path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
130007697/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import easyocr
reader = easyocr.Reader(['en'])
frame = cv.imread(img)
frame = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
result_img = read_plate_number(cordinates, frame, reader)
plt.imshow(result_img)
plt.show() | code |
130007697/cell_14 | [
"text_plain_output_1.png"
] | !python train.py --img 640 --batch 16 --epochs 15 --data /kaggle/working/plate_datasets/dataset.yaml --weights yolov5s.pt --cache ram | code |
130007697/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | !git clone https://github.com/ultralytics/yolov5 # clone
!pip install -r requirements.txt | code |
130007697/cell_5 | [
"image_output_1.png"
] | import cv2 as cv
import matplotlib.pyplot as plt
import cv2 as cv
import matplotlib.pyplot as plt
img = cv.imread('/kaggle/input/car-plate-detection/images/Cars0.png')
img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
rec = cv.rectangle(img, (226, 125), (419, 173), (0, 250, 0), 2)
rec = cv.circle(rec, ((226 + 419) // 2, (125 ... | code |
320908/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
teams = pd.read_sql_query('select * from Teams', conn)
users = pd.read_sql_query('select * from Users', conn)
teammembers = pd.read_sql_query('select * from TeamMemberships', conn)
teams_q = teammembers.groupby('TeamId').UserId.cou... | code |
320908/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.sparse import spdiags, coo_matrix
import networkx as nx
import numpy as np
import numpy as np
import pandas as pd
import plotly
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
teams = pd.read_sql_query('select * from Teams', conn)
users = pd.read_sql_query('select * from Users', conn... | code |
16157889/cell_23 | [
"text_plain_output_1.png"
] | from copy import deepcopy
from sklearn.preprocessing import MinMaxScaler
import math
import numpy as np
import pandas as pd
import warnings
import pandas as pd
import warnings
import os
import numpy as np
import folium
from folium import plugins
from sklearn.preprocessing import MinMaxScaler
from copy import deep... | code |
16157889/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import warnings
import os
import numpy as np
import folium
from folium import plugins
from sklearn.preprocessing import MinMaxScaler
from copy import deepcopy
import math
import time
from sklearn.cluster import AgglomerativeClustering
from sklearn import metrics... | code |
16157889/cell_19 | [
"text_plain_output_1.png"
] | from copy import deepcopy
from sklearn.preprocessing import MinMaxScaler
import math
import numpy as np
import pandas as pd
import warnings
import pandas as pd
import warnings
import os
import numpy as np
import folium
from folium import plugins
from sklearn.preprocessing import MinMaxScaler
from copy import deep... | code |
16157889/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import warnings
import os
import numpy as np
import folium
from folium import plugins
from sklearn.preprocessing import MinMaxScaler
from copy import deepcopy
import math
import time
from sklearn.cluster import AgglomerativeClustering
from sklearn import metrics... | code |
16157889/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import warnings
import os
import numpy as np
import folium
from folium import plugins
from sklearn.preprocessing import MinMaxScaler
from copy import deepcopy
import math
import time
from sklearn.cluster import AgglomerativeClustering
from sklearn import metrics... | code |
122250672/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
car = pd.read_csv('/kaggle/input/car-15/cardata.csv')
car.columns
car.info() | code |
122250672/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
lrmodel = LinearRegression()
lrmodel.fit(X_train, Y_train) | code |
122250672/cell_7 | [
"image_output_1.png"
] | import pandas as pd
car = pd.read_csv('/kaggle/input/car-15/cardata.csv')
car.head() | code |
122250672/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
car = pd.read_csv('/kaggle/input/car-15/cardata.csv')
car.columns
car.isnull().sum()
car.replace({'Fuel_Type': {'Petrol': 0, 'Diesel': 1, 'CNG': 2}}, inplace=True)
car.replace({'Seller_Type': {'Dealer': 0, 'Individual': 1}}, inplace=True)
c... | code |
122250672/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
car = pd.read_csv('/kaggle/input/car-15/cardata.csv')
car.columns
car.isnull().sum()
car.replace({'Fuel_Type': {'Petrol': 0, 'Diesel': ... | code |
122250672/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
car = pd.read_csv('/kaggle/input/car-15/cardata.csv')
car.columns | code |
122250672/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
car = pd.read_csv('/kaggle/input/car-15/cardata.csv')
car.columns
car.isnull().sum() | code |
34124456/cell_20 | [
"text_plain_output_1.png"
] | def printinfo(name, age):
"""This prints a passed info into this function"""
return
def printinfo(name, age=35):
"""This prints a passed info into this function"""
return
def printinfo(arg1, *vartuple):
"""This prints a variable passed arguments"""
print('Output is: ')
print(arg1)
for ... | code |
34124456/cell_11 | [
"text_plain_output_1.png"
] | def printme(str):
"""This prints a passed string into this function"""
return
def printme(str):
"""This prints a passed string into this function"""
return
def printme(str):
"""This prints a passed string into this function"""
print(str)
return
printme() | code |
34124456/cell_7 | [
"text_plain_output_1.png"
] | def changeme(mylist):
"""This changes a passed list into this function"""
print('Values inside the function before change: ', mylist)
mylist[2] = 50
print('Values inside the function after change: ', mylist)
return
mylist = [10, 20, 30]
changeme(mylist)
print('Values outside the function: ', mylist) | code |
34124456/cell_18 | [
"text_plain_output_1.png"
] | def printinfo(name, age):
"""This prints a passed info into this function"""
return
def printinfo(name, age=35):
"""This prints a passed info into this function"""
print('Name: ', name)
print('Age ', age)
return
printinfo(age=50, name='miki')
printinfo(name='miki') | code |
34124456/cell_28 | [
"text_plain_output_1.png"
] | sum = lambda arg1, arg2: arg1 + arg2
def sum(arg1, arg2):
total = arg1 + arg2
return total
total = sum(10, 20)
total = 0
def sum(arg1, arg2):
total = arg1 + arg2
print('Inside the function local total : ', total)
return total
sum(10, 20)
print('Outside the function global total : ', total) | code |
34124456/cell_16 | [
"text_plain_output_1.png"
] | def printinfo(name, age):
"""This prints a passed info into this function"""
print('Name: ', name)
print('Age ', age)
return
printinfo(age=50, name='miki') | code |
34124456/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | def printme(str):
"""This prints a passed string into this function"""
return
def printme(str):
"""This prints a passed string into this function"""
return
def printme(str):
"""This prints a passed string into this function"""
return
def printme(str):
"""This prints a passed string into t... | code |
34124456/cell_22 | [
"text_plain_output_1.png"
] | sum = lambda arg1, arg2: arg1 + arg2
print('Value of total : ', sum(10, 20))
print('Value of total : ', sum(20, 20)) | code |
34124456/cell_12 | [
"text_plain_output_1.png"
] | def printme(str):
"""This prints a passed string into this function"""
return
def printme(str):
"""This prints a passed string into this function"""
return
def printme(str):
"""This prints a passed string into this function"""
return
def printme(str):
"""This prints a passed string into t... | code |
34124456/cell_5 | [
"text_plain_output_1.png"
] | def printme(str):
"""This prints a passed string into this function"""
return
def printme(str):
"""This prints a passed string into this function"""
print(str)
return
printme('This is first call to the user defined function!')
printme('Again second call to the same function') | code |
130027592/cell_42 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
model_1 = Pipeline([('tfidf', TfidfVectorizer()), ('clf', MultinomialNB())])
model_1.fit(train_sentences, train_label)
model_1_score = model_1.score(test_sentences, test_la... | code |
130027592/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
real_data = pd.read_csv('/kaggle/input/fake-news-football/real.csv')
fake_data = pd.read_csv('/kaggle/input/fake-news-football/fake.csv')
all_data = pd.concat([real_data, fake_data], ignore_index=True)
all_data.head() | code |
130027592/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
real_data = pd.read_csv('/kaggle/input/fake-news-football/real.csv')
fake_data = pd.read_csv('/kaggle/input/fake-news-football/fake.csv')
fake_data.info() | code |
130027592/cell_34 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import numpy as np # linear algebra
import random
sent_lens = [len(sentence.split()) for sentence in train_sentences]
sent_lens[:10]
avg_len = np.mean(sent_lens)
avg_len
out_len_seq = np.percentile(sent_lens, 95)
out_len_seq
max_toke... | code |
130027592/cell_33 | [
"text_plain_output_1.png"
] | import random
random_sen = random.choice(train_sentences)
random_sen | code |
130027592/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
real_data = pd.read_csv('/kaggle/input/fake-news-football/real.csv')
fake_data = pd.read_csv('/kaggle/input/fake-news-football/fake.csv')
all_data = pd.concat([real_data, fake_data], ignore_index=True)
all_data = all_data.dropna()
all_data = all... | code |
130027592/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
real_data = pd.read_csv('/kaggle/input/fake-news-football/real.csv')
fake_data = pd.read_csv('/kaggle/input/fake-news-football/fake.csv')
real_data.head() | code |
130027592/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
model_1 = Pipeline([('tfidf', TfidfVectorizer()), ('clf', MultinomialNB())])
model_1.fit(train_sentences, train_label) | code |
130027592/cell_29 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
sent_lens = [len(sentence.split()) for sentence in train_sentences]
sent_lens[:10]
avg_len = np.mean(sent_lens)
avg_len
out_len_seq = np.percentile(sent_lens, 95)
out_len_seq | code |
130027592/cell_26 | [
"text_plain_output_1.png"
] | sent_lens = [len(sentence.split()) for sentence in train_sentences]
sent_lens[:10] | code |
130027592/cell_48 | [
"text_html_output_1.png"
] | from keras.callbacks import EarlyStopping
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import numpy as np # linear algebra
import tensorflow as tf
sent_lens = [len(sentence.split()) for sentence in train_sentences]
sent_lens[:10]
avg_len = np... | code |
130027592/cell_41 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
model_1 = Pipeline([('tfidf', TfidfVectorizer()), ('clf', MultinomialNB())])
model_1.fit(train_sentences, train_label)
model_1_score = model_1.score(test_sentences, test_la... | code |
130027592/cell_54 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import numpy as np # linear algebra
import tensorflow as tf
def calculate_resul... | code |
130027592/cell_60 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import numpy as np # linear algebra
import tensorflow as tf
sent_lens = [len(sentence.split()) for sentence in train_sentences]
sent_lens[:10]
avg_len = np... | code |
130027592/cell_50 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import numpy as np # linear algebra
import tensorflow as tf
sent_lens = [len(sentence.split()) for sentence in train_sentences]
sent_lens[:10]
avg_len = np... | code |
130027592/cell_52 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import numpy as np # linear algebra
import tensorflow as tf
sent_lens = [len(sentence.split()) for sentence in train_sentences]
sent_lens[:10]
avg_len = np... | code |
130027592/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 |
130027592/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
real_data = pd.read_csv('/kaggle/input/fake-news-football/real.csv')
fake_data = pd.read_csv('/kaggle/input/fake-news-football/fake.csv')
real_data.info() | code |
130027592/cell_49 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
real_data = pd.read_csv(... | code |
130027592/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
real_data = pd.read_csv('/kaggle/input/fake-news-football/real.csv')
fake_data = pd.read_csv('/kaggle/input/fake-news-football/fake.csv')
all_data = pd.concat([real_data, fake_data], ignore_index=True)
all_data = all_data.d... | code |
130027592/cell_51 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import numpy as np # linear algebra
import tensorflow as tf
sent_lens = [len(sentence.split()) for sentence in train_sentences]
sent_lens[:10]
avg_len = np... | code |
130027592/cell_59 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
real_data = pd.read_csv(... | code |
130027592/cell_58 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import numpy as np # linear algebra
import tensorflow as tf
sent_lens = [len(sentence.split()) for sentence in train_sentences]
sent_lens[:10]
avg_len = np... | code |
130027592/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | sent_lens = [len(sentence.split()) for sentence in train_sentences]
sent_lens[:10]
(min(sent_lens), max(sent_lens)) | code |
130027592/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
real_data = pd.read_csv('/kaggle/input/fake-news-football/real.csv')
fake_data = pd.read_csv('/kaggle/input/fake-news-football/fake.csv')
fake_data.head() | code |
130027592/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
real_data = pd.read_csv('/kaggle/input/fake-news-football/real.csv')
fake_data = pd.read_csv('/kaggle/input/fake-news-football/fake.csv')
all_data = pd.concat([real_data, fake_data], ignore_index=True)
all_data = all_data.dropna()
all_data.info(... | code |
130027592/cell_38 | [
"text_plain_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import numpy as np # linear algebra
import random
sent_lens = [len(sentence.split()) for sentence in train_sentences]
sent_lens[:10]
avg_len = np.mean(sent_lens)
avg_len
out_len_seq = np.percentile... | code |
130027592/cell_3 | [
"text_plain_output_1.png"
] | import tensorflow as tf
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
from tensorflow.keras import layers
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn... | code |
130027592/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
real_data = pd.read_csv('/kaggle/input/fake-news-football/real.csv')
fake_data = pd.read_csv('/kaggle/input/fake-news-football/fake.csv')
all_data = pd.concat([real_data, fake_data], ignore_index=True)
all_data = all_data.dropna()
all_data['targ... | code |
130027592/cell_35 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import numpy as np # linear algebra
sent_lens = [len(sentence.split()) for sentence in train_sentences]
sent_lens[:10]
avg_len = np.mean(sent_lens)
avg_len
out_len_seq = np.percentile(sent_lens, 95)
out_len_seq
max_tokens = 65000
outp... | code |
130027592/cell_43 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
def calculate_results(y_true, y_pred):
model_accuracy = accuracy_score(y_true, y_pred) * 100... | code |
130027592/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
real_data = pd.read_csv('/kaggle/input/fake-news-football/real.csv')
fake_data = pd.read_csv('/kaggle/input/fake-news-football/fake.csv')
all_data = pd.concat([real_data, fake_data], ignore_index=True)
all_data.info() | code |
130027592/cell_22 | [
"text_plain_output_1.png"
] | (type(train_sentences), type(train_label)) | code |
130027592/cell_53 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.callbacks import EarlyStopping
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import numpy as np # linear algebra
import tensorflow as tf
sent_lens = [len(sentence.split()) for sentence in train_sentences]
sent_lens[:10]
avg_len = np... | code |
130027592/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
sent_lens = [len(sentence.split()) for sentence in train_sentences]
sent_lens[:10]
avg_len = np.mean(sent_lens)
avg_len | code |
130027592/cell_37 | [
"text_plain_output_1.png"
] | from tensorflow.keras import layers
import numpy as np # linear algebra
sent_lens = [len(sentence.split()) for sentence in train_sentences]
sent_lens[:10]
avg_len = np.mean(sent_lens)
avg_len
out_len_seq = np.percentile(sent_lens, 95)
out_len_seq
max_tokens = 65000
output_sequence_length = int(out_len_seq)
embedd... | code |
122244194/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras.callbacks import EarlyStopping
import numpy as np # linear algebra
import os
import tensorflow as tf
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import os
with open('/kaggle/input/german-harry-potter/Stein.txt', 'r') as datei:
TEXT = date... | code |
122244194/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
122244194/cell_7 | [
"image_output_1.png"
] | from tensorflow.keras.callbacks import EarlyStopping
import numpy as np # linear algebra
import os
import tensorflow as tf
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import os
with open('/kaggle/input/german-harry-potter/Stein.txt', 'r') as datei:
TEXT = date... | code |
122244194/cell_8 | [
"text_plain_output_1.png"
] | from tensorflow.keras.callbacks import EarlyStopping
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import os
with open('/kag... | code |
122244194/cell_10 | [
"text_plain_output_1.png"
] | from tensorflow.keras.callbacks import EarlyStopping
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot ... | code |
122244194/cell_12 | [
"text_plain_output_1.png"
] | from tensorflow.keras.callbacks import EarlyStopping
import numpy as np # linear algebra
import os
import tensorflow as tf
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import os
with open('/kaggle/input/german-harry-potter/Stein.txt', 'r') as datei:
TEXT = date... | code |
122244194/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import tensorflow as tf
with open('/kaggle/input/german-harry-potter/Stein.txt', 'r') as datei:
TEXT = datei.read()
with open('/kaggle/input/german-harry-potter/Orden.txt', 'r') as datei:
Orden = datei.read()
with open('/kaggle/input/german-harry-potter/Kammer.txt', 'r') as... | code |
33108213/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
ted_data = pd.read_csv('/kaggle/input/ted-talks/ted_main.csv')
ted_data.isnull().sum()
ted_data.dtypes
ted_data = ted_data.drop(['name'], axis=1)
ted_data.columns
def date_convert(x):
return pd.to_datetime(x, unit='s'... | code |
33108213/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted_data = pd.read_csv('/kaggle/input/ted-talks/ted_main.csv')
ted_data.isnull().sum()
ted_data.dtypes | code |
33108213/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted_data = pd.read_csv('/kaggle/input/ted-talks/ted_main.csv')
ted_data.isnull().sum()
ted_data.dtypes
ted_data = ted_data.drop(['name'], axis=1)
ted_data.columns
def date_convert(x):
return pd.to_datetime(x, unit='s')
ted_data['film_date']... | code |
33108213/cell_2 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted_data = pd.read_csv('/kaggle/input/ted-talks/ted_main.csv')
ted_data.head() | code |
33108213/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
ted_data = pd.read_csv('/kaggle/input/ted-talks/ted_main.csv')
ted_data.isnull().sum()
ted_data.dtypes
ted_data = ted_data.drop(['name'], axis=1)
ted_data.columns
def date_convert(x):
return pd.to_datetime(x, unit='s'... | code |
33108213/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 |
33108213/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
ted_data = pd.read_csv('/kaggle/input/ted-talks/ted_main.csv')
ted_data.isnull().sum()
ted_data.dtypes
ted_data = ted_data.drop(['name'], axis=1)
ted_data.columns
def date_convert(x):
return pd.to_datetime(x, unit='s'... | code |
33108213/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
ted_data = pd.read_csv('/kaggle/input/ted-talks/ted_main.csv')
ted_data.isnull().sum()
ted_data.dtypes
ted_data = ted_data.drop(['name'], axis=1)
ted_data.columns
def date_convert(x):
return pd.to_datetime(x, unit='s'... | code |
33108213/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
ted_data = pd.read_csv('/kaggle/input/ted-talks/ted_main.csv')
ted_data.isnull().sum()
ted_data.dtypes
ted_data = ted_data.drop(['name'], axis=1)
ted_data.columns
def date_convert(x):
return pd.to_datetime(x, unit='s'... | code |
33108213/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted_data = pd.read_csv('/kaggle/input/ted-talks/ted_main.csv')
ted_data.isnull().sum() | code |
33108213/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy.stats as stats
import seaborn as sns
ted_data = pd.read_csv('/kaggle/input/ted-talks/ted_main.csv')
ted_data.isnull().sum()
ted_data.dtypes
ted_data = ted_data.drop(['name'], axis=1)
ted_data.columns
def date_convert(x):
retu... | code |
33108213/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
ted_data = pd.read_csv('/kaggle/input/ted-talks/ted_main.csv')
ted_data.isnull().sum()
ted_data.dtypes
ted_data = ted_data.drop(['name'], axis=1)
ted_data.columns
def date_convert(x):
return pd.to_datetime(x, unit='s'... | code |
33108213/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy.stats as stats
import seaborn as sns
ted_data = pd.read_csv('/kaggle/input/ted-talks/ted_main.csv')
ted_data.isnull().sum()
ted_data.dtypes
ted_data = ted_data.drop(['name'], axis=1)
ted_data.columns
def date_convert(x):
retu... | code |
33108213/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted_data = pd.read_csv('/kaggle/input/ted-talks/ted_main.csv')
ted_data.isnull().sum()
ted_data.dtypes
ted_data = ted_data.drop(['name'], axis=1)
ted_data.columns | code |
128046727/cell_42 | [
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_di... | code |
128046727/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | !pip install openpyxl | code |
128046727/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_diagnosys', 'right_diagnosys', 'normal', 'diab... | code |
128046727/cell_57 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import glob
import imageio
import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex',... | code |
128046727/cell_56 | [
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import glob
import imageio
import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex',... | code |
128046727/cell_30 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_diagnosys', 'right_diagnosys', 'normal', 'diab... | code |
128046727/cell_33 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_diagnosys', 'right_diagnosys', 'normal', 'diab... | code |
128046727/cell_20 | [
"text_html_output_1.png"
] | import glob
import os
print(f"train images: {len(os.listdir('//kaggle//input//ocular-disease-recognition-odir5k//ODIR-5K//ODIR-5K//Training Images'))}")
print(f"test images: {len(os.listdir('//kaggle//input//ocular-disease-recognition-odir5k//ODIR-5K//ODIR-5K//Testing Images'))}")
print(f"train images - left eye: {le... | code |
128046727/cell_40 | [
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_di... | code |
128046727/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_diagnosys', 'right_diagnosys', 'normal', 'diab... | code |
128046727/cell_39 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_di... | code |
128046727/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_diagnosys', 'right_diagnosys', 'normal', 'diab... | code |
128046727/cell_48 | [
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_di... | code |
128046727/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.head() | code |
128046727/cell_50 | [
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_di... | code |
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