path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
17139134/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/IMDB-Movie-Data.csv')
data.rename({'Runtime (Minutes)': 'Duration', 'Revenue (Millions)': 'Revenue'}, axis='columns', inplace=True)
for col in data.columns:
nans = pd.value_counts(data[col].isnull())
Nans = data[pd.isnull(data).any(axis=1)]
print(Nans.head()) | code |
17139134/cell_5 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/IMDB-Movie-Data.csv')
print(data.columns)
print(50 * '-')
print(data.dtypes)
print(50 * '-')
print(data.shape) | code |
90108657/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf
config = {'SEED': 42, 'DEBUG': False, 'test_size': 0.1, 'img_size': 128, 'batch_size': 32, 'num_labels': 0, 'epochs': 10, 'device': 'GPU'}
debug_config = {}
def set_seed(SEED):
os.environ['PYTHONHASHSEED'] = str(SEED)
np.r... | code |
90108657/cell_9 | [
"text_plain_output_100.png",
"text_plain_output_84.png",
"text_plain_output_56.png",
"text_plain_output_137.png",
"text_plain_output_139.png",
"text_plain_output_35.png",
"text_plain_output_130.png",
"text_plain_output_117.png",
"text_plain_output_98.png",
"text_plain_output_43.png",
"text_plain... | import numpy as np
import os
import tensorflow as tf
config = {'SEED': 42, 'DEBUG': False, 'test_size': 0.1, 'img_size': 128, 'batch_size': 32, 'num_labels': 0, 'epochs': 10, 'device': 'GPU'}
debug_config = {}
def set_seed(SEED):
os.environ['PYTHONHASHSEED'] = str(SEED)
np.random.seed(SEED)
tf.random.se... | code |
90108657/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import tensorflow as tf
config = {'SEED': 42, 'DEBUG': False, 'test_size': 0.1, 'img_size': 128, 'batch_size': 32, 'num_labels': 0, 'epochs': 10, 'device': 'GPU'}
debug_config = {}
def set_seed(SEED):
os.environ['PYTHONHASHSEED'] = str(SEED)
np.random.seed(SEED)
tf.random.se... | code |
90108657/cell_6 | [
"text_plain_output_1.png"
] | import glob
import pandas as pd
config = {'SEED': 42, 'DEBUG': False, 'test_size': 0.1, 'img_size': 128, 'batch_size': 32, 'num_labels': 0, 'epochs': 10, 'device': 'GPU'}
debug_config = {}
df_train = pd.read_csv('../input/kaggle-pog-series-s01e02/train.csv')
df_test = pd.read_csv('../input/kaggle-pog-series-s01e02/t... | code |
90108657/cell_11 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf
config = {'SEED': 42, 'DEBUG': False, 'test_size': 0.1, 'img_size': 128, 'batch_size': 32, 'num_labels': 0, 'epochs': 10, 'device': 'GPU'}
debug_config = {}
def set_seed(SEED):
os.environ['PYTHONHASHSEED'] = str(SEED)
np.r... | code |
90108657/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf
config = {'SEED': 42, 'DEBUG': False, 'test_size': 0.1, 'img_size': 128, 'batch_size': 32, 'num_labels': 0, 'epochs': 10, 'device': 'GPU'}
debug_config = {}
def set_seed(SEED):
os.environ['PYTHONHASHSEED'] = str(SEED)
np.r... | code |
90108657/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf
config = {'SEED': 42, 'DEBUG': False, 'test_size': 0.1, 'img_size': 128, 'batch_size': 32, 'num_labels': 0, 'epochs': 10, 'device': 'GPU'}
debug_config = {}
def set_seed(SEED):
os.environ['PYTHONHASHSEED'] = str(SEED)
np.r... | code |
90108657/cell_17 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | !nvidia-smi | code |
74057413/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
AMR_data = pd.read_csv('../input/antibiotic-resistance-genes/Efaecium_AMRC.csv')
AMR_data.dtypes
print(AMR_data.shape)
print('*****************')
print(AMR_data.nunique())
print('*****************')
print(AMR_data[AMR_data['CRISPR_Cas'] == 1]['AM... | code |
74057413/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
AMR_data = pd.read_csv('../input/antibiotic-resistance-genes/Efaecium_AMRC.csv')
AMR_data.dtypes | code |
74057413/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
AMR_data = pd.read_csv('../input/antibiotic-resistance-genes/Efaecium_AMRC.csv')
AMR_data.dtypes
sns.displot(AMR_data, x='CRISPR_Cas') | code |
74057413/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
74057413/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
AMR_data = pd.read_csv('../input/antibiotic-resistance-genes/Efaecium_AMRC.csv')
AMR_data.dtypes
AMR_data.describe(include='all') | code |
74057413/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
AMR_data = pd.read_csv('../input/antibiotic-resistance-genes/Efaecium_AMRC.csv')
AMR_data.dtypes
sns.set(style='darkgrid')
sns.jointplot(y=AMR_data['CRISPR_Cas'], x=AMR_data['AMR']) | code |
74057413/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
AMR_data = pd.read_csv('../input/antibiotic-resistance-genes/Efaecium_AMRC.csv')
AMR_data.head(15) | code |
16153001/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | summarizeColumns(transactions) | code |
16153001/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | library(data.table)
library(mlr) | code |
16153001/cell_3 | [
"text_html_output_1.png"
] | head(image) | code |
129012879/cell_4 | [
"application_vnd.jupyter.stderr_output_7.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | !pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11/index.html | code |
129012879/cell_23 | [
"image_output_1.png"
] | from PIL import Image
from mmcv import Config
from mmseg.apis import inference_segmentor, init_segmentor, show_result_pyplot
from mmseg.apis import set_random_seed
from mmseg.apis import train_segmentor
from mmseg.datasets import build_dataset
from mmseg.datasets.builder import DATASETS
from mmseg.datasets.custo... | code |
129012879/cell_20 | [
"image_output_1.png"
] | from PIL import Image
from mmcv import Config
from mmseg.apis import set_random_seed
from mmseg.apis import train_segmentor
from mmseg.datasets import build_dataset
from mmseg.datasets.builder import DATASETS
from mmseg.datasets.custom import CustomDataset
from mmseg.models import build_segmentor
import matplot... | code |
129012879/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import mmseg
import mmseg
print(mmseg.__version__) | code |
129012879/cell_2 | [
"text_plain_output_1.png"
] | # Check nvcc version
!nvcc -V
# Check GCC version
!gcc --version | code |
129012879/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import mmcv
import mmcv
import matplotlib.pyplot as plt
img = mmcv.imread('nails/images/2C29D473-CCB4-458C-926B-99D0042161E6.jpg')
plt.figure(figsize=(8, 6))
plt.imshow(mmcv.bgr2rgb(img))
plt.show() | code |
129012879/cell_19 | [
"text_plain_output_1.png"
] | from PIL import Image
from mmcv import Config
from mmseg.apis import set_random_seed
import matplotlib.pyplot as plt
import mmcv
import numpy as np
import os.path as osp
import mmcv
import matplotlib.pyplot as plt
img = mmcv.imread('nails/images/2C29D473-CCB4-458C-926B-99D0042161E6.jpg')
import os.path as osp
i... | code |
129012879/cell_1 | [
"text_plain_output_1.png"
] | from google.colab import drive
from google.colab import drive
drive.mount('/content/drive') | code |
129012879/cell_18 | [
"text_plain_output_1.png"
] | from mmcv import Config
from mmcv import Config
cfg = Config.fromfile('configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py ')
print(cfg.dataset_type) | code |
129012879/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | !mkdir checkpoints
# !wget https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth -P checkpoints
!wget https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscape... | code |
129012879/cell_15 | [
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import mmcv
import numpy as np
import os.path as osp
import mmcv
import matplotlib.pyplot as plt
img = mmcv.imread('nails/images/2C29D473-CCB4-458C-926B-99D0042161E6.jpg')
import os.path as osp
import numpy as np
from PIL... | code |
129012879/cell_3 | [
"text_plain_output_1.png"
] | import torch, torchvision
print(torch.__version__, torch.cuda.is_available())
print(torchvision.__version__) | code |
129012879/cell_22 | [
"text_plain_output_1.png"
] | from PIL import Image
from mmcv import Config
from mmseg.apis import inference_segmentor, init_segmentor, show_result_pyplot
from mmseg.apis import set_random_seed
from mmseg.apis import train_segmentor
from mmseg.datasets import build_dataset
from mmseg.datasets.builder import DATASETS
from mmseg.datasets.custo... | code |
129012879/cell_10 | [
"text_plain_output_1.png"
] | !unzip /content/drive/MyDrive/nails.zip -d /content/mmsegmentation/nails | code |
129012879/cell_12 | [
"text_plain_output_1.png"
] | # Install tree first
!apt-get -q install tree
!tree nails | code |
129012879/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | !rm -rf mmsegmentation
!git clone https://github.com/open-mmlab/mmsegmentation.git
!pip install -e . | code |
1004507/cell_4 | [
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing
import re # regular expressions
raw_data = pd.read_csv('../input/train.csv')
raw_data = shuffle(raw_data)
gender_dic = {'male': 0, 'female': 1}
raw_data.ix[:, 4] = raw_data.ix[:, 4].replace(gender_dic)
raw_data.fillna(0, inplace=True)
raw_data... | code |
1004507/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing
import re # regular expressions
raw_data = pd.read_csv('../input/train.csv')
raw_data = shuffle(raw_data)
gender_dic = {'male': 0, 'female': 1}
raw_data.ix[:, 4] = raw_data.ix[:, 4].replace(gender_di... | code |
1004507/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import learning_curve
import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing
import re # regular expressions
raw_data = pd.read_csv('../input/train.csv')
raw_data = shuffle(raw_data)
gender_dic = {'male': 0, 'female': 1}
raw... | code |
1004507/cell_10 | [
"text_html_output_1.png"
] | from sklearn.model_selection import learning_curve
import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing
import re # regular expressions
raw_data = pd.read_csv('../input/train.csv')
raw_data = shuffle(raw_data)
gender_dic = {'male': 0, 'female': 1}
raw... | code |
1008970/cell_6 | [
"image_output_1.png"
] | silly_thresh_value = 55
thresh_image = bone_image > silly_thresh_value
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
ax1.imshow(bone_image, cmap='bone')
ax1.set_title('Original Image')
ax2.imshow(thresh_image, cmap='jet')
ax2.set_title('Thresheld Image') | code |
1008970/cell_3 | [
"image_output_1.png"
] | from skimage.io import imread
bone_image = imread('../input/bone.tif')
print('Loading bone image shape: {}'.format(bone_image.shape)) | code |
1008970/cell_12 | [
"text_plain_output_1.png"
] | threshold_list = [10, 20, 200]
fig, m_ax = plt.subplots(2, len(threshold_list), figsize=(15, 6))
for c_thresh, (c_ax1, c_ax2) in zip(threshold_list, m_ax.T):
bone_thresh = bone_image > c_thresh
c_ax1.imshow(bone_thresh, cmap='jet')
c_ax1.set_title('Bone @ {}, Image'.format(c_thresh))
c_ax1.axis('off')
... | code |
1008970/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
ax1.imshow(bone_image, cmap='bone')
_ = ax2.hist(bone_image.ravel(), 20) | code |
33122071/cell_25 | [
"text_html_output_2.png"
] | from plotly.subplots import make_subplots
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
import translators as ts
df_new = pd.read_json('/kaggle/input/indonesiandata/emotion_id_tweets.json', lines=True)
ex_label = []
for i in range(df_new['annotation'].shap... | code |
33122071/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
df_new = pd.read_json('/kaggle/input/indonesiandata/emotion_id_tweets.json', lines=True)
ex_label = []
for i in range(df_new['annotation'].shape[0]):
if df_new['annotation'][i]['labels'] == []:
ex_lab... | code |
33122071/cell_29 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
import translators as ts
df_new = pd.read_json('/kaggle/input/indonesiandata/emotion_id_tweets.json', lines=True)
ex_label = []
for i in range(df_new['annotation'].shap... | code |
33122071/cell_19 | [
"text_plain_output_1.png"
] | !pip install translators | code |
33122071/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 |
33122071/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_new = pd.read_json('/kaggle/input/indonesiandata/emotion_id_tweets.json', lines=True)
ex_label = []
for i in range(df_new['annotation'].shape[0]):
if df_new['annotation'][i]['labels'] == []:
ex_label.append('no_emotion')
else:
... | code |
33122071/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
import translators as ts
df_new = pd.read_json('/kaggle/input/indonesiandata/emotion_id_tweets.json', lines=True)
ex_label = []
for i in range(df_new['annotation'].shape[0]):
if df_new['annotation'][i]['labe... | code |
33122071/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_new = pd.read_json('/kaggle/input/indonesiandata/emotion_id_tweets.json', lines=True)
ex_label = []
for i in range(df_new['annotation'].shape[0]):
if df_new['annotation'][i]['labels'] == []:
ex_label.append('no_emotion')
else:
... | code |
33122071/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_new = pd.read_json('/kaggle/input/indonesiandata/emotion_id_tweets.json', lines=True)
ex_label = []
for i in range(df_new['annotation'].shape[0]):
if df_new['annotation'][i]['labels'] == []:
ex_label.append('no_emotion')
else:
... | code |
33122071/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_new = pd.read_json('/kaggle/input/indonesiandata/emotion_id_tweets.json', lines=True)
df_new.head() | code |
33122071/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
df_new = pd.read_json('/kaggle/input/indonesiandata/emotion_id_tweets.json', lines=True)
ex_label = []
for i in range(df_new['annotation'].shape[0]):
if df_new['annotation'][i]['labels'] == []:
ex_lab... | code |
33122071/cell_31 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
import translators as ts
df_new = pd.read_json('/kaggle/input/indonesiandata/emotion_id_tweets.json', lines=True)
ex_label = []
for i in range(df_new['annotation'].shape[0]):
if df_new['annotation'][i]['labe... | code |
33122071/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
import translators as ts
df_new = pd.read_json('/kaggle/input/indonesiandata/emotion_id_tweets.json', lines=True)
ex_label = []
for i in range(df_new['annotation'].shape[0]):
if df_new['annotation'][i]['labe... | code |
33122071/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_new = pd.read_json('/kaggle/input/indonesiandata/emotion_id_tweets.json', lines=True)
ex_label = []
for i in range(df_new['annotation'].shape[0]):
if df_new['annotation'][i]['labels'] == []:
ex_label.append('no_emotion')
else:
... | code |
33122071/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
df_new = pd.read_json('/kaggle/input/indonesiandata/emotion_id_tweets.json', lines=True)
ex_label = []
for i in range(df_new['annotation'].shape[0]):
if df_new['annotation'][i]['labels'] == []:
ex_lab... | code |
33122071/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_new = pd.read_json('/kaggle/input/indonesiandata/emotion_id_tweets.json', lines=True)
ex_label = []
for i in range(df_new['annotation'].shape[0]):
if df_new['annotation'][i]['labels'] == []:
ex_label.append('no_emotion')
else:
... | code |
33122071/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_new = pd.read_json('/kaggle/input/indonesiandata/emotion_id_tweets.json', lines=True)
ex_label = []
for i in range(df_new['annotation'].shape[0]):
if df_new['annotation'][i]['labels'] == []:
ex_label.append('no_emotion')
else:
... | code |
34130888/cell_6 | [
"text_plain_output_1.png"
] | import cv2
import os
import pandas as pd
in_part1 = set(os.listdir('../input/skin-cancer-mnist-ham10000/ham10000_images_part_1'))
df = pd.read_csv('../input/skin-cancer-mnist-ham10000/HAM10000_metadata.csv')
x_train = []
y_train = []
lesions = set()
for index, row in df.iterrows():
if row['lesion_id'] in lesio... | code |
34130888/cell_11 | [
"text_html_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten , Conv2D , MaxPool2D
from keras.models import Sequential
input_shape = (75, 100, 3)
num_classes = 7
model = Sequential()
model.add(Flatten(input_shape=input_shape))
model.add(Dense(512, activation='relu'))
model.add(Dense(256, activation='relu'))
model.add(Dense(128, a... | code |
34130888/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import os
import cv2
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras import backend as K
from keras.layers.normalization import BatchNormalization
from keras.optimizers import Adam
from keras.preproces... | code |
34130888/cell_8 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import os
import pandas as pd
in_part1 = set(os.listdir('../input/skin-cancer-mnist-ham10000/ham10000_images_part_1'))
df = pd.read_csv('../input/skin-cancer-mnist-ham10000/HAM10000_metadata.csv')
x_train = []
y_train = []
lesions = set()
for index, row in df.iterrows():
if row['... | code |
34130888/cell_15 | [
"text_plain_output_1.png"
] | from keras.callbacks import ReduceLROnPlateau
from keras.layers import Dense, Dropout, Flatten , Conv2D , MaxPool2D
from keras.models import Sequential
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
import cv2
import numpy as np
import os
import pandas as pd
in_part1... | code |
16120004/cell_9 | [
"text_plain_output_1.png"
] | from PIL import Image
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
infectados = os.listdir('../input/cell_images/cell_images/Parasitized/')
saudaveis = os.listdir('../input/cell_images/cell_images/Uninfected/')
data = []
labels = []
for i in infectados:
try:
image = cv2.imre... | code |
16120004/cell_4 | [
"image_output_1.png"
] | import os
print(os.listdir('../input/cell_images/cell_images')) | code |
16120004/cell_6 | [
"text_plain_output_1.png"
] | from PIL import Image
import cv2
import numpy as np
import os
infectados = os.listdir('../input/cell_images/cell_images/Parasitized/')
saudaveis = os.listdir('../input/cell_images/cell_images/Uninfected/')
data = []
labels = []
for i in infectados:
try:
image = cv2.imread('../input/cell_images/cell_ima... | code |
16120004/cell_8 | [
"text_plain_output_1.png"
] | from PIL import Image
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
infectados = os.listdir('../input/cell_images/cell_images/Parasitized/')
saudaveis = os.listdir('../input/cell_images/cell_images/Uninfected/')
data = []
labels = []
for i in infectados:
try:
image = cv2.imre... | code |
16120004/cell_15 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Dropout
from keras.models import Sequential
model = Sequential()
model.add(Conv2D(filters=16, kernel_size=2, padding='same', activation='relu', input_shape=(50, 50, 3)))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=32, kernel_size=2, padding=... | code |
16120004/cell_3 | [
"image_output_1.png"
] | from PIL import Image
import numpy as np
import os
import cv2
import keras
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout | code |
16120004/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
import cv2
import keras
import matplotlib.pyplot as plt
import numpy as np
import os
infectados = os.listdir('../input/cell_images/cell_images/Parasitized/')
saudaveis = os.listdir('../input/cell_images/cell_images/Uninfected/')
data = []
labels = []
for i in infectados:
try:
im... | code |
1004704/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import tree
from sklearn import tree
clf = tree.DecisionTreeClassifier()
clf.fit(xtrain, ytrain)
clf.score(xtest, ytest) | code |
1004704/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import tree
from sklearn import tree
clf = tree.DecisionTreeClassifier()
clf.fit(xtrain, ytrain) | code |
16136701/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf.windspeed.plot(kind='box') | code |
16136701/cell_9 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum() | code |
16136701/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
... | code |
16136701/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.info() | code |
16136701/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
da... | code |
16136701/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum() | code |
16136701/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
... | code |
16136701/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf['temp'].unique() | code |
16136701/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16136701/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape | code |
16136701/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf.corr() | code |
16136701/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf.registered.plot(kind='box') | code |
16136701/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf['count'].plot(kind='box') | code |
16136701/cell_38 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf.corr()
train_X = dataf.drop(... | code |
16136701/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.head() | code |
16136701/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf['count'].value_counts() | code |
16136701/cell_35 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_squared_log_error, r2_score
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.cs... | code |
16136701/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
model = LinearRegression()
model | code |
16136701/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf.casual.plot(kind='box') | code |
16136701/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum() | code |
16136701/cell_12 | [
"text_html_output_1.png"
] | dataf.season.plot(kind='box') | code |
16136701/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T | code |
16136701/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_squared_log_error, r2_score
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.cs... | code |
74052344/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"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 pandas as pd
pd.set_option.max_rows = 50
import numpy as np
train_path = '/kaggle/input/house-prices-advanced-regression-techniques/train.csv'
test_path = '/kaggle/input/house-prices-advanced-regression-techniques/test.c... | code |
74052344/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 |
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