path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128010675/cell_29 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datasets import load_dataset
from sklearn.metrics import accuracy_score
from torch.utils.data import DataLoader
from transformers import TrainingArguments, Trainer
from transformers import ViTForImageClassification
from transformers import ViTImageProcessor
import numpy as np
import torch
import torch
fro... | code |
128010675/cell_26 | [
"text_plain_output_1.png"
] | from datasets import load_dataset
from sklearn.metrics import accuracy_score
from torch.utils.data import DataLoader
from transformers import TrainingArguments, Trainer
from transformers import ViTForImageClassification
from transformers import ViTImageProcessor
import numpy as np
import torch
import torch
fro... | code |
128010675/cell_2 | [
"text_plain_output_1.png"
] | !pip install -q transformers datasets | code |
128010675/cell_19 | [
"text_plain_output_1.png"
] | from datasets import load_dataset
from transformers import ViTForImageClassification
from datasets import load_dataset
train_ds = load_dataset('miladfa7/5-Flower-Types-Classification-Dataset')
train_ds = train_ds['train'].train_test_split(test_size=0.15)
train_data = train_ds['train']
test_data = train_ds['test']
l... | code |
128010675/cell_28 | [
"text_plain_output_1.png"
] | from datasets import load_dataset
from sklearn.metrics import accuracy_score
from torch.utils.data import DataLoader
from transformers import TrainingArguments, Trainer
from transformers import ViTForImageClassification
from transformers import ViTImageProcessor
import numpy as np
import torch
import torch
fro... | code |
128010675/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from datasets import load_dataset
from datasets import load_dataset
train_ds = load_dataset('miladfa7/5-Flower-Types-Classification-Dataset')
train_ds = train_ds['train'].train_test_split(test_size=0.15)
train_data = train_ds['train']
test_data = train_ds['test']
label = list(set(train_data['label']))
id2label = {id... | code |
128010675/cell_16 | [
"image_output_1.png"
] | from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import ViTImageProcessor
import torch
from datasets import load_dataset
train_ds = load_dataset('miladfa7/5-Flower-Types-Classification-Dataset')
train_ds = train_ds['train'].train_test_split(test_size=0.15)
train_data = tr... | code |
128010675/cell_17 | [
"text_plain_output_1.png"
] | from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import ViTImageProcessor
import torch
from datasets import load_dataset
train_ds = load_dataset('miladfa7/5-Flower-Types-Classification-Dataset')
train_ds = train_ds['train'].train_test_split(test_size=0.15)
train_data = tr... | code |
128010675/cell_10 | [
"text_plain_output_1.png"
] | from transformers import ViTImageProcessor
from transformers import ViTImageProcessor
processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224-in21k') | code |
128010675/cell_12 | [
"text_plain_output_5.png",
"text_plain_output_4.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"
] | from transformers import ViTImageProcessor
from transformers import ViTImageProcessor
processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224-in21k')
from torchvision.transforms import CenterCrop, Compose, Normalize, RandomHorizontalFlip, RandomResizedCrop, Resize, ToTensor
image_mean, image_std = ... | code |
328872/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import trueskill as ts
def cleanResults(numRaces, dfResults):
for raceCol in range(1, numRaces + 1):
dfResults['R' + str(raceCol)] = dfResults['R' + str(raceCol)].str.replace('\\(|\\)|DNF-|RET-|SCP-|RDG-|RCT-|DNS-[0... | code |
328872/cell_7 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import trueskill as ts
def cleanResults(numRaces, dfResults):
for raceCol in range(1, numRaces + 1):
dfResults['R' + str(raceCol)] = dfResults['R' + str(raceCol)].str.replace('\\(|\\)|DNF-|RET-|SCP-|RDG-|RCT-|DNS-[0... | code |
130011524/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import tensorflow as tf
BATCH_SIZE = 32
IMAGE_SIZE = 256
CHANNELS = 3
EPOCHS = 50
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/potato-dataset/Potato', seed=123, shuffle=True, image_size=(IMAGE_SIZE, IMAGE_SIZE), batch_size=BATCH_SIZE)
class_names = dataset.class_names
class_names
for... | code |
130011524/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import tensorflow as tf
BATCH_SIZE = 32
IMAGE_SIZE = 256
CHANNELS = 3
EPOCHS = 50
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/potato-dataset/Potato', seed=123, shuffle=True, image_size=(IMAGE_SIZE, IMAGE_SIZE), batch_si... | code |
130011524/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import tensorflow as tf
BATCH_SIZE = 32
IMAGE_SIZE = 256
CHANNELS = 3
EPOCHS = 50
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/potato-dataset/Potato', seed=123, shuffle=True, image_size=(IMAGE_SIZE, IMAGE_SIZE), batch_si... | code |
130011524/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import tensorflow as tf
BATCH_SIZE = 32
IMAGE_SIZE = 256
CHANNELS = 3
EPOCHS = 50
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/potato-dataset/Potato', seed=123, shuffle=True, image_size=(IMAGE_SIZE, IMAGE_SIZE), batch_si... | code |
130011524/cell_6 | [
"text_plain_output_1.png"
] | import tensorflow as tf
BATCH_SIZE = 32
IMAGE_SIZE = 256
CHANNELS = 3
EPOCHS = 50
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/potato-dataset/Potato', seed=123, shuffle=True, image_size=(IMAGE_SIZE, IMAGE_SIZE), batch_size=BATCH_SIZE) | code |
130011524/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import tensorflow as tf
BATCH_SIZE = 32
IMAGE_SIZE = 256
CHANNELS = 3
EPOCHS = 50
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/potato-dataset/Potato', seed=123, shuffle=True, image_size=(IMAGE_SIZE, IMAGE_SIZE), batch_si... | code |
130011524/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import tensorflow as tf
BATCH_SIZE = 32
IMAGE_SIZE = 256
CHANNELS = 3
EPOCHS = 50
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/potato-dataset/Potato', seed=123, shuffle=True, image_size=(IMAGE_SIZE, IMAGE_SIZE), batch_si... | code |
130011524/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib as plt
import os
"\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n" | code |
130011524/cell_7 | [
"text_plain_output_1.png"
] | import tensorflow as tf
BATCH_SIZE = 32
IMAGE_SIZE = 256
CHANNELS = 3
EPOCHS = 50
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/potato-dataset/Potato', seed=123, shuffle=True, image_size=(IMAGE_SIZE, IMAGE_SIZE), batch_size=BATCH_SIZE)
class_names = dataset.class_names
class_names | code |
130011524/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import tensorflow as tf
BATCH_SIZE = 32
IMAGE_SIZE = 256
CHANNELS = 3
EPOCHS = 50
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/potato-dataset/Potato', seed=123, shuffle=True, image_size=(IMAGE_SIZE, IMAGE_SIZE), batch_si... | code |
130011524/cell_32 | [
"text_plain_output_1.png"
] | from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization, AveragePooling2D, GlobalAveragePooling2D
from keras.models import Model,Sequential, Input, load_model
from keras.preprocessing.image import ImageDataGenerator
from ... | code |
130011524/cell_8 | [
"text_plain_output_1.png"
] | import tensorflow as tf
BATCH_SIZE = 32
IMAGE_SIZE = 256
CHANNELS = 3
EPOCHS = 50
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/potato-dataset/Potato', seed=123, shuffle=True, image_size=(IMAGE_SIZE, IMAGE_SIZE), batch_size=BATCH_SIZE)
class_names = dataset.class_names
class_names
len... | code |
130011524/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import tensorflow as tf
BATCH_SIZE = 32
IMAGE_SIZE = 256
CHANNELS = 3
EPOCHS = 50
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/potato-dataset/Potato', seed=123, shuffle=True, image_size=(IMAGE_SIZE, IMAGE_SIZE), batch_si... | code |
130011524/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import tensorflow as tf
BATCH_SIZE = 32
IMAGE_SIZE = 256
CHANNELS = 3
EPOCHS = 50
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/potato-dataset/Potato', seed=123, shuffle=True, image_size=(IMAGE_SIZE, IMAGE_SIZE), batch_si... | code |
130011524/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import tensorflow as tf
BATCH_SIZE = 32
IMAGE_SIZE = 256
CHANNELS = 3
EPOCHS = 50
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/potato-dataset/Potato', seed=123, shuffle=True, image_size=(IMAGE_SIZE, IMAGE_SIZE), batch_si... | code |
130011524/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import tensorflow as tf
BATCH_SIZE = 32
IMAGE_SIZE = 256
CHANNELS = 3
EPOCHS = 50
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/potato-dataset/Potato', seed=123, shuffle=True, image_size=(IMAGE_SIZE, IMAGE_SIZE), batch_si... | code |
130011524/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import tensorflow as tf
BATCH_SIZE = 32
IMAGE_SIZE = 256
CHANNELS = 3
EPOCHS = 50
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/potato-dataset/Potato', seed=123, shuffle=True, image_size=(IMAGE_SIZE, IMAGE_SIZE), batch_si... | code |
130011524/cell_10 | [
"text_plain_output_1.png"
] | import tensorflow as tf
BATCH_SIZE = 32
IMAGE_SIZE = 256
CHANNELS = 3
EPOCHS = 50
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/potato-dataset/Potato', seed=123, shuffle=True, image_size=(IMAGE_SIZE, IMAGE_SIZE), batch_size=BATCH_SIZE)
class_names = dataset.class_names
class_names
for... | code |
16120680/cell_13 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. ... | code |
16120680/cell_9 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"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 string
df = pd.read_csv('../input/train.csv')
df['CabinPrefix'] = [str(cabinname)[0] for cabinname in df.Cabin]
df.loc[df.Cabin.isnull(), 'CabinPrefix'] = None
df['CabinKnown'] = [value for value in df.Cabi... | code |
16120680/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from tensorflow.ker... | code |
16120680/cell_2 | [
"text_plain_output_1.png"
] | import os
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
16120680/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
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 string
df = pd.read_csv('../input/train.csv')
df['CabinPrefix'] = [str(cabinname)[0] for cabinname in df.Cabin]
df.... | code |
16120680/cell_19 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
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 string
... | code |
16120680/cell_7 | [
"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)
df = pd.read_csv('../input/train.csv')
df['CabinPrefix'] = [str(cabinname)[0] for cabinname in df.Cabin]
df.loc[df.Cabin.isnull(), 'CabinPrefix'] = None
df['CabinKnown'] = [value for value in df.Cabin.isnull()]
df[... | code |
16120680/cell_15 | [
"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"
] | from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
nn_model = Sequential()
nn_model.add(Dense(16, activation='relu', input_shape=(8,)))
nn_model.add(Dropout(0.3, noise_shape... | code |
16120680/cell_16 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
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 string
df = pd.r... | code |
16120680/cell_3 | [
"application_vnd.jupyter.stderr_output_2.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('../input/train.csv')
df.head() | code |
16120680/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as ... | code |
50242100/cell_9 | [
"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/titanic/train.csv')
df.shape
df.isna().count()
df.describe() | code |
50242100/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 |
50242100/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/titanic/train.csv')
df.shape | code |
50242100/cell_8 | [
"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/titanic/train.csv')
df.shape
df.isna().count() | code |
50242100/cell_5 | [
"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/titanic/train.csv')
df.head() | code |
16120872/cell_21 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
pd.set_option('display.max_columns', 1000)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShel... | code |
16120872/cell_6 | [
"text_html_output_1.png"
] | import missingno
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
pd.set_option('display.max_columns', 1000)
from IPython.core.interactiveshell import InteractiveShe... | code |
16120872/cell_11 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
pd.set_option('display.max_columns', 1000)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShel... | code |
16120872/cell_1 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
print(os.listdir('../input'))
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
pd.set_option('display.max_columns', 1000)
from IPython.core.interactiveshell import In... | code |
16120872/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
pd.set_option('display.max_columns', 1000)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShel... | code |
16120872/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
pd.set_option('display.max_columns', 1000)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShel... | code |
16120872/cell_3 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
pd.set_option('display.max_columns', 1000)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShel... | code |
16120872/cell_22 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
pd.set_option('display.max_columns', 1000)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShel... | code |
16120872/cell_36 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(n_estimators=100)
clf.fit(x_train, y_train)
clf.score(x_train, y_train) | code |
89131213/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import ast
data = pd.read_csv('example_data/Belgium_labeled.csv', keep_default_na=False)[['text', 'label']] | code |
89127563/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
sample_submission = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
train.sample(20)
test.sample(... | code |
89127563/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
sample_submission = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
train.sample(20)
test.sample(... | code |
89127563/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
sample_submission = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
train.sample(20)
test.sample(... | code |
89127563/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
sample_submission = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
train.sample(20)
test.sample(... | code |
89127563/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
sample_submission = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test.sample(20) | code |
89127563/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
sample_submission = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
train.sample(20)
test.sample(... | code |
89127563/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
sample_submission = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
train.sample(20)
train['Trans... | code |
89127563/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
sample_submission = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
train.sample(20)
test.sample(... | code |
89127563/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
sample_submission = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
train.sample(20) | code |
89127563/cell_5 | [
"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 |
73095137/cell_21 | [
"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
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape)
df_all = df.append(test)
df... | code |
73095137/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
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape)
df_all = df.append(test)
df... | code |
73095137/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape)
df_all = df.append(test)
df_all.shape
df_all.head(10).T
df_all.columns = [c.repla... | code |
73095137/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape)
df_all = df.append(test)
df... | code |
73095137/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('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape)
df_all = df.append(test)
df_all.shape
df_all.info() | code |
73095137/cell_23 | [
"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
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape)
df_all = df.append(test)
df... | code |
73095137/cell_20 | [
"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
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape)
df_all = df.append(test)
df... | code |
73095137/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape) | code |
73095137/cell_11 | [
"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
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape)
df_all = df.append(test)
df... | code |
73095137/cell_19 | [
"text_plain_output_1.png",
"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
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape)
df_all = df.append(test)
df... | code |
73095137/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 |
73095137/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape)
df_all = df.append(test)
df_all.shape
df_all.head(10).T
df_all.columns = [c.repla... | code |
73095137/cell_18 | [
"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
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape)
df_all = df.append(test)
df... | code |
73095137/cell_28 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_... | code |
73095137/cell_15 | [
"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
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape)
df_all = df.append(test)
df... | code |
73095137/cell_16 | [
"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
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape)
df_all = df.append(test)
df... | code |
73095137/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape)
df_all = df.append(test)
df_all.shape | code |
73095137/cell_17 | [
"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
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape)
df_all = df.append(test)
df... | code |
73095137/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv... | code |
73095137/cell_22 | [
"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
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape)
df_all = df.append(test)
df... | code |
73095137/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape)
df_all = df.append(test)
df_all.shape
df_all.head(10).T
df_all.columns = [c.repla... | code |
73095137/cell_27 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_... | code |
73095137/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
(df.shape, test.shape)
df_all = df.append(test)
df_all.shape
df_all.head(10).T | code |
1003657/cell_9 | [
"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)
directory_data = pd.read_csv('../input/directory.csv')
plt.figure(figsize=(13, 5))
directory_data['Country'].value_counts().head(15).plot(kind='bar') | code |
1003657/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
directory_data = pd.read_csv('../input/directory.csv')
directory_data.head() | code |
1003657/cell_2 | [
"text_html_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1003657/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
directory_data = pd.read_csv('../input/directory.csv')
sns.countplot(data=directory_data, x='Brand') | code |
1003657/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)
directory_data = pd.read_csv('../input/directory.csv')
directory_data['Brand'].value_counts() | code |
1003657/cell_10 | [
"text_plain_output_1.png"
] | !pip install geoplotlib | code |
1003657/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
directory_data = pd.read_csv('../input/directory.csv')
directory_data.describe() | code |
128032771/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age']... | code |
128032771/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age'], ax = axes[0][0])
sns.boxplot(y = train['Height'], ax = axes[0][1])
sns.boxplot(y = train['Weight'], ax = axes[0][2])
sns.boxplot(y = train['Duration'], ax = axes[1][0])
sns.boxplot(y ... | code |
128032771/cell_7 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
exercise = pd.read_csv('/kaggle/input/fmendesdat263xdemos/exercise.csv')
calories = pd.read_csv('/kaggle/input/fmendesdat263xdemos/calories.csv')
exercise['Calories_Burned'] = calories['Calories']
exercise = exercise.drop(['User_ID'], axis=1)
exercise | code |
128032771/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age'], ax = axes[0][0])
sns.boxplot(y = train['Height'], ax = axes[0][1])
sns.boxplot(y = train['Weight'], ax = axes[0][2])
sns.boxplot(y = train['Duration'], ax = axes[1][0])
sns.boxplot(y ... | code |
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