prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import argparse
import sys
import collections
import numpy as np
import pandas as pd
import matplotlib
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from ast import literal_eval
parser = argparse.ArgumentParser(description='Compare predictions from HMM model... | pd.DataFrame.from_dict(d, orient='index') | pandas.DataFrame.from_dict |
# -*- coding:UTF-8 -*-
import os.path
import pandas as pd
import cv2
import requests
def check_rotate(dt_boxes_df):
check_df = dt_boxes_df.copy()
# 如果竖立长方形数量超过一半则认为需要旋转图片
check_df = check_df[check_df['length'] + check_df['hight'] > 50]
if len(check_df[check_df['hight'] > check_df['length']]) > len(che... | pd.Series([item[1][1] for item in ocr_result]) | pandas.Series |
import pandas as pd
import matplotlib.pyplot as plt
import time
import os
def symbol_to_path(symbol, basedir='data'):
return os.path.join(basedir, '{}.csv'.format(symbol))
def get_data(symbols, dates):
df = | pd.DataFrame(index=dates) | pandas.DataFrame |
import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import os
import wandb
from utils import set_seed, parse_training_args
from dataset import ToxicDataset
from trainer import Trainer
from model import convert_regressor_to_binary, convert_binary_to_regressor
if ... | pd.read_csv(config.train_path) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
@author: mesar
"""
import pandas as pd
import json
from datetime import datetime
import numpy as np
import csv
from pathlib import Path
from progressbar import progressbar as pbar
import time
import sys
def parallel_parsing(i, key, number_of_clients, vehicle_capacity, package_data_list,... | pd.DataFrame.from_dict(route_info, orient='index') | pandas.DataFrame.from_dict |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 9 10:54:32 2019
@author: nmei
"""
import os
from glob import glob
import pandas as pd
import numpy as np
import seaborn as sns
sns.set_style('whitegrid')
sns.set_context('poster')
import statsmodels.api as sm
from statsmodels.formula.api import ol... | pd.concat(temp) | pandas.concat |
'''
train_and_eval_sklearn_binary_classifier.py
Usage
-----
$ python train_and_eval_sklearn_binary_classifier.py \
--dataset_path [path] \
--output_path [path] \
[optional args]
Optional arguments
------------------
--dataset_path DATASET_PATH
Path to folder containing:
... | pd.DataFrame(data=cm, columns=[0, 1], index=[0, 1]) | pandas.DataFrame |
import pandas as pd
import tkinter as tk
from tkinter import filedialog
import tkinter.font as font
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from email.mime.base import MIMEBase
from secrets import home_email, password
from email import encoders
import... | pd.read_excel(self.filename) | pandas.read_excel |
# 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, ... | pd.isnull(val[0]) | pandas.isnull |
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import StandardScaler, MinMaxScaler
import numpy as np
import pandas as pd
import logging
logging.basicConfig(format='%(levelname)s - %(message)s', level=logging.INFO)
logger = logging.getL... | pd.read_json(data_path, **read_data_options) | pandas.read_json |
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 24 14:00:26 2019
@author: <NAME>
"""
import pandas as pd
import time
from datetime import timedelta
import datetime
from pandas import *
import random
data = | pd.read_csv('df_Rhythm4analyze_o_37852_1558704625828706.csv') | pandas.read_csv |
# -*- coding:utf-8 -*-
# !/usr/bin/env python
"""
Date: 2021/11/2 21:08
Desc: 同花顺-数据中心-技术选股
http://data.10jqka.com.cn/rank/cxg/
"""
import pandas as pd
import requests
from bs4 import BeautifulSoup
from py_mini_racer import py_mini_racer
from tqdm import tqdm
from akshare.datasets import get_ths_js
def _get_file_co... | ric(big_df["连续涨跌幅"]) | pandas.to_numeric |
import json
import requests
import pandas as pd
import websocket
# Get Alpaca API Credential
endpoint = "https://data.alpaca.markets/v2"
headers = json.loads(open("key.txt", 'r').read())
def hist_data(symbols, start="2021-01-01", timeframe="1Hour", limit=50, end=""):
"""
returns historical b... | pd.DataFrame(data["bars"]) | pandas.DataFrame |
from flask import Flask
from flask import request
from flask_cors import CORS
import pymongo
from flask_pymongo import PyMongo
import json
from pydash import _
import numpy as np
import pandas as pd
STATIC_FOLDER = 'server/static'
# STATIC_FOLDER = '../client/dist'
TEMPLATE_FOLDER = '../client/dist'
app = Flask(__n... | pd.DataFrame.from_records(json_mouse_raw_data[student_id][question_id_]) | pandas.DataFrame.from_records |
#!/usr/bin/env python
# coding: utf-8
# # Data Preprocessing
# ### Importing the libraries
# In[ ]:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# ### Reading the dataset
# In[ ]:
dataset = | pd.read_csv('startups.csv') | pandas.read_csv |
import os
import sys
import torch
import numpy as np
from BrainMaGe.models.networks import fetch_model
from pathlib import Path
import matplotlib.pyplot as plt
from compare_utils import (
postprocess_prediction,
postprocess_save_output,
postprocess_output,
dice,
get_mask_image,
get_input_image
... | pd.DataFrame(ov_int8_stats) | pandas.DataFrame |
def location_at_distance(start_lon, start_lat, direction, distance=5):
'''
The purpose of this is to find a latitude and longitude at a specific distance from
another point. The inputs are the starting latitude and longitude, and the distance
and angle to proceed from that point.
http://www.ed... | pd.concat([df, applied_df], axis='columns') | pandas.concat |
import numpy as np
import pandas as pd
cjxx1 = pd.read_csv('../SourceData/bks_cjxx_out1-1.csv',usecols = ['xh','xn','xqm','ksrq','kch','kxh','kccj','xf','kcsxdm','xdfsdm'])
cjxx2 = pd.read_csv('../SourceData/bks_cjxx_out1-2.csv',usecols = ['xh','xn','xqm','ksrq','kch','kxh','kccj','xf','kcsxdm','xdfsdm'])
cjxx = cjxx1... | pd.DataFrame(columns = ['xh','2014-1','2014-2','2014-3','2015-1','2015-2','2015-3','2016-1','2016-2','2016-3','2017-1','2017-2','2017-3','2018-1']) | pandas.DataFrame |
from numpy.core.numeric import outer
import pandas as pd
import numpy as np
import functools
def outer_fn(keywords):
def filtre(data) -> bool:
for key in keywords:
if key in data.lower():
return True
return False
return filtre
def result(keywords ,file_name : ... | pd.DataFrame(columns=[x for x in dataframe.columns.values]) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# -------------------------------------------------------------------
# **TD DSA 2021 de <NAME> - rapport de <NAME>**
# ------------------------- -------------------------------------
# # Analyse descriptive
# ## Setup
# In[5]:
get_ipython().system('pip install textbl... | pd.Series(neutral_text_prepro) | pandas.Series |
# object that contains the simulation data.
class MonteCarlo:
'''
(OBJECT INFO)
-------------
vandal.MonteCarlo - main class.
(OBJECT FUNCTIONS)
------------------
eg. vandal.MonteCarlo.function()
.execute() - executes a Monte Carlo simulation on a defined data s... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import os
from collections import namedtuple
from strategy.strategy import Exposures, Portfolio
from strategy.rebalance import get_relative_to_expiry_instrument_weights, \
get_relative_to_expiry_rebalance_dates, get_fixed_frequency_rebalance_dates
from strategy.calendar import get_mtm_dates
de... | pd.Timestamp(ed) | pandas.Timestamp |
#! /usr/bin/python3
import numpy as np
import random as rnd
import functions
import pandas as pd
class NAgent:
nnetfileName = "./data/garry_007.nn"
user_id = None
case_id = None
url = None
nnet = None
rmsprop_cache = None
grad_buffer = None
prev_act = None
prev_score = None
prev_hash = None
pr... | pd.concat([self.episode, step1]) | pandas.concat |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os
import matplotlib.ticker as tck
import matplotlib.font_manager as fm
import math as m
import matplotlib.dates as... | pd.to_datetime(Rad_df_348['Fecha_Hora'], format="%Y-%m-%d %H:%M", errors='coerce') | pandas.to_datetime |
import json
import pandas as pd
from datetime import datetime
from src.func import tweet_utils
from src.func import regex
def load_tweets(geotweet_path):
with open(geotweet_path, 'r') as f:
tweets = json.load(f)
return remove_duplicates(tweets)
def remove_duplicates(tweets):
df = pd.DataFrame.fr... | pd.isnull(tweet['pure_text']) | pandas.isnull |
#!/usr/bin/env python
# coding: utf-8
# # Wasserstein Pareto Frontier Experiment on Adult Data Set
# ## Import Data
# The experiment used the Adult experiment_data2 data set as in "Optimized Pre-Processing for Discrimination Prevention" by Calmon and etc. for comparison purpose: https://github.com/fair-preprocessing/... | pd.get_dummies(TestList[i][Z_features+X_features]) | pandas.get_dummies |
# coding: utf-8
# # Resting state analysis
# In[8]:
import pickle
from pathlib import Path
import os
import mne
import numpy as np
import scipy.stats
import matplotlib as mpl
import matplotlib.pyplot as plt
try:
get_ipython().magic('matplotlib inline')
except:
pass
import pandas a... | pd.DataFrame(alldata) | pandas.DataFrame |
import pandas as __pd
import datetime as __dt
from multiprocessing import Pool as __Pool
import multiprocessing as __mp
from functools import reduce as __red
import logging as __logging
from seffaflik.__ortak.__araclar import make_requests as __make_requests
from seffaflik.__ortak import __araclar as __araclar, __dogr... | __pd.DataFrame(json["body"]["bilateralContractSellList"]) | pandas.DataFrame |
# coding:utf-8
#
# The MIT License (MIT)
#
# Copyright (c) 2016-2018 yutiansut/QUANTAXIS
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... | pd.DataFrame({'CDL3WHITESOLDIERS': res}, index=data.index) | pandas.DataFrame |
import pandas as pd
import bioframe
import pyranges as pr
import numpy as np
from io import StringIO
def bioframe_to_pyranges(df):
pydf = df.copy()
pydf.rename(
{"chrom": "Chromosome", "start": "Start", "end": "End"},
axis="columns",
inplace=True,
)
return pr.PyRanges(pydf)
d... | pd.isna(b["index_2"].values) | pandas.isna |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
#ソースコードは、https://nigimitama.hatenablog.jp/entry/2020/01/25/110921
# 表側が順番通りの整数でないデータフレームにも対応した場合
def slice_df(df: pd.DataFrame, size: int) -> list:
"""pandas.DataFrameを行数sizeずつにスライスしてリストに入れて... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pickle as p
import json
import pandas as pd
import os
from flask import Flask, request, redirect, url_for, flash, jsonify
app = Flask(__name__)
@app.route("/")
def hello():
return "Hello, World!"
@app.route('/api/predict', methods=['POST'])
def makecalc():
data = request.get_json()
... | pd.DataFrame(data_json) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pdb
import numba
import six
import pandas as pd
import numpy as np
import inspect
import datetime
from sklearn import preprocessing
from numpy import log
from alphax.singleton import Singleton
# rolling corr of two pandas dataframes
def rolling_corr(x, y, win):
corr_df = pd.DataFram... | pd.DataFrame(data=np.NaN, index=y.index, columns=y.columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 23 11:44:07 2021
@author: <NAME>
"""
from openpyxl import load_workbook
import pandas as pd
if __name__ == '__main__':
'''
writer = pd.ExcelWriter("./jpeg_result.xlsx",engine="openpyxl")
wb = load_workbook(writer.path)
writer.book = wb
df = pd.DataFra... | pd.ExcelWriter("./jpeg_result.xlsx",engine='openpyxl') | pandas.ExcelWriter |
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([df, df], keys=[1, 2], names=["level2"]) | pandas.concat |
# Author: <NAME>, <NAME>, <NAME>
# Date: 2020/11/27
"""Create transformed train and test files .
Usage: src/preprocess.py <input_file> <input_file1> <output_file> <output_file1>
Options:
<input_file> Path (including filename and file extension) to train file
<input_file1> Path (including filename and file exte... | pd.concat([transformed_train, y_train], axis=1) | pandas.concat |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
import sompy
from sompy.sompy import SOMFactory
from sompy.visualization.mapview import View2D
from sompy.visualization.bmuhits import BmuHitsView
from sompy.visualization.hitmap import HitMapView
from ... | pd.DataFrame(scores, index=['score']) | pandas.DataFrame |
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
from scipy.stats import chi2
from sklearn.cluster import KMeans
def calc_IV(data, var_name, var_name_target):
"""
计算各分组的WOE值以及IV值
:param data: DataFrame 输入数据
:param var_name: str 分箱后的变量
:param var_name_target... | pd.DataFrame(count_update, columns=count.columns) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
import json
import pandas as pd
from pandas.api.types import is_numeric_dtype
import numpy as np
from scipy.stats import ks_2samp, chisquare
import plotly.graph_objs as go
import plotly.express as px
from evidently.model.widget import BaseWidgetInfo, AlertStats, AdditionalGraph... | pd.concat([reference_data, production_data]) | pandas.concat |
import numpy as np
import pandas as pd
dict2={}
df = pd.read_csv('../Data/average1.csv')
dict1 = {col:df[col].tolist() for col in df.columns}
temp = []
for key in list(dict1.keys()):
if(key not in dict2.keys()):
dict2[key] = [0]*2
if(int(int(key)/100000)%10 == 8):
temp = dict1[key][34:54]+ dic... | pd.DataFrame({'xh':[key],'mean':[dict2[key][0]],'var':[dict2[key][1]]}) | pandas.DataFrame |
import torch
from torch import nn
import classification_ModelNet40.models as models
import torch.backends.cudnn as cudnn
from classification_ScanObjectNN.models import pointMLPElite
# from cell_dataset import PointCloudDatasetAllBoth
from torch.utils.data import DataLoader
import numpy as np
import pandas as pd
from f... | pd.read_csv(dataframe) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import wx
import os
import time
import sys
# In[ ]:
input_max_temp = input("Please input maximum of temperature: ")
input_min_temp = input("Please input minimum of temperature: ")
input_meandew = input("Please input mean dew point: ")
inp... | pd.DataFrame({'Actual': y_test, 'Predicted': y_pred}) | pandas.DataFrame |
import numpy as np
import pandas as pd
import datetime as datetime
from scipy.signal import find_peaks, peak_prominences
from scipy.interpolate import interp1d
from scipy import signal
from scipy.integrate import trapz
'''
Feature Engineering of Wearable Sensors:
Metrics computed:
Mean Heart Rate... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 3 00:44:36 2022
@author: filot
Create timeseries
"""
import pandas as pd
import glob
# Standard Library imports
import argparse
import gzip
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import netCDF4
import numpy as np
import os
impo... | pd.DateOffset(months=12) | pandas.DateOffset |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
## Converts h5 input to short format
## By: <NAME>
## Bring in system mod
import sys
# In[ ]:
## Set user defined variables
## Check we have three inputs!
assert (len(sys.argv) >= 4), "ERROR: This script must include:\n(1) The full path to a ginteractions (tsv) file... | pd.read_csv(sizepath,sep=mysep,names=['Chrom','Size']) | pandas.read_csv |
import numpy as np
import cv2
import subprocess
import argparse
import os
import sys
from datetime import datetime
import time
from math import sqrt, pi, cos, sin
import pandas as pd
from PIL import Image
import matplotlib.pyplot as plt
from train import process_image, model
oshapeX = 640
oshapeY = 240
NUM_CLASSES =... | pd.read_csv(data_dir + args.img_dir +\
'_log.csv' , names=['img_name', 'command']) | pandas.read_csv |
def removeMissing(filename):
"""Takes a file that contains missing scans and removes those rows, while providing the subject name and reason for removal."""
import pandas as pd
import math
loaded_file = pd.read_csv(filename)
cleaned_list = []
missing_counter = 0
for row in loaded_file.index... | pd.DataFrame(cleaned_list) | pandas.DataFrame |
#%%
import time
from pathlib import Path
import colorcet as cc
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.decomposition import PCA
from sklearn.feature_selection import VarianceThreshold
from sklearn.model_selection import train_test_split
from sklearn.pre... | pd.DataFrame(data=X, columns=columns) | pandas.DataFrame |
import pandas as pd
import numpy as np
import tkinter as tk
from tkinter import filedialog
Response=pd.read_json("1.json",encoding="UTF-8")
carList=Response["response"]["classifieds"]
df=pd.DataFrame(carList)
for each in range(2,295):
try:
Response=pd.read_json(str(each)+".json",en... | pd.DataFrame(carList) | pandas.DataFrame |
import os
import yaml
import argparse
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import h5py
def predict_example_hdf5_file(cfgs):
dup_num = 14
csv_data = pd.read_csv(cfgs['Testing']['pred_csv'], dtype = {'key': str})
csv_output_data = | pd.DataFrame(columns=['key', 'pred_idx', 'prob_idx']) | pandas.DataFrame |
import argparse
import datetime as dt
from glob import glob
from math import ceil
import json
import os.path
from pathlib import Path
import enaml
with enaml.imports():
from enaml.stdlib.message_box import information
from enaml.qt.qt_application import QtApplication
import matplotlib as mp
import matplotlib.py... | pd.DataFrame(epochs.values, index=new_idx, columns=new_col) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 24 14:42:41 2017
@author: <NAME>
"""
# create dummy variables for catagorical variables with two categories like sex can be either male or female
import pandas as pd
path = 'C:/Users/<NAME>/Documents/Shreeya_Programming/Predictive/Chapter 2'
filename1 = 'titanic3.csv'
... | pd.read_csv(fullpath) | pandas.read_csv |
"""
Copyright 2019 <NAME>.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distribut... | pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric') | pandas.Series |
from natsort import natsorted
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy.stats import spearmanr
def load_TargetScan(gene):
# Load TargetScan predictions for gene
return set(open("data/TargetScan_{}.txt".format(gene)).read... | pd.concat(dfs_isomiR, axis=1) | pandas.concat |
# use selenium to simulate web browser (need to download selenium or create a docker image)
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support.select import Select
import requests
from pandas import DataFrame
from datetime import datetime
CURRENT_YEAR = date... | DataFrame.read_csv('../spreadsheets/candidates.csv') | pandas.DataFrame.read_csv |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, time, timedelta
import sys
import os
import unittest
import nose
import numpy as np
randn = np.random.randn
from pandas import (Index, Series, TimeSeries, DataFrame,
isnull, date_range, Timestamp, DatetimeIndex,
... | assert_series_equal(result, expected) | pandas.util.testing.assert_series_equal |
import pandas as pd
import numpy as np
import copy
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV
from sklearn.feature_selection import mutual_info_classif, SelectKBest
import matplotlib.pyplot as plt
from sklearn import svm
from sk... | pd.read_csv(f"{par_article_path}", sep=',', encoding="utf-8") | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This script saves bid and ask data for specified ETFs to files for each day
during market open hours.
It assumes the computer is at US East Coast Time.
@author: mark
"""
import os
import pandas as pd
import numpy as np
from itertools import product
import streaml... | pd.Timestamp('2021-01-01 9:45') | pandas.Timestamp |
#!/usr/bin/env python
##Run this file in terminal. The command is: python3 common_variations.py trait1.txt trait2.txt.txt
#import the data
import sys, os
import pandas as pd
inFile1=sys.argv[1]
inFile2=sys.argv[2]
inFile1t=os.path.splitext(inFile1)[1]
inFile2t=os.path.splitext(inFile2)[1]
outFile1 = os.path.splitext... | pd.read_csv(inFile2) | pandas.read_csv |
import datetime
import pandas as pd
from typing import List
from config import Config
from translation import Translate
from cachetools import cached, TTLCache
cache = TTLCache(maxsize=10, ttl=60)
@cached(cache)
class Data:
data = None
aggregated_data = None
total_regions_data = None
regions_data = No... | pd.read_csv(self.source_config['csv'][0]) | pandas.read_csv |
# --------------
#Importing header files
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#Path of the file
path
data = pd.read_csv(path)
data = pd.DataFrame(data)
data.rename(columns = {'Total':'Total_Medals'}, inplace = True)
data.head(10)
#Code starts here
# --------------
#C... | pd.read_csv(path) | pandas.read_csv |
import numpy as np
import pandas as pd
from sklearn.model_selection import StratifiedKFold
import gc
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
import logging
import itertools
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split
#modify to wor... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
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
import hgc
import os
from hgc import ner
from hgc import io
import tests
# from googletrans import Translator
def test_ner():
'... | pd.read_excel(WD / 'testfile1_io.xlsx', sheet_name='wide') | pandas.read_excel |
###############################################
# Calculate the turnover moments from BLS Data
##############################################
"""
<NAME>
Script that calculates the moments to match
and that draws a lot of graphs using mainly data
from the BLS
"""
import numpy as np
import os
import pandas as pd
impor... | pd.read_csv("LNS.csv") | pandas.read_csv |
import pandas as pd
import numpy as np
from datetime import datetime
def read_data(file_name, skiprows = 0, index_col = False):
df = pd.read_csv(file_name, skiprows = skiprows,error_bad_lines=False,index_col = index_col)
df = df[['bbr_x','bbr_y','fbr_x','fbr_y','fbl_x','fbl_y','bbl_x','bbl_y',
'Fr... | pd.concat([rightToLeftDF, leftToRightDF]) | pandas.concat |
# -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime
from numpy import random
import numpy as np
from pandas.compat import lrange, lzip, u
from pandas import (compat, DataFrame, Series, Index, MultiIndex,
date_range, isnull)
import pandas as pd
from pandas... | assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
import streamlit as st
import altair as alt
import pandas as pd
import numpy as np
import requests
import matplotlib.pyplot as plt
import plotly.express as px
from pathlib import Path
from functools import lru_cache
import statsmodels.formula.api as smf
from datetime import datetime
import pandasdmx as pdmx
plt.style.... | pd.json_normalize(data["quarters"]) | pandas.json_normalize |
# Preprocessing
import os, matplotlib
if 'DISPLAY' not in os.environ:
matplotlib.use('Pdf')
import matplotlib.pyplot as plt
import pandas as pd
pd.set_option('display.max_rows', 50)
import numpy as np
import xgboost as xgb
import xgbfir
import pdb
import time
np.random.seed(1337)
def client_anaylsis():
"""
... | pd.read_csv("../data/cliente_tabla3.csv.gz") | pandas.read_csv |
#!/usr/bin/env python3
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--task', default='yelp', choices=['yelp'])
parser.add_argument('--mode', default='train', choices=['train', 'eval'])
parser.add_argument('--checkpoint-frequency', type=int, default=100)
parser.add_argument('--eval-frequency',... | pd.DataFrame({'predictions': predictions, 'labels': labels, 'examples': examples}) | pandas.DataFrame |
import json
import sys
import pprint
import pandas as pd
from .KeyWordsSearch import search_phrases
from .Preprocessing_tools import full_preprocess_text, prepare_files, open_text
from .constnats import key_phrase, key_meal, key_category, greeting_key, farewell_key, pay_key, \
additional_key, new_key, order_key, ... | pd.concat([all_info, tmp], axis=0) | pandas.concat |
#
# Copyright 2015 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
from quetzal.analysis import on_demand
from tqdm import tqdm
def tp_summary(links, shared):
links = links.copy()
links['index'] = links.index
line_link_dict = links.groupby('trip_id')['index'].agg(lambda s: set(s)).to_dict()
line_list = list(line_link_dict.keys()... | pd.DataFrame({'transfer': transfers, 'exclusivity': exclusivities}) | pandas.DataFrame |
import os
import sys
import glob
import numpy as np
import pandas as pd
MAIN_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(MAIN_DIR)
from src.evaluation.multipleboardingpoints_eval import multiple_boarding_points
from src.misc.globals import *
EURO_PER_TON_OF_CO2 =... | pd.read_csv(f, index_col=0, squeeze=True) | pandas.read_csv |
# -*- coding: utf-8 -*-
#%% NumPyの読み込み
import numpy as np
# SciPyのstatsモジュールの読み込み
import scipy.stats as st
# SciPyのoptimizeモジュールの読み込み
import scipy.optimize as opt
# SciPyのLinalgモジュールの読み込み
import scipy.linalg as la
# Pandasの読み込み
import pandas as pd
# MatplotlibのPyplotモジュールの読み込み
import matplotlib.pyplot as plt
... | pd.DataFrame(stats, index=param_string, columns=stats_string) | pandas.DataFrame |
# Importing packages
import os
import re
from pathlib import Path
import pandas as pd
import numpy as np
# Basic python scripting using object-oriented coding
'''
Using the corpus called 100-english-novels, write a Python programme which does the following:
- The script should take a directory of text files, a keywo... | pd.DataFrame(data=data_dict) | pandas.DataFrame |
import sys
import click
import requests, requests_cache
import configparser
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
from datetime import datetime
from mpl_toolkits.axes_grid1 import make_axes_locatable
from pynance.auth import signed_params
from pynance.util... | pd.DataFrame(trades_list) | pandas.DataFrame |
#!/usr/bin/python -u
# +
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import random
import argparse
SEED = 123
random.seed(SEED)
np.random.seed(SEED)
# -
def train_test_set(df,train_ids,test_ids):
train_df = df.iloc[train_ids,:]
t... | pd.DataFrame(indices_list, columns=['split','ids']) | pandas.DataFrame |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.3'
# jupytext_version: 0.8.6
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Process Data
# ## Lo... | pd.merge(messages, categories, on='id', how='inner') | pandas.merge |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from sklearn.cluster import DBSCAN
from lifelines.statistics import logrank_test
from lifelines import KaplanMeierFitter
from sklearn import metrics
from sklearn.metrics import pairwise_distances
from lifelines.s... | pd.DataFrame(Euclidean_dis) | pandas.DataFrame |
"""
TODO:
copy in data dir
targzip
post
DONE:
clean date_time string
rename types:
type -> type_string
processed_type -> item_type
merge the data
save file
only 1 section (check content is correct): checked
"""
import datetime
import os
import re
import requests
import urllib.parse
import time
from bs4 impor... | pandas.DataFrame(page_lists) | pandas.DataFrame |
import pandas as pd
import numpy as np
import math
from scipy.stats import nct
from copy import deepcopy
import matplotlib.pyplot as plt
from ..estimators.stan_estimator import StanEstimatorMAP
from ..exceptions import IllegalArgument, ModelException
from ..utils.kernels import sandwich_kernel
from ..utils.features im... | pd.DataFrame(out) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
import reciprocalspaceship as rs
@pytest.fixture
def na_value(dtype):
return dtype.na_value
@pytest.fixture
def na_cmp():
return lambda x, y: | pd.isna(x) | pandas.isna |
from datetime import time
import numpy as np
import pytest
from pandas import DataFrame, date_range
import pandas._testing as tm
class TestBetweenTime:
def test_between_time(self, close_open_fixture):
rng = date_range("1/1/2000", "1/5/2000", freq="5min")
ts = DataFrame(np.random.randn(len(rng), ... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
import pandas as pd
import numpy as np
from sklearn.neighbors import NearestNeighbors
from sklearn.metrics import mean_absolute_error
import operator
import random
class RecommenderSystem(object):
def __init__(self, data, metric, algorithm, user):
self.data = data
self.distance, self.neighbors... | pd.merge(user_r, neighbor, how="inner", on="game-title") | pandas.merge |
# -*- coding: utf-8 -*-
"""
Tools producing reports of fairness, bias, or model performance measures
Contributors:
camagallen <<EMAIL>>
"""
import aif360.sklearn.metrics as aif
from functools import reduce
from IPython.display import HTML
import logging
import numpy as np
import pandas as pd
from sklearn.metrics... | pd.DataFrame(grp_res, index=[0]) | pandas.DataFrame |
from contextlib import nullcontext as does_not_raise
from functools import partial
import pandas as pd
from pandas.testing import assert_series_equal
from solarforecastarbiter import datamodel
from solarforecastarbiter.reference_forecasts import persistence
from solarforecastarbiter.conftest import default_observatio... | pd.Timestamp('20190513 1200', tz=tz) | pandas.Timestamp |
"""dynamic user-input-responsive part of mood, and mood graphs"""
from datetime import datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
from scipy.signal impor... | pd.Timestamp("2021-01-04 09:10:00") | pandas.Timestamp |
import datetime
import os
import shutil
import unittest
from copy import deepcopy
from typing import Optional, Tuple, Any, Callable, Dict, Sequence, List
from unittest.mock import patch
import pandas as pd
from pandas.testing import assert_frame_equal
from datacode.models.column.column import Column
from datacode.mod... | pd.to_datetime('1/1/2000') | pandas.to_datetime |
from flask import render_template, request, session, redirect, url_for, jsonify
from app import app
from amshelper import AmsHelper
from datetime import datetime, timedelta
import pandas as pd
from dateutil.relativedelta import relativedelta
import json
@app.route('/')
@app.route('/index', methods=['GET'])
def index()... | pd.to_datetime(start_time) | pandas.to_datetime |
import os
import pandas as pd
import datetime
import time
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.colors as colors
import matplotlib.lines as mlines
import numpy as np
import sys
import random
from collections import OrderedDict
class Vis:
# TODO: Move all drawing helper funct... | pd.to_timedelta(relatived['rel_start'], errors="coerce") | pandas.to_timedelta |
import pandas as pd
import numpy as np
import matplotlib as plt
pd.set_option('display.max_columns', None)
df=pd.read_csv('train_HK6lq50.csv')
def train_data_preprocess(df,train,test):
df['trainee_engagement_rating'].fillna(value=1.0,inplace=True)
df['isage_null']=0
df.isage_null[df.age... | pd.read_csv('train_HK6lq50.csv') | pandas.read_csv |
import torch
import time
import numpy as np
from .utils import accuracy_onehot, save_model
from sklearn.metrics import confusion_matrix
import pandas as pd
import copy
def train(model, optimizer, criterion, train_dl, test_dl,
N_epochs : int, batch_size : int, history=None, history_model_state=[],
... | pd.to_datetime('now') | pandas.to_datetime |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for Period dtype
import operator
import numpy as np
import pytest
from pandas._libs.tslibs.period import IncompatibleFrequency
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas impo... | pd.Period("2011-03", freq="M") | pandas.Period |
import math
import os
import pathlib
from functools import reduce
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
from experiment_definitions import ExperimentDefinitions
from data_collectors import MemtierCollector, MiddlewareCollector
class ... | pd.wide_to_long(get_copy, stubnames='Server', i=names, j='Server_ID') | pandas.wide_to_long |
# Copyright 2020 <NAME>. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agree... | pd.concat(df) | pandas.concat |
"""Methods for training an agent."""
import os
import sys
import datetime
import pandas as pd
from matplotlib import pyplot as plt
from .setup_env import setup_env
def train(env_id: str, output_dir: str, monitor: bool=False) -> None:
"""
Train an agent to actuate a certain environment.
Args:
env_... | pd.concat([rewards, losses], axis=1) | pandas.concat |
# The published output of this file currently lives here:
# http://share.streamlit.io/0.23.0-2EMF1/index.html?id=8hMSF5ZV3Wmbg5sA3UH3gW
import keras
import math
import numpy as np
import pandas as pd
import streamlit as st
from scipy.sparse.linalg import svds
from sklearn.metrics import mean_squared_error
from sklearn... | pd.read_csv('../data/ml-100k/u.item', sep='|', names=movie_cols, encoding='latin-1') | pandas.read_csv |
from data_handler.graph_class import Graph,wl_labeling
import networkx as nx
#from utils import per_section,indices_to_one_hot
from collections import defaultdict
import numpy as np
import math
import os
from tqdm import tqdm
import pickle
import pandas as pd
#%%
def indices_to_one_hot(number, nb_classes,label_dummy=-1... | pd.merge(mean_aggreg_df,std_aggreg_df) | pandas.merge |
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | pd.testing.assert_frame_equal(df, mdf) | pandas.testing.assert_frame_equal |
import matplotlib
import numpy as np
import pandas as pd
from singlecellmultiomics.utils import is_main_chromosome, get_contig_list_from_fasta
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import pysam
import seaborn as sns
from matplotlib.patches import Circle
from itertools import product
imp... | pd.isna(allele) | pandas.isna |
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