Keiku

import pathlib

# get image paths list in a directory
image_dir = pathlib.Path('images').resolve()
exts = ['.jpg', '.png']
image_paths = [path for path in image_dir.rglob('*') if path.suffix.lower() in exts]

# include parent directory
image_paths = [pathlib.Path(*path.parts[-2:]).as_posix() for path in image_dir.rglob('*') if path.suffix.lower() in exts]

Keiku

# Reset the seaborn setting once set. It can be used in the middle of a notebook.
# Reference: python seaborn to reset back to the matplotlib - Stack Overflow https://stackoverflow.com/questions/26899310/python-seaborn-to-reset-back-to-the-matplotlib

# Either of the following may be used

# in matplotlib
import matplotlib as mpl
mpl.rcParams.update(mpl.rcParamsDefault)

# in seaborn

Keiku

import pandas as pd
from io import StringIO


def read_copytext(text):
    text1 = StringIO(text)
    df = pd.read_table(text1)
    df.columns = ["col1"]

    df["col1"] = df["col1"].str.replace("\s+", ",")

Keiku

import string
import numpy as np
import pandas as pd
from sklearn.model_selection import KFold, StratifiedKFold

X_train = np.random.random((10, 2))
y_train = np.array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0])

column = "pred"
n_fold = 5

Keiku

from itertools import chain
from nltk.corpus import wordnet

synonyms = wordnet.synsets('change')
lemmas = set(chain.from_iterable([word.lemma_names() for word in synonyms]))
lemmas
# Out[31]:
# {'alter',
#  'alteration',
#  'change',

Keiku

import numpy as np
import scipy as sp
import pandas as pd

df1 = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
df2 = pd.DataFrame({"C": [5, 6]})

X1 = sp.sparse.csr_matrix(df1.values)
X1_dense = X1.todense()
# Out[28]:

Keiku

import numpy as pd

# Python
list(map(lambda x: x + 1, range(1, 6, 1)))
# Out[1]: [2, 3, 4, 5, 6]

# Numpy
list(np.array(range(1, 6, 1)) + 1)
# Out[2]: [2, 3, 4, 5, 6]

Keiku

from collections import OrderedDict

d = {'A': 3,
     'B': 2,
     'C': 1}

OrderedDict(sorted(d.items(), key=lambda x: x[0])).values()
# Out[1]: odict_values([3, 2, 1])
OrderedDict(sorted(d.items(), key=lambda x: x[1])).values()
# Out[2]: odict_values([1, 2, 3])

Keiku

from sklearn.preprocessing import LabelEncoder, OneHotEncoder

X_str = np.array([['a', 'dog', 'red'], ['b', 'cat', 'green']])
# transform to integer
X_int = LabelEncoder().fit_transform(X_str.ravel()).reshape(*X_str.shape)
# transform to binary
X_bin = OneHotEncoder().fit_transform(X_int).toarray()

print(X_bin)
# [[ 1.  0.  0.  1.  0.  1.]

Keiku

text = ['This is a string', 'This is another string', 'TFIDF computation calculation', 'TfIDF is the product of TF and IDF']

from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(max_df=1.0, min_df=1, stop_words='english', norm = None)

X = vectorizer.fit_transform(text)
X_vovab = vectorizer.get_feature_names()
# Out[1]: ['calculation', 'computation', 'idf', 'product', 'string', 'tf', 'tfidf']
X_mat = X.todense()
# Out[2]: