path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
32063375/cell_5 | [
"image_output_1.png"
] | import pandas as pd
hp = pd.read_csv('../input/london-house-prices/hpdemo.csv')
hp | code |
121148301/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated().sum()
df.isnull().sum()
df.isnull().sum()
df | code |
121148301/cell_4 | [
"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/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.tail() | code |
121148301/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # data visualization library
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # data visualization library
df = pd.read_csv('/kaggle/input/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated... | code |
121148301/cell_6 | [
"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/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated().sum() | code |
121148301/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated().sum()
df.isnull().sum()
df.isnull().sum() | code |
121148301/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # data visualization library
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # data visualization library
df = pd.read_csv('/kaggle/input/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated... | code |
121148301/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import style
style.use('ggplot')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
121148301/cell_7 | [
"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/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated().sum()
df.isnull().sum() | code |
121148301/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # data visualization library
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # data visualization library
df = pd.read_csv('/kaggle/input/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated... | code |
121148301/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated().sum()
df.isnull().sum()
df.isnull().sum()
df.nunique() | code |
121148301/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # data visualization library
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # data visualization library
df = pd.read_csv('/kaggle/input/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated... | code |
121148301/cell_3 | [
"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/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.head() | code |
121148301/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # data visualization library
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # data visualization library
df = pd.read_csv('/kaggle/input/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated... | code |
121148301/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated().sum()
df.isnull().sum()
df.isnull().sum()
df.describe() | code |
121148301/cell_5 | [
"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/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.info() | code |
32068527/cell_2 | [
"application_vnd.jupyter.stderr_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 |
32068527/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
tes... | code |
32068527/cell_7 | [
"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)
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-... | code |
32068527/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
tes... | code |
32068527/cell_16 | [
"image_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
tes... | code |
89127002/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix, classification_report, cohen_kappa_score
from tensorflow.keras.layers import Conv1D, MaxPooling1D, GlobalMaxPooling1D, GlobalAveragePooling1D
from tensorflow.keras.layers import Dense, Input, Activation, Embedding, Dropout
import matplotlib.pyplot as plt
import numpy as... | code |
89127002/cell_6 | [
"text_plain_output_1.png"
] | import os
print(os.listdir('../input')) | code |
89127002/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix, classification_report, cohen_kappa_score
from tensorflow.keras.layers import Conv1D, MaxPooling1D, GlobalMaxPooling1D, GlobalAveragePooling1D
from tensorflow.keras.layers import Dense, Input, Activation, Embedding, Dropout
import matplotlib.pyplot as plt
import numpy as... | code |
89127002/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/drugsComTrain_raw.csv')
test = pd.read_csv('../input/drugsComTest_raw.csv')
ratings = train['rating'].values
labels = 1.0 * (ratings >= 8) + 1.0 * (ratings >= 5)
hot_labels = to_categorical(labels)
print('Shape of la... | code |
89127002/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/drugsComTrain_raw.csv')
test = pd.read_csv('../input/drugsComTest_raw.csv')
train.head() | code |
89127002/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import tensorflow as tf
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensor... | code |
89127002/cell_31 | [
"image_output_2.png",
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix, classification_report, cohen_kappa_score
from tensorflow.keras.layers import Conv1D, MaxPooling1D, GlobalMaxPooling1D, GlobalAveragePooling1D
from tensorflow.keras.layers import Dense, Input, Activation, Embedding, Dropout
import matplotlib.pyplot as plt
import numpy as... | code |
89127002/cell_24 | [
"text_plain_output_1.png"
] | history = model.fit(train_data, train_cat, batch_size=128, epochs=10, verbose=0, validation_data=(val_data, val_cat)) | code |
89127002/cell_14 | [
"text_plain_output_1.png"
] | data = pad_sequences(sequences, maxlen=sequence_length)
print('Shape of data tensor:', data.shape) | code |
89127002/cell_22 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv1D, MaxPooling1D, GlobalMaxPooling1D, GlobalAveragePooling1D
from tensorflow.keras.layers import Dense, Input, Activation, Embedding, Dropout
embedding_dim = 100
model = Sequential([Embedding(max_features + 1, embedding_dim), Dropout(0.25), Conv1D(128, 7, padding='valid', activ... | code |
89127002/cell_27 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
plt.rcParams['figure.figsize'] = [12, 5]
train = pd.read_csv('../input/drugsComTrain_raw.csv')
test = pd.read_csv('../input/drugsComTest_raw.csv')
data = pad_sequences(sequence... | code |
89127002/cell_12 | [
"text_plain_output_1.png"
] | max_features = 5000
sequence_length = 200
samples = train['review']
tokenizer = Tokenizer(num_words=max_features)
tokenizer.fit_on_texts(samples)
sequences = tokenizer.texts_to_sequences(samples)
word_index = tokenizer.word_index
print(f'Found {len(word_index)} unique tokens.') | code |
2039737/cell_13 | [
"text_html_output_1.png"
] | 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='Survived', ascending=False) | code |
2039737/cell_9 | [
"text_html_output_1.png"
] | 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]
test_df.describe() | code |
2039737/cell_30 | [
"text_html_output_1.png"
] | 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]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'])
for dat... | code |
2039737/cell_33 | [
"text_html_output_1.png"
] | 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]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'])
for dat... | code |
2039737/cell_6 | [
"text_html_output_1.png"
] | 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.head() | code |
2039737/cell_29 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | 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]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'])
for dat... | code |
2039737/cell_26 | [
"image_output_1.png"
] | 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]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'])
for dataset in combine:
... | code |
2039737/cell_11 | [
"text_plain_output_1.png"
] | 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]
test_df.describe(include=['O']) | code |
2039737/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
grid = sns.FacetGrid(train_df, col='Survived')
grid.map(plt.hist, 'Age', bins=20)
grid = sns.FacetGrid(train_df, col='Survi... | code |
2039737/cell_7 | [
"text_html_output_1.png"
] | 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]
test_df.head() | code |
2039737/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
grid = sns.FacetGrid(train_df, col='Survived')
grid.map(plt.hist, 'Age', bins=20)
grid = sns.FacetGrid(train_df, col='Survi... | code |
2039737/cell_28 | [
"image_output_1.png"
] | 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]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'])
for dat... | code |
2039737/cell_8 | [
"text_html_output_1.png"
] | 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.describe() | code |
2039737/cell_15 | [
"text_html_output_1.png"
] | 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='Survived', ascending=False) | code |
2039737/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
grid = sns.FacetGrid(train_df, col='Survived')
grid.map(plt.hist, 'Age', bins=20) | code |
2039737/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from subprocess import check_output
import pandas as pd
import numpy as np
import random as rnd
import sklearn.linear_model
import sklearn.svm
import sklearn.ensemble
import sklearn.neighbors
import sklearn.naive_bayes
import sklearn.tree
import sklearn.neural_network
from subprocess import check_output
import seaborn... | code |
2039737/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
grid = sns.FacetGrid(train_df, col='Survived')
grid.map(plt.hist, 'Age', bins=20)
grid = sns.FacetGrid(train_df, col='Survi... | code |
2039737/cell_31 | [
"text_plain_output_1.png"
] | 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]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'])
for dat... | code |
2039737/cell_24 | [
"text_html_output_1.png"
] | 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]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'])
for dataset in combine:
... | code |
2039737/cell_14 | [
"text_html_output_1.png"
] | 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='Survived', ascending=False) | code |
2039737/cell_22 | [
"text_html_output_1.png"
] | 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]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'])
for dataset in combine:
... | code |
2039737/cell_10 | [
"text_plain_output_1.png"
] | 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.describe(include=['O']) | code |
2039737/cell_12 | [
"text_html_output_1.png"
] | 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='Survived', ascending=False) | code |
2039737/cell_5 | [
"text_html_output_1.png"
] | 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.info()
print('_' * 40)
test_df.info() | code |
72116842/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv'
df = pd.read_csv(path, header=None)
df.columns = ['target', 'ids', 'date', 'flag', 'user', 'text']
df.sample(10)
df.shape
sns.countplot(df['tar... | code |
72116842/cell_30 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import SnowballStemmer
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
path = '/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv'
df = pd.read_csv(path, header=N... | code |
72116842/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv'
df = pd.read_csv(path, header=None)
df.columns = ['target', 'ids', 'date', 'flag', 'user', 'text']
df.info() | code |
72116842/cell_29 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import SnowballStemmer
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
path = '/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv'
df = pd.read_csv(path, header=N... | code |
72116842/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv'
df = pd.read_csv(path, header=None)
df.columns = ['target', 'ids', 'date', 'flag', 'user', 'text']
df.sample(10) | code |
72116842/cell_16 | [
"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)
path = '/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv'
df = pd.read_csv(path, header=None)
df.columns = ['target', 'ids', 'date', 'flag', 'user', 'text']
df.sample(10)
df.shape
df['text'].iloc[0] | code |
72116842/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv'
df = pd.read_csv(path, header=None)
df.columns = ['target', 'ids', 'date', 'flag', 'user', 'text']
df.sample(10)
df.shape
df['text'].iloc[1] | code |
72116842/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv'
df = pd.read_csv(path, header=None)
df.columns = ['target', 'ids', 'date', 'flag', 'user', 'text']
df.head() | code |
72116842/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv'
df = pd.read_csv(path, header=None)
df.columns = ['target', 'ids', 'date', 'flag', 'user', 'text']
df.sample(10)
df.shape | code |
2003059/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test (1).csv')
def simplify_ages(df):
df.Age = df.Age.fillna(-0.5)
bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
gr... | code |
2003059/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test (1).csv')
sns.barplot(x='Embarked', y='Survived', data=data_train, hue='Pclass') | code |
2003059/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test (1).csv')
data_train.head(10) | code |
2003059/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test (1).csv')
sns.barplot(x='Pclass', y='Survived', data=data_train, hue='Sex') | code |
90147643/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath... | code |
90147643/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath... | code |
90147643/cell_34 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath... | code |
90147643/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
sba.head(2) | code |
90147643/cell_29 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba... | code |
90147643/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath... | code |
90147643/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath... | code |
90147643/cell_28 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath... | code |
90147643/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath + 'SBAnational.csv'... | code |
90147643/cell_15 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba... | code |
90147643/cell_35 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba... | code |
90147643/cell_31 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath... | code |
90147643/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath... | code |
90147643/cell_22 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath... | code |
90147643/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath + 'SBAnational.csv'... | code |
90147643/cell_27 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath... | code |
90147643/cell_36 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath... | code |
90153696/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
X = df[['BodyFat', 'Age']]
y = df['Density']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
mod... | code |
90153696/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
X = df[['BodyFat', 'Age']]
y = df['Density']
X.head() | code |
90153696/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
df.head() | code |
90153696/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
sns.lmplot(x='BodyFat', y='Age', data=df, ci=None) | code |
90153696/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
X = df[['BodyFat', 'Age']]
y = df['Density']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
mod... | code |
90153696/cell_20 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
X = df[['BodyFat', 'Age']]
y = df['Density']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
mod... | code |
90153696/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
sns.kdeplot(x='Weight', data=df) | code |
90153696/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
len(df) | code |
90153696/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
X = df[['BodyFat', 'Age']]
y = df['Density']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
mod... | code |
90153696/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
sns.kdeplot(x='BodyFat', data=df) | code |
90153696/cell_18 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
X = df[['BodyFat', 'Age']]
y = df['Density']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
mod... | code |
90153696/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df.info() | code |
90153696/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
X = df[['BodyFat', 'Age']]
y = df['Density']
model = LinearRegression()
model.fit(X, y) | code |
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