path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
106208751/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/video-game-sales-with-ratings/Video_Games_Sales_as_at_22_Dec_2016.csv')
col_remove = ['Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating']
data_clear = data.drop(labels=col_remove, axis=1)
data_clear.isnull().sum()
data_clear = data_clear... | code |
106208751/cell_5 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/video-game-sales-with-ratings/Video_Games_Sales_as_at_22_Dec_2016.csv')
data.info()
data.head() | code |
89139202/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df_train['time'] = pd.to_datetime(df_train['time'])
df_train.drop(['row_id'], axis=1, inplace=True)
df_test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv')
print(df_train['day'].value_counts())
pr... | code |
89139202/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df_train['time'] = pd.to_datetime(df_train['time'])
df_train.drop(['row_id'], axis=1, inplace=True)
df_test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv')
print(df_train.isnull().sum())
print(df_... | code |
89139202/cell_2 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df_train['time'] = pd.to_datetime(df_train['time'])
print(df_train.head())
df_train.drop(['row_id'], axis=1, inplace=True)
print(df_train.head())
df_test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.c... | code |
89139202/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df_train['time'] = pd.to_datetime(df_train['time'])
df_train.drop(['row_id'], axis=1, inplace=True)
df_test = pd.read_csv('../input/tabul... | code |
89139202/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df_train['time'] = pd.to_datetime(df_train['time'])
df_train.drop(['row_id'], axis=1, inplace=True)
df_test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv')
df_train.describe() | code |
89139202/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
df_train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df_train['time'] = pd.to_datetime(df_train['time'])
df_train.drop(['row_id'], axis=1, inplace=True)
df_test = pd.read_csv('../input/tabular-playground-series-ma... | code |
89139202/cell_12 | [
"text_html_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
df_train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df_train['time'] = pd.to_datetime(df_train['time'])
df_train.drop(['row_id'], axis=1, inplace=True)
df_test = pd.read_csv('../input/tabular-playground-series-ma... | code |
89139202/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df_train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df_train['time'] = pd.to_datetime(df_train['time'])
df_train.drop(['row_id'], axis=1, inplace=True)
df_test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv')
fig, ax... | code |
74044709/cell_6 | [
"text_plain_output_1.png"
] | pip install openai | code |
74044709/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 |
74044709/cell_8 | [
"text_plain_output_1.png"
] | """prompt=text
t=True
while t:
person=str(input('Morty:'))
prompt+='Morty:'+person+'
'
prompt+='Rick:'
output=model(prompt)
prompt+=output+'
'
print('Rick:',output)
if person=='bey':
print('Rick:ok I'm done, go away')
t=False
""" | code |
74044709/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/rickmorty-scripts/RickAndMortyScripts.csv')
data.head() | code |
74044709/cell_12 | [
"text_html_output_1.png"
] | """prompt=newtext
t=True
while t:
person=str(input('Morty:'))
prompt+='Morty:'+person+'
'
prompt+='Rick:'
output=model(prompt)
prompt+=output+'
'
print('Rick:',output)
if person=='bey':
print('Rick:ok I'm done, go away')
t=False""" | code |
90143425/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train[['Deck', 'Cabin_Num', 'Side']] = train.Cabin.str.split('/', expand=True)
test[['Deck', 'Cabin_Num', 'Side']] = test.Cabin.str.split('/', expand=True)
train[['Group... | code |
90143425/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train[['Deck', 'Cabin_Num', 'Side']] = train.Cabin.str.split('/', expand=True)
test[['Deck', 'Cabin_Num', 'Side']] = test.Cabin.str.split('/', expand=True)
train[['Group... | code |
90143425/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train[['Deck', 'Cabin_Num', 'Side']] = train.Cabin.str.split('/', expand=True)
test[['Deck', 'Cabin_Num', 'Side']] = test.Cabin.str.split('/', expand=True)
train[['Group... | code |
90143425/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
90143425/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train[['Deck', 'Cabin_Num', 'Side']] = train.Cabin.str.split('/', expand=True)
test[['Deck', 'Cabin_Num', 'Side']] = test.Cabin.str.split('/', expand=True)
train[['Group... | code |
90143425/cell_16 | [
"text_html_output_1.png"
] | from category_encoders.ordinal import OrdinalEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from category_encoders.ordinal impo... | code |
90143425/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train.info() | code |
90143425/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train[['Deck', 'Cabin_Num', 'Side']] = train.Cabin.str.split('/', expand=True)
test[['Deck', 'Cabin_Num', 'Side']] = test.Cabin.str.split('/', expand=True)
train[['Group... | code |
90143425/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train[['Deck', 'Cabin_Num', 'Side']] = train.Cabin.str.split('/', expand=True)
test[['Deck', 'Cabin_Num', 'Side']] = test.Cabin.str.split('/', expand=True)
train.head() | code |
106198899/cell_4 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras import layers
pre_trained_model = InceptionV3(input_shape=(256, 256, 3), include_top=False, weights=None)
for layer in pre_trained_model.layers:
layer.trainabl... | code |
106198899/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras import layers
pre_trained_model = InceptionV3(input_shape=(256, 256, 3), include_top=False, weights=None)
for layer in pre_trained_model.layers:
layer.trainabl... | code |
106198899/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/mayo-clinic-strip-ai/train.csv')
test_df = pd.read_csv('../input/mayo-clinic-strip-ai/test.csv')
train_df.head() | code |
106198899/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
"import os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))" | code |
106198899/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras import Model
from tensorflow.keras import layers
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras import layers
pre_trained_model = InceptionV3(input_shape=(256, 256, 3), include_top=False... | code |
106198899/cell_15 | [
"text_plain_output_1.png"
] | from openslide import OpenSlide
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)
import tensorflow as tf
train_df = pd.read_csv('../input/mayo-clinic-strip-ai/train.csv')
test_df = pd.read_csv('... | code |
106198899/cell_3 | [
"text_plain_output_1.png"
] | """!wget --no-check-certificate https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5 -O /tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5""" | code |
106198899/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from openslide import OpenSlide
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)
import tensorflow as tf
train_df = pd.read_csv('../input/mayo-clinic-strip-ai/train.csv')
test_df = pd.read_csv('... | code |
106198899/cell_5 | [
"text_html_output_1.png"
] | from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras import layers
pre_trained_model = InceptionV3(input_shape=(256, 256, 3), include_top=False, weights=None)
for layer in pre_trained_model.layers:
layer.trainabl... | code |
106211900/cell_11 | [
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision import transforms
import numpy as np
import pandas as pd
train_set = pd.read_csv('../input/digit-recognizer/train.csv')
test_set = pd.read_csv('../input/digit-recognizer/test.csv')
valid_size... | code |
106211900/cell_10 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision import transforms
import numpy as np
import pandas as pd
train_set = pd.read_csv('../input/digit-recognizer/train.csv')
test_set = pd.read_csv('../input/digit-recognizer/test.csv')
valid_size... | code |
1004711/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
train_df[['SibSp', 'Survived']].groupby(['SibSp'], as_index=False).mean().sort_values(by='... | code |
1004711/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by... | code |
1004711/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
train_df[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by='Surv... | code |
1004711/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
train_df[['Parch', 'Survived']].groupby(['Parch'], as_index=False).mean().sort_values(by='... | code |
105197876/cell_9 | [
"text_html_output_1.png"
] | from matplotlib.offsetbox import AnchoredText
from scipy.signal import periodogram
from sklearn.model_selection import train_test_split
import math
import matplotlib.pyplot as plt
import numpy as np
import optuna
import pandas as pd
import seaborn as sns
import xgboost as xgb
def seasonal_plot(X, y, period, f... | code |
105197876/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.offsetbox import AnchoredText
from scipy.signal import periodogram
import math
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
def seasonal_plot(X, y, period, freq, ax=None):
if ax is None:
_, ax = plt.subplots()
palette = sns.color_palette("husl", n_color... | code |
105197876/cell_3 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from matplotlib.offsetbox import AnchoredText
from scipy.signal import periodogram
import math
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
def seasonal_plot(X, y, period, freq, ax=None):
if ax is None:
_, ax = plt.subplots()
palette = sns.color_palette("husl", n_color... | code |
105197876/cell_10 | [
"text_html_output_2.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | from matplotlib.offsetbox import AnchoredText
from scipy.signal import periodogram
from sklearn.model_selection import train_test_split
import math
import matplotlib.pyplot as plt
import numpy as np
import optuna
import pandas as pd
import seaborn as sns
import xgboost as xgb
def seasonal_plot(X, y, period, f... | code |
105197876/cell_5 | [
"application_vnd.jupyter.stderr_output_27.png",
"application_vnd.jupyter.stderr_output_35.png",
"application_vnd.jupyter.stderr_output_9.png",
"text_plain_output_30.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"text_plain_output_40.png",
"text_pl... | _, ax = plt.subplots(12, 4, figsize=(14, 50))
test_df['num_sold'] = 0
train_df['num_sold_predicted'] = 0
for country, i in zip(train_df['country'].unique(), range(6)):
for store, k in zip(train_df['store'].unique(), range(2)):
for product, j in zip(train_df['product'].unique(), range(4)):
temp_d... | code |
105193549/cell_42 | [
"text_html_output_1.png"
] | import ast
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
ast.lite... | code |
105193549/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a | code |
105193549/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
genres2_length = list(map(len, df_titles['genres2'].va... | code |
105193549/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
genres2_length = list(map(len, df_titles['genres2'].values))
genres2_length | code |
105193549/cell_33 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
genres2_length = list(map(len, df_titles['genres2'].va... | code |
105193549/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
df_titles['genres2'].values | code |
105193549/cell_40 | [
"text_plain_output_1.png"
] | import ast
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
ast.lite... | code |
105193549/cell_29 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
genres2_length = list(map(len, df_titles['genres2'].va... | code |
105193549/cell_41 | [
"text_html_output_1.png"
] | import ast
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
ast.lite... | code |
105193549/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
type(a[0]) | code |
105193549/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
df_titles['genres'].values | code |
105193549/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles | code |
105193549/cell_18 | [
"text_plain_output_1.png"
] | import ast
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
ast.literal_eval(a[0])
type(ast.literal_eval... | code |
105193549/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
df_titles['title'] | code |
105193549/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
df_titles['genres2'].values | code |
105193549/cell_15 | [
"text_plain_output_1.png"
] | import ast
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
ast.literal_eval(a[0]) | code |
105193549/cell_16 | [
"text_plain_output_1.png"
] | import ast
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
ast.literal_eval(a[0])
type(ast.literal_eval... | code |
105193549/cell_38 | [
"text_plain_output_1.png"
] | import ast
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
ast.lite... | code |
105193549/cell_3 | [
"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 |
105193549/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
genres2_length = list(map(len, df_titles['genres2'].values))
genres2_length
genres2_length | code |
105193549/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
a[0] | code |
105193549/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
df_titles['genres2'].values | code |
105193549/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
a[0] | code |
105193549/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles | code |
105193549/cell_36 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
genres2_length = list(map(len, df_titles['genres2'].va... | code |
48162408/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/tra... | code |
48162408/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/tra... | code |
48162408/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_... | code |
48162408/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.metrics import log_loss
from sklearn.model_selection import KFold
from xgboost import XGBRegressor
import matplotlib.pyplot as plt
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)
import seaborn as sns
sample = pd.read_cs... | code |
48162408/cell_11 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/tra... | code |
48162408/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 |
48162408/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_... | code |
48162408/cell_28 | [
"text_html_output_1.png"
] | from sklearn.metrics import log_loss
from sklearn.model_selection import KFold
from xgboost import XGBRegressor
import matplotlib.pyplot as plt
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)
import seaborn as sns
sample = pd.read_cs... | code |
48162408/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_... | code |
48162408/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/tra... | code |
48162408/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/tra... | code |
48162408/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_... | code |
48162408/cell_10 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/tra... | code |
48162408/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.metrics import log_loss
from sklearn.model_selection import KFold
from xgboost import XGBRegressor
import matplotlib.pyplot as plt
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)
import seaborn as sns
sample = pd.read_cs... | code |
48162408/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/tra... | code |
1004150/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
poke = pd.read_csv('../input/Pokemon.csv')
poke[poke['Dual'] == 0] | code |
1004150/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sn
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1004150/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
poke = pd.read_csv('../input/Pokemon.csv')
poke['Type 2'].fillna('No Type') | code |
1004150/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
poke = pd.read_csv('../input/Pokemon.csv')
poke.head(10) | code |
1004150/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
poke = pd.read_csv('../input/Pokemon.csv')
print(poke.sample(20))
print('===============================================')
print(poke.dtypes) | code |
1004150/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
poke = pd.read_csv('../input/Pokemon.csv')
print('%0.2f percent of the Pokemon dont have a secondary type' % (poke['Type 2'].isnull().sum() / len(poke) * 100))
print('Roughly %0.2f percent are legendary types.' % (len(poke[poke['Legendary'] == Tru... | code |
129005548/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df.info() | code |
129005548/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns | code |
129005548/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 |
129005548/cell_7 | [
"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('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender = pd.pivot_table(df[df['diabetes'] == 1], index='gender', values='age', aggfunc=len).reset... | code |
129005548/cell_8 | [
"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('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender = pd.pivot_table(df[df['diabetes'] == 1], index='gender', values='age', aggfunc=len).reset... | code |
129005548/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('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender = pd.pivot_table(df[df['diabetes'] == 1], index='gender', values='age', aggfunc=len).reset... | code |
129005548/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)
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df.head() | code |
129005548/cell_14 | [
"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('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender = pd.pivot_table(df[df['diabetes'] == 1], index='gender', values='age', aggfunc=len).reset... | code |
129005548/cell_10 | [
"application_vnd.jupyter.stderr_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('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender = pd.pivot_table(df[df['diabetes'] == 1], index='gender', values='age', aggfunc=len).reset... | code |
129005548/cell_12 | [
"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('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender = pd.pivot_table(df[df['diabetes'] == 1], index='gender', values='age', aggfunc=len).reset... | code |
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