prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
"""
Tests for Series cumulative operations.
See also
--------
tests.frame.test_cumulative
"""
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
methods = {
"cumsum": np.cumsum,
"cumprod": np.cumprod,
"cummin": np.minimum.accumulate,
"cummax": np.maximum.accumulate,
}
... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import statsmodels
from matplotlib import pyplot
from scipy import stats
import statsmodels.api as sm
import warnings
from itertools import product
import datetime as dt
from stat... | pd.Series(results[0:3], index=['t-score', 'p-value', '# lags used']) | pandas.Series |
"""
Unit test suite for OLS and PanelOLS classes
"""
# pylint: disable-msg=W0212
from __future__ import division
from datetime import datetime
import unittest
import nose
import numpy as np
from pandas import date_range, bdate_range
from pandas.core.panel import Panel
from pandas import DataFrame, Index, Series, no... | ols(y=y, x=lp, entity_effects=True, window=20) | pandas.stats.api.ols |
#!/usr/bin/env python
import os.path
import os
import sys
import pandas as pd
from schimpy.unit_conversions import *
if sys.version_info[0] < 3:
from pandas.compat import u
from builtins import open, file, str
else:
u = lambda x: x
import argparse
from vtools.data.timeseries import *
station_variables =... | pd.infer_freq(staout.index) | pandas.infer_freq |
from isitfit.cost.ec2_analyze import BinCapUsed
import datetime as dt
import pytest
import pandas as pd
@pytest.fixture
def FakeMm():
class FakeMm:
StartTime = dt.datetime(2019,1,15)
EndTime = dt.datetime(2019,4,15)
return FakeMm
class TestBinCapUsedHandlePre:
def test_preNoBreak(self, FakeMm)... | pd.to_datetime(e[fx]) | pandas.to_datetime |
import math
import logging
import re
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import pygest as ge
from pygest.convenience import bids_val, dict_from_bids, short_cmp, p_string
from pygest.algorithms import pct_similarity
from scipy.stats import ttest_ind
... | pd.DataFrame(summary_list) | pandas.DataFrame |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
"""
@version:
@author: li
@file: factor_operation_capacity.py
@time: 2019-05-30
"""
import gc
import sys
sys.path.append('../')
sys.path.append('../../')
sys.path.append('../../../')
import six, pdb
import pandas as pd
from pandas.io.json import json_normalize
from utili... | pd.merge(factor_derivation, management, how='outer', on="security_code") | pandas.merge |
""" miscellaneous sorting / groupby utilities """
from collections import defaultdict
from typing import (
TYPE_CHECKING,
Callable,
DefaultDict,
Dict,
Iterable,
List,
Optional,
Tuple,
Union,
)
import numpy as np
from pandas._libs import algos, hashtable, lib
from pandas._libs.hasht... | Categorical(k, ordered=True) | pandas.core.arrays.Categorical |
# -*- coding: utf-8 -*-
from __future__ import print_function
from __future__ import division
import sys
import glob
import pandas as pd
import numpy as np
from ngskit.utils import dna
#form dna_util import *
from common import *
# Pipelines
def lentivirus_combine(data_path = '/home/ccorbi/Work/Beagle/optim_lib/Kim/... | pd.merge(poll_of_replicas, raw_data, on=['referenceId'], how='outer') | pandas.merge |
######################################################################
## DeepBiome
## - Reader
##
## July 10. 2019
## Youngwon (<EMAIL>)
##
## Reference
## - Keras (https://github.com/keras-team/keras)
######################################################################
import os
import sys
import json
import timei... | pd.DataFrame(cov) | pandas.DataFrame |
import numpy as np
import pandas as pd
def get_processed_data(sample):
# loading
raw_tb = pd.read_csv('data/fifa.csv')
raw_tb = raw_tb[:sample]
selected_columns = ['Age','Wage','Crossing', 'Finishing', 'BallControl','Curve','LongPassing', 'Agility','ShotPower','Stamina','LongShots','Aggression','Posit... | pd.to_numeric(_tb.loc[:, 'Wage'].str[3:-1]) | pandas.to_numeric |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = | pd.read_csv('mushrooms.csv') | pandas.read_csv |
import numpy as np
import pandas as pd
from utils.random import scaled_inverse_chi_squared
class ThompsonSamplingGaussianSicqPrior(object):
def __init__(self, N, save_log=False):
self.N = N
self.ks = np.ones(N)
self.mus = np.zeros(N)
self.vs = np.ones(N)
self.sigmas = np.o... | pd.DataFrame(self.thetas) | 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("1961-03-01 00:00:00") | pandas.Timestamp |
import pandas as pd
import requests
from bs4 import BeautifulSoup
import time
import random
# create the progress bar function for use later during scraping (credit to github.com/marzukr)
def progbar(curr, total, full_progbar):
frac = curr/total
filled_progbar = round(frac*full_progbar)
print('\r... | pd.DataFrame(records, columns=['date', 'item', 'price']) | pandas.DataFrame |
from __future__ import absolute_import
import collections
import gzip
import logging
import os
import sys
import multiprocessing
import threading
import numpy as np
import pandas as pd
from itertools import cycle, islice
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import StandardScaler, Min... | pd.DataFrame(mat, columns=df.columns) | pandas.DataFrame |
"""Top-level API, including the main LinkedDataFrame class"""
import attr
from collections import deque
from deprecated import deprecated
import numexpr as ne
import numpy as np
import pandas as pd
from pandas import DataFrame, Series, Index, MultiIndex
from typing import Any, Dict, Deque, Hashable, List, Optional, Set... | pd.read_csv(*args, **kwargs) | pandas.read_csv |
#! -*- coding:utf-8 -*-
import os
import re
import gc
import sys
import json
import codecs
import random
import warnings
import numpy as np
import pandas as pd
from tqdm import tqdm
from random import choice
import tensorflow as tf
import matplotlib.pyplot as plt
from collections import Counter
from sklearn.model_selec... | pd.read_csv(data_path + 'round2_test.csv', encoding='utf-8') | pandas.read_csv |
import pandas as pd
from django.shortcuts import render, redirect
import os
import pandas as pd
from .associations import (
generate_associated_dt_annotation,
generate_associated_coord_annotation,
)
import json
import logging
from .form_population import Form
from utils.cache_helper import cache_get, cache_set
... | pd.DataFrame(samples) | pandas.DataFrame |
# Licensed to Modin Development Team under one or more contributor license
# agreements. See the NOTICE file distributed with this work for additional
# information regarding copyright ownership. The Modin Development Team
# licenses this file to you under the Apache License, Version 2.0 (the
# "License"); you may not... | pandas.Series(data) | pandas.Series |
from itertools import product
import numpy as np
import pytest
from pandas.core.dtypes.common import is_interval_dtype
import pandas as pd
import pandas._testing as tm
# Each test case consists of a tuple with the data and dtype to create the
# test Series, the default dtype for the expected result (which is valid
... | pd.Series(data) | pandas.Series |
from __future__ import print_function, division
import itertools
from copy import deepcopy
from collections import OrderedDict
from warnings import warn
import nilmtk
import pandas as pd
import numpy as np
import metrics
import matplotlib.pyplot as plt
from hmmlearn import hmm
from nilmtk.feature_detectors import clu... | pd.DataFrame() | pandas.DataFrame |
""" test the scalar Timedelta """
from datetime import timedelta
import numpy as np
import pytest
from pandas._libs import lib
from pandas._libs.tslibs import (
NaT,
iNaT,
)
import pandas as pd
from pandas import (
Timedelta,
TimedeltaIndex,
offsets,
to_timedelta,
)
import pandas._testing as ... | Timedelta(10, unit="d") | pandas.Timedelta |
from numpy.ma import argmax
from pandas import DataFrame
from sklearn.model_selection import train_test_split
from tensorflow.python.keras import Sequential
from tensorflow.python.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.python.ops.confusion_matrix import confusion_matrix
from audio_utils.u... | DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
import pandas as pd
from pandas.io.json import json_normalize
import json
import argparse
import logzero
import logging
def get_args():
parser = argparse.ArgumentParser(
description="Convert t-SNE matrix to HTML plot.")
parser.add_argument(
"--tissue-type",
type=str,
... | pd.Categorical(diseases['attr_diagnosis_group'], ["Brain Tumor", "Solid Tumor", "Hematologic Malignancy", "Germ Cell Tumor"]) | pandas.Categorical |
# Databricks notebook source
import pandas as pd
import math
import matplotlib.pyplot as plt
import numpy as np
# COMMAND ----------
# MAGIC %md
# MAGIC #REGRESSION MODEL NOTES
# MAGIC ## We Can Conduct a few different version of this regression model by changing the dependent and independent variables
# MAGIC **Depe... | pd.DataFrame(Y_train) | pandas.DataFrame |
import copy
import csv
import io
import os
from pathlib import Path
import socket
import tempfile
import threading
import unittest
import pandas as pd
import pyarrow as pa
from pyarrow import csv as arrow_csv
from cleanup import cleanup_on_shutdown, directories_to_delete
import main
from proto.aiengine.v1 import aien... | pd.Timestamp("1970-01-01") | pandas.Timestamp |
# -*- coding: utf-8 -*-
# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)
# <NAME> (<EMAIL>), Blue Yonder Gmbh, 2016
import warnings
from unittest import TestCase
import pandas as pd
from tsfresh.utilities import dataframe_functions
import numpy as np
import six
... | pd.concat([first_class, second_class], ignore_index=True) | pandas.concat |
""" Este script extrae información de los campos del HTML
del cvlac a partir de una base de datos inicial de los perfiles
"""
# Importar librerias/Modulos
import pandas as pd
import numpy as np
import requests
from bs4 import BeautifulSoup
from lxml import html
import scrapy
import time
# Extrae metadatos de la ... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""goog-stock-prediction.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1AKrijE9xS03KZo8MMsMxbTD9WkwisjYl
#Stock Prediction Using LSTM
<img src = 'https://www.usnews.com/dims4/USNEWS/85cf3cc/2147483647/thumbnail/640x420/... | pd.DataFrame(index=dates) | pandas.DataFrame |
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.externals import joblib
import warnings
warnings.filterwarnings("ignore")
# Choose GBDT Regression mode... | pd.DataFrame(data={two_columns[0]: observation_period, two_columns[1]: result}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 23 11:32:40 2021
@author: bianca
"""
# +++ IMPLIED GROWTH RATES +++ #
import pandas as pd
import os
import numpy as np
from datetime import datetime
## select Merge File US Equity and WRDS
df_assig3 = pd.read_csv("./data/external/assignment_3_sp... | pd.Timestamp(df_CAR_UE['date']) | pandas.Timestamp |
import cv2
from PIL import Image
import numpy as np
import pandas as pd
import torch
from torch import nn
import torchvision
from torchsat.transforms import transforms_seg
import matplotlib.pyplot as plt
from torchvision.transforms import transforms
from torch.utils.data import Dataset
import torch.nn.functional as F
f... | pd.DataFrame.from_dict(results) | pandas.DataFrame.from_dict |
from datetime import datetime, timedelta
import inspect
import numpy as np
import pytest
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
Index,
IntervalIndex,
MultiIndex... | DataFrame(s1) | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
import framework.constants as cs
from io import StringIO
from framework.representations.embedding import Embedding
from framework.util import scaleInRange
from framework.util import drop_duplicates
heads_vad = ['Word','Valence','Arousal','Dominance']
heads_be5 = ['Word'... | pd.DataFrame(columns=['Word','Valence','Arousal']) | pandas.DataFrame |
"""
A denoiser tries to cancel noise. (also water is wet)
"""
__docformat__ = "google"
from scipy.spatial.distance import cdist
import numpy as np
import pandas as pd
from nmoo.wrapped_problem import WrappedProblem
class KNNAvg(WrappedProblem):
"""
Implementation of the KNN-Avg algorithm of Klikovits and Ar... | pd.DataFrame(self._problem._history["X"]) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In this notebook we try to practice all the classification algorithms that we learned in this course.
#
# We load a dataset using Pandas library, and apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.
#
# Lets first ... | pd.to_datetime(df['due_date']) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 15 11:36:48 2021
@author: nb137
"""
# Data Setup
import os
import sys
# Hide my folder tree for publication online, but this is me ensuring I've updated my data
os.system(r'github COVID_GH_FOLDER') # Can't update from terminal, but this will remind me to pull data if... | pd.datetime(2020,6,8) | pandas.datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 2 11:39:57 2018
@author: <NAME>
@Contains : Pre-processing functions
"""
import pandas as pd
import numpy as np
import json
def mapprice(v):
if pd.isnull(v):
return [ np.nan]
try:
vv = v.split('-')
p0 = vv[0].str... | pd.isnull(srs) | pandas.isnull |
from __future__ import division
import pytest
import numpy as np
from pandas import (Interval, IntervalIndex, Index, isna,
interval_range, Timestamp, Timedelta,
compat)
from pandas._libs.interval import IntervalTree
from pandas.tests.indexes.common import Base
import pandas.uti... | pd.timedelta_range('1 day', periods=5) | pandas.timedelta_range |
# File System
import os
import json
from pathlib import Path
from zipfile import ZipFile
import pickle
import gc
import numpy as np
import pandas as pd
from sympy.geometry import *
DATA_PATH = '../data/' # Point this constant to the location of your data archive files
EXPECTED_DATASETS = {'Colorado': [
'county_... | pd.read_pickle(f'../data/covidWind.{state}.pkl') | pandas.read_pickle |
import sys
import pandas as pd
from sqlalchemy import create_engine
def load_data(messages_filepath, categories_filepath):
###
"""
Load Data function
Arguments:
messages_filepath -> path to messages csv file
categories_filepath -> path to categories csv file
Output:
df -> L... | pd.read_csv(categories_filepath) | pandas.read_csv |
import os
import numpy as np
import pandas as pd
from sklearn import linear_model
def allign_alleles(df):
"""Look for reversed alleles and inverts the z-score for one of them.
Here, we take advantage of numpy's vectorized functions for performance.
"""
d = {'A': 0, 'C': 1, 'G': 2, 'T': 3}
a = [] ... | pd.merge(ggr_df, covariates_df[['IID']], on=['IID']) | pandas.merge |
#!/usr/bin/env python
# coding: utf-8
"""
analysis and optimization
"""
import logging
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn
from matplotlib import pyplot as plt
from scipy import stats
from scipy.optimize import differential_evolution
from sklearn.ensemble import RandomForestReg... | pd.DataFrame() | pandas.DataFrame |
"""
Script to check results files for compliance
with a dietary restriction at the recipe level.
To run, first download food.csv from https://www.foodb.ca/downloads.
"""
import os
import re
import csv
import pandas as pd
from apply_tag import apply_tag
def get_ing_list():
"""Get ingredient list from FooDB."""
... | pd.read_csv('/sample_data/food.csv') | pandas.read_csv |
# Steven 05/17/2020
# clustering model design
from time import time
import pandas as pd
import numpy as np
# from sklearn.decomposition import PCA
# from sklearn.cluster import AgglomerativeClustering
from sklearn.cluster import KMeans
# from sklearn.cluster import DBSCAN
# from sklearn.pipeline import make_pipeline
fr... | pd.DataFrame([[dbName, modelName, k, tt, sse, dbValue, csm]], columns=columns) | pandas.DataFrame |
import pandas as pd
from .helpers import pandas_to_json
from .consts import profile_col_names
pd.set_option('display.max_columns', 40)
import sys
# data processing
def process_data(inf_dict, friends_dict, profile_dict, lk_dict, final_data_dict):
# convert dicts to pandas dfs
inf_df = pd.DataFrame(inf_dict, ind... | pd.DataFrame(profile_dict, index=[0]) | pandas.DataFrame |
""" test the scalar Timedelta """
from datetime import timedelta
import numpy as np
import pytest
from pandas._libs import lib
from pandas._libs.tslibs import (
NaT,
iNaT,
)
import pandas as pd
from pandas import (
Timedelta,
TimedeltaIndex,
offsets,
to_timedelta,
)
import pandas._testing as ... | Timedelta("-1 days 02:34:56.789123456") | pandas.Timedelta |
from datetime import datetime
startTime = datetime.now()
import json
import glob
import numpy as np
import sklearn
import pandas as pd
from io import StringIO
import tensorflow as tf
import tensorflowjs as tfjs
from tensorflow import keras
from sklearn.model_selection import train_test_split
from sklearn.preprocessing... | pd.read_csv(path, skipinitialspace=True, low_memory=False) | pandas.read_csv |
# Automated Antibody Search
# <NAME> - UBC March 2020
# This code uses selenium webdriver to automate search for antibodies based on marker genes found via scRNA-seq
# Input: a dataframe containing uniquely upregulated marker genes for a given cluster
# REQUIREMENTS:
# Download selenium in terminal using the command... | pd.DataFrame([[cur_gene, cur_name, cur_region, cur_ab, "check", "check", cur_pct1, cur_pct2]], columns=co) | pandas.DataFrame |
import pandas as pd
import logging
import electricitylci.model_config as config
formatter = logging.Formatter(
"%(levelname)s:%(filename)s:%(funcName)s:%(message)s"
)
logging.basicConfig(
format="%(levelname)s:%(filename)s:%(funcName)s:%(message)s",
level=logging.INFO,
)
logger = logging.getLogger("elect... | pd.concat([netl_gen,hydro_df[netl_gen.columns]],ignore_index=True,sort=False) | pandas.concat |
# --------------
# Importing header files
import numpy as np
import pandas as pd
from scipy.stats import mode
import warnings
warnings.filterwarnings('ignore')
#Reading file
bank_data = | pd.read_csv(path) | pandas.read_csv |
from flask import request, url_for
from flask_api import FlaskAPI, status, exceptions
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import NMF
from surprise import KNNWithMeans
from surprise import accuracy
from surprise.model_selection import K... | pd.read_csv('hack.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
AIDeveloper
---------
@author: maikherbig
"""
import os,sys,gc
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'#suppress warnings/info from tensorflow
if not sys.platform.startswith("win"):
from multiprocessing import freeze_support
freeze_support()
# Make sure to get the right ... | pd.DataFrame(pred_thresh) | pandas.DataFrame |
#python imports
import os
import gc
import string
import random
import time
import pickle
import shutil
from datetime import datetime
#internal imports
from modules.Signal import Signal
from modules.Database import Database
from modules.Predictor import Classifier, ComplexBuilder
from modules.utils import calcula... | pd.DataFrame(lnSpace) | pandas.DataFrame |
import sys
assert sys.version_info >= (3, 5) # make sure we have Python 3.5+
import pandas as pd
import numpy as np
from pathlib import Path
# init input df - fishing gear
def init_fishing_df(path):
fishing_df = pd.read_csv('../data/' + path)
# comment out for real life data--------------
fishing_df = fi... | pd.to_datetime(df['timestamp'], unit='s') | pandas.to_datetime |
'''
'''
import os, glob
try:
from icecube import dataclasses, icetray, dataio
from icecube import genie_icetray
except ModuleNotFoundError:
# Not running in IceTray
pass
import numpy as np
import pandas as pd
from sqlalchemy import create_engine
import sqlalchemy
import time
from multiprocessing import... | pd.DataFrame() | pandas.DataFrame |
# pylint: disable=E1101
from datetime import datetime
import datetime as dt
import os
import warnings
import nose
import struct
import sys
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
from pandas.compat import iterkeys
from pandas.core.frame import DataFrame, Series
from pandas.c... | DataFrame([(1,)], columns=['var']) | pandas.core.frame.DataFrame |
import datetime
import numpy as np
from pandas.compat import IS64, is_platform_windows
from pandas import Categorical, DataFrame, Series, date_range
import pandas._testing as tm
class TestIteration:
def test_keys(self, float_frame):
assert float_frame.keys() is float_frame.columns
de... | tm.assert_series_equal(ser, expected) | pandas._testing.assert_series_equal |
# -*- coding: utf-8 -*-
"""
@brief test log(time=2s)
"""
import unittest
import pandas
import numpy
from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from pyquickhelper.pycode import ExtTestCase
from mlinsights.search_rank import Se... | pandas.DataFrame(res) | pandas.DataFrame |
import numpy as np
from datetime import timedelta
from distutils.version import LooseVersion
import pandas as pd
import pandas.util.testing as tm
from pandas import to_timedelta
from pandas.util.testing import assert_series_equal, assert_frame_equal
from pandas import (Series, Timedelta, DataFrame, Timestamp, Timedelt... | pd.Timedelta('02:30:00') | pandas.Timedelta |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import os
import operator
import unittest
import cStringIO as StringIO
import nose
from numpy import nan
import numpy as np
import numpy.ma as ma
from pandas import Index, Series, TimeSeries, DataFrame, isnull, notnull
from pandas.core.index... | assert_series_equal(result, expected) | pandas.util.testing.assert_series_equal |
import os
import pandas as pd
from typing import Union
import data
import numpy as np
def runs()->pd.DataFrame:
"""Get meta data about the runs
Returns:
pd.DataFrame: Meta data for all runs
"""
dir_path = os.path.join(os.path.dirname(data.__file__),'raw')
df_runs = pd.read_csv(os.path.join... | pd.read_csv(file_path, index_col=0) | pandas.read_csv |
import pandas as pd
import numpy as np
import os
from datetime import datetime
from IPython.display import IFrame,clear_output
# for PDF reading
import textract
import re
import sys
import docx
from difflib import SequenceMatcher
##################################################################################... | pd.to_datetime(xx) | pandas.to_datetime |
from datetime import datetime
import numpy as np
import pytest
from pandas.core.dtypes.cast import find_common_type, is_dtype_equal
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series
import pandas._testing as tm
class TestDataFrameCombineFirst:
def test_combine_first_mixed(self):
... | Series([1, 2, 3, 4, 5, 6], index=mi2) | pandas.Series |
from datetime import datetime
import json
import pandas as pd
import iso8601 as iso
from dateutil import tz
import platform
def generate_excel(file_loc, export_loc):
# file_loc = r"C:\Users\user\PycharmProjects\MVIPostToExcel\mission-victory-india.ghost.2020-12-19-15-33-27.json"
ist = tz.gettz("Asia/Calcutta"... | pd.DataFrame(columns=["Export Date (IST)", "Exported Records", "Input JSON Path", "Excel Export Path"]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
from pandas.compat import range
import pandas as pd
import pandas.util.testing as tm
# -------------------------------------------------------------------
# Comparisons
class TestFrameComparisons(object):
def test_df_boolean_comparison_error(self):
... | pd.DataFrame(data, dtype=dtype) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 5 18:54:29 2019
@author: suvodeepmajumder
"""
import sys
sys.path.append("..")
from pygit2 import clone_repository
from pygit2 import GIT_SORT_TOPOLOGICAL, GIT_SORT_REVERSE,GIT_MERGE_ANALYSIS_UP_TO_DATE,GIT_MERGE_ANALYSIS_FASTFORWARD,GIT_MERGE_ANAL... | pd.Series(metrics) | pandas.Series |
import os
from flask import jsonify, request
from server import app
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from aif360.sklearn.metrics import disparate_impact_ratio, base_rate, consistency_score
def bias_table(Y, prot_attr=None, instance_type=None):
groups =... | pd.DataFrame(np.c_[pct, data], columns=col, index=groups) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 25 10:45:35 2020
@author: yashr
"""
import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation,Layer,Lambda
forestfires = pd.read_csv("fireforests.csv")
#As dummy variables are already created... | pd.Series(test["original_class"]) | pandas.Series |
import pandas as pd
from intake_dal.dal_catalog import DalCatalog
"""
Test setup:
- batch storage mode driver is parquet and '{{ CATALOG_DIR }}/data/user_events.parquet' has ONLY 1 row
- local storage mode driver is csv and '{{ CATALOG_DIR }}/data/user_events.csv' has TWO rows
"""
def test_dal_catalog_defa... | pd.DataFrame({"key": ["a", "first"], "value": [3, 42]}) | pandas.DataFrame |
#!/usr/bin/env python
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 300
import matplotlib.pyplot as plt
import seaborn as sns
import pysam
from pysamiterators import CachedFasta, MatePairIterator
# Molecule modules:
from singlecellmultiomics.molecule import TranscriptMolecule, MoleculeIterator
from singlecel... | pd.DataFrame(four_su_per_gene_per_cell) | pandas.DataFrame |
import datetime
from unittest import TestCase
import numpy as np
import pandas as pd
from mlnext import pipeline
class TestColumnSelector(TestCase):
def setUp(self):
data = np.arange(8).reshape(-1, 2)
cols = ['a', 'b']
self.df = pd.DataFrame(data, columns=cols)
def test_select_col... | pd.testing.assert_frame_equal(result, expected) | pandas.testing.assert_frame_equal |
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
from dash.exceptions import PreventUpdate
from django_plotly_dash import DjangoDash
import dash_bootstrap_components as dbc
import plotly.graph_objs as go
import plotly.express as px
im... | pd.unique(df.county) | pandas.unique |
#! /usr/bin/env python3
import argparse
import re,sys,os,math,gc
import numpy as np
import pandas as pd
import matplotlib as mpl
import copy
import math
from math import pi
mpl.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
f... | pd.DataFrame(counts) | pandas.DataFrame |
import os
import pandas as pd
import numpy as np
import cv2
from ._io_data_generation import check_directory, find_movies, copy_movie
from .LV_mask_analysis import Contour
import matplotlib.pyplot as plt
import networkx as nx
from sklearn.neighbors import NearestNeighbors
from scipy.spatial.distance import cdist
from i... | pd.unique(df_case['Frame']) | pandas.unique |
from datetime import timezone
import pandas as pd
import numpy as np
import datetime
import netCDF4
import time
def _validate_date(date_text):
'''
Checks date format to ensure YYYY-MM-DD format and return date in
datetime format.
Parameters
----------
date_text: string
Date string... | pd.to_datetime(time_range_all[1]) | pandas.to_datetime |
"""
Download data from original sources if they are not already present in the data dir
"""
import argparse
import os
from pathlib import Path
import pandas as pd
import requests
def delete_file(target_dir, filename):
test_path = Path(os.path.join(target_dir, filename))
if test_path.is_file():
os.re... | pd.read_csv('data/ukgov-gpg-2019.csv', dtype={'SicCodes': str}) | pandas.read_csv |
import logging
import pandas as pd
from catboost import CatBoostClassifier
logging.basicConfig(filename='logs/model_development.txt',
filemode='a',
format='%(asctime)s %(message)s',
datefmt="%Y-%m-%d %H:%M:%S")
logging.warning('-'*100)
logging.wa... | pd.DataFrame(feature_dict, index=['Importance']) | pandas.DataFrame |
import cobra
from cobra.core.metabolite import elements_and_molecular_weights
elements_and_molecular_weights['R']=0.0
elements_and_molecular_weights['Z']=0.0
import pandas as pd
import numpy as np
import csv
#### Change Biomass composition
# define a function change a biomass reaction in the model
def update_biomass(m... | pd.DataFrame(data=[gDW,cells],index = ['Biomass','Cells'],columns=['0']) | pandas.DataFrame |
"""
@author: <NAME>
@email: <EMAIL>
this file augments the precomputed features using pyspark and add wordcount and size of article
"""
import pandas as pd
import requests
import sys
def page_search(session, title):
"""
:param session: http session from wikipedia API
:param title: find the page with wik... | pd.read_csv("merged_augmented.csv") | pandas.read_csv |
import pandas as pd
def generate_train(playlists):
# define category range
cates = {'cat1': (10, 50), 'cat2': (10, 78), 'cat3': (10, 100), 'cat4': (40, 100), 'cat5': (40, 100),
'cat6': (40, 100),'cat7': (101, 250), 'cat8': (101, 250), 'cat9': (150, 250), 'cat10': (150, 250)}
cat_pids = {}
... | pd.concat([df_test_pl, df]) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Sat Jun 6 22:23:07 2020
@author: atidem
"""
import pandas as pd
import numpy as np
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from statsmodels.tsa.ar_model import AR,ARResults
from statsmodels.tsa.arima_model import ARIMA,ARMA,ARIMAResults,ARMAResults
from p... | pd.concat([df,Case_mul_mul,Case_add_add,Death_mul_mul,Death_add_add],axis=1) | pandas.concat |
import numpy as np
#import scipy.io #required to read Matlab *.mat file
from scipy import linalg
import pandas as pd
import networkx as nx
#import pickle
import itertools
from sklearn.covariance import GraphLassoCV, ledoit_wolf, graph_lasso
from statsmodels.stats.correlation_tools import cov_nearest
import networkx as... | pd.Series(mu, index=plabels) | pandas.Series |
# -*- coding: utf-8 -*-
#!/usr/bin/env python
# Copyright 2015-2017, Institute for Systems Biology.
# 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... | pd.DataFrame(lines) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri May 20 14:09:31 2016
@author: bmanubay
"""
import cirpy
import numpy as np
import pandas as pd
from sklearn.externals.joblib import Memory
mem = Memory(cachedir="/home/bmanubay/.thermoml/")
@mem.cache
def resolve_cached(x, rtype):
return cirpy.resolve(x, rtype)
# Defin... | pd.merge(a,bb,how='outer',on=['SMILES']) | pandas.merge |
"""
Copyright 2019 <NAME>.
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 to in writing,
software distribut... | assert_series_equal(expected, actual) | pandas.testing.assert_series_equal |
import argparse
import os
import shutil
import subprocess
from datetime import datetime
from pathlib import Path
import pandas as pd
import numpy as np
from sklearn.manifold import TSNE
from sklearn.cluster import KMeans
from mtc.challenge_pipelines.preprocess_data import generate_preprocessed_data
from mtc.settings ... | pd.api.types.is_float_dtype(df.dtypes[2]) | pandas.api.types.is_float_dtype |
from typing import *
import numpy as np
import argparse
from toolz.itertoolz import get
import zarr
import re
import sys
import logging
import pickle
import pandas as pd
from sympy import Point, Line
from skimage import feature, measure, morphology, img_as_float
from skimage.filters import rank_order
from scipy import ... | pd.DataFrame(counts_dict) | pandas.DataFrame |
#!/usr/bin/env python2
import numpy as np
import pandas as pd
import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from os.path import expanduser
import scipy
def treegrass_frac(ndvi, day_rs):
"""
Process based on Donohue et al. (2009) to separate out tree and grass cov... | pd.read_csv(clim_met_file) | pandas.read_csv |
# Copyright (C) 2016 The Regents of the University of Michigan
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# ... | pd.DataFrame(data=forum_output_list, columns=['userID', 'week', 'forum_views']) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytask
from src.config import BLD
from src.config import SRC
from src.shared import create_age_groups
from src.shared import load_dataset
LOCATIONS = [
"cnt_home",
"cnt_work",
"cnt_school",
"cnt_leisure",
"cnt_transport",
"cnt_otherplace",
]
MOS... | pd.Categorical(nice_sr, categories=durations, ordered=True) | pandas.Categorical |
# Copyright 2021 NVIDIA Corporation
#
# 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 to in wr... | pd.StringDtype() | pandas.StringDtype |
from lolesports_api.downloaders import downloadMeta, downloadDetails
from lolesports_api.analysis import diffPlot
import glob as _glob
import json as _json
import os as _os
import numpy as _np
import pandas as _pd
def dictToAttr(self, dict):
for key, value in dict.items():
setattr(self, key, value)
class... | _pd.TimedeltaIndex(time) | pandas.TimedeltaIndex |
'''
US Job Counts by Industry, 2006-2015
===============================
Interactive heat map shows how total US job change compared to
job change by industry surrounding stock crash in 2008.
'''
import pandas as pd
import altair as alt
from datetime import datetime as dt
us_employment = pd.read_csv("https://raw.git... | pd.to_datetime(us_employment["month"]) | pandas.to_datetime |
import SQLiteFunctions as SQL
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Create new database object and connect to database
algo_db = SQL.SqliteDatabase()
algo_db.connect_database(r'C:/Users/jnwag/OneDrive/Documents/GitHub/AlgorandGovernance/AlgoDB.db')
table_headers = ['id', 'account_id',... | pd.DataFrame(test, columns=table_headers) | pandas.DataFrame |
import numpy as np
import pandas as pd
import scipy
from scipy import stats
import matplotlib as mpl
import matplotlib.pyplot as plt
from distutils.version import LooseVersion
pandas_has_categoricals = LooseVersion(pd.__version__) >= "0.15"
import nose.tools as nt
import numpy.testing as npt
from numpy.testing.decora... | pd.DataFrame({'x': self.x, 'y': self.y}) | pandas.DataFrame |
from distutils.version import LooseVersion
from warnings import catch_warnings
import numpy as np
import pytest
from pandas._libs.tslibs import Timestamp
import pandas as pd
from pandas import (
DataFrame,
HDFStore,
Index,
MultiIndex,
Series,
_testing as tm,
bdate_range,
concat,
d... | tm.assert_frame_equal(expected, result) | pandas._testing.assert_frame_equal |
import pandas as pd
import sys
import utils
import config
nrows = None
tr = utils.load_df(config.data+'train.csv',nrows=nrows)
te = utils.load_df(config.data+'test.csv',nrows=nrows)
actions = ['interaction item image','interaction item info','interaction item deals','interaction item rating','search for item']
df = | pd.concat([tr,te]) | pandas.concat |
# 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... | lrange(4) | pandas.compat.lrange |
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