path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2028270/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
wine_df = pd.read_csv('../input/winemag-data_first150k.csv')
wine_df.drop('region_1', axis=1, inplace=True)
wine_df.drop('region_2', axis=1, inplace=True)
wine_df.drop('description', axis=1, inplace=True)
wine_df.drop('designation', axis=1, inplace=True)
newdf = wine_df[... | code |
2028270/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
wine_df = pd.read_csv('../input/winemag-data_first150k.csv')
wine_df.drop('region_1', axis=1, inplace=True)
wine_df.drop('region_2', axis=1, inplace=True)
wine_df.drop('description', axis=1, inplace=True)
wine_df.drop('designation', axis=1, inplace=True)
wine_df['reds'] ... | code |
2028270/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
wine_df = pd.read_csv('../input/winemag-data_first150k.csv')
wine_df.drop('region_1', axis=1, inplace=True)
wine_df.drop('region_2', axis=1, inplace=True)
wine_df.drop('description', axis=1, inplace=True)
wine_df.drop('designation', axis=1, inplace=True)
newdf = wine_df[... | code |
2028270/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
wine_df = pd.read_csv('../input/winemag-data_first150k.csv')
wine_df.drop('region_1', axis=1, inplace=True)
wine_df.drop('region_2', axis=1, inplace=True)
wine_df.drop('description', axis=1, inplace=True)
wine_df.drop('designation', axis=1, inplace=True)
red_df = wine_df... | code |
2028270/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
wine_df = pd.read_csv('../input/winemag-data_first150k.csv')
wine_df.drop('region_1', axis=1, inplace=True)
wine_df.drop('region_2', axis=1, inplace=True)
wine_df.drop('description', axis=1, inplace=True)
wine_df.drop('designation', axis=1, inplace=True)
wine_df['cheaper... | code |
2028270/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
wine_df = pd.read_csv('../input/winemag-data_first150k.csv')
plt.hist(wine_df['points'], bins=15, edgecolor='white') | code |
122260975/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
stop = stopwords.wo... | code |
122260975/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
test_df.head() | code |
122260975/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import re
import nltk.corpus
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from sklearn.feature_extraction.text import TfidfVectorizer
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for file... | code |
122260975/cell_18 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kag... | code |
122260975/cell_8 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
stop = stopwords.wo... | code |
122260975/cell_15 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
train_df = pd.read_csv('/kaggle/input/nlp-ge... | code |
122260975/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
print(train_df.shape)
print(test_df.shape) | code |
122260975/cell_17 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score, confusion_matrix
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
... | code |
122260975/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.head() | code |
17108514/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
reviews = pd.read_csv('../input/ml-1m/ml-1m/ratings.dat', names=['userID', 'movieID', 'rating', 'time'], delimiter='::', engine='python')
rts_gp = reviews.groupby(by=['rating']).agg({'userID': 'count'}).reset_index()
rts_gp.columns = ['Rating', 'Count']
plt.barh(r... | code |
17108514/cell_23 | [
"text_plain_output_1.png"
] | from surprise import KNNBasic, KNNWithMeans, KNNWithZScore
algoritmo = KNNBasic(k=50, sim_options={'name': 'pearson', 'user_based': True, 'verbose': True})
algoritmo.fit(trainset)
uid = str(49)
iid = str(2058)
print('Prediction for rating: ')
pred = algoritmo.predict(uid, iid, r_ui=4, verbose=True) | code |
17108514/cell_29 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from surprise import KNNBasic, KNNWithMeans, KNNWithZScore
from surprise import accuracy
algoritmo = KNNBasic(k=50, sim_options={'name': 'pearson', 'user_based': True, 'verbose': True})
algoritmo.fit(trainset)
uid = str(49)
iid = str(2058)
pred = algoritmo.predict(uid, iid, r_ui=4, verbose=True)
test_pred = algo... | code |
17108514/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
reviews = pd.read_csv('../input/ml-1m/ml-1m/ratings.dat', names=['userID', 'movieID', 'rating', 'time'], delimiter='::', engine='python')
print('No. of Unique Users :', reviews.userID.nunique())
print('No. of Unique Movies :', reviews.movieID.nunique())
print('No. of Unique Ratings :', reviews... | code |
17108514/cell_17 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from surprise import KNNBasic, KNNWithMeans, KNNWithZScore
algoritmo = KNNBasic(k=50, sim_options={'name': 'pearson', 'user_based': True, 'verbose': True})
algoritmo.fit(trainset) | code |
17108514/cell_31 | [
"text_plain_output_1.png"
] | from surprise import KNNBasic, KNNWithMeans, KNNWithZScore
from surprise import accuracy
algoritmo = KNNBasic(k=50, sim_options={'name': 'pearson', 'user_based': True, 'verbose': True})
algoritmo.fit(trainset)
uid = str(49)
iid = str(2058)
pred = algoritmo.predict(uid, iid, r_ui=4, verbose=True)
test_pred = algo... | code |
17108514/cell_27 | [
"image_output_1.png"
] | from surprise import KNNBasic, KNNWithMeans, KNNWithZScore
from surprise import accuracy
algoritmo = KNNBasic(k=50, sim_options={'name': 'pearson', 'user_based': True, 'verbose': True})
algoritmo.fit(trainset)
uid = str(49)
iid = str(2058)
pred = algoritmo.predict(uid, iid, r_ui=4, verbose=True)
test_pred = algo... | code |
17108514/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
reviews = pd.read_csv('../input/ml-1m/ml-1m/ratings.dat', names=['userID', 'movieID', 'rating', 'time'], delimiter='::', engine='python')
print('Rows:', reviews.shape[0], '; Columns:', reviews.shape[1], '\n')
reviews.head() | code |
104124423/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/digit-recognizer/train.csv')
test_data = pd.read_csv('../input/digit-recognizer/test.csv') / 255
print('Number of null values in training set:', train_data.isnull().sum().sum())
print('')
print('Number of null values in test set:', test_data.isnull().sum().sum()) | code |
104124423/cell_33 | [
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import torch
import torch.nn as nn
import torch.optim as optim
BATCH_SIZE = 16
LEARNING_RATE = 0.001
N_EPOCHS = 20
LAYER1_SIZE = 256
LAYER2_SIZE = 256
DROPOUT_RATE = 0.3
... | code |
104124423/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/digit-recognizer/train.csv')
test_data = pd.read_csv('../input/digit-recognizer/test.csv') / 255
print(train_data.shape)
train_data.head(3) | code |
104124423/cell_7 | [
"image_output_1.png"
] | import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device | code |
104124423/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_data = pd.read_csv('../input/digit-recognizer/train.csv')
test_data = pd.read_csv('../input/digit-recognizer/test.csv') / 255
plt.figure(figsize=(8, 8))
for i in range(9):
img = np.asarray(train_data.iloc[i + 180, 1:].values.reshape((2... | code |
104124423/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/digit-recognizer/train.csv')
test_data = pd.read_csv('../input/digit-recognizer/test.csv') / 255
# Figure size
plt.figure(figsize=(8,8))
# Subplot
for i in range(9):
img = np.asarray... | code |
104124423/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
BATCH_SIZE = 16
LEARNING_RATE = 0.001
N_EPOCHS = 20
LAYER1_SIZE = 256
LAYER2_SIZE = 256
DROPOUT_RATE = 0.3
device = torch.device(... | code |
104124423/cell_36 | [
"image_output_1.png"
] | from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
BATCH_SIZE = 16
LEARNING_RATE = 0.001
N_EPOCHS = 20
LAYER1_SIZE = 256
LAYER2_SIZE = 256
DROPOUT_RATE = 0.3
device = torch.device(... | code |
105195130/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/iris-dataset/iris.data.csv')
data.describe().T
X = data.iloc[:, [0, 1, 2, 3]].values
wcss = []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=200, n_init=10, ran... | code |
105195130/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/iris-dataset/iris.data.csv')
data.describe() | code |
105195130/cell_6 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/iris-dataset/iris.data.csv')
data.describe().T
data['Iris-setosa'].unique() | code |
105195130/cell_11 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/iris-dataset/iris.data.csv')
data.describe().T
X = data.iloc[:, [0, 1, 2, 3]].values
wcss = []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=200, n_init=10, ran... | code |
105195130/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/iris-dataset/iris.data.csv')
data.head() | code |
105195130/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/iris-dataset/iris.data.csv')
data.describe().T | code |
328019/cell_4 | [
"text_plain_output_1.png"
] | import hashlib
import os
import pandas as pd
import numpy as np
import pandas as pd
import os
import hashlib
records = []
for name in os.listdir('../input/train/'):
if 'mask' in name or not name.endswith('.tif'):
continue
patient_id, image_id = name.strip('.tif').split('_')
with open('../input/tr... | code |
328019/cell_3 | [
"text_plain_output_1.png"
] | import hashlib
import os
import pandas as pd
import numpy as np
import pandas as pd
import os
import hashlib
records = []
for name in os.listdir('../input/train/'):
if 'mask' in name or not name.endswith('.tif'):
continue
patient_id, image_id = name.strip('.tif').split('_')
with open('../input/tr... | code |
328019/cell_5 | [
"text_plain_output_1.png"
] | import hashlib
import os
import pandas as pd
import numpy as np
import pandas as pd
import os
import hashlib
records = []
for name in os.listdir('../input/train/'):
if 'mask' in name or not name.endswith('.tif'):
continue
patient_id, image_id = name.strip('.tif').split('_')
with open('../input/tr... | code |
17096995/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import datetime
import glob
import math
import numpy as np
import pandas as pd
station = 68241
def Datastations(station, path):
allFiles = glob.glob(path + '/*.csv')
list_ = []
array = ['T', 'MinT', 'MaxT', 'Precip', 'AIR_TEMP', 'AIR_TEMP_MIN', 'AIR_TEMP_MAX', 'PRCP']
for file_ in allFiles:
... | code |
17096995/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import glob
import numpy as np
import pandas as pd
station = 68241
def Datastations(station, path):
allFiles = glob.glob(path + '/*.csv')
list_ = []
array = ['T', 'MinT', 'MaxT', 'Precip', 'AIR_TEMP', 'AIR_TEMP_MIN', 'AIR_TEMP_MAX', 'PRCP']
for file_ in allFiles:
df = pd.read_csv(file_, index... | code |
17096995/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import datetime
import glob
import math
import numpy as np
import pandas as pd
station = 68241
def Datastations(station, path):
allFiles = glob.glob(path + '/*.csv')
list_ = []
array = ['T', 'MinT', 'MaxT', 'Precip', 'AIR_TEMP', 'AIR_TEMP_MIN', 'AIR_TEMP_MAX', 'PRCP']
for file_ in allFiles:
... | code |
17096995/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import glob
import pandas as pd
station = 68241
def Datastations(station, path):
allFiles = glob.glob(path + '/*.csv')
list_ = []
array = ['T', 'MinT', 'MaxT', 'Precip', 'AIR_TEMP', 'AIR_TEMP_MIN', 'AIR_TEMP_MAX', 'PRCP']
for file_ in allFiles:
df = pd.read_csv(file_, index_col=None, header=0)... | code |
17096995/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import glob
import pandas as pd
station = 68241
def Datastations(station, path):
allFiles = glob.glob(path + '/*.csv')
list_ = []
array = ['T', 'MinT', 'MaxT', 'Precip', 'AIR_TEMP', 'AIR_TEMP_MIN', 'AIR_TEMP_MAX', 'PRCP']
for file_ in allFiles:
df = pd.read_csv(file_, index_col=None, header=0)... | code |
34148405/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train_dataset = pd.read_csv('../input/titanic/train.csv')
df_test_dataset = pd.read_csv('../input/titanic/test.csv')
df_gender_submission = pd.read_csv('../input/titanic/gender_submission.csv')
df_complete_data = pd.concat(... | code |
34148405/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train_dataset = pd.read_csv('../input/titanic/train.csv')
df_test_dataset = pd.read_csv('../input/titanic/test.csv')
df_gender_submission = pd.read_csv('../input/titanic/gender_submission.csv')
df_complete_data = pd.concat(... | code |
34148405/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train_dataset = pd.read_csv('../input/titanic/train.csv')
df_test_dataset = pd.read_csv('../input/titanic/test.csv')
df_gender_submission = pd.read_csv('../input/titanic/gender_submission.csv')
df_train_dataset.info() | code |
34148405/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train_dataset = pd.read_csv('../input/titanic/train.csv')
df_test_dataset = pd.read_csv('../input/titanic/test.csv')
df_gender_submission = pd.read_csv('../input/titanic/gender_submission.csv')
df_complete_data = pd.concat(... | code |
34148405/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train_dataset = pd.read_csv('../input/titanic/train.csv')
df_test_dataset = pd.read_csv('../input/titanic/test.csv')
df_gender_submission = pd.read_csv('../input/titanic/gender_submission.csv')
df_complete_data = pd.concat(... | code |
34148405/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train_dataset = pd.read_csv('../input/titanic/train.csv')
df_test_dataset = pd.read_csv('../input/titanic/test.csv')
df_gender_submission = pd.read_csv('../input/titanic/gender_submission.csv')
df_complete_data = pd.concat(... | code |
34148405/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 |
34148405/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train_dataset = pd.read_csv('../input/titanic/train.csv')
df_test_dataset = pd.read_csv('../input/titanic/test.csv')
df_gender_submission = pd.read_csv('../input/titanic/gender_submission.csv')
df_complete_data = pd.concat(... | code |
34148405/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train_dataset = pd.read_csv('../input/titanic/train.csv')
df_test_dataset = pd.read_csv('../input/titanic/test.csv')
df_gender_submission = pd.read_csv('../input/titanic/gender_submission.csv')
df_complete_data = pd.concat(... | code |
34148405/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train_dataset = pd.read_csv('../input/titanic/train.csv')
df_test_dataset = pd.read_csv('../input/titanic/test.csv')
df_gender_submission = pd.read_csv('../input/titanic/gender_submission.csv')
df_complete_data = pd.concat(... | code |
34148405/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train_dataset = pd.read_csv('../input/titanic/train.csv')
df_test_dataset = pd.read_csv('../input/titanic/test.csv')
df_gender_submission = pd.read_csv('../input/titanic/gender_submission.csv')
df_complete_data = pd.concat(... | code |
34148405/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train_dataset = pd.read_csv('../input/titanic/train.csv')
df_test_dataset = pd.read_csv('../input/titanic/test.csv')
df_gender_submission = pd.read_csv('../input/titanic/gender_submission.csv')
df_complete_data = pd.concat(... | code |
34148405/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train_dataset = pd.read_csv('../input/titanic/train.csv')
df_test_dataset = pd.read_csv('../input/titanic/test.csv')
df_gender_submission = pd.read_csv('../input/titanic/gender_submission.csv')
df_complete_data = pd.concat(... | code |
34148405/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train_dataset = pd.read_csv('../input/titanic/train.csv')
df_test_dataset = pd.read_csv('../input/titanic/test.csv')
df_gender_submission = pd.read_csv('../input/titanic/gender_submission.csv')
df_complete_data = pd.concat(... | code |
50227807/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import matplotlib.image as mpimg
import numpy as np
import os
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
IMAGE_WIDTH = 128
IMAGE_HEIGHT = 12... | code |
50227807/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
print(os.listdir('../input/dogs-vs-cats')) | code |
50227807/cell_8 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import random
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
IMAGE_W... | code |
50227807/cell_15 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.image as mpimg
import numpy as np
import os
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
IMAGE_WIDTH = 128
IMAGE_HEIGHT = 12... | code |
50227807/cell_17 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.image as mpimg
import numpy as np
import os
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
IMAGE_WIDTH = 128
IMAGE_HEIGHT = 12... | code |
50227807/cell_10 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.image as mpimg
import numpy as np
import os
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
IMAGE_WIDTH = 128
IMAGE_HEIGHT = 12... | code |
50227807/cell_5 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
filenames = os.listdir('/kaggle/working/train')
filenames[:5] | code |
128027940/cell_6 | [
"text_plain_output_1.png"
] | !python -m spacy train /kaggle/working/config.cfg --output ./spacy_output --paths.train /kaggle/input/ir-silver-data/dev_data.spacy --paths.dev /kaggle/input/ir-silver-data/test_data.spacy --gpu-id 0 | code |
128027940/cell_1 | [
"text_plain_output_1.png"
] | !pip install spacy==3.4.4 | code |
128027940/cell_10 | [
"text_plain_output_1.png"
] | from spacy.tokens import DocBin
import spacy
train_bin = DocBin().from_disk('/kaggle/input/ir-silver-data/dev_data.spacy')
nlp = spacy.load('/kaggle/input/scispacy-model/en_core_sci_sm')
docs = train_bin.get_docs(nlp.vocab)
for doc in docs:
for ent in doc.ents:
print(ent, ent.label_)
break | code |
128027940/cell_5 | [
"text_plain_output_1.png"
] | # Run in case of error
! python -m spacy debug data /kaggle/working/config.cfg --paths.train /kaggle/input/ir-silver-data/train_data.spacy --paths.dev /kaggle/input/ir-silver-data/dev_data.spacy | code |
33109829/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
calendar_df = pd.read_csv('../input/m5-forecasting-accuracy/calendar.csv', parse_dates=['date'], usecols=['date', 'd'])
calendar_stv = calendar_df[:1913]
sales_train_validation = pd.read_csv('../input/m5-forecastin... | code |
33109829/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
calendar_df = pd.read_csv('../input/m5-forecasting-accuracy/calendar.csv', parse_dates=['date'], usecols=['date', 'd'])
calendar_stv = calendar_df[:1913]
sales_train_validation = pd.read_csv('../input/m5-forecastin... | code |
33109829/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
calendar_df = pd.read_csv('../input/m5-forecasting-accuracy/calendar.csv', parse_dates=['date'], usecols=['date', 'd'])
calendar_stv = calendar_df[:1913]
sales_train_validation = pd.read_csv('../input/m5-forecastin... | code |
33109829/cell_19 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
calendar_df = pd.read_csv('../input/m5-forecasting-accuracy/calendar.csv', parse_dates=['date'], usecols=['date', 'd'])
calendar_stv = calendar_df[:1913]
sales_train_validation = pd.read_csv('../input/m5-forecastin... | code |
33109829/cell_1 | [
"text_plain_output_1.png"
] | 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))
import matplotlib.pyplot as plt | code |
33109829/cell_7 | [
"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)
calendar_df = pd.read_csv('../input/m5-forecasting-accuracy/calendar.csv', parse_dates=['date'], usecols=['date', 'd'])
calendar_stv = calendar_df[:1913]
sales_train_validation = pd.read_csv('../input/m5-forecastin... | code |
33109829/cell_18 | [
"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)
calendar_df = pd.read_csv('../input/m5-forecasting-accuracy/calendar.csv', parse_dates=['date'], usecols=['date', 'd'])
calendar_stv = calendar_df[:1913]
sales_train_validation = pd.read_csv('../input/m5-forecastin... | code |
33109829/cell_28 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
calendar_df = pd.read_csv('../input/m5-forecasting-accuracy/calendar.csv', parse_dates=['date'], usecols=['date', 'd'])
calendar_stv = calendar_df[:1913]
sales_train_validation = pd.read_csv('../input/m5-forecastin... | code |
33109829/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
calendar_df = pd.read_csv('../input/m5-forecasting-accuracy/calendar.csv', parse_dates=['date'], usecols=['date', 'd'])
calendar_stv = calendar_df[:1913]
calendar_stv.info() | code |
33109829/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
calendar_df = pd.read_csv('../input/m5-forecasting-accuracy/calendar.csv', parse_dates=['date'], usecols=['date', 'd'])
calendar_stv = calendar_df[:1913]
sales_train_validation = pd.read_csv('../input/m5-forecastin... | code |
33109829/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
calendar_df = pd.read_csv('../input/m5-forecasting-accuracy/calendar.csv', parse_dates=['date'], usecols=['date', 'd'])
calendar_stv = calendar_df[:1913]
sales_train_validation = pd.read_csv('../input/m5-forecastin... | code |
33109829/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)
calendar_df = pd.read_csv('../input/m5-forecasting-accuracy/calendar.csv', parse_dates=['date'], usecols=['date', 'd'])
calendar_stv = calendar_df[:1913]
sales_train_validation = pd.read_csv('../input/m5-forecastin... | code |
33109829/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
calendar_df = pd.read_csv('../input/m5-forecasting-accuracy/calendar.csv', parse_dates=['date'], usecols=['date', 'd'])
calendar_stv = calendar_df[:1913]
sales_train_validation = pd.read_csv('../input/m5-forecastin... | code |
33109829/cell_12 | [
"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)
calendar_df = pd.read_csv('../input/m5-forecasting-accuracy/calendar.csv', parse_dates=['date'], usecols=['date', 'd'])
calendar_stv = calendar_df[:1913]
sales_train_validation = pd.read_csv('../input/m5-forecastin... | code |
33109829/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
calendar_df = pd.read_csv('../input/m5-forecasting-accuracy/calendar.csv', parse_dates=['date'], usecols=['date', 'd'])
calendar_stv = calendar_df[:1913]
sales_train_validation = pd.read_csv('../input/m5-forecasting-accuracy/sales_train_validation... | code |
2042802/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
hr_attrition = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv', header=0)
hr_attrition.columns.values
###########################################################################################
# Define a comm... | code |
2042802/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hr_attrition = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv', header=0)
hr_attrition.columns.values
###########################################################################################
# Define a common module for drawin... | code |
2042802/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
hr_attrition = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv', header=0)
hr_attrition.columns.values
###########################################################################################
# Define a comm... | code |
2042802/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hr_attrition = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv', header=0)
hr_attrition.columns.values
###########################################################################################
# Define a common module for drawin... | code |
2042802/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hr_attrition = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv', header=0)
hr_attrition.columns.values
def draw_subplots(var_Name, tittle_Name, nrow=1, ncol=1, idx=1, fz=10):
ax = plt.subplot(nrow, ncol, idx)
ax.set_title(... | code |
2042802/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
hr_attrition = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv', header=0)
hr_attrition.columns.values
###########################################################################################
# Define a comm... | code |
2042802/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
hr_attrition = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv', header=0)
hr_attrition.columns.values
###########################################################################################
# Define a comm... | code |
2042802/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
hr_attrition = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv', header=0)
hr_attrition.columns.values
###########################################################################################
# Define a comm... | code |
2042802/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
hr_attrition = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv', header=0)
hr_attrition.columns.values | code |
2042802/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
hr_attrition = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv', header=0)
hr_attrition.columns.values
###########################################################################################
# Define a comm... | code |
2042802/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
hr_attrition = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv', header=0)
hr_attrition.columns.values
###########################################################################################
# Define a comm... | code |
2042802/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
hr_attrition = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv', header=0)
hr_attrition.columns.values
hr_attrition.info() | code |
74067375/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
cars = {'Brand': ['Honda Civic', 'Toyota Corolla', 'Ford Focus', 'Audi A4'], 'Price': [22000, 25000, 27000, 35000], 'Year': [2015, 2013, 2018, 2018]}
df = pd.DataFrame(cars, columns=['Brand', 'Price', 'Year'])
df.sort_values(by=['Brand'], inplace=True)
df.head() | code |
74067375/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd
cars = {'Brand': ['Honda Civic', 'Toyota Corolla', 'Ford Focus', 'Audi A4'], 'Price': [22000, 25000, 27000, 35000], 'Year': [2015, 2013, 2018, 2018]}
df = pd.DataFrame(cars, columns=['Brand', 'Price', 'Year'])
df.sort_values(by=['Brand'], inplace=True)
imp... | code |
74067375/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd
cars = {'Brand': ['Honda Civic', 'Toyota Corolla', 'Ford Focus', 'Audi A4'], 'Price': [22000, 25000, 27000, 35000], 'Year': [2015, 2013, 2018, 2018]}
df = pd.DataFrame(cars, columns=['Brand', 'Price', 'Year'])
df.sort_values(by=['Brand'], inplace=True)
imp... | code |
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