Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
|---|---|---|
5,800 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
A = np.array([[56.0, 0.0, 4.4, 68.0],
[1.2,104.0,52.0,8.0],
[1.8,135.0,99.0,0.9]])
print(A)
cal = A.sum(axis=0)
print(cal)
percentage = 100*A/cal.reshape(1,4)
print(percentage)
import numpy as np
a = np.random.randn(5)
print(a)
print(a.shape... | <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: 15 Note on numpy
|
5,801 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'snu', 'sandbox-3', 'atmoschem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "e... | <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... |
5,802 | <ASSISTANT_TASK:>
Python Code:
matplotlib inline
import pandas as pd
import numpy as np
from numpy import log
import statsmodels.formula.api as smf
data = pd.read_csv("trade_data.csv")
data.head()
data.columns
formula = "log(value) ~ log(egdp) + log(igdp) + log(dist)"
model = smf.ols(formula, data)
result = model.f... | <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: First we read in the data.
Step2: Let's see what it looks like.
Step3: Let's get a full list of columns.
Step4: Let's regress 'value' on 'egd... |
5,803 | <ASSISTANT_TASK:>
Python Code:
import nengo
import numpy as np
import cPickle
from nengo_extras.data import load_mnist
from nengo_extras.vision import Gabor, Mask
from matplotlib import pylab
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import random
import scipy.ndimage
# --- load the data... | <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 the MNIST database
Step2: Each digit is represented by a one hot vector where the index of the 1 represents the number
Step3: Load the sa... |
5,804 | <ASSISTANT_TASK:>
Python Code:
# Load library
import numpy as np
import pandas as pd
# Create feature matrix
X = np.array([[1, 2],
[6, 3],
[8, 4],
[9, 5],
[np.nan, 4]])
# Remove observations with missing values
X[~np.isnan(X).any(axis=1)]
# Load data as a 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: Create Data Frame
Step2: Drop Missing Values Using NumPy
Step3: Drop Missing Values Using pandas
|
5,805 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
def random_line(m, x, b, sigma, size=10):
Create a line y = m*x + b + N(0,sigma**2) between x=[-1.0,1.0]
Pa... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Line with Gaussian noise
Step5: Write a function named plot_random_line that takes the same arguments as random_line and creates a random line ... |
5,806 | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_iris
iris = load_iris()
print(iris.data.shape)
measurements = [
{'city': 'Dubai', 'temperature': 33.},
{'city': 'London', 'temperature': 12.},
{'city': 'San Francisco', 'temperature': 18.},
]
from sklearn.feature_extraction import DictVectori... | <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: These features are
Step2: Derived Features
Step3: Here is a broad description of the keys and what they mean
Step4: We clearly want to discar... |
5,807 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('../')
import cPickle as pickle
import re
import glob
import os
from generators import DataLoader
import time
import holoviews as hv
import theano
import theano.tensor as T
import numpy as np
import pandas as p
import lasagne as nn
from utils import hms, archite... | <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: We're going to test on some train images, so loading the training set labels.
Step2: Using the DataLoader to set up the parameters, you could r... |
5,808 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
from matplotlib.ticker import LinearLocator
sns.set_style('whitegrid')
mpl.rcParams['font.size'] = 16
mpl.rcParams['axes.labelsize'] = 16
mpl.rcParams['... | <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 observations
Step2: Model simulation
Step3: Structural options
Step4: Run the model
Step5: Focus on a subperiod to plot
Step6: Plot th... |
5,809 | <ASSISTANT_TASK:>
Python Code:
cities = ["Bristol", "London", "Manchester", "Edinburgh", "Belfast", "York"]
print("The position of Manchester in the list is: " + str(cities.???('Manchester')))
print("The position of Manchester in the list is: " + str(cities.index('Manchester')))
print(cities[2 + ???])
print(cities[2 ... | <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: b) Replace the ??? so that it prints the position of Manchester in the list
Step2: c) Replace the ??? so that it prints Belfast
Step3: d) Use ... |
5,810 | <ASSISTANT_TASK:>
Python Code:
from impact.core.features import BaseAnalyteFeature, BaseAnalyteFeatureFactory
class ODNormalizedData(BaseAnalyteFeature):
# The constructor should accept all required analytes as parameters
def __init__(self, biomass, reporter):
self.biomass = biomass
self.repor... | <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: Then, we can build our features
Step2: Finally, we register the feature
Step3: and test it
|
5,811 | <ASSISTANT_TASK:>
Python Code:
# run this once per session to bring in a required library
!pip --quiet install sparqlwrapper | grep -v 'already satisfied'
from SPARQLWrapper import SPARQLWrapper, JSON
import pandas as pd
import io
import requests
# This function shows how to use rdflib to query a REMOTE sparql dataset
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: <a href="https
Step3: Examples
|
5,812 | <ASSISTANT_TASK:>
Python Code:
### START CODE HERE ### (≈ 1 line of code)
test = "Hello World"
### END CODE HERE ###
print ("test: " + test)
# GRADED FUNCTION: basic_sigmoid
import math
def basic_sigmoid(x):
Compute sigmoid of x.
Arguments:
x -- A scalar
Return:
s -- sigmoid(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:
Step2: Expected output
Step3: Expected Output
Step4: In fact, if $ x = (x_1, x_2, ..., x_n)$ is a row vector then $np.exp(x)$ will apply the exponent... |
5,813 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mpi-m', 'sandbox-1', 'landice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "e... | <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: 1... |
5,814 | <ASSISTANT_TASK:>
Python Code:
texts = [
"Penny bought bright blue fishes.",
"Penny bought bright blue and orange fish.",
"The cat ate a fish at the store.",
"Penny went to the store. Penny ate a bug. Penny saw a fish.",
"It meowed once at the bug, it is still meowing at the bug and the fish",
"... | <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: When you process text, you have a nice long series of steps, but let's say you're interested in three things
Step2: The scikit-learn package do... |
5,815 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
# Imports
from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Common")
AddReference("QuantConnect.Jupyter")
AddReference("QuantConnect.Indicators")
from System import *
from QuantConnect import *
from QuantConnect.Data.Market import TradeBar, ... | <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: Selecting Asset Data
Step2: Historical Data Requests
Step3: Historical Options Data Requests
Step4: Get Fundamental Data
Step5: Indicators
|
5,816 | <ASSISTANT_TASK:>
Python Code:
import emcee
import halomod
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from multiprocess import Pool
import corner
%matplotlib inline
emcee.__version__
halomod.__version__
model = halomod.TracerHaloModel(
z=0.2,
transfer_model='EH',
rnum... | <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: Create Some Mock Data
Step2: Now, let's create some mock data with some Gaussian noise
Step3: Define a likelihood
Step4: Define an emcee-comp... |
5,817 | <ASSISTANT_TASK:>
Python Code:
from IoTPy.agent_types.simple import f_item
def f(item, M, multiples_stream, non_multiples_stream):
if item%M:
multiples_stream.append(item)
else:
non_multiples_stream.append(item)
x = Stream(name='input stream')
y = Stream(name='even numbers in stream x')
z = Str... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Step1: Second step
Step2: Third step
Step3: ANOTHER EXAMPLE of f_item
Step4: SLIDING WINDOWS OF STREAMS
Step5: Synchronous Join
Step6: Asynchronou... |
5,818 | <ASSISTANT_TASK:>
Python Code:
from abc import ABC, abstractmethod
from collections import namedtuple
Customer = namedtuple('Customer', 'name fidelity')
class LineItem:
def __init__(self, product, quantity, price):
self.product = product
self.quantity = quantity
self.price = price
d... | <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: Python 3.4 中，声明抽象基类的最简单方式是子类化 abc.ABC。我们的例子就是这么做的，从 Python 3.0 到 Python 3.3，必须在 class 语句使用 metaclas = 关键字
Step2: 用函数实现 “策略” 模式
Step3: 新的 Order... |
5,819 | <ASSISTANT_TASK:>
Python Code:
baseDir = '/home/nick/notebook/SIPSim/dev/priming_exp/'
workDir = os.path.join(baseDir, 'exp_info')
otuTableFile = '/var/seq_data/priming_exp/data/otu_table.txt'
otuTableSumFile = '/var/seq_data/priming_exp/data/otu_table_summary.txt'
metaDataFile = '/var/seq_data/priming_exp/data/allsamp... | <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: Init
Step2: Loading OTU table (filter to just bulk samples)
Step3: Which gradient(s) to simulate?
Step4: Notes
Step5: Total richness of star... |
5,820 | <ASSISTANT_TASK:>
Python Code:
# Importing pandas and numpy
import pandas as pd
import numpy as np
# Reading the csv file into a pandas DataFrame
data = pd.read_csv('student_data.csv')
# Printing out the first 10 rows of our data
data[:10]
# Importing matplotlib
import matplotlib.pyplot as plt
# Function to help us 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: Plotting the data
Step2: Roughly, it looks like the students with high scores in the grades and test passed, while the ones with low scores did... |
5,821 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy.stats import powerlaw
import scipy as sp
import seaborn as sns
sns.set()
%matplotlib inline
edges =[]
for line in open('facebook_combined.txt'):
if line[0] != '#':
# print(line.replace('\n','').split(' ')) # \n es salto de linea, .split(' ') -> se... | <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: Generative Network Model
|
5,822 | <ASSISTANT_TASK:>
Python Code::
import numpy as np
import gpt_2_simple as gpt2
from datetime import datetime
from google.colab import files
from tensorflow.python.framework import ops
gpt2.download_gpt2(model_name="124M")
from transformers import GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
sess = gp... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
5,823 | <ASSISTANT_TASK:>
Python Code:
from tvb.simulator.lab import *
LOG.info("Configuring...")
#Initialize a Model, Coupling, and Connectivity.
oscillator = models.Generic2dOscillator()
white_matter = connectivity.Connectivity.from_file("connectivity_96.zip")
white_matter.speed = numpy.array([4.0])
white_matter_coupling = ... | <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: Perform the simulation
Step2: Plot pretty pictures of what we just did
|
5,824 | <ASSISTANT_TASK:>
Python Code:
!pip install git+https://github.com/google/starthinker
from starthinker.util.configuration import Configuration
CONFIG = Configuration(
project="",
client={},
service={},
user="/content/user.json",
verbose=True
)
FIELDS = {
'auth_read':'user', # Credentials used for reading... | <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: 2. Set Configuration
Step2: 3. Enter DV360 Report Emailed To BigQuery Recipe Parameters
Step3: 4. Execute DV360 Report Emailed To BigQuery
|
5,825 | <ASSISTANT_TASK:>
Python Code:
primeNumbers = [2, 3, 5, 7]
for prime in primeNumbers:
print(prime)
for x in range(5):
print(x)
for x in range(3, 6):
print(x)
numbers = [
951, 402, 984, 651, 360, 69, 408, 319, 601, 485, 980, 507, 725, 547, 544,
615, 83, 165, 141, 501, 263, 617, 865, 575, 219, 390,... | <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: 2.Drucke alle die Zahlen von 0 bis 4 aus
Step2: 3.Drucke die Zahlen 3,4,5 aus
Step3: 4.Baue einen For-Loop, indem Du alle geraden Zahlen ausdr... |
5,826 | <ASSISTANT_TASK:>
Python Code:
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 = (20, 30)
import numpy as np
print('Dataset Stats')
print('Roughly the number of unique w... | <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: Explore the Data
Step3: Preprocessing
Step5: Tokenize Punctuation
Step6: Preprocess all the data and save it
Step7: Check Point
Step8: Buil... |
5,827 | <ASSISTANT_TASK:>
Python Code:
from learntools.ml_explainability.ex4 import *
print("Setup Complete")
import pandas as pd
data = pd.read_csv('../input/hospital-readmissions/train.csv')
data.columns
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_s... | <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 Scenario
Step2: Here are some quick hints at interpreting the field names
Step3: Now use the following cell to create the materials for th... |
5,828 | <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: 色空間変換
Step2: サンプル画像をダウンロードする
Step3: 使い方
Step4: 画像は以下の方法で表示できます。
Step5: RGB からグレースケールに変換する
Step6: RGB から BGR に変換する
Step7: RGB から CIE XYZ に変... |
5,829 | <ASSISTANT_TASK:>
Python Code:
## Augumenting functions based on Naoki Shibuya's work! Thank you!
## https://github.com/naokishibuya/car-traffic-sign-classification
import cv2
import numpy as np
def resizeImage(image):
return cv2.resize(img, (48,48))
def random_brightness(image, ratio):
hsv = cv2.cvtColor(image... | <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: With this we have expanded our dataset by factor 8. Let's look at the distribution of datapoints over all classes
Step2: All preprocessing step... |
5,830 | <ASSISTANT_TASK:>
Python Code:
print("Exemplo 8.9\n")
from sympy import *
t = symbols('t')
V = 12
C = 1/2
L = 1
#Para t < 0
i0 = 0
v0 = V
print("i(0):",i0,"A")
print("v(0):",v0,"V")
#Para t = oo
i_f = V/(4 + 2)
vf = V*2/(4 + 2)
print("i(oo):",i_f,"A")
print("v(oo):",vf,"V")
#Para t > 0
#desativar fontes independentes
#... | <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: Problema Prático 8.9
Step2: Exemplo 8.10
Step3: Problema Prático 8.10
|
5,831 | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
pr... | <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 data
Step2: By default, ~mne.io.read_raw_fif displays some information about the file
Step3: ~mne.io.Raw objects also have several bui... |
5,832 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path + '/MEG... | <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: Set parameters
Step2: Show event-related fields images
|
5,833 | <ASSISTANT_TASK:>
Python Code:
# IMPORT LIBRARIES
import pandas as pd, numpy as np, os, gc
# LOAD AND FREQUENCY-ENCODE
FE = ['EngineVersion','AppVersion','AvSigVersion','Census_OSVersion']
# LOAD AND ONE-HOT-ENCODE
OHE = [ 'RtpStateBitfield','IsSxsPassiveMode','DefaultBrowsersIdentifier',
'AVProductStatesIdenti... | <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: Statistically Encode Variables
Step2: Example - Census_OEMModelIdentifier
Step3: Predict Test and Submit to Kaggle
|
5,834 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License")
# 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 AS... | <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: Filter
Step2: Examples
Step3: <table align="left" style="margin-right
Step4: <table align="left" style="margin-right
Step5: <table align="le... |
5,835 | <ASSISTANT_TASK:>
Python Code:
def safe_str(obj):
return the byte string representation of obj
if obj is None:
return unicode("")
return unicode(obj)
def dedupe_pings(rdd):
return rdd.filter(lambda p: p["meta/clientId"] is not None)\
.map(lambda p: (p["meta/documentId"], p))\
... | <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: Take the set of pings, make sure we have actual clientIds and remove duplicate pings.
Step2: We're going to dump each event from the pings. Do ... |
5,836 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
points_in = 0
for i in range(100):
x = np.random.rand()
y = np.random.rand()
r = np.sqrt(x**2 + y**2)
if r <= 1:
points_in += 1
pi_4 = points_in/(i+1)
print("pi = {}".format(pi_4 * 4.))
%matplotlib nbagg
import matpl... | <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: Animations
Step2: So animation plotting is based off creating a function. So in this case, we are animating a line plot.
Step3: This works! An... |
5,837 | <ASSISTANT_TASK:>
Python Code:
a_list = [10, 32.4, -14.2, "a", "b", [], [1,2]]
for item in a_list:
try:
print(item * item)
except TypeError:
print(item + item)
item = 0
try:
item / item
except TypeError:
print(item + item)
x = 0
# The bad fix first...
try:
x / x
exc... | <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: So what’s going on here? Well, we have list which contains several different data-types. For every 'item' we try to multiply item by itself. If ... |
5,838 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
df = pd.read_csv('../data/train.csv')
df.head(10)
df = df.drop(['Name', 'Ticket', 'Cabin'], axis=1)
df.info()
df = df.dropna()
df['Sex'].unique()
df['Gender'] = df['Sex'].map({'female': 0, 'male':1}).astype(int)
df['Embarked'].unique()
df['Port... | <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: Pandas - Cleaning data
Step2: We notice that the columns describe features of the Titanic passengers, such as age, sex, and class. Of particula... |
5,839 | <ASSISTANT_TASK:>
Python Code:
class_port = titanic[['PassengerId', 'Pclass', 'Embarked']]
print class_port.isnull().any()
print
class_port = class_port.dropna()
print class_port.isnull().any()
passengers_by_port_class = class_port.groupby(['Embarked', 'Pclass'], as_index=False).count()
print passengers_by_port_class
... | <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 need the number of passengers for each port of embarkment and class
Step2: and the total number of passengers for each port
Step3: Now we c... |
5,840 | <ASSISTANT_TASK:>
Python Code:
from logic import *
%psource dpll_satisfiable
%psource dpll
%psource min_clauses
%psource moms
%psource momsf
%psource posit
%psource zm
%psource dlis
%psource dlcs
%psource jw
%psource jw2
%psource cdcl_satisfiable
%psource conflict_analysis
%psource pl_binary_resolution
%psou... | <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: DPLL with Branching Heuristics
Step2: Each of these branching heuristics was applied only after the pure literal and the unit clause heuristic ... |
5,841 | <ASSISTANT_TASK:>
Python Code:
'{:.2f}'.format(8.499)
'{:.2f}%'.format(10.12345)
import re
def truncate(num,decimal_places):
dp = str(decimal_places)
return re.sub(r'^(\d+\.\d{,'+re.escape(dp)+r'})\d*$',r'\1',str(num))
truncate(8.499,decimal_places=2)
truncate(8.49,decimal_places=2)
truncate(8.4,decimal_pla... | <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: format float as percentage
Step2: truncate to at most 2 decimal places
Step3: left padding with zeros
Step4: right padding with zeros
Step5: ... |
5,842 | <ASSISTANT_TASK:>
Python Code:
x=range(1,10)
y=[1,2,3,4,0,4,3,2,1]
plt.plot(x,y)
# address = some data set
# cars = pd.read_csv(address)
# cars.columns = ['car_names','mpg','cyl','disp','hp','drat','wt','qsec','vs','am',gear',carb']
#mpg = cars['mpg']
#mpg.plot()
plt.bar(x,y)
#Creating bar chart from pandas object
#... | <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: Plotting a line chart from a Pandas object
Step2: Creating bar charts
Step3: Creating a pie chart
Step4: Defining elements of a plot
Step5: ... |
5,843 | <ASSISTANT_TASK:>
Python Code:
df=pd.read_csv("311-2014.csv",nrows=20000)
df.head()
df.columns
df.info()
dateutil.parser.parse('07/16/1990').month
def parse_date (str_date):
return dateutil.parser.parse(str_date)#dateutil is a module, import parser class, then transform a string into a python time object
df['Create... | <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: What was the most popular type of complaint, and how many times was it filed?
Step2: Make a horizontal bar graph of the top 5 most frequent com... |
5,844 | <ASSISTANT_TASK:>
Python Code:
# Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne import io
from mne.connectivity import spectral_connectivity, seed_target_indices
from mne.datasets import sample
from mne.time_frequency import AverageTFR
print(__doc_... | <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 parameters
|
5,845 | <ASSISTANT_TASK:>
Python Code:
df['x4'] = 1
X = df.iloc[:,(0,1,2,4)].values
y = df.y.values
inv_XX_T = inv(X.T.dot(X))
w = inv_XX_T.dot(X.T).dot(df.y.values)
w
qr(inv_XX_T)
X.shape
#solve(X,y)##只能解方阵
def f(w,X,y):
return ((X.dot(w)-y)**2/(2*1000)).sum()
def grad_f(w,X,y):
return (X.dot(w) - y).dot(X)/10... | <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: $y = Xw$
Step2: Results
Step3: 梯度下降法求解
Step4: 随机梯度下降法求解
|
5,846 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from parcels import FieldSet, ParticleSet, JITParticle, Variable, AdvectionRK4
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
import xarray as xr
dims = [5, 4]
dx, dy = 1./dims[0], 1./dims[1]
dimensions = {'lat': np.linspace(0., 1., dims[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: We create a small 2D grid where P is a tracer that we want to interpolate. In each grid cell, P has a random value between 0.1 and 1.1. We then ... |
5,847 | <ASSISTANT_TASK:>
Python Code:
!pip install git+https://github.com/google/starthinker
from starthinker.util.configuration import Configuration
CONFIG = Configuration(
project="",
client={},
service={},
user="/content/user.json",
verbose=True
)
FIELDS = {
'account':'',
'auth_cm':'user', # Credentials us... | <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: 2. Set Configuration
Step2: 3. Enter CM360 Conversion Upload From BigQuery Recipe Parameters
Step3: 4. Execute CM360 Conversion Upload From Bi... |
5,848 | <ASSISTANT_TASK:>
Python Code:
%%writefile Snakefile
rule:
input: 'fileA.txt'
output: 'fileB.txt'
shell: 'cp fileA.txt fileB.txt'
%%sh
snakemake fileB.txt
%%writefile -a Snakefile
rule:
output: 'fileA.txt'
shell: 'touch fileA.txt'
%%sh
snakemake
%%sh
snakemake --dag | dot | display
snakemake --d... | <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: For snakemake the workflow definition needs to be specified in a Snakefile and can be executed by calling snakemake in a terminal in the same lo... |
5,849 | <ASSISTANT_TASK:>
Python Code:
import datetime
print(datetime.datetime.now())
%pylab inline
!ls
#Example of how to compute the sum of two lists
def add(x,y):
add=0
for element_x in x:
add=add+element_x
for element_y in y:
add=add+element_y
return add
my_list = range(0,100)
print(my_li... | <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: More info on notebooks and cells is here.
Step2: The magic %pylab sets up the interactive namespace from numpy and matplotlib and inline adds t... |
5,850 | <ASSISTANT_TASK:>
Python Code:
from straightline_utils import *
%matplotlib inline
from matplotlib import rcParams
rcParams['savefig.dpi'] = 100
(x,y,sigmay) = get_data_no_outliers()
plot_yerr(x, y, sigmay)
def straight_line_log_likelihood(x, y, sigmay, m, b):
'''
Returns the log-likelihood of drawing data val... | <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: Bayesian Solution
Step2: Short Cut #1
Step3: Similarly, one can derived expressions for the uncertainty for of the least squares fit parameter... |
5,851 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
Construtor padrão
pd.Series(
name="Compras",
index=["Leite", "Ovos", "Carne", "Arroz", "Feijão"],
data=[2, 12, 1, 5, 2]
)
Construtor padrão: dados desconhecidos
pd.Series(
name="Compras",
index=["Leite", "Ovos", "Carne", "A... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step9: Construção
Step20: DataFrame
Step21: Acessando valores
Step22: Slicing
Step30: DataFrame
Step33: * Atribuição de Valores em DataFrames
Step... |
5,852 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from matplotlib import pyplot as plt
#
# get Stull's c_1 and c_2 from fundamental constants
#
c=2.99792458e+08 #m/s -- speed of light in vacuum
h=6.62606876e-34 #J s -- Planck's constant
kb=1.3806503e-23 # J/K -- Boltzman's constant
c=3.e8 #speed of light in vacuu... | <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: Day 3 Planck problem
Step2: so good agreement with 10000 points -- make a plot as well
|
5,853 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1)
D = np.random.rand(100,100)
## This is not symmetric, so we make it symmetric
D = (D+D.T)/2
print (D)
import math
N_steps = 10000
def L(sigma):
s=0
for i in rang... | <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: T = 0.05
Step2: T=10
Step3: Correlation plots
Step4: Result
Step5: $\alpha$
Step6: $\beta$
Step7: $\alpha+\beta$
Step8: pol
|
5,854 | <ASSISTANT_TASK:>
Python Code:
labVersion = 'cs190_week4_v_1_3'
# Data for manual OHE
# Note: the first data point does not include any value for the optional third feature
sampleOne = [(0, 'mouse'), (1, 'black')]
sampleTwo = [(0, 'cat'), (1, 'tabby'), (2, 'mouse')]
sampleThree = [(0, 'bear'), (1, 'black'), (2, 'salm... | <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: Part 1
Step2: (1b) Sparse vectors
Step3: (1c) OHE features as sparse vectors
Step5: (1d) Define a OHE function
Step6: (1e) Apply OHE to a... |
5,855 | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu(header="Plan")
from pyquickhelper.loghelper import fLOG
fLOG(OutputPrint=False) # by default
fLOG("not printed")
fLOG(OutputPrint=True)
fLOG("printed")
from pyquickhelper.loghelper import run_cmd
out,err=run_cmd("help", wait... | <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: Log, run_cmd
Step2: The function run_cmd runs a command line and returns the standard output and error
Step3: Ask something to the user in a n... |
5,856 | <ASSISTANT_TASK:>
Python Code:
%%capture
from Q_tool_devo import Q8;
U=Q8([1,2,-3,4])
V=Q8([4,-2,3,1])
R=Q8([5,6,7,-8])
print(U)
print(R)
def rotate_R_by_U(R, U):
Given a space-time number R, rotate it by Q.
return U.triple_product(R, U.invert())
R_rotated = rotate_R_by_U(R,U)
print(R_rotated)
print(R_rotate... | <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: Use the Q8 class that places these 4 numbers in 8 slots like so
Step3: If you are unfamiliar with this notation, the $I^2 = -1,\, i^3=-i,\, j^3... |
5,857 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'sandbox-3', 'aerosol')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "e... | <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: 1... |
5,858 | <ASSISTANT_TASK:>
Python Code:
from sklearn import preprocessing
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Encode text values to dummy variables(i.e. [1,0,0],[0,1,0],[0,0,1] for red,green,blue)
def encode_text_dummy(df,name):
dummies = pd.get_dummies(df[name])
for x in dummies.col... | <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: Training with a Validation Set and Early Stopping
Step2: Calculate Classification Accuracy
Step3: Calculate Classification Log Loss
Step4: Lo... |
5,859 | <ASSISTANT_TASK:>
Python Code:
!pip install "thinc>=8.0.0a0" transformers torch "ml_datasets>=0.2.0a0" "tqdm>=4.41"
from thinc.api import prefer_gpu, use_pytorch_for_gpu_memory
is_gpu = prefer_gpu()
print("GPU:", is_gpu)
if is_gpu:
use_pytorch_for_gpu_memory()
CONFIG =
[model]
@layers = "TransformersTagger.v1"
s... | <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: First, let's use Thinc's prefer_gpu helper to make sure we're performing operations on GPU if available. The function should be called right aft... |
5,860 | <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:]
text[0:500]
view_sentence_range = (0, 10)
DON'T MODIFY ANYTHING IN THIS CELL
... | <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: TV Script Generation
Step3: Explore the Data
Step6: Implement Preprocessing Functions
Step9: Tokenize Punctuation
Step11: Preprocess all the... |
5,861 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
# Creating a series (with different type of data)
s1 = pd.Series([34, 'Material', 4*np.pi, 'Reactor', [100,250,500,750], 'kW'])
s1
# Creating a series with specified index
lt = [34, 'Material', 4*np.pi, 'Reactor', [100,250,500,750], 'kW']
s2 = pd.S... | <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: 8.1 Data Structures
Step2: The index of a Series can be specified during its creation and giving it a similar function to a dictionary.
Step3: ... |
5,862 | <ASSISTANT_TASK:>
Python Code:
import os
import struct
import numpy as np
def load_mnist(path, kind='train'):
Load MNIST data from `path`
labels_path = os.path.join(path,
'%s-labels-idx1-ubyte' % kind)
images_path = os.path.join(path,
'%s-image... | <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: Chapter 12
Step2: Show a bunch of 4s
Step3: Classifying with tree based models
|
5,863 | <ASSISTANT_TASK:>
Python Code:
def n_divide(n):
pass
n_divide(10)
def sen2word(xs):
pass
sen2word("I am learning Python. It's quite interesting.")
def fibo(n):
pass
fibo(5)
def second(t):
return t[1]
def sort_notes(xs):
pass
L = [("Lee", 45), ("Kim", 30), ("Kang", 70), ("Park", 99), ("Cho", ... | <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: 연습 2
Step2: 연습 3
Step3: 연습 4
Step4: 연습 5
|
5,864 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import tensorflow as tf
with open('../sentiment_network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment_network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
from string import punctuation
all_text = ''.join([c for c in reviews if... | <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 preprocessing
Step2: Encoding the words
Step3: Encoding the labels
Step4: Okay, a couple issues here. We seem to have one review with ze... |
5,865 | <ASSISTANT_TASK:>
Python Code:
# only for the notebook
%matplotlib inline
# only in the ipython shell
# %matplotlib
import matplotlib.pyplot as plt
# Make the size and fonts larger for this presentation
plt.rcParams['figure.figsize'] = (10, 8)
plt.rcParams['font.size'] = 16
plt.rcParams['lines.linewidth'] = 2
import ... | <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 order to work with Matplotlib, the library must be imported first. So we do not have to type so much, we give it a shorter name
Step2: Matpl... |
5,866 | <ASSISTANT_TASK:>
Python Code:
import numpy as np # useful for many scientific computing in Python
import pandas as pd # primary data structure library
from PIL import Image # converting images into arrays
df_can = pd.read_excel('https://ibm.box.com/shared/static/lw190pt9zpy5bd1ptyg2aw15awomz9pu.xlsx',
... | <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 download and import our primary Canadian Immigration dataset using pandas read_excel() method. Normally, before we can do that, we would n... |
5,867 | <ASSISTANT_TASK:>
Python Code:
# get package versions
from pkg_resources import require
print 'Package versions'
print '----------------'
print require('genometools')[0]
print require('goparser')[0]
gene_annotation_file = 'Homo_sapiens.GRCh38.82.gtf.gz'
protein_coding_gene_file = 'protein_coding_genes_human.tsv'
go_ann... | <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: Download all required data
Step2: Extract list of human protein-coding genes
Step3: Parse human GO annotations
Step4: Get information about a... |
5,868 | <ASSISTANT_TASK:>
Python Code:
%%capture --no-stderr
!pip3 install kfp --upgrade
import kfp.components as comp
bigquery_query_op = comp.load_component_from_url(
'https://raw.githubusercontent.com/kubeflow/pipelines/01a23ae8672d3b18e88adf3036071496aca3552d/components/gcp/bigquery/query/component.yaml')
help(bigquer... | <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 the component using KFP SDK
Step2: Sample
Step3: Set sample parameters
Step4: Run the component as a single pipeline
Step5: Compile the... |
5,869 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import rcParams
import matplotlib.pyplot as plt
import pandas as pd
import nilmtk
from nilmtk import DataSet, MeterGroup
plt.style.use('ggplot')
rcParams['figure.figsize'] = (13, 10)
redd = DataSet('/data/redd.h5')
elec = redd.buildings[1].elec
elec
ele... | <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: Note that there are two nested MeterGroups
Step2: Putting these meters into a MeterGroup allows us to easily sum together the power demand reco... |
5,870 | <ASSISTANT_TASK:>
Python Code:
from threeML import *
from threeML.analysis_results import *
from threeML.io.progress_bar import progress_bar
from jupyterthemes import jtplot
%matplotlib inline
jtplot.style(context="talk", fscale=1, ticks=True, grid=False)
import matplotlib.pyplot as plt
plt.style.use("mike")
import ast... | <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 take a look at what we can do with an AR. First, we will simulate some data.
Step2: MLE Results
Step3: We can get our errors as always, ... |
5,871 | <ASSISTANT_TASK:>
Python Code:
import DBELA
from rmtk.vulnerability.common import utils
%matplotlib inline
building_model_file = "../../../../../rmtk_data/DBELA/bare_frames.csv"
damage_model_file = "../../../../../rmtk_data/damage_model_dbela_low_code.csv"
no_assets = 100
building_class_model = DBELA.read_building_c... | <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 geometric and material properties
Step2: Number of samples
Step3: Generate the capacity curves
Step4: Plot the capacity curves
Step5: I... |
5,872 | <ASSISTANT_TASK:>
Python Code:
recent_grads = pd.read_csv('recent-grads.csv')
recent_grads.head()
recent_grads.tail()
recent_grads.describe()
recent_grads.shape
recent_grads.shape[0] - recent_grads.dropna().shape[0]
from pandas.tools.plotting import scatter_matrix
scatter_matrix(recent_grads[['ShareWomen', 'Unemploym... | <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: how many rows contain null values?
Step2: Data Visualization
Step3: Let's compare the share of men and women in engineering major.
Step4: fro... |
5,873 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import inspect, sys
# check pydov path
import pydov
from pydov.search.interpretaties import GeotechnischeCoderingSearch
itp = GeotechnischeCoderingSearch()
itp.get_description()
fields = itp.get_fields()
# print available fields
for f in fields.values():
print(f[... | <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: Get information about the datatype 'Geotecnische codering'
Step2: A description is provided for the 'Geotechnische codering' datatype
Step3: T... |
5,874 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import odeint
from IPython.html.widgets import interact, fixed
def lorentz_derivs(yvec, t, sigma, rho, beta):
Compute the the derivatives for the Lorentz system at yvec(t).
# YOUR CODE HERE... | <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:
Step2: Lorenz system
Step4: Write a function solve_lorenz that solves the Lorenz system above for a particular initial condition $[x(0),y(0),z(0)]$. Y... |
5,875 | <ASSISTANT_TASK:>
Python Code:
import datapot as dp
datapot = dp.DataPot()
from datapot.utils import csv_to_jsonlines
csv_to_jsonlines('../data/transactions.csv', '../data/transactions.jsonlines')
ftr = open('../data/transactions.jsonlines')
datapot.detect(ftr, limit=100)
datapot.fit(ftr)
datapot
datapot.remove_trans... | <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: Let's call the fit method. It automatically finds appropriate transformers for the fields of jsonlines file. The parameter 'limit' means how man... |
5,876 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.DataFrame([["A", "Z-Y"], ["B", "X"], ["C", "W-U-V"]], index=[1,2,3], columns=['var1', 'var2'])
def g(df):
return df.join(pd.DataFrame(df.var2.str.split('-', expand=True).stack().reset_index(level=1, drop=True),columns=['var2 '])).\
drop('var2',1).re... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
5,877 | <ASSISTANT_TASK:>
Python Code:
import string
import os
import time
import pickle
import json
import re
import wikipedia
import nltk
import numpy as np
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.manifold import TSN... | <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 this example, we are going to cluster all of the links found in the Wikipedia page "Vital articles." First, we will open the page in main, th... |
5,878 | <ASSISTANT_TASK:>
Python Code:
import sys
def something_dangerous(x):
print("computing reciprocal of", x)
return 1 / x
try:
for x in [2, 1, 0, -1]:
print("1/{} = {}".format(x, something_dangerous(x)))
except ArithmeticError as error:
print("Something went terribly wrong:", error)
input... | <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: This shows how exceptions are raised and caught, but this approach is somewhat limited. Suppose now, that we weren't expecting this expected une... |
5,879 | <ASSISTANT_TASK:>
Python Code:
%load_ext sql
%sql sqlite://
%%sql drop table if exists product;
create table product(
pname varchar primary key, -- имя продукта
price money, -- цена продукта
category varchar, -- категория
manufacturer varchar NOT ... | <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: Давайте создадим таблицу, заполним ее и сделаем какой-нибудь запрос!
Step2: Посмотрим на полученную таблицу.
Step3: Немного SQL терминологии
S... |
5,880 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
import numpy as np
import tensorflow_hub as hub
import tensorflow_datasets as ds
## general checks
print("Tensor Flow Version : {}".format(tf.__version__))
print("Eager Mode : {}".format(tf.executing_eagerly()))
print("Hub Version : {}".format(hub.__version__))
pri... | <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: IMDB Movie Review
Step2: Step 2
Step3: Step 3
Step4: Step 4
|
5,881 | <ASSISTANT_TASK:>
Python Code:
!pip install git+https://github.com/google/starthinker
from starthinker.util.configuration import Configuration
CONFIG = Configuration(
project="",
client={},
service={},
user="/content/user.json",
verbose=True
)
FIELDS = {
'auth_read':'user', # Credentials used for reading... | <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: 2. Set Configuration
Step2: 3. Enter BigQuery Query to Sheet Recipe Parameters
Step3: 4. Execute BigQuery Query to Sheet
|
5,882 | <ASSISTANT_TASK:>
Python Code:
import rebound
sim = rebound.Simulation()
sim.add(m=1)
sim.add(m=0.1, e=0.041, a=0.4, inc=0.2, f=0.43, Omega=0.82, omega=2.98)
sim.add(m=1e-3, e=0.24, a=1.0, pomega=2.14)
sim.add(m=1e-3, e=0.24, a=1.5, omega=1.14, l=2.1)
sim.add(a=-2.7, e=1.4, f=-1.5,omega=-0.7) # hyperbolic orbit
%matpl... | <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: To plot these initial orbits in the $xy$-plane, we can simply call the OrbitPlot function and give it the simulation as an argument.
Step2: Not... |
5,883 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from pydov.types.fields import XmlField, XsdType
from pydov.types.abstract import AbstractDovSubType
from pydov.types.sondering import Sondering
class Techniek(AbstractDovSubType):
rootpath = './/sondering/sondeonderzoek/penetratietest/technieken'
fields ... | <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: While performing CPT measurements, different techniques can be used. Since these can have an impact on the results, it can be interesting to dow... |
5,884 | <ASSISTANT_TASK:>
Python Code:
# This is regular Python comment inside Jupyter "Code" cell.
# You can easily run "Hello world" in the "Code" cell (focus on the cell and press Shift+Enter):
print("Hello world!")
%%bash
echo "Current directory is: "; pwd
echo "List of files in the current directory is: "; ls
# Module ... | <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: There are also other types of cells, for example, "Markdown". Double click this cell to view raw Markdown markup content.
Step2: If you are not... |
5,885 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.feature_selection import RFE
from sklearn.svm import SVR
from sklearn.svm import LinearSVC
from sk... | <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: Columns Interested
Step2: Decriptive Analyss
Step3: Fig 1a shows the sorted issued loan amounts from low to high.<br/>
Step4: Fig 2a and Fig ... |
5,886 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
import numpy as np
from exact_solvers import acoustics, acoustics_demos
from IPython.display import IFrame, HTML, Image
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
import numpy as np
from exact_solvers impo... | <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: In this chapter we consider our first system of hyperbolic conservation laws. We study the acoustics equations that were introduced briefly in ... |
5,887 | <ASSISTANT_TASK:>
Python Code:
%%file my_first_test.py
def f(a):
return a
def test_a():
assert f(1) == 1
!ls *.py
!py.test
!py.test -q
!py.test -v
%%file my_second_test.py
def f(a):
return a
def test_a():
assert f(1) == 1
def test_b():
assert f(2) == 1
def test_c():
assert f(3) == 1 + ... | <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 file has been saved in the current directory
Step2: Launching pytest is as easy as move to the right directory and using the command line
S... |
5,888 | <ASSISTANT_TASK:>
Python Code:
# Get ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2go.gz
from goatools.base import download_ncbi_associations
gene2go = download_ncbi_associations()
from goatools.associations import read_ncbi_gene2go
geneid2gos_human = read_ncbi_gene2go(gene2go, taxids=[9606])
geneid2gos_fly = read_ncbi_ge... | <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: 2. Read associations
Step2: 2b. Or you can read 'gene2go' once and load all species...
Step3: 3. Import protein-coding information for human a... |
5,889 | <ASSISTANT_TASK:>
Python Code:
%%file consumer.py
import sys
import socket
from collections import Counter
HOST = sys.argv[1]
PORT = int(sys.argv[2])
s = socket.socket()
s.bind((HOST, PORT))
s.listen(4)
connection, address = s.accept()
c = Counter()
while True:
line = connection.recv(64)
words = line.split()
... | <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: Producer sends data to server for processing
Step2: Using Spark Streaming
Step3: Monitor a directory for new or renamed files
Step4: Usage
St... |
5,890 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from load_environment import * # python file with imports and basics to set up this computing environment
f0 = np.average(data[:32], axis=0)
plt.imshow(f0); plt.title("Average of First 32 Frames"); plt.show()
plt.subplot(121)
f41 = data[41]
plt.imshow(f41); plt.title(... | <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: First we will take a look at the fluorescence "base line"
Step2: Now compare the fluorescence of a regular signal to its relative fluorescence
... |
5,891 | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
try:
import mkl
mkl.set_num_threads(1)
except ModuleNotFoundError as e:
print('mkl not found', e)
import os
os.environ['MKL_NUM_THREADS']='1'
# int nb = 0;
# for(auto it = values.begin... | <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: numpy is multithreaded. For an accurate comparison, this needs to be disabled. This can be done as follows or by setting environment variable MK... |
5,892 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (20.0, 10.0)
df = pd.read_csv('../../../datasets/movie_metadata.csv')
df.head()
# split each movie's genre list, then form a set from the unwrapped list of all ge... | <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: For the bar plot, let's look at the number of movies in each category, allowing each movie to be counted more than once.
Step2: Basic plot
Step... |
5,893 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
import numpy as np
from scipy.sparse import diags
from scipy.sparse.linalg import svds, eigs
import matplotlib.pyplot as plt
%matplotlib notebook
def pseudo_spec(x, y, mat_A):
Compute the pseudospectra of `mat_A` around the point $x + iy
ps_spec = 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:
Step2: Pseudospectra of matrices
Step3: 1. Jordan block
Step4: 2. Limacon
Step5: 3. Grcar matrix
Step6: 4. Wilkinson matrix
|
5,894 | <ASSISTANT_TASK:>
Python Code:
from sklearn.model_selection import train_test_split
import seaborn as sns
import os
import shutil
import pandas as pd
%matplotlib inline
df = pd.read_csv('list.txt', sep=' ')
df.ix[2000:2005]
train_cat = df[df['SPECIES'] == 1]
train_dog = df[df['SPECIES'] == 2]
x = ['cat', 'dog']
y = [l... | <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: Visualize the size of the original train dataset.
Step2: Shuffle and split the train filenames
Step3: Visualize the size of the processed trai... |
5,895 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'nicam16-7s', 'atmos')
# 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: 1... |
5,896 | <ASSISTANT_TASK:>
Python Code:
label_map = list('abcdefghij')
fig,axes = pl.subplots(3,3,figsize=(5,5),sharex=True,sharey=True)
with h5py.File(cache_file, 'r') as f:
for i in range(9):
ax = axes.flat[i]
idx = np.random.randint(f['test']['images'].shape[0])
ax.imshow(f['test']['image... | <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: Problem 2
Step2: Problem 3
Step3: Problem 4
Step4: OK, so there are about 50000 in each class in the training set
Step5: And about 1870 in e... |
5,897 | <ASSISTANT_TASK:>
Python Code:
from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)
import pandas as pd
from sklearn.datasets import load_iris
data = pd.DataFrame(data=load_iris().data, columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])
data.head()
from sklearn.preprocessing import Binar... | <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: Initial example
Step2: Spark ML
Step3: Load Newsgroup Data
Step4: Spark ML
Step5: Train-Test Split
Step6: Spark
Step7: Feature engineering... |
5,898 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy.special import binom
import matplotlib.pylab as plt
from scipy.misc import factorial as fact
%matplotlib inline
def binomial(p, n, k):
return binom(n, k) * p ** k * (1 - p) ** (n-k)
p = 4. / 100
np.sum(binomial(p, 150, np.arange(5)))
np.sum(binomial(p,... | <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: Exercise 1.
Step2: b.
Step3: Exercise 2.
Step4: Exercise 3.
Step5: X would be -30 when the sample $\omega$ has no 6s, 50 when has one, 75 wh... |
5,899 | <ASSISTANT_TASK:>
Python Code:
num_friends_good = [49,41,40,25,21,21,19,19,18,18,16,15,15,15,15,14,14,13,13,13,13,12,12,11,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,8,8,8,8,8,8,8,8,8,8,8,8,8,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,... | <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: Of course, we need a better way to figure out how well we’ve fit the data than staring at the graph.
Step2: Doing Statistics with statsmodels
|
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