.concat |
"""
.. module:: merge3
:synopsis: merge assemblies from different cell types
jGEM version 3 merger
.. moduleauthor:: <NAME> <<EMAIL>>
"""
# system imports
import subprocess
import multiprocessing
import gzip
import os
import time
import shutil
from functools import reduce
from operator import iadd, iand
fro... | PD.DataFrame({'gname':i2gn, 'tname':i2tn}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from datetime import datetime
import numpy as np
import pandas as pd
from pandas import date_range
try:
import pandas.tseries.holiday
except ImportError:
pass
hcal = pd.tseries.holiday.USFederalHolidayCalendar()
class ApplyIndex(object):
goal_time = 0.2
params = [pd.offset... | pd.offsets.QuarterEnd() | pandas.offsets.QuarterEnd |
#### Filename: Connection.py
#### Version: v1.0
#### Author: <NAME>
#### Date: March 4, 2019
#### Description: Connect to database and get atalaia dataframe.
import psycopg2
import sys
import os
import pandas as pd
import logging
from configparser import ConfigParser
from resqdb.CheckData import CheckData
import numpy... | pd.isnull(x['VISIT_TIME']) | pandas.isnull |
"""
***************************************************************************************
Description: This module is designed to perform calculations that affect production
due to frac hit mitagation operational shut-ins.
*********************************************************************************... | pd.DataFrame() | pandas.DataFrame |
import asyncio
import logging
import os
import time
from datetime import datetime
from enum import Enum
from pathlib import Path
from typing import List, Optional, Tuple
import pandas as pd
from aiohttp import ClientSession
from pydantic import Field, PrivateAttr
from toucan_connectors.common import ConnectorStatus, ... | pd.DataFrame([]) | pandas.DataFrame |
# -*- coding:utf-8 -*-
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import time
FILENAME = {
"train": "./data/train_format1.csv",
"user_log": "./data/user_log_format1.csv",
"user_info": "./data/user_info_format1.csv",
}
TESTNAME = './data/test_format1.csv'
... | pd.merge(test, bought_rate_temp, on="user_id", how="left") | pandas.merge |
from __future__ import division
import pytest
import numpy as np
from pandas import (Interval, IntervalIndex, Index, isna,
interval_range, Timestamp, Timedelta,
compat)
from pandas._libs.interval import IntervalTree
from pandas.tests.indexes.common import Base
import pandas.uti... | pd.Interval(1, 2) | pandas.Interval |
import pandas as pd
import numpy as np
import pytest
from .time_gap_sizes import main
def test_basic():
pd.testing.assert_series_equal(
main(
data=pd.Series(
[10.0, 22.0, 18.0, 2.0],
index=pd.to_datetime(
[
"2019-08-01... | pd.Series(dtype=float) | pandas.Series |
from pyrebase import pyrebase
import collections
import firebase_admin
from firebase_admin import credentials
config = {
"apiKey": "AIzaSyCL8AqkgupmScHROiU8E0cta9YYigdGTaY",
"authDomain": "test1-a06b1.firebaseapp.com",
"databaseURL": "https://test1-a06b1.firebaseio.com",
"projectId": "test1-a06b1",
... | pd.read_excel(flink, usecols = [0,1,2,3,4], names = ['A','B','C','D','E']) | pandas.read_excel |
import sys, os
BASE_DIR = os.path.dirname(os.path.dirname(__file__))
sys.path.append(BASE_DIR)
import pandas as pd
from sklearn import datasets
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler, MaxAbsScaler, StandardScaler
from lightgbm import LGBMClassifier, LGBMRegressor
from skle... | pd.read_csv('nyoka/tests/auto-mpg.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
import numpy as np
import torch
import pandas as pd
import json
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder as LE
import bisect
import torch
from datetime import datetime
from sklearn.model_selection import train_test_split
np.random.see... | pd.merge(matches, df_cards) | pandas.merge |
from __future__ import annotations
from collections import abc
from datetime import datetime
from functools import partial
from itertools import islice
from typing import (
TYPE_CHECKING,
Callable,
Hashable,
List,
Tuple,
TypedDict,
Union,
cast,
overload,
)
import warnings
import nu... | is_list_like(arg) | pandas.core.dtypes.common.is_list_like |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import pandas.compat as compat
###############################################################
# Index / Series common tests which may trigger dtype coercions
###############################################... | pd.Timestamp('2012-01-02') | pandas.Timestamp |
import pandas as pd
from Bio.PDB import Selection, PDBParser
# from Bio.PDB.vectors import rotmat, Vector
import numpy as np
"""
PDB file --> beads center DataFrame --> local structure --> rotated local structure
Functions in this version can handle multiple chains PDB file.
"""
def get_bead_center(residue):
... | pd.read_csv(f'data/dock/beads/{fname}_bead.csv', dtype={'chain_id': str, 'group_num': int}) | pandas.read_csv |
import argparse
import os
import itertools
import logging
import pandas as pd
from tqdm import tqdm
from src.analysis.utils import \
load_squadv2_dev_as_df, \
squad2_evaluation, \
load_squadv1_dev_as_df
logging.basicConfig(format="%(asctime)s - %(message)s", level=logging.INFO)
def create_filepath_dict... | pd.DataFrame(full_metrics) | pandas.DataFrame |
import pandas as pd
import numpy as np
import os
import nltk
tokenizer = nltk.RegexpTokenizer(r"\w+")
from nltk.corpus import stopwords
nltk.download('stopwords')
from nltk.stem import PorterStemmer
ps = PorterStemmer()
from collections import defaultdict
import pickle
import math
from tqdm import tqdm # monitoring p... | pd.read_csv(tsvfile[0],sep='\t') | pandas.read_csv |
import numpy as np
import pandas as pd
import pytest
from prereise.gather.hydrodata.eia.decompose_profile import get_profile_by_state
def test_get_profile_argument_type():
arg = ((1, "WA"), (pd.Series(dtype=np.float64), 1))
for a in arg:
with pytest.raises(TypeError):
get_profile_by_state... | pd.Series(dtype=np.float64) | pandas.Series |
import pandas as pd
import numpy as np
from datetime import datetime
###############
# SELECT DATA #
###############
print("Selecting attributes...")
# GIT_COMMITS
gitCommits = pd.read_csv("../../data/raw/GIT_COMMITS.csv")
attributes = ['projectID', 'commitHash', 'author', 'committer', 'committerDate']
gitCommits =... | pd.merge(dataFrame, sonarMeasures_committer, how='left', on='committer') | pandas.merge |
import pandas
from text_preprocessing import *
from tensorflow import keras
from tensorflow.keras import layers
import wandb
from wandb.keras import WandbCallback
import pathlib
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
import os
import sys
if getattr(sys, 'frozen', Fa... | pandas.DataFrame(data=df_dict) | pandas.DataFrame |
"""
pymake
-------------------------------
- <NAME>
- <EMAIL>
-------------------------------
Created 29-05-2018
"""
import pandas
from pymake.main import printer
import re
import unidecode
import random
import os
def normalize_str(instr):
output = re.sub(r'[^\w]', ' ', instr).strip().lower()
output = re.su... | pandas.Series([df.shape[0], 100], index=dfx.columns, name='Total') | pandas.Series |
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