prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
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#!/usr/bin/python3
import sys
import copy
from pathlib import Path
from datetime import datetime,timedelta
import re
import matplotlib.pyplot as plt
import math
import numpy as np
import random
import pandas as pd
import subprocess
from pickle import dump,load
from predictor.utility import msg2log
from clustgelDL.au... | pd.Timestamp.now() | pandas.Timestamp.now |
#!/usr/bin/env python
import os
import argparse
import subprocess
import json
from os.path import isfile, join, basename
import time
import pandas as pd
from datetime import datetime
import tempfile
import sys
sys.path.append(
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, 'instance_gene... | pd.DataFrame(results) | pandas.DataFrame |
import os
from typing import List, Tuple, Union
import numpy as np
import pandas as pd
DATASET_DIR: str = "data/"
# https://www.kaggle.com/rakannimer/air-passengers
def read_air_passengers() -> Tuple[pd.DataFrame, np.ndarray]:
indexes = [6, 33, 36, 51, 60, 100, 135]
values = [205, 600, 150, 315, 150, 190, 6... | pd.read_csv(f"{DATASET_DIR}air_passengers.csv") | pandas.read_csv |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : ioutil.py
@Desc : Input and output data function.
'''
# here put the import lib
import os
import sys
import pandas as pd
import numpy as np
from . import TensorData
import csv
from .basicutil import set_trace
class File():
def __init__(self,... | pd.DataFrame() | pandas.DataFrame |
import logging
import os
import pickle
import tarfile
from typing import Tuple
import numpy as np
import pandas as pd
import scipy.io as sp_io
import shutil
from scipy.sparse import csr_matrix, issparse
from scMVP.dataset.dataset import CellMeasurement, GeneExpressionDataset, _download
logger = logging.getLogger(__n... | pd.DataFrame(self.ATAC_name) | pandas.DataFrame |
from flask import Flask, render_template, jsonify, request
from flask_pymongo import PyMongo
from flask_cors import CORS, cross_origin
import json
import copy
import warnings
import re
import pandas as pd
pd.set_option('use_inf_as_na', True)
import numpy as np
from joblib import Memory
from xgboost import XGBClassi... | pd.concat([DataRows2, hotEncoderDF2], axis=1) | pandas.concat |
import pandas as pd
import numpy as np
import json
PROCESS_FILE_NAME_LIST = ["taxi_sort_01", "taxi_sort_001", "taxi_sort_002", "taxi_sort_003", "taxi_sort_004", "taxi_sort_005", "taxi_sort_006", "taxi_sort_007", "taxi_sort_008", "taxi_sort_009", "taxi_sort_0006", "taxi_sort_0007", "taxi_sort_0008", "taxi_sort_0009"]
P... | pd.read_csv("precinct_center.csv", index_col=False) | pandas.read_csv |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
target = 'scale'
# IP
plot_mode = 'all_in_one'
obj = 'occ'
# Port
flow_dir = 'all'
port_dir = 'sys'
user_plot_pr = ['TCP']
user_plot_pr = ['UDP']
port_hist = pd.DataFrame({'A' : []})
user_port_hist = pd.DataFrame({'A' : []... | pd.read_csv("./postprocessed_data/%s/%s_piece%d.csv" % (files[data_idx], files[data_idx], piece_idx), index_col=None, header=0) | pandas.read_csv |
# %% [markdown]
# This python script takes audio files from "filedata" from sonicboom, runs each audio file through
# Fast Fourier Transform, plots the FFT image, splits the FFT'd images into train, test & validation
# and paste them in their respective folders
# Import Dependencies
import numpy as np
import pandas... | pd.DataFrame() | pandas.DataFrame |
'''
The analysis module
Handles the analyses of the info and data space for experiment evaluation and design.
'''
from slm_lab.agent import AGENT_DATA_NAMES
from slm_lab.env import ENV_DATA_NAMES
from slm_lab.lib import logger, util, viz
import numpy as np
import os
import pandas as pd
import pydash as ps
import shutil... | pd.concat(session_fitness_data, axis=1) | pandas.concat |
#!/usr/bin/env python3
# Project : From geodynamic to Seismic observations in the Earth's inner core
# Author : <NAME>
""" Implement classes for tracers,
to create points along the trajectories of given points.
"""
import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
from . import data... | pd.DataFrame(data=self.velocity_gradient, columns=["dvx/dx", "dvx/dy", "dvx/dz", "dvy/dx", "dvy/dy", "dvy/dz", "dvz/dx", "dvz/dy", "dvz/dz"]) | pandas.DataFrame |
#!/usr/bin/env python
import sys, time, code
import numpy as np
import pickle as pickle
from pandas import DataFrame, read_pickle, get_dummies, cut
import statsmodels.formula.api as sm
from sklearn.externals import joblib
from sklearn.linear_model import LinearRegression
from djeval import *
def shell():... | get_dummies(yy_df[categorical_features]) | pandas.get_dummies |
import os
import numpy as np
import pandas as pd
from numpy import abs
from numpy import log
from numpy import sign
from scipy.stats import rankdata
import scipy as sp
import statsmodels.api as sm
from data_source import local_source
from tqdm import tqdm as pb
# region Auxiliary functions
def ts_sum(df, window=10):
... | pd.Series(result_industryaveraged_df.index) | pandas.Series |
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