prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
from ...cg.shapes import asShape as pShape
from ...common import requires as _requires
from warnings import warn
@_requires("geopandas")
def to_df(df, geom_col="geometry", **kw):
"""Convert a ``geopandas.GeoDataFrame`` into a normal
``pandas.DataFrame`` with a column containing PySAL shapes.
Parameters
... | pd.DataFrame(df, **kw) | pandas.DataFrame |
import pandas as pd
num_of_parallel_requests = 5
period = 5.0
class RequestQueue:
def __init__(self):
self.items = | pd.DataFrame(columns=["id", "timestamp", "shard", "load", "expected_end_time", "actual_end_time"]) | pandas.DataFrame |
'''
The analysis module
Handles the analyses of the info and data space for experiment evaluation and design.
'''
from slm_lab.agent import AGENT_DATA_NAMES
from slm_lab.env import ENV_DATA_NAMES
from slm_lab.lib import logger, math_util, util, viz
from slm_lab.spec import spec_util
import numpy as np
import os
import ... | pd.DataFrame({max_tick_unit: x, 'mean_reward': mean_sr}) | pandas.DataFrame |
import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
Timestamp,
date_range,
to_datetime,
)
import pandas._testing as tm
import pandas.tseries.offsets as offsets
class TestRollingTS:
# rolling time-series friendly
# xref GH13327
def set... | DataFrame({"column": [3.0, 3.0, 4.0, 4.0, 6.0]}, index=index) | pandas.DataFrame |
"""
author: <NAME>
date: 2020-11-27
This script imports the train and test csv from the proccessed data folder and performing machine learning modelling and alaysis.
Usage: machine_learning_analysis.py --in_train=<in_train> --in_test=<in_test> --out_path=<out_path>
Options:
--in_train=<in_train> path incl... | pd.DataFrame(results_df) | pandas.DataFrame |
import csv
import re
import pandas as pd
from os.path import join, exists
from os import mkdir
from shutil import rmtree
from os import remove as remove_file
from sys import stdout
import zipfile
import ntpath
from .. enums import POSSIBLE_INPUTS, POSSIBLE_COMMANDS, INPUT_DEFAULTS, PATH_TO_STORAGE
from .. sys_functi... | pd.DataFrame(columns=header) | pandas.DataFrame |
from random import randint
import numpy as np
import pandas as pd
def cross_validation(data, column_target, k=10):
column_values = data[column_target].value_counts().index # classes do problema
class_data = [data[data[column_target] == valor] for valor in column_values] # separação das instancias em classes
cla... | pd.DataFrame(train_instances) | pandas.DataFrame |
import pandas as pd
import numpy as np
import math
import os
import time
from DataCleanService.src.main.utils.utils import remove_gz_suffix, remove_gz_suffix_for_condo
from DataCleanService.src.main.config import constants, DataCleanServiceConfig
import glob
# TODO: data format exception (str, float...)
def select_re... | pd.concat([df, df_month], axis=1) | pandas.concat |
#!/usr/bin/env python
# encoding: utf-8
'''
asreml.Gmatrix -- shortdesc
asreml.Gmatrix is a description
It defines classes_and_methods
@author: user_name
@copyright: 2020 organization_name. All rights reserved.
@license: license
@contact: user_email
@deffield updated: Updated
'''
import sys
import ... | pd.DataFrame(columns=df_maf.index, index=df_maf.index) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import library.areamanager as areamanager
import pandas as pd
import json
import time
import collections
import numpy as np
import pickle
import library.cat_utils as cat_utils
import library.geo_utils as geo_utils
from library.parallel_util import run_parallel
from libr... | pd.to_datetime(checkin['date']) | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 15 07:39:40 2020
@author: adonay
"""
import os.path as op
import numpy as np
import pandas as pd
import pickle
import matplotlib.pyplot as plt
import utils_io as uio
import utils_signal_processing as sig_proc
import utils_feature_extraction as fea... | pd.read_csv(paths['beh'], index_col=0) | pandas.read_csv |
import numpy as np
import pandas as pd
import pyprind
import os
from py_stringsimjoin.utils.generic_helper import \
find_output_attribute_indices, get_output_header_from_tables, \
get_output_row_from_tables
def get_pairs_with_missing_value_disk(ltable, rtable,
l_key_attr... | pd.DataFrame(output_rows) | pandas.DataFrame |
import leidenalg
import graphtools
import sklearn
from igraph import Graph
import numpy as np
import seaborn as sns
import pandas as pd
from scipy.spatial.distance import squareform
from scipy.cluster import hierarchy
class AffinityLeiden(sklearn.base.BaseEstimator, sklearn.base.ClusterMixin):
def __init__(
... | pd.testing.assert_index_equal(dfs[0].index, df.index) | pandas.testing.assert_index_equal |
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 09 16:48:04 2016
@author: rakhunzy
"""
import numpy as np
import pandas as pd
import sys
from matplotlib import pyplot as plt
# In[]
def middle_point(lst):
return lst[len(lst)/2]
def point_index(contour, point):
return np.argwhere(np.all(co... | pd.DataFrame([c[0] for c in contours]) | pandas.DataFrame |
import numpy as np
import pandas as pd
from pandas import compat
from pandas.core.series import Series
from pandas.core.frame import DataFrame
from pandas.core.indexing import is_list_like
from pandas.core.arrays.categorical import _factorize_from_iterable
class Smarties:
def __init__(self, main_lookup=None):
... | is_list_like(item) | pandas.core.indexing.is_list_like |
#!/usr/bin/env python
# coding: utf-8
# ## 라이브러리 import
# In[1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
import seaborn as sns
COLORS = sns.color_palette()
import chart_studio.plotly as py
import cufflinks as cf
print(cf.__version__... | pd.read_csv('/Users/wglee/Desktop/DATA ANALYSIS/데이터사이언스school/EDA프로젝트/EDA프로젝트데이터/서울특별시 공공자전거 이용정보(시간대별)_20190601_20191130(10).csv', encoding='utf-8') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 3 23:04:33 2019
当给定了影像数据和量表时，如果量表数据包括而且大于影像数据时，我们需要从中提取与影像数据匹配的部分
@author: lenovo
"""
import sys
import os
cpwd = __file__
root = os.path.dirname(os.path.dirname(__file__))
sys.path.append(root)
print(f'##{root}')
import pandas as pd
import Utils.lc_copy_selected_file_V6 ... | pd.DataFrame(values) | pandas.DataFrame |
'''
Created on Apr 3, 2020
@author: <NAME>, Blue Lightning Development, LLC
'''
import os
import pandas as pd
pathToRepository = 'C:/Users/NOOK/GITHUB/COVID-19' # change to where you checked out https://github.com/CSSEGISandData/COVID-19.git
states = ["Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colo... | pd.read_csv(base + name, encoding='utf8') | pandas.read_csv |
## By <NAME>
## Created 2018. Edited AS 2019. Edited AJ 2020
import sys
import argparse
import gzip
import pandas as pd
import os
import multiprocessing as mp
import re
from traceback import print_exc
def revComp(my_seq): ## obtain reverse complement of a sequence
base_comp = {'A':'T', 'C':'G','G':... | pd.DataFrame(data=tmp_d) | pandas.DataFrame |
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2020, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | pd.Index(['seq1', 'seq2'], name='Feature ID', dtype=object) | pandas.Index |
"""
Collection of tests asserting things that should be true for
any index subclass. Makes use of the `indices` fixture defined
in pandas/tests/indexes/conftest.py.
"""
import re
import numpy as np
import pytest
from pandas._libs.tslibs import iNaT
from pandas.core.dtypes.common import is_period_dtype, needs_i8_conv... | is_period_dtype(index.dtype) | pandas.core.dtypes.common.is_period_dtype |
# ********************************************************************************** #
# #
# Project: FastClassAI workbecnch #
# ... | pd.Series(scatterpoints) | pandas.Series |
import gc
import time
from datetime import datetime
from functools import partial
from heamylab import mini_sample
import pandas as pd
import numpy as np
# import lightgbm as lgb
# from lightgbm.plotting import plot_importance, plot_metric, plot_tree, create_tree_digraph
import xgboost as xgb
from sklearn import me... | pd.read_csv(testf, sep=",") | pandas.read_csv |
"""
Preprocess sites data.
<NAME>
February 2022
"""
import sys
import os
import configparser
import pandas as pd
import geopandas as gpd
import pyproj
from shapely.ops import transform
from shapely.geometry import shape, Point, mapping, LineString, MultiPolygon
from tqdm import tqdm
CONFIG = configparser.ConfigPar... | pd.DataFrame(output) | pandas.DataFrame |
from datetime import datetime
from dateutil.tz import tzlocal
import pytest
from pandas.compat import IS64
from pandas import (
DateOffset,
DatetimeIndex,
Index,
Series,
bdate_range,
date_range,
)
import pandas._testing as tm
from pandas.tseries.offsets import (
BDay,
Day,
Hour,
... | tm.assert_index_equal(cp, self.rng) | pandas._testing.assert_index_equal |
import logging
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
logger = logging.getLogger(__name__)
def evaluate_agents(agent_manager_list,
n_simulations=5,
fignum=None,
show=True,
plot=True,
... | pd.concat(data_list, ignore_index=True) | pandas.concat |
"""
Tests that work on both the Python and C engines but do not have a
specific classification into the other test modules.
"""
from datetime import datetime
from inspect import signature
from io import StringIO
import os
from pathlib import Path
import sys
import numpy as np
import pytest
from pandas.compat import P... | tm.assert_produces_warning(FutureWarning) | pandas._testing.assert_produces_warning |
#!/usr/bin/env python3
# Author: <NAME>
import numpy as np
import pandas as pd
import gzip
import subprocess
import scipy.stats as stats
import argparse
import os
import feather
import rnaseqnorm
def gtf_to_bed(annotation_gtf, feature='gene', exclude_chrs=[]):
"""
Parse genes from GTF, create placeholder Da... | pd.DataFrame(data={'chr':chrom, 'start':start, 'end':end, 'gene_id':gene_id}, columns=['chr', 'start', 'end', 'gene_id'], index=gene_id) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 18 17:37:59 2020
@author: bernice
"""
#%% Final Project
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats, integrate
import seaborn as sns
df = pd.read_csv('middleSchoolData.csv')
#%% 1) What is the co... | pd.DataFrame(beta.T,columns=['Weight']) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/2/24 15:02
Desc: 东方财富网-数据中心-新股数据-打新收益率
东方财富网-数据中心-新股数据-打新收益率
http://data.eastmoney.com/xg/xg/dxsyl.html
东方财富网-数据中心-新股数据-新股申购与中签查询
http://data.eastmoney.com/xg/xg/default_2.html
"""
import pandas as pd
import requests
from tqdm import tqdm
from akshare.utils i... | tetime(big_df['申购日期']) | pandas.to_datetime |
import os
import logging
import json
import glob
import collections
import yaml
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from mathtools import utils
logger = logging.getLogger(__name__)
def load_vocabs(vocab_fn):
def get_part_name(event_name):
return utils.remove_pre... | pd.concat(labels, axis=0) | pandas.concat |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = "<NAME>, <NAME>"
__copyright__ = "Copyright 2020, University of Oxford"
__email__ = "<EMAIL>"
__license__ = "MIT"
import pandas as pd
from haystac.workflow.scripts.utilities import REGEX_BLACKLIST
def entrez_pick_sequences(config, nuccore_file, taxa_file, ... | pd.read_csv(nuccore_file, sep="\t") | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
"""Modified version of 'eval_reco_trkx.py' (runs after 'tracks_from_gnn.py') script from the
exatrkx-iml2020. The code breakdown of the script is given in 'stt6_eval.ipynb' notebook."""
import os
import glob
import torch
import numpy as np
import pandas as pd
from typing import A... | pd.HDFStore(out_array, 'w') | pandas.HDFStore |
# Copyright 2021 AstroLab Software
# Author: <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... | pd.Series(to_return) | pandas.Series |
# pylint: disable=W0102
import unittest
import nose
import numpy as np
from pandas import Index, MultiIndex, DataFrame, Series
from pandas.sparse.array import SparseArray
from pandas.core.internals import *
import pandas.core.internals as internals
import pandas.util.testing as tm
from pandas.util.testing import (
... | DataFrame({"a": [1]}) | pandas.DataFrame |
from butterfree.data import loader
from collections import defaultdict
import networkx as nx
import pygraphviz as pgv
import matplotlib.pyplot as plt
import pandas as pd
import os
import re
import numpy as np
import torch
from scipy import linalg
from networkx.drawing.nx_agraph import write_dot, graphviz_layout
... | pd.DataFrame(index=intersection) | pandas.DataFrame |
from datetime import timedelta
from functools import partial
import itertools
from parameterized import parameterized
import numpy as np
from numpy.testing import assert_array_equal, assert_almost_equal
import pandas as pd
from toolz import merge
from zipline.pipeline import SimplePipelineEngine, Pipeline, CustomFacto... | pd.Timestamp("2015-01-20") | pandas.Timestamp |
from typing import Tuple
import streamlit as st
import pandas as pd
import altair as alt
from codex.utils import to_columnar
from codex.measure import measure_vars1, measure_vars2
from codex.collection import load_collection, get_width_height_pixels
STEPS = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
DEFAULT_SCENARIO_ID = 7
N... | pd.DataFrame(data={'Step': steps1, 'Proportion': props1, 'Quantity': names1}) | pandas.DataFrame |
import os
import copy
import pytest
import numpy as np
import pandas as pd
import pyarrow as pa
from pyarrow import feather as pf
from pyarrow import parquet as pq
from time_series_transform.io.base import io_base
from time_series_transform.io.numpy import (
from_numpy,
to_numpy
)
from time_series_transfor... | pd.DataFrame(expect_collection_noExpand['pad']) | pandas.DataFrame |
from collections import (
abc,
deque,
)
from decimal import Decimal
from warnings import catch_warnings
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
PeriodIndex,
Series,
concat,
date_range,
)
import pandas._testing as tm
fr... | concat([frames[k] for k in sorted_keys], keys=sorted_keys) | pandas.concat |
# -*- coding: utf-8 -*-
import json
import os
from typing import Optional, Union, Iterator, List
from functools import partial
import pystow
import pandas as pd
from tqdm.auto import tqdm
from prodec import Descriptor, Transform
from .utils.IO import locate_file, process_data_version, TypeDecoder
def read_papyrus(... | pd.concat([mold2, mordd, cddds, molfp, moe], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
# Copyright (c) May 2021, Wageningen Environmental Research
# <NAME> (<EMAIL>)
import sys, os
import xarray as xr
import pandas as pd
CMD_MODE = True if os.environ["CMD_MODE"] == "1" else False
from .util import create_agera5_fnames, convert_to_celsius
def extract_point(agera5_dir, point, sta... | pd.to_datetime(df_final.time) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 4 2021, last edited 27 Oct 2021
Fiber flow emissions calculations module - class version
Inputs:
Excel file with old PPI market & emissions data ('FiberModelAll_Python_v3-yields.xlsx')
Outputs:
Dict of keys 'old','new','forest','trade' with emissions calcs
... | pd.Series(newProd['totalCO2'], name='prodImp') | pandas.Series |
import re
import pandas
import cobra
from fractions import Fraction
def ReadExcel(excel_file, parse="cobra_string", Print=False):
""" parse = "cobra_string" | "cobra_position"
cobra_string
% INPUT
% fileName xls spreadsheet, with one 'Reaction List' and one 'Metabolite List' tab
%
% 'Reac... | pandas.notnull(reac_row['Lower bound']) | pandas.notnull |
from os import path, mkdir
import feedparser
import pandas as pd
import datetime
filename = "last.txt"
date = datetime.datetime.now().strftime("%Y-%m-%d")
project_url = "https://github.com/bwilliams18/risky-or-not"
def format_perc(fl):
return f"{int(round(fl * 100,0))}%"
if __name__ == "__main__":
RiskyOrN... | pd.DataFrame(episodes) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# This is a test file intended to be used with pytest
# pytest automatically runs all the function starting with "test_"
# see https://docs.pytest.org for more information
import os
import pytest
import pandas as pd
from nlp.spacy_tokenizer import MultilingualTokenizer
def test_tokenize_df... | pd.DataFrame({"input_text": ["I hope nothing. I fear nothing. I am free. 💩 😂 #OMG"]}) | pandas.DataFrame |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
from numpy import nan
import numpy as np
import pandas as pd
from pandas.types.common import is_integer, is_scalar
from pandas import Index, Series, DataFrame, isnull, date_range
from pandas.core.index import MultiIndex
from pa... | Series([1, 2], index=['one', 'one']) | pandas.Series |
from numpy import NaN
import pandas as pd
from tqdm import tqdm
# Todo essa parte é só para funcionar a orientação à objeto
# Ela não é obrigatória para chegar no resultado
# O jeito mais fácil seria usar o Jupyter e sem Orientação à objeto
# Fiz usando isso para poder aprender
class MicrodadosENEM:
def __init__... | pd.read_csv(nome, sep=';', encoding='latin-1', usecols=colunas) | pandas.read_csv |
import streamlit as st
import numpy as np
import pandas as pd
import sqlite3
conn=sqlite3.connect('data.db')
c=conn.cursor()
import os
import warnings
warnings.filterwarnings('ignore')
import tensorflow.keras as tf
import joblib
import base64
from io import BytesIO
import bz2
import pickle
import _pickle as cPickl... | pd.DataFrame(reccom,columns=["rating"]) | pandas.DataFrame |
"""
Helper functions to convert the data to the format expected by run_robot.py
"""
import sys
import seir
import pandas as pd
import numpy as np
import numpy.linalg as la
import os.path as path
# To use PyJulia
print('Loading PyJulia module...')
from julia.api import Julia
jl = Julia(compiled_modules=False)
from jul... | pd.DataFrame(data=pre_M, index=large_cities, columns=large_cities) | pandas.DataFrame |
from __future__ import division
import pandas as pd
import os.path
import sys
# parentddir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))
# sys.path.append(parentddir)
from base.uber_model import UberModel, ModelSharedInputs
from .earthworm_functions import EarthwormFunctions
class Earthw... | pd.Series([], dtype="float") | pandas.Series |
from pathsetup import run_path_setup
run_path_setup()
import os
import gl
gl.isTrain = False
from model_config import model_argparse
config = model_argparse()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = config['device']
import tensorflow as tf
tf_config = tf.ConfigProto()
tf... | pd.read_csv(config['data_dir'] + 'DailyDial/de_duplicated/df_daily_test_without_duplicates.csv') | pandas.read_csv |
import pandas as pd
#drop unknow artist
import matplotlib as mpl
import matplotlib.pyplot as plt
log_dir ='logs/'
mpl.rcParams['figure.figsize'] = (22, 20)
dataset=pd.read_csv('/content/MultitaskPainting100k_Dataset_groundtruth/groundtruth_multiloss_train_header.csv')
# indexName=pf[pf['artist']=='Unknown photographer'... | pd.DataFrame() | pandas.DataFrame |
#This script is to do kinetic classification.
#Make sure that you have setup your PYTHONPATH environment
#variable as described in the github repository.
from zipfile import ZIP_FILECOUNT_LIMIT
from isort import file
from SBMLKinetics import kinetics_classification
import sys
import numpy as np
import os
from symp... | pd.concat([df_gen_stat_PR_plot[i],df_temp], ignore_index=True) | pandas.concat |
"""The American Gut App."""
import dash
import dash_daq as daq
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objs as go
import plotly.figure_factory as ff
import numpy as np
import pandas as pd
from start import (
samples,
find_closest,
healthiest_sample,
me... | pd.Series(0, index=samples.index) | pandas.Series |
import datetime
import numpy as np
from numpy import nan
import pandas as pd
import pytest
from pandas.util.testing import assert_frame_equal
from numpy.testing import assert_allclose
from pvlib.location import Location
from pvlib import tracking
SINGLEAXIS_COL_ORDER = ['tracker_theta', 'aoi',
... | pd.Series([90]) | pandas.Series |
#!/usr/bin/env python
# coding: utf-8
'''This script finds the best parameters for SVC and LGR models and fits the data to these two models and outputs the classification images and the classification reports as the csv documents.
Usage: src/model.py --data_input=<data_input> --result_output=<result_output>
Argument... | pd.read_csv(data_input+'/y_train.csv',usecols = ["Target"]) | pandas.read_csv |
import numpy as np # Matrise pakke
import pandas as pd # Database pakke
import support # For error handling
import matplotlib.pyplot as plt # Plottepakke
import matplotlib.patches as mpatches # Legend in plot
import sys # For aborting scripts
impo... | pd.DataFrame() | pandas.DataFrame |
# scraper_horse_racing.py
# -*- coding: utf-8 -*-
import os
import time
from selenium.webdriver import Firefox
from selenium.webdriver.firefox.firefox_profile import FirefoxProfile
from selenium.webdriver.firefox.options import Options
from selenium.webdriver.common.by import By
from selenium.webdriver.supp... | pd.DataFrame(dictionary_of_races) | pandas.DataFrame |
from pathlib import Path
import numpy as np
import pandas as pd
import pickle
import lightgbm as lgb
import statsmodels.api as sm
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import NearestNeighbors
##############################################################################
dir = Path(__f... | pd.merge(dataf, draw_df, on=["hid"], how="left") | pandas.merge |
from datetime import datetime, timedelta
from typing import Any
import weakref
import numpy as np
from pandas._libs import index as libindex
from pandas._libs.lib import no_default
from pandas._libs.tslibs import frequencies as libfrequencies, resolution
from pandas._libs.tslibs.parsing import parse_time_string
from ... | is_scalar(key) | pandas.core.dtypes.common.is_scalar |
import pandas as pd
import lenskit.crossfold as xf
import numpy as np
from utils import *
import json
ratings = pd.read_csv('data/Clothing_Shoes_and_Jewelry/Home_and_Kitchen.csv', header=None, index_col=None)
#
dir_exists('data/Clothing_Shoes_and_Jewelry/th_0')
dir_exists('data/Clothing_Shoes_and_Jewelry/th_4')
dir_... | pd.unique(ratings.user) | pandas.unique |
# 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("2012-06-27 00:00:00") | pandas.Timestamp |
#
# 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... | assert_series_equal(expect_out, actual_out, check_names=False) | pandas.util.testing.assert_series_equal |
from datetime import datetime
import re
import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
_testing as tm,
)
def test_extract_expand_kwarg_wrong_type_raises(any_string_dtype):
# TODO: should this raise TypeError
values = Series(["fooBAD__barBAD", ... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
# Concatenate uber1, uber2, and uber3: row_concat
row_concat = pd.concat([uber1, uber2, uber3])
# Print the shape of row_concat
print(row_concat.shape)
# Print the head of row_concat
print(row_concat.head())
# Concatenate ebola_melt and status_country column-wise: ebola_tidy
ebola_tidy = pd.concat([ebola_melt, stat... | pd.merge(left=site, right=visited, left_on="name", right_on="site") | pandas.merge |
def grid_scanfish_wrapper(cast_as_ds,dx=500,dz=10,d_factor=500):
import glob
import os
import numpy as np
import xarray as xr
import pandas as pd
import gsw
# use scipy.interpolate.Rbf to interpolate to grid; use d_factor to put more weight on values on x-axis, and not z-axis
#d_factor ... | pd.to_datetime(cast_as_ds.time.values-719529, unit='D') | pandas.to_datetime |
import os
import urllib
import requests
import zipfile
import pandas as pd
import time
from datetime import datetime
from google.transit import gtfs_realtime_pb2
def RequestsWrite(APIkey, feed_id):
'''
This function takes APIkey and feed_id as an input, and
Requests MTA subway real-time status, and Wr... | pd.merge(df, stop_times[['match_id', 'arrival_time_scheduled', 'departure_time_scheduled']], on='match_id', how='inner') | pandas.merge |
# -*- coding: utf-8 -*-
"""
Created on Sat Jul 29 11:20:57 2017
@author: James
"""
from xgboost import XGBRegressor, XGBClassifier
from sklearn.model_selection import cross_val_predict
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pickle
from sklearn i... | pd.read_csv("filtered_background.csv",encoding="utf-8") | pandas.read_csv |
# coding=utf-8
"""
Log based system ID
"""
from typing import Dict, List
import control
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pyulog
import scipy.optimize
import scipy.signal as sig
import ulog_tools as ut
# pylint: disable=no-member, invalid-name
def ulog_to_dict(log: pyul... | pd.Series(data=dxdt, index=series.index) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 25 14:18:15 2017
@author: 53771
"""
import pandas as pd
import tushare as ts
import fileInfo as fi
import numpy as np
def save_hist_data(code):
data=ts.get_k_data(code,start='2011-01-01')
data.to_csv("./stock/"+code+'.csv')
#data.index=pd.to_datetime(data.ind... | pd.to_datetime(df.index) | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on 2020-11-16
Code for Figure 6
Code runs multiple evolution run, each run is stored on disk independently
Use mlsFig_evolutionFitnessLandscape with same settings to create reference parameter space scans
@author: simonvanvliet
<EMAIL>
"""
import sys
sys.path.... | pd.DataFrame.from_records(outputMat) | pandas.DataFrame.from_records |
import pandas as pd
l = pd.DataFrame({
"id": [0, 1, 3, 4],
"A": ['a', 'b', 'c', 'd'],
"B": ['e', 'f', 'g', 'h'],
})
r = pd.DataFrame({
"id": [0, 1, 3, 4],
"B": ['e', 'f', 'z', 'h'],
"C": ['i', 'j', 'k', 'l'],
"d": ['m', 'n', 'o', 'p'],
})
# when merge on="id" Spalten werden sortiert, ohn... | pd.merge(l, r, on=["id",'B'], how='inner') | pandas.merge |
from .CoreClasses import *
from .InitializeFunctions import *
import numpy as np
import time
import re
import random
import os
import pickle
import sys
import copy
import math
### DEPRECIATED!!!!
def check_mass(original_mass, CRS, concentrations):
''' Checks conservation of mass
Arguem... | pd.read_csv(fname) | pandas.read_csv |
import json
import os
import pickle as pkl
from collections import Counter, defaultdict, OrderedDict
from copy import deepcopy
from itertools import product
from typing import (
Any,
Dict,
Iterable,
List,
Optional,
OrderedDict as OrderedDictType,
Union,
)
import numpy as np
import quaternio... | DataFrame(data=data) | pandas.DataFrame |
"""Classes for representing datasets of images and/or coordinates."""
import copy
import inspect
import json
import logging
import os.path as op
import numpy as np
import pandas as pd
from nilearn._utils import load_niimg
from .base import NiMAREBase
from .utils import (
_dict_to_coordinates,
_dict_to_df,
... | pd.concat(results[k]) | pandas.concat |
"""Covid Model"""
__docformat__ = "numpy"
import warnings
import pandas as pd
import numpy as np
global_cases_time_series = (
"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_"
"covid_19_time_series/time_series_covid19_confirmed_global.csv"
)
global_deaths_time_series... | pd.to_datetime(deaths.index) | pandas.to_datetime |
import pandas as pd
import os.path
import csv
import matplotlib.pyplot as plt
from modules.global_vars import *
class metabolite_pool(object):
def __init__(self, metabolite_name, number_of_carbons, pool_size):
self.metabolite_name = metabolite_name
self.number_of_carbons = number_of_carbon... | pd.concat([have_been_rotated, self.pool]) | pandas.concat |
""" This module provides the BaseScraper class """
# Standard library imports
from abc import ABCMeta, abstractmethod
from datetime import date
import logging
from pathlib import Path
import sys
from typing import Dict, Union, Optional
# Third party imports
import pandas as pd
from sqlalchemy.engine import Connection
... | pd.DataFrame() | pandas.DataFrame |
# from feature_generation.utils import convert_categorical_labels_to_numerical
from feature_generation.Labels import Labels
import pandas as pd
from itertools import takewhile
import time
from feature_generation.datasets.Timeseries import Timeseries
class EMIP(Timeseries):
def __init__(self):
super().__in... | pd.read_csv(f) | pandas.read_csv |
# Import Libraries
import time
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
# Import Libraries
from scipy import stats
import matplotlib.pyplot as plt
# import time
# Import Libraries
import math
class YinsDL:
print("... | pd.DataFrame(X[incidence]) | pandas.DataFrame |
##%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
## Soft sensing via XGBoost on UCI Wastewater Treatment Plant data
## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%% read data
import pandas as pd
data_raw = | pd.read_csv('water-treatment.data', header=None,na_values="?" ) | pandas.read_csv |
# coding: utf8
from .tsv_utils import complementary_list, add_demographics, baseline_df, chi2
from ..deep_learning.iotools import return_logger
from scipy.stats import ttest_ind
import shutil
import pandas as pd
from os import path
import numpy as np
import os
import logging
sex_dict = {'M': 0, 'F': 1}
def create_s... | pd.DataFrame() | pandas.DataFrame |
# -*- 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 numpy as np
import datetime
import math
import os
import o... | pd.np.abs(lat - latx) | pandas.np.abs |
from __future__ import print_function, division
from warnings import warn, filterwarnings
from matplotlib import rcParams
import matplotlib.pyplot as plt
from collections import OrderedDict
import random
import sys
import pandas as pd
import numpy as np
import h5py
import os
import pickle
from keras.models import Seq... | pd.DataFrame({mains.columns.values[0]: padding}) | pandas.DataFrame |
from pathlib import Path
import numpy as np
import pandas as pd
import torch
import cv2
import os
from PIL import Image
from .base_dataset import BaseDataset
from .constants import COL_PATH, COL_STUDY
class SUDataset(BaseDataset):
def __init__(self, data_dir,
transform_args, split, is_training... | pd.read_csv(data_dir / codalab_data_dir / csv_name) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 8 14:37:03 2019
@author: ppradeep
"""
import os
clear = lambda: os.system('cls')
clear()
## Import packages
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import pickle
# Classifiers
from sklearn.ensemble im... | pd.read_csv(path+'data/OPERA2.5_Pred.csv', index_col='MoleculeID') | pandas.read_csv |
from collections import abc, deque
from decimal import Decimal
from io import StringIO
from warnings import catch_warnings
import numpy as np
from numpy.random import randn
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
... | concat(frames) | pandas.concat |
import numpy as np
from .helpers import scale, scale_clean
#from numba import jit
import pandas as pd
defs = {
'r9.4': {
'ed_params': {
'window_lengths': [3, 6], 'thresholds': [1.4, 1.1],
'peak_height': 0.2
}
},
'r9': {
'ed_params': {
'window_len... | pd.Series(raw) | pandas.Series |
#!/usr/bin/env python
# coding: utf-8
# # Converting output files from "11c - Electric Futures Simulations BIFACIAL (PVSC) CLEANUP"
# ## into OpenEi format for the various graphs shown on the PVSC PVICE wiki page
# In[8]:
import PV_ICE
import numpy as np
import pandas as pd
import os,sys
from pathlib import Path
... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# *****************************************************************************/
# * Authors: <NAME>
# *****************************************************************************/
"""transformCSV.py
This module contains the basic functions for creating the content of... | pandas.StringDtype() | pandas.StringDtype |
import pandas as pd
import numpy as np
import math
import os
from scipy.interpolate import interp1d
import time
from sklearn.ensemble import RandomForestRegressor
import xgboost as xgb
from lightgbm import LGBMRegressor
from catboost import CatBoostRegressor
from information_measures import *
from joblib import Para... | pd.DataFrame([0],columns=['entropy']) | pandas.DataFrame |
import json
import pathlib
import altair as alt
import pandas as pd
import rbo
import streamlit as st
from tinydb import TinyDB, Query
DL = "https://github.com/The57thPick/nba/releases/download/{year}-media-awards/{year}.zip"
DB = TinyDB("db/db.json")
YEARS = [
2015,
2016,
2017,
2018,
2019,
... | pd.DataFrame(data, columns=["stat", "rank", "player", "year"]) | pandas.DataFrame |
"""Tests for the sdv.constraints.tabular module."""
import pandas as pd
from sdv.constraints.tabular import (
ColumnFormula, CustomConstraint, GreaterThan, UniqueCombinations)
def dummy_transform():
pass
def dummy_reverse_transform():
pass
def dummy_is_valid():
pass
class TestCustomConstraint()... | pd.testing.assert_series_equal(expected_out, out) | pandas.testing.assert_series_equal |
import pandas as pd
import numpy as np
import tensorflow as tf
import datetime
import pickle
import math
from create_features import Features
from binance import client
class Trader(client.Client):
"""
This class adds functionalities to perform trades in Binance.
It requires the api and secret key from b... | pd.DataFrame(candles) | pandas.DataFrame |
#!C:\Users\RIchardC\Documents\digitizePlots\venv\Scripts\python.exe
# Create Lyman/Fitz style long flat Design Files from plain-text onset files
# EKK / June 2015
# Python 2/3 compatibile, depends on Pandas and Numpy/Scipy
from __future__ import print_function
from pandas import concat, read_csv
from argparse import A... | read_csv(fid) | pandas.read_csv |
# *****************************************************************************
# © Copyright IBM Corp. 2018. All Rights Reserved.
#
# This program and the accompanying materials
# are made available under the terms of the Apache V2.0 license
# which accompanies this distribution, and is available at
# http://www.apac... | pd.api.types.is_numeric_dtype(df_copy[feature].dtype) | pandas.api.types.is_numeric_dtype |
# -*- coding: utf-8 -*-
"""
Reading data for WB, PRO,
for kennisimpulse project
to read data from province, water companies, and any other sources
Created on Sun Jul 26 21:55:57 2020
@author: <NAME>
"""
import pytest
import numpy as np
import pandas as pd
from pathlib import Path
import pickle as pckl
from hgc impor... | pd.ExcelWriter(r'C:\Users\beta6\Documents\Dropbox\008KWR\0081Projects\kennisimpulse/Opkomende stoffen KIWK Roerdalslenk_processed.xlsx') | pandas.ExcelWriter |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 4 15:00:57 2019
@author: <NAME>
Input file: list of quantification tables
Output files: quantification_stats_*.tsv, quantification_results_*.tsv (HTSeq option) or
Description: Used to merge quantification results from all samples
"""
import argp... | pd.read_csv(input_file,sep='\t',header=0) | pandas.read_csv |
'''
Created on 30.5.2017
@author: Markus.Walden
- https://developers.arcgis.com/authentication/accessing-arcgis-online-services/
'''
import requests
import pandas as pd
import numpy as np
def main():
return None
def getStockData():
# df = df.sample(n = 20) # , frac, replace, weights, ran... | pd.read_csv('./data/symbolLatLong.csv', sep = ';', encoding='latin-1', decimal=",", index_col = 'symbol') | pandas.read_csv |
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