markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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Projet | %tensorflow_version 2.x
import tensorflow as tf
print(tf.__version__)
from __future__ import absolute_import, division, print_function, unicode_literals
from google.colab import files
import numpy as np
import tensorflow_datasets as tfds
import tensorflow as tf
import pandas as pd
import matplotlib.pyplot as plt
import... | _____no_output_____ | MIT | ProjetTripAdvisor_Final.ipynb | a-essa/Sentiment-Analysis-and-Satisfaction-Prediction |
Read CSV | Total_data_for_tokenizer=pd.read_csv('/content/gdrive/My Drive/Data/Total_data_for_tokenizer.csv' , sep='\t')
training_set_TripAdvisor=pd.read_csv('/content/gdrive/My Drive/Data/training_set_TripAdvisor.csv' , sep='\t')
test_set_TripAdvisor=pd.read_csv('/content/gdrive/My Drive/Data/test_set_TripAdvisor.csv' , sep='\t'... | _____no_output_____ | MIT | ProjetTripAdvisor_Final.ipynb | a-essa/Sentiment-Analysis-and-Satisfaction-Prediction |
Tokenizer | def create_dataset(dataframe):
msk = np.random.rand(len(dataframe)) < 0.8
train = dataframe[msk]
test = dataframe[~msk]
#print(train_file_path.head)
all_data = np.array(dataframe.values.tolist())
training_set = np.array(train.values.tolist())
test_set=np.array(test.values.tolist())
print("Dataset Leng... | _____no_output_____ | MIT | ProjetTripAdvisor_Final.ipynb | a-essa/Sentiment-Analysis-and-Satisfaction-Prediction |
Embeddings pretraining (Next Word Prediction) | def split_sentences_forward(input_set):
input_sequences = []
for line in input_set:
for i in range(1, len(line)):
if i < 99 :
n_gram_sequence = line[:i+1]
else :
n_gram_sequence = line[i-99:i+1]
input_sequences.append(n_gram_sequence)
return input_sequences
def sp... | Train on 947983 samples, validate on 237895 samples
Epoch 1/2
947900/947983 [============================>.] - ETA: 0s - loss: 5.7982 - accuracy: 0.1446
Epoch 00001: saving model to training_Embadding/cp-0001.ckpt
947983/947983 [==============================] - 547s 577us/sample - loss: 5.7982 - accuracy: 0.1446 - val... | MIT | ProjetTripAdvisor_Final.ipynb | a-essa/Sentiment-Analysis-and-Satisfaction-Prediction |
Dataset slices | from __future__ import absolute_import, division, print_function, unicode_literals
input_train_CL_sequences_1 = pad_sentences_post(input_training_set_IMDB, max_len)
input_train_CL_sequences_2 = pad_sentences_post(input_training_set_TripAdvisor, max_len)
input_test_CL_sequences_1 = pad_sentences_post(input_test_set_IMDB... | _____no_output_____ | MIT | ProjetTripAdvisor_Final.ipynb | a-essa/Sentiment-Analysis-and-Satisfaction-Prediction |
Model | BidirectionalLayer1 = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences = True))
BidirectionalLayer2 = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences = True))
GlobalMaxPool1DLayer = tf.keras.layers.GlobalMaxPool1D()
DenseLayer1 = tf.keras.layers.Dense(40, kernel_regularize... | _____no_output_____ | MIT | ProjetTripAdvisor_Final.ipynb | a-essa/Sentiment-Analysis-and-Satisfaction-Prediction |
From saved weights | '''EmbeddingLayer = tf.keras.layers.Embedding(80008, 64)
BidirectionalLayer1 = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences = True))
BidirectionalLayer2 = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences = True))
GlobalMaxPool1DLayer = tf.keras.layers.GlobalMaxPool1D()
... | _____no_output_____ | MIT | ProjetTripAdvisor_Final.ipynb | a-essa/Sentiment-Analysis-and-Satisfaction-Prediction |
Validation | from sklearn.metrics import mean_squared_error
import nltk
nltk.download("stopwords")
nltk.download("punkt")
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
nltk.download('wordnet')
from nltk.stem import WordNetLemmatizer
def clean_set(set_len):
stop_words = list(stopwords.words('eng... | _____no_output_____ | MIT | ProjetTripAdvisor_Final.ipynb | a-essa/Sentiment-Analysis-and-Satisfaction-Prediction |
NLTK Sentence and Word Tokenization | from nltk.tokenize import sent_tokenize, word_tokenize
EXAMPLE_TEXT = "Hello Mr. Smith, how are you doing today? The weather is great, and Python is awesome. The sky is pinkish-blue. You shouldn't eat cardboard."
# Sentence Tokenization
print(sent_tokenize(EXAMPLE_TEXT))
# Word Tokenization
print(word_tokenize(EXAMPLE_... | ['Hello', 'Mr.', 'Smith', ',', 'how', 'are', 'you', 'doing', 'today', '?', 'The', 'weather', 'is', 'great', ',', 'and', 'Python', 'is', 'awesome', '.', 'The', 'sky', 'is', 'pinkish-blue', '.', 'You', 'should', "n't", 'eat', 'cardboard', '.']
| MIT | NLTK/NLTK.ipynb | jimit105/Python-Libraries |
Stopwords | from nltk.corpus import stopwords
# Printing all stopwords (english)
set(stopwords.words('english'))
example_sent = "This is a sample sentence, showing off the stop words filtration."
stop_words = set(stopwords.words('english'))
word_tokens = word_tokenize(example_sent)
filtered_sentence = [w for w in word_tokens if no... | ['This', 'is', 'a', 'sample', 'sentence', ',', 'showing', 'off', 'the', 'stop', 'words', 'filtration', '.']
['This', 'sample', 'sentence', ',', 'showing', 'stop', 'words', 'filtration', '.']
| MIT | NLTK/NLTK.ipynb | jimit105/Python-Libraries |
Stemming words | # Porter Stemmer is a stemming algorithm
from nltk.stem import PorterStemmer
from nltk.tokenize import sent_tokenize, word_tokenize
ps = PorterStemmer()
example_words = ["python","pythoner","pythoning","pythoned","pythonly"]
for w in example_words:
print(ps.stem(w))
new_text = "It is important to by very pythonly ... | It
is
import
to
by
veri
pythonli
while
you
are
python
with
python
.
all
python
have
python
poorli
at
least
onc
.
| MIT | NLTK/NLTK.ipynb | jimit105/Python-Libraries |
Part of Speech Tagging POS tag list:CC coordinating conjunction CD cardinal digit DT determiner EX existential there (like: "there is" ... think of it like "there exists") FW foreign word IN preposition/subordinating conjunction JJ adjective 'big' JJR adjective, comparative 'bigger' JJS adjective, superlative... | import nltk
from nltk.corpus import state_union
from nltk.tokenize import PunktSentenceTokenizer
# Create training and testing data
train_text = state_union.raw('2005-GWBush.txt')
sample_text = state_union.raw('2006-GWBush.txt')
# Train Punkt tokenizer
custom_sent_tokenizer = PunktSentenceTokenizer(train_text)
# Actual... | [('PRESIDENT', 'NNP'), ('GEORGE', 'NNP'), ('W.', 'NNP'), ('BUSH', 'NNP'), ("'S", 'POS'), ('ADDRESS', 'NNP'), ('BEFORE', 'IN'), ('A', 'NNP'), ('JOINT', 'NNP'), ('SESSION', 'NNP'), ('OF', 'IN'), ('THE', 'NNP'), ('CONGRESS', 'NNP'), ('ON', 'NNP'), ('THE', 'NNP'), ('STATE', 'NNP'), ('OF', 'IN'), ('THE', 'NNP'), ('UNION', '... | MIT | NLTK/NLTK.ipynb | jimit105/Python-Libraries |
Lemmatizing > A very similar operation to stemming is called lemmatizing. The major difference between these is, as you saw earlier, stemming can often create non-existent words, whereas lemmas are actual words. > So, your root stem, meaning the word you end up with, is not something you can just look up in a diction... | from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
print(lemmatizer.lemmatize("cats"))
print(lemmatizer.lemmatize("cacti"))
print(lemmatizer.lemmatize("geese"))
print(lemmatizer.lemmatize("rocks"))
print(lemmatizer.lemmatize("python"))
print(lemmatizer.lemmatize("better", pos="a"))
print(lemmatiz... | cat
cactus
goose
rock
python
good
best
run
run
| MIT | NLTK/NLTK.ipynb | jimit105/Python-Libraries |
Corpora> The NLTK corpus is a massive dump of all kinds of natural language data sets | # Opening the Gutenberg Bible, and reading the first few lines
from nltk.tokenize import sent_tokenize, PunktSentenceTokenizer
from nltk.corpus import gutenberg
#sample text
sample = gutenberg.raw('bible-kjv.txt')
tok = sent_tokenize(sample)
for x in range(5):
print(tok[x]) | [The King James Bible]
The Old Testament of the King James Bible
The First Book of Moses: Called Genesis
1:1 In the beginning God created the heaven and the earth.
1:2 And the earth was without form, and void; and darkness was upon
the face of the deep.
And the Spirit of God moved upon the face of the
waters.
1:3 ... | MIT | NLTK/NLTK.ipynb | jimit105/Python-Libraries |
Wordnet> Wordnet is a collection of words, definitions, examples of their use, synonyms, antonyms, and more. | # Import wordnet
from nltk.corpus import wordnet
# use the term "program" to find synsets
syns = wordnet.synsets('program')
print(syns)
#Print first synset
print(syns[0].name())
# Print only the word
print(syns[0].lemmas()[0].name())
# Definition for that first synset
print(syns[0].definition())
# Examples of the word... | 0.9090909090909091
0.6956521739130435
0.32
| MIT | NLTK/NLTK.ipynb | jimit105/Python-Libraries |
1) CSV Data File Analysis | from os import path
fname = path.expanduser('track.csv') | _____no_output_____ | MIT | 2_Data_Analysis/jogging_ex/pandas_jogging_ex.ipynb | TheManohar/DSci_Pro |
CSV File Info | !ls -lh "$fname"
path.getsize(fname)
path.getsize(fname) / (1<<10)
!head "$fname"
with open(fname) as fp:
for lnum, line in enumerate(fp):
if lnum > 10:
break
print(line[:-1])
!wc -l "$fname"
with open(fname) as fp:
print(sum(1 for line in fp)) | 741
| MIT | 2_Data_Analysis/jogging_ex/pandas_jogging_ex.ipynb | TheManohar/DSci_Pro |
2) Parse Time Series | import pandas as pd
df = pd.read_csv(fname)
len(df)
df.columns
df.info()
df.head()
df.dtypes | _____no_output_____ | MIT | 2_Data_Analysis/jogging_ex/pandas_jogging_ex.ipynb | TheManohar/DSci_Pro |
Parse Dates | df = pd.read_csv(fname, parse_dates=['time'])
df.dtypes | _____no_output_____ | MIT | 2_Data_Analysis/jogging_ex/pandas_jogging_ex.ipynb | TheManohar/DSci_Pro |
3) Access Rows & Columns Data | df['lat'].head()
df.lat.head()
df[['lat', 'lng']].head()
df['lat'][0]
df.loc[0]
df.loc[2:7]
df[['lat', 'lng']][2:7]
df.index | _____no_output_____ | MIT | 2_Data_Analysis/jogging_ex/pandas_jogging_ex.ipynb | TheManohar/DSci_Pro |
NumPy`.loc[]` example | import numpy as np
df1 = pd.DataFrame(np.arange(10).reshape((5,2)), columns=['x', 'y'], index=['a', 'b', 'c', 'd', 'e'])
df1
df1.loc['a']
df1.loc['b': 'd'] | _____no_output_____ | MIT | 2_Data_Analysis/jogging_ex/pandas_jogging_ex.ipynb | TheManohar/DSci_Pro |
Set Index | df.index
df.index = df['time']
df.index | _____no_output_____ | MIT | 2_Data_Analysis/jogging_ex/pandas_jogging_ex.ipynb | TheManohar/DSci_Pro |
Locate Specific Time Data | df.loc['2015-08-20 04:18:54']
# all masures in this particular minute
df.loc['2015-08-20 03:48'] | _____no_output_____ | MIT | 2_Data_Analysis/jogging_ex/pandas_jogging_ex.ipynb | TheManohar/DSci_Pro |
Timezone Localization | # pytz module contains all timezone information
import pytz
ts = df.index[0]
ts.tz_localize(pytz.UTC)
ts.tz_localize(pytz.UTC).tz_convert(pytz.timezone('Asia/Jerusalem'))
df.index = df.index.tz_localize(pytz.UTC).tz_convert(pytz.timezone('Asia/Jerusalem'))
df.index[:10]
%pwd | _____no_output_____ | MIT | 2_Data_Analysis/jogging_ex/pandas_jogging_ex.ipynb | TheManohar/DSci_Pro |
4) Import Custom Modules | import geo
import sys
sys.path
??geo
from geo import circle_dist
lat1, lng1 = df.iloc[0].lat, df.iloc[0].lng
lat2, lng2 = df.iloc[1].lat, df.iloc[1].lng
circle_dist(lat1, lng1, lat2, lng2) | _____no_output_____ | MIT | 2_Data_Analysis/jogging_ex/pandas_jogging_ex.ipynb | TheManohar/DSci_Pro |
5) Calculate Speed (geo) pandas.`shift()` example | s = pd.Series(np.arange(5))
s
s.shift()
s.shift(-1) | _____no_output_____ | MIT | 2_Data_Analysis/jogging_ex/pandas_jogging_ex.ipynb | TheManohar/DSci_Pro |
Calculate Circle Distance of Data | dist = circle_dist(df['lat'], df['lng'], df['lat'].shift(), df['lng'].shift())
dist[:10]
dist.sum()
dt = df['time'] - df['time'].shift()
dt[:10] # NaT means Not a Time
dt.sum()
dt[1].total_seconds()
dt[1] / np.timedelta64(1, 'h')
dt[1].total_seconds()/3600
speed = dist / (dt / np.timedelta64(1, 'h'))
speed[:10]
df['dis... | _____no_output_____ | MIT | 2_Data_Analysis/jogging_ex/pandas_jogging_ex.ipynb | TheManohar/DSci_Pro |
6) Display Speed Box Plot | %matplotlib inline
speed1m.plot()
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (10, 6)
plt.style.available
plt.style.use('seaborn-whitegrid')
speed1m.plot()
speed1m.plot.box() | _____no_output_____ | MIT | 2_Data_Analysis/jogging_ex/pandas_jogging_ex.ipynb | TheManohar/DSci_Pro |
SMOOTHING (LOWPASS) SPATIAL FILTERS | import cv2
import matplotlib.pyplot as plt
import numpy as np | _____no_output_____ | MIT | SMOOTHING (LOWPASS) SPATIAL FILTERS.ipynb | MTandHJ/digital-image-processing |
FILTERSfilters实际上就是通过一些特殊的kernel $w$ 对图片进行如下操作:$$g(x, y) = \sum_{s=-a}^a \sum_{t=-b}^b w(s, t) f(x+s, y+t), \: x = 1,2,\cdots, M, \: y = 1, 2,\cdots N.$$其中$w(s, t) \in \mathbb{R}^{m \times n}, m=2a+1, n = 2b+1$.注: 一般来说kernel的边是奇数, 这样可以确定唯一的中心, 但是偶数其实也是可以的.实际上, 上面可以转换成卷积的形式:$$(w * f) (x, y) = \sum_{s=-a}^a \sum_{t=-b}^... | img = cv2.imread("./pics/alphabeta.png")
img.shape
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 由于是截图, 先转成灰度图
plt.imshow(img, cmap='gray')
# 或者等价地用 cv2.blur(img, (m, n))
kernels = [np.ones((i, i)) / (i * i) for i in [3, 11, 21]]
imgs_smoothed = [cv2.filter2D(img, -1, kernel) for kernel in kernels]
fig, axes = plt.subp... | _____no_output_____ | MIT | SMOOTHING (LOWPASS) SPATIAL FILTERS.ipynb | MTandHJ/digital-image-processing |
Lowpass Gaussian Filter Kernels即$$w(s, t) = G(s, t) = K e^{-\frac{s^2+t^2}{2\sigma^2}},$$高斯分布的特点是绝大部分集中于$(-3\sigma, +3\sigma)$之间, 故一般$w$的大小选择为$(-6\sigma, +6\sigma)$, 需要注意的是, $\sigma$的选择和图片的大小息息相关. | imgs_smoothed = [cv2.GaussianBlur(img, ksize=ksize, sigmaX=sigma) for (ksize, sigma) in [((5, 5), 1), ((21, 21), 3.5), ((43, 43), 7)]]
fig, axes = plt.subplots(1, 4)
axes[0].imshow(img, cmap='gray')
axes[0].set_title("raw")
axes[1].imshow(imgs_smoothed[0], cmap="gray")
axes[1].set_title("5x5, 1")
axes[2].imshow(imgs_sm... | _____no_output_____ | MIT | SMOOTHING (LOWPASS) SPATIAL FILTERS.ipynb | MTandHJ/digital-image-processing |
Order-Statistic (Nonlinear) Filters即$g(x, y)$由$(x, y)$周围的点的一个某个顺序的值代替, 比如median. | imgs_smoothed = [cv2.medianBlur(img, ksize=ksize) for ksize in [3, 7, 15]]
fig, axes = plt.subplots(1, 4)
axes[0].imshow(img, cmap='gray')
axes[0].set_title("raw")
axes[1].imshow(imgs_smoothed[0], cmap="gray")
axes[1].set_title("3x3")
axes[2].imshow(imgs_smoothed[1], cmap="gray")
axes[2].set_title("7x7")
axes[3].imshow... | _____no_output_____ | MIT | SMOOTHING (LOWPASS) SPATIAL FILTERS.ipynb | MTandHJ/digital-image-processing |
Destination's street density INDEX This code allows you to estimate:The street density INDEX at the DESTINATION for only one origin point or for a set of origins. The street density is estimated base on a selection of arcs contained within a 600m buffer around each destination. All the arcs selected in relation to ea... | %matplotlib inline
import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import matplotlib as mpl
import csv
import math
print ("it works") | it works
| MIT | Code/108_D_end_street density_analysis.ipynb | Rise-group/database_cyclists_medellin |
Example for only one route | route = 1002
db = gpd.read_file('../Shapes/end_arcs600/R' +"%s" % (route) +'_end_arcs600.shp')
print (db.crs)
db.head()
db.info()
db.plot()
radio = 0.6
area = 3.14159265358979 * radio**2
D_Lstreet = (db['Long']).sum()/1000
D_streetdens = D_Lstreet / area
print ("D_Lstreet:" + str(D_Lstreet))
print ("D_streetdens:" + s... | _____no_output_____ | MIT | Code/108_D_end_street density_analysis.ipynb | Rise-group/database_cyclists_medellin |
Street density estimated for All routes The code requires the list of the origin points associated to all the routes. This list is located in the Tables folder. If you move this file out of this folder, please modify the code. | routes = np.loadtxt('../Tables/Routes.txt',dtype='str')
for i in routes:
db = gpd.read_file('../Shapes/end_arcs600/R' +"%s" % (i) +'_end_arcs600.shp')
radio = 0.6
area = 3.14159265358979 * radio**2
D_Lstreet = (db['Long']).sum()/1000
D_streetdens = D_Lstreet / area
results = pd.DataFrame ({
... | _____no_output_____ | MIT | Code/108_D_end_street density_analysis.ipynb | Rise-group/database_cyclists_medellin |
This part of the code summarizes the intersection index related to each origin in only one table. Please copy it to a text file and save. | print ('route,D_Lstreet,D_sttdens')
for i in routes:
df=np.loadtxt('../Tables/OUT/R' +"%s" %(i)+'_end_sd600.txt', delimiter=',',skiprows=1)
print ((df[0]),(df[1]),(df[2])) | route,D_Lstreet,D_sttdens
1002.0 24.733079128306873 21.868843339739048
1006.0 26.1401686196655 23.112983605974527
1009.0 16.660137417108547 14.73079568068372
1050.0 16.93572274099809 14.974466605909287
1052.0 21.618809667095352 19.11522456822823
1079.0 25.1494328077544 22.236980818397786
1100.0 24.85527662835564 21.976... | MIT | Code/108_D_end_street density_analysis.ipynb | Rise-group/database_cyclists_medellin |
[Day 15](https://adventofcode.com/2020/day/15): Rambunctious Recitation | data = 0, 20, 7, 16, 1, 18, 15
init = {d: i for i, d in enumerate(data, 1)}
def number_spoken(turns, n=data[-1], l=len(data)):
prev = init.copy()
for i in range(l, turns):
prev[n], n = i, i - prev.get(n, i)
return n | _____no_output_____ | MIT | solutions/15.ipynb | egnha/AoC-2020 |
Part 1 | assert 1025 == number_spoken(2020) | _____no_output_____ | MIT | solutions/15.ipynb | egnha/AoC-2020 |
Part 2 | assert 129262 == number_spoken(30000000) | _____no_output_____ | MIT | solutions/15.ipynb | egnha/AoC-2020 |
Regular Expression Exercises* Debugger: When debugging regular expressions, the best tool is [Regex101](https://regex101.com/). This is an interactive tool that let's you visualize a regular expression in action.* Tutorial: I tend to like RealPython's tutorials, here is their's on [Regular Expressions](https://realpyt... | import re | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
1. Introduction**a)** Check whether the given strings contain `0xB0`. Display a boolean result as shown below. | line1 = 'start address: 0xA0, func1 address: 0xC0'
line2 = 'end address: 0xFF, func2 address: 0xB0'
assert bool(re.search(r'', line1)) == False
assert bool(re.search(r'', line2)) == True | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**b)** Replace all occurrences of `5` with `five` for the given string. | ip = 'They ate 5 apples and 5 oranges'
assert re.sub() == 'They ate five apples and five oranges' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**c)** Replace first occurrence of `5` with `five` for the given string. | ip = 'They ate 5 apples and 5 oranges'
assert re.sub() == 'They ate five apples and 5 oranges' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**d)** For the given list, filter all elements that do *not* contain `e`. | items = ['goal', 'new', 'user', 'sit', 'eat', 'dinner']
assert [w for w in items if not re.search()] == ['goal', 'sit'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**e)** Replace all occurrences of `note` irrespective of case with `X`. | ip = 'This note should not be NoTeD'
assert re.sub() == 'This X should not be XD' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**f)** Check if `at` is present in the given byte input data. | ip = 'tiger imp goat'
assert bool(re.search()) == True | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**g)** For the given input string, display all lines not containing `start` irrespective of case. | para = '''good start
Start working on that
project you always wanted
stars are shining brightly
hi there
start and try to
finish the book
bye'''
pat = re.compile() ##### add your solution here
for line in para.split('\n'):
if not pat.search(line):
print(line)
"""project you always wanted
stars are sh... | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**h)** For the given list, filter all elements that contains either `a` or `w`. | items = ['goal', 'new', 'user', 'sit', 'eat', 'dinner']
##### add your solution here
assert [w for w in items if re.search() or re.search()] == ['goal', 'new', 'eat'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**i)** For the given list, filter all elements that contains both `e` and `n`. | items = ['goal', 'new', 'user', 'sit', 'eat', 'dinner']
##### add your solution here
assert [w for w in items if re.search() and re.search()] == ['new', 'dinner'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**j)** For the given string, replace `0xA0` with `0x7F` and `0xC0` with `0x1F`. | ip = 'start address: 0xA0, func1 address: 0xC0'
##### add your solution here
assert ___ == 'start address: 0x7F, func1 address: 0x1F' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
2. Anchors**a)** Check if the given strings start with `be`. | line1 = 'be nice'
line2 = '"best!"'
line3 = 'better?'
line4 = 'oh no\nbear spotted'
pat = re.compile() ##### add your solution here
assert bool(pat.search(line1)) == True
assert bool(pat.search(line2)) == False
assert bool(pat.search(line3)) == True
assert bool(pat.search(line4)) == False | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**b)** For the given input string, change only whole word `red` to `brown` | words = 'bred red spread credible'
assert re.sub() == 'bred brown spread credible' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**c)** For the given input list, filter all elements that contains `42` surrounded by word characters. | words = ['hi42bye', 'nice1423', 'bad42', 'cool_42a', 'fake4b']
assert [w for w in words if re.search()] == ['hi42bye', 'nice1423', 'cool_42a'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**d)** For the given input list, filter all elements that start with `den` or end with `ly`. | items = ['lovely', '1\ndentist', '2 lonely', 'eden', 'fly\n', 'dent']
assert [e for e in items if ] == ['lovely', '2 lonely', 'dent'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**e)** For the given input string, change whole word `mall` to `1234` only if it is at the start of a line. | para = '''
ball fall wall tall
mall call ball pall
wall mall ball fall
mallet wallet malls'''
assert re.sub() == """ball fall wall tall
1234 call ball pall
wall mall ball fall
mallet wallet malls""" | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**f)** For the given list, filter all elements having a line starting with `den` or ending with `ly`. | items = ['lovely', '1\ndentist', '2 lonely', 'eden', 'fly\nfar', 'dent']
##### add your solution here
assert ___ == ['lovely', '1\ndentist', '2 lonely', 'fly\nfar', 'dent'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**g)** For the given input list, filter all whole elements `12\nthree` irrespective of case. | items = ['12\nthree\n', '12\nThree', '12\nthree\n4', '12\nthree']
##### add your solution here
assert ___ == ['12\nThree', '12\nthree'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**h)** For the given input list, replace `hand` with `X` for all elements that start with `hand` followed by at least one word character. | items = ['handed', 'hand', 'handy', 'unhanded', 'handle', 'hand-2']
##### add your solution here
assert ___ == ['Xed', 'hand', 'Xy', 'unhanded', 'Xle', 'hand-2'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**i)** For the given input list, filter all elements starting with `h`. Additionally, replace `e` with `X` for these filtered elements. | items = ['handed', 'hand', 'handy', 'unhanded', 'handle', 'hand-2']
##### add your solution here
assert ___ == ['handXd', 'hand', 'handy', 'handlX', 'hand-2'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
3. Alternation and Grouping**a)** For the given input list, filter all elements that start with `den` or end with `ly` | items = ['lovely', '1\ndentist', '2 lonely', 'eden', 'fly\n', 'dent']
##### add your solution here
assert ___ == ['lovely', '2 lonely', 'dent'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**b)** For the given list, filter all elements having a line starting with `den` or ending with `ly`. | items = ['lovely', '1\ndentist', '2 lonely', 'eden', 'fly\nfar', 'dent']
##### add your solution here
assert ___ == ['lovely', '1\ndentist', '2 lonely', 'fly\nfar', 'dent'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**c)** For the given input strings, replace all occurrences of `removed` or `reed` or `received` or `refused` with `X`. | s1 = 'creed refuse removed read'
s2 = 'refused reed redo received'
pat = re.compile() ##### add your solution here
assert pat.sub('X', s1) == 'cX refuse X read'
assert pat.sub('X', s2) == 'X X redo X' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**d)** For the given input strings, replace all matches from the list `words` with `A`. | s1 = 'plate full of slate'
s2 = "slated for later, don't be late"
words = ['late', 'later', 'slated']
pat = re.compile() ##### add your solution here
assert pat.sub('A', s1) == 'pA full of sA'
assert pat.sub('A', s2) == "A for A, don't be A" | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**e)** Filter all whole elements from the input list `items` based on elements listed in `words`. | items = ['slate', 'later', 'plate', 'late', 'slates', 'slated ']
words = ['late', 'later', 'slated']
pat = re.compile() ##### add your solution here
##### add your solution here
assert ___ == ['later', 'late'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
4. Escaping metacharacters**a)** Transform the given input strings to the expected output using same logic on both strings. | str1 = '(9-2)*5+qty/3'
str2 = '(qty+4)/2-(9-2)*5+pq/4'
##### add your solution here for str1
assert ___ == '35+qty/3'
##### add your solution here for str2
assert ___ == '(qty+4)/2-35+pq/4' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**b)** Replace `(4)\|` with `2` only at the start or end of given input strings. | s1 = r'2.3/(4)\|6 foo 5.3-(4)\|'
s2 = r'(4)\|42 - (4)\|3'
s3 = 'two - (4)\\|\n'
pat = re.compile() ##### add your solution here
assert pat.sub('2', s1) == '2.3/(4)\\|6 foo 5.3-2'
assert pat.sub('2', s2) == '242 - (4)\\|3'
assert pat.sub('2', s3) == 'two - (4)\\|\n' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**c)** Replace any matching element from the list `items` with `X` for given the input strings. Match the elements from `items` literally. Assume no two elements of `items` will result in any matching conflict. | items = ['a.b', '3+n', r'x\y\z', 'qty||price', '{n}']
pat = re.compile() ##### add your solution here
assert pat.sub('X', '0a.bcd') == '0Xcd'
assert pat.sub('X', 'E{n}AMPLE') == 'EXAMPLE'
assert pat.sub('X', r'43+n2 ax\y\ze') == '4X2 aXe' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**d)** Replace backspace character `\b` with a single space character for the given input string. | ip = '123\b456'
ip
assert re.sub() == '123 456' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**e)** Replace all occurrences of `\e` with `e`. | ip = r'th\er\e ar\e common asp\ects among th\e alt\ernations'
assert re.sub() == 'there are common aspects among the alternations' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**f)** Replace any matching item from the list `eqns` with `X` for given the string `ip`. Match the items from `eqns` literally. | ip = '3-(a^b)+2*(a^b)-(a/b)+3'
eqns = ['(a^b)', '(a/b)', '(a^b)+2']
##### add your solution here
assert pat.sub('X', ip) == '3-X*X-X+3' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
5. Dot metacharacter and QuantifiersSince `.` metacharacter doesn't match newline character by default, assume that the input strings in the following exercises will not contain newline characters.**a)** Replace `42//5` or `42/5` with `8` for the given input. | ip = 'a+42//5-c pressure*3+42/5-14256'
assert re.sub() == 'a+8-c pressure*3+8-14256' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**b)** For the list `items`, filter all elements starting with `hand` and ending with at most one more character or `le`. | items = ['handed', 'hand', 'handled', 'handy', 'unhand', 'hands', 'handle']
##### add your solution here
assert ___ == ['hand', 'handy', 'hands', 'handle'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**c)** Use `re.split` to get the output as shown for the given input strings. | eqn1 = 'a+42//5-c'
eqn2 = 'pressure*3+42/5-14256'
eqn3 = 'r*42-5/3+42///5-42/53+a'
##### add your solution here for eqn1
assert ___ == ['a+', '-c']
##### add your solution here for eqn2
assert ___ == ['pressure*3+', '-14256']
##### add your solution here for eqn3
assert ___ == ['r*42-5/3+42///5-', '3+a'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**d)** For the given input strings, remove everything from the first occurrence of `i` till end of the string. | s1 = 'remove the special meaning of such constructs'
s2 = 'characters while constructing'
pat = re.compile() ##### add your solution here
assert pat.sub('', s1) == 'remove the spec'
assert pat.sub('', s2) == 'characters wh' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**e)** For the given strings, construct a RE to get output as shown. | str1 = 'a+b(addition)'
str2 = 'a/b(division) + c%d(#modulo)'
str3 = 'Hi there(greeting). Nice day(a(b)'
remove_parentheses = re.compile() ##### add your solution here
assert remove_parentheses.sub('', str1) == 'a+b'
assert remove_parentheses.sub('', str2) == 'a/b + c%d'
assert remove_parentheses.sub('', str3) == ... | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**f)** Correct the given RE to get the expected output. | words = 'plink incoming tint winter in caution sentient'
change = re.compile(r'int|in|ion|ing|inco|inter|ink')
# wrong output
assert change.sub('X', words) == 'plXk XcomXg tX wXer X cautX sentient'
# expected output
change = re.compile() ##### add your solution here
assert change.sub('X', words) == 'plX XmX tX ... | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**g)** For the given greedy quantifiers, what would be the equivalent form using `{m,n}` representation?* `?` is same as* `*` is same as* `+` is same as**h)** `(a*|b*)` is same as `(a|b)*` — True or False?**i)** For the given input strings, remove everything from the first occurrence of `test` (irrespective of case) ti... | s1 = 'this is a Test'
s2 = 'always test your RE for corner cases'
s3 = 'a TEST of skill tests?'
pat = re.compile() ##### add your solution here
assert pat.sub('', s1) == 'this is a Test'
assert pat.sub('', s2) == 'always '
assert pat.sub('', s3) == 'a ' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**j)** For the input list `words`, filter all elements starting with `s` and containing `e` and `t` in any order. | words = ['sequoia', 'subtle', 'exhibit', 'asset', 'sets', 'tests', 'site']
##### add your solution here
assert ___ == ['subtle', 'sets', 'site'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**k)** For the input list `words`, remove all elements having less than `6` characters. | words = ['sequoia', 'subtle', 'exhibit', 'asset', 'sets', 'tests', 'site']
##### add your solution here
assert ___ == ['sequoia', 'subtle', 'exhibit'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**l)** For the input list `words`, filter all elements starting with `s` or `t` and having a maximum of `6` characters. | words = ['sequoia', 'subtle', 'exhibit', 'asset', 'sets', 'tests', 'site']
##### add your solution here
assert ___ == ['subtle', 'sets', 'tests', 'site'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**m)** Can you reason out why this code results in the output shown? The aim was to remove all `` patterns but not the `` ones. The expected result was `'a 1 b 2 c'`. | ip = 'a<apple> 1<> b<bye> 2<> c<cat>'
assert re.sub(r'<.+?>', '', ip) == 'a 1 2' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**n)** Use `re.split` to get the output as shown below for given input strings. | s1 = 'go there // "this // that"'
s2 = 'a//b // c//d e//f // 4//5'
s3 = '42// hi//bye//see // carefully'
pat = re.compile() ##### add your solution here
assert pat.split() == ['go there', '"this // that"']
assert pat.split() == ['a//b', 'c//d e//f // 4//5']
assert pat.split() == ['42// hi//bye//see', 'carefull... | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
6. Working with matched portions**a)** For the given strings, extract the matching portion from first `is` to last `t`. | str1 = 'This the biggest fruit you have seen?'
str2 = 'Your mission is to read and practice consistently'
pat = re.compile() ##### add your solution here
##### add your solution here for str1
assert ___ == 'is the biggest fruit'
##### add your solution here for str2
assert ___ == 'ission is to read and practice c... | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**b)** Find the starting index of first occurrence of `is` or `the` or `was` or `to` for the given input strings. | s1 = 'match after the last newline character'
s2 = 'and then you want to test'
s3 = 'this is good bye then'
s4 = 'who was there to see?'
pat = re.compile() ##### add your solution here
##### add your solution here for s1
assert ___ == 12
##### add your solution here for s2
assert ___ == 4
##### add your solution... | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**c)** Find the starting index of last occurrence of `is` or `the` or `was` or `to` for the given input strings. | s1 = 'match after the last newline character'
s2 = 'and then you want to test'
s3 = 'this is good bye then'
s4 = 'who was there to see?'
pat = re.compile() ##### add your solution here
##### add your solution here for s1
assert ___ == 12
##### add your solution here for s2
assert ___ == 18
##### add your solutio... | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**d)** The given input string contains `:` exactly once. Extract all characters after the `:` as output. | ip = 'fruits:apple, mango, guava, blueberry'
##### add your solution here
assert ___ == 'apple, mango, guava, blueberry' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**e)** The given input strings contains some text followed by `-` followed by a number. Replace that number with its `log` value using `math.log()`. | s1 = 'first-3.14'
s2 = 'next-123'
pat = re.compile() ##### add your solution here
import math
assert pat.sub() == 'first-1.144222799920162'
assert pat.sub() == 'next-4.812184355372417' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**f)** Replace all occurrences of `par` with `spar`, `spare` with `extra` and `park` with `garden` for the given input strings. | str1 = 'apartment has a park'
str2 = 'do you have a spare cable'
str3 = 'write a parser'
##### add your solution here
assert pat.sub() == 'aspartment has a garden'
assert pat.sub() == 'do you have a extra cable'
assert pat.sub() == 'write a sparser' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**g)** Extract all words between `(` and `)` from the given input string as a list. Assume that the input will not contain any broken parentheses. | ip = 'another (way) to reuse (portion) matched (by) capture groups'
assert re.findall() == ['way', 'portion', 'by'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**h)** Extract all occurrences of ``, provided there is at least one character in between ``. | ip = 'a<apple> 1<> b<bye> 2<> c<cat>'
assert re.findall() == ['<apple>', '<> b<bye>', '<> c<cat>'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**i)** Use `re.findall` to get the output as shown below for the given input strings. Note the characters used in the input strings carefully. | row1 = '-2,5 4,+3 +42,-53 4356246,-357532354 '
row2 = '1.32,-3.14 634,5.63 63.3e3,9907809345343.235 '
pat = re.compile() ##### add your solution here
assert pat.findall(row1) == [('-2', '5'), ('4', '+3'), ('+42', '-53'), ('4356246', '-357532354')]
pat.findall(row2) == [('1.32', '-3.14'), ('634', '5.63'), ('63.3... | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**j)** This is an extension to previous question.* For `row1`, find the sum of integers of each tuple element. For example, sum of `-2` and `5` is `3`.* For `row2`, find the sum of floating-point numbers of each tuple element. For example, sum of `1.32` and `-3.14` is `-1.82`. | row1 = '-2,5 4,+3 +42,-53 4356246,-357532354 '
row2 = '1.32,-3.14 634,5.63 63.3e3,9907809345343.235 '
# should be same as previous question
pat = re.compile() ##### add your solution here
##### add your solution here for row1
assert ___ == [3, 7, -11, -353176108]
##### add your solution here for row2
assert __... | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**k)** Use `re.split` to get the output as shown below. | ip = '42:no-output;1000:car-truck;SQEX49801'
assert re.split() == ['42', 'output', '1000', 'truck', 'SQEX49801'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**l)** For the given list of strings, change the elements into a tuple of original element and number of times `t` occurs in that element. | words = ['sequoia', 'attest', 'tattletale', 'asset']
##### add your solution here
assert ___ == [('sequoia', 0), ('attest', 3), ('tattletale', 4), ('asset', 1)] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**m)** The given input string has fields separated by `:`. Each field contains four uppercase alphabets followed optionally by two digits. Ignore the last field, which is empty. See [docs.python: Match.groups](https://docs.python.org/3/library/re.htmlre.Match.groups) and use `re.finditer` to get the output as shown bel... | ip = 'TWXA42:JWPA:NTED01:'
##### add your solution here
assert ___ == [('TWXA', '42'), ('JWPA', 'NA'), ('NTED', '01')] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
> Note that this is different from `re.findall` which will just give empty string instead of `None` when a capture group doesn't participate.**n)** Convert the comma separated strings to corresponding `dict` objects as shown below. | row1 = 'name:rohan,maths:75,phy:89,'
row2 = 'name:rose,maths:88,phy:92,'
pat = re.compile() ##### add your solution here
##### add your solution here for row1
assert ___ == {'name': 'rohan', 'maths': '75', 'phy': '89'}
##### add your solution here for row2
assert ___ == {'name': 'rose', 'maths': '88', 'phy': '92... | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
7. Character class**a)** For the list `items`, filter all elements starting with `hand` and ending with `s` or `y` or `le`. | items = ['-handy', 'hand', 'handy', 'unhand', 'hands', 'handle']
##### add your solution here
assert ___ == ['handy', 'hands', 'handle'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**b)** Replace all whole words `reed` or `read` or `red` with `X`. | ip = 'redo red credible :read: rod reed'
##### add your solution here
assert ___ == 'redo X credible :X: rod X' | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**c)** For the list `words`, filter all elements containing `e` or `i` followed by `l` or `n`. Note that the order mentioned should be followed. | words = ['surrender', 'unicorn', 'newer', 'door', 'empty', 'eel', 'pest']
##### add your solution here
assert ___ == ['surrender', 'unicorn', 'eel'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
**d)** For the list `words`, filter all elements containing `e` or `i` and `l` or `n` in any order. | words = ['surrender', 'unicorn', 'newer', 'door', 'empty', 'eel', 'pest']
##### add your solution here
assert ___ == ['surrender', 'unicorn', 'newer', 'eel'] | _____no_output_____ | MIT | Day16/.ipynb_checkpoints/regex_exercises-checkpoint.ipynb | ithakker/CISC367-Projects |
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