path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
32068206/cell_41 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-... | code |
32068206/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32068206/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-... | code |
32068206/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
32068206/cell_50 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-... | code |
32068206/cell_49 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-... | code |
32068206/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
32068206/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
32068206/cell_38 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-... | code |
32068206/cell_47 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-... | code |
32068206/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
32068206/cell_43 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-... | code |
32068206/cell_46 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-... | code |
32068206/cell_14 | [
"image_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
32068206/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
32068206/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
32068206/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
32068206/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
122259364/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
sample = pd.read_csv('sample_solution.csv')
test_data = pd.read_csv('test_data.csv', index_col=0)
train_data = pd.read_csv('train_data.csv', index_col=0)
sample.head() | code |
329837/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import chisquare
from sklearn.decomposition import PCA
from sklearn.decomposition import PCA
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
from scipy.stats import chisquare
chars = [i for i in df.columns.values if 'char_' in i]
flags = []
for feat in df[chars]:
... | code |
329837/cell_4 | [
"text_plain_output_1.png"
] | from scipy.stats import chisquare
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
from scipy.stats import chisquare
chars = [i for i in df.columns.values if 'char_' in i]
flags = []
for feat in df[chars]:
group = df[chars].groupby(feat)
for otherfeat in df[chars].drop(feat, ax... | code |
329837/cell_6 | [
"text_plain_output_1.png"
] | from scipy.stats import chisquare
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
from scipy.stats import chisquare
chars = [i for i in df.columns.values if 'char_' in i]
flags = []
for feat in df[chars]:
group = df[chars].groupby(feat)
for otherfeat in df[chars].drop(feat, ax... | code |
329837/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
print(df.head()) | code |
329837/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.stats import chisquare
from sklearn.decomposition import PCA
from sklearn.decomposition import PCA
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
from scipy.stats import chisquare
chars = [i for i in df.columns.values if 'char_' in i]
flags = []
for feat in df[chars]:
... | code |
318221/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
post = pd.read_sql_query('SELECT * FROM post', con)
comment = pd.read_sql_query('SELECT * FROM comment', con)
like = pd.read_sql_query('SELECT * FROM like', con)
rmember = pd.read_sql_query('SELECT distinct id as rid, name rname FR... | code |
318221/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
post = pd.read_sql_query('SELECT * FROM post', con)
comment = pd.read_sql_query('SELECT * FROM comment', con)
like = pd.read_sql_query('SELECT * FROM like', con)
rmember = pd.read_sql_query('SELECT distinct id as rid, name rname FR... | code |
318221/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
post = pd.read_sql_query('SELECT * FROM post', con)
comment = pd.read_sql_query('SELECT * FROM comment', con)
like = pd.read_sql_query('SELECT * FROM like', con)
rmember = pd.read_sql_query('SELECT distinct id as rid, name rname FR... | code |
318221/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
post = pd.read_sql_query('SELECT * FROM post', con)
comment = pd.read_sql_query('SELECT * FROM comment', con)
like = pd.read_sql_query('SELECT * FROM like', con)
rmember = pd.read_sql_query('SELECT distinct id as rid, name rname FR... | code |
106205992/cell_9 | [
"text_html_output_2.png"
] | train = pd.DataFrame(X, columns=[f'feat_{idx + 1}' for idx in range(X.shape[1])])
num_cols = train.columns.tolist()
categories = ['a'] * 50 + ['b'] * 25 + ['c'] * 25
random.shuffle(categories)
train['feat_5'] = categories
cat_cols = ['feat_5']
target = pd.DataFrame(y, columns=[f'target_{idx + 1}' for idx in range(y.sh... | code |
106205992/cell_4 | [
"text_plain_output_1.png"
] | train = pd.DataFrame(X, columns=[f'feat_{idx + 1}' for idx in range(X.shape[1])])
num_cols = train.columns.tolist()
categories = ['a'] * 50 + ['b'] * 25 + ['c'] * 25
random.shuffle(categories)
train['feat_5'] = categories
cat_cols = ['feat_5']
target = pd.DataFrame(y, columns=[f'target_{idx + 1}' for idx in range(y.sh... | code |
106205992/cell_11 | [
"text_html_output_1.png"
] | train = pd.DataFrame(X, columns=[f'feat_{idx + 1}' for idx in range(X.shape[1])])
num_cols = train.columns.tolist()
categories = ['a'] * 50 + ['b'] * 25 + ['c'] * 25
random.shuffle(categories)
train['feat_5'] = categories
cat_cols = ['feat_5']
target = pd.DataFrame(y, columns=[f'target_{idx + 1}' for idx in range(y.sh... | code |
106205992/cell_3 | [
"text_html_output_1.png"
] | train = pd.DataFrame(X, columns=[f'feat_{idx + 1}' for idx in range(X.shape[1])])
num_cols = train.columns.tolist()
categories = ['a'] * 50 + ['b'] * 25 + ['c'] * 25
random.shuffle(categories)
train['feat_5'] = categories
cat_cols = ['feat_5']
train.head(2) | code |
106205992/cell_10 | [
"text_html_output_1.png"
] | train = pd.DataFrame(X, columns=[f'feat_{idx + 1}' for idx in range(X.shape[1])])
num_cols = train.columns.tolist()
categories = ['a'] * 50 + ['b'] * 25 + ['c'] * 25
random.shuffle(categories)
train['feat_5'] = categories
cat_cols = ['feat_5']
target = pd.DataFrame(y, columns=[f'target_{idx + 1}' for idx in range(y.sh... | code |
106205992/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | train = pd.DataFrame(X, columns=[f'feat_{idx + 1}' for idx in range(X.shape[1])])
num_cols = train.columns.tolist()
categories = ['a'] * 50 + ['b'] * 25 + ['c'] * 25
random.shuffle(categories)
train['feat_5'] = categories
cat_cols = ['feat_5']
target = pd.DataFrame(y, columns=[f'target_{idx + 1}' for idx in range(y.sh... | code |
32065262/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape | code |
32065262/cell_57 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].... | code |
32065262/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].... | code |
32065262/cell_44 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].... | code |
32065262/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales['Returns'].loc['Basket'] | code |
32065262/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.head() | code |
32065262/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum() | code |
32065262/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].... | code |
32065262/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].... | code |
32065262/cell_61 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].... | code |
32065262/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns | code |
32065262/cell_50 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].... | code |
32065262/cell_52 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].... | code |
32065262/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 |
32065262/cell_45 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].... | code |
32065262/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket'] | code |
32065262/cell_59 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].... | code |
32065262/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.tail(10) | code |
32065262/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales['Returns'] | code |
32065262/cell_47 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].... | code |
32065262/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].... | code |
32065262/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales[['Gross Sales', 'Returns']].loc['Basket'] | code |
32065262/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns'] | code |
32065262/cell_53 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].... | code |
32065262/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum() | code |
32065262/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].... | code |
33119952/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 |
50239348/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/digit-recognizer/train.csv')
test = pd.read_csv('../input/digit-recognizer/test.csv')
train.shape | code |
50239348/cell_19 | [
"text_html_output_1.png"
] | import numpy as np
import os
import pandas as pd
import random
import tensorflow as tf
import tensorflow_addons as tfa
train = pd.read_csv('../input/digit-recognizer/train.csv')
test = pd.read_csv('../input/digit-recognizer/test.csv')
train.shape
Y_train = train['label']
X_train = train.drop('label', axis=1).va... | code |
50239348/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import pandas as pd
import random
import tensorflow as tf
import tensorflow_addons as tfa
train = pd.read_csv('../input/digit-recognizer/train.csv')
test = pd.read_csv('../input/digit-recognizer/test.csv')
train.shape
Y_train = train['label']
X_train = train.drop('label', axis=1).va... | code |
50239348/cell_3 | [
"text_plain_output_1.png"
] | import tensorflow as tf
print(tf.__version__) | code |
50239348/cell_5 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/digit-recognizer/train.csv')
test = pd.read_csv('../input/digit-recognizer/test.csv')
train.head() | code |
73072014/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import bernoulli
from scipy.stats import binom
from scipy.stats import norm
from scipy.stats import norm
from scipy.stats import poisson
from scipy.stats import uniform
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(colo... | code |
73072014/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import uniform
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
sns.set(rc={'figure.figsize': (9.5, 5)})
from scipy.stats import uniform
sample_size = 10000
param_loc = 5
param_scale = 10
data_uniform = unifo... | code |
73072014/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import norm
from scipy.stats import norm
from scipy.stats import uniform
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
sns.set(rc={'figure.figsize': (9.5, 5)})
# import uniform distribution
from scipy.st... | code |
73072014/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import bernoulli
from scipy.stats import beta
from scipy.stats import beta
from scipy.stats import binom
from scipy.stats import norm
from scipy.stats import norm
from scipy.stats import poisson
from scipy.stats import uniform
import seaborn as sns
import numpy as np
import pandas as pd
import... | code |
73072014/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import bernoulli
from scipy.stats import norm
from scipy.stats import norm
from scipy.stats import uniform
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
sns.set(rc={'figure.figsize': (9.5, 5)})
# import... | code |
73072014/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import bernoulli
from scipy.stats import binom
from scipy.stats import norm
from scipy.stats import norm
from scipy.stats import uniform
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
sns.set(rc={'figure... | code |
73072014/cell_10 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import bernoulli
from scipy.stats import beta
from scipy.stats import binom
from scipy.stats import norm
from scipy.stats import norm
from scipy.stats import poisson
from scipy.stats import uniform
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
impo... | code |
73072014/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import norm
from scipy.stats import uniform
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
sns.set(rc={'figure.figsize': (9.5, 5)})
# import uniform distribution
from scipy.stats import uniform
# generate... | code |
106194764/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
import gc
import numpy as np # linear algebra
import os
import pandas as pd
import shutil
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import numpy as np
import pandas as pd
import ... | code |
106194764/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import numpy as np
import pandas as pd
import gc
from tqdm import tqdm
cols = {'countryCode': np.float32, 'c... | code |
106194764/cell_5 | [
"text_plain_output_1.png"
] | len(train.columns) | code |
16131921/cell_9 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/insurance.csv')
data.describe().T
num_data = data.select_dtypes(include... | code |
16131921/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/insurance.csv')
data.info() | code |
16131921/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/insurance.csv')
data.describe().T
graphs = sns.pairplot(data)
graphs.set() | code |
16131921/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | !pwd
!ls /kaggle/input
import seaborn as sns
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os | code |
16131921/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/insurance.csv')
data.describe().T
graphs = sns.p... | code |
16131921/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16131921/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/insurance.csv')
data.describe().T
graphs = sns.p... | code |
16131921/cell_12 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/insurance.csv')
data.describe().T
graphs = sns.p... | code |
16131921/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/insurance.csv')
data.describe().T | code |
16136283/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
X_train.shape
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer().fit(X_train)
len(vect.get_feature_names())
vect.get_feature_names()[0:10]
vect.get_feature_names()[::3000] | code |
16136283/cell_13 | [
"text_html_output_1.png"
] | (y_train[0], X_train[0]) | code |
16136283/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
X_train.shape
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer().fit(X_train)
len(vect.get_feature_names())
vect.get_feature_names()[0:10]
vect.get_feature_names()[::3000]
X_train_vectorized = vect.transform(X_train)
X_t... | code |
16136283/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Amazon_Unlocked_Mobile.csv')
df.info() | code |
16136283/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Amazon_Unlocked_Mobile.csv')
df.dropna(inplace=True)
df = df[df['Rat... | code |
16136283/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
X_train.shape
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer().fit(X_train)
len(vect.get_feature_names())
vect.get_feature_names()[0:10] | code |
16136283/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Amazon_Unlocked_Mobile.csv')
df['Brand Name'].value_counts().head() | code |
16136283/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
X_train.shape
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer().fit(X_train)
len(vect.get_feature_names())
vect.get_feature_names()[0:10]
vect.get_feature_names()[::30... | code |
16136283/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Amazon_Unlocked_Mobile.csv')
df.dropna(inplace=True)
df = df[df['Rating'] != 3]
df['Positively Rated'].mean() | code |
16136283/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
X_train.shape
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer().fit(X_train)
len(vect.get_feature_names()) | code |
16136283/cell_18 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
X_train.shape
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer().fit(X_train)
vect | code |
16136283/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
X_train.shape
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer().fit(X_train)
len(vect.get_feature_names())
vect.get_feature_n... | code |
16136283/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Amazon_Unlocked_Mobile.csv')
df.head() | code |
16136283/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
X_train.shape
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer().fit(X_train)
len(vect.get_feature_names())
vect.get_feature_names()[0:10]
vect.get_feature_names()[::3000]
X_train_vectorized = vect.transform(X_train)
X_t... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.