prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import math
from datetime import date, datetime
import pandas as pd
from airflow.models import Variable
from airflow.operators.bash import BashOperator
from airflow.operators.python_operator import PythonOperator
from minio import Minio
from sqlalchemy.engine import create_engine
from airflow import DAG
DEFAULT_ARGS ... | pd.to_datetime(df_["hire_date"]) | pandas.to_datetime |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
#
# ./list-files.py course_id
#
# outputs a summary of the files in a course
# also outputs an xlsx file of the form: files-course_id.xlsx
#
#
#
# with the option "-v" or "--verbose" you get lots of output - showing in detail the operations of the program
#
# Can also be calle... | pd.json_normalize(output) | pandas.json_normalize |
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
hfont = {'fontname':'Helvetica'}
# plt.rcParams['figure.figsize']=(10,8)
# plt.rcParams['font.family'] = 'sans-serif'
# plt.rcParams['font.sans-serif'] = ['Tahoma', 'DejaVu Sans','Lucida Grande', 'Verdana']
plt.rcParams['font.size']=6
# pl... | pd.read_csv(root_path+'/Huaxian_ssa/data/one_step_7_ahead_forecast_pacf/train_samples.csv') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Script to interpolate atmospheric data from UERRA: increase temporal resolution, and save into speed, cosine of angle and sine of angle, pressure and pressure gradient
Created on Mon Apr 11 15:01:26 2022
@author: <NAME> (<EMAIL>)
"""
#%%
import pandas as pd
imp... | pd.DataFrame(data=d) | pandas.DataFrame |
# Project: GBS Tool
# Author: <NAME>, <EMAIL>
# Date: October 24, 2017
# License: MIT License (see LICENSE file of this package for more information)
#Reads a dataframe and ouputs a new dataframe with the specified sampling time interval.
#interval is a string with time units. (i.e. '30s' for 30 seconds, '1T' for 1 mi... | pd.to_timedelta(1, unit='d') | pandas.to_timedelta |
import argparse
import pandas as pd
import numpy as np
from datetime import timedelta
def set_interval(df, interval, agg):
df_sampled = df.resample(interval).agg(agg)
period = {"H": 24, "30min": 48, "10min": 144, "min": 1440}
return df_sampled, period[interval]
"""Script for generating cluster sequence ... | pd.DataFrame() | pandas.DataFrame |
"""Pre-process accessibility-based provincial OD matrix
Purpose
-------
Create province scale OD matrices between roads connecting villages to nearest communes:
- Net revenue estimates of commune villages
- IFPRI crop data at 1km resolution
Input data requirements
-----------------------
1. Correct paths to... | pd.ExcelWriter(flow_output_excel) | pandas.ExcelWriter |
#--------------------------------------------------------------- Imports
from dotenv import load_dotenv
import alpaca_trade_api as tradeapi
import os
from pathlib import Path
import string
import pandas as pd
import numpy as np
import seaborn as sns
import panel as pn
from panel.interact import interact, interactive, f... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
# run in py3 !!
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1";
import tensorflow as tf
config = tf.ConfigProto()
# config.gpu_options.per_process_gpu_memory_fraction=0.5
config.gpu_options.allow_growth = True
tf.Session(config=config)
import numpy as np
from sklearn import preprocessing... | pd.DataFrame(y_test_) | pandas.DataFrame |
# Copyright 2019 Verily Life Sciences LLC
#
# Use of this source code is governed by a BSD-style
# license that can be found in the LICENSE file.
import datetime
import unittest
from re import escape
from typing import Any, List, Optional, Sequence, Tuple, Union, cast # noqa: F401
import numpy as np
import pandas as... | pd.Timestamp('2019-12-01') | pandas.Timestamp |
import dash
from dash import html
from dash import dcc
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
### plot and layout
import pandas as pd
import altair as alt
import geopandas as gpd
from si_prefix import si_format
data = | pd.read_csv("./data/processed/cleaned_salaries.csv") | pandas.read_csv |
import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn import model_selection
from sklearn import preprocessing
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from math import sqrt
import seaborn a... | pd.read_csv(filepath) | pandas.read_csv |
import numpy as np
import pandas as pd
import math
from abc import ABC, abstractmethod
from scipy.interpolate import interp1d
from pydoc import locate
from raymon.globals import (
Buildable,
Serializable,
DataException,
)
N_SAMPLES = 500
from raymon.tags import Tag, CTYPE_TAGTYPES
class Stats(Serializa... | pd.isnull(value) | pandas.isnull |
import pandas as pd
import numpy as np
from dash_website.utils.aws_loader import load_feather
from dash_website.utils.graphs import heatmap_by_sorted_dimensions
from dash_website import DOWNLOAD_CONFIG, GRAPH_SIZE
def get_data_upper_comparison(uni_or_multi, category):
return load_feather(
f"xwas/{uni_or_... | pd.DataFrame(np.nan, index=ORDER_DIMENSIONS, columns=ORDER_DIMENSIONS) | pandas.DataFrame |
# ###########################################################################
#
# CLOUDERA APPLIED MACHINE LEARNING PROTOTYPE (AMP)
# (C) Cloudera, Inc. 2020
# All rights reserved.
#
# Applicable Open Source License: Apache 2.0
#
# NOTE: Cloudera open source products are modular software products
# made up of hu... | pd.concat([in_liers, out_liers], axis=0) | pandas.concat |
# -*- coding: utf-8 -*-
"""
This file combines all data loading methods into a central location.
Each type of data has a class that retrieves, processes, and checks it.
Each class has the following methods:
get - retrieves raw data from a source
adapt - transforms from the raw data to the common process... | pd.Series(1.0, index=index, name="value") | pandas.Series |
'''
'''
from importlib.util import find_spec
import numpy as np
import pandas as pd
from . import __validation as valid
from .__validation import ValidationError
def prep_X(data):
""" Ensures that data are in the correct format
Returns:
pd.DataFrame: formatted "X" data (test data)
"""
if no... | pd.DataFrame(data, columns=['X']) | pandas.DataFrame |
# --------------
import pandas as pd
import scipy.stats as stats
import math
import numpy as np
import warnings
warnings.filterwarnings('ignore')
#Sample_Size
sample_size=2000
#Z_Critical Score
z_critical = stats.norm.ppf(q = 0.95)
# path [File location variable]
data=pd.read_csv(path)
#Cod... | pd.concat([yes.T, no.T], axis=1, keys=['Yes', 'No']) | pandas.concat |
import numpy as np
import pandas as pd
from anndata import AnnData
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.colors import rgb2hex
from matplotlib.patches import ConnectionPatch
from typing import Union, Optional
from scanpy.plotting._utils import savefig_or... | pd.concat(adata.uns[name]["corAB"]["B"]) | pandas.concat |
# AUTOGENERATED! DO NOT EDIT! File to edit: DataPipelineNotebooks/3.PrepMLData.ipynb (unless otherwise specified).
__all__ = ['PrepML']
# Cell
import xarray as xr
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
import time
from functools import partial
from datetime import datetime
import... | pd.Series(X.variable.data) | pandas.Series |
from datetime import datetime
import numpy as np
from pandas.tseries.frequencies import get_freq_code as _gfc
from pandas.tseries.index import DatetimeIndex, Int64Index
from pandas.tseries.tools import parse_time_string
import pandas.tseries.frequencies as _freq_mod
import pandas.core.common as com
import pandas.core... | _gfc(self.freq) | pandas.tseries.frequencies.get_freq_code |
from flask import Flask, render_template, jsonify, request
from flask_pymongo import PyMongo
from flask_cors import CORS, cross_origin
import json
import collections
import numpy as np
import re
from numpy import array
from statistics import mode
import pandas as pd
import warnings
import copy
from joblib import Mem... | pd.DataFrame.from_dict(DataResults) | pandas.DataFrame.from_dict |
import pandas as pd
import numpy as np
import sklearn
import os
import sys
sys.path.append('../../code/scripts')
from dataset_chunking_fxns import add_stratified_kfold_splits
# Load data into pd dataframes and adjust feature names
data_dir = '../../data/adult'
file_train = os.path.join(data_dir, 'adult.data')
file_t... | pd.get_dummies(test_df['workclass']) | pandas.get_dummies |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Deep Recurrent Reinforcement Learning: 1 capa LSTM y 4 capas Dense, Funcion de activacion tanh, 12 episodes, 50 iteraciones
drnnLSTMtanhMakespan0=[799, 798, 799, 799, 805, 806, 799, 805, 805, 800, 798, 798]
drnnLSTMtanhMakespan1=[800, 798, 796, 8... | pd.Series(drlTanhRewardsValues) | pandas.Series |
"""
.. module:: text_processing_methods
:synopsis: Holding processing classes!
.. moduleauthor:: <NAME>
"""
import os
import re
from abc import ABC, abstractmethod
from typing import List
import gensim
import pandas as pd
import numpy as np
from nltk.util import ngrams
from pattern.en import parse
from difflib impo... | pd.DataFrame(all_tokens, columns=['tokens']) | pandas.DataFrame |
import sys
import os
import csv
import math
import json
from datetime import datetime
import cv2
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import PowerTransformer
from sklearn.decompos... | pd.read_csv(path, header=None) | pandas.read_csv |
import numpy as np
from numpy import where
from flask import Flask, request, jsonify, render_template
import pandas as pd
from sklearn.ensemble import IsolationForest
from pyod.models.knn import KNN
import json
from flask import send_from_directory
from flask import current_app
app = Flask(__name__)
class Detect:
... | pd.DataFrame(self.file) | pandas.DataFrame |
from collections import OrderedDict
import numpy as np
import pandas as pd
from .helpers import Interval
from .helpers import path_leaf
from .fasta import FastaReader
from Bio.Seq import Seq
from Bio.Alphabet import generic_dna
def counts_to_tpm(counts, sizes):
"""Counts to TPM
Parameters
----------
c... | pd.read_csv(indexfile, sep="\t", usecols=["gene_id", "ORF_type", "coordinate"]) | pandas.read_csv |
import pandas as pd
import matplotlib.pyplot as plt
def plot_feature_importances(rf, cols, model_dir='.'):
importances = | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import cv2
import csv
import os
import pandas as pd
import time
def calcuNearestPtsDis2(ptList1):
''' Find the nearest point of each point in ptList1 & return the mean min_distance
Parameters
----------
ptList1: numpy array
points' array, shape:(x,2)
Return
... | pd.read_csv( csv_dir+'/'+ picID +'other_lymph_pts.csv') | pandas.read_csv |
import itertools
import pandas
import logging
import requests
import json
from datetime import datetime
_BASE_CRYPTO_COMPARE_URL = 'https://min-api.cryptocompare.com/data'
def load_crypto_compare_data(currencies, reference_currencies, exchange, time_scale):
"""
:param currencies: list of currency pairs to r... | pandas.DataFrame(data) | pandas.DataFrame |
#!/usr/bin/env python
from __future__ import print_function
import sys
import scipy
from numpy import *
from scipy import stats
import pandas as pd
import matplotlib.pyplot as plt
import glob
import re
import networkx as nx
from itertools import combinations, product
from scipy.interpolate import interp1d
import argpar... | pd.read_csv(file_comb[1], sep='\t') | pandas.read_csv |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
import json
from matplotlib import pyplot as plt
import numpy as np
# Configuration
anomaly_color = 'sandybrown'
prediction_color = 'yellowgreen'
training_color = 'yellowgreen'
validation_color = 'gold'
test_color = 'coral'
figsize=(12, 4)
def load_se... | pd.to_datetime(labels) | pandas.to_datetime |
import re
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import IntervalArray
class TestSeriesReplace:
def test_replace_explicit_none(self):
# GH#36984 if the user explicitly passes value=None, give it to them
ser = pd.Series([0, 0, ""],... | tm.assert_produces_warning(FutureWarning) | pandas._testing.assert_produces_warning |
# -*- coding: utf-8 -*-
"""
This file is part of the Shotgun Lipidomics Assistant (SLA) project.
Copyright 2020 <NAME> (UCLA), <NAME> (UCLA), <NAME> (UW).
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 Licen... | pd.read_excel(sp_dict1_loc, sheet_name='POS', header=0, index_col=None, na_values='.') | pandas.read_excel |
from datetime import datetime, timedelta
import warnings
import operator
from textwrap import dedent
import numpy as np
from pandas._libs import (lib, index as libindex, tslib as libts,
algos as libalgos, join as libjoin,
Timedelta)
from pandas._libs.lib import is_da... | _concat._concat_index_asobject(to_concat, name=name) | pandas.core.dtypes.concat._concat_index_asobject |
# coding=utf-8
# Author: <NAME>
# Date: Jul 05, 2019
#
# Description: Maps DE genes to String-DB. Keeps only those genes that we want.
#
# NOTE: For some reason, "dmelanogaster_gene_ensembl" did not retrieve all gene names. Some were manually added at the end.
#
import math
import pandas as pd
pd.set_option('display.ma... | pd.read_csv(rCSVFileCT, index_col=0) | pandas.read_csv |
from context import dero
import pandas as pd
from pandas.util.testing import assert_frame_equal
from pandas import Timestamp
from numpy import nan
import numpy
class DataFrameTest:
df = pd.DataFrame([
(10516, 'a', '1/1/2000', 1.01),
(10516, 'a'... | Timestamp('2000-01-01 00:00:00') | pandas.Timestamp |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import os, sys
import pandas as pd
import numpy as np
from glob import glob
# In[2]:
days = sorted(
glob('./huabei/wanlong/*'),
key = lambda x: int( os.path.basename(x) )
)
# In[3]:
for day in days:
print (os.path.basename(day) )
files = sorte... | pd.read_pickle('./bulk/'+day+'.pkl') | pandas.read_pickle |
# -*- coding: UTF-8 -*-
"""
此脚本用于展示内生性对模型的影响
"""
# 保证脚本与Python3兼容
from __future__ import print_function
import sys
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas.tools.plotting import scatter_matrix
import statsmodels.api as sm
from statsmodels.sandbox.regression.gmm import IV2SL... | pd.DataFrame() | pandas.DataFrame |
# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/dev-05-price-moe.ipynb (unless otherwise specified).
__all__ = ['construct_dispatchable_lims_df', 'construct_pred_mask_df', 'AxTransformer', 'set_ticks', 'set_date_ticks',
'construct_df_pred', 'construct_pred_ts', 'calc_error_metrics', 'get_model_pred_ts', 'we... | pd.to_datetime(df_pred_mask.columns) | pandas.to_datetime |
# coding: utf-8
# CS FutureMobility Tool
# See full license in LICENSE.txt.
import numpy as np
import pandas as pd
#import openmatrix as omx
from IPython.display import display
from openpyxl import load_workbook,Workbook
from time import strftime
import os.path
import mode_choice.model_defs as md
import mode_choice.ma... | pd.concat([town_definition,zone_daily_o],axis=1,join='inner') | pandas.concat |
import os
import cv2
import glob
import csv
import argparse
import pandas as pd
from tqdm import tqdm
import linecache, shutil
from joblib import Parallel, delayed
parser = argparse.ArgumentParser(description='cropping by class')
parser.add_argument('-cn', '--change_name', help='change "OO_checked" to "OO_cropped" (y/... | pd.read_csv(csvpath_read, index_col=0) | pandas.read_csv |
from __future__ import print_function
import pandas as pd
import os
import logging
import argparse
'''
This file reads in data related E. coli levels
in Chicago beaches. It is based on the files
analysis.R and split_sheets.R, and is written
such that the dataframe loaded here will match
the R dataframe code exactly.
'... | pd.concat(dfs) | pandas.concat |
# Copyright 2020, Sophos Limited. All rights reserved.
#
# 'Sophos' and 'Sophos Anti-Virus' are registered trademarks of
# Sophos Limited and Sophos Group. All other product and company
# names mentioned are trademarks or registered trademarks of their
# respective owners.
import torch
import baker
from ne... | pd.DataFrame(results, index=shas) | pandas.DataFrame |
# -*- coding:utf-8 -*-
import numpy as np
import pandas as pd
# import random
# from datetime import datetime, timedelta
# import time
# import re
# from sklearn.externals import joblib
# import requests
# import sys
# from unit import Distance1
# import xlrd
from unit import *
from crawl_data import *
base_path_1 = "... | pd.to_datetime(df['time']) | pandas.to_datetime |
import argparse
import json
import os
import random
from pprint import pprint
import pandas as pd
import soundfile as sf
import torch
import yaml
from tqdm import tqdm
from asteroid_gan_exps.data.metricGAN_dataset import MetricGAN
from asteroid.losses import PITLossWrapper, pairwise_neg_sisdr
from asteroid.metrics im... | pd.DataFrame(series_list) | pandas.DataFrame |
import pandas as pd
from google.cloud.bigquery import Client
def fetch_eth_blocks(client: Client, start_date: str, end_date: str):
sql = f"""
SELECT blocks.timestamp, blocks.number, blocks.transaction_count, blocks.gas_limit, blocks.gas_used, AVG(txs.gas_price) AS mean_gas_price, MIN(txs.gas_price) AS min_ga... | pd.to_datetime(df["timestamp"]) | pandas.to_datetime |
from datetime import timedelta
from functools import partial
from operator import attrgetter
import dateutil
import numpy as np
import pytest
import pytz
from pandas._libs.tslibs import OutOfBoundsDatetime, conversion
import pandas as pd
from pandas import (
DatetimeIndex, Index, Timestamp, date_range, datetime,... | date_range(start=sdate, end=edate, freq='W-SUN') | pandas.date_range |
# -*- coding: utf-8 -*-
from __future__ import print_function
import nose
from numpy import nan
from pandas import Timestamp
from pandas.core.index import MultiIndex
from pandas.core.api import DataFrame
from pandas.core.series import Series
from pandas.util.testing import (assert_frame_equal, assert_series_equal
... | DataFrame([[1, 2], [1, 3], [5, 6]], columns=['A', 'B']) | pandas.core.api.DataFrame |
import pickle
import numpy as np
import pandas as pd
## plot conf
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 7})
width = 8.5/2.54
height = width*(3/4)
###
import os
script_dir = os.path.dirname(os.path.abspath(__file__))
plot_path = './'
male_rarities, female_rarities = pickle.load(open(script_... | pd.read_csv('../R/original_bootstrapped/female_bootstrapped.csv', index_col=0) | pandas.read_csv |
import pathlib
import pandas as pd
from palmnet.visualization.utils import get_palminized_model_and_df, get_df
import matplotlib.pyplot as plt
import numpy as np
import logging
import plotly.graph_objects as go
import plotly.io as pio
mpl_logger = logging.getLogger('matplotlib')
mpl_logger.setLevel(logging.ERROR)
pio.... | pd.read_csv(src_results_path_tucker, header=0) | pandas.read_csv |
import os
import argparse
import sys
sys.path.append('../')
from load_paths import load_box_paths
from processing_helpers import *
import pandas as pd
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
mpl.rcParams['pdf.f... | pd.concat([civis_template_all, civis_template]) | pandas.concat |
import pathlib
import pytest
import pandas as pd
import numpy as np
import gradelib
EXAMPLES_DIRECTORY = pathlib.Path(__file__).parent / "examples"
GRADESCOPE_EXAMPLE = gradelib.Gradebook.from_gradescope(
EXAMPLES_DIRECTORY / "gradescope.csv"
)
CANVAS_EXAMPLE = gradelib.Gradebook.from_canvas(EXAMPLES_DIRECTORY ... | pd.Series(data=[1, 30, 90, 20], index=columns, name="A1") | pandas.Series |
"""
Module for calculating a list of vegetation indices from a datacube containing bands without a user having to implement callback functions
"""
from openeo.rest.datacube import DataCube
from openeo.processes import ProcessBuilder, array_modify, power, sqrt, if_, multiply, divide, arccos, add, subtract, linear_scale... | pd.DataFrame(df_row) | pandas.DataFrame |
import pandas as pd
import numpy as np
import glob
import data.normalise as nm
from data.duplicates import group_duplicates
from data.features import compute_features
nw_features_disc = {
'Time': {
'func': nm.change_time,
'input': 'time'
},
'Date': {
'func': nm.has_dates,
'input': 'message'
},
'Number': ... | pd.concat(human, [sms.iloc[:, [0, 1, 2]]]) | pandas.concat |
import numpy as np
import pandas as pd
from numba import njit
from datetime import datetime
import pytest
from itertools import product
from sklearn.model_selection import TimeSeriesSplit
import vectorbt as vbt
from vectorbt.generic import nb
seed = 42
day_dt = np.timedelta64(86400000000000)
df = pd.DataFrame({
... | pd.RangeIndex(start=0, stop=4, step=1) | pandas.RangeIndex |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import requests
import json
import pandas as pd
import numpy as np
from pandas.io.json import json_normalize
import folium
import matplotlib.pyplot as plt
from folium.plugins import MarkerCluster
import warnings
warnings.filterwarnings(action='ignore')
# In[2]:
add... | pd.DataFrame() | pandas.DataFrame |
# https://scikit-learn.org/stable/auto_examples/inspection/plot_permutation_importance_multicollinear.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-multicollinear-py
# https://orbi.uliege.be/bitstream/2268/155642/1/louppe13.pdf
# https://proceedings.neurips.cc/paper/2019/file/702cafa3bb4c9c86e4a3b6... | pd.DataFrame() | pandas.DataFrame |
"""
This is the dashboard of CEA
"""
from __future__ import division
from __future__ import print_function
import json
import os
import pandas as pd
import numpy as np
import cea.config
import cea.inputlocator
from cea.plots.optimization.cost_analysis_curve_centralized import cost_analysis_curve_centralized
from cea.... | pd.read_csv(data_activation_path) | pandas.read_csv |
import pandas as pd
import streamlit as st
import yfinance as yf
@st.experimental_memo(max_entries=1000, show_spinner=False)
def get_asset_splits(ticker, cache_date):
return yf.Ticker(ticker).actions.loc[:, 'Stock Splits']
@st.experimental_memo(max_entries=50, show_spinner=False)
def get_historical_prices(ticke... | pd.Timestamp.now() | pandas.Timestamp.now |
#%% [markdown] #--------------------------------------------------
## Equity Premium and Machine
#%% #--------------------------------------------------
import warnings
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [10, 5]
import math
import time
import datetime
import pandas as pd
import numpy as... | pd.to_datetime(df['ym'],format='%Y%m') | pandas.to_datetime |
"""For training the comment spam classifier.
"""
import os
import numpy
from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.model_selection import KFold
from sklearn.metrics import con... | DataFrame({'text': [], 'class': []}) | pandas.DataFrame |
from __future__ import print_function
try:
input = raw_input
except NameError:
pass
import argparse
import pc_lib_api
import pc_lib_general
import json
import pandas
import time
import sys
from datetime import datetime, date
# --Execution Block-- #
# --Parse command line arguments-- #
parser = argparse.Argumen... | pandas.json_normalize(alerts_job_number) | pandas.json_normalize |
'''
nose tests for ipysig.sigma_addon_methods.py
'''
import unittest
import networkx as nx
import pandas as pd
import json
from ..sigma_addon_methods import *
from ..exceptions import IPySigmaGraphDataFrameValueError, \
IPySigmaGraphEdgeIndexError, IPySigmaGraphNodeIndexError, \
IPySigmaNodeTypeError, IPySig... | pd.DataFrame(None) | pandas.DataFrame |
import logging
import sys
import pandas as pd
import pytest
import awswrangler as wr
logging.getLogger("awswrangler").setLevel(logging.DEBUG)
@pytest.mark.parametrize("ext", ["xlsx", "xlsm", "xls", "odf"])
@pytest.mark.parametrize("use_threads", [True, False, 2])
def test_excel(path, ext, use_threads):
df = pd... | pd.DataFrame({"c0": [0, 1, 2], "c1": [3, 4, 5]}) | pandas.DataFrame |
#!/usr/bin/env python3
"""
"""
from pathlib import Path
import numpy as np
import pandas as pd
def load_market_quality_statistics(filepath: Path,) -> pd.DataFrame:
"""
"""
daily_stats = pd.read_csv(filepath)
daily_stats["date"] = pd.to_datetime(daily_stats["date"], format="%Y-%m-%d")
daily_stats... | pd.DataFrame(bloomi) | pandas.DataFrame |
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
"""
BLIS - Balancing Load of Intermittent Solar:
A characteristic-based transient power plant model
Copyright (C) 2020. University of Virginia Licensing & Ventures Group (UVA LVG). All Rights Reserved.
Permission is hereby granted, free ... | pd.read_csv(results_filename) | pandas.read_csv |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import datetime, timedelta
import numpy as np
import pytest
from pandas.errors import (
NullFrequencyError, OutOfBoundsDatetime, PerformanceWarning)
import pandas as pd
from pandas import (
DataFrame, ... | tm.assert_index_equal(result, expected) | pandas.util.testing.assert_index_equal |
import decimal
import numpy as np
from numpy import iinfo
import pytest
import pandas as pd
from pandas import to_numeric
from pandas.util import testing as tm
class TestToNumeric(object):
def test_empty(self):
# see gh-16302
s = pd.Series([], dtype=object)
res = to_numeric(s)
... | pd.to_numeric(data) | pandas.to_numeric |
# store_data_file.py - Process JSON files from AWS S3 to PostgreSQL Database
# forked from Joinville Smart Mobility Project and Louisville WazeCCPProcessor
# modified by <EMAIL> for City of Los Angeles CCP
import os
import sys
import json
import numpy as np
import pandas as pd
from pandas.io.json import json_normaliz... | pd.DataFrame(np.nan, index=[0], columns=col_list) | pandas.DataFrame |
"""Main module
# Resources
- Reference google sheets:
- Source data: https://docs.google.com/spreadsheets/d/1jzGrVELQz5L4B_-DqPflPIcpBaTfJOUTrVJT5nS_j18/edit#gid=1335629675
- Source data (old): https://docs.google.com/spreadsheets/d/17hHiqc6GKWv9trcW-lRnv-MhZL8Swrx2/edit#gid=1335629675
- Output example: https://d... | pd.DataFrame(rows2) | pandas.DataFrame |
from .tdx_parser import TDXParser
import pandas as pd
import numpy as np
import json
from collections import deque
class Formula(object):
buy_kw = [r'买入', r'买', 'BUY', 'BUYIN', 'ENTERLONG']
sell_kw = [r'卖出', r'卖', 'SELL', 'SELLOUT', 'EXITLONG']
FIGURE_DATA_LEN = 200
def __init__(self, text, p... | pd.rolling_sum(norm, param[1]) | pandas.rolling_sum |
import logging
import uuid
from typing import Optional
import fire
import pandas as pd
import matplotlib.pyplot as plt
from core import Simulator, Simulation
from settings import *
from utils import list_dir
logger = logging.getLogger(__name__)
class Main:
@staticmethod
def show_strategies():
retu... | pd.DataFrame(results_confirmed) | pandas.DataFrame |
from typing import Dict, Optional, Union, Callable, Tuple, List, Iterable
import pandas as pd
from scipy import stats
from .db import engine
import numpy as np
import seaborn as sns
import warnings
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
class Error(Exception):
"""Base class for exce... | pd.DataFrame(error, columns=["Error"]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Analyzes code age in a git repository
Writes reports in the following locations
e.g. For repository "cpython"
[root] Defaults to ~/git.stats
├── cpython Directory for https://github.com/python/cpython.git... | DataFrame(author_ext_loc, index=authors, columns=exts) | pandas.DataFrame |
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import numpy as np
import pandas as pd
_MESSAGE_X_NONE = "Must supply X"
_MESSAGE_Y_NONE = "Must supply y"
_MESSAGE_X_Y_ROWS = "X and y must have same number of rows"
_MESSAGE_X_SENSITIVE_ROWS = "X and the sensitive feature... | pd.Series(formless) | pandas.Series |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import pandas
from pandas.compat import string_types
from pandas.core.dtypes.cast import find_common_type
from pandas.core.dtypes.common import (
is_list_like,
is_numeric_dtype,
... | pandas.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
import mando
try:
from mando.rst_text_formatter import RSTHelpFormatter as HelpFormatter
except ImportError:
from argparse import RawTextHelpFormatter as HelpFormatter
import pandas as pd
import typic
from tstoolbox import tsutils
from tsgettoolbox.ulmo.twc.kbdi.core import get_data
... | pd.to_datetime(start_date) | pandas.to_datetime |
from sklearn.model_selection import StratifiedKFold
from spacekit.builder.architect import BuilderEnsemble
from spacekit.analyzer.compute import ComputeBinary
from spacekit.skopes.hst.svm.train import make_ensembles
from spacekit.generator.augment import training_data_aug, training_img_aug
from spacekit.preprocessor.tr... | pd.concat([y_train, y_val], axis=0) | pandas.concat |
import decimal
import numpy as np
from numpy import iinfo
import pytest
import pandas as pd
from pandas import to_numeric
from pandas.util import testing as tm
class TestToNumeric(object):
def test_empty(self):
# see gh-16302
s = pd.Series([], dtype=object)
res = to_numeric(s)
... | to_numeric(s) | pandas.to_numeric |
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 31 17:21:26 2020
@author: <NAME>
"""
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 21 21:23:55 2018
@author: <NAME>
"""
import os
import itertools
from operator import itemgetter
import pandas as pd
import numpy as np
from support_modules import nn_support as nsup
fr... | pd.DataFrame.from_dict(ranges, orient='index') | pandas.DataFrame.from_dict |
#!/usr/bin/env python
# coding: utf-8
# # UCI
# # Drug Review Dataset
# In[ ]:
import pandas as pd
# In[2]:
data_train = pd.read_csv('.....\\drugsCom_raw\\drugsComTrain_raw.tsv',delimiter='\t')
data_test = pd.read_csv('......\\drugsCom_raw\\drugsComTest_raw.tsv' ,delimiter='\t')
# In[ ]:
# In[3]:
df ... | pd.DataFrame(data_cat_encod,columns=["vaderSentimentLabel"]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""Seaborn_and_Linear_Regression_(start).ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1KYXQrn37CzHVYaFYYBlRSEJobv0x113q
# Introduction
Do higher film budgets lead to more box office revenue? Let's find out if there's a... | register_matplotlib_converters() | pandas.plotting.register_matplotlib_converters |
import os
from typing import Any, Optional
import gspread
import pandas as pd
from oauth2client.service_account import ServiceAccountCredentials
import socket
from datetime import datetime
from time import sleep
import traceback
_current_experiment = None # type: Optional[ExperimentParams]
def _check_experiment():... | pd.DataFrame(recs) | pandas.DataFrame |
import mysql.connector as conn
import pandas as pd
def remove_address(roll,password):
try:
cnx=conn.connect(user='root',password='<PASSWORD>',host='127.0.0.1',database='library')
cur_address=cnx.cursor()
cur_address.execute("select add_id,address from address where roll=%s",(roll,))
... | pd.DataFrame(temp,columns=['ROLL','F-NAME','L-NAME']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals, print_function
import json
import pandas as pd
from datetimewidget.widgets import DateTimeWidget
from django import forms
from django.contrib.auth import get_user_model
from django.core.exceptions import ObjectDoesNotExist
from dataops import pandas_db... | pd.isnull(x) | pandas.isnull |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 11 18:29:39 2021
@author: Clement
"""
import pandas
import numpy
import os
import sys
import geopandas as gpd
import tqdm
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
from gen_fct import df_fct
from gen_fct import f... | pandas.DataFrame() | pandas.DataFrame |
import math
from collections import OrderedDict
from datetime import datetime
import pytest
from rpy2 import rinterface
from rpy2 import robjects
from rpy2.robjects import vectors
from rpy2.robjects import conversion
class MockNamespace(object):
def __getattr__(self, name):
return None
has_pandas = Fa... | pandas.Int64Dtype() | pandas.Int64Dtype |
from abc import abstractmethod
from analizer.abstract.expression import Expression
from enum import Enum
from storage.storageManager import jsonMode
from analizer.typechecker.Metadata import Struct
from analizer.typechecker import Checker
import pandas as pd
from analizer.symbol.symbol import Symbol
from analizer.symbo... | pd.DataFrame(result, columns=newColumns) | pandas.DataFrame |
from itertools import repeat, chain
import numpy as np
import pandas as pd
import pytest
from scipy import sparse
import scanpy as sc
def test_obs_df():
adata = sc.AnnData(
X=np.ones((2, 2)),
obs=pd.DataFrame({"obs1": [0, 1], "obs2": ["a", "b"]}, index=["cell1", "cell2"]),
var=pd.DataFra... | pd.DataFrame({"genesymbol2": [1, 1], "obs1": [0, 1], "eye-0": [1, 0], "sparse-1": [0, 1]}, index=adata.obs_names) | pandas.DataFrame |
# --------------
# Importing Necessary libraries
import warnings
warnings.filterwarnings("ignore")
from matplotlib import pyplot as plt
plt.rcParams['figure.figsize'] = (10, 8)
import numpy as np
import pandas as pd
from sklearn.model_selection import GridSearchCV
from sklearn import preprocessing
from sklearn... | pd.read_csv(path1) | pandas.read_csv |
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
##find parent directory and import model
#parentddir = os.path.ab... | pd.Series(num_simulations * ['NaN'], dtype='float') | pandas.Series |
#!/usr/bin/env python3
# coding: utf-8
"""Global sequencing data for the home page
Author: <NAME> - Vector Engineering Team (<EMAIL>)
"""
import argparse
import pandas as pd
import numpy as np
import json
from pathlib import Path
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
... | pd.isnull(iso_lookup_df["Province_State"]) | pandas.isnull |
import datetime as dt
import unittest
import pandas as pd
import numpy as np
import numpy.testing as npt
import seaice.nasateam as nt
import seaice.tools.plotter.daily_extent as de
class Test_BoundingDateRange(unittest.TestCase):
def test_standard(self):
today = dt.date(2015, 9, 22)
month_bound... | pd.to_datetime('2010-01-15') | pandas.to_datetime |
import numpy as np
import pandas as pd
from IPython import embed
from keras.models import load_model
from keras import backend as K
from qlknn.models.ffnn import determine_settings, _prescale, clip_to_bounds
def rmse(y_true, y_pred):
return K.sqrt(K.mean(K.square( y_true-y_pred )))
class KerasNDNN():
def __i... | pd.DataFrame() | pandas.DataFrame |
#!/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import shutil
import csv
import zipfile
import tarfile
import configparser
import collections
import statistics
import pandas as pd
import matplotlib.pyplot as plt
import networkx as nx
from datetime import datetime
# Type of printing.
OK ... | pd.Series(train[OS][3]) | pandas.Series |
# Copyright (c) 2018-2022, NVIDIA CORPORATION.
import numpy as np
import pandas as pd
import pytest
from pandas.api import types as ptypes
import cudf
from cudf.api import types as types
@pytest.mark.parametrize(
"obj, expect",
(
# Base Python objects.
(bool(), False),
(int(), False)... | pd.Series(dtype="complex") | pandas.Series |
import numpy as np
import pandas as pd
import lightgbm as lgb
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import StratifiedKFold
import warnings
warnings.filterwarnings('ignore')
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
features = [c for... | pd.DataFrame({"ID_code": test_df.ID_code.values}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from ..utils import get_drift, get_offset, verify_series
def rsi(close, length=None, drift=None, offset=None, **kwargs):
"""Indicator: Relative Strength Index (RSI)"""
# Validate arguments
close = verify_series(close)
length = int(length) if length and length > 0 else 14
dri... | DataFrame(data) | pandas.DataFrame |
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