path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
88087713/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88087713/cell_8 | [
"image_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88087713/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88087713/cell_17 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88087713/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88087713/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88087713/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88087713/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88087713/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88087713/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88087713/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
16154469/cell_9 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import matplotlib.pylab as plt
import networkx as nx
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
structures_df = pd.read_csv('../input/structures.csv')
test_df['scalar_coupling_constant'] = np.nan
df = pd.concat([train_df, test_df])
... | code |
130014083/cell_42 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud
import matplotlib as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = ... | code |
130014083/cell_21 | [
"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 = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean... | code |
130014083/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 = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
filtered_df = df[df['Delay Minutes'] > 60]
filtered_df.head() | code |
130014083/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price'... | code |
130014083/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupb... | code |
130014083/cell_23 | [
"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 = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean... | code |
130014083/cell_30 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupb... | code |
130014083/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupb... | code |
130014083/cell_44 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Custo... | code |
130014083/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean... | code |
130014083/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
df.head() | code |
130014083/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from wordcloud import WordCloud
import matplotlib as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = ... | code |
130014083/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupb... | code |
130014083/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupb... | code |
130014083/cell_26 | [
"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 = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean... | code |
130014083/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from wordcloud import WordCloud
import matplotlib as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = ... | code |
130014083/cell_19 | [
"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 = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean... | code |
130014083/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 |
130014083/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
df.info() | code |
130014083/cell_18 | [
"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 = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean... | code |
130014083/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupb... | code |
130014083/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupb... | code |
130014083/cell_16 | [
"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 = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
sorted_df.head() | code |
130014083/cell_17 | [
"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 = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean... | code |
130014083/cell_35 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupb... | code |
130014083/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupb... | code |
130014083/cell_24 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price'... | code |
130014083/cell_14 | [
"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 = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
filtered_df = df[df['Delay Minutes'] > 60]
filtered_df = df[df['Churned'] == False]
filtered_df.head() | code |
130014083/cell_22 | [
"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 = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean... | code |
130014083/cell_10 | [
"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 = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
df.head() | code |
130014083/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupb... | code |
130014083/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import missingno as msno
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()... | code |
130014083/cell_12 | [
"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 = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
selected_columns = ['Departure City', 'Arrival City', 'Flight Duration', 'Delay Minutes', 'Booking Class']
df_selected = df[selected_columns]
df_selected | code |
130014083/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupb... | code |
72118922/cell_13 | [
"text_html_output_1.png"
] | from keras.models import Sequential
from tensorflow.keras.layers import Dense,Flatten,Dropout,Conv2D,MaxPooling2D,BatchNormalization
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.utils import to_categorical
from tqdm.... | code |
72118922/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
labels = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train_labels.csv')
labels
sample = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/sample_submission.csv')
sample | code |
72118922/cell_6 | [
"text_html_output_1.png"
] | import cv2
import numpy as np
import pydicom
def load_dicom(path):
dicom = pydicom.read_file(path)
data = dicom.pixel_array
data = data - np.min(data)
if np.max(data) != 0:
data = data / np.max(data)
data = (data * 255).astype(np.uint8)
return data
path0 = '../input/rsna-miccai-brain... | code |
72118922/cell_7 | [
"text_html_output_1.png"
] | from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tqdm.notebook import tqdm
import cv2
import numpy as np
import os
import pandas as pd
import pydicom
train_name = os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train')
test_name = os.listdir('../input/rsna-mic... | code |
72118922/cell_18 | [
"text_plain_output_1.png"
] | from keras.models import Sequential
from tensorflow.keras.layers import Dense,Flatten,Dropout,Conv2D,MaxPooling2D,BatchNormalization
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.utils import to_categorical
from tqdm.... | code |
72118922/cell_8 | [
"text_html_output_1.png"
] | from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tqdm.notebook import tqdm
import cv2
import numpy as np
import os
import pandas as pd
import pydicom
train_name = os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train')
test_name = os.listdir('../input/rsna-mic... | code |
72118922/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
labels = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train_labels.csv')
labels | code |
72118922/cell_17 | [
"text_plain_output_1.png"
] | from keras.models import Sequential
from tensorflow.keras.layers import Dense,Flatten,Dropout,Conv2D,MaxPooling2D,BatchNormalization
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.utils import to_categorical
from tqdm.... | code |
72118922/cell_14 | [
"text_plain_output_1.png"
] | from keras.models import Sequential
from tensorflow.keras.layers import Dense,Flatten,Dropout,Conv2D,MaxPooling2D,BatchNormalization
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.utils import to_categorical
from tqdm.... | code |
72118922/cell_12 | [
"text_html_output_1.png"
] | from keras.models import Sequential
from tensorflow.keras.layers import Dense,Flatten,Dropout,Conv2D,MaxPooling2D,BatchNormalization
model = Sequential()
model.add(Conv2D(64, (4, 4), input_shape=(9, 9, 1), activation='relu'))
model.add(Conv2D(32, (2, 2), activation='relu'))
model.add(Dropout(0.2))
model.add(Flatten()... | code |
90103606/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
transactions = pd.read_csv(... | code |
90103606/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
customers.head() | code |
90103606/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')... | code |
90103606/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
transactions = pd.read_csv(... | code |
90103606/cell_30 | [
"text_html_output_2.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
fig = px.pie(articles, values='article_id',
title='Distribution by Index Group Name',... | code |
90103606/cell_6 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import os
from tqdm import tqdm
import matplotlib.pyplot as plt
import seaborn as sns
from tabulate import tabulate
import cufflinks as cf
import plotly.express as px
import plotly.graph_objects as go | code |
90103606/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)
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
articles.head() | code |
90103606/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)
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
transactions = pd.read_csv(... | code |
90103606/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 |
90103606/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')... | code |
90103606/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
fig = px.pie(articles, values='article_id',
title='Distribution by Index Group Name',... | code |
90103606/cell_8 | [
"text_html_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
l = os.listdir('/kaggle/input/h-and-m-personalized-fashion-recommendations/')
print(f'Folders: {l}') | code |
90103606/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
transactions = pd.read_csv(... | code |
90103606/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')... | code |
90103606/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)
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
transactions = pd.read_csv(... | code |
90103606/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
articles.info() | code |
90103606/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
fig = px.pie(articles, values='article_id', title='Distribution by Index Group Name', names='index_... | code |
90103606/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
customers.info() | code |
128009329/cell_9 | [
"text_plain_output_1.png"
] | df0 = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv', nrows=reads[0], dtype=dtyping, low_memory=True)
mem_usage = df0.memory_usage().sum() / 1024 ** 2
print(f'Memory Usage : {mem_usage} MB') | code |
128009329/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np # Import library NumPy (Kalkulasi dsb)
import pandas as pd # Import library Pandas (Baca data)
tmp = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv', usecols=[0], low_memory=True)
tmp = tmp.groupby('session_id').session_id.agg('count')
labels = pd.read_csv('/kaggle... | code |
128009329/cell_6 | [
"text_html_output_1.png"
] | import gc
import gc
gc.enable()
gc.collect() | code |
128009329/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # Import library Pandas (Baca data)
tmp = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv', usecols=[0], low_memory=True)
tmp = tmp.groupby('session_id').session_id.agg('count')
display(tmp) | code |
128009329/cell_11 | [
"text_plain_output_1.png"
] | df0 | code |
128009329/cell_19 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.model_selection import GroupKFold
import gc
import joblib
import numpy as np # Import library NumPy (Kalkulasi dsb)
import pandas as pd # Import library Pandas (Baca data)
tmp = pd.read_csv('/kaggle/input/predic... | code |
128009329/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np # Import library NumPy (Kalkulasi dsb)
import pandas as pd # Import library Pandas (Baca data)
tmp = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv', usecols=[0], low_memory=True)
tmp = tmp.groupby('session_id').session_id.agg('count')
labels = pd.read_csv('/kaggle... | code |
128009329/cell_18 | [
"text_plain_output_1.png"
] | FEATURES = [c for c in train_df.columns if not c in ['level_group']]
ALL_USERS = train_df.index.unique()
train_level_df = train_df.pivot_table(columns='level_group', values=[x for x in train_df.columns if not x == 'level_group'], index='session_id', aggfunc='sum', fill_value=0)
train_level_df.isna().sum() | code |
128009329/cell_15 | [
"text_plain_output_1.png"
] | FEATURES = [c for c in train_df.columns if not c in ['level_group']]
print('We will train with', len(FEATURES), 'features')
ALL_USERS = train_df.index.unique()
print('We will train with', len(ALL_USERS), 'users info') | code |
128009329/cell_16 | [
"text_plain_output_1.png"
] | FEATURES = [c for c in train_df.columns if not c in ['level_group']]
ALL_USERS = train_df.index.unique()
train_df | code |
128009329/cell_3 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np # Import library NumPy (Kalkulasi dsb)
import pandas as pd # Import library Pandas (Baca data)
tmp = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv', usecols=[0], low_memory=True)
tmp = tmp.groupby('session_id').session_id.agg('count')
labels = pd.read_csv('/kaggle... | code |
128009329/cell_17 | [
"text_plain_output_1.png"
] | FEATURES = [c for c in train_df.columns if not c in ['level_group']]
ALL_USERS = train_df.index.unique()
train_level_df = train_df.pivot_table(columns='level_group', values=[x for x in train_df.columns if not x == 'level_group'], index='session_id', aggfunc='sum', fill_value=0)
display(train_level_df) | code |
128009329/cell_14 | [
"text_plain_output_1.png"
] | all_pieces = []
for k in range(ITER):
print(k, ',', end=' ')
SKIPS = 0
if k > 0:
SKIPS = range(1, skips[k] + 1)
train = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv', nrows=reads[k], skiprows=SKIPS, dtype=dtyping, low_memory=True)
df = process_level(train)
... | code |
128009329/cell_12 | [
"text_html_output_1.png"
] | df0.groupby(['session_id', 'level'])['elapsed_time'].sum() | code |
128009329/cell_5 | [
"text_html_output_1.png"
] | import numpy as np # Import library NumPy (Kalkulasi dsb)
import pandas as pd # Import library Pandas (Baca data)
tmp = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv', usecols=[0], low_memory=True)
tmp = tmp.groupby('session_id').session_id.agg('count')
labels = pd.read_csv('/kaggle... | code |
1005554/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/epi_r.csv')
sorted(list(df.columns))
df.describe() | code |
1005554/cell_6 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/epi_r.csv')
sorted(list(df.columns))
col_names = df.columns
bool_cols = []
nonbool_cols = []
for c in col_names:
if len(df[c].unique()) == 2:
bool_cols.append(c)
df[c] = df[c].astype('bool')
else:
nonbool_cols.append(c... | code |
1005554/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/epi_r.csv')
df.head() | code |
1005554/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pylab as pylab
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/epi_r.csv')
sorted(list(df.columns))
col_names = df.columns
bool_cols = []
nonbool_cols = []
for c in col_names:
if len(df[c].unique()) == 2:
bool_cols.append(c)
df[c] = df[... | code |
1005554/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/epi_r.csv')
sorted(list(df.columns)) | code |
1005554/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/epi_r.csv')
sorted(list(df.columns))
col_names = df.columns
bool_cols = []
nonbool_cols = []
for c in col_names:
if len(df[c].unique()) == 2:
bool_cols.append(c)
df[c] = df[c].astype('bool')
else:
nonbool_cols.append(c)
print('{} non-bool... | code |
49120031/cell_2 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
df = pd.read_csv('/kaggle/input/cassava-leaf-disease-classification/train.csv')
[os.mkdir(os.path.join('/kaggle/working', 'label_' + str(x))) for x in df.label.unique()] | code |
49120031/cell_7 | [
"text_plain_output_1.png"
] | from keras_preprocessing.image import ImageDataGenerator
import os
import tensorflow as tf
from keras_preprocessing.image import ImageDataGenerator
from tensorflow import keras
from tensorflow.keras import layers
train_data_dir = '/kaggle/working/'
img_height = 300
img_width = 300
batch_size = 64
num_classes = 5
train... | code |
49120031/cell_3 | [
"text_plain_output_1.png"
] | from shutil import copyfile
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
df = pd.read_csv('/kaggle/input/cassava-leaf-disease-classification/train.csv')
[os.mkdir(os.path.join('/kaggle/working', 'label_' + str(x))) for x in df.label.unique()]
df = pd.read_csv('/kaggle/i... | code |
88100444/cell_4 | [
"text_html_output_1.png"
] | from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.r... | code |
88100444/cell_6 | [
"text_plain_output_1.png"
] | from matplotlib.pyplot import figure
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.