path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105216483/cell_15 | [
"text_html_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.... | code |
105216483/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.... | code |
105216483/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.... | code |
105216483/cell_31 | [
"text_html_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, GridSearchCV
from sklearn.naive_bayes import GaussianNB... | code |
105216483/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, GridSearchCV
from sklearn.naive_bayes import GaussianNB... | code |
105216483/cell_10 | [
"text_html_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.... | code |
105216483/cell_27 | [
"text_html_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, GridSearchCV
from sklearn.naive_bayes import GaussianNB... | code |
33095782/cell_13 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.datasets import imdb
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing import sequence
import numpy as np
(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = np.concatenate(... | code |
33095782/cell_6 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.datasets import imdb
import numpy as np
(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = np.concatenate((X_train, X_test), axis=0)
y = np.concatenate((y_train, y_test), axis=0)
X | code |
33095782/cell_1 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | !pip install --upgrade pandas-profiling
!pip install --upgrade hypertools
!pip install --upgrade pandas | code |
33095782/cell_7 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
import matplotlib.pyplot as plt
import numpy as np
(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = np.concatenate((X_train, X_test), axis=0)
y = np.concatenate((y_train, y_test), axis=0)
print('Review length: ')
result = list(map(len, X))
print('Mean %.2f words (%f)' % (n... | code |
33095782/cell_18 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.convolutional import Convolution1D
from keras.layers.convolutional import MaxPooling1D
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing impor... | code |
33095782/cell_16 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.convolutional import Convolution1D
from keras.layers.convolutional import MaxPooling1D
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing impor... | code |
33095782/cell_17 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.datasets import imdb
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.convolutional import Convolution1D
from keras.layers.convolutional import MaxPooling1D
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing impor... | code |
33095782/cell_14 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing import sequence
import numpy as np
(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = np.concatenate(... | code |
33095782/cell_10 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.preprocessing import sequence
import numpy as np
(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = np.concatenate((X_train, X_test), axis=0)
y = np.concatenate((y_train, y_test), axis=0)
top_words = 10000
vector_size = 32
max_review_lenght = 800
(X_train, y_train... | code |
33095782/cell_12 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing import sequence
import numpy as np
(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = np.concatenate(... | code |
33095782/cell_5 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
import numpy as np
(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = np.concatenate((X_train, X_test), axis=0)
y = np.concatenate((y_train, y_test), axis=0)
print('Training data: ')
print(X.shape)
print(y.shape)
print('Classes: ')
print(np.unique(y))
print('Number of words: ... | code |
105195844/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
housing.info() | code |
105195844/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)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum() | code |
105195844/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.describe() | code |
105195844/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.c... | code |
105195844/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
housing = housin... | code |
105195844/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
housing.hist(bin... | code |
105195844/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing | code |
105195844/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
housing.info() | code |
105195844/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
sns.heatmap(housing.isnull()) | code |
105195844/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
sns.heatmap(hous... | code |
105195844/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 |
105195844/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.head(5) | code |
105195844/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum() | code |
105195844/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
housing = housin... | code |
105195844/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
housing = housin... | code |
105195844/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.info() | code |
105195844/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns | code |
105195844/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
total_bedroom_median = housing['total_be... | code |
105195844/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
housing = housin... | code |
105195844/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
housing = housin... | code |
105195844/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape | code |
105195844/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
housing = housin... | code |
2025030/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
sns.barplot(x='Parch', y='Survived', hue='Sex', data=data_train) | code |
2025030/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
data_train.sample(5)
train_y = data_train['Survived']
train_y.sample(4) | code |
2025030/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5) | code |
2025030/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import LabelEncoder
from sklearn.svm import SVC
import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_... | code |
2025030/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
model = Gaussia... | code |
2025030/cell_11 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
sns.barplot(x='Embarked', y='Survived', hue='Sex', data=data_train) | code |
2025030/cell_19 | [
"image_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
data_train.sample(5)
train_x = data_train[['Pclass', 'Sex', 'Family_Size']]
train_x.sample(5) | code |
2025030/cell_32 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
data_train.sample(5)
train_x =... | code |
2025030/cell_8 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
sns.barplot(x='Pclass', y='Survived', hue='Sex', data=data_train) | code |
2025030/cell_16 | [
"image_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
data_train.sample(5) | code |
2025030/cell_17 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
data_train.sample(5)
sns.barplot(x='Family_Size', y='Survived', hue='Sex', data=data_train) | code |
2025030/cell_35 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
data_train.sample(5)
train_x = data_train[['Pclass', 'Sex', 'Family_Size']]
train_x.sample(5)
from sklearn.preprocessing import LabelEn... | code |
2025030/cell_24 | [
"text_html_output_1.png"
] | from sklearn.cross_validation import train_test_split | code |
2025030/cell_22 | [
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
data_train.sample(5)
train_x = data_train[['Pclass', 'Sex', 'Family_Size']]
train_x.sample(5)
from sklearn.preprocessing import LabelEn... | code |
2025030/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
sns.barplot(x='SibSp', y='Survived', hue='Sex', data=data_train) | code |
2025030/cell_12 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
sns.barplot(x='Age', y='Survived', hue='Sex', data=data_train) | code |
73067972/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
engagement_data.rename(columns={'lp_id': 'LP ID'}, inplace=True)
merged = pd.merge(engagement_data, product_data, on='LP ID')
m = merged.groupby('Product Name')['engagement_index'].sum().sort_values(ascending=False).head(10)
plt.figure(figsize=(15, 6))
plt.bar(m.ind... | code |
73067972/cell_9 | [
"image_output_1.png"
] | from plotly.offline import plot, iplot, init_notebook_mode
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
from plotly.offline import plot, iplot, init_notebook_mode
init_notebook_mode(connected=True)
import pl... | code |
73067972/cell_4 | [
"text_html_output_1.png"
] | from plotly.offline import plot, iplot, init_notebook_mode
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
from plotly.offline import plot, iplot, init_notebook_mode
init_notebook_mode(connected=True)
import plotly
import plotly.graph_obje... | code |
73067972/cell_2 | [
"text_html_output_1.png"
] | from plotly.offline import plot, iplot, init_notebook_mode
import plotly.graph_objects as go
from plotly.offline import plot, iplot, init_notebook_mode
init_notebook_mode(connected=True)
import plotly
import plotly.graph_objects as go
import plotly.express as px
values = [['District', 'District', 'District', 'Distric... | code |
73067972/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from plotly.offline import plot, iplot, init_notebook_mode
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
from plotly.offline import plot, iplot, init_notebook_mode
init_notebook_mode(connected=True)
import pl... | code |
73067972/cell_14 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | def custom_palette(custom_colors):
customPalette = sns.set_palette(sns.color_palette(custom_colors))
sns.palplot(sns.color_palette(custom_colors), size=0.8)
plt.tick_params(axis='both', labelsize=0, length=0)
import matplotlib.pyplot as plt
import seaborn as sns
red = ['#4f000b', '#720026', '#ce4257', '#ff7... | code |
73067972/cell_5 | [
"text_html_output_1.png"
] | from plotly.offline import plot, iplot, init_notebook_mode
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
from plotly.offline import plot, iplot, init_notebook_mode
init_notebook_mode(connected=True)
import plotly
import plotly.graph_obje... | code |
50232057/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgam... | code |
50232057/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgames_stats.csv')
jordan = df.loc[df['Player'] == 'Michael... | code |
50232057/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgames_stats.csv')
jordan = df.loc[df['Player'] == 'Michael... | code |
50232057/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgames_stats.csv')
jordan = df.loc[df['Player'] == 'Michael... | code |
50232057/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgam... | code |
50232057/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgames_stats.csv')
jordan = df.loc[df['Player'] == 'Michael... | code |
50232057/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgam... | code |
50232057/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgames_stats.csv')
jordan = df.loc[df['Player'] == 'Michael... | code |
50232057/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgam... | code |
50232057/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgames_stats.csv')
jordan = df.loc[df['Player'] == 'Michael... | code |
50232057/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgames_stats.csv')
df.head() | code |
122260915/cell_13 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from scipy import ndimage
from skimage import color
import imageio
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
images = ['peppers.png', 'cameraman.tif', 'coins.png']
path = '/kaggle/input/lab-python/Immagini/'
I = imageio.imread(path + images[0])
if len(I.shape) == 3:
... | code |
122260915/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from scipy import ndimage
from skimage import color
import imageio
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
images = ['peppers.png', 'cameraman.tif', 'coins.png']
path = '/kaggle/input/lab-python/Immagini/'
I = imageio.imread(path + images[0])
if len(I.shape) == 3:
... | code |
122260915/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 |
122260915/cell_7 | [
"image_output_2.png",
"image_output_1.png"
] | from skimage import color
import imageio
import matplotlib.pyplot as plt
images = ['peppers.png', 'cameraman.tif', 'coins.png']
path = '/kaggle/input/lab-python/Immagini/'
I = imageio.imread(path + images[0])
if len(I.shape) == 3:
I = color.rgb2gray(I)
plt.figure()
(plt.subplot(1, 2, 1), plt.imshow(I), plt.title... | code |
122260915/cell_15 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from scipy import ndimage
from skimage import color
import imageio
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
images = ['peppers.png', 'cameraman.tif', 'coins.png']
path = '/kaggle/input/lab-python/Immagini/'
I = imageio.imread(path + images[0])
if len(I.shape) == 3:
... | code |
1006144/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
gdp_stats= gdp.describe().T
budget = movie[movie['budget'] < 300000000]
budg... | code |
1006144/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
budget = movie[movie['budget'] < 300000000]
budget = budget[budget['budget'].... | code |
1006144/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
gdp_stats= gdp.describe().T
budget = movie[movie['budget'] < 300000000]
budg... | code |
1006144/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
gross = movie[['title_year', 'gross']].dropna(axis=0)
gross = gross[gross['title_year'] > 1960]
gros... | code |
1006144/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
gdp_stats= gdp.describe().T
budget = movie[movie['budget'] < 300000000]
budg... | code |
1006144/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
gdp_stats= gdp.describe().T
budget = movie[movie['budget'] < 300000000]
budg... | code |
1006144/cell_2 | [
"text_plain_output_1.png",
"image_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 |
1006144/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
movie['budget'].max() | code |
1006144/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
movie.describe() | code |
1006144/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
gdp_stats= gdp.describe().T
budget = movie[movie['budget'] < 300000000]
budg... | code |
1006144/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
gdp.describe() | code |
1006144/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
budget = movie[movie['budget'] < 300000000]
budget = budget[budget['budget'].... | code |
1006144/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
budget = movie[movie['budget'] < 300000000]
budget = budget[budget['budget'].... | code |
1006144/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
gdp_stats= gdp.describe().T
gdp_stats = gdp_stats.reset_index()
gdp_stats = gdp_stats.dropna()
gdp_stats.head() | code |
1006144/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
budget = movie[movie['budget'] < 300000000]
budget = budget[budget['budget'].isnull() == False]
bud... | code |
1006144/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
gdp_stats= gdp.describe().T
budget = movie[movie['budget'] < 300000000]
budg... | code |
1006144/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
gdp_stats= gdp.describe().T
gdp_stats.head() | code |
1006144/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
gdp_stats= gdp.describe().T
budget = movie[movie['budget'] < 300000000]
budg... | code |
1006144/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)
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
gdp | code |
73083438/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.isnull().sum()
features = train.drop(['target'], axis=1)
list(test.columns) == list(features.columns) | code |
73083438/cell_6 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.describe() | code |
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