prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
from scipy.signal import butter, lfilter, resample, firwin, decimate
from sklearn.decomposition import FastICA, PCA
from sklearn import preprocessing
import numpy as np
import pandas as np
import matplotlib.pyplot as plt
import scipy
import pandas as pd
class SpectrogramImage:
"""
Plot spectrogram for each ch... | np.triu_indices_from(matrix) | pandas.triu_indices_from |
import numpy as np
import pandas as pd
import os
def to_categorical(data, dtype=None):
val_to_cat = {}
cat = []
index = 0
for val in data:
if dtype == 'ic':
if val not in ['1', '2', '3', '4ER+', '4ER-', '5', '6', '7', '8', '9', '10']:
val = '1'
if val in... | pd.get_dummies(complete_data["Breast_Tumour_Laterality"], prefix = "btl", dummy_na = True) | pandas.get_dummies |
import sys
import pandas as pd
import numpy as np
import click
sys.path.append('.')
from src.data.preprocess_input import read_tsyg_data, prepare_dataset
def gen_init_states(base, parameters, mu, sigma, num=100, sign=None):
init = base.loc[np.repeat(base.index.values, num)].reset_index(drop=True)
for i, p in... | pd.Timestamp(timestamp) | pandas.Timestamp |
#!/usr/bin/python
# -*- coding: UTF-8 -*-
import json
from django.http import HttpResponse
import pandas as pd
import numpy as np
import tushare as ts
class DateEncoder(json.JSONEncoder):
def default(self, o):
if isinstance(o,np.ndarray):
return o.tolist()
return json.JSONEncoder.defa... | pd.DataFrame(data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import pytest
import re
from numpy import nan as NA
import numpy as np
from numpy.random import randint
from pandas.compat import range, u
import pandas.compat as compat
from pandas import Index, Series, DataFrame, isn... | tm.assert_almost_equal(mixed, exp) | pandas.util.testing.assert_almost_equal |
import pandas as pd
media = | pd.read_excel('./../data/CCLE/CCLE_Summary.xlsx', sheet_name='Media') | pandas.read_excel |
import numpy as np
import pandas as pd
from scipy import stats
stats.norm(10.,2.).rvs()
x = np.ones(10)
x *= 2.4
df = | pd.DataFrame([1,2,3]) | pandas.DataFrame |
# Import the pandas-PACKAGE
import matplotlib.pyplot as plt
import pandas as pd
# gca stands for 'get current axis'
ax = plt.gca()
# 3D-paraboloid can be described with equation:
# (x2/a2) + (y2/a2) = z
# If the coefficient 'a' is set to 1
# then the radius at each cut will be equal to √z (square-root of z).
# You... | pd.DataFrame(my_paraboloid) | pandas.DataFrame |
import json
import operator
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from pandas.core.indexes import base
from scipy import stats
from sklearn.metrics import auc, roc_auc_score, roc_curve
from tqdm.auto import tqdm
from data_prep import gini_weight, normalise_matrix... | pd.DataFrame(self.match, columns=["Match"]) | pandas.DataFrame |
import pandas as pd
import numpy as np
def create_empty_df(columns, dtypes, index=None):
df = pd.DataFrame(index=index)
for c,d in zip(columns, dtypes):
df[c] = | pd.Series(dtype=d) | pandas.Series |
# Fundamental libraries
import os
import re
import sys
import time
import glob
import random
import datetime
import warnings
import itertools
import numpy as np
import pandas as pd
import pickle as cp
import seaborn as sns
import multiprocessing
from scipy import stats
from pathlib import Path
from ast import literal_e... | pd.DataFrame(multihot_matrix,columns=token_labels) | pandas.DataFrame |
import json
from typing import List, Dict
from lppinstru.discovery import Discovery, c_int, trigsrcAnalogOut1
import time, datetime
import zmq, math
import sys, traceback
import functools
import numpy as np
import pandas as pds
import peakutils
import signal,atexit
from threading import Thread
from juice_scm_gse.analys... | pds.DataFrame(data={"Vout": tf_vout}, index=tf_vin) | pandas.DataFrame |
# !/usr/bin/env python3
from math import isnan
import os
import shutil
import numpy as np
import random
from numpy.lib.function_base import average
import pandas as pd
import seaborn as sn
import matplotlib.pyplot as plt
import cv2
# import simpledorff
from pandas.core.frame import DataFrame
from sklearn import decomp... | pd.Series(accident_occurence) | pandas.Series |
"""
Module for interacting with the NHL's open but undocumented API.
"""
import streamlit as st
import pandas as pd
from pandas.io.json import json_normalize
import requests as rqsts
## data ingestion
def get_seasons(streamlit=False):
""" returns all seasons on record """
seasons_response = rqsts.get('https://... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
from random import sample
def random_selection():
fields = ['Index', 'Is Success']
# read specific columns
mhm_path = './results/attack_mhm.csv'
gi_path = './results/attack_genetic.csv'
index_mhm = pd.read_csv(mhm_path, skipinitialspace=True, usecols=fields)
index_gi = pd.r... | pd.concat(data, axis=1, keys=headers) | pandas.concat |
#Z0096
# import standards
import pandas as pd
# import stats tools
from scipy.stats import chi2_contingency, ttest_ind
# import modeling tools
from sklearn.model_selection import GridSearchCV
from sklearn.feature_selection import RFE
from sklearn.metrics import classification_report, confusion_matrix
#############... | pd.crosstab(cat, target) | pandas.crosstab |
import numpy as np
import pandas as pd
import glob
from pmdarima.arima import ndiffs
from pandas.tseries.offsets import QuarterBegin, QuarterEnd
from .hand_select import hand_select
import pandas_datareader.data as web
import xlrd, csv
from openpyxl.workbook import Workbook
from openpyxl.reader.excel import load_workbo... | pd.read_excel(path, header=header, engine='openpyxl') | pandas.read_excel |
# %% [Algorithm 1c Loop]
# # MUSHROOMS
# %% [markdown]
# ## Binary Classification
# %% [markdown]
# ### Imports
# %%
import os
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
# %% [markdown]
# ### Load Data
dataset = pd.read_csv(r"C:\User... | pd.DataFrame(X_df2["y_actual"]) | pandas.DataFrame |
# gsheets_data.py
from dotenv import load_dotenv
import os
import gspread
from oauth2client.service_account import ServiceAccountCredentials
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import io
import sys
from wordcloud import WordCloud, STOPWORDS
import nltk
from nltk.sentiment.vad... | pd.DataFrame(business_search) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8; -*-
# Copyright (c) 2020, 2022 Oracle and/or its affiliates.
# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from IPython.core.display import displ... | pd.concat([self.evaluations[0], pd_res]) | pandas.concat |
from pathlib import Path
import re
import numpy as np
import pytest
from pandas._libs.tslibs import Timestamp
from pandas.compat import is_platform_windows
import pandas as pd
from pandas import (
DataFrame,
HDFStore,
Index,
Series,
_testing as tm,
read_hdf,
)
from pandas.te... | HDFStore(path, "r") | pandas.HDFStore |
from numpy import mean
import pandas as pd
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plot
import matplotlib.mlab as mlab
import matplotlib.pylab as lab
import matplotlib.patches as patches
import matplotlib.ticker as plticker
from matplotlib import rcParams
from matplotlib import gridspec... | pd.Series(stellar) | pandas.Series |
import glob
import math
import uuid
from enum import Enum
from typing import Union, Optional, Tuple, Iterable, List, Dict
import numpy as np
import pandas as pd
import os
import ray
Data = Union[str, List[str], np.ndarray, pd.DataFrame, pd.Series]
class RayFileType(Enum):
CSV = 1
PARQUET = 2
class RaySh... | pd.read_csv(data_source, **self.kwargs) | pandas.read_csv |
# pylint: disable-msg=W0612,E1101,W0141
import nose
from numpy.random import randn
import numpy as np
from pandas.core.index import Index, MultiIndex
from pandas import Panel, DataFrame, Series, notnull, isnull
from pandas.util.testing import (assert_almost_equal,
assert_series_equal... | zip(*arrays) | pandas.compat.zip |
from datetime import datetime, timedelta
from io import StringIO
import re
import sys
import numpy as np
import pytest
from pandas._libs.tslib import iNaT
from pandas.compat import PYPY
from pandas.compat.numpy import np_array_datetime64_compat
from pandas.core.dtypes.common import (
is_datetime64_dtype,
is_... | Timedelta("1 days") | pandas.Timedelta |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# *****************************************************************************/
# * Authors: <NAME>
# *****************************************************************************/
"""transformCSV.py
This module contains the basic functions for creating the content of... | pandas.StringDtype() | pandas.StringDtype |
from pandas_datareader import data
import matplotlib.pyplot as plt
import pandas as pd
import datetime as dt
import urllib.request, json
import os
import numpy as np
import tensorflow as tf # This code has been tested with TensorFlow 1.6
from sklearn.preprocessing import MinMaxScaler
import sys
ds = None
if (len(sys... | pd.DataFrame(columns=['Date','Low','High','Close','Open']) | pandas.DataFrame |
import pandas as pd
import numpy as np
import seaborn as sns
from scipy import stats
import matplotlib.pyplot as plt
import os
import re
from sklearn.model_selection import train_test_split
import random
import scorecardpy as sc
# split train into train data and test data
# os.chdir(r'D:\GWU\Aihan\DATS 6103 Data Mini... | pd.cut(self.df[colname], list_break) | pandas.cut |
#! /usr/bin/env python3
import argparse
import json
import logging
import logging.config
import os
import sys
import time
from concurrent import futures
from datetime import datetime
import numpy as np
import pandas as pd
from sklearn.externals import joblib
from sklearn.preprocessing import StandardScaler
import Ser... | pd.DataFrame(dataList) | pandas.DataFrame |
import boto3
import json
import pandas as pd
import numpy as np
import random
import re
import os
from global_variables import API_PARAMETERS_FILE
from global_variables import print_green, print_yellow, print_red
from global_variables import service_dict
from global_variables import default_feature_list
def read_api... | pd.read_csv(log_dir + log_file) | pandas.read_csv |
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.nonparametric.smoothers_lowess import lowess as smlowess
from statsmodels.sandbox.regression.predstd import wls_prediction_std... | pd.Series(upper * std + y) | pandas.Series |
from datetime import datetime
import pandas as pd
import pytest
def test_params_1():
d1 = {
"PIDN": [1, 1, 3],
"DCDate": [datetime(2001, 3, 2), datetime(2001, 3, 2), datetime(2001, 8, 1)],
"Col1": [7, 7, 9],
}
primary = | pd.DataFrame(data=d1) | pandas.DataFrame |
import csv
import os
import pandas as pd
import math
import numpy as np
POIEdges = {'Sathorn_Thai_1': ['L197#1', 'L197#2'],
'Sathorn_Thai_2': ['L30', 'L58#1', 'L58#2'],
'Charoenkrung_1': ['L30032'],
'Charoenkrung_2': ['L60', 'L73', 'L10149#1', 'L10149#2'],
'Cha... | pd.read_csv(path) | pandas.read_csv |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tests for `hotelling` package."""
import pytest
import numpy as np
import pandas as pd
from pandas.testing import assert_series_equal, assert_frame_equal
from hotelling.stats import hotelling_t2
def test_hotelling_test_array_two_sample():
x = np.asarray([[23, 45... | pd.Index(['calcium', 'iron', 'protein', 'a', 'c'], dtype='object') | pandas.Index |
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.... | pd.read_csv('/kaggle/input/hotel-booking-demand/hotel_bookings.csv') | pandas.read_csv |
import pandas as pd
from pandas import DataFrame
import pandas._testing as tm
class TestConcatSort:
def test_concat_sorts_columns(self, sort):
# GH-4588
df1 = DataFrame({"a": [1, 2], "b": [1, 2]}, columns=["b", "a"])
df2 = DataFrame({"a": [3, 4], "c": [5, 6]})
# for sort=True/None... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
# 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... | pd.date_range('2010-01-01', periods=2, freq='m') | pandas.date_range |
# -*- coding: utf-8 -*-
"""
Automated Tool for Optimized Modelling (ATOM)
Author: Mavs
Description: Module containing utility constants, functions and classes.
"""
# Standard packages
import math
import logging
import numpy as np
import pandas as pd
from copy import copy
from typing import Union
from scipy import sp... | pd.Series(data, index=index, name=name, dtype=dtype) | pandas.Series |
import os
import warnings
from six import BytesIO
from six.moves import cPickle
import numpy as np
from numpy.testing import assert_almost_equal, assert_allclose
import pandas as pd
import pandas.util.testing as tm
import pytest
from sm2 import datasets
from sm2.regression.linear_model import OLS
from sm2.tsa.arima... | tm.assert_index_equal(res.params.index, expected_index) | pandas.util.testing.assert_index_equal |
""" test indexing with ix """
from warnings import catch_warnings
import numpy as np
import pandas as pd
from pandas.types.common import is_scalar
from pandas.compat import lrange
from pandas import Series, DataFrame, option_context, MultiIndex
from pandas.util import testing as tm
from pandas.core.common import Per... | DataFrame({'a': [0, 1, 2]}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""Untitled0.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1uPsIhY5eetnUG-xeLtHmKvq5K0mIr6wW
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import keras
import tensorflow as tf
dataset = | pd.read_csv('Churn_Modelling.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.2'
# jupytext_version: 1.2.4
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# --... | pd.np.zeros(BW) | pandas.np.zeros |
import os
import pandas as pd
FOLDER = 'data'
FILENAME = 'gl.csv'
COLUMNS = ['GL_Account', 'GL_Description', 'Amount']
class GeneralLedger():
def __init__(self, folder=FOLDER, filename=FILENAME, columns=COLUMNS):
base_folder = os.path.abspath(os.path.dirname(__file__))
self.columns = columns
... | pd.DataFrame([new_row], columns=self.columns) | pandas.DataFrame |
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
from featuretools.primitives import (
Age,
EmailAddressToDomain,
IsFreeEmailDomain,
TimeSince,
URLToDomain,
URLToProtocol,
URLToTLD,
Week,
get_transform_primitives
)
def test_time_since():
time... | pd.testing.assert_series_equal(answers, correct_answers) | pandas.testing.assert_series_equal |
# -*- coding: utf-8 -*-
"""
Created on Jan 5 09:20:37 2022
Compiles NDFD data into SQLite DB
@author: buriona,tclarkin
"""
import sys
from pathlib import Path
import pandas as pd
import sqlalchemy as sql
import sqlite3
import zipfile
from zipfile import ZipFile
# Load directories and defaults
this_dir = Path(__fil... | pd.DataFrame(columns=[DEFAULT_DATE_FIELD]) | pandas.DataFrame |
from __future__ import division
import torch
import numpy as np
import os
import math
import argparse
import logging
from collections import OrderedDict
import pandas as pd
import json
'''
Histogram of simalarities:
a) positive
b) Top-k percent
'''
def histogram(sim, top_k_percents, writer, i_epoch, name):
K = np.ar... | pd.DataFrame(a) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[5]:
import time as tm
import os, cx_Oracle
from datetime import *
import numpy as np
import pandas as pd
pt = os.getcwd() + "\\book1.csv"
df = pd.read_csv(pt)
df = df.astype (str)
df = df.rename (columns=str.upper)
df1 = df[['SERIAL','SUMMARY','CUSTOMATTR15','CUSTOMATTR... | pd.to_datetime(x['LASTOCCURRENCE'], dayfirst=True) | pandas.to_datetime |
from io import StringIO
from copy import deepcopy
import numpy as np
import pandas as pd
import re
from glypnirO_GUI.get_uniprot import UniprotParser
from sequal.sequence import Sequence
from sequal.resources import glycan_block_dict
sequence_column_name = "Peptide\n< ProteinMetrics Confidential >"
glycans_column_nam... | pd.DataFrame(component_list) | pandas.DataFrame |
from collections import Counter
import altair as alt
import pandas as pd
import streamlit as st
def stat_explorer(num_players):
global data, board_spaces
st.title('Mpoly Junior Game statistics explorer')
st.write("""
We play 5,000,000 games and see what we can find.
Use the radio buttons at the ... | pd.DataFrame({'Space': board_spaces, 'Visit Count': visit_count}) | pandas.DataFrame |
"""
This script preprocesses data and prepares data to be actually used in training
"""
import re
import os
import pickle
import unicodedata
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
import logging
logging.basicConfig(filename="memo_1.txt", l... | pd.read_csv('../data/pre-processed/audio_features.csv') | pandas.read_csv |
from itertools import product
import numpy as np
from numpy import ma
import pandas as pd
import pytest
from scipy import sparse as sp
from scipy.sparse import csr_matrix, issparse
from anndata import AnnData
from anndata.tests.helpers import assert_equal, gen_adata
# some test objects that we use below
adata_dense... | pd.testing.assert_index_equal(curr.obs_names, curr.raw.obs_names) | pandas.testing.assert_index_equal |
#! /usr/bin/env python3
#SBATCH -J get_csv
#SBATCH -t 4:0:0
#SBATCH --mem=5G
### Get one csv with the normalized expression data
# This Python script to make a csv of the output data from cuffdiff. It extracts the gene_id and expressionvalues for each analysis and writes it to 1 csv file
# This scripts needs os.syste... | pd.concat([TC6_gene_ex, value_2.iloc[:,1]], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 18 14:52:26 2021
@author: IneR
"""
#set inputs
#Folder (Adapt!!)
Folder = 'I:\\Las\\InputRF\\ReferenceData\\FixedDistance\\'
#%%
#import modules
import os
import glob
import pandas as pd
import numpy
from sklearn.model_selection import train_test_split
fr... | pd.concat([TruePos_T, TrueNeg_T, TruePos_H, TrueNeg_H]) | pandas.concat |
import numpy as np
import pytest
from pandas.compat import lrange
import pandas as pd
from pandas import Series, Timestamp
from pandas.util.testing import assert_series_equal
@pytest.mark.parametrize("val,expected", [
(2**63 - 1, 3),
(2**63, 4),
])
def test_loc_uint64(val, expected):
# see gh-19399
... | pd.date_range("2011-01-01", periods=3, tz="US/Eastern") | pandas.date_range |
#!/usr/bin/env python
# coding: utf-8
# ### Explore processed pan-cancer data
# In[1]:
import os
import sys
import numpy as np; np.random.seed(42)
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
import mpmp.config as cfg
import mpmp.utilit... | pd.DataFrame() | pandas.DataFrame |
def get_default_fitkwargs(dataset=None):
return {'d': 10, 'n_iters': 1000, 'max_n': 3000, 'batch_size': 100, 'lr': 1e-2, 'stop_iters':50, 'norm': True, 'ybar_bias': True}
#=====================================For things implemented in DJKP
import pandas as pd
import numpy as np
import os
import ipdb
from sklearn.m... | pd.concat([raw_df, tdf], axis=1) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 1 12:48:08 2020
@author: smith
"""
import spacy
from gensim.test.utils import common_texts, get_tmpfile
from gensim.models import Word2Vec
from gensim.models.phrases import Phrases, Phraser
import os
import multiprocessing
import csv
import re
impo... | pd.DataFrame() | pandas.DataFrame |
from sklearn.metrics import roc_auc_score, roc_curve, auc
import pandas as pd
from typing import Dict, List
from progress.bar import Bar
import os
import pickle
from prismx.utils import read_gmt, load_correlation, load_feature
from prismx.loaddata import get_genes
def calculate_set_auc(prediction: pd.DataFrame, librar... | pd.DataFrame() | pandas.DataFrame |
"""
Author: <NAME>, <NAME>
"""
import math
import pandas as pd
from bloomberg import BBG
from pandas.tseries.offsets import BDay
class BondFutureTracker(object):
futures_ticker_dict = {'US': 'TY',
'DE': 'RX',
'FR': 'OAT',
'IT': 'IK... | BDay(1) | pandas.tseries.offsets.BDay |
import time
import numpy as np
import pandas as pd
from sklearn import pipeline
from sklearn.calibration import CalibratedClassifierCV
from sklearn.kernel_approximation import (RBFSampler)
from sklearn.metrics import log_loss
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
seed ... | pd.read_csv(datadir + "numerai_training_data.csv") | pandas.read_csv |
from infomemes.utils import media_color_schema
from sklearn.cluster import DBSCAN
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import json
def read_sim_results(sim, step_filtered=0):
"""
Basic analysis of a simulation.
sim: simulation object or string with path to json file.
... | pd.Series([], dtype='float') | pandas.Series |
"""Step 1: Solving the problem in a deterministic manner."""
import cvxpy as cp
import fledge
import numpy as np
import os
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import shutil
def main():
# Settings.
scenario_name = 'course_project_step_1'
results_path = os.pat... | pd.DataFrame(0.0, index=der_model_set.timesteps[:1], columns=der_model_set.states) | pandas.DataFrame |
import torch
import pathlib
import pandas as pd
import pytorch_lightning as pl
from datetime import datetime
from collections import OrderedDict
class CSVLogger(pl.Callback):
"""Custom metric logger and model checkpoint."""
def __init__(self, output_path=None):
super(CSVLogger, self).__init__()
... | pd.DataFrame.from_records([data_dict], index=interval) | pandas.DataFrame.from_records |
"""
Holt-Winters from statsmodels
"""
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from hyperopt import hp, fmin, tpe, Trials
# local module
from foresee.models import models_util
from foresee.models import param... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
import networkx as nx
import scipy.sparse as sparse
from base import BaseFeature
class PageRank(BaseFeature):
def import_columns(self):
return ["engaged_user_id", "engaging_user_id", "engagee_follows_engager"]
def make_features(self, df_train_input, df_test_inpu... | pd.DataFrame() | pandas.DataFrame |
from unittest import TestCase
import pandas as pd
import numpy as np
import pandas_validator as pv
from pandas_validator.core.exceptions import ValidationError
class BaseSeriesValidatorTest(TestCase):
def setUp(self):
self.validator = pv.BaseSeriesValidator(series_type=np.int64)
def test_is_valid_wh... | pd.Series([0, 1, 2]) | pandas.Series |
#<NAME> Data Mining Project B00721425
'''
from surprise import SVD
from surprise import Dataset
from surprise.model_selection import GridSearchCV
from surprise import Reader
import pandas as pd #https://pandas.pydata.org/docs/reference/index.html all the methods I used I searched from here
from surprise import N... | pd.isnull(user_item_matrix.iloc[user_id][movie_id]) | pandas.isnull |
import time
import datetime as dt
import pandas as pd
import numpy as np
import logging
import coloredlogs
import pytz
from typing import List, Dict, Tuple, Any
from polygon import RESTClient
from trader.common.helpers import dateify
class PolygonFinancials():
def __init__(self, financials: pd.DataFrame, dividend... | pd.DataFrame(splits) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# @author: Elie
#%% ==========================================================
# Import libraries set library params
# ============================================================
# Libraries
import pandas as pd
import numpy as np
from numpy import std, mean, sqrt
from scipy.stats imp... | pd.read_csv(prob_path, sep='\t', low_memory=False) | pandas.read_csv |
import train
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import preprocessing
import time
from datetime import datetime
import warnings
warnings.filterwarnings('ignore')
# ML libraries
import lightgbm as lgb
import xgboost as xgb
from xgboost imp... | pd.concat([X_train_check, X_test_check]) | pandas.concat |
from matplotlib.pyplot import title
import streamlit as st
import pandas as pd
import altair as alt
import pydeck as pdk
import os
import glob
from wordcloud import WordCloud
import streamlit_analytics
path = os.path.dirname(__file__)
streamlit_analytics.start_tracking()
@st.cache
def load_gnd_top_daten(typ):
gn... | pd.read_csv(f'{path}/../stats/gnd_classification_all.csv', index_col=False) | pandas.read_csv |
import gc
import numpy as np
import pandas as pd
import xgboost as xgb
from pandas.core.categorical import Categorical
from scipy.sparse import csr_matrix, hstack
categorical_features = ['having_IP_Address','URL_Length','Shortining_Service','having_At_Symbol','double_slash_redirecting','Prefix_Suffix','having_Sub_Dom... | pd.DataFrame(data, columns=column_names) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""Structures data in ML-friendly ways."""
import re
import copy
import datetime as dt
import random
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from avaml import Error, setenvironment as se, _NONE, CSV_VERSION, REGIONS, merge, REGION_ELEV
from avaml... | pd.DataFrame(data=data, index=self.data.index, columns=self.data.columns) | pandas.DataFrame |
import os
""" First change the following directory link to where all input files do exist """
os.chdir("D:\\Book writing\\Codes\\Chapter 5")
import numpy as np
import pandas as pd
# KNN Curse of Dimensionality
import random,math
def random_point_gen(dimension):
return [random.random() for _ in range(dimensi... | pd.DataFrame(dummyarray) | pandas.DataFrame |
#Lib for Streamlit
# Copyright(c) 2021 - AilluminateX LLC
# This is main Sofware... Screening and Tirage
# Customized to general Major Activities
# Make all the School Activities- st.write(DataFrame) ==> (outputs) Commented...
# The reason, since still we need the major calculations.
# Also the Computing is n... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
from scipy.stats import poisson
from cooltools.api.dotfinder import (
histogram_scored_pixels,
determine_thresholds,
annotate_pixels_with_qvalues,
extract_scored_pixels,
)
# helper functions for BH-FDR copied from www.statsmodels.org
def _fdrcorrection(pvals, al... | pd.cut(scored_df[f"la_exp.{k}.value"], ledges) | pandas.cut |
##############################################################################################
# PURPOSE
# Read STARLIGHT output files
#
# CREATED BY:
# <NAME> (in R)
#
# ADAPTED BY:
# <NAME> (Conversion from R to Python and adaptation)
#
# CALLING SEQUENCE
# python read_starlight_output.py --> In terminal... | pd.read_csv(starlight_file, skiprows = 63, nrows=75, delim_whitespace=True, engine='python', header = None) | pandas.read_csv |
import logging
import pickle
import uuid
from datetime import datetime
from warnings import warn
from typing import List
import numpy as np
import pandas as pd
from aequilibrae.starts_logging import logger
from .__version__ import binary_version as VERSION
class Graph(object):
"""
Graph class
"""
def... | pd.DataFrame(crosswalk, copy=True) | pandas.DataFrame |
# This code is part of the epytope distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
"""
.. module:: Core.AResult
:synopsis: Contains relevant classes describing results of predictions.
.. moduleauthor:: schubert
"""
__author__ = 'schu... | pandas.Index(peps) | pandas.Index |
# Diffusion Maps Framework implementation as part of MSc Data Science Project of student
# <NAME> at University of Southampton, MSc Data Science course
# Script 3: Principal Component Analysis
import os, math
import string
import openpyxl
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.p... | pd.DataFrame(worksheet.ix[:,:29]) | pandas.DataFrame |
## GitHub: dark-teal-coder
import pandas as pd
import numpy as np
import requests
from bs4 import BeautifulSoup
from fpdf import FPDF
import datetime
import string
import os
## Get datetime information
current_datetime = datetime.datetime.now()
current_year = current_datetime.year
## Get the running script path
... | pd.to_numeric(df_wage_table['high'], errors='coerce') | pandas.to_numeric |
'''
pyjade
A program to export, curate, and transform data from the MySQL database used by the Jane Addams Digital Edition.
'''
import os
import re
import sys
import json
import string
import datetime
import mysql.connector
from diskcache import Cache
import pandas as pd
import numpy as np
from bs4 import Beautiful... | pd.read_sql(statement,DB) | pandas.read_sql |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for datetime64 and datetime64tz dtypes
from datetime import (
datetime,
time,
timedelta,
)
from itertools import (
product,
starmap,
)
import operator
import warnings
import numpy as np
impo... | tm.assert_equal(result, expected) | pandas._testing.assert_equal |
# common.py (flowsa)
# !/usr/bin/env python3
# coding=utf-8
"""Common variables and functions used across flowsa"""
import shutil
import os
import yaml
import pandas as pd
import numpy as np
from dotenv import load_dotenv
from esupy.processed_data_mgmt import create_paths_if_missing
import flowsa.flowsa_yaml as flows... | pd.unique(df_load['MetaSources']) | pandas.unique |
"""
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 ... | fmt.Datetime64Formatter(x) | pandas.io.formats.format.Datetime64Formatter |
"""
Pull my Garmin sleep data via json requests.
This script was adapted from: https://github.com/kristjanr/my-quantified-sleep
The aforementioned code required the user to manually define
headers and cookies. It also stored all of the data within Night objects.
My modifications include using selenium to drive a Chr... | pd.isnull(ms2_df["Total_Dur"]) | pandas.isnull |
"""PyStan utility functions
These functions validate and organize data passed to and from the
classes and functions defined in the file `stan_fit.hpp` and wrapped
by the Cython file `stan_fit.pxd`.
"""
#-----------------------------------------------------------------------------
# Copyright (c) 2013-2015, PyStan dev... | pd.DataFrame() | pandas.DataFrame |
import logging
from functools import lru_cache
from itertools import chain
# from linetimer import CodeTimer
import pandas as pd
from statistics import mean, StatisticsError
from elecsim.role.market.latest_market_data import LatestMarketData
from elecsim.market.electricity.bid import Bid
import elecsim.scenario.scenar... | pd.DataFrame(self.hold_duration_curve_prices) | pandas.DataFrame |
import psycopg2
import psycopg2
import sqlalchemy as salc
import numpy as np
import warnings
import datetime
import pandas as pd
import json
from math import pi
from flask import request, send_file, Response
# import visualization libraries
from bokeh.io import export_png
from bokeh.embed import json_item
from bokeh.p... | pd.DataFrame() | pandas.DataFrame |
from cbs import cbs
import pandas as pd
import pytest
#Get the CBS RB dataframe
@pytest.fixture(scope="module")
def RB():
return cbs.Cbs().parser('RB')
def test_cbs_rb_columns(RB):
assert RB.columns.tolist() == ['Name', 'pass_att', 'pass_cmp', 'pass_yds', 'pass_td', 'intercept', 'rate', 'rush_att',
... | pd.to_numeric(RB.iloc[0].rush_td, errors='ignore') | pandas.to_numeric |
#!/usr/bin/env python
"""
I use this script to determine the ratio of measurements of fluxes compared to
the number of temperature measurements for FLUXNET and LaThuille sites.
This is done for latent heat, sensible heat and NEE. I focus on
extreme temperatures (lower and upper 2.2% of the temperature distribution... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import urllib.request
import numpy as np
import shapefile
from datetime import datetime
from zipfile import ZipFile
import pandasql as ps
import requests
import json
import pkg_resources
def softmax(x):
if np.max(x) > 1:
e_x = np.exp(x/np.max(x))
else:
e_x = np.exp(x - np.max(x... | pd.DataFrame() | pandas.DataFrame |
import concurrent.futures
import pandas as pd
from equities import static as STATIC
from solaris.api import Client as SolarisClient
from pytrends.request import TrendReq as GoogleTrendClient
import yfinance as YahooFinanceClient
__version__ = STATIC.__version__
__author__ = STATIC.__author__
class Client(object):
... | pd.DataFrame() | pandas.DataFrame |
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC,LinearSVC
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
from skl... | pd.get_dummies(title.Title) | pandas.get_dummies |
import pandas as pd
import numpy as np
import pytest
from .arcsine import main
def test_numeric():
assert main(data=0.0)["result"] == pytest.approx(0.0, rel=1e-5)
def test_series():
pd.testing.assert_series_equal(
main(
data=pd.Series(
{
"2019-08-01T15... | pd.DataFrame(dtype=float) | pandas.DataFrame |
import anndata as ad
import logging
import numpy as np
import os
import time
import pandas as pd
import yaml
from pathlib import Path
from collections import namedtuple
from const import PATH, OUT_PATH
#logging.basicConfig(level=logging.INFO)
try:
import git
except:
pass
def get_tasks(phase):
assert phase... | pd.concat([dg,df],axis=1) | pandas.concat |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import duckdb
import pandas as pd
import numpy
# Join from pandas not matching identical strings #1767
class TestIssue1767(object):
def test_unicode_join_pandas(self, duckdb_cursor):
A = pd.DataFrame({"key": ["a", "п"]})
B = pd.DataFrame({"key": ["a", ... | pd.DataFrame(data=d) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 4 16:15:31 2017
This creates the CollegePrograms Dashboard. It calls the Career Bridge class and Matches it to a SOC based on the listed occupation, industry, keywords and lookups.
This requires selenium.
@author: carrie
"""
from selenium import webdriver
import... | pd.concat([ occupation_con,occupation_not_matched], axis=1) | pandas.concat |
from backlight.trades import trades as module
import pytest
import pandas as pd
@pytest.fixture
def symbol():
return "usdjpy"
@pytest.fixture
def trades(symbol):
data = [1.0, -2.0, 1.0, 2.0, -4.0, 2.0, 1.0, 0.0, 1.0, 0.0]
index = pd.date_range(start="2018-06-06", freq="1min", periods=len(data))
tr... | pd.testing.assert_series_equal(trade, expected) | pandas.testing.assert_series_equal |
# License: Apache-2.0
import databricks.koalas as ks
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from gators.feature_selection.correlation_filter import CorrelationFilter
ks.set_option("compute.default_index_type", "distributed-sequence")
@pytest.fixture
def da... | assert_frame_equal(X_new, X_expected) | pandas.testing.assert_frame_equal |
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