prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#!/usr/bin/env python3
import pandas as pd
import sys
import json
with open("data/TALB_2018.geojson") as f:
geojson = json.load(f)
for i in range(len(geojson["features"])):
geojson["features"][i]["properties"]["cancer"] = {}
def find_talb(name):
if pd.isna(name):
return
for i,f in enumerate(geojson["feat... | pd.read_excel("misc/annual_counts_OUTPUT - Checked.xlsx", sheet_name="TALB", skiprows=10, nrows=88, names = keys) | pandas.read_excel |
import argparse
import collections
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import re
import scipy.cluster
import scipy.spatial.distance
import sklearn.cluster
import sklearn.feature_extraction
import sklearn.manifold
import sklearn.metrics.pairwise
if True:
p = argparse.ArgumentParser(... | pd.concat((tb, feature_tb), axis=1) | pandas.concat |
import os
import pandas as pd
import numpy as np
from itertools import chain
from codifyComplexes.CodifyComplexException import CodifyComplexException
from .DataLoaderClass import DataLoader
#TODO: REWRITE PROTOCOLs CLASSES TO REDUCE COUPLING. E.G. Pairwise agregation should be generic for both seq and struct, just ... | pd.DataFrame(aggregatedResults) | pandas.DataFrame |
#!/usr/bin/env python
from __future__ import division, print_function
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from numpy import log
import pandas as pd
import seaborn as sns
from itertools import groupby
def load_result(fn, label):
'''fn is a file name, label i... | pd.concat([err1, err2, err3]) | pandas.concat |
import nose
import unittest
import os
import sys
import warnings
from datetime import datetime
import numpy as np
from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range,
date_range, Index)
from pandas.io.pytables import HDFStore, get_store, Term, IncompatibilityWarning
import pandas... | tm.assert_panel_equal(expected, result) | pandas.util.testing.assert_panel_equal |
# Author: <NAME> (http://falexwolf.de)
# T. Callies
"""Rank genes according to differential expression.
"""
import numpy as np
import pandas as pd
from math import sqrt, floor
from scipy.sparse import issparse
from .. import utils
from .. import settings
from .. import logging as logg
from ..preprocessing imp... | pd.DataFrame(data=X[mask, left:right]) | pandas.DataFrame |
from json import load
from matplotlib.pyplot import title
from database.database import DbClient
from discord import Embed
import pandas as pd
from util.data import load_data
class Analytics:
def __init__(self, server_id: str, db):
self.server_id = server_id
self.db = db
@staticmethod
de... | pd.DataFrame(data) | pandas.DataFrame |
""" Construct dataset """
import math
import pandas as pd
import numpy as np
import keras
import csv
def one_hot_encode_object_array(arr, nr_classes):
'''One hot encode a numpy array of objects (e.g. strings)'''
_, ids = np.unique(arr, return_inverse=True)
return keras.utils.to_categorical(ids, nr_classes... | pd.to_datetime("2011-08-01 00:00:00") | pandas.to_datetime |
import argparse
import pandas as pd
from shapely import geometry
import helpers
import json
from polygon_geohasher import polygon_geohasher
from tqdm import tqdm
CELL_SIZE_X = 360.0 / 4320.0
CELL_SIZE_Y = 180.0 / 2160.0
def parse_args():
parser = argparse.ArgumentParser(
description="Converts SPAM2017 cr... | pd.DataFrame(flattened_gh, columns=["cell", "geohash", "bounds"]) | pandas.DataFrame |
import sys
from pathlib import Path
from itertools import chain
from typing import List
import numpy as np
import pandas as pd
import pandas_flavor as pf
from janitor import clean_names
sys.path.append(str(Path.cwd()))
from config import root_dir # noqa E402
from utils import ( # noqa: E402
get_module_purpose,
... | pd.isna(missing_projection_df["projected_off_pts"]) | pandas.isna |
#! python3
import random
import math
import pandas as pd
from pandas import DataFrame as df
from anytree import Node, RenderTree, NodeMixin, AsciiStyle
from anytree.exporter import DotExporter, JsonExporter
from anytree.importer import JsonImporter
import os
import copy
import time
import json
import queue
import csv
i... | df(data=all_team_data) | pandas.DataFrame |
"""ADDS FUNCTIONALITY TO APPLY FUNCTION ON PANDAS OBJECTS IN PARALLEL
This script add functionality to Pandas so that you can do parallel processing in multiple cores when you use apply method on
dataframes, series or groupby objects.
This file must be imported as a module and it attached following functions to pan... | pd.DataFrame([idx for idx, df in self], columns=self.keys) | pandas.DataFrame |
from collections import OrderedDict
import contextlib
from datetime import datetime, time
from functools import partial
import os
from urllib.error import URLError
import warnings
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, Index, Multi... | pd.ExcelFile("test5" + read_ext) | pandas.ExcelFile |
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 3 12:51:57 2021
@author: Administrator
"""
import pandas as pd
import numpy as np
from pandas import DataFrame, Series
def apply(decorator):
def decorate(cls):
for attr in cls.__dict__:
if callable(getattr(cls, attr)):
... | pd.concat([ohlc["close"], ohlc["volume"], mf], axis=1) | pandas.concat |
import os
import pandas as pd
from scripts.common.configuration import Configuration
from scripts.common.db import DataBase
from scripts.common import periods as taxes_periods
def generate_report_periods():
configuration = Configuration()
db = DataBase(configuration.get_db_directory())
expenses = db.retr... | pd.to_datetime(d[0]) | pandas.to_datetime |
# -*- coding: utf-8 -*-
from datetime import timedelta
import operator
from string import ascii_lowercase
import warnings
import numpy as np
import pytest
from pandas.compat import lrange
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
Categorical, DataFrame, MultiIndex, Serie... | pd.Timedelta('5 hours') | pandas.Timedelta |
import glob
import math
import os
import sys
import warnings
from decimal import Decimal
import numpy as np
import pandas as pd
import pytest
from packaging.version import parse as parse_version
import dask
import dask.dataframe as dd
import dask.multiprocessing
from dask.blockwise import Blockwise, optimize_blockwis... | pd.array([1, None, 2], dtype="Int64") | pandas.array |
#
# Copyright (c) 2021 Airbyte, Inc., all rights reserved.
#
import bz2
import copy
import gzip
import os
import shutil
from pathlib import Path
from typing import Any, List, Mapping
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from source_s3.source_files_abstract.formats.parque... | pd.DataFrame(data) | pandas.DataFrame |
import collections
import itertools
import multiprocessing
import os
import random
import re
import signal
import sys
import threading
import time
import traceback
import click
import numpy
import pandas as pd
from tqdm import tqdm
from estee.common import imode
from estee.schedulers import WorkStealingScheduler
from... | pd.DataFrame([], columns=COLUMNS) | pandas.DataFrame |
import pandas as pd
import numpy as np
import re
import openpyxl
from openpyxl import load_workbook
class DataFrameFeature():
_NaN = "NaN" # represent the NaN value in df
# filter column values by remove string behind "sep", and r-strip space
@staticmethod
def filter_column_value(df, *, column_name,... | pd.isna(df.iloc[i, n_col]) | pandas.isna |
# Load the necessary libraries
# Set the seed to 123
import pandas as pd
import numpy as np
# load the dataset into the memory
data = pd.read_csv('Logistic_regression.csv')
# Pre-processing steps
'''You may need to clean the variables, impute the missing values and convert the categorical variables to one-hot encod... | pd.DataFrame(data=s_data_x,columns=columns) | pandas.DataFrame |
import numpy as np
import pytest
from pandas._libs.tslibs.period import IncompatibleFrequency
from pandas.core.dtypes.dtypes import PeriodDtype
import pandas as pd
from pandas import Index, Period, PeriodIndex, Series, date_range, offsets, period_range
import pandas.core.indexes.period as period
import pandas.util.t... | tm.assert_series_equal(s, exp) | pandas.util.testing.assert_series_equal |
import collections
import copy
import ixmp
import itertools
import os
import warnings
import pandas as pd
import numpy as np
from message_ix import default_paths
from ixmp.utils import pd_read, pd_write
from message_ix.utils import isscalar, logger
DEFAULT_SOLVE_OPTIONS = {
'advind': 0,
'lpmethod': 2,
'... | pd.Series(df) | pandas.Series |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import datetime, timedelta
import numpy as np
import pytest
from pandas.errors import (
NullFrequencyError, OutOfBoundsDatetime, PerformanceWarning)
import pandas as pd
from pandas import (
DataFrame, ... | Timedelta('1s') | pandas.Timedelta |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import stats
from sklearn.linear_model import Ridge, RidgeCV
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.metrics import mean_sq... | pd.read_csv(path + 'bases_ale/anos_iniciais/ideb_municipios_2017_ai.csv') | pandas.read_csv |
import urllib
import pytest
import pandas as pd
from pandas import testing as pdt
from anonympy import __version__
from anonympy.pandas import dfAnonymizer
from anonympy.pandas.utils_pandas import load_dataset
@pytest.fixture(scope="module")
def anonym_small():
df = load_dataset('small')
anonym = dfAnonymize... | pdt.assert_frame_equal(expected, output) | pandas.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# formats: ipynb,py
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.6.0
# kernelspec:
# display_name: deep_ml_curriculum
# language: python
# name: deep_ml_curr... | pd.Index(perf.horizon.dt.days, name='days') | pandas.Index |
#!/usr/bin/env python3
import os
import json
import h5py
import argparse
import pandas as pd
import numpy as np
import tinydb as db
from tinydb.storages import MemoryStorage
from pprint import pprint
import matplotlib.pyplot as plt
plt.style.use('../clint.mpl')
from matplotlib.colors import LogNorm
from pygama import ... | pd.to_datetime(u_start, unit='s') | pandas.to_datetime |
import time
import datetime
import numpy as np
import pandas as pd
import random
import re
from sklearn.pipeline import Pipeline
from sklearn import grid_search
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import FeatureUnion
from sklearn.decomposition import TruncatedSVD
from sklearn... | pd.read_csv('input/test.csv', encoding="ISO-8859-1") | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 17 19:51:21 2018
@author: Bob
"""
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import DBSCAN
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer
from nltk.corpus import stopwords
from sqlalchemy import create_engine
from c... | pd.read_sql(query, conn, index_col='route_id') | pandas.read_sql |
# -*- coding: utf-8 -*-
#
import logging
logger = logging.getLogger(__name__)
import sys, os, time
from datetime import datetime
from timeit import default_timer as timer
try:
from humanfriendly import format_timespan
except ImportError:
def format_timespan(seconds):
return "{:.2f} seconds".format(seco... | pd.DataFrame(self.id_list, columns=['ID'], dtype=str) | pandas.DataFrame |
import pandas as pd
import pickle
import json
import seaborn as sns
import pprint
import numpy as np
import math
def get_builds_from_commits(_commits):
_build_ids = jobs[jobs.commitsha.isin(_commits)].buildid
return builds[(builds.id.isin(_build_ids))]
def get_builds_from_ids(_builds, _build_ids):
return ... | pd.read_csv(f"{csv_folder}/allJobs.csv", index_col=0) | pandas.read_csv |
import sys
import time
import numpy as np
import pandas as pd
from scipy.special import softmax
train_path = sys.argv[1]
test_path = sys.argv[2]
def f(pred,Y_train):
v = np.log(np.sum(Y_train*pred,axis=1))
#print(np.sum(v))
return abs(np.sum(v)/Y_train.shape[0])
def read_and_encode(train_path,test_path):... | pd.get_dummies(data, columns=cols, drop_first=True) | pandas.get_dummies |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Copyright 2014-2019 OpenEEmeter contributors
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/LIC... | pd.Timestamp("2016-01-03") | pandas.Timestamp |
import streamlit as st
import altair as alt
from os import listdir
from os.path import isfile, join
from pydantic import BaseModel
import boto3
import json
import time
import pandas as pd
import numpy as np
import yfinance as yf
import datetime as dt
import plotly.graph_objects as go
from plotly.subplots import make_su... | pd.DataFrame.from_dict(fundInfo, orient='index') | pandas.DataFrame.from_dict |
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 2 17:13:01 2017
@author: kcarnold
"""
import hashlib
import random
import pickle
import numpy as np
import pandas as pd
#%%
#data_file = 'data/analysis_study4_2017-04-02T17:14:44.194603.pkl'
#data_file = 'data/analysis_study4_2017-04-02T20:37:11.374099.pkl'
#data_file = ... | pd.DataFrame(conditions, columns=['author_id', 'cond_A', 'cond_B', 'author_conds']) | pandas.DataFrame |
import requests
from model.parsers import model as m
import pandas as pd
import datetime
dataset = m.initialize()
unique_dates = list()
raw_data = requests.get('https://api.covid19india.org/states_daily.json')
raw_json = raw_data.json()
for item in raw_json['states_daily']:
if item['date'] not in unique_dates:
... | pd.DataFrame(data) | pandas.DataFrame |
import os
import pytest
import pandas as pd
import numpy as np
from collections import OrderedDict
from ..catalog_matching import (crossmatch,
select_min_dist,
post_k2_clean,
find_campaigns,
... | pd.read_csv('catalog_matching/tests/exfiles/select_min_dist_union.csv') | pandas.read_csv |
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
import numpy as np
def compareCounts(fileList, column):
df = pd.DataFrame()
for i in fileList:
path =Path(i)
name = path.stem
src = gpd.read_file(i)
#print(... | pd.melt(df, var_name='Type', value_name='Accuracy') | pandas.melt |
import streamlit as st
import pandas as pd
import joblib
from PIL import Image
model = open("Knn_Classifier.pkl","rb")
model = joblib.load(model)
st.title("Iris flower species Classification App")
setosa= Image.open("setosa.jpg")
versicolor= Image.open('versiclor.jpg')
virginica = Image.open('virginia.jpg')
virgini... | pd.DataFrame([parameter_input_values],columns=parameter_list,dtype=float) | pandas.DataFrame |
from __future__ import annotations
from collections.abc import MutableMapping
from typing import (
Any,
Callable,
ItemsView,
Iterable,
Iterator,
KeysView,
List,
Mapping,
Optional,
Protocol,
Sequence,
Tuple,
TypeVar,
Union,
ValuesView,
)
import numpy as np
fr... | MultiIndex.from_product([nmajor, nminor]) | pandas.MultiIndex.from_product |
import pandas as pd
from flask import Flask, redirect, request, url_for,render_template, Response, jsonify
from application import app
import requests
import hashlib
import json
@app.route('/')
@app.route('/home')
def home():
return render_template('homepage.html')+('<br><br> <a href="/signup_home" type="button"... | pd.DataFrame.from_dict(creds,orient='index') | pandas.DataFrame.from_dict |
import numpy as np
import pandas as pd
import utils
class Indicators:
def __init__(self, stock, start_date, end_date):
self.stock = stock
self.start_date = start_date
self.end_date = end_date
self.data = utils.read_stock_data(stock)
def calculate_all_indicators(self):
i... | pd.DataFrame(trix.values, columns=['TRIX']) | pandas.DataFrame |
import pandas as pd
import numpy as np
# Lendo do data frame
df = pd.read_csv("https://pycourse.s3.amazonaws.com/bike-sharing.csv")
print(df.head())
print('\n************************************************************************************\n')
print(df.info())
print('\n********************************************... | pd.to_datetime(df['datetime']) | pandas.to_datetime |
import os
import shutil
import zipfile
import torch
import torch.utils.data
from dataset import *
import pickle
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import matplotlib.ticker as tck
import numpy as np
import csv
from collections import Counter, defaultdict
import pandas as pd
from utils im... | pd.read_csv(dirname + 'results/' + drug + '_model_selection.csv') | pandas.read_csv |
#!/usr/bin/env/python
# -*- coding: utf-8 -*-
"""
This script defines some useful functions to use in data analysis and visualization
@ <NAME> (<EMAIL>)
"""
def dl_ia_utils_change_directory(path):
"""
path ='path/to/app/'
"""
import os
new_path = os.path.dirname(os.path.dirname(__file__))... | pd.set_option('display.max_columns', 10) | pandas.set_option |
#coding:utf-8
from scipy import stats
import numpy as np
from pandas import Series,DataFrame
from openpyxl import load_workbook
import math
import uuid
class AnovaTestFile:
def __init__(self, data_file):
self.data_file = data_file
self.wb = load_workbook(data_file)
self.sheetnames = [s for ... | DataFrame(data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
This script performs reverse geocoding for post coordinates, fetching the name
of the administrative region to which the post is geotagged.
Usage:
Execute the script from the command line using the following command:
python3 reverse_geocode.py -i input.pkl -o output.pkl
Arguments... | pd.read_pickle(args['input']) | pandas.read_pickle |
from itertools import product
import pandas as pd
from pandas.testing import assert_series_equal, assert_frame_equal
import pytest
from solarforecastarbiter.validation import quality_mapping
def test_ok_user_flagged():
assert quality_mapping.DESCRIPTION_MASK_MAPPING['OK'] == 0
assert quality_mapping.DESCR... | pd.Series([2, 3, 35]) | pandas.Series |
from datetime import datetime, timedelta
from importlib import reload
import string
import sys
import numpy as np
import pytest
from pandas._libs.tslibs import iNaT
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Index,
Series,
... | Series(s_data, name=name, dtype=exp_dtype) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 14 22:43:13 2016
@author: zhouyu
for kaggle challenge - allstate
"""
import pandas as pd
import numpy as np
import seaborn as sns
dataset = | pd.read_csv('/Users/zhouyu/Documents/Zhou_Yu/DS/kaggle_challenge/train.csv') | pandas.read_csv |
#varianta cu pachetul CSV
import csv
import pandas as pd
with open('test.csv', 'r') as f:
r = csv.reader(f, delimiter=',')
for row in r: #loop
for i in range(0, len(row)):
if len(row) == 19: #vreau toate randurile de pe toate coloanele - 19 coloane
print(row[i]+ ",")
# varian... | pd.read_csv('test.csv') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jul 18 13:15:21 2020
@author: jm
"""
#%% required libraries
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
#%% read data
#df_original = pd.read_csv('https://www.gstatic.com/covid19/mobility/Global_Mobility_Report... | pd.Timestamp('2020-05-23') | pandas.Timestamp |
"""
Functions for loading models and generating predictions:
- `load_model` downloads and returns a Simple Transformers model from HuggingFace.
- `predict_domains` generates a multi-label which indicates which of the 9 ICF domains are discussed in a given sentence; the order is ['ADM', 'ATT', 'BER', 'ENR', 'ETN', 'FA... | pd.Series() | pandas.Series |
import json
import warnings
from collections import Counter, defaultdict
from glob import glob
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import pandas as pd
import seaborn as sns
from scipy.optimize import minimize
from scipy.stats import norm
from tqdm import tqdm
plt.style.use('fivet... | pd.DataFrame(te, columns=self.assets, index=self.assets) | pandas.DataFrame |
import argparse
import datetime
import glob
import os
import re
from tqdm import tqdm
import pandas as pd
from textwrap import dedent
def combineDelayFiles(outName, loc=os.getcwd(), ext='.csv'):
files = glob.glob(os.path.join(loc, '*' + ext))
print('Ensuring that "Datetime" column exists in files')
a... | pd.read_csv(filename, parse_dates=['Datetime']) | pandas.read_csv |
#!/usr/bin/env python
from settings import settings
import numpy as np
import pandas as pd
import os
import rospy # ros library for publishing and subscribing
from std_msgs.msg import Int16MultiArray # ros library for string type of msgs
def detection(K, fps, v, X... | pd.DataFrame(data=my_data) | pandas.DataFrame |
#!/usr/bin/python3
import os
import pickle
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV, ShuffleSplit
from... | pd.Series(y_validation) | pandas.Series |
#!/usr/bin/env python3
# ----------------------------------------------------------------------------
# Copyright (c) 2018--, Qurro development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE.txt, distributed with this software.
# --------------------------... | pd.isna(fd) | pandas.isna |
import pandas as pd
import statsmodels.api as sm
import numpy as np
from pathlib import Path
outdir = Path('data')
def download_rivm_r():
df_rivm = | pd.read_json('https://data.rivm.nl/covid-19/COVID-19_reproductiegetal.json') | pandas.read_json |
import ipywidgets as widgets
# import bql
# import bqviz as bqv
from bqplot import Figure, Pie, pyplot as plt
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from components.efficient_frontier import EfficientFrontier
# bq = bql.Service()
class ETFViewer:
def __init__(self, etf_f... | pd.concat([df, _df]) | pandas.concat |
from __future__ import absolute_import, division, print_function
import pytest
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas import DataFrame, Series
from string import ascii_lowercase
from blaze.compute.core import compute
from blaze ... | DataFrame([[3, 350]], columns=['count', 'sum']) | pandas.DataFrame |
"""
Classes for analyzing RSMTool predictions, metrics, etc.
:author: <NAME> (<EMAIL>)
:author: <NAME> (<EMAIL>)
:author: <NAME> (<EMAIL>)
:organization: ETS
"""
import warnings
from functools import partial
import numpy as np
import pandas as pd
from scipy.stats import kurtosis, pearsonr
from sklearn.decomposition... | pd.merge(df_test, df_test_metadata, on='spkitemid') | pandas.merge |
# -*- coding: utf-8 -*-
import re
import os
import shutil
import pandas as pd
import zipfile
from Classifylib import Extraction
from chardet.universaldetector import UniversalDetector
from io import StringIO
import configparser
import tarfile
import subprocess
###########################################... | pd.DataFrame(ToData) | pandas.DataFrame |
import json
from logging import getLogger
import numpy as np
import pandas as pd
import pytest
from whylogs.app.config import load_config
from whylogs.app.session import session_from_config
from whylogs.core.statistics.constraints import (
MAX_SET_DISPLAY_MESSAGE_LENGTH,
DatasetConstraints,
MultiColumnVal... | pd.DataFrame({"col1": [4, 5, 6, 7], "col2": [0, 1, 2, 3]}) | pandas.DataFrame |
import os
from warnings import warn
import networkx as nx
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from recipe_similarities.u... | pd.DataFrame(jaccard_sim, index=df.index, columns=df.index) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pre_deal,model
from mxnet import autograd
from mxnet import gluon
from mxnet import image
from mxnet import init
from mxnet import nd
from mxnet.gluon.data import vision
import numpy as np
from mxnet.gluon import nn
from matplotlib import pyplot as plt
from utils import Visualizer
tr... | pd.DataFrame({'id': sorted_ids, 'label': preds}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import string
from collections import OrderedDict
from datetime import date, datetime
import numpy as np
import pandas as pd
import pandas.util.testing as pdt
import pytest
from kartothek.core.common_metadata import make_meta, store_schema_metadata
from kartothek.core.index import ExplicitSe... | pd.DataFrame({"test": [7, 8, 9]}) | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sys
from sklearn.metrics import mean_squared_error
from math import sqrt
from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt
# 1. 抽取2012年8月至2013年12月的数据，总共14个月
# Index 11856 marks the end of year 2013
df = pd.r... | pd.to_datetime(test.Datetime,format='%d-%m-%Y %H:%M') | pandas.to_datetime |
from pathlib import Path
from pandas.core.frame import DataFrame
import pytest
import pandas as pd
import datetime
from data_check import DataCheck # noqa E402
from data_check.config import DataCheckConfig # noqa E402
# These tests should work on any database.
# The tests are generic, but in integration... | pd.isna(data_types_check.null_test) | pandas.isna |
import pandas as pd
from calendar import monthrange
from datetime import date, datetime
from argparse import ArgumentParser
PROJECT_TASK_CSVFILE = "project_task.csv"
def main():
parser = ArgumentParser()
parser.add_argument('year', type=int, help='year')
parser.add_argument('month', type=int, help='month'... | pd.read_csv(PROJECT_TASK_CSVFILE) | pandas.read_csv |
# 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... | tm.assert_dict_equal(result, ts, compare_keys=False) | pandas.util.testing.assert_dict_equal |
import csv
import re
import string
import math
import warnings
import pandas as pd
import numpy as np
import ipywidgets as wg
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.ticker as mtick
from itertools import product
from scipy.optimize import curve_fit
from plate_mapping imp... | pd.concat([r_df2, i_df2, ab_df2], axis=1) | pandas.concat |
from argparse import Namespace
import pandas
from pandas import DataFrame, Series
from ssl_metrics_git_bus_factor.args import mainArgs
def buildBusFactor(df: DataFrame) -> DataFrame:
daysSince0: Series = df["author_days_since_0"].unique()
data: list = []
day: int
for day in range(daysSince0.max() +... | DataFrame(data) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Retrieve bikeshare stations metadata."""
# pylint: disable=invalid-name
from typing import Dict, List
import pandas as pd
import pandera as pa
import requests
stations_schema = pa.DataFrameSchema(
columns={
"station_id": pa.Column(pa.Int),
"na... | pd.StringDtype() | pandas.StringDtype |
import requests
import pandas
import io
import logging
from scipy import stats
import plotnine
plotnine.options.figure_size = (12, 8)
from plotnine import *
from mizani.breaks import date_breaks
from mizani.formatters import date_format
# Setting up a logger
logger = logging.getLogger('non_regression_tests')
logger.se... | pandas.to_datetime(df['start_time'], unit='s') | pandas.to_datetime |
import datetime as dt
import json
import os
import pandas as pd
from loguru import logger
class Analysis:
CLASS_CONFIG = {
'AMO_CITY_FIELD_ID': 512318,
'DRUPAL_UTM_FIELD_ID': 632884,
'TILDA_UTM_SOURCE_FIELD_ID': 648158,
'TILDA_UTM_MEDIUM_FIELD_ID': 648160,
'TILDA_UTM_CAMPA... | pd.DataFrame(self.transform_data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pandas as pd
import glob
import numpy as np
import matplotlib.pyplot as plt
import os
'''
This function filters out all the rows for which the label column does not match a given value (i.e GetDistribution).
And saves "elapsed" value for rows that do match the specified label text(trans... | pd.read_csv(file) | pandas.read_csv |
# Copyright 2020 Google 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | testing.assert_frame_equal(output, expected) | pandas.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
import numpy as np
from .utils import fast_OLS, fast_optimize, bootstrap_sampler, eval_expression, bias_corrected_ci, z_score, \
percentile_ci
import scipy.stats as stats
from numpy.linalg import inv, LinAlgError
from numpy import dot
from itertools import product, combinations
import pandas... | pd.DataFrame(rows, columns=columns, index=[""] * rows.shape[0]) | pandas.DataFrame |
# -------------------------------------------------- ML 02/10/2019 ----------------------------------------------------#
#
# This is the class for poisson process
#
# -------------------------------------------------------------------------------------------------------------------- #
import numpy as np
import pandas ... | pd.DataFrame() | pandas.DataFrame |
from x2df.fileIOhandlers.__fileIOhandler__ import FileIOhandler
from pandas import DataFrame, read_csv
import json
import pyqtgraph as pg
from pyqtgraph.Qt import QtCore, QtWidgets
import io
import inspect
# we want to do the imports as late as possible to
# keep it snappy once we have more and more fileIOhandlers
du... | DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
from scipy import interpolate
import numpy as np
from numpy.lib.recfunctions import append_fields
import scipy.signal as sig
import scipy.stats as st
import time, os
import pandas as pd
import math
#import report_ctd
import ctdcal.report_ctd as report_ctd
import warnings
import ctdcal.fit_ctd as f... | pd.DataFrame() | pandas.DataFrame |
from io import BytesIO
import pytest
import pandas.util._test_decorators as td
import pandas as pd
import pandas._testing as tm
def test_compression_roundtrip(compression):
df = pd.DataFrame(
[[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
index=["A", "B"],
columns=["X... | tm.ensure_clean() | pandas._testing.ensure_clean |
from datetime import datetime
from pathlib import Path
import numpy as np
import pandas as pd
from tqdm import tqdm
from pyconsolida.budget_reader import read_full_budget
from pyconsolida.postdoc_fix_utils import (
check_consistency_of_matches,
fix_tipologie_df,
isinlist,
)
DIRECTORY = Path(r"C:\Users\lp... | pd.read_excel(DIRECTORY / "categorie_map.xlsx") | pandas.read_excel |
# -*- coding: utf-8 -*-
# This file is part of CbM (https://github.com/ec-jrc/cbm).
# Author : <NAME>
# Credits : GTCAP Team
# Copyright : 2021 European Commission, Joint Research Centre
# License : 3-Clause BSD
import requests
import pandas as pd
import datetime
import numpy as np
import os
from ma... | pd.set_option('precision', 3) | pandas.set_option |
import numpy as np
from scipy.stats import poisson
#lr1,lr2 = [int(x) for x in input().strip().split()]
#lrr1,lrr2 = [int(x) for x in input().strip().split()]
#reward = [10,-2]
gamma = 0.9
V = np.zeros([20+1,20+1])
pie = np.zeros([20+1,20+1])
class samples:
def __init__(self,l1,l2,ep = 0.01):
... | pandas.DataFrame() | pandas.DataFrame |
import json, os, sys
from pprint import pprint as print
from datetime import datetime
from datetime import date, timedelta
from collections import Counter
from collections import OrderedDict
import openpyxl
from openpyxl.worksheet.dimensions import ColumnDimension, DimensionHolder
from openpyxl.utils import get_column... | pd.DataFrame(UNANSWERED_CHATS) | pandas.DataFrame |
# 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_series_equal(result, expected) | pandas.testing.assert_series_equal |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 15 12:49:10 2017
@author: fubao
"""
#main function for creation graph data
import os
import numpy as np
import pandas as pd
from blist import blist
from readCityState import readcitySatesExecute
from extractweatherData import readUSAStationIdToNam... | pd.DataFrame.from_dict(graphCreationClass.graphNodeNameToIdMap, orient='index') | pandas.DataFrame.from_dict |
import pandas as pd
import logging
import os
from collections import defaultdict
from annotation.utility import Utility
_logger = logging.getLogger(__name__)
TYPE_MAP_DICT = {"string": "String", "number": "Quantity", "year": "Time", "month": "Time", "day": "Time",
"date": "Time", "entity": 'WikibaseIt... | pd.DataFrame(columns=['Attribute', 'Property', 'label', 'description']) | pandas.DataFrame |
import numpy as np
import pandas as pd
from src.create_initial_states.make_educ_group_columns import (
_create_group_id_for_non_participants,
)
from src.create_initial_states.make_educ_group_columns import (
_create_group_id_for_one_strict_assort_by_group,
)
from src.create_initial_states.make_educ_group_colum... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 16 16:43:25 2018
@author: nce3xin
"""
from scipy.io import arff
import pandas as pd
# .xlsx data file path
root="../data/"
origin_pt=root+"origin.xlsx"
train_pt=root+"train.xlsx"
test_pt=root+"test.xlsx"
# .arff data file path
train_arff_pt="../data/train.arff"
test_ar... | pd.read_excel(origin_pt,sheetname=0) | pandas.read_excel |
# -*- coding: utf-8 -*-
import fitz
import logging
import os
import pandas as pd
class PdfMerge:
def __init__(self):
formatter = logging.Formatter('%(asctime)s [%(threadName)s] %(levelname)s: %(message)s')
sh = logging.StreamHandler()
sh.setFormatter(formatter)
sh.setLevel(logging... | pd.read_csv(file_dir_path, encoding=encoding) | pandas.read_csv |
""" Creates the index of files from the specific parser objects """
from pathlib import Path
from collections import Counter
from functools import wraps
import datetime
from typing import Tuple, List, Dict, Union, Collection
# import re
# import copy
import logging
logger = logging.getLogger(__name__)
logger.setLe... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import scipy
from sklearn import metrics
from FPMax import FPMax
from Apriori import Apriori
from MASPC import MASPC
import csv
from scipy.cluster.hierarchy import fcluster
from scipy.cluster.hierarchy import linkage
from optbinning import ContinuousOptimalBinning
# pd.set_option... | pd.get_dummies(self.rtDataFrame['sex']) | pandas.get_dummies |
import pandas as pd
import numpy as np
from scipy import integrate, stats
from numpy import absolute, mean
from itertools import islice
import statsmodels.api as sm
from statsmodels.formula.api import ols
import statsmodels.stats.multicomp
import seaborn as sns
import matplotlib.pyplot as plt
headers = [
'parti... | pd.DataFrame(data=t_data, index=t_rows) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import subprocess
import json
import os
import io
from multiprocessing import Pool
import multiprocessing
import multiprocessing.pool
from operator import itemgetter
import random
import string
import pickle
import copy
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import co... | pd.read_table(coordinates_file, index_col=False) | pandas.read_table |
"""
.. 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([['projectd', tc]], columns=['projectd', 'bus factor']) | pandas.DataFrame |
from tl4sm.prepare_data import split_dataset
from numpy import array, stack
from pandas import read_csv, DataFrame
from pathlib import Path
from keras.models import load_model, clone_model
import time
from matplotlib import pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, LSTM, BatchNor... | read_csv(resFile, header=0) | pandas.read_csv |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.