Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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1,900 | <ASSISTANT_TASK:>
Python Code:
#text goes here
corpora = ""
for fname in os.listdir("codex"):
import sys
if sys.version_info >= (3,0):
with open("codex/"+fname, encoding='cp1251') as fin:
text = fin.read() #If you are using your own corpora, make sure it's read correctly
cor... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Agenda
Step2: Constants
Step3: Input variables
Step4: Build the neural network
Step5: Compiling it
Step8: Law generation
Step9: Model trai... |
1,901 | <ASSISTANT_TASK:>
Python Code:
__author__ = 'Adam Foster and Nick Dingwall'
from centering_and_scaling import *
%matplotlib inline
# A dataset:
data = np.random.multivariate_normal(
mean=[4, 0], cov=[[5, 2], [2, 3]], size=250)
X, y = data[:, 0], data[:, 1]
# Subtract the mean from the features:
empirical_mean = X.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: TL;DR
Step2: Notice that we have a different intercept, but the same slope. The predictions from these two models will be identical. For instan... |
1,902 | <ASSISTANT_TASK:>
Python Code:
# Tensorflow
import tensorflow as tf
print('Tested with TensorFlow 1.2.0')
print('Your TensorFlow version:', tf.__version__)
# Feeding function for enqueue data
from tensorflow.python.estimator.inputs.queues import feeding_functions as ff
# Rnn common functions
from tensorflow.contrib.le... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Loading Data
Step2: We can search our word list for a word like "baseball", and then access its corresponding vector through the embedding matr... |
1,903 | <ASSISTANT_TASK:>
Python Code:
with open('../csv_files/metro_edges_no_duplicated_edges_no_cycles_speed_networkx.csv') as f:
f.readline()
# Source,Target,Weight,edge_name,edge_color,travel_seconds,longitude_Source,latitude_Source,longitude_Target,latitude_Target,distance_meters,speed_ms
g = nx.parse_edgelist... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Top 35 stations with more neighbour stations
Step2: Neighbours' count histogram
Step3: Most of the stations are connected to two other station... |
1,904 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
def generate_prob(n = 100, p = 0.8, eps = 0.2):
@param: n (int): 子样个数
@param: p (float): 伯努利分布成功概率
@param: eps (float): 容忍偏差, bias
sample = [np.abs(np.random.binomial(n=1, p=p, size=n).mean() - p) for i in range(10000)] # 10000 仿真次数
Prob = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Dataset
Step2: Plots
Step3: 动态改变 $\epsilon$
Step4: 动态改变 $n$
Step5: 动态改变 $p$
|
1,905 | <ASSISTANT_TASK:>
Python Code:
# The following code is adopted from Pat's Rolling Rain N-Year Threshold.pynb
# Loading in hourly rain data from CSV, parsing the timestamp, and adding it
# as an index so it's more useful
rain_df = pd.read_csv('data/ohare_full_precip_hourly.csv')
rain_df['datetime'] = pd.to_datetime(rain... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Rainfall Equivalent
Step2: Plotting Rainfall vs. n-year storm threshold
Step3: Calculate new n-year storm definitions
|
1,906 | <ASSISTANT_TASK:>
Python Code:
import os
import math
import torch
import pyro
import pyro.distributions as dist
from pyro.infer.autoguide import AutoDelta
from pyro.optim import Adam
from pyro.infer import SVI, Trace_ELBO, config_enumerate
from pyro.contrib.tracking.extended_kalman_filter import EKFState
from pyro.cont... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Next, let's specify the measurements. Notice that we only measure the positions of the particle.
Step2: We'll use a Delta autoguide to learn MA... |
1,907 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import GPy
%matplotlib inline
#%config InlineBackend.figure_format = 'svg'
from pylab import *
np.random.seed(113321)
# Prepare the data
N,D,Q = 500, 100, 3
pi = 0.2
# sample from 3 random sine waves
X = np.sin(2*np.pi*(np.random.rand(Q)[None,:]+.5)*(np.linspace(0.,3.,N... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The obersved data $Y$ is generated by projecting the samples of the 3 sine waves onto a 100D space with a randomly generated weight matrix $W$. ... |
1,908 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib as plt
import seaborn as sns
users = pd.read_csv('timeseries_users.csv')
users.head()
events = pd.read_csv('timeseries_events.csv')
events.index = pd.to_datetime(events['event_date'], format='%Y-%m-%d %H:%M:%S')
d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <a id='data_exploration'></a>
Step2: User's age mean is from 24 to 63 years old, with a mean of 41 years old.
Step3: Many duplicated entries a... |
1,909 | <ASSISTANT_TASK:>
Python Code:
#@title Default title text
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Install the right software
Step2: Decode the Telluride4 EEG Data
Step3: Run complete jackknife test
|
1,910 | <ASSISTANT_TASK:>
Python Code:
synden = np.zeros((len(sorted_x), len(sorted_y), len(sorted_z)))
for r in rows:
if r[-2] != 0:
synden[sorted_x.index(r[0]), sorted_y.index(r[1]), sorted_z.index(r[2])] = np.float(r[-1])/np.float(r[-2])
x_sum = [0] * len(synden[:,0,0])
for i in range(len(synden[:,0,0])):
x_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 2. Looking at the y-layer
Step2: 3. How are synapses distributed within these possible cortex layers? Are they uniform?
Step3: Surprisingly, i... |
1,911 | <ASSISTANT_TASK:>
Python Code:
import os, sys
import itertools
import re
import json
%matplotlib inline
from random import randint
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import gzip
from math import log, e
from scipy import stats
from math import sqrt
mdf = pd.read... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Read the metrics
Step2: Read the results file.
Step3: Find the option with no metrics
Step4: But no metrics == no phages!
Step5: Find the op... |
1,912 | <ASSISTANT_TASK:>
Python Code:
import mltoolbox.image.classification as model
from google.datalab.ml import *
bucket = 'gs://' + datalab_project_id() + '-lab'
preprocess_dir = bucket + '/flowerpreprocessedcloud'
model_dir = bucket + '/flowermodelcloud'
staging_dir = bucket + '/staging'
!gsutil mb $bucket
train_set = C... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Preprocess
Step2: Train
Step3: Check your job status by running (replace the job id from the one shown above)
Step4: Predict
Step5: Online p... |
1,913 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['figure.dpi'] = 150
from skdaccess.framework.param_class import *
from skdaccess.astro.spectra.stream import DataFetcher
ap_spectra_url = AutoList([
'https://dr14.sdss.org/sas/dr14/eboss/spectro/redux/v5_10_0/spectra/li... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Import data fetcher
Step2: Specify list of SDSS spectra URLs to retrieve
Step3: Create data fetcher
Step4: Access data and metadata
Step5: P... |
1,914 | <ASSISTANT_TASK:>
Python Code:
import cvxopt as cvx
from cvxopt import solvers as cvx_solvers
Q = cvx.matrix([[0.,0.],[0.,0.]])
p = cvx.matrix([-1., 4.])
G = cvx.matrix([[-3., 1., 0.],[1., 2., -1.]])
h = cvx.matrix([6., 4., 3.])
sol = cvx_solvers.qp(Q, p, G, h)
print(sol['x'])
import scipy.optimize as opt
import mysti... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: EXERCISE
Step2: EXERCISE
Step3: EXERCISE
Step4: EXERCISE
Step5: EXERCISE
Step6: EXERCISE
Step7: EXERCISE
Step8: EXERCISE
|
1,915 | <ASSISTANT_TASK:>
Python Code:
from nipype.interfaces.io import DataSink
ds = DataSink()
ds.inputs.base_directory = 's3://mybucket/path/to/output/dir'
ds.inputs.creds_path = '/home/neuro/aws_creds/credentials.csv'
ds.inputs.encrypt_bucket_keys = True
ds.local_copy = '/home/neuro/workflow_outputs/local_backup'
<END_TA... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: With the "s3
|
1,916 | <ASSISTANT_TASK:>
Python Code:
census = list(csv.reader(open("census.csv", 'r')))
for index, column in enumerate(census[0]):
print("{} - {}: {}".format(index, column, census[1][index]))
def get_race_count(census, column_indexes):
return sum([int(census[1][index]) for index in column_indexes])
race_percentage = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Death by race
|
1,917 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division, print_function # Python 3
from sympy import init_printing
init_printing(use_latex='mathjax',use_unicode=False) # Affichage des résultats
%matplotlib inline
from sympy import plot
from sympy import sin
from sympy.abc import x
plot(sin(x))
plot(sin(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: La librairie Sympy utilise matplotlib, une autre librairie de Python, pour faire des dessins. Pour activer l'affichage des graphiques dans Jupyt... |
1,918 | <ASSISTANT_TASK:>
Python Code:
%lsmagic
time print("hi")
%time
ls -l -h
!ls -l -h
files = !ls -l -h
files
%%!
ls -l
pwd
who
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
%timeit np.linalg.eigvals(np.random.rand(100,100))
%%timeit a = np.random.rand(100, 100)
np.linalg.eigvals(a)
%%capture ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 缺省情况下，Automagic开关打开，不需要输入%符号，将会自动识别。
Step2: 执行Shell脚本。
Step3: 执行多行shell脚本。
Step4: 下面开始体验一下魔法操作符的威力。
Step5: <!--====--> cell magics的简单例子
Step... |
1,919 | <ASSISTANT_TASK:>
Python Code:
%projects set ml-autoawesome
import os
PROJECT = 'ml-autoawesome' # CHANGE THIS
BUCKET = 'ml-autoawesome-cmle' # CHANGE THIS
REGION = 'us-central1' # CHANGE THIS
os.environ['PROJECT'] = PROJECT # for bash
os.environ['BUCKET'] = BUCKET # for bash
os.environ['REGION'] = REGION # for bas... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <h2> Step 1
Step3: <h2> 2. Preprocessing </h2>
Step4: <h2> Local preprocessing, training and prediction </h2>
Step5: <h3> Training </h3>
Step... |
1,920 | <ASSISTANT_TASK:>
Python Code:
HISTORY = 4
NRUNS = 50
parameters = {
'chaos': 0,
'risk_x_average_variance': 1,
'dividends': 1,
'discount_rate': 0.001,
'intensity_of_choice':2,
'fundamentalist_adaptive_parameter':1,
'chartist_adaptive_parameter':1.9,
'bubble_sensitivity':1800,
'fitnes... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: initialization
Step2: Sequence of events / simulation
Step3: Log returns
Step4: Sequence of events / simulation
Step5: Log returns
|
1,921 | <ASSISTANT_TASK:>
Python Code:
#CCL cosmology
cosmo_ccl = ccl.Cosmology(Omega_c = 0.30711 - 0.048254, Omega_b = 0.048254, h = 0.677, sigma8 = 0.8822714165197718, n_s=0.96, Omega_k = 0, transfer_function='eisenstein_hu')
#ccl_cosmo_set_high_prec (cosmo_ccl)
cosmo_numcosmo, dist, ps_lin, ps_nln, hmfunc = create_nc_obj (c... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Define proxy modelling
Step2: initialize the ClusterAbundance object
|
1,922 | <ASSISTANT_TASK:>
Python Code:
import pandas
import numpy
import toyplot
import toyplot.pdf
import toyplot.png
import toyplot.svg
print('Pandas version: ', pandas.__version__)
print('Numpy version: ', numpy.__version__)
print('Toyplot version: ', toyplot.__version__)
column_names = ['MPG',
'Cylinder... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load in the "auto" dataset. This is a fun collection of data on cars manufactured between 1970 and 1982. The source for this data can be found a... |
1,923 | <ASSISTANT_TASK:>
Python Code:
from poppy.creatures import PoppyHumanoid
creature = PoppyHumanoid(simulator='vrep')
creature.reset_simulation()
import pypot
creature.stop_simulation()
pypot.vrep.close_all_connections()
from poppy.creatures import PoppyHumanoid
poppy = PoppyHumanoid(simulator='vrep')
from __future__ ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 3 - Redémarrer la simulatiuon
Step2: 4 - Eteindre la simulation
Step3: 5 - Moteurs & capteurs
Step4: Explication
Step5: Explication
Step6: ... |
1,924 | <ASSISTANT_TASK:>
Python Code:
new_data = new_data.to_crs("+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=37.5 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs ")
new_data['logBiomass'] = new_data.apply(lambda x : np.log(x.plotBiomass),axis=1)
new_data['newLon'] = new_data.apply(lambda c : c.geometry.x,... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Add log of the Biomass
Step2: Linear Regression
Step3: STOPPPP!!
Step4: Now with distance restriction (experimental!)
Step5: Model Fitting U... |
1,925 | <ASSISTANT_TASK:>
Python Code:
!pip install -I "phoebe>=2.0,<2.1"
import phoebe
from phoebe import u # units
logger = phoebe.logger()
b = phoebe.default_binary()
b.get_setting()
b['setting']
b['plotting_backend@setting']
b['plotting_backend@setting'].choices
b['log_history@setting'].description
b['log_history@set... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: As always, let's do imports and initialize a logger and a new Bundle. See Building a System for more details.
Step2: Accessing Settings
Step3:... |
1,926 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt # package for doing plotting (necessary for adding the line)
import statsmodels.formula.api as smf # package we'll be using for linear regression
import matplotlib.pyplot as plt
import matplotlib
import pandas as pd
im... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 2. Read in the hanford.csv file
Step2: <img src="images/hanford_variables.png">
Step3: 4. Calculate the coefficient of correlation (r) and gen... |
1,927 | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
from cpyquickhelper.examples.vector_container_python import (
RandomTensorVectorFloat, RandomTensorVectorFloat2)
rnd = RandomTensorVectorFloat(10, 10)
result = rnd.get_tensor_vector()
print(result)
res... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Two identical classes
Step2: Scenarii
|
1,928 | <ASSISTANT_TASK:>
Python Code:
from flow.scenarios import MergeScenario
from flow.core.params import VehicleParams
from flow.controllers import IDMController
from flow.core.params import SumoCarFollowingParams
# create an empty vehicles object
vehicles = VehicleParams()
# add some vehicles to this object of type "huma... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: A schematic of the above network is displayed in the figure below. As we can see, the edges at the start of the main highway and of the on-merge... |
1,929 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'sandbox-2', 'atmoschem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
1,930 | <ASSISTANT_TASK:>
Python Code:
randinds = np.random.permutation(len(digits.target))
# shuffle the values
from sklearn.utils import shuffle
data, targets = shuffle(digits.data, digits.target, random_state=0)
# scale the data
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler().fit(data)
data_scaled... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Prep is done, time for the model.
Step2: We've defined the cost and accuracy functions, time to train our model.
|
1,931 | <ASSISTANT_TASK:>
Python Code:
# Authors: Jean-Remi King <jeanremi.king@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD-3-Clause
import matplotlib.pyplot as plt
from sklearn.pipeline import make_pipeline
from sklearn.preprocess... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: We will train the classifier on all left visual vs auditory trials
Step2: Score on the epochs where the stimulus was presented to the right.
St... |
1,932 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import fredpy as fp
import matplotlib.pyplot as plt
plt.style.use('classic')
%matplotlib inline
fp.api_key = '################################'
fp.api_key = fp.load_api_key('fred_api_key.txt')
u = fp.series('UNRATE')
plt.plot(u.data.index,u.data.v... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Load API key
Step2: or by reading from a text file containing only the text of the API key in the first line
Step3: If fred_api_key.txt is not... |
1,933 | <ASSISTANT_TASK:>
Python Code:
grammar =
S -> NP VP
S -> VP
NP -> DET N
VP -> V[SUBCAT=tr] NP
VP -> V[SUBCAT=intr]
DET -> "das"
N -> "Kind" | "Buch"
V[SUBCAT=tr] -> "lies"
V[SUBCAT=tr] -> "liest"
V[SUBCAT=intr] -> "schlaf"
V[SUBCAT=intr] -> "schläft"
pos_sentences = [
"das Kind schläft",
"das Kind liest das B... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Übungsblatt 8
Step2: Hier wurde versucht, Aufforderungssätze zu modellieren. Allerdings akzeptiert diese Grammatik immer noch viele ungrammatis... |
1,934 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'sandbox-3', 'seaice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "em... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 2... |
1,935 | <ASSISTANT_TASK:>
Python Code:
import gensim
import os
import collections
import random
# Set file names for train and test data
test_data_dir = '{}'.format(os.sep).join([gensim.__path__[0], 'test', 'test_data'])
lee_train_file = test_data_dir + os.sep + 'lee_background.cor'
lee_test_file = test_data_dir + os.sep + 'l... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: What is it?
Step2: Define a Function to Read and Preprocess Text
Step3: Let's take a look at the training corpus
Step4: And the testing corpu... |
1,936 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
view_sentence_range = (100, 110)
DON'T MODIFY ANYTHING IN THIS CELL
import ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: TV Script Generation
Step3: Explore the Data
Step6: Implement Preprocessing Functions
Step9: Tokenize Punctuation
Step11: Preprocess all the... |
1,937 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
#print(plt.style.available)
plt.style.use('presentation')
G = 6.67E-11 # Constante de gravitation universelle en m(3)*s(-2)*kg(-1)
Mt = 5.98E24 # Masse de la terre en kg
Rt = 6378E3 # Rayon de la terre en m
Wt = (2*np.pi... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Définissons ensuite les constantes utilisées
Step2: 1. Movement orbital
Step3: 1.2 Fonctions accélération
Step4: Exemple
Step5: Exemple
Step... |
1,938 | <ASSISTANT_TASK:>
Python Code:
# imports
import pandas as pd
import numpy as np
import time
import os
from tabulate import tabulate
import sys
from operator import add
from pyspark import SparkContext
from pyspark.sql import SparkSession
from pyspark.sql import SQLContext
from pyspark.sql import functions as F #https:/... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Data types
Step2: Dealing with Outliers
Step3: Winsorize for Outliers
Step4: New Chart
Step5: Label Encoding
Step6: Feature interaction
Ste... |
1,939 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
%matplotlib inline
temp_cidade1 = np.array([33.15,32.08,32.10,33.25,33.01,33.05,32.00,31.10,32.27,33.81])
temp_cidade2 = np.array([35.17,36.23,35.22,34.33,35.78,36.31,36.03,36.23,36.35,35.25])
temp_cidade3 = np.array([22.17,23.25,24.22,22.31,23.18,23.31,24.11,23.53,24.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Vamos calcular a média de temperatura de cada cidade e utilizá-la para gerar um gráfico
Step2: Agora, vamos criar um gráfico de barras utilizan... |
1,940 | <ASSISTANT_TASK:>
Python Code:
import ipyvolume
import ipyvolume as ipv
import vaex
ds = vaex.example()
N = 10000
ipv.figure()
quiver = ipv.quiver(ds.data.x[:N], ds.data.y[:N], ds.data.z[:N],
ds.data.vx[:N], ds.data.vy[:N], ds.data.vz[:N],
size=1, size_selected=5, color_selec... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We load some data from vaex, but only use the first 10 000 samples for performance reasons of Bokeh.
Step2: We make a quiver plot using ipyvolu... |
1,941 | <ASSISTANT_TASK:>
Python Code:
def get_words(url):
import requests
words = requests.get(url).content.decode('latin-1')
word_list = words.split('\n')
index = 0
while index < len(word_list):
word = word_list[index]
if ';' in word or not word:
word_list.pop(index)
el... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: <h4>Read the text being analyzed and count the proportion of positive and negative words in the text</h4>
Step2: <h4>Compute sentiment by looki... |
1,942 | <ASSISTANT_TASK:>
Python Code:
# Load image
import cv2
import numpy as np
from matplotlib import pyplot as plt
# Load image as grayscale
image = cv2.imread('images/plane_256x256.jpg', cv2.IMREAD_GRAYSCALE)
# Create kernel
kernel = np.array([[0, -1, 0],
[-1, 5,-1],
[0, -1, 0]])
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Load Image As Greyscale
Step2: Sharpen Image
Step3: View Image
|
1,943 | <ASSISTANT_TASK:>
Python Code:
import subprocess
completed = subprocess.run(['ls', '-l'])
completed
completed = subprocess.run(['ls', '-l'], stdout=subprocess.PIPE)
completed
import subprocess
try:
completed = subprocess.run(
'echo to stdout; echo to stderr 1>&2; exit 1',
shell=True,
stdou... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Capturing Output
Step2: Suppressing Output
Step3: Execute on shell
Step4: if you don't run this command on a shell, this is a error, because ... |
1,944 | <ASSISTANT_TASK:>
Python Code:
import pints
import pints.toy as toy
import numpy as np
import matplotlib.pyplot as plt
# Load a forward model
model = toy.LogisticModel()
# Create some toy data
real_parameters = [0.015, 500]
times = np.linspace(0, 1000, 100)
org_values = model.simulate(real_parameters, times)
# Add nois... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Plotting Pints' standard 1d histograms
Step2: Customise the plots
|
1,945 | <ASSISTANT_TASK:>
Python Code:
import logging
import time
from contextlib import contextmanager
import os
from multiprocessing import Process
import psutil
import numpy as np
import pandas as pd
from numpy.random import RandomState
from sklearn import decomposition
from sklearn.cluster import MiniBatchKMeans
from sklea... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Dataset preparation
Step2: Create a train/test split
Step3: We'll use very simple preprocessing with stemming to tokenize each document. YMMV;... |
1,946 | <ASSISTANT_TASK:>
Python Code:
mod = 1000000007
arr =[[ None for i in range(1001 ) ] for j in range(1001 ) ]
def Preprocess() :
arr[0 ][0 ] = 1
for i in range(1 , 1001 ) :
arr[i ][0 ] = 1
for j in range(1 , i ) :
arr[i ][j ] =(arr[i - 1 ][j - 1 ] + arr[i - 1 ][j ] ) % mod
arr[i ][i ] = 1
def ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
|
1,947 | <ASSISTANT_TASK:>
Python Code:
# Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import find_events, fit_dipole
from mne.datasets.brainstorm import bst_phantom_elekta
from mne.io import read_raw_fif
pr... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: The data were collected with an Elekta Neuromag VectorView system at 1000 Hz
Step2: Data channel array consisted of 204 MEG planor gradiometers... |
1,948 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: TensorFlow 2 início rápido para especialistas
Step2: Carregue e prepare o [conjunto de dados MNIST] (http
Step3: Use tf.data para agrupar e em... |
1,949 | <ASSISTANT_TASK:>
Python Code:
# Load libraries
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import AgglomerativeClustering
# Load data
iris = datasets.load_iris()
X = iris.data
# Standarize features
scaler = StandardScaler()
X_std = scaler.fit_transform(X)
# Cre... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load Iris Flower Data
Step2: Standardize Features
Step3: Conduct Agglomerative Clustering
Step4: Show Cluster Membership
|
1,950 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import netCDF4
import matplotlib.pyplot as plt
dataurl = "http://thredds.socib.es/thredds/dodsC/mooring/conductivity_and_temperature_recorder/buoy_canaldeibiza-scb_sbe37006/L1/dep0003_buoy-canaldeibiza_scb-sbe37006_L1_latest.nc"
with netCDF4.Dataset(dataurl) as ds:
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We will use data from a mooring located in the Ibiza Channel.<br/>
Step2: Read the file
Step3: The variable storing the temperature is not cal... |
1,951 | <ASSISTANT_TASK:>
Python Code:
# Librerias utilizadas
import pandas as pd
import sys
import urllib
import os
import numpy as np
# Configuracion del sistema
print('Python {} on {}'.format(sys.version, sys.platform))
print('Pandas version: {}'.format(pd.__version__))
import platform; print('Running on {} {}'.format(platf... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: La descarga de datos se realiza desde el sitio Beta de INEGI. Los datos de la Encuesta Intercensal 2015 se encuentran en http
Step2: Las ligas ... |
1,952 | <ASSISTANT_TASK:>
Python Code:
R=5.
def z(x,y):
return sqrt(x**2+y**2+R**2.)
x = linspace(-10,10,100) #Definiendo el dominio en x
y = linspace(-10,10,100) #Definiendo el dominio en y
X, Y = meshgrid(x, y) #Formando la grilla x,y
fig = figure(figsize=(6,6))
ax = fig.gca(projection='3d')
surf = ax.plot_surface(X, Y, ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Todo luce un poco mejor usando coordenadas polares en el plano xy
Step2: Cono (R=0)
|
1,953 | <ASSISTANT_TASK:>
Python Code:
import numpy as NUM
import pylab as PYLAB
import arcpy as ARCPY
import numpy as NUM
import SSDataObject as SSDO
import scipy as SCIPY
import pandas as PANDAS
inputFC = r'../data/CA_Polygons.shp'
ssdo = SSDO.SSDataObject(inputFC)
ssdo.obtainData(ssdo.oidName, ['PCR2008', 'POPDEN08', 'PERC... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Initialize Data Object, Select Fields and Obtain Data
Step2: Make Use of PANDAS Data Frame
Step3: Push PANDAS Data Frame to R Data Frame - Use... |
1,954 | <ASSISTANT_TASK:>
Python Code:
#Importation des librairies utilisées
import pandas as pd
import numpy as np
import pickle
import functools
from tqdm import tqdm
import keras.models as km
import keras.layers as kl
N = 100000
DATA_DIR = ""
X = np.load(DATA_DIR+"data/description_coque.npy")[:N]
print(X.shape)
print(X[:3]... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Téléchargement des données
Step2: Exercice Vérifiez que toutes les séquences sont bien de tailles 197.
Step3: Mise en forme des données
Step4... |
1,955 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
# The Dataset comes from:
# https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits
# Load up the data.
with open('../Datasets/optdigits.tes', 'r') as f: testing = pd.read_csv(f)
with open('../Datasets/optdigits.tra', 'r') as f: training = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Let's have a look at these bitmaps of handwritten digits
Step2: Train the SVM Classifier
Step3: Checkpoint
Step4: The model's prediction was ... |
1,956 | <ASSISTANT_TASK:>
Python Code:
from fastai.collab import *
from fastai.tabular import *
user,item,title = 'userId','movieId','title'
path = untar_data(URLs.ML_SAMPLE)
path
ratings = pd.read_csv(path/'ratings.csv')
ratings.head()
data = CollabDataBunch.from_df(ratings, seed=42)
y_range = [0,5.5]
learn = collab_learner... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Collaborative filtering example
Step2: That's all we need to create and train a model
Step3: Movielens 100k
Step4: Here's some benchmarks on ... |
1,957 | <ASSISTANT_TASK:>
Python Code:
!pip install -I "phoebe>=2.1,<2.2"
%matplotlib inline
import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_binary()
b['q'] = 0.8
b['ecc'] = 0.1
b['irrad_method'] = 'none'
b.add_dataset('orb', times=np.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: This first line is only necessary for ipython noteboooks - it allows the plots to be shown on this page instead of in interactive mode
Step2: A... |
1,958 | <ASSISTANT_TASK:>
Python Code:
import os
import logging
from datetime import datetime
import numpy as np
import tensorflow as tf
import tensorflow_transform as tft
import tensorflow.keras as keras
from google.cloud import aiplatform as vertex_ai
from google.cloud.aiplatform import hyperparameter_tuning as hp_tuning
fro... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Setup Google Cloud project
Step2: Set configurations
Step3: Create Vertex TensorBoard instance
Step4: Initialize workspace
Step5: Initialize... |
1,959 | <ASSISTANT_TASK:>
Python Code:
import collections
Person = collections.namedtuple('Person', 'name age')
bob = Person(name='Bob', age=30)
print('\nRepresentation:', bob)
jane = Person(name='Jane', age=29)
print('\nField by name:', jane.name)
print('\nFields by index:')
for p in [bob, jane]:
print('{} is {} years old... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Just like a regular tuple, a namedtuple is immutable. This restriction allows tuple instances to have a consistent hash value, which makes it po... |
1,960 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
%matplotlib inline
df1 = pd.read_csv('../data/df1',index_col=0)
df2 = pd.read_csv('../data/df2')
df1['A'].hist()
import matplotlib.pyplot as plt
plt.style.use('ggplot')
df1['A'].hist()
plt.style.use('bmh')
df1['A'].hist()
plt.style.use('dark_back... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: La informacion
Step2: Hojas de estilo
Step3: Utilizando estilos
Step4: Ahora tu grafica se visulizara de la siguiente manera
Step5: Por lo p... |
1,961 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('C:\Anaconda2\envs\dato-env\Lib\site-packages')
import graphlab
sales = graphlab.SFrame('kc_house_data.gl/')
train_data,test_data = sales.random_split(.8,seed=0)
# Let's compute the mean of the House Prices in King County in 2 different ways.
prices = sales['... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load house sales data
Step2: Split data into training and testing
Step3: Useful SFrame summary functions
Step4: As we see we get the same ans... |
1,962 | <ASSISTANT_TASK:>
Python Code:
from fretbursts import *
sns = init_notebook()
filename = "./data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5"
d = loader.photon_hdf5(filename)
loader.alex_apply_period(d)
d.calc_bg(bg.exp_fit, time_s=30, tail_min_us='auto', F_bg=1.7)
d.burst_search()
ph = d.get_ph_times() ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Getting the timestamps
Step2: This are streams of all timestamps (both inside and outside the bursts).
Step3: Masks are arrays of booleans (Tr... |
1,963 | <ASSISTANT_TASK:>
Python Code:
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import json
from collections import defaultdict
from datetime import datetime, date
from random import randint
from networkx.readwrite.json_graph import node_link_data
%matplotlib inline
G = nx.re... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step4: 2009 H1N1 lineage trace
Step5: 2013 H7N9 lineage trace
|
1,964 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
# In Jupyter, all commands starting with ! are mapped as SHELL commands
!head stockholm_td_adj.dat
np.genfromtxt?
st_temperatures = np.genfromtxt('stockholm_td_adj.dat',
skip_header=1)
st_temperatures.shape
st_temperatures[:10, ]
st_te... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Comma-separated values (CSV)
Step2: DYI
Step3: Numpy's native file format
Step4: See also
Step5: NumPy for Matlab Users (really?)
Step6: If... |
1,965 | <ASSISTANT_TASK:>
Python Code:
!wget -O - 'http://www.cs.nyu.edu/~roweis/data/nips12raw_str602.tgz' > /tmp/nips12raw_str602.tgz
import tarfile
filename = '/tmp/nips12raw_str602.tgz'
tar = tarfile.open(filename, 'r:gz')
for item in tar:
tar.extract(item, path='/tmp')
import os, re
from smart_open import smart_open
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: In the following sections we will load the data, pre-process it, train the model, and explore the results using some of the implementation's fun... |
1,966 | <ASSISTANT_TASK:>
Python Code:
text = Als der Abend herbeikam und die Freunde in einer weitumherschauenden Laube saßen, trat eine ansehnliche Figur auf die Schwelle, welche unser Freund sogleich für den Barbier von heute früh erkannte. Auf einen tiefen, stummen Bückling des Mannes erwiderte Lenardo: Ihr kommt, wie imme... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Table of Contents
Step2: Textblob installieren
Step3: Achtung
Step4: Das Gute daran, ist, dass wir - wie oben - über dieses Objekt iterieren... |
1,967 | <ASSISTANT_TASK:>
Python Code:
x = 1
y = 2
z = x + y
z * 3
from math import sin
sin(2)
my_result = sin(2)
my_result = sin(2)
print(my_result)
print('hello')
hello = 'Hello, world!'
print(hello)
print('The man said:', hello, 'How are you?')
name = input('What is your name?')
age = input('What is your age?')
color... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: In good mathematical tradition, I've named various things x, y and z and re-used them in various lines of the program. You are free to choose wh... |
1,968 | <ASSISTANT_TASK:>
Python Code:
from pytadbit.mapping.full_mapper import full_mapping
r_enz = 'MboI'
! mkdir -p results/iterativ/$r_enz
! mkdir -p results/iterativ/$r_enz/01_mapping
# for the first side of the reads
full_mapping(gem_index_path='/media/storage/db/reference_genome/Homo_sapiens/hg38/hg38.gem',
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: The full mapping function can be used to perform either iterative or fragment-based mapping, or a combination of both.
Step2: And for the secon... |
1,969 | <ASSISTANT_TASK:>
Python Code:
from pymldb import Connection
mldb = Connection()
inceptionUrl = 'file://mldb/mldb_test_data/models/inception_dec_2015.zip'
print mldb.put('/v1/functions/fetch', {
"type": 'fetcher',
"params": {}
})
print mldb.put('/v1/functions/inception', {
"type": 'tensorflow.graph',
"... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Loading a TensorFlow graph
Step2: Scoring an image
Step3: This is great! With only 3 REST calls we were able to run a deep neural network on a... |
1,970 | <ASSISTANT_TASK:>
Python Code:
# Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
# Instala o TensorFlow
#!pip install -q tensorflow==2.5
# Imports
import sklearn
import numpy as np
import tensorflow as tf
import matplotli... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Carregando os Dados (Imagens)
Step2: Pré-Processamento dos Dados
Step3: Label encoding (convertendo string para valor numérico)
Step4: Datase... |
1,971 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pyfolio as pf
stock_rets = pf.utils.get_symbol_rets('FB')
out_of_sample = stock_rets.index[-40]
pf.create_bayesian_tear_sheet(stock_rets, live_start_date=out_of_sample)
help(pf.bayesian.run_model)
# Run model that assumes returns to be T-distributed
trace = p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Fetch the daily returns for a stock
Step2: Create Bayesian tear sheet
Step3: Lets go through these row by row
Step4: For example, to run a mo... |
1,972 | <ASSISTANT_TASK:>
Python Code:
# Import external libraries
import matplotlib.pyplot as plt
# Settings
%matplotlib inline
pvarray_parameters = {
'n_pvrows': 4, # number of pv rows
'pvrow_height': 1, # height of pvrows (measured at center / torque tube)
'pvrow_width': 1, # width of ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Prepare PV array parameters
Step2: Create a PV array and its shadows
Step3: Plot the PV array.
Step4: As we can see in the plot above
Step5: ... |
1,973 | <ASSISTANT_TASK:>
Python Code:
import pathlib
import os
from typing import Dict, List, Mapping, Optional, Sequence, Tuple, Union
import uuid
import zlib
from IPython.display import HTML
import matplotlib.animation as animation
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import tensorflow_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Data location
Step2: Create dataset
Step3: Load one example
Step10: Visualize TF Example
Step12: Display animation
Step14: Config
Step15: ... |
1,974 | <ASSISTANT_TASK:>
Python Code:
%%writefile ../../user_models/cylinder_Ascan_2D.in
#title: A-scan from a metal cylinder buried in a dielectric half-space
#domain: 0.240 0.210 0.002
#dx_dy_dz: 0.002 0.002 0.002
#time_window: 3e-9
#material: 6 0 1 0 half_space
#waveform: ricker 1 1.5e9 my_ricker
#hertzian_dipole: z 0.100 ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Geometry of a metal cylinder buried in a dielectric half-space
Step2: By examining the spectrum of a Ricker waveform it is evident much higher ... |
1,975 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import pandas as pd
import textwrap
import os
import sys
import warnings
warnings.filterwarnings('ignore')
# special things
from pivottablejs impo... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Notebook Extensions
Step2: Snippets Menu
Step3: Python Markdown -- Maybe doesn't work right now for some reason?
Step4: Collapsible Headings
|
1,976 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import json
import matplotlib.pyplot as plt
%matplotlib inline
loans = pd.read_csv('lending-club-data.csv')
loans.head(2)
loans.columns
features = ['grade', # grade of the loan
'term', # the term of the loan
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Getting the data ready
Step2: Extracting the target and the feature columns
Step3: Transform categorical data into binary features
Step4: Let... |
1,977 | <ASSISTANT_TASK:>
Python Code:
# First import the model. Here we use the HBV version
from wflow.wflow_sbm import *
import IPython
from IPython.display import display, clear_output
%pylab inline
#clear_output = IPython.core.display.clear_output
# Here we define a simple fictious reservoir
reservoirstorage = 15000
def si... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Set model run-time parameters
Step2: Here we make a pit in the middle of the main river. This will be the inflow to the reservoir
Step3: Run f... |
1,978 | <ASSISTANT_TASK:>
Python Code:
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder()
x0 = np.random.choice(3, 10)
x0
encoder.fit(x0[:, np.newaxis])
X = encoder.transform(x0[:, np.newaxis]).toarray()
X
dfX = pd.DataFrame(X, columns=encoder.active_features_)
dfX
from sklearn.datasets import load_bost... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 더미 변수와 모형 비교
Step2: 분산 분석을 이용한 모형 비교
|
1,979 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.read_csv('tmdb_5000_movies.csv.gz',
compression='gzip')
df.info()
df.head()
df = df[['title', 'tagline', 'overview', 'genres', 'popularity']]
df.tagline.fillna('', inplace=True)
df['description'] = df['tagline'].map(str) + ' ' + df['overview']... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Your Turn
Step2: Extract TF-IDF Features
Step3: Cluster Movies using K-Means
Step4: Affinity Propagation
Step5: Hierarchical Clustering
Step... |
1,980 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
# Our data is cleaned by cleaning utility code
df = pd.read_csv('Clean_Data_Adults_1.csv')
# Separate labels and Features
df_labels = df['Depressed']
df_feats = df.drop(['Depressed', 'Unnamed: 0'], axis=1, inplace=False)
X = df_feats.get_values() # ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 1. State assumptions about your data
Step2: 5. Compute accuracy
Step3: 6. Plot accuracy vs. sample size in simulation
Step4: 7. Apply method ... |
1,981 | <ASSISTANT_TASK:>
Python Code:
# code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', '..', 'notebook_format'))
from formats import load_style
load_style(plot_style=False)
os.chdir(path)
# 1. magic for inline pl... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Approximate Nearest Neighborhood Search with Navigable Small World
Step2: Given the output matrix, we would like to compute each of its nearest... |
1,982 | <ASSISTANT_TASK:>
Python Code:
import math
a = math.sqrt(16.0)
b = math.ceil(111.3)
c = math.floor(89.9)
print(a, b, c)
import math
print(math.pi)
PI = math.pi
a = math.sqrt(PI)
b = math.ceil(PI)
c = math.floor(PI)
print(a, b, c)
import csv
f = open("nfl.csv")
csvreader = csv.reader(f)
nfl = list(csvreader)
print(n... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Answer
Step2: 4
Step3: Answer
Step4: 5
Step5: 6
|
1,983 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.random.seed(42)
import tensorflow as tf
tf.set_random_seed(42)
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
epochs = 20
batch_size = 128
display_progress = 40 # after this many batches, ou... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load data
Step2: Set neural network hyperparameters
Step3: Set parameters for each layer
Step4: Define placeholder Tensors for inputs and lab... |
1,984 | <ASSISTANT_TASK:>
Python Code:
# Setup a target configuration
conf = {
# Platform and board to target
"platform" : "linux",
"board" : "juno",
# Login credentials
"host" : "192.168.0.1",
"username" : "root",
"password" : "",
# Local installation path
"tftp" : {
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Attributes
Step2: Functions
Step3: A special TestEnv attribute is <b>target</b>, which represent a <b>devlib instance</b>.<br>
Step4: Sample ... |
1,985 | <ASSISTANT_TASK:>
Python Code:
import quandl
data = quandl.get('NIKKEI/INDEX')
data[:5]
data_normal = (((data['Close Price']).to_frame())[-10000:-1])['Close Price']
data_normal[-10:-1] # 最新のデータ１０件を表示
data_normal = data_normal.fillna(method='pad').resample('W-MON').fillna(method='pad')
data_normal[:5]
type(data_normal.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 抜けデータが目立ったため、週単位でのデータを入手します
Step2: データの用い方
Step3: 以下のグラフから、2000年ごろのデータからの推測でも十分に予測が行える可能性が伺えます。
Step4: ARIMAモデルでモデル推定を行うための下準備として、株価の変化量を取得しま... |
1,986 | <ASSISTANT_TASK:>
Python Code:
from examples.cfd import plot_field, init_hat
import numpy as np
%matplotlib inline
# Some variable declarations
nx = 50
ny = 50
nt = 100
xmin = 0.
xmax = 2.
ymin = 0.
ymax = 1.
dx = (xmax - xmin) / (nx - 1)
dy = (ymax - ymin) / (ny - 1)
# Initialization
p = np.zeros((nx, ny))
pd = np.z... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We can now pretty much use our previous implementation, although we will use pd instead of pn for consistency with the original. Our boundary co... |
1,987 | <ASSISTANT_TASK:>
Python Code:
# Imports.
from typing import List
import astropy.io.ascii
import astropy.table
import h5py
import numpy
import sklearn.linear_model
import sklearn.cross_validation
# Globals.
# This file stores the ATLAS-CDFS and SWIRE-CDFS catalogues.
CROWDASTRO_PATH = '../data/crowdastro_swire.h5'
# Th... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step4: Crowdastro ATLAS-CDFS Catalogue
Step5: Making predictions
Step6: Generating the catalogue
|
1,988 | <ASSISTANT_TASK:>
Python Code:
%run "../src/start_session.py"
%run "../src/recurrences.py"
c = IndexedBase('c')
checks_recurrence_spec=recurrence_spec(recurrence_eq=Eq(c[n]/(n+1), 2/(n+1) + c[n-1]/n),
recurrence_symbol=c,
variables=[n])
ch... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Introduction
Step2: Function do_unfolding_steps allow us to perform unfolding or unrolling on occurrences of the inductively defined symbol. Do... |
1,989 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import tensorflow as tf
CONTENT_FILE = '/home/ishiyama/image_style_transfer/image/input/test_input_01.JPG'
STYLE_FILE = '/home/ishiyama/image_style_transfer/image/style/test_style_01.jpg'
class Image(np.ndarray):
画像を扱うためのnumpy.ndarray
XX... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: Image Style Transferの実装
Step4: VGGを実装する
Step6: Maxプーリング層を実装する
Step9: 畳込みとプーリング処理の途中経過を保持するクラスを実装する
Step10: VGGの畳込みとプーリング層を構築する
Step11: 画像を合... |
1,990 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import numexpr as ne
import numba
import math
import random
import matplotlib.pyplot as plt
import scipy as sp
import sys
%load_ext Cython
def primes_python(n):
primes = [False, False] + [True] * (n - 2)
i= 2
while i < n:
# We do ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Table of Contents
Step2: Let's evaluate the performance for the first version
Step3: And now we write our first Cython version, by just adding... |
1,991 | <ASSISTANT_TASK:>
Python Code:
data_rang = 9
pr_type = ['a', 'b', 'c', 'd']
p_type = [ np.random.choice(pr_type) for i in range(data_rang) ]
data = {'product_name' : ['x0', 'x1', 'x3', 'x2', 'x4', 'x5', 'x6', 'x7', 'x8'],
'T1': np.random.randint(100, size = [data_rang]),
'T2': np.random.randint(100, size ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: main function
Step2: 查询函数， 通过输入指定产品名和起止时间参数， 返回该产品在该类中的销售排名
Step3: another form of datafram
|
1,992 | <ASSISTANT_TASK:>
Python Code:
import os
from skimage import io
from skimage.color import rgb2gray
from skimage import transform
from math import ceil
IMGSIZE = (100, 100)
def load_images(folder, scalefactor=(2, 2), labeldict=None):
images = []
labels = []
files = os.listdir(folder)
for file in (f... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: Training LeNet
Step3: Training Random Forests
Step4: So training on raw pixel values might not be a good idea. Let's build a feature extractor... |
1,993 | <ASSISTANT_TASK:>
Python Code:
from pynq.overlays.base import BaseOverlay
from pynq.lib.video import *
base = BaseOverlay("base.bit")
hdmiin_frontend = base.video.hdmi_in.frontend
hdmiin_frontend.start()
hdmiin_frontend.mode
hdmiout_frontend = base.video.hdmi_out.frontend
hdmiout_frontend.mode = hdmiin_frontend.mod... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: HDMI Frontend
Step2: Creating the device will signal to the computer that a monitor is connected. Starting the frontend will wait attempt to de... |
1,994 | <ASSISTANT_TASK:>
Python Code:
from flexx import app, ui, react
app.init_notebook()
# A bit of boilerplate to import an example app
import sys
#sys.path.insert(0, r'C:\Users\almar\dev\flexx\examples\ui')
sys.path.insert(0, '/home/almar/dev/pylib/flexx/examples/ui')
from twente_temperature import Twente
ui.Button(text=... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Any widget can be shown by using it as a cell output
Step2: Because apps are really just Widgets, we can show our app in the same way
Step3: A... |
1,995 | <ASSISTANT_TASK:>
Python Code:
def fact(n ) :
res = 1
for i in range(2 , n + 1 ) :
res = res * i
return res
def nCr(n , r ) :
return fact(n ) //(( fact(r ) * fact(n - r ) ) )
n = 2
print("Number ▁ of ▁ Non - Decreasing ▁ digits : ▁ ", nCr(n + 9 , 9 ) )
<END_TASK>
| <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
1,996 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import re
import sys
import numpy as np
textfile = open('tournamentinfo.txt')
text_table = [line.strip() for line in textfile.readlines()]
text_table
player_state = []
player_number = []
id_data = []
name_data = []
state = '([A-Z]{2})'
number = '([0-9]{1})'
dash =... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Reading the text file into the Ipython Notebook
Step2: First i want to seperate the lines so i will strip them, and store them in a list using ... |
1,997 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import iris
import iris.plot as iplt
import iris.coord_categorisation
import cf_units
import numpy
%matplotlib inline
infile = '/g/data/ua6/DRSv2/CMIP5/NorESM1-M/rcp85/mon/ocean/r1i1p1/hfbasin/latest/hfbasin_Omon_NorESM1-M_rcp85_r1i1p1_200601-210012.nc'
cu... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Ocean heat transport in CMIP5 models
Step5: So for any given year, the annual mean shows ocean heat transport away from the tropics.
Step6: So... |
1,998 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from pandas_datareader.data import DataReader
endog = DataReader('UNRATE', 'fred', start='1954-01-01')
hp_cycle, hp_trend = sm.tsa.filters.hpfilter(endog, lamb=129600)
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Hodrick-Prescott (HP) filter
Step2: Unobserved components and ARIMA model (UC-ARIMA)
Step3: Unobserved components with stochastic cycle (UC)
S... |
1,999 | <ASSISTANT_TASK:>
Python Code:
from enum import Enum
class AccountType(Enum):
SAVINGS = 1
CHECKING = 2
AccountType.SAVINGS
AccountType.SAVINGS == AccountType.SAVINGS
AccountType.SAVINGS == AccountType.CHECKING
AccountType.SAVINGS.name
class BankAccount():
def __init__(self,owner,accountType):
se... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: An Enum stands for an enumeration, it's a convenient way for you to define lists of things. Typing
Step2: returns a Python representation of an... |
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