prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# coding=utf-8
# <NAME>
# <EMAIL>
# 2022-03-15
# 100 Days of Code: The Complete Python Pro Bootcamp for 2022
# Day 31 - Flash Card App
# Constants
BACKGROUND_COLOR = "#B1DDC6"
LANGUAGE_A = "English"
LANGUAGE_B = "French"
FONT_SMALL = ("Arial", 40, "italic")
FONT_LARGE = ("Arial", 60, "bold")
DATAFILE = "data/french_w... | pandas.read_csv(DATAFILE) | pandas.read_csv |
"""
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 ... | tm.reset_display_options() | pandas._testing.reset_display_options |
"""Collect commit data from the user's diffs."""
# pyright: reportMissingImports=false
# pylint: disable=E0401
import pandas as pd
from reporover import get_commit_data
def short_stat(decoded_diff):
"""Get the commit data from git shortstat."""
added = None
deleted = None
changes = decoded_diff.split(... | pd.DataFrame([staged_changes_stats]) | pandas.DataFrame |
'''
This script contains examples of functions that can be used from the Pandas
module.
'''
# Series ---------------------------------------------------------------------
import pandas as pd
import numpy as np
# Creating series
pd.Series(data=[1,2,3,4]) # list
pd.Series(data=[... | pd.read_csv(str_inDir+'example') | pandas.read_csv |
import numpy as np
import pandas as pd
import random as random
import pickle
def formatRank_german(df):
tmp = pd.DataFrame()
tmp['y']=df.sort_values('y_pred',ascending=False).index
tmp['y_pred']=tmp.index
tmp['g']=df.sort_values('y_pred',ascending=False).reset_index()['g']
return tmp
def forma... | pd.read_pickle(inpath+'FairRanking04PercentProtected.pickle') | pandas.read_pickle |
import pandas as pd
#import sys
import requests
import numpy as np
import utils
from sodapy import Socrata
import re
'''
MIT License
Copyright (c) 2021 <NAME> - dLab - Fundación Ciencia y Vida
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation f... | pd.concat([idaux, idx1_t], axis=1) | pandas.concat |
import pandas as pd
import requests
from bs4 import BeautifulSoup, Comment
import json
import re
from datetime import datetime
import numpy as np
comm = re.compile("<!--|-->")
class Team: #change team player object
def __init__(self, team, year, player=None):
self.year = year
self.team = team
... | pd.to_numeric(df_sal[column]) | pandas.to_numeric |
#!/usr/bin/env python
from pandas.io.formats.format import SeriesFormatter
from Bio.SeqUtils import seq1
from Bio import SeqIO
import pandas as pd
import argparse
from pathlib import Path
import numpy as np
from summarise_snpeff import parse_vcf, write_vcf
import csv
import re
from functools import reduce
from binding... | pd.read_csv(f'{args.data_dir}/escape_calculator_data.csv') | pandas.read_csv |
"""
Prepares PUMS Data-Dict CVS for use as panda data frames.
todo: review function comments for accuracy
"""
# %%
import pandas as pd
import json
from _constants import recent_years
# dict.column.value
zero_prefix_rules = {
'DetailedAncestryRecode1': 3,
'DetailedAncestryRecode2': 3,
'DetailedHispanicOrigi... | pd.read_csv(vals_file) | pandas.read_csv |
# bchhun, {2020-03-22}
import csv
import natsort
import numpy as np
import os
import xmltodict
from xml.parsers.expat import ExpatError
import xml.etree.ElementTree as ET
import pandas as pd
import math
import array_analyzer.extract.constants as constants
"""
functions like "create_<extension>_dict" parse files of <e... | pd.read_excel(path_, sheet_name='antigen_type') | pandas.read_excel |
# coding: utf-8
__author__ = 'ersh'
__email__ = '<EMAIL>'
__version__ = '1.1113'
#There is a link to group github where you can find library manoelgadi12 and all the files
#and instructions
#https://github.com/ersh24/manoelgadi12
################
#L Automated data cleaning
####################
import pandas as p... | pd.read_csv("https://dl.dropboxusercontent.com/u/28535341/dev.csv") | pandas.read_csv |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
def main():
df = pd.read_csv('../../data/complete_df_7.csv')
if df.columns[0] == 'Unnamed: 0':
df.drop('Unnamed: 0', axis=1, inplace=True)
if 'stock_open' in df.columns:
df['stock_open'] = df['stock_open'].astype(float)
#ag... | pd.DataFrame(categorical[categorical['sku_key'] == i].iloc[0]) | pandas.DataFrame |
from numpy import loadtxt
import streamlit as st
import numpy as np
import pandas as pd
import altair as alt
n = 25
particle = ['NO2', 'O3', 'NO', 'CO', 'PM1', 'PM2.5', 'PM10']
def actual_vs_predictedpj():
select_model = st.sidebar.radio(
"Choose Model ?", ('Xgboost', 'Randomforest', 'KNN', 'Linear Regre... | pd.DataFrame(temp1, x1, columns=['Data', particle[loc], 'X']) | pandas.DataFrame |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pandas_datareader as web
import datetime as dt
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM
#loading data
compa... | pd.concat((data['Close'], test_data['Close']), axis=0) | pandas.concat |
import pytest
import pandas as pd
import pandas._testing as tm
@pytest.mark.parametrize(
"values, dtype",
[
([], "object"),
([1, 2, 3], "int64"),
([1.0, 2.0, 3.0], "float64"),
(["a", "b", "c"], "object"),
(["a", "b", "c"], "string"),
([1, 2, 3], "datetime64[ns]... | pd.array(mask, dtype="boolean") | pandas.array |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Run nonparametric ridge estimation.
"""
import os
from optparse import OptionParser
import networkx as nx
import numpy as np
import pandas as pd
import time
from networkx.algorithms.centrality import betweenness_centrality
from plotnine import *
from scipy.sparse.csg... | pd.Series(nodes_bc) | pandas.Series |
import os
import numpy as np
import pandas as pd
import json
import lib.galaxy_utilities as gu
from astropy.io import fits
from tqdm import tqdm
aggregated_models = pd.read_pickle('lib/models.pickle')['tuned_aggregate']
def get_n_arms(gal):
keys = (
't11_arms_number_a31_1_debiased',
't11_arms_nu... | pd.read_csv(sid_list_loc) | pandas.read_csv |
import pandas as pd
import numpy as np
import scipy.stats as stats
import copy
import sys
import os
from argotools.config import *
import time
''' Auxiliary functions '''
def preds2matrix(preds_dict):
# Receives preds in Predictor object format.
pred_arrays = []
for model, preds in preds_dict.items():
... | pd.read_csv(path_to_file, index_col=0) | pandas.read_csv |
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import os
import json
# Feature selection strategies
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import SelectFromModel
# Scale feature scores
from sklearn.preprocessing import MinMaxScale... | pd.DataFrame(index=X.columns) | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
import pygrib
from tqdm import tqdm
import logging
import datetime
#########################
###### Definitions ######
#########################
abs_base_path = os.path.dirname(os.path.abspath(__file__))
'''/home/collin/visibility-China/time_series_analysis/src/data'''... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import pandas as pd
from trading_ig import IGService
from trading_ig.config import config
from datetime import timedelta
import requests_cache
import time
import os
import json
counter = -1
ig_service = None
list_of_instruments = []
def login():
expire_after = tim... | pd.DataFrame(data) | pandas.DataFrame |
# Copyright (c) 2018 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | pd.DataFrame(pos_class_df, copy=True) | pandas.DataFrame |
#!//usr/local/bin/python2
import math
import operator
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from plotly.graph_objs import Scatter, Figure, Layout
import plotly.plotly as py
import plotly.graph_objs as go
import pandas as pd
import numpy as np
Xmin=3
Xmax=9
for Abuse in ("Spam D... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
CHORUS_DT_DATA_PATH = "/data/cleaned/data_chorus_dt.csv"
class ChorusDtHandler:
""""
Class for loading chorus DT data and returning
"""
def __init__(self):
self.data_path = CHORUS_DT_DATA_PATH
self.prestation_dict = {"A": "Avion", "T": "Trai... | pd.read_csv(self.data_path, dtype=col_types) | pandas.read_csv |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from .plotter import _Plotter
__all__ = [
"bar_plot",
"time_bar_plot",
"line_plot",
"time_line_plot"
]
def bar_plot(data, y, x=None, hue=None, norm=False,
ax=None, figsize=None, orient="v", aggfunc=np.mean,
... | pd.to_datetime(data[x]) | pandas.to_datetime |
from __future__ import print_function
import sklearn
#%%
import lime
import os
import numpy as np
import pandas as pd
pd.set_option('display.max_colwidth', -1)
pd.set_option('display.max_columns', None)
import sklearn
import sklearn.ensemble
import sklearn.metrics
import seaborn as sns
from scipy.special import softma... | pd.concat([zero,one]) | pandas.concat |
import json
import pandas as pd
import os
import fiona
import geopandas as gpd
import numpy as np
from copy import deepcopy
from pathlib import Path
from flatten_dict import flatten
from poi_conflation_tool import POIConflationTool
# load config file
with open(Path(os.path.dirname(os.path.realpath(__file__)), '../conf... | pd.DataFrame() | pandas.DataFrame |
import os
import pandas as pd
from unidecode import unidecode
def evaluate_banner():
print('\n**************************************************')
print('************Evalulting Reconciliations************')
print('**************************************************')
def evaluate_and_clean_mer... | pd.isnull(df['supplier']) | pandas.isnull |
import numpy as np
import os
import pandas as pd
import pickle
class Predictor(object):
"""
Class for predicting.
"""
def predict(self, data=None, linear=True, model_filename="linear-accelerometer.pcl", features="simple", filtering=None, **kwargs):
"""
:param data: data on which predi... | pd.DataFrame() | pandas.DataFrame |
import os
import pandas as pd
from src.commons.process_dataframe import keep_valid_columns, change_col_value_type, insert_new_col_from_two_cols, \
get_sub_df_according2col_value, get_mean, get_std
from src.commons.process_number import get_deviation, get_percent_change
from src.constants.ms2_uniform_prolific_1_con... | pd.concat(df_list_prepro) | pandas.concat |
import ast
import os
import glob
from io import BytesIO
import base64
import json
from IPython import display as ipd
import numpy as np
import pandas as pd
from scipy.interpolate import interp1d
from scipy.misc import imresize
import pretty_midi
import librosa
import PIL.Image
import soundfile as sf
from flask import ... | pd.read_csv(fn_csv, sep=';') | pandas.read_csv |
from python_speech_features import mfcc
import scipy.io.wavfile as wav
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
import os
import random
from tqdm import tqdm
from audiomentations import Compose, AddGaussianNoise, TimeStretch, PitchShift, Shift
augmenter = Compose([
AddGaussianN... | pd.DataFrame({'train_speaker': y_test}) | pandas.DataFrame |
from pathlib import Path
import numpy as np
import pandas as pd
from sykepic.compute.prediction import prediction_dataframe, threshold_dictionary
def parse_evaluations(
evaluations,
pred_dir,
thresholds=None,
threshold_search=False,
search_precision=0.01,
empty="unclassifiable",
unsure="... | pd.read_csv(file, header=None, names=["roi", "actual"]) | pandas.read_csv |
import numpy as np
import pandas as pd
from collections import Counter
from sklearn.utils import resample
from tqdm.notebook import tqdm_notebook
import copy
from sklearn.base import is_classifier
class DSClassifier:
"""This classifier is designed to handle unbalanced data.
The classification is based... | pd.concat([row_by_class[minority], majority_sample]) | pandas.concat |
# -*- coding: utf-8 -*-
"""data-analysis.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1RKjHEUT1uIYiaDQt2YffetZ0gaRCwlk1
"""
from nltk.tokenize import word_tokenize
from nltk.tokenize import sent_tokenize
import nltk
import os
import glob
impor... | pd.DataFrame(train_comments, columns=['comments']) | pandas.DataFrame |
# pylint: disable=W0102
import nose
import numpy as np
from pandas import Index, MultiIndex, DataFrame, Series
from pandas.compat import OrderedDict, lrange
from pandas.sparse.array import SparseArray
from pandas.core.internals import *
import pandas.core.internals as internals
import pandas.util.testing as tm
from ... | Index(mgr_items) | pandas.Index |
import pandas as pd
from typing import List
def check_missing_value(df: pd.DataFrame, cols: List[str]) -> pd.DataFrame:
"""
Count missing values in specified columns.
@param df: dataframe
@param cols: columns to be calculated
return: summary information
"""
res = | pd.DataFrame(cols, columns=['Feature']) | pandas.DataFrame |
import streamlit as st
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClass... | pd.read_csv('dataset-2010-latlong.csv') | pandas.read_csv |
"""Deconvolution plotter for plotting figures from deconvolution"""
import matplotlib
import matplotlib.pyplot
import numpy as np
import torch
import pyro
import math
from matplotlib.pyplot import cm
import pandas as pd
import seaborn as sns
from typing import Optional, Tuple, Dict
from ternadecov.time_deconv import T... | pd.DataFrame({"time": t, "proportion": prop}) | pandas.DataFrame |
import datetime
import numpy as np
import pandas as pd
import pytest
from .utils import (
get_extension,
to_json_string,
to_days_since_epoch,
extend_dict,
filter_by_columns,
breakdown_by_month,
breakdown_by_month_sum_days,
to_bin,
)
@pytest.fixture
def issues():
return pd.DataFram... | pd.Timestamp(2018, 3, 1) | pandas.Timestamp |
from requests import Session
from bs4 import BeautifulSoup
import pandas as pd
HEADERS = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) '\
'AppleWebKit/537.36 (KHTML, like Gecko) '\
'Chrome/75.0.3770.80 Safari/537.36'}
def zacks_extract(ratio_name, period='weekly_'):... | pd.read_csv('../docs/' + ratio_name + '.csv') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 15 11:51:39 2020
This is best run inside Spyder, not as standalone script.
Author: @hk_nien on Twitter.
"""
import re
import sys
import io
import urllib
import urllib.request
from pathlib import Path
import time
import locale
import json
import pan... | pd.Timedelta('1.2 d') | pandas.Timedelta |
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
"""
Geoip ... | pd.DataFrame(data=ip_dicts) | pandas.DataFrame |
"""
Cause-effect model training
"""
# Author: <NAME> <<EMAIL>>
#
# License: Apache, Version 2.0
import sys
import numpy as np
from .estimator import CauseEffectSystemCombination
# import features as f
import pandas as pd
# from scipy.optimize import fmin
# import _pickle as pickle
from .util import random_permutatio... | pd.concat([train]) | pandas.concat |
from datetime import datetime, timedelta
import numpy as np
import pytest
from pandas._libs.tslibs import period as libperiod
import pandas as pd
from pandas import DatetimeIndex, Period, PeriodIndex, Series, notna, period_range
import pandas._testing as tm
class TestGetItem:
def test_ellipsis(self):
#... | notna(i2) | pandas.notna |
import os, sys, re, json, random, copy, argparse, pickle, importlib
import numpy as np
import pandas as pd
from collections import OrderedDict
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
import logomaker as lm
from util import *
import warnin... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import time
def patient(rdb):
""" Returns list of patients """
patients = """SELECT "Name" FROM patient ORDER BY index"""
try:
patients = | pd.read_sql(patients, rdb) | pandas.read_sql |
# _*_ coding: utf-8 _*_
"""
Prepare level-3 distribution data.
Author: <NAME>
"""
import os
import numpy as np
import pandas as pd
from typing import Union, List
from sklearn.preprocessing import LabelEncoder
# Own Customized modules
from base.base_data_loader import BaseDataLoader
from util.data_util import transf... | pd.date_range(start_dt, periods=pred_len, freq='M') | pandas.date_range |
# importing the dependencies
from string import ascii_uppercase
import pandas as pd
from openpyxl import load_workbook
from openpyxl.styles import PatternFill, Alignment, Border, Side
import numpy as np
#Taking data from GUI
Initiative = "GENESIS"
OutMonth = "July"
# Extracting data from the input calender which i... | pd.Series(UniqueCourseCode) | pandas.Series |
"""
Script to make a plot of the feature variances across the bags.
Use the normalized bags as input, and then show which features have low
variances, and are therefore not useful for the model.
"""
import sys
import os
import numpy as np
import pickle as pkl
import matplotlib.pyplot as plt
import pandas as pd
impo... | pd.DataFrame(plotData) | pandas.DataFrame |
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2022, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | pd.Index(['S1', 'S2', 'S3'], name='id') | pandas.Index |
"""
Parses each kind of spreadsheet into our data structures.
"""
from pathlib import PurePath
import logging, sys
logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)
import math
import os
from itertools import accumulate, islice, chain
import pandas as pd
from data_loader import spreadsheets, DATA_DIR
def a... | pd.to_datetime(date) | pandas.to_datetime |
import logging
import pandas as pd
from lib.constant import Datasets
from lib.features.dtypes import dtypes_clean, dtypes_featured
# features computing functions
def _compute_acc_severity(acc_severities: pd.Series) -> str:
"""Groupby method.
Return the worst victim state for each accident.
"""
# a... | pd.merge(acc_df, users, on='Num_Acc', how='inner') | pandas.merge |
"""
Protein sequence alignment creation protocols/workflows.
Authors:
<NAME>
<NAME> - complex protocol, hmm_build_and_search
<NAME> - hmm_build_and_search
"""
from collections import OrderedDict, Iterable
import re
from shutil import copy
import os
import numpy as np
import pandas as pd
from evcouplings.alig... | pd.read_csv(kwargs["override_annotation_file"]) | pandas.read_csv |
from __future__ import print_function, division
import os
os.environ["OMP_NUM_THREADS"] = "1"
import torch
import torch.multiprocessing as mp
import time
import numpy as np
import random
import json
from tqdm import tqdm
from utils.net_util import ScalarMeanTracker
from runners import nonadaptivea3c_val, savn_val
f... | DataFrame(diff_tracked_means) | pandas.DataFrame |
from collections import Counter
import numpy as np
import pandas as pd
from scipy.spatial.distance import cdist
class KNN:
def __init__(self, k: int):
self.k = k # number of nearest neighbors to be found
self.features = pd.DataFrame([]) # feature matrix
self.labels = pd.Series([]) # la... | pd.Index([]) | pandas.Index |
import pyAgrum as gum
from .DiscreteDistribution import DiscreteDistribution
from .DiscreteVariable import DiscreteVariable
from .MLModel import FitParametersBase, MLModel
import colored
import pandas as pd
import tqdm
import typing_extensions
import pydantic
import typing
import copy
import pkg_resources
import warnin... | pd.Series(True, index=data.index) | pandas.Series |
#%%
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_2o_us_scatter, columns = ['fraction', 'cell_type']) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# **1. Load JSON file**<Br>
# **2. Data Exploration and Visualization**<br>
# **3. Select variables and Convert into CSV**<br>
# **4. Text Preprocessing**
# > a) Change to lower cases<Br>
# > b) Transform links (tentative?)<br>
# > c) Remove punctuation<br>
# > d) Remove stopwords... | pd.DataFrame({'from': df_review['text'], 'to': df_pre['text']}) | pandas.DataFrame |
import numpy as np
from datetime import timedelta
from distutils.version import LooseVersion
import pandas as pd
import pandas.util.testing as tm
from pandas import to_timedelta
from pandas.util.testing import assert_series_equal, assert_frame_equal
from pandas import (Series, Timedelta, DataFrame, Timestamp, Timedelt... | pd.offsets.Hour(1) | pandas.offsets.Hour |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from pandas import (Index, Series, DataFrame, date_range)
from pandas.core.index import MultiIndex
from pandas.compat import StringIO, lrange, range, u
from pandas import compat
import pa... | tm.randn(1000) | pandas.util.testing.randn |
# coding=utf-8
# Real-time air quality data from Beijing official environmental department: http://zx.bjmemc.com.cn/getAqiList.shtml
import datetime
import pandas as pd
from selenium import webdriver
import requests
import const
import settings
config = settings.config[const.DEFAULT]
def get_beijing_aq_l... | pd.date_range(start_date, end_date) | pandas.date_range |
import numpy as np
import pandas as pd
import scipy.stats as stats
class Aggregation:
"""Cálculo de padrões de agregação
Argumento:
file: arquivo de dados no formato csv
Retorno:
pandas series e dataframe com os resultados da análise de agregação
determinados pelo ... | pd.crosstab(self._df["Parcela"], self._df["Especie"]) | pandas.crosstab |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/2/2 23:26
Desc: 东方财富网-行情首页-沪深京 A 股
"""
import requests
import pandas as pd
def stock_zh_a_spot_em() -> pd.DataFrame:
"""
东方财富网-沪深京 A 股-实时行情
http://quote.eastmoney.com/center/gridlist.html#hs_a_board
:return: 实时行情
:rtype: pandas.DataFrame
... | numeric(temp_df["成交量"]) | pandas.to_numeric |
import pandas as pd
import numpy as np
from sklearn import preprocessing
from tqdm import tqdm
from scipy.stats import t
def beta_ridge(Y, X, lamb):
"""
Compute ridge coeffs
Parameters
----------
Y : Nx1 Matrix
X : Matrix (with intercept column)
lamb : Lambda value to use for L2
Retu... | pd.DataFrame(X) | pandas.DataFrame |
#!/usr/bin/env python
# Copyright (c) 2020 IBM Corp. - <NAME> <<EMAIL>>
# Based on: masked_language_modeling.py
# https://keras.io/examples/nlp/masked_language_modeling/
# Fixed spelling errors in messages and comments.
# Preparation on dyce2:
# virtualenv --system-site-packages tf-nightly
# source tf-nightly/bin/ac... | pd.DataFrame({"tokens": texts}) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[24]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pickle
import pygame
pygame.init()
# In[25]:
WIDTH = 1200
HEIGHT = 600
THICKNESS = 30
BALL_RADIUS = 20
PAD_WIDTH = 30
PAD_HEIGHT = 120
VELOCITY = 1
FRAMERATE = 150
BUFFER = 5
AI = True
b... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
from sklearn.model_selection import train_test_split
pd.options.mode.chained_assignment = None
def data_preprocessing():
df = | pd.read_csv("data/SCADA_data.csv.gz") | pandas.read_csv |
import numpy as np
import numpy.linalg as linalg
import pandas as pd
def linear_regression(X, y):
return linalg.inv(X.T.dot(X)).dot(X.T).dot(y)
def go():
data = np.loadtxt('quasar_train.csv', delimiter=',')
wavelengths = data[0]
fluxes = data[1]
ones = np.ones(fluxes.size)
df_ones = pd.Data... | pd.DataFrame(fluxes, columns=['flux']) | pandas.DataFrame |
from datetime import datetime
import warnings
import pytest
import pandas as pd
import pyodbc
from mssql_dataframe.connect import connect
from mssql_dataframe.core import custom_warnings, conversion, create
pd.options.mode.chained_assignment = "raise"
class package:
def __init__(self, connection):
self.... | pd.Series(['datetime64[ns]', 'datetime64[ns]'], dtype='string') | pandas.Series |
import torch
import pandas as pd
from Util import data_split, data_split_val
import spacy
import numpy as np
import pickle
# nlp = spacy.load('en_core_web_sm')
import json
import dateparser
# from bson.int64 import Int64
from datetime import datetime
from sklearn.feature_extraction.text import CountVectorizer
from scip... | pd.DataFrame(train_tgt_data, columns=['encoded_text', 'index', 'domain']) | pandas.DataFrame |
import streamlit as st
import pandas as pd
import altair as alt
def clean_summary_data(file_str:str, name:str):
input_df = pd.read_csv(
file_str,
names=['1', '2','3','type','ministry','source','amount'],
thousands=',')
input_df[['amount']] = input_df[['amount']].fillna(value='EM... | pd.merge(data_2018, data_2019, how='outer') | pandas.merge |
#!/usr/bin/env python3
import argparse
import numpy as np
import matplotlib.pyplot as plt
import pandas
from matplotlib.pyplot import cm
import os
import seaborn as sns
from collections import defaultdict
SEQUENCE_IDENTITY_IDX = 13
ALIGNMENT_IDENTITY_IDX = 14
SAMPLE = "Sample"
SEQUENCE_IDENTITY = "Sequence Identity"
... | pandas.DataFrame(all_identities, columns=columns) | pandas.DataFrame |
from .mcmcposteriorsamplergamma import fit
from scipy.stats import norm, gamma
import pandas as pd
import numpy as np
import pickle as pk
from ..shared_functions import *
class mcmcsamplergamma:
"""
Class for the mcmc sampler of the deconvolution gaussian model
"""
def __init__(self, K=1, Kc=1, alpha ... | pd.DataFrame(columns=["Mean","Std","5%","50%","95%","Rhat","Neff"]) | pandas.DataFrame |
import tkinter as tk
import item_database
import transactions_database
import all_transactions_database
from tkintertable import TableCanvas, TableModel
import datetime
import pandas as pd
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
from decimal import *... | pd.to_numeric(new_all_transactions_df['Tax']) | pandas.to_numeric |
import requests as r
import zipfile
import io
import json
import pandas as pd
from datetime import date, datetime, timedelta
from dateutil.parser import parse
from QualtricsAPI.Setup import Credentials
from QualtricsAPI.JSON import Parser
from QualtricsAPI.Exceptions import Qualtrics500Error, Qualtrics503Error, Qualtri... | pd.DataFrame(df[:1].T) | pandas.DataFrame |
import time
import pandas as pd
import numpy as np
CITY_DATA = { 'chicago': 'chicago.csv',
'new york city': 'new_york_city.csv',
'washington': 'washington.csv' }
def get_filters():
"""
Asks user to specify a city, month, and day to analyze.
Returns:
(str) city - name o... | pd.read_csv(CITY_DATA[city]) | pandas.read_csv |
# -*- coding: utf-8 -*-
import os
import sys
import time
import openpyxl as openpyxl
import pandas
import pandas as pd
import tushare as ts
import numpy as np
from datetime import datetime, timedelta
import matplotlib.ticker as ticker
import matplotlib.dates as mdates
import mplfinance as mpf
import matplotlib.pyplot ... | pd.DataFrame({"trade_date": dates_ext}) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pastas as ps
def acf_func(**kwargs):
index = pd.to_datetime(np.arange(0, 100, 1), unit="D", origin="2000")
data = np.sin(np.linspace(0, 10 * np.pi, 100))
r = pd.Series(data=data, index=index)
acf_true = np.cos(np.linspace(0.0, np.pi, 11))[1:]
acf = ps... | pd.date_range(start=0, periods=1000, freq="D") | pandas.date_range |
import argparse
import pandas as pd
from baseline_tools import write_standard_data, read_IMDB_origin_data, read_AGNEWS_origin_data, \
read_SST2_origin_data
parser = argparse.ArgumentParser()
parser.add_argument('--dataset')
parser.add_argument('--path')
parser.add_argument('--output')
args = parser.pars... | pd.read_csv(sst_folder + 'dictionary.txt', sep='|', header=None, names=['sentence', 'phrase ids']) | pandas.read_csv |
import os as os
from lib import ReadCsv
from lib import ReadConfig
from lib import ReadData
from lib import NetworkModel
from lib import ModelMetrics
from lib import SeriesPlot
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from lib import modwt
import keras
from datetime import date,datetime... | pd.Series(C1) | pandas.Series |
################################################################################
# The contents of this file are Teradata Public Content and have been released
# to the Public Domain.
# <NAME> & <NAME> - April 2020 - v.1.1
# Copyright (c) 2020 by Teradata
# Licensed under BSD; see "license.txt" file in the bundle root ... | pd.to_numeric(df['SAMPLE_ID']) | pandas.to_numeric |
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 14 11:21:38 2018
@author: zdiveki
"""
import pandas as pd
import nltk
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.linear_model.logistic import LogisticRegression
from sk... | pd.concat([a0[columns_sel], a1[columns_sel]]) | pandas.concat |
import datetime
import os, sys
import backtrader as bt
import empyrical as emp
import pyfolio as pyf
import numpy as np
import pandas as pd
class Config:
valid_contracts = ["IF00", "IH00", "IC00"]
contract = valid_contracts[0]
data = os.path.abspath("../data.csv")
df = | pd.read_csv(data, index_col='TRADE_DT', parse_dates=True) | pandas.read_csv |
# coding: utf-8
# In[1]:
from __future__ import division, print_function, absolute_import
from past.builtins import basestring
import os
import gzip
import pandas as pd
from twip.constant import DATA_PATH
from gensim.models import TfidfModel, LsiModel
from gensim.corpora import Dictionary
# In[2]:
import matp... | pd.DataFrame() | pandas.DataFrame |
# Finds and scores all framework mutations from input antibody file (csv format). Outputs normalized FR scores.
# Verbose mode prints full pairwise alignment of each antibody.
# output_mutations option creates a csv with all antibody scores
import numpy as np
import pandas as pd
import seaborn as sns
import ma... | pd.read_csv(norm_filename, index_col=0) | pandas.read_csv |
import unittest
import os
import pandas as pd
from cgnal.core.tests.core import TestCase, logTest
from cgnal.core.logging.defaults import getDefaultLogger
from cgnal.core.data.layer.pandas.databases import Database, Table
from tests import TMP_FOLDER
logger = getDefaultLogger()
db = Database(TMP_FOLDER + "/db")
df1 ... | pd.DataFrame([[1, 2, 3], [6, 5, 4]], columns=["a", "b", "c"]) | pandas.DataFrame |
"""Tests for Table Schema integration."""
import json
from collections import OrderedDict
import numpy as np
import pandas as pd
import pytest
from pandas import DataFrame
from pandas.core.dtypes.dtypes import (
PeriodDtype, CategoricalDtype, DatetimeTZDtype)
from pandas.io.json.table_schema import (
as_json_... | build_table_schema(self.df, version=False) | pandas.io.json.table_schema.build_table_schema |
import numpy as np
import pandas as pd
from bach import Series, DataFrame
from bach.operations.cut import CutOperation, QCutOperation
from sql_models.util import quote_identifier
from tests.functional.bach.test_data_and_utils import assert_equals_data
PD_TESTING_SETTINGS = {
'check_dtype': False,
'check_exact... | pd.cut(p_series, bins=10, right=False) | pandas.cut |
import os
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from .. import read_sql
@pytest.fixture(scope="module") # type: ignore
def mysql_url() -> str:
conn = os.environ["MYSQL_URL"]
return conn
def test_mysql_without_partition(mysql_url: str) -> None:
query = "select... | pd.Series([1, 2, 3, 4, 5, 6], dtype="Int64") | pandas.Series |
import ast
import time
import numpy as np
import pandas as pd
from copy import deepcopy
from typing import Any
from matplotlib import dates as mdates
from scipy import stats
from aistac.components.aistac_commons import DataAnalytics
from ds_discovery.components.transitioning import Transition
from ds_discovery.compone... | pd.Timestamp(date) | pandas.Timestamp |
try:
# Error handling if something happens during script initialisation
from csv import QUOTE_ALL # Needed to export data to CSV
from bs4 import BeautifulSoup # Needed to parse the dynamic webpage of the Ducanator
from requests import get # Needed to get the webpage of the Ducanator
from re impor... | DataFrame(item_list) | pandas.DataFrame |
# Copyright 2019 The TensorFlow Authors. 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 applica... | pd.DataFrame(input_a_np) | pandas.DataFrame |
#! /usr/bin/env python3
###############################################################################
import sys
import os
import argparse
import logging
from datetime import date
import time
import requests
import textwrap
import pandas as pd
import pprint
from lib import utils as ut
##########################... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Merge individual result files with the planned experimental design to create a single all-encompassing
dataframe with experiment and results.
"""
import click
import logging
import pandas as pd
import glob
from pathlib import Path
from dotenv import find_dotenv, load_dotenv
@click.command... | pd.read_csv(f'{input_filepath}/experimental_design.csv') | pandas.read_csv |
from collections import abc, deque
from decimal import Decimal
from io import StringIO
from warnings import catch_warnings
import numpy as np
from numpy.random import randn
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
... | pd.concat([a, b, c], axis=1) | pandas.concat |
import pandas as pd
import re
# Creating `text_id `column from index
def make_text_id(df):
df["text_id"] = df.index
df = df[["text_id", "article", "highlights"]]
return df
def split_into_2_dfs(df):
df_1 = df[["text_id", "article"]]
df_2 = df[["text_id", "highlights"]]
return df_1, df_2
def... | pd.read_csv("data/interim/cnn_dm_train.csv.gz", compression="gzip") | pandas.read_csv |
import os
import numpy as np
import pytest
from pandas.compat import is_platform_little_endian
import pandas as pd
from pandas import DataFrame, HDFStore, Series, _testing as tm, read_hdf
from pandas.tests.io.pytables.common import (
_maybe_remove,
ensure_clean_path,
ensure_clean_store,
tables,
)
fr... | tm.ensure_clean("foo.h5") | pandas._testing.ensure_clean |
from PyQt5 import QtWidgets as Qtw
from PyQt5 import QtCore as Qtc
from PyQt5 import QtGui as Qtg
from datetime import datetime, timedelta
from bu_data_model import BU366
import sys
import socket
import time
import pandas as pd
from openpyxl.chart import ScatterChart, Reference, Series
class CheckingThrea... | pd.Series(record, name=time_) | pandas.Series |
import numpy as np
import re
import pandas as pd
from nova.utils import CalcVol
import logging
# create logger
module_logger = logging.getLogger('NOVA.datastruct')
class DataStruct(object):
def __init__(self, model):
"""
:param model: pyCloudy Model object
"""
self.logger = log... | pd.DataFrame(self._model_input, index=[0]) | pandas.DataFrame |
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