prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
"""
サンプルコード
参考: https://programming-info.dream-target.jp/streamlit-start
"""
import streamlit as st
import pandas as pd
import numpy as np
st.title("streamlitのサンプルだお")
DATE_COLUMN = "date/time"
DATA_URL = (
"https://s3-us-west-2.amazonaws.com/"
"streamlit-demo-data/uber-raw-data-sep14.csv.gz"
)
@st.cache
de... | pd.read_csv(DATA_URL, nrows=nrows) | pandas.read_csv |
"""Core utilities"""
import sys
import logging
import inspect
from functools import singledispatch
from copy import deepcopy
from typing import (
Any,
Callable,
Iterable,
List,
Mapping,
Sequence,
Union,
Tuple,
)
import numpy
from numpy import array as Array
import pandas
from pandas i... | DataFrame(data, columns=name) | pandas.DataFrame |
from datetime import datetime
from decimal import Decimal
from io import StringIO
import numpy as np
import pytest
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series, Timestamp, date_range, read_csv
import pandas._testing as tm
from pa... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
# Web Scraping Demo
import time
import os
import string
from datetime import datetime
import requests
from diskcache import Cache
from bs4 import BeautifulSoup
import pandas as pd
from docx import Document
from docx.shared import Pt, RGBColor
class Fox():
"""
A wrapper for requests that automates interaction ... | pd.isnull(spreadsheet.loc[i,"description"]) | pandas.isnull |
import math
import queue
from datetime import datetime, timedelta, timezone
import pandas as pd
from storey import build_flow, SyncEmitSource, Reduce, Table, AggregateByKey, FieldAggregator, NoopDriver, \
DataframeSource
from storey.dtypes import SlidingWindows, FixedWindows, EmitAfterMaxEvent, EmitEveryEvent
tes... | pd.Timestamp('2021-05-30 17:24:15.811000+0000', tz='UTC') | pandas.Timestamp |
# -*- coding: utf-8 -*-
"""
These test the private routines in types/cast.py
"""
import pytest
from datetime import datetime, timedelta, date
import numpy as np
import pandas as pd
from pandas import (Timedelta, Timestamp, DatetimeIndex,
DataFrame, NaT, Period, Series)
from pandas.core.dtypes.c... | maybe_downcast_to_dtype(arr, 'int64') | pandas.core.dtypes.cast.maybe_downcast_to_dtype |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from scipy import stats as scipy_stats
def estimated_sharpe_ratio(returns):
"""
Calculate the estimated sharpe ratio (risk_free=0).
Parameters
----------
returns: np.array, pd.Series, pd.DataFrame
Returns
-------
float, p... | pd.Series(min_trl, index=returns.columns) | pandas.Series |
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([1.e4, 1.e5, 1.e6], dtype = 'float') | pandas.Series |
import collections
import os
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from tqdm import tqdm
# competitors = ['Eigen', 'PrimateAI', 'FATHMM-XF', 'ClinPred', 'REVEL', 'M-CAP', 'MISTIC']
competitors = ['InMeRF', 'ClinPred', 'REVEL', 'MISTIC']
def violin_plot_scores(dir,... | pd.DataFrame() | pandas.DataFrame |
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.DataFrame(index=dts, columns=['c1', 'c2'], data=100.) | pandas.DataFrame |
import pandas as pd
from modules.locale_generator.data import LocaleOutData
from helper.utils.utils import read_sheet_map_file
class LocaleProcessor:
def __init__(self, language_name, english_column_name):
self.language_name = language_name
self.english_column_name = english_column_name
... | pd.notnull(df_row[self.language_name]) | pandas.notnull |
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 29 09:35:14 2019
@author: ACN980
"""
import os, glob, sys
import calendar
import pandas as pd
import numpy as np
import math
import warnings
import scipy
import scipy.stats as sp
import scipy.signal as ss
from sklearn.linear_model import LinearRegression
from datetime i... | pd.concat([all_events_sampled_ind, sampled_month_ind], axis = 0, ignore_index=True) | pandas.concat |
import unittest
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
import numpy as np
from ITMO_FS.embedded import *
np.random.seed(42)
class TestCases(unittest.TestCase):
data, target = np.random.randint(10, size=(100, 20)), np.random.randint(10, size=... | pd.DataFrame(self.target) | pandas.DataFrame |
import pandas as pd
data = pd.read_csv('data/citibike_tripdata.csv', sep=',')
print(data.info)
print(data['starttime'].dtype)
print(round(data['start station id'].mode()[0]))
print(data['bikeid'].mode()[0])
mode_usertype = data['usertype'].mode()[0]
count_mode_user = data[data['usertype'] == mode_usertype].shape[0]
p... | pd.to_datetime(data['starttime']) | pandas.to_datetime |
"""
Author: <NAME>
Date: December 2020
"""
import configparser
import os.path as osp
import tempfile
from tqdm import tqdm
from pandas_plink import read_plink1_bin
import dask.array as da
import pandas as pd
import numpy as np
from scipy import stats
import zarr
from magenpy.AnnotationMatrix import AnnotationMatrix... | pd.DataFrame({'SNP': self.snps[c], 'A1': self.alt_alleles[c]}) | pandas.DataFrame |
import datetime as dt
import pandas as pd
# TODO: Unit tests
def compute_work_item_times(df: pd.DataFrame) -> pd.DataFrame:
"""
Takes a DataFrame with the ticket data and computes the start time, end time, duration and the
duration_in_hours.
:param df: As described above
:return: As described ... | pd.isnull(times.duration) | pandas.isnull |
from typing import List, Tuple
import numpy as np
from nptyping import NDArray
from pandas import DataFrame
from scipy.stats import expon
from dlsys.model import DualSysyem
def expon_equally_spaced(mean_interval: float, _min: float,
n: int) -> NDArray[1, float]:
intervals = expon.ppf(
... | DataFrame(row_of_result, columns=["gk", "hk"]) | pandas.DataFrame |
# SPDX-License-Identifier: Apache-2.0
import unittest
import numbers
from distutils.version import StrictVersion
import numpy as np
from numpy.testing import assert_almost_equal
import pandas
from onnxruntime import InferenceSession
from sklearn.datasets import load_iris
from sklearn.compose import ColumnTransformer
... | pandas.DataFrame(X) | pandas.DataFrame |
import streamlit as st
import plotly.figure_factory as ff
import numpy as np
import pandas as pd
import plotly.express as px
#in this one I'm letting people see all of the items for a portal. So they pic that, the data is filtered
#and then you get a chart with all of the items
def comparebar():
# Add histogram data
... | pd.read_csv("https://raw.githubusercontent.com/tyrin/info-topo-dash/master/data/freshdata.csv") | pandas.read_csv |
import os
from typing import List, Dict, Callable, Tuple
import pandas as pd
from flowpipe import Graph, INode, Node, InputPlug, OutputPlug
from insurance_claims.record_types import *
# let's invent some kind of overhead that goes into processing the claim
CLAIM_VALUE_PROCESSING_OVERHEAD_RATE = 0.05
# threshold to ... | pd.DataFrame.from_records(new_claims) | pandas.DataFrame.from_records |
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
import numpy as np
from scipy import stats as sps
from scipy.interpolate import interp1d
from matplotlib import pyplot as plt
from matplotlib.dates import date2num, num2date
from matplotlib import dates as mdates
from matplotlib import... | pd.to_datetime(casos_pty['date'], format='%Y-%m-%d') | pandas.to_datetime |
from csv2clean import *
from fuzzywuzzy import fuzz
from tqdm import tqdm
import pandas as pd
import pickle
import spacy
nlp = spacy.load("fr_core_news_lg")
#file_dir='../../data/Catalogue.csv'
stop_loc=['Région', 'Métropole', 'Region', 'Metropole','Mer', 'mer', 'Département', 'DEPARTEMENT', 'Agglomération', 'agglomér... | pd.read_csv('../../data/communes-01012019.csv') | pandas.read_csv |
import abc
import math
import numpy as np
import pandas as pd
import tensorflow as tf
from dataclasses import dataclass
from pathlib import Path
try:
from emnist import extract_samples
except ModuleNotFoundError:
pass
from sklearn.model_selection import train_test_split
from sklearn.base import TransformerM... | pd.get_dummies(test_data) | pandas.get_dummies |
import numpy as np
import pandas as pd
from collections import defaultdict
import datetime
import math
import os.path
from sklearn.preprocessing import StandardScaler
def feature_engineering(feature):
# confirmed, death, confirmed_diff, death_diff, confirmed_square, death_square
diff = [0 for _ in range(12)]
... | pd.DataFrame(dict) | pandas.DataFrame |
import os
import sys
import time
import sqlite3
import pyupbit
import pandas as pd
from PyQt5.QtCore import QThread
from pyupbit import WebSocketManager
sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
from utility.setting import *
from utility.static import now, timedelta_sec, strf_time, ti... | pd.DataFrame(columns=columns_cj) | pandas.DataFrame |
import os
import re
import shlex
import numpy as np
import pandas as pd
from scipy.io import mmread, mmwrite
from scipy.sparse import csr_matrix
import tempfile
import subprocess
from typing import List, Dict, Tuple, Union
import logging
logger = logging.getLogger(__name__)
from pegasusio import UnimodalData, CITESeq... | pd.read_csv(barcode_file, sep=sep, header=None) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import requests
import json
import pandas as pd
url = "https://glyconnect.expasy.org/api/glycosylations"
# In[2]:
## send the correct params to query the api
params = {'taxonomy':'Severe acute respiratory syndrome coronavirus 2 (2019-nCoV)', 'protein': 'Recombinant ... | pd.concat([df_dump,df_temp],sort=True,axis=0) | pandas.concat |
from unittest import TestCase
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from datasets.formatting import NumpyFormatter, PandasFormatter, PythonFormatter, query_table
from datasets.formatting.formatting import NumpyArrowExtractor, PandasArrowExtractor, PythonArrowExtractor
from datasets... | pd.Series(_COL_B, name="b") | pandas.Series |
import datetime
from datetime import timedelta
from distutils.version import LooseVersion
from io import BytesIO
import os
import re
from warnings import catch_warnings, simplefilter
import numpy as np
import pytest
from pandas.compat import is_platform_little_endian, is_platform_windows
import pandas.util._test_deco... | concat([df1, df2], axis=1) | pandas.concat |
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from pathlib import Path
from collections import defaultdict
import mudcod.utils.visualization as VIS # noqa: E402
from mudcod.utils import sutils # noqa: E402
MAIN_DIR = Path(__file__).absolute().parent.parent
SIMULATIO... | pd.read_csv(mpath) | pandas.read_csv |
""" This module contains a class GwGxg that calculates some
descriptive statistics from a series of groundwater head measurements
used by groundwater practitioners in the Netherlands
History: Created 16-08-2015, last updated 12-02-1016
Migrated to acequia on 15-06-2019
@author: <NAME>
"""
import math
from... | pd.Series(name=self.srname,dtype='object') | pandas.Series |
"""
Procedures needed for Common support estimation.
Created on Thu Dec 8 15:48:57 2020.
@author: MLechner
# -*- coding: utf-8 -*-
"""
import copy
import numpy as np
import pandas as pd
from mcf import mcf_data_functions as mcf_data
from mcf import general_purpose as gp
from mcf import general_purpose_estimation as... | pd.concat([x_pr, x_add_tmp], axis=0) | pandas.concat |
from __future__ import division, print_function
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn import tree
from Basic import adult, dutch, testdata
from Utility import Utility
from Detection import Judge
from Basic import get_group
def Ranker(X, Y, Epos):
# One-... | pd.get_dummies(X[f], prefix=f) | pandas.get_dummies |
#!/usr/bin/env python
# coding: utf-8
# <img style="float: left;" src="earth-lab-logo-rgb.png" width="150" height="150" />
#
# # Earth Analytics Education - EA Python Course Spring 2021
# ## Important - Assignment Guidelines
#
# 1. Before you submit your assignment to GitHub, make sure to run the entire notebook ... | pd.DataFrame([[site, date_time, ndvi_mean_value]], columns=['site', 'date', 'mean_ndvi']) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# # Python script for automatic quality-control procedures (CEMADEN data)
# # Created on Aug.12.2020
# ### By:
# <NAME>
# <NAME>
# <NAME>
# Importing libraries used in this code
# In[ ]:
import numpy as np
import pandas as pd
from datetime import datetime
import gl... | pd.DataFrame(df_gauge_resample) | pandas.DataFrame |
# Import packages
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import umap.umap_ as umap
from PIL import Image
from matplotlib import offsetbox
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import datetime
# Import packages for Bokeh visualization demo
from bokeh.models import ... | pd.DataFrame(data2) | pandas.DataFrame |
# Import required modules
import requests
import pandas as pd
import json
import subprocess
from tqdm import tqdm
import re
# Set pandas to show full rows and columns
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwi... | pd.DataFrame(icdata) | pandas.DataFrame |
"""Created on Fri Apr 3 11:05:15 2020.
Contains the functions needed for data manipulation
@author: MLechner
-*- coding: utf-8 -*-
"""
import copy
import math
from concurrent import futures
import numpy as np
import pandas as pd
import ray
from mcf import general_purpose as gp
from mcf import general_purpose_estimati... | pd.read_csv(filepath_or_buffer=indatei) | pandas.read_csv |
import pandas as pd
import json
import os
def run_microsoft_parser(path_save, path_source):
print('Consolidating results of Microsoft classifier')
files = [item for item in os.listdir(path_source) if '.pkl' in item]
print(len(files), 'to consolidate')
df = pd.DataFrame()
for file in files:
... | pd.DataFrame(faces_df) | pandas.DataFrame |
import numpy as np
import pystan
import pickle
from pystan import StanModel
import pandas as pd
import os
def stanTopkl():
"""
The function complies 'stan' models first and avoids re-complie of the model.
"""
if os.path.isfile('log_normal.pkl'):
os.remove('log_normal.pkl')
sm = StanModel(f... | pd.DataFrame(chain, columns=index) | pandas.DataFrame |
import unittest
import pandas as pd
import numpy as np
from pandas.testing import assert_frame_equal
from msticpy.analysis.anomalous_sequence import sessionize
class TestSessionize(unittest.TestCase):
def setUp(self):
self.df1 = pd.DataFrame({"UserId": [], "time": [], "operation": []})
self.df1_... | pd.to_datetime("2020-01-05 00:25:00") | pandas.to_datetime |
#%% [markdown]
# ## ECA information theory comparison figures and stuff
#%% [markdown]
# ## Load packages and data
#%%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
cana_df = pd.read_csv("../data/eca/canalization_df.csv")
imin_df = pd.read_csv("../data/eca/imin_df.csv", i... | pd.read_csv("../data/eca/eca_equiv_classes.csv") | pandas.read_csv |
import pandas as pd
import numpy as np
from pathlib import Path
from datetime import datetime as dt
def mergeManagers(managers, gameLogs):
#Sum up doubled data
managers = managers.groupby(['yearID','playerID'], as_index=False)['Games','Wins','Losses'].sum()
#Get visiting managers
visitingManagers ... | pd.read_csv(path+r'\Filtered\_mlb_filtered_Pitching.csv', index_col=False) | pandas.read_csv |
from collections.abc import MutableMapping
from datetime import datetime
from numpy import exp
import pandas as pd
CHANNEL_ERROR = 'Channel not supported.'
DIRECTION_ERROR = 'Direction not supported.'
class SPMImage():
data_headers = [
'sample_id',
'rec_index',
'probe',
'channel',
... | pd.concat([Z_dataframe,I_dataframe]) | pandas.concat |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import datetime
import pandas as pd
from dateutil.relativedelta import relativedelta
from collections import Iterable
ALLOWED_TIME_COLUMN_TYPES = [
pd.Timestamp,
pd.DatetimeIndex,
datetime.datetime,
datetime.date,
]
def is_date... | pd.offsets.Micro() | pandas.offsets.Micro |
import re
import numpy as np
import numpy.testing as npt
import pandas as pd
import pandas.testing as pdt
import pytest
from aneris.convenience import harmonise_all
from aneris.errors import (
AmbiguousHarmonisationMethod,
MissingHarmonisationYear,
MissingHistoricalError,
)
pytest.importorskip("pint")
im... | pd.DataFrame([{"method": "constant_ratio"}]) | pandas.DataFrame |
from abc import ABC, abstractmethod
from math import floor
import datetime as dt
from typing import Dict, List
import pandas as pd
from .events import FillEvent, OrderEvent
from .enums import EventTypes, SignalTypes
from .data import DataHandler
from .enums import OrderTypes
from .events import SignalEvent
class Por... | pd.DataFrame(self.all_holdings) | pandas.DataFrame |
from __future__ import division
from builtins import str
from builtins import object
__copyright__ = "Copyright 2015 Contributing Entities"
__license__ = """
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 c... | pd.notnull(self.fare_rules_df[Route.FARE_RULES_COLUMN_DESTINATION_ID]) | pandas.notnull |
import numpy as np
from scipy.stats import ttest_ind, pearsonr
from sklearn.model_selection import StratifiedKFold
# General packages
import numpy as np
import seaborn as sns
import pandas as pd
# Bunch of scikit-learn stuff
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sk... | pd.DataFrame(arg_dict, index=[i]) | pandas.DataFrame |
# this file contains all components needed to collect, format and save the data from dwd
import os
import re
import requests
from zipfile import ZipFile
from io import TextIOWrapper, BytesIO
import csv
import pandas as pd
import numpy as np
import datetime
# constants
DWD_URL_HISTORICAL = "https://opendata.dwd.de/cli... | pd.to_datetime(meta_df.START, format="%Y%m%d", utc=True) | pandas.to_datetime |
import pandas as pd
import itertools
def get_init_df(re_list, no_re_list, re_col, no_re_col):
# 直接解包re_list,no_re_list就可以处理完有关系的列
# 对于no_re_col中的每一项 需要查出它的可能取值范围
product_list = list(itertools.product(*re_list, *no_re_list))
# print(product_list)
processed_product_list = unzip_tool(product_list)
... | pd.DataFrame(processed_product_list, columns=re_col + no_re_col) | pandas.DataFrame |
import pandas as pd
import numpy as np
df1 = pd.DataFrame(np.ones((3, 4)) * 0, columns=['a', 'b', 'c', 'd'])
df2 = pd.DataFrame(np.ones((3, 4)) * 1, columns=['a', 'b', 'c', 'd'])
df3 = pd.DataFrame(np.ones((3, 4)) * 2, columns=['a', 'b', 'c', 'd'])
print(df1)
print(df2)
print(df3)
# 纵向合并
print(pd.concat([df1, df2, df... | pd.concat([df4, df5], ignore_index=True, join="inner") | pandas.concat |
import pandas as pd
from exams.models import TimeCode
from exams.models import AcademicYear
from exams.models import Period
def dates(start_date, end=254):
num_weeks = end // 100
num_days = (num_weeks - 1) * 5 + (end - 100 * num_weeks) // 10
print(num_days)
lst = []
start_date = pd.to_datetime(sta... | pd.DataFrame(lst, columns=['time_code', 'exam_date']) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 20 09:38:41 2021
@author: daniele.proverbio
Code to monitor the COVID-19 epidemic in Luxembourg and estimate useful
indicators for the Ministry of Health and the Taskforce WP6.
Path to input file at line 186
Path to output file at line 242; to out... | pd.concat([most_likely, hdis], axis=1) | pandas.concat |
#!/usr/bin/env python3
import warnings
from typing import Generator, Optional, Tuple, Union
import findiff.diff
import matplotlib.pyplot as plt
import numpy as np
import numpy.testing as nptest
import pandas as pd
import pandas.testing as pdtest
from scipy.stats import multivariate_normal
from datafold.pcfold.timese... | pdtest.assert_series_equal(X_dt, Y_dt, atol=atol) | pandas.testing.assert_series_equal |
# -*- coding: utf-8 -*-
'''
Site
A site import and analysis class built
with the pandas library
'''
import anemoi as an
import pandas as pd
import numpy as np
import itertools
class Site(object):
'''Subclass of the pandas dataframe built to import and quickly analyze
met mast data.'''
... | pd.concat([ref_data, site_data], axis=1, join='inner', keys=['Ref', 'Site']) | pandas.concat |
# -*- coding: utf-8 -*-
import unittest
import pandas as pd
import numpy as np
# from ThymeBoost.trend_models import (linear_trend, mean_trend, median_trend,
# loess_trend, ransac_trend, ewm_trend,
# ets_trend, arima_trend, moving_average_trend,
... | pd.Series(predictions) | pandas.Series |
# -*- coding: utf-8 -*-
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
import dash_table
import plotly.graph_objs as go
import pandas as pd
import numpy as np
import urllib
import requests
import zstandard as zstd
import orjson
impor... | pd.Timestamp(start_date) | pandas.Timestamp |
import pandas as pd
import geopandas as gpd
import glob
import os
from shapely import wkt
# from optimization_parameters import *
from _variable_definitions import *
import contextily as ctx
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from _utils import pd2gpd
from matplotlib ... | pd.merge(df_to_plot, merged, how="left", on=["POI_ID"]) | pandas.merge |
import sys, os
import unittest
import pandas as pd
import numpy
import sys
from sklearn import datasets
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, Imputer, LabelEncoder, LabelBinarizer, MinMaxScaler, MaxAbsScaler, RobustScaler,\
Binarizer, PolynomialFeatures, OneHotEn... | pd.read_csv('nyoka/tests/auto-mpg.csv') | pandas.read_csv |
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from numpy.polynomial.polynomial import polyfit
from scipy.stats import shapiro
from scipy.stats import ttest_ind as tt
from scipy.stats import spearmanr as corrp
import numpy as... | pd.read_csv('Gillan_Or_full_MF1_decay.csv',header=None) | pandas.read_csv |
import argparse
import utils
import pandas as pd
import time
import os
parser = argparse.ArgumentParser(prog='cleaner',
description="Parser of cleaning script")
parser.add_argument(
'--data_path', help='Provide Full path of data.', type=str)
parser.add_argument(
'--filename',... | pd.read_csv(data_path, header=0) | pandas.read_csv |
# ------------------
# this module, grid.py, deals with calculations of all microbe-related activites on a spatial grid with a class, Grid().
# by <NAME>
# ------------------
import numpy as np
import pandas as pd
from microbe import microbe_osmo_psi
from microbe import microbe_mortality_prob as MMP
from enzyme imp... | pd.concat([MC, MN*MRC/MRN, MP*MRC/MRP],axis=1) | pandas.concat |
import numpy as np
import pytest
import pandas as pd
from pandas import (
CategoricalDtype,
CategoricalIndex,
DataFrame,
Index,
IntervalIndex,
MultiIndex,
Series,
Timestamp,
)
import pandas._testing as tm
class TestDataFrameSortIndex:
def test_sort_index_and_reconstruction_doc_exa... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
import os
import pandas as pd
import datetime
import dateutil.parser
class Utils:
def __init__(self):
pass
# given date in synthea format return the year
def getYearFromSyntheaDate(self, date):
return datetime.datetime.strptime(date, "%Y-%m-%d").year
# given date in synthea format re... | pd.merge(source, target, how='inner', left_on='source_concept_id', right_on='target_concept_id') | pandas.merge |
import datetime
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import linear_model
import numpy as np
name_list = []
ticker_any = input('ticker: ')
print("Warning: the more days you predict into the future, the less accurate the model is")
print("")
day_num = int(input("Amount of days you want to pre... | pd.DataFrame(data['Low']) | pandas.DataFrame |
from datetime import datetime
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas.core.dtypes.base import _registry as ea_registry
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from pandas.core.dtypes.dtypes import (... | Series([1, 2, 3], dtype=float) | pandas.Series |
__author__ = "<NAME>"
import json
import pandas as pd
import sqlite3
import argparse
import os
def BrowserHistoryParse(f):
conn = sqlite3.connect(f)
cursor = conn.cursor()
BrowserHistoryTable = pd.read_sql_query("SELECT events_persisted.sid, events_persisted.payload from events_persisted inner... | pd.DataFrame(WlanScan) | pandas.DataFrame |
import pkg_resources
import pandas as pd
from unittest.mock import sentinel
import osmo_jupyter.dataset.parse as module
def test_parses_ysi_csv_correctly(tmpdir):
test_ysi_classic_file_path = pkg_resources.resource_filename(
"osmo_jupyter", "test_fixtures/test_ysi_classic.csv"
)
formatted_ysi_d... | pd.to_datetime("2019") | pandas.to_datetime |
"""
A toy ML workflow intended to demonstrate basic Bionic features. Trains a
logistic regression model on the UCI ML Breast Cancer Wisconsin (Diagnostic)
dataset.
"""
import re
import pandas as pd
from sklearn import datasets, linear_model, metrics, model_selection
import bionic as bn
# Initialize our builder.
bu... | pd.DataFrame(data=dataset.data, columns=dataset.feature_names) | pandas.DataFrame |
#!/usr/bin/env python
from pathlib import Path
import pandas as pd
import typer
from rich.console import Console
from rich.logging import RichHandler
import logging
def check_sample_names(df: pd.DataFrame) -> None:
have_whitespace = df['sample'].str.contains(r'\s', regex=True)
n_samples_with_whitespace = ha... | pd.read_csv(input_path, dtype="str") | pandas.read_csv |
"""Extract minimal growth media and growth rates."""
import pandas as pd
from micom import load_pickle
from micom.media import minimal_medium
from micom.workflows import workflow
max_procs = 6
processes = []
def media_and_gcs(sam):
com = load_pickle("models/" + sam + ".pickle")
# Get growth rates
sol ... | pd.read_csv("recent.csv") | pandas.read_csv |
#
# 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.Timestamp('2013-1-1', tz='UTC') | pandas.Timestamp |
import numpy as np
from sklearn.datasets import fetch_mldata
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
import time
from sklearn.manifold import TSNE
# import tensorflow.examples.tutorials.mnist.input_data as input_data
datafile = '' #get this either from command line ... | pd.DataFrame(X,columns=feat_cols) | pandas.DataFrame |
import os
import json
import math
import numpy as np
import pandas as pd
import seaborn as sns; sns.set(style="ticks"); sns.set_context("paper") #sns.set_context("talk")
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
from utils.helper import make_dir
from utils.sweeper import Sweeper
cla... | pd.read_feather(result_file) | pandas.read_feather |
from seaborn.utils import locator_to_legend_entries
import random
import glob
import calendar
import pandas as pd
from datetime import datetime
from tqdm import tqdm
import os
print(os.getcwd())
class DataPreprocessing:
"""
This class preprocesses the data and computes
transition probabilities
"""
... | pd.crosstab(df_mc_sub["after"], df_mc_sub["before"], normalize=1) | pandas.crosstab |
"""Classes for report generation and add-ons."""
import os
from copy import copy
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from jinja2 import FileSystemLoader, Environment
from json2html import json2html
from sklearn.metrics import roc_auc_score, precision_recall_fsc... | pd.DataFrame(datetime_features_df) | pandas.DataFrame |
import torch
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
import tqdm
from rec.model.pinsage import PinSage
from rec.datasets.movielens import MovieLens
from rec.utils import cuda
from dgl import DGLGraph
import argparse
import pickle
import os
parser = argparse.Argumen... | pd.Series(test_mrr) | pandas.Series |
import pandas as pd
import numpy as np
import math
import warnings
import scipy.stats as st
from pandas.core.common import SettingWithCopyWarning
warnings.simplefilter(action="ignore", category=SettingWithCopyWarning)
from .utils import *
LULC_COLORS_DEFAULT = pd.DataFrame(
columns=['lulc', 'color'],
data=[
... | pd.DataFrame(result, columns=['lulc', 'area_ha', 'proportion', 'se', 'year', 'region']) | pandas.DataFrame |
"""the simple baseline for autograph"""
import random
import os
import joblib
import numpy as np
import pandas as pd
import torch
import torch.nn.functional as F
import torch_geometric.utils as gtils
from collections import defaultdict
from torch_geometric.data import Data
from sklearn.model_selection import train_tes... | pd.DataFrame([meta_info]) | pandas.DataFrame |
"""Tests for the sdv.constraints.tabular module."""
import uuid
from datetime import datetime
from unittest.mock import Mock
import numpy as np
import pandas as pd
import pytest
from sdv.constraints.errors import MissingConstraintColumnError
from sdv.constraints.tabular import (
Between, ColumnFormula, CustomCon... | pd.to_datetime('2020-02-01') | pandas.to_datetime |
from datetime import datetime
import inspect
import numpy as np
import pytest
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
Index,
IntervalIndex,
Series,
Timestamp... | date_range("2013", periods=6, freq="A", tz="Asia/Tokyo") | pandas.date_range |
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
from MyAIGuide.utilities.dataFrameUtilities import (
subset_period,
insert_data_to_tracker_mean_steps,
adjust_var_and_place_in_data,
insert_rolling_mean_columns,
insert_relative_values_columns
)
def create_test_da... | assert_frame_equal(result1, expected_data1) | pandas.testing.assert_frame_equal |
#%%
import pandas as pd
import numpy as np
import requests
from datetime import datetime as dt
from io import StringIO
import os
import us
import git
from functools import reduce
from datetime import datetime, timedelta, date
#%%
def clean_df(df, date):
"""Cleans up dataframe to get only US counties (i.e. things w... | pd.concat(dfs) | pandas.concat |
#
# Copyright (C) 2014 Xinguard Inc.
#
# 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 the rights
# to use, copy, modify, merge, publish, dis... | pd.ExcelWriter('example2.xlsx', engine='openpyxl') | pandas.ExcelWriter |
from __future__ import absolute_import
import pytest
skimage = pytest.importorskip("skimage")
import numpy as np
import pandas as pd
from datashader.bundling import directly_connect_edges, hammer_bundle
from datashader.layout import circular_layout, forceatlas2_layout, random_layout
@pytest.fixture
def nodes():
... | pd.DataFrame(data, columns=columns) | pandas.DataFrame |
import pandas as pd
import numpy as np
import os
#from urllib import urlopen # python2
#import urllib2 # python2
import urllib.request as urllib2
#import StringIO python2
from io import StringIO
import gzip
import pybedtools
from pybedtools import BedTool
from .gtf import GTFtoBED
from .gtf import readGTF
from .gtf im... | pd.DataFrame(out) | pandas.DataFrame |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | pd.concat(frames) | pandas.concat |
"""
Test for the normalization operation
"""
from datetime import datetime
from unittest import TestCase
import numpy as np
import pandas as pd
import pyproj
import xarray as xr
from jdcal import gcal2jd
from numpy.testing import assert_array_almost_equal
from xcube.core.gridmapping import GridMapping
from xcube.cor... | pd.to_datetime('2012-01-01') | pandas.to_datetime |
# -*- coding: utf-8 -*-
# 検体検査結果データ（患者ごと）の読み込みと検体検査結果データ（検査項目ごと）の出力
# └→RS_Base_laboファイル
#
# 入力ファイル
# └→患者マスターファイル ：name.csv
# └→検体検査結果データファイル：患者ID.txt（例：101.txt,102.txt,103.txt・・・）
#
# Create 2017/07/09 : Update 2017/07/09
# Auther Katsumi.Oshiro
import csv # csvモジュールの読み込み（CSVファイルの読み書き）
import glob... | pd.to_datetime(birth[low[1]]) | pandas.to_datetime |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# In[211]:
import uuid
from pathlib import Path
import pandas_profiling
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import neptune.new as neptune
import neptune.new.types
import seaborn as sns
import sklearn.ensemble
import sklearn.metrics
import ... | pd.set_option('display.expand_frame_repr', True) | pandas.set_option |
import datetime
import math
import os
import glob
import matplotlib.pyplot as plt
import pandas as pd
# Instructions at the bottom of the script
if False:
import matplotlib
matplotlib.rcParams['interactive'] == True
matplotlib.use('MacOSX')
save = True
only_agg = True
fsize = (8, 6)
plot_name = "DEFAUL... | pd.DataFrame() | pandas.DataFrame |
import MELC.utils.myFiles as myF
import pandas as pd
from os.path import join
import cv2
import tifffile as tiff
from numpy import unique, where
from config import *
import sys
SEPARATOR = '/'
class RawDataset:
"""RawDataset loader.
works with RAW folder structure of MELC images.
Basicaly ... | pd.DataFrame(creation_times) | pandas.DataFrame |
import pandas as _pd
import warnings
from apodeixi.text_layout.excel_layout import Palette
from apodeixi.util.a6i_error import ApodeixiError
from apodeixi.util.dataframe_utils im... | _pd.DataFrame({}) | pandas.DataFrame |
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pandas as pd
import pylab as pl
import numpy as np
from scipy import ndimage
from scipy.cluster import hierarchy
from scipy.spatial import distance_matrix
from sklearn import manifold, datasets, preprocessing, metrics
from sklearn.cluster import... | pd.read_csv('movies.csv') | pandas.read_csv |
if "snakemake" in locals():
debug = False
else:
debug = True
if not debug:
import sys
sys.stderr = open(snakemake.log[0], "w")
import pandas as pd
def merge_deep_arg_calls(mapping, meta_data, deep_arg_calls, output):
mapping = pd.read_excel(mapping)
meta_data = pd.read_csv(meta_data, sep="|... | pd.read_csv(deep_arg_calls, sep="\t") | pandas.read_csv |
import typing as T
import pickle
import itertools as it
from enum import Enum
from pathlib import Path
import defopt
import numpy as np
import pandas as pd
import scipy.stats as st
from sklearn.base import BaseEstimator
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.compose import make_c... | pd.Series(y_prob[:, 1], name="crashInjuryProb") | pandas.Series |
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_series_equal(result, expected) | pandas._testing.assert_series_equal |
from statistics import stdev, mean
import pandas as pd
import pickle
from .visualisations import stability_visualizer
import re
def pickle_reader(filename):
accuracies_ = pickle.load(open(filename, 'rb'))
return accuracies_
class ResultAnalysis():
def __init__(self, filename, seq_len):
self.pkl_f... | pd.DataFrame(accuracy_by_person[key]) | pandas.DataFrame |
import os
import warnings
from typing import List
import joblib
import mlflow
import pandas as pd
from fastapi import FastAPI
from pydantic import BaseModel
pokemon_app = FastAPI()
class Pokemon(BaseModel):
hp: int
attack: int
defence: int
special_attack: int
special_defense: int
speed: int
... | pd.DataFrame(columns=["hp", "attack", "defence", "special_attack", "special_defense", "speed"]) | pandas.DataFrame |
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