prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import quantipy as qp
# from matplotlib import pyplot as plt
# import matplotlib.image as mpimg
import string
import pickle
import warnings
try:
import seaborn as sns
from PIL import Image
except:
pass
from quantipy.core.cache import Cac... | pd.DataFrame(self.valdiffs, index=self.ypairs, columns=self.xdef) | pandas.DataFrame |
import folium
import pandas as pd
#helper method to setup any input dataframe into dictionaries that can be input into OSR isochrone methods and folium maps
def dictSetup(dataframe):
station_dict = dataframe.to_dict(orient='index')
for name, station in station_dict.items():
station['locations'] = [stati... | pd.DataFrame.from_dict(maps[i][1]) | pandas.DataFrame.from_dict |
#! /usr/bin/python
import datetime
import json
import os
import pandas
import urllib.request
import time
#define constants
workingDir = os.getcwd()
print(workingDir)
stationID ='114'
yesterdayDate = (datetime.date.today() - datetime.timedelta(1))
todayDate = datetime.date.today()
yesterdayYear = yesterdayDate.year
ur... | pandas.to_datetime(weatherData["Date"]) | pandas.to_datetime |
from collections import Counter
from os import getenv
from pathlib import Path
from matplotlib.pyplot import savefig
from pandas import DataFrame
from . import database
from ..crawler.models import Article
current_path = Path(__file__).parent.resolve()
def test_rank():
# The test is not suitable for CI
if ... | DataFrame(common) | pandas.DataFrame |
# Implementation of random stuff
import json
import torch
import pandas as pd
import pickle
from torch_geometric.data import Data
from pathlib import Path
from itertools import repeat
from collections import OrderedDict
class MetricTracker:
"""
Class implementation for tracking all the metrics.
"""
de... | pd.DataFrame(index=keys, columns=['total', 'counts', 'average']) | pandas.DataFrame |
import xgboost as xgb
from sklearn.impute import SimpleImputer
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error
from src.utils.io import load, save
from src.visualization.visualize import *
def get_X_y(data):
... | pd.DataFrame(data=values, columns=['feature_labels', 'feature_importance']) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 16 17:37:51 2020
@author: sawleen
"""
import pandas as pd
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
import os
os.chdir('/Users/sawleen/Documents/Leen/Python/stock_analysis')
import data.get_yf_data as get_y... | pd.concat([saved_health_metrics, health_metric_symbol]) | pandas.concat |
import pandas as pd
import math
from numpy import nanmin,nanmax
#maximum number of records in a parquet file (except the index file)
max_rows = 500000
states = ["ACT", "NSW", "NT", "OT", "QLD", "SA", "TAS", "VIC", "WA"]
#states = ["ACT", "WA"]
#initiate the index file
index_file = | pd.DataFrame(columns=['IDX','STREET_NAME','STREET_TYPE_CODE','LOCALITY_NAME','STATE','POSTCODE','FILE_NAME','ADDRESS_COUNT','MIN_STREET_NUMBER','MAX_STREET_NUMBER']) | pandas.DataFrame |
import pydoc
import pandas as pd
import os
import random
def read_excel():
df = pd.read_excel('/Users/ls/Downloads/babycare11-1.xlsx')
data = df.head(2)
print(str(data))
# print(df.head(2))
def merge_excel():
dfs = []
dir = '/Users/ls/babycare/'
des = '/Users/ls/babycare/babycare-stats-... | pd.concat(dfs) | pandas.concat |
import streamlit as st
import datetime
import pytz
from datetime import date
from utils.metrics import log_runtime
import pandas as pd
import timeit
short_title = "iterrows() and itertuples()"
long_title = "iterrows() and itertuples()"
key = 6
content_date = datetime.datetime(2021, 10, 5).astimezone(pytz.timezone("US/... | pd.to_datetime(df['date'], format='%Y-%m-%d') | pandas.to_datetime |
import pandas as pd
from output.helpers import *
from datetime import datetime
import emoji
import re
import string
import nltk
from nltk import ngrams, FreqDist
from nltk.sentiment import SentimentIntensityAnalyzer
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.feature_select... | pd.read_csv(input_path) | pandas.read_csv |
# general imports
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
import cv2
import os
from PIL import Image
from pprint import pprint
import time
from tqdm import tqdm
# torch and torchvision
import torch
import tor... | pd.read_csv(cfg['train_csv_path']) | pandas.read_csv |
"""
This file is for playing around with song data from the MSD data set.
In particular, we are interesting in getting all of the data out in
an exportable manner.
We can't get all of the information from the summary file, we have to
open all files and extract the data to do this.
"""
import os
import pandas as pd
im... | pd.read_csv(training_file) | pandas.read_csv |
import json
import os
import matplotlib
from matplotlib import pyplot as plt
from matplotlib.gridspec import GridSpec
from numpy import array as nparray
import countryinfo
import pandas as pd
import datetime
import folium
import torch
import numpy as np
def compare_models():
"""
Output a table showing final tr... | pd.DataFrame(cellText) | pandas.DataFrame |
from sys import set_asyncgen_hooks
import streamlit as st
import plotly.graph_objects as go
import pandas as pd
import numpy as np
featuresAbbrev = {'Points' : 'pts',
'Goal Scored' : 'gs_cum',
'Goal Conceded' : 'gc_cum',
'Goal difference' : 'gd',
'Form' ... | pd.concat([homeTeamData, awayTeamData]) | pandas.concat |
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_series_equal
from src.contact_models.contact_model_functions import _draw_nr_of_contacts
from src.contact_models.contact_model_functions import _draw_potential_vacation_contacts
from src.contact_models.cont... | pd.Series(False, index=a_saturday.index) | pandas.Series |
import urllib.request
import xmltodict, json
# import pygrib
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
import time
import urllib.request
import xmltodict
# Query to extract parameter forecasts for one particular place (point)
#
# http://data.fmi.fi/fmi-apikey/f96cb70b-64d1-4bbc-9... | pd.DataFrame(columns=['Measurement_Number', 'Name', 'DateTime', 'Lat', 'Long', 'Value']) | pandas.DataFrame |
import warnings
import numpy as np
import pandas as pd
import re
import string
@pd.api.extensions.register_dataframe_accessor('zookeeper')
class ZooKeeper:
def __init__(self, pandas_obj):
# validate and assign object
self._validate(pandas_obj)
self._obj = pandas_obj
# define incor... | pd.to_numeric(vals_not_null, errors='coerce') | pandas.to_numeric |
from bert_embedding import BertEmbedding
#from bert_serving.client import BertClient
from flask import Flask, render_template, request
import os
import json
import requests
import pickle
import joblib
import numpy as np
import pandas as pd
#import tensorflow as tf
#all packages
import nltk
import string ... | pd.DataFrame(data,columns=['text']) | pandas.DataFrame |
from datetime import date as dt
import numpy as np
import pandas as pd
import pytest
import talib
import os
from finance_tools_py.simulation import Simulation
from finance_tools_py.simulation.callbacks import talib as cb_talib
from finance_tools_py.simulation import callbacks
@pytest.fixture
def init_global_data():
... | pd.Series.equals(real, pytest.global_data[col]) | pandas.Series.equals |
#-------------------------------------------------------------
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under... | pd.DataFrame(X) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon May 14 17:29:16 2018
@author: jdkern
"""
from __future__ import division
import pandas as pd
import numpy as np
def exchange(year):
df_data = pd.read_csv('../Time_series_data/Synthetic_demand_pathflows/Sim_daily_interchange.csv',header=0)
paths = ['SALBRYNB', 'ROSET... | pd.read_excel('Path_setup/NEISO_path_export_profiles.xlsx',sheet_name='SALBRYNB',header=None) | pandas.read_excel |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 21 23:24:11 2021
@author: rayin
"""
import os, sys
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import math
import re
import random
from collections import Counter
from pprint import pprint
os.chdir("/Users/rayin/Google ... | pd.Series(aa) | pandas.Series |
#!/usr/bin/python
# -*- coding: utf-8 -*-
import subprocess
import os.path
import time
import sys
import pandas as pd
work_path = sys.path[0]+'/work_space'
def hetero_to_homo(filepath,jobid,role,garblers):
"""异构图转同构图，并存储"""
hetero_df = pd.read_csv(filepath,index_col=None,header=None,sep=" ")
fold_name = o... | pd.read_csv(path,index_col=None,header=None,sep=" ",engine = "python") | pandas.read_csv |
from ctypes import sizeof
import traceback
from matplotlib.pyplot import axis
import pandas as pd
import numpy as np
from datetime import datetime
from time import sleep
from tqdm import tqdm
import random
import warnings
from sklearn.metrics import accuracy_score
from sklearn.metrics import mean_squared_error
from skl... | pd.concat(data_collected_dfs, axis=0) | pandas.concat |
#!/usr/bin/python3
# # Data Indexer
# This script sweeps the file index and consolidate channel and site information.
# - Read files on designated folder
# Import standard libraries
import pandas as pd
import h5py
# Import specific libraries used by the cortex system
import h5_spectrum as H5
import cortex_names as ... | pd.DataFrame() | pandas.DataFrame |
from gensim import corpora
import gensim
from gensim.matutils import hellinger
import pyLDAvis
import pyLDAvis.gensim_models as gensimvis
from IPython.core.display import HTML
from collections import defaultdict
from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import TfidfVectorizer
import pprint... | pd.DataFrame(columns=['Course', 'Topics']) | pandas.DataFrame |
import numpy as np
import pytest
from pandas.compat import range, u, zip
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series
import pandas.core.common as com
from pandas.core.indexing import IndexingError
from pandas.util import testing as tm
@pytest.fixture
def frame_random_data_integer_mul... | Series([1, 2, 3]) | pandas.Series |
import pandas as pd
import sparse
import numpy as np
class AnnotationData:
"""
Contains all the segmentation and assignment data
WARNING: self.assignments['Clusternames'] will contain neurite ids (as strings) rather than names
"""
# Todo: if we can preserve segments instead of merging them when two... | pd.DataFrame({"Time": [], "Segment": [], "x": [], "y": [], "z": []}, dtype=int) | pandas.DataFrame |
# --------------
# import the libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
import warnings
warnings.filterwarnings('ignore')
# Code starts here
df= | pd.read_json(path,lines=True) | pandas.read_json |
#!/usr/bin/env python
# coding: utf-8
import torch
import numpy as np
from sklearn import metrics
import pandas as pd
import torch.utils.data as Data
import sklearn
from sklearn import tree
from sklearn.metrics import precision_score, recall_score, f1_score
from sklearn.ensemble import RandomForestClassifier, AdaBoos... | pd.DataFrame(x_test) | pandas.DataFrame |
import numpy as np
import pandas as pd
from sklearn.decomposition import NMF
class ClusterModel:
@property
def clusters(self):
return len(self._cluster_names)
@property
def cluster_names(self):
return self._cluster_names
@clusters.setter
def clusters(self, value):
se... | pd.DataFrame(self._H, index=self._cluster_names, columns=self._X.columns) | pandas.DataFrame |
import json
import pytest
import numpy as np
import pandas as pd
import scipy.spatial.distance as scipy_distance
from whatlies import Embedding, EmbeddingSet
from .common import validate_plot_general_properties
"""
*Guide*
Here are the plot's propertites which could be checked (some of them may not be applicable
f... | pd.DataFrame(chart["datasets"][chart["data"]["name"]]) | pandas.DataFrame |
# coding: utf-8
# In[1]:
import pandas as pd
import findspark
findspark.init('spark24')
from pyspark.sql import SparkSession
import numpy as np
import matplotlib.pyplot as plt
# In[2]:
reviews = pd.read_csv("/home/yashika/Downloads/zomato.csv")
reviews.head(3)
# In[3]:
#pd.show_versions()
#reviews.value_... | pd.DataFrame(data) | pandas.DataFrame |
#!/usr/bin/python
# _____________________________________________________________________________
# ----------------
# import libraries
# ----------------
# standard libraries
# -----
import torch
import numpy as np
import os
import pandas as pd
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, D... | pd.concat(streambits, ignore_index=True) | pandas.concat |
import sys
import pandas as pd
from sqlalchemy import create_engine
def load_data(messages_filepath, categories_filepath):
'''
Load the datasets and merged them to generate a dataframe
to be used for analysis
Args:
messages_filepath: The path of messages dataset.
categories_filepat... | pd.read_csv(categories_filepath) | pandas.read_csv |
import warnings
import yfinance as yf
from pathlib import Path
import numpy as np
import pandas as pd
import requests
import seaborn as sns
import matplotlib as mpl
from matplotlib import pyplot as plt
from datetime import datetime, date
from yahooquery import Ticker
from tensorflow.keras.callbacks import ModelCheckpoi... | pd.DataFrame(X, index=y.index) | pandas.DataFrame |
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... | tm.assert_produces_warning(FutureWarning) | pandas.util.testing.assert_produces_warning |
from sklearn import metrics
import numpy as np
import pandas as pd
import seaborn as sns
from .stats import *
from .scn_train import *
import matplotlib
import matplotlib.pyplot as plt
def divide_sampTab(sampTab, prop, dLevel="cell_ontology_class"):
cts = set(sampTab[dLevel])
trainingids = np.empty(0)
for... | pd.crosstab(true_label, pred_label) | pandas.crosstab |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
from collections import namedtuple
import math
import geopy.distance
pd.set_option('display.max_rows', 10000)
def generate_dataset_gps():
# tx_coord = (63.4073927,10.4775050) #old
tx_coord = (63.40742, 10.47752) #ole... | pd.DataFrame(measurements) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
i... | pd.read_csv("../../../input/ronitf_heart-disease-uci/heart.csv") | pandas.read_csv |
#!/usr/bin/env python
# coding=utf-8
# vim: set filetype=python:
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import os
import posixpath
import sys
import math
import datetime
import string
from functools import wraps
import traceback
import xlrd3 as xl... | pd.DataFrame() | pandas.DataFrame |
import pytest
import numpy as np
import pandas as pd
from pandas import Categorical, Series, CategoricalIndex
from pandas.core.dtypes.concat import union_categoricals
from pandas.util import testing as tm
class TestUnionCategoricals(object):
def test_union_categorical(self):
# GH 13361
data = [
... | Categorical(['x', 'y', 'z']) | pandas.Categorical |
from sqlalchemy import true
import FinsterTab.W2020.DataForecast
import datetime as dt
from FinsterTab.W2020.dbEngine import DBEngine
import pandas as pd
import sqlalchemy as sal
import numpy
from datetime import datetime, timedelta, date
import pandas_datareader.data as dr
def get_past_data(self):
"""
Get raw... | pd.read_sql_query(query, self.engine) | pandas.read_sql_query |
"""
To extract compile time and runtime data from evo-suite dataset
Version 0.3.0
- Project metric computation has been omitted.
To be used in CodART project
"""
import multiprocessing
import sys
import os
import subprocess
import threading
from collections import Counter
from functools import wraps
import warnings
... | pd.DataFrame(data=[dummy_data], columns=columns) | pandas.DataFrame |
import sys
import re
import requests
from bs4 import BeautifulSoup as soup
import pandas as pd
def ItemResults(item):
'''
Function for scrapping list of items available for the desired one.
The function scrapping:
- Item name
- Item link
- Item price per piece
- Minimum orde... | pd.DataFrame(columns=['name','item','link','price','min-order']) | pandas.DataFrame |
# %%
import math
import multiprocessing as mp
import numpy as np
import pandas as pd
import pickle
import string
from sklearn.dummy import DummyClassifier
from sklearn.calibration import CalibratedClassifierCV
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.linear_model import Logist... | pd.read_csv('train\hosts-210311.txt', delim_whitespace=True, usecols=[1], names=['hostname'], skiprows=39, skipfooter=11) | pandas.read_csv |
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScale... | pd.DataFrame(gs_knn.cv_results_) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable=E1101
# flake8: noqa
from datetime import datetime
import csv
import os
import sys
import re
import nose
import platform
from multiprocessing.pool import ThreadPool
from numpy import nan
import numpy as np
from pandas.io.common import DtypeWarning
from pandas import DataFr... | StringIO(data) | pandas.compat.StringIO |
#!/usr/bin/env python3
# coding: utf-8
"""Abstract command classes for hicstuff
This module contains all classes related to hicstuff
commands:
-iteralign (iterative mapping)
-digest (genome chunking)
-cutsite (preprocess fastq by cutting reads into digestion products)
-filter (Hi-C 'event' sorting: l... | pd.DataFrame({"size": size}) | pandas.DataFrame |
#############################################################
# ActivitySim verification against TM1
# <NAME>, <EMAIL>, 02/22/19
# C:\projects\activitysim\verification>python compare_results.py
#############################################################
import pandas as pd
import openmatrix as omx
################... | pd.read_csv(tm1_per_filename) | pandas.read_csv |
import sys
import time
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GroupShuffleSplit
from sklearn.model_selection import GridSearchCV
from glob import glob
from sklearn.neural_network import MLPClassifier
def load_data(file_name):
print('FI... | pd.DataFrame(y_train) | pandas.DataFrame |
import pandas as pd
import scipy.io as sio
import scipy.interpolate
import numpy as np
import scipy.sparse
import scipy
import gzip
import subprocess
import collections
from collections import defaultdict, Counter
import scipy.sparse as sp_sparse
import warnings
import pickle
import os
#warnings.filterwarnings('ignore'... | pd.Series(stat_dict) | pandas.Series |
# 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... | tm.assertRaisesRegexp(ValueError, length_error) | pandas.util.testing.assertRaisesRegexp |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
import calendar
import seaborn as sns
sns.set(style='white', palette='deep')
plt.style.use('grayscale')
warnings.filterwarnings('ignore')
width = 0.35
# Funções
def autolabel(rects,ax, df): #autolabel
for rect in rects:
... | pd.read_excel('Banco de Dados - WDO.xlsx') | pandas.read_excel |
"""
Market Data Presenter.
This module contains implementations of the DataPresenter abstract class, which
is responsible for presenting data in the form of mxnet tensors. Each
implementation presents a different subset of the available data, allowing
different models to make use of similar data.
"""
from typing impo... | pd.Series.ewm(data['close'], span=period) | pandas.Series.ewm |
# pylint: disable=E1101
from datetime import time, datetime
from datetime import timedelta
import numpy as np
from pandas.core.index import Index, Int64Index
from pandas.tseries.frequencies import infer_freq, to_offset
from pandas.tseries.offsets import DateOffset, generate_range, Tick
from pandas.tseries.tools impo... | Index.intersection(self, other) | pandas.core.index.Index.intersection |
import asyncio
import sqlite3
import logging.config
from typing import List, Any
from datetime import datetime
from pathlib import Path
import aiohttp
import xmltodict
import yaml
import pandas as pd
from credentials.credentials import GOODREADS_KEY
# configuring logging
with open('log_config.yaml', 'r') as f:
... | pd.DataFrame(data, columns=['book_id', 'book_title', 'title_without_series', 'publication_year', 'publication_month']) | pandas.DataFrame |
import pandas as pd
import numpy as np
import pdb
class ArucoCorner:
"""
Object which holds corner data for a specific aruco tag id
"""
def __init__(self, id_num, corners, data_attributes=None, file_folder=None):
"""
Creates the object
"""
# TODO: add aruco dictio... | pd.DataFrame(reshaped_c, columns=["x1","y1","x2","y2","x3","y3","x4","y4"]) | pandas.DataFrame |
#libraries
import numpy as np
import pandas as pd
from datetime import datetime as dt
import time
import datetime
import os
import warnings
warnings.filterwarnings("ignore")
import logging
logging.basicConfig(filename='log.txt',level=logging.DEBUG, format='%(asctime)s %(message)s')
pd.set_option('max_colwidth', 50... | pd.concat([train, test], ignore_index=True) | pandas.concat |
import pandas as pd
from collections import defaultdict
import os
import requirements
import numpy as np
import xmlrpc.client as xc
client = xc.ServerProxy('https://pypi.python.org/pypi')
packages = client.list_packages()
datadict = defaultdict(list)
with open('requirements.txt', 'r') as infile:
new_package = Tru... | pd.DataFrame(data=datadict) | pandas.DataFrame |
# starpar.py
import numpy as np
import pandas as pd
from ..load_sim import LoadSim
from ..util.mass_to_lum import mass_to_lum
class StarPar():
@LoadSim.Decorators.check_pickle
def read_starpar_all(self, prefix='starpar_all',
savdir=None, force_override=False):
rr = dict()
... | pd.DataFrame(rr) | pandas.DataFrame |
# Copyright Contributors to the Pyro-Cov project.
# SPDX-License-Identifier: Apache-2.0
import argparse
from collections import defaultdict
import numpy as np
import pandas as pd
import torch
from pyrocov.sarscov2 import aa_mutation_to_position
# compute moran statistic
def moran(values, distances, lengthscale):
... | pd.DataFrame(data=results, index=index, columns=columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import SGD
from sklearn.metrics import classification_report, confusion_matrix
df = pd.read_csv("data/iris.c... | pd.DataFrame.from_dict(trained_model.history) | pandas.DataFrame.from_dict |
import math
import requests
import os
import pandas as pd
import matplotlib.pyplot as plt
import os
import numpy as np
import sys
import math
from datetime import datetime
from glob import glob
from datetime import timedelta
plt.style.use('ggplot')
from mpl_toolkits.basemap import Basemap
from igrf12py.igrf12fun impor... | pd.read_csv(mpath, header=0, sep=' ', parse_dates=0, index_col=0, low_memory=False) | pandas.read_csv |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, time, timedelta, date
import sys
import os
import operator
from distutils.version import LooseVersion
import nose
import numpy as np
randn = np.random.randn
from pandas import (Index, Series, TimeSeries, DataFrame,
isnull, date_ran... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
from flask import *
import pandas as pd
import os
from pandas.tseries.holiday import USFederalHolidayCalendar
from pandas.tseries.offsets import CustomBusinessDay
from keras.models import load_model
from sklearn.preprocessing import MinMaxScaler
from flask_ngrok import run_with_ngrok
import numpy as np
app = Flask(__n... | USFederalHolidayCalendar() | pandas.tseries.holiday.USFederalHolidayCalendar |
from datetime import datetime
from typing import List
import pandas as pd
import pytest
from hyperwave import (
HyperwaveWeekLenghtGrouping,
HyperwavePhaseGrouper,
HyperwaveGroupingPhasePercent,
HyperwaveGroupingPhaseAggregator,
HyperwaveGroupingToPhase4,
HyperwaveGrouperByMedianSlopeIncrease,
... | pd.DataFrame(raw_data) | pandas.DataFrame |
import os
from pathlib import Path
from typing import List, Tuple, Optional, Sequence, Any, Union, Generator
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import penguins as pg
from penguins import dataset as ds # for type annotations
class Experiment:
"""
Generic interface for expe... | pd.DataFrame.from_records(peaklist, columns=("f1", "f2")) | pandas.DataFrame.from_records |
'''
NMF learns topics of documents
In the video, you learned when NMF is applied to documents, the components correspond to topics of documents, and the NMF features reconstruct the documents from the topics. Verify this for yourself for the NMF model that you built earlier using the Wikipedia articles. Previously, yo... | pd.DataFrame(model.components_, columns=words) | pandas.DataFrame |
#import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
# Create an Empty DataFrame
df = pd.DataFrame()
print (df)
# Create a DataFrame from Lists
data = [1,2,3,4,5]
df = pd.DataFrame(data)
print (df)
data = [['Ankit',21],['Bob',24],['Clarke',20]]
df = pd.DataFrame(data,col... | pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd']) | pandas.Series |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for datetime64 and datetime64tz dtypes
from datetime import (
datetime,
time,
timedelta,
)
from itertools import (
product,
starmap,
)
import operator
import warnings
import numpy as np
impo... | tm.assert_series_equal(result, exp) | pandas._testing.assert_series_equal |
import itertools
import pandas as pd
import numpy as np
from log import Log
def gen_EMA(data: pd.Series, n=20):
alpha = 1 / (n + 1)
EMA = []
for t in range(len(data.index)):
if t == 0:
EMA_t = data.iat[t]
else:
EMA_t = alpha * data.iat[t] + (1 - alpha) * EMA[-1]
... | pd.Series(multinomial, index=data.index) | pandas.Series |
# -*- coding: utf-8; py-indent-offset:4 -*-
import os, sys
import datetime as dt
import tabulate as tb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from ..core import get_cn_fund_list, get_cn_fund_daily, get_cn_fund_manager, get_cn_fund_company, get_all_symbol_name, get_daily
from ..utils im... | pd.read_excel(excel_file, dtype=str) | pandas.read_excel |
import pandas as pd
import datetime
import numpy as np
import icd
def get_age(row):
"""Calculate the age of patient by row
Arg:
row: the row of pandas dataframe.
return the patient age
"""
raw_age = row['DOD'].year - row['DOB'].year
if (row['DOD'].month < row['DOB'].month) or ((row['... | pd.to_datetime(patient['DOB']) | pandas.to_datetime |
#!/usr/bin/python
import sys, os;
import argparse;
from os.path import expanduser;
import pandas as pd;
import math;
from datetime import datetime as dt;
from datetime import timedelta;
__author__ = "<NAME>"
def main():
parser = argparse.ArgumentParser(description="This script normalizes the Binance buy history ... | pd.read_excel(args.foreignexchange, sheet_name="sheet1") | pandas.read_excel |
from datetime import timedelta
import numpy as np
import pandas as pd
import pickle
def generate_data(df, freq: str, scenario: int, regr_vars = None,
multiplier = None,
baseline = None,
look_back = None,
look_ahead = None):
'''
fr... | pd.DataFrame(x_test_scaled, columns=testX.columns, index=testX.index) | pandas.DataFrame |
import pandas as pd
import argparse
import pickle
from collections import defaultdict
COL2Label = {0:'transcript', 1: 'dna', 2: 'protein'}
parser = argparse.ArgumentParser(description='Variant Results.')
parser.add_argument('--results_file', type = str, required = True, help = 'paths results')
parser.add_argument('-... | pd.DataFrame(summary) | pandas.DataFrame |
# coding: utf-8
import pandas as pd
from pandas import Series,DataFrame
import numpy as np
import itertools
import matplotlib.pyplot as plt
get_ipython().magic('matplotlib inline')
from collections import Counter
import re
import datetime as dt
from datetime import date
from datetime import datetime
i... | pd.to_datetime(tweets['date']) | pandas.to_datetime |
import sqlite3
import pandas as pd
import numpy as np
from datetime import datetime
class Rankings:
def run(self, database):
print("Starting product ranking...")
start_time = datetime.now()
conn = sqlite3.connect(database)
query = conn.execute("SELECT * From reviews")
cols... | pd.merge(average_by_asin, count, on='asin') | pandas.merge |
"""
Tests dtype specification during parsing
for all of the parsers defined in parsers.py
"""
from io import StringIO
import numpy as np
import pytest
from pandas import Categorical, DataFrame, Index, MultiIndex, Series, concat
import pandas._testing as tm
def test_dtype_all_columns_empty(all_parsers):
# see gh... | Series([], dtype="timedelta64[ns]") | pandas.Series |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.finance as mpf
'''
读入一支股票指定年份的ohlcv数据
输入:baseDir,stockCode为字符, startYear,yearNum为整数，
输出:dataframe
'''
def readWSDFile(baseDir, stockCode, startYear, yearNum=1):
# 解析日期
datepa... | pd.datetime.strptime(x, '%Y-%m-%d') | pandas.datetime.strptime |
# coding=utf-8
from datetime import datetime
from wit import Wit
from string import Template
from time import sleep
from collections import namedtuple
from pathlib import Path
import pandas as pd
import deepcut
import os
import glob
import pickle
import config
toq_key = config.toq_key
say_key = config.say_key
sub_key ... | pd.Series(analyse.sAppear) | pandas.Series |
"""
Pull information using python ColecticaPortal api
"""
from io import StringIO
import xml.etree.ElementTree as ET
import pandas as pd
import json
import api
def remove_xml_ns(xml):
"""
Read xml from string, remove namespaces, return root
"""
it = ET.iterparse(StringIO(xml))
for _, el in it... | pd.DataFrame(columns=['response_type', 'Value', 'Name', 'ID', 'Label']) | pandas.DataFrame |
import json
import os
import sqlite3
import pyAesCrypt
import pandas
from os import stat
from datetime import datetime
import time
import numpy
# Global variables for use by this file
bufferSize = 64*1024
password = os.environ.get('ENCRYPTIONPASSWORD')
# py -c 'import databaseAccess; databaseAccess.reset()'
def reset... | pandas.DataFrame() | pandas.DataFrame |
import numpy as np
import scipy.stats as sp
import os
import pandas as pd
import h5py
import bokeh.io as bkio
import bokeh.layouts as blay
import bokeh.models as bmod
import bokeh.plotting as bplt
from bokeh.palettes import Category20 as palette
from bokeh.palettes import Category20b as paletteb
import plot_results a... | pd.HDFStore(in_h5_file) | pandas.HDFStore |
# Author : <EMAIL>
# Date : 2020-12-03
import logging
import numpy as np
import pandas as pd
import os, glob, time, datetime
import pickle
import gzip
import copy
import json
import cv2
import random
import torch
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedKFold,... | pd.read_csv(f'{input_csv}{val_fold}.csv') | pandas.read_csv |
# Copyright (c) 2018-2020, NVIDIA CORPORATION.
from __future__ import division
import operator
import random
from itertools import product
import numpy as np
import pandas as pd
import pytest
import cudf
from cudf.core import Series
from cudf.core.index import as_index
from cudf.tests import utils
from cudf.utils.d... | pd.DataFrame({}) | pandas.DataFrame |
import os
import pandas_datareader
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from tensorflow import keras
import pandas
import pandas as pd
import plotly.express as px
import pandas_datareader.data as web
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
... | pd.to_datetime(End) | pandas.to_datetime |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
from math import sqrt
from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt
import statsmodels.api as sm
#Importing data
# dataframe
df = pd.read_csv("<NAME>.txt", sep='\t')... | pd.to_datetime(train.DATE,format="%Y-%m-%d") | pandas.to_datetime |
from collections.abc import Sequence
from functools import partial
from math import isnan, nan
import pytest
from hypothesis import given
import hypothesis.strategies as st
from hypothesis.extra.pandas import indexes, columns, data_frames
import pandas as pd
import tahini.core.base
import tahini.testing
names_index_... | pd.Index([(0, 1)]) | pandas.Index |
from datetime import datetime
import pandas as pd
from bs4 import BeautifulSoup
import cloudscraper
from datetime import timedelta
class CalendarDataFeed:
def __init__(self, startYear, endYear, calendarSite = "https://www.forexfactory.com/calendar?day=" ):
self.startYear = startYear
self.en... | pd.DataFrame() | pandas.DataFrame |
from rpy2.robjects import pandas2ri
import numpy as np
import pandas as pd
import wrfpywind.data_preprocess as pp
import xarray as xr
from .util import _get_r_module, _attach_obs, _xr2pd, _fxda, _fxda_grid
def fmt_training_data(wrfda, obsda):
# Get and format data for only north buoy at 100m
data_n = _attach... | pd.DateOffset(days=sim_len) | pandas.DateOffset |
from django.http import JsonResponse
from collections import Counter
import pandas as pd
import json
from datetime import date, timedelta
from django.contrib.auth.decorators import login_required
from django.utils.decorators import method_decorator
from django.urls import reverse
from django.db.models import Avg, Sum, ... | pd.to_timedelta("0 days") | pandas.to_timedelta |
"""
OneSeries is an extended variant of pandas.Seres, which also inherits all the pandas.Series
features and ready to use. It contains many useful methods for a better experience on data analysis.
WARNING: Because this module is still pre-alpha, so many features are unstable.
"""
import pandas as pd
from pandas impor... | pd.concat([self, other], axis=1) | pandas.concat |
from ...utils import constants
import pandas as pd
import geopandas as gpd
import numpy as np
import shapely
import pytest
from contextlib import ExitStack
from sklearn.metrics import mean_absolute_error
from ...models.geosim import GeoSim
from ...core.trajectorydataframe import TrajDataFrame
def global_variables():
... | pd.to_datetime('2020/01/01 08:00:00') | pandas.to_datetime |
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.corpus import wordnet
from datetime import datetime
from numpy.linalg import norm
from tqdm.auto import tqdm
from glob import glob
import pandas as pd
import numpy as np
import subprocess
import ... | pd.concat([subreddits, vectors], axis=1) | pandas.concat |
#!/usr/bin/env python
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | DataFrame(columns=table_curr.columns) | pandas.DataFrame |
import numpy as np
from RecSearch.DataInterfaces.Recommenders.Abstract import IMatrixRankRecommender
from itertools import combinations, permutations
import pandas as pd
class IXCourseDiffRankRecommend(IMatrixRankRecommender):
def iget_recommendation(self, who: dict, possible: pd.DataFrame, n_column: str, ir_colu... | pd.merge(distance_df, neg_df, how='left', on=['Course1', 'Course2']) | pandas.merge |
# Runs after normalization and per_person_ratio_and_factor and pre_plot_aggregation.
import shutil
from pathlib import Path
import itertools
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import collections
def PlotWithSlices(df, data_name, output_dir):
for group_name in ['Gender', 'Age... | pd.read_csv(input_base_dir / 'S_all_plot_raw_data.csv') | pandas.read_csv |
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