path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
32068059/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
from tqdm import tqdm
import gensim
import json
import nltk
import numpy as np
import os
import pandas as pd
import pickle
import re
import spacy
import warnings
import os
import pandas as pd
pd.set_option('max_co... | code |
32068059/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from tqdm import tqdm
import spacy
stop_words = stopwords.words('english')
cord_stopwords = ['doi', 'preprint', 'copyright', 'peer', 'reviewed', 'org', 'https', 'et', 'al', 'author', 'figure', 'rights', 'reserved', 'permission', 'used', 'using', 'biorxiv', 'medrxiv', 'license', 'fig... | code |
32068059/cell_17 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from tqdm import tqdm
stop_words = stopwords.words('english')
cord_stopwords = ['doi', 'preprint', 'copyright', 'peer', 'reviewed', 'org', 'https', 'et', 'al', 'author', 'figure', 'rights', 'reserved', 'permission', 'used', 'using', 'biorxiv', 'medrxiv', 'license', 'fig', 'fig.', 'al... | code |
32068059/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import stopwords
from tqdm import tqdm
import re
import spacy
stop_words = stopwords.words('english')
cord_stopwords = ['doi', 'preprint', 'copyright', 'peer', 'reviewed', 'org', 'https', 'et', 'al', 'author', 'figure', 'rights', 'reserved', 'permission', 'used', 'using', 'biorxiv', 'medrxiv', 'lic... | code |
32068059/cell_5 | [
"image_output_1.png"
] | import nltk
import numpy as np
import os
import pandas as pd
import warnings
import os
import pandas as pd
pd.set_option('max_colwidth', 1000)
pd.set_option('max_rows', 100)
import numpy as np
np.set_printoptions(threshold=10000)
import pickle
import matplotlib.pyplot as plt
from datetime import datetime
import re... | code |
72092559/cell_4 | [
"text_plain_output_1.png"
] | def a():
print('a() starts')
b()
d()
print('a() returns')
def b():
print('b() starts')
c()
print('b() returns')
def c():
print('c() starts')
print('c() returns')
def d():
print('d() starts')
print('d() returns')
a() | code |
72092559/cell_2 | [
"text_plain_output_1.png"
] | for i in range(1, 10):
print(i) | code |
72092559/cell_3 | [
"text_plain_output_1.png"
] | head = 0
tail = 0
for i in range(1):
ran = 0
if ran == 1:
head = head + 1
elif ran == 2:
tail = tail + 1
else:
print('error')
print(str(head) + ' vs ' + str(tail)) | code |
72092559/cell_5 | [
"text_plain_output_1.png"
] | perc = 0.1
def plustip(total):
return total * perc + total
toatlwtip = plustip(12.0)
print(toatlwtip)
print(perc) | code |
2013234/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
f,ax = plt.subplots(1,3,figsize=(15,6))
ax[0].imshow(test.iloc[0].reshape(28,28),cmap='binary')
ax[1].imshow(test.iloc[1].reshape(28,28),cmap='binary')
ax[2].imshow(t... | code |
2013234/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
np.array([np.array([int(i == label) for i in range(10)]) for label in [5, 2, 3, 9]]) | code |
2013234/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
df.describe() | code |
2013234/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
f, ax = plt.subplots(1, 3, figsize=(15, 6))
ax[0].imshow(test.iloc[0].reshape(28, 28), cmap='binary')
ax[1].imshow(test.iloc[1].reshape(28, 28), cmap='binary')
ax[2].imshow(test.iloc[2].... | code |
2013234/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
f,ax = plt.subplots(1,3,figsize=(15,6))
ax[0].imshow(test.iloc[0].reshape(28,28),cmap='binary')
ax[1].imshow(test.iloc[1].reshape(28,28),cmap='binary')
ax[2].imshow(t... | code |
129026593/cell_9 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
BASE_PATH = '/kaggle/input/histopathologic-cancer-detection'
BASE_TRAIN_PATH = f'{BASE_PATH}/train'
BASE_TEST_PATH = f'{BASE_PATH}/test'
BASE_TRAIN_LABELS_PATH = '/kaggle/input/dataset-copy/new_dataset/train_labels.csv'
BASE_TEST_TRAIN_PATH = f'/kaggl... | code |
129026593/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, Dropout, Flatten, Activation
from tensorflow.keras.models import Sequential
from tensorflow.... | code |
129026593/cell_10 | [
"text_html_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
BASE_PATH = '/kaggle/input/histopathologic-cancer-detection'
BASE_TRAIN_PATH = f'{BASE_PATH}/train'
BASE_TEST_PATH = f'{BASE_PATH}/test'
BASE_TRAIN_LABELS_PATH = '/kaggle/input/dataset-copy/new_dataset/train_labels.csv'
BASE_TEST_TRAIN_PATH = f'/kaggl... | code |
129026593/cell_12 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, Dropout, Flatten, Activation
from tensorflow.keras.models import Sequential
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
mode... | code |
129026593/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
BASE_PATH = '/kaggle/input/histopathologic-cancer-detection'
BASE_TRAIN_PATH = f'{BASE_PATH}/train'
BASE_TEST_PATH = f'{BASE_PATH}/test'
BASE_TRAIN_LABELS_PATH = '/kaggle/input/dataset-copy/new_dataset/train_labels.csv'
BASE_TEST_TRAIN_PATH = f'/kaggle/input/dataset-copy/new_dataset/train'
BASE_TES... | code |
74052188/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train) | code |
74052188/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import OneHotEncoder
import numpy as np
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('../input/abalone-dataset/abalone.csv')... | code |
18137750/cell_9 | [
"text_html_output_1.png"
] | from efficientnet import EfficientNetB5
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
import cv2
import numpy as np
import os
import pandas as pd
import sys
import numpy as np
import pandas as pd
import cv2
import os
import sys
test_df = p... | code |
18137750/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from efficientnet import EfficientNetB5
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
import os
import sys
import numpy as np
import pandas as pd
import cv2
import os
import sys
sys.path.append(os.path.abspath('../input/efficientnet/efficient... | code |
18137750/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
import cv2
import os
import sys
print(os.listdir('../input')) | code |
18137750/cell_11 | [
"text_plain_output_1.png"
] | from efficientnet import EfficientNetB5
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
import cv2
import numpy as np
import os
import pandas as pd
import sys
import numpy as np
import pandas as pd
import cv2
import os
import sys
test_df = p... | code |
18137750/cell_7 | [
"text_plain_output_1.png"
] | from efficientnet import EfficientNetB5
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
import os
import pandas as pd
import sys
import numpy as np
import pandas as pd
import cv2
import os
import sys
test_df = pd.read_csv('../input/aptos2019-b... | code |
130025106/cell_42 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
X_train = X_sample.iloc[:, :-1].values
y_train = X_sample.iloc[:, -1].values
X_test = y_sample.iloc[:, :-1].values
y_test = y_sample.iloc[:, -1].values
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from... | code |
130025106/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
train.isnull().sum()
train.drop_duplicates()
train.info() | code |
130025106/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
test | code |
130025106/cell_56 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
test.drop(columns=['id'], inplace=True)
test
test | code |
130025106/cell_30 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
X_train = X_sample.iloc[:, :-1].values
y_train = X_sample.iloc[:, -1].values
X_test = y_sample.iloc[:, :-1].values
y_test = y_sample.iloc[:, -1].values
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.preprocessing i... | code |
130025106/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.svm import SVC
X_train = X_sample.iloc[:, :-1].values
y_train = X_sample.iloc[:, -1].values
X_test = y_sample.iloc[:, :-1].values
y_test = y_sample.iloc[:, -1].values
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
... | code |
130025106/cell_39 | [
"text_html_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
X_train = X_sample.iloc[:, :-1].values
y_train = X_sample.iloc[:, -1].values
X_test = y_sample.iloc[:, :-1].values
y_test = y_sample.iloc[:, -1].values
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from ... | code |
130025106/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train | code |
130025106/cell_52 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
test.drop(columns=['id'], inplace=True)
test
test | code |
130025106/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
130025106/cell_45 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
X_train = X_sample.iloc[:, :-1].values
y_train = X_sample.iloc[:, -1].values
X_test = y_sample.iloc[:, :-1].values
y_test = y_sample.iloc[:, -1].values
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from skl... | code |
130025106/cell_49 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.svm import ... | code |
130025106/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
train.isnull().sum()
train.drop_dupl... | code |
130025106/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
train.isnull().sum() | code |
130025106/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
train.isnull().sum()
train.drop_dupl... | code |
130025106/cell_24 | [
"image_output_1.png"
] | X_train = X_sample.iloc[:, :-1].values
y_train = X_sample.iloc[:, -1].values
X_test = y_sample.iloc[:, :-1].values
y_test = y_sample.iloc[:, -1].values
(X_train.shape, X_test.shape, y_train.shape, y_test.shape) | code |
130025106/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
train.isnull().sum()
train.drop_duplicates()
train.hist(bi... | code |
130025106/cell_53 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
test.drop(columns=['id'], inplace=True)
tes... | code |
130025106/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
train.isnull().sum()
train.drop_duplicates() | code |
130025106/cell_27 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
X_train = X_sample.iloc[:, :-1].values
y_train = X_sample.iloc[:, -1].values
X_test = y_sample.iloc[:, :-1].values
y_test = y_sample.iloc[:, -1].values
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from... | code |
130025106/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
test.drop(columns=['id'], inplace=True)
test | code |
130025106/cell_36 | [
"text_html_output_1.png"
] | from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import StandardScaler
X_train = X_sample.iloc[:, :-1].values
y_train = X_sample.iloc[:, -1].values
X_test = y_sample.iloc[:, :-1].values
y_test = y_sample.iloc[:, -1].values
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.... | code |
324276/cell_9 | [
"image_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
patches, texts = plt.pie(df.Gender.value_counts(), colors=... | code |
324276/cell_6 | [
"image_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x * 1.0 / N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
patches, texts = plt.pie(df.Gender.value_counts(), col... | code |
324276/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import colorsys
plt.style.use('seaborn-talk')
df = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', sep=',') | code |
324276/cell_3 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
df.Age.hist(bins=100)
plt.xlabel('Age')
plt.title('Distribution of Age')
plt.show() | code |
324276/cell_12 | [
"image_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
patches, texts = plt.pie(df.Gender.value_counts(), colors=... | code |
2000572/cell_13 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import string
messages = pd.read_csv('../input/spam.csv', encoding='latin-1')
messages.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
messages = messages.rename(columns={'v1': 'c... | code |
2000572/cell_9 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
import pandas as pd
import string
messages = pd.read_csv('../input/spam.csv', encoding='latin-1')
messages.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
messages = messages.rename(columns={'v1': 'class', 'v2': 'text'})
messages.groupby('class').describe()
... | code |
2000572/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
messages = pd.read_csv('../input/spam.csv', encoding='latin-1')
messages.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
messages = messages.rename(columns={'v1': 'class', 'v2': 'text'})
messages.groupby('class').describe() | code |
2000572/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
messages = pd.read_csv('../input/spam.csv', encoding='latin-1')
messages.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
messages = messages.rename(columns={'v1': 'class', 'v2': 'text'})
messages.groupby('class').describe()
messages.hist(column='length', by='class', bins=50... | code |
2000572/cell_15 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import string
messages = pd.read_csv('../input/spam.csv', encoding='latin-1')
messages.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
messages = messages.rename(columns={'v1': 'c... | code |
2000572/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
messages = pd.read_csv('../input/spam.csv', encoding='latin-1')
messages.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
messages = messages.rename(columns={'v1': 'class', 'v2': 'text'})
messages.head() | code |
1010505/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
null_columns = houses.columns[houses.isnull().any()]
houses[null_columns].isnull().sum()
sns.barplot(houses['TotRmsAbvGrd'], houses['SalePrice'])
plt.title('Sale Price vs Number of rooms') | code |
1010505/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='whitegrid', color_codes=True)
sns.set(font_scale=1)
houses = pd.read_csv('../input/train.csv')
houses.head() | code |
1010505/cell_3 | [
"text_plain_output_1.png"
] | null_columns = houses.columns[houses.isnull().any()]
houses[null_columns].isnull().sum() | code |
128010152/cell_12 | [
"text_plain_output_1.png"
] | from glob import glob
import matplotlib.pyplot as plt
import tensorflow as tf
IMAGE_SIZE = 256
BATCH_SIZE = 16
MAX_TRAIN_IMAGES = 400
train_low_light_images = sorted(glob('/kaggle/input/lol-dataset/lol_dataset/our485/low/*'))[:MAX_TRAIN_IMAGES]
val_low_light_images = sorted(glob('/kaggle/input/lol-dataset/lol_datas... | code |
128010152/cell_5 | [
"image_output_11.png",
"text_plain_output_5.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"image_output_14.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"image_output_13.png",
"image_output_5.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plai... | from glob import glob
import tensorflow as tf
IMAGE_SIZE = 256
BATCH_SIZE = 16
MAX_TRAIN_IMAGES = 400
train_low_light_images = sorted(glob('/kaggle/input/lol-dataset/lol_dataset/our485/low/*'))[:MAX_TRAIN_IMAGES]
val_low_light_images = sorted(glob('/kaggle/input/lol-dataset/lol_dataset/our485/low/*'))[MAX_TRAIN_IMAG... | code |
122256403/cell_4 | [
"text_html_output_1.png"
] | # check coda version
!nvcc --version | code |
122256403/cell_34 | [
"text_plain_output_1.png"
] | from spacy.tokens import DocBin
from spacy.util import filter_spans
from tqdm import tqdm
import fr_core_news_sm
import json
import os
import os
import pandas as pd
import pandas as pd
import re
import re
import spacy
spacy.require_gpu()
import re
def trim_entity_spans(data: list) -> list:
"""Removes l... | code |
122256403/cell_33 | [
"text_plain_output_1.png"
] | from spacy.tokens import DocBin
from spacy.util import filter_spans
from tqdm import tqdm
import fr_core_news_sm
import json
import os
import os
import pandas as pd
import pandas as pd
import re
import re
import spacy
spacy.require_gpu()
import re
def trim_entity_spans(data: list) -> list:
"""Removes l... | code |
122256403/cell_29 | [
"text_plain_output_1.png"
] | !python -m spacy debug data /kaggle/working/config.cfg --paths.train /kaggle/working/train.spacy --paths.dev /kaggle/working/train.spacy | code |
122256403/cell_2 | [
"text_plain_output_1.png"
] | !pip install spacy-transformers | code |
122256403/cell_7 | [
"text_html_output_1.png"
] | import spacy
spacy.require_gpu() | code |
122256403/cell_3 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | !python3 -m spacy download fr_core_news_sm | code |
122256403/cell_35 | [
"text_html_output_1.png"
] | from spacy.tokens import DocBin
from spacy.util import filter_spans
from tqdm import tqdm
import fr_core_news_sm
import json
import os
import os
import pandas as pd
import pandas as pd
import re
import re
import spacy
spacy.require_gpu()
import re
def trim_entity_spans(data: list) -> list:
"""Removes l... | code |
122256403/cell_31 | [
"text_plain_output_1.png"
] | !python -m spacy train /kaggle/working/config.cfg --output /kaggle/working/ --paths.train /kaggle/working/train.spacy --paths.dev /kaggle/working/train.spacy --gpu-id 0 | code |
122256403/cell_22 | [
"text_plain_output_1.png"
] | from spacy.tokens import DocBin
from spacy.util import filter_spans
from tqdm import tqdm
import fr_core_news_sm
import json
import os
import os
import pandas as pd
import pandas as pd
import re
import re
import re
def trim_entity_spans(data: list) -> list:
"""Removes leading and trailing white spaces fr... | code |
122256403/cell_27 | [
"text_plain_output_1.png"
] | !python -m spacy init fill-config /kaggle/input/configs/base_config.cfg /kaggle/working/config.cfg | code |
106198731/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158... | code |
106198731/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158... | code |
106198731/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158... | code |
106198731/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158... | code |
106198731/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158... | code |
106198731/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
106198731/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158... | code |
106198731/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158... | code |
106198731/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158... | code |
106198731/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158... | code |
106198731/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158... | code |
106198731/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158... | code |
33105040/cell_18 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from fbprophet import Prophet
from pmdarima import auto_arima
from statsmodels.tsa.arima_model import ARIMA
import datetime
import pandas as pd
import pandas as pd
import plotly.graph_objects as go
covid_data = pd.read_excel('/kaggle/input/corona-virus-pakistan-dataset-2020/COVID_FINAL_DATA.xlsx')
covid_data.is... | code |
33105040/cell_15 | [
"text_html_output_1.png"
] | from fbprophet import Prophet
import pandas as pd
import pandas as pd
import plotly.graph_objects as go
covid_data = pd.read_excel('/kaggle/input/corona-virus-pakistan-dataset-2020/COVID_FINAL_DATA.xlsx')
covid_data.isnull().sum()
covid_data.dtypes
covid_data['Date'] = pd.to_datetime(covid_data['Date'])
pak_dat... | code |
33105040/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from fbprophet import Prophet
import pandas as pd
import pandas as pd
import plotly.graph_objects as go
covid_data = pd.read_excel('/kaggle/input/corona-virus-pakistan-dataset-2020/COVID_FINAL_DATA.xlsx')
covid_data.isnull().sum()
covid_data.dtypes
covid_data['Date'] = pd.to_datetime(covid_data['Date'])
pak_dat... | code |
33105040/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
covid_data = pd.read_excel('/kaggle/input/corona-virus-pakistan-dataset-2020/COVID_FINAL_DATA.xlsx')
covid_data.isnull().sum()
covid_data.dtypes
pak_data = covid_data.copy()
pak_data.head() | code |
50242450/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.despine(left=True, right=True, bottom=True, top=True)
sns.set_style('white')
df = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_res... | code |
50242450/cell_9 | [
"image_output_11.png",
"image_output_17.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",... | import pandas as pd
df = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv', engine='python', error_bad_lines=False)
df.head() | code |
50242450/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.despine(left=True, right=True, bottom=True, top=True)
sns.set_style('white')
df = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_res... | code |
50242450/cell_7 | [
"image_output_11.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
... | import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.despine(left=True, right=True, bottom=True, top=True)
sns.set_style('white') | code |
106198134/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | glimpse(dailyActivity) | code |
106198134/cell_9 | [
"text_html_output_1.png"
] | colnames(dailyActivity) | code |
106198134/cell_11 | [
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_5.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | head(dailyActivity) | code |
106198134/cell_15 | [
"text_html_output_1.png"
] | skim_without_charts(dailyActivity) | code |
106198134/cell_3 | [
"text_plain_output_1.png"
] | installed.packages('tidyverse')
installed.packages('readr')
installed.packages('here')
installed.packages('skimr')
installed.packages('dplyr')
installed.packages('janitor') | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.