prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pandas as pd
from py_expression_eval import Parser
math_parser = Parser()
def _get_mz_tolerance(qualifiers, mz):
if qualifiers is None:
return 0.1
if "qualifierppmtolerance" in qualifiers:
ppm = qualifiers["qualifierppmtolerance"]["value"]
mz_tol = abs(ppm * mz / 1000000)
... | pd.DataFrame() | pandas.DataFrame |
import datetime
import pandas as pd
import plotly.express as px
import streamlit as st
def clean_dataframe(df):
df = df.drop(columns=[0])
df.rename(
columns={
1: "errand_date",
2: "scrape_time",
3: "rekyl_id",
4: "status",
5: "reporter",
... | pd.to_datetime(df["Datum"]) | pandas.to_datetime |
#!/usr/bin/env python
# coding: utf-8
# ## Pandas
# In[1]:
import pandas as pd
import os
# In[2]:
os.getcwd()
# In[7]:
titanic_df=pd.read_csv('/Users/kangjunseo/python programming/파이썬 머신러닝 완벽 가이드/titanic_train.csv')
titanic_df.head(3)
# In[8]:
print(type(titanic_df))
print(titanic_df.s... | pd.set_option('display.width',1000) | pandas.set_option |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Date : 2020-12
# @Author : <NAME>
'''
This file will help you crawl city's stations and plan route by Dijkstra algorithm
- Amap: https://lbs.amap.com/api/webservice/summary
- 本地宝：http://sh.bendibao.com/ditie/linemap.shtml
'''
import requests
from bs4 import... | pd.DataFrame(columns=['name','site']) | pandas.DataFrame |
"""Classes and functions related to the management of sets of BIDSVariables."""
from copy import copy
import warnings
import re
from collections import OrderedDict
from itertools import chain
import fnmatch
import numpy as np
import pandas as pd
from pandas.api.types import is_numeric_dtype
from .variables import (
... | is_numeric_dtype(v.values) | pandas.api.types.is_numeric_dtype |
# To mine the required data from Reddit
import praw
import pandas as pd
# from textblob import TextBlob
# import re
reddit = praw.Reddit(client_id='O819Gp7QK8_o5A', client_secret='<KEY>', user_agent='Reddit WebScraping')
def top_posts(topic):
posts=[]
try:
f_subreddit = reddit.subreddit(topic)
... | pd.DataFrame(posts,columns=['title', 'score', 'id', 'num_comments']) | pandas.DataFrame |
# Script use to collect incumbents_v5.pkl
# Uses incumbents_v4.pkl and reorders the list in a semi-deterministic manner
import pickle
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.spatial import distance_matrix
# from scipy.spatial.distance import euclidean
from scipy.spatial import... | pd.DataFrame(ranked_list, columns=model.columns) | pandas.DataFrame |
# __author__ : slade
# __time__ : 17/12/21
import pandas as pd
import numpy as np
from xgboost.sklearn import XGBClassifier
import random
from data_preprocessing import data_preprocessing
from sklearn.externals import joblib
# load data
path1 = 'ensemble_data.txt'
train_data = pd.read_table(path1)
# change columns
tr... | pd.get_dummies(meaningful_data[i], prefix=i) | pandas.get_dummies |
import os
from datetime import date
from dask.dataframe import DataFrame as DaskDataFrame
from numpy import nan, ndarray
from numpy.testing import assert_allclose, assert_array_equal
from pandas import DataFrame, Series, Timedelta, Timestamp
from pandas.testing import assert_frame_equal, assert_series_equal
from pymo... | assert_frame_equal(move_df, expected) | pandas.testing.assert_frame_equal |
# coding: utf-8
"""Extract vertical profiles from RHI and PPI.
Authors: <NAME> and <NAME>
"""
from glob import glob
from os import path
from datetime import datetime, timedelta
import pyart
import numpy as np
import pandas as pd
import scipy.io as sio
import matplotlib.pyplot as plt
from radcomp.tools import db2lin... | pd.concat(vps) | pandas.concat |
import math
import sys
import heapq
import time
import re
import pandas as pd
import numpy as np
from collections import namedtuple
from empress.compare import Default_Cmp
from empress.compare import Balace_Cmp
from empress.tree import Tree
from empress.tree import DEFAULT_COLOR
from empress.tree import SELECT_COLOR
im... | pd.DataFrame(triangles) | pandas.DataFrame |
# -*- encoding: utf-8 -*-
#
# Copyright © 2016 Red Hat, Inc.
# Copyright © 2014-2015 eNovance
#
# 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... | pandas.tseries.offsets.Nano(value * 10e8) | pandas.tseries.offsets.Nano |
'''
CONGESTION ANALYSIS TOOL
Approach & Idea : <NAME>
Author : <NAME>
Acknowledgments : Energy Exemplar Solution Engineering Team
'''
import csv
import pandas as pd
import os
import time
import sys, re
import csv
import numpy as np
from pandas.io.common import EmptyDataError
from sympy import symbol... | pd.read_csv(temp_file) | pandas.read_csv |
from IPython.display import HTML
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
from IPython.display import YouTubeVideo
from scipy.spatial.distance import pdist, squareform
from scipy.cluster.hierarchy import linkage, dendrogram
from matplotlib.colors import ListedCo... | pd.read_csv(data_path+ Species_file_name) | pandas.read_csv |
import calendar
from datetime import datetime
import locale
import unicodedata
import numpy as np
import pytest
import pandas as pd
from pandas import (
DatetimeIndex,
Index,
Timedelta,
Timestamp,
date_range,
offsets,
)
import pandas._testing as tm
from pandas.core.arrays import DatetimeArray
... | tm.assert_index_equal(res, exp) | pandas._testing.assert_index_equal |
'''
process_timeseries_files_pipeline.py
Processes precipitation timeseries data from raster files downloaded from the
NASA GPM mission.
Author: <NAME>
Date: 17/01/2022
'''
import numpy as np
import xarray as xr
import rioxarray
import datetime
import re
import sys
import argparse
import glob
impor... | pd.to_datetime(date_list) | pandas.to_datetime |
import auxilary_functions as f
cfg = f.get_actual_parametrization("../src/config-human.json")
#cfg = f.update_cfg("../src/config.json", "NETWORK_TO_SEARCH_IN", "yeast")
import psutil
import os
import numpy as np
import pandas as pd
import sys
import joblib
sys.path.insert(0, "../src")
ART_NET_PATH = "../networks"
impo... | pd.Series(edges_1_part) | pandas.Series |
import json
import pandas as pd
import os
import re
def create_entry(raw_entry,hashfunction,encoding):
return_dict = {}
app_metadata = {'is_god':raw_entry['is_admin']}
if not pd.isna(raw_entry['organisation_id']):
app_metadata['organisation_id'] = round(raw_entry['organisation_id'])
if not pd.... | pd.read_csv('users.csv') | pandas.read_csv |
import json
from django.http import HttpResponse
from .models import (
Invoice,
Seller,
Receiver,
)
from .serializers import (
InvoiceSerializer,
SellerSerializer,
ReceiverSerializer,
)
import re
from django.views import View
from django.http import Http40... | pd.DataFrame({'date': sf.index, 'freight': sf.values}) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2020 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-... | pd.testing.assert_series_equal(expected, res) | pandas.testing.assert_series_equal |
import json
import networkx as nx
import numpy as np
import os
import pandas as pd
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm
from config import logger, config
def read_profile_data():... | pd.read_csv(config.train_query_file, usecols=['sid','req_time']) | pandas.read_csv |
from datetime import datetime
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
CategoricalIndex,
DataFrame,
Index,
MultiIndex,
Series,
qcut,
)
import pandas._testing as tm
def cartesian_product_for_groupers(result, args, names, fill... | Index([1997], name="A") | pandas.Index |
"""
Market data import and transformation functions
"""
import calendar
from collections import Counter
import copy
from datetime import date
import time
from urllib.request import FancyURLopener
import warnings
import datetime as dt
from bs4 import BeautifulSoup
from lxml import html
import numpy as np
import pandas ... | pd.to_datetime(params['start_date']) | pandas.to_datetime |
import pandas as pd
import numpy as np
import multiprocessing as mp
from tqdm import tqdm
import h5py
import os
###########################################
def match_profile_coords():
# After applying profile mask, the masked df_profile should match the df_beads on both coordinates and seq.
amino_acids = pd.r... | pd.read_csv(f'{profile_dir}/{p1}') | pandas.read_csv |
import pandas as pd
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
data= | pd.read_excel(r"C:\Users\PIYUSH\Desktop\data\maindata.xlsx",sheet_name=None) | pandas.read_excel |
# Importing Libraries:
import pandas as pd
import numpy as np
import pickle
# for displaying all feature from dataset:
| pd.pandas.set_option('display.max_columns', None) | pandas.pandas.set_option |
import pandas as pd
import numpy as np
import time
import bs4
import string
import os
from bs4 import BeautifulSoup
from selenium import webdriver
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
#-----GLOBAL-VARIABLES--------
# List of relevant tags
medium_tags_df = pd.read_csv('medium_tag_... | pd.DataFrame(data=main_db) | pandas.DataFrame |
import codecademylib3_seaborn
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Import the CSV files and create the DataFrames:
user_data = pd.read_csv("user_data.csv")
pop_data = pd.read_csv("pop_data.csv")
# Paste print code here:
print(user_data.head(15))
# Paste merge code here:
new_df ... | pd.read_csv("user_data.csv") | pandas.read_csv |
#Creo el dataset para la predicción del boosting
import gc
gc.collect()
import pandas as pd
import seaborn as sns
import numpy as np
#%% marzo
marzo = pd.read_csv(r'C:\Users\argomezja\Desktop\Data Science\MELI challenge\Project MELI\Dataset_limpios\marzo_limpio.csv.gz')
marzo = marzo.loc[marzo['day']>=4].r... | pd.merge(final, subtest7, left_index=True, right_index=True) | pandas.merge |
from datetime import datetime
import numpy as np
import pandas as pd
import pygsheets
import json
with open('./config.json') as config:
creds = json.load(config)['google']
def convert_int(value):
value = str(value).lower()
value = value.replace('fewer than five', '0')
value = value.replace('fewer than... | pd.read_csv(f'./data/raw/{fname}.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 21 23:30:41 2019
@author:
using to select event from semantic lines and visiuize LDA to check consistency
"""
import os
#import sys
import argparse
import json
import numpy as np
from LDA import lda_model, corp_dict
#import random as rd
#from gensim.models import Coh... | pd.to_datetime(time_index) | pandas.to_datetime |
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def object_creation():
s = pd.Series([1, np.nan])
dates = pd.date_range('20130101', periods=2)
df = pd.DataFrame(np.random.randn(2, 3), index=dates, columns=list('ABC'))
df2 = pd.DataFrame({'A': pd.Timestamp('20130102'),
... | pd.read_csv('tmp/foo.csv') | pandas.read_csv |
import pandas as pd
import os
from configparser import ConfigParser, NoOptionError, NoSectionError
from datetime import datetime
import statistics
import numpy as np
import glob
from simba.drop_bp_cords import *
from simba.rw_dfs import *
def time_bins_movement(configini,binLength):
dateTime = datetime.... | pd.concat([csv_df, csv_df_shifted], axis=1) | pandas.concat |
import numpy as np
import pandas as pd
url = 'Reordered Linescan_nro 31 label JORDAN_234_P1_201901271901_MGA94_55.csv'
dfdos = | pd.read_csv(url) | pandas.read_csv |
import pandas as pd
#import arrow
def fips_glue(row):
x = int(str(row['STATE']) + str(row['COUNTY']).zfill(3))
return x
def pop_mortality(row):
x = float("{:.4f}".format(row['Deaths'] / row['POPESTIMATE2019']* 100))
return x
def case_mortality(row):
if row['Confirmed'] == 0:
return 0
... | pd.merge(dem_df,cvd_df, on='FIPS') | pandas.merge |
import pandas as pd
import numpy as np
svy18 = | pd.read_csv('Survey_2018.csv') | pandas.read_csv |
from collections import Counter
import pandas as pd
import networkx as nx
from biometrics.utils import get_logger
logger = get_logger()
class Cluster:
def __init__(self, discordance_threshold=0.05):
self.discordance_threshold = discordance_threshold
def cluster(self, comparisons):
assert... | pd.isna(x) | pandas.isna |
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 |
import datetime
try:
import pandas as pd
from pandas.testing import assert_index_equal
except ImportError:
pd = None
import numpy as np
import bsonnumpy
from test import client_context, unittest
def to_dataframe(seq, dtype, n):
data = bsonnumpy.sequence_to_ndarray(seq, dtype, n)
if '_id' in dty... | pd.DataFrame(data) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import os
import joblib
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_selection import mutual_info_classif
from dl_omics import create_l1000_df
from utils import create_umap_df, scatter... | pd.DataFrame({'Gene': selected_features, 'Coefficient': coefficients}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
#
# Copyright (c) 2018 Leland Stanford Junior University
# Copyright (c) 2018 The Regents of the University of California
#
# This file is part of the SimCenter Backend Applications
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provi... | pd.DataFrame({'del_par': del_par}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 25 16:42:21 2019
@author: owen.henry
"""
from __future__ import division
#Pyodbc is used to connect to various databases
from pyodbc import connect
#CespanarVariables is my own script to track variables like database names and
#drivers between scripts. For general use t... | pandas.to_datetime(df[datecolumn]) | pandas.to_datetime |
# coding=utf-8
# Author: <NAME>
# Date: Jun 30, 2019
#
# Description: Indexes certain genes and exports their list.
#
#
import math
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
import argparse
from utils impo... | pd.isnull(x) | pandas.isnull |
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
import dash_html_components as html
import dash
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import pandas as pd
import metabolyze as met
from dash.dependencies import... | pd.DataFrame(row) | pandas.DataFrame |
import numpy as np
from datetime import timedelta
from distutils.version import LooseVersion
import pandas as pd
import pandas.util.testing as tm
from pandas import to_timedelta
from pandas.util.testing import assert_series_equal, assert_frame_equal
from pandas import (Series, Timedelta, DataFrame, Timestamp, Timedelt... | TimedeltaIndex([], freq='D') | pandas.TimedeltaIndex |
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 14 17:55:40 2020
@author: Erick
"""
import pandas as pd
import numpy as np
from scipy import optimize
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy.linalg import svd
import matplotlib.gridspec as gridspec
import os
import matplotlib.tic... | pd.read_csv(csv_data) | pandas.read_csv |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# Created by <NAME>
import unittest
import pandas as pd
import pandas.testing as pdtest
from allfreqs import AlleleFreqs
from allfreqs.classes import Reference, MultiAlignment
from allfreqs.tests.constants import (
REAL_ALG_X_FASTA, REAL_ALG_X_NOREF_FASTA, REAL_RSRS_F... | pdtest.assert_frame_equal(self.af.frequencies, exp_freqs) | pandas.testing.assert_frame_equal |
import matplotlib
import matplotlib.pyplot as plt
import nibabel as nib
import numpy as np
import os
import pandas as pd
matplotlib.use('agg')
def get_whole_tumor_mask(data):
return data > 0
def get_tumor_core_mask(data):
return np.logical_or(data == 1, data == 4)
def get_enhancing_tumor_mask(data):
... | pd.DataFrame.from_records(rows, columns=header, index=subject_ids) | pandas.DataFrame.from_records |
import json
from datetime import datetime
import os
import shutil
import re
import pandas as pd
import seaborn as sns
import matplotlib.ticker as ticker
import matplotlib.pyplot as plt
import configuration as c
from scipy.stats import mannwhitneyu
import numpy as np
all_apps_permissions_counts = []
permission_counts... | pd.Series(protection_level_app_frequencies_covid) | pandas.Series |
from flask import Response, url_for, current_app, request
from flask_restful import Resource, reqparse
import pandas as pd
import os
from pathlib import Path
from flask_mysqldb import MySQL
from datetime import datetime
import random
import string
from flask_mail import Mail, Message
db = MySQL()
parser = reqparse.Req... | pd.DataFrame(json_data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Tue May 18 21:25:39 2021
@author: alber
"""
import sys, os
import pandas as pd
import numpy as np
import time
import pickle
import six
sys.modules["sklearn.externals.six"] = six
from joblib import Parallel, delayed
from itertools import combinations, permutations, product
fro... | pd.DataFrame() | pandas.DataFrame |
import unittest
import qteasy as qt
import pandas as pd
from pandas import Timestamp
import numpy as np
from numpy import int64
import itertools
import datetime
from qteasy.utilfuncs import list_to_str_format, regulate_date_format, time_str_format, str_to_list
from qteasy.utilfuncs import maybe_trade_day, is_market_tr... | pd.to_datetime(date_christmas) | pandas.to_datetime |
import os
import pandas as pd
import numpy as np
import datetime
import gc
class Dataset(object):
def __init__(self, train_path = 'train.csv', test_path = 'test.csv', hist_trans_path = 'historical_transactions.csv', new_trans_path='new_merchant_transactions.csv',
new_merc_path='merchants.csv', ba... | pd.to_datetime(df['purchase_date']) | pandas.to_datetime |
import numpy as np
import csv
import pandas as pd
import matplotlib.pyplot as plt
import math
import tensorflow as tf
import seaborn as sns
import itertools
import operator
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.feature_se... | pd.set_option('display.expand_frame_repr', False) | pandas.set_option |
from typing import Callable
import numpy as np
import pandas as pd
from tqdm import tqdm
# filter document types
DOC_TYPES_TO_REMOVE = [
'aaib_report',
'answer',
'asylum_support_decision',
'business_finance_support_scheme',
'cma_case',
'countryside_stewardship_grant',
'drug_safety_update',... | pd.DataFrame(collected_doc_embeddings) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sun Oct 10 08:48:34 2021
@author: PatCa
"""
import numpy as np
import pandas as pd
import joblib
from pickle import dump
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
fro... | pd.DataFrame(data=data_pca_array) | pandas.DataFrame |
import sys
sys.path.insert(0, '..')
import pandas as pd
from tqdm import tqdm
from config.config import *
def create_whole_train_split(train_meta, split_name):
train_meta = train_meta.copy()
split_dir = f'{DATA_DIR}/split/{split_name}'
os.makedirs(split_dir, exist_ok=True)
print('train nums: %s' % train_meta.... | pd.merge(train_df, train_split_df, on=[ID, TARGET], how='left') | pandas.merge |
# -*- 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... | DataFrame({'A': [1, 0, 1, 0], 'B': [1, 1, 0, 0]}) | pandas.DataFrame |
"""
This is a place to create a python wrapper for the BASGRA fortran model in fortarn_BASGRA_NZ
Author: <NAME>
Created: 12/08/2020 9:32 AM
"""
import os
import ctypes as ct
import numpy as np
import pandas as pd
from subprocess import Popen
from copy import deepcopy
from input_output_keys import param_keys, out_co... | pd.api.types.is_integer_dtype(days_harvest.doy) | pandas.api.types.is_integer_dtype |
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 7 13:38:07 2021
@author: bferrari
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.svm import SVC
from itertools import combinations
from xgboost import XGBClassifier
from sklearn.pipeline import Pipeline
from sklearn.model_select... | pd.read_excel('final_results.xlsx') | pandas.read_excel |
# Copyright 2018 Amazon.com, Inc. or its affiliates. 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.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | pd.to_datetime("1999-01-04") | pandas.to_datetime |
# Import required packages
import requests
import json
from spatialite_database import SpatialiteDatabase
import sqlite3
import csv
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
def get_report(data_url_path):
"""
Reads the data from the url and converts to text.
Additionally, i... | pd.read_csv(data_file_path, sep=';') | pandas.read_csv |
from __future__ import division
from datetime import datetime
import sys
if sys.version_info < (3, 3):
import mock
else:
from unittest import mock
import pandas as pd
import numpy as np
from nose.tools import assert_almost_equal as aae
import bt
import bt.algos as algos
def test_algo_name():
class Tes... | pd.date_range('2010-01-01', periods=35) | pandas.date_range |
import unittest
import pandas as pd
import numpy as np
from tests.context import algotrading
from tests.context import dates
from tests.context import get_test_market_a
from tests.context import get_test_market_b
from tests.context import assert_elements_equal
import algotrading.data.features.intra_bar_features as ib... | pd.Series([2.3, 3.4, 3.4, 2.0, np.nan], index=dates) | pandas.Series |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
'''
Created on 2017年06月04日
@author: debugo
@contact: <EMAIL>
'''
import json
import datetime
from bs4 import BeautifulSoup
import pandas as pd
from tushare.futures import domestic_cons as ct
try:
from urllib.request import urlopen, Request
from urllib.parse import u... | pd.DataFrame(json_data['o_currefprice']) | pandas.DataFrame |
import math
import pandas as pd
import csv
import pathlib
import wx
import matplotlib
import matplotlib.pylab as pL
import matplotlib.pyplot as plt
import matplotlib.backends.backend_wxagg as wxagg
import re
import numpy as np
import scipy
import scipy.interpolate
import sys
#from mpl_toolkits.mplot3d import Axes3D
#i... | pd.DataFrame(dataLeadList) | pandas.DataFrame |
# Copyright 2019 Systems & Technology Research, LLC
# Use of this software is governed by the license.txt file.
#!/usr/bin/env python3
import os
import glob
import dill as pickle
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import pdb
import itertools
from xfr import inpainting_game ... | pd.DataFrame(csv_rows) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""L05 Welliton - KNN with Time Audio Features.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1THyHhrvgkGnNdoTOdrDm7I3JMIiazjz4
"""
import os
import random
import librosa
import scipy
import numpy as np
import pandas as p... | pd.DataFrame(y) | pandas.DataFrame |
#Loading libraries
import cv2
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
from scipy import ndimage
import math
import keras
import ast
import operator as op
import re
from tensorflow.keras.preprocessing.image import ImageDataGenerator
#Suppressing warning
def warn(*args, **kwargs)... | pd.concat([df_lines, df]) | pandas.concat |
"""
Medical lexicon NLP extraction pipeline
File contains: Compares the validation set with the NLP pipeline's labeling and outputs some relevant statistics afterwards.
-- (c) <NAME> 2019 - Team D in the HST 953 class
"""
from na_pipeline_tool.utils import logger
from na_pipeline_tool.utils import config
from na_pi... | pd.Series([[1]]*validset.shape[0]) | pandas.Series |
import numpy as np
import pandas as pd
import sys
import os
import pandas.core.indexes
sys.modules['pandas.indexes'] = pandas.core.indexes
import time
import yaml
import json
import matplotlib.pyplot as plt
import keras
import tensorflow as tf
from keras.models import Sequential, load_model, Model
from keras.layers im... | pd.get_dummies(y_val) | pandas.get_dummies |
# CHIN, <NAME>. How to Write Up and Report PLS Analyses. In: Handbook of
# Partial Least Squares. Berlin, Heidelberg: Springer Berlin Heidelberg,
# 2010. p. 655–690.
import pandas
import numpy as np
from numpy import inf
import pandas as pd
from .pylspm import PyLSpm
from .boot import PyLSboot
def isNa... | pd.DataFrame.mean(data2_) | pandas.DataFrame.mean |
# 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.concat([inp, extra_dataframe], axis=0) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 31 02:10:49 2021
@author: mofarrag
"""
import numpy as np
import pandas as pd
import datetime as dt
import os
import gdal
from types import ModuleType
import matplotlib.pyplot as plt
import matplotlib.dates as dates
from Hapi.raster import Raster
from Hapi.giscatchment ... | pd.date_range(self.StartDate, self.EndDate, freq="D") | pandas.date_range |
import io
import requests
import pandas as pd
def request_meteo(year, month, stationID):
"""
Function that calls Meteo Canada's API to extract a weather station's hourly weather data at a given year and
month.
:param year: (int) year
:param month: (int) month
:param stationID: (int) id of the ... | pd.to_datetime(df_meteo["Date/Time"]) | pandas.to_datetime |
import collections
import numpy as np
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Index,
Series,
isna,
)
import pandas._testing as tm
class TestCategoricalMissing:
def test_isna(self):
exp = np... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
from isitfit.utils import logger
from .tagsSuggestBasic import TagsSuggestBasic
from ..utils import MAX_ROWS
import os
import json
from ..apiMan import ApiMan
class TagsSuggestAdvanced(TagsSuggestBasic):
def __init__(self, ctx):
logger.debug("TagsSuggestAdvanced::constructor")
# api manager
self.api_... | pd.read_csv(self.csv_fn, nrows=MAX_ROWS) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Mon May 14 17:29:16 2018
@author: jdkern
"""
from __future__ import division
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
def exchange(year):
df_Path66 = pd.read_csv('../Stochastic_engine/Synthetic_demand_pathflows/syn_Path66.csv',header=0,index... | pd.read_csv('../Stochastic_engine/Synthetic_demand_pathflows/syn_Path3.csv',header=0,index_col=0) | pandas.read_csv |
""" test fancy indexing & misc """
from datetime import datetime
import re
import weakref
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas.core.dtypes.common import (
is_float_dtype,
is_integer_dtype,
)
import pandas as pd
from pandas import (
DataFrame,
Index,... | _mklbl("A", 20) | pandas.tests.indexing.common._mklbl |
import numpy as np
import pandas as pd
import seaborn as sns
import xarray as xa
import tensorly as tl
from .common import subplotLabel, getSetup
from gmm.tensor import minimize_func, gen_points_GMM
def makeFigure():
"""Get a list of the axis objects and create a figure."""
# Get list of axis objects
ax, ... | pd.DataFrame({"Cluster": points[1], "X": points[0][:, 0], "Y": points[0][:, 1]}) | pandas.DataFrame |
import threading
from sklearn import preprocessing
import settings
import pandas as pd
from bson import ObjectId
import json
import datetime
from constants import MAX_FILE_SIZE
from db.encoding import EncodingHelper
class MongoDataStream(object):
def __init__(self, collection, start_date, end_date, ch... | pd.DataFrame(data) | pandas.DataFrame |
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from matplotlib import gridspec
import warnings
import nltk
from shift_detector.checks.check import Report
from shift_detector.checks.statistical_checks.categorical_statistical_check import CategoricalStatisticalCheck
from shift_detector.checks.sta... | pd.DataFrame(columns=df1.columns, index=['pvalue']) | pandas.DataFrame |
from __future__ import division #brings in Python 3.0 mixed type calculation rules
import datetime
import inspect
import numpy as np
import numpy.testing as npt
import os.path
import pandas as pd
import sys
from tabulate import tabulate
import unittest
print("Python version: " + sys.version)
print("Numpy version: " +... | pd.Series([100., 125., 90.], dtype='float') | pandas.Series |
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2021, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | pd.Index(['feat1', 'feat2'], name='id') | pandas.Index |
# ********************************************************************************** #
# #
# Project: FastClassAI workbecnch #
# ... | pd.DataFrame(test_scores) | pandas.DataFrame |
import os
import pandas as pd
import numpy as np
from typing import List
from datetime import datetime
PATH: str = os.path.join('data', 'raw')
DAOSTAK_SRCS: List[str] = [os.path.join(PATH, 'daostack_members.csv')]
DAOHAUS_SRCS: List[str] = [os.path.join(PATH, 'daohaus_members.csv'), os.path.join(PATH, 'daohaus_rage_qu... | pd.to_datetime(dff.loc[:, 'date']) | pandas.to_datetime |
#%%
############################################################################
# IMPORTS
############################################################################
import pandas as pd
import numpy as np
from utils import model_zoo, data_transformer
import argparse
import pickle
import os
#%%
####################... | pd.DataFrame(df) | pandas.DataFrame |
# Imports
from sqlalchemy import String, Integer, Float, Boolean, Column, and_, ForeignKey
from connection import Connection
from datetime import datetime, time, date
import time
from pytz import timezone
import pandas as pd
import numpy as np
import os
from os import listdir
from os.path import isfile, join
from open... | pd.concat(pre_date, ignore_index=True) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 25 16:14:12 2019
@author: <NAME>
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#import graphviz
import os
import seaborn as sns
from scipy.stats import chi2_contingency
os.chdir("E:\PYTHON NOTES\projects\cab fare prediction")
d... | pd.concat([dataset_train2,temp],axis=1) | pandas.concat |
import pandas as pd
import numpy as np
df =pd.read_csv('movies_metadata.csv',low_memory=False)
data={ 'id': df['id'],
'title':df['title'],
'overview':df['overview'],
'poster_path':df['poster_path']
}
mdbEnd= | pd.DataFrame(data) | pandas.DataFrame |
# -*- coding:utf-8 -*-
# !/usr/bin/env python
"""
Date: 2022/1/12 14:55
Desc: 东方财富网-数据中心-股东分析
https://data.eastmoney.com/gdfx/
"""
import pandas as pd
import requests
from tqdm import tqdm
def stock_gdfx_free_holding_statistics_em(date: str = "20210930") -> pd.DataFrame:
"""
东方财富网-数据中心-股东分析-股东持股统计-十大流通股东
... | c(big_df["流通市值统计"]) | pandas.to_numeric |
# -*- coding: utf-8 -*-
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import sklearn
import json
import datetime
import math
from random import randint
import sklearn
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.model_selection import train_test_split
from sklear... | pd.to_datetime(matches_team["Date"]) | pandas.to_datetime |
'''
This convert data from txt to csv
'''
import argparse
import csv
import pandas as pd
parser = argparse.ArgumentParser(
description="data name"
)
parser.add_argument(
"--data",
type=str,
help="choose dataset: spheres, mnist, fmnist, cifar10",
default="spheres",
)
args = parser.parse_args()
... | pd.DataFrame(y) | pandas.DataFrame |
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
# import pandas_datareader.data as web
import plotly.graph_objs as go
from datetime import datetime
import pandas as pd
import numpy as np
import os
import flask
import psycopg2
from pat... | pd.read_sql_query(sql_query_2_3, con) | pandas.read_sql_query |
import sdi_utils.gensolution as gs
import sdi_utils.set_logging as slog
import sdi_utils.textfield_parser as tfp
import pandas as pd
EXAMPLE_ROWS =5
try:
api
except NameError:
class api:
class Message:
def __init__(self,body = None,attributes = ""):
self.body = body
... | pd.DataFrame({'icol': [1, 2, 3, 4, 5], 'col 2': [1, 2, 3, 4, 5], 'col3': [100, 200, 300, 400, 500]}) | pandas.DataFrame |
import pandas as pd
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
... | pd.DataFrame(user_data, index=[0]) | pandas.DataFrame |
#!/usr/bin/python
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
def EMA(DF, N):
return pd.Series.ewm(DF, span=N, min_periods=N - 1, adjust=True).mean()
def MA(DF, N):
return pd.Series.rolling(DF, N).mean()
def SMA(DF, N, M):
DF = DF.fillna(0)
z = len(DF)
var =... | pd.DataFrame(DICT) | pandas.DataFrame |
# Imports
import streamlit as st
import streamlit.components.v1 as components
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import time
import os.path
# ML dependency imports
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.manif... | pd.read_csv("./data/clean/fire_data_clean.csv") | pandas.read_csv |
import pytest
import pandas as pd
from pandas import compat
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from pandas.util.testing import assert_frame_equal, assert_raises_regex
COMPRESSION_TYPES = [None, 'bz2', 'gzip',
pytest.param('xz', marks=td.skip_if_no_lzma)]
... | pd.read_json('{"a": [1, 2, 3], "b": [4, 5, 6]}') | pandas.read_json |
import pandas as pd
import numpy as np
from mvn_historical_drawdowns import read_data
from db_connection import create_connection, odbc_engine
from dateutil.relativedelta import relativedelta
import re
def write_data(df):
engine = create_connection()
tsql_chunksize = 2097 // len(df.columns... | pd.tseries.offsets.YearEnd() | pandas.tseries.offsets.YearEnd |
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