path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
17111876/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
plt.imshow(train_x[10][:, :, 0]) | code |
17111876/cell_41 | [
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix
from tensorflow import keras
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import tensorflow as tf
train_imgs = pd.read_csv('../input/train.csv')
test_im... | code |
17111876/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_imgs = pd.read_csv('../input/train.csv')
test_imgs = pd.read_csv('../input/test.csv')
img_train = train_imgs.drop(labels='label', axis=1)
del train_imgs
img_train.max().max() | code |
17111876/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from tensorflow import keras
import tensorflow as tf
import os
print(os.listdir('../input')) | code |
17111876/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow import keras
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
train_imgs = pd.read_csv('../input/train.csv')
test_imgs = pd.read_csv('../input/test.csv')
label_train = train_imgs['label']
label_train = keras.utils.to_categorical(label_train, num_classes... | code |
17111876/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_imgs = pd.read_csv('../input/train.csv')
test_imgs = pd.read_csv('../input/test.csv')
label_train = train_imgs['label']
sns.countplot(label_train) | code |
17111876/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_imgs = pd.read_csv('../input/train.csv')
test_imgs = pd.read_csv('../input/test.csv')
img_train = train_imgs.drop(labels='label', axis=1)
del train_imgs
img_train.max().max()
test_imgs.max().max()
img_train = img_train / 255.0
test_imgs =... | code |
17111876/cell_38 | [
"text_plain_output_1.png"
] | from tensorflow import keras
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import tensorflow as tf
train_imgs = pd.read_csv('../input/train.csv')
test_imgs = pd.read_csv('../input/test.csv')
label_train = train_imgs['label']
label_tra... | code |
17111876/cell_47 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_imgs = pd.read_csv('../input/train.csv')
test_imgs = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
sub.head() | code |
17111876/cell_22 | [
"text_plain_output_1.png"
] | from tensorflow import keras
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_imgs = pd.read_csv('../input/train.csv')
test_imgs = pd.read_csv('../input/test.csv')
label_train = train_imgs['label']
label_train = keras.utils.to_categorical(label_train, num_classes=10)
label_train[0] | code |
17111876/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_imgs = pd.read_csv('../input/train.csv')
test_imgs = pd.read_csv('../input/test.csv')
test_imgs.max().max() | code |
17111876/cell_36 | [
"text_plain_output_1.png"
] | from tensorflow import keras
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
train_imgs = pd.read_csv('../input/train.csv')
test_imgs = pd.read_csv('../input/test.csv')
label_train = train_imgs['label']
label_train = keras.utils.to_categorical(label_train, num_classes... | code |
32068525/cell_11 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
shops_data = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sales_train = pd.read_cs... | code |
32068525/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
l = []
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
l.append(os.path.join(dirname, filename))
l | code |
32068525/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
shops_data = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sales_train = pd.read_cs... | code |
32068525/cell_10 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
shops_data = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sales_train = pd.read_cs... | code |
34139025/cell_42 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_63 | [
"text_plain_output_1.png"
] | from googlesearch import search
from googlesearch import search
from googlesearch import search
from googlesearch import search
try:
from googlesearch import search
except ImportError:
print('Error/Not found')
query4 = 'recommendations for PPE problems'
for j4 in search(query4, tld='co.in', num=10, stop=10,... | code |
34139025/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_13 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_56 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_44 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_48 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_61 | [
"text_html_output_1.png"
] | from googlesearch import search
from googlesearch import search
try:
from googlesearch import search
except ImportError:
print('Error/Not found')
query2 = 'recommendations for COVID 19 resources limits'
for j2 in search(query2, tld='co.in', num=10, stop=10, pause=2):
print(j2) | code |
34139025/cell_54 | [
"text_plain_output_1.png"
] | from googlesearch import search
try:
from googlesearch import search
except ImportError:
print('Error/Not found')
query = 'COVID 19 population studies'
for j in search(query, tld='co.in', num=10, stop=10, pause=2):
print(j) | code |
34139025/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_52 | [
"text_plain_output_1.png"
] | pip install beautifulsoup4 | code |
34139025/cell_64 | [
"text_plain_output_1.png"
] | from googlesearch import search
from googlesearch import search
from googlesearch import search
from googlesearch import search
from googlesearch import search
try:
from googlesearch import search
except ImportError:
print('Error/Not found')
query5 = 'recommendations for improving access to COVID 19 resour... | code |
34139025/cell_45 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_62 | [
"text_plain_output_1.png"
] | from googlesearch import search
from googlesearch import search
from googlesearch import search
try:
from googlesearch import search
except ImportError:
print('Error/Not found')
query3 = 'recommendations for COVID 19 testing problems'
for j3 in search(query3, tld='co.in', num=10, stop=10, pause=2):
print... | code |
34139025/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
tablesTable = t[['Question', 'Table Format']]
tablesTable | code |
34139025/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_38 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_47 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_66 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_46 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_53 | [
"text_plain_output_1.png"
] | pip install google | code |
34139025/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape | code |
34139025/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_12 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
34139025/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape
journals = df1[['title', 'abstract',... | code |
130008103/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
lung_df = pd.read_csv('/kaggle/input/lung-cancer/survey lung cancer.csv')
lung_df = lung_df.dropna(how='any')
lung_df['LUNG_CANCER'] = lung_df['LUNG_CANCER'].map({'NO': 0, 'YES': 1})
sns.bar... | code |
130008103/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
lung_df = pd.read_csv('/kaggle/input/lung-cancer/survey lung cancer.csv')
lung_df = lung_df.dropna(how='any')
sns.countplot(data=lung_df, x='GENDER')
plt.xlabel('Gender')
plt.ylabel('Count')... | code |
130008103/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.... | code |
130008103/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lung_df = pd.read_csv('/kaggle/input/lung-cancer/survey lung cancer.csv')
lung_df.describe()
lung_df.info() | code |
130008103/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
lung_df = pd.read_csv('/kaggle/input/lung-cancer/survey lung cancer.csv')
lung_df = lung_df.dropna(how='any')
lung_df['LUNG_CANCER'] = lung_df['LUNG_CANCER'].map({'NO': 0, 'YES': 1})
sns.sc... | code |
130008103/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lung_df = pd.read_csv('/kaggle/input/lung-cancer/survey lung cancer.csv')
lung_df.head(5) | code |
73096402/cell_13 | [
"text_plain_output_1.png"
] | from itertools import product
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/tmnist-typeface-mnist/TMNIST_data.csv')
unique_typefaces = df.names.unique()
typeface_map = {}
for i in range(len(unique_typefaces)):
typeface_... | code |
73096402/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/tmnist-typeface-mnist/TMNIST_data.csv')
print(df.shape)
df.head() | code |
73096402/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/tmnist-typeface-mnist/TMNIST_data.csv')
unique_typefaces = df.names.unique()
typeface_map = {}
for i in range(len(unique_typefaces)):
typeface_map[unique_typefaces[i]] = i
df = df.replace({'names': typeface_map}... | code |
73096402/cell_11 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/tmnist-typeface-mnist/TMNIST_data.csv')
unique_typefaces = df.names.unique()
typeface_map = {}
for i in range(len(unique_typefaces)):
typeface_map[unique_typefaces[i]] = i
df... | code |
73096402/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from itertools import product
from tqdm import tqdm
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
73096402/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/tmnist-typeface-mnist/TMNIST_data.csv')
unique_typefaces = df.names.unique()
typeface_map = {}
for i in range(len(unique_typefaces)):
typeface_map[unique_typefaces[i]] = i
df = df.replace({'names': typeface_map}... | code |
73096402/cell_15 | [
"text_html_output_1.png"
] | from itertools import product
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/tmnist-typeface-mnist/TMNIST_data.csv')
unique_typefaces = df.names.unique()
typeface_map = {}
for i in range(len(unique_typefaces)):
typeface_... | code |
73096402/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/tmnist-typeface-mnist/TMNIST_data.csv')
unique_typefaces = df.names.unique()
typeface_map = {}
for i in range(len(unique_typefaces)):
typeface_map[unique_typefaces[i]] = i
df = df.replace({'names': typeface_map}... | code |
2010817/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.describe(include='all') | code |
2010817/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import tensorflow as tf | code |
2010817/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
train.head() | code |
2010817/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
2010817/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
train['Name'] = train.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
test['Name'] = test.Name.str.extract(' ([A-Za-z]+... | code |
2010817/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.describe(include='all') | code |
73081521/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import warnings
import warnings
warnings.simplefilter('ignore')
import numpy as np
import pandas as pd
from PIL import Image, ImageFilter, ImageOps
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm
import time
from contextlib import contextmanager
import gc
import os
impo... | code |
73081521/cell_20 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import warnings
import warnings
warnings.simplefilter('ignore')
import numpy as np
import pandas as pd
from PIL import Image, ImageFilter, ImageOps
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm
import ... | code |
73081521/cell_6 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
import warnings
import warnings
warnings.simplefilter('ignore')
import numpy as np
import pandas as pd
from PIL import Image, ImageFilter, ImageOps
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm
import time
from contextlib import contextmanager
import gc
import os
impo... | code |
73081521/cell_11 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import warnings
import warnings
warnings.simplefilter('ignore')
import numpy as np
import pandas as pd
from PIL import Image, ImageFilter, ImageOps
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm
import ... | code |
73081521/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import warnings
import warnings
warnings.simplefilter('ignore')
import numpy as np
import pandas as pd
from PIL import Image, ImageFilter, ImageOps
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm
import time
from contextlib import contextmanager
import gc
import os
impo... | code |
73081521/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import warnings
import warnings
warnings.simplefilter('ignore')
import numpy as np
import pandas as pd
from PIL import Image, ImageFilter, ImageOps
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm
import time
from contextlib import contextmanager
import gc
import os
impo... | code |
73081521/cell_17 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import warnings
import warnings
warnings.simplefilter('ignore')
import numpy as np
import pandas as pd
from PIL import Image, ImageFilter, ImageOps
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm
import ... | code |
73081521/cell_14 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import warnings
import warnings
warnings.simplefilter('ignore')
import numpy as np
import pandas as pd
from PIL import Image, ImageFilter, ImageOps
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm
import ... | code |
73081521/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import warnings
import warnings
warnings.simplefilter('ignore')
import numpy as np
import pandas as pd
from PIL import Image, ImageFilter, ImageOps
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm
import time
from contextlib import contextmanager
import gc
import os
impo... | code |
73081521/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings
import warnings
warnings.simplefilter('ignore')
import numpy as np
import pandas as pd
from PIL import Image, ImageFilter, ImageOps
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm
import time
from contextlib import contextmanager
import gc
import os
impo... | code |
325602/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
"""
Boiler-Plate/Feature-Engineering to get frame into a testable format
"""
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pass', 'Run', 'Sack']
df = df[df['PlayType'].isin(valid_pla... | code |
325602/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn import grid_search
from sklearn.preprocessing import LabelEncoder
df = pd.read_csv('../input/nflplaybyplay20... | code |
325602/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
"""
Boiler-Plate/Feature-Engineering to get frame into a testable format
"""
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pass', 'Run', 'Sack']
df = df[df['PlayType'].isin(valid_pla... | code |
32068016/cell_9 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express ... | code |
32068016/cell_6 | [
"text_html_output_2.png",
"text_html_output_3.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('mode.chained_assignment', None)
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train = pd.read_csv('/kaggle/input/covid19-global-... | code |
32068016/cell_11 | [
"text_html_output_1.png"
] | from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express ... | code |
32068016/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 |
32068016/cell_7 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('mode.chained_assignment', None)
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train = pd.read_csv('/kaggle/input/covid19-global-... | code |
32068016/cell_10 | [
"text_html_output_1.png"
] | from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express ... | code |
334762/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
334762/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cross_validation import train_test_split, cross_val_score
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, AdaBoostRegressor
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd... | code |
334762/cell_4 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text... | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
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