prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
'''
Data Pipeline
tocsv.py: input: video or photo, output: csv containing the bottleneks
train.py: input: geo directory, output: softmax model
predict.py: input: photo, output: label
directory structure:
geo
|
petid_1, petid_2, petid_n, model, found
The "petid_n" directory contains uploaded photos and videos for p... | pd.concat([bottleneck_df, new_df], axis=0) | pandas.concat |
# %%
import warnings
warnings.filterwarnings("ignore")
from folktables import (
ACSDataSource,
ACSIncome,
ACSEmployment,
ACSMobility,
ACSPublicCoverage,
ACSTravelTime,
)
import pandas as pd
from collections import defaultdict
from scipy.stats import kstest, wasserstein_distance
import seaborn ... | pd.DataFrame(ca_features, columns=ACSEmployment.features) | pandas.DataFrame |
import numpy as np
import pandas as pd
from sklearn import preprocessing
from sklearn.model_selection import GridSearchCV
import lightgbm as lgb
data = {
'reserve': pd.read_pickle('../features/reserve.pkl'),
'store_info': pd.read_pickle('../features/store_info.pkl'),
'visit_data': pd.read_pickle('../featur... | pd.read_csv('../data/sample_submission.csv') | pandas.read_csv |
import os
import numpy as np
import pandas as pd
from scipy.sparse import csr_matrix, hstack
CACHE_LOCATION = 'dataset/data_cache'
OUTPUT_LOCATION = 'dataset/data'
def get_synthetic_data( dim, n, flag='sign'):
w = pd.Series(np.random.randint(2, size=dim+1))
if flag == 'sign':
w = 2*w-1
# genera... | pd.Series([b if b != 0 else -1 for b in y]) | pandas.Series |
import abc
from typing import List, Tuple
import numpy as np
import pandas as pd
from scipy.optimize import minimize
from fipie.common import ReprMixin
from fipie.date import infer_ann_factor
class Weighting(ReprMixin, metaclass=abc.ABCMeta):
@abc.abstractmethod
def optimise(self, ret: pd.DataFrame, *args,... | pd.Series(weights, index=ret.columns) | pandas.Series |
"""Create PV simulation input for renewables.ninja."""
import click
import pandas as pd
import geopandas as gpd
from src.utils import Config
from src.capacityfactors import point_raster_on_shapes
@click.command()
@click.argument("path_to_shapes_of_land_surface")
@click.argument("path_to_roof_categories")
@click.argu... | pd.DataFrame(index=index) | pandas.DataFrame |
import os
import fnmatch
import shutil
import csv
import pandas as pd
import numpy as np
import glob
import datetime
print(os.path.realpath(__file__))
def FindResults(TaskList, VisitFolder, PartID):
for j in TaskList:
TempFile = glob.glob(os.path.join(VisitFolder,(PartID+'_'+j+'*.csv')))
# Ideal... | pd.read_csv(InputFile) | pandas.read_csv |
# -*- coding: utf-8 -*-
# -*- python 3 -*-
# -*- <NAME> -*-
# Import packages
import re
import numpy as np
import pandas as pd
import os ##for directory
import sys
import pprint
'''general function for easy use of python'''
def splitAndCombine(gene, rxn, sep0, moveDuplicate=False):
## one rxn has several gen... | pd.read_excel('/Users/luho/PycharmProjects/model/cobrapy/result/met_yeastGEM.xlsx') | pandas.read_excel |
"""
Provides processing functions for CRSP data.
"""
from pilates import wrds_module
import pandas as pd
import numpy as np
import numba
from sklearn.linear_model import LinearRegression
class crsp(wrds_module):
def __init__(self, d):
wrds_module.__init__(self, d)
# Initialize values
se... | pd.to_datetime(dfret.year, format='%Y') | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 21 11:31:36 2016
@author: zbarge
"""
import os
import sqlite3
import requests
import pandas as pd
from time import sleep
from .SimpleSQLite3 import SimpleSQLite3
#======================================================#
"""These lists provide inf... | pd.notnull(x) | pandas.notnull |
import numpy as np
import pandas as pd
from anubis.models import TheiaSession, Assignment
def get_theia_sessions(course_id: str = None) -> pd.DataFrame:
"""
Get all theia session objects, and throw them into a dataframe
:return:
"""
# Get all the theia session sqlalchemy objects
if course_i... | pd.to_datetime(date) | pandas.to_datetime |
#!/usr/bin/env python
import matplotlib.pyplot as plt
import json
import pandas as pd
import sys
import signal
import time
fname = sys.argv[1]
plt.ion()
fig = plt.figure()
def readStats():
f = open(fname, 'r')
m = json.load(f)
f.close()
plt.clf()
data = | pd.DataFrame(m['heap']) | pandas.DataFrame |
from .neural_model import NeuralModel
from warnings import warn, catch_warnings
import numpy as np
import pandas as pd
from sklearn.linear_model import PoissonRegressor
from tqdm import tqdm
class PoissonGLM(NeuralModel):
def __init__(self, design_matrix, spk_times, spk_clu,
binwidth=0.02, metri... | pd.Series(index=cells, name='intercepts') | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 16 18:03:13 2017
@author: lfiorito
"""
import pdb
import os
import logging
from collections import Counter
from functools import reduce
import numpy as np
import pandas as pd
from sandy.formats.records import read_cont
from sandy.formats import (mf1,
mf3,
... | pd.DataFrame(sec["RECORDS"], columns=["MF","MT","NC","MOD"]) | pandas.DataFrame |
# !/usr/bin/env python
# coding: utf-8
"""
Some utility functions aiming to analyse OSM data
"""
import datetime as dt
from datetime import timedelta
import re
import math
import numpy as np
import pandas as pd
import statsmodels.api as sm
from osmdq.extract_user_editor import editor_name
### OSM data exploration... | pd.merge(metadata, md_ext, on=grp_feat, how='outer') | pandas.merge |
import text_process
import os
import sys
import gzip
import json
import argparse
import itertools
import _thread
import threading
sys.path.append(os.getcwd())
import pandas as pd
import numpy as np
def get_df(path):
""" Apply raw data to pandas DataFrame. """
idx = 0
df = {}
g = gzip.open(path, 'rb... | pd.DataFrame.from_dict(df_enlarge, orient='index') | pandas.DataFrame.from_dict |
import kabuki
import hddm
import numpy as np
import pandas as pd
from numpy.random import rand
from scipy.stats import uniform, norm
from copy import copy
def gen_single_params_set(include=()):
"""Returns a dict of DDM parameters with random values for a singel conditin
the function is used by gen_rand_para... | pd.DataFrame(rts, columns=['rt']) | pandas.DataFrame |
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
from numba import njit
import vectorbt as vbt
from tests.utils import record_arrays_close
from vectorbt.generic.enums import range_dt, drawdown_dt
from vectorbt.portfolio.enums import order_dt, trade_dt, log_dt
day_dt = np.timedelta64... | pd.Timedelta('3 days 00:00:00') | pandas.Timedelta |
from datetime import datetime
from decimal import Decimal
from io import StringIO
import numpy as np
import pytest
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series, Timestamp, date_range, read_csv
import pandas._testing as tm
from pa... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
from __future__ import division
from datetime import datetime
import sys
if sys.version_info < (3, 3):
import mock
else:
from unittest import mock
import pandas as pd
import numpy as np
from nose.tools import assert_almost_equal as aae
import bt
import bt.algos as algos
def test_algo_name():
class Tes... | pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100.) | pandas.DataFrame |
import os
import pickle
import pathlib
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
import joblib
PATH = pathlib.Path(os.path.abspath(os.path.dirname(__file__)))
BIN_PATH = PATH / "bin"
DATA_PATH = PATH / "_data"
NO_FEATURES = ['id', 'tile', 'cnt', 'ra_k', 'dec_k']
... | pd.read_csv(p) | pandas.read_csv |
import discord
import requests
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import random
TOKEN = 'YOUR TOKEN HERE'
KEY = 'YOUR API KEY HERE'
client = discord.Client()
command = '!COVID:'
@client.event
async def on_ready():
await client.change_p... | pd.DataFrame(response['metricsTimeseries']) | pandas.DataFrame |
import sys
sys.path.append('../')
from matplotlib import figure
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.collections import PatchCollection
from matplotlib.colors import ListedColormap
import os
from tqdm import tqdm
### Co... | pd.DataFrame(columns=['iter','Mask','interven_eff','ventilation','end_dead']) | pandas.DataFrame |
# -*- 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... | Index(i.values) | pandas.Index |
# AUTOGENERATED! DO NOT EDIT! File to edit: 00_core.ipynb (unless otherwise specified).
__all__ = ['universal_key', 'find_date', 'find_float_time', 'week_from_start', 'load_public_data',
'filtering_usable_data', 'prepare_baseline_and_intervention_usable_data', 'in_good_logging_day',
'most_active_... | pd.DatetimeIndex(public_all.original_logtime_notz) | pandas.DatetimeIndex |
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import seaborn as sns
import tqdm
def load_data(index=0):
""" 0: C7
1: C8
2: C9
3: C11
4: C13
5: C14
6: C15
7: C16
... | pd.concat(samples, axis=0) | pandas.concat |
from bs4 import BeautifulSoup
import requests
import pandas as pd
import matplotlib.pyplot as plt
url = "https://www.mohfw.gov.in/"
def get_url() -> str:
return url
def get_response(url) -> "response":
return requests.get(url, timeout=10)
def return_content() -> "html":
return BeautifulSoup(get_respo... | pd.DataFrame(plot_data, index=states) | pandas.DataFrame |
################################################################################
# Module: dataportal.py
# Description: Various functions to acquire building archetype data using
# available APIs
# License: MIT, see full license in LICENSE.txt
# Web: https://github.com/samuelduchesne/archetypal
###########... | pd.DataFrame(json_response) | pandas.DataFrame |
import operator
from shutil import get_terminal_size
from typing import Dict, Hashable, List, Type, Union, cast
from warnings import warn
import numpy as np
from pandas._config import get_option
from pandas._libs import algos as libalgos, hashtable as htable
from pandas._typing import ArrayLike, Dtype, Ordered, Scal... | is_iterator(list_like) | pandas.core.dtypes.common.is_iterator |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
from pandas.plotting import scatter_matrix
from sklearn import model_selection, preprocessing, svm
from sklearn.linear_model import LinearRegression
from sklearn.metrics import classification_report
from sklearn.metrics ... | pd.read_csv(r'tests\mean_sunspots.csv', parse_dates=True, index_col=0) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Authors: <NAME>, <NAME>, <NAME>, and
<NAME>
IHE Delft 2017
Contact: <EMAIL>
Repository: https://github.com/gespinoza/hants
Module: hants
"""
from __future__ import division
import netCDF4
import pandas as pd
import math
from .davgis.functions import (Spatial_Reference, Lis... | pd.np.empty((rows, cols, ztime)) | pandas.np.empty |
import numpy as np
import pandas as pd
from numpy import nan
from pvlib import modelchain, pvsystem
from pvlib.modelchain import ModelChain
from pvlib.pvsystem import PVSystem
from pvlib.tracking import SingleAxisTracker
from pvlib.location import Location
from pandas.util.testing import assert_series_equal, assert_f... | assert_series_equal(ac, expected) | pandas.util.testing.assert_series_equal |
import matplotlib.cm as cm
import pandas as pd
import seaborn as sns
import matplotlib.dates as mdates
from matplotlib.dates import DateFormatter
import matplotlib.pyplot as plt
import numpy as np
###############################################################################################################
# IMPORTA... | pd.to_numeric(tweets.followers) | pandas.to_numeric |
import os.path as osp
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import yaml
from matplotlib import cm
from src.furnishing.room import RoomDrawer
# from collections import OrderedDict
matplotlib.rcParams['xtick.direction'] = 'out'
matplotlib.rcParams['ytick.direction'] ... | pd.to_numeric(self.log_df['Epoch'], downcast='integer') | pandas.to_numeric |
import os
import pandas as pd
import re
def load_diffs(keep_diff = False):
nick_map = {
'talk_diff_no_admin_sample.tsv': 'sample',
'talk_diff_no_admin_2015.tsv': '2015',
'all_blocked_user.tsv': 'blocked',
'd_annotated.tsv': 'annotated',
}
base = '../../data/samples/'
... | pd.to_datetime(df['rev_timestamp']) | pandas.to_datetime |
# Question 07, Lab 07
# AB Satyaprakash, 180123062
# imports
import pandas as pd
import numpy as np
# functions
def f(t, y):
return y - t**2 + 1
def F(t):
return (t+1)**2 - 0.5*np.exp(t)
def RungeKutta4(t, y, h):
k1 = f(t, y)
k2 = f(t+h/2, y+h*k1/2)
k3 = f(t+h/2, y+h*k2/2)
k4 = f(t+h, y+... | pd.Series(yact) | pandas.Series |
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# 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 a... | pd.testing.assert_frame_equal(result, expected) | pandas.testing.assert_frame_equal |
import pathlib
import pytest
import pandas as pd
import numpy as np
import gradelib
EXAMPLES_DIRECTORY = pathlib.Path(__file__).parent / "examples"
GRADESCOPE_EXAMPLE = gradelib.Gradebook.from_gradescope(
EXAMPLES_DIRECTORY / "gradescope.csv"
)
CANVAS_EXAMPLE = gradelib.Gradebook.from_canvas(EXAMPLES_DIRECTORY ... | pd.DataFrame([p1, p2]) | pandas.DataFrame |
from PIL import Image
from io import BytesIO
import pickle
import json
import numpy as np
import pandas as pd
from pykafka import KafkaClient
from pykafka.common import OffsetType
import requests
import os
from tornado import gen, httpserver, ioloop, log, web
import random
import time
import sys
IMAGE_FREQUENCY = 30
... | pd.DataFrame(text_file['predictions']) | pandas.DataFrame |
"""
@author: <NAME>, portions originally by JTay
"""
import numpy as np
import pandas as pd
import sklearn.model_selection as ms
from collections import defaultdict
from sklearn.metrics import make_scorer, accuracy_score
from sklearn.utils import compute_sample_weight
import matplotlib.pyplot as plt
cv_folds = 5
# ... | pd.DataFrame(index=train_size, data=test_scores) | pandas.DataFrame |
import pandas as pd
import re
from programs.data_cleaning.data_cleaning import box_data_cleaning
trends = pd.read_csv('../data/HWSysTrends11_5to11_12.csv')
# creates a list of building names based on regex patterns from the "Name Path Reference" column in the csv file
reg_list = [re.findall(r"(?<=\.)(B.*?)(?=\.)", ... | pd.DataFrame() | pandas.DataFrame |
import sys
sys.path.append('../src/meta_rule/')
sys.path.append('../dd_lnn/')
import random
import time
import copy
import argparse
from meta_interpretive import BaseMetaPredicate, MetaRule, Project, DisjunctionRule
from train_test import score, align_labels
from read import load_data, load_metadata, load_labels
impo... | pd.concat([labels_df_train, labels_df_test]) | pandas.concat |
from sklearn.manifold import TSNE
from kaldi_io import read_vec_flt_scp
import sys
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
# example usage
# python local/visualize_trait_emb.py age/accen... | pd.DataFrame(X_emb,columns=feat_cols) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
description: cleaning tools for tidals (tidepool data analytics tools)
created: 2018-07
author: <NAME>
license: BSD-2-Clause
"""
import pandas as pd
import numpy as np
#Cleaning Functions
#removeNegativeDurations (Duplicate with differences)
#tslimCalibrationFix (Du... | pd.Timedelta("1microseconds") | pandas.Timedelta |
# The MIT License (MIT)
#
# Copyright (c) 2015 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, me... | pd.Series(sol['x'], index=cov_mat.index) | pandas.Series |
"""
Implement FlexMatcher.
This module is the main module of the FlexMatcher package and implements the
FlexMatcher class.
Todo:
* Extend the module to work with and without data or column names.
* Allow users to add/remove classifiers.
* Combine modules (i.e., create_training_data and training functions)... | pd.DataFrame(datafr.columns) | pandas.DataFrame |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import operator
import numpy as np
import pytest
import pandas.compat as compat
from pandas.compat import range
import pandas as pd
from pandas import (
Categorical, DataFrame, Index, NaT, Series, bdate_range, date_range, ... | Series([True, False, True], index=index) | pandas.Series |
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.cut(complete_data["NPI"],10, labels=[1,2,3,4,5,6,7,8,9,10]) | pandas.cut |
"""
This step should only be performed afer handling Catagorical Variables
"""
import numpy as np
import pandas as pd
def Get_VIF(X):
"""[summary]
PARAMETERS :-
X = Pandas DataFrame
Return :-
Pandas DataFrame of Features and there VIF value... | pd.DataFrame() | pandas.DataFrame |
import concurrent.futures
import math
import multiprocessing
import os
import numba as nb
import numpy as np
import pandas as pd
EXPERIMENT=False
class SpectrumMatcher:
"""
Handles the creation of a uniqueness matrix.
"""
def __init__(self, provider, density, local, cutoff, validate, output_director... | pd.DataFrame.to_csv(data,output,index=False) | pandas.DataFrame.to_csv |
from pathlib import Path
import sklearn
import numpy as np
import pandas as pd
from scipy.stats import pearsonr, spearmanr
def calc_preds(model, x, y, mltype):
""" Calc predictions. """
if mltype == 'cls':
def get_pred_fn(model):
if hasattr(model, 'predict_proba'):
return ... | pd.Series(y_true, name='y_true') | pandas.Series |
#!/usr/bin/env python3
'''This module includes julia method and JuliaPlane class. The complex plane generated by the parent Class (ArrayComplexPlane) will be transformed by a returned function by julia() method to create a Julia plane. After the Julia plane is created, the method toCSV exports the plane to a plane.csv... | pd.Series( [self.c, self.xmin, self.xmax, self.xlen, self.ymin, self.ymax, self.ylen], index=['c','xmin','xmax','xlen', 'ymin', 'ymax', 'ylen'] ) | pandas.Series |
'''
Author: <NAME>
GitHub: https://github.com/josephlyu
The figures for the UK page, using data from
Public Health Englend's COVID-19 UK API and
Oxford University's GitHub repository.
Link1: https://coronavirus.data.gov.uk/developers-guide
Link2: https://github.com/OxCGRT/covid-policy-tracker
'''
import ... | pd.read_csv(url_vaccination_uk, index_col=3) | pandas.read_csv |
import builtins
from io import StringIO
from itertools import product
from string import ascii_lowercase
import numpy as np
import pytest
from pandas.errors import UnsupportedFunctionCall
import pandas as pd
from pandas import (
DataFrame, Index, MultiIndex, Series, Timestamp, date_range, isna)
import pandas.cor... | tm.assert_series_equal(count_B, expected['B']) | pandas.util.testing.assert_series_equal |
from txtai.embeddings import Embeddings
from txtai.pipeline import Similarity
from txtai.ann import ANN
import os
import json
import numpy as np
import pandas as pd
import logging
import pickle
from gamechangerml.src.text_handling.corpus import LocalCorpus
import torch
logger = logging.getLogger(__name__)
class ... | pd.concat([old_df, df]) | pandas.concat |
import hbase
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from io import StringIO
import matplotlib.pyplot as plt
zk = '192.168.1.19:2181,192.168.1.20:2181,192.168.1.21:2181'
def timespan(series):
return series[-1] - series[0]
def lastele(series):
return se... | pd.concat(dfs) | pandas.concat |
#Python wrapper / library for Einstein Analytics API
#core libraries
import sys
import logging
import json
import time
from dateutil import tz
import re
from decimal import Decimal
import base64
import csv
import math
import pkg_resources
# installed libraries
import browser_cookie3
import requests
import unicodecsv
... | pd.to_datetime(x) | pandas.to_datetime |
import re
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import IntervalArray
class TestSeriesReplace:
def test_replace_explicit_none(self):
# GH#36984 if the user explicitly passes value=None, give it to them
ser = pd.Series([0, 0, ""],... | pd.Series([0, np.nan, 2, 3, 4]) | pandas.Series |
import addfips
import os
import pandas as pd
import datetime
ageVariables = {
'DATE': 'date_stamp',
'AGE_RANGE': 'age_group',
'AR_TOTALCASES': 'cnt_confirmed',
'AR_TOTALPERCENT': 'pct_confirmed',
'AR_NEWCASES': 'cnt_confirmed_new',
'AR_NEWPERCENT': 'pct_confirmed_new',
'AR_TOTALDEATHS' : 'cnt_death',
'AR_NEWDE... | pd.Int32Dtype() | pandas.Int32Dtype |
from statsmodels.compat.pandas import Appender, is_numeric_dtype
from typing import Sequence, Union
import numpy as np
import pandas as pd
from pandas.core.dtypes.common import is_categorical_dtype
from scipy import stats
from statsmodels.iolib.table import SimpleTable
from statsmodels.stats.stattools import jarque_... | pd.DataFrame(top, dtype="object", index=index, columns=cols) | pandas.DataFrame |
import sys
import pandas as pd
inputdat=sys.argv[1]
outputf=sys.argv[2]
dat=pd.read_csv(inputdat,sep='\t',index_col=0)
print([i for i in dat.index][0])
ex=[i for i in dat.index if 'ae' in i]
no=[i for i in dat.index if 'aw' in i]
dat_ex=dat.loc[dat.index.isin(ex)]
dat_no=dat.loc[dat.index.isin(no)]
dat_ex['label']=[1 f... | pd.concat([dat_ex,dat_no]) | pandas.concat |
import pandas as pd
import pytest
from kartothek.io.dask.dataframe import collect_dataset_metadata
from kartothek.io.eager import (
store_dataframes_as_dataset,
update_dataset_from_dataframes,
)
from kartothek.io_components.metapartition import _METADATA_SCHEMA, MetaPartition
from kartothek.io_components.write... | pd.DataFrame(data={"A": [1, 1, 1, 1], "b": [1, 1, 2, 2]}) | pandas.DataFrame |
import numpy as np
import pandas as pd
# from pyranges.methods.join import _both_dfs
np.random.seed(0)
def sort_one_by_one(d, col1, col2):
"""
Equivalent to pd.sort_values(by=[col1, col2]), but faster.
"""
d = d.sort_values(by=[col2])
return d.sort_values(by=[col1], kind='mergesort')
def _ins... | pd.Series(dist, index=ocdf.index) | pandas.Series |
import csv
import json
import os
import shutil
import uuid
from functools import partial
from io import StringIO
import pandas as pd
from pandas import DataFrame
import pyproj
import requests
import shapely.geometry as shapely_geom
import shapely.wkt as shapely_wkt
import app.helper
from app import ce... | DataFrame(columns=columns_name) | pandas.DataFrame |
### 공시지가 K-NN ###
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score, classification_report
import sklearn.neighbors as neg
import matplotlib.pyplot as plt
import folium
import json
import sklearn.preprocessing as pp
## 데이터 전처리 ## --> 이상치 제거, 표준화 필요 ##
all_data = pd.read_csv("data-set/h... | pd.DataFrame(mean.iloc[:, -1]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
try:
import json
except ImportError:
import simplejson as json
import math
import pytz
import locale
import pytest
import time
import datetime
import calendar
import re
import decimal
import dateutil
from functools import partial
from pandas.compat import range, StringIO, u
from pandas.... | ujson.encode(i, orient="records") | pandas._libs.json.encode |
import pandas as pd
import networkx as nx
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
#funtions
def degree(G,f):
"""
Adds a column to the dataframe f with the degree of each node.
G: a networkx graph.
f: a pandas dataframe.
"""
if not(set(f.name) == set(G.nodes()... | pd.DataFrame(data = {'name': X['name'], 'participation_coefficient': [1 for _ in X['name']] }) | pandas.DataFrame |
#!/usr/bin/env python3.6
"""This module describes functions for analysis of the SNSS Dataset"""
import os
import pandas as pd
from sas7bdat import SAS7BDAT
import numpy as np
import subprocess
from datetime import datetime, date
from csv import DictReader
from shutil import rmtree
from json import load as jsonLoad
impo... | pd.DataFrame.from_dict(sigDict, orient='index', columns=['p', 'stat', 'effect', 'effectCI']) | pandas.DataFrame.from_dict |
import datetime
from datetime import timedelta
from distutils.version import LooseVersion
from io import BytesIO
import os
import re
from warnings import catch_warnings, simplefilter
import numpy as np
import pytest
from pandas.compat import is_platform_little_endian, is_platform_windows
import pandas.util._test_deco... | tm.makeTimeSeries() | pandas.util.testing.makeTimeSeries |
import requests
from bs4 import BeautifulSoup
import pandas as pd
import datetime
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
# Get all foodpanda orders
def get_foodpanda_orders(orders, cookie):
url = "https://www.foo... | pd.DataFrame(orders) | pandas.DataFrame |
#!/usr/bin/env python3.6
import pandas as pd
from collections import defaultdict, Counter
import argparse
import sys
import os
import subprocess
import re
import numpy as np
from datetime import datetime
from itertools import chain
from pyranges import PyRanges
from SV_modules import *
pd.set_option('display.max_colum... | pd.DataFrame() | pandas.DataFrame |
from __future__ import division #brings in Python 3.0 mixed type calculation rules
import datetime
import inspect
import numpy as np
import numpy.testing as npt
import os.path
import pandas as pd
import sys
from tabulate import tabulate
import unittest
print("Python version: " + sys.version)
print("Numpy version: " +... | pd.Series([[0.34], [0.78, 11.34, 3.54, 1.54], [2.34, 1.384]], dtype='object') | pandas.Series |
import warnings
from copy import deepcopy
from typing import Any
from typing import Dict
from typing import List
from typing import Optional
from typing import Set
from typing import Tuple
from typing import Union
import numpy as np
import pandas as pd
from joblib import Parallel
from joblib import delayed
from sklear... | pd.concat([features_df.loc[:, segment] for segment in self.ts.segments], axis=0) | pandas.concat |
""" Module for data preprocessing.
"""
import datetime
import warnings
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Set
from typing import Union
import numpy as np
import pandas as pd
from sklearn.base import BaseEstim... | pd.RangeIndex(0, X.shape[0]) | pandas.RangeIndex |
"""
This script does a quick sanity check about how the communities are disconnected (i.e., how many connections exist
among different communities), using the pickle files generated in script `04_01`.
"""
import pickle
import numpy as np
import pandas as pd
from definitions import TISSUES
# python -u 04_02_quick_sum... | pd.DataFrame(corr_arr, index=corr_mat.index, columns=corr_mat.columns) | pandas.DataFrame |
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
def test_group_by(c):
df = c.sql(
"""
SELECT
user_id, SUM(b) AS "S"
FROM user_table_1
GROUP BY user_id
"""
)
df = df.compute()
expected_df = pd.DataFrame({"user_id": [1, 2, 3], "S": [3... | pd.DataFrame({user_id_column: [2, 3, 4], "S": [1, 1, 1]}) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
import orca
from urbansim_templates import utils
def test_parse_version():
assert utils.parse_version('0.1.0.dev0') == (0, 1, 0, 0)
assert utils.parse_version('0.115.3') == (0, 115, 3, None)
assert utils.parse_version('3.1.dev7') == (3, 1, 0, 7)
a... | pd.testing.assert_frame_equal(df[['val1']], df_out) | pandas.testing.assert_frame_equal |
##############################################################################
# Copyright 2020 IBM Corp. 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
#
# htt... | assert_frame_equal(out, result_df) | pandas.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
# !/usr/bin/env python
#
# @file multi_md_analysis.py
# @brief multi_md_analysis object
# @author <NAME>
#
# <!--------------------------------------------------------------------------
# Copyright (c) 2016-2019,<NAME>.
# All rights reserved.
# Redistribution and use in source and bina... | pd.to_numeric(self.df['Y']) | pandas.to_numeric |
#Library of functions called by SimpleBuildingEngine
import pandas as pd
import numpy as np
def WALLS(Btest=None):
#Building height
h_building = 2.7#[m]
h_m_building = h_building / 2
h_cl = 2.7# heigth of a storey
#number of walls
n_walls = 7
A_fl = 48
#WALLS CHARACTERISTICS
#Orie... | pd.Series([12, 0, 0, 0, 0, 0, 0]) | pandas.Series |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import os
import argparse
from pathlib import Path
import joblib
import scipy.sparse
import string
import nltk
from nltk import word_tokenize
nltk.download('punkt')
from sklearn.feature_extraction.text import Coun... | pd.to_numeric(admissions['DAYS_NEXT_ADMIT']) | pandas.to_numeric |
"""
Demultiplexing of BAM files.
Input: BAM file, fasta file of terminal barcodes and/or internal barcodes
Output: Multiple BAM files containing demultiplexed reads, with the file name indicating the distinguishing barcodes.
If terminal barcodes are not used, output name will be all_X.input.bam, where X is ... | pd.DataFrame(rows, columns=colNames) | pandas.DataFrame |
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import lightgbm as lgb
from catboost import CatBoostRegressor
def featureModify(isTrain):
rowstoread = None
if isTrain:
train = pd.read_csv('../input/tr... | pd.read_csv('../input/test.csv',nrows=rowstoread) | pandas.read_csv |
"""Tests for the sdv.constraints.base module."""
import warnings
from unittest.mock import Mock, patch
import pandas as pd
import pytest
from copulas.multivariate.gaussian import GaussianMultivariate
from copulas.univariate import GaussianUnivariate
from rdt.hyper_transformer import HyperTransformer
from sdv.constrai... | pd.DataFrame([[1, 2], [3, 4]], columns=['b', 'c']) | pandas.DataFrame |
import dash
from dash import dcc, html, dash_table, callback
from dash.dependencies import Input, Output
import dash_bootstrap_components as dbc
import plotly.graph_objects as go
import plotly.graph_objects as go
import pandas as pd
df = | pd.read_csv("Amazon.csv") | pandas.read_csv |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# @Time : 2021/3/25 12:42 PM
# @Author : <NAME>
# @File : getwordC.py
import jieba
import pandas as pd
import wordcloud
import matplotlib.pyplot as plt
from imageio import imread
# 读取数据
file_name = '/Users/zhihuyang/IdeaProjects/ddmcData/dataset/result.csv'
df = pd.r... | pd.DataFrame({'segment': segment}) | pandas.DataFrame |
from backend.lib import sql_queries
import pandas as pd
from pandas.testing import assert_frame_equal, assert_series_equal
def test_get_user_info_for_existing_user(refresh_db_once, db_connection_sqlalchemy):
engine = db_connection_sqlalchemy
user_id = sql_queries.get_user_id(engine, email='<EMAIL>', password... | assert_series_equal(df['in_progress'], df_test['in_progress']) | pandas.testing.assert_series_equal |
import numpy as np
from tenbagger.src.passiveIncome.calculator import PassiveIncomeCalculator
import pandas as pd
class PassiveDividends(PassiveIncomeCalculator):
def __init__(self, port):
super().__init__(port=port)
def calulate_dividends(self, n: int, growth_stock, growth_dividend, monthly_payment,... | pd.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pandas as pd
import datetime as dt
import numpy as np
from collections import OrderedDict
import os
import pickle
from errorplots import ErrorPlots
class ErrorAnalysis(object):
""" Reads log and o... | pd.merge(df, df_tmp, how='outer', left_index=True, right_index=True) | pandas.merge |
import sciwing.constants as constants
from sciwing.metrics.precision_recall_fmeasure import PrecisionRecallFMeasure
from sciwing.infer.classification.BaseClassificationInference import (
BaseClassificationInference,
)
from sciwing.data.datasets_manager import DatasetsManager
from deprecated import deprecated
from t... | pd.DataFrame(self.output_analytics) | pandas.DataFrame |
import warnings
from collections import OrderedDict
from datetime import time
import tables as tb
import pandas as pd
import pandas.lib as lib
import numpy as np
import pandas.io.pytables as pdtables
from trtools.compat import izip, pickle
from trtools.io.common import _filename
from trtools.io.table_indexing import ... | pd.DataFrame(sdict, columns=columns, index=index) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Routine to read water quality data of different formats and transform to HGC format
<NAME>, <NAME>
KWR, April-July 2020
Edit history: 24-08-2020: by Xin, check unit conversion,
"""
import copy
import logging
import numpy as np
import pandas as pd
# from unit_converter.converter import conv... | pd.DataFrame() | pandas.DataFrame |
"""Unittests for the functions in raw, using example datasets."""
import unittest
import pandas.testing as pt
import pandas as pd
from io import StringIO
from gnssmapper import log
import gnssmapper.common.time as tm
import gnssmapper.common.constants as cn
class TestReadCSV(unittest.TestCase):
def setUp(self):
... | pd.Series([0]) | pandas.Series |
import copy
import logging
import pandas as pd
from pandas import DataFrame, Series, Int64Index
from roughsets_base.roughset_si import RoughSetSI
class RoughSetDT(RoughSetSI):
"""Class RoughSet to model a decision table (DT).
DT = f(X, A, y),
where:
X - objects of universe,
A -... | pd.concat([X, y], axis=1) | pandas.concat |
import numpy as np
import pandas as pd
from sklearn.metrics import roc_curve, precision_recall_curve, auc
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
def viz_train_val_data(hist_scores, model_str, model_timestamp):
# Plot training & validation metrics
loss_train, kl_train, acc... | pd.DataFrame(data=adj, index=gene_names, columns=gene_names) | pandas.DataFrame |
import os
import requests
import warnings
import numpy as np
import pandas as pd
from functools import reduce
from io import BytesIO
import sys
sys.path.append("..")
NUM_TESS_SECTORS = 27
TESS_DATAPATH = os.path.abspath(os.path.dirname(os.getcwd())) + "/data/tesstargets/" # or change
assert TESS_DATAPATH[-1] == os.pat... | pd.read_csv(fullpath, comment='#') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 1 14:13:20 2022
@author: scott
Visualizations
--------------
Plotly-based interactive visualizations
"""
import pandas as pd
import numpy as np
import spiceypy as spice
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import plotly.graph_object... | pd.isnull(dftopo1['size']) | pandas.isnull |
"""
.. module:: projectdirectory
:platform: Unix, Windows
:synopsis: A module for examining collections of git repositories as a whole
.. moduleauthor:: <NAME> <<EMAIL>>
"""
import math
import sys
import os
import numpy as np
import pandas as pd
from git import GitCommandError
from gitpandas.repository import... | pd.DataFrame(ds, columns=['repository', 'is_bare']) | pandas.DataFrame |
""" test the scalar Timedelta """
import numpy as np
from datetime import timedelta
import pandas as pd
import pandas.util.testing as tm
from pandas.tseries.timedeltas import _coerce_scalar_to_timedelta_type as ct
from pandas import (Timedelta, TimedeltaIndex, timedelta_range, Series,
to_timedelta,... | ct(10, unit='s') | pandas.tseries.timedeltas._coerce_scalar_to_timedelta_type |
import re
import warnings
import numpy as np
import pandas as pd
from Amplo.Utils import clean_keys
class DataProcesser:
def __init__(self,
target: str = None,
float_cols: list = None,
int_cols: list = None,
date_cols: list = None,
... | pd.api.types.is_datetime64_any_dtype(data[key]) | pandas.api.types.is_datetime64_any_dtype |
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