prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import numpy as np
import pandas as pd
from numba import njit
import pytest
import os
from collections import namedtuple
from itertools import product, combinations
from vectorbt import settings
from vectorbt.utils import checks, config, decorators, math, array, random, enum, data, params
from tests.utils import hash... | pd.Series([1, 2]) | pandas.Series |
'''
Created on Mar. 9, 2021
@author: cefect
'''
import configparser, os, inspect, logging, copy, itertools, datetime
import pandas as pd
idx = pd.IndexSlice
import numpy as np
from scipy import interpolate, integrate
from hlpr.exceptions import QError as Error
from hlpr.plot import Plotr
from model.modcom import Mod... | pd.DataFrame(scenFail_ar, columns=inde_df[bxf].index) | pandas.DataFrame |
import json
import pandas as pd
try:
from urllib.request import urlopen
from urllib.error import URLError, HTTPError
except ImportError:
from urllib2 import urlopen, URLError, HTTPError
def get_form(api_key, typeform_id, options=None):
typeform_url = "https://api.typeform.com/v1/form/"
typeform_url += str(type... | pd.DataFrame(qs) | pandas.DataFrame |
# Local
from sheetreader import get_index
# Internal
from collections import Counter, defaultdict
import json
import operator
import os
import re
from itertools import combinations, cycle
# External
from tabulate import tabulate
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
########... | pd.DataFrame(rows, columns=['Task', 'Verbatim Criterion', 'Count']) | pandas.DataFrame |
import numpy as np
import pandas as pd
import datetime
import requests
import json
fr_grade = {13:'3a',
21:'4a',
23:'4b',
25:'4c',
29:'5a',
31:'5b',
33:'5c',
36:'6a',
38:'6a+',
40:'6b',
42:'6b+',
... | pd.to_datetime(start_date) | pandas.to_datetime |
# -*- coding: utf-8 -*-
import re
import warnings
from datetime import timedelta
from itertools import product
import pytest
import numpy as np
import pandas as pd
from pandas import (CategoricalIndex, DataFrame, Index, MultiIndex,
compat, date_range, period_range)
from pandas.compat import PY... | tm.assert_index_equal(result, expected) | pandas.util.testing.assert_index_equal |
import sklearn
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import scale
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score, precision_score
from sklearn.preprocessing impor... | pd.read_csv('data/Xte_mat100.csv',sep=' ',header=None) | pandas.read_csv |
import os
import time
import pandas as pd
import numpy as np
import json
from hydroDL import kPath
from hydroDL.data import usgs, gageII, gridMET, ntn
from hydroDL.post import axplot, figplot
import matplotlib.pyplot as plt
dirInv = os.path.join(kPath.dirData, 'USGS', 'inventory')
fileSiteNo = os.path.join(dirInv, 'si... | pd.date_range(start='1979-01-01', end='2019-12-30', freq='D') | pandas.date_range |
#! -*- coding: utf-8 -*-
import ToolsNLP
import gensim
import numpy as np
from collections import Counter
import matplotlib.pyplot as plt
from wordcloud import WordCloud
import pandas as pd
import re
import site
import os
class TopicModelWrapper:
'''
Description::
トピックモデルを実行する
:param data:
... | pd.DataFrame(topic_counnter) | pandas.DataFrame |
from dateutil import parser
import numpy as np
import pandas as pd
import urllib3
import json
import datetime as dt
import time
import warnings
import math
#######################################################################
# drops invalid data from our history
def dropDirty(history, exWeekends):
history = hi... | pd.to_datetime(history.index) | pandas.to_datetime |
#!/usr/bin/env python
import argparse
import pandas as pd
from Bio import SeqIO
import os
import numpy as np
from collections import OrderedDict
from tqdm import tqdm
from abnumber import Chain
import re
import requests
import time
SCORE_REGEX = re.compile('<h3>The Z-score value of the Query sequence is: (-?[0-9.]+)<... | pd.DataFrame(results) | pandas.DataFrame |
# importing all the required libraries
import numpy as np
import pandas as pd
from datetime import datetime
import time, datetime
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler, LabelEncoder, MinMaxScaler
from chart_studio.plotly import plotly
import plot... | pd.merge(air_visit_data,date_info,how='left',on=['visit_date']) | pandas.merge |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import streamlit as st
from scipy import stats
from fairlearn.metrics import MetricFrame
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_s... | pd.DataFrame('', index=x.index, columns=x.columns) | pandas.DataFrame |
from matplotlib import pyplot as plt
import csv
from absl import app, flags, logging
from absl.flags import FLAGS
import os
import scipy.io
import numpy as np
import cv2
import tqdm
from sklearn.metrics import average_precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import accuracy_score
fr... | pd.DataFrame.from_dict(new_dict, orient='index') | pandas.DataFrame.from_dict |
from zipfile import ZipFile
import datetime
import calendar
import json
import pandas as pd
class DateNotValidException(Exception):
pass
class FeedNotValidException(Exception):
pass
REQUIRED_FILES = [
'agency.txt', 'stops.txt', 'routes.txt', 'trips.txt', 'stop_times.txt'
]
OPTIONAL_FILES = [
'ca... | pd.Series() | pandas.Series |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scikit_posthocs as sp
import warnings
import seaborn as sns
import statsmodels.api as sm
from bevel.linear_ordinal_regression import OrderedLogit
import scipy.stats as stats
warnings.filterwarnings("ignore")
from statsmodels.miscmodels.ordina... | pd.read_csv("df_complet.csv") | pandas.read_csv |
import time
import pandas as pd
import copy
import numpy as np
from shapely import affinity
from shapely.geometry import Polygon
import geopandas as gpd
def cal_arc(p1, p2, degree=False):
dx, dy = p2[0] - p1[0], p2[1] - p1[1]
arc = np.pi - np.arctan2(dy, dx)
return arc / np.pi * 180 if degree else arc
def... | pd.Series([a2 if longer else a1 for a1, a2, longer in df_mbr[['a1', 'a2', 'longer']].values]) | pandas.Series |
from __future__ import division
import pytest
import numpy as np
from datetime import timedelta
from pandas import (
Interval, IntervalIndex, Index, isna, notna, interval_range, Timestamp,
Timedelta, compat, date_range, timedelta_range, DateOffset)
from pandas.compat import lzip
from pandas.tseries.offsets imp... | Timedelta(days=1) | pandas.Timedelta |
from datetime import datetime
import warnings
import numpy as np
import pytest
from pandas.core.dtypes.generic import ABCDateOffset
import pandas as pd
from pandas import (
DatetimeIndex,
Index,
PeriodIndex,
Series,
Timestamp,
bdate_range,
date_range,
)
from pandas.tests.test_base import ... | Index(t2.values) | pandas.Index |
import sqlite3
import pandas as pd
import numpy as np
from pandas import Series, DataFrame
#@Author: <NAME>
#@Version: 1.0
#@Description: Function for show up the odds history for 2 team
def getOddsHistoryByTeam(team1_id,team2_id):
db_con = sqlite3.connect("database.sqlite")
Liga_match_history = pd.read_sq... | pd.read_sql_query("SELECT team_api_id, date,buildUpPlaySpeed,chanceCreationShooting,defenceAggression from Team_Attributes", db_con) | pandas.read_sql_query |
from scseirx.model_school import SEIRX_school
import scseirx.analysis_functions as af
import pandas as pd
import numpy as np
import networkx as nx
from os.path import join
from scipy.stats import spearmanr, pearsonr
def weibull_two_param(shape, scale):
'''
Scales a Weibull distribution that is defined soely by... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 27 19:51:30 2018
@author: alber
"""
import os
import glob
import pandas as pd
import re
from nltk.corpus import stopwords
from nltk.stem import SnowballStemmer
import numpy as np
global stemmer
import pickle
stemmer = SnowballStemmer("english")
def word_tf_idf(document... | pd.DataFrame(df) | pandas.DataFrame |
import argparse
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scikit_posthocs as sp
import seaborn as sns
from pingouin import kruskal
from statannot import add_stat_annotation
def parse_args(args):
parser = argparse.ArgumentParser(description="GC_content_plots")
par... | pd.read_table(GC_content_tsv, sep="\t", header=None) | pandas.read_table |
'''
Created on April 15, 2012
Last update on July 18, 2015
@author: <NAME>
@author: <NAME>
@author: <NAME>
'''
import pandas as pd
class Columns(object):
OPEN='Open'
HIGH='High'
LOW='Low'
CLOSE='Close'
VOLUME='Volume'
# def get(df, col):
# return(df[col])
# df['Close'] =... | pd.Series(df['Low'] - 2 * (df['High'] - PP)) | pandas.Series |
import numpy as np
import pandas as pd
from tqdm import tqdm
from collections import Counter
class AutoDatatyper(object):
def __init__(self, vector_dim=300, num_rows=1000):
self.vector_dim = vector_dim
self.num_rows = num_rows
self.decode_dict = {0: 'numeric', 1: 'character', 2: 'time', 3: ... | pd.Series(iterable) | pandas.Series |
from __future__ import division
import copy
import bt
from bt.core import Node, StrategyBase, SecurityBase, AlgoStack, Strategy
import pandas as pd
import numpy as np
from nose.tools import assert_almost_equal as aae
import sys
if sys.version_info < (3, 3):
import mock
else:
from unittest import mock
def te... | pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100) | pandas.DataFrame |
# Copyright 2019-2021 VMware, Inc.
# SPDX-License-Identifier: Apache-2.0
import logging
import traceback
import pandas as pd
from src.al.project_service import find_project_by_name, update_project
from src.al.sr_service import query_all_srs
import numpy as np
from sklearn.preprocessing import OneHotEncoder
import p... | pd.DataFrame(sr_text) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 18 21:10:42 2022
@author: Nehal
"""
# -*- coding: utf-8 -*-
import streamlit as st
import pandas as pd
import altair as alt
import numpy as np
import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squa... | pd.to_datetime(df['date']) | pandas.to_datetime |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
data_file = 'mortality_germany.xlsx'
months = ['Jan', 'Feb', 'März', 'Apr', 'Mai', 'Jun', 'Jul', 'Aug', 'Sept', 'Okt', 'Nov', 'Dez']
days_per_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] # Ignoring that Feb has 27... | pd.read_excel(data_file, index_col='Jahr', sheet_name=1) | pandas.read_excel |
import glob
import os
import numpy as np
import pandas as pd
from xml.etree import ElementTree
from ..generic.mapping_io import read_geo_image
def list_central_wavelength_re():
""" create dataframe with metadata about RapidEye
Returns
-------
df : datafram
metadata and general multispectral... | pd.Series(irradiance) | pandas.Series |
import numpy as np
import pandas as pd
from reshape_tools.make_recurrent import make_recurrent
from sample_data.make_sample_data import sample_data1, sample_data2
from nptyping import NDArray
from typing import Any, Optional
def check_results(
output: NDArray[(Any, Any, Any)],
data_input: pd.DataFrame,
n_... | pd.to_datetime(df["times"]) | pandas.to_datetime |
# -*- coding: utf-8 -*-
# Copyright (c) 2018-2021, earthobservations developers.
# Distributed under the MIT License. See LICENSE for more info.
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
import pytz
from freezegun import freeze_time
from pandas import Timestamp
from pandas._tes... | pd.Categorical(["climate_summary"] * 28) | pandas.Categorical |
from re import S
from numpy.core.numeric import NaN
import streamlit as st
import pandas as pd
import numpy as np
st.title('world gdp')
@st.cache
def load_data(path):
data = pd.read_csv(path)
data.columns = data.columns.str.lower()
return data
data = load_data("data/gdp.csv")
if st.checkbox('show raw dat... | pd.DataFrame(data.values.T, index=data.columns, columns=data.index) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
This file combines all data loading methods into a central location.
Each type of data has a class that retrieves, processes, and checks it.
Each class has the following methods:
get - retrieves raw data from a source
adapt - transforms from the raw data to the common process... | pd.read_csv(settings['address'],index_col=0) | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 18 10:44:47 2019
@author: tawanda
"""
import sys
import time
import pandas
import argparse
from selenium import webdriver
from selenium.common.exceptions import NoSuchElementException
BASE_URL = 'https://azure.microsoft.com/en-us/pricing/calculato... | pandas.DataFrame(all_instances) | pandas.DataFrame |
# module model
import pandas as pd
from fbprophet import Prophet
import matplotlib.pyplot as plt
from sklearn import metrics, ensemble, model_selection
from sklearn.preprocessing import MinMaxScaler
from math import sqrt
import numpy as np
import datetime
from dateutil import relativedelta
import os
import io
import j... | pd.DataFrame(data) | pandas.DataFrame |
"""Tools for generating and forecasting with ensembles of models."""
import datetime
import numpy as np
import pandas as pd
import json
from autots.models.base import PredictionObject
from autots.models.model_list import no_shared
from autots.tools.impute import fill_median
horizontal_aliases = ['horizontal', 'probab... | pd.Series(models_pos) | pandas.Series |
import re
from datetime import datetime, timezone
import pandas as pd
import numpy as np
"""
Created on Thu Feb 27 14:05:58 2020
@author: <NAME>
Partly Adopted from Meng Cai
A few functions for processing text data.
"""
def import_comment(file, text_column):
"""
Load a csv file with survey comments,
remove... | pd.read_csv(file, encoding="ISO-8859-1") | pandas.read_csv |
import gzip
import json
from enum import Enum
from typing import Optional, Dict, Union, Iterator, Set, List
from problog.logic import Clause as ProblogClause, Term as ProblogTerm
from pylo.language.lp import Clause as PyloClause, Literal as PyloLiteral
from pylo.language.lp import global_context as pylo_global_contex... | pd.DataFrame(data=row_data, columns=columns_header) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Combine raw tweets data, per hour, into single CSV file."""
# pylint: disable=invalid-name,too-many-locals,too-many-arguments
import os
from datetime import datetime
from io import StringIO
from typing import Dict, List, Union
import boto3
import pandas as pd
def... | pd.DataFrame(all_buffer_contents, columns=headers) | pandas.DataFrame |
# Run this script as a "standalone" script (terminology from the Django
# documentation) that uses the Djano ORM to get data from the database.
# This requires django.setup(), which requires the settings for this project.
# Appending the root directory to the system path also prevents errors when
# importing the models... | pd.isnull(level5) | pandas.isnull |
#from rest_client import get_currency_data
import pandas as pd
from functools import reduce
import seaborn as sns
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.svm import SVR
from sk... | pd.read_csv('files/data_lisk.csv', sep=',', decimal='.', index_col=0) | pandas.read_csv |
import os
import numpy as np
import pandas as pd
from pandas.core.common import array_equivalent
from plio.utils.utils import file_search
# This function reads the lookup tables used to expand metadata from the file names
# This is separated from parsing the filenames so that for large lists of files the
# lookup t... | pd.read_csv(LUT_files['spect'], index_col=0) | pandas.read_csv |
from avatar_models.utils.util import get_config
import pandas as pd
import os
from avatar_models.captioning.evaluate import CaptionWithAttention
from pycocoevalcap.bleu.bleu import Bleu
from pycocoevalcap.spice.spice import Spice
from tqdm import tqdm
import json
import collections
import random
import pickle
from tens... | pd.read_csv(captions_file, sep="\t",header=0) | pandas.read_csv |
import pandas as pd
from numpy import isnan
'''
@Author <NAME>
Reads in the final derived file in order to find
bluebook treatments, and then compares with statement outcome data
in order to determine which alternative was chosen at each meeting
'''
def main():
derived_df = pd.read_csv("../../../derivation/pyth... | pd.to_numeric(x, errors="coerce") | pandas.to_numeric |
from itertools import product
import networkx as nx
import numpy as np
import pandas as pd
from .probability import (
Variable, ProbabilityTree, JointDist, TreeDistribution)
class Equation(object):
"""Maps input variable(s) to output variable(s)"""
INPUT_LABEL = 'Input'
OUTPUT_LABEL = 'Output'
... | pd.DataFrame(data=data) | pandas.DataFrame |
import sys
import pandas as pd
import numpy as np
from sqlalchemy import create_engine
import nltk
from nltk.tokenize import word_tokenize
from nltk.stem.porter import PorterStemmer
from nltk.stem.wordnet import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from nltk.corpus import stopwo... | pd.read_sql_table('message_and_category', engine) | pandas.read_sql_table |
# -*- coding: utf-8 -*-
"""
@author: Adam
"""
import numpy as np
import pandas as pd
from tqdm import tqdm
from .trajectory import trajectory, final_position
def fly(fa, vol_t, initial, charge, mass, dt, **kwargs):
""" Calculate the trajectories of charged particles in a
time-varying electric field
... | pd.concat(result, names=["particle"]) | pandas.concat |
import argparse
import csv
import json
import joblib
import os
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from time import time
import sklearn
# https://www.kaggle.com/mantri7/i... | pd.Series(data) | pandas.Series |
import pandas as pd
import geopandas as gpd
import numpy as np
import sqlite3
from sklearn.cluster import DBSCAN
import os
import osmnx as ox
import math
def osm_downloader(boundary=None, osm_path=None, regenerating_shp=False):
"""
Download drive network within a certain geographical boundary.
:param boun... | pd.read_csv(path) | pandas.read_csv |
import asyncio
import logging
import os
from enum import Enum
from typing import List, Optional, Tuple
import pandas as pd
from aiohttp import ClientSession
from pydantic import Field
from toucan_connectors.common import get_loop
from toucan_connectors.toucan_connector import ToucanConnector, ToucanDataSource
from .... | pd.DataFrame([]) | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from emissions.trainer import Trainer
from emissions.data import load_data, clean_data
from sklearn.metrics import precision_score
class ImpSearch():
"""
this class is built to facilitate analysis for answering following question:
How... | pd.DatetimeIndex(df.TEST_SDATE) | pandas.DatetimeIndex |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
import os
# In[2]:
train_encoded = pd.read_csv("../data/train_store_encoded_onehot.csv")
# In[3]:
train_df = pd.read_csv("../data/train.csv")
store_df = pd.read_csv("../data/store.csv")
# In[4]:
cate_df = store_df.apply... | pd.Series(ordered_intersection_dates) | pandas.Series |
#!/usr/bin/env python
import pandas as pd
from app.solr import get_collections, get_connection, get_query, get_count, get_schema, set_schema
import requests
import json
DEBUG = True
if __name__ == '__main__':
DEBUG = False
if DEBUG:
pd.set_option('display.max_columns', None)
MONDAY = pd.offsets.Week(weekday... | pd.offsets.Day() | pandas.offsets.Day |
# coding: utf-8
# # Chart presentation (6) - Creating custom hovertext
# In the last lesson we found out how to control what, how and where the hover information is displayed on a chart.
#
# In this lesson we'll learn how to create a custom text field in a Pandas DataFrame using the <code>apply()</code> and <code>l... | pd.read_csv("http://www.richard-muir.com/data/public/csv/RegionalHousePricesAndRanksJan16.csv") | pandas.read_csv |
"""LogToDataFrame: Converts a Zeek log to a Pandas DataFrame"""
# Third Party
import pandas as pd
# Local
from zat import zeek_log_reader
class LogToDataFrame(object):
"""LogToDataFrame: Converts a Zeek log to a Pandas DataFrame
Notes:
This class has recently been overhauled from a simple l... | pd.to_datetime(self._df[name], unit='s') | pandas.to_datetime |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy import interpolate
import pickle # to serialise objects
from scipy import stats
import seaborn as sns
from sklearn import metrics
from sklearn.model_selection import train_test_split
sns.set(style='whitegrid', palette='muted', font_scal... | pd.get_dummies(l) | pandas.get_dummies |
import datetime
import os
import sys
import tkinter as tk
import warnings
from tkinter import filedialog, messagebox
import ipywidgets as widgets
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from ipywidgets import Button, HBox, Layout, VBox
sys.path.i... | pd.to_numeric(self.windsonic_dataframe['mean_wind_direction']) | pandas.to_numeric |
import pandas as pd
from datacollection.models import Event, URL, CustomSession
from django_pandas.io import read_frame
import numpy as np
import json
import hashlib
import collections
from datetime import datetime
from datetime import timedelta
from collections import OrderedDict
from math import nan
import copy
pd.o... | pd.to_datetime(dataEvents['time']) | pandas.to_datetime |
from operator import methodcaller
import numpy as np
import pandas as pd
import pytest
from pandas.util import testing as tm
import ibis
import ibis.common.exceptions as com
import ibis.expr.datatypes as dt
import ibis.expr.operations as ops
from ibis.expr.scope import Scope
from ibis.expr.window import get_preceding... | pd.date_range('20170501', '20170507') | pandas.date_range |
# ###########################################################################
#
# CLOUDERA APPLIED MACHINE LEARNING PROTOTYPE (AMP)
# (C) Cloudera, Inc. 2021
# All rights reserved.
#
# Applicable Open Source License: Apache 2.0
#
# NOTE: Cloudera open source products are modular software products
# made up of hun... | pd.DataFrame(data, index=[0]) | pandas.DataFrame |
import warnings
import itertools
from copy import copy
from functools import partial
from collections import UserString
from collections.abc import Iterable, Sequence, Mapping
from numbers import Number
from datetime import datetime
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
impo... | pd.api.types.is_datetime64_dtype(vector) | pandas.api.types.is_datetime64_dtype |
import os
import unittest
import pandas as pd
from context import technical as ti
# Change working directory
# This enable running tests from repository root
if os.getcwd() != os.path.abspath(os.path.dirname(__file__)):
os.chdir('tests/')
# Test results
class ResultsRSI(unittest.TestCase):
# Input data
te... | pd.read_csv('test_data/correct_ohlc.csv') | pandas.read_csv |
"""
Functions to make all of the figures for Solar Forecast Arbiter reports using
Bokeh.
This code is currently unreachable from the rest of the Solar Forecast Arbiter
Core library. It may be used in place of the plotly_figures to generate bokeh
plots for the `plots` attribute of the RawReport object. See
:py:mod:`sol... | pd.DataFrame(meta_rows) | pandas.DataFrame |
from itertools import product
from string import ascii_uppercase
import pandas as pd
from pandas.tseries.offsets import MonthBegin
from .futures import CMES_CODE_TO_MONTH
def make_rotating_equity_info(num_assets,
first_start,
frequency,
... | pd.DataFrame.from_records(contracts, index='sid') | pandas.DataFrame.from_records |
from functools import reduce
import pandas_profiling
from pandas import read_csv, read_table, merge, concat
def fn_to_df_(filename, from_='raw_datasets', samples=0, describe=False):
fn = f'{from_}/{filename}'
if '.csv' in filename:
df = read_csv(fn)
elif '.xyz' in filename:
df = read_ta... | concat(dfs) | pandas.concat |
# Module deals with creation of ligand and receptor scores, and creation of scConnect tables etc.
import scConnect as cn
import scanpy as sc
version = cn.database.version
organism = cn.database.organism
# Scoring logic for ligands
def ligandScore(ligand, genes):
"""calculate ligand score for given ligand and gen... | pd.DataFrame(adata.uns["receptors"]) | pandas.DataFrame |
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd
import numpy.linalg as LA
from scipy.sparse import csr_matrix
from sklearn.preprocessing import MinMaxScaler
def show_mtrx(m, title = None):
fig, ax = plt.subplots(figsize = (10, 5))
min_val = int(m.min())
max_val... | pd.DataFrame(mse) | pandas.DataFrame |
from datetime import datetime
from io import StringIO
import itertools
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
Period,
Series,
Timedelta,
date_range,
)
import pandas._testing as tm
... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
# -*- coding: utf-8 -*-
# pylint: disable=E1101
# flake8: noqa
from datetime import datetime
import csv
import os
import sys
import re
import nose
import platform
from multiprocessing.pool import ThreadPool
from numpy import nan
import numpy as np
from pandas.io.common import DtypeWarning
from pandas import DataFr... | tm.assert_almost_equal(df2.values, expected) | pandas.util.testing.assert_almost_equal |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import os.path as op
import sys
import pandas as pd
import logging
#import simplejson as json
import yaml
from jcvi.apps.base import sh, mkdir
def get_gsize(fs):
cl = pd.read_csv(fs, sep="\t", header=None, names=['chrom','size'])
return sum(cl['size'])
... | pd.isna(gl['status'][i]) | pandas.isna |
"""
Functions used for pre-processing
"""
#import math
import pickle
#import copy
#import config
import os
# for multiprocessing
from functools import partial
from multiprocessing import Pool, cpu_count
from joblib import Parallel, delayed
import joblib
import numpy as np
import pandas as pd
from sklearn.decomposit... | pd.date_range(train_start_shift, train_end) | pandas.date_range |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 7 17:09:57 2019n
@author: abhik
"""
import os
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
#heatmap
df = | pd.read_excel("Excel/Final_result.xlsx") | pandas.read_excel |
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,... | Timestamp('2011-01-01 10:00') | pandas.Timestamp |
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import os
import glob
import pandas as pd
import xml.etree.ElementTree as ET
import io
import tensorflow as tf
from PIL import Image
from utils import dataset_util #ImportError: No module named 'object_detection... | pd.read_csv(FLAGS.csv_input) | pandas.read_csv |
###-----------###
### Importing ###
###-----------###
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime
from scipy import integrate
import seaborn as sns; sns.set()
###------------------###
### Helper Functions ###
###------------------###
## Time series management
def statal_tim... | pd.read_csv(DATA_URL_MEX+'covid19_mex_recuperados.csv', ) | pandas.read_csv |
from __future__ import unicode_literals
import copy
import io
import itertools
import json
import os
import shutil
import string
import sys
from collections import OrderedDict
from future.utils import iteritems
from unittest import TestCase
import pandas as pd
import pytest
from backports.tempfile import TemporaryD... | pd.read_csv(f) | pandas.read_csv |
# Copyright (C) 2019-2020 Zilliz. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | pandas.Series(arr2) | pandas.Series |
#!/usr/bin/env python3
# import numpy as np
import requests
import pandas as pd
import datetime
import json
# import matplotlib.pyplot as pp
import time
# import pymongo
import sys
import os
import sqlite3
MONGO_HOST = 'localhost'
MONGO_DB = 'TwStock'
MONGO_COLLETION = 'twse'
# from pymongo import MongoClient
def co... | pd.DataFrame(_RowDF) | pandas.DataFrame |
import numpy as np
import pandas as pd
from scipy.spatial import Delaunay
from scipy.spatial.distance import cdist
from sklearn.linear_model import RANSACRegressor, LinearRegression
import ops.utils
def find_triangles(df):
v, c = get_vectors(df[['i', 'j']].values)
return (pd.concat([
pd.DataFrame(v).... | pd.Series(df_info_1.index) | pandas.Series |
"""
Classes and methods to load datasets.
"""
import numpy as np
import struct
from scipy.misc import imresize
from scipy import ndimage
import os
import os.path
import pandas as pd
import json
from collections import defaultdict
from pathlib import Path as pathlib_path
import pickle
'''
Contains helper methods and c... | pd.Categorical(y) | pandas.Categorical |
def test_get_number_rows_cols_for_fig():
from mspypeline.helpers import get_number_rows_cols_for_fig
assert get_number_rows_cols_for_fig([1, 1, 1, 1]) == (2, 2)
assert get_number_rows_cols_for_fig(4) == (2, 2)
def test_fill_dict():
from mspypeline.helpers import fill_dict
def test_default_to_regular... | pd.Series([1, 0, 0, 0], dtype=bool) | pandas.Series |
from __future__ import absolute_import, division, print_function, unicode_literals
import pandas as pd
import numpy as np
import re
import pathlib
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from sklearn.metri... | pd.DataFrame(grid_result.cv_results_["params"]) | pandas.DataFrame |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# import tensorflow as tf
# from tensorflow.keras import layers, optimizers
from matplotlib.pyplot import MultipleLocator
import os
from collections import defaultdict
# import __main__
# __main__.pymol_argv = ['pymol', '-qc']
# import pymol as pm
i... | pd.DataFrame(rmsd_mat) | pandas.DataFrame |
from collections import Counter
import pandas as pd
import pytest
from simplekv import KeyValueStore
from kartothek.api.discover import (
discover_cube,
discover_datasets,
discover_datasets_unchecked,
discover_ktk_cube_dataset_ids,
)
from kartothek.core.cube.constants import (
KTK_CUBE_DF_SERIALIZ... | pd.DataFrame({"x": [0], "y": [0], "p": [0], "q": [0], "v1": 100}) | pandas.DataFrame |
import pandas as pd
import sasoptpy as so
import requests
from subprocess import Popen, DEVNULL
# Solves the pre-season optimization problem
def get_data():
r = requests.get('https://fantasy.premierleague.com/api/bootstrap-static/')
fpl_data = r.json()
element_data = pd.DataFrame(fpl_data['elements'])
... | pd.read_csv('../data/fplreview.csv') | pandas.read_csv |
import glob
import os
import pandas as pd
import yaml
from flatten_dict import flatten
from ensembler.p_tqdm import t_imap as mapper
import re
from functools import partial
from ensembler.Dataset import Dataset
from ensembler.datasets import Datasets
def process_file(file_path: str) -> pd.DataFrame:
file_dir = o... | pd.concat(combined_metrics, ignore_index=True) | pandas.concat |
import sys
sys.path.append('../')
#code below used to deal with special characters on the file path during read_csv()
sys._enablelegacywindowsfsencoding()
import numpy as np
import seaborn as sns
import pandas as pd
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import py... | pd.read_csv('faults.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
#
# License: This module is released under the terms of the LICENSE file
# contained within this applications INSTALL directory
"""
Defines the ForecastModel class, which encapsulates model functions used in
forecast model fitting, as well as their number of parameter... | pd.Timedelta(min_periods * 365, unit='d') | pandas.Timedelta |
import pandas as pd
import numpy as np
from churn_const import out_col, no_plot, save_path, schema_data_dict, skip_metrics,key_cols,max_clips,min_valid
def data_load(schema):
data_file = schema_data_dict[schema]
schema_save_path = save_path(schema) + data_file
churn_data = pd.read_csv(schema_save_path + ... | pd.DataFrame({var_to_plot: midpoints.values, 'churn_rate': churns}) | pandas.DataFrame |
#!/usr/bin/env python
"""
CreateNetwork: Creates a TF-TF gene regulation network from annotated transcription factor binding sites
@author: <NAME>
@contact: mette.bentsen (at) mpi-bn.mpg.de
@license: MIT
"""
import os
import sys
import argparse
import pyBigWig
import numpy as np
import glob
#impo... | pd.read_csv(args.origin, sep="\t", header=None) | pandas.read_csv |
#
# Copyright 2020 Capital One Services, LLC
#
# 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([{"a": 1, "b": 2}, {"a": 2, "b": 2}, {"a": 3, "b": 2}]) | pandas.DataFrame |
import pandas as pd
import numpy as np
import datetime
class Durations(object):
@classmethod
def set(cls, X, extract_cols, dataset):
print("... ... Durations")
all_df = dataset["all_df"]
# duration from first action to clickout
dffac_df = all_df[["session_id", "timestamp", "tim... | pd.merge(preref_df2, preref_df3, on="session_id", how="left") | pandas.merge |
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2021, 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.
# ------------------------------------------------... | pdt.assert_frame_equal(obs, table, check_like=True) | pandas.util.testing.assert_frame_equal |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Compare one dataset to another at a variety of p-value cutoffs.
Author: <NAME> (Fraser Lab, Stanford University)
License: MIT
Version: 1.0b2
Created: 2018-05-30
Updated: 2018-05-31
See the README at:
https://github.com/TheFraserLab/enrich_pvalues/blob/master/READM... | pd.read_csv(fin, sep=conf['test_sep']) | pandas.read_csv |
from datetime import datetime
from pandas.api.types import is_datetime64_any_dtype
from pandas.api.types import is_period_dtype
from pandas.core.common import flatten
from functools import wraps
from copy import deepcopy
import logging
import numpy as np
import pandas as pd
import re
from typing import (
Any,
... | pd.concat([df, split_cols], axis=1) | pandas.concat |
"""Univariate anomaly detection module."""
__version__ = '1.0.0'
from typing import Dict
from fastapi import FastAPI
from pydantic import BaseModel
from adtk.detector import PersistAD, ThresholdAD, LevelShiftAD, VolatilityShiftAD
import numpy
import pandas
from . core.tools import aggregate_anomalies
app = FastAPI(
... | pandas.Series(time_series_data.score_data) | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 2 15:41:04 2021
Run MLR hedonic with run_MLR_on_all_years(features=best1)
use plot_price_rooms_new_from_new_ds for time_series new rooms MLR
for standertized betas use plot_regular_feats_comparison_from_new_ds
For RF, HP tuning :
run_CV_on_all_years... | pd.concat([df_scaled, df_rest], axis=1) | pandas.concat |
"""
Copyright 2020 The Google Earth Engine Community Authors
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
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed ... | pd.DataFrame(ds) | pandas.DataFrame |
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# This file contains dummy data for the model unit tests
import numpy as np
import pandas as pd
AIR_FCST_LINEAR_95 = pd.DataFrame(
{
... | pd.Timestamp("2012-05-28 00:00:00") | pandas.Timestamp |
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