path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
16136283/cell_14 | [
"text_plain_output_1.png"
] | X_train.shape | code |
16136283/cell_10 | [
"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/Amazon_Unlocked_Mobile.csv')
df.dropna(inplace=True)
df = df[df['Rating'] != 3]
df.head() | code |
16136283/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/Amazon_Unlocked_Mobile.csv')
df.describe() | code |
2016758/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from keras.applications.vgg16 import VGG16
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Input, Dense, Dropout, Flatten
from keras.layers import Conv2D... | code |
2016758/cell_5 | [
"text_plain_output_1.png"
] | from keras.applications.vgg16 import VGG16
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Input, Dense, Dropout, Flatten
from keras.models import Sequential, Model
from keras.optimizers import Adam
from keras.pre... | code |
32062145/cell_21 | [
"text_plain_output_1.png"
] | from six.moves import xrange
import collections
import math
import numpy as np
import os
import random
import tensorflow as tf
import tensorflow.compat.v1 as tf
folder_dir = '/kaggle/input/108-2-ntut-dl-app-hw2'
filename = 'tag_list.txt'
vocabulary_size = 990
batch_size = 128
embedding_size = 64
skip_window = ... | code |
32062145/cell_23 | [
"text_plain_output_1.png"
] | from IPython.display import FileLink
from IPython.display import FileLink
from IPython.display import FileLink
FileLink('meta.tsv') | code |
32062145/cell_26 | [
"text_html_output_1.png"
] | from IPython.display import FileLink
from IPython.display import FileLink
from IPython.display import FileLink
folder_dir = '/kaggle/input/108-2-ntut-dl-app-hw2'
filename = 'tag_list.txt'
vocabulary_size = 990
batch_size = 128
embedding_size = 64
skip_window = 1
num_skips = 2
from IPython.display import FileLink
F... | code |
32062145/cell_11 | [
"text_plain_output_1.png"
] | import collections
import os
folder_dir = '/kaggle/input/108-2-ntut-dl-app-hw2'
filename = 'tag_list.txt'
vocabulary_size = 990
file_path = os.path.join(folder_dir, filename)
with open(file_path, 'r', encoding='utf-8') as f:
words = f.read().split()
word_count = [['UNK', -1]]
word_count.extend(collections.Count... | code |
32062145/cell_7 | [
"text_html_output_1.png"
] | import os
folder_dir = '/kaggle/input/108-2-ntut-dl-app-hw2'
filename = 'tag_list.txt'
vocabulary_size = 990
file_path = os.path.join(folder_dir, filename)
with open(file_path, 'r', encoding='utf-8') as f:
words = f.read().split()
words | code |
32062145/cell_18 | [
"text_plain_output_1.png"
] | from six.moves import xrange
from sklearn.manifold import TSNE
import collections
import math
import matplotlib.pyplot as plt
import numpy as np
import os
import random
import tensorflow as tf
import tensorflow.compat.v1 as tf
folder_dir = '/kaggle/input/108-2-ntut-dl-app-hw2'
filename = 'tag_list.txt'
vocabu... | code |
32062145/cell_8 | [
"text_html_output_1.png"
] | import collections
import os
folder_dir = '/kaggle/input/108-2-ntut-dl-app-hw2'
filename = 'tag_list.txt'
vocabulary_size = 990
file_path = os.path.join(folder_dir, filename)
with open(file_path, 'r', encoding='utf-8') as f:
words = f.read().split()
word_count = [['UNK', -1]]
word_count.extend(collections.Count... | code |
32062145/cell_16 | [
"text_plain_output_1.png"
] | import math
import numpy as np
import tensorflow as tf
import tensorflow.compat.v1 as tf
folder_dir = '/kaggle/input/108-2-ntut-dl-app-hw2'
filename = 'tag_list.txt'
vocabulary_size = 990
batch_size = 128
embedding_size = 64
skip_window = 1
num_skips = 2
valid_size = 32
valid_window = 200
valid_examples = np.rand... | code |
32062145/cell_17 | [
"text_plain_output_1.png"
] | from six.moves import xrange
import collections
import math
import numpy as np
import os
import random
import tensorflow as tf
import tensorflow.compat.v1 as tf
folder_dir = '/kaggle/input/108-2-ntut-dl-app-hw2'
filename = 'tag_list.txt'
vocabulary_size = 990
batch_size = 128
embedding_size = 64
skip_window = ... | code |
32062145/cell_22 | [
"image_output_1.png"
] | from IPython.display import FileLink
from six.moves import xrange
import collections
import math
import numpy as np
import os
import random
import tensorflow as tf
import tensorflow.compat.v1 as tf
folder_dir = '/kaggle/input/108-2-ntut-dl-app-hw2'
filename = 'tag_list.txt'
vocabulary_size = 990
batch_size = ... | code |
32062145/cell_12 | [
"text_plain_output_1.png"
] | import collections
import os
folder_dir = '/kaggle/input/108-2-ntut-dl-app-hw2'
filename = 'tag_list.txt'
vocabulary_size = 990
file_path = os.path.join(folder_dir, filename)
with open(file_path, 'r', encoding='utf-8') as f:
words = f.read().split()
word_count = [['UNK', -1]]
word_count.extend(collections.Count... | code |
32071559/cell_2 | [
"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))
import pandas as pd
import numpy as np
from keras import backend as K
from keras.models import Model
from keras.callbacks impor... | code |
32071559/cell_11 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.initializers import Identity
from keras.layers import Input, Dense, Flatten, Dropout, Lambda, \
from keras.layers import Input, LSTM, Dense, BatchNormalization, Lambda, Flatten, Reshape
from keras.layers.normalization import BatchNormalization
from keras.models ... | code |
32071559/cell_16 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.initializers import Identity
from keras.layers import Input, Dense, Flatten, Dropout, Lambda, \
from keras.layers import Input, LSTM, Dense, BatchNormalization, Lambda, Flatten, Reshape
from keras.layers.normalization import BatchNormalization
from keras.models ... | code |
32071559/cell_14 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.initializers import Identity
from keras.layers import Input, Dense, Flatten, Dropout, Lambda, \
from keras.layers import Input, LSTM, Dense, BatchNormalization, Lambda, Flatten, Reshape
from keras.layers.normalization import BatchNormalization
from keras.models ... | code |
34122628/cell_13 | [
"text_html_output_1.png"
] | from keras.preprocessing import image
from tqdm import tqdm
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/coronahack-chest-xraydataset/Chest_xray_Corona_Metadata.csv')
train.columns
... | code |
34122628/cell_9 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
from tqdm import tqdm
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/coronahack-chest-xraydataset/Chest_xray_Corona_Metadata.csv')
train.columns
... | code |
34122628/cell_25 | [
"text_plain_output_1.png"
] | """
model = Sequential()
model.add(Conv2D(filters=64, kernel_size=(5, 5), input_shape=(256, 256, 3), activation='relu'))
model.add(BatchNormalization(axis=3))
model.add(Conv2D(filters=64, kernel_size=(5, 5), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(BatchNormalization(axis=3))
model.add(Dropout(0.1)... | code |
34122628/cell_20 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
from tqdm import tqdm
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/coronahack-chest-xraydataset/Chest_xray_Corona_Metadata.csv')
train.columns
... | code |
34122628/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.preprocessing import image
from tqdm import tqdm
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/coronahack-chest-xraydataset/Chest_xray_Corona_Metadata.csv')
train.columns
... | code |
34122628/cell_1 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"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 |
34122628/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)
train = pd.read_csv('../input/coronahack-chest-xraydataset/Chest_xray_Corona_Metadata.csv')
train.columns | code |
34122628/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.preprocessing import image
from tqdm import tqdm
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/coronahack-chest-xraydataset/Chest_xray_Corona_Metadata.csv')
train.columns
... | code |
34122628/cell_15 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
from tqdm import tqdm
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/coronahack-chest-xraydataset/Chest_xray_Corona_Metadata.csv')
train.columns
... | code |
34122628/cell_3 | [
"text_html_output_1.png"
] | import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import to_categorical
from keras.preprocessing import image
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection... | code |
34122628/cell_31 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
model = Sequential()
model.add(Conv2D(filters=16, kernel_size=(5, 5), activation='relu', input_shape=(256, 256, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
mo... | code |
34122628/cell_27 | [
"text_html_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
model = Sequential()
model.add(Conv2D(filters=16, kernel_size=(5, 5), activation='relu', input_shape=(256, 256, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
mo... | code |
34122628/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/coronahack-chest-xraydataset/Chest_xray_Corona_Metadata.csv')
train.head() | code |
105201902/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chocolate-bar-ratings/chocolate_bars.csv')
df.info() | code |
105201902/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.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 |
105201902/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/chocolate-bar-ratings/chocolate_bars.csv')
plt.xticks(rotation=90)
y = df['cocoa_percent']
x = df['rating']
correlation = y.corr(x)
plt.xticks(rotation=0)
plt.figure(figsize=(25, 10))
df.groupby('company_location'... | code |
105201902/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chocolate-bar-ratings/chocolate_bars.csv')
df.head(-5) | code |
105201902/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/chocolate-bar-ratings/chocolate_bars.csv')
plt.xticks(rotation=90)
y = df['cocoa_percent']
x = df['rating']
correlation = y.corr(x)
print(correlation)
plt.figure(figsize=(25, 10))
sns.regplot(x='cocoa_percent', y='... | code |
105201902/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chocolate-bar-ratings/chocolate_bars.csv')
df.describe() | code |
105201902/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/chocolate-bar-ratings/chocolate_bars.csv')
plt.figure(figsize=(10, 25))
sns.catplot(x='bean_origin', y='rating', kind='bar', height=10, aspect=2, data=df.head(2530)).set(title='Bean Origin vs Ratings')
plt.xticks(ro... | code |
32069765/cell_21 | [
"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/aipowered-literature-review-csvs/kaggle/working/TIE/Proportion of patients who were asymptomatic.csv')
df = df.rename(columns={'Unnamed: 0': 'unnamed', 'Asymptomati... | code |
32069765/cell_25 | [
"text_html_output_2.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import seaborn as sns
df = pd.read_csv('../input/aipowered-literature-review-csvs/kaggle/working/TIE/Proportion of patients who were asymptomatic.csv')
df = df.rename(columns={'Unnamed... | code |
32069765/cell_4 | [
"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/aipowered-literature-review-csvs/kaggle/working/TIE/Proportion of patients who were asymptomatic.csv')
df = df.rename(columns={'Unnamed: 0': 'unnamed', 'Asymptomati... | code |
32069765/cell_26 | [
"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 plotly.express as px
import seaborn as sns
df = pd.read_csv('../input/aipowered-literature-review-csvs/kaggle/working/TIE/Proportion of patients who were asymptomatic.csv')
df = df.rename(columns={'Unnamed... | code |
32069765/cell_2 | [
"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/aipowered-literature-review-csvs/kaggle/working/TIE/Proportion of patients who were asymptomatic.csv')
df.head() | code |
32069765/cell_19 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/aipowered-literature-review-csvs/kaggle/working/TIE/Proportion of patients who were asymptomatic.csv')
df1 = pd.read_csv('../input/aipowered-literature-review-csvs/kaggle/working/TIE/Pediatric patients who were asymptoma... | code |
32069765/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objs as go
import plotly.offline as py
import plotly.express as px
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirnam... | code |
32069765/cell_18 | [
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
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/aipowered-literature-review-csvs/kaggle/working/TIE/Proportion of patients who were asymptomatic.csv')
df = df.renam... | code |
32069765/cell_28 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/aipowered-literature-review-csvs/kaggle/working/TIE/Proportion of patients who were asymptomatic.csv')
df = df.rename(columns={'Unnamed: 0': ... | code |
32069765/cell_15 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
import lightgbm as lgb
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 random
import seaborn as sns
import shap
df = pd.read... | code |
32069765/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
import lightgbm as lgb
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 random
import seaborn as sns
import shap
df = pd.read... | code |
32069765/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
import lightgbm as lgb
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 random
import seaborn as sns
import shap
df = pd.read... | code |
32069765/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import seaborn as sns
df = pd.read_csv('../input/aipowered-literature-review-csvs/kaggle/working/TIE/Proportion of patients who were asymptomatic.csv')
df = df.rename(columns={'Unnamed... | code |
32069765/cell_14 | [
"image_output_1.png"
] | from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
import lightgbm as lgb
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 random
import seaborn as sns
df = pd.read_csv('../inpu... | code |
32069765/cell_27 | [
"text_html_output_1.png"
] | import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/aipowered-literature-review-csvs/kaggle/working/TIE/Proportion of patients who were asymptomatic.csv')
df1 = pd.read_csv('../input/aipowered-literature-review-csvs/kaggle/working/TIE/Pediatric pati... | code |
32069765/cell_12 | [
"text_html_output_1.png"
] | from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
import lightgbm as lgb
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 random
import seaborn as sns
df = pd.read_csv('../inpu... | code |
128004504/cell_13 | [
"text_plain_output_1.png"
] | from keras.callbacks import ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MultiLabelBinarizer
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
from vit_keras ... | code |
128004504/cell_9 | [
"image_output_1.png"
] | from keras.callbacks import ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MultiLabelBinarizer
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
from vit_keras ... | code |
128004504/cell_4 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MultiLabelBinarizer
import cv2 as cv
import numpy as np
import os
import re
X = []
Y = []
input_shape = (96, 96, 3)
path_to_subset = f'../input/apparel-images-dataset/'
... | code |
128004504/cell_6 | [
"image_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
from vit_keras import vit, utils
import cv2 as cv
import numpy as np
import os
import re
X = []
Y = []
input_shape = (96, 96, 3)
path_to_subset = f'../input/apparel-im... | code |
128004504/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import matplotlib.pyplot as plt
import os
import cv2 as cv
import re
import requests
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.model_selection import train_test_split
import tensorflow
from vit_keras import vit, utils
from tensorflow.keras.models import Model
from tensorflow.... | code |
128004504/cell_7 | [
"image_output_1.png"
] | from keras.callbacks import ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MultiLabelBinarizer
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
from vit_keras ... | code |
128004504/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import cv2 as cv
import numpy as np
import os
import re
X = []
Y = []
input_shape = (96, 96, 3)
path_to_subset = f'../input/apparel-images-dataset/'
for folder in os.listdir(path_to_subset):
for image in os.listdir(os.path.join(path_to_subset, folder)):
... | code |
128004504/cell_12 | [
"text_plain_output_1.png"
] | from keras.callbacks import ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MultiLabelBinarizer
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
from vit_keras ... | code |
128001783/cell_21 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv')
tc = df.sort_values(by='Total Cases', ascending=False)
tc
ta = df.sort_values(by='Active', ascending=False)
ta
td = df.sort_values(by='Discharged', ascending=False)
td
tdh = df.sort_values(by=... | code |
128001783/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv')
tc = df.sort_values(by='Total Cases', ascending=False)
tc
plt.xticks(rotation=90)
plt.ticklabel_format(style='plain', axis='y')
ta = df.s... | code |
128001783/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv')
tc = df.sort_values(by='Total Cases', ascending=False)
tc | code |
128001783/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv')
tc = df.sort_values(by='Total Cases', ascending=False)
tc
plt.xticks(rotation=90)
plt.ticklabel_format(style='plain', axis='y')
ta = df.s... | code |
128001783/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv')
df.info() | code |
128001783/cell_2 | [
"image_output_1.png"
] | import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore') | code |
128001783/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv')
tc = df.sort_values(by='Total Cases', ascending=False)
tc
plt.xticks(rotation=90)
plt.ticklabel_format(style='plain', axis='y')
plt.pie(x... | code |
128001783/cell_19 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv')
tc = df.sort_values(by='Total Cases', ascending=False)
tc
plt.xticks(rotation=90)
plt.ticklabel_format(style='plain', axis='y')
ta = df.s... | code |
128001783/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv')
df.describe() | code |
128001783/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv')
tc = df.sort_values(by='Total Cases', ascending=False)
tc
ta = df.sort_values(by='Active', ascending=False)
ta
td = df.sort_values(by='Discharged', ascending=False)
td
tdh = df.sort_values(by=... | code |
128001783/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv')
for col in df.describe(include='object').columns:
print(col)
print(df[col].unique())
print('--' * 50) | code |
128001783/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv')
tc = df.sort_values(by='Total Cases', ascending=False)
tc
ta = df.sort_values(by='Active', ascending=False)
ta
td = df.sort_values(by='Discharged', ascending=False)
td | code |
128001783/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv')
tc = df.sort_values(by='Total Cases', ascending=False)
tc
plt.xticks(rotation=90)
plt.ticklabel_format(style='plain', axis='y')
ta = df.s... | code |
128001783/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv')
tc = df.sort_values(by='Total Cases', ascending=False)
tc
plt.xticks(rotation=90)
plt.ticklabel_format(style='plain', axis='y')
ta = df.s... | code |
128001783/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv')
tc = df.sort_values(by='Total Cases', ascending=False)
tc
plt.xticks(rotation=90)
plt.ticklabel_format(style='plain', axis='y')
ta = df.s... | code |
128001783/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv')
tc = df.sort_values(by='Total Cases', ascending=False)
tc
sns.barplot(x='State/UTs', y='Total Cases', data=tc)
plt.xticks(rotation=90)
plt... | code |
128001783/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv')
tc = df.sort_values(by='Total Cases', ascending=False)
tc
ta = df.sort_values(by='Active', ascending=False)
ta | code |
128001783/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv')
df | code |
32064609/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
base = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv', parse_dates=['Last Update'])
df = base[base['Country/Region'] == 'Brazil']
df['Contamined'] = df['Confirmed'] - df['Deaths'] - df['Recovered'] | code |
32064609/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
base = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv', parse_dates=['Last Update'])
df = base[base['Country/Region'] == 'Brazil']
df[df['Last Update'] == '2020-03-08 05:31:00'] | code |
32064609/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
base = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv', parse_dates=['Last Update'])
df = base[base['Country/Region'] == 'Brazil']
df | code |
322480/cell_9 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', '... | code |
322480/cell_4 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
print(titanic.head()) | code |
322480/cell_11 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', '... | code |
322480/cell_1 | [
"application_vnd.jupyter.stderr_output_9.png",
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_6.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_8.png",
"text_plain_output_1.png... | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
titanic = pd.read_csv('../input/train.csv')
print(titanic.describe()) | code |
322480/cell_7 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
print(titanic['Sex']) | code |
322480/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
print(titanic.describe()) | code |
322480/cell_10 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', '... | code |
322480/cell_12 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', '... | code |
322480/cell_5 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
print(titanic['Cabin'].count()) | code |
18161218/cell_21 | [
"text_plain_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)
import seaborn as sns
data = pd.read_csv('../input/train.csv', dtype={'YearBuilt': 'str', 'YrSold': 'str', 'GarageYrBlt': 'str', 'YearRemodAdd': 'str'})
data.shape
missing_list... | code |
18161218/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv', dtype={'YearBuilt': 'str', 'YrSold': 'str', 'GarageYrBlt': 'str', 'YearRemodAdd': 'str'})
data.shape
missing_list = data.columns[data.isna().any()].tolist()
data.columns[data.isna().any()].tolist() | code |
18161218/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv', dtype={'YearBuilt': 'str', 'YrSold': 'str', 'GarageYrBlt': 'str', 'YearRemodAdd': 'str'})
data.shape
data.info(verbose=True) | code |
18161218/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv', dtype={'YearBuilt': 'str', 'YrSold': 'str', 'GarageYrBlt': 'str', 'YearRemodAdd': 'str'})
data.shape
data.describe() | code |
18161218/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv', dtype={'YearBuilt': 'str', 'YrSold': 'str', 'GarageYrBlt': 'str', 'YearRemodAdd': 'str'})
data.shape
missing_list = data.columns[data.isna().any()].tolist()
data.columns[data.isna().any()].tolist()
data.sh... | code |
18161218/cell_19 | [
"text_plain_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)
import seaborn as sns
data = pd.read_csv('../input/train.csv', dtype={'YearBuilt': 'str', 'YrSold': 'str', 'GarageYrBlt': 'str', 'YearRemodAdd': 'str'})
data.shape
missing_list... | code |
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