path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
16144426/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.metrics import accuracy_score
model = RandomForestClassifier(n_estimators=800)
model.fit(xtrain, ytrain)
test_pred = mo... | code |
16144426/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import gensim
import nltk
import os
print(os.listdir('../input/embeddings/GoogleNews-vectors-negative300/')) | code |
16144426/cell_7 | [
"text_plain_output_1.png"
] | import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5])
url = 'ht... | code |
16144426/cell_28 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2v... | code |
16144426/cell_8 | [
"text_plain_output_1.png"
] | import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5])
url = 'ht... | code |
16144426/cell_15 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Seri... | code |
16144426/cell_16 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Seri... | code |
16144426/cell_3 | [
"text_html_output_1.png"
] | import gensim
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
list(embeddings['modi'][:5]) | code |
16144426/cell_31 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
from nltk.stem import PorterStemmer
from sklearn.feature_extraction.text import CountVectorizer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negativ... | code |
16144426/cell_24 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Seri... | code |
16144426/cell_14 | [
"text_html_output_1.png"
] | from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Seri... | code |
16144426/cell_22 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Seri... | code |
16144426/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5])
embedding... | code |
16144426/cell_27 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Seri... | code |
16144426/cell_12 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Seri... | code |
16144426/cell_5 | [
"text_plain_output_1.png"
] | import gensim
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
embeddings.most_similar('modi', topn=10) | code |
16123290/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict... | code |
16123290/cell_9 | [
"image_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict... | code |
16123290/cell_4 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict... | code |
16123290/cell_20 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import matplotlib.pyplot as plt
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJ... | code |
16123290/cell_29 | [
"image_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import matplotlib.pyplot as plt
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJ... | code |
16123290/cell_26 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data =... | code |
16123290/cell_11 | [
"image_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict... | code |
16123290/cell_1 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import xgboost as xgb
from xgboost import plot_importance, plot_tree
from sklearn.metrics import mean_squared_error, mean_absolute_error
import plotly.plotly as p... | code |
16123290/cell_7 | [
"image_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict... | code |
16123290/cell_18 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict... | code |
16123290/cell_32 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from xgboost import plot_importance, plot_tree
import matplotlib.pyplot as plt
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
import xgboost as xgb
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
... | code |
16123290/cell_28 | [
"image_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data =... | code |
16123290/cell_8 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict... | code |
16123290/cell_15 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from xgboost import plot_importance, plot_tree
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
import xgboost as xgb
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
t... | code |
16123290/cell_3 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
df.plot(figsize=(15, 8)) | code |
16123290/cell_31 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data =... | code |
16123290/cell_14 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
import xgboost as xgb
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
dat... | code |
73070243/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data[['Pclass', 'Survived']].groupby('Pclass').mean() | code |
73070243/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
test_data.head() | code |
73070243/cell_57 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket... | code |
73070243/cell_33 | [
"text_plain_output_2.png",
"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)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test... | code |
73070243/cell_44 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test... | code |
73070243/cell_20 | [
"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
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
v = sns.FacetGrid(train_data, col='Survived')
v.map(plt.hist, 'Age', bin... | code |
73070243/cell_55 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket... | code |
73070243/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test... | code |
73070243/cell_26 | [
"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
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
v = sns.FacetGrid(train_data, col='Survived')
v.map(plt.hist, 'Age', bin... | code |
73070243/cell_48 | [
"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)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket... | code |
73070243/cell_41 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test... | code |
73070243/cell_50 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test... | code |
73070243/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 |
73070243/cell_45 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
guess = np.zeros(5)
guess | code |
73070243/cell_51 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket... | code |
73070243/cell_28 | [
"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
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
v = sns.FacetGrid(train_data, col='Survived')
v.map(plt.hist, 'Age', bin... | code |
73070243/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
print(train_data.columns)
print(train_data.info())
print(test_data.info()) | code |
73070243/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data[['Sex', 'Survived']].groupby('Sex').mean() | code |
73070243/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data[['SibSp', 'Survived']].groupby('SibSp').mean().sort_values(by='Survived', ascending=False) | code |
73070243/cell_47 | [
"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)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket... | code |
73070243/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)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.head() | code |
73070243/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data[['Parch', 'Survived']].groupby('Parch').mean().sort_values(by='Survived', ascending=False) | code |
73070243/cell_35 | [
"text_plain_output_2.png",
"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)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test... | code |
73070243/cell_24 | [
"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
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
v = sns.FacetGrid(train_data, col='Survived')
v.map(plt.hist, 'Age', bin... | code |
73070243/cell_22 | [
"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
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
v = sns.FacetGrid(train_data, col='Survived')
v.map(plt.hist, 'Age', bin... | code |
73070243/cell_53 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket... | code |
73070243/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.describe(include=['O']) | code |
73070243/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test... | code |
1005662/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from sklearn import tree
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1005662/cell_3 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
combine = [train_data, test_data]
print(train_data.columns.values)
print(train_data.head())
print(train_data.describe())
train_data.shape | code |
17136141/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
print('Max year :', max_year)
print('Min year :', min_year) | code |
17136141/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
suicide_df.plot(x='generation', y='suicides_no', linestyle='', m... | code |
17136141/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.info() | code |
17136141/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
suicide_df.groupby('year')['suicides_no'].sum().plot(kind='bar',... | code |
17136141/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any() | code |
17136141/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
suicide_df.groupby('age')['suicides_no'].sum().plot(kind='bar', ... | code |
17136141/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
sns.catplot('country', 'population', hue='age', data=suicide_df) | code |
17136141/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
suicide_df.groupby('sex')['suicides_no'].sum().plot(kind='bar', ... | code |
17136141/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
df = suicide_df[['country', 'suicides_no']]
df1 = df.groupby('c... | code |
17136141/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.head() | code |
17136141/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
df = sui... | code |
17136141/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
df = sui... | code |
17136141/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
pop = suicide_df[['country', 'population', 'suicides_no']]
pop.... | code |
17136141/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
df = suicide_df[['country', 'suicides_no']]
df.head() | code |
17136141/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.describe() | code |
73067872/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df | code |
73067872/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.Survived | code |
73067872/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df | code |
73067872/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.info() | code |
32062482/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
apple_mobility_df = pd.read_csv('../input/apple-mobility-trends-updated-daily/Apple_Mobility_2020-04-13.csv')
apple_mobility_df.drop('Unnamed: 0', axis=1, inplace=True)
geo_mask = apple_mobility_df['geo_type'] == 'country/region'
mobility_countries = apple_mobility_df[geo_mask]... | code |
32062482/cell_6 | [
"text_html_output_2.png"
] | import pandas as pd
import plotly.express as px
import pandas as pd
apple_mobility_df = pd.read_csv('../input/apple-mobility-trends-updated-daily/Apple_Mobility_2020-04-13.csv')
apple_mobility_df.drop('Unnamed: 0', axis=1, inplace=True)
geo_mask = apple_mobility_df['geo_type'] == 'country/region'
mobility_countries ... | code |
32062482/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
apple_mobility_df = pd.read_csv('../input/apple-mobility-trends-updated-daily/Apple_Mobility_2020-04-13.csv')
apple_mobility_df.drop('Unnamed: 0', axis=1, inplace=True)
apple_mobility_df.head() | code |
32062482/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
apple_mobility_df = pd.read_csv('../input/apple-mobility-trends-updated-daily/Apple_Mobility_2020-04-13.csv')
apple_mobility_df.drop('Unnamed: 0', axis=1, inplace=True)
geo_mask = apple_mobility_df['geo_type'] == 'country/region'
mobility_countries = apple_mobility_df[geo_mask]... | code |
32062482/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
apple_mobility_df = pd.read_csv('../input/apple-mobility-trends-updated-daily/Apple_Mobility_2020-04-13.csv')
apple_mobility_df.drop('Unnamed: 0', axis=1, inplace=True)
geo_mask = apple_mobility_df['geo_type'] == 'country/region'
mobility_countries = apple_mobility_df[geo_mask]... | code |
106211205/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data.dtypes
data.isnull().sum()
data.columns | code |
106211205/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data.dtypes
data.isnull().sum() | code |
106211205/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data | code |
106211205/cell_6 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data.describe() | code |
106211205/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data.dtypes
data.isnull().sum()
data.columns
columns = ['Age', 'Annual Income (k$)', 'Spending Score (1-100)']
sns.pairplot(data) | code |
106211205/cell_7 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data.dtypes | code |
106211205/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data.dtypes
data.isnull().sum()
data.columns
columns = ['Age', 'Annual Income (k$)', 'Spending Score (1-100)']
for i in columns:
plt.figure()
sns.kdeplot(data[i], hue... | code |
106211205/cell_15 | [
"text_plain_output_1.png"
] | columns = ['Age', 'Annual Income (k$)', 'Spending Score (1-100)']
columns | code |
106211205/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data.dtypes
data.isnull().sum()
data.columns
columns = ['Age', 'Annual Income (k$)', 'Spending Score (1-100)']
for i in columns:
plt.figure()
sns.distplot(data[i]) | code |
106211205/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data.dtypes
data.isnull().sum()
sns.distplot(data['Annual Income (k$)']) | code |
322326/cell_2 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
act_df = pd.read_csv('../input/act_train.csv', sep=',')
sns.countplot(x='outcome', data=act_df)
sns.plt.show() | code |
322326/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
act_df = pd.read_csv('../input/act_train.csv', sep=',')
sns.countplot(x='activity_category', data=act_df, hue='outcome')
sns.plt.show() | code |
32068059/cell_4 | [
"text_plain_output_1.png"
] | !pip install scispacy
!pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.2.4/en_core_sci_lg-0.2.4.tar.gz
!jupyter nbextension enable --py --sys-prefix widgetsnbextension | code |
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